From 45fe90a23fe314626f9a2e9ade8fa3a9de62a63f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 11 Nov 2020 17:58:21 +0530 Subject: [PATCH 001/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index c80e12f..26ed77b 100644 --- a/README.md +++ b/README.md @@ -57,3 +57,5 @@ https://docs.google.com/document/d/12h9bcIdBPJUFc_fJssJe8hVBzledq2Dtk5-9OpKHbfg/ Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python + +2. Numbering the subplots https://matplotlib.org/3.1.1/gallery/axes_grid1/simple_anchored_artists.html From f23dc3613daccccd80e6b9fb8236e7b9c41de08b Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 4 Jan 2021 15:42:55 +0530 Subject: [PATCH 002/171] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 26ed77b..2d041c2 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,7 @@ Some of the tutorials to follow 43. https://github.com/giswqs/earthengine-py-notebooks 44. Google Earth Engine image to numpy https://mygeoblog.com/2019/08/21/google-earth-engine-to-numpy/ 45. Stippling to show statistical significance https://github.com/bradyrx/esmtools/issues/13 +46. Resampling from swath to grid https://github.com/TerraFusion/pytaf Projects for the class From 07266b3f273e4fc0f231daa7d9e9b4aec2236a5a Mon Sep 17 00:00:00 2001 From: msingh Date: Thu, 7 Jan 2021 16:49:00 +0530 Subject: [PATCH 003/171] JAXA GSMAP data --- satellite/gsmap_reader.ipynb | 122 +++++++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 satellite/gsmap_reader.ipynb diff --git a/satellite/gsmap_reader.ipynb b/satellite/gsmap_reader.ipynb new file mode 100644 index 0000000..8e794c5 --- /dev/null +++ b/satellite/gsmap_reader.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import gzip\n", + "import numpy as np\n", + "from mpl_toolkits.basemap import Basemap\n", + "import matplotlib.colors as colors\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "filename1='/home/cccr/msingh/satellite_data/hokusai.eorc.jaxa.jp/standard/v6/hourly/2002/01/01/gsmap_rnl.20020101.0000.v6.5133.0.dat.gz'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "gz = gzip.GzipFile(filename1,'rb')\n", + "dd=np.frombuffer(gz.read(),dtype=np.float32)\n", + "pre=dd.reshape((1200,3600))\n", + "lon=np.linspace(0.05,359.95,3600)\n", + "lat=np.linspace(59.95,-59.95,1200)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cccr/msingh/anaconda3/envs/gsmap/lib/python3.6/site-packages/matplotlib/colors.py:263: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " c = np.array(c)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,6))\n", + "lon_leftup=0;lat_leftup=60\n", + "lon_rightdown=360.1;lat_rightdown=-60\n", + "m = Basemap(projection='cyl', llcrnrlat=lat_rightdown, urcrnrlat=lat_leftup, llcrnrlon=lon_leftup, urcrnrlon=lon_rightdown, resolution='l')\n", + "m.drawcoastlines(linewidth=0.3, color='black')\n", + "parallels = np.arange(-60,80,20)\n", + "m.drawparallels(parallels,labels=[True,False,False,False],linewidth=0.2,dashes=[1,4])\n", + "meridians = np.arange(0,400,30)\n", + "m.drawmeridians(meridians,labels=[False,False,False,True],linewidth=0.2,dashes=[1,4])\n", + "\n", + "cdict = ['w',(151 / 255, 250 / 255, 151 / 255), (49/ 255, 204 / 255, 49/ 255), (126/ 255, 191 / 255, 237 / 255)\n", + " , (0 / 255, 0 / 255, 255 / 255), (237/ 255, 0 / 255, 237 / 255)]\n", + "my_cmap = colors.ListedColormap(cdict,'pre')\n", + "my_cmap.set_under('gray')\n", + "my_cmap.set_over((135/ 255, 25 / 255, 25 / 255))\n", + "lev=np.array([-60,0.1,10,25,50,100,250])\n", + "norm3 = mpl.colors.BoundaryNorm(lev, my_cmap.N)\n", + "\n", + "lons, lats = np.meshgrid(lon,lat)\n", + "x, y = m(lons, lats)\n", + "pp=m.pcolormesh(x,y,pre*24,cmap=my_cmap,norm=norm3)\n", + "\n", + "cb=fig.colorbar(pp,ax=ax,pad=0.07,shrink=0.6,aspect=25,orientation='horizontal',extend='both')\n", + "cb.set_ticks(lev)\n", + "cb.set_ticklabels(['ND',0.1,10,25,50,100,250])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a55e76f63cef5a1a81bcfeeee9a664de28307488 Mon Sep 17 00:00:00 2001 From: msingh Date: Thu, 7 Jan 2021 17:20:46 +0530 Subject: [PATCH 004/171] add a day of gz gsmap data to netcdf --- satellite/gsmap_reader_v3.py | 61 ++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 satellite/gsmap_reader_v3.py diff --git a/satellite/gsmap_reader_v3.py b/satellite/gsmap_reader_v3.py new file mode 100644 index 0000000..1e29324 --- /dev/null +++ b/satellite/gsmap_reader_v3.py @@ -0,0 +1,61 @@ +# https://wiki.mpimet.mpg.de/doku.php?id=analysis:pot_pourri:processing_binary_data +import gzip +import xarray as xr +import numpy as np +import pandas as pd +import os + +#filename1='/home/cccr/msingh/satellite_data/hokusai.eorc.jaxa.jp/standard/v6/hourly/2002/01/01/gsmap_rnl.20020101.0000.v6.5133.0.dat.gz' + + +# In[3]: + + +#gz = gzip.GzipFile(filename1,'rb') +#dd=np.frombuffer(gz.read(),dtype=np.float32) +#pre=dd.reshape((1200,3600)) + + +#lon=np.linspace(0.05,359.95,3600) +#lat=np.linspace(59.95,-59.95,1200) + + + +#unit_conversion = 1./3600. +#gsm_out = '/home/cccr/msingh/satellite_data/GSMaP.v7_0.10deg_2001.nc' +gsm_out = '/home/cccr/msingh/satellite_data/hokusai.eorc.jaxa.jp/standard/v6/hourly/test.nc' +year = '2002' +if (os.path.isfile(gsm_out)): + print (gsm_out+' exists, not overwriting') +else: +# time= pd.date_range(ym+'2001-01-01','2001-12-31', freq='1H') + #time= pd.date_range('2001-01-01','2001-12-31 23:00:00', freq='1H') + time= pd.date_range('2002-02-01','2002-02-01 23:00:00', freq='1H') + lat = xr.Coordinate('lat', np.linspace(59.95,-59.95,1200) , attrs={'long_name':'latitude','standard_name':'latitude','units': "degrees_north"}) + lon = xr.Coordinate('lon', np.linspace(0.05,359.95,3600) , attrs={'long_name':'longitude','standard_name':'longitude','units': "degrees_east"}) + da = np.ndarray(shape=(time.size,lat.size,lon.size)) + + path = '/home/cccr/msingh/satellite_data/hokusai.eorc.jaxa.jp/standard/v6/hourly/2002/' + for i in np.arange(time.size): + gsm_in = path + time[i].strftime("%m/%d")+ '/gsmap_rnl.2002'+time[i].strftime("%m%d.%H")+'00.v6.5133.0.dat.gz'# gsmap_mvk.2016'+time[i].strftime("%m%d.%H")+'00.v7.0001.0.dat' + if (os.path.isfile(gsm_in)): + gz = gzip.GzipFile(gsm_in,'rb') + dd = np.frombuffer(gz.read(),dtype=np.float32) + data = dd.reshape((1200,3600)) + #with open(gsm_in,'rb') as f: + # data = np.fromfile(f, dtype=np.float32, count = lon.size*lat.size) + #d2 = pd.DataFrame(np.reshape(data,(lat.size,lon.size)), lat, lon) + d2 = pd.DataFrame(data, lat, lon) + da[i,:,:] = d2.where(d2 != -99.0)# * unit_conversion + print("read successfull ", d2, d2.shape) + else: + print(gsm_in, " file not found: filing nan") + da[i,:,:] = np.nan + + da[da<0] = np.nan + ds = xr.Dataset({'pr': (['time', 'lat', 'lon'], da.astype('float32'))}, coords={'time': time,'lat': lat,'lon': lon}) + + ds.pr.attrs['long_name'] ='precipitation' + ds.pr.attrs['standard_name']='precipitation_flux' + ds.pr.attrs['units'] ='kg m-2 s-1' + ds.to_netcdf(gsm_out) From ede19ce196ae12a6ffe1daa3ebbfb8fe70604baa Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 12 Jan 2021 13:15:07 +0530 Subject: [PATCH 005/171] fortran compilation --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 2d041c2..b6d9e8b 100644 --- a/README.md +++ b/README.md @@ -60,3 +60,5 @@ Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python 2. Numbering the subplots https://matplotlib.org/3.1.1/gallery/axes_grid1/simple_anchored_artists.html + +3. Fortran compilation may sometimes be solved by running the command ulimit -s unlimited From 6b8982a67a410f0188eb3a9c87ea57949c49b429 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 15 Jan 2021 17:35:12 +0530 Subject: [PATCH 006/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b6d9e8b..83030f4 100644 --- a/README.md +++ b/README.md @@ -62,3 +62,5 @@ Troubleshooting 2. Numbering the subplots https://matplotlib.org/3.1.1/gallery/axes_grid1/simple_anchored_artists.html 3. Fortran compilation may sometimes be solved by running the command ulimit -s unlimited + +4. There are visualization problems in cartopy if the lon is from 0 to 360 and not from -180 to 180 From e0bc3881d02f5c6a6155537e1094a74aebc16f35 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 21 Jan 2021 20:10:35 +0530 Subject: [PATCH 007/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 83030f4..15dc984 100644 --- a/README.md +++ b/README.md @@ -64,3 +64,5 @@ Troubleshooting 3. Fortran compilation may sometimes be solved by running the command ulimit -s unlimited 4. There are visualization problems in cartopy if the lon is from 0 to 360 and not from -180 to 180 + +5. Run docker as a non-root user https://docs.docker.com/engine/install/linux-postinstall/ From b1ab985099e56c4cc91150249b4407439dd33d29 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 21 Jan 2021 20:37:50 +0530 Subject: [PATCH 008/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 15dc984..b926482 100644 --- a/README.md +++ b/README.md @@ -66,3 +66,5 @@ Troubleshooting 4. There are visualization problems in cartopy if the lon is from 0 to 360 and not from -180 to 180 5. Run docker as a non-root user https://docs.docker.com/engine/install/linux-postinstall/ + +6. In the first instance of an image sometimes docker hub may deny you to push the image https://stackoverflow.com/questions/41984399/denied-requested-access-to-the-resource-is-denied-docker From ecd2d4f854c713953e8a9100e287d739ae559d55 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 21 Jan 2021 21:01:00 +0530 Subject: [PATCH 009/171] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b926482..4032774 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,7 @@ Some of the tutorials to follow 44. Google Earth Engine image to numpy https://mygeoblog.com/2019/08/21/google-earth-engine-to-numpy/ 45. Stippling to show statistical significance https://github.com/bradyrx/esmtools/issues/13 46. Resampling from swath to grid https://github.com/TerraFusion/pytaf +47. Making a docker container for data science https://towardsdatascience.com/docker-for-data-scientists-5732501f0ba4 Projects for the class From b2a1e924520c5f527167b4cebbcba57f3431da9b Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 30 Jan 2021 11:34:46 +0530 Subject: [PATCH 010/171] Update README.md --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index 4032774..de9e025 100644 --- a/README.md +++ b/README.md @@ -49,6 +49,17 @@ Some of the tutorials to follow 45. Stippling to show statistical significance https://github.com/bradyrx/esmtools/issues/13 46. Resampling from swath to grid https://github.com/TerraFusion/pytaf 47. Making a docker container for data science https://towardsdatascience.com/docker-for-data-scientists-5732501f0ba4 +48. Docker commands: + +Run interactively: docker run -it manmeet3591/dl:iitm:latest + +Install the necessary libraries + +Open a new terminal and do docker images to see the id and run the following command + +$ docker tag id_ manmeet3591/dl_iitm:v2 + +$ docker push manmeet3591/dl_iitm:v2 Projects for the class From 9c8a9f476c2bf6fbe501edf4b6be44f67facd664 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 19 Feb 2021 12:17:15 +0530 Subject: [PATCH 011/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index de9e025..66dad56 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,8 @@ https://docs.google.com/spreadsheets/d/1m2ZIJ_To8IbE18Teb70a7BVZg0o29sOM6rlgFkE2 https://docs.google.com/document/d/12h9bcIdBPJUFc_fJssJe8hVBzledq2Dtk5-9OpKHbfg/edit +49. Homogenous regions India shape files: https://github.com/Cassimsannan/Shapefiles + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 5815385a2966a3fc510b7e49e6228786fceb393c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Feb 2021 13:34:15 -0600 Subject: [PATCH 012/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 66dad56..6d24c76 100644 --- a/README.md +++ b/README.md @@ -82,3 +82,5 @@ Troubleshooting 5. Run docker as a non-root user https://docs.docker.com/engine/install/linux-postinstall/ 6. In the first instance of an image sometimes docker hub may deny you to push the image https://stackoverflow.com/questions/41984399/denied-requested-access-to-the-resource-is-denied-docker + +7. Numpy to xarray : foo = xr.DataArray(data, coords=[times, locs], dims=["time", "space"]) From c5063b478c12a4f00e1b22b21dd418f9edee4181 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Feb 2021 14:25:48 -0600 Subject: [PATCH 013/171] Update README.md --- README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/README.md b/README.md index 6d24c76..ba99b0b 100644 --- a/README.md +++ b/README.md @@ -84,3 +84,17 @@ Troubleshooting 6. In the first instance of an image sometimes docker hub may deny you to push the image https://stackoverflow.com/questions/41984399/denied-requested-access-to-the-resource-is-denied-docker 7. Numpy to xarray : foo = xr.DataArray(data, coords=[times, locs], dims=["time", "space"]) + + data = ds_merra2_jjas.DUSCATAU.sel(time='2002').values[0,:,:] + lats_ = ds_merra2_jjas.DUSCATAU.sel(time='2002').lat.values + lons_ = ds_merra2_jjas.DUSCATAU.sel(time='2002').lon.values + ds_merra2_jjas_new = xr.DataArray(data, coords=[lats_, lons_], dims=["lat", "lon"]) + +8. Using matplotlib to make map plots + plt.contourf(ds_merra2_jjas.DUSCATAU.sel(time='2002').lon.values,\ + ds_merra2_jjas.DUSCATAU.sel(time='2002').lat.values ,\ + ds_merra2_jjas.DUSCATAU.sel(time='2002').values[0,:,:],\ + cmap='bwr') +plt.colorbar() + +9. Sometimes xarray plot might show blank, the way to resolve that is select the area and that should work. From f1945f15d84b6518047301ee8e8536988b5ff150 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Feb 2021 20:01:31 -0600 Subject: [PATCH 014/171] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index ba99b0b..fbbd280 100644 --- a/README.md +++ b/README.md @@ -98,3 +98,7 @@ Troubleshooting plt.colorbar() 9. Sometimes xarray plot might show blank, the way to resolve that is select the area and that should work. + +10. Pattern correlation formula: https://www.mdpi.com/2073-4441/10/1/28 may use weights as well for the pattern correlation + +For the weights, the following can be followed: https://stackoverflow.com/questions/58881607/calculating-the-cosine-of-latitude-as-weights-for-gridded-data From d81ecfbe163f77212de62de03f561bcfe19175c1 Mon Sep 17 00:00:00 2001 From: msingh Date: Thu, 25 Feb 2021 08:59:17 +0530 Subject: [PATCH 015/171] added pattern correlation --- xarray_tutorial/pattern_cor.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 xarray_tutorial/pattern_cor.py diff --git a/xarray_tutorial/pattern_cor.py b/xarray_tutorial/pattern_cor.py new file mode 100644 index 0000000..45902ff --- /dev/null +++ b/xarray_tutorial/pattern_cor.py @@ -0,0 +1,32 @@ +def pattern_cor(ds1, ds2): + # Assume latxlon grid + nlat = ds1.lat.values.shape[0] + nlon = ds1.lon.values.shape[0] + weight = np.zeros((nlat, nlon)) + for i_lat in range(nlat): + weight[:,0] = np.cos(ds1.lat.values[i_lat]*(np.pi/180)) + ds1_ = np.multiply(ds1.values[0,:,:],weight) + ds1_sum = np.sum(ds1_.flatten()) + ds1_mean = ds1_sum/(np.sum(weight.flatten())) + + ds2_ = np.multiply(ds2.values[0,:,:],weight) + ds2_sum = np.sum(ds2_.flatten()) + ds2_mean = ds2_sum/(np.sum(weight.flatten())) + + xycov = 0.0 + xanom2 = 0.0 + yanom2 = 0.0 + w = weight + x = ds1.values[0,:,:] + y = ds2.values[0,:,:] + xave = ds1_mean + yave = ds2_mean + print(weight.shape, x.shape, y.shape, xave, yave) + for ml in range(nlat): + for nl in range(nlon): + xycov = xycov + w[ml,nl]*(x[ml,nl]-xave)*(y[ml,nl]-yave) + xanom2 = xanom2 + w[ml,nl]*(x[ml,nl]-xave)**2 + yanom2 = yanom2 + w[ml,nl]*(y[ml,nl]-yave)**2 + + r = xycov/(np.sqrt(xanom2)*np.sqrt(yanom2)) + return r From ad7cb7b562498798dd28573721334ee17ab27831 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 25 Feb 2021 03:37:34 -0600 Subject: [PATCH 016/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index fbbd280..328572b 100644 --- a/README.md +++ b/README.md @@ -69,6 +69,8 @@ https://docs.google.com/document/d/12h9bcIdBPJUFc_fJssJe8hVBzledq2Dtk5-9OpKHbfg/ 49. Homogenous regions India shape files: https://github.com/Cassimsannan/Shapefiles +50. Download CMIP6 data: https://github.com/TaufiqHassan/acccmip6 + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From d305f496dffc7d92eb51f7b39bbcdf5bf303ceeb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 25 Feb 2021 13:47:19 -0600 Subject: [PATCH 017/171] Update README.md --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 328572b..f0263c7 100644 --- a/README.md +++ b/README.md @@ -71,6 +71,12 @@ https://docs.google.com/document/d/12h9bcIdBPJUFc_fJssJe8hVBzledq2Dtk5-9OpKHbfg/ 50. Download CMIP6 data: https://github.com/TaufiqHassan/acccmip6 +51. Download MSWEP data from Google drive: + +Setup rclone: https://www.youtube.com/watch?v=vPs9K_VC-lg + +$ rclone sync -v --exclude 3hourly/ --drive-shared-with-me GoogleDrive:/MSWEP_V280 /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/mswep/. + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 1e532ed34e689056702b08f07a5de82f72865d89 Mon Sep 17 00:00:00 2001 From: CCCR RND Date: Fri, 26 Feb 2021 03:07:12 +0530 Subject: [PATCH 018/171] download cmip6 data --- download_data/download_cmip6.py | 9 +++ .../models_esgf_cmip_historical_pr.txt | 60 +++++++++++++++++++ 2 files changed, 69 insertions(+) create mode 100644 download_data/download_cmip6.py create mode 100644 download_data/models_esgf_cmip_historical_pr.txt diff --git a/download_data/download_cmip6.py b/download_data/download_cmip6.py new file mode 100644 index 0000000..bf17bb9 --- /dev/null +++ b/download_data/download_cmip6.py @@ -0,0 +1,9 @@ +import os +import pandas as pd +#os.system(" acccmip6 -o S -m CanESM5 -e historical -v pr -f day" +models = pd.read_csv("/lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/CMIP6/models_esgf_cmip_historical_pr.txt", header=None).values +print(models.shape) +for i in range(models.shape[0]): + print(models[i,0]) + os.system("/lus/dal/cccr_rnd/manmeet/anaconda3/envs/py36/bin/acccmip6 -o S -m "+models[i,0]+" -e historical -v pr -f day") + os.system("/lus/dal/cccr_rnd/manmeet/anaconda3/envs/py36/bin/acccmip6 -o D -m "+models[i,0]+" -e historical -v pr -f day") diff --git a/download_data/models_esgf_cmip_historical_pr.txt b/download_data/models_esgf_cmip_historical_pr.txt new file mode 100644 index 0000000..2d94b38 --- /dev/null +++ b/download_data/models_esgf_cmip_historical_pr.txt @@ -0,0 +1,60 @@ +ACCESS-CM2 +ACCESS-ESM1-5 +AWI-CM-1-1-MR +AWI-ESM-1-1-LR +BCC-CSM2-MR +BCC-ESM1 +CAMS-CSM1-0 +CESM2 +CESM2-FV2 +CESM2-WACCM +CESM2-WACCM-FV2 +CIESM +CMCC-CM2-HR4 +CMCC-CM2-SR5 +CMCC-ESM2 +CNRM-CM6-1 +CNRM-CM6-1-HR +CNRM-ESM2-1 +CanESM5 +CanESM5-CanOE +E3SM-1-0 +E3SM-1-1 +E3SM-1-1-ECA +EC-Earth3 +EC-Earth3-AerChem +EC-Earth3-CC +EC-Earth3-Veg +EC-Earth3-Veg-LR +FGOALS-f3-L +FGOALS-g3 +FIO-ESM-2-0 +GFDL-CM4 +GFDL-ESM4 +GISS-E2-1-G +GISS-E2-1-H +HadGEM3-GC31-LL +HadGEM3-GC31-MM +IITM-ESM +INM-CM4-8 +INM-CM5-0 +IPSL-CM5A2-INCA +IPSL-CM6A-LR +IPSL-CM6A-LR-INCA +KACE-1-0-G +KIOST-ESM +MCM-UA-1-0 +MIROC-ES2H +MIROC-ES2L +MIROC6 +MPI-ESM-1-2-HAM +MPI-ESM1-2-HR +MPI-ESM1-2-LR +MRI-ESM2-0 +NESM3 +NorCPM1 +NorESM2-LM +NorESM2-MM +SAM0-UNICON +TaiESM1 +UKESM1-0-LL From 3248422c8279cb4f66713715e6534451ecd1b401 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 11 Mar 2021 20:11:31 +0530 Subject: [PATCH 019/171] run jupyter from docker container --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index f0263c7..63c0444 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,13 @@ https://docs.google.com/document/d/12h9bcIdBPJUFc_fJssJe8hVBzledq2Dtk5-9OpKHbfg/ Setup rclone: https://www.youtube.com/watch?v=vPs9K_VC-lg +52. Run jupyter notebook from docker container + +docker run --rm -it --entrypoint bash -p 8890:8890 manmeet3591/drl + +jupyter-notebook --ip 0.0.0.0 -p 8890 --no-browser --allow-root & + + $ rclone sync -v --exclude 3hourly/ --drive-shared-with-me GoogleDrive:/MSWEP_V280 /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/mswep/. Troubleshooting From 9eaccf981b0095d351a1c19b380c464801e55469 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 14 Mar 2021 08:52:29 +0530 Subject: [PATCH 020/171] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 63c0444..d92a489 100644 --- a/README.md +++ b/README.md @@ -117,3 +117,6 @@ plt.colorbar() 10. Pattern correlation formula: https://www.mdpi.com/2073-4441/10/1/28 may use weights as well for the pattern correlation For the weights, the following can be followed: https://stackoverflow.com/questions/58881607/calculating-the-cosine-of-latitude-as-weights-for-gridded-data + +11. When installing packages otherwise difficult to install like ESMF we can set the compiler environment variables such as CC and FC to force conda to install using that particular compiler. This saves a lot of time and effort. https://stackoverflow.com/questions/59284298/conda-install-c-anaconda-gcc-linux-64-not-being-used + From d905c25d8e832530a5ad46eea747c29482c7a381 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 14 Mar 2021 08:52:52 +0530 Subject: [PATCH 021/171] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d92a489..969635e 100644 --- a/README.md +++ b/README.md @@ -118,5 +118,7 @@ plt.colorbar() For the weights, the following can be followed: https://stackoverflow.com/questions/58881607/calculating-the-cosine-of-latitude-as-weights-for-gridded-data -11. When installing packages otherwise difficult to install like ESMF we can set the compiler environment variables such as CC and FC to force conda to install using that particular compiler. This saves a lot of time and effort. https://stackoverflow.com/questions/59284298/conda-install-c-anaconda-gcc-linux-64-not-being-used +11. When installing packages otherwise difficult to install like ESMF we can set the compiler environment variables such as CC and FC to force conda to install using that particular compiler. This saves a lot of time and effort. https://stackoverflow.com/questions/59284298/conda-install-c-anaconda-gcc-linux-64-not-being-used Many build tools such as make and CMake search by default for a compiler named simply gcc, so we set environment variables to point these tools to the correct compiler. + + From 788ef34cf1c461a48a0f242b8fd578ac31aef07e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 18 Mar 2021 09:41:17 +0530 Subject: [PATCH 022/171] Update README.md --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 969635e..e88de1d 100644 --- a/README.md +++ b/README.md @@ -77,10 +77,13 @@ Setup rclone: https://www.youtube.com/watch?v=vPs9K_VC-lg 52. Run jupyter notebook from docker container -docker run --rm -it --entrypoint bash -p 8890:8890 manmeet3591/drl +docker run --rm -it --entrypoint bash -p 8891:8891 manmeet3591/tensortrade -jupyter-notebook --ip 0.0.0.0 -p 8890 --no-browser --allow-root & +Inside the container +jupyter-notebook --ip 0.0.0.0 -p 8891 --no-browser --allow-root & +In the browser +http://localhost:8891/ $ rclone sync -v --exclude 3hourly/ --drive-shared-with-me GoogleDrive:/MSWEP_V280 /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/mswep/. From e34f9f24048d134bf313342668a83c105c9377bf Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 18 Mar 2021 10:24:08 +0530 Subject: [PATCH 023/171] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e88de1d..9a3315b 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ Setup rclone: https://www.youtube.com/watch?v=vPs9K_VC-lg docker run --rm -it --entrypoint bash -p 8891:8891 manmeet3591/tensortrade Inside the container -jupyter-notebook --ip 0.0.0.0 -p 8891 --no-browser --allow-root & +jupyter-notebook --ip 0.0.0.0 --port=8891 --no-browser --allow-root & In the browser http://localhost:8891/ From 1dcb3291bea8fa8eb8000c726aea145d2efa42fe Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 22 Mar 2021 02:27:11 -0500 Subject: [PATCH 024/171] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 9a3315b..44f7403 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,10 @@ http://localhost:8891/ $ rclone sync -v --exclude 3hourly/ --drive-shared-with-me GoogleDrive:/MSWEP_V280 /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/mswep/. +53. Create any number of subplots matplotlib + +$ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'projection': ccrs.PlateCarree()}) + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 4c4cd459dcfd19a5e88602abf3b8a17d22c8bd5a Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 22 Mar 2021 17:39:14 -0500 Subject: [PATCH 025/171] Create download_era5.py --- download_data/download_era5.py | 37 ++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 download_data/download_era5.py diff --git a/download_data/download_era5.py b/download_data/download_era5.py new file mode 100644 index 0000000..affc1fa --- /dev/null +++ b/download_data/download_era5.py @@ -0,0 +1,37 @@ +import cdsapi +import sys + +year=sys.argv[1] +var = sys.argv[2] +c = cdsapi.Client() + +# More complex request + +#c.retrieve("reanalysis-era5-single-levels", { +c.retrieve("reanalysis-era5-land", { + + "product_type": "reanalysis", + + "format": "netcdf", + + + "variable": var, + #"variable": ["Evaporation"], + + + "year": year, + #"year": "2017", + + "month": ["01","02","03","04","05","06","07","08","09","10","11","12"], + + "day": ["01","02","03","04","05","06","07","08","09","10","11", + + "12","13","14","15","16","17","18","19","20","21","22", + + "23","24","25","26","27","28","29","30","31"], + + "time": ["00:00","01:00","02:00","03:00","04:00","05:00","06:00","07:00","08:00","09:00","10:00","11:00", + "12:00", "13:00","14:00","15:00","16:00","17:00","18:00","19:00","20:00","21:00","22:00","23:00"] + + }, "era5-hourly-"+var+"-"+year+".nc") +# }, "output.nc") From 5fc132940429a3f0befd5b959c1a259c57042298 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 22 Mar 2021 17:40:07 -0500 Subject: [PATCH 026/171] Create run_download_era5.sh --- download_data/run_download_era5.sh | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 download_data/run_download_era5.sh diff --git a/download_data/run_download_era5.sh b/download_data/run_download_era5.sh new file mode 100644 index 0000000..9e46475 --- /dev/null +++ b/download_data/run_download_era5.sh @@ -0,0 +1,20 @@ +#!/bin/bash +# +#for year in {1990..2008} +#do +#python download_era5.py $year evaporation_from_vegetation_transpiration +#done +# +#for year in {2010..2020} +#do +#python download_era5.py $year evaporation_from_vegetation_transpiration +#done +# +for year in {1981..2020} +do +python download_era5.py $year total_evaporation +python download_era5.py $year surface_latent_heat_flux +python download_era5.py $year surface_sensible_heat_flux +python download_era5.py $year volumetric_soil_water_layer_1 +python download_era5.py $year forecast_albedo +done From a7490506cfeb65c04582e534204d5b95c5cbea7c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 23 Mar 2021 12:09:43 -0500 Subject: [PATCH 027/171] Create compute_terrestrial_coupling_index.py --- .../compute_terrestrial_coupling_index.py | 25 +++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 xarray_tutorial/compute_terrestrial_coupling_index.py diff --git a/xarray_tutorial/compute_terrestrial_coupling_index.py b/xarray_tutorial/compute_terrestrial_coupling_index.py new file mode 100644 index 0000000..84c8789 --- /dev/null +++ b/xarray_tutorial/compute_terrestrial_coupling_index.py @@ -0,0 +1,25 @@ +# References +# https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL048268 +# http://www.coupling-metrics.com/terrestrial-coupling-index/ + +# Calculate the standard deviation of soil moisture. +# Calculate the slope between soil moisture and surface flux (either sensible or latent) with soil moisture as the independent variable. +# Multiple the standard deviation by the slope returning the terrestrial coupling index with the same units as the surface flux variable. + +def linear_trend(x, y): + if np.sum(np.isnan(x))>0: + pf = np.empty((x.shape[0])) + else: + #idx = np.isfinite(x) & np.isfinite(y) + pf = np.polyfit(x, y, 1) + return xr.DataArray(pf[0]) + +def compute_tci(sm, lh): + sm_std = sm.std(dim='time').plot() + slopes = xr.apply_ufunc(linear_trend, + sm, lh, + vectorize=True, + input_core_dims=[['time'], ['time']],# reduce along 'plev' + ) + tci = slopes*sm_std +#mrsos_control[0].std(dim='time').plot() From f98337da484369f13e23a17591e66f09ecfb9eb6 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 23 Mar 2021 12:10:49 -0500 Subject: [PATCH 028/171] corrected compute_tci function - removed .plot() --- xarray_tutorial/compute_terrestrial_coupling_index.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/xarray_tutorial/compute_terrestrial_coupling_index.py b/xarray_tutorial/compute_terrestrial_coupling_index.py index 84c8789..f755406 100644 --- a/xarray_tutorial/compute_terrestrial_coupling_index.py +++ b/xarray_tutorial/compute_terrestrial_coupling_index.py @@ -15,7 +15,7 @@ def linear_trend(x, y): return xr.DataArray(pf[0]) def compute_tci(sm, lh): - sm_std = sm.std(dim='time').plot() + sm_std = sm.std(dim='time') slopes = xr.apply_ufunc(linear_trend, sm, lh, vectorize=True, From 6a7d988d17737cb01083e0d9857ba623bda85300 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 23 Mar 2021 12:14:28 -0500 Subject: [PATCH 029/171] return tci was missing --- xarray_tutorial/compute_terrestrial_coupling_index.py | 1 + 1 file changed, 1 insertion(+) diff --git a/xarray_tutorial/compute_terrestrial_coupling_index.py b/xarray_tutorial/compute_terrestrial_coupling_index.py index f755406..aeb9749 100644 --- a/xarray_tutorial/compute_terrestrial_coupling_index.py +++ b/xarray_tutorial/compute_terrestrial_coupling_index.py @@ -22,4 +22,5 @@ def compute_tci(sm, lh): input_core_dims=[['time'], ['time']],# reduce along 'plev' ) tci = slopes*sm_std + return tci #mrsos_control[0].std(dim='time').plot() From 6661e3148af7f7646c030dab6f6a0a51e3c56df2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 23 Mar 2021 14:34:12 -0500 Subject: [PATCH 030/171] updated the compute_tci function --- .../compute_terrestrial_coupling_index.py | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/xarray_tutorial/compute_terrestrial_coupling_index.py b/xarray_tutorial/compute_terrestrial_coupling_index.py index aeb9749..8ffaf35 100644 --- a/xarray_tutorial/compute_terrestrial_coupling_index.py +++ b/xarray_tutorial/compute_terrestrial_coupling_index.py @@ -6,21 +6,21 @@ # Calculate the slope between soil moisture and surface flux (either sensible or latent) with soil moisture as the independent variable. # Multiple the standard deviation by the slope returning the terrestrial coupling index with the same units as the surface flux variable. -def linear_trend(x, y): - if np.sum(np.isnan(x))>0: - pf = np.empty((x.shape[0])) - else: - #idx = np.isfinite(x) & np.isfinite(y) - pf = np.polyfit(x, y, 1) - return xr.DataArray(pf[0]) - def compute_tci(sm, lh): + print(sm.shape, lh.shape) sm_std = sm.std(dim='time') - slopes = xr.apply_ufunc(linear_trend, - sm, lh, - vectorize=True, - input_core_dims=[['time'], ['time']],# reduce along 'plev' - ) - tci = slopes*sm_std + slopes = np.zeros_like(sm_std.values) + + for i_tci in range(sm.values.shape[1]): + for j_tci in range(sm.values.shape[2]): + mask = ~np.isnan(sm.values[:,i_tci,j_tci]) & ~np.isnan(lh.values[:,i_tci,j_tci]) + if np.sum(np.isnan(sm.values[:,i_tci,j_tci]))>=sm.values.shape[0]-2: + slope=np.nan + else: + slope, intercept, r_value, p_value, std_err = stats.linregress(sm.values[:,i_tci,j_tci][mask], lh.values[:,i_tci,j_tci][mask]) + slopes[i_tci,j_tci] = slope + + + sm_std['slopes'] = (('lat', 'lon'), slopes) + tci = sm_std.slopes*sm_std return tci -#mrsos_control[0].std(dim='time').plot() From 0a6eb109b2cf28f6c551adb0e958df3699886473 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Mar 2021 23:18:54 -0500 Subject: [PATCH 031/171] Create multiple_subplots.py --- xarray_tutorial/multiple_subplots.py | 29 ++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 xarray_tutorial/multiple_subplots.py diff --git a/xarray_tutorial/multiple_subplots.py b/xarray_tutorial/multiple_subplots.py new file mode 100644 index 0000000..2be771c --- /dev/null +++ b/xarray_tutorial/multiple_subplots.py @@ -0,0 +1,29 @@ +fig,ax = plt.subplots(ncols=3,nrows=3, figsize=(11.69,8.27), subplot_kw={'projection': ccrs.PlateCarree()}) +cnt = 0 +for i in range(3): + for j in range(3): + #print(pr_aer[0].sel(time=)) + tci_control = compute_tci(mrsos_control[cnt].sel(time=mrsos_control[cnt].time.dt.month.isin([5,6, 7, 8,9])), \ + hfls_control[cnt].sel(time=mrsos_control[cnt].time.dt.month.isin([5,6, 7, 8,9]))) + tci_aer = compute_tci(mrsos_aer[cnt].sel(time=mrsos_aer[cnt].time.dt.month.isin([5,6, 7, 8,9])), \ + hfls_aer[cnt].sel(time=mrsos_aer[cnt].time.dt.month.isin([5,6, 7, 8,9]))) +# tci_control = compute_tci(mrsos_control[cnt], hfls_control[cnt]) +# tci_aer = compute_tci(mrsos_aer[cnt], hfls_aer[cnt]) + + diff_ = tci_aer - tci_control + #diff_ = (pr_aer[cnt]-pr_control[cnt]).sel(time=pr_aer[cnt].time.dt.month.isin([5,6, 7, 8,9])).mean(dim='time')*86400 + diff_.sel(lon=slice(60,100),lat=slice(5,40)).plot(ax=ax[i,j], \ + cmap='BrBG', extend='both', vmin=-10, vmax=10) + ax[i,j].coastlines() + ax[i,j].set_title(titles[cnt]) + cnt=cnt+1 + #print(i,j) + if cnt>=6: + break + + +fig.delaxes(ax[2,1]) +fig.delaxes(ax[2,2]) +fig.patch.set_linewidth(10) +fig.patch.set_edgecolor('cornflowerblue') +fig.suptitle('Terrestrial coupling index (Dirmeyer et al 2011) (AER - Control) MJJAS', size=16) From 23aa00290a215c4c559a457ee419a09190dff1ee Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Mar 2021 23:25:13 -0500 Subject: [PATCH 032/171] Create multiple_files_ensemble --- xarray_tutorial/multiple_files_ensemble.py | 92 ++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 xarray_tutorial/multiple_files_ensemble.py diff --git a/xarray_tutorial/multiple_files_ensemble.py b/xarray_tutorial/multiple_files_ensemble.py new file mode 100644 index 0000000..4e4ee21 --- /dev/null +++ b/xarray_tutorial/multiple_files_ensemble.py @@ -0,0 +1,92 @@ +data_dir = '/home/cccr/msingh/phd_data_cmip_account/anthrop/ATM/MON/atm_iitmesm_seasonal_hist_clim_mon_jjas_ensmean.nc' +ds_anthrop_clim = xr.open_dataset(data_dir).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon') + +data_dir = '/home/cccr/msingh/phd_data_cmip_account/no_anthrop/ATM/MON/atm_iitmesm_seasonal_hist_clim_mon_jjas_ensmean.nc' +ds_no_anthrop_clim = xr.open_dataset(data_dir).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon') + + +for i_y,year_ in enumerate(range(1982,2014)): + #lats, late, lons, lone = 14, 28, 74, 87 + print(year_) + year = str(year_) + ds_imd_ = ds_imd.sel(time=slice(year+'-06-01T00:00:00.000000000',year+'-09-30T00:00:00.000000000')).rf.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).mean(dim='lat', skipna=True).mean(dim='lon', skipna=True) + + #ds_imd_.mean(dim='time').plot() + #################################### + data_dir = '/home/cccr/msingh/phd_data_cmip_account/anthrop/ATM/MON/'+str(year_) + filenames_a = glob.glob(data_dir+'/atm_iitmesm_seasonal_hist_*_mon_jjas.nc') + if len(filenames_a)==0: + continue + #print(len(filenames)) + ens_list = [] + for file in filenames_a: + ens_list.append(xr.open_dataset(file).pr.sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon')) + ds_anthrop_ = xr.concat(ens_list, dim='time') + ds_anthrop_anom = ds_anthrop_.values + for i_ens in range(len(filenames_a)): + ds_anthrop_anom[i_ens*4:(i_ens+1)*4] = ds_anthrop_.values[i_ens*4:(i_ens+1)*4] - ds_anthrop_clim.pr.values + #print('check') + + #################################### + + #################################### + data_dir = '/home/cccr/msingh/phd_data_cmip_account/no_anthrop/ATM/MON/'+str(year_) + filenames_na = glob.glob(data_dir+'/atm_iitmesm_seasonal_hist_*_mon_jjas.nc') + if len(filenames_na)==0: + continue + ens_list = [] + #print(len(filenames)) + for file in filenames_na: + ens_list.append(xr.open_dataset(file).pr.sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon')) + ds_no_anthrop_ = xr.concat(ens_list, dim='time') + + ds_no_anthrop_anom = ds_no_anthrop_.values + for i_ens in range(len(filenames_na)): + ds_no_anthrop_anom[i_ens*4:(i_ens+1)*4] = ds_no_anthrop_.values[i_ens*4:(i_ens+1)*4] - ds_no_anthrop_clim.pr.values + + #################################### + + #print(np.sum(np.isnan(ds_imd_.values[0,:,:])), ds_imd_.values[0,:,:].shape[0]* ds_imd_.values[0,:,:].shape[1]) + ds_imd_o = ds_imd_ + ds_imd_jjas_o = ds_imd_o.resample(time='m').mean() + #print(np.sum(np.isnan(ds_imd_jjas_o.values[0,:,:])), ds_imd_jjas_o.values[0,:,:].shape[0]* ds_imd_jjas_o.values[0,:,:].shape[1]) + #lats, late, lons, lone = 14, 28, 74, 87 + ds_anthrop_mt = ds_anthrop_anom#.sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)) + ds_no_anthrop_mt = ds_no_anthrop_anom#.sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)) + #print(lats,late,lons,lone) + #print(ds_anthrop_anom.shape, ds_no_anthrop_anom.shape) + ds_imd_mt = ds_imd_jjas_o#.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)) + #print(ds_imd_mt) + #print(np.sum(np.isnan(ds_imd_mt.values[0,:,:])), ds_imd_mt.values[0,:,:].shape[0]* ds_imd_mt.values[0,:,:].shape[1]) + #print(ds_imd_mt.values[0,0,0], ds_anthrop_mt[0,0,0]) - this is fine + + ds_imd_mt_rep_anthrop = np.repeat([ds_imd_mt.values], [len(filenames_a)], axis=0) + ds_imd_mt_rep_anthrop_ = ds_anthrop_mt.copy()#.values + #print(ds_imd_mt.values[0], ds_anthrop_mt[0]) + + for i_rep in range(len(filenames_a)): + ds_imd_mt_rep_anthrop_[i_rep*4:(i_rep+1)*4,] = ds_imd_mt_rep_anthrop[i_rep,:] + + #print(ds_imd_mt.values[0,0,0], ds_anthrop_mt[0,0,0]) + ds_imd_mt_rep_no_anthrop = np.repeat([ds_imd_mt.values], [len(filenames_na)], axis=0) + ds_imd_mt_rep_no_anthrop_ = ds_no_anthrop_mt.copy()#.values + for i_rep in range(len(filenames_na)): + ds_imd_mt_rep_no_anthrop_[i_rep*4:(i_rep+1)*4, ] = ds_imd_mt_rep_no_anthrop[i_rep,:] + + #print(ds_imd_mt.values[0,0,0], ds_anthrop_mt[0,0,0]) + + #print(y_actual, y_predicted) + y_actual = ds_imd_mt_rep_anthrop_.flatten()# ds_imd_mt.values.flatten() + y_predicted = ds_anthrop_mt.flatten()*86400 + #print(y_actual.shape, y_predicted.shape) + corr_anthrop = ma.corrcoef(ma.masked_invalid(y_actual), ma.masked_invalid(y_predicted)) + + y_actual = ds_imd_mt_rep_no_anthrop_.flatten()# ds_imd_mt.values.flatten() + y_predicted = ds_no_anthrop_mt.flatten()*86400 + #print(y_actual.shape, y_predicted.shape) + corr_no_anthrop = ma.corrcoef(ma.masked_invalid(y_actual), ma.masked_invalid(y_predicted)) + + + + corr_anthrop_[i_y] = corr_anthrop[0,1] + corr_no_anthrop_[i_y] = corr_no_anthrop[0,1] From 625bbeda34faa8f185a64527d2998acbe0908bcb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Mar 2021 23:25:52 -0500 Subject: [PATCH 033/171] Create regrid_xesmf --- xarray_tutorial/regrid_xesmf.py | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 xarray_tutorial/regrid_xesmf.py diff --git a/xarray_tutorial/regrid_xesmf.py b/xarray_tutorial/regrid_xesmf.py new file mode 100644 index 0000000..a419626 --- /dev/null +++ b/xarray_tutorial/regrid_xesmf.py @@ -0,0 +1,7 @@ +ds_out = xr.Dataset({'lat': (['lat'], ds_anthrop.lat.values), + 'lon': (['lon'], ds_anthrop.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_) From e85127e1eede91b7cc5213070403cb3a6100905a Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 24 Mar 2021 23:47:08 -0500 Subject: [PATCH 034/171] Create stipple_significant.py --- xarray_tutorial/stipple_significant.py | 29 ++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 xarray_tutorial/stipple_significant.py diff --git a/xarray_tutorial/stipple_significant.py b/xarray_tutorial/stipple_significant.py new file mode 100644 index 0000000..b9db2ad --- /dev/null +++ b/xarray_tutorial/stipple_significant.py @@ -0,0 +1,29 @@ +fig = plt.figure(figsize=(11,8)) +ax = fig.add_axes([0.1,0.1,0.8,0.8],projection=ccrs.PlateCarree()) +a = ds_merra2_mam.dust_trend_mam.sel(lon=slice(60,100), lat=slice(5,40)).plot(ax=ax, \ + vmin=-0.01,\ + vmax=0.01,extend='both', \ + cmap='twilight_shifted') +ds_merra2_mam['dust_p_value_manm_'] = ds_merra2_mam['dust_p_value_mam']<0.05 +#ds_merra2_mam['dust_p_value_manm_'].sel(lon=slice(60,100), lat=slice(5,40)).plot.scatter(ax=ax) + +[m,n] = np.where(ds_merra2_mam['dust_p_value_mam'].sel(lon=slice(60,100), lat=slice(5,40)).values<0.05 ) +z1 = np.zeros(ds_merra2_mam['dust_p_value_mam'].sel(lon=slice(60,100), lat=slice(5,40)).values.shape) +z1[m, n] = 99 +x = ds_merra2_mam.sel(lon=slice(60,100), lat=slice(5,40)).lon.values +y = ds_merra2_mam.sel(lon=slice(60,100), lat=slice(5,40)).lat.values + +cs = ax.contourf(x, y, z1 ,1 , hatches=['', '..'], alpha=0) + +ax.coastlines(resolution='10m') +plt.title('Dust AOD(550nm) MAM trend from MERRA2 reanalysis 2002-2020') +ax_ = [ax] +for i in range(1): + gl = ax_[i].gridlines() + gl.xlabels_bottom = True + gl.ylabels_left = True + gl.xformatter = LONGITUDE_FORMATTER + gl.yformatter = LATITUDE_FORMATTER + gl.xlabel_style = {'size': 'medium'} + gl.ylabel_style = {'size': 'medium'} +#plt.savefig('merra2_mam_dust_22_34n_65_90e_2002_2014_spatial_reproduce_vinoj.png') From 7f8926bbff14a07539cd36153ef7b7ec8fe98fdb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 25 Mar 2021 01:19:18 -0500 Subject: [PATCH 035/171] update multiple_subplots.py --- xarray_tutorial/multiple_subplots.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/xarray_tutorial/multiple_subplots.py b/xarray_tutorial/multiple_subplots.py index 2be771c..252e873 100644 --- a/xarray_tutorial/multiple_subplots.py +++ b/xarray_tutorial/multiple_subplots.py @@ -27,3 +27,28 @@ fig.patch.set_linewidth(10) fig.patch.set_edgecolor('cornflowerblue') fig.suptitle('Terrestrial coupling index (Dirmeyer et al 2011) (AER - Control) MJJAS', size=16) + + + +############### Also + +data = [ds_trmm.r.sel(lon=slice(0,150), lat=slice(-20,60)).mean(dim='time'), \ + ds_anthrop.sel(lon=slice(0,150), lat=slice(60,-20)).mean(dim='time'), \ + ds_no_anthrop.sel(lon=slice(0,150), lat=slice(60,-20)).mean(dim='time')] + +fig,ax = plt.subplots(ncols=2,nrows=3, figsize=(11.69,8.27), subplot_kw={'projection': ccrs.PlateCarree()}) +fig.delaxes(ax[1,1]) +#ax_ = [ax1, ax2, ax3, ax4] +tit_ = ['TRMM', 'Anthrop', 'No-Anthrop'] +cnt=0 +for i_fig in range(2): + for j_fig in range(2): + if cnt>2: + break + data[cnt].plot(ax=ax[i_fig, j_fig], cmap='BrBG', vmin=0, vmax=15) + ax[i_fig, j_fig].set_title(tit_[cnt]) + ax[i_fig,j_fig].coastlines() + cnt=cnt+1 + +fig.patch.set_linewidth(10) +fig.patch.set_edgecolor('cornflowerblue') From 48bfc2793830247452c131ac12478b38fe9de102 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 26 Mar 2021 15:10:20 -0500 Subject: [PATCH 036/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 44f7403..3423d38 100644 --- a/README.md +++ b/README.md @@ -91,6 +91,8 @@ $ rclone sync -v --exclude 3hourly/ --drive-shared-with-me GoogleDrive:/MSWEP_V2 $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'projection': ccrs.PlateCarree()}) +54. Google Earth Engine timelapse gif generator: https://9611d0317f71.ngrok.io/voila/render/timelapse.ipynb + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 1f7055611b133647aecbf6fbeecac1502e6a7b69 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 28 Mar 2021 09:43:58 -0500 Subject: [PATCH 037/171] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 3423d38..15135e5 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,10 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 54. Google Earth Engine timelapse gif generator: https://9611d0317f71.ngrok.io/voila/render/timelapse.ipynb +55. Handling expver dimension in a netcdf file downloaded as ERA5 data + + ds.reduce(np.nansum, 'expver') Solution from marco venturini https://confluence.ecmwf.int/pages/viewpage.action?pageId=173385064 + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 0811a4c9db893c63e52d242dc38fed92dad3c235 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 31 Mar 2021 21:46:43 -0500 Subject: [PATCH 038/171] Create active_break.py --- xarray_tutorial/active_break.py | 205 ++++++++++++++++++++++++++++++++ 1 file changed, 205 insertions(+) create mode 100644 xarray_tutorial/active_break.py diff --git a/xarray_tutorial/active_break.py b/xarray_tutorial/active_break.py new file mode 100644 index 0000000..aa55993 --- /dev/null +++ b/xarray_tutorial/active_break.py @@ -0,0 +1,205 @@ +import xarray as xr +import numpy as np +import matplotlib.pyplot as plt +import random +from scipy import stats +from datetime import datetime, timedelta +import cartopy.crs as ccrs +from matplotlib.axes import Axes +from cartopy.mpl.geoaxes import GeoAxes +import cartopy.io.shapereader as shapereader +from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER +GeoAxes._pcolormesh_patched = Axes.pcolormesh +# fname = '/home/cccr/msingh/phd_obj2_data/ensemble_data/Admin2.shp' +# adm1_shapes = list(shapereader.Reader(fname).geometries()) +# %matplotlib inline +import warnings +warnings.filterwarnings("ignore") +import xesmf as xe +from sklearn.metrics import mean_squared_error +import os +import matplotlib.pyplot as plt +import numpy.ma as ma +import seaborn as sns +import pandas as pd +import glob +import matplotlib.pyplot as plt + +lats, late, lons, lone = 14, 28, 74, 87 +ds_anthrop_pr = [] +ds_no_anthrop_pr = [] +for i_y,year_ in enumerate(range(1982,2014)): + print(year_) +#################################################################################### + data_dir = '/home/cccr/msingh/phd_data_cmip_account/anthrop/ATM/DAY/'+str(year_) + filenames_a = glob.glob(data_dir+'/atm_*_day.nc') + + if len(filenames_a)==0: + continue + ens_list = [] + for file in filenames_a: + + #print(file) + + data_ = xr.open_dataset(file).pr.sel(lat=slice(late,lats))\ + .sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon') + datetimeindex = data_.indexes['time'].to_datetimeindex() + data_['time'] = datetimeindex + if data_.sel(time=slice(str(year_)+'-05-20', str(year_)+'-10-15')).time.shape[0]==148: + ens_list.append(data_.sel(time=slice(str(year_)+'-05-20', str(year_)+'-10-15'))) + #print('test') + if len(filenames_a)>0: + ds_anthrop_pr_ = xr.concat(ens_list, dim='ens').mean(dim='ens') + ds_anthrop_pr.append(ds_anthrop_pr_) +###################################################################################### + data_dir = '/home/cccr/msingh/phd_data_cmip_account/no_anthrop/ATM/DAY/'+str(year_) + filenames_a = glob.glob(data_dir+'/atm_*_day.nc') + if len(filenames_a)==0: + continue + ens_list = [] + #print(filenames_a) + for file in filenames_a: + #ens_list = [] + #print(file) + data_ = xr.open_dataset(file).pr.sel(lat=slice(late,lats))\ + .sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon') + datetimeindex = data_.indexes['time'].to_datetimeindex() + data_['time'] = datetimeindex + if data_.sel(time=slice(str(year_)+'-05-20', str(year_)+'-10-15')).time.shape[0]==148: + ens_list.append(data_.sel(time=slice(str(year_)+'-05-20', str(year_)+'-10-15'))) + if len(filenames_a)>0: + ds_no_anthrop_pr_ = xr.concat(ens_list, dim='ens').mean(dim='ens') + ds_no_anthrop_pr.append(ds_no_anthrop_pr_) +###################################################################################### +ds_anthrop = xr.concat(ds_anthrop_pr, dim='time') +ds_no_anthrop = xr.concat(ds_no_anthrop_pr, dim='time') + +data_dir = '/home/cccr/msingh/data/IMD/imd-dly-p25-1901-2019.nc' +ds_imd = xr.open_dataset(data_dir) +ds_imd['time'] = ds_imd.indexes['time'].normalize() +ds_anthrop['time'] = ds_anthrop.indexes['time'].normalize() +ds_no_anthrop['time'] = ds_no_anthrop.indexes['time'].normalize() +ds_imd_ = ds_imd.sel(time=ds_anthrop.time).sel(lat=slice(lats,late)).\ + sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon') +ds_anthrop_ = ds_anthrop*86400 +ds_no_anthrop_ = ds_no_anthrop*86400 + +imd_active = np.zeros((2014-1982)) +anthrop_active = np.zeros((2014-1982)) +no_anthrop_active = np.zeros((2014-1982)) + +imd_break = np.zeros((2014-1982)) +anthrop_break = np.zeros((2014-1982)) +no_anthrop_break= np.zeros((2014-1982)) +years = np.arange(1982,2014) +months=[6,7,8,9] +print(years.shape) + +ds_imd_active= ds_imd_.rf.where(ds_imd_.rf>ds_imd_.rf.mean(dim='time')+ds_imd_.rf.std(dim='time'), drop=True) +ds_imd_active_ja = ds_imd_active.time.sel(time=ds_imd_active.time.dt.month.isin(months)) +#print(ds_imd_active_ja.time) +cnt=0 +for year in range(1982,2014): + #print(ds_imd_active_ja.time.sel(time=ds_imd_active_ja.time.dt.year.isin([year])).values) + #print(cnt) + imd_active[cnt] = ds_imd_active_ja.time.sel(time=ds_imd_active_ja.time.dt.year.isin([year])).values.shape[0] + cnt=cnt+1 +#np.savetxt('imd_active.txt',ds_imd_active.time.sel(time=ds_imd_active.time.dt.year.isin([1982])).values ) + +ds_anthrop_active= ds_anthrop_.where(ds_anthrop_>ds_anthrop_.mean(dim='time')+ds_anthrop_.std(dim='time'), drop=True) + +ds_anthrop_active_ja = ds_anthrop_active.time.sel(time=ds_anthrop_active.time.dt.month.isin(months)) +cnt=0 +for year in range(1982,2014): + #print(ds_anthrop_active_ja.time.sel(time=ds_anthrop_active_ja.time.dt.year.isin([year])).values) + anthrop_active[cnt] = ds_anthrop_active_ja.time.sel(time=ds_anthrop_active_ja.time.dt.year.isin([year])).values.shape[0] + cnt=cnt+1 +#ds_anthrop_.mean(dim='time') + +ds_no_anthrop_active= ds_anthrop_.where(ds_no_anthrop_>ds_no_anthrop_.mean(dim='time')+ds_no_anthrop_.std(dim='time'), drop=True) + +ds_no_anthrop_active_ja = ds_no_anthrop_active.time.sel(time=ds_no_anthrop_active.time.dt.month.isin(months)) +cnt=0 +for year in range(1982,2014): + #print(ds_no_anthrop_active_ja.time.sel(time=ds_no_anthrop_active_ja.time.dt.year.isin([year])).values) + no_anthrop_active[cnt] = ds_no_anthrop_active_ja.time.sel(time=ds_no_anthrop_active_ja.time.dt.year.isin([year])).values.shape[0] + cnt=cnt+1 +#ds_anthrop_.mean(dim='time') + +ds_imd_break= ds_imd_.rf.where(ds_imd_.rf Date: Thu, 1 Apr 2021 02:24:51 -0500 Subject: [PATCH 039/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 15135e5..5647fa5 100644 --- a/README.md +++ b/README.md @@ -133,5 +133,7 @@ For the weights, the following can be followed: https://stackoverflow.com/questi 11. When installing packages otherwise difficult to install like ESMF we can set the compiler environment variables such as CC and FC to force conda to install using that particular compiler. This saves a lot of time and effort. https://stackoverflow.com/questions/59284298/conda-install-c-anaconda-gcc-linux-64-not-being-used Many build tools such as make and CMake search by default for a compiler named simply gcc, so we set environment variables to point these tools to the correct compiler. +12. When using the isin function with sel we can at present use it only once in a call. Need to instantiate a new variable for doing it twice. + From dd873832a3c63f725a54e9f4d467921cc3cdfa36 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 1 Apr 2021 05:19:54 -0500 Subject: [PATCH 040/171] Create taylor_diagram.py --- xarray_tutorial/taylor_diagram.py | 266 ++++++++++++++++++++++++++++++ 1 file changed, 266 insertions(+) create mode 100644 xarray_tutorial/taylor_diagram.py diff --git a/xarray_tutorial/taylor_diagram.py b/xarray_tutorial/taylor_diagram.py new file mode 100644 index 0000000..8be8c6b --- /dev/null +++ b/xarray_tutorial/taylor_diagram.py @@ -0,0 +1,266 @@ +import xarray as xr +import numpy as np +import matplotlib.pyplot as plt +import random +from scipy import stats +from datetime import datetime, timedelta +import cartopy.crs as ccrs +from matplotlib.axes import Axes +from cartopy.mpl.geoaxes import GeoAxes +import cartopy.io.shapereader as shapereader +from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER +GeoAxes._pcolormesh_patched = Axes.pcolormesh +# fname = '/home/cccr/msingh/phd_obj2_data/ensemble_data/Admin2.shp' +# adm1_shapes = list(shapereader.Reader(fname).geometries()) +# %matplotlib inline +import warnings +warnings.filterwarnings("ignore") +import xesmf as xe +from sklearn.metrics import mean_squared_error +import os +import matplotlib.pyplot as plt +import numpy.ma as ma +import seaborn as sns +import pandas as pd +import glob +import matplotlib.pyplot as plt +import skill_metrics as sm + +data_dir = '/lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/CMIP6/CMIP6/' +ds_ipsl = xr.open_dataset(data_dir+'pr_day_IPSL-CM6A-LR_historical_r11i1p1f1_gr_18500101-20141231.nc').pr.resample(time="1MS").mean(dim="time") + +ds_miroc6 = xr.open_mfdataset(data_dir+'pr_day_MIROC6_historical_r1i1p1f1_gn_*.nc', concat_dim='time')\ + .pr.resample(time="1MS").mean(dim="time") + +ds_noresm2 = xr.open_mfdataset(data_dir+'pr_day_NorESM2-LM_historical_r1i1p1f1_gn_*.nc', concat_dim='time')\ + .pr.resample(time="1MS").mean(dim="time") + +ds_ukesm1 = xr.open_mfdataset(data_dir+'pr_day_UKESM1-0-LL_historical_r1i1p1f2_gn_1*.nc', concat_dim='time')\ + .pr.resample(time="1MS").mean(dim="time") + +ds_mpiesm = xr.open_mfdataset(data_dir+'pr_day_MPI-ESM1-2-HR_historical_r1i1p1f1_gn_*.nc', concat_dim='time')\ + .pr.resample(time="1MS").mean(dim="time") + +ds_cnrm = xr.open_mfdataset(data_dir+'pr_day_CNRM-ESM2-1_historical_r1i1p1f2_gr_1*.nc', concat_dim='time')\ + .pr.resample(time="1MS").mean(dim="time") + +data_dir = '/lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/IMD/imd-dly-p25-1901-2019.nc' +ds_imd = xr.open_dataset(data_dir).rf.resample(time="1MS").mean(dim="time") + +lats, late, lons, lone = 14, 28, 74, 87 +months = [6,7,8,9] + +ds_ipsl_jjas_mt_ = ds_ipsl.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_ipsl.time.dt.year.isin(range(1901, 2015)))#.mean(dim='lat').mean(dim='lon') + +ds_ipsl_jjas_mt = ds_ipsl_jjas_mt_.sel(time=ds_ipsl_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + +ds_miroc6_jjas_mt_ = ds_miroc6.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_miroc6.time.dt.year.isin(range(1901, 2015))).mean(dim='lat').mean(dim='lon') + +ds_miroc6_jjas_mt = ds_miroc6_jjas_mt_.sel(time=ds_miroc6_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + +ds_noresm2_jjas_mt_ = ds_noresm2.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_noresm2.time.dt.year.isin(range(1901, 2015))).mean(dim='lat').mean(dim='lon') + +ds_noresm2_jjas_mt = ds_noresm2_jjas_mt_.sel(time=ds_noresm2_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + +ds_ukesm1_jjas_mt_ = ds_ukesm1.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_ukesm1.time.dt.year.isin(range(1901, 2015))).mean(dim='lat').mean(dim='lon') + +ds_ukesm1_jjas_mt = ds_ukesm1_jjas_mt_.sel(time=ds_ukesm1_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + +ds_mpiesm_jjas_mt_ = ds_mpiesm.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_mpiesm.time.dt.year.isin(range(1901, 2015))).mean(dim='lat').mean(dim='lon') + +ds_mpiesm_jjas_mt = ds_mpiesm_jjas_mt_.sel(time=ds_mpiesm_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + +ds_cnrm_jjas_mt_ = ds_cnrm.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_cnrm.time.dt.year.isin(range(1901, 2015))).mean(dim='lat').mean(dim='lon') + +ds_cnrm_jjas_mt = ds_cnrm_jjas_mt_.sel(time=ds_cnrm_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))).mean(dim='lat').mean(dim='lon') + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time').values*86400 + + +year = np.arange(1901,2015) + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))) + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + + +ds_ipsl_jjas_mt = ds_ipsl_jjas_mt_.sel(time=ds_ipsl_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_out = xr.Dataset({'lat': (['lat'], ds_ipsl_jjas_mt.lat.values), + 'lon': (['lon'], ds_ipsl_jjas_mt.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_jjas_mt, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_jjas_mt) + +print(np.corrcoef(ds_imd_o.mean(dim='year').values.flatten(), ds_ipsl_jjas_mt.mean(dim='year').values.flatten()),\ + np.std(ds_ipsl_jjas_mt.mean(dim='year').values.flatten())*86400,\ + centered_rms_dev(ds_ipsl_jjas_mt.mean(dim='year').values.flatten(),ds_imd_o.mean(dim='year').values.flatten())) + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))) + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_miroc6_jjas_mt_ = ds_miroc6.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_miroc6.time.dt.year.isin(range(1901, 2015))) + + +ds_miroc6_jjas_mt = ds_miroc6_jjas_mt_.sel(time=ds_miroc6_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_out = xr.Dataset({'lat': (['lat'], ds_miroc6_jjas_mt.lat.values), + 'lon': (['lon'], ds_miroc6_jjas_mt.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_jjas_mt, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_jjas_mt) + +print(np.corrcoef(ds_imd_o.mean(dim='year').values.flatten(), ds_miroc6_jjas_mt.mean(dim='year').values.flatten()),\ + np.std(ds_miroc6_jjas_mt.mean(dim='year').values.flatten())*86400,\ + centered_rms_dev(ds_miroc6_jjas_mt.mean(dim='year').values.flatten(),ds_imd_o.mean(dim='year').values.flatten())) + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))) + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_noresm2_jjas_mt_ = ds_noresm2.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_noresm2.time.dt.year.isin(range(1901, 2015))) + + +ds_noresm2_jjas_mt = ds_noresm2_jjas_mt_.sel(time=ds_noresm2_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_out = xr.Dataset({'lat': (['lat'], ds_noresm2_jjas_mt.lat.values), + 'lon': (['lon'], ds_noresm2_jjas_mt.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_jjas_mt, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_jjas_mt) + +print(np.corrcoef(ds_imd_o.mean(dim='year').values.flatten(), ds_noresm2_jjas_mt.mean(dim='year').values.flatten()),\ + np.std(ds_noresm2_jjas_mt.mean(dim='year').values.flatten())*86400,\ + centered_rms_dev(ds_noresm2_jjas_mt.mean(dim='year').values.flatten(),ds_imd_o.mean(dim='year').values.flatten())) + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))) + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_ukesm1_jjas_mt_ = ds_ukesm1.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_ukesm1.time.dt.year.isin(range(1901, 2015))) + + +ds_ukesm1_jjas_mt = ds_ukesm1_jjas_mt_.sel(time=ds_ukesm1_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_out = xr.Dataset({'lat': (['lat'], ds_ukesm1_jjas_mt.lat.values), + 'lon': (['lon'], ds_ukesm1_jjas_mt.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_jjas_mt, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_jjas_mt) + +print(np.corrcoef(ds_imd_o.mean(dim='year').values.flatten(), ds_ukesm1_jjas_mt.mean(dim='year').values.flatten()),\ + np.std(ds_ukesm1_jjas_mt.mean(dim='year').values.flatten())*86400,\ + centered_rms_dev(ds_ukesm1_jjas_mt.mean(dim='year').values.flatten(),ds_imd_o.mean(dim='year').values.flatten())) + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))) + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_mpiesm_jjas_mt_ = ds_mpiesm.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_mpiesm.time.dt.year.isin(range(1901, 2015))) + + +ds_mpiesm_jjas_mt = ds_mpiesm_jjas_mt_.sel(time=ds_mpiesm_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_out = xr.Dataset({'lat': (['lat'], ds_mpiesm_jjas_mt.lat.values), + 'lon': (['lon'], ds_mpiesm_jjas_mt.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_jjas_mt, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_jjas_mt) + +print(np.corrcoef(ds_imd_o.mean(dim='year').values.flatten(), ds_mpiesm_jjas_mt.mean(dim='year').values.flatten()),\ + np.std(ds_mpiesm_jjas_mt.mean(dim='year').values.flatten())*86400,\ + centered_rms_dev(ds_mpiesm_jjas_mt.mean(dim='year').values.flatten(),ds_imd_o.mean(dim='year').values.flatten())) + +ds_imd_jjas_mt_ = ds_imd.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_imd.time.dt.year.isin(range(1901, 2015))) + +ds_imd_jjas_mt = ds_imd_jjas_mt_.sel(time=ds_imd_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_cnrm_jjas_mt_ = ds_cnrm.sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).\ +sel(time=ds_cnrm.time.dt.year.isin(range(1901, 2015))) + + +ds_cnrm_jjas_mt = ds_cnrm_jjas_mt_.sel(time=ds_cnrm_jjas_mt_.time.dt.month.isin(months))\ +.groupby('time.year').sum(dim='time') + +ds_out = xr.Dataset({'lat': (['lat'], ds_cnrm_jjas_mt.lat.values), + 'lon': (['lon'], ds_cnrm_jjas_mt.lon.values), + } + ) +regridder = xe.Regridder(ds_imd_jjas_mt, ds_out, 'bilinear') +#regridder.clean_weight_file() +ds_imd_o = regridder(ds_imd_jjas_mt) + +print(np.corrcoef(ds_imd_o.mean(dim='year').values.flatten(), ds_cnrm_jjas_mt.mean(dim='year').values.flatten()),\ + np.std(ds_cnrm_jjas_mt.mean(dim='year').values.flatten())*86400,\ + centered_rms_dev(ds_cnrm_jjas_mt.mean(dim='year').values.flatten(),ds_imd_o.mean(dim='year').values.flatten())) + +print(np.std(ds_imd_o.mean(dim='year').values.flatten())) + +ccoef = np.array([1.0, 0.14, 0.29, -0.14, 0.35, -0.3, 0.43]) +sdev = np.array([14.78, 9.60, 14.87, 9.87, 7.86, 13.43, 16.09]) +crmsd = np.array([0.0, 12.62, 14.78,13.55, 14.99, 13.84, 14.78, ]) + + # Specify labels for points in a cell array (M1 for model prediction 1, + # etc.). Note that a label needs to be specified for the reference even + # though it is not used. +label = ['Obs', 'IPSL', 'MIROC6', 'NORESM2', 'UKESM1', 'MPIESM', 'CNRM'] + +plt.figure(figsize=(12,8)) +sm.taylor_diagram(sdev,crmsd,ccoef, markerLabel = label, + markerLabelColor = 'r', + markerColor = 'r', markerLegend = 'on', + tickRMS = range(0,20,4), + colRMS = 'm', styleRMS = ':', widthRMS = 2.0, + titleRMS = 'on', titleRMSDangle = 40.0, tickSTD = range(0,20,4), + axismax = 20.0, colSTD = 'b', styleSTD = '-.', + widthSTD = 1.0, titleSTD = 'on', + colCOR = 'k', styleCOR = '--', widthCOR = 1.0, + titleCOR = 'on', markerSize = 10, alpha = 0.0) From 05e96bca4ad6fe3c859220ef58d40e75e68deb0a Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 01:35:25 -0500 Subject: [PATCH 041/171] Get data from GEE to netcdf --- xarray_tutorial/read_gee2nc.py | 49 ++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 xarray_tutorial/read_gee2nc.py diff --git a/xarray_tutorial/read_gee2nc.py b/xarray_tutorial/read_gee2nc.py new file mode 100644 index 0000000..ad35b9d --- /dev/null +++ b/xarray_tutorial/read_gee2nc.py @@ -0,0 +1,49 @@ +import ee +import numpy as np +ee.Initialize() +import xarray as xr +data_dir = '/home/cccr/msingh/data/merra_aod_global_1980_2020/MERRA2_100.tavg1_2d_aer_Nx.1981_monmean.nc4' +import sys +dates = sys.argv[1]# '2020-03-23' +datee = sys.argv[2]# '2020-04-24' +ds_merra2 = xr.open_dataset(data_dir) +collection_evi = ee.ImageCollection('MODIS/MYD09GA_006_EVI').select('EVI').filterDate(dates,datee ).mean() +collection_ndsi = ee.ImageCollection('MODIS/MYD09GA_006_NDSI').select('NDSI').filterDate(dates,datee ).mean() +collection_ndwi = ee.ImageCollection('MODIS/MYD09GA_006_NDWI').select('NDWI').filterDate(dates,datee ).mean() +collection_ndvi = ee.ImageCollection('MODIS/MYD09GA_006_NDVI').select('NDVI').filterDate(dates,datee ).mean() +#p = ee.Geometry.Point(32.3, 40.3) +lats = ds_merra2.lat.values +lons = ds_merra2.lon.values +print(lats.shape, lons.shape, ds_merra2.TOTEXTTAU.values.shape) +evi = np.zeros((lats.shape[0], lons.shape[0])) +ndsi = np.zeros((lats.shape[0], lons.shape[0])) +ndwi = np.zeros((lats.shape[0], lons.shape[0])) +ndvi = np.zeros((lats.shape[0], lons.shape[0])) + +#data = collection_evi.reduceRegion(ee.Reducer.first(),p,50000).get("EVI")# 0.5 degree = 50km =50000 +#dataN = ee.Number(data) +#print(type(dataN.getInfo())) +#print(dataN.getInfo()) +for i_lat in range(lats.shape[0]): + for j_lon in range(lons.shape[0]): + p = ee.Geometry.Point(lons[j_lon], lats[i_lat]) #p = ee.Geometry.Point([lon, lat]) + data = collection_evi.reduceRegion(ee.Reducer.first(),p,50000).get("EVI")# 0.5 degree = 50km =50000 + evi[i_lat, j_lon] = ee.Number(data).getInfo() + data = collection_ndsi.reduceRegion(ee.Reducer.first(),p,50000).get("NDSI")# 0.5 degree = 50km =50000 + ndsi[i_lat, j_lon] = ee.Number(data).getInfo() + data = collection_ndwi.reduceRegion(ee.Reducer.first(),p,50000).get("NDWI")# 0.5 degree = 50km =50000 + ndwi[i_lat, j_lon] = ee.Number(data).getInfo() + data = collection_ndvi.reduceRegion(ee.Reducer.first(),p,50000).get("NDVI")# 0.5 degree = 50km =50000 + ndvi[i_lat, j_lon] = ee.Number(data).getInfo() + print(lats[i_lat], lons[j_lon]) +# evi[] print(dataN.getInfo()) + +ds_merra2['evi'] = (('lat', 'lon'), evi) +ds_merra2['ndsi'] = (('lat', 'lon'), ndsi) +ds_merra2['ndwi'] = (('lat', 'lon'), ndwi) +ds_merra2['ndvi'] = (('lat', 'lon'), ndvi) + +ds_merra2.evi.to_netcdf('evi.nc') +ds_merra2.ndsi.to_netcdf('ndsi.nc') +ds_merra2.ndwi.to_netcdf('ndwi.nc') +ds_merra2.ndvi.to_netcdf('ndvi.nc') From 5853fff5691d60734df48b446087c6c6f57717f5 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 16:38:06 -0500 Subject: [PATCH 042/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 5647fa5..da5dd1d 100644 --- a/README.md +++ b/README.md @@ -96,6 +96,8 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 55. Handling expver dimension in a netcdf file downloaded as ERA5 data ds.reduce(np.nansum, 'expver') Solution from marco venturini https://confluence.ecmwf.int/pages/viewpage.action?pageId=173385064 + + 56. GeoTIFF to netcdf and exporting data from Google Earth Engine https://medium.com/@wenzhao.li1989/nco-translate-geotiff-files-exported-from-gee-to-a-netcdf-file-with-correct-time-dimension-ce97a8f3043f Troubleshooting From c88c071725bc99639325378ca1bd45fb8942fad0 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 19:34:08 -0500 Subject: [PATCH 043/171] create a dataarray --- xarray_tutorial/create_xarray_dataarray.py | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 xarray_tutorial/create_xarray_dataarray.py diff --git a/xarray_tutorial/create_xarray_dataarray.py b/xarray_tutorial/create_xarray_dataarray.py new file mode 100644 index 0000000..9405448 --- /dev/null +++ b/xarray_tutorial/create_xarray_dataarray.py @@ -0,0 +1,8 @@ +import numpy as np +import xarray as xr +import pandas as pd +times = pd.date_range("2000-01-01", periods=1) +data = np.random.rand(1, 4, 3) +lats = np.arange(0,4,1) +lons = np.arange(0,3,1) +foo = xr.DataArray(data, coords=[times, lats, lons], dims=["time", "lat", "lon"]) From 53796da1f7e39f258f6d96c1122755717ee0b670 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 19:35:38 -0500 Subject: [PATCH 044/171] create download_daily_modis_gee.py --- download_data/download_daily_modis_gee.py | 40 +++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 download_data/download_daily_modis_gee.py diff --git a/download_data/download_daily_modis_gee.py b/download_data/download_daily_modis_gee.py new file mode 100644 index 0000000..3285f1f --- /dev/null +++ b/download_data/download_daily_modis_gee.py @@ -0,0 +1,40 @@ +import ee +import numpy as np +ee.Initialize() +import xarray as xr +#data_dir = '/home/cccr/msingh/data/merra_aod_global_1980_2020/MERRA2_100.tavg1_2d_aer_Nx.1981_monmean.nc4' +import sys +dates = sys.argv[1]# '2020-03-23' +datee = sys.argv[2]# '2020-04-24' +#ds_merra2 = xr.open_dataset(data_dir) +collection_evi = ee.ImageCollection('MODIS/MYD09GA_006_EVI').select('EVI').filterDate(dates,datee ).mean() +collection_ndsi = ee.ImageCollection('MODIS/MYD09GA_006_NDSI').select('NDSI').filterDate(dates,datee ).mean() +collection_ndwi = ee.ImageCollection('MODIS/MYD09GA_006_NDWI').select('NDWI').filterDate(dates,datee ).mean() +collection_ndvi = ee.ImageCollection('MODIS/MYD09GA_006_NDVI').select('NDVI').filterDate(dates,datee ).mean() +#p = ee.Geometry.Point(32.3, 40.3) +#lats = ds_merra2.lat.values +#lons = ds_merra2.lon.values +lats = np.arange(14,28,0.25) +lons = np.arange(74,87,0.25) +#print(lats.shape, lons.shape, ds_merra2.TOTEXTTAU.values.shape) +evi = np.zeros((lats.shape[0], lons.shape[0])) +ndsi = np.zeros((lats.shape[0], lons.shape[0])) +ndwi = np.zeros((lats.shape[0], lons.shape[0])) +ndvi = np.zeros((lats.shape[0], lons.shape[0])) + +#data = collection_evi.reduceRegion(ee.Reducer.first(),p,50000).get("EVI")# 0.5 degree = 50km =50000 +#dataN = ee.Number(data) +#print(type(dataN.getInfo())) +#print(dataN.getInfo()) +for i_lat in range(lats.shape[0]): + for j_lon in range(lons.shape[0]): + p = ee.Geometry.Point(lons[j_lon], lats[i_lat]) #p = ee.Geometry.Point([lon, lat]) + data = collection_evi.reduceRegion(ee.Reducer.mean(),p,25000).get("EVI")# 0.5 degree = 50km =50000 + evi[i_lat, j_lon] = ee.Number(data).getInfo() + data = collection_ndsi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDSI")# 0.5 degree = 50km =50000 + ndsi[i_lat, j_lon] = ee.Number(data).getInfo() + data = collection_ndwi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDWI")# 0.5 degree = 50km =50000 + ndwi[i_lat, j_lon] = ee.Number(data).getInfo() + data = collection_ndvi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDVI")# 0.5 degree = 50km =50000 + ndvi[i_lat, j_lon] = ee.Number(data).getInfo() + print(lats[i_lat], lons[j_lon], ndvi[i_lat, j_lon]) From ff0f8ce1e050bbbdfae95330458a46e5cbc35e00 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 19:55:10 -0500 Subject: [PATCH 045/171] Create run_modis_daily_gee.py --- download_data/run_modis_daily_gee.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 download_data/run_modis_daily_gee.py diff --git a/download_data/run_modis_daily_gee.py b/download_data/run_modis_daily_gee.py new file mode 100644 index 0000000..34168d0 --- /dev/null +++ b/download_data/run_modis_daily_gee.py @@ -0,0 +1,20 @@ +import os +import numpy as np +import xarray as xr + +from datetime import timedelta, date + + + +def daterange(start_date, end_date): + for n in range(int((end_date - start_date).days)): + yield start_date + timedelta(n) + +start_date = date(2000, 2, 24) +#end_date = date(2020, 12, 31) +end_date = date(2000, 12, 31) +for single_date in daterange(start_date, end_date): + print(single_date.strftime("%Y-%m-%d"), (single_date + timedelta(days=1)).strftime("%Y-%m-%d")) + dates = single_date.strftime("%Y-%m-%d") + datee = (single_date + timedelta(days=1)).strftime("%Y-%m-%d") + os.system('python generate_modis_ndvi_gee.py '+dates+' '+datee+' &') From 5581441e92f07a80c5940834fc7d250f5c2281c8 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 20:08:03 -0500 Subject: [PATCH 046/171] update download from gee --- download_data/download_daily_modis_gee.py | 32 +++++++++++++++-------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/download_data/download_daily_modis_gee.py b/download_data/download_daily_modis_gee.py index 3285f1f..99e3172 100644 --- a/download_data/download_daily_modis_gee.py +++ b/download_data/download_daily_modis_gee.py @@ -14,13 +14,13 @@ #p = ee.Geometry.Point(32.3, 40.3) #lats = ds_merra2.lat.values #lons = ds_merra2.lon.values -lats = np.arange(14,28,0.25) -lons = np.arange(74,87,0.25) +lats = np.arange(10,35,0.25) +lons = np.arange(70,90,0.25) #print(lats.shape, lons.shape, ds_merra2.TOTEXTTAU.values.shape) -evi = np.zeros((lats.shape[0], lons.shape[0])) -ndsi = np.zeros((lats.shape[0], lons.shape[0])) -ndwi = np.zeros((lats.shape[0], lons.shape[0])) -ndvi = np.zeros((lats.shape[0], lons.shape[0])) +evi = np.zeros((1, lats.shape[0], lons.shape[0])) +ndsi = np.zeros(1, (lats.shape[0], lons.shape[0])) +ndwi = np.zeros((1, lats.shape[0], lons.shape[0])) +ndvi = np.zeros((1, lats.shape[0], lons.shape[0])) #data = collection_evi.reduceRegion(ee.Reducer.first(),p,50000).get("EVI")# 0.5 degree = 50km =50000 #dataN = ee.Number(data) @@ -30,11 +30,21 @@ for j_lon in range(lons.shape[0]): p = ee.Geometry.Point(lons[j_lon], lats[i_lat]) #p = ee.Geometry.Point([lon, lat]) data = collection_evi.reduceRegion(ee.Reducer.mean(),p,25000).get("EVI")# 0.5 degree = 50km =50000 - evi[i_lat, j_lon] = ee.Number(data).getInfo() + evi[0, i_lat, j_lon] = ee.Number(data).getInfo() data = collection_ndsi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDSI")# 0.5 degree = 50km =50000 - ndsi[i_lat, j_lon] = ee.Number(data).getInfo() + ndsi[0, i_lat, j_lon] = ee.Number(data).getInfo() data = collection_ndwi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDWI")# 0.5 degree = 50km =50000 - ndwi[i_lat, j_lon] = ee.Number(data).getInfo() + ndwi[0, i_lat, j_lon] = ee.Number(data).getInfo() data = collection_ndvi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDVI")# 0.5 degree = 50km =50000 - ndvi[i_lat, j_lon] = ee.Number(data).getInfo() - print(lats[i_lat], lons[j_lon], ndvi[i_lat, j_lon]) + ndvi[0, i_lat, j_lon] = ee.Number(data).getInfo() + #print(lats[i_lat], lons[j_lon], ndvi[i_lat, j_lon]) + +times = pd.date_range(dates, periods=1) +ds = xr.DataArray(evi, coords=[times, lats, lons], dims=["time", "lat", "lon"]) +ds.to_netcdf('evi_'+dates+'_.nc') +ds = xr.DataArray(ndsi, coords=[times, lats, lons], dims=["time", "lat", "lon"]) +ds.to_netcdf('ndsi_'+dates+'_.nc') +ds = xr.DataArray(ndwi, coords=[times, lats, lons], dims=["time", "lat", "lon"]) +ds.to_netcdf('ndwi_'+dates+'_.nc') +ds = xr.DataArray(ndvi, coords=[times, lats, lons], dims=["time", "lat", "lon"]) +ds.to_netcdf('ndvi_'+dates+'_.nc') From 412dcd901c1c7869ec09043dc7fa0be9550934e6 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 3 Apr 2021 20:25:35 -0500 Subject: [PATCH 047/171] update run_modis_daily_gee.py --- download_data/run_modis_daily_gee.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/download_data/run_modis_daily_gee.py b/download_data/run_modis_daily_gee.py index 34168d0..e119dc0 100644 --- a/download_data/run_modis_daily_gee.py +++ b/download_data/run_modis_daily_gee.py @@ -10,9 +10,10 @@ def daterange(start_date, end_date): for n in range(int((end_date - start_date).days)): yield start_date + timedelta(n) -start_date = date(2000, 2, 24) +start_date = date(2003, 1, 1) #end_date = date(2020, 12, 31) -end_date = date(2000, 12, 31) +#end_date = date(2000, 12, 31) +end_date = date(2004, 1, 1) for single_date in daterange(start_date, end_date): print(single_date.strftime("%Y-%m-%d"), (single_date + timedelta(days=1)).strftime("%Y-%m-%d")) dates = single_date.strftime("%Y-%m-%d") From d7b91d5b1feb47bf8e07855f87f7a80ab468c3e2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 4 Apr 2021 00:40:07 -0500 Subject: [PATCH 048/171] Create gee_extract.py --- xarray_tutorial/gee_extract.py | 75 ++++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 xarray_tutorial/gee_extract.py diff --git a/xarray_tutorial/gee_extract.py b/xarray_tutorial/gee_extract.py new file mode 100644 index 0000000..d9a4866 --- /dev/null +++ b/xarray_tutorial/gee_extract.py @@ -0,0 +1,75 @@ +import ee +import numpy as np +ee.Initialize() +import xarray as xr +import sys +#data_dir = '/home/cccr/msingh/data/merra_aod_global_1980_2020/MERRA2_100.tavg1_2d_aer_Nx.1981_monmean.nc4' +import sys +#dates = sys.argv[1]# '2020-03-23' +#datee = sys.argv[2]# '2020-04-24' +#ds_merra2 = xr.open_dataset(data_dir) +from datetime import timedelta, date +import pandas as pd + + +def daterange(start_date, end_date): + for n in range(int((end_date - start_date).days)): + yield start_date + timedelta(n) + +start_date = date(2003, 1, 1) +end_date = date(2021, 1, 1) +#end_date = date(2000, 12, 31) +#end_date = date(2021, 1, 1) + + +times = pd.date_range('2003-01-01', '2021-01-01') +print(times, times.shape) +#sys.exit() +evi = np.zeros((times.shape[0])) +ndsi = np.zeros((times.shape[0])) +ndwi = np.zeros((times.shape[0])) +ndvi = np.zeros((times.shape[0])) +monsoon_region = ee.Geometry.Polygon([ + + [[74, 14], [87, 14],[87, 28], [74, 28], [74, 14]] + + ]); + + +cnt = 0 +for single_date in daterange(start_date, end_date): + print(single_date.strftime("%Y-%m-%d"), (single_date + timedelta(days=1)).strftime("%Y-%m-%d")) + dates = single_date.strftime("%Y-%m-%d") + datee = (single_date + timedelta(days=1)).strftime("%Y-%m-%d") + + + collection_evi = ee.ImageCollection('MODIS/MYD09GA_006_EVI').select('EVI').filterDate(dates,datee ).mean() + collection_ndsi = ee.ImageCollection('MODIS/MYD09GA_006_NDSI').select('NDSI').filterDate(dates,datee ).mean() + collection_ndwi = ee.ImageCollection('MODIS/MYD09GA_006_NDWI').select('NDWI').filterDate(dates,datee ).mean() + collection_ndvi = ee.ImageCollection('MODIS/MYD09GA_006_NDVI').select('NDVI').filterDate(dates,datee ).mean() + + data = collection_evi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('EVI') + evi[cnt] = ee.Number(data).getInfo() + + data = collection_ndsi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('NDSI') + ndsi[cnt] = ee.Number(data).getInfo() + + data = collection_ndwi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('NDWI') + ndwi[cnt] = ee.Number(data).getInfo() + + data = collection_ndvi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('NDVI') + ndvi[cnt] = ee.Number(data).getInfo() + + cnt = cnt+1 +np.savetxt('evi.out', evi, delimiter=',') +np.savetxt('ndsi.out', ndsi, delimiter=',') +np.savetxt('ndwi.out', ndwi, delimiter=',') +np.savetxt('ndvi.out', ndvi, delimiter=',') +ds = xr.DataArray(evi, coords=[times], dims=["time"]) +ds.to_netcdf('evi_.nc') +ds = xr.DataArray(ndsi, coords=[times], dims=["time"]) +ds.to_netcdf('ndsi_.nc') +ds = xr.DataArray(ndwi, coords=[time], dims=["time"]) +ds.to_netcdf('ndwi_.nc') +ds = xr.DataArray(ndvi, coords=[times], dims=["time"]) +ds.to_netcdf('ndvi_.nc') From bd1606ac87ef678296402370099bc51486777e89 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 7 Apr 2021 13:59:12 -0500 Subject: [PATCH 049/171] create dataset --- xarray_tutorial/create_xarray_dataarray.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/xarray_tutorial/create_xarray_dataarray.py b/xarray_tutorial/create_xarray_dataarray.py index 9405448..38b8f66 100644 --- a/xarray_tutorial/create_xarray_dataarray.py +++ b/xarray_tutorial/create_xarray_dataarray.py @@ -6,3 +6,15 @@ lats = np.arange(0,4,1) lons = np.arange(0,3,1) foo = xr.DataArray(data, coords=[times, lats, lons], dims=["time", "lat", "lon"]) +ds = xr.Dataset({ + 'ndvi': xr.DataArray( + data = data, # enter data here + dims = ['time'], + coords = {'time': times}, + attrs = { + '_FillValue': -999.9, + 'units' : 'W/m2' + } + ), + attrs = {'example_attr': 'this is a global attribute'} + ) From 2112f63416749f0d4fa525100ebc9578ae14a291 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 7 Apr 2021 14:16:08 -0500 Subject: [PATCH 050/171] update xarray dataset --- xarray_tutorial/create_xarray_dataarray.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/xarray_tutorial/create_xarray_dataarray.py b/xarray_tutorial/create_xarray_dataarray.py index 38b8f66..60916ad 100644 --- a/xarray_tutorial/create_xarray_dataarray.py +++ b/xarray_tutorial/create_xarray_dataarray.py @@ -7,8 +7,8 @@ lons = np.arange(0,3,1) foo = xr.DataArray(data, coords=[times, lats, lons], dims=["time", "lat", "lon"]) ds = xr.Dataset({ - 'ndvi': xr.DataArray( - data = data, # enter data here + 'SWdown': xr.DataArray( + data = np.random.random(6), # enter data here dims = ['time'], coords = {'time': times}, attrs = { @@ -16,5 +16,15 @@ 'units' : 'W/m2' } ), - attrs = {'example_attr': 'this is a global attribute'} + 'LWdown': xr.DataArray( + data = np.random.random(6), # enter data here + dims = ['time'], + coords = {'time': times}, + attrs = { + '_FillValue': -999.9, + 'units' : 'W/m2' + } + ) + }, + attrs = {'example_attr': 'this is a global attribute'} ) From f4c38ed6c08e81bfe370bedc214bb4a2267fcaa9 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 15 Apr 2021 23:24:33 -0500 Subject: [PATCH 051/171] create terrestrial coupling index --- xarray_tutorial/terrestrial_coupling_index.py | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 xarray_tutorial/terrestrial_coupling_index.py diff --git a/xarray_tutorial/terrestrial_coupling_index.py b/xarray_tutorial/terrestrial_coupling_index.py new file mode 100644 index 0000000..733ad07 --- /dev/null +++ b/xarray_tutorial/terrestrial_coupling_index.py @@ -0,0 +1,62 @@ +# TCI for control +# mrsos_control[0].shape +# mrsos_std = np.zeros((mrsos_control[0].shape[1], mrsos_control[0].shape[2])) +# for i_lat in range(mrsos_control[0].shape[1]): +# for j_lon in range(mrsos_control) +# https://stackoverflow.com/questions/58546999/calculate-correlation-in-xarray-with-missing-data +def linear_trend(x, y): + #print(x.shape) +# if np.sum(np.isnan(x))>0: +# pf = np.empty((x.shape[0])) +# else: +# try: + idx = np.isfinite(x) & np.isfinite(y) + #print(x.shape) + pf = np.polyfit(x[idx], y[idx], 1) +# except: +# pf = np.empty((x.shape[0])) + return xr.DataArray(pf[0]) + +def compute_tci(sm, lh): + print(sm.shape, lh.shape) + sm_std = sm.std(dim='time') + slopes = np.zeros_like(sm_std.values) +# slopes = np.zeros_like(sm_std) +# for i_tci in range(sm.shape[1]): +# for j_tci in range(sm.shape[2]): +# mask = ~np.isnan(sm) & ~np.isnan(lh) +# slope, intercept, r_value, p_value, std_err = stats.linregress(varx[mask], vary[mask]) +# slopes[i_tci,j_tci] = slope +# for i_tci in range(sm.values.shape[1]): +# for j_tci in range(sm.values.shape[2]): +# mask = ~np.isnan(sm.values[:,i_tci,j_tci]) & ~np.isnan(lh.values[:,i_tci,j_tci]) +# if np.sum(np.isnan(sm.values[:,i_tci,j_tci]))>=sm.values.shape[0]-2: +# slope=np.nan +# else: +# slope, intercept, r_value, p_value, std_err = stats.linregress(sm.values[:,i_tci,j_tci][mask], lh.values[:,i_tci,j_tci][mask]) +# slopes[i_tci,j_tci] = slope + #print(i_tci, j_tci) +# slopes = xr.apply_ufunc(linear_trend, +# sm, lh, +# vectorize=True, +# input_core_dims=[['time'], ['time']],# reduce along 'plev' +# ) + x = sm + y = lh + n = y.notnull().sum(dim='time') + xmean = x.mean(axis=0) + ymean = y.mean(axis=0) + xstd = x.std(axis=0) + ystd = y.std(axis=0) + cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n) + slopes = cov/(xstd**2) + intercept = ymean - xmean*slope + sm_std['slopes'] = (('lat', 'lon'), slopes) + tci = sm_std.slopes*sm_std + return tci +#mrsos_control[0].std(dim='time').plot() + +mrsos_control = ds_piClim_control_MPIESM_r1i1p1f1_mrso.mrso +hfls_control = ds_piClim_control_MPIESM_r1i1p1f1_hfls.hfls +tci_control = compute_tci(mrsos_control.sel(time=mrsos_control.time.dt.month.isin([5,6, 7, 8,9])), \ + hfls_aer.sel(time=mrsos_control.time.dt.month.isin([5,6, 7, 8,9]))) From 87429dbe1e6114746b9610254f92f2964992d47b Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 17 Apr 2021 18:35:58 -0500 Subject: [PATCH 052/171] Create soil_moisture_memory.py --- xarray_tutorial/soil_moisture_memory.py | 65 +++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 xarray_tutorial/soil_moisture_memory.py diff --git a/xarray_tutorial/soil_moisture_memory.py b/xarray_tutorial/soil_moisture_memory.py new file mode 100644 index 0000000..3ca06db --- /dev/null +++ b/xarray_tutorial/soil_moisture_memory.py @@ -0,0 +1,65 @@ +def soilm_memory(ds_soilm): + threshold = 1./np.exp(1.) + soilm = ds_soilm.values + smemory = np.zeros_like((ds_piClim_control_MPIESM_r1i1p1f1_mrso.mrso.values[0,:,:])) + ntim = 150 # ds_piClim_control_MPIESM_r1i1p1f1_mrso.mrso.values.shape[0] + correlation_ = np.zeros((ntim, \ + ds_piClim_control_MPIESM_r1i1p1f1_mrso.mrso.shape[1], \ + ds_piClim_control_MPIESM_r1i1p1f1_mrso.mrso.shape[2])) + for tt in range(2,ntim): + soilm_lagged = soilm[tt:,:,:] + times = ds_piClim_control_MPIESM_r1i1p1f1_mrso.time.values[tt:] + lats = ds_piClim_control_MPIESM_r1i1p1f1_mrso.lat.values + lons = ds_piClim_control_MPIESM_r1i1p1f1_mrso.lon.values + ds = xr.Dataset({ + 'soilm': xr.DataArray( + data = soilm[:-tt], # enter data here + dims = ['time', 'lat', 'lon'], + coords = {'time': times, 'lat':lats, 'lon':lons}, + ), + 'soilm_lagged': xr.DataArray( + data = soilm_lagged, # enter data here + dims = ['time', 'lat', 'lon'], + coords = {'time': times, 'lat':lats, 'lon':lons}, + + ) + }, + ) + print('lag = ', tt) + x = ds['soilm'] + y = ds['soilm_lagged'] + n = y.notnull().sum(dim='time') + xmean = x.mean(axis=0) + ymean = y.mean(axis=0) + xstd = x.std(axis=0) + ystd = y.std(axis=0) + + #4. Compute covariance along time axis + cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n) + + #5. Compute correlation along time axis + cor = cov/(xstd*ystd) + correlation_[tt-2,:,:] = cor.values + for i_lat in range(correlation_.shape[1]): + for j_lon in range(correlation_.shape[2]): + idx = np.where(correlation_[:,i_lat, j_lon] < 1/np.exp(1.))[0] + #print(idx) + #print(len(idx)) + #print(np.sum(np.isnan(soilm[:,i_lat,j_lon]))) + if len(idx)==0: + smemory[i_lat, j_lon] = np.nan + elif len(idx)==2: + smemory[i_lat, j_lon] = np.nan + else: + print(idx) + smemory[i_lat, j_lon] = idx[0]+1 + ds = xr.Dataset({ + 'smemory': xr.DataArray( + data = smemory, # enter data here + dims = [ 'lat', 'lon'], + coords = {'lat':lats, 'lon':lons}, + ) + }, + ) + + return ds From 95ad53b4e87f848c24d81340ba4946a2bf90fdcd Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 19 Apr 2021 14:53:09 -0500 Subject: [PATCH 053/171] create imerg 30 min file to netcdf --- download_data/imerg_hdf5_to_nc.py | 50 +++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 download_data/imerg_hdf5_to_nc.py diff --git a/download_data/imerg_hdf5_to_nc.py b/download_data/imerg_hdf5_to_nc.py new file mode 100644 index 0000000..ae6be60 --- /dev/null +++ b/download_data/imerg_hdf5_to_nc.py @@ -0,0 +1,50 @@ +import h5py +import numpy as np +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER +import matplotlib.ticker as mticker +import sys +from datetime import datetime +import xarray as xr +import pandas as pd +# Open the IMERG data file; it may be necessary to add a path if the file is not in the working directory. + +file_ = sys.argv[1] +#f = h5py.File('3B-MO.MS.MRG.3IMERG.20140101-S000000-E235959.01.V06B.HDF5', 'r') +f = h5py.File(file_, 'r') + +# View the available groups in the file and the variables in the 'Grid' group: + +groups = [ x for x in f.keys() ] +print(groups) +gridMembers = [ x for x in f['Grid'] ] +print(gridMembers) + +#print(file_[20:28],file_[31:35] ) + +precip = f['Grid/precipitationCal'][0][:][:] +precip = np.transpose(precip) +theLats = f['Grid/lat'][:] +theLons = f['Grid/lon'][:] +#theTime = f['Grid/time'][:] +#date_time_str = file_[26:30]+'-'+file_[30:32]+'-'+file_[32:34]+' '+file_[36:38]+'-'+file_[38:40] +#date_time_obj = datetime.strptime(date_time_str, '%Y-%m-%d %H-%M') +date_time_str = file_[26:30]+'-'+file_[30:32]+'-'+file_[32:34]+' '+file_[36:38]+':'+file_[38:40] +theTime =pd.Timestamp(date_time_str) +precip[precip==-9999.9]=np.nan +print(theTime) +ds = xr.Dataset({ + 'pr': xr.DataArray( + data = precip[np.newaxis,:,:] , # enter data here + dims = ['time', 'lat', 'lon'], + coords = {'time': np.atleast_1d(theTime), 'lat':theLats, 'lon':theLons}, + ) + }, + ) +#print(ds) +ds.to_netcdf(file_[:-4]+'.nc') +#print(theLats, theLons, theTime, precip) +#print(theLats.shape, theLons.shape, theTime.shape, precip.shape) +#print(file_[26:34],file_[36:40] ) +#print(date_time_obj) From a21ed2cf97f7b8013ffaa1c87331f57fe095c146 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 19 Apr 2021 14:57:32 -0500 Subject: [PATCH 054/171] download_imerg.sh --- download_data/download_imerg.sh | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 download_data/download_imerg.sh diff --git a/download_data/download_imerg.sh b/download_data/download_imerg.sh new file mode 100644 index 0000000..f0fed56 --- /dev/null +++ b/download_data/download_imerg.sh @@ -0,0 +1,5 @@ + #!/bin/bash + # 1. Go to https://disc.gsfc.nasa.gov/datasets?keywords=imerg&page=1 + # 2. Click Subset/Get data + # 3. Click get data after applying filters and download the subset...txt file + wget --load-cookies ~/.urs_cookies --save-cookies ~/.urs_cookies --auth-no-challenge=on --keep-session-cookies --content-disposition -i subset_GPM_3IMERGHH_06_20210411_212529.txt From aa673b88158765d001d7292c13b9b6b93ae53c29 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 19 Apr 2021 15:12:57 -0500 Subject: [PATCH 055/171] update preprocess hdf5 file imerg --- download_data/imerg_hdf5_to_nc.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/download_data/imerg_hdf5_to_nc.py b/download_data/imerg_hdf5_to_nc.py index ae6be60..a3c66df 100644 --- a/download_data/imerg_hdf5_to_nc.py +++ b/download_data/imerg_hdf5_to_nc.py @@ -30,7 +30,8 @@ #theTime = f['Grid/time'][:] #date_time_str = file_[26:30]+'-'+file_[30:32]+'-'+file_[32:34]+' '+file_[36:38]+'-'+file_[38:40] #date_time_obj = datetime.strptime(date_time_str, '%Y-%m-%d %H-%M') -date_time_str = file_[26:30]+'-'+file_[30:32]+'-'+file_[32:34]+' '+file_[36:38]+':'+file_[38:40] +s_i = 21 +date_time_str = file_[s_i:s_i+4]+'-'+file_[s_i+4:s_i+6]+'-'+file_[s_i+6:s_i+8]+' '+file_[s_i+10:s_i+12]+':'+file_[s_i+12:s_i+14] theTime =pd.Timestamp(date_time_str) precip[precip==-9999.9]=np.nan print(theTime) @@ -43,8 +44,4 @@ }, ) #print(ds) -ds.to_netcdf(file_[:-4]+'.nc') -#print(theLats, theLons, theTime, precip) -#print(theLats.shape, theLons.shape, theTime.shape, precip.shape) -#print(file_[26:34],file_[36:40] ) -#print(date_time_obj) +ds.to_netcdf(file_[:-5]+'.nc') From 7450478dc03c901baf91034ec663f327595d7b25 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 21 Apr 2021 16:23:37 -0500 Subject: [PATCH 056/171] Create potential_evaporation.py --- xarray_tutorial/potential_evaporation.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 xarray_tutorial/potential_evaporation.py diff --git a/xarray_tutorial/potential_evaporation.py b/xarray_tutorial/potential_evaporation.py new file mode 100644 index 0000000..3a7bef1 --- /dev/null +++ b/xarray_tutorial/potential_evaporation.py @@ -0,0 +1,16 @@ +import pandas as pd +import numpy as np +import pyet as pyet + +months = [6,7,8,9] +tas_control = ds_piClim_control_MPIESM_r1i1p1f1_tas.tas.sel(time=ds_piClim_aer_MPIESM_r1i1p1f1_hfss.time.dt.month.isin(months)) +pet_control = np.zeros_like((tas_control.values)) +time_pd = pd.to_datetime(tas_control.time.values) +for i_lat in range(tas_control.shape[1]): + print(i_lat) + for j_lon in range(tas_control.shape[2]): + + df = pd.DataFrame({'YYYYMMDD':time_pd, 'data':tas_control.values[:,i_lat,j_lon]-273.15}) + df.set_index('YYYYMMDD', inplace=True) + et_oudin = pyet.oudin(df['data'], lat=np.radians(tas_control.lat.values[i_lat])).values + pet_control[:,i_lat,j_lon]= et_oudin From 26f818b5a78ab617faebfe3c3b637b138c031203 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 21 Apr 2021 16:33:05 -0500 Subject: [PATCH 057/171] update potential evaporation --- xarray_tutorial/potential_evaporation.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/xarray_tutorial/potential_evaporation.py b/xarray_tutorial/potential_evaporation.py index 3a7bef1..c55eeec 100644 --- a/xarray_tutorial/potential_evaporation.py +++ b/xarray_tutorial/potential_evaporation.py @@ -1,6 +1,18 @@ import pandas as pd import numpy as np import pyet as pyet +def calc_pet_oudin(tas_control): + pet_control = np.zeros_like((tas_control.values)) + time_pd = pd.to_datetime(tas_control.time.values) + for i_lat in range(tas_control.shape[1]): + print(i_lat) + for j_lon in range(tas_control.shape[2]): + + df = pd.DataFrame({'YYYYMMDD':time_pd, 'data':tas_control.values[:,i_lat,j_lon]-273.15}) + df.set_index('YYYYMMDD', inplace=True) + et_oudin = pyet.oudin(df['data'], lat=np.radians(tas_control.lat.values[i_lat])).values + pet_control[:,i_lat,j_lon]= et_oudin + return pet_control months = [6,7,8,9] tas_control = ds_piClim_control_MPIESM_r1i1p1f1_tas.tas.sel(time=ds_piClim_aer_MPIESM_r1i1p1f1_hfss.time.dt.month.isin(months)) From 0d398731663ce218dba109d0a37bce1cfb7d4435 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 21 Apr 2021 18:55:26 -0500 Subject: [PATCH 058/171] Create latent_heat_vaporization.py --- xarray_tutorial/latent_heat_vaporization.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 xarray_tutorial/latent_heat_vaporization.py diff --git a/xarray_tutorial/latent_heat_vaporization.py b/xarray_tutorial/latent_heat_vaporization.py new file mode 100644 index 0000000..0e7d5ab --- /dev/null +++ b/xarray_tutorial/latent_heat_vaporization.py @@ -0,0 +1,20 @@ +# https://github.com/phydrus/pyet/blob/master/pyet/radiation.pyFrom +def calc_lambda(tmean): + """ Latent Heat of Vaporization [MJ kg-1]. + Parameters + ---------- + tmean: pandas.Series/float, optional + average day temperature [°C] + Returns + ------- + pandas.Series containing the calculated Latent Heat of Vaporization + [MJ kg-1]. + Examples + -------- + >>> lambd = calc_lambda(tmean) + Notes + ----- + Based on equation (3-1) in [allen_1998]_. + """ + return 2.501 - 0.002361 * tmean + From adce0da2c3cbd4027281d0c2f4a24199addd8b6a Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 8 May 2021 02:10:34 -0500 Subject: [PATCH 059/171] Create parallelize.py --- xarray_tutorial/parallelize.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 xarray_tutorial/parallelize.py diff --git a/xarray_tutorial/parallelize.py b/xarray_tutorial/parallelize.py new file mode 100644 index 0000000..7576a69 --- /dev/null +++ b/xarray_tutorial/parallelize.py @@ -0,0 +1,28 @@ +import time +import multiprocessing + +def basic_func(x): + if x == 0: + return 'zero' + elif x%2 == 0: + return 'even' + else: + return 'odd' + +def multiprocessing_func(x): + y = x*x + time.sleep(2) + print('{} squared results in a/an {} number'.format(x, basic_func(y))) + +if __name__ == '__main__': + starttime = time.time() + processes = [] + for i in range(0,10): + p = multiprocessing.Process(target=multiprocessing_func, args=(i,)) + processes.append(p) + p.start() + + for process in processes: + process.join() + + print('That took {} seconds'.format(time.time() - starttime)) From 67950f0655426b0f620af6a97f11f404221771fd Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 8 May 2021 02:46:09 -0500 Subject: [PATCH 060/171] use pool and return data --- xarray_tutorial/parallelize.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/xarray_tutorial/parallelize.py b/xarray_tutorial/parallelize.py index 7576a69..fca52b9 100644 --- a/xarray_tutorial/parallelize.py +++ b/xarray_tutorial/parallelize.py @@ -1,3 +1,12 @@ +from multiprocessing import Pool +def job(num): + return num * 2 +if __name__ == '__main__': + p = Pool(processes=100) + data = p.map(job, [i for i in range(100)]) + p.close() + print(data) + import time import multiprocessing From 7e068eb2d4e215dff7793b04cc2ce71110b516b3 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 8 May 2021 03:26:52 -0500 Subject: [PATCH 061/171] Create parallel_transfer_entropy --- xarray_tutorial/parallel_transfer_entropy.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 xarray_tutorial/parallel_transfer_entropy.py diff --git a/xarray_tutorial/parallel_transfer_entropy.py b/xarray_tutorial/parallel_transfer_entropy.py new file mode 100644 index 0000000..b069e94 --- /dev/null +++ b/xarray_tutorial/parallel_transfer_entropy.py @@ -0,0 +1,18 @@ +from copent import transent +X = np.random.randn(100,1000) +Y = np.random.randn(100,1000) +print(X.shape, Y.shape) + +#from itertools import product +def transfer_entropy(x,y): + return transent(x,y,1) +if __name__ == '__main__': + iterable = [1, 2, 3, 4, 5] + iterable = [] + for i in range(10): + iterable.append((X[i,:], Y[i,:])) + p = Pool(processes=100) + data = p.starmap(transfer_entropy, [(X[i,:],Y[i,:]) for i in range(100)]) + #data = p.map(transfer_entropy, iterable) + p.close() + print(data) From 29e22479d7a0d5eff79f00e150b2de37140fafef Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 13 May 2021 01:42:45 -0500 Subject: [PATCH 062/171] Installing PyRQA on python 2.7 --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index da5dd1d..4d8d508 100644 --- a/README.md +++ b/README.md @@ -137,5 +137,15 @@ For the weights, the following can be followed: https://stackoverflow.com/questi 12. When using the isin function with sel we can at present use it only once in a call. Need to instantiate a new variable for doing it twice. +13. Installing PyRQA (Runs only with python 2.7) https://github.com/szhan/pyrqa + + conda install https://anaconda.org/conda-forge/pytools/2017.2/download/linux-64/pytools-2017.2-py27_0.tar.bz2 + conda install https://anaconda.org/conda-forge/pyopencl/2018.1.1/download/linux-64/pyopencl-2018.1.1-py27_1.tar.bz2 + conda install -c conda-forge pocl + pip install Mako + pip install PyRQA + + + From b297ffb1b1312a309861f6d8dd5cb67b8ac2c871 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 13 May 2021 01:43:05 -0500 Subject: [PATCH 063/171] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 4d8d508..049b976 100644 --- a/README.md +++ b/README.md @@ -140,9 +140,13 @@ For the weights, the following can be followed: https://stackoverflow.com/questi 13. Installing PyRQA (Runs only with python 2.7) https://github.com/szhan/pyrqa conda install https://anaconda.org/conda-forge/pytools/2017.2/download/linux-64/pytools-2017.2-py27_0.tar.bz2 + conda install https://anaconda.org/conda-forge/pyopencl/2018.1.1/download/linux-64/pyopencl-2018.1.1-py27_1.tar.bz2 + conda install -c conda-forge pocl + pip install Mako + pip install PyRQA From 29c2eb27d913677703fe54257921c97e5bb54fe6 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 13 May 2021 01:54:26 -0500 Subject: [PATCH 064/171] running pyrqa --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 049b976..94a0341 100644 --- a/README.md +++ b/README.md @@ -148,6 +148,8 @@ For the weights, the following can be followed: https://stackoverflow.com/questi pip install Mako pip install PyRQA + + Even after all this, unable to run pyrqa smoothly. However, this activity ensured that the environment to run pyrqa was perfect. So then clone the github repository and inside the main github repository pyrqa, there is a folder pyrqa. Copy that to your desired location, rename it lets say PYRQA. And use the library as PYRQA. From 0ed4f11cb1d6d8513fd810807e2087a32b18bdcb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 21 May 2021 07:09:04 -0500 Subject: [PATCH 065/171] create multivariate mutual information --- .../multivariate_mutual_information.py | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 xarray_tutorial/multivariate_mutual_information.py diff --git a/xarray_tutorial/multivariate_mutual_information.py b/xarray_tutorial/multivariate_mutual_information.py new file mode 100644 index 0000000..27d50f7 --- /dev/null +++ b/xarray_tutorial/multivariate_mutual_information.py @@ -0,0 +1,27 @@ +def multivariate_mutual_information(xs1,xs2,xtar): + # https://stackoverflow.com/questions/20332750/python-joint-distribution-of-n-variables + numBins =int(np.sqrt(xs1.shape[0]/5)) # number of bins in each dimension + #print(xtar) + xs1 = xs1[np.isfinite(xs1)] + xs2 = xs2[np.isfinite(xs1)] + xtar = xtar[np.isfinite(xs1)] + + xs2 = xs2[np.isfinite(xs2)] + xs2 = xs2[np.isfinite(xtar)] + + xtar = xtar[np.isfinite(xs2)] + xtar = xtar[np.isfinite(xtar)] + #print(xtar) + data = np.stack((xs1, xs2, xtar)).T + #data = np.random.randn(100000, 3) # generate 100000 3-d random data points + jointProbs3, edges = np.histogramdd(data, bins=numBins) + jointProbs3 /= jointProbs3.sum() + jointProbs2, edges = np.histogramdd(data[:,:2], bins=numBins) + jointProbs2 /= jointProbs2.sum() + jointProbs1, edges = np.histogramdd(data[:,2:], bins=numBins) + jointProbs1 /= jointProbs1.sum() + jointProbs3_ = jointProbs3.copy() + for i in range(jointProbs1.shape[0]): + jointProbs3_[i,:,:] = jointProbs1[i]*jointProbs2[:,:] + arr = jointProbs3*(np.log2(jointProbs3)-np.log2(jointProbs3_)) + return np.mean(arr[np.isfinite(arr)]) From 39be4363277d9677f75c7dd10e34306d15c47a68 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 8 Jun 2021 14:37:02 -0500 Subject: [PATCH 066/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 94a0341..e370de3 100644 --- a/README.md +++ b/README.md @@ -151,6 +151,8 @@ For the weights, the following can be followed: https://stackoverflow.com/questi Even after all this, unable to run pyrqa smoothly. However, this activity ensured that the environment to run pyrqa was perfect. So then clone the github repository and inside the main github repository pyrqa, there is a folder pyrqa. Copy that to your desired location, rename it lets say PYRQA. And use the library as PYRQA. +14. Logging to a remote server without password https://www.thegeekstuff.com/2008/11/3-steps-to-perform-ssh-login-without-password-using-ssh-keygen-ssh-copy-id/ + From 043d5d1d748a8f6a00d08a355dc7417d1c880cd5 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 8 Jun 2021 16:37:42 -0500 Subject: [PATCH 067/171] Running parallel jobs on Pratyush HPC --- parallel/run_generate_cs.sh | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 parallel/run_generate_cs.sh diff --git a/parallel/run_generate_cs.sh b/parallel/run_generate_cs.sh new file mode 100644 index 0000000..afecd69 --- /dev/null +++ b/parallel/run_generate_cs.sh @@ -0,0 +1,18 @@ +# Running parallel jobs on Pratyush HPC + +#!/bin/bash + +for file in 2m_temperature_2018_1.40625deg_12_31.nc +do + tmpfile=$(mktemp --suffix=".sh") + echo "#!/bin/sh" > $tmpfile + echo "#PBS -N test_job" >> $tmpfile + echo "#PBS -q research" >> $tmpfile + echo "#PBS -l select=1:ncpus=1:vntype=cray_compute" >> $tmpfile + echo "#PBS -l walltime=500:00:00" >> $tmpfile + echo "cd /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/1.40625deg/2m_temperature" >> $tmpfile + # echo "cd $PBS_O_WORKDIR" >> $tmpfile +# echo "module load cudatoolkit" + echo "aprun -n 1 -N 1 /lus/dal/cccr_rnd/manmeet/anaconda3/envs/py36/bin/python /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/1.40625deg/2m_temperature/preprocess_and_to_cs512.py $file > /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/1.40625deg/2m_temperature/output.log" >> $tmpfile + qsub $tmpfile +done From b24d0753253cd906a02d35d9bfab036cb8c91d00 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 22 Jun 2021 03:37:16 +0530 Subject: [PATCH 068/171] Create gee_landsat_ts.py --- download_data/gee_landsat_ts.py | 37 +++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 download_data/gee_landsat_ts.py diff --git a/download_data/gee_landsat_ts.py b/download_data/gee_landsat_ts.py new file mode 100644 index 0000000..c5cb2fc --- /dev/null +++ b/download_data/gee_landsat_ts.py @@ -0,0 +1,37 @@ +from geextract import ts_extract, get_date +from datetime import datetime +import numpy as np +import matplotlib.pyplot as plt +plt.figure(figsize=(10,5)) + +# Extract a Landsat 7 time-series for a 500m radius circular buffer around +# a location in Yucatan +lon = 73.97 +lat = 18.394806 +raw_dict = ts_extract(lon=lon, lat=lat, sensor='LE7', + start=datetime(1999, 1, 1), radius=500) + +# Function to compute ndvi from a dictionary of the list of dictionaries returned +# by ts_extract +def ndvi(x): + try: + return (x['B4'] - x['B3']) / (x['B4'] + x['B3']) + except: + pass + +# Build x and y arrays and remove missing values +x = np.array([get_date(d['id']) for d in raw_dict]) +y = np.array([ndvi(d) for d in raw_dict], dtype=np.float) +x = x[~np.isnan(y)] +y = y[~np.isnan(y)] + +# Make plot +plt.plot_date(x, y, "--") +plt.plot_date(x, y) +plt.title("Landsat 7 NDVI time-series Uxmal") +plt.ylabel("NDVI (-)") +plt.grid(True) +plt.show() +import pandas as pd +dataset = pd.DataFrame({'date': x, 'landsat7_ndvi': y}) +dataset.to_csv('landsat7.csv', sep='\t') From 4338932df4874c481176da4242f0e7771e5b3eee Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 22 Jun 2021 13:41:05 -0500 Subject: [PATCH 069/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e370de3..82a9073 100644 --- a/README.md +++ b/README.md @@ -99,6 +99,8 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 56. GeoTIFF to netcdf and exporting data from Google Earth Engine https://medium.com/@wenzhao.li1989/nco-translate-geotiff-files-exported-from-gee-to-a-netcdf-file-with-correct-time-dimension-ce97a8f3043f +57. Make pipeline to avoid test data leaking into train https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From be144d228a8a54afeaf47ac1b333ea380048b8bb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 23 Jun 2021 18:04:50 -0500 Subject: [PATCH 070/171] Create swathhdf2nc.py --- satellite/swathhdf2nc.py | 67 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 satellite/swathhdf2nc.py diff --git a/satellite/swathhdf2nc.py b/satellite/swathhdf2nc.py new file mode 100644 index 0000000..75f2952 --- /dev/null +++ b/satellite/swathhdf2nc.py @@ -0,0 +1,67 @@ +import h5py +import numpy as np +import datetime as dt +import xarray as xr +import sys + +input_file = sys.argv[1] +f = h5py.File(input_file, 'r') + +yy = np.max(f['Grid']['GridTime']['Year'][:]) +mm = np.max(f['Grid']['GridTime']['Month'][:]) +dd = np.max(f['Grid']['GridTime']['DayOfMonth'][:]) + +time_ = dt.datetime(yy, mm, dd) +lat = np.linspace(-67+0.25/2,67-0.25/2,536) +lon = np.linspace(-180+0.25/2,180-0.25/2,1440) +lev = np.arange(0.125,0.125+0.25*80,0.25) + +lh = f['Grid']['latentHeating'][:] +spr = f['Grid']['surfacePrecipRate'][:] +stratfrac = f['Grid']['stratiformFraction'][:] +lh[lh==-9999.9] = np.nan +spr[spr==-9999.9] = np.nan +stratfrac[stratfrac==-9999.9] = np.nan + +stratfrac = stratfrac.T +spr = spr.T +lh = lh.swapaxes(1,2) +lh = lh[np.newaxis,...] + +stratfrac = stratfrac[np.newaxis,...] +spr = spr[np.newaxis,...] + +df = xr.Dataset( + data_vars={'latentHeating': (('time', 'level', 'latitude', 'longitude' ), lh), + 'surfacePrecipRate': (('time', 'latitude', 'longitude' ), spr), + 'stratiformFraction': (('time', 'latitude', 'longitude'), stratfrac)}, + coords={ 'time':np.atleast_1d((time_-dt.datetime(1,1,1)).days+2), + 'level': lev, + 'longitude': lon, + 'latitude': lat, + }) +df.latentHeating.attrs['long_name'] = 'Latent Heating' +df.surfacePrecipRate.attrs['long_name'] = 'Surface Precipitation Rate' +df.stratiformFraction.attrs['long_name'] = 'Stratiform Fraction' + +df.time.attrs['units'] = 'days since 01-01-01 00:00:00' +df.time.attrs['standard_name'] = 'time' +df.time.attrs['long_name'] = 'Year AD' +df.time.attrs['calendar'] = 'standard' + +df.longitude.attrs['standard_name'] = 'longitude' +df.longitude.attrs['long_name'] = 'longitude' +df.longitude.attrs['units'] = 'degrees_east' +df.longitude.attrs['axis'] = 'X' +df.latitude.attrs['standard_name'] = 'latitude' +df.latitude.attrs['long_name'] = 'latitude' +df.latitude.attrs['units'] = 'degrees_north' +df.latitude.attrs['axis'] = 'Y' + +df.level.attrs['standard_name'] = 'air_pressure' +df.level.attrs['long_name'] = 'pressure_level' +df.level.attrs['units'] = 'km' +df.level.attrs['axis'] = 'Z' +df.level.attrs['positive'] = 'up' + +df.to_netcdf(input_file[:-4]+'nc') From e95642036d144b7d309c9bb5d218125d0a427080 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 23 Jun 2021 18:07:25 -0500 Subject: [PATCH 071/171] Create swath2grid.ncl --- satellite/swath2grid.ncl | 208 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 satellite/swath2grid.ncl diff --git a/satellite/swath2grid.ncl b/satellite/swath2grid.ncl new file mode 100644 index 0000000..62057b5 --- /dev/null +++ b/satellite/swath2grid.ncl @@ -0,0 +1,208 @@ +;*************************************************************** +; hdf4sds_3.ncl +; +; Concepts illustrated: +; - Reading multiple files with data from Trmm satellite +; - Binning and summing the data from each pass [swath] +; - Averaging binned data +; - Writing data to a NetCDF file using the easy but inefficient method +;*************************************************************** + +;*****************Load Libraries ************************************ +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl" +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl" +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" + +;******************************************************************** +; Utility functions to get (1) date information (2) facilitate netCDF +;******************************************************************** +function parseFileName ( fNam:string ) +; parse the file names and extract data information +; 1 2 +; 01234567890123456789012345678 +; MYDATML2.A2005364.1820.005.nc +local onNam, n, output, tmp_c +begin + nNam = dimsizes( fNam ) + + if (nNam.eq.1) then + output = new( 4,integer,"No_FillValue") + tmp_c = stringtochar(fNam) + + output(0) = stringtointeger((/tmp_c(10:13)/)) ; YYYY + output(1) = stringtointeger((/tmp_c(14:16)/)) ; MM + output(2) = stringtointeger((/tmp_c(18:19)/)) ; HH + output(3) = stringtointeger((/tmp_c(20:21)/)) ; MN + else + output = new( (/nNam,4/),integer,"No_FillValue") + do n=0,nNam-1 + tmp_c = stringtochar(fNam(n)) + output(n,0) = stringtointeger((/tmp_c(10:13)/)) ; YYYY + output(n,1) = stringtointeger((/tmp_c(14:16)/)) ; MM + output(n,2) = stringtointeger((/tmp_c(18:19)/)) ; HH + output(n,3) = stringtointeger((/tmp_c(20:21)/)) ; MN + end do + end if + + return (output) +end +;--------------------------------------------------- +function create3d (x[*][*]:numeric, time[*]:numeric) +; convert a 2-dimensional to a 3-dimensional variable +; with time as a coordinate variable. +local dimx, ntim, nlat, mlon, x3d +begin + dimx = dimsizes(x) + + ntim = dimsizes(time) + nlat = dimx(0) + mlon = dimx(1) + ; perform expansion + x3d = conform_dims( (/ntim,nlat,mlon/), x, (/1,2/)) + copy_VarAtts(x, x3d) + x3d!0 = "time" + x3d&time= time + copy_VarCoords(x, x3d(0,:,:) ) + return(x3d) +end + +;************************************************************** +; MAIN +;************************************************************** +begin + vNam = "Cloud_Water_Path_1621" + vNam = "nearSurfZ" + vNam = "correctZFactor" + + PLOT = True + netCDF= True + +;***************************************************************** +; binned grid definition +; The grid must be constant spacing in lat and in lon. +; The spacing in the lat and lon directions may be different. +; so: 2x2, 3x5, etc +;***************************************************************** + + nlat = 480 ; grid coordinates + mlon = 1440 + nlev = 80 + lat = latGlobeFo(nlat,"lat","latitude","degrees_north") + lat = lat(::-1) ; North to South + lon = lonGlobeFo(mlon,"lon","longitude","degrees_east") + lon = (/ lon - 180. /) ; subtract 180 from all values + lon&lon = lon ; update coordinates + lev=new(80,float) +; printVarSummary(lev) + + tunits = "hours since 1990-1-1 00:00:0.0" + time = new((/1/),integer) + time = 2005 + time!0 = "time" + time@units = tunits + + do k=0,nlev-1 + + lev(k) = 0.25 * (k+1) + + end do + +; print(lev) +;exit +;end +; to add lev + +;***************************************************************** +; Variables to hold binned quantities +;***************************************************************** + GBIN = new ( (/nlat,mlon/), float ) + GKNT = new ( (/nlat,mlon/), integer ) + Z = new ( (/nlat,mlon,nlev,1/),float ) + Z = 0.0 +;***************************************************************** +; File info +;***************************************************************** + + diri = "./" + fili = systemfunc("cd "+diri+" ; ls 2A25*.HDF") + nfil = dimsizes( fili ) + ;print("nfil="+nfil) + + + diro = "./" ; output directory + filo = "TrmmBin_bin_sum" ; output file name + fout = diro+filo+".nc" + + system("/bin/rm -f "+fout) ; remove any pre-existing file + ncdf = addfile(fout ,"c") ; open output netCDF file + + filedimdef(ncdf,"time",-1,True) ; make time UNLIMITED dimension + ncdf->time = time + +;***************************************************************** +; Loop over the files: Sum the quantities +;***************************************************************** + + + do k=0,nlev-1 + vNam = "correctZFactor" + GBIN = 0.0 ; initialization + GKNT = 0 + + do nf=0,nfil-1 + print(nf+" "+fili(nf)) + f = addfile(diri+fili(nf), "r") + ; read data + lat2d = f->Latitude + lon2d = f->Longitude + x = f->$vNam$ + nx = product(dimsizes(x)) + bin_sum(GBIN,GKNT,lon,lat \ + ,ndtooned(lon2d), ndtooned(lat2d),ndtooned(x(:,:,k)) ) + + delete( x ) ; size may change + delete(lat2d) + delete(lon2d) + end do +;***************************************************************** +; Perform averaging +;***************************************************************** + ; avoid division by 0.0 + GKNT = where(GKNT.eq.0 , GKNT@_FillValue, GKNT) + + GBIN = GBIN/GKNT + + GBIN!0 = "lat" + GBIN!1 = "lon" + GBIN&lat = lat + GBIN&lon = lon + + Z!0 = "lat" + Z!1 = "lon" + Z!2 = "lev" + Z!3 = "time" + Z&lat = lat + Z&lon = lon + Z&lev = lev + Z&time = time + copy_VarCoords(GBIN, GKNT) ; copy coords + + if (isfilevaratt(f, vNam, "long_name")) then + GBIN@long_name = "BINNED: "+vNam + GKNT@long_name = "BINNED COUNT: "+vNam + end if + + if (isfilevaratt(f, vNam, "units")) then + GBIN@units = f->$vNam$@units + end if + + +;Z(:,:,k,:) = create3d(GBIN,time) + + vNam = vNam + k + ncdf->$vNam$ = create3d(GBIN,time) + + +end do + +end From 221dea51ab33e93597b7baf404d943bfa8ef9520 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 23 Jun 2021 18:08:07 -0500 Subject: [PATCH 072/171] Create swath2grid.sh --- satellite/swath2grid.sh | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 satellite/swath2grid.sh diff --git a/satellite/swath2grid.sh b/satellite/swath2grid.sh new file mode 100644 index 0000000..44d0c03 --- /dev/null +++ b/satellite/swath2grid.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#combine_lev.ncl jjas_2007.sh jjas_2009.sh jjas_2011.sh jjas.sh swath2grid.ncl +for year in {2006..2011} +do + for dd in {153..275} + do + cp swath2grid.ncl combine_lev.ncl ${year}/${dd}/. + done +done From fbc5d67bff3fe033b90535fbc00fb48aa464ae3c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 23 Jun 2021 18:08:33 -0500 Subject: [PATCH 073/171] Create run_swath2grid.sh --- satellite/run_swath2grid.sh | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 satellite/run_swath2grid.sh diff --git a/satellite/run_swath2grid.sh b/satellite/run_swath2grid.sh new file mode 100644 index 0000000..9187239 --- /dev/null +++ b/satellite/run_swath2grid.sh @@ -0,0 +1,13 @@ +#!/bin/bash +for year in {2006..2011} +do + for dd in {153..275} + do + cd ${year}/${dd}/ + tmpfile=$(mktemp --suffix=".sh") + echo "ncl swath2grid.ncl" > $tmpfile + echo "ncl combine_lev.ncl" >> $tmpfile + bsub -q "cccr-res" -W 10:00 -J "swath_${year}_${dd}" -o "swath.out" < $tmpfile + cd - + done +done From 26b1a26a0b3af32f5d4908abeafb0c65e2805cb6 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 24 Jun 2021 11:12:56 +0530 Subject: [PATCH 074/171] Created using Colaboratory --- deep_learning/nlp_experiments.ipynb | 232 ++++++++++++++++++++++++++++ 1 file changed, 232 insertions(+) create mode 100644 deep_learning/nlp_experiments.ipynb diff --git a/deep_learning/nlp_experiments.ipynb b/deep_learning/nlp_experiments.ipynb new file mode 100644 index 0000000..e16499c --- /dev/null +++ b/deep_learning/nlp_experiments.ipynb @@ -0,0 +1,232 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "nlp_experiments.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyMy6cfJXMpFrBs++y2ReLG2", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IK9JOPTfYxLk" + }, + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zzu9tBdgY1AA" + }, + "source": [ + "corpus = [\n", + " 'This is the first document.',\n", + " 'This document is the second document.',\n", + " 'And this is the third one.',\n", + " 'Is this the first document?',\n", + "]" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q4oy_HmrhjOO", + "outputId": "d7e0b14e-fda7-4125-fada-339d91aa185c" + }, + "source": [ + "len(corpus)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "y_MGq4VbhnCq" + }, + "source": [ + "vectorizer = CountVectorizer()\n", + "X = vectorizer.fit_transform(corpus)" + ], + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qSbags0_hszc", + "outputId": "fdab4541-f6d1-4c43-f24d-bb7b46a90702" + }, + "source": [ + "X" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<4x9 sparse matrix of type ''\n", + "\twith 21 stored elements in Compressed Sparse Row format>" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B43PwLX5htOS", + "outputId": "10b50540-a767-4d9b-affd-97da2b7a1edd" + }, + "source": [ + "print(vectorizer.get_feature_names())" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wW_w4XHviGPv", + "outputId": "9558f097-9b6e-4376-cae9-9eb84f31d59f" + }, + "source": [ + "print(X.toarray())" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0 1 1 1 0 0 1 0 1]\n", + " [0 2 0 1 0 1 1 0 1]\n", + " [1 0 0 1 1 0 1 1 1]\n", + " [0 1 1 1 0 0 1 0 1]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VbI48KiEiJvR" + }, + "source": [ + "vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2))" + ], + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3pXTV3EGiVIB" + }, + "source": [ + "X2 = vectorizer2.fit_transform(corpus)" + ], + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ce2GVFmjiXE3", + "outputId": "f00b19bc-b9ed-4e1a-a0d7-c1280a159327" + }, + "source": [ + "print(vectorizer2.get_feature_names())" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['and this', 'document is', 'first document', 'is the', 'is this', 'second document', 'the first', 'the second', 'the third', 'third one', 'this document', 'this is', 'this the']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dwMzGx-eiZDx" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 3a84498d15b9d349ff27fb24ab3e476a23ef093f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 24 Jun 2021 11:53:42 +0530 Subject: [PATCH 075/171] Created using Colaboratory --- deep_learning/nlp_experiments.ipynb | 199 +++++++++++++++++++++++++--- 1 file changed, 180 insertions(+), 19 deletions(-) diff --git a/deep_learning/nlp_experiments.ipynb b/deep_learning/nlp_experiments.ipynb index e16499c..9ec0661 100644 --- a/deep_learning/nlp_experiments.ipynb +++ b/deep_learning/nlp_experiments.ipynb @@ -5,7 +5,7 @@ "colab": { "name": "nlp_experiments.ipynb", "provenance": [], - "authorship_tag": "ABX9TyMy6cfJXMpFrBs++y2ReLG2", + "authorship_tag": "ABX9TyNz5VZ8NEIpyDaAjxyouG0q", "include_colab_link": true }, "kernelspec": { @@ -35,7 +35,7 @@ "source": [ "from sklearn.feature_extraction.text import CountVectorizer" ], - "execution_count": 1, + "execution_count": 19, "outputs": [] }, { @@ -51,7 +51,7 @@ " 'Is this the first document?',\n", "]" ], - "execution_count": 2, + "execution_count": 20, "outputs": [] }, { @@ -61,12 +61,12 @@ "base_uri": "https://localhost:8080/" }, "id": "q4oy_HmrhjOO", - "outputId": "d7e0b14e-fda7-4125-fada-339d91aa185c" + "outputId": "19394384-77c3-499c-82e5-042363b7e8fc" }, "source": [ "len(corpus)" ], - "execution_count": 4, + "execution_count": 21, "outputs": [ { "output_type": "execute_result", @@ -78,7 +78,7 @@ "metadata": { "tags": [] }, - "execution_count": 4 + "execution_count": 21 } ] }, @@ -91,7 +91,7 @@ "vectorizer = CountVectorizer()\n", "X = vectorizer.fit_transform(corpus)" ], - "execution_count": 5, + "execution_count": 22, "outputs": [] }, { @@ -101,12 +101,12 @@ "base_uri": "https://localhost:8080/" }, "id": "qSbags0_hszc", - "outputId": "fdab4541-f6d1-4c43-f24d-bb7b46a90702" + "outputId": "870c9b15-2d1d-48b4-9203-54e80f548716" }, "source": [ "X" ], - "execution_count": 13, + "execution_count": 23, "outputs": [ { "output_type": "execute_result", @@ -119,7 +119,7 @@ "metadata": { "tags": [] }, - "execution_count": 13 + "execution_count": 23 } ] }, @@ -130,12 +130,12 @@ "base_uri": "https://localhost:8080/" }, "id": "B43PwLX5htOS", - "outputId": "10b50540-a767-4d9b-affd-97da2b7a1edd" + "outputId": "c725f85a-f4af-4deb-b02a-7370cd03d207" }, "source": [ "print(vectorizer.get_feature_names())" ], - "execution_count": 14, + "execution_count": 24, "outputs": [ { "output_type": "stream", @@ -153,12 +153,12 @@ "base_uri": "https://localhost:8080/" }, "id": "wW_w4XHviGPv", - "outputId": "9558f097-9b6e-4376-cae9-9eb84f31d59f" + "outputId": "29efe07b-2fa8-4b67-9fc2-aad2fe12831b" }, "source": [ "print(X.toarray())" ], - "execution_count": 15, + "execution_count": 25, "outputs": [ { "output_type": "stream", @@ -180,7 +180,7 @@ "source": [ "vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2))" ], - "execution_count": 16, + "execution_count": 26, "outputs": [] }, { @@ -191,22 +191,22 @@ "source": [ "X2 = vectorizer2.fit_transform(corpus)" ], - "execution_count": 17, + "execution_count": 27, "outputs": [] }, { "cell_type": "code", "metadata": { + "id": "Ce2GVFmjiXE3", "colab": { "base_uri": "https://localhost:8080/" }, - "id": "Ce2GVFmjiXE3", - "outputId": "f00b19bc-b9ed-4e1a-a0d7-c1280a159327" + "outputId": "40900e81-b9a0-429d-f1db-6cd843d523cb" }, "source": [ "print(vectorizer2.get_feature_names())" ], - "execution_count": 18, + "execution_count": 28, "outputs": [ { "output_type": "stream", @@ -222,6 +222,167 @@ "metadata": { "id": "dwMzGx-eiZDx" }, + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer" + ], + "execution_count": 29, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "t_v1fkw6rgyy" + }, + "source": [ + "corpus = [\n", + " 'This is the first document.',\n", + " 'This document is the second document.',\n", + " 'And this is the third one.',\n", + " 'Is this the first document?',\n", + "]" + ], + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "62Sz-9fRri-6", + "outputId": "6f7a9b73-71d6-4caf-d342-d27b49b0876e" + }, + "source": [ + "vectorizer = TfidfVectorizer()\n", + "X = vectorizer.fit_transform(corpus)\n", + "print(vectorizer.get_feature_names())" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xVPFDyrBrkvJ", + "outputId": "fd4d3afa-fe87-4306-c9c1-d453d5792745" + }, + "source": [ + "print(X.shape)" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(4, 9)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nlf9Sp-zrm_R", + "outputId": "b60c5ad2-6b70-4c98-f943-9c46f07a59a0" + }, + "source": [ + "print(X, type(X))" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "text": [ + " (0, 1)\t0.46979138557992045\n", + " (0, 2)\t0.5802858236844359\n", + " (0, 6)\t0.38408524091481483\n", + " (0, 3)\t0.38408524091481483\n", + " (0, 8)\t0.38408524091481483\n", + " (1, 5)\t0.5386476208856763\n", + " (1, 1)\t0.6876235979836938\n", + " (1, 6)\t0.281088674033753\n", + " (1, 3)\t0.281088674033753\n", + " (1, 8)\t0.281088674033753\n", + " (2, 4)\t0.511848512707169\n", + " (2, 7)\t0.511848512707169\n", + " (2, 0)\t0.511848512707169\n", + " (2, 6)\t0.267103787642168\n", + " (2, 3)\t0.267103787642168\n", + " (2, 8)\t0.267103787642168\n", + " (3, 1)\t0.46979138557992045\n", + " (3, 2)\t0.5802858236844359\n", + " (3, 6)\t0.38408524091481483\n", + " (3, 3)\t0.38408524091481483\n", + " (3, 8)\t0.38408524091481483 \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SAqA-mGgroKB", + "outputId": "f0b2be96-428c-4f53-b057-5aac91ebb9b2" + }, + "source": [ + "print(X.toarray(), X.toarray().shape)" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0. 0.46979139 0.58028582 0.38408524 0. 0.\n", + " 0.38408524 0. 0.38408524]\n", + " [0. 0.6876236 0. 0.28108867 0. 0.53864762\n", + " 0.28108867 0. 0.28108867]\n", + " [0.51184851 0. 0. 0.26710379 0.51184851 0.\n", + " 0.26710379 0.51184851 0.26710379]\n", + " [0. 0.46979139 0.58028582 0.38408524 0. 0.\n", + " 0.38408524 0. 0.38408524]] (4, 9)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kdLdat9mrtL3" + }, + "source": [ + "# TF-IDF is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. \n", + "# This is done by multiplying two metrics: how many times a word appears in a document, and the inverse document \n", + "# frequency of the word across a set of documents." + ], + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MBcBe0w7r5BR" + }, "source": [ "" ], From b408d7415ce358fa87d60f6226fd6a6515c765b8 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 26 Jun 2021 20:31:18 -0500 Subject: [PATCH 076/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 82a9073..b23c741 100644 --- a/README.md +++ b/README.md @@ -101,6 +101,8 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 57. Make pipeline to avoid test data leaking into train https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html +58. Potential evapotranspiration (PET) from netcdf file https://climate-indices.readthedocs.io/en/latest/# + Troubleshooting 1. 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"nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + }, + "colab": { + "name": "nn_tutorial.ipynb", + "provenance": [] + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "46TlD32UEH8-" + }, + "source": [ + "%matplotlib inline" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D-gUeu9wEH9A" + }, + "source": [ + "\n", + "What is `torch.nn` *really*?\n", + "============================\n", + "by Jeremy Howard, `fast.ai `_. Thanks to Rachel Thomas and Francisco Ingham.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ngj3iTm3EH9B" + }, + "source": [ + "We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file,\n", + "click the link at the top of the page.\n", + "\n", + "PyTorch provides the elegantly designed modules and classes `torch.nn `_ ,\n", + "`torch.optim `_ ,\n", + "`Dataset `_ ,\n", + "and `DataLoader `_\n", + "to help you create and train neural networks.\n", + "In order to fully utilize their power and customize\n", + "them for your problem, you need to really understand exactly what they're\n", + "doing. To develop this understanding, we will first train basic neural net\n", + "on the MNIST data set without using any features from these models; we will\n", + "initially only use the most basic PyTorch tensor functionality. Then, we will\n", + "incrementally add one feature from ``torch.nn``, ``torch.optim``, ``Dataset``, or\n", + "``DataLoader`` at a time, showing exactly what each piece does, and how it\n", + "works to make the code either more concise, or more flexible.\n", + "\n", + "**This tutorial assumes you already have PyTorch installed, and are familiar\n", + "with the basics of tensor operations.** (If you're familiar with Numpy array\n", + "operations, you'll find the PyTorch tensor operations used here nearly identical).\n", + "\n", + "MNIST data setup\n", + "----------------\n", + "\n", + "We will use the classic `MNIST `_ dataset,\n", + "which consists of black-and-white images of hand-drawn digits (between 0 and 9).\n", + "\n", + "We will use `pathlib `_\n", + "for dealing with paths (part of the Python 3 standard library), and will\n", + "download the dataset using\n", + "`requests `_. We will only\n", + "import modules when we use them, so you can see exactly what's being\n", + "used at each point.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BnruD83qEH9B" + }, + "source": [ + "from pathlib import Path\n", + "import requests\n", + "\n", + "DATA_PATH = Path(\"data\")\n", + "PATH = DATA_PATH / \"mnist\"\n", + "\n", + "PATH.mkdir(parents=True, exist_ok=True)\n", + "\n", + "URL = \"https://github.com/pytorch/tutorials/raw/master/_static/\"\n", + "FILENAME = \"mnist.pkl.gz\"\n", + "\n", + "if not (PATH / FILENAME).exists():\n", + " content = requests.get(URL + FILENAME).content\n", + " (PATH / FILENAME).open(\"wb\").write(content)" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QLVs7Gl9EH9C" + }, + "source": [ + "This dataset is in numpy array format, and has been stored using pickle,\n", + "a python-specific format for serializing data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5caNri-lEH9C" + }, + "source": [ + "import pickle\n", + "import gzip\n", + "\n", + "with gzip.open((PATH / FILENAME).as_posix(), \"rb\") as f:\n", + " ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding=\"latin-1\")" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0DUpE8BEEH9C" + }, + "source": [ + "Each image is 28 x 28, and is being stored as a flattened row of length\n", + "784 (=28x28). Let's take a look at one; we need to reshape it to 2d\n", + "first.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RkNehBpLEH9C", + "outputId": "ff6f730f-555b-4ecd-a40d-1a1e71a0fa14", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + } + }, + "source": [ + "from matplotlib import pyplot\n", + "import numpy as np\n", + "\n", + "pyplot.imshow(x_train[0].reshape((28, 28)), cmap=\"gray\")\n", + "print(x_train.shape)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(50000, 784)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JFGPdGmIEH9D" + }, + "source": [ + "PyTorch uses ``torch.tensor``, rather than numpy arrays, so we need to\n", + "convert our data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sKb5kZkgEH9D", + "outputId": "3121bcad-548b-416b-e18d-2a8b820c7340", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "import torch\n", + "\n", + "x_train, y_train, x_valid, y_valid = map(\n", + " torch.tensor, (x_train, y_train, x_valid, y_valid)\n", + ")\n", + "n, c = x_train.shape\n", + "print(x_train, y_train)\n", + "print(x_train.shape)\n", + "print(y_train.min(), y_train.max())" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor([[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]]) tensor([5, 0, 4, ..., 8, 4, 8])\n", + "torch.Size([50000, 784])\n", + "tensor(0) tensor(9)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gVkYogpdEH9D" + }, + "source": [ + "Neural net from scratch (no torch.nn)\n", + "---------------------------------------------\n", + "\n", + "Let's first create a model using nothing but PyTorch tensor operations. We're assuming\n", + "you're already familiar with the basics of neural networks. (If you're not, you can\n", + "learn them at `course.fast.ai `_).\n", + "\n", + "PyTorch provides methods to create random or zero-filled tensors, which we will\n", + "use to create our weights and bias for a simple linear model. These are just regular\n", + "tensors, with one very special addition: we tell PyTorch that they require a\n", + "gradient. This causes PyTorch to record all of the operations done on the tensor,\n", + "so that it can calculate the gradient during back-propagation *automatically*!\n", + "\n", + "For the weights, we set ``requires_grad`` **after** the initialization, since we\n", + "don't want that step included in the gradient. (Note that a trailing ``_`` in\n", + "PyTorch signifies that the operation is performed in-place.)\n", + "\n", + "

Note

We are initializing the weights here with\n", + " `Xavier initialisation `_\n", + " (by multiplying with 1/sqrt(n)).

\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BkSZAC-OEH9E" + }, + "source": [ + "import math\n", + "\n", + "weights = torch.randn(784, 10) / math.sqrt(784)\n", + "weights.requires_grad_()\n", + "bias = torch.zeros(10, requires_grad=True)" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pDt_cxhTEH9E" + }, + "source": [ + "Thanks to PyTorch's ability to calculate gradients automatically, we can\n", + "use any standard Python function (or callable object) as a model! So\n", + "let's just write a plain matrix multiplication and broadcasted addition\n", + "to create a simple linear model. We also need an activation function, so\n", + "we'll write `log_softmax` and use it. Remember: although PyTorch\n", + "provides lots of pre-written loss functions, activation functions, and\n", + "so forth, you can easily write your own using plain python. PyTorch will\n", + "even create fast GPU or vectorized CPU code for your function\n", + "automatically.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a0ZRbJW9EH9F" + }, + "source": [ + "def log_softmax(x):\n", + " return x - x.exp().sum(-1).log().unsqueeze(-1)\n", + "\n", + "def model(xb):\n", + " return log_softmax(xb @ weights + bias)" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_bDLbCHJEH9F" + }, + "source": [ + "In the above, the ``@`` stands for the dot product operation. We will call\n", + "our function on one batch of data (in this case, 64 images). This is\n", + "one *forward pass*. Note that our predictions won't be any better than\n", + "random at this stage, since we start with random weights.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l-hu806pEH9F", + "outputId": "e38927e2-bda7-4763-802b-c3f53b8ac0ad", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "bs = 64 # batch size\n", + "\n", + "xb = x_train[0:bs] # a mini-batch from x\n", + "preds = model(xb) # predictions\n", + "preds[0], preds.shape\n", + "print(preds[0], preds.shape)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor([-2.2635, -2.2963, -2.0675, -2.5985, -2.2197, -2.2012, -2.1712, -2.2893,\n", + " -2.1681, -3.0918], grad_fn=) torch.Size([64, 10])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8TF14KfMEH9F" + }, + "source": [ + "As you see, the ``preds`` tensor contains not only the tensor values, but also a\n", + "gradient function. We'll use this later to do backprop.\n", + "\n", + "Let's implement negative log-likelihood to use as the loss function\n", + "(again, we can just use standard Python):\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4klpWk3iEH9G" + }, + "source": [ + "def nll(input, target):\n", + " return -input[range(target.shape[0]), target].mean()\n", + "\n", + "loss_func = nll" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2cbOEkx1EH9G" + }, + "source": [ + "Let's check our loss with our random model, so we can see if we improve\n", + "after a backprop pass later.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xopUypzgEH9G", + "outputId": "247aed09-9729-4810-82ca-b797135f3060", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "yb = y_train[0:bs]\n", + "print(loss_func(preds, yb))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(2.3331, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7tIU2Eq_EH9G" + }, + "source": [ + "Let's also implement a function to calculate the accuracy of our model.\n", + "For each prediction, if the index with the largest value matches the\n", + "target value, then the prediction was correct.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "E47foMWTEH9G" + }, + "source": [ + "def accuracy(out, yb):\n", + " preds = torch.argmax(out, dim=1)\n", + " return (preds == yb).float().mean()" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XLVd0GycEH9H" + }, + "source": [ + "Let's check the accuracy of our random model, so we can see if our\n", + "accuracy improves as our loss improves.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0rb6QlyYEH9H", + "outputId": "61fed1fd-aef4-47be-976b-4ffb772695fa", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(accuracy(preds, yb))" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0938)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wc2wtF5FEH9H" + }, + "source": [ + "We can now run a training loop. For each iteration, we will:\n", + "\n", + "- select a mini-batch of data (of size ``bs``)\n", + "- use the model to make predictions\n", + "- calculate the loss\n", + "- ``loss.backward()`` updates the gradients of the model, in this case, ``weights``\n", + " and ``bias``.\n", + "\n", + "We now use these gradients to update the weights and bias. We do this\n", + "within the ``torch.no_grad()`` context manager, because we do not want these\n", + "actions to be recorded for our next calculation of the gradient. You can read\n", + "more about how PyTorch's Autograd records operations\n", + "`here `_.\n", + "\n", + "We then set the\n", + "gradients to zero, so that we are ready for the next loop.\n", + "Otherwise, our gradients would record a running tally of all the operations\n", + "that had happened (i.e. ``loss.backward()`` *adds* the gradients to whatever is\n", + "already stored, rather than replacing them).\n", + "\n", + ".. tip:: You can use the standard python debugger to step through PyTorch\n", + " code, allowing you to check the various variable values at each step.\n", + " Uncomment ``set_trace()`` below to try it out.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CfFmh5jREH9I" + }, + "source": [ + "from IPython.core.debugger import set_trace\n", + "\n", + "lr = 0.5 # learning rate\n", + "epochs = 2 # how many epochs to train for\n", + "\n", + "for epoch in range(epochs):\n", + " for i in range((n - 1) // bs + 1):\n", + " # set_trace()\n", + " start_i = i * bs\n", + " end_i = start_i + bs\n", + " xb = x_train[start_i:end_i]\n", + " yb = y_train[start_i:end_i]\n", + " pred = model(xb)\n", + " loss = loss_func(pred, yb)\n", + "\n", + " loss.backward()\n", + " with torch.no_grad():\n", + " weights -= weights.grad * lr\n", + " bias -= bias.grad * lr\n", + " weights.grad.zero_()\n", + " bias.grad.zero_()" + ], + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WyjWprwmEH9J" + }, + "source": [ + "That's it: we've created and trained a minimal neural network (in this case, a\n", + "logistic regression, since we have no hidden layers) entirely from scratch!\n", + "\n", + "Let's check the loss and accuracy and compare those to what we got\n", + "earlier. We expect that the loss will have decreased and accuracy to\n", + "have increased, and they have.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VCUowZfSEH9J", + "outputId": "0c29a50a-7372-4e41-8d03-27be8a882a38", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(loss_func(model(xb), yb), accuracy(model(xb), yb))" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0821, grad_fn=) tensor(1.)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jQVfk28zEH9J" + }, + "source": [ + "Using torch.nn.functional\n", + "------------------------------\n", + "\n", + "We will now refactor our code, so that it does the same thing as before, only\n", + "we'll start taking advantage of PyTorch's ``nn`` classes to make it more concise\n", + "and flexible. At each step from here, we should be making our code one or more\n", + "of: shorter, more understandable, and/or more flexible.\n", + "\n", + "The first and easiest step is to make our code shorter by replacing our\n", + "hand-written activation and loss functions with those from ``torch.nn.functional``\n", + "(which is generally imported into the namespace ``F`` by convention). This module\n", + "contains all the functions in the ``torch.nn`` library (whereas other parts of the\n", + "library contain classes). As well as a wide range of loss and activation\n", + "functions, you'll also find here some convenient functions for creating neural\n", + "nets, such as pooling functions. (There are also functions for doing convolutions,\n", + "linear layers, etc, but as we'll see, these are usually better handled using\n", + "other parts of the library.)\n", + "\n", + "If you're using negative log likelihood loss and log softmax activation,\n", + "then Pytorch provides a single function ``F.cross_entropy`` that combines\n", + "the two. So we can even remove the activation function from our model.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5MNvrCZEEH9K" + }, + "source": [ + "import torch.nn.functional as F\n", + "\n", + "loss_func = F.cross_entropy\n", + "\n", + "def model(xb):\n", + " return xb @ weights + bias" + ], + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-KEmgt0yEH9K" + }, + "source": [ + "Note that we no longer call ``log_softmax`` in the ``model`` function. Let's\n", + "confirm that our loss and accuracy are the same as before:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tJyXAPACEH9K", + "outputId": "297d5792-f738-4f44-e388-d7423b776c47", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(loss_func(model(xb), yb), accuracy(model(xb), yb))" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0821, grad_fn=) tensor(1.)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "54A--5CQEH9K" + }, + "source": [ + "Refactor using nn.Module\n", + "-----------------------------\n", + "Next up, we'll use ``nn.Module`` and ``nn.Parameter``, for a clearer and more\n", + "concise training loop. We subclass ``nn.Module`` (which itself is a class and\n", + "able to keep track of state). In this case, we want to create a class that\n", + "holds our weights, bias, and method for the forward step. ``nn.Module`` has a\n", + "number of attributes and methods (such as ``.parameters()`` and ``.zero_grad()``)\n", + "which we will be using.\n", + "\n", + "

Note

``nn.Module`` (uppercase M) is a PyTorch specific concept, and is a\n", + " class we'll be using a lot. ``nn.Module`` is not to be confused with the Python\n", + " concept of a (lowercase ``m``) `module `_,\n", + " which is a file of Python code that can be imported.

\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NL8NGJ_AEH9L" + }, + "source": [ + "from torch import nn\n", + "\n", + "class Mnist_Logistic(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.weights = nn.Parameter(torch.randn(784, 10) / math.sqrt(784))\n", + " self.bias = nn.Parameter(torch.zeros(10))\n", + "\n", + " def forward(self, xb):\n", + " return xb @ self.weights + self.bias" + ], + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qVEaL_ekEH9L" + }, + "source": [ + "Since we're now using an object instead of just using a function, we\n", + "first have to instantiate our model:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "N4Mwv6myEH9L" + }, + "source": [ + "model = Mnist_Logistic()" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ATMAsh0FEH9M" + }, + "source": [ + "Now we can calculate the loss in the same way as before. Note that\n", + "``nn.Module`` objects are used as if they are functions (i.e they are\n", + "*callable*), but behind the scenes Pytorch will call our ``forward``\n", + "method automatically.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ceQuT1zbEH9M", + "outputId": "c2eaffd7-f35e-49ec-9777-c97ca97ee011", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(loss_func(model(xb), yb))" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(2.3546, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mJDfCOKEH9M" + }, + "source": [ + "Previously for our training loop we had to update the values for each parameter\n", + "by name, and manually zero out the grads for each parameter separately, like this:\n", + "::\n", + " with torch.no_grad():\n", + " weights -= weights.grad * lr\n", + " bias -= bias.grad * lr\n", + " weights.grad.zero_()\n", + " bias.grad.zero_()\n", + "\n", + "\n", + "Now we can take advantage of model.parameters() and model.zero_grad() (which\n", + "are both defined by PyTorch for ``nn.Module``) to make those steps more concise\n", + "and less prone to the error of forgetting some of our parameters, particularly\n", + "if we had a more complicated model:\n", + "::\n", + " with torch.no_grad():\n", + " for p in model.parameters(): p -= p.grad * lr\n", + " model.zero_grad()\n", + "\n", + "\n", + "We'll wrap our little training loop in a ``fit`` function so we can run it\n", + "again later.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "odQkIkJkEH9N" + }, + "source": [ + "def fit():\n", + " for epoch in range(epochs):\n", + " for i in range((n - 1) // bs + 1):\n", + " start_i = i * bs\n", + " end_i = start_i + bs\n", + " xb = x_train[start_i:end_i]\n", + " yb = y_train[start_i:end_i]\n", + " pred = model(xb)\n", + " loss = loss_func(pred, yb)\n", + "\n", + " loss.backward()\n", + " with torch.no_grad():\n", + " for p in model.parameters():\n", + " p -= p.grad * lr\n", + " model.zero_grad()\n", + "\n", + "fit()" + ], + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kjm6r1X8EH9N" + }, + "source": [ + "Let's double-check that our loss has gone down:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XCNjZWeZEH9N", + "outputId": "3cdea7a6-1d76-4108-a390-b8c9f31a8215", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(loss_func(model(xb), yb))" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0825, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WMCicTWpEH9N" + }, + "source": [ + "Refactor using nn.Linear\n", + "-------------------------\n", + "\n", + "We continue to refactor our code. Instead of manually defining and\n", + "initializing ``self.weights`` and ``self.bias``, and calculating ``xb @\n", + "self.weights + self.bias``, we will instead use the Pytorch class\n", + "`nn.Linear `_ for a\n", + "linear layer, which does all that for us. Pytorch has many types of\n", + "predefined layers that can greatly simplify our code, and often makes it\n", + "faster too.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vb-f3QruEH9N" + }, + "source": [ + "class Mnist_Logistic(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.lin = nn.Linear(784, 10)\n", + "\n", + " def forward(self, xb):\n", + " return self.lin(xb)" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7LsAdeM-EH9N" + }, + "source": [ + "We instantiate our model and calculate the loss in the same way as before:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vGunKRGdEH9O", + "outputId": "09e7f9be-006c-4bd2-803e-c33e10b99071", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "model = Mnist_Logistic()\n", + "print(loss_func(model(xb), yb))" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(2.2689, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bdA5vuqlEH9O" + }, + "source": [ + "We are still able to use our same ``fit`` method as before.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OMq17Li_EH9O", + "outputId": "2bf4d84a-d99f-45a6-b5fc-4e2e46e8698d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "fit()\n", + "\n", + "print(loss_func(model(xb), yb))" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0823, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8e9j1bfTEH9O" + }, + "source": [ + "Refactor using optim\n", + "------------------------------\n", + "\n", + "Pytorch also has a package with various optimization algorithms, ``torch.optim``.\n", + "We can use the ``step`` method from our optimizer to take a forward step, instead\n", + "of manually updating each parameter.\n", + "\n", + "This will let us replace our previous manually coded optimization step:\n", + "::\n", + " with torch.no_grad():\n", + " for p in model.parameters(): p -= p.grad * lr\n", + " model.zero_grad()\n", + "\n", + "and instead use just:\n", + "::\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + "(``optim.zero_grad()`` resets the gradient to 0 and we need to call it before\n", + "computing the gradient for the next minibatch.)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5vdHaipqEH9O" + }, + "source": [ + "from torch import optim" + ], + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SmNLkgB8EH9O" + }, + "source": [ + "We'll define a little function to create our model and optimizer so we\n", + "can reuse it in the future.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sC98V6NyEH9P", + "outputId": "de3e6e5d-e0f7-476f-bbdb-12c75f250a29", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "def get_model():\n", + " model = Mnist_Logistic()\n", + " return model, optim.SGD(model.parameters(), lr=lr)\n", + "\n", + "model, opt = get_model()\n", + "print(loss_func(model(xb), yb))\n", + "\n", + "for epoch in range(epochs):\n", + " for i in range((n - 1) // bs + 1):\n", + " start_i = i * bs\n", + " end_i = start_i + bs\n", + " xb = x_train[start_i:end_i]\n", + " yb = y_train[start_i:end_i]\n", + " pred = model(xb)\n", + " loss = loss_func(pred, yb)\n", + "\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + "print(loss_func(model(xb), yb))" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(2.3409, grad_fn=)\n", + "tensor(0.0811, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bszNdoqHEH9P" + }, + "source": [ + "Refactor using Dataset\n", + "------------------------------\n", + "\n", + "PyTorch has an abstract Dataset class. A Dataset can be anything that has\n", + "a ``__len__`` function (called by Python's standard ``len`` function) and\n", + "a ``__getitem__`` function as a way of indexing into it.\n", + "`This tutorial `_\n", + "walks through a nice example of creating a custom ``FacialLandmarkDataset`` class\n", + "as a subclass of ``Dataset``.\n", + "\n", + "PyTorch's `TensorDataset `_\n", + "is a Dataset wrapping tensors. By defining a length and way of indexing,\n", + "this also gives us a way to iterate, index, and slice along the first\n", + "dimension of a tensor. This will make it easier to access both the\n", + "independent and dependent variables in the same line as we train.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SM0pyFJLEH9P" + }, + "source": [ + "from torch.utils.data import TensorDataset" + ], + "execution_count": 33, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XTm8sxuyEH9P" + }, + "source": [ + "Both ``x_train`` and ``y_train`` can be combined in a single ``TensorDataset``,\n", + "which will be easier to iterate over and slice.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7MjG-kbiPgGh", + "outputId": "6f4356e9-ce07-4289-d3cb-550a9b7051f0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(type(x_train), x_train.shape, x_train)" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "stream", + "text": [ + " torch.Size([50000, 784]) tensor([[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jXVtkTaIEH9P" + }, + "source": [ + "train_ds = TensorDataset(x_train, y_train)" + ], + "execution_count": 39, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SZ1ZgaOrEH9P" + }, + "source": [ + "Previously, we had to iterate through minibatches of x and y values separately:\n", + "::\n", + " xb = x_train[start_i:end_i]\n", + " yb = y_train[start_i:end_i]\n", + "\n", + "\n", + "Now, we can do these two steps together:\n", + "::\n", + " xb,yb = train_ds[i*bs : i*bs+bs]\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ad2G-zO6EH9Q", + "outputId": "89c1e660-c64f-40ee-dc41-ba8ef79809f0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "model, opt = get_model()\n", + "\n", + "for epoch in range(epochs):\n", + " for i in range((n - 1) // bs + 1):\n", + " xb, yb = train_ds[i * bs: i * bs + bs]\n", + " pred = model(xb)\n", + " loss = loss_func(pred, yb)\n", + "\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + "print(loss_func(model(xb), yb))" + ], + "execution_count": 40, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0818, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ClNyGwQOEH9Q" + }, + "source": [ + "Refactor using DataLoader\n", + "------------------------------\n", + "\n", + "Pytorch's ``DataLoader`` is responsible for managing batches. You can\n", + "create a ``DataLoader`` from any ``Dataset``. ``DataLoader`` makes it easier\n", + "to iterate over batches. Rather than having to use ``train_ds[i*bs : i*bs+bs]``,\n", + "the DataLoader gives us each minibatch automatically.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YNOjegf2EH9Q" + }, + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "train_ds = TensorDataset(x_train, y_train)\n", + "train_dl = DataLoader(train_ds, batch_size=bs)" + ], + "execution_count": 41, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I1ftWz3uEH9Q" + }, + "source": [ + "Previously, our loop iterated over batches (xb, yb) like this:\n", + "::\n", + " for i in range((n-1)//bs + 1):\n", + " xb,yb = train_ds[i*bs : i*bs+bs]\n", + " pred = model(xb)\n", + "\n", + "Now, our loop is much cleaner, as (xb, yb) are loaded automatically from the data loader:\n", + "::\n", + " for xb,yb in train_dl:\n", + " pred = model(xb)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hOl6LP1zEH9Q", + "outputId": "d9f46478-ac64-4de2-a959-a47541031e09", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "model, opt = get_model()\n", + "\n", + "for epoch in range(epochs):\n", + " for xb, yb in train_dl:\n", + " pred = model(xb)\n", + " loss = loss_func(pred, yb)\n", + "\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + "print(loss_func(model(xb), yb))" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor(0.0815, grad_fn=)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gWcOTdxXEH9Q" + }, + "source": [ + "Thanks to Pytorch's ``nn.Module``, ``nn.Parameter``, ``Dataset``, and ``DataLoader``,\n", + "our training loop is now dramatically smaller and easier to understand. Let's\n", + "now try to add the basic features necessary to create effective models in practice.\n", + "\n", + "Add validation\n", + "-----------------------\n", + "\n", + "In section 1, we were just trying to get a reasonable training loop set up for\n", + "use on our training data. In reality, you **always** should also have\n", + "a `validation set `_, in order\n", + "to identify if you are overfitting.\n", + "\n", + "Shuffling the training data is\n", + "`important `_\n", + "to prevent correlation between batches and overfitting. On the other hand, the\n", + "validation loss will be identical whether we shuffle the validation set or not.\n", + "Since shuffling takes extra time, it makes no sense to shuffle the validation data.\n", + "\n", + "We'll use a batch size for the validation set that is twice as large as\n", + "that for the training set. This is because the validation set does not\n", + "need backpropagation and thus takes less memory (it doesn't need to\n", + "store the gradients). We take advantage of this to use a larger batch\n", + "size and compute the loss more quickly.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kX5LtAINEH9R" + }, + "source": [ + "train_ds = TensorDataset(x_train, y_train)\n", + "train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)\n", + "\n", + "valid_ds = TensorDataset(x_valid, y_valid)\n", + "valid_dl = DataLoader(valid_ds, batch_size=bs * 2)" + ], + "execution_count": 43, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jtiOhCChEH9R" + }, + "source": [ + "We will calculate and print the validation loss at the end of each epoch.\n", + "\n", + "(Note that we always call ``model.train()`` before training, and ``model.eval()``\n", + "before inference, because these are used by layers such as ``nn.BatchNorm2d``\n", + "and ``nn.Dropout`` to ensure appropriate behaviour for these different phases.)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "P9pE4DBMEH9R", + "outputId": "beb65ac5-da4b-4655-f13f-7e42a18db809", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "model, opt = get_model()\n", + "\n", + "for epoch in range(epochs):\n", + " model.train()\n", + " for xb, yb in train_dl:\n", + " pred = model(xb)\n", + " loss = loss_func(pred, yb)\n", + "\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + " model.eval()\n", + " with torch.no_grad():\n", + " valid_loss = sum(loss_func(model(xb), yb) for xb, yb in valid_dl)\n", + "\n", + " print(epoch, valid_loss / len(valid_dl))" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 tensor(0.3378)\n", + "1 tensor(0.4590)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Av5yhHNEH9R" + }, + "source": [ + "Create fit() and get_data()\n", + "----------------------------------\n", + "\n", + "We'll now do a little refactoring of our own. Since we go through a similar\n", + "process twice of calculating the loss for both the training set and the\n", + "validation set, let's make that into its own function, ``loss_batch``, which\n", + "computes the loss for one batch.\n", + "\n", + "We pass an optimizer in for the training set, and use it to perform\n", + "backprop. For the validation set, we don't pass an optimizer, so the\n", + "method doesn't perform backprop.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iUFeb2QHEH9R" + }, + "source": [ + "def loss_batch(model, loss_func, xb, yb, opt=None):\n", + " loss = loss_func(model(xb), yb)\n", + "\n", + " if opt is not None:\n", + " loss.backward()\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + " return loss.item(), len(xb)" + ], + "execution_count": 45, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FlJUIS_lEH9R" + }, + "source": [ + "``fit`` runs the necessary operations to train our model and compute the\n", + "training and validation losses for each epoch.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gbMgZcGOEH9R" + }, + "source": [ + "import numpy as np\n", + "\n", + "def fit(epochs, model, loss_func, opt, train_dl, valid_dl):\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " for xb, yb in train_dl:\n", + " loss_batch(model, loss_func, xb, yb, opt)\n", + "\n", + " model.eval()\n", + " with torch.no_grad():\n", + " losses, nums = zip(\n", + " *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]\n", + " )\n", + " val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)\n", + "\n", + " print(epoch, val_loss)" + ], + "execution_count": 46, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A4CyMNXnEH9S" + }, + "source": [ + "``get_data`` returns dataloaders for the training and validation sets.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zRo5s-v3EH9S" + }, + "source": [ + "def get_data(train_ds, valid_ds, bs):\n", + " return (\n", + " DataLoader(train_ds, batch_size=bs, shuffle=True),\n", + " DataLoader(valid_ds, batch_size=bs * 2),\n", + " )" + ], + "execution_count": 47, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_sQwui26EH9S" + }, + "source": [ + "Now, our whole process of obtaining the data loaders and fitting the\n", + "model can be run in 3 lines of code:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yFl522zAEH9S", + "outputId": "e8afd502-8300-42ff-9e2a-7aa056fce1f4", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "train_dl, valid_dl = get_data(train_ds, valid_ds, bs)\n", + "model, opt = get_model()\n", + "fit(epochs, model, loss_func, opt, train_dl, valid_dl)" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0.2945808494091034\n", + "1 0.31396189473867414\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MqMwGIqCEH9S" + }, + "source": [ + "You can use these basic 3 lines of code to train a wide variety of models.\n", + "Let's see if we can use them to train a convolutional neural network (CNN)!\n", + "\n", + "Switch to CNN\n", + "-------------\n", + "\n", + "We are now going to build our neural network with three convolutional layers.\n", + "Because none of the functions in the previous section assume anything about\n", + "the model form, we'll be able to use them to train a CNN without any modification.\n", + "\n", + "We will use Pytorch's predefined\n", + "`Conv2d `_ class\n", + "as our convolutional layer. We define a CNN with 3 convolutional layers.\n", + "Each convolution is followed by a ReLU. At the end, we perform an\n", + "average pooling. (Note that ``view`` is PyTorch's version of numpy's\n", + "``reshape``)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tq9kBqmUEH9S" + }, + "source": [ + "class Mnist_CNN(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1)\n", + " self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1)\n", + " self.conv3 = nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1)\n", + "\n", + " def forward(self, xb):\n", + " xb = xb.view(-1, 1, 28, 28)\n", + " xb = F.relu(self.conv1(xb))\n", + " xb = F.relu(self.conv2(xb))\n", + " xb = F.relu(self.conv3(xb))\n", + " xb = F.avg_pool2d(xb, 4)\n", + " return xb.view(-1, xb.size(1))\n", + "\n", + "lr = 0.1" + ], + "execution_count": 49, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z7aq5HLJEH9T" + }, + "source": [ + "`Momentum `_ is a variation on\n", + "stochastic gradient descent that takes previous updates into account as well\n", + "and generally leads to faster training.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JEcLodthEH9T", + "outputId": "9bb7939e-2df9-4496-821f-e2d17b3a9dc0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "model = Mnist_CNN()\n", + "opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)\n", + "\n", + "fit(epochs, model, loss_func, opt, train_dl, valid_dl)" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0.3381934159517288\n", + "1 0.2412288737297058\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0QhEBoWhEH9T" + }, + "source": [ + "nn.Sequential\n", + "------------------------\n", + "\n", + "``torch.nn`` has another handy class we can use to simplify our code:\n", + "`Sequential `_ .\n", + "A ``Sequential`` object runs each of the modules contained within it, in a\n", + "sequential manner. This is a simpler way of writing our neural network.\n", + "\n", + "To take advantage of this, we need to be able to easily define a\n", + "**custom layer** from a given function. For instance, PyTorch doesn't\n", + "have a `view` layer, and we need to create one for our network. ``Lambda``\n", + "will create a layer that we can then use when defining a network with\n", + "``Sequential``.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Bu3Gu2xEEH9T" + }, + "source": [ + "class Lambda(nn.Module):\n", + " def __init__(self, func):\n", + " super().__init__()\n", + " self.func = func\n", + "\n", + " def forward(self, x):\n", + " return self.func(x)\n", + "\n", + "\n", + "def preprocess(x):\n", + " return x.view(-1, 1, 28, 28)" + ], + "execution_count": 51, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ij3kmqJgEH9T" + }, + "source": [ + "The model created with ``Sequential`` is simply:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xl4Fn45UEH9T", + "outputId": "c2816630-17f0-4d47-c41e-ea1fd1c5d5dc", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "model = nn.Sequential(\n", + " Lambda(preprocess),\n", + " nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),\n", + " nn.ReLU(),\n", + " nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1),\n", + " nn.ReLU(),\n", + " nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1),\n", + " nn.ReLU(),\n", + " nn.AvgPool2d(4),\n", + " Lambda(lambda x: x.view(x.size(0), -1)),\n", + ")\n", + "\n", + "opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)\n", + "\n", + "fit(epochs, model, loss_func, opt, train_dl, valid_dl)" + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0.319701298558712\n", + "1 0.2780502150177956\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-YEEI3n3EH9U" + }, + "source": [ + "Wrapping DataLoader\n", + "-----------------------------\n", + "\n", + "Our CNN is fairly concise, but it only works with MNIST, because:\n", + " - It assumes the input is a 28\\*28 long vector\n", + " - It assumes that the final CNN grid size is 4\\*4 (since that's the average\n", + "pooling kernel size we used)\n", + "\n", + "Let's get rid of these two assumptions, so our model works with any 2d\n", + "single channel image. First, we can remove the initial Lambda layer by\n", + "moving the data preprocessing into a generator:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Iad-G1MCEH9U" + }, + "source": [ + "def preprocess(x, y):\n", + " return x.view(-1, 1, 28, 28), y\n", + "\n", + "\n", + "class WrappedDataLoader:\n", + " def __init__(self, dl, func):\n", + " self.dl = dl\n", + " self.func = func\n", + "\n", + " def __len__(self):\n", + " return len(self.dl)\n", + "\n", + " def __iter__(self):\n", + " batches = iter(self.dl)\n", + " for b in batches:\n", + " yield (self.func(*b))\n", + "\n", + "train_dl, valid_dl = get_data(train_ds, valid_ds, bs)\n", + "train_dl = WrappedDataLoader(train_dl, preprocess)\n", + "valid_dl = WrappedDataLoader(valid_dl, preprocess)" + ], + "execution_count": 53, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fzAfmPIREH9U" + }, + "source": [ + "Next, we can replace ``nn.AvgPool2d`` with ``nn.AdaptiveAvgPool2d``, which\n", + "allows us to define the size of the *output* tensor we want, rather than\n", + "the *input* tensor we have. As a result, our model will work with any\n", + "size input.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oF9b200JEH9U" + }, + "source": [ + "model = nn.Sequential(\n", + " nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),\n", + " nn.ReLU(),\n", + " nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1),\n", + " nn.ReLU(),\n", + " nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1),\n", + " nn.ReLU(),\n", + " nn.AdaptiveAvgPool2d(1),\n", + " Lambda(lambda x: x.view(x.size(0), -1)),\n", + ")\n", + "\n", + "opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)" + ], + "execution_count": 54, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MLTvZScXEH9U" + }, + "source": [ + "Let's try it out:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "f3Gyy2QOEH9U", + "outputId": "63a5a8a3-9e7a-4e75-ab99-7f456be0aeac", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "fit(epochs, model, loss_func, opt, train_dl, valid_dl)" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0.3804978719711304\n", + "1 0.28147055239677427\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WV__kkPTEH9V" + }, + "source": [ + "Using your GPU\n", + "---------------\n", + "\n", + "If you're lucky enough to have access to a CUDA-capable GPU (you can\n", + "rent one for about $0.50/hour from most cloud providers) you can\n", + "use it to speed up your code. First check that your GPU is working in\n", + "Pytorch:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HUAuf_QeEH9V", + "outputId": "863941ff-a380-4d0b-c397-83be8f19cb0a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "print(torch.cuda.is_available())" + ], + "execution_count": 56, + "outputs": [ + { + "output_type": "stream", + "text": [ + "False\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U9s3sRoKEH9V" + }, + "source": [ + "And then create a device object for it:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "btpoUH05EH9V" + }, + "source": [ + "dev = torch.device(\n", + " \"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")" + ], + "execution_count": 57, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WJM1S-rsEH9V" + }, + "source": [ + "Let's update ``preprocess`` to move batches to the GPU:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vuDsDNYpEH9V" + }, + "source": [ + "def preprocess(x, y):\n", + " return x.view(-1, 1, 28, 28).to(dev), y.to(dev)\n", + "\n", + "\n", + "train_dl, valid_dl = get_data(train_ds, valid_ds, bs)\n", + "train_dl = WrappedDataLoader(train_dl, preprocess)\n", + "valid_dl = WrappedDataLoader(valid_dl, preprocess)" + ], + "execution_count": 58, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3uFTJm4yEH9V" + }, + "source": [ + "Finally, we can move our model to the GPU.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3GWOTgBnEH9W" + }, + "source": [ + "model.to(dev)\n", + "opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)" + ], + "execution_count": 59, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Orvs0q-EH9W" + }, + "source": [ + "You should find it runs faster now:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "akTHe2jOEH9W", + "outputId": "30b2a005-92af-4878-8322-069c7b7ac07a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "fit(epochs, model, loss_func, opt, train_dl, valid_dl)" + ], + "execution_count": 60, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0.2620402427375317\n", + "1 0.21865889232754707\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XNWhTwNDEH9W" + }, + "source": [ + "Closing thoughts\n", + "-----------------\n", + "\n", + "We now have a general data pipeline and training loop which you can use for\n", + "training many types of models using Pytorch. To see how simple training a model\n", + "can now be, take a look at the `mnist_sample` sample notebook.\n", + "\n", + "Of course, there are many things you'll want to add, such as data augmentation,\n", + "hyperparameter tuning, monitoring training, transfer learning, and so forth.\n", + "These features are available in the fastai library, which has been developed\n", + "using the same design approach shown in this tutorial, providing a natural\n", + "next step for practitioners looking to take their models further.\n", + "\n", + "We promised at the start of this tutorial we'd explain through example each of\n", + "``torch.nn``, ``torch.optim``, ``Dataset``, and ``DataLoader``. So let's summarize\n", + "what we've seen:\n", + "\n", + " - **torch.nn**\n", + "\n", + " + ``Module``: creates a callable which behaves like a function, but can also\n", + " contain state(such as neural net layer weights). It knows what ``Parameter`` (s) it\n", + " contains and can zero all their gradients, loop through them for weight updates, etc.\n", + " + ``Parameter``: a wrapper for a tensor that tells a ``Module`` that it has weights\n", + " that need updating during backprop. Only tensors with the `requires_grad` attribute set are updated\n", + " + ``functional``: a module(usually imported into the ``F`` namespace by convention)\n", + " which contains activation functions, loss functions, etc, as well as non-stateful\n", + " versions of layers such as convolutional and linear layers.\n", + " - ``torch.optim``: Contains optimizers such as ``SGD``, which update the weights\n", + " of ``Parameter`` during the backward step\n", + " - ``Dataset``: An abstract interface of objects with a ``__len__`` and a ``__getitem__``,\n", + " including classes provided with Pytorch such as ``TensorDataset``\n", + " - ``DataLoader``: Takes any ``Dataset`` and creates an iterator which returns batches of data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KswMl0BTiCEU" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 3512256d373f57d3e5f838094cb7c109577a09a2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 8 Jul 2021 16:46:07 +0530 Subject: [PATCH 079/171] CAPE and CIN calculation --- xarray_tutorial/cape_cin.ipynb | 589 +++++++++++++++++++++++++++++++++ 1 file changed, 589 insertions(+) create mode 100644 xarray_tutorial/cape_cin.ipynb diff --git a/xarray_tutorial/cape_cin.ipynb b/xarray_tutorial/cape_cin.ipynb new file mode 100644 index 0000000..2f073e3 --- /dev/null +++ b/xarray_tutorial/cape_cin.ipynb @@ -0,0 +1,589 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "cape_cin.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyNF6quuqPnT+zGJy2YMka8k", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u5S1EZhzrk81" + }, + "source": [ + "#!pip install siphon\n", + "from datetime import datetime\n", + "from siphon.simplewebservice.wyoming import WyomingUpperAir" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sH6PMDlyzOFY" + }, + "source": [ + "df = WyomingUpperAir.request_data(datetime(2012,4,14,12), 'OUN')" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 790 + }, + "id": "PbD_gDRzzvf7", + "outputId": "2b292713-1339-4ac9-cccd-fad3e4d87ffc" + }, + "source": [ + "df" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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pressureheighttemperaturedewpointdirectionspeedu_windv_windstationstation_numbertimelatitudelongitudeelevationpw
0969.034521.018.2165.08.0-2.070552e+007.727407e+00OUN723572012-04-14 12:00:0035.18-97.44345.029.21
1964.039020.818.3167.012.0-2.699413e+001.169244e+01OUN723572012-04-14 12:00:0035.18-97.44345.029.21
2939.661019.117.8175.031.0-2.701828e+003.088204e+01OUN723572012-04-14 12:00:0035.18-97.44345.029.21
3925.074418.017.5175.032.0-2.788984e+003.187823e+01OUN723572012-04-14 12:00:0035.18-97.44345.029.21
4906.891416.616.6180.040.0-4.898587e-154.000000e+01OUN723572012-04-14 12:00:0035.18-97.44345.029.21
................................................
11710.830480-43.1-79.1270.023.02.300000e+014.225031e-15OUN723572012-04-14 12:00:0035.18-97.44345.029.21
11810.530681-42.5-78.5262.021.02.079563e+012.922635e+00OUN723572012-04-14 12:00:0035.18-97.44345.029.21
11910.031010-42.9-78.9250.018.01.691447e+016.156363e+00OUN723572012-04-14 12:00:0035.18-97.44345.029.21
1209.031699-44.4-79.5240.024.02.078461e+011.200000e+01OUN723572012-04-14 12:00:0035.18-97.44345.029.21
1218.831868-44.7-79.7NaNNaNNaNNaNOUN723572012-04-14 12:00:0035.18-97.44345.029.21
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122 rows × 15 columns

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" + ], + "text/plain": [ + " pressure height temperature ... longitude elevation pw\n", + "0 969.0 345 21.0 ... -97.44 345.0 29.21\n", + "1 964.0 390 20.8 ... -97.44 345.0 29.21\n", + "2 939.6 610 19.1 ... -97.44 345.0 29.21\n", + "3 925.0 744 18.0 ... -97.44 345.0 29.21\n", + "4 906.8 914 16.6 ... -97.44 345.0 29.21\n", + ".. ... ... ... ... ... ... ...\n", + "117 10.8 30480 -43.1 ... -97.44 345.0 29.21\n", + "118 10.5 30681 -42.5 ... -97.44 345.0 29.21\n", + "119 10.0 31010 -42.9 ... -97.44 345.0 29.21\n", + "120 9.0 31699 -44.4 ... -97.44 345.0 29.21\n", + "121 8.8 31868 -44.7 ... -97.44 345.0 29.21\n", + "\n", + "[122 rows x 15 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1-BMjkJOzwwU" + }, + "source": [ + "#!pip install metpy" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sJZmz3ezz67C" + }, + "source": [ + "from metpy.units import units" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KS3XbaLm0DPS" + }, + "source": [ + "p = df['pressure'].values * units(df.units['pressure'])\n", + "T = df['temperature'].values * units(df.units['temperature'])\n", + "Td = df['dewpoint'].values * units(df.units['dewpoint'])\n", + "heights = df['height'].values * units(df.units['height'])" + ], + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hQ07hPc20Zmu" + }, + "source": [ + "import metpy.calc as mpcalc" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "E29BMrFp0oiX", + "outputId": "86d4dbbb-1df9-41b9-8a03-fa5605d5c81f" + }, + "source": [ + "mpcalc.surface_based_cape_cin(p, T, Td)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1153.8204819179555 joule/kilogram, -8.634797296894165 joule/kilogram)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 218 + }, + "id": "TfIx4fQC0s9q", + "outputId": "0797e3bb-38d6-4d7d-a89b-0d2ef98017bf" + }, + "source": [ + "p" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/latex": "$\\begin{pmatrix}969.0 & 964.0 & 939.6 & 925.0 & 906.8 & 904.0 & 874.9 & 850.0 & 844.0 & 819.0 & 814.0 & 784.8 & 757.0 & 756.8 & 729.1 & 722.0 & 700.0 & 690.0 & 661.0 & 652.0 & 651.5 & 638.0 & 603.6 & 558.9 & 550.0 & 514.0 & 500.0 & 496.6 & 476.8 & 410.0 & 405.2 & 400.0 & 394.0 & 388.6 & 386.0 & 373.0 & 326.0 & 313.2 & 300.0 & 299.6 & 261.4 & 250.0 & 200.0 & 196.4 & 196.0 & 189.0 & 188.0 & 182.0 & 169.2 & 166.0 & 150.0 & 145.3 & 142.0 & 137.0 & 131.3 & 131.0 & 130.0 & 126.0 & 122.0 & 119.0 & 114.0 & 113.3 & 108.0 & 106.0 & 104.0 & 102.0 & 100.0 & 80.7 & 76.1 & 74.2 & 72.4 & 70.0 & 68.8 & 67.6 & 65.5 & 63.7 & 62.3 & 59.2 & 58.5 & 56.3 & 50.0 & 49.3 & 48.5 & 43.9 & 43.2 & 39.8 & 37.9 & 37.4 & 36.1 & 34.4 & 33.5 & 32.8 & 31.2 & 30.7 & 30.0 & 29.1 & 28.4 & 27.1 & 27.1 & 25.8 & 25.5 & 23.6 & 23.1 & 21.5 & 20.3 & 20.0 & 19.6 & 18.7 & 18.3 & 17.1 & 17.1 & 15.7 & 14.9 & 14.4 & 14.0 & 12.1 & 11.4 & 10.8 & 10.5 & 10.0 & 9.0 & 8.8\\end{pmatrix}\\ \\mathrm{hectopascal}$", + "text/html": [ + "
Magnitude
[969.0 964.0 939.6 925.0 906.8 904.0 874.9 850.0 844.0 819.0 814.0 784.8
 757.0 756.8 729.1 722.0 700.0 690.0 661.0 652.0 651.5 638.0 603.6 558.9
 550.0 514.0 500.0 496.6 476.8 410.0 405.2 400.0 394.0 388.6 386.0 373.0
 326.0 313.2 300.0 299.6 261.4 250.0 200.0 196.4 196.0 189.0 188.0 182.0
 169.2 166.0 150.0 145.3 142.0 137.0 131.3 131.0 130.0 126.0 122.0 119.0
 114.0 113.3 108.0 106.0 104.0 102.0 100.0 80.7 76.1 74.2 72.4 70.0 68.8
 67.6 65.5 63.7 62.3 59.2 58.5 56.3 50.0 49.3 48.5 43.9 43.2 39.8 37.9
 37.4 36.1 34.4 33.5 32.8 31.2 30.7 30.0 29.1 28.4 27.1 27.1 25.8 25.5
 23.6 23.1 21.5 20.3 20.0 19.6 18.7 18.3 17.1 17.1 15.7 14.9 14.4 14.0
 12.1 11.4 10.8 10.5 10.0 9.0 8.8]
Unitshectopascal
" + ], + "text/plain": [ + "array([969. , 964. , 939.6, 925. , 906.8, 904. , 874.9, 850. , 844. ,\n", + " 819. , 814. , 784.8, 757. , 756.8, 729.1, 722. , 700. , 690. ,\n", + " 661. , 652. , 651.5, 638. , 603.6, 558.9, 550. , 514. , 500. ,\n", + " 496.6, 476.8, 410. , 405.2, 400. , 394. , 388.6, 386. , 373. ,\n", + " 326. , 313.2, 300. , 299.6, 261.4, 250. , 200. , 196.4, 196. ,\n", + " 189. , 188. , 182. , 169.2, 166. , 150. , 145.3, 142. , 137. ,\n", + " 131.3, 131. , 130. , 126. , 122. , 119. , 114. , 113.3, 108. ,\n", + " 106. , 104. , 102. , 100. , 80.7, 76.1, 74.2, 72.4, 70. ,\n", + " 68.8, 67.6, 65.5, 63.7, 62.3, 59.2, 58.5, 56.3, 50. ,\n", + " 49.3, 48.5, 43.9, 43.2, 39.8, 37.9, 37.4, 36.1, 34.4,\n", + " 33.5, 32.8, 31.2, 30.7, 30. , 29.1, 28.4, 27.1, 27.1,\n", + " 25.8, 25.5, 23.6, 23.1, 21.5, 20.3, 20. , 19.6, 18.7,\n", + " 18.3, 17.1, 17.1, 15.7, 14.9, 14.4, 14. , 12.1, 11.4,\n", + " 10.8, 10.5, 10. , 9. , 8.8]) hectopascal" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 218 + }, + "id": "UD7rBh-M0um0", + "outputId": "b809d3a4-c7da-4e45-a539-dee985dc4c1a" + }, + "source": [ + "T" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/latex": "$\\begin{pmatrix}21.0 & 20.8 & 19.1 & 18.0 & 16.6 & 16.4 & 15.0 & 13.8 & 13.6 & 12.6 & 12.3 & 10.2 & 8.2 & 8.2 & 4.6 & 3.6 & 4.8 & 5.2 & 3.4 & 3.0 & 3.0 & 2.2 & -1.1 & -5.6 & -6.5 & -11.1 & -12.3 & -12.7 & -14.7 & -22.5 & -23.2 & -23.9 & -24.7 & -25.1 & -25.3 & -27.3 & -35.3 & -37.5 & -39.9 & -40.0 & -47.3 & -49.7 & -61.5 & -62.4 & -62.5 & -63.5 & -62.5 & -63.1 & -66.9 & -67.9 & -68.5 & -68.6 & -68.7 & -67.9 & -63.6 & -63.3 & -63.2 & -62.7 & -60.9 & -61.1 & -61.5 & -61.5 & -61.3 & -60.3 & -61.1 & -59.9 & -60.5 & -68.3 & -65.9 & -64.9 & -65.5 & -66.3 & -66.7 & -67.1 & -65.7 & -64.5 & -65.3 & -67.1 & -67.5 & -66.7 & -64.3 & -63.7 & -63.7 & -63.5 & -63.5 & -60.3 & -58.4 & -57.9 & -58.2 & -58.5 & -58.7 & -58.3 & -57.4 & -57.1 & -55.5 & -53.1 & -52.0 & -50.1 & -50.1 & -50.6 & -50.7 & -47.3 & -46.5 & -46.8 & -47.1 & -46.3 & -45.7 & -46.9 & -47.5 & -45.8 & -45.7 & -48.1 & -47.1 & -46.5 & -44.1 & -42.9 & -44.1 & -43.1 & -42.5 & -42.9 & -44.4 & -44.7\\end{pmatrix}\\ \\mathrm{degree\\_Celsius}$", + "text/html": [ + "
Magnitude
[21.0 20.8 19.1 18.0 16.6 16.4 15.0 13.8 13.6 12.6 12.3 10.2 8.2 8.2 4.6
 3.6 4.8 5.2 3.4 3.0 3.0 2.2 -1.1 -5.6 -6.5 -11.1 -12.3 -12.7 -14.7 -22.5
 -23.2 -23.9 -24.7 -25.1 -25.3 -27.3 -35.3 -37.5 -39.9 -40.0 -47.3 -49.7
 -61.5 -62.4 -62.5 -63.5 -62.5 -63.1 -66.9 -67.9 -68.5 -68.6 -68.7 -67.9
 -63.6 -63.3 -63.2 -62.7 -60.9 -61.1 -61.5 -61.5 -61.3 -60.3 -61.1 -59.9
 -60.5 -68.3 -65.9 -64.9 -65.5 -66.3 -66.7 -67.1 -65.7 -64.5 -65.3 -67.1
 -67.5 -66.7 -64.3 -63.7 -63.7 -63.5 -63.5 -60.3 -58.4 -57.9 -58.2 -58.5
 -58.7 -58.3 -57.4 -57.1 -55.5 -53.1 -52.0 -50.1 -50.1 -50.6 -50.7 -47.3
 -46.5 -46.8 -47.1 -46.3 -45.7 -46.9 -47.5 -45.8 -45.7 -48.1 -47.1 -46.5
 -44.1 -42.9 -44.1 -43.1 -42.5 -42.9 -44.4 -44.7]
Unitsdegree_Celsius
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Magnitude
[18.2 18.3 17.8 17.5 16.6 16.4 15.0 13.8 13.6 12.6 12.3 10.2 8.2 7.9 -24.0
 -32.4 -29.2 -21.8 -23.6 -46.0 -46.0 -46.8 -43.7 -39.4 -38.5 -23.1 -25.3
 -25.9 -29.2 -41.5 -40.3 -38.9 -39.7 -44.8 -47.3 -62.3 -62.3 -62.6 -62.9
 -62.9 -65.8 -66.7 -74.5 -75.4 -75.5 -76.5 -75.5 -76.1 -79.1 -79.9 -80.5
 -80.6 -80.7 -79.9 -76.5 -76.3 -76.2 -75.7 -74.9 -75.5 -76.5 -76.6 -77.3
 -76.3 -77.1 -76.9 -77.5 -83.3 -82.3 -81.9 -82.5 -83.3 -83.7 -84.1 -83.2
 -82.5 -83.0 -84.2 -84.5 -84.7 -85.3 -85.7 -86.2 -89.0 -89.5 -89.7 -89.9
 -89.9 -89.8 -89.8 -89.7 -89.5 -89.2 -89.1 -87.5 -86.1 -85.4 -84.1 -84.1
 -84.6 -84.7 -82.1 -81.5 -81.8 -82.1 -81.3 -80.7 -81.9 -82.5 -80.7 -80.7
 -82.1 -81.7 -81.5 -80.1 -78.9 -80.1 -79.1 -78.5 -78.9 -79.5 -79.7]
Unitsdegree_Celsius
" + ], + "text/plain": [ + "array([ 18.2, 18.3, 17.8, 17.5, 16.6, 16.4, 15. , 13.8, 13.6,\n", + " 12.6, 12.3, 10.2, 8.2, 7.9, -24. , -32.4, -29.2, -21.8,\n", + " -23.6, -46. , -46. , -46.8, -43.7, -39.4, -38.5, -23.1, -25.3,\n", + " -25.9, -29.2, -41.5, -40.3, -38.9, -39.7, -44.8, -47.3, -62.3,\n", + " -62.3, -62.6, -62.9, -62.9, -65.8, -66.7, -74.5, -75.4, -75.5,\n", + " -76.5, -75.5, -76.1, -79.1, -79.9, -80.5, -80.6, -80.7, -79.9,\n", + " -76.5, -76.3, -76.2, -75.7, -74.9, -75.5, -76.5, -76.6, -77.3,\n", + " -76.3, -77.1, -76.9, -77.5, -83.3, -82.3, -81.9, -82.5, -83.3,\n", + " -83.7, -84.1, -83.2, -82.5, -83. , -84.2, -84.5, -84.7, -85.3,\n", + " -85.7, -86.2, -89. , -89.5, -89.7, -89.9, -89.9, -89.8, -89.8,\n", + " -89.7, -89.5, -89.2, -89.1, -87.5, -86.1, -85.4, -84.1, -84.1,\n", + " -84.6, -84.7, -82.1, -81.5, -81.8, -82.1, -81.3, -80.7, -81.9,\n", + " -82.5, -80.7, -80.7, -82.1, -81.7, -81.5, -80.1, -78.9, -80.1,\n", + " -79.1, -78.5, -78.9, -79.5, -79.7]) degree_Celsius" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ptQl-22r0wI8", + "outputId": "c0d93eec-8ce5-40a0-c947-a171320ef464" + }, + "source": [ + "mpcalc.most_unstable_cape_cin(p, T, Td)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1400.0053934540974 joule/kilogram, -0.1321154666668731 joule/kilogram)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pKgl2NWk05-p" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From badd06deac6dde983475698755cf34833343e881 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 8 Jul 2021 06:22:56 -0500 Subject: [PATCH 080/171] Create cape_cin.py --- xarray_tutorial/cape_cin.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 xarray_tutorial/cape_cin.py diff --git a/xarray_tutorial/cape_cin.py b/xarray_tutorial/cape_cin.py new file mode 100644 index 0000000..7332dac --- /dev/null +++ b/xarray_tutorial/cape_cin.py @@ -0,0 +1,18 @@ +# Source: https://unidata.github.io/MetPy/latest/tutorials/xarray_tutorial.html + +data_at_point = data.metpy.sel( + time1='2017-09-05 12:00', + latitude=40 * units.degrees_north, + longitude=260 * units.degrees_east +) +dewpoint = mpcalc.dewpoint_from_relative_humidity( + data_at_point['Temperature_isobaric'], + data_at_point['Relative_humidity_isobaric'] +) +cape, cin = mpcalc.surface_based_cape_cin( + data_at_point['isobaric3'], + data_at_point['Temperature_isobaric'], + dewpoint +) +cape + From 0a51be97bcfb159c4b5854aa4e7789a8149355ba Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 8 Jul 2021 07:36:53 -0500 Subject: [PATCH 081/171] Create advection.py --- xarray_tutorial/advection.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 xarray_tutorial/advection.py diff --git a/xarray_tutorial/advection.py b/xarray_tutorial/advection.py new file mode 100644 index 0000000..181e704 --- /dev/null +++ b/xarray_tutorial/advection.py @@ -0,0 +1,12 @@ +import metpy.calc as mpcalc +temp_850 = temp[lev_850] +u_wind_850 = u_wind[lev_850] +v_wind_850 = v_wind[lev_850] + +# Use helper function defined above to calculate distance +# between lat/lon grid points +dx, dy = mpcalc.lat_lon_grid_deltas(lon_var, lat_var) + +# Calculate temperature advection using metpy function +adv = mpcalc.advection(temp_850 * units.kelvin, [u_wind_850, v_wind_850], + (dx, dy), dim_order='yx') * units('K/sec') From 43a7a8f1045e8b7a4c57c4e0e8e26dabb462860e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 8 Jul 2021 07:43:39 -0500 Subject: [PATCH 082/171] Create moisture_flux_convergence.py --- xarray_tutorial/moisture_flux_convergence.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 xarray_tutorial/moisture_flux_convergence.py diff --git a/xarray_tutorial/moisture_flux_convergence.py b/xarray_tutorial/moisture_flux_convergence.py new file mode 100644 index 0000000..f5a529b --- /dev/null +++ b/xarray_tutorial/moisture_flux_convergence.py @@ -0,0 +1,14 @@ +# https://earthscience.stackexchange.com/questions/9634/how-to-compute-du-dx-and-dv-dy-in-moisture-flux-convergence + +import metpy.calc as mpcalc +q_850 = q[lev_850] +u_wind_850 = u_wind[lev_850] +v_wind_850 = v_wind[lev_850] + +# Use helper function defined above to calculate distance +# between lat/lon grid points +dx, dy = mpcalc.lat_lon_grid_deltas(lon_var, lat_var) + +# Calculate temperature advection using metpy function +adv = mpcalc.advection(q_850 * units.kelvin, [u_wind_850, v_wind_850], + (dx, dy), dim_order='yx') * units('K/sec') From 3247790c2bc034b687a362712e3efa125c81e858 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 11 Jul 2021 19:07:31 +0530 Subject: [PATCH 083/171] Created using Colaboratory --- deep_learning/stratifiedkfold.ipynb | 166 ++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 deep_learning/stratifiedkfold.ipynb diff --git a/deep_learning/stratifiedkfold.ipynb b/deep_learning/stratifiedkfold.ipynb new file mode 100644 index 0000000..8aeac00 --- /dev/null +++ b/deep_learning/stratifiedkfold.ipynb @@ -0,0 +1,166 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "stratifiedkfold.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyOrTwfcnuJ7V7v1rj30+3bM", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aF7IjGuSyB-h", + "outputId": "b6a5d145-0324-4d24-aea9-abba21be6002" + }, + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import StratifiedKFold\n", + "X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])\n", + "y = np.array([0, 0, 1, 1])\n", + "skf = StratifiedKFold(n_splits=2)\n", + "skf.get_n_splits(X, y)\n", + "\n", + "print(skf)\n", + "\n", + "for train_index, test_index in skf.split(X, y):\n", + " print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", + " X_train, X_test = X[train_index], X[test_index]\n", + " y_train, y_test = y[train_index], y[test_index]" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "StratifiedKFold(n_splits=2, random_state=None, shuffle=False)\n", + "TRAIN: [1 3] TEST: [0 2]\n", + "TRAIN: [0 2] TEST: [1 3]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sIZPmuxOyHG0", + "outputId": "f5d07bb6-3375-41be-dea4-9d8ae6b15cca" + }, + "source": [ + "X" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [1, 2],\n", + " [3, 4]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4WEaOCYWyKx3", + "outputId": "4d464763-0356-4853-e499-55a4aa0ef8c0" + }, + "source": [ + "X.shape" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4, 2)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ayXzzCwTyL10", + "outputId": "d93d2e5e-d6bf-400d-9fe5-e185120dd36c" + }, + "source": [ + "X[1]" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([3, 4])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i_hjj_BIyN3w" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 964792a8c8dad34da8a64c074300d151a4c2c680 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 13 Jul 2021 10:40:22 +0530 Subject: [PATCH 084/171] AHPS precipitation processing NWS --- xarray_tutorial/ahps_v1.ipynb | 246 ++++++++++++++++++++++++++++++++++ 1 file changed, 246 insertions(+) create mode 100644 xarray_tutorial/ahps_v1.ipynb diff --git a/xarray_tutorial/ahps_v1.ipynb b/xarray_tutorial/ahps_v1.ipynb new file mode 100644 index 0000000..739528a --- /dev/null +++ b/xarray_tutorial/ahps_v1.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dd8f5229", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b261286c", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4fa13d36", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.open_dataset('nws_precip_1day_20180914_conus.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e2478593", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ds_imerg.precipitationCal.T.sel(lat=slice(25,55)).sel(lon=slice(-110, -60)).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7aaffb10", + "metadata": {}, + "outputs": [], + "source": [ + "from pyproj import Proj\n", + "p2 = Proj('+proj=stere +lat_0=90 +lat_ts=60 +lon_0=-105 +x_0=0 +y_0=0 +a=6371200 +b=6371200 +units=m +no_defs')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ed75d4d4", + "metadata": {}, + "outputs": [], + "source": [ + "points = []\n", + "values = []\n", + "for i_x in range(ds.x.values.shape[0]):\n", + " for j_y in range(ds.y.values.shape[0]):\n", + " points.append([p2(ds.x.values[i_x],ds.y.values[j_y],inverse=True)[0],p2(ds.x.values[i_x],ds.y.values[j_y],inverse=True)[1]])\n", + " values.append(ds.observation.values[j_y, i_x])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "83e0270d", + "metadata": {}, + "outputs": [], + "source": [ + "ds_imerg_ = ds_imerg.sel(lat=slice(25,55)).sel(lon=slice(-110, -60))\n", + "lat_ = ds_imerg_.lat.values\n", + "lon_ = ds_imerg_.lon.values\n", + "grid_x, grid_y = np.meshgrid(lon_, lat_)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "352f6c5e", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.interpolate import griddata" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ad88693e", + "metadata": {}, + "outputs": [], + "source": [ + "grid_z0 = griddata(np.array(points), np.array(values), (grid_x, grid_y), method='nearest')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f7ac0c2c", + "metadata": {}, + "outputs": [], + "source": [ + "ds_imerg_['ahps_observation'] = (('lat', 'lon'), grid_z0)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6c10cb3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ds_imerg_.ahps_observation.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "960979d3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 0c6ed6f054ae284d0a1bf61144a6fdd154df0e5f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 13 Jul 2021 17:29:38 +0530 Subject: [PATCH 085/171] Created using Colaboratory --- .../cape_cin_dewpoint_xarray_metpy.ipynb | 12161 ++++++++++++++++ 1 file changed, 12161 insertions(+) create mode 100644 xarray_tutorial/cape_cin_dewpoint_xarray_metpy.ipynb diff --git a/xarray_tutorial/cape_cin_dewpoint_xarray_metpy.ipynb b/xarray_tutorial/cape_cin_dewpoint_xarray_metpy.ipynb new file mode 100644 index 0000000..b3ae4ab --- /dev/null +++ b/xarray_tutorial/cape_cin_dewpoint_xarray_metpy.ipynb @@ -0,0 +1,12161 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "cape_cin_dewpoint_xarray_metpy.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyN4Yw6qlE5mFSlau0oydbyk", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EJSxXOm7eEDO", + "outputId": "722e1317-e109-4870-dbf0-cde0041e0bb5" + }, + "source": [ + "#!pip install condacolab\n", + "import condacolab\n", + "condacolab.install()\n", + "condacolab.install_anaconda()\n", + "import numpy as np\n", + "import xarray as xr\n", + "#!conda install --yes -c conda-forge cartopy\n", + "#!pip install metpy\n", + "# Any import of metpy will activate the accessors\n", + "import metpy.calc as mpcalc\n", + "from metpy.cbook import get_test_data\n", + "from metpy.units import units" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "✨🍰✨ Everything looks OK!\n", + "✨🍰✨ Everything looks OK!\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + }, + "id": "zYgPeikJeIjj", + "outputId": "7e1a4a2f-e515-457e-b73f-103d6aefce4f" + }, + "source": [ + "# Open the netCDF file as a xarray Dataset\n", + "data = xr.open_dataset(get_test_data('irma_gfs_example.nc', False))\n", + "\n", + "# View a summary of the Dataset\n", + "data" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+              "Dimensions:                              (isobaric1: 21, isobaric3: 31, latitude: 81, longitude: 131, time1: 9)\n",
+              "Coordinates:\n",
+              "  * time1                                (time1) datetime64[ns] 2017-09-05T12...\n",
+              "    reftime                              datetime64[ns] 2017-09-05T12:00:00\n",
+              "  * latitude                             (latitude) float32 50.0 49.5 ... 10.0\n",
+              "  * isobaric3                            (isobaric3) float64 100.0 ... 1e+05\n",
+              "  * isobaric1                            (isobaric1) float64 1e+04 ... 1e+05\n",
+              "  * longitude                            (longitude) float32 250.0 ... 315.0\n",
+              "Data variables:\n",
+              "    Vertical_velocity_pressure_isobaric  (time1, isobaric1, latitude, longitude) float32 ...\n",
+              "    Relative_humidity_isobaric           (time1, isobaric3, latitude, longitude) float32 ...\n",
+              "    Temperature_isobaric                 (time1, isobaric3, latitude, longitude) float32 ...\n",
+              "    u-component_of_wind_isobaric         (time1, isobaric3, latitude, longitude) float32 ...\n",
+              "    v-component_of_wind_isobaric         (time1, isobaric3, latitude, longitude) float32 ...\n",
+              "    Geopotential_height_isobaric         (time1, isobaric3, latitude, longitude) float32 ...\n",
+              "    LatLon_361X720-0p25S-180p00E         int32 -2147483647\n",
+              "Attributes: (12/13)\n",
+              "    Originating_or_generating_Center:                                        ...\n",
+              "    Originating_or_generating_Subcenter:                                     ...\n",
+              "    GRIB_table_version:                                                      ...\n",
+              "    Type_of_generating_process:                                              ...\n",
+              "    Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n",
+              "    Conventions:                                                             ...\n",
+              "    ...                                                                                ...\n",
+              "    featureType:                                                             ...\n",
+              "    History:                                                                 ...\n",
+              "    geospatial_lat_min:                                                      ...\n",
+              "    geospatial_lat_max:                                                      ...\n",
+              "    geospatial_lon_min:                                                      ...\n",
+              "    geospatial_lon_max:                                                      ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (isobaric1: 21, isobaric3: 31, latitude: 81, longitude: 131, time1: 9)\n", + "Coordinates:\n", + " * time1 (time1) datetime64[ns] 2017-09-05T12...\n", + " reftime datetime64[ns] ...\n", + " * latitude (latitude) float32 50.0 49.5 ... 10.0\n", + " * isobaric3 (isobaric3) float64 100.0 ... 1e+05\n", + " * isobaric1 (isobaric1) float64 1e+04 ... 1e+05\n", + " * longitude (longitude) float32 250.0 ... 315.0\n", + "Data variables:\n", + " Vertical_velocity_pressure_isobaric (time1, isobaric1, latitude, longitude) float32 ...\n", + " Relative_humidity_isobaric (time1, isobaric3, latitude, longitude) float32 ...\n", + " Temperature_isobaric (time1, isobaric3, latitude, longitude) float32 ...\n", + " u-component_of_wind_isobaric (time1, isobaric3, latitude, longitude) float32 ...\n", + " v-component_of_wind_isobaric (time1, isobaric3, latitude, longitude) float32 ...\n", + " Geopotential_height_isobaric (time1, isobaric3, latitude, longitude) float32 ...\n", + " LatLon_361X720-0p25S-180p00E int32 ...\n", + "Attributes: (12/13)\n", + " Originating_or_generating_Center: ...\n", + " Originating_or_generating_Subcenter: ...\n", + " GRIB_table_version: ...\n", + " Type_of_generating_process: ...\n", + " Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n", + " Conventions: ...\n", + " ... ...\n", + " featureType: ...\n", + " History: ...\n", + " geospatial_lat_min: ...\n", + " geospatial_lat_max: ...\n", + " geospatial_lon_min: ...\n", + " geospatial_lon_max: ..." + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 256 + }, + "id": "BJBvRcIVeV_Q", + "outputId": "b4cf284e-501f-4b3f-d7ee-e2215f90b810" + }, + "source": [ + "data.isobaric3" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "  * isobaric3  (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n",
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<xarray.DataArray 'Temperature_isobaric' (time1: 9, isobaric3: 31, latitude: 81, longitude: 131)>\n",
+              "[2960469 values with dtype=float32]\n",
+              "Coordinates:\n",
+              "  * time1      (time1) datetime64[ns] 2017-09-05T12:00:00 ... 2017-09-06T12:0...\n",
+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "  * latitude   (latitude) float32 50.0 49.5 49.0 48.5 ... 11.5 11.0 10.5 10.0\n",
+              "  * isobaric3  (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n",
+              "  * longitude  (longitude) float32 250.0 250.5 251.0 251.5 ... 314.0 314.5 315.0\n",
+              "Attributes:\n",
+              "    long_name:                      Temperature @ Isobaric surface\n",
+              "    units:                          K\n",
+              "    Grib_Variable_Id:               VAR_0-0-0_L100\n",
+              "    Grib2_Parameter:                [0 0 0]\n",
+              "    Grib2_Parameter_Discipline:     Meteorological products\n",
+              "    Grib2_Parameter_Category:       Temperature\n",
+              "    Grib2_Parameter_Name:           Temperature\n",
+              "    Grib2_Level_Type:               100\n",
+              "    Grib2_Level_Desc:               Isobaric surface\n",
+              "    Grib2_Generating_Process_Type:  Forecast\n",
+              "    grid_mapping:                   LatLon_361X720-0p25S-180p00E
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}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "FHEAKNCZefFo", + "outputId": "ab8870b7-abcc-4a14-ccd5-f7a7da13013b" + }, + "source": [ + "temperature.isel(time1=0).isel(isobaric3=-1).plot()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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/vJOVtUZso2rSSGc+fbZRKuKdRvqWlJ+9jzzuqUQHaZMu63v6iKBQNMef+23qMfEJQ1WvtO11IvJBAPcEcK2InKqqV0vIYnfdpPuRMQb4CScj4zjGTU16GAUTnTAsOFhFVQ/b/x+MEGriwwhhvV9u238YpT7yq3EVVS2s9sgbtx2n2g6FqcvoKjqtjlp/CYLcbGUjXenG8tR9xM6V8Ku2EqY00SlIGKwrXkHlKfUhe3aFcib9yNp6KGa6hgpX1yw/a/oTrvrjyssGx+tdfF+tHjHdh3KV3+zGsYs6CqeDIKLkwL6wHK9ruPFkn3g+6lVsnKiH8X3l5MUtnwslDx5v964S1caN40rlLBW6FdM5UNfQtFV9e4YVWJdoI2D3WDNdBqXT5lKof2OXJOW5Ot/YZV00/cTGnlB+6cruh4qSc/c9p0TAtm1blb7HPajDqDS5z76lEjn7yHtuz/Rf3S/sS9tvL82i0uiv34g6CFCSsPe9ZB2ilf5tElGyaI9/IaNQtLNTcw8mLWGcAuCDliKihhBk7RMi8gUA7xORpyDEePnlI6lcB79PGWOG9vn4ZmQUwcnieEBnrIGEjw9MdMLQELb7Ln2O34CQZjQjIyNj20EBtPOE0YNjZVa7aWilYBJYOEZ0vCmeicPteXeYVFKVor5RTzSrNTG4YjROVNDRBp/0UIVK71SbqLP9zUNJWxTRmQ/0S3VpMRygWStNefcfCFWRnqEIPuPoHtI2Fa8IttUeGSUqyVlfx3G0NK9lvY6uEwC4KqibOqaoJy0mkQ6zPpg0oqtryT7biD4nRntFiot9Y31lYN/pJ1M1U1gaL1DBXe0+B6URAO+LJqfz4XjHPetI19g7U22E/eZ8qpStmsn28mmhnpY9zoo9xro91vpa+gFi/VXTE9dsqIrvdVS085hTZlOx3qMgZnEq4Otp3bF8VHLzXm3f+ZiQDu5EystotF3VZL9+qEtfktrr0mWeLu7fVqlym5Rti8+eDTn/mDEhSxi9mJoJI2Mb4Kpsm5Bx04ACaGYdRg/yhJGRkZHhoNBMSfXBVE0YtCah2F0riPgUR2nNQeuOqlFGFfOqJX3QDXlAe3ZuTYx21EakQCI1ZVTBYvCRoGUMKS6J1j/kLayjRbGZ1MeS2bZ3UqumzmnBgbSyZiYty8ETXIyy0tWwr+b7QesqIcVFkOYh5TRP0xfrI6maan9aL1JUZ58R76u637JvGuUUqSWOn1lWKX1GuFpzllrivK5jPfTw9v4W3I9+MuRkvBVVJz0PFCzjSJGEA6SiSE01dqRWUfGdMgMuWhDxe9LY6fwzqPe19po7wnbtZKOgjKqaOWTbw6Gi+rI994JFUttum9QRaaxosVXmflLinyFDdNI+7Akr6HhTJlcvx0iWuuNdaTiOiM/cV2W/ka4nt+sUf5vt9Lfa4W+wOmYuCgA0cbjPMEzVhJFxbCHZSirjJoLg6Z3hMT0ThnY9YilptOYlrry46osrKOrPXMAz6UhyXOuUCNJVSlxo0Va84/ws/ArXLUfiig7tpD1ZbxXs/9mYrZTmzFu6ZRLHjdSW2mOi0jqu1GxLD20qgOltvivEsWrvWUB/WL0Unpx0Fe3oecv1SlQYd0xKqayYh+++A2nfzJ9C1Nr28a2o4KRXb90p5L2vCCWMGLgv9QOJEqGPbdTWQowiU25b2c6MSRJ8n+bS1Tu9qylZ8Hh91fZTp/b47nkFcceui1ICXUwWuE/ld6psL17jld7dAna4k5YnKibg1VecZ3hZ4D9XL8dAXQFGUuB5GqQ0dlRRC+5CmDls7z6jKkT389S7PAbV5L04SSJKJPzN8pnzXaQ/01h1DoL28CiINzlMz4TRB/7HMQ3Q+hR22hCti6YI09jnaQYni2mHIgc26IepnjAyMjIyJgEF0Mj55XowPROGFMJ/9BG/Kf5HyZ0Rv6nopK02GST6X7SoQOP51K68G6La9qkkR1pvDBzoAvqNJFF00hAHujCftM1wIqSMoqDsdArx+M6l5LqoXK8zBEY4XD0UFNayEQarszO022Fwvj5B3+I4Mtz2YniFZjlO11isCFJLVFKbX0WsyVNTPvS4z1nhwpl3fSncPsedtv+17vjz/klFUbncsiCWkZKaTd8VUiVUVrfm7DAZPU+Lmj8Fb5a0EP0tov8B30VSYL7dQp09/hWkv5ppX1rmdzRjNgm19ZLwG57yc+d9KBL2nVQUxyjei9UTQ7yjSyHHOl0Yk9imp56c4j3+xqI/R0pFdQMujvcD38mhJHowPRNGxjHHsNg+GRnHC4Knd37fPfKEkZGRkeGgELQzJdWDqZkwtAJs7CEtFI4Vxewo3qdMBdBMI2PGcAw96RYsz0CDtvAuG1rMMaHJ8R5Rn+J2tPRwaU8L4TjiffgwJLwnW+HELIAUyUnvbJDjcC+2+UAIrVAsimq14TLwOYuiyspG0l5nIbRTjDrK6L0xixwtVDbSXBw9FJPP3hejydp1tOxi5F1fnlRUpCWMNpslhZimA43RU+e6rzgz59FngDRkq2B1F/Z5s9ZVlyOi46KWVFxoD9JIMTKsp0ed5ROtppgVr0jl+FAepIDqRg2RkmouuTAjq6mlUa+PQ8nqmcZrDIdi1JbPNUGfkdmDqfVVkkVv1JS2zqcjUktV0mH8faSZD+O7wHGcyZTUpDE1E0bGsYcP9Z6RcbxCIWhMoxnmhDFVE4a0ijtBMedXbRGtdDcqreNqxq1yXDLkuKLr9Pd07ckfwIX1upMGKLmsdzORRUVtH5+B0Hd6YKeKXFBBP88EzHaaIaWZt8LyZjCvRak3tN+n1LQSrq8eKqTFZR30FmcQRutzZzEsyzsnnBa6upC+WsxHUl1lIgZGxbNbWbblecxgaPdkEgczFcYx6wkKmSq0i/qWeCxKHWG/sZj6WXjJwUsWHO/aalouShCUct272PYLX+c7wSCF9Oto16UrKaxTQuA16XtIRX3siw1fa8EkjvW0LaJXCe52WW1s17bOR8KX15lKt273G4nHo2GCneb4eB1ZNA6w3yC3M06a5M9jbnwSQXDcy5SUx1RNGBFOhJ0q9Ilau+3hJotpwrhpiq1AtTG8zHZFWTKkaURWevdi+r4AGRkZGROGqqA9lSvSyWKqJgwf/K2YH4MrG+YeoJjKa2olIRGo8GQqCZlhSlenzHOpLIX27zG0iFEHtZSPqDSdkrha6MCcKZWjwtDFaiI1RamEuTrWnC6BAf5ISbEPVG5z6wP4GWKfIwMWuJHOYvDnaO3ocjXNHZb/ohnuY/bGjaTvbVNCty2PA9ObdmqhzuqGhT0hvValsja0tfBjSz97cDW5JyGNQeprhmE+0gB07UhN9YbZIBVFPwrmSum474KnoliOlGjd0zx811wIkPjuOIU1/TkYPLNqY9lc6F3Rtuuk0axvdK1h23y066y7fwiQbniNniYSeIV09C3xvhI9F1r1xVugoQjtPfzt+WvE/casAK2V+FvjmHilek9OnKNEJ0sYPdiSKVREqiLyZRH5qO2/RUS+LyJfsb9ztqIfGUcHThYZGcc7gh9GZaS/mxK26gvwTADfBLCzcOw5qvqBLWo/IyMjY2QoBE3NCySPiY+IiJwB4KEAXgrg2Udaj9aAxm5WGjaVouERraIoopvYT3GYNAStSCpNir92mRP1uxFEGRokPa+zjJBp9c2kEWRpIUL/jZgno1GgnYzX6kavTf1MeqyYjKKKITzMcqgWc0cweYL1gX4aPhUrLZF8uA4D83M09oRBaxZyHHBc6tZmDNNgFFHNrKCqa+Em2jOhr/RtaO4lfxA2TFvKNloLIXnE4pWhb9XljaRvPt0nraNaTLNK2sKoxnYhtwTpRz7rSEUJ0uPMcMsQKmbART8LUkr0VYj+HAwZ4vwOSJPO38DIxWF/fU8ouLGbuVrS68J9Jrcf3w2WrcRIsOi7HW4d5ajXEuo2+j50TZ3611e8xhvhOV8QryLoWmKFLaP+opY+yxjGx9XjUzgfLdrZD6MHWyFPvQrA76M3vPxLReSrIvJKEZntc13GNgN/qBkZxzvo6Z0pqRQTlTBE5GEArlPVS0TkAYVTzwdwDYAZABcCeC6AF/e5/nwA5wNAbdceAAV79HZYUXCltbEnLHVqXEUuWzmfL8Dplev0gt7wq8XUC1hm2K6ze+cqk6ufmRpqy22gKqi0Omgv1FFZa0EXZ1FdXg++FQ1ThDMwnPOa1llTLPuUXyYpVK4/GLbMj0F797W1tPy8aWup1I6+D67edrvraV2vobK/Ad2xgNlrDmPt5jtR3ehECWD++jDgtUNW3gVIpNI7BjxkgL4abelt3Kwri1eH+qisbi2apDFPD27LaBht+ote0AJpdtCZqaK63kF7voJKS9FcqACa5k0J23Bd0xISciXLbfTMpquIPfOavUvMjMeVbmMhXTFzRVyxxzC/n0ElkUAFWN9r2f2MpK1bG5RoYx9cxr3uSt/OUyqNy7H+S/74/rPLJUrs2I5TRIeDilIP8aSxtI/d464t7xbkFO0c/041HeeY5S8GDO1f/9GiMyYrKRGZA/CvAGYRvrkfUNULROSWAN4D4AQAlwA4T1UbIvIkAH8G4Eqr4v+p6t+MpTNHiUlPj/cB8AgRuQJhYM4VkXeo6tUasAHgzQDu2e9iVb1QVe+uqnevLi72pJncjvbqteXQuYpNAhWjZqrLZsbS2Ibe0oXJAgB0R8jss3bz8DXzk8V2AD+cpMKiVdaCS4o0v/2kolEni22FUZMTeRp1SjFmpfcGgHNV9S4AzgHwEBG5F4A/BfBKVb01gBsBPKVwzXtV9Rz72xaTBTDhCUNVn6+qZ6jqWQAeC+DTqvorInIqAEiwlXwUgK9Psh8ZGRkZm4FC0NbR/obWFWDLAtTtTwGcC4CGP29F+BZuaxwrM4B3ishJCELkVwA8Y9gFWgVaS72+DNVVUz63uaI0amrVxFUyGZRqeccMoWDU08xhow9MLO6mnqQczOttFUixGaSw0rmXNIbQX8Aom0qzELOEFFGD1IXRMDVu0wCIDG8Sw2as2E2Qepolh2I3ScmB1h4M7MdVIPti+TO4316as8tCudp66F9zRw3zV6VxMVo7TdMbDRFCWa745/bTWiD0Yc2MBWZtvCkpUHk7c5Dlrf6F1CCgR1EajRrScBGkjdoF7Vgj6NPjcFCZ7dP8dnM/pG2t70l9IigJzB7gPl8Sa2+R+UfsnTQhMxpYtNlXJOeL1EoMeEhapmSJFxXALoClV353/Sw8lWXFy1LBlvA9XeqK+yN8QN09kNbtUoPps+4aOoT9mBrX1TNOHbUqNmMldaKIfLGwf6GqXlgsICJVBNrp1gD+GsBlAA6oKj8IPwZweuGSXxKR+wH4DoBnqeqP+jUsIqcAeBmA01T1F0TkDgDurapvGrXzm8GWTRiqejGAi+3/546jTk4WGVsDP1lkZBy/kM047u1T1bsPKqCqbQDniMhuAB8EcLsBxT8C4N2quiEiT0eQPsq+mW9BoPX/wPa/A+C9ACYyYeQvbkZGRoaDAmhrZaS/TdWregDARQDuDWC3SOQ8zoApuVX1BtPvAsDfALjbgCpPVNX3waxQTWJpDyh/VJgezxQB1HwdKutm+z/bpaaizfxqKr56Y952yjChZiL0yimm8I1WPWEb02s2XcgEWtiURO2kP0YUq5m3oVGPZYUKcIbusOfMdY0yXknMKWHlaB3F6xiVdqZbd+hkJS132BwC5pwVM/th5dvz4ea7lk1he/iWS1j8MZM+kJ5xjgKkQFrMZRD6Vp9lhFhSiNY1UklOp8rn0TKLJu8z0R0k6w5dUewWSfvQIgrovhMswz60llzf47O1uvhOWJ2koGKU1AWWN98d/tRdJGTSYzOHQkOkQ2NEWpeyFSj4LrjcHD0WRz7XhhufbngSUoDp8VKkbhc9vg/lFNbwOvnMOrzHelpnj4VYif9GxJj17eMymTX6vamqB0RkHsCDEBTeFwF4NIJB0BMB/IOVP1VVr7bLH4Hg9FyGFRE5ATaqpkw/OJaO98H0TBgZxxxxssjIOM6hkHEmUDoVwFtNj1EB8D5V/aiIfAPAe0TkJQC+jC6N9Nsi8giEJA37ATxpQN3PBvBhALcSkX8HcBLCJDQR5AkjI4lL5n4AACAASURBVCMjw0GxKaX34LpUvwrgrn2OX44+LgWq+nwEX7VR6v6SiNwfwE8gyFjfVtWJGWZPzYQh9Q4qOwI/1DGZXXrCX/amw4zWOz60h905LV7aPqwDQ4ysMqKoHWfKVtvvUlVpiIaWUTXVJjkqhv0ocGQ07Flz/g0xvAjLkU+wujylRGsohgJh4iWK+qSkfD4L9pn00t7A37Rn0yRETB166NYLmNsXBrK2bgmRbEuLKuafiFY95oBHC6Klq8LA0SoqOqI5i5iZFbMcU9I2SOppOqc5orEjHCAVlYTW4CVMzMPx5ztjw0OKyb9DtHwjZeRDUfCdICsd3zW+GjHEhV1gx5letZuitFtnPFZLLumh5mKEXUfrxOJ2j9G6z4cASQ28ev3/SqiogZSUq8OH/Og4mq0noZKHp6Qc7TZeyFTkwxCR3wTwTlW91Pb3iMjjVPW1k2gvK70zRgYni4yM4x2K4Ok9yt8xxtNMkQ4AUNUbATxtUo1NjYShHaA2G5YmjWhzrlEBHldzVBy6AHBxZeX0wn5VQyX3jNH1PqRCN72kSRY+b0ar/3In5suoF5a89K9gQMINJkqw0CBUnLec5j72waQWH3SQyvKYaIPaWWqa3dLXzlf3HQ67FnSQY8XwKMun17Gwz/JeuNASXnndXAr7qyeGbX0lnJi7MbTd2GWSgMsBQf+JqKBeTUOCRP+KmVSS6JjQFf0u4qq+W3cMLmjbbhBBO+6kTl4bJRL6RLiVbgz9EUNW2HE77HNXRJSssPth2Kq74yWLkjAcMVfHsPwYTtldJmEMgg9oGEN6uPHtacsbH1Dg9gp/pzzvGd+jxDRIGACqIiKqSqV3FSHk0kQwNRNGP3CyyNgacLLIyDjeoSrbQXoYBZ8A8F4ReYPtP92OTQRTPWFkZGRkTApTkqL1uQiTxK/b/icRfDcmgqmZMCoVRYdOFPNMB9qVQRk2g0rXyobRLOsM8QHbh5WD1ZG248NBkPqgUptKbka1bSyluQy6+QnSmCQVT18A0BnzdzBFbYXXUIltF0nN+zpQRjf6ZmGWFYbyG620HOOgrDH2hGEmDSXSullIOBIj71bTe1/bW8HsQVNGz6Y/JiqvWbZuSuvqLqZqDec39liwQEcdcdxnD1oYjbVUKd7YYX4cFt6jaYH7ouKZimk6o5PWKNiLMBRINIywYWUUW3iFLhkJn6qVylvaKpAW8hQLT/fklrDypJH4eLV/+aQvZXUOUxg7Y47ISG3ym9j1M3L9cbQT0Ou/orVUs96NFpw6R3kDlY67N091xd/UGJXfIYHSAI5wm0BVOwBeZ38Tx9RMGBnHHpwsMjKOdwSl9/bXYYjIbQD8XwB3ADDH46p69iTayxNGRkZGRh9MSXKkNwO4AMArAfwsgF/DBK1fp2bCEAFmZoKsqmbA3W4UxkXs/2RzjKKibXy0cHHWJFH8jX4WYUNLF1pgMIyDty6J/hiNdPUt9M8ge+QSCwEFyyn6aBi1JLRmopVUTCBDvwpaZlnEV1q7+teE55cClxJbppUUrauqpPFsfC1bVG2NyaXoJyCRcuu4VKnsK6OXtJjClhZI5tvgfRAYZoMUFMeLaUs3doX9LgWlSZ+YS8L7K5BmqhXiJVZtnEiHeaupbiIeRk8l12SHnYAVLe5KwpUMs1Qqo1iS1BMli1zPvvSEBHHX91BJA+ivQVBXX7d9ey8KXxRSUPE+XeTc+N5zvEmx8p3hcymhwXyfxmnUNGZP70liXlU/ZZZSPwDwIhG5BMAfTaKxqZkwMo49KiUmwxkZxyM60yFhbIhIBcB3ReS3EAIYLg255ogxNRPGTLWF+dmwnN9YD8vU6kwHtZnUmWyjFs61zeVVWuGhx1wDVDjaSpeLCLouiK1Io0Ril1WdYo0pXWMsf+eHUYk5LtJlaeLFW3f5Lmx5VrElcEzRasrvmB+DK1uet/wY0Z+D+TLoK3HYbqpuFzK1paWCpfKdK77Z/eZRz4CJlW6nGZCwaalU6QUeJTl6btubxTwjlAhi6lt6U9s4b1iuiaa96pQQ/CqV9UTJziRKKkYtpQjqwaUkkQqaVJgv9Z/4orTplLDRf4C2BSWr+GGrdq7CvWSBVurpnVTtldtDJIiutJNeGCUwno7STHqvvl7WE+v3hgG8p1qfm/aK9ijdULJwdVpb9KmBi+RQcUEhJwlVjJQcaRvgmQAWAPw2gD9BoKWeOKnGpmbC6Ac/WWRMFjGZVEbGcQ6FoNXZvlZSIvJ2VT0PwE+r6hcALCPoLyaKqZ4wMjIyMiaFbe7pfTcROQ3Ak0XkbXAaHFXdP4lGB04YIvLhEerYr6pPGk93yrG7toY7n3gVAOC79ZMAFMRpAMsbgWNqbpBeCceZM6NhvgWkMJjKleJw5VCq1I6KNqu/2kiVevTPYHBBNTm51nGK6tiA1Vvp8xJGnsD6QGU4I8RRdudWU5pAGCrE/DWkmlJdMCU2w5KQ2mLa2K5C3jYWiiSGOaFuvKFoz5AeQ9KHmFaTVJ+jLkhdtV0Ijw2nDCfFUbX4Lx1P0zjmoz2X7scQMKSoCn42PjWqR2Rn6qm/gKdjSnmijjtdRvP4tKg199IVrusNNuhf0P7Uk2/T30OnQiOD1KcnouKoqKiwJyeb0muxuX50XBwf70zCOl1x/79I3SGpJ8bkHBbm5AgwBWa1rwfwKQBnI6R+LXZW7fjYMUzCuD2Apw44Lwj5aQfC4pt8EcCVqvowEbklQtKQExBu9jxVbQyqI+PYg5NFRsbxj+0dGkRVXwPgNSLyOlX99aEXjAnDJow/UNXPDCogIn88QjvPRMgaZcaR+FMAr1TV94jI6wE8BVvkqZiRkZExCjaR0/uYwBbiP7uVbQ6cMCxX7EAMKyMiZwB4KICXAni2BP7kXACPtyJvBfAiDJkwqtLB7RevAQDsNJOnH6/tiecbln9i4YTrAQBfX7gZAGDlqtTCTI2u8WIwqQ1aT9EKp8JQIQx9QRcJitO0vnKrkWottZ6KvhN9xGaG0+haGJkPQ8NRUrSmctFrYwRcRnp19Ucqyu49pl4lFRV9SjpJ+5F+qnfvbWNXuKYV84c4iyIXUXTdHhHHtbWoSTmGbqkxmZ+k1BVRW3Xj79J4+nATtMKKvAUQH1rsY9XRLizG2624kfTfj0iFOL8NHu+k+/Fd4OEev5ned6TH+imyZWnf4z1FKrCEqoo8TtL1An2Z1hOpKUZnrqblorVVp/+7B3THRTxlFwsMpr94fSfmTqGzjuONx2g9pQo0t7HSGwBUtS0i3xaRM1X1h1vR5khKbxG5LYDnALhF8RpVPXeEy18F4PcBmFEjTgBwwJKVA8CPAZxe0u75AM4HgBNPm1jE3owRwckiI+N4xxQ57u0BcKmIfB7ACg+q6iMm0dioVlLvR1CyvBHAyDGuReRhAK5T1UtE5AGb7ZyqXgjgQgA48047dVc1+BPMzofl/9nz1+NHGycAAL524FQAwJU3BtfgZtMyve1qWF22crUgeEyYUTloAQDrJatE29IRnEpV70HeXRHbKn6G0kA4Kn1Wj3SEo5e52AqqZY4W8eHQ34KSRj19kTtRmZ0qzTv1VEJoc9+Zx0Y9Kr23qcB2ivr6Sgfre9JggnHFTyW4y0PC480dmuzXVkyy4CtuXYqSi1vJegmGgf/i+Nt6oieHQjEY3myJTwxXrF6RK64P8UI7TAmizdW1UyBL+sx7DB68sjYV+AYielZHyWJEjS/v0UsMlZJ7ZfWUAkpcvaVP++p/S/FEfz+XOO6uT701m8QRgxcO7PoRY7tTUoYXbmVjo04YLVU9Eh3DfQA8QkT+J0JgrJ0AXg1gt4jUTMo4A8E7cdPgZJGxNeBkkZFxvGMKrKQAAMN0zOPGwC+AiOwVkb0APiIivyEip/KYHR8IVX2+qp6hqmcBeCyAT6vqEwBcBODRVuyJAP7h6G4jIyMjY7yYhhStInJYRA7Z37qItEXk0KTaGyZhXIIw2XKqfU7h3NHY+j4XwHtE5CUAvgzgTcMuOLG6gXMXvwsA+EbjZABIHtbnW2cCAGqVQDvMWRgRNW/wRssoKhOtV/dbQD4SbDQVn0mVslTWypwLU2A0UoMUlYV3YLA7Kmnpv8HzRZCqYJC/bjBCo5DmAs+1ekp4TDt+0LC6TDlNiiMqCSU53t2y005JaIgiv/edMKV3e5b3AjTnU8UxKaS2KZlJwXHcYhgOuqMwtAeDRNo4MmwHleIsT+rKp0v1AQO7QR7d86t3CQ31tAvHrZbSM9Jj4I/0AheUsKc+r4uNr6mjfyIl1Z8iLKKbmyOlzyL95RW+vny9k7Rd8UrxktU0AwNqbMffpKun4GvRQ+/GQq6PnhoknN8Gn1+v8YAkXRoLdDp0GKpK3TDMoOiRAO41qfaGWUnd0joyp6pJ9h0Rmet/VWldFwO42P5/OYB7bub6jGMPThYZGcc7FEBrG/th9IPl9f6QiFwA4HmTaGNUHcZ/APjJEY5lZGRkTD2mRYchIv+rsFsBcHcA6yXFjxrDQoPcDMHkdV5E7oqu8LgTIULilqECwVWt4FNxyJwmrljvKr0X64GuuXEldKtqiRnWGSrExNu1NUtjalRF+0TjRDbIwxhl0qTZTmoNVHF5MygeR6spshZmNdVcSm3Qa4VHWVu1PhjlwzSz0bqG6U6Xw7a5w0J/dBhFFgl6qAzbXz49jAEj7C5cmwZtpFUU087SuqplEkVrtvvDYRRZ+jlwG9No1mmhlXamumZRg80qiiFaNsxPo7VkUW3XU8ojWsA467RIJdoYdCkt0nuOjiuU9RSI1FK6hpRUtCAi+9OuJNf7PA/xhLfuce1FCzDeYyell5KRY1kXDZYWSBLrRN97KbPwUh+mw0NcObt3VV/M+hzTpRbqjf4X6bZ7Ma3SUhrSn/f1eO7Jp7gdF6ZhwgDw8ML/WwCuQKClJoJhEsbPA3gSgiXTX6D7+h0C8IJJdSpjeyLmvs7IOM4xLX4YqvprW9neMB3GW0Xk7QAep6rv3KI+9UUDbdRtmblqAfPvsHAV/uPgrQEAS7UgYdzhpGsBAN87kJrcLi2EJe0hW6W0ViynxkJYsrY5FCZpRBtyF/dfTGlbZSa3GMs/bJk3o+K3lExQsDunYpYrsB7K1I47O34GPowrU/PPoF9HzPFhSvS5GzpJe94Po7mQeoBTOqKEUfS6jit5+ktQophxK2AbL0pq1egxb/dMj3qTHBauqiTlukELrZz5XVBSi32iUEaJg/dYLfTHK1kr6aqdq/KKKYYpnUbfnZYX5dJtlDQklUh6A/9ZezzOIJJOMpFi3+P/0/EVGgHwGpZy3uVDV91l30ReR6mnpJ4oWUTfE3SNApwU7o0Euop82+dvrMxj292TemOCMX/gp8EPQ0ReAeAlANYAfALAnQE8S1XfMYn2hmp1VLUD4FmTaPxowclimjBlerQEzR3Dy2w7jJmmyBiC9vb/yI4EDZTUKH/HGA9W1UMAHoZAR90aqTXrWDGq0vtfROT3ALwXqfv5RGKuZ2RkZBxLKIBWT2z9bQl+wx8K4P2qelBkcpPYqBPG/7btbxaOHY0fxqaxoVWcVA0a47NmQoDBy2ZPjucva4UcGQc2AofRMiVdvRa4ivVGuNV2w+Rf5rHopHQAEQOlRaWrU8JG5WHaT/oHcBtXuEzvWRjxuvkYMD0sfTZ0tiDeA6g0w/EqA71FHxGjzyIt1t+e38dQi6FESPuYstsrtEkHFXNOtOnnUEu3MUeBjVN1xUKvGOVEqonj1/RZhztpX9n3iqOoSGWRkqo20vNwfjVaUL5H5bRRT2Lb+mzopA9v0WHq1BJ/gjjc1RIxhsYLku53T9uJmuNa+vzeSXeJaysG/SvJNSH9nDpGQE8IkBKXFB+6pVhOvO/GkGtjV32d/rpoVJAaNkSKdgyYFh0GgI+KyLcQKKlfF5GTcKyspAj6Y2TctMHJIiPjpoAyZ8bNwnzW/hVh2VgD8AFVvaAsL5CIzAJ4G4C7AbgBwP9W1Sv691GfZ3qMgxa9dhUTtJIaSeYSkbqI/LaIfMD+fktE6sOvzMjIyJhOdCAj/Y2ADQDnqupdAJwD4CEici908wLdGsCNCHmBYNsb7fgrrVwpVHW/qrbt/yuqes0R3fAIGJWSeh2AOoDX2v55dmxQNr6xYhZtXGNcyaFO4E721KM6BTOVYNC/3Ag5mmbMXGlmNnAWdTNXus7Kr65ZlNrDzNtpJ2i/z31aAUW3DPoqpP0j1eJD+aq3/S+K7FY4ZlQ1K6WKUVPRDN2sohpL9I8Ixys+N0cUzdM22/W0PGmi6DNilFO0gFqgLwVsW+h0pKCsrobl7qB/RKSm0r5EK6YSXxGtp6e9VZTP+1C0OiteWF23cCYLvaY20XqMVNRc6GTF0Ta0WqPfRZfuSanCnmi2Zd8O0kk+9EWZ6VGhHnG+ITGnh7u9WM71ybfZQ12V0D5cXXfzZbBC1x/nr9E3/a3ve08IEJazYu430zU+074nSqMBHwVUx+eHYR7Yy7Zbtz9FeV6gR9r/AeADAP6fiIhq2QuzdRh1wriHzY7Ep0XkvyfRoYxtjNoxf18zMrYIgvboSu8TReSLhf0LLTVDt7aQHe8SBCumvwZwGcrzAp0O4EcAoKotETmIQFvtO5I7GSdGnTDaInIrVb0MAETkbGwiL8Y4UJcqblUPzg+3qq/iCxsn4w5zV2F3JRw7ox4MtvbtChLGpSunAQA22uEWuYo8sB6W5629QS/UMiV452CaoCkqdVNXhx7la/QAb6TKwY7L99SzIi7URZ8Gcy+JAfqo0I25HjruOj49rrCYk8KtzDruKcdcEna8q9xOfSqSlWDF2jEJrLqWrr6isjtmILQtpRgq/avptrqWlo+e3O5eibKsdRwrBljsBkOUbnBB87eo1ttW1HxFLGBlxz4QHefLEL2pvWI39q1kJcr6a04ciCtmZ6RQkHR4Dd+7zhClNstVqp3kuEcM1Mc+DMtv4e/dS1NeGV/VrkEJ2xT6E5U5c/AmkbTZtXyIvbJ6U0OLrn/HeBc0m9Bh7FPVuw+uS9sAzhGR3QA+COB2R9k9AICI/CJCFPCDtr8bwANU9UPjqN9j1Cn0OQAuEpGLReQzAD4N4Hcn0aFR8IWNYB3FySJjwogWSFNhNZJAp1Aq6plgpgnN6XtH+kExGT8MVT2AkN7h3rC8QHaqmBfoSgA3BwA7vwtB+d0PF3CyKNR/waY6tQmMaiX1KRG5DYCfsEPfVtWNQddkZGRkTC20XMW0WZipa1NVD4jIPIAHISiyL0LIC/QepHmBPmz7n7Pznx6gv+i36B+VOdo0NlPx3QCcZdecIyJQ1bdNpFd9sK4dXN8OPAYli5tVl+P5HZXAhVxmnMa1zUBNrbQCF3LPHZcBAL538EQAQMNSuLbIjcyRWwobpd9FwwVdizyP7RdDIgB9lIjhQJtxmAqLx/UTU+qJoO9BpHFIgzkfj55cEC5Nao/CnfTBbLoq8qvwGLjPBd9DRVFdZWILlg1bUlHMfxGV02Ro3TixfLRboB8GjQlIu3HceDkV/S68SkzxakEM6RshM90Br86Ei2ukpqrpSj5SKAwK6FK1klKJQQjVX2nFGRYl5tfopNexnFEvVdJPPk8GCmFjHL0V81Roet5DnR9FbygRlrP6jJaL9fsAi2WK/qrTXBfPsY3NCk6Rdkz77H9scZG/fUODnArgrabHqAB4n6p+VES+gf55gd4E4O0i8j0A+xGSz5XhiyLylwh6ESD4yl0yro57jDRhWDypWwH4Crq6C0WwFc64iSBOFhkZxzkU4/PDUNWvArhrn+N98wJZ7qHHjFj9/0HI6/1e2/8kUgfrsWJUCePuAO6wHcy6MjIyMiYPQXtYCPhtAFVdwYSSJfXDqBPG1wHcDMDVE+zLQDS1iq9uBKuzjom9l26cHs8vGo/TNhn6lrMhfEh1LsjBB81s5swdN4b6jN6idUlrzuz3LTJpi3k0ogTOxAs0+zHaxkawZdRHZABomdMnNSvRtShKy7QWwUbD/VobkYpyYTn8QoiUUod0TMy7YOejiYwL+xDt2Y3+sMi9/SLtEoxGS+qI/hTeHr/iqCumsqWFVsdd5+k0T8upi3BKCy9dMgsoC/dRrRcoKaNNGC6GtEujaT45jjLyoT2ir4EPI2PlapHyCttodWXjXKmm7RKeNuqxiEKX3qqS3nJUVGSIbN+f9/4VPtQI763tzAJJp3mLrp7QI/EWCryT0GeELzKtpXpuL5RjEy6qb7TU8u4wnooaY2gQYHwSxiQgIq9S1d8RkY+glwiHqj5iEu2OOmGcCOAbIvJ5BK/FkTo1wCX+LQDuD4Da/Sep6lc22feMLcY0R9rNyNgMVLf3hAHg7bb9861sdNQJ40VHWD9d4pctlMhnReTjdu45qvqBzVR2cu0QDrQXAbRxuDOHGWlFiWLOXI13W0q3hi39r28F5fcVGyE4YcfKn7QQFOaqlsVvLSx1qfSLSkJnShoVwYXD0V8h5pvoxDI6W5Ay2ulKi6tkKre9j0EnShJsKD1P9KzYnGdtt5/daug3Eq8tbKUtPXX2+EPQs50+G1H5TMnKXWD1URKhb0lrUZP64nhzxdonsF+8D/NAV5OkZKEFQdeDm5JAzSQLrs4JOmb1DJ8PQuhW/F6ZzX1KLh4V0YK/hEkAhbZFNHycJKzGRQqSbY+/hUl/ro2uZ3b/j1w86iQS+qC0rcYKG64oOh2BVDT0qao9XuL9FO1S0W5fpHssOVAiCPTmg/H3oMnllHp8XphxYTsHH1TVS0yJfr6qPmGr2h3VrPYzg86LyOdU9d59rlP0d4k/IoTJAjjcCR/39jZY8g6aLIDyyeJYYtBkkRzn4WM/zBGDJgugfLI4lhg0WQCFj/2QZEVbiY6LgjvKZAFs+1X5prAdnsMgWLDBW4jIjKo2hl9x9BiXve5c2QnvEq+q/yUivw7gpSLyRwA+BeB5/fw6ROR8AOcDwMmnTcy0OCMjIyOBQuLEvs1xOYB/F5EPI81V9JeTaGxcX+HSudi7xIvInQA8H8A1AGYAXAjguQBe3OfaC+08zv4fi7q7SrqJDghA1VY6PLcgYaKlNEJl95JpW/fMhHIUN3fPMzZFwKE491n+DPaFFAtXXCbKi8tlENdXUR53+4Whas/bNZaPwvs/lCkHGQyQ5SkxtOep5LaCfN8pBDmz2G5gwLTdqFDmLRT64ReQbFs5UK7NilNix0CGLnRKDGviFPVsPOb0YBrVnYGCnJkNWy9JkGrpx1Jw9dwu8YuIfeqhooyCcgEB2TZDjfhcLETLK9XZrvdtKHCOFVd37JunG3uuHLzvJYRYfwwtYmM0cjCI3nq9rwiir8cQKcTRcN2cNTyelusqyzfV1aHY5gIGcZn9VQBMPCfmli3bzcvxIgAPUVUqajZE5M0Afu9I6qyWfVEzJoLjiG3IyBiM7a/0BgCo6h9vZXvjmjD6jmyZS7yInKqqV0tYxjwKwWw3IyMjY/tgCtaj9o39fQB3REE1oKrnTqK9UT29FwGsqWpHRG6LEGnx46pKC/3zSi4tc4n/tN2oIHiPP2NYH1Y7s/jEwTsDAE6sBz36UrWbibBpPAq3FXvadQlK0F0WFnXXQtgenD0U6jVznebucN1XbwxRbq87HKS7tUow/O+0GCIktfRguAlPU7Ac/TqYTrWIqARlmaZZqqyZjwiV0y5ya9V8H6LvgUuTqgxzwmncWWWJ0wMzk17MYcGwKJJSVKKIYR5iDgJru3mCKZtN6V/ZH8aVTUWfEW9lRvqB0X67nF64fiHcS3XelNrmXzFbbyV9JKoMt+FvEkDTeK1NhK1O2ojWUeJ8Iji+1ndPRZWtVHvycMSoK93jFWfZdaRUVGk570/howRLSnPGe/X35PNnFBqruOizpWllXWd7KEJP60ZrtZTyGhemQcIA8E4EL++HIXxHnwjg+kk1NqqE8a8A7isiewD8M4AvIOT5fgIAqGpfCWGAS/xEZr+MCePYGxxlZGwZtruVlOEEVX2TiDzTrFk/IyJfmFRjo04YoqqrIvIUAK9V1VeISHa0y8jIOC6hWvDJ2t4gy3O1iDwUwFUA9k6qsZEnDBG5N4JEwbyz1QHlx46atLFUDZa3C5YB6KClbAWAVctYVDcPsNPqB5Ljhy0GBfdZx4bF3ThkWYzOXAqhQ5bq4fy1K4GaWm+5UCEmrTIV7OJM6BtpBlphrTSCV97yemi31eoOG610KrZ0Z3RMbYU62vWUJoCFOYGlIWUGvI5ZDqlZO0XLLa6QeNw5x3Vm0+vF+kN2orXYScoDQHWdVmJI2pAN8ilW56wTR0hF8R5Wq8m90a+CUYMrs2E7ZxTU3Ez4XXDcvRMeaRxvFVVcJcbUq6RE0qI99JY4p0FPUNDssuocA309ngLzNBPr6SZ06uOs6A6xjmEOez312LasdCVSqrTUS+uPlGsJnVR0cvRhSGLdlfQ59DiYej+gkjAn3fSw7ubGhCmRMF4iIrsQ8hP9FYCdAJ41qcZGnTB+B8EU9oOqeqll3LtoUp3K2J7gZJGRcZPAFEwYqvpR++9BAD876fY24+n9GRFZsP3LAfz2JDvmsVjZwL2XvgugG+5jb205Sg77WkES2GsJFg6Y/8Uq854a9jeCf0bV7P1nTdNLE93DzVBfy1Z9J8yH+mou6Bu365YClvtcTVLBumESBSWL5np3yHfuDhH41htBsb6xjwkvrIBfLXOlTz8MTY+LV37zOPet6RhKgT8Iu65tEgWDDtZWykVyr4hnEMIOBU/6dPh8CuuuTreKn9sZJDUfKJCr9zJT6qpbcTf5HAoSHf/f8avmEp+EMkmBj2Wm3sIgqFME+/o6LvdEWWiRYpmWBc304UrKPK+7nelf3kvEw1bVcXFPacGdrxSaKct/EfOAUBpt++fh6jRDijKppl8ekaOHTIXS2xbvr0bI4NdBSLr0LPtGjx0jkXQicm9L9vEt27+L/6WvJgAAIABJREFUiLx2Eh3aDDhZZGRkZIwdOuLfscW7ALwPIZr4aQDeD+Ddk2psVK3OqwD8PCyvrKr+N4D7TapTGRkZGccU5rg3yt8xxoKqvl1VW/b3DgwI1XS0GNlxT1V/JKmsWC4/TwCqgh0S/C5WKoFmuqq5O56nEnu/hQRhVFpKIXOW1GGxloas2mMUFv03aqYZnjGqqmX1tIxianRSXb+nN1abQbm9bjkWSDe1jQ4pitUHbwx9VUsDSyoo+lX0RhMJh5njYTm0Eamoetv6YuVIYZlfBfNjMJIs81zQ/0NnSbvR2cIok+KTpgn8hlE/O4wuqKed9Glf4317u38rN7MjPJdIQTlFcr1EoezpoxiBts8POSqTI+VXrmRO+m6nZ2r0uUnpr2GI1AvpTBuLsvAlaXgNJNeUUUY+DEeZv0bcj9Rq2I/UFJxhQFn0W09pdTz32DUaGFZH1b0rPalsfVpab7wQn3nfZo4cx34yKIWI0BLq4yLyPIS84Irg7vCxSbU76oTxIxH5aQBqYcqfCeCbk+pUxjbFsRe/MzK2Dtv7fb8EoYec1Z5eOKcIRkpjx6gTxjMQFCunA7gSwXlvYnljMzIyMo45tvGEoaq3PBbtjmoltQ/m1X2sUBHFuuX/JN1UL3AlyyUKcFJV1zSCZRWppV1za33rWtHZ2B4ANMwKin4YK0Y5karaMOqpYdYrFL95vEvFmAhfSBlKKihGizVKqGKWRD15KJgLgjzFvGMFmXsjpsS0y1iMEXdpv+5DhfAHwnYY7qSY1Ig+GhZp1yc4Ut4fabE+CaeALn1QtfwVcxZ1draWWh4xBIi35iEqjooiatGqqjDepd4H7FM476PKerrHWzf1+G+48CYxBIy9I6TduGV7vK4YMsRTnr6vpfv9b7GnvGr6fLr+LZXkvA/17duLY9CPxnHj1zNerg6pMi1sf0SrqEnmllFsa0qKMMbn19HVKV8M4A2FsE1jxahWUrcVkU+JyNdt/84i8oeT6FDGNsY2XnFlZIwbIU3r8L9jjNcBuBuA19rf3ezYRDAqJfVGAM8B8AYAUNWvisi7ALxkUh3zUAC7LYAgV40n1Q5FqYP5L643fwxKHFRm00t8Q8It39AIqVlnq7aCteV405b1VG5TclhthXZWGkHC2DCJo+0UbnGV2jR7eSqY+6QajWur6PFqG+7WBr+NXb+L9Hj3JU49aenRjRqXsunqskeiMDA4Yd9r6CVOpXcMXMjytu2kCsvoKW+e3FQ8M4ui9zsY5kXNLVfkXokLAFU343Gc2GanZAXs4T21Cd6T/4jweM2iAjA6QOwXx93njyjU1SNZDVO8U4IouQcej9n/NDUAiMYEfC49yvdypbiXvNT1pUcBz+2QFb0/H39TJkL7/CZHjW2QHXME3ENV71LY/7SI/PekGht1hBdU9fPu2GCvpS0AJ4uMLcIQa6KMjKEOhFME0dH+jjHaInIr7pgj38QsWEeVMPZZp9Q69WgAV0+qUxkZGRnHFNvDKW8UPAfARSJyOYLcfwsAvzapxkadMH4TIVXq7UTkSgDfxxYrwRtaw3cbpwDo0kx7q8vxPI/tqARfjY1OkD4qRl8xVAdlqv3NQGEtWz4MhgihUny5GZTfDP1B5Tfpi0iFtFM6o7WRUlGERhE/ORo2Lpgac0DEkB6M+0+6h3Uw4B/TmrIeF9CvPWO5IxrOVt4HECStZOnkdaaX8mIKVfahJyxDVJyT7+nvPyD1VPFLtDvpYJCGqDmqycNTNn6/eG2/c0UM9RsYJmk5XxMfGNEHsKzZO9p2YWeKffGhT8ros7JQH2U5QDwdxHAyHeejUnHvDCksT01pn74TAxXjGP597qG4evwxxkkhyVQovVX1UyJyGwA/YYe+raobg645GgylpCz50W+o6gMBnATgdqr6M6r6g0l1KmN7Iubbzsi4KUBH/DuGEJHHAJix3EOPAPBuEfnJSbU3dMJQ1TaAn7H/r6jq4Ul1JiMjI2PbYAomDAAvVNXDIvIzAH4OwJuwDaykviwiH0YIbLXCg6r69xPpVR+sdWbwuUO3To61CxYwN5/bn5y70Sinjs2JTOe62rKUrEY9XbMWrKpICyzUgvkyqagN21Icpn0/ZT6+LzEVKy01KM66GP6dogWS0VfR0ojRZWn9weO82Oe58DknDLSeos9Hxeza2bdOk+FNUkuYCqPWlvwKpHgjRd+MfvtE1d0DmY162vfol2F98f4YnoohYg4JHwl2gPVONNyKdzSYKvFUVL/0r8X6yiLqHokJpg9bEv0mSqzCYl97QoV4qqo/peUpKr6vVRfGpBvuJO3XIEpqs74iPdfx1icdEoRtTYeVFDndhwJ4o6r+o4iUWq+KyGtGqPOQqvZ1mxh1wphDCDxYTK2qAAZOGCIyh5Deddba+oCqXiAit0SIfXICgov7earaGLEvGccMU/EDysgYC7aBBdQouFJE3gDgQQD+VERmMZg5eiSAPxpS5/MAHPmEoapHqnXfAHCuqi6bR+JnReTjAJ4N4JWq+h4ReT1CFr+BYtRycxaXL5/Qc/zAxjwA4PDO4Hexo77e93oqFNfaQRlOJfeJcyuxfqDrwT1jtt01Z3O/Ym/RmgUVjKt2Krn5kkWvVVt5UflbUIarV57aqntmR5g7qcRr0yPcLo1SSmwrraYb1C2VMGbN5yEG06Pi2e61TCnbKihMmVNifSPcf6sR9inVROnISVgcH7Xx8gpKrlijh3bFSyC2Ii6RMAiuoKt9JI8ej23nnR/r9L4ITnndrS/ZRV1SX5FoGOH61PXZQV/0U+yXeXyXGQF4RN8I91zKlgC+Vp8LRJ3E0d12r+wndRT3Y12+bSdR+Lwivcp0JOfHhjHVJyI3B/A2AKdYrReq6qtF5C4AXg9gCcAVAJ6gqodE5CyEWH3ftir+U1WfUVL9LwN4CIA/V9UDInIqguVUGV6pqm8d0t89ZedGmjBKxJiDAL6oqv9Qdp2GN4amTHX7UwRJ5fF2/K0AXoQj4N04WWRsDYrJiDIyMkZGC8DvquqXRGQHgEtE5JMA/gbA76nqZ0TkyQgf+hfaNZep6jllFRai1QIhHAiPbQD44oC+/N2AOh+mqh9V1VeVlRnVcW8OwDkAvmt/dwZwBoCniEhp5daJqoh8BcB1AD4J4DIAB1SVJPWPEYIa9rv2fBH5ooh8sXlwdcSuZmRkZBw9xuW4p6pXq+qX7P+HEaSH0wHcFoGyB8K38Zc20b1LECaG4vaSwn4ZPmkSTAKbsF49rNFRdRh3BnAfs5iCiLwOwL8hWE99bdCFds05IrIbwAcB3G7ENqGqFyL4f2DxtqfqwUagnU6YC5MHw3QAwPcOnggAOG3pUCgzE6im3fVQlnkvrtnYBaCbX+GU2VD+2o0QnLBm9AwpgBs2QggRhgahwnN+JijHG7bqrprTuVZTX4dZC6q3vhb6WkxbGX0YnD8G6ZoZ85+gQ3vNqCVuueKPfSix1Wd9LVsfeBoj0kHVdt/rWX6m2u5SPtaHRt0oKes7gwUyoB772BTzT3E5Deh3sWRKbt5DzSuWS7gTH1BQXJ+Tsi6NLuFpR2IY3VMWroTjR20kqazyXBZWLnKB3YLx/pySuqzNMmV2N/wI35HBfYm6dSqW2Y5t/Rj2ezxlXt/9QqD0vc5xVhL7lPYlYtx+E6PXd6KIFD/SF9q3qwf2sb4rgP8CcCmCTuFDAB4D4OaForcUkS8DOATgD1X135KuHXm02mcD+GcReaiqftf69HwExuf+wy4edcLYg8CzHbT9RQB7VbUtIiM5iRi/dhFC7tndIlIzKeMMhJDpGdscwxzeMjKOGyh6YrQNwD5VvfuwQiKyhEAJ/Y7pKp4M4DUi8kIAH0Z0mcXVAM5U1RtE5G4APiQid1TVQ5u9DQ9V/Zh9sz8uIo8C8FQA9wRwP1W9cdj1o1JSrwDwFRF5s4i8BcCXAfyZiCwC+Jeyi0TkJJMsICLzCJr8bwK4CMCjrdgTAZTqQTIyMjKOBcYZS8qMfv4OwDvpjqCq31LVB6vq3RDycF9mxzdUlemwL7Hjtx3XfanqpxDCh1wM4GwEw6ShkwUwupXUm0TkYwgzEQC8QFWvsv8P0sifCuCt5i1eAfA+Vf2oiHwDwHvMXvjLCM4mQ/oA7DsU6KEbDgcfi9mZrq3+zln6WTBnxlJy/Yn1oHtn1NrrG8H/4tp22K6b9dRJc8EvkVZUPzoc0sCSOiEdNGMUytJ8qG91PVBO7bZZyBgls2HHu/JzwWrH+T/EnBnWxuJsWHDUqylNFmmb2ZQSIYVC35G1poVHsTaZYrRp1kEtl9fBg7QSI/OGcagkfZizZ+Ctm5Zm07b2HVyyew71VKv9Q4PUK/3jpo2aDpVUVqsnmUgvXVPmL0GUhRLhPfaG4UjL8R1ptEYT5L3VFVCgqRzd6C23SF2VRU/2fffH/T1uNld1v5HssWYa4Zq+17t78flGJpZXe3xWUoLwjfumqv5l4fjJqnqdiFQQzFhfb8dPArDfGJyzAdwGwOVj6sthIGbqm0Vw9rvO+qiqunPQ9aNaSYlVfLaqvlhEzhSRe/aJYJvA3NXv2uf45ehOPhlTgrJ4RBkZxyXGZ6Z7HwDnAfiaGQABwAsA3EZEmLn07wG82f5/PwAvFpEmAjH2DFVNPZMLMOuoWwK4TlV/NKgjqrrjyG9jdB3GaxE6fi6AFwM4jCBe3eNoGt8cJCpa52a6yaTWzB/gxrUFAN1Mely1M/jg5WtBKd6yIIWHm0GBvmwZ9Jij4MrVIFFwxbWy0VWsA8AcmlZvqH+OHsmW8G/d/DNoy893jv4a1Xqn69VcS++H0kvdKVOp1I6exPbhrtRtHJw3L1fZO2ZS9RJX3dUSXwe/Eu4qw1NlOwAszYa6OYmwjxs9dfRXDDPDns8yx/FkHpJhehPvGzEoq56XLIYFI/SKZL86b5es0n25UQMm9utP9Kz2ml8H+oDw3enWQUMG24vBM929lWQu9NjUap5ddW5DQ8sPK+alpwlIGOMMXa6qn0W5y0uPZZKq/h0GmL8S5gD9lwgP+XsATjbp5Mmqen3JNV9S1YGxpgaVGXXC+ClV/UnT2kNVbxSRmWEXTRqcLKYJ2yBDV0ZGxijYxqFBROQMAO8F8Cuq+p3C8TsBeIWIfADAV/tIHLcXka8OqhrArrKTo04YTdNDqHXqJGzGhiAjIyNjyrDNQ4P8EYDnqep3bHJ4EIBvALg9gsXV1QhOgOe760ZxayhNwDTqhPEaBB+Kk0XkpQgWTluc01ujTf/KwcD/zC51w08tm3KZK/i9s4Ga6jjlJ4MOMoQIaZqWE8VJVVEpu94IQ0Wapu0UxvNGD5EiITXTaIYtfSqKSt6oPHX5EkgXtDWlZWaqKb1ACmmu1j/fO+kZKp7L6Ia2o15qjtYo+ilQkVvm0xDrjMrXUI4GCmvMYWDHGyYl+rSlPM/n5Wmd7pilik9P8xT9Ocrs/4cpsTtu60OGDAuyV2ZU0M1em9bvqcJ+dfbku4g0Wf+ghPQf4rZDvxj3PEaWgEcpV6LlHjY+ZbRXGRUV+zzuD/z2njB+UlU5GSiA/6GqPxSRMxHChHxJRHr0xEeblmJUK6l3isglCIpvAfAoVf3m0TScMX2YcRFkMzKOW4xRhzEh1Au+bGcDoFnsAdsHJsACDZwwXLyS6xBsheO5QZr7jIyMjKnG9p4wLkLwEv87ABcA+JSIXIYwWbxYRH4OwZt8rBgmYVyCrs3umQizmADYDeCHCKZcWwJBl85pVmrxWDwv6Zb+GI1O6nMwV23ZtplsGzYUNRelltZXjOzqV9lNF/GUvg6rFrYkptJ06SQBoF5JqSgPUlOkaxbN6mmtmda93kqV/7029Wm9vj1ayNA3wlMzlQL1UkbD+HSj9B1pllg7kV5kKJEy6YXt0GqK4Uu6YSP6WxoNgs9t4ik4vitcns3aO0P6sl860iLKrJ/KzhP9rNdiG86KKT4jOFrGgeO8oeH95m+ox9+lnUbWJfzzjvtst9O//CgYNfqsetpzq5b+23vCeBmAT4jIt8y37WMATgSwDyFd6zsAPHzcjQ6cMBivRETeCOCDqvox2/8FAI8ad2cytjc28zHIyJh2bOfX3Rz+HgPgtSJyHYD/RFBW3wshJtUTCs7VY8OoSu97qerTCp39uIi8YtydGYRapYMzdh8IO7uBK27YCwWwcTjksZjdEVbfB1eDQpwr3Jlq6ok8F/fD+RNmQnDCmoR9+mnc0AiSxZ75NEouV/sH10No9XVTPPcobd3Ki6vKjWYt8Wcowis7o/eurWwpSfRkjhtih+4zwXE1X7ZC9orstgrm642kL2234uQVlADmTRHfsmCMlBDoAR+NBcwHRd3qnvtdCY73aitla8cbDoyCsix9FSd5tFw+C54vW4X7LHf+PNHj8zBAkvPXltUV22RkPj4RZltkEEjbVurhul7PeirNWZ09ZzvrR3ngIsKd8nkteoprSSu+jUkru2M7E6p3TFDVywD8vIjcBsBd7PDLVfVbk2pz1AnjKhH5QwQxBwCeAGDss9eouOKGoFrhZDFNKJsstjP8ZJExWWRJbhtg+yu9Iyzq7He3oq1RvwCPA3ASgmnt39v/HzepTmVkZGQcc+iIfzchjGpWux/AMyfcl4GoimLBaI4z9wYLssOLc/H8NVeHkB7VuSBi77MAhVzR71kM1NKSrZZXW0E6WaqG/Vmrm8rBZQsdstMUzTesB4pq33IIolcx2oKhQBoV5qRIqYSqU6iSkgG6vhpe6qg4igOOdojKUSqp3VvL4IM95Z2/AestC/jXq/zu5sNg6JMu3WVBGa1PpP6iv4X1iffPwIbe/6VTC+VJOTEUi/d7GdXXoVjOU1FlCnJ/PNbtFb9lyu0Sf46etLOsp4R+2lTfDNUK/WPCcVKBbNvTjT6UCt9rsBzTAjMHRYnyu9+Hc3QKarTjw6iosQchvIlNBqNgoIQhIi8aVsEoZTKOD+R8GBk3FQjGG958khCRW4jIA+3/85YGdiIYJmE8VUQGJe0QAI9FyMmdkZGRcXxAAZ/0cTtCRJ6GEP5jL4BbISSkez2Ck/XYMWzCeCOAYbPVG8fUl8EQjeL19SuBFipaJs3sCNRSjHpq0VA93UL/jBtbwcqJ1lJrnXCcFjD0z/C296RSlOlR18J12g7nF3auJ+V7wkYUb6knxASSPvhIruKoDtIGVaN/OB4sR6uqGF010gxpGlTSR+vt9HUY6E9AOouWV85/ZaEWnkejE+pcMmqPVmXtDvOW2HhbaBUfEoRYcpF3+S6U+ThE2qlAA3mKzZctq4PQmEI1HYMyCy1P9/h6Y+gVe26Dcnl4eCrK02G91lShbtKgbJtRbkmLsc/e+q1rkpfeSw81VcQwysjvly3Xt5qKihVPptox4zcRUkX8FxAU4CJy8qQaG+aH8ceTajgjIyNjW2M6JowNVW2IcAKXGibY81HNarcFluoNtLSCW+25IUoK+1aCcrtjK3zYwrTRNLv/Srpaa9nKlAH7DjWCcrtVC8d31UPQQgYT3FFfxw0bSzhj6SCuXV3CKTsP49BGuKZW6QAL61i3AIOHD81jbWUW9dmwYq9WOxDRHoUt10O0iSd6lN+arvq8rwNXpO0YQLGrzJ2rtrBQa+Kg3V9R2dnuVFARRVOrWKg30OhUURFFrdLpejmXrNrKfBf8fiVKHvSsD/XutnwlVOhz7KoF+T9ISu1uXS6rXKNdTdr141tzyn1uW85LmvASR4+UwtW3U37H6yF983JUCs++owKoHVPpCTRISbitlaR/XnFephgv63MtZmsM4zxrPjje278mHbRVUBWFqqBSVatT0emkx9u236HPjPWrxxCBBiDRpwPJvfRgSNDBbsUoDWQ4zk/ldtBPjIDPiMgLAMyLyIMA/AaAj0yqsakyrKeo7ieLSeKGjUB/XbsatslkASSTBYBksgB6P2ZboTYmxdRvsgC6H5OFekrjbXaymARmXHiSfpNFEaNOFpNEWRKnthtPT0X1myyOFXzE4jh5ukRL7RjipjtZFM9vCbZgsoj1jfJ3bPFcANcD+BqApwP4GCYYSXyiEoaI3BzA2wCcgjC0F6rqq82y6mkINwqEHOEfm2RfMjIyMkbGFCi9LUfRpap6O2yRLnnUnN63BfA6AKeo6p1E5M4AHqGqLxlyaQvA71ps9h0ALhGRT9q5V6rqn2+ms1z17KwHBejO3RsxBeu3Neh5btwfpI6mWAiJuTSo3eJsWFVTqdoTFM9WeYcbwU+DFNUJe5YBdCUO5sug1LNm+ThazTQEBoOzUfIo5sOgQparalJSXsndoyRlToNIVaXnKYHF1bVLGeppDb9qL0PHKBV/LFSa1tXR2eS8z63BQIpUdtPfhfVTAe+DR5b5IdScj4OXUIplhqZ9LRn3btvpePZcz+fhFMeeXuJzHGV1PsxHg9IM366yvpMm8pJEr8+P9bmSSkvkyskAx3wa1m7fe+ErQpeNMt32ka7WyySOo8Wxlx4GQlXbIvJtETlTVX+4FW2OKgO/EcDzgeBFpapfRTCnHQhVvVpVv2T/PwzgmwBOP7Ku9qKy3ZcAxxm2gt7JyNgumBI/jD0ALhWRT4nIh/k3qcZGpaQWVPXzkmrKNpVNR0TOAnBXBPOv+wD4LRH5VQBfRJBCbuxzzfmwFIMLpyxtprmMjIyMo8OxnwxGwQu3srFRJ4x9InIrIOb0fjRCztiRICJLCIk+fkdVD4nI6wD8idX3JwD+AsCT/XWqeiGACwFg7+1Ojo+PNEWtMIGdsnQYQNfy6PD+EMqjUzc7/4VAfdAPwK+WWeehZqBSSCssN1NqxZePCuT5QKkcOrhgnQ+bTovRWi1nxUK3jrkZ84cwKopKaE+d+HSoVJbKkLwMMR2nReAlrRHzO5QowwehjM4hNUeapeEovxj51ba01tk9szKwHsrAMc2s1bdoY+V9FrqhSOwdKVBVZX4lZQryYXktfNRaj56QIKR1KimlWKYU74eyVLWkOmM0YUfhiaMlXUzbnjb506ryRY7GBNZXhobppP5JncI7FRXinSOkikqpqy1QsG8PhfZQqOpntrK9USeM30T4cN9ORK4E8H0AvzLKhSJSR5gs3qmqfw8Aqnpt4fwbAXx0M53OODbIoUEybioQbI1F49FCRA6jO7XNAKgDWFHVnZNob9Tgg5cDeKCILAKomD5iKCRwWG8C8E1V/cvC8VNVlRLKLwL4+ua6nZGRkTFZTIOKVFVjJA773j4SIYnSRDAsp/ezS44DAIqTQAnuA+A8AF8Tka/YsRcAeJyInIMwM16BYD88EG1I1wKnz9xP0fzEpWDN1GBqyuVAKW3Mh1udq/dPsbpsVj1VFzaDNA4j5RIL0fHPEjgZJVCpmqjeivEjAADaJEXQiHVEsd9Z+HiqqNQCqNSKB5sq7ymvQZZHpcmHnPweKSVa07BNJrYqCdlS85ZjmiZIijROyfqvjM4r9rmfBdWgOkehivrV48PSVNx+WQiQfo578RwGW9BVHIVUVs8wDLNY6lrghU3NSK52oZkYmblCfhbWd2sjNibJ8XiYW09pub7dxEODRGiIXfMhEbkAwPMm0cYwCYOz108AuAfw/9v78njJqurcb1XVnYduegAaREAe4MMJgRhwRI0kYAQTceD58hzyC3k4BMenxiRPY1Qcok/EIQg80BAUnEJQwyMGbEMYROZR8AVkaLrp4fbtO99bd+WPvdauc1adXefc7qpbVX339/vVr6rOuM45de/e61trfQsafX8NgJvzDs7M/4Zszy7WXHQhYpZUxIpCF/zciegPE19LAI4DMNOq8xXSkiKijQCOUSpKCu9+1CqjsrCwWPKz0KQwna1J0FndmmHX/2KTeBiTk67quTTi6ip0BjXYm/YcVDqES+nj6bkVOgPWPg++JayIHk5NDKa2p1kJ5q6tzfx8ha8GhCk949TAsIV6IP2yvibol/YQFL5KV23Jy+nXwDNM/wzUz85tZbXdTm21HoVvgyrn6jW9Per6NHhZDmQut0H2uv0ybLR1LPoeCqSHEBIZ1MXlALeh9zfk2SRtttXhebDeU7ku6J32KmGq0vNqJoqoAbB6e4tpT6Okx9ZcAF/EkX3Mut+rt01sRbq2pCnojJTZInhN4vMCHGNzWqtOVjTovR+AucT3OVnWVlS6gWTcixA9jIgVhe74uV/AzNcnFxDRiwBsacXJig4Y3wRwMxH9QL6/FsAlrTAoIiIiohPQJfPRLwM4psCypqBoltQniegnAF4ii97GzLe1wqAQCDX6RwPVC4kZr+0Vof0TNuzv6gE3b3VZZmPbXAHg+vU7AdRccpWomDP0jg1cToskyMxC2g3eNeOor5kpt576ZD91kyUY3psIuvdWVEnUbaNUm23rWqJs+idECyilZKUoFDZgaumLumBwIoCs1JK1QaknlfSYkvukiQraynZepFP6ekXFVu67trbtFZptpCctFZKH0HbJe2Tvlw3q51FRdZSW3gObpCD/aXrK6d+OP4+hDsvmfiefl9o0IEkWebUiIR4lFDzX4Ljaor97S12pcnLV0GeWsqLU36TWbBhqygTMfS1IDjW13Ohkh5qITgDwQgDrTXLSKLxmd/NRSBqEiJ4OYCuAH8hrmyyLWEGwcYiIiL0WvIRXDojoICK6lojuJaJ7iOhsWf48IrqBiO4ion8iotHEPh8hoodEK+p3Mw7bC2AYbtI/kniNAzh9dy87D0UpqR+hdmsGABwK4AEAz2qFURERERFtR/M8jJAI6wUAPsDMPyOitwP4IIC/JKKj4LT6ngXgAAD/QkRHMLOfsUmF98+I6GJmfqRpluagKCX1nOR3IjoGrlHHsmGR67OBkhkjqrZZNrTNmn6XLdWzn7vXm7avAlDLAlE5jsk5R6H0CWWkVJS2OVU5DT2+0mKTs26/RZsrPiW3dkCecSVdX+CuySiJGopIz2GvSWHrClSqwtMNYpJwvKg6AAAgAElEQVQut1lUvi2nOY5m9XgqLEHm1lFRQpnsmHZZYU9tdZnY5V7JVht19390wGX6TZCj7qZnelLvXkZDZFJmhl0220if2y+YjRWgz7IoKrvO1rvkIURp2dqPPFXcPJotKTmiVNRSj7nUuouS+U3ky57oe4EML+weNUVaw+RP2vga8rL/lgJC8ygpKVLeJJ93EZGKsB4BYKNsdg2Aq+G0oU4D8G1mngXwH0T0EFwb1hsyDj9FRJ+DG1z6E+d8RXOsT2O3OraIAu1vN9mWiA5HzJKKWFFoEiWVhBFhvQe1FNjXAzhIPh8I4NHEbo8hrPJ9KYD74Vifj8Ol1f5iaVYVR9F+GMmgSgkuAv9ESywKW+GFBX1dQbXkZ6RlmbmrZ2BTbjWI+qT809vylKMLh0bdDFb7ZCgmpYJb0SMzZjuL0dn/ggR72TcKQPpdgr0TM7Xj6qxbj6k1IBqy8jN+2d7OMut6RJTSXf5CrUc1qGgjEt5j0za3uoEawPWDxqx4fbPybFbt4zyKfQamUjYrtDUuD6UD/epNbdvp+pls3e6SE8b6XCfDwX73fLTFq7bQVQQr0BsMcqH7E2p3GoK9xqKV99YL0O/JYLk99u6iNts3y8VEWwvif+emLkNrHqTjLhaqpp4DNe9DExks7LGtp6Fsgffaze9Wj0/Gk256HcZi4dFgHRHdkvh+vginppAhwvp2AOcS0V/CFUXP2X0KYC0zX0hEZydoqvYOGKhVfAOOj/sR3IW3FeVyc/6YIoohehgRedjtJkgdiCX83Lcy83ENj5Utwno/gJNk/REAXi2bP46atwEAT5NlWdCZ0yYiejXcRH5NYcuXiKIDxr3MfEVyARG9HsAVge0jIiIiuhtNGvwaiLDuy8xbiKgE14f767LqSgD/QERfgAt6H46wFNPfENEqAO+Hq78YBfDe5lhej6IDxkdQPzhkLWsZmOs1/pNB71rg121TJ2YnVNW+q53Q7qaF1QASQW6hAdSt1WD4rKn9UHj6QGoptM7AY1BohVn13aVeY0dNMkQFElcNOmpKaxC0FkS/hygML69hKSjx0ZXi0loI7VlgexeoSJy276zIvRgWGqhX6KVFpjqqT/tSjEjdi9qk59w55+Jwq3rdNWorVtu3YlLqNkqr09eqM1al8jbPOWd3RPqPeOprd6qsAgxGSKwxBNviVhMb8gLUeQH8JHzgOEdwMiQsGbLFJ1roYUzvDktz2lauuj9V62tSQj026tUD09QUw4oRcmovDYbXqCj92lz3pokOdUiE9XAieqd8/z6A/wsAzHwPEV0O4F44RuedyQwpb5/r6X04M18FYCeAlzfN4gDy1GpPBnAKgAOJ6NzEqlEsseNeRPcjSrFErCg0L0sqJMIKAF8K7PNJAJ/MOW6ViM4A8MU9s7A48jyMJ+BaqJ4K4JeJ5bvQQrcnIiIioq3gzq70TuB6IjoPwHcA+PaVksnadOSp1d4B4A4iupSZ2+5ReCpKM6MST7RiZBiUWlJKSTE7774vSnbH5IyjQqqSkaF9LbyCrK+vcPtpPYauV/pB1XHHy2mJEJ9oIW1SPUUF+FQCpZR6cigMdfFtxXV9PQZStus1am+QupmTcf0XKs7W3h4jA5KYJHmF15xeG2ulDsZKiiwgff80QUQVer2kiFCLKqmi1N/4lKO6ZubTfU5UakTrF7Kq023vCG87pbPPQllToeNZhO5JI+ophLp9mqye4Vu2FqSmrF2aEUVJL7Savo8h5GVNYZFS2zHru8miaiIIXaMldbS8/3ViGQNoSR1GHiV1OTO/AcBtlFEVw8zPbYVREREREW1HF6R8MXPL4xZJ5FFSZ8v777fakCJQzyJZLW37A9iOZFWZqc7KpY5Pu5np8IgEmmVmqjUe0zKD1dm5zpyGSm4GPDYzkLJhVCqRdUasdQazg5XU8rFxF+xe3OKLMTE7ma4uny+lK7s5Z4brK7G1b0U5PaOt9TQwtSGBCZkPekvNidY6ZM4Q9ZB+lic2BfpkeO9EttN4iFb/6nffH0M8A+1sONwrz1E8Rq3M3yWehgby9fntSJiq59h/xCU89IuXWHddalsgmaBoXUWojiPPs1hKZ7/QsbK8QSBcK1JXC6LHMZ4GmWvytT5ynjLU668lnkD+XmlRExikZsOcs04c038wngYHttevTXY0uoGSIqL9AHwKwAHMfLLIipzAzBe24nwNK70TfbffwcyPJF9YZmmQLBRtJhOxclGKv5FlRTXQdrbrkFfdnXy1FxfDSYocIN9/BeA9rTpZ0af7qoxlJ+ft1EClcQ0RXUNED8r7PksxOiIiIqLVoMVirzZjHTNfDnEOJdbcMlnpvBjGWXCexDOI6M7EqhEA12fvlUJIpfGtAH7KzOcQ0YfhGpZ/qLEtXGvjmeEras2BtjnVugqlppTCmJtylMXsovu+dr3QFBpUlSCq1fL39QJTEjgedHUAvg+EBGdVKkMpgDVSJ9Aj38d6as9ybkbkRIwkRDkg8VEn5ibTG23VaiVDFNUF6XEggX7qUd9etxAaTyitNSPO5mFTMwGEJc4tBWJlS+x7pUZ+pJb3lxwFtUDufms9jVJT/SbpgKUeQ6FJDVpbMjpYkxCpGFFFhZ5DbdZZsg3wqlSKys/05dTJFEVRCZKlnMMG9OuQcxhLTS362oh0DZRSVb5OIyU+klYVVFFBlRWpynf9/QdzMUx9RqmkNR/p/ZuNDhgMimCSiNZCbh8RHQ9Xk9ES5MUw/gHATwB8Gu6fumIXM2/PO3gDlcbTAJwom10C4DrkDBgR7UfshxGxYsDoiqA3gPfBVYYfRkTXA1iPdvXDYOadcKPVGYArZYeT0B0momFm/k3RExmVxv0S8ZEnEegPTkRnAjgTAHrWj2ZtEhEREdESdEPQW9iblwE4Es4pe4CZ53N2220UVat9DQDVNdkC4GAA96FgA6UMlUa/jpk5K2VX1p0P4HwAGD5iAzcKcus63zvCuORaszG62tEtO7c7VdRt0r+hbygtFDk/627NyIhTR9WsqUGhQPQ84zMuS0czndYPTQAAnjm6GQBwy1bXmFDrRI7ct9abfduMy5x6bJPTCtPbMNJfTwUlYa+ttyS1C4uiHFtNK8hWp9KyJTwv9IIyLhWhiYQuG+pN0zxeJbdBHUYo+BeiROZEpdaur1CaflMa0tJJlUF3ny0Np1lWSiGOJq7Fekgzcr90X1Uo1onlcF9a7uSRbe456bNUSRfNlMu75hBsH40i24Yy5kK/mWCL1oLUlFKI+hvQ5WVjR5LG81IdnE0l6f8BW4fhS5eMLRSgpqqc3r9p6IIBg4j64cIGL4az+OdE9HVmnmm85+6haND7bwAcD+BXzHwogFcCuLHIjlkqjQA2E9EGWb8BbhCKiIiI6AgQnIdR5NVmfBNu4v5lAOfJ52+16mRFxQfnmXkbEZWIqMTM1xLR/8nbKaTSCMe5vQXAOfL+j/kmsA8KN8pn11mI7R2hYoIlcrNtWjuZ2m9MPI6SdMbT2fauXa7uQmsTdHa5a9wt11m6eiIafL9h+lA5X3rmt2li1NsyJYF4jDubJsrOW9GZ01Bv2uvRWbatExibG0jdFw3wqxeFaREX1GB3RStlkTqf1rk0Et/L6+yWV6tgYfefk4p4DW4PV9wsX70nDVCHjq8z4Uqldo90nd1XZ8vqxegvwndjlEC7bnfAahdLfHKno0fVi9FEh7L5zVntLXs/1cvqNT1c9B4kEeoOWAr83vOei612954DpfevJQI0FjH0v5lytS5ZgM11l3yRRzrxIRTEzgtq1zyPJoK5W2IYz2bmoxLfryWie1t1sqIDxpjQShsBXEpEW5DQLWmAkErjOQAuJ6I/BvAIgDcszezuhQ4W3Yhu7IfRjTZ3M+xg0c3okiypW4noeGa+EQCI6Lfh9P9agqIDxmkAZuAEB98MYBXS2iWZyFFpfGXBc0dEREQsO7pkrnEsgH8nIk1AejqAB4joLrgQcVPlmwoNGMyc9CYuaaYBRUFUy39XZM0e7TJ1qdVtVekOTydI3r4GvXV0m5tN35p5qZmolqVNrATBF2fd90VpOTo26eihGRH6K4mY4cioo6zWJuoCxmcdBcV9QidIkHqn9MyYHXLn3H/VuLsWCehum3RU0+Sso56mpt17j9BovhPhjFAbqspRTtdb6K3qHXA2rR91geQ9aQu61H4MihBlotc8If0ynppyrVuVLdBkA+3LYWmg5HmVktI6C2vTiASvlQ7T7bXGRn87/X3ufWLaPWMN/K7XQDzSlJP+Bu191R4rVkYluV2IilLb1VYPuaSivTZskkBQcNHXXSidqcFvEwRP2L4oxyyToZ68dI1JnPBig+lr9DCXwP44gQ32BIyEcmhH4/eW82R5hXu7kP0UCG70irmuEREReye6YLxg5kdEKeMgJP6ft0vefKTR+oiIiIi9Fd1ASRHRJ+CUM36NdEby8subdxKS7RezqCjrSs8LHWBbq2qmlbr9lT5HZVjaoHdUaAnJOBobk9aqcu7BEbffolBTFaGBZmbFlRd6SKkrVcPdMV1r0ap5/juHHcVRnSul9pkRNdvHFpzUlqrJ2pnPokh/zMzJtaor35PekComw13z4GXpSE+6/iILVgJEkZcFVTR7Sr8rnTNj+pno89sh1J9iet7RRvsP7QraXjtXTVkVqFcF1mvULCmtz9DflGZV9Q5m161Y+kf30/MofWbpJqWHltIvo5bRpllh5Ybr7fJ+OddcKZ2FZum0OspQj6OL5V4mlWitxE3ZZEXZzCubteepKdnLPyejftsydEeW1BsAHMbMy5JN0zUDRkT7ERosIiL2OnDXZEndDWA1lqmWrWsGDE78s1pkCs5cdTY3M68BTgkOVtKidbq8bAKNVu9fvYDh/WZTy58aHzbba/W0+97X72anPZW0COL0fAWDvW6dVomvXuVyCrZvl4CuegoS5J/XSu2S5ro7W/sH3aRCq9K9Z2EnRiI6yAvZjQNs7Uow538JM9+6PguB5xU6l4XOvjUtefWa6ezzJq7Nzo57TVdGez0+WG0CwP2mL4jOnLX7ov62LCpmu1Aygdo5l/CGNZhtBSdtjUftviK1vb9GWyEv3wfKpsZnsZrafmK+L7U+lMzgxQjBYCaUStXaM1CBSHMN3tMoaf2FW69sgAp+6rFV4FB//2Q8kpZ13OsOD+PTcA3u7gbgXV1mPrUVJ+uaASOJPJqjk6GDRURrEaJSugF1mU9dAJs1tVegOzyMSwB8BsBdWAaLu3LAiIiIiGg1usTDmGLmc5frZF01YCjdNCdSDI1y+/VZq7yG5oJPSz5/j6EHPP0ggU4NroYkvQfXuuOq0N+TO0QuYptIhgxJX4e+dG1EMlCnbUX7e4UykXqI2Wo6+K2ueLlHgohz6sq79/VrXaB3y1POBpYgOC0Y6mlejqt1GCJ3Mi01Iwtrd79KNyRFEaKm/HdDteR5BFYI0SY7BOsIMmwLSaDU76OClm691n705kjV+H4lhl7yAWehsiwFNpcIXNcEJt25vLyJ+en7FrehaxVbtJbpvw45seidCyKAOeMSK1Sxzno5mnwQoqaWAq2bUGpqsZx+ZtrLxFNS+q4H0NavKmPSiiA4oyvSauHEBj8NJ7eUpKSWP602IiIiYmWCQd1RuPd8eT8+sSym1UZEREQsK7qAkmLmly/n+bpmwGAmr8KqSFIBZaFterVtJtLurNJZqjarrrlSVPa3YXPyLe1gqSptazrZm3bld21zMh7zQiPNDtWC3r0iE6LUxrxkVC0IVVSWWo5+CZSrBAiJCz8z7qikIenRsWatk6bYvtVlW2HOPF7NMukVl17v2WBalTWUPQSEqSe7j2Y12f3yqKcQtWTPF9reIimGpzbps8+TQMlbn9ebwsIuD7a7zbgWKxviKSKQWZ99TF2ufwc7FtzvcqrqflPT1WwVYP1tqk2WmmoESynZbCalpvR5lKRuaEGyp7R2yVJNng7TDrAtkgbphrRaItoPwKcAHMDMJxPRUQBOYOYLW3G+vUdaMqLl2N2e1RERXQmVOM97tRcXA7garrkdAPwKwHtadbKuGzB0xjMgufEDvfMYkBl4byAfvmpmuwMqIifba6WuD5Cb3gIzEmS3szpvk8yQ1g64eoqnrR4DAOwz5LyOfTe476uf5vopDI7MYnDEnVOPtH3CBR91NjY05MKP+65yAe01g+5Ya0bd+8CQdIMT72DbU07FRQXxBuT4mmhHWothqmjVgAER1Ns25Waej+5cDQDYOedqRbZODqFEjMfGVuOxMbfu0R3u/Tc7XMD0sfFVqfvkey2YmXhImLCue57WzJj30Hqdierzts8dCIsTLnUwtNeyp9vpb6iIikHIM1GBxjm5/3OmSn5R7kefdGh8ZNp1EHx8ZnXm8dQjsyoI/YG6kqWk1Nr/s+pteEFC+Z1WyotmeXbFesl6M80AF3y1AUSkD3cdM18O+Ytm5gUA2T+QJqCrBoyswcKv66LBQtFNgwVQGxiAOFgk3/d0uzhY6HtzBotmDRrEXOjVJtws75NEtBYydBHR8QB2tuqkXRPDiIiIiFg2MIBq2+mmRtAR+n1wKbWHEdH1ANYDOL1VJ+2aAYOIvcTCQkY/g7qZa2DGozMmL7FgJEFshbCVEplWITUVLzT768xahfyGerLbrAK1fHtt8akzIxW9U49K95kQz0q9Ip1Fb5uQwLoECdcMO09ky3r3vSp1GWV1MLTOQzyPMQ2S68xM1o9XnOfDcxmOqEpDVNSNcW/T0qNDvSMrtJc3m8+r01AKIFQHUOuRwCm7krCO1lJRtGZkqe1rs64plCxgg9H6W/JtZ8UT0e++TkN+M77dr7aF1RqnRfVQsnt1hFrCcsZMW88dqo/Qvyn9m1GZHu294cUIzf41UcP08ZtZ1U9oq/dQBOuJ6H3y+QcAfgz3a58F8DsA7mzFSVtKSRHRRUS0RXROdNnHiOhxIrpdXqe00oaIiIiI3UKTgt5EdBARXUtE9xLRPUR0tiw/mohulP+DtxDRC2T5iUS0M/E/8q8yDlsGMAxgBMAQ3OS/DGBQlrUErfYwLgZwHoBvmuVfZObPt/jcEREREbuP5nkYCwDez8y3EtEIgF8S0TUAPgvg48z8E5k4fxbAibLPz5n59xsccxMz57bJbjZaOmAw80YiOqRZx1N3OsvFtfSCblM2gTK/b4DSsMG8Ujk7Z9/up8Fx30+jlC0bkdxPA4hWhsFSEwtI0whKWY3NpntCDA+k+zKoHMn8tMux75U2tPPT4sobCRISf5N6tN+DSJVwTblU+3wsiqKuV8BVamTCUVJbpPfWweu2u2soqEobErGzz3w+53im26dbZlRTlZryfRkC1JClZYLnzJEaCSUAhI6ThKWmvFqt78nq3pRKmjN9YHz/F6NeWycBIr/jXBuRXt8oLcf+rWiQu2T+IXsKSikq32xD6pO05av8rqVcw6vdKkXVFDCaJuXHzJsAbJLPu4joPgAHylm0a+kqAE8s4bBtyXFvV5bUu4joTqGs9snfPKIToINFRMRKwBKypNYJpaSvM4PHdBPo5wO4Ca5e4nNE9CiAzwP4SGLTE4joDiL6CRE9K+NQr2zWdS4F7RgwvgbgMABHw426fxvakIjO1IcwPza1XPZFRESseDCwuFjsBWxl5uMSr/OzjkhEwwC+B+A9zDwO4CwA72XmgwC8F4BWZ98K4GBmfh6ALwP4YZ11zNubf835WPYsKWberJ+J6BsArmqw7fkAzgeAkSP39/5roywV76oTNdw2L4OlqCvuz1tJSzZMSl582dAZWXITIdssjaPbKSWl75rJNS51GBNT7l2lP3oGTA8OufS+0eyWrHNCYc0KvaS0U3WhAhi3X2s81H1nmYMszKUVWC1CmTM2K8puFzqevYdZufihDCx/DE8fNqagijaSCkmL5NE9Wdl/itBvYrTinqVtrWrRG1Bh1jqOkO159z3rflfkPrJK3xiajHJIFZ/JJZeiFJWn5VQBWtWcm65W28SsK6IeuMHiUmb+vix+C4Cz5fMVAC4AABlMIJ9/TERfJaJ1zLy1aQbtJpbdwyCiDYmvfwDXYjCiG9BMjjgiotOxWPCVA3I67BcCuI+Zv5BY9QSAl8nnVwB4ULbfX/aBZE6VAGzb4+tpAlrqYRDRZXBR/3VE9BiA/w3gRCI6Gm4MfxjAnxY6Fmp1GIs1tbG6it6i/RVs8DsPfoblY4zZ++kMbsH0MchCqCtcnvfjq6ll+UEjrpp8vN95FlqFrZ5HpZLue8GzEhiVw/b0u/vqxQjFI/GtXyu1vwq9bzzl1rGsI1uroY5HXt1FTuzOJi1Q4N5YsUlrb/JcdYkOZCqKTZDaBpDzahLsT0NtC7XAbSRyuNQq9N6AsKL9e9DfUMiWPLHIkCeY9TytekI18Lu3tlrYa1IHoIrGf+u7iybWYbwIwB8BuIuIbpdlfw7gTwB8SWQ+ZgBo7ON0AGcR0QKAaQBv4qxClzag1VlSZ2QsbpqKYpb8Q0Tr0NQGNRERnY4m/Y9m5n9DOKvp2Iztz4MrR+g4dE2ld0RERMSygRmodoG++TKjawYMBnlXNotayM2RD1BIRVuLBnPrAxMH20pUz5/sE2GPZcXxQrbWyZnIMZWOOHTN9tRyT6k4fUDsnHH1G1MzLtA5s9OJDGr9RUlawS6KpAiqGdeotRumDawWN/T2pUXqLPWh9E8PGTpHDxO4rzbIulRaIwv+mRScUFoazAr01dkkpuwOBZlX05Frq0nSsH1KQhRUCCGxyEZ/k3W0lpf+KHbOsFihe/etXptdmtAZLFBHoWsGjIiIiIhlRRww6hAHjIiIiAgLBtAdPb2XFV01YISyXELLsqBUxlIDuHX9G6hYfYciizqpoyhU6sCcS+UbrKKupRNmTA8EpagWKH2eDcMuzXtx2O03uSqdg791l1O/rQpF5bOlkJAEEdrK12EINaXfSzZzyMBmqdXTOI2pl93JiKnL6CmoJhvqr6D0jtI+qv5ra2/8+ZUyMYer1RvUU5KFW9UaVVqr6mupqBCK1i35fiTmuD2JrEDbo0RhM99U8kPlXmoZb0i9hxCSGtkzMMAxhmHRVQNGRHuhg0VExIpApKTq0FUDhs3jzoINitr6ibKZ3tXyx9P7582wtGo1NPu0HkxmJWwg4Di94Cqt57Tbn9R2rO6bFluzK8FtDwSt+g1VmetsVHt3aA2J3t/NO0QlWWerPVVUZ+Qno70IJMhNUhujlz3cP5c6t0W1oIfXzNz6WqKCQ9k863Ig6SAUaC6bSwg9z7yAsnqAFc4XObRBbH1mmtgww835kw7Zqr+t+cX05CF572xPEn334oFyn3xPGdPbQ3/XZPtk5CDPE1kSGDFLKgNdNWBEtBd+sIiICGCvqtWJHkYd4n+AiIiIiDpwHDAy0DUDBnPNpW0U4NaQmw081noepIN6oX4V+j5rZDh0ex+AN4HruuP5QGe9zVXpR6HuvFJBGjjsM7UMGtT2wfBKupdBSHDOuvqemuK0jVNChSn1RWvcBpt3OMn+cs8cqvPaNEOuYdLtw9r/VYXg5Bx6/+y1hag8RYgS3JOeEhb2d6T0joo6+t4ShtrT9SrYF+qfYc+jx6uaFsN6Txq1sfX9LMyzs+vtb8C2LC4qnGihQXNtJ2yfZ508SgK6jW93bNoce0rQ3DdLW+bRxHb/PQIDokQbkUDXDBgR7YcfLCIiVgKih1GHOGBEREREZCEOGHXomgGDQYk8+vr1NkNioZqWLCgbt1epqzw312Z8hPT/fQaXuO4hyiWLKqkTUfTXmU276Pbac2NAKBLdTmkDpRGUEqjL9jGSFZ6yEgXaVb0zboNET8St48MAgJEBt247ue8LkxW57vR1lk1/EkWIMgrJa4T6QNjvdcszKBhL7alUykCPu48+2yzQDlWpqIk5p/6rv6kBQ2UpQpI29XUKackMoPY7sjSXPlvbUrVuO71vS/zft2Bos3r6TtoLCy2adZ9DdVMzQn3a+1su1V+/+y4fNNNRs6xksd7fpgbcmcHV2GHSomsGjIj2QweLiIgVgVjpXYfuGTC4FgTLmkmoAJn1NHwVrjx7W8NRdA6RngMC1Wp2EVueOFyjYKz1SqzAYQnpWbveB/UofN8BpO+TvtvK8lBXO52Ba4W59iE5cJ8xTMqseutOVw1Ock7SG1ROB/ArPelgbUiAz9qk770h78jsZ4UbNYCdxLjYrrbs2z8BoOZhjE+LCKMccx+te5FzarB7xgR+J2adx7HY77ZTT8PXE2jihQbRF9x7X89C6vjz1Z7UNbllbttp8Wp6K+lnYjtLLlaz749FqNtg3XLb18X87HtNDUXqs0mqUPRrp8gGVeJALcmlpmDg1pdVE9Pc32b3w4iUVD26Z8CIaDt0sIiICGGpDZ86FswxSyoDccCIiIiIyEL0MOrQNQMGg3wg2waiAYBEp0EFyCwtozMfn/uNbLon5KrPG/c5ywYgLHmhx8la72k0s04ddEujhfLNvRieCfBbGQdFSIyvx0iE6HEr5aqXK6nOyzHl8qnX7cOSevvkFtd8Y36d+z7a54LkSmFowF5hA5+ewjLXZIPiNtiq6zXwvGli1K+blP4fL3v6r+UYbtuDV+0AUAtmPznu9vH1A5y+D0oB6rPvkxa4miSgvyGl9LSOY8f0oFu/mJbX8LUM5frnus/AVGrfnUKbrRuadNckNit1Ndgzlzp3kBrNiYLr81hqckISSxUErTt3Jb1cn+m8/GHouXV5UQmRYohB7yy0NLGeiC4ioi1EdHdi2RoiuoaIHpT3fRodI6JzoINFRMReD4YLehd5rSC0uhLrYgC/Z5Z9GMBPmflwAD+V7xERERGdBV4s9lpBaOmUkZk3EtEhZvFpAE6Uz5cAuA7Ah4ocT7MmlFrsqdRcRp+LXZcB5NarhIfKcWi/BqWDrKSIlzGQ5V5iROgEq7xZV69RSmduzHN9dl2LNhEAAAu9SURBVJB3xVVVw9AEapt17RdzMrSsi5/XCrOuzmMxO3ul0jPnc+hJrq+nLy1PUh4SKkkygZRGnDMSK5rlo1AJEd0eSMuihKRBQteiVI0+LwBg+bzxN4cBAEYHHYU0InSZUkQDvS6L55AR1+p2y/RI6pgHDu8EADw6sRoAsGPC0UWzcs0+c0iVXc29UOppds5d87hkaR2wfqzuevSc41P9qevfoa1w5VyaPaVUlL1vea1s/XZIH1fpnrp+G2b/Rghl4+m5bO2I0mveFiOBEzqnrX/ZEzAAXmHeQxG0g2PYj5k3yecnAezXBhsidgM6WERE7PVgXnHeQxG0lZRmZqYGKnREdCaAMwGgZ/1oaLOIZUJ/ZT4OGhErBtHDqAdxi1PHhJK6ipmfLd8fAHAiM28iog0ArmPmIwsc5ykAkwC2ttDcZmAdoo3NQKfb2On2ASvbxoOZef3u7kxE/wxnWxFsZWYbq90r0Q4P40oAbwFwjrz/Y5GdmHk9Ed3CzMe10rg9RbSxOeh0GzvdPiDauCdYKQPAUtHqtNrLANwA4EgieoyI/hhuoHgVET0I4Hfke0REREREh6PVWVJnBFa9spXnjYiIiIhoPrqtI8757TagAKKNzUGn29jp9gHRxogmo+VB74iIiIiIvQPd5mFERERERLQJccCIiIiIiCiEjhkwiOggIrqWiO4lonuI6GxZ/jEiepyIbpfXKYl9PkJEDxHRA0T0u+2yUda9m4jul+Wf7TQbieg7iXv4MBHd3oE2Hk1EN4qNtxDRC2Q5EdG5YuOdRHRMG218HhHdQER3EdE/EdFoYp/lvo/9RHQzEd0hNn5clh9KRDeJLd8hol5Z3iffH5L1h7TRxneJHUxE6xLbL/uzjlgCmLkjXgA2ADhGPo8A+BWAowB8DMAHMrY/CsAdAPoAHArg1wDKbbLx5QD+BUCfrNu302w02/wtgL/qNBsB/D8AJ8vyU+CKOvXzT+C6Oh8P4KY2/h5/AeBlsvztAD7RxvtIAIblcw+Am+T+XA7gTbL86wDOks/vAPB1+fwmAN9ZhvsYsvH5AA4B8DCAdYntl/1Zx1fxV8d4GMy8iZlvlc+7ANwH4MAGu5wG4NvMPMvM/wHgIQAvaJONZwE4h5lnZd2WDrQRgJvBAXgDgMs60EYGoDP2VQCeSNj4TXa4EcBqUQloh41HANgom10D4HUJG5f7PjIzT8jXHnkxgFcA+K4svwTAaxM2XiKfvwvglfJ7WHYbmfk2Zn44Y5dlf9YRxdExA0YS4io/H242AgDvEvf0Iqr1zzgQwKOJ3R5D4wGmlTYeAeAl4ub/jIh+qwNtVLwEwGZmflC+d5KN7wHwOSJ6FMDnAXykA228B+6fGgC8HsBB8rktNhJRWejFLXAD2K8BjDGzSrwm7fA2yvqdANYut43MfFODzdv6rCMao+MGDCIaBvA9AO9h5nEAXwNwGICjAWyCo1PaigwbKwDWwLnQHwRweatnbnnIsFFxBmreRVuRYeNZAN7LzAcBeC+AC9tpH5Bp49sBvIOIfglHVc210z5mrjLz0QCeBufRPLOd9mTB2khEz263TRG7h44aMIioB+6P81Jm/j4AMPNm+cEtAvgGam7+46jN7gD3Y3y8HTbCzYK+L270zQAW4YTLOslGEFEFwB8C+E5i806y8S0A9PMV6MBnzcz3M/NJzHws3MD763baqGDmMQDXAjgBjsZRFYekHd5GWb8KwLY22NhIp6mt9zGiMTpmwJAZ+YUA7mPmLySWJ/nLPwCg7V6vBPAmyfw4FMDhAG5uh40AfggX+AYRHQGgF06Bs5NsBJx21/3M/FhiWSfZ+ASAl8nnVwBQ2uxKAP9DMmiOB7CTaz1VltVGItpX3ksA/gIuqKw2Lvd9XE9Eq+XzAIBXwcVargVwumyWFPhU4U/I+n9l5pZW7gZsvL/BLsv+rCOWgHZH3fUF4MVwAbs7Adwur1MAfAvAXbL8SgAbEvt8FG6G9wAku6ZNNvYC+Hu4wexWAK/oNBtl3cUA/mfGPh1hoyz/JVy20U0AjpXtCcBXxMa7ABzXRhvPhsuY+hWccCa18T4+F8BtYuPdqGW+PQNusHoIzlPT7L1++f6QrH9GG238MzjPfAFuonBBu551fBV/RWmQiIiIiIhC6BhKKiIiIiKisxEHjIiIiIiIQogDRkREREREIcQBIyIiIiKiEOKAERERERFRCHHAiIiIiIgohDhg7CUgoon8rZZ8zFOJ6MPy+bVEdNRuHOM6Ijpuids/QESnZqw7hIjuztpvbwQR/Xni8wA52fe5pBx4RMRyIg4YEUEw85XMfI58fS2chPdy4M3MfGUrT0BE5VYev0nwAwYzT7PTY3qiwfYRES1FHDD2MoikwueI6G5yTX7eKMtPlNn7d8k1erpUBRKJ6BRZ9ktpXnOVLH8rEZ1HRC8EcCqckuztRHRY0nMgonVE9LB8HiCibxPRfUT0AwADCdtOItd86FYiukKE/fKu51hyzXfuAPDOxPKyXOcvyCkZ/6ksLxHRV+V6riGiHxPR6bLuYSL6DBHdCuD1IXvknD+T+3G1ytMQ0Z+Ra6h0JxF9u4HNQ+SUlW8motuI6DRZfggR/VzOd6vcVxDRBiLaKPf2biJ6CRGdA0C9iksLPfyIiFaj3aXm8dWcF4AJeX8dnMx1GcB+AH4D1wzoRDg566fBTRRugJO/6IeTkz5U9r8MwFXy+a0AzpPPFwM4PXG+6yCyDXBCiw/L5/cBuEg+PxdO+uE42WYjgCFZ9yGITIS5Dn9c+X4ngJfK588BuFs+nwngL+RzH4Bb4BoXnQ7gx3KN+wPYoXbDNev5Xwmb6+yB69fw7wDWy/I3Jq7nCdRkNlY3eBafAvDfdTs4GZEhAIMA+mX54QBukc/vB/BR+VwGMJJ8pubYDyPRcCi+4ms5X6poGbH34MUALmPmKoDNRPQzAL8FYBzAzSzCg+T6ExwCYALA/2fX9AdwA8aZe3D+lwI4FwCY+U4iulOWHw9HaV0vjk0v3KAVhIjWrWZmbVj0LQAny+eTADxXvQc45dXD4a7/Cnbqxk8S0bXmsKrUG7LnSADPBnCNLC/DyeoDbvC6lIh+CCc4GcJJAE4log/I934AT4cbcM4joqMBVOH6qACui99F5NRxf8jMt9sDRkR0AuKAsbIwm/hcxZ49/wXUKM3+AtsTXPOcM/bgnPZ472bmq1MLEz3fA5hsZA8RPQfAPcx8Qsa+r4YbEF8D4KNE9ByuNSqytr2OmR8wx/4YgM0Angd372YAgJk3EtFL5fgXE9EXmPmbOdcREbHsiDGMvQ8/B/BG4fjXw/2DaySz/QCAZ5DrKgc4CiYLu+AaBikeBnCsfD49sXwjgP8GAOQa5TxXlt8I4EVE9F9k3RA5Kfgg2PVPGCOiF8uiNydWXw3gLJmVg4iOIKIhANcDeJ3EMvaDo+KyELLnAQDriegEWd5DRM8iJ2d+EDNfC0dfrQIQisFcDeDdiRjR82X5KgCbxPv5IzjvBUR0MFwXxG8AuADAMbL9vF5fREQnIA4Yex9+AEed3AHgX+E4+ydDGzPzNIB3APhncl3kdsHFOiy+DeCDEsQ9DK6F6llEdBtcPEDxNQDDRHQfgL+GkysHMz8FFxO5TGiqG1CsO9zbAHxFKLRkF8MLANwL4FZyqbZ/B+cxfQ9ONvteOMn5W7OuJ2QPM8/BDYCfkUD77QBeCPfP/e+J6C44ue5zZUDLwifgYiF3EtE98h0AvgrgLXLcZ6Lm7ZwI4A65l28E8CVZfr4cIwa9IzoCUd48AkQ0zMwTMiP+CoAHmfmLbbLlOgAfYOZb9uAYej1r4byrFzUaNLsJko12HDNvbbctESsP0cOIAIA/kRn8PXC0yd+10ZbtcDx+XeHeEnCVXM/PAXxibxgsJF35djjPZbHd9kSsTEQPIyJiN0FEb4PrwJfE9cz8zqztIyK6HXHAiIiIiIgohEhJRUREREQUQhwwIiIiIiIKIQ4YERERERGFEAeMiIiIiIhC+E9VfIUf/3y0dAAAAABJRU5ErkJggg==\n", 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<xarray.DataArray 'time1' (time1: 9)>\n",
+              "array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000',\n",
+              "       '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000',\n",
+              "       '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000',\n",
+              "       '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000',\n",
+              "       '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')\n",
+              "Coordinates:\n",
+              "  * time1    (time1) datetime64[ns] 2017-09-05T12:00:00 ... 2017-09-06T12:00:00\n",
+              "    reftime  datetime64[ns] 2017-09-05T12:00:00\n",
+              "Attributes:\n",
+              "    standard_name:  time\n",
+              "    long_name:      time\n",
+              "    udunits:        Hour since 2017-09-05T12:00:00Z\n",
+              "    _metpy_axis:    time
" + ], + "text/plain": [ + "\n", + "array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000',\n", + " '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000',\n", + " '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000',\n", + " '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000',\n", + " '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')\n", + "Coordinates:\n", + " * time1 (time1) datetime64[ns] 2017-09-05T12:00:00 ... 2017-09-06T12:00:00\n", + " reftime datetime64[ns] 2017-09-05T12:00:00\n", + "Attributes:\n", + " standard_name: time\n", + " long_name: time\n", + " udunits: Hour since 2017-09-05T12:00:00Z\n", + " _metpy_axis: time" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KuwqwYwOgH3g" + }, + "source": [ + "x, y = temperature.metpy.coordinates('x', 'y')" + ], + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vSniQlgPgNuP" + }, + "source": [ + "heights = data['Geopotential_height_isobaric'].metpy.sel(\n", + " time='2017-09-05 18:00',\n", + " vertical=50000.\n", + ")" + ], + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xXnsV2xUgP1H" + }, + "source": [ + "# Parse full dataset\n", + "data_parsed = data.metpy.parse_cf()\n", + "\n", + "# Parse subset of dataset\n", + "data_subset = data.metpy.parse_cf([\n", + " 'u-component_of_wind_isobaric',\n", + " 'v-component_of_wind_isobaric',\n", + " 'Vertical_velocity_pressure_isobaric'\n", + "])\n", + "\n", + "# Parse single variable\n", + "relative_humidity = data.metpy.parse_cf('Relative_humidity_isobaric')" + ], + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 257 + }, + "id": "S2h_iTxng_Ce", + "outputId": "c9205bfb-1c42-4215-e151-1780892ebda4" + }, + "source": [ + "temperature = data['Temperature_isobaric'].metpy.assign_crs(\n", + " grid_mapping_name='latitude_longitude',\n", + " earth_radius=6371229.0\n", + ")\n", + "\n", + "temperature" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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<xarray.DataArray 'Temperature_isobaric' (time1: 9, isobaric3: 31, latitude: 81, longitude: 131)>\n",
+              "[2960469 values with dtype=float32]\n",
+              "Coordinates:\n",
+              "  * time1      (time1) datetime64[ns] 2017-09-05T12:00:00 ... 2017-09-06T12:0...\n",
+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "  * latitude   (latitude) float32 50.0 49.5 49.0 48.5 ... 11.5 11.0 10.5 10.0\n",
+              "  * isobaric3  (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n",
+              "  * longitude  (longitude) float32 250.0 250.5 251.0 251.5 ... 314.0 314.5 315.0\n",
+              "    metpy_crs  object Projection: latitude_longitude\n",
+              "Attributes:\n",
+              "    long_name:                      Temperature @ Isobaric surface\n",
+              "    units:                          K\n",
+              "    Grib_Variable_Id:               VAR_0-0-0_L100\n",
+              "    Grib2_Parameter:                [0 0 0]\n",
+              "    Grib2_Parameter_Discipline:     Meteorological products\n",
+              "    Grib2_Parameter_Category:       Temperature\n",
+              "    Grib2_Parameter_Name:           Temperature\n",
+              "    Grib2_Level_Type:               100\n",
+              "    Grib2_Level_Desc:               Isobaric surface\n",
+              "    Grib2_Generating_Process_Type:  Forecast\n",
+              "    grid_mapping:                   LatLon_361X720-0p25S-180p00E
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+              "  ...\n",
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+              "  [ 21.950012  21.75      22.149994 ...  22.550018  22.450012  22.050018]]\n",
+              "\n",
+              " [[ 15.649994  15.149994  14.649994 ...  11.950012  11.850006  11.75    ]\n",
+              "  [ 15.149994  15.149994  15.050018 ...  11.950012  11.649994  11.450012]\n",
+              "  [ 15.050018  14.850006  15.050018 ...  11.850006  11.75      11.649994]\n",
+              "  ...\n",
+              "  [ 24.149994  24.25      24.149994 ...  24.649994  24.649994  24.850006]\n",
+              "  [ 23.950012  23.950012  23.850006 ...  24.649994  24.25      24.450012]\n",
+              "  [ 23.850006  23.649994  23.75     ...  24.649994  24.25      24.050018]]\n",
+              "\n",
+              " [[ 17.050018  16.550018  16.050018 ...  13.450012  13.450012  13.550018]\n",
+              "  [ 16.550018  16.550018  16.450012 ...  13.350006  13.050018  12.950012]\n",
+              "  [ 16.450012  16.25      16.450012 ...  13.050018  12.850006  12.850006]\n",
+              "  ...\n",
+              "  [ 26.350006  26.350006  26.25     ...  26.75      26.850006  26.950012]\n",
+              "  [ 26.050018  26.050018  25.950012 ...  26.75      26.350006  26.550018]\n",
+              "  [ 26.050018  25.75      25.649994 ...  26.75      26.350006  26.149994]]], 'degree_Celsius')>\n",
+              "Coordinates:\n",
+              "    time1      datetime64[ns] 2017-09-05T12:00:00\n",
+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "  * latitude   (latitude) float32 50.0 49.5 49.0 48.5 ... 11.5 11.0 10.5 10.0\n",
+              "  * isobaric3  (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n",
+              "  * longitude  (longitude) float32 250.0 250.5 251.0 251.5 ... 314.0 314.5 315.0\n",
+              "    metpy_crs  object Projection: latitude_longitude\n",
+              "Attributes:\n",
+              "    long_name:                      Temperature @ Isobaric surface\n",
+              "    Grib_Variable_Id:               VAR_0-0-0_L100\n",
+              "    Grib2_Parameter:                [0 0 0]\n",
+              "    Grib2_Parameter_Discipline:     Meteorological products\n",
+              "    Grib2_Parameter_Category:       Temperature\n",
+              "    Grib2_Parameter_Name:           Temperature\n",
+              "    Grib2_Level_Type:               100\n",
+              "    Grib2_Level_Desc:               Isobaric surface\n",
+              "    Grib2_Generating_Process_Type:  Forecast\n",
+              "    grid_mapping:                   LatLon_361X720-0p25S-180p00E
" + ], + "text/plain": [ + "\n", + "\n", + "Coordinates:\n", + " time1 datetime64[ns] 2017-09-05T12:00:00\n", + " reftime datetime64[ns] 2017-09-05T12:00:00\n", + " * latitude (latitude) float32 50.0 49.5 49.0 48.5 ... 11.5 11.0 10.5 10.0\n", + " * isobaric3 (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n", + " * longitude (longitude) float32 250.0 250.5 251.0 251.5 ... 314.0 314.5 315.0\n", + " metpy_crs object Projection: latitude_longitude\n", + "Attributes:\n", + " long_name: Temperature @ Isobaric surface\n", + " Grib_Variable_Id: VAR_0-0-0_L100\n", + " Grib2_Parameter: [0 0 0]\n", + " Grib2_Parameter_Discipline: Meteorological products\n", + " Grib2_Parameter_Category: Temperature\n", + " Grib2_Parameter_Name: Temperature\n", + " Grib2_Level_Type: 100\n", + " Grib2_Level_Desc: Isobaric surface\n", + " Grib2_Generating_Process_Type: Forecast\n", + " grid_mapping: LatLon_361X720-0p25S-180p00E" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 246 + }, + "id": "vkgm-HeJtDNk", + "outputId": "2dfe2ff9-f707-4e7a-cbe5-a7004d74e42d" + }, + "source": [ + "heights_on_hPa_levels = heights.metpy.convert_coordinate_units('isobaric3', 'hPa')\n", + "heights_on_hPa_levels['isobaric3']" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'isobaric3' ()>\n",
+              "array(500.)\n",
+              "Coordinates:\n",
+              "    time1      datetime64[ns] 2017-09-05T18:00:00\n",
+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "    isobaric3  float64 500.0\n",
+              "Attributes:\n",
+              "    units:        hPa\n",
+              "    positive:     down\n",
+              "    _metpy_axis:  vertical
" + ], + "text/plain": [ + "\n", + "array(500.)\n", + "Coordinates:\n", + " time1 datetime64[ns] 2017-09-05T18:00:00\n", + " reftime datetime64[ns] 2017-09-05T12:00:00\n", + " isobaric3 float64 500.0\n", + "Attributes:\n", + " units: hPa\n", + " positive: down\n", + " _metpy_axis: vertical" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 270 + }, + "id": "cp8A_K0CtHW7", + "outputId": "f938ff24-5380-40d1-8e06-9acc10598a15" + }, + "source": [ + "heights_unit_array = heights.metpy.unit_array\n", + "heights_unit_array" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/latex": "$\\begin{pmatrix}5883.3564453125 & 5879.31640625 & 5875.3564453125 & 5869.67626953125 & 5863.9560546875 & 5857.15625 & 5850.1162109375 & 5842.0361328125 & 5833.3564453125 & 5822.9560546875 & 5813.3564453125 & 5801.43603515625 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5494.71630859375 & 5496.15625 & 5497.63623046875 & 5499.75634765625 & 5502.1162109375 & 5503.59619140625 & 5506.15625 & 5509.15625 & 5512.31640625 & 5515.55615234375 & 5518.79638671875 & 5523.2763671875 & 5528.15625 & 5533.236328125 & 5537.99609375 & 5543.396484375 & 5548.2763671875 & 5554.55615234375 & 5562.396484375 & 5570.396484375 & 5578.79638671875 & 5589.076171875 & 5598.236328125 & 5605.51611328125 & 5613.67626953125 & 5622.076171875 & 5630.87646484375 & 5638.3564453125 & 5646.83642578125 & 5652.55615234375 & 5659.076171875 & 5667.51611328125 & 5675.79638671875 & 5683.3564453125 & 5690.83642578125 & 5696.31640625 & 5702.1162109375 & 5707.87646484375 & 5712.9560546875 & 5718.79638671875 & 5725.2763671875 & 5730.076171875 & 5734.51611328125 & 5740.1962890625 & 5744.3564453125 & 5746.75634765625 & 5751.9560546875 & 5757.236328125 & 5762.3564453125 & 5766.0361328125 & 5769.1162109375 & 5771.99609375 & 5775.59619140625 & 5778.59619140625 & 5780.67626953125 & 5784.15625 & 5786.47607421875 & 5788.59619140625 & 5791.75634765625 & 5795.43603515625 & 5798.43603515625 & 5800.55615234375 & 5802.63623046875 & 5804.83642578125 & 5806.83642578125 & 5808.43603515625 & 5809.55615234375 & 5810.43603515625 & 5811.1962890625 & 5811.9560546875 & 5812.59619140625 & 5813.15625 & 5813.59619140625\\\\ \n5895.75634765625 & 5891.31640625 & 5887.9560546875 & 5880.83642578125 & 5876.79638671875 & 5870.75634765625 & 5863.59619140625 & 5855.99609375 & 5847.83642578125 & 5837.87646484375 & 5826.31640625 & 5814.63623046875 & 5802.75634765625 & 5790.67626953125 & 5778.43603515625 & 5765.67626953125 & 5753.1162109375 & 5740.3564453125 & 5728.55615234375 & 5716.1962890625 & 5704.79638671875 & 5694.51611328125 & 5685.59619140625 & 5677.076171875 & 5668.83642578125 & 5658.916015625 & 5647.83642578125 & 5636.83642578125 & 5626.396484375 & 5615.67626953125 & 5604.59619140625 & 5593.1962890625 & 5580.87646484375 & 5568.396484375 & 5556.47607421875 & 5545.55615234375 & 5535.3564453125 & 5527.31640625 & 5521.9560546875 & 5516.9560546875 & 5511.1162109375 & 5504.87646484375 & 5499.9560546875 & 5494.55615234375 & 5490.15625 & 5486.1962890625 & 5482.79638671875 & 5480.99609375 & 5480.15625 & 5481.236328125 & 5481.9560546875 & 5483.59619140625 & 5485.87646484375 & 5488.67626953125 & 5491.1962890625 & 5493.67626953125 & 5496.3564453125 & 5498.3564453125 & 5500.55615234375 & 5502.59619140625 & 5505.1162109375 & 5507.3564453125 & 5509.396484375 & 5511.916015625 & 5514.236328125 & 5516.87646484375 & 5520.15625 & 5524.0361328125 & 5528.63623046875 & 5532.79638671875 & 5538.15625 & 5543.916015625 & 5549.75634765625 & 5556.99609375 & 5563.67626953125 & 5571.0361328125 & 5578.79638671875 & 5589.63623046875 & 5598.75634765625 & 5606.79638671875 & 5615.71630859375 & 5627.1962890625 & 5634.47607421875 & 5643.3564453125 & 5649.9560546875 & 5659.1962890625 & 5664.99609375 & 5670.71630859375 & 5677.99609375 & 5684.31640625 & 5691.0361328125 & 5697.67626953125 & 5704.1962890625 & 5709.9560546875 & 5715.9560546875 & 5721.79638671875 & 5727.9560546875 & 5735.3564453125 & 5741.63623046875 & 5745.47607421875 & 5749.916015625 & 5753.99609375 & 5758.31640625 & 5762.076171875 & 5765.71630859375 & 5770.0361328125 & 5774.47607421875 & 5778.59619140625 & 5781.1962890625 & 5784.99609375 & 5788.236328125 & 5790.9560546875 & 5793.67626953125 & 5797.0361328125 & 5798.55615234375 & 5801.1162109375 & 5804.1962890625 & 5807.43603515625 & 5810.1962890625 & 5812.916015625 & 5815.47607421875 & 5817.63623046875 & 5819.31640625 & 5820.71630859375 & 5822.076171875 & 5823.2763671875 & 5824.396484375 & 5825.1962890625 & 5825.83642578125 & 5826.396484375 & 5826.99609375\\\\ \n5897.31640625 & 5893.916015625 & 5890.1962890625 & 5885.67626953125 & 5879.99609375 & 5873.2763671875 & 5866.71630859375 & 5858.9560546875 & 5850.51611328125 & 5841.51611328125 & 5830.1962890625 & 5818.51611328125 & 5806.0361328125 & 5794.1962890625 & 5782.076171875 & 5769.75634765625 & 5756.99609375 & 5744.43603515625 & 5731.63623046875 & 5718.9560546875 & 5707.43603515625 & 5696.9560546875 & 5687.9560546875 & 5679.31640625 & 5670.3564453125 & 5660.55615234375 & 5649.79638671875 & 5638.43603515625 & 5627.63623046875 & 5617.396484375 & 5606.1162109375 & 5594.83642578125 & 5583.31640625 & 5571.076171875 & 5559.2763671875 & 5547.83642578125 & 5539.0361328125 & 5532.71630859375 & 5524.71630859375 & 5518.15625 & 5511.396484375 & 5506.47607421875 & 5501.83642578125 & 5496.236328125 & 5491.55615234375 & 5486.83642578125 & 5483.59619140625 & 5481.916015625 & 5481.83642578125 & 5483.0361328125 & 5484.51611328125 & 5487.2763671875 & 5489.9560546875 & 5493.79638671875 & 5496.87646484375 & 5499.99609375 & 5503.236328125 & 5506.0361328125 & 5507.396484375 & 5508.87646484375 & 5512.1962890625 & 5514.3564453125 & 5516.99609375 & 5519.31640625 & 5522.236328125 & 5525.3564453125 & 5528.71630859375 & 5533.47607421875 & 5537.87646484375 & 5543.236328125 & 5549.63623046875 & 5557.15625 & 5564.79638671875 & 5572.47607421875 & 5581.0361328125 & 5591.31640625 & 5600.236328125 & 5609.0361328125 & 5619.43603515625 & 5626.59619140625 & 5634.99609375 & 5645.236328125 & 5653.71630859375 & 5661.31640625 & 5668.63623046875 & 5675.1162109375 & 5681.15625 & 5686.916015625 & 5694.236328125 & 5700.79638671875 & 5707.076171875 & 5714.43603515625 & 5721.076171875 & 5726.916015625 & 5732.0361328125 & 5736.87646484375 & 5742.59619140625 & 5749.47607421875 & 5755.31640625 & 5759.3564453125 & 5763.83642578125 & 5766.75634765625 & 5769.43603515625 & 5774.1162109375 & 5778.236328125 & 5782.51611328125 & 5787.79638671875 & 5791.47607421875 & 5794.3564453125 & 5796.67626953125 & 5800.15625 & 5801.9560546875 & 5805.83642578125 & 5808.55615234375 & 5811.83642578125 & 5812.3564453125 & 5815.2763671875 & 5818.31640625 & 5821.71630859375 & 5824.83642578125 & 5827.076171875 & 5828.916015625 & 5830.71630859375 & 5832.47607421875 & 5834.15625 & 5835.47607421875 & 5836.55615234375 & 5837.396484375 & 5838.1162109375 & 5838.83642578125 & 5839.51611328125\\\\ \n5899.51611328125 & 5896.79638671875 & 5892.916015625 & 5888.31640625 & 5883.31640625 & 5876.83642578125 & 5870.15625 & 5862.63623046875 & 5853.99609375 & 5844.15625 & 5834.236328125 & 5822.75634765625 & 5810.3564453125 & 5798.2763671875 & 5785.51611328125 & 5773.3564453125 & 5760.9560546875 & 5748.2763671875 & 5735.75634765625 & 5722.67626953125 & 5710.71630859375 & 5699.59619140625 & 5690.55615234375 & 5681.75634765625 & 5672.63623046875 & 5662.79638671875 & 5652.396484375 & 5641.15625 & 5629.99609375 & 5619.55615234375 & 5608.99609375 & 5597.99609375 & 5586.47607421875 & 5574.99609375 & 5564.0361328125 & 5553.916015625 & 5544.87646484375 & 5535.59619140625 & 5527.396484375 & 5520.236328125 & 5514.79638671875 & 5509.396484375 & 5504.63623046875 & 5498.1962890625 & 5493.71630859375 & 5489.916015625 & 5486.87646484375 & 5485.1962890625 & 5485.1162109375 & 5486.396484375 & 5488.99609375 & 5492.31640625 & 5495.9560546875 & 5499.51611328125 & 5502.83642578125 & 5506.63623046875 & 5510.076171875 & 5512.916015625 & 5514.3564453125 & 5516.1962890625 & 5519.3564453125 & 5522.396484375 & 5525.2763671875 & 5527.83642578125 & 5530.75634765625 & 5534.67626953125 & 5538.67626953125 & 5542.87646484375 & 5548.3564453125 & 5555.31640625 & 5564.1962890625 & 5573.15625 & 5581.71630859375 & 5590.916015625 & 5600.1962890625 & 5610.63623046875 & 5620.47607421875 & 5628.916015625 & 5637.71630859375 & 5648.0361328125 & 5655.51611328125 & 5663.79638671875 & 5671.9560546875 & 5679.2763671875 & 5684.51611328125 & 5689.67626953125 & 5696.076171875 & 5701.59619140625 & 5708.75634765625 & 5715.59619140625 & 5722.51611328125 & 5729.63623046875 & 5736.75634765625 & 5742.79638671875 & 5747.31640625 & 5751.83642578125 & 5756.63623046875 & 5762.1962890625 & 5768.396484375 & 5773.15625 & 5776.31640625 & 5779.59619140625 & 5782.67626953125 & 5786.55615234375 & 5790.47607421875 & 5794.15625 & 5798.15625 & 5802.076171875 & 5805.79638671875 & 5809.31640625 & 5811.87646484375 & 5813.79638671875 & 5816.9560546875 & 5820.9560546875 & 5821.916015625 & 5824.31640625 & 5826.71630859375 & 5829.71630859375 & 5832.79638671875 & 5835.59619140625 & 5837.916015625 & 5840.076171875 & 5842.076171875 & 5843.87646484375 & 5845.3564453125 & 5846.63623046875 & 5847.75634765625 & 5848.71630859375 & 5849.67626953125 & 5850.55615234375 & 5851.31640625\\\\ \n5902.15625 & 5898.9560546875 & 5895.1962890625 & 5890.83642578125 & 5886.0361328125 & 5879.87646484375 & 5873.75634765625 & 5865.99609375 & 5857.99609375 & 5848.396484375 & 5837.67626953125 & 5827.076171875 & 5814.79638671875 & 5802.79638671875 & 5790.076171875 & 5777.236328125 & 5764.916015625 & 5752.51611328125 & 5739.55615234375 & 5726.9560546875 & 5714.63623046875 & 5703.1962890625 & 5693.63623046875 & 5684.59619140625 & 5675.47607421875 & 5665.3564453125 & 5655.236328125 & 5644.55615234375 & 5633.51611328125 & 5622.396484375 & 5612.1162109375 & 5602.076171875 & 5591.3564453125 & 5580.396484375 & 5569.67626953125 & 5559.236328125 & 5550.396484375 & 5540.55615234375 & 5531.916015625 & 5525.43603515625 & 5519.076171875 & 5513.3564453125 & 5508.71630859375 & 5504.47607421875 & 5499.67626953125 & 5495.59619140625 & 5491.9560546875 & 5490.236328125 & 5490.1962890625 & 5491.79638671875 & 5494.31640625 & 5497.75634765625 & 5500.9560546875 & 5505.236328125 & 5508.99609375 & 5513.0361328125 & 5516.47607421875 & 5518.75634765625 & 5521.3564453125 & 5524.59619140625 & 5528.076171875 & 5531.3564453125 & 5532.916015625 & 5536.3564453125 & 5539.55615234375 & 5543.59619140625 & 5548.55615234375 & 5554.916015625 & 5561.87646484375 & 5571.15625 & 5580.63623046875 & 5591.63623046875 & 5600.47607421875 & 5610.63623046875 & 5620.236328125 & 5629.55615234375 & 5639.076171875 & 5648.0361328125 & 5656.15625 & 5665.236328125 & 5672.63623046875 & 5678.67626953125 & 5686.83642578125 & 5692.916015625 & 5698.55615234375 & 5705.1962890625 & 5709.99609375 & 5717.71630859375 & 5723.9560546875 & 5730.1962890625 & 5736.83642578125 & 5744.1962890625 & 5750.55615234375 & 5756.076171875 & 5761.396484375 & 5765.99609375 & 5770.0361328125 & 5774.0361328125 & 5780.83642578125 & 5785.99609375 & 5790.1962890625 & 5791.9560546875 & 5795.15625 & 5798.67626953125 & 5802.1962890625 & 5805.87646484375 & 5809.55615234375 & 5813.0361328125 & 5816.51611328125 & 5820.1162109375 & 5823.2763671875 & 5825.916015625 & 5828.67626953125 & 5831.71630859375 & 5834.15625 & 5836.0361328125 & 5838.59619140625 & 5841.076171875 & 5843.71630859375 & 5846.2763671875 & 5848.67626953125 & 5850.83642578125 & 5852.75634765625 & 5854.47607421875 & 5855.99609375 & 5857.31640625 & 5858.51611328125 & 5859.63623046875 & 5860.71630859375 & 5861.71630859375 & 5862.55615234375\\\\ \n5905.47607421875 & 5900.43603515625 & 5897.47607421875 & 5893.396484375 & 5888.79638671875 & 5883.0361328125 & 5876.51611328125 & 5869.71630859375 & 5861.55615234375 & 5852.3564453125 & 5842.396484375 & 5830.79638671875 & 5819.55615234375 & 5807.15625 & 5794.87646484375 & 5781.99609375 & 5769.3564453125 & 5756.916015625 & 5744.076171875 & 5731.47607421875 & 5718.79638671875 & 5706.83642578125 & 5696.83642578125 & 5687.67626953125 & 5678.67626953125 & 5669.1162109375 & 5658.83642578125 & 5648.1962890625 & 5637.31640625 & 5626.43603515625 & 5616.15625 & 5606.1962890625 & 5596.83642578125 & 5586.83642578125 & 5576.1962890625 & 5565.71630859375 & 5556.71630859375 & 5548.076171875 & 5540.31640625 & 5533.396484375 & 5527.396484375 & 5522.1962890625 & 5517.75634765625 & 5512.59619140625 & 5506.71630859375 & 5501.63623046875 & 5498.15625 & 5496.63623046875 & 5496.916015625 & 5498.55615234375 & 5501.3564453125 & 5504.55615234375 & 5507.75634765625 & 5511.0361328125 & 5514.9560546875 & 5518.916015625 & 5522.47607421875 & 5525.3564453125 & 5528.71630859375 & 5532.47607421875 & 5535.55615234375 & 5538.51611328125 & 5541.076171875 & 5544.63623046875 & 5548.076171875 & 5552.87646484375 & 5561.51611328125 & 5569.71630859375 & 5578.67626953125 & 5589.51611328125 & 5599.43603515625 & 5610.2763671875 & 5620.47607421875 & 5630.59619140625 & 5639.67626953125 & 5648.63623046875 & 5657.75634765625 & 5665.67626953125 & 5673.59619140625 & 5680.396484375 & 5686.9560546875 & 5692.99609375 & 5701.55615234375 & 5706.15625 & 5712.59619140625 & 5719.75634765625 & 5726.51611328125 & 5732.59619140625 & 5737.43603515625 & 5744.2763671875 & 5751.1162109375 & 5757.916015625 & 5763.87646484375 & 5769.15625 & 5773.67626953125 & 5778.3564453125 & 5783.1962890625 & 5786.79638671875 & 5790.15625 & 5796.51611328125 & 5801.076171875 & 5804.47607421875 & 5807.59619140625 & 5811.0361328125 & 5814.43603515625 & 5818.0361328125 & 5821.71630859375 & 5825.236328125 & 5828.55615234375 & 5831.916015625 & 5835.15625 & 5838.1162109375 & 5840.87646484375 & 5843.67626953125 & 5846.1962890625 & 5848.31640625 & 5850.43603515625 & 5852.63623046875 & 5854.87646484375 & 5857.15625 & 5859.396484375 & 5861.47607421875 & 5863.2763671875 & 5864.9560546875 & 5866.3564453125 & 5867.63623046875 & 5868.79638671875 & 5869.9560546875 & 5871.076171875 & 5872.076171875 & 5872.916015625\\\\ \n5906.59619140625 & 5903.51611328125 & 5899.51611328125 & 5895.31640625 & 5891.236328125 & 5885.83642578125 & 5880.1162109375 & 5871.99609375 & 5864.71630859375 & 5856.47607421875 & 5846.47607421875 & 5835.83642578125 & 5823.99609375 & 5812.3564453125 & 5799.55615234375 & 5786.99609375 & 5774.47607421875 & 5761.916015625 & 5749.236328125 & 5736.1162109375 & 5723.396484375 & 5711.1162109375 & 5700.31640625 & 5690.9560546875 & 5682.236328125 & 5673.076171875 & 5663.83642578125 & 5652.9560546875 & 5642.076171875 & 5631.51611328125 & 5621.1962890625 & 5611.396484375 & 5601.99609375 & 5592.87646484375 & 5582.99609375 & 5573.71630859375 & 5564.75634765625 & 5557.0361328125 & 5549.236328125 & 5541.47607421875 & 5534.83642578125 & 5529.9560546875 & 5524.51611328125 & 5518.9560546875 & 5513.0361328125 & 5508.916015625 & 5506.1962890625 & 5504.63623046875 & 5504.51611328125 & 5506.2763671875 & 5509.916015625 & 5512.31640625 & 5514.99609375 & 5518.51611328125 & 5522.396484375 & 5526.076171875 & 5529.71630859375 & 5532.87646484375 & 5537.0361328125 & 5540.63623046875 & 5544.15625 & 5547.31640625 & 5551.31640625 & 5554.2763671875 & 5558.83642578125 & 5565.9560546875 & 5575.236328125 & 5588.71630859375 & 5600.0361328125 & 5609.15625 & 5619.0361328125 & 5629.59619140625 & 5640.236328125 & 5649.236328125 & 5658.2763671875 & 5666.9560546875 & 5674.47607421875 & 5681.51611328125 & 5687.31640625 & 5695.1962890625 & 5701.2763671875 & 5707.99609375 & 5713.71630859375 & 5720.15625 & 5727.59619140625 & 5734.43603515625 & 5741.076171875 & 5747.076171875 & 5752.59619140625 & 5759.47607421875 & 5765.43603515625 & 5770.87646484375 & 5777.71630859375 & 5783.3564453125 & 5787.83642578125 & 5792.43603515625 & 5795.59619140625 & 5799.396484375 & 5802.55615234375 & 5805.79638671875 & 5810.51611328125 & 5814.916015625 & 5818.916015625 & 5822.79638671875 & 5826.59619140625 & 5830.31640625 & 5833.99609375 & 5837.55615234375 & 5840.916015625 & 5844.1962890625 & 5847.3564453125 & 5850.396484375 & 5853.1962890625 & 5855.87646484375 & 5858.2763671875 & 5860.43603515625 & 5862.43603515625 & 5864.31640625 & 5866.236328125 & 5868.15625 & 5870.1162109375 & 5871.916015625 & 5873.59619140625 & 5875.0361328125 & 5876.236328125 & 5877.396484375 & 5878.55615234375 & 5879.71630859375 & 5880.79638671875 & 5881.75634765625 & 5882.67626953125\\\\ \n5911.1962890625 & 5905.1162109375 & 5904.076171875 & 5898.2763671875 & 5895.59619140625 & 5890.87646484375 & 5882.75634765625 & 5876.67626953125 & 5868.2763671875 & 5859.67626953125 & 5849.87646484375 & 5840.51611328125 & 5828.75634765625 & 5817.31640625 & 5804.99609375 & 5791.99609375 & 5779.67626953125 & 5767.2763671875 & 5754.47607421875 & 5741.47607421875 & 5728.63623046875 & 5716.076171875 & 5704.63623046875 & 5694.9560546875 & 5686.1162109375 & 5677.55615234375 & 5668.31640625 & 5658.0361328125 & 5647.55615234375 & 5636.916015625 & 5626.71630859375 & 5617.1962890625 & 5607.916015625 & 5598.75634765625 & 5590.076171875 & 5582.0361328125 & 5573.79638671875 & 5566.076171875 & 5558.51611328125 & 5551.1162109375 & 5544.43603515625 & 5538.55615234375 & 5533.0361328125 & 5527.43603515625 & 5522.15625 & 5518.1962890625 & 5515.55615234375 & 5514.55615234375 & 5514.83642578125 & 5516.75634765625 & 5520.236328125 & 5522.79638671875 & 5525.1162109375 & 5527.51611328125 & 5530.63623046875 & 5534.31640625 & 5538.67626953125 & 5543.1962890625 & 5546.396484375 & 5550.236328125 & 5553.83642578125 & 5558.31640625 & 5562.47607421875 & 5567.076171875 & 5573.59619140625 & 5583.396484375 & 5594.076171875 & 5606.63623046875 & 5619.15625 & 5629.15625 & 5638.916015625 & 5649.1162109375 & 5658.396484375 & 5667.0361328125 & 5675.9560546875 & 5683.75634765625 & 5691.1162109375 & 5695.1962890625 & 5701.0361328125 & 5708.79638671875 & 5715.076171875 & 5722.15625 & 5729.1962890625 & 5736.236328125 & 5743.47607421875 & 5750.63623046875 & 5755.916015625 & 5761.0361328125 & 5766.67626953125 & 5773.1962890625 & 5778.3564453125 & 5783.51611328125 & 5788.63623046875 & 5793.076171875 & 5798.396484375 & 5803.396484375 & 5807.79638671875 & 5811.59619140625 & 5814.9560546875 & 5818.1962890625 & 5822.076171875 & 5826.3564453125 & 5830.51611328125 & 5834.55615234375 & 5838.3564453125 & 5842.0361328125 & 5845.71630859375 & 5849.31640625 & 5852.75634765625 & 5855.99609375 & 5859.076171875 & 5862.0361328125 & 5864.87646484375 & 5867.51611328125 & 5869.9560546875 & 5872.1162109375 & 5874.1162109375 & 5875.99609375 & 5877.67626953125 & 5879.31640625 & 5880.79638671875 & 5882.1962890625 & 5883.47607421875 & 5884.59619140625 & 5885.63623046875 & 5886.71630859375 & 5887.83642578125 & 5889.0361328125 & 5890.1162109375 & 5891.076171875 & 5892.0361328125\\\\ \n5913.51611328125 & 5910.2763671875 & 5906.63623046875 & 5901.51611328125 & 5898.47607421875 & 5894.0361328125 & 5890.31640625 & 5879.916015625 & 5873.076171875 & 5864.3564453125 & 5854.0361328125 & 5843.59619140625 & 5835.83642578125 & 5823.63623046875 & 5811.0361328125 & 5798.15625 & 5785.99609375 & 5773.47607421875 & 5760.47607421875 & 5747.51611328125 & 5734.396484375 & 5721.75634765625 & 5709.83642578125 & 5699.236328125 & 5689.99609375 & 5681.55615234375 & 5672.9560546875 & 5663.87646484375 & 5653.83642578125 & 5643.396484375 & 5633.15625 & 5623.47607421875 & 5614.1962890625 & 5605.67626953125 & 5597.47607421875 & 5590.43603515625 & 5583.2763671875 & 5576.1962890625 & 5569.67626953125 & 5562.71630859375 & 5555.99609375 & 5549.87646484375 & 5544.076171875 & 5538.71630859375 & 5533.75634765625 & 5529.87646484375 & 5527.87646484375 & 5526.1962890625 & 5527.15625 & 5529.1162109375 & 5531.396484375 & 5532.916015625 & 5536.0361328125 & 5537.9560546875 & 5540.31640625 & 5544.1962890625 & 5549.1962890625 & 5554.1162109375 & 5558.63623046875 & 5561.83642578125 & 5564.99609375 & 5570.2763671875 & 5576.15625 & 5581.43603515625 & 5591.75634765625 & 5604.236328125 & 5615.916015625 & 5626.71630859375 & 5637.55615234375 & 5648.2763671875 & 5658.79638671875 & 5667.47607421875 & 5675.916015625 & 5685.236328125 & 5692.9560546875 & 5698.43603515625 & 5703.67626953125 & 5709.2763671875 & 5715.9560546875 & 5721.55615234375 & 5729.9560546875 & 5736.9560546875 & 5743.79638671875 & 5751.1162109375 & 5757.79638671875 & 5764.31640625 & 5770.076171875 & 5774.63623046875 & 5780.236328125 & 5786.83642578125 & 5791.99609375 & 5796.1162109375 & 5800.83642578125 & 5804.71630859375 & 5808.83642578125 & 5813.31640625 & 5817.75634765625 & 5821.71630859375 & 5825.43603515625 & 5829.3564453125 & 5833.43603515625 & 5837.59619140625 & 5841.79638671875 & 5845.83642578125 & 5849.63623046875 & 5853.31640625 & 5856.916015625 & 5860.47607421875 & 5863.87646484375 & 5867.076171875 & 5870.076171875 & 5872.9560546875 & 5875.71630859375 & 5878.3564453125 & 5880.87646484375 & 5883.15625 & 5885.1962890625 & 5886.99609375 & 5888.59619140625 & 5889.9560546875 & 5891.236328125 & 5892.31640625 & 5893.15625 & 5893.99609375 & 5894.916015625 & 5895.9560546875 & 5897.1162109375 & 5898.1962890625 & 5899.1962890625 & 5900.2763671875 & 5901.31640625\\\\ \n5917.2763671875 & 5913.31640625 & 5909.55615234375 & 5905.396484375 & 5900.47607421875 & 5897.0361328125 & 5890.59619140625 & 5884.83642578125 & 5877.55615234375 & 5868.99609375 & 5859.396484375 & 5850.55615234375 & 5838.79638671875 & 5829.31640625 & 5815.67626953125 & 5804.47607421875 & 5792.31640625 & 5780.51611328125 & 5767.83642578125 & 5754.43603515625 & 5741.0361328125 & 5727.9560546875 & 5715.076171875 & 5703.99609375 & 5694.1162109375 & 5685.63623046875 & 5677.83642578125 & 5669.51611328125 & 5660.396484375 & 5650.47607421875 & 5640.87646484375 & 5631.1162109375 & 5621.79638671875 & 5613.1162109375 & 5605.67626953125 & 5599.2763671875 & 5593.2763671875 & 5587.75634765625 & 5581.83642578125 & 5575.99609375 & 5570.1162109375 & 5563.99609375 & 5558.396484375 & 5553.0361328125 & 5548.076171875 & 5544.31640625 & 5541.916015625 & 5540.47607421875 & 5540.63623046875 & 5542.0361328125 & 5543.2763671875 & 5544.0361328125 & 5546.87646484375 & 5548.99609375 & 5551.9560546875 & 5555.51611328125 & 5560.59619140625 & 5566.55615234375 & 5570.79638671875 & 5574.99609375 & 5579.79638671875 & 5585.59619140625 & 5591.63623046875 & 5601.43603515625 & 5612.99609375 & 5625.31640625 & 5636.67626953125 & 5647.31640625 & 5657.076171875 & 5666.55615234375 & 5676.396484375 & 5684.67626953125 & 5693.916015625 & 5699.1162109375 & 5709.9560546875 & 5713.916015625 & 5716.71630859375 & 5723.9560546875 & 5728.47607421875 & 5737.396484375 & 5744.79638671875 & 5751.51611328125 & 5759.0361328125 & 5765.63623046875 & 5771.87646484375 & 5777.87646484375 & 5783.1162109375 & 5787.1962890625 & 5792.2763671875 & 5799.43603515625 & 5805.076171875 & 5809.31640625 & 5812.79638671875 & 5816.63623046875 & 5820.71630859375 & 5824.87646484375 & 5828.9560546875 & 5832.87646484375 & 5836.83642578125 & 5840.916015625 & 5844.9560546875 & 5848.83642578125 & 5852.63623046875 & 5856.47607421875 & 5860.31640625 & 5864.076171875 & 5867.67626953125 & 5871.076171875 & 5874.3564453125 & 5877.47607421875 & 5880.47607421875 & 5883.2763671875 & 5885.9560546875 & 5888.51611328125 & 5890.99609375 & 5893.2763671875 & 5895.31640625 & 5897.15625 & 5898.75634765625 & 5900.076171875 & 5901.1162109375 & 5901.83642578125 & 5902.43603515625 & 5903.15625 & 5904.0361328125 & 5905.076171875 & 5906.076171875 & 5907.0361328125 & 5908.1162109375 & 5909.076171875 & 5909.67626953125\\\\ \n5919.67626953125 & 5917.31640625 & 5913.0361328125 & 5907.79638671875 & 5903.1962890625 & 5899.63623046875 & 5894.47607421875 & 5888.55615234375 & 5881.67626953125 & 5874.1962890625 & 5865.51611328125 & 5854.9560546875 & 5845.1162109375 & 5833.99609375 & 5823.3564453125 & 5811.076171875 & 5799.15625 & 5786.9560546875 & 5774.79638671875 & 5762.1962890625 & 5748.2763671875 & 5734.71630859375 & 5721.63623046875 & 5709.83642578125 & 5698.79638671875 & 5689.87646484375 & 5682.15625 & 5674.916015625 & 5666.87646484375 & 5658.15625 & 5648.9560546875 & 5639.55615234375 & 5630.59619140625 & 5622.3564453125 & 5615.2763671875 & 5608.83642578125 & 5603.31640625 & 5597.79638671875 & 5593.47607421875 & 5588.71630859375 & 5583.47607421875 & 5578.1962890625 & 5573.1162109375 & 5568.83642578125 & 5563.87646484375 & 5560.0361328125 & 5557.47607421875 & 5555.79638671875 & 5555.3564453125 & 5554.99609375 & 5555.75634765625 & 5557.396484375 & 5559.79638671875 & 5562.43603515625 & 5565.076171875 & 5567.83642578125 & 5573.55615234375 & 5578.916015625 & 5583.31640625 & 5588.31640625 & 5594.75634765625 & 5602.47607421875 & 5613.076171875 & 5623.9560546875 & 5635.15625 & 5646.99609375 & 5657.076171875 & 5666.55615234375 & 5676.3564453125 & 5685.87646484375 & 5693.236328125 & 5698.9560546875 & 5707.9560546875 & 5712.63623046875 & 5717.63623046875 & 5723.79638671875 & 5731.3564453125 & 5737.71630859375 & 5744.67626953125 & 5752.9560546875 & 5759.67626953125 & 5765.59619140625 & 5773.15625 & 5780.076171875 & 5786.236328125 & 5791.83642578125 & 5796.99609375 & 5801.2763671875 & 5805.396484375 & 5811.1162109375 & 5816.9560546875 & 5821.59619140625 & 5825.55615234375 & 5829.3564453125 & 5833.2763671875 & 5837.2763671875 & 5841.1162109375 & 5844.9560546875 & 5848.79638671875 & 5852.75634765625 & 5856.51611328125 & 5860.1162109375 & 5863.59619140625 & 5867.1962890625 & 5870.83642578125 & 5874.55615234375 & 5878.0361328125 & 5881.2763671875 & 5884.31640625 & 5887.1962890625 & 5890.0361328125 & 5892.79638671875 & 5895.51611328125 & 5898.1162109375 & 5900.55615234375 & 5902.75634765625 & 5904.79638671875 & 5906.59619140625 & 5908.1162109375 & 5909.2763671875 & 5910.0361328125 & 5910.59619140625 & 5911.15625 & 5911.79638671875 & 5912.59619140625 & 5913.396484375 & 5914.236328125 & 5915.1962890625 & 5915.99609375 & 5916.43603515625 & 5916.79638671875\\\\ \n5923.55615234375 & 5919.99609375 & 5917.71630859375 & 5911.31640625 & 5907.1962890625 & 5903.31640625 & 5898.79638671875 & 5892.47607421875 & 5886.1962890625 & 5879.1962890625 & 5871.15625 & 5861.43603515625 & 5851.076171875 & 5841.2763671875 & 5830.55615234375 & 5818.67626953125 & 5806.79638671875 & 5794.51611328125 & 5781.9560546875 & 5769.47607421875 & 5756.71630859375 & 5742.916015625 & 5729.43603515625 & 5716.1962890625 & 5704.0361328125 & 5694.2763671875 & 5686.2763671875 & 5679.31640625 & 5672.55615234375 & 5665.076171875 & 5657.076171875 & 5648.396484375 & 5640.3564453125 & 5632.79638671875 & 5625.47607421875 & 5618.9560546875 & 5613.0361328125 & 5607.87646484375 & 5603.83642578125 & 5599.75634765625 & 5595.75634765625 & 5591.396484375 & 5587.236328125 & 5583.076171875 & 5578.63623046875 & 5575.076171875 & 5572.63623046875 & 5571.236328125 & 5569.99609375 & 5569.15625 & 5569.9560546875 & 5571.59619140625 & 5573.55615234375 & 5576.31640625 & 5579.67626953125 & 5582.076171875 & 5585.396484375 & 5589.916015625 & 5596.31640625 & 5604.916015625 & 5614.75634765625 & 5624.43603515625 & 5634.9560546875 & 5645.71630859375 & 5656.15625 & 5667.2763671875 & 5677.51611328125 & 5686.31640625 & 5694.31640625 & 5701.2763671875 & 5707.2763671875 & 5715.1162109375 & 5719.71630859375 & 5723.71630859375 & 5731.1162109375 & 5736.236328125 & 5744.1162109375 & 5752.63623046875 & 5759.63623046875 & 5768.1162109375 & 5774.076171875 & 5780.47607421875 & 5787.31640625 & 5793.67626953125 & 5799.71630859375 & 5805.2763671875 & 5810.47607421875 & 5815.3564453125 & 5819.87646484375 & 5824.51611328125 & 5829.236328125 & 5833.71630859375 & 5837.87646484375 & 5841.9560546875 & 5845.916015625 & 5849.83642578125 & 5853.63623046875 & 5857.3564453125 & 5861.0361328125 & 5864.67626953125 & 5868.1162109375 & 5871.3564453125 & 5874.55615234375 & 5877.87646484375 & 5881.2763671875 & 5884.63623046875 & 5887.87646484375 & 5890.87646484375 & 5893.75634765625 & 5896.43603515625 & 5898.9560546875 & 5901.59619140625 & 5904.31640625 & 5906.916015625 & 5909.31640625 & 5911.55615234375 & 5913.51611328125 & 5915.1162109375 & 5916.3564453125 & 5917.2763671875 & 5918.0361328125 & 5918.67626953125 & 5919.1162109375 & 5919.55615234375 & 5920.2763671875 & 5920.916015625 & 5921.59619140625 & 5922.2763671875 & 5922.63623046875 & 5922.83642578125 & 5923.1162109375\\\\ \n5926.67626953125 & 5923.15625 & 5918.71630859375 & 5915.15625 & 5910.83642578125 & 5907.2763671875 & 5902.31640625 & 5897.31640625 & 5889.1962890625 & 5884.79638671875 & 5876.59619140625 & 5866.916015625 & 5857.75634765625 & 5847.75634765625 & 5836.1962890625 & 5825.83642578125 & 5814.55615234375 & 5802.71630859375 & 5790.31640625 & 5777.43603515625 & 5765.236328125 & 5752.236328125 & 5738.1962890625 & 5724.396484375 & 5711.15625 & 5700.0361328125 & 5691.43603515625 & 5684.0361328125 & 5677.916015625 & 5671.31640625 & 5664.3564453125 & 5657.51611328125 & 5650.43603515625 & 5643.3564453125 & 5636.31640625 & 5629.47607421875 & 5623.3564453125 & 5618.1962890625 & 5614.31640625 & 5610.55615234375 & 5606.79638671875 & 5602.59619140625 & 5598.63623046875 & 5594.99609375 & 5591.43603515625 & 5588.396484375 & 5586.31640625 & 5584.9560546875 & 5583.916015625 & 5583.63623046875 & 5584.2763671875 & 5585.79638671875 & 5588.1162109375 & 5591.2763671875 & 5594.3564453125 & 5597.236328125 & 5600.43603515625 & 5605.47607421875 & 5614.71630859375 & 5625.67626953125 & 5636.236328125 & 5646.51611328125 & 5656.3564453125 & 5666.9560546875 & 5676.87646484375 & 5687.15625 & 5696.31640625 & 5703.79638671875 & 5710.87646484375 & 5716.396484375 & 5721.51611328125 & 5727.63623046875 & 5734.55615234375 & 5739.59619140625 & 5744.9560546875 & 5751.63623046875 & 5759.15625 & 5766.79638671875 & 5774.3564453125 & 5782.1162109375 & 5788.2763671875 & 5794.51611328125 & 5800.87646484375 & 5806.99609375 & 5812.916015625 & 5818.43603515625 & 5823.67626953125 & 5828.75634765625 & 5833.67626953125 & 5838.1962890625 & 5842.396484375 & 5846.47607421875 & 5850.63623046875 & 5854.71630859375 & 5858.63623046875 & 5862.3564453125 & 5865.87646484375 & 5869.236328125 & 5872.67626953125 & 5876.0361328125 & 5879.1962890625 & 5882.1962890625 & 5885.236328125 & 5888.31640625 & 5891.31640625 & 5894.236328125 & 5897.1162109375 & 5899.87646484375 & 5902.47607421875 & 5904.9560546875 & 5907.47607421875 & 5910.0361328125 & 5912.55615234375 & 5914.9560546875 & 5917.236328125 & 5919.236328125 & 5920.99609375 & 5922.3564453125 & 5923.43603515625 & 5924.396484375 & 5925.15625 & 5925.55615234375 & 5925.87646484375 & 5926.43603515625 & 5927.0361328125 & 5927.47607421875 & 5927.79638671875 & 5928.076171875 & 5928.236328125 & 5928.43603515625 & 5928.63623046875\\\\ \n5929.3564453125 & 5926.31640625 & 5923.1162109375 & 5918.83642578125 & 5915.1962890625 & 5910.916015625 & 5905.71630859375 & 5900.2763671875 & 5895.3564453125 & 5888.1962890625 & 5882.236328125 & 5873.396484375 & 5864.99609375 & 5855.1162109375 & 5844.83642578125 & 5833.3564453125 & 5821.9560546875 & 5810.1962890625 & 5798.51611328125 & 5786.75634765625 & 5774.75634765625 & 5762.236328125 & 5748.9560546875 & 5735.1162109375 & 5721.0361328125 & 5707.83642578125 & 5697.1962890625 & 5689.2763671875 & 5682.63623046875 & 5676.99609375 & 5671.99609375 & 5666.076171875 & 5659.916015625 & 5653.43603515625 & 5646.9560546875 & 5640.3564453125 & 5634.71630859375 & 5629.3564453125 & 5624.83642578125 & 5620.63623046875 & 5616.0361328125 & 5611.83642578125 & 5608.83642578125 & 5605.79638671875 & 5602.51611328125 & 5600.3564453125 & 5598.916015625 & 5598.236328125 & 5597.75634765625 & 5597.59619140625 & 5598.75634765625 & 5600.87646484375 & 5602.87646484375 & 5605.31640625 & 5608.47607421875 & 5613.47607421875 & 5620.1962890625 & 5628.43603515625 & 5638.15625 & 5647.75634765625 & 5657.83642578125 & 5667.3564453125 & 5677.31640625 & 5686.1162109375 & 5696.47607421875 & 5706.3564453125 & 5714.55615234375 & 5720.0361328125 & 5724.1962890625 & 5729.83642578125 & 5735.55615234375 & 5741.63623046875 & 5746.75634765625 & 5753.59619140625 & 5761.67626953125 & 5768.67626953125 & 5774.83642578125 & 5782.1162109375 & 5789.2763671875 & 5796.55615234375 & 5802.63623046875 & 5808.1162109375 & 5813.9560546875 & 5819.83642578125 & 5825.55615234375 & 5830.9560546875 & 5836.236328125 & 5841.3564453125 & 5846.15625 & 5850.55615234375 & 5854.67626953125 & 5858.71630859375 & 5862.71630859375 & 5866.63623046875 & 5870.3564453125 & 5873.79638671875 & 5876.99609375 & 5880.15625 & 5883.3564453125 & 5886.47607421875 & 5889.396484375 & 5892.31640625 & 5895.31640625 & 5898.1162109375 & 5900.75634765625 & 5903.2763671875 & 5905.75634765625 & 5908.1162109375 & 5910.51611328125 & 5912.87646484375 & 5915.31640625 & 5917.71630859375 & 5920.076171875 & 5922.31640625 & 5924.3564453125 & 5926.076171875 & 5927.47607421875 & 5928.67626953125 & 5929.75634765625 & 5930.51611328125 & 5930.9560546875 & 5931.236328125 & 5931.83642578125 & 5932.396484375 & 5932.51611328125 & 5932.55615234375 & 5932.75634765625 & 5932.87646484375 & 5933.0361328125 & 5933.15625 & 5933.076171875\\\\ \n5932.43603515625 & 5928.75634765625 & 5925.55615234375 & 5922.076171875 & 5918.3564453125 & 5914.51611328125 & 5909.3564453125 & 5904.63623046875 & 5897.916015625 & 5891.67626953125 & 5885.396484375 & 5878.15625 & 5870.2763671875 & 5861.99609375 & 5852.83642578125 & 5841.67626953125 & 5830.43603515625 & 5818.75634765625 & 5806.59619140625 & 5794.99609375 & 5783.59619140625 & 5772.0361328125 & 5759.59619140625 & 5746.31640625 & 5732.3564453125 & 5718.236328125 & 5705.47607421875 & 5695.3564453125 & 5687.83642578125 & 5682.59619140625 & 5677.83642578125 & 5673.1162109375 & 5667.43603515625 & 5661.55615234375 & 5655.9560546875 & 5650.59619140625 & 5645.3564453125 & 5640.15625 & 5635.2763671875 & 5630.1962890625 & 5626.076171875 & 5622.71630859375 & 5619.51611328125 & 5616.99609375 & 5614.79638671875 & 5613.15625 & 5612.67626953125 & 5611.79638671875 & 5611.396484375 & 5612.236328125 & 5613.75634765625 & 5615.15625 & 5617.236328125 & 5620.59619140625 & 5626.31640625 & 5634.51611328125 & 5643.67626953125 & 5652.71630859375 & 5660.87646484375 & 5670.3564453125 & 5678.99609375 & 5687.1962890625 & 5695.79638671875 & 5705.99609375 & 5715.63623046875 & 5722.15625 & 5728.396484375 & 5733.396484375 & 5737.71630859375 & 5744.87646484375 & 5750.47607421875 & 5754.916015625 & 5759.67626953125 & 5766.1162109375 & 5774.75634765625 & 5782.31640625 & 5788.83642578125 & 5795.3564453125 & 5802.1162109375 & 5809.15625 & 5815.67626953125 & 5821.43603515625 & 5826.87646484375 & 5832.236328125 & 5837.51611328125 & 5842.63623046875 & 5847.63623046875 & 5852.47607421875 & 5857.0361328125 & 5861.31640625 & 5865.31640625 & 5869.15625 & 5872.99609375 & 5876.75634765625 & 5880.236328125 & 5883.47607421875 & 5886.63623046875 & 5889.75634765625 & 5892.75634765625 & 5895.67626953125 & 5898.59619140625 & 5901.55615234375 & 5904.31640625 & 5906.87646484375 & 5909.31640625 & 5911.59619140625 & 5913.63623046875 & 5915.71630859375 & 5917.9560546875 & 5920.1962890625 & 5922.43603515625 & 5924.75634765625 & 5927.0361328125 & 5929.1162109375 & 5930.75634765625 & 5932.1162109375 & 5933.2763671875 & 5934.3564453125 & 5935.076171875 & 5935.43603515625 & 5935.83642578125 & 5936.396484375 & 5936.9560546875 & 5936.916015625 & 5936.67626953125 & 5936.83642578125 & 5937.0361328125 & 5937.076171875 & 5937.076171875 & 5936.99609375 & 5936.63623046875\\\\ \n5934.31640625 & 5930.63623046875 & 5930.31640625 & 5925.87646484375 & 5921.87646484375 & 5917.51611328125 & 5912.0361328125 & 5907.71630859375 & 5902.9560546875 & 5895.51611328125 & 5889.75634765625 & 5882.83642578125 & 5875.916015625 & 5868.236328125 & 5859.1162109375 & 5849.63623046875 & 5839.31640625 & 5828.1162109375 & 5816.47607421875 & 5804.71630859375 & 5792.71630859375 & 5781.55615234375 & 5769.79638671875 & 5757.236328125 & 5743.9560546875 & 5730.1962890625 & 5716.916015625 & 5704.87646484375 & 5695.236328125 & 5688.076171875 & 5683.076171875 & 5679.076171875 & 5674.47607421875 & 5669.396484375 & 5664.43603515625 & 5659.396484375 & 5654.75634765625 & 5650.076171875 & 5645.1962890625 & 5641.43603515625 & 5637.63623046875 & 5634.396484375 & 5631.71630859375 & 5629.83642578125 & 5628.3564453125 & 5627.0361328125 & 5625.67626953125 & 5625.1162109375 & 5625.55615234375 & 5626.75634765625 & 5628.2763671875 & 5630.71630859375 & 5635.1962890625 & 5641.3564453125 & 5649.47607421875 & 5658.1162109375 & 5666.43603515625 & 5674.3564453125 & 5682.3564453125 & 5690.9560546875 & 5699.2763671875 & 5706.1962890625 & 5715.076171875 & 5724.43603515625 & 5731.51611328125 & 5736.3564453125 & 5739.9560546875 & 5745.71630859375 & 5751.396484375 & 5758.3564453125 & 5764.55615234375 & 5770.87646484375 & 5775.55615234375 & 5782.0361328125 & 5789.2763671875 & 5795.2763671875 & 5800.83642578125 & 5806.83642578125 & 5813.43603515625 & 5819.916015625 & 5825.99609375 & 5831.75634765625 & 5837.3564453125 & 5842.75634765625 & 5847.9560546875 & 5852.916015625 & 5857.67626953125 & 5862.2763671875 & 5866.71630859375 & 5870.79638671875 & 5874.55615234375 & 5878.2763671875 & 5881.99609375 & 5885.55615234375 & 5888.87646484375 & 5892.0361328125 & 5895.15625 & 5898.15625 & 5901.076171875 & 5903.99609375 & 5906.83642578125 & 5909.47607421875 & 5911.916015625 & 5914.3564453125 & 5916.75634765625 & 5918.87646484375 & 5920.63623046875 & 5922.59619140625 & 5924.75634765625 & 5926.916015625 & 5929.076171875 & 5931.236328125 & 5933.2763671875 & 5934.916015625 & 5936.15625 & 5937.2763671875 & 5938.2763671875 & 5939.076171875 & 5939.67626953125 & 5940.1962890625 & 5940.75634765625 & 5941.076171875 & 5940.99609375 & 5940.67626953125 & 5940.59619140625 & 5940.67626953125 & 5940.63623046875 & 5940.51611328125 & 5940.3564453125 & 5939.99609375 & 5939.3564453125\\\\ \n5934.9560546875 & 5932.1162109375 & 5932.75634765625 & 5929.1962890625 & 5924.79638671875 & 5920.87646484375 & 5914.43603515625 & 5912.43603515625 & 5908.79638671875 & 5900.55615234375 & 5895.15625 & 5887.67626953125 & 5881.1962890625 & 5873.83642578125 & 5865.47607421875 & 5856.87646484375 & 5847.71630859375 & 5837.63623046875 & 5826.67626953125 & 5815.55615234375 & 5803.83642578125 & 5792.75634765625 & 5781.2763671875 & 5769.3564453125 & 5757.1162109375 & 5744.3564453125 & 5731.0361328125 & 5718.3564453125 & 5707.0361328125 & 5697.1162109375 & 5689.67626953125 & 5684.236328125 & 5680.31640625 & 5676.2763671875 & 5671.63623046875 & 5667.15625 & 5662.75634765625 & 5658.43603515625 & 5654.63623046875 & 5651.236328125 & 5648.1962890625 & 5645.916015625 & 5644.15625 & 5642.55615234375 & 5640.67626953125 & 5638.47607421875 & 5637.3564453125 & 5638.71630859375 & 5640.2763671875 & 5642.2763671875 & 5645.75634765625 & 5650.9560546875 & 5658.1162109375 & 5665.63623046875 & 5673.63623046875 & 5680.71630859375 & 5687.87646484375 & 5695.1962890625 & 5703.076171875 & 5710.99609375 & 5717.15625 & 5724.47607421875 & 5733.63623046875 & 5739.236328125 & 5743.59619140625 & 5747.31640625 & 5752.9560546875 & 5759.87646484375 & 5765.43603515625 & 5771.15625 & 5777.1162109375 & 5783.87646484375 & 5789.83642578125 & 5795.1162109375 & 5800.87646484375 & 5806.67626953125 & 5812.3564453125 & 5818.43603515625 & 5824.59619140625 & 5830.3564453125 & 5835.87646484375 & 5841.31640625 & 5846.75634765625 & 5851.99609375 & 5856.9560546875 & 5861.67626953125 & 5866.236328125 & 5870.71630859375 & 5874.99609375 & 5878.9560546875 & 5882.59619140625 & 5886.236328125 & 5889.71630859375 & 5893.076171875 & 5896.31640625 & 5899.47607421875 & 5902.55615234375 & 5905.51611328125 & 5908.396484375 & 5911.1162109375 & 5913.63623046875 & 5915.9560546875 & 5918.236328125 & 5920.59619140625 & 5922.87646484375 & 5924.99609375 & 5926.79638671875 & 5928.71630859375 & 5930.67626953125 & 5932.67626953125 & 5934.55615234375 & 5936.31640625 & 5937.83642578125 & 5939.1162109375 & 5940.1962890625 & 5941.15625 & 5941.83642578125 & 5942.43603515625 & 5943.3564453125 & 5944.236328125 & 5944.75634765625 & 5944.916015625 & 5944.79638671875 & 5944.47607421875 & 5944.1962890625 & 5943.9560546875 & 5943.55615234375 & 5943.236328125 & 5942.87646484375 & 5942.2763671875 & 5941.3564453125\\\\ \n5940.99609375 & 5938.1162109375 & 5935.3564453125 & 5934.916015625 & 5926.99609375 & 5924.47607421875 & 5922.87646484375 & 5915.9560546875 & 5910.59619140625 & 5905.71630859375 & 5901.67626953125 & 5893.3564453125 & 5887.076171875 & 5879.71630859375 & 5872.1162109375 & 5863.9560546875 & 5855.75634765625 & 5846.83642578125 & 5836.75634765625 & 5826.236328125 & 5815.59619140625 & 5804.83642578125 & 5793.59619140625 & 5782.3564453125 & 5770.59619140625 & 5759.15625 & 5746.916015625 & 5734.83642578125 & 5722.75634765625 & 5711.79638671875 & 5702.0361328125 & 5693.79638671875 & 5687.1962890625 & 5682.0361328125 & 5677.75634765625 & 5673.55615234375 & 5669.67626953125 & 5665.916015625 & 5663.1962890625 & 5660.1962890625 & 5658.63623046875 & 5656.99609375 & 5655.1162109375 & 5652.83642578125 & 5650.87646484375 & 5650.43603515625 & 5652.47607421875 & 5655.236328125 & 5657.83642578125 & 5662.51611328125 & 5668.63623046875 & 5675.31640625 & 5682.99609375 & 5689.79638671875 & 5695.51611328125 & 5701.87646484375 & 5708.1162109375 & 5714.79638671875 & 5722.15625 & 5728.99609375 & 5733.79638671875 & 5741.55615234375 & 5748.43603515625 & 5749.9560546875 & 5754.9560546875 & 5760.75634765625 & 5764.71630859375 & 5770.2763671875 & 5777.55615234375 & 5783.236328125 & 5788.99609375 & 5795.0361328125 & 5800.3564453125 & 5805.396484375 & 5810.99609375 & 5816.79638671875 & 5822.71630859375 & 5828.55615234375 & 5834.1962890625 & 5839.59619140625 & 5844.87646484375 & 5850.1162109375 & 5855.2763671875 & 5860.15625 & 5864.67626953125 & 5869.076171875 & 5873.3564453125 & 5877.63623046875 & 5881.75634765625 & 5885.55615234375 & 5889.15625 & 5892.63623046875 & 5896.0361328125 & 5899.31640625 & 5902.55615234375 & 5905.63623046875 & 5908.63623046875 & 5911.51611328125 & 5914.236328125 & 5916.67626953125 & 5919.0361328125 & 5921.31640625 & 5923.63623046875 & 5925.9560546875 & 5928.15625 & 5930.1962890625 & 5932.15625 & 5933.99609375 & 5935.75634765625 & 5937.47607421875 & 5939.15625 & 5940.75634765625 & 5942.1162109375 & 5943.2763671875 & 5944.1962890625 & 5944.71630859375 & 5944.916015625 & 5945.2763671875 & 5946.076171875 & 5946.99609375 & 5947.71630859375 & 5948.076171875 & 5948.0361328125 & 5947.59619140625 & 5947.15625 & 5946.59619140625 & 5945.9560546875 & 5945.43603515625 & 5944.79638671875 & 5943.9560546875 & 5942.71630859375\\\\ \n5940.1962890625 & 5939.15625 & 5938.83642578125 & 5937.0361328125 & 5934.076171875 & 5927.3564453125 & 5928.076171875 & 5922.87646484375 & 5918.75634765625 & 5910.15625 & 5905.71630859375 & 5898.31640625 & 5893.0361328125 & 5886.0361328125 & 5879.3564453125 & 5871.55615234375 & 5863.55615234375 & 5854.916015625 & 5845.9560546875 & 5836.55615234375 & 5827.236328125 & 5817.1162109375 & 5806.63623046875 & 5795.67626953125 & 5784.9560546875 & 5774.076171875 & 5762.67626953125 & 5751.43603515625 & 5739.916015625 & 5729.076171875 & 5718.87646484375 & 5709.99609375 & 5702.2763671875 & 5695.31640625 & 5689.1162109375 & 5684.1162109375 & 5679.67626953125 & 5676.9560546875 & 5674.51611328125 & 5672.67626953125 & 5671.0361328125 & 5668.75634765625 & 5666.83642578125 & 5666.396484375 & 5667.59619140625 & 5669.79638671875 & 5672.67626953125 & 5675.47607421875 & 5680.236328125 & 5686.83642578125 & 5693.916015625 & 5700.076171875 & 5705.63623046875 & 5710.75634765625 & 5715.83642578125 & 5721.71630859375 & 5728.1162109375 & 5734.15625 & 5739.1962890625 & 5744.236328125 & 5749.83642578125 & 5755.076171875 & 5759.67626953125 & 5762.0361328125 & 5765.31640625 & 5772.0361328125 & 5776.396484375 & 5781.2763671875 & 5787.236328125 & 5793.076171875 & 5798.79638671875 & 5804.3564453125 & 5809.916015625 & 5815.15625 & 5820.75634765625 & 5826.47607421875 & 5831.9560546875 & 5837.1962890625 & 5842.47607421875 & 5847.75634765625 & 5852.79638671875 & 5857.67626953125 & 5862.3564453125 & 5866.67626953125 & 5870.83642578125 & 5874.916015625 & 5878.99609375 & 5883.076171875 & 5887.076171875 & 5890.79638671875 & 5894.396484375 & 5897.916015625 & 5901.31640625 & 5904.51611328125 & 5907.59619140625 & 5910.51611328125 & 5913.396484375 & 5916.15625 & 5918.75634765625 & 5921.236328125 & 5923.67626953125 & 5926.076171875 & 5928.43603515625 & 5930.67626953125 & 5932.83642578125 & 5934.87646484375 & 5936.83642578125 & 5938.71630859375 & 5940.47607421875 & 5942.1962890625 & 5943.87646484375 & 5945.47607421875 & 5946.83642578125 & 5947.87646484375 & 5948.63623046875 & 5949.076171875 & 5949.3564453125 & 5949.59619140625 & 5949.9560546875 & 5950.43603515625 & 5950.75634765625 & 5950.71630859375 & 5950.396484375 & 5949.9560546875 & 5949.31640625 & 5948.55615234375 & 5947.83642578125 & 5947.15625 & 5946.3564453125 & 5945.15625 & 5943.59619140625\\\\ \n5941.15625 & 5939.9560546875 & 5944.63623046875 & 5933.55615234375 & 5941.0361328125 & 5928.2763671875 & 5931.55615234375 & 5928.1962890625 & 5919.75634765625 & 5911.396484375 & 5912.43603515625 & 5905.2763671875 & 5900.2763671875 & 5893.83642578125 & 5886.9560546875 & 5879.67626953125 & 5872.15625 & 5864.0361328125 & 5855.3564453125 & 5846.75634765625 & 5837.83642578125 & 5828.9560546875 & 5819.396484375 & 5809.55615234375 & 5799.63623046875 & 5789.47607421875 & 5779.0361328125 & 5768.83642578125 & 5758.236328125 & 5747.916015625 & 5738.236328125 & 5729.396484375 & 5721.79638671875 & 5714.55615234375 & 5708.59619140625 & 5703.59619140625 & 5699.9560546875 & 5697.0361328125 & 5693.79638671875 & 5691.0361328125 & 5688.63623046875 & 5687.3564453125 & 5687.79638671875 & 5689.1162109375 & 5690.63623046875 & 5692.396484375 & 5695.076171875 & 5698.75634765625 & 5704.51611328125 & 5711.47607421875 & 5717.55615234375 & 5721.79638671875 & 5725.63623046875 & 5729.59619140625 & 5735.1962890625 & 5741.396484375 & 5746.51611328125 & 5750.63623046875 & 5753.59619140625 & 5758.31640625 & 5762.59619140625 & 5766.99609375 & 5767.59619140625 & 5770.67626953125 & 5776.0361328125 & 5780.51611328125 & 5786.1962890625 & 5791.99609375 & 5797.51611328125 & 5803.1162109375 & 5808.51611328125 & 5813.83642578125 & 5819.31640625 & 5824.67626953125 & 5830.0361328125 & 5835.1962890625 & 5840.15625 & 5845.0361328125 & 5850.0361328125 & 5854.87646484375 & 5859.47607421875 & 5863.79638671875 & 5867.79638671875 & 5871.63623046875 & 5875.59619140625 & 5879.71630859375 & 5883.83642578125 & 5887.87646484375 & 5891.71630859375 & 5895.3564453125 & 5898.99609375 & 5902.47607421875 & 5905.67626953125 & 5908.71630859375 & 5911.63623046875 & 5914.43603515625 & 5917.15625 & 5919.79638671875 & 5922.3564453125 & 5924.9560546875 & 5927.59619140625 & 5930.1162109375 & 5932.55615234375 & 5934.9560546875 & 5937.236328125 & 5939.3564453125 & 5941.396484375 & 5943.31640625 & 5945.1162109375 & 5946.75634765625 & 5948.3564453125 & 5949.79638671875 & 5950.99609375 & 5951.916015625 & 5952.67626953125 & 5953.2763671875 & 5953.63623046875 & 5953.87646484375 & 5953.9560546875 & 5953.99609375 & 5953.75634765625 & 5953.236328125 & 5952.55615234375 & 5951.75634765625 & 5950.99609375 & 5950.2763671875 & 5949.67626953125 & 5948.71630859375 & 5947.47607421875 & 5945.99609375 & 5944.1162109375\\\\ \n5943.59619140625 & 5939.43603515625 & 5940.59619140625 & 5945.71630859375 & 5936.1162109375 & 5936.2763671875 & 5930.396484375 & 5926.31640625 & 5921.47607421875 & 5919.236328125 & 5915.47607421875 & 5911.79638671875 & 5905.55615234375 & 5899.63623046875 & 5893.87646484375 & 5887.2763671875 & 5880.55615234375 & 5872.916015625 & 5865.076171875 & 5857.15625 & 5848.75634765625 & 5840.43603515625 & 5831.47607421875 & 5822.55615234375 & 5813.83642578125 & 5804.396484375 & 5794.79638671875 & 5785.43603515625 & 5776.2763671875 & 5767.59619140625 & 5759.076171875 & 5751.076171875 & 5744.2763671875 & 5737.83642578125 & 5732.2763671875 & 5727.15625 & 5722.83642578125 & 5718.83642578125 & 5715.396484375 & 5713.1162109375 & 5712.43603515625 & 5712.076171875 & 5712.0361328125 & 5711.99609375 & 5712.3564453125 & 5714.59619140625 & 5718.51611328125 & 5723.31640625 & 5729.0361328125 & 5734.63623046875 & 5737.99609375 & 5740.15625 & 5743.79638671875 & 5749.3564453125 & 5754.87646484375 & 5758.87646484375 & 5762.0361328125 & 5764.236328125 & 5766.47607421875 & 5769.67626953125 & 5773.076171875 & 5774.87646484375 & 5778.396484375 & 5784.55615234375 & 5786.076171875 & 5792.1162109375 & 5797.51611328125 & 5802.15625 & 5807.87646484375 & 5813.83642578125 & 5818.916015625 & 5823.47607421875 & 5828.3564453125 & 5833.1962890625 & 5838.0361328125 & 5842.83642578125 & 5847.55615234375 & 5852.1962890625 & 5856.71630859375 & 5860.75634765625 & 5864.67626953125 & 5868.1162109375 & 5871.59619140625 & 5875.47607421875 & 5879.75634765625 & 5884.236328125 & 5888.396484375 & 5892.236328125 & 5895.916015625 & 5899.43603515625 & 5902.99609375 & 5906.3564453125 & 5909.31640625 & 5912.1162109375 & 5914.87646484375 & 5917.67626953125 & 5920.3564453125 & 5922.99609375 & 5925.79638671875 & 5928.59619140625 & 5931.31640625 & 5933.916015625 & 5936.396484375 & 5938.83642578125 & 5941.076171875 & 5943.15625 & 5945.15625 & 5946.99609375 & 5948.67626953125 & 5950.236328125 & 5951.71630859375 & 5953.076171875 & 5954.1962890625 & 5955.15625 & 5955.87646484375 & 5956.43603515625 & 5956.83642578125 & 5956.9560546875 & 5956.9560546875 & 5956.79638671875 & 5956.43603515625 & 5955.67626953125 & 5954.63623046875 & 5953.63623046875 & 5952.916015625 & 5952.1962890625 & 5951.076171875 & 5949.63623046875 & 5948.076171875 & 5946.31640625 & 5944.2763671875\\\\ \n5944.59619140625 & 5943.63623046875 & 5944.0361328125 & 5942.51611328125 & 5939.31640625 & 5940.47607421875 & 5938.99609375 & 5934.0361328125 & 5924.916015625 & 5924.916015625 & 5920.63623046875 & 5915.71630859375 & 5909.67626953125 & 5904.076171875 & 5898.3564453125 & 5892.99609375 & 5887.1962890625 & 5880.9560546875 & 5874.2763671875 & 5867.0361328125 & 5859.1962890625 & 5851.2763671875 & 5843.2763671875 & 5835.3564453125 & 5827.15625 & 5818.47607421875 & 5809.916015625 & 5801.396484375 & 5793.43603515625 & 5785.916015625 & 5778.236328125 & 5771.076171875 & 5764.59619140625 & 5758.2763671875 & 5752.3564453125 & 5747.59619140625 & 5743.2763671875 & 5740.51611328125 & 5738.55615234375 & 5736.31640625 & 5734.79638671875 & 5734.1162109375 & 5733.2763671875 & 5733.1962890625 & 5734.9560546875 & 5738.43603515625 & 5743.0361328125 & 5747.1162109375 & 5750.43603515625 & 5753.2763671875 & 5755.076171875 & 5758.3564453125 & 5762.67626953125 & 5766.87646484375 & 5770.9560546875 & 5772.47607421875 & 5773.9560546875 & 5776.47607421875 & 5778.15625 & 5779.75634765625 & 5784.15625 & 5786.0361328125 & 5789.43603515625 & 5792.75634765625 & 5798.076171875 & 5802.99609375 & 5807.67626953125 & 5812.396484375 & 5817.51611328125 & 5822.75634765625 & 5827.71630859375 & 5832.1962890625 & 5836.59619140625 & 5840.9560546875 & 5845.47607421875 & 5849.916015625 & 5854.1962890625 & 5858.396484375 & 5861.87646484375 & 5865.0361328125 & 5867.87646484375 & 5870.9560546875 & 5874.83642578125 & 5879.236328125 & 5883.83642578125 & 5888.236328125 & 5892.3564453125 & 5896.1962890625 & 5899.71630859375 & 5903.076171875 & 5906.43603515625 & 5909.47607421875 & 5912.15625 & 5914.87646484375 & 5917.79638671875 & 5920.67626953125 & 5923.55615234375 & 5926.47607421875 & 5929.43603515625 & 5932.15625 & 5934.55615234375 & 5936.83642578125 & 5939.076171875 & 5941.3564453125 & 5943.59619140625 & 5945.67626953125 & 5947.63623046875 & 5949.43603515625 & 5951.1162109375 & 5952.75634765625 & 5954.2763671875 & 5955.59619140625 & 5956.71630859375 & 5957.63623046875 & 5958.3564453125 & 5958.87646484375 & 5959.1962890625 & 5959.3564453125 & 5959.2763671875 & 5959.0361328125 & 5958.47607421875 & 5957.63623046875 & 5956.71630859375 & 5955.75634765625 & 5954.79638671875 & 5953.43603515625 & 5951.87646484375 & 5950.15625 & 5948.3564453125 & 5946.31640625 & 5944.0361328125\\\\ \n5945.2763671875 & 5942.9560546875 & 5943.63623046875 & 5939.55615234375 & 5940.15625 & 5937.79638671875 & 5936.75634765625 & 5935.51611328125 & 5930.2763671875 & 5926.916015625 & 5921.87646484375 & 5917.59619140625 & 5912.916015625 & 5907.47607421875 & 5902.71630859375 & 5897.87646484375 & 5892.99609375 & 5887.75634765625 & 5881.99609375 & 5875.79638671875 & 5868.75634765625 & 5861.1162109375 & 5853.83642578125 & 5846.75634765625 & 5838.916015625 & 5831.31640625 & 5823.79638671875 & 5816.15625 & 5809.31640625 & 5802.47607421875 & 5795.396484375 & 5789.076171875 & 5783.31640625 & 5777.9560546875 & 5773.2763671875 & 5769.71630859375 & 5766.55615234375 & 5763.63623046875 & 5760.59619140625 & 5757.99609375 & 5756.83642578125 & 5756.51611328125 & 5755.67626953125 & 5756.55615234375 & 5758.83642578125 & 5761.75634765625 & 5764.55615234375 & 5766.47607421875 & 5767.71630859375 & 5769.2763671875 & 5771.83642578125 & 5775.47607421875 & 5778.59619140625 & 5780.47607421875 & 5784.076171875 & 5785.83642578125 & 5786.43603515625 & 5787.67626953125 & 5787.87646484375 & 5791.31640625 & 5792.63623046875 & 5797.71630859375 & 5799.9560546875 & 5804.51611328125 & 5808.59619140625 & 5813.076171875 & 5817.79638671875 & 5822.2763671875 & 5826.59619140625 & 5830.9560546875 & 5835.43603515625 & 5839.67626953125 & 5843.75634765625 & 5847.9560546875 & 5852.1162109375 & 5856.0361328125 & 5859.87646484375 & 5863.0361328125 & 5865.2763671875 & 5867.236328125 & 5869.99609375 & 5873.9560546875 & 5878.51611328125 & 5883.076171875 & 5887.63623046875 & 5891.9560546875 & 5896.0361328125 & 5899.79638671875 & 5903.1962890625 & 5906.396484375 & 5909.3564453125 & 5911.9560546875 & 5914.55615234375 & 5917.55615234375 & 5920.67626953125 & 5923.47607421875 & 5926.236328125 & 5929.15625 & 5931.83642578125 & 5934.0361328125 & 5936.1162109375 & 5938.2763671875 & 5940.47607421875 & 5942.9560546875 & 5945.31640625 & 5947.55615234375 & 5949.55615234375 & 5951.43603515625 & 5953.1162109375 & 5954.71630859375 & 5956.1962890625 & 5957.47607421875 & 5958.55615234375 & 5959.396484375 & 5960.0361328125 & 5960.43603515625 & 5960.75634765625 & 5960.87646484375 & 5960.79638671875 & 5960.47607421875 & 5959.916015625 & 5959.1962890625 & 5958.31640625 & 5957.236328125 & 5955.87646484375 & 5954.2763671875 & 5952.59619140625 & 5950.67626953125 & 5948.47607421875 & 5946.1162109375 & 5943.63623046875\\\\ \n5945.55615234375 & 5944.1162109375 & 5940.59619140625 & 5943.15625 & 5940.2763671875 & 5937.71630859375 & 5936.15625 & 5934.0361328125 & 5930.87646484375 & 5928.47607421875 & 5921.55615234375 & 5921.59619140625 & 5916.076171875 & 5911.396484375 & 5907.1962890625 & 5902.71630859375 & 5898.55615234375 & 5894.15625 & 5889.3564453125 & 5884.076171875 & 5878.15625 & 5871.3564453125 & 5864.916015625 & 5857.99609375 & 5851.396484375 & 5844.75634765625 & 5837.83642578125 & 5831.15625 & 5825.2763671875 & 5819.2763671875 & 5813.396484375 & 5808.3564453125 & 5803.3564453125 & 5798.83642578125 & 5794.83642578125 & 5791.1162109375 & 5787.83642578125 & 5784.83642578125 & 5781.9560546875 & 5779.67626953125 & 5778.79638671875 & 5777.51611328125 & 5776.43603515625 & 5777.47607421875 & 5779.71630859375 & 5781.1962890625 & 5781.236328125 & 5781.87646484375 & 5783.1162109375 & 5784.71630859375 & 5786.916015625 & 5789.3564453125 & 5790.59619140625 & 5792.47607421875 & 5795.31640625 & 5796.79638671875 & 5797.1962890625 & 5797.236328125 & 5797.71630859375 & 5800.59619140625 & 5803.47607421875 & 5806.79638671875 & 5810.3564453125 & 5814.2763671875 & 5818.1162109375 & 5822.51611328125 & 5827.1162109375 & 5831.236328125 & 5835.0361328125 & 5838.75634765625 & 5842.67626953125 & 5846.55615234375 & 5850.47607421875 & 5854.43603515625 & 5858.076171875 & 5861.47607421875 & 5864.396484375 & 5866.0361328125 & 5867.0361328125 & 5868.87646484375 & 5872.396484375 & 5877.1962890625 & 5882.076171875 & 5886.63623046875 & 5891.0361328125 & 5895.31640625 & 5899.2763671875 & 5902.9560546875 & 5906.1962890625 & 5909.236328125 & 5912.0361328125 & 5914.59619140625 & 5917.2763671875 & 5920.1962890625 & 5922.9560546875 & 5925.43603515625 & 5927.99609375 & 5930.83642578125 & 5933.2763671875 & 5935.3564453125 & 5937.15625 & 5938.55615234375 & 5941.236328125 & 5944.076171875 & 5946.75634765625 & 5949.0361328125 & 5951.1162109375 & 5952.9560546875 & 5954.59619140625 & 5956.15625 & 5957.55615234375 & 5958.79638671875 & 5959.83642578125 & 5960.55615234375 & 5961.1162109375 & 5961.47607421875 & 5961.71630859375 & 5961.75634765625 & 5961.63623046875 & 5961.2763671875 & 5960.79638671875 & 5960.076171875 & 5959.15625 & 5957.99609375 & 5956.51611328125 & 5954.87646484375 & 5953.0361328125 & 5950.916015625 & 5948.55615234375 & 5945.99609375 & 5943.236328125\\\\ \n5944.1162109375 & 5940.3564453125 & 5944.916015625 & 5942.15625 & 5940.1962890625 & 5937.1162109375 & 5937.55615234375 & 5934.55615234375 & 5932.67626953125 & 5930.51611328125 & 5926.31640625 & 5924.47607421875 & 5919.87646484375 & 5916.076171875 & 5911.75634765625 & 5907.87646484375 & 5904.076171875 & 5900.2763671875 & 5896.1162109375 & 5891.67626953125 & 5886.75634765625 & 5881.1162109375 & 5875.47607421875 & 5869.55615234375 & 5864.15625 & 5858.076171875 & 5852.0361328125 & 5846.396484375 & 5840.83642578125 & 5835.67626953125 & 5830.71630859375 & 5826.0361328125 & 5821.67626953125 & 5817.15625 & 5812.916015625 & 5809.87646484375 & 5807.076171875 & 5803.9560546875 & 5800.55615234375 & 5798.1162109375 & 5796.1162109375 & 5795.3564453125 & 5796.1162109375 & 5796.99609375 & 5796.31640625 & 5795.1962890625 & 5795.67626953125 & 5796.396484375 & 5796.83642578125 & 5797.51611328125 & 5799.75634765625 & 5800.43603515625 & 5801.396484375 & 5801.1962890625 & 5803.15625 & 5804.55615234375 & 5805.87646484375 & 5804.43603515625 & 5808.47607421875 & 5809.51611328125 & 5812.63623046875 & 5815.3564453125 & 5819.3564453125 & 5823.3564453125 & 5827.87646484375 & 5831.87646484375 & 5835.63623046875 & 5839.3564453125 & 5842.75634765625 & 5846.076171875 & 5849.47607421875 & 5853.15625 & 5856.87646484375 & 5860.31640625 & 5863.396484375 & 5865.99609375 & 5867.71630859375 & 5868.15625 & 5868.9560546875 & 5871.1962890625 & 5875.2763671875 & 5880.55615234375 & 5885.59619140625 & 5890.076171875 & 5894.43603515625 & 5898.55615234375 & 5902.43603515625 & 5905.83642578125 & 5908.99609375 & 5911.916015625 & 5914.55615234375 & 5917.0361328125 & 5919.47607421875 & 5922.076171875 & 5924.55615234375 & 5926.87646484375 & 5929.076171875 & 5932.47607421875 & 5934.55615234375 & 5936.916015625 & 5938.59619140625 & 5940.2763671875 & 5942.99609375 & 5945.63623046875 & 5947.99609375 & 5950.1162109375 & 5952.1962890625 & 5954.0361328125 & 5955.71630859375 & 5957.1962890625 & 5958.55615234375 & 5959.71630859375 & 5960.63623046875 & 5961.3564453125 & 5961.83642578125 & 5962.1162109375 & 5962.236328125 & 5962.236328125 & 5961.99609375 & 5961.63623046875 & 5961.1162109375 & 5960.396484375 & 5959.47607421875 & 5958.31640625 & 5956.83642578125 & 5955.1162109375 & 5953.15625 & 5950.9560546875 & 5948.55615234375 & 5945.83642578125 & 5942.79638671875\\\\ \n5942.236328125 & 5940.2763671875 & 5940.75634765625 & 5939.43603515625 & 5939.076171875 & 5937.59619140625 & 5934.0361328125 & 5937.63623046875 & 5929.43603515625 & 5930.63623046875 & 5928.87646484375 & 5925.43603515625 & 5922.916015625 & 5919.31640625 & 5915.99609375 & 5912.1962890625 & 5908.51611328125 & 5904.99609375 & 5900.916015625 & 5897.1162109375 & 5893.0361328125 & 5888.79638671875 & 5884.15625 & 5879.15625 & 5873.916015625 & 5868.63623046875 & 5863.51611328125 & 5858.59619140625 & 5853.2763671875 & 5848.59619140625 & 5844.236328125 & 5839.9560546875 & 5835.236328125 & 5831.076171875 & 5826.63623046875 & 5823.83642578125 & 5820.43603515625 & 5818.15625 & 5815.63623046875 & 5813.79638671875 & 5812.75634765625 & 5812.1962890625 & 5811.396484375 & 5809.99609375 & 5808.51611328125 & 5808.2763671875 & 5807.99609375 & 5807.396484375 & 5808.1162109375 & 5808.67626953125 & 5808.79638671875 & 5807.99609375 & 5809.55615234375 & 5809.79638671875 & 5809.236328125 & 5811.51611328125 & 5814.236328125 & 5814.2763671875 & 5817.396484375 & 5818.31640625 & 5820.79638671875 & 5823.9560546875 & 5827.83642578125 & 5832.31640625 & 5835.9560546875 & 5839.2763671875 & 5842.63623046875 & 5846.2763671875 & 5849.59619140625 & 5852.51611328125 & 5855.63623046875 & 5859.1162109375 & 5862.3564453125 & 5865.31640625 & 5867.79638671875 & 5869.75634765625 & 5870.75634765625 & 5871.2763671875 & 5872.67626953125 & 5875.2763671875 & 5879.396484375 & 5884.71630859375 & 5889.63623046875 & 5893.87646484375 & 5897.83642578125 & 5901.79638671875 & 5905.47607421875 & 5908.71630859375 & 5911.63623046875 & 5914.076171875 & 5916.3564453125 & 5918.59619140625 & 5920.83642578125 & 5923.3564453125 & 5925.59619140625 & 5927.67626953125 & 5930.2763671875 & 5932.83642578125 & 5935.47607421875 & 5937.63623046875 & 5939.9560546875 & 5942.31640625 & 5944.67626953125 & 5947.0361328125 & 5949.15625 & 5951.15625 & 5953.1962890625 & 5955.076171875 & 5956.75634765625 & 5958.15625 & 5959.3564453125 & 5960.396484375 & 5961.236328125 & 5961.83642578125 & 5962.2763671875 & 5962.51611328125 & 5962.59619140625 & 5962.43603515625 & 5962.15625 & 5961.79638671875 & 5961.236328125 & 5960.47607421875 & 5959.51611328125 & 5958.31640625 & 5956.87646484375 & 5955.15625 & 5953.15625 & 5950.79638671875 & 5948.1962890625 & 5945.396484375 & 5942.236328125\\\\ \n5939.2763671875 & 5939.71630859375 & 5937.99609375 & 5938.43603515625 & 5937.55615234375 & 5935.076171875 & 5933.71630859375 & 5931.076171875 & 5930.396484375 & 5928.75634765625 & 5927.916015625 & 5925.43603515625 & 5922.83642578125 & 5919.87646484375 & 5916.87646484375 & 5913.75634765625 & 5910.236328125 & 5907.43603515625 & 5903.99609375 & 5900.59619140625 & 5897.15625 & 5893.51611328125 & 5888.9560546875 & 5884.71630859375 & 5880.076171875 & 5875.396484375 & 5870.83642578125 & 5866.2763671875 & 5861.71630859375 & 5857.59619140625 & 5853.31640625 & 5848.99609375 & 5844.55615234375 & 5840.51611328125 & 5838.0361328125 & 5835.31640625 & 5833.1162109375 & 5830.9560546875 & 5828.55615234375 & 5826.87646484375 & 5825.59619140625 & 5824.31640625 & 5822.59619140625 & 5820.87646484375 & 5819.31640625 & 5818.83642578125 & 5818.236328125 & 5817.0361328125 & 5816.83642578125 & 5817.1162109375 & 5814.99609375 & 5816.236328125 & 5818.43603515625 & 5819.1962890625 & 5817.75634765625 & 5819.63623046875 & 5821.67626953125 & 5823.2763671875 & 5824.79638671875 & 5826.55615234375 & 5829.236328125 & 5832.0361328125 & 5835.59619140625 & 5838.79638671875 & 5841.99609375 & 5845.0361328125 & 5848.0361328125 & 5851.71630859375 & 5854.83642578125 & 5857.71630859375 & 5860.75634765625 & 5863.916015625 & 5866.75634765625 & 5869.3564453125 & 5871.51611328125 & 5873.1962890625 & 5874.396484375 & 5875.67626953125 & 5877.75634765625 & 5880.63623046875 & 5884.59619140625 & 5889.31640625 & 5893.71630859375 & 5897.55615234375 & 5901.0361328125 & 5904.47607421875 & 5907.87646484375 & 5910.79638671875 & 5913.15625 & 5915.236328125 & 5917.43603515625 & 5919.55615234375 & 5921.71630859375 & 5923.87646484375 & 5926.236328125 & 5927.9560546875 & 5930.47607421875 & 5933.43603515625 & 5936.236328125 & 5938.83642578125 & 5941.1962890625 & 5943.43603515625 & 5945.71630859375 & 5948.43603515625 & 5950.15625 & 5952.1162109375 & 5954.1162109375 & 5955.916015625 & 5957.47607421875 & 5958.79638671875 & 5959.87646484375 & 5960.79638671875 & 5961.51611328125 & 5962.1162109375 & 5962.51611328125 & 5962.75634765625 & 5962.71630859375 & 5962.51611328125 & 5962.1962890625 & 5961.83642578125 & 5961.236328125 & 5960.43603515625 & 5959.43603515625 & 5958.15625 & 5956.67626953125 & 5954.9560546875 & 5952.9560546875 & 5950.51611328125 & 5947.75634765625 & 5944.75634765625 & 5941.47607421875\\\\ \n5938.2763671875 & 5937.67626953125 & 5936.1962890625 & 5934.71630859375 & 5935.1162109375 & 5931.31640625 & 5932.1162109375 & 5929.1962890625 & 5927.3564453125 & 5927.55615234375 & 5925.67626953125 & 5923.87646484375 & 5922.1962890625 & 5920.31640625 & 5918.1162109375 & 5915.47607421875 & 5912.63623046875 & 5909.55615234375 & 5906.63623046875 & 5903.236328125 & 5899.71630859375 & 5895.75634765625 & 5891.71630859375 & 5887.916015625 & 5883.67626953125 & 5879.3564453125 & 5875.43603515625 & 5871.67626953125 & 5868.3564453125 & 5864.63623046875 & 5860.916015625 & 5856.99609375 & 5853.47607421875 & 5849.67626953125 & 5847.916015625 & 5845.396484375 & 5843.1162109375 & 5840.83642578125 & 5839.1962890625 & 5837.3564453125 & 5835.3564453125 & 5833.0361328125 & 5830.55615234375 & 5828.71630859375 & 5828.0361328125 & 5826.63623046875 & 5824.87646484375 & 5823.67626953125 & 5824.2763671875 & 5824.236328125 & 5824.83642578125 & 5825.1162109375 & 5823.55615234375 & 5827.396484375 & 5826.71630859375 & 5826.99609375 & 5829.99609375 & 5830.59619140625 & 5831.71630859375 & 5833.75634765625 & 5836.3564453125 & 5838.83642578125 & 5841.67626953125 & 5844.83642578125 & 5847.87646484375 & 5850.87646484375 & 5853.55615234375 & 5856.396484375 & 5859.236328125 & 5862.15625 & 5865.0361328125 & 5867.67626953125 & 5870.1962890625 & 5872.51611328125 & 5874.55615234375 & 5876.3564453125 & 5878.1162109375 & 5880.15625 & 5882.63623046875 & 5885.87646484375 & 5889.9560546875 & 5893.87646484375 & 5897.51611328125 & 5900.79638671875 & 5903.87646484375 & 5906.87646484375 & 5909.67626953125 & 5912.0361328125 & 5914.0361328125 & 5916.076171875 & 5918.1962890625 & 5920.236328125 & 5922.43603515625 & 5924.1962890625 & 5926.2763671875 & 5928.79638671875 & 5931.43603515625 & 5934.076171875 & 5936.83642578125 & 5939.3564453125 & 5941.79638671875 & 5943.99609375 & 5946.1962890625 & 5948.43603515625 & 5950.59619140625 & 5952.63623046875 & 5954.55615234375 & 5956.2763671875 & 5957.75634765625 & 5959.0361328125 & 5960.0361328125 & 5960.87646484375 & 5961.51611328125 & 5962.076171875 & 5962.47607421875 & 5962.75634765625 & 5962.75634765625 & 5962.51611328125 & 5962.1162109375 & 5961.71630859375 & 5961.076171875 & 5960.236328125 & 5959.1962890625 & 5957.9560546875 & 5956.3564453125 & 5954.51611328125 & 5952.43603515625 & 5950.0361328125 & 5947.236328125 & 5944.15625 & 5940.916015625\\\\ \n5934.83642578125 & 5935.236328125 & 5933.3564453125 & 5931.71630859375 & 5930.47607421875 & 5927.67626953125 & 5929.75634765625 & 5927.55615234375 & 5926.076171875 & 5924.87646484375 & 5923.83642578125 & 5923.0361328125 & 5921.2763671875 & 5919.99609375 & 5917.916015625 & 5915.9560546875 & 5913.55615234375 & 5910.47607421875 & 5907.79638671875 & 5904.1962890625 & 5900.71630859375 & 5897.1962890625 & 5894.0361328125 & 5890.236328125 & 5886.67626953125 & 5883.2763671875 & 5880.0361328125 & 5876.9560546875 & 5873.75634765625 & 5869.83642578125 & 5866.59619140625 & 5862.236328125 & 5860.79638671875 & 5858.43603515625 & 5855.75634765625 & 5853.51611328125 & 5851.1162109375 & 5848.79638671875 & 5846.55615234375 & 5843.75634765625 & 5841.236328125 & 5838.47607421875 & 5836.1162109375 & 5834.87646484375 & 5834.076171875 & 5832.43603515625 & 5831.55615234375 & 5831.43603515625 & 5831.55615234375 & 5830.87646484375 & 5832.15625 & 5833.3564453125 & 5831.87646484375 & 5834.43603515625 & 5835.1162109375 & 5835.31640625 & 5836.1162109375 & 5836.79638671875 & 5837.63623046875 & 5839.55615234375 & 5842.396484375 & 5844.396484375 & 5846.51611328125 & 5850.1162109375 & 5853.87646484375 & 5856.59619140625 & 5859.1162109375 & 5861.1962890625 & 5863.51611328125 & 5866.1162109375 & 5868.51611328125 & 5870.67626953125 & 5872.87646484375 & 5875.0361328125 & 5877.2763671875 & 5879.43603515625 & 5881.75634765625 & 5884.396484375 & 5887.31640625 & 5890.87646484375 & 5894.55615234375 & 5897.71630859375 & 5900.71630859375 & 5903.55615234375 & 5906.2763671875 & 5908.83642578125 & 5910.9560546875 & 5912.83642578125 & 5914.79638671875 & 5916.71630859375 & 5918.75634765625 & 5920.87646484375 & 5922.75634765625 & 5925.1162109375 & 5927.47607421875 & 5929.916015625 & 5932.43603515625 & 5934.99609375 & 5937.47607421875 & 5939.87646484375 & 5942.15625 & 5944.2763671875 & 5946.3564453125 & 5948.43603515625 & 5950.63623046875 & 5952.75634765625 & 5954.51611328125 & 5956.1162109375 & 5957.71630859375 & 5958.916015625 & 5959.9560546875 & 5960.71630859375 & 5961.3564453125 & 5961.916015625 & 5962.31640625 & 5962.55615234375 & 5962.55615234375 & 5962.396484375 & 5961.99609375 & 5961.47607421875 & 5960.79638671875 & 5959.916015625 & 5958.916015625 & 5957.59619140625 & 5955.9560546875 & 5954.0361328125 & 5951.87646484375 & 5949.43603515625 & 5946.67626953125 & 5943.63623046875 & 5940.3564453125\\\\ \n5931.1962890625 & 5935.1162109375 & 5932.47607421875 & 5930.87646484375 & 5927.55615234375 & 5926.59619140625 & 5929.1162109375 & 5924.63623046875 & 5924.076171875 & 5923.916015625 & 5922.236328125 & 5921.1962890625 & 5919.63623046875 & 5918.43603515625 & 5917.0361328125 & 5914.83642578125 & 5912.43603515625 & 5909.55615234375 & 5906.99609375 & 5903.9560546875 & 5901.15625 & 5898.43603515625 & 5895.67626953125 & 5892.79638671875 & 5889.79638671875 & 5886.59619140625 & 5883.43603515625 & 5880.55615234375 & 5877.916015625 & 5874.75634765625 & 5871.83642578125 & 5868.75634765625 & 5866.55615234375 & 5863.51611328125 & 5860.71630859375 & 5858.236328125 & 5856.3564453125 & 5853.916015625 & 5850.71630859375 & 5847.59619140625 & 5846.59619140625 & 5844.43603515625 & 5841.9560546875 & 5840.75634765625 & 5840.43603515625 & 5838.99609375 & 5838.0361328125 & 5837.71630859375 & 5838.3564453125 & 5839.2763671875 & 5839.1162109375 & 5840.236328125 & 5840.67626953125 & 5840.99609375 & 5840.83642578125 & 5840.83642578125 & 5841.396484375 & 5842.3564453125 & 5843.076171875 & 5845.31640625 & 5848.1962890625 & 5849.71630859375 & 5851.67626953125 & 5854.87646484375 & 5858.236328125 & 5861.31640625 & 5863.59619140625 & 5865.2763671875 & 5867.3564453125 & 5869.63623046875 & 5871.67626953125 & 5873.43603515625 & 5875.47607421875 & 5877.79638671875 & 5880.236328125 & 5882.83642578125 & 5885.79638671875 & 5888.83642578125 & 5891.916015625 & 5894.99609375 & 5897.9560546875 & 5900.71630859375 & 5903.236328125 & 5905.63623046875 & 5908.0361328125 & 5910.076171875 & 5911.79638671875 & 5913.63623046875 & 5915.3564453125 & 5916.9560546875 & 5918.87646484375 & 5920.916015625 & 5922.99609375 & 5925.47607421875 & 5928.076171875 & 5930.43603515625 & 5933.0361328125 & 5935.51611328125 & 5937.83642578125 & 5940.076171875 & 5942.1962890625 & 5944.1962890625 & 5946.15625 & 5948.1962890625 & 5950.396484375 & 5952.43603515625 & 5954.31640625 & 5955.99609375 & 5957.43603515625 & 5958.67626953125 & 5959.71630859375 & 5960.55615234375 & 5961.1962890625 & 5961.71630859375 & 5962.0361328125 & 5962.15625 & 5962.1162109375 & 5961.99609375 & 5961.71630859375 & 5961.1962890625 & 5960.396484375 & 5959.51611328125 & 5958.43603515625 & 5956.99609375 & 5955.3564453125 & 5953.43603515625 & 5951.236328125 & 5948.83642578125 & 5946.0361328125 & 5942.916015625 & 5939.59619140625\\\\ \n5930.2763671875 & 5927.83642578125 & 5927.31640625 & 5928.51611328125 & 5925.99609375 & 5924.9560546875 & 5923.63623046875 & 5923.51611328125 & 5922.15625 & 5920.55615234375 & 5919.63623046875 & 5917.83642578125 & 5916.87646484375 & 5916.1962890625 & 5915.2763671875 & 5913.75634765625 & 5911.9560546875 & 5909.63623046875 & 5907.1962890625 & 5904.75634765625 & 5902.2763671875 & 5899.71630859375 & 5897.1962890625 & 5894.47607421875 & 5891.71630859375 & 5888.67626953125 & 5885.916015625 & 5883.55615234375 & 5880.87646484375 & 5877.99609375 & 5875.15625 & 5872.31640625 & 5869.63623046875 & 5866.916015625 & 5864.2763671875 & 5861.87646484375 & 5860.51611328125 & 5858.1162109375 & 5853.63623046875 & 5851.63623046875 & 5851.47607421875 & 5849.63623046875 & 5847.236328125 & 5847.1962890625 & 5847.67626953125 & 5847.43603515625 & 5846.1162109375 & 5845.3564453125 & 5845.55615234375 & 5845.67626953125 & 5845.87646484375 & 5846.3564453125 & 5846.15625 & 5846.236328125 & 5846.31640625 & 5846.59619140625 & 5847.15625 & 5848.1162109375 & 5849.1162109375 & 5850.87646484375 & 5853.3564453125 & 5855.63623046875 & 5858.0361328125 & 5861.0361328125 & 5863.75634765625 & 5865.67626953125 & 5867.236328125 & 5868.75634765625 & 5870.75634765625 & 5872.79638671875 & 5874.67626953125 & 5876.47607421875 & 5878.63623046875 & 5881.1962890625 & 5883.916015625 & 5886.79638671875 & 5889.87646484375 & 5892.71630859375 & 5895.3564453125 & 5897.99609375 & 5900.55615234375 & 5902.916015625 & 5905.076171875 & 5907.2763671875 & 5909.2763671875 & 5910.916015625 & 5912.47607421875 & 5913.99609375 & 5915.1162109375 & 5916.59619140625 & 5918.43603515625 & 5920.15625 & 5922.43603515625 & 5925.31640625 & 5927.83642578125 & 5930.43603515625 & 5933.076171875 & 5935.43603515625 & 5937.75634765625 & 5939.99609375 & 5942.0361328125 & 5943.83642578125 & 5945.75634765625 & 5947.75634765625 & 5949.75634765625 & 5951.63623046875 & 5953.43603515625 & 5955.236328125 & 5956.87646484375 & 5958.31640625 & 5959.43603515625 & 5960.2763671875 & 5960.83642578125 & 5961.236328125 & 5961.51611328125 & 5961.63623046875 & 5961.55615234375 & 5961.396484375 & 5961.1162109375 & 5960.63623046875 & 5959.83642578125 & 5958.916015625 & 5957.75634765625 & 5956.1962890625 & 5954.55615234375 & 5952.59619140625 & 5950.43603515625 & 5948.076171875 & 5945.31640625 & 5942.15625 & 5938.83642578125\\\\ \n5919.99609375 & 5926.31640625 & 5926.9560546875 & 5920.59619140625 & 5923.31640625 & 5921.79638671875 & 5921.076171875 & 5919.87646484375 & 5919.1962890625 & 5918.15625 & 5916.87646484375 & 5914.9560546875 & 5913.99609375 & 5913.1962890625 & 5912.71630859375 & 5911.71630859375 & 5910.236328125 & 5908.67626953125 & 5906.9560546875 & 5904.51611328125 & 5902.31640625 & 5899.83642578125 & 5897.3564453125 & 5894.59619140625 & 5892.31640625 & 5889.43603515625 & 5887.15625 & 5884.67626953125 & 5882.1962890625 & 5879.916015625 & 5876.79638671875 & 5874.31640625 & 5871.63623046875 & 5869.1962890625 & 5866.71630859375 & 5864.71630859375 & 5862.75634765625 & 5859.83642578125 & 5857.1162109375 & 5855.83642578125 & 5855.31640625 & 5854.83642578125 & 5853.15625 & 5852.31640625 & 5852.99609375 & 5853.43603515625 & 5853.236328125 & 5853.3564453125 & 5853.1162109375 & 5852.15625 & 5851.99609375 & 5851.71630859375 & 5851.83642578125 & 5851.87646484375 & 5851.83642578125 & 5852.47607421875 & 5853.43603515625 & 5854.3564453125 & 5855.55615234375 & 5857.1162109375 & 5858.9560546875 & 5861.236328125 & 5863.43603515625 & 5866.3564453125 & 5868.59619140625 & 5869.75634765625 & 5871.076171875 & 5872.3564453125 & 5874.0361328125 & 5875.87646484375 & 5877.83642578125 & 5879.79638671875 & 5882.0361328125 & 5884.71630859375 & 5887.47607421875 & 5890.15625 & 5892.916015625 & 5895.55615234375 & 5897.916015625 & 5900.2763671875 & 5902.55615234375 & 5904.67626953125 & 5906.67626953125 & 5908.71630859375 & 5910.31640625 & 5911.63623046875 & 5912.67626953125 & 5913.47607421875 & 5914.236328125 & 5915.63623046875 & 5917.236328125 & 5919.31640625 & 5921.9560546875 & 5924.31640625 & 5926.87646484375 & 5929.75634765625 & 5932.43603515625 & 5934.9560546875 & 5937.47607421875 & 5939.71630859375 & 5941.63623046875 & 5943.396484375 & 5945.1962890625 & 5947.0361328125 & 5948.916015625 & 5950.75634765625 & 5952.51611328125 & 5954.2763671875 & 5956.0361328125 & 5957.75634765625 & 5959.0361328125 & 5959.83642578125 & 5960.31640625 & 5960.51611328125 & 5960.79638671875 & 5960.916015625 & 5960.83642578125 & 5960.67626953125 & 5960.3564453125 & 5959.83642578125 & 5959.076171875 & 5958.1162109375 & 5956.87646484375 & 5955.3564453125 & 5953.71630859375 & 5951.75634765625 & 5949.63623046875 & 5947.31640625 & 5944.63623046875 & 5941.51611328125 & 5938.076171875\\\\ \n5917.236328125 & 5919.31640625 & 5920.71630859375 & 5920.71630859375 & 5917.916015625 & 5917.79638671875 & 5917.9560546875 & 5917.71630859375 & 5915.076171875 & 5913.83642578125 & 5915.236328125 & 5912.1162109375 & 5911.71630859375 & 5910.79638671875 & 5909.87646484375 & 5908.83642578125 & 5907.67626953125 & 5906.83642578125 & 5904.87646484375 & 5903.15625 & 5901.1162109375 & 5899.47607421875 & 5896.87646484375 & 5893.9560546875 & 5892.0361328125 & 5889.87646484375 & 5887.31640625 & 5884.71630859375 & 5882.79638671875 & 5880.59619140625 & 5877.87646484375 & 5876.43603515625 & 5873.3564453125 & 5870.55615234375 & 5867.67626953125 & 5865.79638671875 & 5864.31640625 & 5862.31640625 & 5861.236328125 & 5860.1962890625 & 5859.47607421875 & 5859.3564453125 & 5859.51611328125 & 5858.87646484375 & 5858.71630859375 & 5858.59619140625 & 5858.55615234375 & 5858.67626953125 & 5858.43603515625 & 5857.75634765625 & 5857.396484375 & 5857.71630859375 & 5857.47607421875 & 5857.236328125 & 5857.59619140625 & 5858.396484375 & 5859.1962890625 & 5860.076171875 & 5861.67626953125 & 5863.51611328125 & 5865.59619140625 & 5866.99609375 & 5868.59619140625 & 5871.396484375 & 5872.51611328125 & 5873.67626953125 & 5874.916015625 & 5875.99609375 & 5877.3564453125 & 5879.1162109375 & 5881.076171875 & 5883.1962890625 & 5885.47607421875 & 5887.9560546875 & 5890.51611328125 & 5892.83642578125 & 5895.3564453125 & 5897.83642578125 & 5900.076171875 & 5902.2763671875 & 5904.3564453125 & 5906.3564453125 & 5908.1962890625 & 5909.9560546875 & 5911.15625 & 5911.9560546875 & 5912.396484375 & 5912.59619140625 & 5913.47607421875 & 5914.71630859375 & 5915.99609375 & 5917.87646484375 & 5920.15625 & 5922.71630859375 & 5925.67626953125 & 5928.67626953125 & 5931.63623046875 & 5934.51611328125 & 5937.076171875 & 5939.31640625 & 5941.1962890625 & 5942.87646484375 & 5944.55615234375 & 5946.2763671875 & 5948.1162109375 & 5949.99609375 & 5951.87646484375 & 5953.63623046875 & 5955.2763671875 & 5956.99609375 & 5958.43603515625 & 5959.2763671875 & 5959.63623046875 & 5959.87646484375 & 5959.87646484375 & 5959.87646484375 & 5959.87646484375 & 5959.75634765625 & 5959.43603515625 & 5958.99609375 & 5958.2763671875 & 5957.2763671875 & 5956.0361328125 & 5954.63623046875 & 5952.99609375 & 5950.9560546875 & 5948.75634765625 & 5946.396484375 & 5943.63623046875 & 5940.51611328125 & 5937.2763671875\\\\ \n5919.87646484375 & 5916.916015625 & 5914.99609375 & 5916.83642578125 & 5916.15625 & 5914.916015625 & 5913.99609375 & 5912.47607421875 & 5911.51611328125 & 5910.99609375 & 5907.396484375 & 5908.2763671875 & 5908.916015625 & 5908.59619140625 & 5907.3564453125 & 5906.31640625 & 5905.15625 & 5903.67626953125 & 5902.71630859375 & 5900.75634765625 & 5899.47607421875 & 5897.9560546875 & 5896.15625 & 5893.67626953125 & 5891.87646484375 & 5889.63623046875 & 5887.47607421875 & 5885.15625 & 5883.15625 & 5880.916015625 & 5878.236328125 & 5876.55615234375 & 5873.51611328125 & 5870.99609375 & 5869.0361328125 & 5867.99609375 & 5866.87646484375 & 5865.236328125 & 5864.87646484375 & 5864.43603515625 & 5864.43603515625 & 5864.75634765625 & 5864.87646484375 & 5864.47607421875 & 5863.9560546875 & 5863.51611328125 & 5863.31640625 & 5863.1162109375 & 5862.79638671875 & 5862.59619140625 & 5862.55615234375 & 5862.2763671875 & 5862.3564453125 & 5862.51611328125 & 5863.15625 & 5863.99609375 & 5864.59619140625 & 5865.51611328125 & 5867.0361328125 & 5868.83642578125 & 5870.55615234375 & 5871.83642578125 & 5873.076171875 & 5874.236328125 & 5875.15625 & 5876.67626953125 & 5878.1962890625 & 5879.47607421875 & 5880.9560546875 & 5882.55615234375 & 5884.2763671875 & 5886.236328125 & 5888.47607421875 & 5890.75634765625 & 5892.99609375 & 5895.2763671875 & 5897.67626953125 & 5899.9560546875 & 5902.1162109375 & 5904.236328125 & 5906.1962890625 & 5907.9560546875 & 5909.51611328125 & 5910.83642578125 & 5911.63623046875 & 5911.99609375 & 5912.076171875 & 5912.1162109375 & 5912.236328125 & 5913.1162109375 & 5914.396484375 & 5916.1962890625 & 5918.51611328125 & 5921.31640625 & 5924.43603515625 & 5927.67626953125 & 5931.076171875 & 5933.99609375 & 5936.47607421875 & 5938.67626953125 & 5940.67626953125 & 5942.2763671875 & 5943.75634765625 & 5945.43603515625 & 5947.2763671875 & 5949.31640625 & 5951.2763671875 & 5953.0361328125 & 5954.63623046875 & 5956.1962890625 & 5957.55615234375 & 5958.396484375 & 5958.75634765625 & 5958.79638671875 & 5958.87646484375 & 5958.9560546875 & 5958.87646484375 & 5958.75634765625 & 5958.3564453125 & 5957.916015625 & 5957.3564453125 & 5956.3564453125 & 5955.15625 & 5953.75634765625 & 5952.1162109375 & 5950.076171875 & 5947.83642578125 & 5945.47607421875 & 5942.55615234375 & 5939.396484375 & 5936.2763671875\\\\ \n5916.916015625 & 5916.236328125 & 5916.2763671875 & 5914.076171875 & 5913.2763671875 & 5912.51611328125 & 5911.1962890625 & 5909.9560546875 & 5908.31640625 & 5908.67626953125 & 5909.15625 & 5907.9560546875 & 5906.396484375 & 5905.75634765625 & 5905.396484375 & 5904.3564453125 & 5903.3564453125 & 5901.916015625 & 5900.71630859375 & 5899.1162109375 & 5898.1162109375 & 5896.59619140625 & 5895.63623046875 & 5894.396484375 & 5892.1962890625 & 5890.2763671875 & 5887.87646484375 & 5885.67626953125 & 5883.79638671875 & 5882.076171875 & 5879.79638671875 & 5877.43603515625 & 5875.3564453125 & 5873.31640625 & 5871.236328125 & 5869.83642578125 & 5868.71630859375 & 5868.47607421875 & 5868.59619140625 & 5868.916015625 & 5868.67626953125 & 5868.87646484375 & 5868.71630859375 & 5868.3564453125 & 5868.1162109375 & 5867.63623046875 & 5867.1962890625 & 5866.9560546875 & 5866.67626953125 & 5866.67626953125 & 5866.71630859375 & 5866.71630859375 & 5866.79638671875 & 5866.9560546875 & 5867.75634765625 & 5868.55615234375 & 5869.3564453125 & 5870.396484375 & 5871.79638671875 & 5873.47607421875 & 5874.9560546875 & 5875.59619140625 & 5876.63623046875 & 5877.236328125 & 5878.236328125 & 5880.076171875 & 5881.43603515625 & 5882.71630859375 & 5884.076171875 & 5885.3564453125 & 5886.75634765625 & 5888.55615234375 & 5890.71630859375 & 5892.99609375 & 5895.236328125 & 5897.59619140625 & 5899.87646484375 & 5901.99609375 & 5904.1162109375 & 5906.1162109375 & 5907.87646484375 & 5909.31640625 & 5910.59619140625 & 5911.55615234375 & 5911.87646484375 & 5911.71630859375 & 5911.63623046875 & 5911.1162109375 & 5910.87646484375 & 5911.43603515625 & 5912.3564453125 & 5914.3564453125 & 5917.15625 & 5920.1962890625 & 5923.31640625 & 5926.75634765625 & 5930.31640625 & 5933.3564453125 & 5935.67626953125 & 5937.87646484375 & 5939.916015625 & 5941.47607421875 & 5942.9560546875 & 5944.63623046875 & 5946.47607421875 & 5948.51611328125 & 5950.51611328125 & 5952.31640625 & 5953.916015625 & 5955.31640625 & 5956.59619140625 & 5957.43603515625 & 5957.63623046875 & 5957.1162109375 & 5958.15625 & 5957.87646484375 & 5957.67626953125 & 5957.67626953125 & 5957.2763671875 & 5956.79638671875 & 5956.31640625 & 5955.43603515625 & 5954.15625 & 5952.75634765625 & 5951.1162109375 & 5949.1162109375 & 5946.9560546875 & 5944.51611328125 & 5941.9560546875 & 5939.1962890625 & 5935.75634765625\\\\ \n5915.99609375 & 5914.51611328125 & 5918.1962890625 & 5911.99609375 & 5911.51611328125 & 5910.43603515625 & 5910.15625 & 5909.15625 & 5907.87646484375 & 5905.076171875 & 5906.2763671875 & 5904.31640625 & 5904.43603515625 & 5903.2763671875 & 5903.0361328125 & 5902.43603515625 & 5900.9560546875 & 5900.31640625 & 5899.236328125 & 5898.076171875 & 5896.79638671875 & 5895.87646484375 & 5895.15625 & 5893.3564453125 & 5890.99609375 & 5889.1962890625 & 5887.396484375 & 5885.75634765625 & 5883.99609375 & 5882.2763671875 & 5880.59619140625 & 5878.63623046875 & 5876.71630859375 & 5873.9560546875 & 5872.3564453125 & 5871.43603515625 & 5871.47607421875 & 5871.87646484375 & 5871.83642578125 & 5871.87646484375 & 5872.1162109375 & 5871.916015625 & 5871.55615234375 & 5871.2763671875 & 5870.916015625 & 5870.87646484375 & 5870.79638671875 & 5870.59619140625 & 5870.43603515625 & 5870.59619140625 & 5870.79638671875 & 5870.75634765625 & 5870.67626953125 & 5871.0361328125 & 5871.59619140625 & 5872.1162109375 & 5872.9560546875 & 5874.15625 & 5875.63623046875 & 5877.55615234375 & 5878.67626953125 & 5878.9560546875 & 5879.79638671875 & 5881.1162109375 & 5882.2763671875 & 5883.43603515625 & 5884.43603515625 & 5885.31640625 & 5886.31640625 & 5887.43603515625 & 5888.83642578125 & 5890.71630859375 & 5892.9560546875 & 5895.236328125 & 5897.51611328125 & 5899.83642578125 & 5901.99609375 & 5903.9560546875 & 5905.916015625 & 5907.75634765625 & 5909.2763671875 & 5910.47607421875 & 5911.43603515625 & 5912.15625 & 5912.076171875 & 5911.51611328125 & 5910.87646484375 & 5909.87646484375 & 5909.396484375 & 5909.396484375 & 5910.31640625 & 5912.67626953125 & 5915.87646484375 & 5919.15625 & 5922.2763671875 & 5925.67626953125 & 5929.1962890625 & 5932.2763671875 & 5934.83642578125 & 5937.15625 & 5939.0361328125 & 5940.59619140625 & 5942.15625 & 5943.79638671875 & 5945.55615234375 & 5947.51611328125 & 5949.47607421875 & 5951.236328125 & 5952.79638671875 & 5954.1162109375 & 5955.2763671875 & 5956.1162109375 & 5956.55615234375 & 5956.31640625 & 5957.1162109375 & 5956.87646484375 & 5956.55615234375 & 5956.43603515625 & 5956.1162109375 & 5955.71630859375 & 5955.236328125 & 5954.3564453125 & 5953.1162109375 & 5951.55615234375 & 5949.87646484375 & 5947.87646484375 & 5945.51611328125 & 5943.076171875 & 5940.47607421875 & 5937.67626953125 & 5934.31640625\\\\ \n5909.79638671875 & 5912.396484375 & 5913.2763671875 & 5911.15625 & 5908.1962890625 & 5909.31640625 & 5907.916015625 & 5906.51611328125 & 5905.59619140625 & 5904.1162109375 & 5904.3564453125 & 5903.396484375 & 5901.43603515625 & 5899.71630859375 & 5900.87646484375 & 5899.47607421875 & 5898.83642578125 & 5898.076171875 & 5897.15625 & 5896.1962890625 & 5895.076171875 & 5894.31640625 & 5893.47607421875 & 5891.87646484375 & 5889.99609375 & 5888.71630859375 & 5887.1962890625 & 5885.71630859375 & 5884.31640625 & 5882.43603515625 & 5880.236328125 & 5877.99609375 & 5876.31640625 & 5874.63623046875 & 5873.9560546875 & 5874.1162109375 & 5874.2763671875 & 5874.47607421875 & 5874.71630859375 & 5874.31640625 & 5873.51611328125 & 5873.43603515625 & 5873.1962890625 & 5873.0361328125 & 5873.236328125 & 5873.55615234375 & 5873.79638671875 & 5873.87646484375 & 5874.076171875 & 5874.3564453125 & 5874.47607421875 & 5874.43603515625 & 5874.67626953125 & 5874.75634765625 & 5874.75634765625 & 5875.396484375 & 5876.67626953125 & 5877.75634765625 & 5878.9560546875 & 5880.31640625 & 5881.3564453125 & 5881.79638671875 & 5882.63623046875 & 5884.076171875 & 5885.1162109375 & 5886.0361328125 & 5886.67626953125 & 5887.396484375 & 5888.31640625 & 5889.47607421875 & 5891.0361328125 & 5893.0361328125 & 5895.31640625 & 5897.55615234375 & 5899.79638671875 & 5902.076171875 & 5904.1962890625 & 5905.916015625 & 5907.67626953125 & 5909.2763671875 & 5910.51611328125 & 5911.51611328125 & 5912.2763671875 & 5912.67626953125 & 5912.55615234375 & 5911.71630859375 & 5910.396484375 & 5908.83642578125 & 5907.87646484375 & 5907.55615234375 & 5908.67626953125 & 5911.15625 & 5914.47607421875 & 5917.83642578125 & 5920.9560546875 & 5924.31640625 & 5927.916015625 & 5931.31640625 & 5934.15625 & 5936.59619140625 & 5938.396484375 & 5939.71630859375 & 5941.1962890625 & 5942.71630859375 & 5944.31640625 & 5946.1962890625 & 5948.1162109375 & 5949.79638671875 & 5951.1962890625 & 5952.47607421875 & 5953.59619140625 & 5954.396484375 & 5954.9560546875 & 5955.1162109375 & 5955.1962890625 & 5955.31640625 & 5955.15625 & 5954.9560546875 & 5954.83642578125 & 5954.51611328125 & 5953.83642578125 & 5952.87646484375 & 5951.63623046875 & 5950.1962890625 & 5948.51611328125 & 5946.63623046875 & 5944.51611328125 & 5942.0361328125 & 5939.31640625 & 5936.3564453125 & 5933.15625\\\\ \n5909.31640625 & 5910.3564453125 & 5907.31640625 & 5904.71630859375 & 5904.83642578125 & 5904.3564453125 & 5904.63623046875 & 5905.71630859375 & 5903.076171875 & 5901.9560546875 & 5900.79638671875 & 5900.1962890625 & 5898.67626953125 & 5899.0361328125 & 5898.59619140625 & 5896.47607421875 & 5896.59619140625 & 5895.396484375 & 5894.63623046875 & 5893.99609375 & 5893.3564453125 & 5892.59619140625 & 5891.59619140625 & 5890.75634765625 & 5889.87646484375 & 5888.71630859375 & 5887.076171875 & 5885.63623046875 & 5883.87646484375 & 5881.916015625 & 5879.87646484375 & 5878.076171875 & 5876.75634765625 & 5876.396484375 & 5876.67626953125 & 5876.67626953125 & 5876.236328125 & 5875.916015625 & 5875.75634765625 & 5875.0361328125 & 5874.396484375 & 5874.2763671875 & 5874.3564453125 & 5874.87646484375 & 5875.43603515625 & 5876.15625 & 5876.55615234375 & 5876.916015625 & 5877.396484375 & 5877.87646484375 & 5878.396484375 & 5878.31640625 & 5878.43603515625 & 5877.9560546875 & 5877.916015625 & 5879.31640625 & 5880.75634765625 & 5880.79638671875 & 5881.396484375 & 5882.43603515625 & 5883.51611328125 & 5884.2763671875 & 5885.2763671875 & 5886.59619140625 & 5887.59619140625 & 5888.236328125 & 5888.79638671875 & 5889.47607421875 & 5890.396484375 & 5891.67626953125 & 5893.396484375 & 5895.47607421875 & 5897.79638671875 & 5900.076171875 & 5902.31640625 & 5904.3564453125 & 5906.31640625 & 5907.99609375 & 5909.3564453125 & 5910.51611328125 & 5911.55615234375 & 5912.2763671875 & 5913.0361328125 & 5913.55615234375 & 5912.75634765625 & 5911.79638671875 & 5910.236328125 & 5908.31640625 & 5906.87646484375 & 5906.47607421875 & 5907.59619140625 & 5909.99609375 & 5913.236328125 & 5916.71630859375 & 5920.0361328125 & 5923.3564453125 & 5926.83642578125 & 5930.236328125 & 5933.1962890625 & 5935.79638671875 & 5937.67626953125 & 5938.75634765625 & 5940.0361328125 & 5941.396484375 & 5942.75634765625 & 5944.3564453125 & 5946.31640625 & 5948.076171875 & 5949.396484375 & 5950.59619140625 & 5951.79638671875 & 5952.63623046875 & 5953.15625 & 5953.47607421875 & 5953.63623046875 & 5953.63623046875 & 5953.55615234375 & 5953.51611328125 & 5953.43603515625 & 5953.076171875 & 5952.31640625 & 5951.2763671875 & 5950.0361328125 & 5948.63623046875 & 5947.076171875 & 5945.31640625 & 5943.2763671875 & 5940.9560546875 & 5938.3564453125 & 5935.43603515625 & 5932.236328125\\\\ \n5902.63623046875 & 5901.0361328125 & 5900.75634765625 & 5904.1962890625 & 5902.076171875 & 5902.0361328125 & 5900.0361328125 & 5901.31640625 & 5899.87646484375 & 5899.87646484375 & 5897.79638671875 & 5897.3564453125 & 5898.59619140625 & 5895.87646484375 & 5898.076171875 & 5895.31640625 & 5893.79638671875 & 5893.63623046875 & 5892.236328125 & 5891.59619140625 & 5891.0361328125 & 5890.47607421875 & 5889.87646484375 & 5889.2763671875 & 5888.75634765625 & 5887.55615234375 & 5886.31640625 & 5884.99609375 & 5883.63623046875 & 5882.1962890625 & 5880.75634765625 & 5879.83642578125 & 5879.47607421875 & 5879.3564453125 & 5879.1962890625 & 5878.0361328125 & 5876.9560546875 & 5876.51611328125 & 5876.236328125 & 5875.916015625 & 5875.71630859375 & 5875.47607421875 & 5875.71630859375 & 5876.55615234375 & 5877.396484375 & 5878.43603515625 & 5879.236328125 & 5879.71630859375 & 5880.2763671875 & 5880.87646484375 & 5881.31640625 & 5881.59619140625 & 5881.67626953125 & 5881.9560546875 & 5882.3564453125 & 5882.83642578125 & 5882.99609375 & 5883.1162109375 & 5883.43603515625 & 5884.1962890625 & 5885.3564453125 & 5886.396484375 & 5887.59619140625 & 5888.87646484375 & 5889.87646484375 & 5890.51611328125 & 5891.1162109375 & 5891.916015625 & 5893.0361328125 & 5894.51611328125 & 5896.3564453125 & 5898.43603515625 & 5900.59619140625 & 5902.83642578125 & 5904.916015625 & 5906.63623046875 & 5908.31640625 & 5909.67626953125 & 5910.67626953125 & 5911.63623046875 & 5912.43603515625 & 5912.47607421875 & 5913.3564453125 & 5913.59619140625 & 5912.75634765625 & 5911.51611328125 & 5910.076171875 & 5908.2763671875 & 5906.75634765625 & 5906.236328125 & 5907.15625 & 5909.3564453125 & 5912.51611328125 & 5916.1962890625 & 5919.71630859375 & 5922.9560546875 & 5926.236328125 & 5929.3564453125 & 5931.79638671875 & 5934.0361328125 & 5936.43603515625 & 5937.67626953125 & 5938.55615234375 & 5939.79638671875 & 5940.916015625 & 5942.236328125 & 5944.1162109375 & 5945.9560546875 & 5947.31640625 & 5948.55615234375 & 5949.87646484375 & 5950.83642578125 & 5951.31640625 & 5951.63623046875 & 5951.87646484375 & 5951.916015625 & 5951.83642578125 & 5951.75634765625 & 5951.59619140625 & 5951.1162109375 & 5950.396484375 & 5949.51611328125 & 5948.396484375 & 5947.076171875 & 5945.59619140625 & 5943.916015625 & 5941.99609375 & 5939.71630859375 & 5937.15625 & 5934.31640625 & 5931.236328125\\\\ \n5897.31640625 & 5899.3564453125 & 5900.75634765625 & 5898.396484375 & 5897.3564453125 & 5896.15625 & 5898.0361328125 & 5897.916015625 & 5897.59619140625 & 5895.75634765625 & 5894.396484375 & 5894.1162109375 & 5895.1962890625 & 5894.1162109375 & 5894.1162109375 & 5896.83642578125 & 5893.47607421875 & 5891.47607421875 & 5890.63623046875 & 5889.83642578125 & 5889.1962890625 & 5888.99609375 & 5888.59619140625 & 5887.99609375 & 5887.396484375 & 5886.9560546875 & 5886.15625 & 5885.2763671875 & 5884.2763671875 & 5883.15625 & 5882.43603515625 & 5881.99609375 & 5881.59619140625 & 5881.2763671875 & 5880.71630859375 & 5879.076171875 & 5877.99609375 & 5877.51611328125 & 5877.43603515625 & 5877.1962890625 & 5876.79638671875 & 5876.87646484375 & 5877.916015625 & 5879.15625 & 5880.0361328125 & 5880.916015625 & 5881.31640625 & 5881.63623046875 & 5882.076171875 & 5882.59619140625 & 5883.2763671875 & 5884.3564453125 & 5885.236328125 & 5885.55615234375 & 5885.55615234375 & 5885.236328125 & 5885.1162109375 & 5885.1162109375 & 5885.396484375 & 5886.51611328125 & 5887.43603515625 & 5888.59619140625 & 5890.15625 & 5891.43603515625 & 5892.2763671875 & 5893.15625 & 5894.1162109375 & 5895.2763671875 & 5896.55615234375 & 5897.99609375 & 5899.63623046875 & 5901.47607421875 & 5903.47607421875 & 5905.55615234375 & 5907.3564453125 & 5908.63623046875 & 5909.83642578125 & 5910.83642578125 & 5911.63623046875 & 5912.2763671875 & 5912.47607421875 & 5913.1962890625 & 5913.47607421875 & 5913.43603515625 & 5912.71630859375 & 5911.55615234375 & 5910.1962890625 & 5908.63623046875 & 5907.236328125 & 5906.59619140625 & 5907.2763671875 & 5909.2763671875 & 5912.31640625 & 5915.99609375 & 5919.47607421875 & 5922.51611328125 & 5925.51611328125 & 5928.43603515625 & 5931.0361328125 & 5932.67626953125 & 5934.63623046875 & 5936.3564453125 & 5937.236328125 & 5937.99609375 & 5938.99609375 & 5940.1162109375 & 5941.67626953125 & 5943.55615234375 & 5945.076171875 & 5946.43603515625 & 5947.87646484375 & 5948.9560546875 & 5949.51611328125 & 5949.75634765625 & 5949.83642578125 & 5949.79638671875 & 5949.67626953125 & 5949.55615234375 & 5949.2763671875 & 5948.79638671875 & 5948.1162109375 & 5947.3564453125 & 5946.43603515625 & 5945.2763671875 & 5943.9560546875 & 5942.396484375 & 5940.59619140625 & 5938.396484375 & 5935.916015625 & 5933.236328125 & 5930.3564453125\\\\ \n5893.59619140625 & 5892.79638671875 & 5893.2763671875 & 5893.43603515625 & 5893.83642578125 & 5892.83642578125 & 5893.1962890625 & 5891.47607421875 & 5892.47607421875 & 5890.83642578125 & 5891.71630859375 & 5891.3564453125 & 5890.1962890625 & 5893.15625 & 5890.9560546875 & 5891.916015625 & 5889.076171875 & 5888.99609375 & 5887.63623046875 & 5887.71630859375 & 5887.63623046875 & 5887.2763671875 & 5887.15625 & 5887.1162109375 & 5886.99609375 & 5886.47607421875 & 5886.076171875 & 5885.1962890625 & 5884.3564453125 & 5883.9560546875 & 5883.67626953125 & 5883.59619140625 & 5883.1162109375 & 5882.63623046875 & 5881.99609375 & 5880.67626953125 & 5879.63623046875 & 5879.51611328125 & 5879.63623046875 & 5879.87646484375 & 5880.1962890625 & 5880.55615234375 & 5880.51611328125 & 5880.67626953125 & 5881.396484375 & 5881.9560546875 & 5882.15625 & 5882.75634765625 & 5883.0361328125 & 5883.43603515625 & 5884.71630859375 & 5886.3564453125 & 5887.79638671875 & 5888.1962890625 & 5887.83642578125 & 5887.59619140625 & 5887.43603515625 & 5887.47607421875 & 5888.31640625 & 5889.75634765625 & 5890.59619140625 & 5891.3564453125 & 5893.15625 & 5894.31640625 & 5895.076171875 & 5896.1962890625 & 5897.43603515625 & 5898.67626953125 & 5899.87646484375 & 5901.1962890625 & 5902.63623046875 & 5904.31640625 & 5906.1962890625 & 5908.396484375 & 5909.1962890625 & 5910.15625 & 5911.2763671875 & 5911.87646484375 & 5912.396484375 & 5912.87646484375 & 5913.31640625 & 5913.55615234375 & 5913.43603515625 & 5913.2763671875 & 5912.51611328125 & 5911.47607421875 & 5910.3564453125 & 5909.1162109375 & 5908.15625 & 5907.63623046875 & 5908.076171875 & 5909.83642578125 & 5912.55615234375 & 5915.79638671875 & 5919.15625 & 5922.1162109375 & 5924.71630859375 & 5927.71630859375 & 5929.79638671875 & 5930.9560546875 & 5933.1962890625 & 5934.75634765625 & 5935.916015625 & 5936.43603515625 & 5937.1162109375 & 5938.0361328125 & 5939.2763671875 & 5940.99609375 & 5942.71630859375 & 5944.2763671875 & 5945.79638671875 & 5946.9560546875 & 5947.55615234375 & 5947.75634765625 & 5947.59619140625 & 5947.3564453125 & 5947.1962890625 & 5946.9560546875 & 5946.67626953125 & 5946.236328125 & 5945.71630859375 & 5945.076171875 & 5944.236328125 & 5943.31640625 & 5942.076171875 & 5940.63623046875 & 5938.916015625 & 5936.916015625 & 5934.59619140625 & 5932.0361328125 & 5929.3564453125\\\\ \n5889.67626953125 & 5890.076171875 & 5892.2763671875 & 5887.076171875 & 5889.75634765625 & 5889.2763671875 & 5888.396484375 & 5888.916015625 & 5888.31640625 & 5888.51611328125 & 5888.3564453125 & 5887.67626953125 & 5887.59619140625 & 5889.31640625 & 5889.75634765625 & 5886.87646484375 & 5886.59619140625 & 5885.55615234375 & 5885.15625 & 5884.51611328125 & 5884.75634765625 & 5885.076171875 & 5885.236328125 & 5885.9560546875 & 5885.71630859375 & 5885.63623046875 & 5885.1962890625 & 5884.31640625 & 5883.63623046875 & 5883.47607421875 & 5883.916015625 & 5884.236328125 & 5883.83642578125 & 5883.47607421875 & 5882.9560546875 & 5882.47607421875 & 5881.99609375 & 5882.396484375 & 5882.67626953125 & 5882.51611328125 & 5882.396484375 & 5882.3564453125 & 5882.63623046875 & 5882.59619140625 & 5883.43603515625 & 5884.0361328125 & 5884.396484375 & 5883.99609375 & 5883.47607421875 & 5884.31640625 & 5886.1162109375 & 5887.87646484375 & 5889.15625 & 5889.67626953125 & 5889.71630859375 & 5889.83642578125 & 5889.9560546875 & 5890.1162109375 & 5890.87646484375 & 5892.47607421875 & 5894.236328125 & 5894.59619140625 & 5895.59619140625 & 5896.71630859375 & 5897.71630859375 & 5898.916015625 & 5900.236328125 & 5901.47607421875 & 5902.63623046875 & 5903.83642578125 & 5905.2763671875 & 5906.75634765625 & 5908.71630859375 & 5910.47607421875 & 5910.3564453125 & 5910.916015625 & 5911.99609375 & 5912.75634765625 & 5913.236328125 & 5913.51611328125 & 5914.0361328125 & 5914.55615234375 & 5913.75634765625 & 5913.1162109375 & 5912.396484375 & 5911.55615234375 & 5910.71630859375 & 5910.0361328125 & 5909.55615234375 & 5909.236328125 & 5909.51611328125 & 5910.79638671875 & 5913.0361328125 & 5915.87646484375 & 5919.076171875 & 5922.0361328125 & 5924.43603515625 & 5926.916015625 & 5928.79638671875 & 5930.2763671875 & 5930.71630859375 & 5932.31640625 & 5934.47607421875 & 5935.0361328125 & 5935.2763671875 & 5936.1162109375 & 5937.076171875 & 5938.59619140625 & 5940.3564453125 & 5942.0361328125 & 5943.67626953125 & 5944.79638671875 & 5945.396484375 & 5945.51611328125 & 5945.236328125 & 5944.75634765625 & 5944.396484375 & 5944.1162109375 & 5943.916015625 & 5943.63623046875 & 5943.236328125 & 5942.75634765625 & 5942.0361328125 & 5941.1162109375 & 5939.9560546875 & 5938.59619140625 & 5937.0361328125 & 5935.1962890625 & 5933.076171875 & 5930.75634765625 & 5928.3564453125\\\\ \n5886.1162109375 & 5885.83642578125 & 5886.71630859375 & 5883.51611328125 & 5887.396484375 & 5886.0361328125 & 5887.0361328125 & 5885.59619140625 & 5885.9560546875 & 5885.15625 & 5885.396484375 & 5886.2763671875 & 5885.51611328125 & 5886.236328125 & 5886.1162109375 & 5887.87646484375 & 5885.67626953125 & 5884.67626953125 & 5883.55615234375 & 5883.0361328125 & 5882.79638671875 & 5882.71630859375 & 5884.3564453125 & 5884.63623046875 & 5884.83642578125 & 5884.1962890625 & 5883.87646484375 & 5883.43603515625 & 5882.59619140625 & 5882.99609375 & 5883.916015625 & 5884.15625 & 5883.916015625 & 5883.75634765625 & 5883.99609375 & 5884.1962890625 & 5884.3564453125 & 5884.396484375 & 5884.43603515625 & 5884.3564453125 & 5884.236328125 & 5884.396484375 & 5884.63623046875 & 5885.236328125 & 5885.75634765625 & 5885.916015625 & 5885.1162109375 & 5884.75634765625 & 5885.396484375 & 5886.75634765625 & 5888.1162109375 & 5889.396484375 & 5890.396484375 & 5890.99609375 & 5891.3564453125 & 5891.67626953125 & 5892.396484375 & 5892.87646484375 & 5893.75634765625 & 5895.55615234375 & 5896.63623046875 & 5897.236328125 & 5898.31640625 & 5899.43603515625 & 5900.47607421875 & 5901.63623046875 & 5902.87646484375 & 5903.916015625 & 5904.9560546875 & 5906.076171875 & 5907.47607421875 & 5908.71630859375 & 5909.87646484375 & 5910.99609375 & 5911.15625 & 5911.67626953125 & 5913.0361328125 & 5913.51611328125 & 5913.99609375 & 5913.99609375 & 5915.1162109375 & 5914.3564453125 & 5913.67626953125 & 5912.87646484375 & 5912.2763671875 & 5911.71630859375 & 5911.1962890625 & 5910.87646484375 & 5910.67626953125 & 5910.51611328125 & 5910.87646484375 & 5911.79638671875 & 5913.47607421875 & 5916.076171875 & 5919.076171875 & 5921.83642578125 & 5923.9560546875 & 5925.396484375 & 5927.31640625 & 5928.63623046875 & 5929.47607421875 & 5928.55615234375 & 5931.0361328125 & 5933.43603515625 & 5933.83642578125 & 5934.63623046875 & 5935.43603515625 & 5936.59619140625 & 5938.1162109375 & 5939.75634765625 & 5941.31640625 & 5942.396484375 & 5942.9560546875 & 5943.1162109375 & 5942.83642578125 & 5942.15625 & 5941.55615234375 & 5941.31640625 & 5941.1962890625 & 5941.0361328125 & 5940.79638671875 & 5940.43603515625 & 5939.75634765625 & 5938.87646484375 & 5937.79638671875 & 5936.51611328125 & 5935.1162109375 & 5933.43603515625 & 5931.43603515625 & 5929.31640625 & 5927.1162109375\\\\ \n5883.87646484375 & 5883.3564453125 & 5885.396484375 & 5884.3564453125 & 5882.67626953125 & 5888.63623046875 & 5883.79638671875 & 5885.15625 & 5883.9560546875 & 5883.3564453125 & 5883.83642578125 & 5884.916015625 & 5884.1962890625 & 5884.43603515625 & 5883.916015625 & 5883.43603515625 & 5883.1962890625 & 5883.55615234375 & 5883.1162109375 & 5881.55615234375 & 5881.59619140625 & 5882.87646484375 & 5883.51611328125 & 5884.15625 & 5884.63623046875 & 5884.71630859375 & 5884.1962890625 & 5884.076171875 & 5883.3564453125 & 5883.51611328125 & 5884.71630859375 & 5884.75634765625 & 5884.47607421875 & 5884.2763671875 & 5884.63623046875 & 5884.9560546875 & 5885.3564453125 & 5885.67626953125 & 5886.076171875 & 5885.43603515625 & 5885.75634765625 & 5886.59619140625 & 5887.67626953125 & 5887.916015625 & 5886.99609375 & 5886.43603515625 & 5886.55615234375 & 5887.1162109375 & 5887.99609375 & 5889.1962890625 & 5890.2763671875 & 5891.47607421875 & 5892.2763671875 & 5892.79638671875 & 5893.2763671875 & 5894.076171875 & 5894.79638671875 & 5895.2763671875 & 5896.67626953125 & 5898.1962890625 & 5898.71630859375 & 5899.55615234375 & 5900.71630859375 & 5901.916015625 & 5903.1162109375 & 5904.076171875 & 5904.99609375 & 5905.87646484375 & 5906.75634765625 & 5907.83642578125 & 5909.1162109375 & 5910.15625 & 5910.99609375 & 5911.396484375 & 5912.076171875 & 5912.75634765625 & 5913.51611328125 & 5914.236328125 & 5914.47607421875 & 5914.15625 & 5914.67626953125 & 5914.15625 & 5913.59619140625 & 5912.63623046875 & 5911.9560546875 & 5911.51611328125 & 5911.236328125 & 5911.1962890625 & 5911.2763671875 & 5911.3564453125 & 5911.63623046875 & 5912.396484375 & 5913.87646484375 & 5916.2763671875 & 5919.0361328125 & 5921.47607421875 & 5923.3564453125 & 5924.63623046875 & 5925.75634765625 & 5927.75634765625 & 5928.75634765625 & 5928.3564453125 & 5927.79638671875 & 5931.79638671875 & 5932.55615234375 & 5933.3564453125 & 5934.076171875 & 5934.9560546875 & 5936.1962890625 & 5937.63623046875 & 5938.916015625 & 5939.83642578125 & 5940.3564453125 & 5940.51611328125 & 5940.2763671875 & 5939.59619140625 & 5938.916015625 & 5938.63623046875 & 5938.59619140625 & 5938.51611328125 & 5938.3564453125 & 5937.99609375 & 5937.55615234375 & 5936.75634765625 & 5935.71630859375 & 5934.47607421875 & 5933.1962890625 & 5931.71630859375 & 5929.916015625 & 5927.916015625 & 5925.87646484375\\\\ \n5882.3564453125 & 5882.0361328125 & 5882.396484375 & 5882.31640625 & 5882.59619140625 & 5884.99609375 & 5883.396484375 & 5883.67626953125 & 5883.9560546875 & 5882.47607421875 & 5882.67626953125 & 5883.59619140625 & 5884.15625 & 5884.396484375 & 5882.99609375 & 5882.63623046875 & 5882.79638671875 & 5884.3564453125 & 5882.47607421875 & 5882.59619140625 & 5882.51611328125 & 5883.87646484375 & 5883.63623046875 & 5883.63623046875 & 5884.076171875 & 5884.236328125 & 5883.83642578125 & 5883.87646484375 & 5883.75634765625 & 5883.99609375 & 5884.59619140625 & 5884.79638671875 & 5884.87646484375 & 5885.076171875 & 5885.2763671875 & 5885.3564453125 & 5885.83642578125 & 5886.2763671875 & 5887.2763671875 & 5887.9560546875 & 5888.1162109375 & 5888.51611328125 & 5888.55615234375 & 5888.67626953125 & 5888.236328125 & 5887.1962890625 & 5887.51611328125 & 5888.87646484375 & 5890.71630859375 & 5891.67626953125 & 5892.55615234375 & 5893.51611328125 & 5894.67626953125 & 5895.31640625 & 5895.9560546875 & 5896.71630859375 & 5897.396484375 & 5897.71630859375 & 5898.47607421875 & 5900.076171875 & 5900.87646484375 & 5901.75634765625 & 5902.9560546875 & 5904.15625 & 5905.2763671875 & 5905.9560546875 & 5906.63623046875 & 5907.43603515625 & 5908.31640625 & 5909.1962890625 & 5909.99609375 & 5910.59619140625 & 5911.076171875 & 5911.63623046875 & 5912.43603515625 & 5913.1962890625 & 5913.87646484375 & 5913.59619140625 & 5914.1162109375 & 5914.1962890625 & 5914.43603515625 & 5913.99609375 & 5913.9560546875 & 5912.47607421875 & 5911.87646484375 & 5911.1162109375 & 5910.87646484375 & 5910.9560546875 & 5911.15625 & 5911.396484375 & 5911.83642578125 & 5912.59619140625 & 5914.076171875 & 5916.31640625 & 5918.83642578125 & 5920.99609375 & 5922.59619140625 & 5924.1162109375 & 5925.43603515625 & 5926.63623046875 & 5927.83642578125 & 5928.2763671875 & 5928.59619140625 & 5930.67626953125 & 5931.51611328125 & 5932.0361328125 & 5932.71630859375 & 5933.47607421875 & 5934.55615234375 & 5935.71630859375 & 5936.59619140625 & 5937.236328125 & 5937.59619140625 & 5937.79638671875 & 5937.55615234375 & 5936.9560546875 & 5936.3564453125 & 5936.0361328125 & 5935.916015625 & 5935.87646484375 & 5935.71630859375 & 5935.43603515625 & 5935.15625 & 5934.59619140625 & 5933.71630859375 & 5932.59619140625 & 5931.3564453125 & 5930.076171875 & 5928.55615234375 & 5926.83642578125 & 5924.9560546875\\\\ \n5880.9560546875 & 5880.15625 & 5880.396484375 & 5880.916015625 & 5881.2763671875 & 5881.99609375 & 5885.51611328125 & 5882.0361328125 & 5883.3564453125 & 5881.47607421875 & 5882.236328125 & 5881.75634765625 & 5882.3564453125 & 5882.43603515625 & 5882.15625 & 5883.3564453125 & 5883.396484375 & 5885.396484375 & 5882.15625 & 5882.55615234375 & 5883.15625 & 5883.47607421875 & 5884.15625 & 5884.076171875 & 5883.99609375 & 5883.916015625 & 5883.67626953125 & 5883.55615234375 & 5883.75634765625 & 5883.71630859375 & 5883.83642578125 & 5883.916015625 & 5884.2763671875 & 5885.236328125 & 5885.87646484375 & 5886.67626953125 & 5887.87646484375 & 5888.55615234375 & 5889.396484375 & 5890.15625 & 5890.396484375 & 5889.59619140625 & 5888.83642578125 & 5889.83642578125 & 5890.076171875 & 5888.99609375 & 5889.71630859375 & 5891.396484375 & 5892.83642578125 & 5893.83642578125 & 5894.51611328125 & 5894.99609375 & 5896.236328125 & 5897.63623046875 & 5898.1162109375 & 5898.916015625 & 5899.79638671875 & 5900.51611328125 & 5901.1162109375 & 5902.43603515625 & 5903.67626953125 & 5904.31640625 & 5905.076171875 & 5905.79638671875 & 5906.63623046875 & 5907.2763671875 & 5907.9560546875 & 5908.79638671875 & 5909.59619140625 & 5909.9560546875 & 5910.236328125 & 5910.87646484375 & 5911.31640625 & 5911.99609375 & 5912.71630859375 & 5913.31640625 & 5913.59619140625 & 5913.396484375 & 5913.9560546875 & 5913.9560546875 & 5913.916015625 & 5913.916015625 & 5912.99609375 & 5912.1162109375 & 5911.3564453125 & 5910.83642578125 & 5910.47607421875 & 5910.51611328125 & 5910.87646484375 & 5911.2763671875 & 5911.75634765625 & 5912.63623046875 & 5914.1962890625 & 5916.2763671875 & 5918.63623046875 & 5920.1962890625 & 5921.55615234375 & 5923.2763671875 & 5924.87646484375 & 5924.83642578125 & 5926.1962890625 & 5927.236328125 & 5927.67626953125 & 5929.1162109375 & 5930.1962890625 & 5930.51611328125 & 5931.15625 & 5931.79638671875 & 5932.67626953125 & 5933.59619140625 & 5934.15625 & 5934.51611328125 & 5934.75634765625 & 5934.916015625 & 5934.71630859375 & 5934.1962890625 & 5933.71630859375 & 5933.396484375 & 5933.1962890625 & 5933.2763671875 & 5933.1962890625 & 5932.87646484375 & 5932.55615234375 & 5932.1962890625 & 5931.55615234375 & 5930.59619140625 & 5929.51611328125 & 5928.3564453125 & 5927.1162109375 & 5925.67626953125 & 5924.076171875\\\\ \n5879.67626953125 & 5878.79638671875 & 5878.47607421875 & 5878.63623046875 & 5879.076171875 & 5880.1162109375 & 5883.75634765625 & 5880.71630859375 & 5880.83642578125 & 5883.51611328125 & 5880.87646484375 & 5882.396484375 & 5883.0361328125 & 5881.0361328125 & 5881.1962890625 & 5883.63623046875 & 5881.916015625 & 5881.47607421875 & 5881.916015625 & 5884.236328125 & 5882.916015625 & 5883.75634765625 & 5884.87646484375 & 5884.59619140625 & 5884.076171875 & 5883.75634765625 & 5884.67626953125 & 5884.916015625 & 5884.55615234375 & 5883.83642578125 & 5883.63623046875 & 5883.87646484375 & 5883.916015625 & 5884.83642578125 & 5886.1162109375 & 5887.51611328125 & 5888.916015625 & 5889.79638671875 & 5890.55615234375 & 5890.9560546875 & 5891.59619140625 & 5891.75634765625 & 5891.51611328125 & 5890.9560546875 & 5891.2763671875 & 5892.0361328125 & 5893.1962890625 & 5894.31640625 & 5895.79638671875 & 5895.79638671875 & 5896.43603515625 & 5896.55615234375 & 5897.59619140625 & 5898.99609375 & 5899.83642578125 & 5900.75634765625 & 5901.59619140625 & 5902.43603515625 & 5903.3564453125 & 5904.63623046875 & 5905.87646484375 & 5906.51611328125 & 5906.83642578125 & 5907.31640625 & 5907.916015625 & 5908.55615234375 & 5909.2763671875 & 5909.83642578125 & 5910.15625 & 5910.55615234375 & 5910.83642578125 & 5911.2763671875 & 5911.75634765625 & 5912.31640625 & 5912.75634765625 & 5913.0361328125 & 5913.3564453125 & 5913.67626953125 & 5913.71630859375 & 5913.236328125 & 5912.916015625 & 5912.79638671875 & 5912.43603515625 & 5911.75634765625 & 5911.15625 & 5910.67626953125 & 5910.236328125 & 5910.236328125 & 5910.59619140625 & 5911.0361328125 & 5911.55615234375 & 5912.51611328125 & 5914.076171875 & 5915.83642578125 & 5917.87646484375 & 5919.3564453125 & 5920.59619140625 & 5922.31640625 & 5922.71630859375 & 5924.15625 & 5925.3564453125 & 5925.51611328125 & 5926.1962890625 & 5927.51611328125 & 5928.396484375 & 5928.79638671875 & 5929.2763671875 & 5929.67626953125 & 5930.31640625 & 5931.0361328125 & 5931.51611328125 & 5931.67626953125 & 5931.75634765625 & 5931.79638671875 & 5931.67626953125 & 5931.31640625 & 5930.99609375 & 5930.75634765625 & 5930.59619140625 & 5930.67626953125 & 5930.63623046875 & 5930.396484375 & 5930.0361328125 & 5929.75634765625 & 5929.2763671875 & 5928.55615234375 & 5927.59619140625 & 5926.47607421875 & 5925.3564453125 & 5924.1162109375 & 5922.75634765625\\\\ \n5879.3564453125 & 5878.55615234375 & 5878.1162109375 & 5878.31640625 & 5878.51611328125 & 5879.31640625 & 5879.15625 & 5882.15625 & 5882.15625 & 5882.55615234375 & 5881.55615234375 & 5882.1162109375 & 5883.15625 & 5882.1962890625 & 5881.71630859375 & 5881.9560546875 & 5881.67626953125 & 5881.9560546875 & 5882.0361328125 & 5882.396484375 & 5884.31640625 & 5884.75634765625 & 5884.47607421875 & 5883.79638671875 & 5881.43603515625 & 5881.43603515625 & 5882.83642578125 & 5884.236328125 & 5884.55615234375 & 5884.236328125 & 5884.1962890625 & 5884.43603515625 & 5884.51611328125 & 5885.51611328125 & 5887.1962890625 & 5888.9560546875 & 5889.916015625 & 5890.31640625 & 5890.87646484375 & 5891.916015625 & 5892.79638671875 & 5893.2763671875 & 5893.916015625 & 5893.67626953125 & 5894.1962890625 & 5895.1962890625 & 5896.0361328125 & 5896.67626953125 & 5897.076171875 & 5897.71630859375 & 5898.75634765625 & 5899.43603515625 & 5899.83642578125 & 5900.87646484375 & 5901.99609375 & 5902.75634765625 & 5903.67626953125 & 5904.67626953125 & 5905.3564453125 & 5906.3564453125 & 5907.236328125 & 5907.75634765625 & 5908.076171875 & 5908.31640625 & 5908.99609375 & 5909.59619140625 & 5910.1162109375 & 5910.1962890625 & 5910.2763671875 & 5910.55615234375 & 5910.9560546875 & 5911.3564453125 & 5911.75634765625 & 5911.99609375 & 5912.15625 & 5912.3564453125 & 5912.51611328125 & 5912.79638671875 & 5912.83642578125 & 5912.1962890625 & 5912.31640625 & 5911.83642578125 & 5911.63623046875 & 5911.47607421875 & 5910.916015625 & 5910.51611328125 & 5910.1162109375 & 5910.1162109375 & 5910.3564453125 & 5910.71630859375 & 5911.236328125 & 5912.1962890625 & 5913.51611328125 & 5915.236328125 & 5916.916015625 & 5918.67626953125 & 5919.396484375 & 5920.31640625 & 5921.236328125 & 5922.31640625 & 5923.236328125 & 5924.0361328125 & 5924.83642578125 & 5925.59619140625 & 5926.236328125 & 5926.43603515625 & 5926.75634765625 & 5927.1962890625 & 5927.59619140625 & 5928.2763671875 & 5928.67626953125 & 5928.75634765625 & 5928.79638671875 & 5928.75634765625 & 5928.59619140625 & 5928.43603515625 & 5928.31640625 & 5928.1162109375 & 5927.83642578125 & 5927.83642578125 & 5927.87646484375 & 5927.71630859375 & 5927.396484375 & 5927.15625 & 5926.87646484375 & 5926.31640625 & 5925.51611328125 & 5924.51611328125 & 5923.51611328125 & 5922.47607421875 & 5921.31640625\\\\ \n5880.1962890625 & 5879.2763671875 & 5878.63623046875 & 5877.9560546875 & 5878.1962890625 & 5878.9560546875 & 5879.55615234375 & 5882.15625 & 5881.0361328125 & 5882.43603515625 & 5883.71630859375 & 5883.2763671875 & 5881.916015625 & 5882.396484375 & 5882.916015625 & 5882.59619140625 & 5882.47607421875 & 5883.31640625 & 5882.2763671875 & 5882.59619140625 & 5884.67626953125 & 5884.236328125 & 5885.87646484375 & 5885.63623046875 & 5881.79638671875 & 5880.1962890625 & 5881.79638671875 & 5883.79638671875 & 5884.59619140625 & 5885.31640625 & 5885.3564453125 & 5885.396484375 & 5886.1162109375 & 5887.0361328125 & 5888.396484375 & 5889.67626953125 & 5890.236328125 & 5891.3564453125 & 5892.2763671875 & 5893.55615234375 & 5894.79638671875 & 5895.71630859375 & 5896.3564453125 & 5896.83642578125 & 5897.59619140625 & 5898.1162109375 & 5898.396484375 & 5898.59619140625 & 5898.67626953125 & 5899.076171875 & 5899.9560546875 & 5900.43603515625 & 5901.2763671875 & 5902.2763671875 & 5903.1962890625 & 5904.3564453125 & 5905.15625 & 5905.79638671875 & 5906.31640625 & 5907.0361328125 & 5907.79638671875 & 5908.47607421875 & 5908.916015625 & 5909.3564453125 & 5909.67626953125 & 5909.87646484375 & 5909.83642578125 & 5909.75634765625 & 5909.916015625 & 5910.236328125 & 5910.55615234375 & 5910.79638671875 & 5911.0361328125 & 5911.15625 & 5911.3564453125 & 5911.51611328125 & 5911.67626953125 & 5911.87646484375 & 5912.15625 & 5911.99609375 & 5911.79638671875 & 5911.43603515625 & 5911.31640625 & 5910.87646484375 & 5910.51611328125 & 5910.15625 & 5909.87646484375 & 5909.916015625 & 5910.076171875 & 5910.236328125 & 5910.67626953125 & 5911.51611328125 & 5912.71630859375 & 5914.236328125 & 5915.55615234375 & 5916.79638671875 & 5918.0361328125 & 5919.47607421875 & 5919.59619140625 & 5920.2763671875 & 5921.076171875 & 5922.076171875 & 5922.63623046875 & 5923.076171875 & 5923.55615234375 & 5923.63623046875 & 5924.0361328125 & 5924.51611328125 & 5924.75634765625 & 5925.2763671875 & 5925.59619140625 & 5925.59619140625 & 5925.63623046875 & 5925.67626953125 & 5925.63623046875 & 5925.63623046875 & 5925.71630859375 & 5925.63623046875 & 5925.2763671875 & 5925.0361328125 & 5924.9560546875 & 5924.87646484375 & 5924.71630859375 & 5924.43603515625 & 5924.1162109375 & 5923.71630859375 & 5923.1162109375 & 5922.31640625 & 5921.47607421875 & 5920.63623046875 & 5919.71630859375\\\\ \n5881.67626953125 & 5880.43603515625 & 5878.79638671875 & 5877.83642578125 & 5877.75634765625 & 5878.43603515625 & 5878.9560546875 & 5880.1962890625 & 5882.1162109375 & 5881.916015625 & 5882.79638671875 & 5882.51611328125 & 5882.71630859375 & 5881.9560546875 & 5882.15625 & 5882.75634765625 & 5882.3564453125 & 5882.71630859375 & 5881.87646484375 & 5883.47607421875 & 5884.83642578125 & 5887.47607421875 & 5887.396484375 & 5889.236328125 & 5885.31640625 & 5881.59619140625 & 5883.1162109375 & 5884.396484375 & 5885.2763671875 & 5885.87646484375 & 5886.1962890625 & 5886.87646484375 & 5887.47607421875 & 5888.0361328125 & 5889.1162109375 & 5890.31640625 & 5890.63623046875 & 5892.3564453125 & 5893.59619140625 & 5894.9560546875 & 5896.63623046875 & 5897.99609375 & 5898.9560546875 & 5899.3564453125 & 5899.9560546875 & 5900.43603515625 & 5900.396484375 & 5900.47607421875 & 5900.396484375 & 5900.59619140625 & 5901.1162109375 & 5901.87646484375 & 5902.59619140625 & 5903.51611328125 & 5904.2763671875 & 5905.31640625 & 5905.75634765625 & 5906.47607421875 & 5907.076171875 & 5907.51611328125 & 5908.236328125 & 5908.87646484375 & 5909.31640625 & 5909.55615234375 & 5909.43603515625 & 5909.15625 & 5908.916015625 & 5908.99609375 & 5909.3564453125 & 5909.67626953125 & 5909.9560546875 & 5910.15625 & 5910.31640625 & 5910.43603515625 & 5910.59619140625 & 5910.67626953125 & 5910.75634765625 & 5911.076171875 & 5911.31640625 & 5911.236328125 & 5911.1962890625 & 5910.71630859375 & 5910.51611328125 & 5910.076171875 & 5909.79638671875 & 5909.55615234375 & 5909.43603515625 & 5909.396484375 & 5909.43603515625 & 5909.51611328125 & 5909.87646484375 & 5910.63623046875 & 5911.87646484375 & 5913.1162109375 & 5914.1162109375 & 5915.1962890625 & 5916.076171875 & 5916.83642578125 & 5917.43603515625 & 5918.0361328125 & 5918.916015625 & 5919.59619140625 & 5919.9560546875 & 5920.3564453125 & 5920.63623046875 & 5920.79638671875 & 5921.1962890625 & 5921.67626953125 & 5921.79638671875 & 5922.1962890625 & 5922.43603515625 & 5922.396484375 & 5922.3564453125 & 5922.47607421875 & 5922.63623046875 & 5922.87646484375 & 5923.0361328125 & 5923.1162109375 & 5922.916015625 & 5922.59619140625 & 5922.2763671875 & 5921.9560546875 & 5921.71630859375 & 5921.51611328125 & 5921.236328125 & 5920.916015625 & 5920.43603515625 & 5919.79638671875 & 5919.076171875 & 5918.3564453125 & 5917.63623046875\\\\ \n5887.43603515625 & 5880.71630859375 & 5878.83642578125 & 5877.75634765625 & 5877.9560546875 & 5878.3564453125 & 5879.396484375 & 5880.51611328125 & 5884.916015625 & 5883.0361328125 & 5882.3564453125 & 5883.71630859375 & 5882.1962890625 & 5881.3564453125 & 5881.1962890625 & 5880.67626953125 & 5881.51611328125 & 5882.3564453125 & 5882.916015625 & 5885.31640625 & 5885.71630859375 & 5885.916015625 & 5888.63623046875 & 5888.83642578125 & 5886.43603515625 & 5885.396484375 & 5885.236328125 & 5885.51611328125 & 5885.916015625 & 5886.236328125 & 5886.2763671875 & 5886.75634765625 & 5887.916015625 & 5888.51611328125 & 5889.59619140625 & 5890.83642578125 & 5892.0361328125 & 5893.1962890625 & 5894.59619140625 & 5896.1962890625 & 5898.1162109375 & 5899.63623046875 & 5900.71630859375 & 5901.43603515625 & 5901.916015625 & 5902.236328125 & 5902.2763671875 & 5902.3564453125 & 5902.43603515625 & 5902.83642578125 & 5902.9560546875 & 5903.75634765625 & 5904.1162109375 & 5904.47607421875 & 5905.076171875 & 5905.87646484375 & 5906.51611328125 & 5907.236328125 & 5907.59619140625 & 5908.1962890625 & 5908.67626953125 & 5909.236328125 & 5909.0361328125 & 5908.83642578125 & 5908.396484375 & 5908.0361328125 & 5907.9560546875 & 5908.31640625 & 5908.71630859375 & 5908.99609375 & 5909.1962890625 & 5909.47607421875 & 5909.71630859375 & 5909.75634765625 & 5909.79638671875 & 5909.63623046875 & 5909.63623046875 & 5909.99609375 & 5910.1962890625 & 5909.99609375 & 5909.87646484375 & 5910.59619140625 & 5910.0361328125 & 5909.43603515625 & 5909.076171875 & 5908.87646484375 & 5908.67626953125 & 5908.51611328125 & 5908.43603515625 & 5908.47607421875 & 5908.83642578125 & 5909.67626953125 & 5910.87646484375 & 5911.75634765625 & 5912.59619140625 & 5913.47607421875 & 5914.076171875 & 5914.59619140625 & 5914.9560546875 & 5915.63623046875 & 5916.3564453125 & 5916.71630859375 & 5916.9560546875 & 5917.236328125 & 5917.396484375 & 5917.55615234375 & 5917.83642578125 & 5918.3564453125 & 5918.51611328125 & 5918.67626953125 & 5918.9560546875 & 5918.9560546875 & 5918.9560546875 & 5919.1162109375 & 5919.55615234375 & 5920.0361328125 & 5920.2763671875 & 5920.47607421875 & 5920.43603515625 & 5920.2763671875 & 5919.99609375 & 5919.55615234375 & 5918.99609375 & 5918.55615234375 & 5918.236328125 & 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5904.3564453125 & 5904.87646484375 & 5905.3564453125 & 5905.83642578125 & 5905.75634765625 & 5905.83642578125 & 5906.55615234375 & 5907.55615234375 & 5907.51611328125 & 5907.83642578125 & 5908.47607421875 & 5908.67626953125 & 5908.43603515625 & 5908.1162109375 & 5907.75634765625 & 5907.396484375 & 5907.1162109375 & 5907.1962890625 & 5907.43603515625 & 5907.71630859375 & 5907.916015625 & 5908.0361328125 & 5908.2763671875 & 5908.47607421875 & 5908.396484375 & 5908.15625 & 5907.99609375 & 5908.236328125 & 5908.67626953125 & 5908.55615234375 & 5908.75634765625 & 5908.87646484375 & 5909.2763671875 & 5908.99609375 & 5908.51611328125 & 5908.1162109375 & 5907.83642578125 & 5907.59619140625 & 5907.3564453125 & 5907.236328125 & 5907.31640625 & 5907.71630859375 & 5908.63623046875 & 5909.51611328125 & 5910.15625 & 5910.916015625 & 5911.47607421875 & 5911.87646484375 & 5912.0361328125 & 5912.1962890625 & 5912.87646484375 & 5913.1162109375 & 5913.0361328125 & 5913.1962890625 & 5913.43603515625 & 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5907.0361328125 & 5906.83642578125 & 5906.51611328125 & 5906.1962890625 & 5905.9560546875 & 5905.87646484375 & 5906.076171875 & 5906.63623046875 & 5907.3564453125 & 5907.916015625 & 5908.51611328125 & 5909.0361328125 & 5909.236328125 & 5909.1962890625 & 5909.0361328125 & 5909.1962890625 & 5909.3564453125 & 5908.9560546875 & 5908.51611328125 & 5908.63623046875 & 5908.916015625 & 5909.15625 & 5909.59619140625 & 5910.2763671875 & 5911.0361328125 & 5911.63623046875 & 5911.75634765625 & 5911.99609375 & 5912.1962890625 & 5912.1162109375 & 5912.2763671875 & 5912.83642578125 & 5913.9560546875 & 5914.916015625 & 5915.2763671875 & 5915.31640625 & 5915.3564453125 & 5915.31640625 & 5914.99609375 & 5914.55615234375 & 5914.0361328125 & 5913.31640625 & 5912.51611328125 & 5911.87646484375 & 5911.55615234375 & 5911.3564453125 & 5911.076171875 & 5910.67626953125\\\\ \n5887.15625 & 5885.67626953125 & 5883.9560546875 & 5882.59619140625 & 5882.1962890625 & 5882.51611328125 & 5882.63623046875 & 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5905.59619140625 & 5905.31640625 & 5905.55615234375 & 5905.396484375 & 5905.51611328125 & 5905.63623046875 & 5905.67626953125 & 5905.71630859375 & 5905.63623046875 & 5905.55615234375 & 5905.43603515625 & 5905.396484375 & 5905.3564453125 & 5905.31640625 & 5905.396484375 & 5905.55615234375 & 5905.71630859375 & 5905.75634765625 & 5905.67626953125 & 5905.55615234375 & 5905.71630859375 & 5905.47607421875 & 5905.0361328125 & 5904.71630859375 & 5904.47607421875 & 5904.51611328125 & 5904.83642578125 & 5905.3564453125 & 5905.79638671875 & 5906.236328125 & 5906.63623046875 & 5906.79638671875 & 5906.51611328125 & 5906.0361328125 & 5905.67626953125 & 5905.43603515625 & 5904.83642578125 & 5903.9560546875 & 5903.51611328125 & 5903.67626953125 & 5903.87646484375 & 5904.1162109375 & 5904.916015625 & 5905.916015625 & 5906.916015625 & 5907.9560546875 & 5908.43603515625 & 5908.63623046875 & 5908.99609375 & 5909.236328125 & 5909.396484375 & 5909.71630859375 & 5910.75634765625 & 5911.99609375 & 5912.63623046875 & 5912.75634765625 & 5912.83642578125 & 5912.99609375 & 5912.9560546875 & 5912.59619140625 & 5912.1162109375 & 5911.55615234375 & 5910.83642578125 & 5909.99609375 & 5909.236328125 & 5908.75634765625 & 5908.47607421875 & 5908.31640625\\\\ \n5889.75634765625 & 5888.43603515625 & 5886.79638671875 & 5885.59619140625 & 5885.31640625 & 5885.31640625 & 5884.9560546875 & 5884.63623046875 & 5884.63623046875 & 5885.0361328125 & 5888.15625 & 5883.1962890625 & 5883.63623046875 & 5883.87646484375 & 5883.63623046875 & 5885.1962890625 & 5887.916015625 & 5888.99609375 & 5890.396484375 & 5890.31640625 & 5890.75634765625 & 5890.83642578125 & 5890.51611328125 & 5891.43603515625 & 5890.79638671875 & 5890.47607421875 & 5889.396484375 & 5887.51611328125 & 5885.31640625 & 5883.83642578125 & 5883.9560546875 & 5884.55615234375 & 5884.99609375 & 5887.0361328125 & 5891.076171875 & 5893.47607421875 & 5895.83642578125 & 5897.916015625 & 5900.0361328125 & 5901.51611328125 & 5903.0361328125 & 5904.2763671875 & 5905.59619140625 & 5906.15625 & 5906.31640625 & 5906.1162109375 & 5906.1162109375 & 5907.31640625 & 5907.43603515625 & 5907.1962890625 & 5907.076171875 & 5907.15625 & 5907.0361328125 & 5907.0361328125 & 5907.2763671875 & 5907.63623046875 & 5907.31640625 & 5906.79638671875 & 5906.2763671875 & 5905.75634765625 & 5905.076171875 & 5904.3564453125 & 5904.1162109375 & 5904.15625 & 5904.1162109375 & 5904.3564453125 & 5904.43603515625 & 5904.67626953125 & 5904.99609375 & 5905.1162109375 & 5905.15625 & 5905.2763671875 & 5905.15625 & 5904.99609375 & 5904.87646484375 & 5904.55615234375 & 5904.2763671875 & 5904.31640625 & 5904.396484375 & 5904.31640625 & 5904.15625 & 5904.1962890625 & 5904.3564453125 & 5904.3564453125 & 5904.076171875 & 5903.59619140625 & 5903.31640625 & 5903.236328125 & 5903.2763671875 & 5903.55615234375 & 5903.87646484375 & 5904.15625 & 5904.396484375 & 5904.43603515625 & 5904.0361328125 & 5903.1962890625 & 5902.396484375 & 5901.71630859375 & 5900.75634765625 & 5899.59619140625 & 5898.67626953125 & 5898.3564453125 & 5898.47607421875 & 5898.67626953125 & 5899.2763671875 & 5900.31640625 & 5901.51611328125 & 5902.71630859375 & 5903.9560546875 & 5905.0361328125 & 5905.63623046875 & 5906.076171875 & 5906.71630859375 & 5907.15625 & 5907.31640625 & 5907.9560546875 & 5908.99609375 & 5909.83642578125 & 5910.31640625 & 5910.47607421875 & 5910.67626953125 & 5910.83642578125 & 5910.79638671875 & 5910.47607421875 & 5910.0361328125 & 5909.47607421875 & 5908.79638671875 & 5908.0361328125 & 5907.236328125 & 5906.63623046875 & 5906.2763671875\\\\ \n5891.59619140625 & 5890.43603515625 & 5889.2763671875 & 5888.2763671875 & 5887.55615234375 & 5887.1162109375 & 5886.79638671875 & 5886.3564453125 & 5886.1962890625 & 5886.076171875 & 5886.31640625 & 5886.63623046875 & 5885.076171875 & 5885.3564453125 & 5886.1162109375 & 5886.236328125 & 5888.0361328125 & 5889.3564453125 & 5890.396484375 & 5891.31640625 & 5891.916015625 & 5892.2763671875 & 5892.396484375 & 5892.1962890625 & 5891.396484375 & 5890.67626953125 & 5890.15625 & 5889.236328125 & 5887.47607421875 & 5885.9560546875 & 5885.67626953125 & 5886.076171875 & 5886.87646484375 & 5889.3564453125 & 5893.0361328125 & 5894.75634765625 & 5896.67626953125 & 5898.83642578125 & 5901.0361328125 & 5902.31640625 & 5903.47607421875 & 5904.99609375 & 5906.31640625 & 5906.75634765625 & 5906.63623046875 & 5906.51611328125 & 5906.79638671875 & 5907.67626953125 & 5907.63623046875 & 5907.236328125 & 5907.0361328125 & 5906.9560546875 & 5906.67626953125 & 5906.3564453125 & 5906.31640625 & 5907.236328125 & 5906.2763671875 & 5905.9560546875 & 5905.71630859375 & 5904.2763671875 & 5903.236328125 & 5903.0361328125 & 5902.9560546875 & 5902.99609375 & 5902.916015625 & 5903.1962890625 & 5903.75634765625 & 5903.75634765625 & 5904.1962890625 & 5904.63623046875 & 5904.59619140625 & 5904.59619140625 & 5904.43603515625 & 5904.43603515625 & 5904.43603515625 & 5904.0361328125 & 5903.67626953125 & 5903.47607421875 & 5903.076171875 & 5903.076171875 & 5903.0361328125 & 5903.15625 & 5903.2763671875 & 5902.916015625 & 5902.59619140625 & 5902.2763671875 & 5902.076171875 & 5901.99609375 & 5901.9560546875 & 5902.15625 & 5902.31640625 & 5902.31640625 & 5902.1162109375 & 5901.67626953125 & 5900.71630859375 & 5899.43603515625 & 5898.3564453125 & 5897.15625 & 5895.75634765625 & 5894.31640625 & 5893.47607421875 & 5893.1162109375 & 5892.916015625 & 5893.236328125 & 5894.076171875 & 5895.396484375 & 5896.916015625 & 5898.51611328125 & 5900.0361328125 & 5901.47607421875 & 5902.55615234375 & 5903.2763671875 & 5904.0361328125 & 5904.67626953125 & 5905.1162109375 & 5905.75634765625 & 5906.3564453125 & 5907.076171875 & 5907.71630859375 & 5908.2763671875 & 5908.67626953125 & 5908.87646484375 & 5908.916015625 & 5908.83642578125 & 5908.63623046875 & 5908.2763671875 & 5907.75634765625 & 5907.1962890625 & 5906.59619140625 & 5905.99609375 & 5905.47607421875\\\\ \n5892.87646484375 & 5891.83642578125 & 5890.83642578125 & 5889.63623046875 & 5888.71630859375 & 5888.47607421875 & 5888.0361328125 & 5887.47607421875 & 5887.236328125 & 5886.916015625 & 5887.3564453125 & 5886.79638671875 & 5886.236328125 & 5887.55615234375 & 5887.396484375 & 5887.51611328125 & 5889.47607421875 & 5890.75634765625 & 5891.1162109375 & 5891.83642578125 & 5893.1962890625 & 5893.1162109375 & 5893.076171875 & 5893.916015625 & 5892.55615234375 & 5891.236328125 & 5890.75634765625 & 5890.3564453125 & 5889.396484375 & 5889.15625 & 5888.79638671875 & 5888.47607421875 & 5889.1162109375 & 5891.51611328125 & 5894.2763671875 & 5895.87646484375 & 5897.3564453125 & 5899.75634765625 & 5901.916015625 & 5903.0361328125 & 5903.87646484375 & 5905.1962890625 & 5906.55615234375 & 5906.916015625 & 5906.79638671875 & 5907.3564453125 & 5907.59619140625 & 5907.79638671875 & 5907.47607421875 & 5907.076171875 & 5906.83642578125 & 5906.63623046875 & 5906.15625 & 5905.63623046875 & 5905.396484375 & 5904.83642578125 & 5905.15625 & 5904.83642578125 & 5904.15625 & 5902.75634765625 & 5901.71630859375 & 5901.71630859375 & 5901.87646484375 & 5901.916015625 & 5902.0361328125 & 5902.43603515625 & 5903.1162109375 & 5903.236328125 & 5902.9560546875 & 5904.79638671875 & 5904.79638671875 & 5903.3564453125 & 5903.75634765625 & 5903.55615234375 & 5903.47607421875 & 5903.31640625 & 5903.076171875 & 5902.67626953125 & 5902.076171875 & 5901.99609375 & 5902.236328125 & 5902.236328125 & 5901.916015625 & 5901.59619140625 & 5901.3564453125 & 5901.1162109375 & 5900.916015625 & 5900.79638671875 & 5900.75634765625 & 5900.83642578125 & 5900.71630859375 & 5900.1962890625 & 5899.43603515625 & 5898.43603515625 & 5896.9560546875 & 5895.396484375 & 5893.87646484375 & 5892.15625 & 5890.43603515625 & 5888.83642578125 & 5887.75634765625 & 5886.99609375 & 5886.55615234375 & 5887.076171875 & 5888.31640625 & 5890.076171875 & 5892.0361328125 & 5893.87646484375 & 5895.71630859375 & 5897.55615234375 & 5899.1162109375 & 5900.31640625 & 5901.3564453125 & 5902.1962890625 & 5902.83642578125 & 5903.79638671875 & 5904.15625 & 5904.51611328125 & 5905.1962890625 & 5905.916015625 & 5906.63623046875 & 5907.0361328125 & 5907.1962890625 & 5907.2763671875 & 5907.236328125 & 5907.076171875 & 5906.79638671875 & 5906.31640625 & 5905.79638671875 & 5905.3564453125 & 5904.99609375\\\\ \n5893.43603515625 & 5892.55615234375 & 5891.63623046875 & 5890.71630859375 & 5890.076171875 & 5889.916015625 & 5889.31640625 & 5888.63623046875 & 5888.43603515625 & 5888.31640625 & 5888.47607421875 & 5887.99609375 & 5887.43603515625 & 5886.71630859375 & 5887.9560546875 & 5888.67626953125 & 5890.63623046875 & 5892.1962890625 & 5892.1962890625 & 5892.67626953125 & 5894.15625 & 5893.99609375 & 5894.47607421875 & 5893.83642578125 & 5893.9560546875 & 5893.63623046875 & 5892.87646484375 & 5892.1962890625 & 5891.87646484375 & 5891.79638671875 & 5891.43603515625 & 5891.43603515625 & 5891.71630859375 & 5892.51611328125 & 5894.79638671875 & 5896.43603515625 & 5898.43603515625 & 5900.99609375 & 5902.79638671875 & 5903.59619140625 & 5904.396484375 & 5905.43603515625 & 5906.47607421875 & 5907.1162109375 & 5907.43603515625 & 5907.59619140625 & 5907.79638671875 & 5907.59619140625 & 5907.1962890625 & 5906.75634765625 & 5906.43603515625 & 5906.076171875 & 5905.59619140625 & 5904.99609375 & 5904.47607421875 & 5903.9560546875 & 5903.396484375 & 5902.916015625 & 5901.9560546875 & 5901.0361328125 & 5899.9560546875 & 5900.0361328125 & 5900.51611328125 & 5900.59619140625 & 5900.9560546875 & 5901.75634765625 & 5904.43603515625 & 5903.51611328125 & 5903.1962890625 & 5904.3564453125 & 5903.59619140625 & 5902.59619140625 & 5902.75634765625 & 5902.396484375 & 5901.75634765625 & 5902.1162109375 & 5902.87646484375 & 5901.75634765625 & 5900.87646484375 & 5901.236328125 & 5901.236328125 & 5900.79638671875 & 5900.63623046875 & 5900.59619140625 & 5900.3564453125 & 5899.9560546875 & 5899.59619140625 & 5899.43603515625 & 5899.396484375 & 5899.236328125 & 5898.83642578125 & 5897.75634765625 & 5896.396484375 & 5894.83642578125 & 5892.87646484375 & 5891.0361328125 & 5889.1162109375 & 5886.9560546875 & 5884.916015625 & 5882.9560546875 & 5881.0361328125 & 5879.59619140625 & 5879.1962890625 & 5879.83642578125 & 5881.2763671875 & 5883.396484375 & 5885.916015625 & 5888.55615234375 & 5890.71630859375 & 5892.916015625 & 5895.1162109375 & 5896.99609375 & 5898.59619140625 & 5899.67626953125 & 5900.55615234375 & 5901.47607421875 & 5902.236328125 & 5902.396484375 & 5902.9560546875 & 5903.63623046875 & 5904.31640625 & 5904.99609375 & 5905.43603515625 & 5905.67626953125 & 5905.79638671875 & 5905.79638671875 & 5905.63623046875 & 5905.31640625 & 5904.916015625 & 5904.55615234375 & 5904.236328125\\\\ \n5893.99609375 & 5893.3564453125 & 5892.67626953125 & 5892.0361328125 & 5891.47607421875 & 5891.1162109375 & 5890.43603515625 & 5889.75634765625 & 5889.43603515625 & 5888.9560546875 & 5888.47607421875 & 5886.83642578125 & 5886.83642578125 & 5887.076171875 & 5887.63623046875 & 5888.076171875 & 5890.2763671875 & 5890.67626953125 & 5892.31640625 & 5893.916015625 & 5895.15625 & 5897.47607421875 & 5895.71630859375 & 5895.0361328125 & 5896.51611328125 & 5895.1962890625 & 5894.67626953125 & 5894.2763671875 & 5894.31640625 & 5893.99609375 & 5893.59619140625 & 5893.916015625 & 5894.43603515625 & 5895.076171875 & 5896.75634765625 & 5898.43603515625 & 5900.2763671875 & 5902.15625 & 5903.59619140625 & 5904.47607421875 & 5905.396484375 & 5905.3564453125 & 5906.396484375 & 5907.0361328125 & 5907.3564453125 & 5907.51611328125 & 5907.55615234375 & 5907.396484375 & 5906.916015625 & 5906.3564453125 & 5905.9560546875 & 5905.55615234375 & 5905.076171875 & 5904.3564453125 & 5903.51611328125 & 5902.67626953125 & 5902.15625 & 5901.15625 & 5900.2763671875 & 5899.59619140625 & 5898.3564453125 & 5898.31640625 & 5899.396484375 & 5899.63623046875 & 5899.916015625 & 5900.55615234375 & 5901.1162109375 & 5901.2763671875 & 5901.51611328125 & 5901.83642578125 & 5902.1962890625 & 5902.43603515625 & 5902.236328125 & 5901.75634765625 & 5900.63623046875 & 5902.67626953125 & 5902.15625 & 5900.396484375 & 5899.0361328125 & 5902.076171875 & 5899.1962890625 & 5899.43603515625 & 5899.43603515625 & 5899.51611328125 & 5899.47607421875 & 5898.83642578125 & 5898.236328125 & 5898.076171875 & 5897.916015625 & 5897.43603515625 & 5896.75634765625 & 5895.1962890625 & 5893.43603515625 & 5891.3564453125 & 5888.9560546875 & 5886.87646484375 & 5884.47607421875 & 5881.916015625 & 5879.15625 & 5875.87646484375 & 5872.2763671875 & 5870.3564453125 & 5870.3564453125 & 5871.79638671875 & 5873.59619140625 & 5875.75634765625 & 5878.79638671875 & 5882.59619140625 & 5886.076171875 & 5888.75634765625 & 5891.3564453125 & 5893.9560546875 & 5896.15625 & 5897.63623046875 & 5898.59619140625 & 5899.15625 & 5899.87646484375 & 5900.75634765625 & 5901.1162109375 & 5901.59619140625 & 5902.1962890625 & 5902.916015625 & 5903.43603515625 & 5903.79638671875 & 5904.0361328125 & 5904.236328125 & 5904.236328125 & 5904.0361328125 & 5903.59619140625 & 5903.2763671875 & 5903.076171875\\\\ \n5894.43603515625 & 5893.916015625 & 5893.2763671875 & 5892.75634765625 & 5892.31640625 & 5891.67626953125 & 5890.916015625 & 5890.1962890625 & 5889.63623046875 & 5889.076171875 & 5888.396484375 & 5887.79638671875 & 5887.31640625 & 5886.3564453125 & 5887.0361328125 & 5888.236328125 & 5890.3564453125 & 5891.31640625 & 5893.916015625 & 5895.31640625 & 5896.59619140625 & 5897.1962890625 & 5897.1162109375 & 5896.1162109375 & 5897.47607421875 & 5897.236328125 & 5896.99609375 & 5895.71630859375 & 5896.1162109375 & 5895.71630859375 & 5895.47607421875 & 5895.396484375 & 5896.31640625 & 5897.59619140625 & 5899.15625 & 5900.3564453125 & 5901.79638671875 & 5903.1962890625 & 5904.236328125 & 5905.0361328125 & 5904.9560546875 & 5905.55615234375 & 5906.2763671875 & 5907.0361328125 & 5907.236328125 & 5907.236328125 & 5907.236328125 & 5907.076171875 & 5906.55615234375 & 5905.87646484375 & 5905.396484375 & 5904.99609375 & 5904.51611328125 & 5903.75634765625 & 5902.71630859375 & 5901.75634765625 & 5900.916015625 & 5899.9560546875 & 5899.3564453125 & 5899.0361328125 & 5898.55615234375 & 5898.47607421875 & 5898.916015625 & 5898.99609375 & 5899.1962890625 & 5899.71630859375 & 5899.99609375 & 5900.1962890625 & 5900.83642578125 & 5901.1162109375 & 5901.51611328125 & 5901.71630859375 & 5901.87646484375 & 5901.236328125 & 5900.87646484375 & 5902.87646484375 & 5900.59619140625 & 5898.43603515625 & 5902.79638671875 & 5899.59619140625 & 5898.87646484375 & 5898.1962890625 & 5898.1962890625 & 5898.31640625 & 5898.3564453125 & 5897.51611328125 & 5896.75634765625 & 5896.51611328125 & 5896.1962890625 & 5895.59619140625 & 5894.51611328125 & 5892.67626953125 & 5890.47607421875 & 5887.83642578125 & 5885.1162109375 & 5882.67626953125 & 5879.916015625 & 5876.99609375 & 5872.43603515625 & 5866.55615234375 & 5862.71630859375 & 5861.3564453125 & 5860.83642578125 & 5862.2763671875 & 5865.55615234375 & 5868.9560546875 & 5872.55615234375 & 5876.55615234375 & 5881.43603515625 & 5885.31640625 & 5888.0361328125 & 5890.71630859375 & 5893.2763671875 & 5895.236328125 & 5896.51611328125 & 5897.3564453125 & 5897.83642578125 & 5898.83642578125 & 5899.396484375 & 5899.83642578125 & 5900.31640625 & 5900.9560546875 & 5901.59619140625 & 5901.99609375 & 5902.2763671875 & 5902.43603515625 & 5902.55615234375 & 5902.51611328125 & 5902.1962890625 & 5901.79638671875 & 5901.51611328125\\\\ \n5894.2763671875 & 5893.87646484375 & 5893.47607421875 & 5893.0361328125 & 5892.51611328125 & 5891.75634765625 & 5890.99609375 & 5890.43603515625 & 5890.0361328125 & 5889.1162109375 & 5888.83642578125 & 5888.2763671875 & 5887.0361328125 & 5887.55615234375 & 5887.43603515625 & 5889.916015625 & 5891.1962890625 & 5892.79638671875 & 5893.916015625 & 5895.51611328125 & 5896.076171875 & 5898.15625 & 5898.1162109375 & 5898.236328125 & 5897.75634765625 & 5896.59619140625 & 5898.2763671875 & 5898.1962890625 & 5897.47607421875 & 5896.75634765625 & 5896.0361328125 & 5897.2763671875 & 5897.916015625 & 5899.31640625 & 5900.55615234375 & 5901.51611328125 & 5902.75634765625 & 5904.076171875 & 5904.83642578125 & 5905.31640625 & 5905.67626953125 & 5905.63623046875 & 5906.236328125 & 5907.0361328125 & 5907.076171875 & 5906.9560546875 & 5906.9560546875 & 5906.67626953125 & 5906.15625 & 5905.31640625 & 5904.71630859375 & 5904.396484375 & 5903.99609375 & 5903.31640625 & 5902.3564453125 & 5901.2763671875 & 5900.43603515625 & 5899.63623046875 & 5899.0361328125 & 5898.75634765625 & 5898.67626953125 & 5898.59619140625 & 5898.3564453125 & 5898.51611328125 & 5898.1162109375 & 5898.31640625 & 5898.9560546875 & 5900.31640625 & 5900.236328125 & 5900.43603515625 & 5900.79638671875 & 5900.916015625 & 5902.51611328125 & 5901.2763671875 & 5899.31640625 & 5899.63623046875 & 5896.396484375 & 5896.83642578125 & 5899.47607421875 & 5897.236328125 & 5896.71630859375 & 5897.0361328125 & 5896.916015625 & 5897.15625 & 5897.076171875 & 5896.396484375 & 5895.1962890625 & 5894.076171875 & 5894.0361328125 & 5893.43603515625 & 5892.3564453125 & 5890.31640625 & 5887.51611328125 & 5884.51611328125 & 5881.75634765625 & 5879.076171875 & 5875.87646484375 & 5871.55615234375 & 5864.59619140625 & 5857.47607421875 & 5851.396484375 & 5845.15625 & 5845.9560546875 & 5852.236328125 & 5858.15625 & 5862.31640625 & 5866.75634765625 & 5871.63623046875 & 5876.51611328125 & 5881.63623046875 & 5885.1962890625 & 5887.99609375 & 5890.55615234375 & 5892.79638671875 & 5894.55615234375 & 5895.67626953125 & 5896.236328125 & 5896.916015625 & 5898.0361328125 & 5898.396484375 & 5898.47607421875 & 5898.87646484375 & 5899.51611328125 & 5900.076171875 & 5900.51611328125 & 5900.79638671875 & 5900.87646484375 & 5900.87646484375 & 5900.67626953125 & 5900.2763671875 & 5899.916015625\\\\ \n5893.99609375 & 5893.47607421875 & 5892.9560546875 & 5892.47607421875 & 5892.076171875 & 5891.71630859375 & 5891.1962890625 & 5890.87646484375 & 5890.2763671875 & 5889.3564453125 & 5888.9560546875 & 5888.1962890625 & 5887.236328125 & 5887.83642578125 & 5888.396484375 & 5890.87646484375 & 5891.87646484375 & 5892.71630859375 & 5893.9560546875 & 5896.076171875 & 5896.396484375 & 5897.79638671875 & 5898.55615234375 & 5898.79638671875 & 5898.67626953125 & 5898.31640625 & 5895.99609375 & 5897.1962890625 & 5898.1162109375 & 5898.1162109375 & 5897.75634765625 & 5898.59619140625 & 5899.236328125 & 5900.1962890625 & 5901.236328125 & 5902.1962890625 & 5903.43603515625 & 5904.51611328125 & 5905.1162109375 & 5905.31640625 & 5905.396484375 & 5905.87646484375 & 5906.31640625 & 5906.9560546875 & 5907.15625 & 5906.87646484375 & 5906.67626953125 & 5906.3564453125 & 5905.79638671875 & 5904.75634765625 & 5903.9560546875 & 5903.71630859375 & 5903.31640625 & 5902.71630859375 & 5901.87646484375 & 5900.75634765625 & 5899.79638671875 & 5899.0361328125 & 5898.55615234375 & 5898.43603515625 & 5898.31640625 & 5897.99609375 & 5897.916015625 & 5898.31640625 & 5898.0361328125 & 5900.15625 & 5898.1162109375 & 5901.3564453125 & 5899.55615234375 & 5899.71630859375 & 5899.916015625 & 5900.3564453125 & 5900.43603515625 & 5900.396484375 & 5899.55615234375 & 5899.75634765625 & 5899.396484375 & 5897.59619140625 & 5896.47607421875 & 5896.916015625 & 5896.1962890625 & 5895.75634765625 & 5895.71630859375 & 5895.75634765625 & 5895.63623046875 & 5895.31640625 & 5892.47607421875 & 5891.31640625 & 5892.43603515625 & 5891.51611328125 & 5890.1162109375 & 5888.1162109375 & 5885.15625 & 5881.87646484375 & 5878.99609375 & 5876.076171875 & 5872.31640625 & 5866.43603515625 & 5857.2763671875 & 5846.71630859375 & 5830.99609375 & 5816.9560546875 & 5800.396484375 & 5810.396484375 & 5836.43603515625 & 5853.63623046875 & 5861.75634765625 & 5868.1162109375 & 5873.15625 & 5878.236328125 & 5882.67626953125 & 5885.83642578125 & 5888.87646484375 & 5891.1962890625 & 5893.076171875 & 5894.396484375 & 5895.1962890625 & 5895.87646484375 & 5896.67626953125 & 5896.916015625 & 5896.83642578125 & 5896.87646484375 & 5897.076171875 & 5897.79638671875 & 5898.47607421875 & 5898.916015625 & 5899.076171875 & 5899.1162109375 & 5898.99609375 & 5898.63623046875 & 5898.236328125\\\\ \n5893.63623046875 & 5893.15625 & 5892.15625 & 5891.63623046875 & 5891.47607421875 & 5891.71630859375 & 5891.75634765625 & 5890.79638671875 & 5889.1962890625 & 5888.71630859375 & 5888.87646484375 & 5888.75634765625 & 5887.59619140625 & 5887.076171875 & 5889.71630859375 & 5891.71630859375 & 5893.236328125 & 5894.55615234375 & 5894.83642578125 & 5897.1162109375 & 5897.0361328125 & 5898.0361328125 & 5899.3564453125 & 5897.63623046875 & 5898.47607421875 & 5898.31640625 & 5898.43603515625 & 5901.67626953125 & 5899.1962890625 & 5899.1962890625 & 5899.15625 & 5899.87646484375 & 5900.3564453125 & 5900.87646484375 & 5900.75634765625 & 5902.59619140625 & 5904.51611328125 & 5904.55615234375 & 5905.076171875 & 5905.43603515625 & 5905.31640625 & 5905.71630859375 & 5906.79638671875 & 5907.1162109375 & 5907.076171875 & 5906.75634765625 & 5906.2763671875 & 5905.87646484375 & 5905.31640625 & 5904.2763671875 & 5903.3564453125 & 5902.83642578125 & 5902.31640625 & 5901.63623046875 & 5900.79638671875 & 5899.83642578125 & 5898.9560546875 & 5898.3564453125 & 5898.15625 & 5898.15625 & 5897.99609375 & 5897.63623046875 & 5897.51611328125 & 5897.71630859375 & 5897.87646484375 & 5898.236328125 & 5898.2763671875 & 5898.47607421875 & 5898.51611328125 & 5898.71630859375 & 5898.71630859375 & 5898.87646484375 & 5899.0361328125 & 5899.0361328125 & 5898.9560546875 & 5898.83642578125 & 5897.71630859375 & 5897.3564453125 & 5896.79638671875 & 5895.75634765625 & 5895.076171875 & 5894.63623046875 & 5894.396484375 & 5894.31640625 & 5893.99609375 & 5893.15625 & 5891.916015625 & 5891.31640625 & 5890.55615234375 & 5889.236328125 & 5887.87646484375 & 5886.076171875 & 5883.47607421875 & 5880.076171875 & 5877.0361328125 & 5873.83642578125 & 5869.15625 & 5862.0361328125 & 5850.75634765625 & 5834.236328125 & 5806.1962890625 & 5730.9560546875 & 5680.15625 & 5748.59619140625 & 5808.31640625 & 5840.236328125 & 5856.396484375 & 5864.51611328125 & 5870.79638671875 & 5875.99609375 & 5880.71630859375 & 5884.31640625 & 5887.396484375 & 5889.87646484375 & 5892.0361328125 & 5893.67626953125 & 5894.15625 & 5894.59619140625 & 5895.076171875 & 5895.3564453125 & 5895.3564453125 & 5894.99609375 & 5894.99609375 & 5895.87646484375 & 5896.79638671875 & 5896.916015625 & 5897.0361328125 & 5897.1162109375 & 5897.1962890625 & 5896.99609375 & 5896.59619140625\\\\ \n5893.076171875 & 5892.396484375 & 5891.51611328125 & 5890.87646484375 & 5891.236328125 & 5891.83642578125 & 5891.75634765625 & 5891.2763671875 & 5889.67626953125 & 5889.3564453125 & 5889.9560546875 & 5889.75634765625 & 5888.83642578125 & 5890.2763671875 & 5891.67626953125 & 5892.1962890625 & 5893.59619140625 & 5894.67626953125 & 5895.63623046875 & 5896.63623046875 & 5897.3564453125 & 5898.236328125 & 5898.9560546875 & 5898.47607421875 & 5897.79638671875 & 5898.71630859375 & 5901.31640625 & 5899.1162109375 & 5901.2763671875 & 5898.59619140625 & 5899.076171875 & 5899.83642578125 & 5901.71630859375 & 5901.43603515625 & 5901.1962890625 & 5900.2763671875 & 5905.67626953125 & 5905.2763671875 & 5904.916015625 & 5904.87646484375 & 5904.79638671875 & 5905.99609375 & 5906.3564453125 & 5906.87646484375 & 5906.83642578125 & 5906.236328125 & 5905.67626953125 & 5905.31640625 & 5904.59619140625 & 5903.71630859375 & 5902.75634765625 & 5901.87646484375 & 5901.1962890625 & 5900.63623046875 & 5899.916015625 & 5898.9560546875 & 5898.0361328125 & 5897.47607421875 & 5897.3564453125 & 5897.31640625 & 5897.1962890625 & 5897.0361328125 & 5896.79638671875 & 5896.63623046875 & 5896.75634765625 & 5896.916015625 & 5897.1162109375 & 5897.1162109375 & 5897.2763671875 & 5897.47607421875 & 5897.63623046875 & 5897.87646484375 & 5898.1162109375 & 5898.076171875 & 5898.3564453125 & 5897.67626953125 & 5896.916015625 & 5896.31640625 & 5895.67626953125 & 5894.916015625 & 5894.2763671875 & 5893.67626953125 & 5893.076171875 & 5892.83642578125 & 5892.31640625 & 5891.31640625 & 5890.2763671875 & 5889.47607421875 & 5888.51611328125 & 5887.396484375 & 5885.9560546875 & 5884.236328125 & 5882.076171875 & 5878.47607421875 & 5876.15625 & 5872.51611328125 & 5867.75634765625 & 5859.67626953125 & 5846.916015625 & 5825.99609375 & 5776.87646484375 & 5606.1162109375 & 5401.1162109375 & 5648.31640625 & 5790.99609375 & 5830.63623046875 & 5850.87646484375 & 5860.99609375 & 5868.3564453125 & 5874.71630859375 & 5880.0361328125 & 5883.63623046875 & 5886.31640625 & 5888.3564453125 & 5890.15625 & 5892.076171875 & 5893.63623046875 & 5893.9560546875 & 5894.236328125 & 5894.31640625 & 5894.0361328125 & 5894.0361328125 & 5894.15625 & 5894.79638671875 & 5895.1162109375 & 5895.236328125 & 5895.1162109375 & 5895.076171875 & 5895.1162109375 & 5895.0361328125 & 5894.83642578125\\\\ \n5890.71630859375 & 5890.67626953125 & 5891.3564453125 & 5891.47607421875 & 5891.99609375 & 5892.1962890625 & 5892.236328125 & 5892.396484375 & 5891.31640625 & 5891.0361328125 & 5891.236328125 & 5890.59619140625 & 5890.51611328125 & 5891.67626953125 & 5892.47607421875 & 5892.59619140625 & 5893.87646484375 & 5894.79638671875 & 5895.71630859375 & 5896.75634765625 & 5897.75634765625 & 5898.396484375 & 5898.67626953125 & 5898.75634765625 & 5899.1162109375 & 5898.59619140625 & 5899.2763671875 & 5899.076171875 & 5897.916015625 & 5897.67626953125 & 5899.75634765625 & 5898.83642578125 & 5901.43603515625 & 5900.916015625 & 5902.63623046875 & 5902.396484375 & 5903.236328125 & 5903.0361328125 & 5904.15625 & 5904.15625 & 5904.67626953125 & 5904.916015625 & 5907.076171875 & 5905.75634765625 & 5905.79638671875 & 5905.3564453125 & 5904.99609375 & 5904.55615234375 & 5903.9560546875 & 5903.076171875 & 5902.396484375 & 5902.1162109375 & 5900.51611328125 & 5899.79638671875 & 5899.076171875 & 5898.076171875 & 5897.236328125 & 5896.63623046875 & 5896.31640625 & 5896.2763671875 & 5896.15625 & 5895.9560546875 & 5895.83642578125 & 5895.67626953125 & 5895.55615234375 & 5895.59619140625 & 5895.79638671875 & 5895.916015625 & 5896.15625 & 5896.43603515625 & 5896.67626953125 & 5896.916015625 & 5897.076171875 & 5896.87646484375 & 5896.55615234375 & 5896.1962890625 & 5895.79638671875 & 5895.31640625 & 5894.71630859375 & 5894.1962890625 & 5893.59619140625 & 5892.67626953125 & 5891.79638671875 & 5891.236328125 & 5890.67626953125 & 5889.63623046875 & 5888.51611328125 & 5887.67626953125 & 5886.71630859375 & 5885.75634765625 & 5884.43603515625 & 5882.59619140625 & 5880.43603515625 & 5877.0361328125 & 5874.83642578125 & 5871.83642578125 & 5867.396484375 & 5859.43603515625 & 5847.236328125 & 5827.87646484375 & 5785.83642578125 & 5676.15625 & 5570.31640625 & 5705.99609375 & 5798.71630859375 & 5827.3564453125 & 5850.0361328125 & 5858.9560546875 & 5866.3564453125 & 5874.0361328125 & 5879.87646484375 & 5883.2763671875 & 5885.31640625 & 5887.0361328125 & 5888.63623046875 & 5890.51611328125 & 5892.1962890625 & 5893.2763671875 & 5893.75634765625 & 5893.59619140625 & 5893.43603515625 & 5893.31640625 & 5893.0361328125 & 5893.47607421875 & 5893.79638671875 & 5893.71630859375 & 5893.51611328125 & 5893.31640625 & 5893.236328125 & 5893.15625 & 5893.0361328125\\\\ \n5890.9560546875 & 5891.79638671875 & 5892.55615234375 & 5891.79638671875 & 5892.43603515625 & 5892.916015625 & 5892.75634765625 & 5892.59619140625 & 5891.63623046875 & 5891.1962890625 & 5891.59619140625 & 5892.076171875 & 5892.0361328125 & 5892.55615234375 & 5893.15625 & 5893.71630859375 & 5894.2763671875 & 5895.236328125 & 5896.1962890625 & 5897.236328125 & 5897.87646484375 & 5898.236328125 & 5898.47607421875 & 5898.75634765625 & 5898.99609375 & 5897.99609375 & 5898.43603515625 & 5901.0361328125 & 5899.75634765625 & 5899.31640625 & 5899.9560546875 & 5900.236328125 & 5901.63623046875 & 5903.71630859375 & 5900.47607421875 & 5903.15625 & 5903.71630859375 & 5903.47607421875 & 5903.43603515625 & 5903.1962890625 & 5904.83642578125 & 5905.31640625 & 5905.0361328125 & 5904.99609375 & 5904.55615234375 & 5904.076171875 & 5904.79638671875 & 5903.59619140625 & 5903.15625 & 5902.63623046875 & 5901.63623046875 & 5901.1962890625 & 5899.9560546875 & 5898.916015625 & 5897.99609375 & 5897.1962890625 & 5896.43603515625 & 5895.83642578125 & 5895.47607421875 & 5895.43603515625 & 5895.31640625 & 5895.1162109375 & 5894.99609375 & 5894.75634765625 & 5894.67626953125 & 5894.71630859375 & 5894.99609375 & 5895.15625 & 5895.43603515625 & 5895.63623046875 & 5895.75634765625 & 5895.83642578125 & 5895.79638671875 & 5895.31640625 & 5895.1962890625 & 5894.916015625 & 5894.71630859375 & 5894.43603515625 & 5894.1162109375 & 5893.59619140625 & 5892.83642578125 & 5891.87646484375 & 5890.79638671875 & 5889.83642578125 & 5889.0361328125 & 5888.0361328125 & 5887.0361328125 & 5886.2763671875 & 5885.3564453125 & 5884.31640625 & 5883.0361328125 & 5881.2763671875 & 5878.79638671875 & 5876.2763671875 & 5873.63623046875 & 5870.55615234375 & 5867.63623046875 & 5861.396484375 & 5852.15625 & 5837.9560546875 & 5814.79638671875 & 5781.63623046875 & 5767.1162109375 & 5794.3564453125 & 5817.236328125 & 5835.9560546875 & 5852.1162109375 & 5858.236328125 & 5865.79638671875 & 5873.99609375 & 5879.75634765625 & 5882.63623046875 & 5884.1962890625 & 5885.75634765625 & 5887.51611328125 & 5889.55615234375 & 5891.1962890625 & 5892.51611328125 & 5892.83642578125 & 5892.75634765625 & 5892.076171875 & 5892.59619140625 & 5892.51611328125 & 5892.71630859375 & 5892.47607421875 & 5892.87646484375 & 5892.51611328125 & 5892.2763671875 & 5892.1162109375 & 5891.916015625 & 5891.55615234375\\\\ \n5892.55615234375 & 5892.71630859375 & 5892.87646484375 & 5893.47607421875 & 5893.396484375 & 5893.71630859375 & 5892.99609375 & 5892.396484375 & 5891.75634765625 & 5891.47607421875 & 5892.076171875 & 5892.51611328125 & 5892.75634765625 & 5893.236328125 & 5894.0361328125 & 5894.43603515625 & 5894.71630859375 & 5895.75634765625 & 5896.55615234375 & 5897.31640625 & 5897.59619140625 & 5897.916015625 & 5898.076171875 & 5898.1962890625 & 5898.0361328125 & 5897.71630859375 & 5898.1162109375 & 5898.916015625 & 5899.31640625 & 5899.076171875 & 5899.2763671875 & 5899.51611328125 & 5899.59619140625 & 5900.9560546875 & 5899.0361328125 & 5902.79638671875 & 5903.79638671875 & 5906.99609375 & 5904.47607421875 & 5902.15625 & 5903.15625 & 5904.67626953125 & 5903.75634765625 & 5903.0361328125 & 5902.9560546875 & 5902.87646484375 & 5903.43603515625 & 5902.1162109375 & 5903.47607421875 & 5901.1962890625 & 5901.0361328125 & 5900.2763671875 & 5899.31640625 & 5898.31640625 & 5897.3564453125 & 5896.59619140625 & 5895.83642578125 & 5895.1962890625 & 5894.87646484375 & 5894.75634765625 & 5894.55615234375 & 5894.3564453125 & 5894.2763671875 & 5894.1962890625 & 5894.15625 & 5894.31640625 & 5894.43603515625 & 5894.55615234375 & 5894.67626953125 & 5894.75634765625 & 5894.79638671875 & 5894.75634765625 & 5894.396484375 & 5893.9560546875 & 5894.1962890625 & 5894.1962890625 & 5893.75634765625 & 5893.55615234375 & 5893.396484375 & 5892.51611328125 & 5891.71630859375 & 5890.75634765625 & 5889.83642578125 & 5888.75634765625 & 5887.75634765625 & 5886.71630859375 & 5885.75634765625 & 5884.9560546875 & 5884.15625 & 5883.1162109375 & 5881.75634765625 & 5880.236328125 & 5878.1162109375 & 5875.59619140625 & 5872.75634765625 & 5869.79638671875 & 5867.47607421875 & 5864.15625 & 5857.1962890625 & 5847.3564453125 & 5836.51611328125 & 5827.2763671875 & 5824.43603515625 & 5826.87646484375 & 5836.916015625 & 5847.47607421875 & 5853.15625 & 5859.47607421875 & 5867.87646484375 & 5874.83642578125 & 5879.43603515625 & 5882.076171875 & 5883.236328125 & 5884.51611328125 & 5886.396484375 & 5888.47607421875 & 5890.43603515625 & 5891.71630859375 & 5892.2763671875 & 5891.87646484375 & 5892.0361328125 & 5891.99609375 & 5891.916015625 & 5891.2763671875 & 5891.31640625 & 5891.31640625 & 5891.67626953125 & 5891.59619140625 & 5891.3564453125 & 5890.99609375 & 5890.55615234375\\\\ \n5892.75634765625 & 5892.2763671875 & 5892.0361328125 & 5892.63623046875 & 5893.396484375 & 5893.83642578125 & 5893.51611328125 & 5893.2763671875 & 5892.396484375 & 5892.1962890625 & 5892.47607421875 & 5892.83642578125 & 5893.2763671875 & 5893.87646484375 & 5894.67626953125 & 5895.1962890625 & 5895.3564453125 & 5895.9560546875 & 5896.83642578125 & 5897.1962890625 & 5897.236328125 & 5897.1962890625 & 5897.236328125 & 5897.2763671875 & 5897.1162109375 & 5896.83642578125 & 5897.0361328125 & 5897.31640625 & 5897.71630859375 & 5897.99609375 & 5897.9560546875 & 5897.916015625 & 5898.396484375 & 5899.0361328125 & 5899.87646484375 & 5900.43603515625 & 5898.0361328125 & 5902.15625 & 5901.43603515625 & 5903.71630859375 & 5904.51611328125 & 5899.1962890625 & 5903.55615234375 & 5903.9560546875 & 5903.51611328125 & 5901.99609375 & 5904.75634765625 & 5900.916015625 & 5902.47607421875 & 5900.2763671875 & 5899.55615234375 & 5899.59619140625 & 5898.71630859375 & 5897.55615234375 & 5896.67626953125 & 5895.916015625 & 5895.2763671875 & 5894.75634765625 & 5894.43603515625 & 5894.1962890625 & 5893.99609375 & 5893.79638671875 & 5893.71630859375 & 5893.67626953125 & 5893.59619140625 & 5893.51611328125 & 5893.59619140625 & 5893.71630859375 & 5893.83642578125 & 5893.99609375 & 5894.0361328125 & 5893.75634765625 & 5893.3564453125 & 5892.916015625 & 5892.916015625 & 5892.9560546875 & 5892.67626953125 & 5892.2763671875 & 5891.43603515625 & 5891.3564453125 & 5890.55615234375 & 5889.67626953125 & 5888.67626953125 & 5887.55615234375 & 5886.55615234375 & 5885.55615234375 & 5884.51611328125 & 5883.51611328125 & 5882.71630859375 & 5881.916015625 & 5880.75634765625 & 5879.2763671875 & 5877.63623046875 & 5875.71630859375 & 5872.916015625 & 5869.63623046875 & 5866.31640625 & 5863.1162109375 & 5858.99609375 & 5854.1962890625 & 5848.99609375 & 5845.59619140625 & 5843.75634765625 & 5845.0361328125 & 5848.75634765625 & 5851.83642578125 & 5857.31640625 & 5864.1162109375 & 5870.87646484375 & 5875.916015625 & 5879.1162109375 & 5880.83642578125 & 5882.076171875 & 5883.79638671875 & 5885.71630859375 & 5887.63623046875 & 5889.2763671875 & 5890.55615234375 & 5891.1962890625 & 5891.3564453125 & 5891.3564453125 & 5891.236328125 & 5891.236328125 & 5890.63623046875 & 5890.15625 & 5889.83642578125 & 5890.396484375 & 5890.67626953125 & 5890.3564453125 & 5890.0361328125 & 5889.51611328125\\\\ \n5893.1962890625 & 5892.51611328125 & 5892.51611328125 & 5892.67626953125 & 5893.1962890625 & 5893.51611328125 & 5893.3564453125 & 5893.1962890625 & 5893.2763671875 & 5893.51611328125 & 5893.51611328125 & 5893.396484375 & 5893.55615234375 & 5893.87646484375 & 5894.47607421875 & 5895.3564453125 & 5895.79638671875 & 5896.43603515625 & 5897.1162109375 & 5897.0361328125 & 5896.47607421875 & 5896.0361328125 & 5896.0361328125 & 5896.15625 & 5895.99609375 & 5895.87646484375 & 5895.9560546875 & 5895.83642578125 & 5895.87646484375 & 5896.43603515625 & 5896.67626953125 & 5896.916015625 & 5897.47607421875 & 5897.9560546875 & 5898.47607421875 & 5898.71630859375 & 5899.71630859375 & 5898.75634765625 & 5899.916015625 & 5900.1162109375 & 5901.916015625 & 5901.67626953125 & 5901.076171875 & 5898.0361328125 & 5897.63623046875 & 5902.236328125 & 5899.63623046875 & 5898.99609375 & 5900.1162109375 & 5898.75634765625 & 5898.236328125 & 5897.9560546875 & 5896.9560546875 & 5896.31640625 & 5895.71630859375 & 5895.15625 & 5894.63623046875 & 5894.236328125 & 5893.916015625 & 5893.71630859375 & 5893.47607421875 & 5893.31640625 & 5893.236328125 & 5893.076171875 & 5892.87646484375 & 5892.75634765625 & 5892.67626953125 & 5892.75634765625 & 5892.87646484375 & 5892.99609375 & 5893.076171875 & 5892.87646484375 & 5892.51611328125 & 5892.1162109375 & 5892.076171875 & 5892.076171875 & 5891.71630859375 & 5891.076171875 & 5890.47607421875 & 5889.916015625 & 5889.15625 & 5888.43603515625 & 5887.47607421875 & 5886.51611328125 & 5885.55615234375 & 5884.55615234375 & 5883.396484375 & 5882.15625 & 5881.1962890625 & 5880.51611328125 & 5879.71630859375 & 5878.51611328125 & 5877.0361328125 & 5875.55615234375 & 5873.75634765625 & 5871.3564453125 & 5868.59619140625 & 5865.236328125 & 5861.99609375 & 5858.916015625 & 5855.79638671875 & 5854.1962890625 & 5853.67626953125 & 5854.47607421875 & 5856.31640625 & 5859.59619140625 & 5863.99609375 & 5868.916015625 & 5873.2763671875 & 5877.076171875 & 5878.87646484375 & 5879.51611328125 & 5880.63623046875 & 5882.67626953125 & 5884.916015625 & 5886.79638671875 & 5888.076171875 & 5889.1962890625 & 5889.71630859375 & 5890.1962890625 & 5890.2763671875 & 5889.9560546875 & 5889.79638671875 & 5889.79638671875 & 5889.63623046875 & 5889.67626953125 & 5889.71630859375 & 5889.79638671875 & 5889.43603515625 & 5888.83642578125 & 5888.15625\\\\ \n5893.236328125 & 5892.87646484375 & 5892.83642578125 & 5893.076171875 & 5893.3564453125 & 5893.2763671875 & 5893.1162109375 & 5893.31640625 & 5893.51611328125 & 5894.0361328125 & 5894.076171875 & 5893.75634765625 & 5893.55615234375 & 5893.63623046875 & 5894.15625 & 5894.87646484375 & 5895.63623046875 & 5896.15625 & 5896.59619140625 & 5896.3564453125 & 5895.2763671875 & 5894.43603515625 & 5894.71630859375 & 5894.79638671875 & 5894.51611328125 & 5894.51611328125 & 5894.59619140625 & 5894.236328125 & 5894.1962890625 & 5895.2763671875 & 5896.15625 & 5896.396484375 & 5896.67626953125 & 5896.63623046875 & 5896.9560546875 & 5897.916015625 & 5898.15625 & 5898.15625 & 5899.0361328125 & 5898.63623046875 & 5900.43603515625 & 5899.59619140625 & 5898.87646484375 & 5898.83642578125 & 5900.63623046875 & 5899.67626953125 & 5898.67626953125 & 5897.236328125 & 5896.9560546875 & 5896.71630859375 & 5896.9560546875 & 5896.71630859375 & 5895.71630859375 & 5895.1162109375 & 5894.71630859375 & 5894.2763671875 & 5893.87646484375 & 5893.55615234375 & 5893.236328125 & 5892.99609375 & 5892.75634765625 & 5892.55615234375 & 5892.59619140625 & 5892.47607421875 & 5892.2763671875 & 5891.99609375 & 5891.83642578125 & 5891.71630859375 & 5891.71630859375 & 5891.916015625 & 5891.99609375 & 5891.99609375 & 5891.67626953125 & 5891.2763671875 & 5891.396484375 & 5891.31640625 & 5890.916015625 & 5890.1962890625 & 5889.47607421875 & 5888.71630859375 & 5887.87646484375 & 5886.9560546875 & 5886.1962890625 & 5885.396484375 & 5884.59619140625 & 5883.67626953125 & 5882.71630859375 & 5881.67626953125 & 5880.71630859375 & 5879.79638671875 & 5878.99609375 & 5878.0361328125 & 5876.83642578125 & 5875.3564453125 & 5873.916015625 & 5872.59619140625 & 5870.67626953125 & 5867.916015625 & 5865.67626953125 & 5863.396484375 & 5861.396484375 & 5860.51611328125 & 5860.55615234375 & 5861.71630859375 & 5863.47607421875 & 5865.79638671875 & 5868.99609375 & 5872.55615234375 & 5875.71630859375 & 5877.71630859375 & 5878.31640625 & 5878.87646484375 & 5880.2763671875 & 5882.1962890625 & 5884.076171875 & 5885.79638671875 & 5886.75634765625 & 5887.71630859375 & 5888.63623046875 & 5888.99609375 & 5889.1962890625 & 5889.15625 & 5889.15625 & 5889.31640625 & 5888.916015625 & 5889.1162109375 & 5889.15625 & 5889.1962890625 & 5888.63623046875 & 5887.67626953125 & 5886.79638671875\\\\ \n5892.67626953125 & 5892.87646484375 & 5892.87646484375 & 5892.87646484375 & 5892.9560546875 & 5892.99609375 & 5893.31640625 & 5893.63623046875 & 5894.0361328125 & 5894.43603515625 & 5894.236328125 & 5893.71630859375 & 5893.2763671875 & 5893.2763671875 & 5893.63623046875 & 5894.15625 & 5894.67626953125 & 5895.15625 & 5895.47607421875 & 5895.1962890625 & 5893.83642578125 & 5893.0361328125 & 5893.47607421875 & 5893.3564453125 & 5893.0361328125 & 5893.0361328125 & 5892.79638671875 & 5892.076171875 & 5892.75634765625 & 5893.9560546875 & 5894.59619140625 & 5894.99609375 & 5895.2763671875 & 5895.51611328125 & 5896.15625 & 5896.59619140625 & 5896.99609375 & 5897.236328125 & 5897.43603515625 & 5897.31640625 & 5897.99609375 & 5896.916015625 & 5896.236328125 & 5899.43603515625 & 5896.916015625 & 5896.99609375 & 5895.396484375 & 5894.63623046875 & 5895.236328125 & 5896.0361328125 & 5895.83642578125 & 5895.15625 & 5894.43603515625 & 5894.1162109375 & 5893.75634765625 & 5893.43603515625 & 5893.0361328125 & 5892.63623046875 & 5892.1962890625 & 5891.916015625 & 5891.75634765625 & 5891.71630859375 & 5891.79638671875 & 5891.79638671875 & 5891.55615234375 & 5891.2763671875 & 5891.0361328125 & 5890.87646484375 & 5890.71630859375 & 5890.75634765625 & 5890.99609375 & 5891.1162109375 & 5890.99609375 & 5890.67626953125 & 5890.71630859375 & 5890.59619140625 & 5890.236328125 & 5889.63623046875 & 5888.79638671875 & 5887.99609375 & 5886.9560546875 & 5885.75634765625 & 5884.79638671875 & 5883.9560546875 & 5883.15625 & 5882.1962890625 & 5881.1962890625 & 5880.47607421875 & 5879.916015625 & 5879.3564453125 & 5878.67626953125 & 5877.87646484375 & 5876.83642578125 & 5875.63623046875 & 5874.236328125 & 5872.916015625 & 5871.71630859375 & 5870.43603515625 & 5868.79638671875 & 5867.31640625 & 5866.15625 & 5865.71630859375 & 5865.79638671875 & 5866.3564453125 & 5867.63623046875 & 5869.75634765625 & 5872.3564453125 & 5874.87646484375 & 5876.67626953125 & 5877.2763671875 & 5877.396484375 & 5877.9560546875 & 5879.51611328125 & 5881.59619140625 & 5883.1162109375 & 5884.1962890625 & 5885.396484375 & 5886.15625 & 5886.79638671875 & 5887.63623046875 & 5888.1962890625 & 5888.236328125 & 5888.51611328125 & 5888.2763671875 & 5888.236328125 & 5888.2763671875 & 5888.3564453125 & 5888.31640625 & 5887.55615234375 & 5886.55615234375 & 5885.63623046875\\\\ \n5892.87646484375 & 5892.9560546875 & 5893.0361328125 & 5892.79638671875 & 5893.0361328125 & 5893.236328125 & 5893.63623046875 & 5893.99609375 & 5894.47607421875 & 5894.83642578125 & 5894.1162109375 & 5893.3564453125 & 5892.87646484375 & 5892.75634765625 & 5892.9560546875 & 5893.31640625 & 5893.75634765625 & 5894.15625 & 5894.2763671875 & 5893.71630859375 & 5893.0361328125 & 5892.15625 & 5892.076171875 & 5891.79638671875 & 5891.71630859375 & 5891.396484375 & 5890.916015625 & 5891.15625 & 5890.916015625 & 5892.15625 & 5892.9560546875 & 5893.63623046875 & 5894.076171875 & 5894.83642578125 & 5895.51611328125 & 5896.076171875 & 5896.1162109375 & 5895.916015625 & 5895.87646484375 & 5896.0361328125 & 5895.79638671875 & 5895.71630859375 & 5895.83642578125 & 5896.1162109375 & 5895.55615234375 & 5895.59619140625 & 5894.59619140625 & 5894.51611328125 & 5892.99609375 & 5894.67626953125 & 5894.43603515625 & 5894.236328125 & 5893.916015625 & 5893.43603515625 & 5892.916015625 & 5892.47607421875 & 5892.076171875 & 5891.59619140625 & 5891.15625 & 5890.87646484375 & 5890.79638671875 & 5890.79638671875 & 5890.99609375 & 5891.15625 & 5890.9560546875 & 5890.63623046875 & 5890.2763671875 & 5890.1162109375 & 5890.0361328125 & 5890.0361328125 & 5890.15625 & 5890.31640625 & 5890.31640625 & 5890.1162109375 & 5889.99609375 & 5889.75634765625 & 5889.396484375 & 5888.99609375 & 5888.236328125 & 5887.31640625 & 5886.3564453125 & 5884.99609375 & 5883.75634765625 & 5882.83642578125 & 5881.99609375 & 5880.9560546875 & 5879.87646484375 & 5878.916015625 & 5878.15625 & 5877.63623046875 & 5877.31640625 & 5876.99609375 & 5876.43603515625 & 5875.71630859375 & 5874.79638671875 & 5873.79638671875 & 5872.79638671875 & 5871.99609375 & 5871.1162109375 & 5870.236328125 & 5869.59619140625 & 5869.2763671875 & 5869.31640625 & 5869.916015625 & 5871.1962890625 & 5872.75634765625 & 5874.3564453125 & 5875.63623046875 & 5876.1962890625 & 5876.43603515625 & 5876.79638671875 & 5877.59619140625 & 5878.83642578125 & 5880.79638671875 & 5882.15625 & 5882.9560546875 & 5884.0361328125 & 5884.51611328125 & 5884.99609375 & 5885.99609375 & 5886.67626953125 & 5887.076171875 & 5887.43603515625 & 5887.43603515625 & 5887.3564453125 & 5887.51611328125 & 5887.15625 & 5886.67626953125 & 5886.1962890625 & 5885.51611328125 & 5884.63623046875\\\\ \n5893.15625 & 5893.31640625 & 5893.0361328125 & 5892.99609375 & 5893.3564453125 & 5893.55615234375 & 5893.916015625 & 5894.076171875 & 5894.2763671875 & 5893.0361328125 & 5893.59619140625 & 5892.83642578125 & 5892.396484375 & 5892.1962890625 & 5892.31640625 & 5892.63623046875 & 5892.9560546875 & 5893.1962890625 & 5893.1162109375 & 5892.75634765625 & 5892.396484375 & 5891.71630859375 & 5890.63623046875 & 5890.51611328125 & 5890.59619140625 & 5890.15625 & 5890.0361328125 & 5890.0361328125 & 5890.55615234375 & 5891.2763671875 & 5892.15625 & 5892.67626953125 & 5893.1962890625 & 5893.9560546875 & 5894.59619140625 & 5895.59619140625 & 5895.396484375 & 5894.99609375 & 5894.55615234375 & 5894.3564453125 & 5894.3564453125 & 5894.43603515625 & 5894.47607421875 & 5894.43603515625 & 5894.31640625 & 5894.0361328125 & 5892.47607421875 & 5893.3564453125 & 5893.43603515625 & 5893.47607421875 & 5893.3564453125 & 5894.43603515625 & 5893.71630859375 & 5892.31640625 & 5892.236328125 & 5891.83642578125 & 5891.3564453125 & 5890.916015625 & 5890.59619140625 & 5890.3564453125 & 5890.3564453125 & 5890.2763671875 & 5890.3564453125 & 5890.396484375 & 5890.15625 & 5889.71630859375 & 5889.47607421875 & 5889.55615234375 & 5889.43603515625 & 5889.51611328125 & 5889.63623046875 & 5889.71630859375 & 5889.75634765625 & 5889.43603515625 & 5889.1162109375 & 5888.83642578125 & 5888.3564453125 & 5888.0361328125 & 5887.55615234375 & 5886.55615234375 & 5885.67626953125 & 5884.3564453125 & 5883.076171875 & 5882.1162109375 & 5881.236328125 & 5880.31640625 & 5879.3564453125 & 5878.51611328125 & 5877.79638671875 & 5877.0361328125 & 5876.55615234375 & 5876.2763671875 & 5876.0361328125 & 5875.63623046875 & 5875.1162109375 & 5874.51611328125 & 5873.916015625 & 5873.236328125 & 5872.59619140625 & 5872.15625 & 5871.87646484375 & 5871.83642578125 & 5872.076171875 & 5872.63623046875 & 5873.51611328125 & 5874.236328125 & 5874.55615234375 & 5874.83642578125 & 5875.59619140625 & 5876.15625 & 5876.71630859375 & 5877.15625 & 5877.9560546875 & 5879.75634765625 & 5881.43603515625 & 5881.87646484375 & 5882.55615234375 & 5882.99609375 & 5883.55615234375 & 5884.59619140625 & 5885.31640625 & 5886.0361328125 & 5886.236328125 & 5886.0361328125 & 5886.15625 & 5886.31640625 & 5885.916015625 & 5885.51611328125 & 5885.236328125 & 5884.71630859375 & 5883.9560546875\\\\ \n5892.9560546875 & 5892.9560546875 & 5892.79638671875 & 5892.79638671875 & 5893.1162109375 & 5893.076171875 & 5893.43603515625 & 5893.1962890625 & 5892.51611328125 & 5892.51611328125 & 5892.59619140625 & 5892.31640625 & 5891.79638671875 & 5891.67626953125 & 5891.71630859375 & 5891.916015625 & 5892.43603515625 & 5892.31640625 & 5892.1962890625 & 5891.87646484375 & 5891.43603515625 & 5890.75634765625 & 5890.0361328125 & 5889.59619140625 & 5889.396484375 & 5889.1162109375 & 5888.9560546875 & 5889.0361328125 & 5889.67626953125 & 5890.55615234375 & 5891.236328125 & 5892.076171875 & 5892.59619140625 & 5893.3564453125 & 5893.75634765625 & 5893.99609375 & 5894.0361328125 & 5893.87646484375 & 5893.51611328125 & 5893.396484375 & 5893.31640625 & 5893.1962890625 & 5893.1962890625 & 5893.15625 & 5893.15625 & 5892.9560546875 & 5892.83642578125 & 5891.3564453125 & 5891.55615234375 & 5891.71630859375 & 5891.71630859375 & 5890.9560546875 & 5891.1162109375 & 5890.9560546875 & 5891.1162109375 & 5891.1162109375 & 5890.79638671875 & 5890.43603515625 & 5890.3564453125 & 5890.15625 & 5890.15625 & 5890.0361328125 & 5889.75634765625 & 5889.43603515625 & 5888.9560546875 & 5888.71630859375 & 5888.71630859375 & 5888.87646484375 & 5888.63623046875 & 5888.83642578125 & 5888.99609375 & 5889.1162109375 & 5889.2763671875 & 5889.076171875 & 5888.47607421875 & 5888.1162109375 & 5887.43603515625 & 5886.83642578125 & 5886.3564453125 & 5885.87646484375 & 5885.1962890625 & 5883.67626953125 & 5882.396484375 & 5881.396484375 & 5880.51611328125 & 5879.63623046875 & 5878.75634765625 & 5877.9560546875 & 5877.51611328125 & 5877.0361328125 & 5876.59619140625 & 5876.236328125 & 5875.83642578125 & 5875.43603515625 & 5874.9560546875 & 5874.55615234375 & 5874.1162109375 & 5873.71630859375 & 5873.43603515625 & 5873.1962890625 & 5873.15625 & 5873.236328125 & 5873.43603515625 & 5873.75634765625 & 5873.99609375 & 5873.75634765625 & 5873.47607421875 & 5873.79638671875 & 5874.9560546875 & 5875.59619140625 & 5875.99609375 & 5876.43603515625 & 5877.15625 & 5879.076171875 & 5880.51611328125 & 5881.51611328125 & 5881.3564453125 & 5881.87646484375 & 5882.59619140625 & 5883.55615234375 & 5884.31640625 & 5884.916015625 & 5884.63623046875 & 5884.47607421875 & 5884.55615234375 & 5884.79638671875 & 5884.31640625 & 5883.71630859375 & 5884.15625 & 5883.79638671875 & 5882.59619140625\\\\ \n5892.916015625 & 5892.63623046875 & 5892.71630859375 & 5892.59619140625 & 5892.59619140625 & 5893.236328125 & 5892.83642578125 & 5892.076171875 & 5892.0361328125 & 5891.916015625 & 5891.83642578125 & 5891.51611328125 & 5891.0361328125 & 5891.15625 & 5891.31640625 & 5891.396484375 & 5891.47607421875 & 5891.59619140625 & 5891.3564453125 & 5890.83642578125 & 5890.3564453125 & 5889.75634765625 & 5889.236328125 & 5888.71630859375 & 5888.396484375 & 5888.1162109375 & 5888.076171875 & 5888.43603515625 & 5889.236328125 & 5889.99609375 & 5890.55615234375 & 5891.1162109375 & 5891.916015625 & 5892.396484375 & 5892.47607421875 & 5892.3564453125 & 5892.396484375 & 5892.47607421875 & 5892.47607421875 & 5892.63623046875 & 5892.51611328125 & 5892.2763671875 & 5892.076171875 & 5892.1162109375 & 5891.59619140625 & 5891.67626953125 & 5891.63623046875 & 5891.47607421875 & 5890.83642578125 & 5889.916015625 & 5888.83642578125 & 5888.1962890625 & 5888.75634765625 & 5889.1162109375 & 5889.71630859375 & 5889.71630859375 & 5889.51611328125 & 5889.396484375 & 5889.55615234375 & 5889.67626953125 & 5889.55615234375 & 5889.396484375 & 5889.0361328125 & 5888.396484375 & 5887.916015625 & 5887.79638671875 & 5887.67626953125 & 5887.43603515625 & 5887.236328125 & 5887.83642578125 & 5888.15625 & 5888.3564453125 & 5888.55615234375 & 5889.3564453125 & 5887.87646484375 & 5887.3564453125 & 5886.67626953125 & 5886.0361328125 & 5885.43603515625 & 5884.9560546875 & 5884.1962890625 & 5882.71630859375 & 5881.71630859375 & 5880.59619140625 & 5879.71630859375 & 5878.87646484375 & 5878.15625 & 5877.3564453125 & 5876.87646484375 & 5876.55615234375 & 5876.236328125 & 5875.9560546875 & 5875.67626953125 & 5875.3564453125 & 5874.87646484375 & 5874.43603515625 & 5873.9560546875 & 5873.59619140625 & 5873.31640625 & 5873.15625 & 5873.236328125 & 5873.31640625 & 5873.1962890625 & 5873.076171875 & 5872.71630859375 & 5872.1962890625 & 5872.396484375 & 5873.1962890625 & 5874.3564453125 & 5874.79638671875 & 5875.1162109375 & 5875.71630859375 & 5876.9560546875 & 5878.396484375 & 5879.1962890625 & 5879.83642578125 & 5880.2763671875 & 5880.99609375 & 5881.75634765625 & 5882.43603515625 & 5882.87646484375 & 5883.2763671875 & 5883.1962890625 & 5882.99609375 & 5882.67626953125 & 5882.87646484375 & 5882.87646484375 & 5882.51611328125 & 5882.67626953125 & 5882.51611328125 & 5881.3564453125\\\\ \n5892.51611328125 & 5892.236328125 & 5891.99609375 & 5892.31640625 & 5892.43603515625 & 5892.3564453125 & 5891.83642578125 & 5891.51611328125 & 5891.31640625 & 5891.31640625 & 5891.1962890625 & 5890.55615234375 & 5890.2763671875 & 5890.59619140625 & 5890.83642578125 & 5890.79638671875 & 5890.79638671875 & 5890.59619140625 & 5890.076171875 & 5889.71630859375 & 5889.15625 & 5888.87646484375 & 5888.51611328125 & 5888.1962890625 & 5887.79638671875 & 5887.3564453125 & 5887.396484375 & 5887.9560546875 & 5888.75634765625 & 5889.47607421875 & 5889.75634765625 & 5889.83642578125 & 5890.47607421875 & 5891.1962890625 & 5891.1962890625 & 5890.79638671875 & 5890.67626953125 & 5890.75634765625 & 5890.9560546875 & 5891.2763671875 & 5891.59619140625 & 5892.076171875 & 5892.67626953125 & 5891.71630859375 & 5890.1962890625 & 5889.83642578125 & 5890.076171875 & 5889.51611328125 & 5889.076171875 & 5888.3564453125 & 5886.15625 & 5886.99609375 & 5886.43603515625 & 5887.59619140625 & 5888.2763671875 & 5888.3564453125 & 5888.31640625 & 5888.0361328125 & 5888.396484375 & 5888.79638671875 & 5888.236328125 & 5888.1962890625 & 5888.2763671875 & 5887.67626953125 & 5887.236328125 & 5887.15625 & 5886.55615234375 & 5885.99609375 & 5885.99609375 & 5886.87646484375 & 5887.1162109375 & 5887.236328125 & 5888.71630859375 & 5885.396484375 & 5887.75634765625 & 5887.1962890625 & 5885.71630859375 & 5885.2763671875 & 5884.9560546875 & 5883.83642578125 & 5881.63623046875 & 5880.076171875 & 5881.59619140625 & 5879.236328125 & 5878.87646484375 & 5878.0361328125 & 5877.59619140625 & 5877.396484375 & 5876.43603515625 & 5876.0361328125 & 5875.75634765625 & 5875.396484375 & 5875.236328125 & 5874.9560546875 & 5874.63623046875 & 5873.67626953125 & 5873.51611328125 & 5873.076171875 & 5872.9560546875 & 5872.9560546875 & 5872.916015625 & 5872.79638671875 & 5872.3564453125 & 5871.83642578125 & 5871.47607421875 & 5871.47607421875 & 5871.99609375 & 5873.1162109375 & 5874.076171875 & 5874.236328125 & 5874.55615234375 & 5875.43603515625 & 5876.71630859375 & 5877.79638671875 & 5878.55615234375 & 5879.236328125 & 5879.75634765625 & 5880.43603515625 & 5881.2763671875 & 5881.63623046875 & 5881.75634765625 & 5881.83642578125 & 5881.9560546875 & 5881.75634765625 & 5881.1962890625 & 5881.3564453125 & 5881.9560546875 & 5881.75634765625 & 5881.55615234375 & 5881.43603515625 & 5880.59619140625\\\\ \n5891.83642578125 & 5891.59619140625 & 5891.71630859375 & 5891.87646484375 & 5891.67626953125 & 5891.396484375 & 5891.396484375 & 5890.87646484375 & 5890.83642578125 & 5890.79638671875 & 5890.71630859375 & 5890.236328125 & 5889.67626953125 & 5890.0361328125 & 5890.0361328125 & 5890.1162109375 & 5889.99609375 & 5889.67626953125 & 5889.1962890625 & 5888.75634765625 & 5888.55615234375 & 5888.2763671875 & 5887.99609375 & 5888.15625 & 5887.47607421875 & 5887.236328125 & 5886.99609375 & 5887.51611328125 & 5888.1162109375 & 5889.396484375 & 5889.43603515625 & 5889.43603515625 & 5889.396484375 & 5890.15625 & 5889.71630859375 & 5889.59619140625 & 5890.1162109375 & 5889.87646484375 & 5890.0361328125 & 5890.1962890625 & 5890.55615234375 & 5891.1162109375 & 5890.55615234375 & 5889.55615234375 & 5888.67626953125 & 5888.59619140625 & 5888.55615234375 & 5887.9560546875 & 5887.47607421875 & 5886.83642578125 & 5884.83642578125 & 5881.79638671875 & 5884.87646484375 & 5886.396484375 & 5886.47607421875 & 5887.15625 & 5887.1962890625 & 5886.79638671875 & 5887.1962890625 & 5887.31640625 & 5886.9560546875 & 5886.71630859375 & 5887.55615234375 & 5887.0361328125 & 5886.71630859375 & 5886.75634765625 & 5886.47607421875 & 5886.1162109375 & 5885.63623046875 & 5885.51611328125 & 5886.43603515625 & 5885.67626953125 & 5886.15625 & 5887.916015625 & 5884.9560546875 & 5883.75634765625 & 5885.31640625 & 5884.31640625 & 5883.31640625 & 5882.43603515625 & 5883.83642578125 & 5881.99609375 & 5880.236328125 & 5877.79638671875 & 5876.87646484375 & 5876.0361328125 & 5877.51611328125 & 5879.0361328125 & 5876.67626953125 & 5875.63623046875 & 5875.2763671875 & 5875.0361328125 & 5874.43603515625 & 5873.43603515625 & 5873.75634765625 & 5872.79638671875 & 5872.9560546875 & 5872.236328125 & 5872.3564453125 & 5872.47607421875 & 5872.51611328125 & 5872.1162109375 & 5871.51611328125 & 5871.2763671875 & 5871.3564453125 & 5871.236328125 & 5871.916015625 & 5872.47607421875 & 5873.236328125 & 5873.63623046875 & 5874.236328125 & 5875.2763671875 & 5876.3564453125 & 5877.43603515625 & 5878.1962890625 & 5878.67626953125 & 5878.99609375 & 5879.55615234375 & 5880.67626953125 & 5880.83642578125 & 5880.71630859375 & 5880.63623046875 & 5880.47607421875 & 5880.31640625 & 5880.31640625 & 5880.9560546875 & 5881.2763671875 & 5880.916015625 & 5880.2763671875 & 5880.3564453125 & 5879.916015625\\\\ \n5891.3564453125 & 5891.59619140625 & 5891.3564453125 & 5891.396484375 & 5891.15625 & 5890.67626953125 & 5890.3564453125 & 5890.0361328125 & 5889.75634765625 & 5890.15625 & 5889.79638671875 & 5889.79638671875 & 5889.47607421875 & 5889.236328125 & 5889.3564453125 & 5889.2763671875 & 5889.0361328125 & 5888.67626953125 & 5888.2763671875 & 5888.076171875 & 5887.916015625 & 5887.55615234375 & 5887.15625 & 5887.75634765625 & 5887.0361328125 & 5886.71630859375 & 5886.71630859375 & 5886.9560546875 & 5887.59619140625 & 5888.31640625 & 5889.1162109375 & 5888.75634765625 & 5888.55615234375 & 5889.15625 & 5889.75634765625 & 5889.59619140625 & 5889.0361328125 & 5888.83642578125 & 5889.076171875 & 5888.916015625 & 5889.1162109375 & 5889.236328125 & 5889.15625 & 5888.1962890625 & 5887.43603515625 & 5887.63623046875 & 5887.43603515625 & 5886.43603515625 & 5885.83642578125 & 5886.0361328125 & 5884.43603515625 & 5883.1962890625 & 5882.71630859375 & 5884.43603515625 & 5884.67626953125 & 5885.43603515625 & 5885.99609375 & 5885.79638671875 & 5885.916015625 & 5886.076171875 & 5885.83642578125 & 5885.47607421875 & 5885.71630859375 & 5886.15625 & 5886.1962890625 & 5886.43603515625 & 5886.3564453125 & 5886.1162109375 & 5885.47607421875 & 5885.236328125 & 5885.99609375 & 5884.47607421875 & 5883.99609375 & 5886.396484375 & 5889.076171875 & 5884.47607421875 & 5883.2763671875 & 5883.87646484375 & 5882.1962890625 & 5884.1162109375 & 5882.75634765625 & 5880.47607421875 & 5879.1962890625 & 5879.63623046875 & 5877.1162109375 & 5877.9560546875 & 5879.1162109375 & 5878.55615234375 & 5876.396484375 & 5876.0361328125 & 5875.55615234375 & 5874.31640625 & 5875.47607421875 & 5873.87646484375 & 5873.31640625 & 5873.0361328125 & 5872.59619140625 & 5872.15625 & 5872.076171875 & 5871.99609375 & 5871.9560546875 & 5871.31640625 & 5871.076171875 & 5871.15625 & 5871.2763671875 & 5870.87646484375 & 5870.9560546875 & 5871.71630859375 & 5872.63623046875 & 5873.2763671875 & 5873.916015625 & 5874.9560546875 & 5876.0361328125 & 5876.87646484375 & 5877.43603515625 & 5877.79638671875 & 5877.79638671875 & 5878.1962890625 & 5878.99609375 & 5879.51611328125 & 5879.15625 & 5879.15625 & 5879.236328125 & 5879.63623046875 & 5879.79638671875 & 5880.55615234375 & 5880.9560546875 & 5880.51611328125 & 5879.87646484375 & 5879.59619140625 & 5878.99609375\\end{pmatrix}\\ \\mathrm{meter}$", + "text/html": [ + "
Magnitude
[[5883.3564453125 5879.31640625 5875.3564453125 ... 5769.396484375
  5769.99609375 5770.43603515625]
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  5879.59619140625 5878.99609375]]
Unitsmeter
" + ], + "text/plain": [ + "array([[5883.3564, 5879.3164, 5875.3564, ..., 5769.3965, 5769.996 ,\n", + " 5770.436 ],\n", + " [5885.476 , 5882.2363, 5877.6763, ..., 5783.916 , 5784.516 ,\n", + " 5785.1963],\n", + " [5888.8765, 5885.1562, 5880.916 , ..., 5798.476 , 5799.076 ,\n", + " 5799.7163],\n", + " ...,\n", + " [5892.516 , 5892.2363, 5891.996 , ..., 5881.556 , 5881.436 ,\n", + " 5880.596 ],\n", + " [5891.8364, 5891.596 , 5891.7163, ..., 5880.2764, 5880.3564,\n", + " 5879.916 ],\n", + " [5891.3564, 5891.596 , 5891.3564, ..., 5879.8765, 5879.596 ,\n", + " 5878.996 ]], dtype=float32) meter" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "MOhXgsNntKVL", + "outputId": "4c94ce86-9895-4f04-890a-5d0f4c998406" + }, + "source": [ + "height_units = heights.metpy.units\n", + "height_units" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/latex": "$\\mathrm{meter}$", + "text/html": [ + "meter" + ], + "text/plain": [ + "meter" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "HT7UmzXRtNQ7", + "outputId": "43d3cabf-dde8-4aa5-d1ee-0598c258e2c2" + }, + "source": [ + "heights = data_parsed.metpy.parse_cf('Geopotential_height_isobaric').metpy.sel(\n", + " time='2017-09-05 18:00',\n", + " vertical=500 * units.hPa\n", + ")\n", + "u_g, v_g = mpcalc.geostrophic_wind(heights)\n", + "u_g" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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<xarray.DataArray (latitude: 81, longitude: 131)>\n",
+              "<Quantity([[ 2.33535886  4.60981877  2.93625413 ... 22.89067952 22.89183583\n",
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+              "  22.75482798]\n",
+              " ...\n",
+              " [-3.42134235 -3.29600145 -3.16911313 ... -7.60556204 -6.84423212\n",
+              "  -4.56488464]\n",
+              " [-3.84801887 -2.12410641 -2.1224862  ... -5.57355154 -6.1049832\n",
+              "  -5.30945593]\n",
+              " [-2.64743317  2.22914893 -2.78516091 ...  0.27885616 -4.18284239\n",
+              "  -7.24175843]], 'meter / second')>\n",
+              "Coordinates:\n",
+              "    time1      datetime64[ns] 2017-09-05T18:00:00\n",
+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "  * latitude   (latitude) float32 50.0 49.5 49.0 48.5 ... 11.5 11.0 10.5 10.0\n",
+              "  * longitude  (longitude) float32 250.0 250.5 251.0 251.5 ... 314.0 314.5 315.0\n",
+              "    metpy_crs  object Projection: latitude_longitude\n",
+              "    isobaric3  float64 5e+04
" + ], + "text/plain": [ + "\n", + "\n", + "Coordinates:\n", + " time1 datetime64[ns] 2017-09-05T18:00:00\n", + " reftime datetime64[ns] 2017-09-05T12:00:00\n", + " * latitude (latitude) float32 50.0 49.5 49.0 48.5 ... 11.5 11.0 10.5 10.0\n", + " * longitude (longitude) float32 250.0 250.5 251.0 251.5 ... 314.0 314.5 315.0\n", + " metpy_crs object Projection: latitude_longitude\n", + " isobaric3 float64 5e+04" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yvBf6uiutSY6" + }, + "source": [ + "" + ], + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + }, + "id": "U2BRJpx9tYJD", + "outputId": "9c670075-4a5e-48b4-d544-34cc9f0d07a0" + }, + "source": [ + "data_at_point = data.metpy.sel(\n", + " time1='2017-09-05 12:00',\n", + " latitude=40 * units.degrees_north,\n", + " longitude=260 * units.degrees_east\n", + ")\n", + "dewpoint = mpcalc.dewpoint_from_relative_humidity(\n", + " data_at_point['Temperature_isobaric'],\n", + " data_at_point['Relative_humidity_isobaric']\n", + ")\n", + "cape, cin = mpcalc.surface_based_cape_cin(\n", + " data_at_point['isobaric3'],\n", + " data_at_point['Temperature_isobaric'],\n", + " dewpoint\n", + ")\n", + "cape" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/metpy/interpolate/one_dimension.py:147: UserWarning: Interpolation point out of data bounds encountered\n", + " warnings.warn('Interpolation point out of data bounds encountered')\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/latex": "$0\\ \\frac{\\mathrm{joule}}{\\mathrm{kilogram}}$", + "text/html": [ + "0 joule/kilogram" + ], + "text/plain": [ + "0 joule/kilogram" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 416 + }, + "id": "u_GfeasGtjub", + "outputId": "742086e4-d4a5-4dcd-f234-8ba0bca6b29e" + }, + "source": [ + "dewpoint" + ], + "execution_count": 35, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (isobaric3: 31)>\n",
+              "<Quantity([-99.82935    -96.059525   -92.33535    -88.63002    -87.07465\n",
+              " -85.15522    -80.00673    -78.03093    -77.61473    -75.08857\n",
+              " -79.37687    -70.56154    -58.95       -51.376232   -46.689957\n",
+              " -37.572388   -32.0401     -29.612907   -28.455292   -19.897642\n",
+              " -10.623803    -5.35966     -3.6750817   -7.891809    -7.5533085\n",
+              "  -5.4180036   -3.9739802   -2.8671355   -1.6815517   -0.44102302\n",
+              "   0.7987133 ], 'degree_Celsius')>\n",
+              "Coordinates:\n",
+              "    time1      datetime64[ns] 2017-09-05T12:00:00\n",
+              "    reftime    datetime64[ns] 2017-09-05T12:00:00\n",
+              "    latitude   float32 40.0\n",
+              "  * isobaric3  (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n",
+              "    longitude  float32 260.0
" + ], + "text/plain": [ + "\n", + "\n", + "Coordinates:\n", + " time1 datetime64[ns] 2017-09-05T12:00:00\n", + " reftime datetime64[ns] 2017-09-05T12:00:00\n", + " latitude float32 40.0\n", + " * isobaric3 (isobaric3) float64 100.0 200.0 300.0 ... 9.5e+04 9.75e+04 1e+05\n", + " longitude float32 260.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 342 + }, + "id": "dDKhr2TbtlIa", + "outputId": "4bc6ff88-15be-475f-a999-00878aba93ab" + }, + "source": [ + "# Load data, parse it for a CF grid mapping, and promote lat/lon data variables to coordinates\n", + "data = xr.open_dataset(\n", + " get_test_data('narr_example.nc', False)\n", + ").metpy.parse_cf().set_coords(['lat', 'lon'])\n", + "\n", + "# Subset to only the data you need to save on memory usage\n", + "subset = data.metpy.sel(isobaric=500 * units.hPa)\n", + "\n", + "# Quantify if you plan on performing xarray operations that need to maintain unit correctness\n", + "subset = subset.metpy.quantify()\n", + "\n", + "# Perform calculations\n", + "heights = mpcalc.smooth_gaussian(subset['Geopotential_height'], 5)\n", + "subset['u_geo'], subset['v_geo'] = mpcalc.geostrophic_wind(heights)\n", + "\n", + "# Plot\n", + "heights.plot()" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading file 'narr_example.nc' from 'https://github.com/Unidata/MetPy/raw/v1.0.1/staticdata/narr_example.nc' to '/root/.cache/metpy/v1.0.1'.\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1zTjnLGituMl" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From daf0ce3e88eaeed33f338440d5a23ebc5eb8efb0 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 13 Jul 2021 22:16:02 +0530 Subject: [PATCH 086/171] Created using Colaboratory --- deep_learning/wrf_python.ipynb | 611 +++++++++++++++++++++++++++++++++ 1 file changed, 611 insertions(+) create mode 100644 deep_learning/wrf_python.ipynb diff --git a/deep_learning/wrf_python.ipynb b/deep_learning/wrf_python.ipynb new file mode 100644 index 0000000..fa7ef2d --- /dev/null +++ b/deep_learning/wrf_python.ipynb @@ -0,0 +1,611 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "wrf_python.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyPvP5EsuVVDbKKnOgpesla8", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ygDc2yitvaN5", + "outputId": "1df69458-4a7f-40c2-fe7a-bfdbbea93526" + }, + "source": [ + "!pip install condacolab\n", + "!conda install --yes -c conda-forge wrf-python\n", + "##!pip install -U numpy\n", + "import condacolab\n", + "condacolab.install()\n", + "condacolab.install_anaconda()\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "!conda install --yes -c conda-forge cartopy\n", + "!pip install metpy\n", + "# Any import of metpy will activate the accessors\n", + "import metpy.calc as mpcalc\n", + "from metpy.cbook import get_test_data\n", + "from metpy.units import units\n", + "from google.colab import drive\n", + "drive.mount('/content/drive/')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Requirement already satisfied: condacolab in /usr/local/lib/python3.7/site-packages (0.1.3)\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "\n", + "# All requested packages already installed.\n", + "\n", + "✨🍰✨ Everything looks OK!\n", + "✨🍰✨ Everything looks OK!\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "# All requested packages already installed.\n", + "\n", + "Requirement already satisfied: metpy in /usr/local/lib/python3.7/site-packages (1.0.1)\n", + "Requirement already satisfied: pandas>=0.22.0 in /usr/local/lib/python3.7/site-packages (from metpy) (1.3.0)\n", + "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/site-packages (from metpy) (1.21.0)\n", + "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/site-packages (from metpy) (1.7.0)\n", + "Requirement already satisfied: traitlets>=4.3.0 in /usr/local/lib/python3.7/site-packages (from metpy) (5.0.5)\n", + "Requirement already satisfied: pint>=0.10.1 in /usr/local/lib/python3.7/site-packages (from metpy) (0.17)\n", + "Requirement already satisfied: xarray>=0.14.1 in /usr/local/lib/python3.7/site-packages (from metpy) (0.18.2)\n", + "Requirement already satisfied: importlib-resources>=1.3.0 in /usr/local/lib/python3.7/site-packages (from metpy) (5.2.0)\n", + "Requirement already satisfied: pooch>=0.1 in /usr/local/lib/python3.7/site-packages (from metpy) (1.4.0)\n", + "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.7/site-packages (from metpy) (3.4.2)\n", + "Requirement already satisfied: importlib-metadata>=1.0.0 in /usr/local/lib/python3.7/site-packages (from metpy) (4.6.1)\n", + "Requirement already satisfied: pyproj>=2.3.0 in /usr/local/lib/python3.7/site-packages (from metpy) (3.1.0)\n", + "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/site-packages (from importlib-metadata>=1.0.0->metpy) (3.5.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/site-packages (from importlib-metadata>=1.0.0->metpy) (3.10.0.0)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.1.0->metpy) (8.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.1.0->metpy) (0.10.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.1.0->metpy) (2.4.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.1.0->metpy) (1.3.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.1.0->metpy) (2.8.1)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=2.1.0->metpy) (1.15.0)\n", + "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas>=0.22.0->metpy) (2021.1)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/site-packages (from pint>=0.10.1->metpy) (21.0)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.7/site-packages (from pooch>=0.1->metpy) (2.25.1)\n", + "Requirement already satisfied: appdirs in /usr/local/lib/python3.7/site-packages (from pooch>=0.1->metpy) (1.4.4)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.7/site-packages (from pyproj>=2.3.0->metpy) (2021.5.30)\n", + "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/site-packages (from traitlets>=4.3.0->metpy) (0.2.0)\n", + "Requirement already satisfied: setuptools>=40.4 in /usr/local/lib/python3.7/site-packages (from xarray>=0.14.1->metpy) (49.6.0.post20210108)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests->pooch>=0.1->metpy) (2.10)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests->pooch>=0.1->metpy) (1.26.3)\n", + "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/site-packages (from requests->pooch>=0.1->metpy) (4.0.0)\n", + "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WG51BGbSvetK" + }, + "source": [ + "from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,\n", + " cartopy_ylim, latlon_coords)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vpF4Pl0P8Lx9" + }, + "source": [ + "from netCDF4 import Dataset\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.cm import get_cmap\n", + "import cartopy.crs as crs\n", + "from cartopy.feature import NaturalEarthFeature\n", + "\n", + "from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,\n", + " cartopy_ylim, latlon_coords)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vhomZZ5P_O_l" + }, + "source": [ + "# Open the NetCDF file\n", + "import os\n", + "os.chdir(\"drive/My Drive/\")\n", + "ncfile = Dataset(\"wrfout_d01_2016-10-07_00_00_00_subset.nc\")\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + }, + "id": "3cWgt7kA_a3i", + "outputId": "724aca71-e475-47cb-ae43-96b582a68a00" + }, + "source": [ + "# Get the sea level pressure\n", + "slp = getvar(ncfile, \"slp\")\n", + "\n", + "# Smooth the sea level pressure since it tends to be noisy near the\n", + "# mountains\n", + "smooth_slp = smooth2d(slp, 3, cenweight=4)\n", + "\n", + "# Get the latitude and longitude points\n", + "lats, lons = latlon_coords(slp)\n", + "\n", + "# Get the cartopy mapping object\n", + "cart_proj = get_cartopy(slp)\n", + "\n", + "# Create a figure\n", + "fig = plt.figure(figsize=(12,6))\n", + "# Set the GeoAxes to the projection used by WRF\n", + "ax = plt.axes(projection=cart_proj)\n", + "\n", + "# Download and add the states and coastlines\n", + "states = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n", + " facecolor=\"none\",\n", + " name=\"admin_1_states_provinces_shp\")\n", + "ax.add_feature(states, linewidth=.5, edgecolor=\"black\")\n", + "ax.coastlines('50m', linewidth=0.8)\n", + "\n", + "# Make the contour outlines and filled contours for the smoothed sea level\n", + "# pressure.\n", + "plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors=\"black\",\n", + " transform=crs.PlateCarree())\n", + "plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10,\n", + " transform=crs.PlateCarree(),\n", + " cmap=get_cmap(\"jet\"))\n", + "\n", + "# Add a color bar\n", + "plt.colorbar(ax=ax, shrink=.98)\n", + "\n", + "# Set the map bounds\n", + "ax.set_xlim(cartopy_xlim(smooth_slp))\n", + "ax.set_ylim(cartopy_ylim(smooth_slp))\n", + "\n", + "# Add the gridlines\n", + "ax.gridlines(color=\"black\", linestyle=\"dotted\")\n", + "\n", + "plt.title(\"Sea Level Pressure (hPa)\")\n", + "\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naciscdn.org/naturalearth/50m/physical/ne_50m_coastline.zip\n", + " warnings.warn('Downloading: {}'.format(url), DownloadWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "0ZDhBHSEAokJ", + "outputId": "71627018-a6a6-4b05-a1b0-3d180b239860" + }, + "source": [ + "from wrf import (getvar, interplevel, to_np, latlon_coords, get_cartopy,\n", + " cartopy_xlim, cartopy_ylim)\n", + "\n", + "# Extract the pressure, geopotential height, and wind variables\n", + "p = getvar(ncfile, \"pressure\")\n", + "z = getvar(ncfile, \"z\", units=\"dm\")\n", + "ua = getvar(ncfile, \"ua\", units=\"kt\")\n", + "va = getvar(ncfile, \"va\", units=\"kt\")\n", + "wspd = getvar(ncfile, \"wspd_wdir\", units=\"kts\")[0,:]\n", + "\n", + "# Interpolate geopotential height, u, and v winds to 500 hPa\n", + "ht_500 = interplevel(z, p, 500)\n", + "u_500 = interplevel(ua, p, 500)\n", + "v_500 = interplevel(va, p, 500)\n", + "wspd_500 = interplevel(wspd, p, 500)\n", + "\n", + "# Get the lat/lon coordinates\n", + "lats, lons = latlon_coords(ht_500)\n", + "\n", + "# Get the map projection information\n", + "cart_proj = get_cartopy(ht_500)\n", + "\n", + "# Create the figure\n", + "fig = plt.figure(figsize=(12,9))\n", + "ax = plt.axes(projection=cart_proj)\n", + "\n", + "# Download and add the states and coastlines\n", + "states = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n", + " facecolor=\"none\",\n", + " name=\"admin_1_states_provinces_shp\")\n", + "ax.add_feature(states, linewidth=0.5, edgecolor=\"black\")\n", + "ax.coastlines('50m', linewidth=0.8)\n", + "\n", + "# Add the 500 hPa geopotential height contours\n", + "levels = np.arange(520., 580., 6.)\n", + "contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500),\n", + " levels=levels, colors=\"black\",\n", + " transform=crs.PlateCarree())\n", + "plt.clabel(contours, inline=1, fontsize=10, fmt=\"%i\")\n", + "\n", + "# Add the wind speed contours\n", + "levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]\n", + "wspd_contours = plt.contourf(to_np(lons), to_np(lats), to_np(wspd_500),\n", + " levels=levels,\n", + " cmap=get_cmap(\"rainbow\"),\n", + " transform=crs.PlateCarree())\n", + "plt.colorbar(wspd_contours, ax=ax, orientation=\"horizontal\", pad=.05)\n", + "\n", + "# Add the 500 hPa wind barbs, only plotting every 125th data point.\n", + "plt.barbs(to_np(lons[::125,::125]), to_np(lats[::125,::125]),\n", + " to_np(u_500[::125, ::125]), to_np(v_500[::125, ::125]),\n", + " transform=crs.PlateCarree(), length=6)\n", + "\n", + "# Set the map bounds\n", + "ax.set_xlim(cartopy_xlim(ht_500))\n", + "ax.set_ylim(cartopy_ylim(ht_500))\n", + "\n", + "ax.gridlines()\n", + "\n", + "plt.title(\"500 MB Height (dm), Wind Speed (kt), Barbs (kt)\")\n", + "\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J7liOHMRMMf8" + }, + "source": [ + "# from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds,\n", + "# get_cartopy, latlon_coords, cartopy_xlim, cartopy_ylim)\n", + "\n", + "# # Get the WRF variables\n", + "# slp = getvar(ncfile, \"slp\")\n", + "# smooth_slp = smooth2d(slp, 3)\n", + "# ctt = getvar(ncfile, \"ctt\")\n", + "# z = getvar(ncfile, \"z\")\n", + "# dbz = getvar(ncfile, \"dbz\")\n", + "# Z = 10**(dbz/10.)\n", + "# wspd = getvar(ncfile, \"wspd_wdir\", units=\"kt\")[0,:]\n", + "\n", + "# # Set the start point and end point for the cross section\n", + "# start_point = CoordPair(lat=26.76, lon=-80.0)\n", + "# end_point = CoordPair(lat=26.76, lon=-77.8)\n", + "\n", + "# # Compute the vertical cross-section interpolation. Also, include the\n", + "# # lat/lon points along the cross-section in the metadata by setting latlon\n", + "# # to True.\n", + "# z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point,\n", + "# end_point=end_point, latlon=True, meta=True)\n", + "# wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,\n", + "# end_point=end_point, latlon=True, meta=True)\n", + "# dbz_cross = 10.0 * np.log10(z_cross)\n", + "\n", + "# # Get the lat/lon points\n", + "# lats, lons = latlon_coords(slp)\n", + "\n", + "# # Get the cartopy projection object\n", + "# cart_proj = get_cartopy(slp)\n", + "\n", + "# # Create a figure that will have 3 subplots\n", + "# fig = plt.figure(figsize=(12,9))\n", + "# ax_ctt = fig.add_subplot(1,2,1,projection=cart_proj)\n", + "# ax_wspd = fig.add_subplot(2,2,2)\n", + "# ax_dbz = fig.add_subplot(2,2,4)\n", + "\n", + "# # Download and create the states, land, and oceans using cartopy features\n", + "# states = cfeature.NaturalEarthFeature(category='cultural', scale='50m',\n", + "# facecolor='none',\n", + "# name='admin_1_states_provinces_shp')\n", + "# land = cfeature.NaturalEarthFeature(category='physical', name='land',\n", + "# scale='50m',\n", + "# facecolor=cfeature.COLORS['land'])\n", + "# ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean',\n", + "# scale='50m',\n", + "# facecolor=cfeature.COLORS['water'])\n", + "\n", + "# # Make the pressure contours\n", + "# contour_levels = [960, 965, 970, 975, 980, 990]\n", + "# c1 = ax_ctt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels,\n", + "# colors=\"white\", transform=crs.PlateCarree(), zorder=3,\n", + "# linewidths=1.0)\n", + "\n", + "# # Create the filled cloud top temperature contours\n", + "# contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]\n", + "# ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt),\n", + "# contour_levels, cmap=get_cmap(\"Greys\"),\n", + "# transform=crs.PlateCarree(), zorder=2)\n", + "\n", + "# ax_ctt.plot([start_point.lon, end_point.lon],\n", + "# [start_point.lat, end_point.lat], color=\"yellow\", marker=\"o\",\n", + "# transform=crs.PlateCarree(), zorder=3)\n", + "\n", + "# # Create the color bar for cloud top temperature\n", + "# cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)\n", + "# cb_ctt.ax.tick_params(labelsize=5)\n", + "\n", + "# # Draw the oceans, land, and states\n", + "# ax_ctt.add_feature(ocean)\n", + "# ax_ctt.add_feature(land)\n", + "# ax_ctt.add_feature(states, linewidth=.5, edgecolor=\"black\")\n", + "\n", + "# # Crop the domain to the region around the hurricane\n", + "# hur_bounds = GeoBounds(CoordPair(lat=np.amin(to_np(lats)), lon=-85.0),\n", + "# CoordPair(lat=30.0, lon=-72.0))\n", + "# ax_ctt.set_xlim(cartopy_xlim(ctt, geobounds=hur_bounds))\n", + "# ax_ctt.set_ylim(cartopy_ylim(ctt, geobounds=hur_bounds))\n", + "# ax_ctt.gridlines(color=\"white\", linestyle=\"dotted\")\n", + "\n", + "# # Make the contour plot for wind speed\n", + "# wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap(\"jet\"))\n", + "# # Add the color bar\n", + "# cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)\n", + "# cb_wspd.ax.tick_params(labelsize=5)\n", + "\n", + "# # Make the contour plot for dbz\n", + "# levels = [5 + 5*n for n in range(15)]\n", + "# dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels,\n", + "# cmap=get_cmap(\"jet\"))\n", + "# cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)\n", + "# cb_dbz.ax.tick_params(labelsize=5)\n", + "\n", + "# # Set the x-ticks to use latitude and longitude labels\n", + "# coord_pairs = to_np(dbz_cross.coords[\"xy_loc\"])\n", + "# x_ticks = np.arange(coord_pairs.shape[0])\n", + "# x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)]\n", + "# ax_wspd.set_xticks(x_ticks[::20])\n", + "# ax_wspd.set_xticklabels([], rotation=45)\n", + "# ax_dbz.set_xticks(x_ticks[::20])\n", + "# ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4)\n", + "\n", + "# # Set the y-ticks to be height\n", + "# vert_vals = to_np(dbz_cross.coords[\"vertical\"])\n", + "# v_ticks = np.arange(vert_vals.shape[0])\n", + "# ax_wspd.set_yticks(v_ticks[::20])\n", + "# ax_wspd.set_yticklabels(vert_vals[::20], fontsize=4)\n", + "# ax_dbz.set_yticks(v_ticks[::20])\n", + "# ax_dbz.set_yticklabels(vert_vals[::20], fontsize=4)\n", + "\n", + "# # Set the x-axis and y-axis labels\n", + "# ax_dbz.set_xlabel(\"Latitude, Longitude\", fontsize=5)\n", + "# ax_wspd.set_ylabel(\"Height (m)\", fontsize=5)\n", + "# ax_dbz.set_ylabel(\"Height (m)\", fontsize=5)\n", + "\n", + "# # Add a title\n", + "# ax_ctt.set_title(\"Cloud Top Temperature (degC)\", {\"fontsize\" : 7})\n", + "# ax_wspd.set_title(\"Cross-Section of Wind Speed (kt)\", {\"fontsize\" : 7})\n", + "# ax_dbz.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 7})\n", + "\n", + "# plt.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "443eoGnhOXu-" + }, + "source": [ + "from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair,\n", + " get_basemap, latlon_coords)\n", + "\n", + "\n", + "# Get the WRF variables\n", + "slp = getvar(ncfile, \"slp\")\n", + "smooth_slp = smooth2d(slp, 3)\n", + "ctt = getvar(ncfile, \"ctt\")\n", + "z = getvar(ncfile, \"z\")\n", + "dbz = getvar(ncfile, \"dbz\")\n", + "Z = 10**(dbz/10.)\n", + "wspd = getvar(ncfile, \"wspd_wdir\", units=\"kt\")[0,:]\n", + "\n", + "# Set the start point and end point for the cross section\n", + "start_point = CoordPair(lat=26.76, lon=-80.0)\n", + "end_point = CoordPair(lat=26.76, lon=-77.8)\n", + "\n", + "# Compute the vertical cross-section interpolation. Also, include the\n", + "# lat/lon points along the cross-section in the metadata by setting latlon\n", + "# to True.\n", + "z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point,\n", + " end_point=end_point, latlon=True, meta=True)\n", + "wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,\n", + " end_point=end_point, latlon=True, meta=True)\n", + "dbz_cross = 10.0 * np.log10(z_cross)\n", + "\n", + "# Get the latitude and longitude points\n", + "lats, lons = latlon_coords(slp)\n", + "\n", + "# Create the figure that will have 3 subplots\n", + "fig = plt.figure(figsize=(12,9))\n", + "ax_ctt = fig.add_subplot(1,2,1)\n", + "ax_wspd = fig.add_subplot(2,2,2)\n", + "ax_dbz = fig.add_subplot(2,2,4)\n", + "\n", + "# Get the basemap object\n", + "bm = get_basemap(slp)\n", + "\n", + "# Convert the lat/lon points in to x/y points in the projection space\n", + "x, y = bm(to_np(lons), to_np(lats))\n", + "\n", + "# Make the pressure contours\n", + "contour_levels = [960, 965, 970, 975, 980, 990]\n", + "c1 = bm.contour(x, y, to_np(smooth_slp), levels=contour_levels,\n", + " colors=\"white\", zorder=3, linewidths=1.0, ax=ax_ctt)\n", + "\n", + "# Create the filled cloud top temperature contours\n", + "contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]\n", + "ctt_contours = bm.contourf(x, y, to_np(ctt), contour_levels,\n", + " cmap=get_cmap(\"Greys\"), zorder=2, ax=ax_ctt)\n", + "\n", + "point_x, point_y = bm([start_point.lon, end_point.lon],\n", + " [start_point.lat, end_point.lat])\n", + "bm.plot([point_x[0], point_x[1]], [point_y[0], point_y[1]], color=\"yellow\",\n", + " marker=\"o\", zorder=3, ax=ax_ctt)\n", + "\n", + "# Create the color bar for cloud top temperature\n", + "cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)\n", + "cb_ctt.ax.tick_params(labelsize=5)\n", + "\n", + "# Draw the oceans, land, and states\n", + "bm.drawcoastlines(linewidth=0.25, ax=ax_ctt)\n", + "bm.drawstates(linewidth=0.25, ax=ax_ctt)\n", + "bm.drawcountries(linewidth=0.25, ax=ax_ctt)\n", + "bm.fillcontinents(color=np.array([ 0.9375 , 0.9375 , 0.859375]),\n", + " ax=ax_ctt,\n", + " lake_color=np.array([0.59375 ,\n", + " 0.71484375,\n", + " 0.8828125 ]))\n", + "bm.drawmapboundary(fill_color=np.array([ 0.59375 , 0.71484375, 0.8828125 ]),\n", + " ax=ax_ctt)\n", + "\n", + "# Draw Parallels\n", + "parallels = np.arange(np.amin(lats), 30., 2.5)\n", + "bm.drawparallels(parallels, ax=ax_ctt, color=\"white\")\n", + "\n", + "merids = np.arange(-85.0, -72.0, 2.5)\n", + "bm.drawmeridians(merids, ax=ax_ctt, color=\"white\")\n", + "\n", + "# Crop the image to the hurricane region\n", + "x_start, y_start = bm(-85.0, np.amin(lats))\n", + "x_end, y_end = bm(-72.0, 30.0)\n", + "\n", + "ax_ctt.set_xlim([x_start, x_end])\n", + "ax_ctt.set_ylim([y_start, y_end])\n", + "\n", + "# Make the contour plot for wspd\n", + "wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap(\"jet\"))\n", + "# Add the color bar\n", + "cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)\n", + "cb_wspd.ax.tick_params(labelsize=5)\n", + "\n", + "# Make the contour plot for dbz\n", + "levels = [5 + 5*n for n in range(15)]\n", + "dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels,\n", + " cmap=get_cmap(\"jet\"))\n", + "cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)\n", + "cb_dbz.ax.tick_params(labelsize=5)\n", + "\n", + "# Set the x-ticks to use latitude and longitude labels.\n", + "coord_pairs = to_np(dbz_cross.coords[\"xy_loc\"])\n", + "x_ticks = np.arange(coord_pairs.shape[0])\n", + "x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)]\n", + "ax_wspd.set_xticks(x_ticks[::20])\n", + "ax_wspd.set_xticklabels([], rotation=45)\n", + "ax_dbz.set_xticks(x_ticks[::20])\n", + "ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4)\n", + "\n", + "# Set the y-ticks to be height.\n", + "vert_vals = to_np(dbz_cross.coords[\"vertical\"])\n", + "v_ticks = np.arange(vert_vals.shape[0])\n", + "ax_wspd.set_yticks(v_ticks[::20])\n", + "ax_wspd.set_yticklabels(vert_vals[::20], fontsize=4)\n", + "ax_dbz.set_yticks(v_ticks[::20])\n", + "ax_dbz.set_yticklabels(vert_vals[::20], fontsize=4)\n", + "\n", + "# Set the x-axis and y-axis labels\n", + "ax_dbz.set_xlabel(\"Latitude, Longitude\", fontsize=5)\n", + "ax_wspd.set_ylabel(\"Height (m)\", fontsize=5)\n", + "ax_dbz.set_ylabel(\"Height (m)\", fontsize=5)\n", + "\n", + "# Add titles\n", + "ax_ctt.set_title(\"Cloud Top Temperature (degC)\", {\"fontsize\" : 7})\n", + "ax_wspd.set_title(\"Cross-Section of Wind Speed (kt)\", {\"fontsize\" : 7})\n", + "ax_dbz.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 7})\n", + "\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6JoxLzsHUsMB" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From e882ce51d86bd623d7385c7c729676886ea6cc83 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 13 Jul 2021 23:03:56 +0530 Subject: [PATCH 087/171] Created using Colaboratory --- .../cape_cin_wrf_python_lcl_lfc.ipynb | 1902 +++++++++++++++++ 1 file changed, 1902 insertions(+) create mode 100644 python_class/cape_cin_wrf_python_lcl_lfc.ipynb diff --git a/python_class/cape_cin_wrf_python_lcl_lfc.ipynb b/python_class/cape_cin_wrf_python_lcl_lfc.ipynb new file mode 100644 index 0000000..4f8e644 --- /dev/null +++ b/python_class/cape_cin_wrf_python_lcl_lfc.ipynb @@ -0,0 +1,1902 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "cape_cin_wrf_python_lcl_lfc.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyNgdD9DuIK4c33VU24esB7q", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "kL3fhBb_xIKk", + "outputId": "6b075da8-6259-4329-f053-3547717951fa" + }, + "source": [ + "!pip install condacolab\n", + "import condacolab\n", + "condacolab.install()\n", + "condacolab.install_anaconda()\n", + "import numpy as np\n", + "import xarray as xr\n", + "!conda install --yes -c conda-forge cartopy\n", + "!pip install metpy\n", + "# Any import of metpy will activate the accessors\n", + "import metpy.calc as mpcalc\n", + "from metpy.cbook import get_test_data\n", + "from metpy.units import units" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting condacolab\n", + " Using cached condacolab-0.1.3-py3-none-any.whl (6.8 kB)\n", + "Installing collected packages: condacolab\n", + "Successfully installed condacolab-0.1.3\n", + "✨🍰✨ Everything looks OK!\n", + "✨🍰✨ Everything looks OK!\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - cartopy\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " ca-certificates-2021.5.30 | ha878542_0 136 KB conda-forge\n", + " cartopy-0.19.0.post1 | py37h0c48da3_0 1.8 MB conda-forge\n", + " certifi-2021.5.30 | py37h89c1867_0 141 KB conda-forge\n", + " conda-4.10.3 | py37h89c1867_0 3.1 MB conda-forge\n", + " cycler-0.10.0 | py_2 9 KB conda-forge\n", + " freetype-2.10.4 | h0708190_1 890 KB conda-forge\n", + " geos-3.9.1 | h9c3ff4c_2 1.1 MB conda-forge\n", + " jpeg-9d | h36c2ea0_0 264 KB conda-forge\n", + " kiwisolver-1.3.1 | py37h2527ec5_1 78 KB conda-forge\n", + " lcms2-2.12 | hddcbb42_0 443 KB conda-forge\n", + " libblas-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libcblas-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libgfortran-ng-9.3.0 | hff62375_19 22 KB conda-forge\n", + " libgfortran5-9.3.0 | hff62375_19 2.0 MB conda-forge\n", + " liblapack-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libopenblas-0.3.15 |pthreads_h8fe5266_1 9.2 MB conda-forge\n", + " libpng-1.6.37 | h21135ba_2 306 KB conda-forge\n", + " libtiff-4.2.0 | h85742a9_0 502 KB\n", + " libwebp-base-1.2.0 | h7f98852_2 815 KB conda-forge\n", + " matplotlib-base-3.4.2 | py37hdd32ed1_0 7.2 MB conda-forge\n", + " numpy-1.21.0 | py37h038b26d_0 6.1 MB conda-forge\n", + " olefile-0.46 | pyh9f0ad1d_1 32 KB conda-forge\n", + " openjpeg-2.4.0 | hb52868f_1 444 KB conda-forge\n", + " openssl-1.1.1k | h7f98852_0 2.1 MB conda-forge\n", + " pillow-8.2.0 | py37h4600e1f_1 684 KB conda-forge\n", + " proj-7.2.0 | h277dcde_2 3.0 MB conda-forge\n", + " pyparsing-2.4.7 | pyh9f0ad1d_0 60 KB conda-forge\n", + " pyshp-2.1.3 | pyh44b312d_0 36 KB conda-forge\n", + " python-dateutil-2.8.1 | py_0 220 KB conda-forge\n", + " python_abi-3.7 | 2_cp37m 4 KB conda-forge\n", + " scipy-1.7.0 | py37h29e03ee_0 21.7 MB conda-forge\n", + " shapely-1.7.1 | py37h2d1e849_5 438 KB conda-forge\n", + " tornado-6.1 | py37h5e8e339_1 646 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 63.3 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " cartopy conda-forge/linux-64::cartopy-0.19.0.post1-py37h0c48da3_0\n", + " cycler conda-forge/noarch::cycler-0.10.0-py_2\n", + " freetype conda-forge/linux-64::freetype-2.10.4-h0708190_1\n", + " geos conda-forge/linux-64::geos-3.9.1-h9c3ff4c_2\n", + " jpeg conda-forge/linux-64::jpeg-9d-h36c2ea0_0\n", + " kiwisolver conda-forge/linux-64::kiwisolver-1.3.1-py37h2527ec5_1\n", + " lcms2 conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n", + " libblas conda-forge/linux-64::libblas-3.9.0-9_openblas\n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-9_openblas\n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-9.3.0-hff62375_19\n", + " libgfortran5 conda-forge/linux-64::libgfortran5-9.3.0-hff62375_19\n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-9_openblas\n", + " libopenblas conda-forge/linux-64::libopenblas-0.3.15-pthreads_h8fe5266_1\n", + " libpng conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n", + " libtiff pkgs/main/linux-64::libtiff-4.2.0-h85742a9_0\n", + " libwebp-base conda-forge/linux-64::libwebp-base-1.2.0-h7f98852_2\n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.4.2-py37hdd32ed1_0\n", + " numpy conda-forge/linux-64::numpy-1.21.0-py37h038b26d_0\n", + " olefile conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n", + " openjpeg conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n", + " pillow conda-forge/linux-64::pillow-8.2.0-py37h4600e1f_1\n", + " proj conda-forge/linux-64::proj-7.2.0-h277dcde_2\n", + " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n", + " pyshp conda-forge/noarch::pyshp-2.1.3-pyh44b312d_0\n", + " python-dateutil conda-forge/noarch::python-dateutil-2.8.1-py_0\n", + " scipy conda-forge/linux-64::scipy-1.7.0-py37h29e03ee_0\n", + " shapely conda-forge/linux-64::shapely-1.7.1-py37h2d1e849_5\n", + " tornado conda-forge/linux-64::tornado-6.1-py37h5e8e339_1\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates 2020.12.5-ha878542_0 --> 2021.5.30-ha878542_0\n", + " certifi 2020.12.5-py37h89c1867_1 --> 2021.5.30-py37h89c1867_0\n", + " conda 4.9.2-py37h89c1867_0 --> 4.10.3-py37h89c1867_0\n", + " openssl 1.1.1j-h7f98852_0 --> 1.1.1k-h7f98852_0\n", + " python_abi 3.7-1_cp37m --> 3.7-2_cp37m\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "liblapack-3.9.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 11.68it/s]\n", + "libblas-3.9.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 32.11it/s]\n", + "libtiff-4.2.0 | 502 KB | : 100% 1.0/1 [00:00<00:00, 9.78it/s]\n", + "python-dateutil-2.8. | 220 KB | : 100% 1.0/1 [00:00<00:00, 11.12it/s]\n", + "ca-certificates-2021 | 136 KB | : 100% 1.0/1 [00:00<00:00, 18.42it/s]\n", + 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127.985115,\n", + " 127.525116, 126.24512 ],\n", + " [ 96.625114, 96.38512 , 95.72512 , ..., 130.02512 ,\n", + " 129.40512 , 128.34512 ]]]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i4Wgjedt3THp" + }, + "source": [ + "MCAPE, MCIN, LCL, LFC = cape_2d(pres_hpa=pres_hpa_.values \\\n", + " , tkel=tkel_.values[0,:,0,0], \\\n", + " qv=qv_.values[0,:,0,0], \\\n", + " height=height_.values[0,:,0,0], \n", + " terrain=terrain_, psfc_hpa=psfc_hpa_, \\\n", + " ter_follow=ter_follow_, missing=9.969209968386869e+36, meta=meta_)" + ], + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Fyt9KaBu5SMY", + "outputId": "25a3338f-8565-45b7-895c-35bf77228fd3" + }, + "source": [ + "LCL" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[1592.57080078125]],\n", + " mask=[[False]],\n", + " fill_value=9.969209968386869e+36)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GJRF6OMb5TJP", + "outputId": "08c697f6-c77a-4f88-f58a-91041d058571" + }, + "source": [ + "LFC" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[47610.48828125]],\n", + " mask=[[False]],\n", + " fill_value=9.969209968386869e+36)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bhCWRhGF5Wu_", + "outputId": "82de75f7-e2d9-47f2-9aa6-e898cbe55577" + }, + "source": [ + "MCIN" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[--],\n", + " mask=[ True],\n", + " fill_value=9.969209968386869e+36,\n", + " dtype=float64)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vNsGABNr5jqu" + }, + "source": [ + "# A smaller difference between the LCL and LFC (LCL-LFC) is conducive to the rapid formation of thunderstorms." + ], + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WW8KXzpL7A0d" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 459bfa093780774adc80ee7d2ae980da3c7309be Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 14 Jul 2021 15:50:45 +0530 Subject: [PATCH 088/171] Created using Colaboratory --- .../cape_cin_wrf_python_lcl_lfc.ipynb | 1902 +++++++++++++++++ 1 file changed, 1902 insertions(+) create mode 100644 xarray_tutorial/cape_cin_wrf_python_lcl_lfc.ipynb diff --git a/xarray_tutorial/cape_cin_wrf_python_lcl_lfc.ipynb b/xarray_tutorial/cape_cin_wrf_python_lcl_lfc.ipynb new file mode 100644 index 0000000..69b1381 --- /dev/null +++ b/xarray_tutorial/cape_cin_wrf_python_lcl_lfc.ipynb @@ -0,0 +1,1902 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "cape_cin_wrf_python_lcl_lfc.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyNgdD9DuIK4c33VU24esB7q", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "kL3fhBb_xIKk", + "outputId": "6b075da8-6259-4329-f053-3547717951fa" + }, + "source": [ + "!pip install condacolab\n", + "import condacolab\n", + "condacolab.install()\n", + "condacolab.install_anaconda()\n", + "import numpy as np\n", + "import xarray as xr\n", + "!conda install --yes -c conda-forge cartopy\n", + "!pip install metpy\n", + "# Any import of metpy will activate the accessors\n", + "import metpy.calc as mpcalc\n", + "from metpy.cbook import get_test_data\n", + "from metpy.units import units" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting condacolab\n", + " Using cached condacolab-0.1.3-py3-none-any.whl (6.8 kB)\n", + "Installing collected packages: condacolab\n", + "Successfully installed condacolab-0.1.3\n", + "✨🍰✨ Everything looks OK!\n", + "✨🍰✨ Everything looks OK!\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - cartopy\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " ca-certificates-2021.5.30 | ha878542_0 136 KB conda-forge\n", + " cartopy-0.19.0.post1 | py37h0c48da3_0 1.8 MB conda-forge\n", + " certifi-2021.5.30 | py37h89c1867_0 141 KB conda-forge\n", + " conda-4.10.3 | py37h89c1867_0 3.1 MB conda-forge\n", + " cycler-0.10.0 | py_2 9 KB conda-forge\n", + " freetype-2.10.4 | h0708190_1 890 KB conda-forge\n", + " geos-3.9.1 | h9c3ff4c_2 1.1 MB conda-forge\n", + " jpeg-9d | h36c2ea0_0 264 KB conda-forge\n", + " kiwisolver-1.3.1 | py37h2527ec5_1 78 KB conda-forge\n", + " lcms2-2.12 | hddcbb42_0 443 KB conda-forge\n", + " libblas-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libcblas-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libgfortran-ng-9.3.0 | hff62375_19 22 KB conda-forge\n", + " libgfortran5-9.3.0 | hff62375_19 2.0 MB conda-forge\n", + " liblapack-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libopenblas-0.3.15 |pthreads_h8fe5266_1 9.2 MB conda-forge\n", + " libpng-1.6.37 | h21135ba_2 306 KB conda-forge\n", + " libtiff-4.2.0 | h85742a9_0 502 KB\n", + " libwebp-base-1.2.0 | h7f98852_2 815 KB conda-forge\n", + " matplotlib-base-3.4.2 | py37hdd32ed1_0 7.2 MB conda-forge\n", + " numpy-1.21.0 | py37h038b26d_0 6.1 MB conda-forge\n", + " olefile-0.46 | pyh9f0ad1d_1 32 KB conda-forge\n", + " openjpeg-2.4.0 | hb52868f_1 444 KB conda-forge\n", + " openssl-1.1.1k | h7f98852_0 2.1 MB conda-forge\n", + " pillow-8.2.0 | py37h4600e1f_1 684 KB conda-forge\n", + " proj-7.2.0 | h277dcde_2 3.0 MB conda-forge\n", + " pyparsing-2.4.7 | pyh9f0ad1d_0 60 KB conda-forge\n", + " pyshp-2.1.3 | pyh44b312d_0 36 KB conda-forge\n", + " python-dateutil-2.8.1 | py_0 220 KB conda-forge\n", + " python_abi-3.7 | 2_cp37m 4 KB conda-forge\n", + " scipy-1.7.0 | py37h29e03ee_0 21.7 MB conda-forge\n", + " shapely-1.7.1 | py37h2d1e849_5 438 KB conda-forge\n", + " tornado-6.1 | py37h5e8e339_1 646 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 63.3 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " cartopy conda-forge/linux-64::cartopy-0.19.0.post1-py37h0c48da3_0\n", + " cycler conda-forge/noarch::cycler-0.10.0-py_2\n", + " freetype conda-forge/linux-64::freetype-2.10.4-h0708190_1\n", + " geos conda-forge/linux-64::geos-3.9.1-h9c3ff4c_2\n", + " jpeg conda-forge/linux-64::jpeg-9d-h36c2ea0_0\n", + " kiwisolver conda-forge/linux-64::kiwisolver-1.3.1-py37h2527ec5_1\n", + " lcms2 conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n", + " libblas conda-forge/linux-64::libblas-3.9.0-9_openblas\n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-9_openblas\n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-9.3.0-hff62375_19\n", + " libgfortran5 conda-forge/linux-64::libgfortran5-9.3.0-hff62375_19\n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-9_openblas\n", + " libopenblas conda-forge/linux-64::libopenblas-0.3.15-pthreads_h8fe5266_1\n", + " libpng conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n", + " libtiff pkgs/main/linux-64::libtiff-4.2.0-h85742a9_0\n", + " libwebp-base conda-forge/linux-64::libwebp-base-1.2.0-h7f98852_2\n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.4.2-py37hdd32ed1_0\n", + " numpy conda-forge/linux-64::numpy-1.21.0-py37h038b26d_0\n", + " olefile conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n", + " openjpeg conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n", + " pillow conda-forge/linux-64::pillow-8.2.0-py37h4600e1f_1\n", + " proj conda-forge/linux-64::proj-7.2.0-h277dcde_2\n", + " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n", + " pyshp conda-forge/noarch::pyshp-2.1.3-pyh44b312d_0\n", + " python-dateutil conda-forge/noarch::python-dateutil-2.8.1-py_0\n", + " scipy conda-forge/linux-64::scipy-1.7.0-py37h29e03ee_0\n", + " shapely conda-forge/linux-64::shapely-1.7.1-py37h2d1e849_5\n", + " tornado conda-forge/linux-64::tornado-6.1-py37h5e8e339_1\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates 2020.12.5-ha878542_0 --> 2021.5.30-ha878542_0\n", + " certifi 2020.12.5-py37h89c1867_1 --> 2021.5.30-py37h89c1867_0\n", + " conda 4.9.2-py37h89c1867_0 --> 4.10.3-py37h89c1867_0\n", + " openssl 1.1.1j-h7f98852_0 --> 1.1.1k-h7f98852_0\n", + " python_abi 3.7-1_cp37m --> 3.7-2_cp37m\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "liblapack-3.9.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 11.68it/s]\n", + "libblas-3.9.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 32.11it/s]\n", + "libtiff-4.2.0 | 502 KB | : 100% 1.0/1 [00:00<00:00, 9.78it/s]\n", + "python-dateutil-2.8. | 220 KB | : 100% 1.0/1 [00:00<00:00, 11.12it/s]\n", + "ca-certificates-2021 | 136 KB | : 100% 1.0/1 [00:00<00:00, 18.42it/s]\n", + "numpy-1.21.0 | 6.1 MB | : 100% 1.0/1 [00:01<00:00, 1.46s/it]\n", + "libcblas-3.9.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 30.20it/s]\n", + "python_abi-3.7 | 4 KB | : 100% 1.0/1 [00:00<00:00, 36.35it/s]\n", + "cycler-0.10.0 | 9 KB | : 100% 1.0/1 [00:00<00:00, 35.89it/s]\n", + "geos-3.9.1 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 3.16it/s]\n", 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127.985115,\n", + " 127.525116, 126.24512 ],\n", + " [ 96.625114, 96.38512 , 95.72512 , ..., 130.02512 ,\n", + " 129.40512 , 128.34512 ]]]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i4Wgjedt3THp" + }, + "source": [ + "MCAPE, MCIN, LCL, LFC = cape_2d(pres_hpa=pres_hpa_.values \\\n", + " , tkel=tkel_.values[0,:,0,0], \\\n", + " qv=qv_.values[0,:,0,0], \\\n", + " height=height_.values[0,:,0,0], \n", + " terrain=terrain_, psfc_hpa=psfc_hpa_, \\\n", + " ter_follow=ter_follow_, missing=9.969209968386869e+36, meta=meta_)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Fyt9KaBu5SMY", + "outputId": "25a3338f-8565-45b7-895c-35bf77228fd3" + }, + "source": [ + "LCL" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[1592.57080078125]],\n", + " mask=[[False]],\n", + " fill_value=9.969209968386869e+36)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GJRF6OMb5TJP", + "outputId": "08c697f6-c77a-4f88-f58a-91041d058571" + }, + "source": [ + "LFC" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[47610.48828125]],\n", + " mask=[[False]],\n", + " fill_value=9.969209968386869e+36)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bhCWRhGF5Wu_", + "outputId": "82de75f7-e2d9-47f2-9aa6-e898cbe55577" + }, + "source": [ + "MCIN" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[--],\n", + " mask=[ True],\n", + " fill_value=9.969209968386869e+36,\n", + " dtype=float64)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vNsGABNr5jqu" + }, + "source": [ + "# A smaller difference between the LCL and LFC (LCL-LFC) is conducive to the rapid formation of thunderstorms." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WW8KXzpL7A0d" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 3808e739909d95313b4b2747e25dba8d9f10f6b4 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 14 Jul 2021 18:29:53 +0530 Subject: [PATCH 089/171] Created using Colaboratory --- ...ape_cin_wrf_python_metpy_lcl_lfc_mse.ipynb | 2070 +++++++++++++++++ 1 file changed, 2070 insertions(+) create mode 100644 xarray_tutorial/cape_cin_wrf_python_metpy_lcl_lfc_mse.ipynb diff --git a/xarray_tutorial/cape_cin_wrf_python_metpy_lcl_lfc_mse.ipynb b/xarray_tutorial/cape_cin_wrf_python_metpy_lcl_lfc_mse.ipynb new file mode 100644 index 0000000..6c73c99 --- /dev/null +++ b/xarray_tutorial/cape_cin_wrf_python_metpy_lcl_lfc_mse.ipynb @@ -0,0 +1,2070 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "cape_cin_wrf_python_metpy_lcl_lfc_mse.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyOrQyO6apSVEsZhPSZCO0iv", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "kL3fhBb_xIKk", + "outputId": "356b9099-e35f-4629-c4fa-c8ac7f260176" + }, + "source": [ + "!pip install condacolab\n", + "import condacolab\n", + "condacolab.install()\n", + "condacolab.install_anaconda()\n", + "import numpy as np\n", + "import xarray as xr\n", + "!conda install --yes -c conda-forge cartopy\n", + "!pip install metpy\n", + "# Any import of metpy will activate the accessors\n", + "import metpy.calc as mpcalc\n", + "from metpy.cbook import get_test_data\n", + "from metpy.units import units" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting condacolab\n", + " Using cached condacolab-0.1.3-py3-none-any.whl (6.8 kB)\n", + "Installing collected packages: condacolab\n", + "Successfully installed condacolab-0.1.3\n", + "✨🍰✨ Everything looks OK!\n", + "✨🍰✨ Everything looks OK!\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - cartopy\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " ca-certificates-2021.5.30 | ha878542_0 136 KB conda-forge\n", + " cartopy-0.19.0.post1 | py37h0c48da3_0 1.8 MB conda-forge\n", + " certifi-2021.5.30 | py37h89c1867_0 141 KB conda-forge\n", + " conda-4.10.3 | py37h89c1867_0 3.1 MB conda-forge\n", + " cycler-0.10.0 | py_2 9 KB conda-forge\n", + " freetype-2.10.4 | h0708190_1 890 KB conda-forge\n", + " geos-3.9.1 | h9c3ff4c_2 1.1 MB conda-forge\n", + " jpeg-9d | h36c2ea0_0 264 KB conda-forge\n", + " kiwisolver-1.3.1 | py37h2527ec5_1 78 KB conda-forge\n", + " lcms2-2.12 | hddcbb42_0 443 KB conda-forge\n", + " libblas-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libcblas-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libgfortran-ng-9.3.0 | hff62375_19 22 KB conda-forge\n", + " libgfortran5-9.3.0 | hff62375_19 2.0 MB conda-forge\n", + " liblapack-3.9.0 | 9_openblas 11 KB conda-forge\n", + " libopenblas-0.3.15 |pthreads_h8fe5266_1 9.2 MB conda-forge\n", + " libpng-1.6.37 | h21135ba_2 306 KB conda-forge\n", + " libtiff-4.2.0 | h85742a9_0 502 KB\n", + " libwebp-base-1.2.0 | h7f98852_2 815 KB conda-forge\n", + " matplotlib-base-3.4.2 | py37hdd32ed1_0 7.2 MB conda-forge\n", + " numpy-1.21.0 | py37h038b26d_0 6.1 MB conda-forge\n", + " olefile-0.46 | pyh9f0ad1d_1 32 KB conda-forge\n", + " openjpeg-2.4.0 | hb52868f_1 444 KB conda-forge\n", + " openssl-1.1.1k | h7f98852_0 2.1 MB conda-forge\n", + " pillow-8.2.0 | py37h4600e1f_1 684 KB conda-forge\n", + " proj-7.2.0 | h277dcde_2 3.0 MB conda-forge\n", + " pyparsing-2.4.7 | pyh9f0ad1d_0 60 KB conda-forge\n", + " pyshp-2.1.3 | pyh44b312d_0 36 KB conda-forge\n", + " python-dateutil-2.8.1 | py_0 220 KB conda-forge\n", + " python_abi-3.7 | 2_cp37m 4 KB conda-forge\n", + " scipy-1.7.0 | py37h29e03ee_0 21.7 MB conda-forge\n", + " shapely-1.7.1 | py37h2d1e849_5 438 KB conda-forge\n", + " tornado-6.1 | py37h5e8e339_1 646 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 63.3 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " cartopy conda-forge/linux-64::cartopy-0.19.0.post1-py37h0c48da3_0\n", + " cycler conda-forge/noarch::cycler-0.10.0-py_2\n", + " freetype conda-forge/linux-64::freetype-2.10.4-h0708190_1\n", + " geos conda-forge/linux-64::geos-3.9.1-h9c3ff4c_2\n", + " jpeg conda-forge/linux-64::jpeg-9d-h36c2ea0_0\n", + " kiwisolver conda-forge/linux-64::kiwisolver-1.3.1-py37h2527ec5_1\n", + " lcms2 conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n", + " libblas conda-forge/linux-64::libblas-3.9.0-9_openblas\n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-9_openblas\n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-9.3.0-hff62375_19\n", + " libgfortran5 conda-forge/linux-64::libgfortran5-9.3.0-hff62375_19\n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-9_openblas\n", + " libopenblas conda-forge/linux-64::libopenblas-0.3.15-pthreads_h8fe5266_1\n", + " libpng conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n", + " libtiff pkgs/main/linux-64::libtiff-4.2.0-h85742a9_0\n", + " libwebp-base conda-forge/linux-64::libwebp-base-1.2.0-h7f98852_2\n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.4.2-py37hdd32ed1_0\n", + " numpy conda-forge/linux-64::numpy-1.21.0-py37h038b26d_0\n", + " olefile conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n", + " openjpeg conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n", + " pillow conda-forge/linux-64::pillow-8.2.0-py37h4600e1f_1\n", + " proj conda-forge/linux-64::proj-7.2.0-h277dcde_2\n", + " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n", + " pyshp conda-forge/noarch::pyshp-2.1.3-pyh44b312d_0\n", + " python-dateutil conda-forge/noarch::python-dateutil-2.8.1-py_0\n", + " scipy conda-forge/linux-64::scipy-1.7.0-py37h29e03ee_0\n", + " shapely conda-forge/linux-64::shapely-1.7.1-py37h2d1e849_5\n", + " tornado conda-forge/linux-64::tornado-6.1-py37h5e8e339_1\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates 2020.12.5-ha878542_0 --> 2021.5.30-ha878542_0\n", + " certifi 2020.12.5-py37h89c1867_1 --> 2021.5.30-py37h89c1867_0\n", + " conda 4.9.2-py37h89c1867_0 --> 4.10.3-py37h89c1867_0\n", + " openssl 1.1.1j-h7f98852_0 --> 1.1.1k-h7f98852_0\n", + " python_abi 3.7-1_cp37m --> 3.7-2_cp37m\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "libgfortran-ng-9.3.0 | 22 KB | : 100% 1.0/1 [00:00<00:00, 7.87it/s] \n", + "freetype-2.10.4 | 890 KB | : 100% 1.0/1 [00:00<00:00, 3.92it/s]\n", + "lcms2-2.12 | 443 KB | : 100% 1.0/1 [00:00<00:00, 7.62it/s]\n", + "cartopy-0.19.0.post1 | 1.8 MB | : 100% 1.0/1 [00:00<00:00, 2.00it/s]\n", + "python-dateutil-2.8. | 220 KB | : 100% 1.0/1 [00:00<00:00, 10.76it/s]\n", + "libpng-1.6.37 | 306 KB | : 100% 1.0/1 [00:00<00:00, 10.34it/s]\n", + "kiwisolver-1.3.1 | 78 KB | : 100% 1.0/1 [00:00<00:00, 19.20it/s]\n", + "openssl-1.1.1k | 2.1 MB | : 100% 1.0/1 [00:00<00:00, 2.54it/s]\n", + "pyshp-2.1.3 | 36 KB | : 100% 1.0/1 [00:00<00:00, 19.64it/s]\n", + 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471 + }, + "id": "s6TQNu7VxZDS", + "outputId": "578d8e84-28ef-4d93-dead-02275f32f974" + }, + "source": [ + "# Open the netCDF file as a xarray Dataset\n", + "data = xr.open_dataset(get_test_data('irma_gfs_example.nc', False))\n", + "\n", + "# View a summary of the Dataset\n", + "data" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading file 'irma_gfs_example.nc' from 'https://github.com/Unidata/MetPy/raw/v1.0.1/staticdata/irma_gfs_example.nc' to '/root/.cache/metpy/v1.0.1'.\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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127.985115,\n", + " 127.525116, 126.24512 ],\n", + " [ 96.625114, 96.38512 , 95.72512 , ..., 130.02512 ,\n", + " 129.40512 , 128.34512 ]]]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i4Wgjedt3THp" + }, + "source": [ + "MCAPE, MCIN, LCL, LFC = cape_2d(pres_hpa=pres_hpa_.values \\\n", + " , tkel=tkel_.values[0,:,0,0], \\\n", + " qv=qv_.values[0,:,0,0], \\\n", + " height=height_.values[0,:,0,0], \n", + " terrain=terrain_, psfc_hpa=psfc_hpa_, \\\n", + " ter_follow=ter_follow_, missing=9.969209968386869e+36, meta=meta_)" + ], + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Fyt9KaBu5SMY", + "outputId": "a7b81879-34f0-472f-b1df-68d3a6172ef3" + }, + "source": [ + "LCL" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[1592.57080078125]],\n", + " mask=[[False]],\n", + " fill_value=9.969209968386869e+36)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GJRF6OMb5TJP", + "outputId": "840ab4e0-3aa4-4e2a-f35e-78ad85e47a5b" + }, + "source": [ + "LFC" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[47610.48828125]],\n", + " mask=[[False]],\n", + " fill_value=9.969209968386869e+36)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bhCWRhGF5Wu_", + "outputId": "95d6982c-0170-4064-d816-ce8fcc46e080" + }, + "source": [ + "MCIN" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "masked_array(data=[[--]],\n", + " mask=[[ True]],\n", + " fill_value=9.969209968386869e+36,\n", + " dtype=float64)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vNsGABNr5jqu" + }, + "source": [ + "# A smaller difference between the LCL and LFC (LCL-LFC) is conducive to the rapid formation of thunderstorms." + ], + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WW8KXzpL7A0d" + }, + "source": [ + "# Moist static energy\n", + "mse=0\n", + "for i_lev in range(height_.values.shape[1]):\n", + " mse = mse+mpcalc.moist_static_energy(height_[0,i_lev,0,0].values*units('m'), tkel_.values[0,i_lev,0,0]*units('K'), qv_.values[0,i_lev,0,0]*units('kg/kg'))" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "il-tqHuaxgUf", + "outputId": "6fa89998-ba91-458e-c227-719a704ad057" + }, + "source": [ + "mse" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/latex": "$1770716.740489295\\ \\frac{\\mathrm{kilojoule}}{\\mathrm{kilogram}}$", + "text/html": [ + "1770716.740489295 kilojoule/kilogram" + ], + "text/plain": [ + "1770716.740489295 kilojoule/kilogram" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vWXJYgSCxr2g", + "outputId": "36a8b2ee-9376-4cfd-dbcd-dc54d93231ff" + }, + "source": [ + "dewpoint = mpcalc.dewpoint_from_relative_humidity(\n", + " tkel_.values[0,:,0,0]*units('K'),\n", + " qv_.values[0,:,0,0]*units('kg/kg'))" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/metpy/calc/thermo.py:1022: UserWarning: Relative humidity >120%, ensure proper units.\n", + " warnings.warn('Relative humidity >120%, ensure proper units.')\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J8GAN1Kg64uw", + "outputId": "eb531d6f-5972-4691-b0d2-a9ea10c8c0d9" + }, + "source": [ + "cape, cin = mpcalc.surface_based_cape_cin(\n", + " pres_hpa_*units('hPa'),\n", + " tkel_.values[0,:,0,0]*units('K'),\n", + " dewpoint)" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/metpy/interpolate/one_dimension.py:147: UserWarning: Interpolation point out of data bounds encountered\n", + " warnings.warn('Interpolation point out of data bounds encountered')\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OIZkxSLT66Vt", + "outputId": "208ee1f4-859f-4c42-9b95-b7f16b02efa3" + }, + "source": [ + "print(cape, cin)" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 joule / kilogram 0 joule / kilogram\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "E2oWe7Jn7e1-" + }, + "source": [ + "pw = mpcalc.precipitable_water(pres_hpa_*units('hPa'), dewpoint)" + ], + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "m0bvyHiZ7qCS", + "outputId": "d06479ec-518c-47c4-bded-e7f72d2a575b" + }, + "source": [ + "pw" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/latex": "$1986.985242428795\\ \\mathrm{millimeter}$", + "text/html": [ + "1986.985242428795 millimeter" + ], + "text/plain": [ + "1986.985242428795 millimeter" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_wL9qA377qb3" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From b0d23fd98b95123bdb4a7e5e90fa6d04ef5998ce Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 28 Jul 2021 01:37:17 +0530 Subject: [PATCH 090/171] Degrees of freedom --- .../pattern_corr_degrees_of_freedom.ipynb | 302 ++++++++++++++++++ 1 file changed, 302 insertions(+) create mode 100644 xarray_tutorial/pattern_corr_degrees_of_freedom.ipynb diff --git a/xarray_tutorial/pattern_corr_degrees_of_freedom.ipynb b/xarray_tutorial/pattern_corr_degrees_of_freedom.ipynb new file mode 100644 index 0000000..84ea465 --- /dev/null +++ b/xarray_tutorial/pattern_corr_degrees_of_freedom.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "id": "2fe942ed", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "from scipy import stats\n", + "from datetime import datetime, timedelta\n", + "import cartopy.crs as ccrs\n", + "from matplotlib.axes import Axes\n", + "from cartopy.mpl.geoaxes import GeoAxes\n", + "import cartopy.io.shapereader as shpreader\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "import math\n", + "import scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fb14bf04", + "metadata": {}, + "outputs": [], + "source": [ + "ds_aod = xr.open_dataset('aodana_ano_2020_gpcp.nc', decode_times=False)\n", + "ds_pr = xr.open_dataset('gpcp_rf_ano_2020.nc', decode_times=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "97c81933", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5NTMZsSUxw5WPQX/8rJjCixHbDAXn4k75/ii2E2yxD5F0Kw7Nn9sutSqvVtSO3Q9SaLBPcTmgPSu6rm2qSVM3+hh2fQ6p9CFOLRBoz+C1mYXfAR0HXUaSMDfBATTJJCQkJCwhpDzJJlRj2dd6IrJZRP5FRL4tIteLyE+IyKEicpGIfMe/H7LcdiYkJCSUIWStrNHrYMZKWMm8Ffisqj7X51JYC7wOuFhVzxGRs4GzgT9qVJtfHht11hPHZ8x6aqWn+bJ+NqKkJqN7oYpWmzCnyLrm/XvxeF0myV6BfrFz56JGWzHVEHESRXFLndJozYTr2K4Zpyua7WpoZ6fzqyyMgfuw1p9zrz/Hi8zozvWC3fvm3M579rvw/eYUuXW9U39tnHLGtTNoZZ4G8zZtWOOOTa11/x/kyIISCNCszYx6Z0zb598nO05lNuGpw7mJyfy88Mmda3TZVEFNBmXnWrsu6/0NYDTZtD++pt0u1TXX0/w+EBuvYqs5vTnAr7LPZrMpjwWmfeeEe8Y3VHc/VinTgqrN6g+pJvP91h+jA+N7NjgoN6CD4zh8xZqmrH5Tf2YTvt6y4rNIheb0ddyMlM41aly6vfA7EKeyGAUkgyz+0Ujow7KOkIhsBJ4AvBdAVWdV9V7gDOA8X+w84DnLYV9CQkLCIKzWlYyIvEZENorDe0XkChF56jjaWu7enwDcCbxfRL4pIu8RkXXAkap6G4B/P2JQJQkJCQlLDhGk1ey1AvGrqnof8FTgcOBlwDnjaGi56bI2cCrwW6r6NRF5K44aawQROQs4C2Dbtm30vAoJ8mW20WR5GPKCGiU4lPn6sP1lqqCn+VLf1DVGU8QqJqsjk/7Q9LMRn9VuGT1itkmod1h2waIKqBPVb+92b++aNaowd9rc79VJ1p5RRPd1HT2W7ff98/t37J0FYKbb63MytXrXeu5u71y39D7RkgINUu6HXYOpljt32ke1bWVdet6L0GzcMOWOzXnqbbKgXgN3Lcy2iYhOyilS974/ui8AZve7g3aN+5008zGLfzesW3Y/WL05rUYf4hhitp1TpO79nv0dbts1U7LbxumEQ53ibo3fNporK3wOisXonqrKlNmJ7ue4f7E6r2R3w3tXyL9PsUN0n0KywOiFdAchNpo1UL7mIYafZDlNllWr1hYDAbLV++DfDH8m8H5V/ZbICAengOVeydwC3KKqX/Pb/4KbdO4Qka0A/n171cmqeq6qnqaqp205/PAlMTghISEB8LHLskavFYhviMjncZPM50RkA6PPzA0s8ySjqrcDPxSRB/ldTwKuAy4EXur3vRT45DKYl5CQkDAQWUsavVYgzsSxRo9S1b3AJI4yGzmWmy4D+C3gfK8s+x6uoxlwgYicCdwMPG9YJT1V9s71UzkxuuRxp8Iy35QuVqZXVp21sn6HvXhhGVNtnQJ1U5cWYM2ApXar0Db00y5FmqFbUD0VEVM31u99c71Au8SKJsNuzysZTWbt/+DefWyccrfNhP+HNhH1w+o29FTZsnaytG+iZRRYu1THHk/tbd8zw6Zpd2z9ZNuPhe+7Lzvt1VHtAiUWD6kXwOXjFWW0bEl/yoVAGVFWohXjkdn16fvrZxRor1yXan2YfqN3YvrMbD187QTrvdfnPnOWNWdMf+cZXVa8b+K4Y1lEq/bdl3FfCvUF1ZxRUYVzbZ9ln7WyIYVGqCunu2I6LjhsNnEcjVRrdmzKj4GlniiW7dQoPBcDyYQs9sZd4RCRk1X128ApftcJY2LJApZ9klHVK4HTKg49aYlNSUhISJgXVugqZRB+F/cc+28qjinwxFE3uOyTTEJCQsKqhKw+j39VPcu//8xStXnATDKqjgaoU7bkcaDy5bKpb1qYykYqz3WKNFPtGAfgVT6UszQaZjq9PoWMtRfCqZvt/r3IIATaw5RAIRVhuX9ZSwL15JmnENNrj08DOe2P7/GcwR27ZyucPt379j2OHvvC9Xc4m9vmzOrIlFaWsf2+/aVzja57yNGbADhs/aQv62mmdsZez1sdt9mpoTZNufFqR2yDqaZOOmxtoFTMiTSEgTcVk9luKjPVQI/FCGH1C0q+GOZXF9MHxXD9MeKw9uF6+bqKdF0dLdHxN4qpDYno3Im2sCYMlHu3Pk/jnFjnIrppptNjn7/eRiO1s/L9F6sSi1TzXKCM7d4q226mFvtl6q6QxdWXjWnoImLFmH0Hg8Kv8GUcFlctpMOwbK7aC6qy0K8muREaQrzH/0jqEnk6zjG9BbxHVc+Jjr+Y3CF9N/AKVf3WItp7Hs4JfpeIvB4nuPpzVf3mQuusw4qUPSQkJCSseAgj8ZMRkRbwduAZwIOBF4nIg6Ni3wd+WlUfBvw5cO4irf/ffoJ5HPA0nNP7uxZZZyXSJJOQkJCwEIiQTbQbvYbg0cCNqvo9VZ0FPoKLehKgqv+tqvf4za8CxyzSetN5/CzwTlX9JE5hNnIcMHRZT53yJo71ZRSEUS1Fh0hbdudZ+Sida1RHO8tpshCS3ELX+6V5r49eyPoUad0oLlNfJk7NaYOgZIpyCMbOZN2e9qlsrK9zPVfHTLesJLv/5mnu3ufije3Y697v2ef6tX23c/rbtMbdb8d4Z7/Jdv5/ZN/segA+e8WPAJj1535v0mKVbXDtetpuw3R+mx2xztVrtBhzpe6Esdo7p8G5c12o15Rbfhwlp4YgVnqVaRgbNovjltOQBRVeDdUWIsgXLoV9Q41qjR0RDTbm0xUx5qzeqUgZZijeF0WlI8CE2eS/wjbCvVC3YESV3fN2XxhNViXE3NOxFBn2vfE2R2Mz2c7v89heCduRcrHgHBorL+scOatSacTKywCj3LxTdlHBZmNgTsijgAjzocu2iMjlhe1zVdVWI0cDPywcuwV4zIC6zgQ+09jQavxIRP4BeDLwJhGZYkyLjgNmkklISEhYWswradkOVa1S0bqK+lE5G4rIz+Ammcc1bbgGzweeDvy1qt7rnd7/YJF1ViJNMgkJCQkLgTAqb/5bgG2F7WOAW/uaE3kY8B7gGap61yLb/DNcOJnvQIgRedsi66zEATfJBOpDytuxUyPkdEUnxPoqK1rs/slEUDvNQod72gxPm030nCpLuo7/abWnQpa/ThRevhf9bzHqoycSKLpeVZAo8jDtMufC3be0l4cvt9ht3iajOqa9MmkuqLV6gYo6brMra3HGtviY9du8Cmym088hXbHjXnfuVkeLmQKtG4311s0uxtijt20OqjWj7sxh0+zYPevGs0i/tAN9GSmbousT1EyaK5wC7VIjMS1SpaHFKJad0Twzc+XrJ1I8p0zD5nHPKL33VAI11I6vf/Q7Zf0p0oPx39qghPTx3UJqiwIla/eS+vhd++yW9XW0C/SVba/3qTGMZozpRbPJlGudTi+kheijr6x//l0Lqjb7PsX96gvjb7YKeRoPg236vsdUckvymHlNs+HOD4JkI5lkvg6cJCLHAz8CXgj8YqklkWOBjwMvUdX/GUGb3wbOFZE28H7gw6q6cwT19uGAm2QSEhISlgQC0lq8x7+qdkTkN4HP4STM71PVa0Xk5f74u4D/AxwGvMM/y+0MoN+atPke4D0+pNfLgKtE5L+Ad6vqFxfXozIGTjIicmGDOu5W1V8ZjTkJCQkJqwMiQmu4cqwRVPXTwKejfe8qfP414NdG0piHl06f7F87gG8Bvysiv6GqLxxVO8NG6McY3DHB6btXBITc8dCwey5wH4BT2KyfKDunWdynPMx+Ob5VTzXEimplFlLd/YPJfJwk6c6Wjel1w1J/wi/z94tTVhkFFZwI/TJ/f6eXpwwIdEtZJWXnrDdqbHZPsKHns0x2ovhMRuUYXeYc68oUmqm9LE6Y0Vj7I1t37J3lZE+T3XyXK3M/T4tZWoVD1jjb7rfB7d871+PkLeuAnIoqUpHFc4x62dfphbLepEDdxDG+ipc8dgyMs5LGzn9d1T7llEbOkHafDAp7ZVV0IsowOOBqISOlMaF2bqSwiunBYpbLLDgO+/awMSm7PmaS92M2UpOF+G1e2Rffa+6zt81/f/LYeWXqcLIleebLmvHpRteg29O+Mcj7ajZJoTcWe8xix5XPCtRy9B0pnl+VbXQUWKERlodCRN4CPBu4GPgLVb3MH3qTiNwwyraGTTJ/rKpfGlRARP50hPYkJCQkrA7IvNRlKw3XAK/3EZhjPHqUDQ2cZFT1gmEVNCmTkJCQcKBBYFQP/pcMInKq/3glcHIc6khVrxi1AKARoSgiD8RpqO9fPEdVRx6xc6EQystk+xyULwXHR1MwTRmlEi3aJ7RT2tasTSuSAFksrpanwNqTU+5crzKbzSbz8P+WrdDzFRa23Zb5M547UFXWtM2BzZ8bHM88peeJkmyPc/6VmV1Ix/Ee2vYxw6YdbTYX0SRFJ7wqB0PoV3sZNvnAaLtmOuyecccefswmII9HFjJhelvv2T8X+mvOn/dbP1WqN27fqL017SxQTRMRjRRC5EfOmUJOyQRlla83OF9GGUxbIpDZ+PudpgK0a2DGFRwE66KjB8GTP3dQrKzQXLTfKJ0WOX2bOyuK75/RVoQykF/jfZ2ckoqdTa2fMXU01+0FJ8vp8A/d36MdU8+5vXZNtCWI0Zo1z8DjrLFVfY5VZX1+lhQyeVbQYiUUHD2LqRZGjtW5kqmKvmxQljEK80dxcW3eTXXaiYSEhISDC6Pzk1kyLGX0ZUPTSaajqu8cqyUJCQkJqwijVJctB0TkobiAnNO2T1U/MOp2hkmYD/Uf/01EXgl8ApgpGHT3KIzwUrrLgR+p6rN8u/8MHAfcBDy/EByupg5HfcxGUqHJSG3WEgmUyt7ACJWX37bql47ramvv7fSmnaKqN+0ooinLgBg52O3zrmfdrrLPczbrfQz5nTNu+8i13nnRt1MMgR5ilnknO3PulP3u+Vxrj3P0vecT/+janZ5i5t5dAGx59vNdHUf/uK/LO9Z5jmPOX+77ZnqB7lvrPUHvdr6dgSaLFTyX3+po2i9cdwf37naU4JaNjvo6/eQjALhzlxuvYw5x9JnRZrtnOhzmM2Pe6Z0yLevldLv8T7B4vWr8Ufvov6oQ8iGsfXROXxqHTIj9TePqqliZeFcd9VU8N65XojJxuPti7Lu6LKu2WUXL5RlgvcNo5MxY5WhrmUl3ecp487S7n/M/7KYy8/QduTNx7IxriGnNIuKYbOVWcmixTFRPJ6IOTdc5UchoG5xJY0/oRWK1rWQMIvInwOm4SebTuAjQXwaWdpIBvoG7vnZlirFtFDhhRHa8Brge2Oi3zwYuVtVzRORsv/1HdScnJCQkLDlW5zMZw3OBhwPfVNWXiciRuJA1I8cwddnxACIyraqlTFUiMl191vwgIsfgwk2/EZcaFFyY69P95/OAS0iTTEJCwgrDalOXFbBPVXsi0hGRjcB2RrdoKKEpofjfuMxpw/YtBP8P+ENgQ2HfkT5gG6p6m4gcUXWiiJyFy1fNUcdsY/dcrkkw9mBdFAcqE/pUN6aUCXHNfBZIvINld/3haNtRQxYp3BbsRu+YQmy3pxt2z/W41dNHptja4L0KQ9h5TxWZk+H+jhYyVnpVXMu1O33jVwHYcdG/u/bu2e3q3nooG068v7Np0/38qZ6OyxzV0YukNesns0B/GKW3fY+z9TYf6v8QT5N85jqXIdOojvsfto5HHe9Y1H2zbpxu3+n+fxzu6bMQet+rttZPtdkw1fJtt0s2BWfQlsXecnYVqY4YuZMhpXOKyDM7um0b81yplpeN6ZyFZNRtRydpNOYiEvi/uPq6jJn58aIyrOxUajSZtVYcC7NpzmdxNZNs7C2jqjncTrczdnoO2RSQ1q3JKFOliS2L/bbvVSdy4DVUUZa9yPDcmTZX1oGjuWM1nEbjae33gqIsL29qwyqKcKEQEbIRhJVZJlwuIptxYq5v4LJtXjbwjAVi2DOZ++FyHawRkUeQX8+NwNrFNi4izwK2q+o3ROT0+Z7v8zGcC/DQUx4xDpFiQkJCQjUEssnV+eBfVV/pP75LRD4LbFTVq8bR1rARehrwK7jQ039DPsncB7xuBO3/FPBsEXkmTuGwUUT+CbhDRLb6VcxW3FIuISEhYQVhZFGYlwUicjQF30cReYKqXjrqdoY9kzlPRD4IvEhVzx9146r6WuC1AH4l8/uq+ksi8mbgpcA5/v2Tw+oShMksD6duii6LZWZL9/0d7Vsyh9DuPUf/rJlwtE97In/sZDSYOQuaOmuPp+gO8Wqp/d283BavqDLFltESsSPkrllPK01kTHk7d82VqZvuA3/anXPC4wHYuu92AGY2HsU+b9NO/+79QZnIuqW+G9qZhLbtmDmo2tjccKej4ywemcUrO/2EwwJFc9O9TpJm6QGm2tVfuA2Ff3sbJsvxp+KxKMbRCtkR/bnBCdNTa32Ol6rEZEjswBmjuD+LQt83QSx4iumsbn6g9lwrY9SrjU2r66jLuWwy1Gv0VCcy0uoItFC3V7ivy+3a9bN2LPPo/rleuJZGl9n3py+Lq69rX0drx7bbtbHw/cXOLai+ssHXpx21V6q/5jrZWO3r5LSpOZMOYSbnBVmFfjIGEXkT8ALgOnKhqwJLO8kA+IdDvwGMfJIZgHOAC0TkTOBm4HlL2HZCQkLCcKxuddlzgAep6sywgotFU0LxIhH5fZzvyh7bOSo/GV/XJTgVGT7r25NGVXdCQkLCOLCK6bLv4fLJrZhJ5lf9+6sK+0bpJ7NoTGTCkesmgjNmoIH2OudFnVoPwD6dCKHPQxZDX4ctye/zTpPFGFl1ocKtHaPTNk1ZrDRlX8fCpJdvxImg+ik7ke3vKvv2upWr0X33zZQpL6NUbp04EgDZ3+0LfR9omBrlTqenoc27fV/jMPDb73P33v0Pc/qO0084DIAHHjod6j96o6MDjUJsRRTY7lnrSyso6WK1V1BNRSHsVTUU6qOePKwPeVg5qXW6jFEV9yrEKouonDjWl1CTgL1ga0gXUO0/WCpr9luR/B6eDGXX7XaZcXsbnNCy1Xa01r37u/6cslpvtquF+6B8v7ejIGPWv3WTLdZNWCqLsgqvF5XtdPN7KlbWGYKjZYXDpX2qc2I1FOMK9qnyou3gmOqN3dfphbYPXeP6vDZOQ7oYiCDZqlWX7QWuFJGLKTvYv3rUDTWaZMxfJiEhISHBINCeGF5sZeJC/ypiLArdplGYJ4BXAE/wuy4B/kHV0h4lJCQkHGSQ0aRfXg6o6nnFbRHZBowsG2YRTemyd+L4u3f47Zf4fSNNB7oYZJ39TO34DpPiaZlZp3zqTfmMjJOO9lk7kZWUZkVoRLUEVYzkMbYmvYNhxyvRZmfM4dAVPmytG9K793UDTTHhl9RGJ+XZKMvKt+mWcJ+n3YwKMGrt6u3uUdj9fVh9SwmQIaUQ9FB2PC3WZegqXHOni4V29R0u7tlWn8XyO7d7J09PqZzgM1re5h1LTzl8imzvPc4GoyB9uoM9c9Zf186mKet3Ic5YRLcYDWP9bAeKRfqopkBvVjhUgo9/FKm8griL8lj0Ck6Mti/QY357NqiwqEU3oiJjJ8xBSrU4VlmMQGH2lLl1WwFY4x80mxNt3r4vW2jf7jdLNWHivzgDp9FzG6dahdQBVp8/x9cZmN/C4NunOA6Z7bdrKj2nslTJUHMY7uXXoYhu5GhZREwzxv2wc6dbEmzZ0POx/+68raLGhUKC4/NqhIhswYmqXoTzh/zEONppOsk8SlUfXtj+DxH51jgMSkhISFgVEFbdJCMiG4CfB34ReCBuYjlBVY8ZV5tNJ5muiJyoqt8FEJETSHllEhISDmLI6nTG3I4LH/N64MuqqiLy8+NssOkk8wfAF0Xke7j5+/7Ay8Zm1QKg+3czd+1XaB9+NFBQw2x7SLkc/aHigzrK3y+mhNrsFV7SnQ0Vtr2j5j5Pta2b9I5fnr64+b7ZsG2qss3Tnhrwy/j9XtE16Y/vL3iVbfEqGFvmb2m77UdsddSU0Uxfv9XRXD9+5HrWmJ0RjRCojojv6XaVe3ymyh8/0jlZGh146rGbAUJofmNFDlnjHnDeurfHUeuc0sxSIZgTpkjkVOjTFHSzXPUX6KuI66pSCgUaK6IDjWaykO623cqkjybrC40fOUtmklM1ccj4uphixXuoTvkWI5N+6s5sk4hii500i30wZ9lu1L79n14/YfdP7khp18eamS04DPsWAVjXlkDdxfHuDFVqsFiFFw+bzLnYdtI1L+E1KOUVQDx8VbHfpnF025RPdyFz+0plWhsdpShT7ju6/u4bYZcrO3P1VwCY7YzwMfIqXMngIrW8EPe440Mi8s/jbrCpuuxiETkJeBBuaL+9FE48CQkJCSsXgqwydZmq/i3wt56NehHwr8BRIvJHwCdU9X9G3eZ81nqPBB6Ky0HwAhH55VEbk5CQkLBqIP7Bf5PX0Krk6SJyg4jc6HNoxcdPFpGviMiMd4xfFFT1e6r6RlX9ceBRwCbgM4uttwpNJcwfBE4ErqQc52bkWdQWCu326O25D0t2OXnSKQDMrdkMwB1zrqsz+zvBSdIoAQu1v8lTDSE7o88kKLN7kI5f6vv61rQdnTQRbiAf/8lTEHftnePufV6RdaRTaBk9sWWNsyWOq5QJrMEt51v33OrOOdS5KMXshcVF275nljVtpwxb5/9UmULN+rHexzDrtSZ8ez0edfTGkk3bfcbKOU+lHOptPGKda2ejp+R65OkO2r6+mGYKtIy4OrqF4Fl1zonx/io6Zlgdc13tC3kf0y4xelqvwpsPsohii2k5gIx++quIeHegByk4WXq2p5WV340as8yqIpAZTbV3rzfOX8OpTaV2ptq5ja2ez8Tqr22s/iqmYgh2qx3zTp/2vfF0abb/Prcd6K17yFqejp1cU7ItvFsWT69Ia+25K1Bf3bt83L6b3Z/uno8N2F67xvfXtfODq24MEuOJdSNJfxVhNOoynxn47cBTgFuAr4vIhap6XaHY3cCrceFgRgpVvRq4mtEEPe5D02cypwEP1mHf2ISEhISDBaPzk3k0cKOqfg9ARD6CS9wYJhlV3Q5sF5GfHUWDS4mmdNk1wP3GaUhCQkLC6oI4x6EmL9giIpcXXmcVKjoa+GFh+xa/74BA05XMFuA6EbmMcpybZ4/FqkWgvfU4ADqHuff9mVOa3LHbUQe7ZjvBeWtLUFBFSie/7cOIsXbNIWQzuyrbs1hIU7tdBslda1xsqa3rJ7ljt6Og9nh6zmiyH+1yNIIpvCwFwFxP+Y6nIY7Z6O6xDfscFXC3fzfF2iafWmDH3tkQv6rll+5GXxmFsk+94qgQUt4oKHMqPdSrxx7q1WZHeO7NRsYoOHApCQBmtJwewOrs+kyMxfDscXy1GLZErlJ2xfRYrOQKGRELu6voqiKKFNmwNAB1YfyL+2IMynY5jJaz2Futgo1xRs9dPi6c3X/mCDvd8tTrvp2Id0g2VVdv2l3byZmd7j1z95BlfaXT7ZOG1cUWKzpLWsqAtr/imafFZL/7zsico+uyGedQrPt2wZynhdf5hLiensNTbL19rqytFLr33Mn2L1wMwK1f+66rz4/J7B53zqS/Z1vekXj91o20pv133L9no4yaLIK0J4eXc9ihqqfV1VSx74BhjZpOMm8YpxEJCQkJqxKj8ZO5BdhW2D4GuHUUFddBRH4K97t+f9w8IICq6siDHjeVMH9p0HER+Yqq/sRoTEpISEhYBRhdFOavAyeJyPHAj3B+LL84iooH4L3A7wDfYMyO9aNKUL0g6YYPyvYB3POeHnCuqr5VRA7F5a45DrgJeL6q3jOormzteqYf/jj2HOWi39y519FLu/c6qsAoKRcTy8dW8otUo5eM3tnlw+tbSoCZbsbaCbesD1n35sqOh+qzaK7DtbeTFhN+aX6fp7Pu2uuW9f96tVPHmOOjUSH37JsLmTV/uNNRDg/a4pwwTbljtk/4dg9dM8Eur67pRSvse/a5dtd5ZZhRK+snsqBWsnhgRoGZ46j9P9szZ+NGsHXG4o5FDoEGG8essB2oszhVgl0LK6t2bl6uig6rQpH2qosHFivJqqiyuD95Nsjq+GTFc5pQbHGb1r+YBjQVX1VWyEnLoDpTVhL625LpmZD2CZ3wCi5PY0qnTKOV+uHVXL1Jd9+1fOw/o9RaRq1ZXzozQTUW03NBnTnnGHb172QtxLNjOrvfH9tV2u7tcYo09c6Td192OT+4+NsA7PZx/G7Z4Wi4w6fcz9hJT3d/wjedmD/OaPmsrJ19zqZDH/9TfX1eOEajLlPVjoj8JvA5nEz1fap6rYi83B9/l4jcD7gc2Aj0ROS3cWKs+xbY7E5VHYtkOcaoJpmF8ocd4PdU9QofU+cbInIR8CvAxap6jteMnw380WhMTUhISBgBhFHRZajqp4FPR/veVfh8O45GWxRE5FT/8Ys+zf3HKT9nv2KxbcQY1SSzIKjqbcBt/vMuEbkep6o4AzjdFzsPl1ogTTIJCQkrBoKsxlD/fxNtF8UICjxx1A2OapJZjB+bq0DkOOARwNeAI/0EhKreJiJH1JxzFnAWwLHHHEV3w5EhE+bha118ralWz2+7rrYkp4CMeopDxpsazJwku72cbjtqylFQu9XVN6GOXtg74ZwbjX6abPXYstZxAnvn3Dk7Z1zZp57surPeq2Ds+PrJNrs99TXlY5aZiuzmnY5GMGptg6UPyISd+905Ozwdt8HXa/HGzBfSKJW1bWGdV/HMTTh1zEzESZlTqbUXYnSp9mWQtFONggvR4O3cAevcmDoqUV5DHDbjc4vlq9IAFBEoKq2iv9y2qbwsJcOUUaMNlGN9zpnkdFsfRWfh+k2dF1FtqvnY5vHHjO5020eu9TTnrKOSspnd9KbdPaktdw8ZbZXtc+oyiRweydr0vHNkNutSPuAdKbs+Xl2r5R0qJf8HHxyVtUzsGfWWJ/ZaF/brPk+Pdb2j8H6vPJvx9JmnyeZud8rezp79dP335Nu3u7LX+/QTP7bBUXiHfccx6lsediIAa+93GL0578y5ztF/smYdI4MINFeXrQio6s+AC3JsfjkGH2pm5Gi01hORdSLurhKRB4rIs30iM8NLFmOEiKwHPgb89nw4RlU9V1VPU9XTthx66GJMSEhISJg3JMsavVYg/qVi30fH0VDTlcylwONF5BDgYtwDqBcALwZQ1WsWaoCfrD4GnK+qH/e77xCRrX4VsxUXnjohISFh5cBil60iiMjJwEOATSLyvwqHNrJAAdcwNJ1kRFX3isiZwN+p6l+JyDcX27g4LuG9wPWq+pbCoQuBlwLn+PdPDq2rM0t25/eQabccNtrA62qCI9q+qUPY4VVXs91q5d5cFFxq6/oJNkx4KgO3gFtrmh+fgVp8nK7gCNkWDvN0lVEdJx7irqGpvEwhdo9Xn92xe44j17vl9yGeJjOKS8Sdawoko+/u3DPLBp+B0vbFWOttt0yV2cxu8Aqg6b23uG4c6lbKpjfyLGNIG2Bt3Lu/20dFGVoR/RgyJTagwGKISCEWWnUo/D4VGDpUgTYoplmcKsHGzVCM3xWr1PrSKRh1GCkZi+hExrYiPtBSGQgaaMtcFeiKrvPGtu++yZ36vW+5c3/scXTXbwFgr+f9NvTudvV5NZhRUjrnrnq2Zh2esEB9SgudXl82umvUmjeg1ws0We7U6cuausxoM6n4R+9tMClwoM28yqzlw/ZvPH4rx55evr9P/KHP6vpIF4zkhGc+0pV9/FNcndPr6O2615lrTp+jRlWfVjYeBDwL2Az8XGH/LuDXx9Fg40lGRH4Ct3I5c57nDsJP4ai2q0XkSr/vdbjJ5QI/qd2MSxGakJCQsIIgq26SUdVPAp8UkZ9Q1a8sRZtNJ4rfBl6LyzdwrX9A9MXFNq6qX6ZeNPCkxdafkJCQMDYIaLasAt3F4A4R+TfgsbhF+leA34nFAKPAfDz+vyQi6/z293Bhp1cMtD2BHnJUUMoExzDb9s5p67mdE+73YwB8+26nTjF1l9FNa33I/63r8+yQt+7xzp0+hpc5La7PXB0zni6ztAGtQhlLLWDH1omP1+5jf23yzmQTmQRaxZwjLR5UOzOlmts2p80taydCJs+7vcos8/P2mglTurnjRs90JtYx6amN4CDnYTP+RPQHbV/BKdPqsYyLIdmB0VvlU0ux4YZlXMzLFWyqcXQM51bUWUfD5bHR+v+B1jlJxser6MI4K2QePV9Lb5Cr02JnVg0Ot1F7KoE2tfvBsmlmXk2Z+ThhRii1d94aYoht9H2174JOeQpsjQ/9f4sLmd+9585ckusVYdmko2lbGza7sms3e6MmfV+y8G/eMqVKxzJhdkqDE9rP2sgaT195dZbuvMt32lO661074jPdtjtzTB/jIq+Yekz9d6C1Zi0Akye4LLi9Lce5Ou69ldnvucfFrU1OHdc+6nhGB6nmQVcHPoRLL/DzfvuFwIeBx4y6oabqsp8QkeuA6/32w0XkHaM2JiEhIWFVoXkU5pUGUdUPqmrHv/6JMQXlbNr7/wc8DbgLQFW/BTxhHAYlJCQkrAYobiXX5LUC8UUROVtEjhOR+4vIHwL/LiKH+rBeI0NjQlFVfxipecYaVG3emJuF7T9Afej6zC/vbflt9Eh3wxFh2X6/9Y4SmO2WhyEO/X/3/m6gLoya+sG9jhq432ZX16YpU5u59z29VgjHfpgP8b+mk8eTAuhNOiWcj87OhsksD5u+7+5S2Y2TFkPK1WXOmnvneqyftJvYZ6LUclw164+p5iaKfI8pgPxmN6JwYtps7UQWykx3y455QV3U55SXbwfBp5Wxc6PMiJpJX8ZFQ18my0CnSShb95dsELlRl5kyRiZSy5JIZKsGKiyPxxaze3mWSd8PLX+1RHuB7s0zRlocMh8XzD8baB+V+9OFUPt+u7f2EPc+WVZgGpU0d8t36e31bmqexursK9+zRklx5HGu7opnEuoztXp/5TyWWS+X+6pXaZpDaP59LajWADFHzslpWkadbXCqOaMIuztcwOKOz5jZtpQX92wnW+ccUluHHFGubxSQ1ffgv4AX+PffiPb/Ku6rMDLHzKaTzA9F5CcBFZFJ3POY60dlREJCQsKqxCqdZFR1lA+nBqLpCL0ceBUurtgtwCl+OyEhIeEghaBZu9FrpUFE1orI60XkXL99kog8axxtNVWX7cB7969YaJfe/j20tjrlCV7hoj5UuS2/W7u2B4rhsCgzYFDdmAOdV+cw2Q7Uw13Tbtk9Zed4Jdp+P5QhFUBHc5rMhzHHx3jSSaeGMdqu5bMCquZUiVEOljFwzqvX9nsKzhwrVXPHv3XmMKplx704xlhLoefrnd14lDvWKyubLAabUTnF0PshA2bm+hHTO4Y8Nla3j0ILiqSuhX83bsXinWmuNLLrVDxW3PYUqfR6ZL7ennjnvj6qLY/BFvoTpyqIKLdYzdZTHZqtM88omY9rO9BiZToz83HkLCtkyBIZGszHN8QJ8/vCD5h3NsayT05MBXpMvRIsqLt86P+OP575c9ubjqRz9X8CsPOqq52NG9z3Z8NjT3dlNzq6vjO9ydWpvRAbjYjyjMcioDObO2hO+O+Cp9SyDYeVu+6P69S6vF5z/vT9yHz/Mk/h2fH2mnX0dpUzhHRNxTYqrNKVDPB+XC6Zn/Tbt+DCynxq1A01VZc9UEQuFpFr/PbDROT1ozYmISEhYdVApPlr5eFEVf0rYA5AVfcxgkDHVWg6Db8b54xpBl2F01UnJCQkHLwwP6Fhr5WHWRFZg19oisiJFPLKjBJNycK1qnpZpC7r1BVeFkxMk207ma6nAMKFNTrBluP7dpLtLS+hbdmt+3eWzinSDKZ+OnTOnbtrytEGXU9rdbzDZbcQu2xaZ0u26JpNvkG/3DfKyJzVipRSFHjPnPC2eArOKB1z+Cy2baH1Y8rLKCIhd67MaZ1qGOUWYqj1uqF+9ZSUmoIv6kfoX0buJRjRZn3yziK9ZTRYpIAzBNrM6EbJgi3WMXOKDFSRlGk5GE6P2bgWx0ojuiweRxv7ohLObIlpv7xDpqzy9FhRgefvSQvfH06xsTYFl93/FNR+th3Gxiu6PG1sjpW9NZuQx78IgMMe6jNI3vUj187GLaU6zNFT21P5/e3rCXSfXTdPA4ZrPdkO38fwXcg2lLdb5RD6pe+GlfH0dtePTYmeBWRqHbLpfn6fd1TujfZna4XKk5vgT4DPAttE5HxciK9fGUdDTSeZHX6ms1nvufhkYwkJCQkHJ2SlOloOhapeJCJX4MLKCPAa/+x95Gg6ybwKOBc4WUR+BHyflS4ESEhISBgnRHKmZBVBXMj4ZwAn+13XA/eOq72hIyQiLeAVqvpkH7ssU9Vd4zJoodD2FPsPzf2HLGNlUIgFJdk0vWlzZPPL+AnvPGbLbou9FEKYT9Pd4FRluzqmtnK4z6u9LPukqcsOm2ox2/M0hJSX/pPt3EEPcuZGyPqcFE1xlBndZ8cLsaOMFmn7c0LcLF92zsdI2+8zfe6a7QZ7zZEzj+nlPnRimiwYmwW6x5w7J2ti/0usKCv2K95tNEagT9r5+EfKs3y7vy5Tmhnd2DNey8d+s7HuFSiwONOmXUNT0U16j9Ti9YoVbn3Ml41n8YDRR3Hstbr+FLaVyNHVKKpQoj7jYyfi8kL6gYhOc1SoH3PvsClHHO+bzbNnQk5Ryuye3CHUbDKlW6DRJkr91KyF6BrfZjV9FahWo9qqytpY2Pc3SimgnYmw0ojVmiPDKqPLROQoXHDj24Bv4u6KZwF/IyI/o6q3jrrNoZOMqnZF5JH+855h5RMSEhIOFqzCZzJ/AbxTVf9fcaeIvBr4S1z+rpGi6VrvmyJyIU5HHSaaQibLhISEhIMLqzOszGNV9Vfinar6NhG5oeoEEfndBvXuUdV/qDrQdJI5FBcc84lFu4CxTTIi8nTgrbhwV+9R1XMGlVccxdOOAk61dt0BQHf94W5H1gqKGSY8jeWzALa86qx3jztHj3mwe59aF9Q0k21HI8wENZmrwigji23W6Smznk4ydZLRL0ZFmaWBlmlJTqVEyqrYES2nXnohJpTGKhtzSvO0SOYr/6+bd/KE+292ZY0+sgHz9I71w9CzLI1C6Fcc2yvEqKoI7V7njGm0llpUs0Issz4nvnCsRp1FQUEVIThl9kfe74uFZqkRYvcGCXRa/8EQlyzaX9y2PpbqKTUQOZBWBGBTTJFYWyTAaMCZKNWE1Z9Fer29XWHDXJkJj++p+J+7tqdzGtPHO4tj2WnkTAv5/WSOtoEKs/u+IjRiUJwFLtL3p+DMXOyfFlhq+w52KxjcRWFEPjDDfu98FuG3As8E9gK/oqpXLKCpfQOO7a3Z/wfAOxnsR/NyYOGTjKq+rEm5UcE/B3o78BScJ+rXReRCVb1uKe1ISEhIqIeMJGRMw9+7ZwAn+ddjcD/6C8n9sklE/leVGcDGiv0AH1TVPxtUqeUaq0KjERKRt1Xs3glc7tN5jhqPBm60LG0i8hHgDCBNMgkJCSsHo6HLmvzenQF8QJ0q5asisllEtqrqfF1JvgT8XM2xS6t2quofDqt0UJmm0/A0Tu72Ub/9C8C1wJlekfDbDetpiqOBHxa2b6Fi1haRs4CzAI46Zhs79nXZ6NVSm9WtCrs+69/Or74PgPUnPSDEEGsf7eKctXz2v9n/+SYAEw/+CQD2rHeOXFPSCyqXWIW1ziKRt8o321xP+zIfxk5+RtPYUn4WmGyZY6Mpq8oOdMiaUh3FzIQxlRbig2mZnnvsMZvydACmvvHHzMZ9HYuH5o5nBZ6p6JgJIN2yA52pjQzS2d/nqBeH9i9RazUYREUNQ1ZTtipTZ0xXxef2VGt5KlOm1V3rYr15feVzq+0cUF9NOStj1yuOs9bpWZZVt33odAtmyg6NweRaBZzUHwsUW3TdtEAZ+ruy451ks5pQ/D3VEHvP6GZTN6rPbGv9K34VjR7rBopwdFCR+dyDW0Tk8sL2uap6rv/c5PeuqszRzNNfcdSslIi8TFXfP6hM00nmAcATVZ0uWETeCXwet7y7elFWVqPqyvV9A/1FOhfgx085dRA9nZCQkDBaaMVztXrsUNXTao41+b1r9Ju4DPhTXLDNWjSdZI7GCfF93BXWAUd5efM44t3cAmwrbB8DjFy/nZCQkLBwaOVKeAFo8nu3bL+JInJV3SHgyGHnN51k/gq4UkQu8RU/AfgL/7DnCw3rmA++DpwkIscDP8IF4/zFQScoSleVnX65P7XOOYRNP+RnANh8wqkAyK030L1nOwDdO53KTL1T5uQDHwHAniMeBMDO/W4ZPpEJa7xyxgbMMkZOG39m4e4ld26MnRTjcPCGYqbKoDzz9IEpuYL6ylcx1cqVakYP9LzTZSvzoc9NheWrn/ZemoeuyWOXmbJtqoZHkJ4bG1PwdHsaKMMZNSdFr47zf7bmMq9ma3kn0UwCtTbnCYtAEQa5j9s/6dOEtjPpsz/YVOMIWSwXf/eHxScbhEC1FOKVDaK2quvIP0sNxRLvL7YRFHAhC2huS7FsoGC1kNpBqsvYtc8sY2pnX6CFc6MiKrbP6Kzf4dEUg4EKtbQEuXrOlG2Btu34WGg+juCctz1QYuT3jFkSO0AT9vePb1CXVfdiwRjRUqLJ792FwG/65zWPAXYu4HnMQnEk8DTgnmi/AP897OSm6rL3isincQ+oBHhdwTP0D5rb2gyq2hGR3wQ+h5P0vU9Vrx11OwkJCQkLRXHiW1Q9Nb93IvJyf/xdwKdx8uUbcVLjBT1bqVGWFW2pckv5FLBeVa+sqO+SYW02VZcJ8CTgBFX9MxE5VkQeraqXNTl/IVDVT+MGNiEhIWHFoRiSaNF1Vfze+cnFPiujyUZcpyyDGt9HVT2z9gTVgQwTNKfL3oFbpT4R+DNgF/Ax4FENzx87MoTpVhYok92zbi0946mbTetdqPL2lt3sPOHxbl/mFWM/+AYA+48+BYB3X+5otJc8fKuro9Njrpc7TEKFD5anBjKfLmCqM0vPh/Y3ZVUnZGV0p8Qhv4qUSk04sLDfqKR2VqQ9LKS6v6xG4ZlKyiu41muX9b7IrFerZbMukENn0oVPN3rJsnYaRdWmF9RknhyhFymCLLWAjVGnpwUKw9UzFZw9fXw1EzV5W2e7ik86aklN+2KKxZyYFCiiQAkZpWatVfwoxEqtuIy1a3VXKbiG/dhkklNdMb0TU2CFA/lHs6XO9uiGjOOjQf89NSnRPb13b58yMdSblcP3G7TVDnH0Ak0WKQRNOWj3jUiG0aMtLWfrDGqt6FqrKhINULdADRZOKbfty6zzlF53RJOCYSU8eZ8PFqIuE5ErVPXUhZZpOsk8RlVPFZFvAqjqPSJR1MeEhISEgwk6upXMckBEfhZ4CPn/RWqcLn9swMN/cP+BNtUdbDrJzHmvVPXGHU4hEklCQkLCwYj5CkBWCkTkXcBa4GeA9wDPBeoef5xcs7+IWk1F00nmbcAngCNE5I3eoNc3PHdJkAmsaUugHky1YjPhHh/fa/3GrYEmu2POdf/IY52qrDXnQvc8/yFOlWdh8DdMFGgLU1uZ85inmXo+NLpRY9n+XWT7HHVmtFm7Va36Cqj4W1SnKrJz29pDZp2K3GKHZXP7fSFT/fhFZ8hU2EEn1wIwNWOq9GpY3Ks8A6Qw2XJ9NyqqFSuSPExdNCEZE5lXzRml13P0SzskfeqnpLreITSoo3zJXsR52FY7k6DOs3NaRgX5MhMR5TJXGPNah8fImTZD+5RgE0HhRyVKzcbUVvUp9fdJAX3xuvz+IjVm+2KaNk974N5vzzaztXW3PylKKeCveShs1y1r0/NUV+yMmWcjNafgPFOmRk65cTw6i0EYHqyXYsD5foSxzpWWbix8Oc2vk/V5lNlflFX9T/snVfVhInKVqv6piPwNNbEoVfUHi2moqbrsfBH5Bu7hvwDPUdXrF9NwQkJCwmrHyANuLh0sUOZen2PmLuD4cTQ0cJIRkUMLm9uBDxePqerd4zAqISEhYaVDF+AvtYLwKRHZDLwZuAK3MHvPOBoatpL5hm9cgGNxzjgCbAZuZkwz30Ig4hRLtmSOFSe2f1e2NqhpNlnyycxRR5lXYx3R8ylzZgrL/yhL40RmcihTzjiqqudpKCQj86kDZL8Lny6etgoEURS6PJOsr5084r6nAnw4eKMRlIzWhLff0xbineLMpqD2CWH1qU0L0Oo66s0C5k9EdEkxVlNfqH2jzXw7wQmvNREc8eJsmZbFM2tZ9k4flr4tfUqgvM9lFKkhc1JtGqtsKquOrVVEXJWq9NFTsS2DYoL0K8QG8GF2fmSURlRaUNGVnD5ju6vrtu/DRCaoWPw5f+/4rJOBPrP7MWTInAzXdueMu96bTYgWqcwCbVbc58/NapxA2wUKLna+jdVyRoV2Q0cl3DO1Cr5FYrUuZFT1z/3Hj4nIp4BpVR3MnS8QA+PFqerxqnoCzkno51R1i6oehkvXmRKWJSQkHNRwq5nhr5UGEXmVX8mgqjNAJiKvHEdbTYOSPso7C+GN+gzw0+MwKCEhIWE1wDljaqPXCsSvq+q9tqGq9wC/Po6GmootdojI64F/wo3tL+EeFK0YZMA0HbreMXAyCrludFkx42MvlLGYTp4a8sv8bGZ3KGt0UXCKCzSSpxf8cl/8uXOtKaaM0rCsf7PlcPahrkAr5CrAQB/YuXaO2WbqnKyVh/K3skYrmOonVgpJVogjVc7WGCPQj918rIrxsQBaltHRv9sYh1QAZhf9SiOzzcZ+jffAdF0qU1JT7bKRPWJ6pOhsZ9e0fI5tW+oC6c7m49fZXyobYnEZbFyzdrA3L1xOzWB9b5RvRGvUnyFuWMWhuh+uokqrVyZzwkjU1Lt5usXte50z7hHrrR+eDrb0DXYtPZ0mkoW+bpy0mGVlG4vX3x3PxySb9ckYaxw543EtdDEvEvWjmB23FdGK0htt9LJRO3cuITIRER9JwBKnjcX3selK5kXA4TgZ8yf85xeNw6CEhISE1YLVSpfhHoFcICJPEpEn4kRdnx1HQ00lzHcDrxmHAQkJCQmrEYrSW3WBZQL+CPgN4BW4Re7nWQ51mYi8QVXfsNgySwOFXpeWXw63jA4yWsmW7pIv4y28fceUaLa0thptWd+eZGCocwC/7Je2hbfP6E27lNlilICdYvTBjKfEvI202jmlZbRBRNmYYszeS2WjsOzSmylth+OtyTykexSW3WgJo8lmA03mq1LtU9Ts9zss5UDWsTQC3hmzla/Cwxh3y3SZRO8qGeJtMYdQC/+eh6r33bK6ya+pUWkhBlssK7L+tPqvrUSKwZzCKWTxjEPgGw3Y8de2Kv9Aga4sbvddtzjD6QBoRMcJkV2VJ0XHCiH5D512Y759n/serfP05dSEczZu+6tvTsh0Z3M6xDsk9wJf5ulFi39XoBDDfWD3fgW12oeor7HCLlCIBZowjL5djkH1zxcrd5UyFKraA97pX2PFsJXMr4nIfQOOCy73wRtGZlFCQkLCKsFqi10mIheo6vNF5Goqnvap6sNG3eawSebdwIYGZRISEhIOKiirciVjjz2etVQNDpxkVPVPx9WwiLwZl9tgFvgu8DKT1InIa4EzcXKrV6vq5xpXbFSAhRX3VEceMrzgqOlPCTGpQlAkT7H5mGOlem3Jbkv/4KQWUSGAeuospyO8usyW7DFtUdiupUwq1EpD6ZWYjunsD2q1oBbytprazBDHkOoiqH2O/sZ1/JAYkSfenFnJ65m08P1RCoEq2KHJloVp95SnPx7H4mpnkv81C9RaFAPL+lWlpis4qzr7O6X3oArs9fodXMMxU/hVdSiiw2IMUqLVUlyd0jaD7oW6+gtlbVwCbbbX1X/4Wq+a9OTYhKfGspldEChco33dvdTHGPr2ewg9ux52H1h/+pwyI4UmhbG2+uvGs7i/icpvAeiuslmmkE3zlar6R8VjIvIm3LOakWI8I98MFwEP9cuz/wFeCyAiD8ZRcA8Bng68w8vrEhISElYMVrmfzFMq9j1jHA0t2ySjqp9XVft78lXgGP/5DOAjqjqjqt/HpRt99HLYmJCQkFALdYRJk9dKgYi8wj+PeZCIXFV4fR8YlDNmwRhl5OvF4FeBf/afj8ZNOoZb/L4+iMhZwFkA27YdU1UkV2NlTuHUVe1z0CxmYwTI/MIphIvPhHYriptkDag5PuYxy/LGLdS+p816xhVVO5yVzq2hVvqVSZIbVVVPFQbQCOakaCXieGEufLr7HCu2Yie5kJaAfKzjY+0422Evd6KM67P8m6Yqaod0Ds0DUuXOmfmY9Y2xKZ7MATBQUH4sJKvwCPQpEfy5mTkvFhVlsRIsprYixV+xfF+suVBJzbUuxMHL7w9Td9V4M4qEkbQUDGt9DgNzagwx2wrOkbFKzqoPFKs3w2KL9VQL95M1bdLE4aRFLT0W+lM4Hvd9hLCVzCrDh4DPAH8JnF3Yv2tcAY8brWRE5IEicrGIXOO3H+YjAAw77wsick3F64xCmT8GOsD5tquiqsorqarnquppqnra4Vu2NOlKQkJCwoigdLXZa6VAVXeq6k2q+iLgMBxz9GzguHG12XQl827gD4B/AFDVq0TkQ8D/HXSSqj550HEReSlO5fAkC2+AW7lsKxQ7Bri1oZ0JCQkJS4JVupIBQET+N/B88kDH7xeRj6rqwN/0haDpJLNWVS+LsgEuyqtJRJ6OUzL8tKoWvRUvBD4kIm8BjgJOoj4taEXFcUwpdxOYakZFaqmbPEtiWZE0N6eFcOiuxFS7rI4yyqboGNYXQt6UNKZMip0AtVfY169WK50TGsoI5Fasj4gVR6HDE2Ffnyoqaj9U5d9bmQRVj0YLzDhbpFFhE5mEEP5xfTbG7QoKTiIaUGroQBlAsZhJWXR9cglZ1j8+ViSmJu2UokOfORGG/A2R+tAPViWr1eckO9yRsi+2V1xX0c7cSF9Gy++BDqyIsxbfm9E4lkLzRw7E9v2y9u2SznrebLolhfvNv8WOqnFfqsakKT08JqjC3OoNXvaLwCNUdT+AiJyDyysz8kmm6dXZISIn4m8JEXkucNvgU4bi73E+OBeJyJU+5zSqei1wAXAdLpbOq1THQKgmJCQkLBJLQZeJyKEicpGIfMe/H1JT7n0ist0eawzBTUAxnMgUzpVk5Gi6knkVcC5wsoj8CPg+LhLzgqGqDxhw7I3AGxdTf0JCQsI44eiyJWnqbOBiVT1HRM7221X+LP+I+/P+gQZ1zgDXishFuK48BfiyiLwNQFVfPQrDoXmAzO8BTxaRdUCmqrtGZcDoIGVlSk08KrJ2uDFi5mQiOAy691m/fprrKfuM7vFh7Nu9ckys4G9W+Ndin2PaLC9QQYWFfRHFEfiKibxsXE+dc2ffP6le2BfTEDZOmXeoy6SswKvsRpR1MIyrb6OrRWfY8pgEdVkIC1+gbirimVXZXAqmVlTdVdna4F/lUOc+1QrbIqdBC/kfp2yggoazhXqkKiuWq72HgtFlylAKCq48flfkQGzdtLor7ql1EzVkR9EZOcTKc31s+XvUnlfkijV/DxRUbLm9ZlOkgBtEhdXEjyvtj8dtlNSa9jv5jglnAKf7z+cBl1AxyajqpSJyXMM6P+FfhksWbN0QDAuQ+bs1+wFQ1beMwaaEhISEFQ9lyRwtjzRPfVW9TUSOGEGd/ww8APcf+7v2bGYcGLaSsbhlDwIehXsoDy4czKXjMiohISFhpUMpioWGYouIXF7YPldVz7UNEfkCcL+K8/544Rb2Q0TawF/gfBN/gHsuf4yIvB/4Y1WdG3T+QtAodpmIfB441WgyEXkD8NFRGzMK1CqCCo5vExZvquZfiClGcmdMoFemhPraDdxErr4J9E6dvqIiY2W/gqqB41mgB6L+1FEsvW5+ThwryqMlbn/LK4Z6BRWNjUHsBFlUoEFOJfS6yoQNZpROodSPYl29Tk6dhPoj/YdRUiXnu16p/qFOjNqrVTSFohU0ZEi1EEL8l50v+2KIFa5TX4ZKu/4hDl6r3Afye6hvzMtsY3CmrYTRcXW3lGp+fbwtrRrlXnAwhgL95pV25pBak26hkrKsVE1WHC8eGxD7r6+OeaRPaIz50WU7VPW02qoGuHuIyB0istWvYrYC2+dpaRFvxi0eji/8nm8E/tq/XjPg3AWhKUF5LHncQ/zn40ZtTEJCQsJqwRLGLrsQeKn//FLgk4uo61nArxefq6vqfbjkZc9cRL21aDrJfBC4TETeICJ/AnyNZgqGhISEhAMWXW32WiTOAZ4iIt/BqcDOARCRo0Tk01ZIRD4MfAUXl+wWETmzoi7VCgrHu4mM5QFTU3XZG0XkM8Dj/a6Xqeo3x2HQwuEUPzENk2d8zB0gjVIIYeyDj1p5jOf8ynoyk+BjF8PUUCH2ktWVtWuX6H2xsmr2u5011EBxf0w1xE53oaGCImqYA6Kn0Sw0v7RyR7uQGiFSk8WxzIrbIbJ7LPaJVW6FDIlDaay5OHNlsV6jzWaqy1QpEevai9I6lJwx42vcq77WWnSaNbTa1WWDjX5/YV+w0O7ryMlUC98Ao9jC/RXSAhjfaf0q1BnUjL5IuJfcWze69hPtqX7qMLqmLaNkje0cpJSru9+hcD/HNFmkwAz7e/3fgWEqvXlgqTz+VfUu4EkV+2+lsPrwoWKG4ToR+WVVLS0SROSXgG8v1tYqNJpkRORYYAcFyZuIHKuqN4/DqISEhIQVD9U8L87qwauAj4vIrwLfwM2VjwLWAD8/jgabOmP+O/n/nTXA8cANuJwvCQkJCQcd5qkuWxFQ1R8BjxGRJ+J+vwX4jKpePK42m9JlP17cFpFTgd8Yi0WLgWqfbKmPgoCwFG/7pbnROsHf0hezsPDFaPoWIiLL+YlSnVVtD6NjBoUu11iRVOV4Vud0WeeQWmMvFGg/gz+n7TMhtluF4331l5Vq2pr0fcjjxdm4BRVUn6NloY4QW6tcNm8wK/WrijbTmH6py2xaaLuJQrGv793BofyqFHB9P09Z9bXWrI3I4OsVO6qWlIqhcM1YNEBMKRsxPaMw6ZVmgSaz+6xGuVjF/Euhr5U2Fq9Tr3zP9NVbdMiN6bHe6CJUKasvM6ZBVf8D+I+laGtB+WRU9QoRedSojUlISEhYNVBWI1225Gj6TKbo+Z8BpwJ3jsWihISEhFUAt5JZbitWPpquZDYUPndwz2g+NnpzFgFVp0iqCwcvBUopKmNhciYHLX19mVagW+rigpXLl23sd76EaiolVFNHbQxa9oc4WmVaoeSYGPoR0Tzxtke2f2e/HTW0UuhXb583J2PCUypaFWqffmdGLSqdovYCPRZTidBPEdWlAQg258e1F8UQM8RjnbXzcapzCOxzyuy/T0Kf+5wkvWqu7ejGYp8CnRQ568YUm1Qpq2IMUDPa2AZ60zewv+PqLIY068Zx/Irx52KbYhN8f4KlcdlBFGVkc2UfxpwGYLXmk1lKNJ1krlPVkoe/iDyPFer1n5CQkDBuqGrIkbNaICK7KD8VM4W8AKqqG0fdZtNJ5rX0TyhV+xISEhIOCihLFoV5ZFDVDcNLjRbDojA/A+fsc7TlGfDYyCIzYxba+H1cPJ3DVXWH3/da4Eyc+9arVfVzDSoqOUBKDe0D1KqUwtLdMlj6OqQz0+90Z8qpusyShWV6v+qphj4oLu/raJiYiqjsT6u63SrlUU2Wzj47TD2VFY51ZsvnmDrKjtt4ShaooZDFskb5VrK5jjqM4pPl7bfoZVOlsjlVWKbAiixHCJdVp/6zoTAasqiaiijJfpqnoIyroXX6jAppAkyl1UFb5RhsptwL7plxGP+qfjRRO8bnt50tpqKK49JNtrKgFJQaNVl/1tUKm8KOAbHf4jKxzVm/c2vfFDBC+kyXLtT/WCAiDyd3sL9UVa8aRzvDRvxW4HJgP85xx14XAk9bbOMisg0XJuHmwr4HAy/EabifDrxDJM4rnJCQkLD86Pa00WulQUReA5wPHOFf54vIb42jrWFRmL8FfEtEzlfVkaxcIvwt8IeUA76dAXxEVWeA74vIjcCjcTF5EhISElYElJU5gTTEmcBjVHUPgIi8Cfcb+3ejbmgYXXaBqj4f+KaIVAVVe9hCGxaRZwM/UtVvSVmJdTTw1cL2LX5fVR1nAWcBbNt2DGivnxKoPNFTKJ4WC7ttuT/o3AqHPygqqgpz8RB1TZ8zWf8Q19NYlbbVLPiqKLy4TGz/oHGsc/LUsjKpkvqzB6XR2Pc5TVbYFmwOSrVyO9KZ6Xfqs/r9ybEaKBPJqbOQOqBcptI2o0mz8nhpr6w+zOOFtYY7Q8ahtwbQWcPuc5VsIE01DDHdbKaFcHQFR+XgVxvUmmWqMFB7VbHzhqnzCpRrc+OLCsh4UEc4KaxuukyglDujSwUDPQoMe/BvuQWetZDKhyTieR3w1KrTKvZVXkmf9OdcgEc+4pRVe7UTEhJWH3oKs535T+ArBO8HviYin8D95p4BvHccDQ2jy27zH1+pqqWc0n551ZdnOjq/MhGPiPw4Lv6ZrWKOAa4QkUfjVi7bCsWPwT0bSkhISFhRWK0rGVV9i4hcAjwON8mMLbJ+0zXoUyr2PWOhjarq1ap6hKoep6rH4SaWU1X1dpyo4IUiMiUixwMnAZcttK2EhISEccCeyazSB/8nAteq6tuAbwGPF5HN42hr2DOZVwCvBE4QkaK8bQPwX+MwSFWvFZELgOtwMulX+YQ6gyECkiefrcsrQ4Gr7kuZG+qKPNizdoFbj7z1B0iXh8mpK2XEcUreWrlmq1AukgVbTpb4GVCh3TgQYa2UNLzXRyWohY1zVX+CFFsq+lPdjrb8s5jo2VMWrmM9pRyaiVIC91SDV3uUSqVeQi0ZmpWf64Vj/rleeDYUrknFszovUR76rC7L+q9HVLZP3l313CPKdWOS81KdNc8cY9gYdXtaSKvsm4tl/eHaT+T2RBLvkJMmNFDx/7fvexpHwx3wXG8MUCUEfl2F+Bhwmog8AHgP8G/AhxhDdsxhd9SHgM8AfwmcXdi/S1XvHpURfjVT3H4j8MZR1Z+QkJAwDqzEVUpD9FS1IyL/C3irqv6diIyFLhv2TGYnsBN4EYCIHAFMA+tFZH1KWpaQkHCwYpU7Y86JyIuAXwZ+zu+bGFB+wWgahfnngLcARwHbgfsD17OikpZJWZIce8hXBRmMqKI8+GCUxliygod1GeEWq6It6uSZ0TkBFSmb+6i2CkqgT8pb064Uywf+aKLcj75z+3PU5DSIjVsUNNQQtju5bUMkqaW+1EVmiHL7SOauTVvoG79cml1Oky1ZPbUWUhrb5a+iSv1ny6tikQxC/2Iastftv5Y1wSEH3ieDgqsWyxb32707zKVZe3mEhIk1rjo/TK0GaYtrZdXx/Ve8z21fFGWjMgBsX/DR6rTLJfn6GANkKqsvdlkBLwNeDrxRVb/vn3//0zgaanoF/i/wWOB/VPV4XL7psTyTSUhISFgNsJXManzwr6rXAb8PXC0iDwVuUdVzxtFW0wCZc6p6l4hkIpKp6he9hDkhISHhoMVKnECaQEROB84DbsItMreJyEtV9dJRt9V0krlXRNYDl+Ji3GxnRAEyRwVF6EkrZ7o8NxAHPSypbiyQZB2PUOVBXxOMMrfDcoN0h0cfiAJzIlkht0iZ5uurq0C9SESXhfrqIgBAbeDIUGecSjeo2ibCOUGdZ6hbF1fl9QhjUnF9KHvfxn3PYlrTqhQQjE4yyiunPIt1lIIrxPdIX9QDK5zTPrl3e7mIUW12X2RFz/VepAKsG5NBXunDlGihE0VFmuT7BpxTldcoHs/+c3Jb4ygLtUFqCyrK+DoFZdogmitSJsY5djRSC44LLgrzqqXL/gZ4qqreACAiDwQ+DDxy1A01nWTOwAXJ/B3gxcAm4M9GbUxCQkLCqoGuTCqsISZsggFQ1f8RkeV78G9B1DzOG4chCQkJCasJPYWZJQgrIyKHAv8MHIejt56vqvdEZbYBH8CF8eoB56rqWwdU+w0ReS/wQb/9YlyE/ZFjmDNmnEUtHILxZFFbMKTMngT6Is4SUNjO4q41yMXRnxvGCsRKsV6f8qy2HamgJFqD/1TEQRxL1dfQBYE1Kaagjs/VqHCVEi8EGPXpgc2RzvKftCMl3gDqoy+PTJVzaDEnS+FYK1ZaFamLmiCdlRRmjfNqaD7Uld87gQoKCrEynZTTtoX9fQ6uEWUUKaqq6dZqCqzf1qyP4orvh75goQXqdM5SKpdb7Y83KVIRUNQ7vHrlorVTcpS21Dl2TkQz2nvoZq/bP36GOvqv4veggUiuMZYwadnZwMWqeo6InO2345BeHeD3VPUKEdmAm0Qu8g/4q/By4FXAq3GX5FLgHeMwfpifzJJnUUtISEhYFVg6P5kzgNP95/OAS4gmGR9n8jb/eZeIXI+LXt83yYh74PUNVX0ozjVlrGj6TCYhISEhoYB55pPZIiKXF7bP9VHkm+BIC1asqrd5p/haiMhxwCOAr1UdV9WeiHxLRI5dCof6A26SGaYoEcnL9CKKoxcpnSymVU91qFOXxcQKy/ECtdJnU02iz17FDTvsFpaCnYPqiStTjGJyO+M6QjmJaAztdwwMKjajzwalvrZT6xz2qtQ6cdnIObLK8lpWpCqtb69MpQXKKYvqN9ta7ZxGFBerrBWnd66iVWtVXRptxzb254oOqcKjdu1HL0NK8cVKtll3jIUsxHGLj7XMGdObPtkqp1+G/nsnbi9up5g/yloMjprWzdhYsvw7Fuf96YsxV9woX8OY1lws5jHJ7FDV0+oODkmL0hheBfwx4LdV9b4BRbcC14rIZUB45q6qz55Pe01wwE0yCQkJCUuBUYaVqUuLAiAid4jIVr+K2YqLulJVbgI3wZyvqh8f0uSfLtza+SFNMgkJCQkLgCp0lyZp2YXAS4Fz/Psn4wLilofvBa5X1aHPWVT1SyJyf+AkVf2CiKxleOChBeHAmWTUXfRYMVNVzlCiw+inproVddkfFwt9ZTGdSsotKC3hs6Fpl/N4WnXm19moNXZCP2VUpEL6qYtYaedtj6kQJQ/tHhz1Cg6HkDsdRmHvXeFq6quPxKhKmdAdQsMNCvFeEYOtD3H6YCtSca6KtzuL+m51+L6XYrF5mqfWaTGmx4o2VjkVu8p8O24Ei/d0nnK6bH7dv+9u4b4IUeeMIfTndHqu/pxGk77vXHxHxSmbizbFpljZ2MJi7LQ6x9e+7yD1TqSjgYbxGjPOAS4QkTOBm4HnAYjIUcB7VPWZwE8BL8GFibnSn/c6Vf10VYUi8uu41PWHAifiRALvwoUMGykOnEkmISEhYYmhS6AuU9W7qPjxV9Vb8flfVPXLDHgUWYFXAY/GiwNU9TvDBAULxfhClDaAiPyWiNwgIteKyF8V9r9WRG70x562nDYmJCQkVEKdyKbJawViRlVDpjgRaTNcZ7QgLNtKRkR+Bqf/fpiqztgsKiIPBl6ISyNwFPAFEXngsOyYgpKhIYR7ThXUj1tMMzX5GzDh6zdHTunNlQsUYo71q5VMFVND62TtELOp1qEy2O7fB9zARj1IBWUgoX53frem91VUXPzvzc40qsZiiwXapCrMfTAyugWL1FFdXKg4q2LxnAFjCxF9BWXvvDhMft29oz2nMKuyaVB8sED7zZbb62u/2388jj/m+xMUkhVsZ6zq6jPHv5co4BBLzMqUadWc+RqUKqEaxdumSmkWlwk2Uc5gGqPKQRQcbRY7uI4y9L8ymKFd4fiSiLwOWCMiT8FlQP63cTS0nCuZVwDnqOoMgKqaYuIM4COqOqOq3wduxC3rEhISElYUVLXRawXibOBO4GrgN4BPq+q85NJNsZyTzAOBx4vI10TkSyLyKL//aOCHhXK3+H0JCQkJKweqdDu9Rq+VBlXtqeq7VfV5qvpc4HoRefs42horXTbEwagNHIJLhvYonHriBKpZq8q/AiJyFk4hwbZt21DpV2fFS+yiM6ahjlIrnlurHosycFZRYYE261FbBpwiKQ8hX6bYskCTeIWS5xVUcuosp63K9QYHvYqRDVQJ1fRFE+S0SxQgyseuktZE37jlFFe0v0hZSXmctE+ZZgNaiLcfZzutjXdV0c84BL9VG2eh7HWRTpny0lYUe43yueW2B4fcN8/HIsUYh7M3miy+d22rSKMG2izatvuheF/Yx1bOi/WVKW43cYTtU5kV7I8xqN5a6nvgQiHz9UbO0iOAo8tW5CqlEUTkFOBFwAuA7wPDfGsWhLFOMkMcjF4BfFzdWvIycbrRLbiVy7ZC0WOAW2vqPxc4F+DURz5y9V7thISE1YcmLhMrDD5vzAtxk8tduOjOoqo/M642l5Mu+1fgiRA6PgnswDkevVBEpnze6ZOAy5bLyISEhIQ6aE8bvVYQvo2TQ/+cqj5OVf8OGCiqWiyW00/mfcD7ROQaYBZ4qV/VXCsiF+Cih3aAVw1TloFXkqgWaIr6dXFMfUkVj0SZVgtMUOxY2aekqig3KMx8n21Rdks1TqPdZ5MrL7Qiqit+0NiK+lfcHObiW/X9qFMAxQipFHq9AgUV9c+jLyNmE9mOOagWyubUZOTgOCCDYTinVaak+myusi2mvgaplyoybA5CuLeqlIoRjDLN45D1l6mjU4vHa49FtFlRUThfCkq1QMc1xKAVgx2JqxxId48IK2wCaYJfwK1kviginwU+wvz8a+aNZZtkvEb7l2qOvRF449JalJCQkNAcqkq3u/Ie6g+Cqn4C+ISIrAOeg8t2fKSIvBP4hKp+ftRtLqszZkJCQsJqhrlmDXutNKjqHlU9X1WfhXvufSVO1jxyHHBhZeposqLwqT+0ennblC/dCuphwtRetkiPnfDaucoopjZqnTBL9ldnTYyFWy1vYzsD1bjPg53WqiiDogNbcbsJ+uuriL01pO8D6aa+UO425ranmFbBH4sybfadW0CvjuKKHTYHqcL6MlSWx7NoS1+ssrrsloVtic41tWH+bmozfw79KrJ4uwqDKCcYrTprPqhzxISKDJ+DHEVH2AHVAWk1VhFU9W7gH/xr5DjgJpmEhISEpcIqfCaz5EiTTEJCQsJCoGmSaYIDbpKpi1UUFslF9qImPpgtv9tZgfIIyrC4wWoKpYpSCfsGqctiBVqN/fmiP2suDRnwfRhGky1IldOA+mp0vGZsByoI6+J1NRn7OtMaxL2KbSptW3bRBvdBqRz1904ffVY8mEX2xhLIge0tnD4dhmJVdbfVoObqHK6b+KyMVl2mq85PZjlwwE0yCQkJCUsBVeitwJAxKw1pkklISEhYIA6EB//jxoE3yfSFa6//p5HVOOgFrZJF7W+gJurDAnWLTSiZ2nYWEMa8KQ1SLLcoymGEodZjO1RkuG1x+4Xxazz2A8oNbL8BTTXfthd0JQa0N1+6dND9EysiK+sdoVptOZRwKzTC8orCgTfJJCQkJCwBVFdcyJgViTTJJCQkJCwQiS4bjgN3kqmjBBZCjwzCSnDnHSEF1QSVjoYrAPOyZzHXbREKtVHbsqB7eKD99mFwvU1o1nHTVcPqX4r7U2M1a0IfDtxJJiEhIWGMUO3Rs7xCCbVIk0xCQkLCQqCg3bSSGYaDb5Ip0gDzoSmGxbdapahSaM33nLEjvk5NxnzMNGYcer+OJhsJJbtQjJIS7IvDl98Do3TUbIKmzVXZNWpnzKWgy0TkUFxyseOAm4Dnq+o9UZlp4FJgCve7/i+q+idjN64BVvcvZEJCQsJyQd0k0+S1SJwNXKyqJwEXUx0teQZ4oqo+HDgFeLqIPHaxDY8CaZJJSEhIWCCWaJI5AzjPfz4PlwembIfDbr854V8rQpmzbHSZiJwCvAuYxmXAfKWqXuaPvRY4E+cO+WpV/dx4jKh3zOvDPOJMrWasGMXYYq7FAFTRWvNNyaCS1dJky0qPNUXdfb9A28M9s0zfkZHG25tPu6rzefC/RUQuL2yfq6rnNjz3SFW9zbd5m4gcUVVIRFrAN4AHAG9X1a81NW6cWM5nMn8F/KmqfkZEnum3TxeRB+PSgz4EOAr4gog8sEkK5oSEhISlg9JrvkrZoaqn1R0UkS8A96s49MeNrXG/kaeIyGZc9suHquo1Tc8fF5ZzklFgo/+8CbjVfz4D+IiqzgDfF5EbgUcDX1l6ExMSEhJqoKN78K+qT647JiJ3iMhWv4rZCmwfUte9InIJ8HTgoJ5kfhv4nIj8Ne7Z0E/6/UcDXy2Uu8Xv64OInAWcBbBt27bFW9TAgbMWo1CqHcxYIqfWZuH6m1+fFUuPLSIe2rzR9No1SOOwECwXxassmTPmhcBLgXP8+yfjAiJyODDnJ5g1wJOBNy2FccMw1klmyBLwScDvqOrHROT5wHtxA1NFsFbeRZ7TPBfgkaeeukIeJiQkJBwUUF0qP5lzgAtE5EzgZuB5ACJyFPAeVX0msBU4zz+XyYALVPVTS2HcMIx1khmyBPwA8Bq/+VHgPf7zLUBxWXIMOZWWkJCQsGKwFCsZVb0L96c83n8r8Ez/+SrgEWM3ZgFYTrrsVuCngUuAJwLf8fsvBD4kIm/BPfg/Cbhs0a2thBhjhkSt9WMFOFgeUFgtY7WaVZsprEwjLOck8+vAW0WkDezHP1tR1WtF5ALgOpy0+VVJWZaQkLDS4J7JrJLJfBmxbJOMqn4ZeGTNsTcCb1xaixISEhLmgRGqyw5kHFixy1YLRTAfLKZP46Aaxj3Gg2yeT38OxHvhYMQKv45pkhmOA2uSSUhISFgq6LycMQ9apEkmISEhYQFQVXpz6cH/MKRJ5kDGCqcaKrEabU44SJGeyTRBmmQSEhISFog0yQxHmmQSEhISFoKkLmsE0ZUS2n2REJFdwA3LbUeELcCO5TaiAivRrmRTMySbmmOQXfdX1cMXU7mIfNa30QQ7VPXpi2lvteJAmmQuHxRKezmwEm2ClWlXsqkZkk3NsVLtOtiwgmM2JCQkJCSsdqRJJiEhISFhbDiQJpmmqUyXEivRJliZdiWbmiHZ1Bwr1a6DCgfMM5mEhISEhJWHA2klk5CQkJCwwpAmmYSEhISEseGAmGRE5OkicoOI3CgiZy+jHTeJyNUicqWIXO73HSoiF4nId/z7IWO24X0isl1Erinsq7VBRF7rx+0GEXnaEtr0BhH5kR+rK0XkmUts0zYR+aKIXC8i14rIa/z+ZRurATYt91hNi8hlIvItb9ef+v3LOVZ1Ni3rWCVUQFVX9QtoAd8FTgAmgW8BD14mW24CtkT7/go4238+G3jTmG14AnAqcM0wG4AH+/GaAo7349haIpveAPx+RdmlsmkrcKr/vAH4H9/2so3VAJuWe6wEWO8/TwBfAx67zGNVZ9OyjlV69b8OhJXMo4EbVfV7qjoLfAQ4Y5ltKuIM4Dz/+TzgOeNsTFUvBe5uaMMZwEdUdUZVvw/ciBvPpbCpDktl022qeoX/vAu4HjiaZRyrATbVYanGSlV1t9+c8C9leceqzqY6LMlYJfTjQJhkjgZ+WNi+hcFfzHFCgc+LyDdE5Cy/70hVvQ3cjwhwxDLYVWfDco/db4rIVZ5OM6plyW0SkeOAR+D+Da+IsYpsgmUeKxFpiciVwHbgIlVd9rGqsQlWyH2V4HAgTDJSsW+5dNk/paqnAs8AXiUiT1gmO5piOcfuncCJwCnAbcDfLIdNIrIe+Bjw26p636CiFfvGYleFTcs+VqraVdVTgGOAR4vIQwcUXxK7amxa9rFKKONAmGRuAbYVto8Bbl0OQ1T1Vv++HfgEbjl+h4hsBfDv25fBtDoblm3sVPUO/yPRA95NTl0smU0iMoH7MT9fVT/udy/rWFXZtBLGyqCq9wKXAE9nhdxXRZtW0lglOBwIk8zXgZNE5HgRmQReCFy41EaIyDoR2WCfgacC13hbXuqLvRT45FLbNsCGC4EXisiUiBwPnARcthQG2Y+Tx8/jxmrJbBIRAd4LXK+qbykcWraxqrNpBYzV4SKy2X9eAzwZ+DbLO1aVNi33WCVUYLmVB6N4Ac/EKXG+C/zxMtlwAk698i3gWrMDOAy4GPiOfz90zHZ8GEcTzOH+vZ05yAbgj/243QA8Ywlt+iBwNXAV7gdg6xLb9DgcXXIVcKV/PXM5x2qATcs9Vg8Dvunbvwb4P8Pu7SUYqzqblnWs0qv/lcLKJCQkJCSMDQcCXZaQkJCQsEKRJpmEhISEhLEhTTIJCQkJCWNDmmQSEhISEsaGNMkkJCQkJIwNaZJJSEhISBgb0iRzEEFEdg8vNe86ny0+vYKIPEdEHryAOi4RkdPmWf4GEXl2xbHjpJBS4ECHiLyu8HmND28/KyJbltOuhARDmmQSFgVVvVBVz/Gbz8GFVF8KvFhVxxrZQURa46x/RAiTjKruUxfLK4VLSVgxSJPMQQhxeLOIXCMuydoL/P7T/SrhX0Tk2yJyvg91gog80+/7soi8TUQ+5ff/ioj8vYj8JPBs4M3+3/SJxRWKiGwRkZv85zUi8hEfKfefgTUF254qIl8RkStE5KM+WOSw/jxSXPKqrwCvKuxv+X5+3bf1G35/JiLvEJfs6lMi8mkRea4/dpOI/B8R+TLwvDp7fJtfEhdx+3OFGF6vFpHrfHsfGWDzOnFRgr8uIt8UkTP8/uNE5D99e1f4cUVEtorIpX5srxGRx4vIOYCtXs5vdPETEpYayx1yIL2W7gXs9u+/AFyES/h2JHAzLmHW6cBOXPDADPgKLtTJNC5M+vH+/A8Dn/KffwX4e//5H4HnFtq7BDjNf94C3OQ//y7wPv/5YUAHOM2XuRRY54/9ET5cSNSPUK/fvgr4af/5zfjkaMBZwOv95yngclzCqucCn/Z9vB9wj9mNSzz3hwWb++zB5S75b+Bwv/8Fhf7cCkz5z5sHXIu/AH7JyuHCIq0D1gLTfv9JwOX+8++RhypqARuK1zSq+yai5HnplV7L9WqTcDDiccCHVbWLi6T7JeBRwH3AZap6C4C4XB3HAbuB76lL9gRukjkrrnQeeALwNgBVvUpErvL7H4uj2/7LL6AmcRNdLURkE+7H/Et+1wdxqRbABSl9mK1SgE24H+7HAR9VF6n3dhH5YlTtPw+x50HAQ4GL/P4WLjYbuAnvfBH5V+BfB5j+VODZIvL7fnsaOBY3Sf29iJwCdIEH+uNfB94nLkrzv6rqlQPqTkhYMUiTzMGJqtwahpnC5y7uHhlUfhA65JTsdHSsKmie4JJPvWgebUhNXXbst1T1c6WdIj87pM49g+wRkR8HrlXVn6g492dxk+izgf8tIg9R1U6Nbb+gqjdEdb8BuAN4OG7s9oPLLiouP9HPAh8UkTer6geG9CMhYdmRnskcnLgUeIF/ZnE47kdxUNjzbwMniMvWCI4eqsIuXG56w03AI/3n5xb2Xwq8GEBcoqmH+f1fBX5KRB7gj60VkQcyAOpyiewUkcf5XS8uHP4c8Ar/7x8ReaC4NAxfBn7BP5s5EkcTVqHOnhuAw0XkJ/z+CRF5iIhkwDZV/SLwhzgarO6Z0ueA3yo883qE378JuM2vsl6CWyUhIvcHtqvqu3HpAE715eesfwkJKxFpkjk48QkcrfMt4D9wzyBuryusqvuAVwKf9Q/E78A9u4nxEeAP/IPsE4G/xv3I/zfu+YbhncB6T5P9IX6CU9U7cc94PuyPfRU4uUF/Xga83T/431fY/x7gOuAKcbLmf8CtzD6GSzlg+75W1Z86e1R1FjdpvklEvoULyf+TuAnhn0TkalwY+r/1k2AV/hz3bOcqb9uf+/3vAF4qIl/FUWW2qjoduFJEvol7pvZWv/9cX0d68J+wIpFC/Sc0goisV9Xd/p/324HvqOrfLpMtlwC/r6qXL6IO689huEnupwZNtKsJXsV3mqruWG5bEhLSSiahKX7dCwGuxVE6/7CMttwN/KNUOGPOA5/y/flP4M8PhAlGvDMmboXUW2ZzEhKAtJJJSBgrRORlwGui3f+lqq+qKp+QcKAhTTIJCQkJCWNDossSEhISEsaGNMkkJCQkJIwNaZJJSEhISBgb0iSTkJCQkDA2/H+RyX9lacYxigAAAABJRU5ErkJggg==\n", 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dkGOmlcOy87Rrmvd68kzpUVF+yldu+fRRvh4jHNmnccyDrVTNGuqSXTJKi3RLDaoiysuZlLaJlIppaleM+ut01Xy6bs4oQe1rOVfOxt/oPKO5jAZrNovjWIvIXR/3XTAvKjtdpfVaGuA612KMli2gU8rESvdG84eKndcAw1LSxHRJAoUbkXyPJvRCGdVnfRxTiprjslPoZdb2bcOzZBABcblzaogO2EpEV+S2L2DmC/R9CcBZAH5f/QH/DpKF9c+G19uVwYaaQAICAgJWE4ugsPYw8zldPrsdwO3M/F3d/jRkAln3CBNIQEBAwFJANJSH6My8m4huI6JTmfk6iJHsT5dd8Spgw0wgKYSmsB8Eu+dkSX/0qFI+VVn+zjRTlJ3Spxjs5dySPHtopC3nURXpKj3LeqZldJmeVmV5PdtiRz2wkgSpRx0YpTDbMlqgmlE3LiBRK3HeS0q9zB2Q/fUpIBI6w9FPLsufl2VQQWmCslm7m5pHmzFKxql+jELLHe+XaTrhk51fcbsUUbsqKatMyuiFM3fTcpyjjFTplJRUkWNKqlwwIGCeZbI1ohRJ4vqoAXbOrlzPj7K2E+987H9Dmw16bruiwag9s0oCorDzrcQ9Ksv3brJgyKi14KiYBf3KctI5sNVVjVywn9XnlUk81V7KMNKsjfaye3ayqtdpVuzYC496I61Ivws0f1ja0YDZZq4/Rrn5isjUvLG87yBThIYef9tBadRUhkan7RzX4FESitq+B0DxHhkWCBhIojsgfh/AR1WBdSOA3xhWxSuJDTOBBAQEBKwqCKAh2bkz848AdKO41i3WXIXVKYCGiDYT0SVEdIO+bupfU0BAQMBqghDF0UB/GxXrYQXSKYDmTwF8lZnfQUSvgzxQem2vSiLIUrbakGXzzlFRwdmSPCkoZ+TVTt7oB/YspJGzljaPKhcc2DSlRzFjoH0+GsWY1xaSLutm36OokTDMhNuW5UbfxM76W1Q9Zm3Ncdn9CjC1lbOL5yIX484vLrs1vVFUdX01tZL1zP+B1UyBlkc7GQXivLi0rB3aSrlAX8gxZgGuahwbg1zQW6L9t7I2Xj5tYyxCJSZXxvpS8o5NHDVofeY2ZZiVsWC5BopjEkfkrmlEQi1VKsUgtlJjWurPeZeZEsjEcs46X6kedx+qStD5QkURUFalnQXjmdeX62sxKDAmyjIOas/tGjivKne+cPubHs9l//vGtNPWd/Nxi2b3O8oq0QBZUxnWxuuFukztyBE5+olToV8dTannU6l69F5UwpTWM7FN6s3b0AM5z7ccdXXPnLzfXpf6D/knuAxQBESVjTs5DII1nUC6BdAQ0XkAztViFwK4DH0mkICAgIDVxkZeXQyCtT77bgE0RzHzXQCgr9vXspMBAQEBbSACxYP9bVSsNYXVLYBmIBDR+QDOB4Bdu3YhZWA6lmX0+PQ9UmZ0CwBgOs2WyA3z8dHreo8KQaqxLW9lWT1aVnqHkjYrdkdz6fLdssuZV1bC7PySjAeKzO+H1MNHKSULBmsk7OgFE7dULajNeWG1+ydVnIW4p+KpFO10six6mYeTT32MesGIRiE09bdGM80y0C049ZjSJl5kYVlpiiRlR40ZjWLZCmdTs+3W5nQs6rl+OLpLT++w5xPuqCXklGC6z4Irmy66UaksZDScZVi0IET7Uekrd+z0qhEwb/1ko6HkJjoYa7BbKjSLBenVowhlo648yhRK/Vj2SXhZIbk66rLwGZUat6Q9U4iVykWPpyhpunvFBelp8OGCqqUSL+IzzZ27Xa+alnUBp84bSynVyojzq/I9t/bqbWnXsq5cJzE7NeGs8mjTTTl3u//tWjh7f86UdT79WnIZPfVEct+Dzfr1tO/YWGXgwL++IGRZKo9UrPUKpFMAzVkA7iaiHQCgr/d0OpiZL2Dmc5j5nC1bt65KhwMCAgIAqBdWNNDfRsWanhkz7wZwGxGdqrssgOZiAC/RfS8B8Pk16F5AQEBATwzLzv2+irWmsIDOATQRgIuI6GUAbgXwK/0qISKxvdYlZWviaADZkphgQVOZOsWW5ZPV4rLWKAujmqLpPY5CMK+qZPJYAMBcpGtkPaZm1ulIHe1j9085mS/UgUjqT6OMfnAKJm+ZnlBN6zUVUUvLsbOxL+nRUSrcQemwLNycqsc8iuISyKgQpVGMCrHzhGcrX0mFVimXKzisIi/fzt3Guq7KFMoF3G2uF281P5Gbb3E+02JHQ9U9mdckSx8XykW/uVJEbcFzvnLLrrT1uZmyu/41pdwsyM336bKguvkk89xqsmZL1MDMMUdTyfnMp3r9UnY0yqj3sy0+fLf0UYPonC25UVxpC6Q+U/aZ0UZGc8UWWGrBo3Epu88UZmVulKO95i3ubd+Uxslan939YV5lRpvm+lDWe8Woy6rWNadU5z5VRNVKBHPdtyyW9VLxu2JoeLQYkPsOqEqN1C/LxiTNWbZX5vYVx0ADfYcBigjRECmx+yLWfALpEUDzpFXuSkBAQMCisJFXF4NgzSeQgICAgPskhhiJfl/FhplAKE0QNWad8siCmZxley53cclT9ZjSyIKZ5vRY8yKi2ji4IstjWyY7BY2XDdAQR5FT/hgtYH4/Sc7jKN+PShxlSpa0SLO5eo1WySlNTLAULYh/EDVmdVu9iLSsBRhScw6pUh3FUC+00R6pKoRc0FtjBhNKo5gyzdFdqgTiWKilNNZtzgLEXECfKbXSrF4goxhKUSmz01c6KD60W8rotagoVWHXYjYXJOaoPx0/Gzffar6cy6hn2QxHdECjOTm/hZoEzVXYAjQZDaWunBeWqqIy+klpMVUxxTN7M58ntaW3fz1prUirJGW5hy1QrtyaA+s+45ActWTXwsuuyRS562P3QaQBixYEaP5aVsdkehisgZHRrNiqk5Y1zze7h7IsgFVH1TqVmd4rDZYyE1FTz0vG7N7ZFo6qS+GRkqq5WkVPMQtKHNV7uVHK7tRoRn24TAFpqRXmDsq23bNpy92bRgEizrKWLhekkehHMjbMBBIQEBCwqqDhZCS8LyNMIAEBAQFLARGi8pH9L3TDnD1TjKQ8gobST/OJqaEE+Ri3Wmp+VrLUNgqkpMvnUVUoGSWS0AiSSAP5VOSSeTup8qhUVB4lKTtVjyGNjGbgQruckyQZxVNpyZK7zafLKDSl26Kk6bLeOe8ka08zwJnNdlpRCqU1n6l6lA4yxQ8pDeBogrFt8mpUQGseYKV2/vtzAIDy8Q8EAMSbxDAgnTpGypoaJi45moQa0sfYszJ32QaVKqnHJdcnC4hLxyTWJ1FqzDyPbPzKcUaNGS1oVvl2Axg9aShH1BYoaN5RkdId1abSa5YFMCpl/mZOvVbMwmf0UKR0DldGMK/00Mj8HtmnlFZa31Q4D7uHnUdVXHd0ngVaWpmmjteYKtVKeq1bDJQ0iLY1WoyR8oM2SwtCV6WVUUd3uXQFdm95qryDiar3mDDq/RcxFdSIBa7OSp2xMk7bR6tY0JOrmy28tmfjGFnmzQXxFKsmDUcPGk2X2j1rvnRKXzuFWmPGXTODv70cEAUrkw0zgQQEBASsLoaTUOq+jDCBBAQEBCwFGol+JKPnBEJEzxmgjnlm/o8h9WfJSJhxqJG4ACUL+nJZy0wtkzScWiOtqkW7Ls9tvy1zF3SZPt9KXeBY5C3//WAsQ61EGcViPllaximEOJcKLrdfNsqFY40WMEVSZAFjzE4BBLOWN/rBaAc7VgOuqNVwKiGD8/oytYqzpy9SM9H8YTRv/zkAoLRNginT/RKwWDrqftKN6XulXR3ftDruaAUHpR1MEUSmajOqrDmb0Xbaf6OjWmpmZmTUbFNolmqHL7MForWa5rlltvVKldVLqCVzem5Cl8QH75SDdazZsk7a/TJ1jMvoaIojC1w11ZzdDw2Svo+XI9RaNrYy9i2lGM36PbWUAdrHWOlRZnb3dT6TogyX+pJ5XlU1tBxlOmfnrmNit9lIMlsYK0pb7jq574JuT6uiaq5h2TzlmLunmy4Q00Z/XLfrC1qHqqKiaaHuyod2I94s94pPR8FoKL3mpgK07wMAJErN2bH2OhPLsSUbo+oYIlVxWaZQjoYZ+EegaH1MIET0VWZ+EhH9JTOvmnN5vxXI+yA2Ir2kBo8DsOYTSEBAQMCqggCK100k+g4iejyAZxPRJ+D9z2bmH6xEo/0mkC8x82/2KkBEHxlifwICAgLuEyAixOtHhfXnECfznQDe7X3GAJ64Eo32PHtmflG/CgYpsxogEkVN2a0oi/bk0eEDspvZ0SZGGVjAmMsqqMqTklIAY5XYUVVGQ42r35NRB0YTOGoh9wMg7zWUfxN5hlDMWfCYUTousMp5AimVpv5XlTRTpzgba09mY+eV1sRqvFUZd32pqR15atSRKViU7nJjpNuN638IbqjqJRGKp3nggGy3ZNxMjRWd+FCpK2k6OsjqOxwJNVFWaqamlMWM2ntPtA64wMi5yZ167lK26dGUpkCKoywjYb0lx1ogmsscWRrRPilddcvPgJEp5JHOiCqpddfNAIDKKWfpsRmNYuqxhYpQI2SBkUYh2bjWMtWP0UFNDQqM1XfMxsTqNAtzZqM6M3rU1GVN+8yoK6NB9dIfSmOMq5eb3YtjaZGyyuzj9bVcd8Gz8yOivrOA3P0LUsYoulEd803V2NHEE1CvtwWlJ2um+lP6WM+vddSp7p7cl6p/lt6zRksuqG9WVe/tMrfTd+ZsVhpVpaCOgY1WI2HUctRXfpyGhWE+AyGiGMAVAO5g5mcu5lhm/jSATxPRnzHz24bWqT4YePokokcDOD5/DDP/6wr0KSAgIGD9g4auwnoVgGsATCy1AmZ+GxE9G/JoAQAuY+YvDqNznTDQBEJEHwZwEoAfAbB0PgwgTCABAQFHJAgY2kN0ItoJ4BkA3g7gD5dRz18AeDiAj+quVxHRY5j59V65iweobh8zv7RXgUFXIOcAOI2Zh7v+GyIiiCLKVrfmD+WoGFVuIE1cMJRbWqvyqDWlFu3KkZi3z8GFFLqCd8v1aS1zcEHmU1OiuEBCBmKlIuK2pbdup+oRZAGGlGXqa9lQk1wiow5MQRPbjZsmGXVlChOznNflv9FiRmmlnPXF/KqgFFI8L/SNUT7mn2R0R+WUM9H42ZUyPjuOl316Pukhsc4u6f7mdd+TOh/4KByC9KXVUEv0loybjd/Bean//puF5tk0vQfN7ScDyK6HZZNrKR22uSyvpXtFFZaObHLqIaP+rN+tzcfJflWV3ZIKvXL08Y/A6J0/lqJKc7buuknHRM4sGd9eGBNw6tRXdu6RjltzXNRYFjRYdZkkCQvKz8Tq1U+mOLKAQdUx2ZfSKC6qjDgaNJ+9EsgCT8dI7qUZVUsttFLU1GdqbEGC9UwNtV9poym99EYtzaOEAzMt7aN8dlA7vV2jBXfPGL2rtu+1GGVVOHLJLOY1W6IGo1qAX6JBqddPkzvnLXVrx6ha2bZMkqlSaOM1cvSmUVnzLaMNPZ81C1IsRWClfE191cj5pS0bi1uBbCWiK3LbFzDzBbntvwXwJwCW6zf/DABnMMtFJaILAfwQwOu9cg8E8L971EMA/qFfY4NOIFcBOBrAXQOWDwgICNjYWFwcyB5m7pS2AkT0TAD3MPP3iejcIfRsCoAlQpnsUuYNzPz1XpUQ0Vv6NdQvDuQLEKpqHMBPiehyAC6TDDM/u18DAQEBARsRQ1RhPQYiv306gBqACSL6yBIFSn8B4IdE9DXIKuJxaF99gJkvAgAiehAzX9WpIivTC/3O/q/7dncI8NUHRLQZwCchD+1vBvB8ZjVg6gOzB2+YSsk+UGVVWhlxXlQNpW8q6rFEHkNngo3N9dipN1x2OvNL8tvXHdUoU4OYKIpydAaQs4vPHe8yvuneWOmATEuix2qAmtnXF/pvwVnWntfHmDIFWKxeQ+aBZdSMeTkZBWSKmnRsGyqmqDJl2477y+uuBwEAWnrsbZOnAwCOHS9jUoPIDpRFAWfqtZM2mS26VFHae4PUsWln5qlkFIV6Nm1VWs0Fu2mAX/Lj/0J84umFfa3JE6VipTBKNwuLsPP+j3Hnb6or8wyLJoXujPU1USrL/MHmN5+Iino4pfUpKaPKqqanhorMpj4uO+Vc7FnaG+w+sOto1zZfzlIE2C7z9ipXZKwsyDJh4OC8vN9q9K2ipQqnGT3fMZI65pLUqQtHlHclMtpQtneNy5040lSlGk2hqd8jy1oY3SIUJ+84WTst9/B8XfqxtZ46Cm5CZWVO5chcqMuoLOIWxrz0C/Va0RNtVu8h86WTa6HZEfWeLQ85cnwYD9H1+cTrAUBXIK9ZqrqVmT9ORJcBeBjklnqtpg7vhn/SbLD/AuBjzHxgMe31PHtm/rouc55u7/P7FtNQH5j6wPA6AF9l5pMBfFW3AwICAtYP9BnIIH+rCWa+i5kvZubP95k8wMyPBfDrAHYBuIKIPkZEvzBoW4OeWacKnzZoI72QUx+8P7f7PAAX6vsLAfzSMNoKCAgIGCYoigb6GxTMfNliY0CWC2a+AcAbAbwWwOMB/D0RXTuIlVW/ZyC/A+B3AZxERFfmPhoH8K2ld7mAv0W7+uAoZr4LkNmUiLZ36d/5AM4HgF27dsmyVxVJZRTVFkYxJCm7z8rOolwe65hNuIp9Ct5U9s6nH0wZFClR5AL/UG7jjkw9UkKRyiqckwUSGg1lWdfsc7PIztFhti8rpFSM0V0elRUROQWYf4zL7nZA/aA0W54FV0Yze9HaKz9q9l72X3KISme2v/T3AAAzm4Q22mb2RruvdRkHN4/J68RV35RjNSjRrhad9ggAQGnfrYgOfF/23XuHvFqA4viUbFtgn76m/+vXsUcpHKNI4lTGYJNyi8nRp8r+uQNSx8IMaELtzk3FduxpAIAFtUE3WqWsNF4Ecqq+OU/VEzuKU2B+Vwlzmx+QK1t0hndj3fQUeEC7n5V9ZPffRNnqjlzfLLWBtbe1LudpqrAZVWVtTg8hPnC31DsuiqnNI5sLfTQaEVddBgCobjkaPLVD+qv+VskpQg82tP9GtxmtV4mpLTNlXamq2Ptna5/bWABZHEHV+86NKNVoVySi7PgF/a08TCEpESFaP1YmSwIRPQTAb0B+xF8C4FnM/AMiOgbAdwB8ttfx/Z6BfAzAlyAPZvI00mFm3tf5kMGxXPWByuAuAICzzjp73UqMAwICNiAIiCrrxsrEgYgeC+BkZv4QEW0DMMbMN3Up/h6I5+GfMvOc7WTmO4nojf3a6mdlcpCIDgN4MDPfMvgpDIyO6gMAdxPRDl197ABwzwq0HRAQELAMrB83XgMRvQkSt3cqgA9B9DcfgfyvbQMzP67Tfv3sw/3a6zt9MnNKRD8mouOY+dZ+5ReDbuoDIvorAC8B8A59/XzfusBoJClIl7rmrROpkqc0f0C2q+OOIjDL6FZVnAOMQrBBaTn/qaydVsv8ioo3jiW+K1kmNbDLbOcrS7iLKgvIAsVKnu200Rhls3fPBQdG1HkZbbRG5HEn1JrPgr3Uet0yAZoSiceEurBAQr7+Wvl827GIjxHV1banql377KFC2bE918v+3fqjZ/txLptgeosoBqNRocaiB8v9a95IUCt1ro4hOflRAID5U2s6JnpNdbxmlKIxv6aDBxpI9Zxren221JXOmxfFVqy+Z2bvTa15FyhoHmmHlN1LlQ6raLupa5+dTbzdGk51ZfeQT0/l+t1GWXn3g6nzkPNQSzzqxamULFOhZUa0YMQI2DJSKtRrMKVdrKqwGquHVWMOiQbTNspybe+Zbrr+A8CkBiOOnimPQGejCiqqfDS6aDqR+9GoYKONpvVLwpyp1RpuTIpU4FjF1FnZ98zuZxckrOdhFLRTDtp5Ihdc6Ma8l7H44kCLiwNZLfwygDMB/ABwK4muwYlEdDKEYToNgOPLmfnEQRobdP21A8DVGgcyk2tkpeJA3gHgIiJ6GYBbAfzKCrUTEBAQsDQM3wtrGGgwMxOJ3QERjfYp/yEAbwLwNwCeAHkeMvAsO+gE0jcicblg5ssAXKbv9wJ40kq3GRAQELAcrDcKC/LD+58BTBHRywH8JooKVx91Zv4qEZE+pngzEf03ZFLpi4EmEGb+OhEdBQlOAYDLmXndPZcgooxKMHLBsvJZtrfWglNtGIXlFFRqa210igu2i/K0Q+fJ2SgF5NVR+pkFAfrUlQ/mjKoyBY15b+2bk77VVU1ktuGliDJ/LKWljJ4yjy/bNnv01l03uiBArsm4lE56iFSiVBbvFn+phbuFtbRgu/3f+RamHv5IaU8D7X7+fkkJs/1sUThteupzpb3t4j/VvOa7KB93irSjWQstg990TaiyO6fl/GZqU3IsEWb3CIV0wlRaOHcbi0Pqo2V03kQtxqj6P5nSyKz6qdUonJ+7TtVxp6g6oNn2Wp5qyYIeXaY7yj6zrIFu27zQPMv+iAj92BNflWd1xcRQNs1lUjQ7dbWmQqIU0HhLrlMdyJR1Vr9H8ZRZxtFl9ksaSNRyfp8GIRrV5DzajFarynennCbOqr8MGWMLpp1Wdde0jqvRcJWIMnrYFInaJ0cXel+RKCdpNOrKz7jpPveCcaX+YgDmUEAEGmqGw+WDmf9a4zgOQZ6D/DkzX9LjkHmSlKw3ENHvAbgDQEfVaycMNH0S0fMBXA6hkp4P4LtE9LxBGwkICAjYeCCRkA/yt1o9kpS2lzDzHzPza5j5EiL6yx6H/AGAEQCvBHA2gBdDnjsPhEEprDcAeJitOlQadimATw/aUEBAQMCGAmE9pbQ1/AIkIDCPp3XYBwBg5u/p22nI849FYdAJJPIoq70YPIp9VUBEzk8HQIFKAuAs3BG3/xrw1S8umC9Xna+Ucc34WQU78BROIdPnWKLME6hsUhOlF4wi2auGP3X1mh8pRyir3MWW9uYR5TyxTHF0WPyoWnfejLm7RX01+ahz5bOmKFka10vq5D3fkfvqhoslfnTTiVMAgGMe9QCUjlZq6kZRVNmDxHu+fx0AYOpZQoMkE0cBAEpnPhkw+kzVWLsjqe/gYVXEdaAW7qd+46ag2a0012xTxqCmXMm2EbluE5XYBUhGquaKLOufjoEFyLEGlkaz+52Kx8hGG2u7PIljvZTOyQeYetfQ6DQ/22Qv+srdf6COZSPK6J+a83nSvtm9qscuVCXYMSbKVHgaxBnNCp0X7b9djt16gpxXXY6ZmTzOWaFvqkk7s4e1Ie2TC9KzMaMI0yTinVH1LDPr9FSpKwsSVFasEOJrVKyNubv/4+JYUNLK1Gmm2LNAYKOkOwyyy/DptTccUBZ8usbIBX2fOEjQd84otyMGFUgNOoF8mYi+AuDjuv2rAP5jwGMDAgICNh4I62YCweKDvs0o9zmQVB0f0e1fgxjYDoRBH6L/MRE9FxKMQpBkKJ8btJGAgICAjQZaR4GEzHwQwEHIBAC1f6oBGCOiMT+Gz3KBENHbvGDCLxDRNwZtd+A4fGb+DIDPDFp+rdFpOdsPbQFXHXgVn47qFAy42L6Zl1XUmEFsyhJdrpsizJb2TVUR/eQOoSVO3z7mVENHtYSuscxvTo2l6rLmcWdJXXMzQCT0U+PGqwEAM7dfCgBozQsNNn6c0E+7HiOqqUjzHmx+0tMc9XHwYkkXMH2nBBCe+oL/pe0WMz1yXMa9U2LtvV95jNsOSl+3jogyyBRW1Zr8opuoxE4xs5Ba8KbRGqqw0mGcUmZjtpWiolkDy6qsSkkoM5ch0JI3qjqLa+PObyxSVRc5RZWUbVkAm6O2sutnfcg8qrrfB34AYfaB+o85P7JinXFEWGhZ4F0xOHSkXPwHNp2jjQ7p+6MjVUH9WLzL0oqcL+l1tIyYEbNTCu6ZVRt/7YsFBZpKKpqRH7Xx9L0Y37RT6ilvLvTFggFNURjnuCv7v1syysobc4Op2TgqI+qiXnQZPXU7P85RF1pwKFhfKxAAABE9C8C7ARwDcfC4H8Tp/PQuh2wjohOZ+UY9/gQA2wZtb9Cc6M8B8JcQeRfpHzPzkpO/BwQEBNy3QZmh5/rB/wHwSACXMvOZRPQE6KqkC14N4DIiulG3j4ca1A6CQVcg74S4NF7Tt2RAQEDAkQBaPw/Rc2gy814iiogoYuavdZLxmtcgM39Z7UweoB9dy8wLfvluGHQCufu+Mnn4Wd4GUcP0o5+6Bf71KttLjWXVWSa1WDPd0a0/QbRJqCMuq0/XpHgT3TtbvFGfePwUAGBTYy/oNvGpuvc/Pg8A2P5C+QFhvkakGeJIbbaxbReqdQkgPPS1fwcAHL5VbLx3vfAF0ie1Th896wAAIL2fBBpetreMx6dCn02ecQYAYOI0uffKZz8FANCYEkrDbMT3zSe44lZRhu2cKAZ9mUrGAuQitV+fi1IXSGfUUcVoD6VCjPaaSTL6w673fFTVes1DTClHDSg0FVZSqqHRKirdMs+tQlfdNjMv2lOJuYOSzwL49JWNvuyg2quqkskyENp5mhdWlinTykfYMSeqO9wgyrrDVwttuencXwSQpQowWrEcARM6yM4mXvs8rxRaYokqVU3nFH/Isls6XzNFWdMkmIJsvpUi4eIYW//LngKugC5fQ/sepSh+R4bo3N4F63ICOUBEYwC+AeCjRHQPgFaHch8kok0Q948vA/gmM3cq1xODTiBXENEnAfwbijnRe3rFBwQEBGxYrM84kPMAzEOoqV8HMIkOVlTM/DQiqgE4F2LA+NdEdCtkMvnyoMa5g04gEwBmATwl3wf0STYSEBAQsHFBmRpgnYCZZ3KbFxLRAyDPr1/eoew8dMIA3AP0pwF4DxEdzcwP79feoDLenhGKRPR6Zv6LQepaMTAjSdmtcm0F3C2waxAUqCujG+AFL0VFy+zFqLGipqiUYg16O3j5f7sENSMPkWsXKdVyyrhQBvta8tBuMlaVTH0SuL/Yns+9XF7vVWpnt1pxn3Tq4wEAo3f+WPrYajq79vFnvVhe90rWPz5G6Kim2nbjKKnrW3dLXa/50PfwqT8QtdX9Hv4sAMBCRYL0btdAvy1Kd4ySHDPdAO6ZkYXrVg362zmhSiAdrntnhFo6rKZP45UYm7Ws0RoW0GeW7aYUGq/Kr8CjR0susM6oK9seU4+sVB2rTRmUNFOn7rLYTacI0qDEsgZKtlCkd4BO3krF+y3N3UNGydm9gy6ZJBOOCnWUIqCu9M8BpZv2N6WO7WrZXlZLdajfVeXu6zIK8/iHAgA2Hy2qK1PJpXpPby2rDxmXHe1oVJYpuew0bDynUw1e3XIKRqOkUC97wbqVxIIO5Xzq5aq7Pi2fcjZfOg1sdT5XnGbeVx5txB51lf/Ou/dovx7LBhGoVOlfrm81tAvAv0LiMVJImMTfLbKOh0BiO46BMEX/D8B7ATwCwLsGOH4CIgP+hP5ND9LusKbPYLceEBBw5CGKBvvrjRaAP2LmB0IUVK8gotMW2ZP3QYIJnwvgXkg+kBsB3J+Z/6bbQUT0W0R0N4ArAXxf/65g5sYgjQ4rH+NKqKwDAgIC1i+G5MbLzHcBuEvfHyaiawAcC+Cni6imysz/ou+vI6LXAHgdMyc9jgGA1wA4nZn3LLLbAIY3gSxpXdht6UZEmwF8EqJJvhnA85l5f5/aEOX8f1zHPOUTkAXutVfh/VIw2iptgdRPiEtKvZhyxgK5vGN7BSGmngqHq6KIGjn1NBz8wfcBAOWbRVlVUzWU+RVtUQ1DGmk2QIpxt3p671VKpzqumeaU9jiotEdd60imdjr65Ppp6dPJJx9f6OusKpu+d6esZG/cL4F/9bEq/vtWuRS7N0sfbjsoKjKznn/gNqG/ttbl9tozO4s5paYse+C4RvQd1OC9ed1vPlezzQSb6kKFVC3rnn5X59TC3PySJpXCqqPp+KU5LtIo9+rYGI3kLPAJ8H//GE2TanbLkro32Wscx5kdedr5fnN15NRMVpbMwwnF+9BlyoxNQWY26EBN+bWd43Je1+7V89ETOaTU5u7D8sNx29T9MaHRf5Wbf6AnK9vJURLUmdQmCmOzuUbYO6f3irZntvFGNdp2noKaUQppRK3gs/OQPpnay74TUWsBVaV+7Z5x31MbRwvuTCw9ZJIF12p7WcqG4vXL27iX3NtuUZzLwaJUWFuJ6Irc9gXMfEFbjUTHQzIKfneRnakR0ZnIbuZpAA8hlQsy8w+6HPdzyPPtJWGtVyC2dPuBpl38PhFdAuClAL7KzO8gotdBvF06ukkGBAQErAkIi3mIvoeZz+lZnchvPwPgD5j50CJ7cxckAt2wO7fNAJ7Y5bjXA/g2EX0XRYXtKwdpdFgTyKeWclCPpdt5EHkZAFwI0SqHCSQgIGDdgEBDk/ESURkyeXx0KeERzPyEJTb9zwD+C8BPUDRKHgiDWpm8ExIiPweRfD0UMkt+BACY+f8utuEObRyPbOl2lE4uYOa71Bis0zHnQ8Puj9t5LEozORrP+2XA1VxeeaOwbDlr2x2s3gFZkpufFFJd7dmy3GytbSlrWd+4/VqQYzuKmdSMFisfdwroxz8EALSmO4sgommxYY9IaKNromMcLXPDXqHZvniNBAX+2hnHAAC21pRWKe+Q9ijC1QfknH++X47ZVBU6wzLQXX2vnOfuw+KntfugvB5z9BjuOSzn3FQa4+5D8tlRqqy65l7p+2RNxnPPbAMT+n6/8k+mJrI6jI7aMZYFGpryyCgqywzYSOTYin55HStBEVive2yqm9QCCb2Auw4Uo9FSjjLT/bdNq6fUmHxd0oTbjjcqyVdlWbkUWeZIU3PFeg+5dueFGqwfvrtQx/TELqdkquh9duqWutYvZcY1XWJ9Uu6/2Wbq6MGtpqwbl6+RKdCmlboqu8BJcgGLdj52Qvu1rhv3y7U+8+hR13e7hk09L9Mlmcow1Wyg0bwEHVJzPlNUeSqmNP89RRZcSUjcMVkGUaHryHmImfW8bqdJ276S+sMNBUTAcFRYBOADAK5h5nf3Kz9ktJj5D5d68KDrr6fokuqZAG4HcAqAP15qoz6WunRj5guY+RxmPmfrls39DwgICAgYIiiKBvrrg8dAMgE+kYh+pH9PX/neAwC+RkTnE9EOItpsf4MePCiFZT/Nnw7g48y8b7FWDt3QZel2t3m1ENEOiKtkQEBAwPrBkLywmPmbWDsl6wv19fW5fQzgxEEOHnQC+QIRXQuhsH5XU9ouey3YY+l2MSQv7zv09fOD1mnLZKOFsuWsLmXj7JQ5Li4/aUFsyY2ucstnThEfvkePkbnUMttFZlluAU8VCfyjtOWW7n7gk2V9i/UYs/FeuPb7+MmHJHmYZQCMviSZAU9/w6ul3qpQF1BPp1OPmUSrLj8YNtc3AQDOPlboqOPrcs7XHdDgL1VC3X9TGRobh0PzQil94Xqh/x5/vNR1wpS0Y/TGJy6/Tfpx7CSe8QChQvbNyrH7Z6UvP7rtgJyX/rioKAW1ZayCHZNyPcaqpsySY4zyObwgY37cZF3PJXaKH6NT6u4rpmqitBg0eIhip34y6sh+51iwoNmFlzr8Kqxoe1avnccOpa4sgLCRMEx8Z5kBrR1HB5l6UtuZbTEOaFCe64PnXV61++Dr8kixtEMC/0ZPryMdkWtrlMx0Q441BZW1X4ulk+OVCHWt/65Uf1BqWaME7ZVzfmSbNEC1Ecl3I7Wx15M36szUUXOtKPMFMzrSvgs6OPadNHBlBDQv3zVTF1p7Jc+ALJ7PCAn77lmGRWhApvlxOZ8z/Q5yVHL/4C3FgR/kuGz4ys01BhF9BsAHAXyJuQOP7oGZT1hOe4NGor9OHR0PMXNCRLOQB93LhS3dfkJEP9J9fwqZOC4iopcBuBUhUDEgIGDdgdbdBALgHyG5zf+eiD4F4F+Y+dpuhUmS6zwDEjLh5oNBn8UM+hB9BMArABwHeWh9DIBTAXxxkOO7oc/S7UnLqTsgICBgRUHtzsNrDWa+FMClRDQJyQNyCRHdBolU/wgzN71DvgBhk1ZOhQXgQ5AQ90fr9u0Q6e6yJpBhgtJEbNFddj9VgGiQnq3xqTnnlr5Gc5l1ulFXRmVFtgKcOYDWveIVFe8SRYvRRpUFoXbgWXKn1fHMllv7lFKRMmgZq6Z95JlDGDtGgvCu+qbU+629ch5/+5s3S3v3F1v1lmaTixZm3EW0J1+jY0J3UEPOY7oh7Y6pz9YU5rC/LJTByVukbcsMuGtClvim1Jlvyf4XP0oyE24frWCTUh+ba1KfBf8ZZXX7PumzUUGTI2VMKnVlAYNV5dCmGxr8qF5VVtdENcY+VWpVNZWdUVqW6c7UP9NNC0JMXZCmBVFaBr2R2JQ78v0h9ZKajkacAszoktKCBo0q3WFBo6nSLdVSCZHeIzAvtNlZrVfO3e6H5ha9TsQ4uqL3lyqBolmpI7n6mwCA277wFQDA4dtFjbX9TLnnNs/PIBqfkmPuJ4nlpjYJRV1SKss8xA7p60g5wogLliQdU703c1QcAEyQUD/zURWswZOZlb6U3aSZIp2KzZRjpcgFh8Z6TZ06Ss8zPnxvYWzS0S3ue1Lac5M0oPdzUzVcJmZrqc9aJW1kikf9/rqMl3feILtP0jALpavjuSz22F2XoYKGHJg4HBDRFgAvgrA7PwTwUQCPhTwOONcrvpOZH7LUtgZdf53EzO8E0AQAZp5DsC8JCAg40jEcL6yhgYg+C+C/AYxAkgA+m5k/ycy/D2CswyFfIqKndNg/EAZdgTSIqA79YUBEJyEXtRgQEBBwpIGRMQ7rCO9h5v/q9EGXSPj/AfA5kmCaJrC4dOWDTiBvggQQ7iKij0Iefr90wGNXBUwR0upYZrOu+516JW+/rkosVpWUo5+sMj3G8ZtbgXiLUDisS2ELWkxGt0gZVXsYRYJW5udjKiwLavMz3xlKx52Ck88TRUl1QqzXn/XkM6V6pTBSbc+CtGj/HWhdfbm8LwvlMvJQsW9v3iBBiSc/UlIiW3MLcQXX3SHtPOUEoWUWlE4b2fszAEBFMyE2NbBv/7ycywO3jmBLTakQXYTef7OoXox+qpflfM3LabJadpboB1V9NaJljLoy9VJT6Ye9c01Ho8RKldm4VTRxmmUoLNeMHmMXPGdUzpi2M6pZOmMN0jPlzkR9EqkqdMwbyiisktrsL1z9P9LXB5wNAOA0Bc9JGdLMjjPfuQQAcMNnvwMA2HR/qdOw6ZTjcM2lGiQ6p4qzJz5I+vAQYRB2Pf850rcT5Zqn6i1VOngnkn27pY/XyrUeO0nOZ1Tpz6P33V5oD/Ob0NhRNHS1zIPHzAk9ek9ZMkfW9BrXZ/ficFWpWaX8SnoN7jgs97XRX0eNypiPUQPlipdlkmS7rMpFo5XTGaEIee9usCqmWjqO9o8o1hzjdEhor/TwATkmTVA6Sr6DjWtFmVg66jg5SIP57iG5l0c1cHakXCsoKQGAFgZyKR8MtH4eohPRczq9N/SIbn8XgEcB+Al3iq7tg0FVWJcQ0Q8gVsME4FVLdW8MCAgI2DBYJxMIgGf1+KxX8r8bAFy1lMkDGFyFRZBMVScy81uJ6DgiejgzX76URgMCAgLu+6B1o8Lql/SvB+4CcBkRfQlFM8XhyXghma1SiKPjWwEchkSPP2xRXV1JxGUkk8e4DGqWUc0yrFmw03QjdWqUsqnWTEmj6pEkag82inSZnGqmPlOYUEO9sfRzC050gYU52BRv6qSKZpEzNQlVaph46vMBAKed9Uhpd1IpsrrQKy2lsEoH79COxZi+Rd7P7N4HANhREbqp9NBzAQBTC7JYtGBItBbw1GNPlfcalFXXc3cZFvW8towKjfIADSCrlchRfpX9kjZ5bFSohGPVC8voKqOl5lspDs4LbWNKsM1q9e6yDOoPIFODjVUiR0fF3o+81ALKVJVTbck5VNMWxkdFtdP09CHlO6+XvvzwGwCAe664GgBw1GPORnnXKdKO0lFGT6VKvczeKpTP/B1CadVPOhnxQ8XctKX+UuXrhZ560KsksDfZr8ojpRXLu07Bg37pZQCAO6viSbZP1WNzKnXaOXtT4fxao5KFMt5zE6JRuf7J6aJu/+Zdcs/crK8v0tst3SbqLK7Unapsq6oMdy9oUN2EtF9tkr7q+Y5swtTdGjIwIwqm1gmSGfOm/dLOo3bK+BodVh2pYLZh3mRSXy3S75zeQ4mOUaR0W3TgLkdNxTulL5iVbVMszv1YAmpvu1Qc0A/cchDbHyxldz3vl6TeUx8LALhOYw2v+Ln4w520SSjV4yarTj1miMdGMVSsnxUIAICIqpCkUsejGNfx1i6H3KR/FWQ2ZgNj0AnkEcx8FhH9UDuzn4iW7yIWEBAQcF8FrUsZ7+chqWm/jwGETsz8luU0NugE0tSIRVNhbcMSgk4CAgICNhTW2QoEEtfx1H6FiOjNzPzm5ZYZdAL5ewCfA7CdiN4O4HkA3jjgsauCFgN751PsVYXLdFN9lPQXwnZVjTSSFAfnNcBNl7dVz6uHlH4yqqnMLcCCDCNPweXdQKb64NwvEz9rXVbY0tWpgqs+CqgKJTpaAqtMRWZBbEZDpWVZpselGcQ19RHSV0enqYoHqgxLK7Kd3nUjaOrYQn2mNMKWowEAzVuF8qk/7MkAgPEdDwUAVCl1AV3JpNjFbyb9LTEp42ZBfIeVgjrcWHD+T0eNVgqf7VTaa6cGMNpY7ZtPnFfTUWqCVdonFI8pdHjyKDlm/Cg97xrmUylrCrC6Bh+2NgvNVjlRAvF2PfJpsn/yWFx1OCqUPWpErvFIU7iR8RNEFXUglmtw+e4Z/PCncs+cvFVef/HJLwcA7J03m3yhOE1t9qXr7sHYrNR/zIQq7ZSbM9ruYE36mDZlDG76udBIJ21+CG5VO/3ybUI3XX2PBCF+6wahJ2/YIdciuUWophvvvRdjaqG/Rcd8ckS2904LXfnM02XcvqcW+884qY5ExynWe/JnhzSLoN7Pu2fkezBeySz2LatgVe8D86oyujd1SkUNHiztyVSFm0QJZurJlt5TOFFilnecJ+0fX4ocJXd7S+7jRNnjptr72zgeOyH3YSWOnH+ZpQYwdd6wsA5lvN8mogcz80/6lPvfRNTL+ZwAvADAm3tV0ncCUX3wTQD+BGIvQgB+iZmv6XdsQEBAwMYFrWqQYC8Q0U8gDFEJwG8Q0Y0QCsviOvxo8/cBGEdvvK9fu30nEGZOiehdzPwoAF1NuQICAgKOKBA5Ac46wDMXU3i5zz4MNIj8l4jeAuBKAJ9dql54pXHmWWfx177xTdw7K0vU+5V1Gf2dzwEASmcKFZOMb0fTrKPh2U27LGlmBa9Z5krVjG0ym25v6WoKLgua4nIdLT3GltEtz358Sik0W5rT/GG33G/Esgw3v6cRzRRnqrKRlgRERTN7kVwngVU8r0FZGmCVPOBxUq/6Ns1VhQ4bPXirU1ulPxW1y8ItEkC4cEDK1rdrAOYvS96w6sy9WV3elya95So5D6UlqCb0XnOH0EWX391w9u0LSi3Z9rHjMtab60KvGFWzfbSSeVTpdTAF14RSW7uVijFsGSk7O/eGDr61c9SYXHPz4tqjVvSWcVGOlzJGO9kxP983W9ifcKYiq2pWRL+vltnPlGgJs/P+Maqs2zfJ2M8FPYeU2fmmGSxzpJ2vT5POt1Jn33+HZow8rL5jVramQZx3HhDa6/yH73KqJaMAzY9sqmoBmVKX0aPzCbt70QXrKr07zXJNxyKpYx7mswWU06Lq73BLTtq+I6bAM4yUI+eFtm9OPjOfLrsfLDC37LJDsqPebEzdd29s5Pv9cpT3w9lnnsHfuezSgcpWp7Ytu71BQEQfZuYX99s3LAw6ff4hgFEALSKaxyLD3QMCAgI2ItbhM5DT8xsqfjp7pRobNBK9H1cWEBAQcGRhfVmZvB6SS6meezhOABoALlipdgeNRD+rw+6DAG5hVmOiIYOIngrg7yDp597PzO/oVZ5ZEqBtrqvvVElVS+eI2ibVpXJ8+B5QTebDtKrzotE5uvQ2P6sZXYI3FhJMqOokmhVlTDImQV5kme9UmURNXeKnLZS0fltGW6DVGKwMCu1GcuLSpi6155XGMIpkRKUn8X7xPuLaOOJNEqiVHJRAqmhKvZ2UTjObelNCJRNHo3SzBGgd+qmINe79kVhil0c1c+AJqsaZlTpb3/k3AMD0z2/C1KPFa6txo1BXpKqvRIPmqqdJ8Fl8QPp4+rb74Zu3yZhedZvc2z/W7IVGp8zMCKVx3FGi3ImjCI2W2cTL2O/cJLTJWK1UOHazqox2TdadbfxBzXBoijALZLRrYRTUUaMT2fVR2qmm6ii7Xg/TDI9GOeWpEYOfZdD+rWTMEzl6y7IZdksLbZ5lo7l4Vl/I141Jjh19k+07Z4fcX3ZPdcN4NXYZDo0uGtHXUajleyzXoK5W6fXyiPseOcv01K6bXJd9DaljsppTyCmNbONo5zqnXN0WvT52nqWIHI1bH1fFHhvFJ8cadWXDGoFcgGq0Uubhw0vtvaj/dz6Y+S8A/AUR/QUzv77vAVm72wC8HO2Bh785yPGLiUQ/C5J0BAAeDODHALYQ0W8z838O2uFBoMuufwDwC5DcI98joouZ+afDbCcgICBg6RiOlckw/98x8+uJ6FgA90NxQvhGl0M+D7F/vxTAojXOg579zQBexsxXAwARnQbgjwG8DWLSNdQJBMDDAfyMmW/U9j4BSaEbJpCAgID1g+FQWEP7f0dE74DEb/wU2YTAALpNICPM/NpF91gx6ATyAJs8AICZf0pEZzLzjd2W4cvEsQBuy23fDuARfiEiOh+SYhe7du1ymeq0kwByQWaGhcMgtesmpY5sCW6W5jU9pwOqQNlSLyFOhKLiigTwmWePfwOlagkOTl1AlSuhKhVrz4Leakp7cFRy9c1rMJkt4c3CHOZZpb5CNH8YLaWuyrtOls/mRElFTaG7WKmE2r4bpf2RTaC69KG+Te27x+W8aluE+quoJfzC1z5WOL/NT3sOkv0SfGhUVeuum6XPt8pr/IRTtV1Rko01p/HwY6W9aaWWbrhb+miqmy1jQl2Zr1KStnBYlVLjGgC3fVzqm1TFlgWwlSPLtMeO6tupwWTznnzJZSrM+XWl6lLm7OONjlJKxPfiytNRRjfZt4BR3DZElNFL/b4xvnKoE5iLHw5SVlnYtrJGBe6dbbmAuzHSzI2pZv2LhNqsq+8YzPMtLuNw0zJsVgv1L6haal4VXba/EpEbCx+jSlNplSAdLUb2/Ywpo7WAjEb2hgRE2TU1lLu0uxQwUSFguA+2EtEVue0LmNmeTQz0/25A/DKAU5l50HxNXySipzPzfyylsUEnkOuI6B8BfEK3fxXA9Wrc5efYHQY6XZU2AlcvwAUAcNbZZ69LeXFAQMAGBXeXYnfAnh4y3oH+3w2IGwGUMXjCv1cB+FMiaiD7Xz70hFIvBfC7AP4AcrLfBPAabfAJA9axGNwOYFdueyeAO1egnYCAgIAlgt1D+mVimP/vZgH8iIi+iqI9+ys7FV6uwnZQGe8cEb0XwBeZ+Trv4yGm+HL4HoCTiegEAHdAOL0X9jqAmN1SFkCWZVA9fdxH1XFERjspDbUA88lSxUbJAsrUtjwmMMu+lh5qQYjxtGZOU+qKPFt0AIAqsywrWgShb0a0H/F+yZJHCzOYPUY8pxINWDT2ZPe8/Eg5WhVkltGNDtwFbkj9rd03AwDmr9fAvppkxyvvvL/sV3+r2rm/4ry1amfL/L/r+AfKeRzeX+hr+TixOucHiHU27bkRrJnl0gc8Rl5/diUAoP5CoVLn1Ki5/qMvSOcf+FgcaBafz73miUK3WQbCm9Uu/F4N/Nt9YB4LSj9NKYV1ynahubar6sroKAu2jIgwpQotozeMDjIVVsrFfpSjOFPv6L7IU0sZVWZ0W5Kyo7kchaXHGOXoU7u9mBNf0TUIKzIM5ti+L84vqhy5cWNSW3/vIbGLezDLeUQoR8V67LsW5yir/H4gG0ujmEoLqjzV+7qi96cLeAW5wEFHWXU5H6OViLmNYho24z4k2mPR/+964GL9GxhE9GwAj9PNy5j5i4MeO6iM99kA/griF38CEZ0B4K3M/OzFdHRQMHOLiH4PwFcgsrYP5p/BBAQEBKw1GD2MUhdTzxD/3zHzhZpq4xTddR0zd33MoA/dHwbgo7rrVUT0WGZ+3SDtLSYn+sMBXKad/BERHT/gsUuCPtRZ0oOdgICAgJUGoz0+Z8l1Den/HRGdC+BCiHKWAOwiopf0kPE+HcAZzEI5ENGFAH4IYKgTSIuZD66Q4mooaFNEUDETmX3EIBe4ZzSXlbQlt1FZFkzlFFfIbK7ZjtKgQ6OyMuqshMhUUPvu0i5pkKNmjYsawv7RnCzfE7VYBzJllmEy1mDA64WWat0l1uYJgGhcfKtIMxHW7n9aYTveJIGF8a4HyP75w0hVxdXaJvRWesyDdaDknA9oAGOyVeoyOife8WBUjha6q6E0x+yTf0c+UxlM3YLpTjxTDrrlSpw8JcGOJ5x0PABgXyo01PbDogxrvPx8AMCWj34eAPCihxztgtruVs+rTaq+2qZ26xXnP6V9iwhlo5QssDOWa9CYkjEyuijWjJKgFA0NajukMh9TXdn9blSaeVhRTFCxkPM7Mwyiyumm1HKfmwJpSF+3bvVYX02dFREwY1kStcxERQM79V52fnH6GhE5lZVPXaVav2UAzV8n65J5eu1Wi/ZjRjSoVhWMZJRZZRSpyaxyFFWn8+m2vRJYh8qddwF4ij1qIKJTAHwcve1MpgDs0/eTi2ls0AnkKiJ6IYCYiE4G8EoA315MQwEBAQEbCjy8FcgQUc4/p2bm64moPUd3hr8A8EMi+hrkN83jAAwcyT7oBPL7AN4Aear/cQhX97ZBGwkICAjYiFiH5uRXENEHAHxYt38dkt62I5j540R0GeQ5CAF4LTPvHrSxQVVYs5AJ5A2DVryu4Sm0IvPS0aV8yZPMOLoKHegFVaNEmtnPVCNRax6tu28FABz4jlim771aaKfJk4Sqmrnj3sL21CMeheqpj5S+1Kekfu1j+W7xqlq4RfJ4HbpeqJ/JB52G6ERRbtF+Uf6lu8SQM1W/rlRpgOlU6jq4kLiAsaqe64guxi1Yy+gap0zT8y0T0HC23LLT6DVDAgkoOzQm51U5dadT3ZiiabNSTGlNVsyPvfhfAAD7PvZ2AMDYGWdg6mjx4zpZVXHTR59T6Eu1qYGaM+oBNr3HKdLSGVG6RaOiWqtuPV6252V/83r5TkX1UYzslOeNNc3+yOWqvkpwJZwyqeLO0TLolXIqIQA9ZZ0+deUH0w2TuuqkQOp7DLLMfdY1S1+QWAbMVCjbBVYqK03dfdDyzt1RgVa/C9SkjFLW+8KorGv3S/3HTwp1VrMvJ6egqEhLrzUY6zKv9+8AeAWEJSJIBPp7/UJE9ABmvjbnc3i7vh5DRMcw8w8GaaznBEJEX0APmm+lVFgBAQEB9wUk62wG0Qj0d+tfL/whxMXjXZ2qAfDEQdrrtwL5a319DoCjAXxEt38N8pQ/ICAg4IgE8/qhsIjoyl6f+yltmfl8ffs0Zp7Pf0ZEtUHb7TmBMPPXtcK3MfPjch99gYi6ycLWJfKKDfaWwpEusqhHtJd/n5j+m8dEXdTUV1P3RHMHEB0+AACobhIa5fjn/iIAYP42sb2ZOGEHAKByvKijopGJ7IbUNX5TlU6R0lHVB0vw3tZTVOE0vsXRZjx1tLyqwmp/YrbnUnRO7dFbKaORyHuz615IjF5ItYwGeHlBdi1uVw+x+iJZEKXRExboJ/UUx42cl5IGrKlH1uZfeAYA4KZ/ugCbTz8BAHDXd8RT7tRXvlzGYlKyNvKcUFite++Qsdqd2Qk1Dsln9R3ihVZZUAt9vfYLtwmdOHrGo4AZCZ6MSkJHphbwqZ5phtbkMbK/Oed8vny/M9ufRFZXduP4gYo+lkNd+UF0vegrX71kiIjQ0sDOOZOX6f2R6E1U1TFiVaYlnNFdzjusS5Bl7LaBSClMs3XfrAGgtTGjAuXjlo5aHFFf1ZV/fvnPO+0bBtbRAiSFrBw+BuALyIR0/fBtiNN6v30dMehD9G1EdGLOLfIEANsGPDYgICBgQ2KdLEDAzGcQ0QMg7NDHIG68HwPwn51yNhHR0RATxzoRnYns980EgJFB2x10Ank1gMuI6EbdPh7qghsQEBBwJEICCdfJDAKAma+FBH2/iYh+FcC/AvhLiIuIj1+EeBzuRPF5yWFIZsOBMKgK68sa//EA3XXtIuyCVwVtXlgelrt09Q/3FTNuSW6flyrgXQ8CAIxpEJ0pd6KaTPAl9agiVf0kY9vAFQmoMp+usmaCM1v6dFTomyz7WwvsZVK0IK+Kdi5xlILZngMLXvCXD6MQnFWD85bKq2r0VTMeWpBZ5lWUDZrRNvaFm1d1V1qeAgAsjIkaa3LLSQCA4998slPLTT1TlFOmjmrdIAKR0nZRecUPOVfaOKcOXPNNAEBVqRhuqjdZS8emIXVVd8ixvOt0wBRh6r9k7dLcQXm166bZKBGXULpHFHbp6GY9Vs7Dxt6y5eVpq64Bfd41GCaV1bG9btQP2hWIlhFzItLxU0+4Zo67aXsO0MUHLE/dGe3ZbBWDdg2+71WnG3VQSqvXMctFnySPqwpNJPUCiKX7fsgP/891KsvMFwK4kIiey8yfWWqb/VRYZ5mcSyeMH/cqExAQEHAkYb0sQIjo6wDGAVwEWVlYZHmFiDYz875OxzHzZ4joGQBOB1DL7X/rIO32W4F8SL1Vek3tHwBw5iCNBQQEBGwUMNglI1sHuB+EVfstFB8vkO4/sdNBRPRPkGceTwDwfgDPA3D5oI32m0AmIVGMvSaQewdtbMXBPTQR1D0Iqd/SNx+U1eriXeCW5PpxExWMxEb/2HpcKZ5NOwF0TkBs9bSlytRzM1VWSbMaUtLIVFimbNK+jiqlZcdosj8wgDgq1m9bftY8ox+MpkhB8Gy6Motvt12sQ463+kxVI9upcgBVrXRaO1kfP9rRJ62aqMvsMm0+Q2i8lp5vUpIfTgcXEtQf+kypV9sbVQqQ1cuJGkL9lZSWao1tcxTVHERhZD5kZJb5swfkVctxFLn3ZJ5NVQl8i+akbKwUpFGSrfrmbAz8fzq+h1OP/0ndqNRBeK/F/Fq2+8CuUxMy1iW9D0c16raZMhbMN03rT3XwzSbfrrHF48YgV9angIzi9DMJAounoYdNV7WB188KhJmPX+Khj2bmhxDRlcz8FiJ6FyRN+UDoJ+NdaqcCAgICNjzWoRfWYmFy31kiOgbAXgAnDHrwoCqsgICAgIAcGOtnBbIMfJGIpiBKrR9ATuv9gx68ZhMIEf0VgGcBaAD4OYDfYOYD+tnrAbwMwvK8kpm/suj6fTrLvHuWEGDFRDmraqU3ulRjduIHmylqJaWWRjYX6jf6w5RWbhtw3lfsZYIzSit2aiYrXsksxd2rBUYq3WVjoXwEI1PbGCXnU1YW/OXHVuZ/cbnh0j73ogxsvBIvE11CXGgv304mzCn29QCJ6iy1AMkZoeoSBppp5rcEAC0N6JtUyo+V7nJ0YtJ0FFiiwXHmB5bURT03buel1yutTaK1WY9XmovVE82Noyq4oNRZq74ZDVW++dkSU+9nrO03qit///kJjKxsLx+t7kq79rp8tVzk3+/6PTJ796hURWK0p1Y3p+PYUn6qFBevX7UUtQUbmmrNzs+1a/dujibtdj7OX6tDAOFKIbnvzyDvVIHUZ4joi5AH6fN9jnGI+hdZMVwC4EEaYn891EKYiE6DSNFOB/BUAO8l6vEAIyAgIGANYHEgg/ytJojosUT0G/p+mwZ+d8N37A0zLzDzwfy+fhg0pS1BbIFPZOa3EtFxAI5m5oGf1vtg5v/Mbf4P5Ok/AJwH4BM6K95ERD+DZEMc+KQCAgICVhy8/swUiehNAM4BcCqADwEoQzwMH+OVW9VI9PdCvFaeCOCtkGjFz0A85IeB3wTwSX1/LGRCMdyu+9pAROdDJWu7du2Sfbbk9Ze+bru/GssyELq6opILirMla+wL04x20KC0bdUI8+aX1WZzrXbo6ivklt65Yr5Sx6fe3GaaZlkSnbJF1UtO+aS29bljM1UUOsJ/ONjpYaH7ZeUprDqJjLpRCT5VZsq0JGVHeVjQ4axGr80rRTJRLV7L2WaK6YYpgORsJzUbHkGuS3T4btm2wMJyFZgQT7I4Z9cOAKOkZVRJFR+SY7kxg0QDB62PlWRB61OKTH20KJXgx2bK2DOnCjONkhurFC3zDbFyQWVTgzG3+WfZEW0Bi8goJ8e8eXRUL1iRTiooIOe1VZH/McxARdWGNhYWUmtKLju/co42jbxskv4YNJ2SK87O05SAjk72O4f28/S/N0NcDdgKZKXRi+7vgF+GhFVY/N6dRDTeodzqRaIDeAQzn0VEP9RO7dfE7T1BRJdCXHx9vIGZP69l3gCghSype6c7t+NVYuYLAFwAAGefddZ9nowMCAi4L4FX6xnIJQBez8wtIvpLCN3/2i5lG8zMRPIrhEgfGHpYlUj0HJr6HMI6tQ0DGFEy85N7fU5ELwHwTABP4uxn+u0AduWK7QRw54D9DAgICFgVrNYKpAfd3wkXEdE/A5giopdD2J33+YWI6EXM/BEAxxPRH3Zos18+EQCDTyB/D/FU2U5Eb4ecwBsHPLYjiOipkFn08Zrx0HAxgI8R0bsBHAPgZAwaGUkZ8dNtsb6YTG2mhKK0hcgyDeq82eJM0ST16jGx+loxuw99+ofcNhe2C15BlNEXhT55ahviFGS+VqYs8uzqnfW8bpeR0XMpiufRDXmqyw/+ss+aTkGjfbPPQa49u+Fs/AxWtqK8RImAeeWzjN4YU1mWlZlvFTuSz3TXVInWvBqBzanFfV0zIBq4No5mVPRlYu1rXNbguZq32OYUkQZpNlKLjpPr7i/LmzddDQCobbs/Gmqvf8+MiFyOHpPSVVXHtZxCTavUvotqSz6rlZRKTYt0TqaeK27ny7Ddb7AyRcWVnHuxnm7I02N2P5gF/IxRjBXNpqmdsWDRckyu/+apZb8ffet3y4jY5KgtiNfoTl85mHB2H7Z9t4Zo584MNAc3w9pKRFfkti9QBmWxyNP9HfrEf01EvwDgEOQ5yJ8z8yUditrKZGwJfXAY1Ezxo0T0fQBPgtx/v8TM1yynYQDvAVAFcIle+P9h5t9m5quJ6CKIHXELwCuYuVPQdkBAQMCaYhEU1h5mPqfbh0uk+ztCJ4xOk0a+zD/r61v69Lsn+pkpbs5t3gPg4/nPuhl0DQJmvn+Pz94O4O1LrTsgICBgpSEU1pDqWhrd36ncYbQTCgcBXAHgjyynU678iQD+DsAj9bjvAHi1X64b+q1Avq+VEoDjIBbBBGAKwK1YRMj7SoNBSEFOZRW1qa+6BxK2q1Xiwjao5C4JI7NEl2OVduhgg92mEnHtFZfrbcFa6KVa6vC5qsaiRFwJLCteqq/uWCsOymiutBjA5VMiiaM02k+mRwLHAlLmTAGm1yMx6swCyLxgwRZlAXcWiDbXLD52M58mUx41U0ZFjymXi4GX1td0ZJO0q+d/cCHB9JwEcpo6alSjQU31tU+5tGosq/4dIxHiabGAG3dqpFLh/KCUWbxpu45B6qicfdqevR41KlRWzeuzUXX1UuTGyTJFdvPEijN+tO2aOWpM++FTWmkHtZdfv8/5t1LGdCPLTggA0xqJaeNpgYUnlNWPbGYGiWZ3nNExtvQCdkzCeh01F1I5rrrr0XRUn9Jd7j4ofq+M7ut0HkMBtwd2rgR60P2d8G7IM+OPQS7tCyArm+sAfBDAuV75jwH4B4h6C1r+4wAeMUjfegYSMvMJzHwigK8AeBYzb2XmLZCZcGDDrYCAgICNBsZgQYRDeND+HohV+yVE9CN10O2GpzLzPzPzYWY+pM9Zns7MnwSwqUN5YuYPM3NL/z6C/o9EHQZ9iP4wZv5t22DmLxHR2wZtJCAgIGCjgZGthla0nR50fwekRPR8AJ/W7bxiq1Nnv0ZErwPwCf38VwH8uz2+6PeYYtAJZA8RvRES0cgAXgRxbVx3yGgnoy6KVBblFl0+nWWj2/RonTjKLMxNdWFl7f5xxxhFk2ZL60pUpFEMzjrdKK2klVmzeyosC25sC4zktPgecJkJSe3OnbeSahEK7cAog2LfMuWO9hXt1Ii/fK/oCXdkQYx90j5WSkZZyW5TWrlxzo2fwQ9My/ojr7USuX77XbBjUg0wzKulah7XeHDBKBnW8zJ1j3w+kxAm9LPSHqWKdTzTmnpuHdoj2w1RXHFcdvWcMFXXduQ6zTZVaaTKMbtem+p6jXJKp4YOmCnSbEz2K81m91hM5LL62Tiyf009SovQnRrzAxYNpYgcxUh6LR+0Sbcbh2QMNNukRRimo1vc/W19i1NThMm20V4LyPzcLIDU7pmKExvKsQuO2rLXCLVSj3tyuVglCmuR+HXIM433Qi71/wB4ERHVAfxeh/K/qq+/5e3/TT2+Yx4Rw6ATyK9Bcu1+Tre/ofsCAgICjkisVhzIYqAPv5/V5eNvdii/rOfYg8p49wF41XIaCggICNhoWE850QGAiGoQJ3M/Re1veuWeyMz/RUTP6VQPMw/0jHtQM8WvoQN/xsxPHOT4VQHpMtXrZUYFabGkkWXu85Qltsq1Zem0qj7E76e4Bu6nwgIyO2tjYqolU9loX8xzS/2zEMVtnl2p9ip22fCy4EYpkIBa81qPWouXKrqtnltuMMzWvdx2Hnm6Dmin8+IOkrJulJIfjMYMZ/nubNT13GM9T6MjTNFDBJCeu1GAzgJej60p/2HsWJKyo6N82snOw4LZoqaM2Wa0wHpduCzUkmWVPKi0kJ1nnh4zfyw7BnNC10ApLGyXH3ZkiriohKr7tpkXlnx217RctwXlZjTZX44+yjy9rC82TnP6un8ulxIAougyRZO9sinrPLqwlFPe2ZW0q72QFGk8n7pNcso3iw2dZrm/JppyX5du+W89SO/3LcciHZUIgalRyS7JOjg+JZRvz87dlHoznirPVFe2O80lnB01RVavrKWLxHpcgQD4MIBrIV5Xb4VQWp1i9h4P4L/QebXCGFAkNSiF9Zrc+xqA50ICWgICAgKOTDC35XJZB7g/M/8KEZ3HzBcS0ccgKtoCmPlN+voby2lsUArr+96ubxHR15fTcEBAQMB9Gaulwlokmvp6gIgeBGA3gOO7FSai/wtJKnVAtzdBAg4HsqoalMLKR6RHAM5G57D7NQMxg9KkLQjQnaCpl3KZzWKlfiKlGSwTnXn6uGU7ZzSNr0Yx5ZHdR+WccsjopyS33AdyluWRSVDq7hi2ilSRY8t2o6Gi+YN6TMUOQDR/WI8pLgopKWY+5LKcJ1MEUslMzYIQy/XCsfPOF0q2jX5jzqgpo1xce7rfAv6MaihF5NQwVsjUcHHa1Ffpx4j2sUopqCWBkWW9ZnPqWTWpNu4jZiOudR2YT9DwqLiRcjFg0flNqe16PH8I8UxRUBhpsOF4teiCbfdARECqn9GI0E+oTcirR5FwrJRj0sRkVdpslaUeG+Md6oll/4ymGzIWMxagl2b3ojnY+5SqBSE2ctfNZ1cqnoW6UT15+tXOseRRso7e9eosx+TOw+qvsVKpGkxJR+lzWr2OaamKtDpW2GfXxTJ6Olq3cI7ah7qMo43TvKPk7Psm26PlKPsOmxIxGR5xwrm21hEu0EngjRBfwTEAf9aj/NOY2dm3q9P60zGg1+GgFFY+Ir0F4CbIg5qAgICAIxPcno54LUFEEYBDzLwfopTtKcFVxERU1QR+ULlvtc8xDoNOIA9k5kKeXCIauJGAgICAjQZZgax1LzIwc0pEvwfgokUc9hEAXyWiD0FO6TcBXDjowYNOIN8GcJa37zsd9q0dOEXUnAOZysc8ivRjs99G0nR+RU7JpLbZRudU6nKsUU4LCbd5RTlqyQsWHIUs35tx1VFhRvXEzuuoWIcpamLKqaC87GtmOV6JZGlPC9PZqSud5UgHo6xKennLGpBWEUohiatoqPdQVZVAFaUMLAixrGXNUt3OoU5JZhuvr02t6/CCFDb6w6iFCoAoMXVVUUkzZsxWUyx+6rGqzeJydn1UXVY3hZNSf6QqHwsKLMV1HNKx3FKPCv32aY65lvR1vDKOqiqyotn9Um9D+lLTcU1KQpmUc7SOEVVU1yA5RxOqOsvLbslR7O4ho2kqkHYnMlEcAGCmJu0d1PFMuD2w08/KZ9bpzVJGtZoGyYLyZvUrUC/ZvVuktGqlyAUi2j1pFKDzpLJA2Tj7/WiBe3ZNqSnZGOND9+gYyLGJKq4WRrdl6i67v3X7kH5nypEMSj3nDxZr1kd7najI9Rnx/olbP2oRg5QijWaE+jVKcVhYhyqsS4joNRDL9xnb2S2inJnfSURXAngy5F/I25i57aF7N/Rz4x1K3tyAgICAjQZmdj/E1hEs3uMVuX39IsqvAdBi5kuJaISIxpn58CCN9ZuOh5I3NyAgIGCjgbH+rEwWG1muWQvPB7AZwEmQBcM/QXI/9UXPCWRYeXN7QZdbfwVgGzPv0X2vhzykTwC8cqAlFaeghenMM8rqTyy4TmgB4hRoKTViVExDVnpGYURa1ganNHE0RpQGMoWR3TaZskUpizmlPw7fjbq2nYxtk8Iq2HHBZ0aj6DYlDdDcYX2vS++GUFWOpmqoZbsFssU5VyejfDTYEKVifjwLQkw4s+A2mi0m+cyC20xRVSepc46FItnfyhtUSR0Njx5y3lRxRpUYtWQeTiUXfKbjWpkq9JWIUK2oginKlExyzh7no9g7m2DbiPTTrL+Nnil7qiJTPB1upMCoXJ+yKak06HFe74A5Vfvkzzzzj5K+jJSKAZ7WR7ONn2umjloxRVtFadZ47oAcq1TaWCT3UHlkizvWYP+wjJKzsW95gZLlKHL0itFA/o9luwajpNxWM0Gs/a1q/13Qq9532QBk94k9BzDada4qKrb6lGbx1O+VodyaQyuqFfqfqRqL18llLmRGJS7ez/b99ANabWxSJndMRVV30dxBDAvM628CIaIRAH8I4DhmPp+ITgZwKjN/scshrwDwcADfBQBmvoGItg/aXj8Kayh5c3vUvwvAL0Byi9i+0yCe9KdDUtpeSkSnhKyEAQEB6w3rbQIB8CGIavbRun07gE8B6DaBLDBzg9yPPyphEXbuPfOBoJg3d9z7W1YuXcXfAPgTFDt8HoBPMPMCM98E4GeQGTIgICBg3YAh4ppB/lYRJzHzO6EBhcw8h3YT5Ty+TkR/CnnO/QuQyeYLgzbWj8L6Z317KTN/K/8ZET1m0EY6gYieDeAOZv6xlzntWIgFseF23depjvMh/B2O3bULe6vbsKmk1MWB2wEAycQOKauqpdboVpTmDwAADkYyB06mQh2k40cBAA5A6CFToEREiI1S0kBFX77nrL8XhA5rTe3ErKq7jEKIGl4yMaUJooXDrj/lmsRs2jJ8k6p8ovlDxfYmNI6TIpAeb4FbqSqoSvt1YWeUltIrleo4tqmIpqHnY3SGeUSRF5RY175Wq5n6xqmKSlZWqQMtazRSyhmFdO3eOT1WjnjAVhnrURsjyyqXNMBKq9k4secd1qoK5XTbYeEGjx4rOX8kP6vfzEJxAZu/fHvn5LN6Sdpp6uksaNDZFrVVLxcC7op8UKrj2FRDqLpdL1WQjYxsApezLIgAsKDt1KtyjefjiUKfF1wGvkw5ZV3wfaB8NNMsI6ZRO9Z/+7oZ7XVQsymOlCtObWWqQkdPKc2aqDrK6oia8xjXcTN1YxrLeTZIVHORXidLJwCKUNH/abbP0cd6L1vGQvPVGi1Rwf9NOqVjoH2rkikLVR2Yu8iJ0pP23RgK1iGFBaChsRwMAER0EpyRfke8FsD/BvATiKX7fwB4/6CNDapp+39ol+x22ldAr0TxkIfwT+l0WId9Ha+SZtu6AAAeeuZZ6+5KBgQEbFyknOVnWUd4M4AvA9hFRB8F8BiIEKoNGnh4JTM/CMD7ltJYv2cgj4Jwadu8ZyATyJm8dkO3RPFE9GBIPnVbfewE8AMiejhkxbErV3wnJMdvQEBAwLrCeluBMPN/EtH3ATwS8mP8VSZO6lA2JaIfE9FxzHxrpzL90G8FUoE86yhBnnsYDqGYKnFRYOafAHBP+onoZgDnMPMeIroYwMeI6N2Qh+gnA7h8qW0FBAQErATsGchqoZNitUOZiwF8HMDFzDzTqYyHHQCuJqLLUQw8fPYgfer3DOTrkIcs/8LMtwxS4XLBzFcT0UUAfgrx3XrFIAqsEgGbSimaypvzpuPkAy8FbJwsOCO8MYtoVhfFVMuOmMxyQSR/h0sTGFMLxLpZIZpqYUE4/Ybx2JM7XX8sP0Gsz1+Mf7Uo5fL+22S/mvdN8izumJdnDJaLZHNdeN/5MXk+Y/er8eSlA3eA1QySWrN6yppe1TMDdLLl5px7fmBePk7+bHk7vGcgWR6SJOOitV1Oi1oMS8NrktJGwqgrL33ClMgpLT3pRKzpXDVZBbmcrBVnblk1vr1VlFtX1ARx09hxrm17lmIyXTP6c2aOCms/JnLpdZ0c2dwF2KLWtSNaRzmiNumoeybgEbBurChy5ob2fMbK2vOnmn4brQqTWNdL5O6Hrfo8ZnO92P5cq/jsJ6KcpLeLNDZLH2vR60BZ74OSDkKiOht7rmU12f2SlmtZXh1Lj2zGo6nJiLVP+nwwrY5npqR2v8XFe9Yk2+Ni0QRGBfOR3G8NlZTbvWMGo/acZoYzmfdYMl2oN2p6cuRlgDl7jrTS6KRY7YJ3QdLUvkMnhU8C+KJvRZXDW5bTr0GfgcwS0V+hPcvVUBJKMfPx3vbbAbx9GHUHBAQErBRWcQViitXP9yqU+9EfA3gigJcD+CDksYODZi78bQD3hzxA/wAzL9qquJ+M1/BRSJarEyAz1s0AvrfYxgICAgI2CiyQcKVlvHnF6oDl65Ckf78N4GHobI54IYBzIJPH0yArl0Vj0BXIFmb+ABG9KjfDfX0pDa4YOAE1ZpwJ4HQqy9zxhWKuB5QqoJZGe5upoi1rVTJb1jwUJn8dixJQU+SYRv3EzaIk1+SORktQ0kDJDBy1nSy9ri7xVYprtFtldi92jMgy3OX40EtUSTWqXSOAzRxydvwYR2dZvbakb41ulfM5eAcAuBwM0fwhpBVZ0tc0saRJZpv6m6JULv62MJlqnDYLOVWkYUtTazJLM96T/nDOeG+sYkaMsn0oMbmopnPVAazG5OgBG7e6XttUr4uZOk5GWbpf62eqlE+iAr6SZ1K5qWZ5Y7LTMKmstevGlYrHzrfYfWZSb6OSXP4Ji5ouSz+ixgxa5dFCOyWzZPSMM619K0dEGFMayKg5l9fEjBk9R4RKTI7WsnvTLqm5EJT0GLsmzOzk4navlMtFY9GyGhk29ZqW0gTsSdvtGpqMu6x5WyIdk2hmL6BR9mZ2mZbkezUmeY3Qqui9arLu1oLLM1JRt4REJdNRTb63ln/HjDtLEaERy31u/+gSy0MyBDAW5YW1lYiuyG1foCpSAEtWrLaBiD4J4BEQJdY/ALiMuWMe39OY+cF6zAewxOfMg04gluXqLiJ6BkQVtXMpDQYEBARsBCzSymQPM5/Tva7FK1aZeXeHQz4E4IX23JiIHkNEL2TmV3jl7H86mLnlxeINjEEnkP9DRJMA/ggS/zEB4A+W1GJAQEDABsFKPwPppVjtUv7LRHQGEf0a5GH6TQA+26HoQ4nIopMJEol+SN8zM090OKYNg+ZENx+VgwCeoCfyB4Mcu1pgitGoTrqlvS315+pbCuXqjYNO8ZHo0tcMF1NVFZUO3qWVmgxo1KmsjG6wNLgGo4JSi9RtNuHD0QBKR5myKbLZPy65SFxLWRtN3ytlLc+EGTNq30Zm70Zan5JdporSsrb8N6rMlC3pyCZUNGeD5b6wXA0VpfMaSre0nGJH2huNMurI1GXQ8WvVJYreTqfhwpjbhsKZKmZpYknbkc8XEm6jZUwlF3mRyHlDSUtTPFYRrYdvfmlGhgs5Ez+XBpeKlJWjI61ui+TmrL9++l5W5VasFE/ZjB8pQskoU42kT5QesvEypqGUo/EAoYI0WB6a9sPRUTZemaElXH/cPzc9ZqFhirGigitSKq1ejrCgxpKmzIqUYhqN7Vp6KXujGJF9J7RtU1hZ6mFLW4yc4qqiVJjtS2yc1KzTqeTMISGuuuvT9Gwgstw5eg10P+WKGZ221F/ancAAknR9BBIS0SkQD8FfA7AXor4iZn5Cp/LM3DeObxAM+hC9E9rMFQMCAgKOGPDqe2Ex8/FdVh/XQizYn8XMj2Xm/wegb/jDcrGc9FzDm8oDAgIC7mNIORMBrAM8F7IC+RoRfRnAJ7AK/6OXM4Gsrxh+SIds+V/W3mXBTLLYatWm3BK72TJFiwxDPVIaQHNtQM30wKkLKnTGhLaUN+WRUkGx0mMcVxylZDAFCxwlU1xFprVJp2RyNIcaPJJSM6awsn5wqeIoOHvNBoQL7cFRKK0c/aM0muZEsT6bqV5JP7f+UGPemdeZosUMGH1lWk1piSaVcvSPd9uYcaBHSxBy/LKuk0v+edhYWSBZZdTl4SAvF4aPOpn6i9sC7bKuZWXyKEfk6BJHsRgLafefbqc5xx+/J+Xpe7TfQmXVdewtaC+vBrNgPOuTUTxWxq+7mbLb17L8GLrNFjzqKCdonYzdM03tixw9oQqtlgYWlkrVwjGccpbLQ+/vLF+LqruUenT3e20KJc1zA6VBSxrUy0rrjagCLW+IaLS0o4JjU6YVz936k6TcFqwZ+5GeywBj5Z+BDApm/hyAzxHRKIBfAvBqAEcR0T8C+Bwz/+dKtNvPC+swOk8UBKhlbUBAQMCRiHXoxqv2JR8F8FEi2gzgVwC8DsDqTyDMPN7r84CAgIAjFavthbVYMPM+AP+sfyuC5VBY6wqNlHHndBM+Q2LodJ2dakSXwJVY3hw7PgUgR8lEGQ2RpVXVFLOuMv1c/X6i5GCWeyAyyYw2pLRQSWkhRydVRrO0sxWhAVyeEc/TK+tT1dFLLsDPqB2jtFzqXjkfpsj1wT8mmhM6ijUFqKnBCvSY0TemwvJglIWdC3OOTet4REaZ5APljJrIVEpa2CgSj0akxgwizb+BpKiCsxTARv1Z+tgyp45CsmvqvMVUPRR7yqNGlKm+aqlSey3PasgUdtZ+js50aZatXdcnUcDFqsSLtT9puQ6OLC1tkb6x17oOSeZzlfl1+crEBaVuLeDP0TsEHD0qFR2Yl+evRlU5zyevH4jI5eFw/nBe6mGjGmOygNrIBQre3ZRxsftbv4KYnta8JBbsWI6cn5n126m74mK6aVOXTTfTtu+9n9p4uVjPE8hqYMNMIAEBAQGrifWYE321ESaQgICAgCWAGUjWjwprTbBxJhAWKspojqbSRUZPmUV3ROQCkVLnbSSFbIk8XtW0pKVMJ2C/NOKS0Aq2pJ9TBZcFKI2NyWOjSkxZIJ3CLfWty1FRhUXMmdpJKSRTipEGpllA1JimDS0tHMoC6YxCcA2oz5AFFhoVQ1GmZFLKzVdy0UHJ4ZWqP5i9UnPBHWM0mvXZUTGWglbbqOY+c12zdL6qzLHUpjGZjXhGX1SNHkw9bzGjheyVU/dZ5PXJ0YdoFeuISi7tLLU6q9kctaV9LiN11y6Natp/HU8/bbFPPebfW/pgU7o15vQ8NShVfbSoVEXqxX0523YU/cIy4VqmQLJxtM9GylTYb/dUCxldmH0HirSRfWdGoyyYk5XSs0DcyKMPbQzMn+7gQoJGIsdndvh6Pez7q/vtO9lKGXHDUvwWfcdaOl4NZyMvdSTMrr+R8zPDEMEuiPFIxcaZQAICAgJWGXyEU1jLiURfNojo94noOiK6mojemdv/eiL6mX72i2vZx4CAgICOYEnINsjfRsWarUCI6AkAzgPwEGZeIKLtuv80SETl6ZCUtpcS0Sn9shJGRC4bHSAqFKA9iCVlAOXiPqOjbAm8e1qW4GVVnMRRu4rL1FGREkYlZRhmc0qQpueTY8txU71MVotBaJWY0IDQAdMzrUI7BqOnzKtopDzmfKUsoM8oKwvcOrigWf/SET02ddTE6IioYcx3qTK/X9oxnyYX2Ki0ThRnEWmmtjKllpZlpTAscyBHJcBUUEZRlYT62Ten9TsbclMItWf3M2UVuSBIU2MpDZI0QIn1V9VkqvZBuYY8TCkGipwiy9FaXlodO69mvEn6PJ9gTsejop2cqEr9Y3XzQtOUAFouWpjJxsfUaeUc9QaAVPVnmfuooteTU0dzOSrOlH2ecqzi6ENqU6+NVYrBnGZ/bp5V8zk+3w+4izVIMDa/KW2vxUCjZR5U5scl9U1rSkm9xGim2T3t6EnL2kmZEiwP+87USxklHHv/Cewa2Gsnvyv7Hg2TcWK0WYMdcVjLFcjvAHgHs+SsZOZ7dP95AD7BzAvMfBOAnwF4+Br1MSAgIKArmHmgv42KtZxATgHwv4jou0T0dSJ6mO4/FsBtuXK3676AgICA9QNmJK10oL+NihWlsPpk2SoB2ATgkZC0ixcR0YnoHGvWcQonovMBnA8Au3btwlQtzlk7dz6wEx1px5hoqlMZPxjLumlLbv+QKhHqlsXPW56T57EU59RTtrTfUi+qbqz+UodAKJfhsFRr+wzIKAprr96KnAmcZaebc30TtVVSkXQAtbho/Z0w0ODi4FYsw57XN2tvocWOdqiqHK41K3SGZc2zMZhtZsfavklVBI0oNef8p1zGxxyn4RRaMhZGcxmt56uiXBbF3D4rk1ZFUbdfqZhD05m6qOx5K80rjTPHRmlKEGlZ/cLGosRRekZv2bbDjGynC6rOGp3Q80zA1dGOfeRIVXvQLJtGi8WVAk0H5AIYlUKLTQWon49Gufo9H6uZVMbRqNOkKWNeL0WolYr+X1ZHdUSDKU1ZqFk2uTqGtFz0ifNt28gs++1agEDqz17V/1rOf8yjrPLKK3vvKyKHAaGwNu7qYhCs6ATSLcsWABDR7wD4LMt/78tJdHxbISuOXbmiOyEZEDvVfwGACwDgzLPOOrKvZEBAwOqCs8nqSMVaUlj/BuCJgEuGUgGwB8DFAF5ARFUiOgHAyVhivt6AgICAlQSnPNDfRsVaxoF8EMAHiegqAA0AL9HVyNVEdBGAn0Jim17RT4EFCP0TUaYscdSS9wshT7Nkb4uBVnYMueAjdqquxCvTjd2McsdbX9qULWY17ZbtHegURxcp5eLLPjh1Sp849eRDSgM49Y8qZ6qliqN44AUzOorHqrcxUN6gkfQ3kLNPLaDLFF95jJaL9JQ/VqXcULnAPhsSvW2d7TnpbRxXkZSEMrLr5Ky9dWgil6bOrnkZkXk2eX2MElO6SR/H1NqckGWVNDrKxtNoxEZiQW9S10waY0SzPlrWSRsVpwIbFZVXVLPzlevH03tB03ulbE2orLSmPqemuipl/lyGVL22jH4tW8ZKL5uieWOlDMRR8Vol7jYsZgjcWpFjSntuyNpTuo416LQt5YE739liUGuurPsOePchRSV3nRwlZ2W92zFyx0buOx7pPTjsFcNGnhwGwZpNIMzcAPCiLp+9HcDbV7dHAQEBAYODmZEkG/cB+SBY00DCgICAgPsyzD2n399y0S3oeq2x4axMbMmaOlVHu2opo5+Kn9mx/jFMBMd8pEXvoZJ3bCEjnXfnOMtvFwCH4ityxzgFjbVb/JzmDmblzBdJqRFqqr27eSoZpWCUVtICUgla8ykDR4VQRtcAWbBgPWm00wtq695Go1jAXFRxfWzzxFIKreVRAa3cZsmzlPc9xGLnA8WoxMW++V5I5rnlghEpymjBbt90/zoxt19bDQIs6TUuUYffZmYfpTRXa2q8cD7Grlif88o7U9rZuFgGx8z/qagkpARIVDVm9fhqwNTfH2W/KF1mT/PNUsoumtknx5Y2S3+2nujGYp4qhfrdmGsP3L2W5hhp+65FSsX5Vv0K/77J77PvYlu2S2TfcX9MhwHWSPSVRreg6/WADTeBBAQEBKwWVukZSLeg6zVHoLACAgIClgJeNRVWt6DrNceGWYEQGJW0kVlvR93tm7NkasWAvlySNa1TEBOhrBxW6qlUbAXuMsWZkVKaZjbrBluem6dTw6ORmLOAMd8G3DyplJ6KlDJJyyNu+W+Bb1zfpO15WQc7II1GimWMTvGC0Az5YEVHD6nflD8WRb2TVu9RSQY/GCy/6ehIUxF5VI+B0e4d5vrtaMvieUVEIF+J1gd5qiTLXlikZ9posdw4On8xXzXngfPHaD12R5UtQLKmnlRpkcrq2G8be4/Vtc2IyHleOX+x2elC++nYVvk4Rx9ZoKDV61R4FuDq2tfAwlI5Cxj0r2EHytn1s0vZTtSV65sF63IH+mzZ4MWourYS0RW57Qs0jg3A0oKueR14pGyYCSQgICBgNcEMpIPblOxh5nO617WkoOt7F9PflUCgsAICAgKWiFWyc/83dA66XnNsnBUIM5AmoFR9hszOWykXC6qiNMnNmkZj2LbnM5RbTjMyvyprLg8LfGMLauug3zNKwmXs08ArWKBcK5cJz1FWeh5GGagtelIbz/Z7FEyb0qnPUr9T2W4gZOcedSnrB0wC7eNlVvNW0q8qXz429ZrjPdRm3+gIyw4Yl9vb6XLOUU65ZZxLt9HJUzxtndXzMCWVG+NO9JQfHNoN/mBQlPXNu6diVcBFds1zY9SmXPKCUdlX07WamT+XqePGi4If8x+zLJTRwnRmla+ZCY1G8ynhXEe602ne2BTuMe8+dl5oTl1owZ36nYlLTgmYfaeH+5t5lVikbkHXa46NM4EEBAQErCKYV8empFfQ9VojTCABAQEBS8RGzjY4CDbOBEIERHEu21u9Y7GU4jaGwMAoUkGdirkldodgw2LBuJ0y8pU5fuBaFGXUhy25K0WVFHeiKjzFVDcaqh89BbSzK50OGaCavnAKLm+UO7FEqVIk3SgLpqI1eCd0o9vylt9Z34plMrag6JEmxxfLxo6aUbrSrnHSzKntvOvfFlzpfy2TNprSt4RPR0R5t1CVoFFCNrZ2fs6+vauBW4zEKCvtvx+oGCllVyuVtB+z7v6zcfNTKfhO6kKDdv7H65SPpqJ0WQ4ztZfRZwYbrwJ1BQBRKVNCDuOm7QAeqqrrvoeNM4EEBAQErCKYU6T555ZHIMIEEhAQELAUMMBJWIFsEFDHYC2nfCrYe3tBenaMb2WOouqjULZH8FKn99K2Uky2rI5UDQMNRuvlvNZFPdJJVWJ9G4Sy8mF6H1+5NUhdvcr6u7Ibz+eL7DUbh6gxI3UsyCtrNrss62Ala99ryGicXt1v/xJ4aqXIMvpZBsSspKPRLMjR6BQLvDPFXVRu8+XqphDrFYDpjqnKOUe1qcKxeaLLzrlk6qtuzrHuPEvwrdKrej4WQGt1OppqZLPLHljVcUtLcaGvRmH5AaZ52LmWY/Pr8rzL0qzvNtZ+UKDRvZwPivUDcoeqwuJAYa11BwICAgLuk+AwgYQJJCAgIGCJCBPIGoGIzgDwTwBqkMyDv8vMl+tnrwfwMoiNziuZ+SsD19stSCpHiXQV3vmUVi7kcCl0UFv1HZRahc+xOE+mlYDfx8V4E/Xbv1SkFfEHI99bbJEeVt3QjWpMPZt1KDWT9/FiR1F5luxaR5KTIPlqLteeT7t5nlIpd/f4aqWdaSkiynm6acCs9jHy2jeLeM41muq/Bk469zmvVGOn9tLtLglEOwUPZkGa8ups6T0ij6gEc+p3fmqxNdhF1TiMRBw9wMzhIfoatv1OAG9h5i8R0dN1+1wiOg3ACwCcDuAYAJcS0SmDpLUNCAgIWD0w0iN8BbKWXlgMYELfTwK4U9+fB+ATzLzAzDcB+BmAh69B/wICAgK6Q5+BDPK3UbGWK5A/APAVIvpryET2aN1/LID/yZW7Xfe1gYjOB3A+AOzatav4WY/lazeaKyuwNvNqL+ZnheKgure3VlY7nl8TkF0nl63OV8stIUCyZ1l4HJLCVEVpp99dXSKS43zXLCtjl3b9QLw8OtiLFers3HZnBVrK/jGc+0z3eCkO/C4VKKw+g+tTdCl3pumADt5YBT86gaPz7PyMymTvuuTuoZX4+jDCM5AVnUD6eNw/CcCrmfkzRPR8AB8A8GR0vtYd71D1078AAM4+66wj21MgICBgdcEc4kBWsvI+Hvf/CuBVuvkpAO/X97cDyC8ndiKjtwICAgLWDcIKZO1wJ4DHA7gM4nV/g+6/GMDHiOjdkIfoJwO4fJAK2z2EBsMwFFbDxkp0ac1oKfQf48xjTOkqZDRWW+Y+n8IoVOTTW50zK3bqVz8r+OWMHnV5n0fsfZD372oT8A2Una8z/+RSEpiaKeeZ5dRQHqXkU1n5/Z3s+zu11wt9qugId326pBfIZ5o0heNQvwPBymRNJ5CXA/g7kjyX89BnGcx8NRFdBOCnEHnvK4ICKyAgYL1BnoGsrFR4vWPNJhBm/iaAs7t89nYAb1/dHgUEBAQsAiESfeNEojOygKh+aLP2XuPH750szJdUz/pIUtYOTp0XWT90VM/59JPjYPr7G1EfC/2OQ27258u4IL4n1qKOdf5N+Z29j3Febx3Ua9364J9dlBvHUskLcu1D7w3QxUVhKSPv9ylCu1X/sBEmkICAgICAxYNDIGGYQAICAgKWAGZG2gwP0TcIqGvWuWXXTO1L+LUSbvmW6XnaqrMSZ4nw6aE8DdJpX6f9SwjI7Kia6mdxT/09y7qNGxP1Ha9BxrVbGV5C3/z9iwF3GLeuAbM9QN4v6150V9f7oAuGrXr0k4Tmh22l/idoS4HCWusOBAQEBNxXESaQgICAgIDFI6iwQP18bO4rIKLDAK5b63542Apgz1p3ogPWY79CnwZD6NPg6NWv+zHztuVUTkRf1jYGwR5mfupy2luP2EgTyBXMfM5a9yOP9dgnYH32K/RpMIQ+DY712q+NhLW0cw8ICAgIuA8jTCABAQEBAUvCRppALljrDnTAeuwTsD77Ffo0GEKfBsd67deGwYZ5BhIQEBAQsLrYSCuQgICAgIBVRJhAAgICAgKWhA0xgRDRU4noOiL6GRG9bg37cTMR/YSIfkREV+i+zUR0CRHdoK+bVrgPHySie4joqty+rn0gotfruF1HRL+4in16MxHdoWP1IyJ6+ir3aRcRfY2IriGiq4noVbp/zcaqR5/WeqxqRHQ5Ef1Y+/UW3b+WY9WtT2s6VkccmPk+/QcgBvBzACcCqAD4MYDT1qgvNwPY6u17J4DX6fvXAfjLFe7D4wCcBeCqfn0AcJqOVxXACTqO8Sr16c0AXtOh7Gr1aQeAs/T9OIDrte01G6sefVrrsSIAY/q+DOC7AB65xmPVrU9rOlZH2t9GWIE8HMDPmPlGZm4A+ASA89a4T3mcB+BCfX8hgF9aycaY+RsA9g3Yh/MAfIKZF5j5JgA/g4znavSpG1arT3cx8w/0/WEA1wA4Fms4Vj361A2rNVbMzNO6WdY/xtqOVbc+dcOqjNWRho0wgRwL4Lbc9u3o/aVbSTCA/ySi7xPR+brvKGa+C5B/EAC2r0G/uvVhrcfu94joSqW4jP5Y9T4R0fEAzoT8il0XY+X1CVjjsSKimIh+BOAeAJcw85qPVZc+AevkvjoSsBEmkE5+zWulTX4MM58F4GkAXkFEj1ujfgyKtRy7fwRwEoAzANwF4F1r0SciGgPwGQB/wMyHehXtsG9F+tWhT2s+VsycMPMZAHYCeDgRPahH8VXpV5c+rflYHUnYCBPI7QB25bZ3ArhzLTrCzHfq6z0APgdZIt9NRDsAQF/vWYOudevDmo0dM9+t/wBSAO9DRiesWp+IqAz5R/1RZv6s7l7TserUp/UwVgZmPgDgMgBPxTq5r/J9Wk9jdSRgI0wg3wNwMhGdQEQVAC8AcPFqd4KIRolo3N4DeAqAq7QvL9FiLwHw+dXuW48+XAzgBURUJaITAJwM4PLV6JD941H8MmSsVq1PREQAPgDgGmZ+d+6jNRurbn1aB2O1jYim9H0dwJMBXIu1HauOfVrrsTrisNZP8YfxB+DpEMXKzwG8YY36cCJE5fFjAFdbPwBsAfBVADfo6+YV7sfHIUv3JuRX18t69QHAG3TcrgPwtFXs04cB/ATAlZAv945V7tNjIRTGlQB+pH9PX8ux6tGntR6rhwD4obZ/FYA/73dvr8JYdevTmo7VkfYXrEwCAgICApaEjUBhBQQEBASsAcIEEhAQEBCwJIQJJCAgICBgSQgTSEBAQEDAkhAmkICAgICAJSFMIAEBAQEBS0KYQI4AENF0/1LLqv8/iGhK/353CcefS0RfXGT5g0T0H10+/xciet5i+3FfhI7Fo3PbryaiW4noPWvZr4AjA2ECCVg2mPnpLHYSUwAWPYEsEf/NzE/vX2zpIKLSStY/JJwLwE0gzPw3AP58zXoTcEQhTCBHKIjoDCL6H3Ut/Zy5lhLRZUT0l5qs53oi+l+6f4SILtLynySi7xLROfrZzUS0FcA7AJykiXz+yl9ZENF7iOil+v6pRHQtEX0TwHNyZUbVRfV7RPRDIuprzU+C9xDRT4no35FzPCais4no6+qQ/JWcd9PD9Fy+o329Sve/lIg+RURfgDgrd+wPiRPsX+n+K4not3T/DiL6ho7BVTZ+Xfr9FG3/B9rmmO7/c633KiK6QC1OQESv1HO8kog+QeLY+9sAXq3tdW0rIGBFsNah8OFv5f8ATHfYdyWAx+v7twL4W31/GYB36funA7hU378GwD/r+wcBaAE4R7dvBrAVwPEoJo06F8AXc9vvAfBSADWItfbJEJfUi6wcgP8L4EX6fgpiUTPq9d2v9zkALoEkFzsGwAEAz4PkiPg2gG1a7lcBfFDfXwXg0fr+HdZv7d/tUFuObv0BcD6AN+r+KoArIImK/giZjU0MYLzLNdkK4Bt2bgBei8yOI28J8mEAz9L3dwKoWl/09c3wEijpObxnre+78Lfx/+4LS/SAIYOIJiH/gL6uuy4E8KlcEXOm/T5kUgDEp+nvAICZryKiK5fRhQcAuImZb9D+fATyDxkQE8pnE9FrdLsG4DhIcqVueByAjzNzAuBOIvov3X8qZLK7RH/ExwDuUhO+cWb+tpb7GIBn5uq7hJktAVa3/jwFwENyz1omIRPi9wB8kMRV99+Y+Udd+vxISJa8b2nfKgC+o589gYj+BMAIgM0Qb7UvQCb9jxLRvwH4tx7jERCwKggTSEAnLOhrguwe6ZRPoR9aKNKktdz7biZsBOC5zHzdItvqVB8BuJqZH1XY2T8v/Uy//iit9PvM/JW2RiUPzDMAfJiI/oqZ/7VL3y5h5l/zjq0BeC9kdXcbEb0Z2bg9AzJZPhvAnxHR6X3OIyBgRRGegRyBYOaDAPbnOPMXA/h6j0MA4JsAng8ARHQagAd3KHMYksvbcAuA00gstCcBPEn3XwvgBCI6Sbfz/0S/AuD3c7z/mQOc0jcgVt2xPuN4gu6/DsA2InqU1lUmotOZeT+Aw0T0SC33gh51d+vPVwD8jq40QESn6POS+wG4h5nfB7FmP6tLvf8D4DFEdH89foSITkE2WezRZyLP088jALuY+WsA/gRCp42hfcwDAlYNYQVyZGCEiG7Pbb8bkr/hn4hoBMCNAH6jTx3vBXChUldmo30wX4CZ9xLRt/SB9JeY+Y+J6CIte4MeB2aeJ0n5++9EtAcyOVmGu7cB+FsAV+o/7ZtRpJc64XMAngix8b4eOhkyc0Mppr/XCaykdV8NsZR/HxHNQJ77HGyvtmd/3g+h936g+++F5AQ/F8AfE1ETwDSA/69Tpcx8L4mg4ONEVNXdb2Tm64nofXouN0MoMUDot4/oeRCAv2HmA/qw/9P6cP/3mfm/+4xVQMDQEOzcAwYCEcUAyvrP/yRI/odTmLmxBn05F/LguN/E0quOMWae1vevg+SNeNVweri20InpHGb+vbXuS8DGRliBBAyKEQBfU8qGAPzOWkweigaABxHRf/DSY0GeQUSvh3wHboEol+7zIKJXQ6S9n1nrvgRsfIQVSEDACoOIvguR+ubxYmb+yVr0JyBgWAgTSEBAQEDAkhBUWAEBAQEBS0KYQAICAgICloQwgQQEBAQELAlhAgkICAgIWBL+f/9pyBYWGJslAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ds_pr.precip.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "acfd5934", + "metadata": {}, + "source": [ + "# Degrees of Freedom\n", + "\n", + "https://stackoverflow.com/questions/49473757/python-degrees-of-freedom/49473949" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ce232e24", + "metadata": {}, + "outputs": [], + "source": [ + "def stdev(X):\n", + " m = np.mean(X)\n", + " return math.sqrt(sum((x-m)**2 for x in X) / len(X))\n", + "\n", + "def degreesOfFreedom(X, Y):\n", + " s1 = (stdev(X)**2)\n", + " s2 = (stdev(Y)**2)\n", + " df = (s1 / len(X) + s2 / len(Y))**2 / ((s1 / len(X))**2 / (len(X) - 1) + (s2 / len(Y))**2 / (len(Y) - 1))\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "id": "9b77f886", + "metadata": {}, + "source": [ + "# Domains\n", + "\n", + "d1. Asian domain (10-50N, 50-150E)\n", + "\n", + "d2. South Asia (10-30N, 60-100E)\n", + "\n", + "d3. West Central India (15-25N, 70-85E)\n", + "\n", + "d4. East Asia (20-40N, 100-140E)\n", + "\n", + "d5. Yangzte River Valley (25-35N, 105-120E)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e05ecd0c", + "metadata": {}, + "outputs": [], + "source": [ + "latd1s, latd1e, lond1s, lond1e = 10, 50, 50, 150\n", + "latd2s, latd2e, lond2s, lond2e = 10, 30, 60, 100\n", + "latd3s, latd3e, lond3s, lond3e = 15, 25, 70, 85\n", + "latd4s, latd4e, lond4s, lond4e = 20, 40, 100, 140\n", + "latd5s, latd5e, lond5s, lond5e = 25, 35, 105, 120" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "31e76aaa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For domain d1 Degrees of freedom: 2312.5214312610246 pattern correlation: -0.12228599707002985 p-value: 3.893520668569451e-09\n" + ] + } + ], + "source": [ + "x = ds_aod.sel(latitude=slice(latd1s, latd1e)).AODANA.values.flatten()\n", + "y = ds_pr.sel(lat=slice(latd1s, latd1e)).precip.values.flatten()\n", + "dof_d1 = degreesOfFreedom(x, y)\n", + "pattern_corr = scipy.stats.pearsonr(x, y)\n", + "print(\"For domain d1 Degrees of freedom: \", dof_d1, \" pattern correlation: \", pattern_corr[0], \" p-value: \", pattern_corr[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "539c00f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For domain d2 Degrees of freedom: 1155.4437968252507 pattern correlation: 0.014733238875865817 p-value: 0.6173941210531376\n" + ] + } + ], + "source": [ + "x = ds_aod.sel(latitude=slice(latd2s, latd2e)).AODANA.values.flatten()\n", + "y = ds_pr.sel(lat=slice(latd2s, latd2e)).precip.values.flatten()\n", + "dof_d2 = degreesOfFreedom(x, y)\n", + "pattern_corr = scipy.stats.pearsonr(x, y)\n", + "print(\"For domain d2 Degrees of freedom: \", dof_d2, \" pattern correlation: \", pattern_corr[0], \" p-value: \", pattern_corr[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "4f4d3693", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For domain d3 Degrees of freedom: 578.8769938985359 pattern correlation: 0.04346799922785351 p-value: 0.2976630119521465\n" + ] + } + ], + "source": [ + "x = ds_aod.sel(latitude=slice(latd3s, latd3e)).AODANA.values.flatten()\n", + "y = ds_pr.sel(lat=slice(latd3s, latd3e)).precip.values.flatten()\n", + "dof_d3 = degreesOfFreedom(x, y)\n", + "pattern_corr = scipy.stats.pearsonr(x, y)\n", + "print(\"For domain d3 Degrees of freedom: \", dof_d3, \" pattern correlation: \", pattern_corr[0], \" p-value: \", pattern_corr[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "d4b504e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For domain d4 Degrees of freedom: 1156.3094132407402 pattern correlation: -0.20449683135380875 p-value: 2.4261635556227888e-12\n" + ] + } + ], + "source": [ + "x = ds_aod.sel(latitude=slice(latd4s, latd4e)).AODANA.values.flatten()\n", + "y = ds_pr.sel(lat=slice(latd4s, latd4e)).precip.values.flatten()\n", + "dof_d4 = degreesOfFreedom(x, y)\n", + "pattern_corr = scipy.stats.pearsonr(x, y)\n", + "print(\"For domain d4 Degrees of freedom: \", dof_d4, \" pattern correlation: \", pattern_corr[0], \" p-value: \", pattern_corr[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f5ffde59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For domain d5 Degrees of freedom: 576.3308357999422 pattern correlation: -0.24225884432069728 p-value: 3.872824485723952e-09\n" + ] + } + ], + "source": [ + "x = ds_aod.sel(latitude=slice(latd5s, latd5e)).AODANA.values.flatten()\n", + "y = ds_pr.sel(lat=slice(latd5s, latd5e)).precip.values.flatten()\n", + "dof_d5 = degreesOfFreedom(x, y)\n", + "pattern_corr = scipy.stats.pearsonr(x, y)\n", + "print(\"For domain d5 Degrees of freedom: \", dof_d5, \" pattern correlation: \", pattern_corr[0], \" p-value: \", pattern_corr[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0df82ad0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2239de03978e81c2ddc2e89f6f274361a591d47c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 2 Aug 2021 00:50:12 -0500 Subject: [PATCH 091/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b23c741..83d0f8b 100644 --- a/README.md +++ b/README.md @@ -103,6 +103,8 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 58. Potential evapotranspiration (PET) from netcdf file https://climate-indices.readthedocs.io/en/latest/# +59. t-distributed Stochastic Neighbourhood Embedding (tSNE) versus PCA https://stats.stackexchange.com/questions/238538/are-there-cases-where-pca-is-more-suitable-than-t-sne + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From f5a6abfeb54f4d0e9a5ad3842fd1fe5847efd3cb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 2 Aug 2021 12:56:14 -0500 Subject: [PATCH 092/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 83d0f8b..d642b29 100644 --- a/README.md +++ b/README.md @@ -105,6 +105,8 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 59. t-distributed Stochastic Neighbourhood Embedding (tSNE) versus PCA https://stats.stackexchange.com/questions/238538/are-there-cases-where-pca-is-more-suitable-than-t-sne +60. Stationarity of time series: https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322 + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 95318d05a67b292574a42f36af4e8f23b9bdb1ae Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 2 Aug 2021 13:45:32 -0500 Subject: [PATCH 093/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index d642b29..fb27dbd 100644 --- a/README.md +++ b/README.md @@ -107,6 +107,8 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 60. Stationarity of time series: https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322 +61. SARIMAX model: https://towardsdatascience.com/end-to-end-time-series-analysis-and-forecasting-a-trio-of-sarimax-lstm-and-prophet-part-1-306367e57db8 + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From c4bbf94e3255d0ed59f98ada1c2b8bc29bb35b77 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 3 Aug 2021 15:06:45 -0500 Subject: [PATCH 094/171] Update README.md --- README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/README.md b/README.md index fb27dbd..452ca13 100644 --- a/README.md +++ b/README.md @@ -109,6 +109,15 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj 61. SARIMAX model: https://towardsdatascience.com/end-to-end-time-series-analysis-and-forecasting-a-trio-of-sarimax-lstm-and-prophet-part-1-306367e57db8 +62. Prevent kaggle from disconnecting https://stackoverflow.com/questions/57113226/how-to-prevent-google-colab-from-disconnecting + +function ClickConnect(){ + console.log("Working"); + document.querySelector("colab-toolbar-button#connect").click() +} +setInterval(ClickConnect,60000) + + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 28bc01555baa9bb0224c3db47cdea01b6a67f9e9 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 3 Aug 2021 18:04:11 -0500 Subject: [PATCH 095/171] Update README.md --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 452ca13..16e9880 100644 --- a/README.md +++ b/README.md @@ -117,6 +117,12 @@ function ClickConnect(){ } setInterval(ClickConnect,60000) +63. Solving NVIDIA driver installation issues https://stackoverflow.com/questions/42984743/nvidia-smi-has-failed-because-it-couldnt-communicate-with-the-nvidia-driver/51113428#51113428 + +64. Installing NVIDIA drivers https://www.itzgeek.com/post/how-to-install-nvidia-drivers-on-ubuntu-20-04-ubuntu-18-04.html + +65. Install cuda https://www.tensorflow.org/install/gpu + Troubleshooting From 27b3f7b47f425d98d3dbf407cab35022cda07c75 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 4 Aug 2021 03:27:55 -0500 Subject: [PATCH 096/171] Update README.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index 16e9880..aec8efb 100644 --- a/README.md +++ b/README.md @@ -123,6 +123,13 @@ setInterval(ClickConnect,60000) 65. Install cuda https://www.tensorflow.org/install/gpu +66. bashrc commands for cuda + +export CUDA_HOME=/usr/local/cuda-11.0 +export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0/lib:$LD_LIBRARY_PATH +export PATH=/usr/local/cuda-11.0/bin:$PATH + + Troubleshooting From 297040f196ebb5587b0d17fb92b34248c6e0e7d5 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 4 Aug 2021 03:43:39 -0500 Subject: [PATCH 097/171] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index aec8efb..2d7ba35 100644 --- a/README.md +++ b/README.md @@ -126,9 +126,13 @@ setInterval(ClickConnect,60000) 66. bashrc commands for cuda export CUDA_HOME=/usr/local/cuda-11.0 + export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0/lib:$LD_LIBRARY_PATH + export PATH=/usr/local/cuda-11.0/bin:$PATH +sudo ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.11 # https://stackoverflow.com/questions/63199164/how-to-install-libcusolver-so-11 + Troubleshooting From f82d892d9bb0dddf9498467125e38e8fe2db70c9 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 4 Aug 2021 03:46:45 -0500 Subject: [PATCH 098/171] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2d7ba35..351f6d0 100644 --- a/README.md +++ b/README.md @@ -133,7 +133,7 @@ export PATH=/usr/local/cuda-11.0/bin:$PATH sudo ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.11 # https://stackoverflow.com/questions/63199164/how-to-install-libcusolver-so-11 - +echo | sudo -S ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.11 Troubleshooting From e08eafd53300226ef362659cd907eddc6c02dd29 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 13 Aug 2021 17:54:18 -0500 Subject: [PATCH 099/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 351f6d0..780aff4 100644 --- a/README.md +++ b/README.md @@ -135,6 +135,8 @@ sudo ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/ echo | sudo -S ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.11 +67. Test time augmentation https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 3544a84d04e2990464caf84abd923b3c9e2052c4 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 14 Aug 2021 16:31:34 -0500 Subject: [PATCH 100/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 780aff4..0b59fd6 100644 --- a/README.md +++ b/README.md @@ -137,6 +137,8 @@ echo | sudo -S ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/li 67. Test time augmentation https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d +68. Geometric Deep Learning https://geometricdeeplearning.com/lectures/ + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 0b92b8371bca6e0b162f104c329a835e82453dfa Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 14 Aug 2021 16:35:33 -0500 Subject: [PATCH 101/171] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0b59fd6..e9a9f19 100644 --- a/README.md +++ b/README.md @@ -137,7 +137,9 @@ echo | sudo -S ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/li 67. Test time augmentation https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d -68. Geometric Deep Learning https://geometricdeeplearning.com/lectures/ +68. Geometric Deep Learning https://geometricdeeplearning.com/lectures/\\ + +69. DeepSphere Spherical convolutions using Graph convolutions https://github.com/deepsphere/deepsphere-pytorch Troubleshooting From 89306889e55c667ec052f31625eaefdd1392c73e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 25 Aug 2021 21:09:16 -0500 Subject: [PATCH 102/171] Update README.md --- README.md | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/README.md b/README.md index e9a9f19..30f7ed5 100644 --- a/README.md +++ b/README.md @@ -141,6 +141,36 @@ echo | sudo -S ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/li 69. DeepSphere Spherical convolutions using Graph convolutions https://github.com/deepsphere/deepsphere-pytorch +70. Interactively logging into Pratyush GPU + +$ qsub -I -l select=1:ncpus=1:naccelerators=1:accelerator_model="Tesla_P100-PCIE-12GB" -q gpu + +$ source activate py36 + +$ module load cudatoolkit + +$ aprun -n 1 jupyter-notebook --no-browser --ip=0.0.0.0 --port=8890 >> NOTEBOOK_LOGFILE1 2>&1 + +$ tail -f NOTEBOOK_LOGFILE1 + +$ Ctrl+C + +$ ssh -N -f -L localhost:8888:node:8890 cccr_rnd@nid00019 (Here nid should be the one as seen from NOTEBOOK_LOGFILE1) + +$ source activate py36 + +$ module load cudatoolkit + +$ firefox& + +To check if a port is being used + +$ netstat -antp ! grep -i port_id + +The above statement will only work in the interactive login node. + +Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 42a0274fd18bfe5eb62c2422137be2baa97b1338 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 2 Sep 2021 11:23:41 -0500 Subject: [PATCH 103/171] Create README.md --- deep_learning/README.md | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 deep_learning/README.md diff --git a/deep_learning/README.md b/deep_learning/README.md new file mode 100644 index 0000000..fd8c7d5 --- /dev/null +++ b/deep_learning/README.md @@ -0,0 +1,3 @@ +# Important resources + +1. Microsoft COCO paper: https://arxiv.org/pdf/1405.0312.pdf From be55c90a6a4e5af5594086076e4863822b7e93a2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 2 Sep 2021 12:55:26 -0500 Subject: [PATCH 104/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index fd8c7d5..669358c 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -1,3 +1,5 @@ # Important resources 1. Microsoft COCO paper: https://arxiv.org/pdf/1405.0312.pdf + +2. Color models: https://www.wigglepixel.nl/en/blog/what-are-color-models/ From 84b321b306a5f075fbc9b3fbdb3196500dfbf497 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 2 Sep 2021 14:53:27 -0500 Subject: [PATCH 105/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index 669358c..de4849a 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -3,3 +3,5 @@ 1. Microsoft COCO paper: https://arxiv.org/pdf/1405.0312.pdf 2. Color models: https://www.wigglepixel.nl/en/blog/what-are-color-models/ + +3. Scale invariant feature transform (SIFT): https://www.analyticsvidhya.com/blog/2019/10/detailed-guide-powerful-sift-technique-image-matching-python/ From 541e0fb4a2cfb2f1a8b513e23fed089dbe851d91 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 2 Sep 2021 16:35:15 -0500 Subject: [PATCH 106/171] Update README.md --- deep_learning/README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index de4849a..17c5832 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -5,3 +5,17 @@ 2. Color models: https://www.wigglepixel.nl/en/blog/what-are-color-models/ 3. Scale invariant feature transform (SIFT): https://www.analyticsvidhya.com/blog/2019/10/detailed-guide-powerful-sift-technique-image-matching-python/ + +4. Selective search for object detection: https://sahiltinky94.medium.com/selective-search-for-object-recognitions-eca15dc9c47c + +5. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition: https://arxiv.org/pdf/1406.4729.pdf + +6. R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms: https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e + +7. Region of Interest (RoI) pooling: https://towardsdatascience.com/region-of-interest-pooling-f7c637f409af + +8. YOLO, YOLOv2, YOLOv3: https://amrokamal-47691.medium.com/yolo-yolov2-and-yolov3-all-you-want-to-know-7e3e92dc4899 + +9. Faster RCNN: https://arxiv.org/pdf/1506.01497.pdf + +10. From f3cb126d30aa9d3b240d0abc4badef7e3820a575 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 2 Sep 2021 17:29:19 -0500 Subject: [PATCH 107/171] Update README.md --- deep_learning/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deep_learning/README.md b/deep_learning/README.md index 17c5832..3927638 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -18,4 +18,4 @@ 9. Faster RCNN: https://arxiv.org/pdf/1506.01497.pdf -10. +10. Mask R-CNN: https://arxiv.org/pdf/1703.06870.pdf From 46aa3fb9b98d779e19a1d2aee779f5e0b7d78889 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 3 Sep 2021 12:53:23 -0500 Subject: [PATCH 108/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index 3927638..bf6c208 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -19,3 +19,5 @@ 9. Faster RCNN: https://arxiv.org/pdf/1506.01497.pdf 10. Mask R-CNN: https://arxiv.org/pdf/1703.06870.pdf + +11. Anchor boxes: https://towardsdatascience.com/anchor-boxes-the-key-to-quality-object-detection-ddf9d612d4f9 From 7f2c575aaebc8f917fdbd4065f758eb9cc81fc8c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 7 Sep 2021 18:07:47 -0500 Subject: [PATCH 109/171] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 30f7ed5..1817e1c 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # python_class This repository contains all the scripts used for the python class for JRFs at IITM -Some of the tutorials to follow +Some of the tutorials and resources to follow 1. http://pure.iiasa.ac.at/id/eprint/14952/1/xarray-tutorial-egu2017-answers.pdf 2. https://rabernat.github.io/research_computing/xarray.html 3. Ocean Data Analysis https://currents.soest.hawaii.edu/ocn_data_analysis/exercise_data.html#id1 From a9b75ea65b5284cf9413eb284da35b5611645f20 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 10 Sep 2021 13:00:01 -0500 Subject: [PATCH 110/171] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1817e1c..b01667d 100644 --- a/README.md +++ b/README.md @@ -97,7 +97,7 @@ $ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'proj ds.reduce(np.nansum, 'expver') Solution from marco venturini https://confluence.ecmwf.int/pages/viewpage.action?pageId=173385064 - 56. GeoTIFF to netcdf and exporting data from Google Earth Engine https://medium.com/@wenzhao.li1989/nco-translate-geotiff-files-exported-from-gee-to-a-netcdf-file-with-correct-time-dimension-ce97a8f3043f +56. GeoTIFF to netcdf and exporting data from Google Earth Engine https://medium.com/@wenzhao.li1989/nco-translate-geotiff-files-exported-from-gee-to-a-netcdf-file-with-correct-time-dimension-ce97a8f3043f 57. Make pipeline to avoid test data leaking into train https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html @@ -171,6 +171,8 @@ The above statement will only work in the interactive login node. Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 +71. Markdown tool https://dillinger.io/ + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 184d3563c9d99e3499661d00fd7b58209b82b5b9 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 13 Sep 2021 15:37:24 -0500 Subject: [PATCH 111/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b01667d..827d539 100644 --- a/README.md +++ b/README.md @@ -173,6 +173,8 @@ Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 71. Markdown tool https://dillinger.io/ +72. LRP for explainable AI (XAI): https://github.com/albermax/innvestigate + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From b502b40aba55d83a7109965230701f404ada3e2f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 13 Sep 2021 15:39:09 -0500 Subject: [PATCH 112/171] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 827d539..e2370ec 100644 --- a/README.md +++ b/README.md @@ -173,7 +173,7 @@ Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 71. Markdown tool https://dillinger.io/ -72. LRP for explainable AI (XAI): https://github.com/albermax/innvestigate +72. LRP for explainable AI (XAI): https://github.com/albermax/innvestigate Application paper: https://arxiv.org/abs/2103.10005 Troubleshooting From f3c23cd6a901fecc4bb14d3674b802941535f903 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 21 Sep 2021 23:36:35 -0500 Subject: [PATCH 113/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e2370ec..bc7acb6 100644 --- a/README.md +++ b/README.md @@ -175,6 +175,8 @@ Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 72. LRP for explainable AI (XAI): https://github.com/albermax/innvestigate Application paper: https://arxiv.org/abs/2103.10005 +73. Lower tropospheric stability (LTS = θ700hPa − θ1000hPa; Kelvin), which is defined as the difference in potential temperature (θ) between the 700-hPa level and the surface + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From 787e90eb5be0c6fbab42f03b05ae772bd7bb460a Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 25 Sep 2021 15:02:27 -0500 Subject: [PATCH 114/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index bc7acb6..e6a7297 100644 --- a/README.md +++ b/README.md @@ -230,6 +230,8 @@ For the weights, the following can be followed: https://stackoverflow.com/questi Even after all this, unable to run pyrqa smoothly. However, this activity ensured that the environment to run pyrqa was perfect. So then clone the github repository and inside the main github repository pyrqa, there is a folder pyrqa. Copy that to your desired location, rename it lets say PYRQA. And use the library as PYRQA. 14. Logging to a remote server without password https://www.thegeekstuff.com/2008/11/3-steps-to-perform-ssh-login-without-password-using-ssh-keygen-ssh-copy-id/ + +15. Create xarray dataset from dataarrays From 253bda2450a674607d6010593d027aae3dc830b9 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 29 Sep 2021 15:10:31 -0500 Subject: [PATCH 115/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e6a7297..52c83d8 100644 --- a/README.md +++ b/README.md @@ -232,6 +232,8 @@ For the weights, the following can be followed: https://stackoverflow.com/questi 14. Logging to a remote server without password https://www.thegeekstuff.com/2008/11/3-steps-to-perform-ssh-login-without-password-using-ssh-keygen-ssh-copy-id/ 15. Create xarray dataset from dataarrays + +16. Use tmux to run a process in the background: https://medium.com/@praveendhawan/tmux-run-commands-in-the-background-bad007810318 From 6ec2985f5fb4342090d27377347112c6599c538e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 5 Oct 2021 01:17:17 -0500 Subject: [PATCH 116/171] modis hdf4 --- satellite/modis_hdf4.ipynb | 5890 ++++++++++++++++++++++++++++++++++++ 1 file changed, 5890 insertions(+) create mode 100644 satellite/modis_hdf4.ipynb diff --git a/satellite/modis_hdf4.ipynb b/satellite/modis_hdf4.ipynb new file mode 100644 index 0000000..c6785b3 --- /dev/null +++ b/satellite/modis_hdf4.ipynb @@ -0,0 +1,5890 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "id": "7b00ea39", + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.earthdatascience.org/courses/use-data-open-source-python/hierarchical-data-formats-hdf/open-MODIS-hdf4-files-python/\n", + "# conda install -c conda-forge earthpy\n", + "# conda install -c conda-forge geopandas\n", + "# conda install -c conda-forge shapely\n", + "# conda install //anaconda.org/conda-forge/rioxarray/0.3.1/downlohttps://anaconda.org/conda-forge/rioxarray/0.3.1/download/noarch/rioxarray-0.3.1-pyhd8ed1ab_0.tar.bz2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a814033f", + "metadata": {}, + "outputs": [], + "source": [ + "# Import packages\n", + "import os\n", + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy.ma as ma\n", + "import xarray as xr\n", + "import rioxarray as rxr\n", + "from shapely.geometry import mapping, box\n", + "import geopandas as gpd\n", + "import earthpy as et\n", + "import earthpy.plot as ep\n", + "\n", + "warnings.simplefilter('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0ef3b948", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading from https://ndownloader.figshare.com/files/10960112\n", + "Extracted output to /home/manmeet/earth-analytics/data/cold-springs-modis-h4/.\n", + "Downloading from https://ndownloader.figshare.com/files/10960109\n", + "Extracted output to /home/manmeet/earth-analytics/data/cold-springs-fire/.\n" + ] + } + ], + "source": [ + "# Get the MODIS data\n", + "et.data.get_data('cold-springs-modis-h4')\n", + "\n", + "# This download is for the fire boundary\n", + "et.data.get_data('cold-springs-fire')\n", + "\n", + "# Set working directory\n", + "os.chdir(os.path.join(et.io.HOME,\n", + " 'earth-analytics',\n", + " 'data'))\n", + "#Downloading from https://ndownloader.figshare.com/files/10960112\n", + "#Extracted output to /root/earth-analytics/data/cold-springs-modis-h4/." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b62cb0cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a path to the pre-fire MODIS h4 data\n", + "modis_pre_path = os.path.join(\"cold-springs-modis-h4\",\n", + " \"07_july_2016\",\n", + " \"MOD09GA.A2016189.h09v05.006.2016191073856.hdf\")\n", + "modis_pre_path" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "522284de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open data with rioxarray\n", + "modis_pre = rxr.open_rasterio(modis_pre_path,\n", + " masked=True)\n", + "type(modis_pre)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "df6bb593", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    granule_pnt_1         (band, y, x) float64 ...\n",
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+       "  * band                   (band) int64 1\n",
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+       "    num_observations_500m  (band, y, x) float64 ...\n",
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+       "    sur_refl_b02_1         (band, y, x) float64 ...\n",
+       "    sur_refl_b03_1         (band, y, x) float64 ...\n",
+       "    sur_refl_b04_1         (band, y, x) float64 ...\n",
+       "    sur_refl_b05_1         (band, y, x) float64 ...\n",
+       "    sur_refl_b06_1         (band, y, x) float64 ...\n",
+       "    sur_refl_b07_1         (band, y, x) float64 ...\n",
+       "    QC_500m_1              (band, y, x) float64 ...\n",
+       "    obscov_500m_1          (band, y, x) float64 ...\n",
+       "    iobs_res_1             (band, y, x) float64 ...\n",
+       "    q_scan_1               (band, y, x) float64 ...\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
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MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reflectance data\n", + "modis_pre_bands = modis_pre[1]\n", + "modis_pre_bands" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d8320133", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:num_observations_1km\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:granule_pnt_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:num_observations_500m\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b01_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b02_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b03_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b04_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b05_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b06_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:sur_refl_b07_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:QC_500m_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:state_1km_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:obscov_500m_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:iobs_res_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_500m_2D:q_scan_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:SensorZenith_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:SensorAzimuth_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:Range_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:SolarZenith_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:SolarAzimuth_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:gflags_1\n", + "HDF4_EOS:EOS_GRID:cold-springs-modis-h4/07_july_2016/MOD09GA.A2016189.h09v05.006.2016191073856.hdf:MODIS_Grid_1km_2D:orbit_pnt_1\n" + ] + } + ], + "source": [ + "# Use rasterio to print all of the subdataset names in the data\n", + "# Here you can see the group names: MODIS_Grid_500m_2D & MODIS_Grid_1km_2D\n", + "import rasterio as rio\n", + "with rio.open(modis_pre_path) as groups:\n", + " for name in groups.subdatasets:\n", + " print(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9b02df18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:                (x: 2400, y: 2400)\n",
+       "Coordinates:\n",
+       "  * y                      (y) float64 4.448e+06 4.447e+06 ... 3.336e+06\n",
+       "  * x                      (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06\n",
+       "    band                   int64 1\n",
+       "    spatial_ref            int64 0\n",
+       "Data variables:\n",
+       "    num_observations_500m  (y, x) float64 ...\n",
+       "    sur_refl_b01_1         (y, x) float64 ...\n",
+       "    sur_refl_b02_1         (y, x) float64 ...\n",
+       "    sur_refl_b03_1         (y, x) float64 ...\n",
+       "    sur_refl_b04_1         (y, x) float64 ...\n",
+       "    sur_refl_b05_1         (y, x) float64 ...\n",
+       "    sur_refl_b06_1         (y, x) float64 ...\n",
+       "    sur_refl_b07_1         (y, x) float64 ...\n",
+       "    QC_500m_1              (y, x) float64 ...\n",
+       "    obscov_500m_1          (y, x) float64 ...\n",
+       "    iobs_res_1             (y, x) float64 ...\n",
+       "    q_scan_1               (y, x) float64 ...\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 2400, y: 2400)\n", + "Coordinates:\n", + " * y (y) float64 4.448e+06 4.447e+06 ... 3.336e+06\n", + " * x (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + "Data variables:\n", + " num_observations_500m (y, x) float64 ...\n", + " sur_refl_b01_1 (y, x) float64 ...\n", + " sur_refl_b02_1 (y, x) float64 ...\n", + " sur_refl_b03_1 (y, x) float64 ...\n", + " sur_refl_b04_1 (y, x) float64 ...\n", + " sur_refl_b05_1 (y, x) float64 ...\n", + " sur_refl_b06_1 (y, x) float64 ...\n", + " sur_refl_b07_1 (y, x) float64 ...\n", + " QC_500m_1 (y, x) float64 ...\n", + " obscov_500m_1 (y, x) float64 ...\n", + " iobs_res_1 (y, x) float64 ...\n", + " q_scan_1 (y, x) float64 ...\n", + "Attributes: (12/136)\n", + " ADDITIONALLAYERS1KM: 11\n", + " ADDITIONALLAYERS500M: 1\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Subset by group only - Notice you have all bands in the returned object\n", + "rxr.open_rasterio(modis_pre_path,\n", + " masked=True,\n", + " group=\"MODIS_Grid_500m_2D\").squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "fcc79536", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:         (x: 2400, y: 2400)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 4.448e+06 4.447e+06 ... 3.337e+06 3.336e+06\n",
+       "  * x               (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06 -8.896e+06\n",
+       "    band            int64 1\n",
+       "    spatial_ref     int64 0\n",
+       "Data variables:\n",
+       "    sur_refl_b01_1  (y, x) int16 ...\n",
+       "    sur_refl_b02_1  (y, x) int16 ...\n",
+       "    sur_refl_b03_1  (y, x) int16 ...\n",
+       "    sur_refl_b04_1  (y, x) int16 ...\n",
+       "    sur_refl_b07_1  (y, x) int16 ...\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 2400, y: 2400)\n", + "Coordinates:\n", + " * y (y) float64 4.448e+06 4.447e+06 ... 3.337e+06 3.336e+06\n", + " * x (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06 -8.896e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + "Data variables:\n", + " sur_refl_b01_1 (y, x) int16 ...\n", + " sur_refl_b02_1 (y, x) int16 ...\n", + " sur_refl_b03_1 (y, x) int16 ...\n", + " sur_refl_b04_1 (y, x) int16 ...\n", + " sur_refl_b07_1 (y, x) int16 ...\n", + "Attributes: (12/136)\n", + " ADDITIONALLAYERS1KM: 11\n", + " ADDITIONALLAYERS500M: 1\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open just the bands that you want to process\n", + "desired_bands = [\"sur_refl_b01_1\",\n", + " \"sur_refl_b02_1\",\n", + " \"sur_refl_b03_1\",\n", + " \"sur_refl_b04_1\",\n", + " \"sur_refl_b07_1\"]\n", + "# Notice that here, you get a single xarray object with just the bands that\n", + "# you want to work with\n", + "modis_pre_bands = rxr.open_rasterio(modis_pre_path,\n", + " variable=desired_bands).squeeze()\n", + "modis_pre_bands" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2c304e51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-28672" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view nodata value\n", + "modis_pre_bands.sur_refl_b01_1.rio.nodata" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0554e0f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:         (x: 2400, y: 2400)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 4.448e+06 4.447e+06 ... 3.337e+06 3.336e+06\n",
+       "  * x               (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06 -8.896e+06\n",
+       "    band            int64 1\n",
+       "    spatial_ref     int64 0\n",
+       "Data variables:\n",
+       "    sur_refl_b01_1  (y, x) float64 ...\n",
+       "    sur_refl_b02_1  (y, x) float64 ...\n",
+       "    sur_refl_b03_1  (y, x) float64 ...\n",
+       "    sur_refl_b04_1  (y, x) float64 ...\n",
+       "    sur_refl_b07_1  (y, x) float64 ...\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 2400, y: 2400)\n", + "Coordinates:\n", + " * y (y) float64 4.448e+06 4.447e+06 ... 3.337e+06 3.336e+06\n", + " * x (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06 -8.896e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + "Data variables:\n", + " sur_refl_b01_1 (y, x) float64 ...\n", + " sur_refl_b02_1 (y, x) float64 ...\n", + " sur_refl_b03_1 (y, x) float64 ...\n", + " sur_refl_b04_1 (y, x) float64 ...\n", + " sur_refl_b07_1 (y, x) float64 ...\n", + "Attributes: (12/136)\n", + " ADDITIONALLAYERS1KM: 11\n", + " ADDITIONALLAYERS500M: 1\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open just the bands that you want to process\n", + "desired_bands = [\"sur_refl_b01_1\",\n", + " \"sur_refl_b02_1\",\n", + " \"sur_refl_b03_1\",\n", + " \"sur_refl_b04_1\",\n", + " \"sur_refl_b07_1\"]\n", + "# Notice that here, you get a single xarray object with just the bands that\n", + "# you want to work with\n", + "modis_pre_bands = rxr.open_rasterio(modis_pre_path,\n", + " masked=True,\n", + " variable=desired_bands).squeeze()\n", + "modis_pre_bands" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a9726cfe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now your nodata value returns a NAN as it has been masked\n", + "# and accounted for\n", + "modis_pre_bands.sur_refl_b01_1.rio.nodata" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "41f5dea1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot band one of the data\n", + "ep.plot_bands(modis_pre_bands.sur_refl_b01_1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "13e2ba71", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ERAgqpBNwAgPOXAEYSYmThsRJs+7uQHGYgGnWEpBHcMAcTk5O6ty5c8EJAECPj49DhgEQVSqVdO7cOeXzebXb7TDWdrutdrut2dlZTU1NaTAYhJQ6MsnnJYWxuYGs1WoqlUoaDoeB3cYYNpvNICswR/1+P9TJOluHwwTMABocVDr77HrpgJprElS6XPK3Aw5AMWAE48xa+d8AxX6/r3a7nWB4Y5Dl7BmsmNeku864E0IupSQgQ6c98Pc5Yyw+B94P9Ardmpyc1MbGRriOM070q91u6+DgIIz5+vXr+ta3vqWbN2/q+PhYW1tb+uKLL9TtdtVutwNQAPB4kFKr1YIuzc7O6vHjx1pZWQm6cvHiRV27dk3Hx8fa2dnR6upqCOg8Nc3cxMxXHJz4HDuDRXPb6nPHXDHXvMdYsEXxtfAPlKPg8AED3sc42Inl0wka7udZSQ8W/TpuT11HGScyyPs+7jhw4H2Xac/cxu/HQNrlLwZ8Lu8xyI4BHN/17ICTLk56xddm3VjPSqWis2fPJuw/Y8dnsx9gf38/2NB0+qQk8JNPPglkTKVS0Q9/+EM1Gg0dHR2pVCrp6OhIjx49Cj6h3+9raWkpZKHX1tYSunhwcKDBYKByuazJyUn1+33duHFD5XJZn376acgKbW1tqd1uJ9hZiA9JiUBfUiIT6tmncrmcKC91WxPLpBNYzGUc6OHLWScn7lwmnhdMYaeQZzLYjBNfmEqlAnEFQMaPHh0dBYxA5tHnCV11YE0wdNrGtxcC1dJIeIgGPRLzqKpUKgUmTdIzqXpJiXpfhIZyAT7L//Pz85qZmdHR0VGo3+31elpcXNT29nZIkbihRABgBB1ExmwpjszZdNjNQqGgs2fP6ty5c6pUKmo0Gtrb29PKyooWFxdFUT4bKGDiKcdAIJ0VxkHCFnoqkVpJ6QQYAFgcAACOcKoIPuCUMXE/SSHwIMKXkixGqVRSPp9XqVRSvV4PQBNDvLW1pVarFa4BAAGwV6vVRK0eih9nDrhXpVLR5ORkUOJer5dgIZwBwvDEBsadg9fjOgNQqVRC9A2bweYtDNnk5KQkaXt7O3yf71YqFZ07d06Hh4daWVlRu91Wq9VSNpvV/Py8Jicn1W63tba2psPDQy0uLgZDTR89jQ0j5ptvkAFnlL0WGCOIjPG/1+ABHpkrdI85R2b4PGwEjSDC2aZY96WRs0b/CRidPYEJZ56RW+rvnd1kvl13GQvBmadzx4EO/9/76jqO/vG+z4FnOuJ+8HlntxYWFhIyDehygOisYzqd1sOHD/Xw4cOQhbpz507Ckfv8uj2F2ep0Ojpz5oxu374dAMaFCxeUz+c1OzurxcVFLS4uql6v68yZM4GIgPV1lt6Bms9RzCx7G5d+9tf52wE142GOnYCJP893PIPngN7nO74v6+VjizOn2CnkG18Vg1aXSWfc3X94qdC4YGPc/DnTHb8fj8/nFrnycdP3cdenf973mOhiHiYmJjQ/P69XX31VnU5Ht27dCj4EucGvdrtdPXz4UNIo65vL5bS7u6t6va7vfOc7ajQaWlpa0m9/+1v1ej3NzMzo7bffDuD60aNH6nQ6+v73v68f/vCHmpiY0P7+vjY2NrS/v69vfetb6nQ6arfbWl1d1f3793Xr1i11u12dO3dO6+vr2tvbC/YnlTrZpLu2thYCxe3t7eDzfW8P+20o7ZMUAKXPNeOjsQes3++r1WqFOZ2amlK73Q7+3m2Fk16ZTCbYL+QTe+dlOXFQ6mDb/T6fRY4dvNM3CAMqBDxgdH0jYKUCwMkQ9yVOXrTb7eBbTtuz7YVBNaCPBihBoHGipHmcwcYIuPF0dho2yb9TLpd17tw51Wq1EB12u13dvXs3LC4OmGsjAAiP19oizB6ZplKjMgmvO2Iz3muvvaZGoxEM6fHxsVZWVkLdNEAQwMO4mRvAvaf+ANVewy0pgFoCDubWFRQAQHBBXz1t444Jphi209luFJIAoFwua3p6Osxbv9/X3t6ednd3EydYMHeklLg3StvtdrW3t5fYpDkxMRHST+VyWfV6PVHrRRAWs5E+HubJwRGnHDAO+sc96Fu/39f+/r6y2azOnz+v4XAY6sOQRY/ScSaPHj3SzZs3w+t7e3s6c+aMqtWqJiYmtLCwEBzByspKWGcMF+uNYykWi+G0GAyu12Y7s8R4yGrA2Dt7xzqQBWDNHQi4Y3WGKXa0nmly4OByiw1Ab4rFYugvDmZ/fz8EqrwOwz4ODDj45DtebhYDadrzQLY7NmQsbuiBy5VfV0rWDvOas3Tck36Xy+VAOBD0IEfpdFrf//73JUm/+tWvEqUzOFzWDweG00W+l5aWwloWCgU9efJEh4eHeuutt1Sv1/XWW29pdnZW9+7d01dffRXAg3QCFJgHL3n4OnDGZ2NmehzoY6486+DBhjPIzDsBiJd5eVbFyzRiYMmauC33e9BPDyi9395nPhv7Dz4TA2ofj+tRLEN+7XEMdUw2+HfjdRk37/H9fK78t5esYHcqlYrefPNNnT9/XtVqVcvLy5Kk69evq1ar6cmTJ6pWqzp//ryWlpb0+eefq9frhSDujTfe0MTEhG7duqU33nhD169fDwHfv/yX/1L1el2Tk5O6fPmyJGl1dVWVSiWQAh988IFee+01nTlzRuVyOWz4q1QqevTokf72b/9W7XY7zDcbxgkymYPj4+NABqytrQU7XigUgh0i4zcxMZHI6k5OTqpYLAYf5IDdy108M8jpO5TKOKZgvn3/V61Wk6TQB3TXa6CRd/53e+uZddbZAzNeBwBLowDa7S+6yGscHuD6LY38HnLD9TudTugTdv55LZbNP6T2wuUfMevhqQeEBWNJc6FDoN3hxwyCA8z5+flg4DqdjjY3N/X48eOQVnf2NjYqzm55H7zWF6BHWl1SAvzNz8+r2+3qyZMnAVhms1ltbW0FZ+313MyJM+4O3p1BeWYx/tdcskOZKNPn8ujoKOG0uY9/BsDDHLCJkLGTTsdAcV8Yina7HdaUzXh+DV8fQCj3AvwBnugX5RdeRtPtdkP9OYYsnhtnRJlHxg5LD5ONTHIvyj0I+JrNpqrVqi5fvhy+T5DCdQGK6fTJptMvvvhCq6uroQ5tYmJCjUZD1WpVnU5HrVYr1Oc7mHZwTDCSyZzsgifFls/nQ/aAz3o5AWUre3t7wdF7WQaGEDANO8N43AE7Wxw35AKQ599DDpgfr6UmEMWYo8/Org4Gg5AyjZlmZ4QZh8u7Ax/0+58CPKRkLbanadELgkEpuccjBkG0uBTLx+BAfnt7O/S/Uqno+vXr6na7Wl5eVjqd1uPHj7W9vR36GPeX6/X7/UR9rzRKxaZSqbBhPJVK6Y033tDVq1cDoJEUar2RWzYUx0y9l0k50IvTzXF/+d/ZNxrXQV+dtfegD0Dtx45yXYIU+jOuT4zBgYGDTfrnwZWD33G+wWXNg0hfcwcU4wD1ODZbejY4iWWMufMgxD/jQUEqlUoA/XENQFSr1VStVrWzs6OJiQnNzc2p0Whoe3tbn3zyiT7++ONw2tHs7GwAnufPnw+gUlK4zve//329/fbbWl1d1dbWlv78z/9cjUZDx8fHunLlir7//e/r3Llzkk5qqXd2dvTVV1/p4OBA6XRaKysrKpfLunLlSgioPv7447APgewvmQ38NPW+vh+KdcMXY3uGw9G+D8ieer0e/F4+n9fMzIxeffVVdbvdgCPYE7W2tqZKpRKANnpIQIjfICMfZ3FcxthEScbadR3byv+9Xi/IszPgbpd5DV/ueosfp9QU3xODZM/UO6gfl53EL6GjvV4v2J/T9mx7YVDtDJan5D2VjBC7ASHiwljEm9pIt/iiplIntVmNRkOLi4t68uSJdnZ2giPwlIY72Jjd8tMz6DOgFeHrdDpKp9Ph6B+U6P79+6Fcwo06intwcJA4aYMaXMCT70B39smPhcOBIOyefnf2D7aPz5HeKRQKwcACZtmwGadzOU5rf38/pPCYI2lUbnJ0dBTSZQ6oGTuAyo2Jz4OXVniN9pkzZ9TtdrW+vh766Cw0AM/lxIGGg2lP2zmbz1FmgBL6c/nyZTUaDbXb7bBpkcb9d3d39eTJEy0vLweABFOYz+d14cIFzc3NaXNzU0tLS9rf31e1WtXU1FQYD2sAoIM54Sgm+txsNkOQirGnIYPU72Es+a4beYI4lzWXfwcNvOfOyOeI6/M9BxesDywLdsCPdoRBp2+Ua8XMGc3lxBkSskYefI9jpZ8XvHMt5sdlycukqNn14M2Bi1+XdYrHMA7w5fN5vfbaa9re3tbjx4+DvVpcXAy2R1LIRhEskU0pFAoqlUoh8PNjMj24HQ6HoSxrdXU1EA35fD7YXEgLMlXMG2vojKzPJWNjneLmmRgHp3GQ5c4eGUCfY72PAarP6zgAGWczWB9e9wyfz1ksf3FWhrWhf86QjwvkxsnlOGbdAxH/nI/FQTRzUKlUNDMzo6dPnz4TmMb3dx9aq9X0/e9/X6VSSR9++GEgfs6dO6fBYKBWq6VKpaLXX3897OlYWVlRNpsNZW37+/uq1+v6i7/4CzUaDV25ckWtVkuTk5O6du1asFOVSkWXLl0KtceA6c8++0zNZlO9Xk/ValVvv/222u22bt68qV/+8pc6f/682u22lpaWAhvqgVCpVErs4WH+aJzo1O+f7KmpVCqhXhrSqN/vh026bEofDAa6d++ems2mpqamlMvl1Gg0wnow39Vq9ZlAmPtD3FEKA0ni2eB8Pp8gfvBL2Cb8Onbc8ZXrA3qC72e86B1ZbrfbzjJnMplEKan7XsbS7/dDKR8ydXBwEOww5TPxkcSnbdReuPzDjQWC72wsCh+fDesg0U/T8GPgJAVQilAUCgU9ffpUT548CYvOfaWRkePzcUqPz/j9XWFJqcOy53K5wLS64QME06derxeEzaM/nB9pYJqzcvE4AIUEFqlUKpRUxMcESkownfTFhZ6aYTe8AGE2FlI/LSnsPIYJ4OxPgg1n+qgFhhXFefI3kTOsI6x/vV5XNptVs9kMZxZ7yQOyk81mQ+Tvis39OdkDQwbL4BtZy+VyAGke0fd6PTWbTQ2Hw/DaYDAIG8rW1tbUarUCYEcWdnd3ValU9I1vfEOFQkGfffZZ2HBDNmJzczOkDqVR4ISxYz2Pjo60t7eXqO+FscDIApgJfDwF7g7fgWocZLhexLpC/zC6zuCN6xPfwSHwOQ+Mvd/oG+P09XW95G+/P7aEQM4ZYgdMDoScPR4XHHhA4JugGRM6yXfctnkWzAMSb8wlrJXXXXttvY8vlUoFR3rmzJkg271eT+VyWTdu3ND+/n44R9fnqFQqqd8/OR8+lUqFrF18jq1nESkfIfh0FhT9dvkZx7j5WrlthzDwPS0eaKTT6UQJFs113zdQjcu0xDLj6+z3Y83i7ATXiVk9+od/cOY9BuB8L26xTDMOl5lx14jf82CWftEPAq3Lly9rd3c3gMY4ePbyHAiRzz77TNLJ+cH9/kld8OLiYgh62+12YLG5z2uvvaYbN25IGpUyXLt2TYPBQHfu3FGtVtOFCxfU6XS0urqqbrerl19+WYVCIbDOOzs7+uSTT8IZ0W+99ZYuXryoZrOpO3fuqFQqBX/y4MED7e3thXEzNgINlznfSIyMU1rGZvJisRj8Aic5eebPGWRJ2traUrVa1dzcXCjZWFlZSexnQcch2TgSNJUaHU+LTUUOwDj0Fb/t9hr9ifFJvL4xhnJb5Yw1Phlb3e12wwOh0NG4rMQzvdKofBB9pDTN6+hP2/j2wqAaQOgb8Ij6WIC45tBTftIodeFCxGszMzNKp9Nh899XX30VdsZLSnyHe7rSxUyT/8TOFqEGhDmj7sYf4zMxMaFyuRxOpvAaNRhNdtki1JlMJqRiAT/x0UsIKuPz+l/SNzDIgEGYcECyNCrPgZHy61Ff1uv1QqQ5MTERItudnR2lUqmg7IB4d0QAc0nhcwQl0mhjQ7VaDYahXC4H8EsazZ0884uB8IDDxwBDTeoOw5BKpUKNNsfZwcwB9DB+yC2Aj7KKfr+fOEEGgIJxmp+f17Vr13R0dKTFxUW1Wi0NBgPVarVwrTgFiPwA4JhPB6PIIGyHbyRDVgl4CKicmQNUesbDgbCXfHi2hNe8lMR1JmYQCQyceeezXq5xeHgYasBJ6bt9iBuAH512EOjlADHQ5d6uQzGw4zNekuIbkZljDwjQfcAOcgZB4OUENGf5cT6pVEpTU1OBhSKgwyagz9VqVdevX9f58+f15ZdfhqAyl8vp0aNHajabqlQqoQyr1+upVquFtXBGGHk7Pj4O+ytIrfvcMGbkkP6z5p7piJuXzY0r14jLVHiPzBOy7hknX0vGgrww1y6zyLyXUmD3+Rxy5+AmHo+THNzHg9E4W4Mc0mLm3mViXCnK1zVnq8vlcmJ/SalU0tTUlGZnZ/XKK69oYWFBm5ubCSayWCyqVqtpZ2cn2GX61+l0tLKykghC0TdqgvFvjBXZQXZLpZIWFxd169YtPXnyRMViUe+++64+/fRTPXnyRNlsVj/84Q/1xRdf6P3339fq6mpgQQkc5+fntbm5qZs3b4aAYDg82eTM5+OgO2btfU+JB/CeUUCm0PXt7W0dHR0FkM28ARC5D5lL9sb4hnCfD2QcP+LkG1kv+stn8d3YRh8r6+666cG7B1hcM84AxSV4TkBJCj6eMhpOoXL77eSFny6FTSIzzWfHZa5O20n7Z5V/8AAGaeTIqZHz6AuFiB20lzhQA1WtVsODD5aXl0P9MobeozRpVGs7btMQ9/eUHgrnacB0+uSYoGw2Gzbz4SziYMGfAOn1wjCVXIsNFwAonIqDKQ82YHM9UvSUEiAC4A2b3u/3g6NljjHEDlZKpZLS6bR2d3eVSqXCQ0aGw5MD3kulUtjRzFi9hIH++mZUB8CwGKwpp6UgJxhDZ9FZW+7n9WQuNwAh6sgByzhVjjmirIL7cU1PfWHUOO+ZNOjh4aFarVZYLwIN6rGnp6dDIMAccmLJwcFBOEc8Zow8lecbOzCqNE9lIq8wkcy9lyiwvrGcM3+eOqUvPqeASk8txkwX68V7zLnrhoNUQJYHD9T6xxsT3Q5wDQ/Q/7H2jwXPfh8PJADsHoQ7axzbJQdS44Coz6XbFvaUNJvNQAwwf1NTUzp37pyWl5d1/fp1feMb39De3p4+/vjjEBijy4VCQVNTUxoOh1pbWwtP50SeKAtyO4SN9AAdW+E1lA4suB5z6tkAD2aQZQcxMaPm9tlBrdfkO1CK7bMHUW4XfI3RB2fasUkAi3Esu8uIy4/PiQcKPh+xXDKXHtCin/TNMz4ODOkP13W2XVI4GYdrYv/W1ta0tLQUjj3jXGdIl3w+r3q9HmzUzs7OM9kX/BFjRbcdIKVSJyUWDx480GeffRZIER64wlzSz1arpWKxqI2NDR0eHiYeyuKB0cHBgVZXVxNHgwLsXd48IzNOB2OSwO/htrLZbAabGBM56Dqf5dSL9fX1QEx51sbXGJnA942TY0pK3cZJI8bf5Yvvo2NOGvr4XT7pG74KAimWUyc1fK3pO3ODz3dAzni91IMgyW3g89o/JZj8P7W9EKgGlDmb46zFOCOBUHkKAecFIOI7sIB+cgEKAAB3w49h4HrS6GgqT6OggABTrktangPlpREb4QwOGxb89AJPhQNOj46O1Gw2Q7+IcGFEPYoFkFDf6WwZGy4YI+lTokVSzO7giOIB8YAaSWHjYblcTmwwgKEl4mctY2PhAVAMqOmbH9GUzWbDOaMoK/3jtx/X5GvqbTAYaG9vLxgcd/AcyQdz5+ljn08YcmrmGYOX8PgcV6tVzc/Pq16vB0CAIaFcpN/vh5QpUTuGn+wEBsxTaF7j7mwWbBNyFAMWZ7Nj+YznCz1l7egv8o5exN/FAXn5AmtPH5w19DQ7/1M2hI0Y9+he7uVlY4BT5suDMB/TOOCM80YunHF0B8r8OMCJwZVfH5tFKxQK6nQ6wYnxwAnO4z1z5ozeeust3bp1K5xzPhwOQ21/KpXSq6++GjY1Hxwc6KOPPgpAA12YnJwMDyWq1Wq6d++eDg8PNTs7q3fffVf1el0fffRRCEypvWYOxoFoQIWz7P6wB/ZO+NzEgJo5ZV2cPeY72FoHb17XHX+H9eZz3MOzhHwnBv6MI9aHGBjE4NnBrsuWEz8uq55Bcgae99zHONBz8ITejCN/Yp1Fz+grJ0bwxNtisajJyUlduXJFDx480OHhoc6cOaNr164FMDkYnDyB8+rVq3r48KE2NzcDWM7n81pdXQ2kRzweWGt0mcfX+zqn0+nwkBZJgVzwTA66y16SjY2NRNDiG6uZM7eLbqd8rXxdHfhmMpkAZrlGtVpVo9EI/t1ZYez5xMREOBYP0Ovrh/3g/zig48c/gxzgC3xeYnl0Ow4WcD/rBBH+xgkzz3L7tcEQ6C79Q5ec8AFz+F4M96OMidcgUk7b+PbCoDo2PqS93fnyWQQOwFoulwP4mpycDJsLDg4OtLm5GXb1eqSOUHJ9ALdvnMOwoRTOCAwGg7ARCKMIq8PmHS+X8PtKCqAbR48xwjlcvnxZs7OzWltbS4B3DIaDfOnESaAM1CZjVAAigDlOAUHYSTfRF+qLPf3Pb1LPgFdA6MTEhCqViprNpvb394MCM6d83h0TIF0aHWFHGQaKxz1LpZKq1aqGw2ECULPzm2tgGJxt4L44Rz8xg7mvVCphLH4CgoMFmJHDw8NQb3p8fBzqmgeDQZgbxluv19VoNDQzMxOO2CMVSG0zxrrT6QSG2tPqXnIiKXHUIulVDJk7nWq1Gth9dMVTfV7i48wuGyhdZ2InFNfKeeDrIMbLeNBddIbPOpuC0cfhU1rkaWVfl5h9IgCB/UG36QfO63nf9f+9DA155Xo+Tr7rjg4HDhAF1MQOmBKu4fDkoS7ck9R7qVTSJ598EupCq9Wqzp07pzfffFM3b95Uu93WwsJCYMNWVlZ0eHioYrGoa9eu6eWXX9b8/HzYcLq2tqYvv/xSy8vLmp6e1htvvKFCoaClpSVduHBBg8EgODdn08i2wEgxHuylB5Ze4sU8Ownh/7PecVqav5ETvueBOv3ygMczLjFJgtw6keL3B3C5DJJFdDn3H3TEgwcPPvnfA91xASHy5DoRA2ov54jBc3w93nfghXxPTk7q+vXrevjwYZArgrjNzc1gb/v9k4ekFIvFEGy99dZbqtVqmpubC6wsNf6MD6ICe+RrFgc3Hhi4P47lL2aaAXjestlsKGvyoMNJB18Drj1uDl2XpdHGRDK6ni0BTOIj8M08yAms4GSCB+VeVuPj86yo26Q4EwZA9rXmO/SLcaJ3jAc99RJMALYfzcfrvV4vBBm9Xi8QR76GZI4ds8UlNo4NfJ18Hk5bsr1wTTVAjyiaKC1OZbjBIQ1JlENJwt7enprNZiKFjlHix8GcL7CzUNzPhdjT59Io0kJ4h8NhqFmOG8KFQKLoRMOVSkWvvvqqyuWyOp2OFhcXg7HDAAD+pVH9FZsyKC9AqAHgMNoYfwAUGzfZmATgo48e1buDYb6Yo1TqJNPA+d7uRNiBzJyybpT6oLDMO7XLjMuf1gSYpCwINswdogNqlx36jMEgs0A9++TkZKgH9xTe5OSkhsOTI812d3cD04IcsTNaUgCpzOfMzIzm5uZUrVY1GAyCAUM+CFAw+vwm2AHEMB/OShP0uJxKCsECP2Qg/KEDzKGPg3syz8xjDITcWSGHXtpAoIEMsd6uA+gSso+e44yQW9ZbGgGNceww1yWLghPxh93g/Fyu+V4MbuLsFXPgQey4NKVv1uF7/M+6+FpTl1woFNTtdlUoFMJjwh89eqRut6vV1dXgBDOZjF577TXl83ltb2+r0WhoZWUlbAJrNBrq9/uqVqt67733NDU1FUiGe/fu6b/+1/+q4XCoGzdu6Mc//rFmZ2eVTqd1+/ZtZbNZ7e/vJwI4mG6/P4E3nx8MBokHDR0fHwcdcaDkTLMDS4JB1jS29S4jfhQacoSjd1vubJzrvwfcXi8+jvHF/uDsvV8OWmKyhrIKv65/hj6ih3yGPntAMI6188B1XHP9pGFHyc4dHh7qiy++CPPGZ9BdGOWNjY0g8+jA4uJikHXWw9cJsOab0mNdZW6cTfVsg88XzWUDG8ozKwjUkUOYYt+f5PMjPQtkXebwDYBSrjk5OalutxuY3RikA/R5nWxtbHsZq5+2I41sittExo7s4vN53QOxWCaYB+7DNbm/Yxc/BhRMhe30OYLYixlxSDnf/+Flk9hisnHS6OhcbMpwODw9Uu9r2gvXVAN4mHB3uhTAA558A5+za7BZHObui+lPU/T6IneyKAYMNOAK5YrrgTF6/vhsZ2ji8XEv31CIYM3Pz+ub3/ymUqmUFhcXtbS0FNhM+kC/C4VCmAvO+4Qt5dg6zt3c2toKgLLf7weQyrFaGJ34qY1+9iXfJZIEwExMnDztMJfLhaONGC+gwR0DNXFscHD2BYXFmTEvOKXj4+PAVDqodMeF8eZ1Z6E8+qb/BCIENH5KSyaTUb1eV6/X09LS0jNnRkuj8hxAGw/syWazmpmZ0czMTAC3GFHqB5lzaq7pN4DGWXrAjB9ryFp6psVPbwEgkzWQRgFgnIHwshZnANFDriUl2TRnSwBHriMxUPFMgqcO+QxGn0DSmT6veUdPuSY2wVOWPl+MOwbUPh7GjI7R/9hZucN3O8IaxEyRX4eUN3rXaDT0J3/yJxoMTk4+uHHjhjKZjB49ehQYLq6DTH/66aeq1Wq6dOmSHj9+HHRnODypQ83lcrp69ao2Nze1srKimZkZ7e3t6f3339c3vvEN/bt/9+904cKFMEd3794Nsl8sFnX27Fk9fPgwHBOGPUylUsG2edDppUuUsjhI9cyIzy3XdtDlgXq8LvgGt4Ou825jXW742wMe7slnkAMHcmRKkNcY8Dn4ZQ3QIZdP5Jj+er+xdc6iopseeNJXD1R8Hr3vqdTJo6MHg0HINnl5Fnoa9517x3OH/NOH/f39UPYT+2iAFP10PfM19zF4tiue43GBw/PKA3g6YS6XCxkuD8C9vME34BGsMYfMD/YGAuj69euq1+t68uRJIE2wF9lsNhx6EG/QQ+ac2BtnvzzIdzvMHMV2lrWK9Qlbydh9D5bbK8/YuIxTOorP5TVwFPNIfykDwUZJo6wudiEG8mSjsYWZTCYQOaft+e2FQDWMJoqE8Hg6h9SLlDxoHAal3z850oeTFpyxoGYYQXNh4nrpdDoUz1PGQaRbKBSCgqHo9MfPheW+rjCwgjyamxpcjFAul9Pc3JyuXr2qTqejp0+fan19Xbu7u4FhJspE4Nm8Eke2ksKxcjD1lK4Axp1xwglx6ki/P6p3glX3zZPO+ABKCTZQdKJWrke/AGdsuOI15hJQ5EoOE8XDUPwpbl4O4fONw3RH4fOG4efR5zAQ+Xw+sDQEFg8fPnwmjZXJZMLjyTESg8EgAGoYw7m5OU1PT2tqaiqcb9pqtcIxTDB6HN3ndf6MHZaEPjEesgxeQuF7ClhDHKDX1WOEu91uyO74gf7IE/3xbAHsEGuJ7vA73iSKkSVAcEftuugBLewtY/P3YwYLB+JnpALsMeYOtNxou9P2DJa3+DSdmHn08eGEWQ9avJkYW/Dtb39bf/EXf6F+v69bt27pnXfe0c2bN0NGJA4epFHqd3d3V59//nl4HTnELnz22Wc6e/asfvKTn+j8+fNaXl5WtVrVW2+9pWKxqKdPn6rVaunmzZt6//33g81zx+aOnSCZwMcZaNYee8smJAev7lT9e2yAjrMQDsqQeX7iOlQHg/zvdsXXzJlV5s19jgNBgtRaraZisaitra3AQI9jOz3YjYF3sVhUpVIJrP7W1pb6/X544t/m5mYIorLZrM6ePRvqhVutVtAnnlSYzWb14MGDwBrTMpmTDa31el2VSkWPHz9Wv3/y2GsyUB4oOgOKfMeg1Zlk/vc5dV/GnKDDrq8evCAHfi3m1YPOmK2Om18bOw/JQVaI/TkepIAtkCcCQmTcSYZ0Oq2pqSkdHx/r8ePHajabIcNL+d3h4WEgMvCFEAQAVOwpQNXJgDhTgdzQZ8+OeUDiAN2DJtaTem7XWycv6Iv7T/w+9rLf7wdg7pUE9B07j2w7keXjduzgpx9BMsZy9I+t+R9ie+HyDyl5Li3paAfD0uihBnym3+9rZ2cnceQXisv1cHrxgsUMMIohnUR4sI4O9AEmsKmlUik8HRChdKMvjeon2RxCJDkzM6P5+XnVajUdHh7q4cOHarVaYYODKxTz4QDaj8CDWdnc3Azf9zIRB2GMK50enTriqR3Av9eXSyN2kk1M3NNBhB+bwxwApmGoSSvTN9YHRXXWf39/P7AAHtDEzJCfEhM7Z+Yql8sFNgOwCFsN8KHO1U9sccYQw809Ob2DM1Pr9bqmpqbCwwskhU09Lp+AdZgSAhrWm/lgLpG9OP3GfCDDk5OTOjw81M7OzjNsmMsSpSGMmfcxutVqNcjq0dFROBHG7+VG2R0CAJP0OaDdN85JCmctwy47q+cMNnPmaX9+O/sSM+UOstxx89sZaZyRs0Bul7AlOAf65nMyjj2izzjXy5cv6y//8i/1zjvv6M6dO/r5z38uSbp582Y4WQD5ZT5xNji1OBgAjFF+BsD74IMP1O/3df78eZ05c0a/+MUvdP/+fZ09e1aPHj1SqVTS22+/rYsXL4ba7O3t7USAFYNUZMpZU0iRGLD5fDsTh+w4gPB0t8+d1+S7vnsJk4Nct7/87RvZmDfG5uvl/cnlcpqfn9eFCxe0uLiog4ODBDDjOj4fzFWpVNJLL70UNjzX63U9ePBA29vbwS7X63VJCg/YyWQyunr1qt577z1Vq1VtbGzo7t27evPNN7W6uqpXX31Vs7Oz2t7e1j/8wz9oc3MzlP15Cj+VSunixYtaXl7W8fGxrl+/rtdff13vv//+M+Pkfw8EHFj7eJE/bInL33A4OrKUTAWBlbPiroeu3zTPnLqePg9o0Sfa9PT0M0DRyyXdNzjxIo0e9c1+JPxBKnVy4snW1lbAI/g4xowvISCYm5sLG9kBo5A2ThaAX2KSyTMTzDE2kvfI3vu8uh31jD92kr74/BI8ct2YhOGeEFDsE/MjcAmkIaUGg0GQE0gPGG4nINzOgAMcG5y2ZPtngWqcMAKCoUX4WDTqbtgw5gYTA+1OMGagaK6wKAwCibCgBESmDvxhLuiXlxgMh8Pw0AavJ0I4z58/r3PnzqlUKmljYyMcas/GMzdADiD5fpyyhwkFJGM8AEiepuGaBALlcjmRmvHjbdz4AEoxtp5dgFF2hg1GkxIISQm2Jw4UAA+AOA8u6AeADHCLYS4UCokTNxg7LBxzwDWq1WpwQgBcrwXGwLts4FTISjjYzOVyqtfrmpmZ0blz50JJzNbWVqg9dcADAMAZu6wxJ173zBhdHglwmDuOvqK2nfktl8vBoGNkh8NhgvkHSBAwkNJstVrhXn6ahrOGw+Ew6KEDL0/lc3/ed10iQKEfUvJR1B4cI1v0xdPlAAWfY2dwYqDH/RhDXD8YM0DuxJH5UqkUHsjkQagz9tigiYkJffOb39Ti4qL++3//73r06FGQM69v9X4wf+NYRJ8Lgrm1tTW122212209efIkAfrp69WrV/Uf/sN/CMBucXFR9+/f16NHj8KJBrlcLpyg5CAjBpIAX4IaPkdfPQDgOg4i/JSeuCzJbQnzCGD3mlZ+/Hqub/TB7aoHbASv6FO1WtXk5KRqtZoePHigTqej69evh2M7vdbfWUaOcL106ZKKxWJImz969CgE1cvLyxoMBtra2gpync/n9eqrr+o73/lOomzx3Xff1eHhYWDMP/74Y928eVOlUkk//vGPQxb1s88+C7Wzm5ub+uijj4KdffTokR4/fpxg6L3F9trl3wGw/0YP3fa7bKbT6VBT7dcel/WhDwRJz2MiY2DN58iiXbp0SWfOnNHW1pYymUx48BbrKymw/U6A+RFw0ugMZvSXIME38fl48VnYyXQ6rfX19YSvpb8+F4DQWEecVZeU2OPDZ7AJnmFAH/30K2ynf5654DQOz+j7fcFAHkCxOX8wGAT2Gh9KVt19tDQqiYOwxBdAJu3t7QVf3u/3Q538aXu2pV6Eps/lcsMLFy4kWDAXAB7uQf0eBhjD6mmmuFYoNggxS+GpDwQGZ+ipDY/eOd3DN5vh9D0Fw7nMsHewCZcvX9a5c+fCbuutra3wRD5pdA4kffWzkEnXw0pR8gK7Cljz4+9gyXBoBBuetkUhvdyDOeI68VFJKAwAk8a8wgI7w4fTYDwonIMoGGrKFwCYzmpRQ8f6kZbydF82e/IESk6DwciQjgVY+uYxZIuH8vg9isViIuVHvfvExISmpqZ09uxZTU9PB6PDdbk3jA5GGUeDnMJEO3sdMwju6KiFhXmGRQBUUbsGm+XAlUCP/5kT9G1nZyeAbjIhvr6exoUVZ8MdASTr4s48PjKx10setwcYR76dPXPZZZ1Ya2TYHy7gOuQ67uypOyleww6NAwLOjOdyOV2+fFkXLlzQ1taWbt++HZhRnFu5XA5y4OA4droOSrzvOEX0wFlyD1y4L3W0lUpFU1NTqtfrKpfLevPNN1Uul7W/v69vfvOb+sY3vqH19XX97d/+rX77298GAAK7dPnyZQ2HQ21ubgZ5dRlw9s/XyZlGB8QOYr0eF7sUAzOfe+bcr+1gIAZuAAgH2273HUBC2OTzJ49/B/hvbm5qYmJCN27c0DvvvKPhcKjl5WV99NFHevjwYbAVfj/IDkgOAsh8Ph9YZs57pk/0a2pqKuhFuVzW5cuXdfHiRX300UdaXl5WpVLR+vq6Lly4oDfffFNPnz7V6uqqWq1WKPdYXFxMkBYxmPX5dJ3itVivY1Adr38cnMTA3ed5XB88yPq6cg/33a432Pd8Pq+pqSkVi0Wtra0FG429w0b4tSDwyNrij7Ah9MlBOdeSlKi7jgmBeBxeSsWcxKy9Z8Dw306axXYrtg8Ehp6ld8be++bzARFCBhe5dhvm5YnoIX6LzD7BB0EGrLPbMq8I6PV6gYgBJ+B3v/rqq4+Gw+G3Yjn41re+Nfzd7343VkZetGWz2bH3+P9ye+Ej9WDhMFDOHPpu/lgBnXkFeMQO1QXSm6dC/HoOqN2hkLrgdYBeNnty0LuXKDhzRfnDhQsXdOnSJaVSJxvI1tbWwuH3khJOwIEFxoOn+2Wz2ZCS8rOwUfJ0Oh0YWgy7jxn2insCEj1ilkbpVgfxnu71oMGZBEo9OMGCa1JmwjFvsEPMZRzgAHZ8HXHS7CyWRkyH10oTNJCW87IdSQH00n/AGPVerBnrnc/nAwCnFjqdTgfgwhFTMCTeN0+LsQY0ACtpRJwUxpKAyBkAZJbMwNmzZ7W7uxuu5SVMGEh0hyAJvSN1l81mw9MgfZNgnOlBxrkGDAXzCrhgPtFnQB9jhpVgfrgfgSOG3HUMpsN10llU30Ues8q8No6Vc2fG9ygXQye9BANnlc2enN9LQMx64SjQg42NjTB+QESsM8i3N+aOH+7v+uwp2uPjY9VqNf3gBz/Q1atXtb+/r0uXLunNN99ULpfTZ599pu3tbS0tLemLL77Qxx9/rMXFxVALenh4qFqtposXL+r8+fO6f/9+OLLSgYFn5VzOXT48EACA+TqxFjD18VzEMsfnff5cn3yt0umTB2YBYIvFYuJEI9hKfshcAVLPnDmjd999V1evXtUrr7yiZrOpL7/8Uvfu3dPdu3eDDYPJwzdw0pGTI5ISJ1WwzvQVEL62thaALXby888/19bWVsgqSifg5Ze//GU4pSmdTmt7e1vr6+uJQJ35ZI4cbKEPMaD2v2P2FLsfg0P+Zn1dhzyQ8UDSiRC3M3GgOa45sGQ+WFdKNj1Aw95hNyQFrIHd8hJTfOPExETiGFju69iDNRkHfBkTwb9fIwa8zLcHerHd5doxUHZ/jN1y++B+1OfNMRJ2lWACfwUWiglHB9TYfeafTDv+DJ3Hlru/Zx+VZ8THnZp22k7aC4NqDCOCSEQE+PJoGGNMRIfR92jU0+RSMjpHKHjfFYEF9s13vsnNUyKcz4xSueKinOn0yQbIq1evhqfora6uhrIAnJZH1a7sxWJR9Xo9/N3tdrWxsaH9/f3wlCnGf3x8HICgn4jihsxZcJgtNpa4kqI0AHOedMX7bDJirh3k+IkXGFdKUtj0yUkefk1+s4YEBLznDAXXkU6MNulWP74IYMYpHZlMJlGOQ+kGkTPHCgFgXbb4HqwqxxfWajVNTk4qnU6Hh8HA4Htw6MEBBpSgwx0eso2MetkPjqvT6WhnZ0e1Wk3nzp3Tzs5O2DTLUzeRwVRqdBQhsorM+OZZHDnzzDpQN+66hZFGR2LA4w88oP8AU2THa2Jd90qlUiixYS2QMQ9aAb3uBDxVGYMDdD12KvxmblhzGkEWwdlwOAwOrN/vh8DFWWg/h3tnZydhj5g/B/9uf5zBdaAfj0VSYryp1Ekt75/8yZ/o1Vdf1fr6urrdrhqNhg4ODvQ3f/M3un//vhYWFrS3t6d2u62pqSl997vfVbPZ1MLCgqSTc9Xb7bZ+97vfBZlgbdFbgKjXhLrjRfek0bGGzJdvIve1Z70hRfw9HDB/EyAjM5TtDYcnJVLvvPOOLly4oA8++EBvv/22qtWqVlZWQs360dGR7ty5E/pN2USpVNLFixc1MzOjq1ev6ty5c/rwww/14MED3bp1S+vr66pUKpqbm9Py8nJ4cIoHdk7O+DrGLKN0csb/pUuXtLy8nHjIydHRkRYWFhJ+ivGtrq6G62BLnKEcF7CNC+C4htudmNX0wMjJHr7H+seAzeXVfbbPR9wH/nagHTcH9E784Kc4ni0OGgC/yCo2CHYau4Kvz+fzYUM244xLtBw3xBkWzwLH8+1YhzlyZhmAjy12m8d6E7zn8/nEiV0eCHmgFN+HH/rpdcyuwy637pshp2LsA4j2OmmfG0kh4OFQCLK6yJXPcdyeR47+obQXBtXumP1vnC/smLOXzysXobnD9J25vtnCmRZXOu6LcKNY9Akmitd80wz9w0lfv3491Nmur6+HzSn0AUfhp1+Q0qLW6OjoKKTkUXBP1eG4cHZE284g+Zj9iYBuvKXREYGennPA7Q8d8SgYA4eCwJhKCo/n5sEQw+EwMJKsVQyw+DwywrjYTML3YFoxRhhLDyioh2w2m2FjCfXD9J1xsA7O7PgmGjYgTU9Ph3p0ZAuQjIFgnpxhcCDvwRdyyLoWi8XAkAKOd3Z2dHh4qHq9rsuXL6vb7YYAZmdnRzs7O5JGRyn5fLJ+nkEgy8Fued+kCZgmAIPVkJSoWfaNhwQEzB/fp6SFefSNtJIC+wdz52yzAxZsAOy764LbkricgvfcqXvwRD8x3Gw8rVQq4aFM+Xw+bD5ysE//kB3W2HUU/QQQOriPbaD3m3u543XWmPQt9vHWrVv68MMP1W63VS6Xtb29raOjI21sbGhlZUWDwUCTk5P64Q9/GNKuDx480CuvvKIrV66o0+no888/197enqanp9Xv90P9L06YYK7dbmtlZSXMnRMCExMnT5TDAfMAEWct/W/WI840MF/1ej3Yq1qtpmq1GtaoUChobW0tsPIXLlxIHM85NzenfD6vdrutjY0NXbp0ST/4wQ/0ySefKJvNampqSrVaTcPhMMzT8vKyhsOhdnZ2VC6Xdfbs2WB779+/n2Dq0XXG4Ws4LkhwWeTBKf75uORAGmV3YtDpQCNmguN7+TzH4M5lkM/4+Pw+Huw7UHYb5oGYf9fv7ez3uOCS6/E5dIixUIvLaUxud903Yg88iHJmmjEhv16XjO91pjguL4uBqM95PJZ4PeLXPYh15p+GLU2n04EMifs/LmD3DJt/xvvsaxSvG7LlBz7Ee1A84yopZOA9aOIazKu3rwPUp+2fsVHRjQVgxNOMAGFXSE+NuvLjdGEw/YgnBzDSqCwBxxhHcu7oUeparRaAKX11R4FBr1arun79ui5evKhMJqPFxUUtLCyEtDfGCecAIOVYtmKxGM41hiFC4Tw94wKPQwMIeXqVuaMe2I8MwwGRGnMB529P2aPcGDHuzXwDLgDHMKQoHUYwBn7jjBGv+Xq483BA7Q+LYY68jhkgj3H2wE0aMaAOfPz0AB73DNCizAe2iGvC1rqc0QcvZ/KSDlqpVAobY/b390MamzGTopcU2F9OIUFeuGZcJgFDQO06fdjd3U0c4I/jcV3woNYDNIJQzzpwTww084Fee6qXdeOM27j/3B+95V6eBeDe0rMbEZ2x8fVgrwBMpzOq586dCxvHbt68qU6nE0qLWBtnn0mFks1w+aWR2XEGj8/F/eN1n0cHLW4/aNyXAODy5cuqVqv69NNPw3GPyFen09Hdu3e1tbWlRqOhyclJbW5u6vPPP1e73daPfvQjFYtF/fVf/7W63ZNHp6Pr+Xw+HC+G3qdSozp97IQTGGSmGGcM8mgxME2n03rppZf00ksv6Re/+IUuX76sn/zkJ+p0Ovr000+1tbWls2fP6vz587py5Yqy2WxgfqWTJ9O2Wi1tbW2p2+2G84a3trb0rW99Szdu3FA6ffIAnN/85jfhbGrOpZ+YmNDVq1fDk1R538/i9eyfp/HT6bSmp6dDVtHZZV83B8weODlgY1681IB5ctsxDpz6606CxGUfEBesh2denShyIseJJPwR740D1LEuSCP7TaAd21+/Pr4HPSDQgkjBvvh1YwDp2TNn4PEtMUHG/DjpQr/ch8ZByfNaTCZgu7CvPm4AMOPB3vvTZdE//I4TVciwk2Yx4/u8QB6ZIAPumSnmDX/jAQx+DvtF331tGbsTEv1+X+Vy+bnz9ofe/lmnf7BgbAIcDofPpPoREFdugAl1r0Sq+Xw+1N76U/2k5JO8cMzuHFKpVDiHWRrVimJIer3RY6JRPkBLNnty4sbrr7+umZkZ5XI53b9/X6urqwGsFAqFxFFqMMswYyg3DCSGwtkIZ2L9YSkOLjBUzBubAGGAGBc10F4z6+w2YAEF8pIYAAWb4qiTqlariX5TwhKnSlkHasJZ33G7iGGgMXIYSMYAcCMwcEbdFdzlzTMhvkGEgCeVGm3kg4nmaZ3uHGH93fCQuuY9TgRgDt1JUv7QaDS0u7ur5eXlkIbks5lMRjMzM4n6+VQqFTYnwhI4I+M7yGF4neWlpIn/6aezzYzF0320mInzEorhcJgIkH3DqXTiiLxcKJYJ7s+1vGbVN1vGIIS5GcfYeKmHs948fVCSvvWtb+n8+fP66KOPwqad1dXVELQ6s0yQx/x59stZ5ZhlZvwx+OB1QIFnOHiP/9FtT8sTzG1sbOirr74KQS92bXV1Vaurq+GeBwcHWltbk3QSpH3ve99Tp9PRr3/9aw0GA33zm9/UhQsX9POf/1xbW1va3NxUtVrVlStXdO7cuWD77t27p93d3QTYh/nD3hK8egAWp7eZK+ZmcXFRy8vLyufz+vM//3NdvHhRCwsL2tra0oULF/Tuu+9qYWEhnKC0sbGha9euKZvN6te//rX29/dDmdvt27dDYFksFtVsNtVqtbSwsBDm0YH9cDjU3bt3w6YqZwOd3EHf4mzI5ubmMzLo4BUm27Og/hmu4ySAM53YDfoblxx4ixlP/9tBEdeNwZKz8vgJxsYP6+n2neasp/fHGduYlXa5dxmhL57xgAzwvvj/ztayjtlsNpScMd8AQCejKC2J5XrcfI4Lavxvf5+xu6/zz4MRsFme5fPx4EOQ4XH3ZD58TccF8oD32Odit5zl7/f7YY+UzxmllFwDPMbGbUhJ/CubJeP+nLZRe+EnKnpZg4MFZ8tYKECUNNqgI43Ov6UMoVarhTIH2t7eXgCxCApOACEG8HjE7KdWUIvsZ6hKo8fBwm7Mz88rkxk9IW04HAYmFWACqKYm2EEbpRZewwlQlkZMmJdqeP0rddCc2rG/vx+OMuN+XrIBAMYx4wQpB3CmgjXhRBPYGwe5zsL6ZjnW2ZXQDQFG2RlWGDGPuPkfYIbjAUx7YObsnjQyCETlBCQOQrvdk0e2NhqNkNHwOnQ3PvSbdZFGDw+hnhxw6oASg0Maulqtam1tTYuLiyHwYewYr06nE0o4hsOT0z52dnYSDAr6wNjpi7PnPlfuCFk7nKYzcQ6g3bkjD6wj3/UHC3FPZ678QToxCx6zs15CQYCHTjoD532L06vIKWNBHkqlkm7cuKHBYKBms6mHDx/q3r17iUfK0+JgIw4SmXN+Y2dihtaBuYMYZ6v8M/491iGTySTK0NCbwWCg9fX10F937s4i8r8Dxs8++yzYD0D4rVu3wilMFy5c0E9/+lMNBidHxLXb7fAURub3/PnzKhQKWlxc1M7OTrBNyJUDt2w2q0ajoePjY21vbz+jj0dHR8rlcvrpT3+qmZkZ3b17V3//93+v2dlZvfzyy3r//fe1ubmpg4MDbWxsSJJ2d3cDyMIGAAQHg0HYm8Jxc/4wJ5elXq+XqIvnPUoK8QsxCx+zfzGLGWcx3K4hS9gVB9TjmHy3I8hH3ACrLgMua/63g1pAPz/oJvLs38UXcNa538/1mO+5veZ/DxSQc3/d56tUKiX0L14DH1s8Bidy3Ncgb57hZbM4AVf8oDpf53j+xwFpB7PIfywjvOeygSxj1+ivs/exXIFlAOOOreiDZzNpHOUI5oKc8rOwM5mTpw5DOngw7b4dWeI4Xn+fLCU2LC4JOW2j9sJPVISB8rSDlExL+esucIA4gDIgxA0PhsrrSWk4QGezjo6OwlFkAK1erxecrNfYwihR3/fGG29oenpa3W5XrVYrHMTvoINxAMIp6SByhjF3p+xMH6BfUsKgpNPpxEY5nBg7cxFiokIUAcPipzZQDuCKyD1gI0mhswb0E7CJwnka0csxvAbL7+FMtbPHzsxPTEwEsA8wBMB7eQLr787JARBGiAwC8lOpVMKaU7/G5j765qdqxIafcgAHhOPGl0qlwkMiFhcXwzmnBEtzc3Oq1+shkJNOWHH0ho0e7nhxUswFxo37T0xMBJlgzghSYoDrwZ4HV4zXz5Qnc9Pv90NA6KwWbWJiImQ1HND5mqRSqcSDDAhofVOiA51xzoyGnDMWf4QzBn96elp7e3uh5h+G3dlBDxq4h6eyHTS47aH/seP1efEgwsEA94pBGIHW7OxsANAEoawrciAlmULvY9wXWGxk6enTpyH4n5qa0ve+9z09efJEX375Zdg4h21hD8lwONRXX30V7j09PR0eVMWJRdVqVeVyWTMzM1pdXdX6+rq++c1v6uDgQNvb22H/xNbWlq5fv65ut6vd3V1NT0/rpz/9qY6Pj/Xf/tt/0/r6ujKZTNhwLY02iLouskcjDrbcnjlJw5w54OT6pVJJ8/Pzevz4cQCUntlz9tVlAdYPGY/BsZdPOIDlPYDdOMbax/R1jLV/hnvxfUAufeL6HpC4DHNP7Pru7m74H99MRskZcT6DjGIL/dpxsEfDjkPEOW4YV1rhRIKPm8wpT/nls54tcbxwdHQU7O7zWPjYBo2bK18fB7PjAl2/nq+HpBDYeZDs4/frY8e5tgNx7L5nGZwko9TEiSVwBIQj2XlJoayQ8/vBSH4Cjm8sTqVSz2zofl77Q2ayX5iplkaLCyBGKOPJxtg2Go1w4kG73ZZ0ImjU/VI2ggJxL4TPAQiRWLFYTJzZKo02fcVp/VQqFWqNODLvwoULGg6H4RGxOG/uj0HgHGT+R/BgP2D5/L5+KgRg140y5SkYKaJuyj0wIpy9jBGBCXeg6w84cOMAGKSvlH4AGHgdRtXHII2OmPOoGxDuKUBPUTn77cEVn/Gjypyt43PugLyRxkdGSqVSqAmfmJhQq9XS9vZ24rxNruWyxNzhWIjoSbkzdq/rBtxhFEulUgDUqdTJGdSzs7OanZ1VKnVSu8aRgWQcMFJe2uHOxYML7ycBD8aS1JyzeYzNa2a5vjRymvzPvQBXnimgOaNVLpcDg8KaAtwAXgBDgjU2QjrIcObGwUEss+7QcKA3btzQ3Nycvvzyy7A2jIuNreiIH6nn6Veux+sO8rkeTtzLH5hD1+EY5Hp/PCBDJ8hkLS4uJphVn2/GHd/HGSWXbdYNJjCdTocHVQDif/3rX2t7e1u1Wk1XrlzR8vJyeLS6dPLAEY6mq1areuedd/Tqq6+GEzO474ULF7Szs6O1tTUtLy/r6tWrmpubC+fTP3z4UN1uV1euXEkcX3b79m11u119+OGH4ShJ39DszYNx1/s4eHGwGjfWnfcmJk6OW7t7925g4zw9HrPAXiaCbYznnvXjNQ9svQ/ODI8DbM7SxgDNm+sDgCkGca4Tz7tGDOgymZPyNOzM0dGRWq1WQl99Xv2+jNc/g3+AuGLusbO+xlyL6znZwRrD7EoKZA6kjweXyD0yR1lhnCngb9dN5t5fd8DLOL28ywklt2m+1h5cj8uWuTx4UO4kRLyGzKnvTYHYww5is/Ap9Al7xpzv7+8HPMB9+a7rBz7G/RRtnP6dtpP2QqAaQOPOIzaORI31ej0IAec0Hx0dBVDA6QVuVLxGipINN9J8BlDhf3uE7sCNzwAir169qomJCe3s7ITvIHCAAJTYNzcAAAGMfqqBg0T+RzkB0A5UiDJ99y1ODAa2UqkkIn8HBNwHEOlGk7nCOPKkREo/KGs5Pj554Mj29nai3w6qPcXjhgGwDeB04+D3Bug4wPPmkbczOB7lSwprks1mwxPpJIVTDdrtdkiBE5B4cOZAhvmjvz52PkPQVqvVAnjs9XqJx7cXCgWdPXtWMzMzIVOytbWlwWAQUtoeOOC43GjGQI4MCIBeGjHdXkPqjCtMuZ+d7ZkBSk98ky8y4sdR+hqgw6QBkWP0PpPJhBNOpOQZ3jDW7lhiQI08sT70h9c863L27Fl9+9vf1sbGRqiXXFxcDPNEqQNrTw0uQJb9BzSvt3TAEuuRM3Cx3I5jvxxMYaucUXQw8rzrxY6ZANwdsc/xuXPn9Gd/9meSThjf4XCo3/zmN+FM/eHw5HHrs7Ozun37tlZXV1UqlfT9739f3/nOd9RsNvU//sf/0Pe+9z29++67qlararVayuVyunr1qtbX1/XkyRNtbGzoiy++CGtw584dff7554m1KhQKQQ+azab+9m//NlyL8rZ4wzUNGyCNniDntpIWlw/F4Mb9CAwzT0nlc349AJxn4OhPzC470PK++xj4PON0MIosOJsat3FANmbefbzeZ7fdz2te+sQRsCsrK4kTrlgLwBWvcwgAfanX6wGgeeBOiaSDafrMunr/fU2cVZUUyhrS6fQzR8p6uQtYYBzQdxmJ+8PvOFNBc5Dv6+K6yXjcrtK/cQRBnJXBH3mLg0O+48cQ4lM8O8F6YaMh1WISzMk41x0nZFhT9+VeEvuPMdV/yO2fdU61152i+DwGkwio0+mE+mQWiMfpAmgRFmd0AYsePSG47jBzuVyCEXO2DQH2kpPJyUm9/vrrkhSebujgl9QILKFHdy7kRH5+wgd1o3wPIAWzjjPgGoAbACrlCtQruRFHWWDGvQ6ZuXOFJaJlrgDVKBaPjGeuvSyC+3F/NwoAaPoOkHKWh7l2gxD/7SlLZwqdAYh/53KjR4tPTU2p0+loeXlZzWYzlHywdpS4YOR9vtnE2e12E7WgXvKSTqdDuntyclL9/snRZGxY7fV6unbtWjhGbzg82UB2cHCgVqsV5i+Xy4V5BtT6PdAL5Adw6EwE8hODBr6PcWPz6XA4ekgHBh95ccfj2Qfmnvuhy+VyOXyfgKbb7Qb2nTF68OT1+J4C9z772sfglMY5xMViUfPz89ra2tLq6qry+bwajUYIGljHXq8XTm0g4GC8BNbIgjNg9NPBPnrMnLn98eCQuXIw7YDB5S9O149z7q4P/hr9cceXz+d17tw5/at/9a/0zjvv6O7du/r444/16NGjcL96va7Z2VkNBgP96le/Urfb1fT0tP79v//3eu+993T79m39/ve/Vzab1aVLl3T+/HllMhmtra2pVCrp8PAwPKJ9cnJS165d0w9+8IPAuFM/Tet0Orp//37YGMx8Pi+lP44ZBCh5VoQ5cxn06zOfXiaQSqXCHoFxAYwH6dzH++e/kROXVf/f2WYCJgfjXNs3/nqwFusA8+Jz5DrkAUJMasXkSixr/v7BwYEePHgQxoO8oiteP81JTBx3KElXrlzRnTt3wnXZDwTD74BwHCHm8+2+GuDH+Pr90Yk0Lg8+duYSXPE8u+I2xxlln6d47pyhdpIoLgVhvdyXud0GO6DP2CgPvrwfzJ8HTfE6uk9BLjwrR//I+Hj5qTR6uI6Xj9JX9zf9/mivFBkRDzROW7K9MKhGMXDkMVD0h67E7wMeeaqhdBI9o6hEUFwjZn24Zzab1e7ubmBqpfGPV81mT3aqzszM6MqVKxoMBlpbW3vmEH5ptHkS0OWKkE6PdurzNwDGa7a5vzMq7Fj29IunaWBAa7VaAM8YMRSHoAO2P1ZiZ2goVcjlcuHJjrCV3niiIAfKu2NxhoVgxjeNwdhiBJ1VAbQQTMACU0JAP9zAOcPtYBHmo1ar6ezZs+HxzSsrK2HD0/7+fkI2WHfml3Qgv1kvB1oeKDD3zPvVq1c1NTWlpaWlwO5zJjl1+7C2yBWG1ctaYqfMbz/+i/Qu/fd6W3civt6FQiExDphr6dmHDDiL7AwRTi2VOimTQj/Z5Ml6Ejxy5Bs6QGkS8o2D9ObM9Ti2ipbP53XlyhXNzc2FTATzUKlUQu0gD1RCTtjsjKNhM6jLAmvh/fPNxL1eL+iYgwIH1bEj5nswq8gfQY7XD8csKw7VmVoHVQDMOJPDerdaLf3ud78LD5ny+dza2tL6+nro23e/+1396Ec/UqVS0X/5L/9FP/vZz7S7u6szZ86o2Wzqk08+0d27d7WysqLz589rZ2dHT548UTZ7sjnx4sWLqtVq+v3vfx/66aUcPCArZuM8UGD9Y2KGufC0P4CdQAid4+ErDqiwO8y5P9gpZpYJGrHRPmfMt4M7Xwtsk+uNpGfAkb/vINBr9cdl7+J+xmy53zMOzOLP+WdiO+D34z7YWnwFx3cSoKTT6fC72+3q1q1bibn1Y1ElhQCHbDTrh9+lf+glvksalfs5ARTLEWNzxj8GqF83F9yTNi74Yn48aMb2+X2QM8bGHPOZuGRlHICPM3hx4OmvuYxh9+Mx0x/8nQf7yBW2CdnxQMHnzX2qs9WnbXx74XCDI92kUSrSDT+C5Scz+FFoOEKckTQ6gxqAwTVcqNPp0eNNibSd3XFnBEs3PT2tmZkZVSoVNZtNHR4ehqNhHAQ6+0tfaW5oEUYHEAis16E5+EQAYcMRdMbFY83defAEJsZKKtvTZ9yb+0kK5R5scKxWq4mnAZIS3tnZUavVCsEL/cWwMZfULDvLSFDjCkqNKk+4osyH+WQOYiaF7/M3BpmAgDKY6elp1Wo1bW9va2NjQxsbG+GJhHzWa/mQTcpqYE/j9Bh9x4ACbFOplKrVqs6cOaN+vx82sFL6dHR0pM3NzRAMIrOsa7fbDXV+zg640/S6XX+UO0DDS3v4nqcYYZBcBvms64Mz9F6aJI0ApxvOYrEYQIkfrcTYYAIBoF6Lzv29D64TyI0zdWSdkFl09+nTp0FuYRad1cf2sIkynT6p2ad8SlIimKTPXmYSlz2R+XIZdSAcAzDG5NksAmFnhGKQGc+LN7dNzmZhX7BHW1tb+sUvfqFXXnlFu7u72tzcDJ/1QIyNej/5yU+USqX0H//jf9SjR48CqGy32/rrv/5rTUxM6Lvf/a7ee+89/fznP9cXX3yhwWAQbP3a2ppu3ryZcL4ONh1kOEvmAMjnwoMcbA/9Zg2RFwAWZ7Wjg7zHZuVUKpU4Vcnn0n0Jcs9aj8sg0BzQ+WsOavx19x8xmPXMxT+ljWOzxwUCcYvHETOOAENsFnPi9cgeKGAzx80P13V2mQzp3t5eorQGf+MMLtlFL9fDD8R7PRgb/fUMo+MEZ27j4MMBcjyHrvNgGD+ylL1fkDBxc3l3djgOkH0d/Tsut1zPZYisIVlTnx8PVjxoie0B+hI/g8ADDLfhbueQBcikr2vj5PIPpb1wTTWTKo1KE8a1wWCQKLHAMFGb5Wwd9T3U6eJAqO/hb5qnryWFzXNE2ADLRqMh6aTeEEXzgABFh+XxmjGUOzaC8cNCmBc3mM6CA1DGpXrYQCiNgpVer6dmszn2+Ddn/bkHfeXJg6TIYZMB5ICRg4ODMB/0IQ5iYGyp2ZKSBgfjDKtEWc/m5qZSqVQ4GN7rxlFuZ3E9GHCwy/xWKhVduHBBhUIhbEZcW1vTxsZGqJd11gkwlU4/+yhyxsFnMHZ++gwgBCAijTa/plIptVqt8HAJDIsbJX8oEIGap7aZh9goxwEbIB0ngEzhTACe7mA9qGQdyUSwARbZc+eCwURvAC+A51arFTbrIp8Add+MiBPCcbhBd6fizEij0dD3vvc9Xb9+XVNTU5qcnNTa2pq++uorPXz4MAFcyCQwXmo6efInQMvL0BxII1/Mgx9Byekhblfi+cXhuYMiJepBt4NF1tvnIg5AYjvCPZwFdfDumart7W29//77oX+eKZiZmQlj2tra0n/6T/9J6+vr2tzcDGuFfa1UKvrTP/1TnTt3Tvfu3dP29naY65mZGZ09e1bLy8taXl6WpHDcZxz0sVaub+Ma943LzlgXPzGJmmxq4b1UEL3yPRQ+j8y9zylAEjvqfsSBFLLDWPyzvgeD7zlTHLPPXmLicuR6+481B1r8DYDN5U4e5sXekjgAoLldp3wHoAjj6+PwMXjQ6PLom6KZP0nhqFkCYy+F4z79fj8QCu5v0S8P4PA5+Pbj4+NQptZsNsP9x7HZ4+YtBtK8zg/2nLES4GGLkCGvWXbs4AGRA+N4Xr3553wcjJ/+kh30ezBffk2/HjYaG0hQ4mvkcsN1IVa8JPb0OL2vby98pJ5HvRgaB5U4MBaM94iQSQfBDOGcMY5uNL0elxZHkwBTHNvs7KwqlUpIR5ISjNkF+oiTwtm60XUgT5kEAu9gG8EHZDqLzT3dOKTT6TBWxp5KpQLD5eDez9qMS02YX8B0v99XpVJRqVQKzOrx8XEoTdjb20uk0/jxpyr6jmIMKfd0phGFO3PmjA4ODrS+vp4IWFi/OIPhUS/r6aBkf39ftVpN169f1/T0tIbDoVqtlhYXF7W0tKS1tbVgFGCzib4xwgQQvunCnb+nQJ3t4qSaarWqQqEQrnd0dJSo1+v3+yE1yvx5JsLZaZgYZIisDOwcOuFryz1cvvxoQjIBgPa41IKsDwGg66hnaPhdrVY1HI7S5gAU2G1AjusrOgAgd3bLA9h0Oh0A0fT0dPh/ampKP/rRj/Td735X165d0+HhoT799NNENgkdYIxeG05AxHx6JsHBs685a+2bjP04PrcRLp/8H7MvbqdidtKzBTFIcdnz1+M9JPyOGa50+qTuf2tr6xkHnk6nNTs7q+vXr4d9LZcvX9Yrr7yiL7/8Uru7u+p0OsrlcpqZmQn7V27fvq1PP/1U9Xpdm5ub2t/fDwHmw4cPtbKy8kyA4g6f+8fz5fPogbSz8di2OPBABgeDgarV6jPsGOyhbxiLAa4DYOYuzlrRJ4Ik1jbOqPha+LrF/sX751mx+Dt8hubXjckM5NfnJpPJJGrI47Il2E1JoRRyamoqcRoR88v1nATwYBifjQ1xu80YsAW9Xi9xpj1lZLF8A5IhtzwT6/MEgYOs8TwL9vjEoBK5YvzxujigZiz4buQUptmz6sylHz/H/Vw24mN0fcz8zXh433XHA3fP+vGel8c6AI/lEf/N3Hh5IGMj4OOgBP8u/0PK0F/m7bSNby9c/uFsjjMOnt6KDQyMIoJ4fHysdrudMADuyAG8zqhJScGD0fafqakp5XI5ra6uajAYhKeu9fv9xCkUpI4BNzBxuVwubIqIC/dhplAON3IogBtwF+rYCHltOCUZXotOn5wpcXCNUFM7jXOBoSZNynFjnU4nsNSSEoae/rBe7EiGEWWMKB9po5mZGc3OzqrT6YSzap3Bj1NZHi2zfig6wO3o6EgvvfSSrl69Gq6zvLwcnsK2vb0dyhJgpzAOHtA1m83wvxtOxu2ymUqlwqbOer0eWHYehMKZpwQmPJKetSIzMBwOw9FubpSQaRwKzCMy486Q93wOcTzMfafTCVmIeF4Hg9E558hwnCFBjgluyuWyMplMOLe2XC4HZwSYd4YCw0zfkHv0Eof65ptvan5+Xr/61a9Uq9X0V3/1V+GBE/V6XZOTk4Fd++yzz/Sb3/wmPMk0lUppcnJSx8cnZ6fHTsqzZTAuHli5jro9InAhAAJcj8sK4YhoMePkDGQ6nQ52EeDhTtQDLJe7ccx0zJDzGnaGMisPbCWpVqsF5vKVV17R/fv31el09MYbb+jy5cshJT8YDMIG0FQqpSdPnmhlZUVbW1uan5/XwsJCOJownU6H0hLknbUfl31gbuJgwVlDD0KwMQAaD959wzOEQ1yag/474I2zgqxjDFrideVayAOfj8f4dSy8633sm7gOn/P+uQyMY5ljueF1gnt/D33wGuZ8Pq/Z2dmwHwE/BFsdkzWsL59BTnO5XDhmFBlhblhHwCd2xgFivV5PHNVI8OvA3XXDMw4eUDgG8Lp+dJ3nFsQBmttAD1r9cw5mkVPPUvB/DHoB9GR48IXxGsbf8fHFDbmJZYw5BW/5+FwOuSdzy/uczMJnWJMYnKN/vp/M5+y0jW8vfE416XBnaz3Cjx1PtVoNtVooUbvdTqQxed2NzjgFcEDG9zhLt1qtand3V+vr65qYmNDk5GQiZQNQlZLHDzlQ8GAhPukCEEd6EiDskayUPFqJsfB/zIRwbdJZOAAvW5CSDBZzS584vrBWq4WxwfYDsCgD4H4YTYAB1yRlDojzoIBd/ZzrPBwOwxFyzBkOk+sxH95ipUyn05qZmVG329XFixd17tw5ZTInj2l/9OiRFhYWtL29HZwHtbfsXHYH5U7Gyy24F+ysM8znzp1TqVTS7u6u0um02u12AtzGYJLPsZ6VSkWDwUl6cxzL4qU6bogAd7GTpm/ICiw4GR4O7kfeisWier2T0y/YiEimhT74dZkzwBmgnlSws1ewFB5gOsPhDoex12o1/fmf/7kODg70wQcfqFAo6K/+6q9UKpX0/vvv6y//8i919epVffjhh/rd736nhYWFoGcAw0wmE+rVGaPLl+s1PzB27rywKc7SYKMGg1HtcxwIjWOlkVV3+C4jbmfQM/TZZd6BQuyYnE2PP8drkAHT09P6zne+o4mJibChcGtrS8ViUb/73e/COJeWllQoFPT666+HoJu5uHbtWqhdd/DnbCQBqtffc+0YiLi8+Xhjffdxx8CAQMXBLnM7rvbZbY//7eVVvr7+N+uJjXN2WtIzMjNOJp4nJy4rntV1+XEg7Ww4zYkcWkyq8D5HJ968eVPZbFbvvPOObt68qfX1dc3Ozury5cva29vT3t5eOH60XC6r3W4nCJ14fekrJ2h5Bgi/RGaPPSwOkL2sETDnjPq4TCJz4feCBY8DCmTTg5RxdeFScg9PLEPekJOYkOCa/hkH3C7rsYz7/w7K4/syfil5lr33gT6DGeK5Y6zYDd8g62PGZ7pcuX+U9Aygxk7EFQSnbdReCFSTeokVO95h7ywOj9HEIezu7oZdp878Oqjk2jQXGtg10tY8cXBlZUV7e3thc56kBAuFgYzrgjw9hhLhoLm3O2uUCAPhTiFWEgeZGG7qEWGNAEw4eY9a5+bmtLq6GuYFJXGQPzc3Fw7x56l9MA+Ufvg9HVD7vMDywVAxZpjrbrcbTgFB0QgAmAtKbWJldmXFMLC5qFgs6vDwUOVyWWfOnFEul9Pa2ppu376tzc1NNZtNdTodSQoPxOGEDuYb+eKsXq8vdUaZMox0Oh1OXBkMTh537aUqbNbzB/L4E6owXNRO7+zshMAQB+oAiiAIthTn7SAMfYhPkODevMf3mFvfjOmZDZ9zagJh45ANxkPGI5VKBVad2m5PuftG0Dityv0Gg4F+/vOfB5krFAr6/e9/r+3tbU1NTenJkydaWlrSL3/5S21vbwc5y2aziRNGBoNBANmc+IDO4tCZR38PucT40zdnmMalhd3xxmA3dsQEFqwd4AO9jRkdd+z+v2csYnCFzDLXBLOSdP78ef30pz/V48eP9ctf/jI8vXAwOCmT+OM//mNtbGxoZWVFb7/9tl566SUdHx/rwYMHgaVfWloKNpMSimazqXa7/cxc0Q8PZLDR7pT9t9s8nz/3Bf1+P7Bk0ujcepy9l+k4m+z7RdxeEkTFYDhOVXuA6WsSM5vOssbjHAemHeA8D4jFfoRrxoyjB74EzhBTHhz4foKVlRUNhycPS7p37562traCf+ZULUoA0XX2hmAnYKYpNeNelHX4STzYPJ77QFkG8k2W7fj4OJTjxfMaM8boTwwgveTIg14vwfBSB78WsuhgMV4X7hOXJsaAn/FwbfoWEzYuM26HxtkItw/PkyuXvTjgcTlynxPbZ0jIwWCQ2GDua+aZJnTc9yp5ievXtXHj+ENpL8xUu7H1Y8xoGAKckx8t1W63E+c8erTnhtfBAMqCENVqtfDT7XbVbDZDvaufHuBpQE8Lks5ASblnPC7fnAD7AatHMBCneZkjjDQAFSWkLIMD7ovFYjhnOXb4w+FQa2trkk5S8gALgK0knTlzRlNTUyqXy6F/nBjS6/VCAONAGWVxheJ6zkJ66cHR0ZEuXbqkUqmk5eXlsHYAz1Qqldjw5+l6mrNvbH4CQE1PT+vMmTMaDk8emfzVV1+FgIAj82BuYT7i8pJms5k4EcAdFdkM5gCnzHz4Zp1erxecz3A4esQuzp+ac98Jznx7PTyyi+HGkHEP7yt6449eJytCuQvrwHp5AOVAPnZMMWh2gMJaIx9kNLzUyAGkpETwy/zHLJE/sOng4ECfffaZ8vm8Wq2Wms1m0CuMdS6XC6VXzWZTqdQJI++BHc6b4BHmy5kuH7uDFy9ZkZR48IHPmYOtGBB7MMq9XMb8Gp5BQeac0XLbgc1gfkmrA1oODw/DEzt5auilS5f0xRdf6De/+U04AadarepP/uRPNDs7q263qzt37ujdd9/VtWvXtL29rbt374aHv2BvMpmM6vW6crmcdnd3nzmu07NV6IfbjZjlcplxW+gM4WAwyvAgJ6wjcu+ZIC8t9HNyPYhijdE1B6zOrNG3mI1zQOeAYlygwGdi28ZrLkPIq9sDAmvXSxhXJ5kGg4HK5bKmpqb06quv6uHDh9rY2NBrr72mubk57ezs6MGDB1pbWwv2jWMo33///TBf5XJZhUJB29vbwX8RqHCQAM1P5vK1xYfhf7BJhUJB+/v74SSpdDodHvzC+CEmPNvq/j8uH8IWOdvNnHtpFfPn+gQgdL/tazJubV1+vewlXncvMSsUCoHdR06RWS83oy/jgvc4iIhxxLgW2yS3vZ5d8XljLbH37i9cB8bZIy81k5LZXzDDaXu2vXBNNQYW4UKQMRw8CQ5n2O/3QxkC0SsgxFlplNePw/INUKVSSVNTU6pUKup0OuGpdfv7+4naPEkJhwnQAfSgaAAK2BYUk5IKDAIAg3pWWBZ3Pu5kJSV2VQPGGBvlC76BzFP1KCtGbGJiIjAAlD30ej3V63WdO3cubD7hQSgYQGqpXWGdnUbRMI6wkHy+Xq8rn8/r4OBAc3NzqlarWl1dVSaTUbVaDWlod1qeoXBHi7KWSiVNT09rampKq6uryuVyunLligqFgtbW1vTll19qbW0tcVKJR+TMo7OuksJxibHDZzyAcOQWkOqsLGvIg4GQHeoP6T/zhAw4iMBIOZDyGkNOYUHeMLr0h+PhhsPRw1R4opg/MZIHsGDU/QE+znKhQ4VCQYVCIQSUZDqoY8fBArY8hejlSm7MmS+/J+sjnbD4MzMzqtVqQZb9KWselEgKT2eEnYeBQ7elUZ17bF/iYB8Z9EwLjodABLbMwRAOlfekZDmbH9c4jq3xwBG7OI7RiQNOB5/lcll/9Ed/pDfeeEODwUB///d/L0m6ePGiZmdn9dvf/lY/+9nPNDU1FUiGiYkJvfbaa7p+/boWFha0u7ur+fl53blzR1988YWuXbume/fuJbJEzG2/f3JCEQ7UgwgPBry8LQYhkp6xicwr340DFuwPgTIlV14+xt4OB0TO5mPb/b0YmLhd9vmn7xAmnlVz5s/lg/GMW8M4UPKxI5uVSiWcWjQ/P6933nlHn376qdrttgqFgqampsLzAyYmJtRoNFQul8ODpCYnJ8Om0mKxGIgEgqqHDx+GfSfYPWwHgJpMhMu2j8ntCAENwQ1Zu3K5HLJEvFatVsOaIMeZzMleDa+x9hOCWFv3heie2xdfU9fHOEjCl8aEReyrYyDvv339vBzKGWyylv6ZuNREStb8Y7dcnpnzWO5ieRsnj95fdPB5GTaCt4ODg2f0IbbfDrQlJWwnNiAmUk9bsr3ww19gmADUkoKh5uEMkhIGO37yoTSKqIjOPUr0TQ/SSboTw7u5uRmUCVbWAVa8YQhj68fjxUwM9yVV5oAKVsTLRRgvJRMoFAAJtpGTNpx9RKlI2TN/KB6KQxkABqdcLodziefn5/X666+HcoZWq6XHjx8H9pKMAODC145xAQ5ROGeDON6Mp/yxMQUnDuvvZT/M37iIO5VKqdFo6Ny5c+r3+1pdXVWj0dCFCxfU6/V0+/Zt3b17VwcHB9rd3Q3MBgY2mz05GYZNMl7/yXhZl5ht8COyHDhQew3wzWQygXFxZgsnw0ZbB29u1D26dwAJG0PJkTOmsNPIjcuFGzs/scXrgGG8vfYO3SNt7E89BJQyh51OJ/y4U3BWFtn096Rnjyvr9/tqNBohW/TKK6/o6tWrGg6Hevr0qdbW1sK8+qYwP3kjlUqFIA9mHSDs7GMcMCNjBFGMDx1l3pyJp8/OojrQdCfjAXjMinsqm887U8tvXnN9x2bAgHGfarWqlZUVffDBB9re3lY+n9fq6mo4Pebb3/623nnnHX311Ve6ffu2XnnlFVUqFW1ubmo4HOrx48c6OjoKG4iXl5fDWD0YkUbn+vf7/ZBtirMdzJfXUmJbx+l6DHQcvPk8oRv875+hX9hyZ6PxI/TVZSAGGTFQoW8eVPHb7a+Dhhh00Lyv/roDf+mEZGk0GkqnT44vrFQqOn/+vG7duqXFxUVls9nAAGMj+v2+lpaWlE6nde/ePaVSJzXQm5uboRyOvrbbbf36178O/+PnKL2EZGH+nEDxmlnm0+eRwAf9Ys+FH0EJUCZ7BOmAfXK7gf3Ed8U1yvzENo3XnRii377/ye0W4/A6/XhTpq8xc+Lr6DiBfuETkQkvASPo9MyEl5g+T47i11y+XCddZt3+xuQAvyFo4oMXaL6+BCVO+rj+8DlKA0/b+PbCTDUOD/BJSsgfdCKNnkzo6SZSuJ5GlUYlI7zG/0S7g8HJRrDt7e1wDQQNoColn1QHCwET6dEqTteV3Y//AgQhYBh4f1oURgHwh6IhpCg4x76REut2uwE0IsTOrHMvlN9ZxU6no6mpKb3++uuhzq7b7erBgwfhDGXO3PY6WtI+HigwZp8LwP/ExEQAqoAv1jFm6egv94ENpWUymfAUTemE7b1w4YLy+bwWFhZC7TTsJCeUeElKLpdTrVYLTC4MeqvVStRQssbIFSwYzAnA9ejoSPV6XcfHx9rY2AjrwLp4sIQ8MqdueGE9fEMNRswDGEAJ8gZ4Yr55DXnGqFF24nItKQGu3QE6M1Gr1UItpa8XwbCXYzmzzRgIhJ1hdEDEb+blrbfe0l/8xV9obW1Nv/71r7WwsKD19fWEM/TyFZwyegEIAfQ5y4c8xVkuQLgDqTg4BwD4ed303fXN1wZH4kG3lNxABKBwJ+yfc8DuTpHPeZrWWeGjoyP9zd/8TQjkyEz0+yebMX/yk5/ovffe09OnT3X//n01m82wIa1QKGhlZUXdbjdR8oMuSEnmOJMZHZfpsuWsIf3DhjhrhrzGTt3BAXbFHTTALpYnZyb9PjRkwe0l33f5j4MiD3YdlDnYj0mNeD5o8ThjQC2d+IdGoxFkvF6v69q1a8rnT57Gur29rU8++SRRruL1ylyTo2GRFU4X4jv0x0ESzcfmj2unvIJ1QB757YFQqVTSzMxMOFWL8ixnX+OAHb3CL3r204NS31zqQSi+3Oc6DuB9zbHBTtKRUYptsAdO3rABXiY5DqgSIMQBYNxfz4B76YXLWgxaXRewh3w+tjN8Lp4f/wzEyvM2rvvnSqVSqCaQRs+uYD3oD3MNefro0aNY7E6b/hkPf/FTF6jlQiBQJJQKVihmct3YOwPEIfaAjW63Gx4+QRqpWCwGxQCYcD9vKJJHia50rhAIjh8+78yOA6NMJhNqcjFWgGl+AOIczyaNjvFjk6anoZizVCoVGPnhcBgAJArK2c3tdjsEDKR7AQ0xAPJUl4OIUqkUNmTClGWzWU1OTgZQw6kLW1tbCefrtW0YTWeR6AMK3O12tbu7q3PnzmlyclKdTke3b9/WvXv3QkkEc0+2gGvyPw5penpa+/v72tzcDONzeYC5IBipVCqq1WohPXl4eKhCoRBOoCmXy888mpo5Y04cSMepUwdH7pCZD+SG+XdA7Zv+ABp+6os7I+aczIkzyzEoJCDxs7qReWeQ3NECtjHADiy9n9VqVf1+P/FIanTwF7/4hZaXl8PRiKlUSmtra2FzswfezWZTg8EgBFu+4chLDjwrAmPtwSJ9lpIlHO5QeBKrg0SYWZgsshkEUj7v7pQcjLP+zIN/xt+juXN0UEkmjc9kMhm9/vrrevnll/Xo0aPANL7yyit69dVX9dvf/lYLCwtqt9tBRgkwySLSWJ+4f8xBDGxj5+0ZAcaOvmE7cMJxkMd4+LyDN97j864jXtLh7Jrf278fs5fxnMcssr/H+5498Bb/H8+V/41+z8/Phw1tlOZ9/vnn4d5HR0eqVquan59Xs9nU3NxcKNcrlUqam5vT/Py8crmc/u7v/k4bGxvP9MODnnH940m02DW3yV56KY0CUWm0YZ/55ChR/CClFfilWD7QP880xKDQWWbkwtcuDlLiIM3X8ujoKOivB6f8+LzF/9MHvsv947mBKKHhe/3z0ujx6q4n2GRnmD3LEzPO/B8Hl7Ge+neZW8czqVQqEDI+t/H89nq9YDsIUjj9zNl4J1Ah/b6ujdObP5T2wqAawQHo8LorHUfCAWpxzOVyOfFIZhSdMg4MESxkqVQK18Mps/AwfM4auTDGxsRTQPv7+4mIHYDLdV0ISY/TP5wYm+xw8H4KCs4Z5ST9wmse6ccA2lNoGK5Go6GLFy+GVF4mk9HOzo6ePn0a6uT8eD9JiSCAkgfKDYrFYuLRsNyr0WiEa6RSqbARNGaemUc3Bs4gSEpE6OfOndO1a9fUaDS0tramzz//XEtLSyE6Zp6oH+ba1DDTbx7Isrq6mjDEyFetVgtAvNFoqFgsBpbfSyUA0YPBIJQnxee9evkIwMHrqN2YxmAXp8Ra0kcH6A6kmDMP0qj9Pj4ePYyF+YqNLWwtwBSZ8LIeZ58Bic5OYyR5D12HiYTBJ4vha97v9/XgwQPNzMzowoULOnPmjLLZrL788stQQjQ9PZ146ls2mw0yyrpzHwdw6ImfMOOy60DRgQE60G63E7XYzAWfcRAAAEWm/UE3BCf083mABlbH7RHyxD1YP4IF7sNpOMxjr9fTt7/9ba2trWlhYUGXLl3S8vKyhsOTTczZbFbXrl3T5OSkHj58mACrfl/PKiInrD9pXGf+xs1rzFQzDme5PRiOATb2EYDlIGs4HCau7b+Z+7gcIF5zB9quU6yJgxG/r8vZ17UY6DlQQq/7/b6mpqZ07do1PXjwQP/iX/wLlUolLS4uam9vT4uLizo6OtIrr7yiYrGoarWqqakp7e7u6mc/+5kymYwajYYmJye1vb2tR48eaX9/X+VyObGpcByQioPgarUaHoAGYeVZSuTYg333g5S7OamED8NPujxj732zKf3yjB/A1n2gg9jYj8fz7q95wCUpAfQcrMZBGo1xxHX7vIevo4/0M51OPlFSUsJu8DmXka+TJR/XOEZ6HBv+vIbfcgbf58yvF2ejXNedLOVEmH4/uf/itI1vL1z+4ZvbcDQYXBghHDdKWalUVK1WA3OLIrtA7+3thRoyaQRMJYVrOBuH04Mxl0ZnP+OYAb1eV+mpR67FiRI8glo6UURO02DcgAqAMEoJ8EJAMSKwXHHZhTOZg8EgsLCUy3A/wC/jI9hIp08eyjAYDMLjuN24ANb8mDRJiU2YsNXMAw/KwYjs7+8nWIpYsVHAcUaDe585cyZsqCyVSnry5Ik+//xzra6uhpNJvAyCsguAgDRitdhUR7mGO1OyG+n0Sc3i2bNnQ78mJiYCG45cesDHE/wc5MJiMu84Kk/beSAHKHIQBoCIQYWzCRg8N858x9e91+uFchxnDpgnAhDkod/vh9NOJIXAMA6OYCKQUxwFa8F93Am2Wi0dHx9renpa9Xpd58+fD78bjYYWFxdVLBb1xRdfqNPpKJVKaWpqKgTI0smGW9g6D0i4X5zGdOaEteazvoeC0w4IIPb29hK17TFw9NfdUbvOoqfcL2a7YmflqXiCA7c30uiJlzywiXn68Y9/rHK5rC+++CLo6vvvv692u60333wzHIe2sLCgvb09VSoVTUxM6OnTp4lNoOgN/3vJSyqVShxtxhzTX2ehmQ9kGaAcA1fsnQNsZMwDSXfm3N/nlPVxWxlnbsY1B8TxHMTgkzHFwP15wCcG+vgMvy/3uHbtml577TVtbW1pdnZWrVZLT5480dmzZ5VOn2wYREcGg4HOnz+vra0t3bp1K9TDr6ysJNaMoxJj0D+uz5QlptPp8HA1B5wOon08Dow5mYVMMevuvgkf6Bu8AVrObrrM4x9jJtf7Mm6c4+TGy+G8715ax5jjmn3+hjRw8E+jz9yHPnKfGJg6ceEB5jhdiYG933vcZzwwjz/jc+KkFtgJciR+38lE/x9dx9cylxA1lOQeHx8HXHTanm3/rNM/qNdk4djIRwof48tDWTjWRxpFR/l8XtPT0+p0OuEpXi741O10Oh1VKpVE5BgzJH4eNQqAkgFeYPg4kq5cLqtWqwWBxQghQAAxaWQc+I3RR5n8b+aIuSA95jVk3BPFg63CueJQ6Wu1WtXGxkZgK5w5oM5LGhlO2FrmjLVgJzkGkHOa2R0OIwLQ9I11gG1vjI33SaPBOgM0W62WvvzySz148EDtdjsEBKVSKaxtnB50ZpFUvp9yQVBAH4vFos6cOaNyuaxWq6VWq6WpqakEYCV7EYMb5o1yGOQXJ+Cg04GHpMRaeIqaa0gKAB2GA9bbmW+Xa2eKdnZ2wokV0giMA1Zg8nlIC/eDhXedRDaRO2qmkXHm2l/DSZLBSaVSeumll/Rv/+2/Vbvd1vz8vGq1mu7cuaMPP/xQi4uLmp2d1cHBQTjhA0PPUYTHx8cqFAohAGed3Yk5E+olYe5UfMMh8s0mmvgYLwKG2HHHLJUDai/7oF/eYqA2ji31GlHkg6Dk3/ybf6NLly7pP//n/6z5+Xm9/PLLQfd+9atfhYC4WCxqYWEhcW468sQmRGRoHIh3IOVMoc+7gx2fY8CCXws5ctafcfI+8xbX1CJ3fnwb1/P36B/ZuRhMuIz6uowLmGIA6nrm14zbOKBNCRObh8kscLTc//yf/1ODwUD1el137twJrPHW1paOjo7UarWCDPLgo1huvFHmxrq6PHqA7qn5+GEuHrTE+u6gjWwptgO7CLj26/n1vR6Z5vckKENWnHyI1zXOgsZg2N9z0oM5mJqa0pkzZ/TkyZNnzq73ccdZOK6Dn4uzZdw3ZnS9lAM98Xngs+73x4HtceNjXvw78XVoMXjGHsRz63qRyWQCHvDyS0hSb5T9kTU8Zaqf3164/IMj7fgfpXNFKxQKwcn5KSEI7MTEhOr1ulqtlh4+fBiMJsaOo+M2NzfDZh1ptNmGelh/8Iin67kXRoiImhM0arVaAPnD4TBslBsOh6EcIE43ojC+MQ0n4CcVeATOvQHhzgwxXgAlO7UZL4B1YmJCGxsbiRpxzwQ4Q8Q89Xq9RBquWq2qWq2GTQk8NGcwONkACmMNE+bgVXq25pPX3GjyQJ5MZnTkHgb45s2bWllZCbXg1PDiBJgDMgnOIDAXLoNsnGm1WoHlr9Vqqtfr4YExfj46TqHT6YR6MAyurxWyw0k2yKuvn6cCvTTAS3mcaWCc9JM6Rd8058CFOfGTKuiTszLOJvO0TEBxv99/5ug4Z2aQLRwH+oJDo/E9dIU9D2fOnNHy8rKmpqZ07949ra+vh2BJkpaWlkKWgM3GLjNs1pVGG1LjWm765CfUSKNsFP1nLvwBGYzdHc84gMR79Mvr1B0kekaB9YptQwwQ/NoOrMkK/fEf/7Fef/31sFF3e3tb9+7dUz6fDyccwc4PBoPwECgPfAhonRGXlAhumTP6zLw4G+f9xjbFawGI8GyHj9+ZZq6PDHAfZ/zHgXC3M3FzwOH9cnDsn3GQEYNoBx7Pu1d8X+bAv1csFgMj3Wq1EsALXSCryLXiTMw4QP11/Y8DJcgF9jo1m81ERgu7AphFZjzYoM8w0G6L46CEz8JKxwGTNNqT4ptYAd+xzXPZiQG1l6j4XDmohaTguRXdblf3798Pe5fiNfX9T9Ioq8u8ug1w5pn+eabCdSRmlseB4XEyNs5Gxf329Y+v4zbp6wKmONj0DJJjN2n0kC9ffzL33GtcOdZpO2kvDKphfREsGgwgdYGwPqQNUPpcLhcA9dLSUjjOS1Jgo46Pj9VsNp/ZYMMZnn5Enwu6p8tdWABJk5OTYWOUpMDSAlL9+D9ptFMape33++F8bPrr9/fyAj9yCOV0Q+DsULfbDfPT6XRULpc1OzsbQDkMKyyagyUcptczAsyk0VMIs9lsqOFzNoJ1jE+DkEYK7Qw4hg4lpayBOlnS2TBuW1tb4TzVbDYb5MMZUdgo/ubEDgfysD8XLlxQOp3Wo0ePVCwWQ+1Xo9FQq9UKTzck4KF+Omal/ThDN8wwxJxjzWkkyLiXBOCkYEyZM2m0q9qzNO12O5TreCoUvaBWOJVKhZIUDGIcaLFh188KT6VGD+Rxw+6BZpziRyYBt+4EMb6sF2u+vb2tnZ2dRMDc7/cTjxNnXjOZTMgAHB4ehid8DofDkIHw2j/0Avn1lLJnfgALOG/ppMyFbE5snxhXnBWhrw5EGI871BhYuSOMwefzgB9zTanO7du3dfv2bWWz2SAbzLufzONOHCYpnT4pPYs3JztTtr+/HzI5HpRhZ2GzCFLon2fekHXfnMWYYhvhssp7nn1gfmJQ+XUlHs6WjwuQvo7Vi7MSMTj0a8RgxQEOOsRmZ86xlxQ2ifs1sZMOHCF1nlc7Ho/P+82YYlBNwEXwxSPlfZzuG5Arz2D6xkTWPj6m06/lvsXlLg6GpdEmci/Z/KcENL5+Pq8xmEbOsNlk6yDa4rX2zc2My/9GVt12+Hfd1sVrNK45OI9l1sE0PtXfj2XEdcBll/dcRsaB8nhuvSwmzryi7xBBBEPYYY7rPW3j2ws/pnx/fz8YYhaZOuVarRZYw2azmYhAB4NBYDPX1ta0tbUVQBzKzuf8YR1EzQ4WYsYXBXOjz70BNjyF0aPfvb097e3thY1dXgfOSRMocC538vhs7gfzh1HxujNAsDsKnJizSYyvWCwGQ0YJgx8BBLj2MgNplJ5HCdiYh6Gs1+thYyjrwn07nU4Ak1tbW+EcU0/DMxafby8fYDMdDoS6dFKhbJQh2KJ2HUXme7DEvgubPsCs1Ot1zczMBMM2MzOTeLLh+vq6tra2JJ0EQ3t7ewGcszmOOc3n8zp//nwAesifP54eQOJzwo/XyHttNQYrlzt5GAMPQgLQE2zEcwmTHbNCnmJkXrgH8sIc+gZEmhtvZBSH4DXrrCHBHtcibYtewP5zMgGAwhkwgidki6MgeRqgOwvAtbO8BAz0zcHuuI03HiA4k+K6F3/HAxp3Zs6cxa9zrRjE+Vw7eGCOi8ViCOY82Nvf39fdu3f14MGD8FkcFwEWcoIMoW/IuG98dv1JpVLBVsYbOr0xz9hvL8dwdpD5iJnFeA4gNhw4+/o4WEKeHZT5Pfze4/rufWD+x61fzPjy2bjvXI9Nzr6/Blt3/fp17e7uamlpSdlsNpyu0W63w3Uc4MTNTxjCDrHBfByIZU3iAJH3kAlptMmZufZ1Qh8cQKVSqXDMKA8Jw9dC1KA72CBkk2v7aRfYCRokjDfPkPlaxevpfeeHe/lxvthhjtr1fvo8Pw88e3kY/XXc4fJD/3283mIg7Nfiff+JsyveYiBNGxeQxjrrmSf/HPfyEpDYV0C0+b3xPfjyQqEQcNRpG99e+DHl0ojFQVnZIOR1oICFbrerUqmker2u/f19LS4uJk4xcLAgjZ7WE7OBfhSdNFp0P70AQXZgDFNYrVYDmATgsLnS2S3Yt4mJCW1ubgamO50+eYwz0RuACuABcPLD9BFUZ58Yg9c27+/vq1qtam5uTr3e6PGxsPjOHjgTNhgMAigkooRJoD7OswOpVEqtVisBiLe3t9VsNsMaYPA44omxcC0YdTcMAKherxeeiEhpDo4e5o11dkcBgOARt2zKg+2dm5vTcDjU9va2Op1OOLN6MBio2WyGR1/zaGvui+zgOKST2rBr166F9Ws0GmEMAMpmsxnYJ2TGnR7yD2hxR8UTMynDIGsBsPK0GzIKIGTNYJ9hmJln1wtkitr/GAR60EfAidw46GRDJE6KdfXvovec4HH27NmwB4Dv0V/kg0B0c3MzbBhEdgGblGu4PKdSJ2w74NGDLLc70smZ56VSKQRNXsfOvLlDcbDjwCtmwMeBLV97B2o4Ol6jHpiSKOpHs9ms6vW65ubmtLW1pbt374Zxc03KWAiEvayItQLodLvd8KRV10cyPAB0+uQbYf2aUvIoMBrg38sTHOxxv5gF9IbtYg6dwY7Z83ElVg60xwU53mJW2vsWl7KM+342m9XFixf13nvvaXJyUp988onW1tZUq9U0MzOjfr8fMohbW1uh3G5nZ+eZoG0cM8mY8FsQIXw+nnv8Bf+zblzby/14KJDvT+A35Q6sOTJCVsxLpXzzIcAUnfbAFuKA+3j2lLXyQI/XuabrmRMG7jMdG7i/dCJDGj2BlXn0OR23zrxH+Z/bCOQ6bv+U1zwI4FreXB7jQM/Xf5wextfwYMmDyBh0+2fpo2cl/P2YGMLGkDnHt1FJENdcn7ZRe2EO340xhhEGkoWamJgIQgtoAbwBAtld7AYEJ+CbHkmt47hRRKKudDodGEiUgh3MXntIahgwj8Bwn1qtpnw+H8729U0OAG6vY6UGmj4CqJ059CjV079cBwNy5swZVavVkAKG8cZQOTDg/5ghwokzDtiPo6MjlUql8BhqjFyn09Hq6mqi3tJBpTOObBTDQDsbxVru7u5qe3s7zAWBDfLhKUga80GwQa0tBoKTPHK5nLa2tsI1YTkpz+A+rOXh4aFarZYqlUo4DQLAPjMzE+QYhpiAhSc6OkilfMHXYxx7wGswKdQwsrlsOBwGo4TDKJfLIWB0Rpw5lUbGmc+hD2Rv3KHTB9ZJGj1hFKbUHaYbSRiz+KmoHKFHsNJsNsO5uQRyw+FQk5OTQU+RpY2NjUTwzL0p+/DTeHAOmUwm1P/H5SowKwTs6XQ6lIS5g0LWfK08be4/HiD5dxzIxf/Ha8/6I7f0nUCqWq3q1Vdf1R//8R+Ho9WazaYajYY+/vjjREodXfGaf8bk+zl8c5WDFDJLvg5e6sQ4CbQ8re3gNwZxsTP2MjZsk2cCPPU/jt1zEOGso4PwmNGMr8Hf49aF92N9HdecDZWkO3fuaGVlRdvb22HPye7uru7fvx/Kt/r9vu7du/cM2Hem0EEQcxfLn9tDBzbxpkRfa7JGMMs+ZwBPbJCDd3SAZxSwkdyzkXwOucZP4UdjQE0Jmq8Fc+l7YzzbGm+ajgEp90WvXJ/9u9yPeXJ5iYGpl33EbL7LQMzmjmvjgib+9/vHbLRnaDyT7bowrjlQ9vt4NmZclgl55G/vpwecvhbYF2wYNpq55Xg9dOWfMj9/aO2FmWpn0UiZO6CWFJQnmx090pTNVM5moFBEzV46ETs1ALMzMqnUKM3p9c+AQJzBcHjCvDqg9h3SODTqqzGMMSgm7XR4eBj64EwhffS0EgYGVhIwwNjYYNdsNoPBht1yIO5jccYRdnpycjIcXYYh7ff7qtfrAUQTLABWuAbj7XQ62tvbCxsdYU4BVxgmQB+Kt7KyEo5aI2WKQfV6+ljRYDMx7l7bWSqVNDU1pXQ6HVhcxkl5CYDf5ZOad44jRC5gsd2401cyKjyQBOMNgx6zZw4qpNGpK84o8/h0mFhKn5Bl1onjEP1BQa5LnrHx+lbvCzLi4J5AE2eIk0X//KzndDqd2GjKNZHpWq2my5cvq9frhawAjBH7AZjXGNh77Z5ndZzBRUeq1WqwJw60+D4MGyUlHiTH4CQORN2J4myc4YoBnINtBzdxWthZHtg0v1exWNTly5d19epVPXnyRKlUShsbG5KkTz/9NOwh8LIO7CDkADbRAVIcoPqpGQ5SXE6cXfYaaM+m+fusJU7VbSFyhbwSuPl8eLAUO/x0Oh2C/piowSb5uvm6Ojjya/p6+3f8PWSNYBqAcHR0pPv372thYSHMZzp9Upr3u9/9LvQHeXWyw8FNDBKxOWTzisWi9vb2wkZd5i9O0VO6xneZJ87qR49iP+H2HP9LwA5B0Gg0QkbUTy1yppNMIGMGzGPLkYsY5AKA3TZ6Ngqf474kZvo9S8b8+lGz9BEbEmOFcevO2JAplwcH07zONVxeXe48a/y8+40LAF1e/N6xTPv/42TeSS2/FnYrJq9cf6Xk6Swe+PA/5argDnyWpEC4nbbx7YVBNVEykSjKgoEk8sW5+OkLfNYdN8ypH4GF0uEo+A7REUYfQUHAPHUqJXd5uyFCQb1ezWuY3dk6MyuNDkKHicSouUDzXcbnJQEYSknhNAge6OAbHRknrLc7ChwD7PT09HQATfQB1rHT6YQn0fl4UTT64mdyA7hwPnFKCQAFcGy1Wokn1vEZ3/zGvXmPPmCskR8eToNhpfzmzJkzSqfT4WgqGGicGA9wQU444YVSEBxTp9PRzs5O2NRTrVaD4XfQghxTxuGOH9mRkqeHILc8cASGkEfV85l0Oh1KfNLpdCjNccDe6/XCMY8OYHGCbig94PTrOOPg9erSKJAk24Sz4eg8nCeBBWUcrBXBDCVf6OPx8cljmQFasB30NX5sOsDRA2LAAsYcmYEZ39nZCccyjnOA7qDjVKvLMXMYs0yABXfW49KlXAfdA2y5U+v3+1pfXw+ZHALJ4fCE6fdaaOSd/Q70jzF4atuZReyb9wv75M7fPzcOyNFiFi3exEnDxrkDd9n0jKKDXQ/64+vGDv554x3XHx+/v8Z8cGpQr9fT5OSkNjc3NTMzo7Nnz+ru3buhrMP9BqAUWQYMUofsWbWrV68qm81qfX1dk5OT4WnAmUxGFy9eVL1eD6e88N2YZMJn8Jhz5iSXy6nRaKhSqWh9fT3sWfJjNVkPD16xkRyn2uv1tLW1lQgk8Sd8F53H9jo77mC52+0G2+LMrpepxTKIjWQvENdGRtAlB9SQcbHN9SAYOXHZjANFJ6icoY3lxUkrZ8IdR8TXjmXveaCe68Q66GA5zka77fFgPpbvuHmmz5vLngfhvJdOp0MVgQciXj7kOn/aku2FQHUqlVK9Xg/OE8Dlm+dIx0tK1Dl6+sUFoN/vh/Qt7DJOFObDWVJPBztg8KP3+J4bQ5y0KziCiuLyGY9q+RtwCxvl7OS42kMMISdU+OYJzuoFGAyHoyc3YkDcqKB8GD4cMbXiqVQqHF1G7Ti1yRh/QFTMvjF+yjBwhhhQr6n1c4IBsZRLODMFcKBPrrhxGQjpQ2qfmS9AAaUAnNfMehNAYJz39/dDEDA5OalMJhOAfr/f19LSUgioAMgcNQiLT5kG/eOEDRy8s1POeCMHqVQqAEIcjh/Nx9jJbHDspBvbiYmJEAg5UI2DFnSK/uJEaQ6i3aF43TEOn/rber2uWq0WAsVM5uSILuSdR7oXCoVE1oSMxnA4yhjxYB/uT/mR6wDHO3rGiMAcPaRhd1qtVqi5R35jxgmZJvh2B+WMtDsvtwMONrzO0p2PBy3IRsw6MYcc0UnAH/+wll6Gk0qlwjw4sI1BLP12u+PBAifrkDnErjmrzHj8WqwJdpH7+jWcQfN5dRDi8+W64yDZgVG8pn7E3jggzbX9/Xh+AJVvvPGGFhcXtbi4GMrMtre3tbGxEWxx3C/fv4BO9Xo9lUolNRoN9ft9bW9vq1qtqlAoaHV1VUdHR1pZWQkPe3nzzTfV6XR09+7dkEHzE0Owz2QxsXeSNDk5qW63G/aRNJtN7ezshAyX22+Xf+xGOn1yYke9Xk+cfuRzi7/BbqM37o8LhUK4tvsDZ45530mKuDyIH8C0E0T4cwfPkhJ+3RvXcF+Gv49JqBgEup4iqzEZ5yx1HMihD25rXJ9cF+MWs87xNR30xraH98kW8jfZALAQ/o+yP8bMfSAz/XX/24MNSC5IJk53O23j2wsfqccGI1hXr/3DeaTTo3Mu47opZ2D8XGIAgjs2B3QYN66D8+bEhsFgdEyaKwHgmh9Pm2Dc41ovruenWwyHw5B6A3x7+omGgrGhEQGnpKLRaKjdboeHBzBeL5sApGDcADSMGYGnf7wOMwvLKiUf0uAGKDYyMPWMJXaIPMRnZ2dHrVYrpDCJaDGIsDZswAuC9r/W3YMrxj0YDMIDhegzbGmr1UrUPzOWw8PDBGCllhxnAuDnCXUcH0g5A0w8LD6pTS9r8dMqHIAhu7Axzta47JZKpTCn1E/DQnK0oLMhg8EglK+gI4BrN/Dci7l3kIKRZQzO8niaF/kA1FcqlbAJi80pPKKePQDIsG86hHWlXCGVSoWSL09Ds9nJn+gpjVKJXBfwSAkIcyUpcYa8szbu1J11dRDn6+hgZFxz5yONP8INB+bvx+w1OsyaxEEl8uhEAbbHbZGnZePAwfvlDJ6f3++BuQMMbOG4tDf6GQMZxk2fADauI67jMUBx4B4HqrxHn7yciOaBTfz6uM/k83m9/vrreuWVV/T06dPw2sWLF7W6uqpWq/UMyOcaXtfMw8qYA4LC5eVlZTIn51LfvHkzkeEgs/aLX/xCw+Ew+AL2KDiIJMvK3PCAMkmJ40nJtrk98EDLs7v+OlkvX1P3oxA2rAegjcDMgyJJCXlyPcE2+slJDvhddj1j7SWG3Bdb75kSdBc7GwNqaQTG/d5xUO2kDn3wa8QyFuuayxz/+2ecjEMWXM5jUO/NbVj8eeym99XJLGwppI8HRn4t+uivPy+gISuM/SXwP23j2z8LVCPM7mRRJBhGafQkORbMa5L5TLFYTDzYgY16UnK3KoqBweYn3kCDc3HHgAD4b1hrmLE4zYtwomz1ej2RlnbG0yP4TCYTTiUA5KVSKdVqNVWrVW1ubobHf3t6249Vg3X3sgM3QNyTc5UBKe12OzCLMAYEOdzHlZvrMEeAQeaNdYFJ2NnZ0dbWVnjqYsx0ABI9HYgh8JMsHETyoCBAFJkPGD6CGer6fBMpgI4MwvHxcahj3t/fD8wRgDqXy2lyclLlcjmUu3gAVqvVgkECUGez2eCkkEcH1AQz9A+2lvUjW0G/mSdYaN8o6RkQHCmy6YYbh8MJJ8wrjXVkDWCfkEVfZ/YH0B+yA2RkpqamgmzE9fIOkNBtNrmyPuiGOwOXMfSM92isabValaTwMBw/mi52SvSTeYrlfByQc5DtznscQ+SstF/XnRQOyIElWSCcHzrN2H2jGLrhqWGXUdbQQVPMLAMmAOfIMJkdWsx+OXOF/mFnWTfXX2R1XBkHsjEOVMd/xyyggyWfW18rfy/+WzoJFicnJ5VKpXTjxo0Q6BEcLi8vq91uP9NH5oISCGSZrIokra+va21tLci3l7d4JsiD2FQqFU6a4jsxMbS3txfsA+fmb29vhwyG+zb66broJZEOtIbDYdAdz8SS8cS+Y2sciPoJMnFGDd/ODxk1giHPxPDDGqID2Ep/zwEw8u5g3AH1uICXhg7yupeaxCDX7+26jx13YM4cuG6gs65Pzjwjw3x3XH/j5uuMD2eeB4NBCLximfPvx82Bvjf3Lz6vLrc+xnHXjvv+h9peuNrcSyqYODYi4vyJOjECLsR8xsFgr9dLOE0+5wBCepYlcmCDQ41TUbED7vf7IS0CsI6ZKQzbxMSEDg8PA0ONUjoT4GCckgw/yYRIL5/Pa2NjQ1tbWyH1B5tFhI6R9jKMuFxCGgUrMNqAyb29vUSWgLkHxDi7xfqRNnL2CGYY4z8/P6+NjQ1tbGwEBcM4cbqHbw5l/mHSYwMPqwmbz1pS2wsg9lpcXmfu8/m81tbWlM/nValUdHBwoM3NzQTAWFlZUbfbDQw3zovyAcAkhnBjYyOwSRhSAin6gsw5g0W/cVQ0sguU9iCTHIFFcAcg57oAAGeUMPgEkrHxc4PtbAWfd51l3nd3d5VKpXT+/HnNzc0F8FEul/XVV1+FR2QD+txJxmVU9JtSMEAdoMT3Q7gcsgYEK1wzn8+rWq0qnU6H8+SxMV4axbU9UHNZRs/cprgD9R90hH7igLiX2zI+42CdMTsTzQ9yj53wbAMAg/IYt1ke0HFNL7WgocP0ic95+UQ8bx4QxLbW5YW9H1zfr+tHbDm7SB89IHSgHTtdt2/+Xhw4EaDNzs4GPUin09rZ2QlgrVgs6t1339XU1JQ+/fRT/d3f/V2YE04o8o2C7lfIZHHWPdnFmZkZHR4eanNzU/v7+4msGb7BQZg0YnPd5hJAukwRUJPdww+SKWKMngXzzXyDwSDIDrrD/QjUvU+sm2diPcvANR3sIgvYK2+DwSAQax7ke3ZSGh3Hx3gpX6RUEb9Olo4stvsnbAVjGRcIS6PyNw+iWd8Y5ErJ89SRMwf/HsTGGSPW3XEEn4mv62tAnz24HRckMC/4CdaN8VO2yKZnX0sCHCcikRO/jwetDtDJJjMen8fTNr698EZFDBngEkYNZ01KS0puBHRnGLPTHCyO8XGw7E7YHRSRu6crvNQhZpokBbCJIgKovG6Ow/85Js6BK/1CUBF0vhePn0bqDmPpmzE8Bch4xgFiDCsMIvPbarW0urqqRqORSJ9LI/CNInEN5sUZegwjZQsoLU9IXFtbC4GPNGKv+Zw7EIAD8+GsdyqVCvObTqfD8WqAgHa7rXT6ZCMQJTLUcDlY6Pf7qlQqqtfrOjg40NLSkg4ODjQ5ORmeDukyQ2mHB4WUYzCntVotAZ44DSWWG4wUZ7M7SJRGT1Ok1o1rOlBjbbLZrFqtVlijOHXqAIh+Ara4F7LrzC966puLUqlUeKoon3njjTf0yiuv6Pbt27pz504ACEtLS4GNjPdOeKCHHfBg04MHd870gTWgb6S0ccSc9AJ7F9djw/Bzf+R5HDB08OmBMf/HIH8cI4WuSc9ufHQA7cEu4AqAiUyhG4ANAhGcNzbR7+9MH+OPAazbTgf26AAntDiTRRCIPjAm1pLPOIGA/fPMisuiBzc+764fMaDG9nmwkE6nw2lJg8Eg1CH3ej1NT0/rpZdeCk9QJThk/ovFoh49eqTbt2+r2WyGB3nx+HfW2WVEUpA79geUy2VVKhXNzc2p2+3q6dOnOjw8DCdzUIrkwa0HQD4HvnY+VrfnBNK1Wi0AImTGQbv7HWyGl1pxTzIUlAIBtiUFe4i8e2aAz3rAzpr64QR8D5vlcsD3+NvlDv0gIBsOh+G8/Lm5ObXbbW1tbQUZcybe5zQG1B7I8hprEYN1xw5e3+12ysfp143lBvn199zGxH30v/01xunzznu+t4EHSnnQ4YE+a+X2OO4Ln3Xw73Ia9xXiAp8dM92nbdReONzgwS0wSy5Y0uhBISg34NML4/0hH/7QFE/teDTo4MgdiNc0umDh9BGquMQDQMm1ATqFQkGNRkP5fF6tVisAZWqn/UxPnKKD6XGpFb5D7TDKjMEBVPO3pIRTdKMAu1KpVML5wTw4xqN3nNLz0lyw2V53DXglpeQGanl5Oay5pzxRaMY5jlVPp9PhNAjmhzkdDofh4Qn0h82a3GNycjIcQyWNNqEy98gk7BFH+3kdvwctNO6F7E1NTSXOGidCx1nEToESHQfMAELmz0uMWIdMJpM4Ps9rOpkDD67cILusohsxe4rzwODCfnW7XVUqFU1OToZxp1Ipra6uant7W6urqwG0UCbg6X76js5zP9+w6w4hZmBo6AAZGlhPgDtzmE6fPGyJcjCAIeP38gjAhoM4Z63dyThLzXeRU77rssu93Kk6QwowJesGGJRGpyekUqmQFkdmCBio6afFDLSDNHe+9GOcMwSA+PewlQ7eSfnTLwCHn8zhaweYZj5jIOb1xLEDj68VywSfyefzeumll/TKK6/oypUr2tvb0wcffKCzZ89qamoqbPS7ffu2FhYW1Ol0VK/XdeHCBe3v7yeC8t3d3XBff0DQuL4AVikbKRaLarVaOjw81Pb2ttbW1tTpdAITuLe39wx7P45xdHvvJW9+GpAzmew9IN0u6Zlj8RwkOaBG/gjK/HvO+DpQdeZxnJz5mvIaetDtdkOw6zaQMfO3zwX64KV72LDz58+rUCjo6dOniWwpn3lehoYf96n+PQf28X6MuITESUDmw4MPv68z3T53bjOZM+Y0LklxefETmSCj+Cz+EtnjM57JoJQMrBHbObfJ9Iv3+N9LXVKpVDjlybFCr9dLBGinLdle+DHlpKQwgG602bzhKTBnCFHuo6OjwAAi+J7WdmbL04gIitdzxWDU0yR83xUXpkVKPsiAGjaYV8bhqWmPxJ2pxYliXPkNMPOHiaBwnh5zFotgwNno4XAYWFsA9WAwCBtb/Pgbb3yXSJtxA/YAJjhVD4aYk6dPn4ZTN6rVagBWzJuPhXEUi8Vw5nD8FCa+Tz+2trZCrS4nXABQ2FC4s7MT+s+udGSo0+mo2WyGJ2u50RgHNHBsGKxqtRpO3HDDxbo7wIAlqFarCdbTT2XheLhUKhV227vTZvMiKTvfJOtMogdWyE0MvpBfGCMPojCypVIpGN7B4KRWe2NjI8geQJ6sBIGrp6m5vj/hk1MlMLAO/pC7mL3BTuBw0+l02PTizhYQio1g/qi/9CwE4AT9Ys3oD/Pqr31d8+CH6/vpQ+5wHFDzOkDbG3bD9QBQ6gCdeXJm2DMc2DN32v551s6ZR2SJzzgIgShwJtABkNfaEnA5C8t8eb8dTMcgxP9Gdjl15ujoSI1GQ9/97nf1xhtvKJVK6fHjx/rggw90eHioK1eu6J133lG9Xtf6+rq2trb0gx/8IGS9nj59qi+++CLo3cTEhKanp9XrjR5j7UApbgDU7e1t7e/vB2COTDFWAI2DXNaC6/oceraO14+Pj0Ow62UO2Dh/PHqcffHsJYAan8k4HOjHfaJMj4bd4B6soQdZjB2/TF/iTIf743iOvf9euomsDgYDLS8vh1NQIFrcvnnA6+DfbU82mw2nE+Fn/HsO9N3eOlkxTo/HEQSxbHuw48x3rHd8xjEOcsGcYm+cSHTSwG2sl7oQpMfA3dfheX7F2XVsUrynh8Ay3sB82kbthUG1PxERJWRDkac5cTQYZQTAjwpzAxcz1M4weQTlQBSWF4DopRoOohBUnIg0Ui4AXKVSCUCCVPTx8XFwJp7yZHxsSPS0EulsQB/zxT3ZrIahJV2Mkrsikxrs9UZHOGH4Z2dnEywFn0cJ2FCKAsL2wQJjeHwn78HBQTifuFKphIdrDAaDEEDFTsmNNI/0nZmZCXXOe3t7arfbCfYYowpjCivjxo+Ho+zu7gb5KpfLYTNMv98PJ4O4gUSukA0HIRgPZBdgTRkIDoRAI65FJFAg+8H3qcGPHyONrE9MTIRNTv1+Pxg+NiLGbAn3g9XE0TnjjrPGCQ+Hw8Ds0CcA9f7+vvb39wMAYB2RwWw2q+npaeXz+US9N+wjeyaQHXQDmfYMBeMYx1Jh0B00OvPGhk/GBOBzhw4T6Y8Ad3CKDDGv45zxOHAdpzNZ03i+fX2wKwQnyA96iV4QpDFmz2BISmz48v444PbAOwawsXNmnOi7A3i/Buvpdjtex16vl9jrEgcMzjL7/Ho/kA+C91wup/n5eb300kuamppStVrV6upqCNp//etfa2trS0+fPtVwONSrr76qq1evamZmJmw2fvvtt8NehUePHkmSvvnNb2pxcVGbm5thHfxYVydb4nG4rKDX+BhsiI+LdUdHmPsYUDvoxAf5/cj8pFKpwH7TkJc4WxGDHd+/FAc1HkyNYyYd4CL32Gn0yZlaxsJnkSnkzOd3XIDFfHkQzHzs7e2Fa/EZDxT5ftzcJ8P0YxPddsRrzft++pj7TJdlxjyOrXYi0AGp3y8ORrg+8+Drl8/nE6cceRDv/t71H2zDHiV/ToLLU/z3OJmhT47RPFAgs/h17R8jL/5Pbi9c/oGxxtkVi8UEU8tkOyvqaXJptOkMw+AG3qN3N9ie4ube7mxgsQBcbkhJ43NNUk/ZbDacL4ohcWaLshGMKiAXkBcrIkEDQNHrDfmsK2x8zqc0YqI8XQeLzBzPzMwok8kEFtRT/jgtnzs/rYKAw9k7PotxIdCg7/50L+aVzRKA6nq9rmq1qunpaV28eFE7OztaXFx8poYVtmd1dTWk9t24FAqFkPEA8FMDTP0+48CQOBPrRh95c8CQz+cDgCbwcvYQAOSsXTabTRzZh4zBqvjpJYBYAjE23DlbTrDmpz6wtjhAZ8DG6V3stAiGvJaa0ppsNqvXXntN58+fTxzNhfzl83nV63Vtbm6GUzdg0UkhewDqgUbMjjuAc0eCbfDggOuSGSLQ49rMW7FY1MHBQSiVcYbM2R/kIrZXMePkoCL+n/UGEDk4coYvLo0BCGD3OKMdIgI5xJ74aQqxY3Mmjb/dHqBL8ZiYC9/XEWcQmCMH4un0yZGKXD/eP+JkB/16ntPkXh5g1uv1cK1Go6G33npL58+fV7lc1ksvvaTt7W0NBieb7T788EMtLS3p/Pnz+sEPfqDV1VXlcrnEw4cuXLigc+fOaWdnR/fv39ejR49UqVT0jW98Q8fHx3ry5EnQQewXJWfIc8y0u5x4BhZAyffGZUVhtJnz2H773PlJPOiRgzcPmGgAQ/qFzcPW4QsIBrw52+n+lfvGAM3nw9/ne9glwLSkxHMm0CkCXsZPPwj+saM0B/YxAHYdjYML5N0zSv7AHn57+ZOzyPhL7uXERQzg47mlxXtGnvc5Z4ediPDS11QqFfb58B7NZcKDAg+uPGtI80wC/cBO+mdjZpvf/r4HEqdtfHvhh7/gWAFUXp+VyWS0s7OjXq8XjleDeXJG8ejoKHzOI+1xLAfv++YH7sVr9AFAjHEBvOMk/F7sPAaMuJED/DhTxCYYnvgnJXdv89tT/hgSZzIymUziqYEYXsbtqR83wsPhyTmRnHYRb9SUFEpX6IufAkIJihsvZ2XY4MPaAgBw0B71Z7PZsOEtlTo52m9qakrT09OqVqvhSYvlcjnUQgM+OBuW0zDoL8yql/MAqHCIcS2ZGxY+j9F1JtYBRLvdDvNFvbuDXWSU6wE4AfmsLRuaeFDNYDCqdSPIJMihn2z0HAemfS0BIw5mYsCNLBHkSQpzXa1Wdf36ddVqNUnSH/3RHymbzWpxcTGckuCbHofDodbW1oKjdofI9Z0hcT1Cbh1U41zRJX48i4V+Edhy/j0ZjUajoXT65FQHTnlhnBx35pvpvF8OJP3+rAlzyPv0nX7HJV2+jwBQwdwB0NjXQKkU7LXXcQO8mRdnf2In5ewU+uD10My1g3MpmTlAN1hTHyvX8FMEsA9OJvg1HOy5w43/BuCgWxwjR5C0vLysUqmkl156ScPhUFNTU9rb29Pf/M3faGFhIcj+rVu31Gq1dPbsWS0sLGh7e1uvv/66JiZOTmVaWVnR0tKS1tbW9PDhQ33++edBdpmXSqWicrkcdM4DKIA/QQ+vO2ngrKnLPD5gb28vZByRH9YLogbW1PeAOBmEL/NAxrM6zCky4EffSc/WBTuQddkFfMbrR1+dfJBGBwqkUqMTochScW/ANddx4gzZQbe4B0+DdNvhchS3GBN4yRAy5kSRn5rEbyco2KTt/sf1DTnwe3o/vJ/jcAv3idluz3b4bzLM7qfiz8XfifuKjhOc+oPAGD9BhJOAsd3xYCIOZpDt0/KPr28vDKopk3BFTaVGDG2/3w+7poluAS1sTHQFdiCEUXcAGm96hB339DHGD4WamDh5yh4Gz1PeAKWpqalQ3+ostjN0GCfKSgB97qwdfDswc2DiAJtreZqLfnnE7ec2cw1KDBqNhra3txMOXhptLKGsACDuG0pjpgpQU6lUAuuCU3YmIQ5mUK75+XldvHgxALjFxUXt7u4mzljmKXieMfDgwY8hBDQfHBwExh0nBJja3t4OD0BgPWGTqIdkfqXREyxxVgAH+gJYiI0rdd3xMUWsj5/w0W63lcmc1CXzmjQqmeJc7HgHu4NmWlyyQGDDetOccRkOh5qcnNTVq1d148YNvfzyy1pdXQ0y9uTJkwCcPSs0GAzCkUnIUZz+hPn2IAWQ6BkC+jrOGLOx0x0BZTHUlw6HwxCkHB8fa21tTRsbG6rX66pUKuHISC9zcnbYSyOwT/QfYIAdI+0dgxuyD/l8PmwMdhDO3ACqeB9di+tdJSWYXz7vjLOzgswbfed6HjwiI842uf3wwDDO2jBO5I15cNDvmZ+4jQMdfr9s9uRhPWRJCoWCbty4oampKT19+lQPHz7U6uqqtra2dOvWLZ09e1aDwUALCwuJYxPv3LkT+rixsRHWdX9/Xzdv3gzBKX1x0O9ZL/Y4xAxlsVjU3NyccrmclpeXExsDHRRh3zmFhPv5Q1gAG6wJJIH7PwA1AJ7sqANSru9sOGvLWnsgxrp6sEO/+WHNnSHH/rpv8ZIObBK2GBmOMzPsgXFmN/Z3bkshg/yhYc+TL3TBy2F4vdc72YsDYEefAciAfi/JwM47iROXdMT9ie2yvxaDaPoZ6wW/fS0Zl/tBXw+Xs7genz4wt1zHwbnbHuSLfuP7HQ+NWwe34f6ab6Y/bePbC4FqUrLhy/9LMNvtdjhqjTpjhME3lDlbwnddSWNHACvuiw+YQXFgvWAieA8j6VGXp+vj81Uxnv4oVz5PnRLNayIBGQizsyQAOAwM0bpvXvN6WD9/EiaaAILPs3mvVqspnU6r2WwmDCzz7GUKKDHAH4Xi+EA/q7RUKimXyyWOQ2OdMLiFQkEXL17UjRs3NDs7G2qdHz9+rM3NzcDI7uzsaH19PTBfbIjzowed1cRYwPZhsJnPXq+nVqsVgoRM5uRJZvv7+4GNOjo6ClkAghLPOBwfHweQ6zVrcfTPurtRIstAUEFf9/f3g9PA8PjJIgAVLymRkidOeBkFc8FvZ3FwFDw9E+fBqQXU2t+8eVOdTiccV+nnkTtj4enhmM2lP8ynp3yRMfrvDgt9AwhwRBgNPSb1yr3Qsa2tLa2urqrb7YZsx87OTuKBHs7EuG5+XYrUSzhw9M7+I49k4HhID7oMs+4PZ4KRJQj3bFA6nU6k6umbO1cP0LFx3qeYXXbW2+XE09Z+La6DXeF/7C0ZKS97877GwMPvyevMMxkbmEic+P3795XNZkOZRzqd1uLiYgDPsfzFoMUZep6U6uOMA0DWdzgcam9vL+yzYS3JOvp1kV8Hgc5ae+085IDPDb4Gm4+t5G/P1uIfmV+IDg+04sA0Bt4OOOOslwfhDh65HzoQ2yIPTNwuojf4EAgC7EfMqscyzNoC9iqVSihT4JkH9N1BumcMpZOSqnq9rp2dncQ54+5DYhLNZcoDSc/kxECe+XDsQPOAxnXM59EZaZ93rum6TFDvtfYQeR4QuJw6hiKjRvO9Pt43xoa9dyIk7mv8Whx0jAt2TtuovTBT7c5dUuI0kEKhkDgD18G0R1sYsJgh5LpEaOVyOREFsjmQ9wHUHiV2u13t7u4m7odD9TpJL5HAmeBYAOCAasDwYDAIgC52RC7MnHfsDpkAgd8Yjnq9rnq9HuYVhWN+3VgPh0Otr69LUqjp5ul3u7u7YSwYsFj53TlzUgVBS7/f19TUlCqVSnhqojuser0eGMcrV67oxo0bqtVqgT1+8uSJlpaWtL29HYwugJoaZmcBnTmJHT1zLyVPKnG2lzGys7/X64VSDWSL77EWMNQETrEBxWA5s8P8EYTQr/hpbJnM6FQTf5yrZ0DGRf9cH7CMgfWNUM6S4uSz2WwIENGJvb09ra+v69y5c+r3+1pdXQ1BFM4dJhPZ9DITUuH0kRIv2CFnWDz958dMMj/UZlNexTjIPjBGl3P0a3t7W0dHR8GWAGRrtVqihMLXhr+9ORPna4n8jwvovD7SM2pew418AYQ4p9+zIIzH+8p9CQqc9fc+xKnZmB329Da21AHeOGfPeCA7nOGir+5oabzmwI1x+mdx8H50KN/n5CIfgwcU8RzFn3tei8F9/FlknBLAYrEYjruUFEoQ/bhVAj30ZnJyMrDujAcdlEbHDMY6GoMSdNzZPv73jfsxqymNAiYHMnHmxO/hYJqMI0SSAyI+x3pKo7p//BwBsdsKfCVr8DwwKilh/46OjhLll5zk4lld98/eOC98fX096CP9931cYJE4wPLsL3IPjmCNnGV2GXNdfB5DG4/bZZJ18Wc1YGfdnkFYsdkSPMERxPhKx1FsQKc5UeKyQj/i8p84IHOfzD2kEYHqZMnXta/T2//T2wtvVARc9Pv9AORgs6anp4MD9to2T2ez4K6Y0mjRvLTDlYt6ZhpOketzDwCc11VjBGGCMbR8zh0M10YJPCXO+Dc3N0NJAGOFxeJ1xo6jlEZP1MKY+vFBOHPYHTf0GFWPPo+OjjQ5ORkedrK7uxvAIsCd+fWHiUgn6cBGoxHGMxgMVKlUdObMmVB360wH80at7tWrVwMgPz4+1sOHD/XRRx9pe3tb/f7JqRb0BwWFnXFg6obMT0UB0HrARE21b/SCreDJexhOdxIANQwuRpfmhoS5Rhb9YRKeEeE8XPqKPDkTHjtKvx964AbSZcUZfD7vARrMo5embG1thaCRwMZZ2th5EiQ6GCMIQAdc7n0s7vBgsrgmukjtP/cAGHPmOPqG/eAkh93dXWWz2VCeRYmWX5t59NpAZ8fop6eHAcee9fHUuwcvPveMBccG4JCUKK3CFjhj7al6ZJt59hIs3ncCgfXiuh4ckjbmO/wdA2onLbBlEALOSHvA5/PBWsfy6+DbmTdOTfJTLmK5R2b47a+PAyzxa7E99uu5jY7XwNeb8jIyYsiRB5GcJQ8xhA1kbpFhacTyOrkzGAyCDfeN5Ngu/KQDZ/ru5A72ILYjcZkBso0NYB04Ix/98fnyueKa+ObDw8NQOuMMOvLnJR+xfDyvr+gjLDenygyHw5AVGhdkca/l5eWQnXN773rs5TBuSwHPlDQy1jgg9/GMm/c4OHedc7lGHjwDANsvnWRCKR9i/crlclg7l3OCdifG+ME+5XK5RDkOn/cMAgGLy5s3ZNTHRx+wvS6Pp218e2Gmulwuq9/vJ45b44lVOCFqTFkgrxuWRtG9NEqLeIoSpcV5wW7GrJJfB4fmzBsOrVQqhZphImKvcwSAe19QxJjt7XQ6CUPM9wGMnK2LomA8Oe4M5WauYHAxJru7u5KUUGwpydygmDxprtVqhfklaveNLw4wYDZZh0wmo4sXLyqfz+v+/fvB0TjTxUY9ADWnXXQ6Hd25c0cfffSRlpeXA4DFWBJlw1Z6WtJlQdIzGyTdaBwdHYWnpzGvkhK7nQlocFqsL3/jqGKWzUGug3rvK2uMg8UBA2R8g58HeOP0x0FIbJB5z8E0AMazN2QpKNPJZDKanp7WwcGBtre3E+UpzA1Biqc7nS30gIPsDEECjXF5X934uj4hf/QFltDtAOUx/n1kS1LYf4CT9FIa1xuMPcE9csVaYC88oGXevXQAOcB5ORBHDn2tx9XlOxB1porPwNy5HWPeaM7e8zmyAz63yIczpHFqn2v5HDMOB6Fcy1nrGDzTHNA5iPfxe4rbvxczub4W/1jza3KtODj1vtIXtzUw59hoxusgJpUaHf3qZx2zRr5+jNXXknmkTMiZWGm00db1zwMzro3OewBJAyy7z0JO3U8STHnGk3ui24ydLFI2mw0ZUOQVmYsf/uTywRy4/PhnsKWDwSDUlhMYAnYd9KGr6XQ6ZF4hMbwG2ec0zr65D/YgiH7FNszlKNYBB9gObP09D0DcNkmjsjon8FiTYrGoSqUSPsc+Il8nbDn+lLEyT457kB33H64ffAbZcBvkAR7+Fv+KT6Dc97Q9214IVLNQzWYzOPNGoxGieo/QcIJ+/i8KJ41qQ1lYB1qe6uL4OgwiCuYOjrIAr51FkACRGEpXLu8rggaogp10A0WddjabDc4U4zYxMRHSjOMcBYDXxzkcnjzUJX54BgpHIEGECVBkvvgfQffXmUcUC0AGYKFc4uWXX9bR0ZEePXoUQDZMcjqd1uTkZCgVmZ+fD6Dl7t27unfvnp4+far19fXEJjzYmVKpFJifTCYTgiOCB8YLyD84OAiGExaQMgovS0C5fRd8fCSYb9jAWTkr52AAVjCdHpUvEAgNh6PNnmQBXNbjTEfM1LgcxCwi90T+3EHilF0nSA0CNpEFl036TB9xlC7zrA86hE6wFp6mjoNDd57OdgD6KpVKkEVkDYYagEdKmRIj+sTcAwQ8rUvQTFALQGdzIIE4a4G+UVvf7/cTjCH9Zx5xXg5+HKCwzgADABenlbgDHueMGYc7Yq4rjUrUHNB7YOXO0QNrABf66kELgIHflOJ5RstZVT9rm+87i+m2bFyAyN8Ec3FgGf//PDA9DpDHzQH1uO/6/JDlhAwalyKnv8fHx4kN2jGg8g2uHtgg23Hf0Ru3rfgQiCMPVn3tCUhjBpy/sRf4ZUqUvGY8Du78b/qHjtMHP+WEzcGcC+9y6c0DM1/f+F7lclnlclmbm5uBqWUtuY77R8aG/fbMjGeppBGRxHq4LMT2mblzQOpj8T4728xrLoeu8/TJv0s/ut1uYiOxl8ox137IAtdyPIN99yNRkSNsk/eNfvg8+bwxLnDG84gT7Adkpgcfpy3ZXghUD4dDrayshHTY7OxsIm1zfHwcdnDjAKWREDprhEDj3HCkrrg4PQcEvqmDyNnZIhQLgJDP58P7gFAXVN8Nm8/nE49Chg2jfotzkwGQOMlsNhsYZt/U4WkqwBD9g1VnE42zL5lMJvFQFTfebkTdsHMvKXk2KdfyY96Ym/n5ebVaLa2srAQwizGXFOYjl8vpypUrKpfLarVa+uyzz7S0tKS9vb1Qfw0LCtPprIU0Smd3Op2wCZK+4OTov5/xjZFHjugjjHEqNXqQjR8F5sfWOWvi8udALpVKJY45rFarGg5PSn28HMTlxxkRUt5xvSt/syYYYA+umBsPLnGSGDVkh+MFue7BwUEI1txI0w4ODoLjQJbQI5w9Bt7rfWnOQCJntDigI7imflMaPcrYwQAZBGdsYKAkJU5VQXdxqhzzxTgnJiYSrCvzmclkQuYCOSQg9yCLNXQnTL8orSF7wtq5vXGQ5UyvOyTGyzy6HLiMeNAXM5fOkMXss8tYnOVwMA5olBROsyBD4TYobg6WY+CMnLvN9nWNr+d6GF8/BmvIbQzYYsY6fs3nBADmpX4+N75GPj8OYNFJ5Ij1joFHzJYPh6O9N+l0Oth6fAvsK/rugNpLenye40CJ11x/0dEYWDJuD5SlZPaTMeBfOKUDsobvxmvphJrPBVlf6SSYnpubC0/p9fmK++P4AHvJDzaEfvncoacOsD0ocVnzgN7H7msYE2M+NtdVrs9a0c9YdmPwznUPDw8TD1TBX/tRkIPBSSmIB9/SKCs3jshhvF5OxnueYXO77PI4HI5OrvFg47Q9v70QqMYQ1Gq1UO7hG/06nU44CQQGB6cWC6ynT2Dk3NE6E4YCEY3hHP2kDmkkTNQY8TmcBvdF+d3YVavVwAygFJx0wuZHTlKgbhKFJxXC96VRXTaA2s+bZnyAAwd/lDTEDIWUPBUFppFxu6N2Z+wbb/y+hUIh8QhyruPsOIbu7Nmzmpyc1FdffaV79+5pa2sr1L/yuG2UjTFxHxyIl7agrF6i4qlTSWF9GYefwY3ik/LzNR5XI+8y5ywC9fs+L6zT/v5+CKIAi5zyISm8R0lRvBmXezjQYX0c5EqjtB4ZFWcgCOAodWDTFOsEc4GOeIYHea3X688wa8wRZ5Hj+Bz8xeDNmQyCAAA1xzlxcgZyCGgjyHZw5yDH58oBhZ+5yrVZc3eazAXjov7VN8SiD9gkf5KplwEcHx+HIBhHzWeczfVskTNVPscxOHZgFjNiODz6FINurottjR2+p21jsIltdGYa/XJmlHv4GvM9L89zdp6gxPXM9TwGL3G/XC//MYY7ZjZZM2/Irp+kNI5Z8/ny31KS6eY+Lgfe9/g6fB67RsbEx+0n93iA6/OMHLh9dr/Ja9iLeA4InrmvzyPyN24OfC342wP5rwOhjD2eh+FwqFarpVarFYC6y2MM2HyM/M1nnKiRRuDQ92l5Fs8BpgPdeNzxOMY110n/fAzA42CS92Nmm3t7loATzlg/B+r+gKzBYPDMk3KdyHJAjTzFwTX642SAE3bIlh/T909hqf+QmewX3qh45syZ8KADTx/zKGQiUN+A54bQHSfADWOFwPP6cDgMkRmGnDIL3+TAtTGkXIejj9wxuZDy+UqlkqjJoqaMvjWbzQDgSVnRJ1iC2JEBsAG1LuwwxQQKfNdPBnFjGDMKAOpxTgtFBIjAbDrYymQy2t7eDoZdUmD2arVaCDC2t7fVaDR0eHioX/ziF9rY2Ah13PxgLJxlJajB8RIFMwY+5wwjjh0mkFKQer0enqjHY2w5Tg5m2NnM2Eg6m+QBEw6N+WX9/cQOjtUjHYcR9xo3DPk4cORp1KBwBkbYBBnXciP3gGevtaPfcX0whpHxID+NRkMzMzPhCDNPXXvWgnHFc+V99/lF1sgGOQPjewxgst0ZAnJiRtaZGmfgmAd02k/e4T3WJq4nd+bM2TAveUEeANAxS4StYQNXXB7DXDInXGecA3ZmC3lx/XW2Ov5e7OxZD+bNsyEx8+fg3+XW2VrWVRodZyqdnLzgZIanmBmD9HwWjt8O/ByE+Nhj4P28OYzlzRvAxG2Ms4nY6biP48bkJEzM5DNv476PTLMmPs+Afl93dCY+bcfJCuQCX+G672PDf3iA9nUZCNdFHzt98vfigInv++vxGvpY8AX8HwM1Z5/jY2y5ngdScWmHv+92kL75eMbpGdfw7/h7sb7Sd9Y3Jk2c8HrePRkXvjubzQYSwtfcWXsveZOUsHe+TvTB30MnGAtyB57is2ycZJ4J5iETTtv49kKgmtpa0vNE7/v7+4lHTnuNMALkig9T6kwlQgkb1e/3A0tF+UI2m9XOzk6ojeb6OEUMKcLozKdHqAglu71JhabTaZVKJdXrdR0eHoaHllA6AisL6HFjCbuA0HEtF0r6ihP02mePogHBXM8VgjHymoNqT+dLCoEN16K0ZW9vL8FSlEolNRoNFYvF8JuTNe7cuRP6xPF48Rml7sCdNcK4k/LkNWfqpFG07A6gUqmEB8oQsGHw4k1iXifv64zBANy6I2PN/PgiGGGMCRtGDg8PE3WKnip2FqzX64X1xglKSYfhoIUMBptPaJzDS3DC/XyTCg1D7veOz09fWFh4ZsOhyxcGmut7etVTjC5rsCk4dww8zBn3AQC4EZdGGRQ/koy5IiPAHBQKBe3t7YX6Tg8MYnljThiTs/PObrvzj5lOH8u48/UBpYzRASpz8TxGy3/TmJtxzDRz4t9FlwC+Fy9eDIAX3WQd6IuvPbXgMaPIvdLptGZnZzUxMRFkH5uBLqNPXkYzDjR4f2PGELl19s6bA2e/3rh70Fg3HmjED3MSNycjsE1+fZ+XcUykByM+BuwUQAQd4DqALbJcyLozyg4wsXHoE3PjJTn0E9/opImDPkkJWxjLAu8zDret3MPnAZsbA2rPUMfzhK8nyxuz4x7cOwjEVzgIjQE/1yF4cdDNvLlseVBJe56u+rX5O76Wjzn+vs8d8+DfwU7xN9d0Ioi14XO850ebcn/myskr+s9+M2y4l4G5nUBHXZb84VenLdlemKkGZAJ69/b2Qn0wgHGcwQZs+jnFvvufCB1FAbzxMBJKMPwsZgw7hkYaPb3M00UO6Jyhpi+SwpMKC4VCeCIg7DQnnUgKpRS0WOAzmdET9zzSRoEccLgTxWDiwLmOO376yznIXFd69tg8B2nUuHIqC9/LZk82L87Ozobd3sfHx1pfXw/nj8NCMgde+uKRO4YJGfCUUcweYEhippp1QYkB9vQZ4IDcuJzFDBK/yYjEhhpQBzh2dhUwjTF2pt03ZBLZ+zoAOJ11RAeYI/pFUOcsbDabVb1eD+ANh+YAnGvQb8bLA4PoXyaTCeOI5wZjy28HizH75Yyuy9NgMAh99sCKuYkdNXsU/Hi3YrGYSEfC8jprh+OtVqtB7lOpk01PbDwD7FGH7mCadcRuxKl9L4FwEBEz0D4+B4Ssu8tEDMJcRsa95oAsZo9p2C7PVFSrVV26dEmbm5va3d0Na4Tj9TGQ1YkzXC4XZGg4LQlyAXvh4JD55LtxYOslZHHJhAPsmFEcN0/YWEkBhI4LWmJgx5hicOkBgQO657WYyXd7Ni6Acv33gNIDPRoB9rjSIA8O8R9cm/n1ufF1QaeRBSehsC1eQkWjpM3LKcbNh4N+ALIf/0Y/2FgNEUJf2HOF/fB1RobIhjtp4zLlAfE4WXZZZ9yxPrssPk8OPJiLQfO4LED8HX+N386mu35gi/yzflysg1zW18uS/PqSEtfD5rsN5+FgDs6Zc+8XY+Go09M2vr0QqMbhDYcnp1bAWMOKxKkeBBhGBTYXhm8wGCQe9IHBAszWarWgjKRgUQg/z9Gdox/RFTMYOAWADEJVqVRUr9e1u7urp0+fhloiasT9zF+AooMSHx9BgAskhsxfx9nFRhYG1QG5g8TDw0MdHBwEoBobaKJIPx0imz3ZSEnNnnRiyKvVqq5cuSLphA3e3NwM52dms9kQWHS73VCOwFw6gGbdvBQAJfUaMAeF1Aaj+LHTkk7OiHWD4KdwxMCItYhr6XzuPOABMPAem9p2d3eDTAN0CQrRgXjDqwc+vmnGAZfLH7JCWp2gkbkEaPZ6vYTxYm6ZX3dezDFO3o8D80Z/WSs33Mi2l1A4E5RKpUJpUL/f197eXtiU6nrn94rZvRh8s44O5j2b4xtz4ie5wcgzVzgoQKc0eviEB28Aq7hcwYGtz0PM9o5jVh28x+3rXnNmyeeJz3gQivyQUcB+3r59O5Ri8Vkvj3HHDlhxJjIuhZqfn9ePfvQjra6u6mc/+5kkBdvlLL/rhJRk/D1Dge2kD84WxgynBxkOpPyscNaOIM0ZdLcLXpbFvHDt+AQDz5zRfDzuz6TRU/mex/a7nI+raaWf8VnLrJsHML5OPlbs2Tjw5rrnQZCDOAIsB90+3vg39/HXWFcePc59scOcpIW+xkytA3MPbgB1Tpghox74MrfYPGyD2363QXHfxzHSPm8+7vhzYICYtHCdi+fM55mx+2EJ/uPXhEThdeTH90FwL95zUije9A5h4ScugevYJ8N1vc+5XE6Tk5PPBK6nbdRe+JxqlMNTMjRPWyFIzztVAMeAg8Zgc+wMDp8NY14r5CkT0t4OtNxJEe076OWs7X6/r0ajoampKa2tranZbIZjbXjYiLPwOEAcG4aNkgF//Kw0espeDD5J8fhGPBwkc0LfpVGk6Wdg8z0AOACdfjhIgc3jmjw0ZGpqSt1uVysrK0qn0/8Xe/8SY9uWnWdiY+29I2K/I+I87iN5M0klMyVQJCVRKolWT4ABq1wwUIYaBbjjqoLhjm3ATZcBAwZsw1DXsAED1RCq1LEttVyNKpgFA5RFgRSclAQSpEgxk/k8994858RjP+O1915uxP3m/taIFScZF7KqoHsmEIiI/VhrzjHHHOMf/xhzrvjggw+irutSQ84DcpAtc+p5jYjSbzsOyniqqmpsiiDwIdJ26QblF9wTA+Cj9eykkQvGbbvdFmbaOkIAxf3YpEWARTbCzIYf6mH9NCvFa8y1z1TOn+OIQbIN3e7+UfTD4TDW63VcXl4WVmcwGDRAkdkIXzcznJmBsrNGVj6dw6wszLHZTdY4aUJ08ubmpgBzb7oyE05/CdB8EgN9MbiwTufsQKfTidlsVh4S5LQk65L5BtQA9M1cORDKLLEdPs6aa5jFQZ4G4C5HyyywHfNjTCoOLzt3ByJ+j3VzeXn5IHhpA/0GJ9hErxNszsHBQXzzm9+M+Xwe3/nOd8qG3Bw0WIYODDxmB9N5TTEXvi79MjDw2GHRDw/vnyCLbPr9fimB+ZM/+ZOi/87i0DfbTr9On7O+OLAxyM/2gN85g8fax6dkP2b2HZl646HLOwCOtr/0KwNqM+SZsYXUwq5lIGq7Yf3lb+wv37Wu8T7+nYyws5ImPCwjdAY5OtNhGRgsMj4ztV5vBtbW3QyWHwO+/ts6z3pytoZ14v76ugbHzkgwJs+158LYCZmwrvhuPrABWRlD0Ec/xMhzyLHEeX4c2PFALx87mVsO8L5q7cnlHzhrb6iwAqHcLsHg6CCXR7DgeI+FP5lMGmfA2hCjdChCxJ69Q7G5p1lal3q4LGI8HsdgMIjPPvus1CPC1PqJWVYyHBBj5sEyrjFy1OxzfzHoTpPhkAAofDY7Kp/LzUI1kAWMwRzidKkD3+32TwwbDofx4sWLqKp7xvjk5CQODw/jzZs3JVJte5AJ4NQsjYESxtRHO6EHPn3FKcDZbFbKUDDyvqbPIM+AMoM4M+iwjMiD00roP8aJ02o6nU5hjM2oMA+Z8eQ93w9Z2YERIB4eHpa0GfKLuN9IeHh4GG/fvo2Tk5P4xV/8xbi6uorZbHa/QLXpxfrIWkTXkb9Lk/ifteGabMbjujxkZqaTtWrnd3d31ziNxRkZ+sx6oSaSfiM/s5wOLmBgYdFwPgR4Xvu0DCoiotgV5okfAzh0BpmZAYzYH1XF55xhsszRkezUkIn7lgG1najBmvXbwaxZXta+AZTva3KDezlj6HkHbN3c3MTv/u7vFuYRB2x75HVg55znxHbdR2TahqHDLtPK5yGjIxAUDmTpV13f73P5xV/8xTg/P4+zs7MC5s1KZ/BpEOQ+c/0cIHnTeZ5L/Fsu49jtdqX/XJfA0Sw2AajBm+eP9Z+JA94zO85384YyZ7XagDt6xzXeBUrRBesgMrLdurm5idls9iCbZZ9u357Hwr4S7+vINpB75TUEQMc+Z/BsXc06kseZiZK8z8SZkcz0cj3siAMB+uj72a74fb7vvVouUfMa9T2tYzz91yQB71n3LZ+IKKTker1+J6j+qrcngWqcHZEyC4CUeERzd25OcbmuGqBl5aEem/pdKzD3i9jXQPL5tvMzSbNTCoESkdow4GIjXEQUQA0YjGhuuOD6nU4nptNpeVw3DgLQHHFfA4qMcOikrFF0MxYsTuTHWAH83IeaRwAggQqf9yN4HcgcHx/H6elpefjG1dVVjMfjWK/X5dxpsgUGkXW9P+XB1+x0OqUeywyLH9SDzDEgNqyUlMCOAqQZB+UeXuxmTOwI7aAADvTF56W7rIHrIx8/xcoG1CDOxtfALKLpsHF8o9GoZB+YP2cwnj17Fuv1Ok5PT8vrHJOUjTYy9vF9BrRm/w20WFucOGNAaINrx0Bw5J9ut9sA+xh3O95cFsN8+Dg+SoJcn97pdEpQa9aH9WYHE7HfQ4CdofwsM9AGwm3zaufh1+2UYHEAaPmBHZZ/Zj3tJGl2ntZfxor+tOkV80XZ3NHRUeNYTK7BHNpZska8drweON0Iu4m+0AcH922gL592w76HDGqzrHx8IRmIbvf+fH3uiX3ALvvzZ2dnMZvNyl6E9XodX//616PX68UPfvCD1nlwRtH65bQ6gYbnjc/muWGzF2uI35AIzDPjYr6zjzGL776YnW0DYBkU+shKdAYfg72loQ85eDP4rKqq+ByXQ1r3PR7bcYKq3FfrPRs6DbQhV5jnHMRkttXznN/P/czXaQPbXnd5XZs4oWWbhW/nNWyR5c33MrNtuxyx1y9sLngir0XPq69r4O254oFwft/98Njr+j6L3TYX79u+PZmp9sYJJpYUIUbaUSoOAJbLqamIJtvHg2Nc02MH0Onsd1QDyvNiwuDCsPmhJyg5IL+u7x/uASjkwST0O7MqlsHJyUl5sh9AbLlcRsQ+oovYO2g7dvqG8cNh8PmIaIzNn6fcBSbdqUIYxru7u8KcE5CMRqMGo045xXq9jrOzsyL37XZfD43hzIC6rvfHFQJoKCdYLpfFkHnzmZ0hYBdm0hkN5spH22UDZofiM50BAwYINzc35dQajA6vG6hTIpNBbET72a2Z3fD7bUwUhgtnQz33v/pX/6psEr2+vi4nf/hUDAMvA5S2dCf6ifE1A22jj7Hl+zbK3qjGD33a7XbF+dEHM2Zmum5vbwtDz3u73a487p05s9y9kY7rs965N7LBfhCoI18z8W1sPvOEfuO820oPrAOu5zcIy2D6XY4cWbWBbuYYZ2gHa0eMLHBuBsx+v673mSP3jwwhdmWz2cRisSjZLBMgyI01mckOgnmnlQlCcqbLgYOvgT2xnTg9PY3nz5/H+fl5CfTwNxHNBxzhKxhDv9+Ps7OzB/sKMmBuyzzRT+7DXBnwMkcuV+L6+DM+ZxBGXw2W+I1PpTE+rzXvyfB6twzQi5zZc/M42oI+Z34Bgaenp9Hr9cqDnNBFPkd/mGPm0nXQvG9b4f4zVmyos5UGqRk4W9cdeBmoZiCY16mvybjy/3ld5zXp61p2ZqfzvbA/zDnfxYfhMyP2JULodJ7ffF37hccyK+xT8J4p5sPXZ42i+zzD43172L5U+Qe/mQgWNAoCcHQNpgEZwI0038HBQSwWi1I7yTVwmDg3HxWWHQ7gyk4SNsMLkiiPDWlcMwPqthQzDun4+DiGw2EZN9egvIL+upYYsEE/kAHXpMGGGRxTPuDNbLCWTpPB/CF3g1j+hqG+u7uL9XpdHjHuB3MA4GFX+T5GDgBK3xlvZgO9KGGDAWfedOonQiGXtmtgKNAJp0GRwWKxaByBhuxg/PPDO5gnz3dO9WGk2wxxNmoeZ6/Xiw8++KAwChHR2IBKcLfb7WKxWDxgSuz8cVLopgE1/7ssh4cH5HEZ5Ll2nzVDYELq3GUGnMZjoFtVVWMtIIObm5u4uLhoyIl1hhPiNXQHJ2zQQDDsTToug3HQ7lSw2Zu2h3ZYJlyP73Fv35PXWQ8ZpGfdYF06k/CYE3fgZEfI+BzcOwCdTCbl+MvMumf9pB+dTqcEdNvtNmazWTk2kPFxHdYia8f15QZ3nABDIL1erxtrMAM4M8KWu+v7Z7NZzOfzRlCT58+2j3VcVfd7T1hPZgtN5PjefM5yM4NcVVUDZEfc23wIB2r8vTaZO8aDPYBIIFAx6OFvy8bllcicfvs38nPmJ+JhAIEtMfC3f8zgmsbGcnwyum35mnxAlwiUkSXv8UMWC9m4zNF7qTI7byKAecnA3uPOdjoHBZYlcgPcu2TM92C+uR5zaP3ObLMJBOuI7ZXfN8B20If9cctAP//4c+gWD2Szr/XmR+TFZ8AH2db8N9Wqqvp7EfE/iIjXdV3/yhevPYuI/0dE/EJE/CAi/oO6ri+q+4H8nyLi34uIdUT8R3Vd/7MvvvMfRsT/5ovL/h/quv7Pv3j9r0XEfxYRg4j4LyPif1m3RapqT96oSLTKbnMWOYC10+k02EOUEEYKQAegxFnXdV02Pbk+F0Pth6RE7FONADvAo53VdrttnHiBgQIUoCSMJ0fSKBlOZDgcxmQyaZweAZgGGAM8M9PhdBrgjr4bBCDniCgMMOPiHlnxMT7I0M7/o48+apQdIP83b97EfD4v/9O8E90Lv6qqUuphmfBZnK9PgWEsyIBx53QrgMogwKCGBey0t+eaEhNOjcHYAZphzRinwRnADZbEzKPXDs4kOzOf4sFmx8PDw3jx4kUBKABeByKsGwIEp2ptxGw4HexhxPnfB/XTf2Sda2gtZ9dRM6e73e4BI35xcREvXrwo/49Go0YpgYNkxjIcDhsBG4E112BO7IAYG0COzxkgO+uEMwBUOC3v00EMNHC2+fO5WU7YN7OvBrl28vy4XtZgn+vx49R/dlbIstPplAcFce/xeBzf/va347vf/W4py0GO3ptBCYZLfhg/8+dsmG2zM4lkX8xku3YdQO1zdh0YGKTZTmOLLUMHOnlOkJ8BtdeFgxn+b8vQ0D83A270Gx3y+z660Ru50Qk2VG6328YxpKzbiOZGM8ZKNoZAkv46E+G+eo4N/gxwbbPymB3w0H8Hgvw2eOQ1B5/oUg4WrWN5Hsno0q/1el3m0uWk9M/9zEwq18wlGG3647FaV9x/xptJCffDr9nf+eQSyzIzwazTXOZHoGG9cnCFPfM4rN95vh9rzuw5WCDIwX/Y74HHMqDP7Wfgzn+d7T+LiP9LRPx9vfafRMT/u67rv1tV1X/yxf//q4j470fEt7/4+fWI+L9GxK9/AcL/txHx70REHRG/W1XVf1HX9cUXn/mfRsQ/jXtQ/e9GxH/1rg49makmBWpQ4E0TAB8aTsGPCq+qe2bMAI2j21AWA9D8Gmkw7o0zcNrUhqqq7uvIDBhYvGyCi2ge+WUlOzo6KidmYNgwFpQV+ExnGxgbE2Rwc3PTCAKyEQAc8+NImPH4oSDMA+mco6OjePbsWYxGo5hMJtHpdOLNmzclu3B+fl42JNr5OwWHgQIw2cjB1OWULePJKV8DLYMoAyY7Hc+z5cjYmXtvMOO6jIFyi+zgbUyZR+bPn8sAlsbrR0dHjZp5NjuORqP45JNP4sMPP4zxeBw/+clPSr1+t9ttPEkTsEvg501QZqadBcgMh4MesxpO+1uuZk2c0eA6MMV5LTnQ8oZazo630fXmTGowuWenc1/CYtBuxsdsn3/oDy07WvQHXTPIcIo1g6Ddbtc4FcPXfyzQycAuB2KMywDGNszEA+l05pP1zuk0BwcHcXV1VfQN8MzJNT/+8Y9jNps90Im28imagR/zhP32g7P4rNk1+mQwxfr3UZMGGTlgMHBxM1kAIMtgzWuA/7lmBmD+bAZDXtcOunwN+xHbXxhjl2xY/qPRKE5PT6Ou6/j888+LvB3MsX78NGHug8yRRd6UiDwdKJlVtI3IwJM+5nFZFpabAwV8LYcUvH79ujCo1nPrP2z+cDiMi4uLUr6IPh8eHpbN9Hl/VAbMXBPd8bx5jfJ+Xqu+Vl6rjwUgbpAjGXjbpzhItsxNLNE48ciytw7QMgOegbTnyH2nn23j9fxYh8E7Pq2lruvGQ/lyIPrfZKvr+v9TVdUvpJf//Yj4W1/8/Z9HxG/GPaj+9yPi79f3QvidqqpOqqr6+IvP/td1XZ9HRFRV9V9HxL9bVdVvRsS0ruvf+eL1vx8R/8P41wmqUXYMCpv9WKiwiGasiKK/6FRENJ/4BtCz87dh8feqar+BijQ1TBtAAifl4968CMyUGlATnUXsGUwc13g8LgtgtVoVdtKftQGzYQY0scFpt9uVEyD8HWrHMEIG3TkqZJEgDxYTjvro6ChGo1HU9X3NOPdfLBYxn88LIAYImx0zSMdRmklyBO26r4goRt7gje8TUNjg2vGiX8x5ZlBIO41Go3Jf5Jb7jLOin4yT/vDbhsbGFb20c7bcP/roo/jbf/tvx+///u/Hv/yX/zKm02n82q/9WhwdHZUjzih/WK1WD2qae71eOeUGcJmdWkQz69GmV7kUhPnifYI8ZIsc2eBmkAygNrtqMB8RJVWY2TXux5rMjJxBPwG2wZiZkrb7cg0DBDtX3kcHcVysD+uW65CZC6fs7Rx9P8aTGeoMpt3nDJY8d+PxuNgjp3JxZradBwcH5Xxw+nxzcxM/+tGPGseSwhrnzYEO7rvdbjkByXXkEBvOshmoGcRYbmQoXMJlYIKssFMAS1/b+m6WHfn5eg6uMtFC8zX9WRrX5jXWktdfG3DodruNhzXZPmRd4PjTiCjkiMshXP6W2XPG6X7n63suHJA6uLaOor+Um7HJP+s1hE0OKHq9/YPCbm5u4vXr1w0W2gDMQBa7QIkSx6+ORqNSz2tdwW87AMqA3cGJ55jPZDDsOfI1kZdLPPxdy86f9xq3zlk3uQ5z7+w0es51nYHAPjP/9pltY8y+KQcT9p8Onmz3Ydb5LHsTaJkk+Vks9b/m9qKqqu/o//+0ruv/9Gd858O6rj/74u/PI+LDL/7+uYj4sT73ky9ee9frP2l5/Z3tyaAa5WOTHkYQJw5YZbKJuFkIi8WicdarDYJT+hgbzsD1QmVRsskNZeUYKBTWLLoVAaBqZhNlc4qezVqwore3t8W5UX9tUOD0tQMIbzqDMccQEVSwcWs8HjfqplFunBiyidgfowQI90L4/PPPS2kHDJTPJiUYMhNKhoH7mFHEeNphu2acyNjOklrizAyaDablRZ+NKgwZwYedN4Blt9u1noWbDR1zb3mZ3bFR4m9+9/v9+NVf/dX4/ve/H2/evImTk5P4+OOPo9/vxw9/+MM4Pz+PDz74ILrd++P6MJgEF5n9PTo6KoGO2TD6a6Pp8om6rksAaYNops5BT1XdP7gFXWVdGkz7BAMDJtglG3LG5XVpNtiBs4GR623RP59jyyZZxkxgYaCZATiA0OvFDhBWz7Kiz5ZzBnAOrFirBkGZBaTPXkNZv7juYrFoOEEzXNifiP0muLdv30a3243j4+OyyRBWCV0ywAJcR0QDqMC08drBwf5MfgMcA2uXmpGVwWZaLozH68r66JSyZWWgwckYbPo24PHc+W/PYRuwoRmY8r9JIq5LMJMzIxAvvG+dyoCUUgYC2H6/3ygF4f7uOz+QI8yVwTNjcAoe3bE9BcjlNZq/w1rOYDHvF2DD8Zs3b+Ly8rLhi9oANb9dtwvTjSw4ZSuvPfcPW8VrllVmpy3TfD3PufWnjZxwawPSvhY+ywx27gN7FrCzrKPtdn/y1Xq9LnKOaJ59b9+ag758T/rMa54f/CS2O4Nw2yfLh+yZScd/g8D6bV3X/86X/XJd13VVVf/GalEivkT5h9PrLhu4vLxs1O7x2Yh7cAbgzaUBOIfMEvM9Jtyb/KjFQgE5hszn4XojjReMDZR3rluhuL6BBxHnbrdrPGUP5cMAU3cI+AMswapHRKmj4uxkP4ikqqqGc6M/LF7u2e3eP8RjOp1GxH1Ee3l5GRFRShE4dYG5QXY4YDtM5MbcuayDz5u1tnNy2om+OM1up2uQ1bbRgnGadXHdb5vxcAbC4zBDxucM8LmeDVQeFzI4Pj4u3/vn//yfx3K5LA8O+ta3vhUffvhhfPbZZ/ELv/AL8ezZswKCYI4pHQKUoCsOQM2+28DyWXSRDIvBGA3Hw30dpLFWbSCRC311sGsQix66JMOOBBmbIUP2fJ6jDQEezDXfsZP0/He73cLosoadxbKOWS8YS9YzB+/Wc+sPa9D7GGjW65wJeIzJcsBDsOk5c58AH8vlMp49exZnZ2dFDmSYIBcoCWF9O2C0LH1Wv4Mdjuv0sW/OAgDO0RlnASi74/t24MyfwYA3W1lG9I3P5aMtGYdlZFvAZ63f2E6aAQn9x+YaoGHzXX6BbJEXzJ6vbVKIgBJW23WzrnXPeszcZ/uM7Om/gyXbN2QR0fSdno8MDnu9Xkyn00K65Fp2B+g+KYv3HtP1bFdtv3Itbw4AHVRnAGsyxcAvExK0TPQQSPg6bey2fTrNRE8mh5C754nPeq07WIUMI4BEznw+l2X47xwc+j2PjddNyrmkyplls/K0w8PDOD09LXY86/5/S9tPq6r6uK7rz6r78o7XX7z+KiK+rs998sVrr2JfLsLrv/nF65+0fP6d7ckbFV2HR13vfD6Put5vjCJC32w2JQ3mEoKIfarewBTj5FpYlyfAPANOAFMG1Ab8KAlG1w7QtXv8Njix8kY8PPKIz6OELC4CC4BKvj4O+/r6uhwXBYjPKe5er1fAGHIDtPusah6ljqxZgDCglOkY5CADjj2zQ7Yxo79+SA+vez5ZyDDTLlvg83wng3LeR0bOHrgUwY4/38vG1SCNOaQ/LhewUbIxMujv9/vxN//m34yjo6P4vd/7vfjzf/7Pxw9/+MPYbDZxenoaX/va12K9Xsdv/uZvxnw+j/F4XJhEl1NE3Ad/HGOIzF1DGNE8KcA673Xh7I4BEPNBuo7v5UyKnRprzq9bD3D8+TMOQEiHtwWq9M2OxUEymxgB/nZ0AB7ubwCd9TQ7UwNqOwtk7kyQAw47RMaSgwyDonxvB2QGgJaXQQdjYINtxH7DX0TEZ5/dZzHZGGpbRsYGJp51730o2BHOo0X+nc79GfPsZWkLThiPmTLA5Te+8Y0YjUbxx3/8x4WxNVvMuAzSAPfMOzKAwDCYyJlFB9V26rZlOdPiIMe2G5DH8aLYTDaIYZP5LsFttjXO8jCHyNBHnboszv3OoN/sfz4liM8SAPqYSsbnANLr3Q0gZbtxeXn5oNwq2+q2oKQNUOc5Q0bGC7bjHqPtNX6K1gamDTb5jteYx2BAbeY7B2TModl7r2nrln0MNioDWme73FdsNLaPvwlyvX4fk7Xfp2Ubyn1t2yxDdMt70wD5lJ0xvsvLy0LY+SF6be1duvFvoP0XEfEfRsTf/eL3/1Ov/y+qqvq/x/1GxdkXwPv/FRH/x6qqTr/43H8vIv7XdV2fV1U1r6rqvxP3GxX/xxHxf/5ZN38SqIZxBaDtdvuTFcwSAbB5iAvNRt6lGVZy10Q6LY1CD4fDmE6nZcNO3mhIowwk99/HQmXATh+p0WZBGGxE7E/I4Dvc1+dpuobQgIlIFMAL4871WKw+QYXv9/v9ODk5KazO2dlZvHnzprFh0E6X1LpLTnxsIM7Eu9NpjJX+AvjbwAHX4/4ZUJsVNRjJxpFUrBkYAA2AgpKGzJijR2a6nf1w7XI20NZPrnl4eBi/9Eu/FL/yK78Sz549iz/4gz+Iv/N3/k50u9343ve+F3Vdx9nZWePR4tPptOgCxyFyf2TDtfkcOmJwwHseg9eRjSb/+4E3dsK8zz1yHTwACBnZKWdA6wAA1hLdyKy0wYfT9ziPu7u7YrwJ7gD6yMVAxZkSs5geozMS9N37OXw9g3EcqAG4T1IhKHMpTRsAyS2zShlI5IDAnyHThi6zwfX8/Dx2u13jUfOcHz0YDBpBEPYDQE2m4ebmppSc+eERWS5tAXHEvf1br9fx2WefFfufx0g/0AGAvdl27N9kMomzs7MGeLMMM3lhXfNnDC5zYMBc+Br5mEB0CICLDvhBQ4yBLCiPbT4/Py/BGgGt1zj+ErBkO4QOMk7vt8mlag5W0U9vGM021Yy+iRC/Z8KL9ZeZ0GzvM8h2YyyuDbaddsBiQorP+Xf+rsdOM2hERpB6zuhZJ7nOdDqNjz76KFarVfzJn/xJOYTBupKDd2TnrLQDXmcY0TMHALZF9N8kXAbnOUBoW5dt/syYhma5UyNNI1PB2CixjYhyqo+rA/7b0Kqq+r/FPcv8oqqqn8T9KR5/NyL+QVVV/5OI+GFE/AdffPy/jPvj9L4b90fq/ccREV+A5/99RPx/v/jc/67+YtNiRPzPYn+k3n8VP2OTYsSXYKoBlKSxfe4vjhsmll3/NnpcA6Pl49xwmhF7RtZM22QyiclkUsAggJpFR8oNJcspJhx9Pt8YpXPa2Abe0SObDFFOFiv9xqDYGbCYMZKkS/v9fuMEBgNxGHcM09HRUXz44YcxGo3i008/jc8//zzevHnTSN84dTQYDMp5w2wI8cNVANGZ0cEhuh0eHjY29iEjWJOIKIwXxh3ZeNNRjvotd2cYDI7QLVjpDIroCzLzfSOaQVceo40msjg8PIxf/uVfjqOjo/jggw/ie9/7Xvzpn/5p/Nqv/VpcXV3F27dv4xd+4Rfi888/L8Yb54Rh3263pfaedcDYfSa59d4GF5l4LnjdzhOw5RIFgwYDdcbn+bBM0BuzcnzeLLWDL3QKJwlD6hpe9NOBFbWUrA+AGTaBvQD0H73yg5/oj9et2T+ACADJ4MMAijnztXB41JG7bKENTNjxuBn4M3+ZLUJOXge2Q8yVT4kYDAaFuebxz9vttvFgDmxGVVXFzjBnZEsIxs2iOhCw02YcBOE8fTVno2iUPgBcCQo4AYLvXF9fx6tXrx7YHK5ncGpAij5lgAmQsr6ZmHHwBIDgGrD9sIjoEPqLL+IITTblXlxcFNvKU3qt8/ZR1lvWt/2c/ZazdJYB13EwkwGYG9/NQbJB5mN22ve0LmQwnW25TxdiDW2390dxsg4zGPY4/b+BLOP22vE1kD99yvPAtU5OTuLFixfxy7/8y/GTn/wk/vAP/zAiIr72ta9FxH3JZFVVjafQGtijD3VdNzbvM27k3VYuwXoDMzhowg/mIPex+WzLHrQFA8YJDmwiohwX7KNP8WuQUdvttpSO5axLW2vr9/8/Wl3X/6NH3vrvtny2joj/+SPX+XsR8fdaXv9ORPzKU/r05Jrq5XJZjC7sDY4LZ+SHCZhBZLHx25G7038GV6S62BgJwOVoOqcROUMaIJEBYE7Htyk7Y6Bvy+Uyrq+vo6qqGI1GhakwY8X4MCKw8JPJpLDSgKHVahXz+byAntvb28I84wwc6cPMj0ajuLi4iN/7vd+L8/PzxjFmbrDTsCXD4bDBsjv1Z5BkgxvRfDCI6yj5HIbNpR4Ab5yeQWaWN7qB8+c1BwgYd5cPtYES5G0A6Y1EtGyU6QNGsaqq+OY3v1ke2PJP/+k/LQD5H/7Dfxh1XcfXvva1clQhess4YG0p5bm6uorFYhGbzabUb3JvGEP3mXEh+8wgMpbRaNSo0WZsdiC5rMTzBEsRsa/3c6BIcMG84BQJiCP2WRn+X61WjftbR8ze5z4xRn4DDF0DaGCJTrQxWoBLdNtlKvTJDJ1BYWaSnH73ezkY4b0MqM1EZjaJ91kntoXofgaTs9mslHGNRqP41re+FX/wB38Qs9msMNSsZYNH9JDrLBaLWK/XZdwASa8H+sy1MvHB+O38GdN2e/9Ezm9/+9vx0Ucfxe///u/Her0uJW69Xi/Ozs7K/dtsQ0SzvM6sNHbdes91ADZkuzw/BiLMPUeHZTtCNpY6Y8oW+NxyuYxerxer1ar4InTWcnOQ5yDFPpFA2xlB29ysU85OmWXMDX1gnRtgOaOJDmLL6edjoDk3g1tsqe0/7yFfr8uc/fQ1bfOQif/3Zw3GHYg6W+egMOK+NPL29jYuLi5isViU7ICfEuwDBWgEDfQHG4z/t91w6RP/23ZxEgrzzfW4to+ydWCRSQ7k0EaI2faY0AKPsM68edxjR1+y328jh963+/bk0z9YrJwMEbGfJBTBzpQF5BpPJszHm1FTyIJEuSaTSYxGo0bqmnQn6Qoew009XEQTmBD9WSFxsDZSKCnjo1YbQ0uphhXVgLrT6ZTC/ul0GrvdrmzugN1hVziOEyABO9Lr9eL4+DgGg0GcnJzEZDKJy8vL+M53vhPz+bycnoDcXSLjukIWhGuh7YjM+sBY2QDzGW8QQ540HIEZD8vYabtsmFi8+eFBnOkMKPBRfG0RuFkrOyQfReQAKDsL5prv/uhHP4of/ehHxXn3+/24vLyMbrcbz58/j8PDwwKguB6ydor9/Py8PBEOVivfFyMGoLbOsi6sWzmlarCDA7XhRlbOYNgpOBDEcBLkEJgApr1L3Rtw67ouT9RjnjnZgrVnUOR5aHMAEdE4r5sg3SfNOLvCWjajnB1qZvLaGCBkQsDB/e2szAry/d1uV043yOndtqDXLJqdoz+bQQP9Y72sVqv4Z//sn8Xd3V380i/9UqzX6/jud79bGHkHtD4qdL1eN+xHG+NoJ22ZOug7OjqKr3/96zGbzeLTTz8tcuXe2Kwf/OAH5bjUqqpiOp3Gcrl8wG57nVoG6HyWeZ5XbJsDLTPwBrLck7S8gx4DSzKwWU/RD/wQ2Vh0Jmd2rIdmxW078gN0zGwzZrPLDvLwsdYbB3OsX2dMLWPWfyYg3M/8v/Xc8rVddwkLhJZtLQDb1zcwdgmbQbN1BLvlfpqoi4hSyum1zOdWq1WsVqtGRhV5ec1zjzy/EQ9ZZnCNQTz953o0/sZfYXcZ12azeXD8pQE8JIL3IuQx0m/jHGTBPPBdPsdTPzudTnniaiZ+PA/vW7M9GVRH7Bc+u/hZKEShZoZwBl4wZhdch8wipw4YwEXJBA7BiwQGBLaHa9PMelthI5r1Wz75AEAN6wKgxkjA7uCI/ehYUv4+D3q1WsXFxUUDDBPVAqbp6/Pnz+P09DROT09juVzG7//+78fr16/L2AEzfrKgS05spMwmcQwW82cgAkOObOxcbPiYO/fD4MMsIO/TDwdMPjs8Yn+WOE8kpI8+LcYLOgNqg0TXBWdW0sCc6/h/syEAagwPJTSXl5eNcgruwRhXq1UBnv1+v2woAqS6zo9xcS0HpKwDdKONXTBLg+5m5oj7+AEfjJO/0X0cIYEe6UzuvV6vGyfJYNxZ+3bM9MtOD3k7kELvHbx5LFzH48+lGgbKZuG4n9k36yM2ivpldIDXcbIZzDEuM61m+rMD93UNRGgOSDOYYV46nU588MEH8cknn8Tl5WW8efMmnj9/HicnJ/HHf/zHJWgDMCKzk5OTqKoqZrNZCejzuPJ6yaCCbBe10EdHR/H69es4Pz9vBDjInLpvg4jz8/M4Pz9vAMXHGFGz1J5bf74NUBuI5vlxg5kzs2dQ5/nAntjGXl1dFVuFLQdwYW+dtbMOe+wGZ8gff9rG3NJ3rsW1DSitV7Z5yIz7ep+SSYscvGSw678doGOzxuNxI8PC/fIxms5uGKAZO3gsbS1nSCwzxuNTUB6z/RAPrB3LPBMnlgv2yL6QjAlzlckkz9lms4nZbBZHR/cPayOLhI08PT0tmaZMIpjBzvPs1233kBU+k76D09zHbrdbDkBgP4TXwvv2eHuydFz37IlzxIQi+Vg7JpZD56+vr4uyACyJkKhZe/v2bUREAVw4XgwowIOUC9Gwj42L2EeLdqQGIx4PR93VdV12iDtFw3mTBlKWBU83oy/r9bqwMywWaqUxwhi48Xgc0+k0+v1+/Omf/mn89Kc/bWwowsCZnWYRlAlV7d9ut4vj4+P49NNPi4zMVGEwuIbrrliodowYEm9u8vuZITAT4sCGejqn11zf6Tnmmu6DmRE7f75L0NHmVNFDsy2+lp0jpT4YHWQDCAHwIj/0czgcxrNnz4qu+wEfZsCcDWBteP7cPN+Mg37ApnjdodvonMfMWmAO0SmCQEAKrDvrwU/bY/5YM3aOBjxegw7CLH/ucXV11SjhwnlQq02/zMgYUOXaVRo66QyKf8MUUWKGTuWAwGCGe1VVVezWarUqwWhm1NqagbbXj8G1Qe7Z2Vnc3t7G6elpfPvb347NZhO/+7u/Wx5IhS0FJPlhI35ke05L0wdkbnnB4PmR8W/evGl832syopmWNojIICOP0fc0+OG7OXAx6PF1Mqvp+5uxzbaKv/N1fNY+zKeDUAIN95vWRuB43dFYRw5w3HfbJo+f+aT/BsYZEGdAzfrymmmb07br4YNoR0f3T0hkzAaAWdfMBrcFFnyPOaM/OUMVEYVMszydkchBKq+7cU2z1SYdmFfPr+eKlp887EDIAaHnFoyx2WxKNt393G63JWNuEgJbaj/CWLiPsYEDfTPYlBmhU51OJy4vL2M+n8dgMIjpdFrWPdUEHOP7vrW3L1X+EdE8KxagBNtmBTVgoIZtvV4X5aOu6OTkpMEGLxaLYoR9ugWAhEPkDagBEDgH2DnSJj7ayLWfLAAAcMQ+IEBxXUNq5t1GEiPR6XQKKwnjRx/9UBmc3uHhYSn5uLq6iu9///ul5ov6R0CMAbnBILKEVdput/H8+fOSnmRTmaNTOzMMltkhZI1hI6hwJG4Aj+HAcPsEBQdgZqoJ0rg28mwzoAbA1CECcDCETpPZwBg8ci07CcZDVM5GDgITQCz9AlBi4GD00M3Dw8PG6QKWMzJwyQ7Ogc8CaD12PuOMkEEX8rIBNTNtZ+MTDfy/5eTTYRaLRem3WXf02AGRZW6AiQxxFuj0ZrMpdY0wxhkcG6SbBfe+DgM5z7F10ww0nx0Oh/GNb3wjXr58GXd3d/G9732vURqAfnt8Bods/LO+ZmDt/vCZXA9v0E7jPYDTN7/5zXj+/HksFov4oz/6o6JnNzc3BfAhx/F4XNYB73nDcWYxDahs35xBcp+8Rq1ntod24Hkd+1oGWQa+EdHYDGw2mms5i+V7tjGg/J1ZPds5SvMimo+iZrwG4c40ci3u4e96PIzV65vPOKj2fOVmXWQNe0zYwSwr/AYBvm2O5WNmne+brUc2nc7+vHTsLmP2uoTxzPfy2HNwxdzCNvvzvIcNgEhh/5NtYts90RPjg1yuwz08ZjBNXg+eX4K/vGeJz3DPyWQSw+Ewlstl4+x+X/fu7q5gG4IhdJMH6bkP7nvGa5aLdZH3mdOIKJlZXqcM7+Tk5EHt+PvWbF/qnOqIKOURTBApbxSVFHG3u984t1qtCkBmYobDYYzH47JbPGK/i57FiNMgwiWtbkOM4uF8qLOOiMbDCVy3isLwRDFOabBD8ZnTBokRzaem8T6MXkQURt6L3owIIOXDDz+Mbrcbn332WZyfn5caUtfUskkJEJcXPNel7OTFixdxe3vbuB59zsbcjJHLQjKb6vIEjDYMpwErDePjiJqAgPtQl5hTnuhbdrj8TeROmQhzy3uZsfH8OajKrDFGGAN7cHDQeLAFrJSNG4ALkF3XdaN2FBCx2Wwap3UwH8g3Yn9eq89vRr8y60Rwgp4yh24ANzsZB8Nm0jHsPmHDj1u+vr4uRt610gbkyCjLkjWATQD40ff8oCbkD3hvO/kFkM5Y3JytMEA2g9/v9+Ply5fxV//qX41vfvObERHxO7/zOw1H1u/3Y7FYNABC1tM2YNLWrI/v+iz9RMYRUdb/1dVVHB0dxfX1dXzwwQfxwx/+MKqqiuPj4+j1eiV9zJMJsV1kzgxKDTry/ZGtQRVjzayfgTGy9+cNuP3ZPEbLCRDhzzo45z7c078zA8tv1loOJiL24Bng44Cd6+JXsEO2Afgml0PwGeusgaoBOGsi12S3AbvHwDMyzrYNGSNP1ryZVs9B3qTrYMc60O3en4QCoDZwdNbIIN2ytPxd1sLrBLP4DxNW/q6zbJkN9npjPmBo3V+CTsvDcrdMsx56DHwe2To4y6TFaDQqeCUHVej7brcrpV22MxAaZKas19aPXEYDIUSw4j5xj15v/7CibrdbmHQOXzAL39ayzfiqtSeD6ojmwwXqui6lGVY2SjPG43HUdV2Y14h9nTPHvuEwWOiUO3C6B4uFDYMYNhYhhoyHbgCouZYVGSVC2dbrdSwWiwZ49thQLIyAFw4GEuaUWuzlclnkAjBjAeT6a4D/j3/84zg/Py+yRNZmNOkbim92BfYflpRjnpwetxHIjo+xZhAGu59ZNX+GSN+byZAhWQwYTcYyn88LULez57o2wK73spGeTCbx9u3bYkyy88qA3MAKI+dIHRCby4dcqwwQBewaTDrdjjPg/dvb23Jt61EbWwWog5lG3wCVOEiuCwg2W+G0K+ASWcAsA5LRB6dPndlgHqgPdxbDfWdcOCTL2OCLWnWn79Fn9AywbjDkenTWJ0afwJH7AyDM5KB79PX58+fxV/7KX4nDw8P4x//4H8fnn38e8/m84Zi8NrKjyOxQfi+3NjBnxszyM5vk12azWfyjf/SPSuYAhpyxTafTOD4+jm63Wxhm5IouWW8zQGbMBtwGpvmzeZ3l7+XMEH87y5YDDO7nQBodtT5ZjvxmjVj/8z2siwTCtvdtAMg65R/WY9Z520Lbs7ZmnaWhV9y7DdR5v5IBIg0bYtbZpyJZ/vZzrDcz09hhZNTpdGI0GpUaaq5NELzdbsteCe/ZMVHgTIfJFs9ftt34E8pFOaIRcJpPMWJerFfWX34zZgcx6Ahz4GcdIF9wQ87YMWYCM65NBgncA+lIJp0H6XkvWL4H/olSVALAvLY9PusavsOf4e/BYBDHx8dFxugJTz2tqirm8/n70z/e0Z5c/gEL2O12CzvtOkKMw2AwiMlkUorxcXgYAZRjOBzGixcvSpkDQNiPzwUYtAFqFInrk44DFGAsstGEwVosFmUh0FA6/na0mjfZYfBYEMvlshhbb35i8VCiwTVvb2/j+9//frx9+/ZBqQgglL4Dnli8gC4MF6UhpGpylMo4ubfHnZkg7mdg5DQrf3vjqB0HBt2MFPNExsInCwBiDXwBTAYXbIo9OTlp3NepPLM6EQ8dmwGhQTiGA+fhEhuDVWr+cU6udabcxtem74wVI0o/YWxdKsVrzJEfiIShprzE4M6pSNYNBpmx4Pj4PNd2aYQBEWNibswu4oyQs3+j+1kOPgrMfQU4W1ddj+7gC308ONifa+1AzEwUZVesXeZ/NpvFb/3Wb5X7tQFJs0B2ynmcueU07mOA2uDUtfD+PO3u7i4+++yz2O128Y1vfCPu7u7izZs3sVqtYjabxeHhYUwmk5IBYpxXV1flbHCztXl+cpDUljnKLQfA/N8WJPM52wmPlQDDMnTAx5x7Ptxv958+tJUrOFDnf5jm7Avs0/K9TGrQCEjdDHY8bgf8GRAZuEdEw15Y//Gn3ryPTWo7qcqBue/N9xif98Q4y3d9fR3dbjcmk0kMBoPyPRMktjN3d/uHqSBHkwmAb48/22f7JAiOzWYTFxcX5TqsG6/jqtqXTDj7yvxjx3gv988suctwPG9k2cAMYBivC9efj8fjApjZOMzeGwKx/CCsu7u7GI/H5XQnZD4YDOLm5qbgoe32vv7aPo115MDImU3rebfbLQ+VckacNcj3CGze1TIB8VVqT96oCBglLcwi8kkW/LYxJzKLuFey8Xgcz58/j+l0WnZT+6ExlARE7OuunXpzZA7DTHlETodE7HfMsjjYkOhFjPNlkZsh5jW+G7HfqOZjxijsByBgeNmQANuH45vNZvH27dsCXEjVslCJ+g00kUld16VUhr64NtkRO5EwCyV/hr+dOmUcdmo0xufI36yDgwE7q5zGN+DIRp4yHxg3O46zs7NG0OCjw9qYKY8zAwruRQo9OzaMHyVKgDvAKtfxZlLmyakyXvNjkTHc+bxgWBHu48DED8JBtw0emHMHg9zbpUU+6xzZR+zLNXyGtbMz1m3uabk6I5JBB69l3UFveJ3++aQaAynWqusgDcwcFB8fH8c3vvGN2Gw25bHffC5ncQwQLcs8hjaA+VhzwJkZJDOOzCVzZ4YNncTZUl60Xq8LuGE9UToUEeUozwyocz8cnOaWs1q8lllA67xBIbple2Q2jX4APmxzmH8DlceCkxw853t6/lhTbaUKHp99jkG/gyGDH9axyQHmtS2Qt3/ye/zt/zOzS3CNraXhLz1u98dkh+fW5AQ/JpLQPa7ts7Rdfma2GSyAX/P7EBnMOTaHvjpryvyz5rkX68eZcr6DfFnf1s+s095oil33+8ia7zPf6IZtqYE8feHey+WygGTqzL3/iqcW9nq9EhgfHByUhz2RYTQxCINsIs1ZU3SDtZBf4zfPP2A+OULYR/exzrNdf9/27clMNWc7ouQG01ZEPzSFdK+dOyCT4+d8hiospplHnDjXiogyyWZ/AaeOMnksL2wuLLgde6fTKeUqKCSLw2AII+VUD44LVghw75M9vMAB9JxfjTNAlhgyjy+iuXEiAzWPBefqI9zMYiAjG3xHoo7Kc9/5fDZg/PZTIJ1iNjPt75j9xuja0fPwFDsx9w2jinFzqpC+mnnIrBjzzqYRn2SB3lXV/Rm7k8mkAFlKNLgf+uYyHe7ltKJTl6wXs2MwBHZMOEJAFXJEL5AnzgNd8j4Dl1KYqaa/yIjrM5eZQcuAyNfznFr+GGKcLM6BZgPNPR4rXWI90azb/O86U0rBZrNZ2W9gFpNr+5r0zYGl+2BQZzDTBrYzi9sGrLye8/X9m2AIxprvV1VVGEQDauY+A0c3A8m2PmQQmxlOfz7/8P0cUEREQ3eRv+XD69zDTKUDPLNo3INrGPB7Lrge/bZc+I7XsoOEDJYc8Ox2u+j3+w/KF2wHs8302PLc+zPOskH4GPhiOwGqzv4ZhPE6disHGxFR/BCZUPwF4M723uy35crc8dRJ72VCDm2BvJlp5sFrHCBqMszg27oU0QxSPa/cs67r4rOcybVsGGdEM1OYdcz+hzljfTt7wz6xo6OjBil2d3dXAuRO534DNcA94h5/kOXldCoyq+fn5wWA+whM+m9SxusROXGCkfdtdTr7LCb+xlmR9629PQlUb7f3p3KguKQJMD4oHjXRTILBIobn9PQ0ttttzGazmM1mxYkCWjh8PKekmExv4uOkBB8sDzDh6D4WFqyy0xfUNk+n05LmyoY7A5yIKNeGtTezQbRZVfvHkR4cHDSYeI7gYgGxAAH1bXVLBp5mn2B6mAPXt2VGkrm0oSEaNThHjpmdtAGxg7Ex9tzRj+w8XJJAQ4Y5DetmB2cQ6zljjNwfgMR9I6KUE43H4xiPx2WeMuBGz8wOc28cK2PwHFDXj9zMoNEf5sdMjgEi68aM7GPjtpNBN/I+AubLTx6N2J8OY8DO306bZibNDpVrOQDIzhM98jVyQGh2MgNabybyqQDIbDwex0cffRQXFxexXq9L8OI16mCHekTGlhl363e2R2166ebrZtbRDO5j7+VNTsitrutG/a3PsfVpM3VdN5g/yzwHEvn+7r//z+NFJozFY6a/DjAjHh5t6L8dIHltOVD1e5bvY9kTf873zOPMQMGgmetZTxk/MvCa5DUDasZmG2XZ89vzzLWQg49ipaEf+Ayu7X5sNpvyXdsi+mbbiG5BcFXV/thIfCD2iofAGcxik8zyG7wjE4PbnEVxsNkmH+sUG/k4IAD5Wt/4n2CONYN9MPi2Hlg+7i9YxPrlLIt9J3NhAqbT6cRqtSpjhMiAdCEbwPc5cjQiStaeflLyQTVAXe+zCC5xsf+g7Xa78tRm7G4uoSQj9vz589LX9+3x9mSmGtBDzRaThkPGAfsQ/IgoSnJ6ehrHx8flmC6+x0JhsfJd7oniZ6PGI3pZ7BgtFMFOxg8T4TWibx4g49QXzVE+/YDtZmMhzLtlQj9cx3Rzc1OYeUBbWzqF12CODboAWTkiZsFyHxgKOxk7cQyb2SzknRnq7XZbNoQZBBA4YcxzTawBHjKnz1mvAGP0GYOTI2t+e+PZdrstEbo3P5qVNVuMLDFQPDXKRojxAWLq+r4Olz0DmS224aIWnLImOztnPDBqZoSRG4bZgBrd4FouG6jruhFAunyIINMAF/0yyMXgw9i31d4ZoNhBZgYI+WTwRADt+fB79MkO1SnRDAjNjh4cHMTnn39e5nY0GkVd1w8e1oFe+2/0EFtkltotg+d3Nc9bliFBma8PGLEzdmDD9+mHX/MJKpTPPBYEtAHk3L/MOkc8rCn2mfeALo8pM4e85mvkseR78p7nBl3DFvBjAMt9PFbWdu6T38vyiGgCudwvXnNJHXJwoPYuQB3RzPD4NXwP64U1lQkns9UR+zOcDw8Pi05ZJu6fCSH0bbvdllISDhLw3EAOZRCa15h9m8kpbF2Wo3142+vuP2Sa5w07wD2QI69hj2n2F9YjB1W27YwHoqWu92fCuxTHQTMyYb+T+4D9xN5x6EC32y2f2Ww2ZVOon5K8Xq9LSQ5rAh9Ny3pr+zmZTOL09DSGw2HM5/OS7WfcV1dXZfPkcrlsyOh9a29PPv2DTVpExXVdl8WFInS73VJfdnR0VBhgWGV22Tv9GrFXUj/kxYCUhY9CwCLCNhmYOE1t0E5DyYfDYZycnERE82EHfD4bYYIA14tTj0vUi0KSkqUvPhuYz3Jdp+BZBAZPLGqAkuWGDAgaCG7M+ngh4dDNZLn+2yksfj9//rw8dptrcMILBoWSCANZM1Zm57woMXhmRD0fEc3jpfy+GWjkYGBqg4xDcn07mz24HnMFiEMWyHM6nTaAt50k+sepNqvVqvTfgSg71l2jzTzY0XS73cbueacl0YUcvHijjRksdMC17rBMBF+eD+uxnTGvmaGx8wH4+J40HASMC2uVz2Ez0GP0FHlfXV09ANQGR7vdrpzegWzOzs4aoCYDRr6Xf6w3tOxI8mcspzz23F++Y2dHwEwfbHseuydrlgCTjcDeV8Lnva4fA51ZNg5umY88Zq7bthbcb+tibmYonU0w2M6yNVvIPc3oZ+DoxtrNNsZjMsNnUsCZMDObttd5fg2oH9OttvI3B6gE87Y9nhcAJgGlmVMDQr7Db/TODDQ+FeY6Z4GpATabGtEE9y6N4H/AsokDXqdPXM+ZB+ujr02ZbsWZwwABAABJREFUJPLAJ7EWGDMnvPikJAgY6zJ6aF/s91x+xnsul/Dr3MMM+9XVVQwGgxiPx7Hb7WK5XBZ5o9OsZcpSOEWM97Dz3hSPz21bd/apNK5HCQrBsUtlAexVVcXz58+Ljq3X6waZ1NbaAvmvSnsSqAYMYqiYAJdFALIxAtPpNMbjcUTcT/yrV68KSDDLkYEhxsC1SxhBgIEXXV7E/GRAzWQfHR3F8fFxTKfTBoDMZxK7TAJAvVqt4urqKqrqvrj/9PS0ERm7X2bzuK4NaE7F4CTM+NqxGsgZUBmQRDQ3R8AW8JpZOPrW5iwwyqPRqGwcxQH1er3C7nOt7NQyuOf+dgqAcgNv5ruhqL39sXroX2bWmWczoXyH+3izKADbjDbALxtyj9uZC/eZdN7x8XGRg4E8zDXGDFaAOTT4i9g7FYNp9BQHwRw6mLU+0TDEVVU1TsZx6QYyxYE6+5NZvMfAgvvi88OdBnYWhEyM9Rhnz9NM67outfUGHQZNuT92bAZ99C0HXL5OXv9tANhMVpu8/yyN8XhMtnd5XDlINohg7tfrdaPcKzOhyATdMXg3KOSeWcf922Apgx/f2/bP17Dcsg23jH0NB4TcH71zradta9u8Yd98NjsyysRCDsroD33hWswHY/RaxIbwnaxTGZDlzISPk8UWA3pZV/hdQGUOYrJ/cXaJexDIY7NgxVkLlFDxmuWUr2ciiPkgE+VsWp4v5Gu5WN+yXeP9Tqe58RGddnABuIZI8Ppz4OF72N7k+5MFcH9MPJjwu7u7K4TObndfq86cgilgvdGN1WoV8/m8yBfikmsjQ/sEyLzsT6z/DoY4MQg/SJAwGo3KmiL4YFxPtXVfpfZkUN3v9+Pu7i7m83mZPDPEgDrqpsfjcYmqnD6w8sHU5RSbDT7Xp/FZ2G8+Z+ecC+pRIp5qSM0rzde3IqKopGVgMtnc1lYb5n7iwHF21IujnLvdfhOAGR0/mtSgyY4fZt8BCcbQ4IAx2cE4vZ4BOI9LXywWsVgsGovRR88xdxgzs60u37HxIiL3wzkMVNvYQQNkj8MBD0bfQJTvsnGUHc0uE8lMDsyB9d6A1dc2iOx0OmUzI+w35QfoEMDdj4fFuANE0VUHQQZeDtZw1nzPgWUbU+01h27TAMBmy32tNsCELmZm2YGlQbWNP/bCzJWDEfY/WI9zHww8fA/GaNbf2aDcvC4y42q20wFh27UeA465cS1/PoN8z78BpAMV3ud4UAcLuV/Iguv6mplhNZCwXByAWDcMYG0r6HsGtm0ydzCUgxUDb/p2e3vbqDs3uPMaaWuQAugZMvX4HVgyJt/HfeboOOtIGxjnWs6EYh9ycGfQjs2lrM9rLRM5rrev6z27ylzxPIdcqsBnsCH2CQamDp55Pz9/wXPIHJAVs9+uqj1zne1TXkPIGvlaryAvmIter1dK8JhLcAJH33ozd5u+4b8c6PG3gxLjE8ZCf5HF9fV18Zkum2HtOqDEJlJHvdvtSukl/UaXxuNxw3+yP6fNBtCniHt8Np1Oy3MysCWWhx/2hp/wnqH3rb09ufyjrus4OzsrihuxN9hEZRS+UyqyXC5LSiHi4dm2MMAGixEP6/5QOlI4rgHz04EAE2bWcI42Jn50qp2mWQPAF+dZk1qDpWXsGcAa2HOtiCgLy6Az18jltJiDFn74nGtQI6IwGnze3zVIsqPCQROdYkgWi0U5gg2D7ZMrAKOwrhiCbFi4JyknmDXm28Anz3kbQ2YA6fpXPzmPMfgHXXMQBkODscBpUEtoZ5iP06MfPMacNOnV1VWD7QJQ57IGdILP+Qg7gxL0B52mVtJzS3N9cmbueB0dt26ancnAzWnuDBa4NjIDeJgZ4ho+xo81m0/5QDZm5bMuGFxW1f7BEWaunSGB0cuBVmYNDSz432N2H7Lcc1/z64+B7Dad97WZKwesZDqwT6SH8wYi+u/x5b9zn3MQ7Gv5+/kaj5V2ZFarbT4NWjIT6MDMARRrgM8YvLYB6sxeYjsAN2bkDSy4B8DUto15AhCxNjPIRZbMtYOYDLBt7y3z3W5XSB3KALxfgWujB7DOrA3A3NXVVQFStpWPZSgiothrALDtAXMR0QzQsQHMmX2hr2fm0789/8jO/p0x4zvog4EyIJE5IiDhhByDc/ps32i5AMrpM2sSWdgfU97R6dyXrF1cXESn0ykne7Be7QMBzhHRyMph16zHzFu32y3Xov9kRH1qiNcJNplzxiPuy/Gc/fdaAy+xV8flPe9qj9nCr0J7EqjebDbx05/+tJFKtvNmY9BgMCjnNfNQE4MYKy+MlZUrMxkYQdjcDDT9m2u6phdlwsDhkLLBNwtCTREbzXDe9AHWEQMVsT/CypuEzGxRg06AYXbERtubBxlbNjyuPYfloH8YBa7thdL2hESDNAA0j2bGqGOkqT/33Dg1aDCdHagfwGFwmtlpf8cgINeNAcxscM1Y8jmfQsMYzQyQ8jSIhHlhHjCglNnAznzwwQelHxhV64lLNJAn4JmAzrX21muz+zhey48Ay6wyzeAVB5iBPvPq9WJwwDwb6Bhcei36LFvmglNcSE3TT1gQs9MG/16v1iMzfmblABkZ8FkXrQ8GyQbo+SenOm1LbDd8z2y7eN0gxf30ddoAaw4qmSPmkE1Pvp6De8uNdd6WkcvX9/eRtQNkv9f2Nzrqkh3k6QA6620G1JYnn2UM1gt+twGziGZZDeuSNZCzQVzP2TPsqgF1DliwHQZ69jsGbGZjDbwZgwMI/ALzZltEw3YxLnwu9pJNpN4PYrkaSHI99ycDM+SVM3gOfLOPftcaMFh2MMK1IqJRauhAjusC+PCFXLvX6xVA/Vjdsa/pbB+ywb7wvoEngc52u43RaBSTyaTUene73Xjx4kWZLzJv1FSbhPLTX589e9Y4Ac0nMyFLl9HgL9BRj4F2cHAQL1++LMTZer1+UHJC1tMBA5jAWaGfBaq/yu3JoBoljWiyYd1ut6TWu91uvHnzpnFmagaxNjB2BAahOGo7zzY2yRGlH6/sz7P42lK2LBLGSH0TYwOIw3bABMBUc28Al5Xf4MgLAyfnQ+cZkx8E48Vihs9gBLDKkyQZj+eL77GokalZPcZig+oaOa6JU0emj7Hyri+k3AMZG5TbWRu0mclycEBt2WazKU8nhJHhvp5vj8WsBsEhAG8ymTRKF2yQLi8vy9xH3LPT3liLzuF8MZY+LcUlMxg/HLf1OyIaJSjImnrvXNrklGwGfF5/doTZWdLMcPA/12HeDdrQNa7vYJJjNbkXY2PuDIxoZpf9vQzyDFYcROVx+XXXN/p+tJwRct/8uTZATb9yy33PIJZ1ZxbdWSpf18ETZ90S4GWA6SDK69gEhq9PH7KztI75f3+3LSC2DA2WbDf83cwaYq8yW2hA7c9iY991TYBn7rdZ9nf9zoFJmw7Sl2zTc3bWgUpmLOk7dtj22f7OjCljw6a7PKiqqsaZxd7rgL3Kuo4vg6HNAQWyxT44qM3gOesHMgT0c8+83i1fXwNdNcDEttrPuBwjn1KSgwvkbLCex+OSU/ymx0lZqbOigG8Ycs7Nj4iYzWYPyCkANcCX+1hfndlDdwyuAf/0i7ptHitfVfcHLkwmk0bmAh82GAwatdQcjoCvtK99rLXZwq9Ke3L5h489g8Gl+L6u94X1POmL1pbWM1vAhiRvtDMY4vs5yrThAQyb/bTRiWjWw2LQMCwcrA7Lh7HMmx+obzNgyKnsiKbDxMkwHlK4sBl3d3flLF3kQR8xyBgJgHun0ymPFYWFNYtV13VJBwH8slMzY43BcJrecnbdtwMMbz6xg+O6nj/XZmXHnB1YBh5E7tzLxpm55bp2gjm9zG8MGfXWfjys780TFXFaZCpub2/LUW30g37RYIhsNJGrHwWL3mRdwTjycADG7mZQ5OOl/DnKMGBLPJ78eGFkbGCWZYdhZc4dJCDzzBp6XdDvPN/WHcbA62YTmW/66/IOgw3666AggzL6YFaU1sZ60vIYMwjOYCIDA79u25Adu9/H/nqekb/Hiu7nmtu2H/rgwNPz5HFaDzwmj4f+u18ei7MNljONIB2gxbx7c2VuWQY50LB+5HkwmLPMPD73xfaKLAnzltldvkMzW5+JDfqOTjPP2Ho+7/IwbDmNfhhQ0y8HFwazjBPfgiz9EC8a/WN+bR/sVzxWbAP9tY/H1jloyzXZXpsRzUMLIPNywLTdbguxYXtqH+ExtREp1lXrlzOLnU6nHMRA2QYbEXu9Xsxms7Ln6OTkJJbLZczn88bZ3mbVu91uY7MxMsp663JZ67IBNrI/OjqK09PTgsc+/fTTRnCQdYfDJ5gnNuBz9LD9+Pv2sD0ZVONwYWsB1TCxHETuJy9F7JWShYuyA1ht+HH2NhiZIYuI8nhONkG6sJ5mBiADcgwEZR70IRsR/+YYPsAVGwlYwAZJLAQDGMA0BmGz2ZSFZ6MZ0TzvF8OAonNsmwE/4AgZ8AAPM5uWR2Z7eASsWY2I/SaQzJLsdruy6QIWDTlxf4w5fcCIeI7a2DwHYW3GFQNgA8mcmMVwEECfAPnMAQEdj5A1uMX5MLf0x5tgOE3ETozGKTHMbUQ0ntoYEQ2Db/2zTObzeaOcwe/xOs4y17MjS/THDHjeGMncen08xrTm2lICEgcVvN4GhrJjQ0ZmIz1WH2dl+QCg7VSyjLO+A4joo+Vk9ijrnt/Lc41M89/ZbmXd9zw5KHAa1/rn0i8+701TXqO2Q6x7rw0DXdZNZvCwPe5j2zxa/jmgapszEwZcwzY4A5rHMgRZ3gbE2FPfj/XmvpHpy6wo14KtM3tuNtWZDeTneTVzjx/gcwCwrGuURtJYWwaVjJvvGByS2cPv+tHVuWyAYJ0xGaR6Xpyd9VnPeb7BCIwfH0/fsB+2VV7DZMzynifL1KQUffSa8HGxZBEfI+foQ8Q+o2H9M8GBLGGA6/q+XDJivzYhOCi5uL6+jrOzsyL3o6OjQgCCeSCubEsdFICtnj9/3sgM48dYH5yaxElTx8fHMZvN4uLiopQs3tzcxOXlZQyHw1KaMhwOY7lcxmw2i16vV44bPjs7i9VqFRcXF3F8fNxaQvO+7duTQHVENBgEjDkOgEkmcsubOohwbEi86CP2x/aZmcosN1E0zDCsYcTDGjGXgKCwgKyIKKx6mxPHcOHoYdUZMw+AYcF797EVnTH4CZS73f2ZurPZLCKibDxBlkSy2elQMgJTTmouA2EbF5jkzKA5+oVBMIuCMcy10PymntOZgYj9I9+ZK476c7Dk+2QGK4NB+sppM05tmklx5O0NHQ7UvLmnqvZP41qtVuX+m82mnFTDdXwUn0/vYPOKHQf991OtnB7OJSBmjQy+Mngy0+L1xRidMaD+EN1zaY7HaSdTVVU5Q9pBBz/eSGnm0rrk0jCco0uSkAeMuRsO1eCSho7gxBxk43CQT2bEM3DNmQhfz/Yq94trOfBoA9EGbI+xon7PNstAjfe4F5kGB898FoIBnTMDCBDjkcheZ76PgXvun9erAW5boNQGgpG558glTPletJyVeVfL8sRucfqUfQygjqAYO0Awhn9zsGv2kLHbPmfZmRBBz8zsOqvJPHMd/CnfcaaFMTCX3Mu2x7rEvcx8OvDlPR8Ph9zRo7u7uwYZ5Pswr8jXpx/ZhiNHrsvY3FcHcPSNhq23H0Xf6CPkFM3BhUkCB0L0gxM2Dg8PYzgcxmg0ivV6HRcXF40gDzaXTe/2uRmHHB0dxfX1dXmASsTef3hde38NfSNoOTw8LP4IAoiAkTGZtOr1euWMaeq5R6NR3NzcxLNnz8r10D8efMbD7NzvV69eRVVV5fkMxkDvW3t78hMViZ5ZCKTNiYpGo1FR8ByJ2jAbQDrK95PncqoJMBKxPzPaDCzvOVXKAuI6GCM2LthQ2uE5WiddPhgM4uTkJOq6Lg9Y8KLEKcNEkGJxrTmObzabFZYbkFxVVemTFz3GzGUjOAvSXMjb/b+9vW1simQePBdm5/ifRUPkjNHA6bqGODNcGfQB8ryJJP/QX/rHHFrvMGAwzfTVQZrZNTOJdkoYbdgCdAFAvN1uH5RHuPQlIgrDwIMYzJBhxAE+rk/Pskb36aedMoayLWsSsXfoZjPMciNzNsfmshGDUteYe064vuUGqM1sn/vPejVwgqFDXsjDQZEZqgzanI1xCZrHQPDrOUdOEdG4f3beWcYui6qqKiaTSYzH4xIIm400CHSQypgMmHMwyWf4Dj9eQ3yGMiT3Hfk4I5IBbQ4W3EeDI7NzuY9mVg0Sc9+dZeD1bBeYZ8vYAUcbYDdr2DZnft12EtBg9hrbGhGNR0LzlFsDbvc3z2vEfo3YjjEOs8aARpfvcA2Dcu5lIJrnwvf1+jTgtv2zXTILaxuDTLCz3W632HhkgVyZo7yuI6L4G9sq2xj7YdsVdMdjcKCeiS5kELEnF7we7R+w4QbUnkN8OwEW5SQXFxcl8PdpM84qQKZF7P0C96a0ziWPnP3szC44iLE4+Hnx4kWph8bvZpwCmPf6Iei4vb2N0WjU0L/FYlFOt2JfElntZ8+elazodDqNxWJRaq4htbLNe9+a7cmgmjMVmSDACKd9GEhhfCP2oDozv7yXgY8ZajtPR/ne8GRwAyvMgkQRYKoBrmaAbfwMRgygJpNJAzB5XGZIuSbKaoA+n8/L4oiIslM4Yr/5yOAI9oBjiJAHrCTA3I2InVT/Y46cBcjrdn4ODLifDbQBOGN12QrpP4IbA2Hk5n6YAfI8o0cuE6IPBlQOuDD4BuHMDcar7TB75OZruvTF7BByMtvAdX3knzfwbDb3J8rA4uN8zDChy2QB7GQYr42yN8b6sy7XsYPODLAdUWa3+J5rzQ3oDKKZX66TGUcDd2dRzNTZDhhoIVsz6jBr3Me18/4NSM/2xCUzbl4THiO27/r6upQCtDmW/JqBkLME1nkDEL7vEz0iohE85uAog2PrQg4228aKrJzSNcjzPL9r3NYtg3GDP0BjJkx+VvOYuG4eA+NwGr3tZArmnjXOdeh3r9crYOhdaW5/xxk4B5UGowbbvobtaESzxAadyfabNW59yuM00EYWnU6ngKxsuyxXToagP6PRqBHA0ycHzvguM+oQbz6yLZMBril2UMJ6R8bup4N6PxHXcuRUjlzKgg5gX3e7+ycbYidyRscyNmjO1yHLTtAUEaW8AjLg7u7+AU0ZI1RVVco70InpdFp8xmKxKEw1fckYyuTEyclJuTfXODk5iaurq3jz5k2Za1h/k0EHBwcxGo3i6uqqcQIJGdxOZ5+JbmvZ9n/V2pNBNc6p220+b56IFyefI2cMDIqVQZTZRe6FsQRA2CiaBbdRYvGyCL2xA0DN05RsqDOLCsDtdO43InAEDtG7QXVOccGCWkZOAZlJaDvRAVkBpvP5v35yYjaiZoCyg4toOifG6lM6AMc4PAylwRKOAibWzolyHDtOMwSPMXkZcNtBI0tApAMwz71ZCjvviChsFIyGjQjXcdmON5BuNpvyJFE7hQx07cBxCDbCzFuO9JlrdJr6fOTjEimcodl5ZwzQe2dtXAPXFgy2Mdbcn9NiACl2fPQFIJMBtVkmzydgl7/dB/fPwI51bgbL7Jb75YDMwDUHAVzT93S2yfdmA7YBnNdVG5jOziUzZF4TXpP+nseYAaSbZd/WP899Zv0sG8ue63o9cv08Nts/X8Prn9cdrD+lMY857U//rZfebG6W3YE+9pUgaTQaNYJzB81tIME+hv5kOXOfiObTca1zNAetlnUmf7KvxCbyWf72uqB0bT6fl1I3B9H4pYg9oMa+85Ac+z3bFWwi9jPPfT5GFZ+HX7ceeD04GMtrBLuD3GiMi0Cbhp5kgi5vyM8BAzLELtN/94m+EuRjz3m+BToI/mC8vV6vgNP5fF7mfDgcNkoM1+t1zOfzQuo5SONBQIzp5cuX8eLFi0bJy3w+L/5vNpuVrPnNzf6R5DDuJycn5fkU19fX5WmLXO/4+LhRS/++PWxPLozBQOZNDywql0Sg+AADgE42yGa+MnOBYpPKc0rKLJs3pPjaAEJHY5nNcXoJ0FpV+6NoOMEhl3vYADswwJiOx+PYbrcxm81itVo1lBGQwvjNAGGIs0PC2CEjg2ED7ex4Wejuq+VG32E+eJ/gKDMn3Jf6bowcIBzDboOYWRbkb4Nog+0NgwQnjuL9eTsdnCrjykwF5UrIl+/wG+MIGO73+8XwccIL16Z/NpTcn9cdLBDlOzCwPNFXO3KDDxgQdJh5ciDpkwjMYhtwu+zE/XXQYKeCnudAxkGz+2pQ7ca884NeMafog8Ge9SQDI8ZpYODmMTqIol/OKlkncR63t7exXC4bsrC+eC1l8PUYU5Nlh53MbHXWgWyzeM8Bde5bbrYD2C8HIPSPe9pZZzBtW5LtCvL0POZg7zHGm+u4+fpc1ySNMzDuu+0PsiPzh60zkLUOP6bfloODoqz7jJtyRr/nYI9+cj8ATJsuM2c5oEC3mX/Gz9q2nSTDO5/PC2DmTGWCST9UbbPZxGKxKLbBdoC5Znye77bxGw+4jNS2jObMpuei02mWSiAb9iDxPU7gsK5muXr9tNks5omTuTabTYzH43KPvOYA6H74Gu9DmNAf5DObzRrj3263ZY+X5XpyclLqmrnXcrksfT04OCgPdIGZ5qQO2OZPP/20PJFxOp0WH79er2M6ncbR0VGcn5+XoMskCOuM8qD37fH2ZFBNFEY9LwvNCstvInQmxICBa/F5wCz/mwV0uQjGxClzsy35Ndcg41C9cDAELg0AmPgkDjNevr6jahy9H39LpMrRdpm9wcjCiPoYI1LNLEpOKPEJFmwwIAXln+y8M/BkjIwnopnyN0hnkdMfZwAwGD76CcMa8fAIrmyMzMQZUPM6r3ljJmPiswbS/PbTxLbb/RF8draMPadCMSQAbDaloiuuYc9OOGJfXpIZf6fv6Dsgkc/hCA2sCJz4rPWNdUbzWmQcXIdrmZW2LLgm72UdN/jFiWVmjfm2jLzuAK7oTga52cHSF3/XTJ8ZaD5DBoJ1T21lPpKOMbqfPML3s88+K/PC/QAJpMbN9j3GIud+ZuBqAOSgxmCEcfl6pORXq1WjRtNsKOvQR2DZ/uSsmwEs18zghJbHzP299g06aQYXvpY/n2Vne4bcYAzxP6y1bOt4DTvsINRjMOBj7B6Hr4+uZgaXtUG5WsQ+i5uBOfdEj/K+GDeIBQcrftDXY0FsRPN5AYPBoMx9v98v93UgDTg0iLYu7Xa7xrnPDpq81v3kWvt9Z2UtY8bmIJm1aj1mzNYV3seHZsLK9+Hz+Ie2zC73ZkMicnOQ7nvjT+mvx5sxD34MH29dZQ6MBSCuWLf2HRH3p308f/68YZvYfNjtduPi4qIccQxZgLzADZS28ZTpbve+lAWGerfbxWKxKLjmZ21UfCxg/iq0Jx+ph1FyyodFnYvvDUpYoBghgxAbbgNUrhHx8BGiBm4slgxuMUQcf+ZaNrMM/j7/44DNhNBXxmomjLTQZDIpGwQxvu6bDQyRH2keHyPIGKy8RMAENBHx4PzpNjbN4+X7BlvIF9DXdsJABtM2Vt60aGdledlQuX92SGZWuQ5OhiMEAaeZwcjNTD/gD6eFMcFg+Ts2cC5lMWCk5i7XNCIfnkgVsc98cGi+dQcdiGieBIBMuCfAF1BgA2ug4c+ik3zWWSQ//dC6xjjoN/ekOQihz06RGwjaWZiJsQ5ERAOc0LJe0riO+5LZRe7T7/dLqvJ73/teyTptt9tYLpcPslMGPFdXV/HjH/84IqLsgmfumbs2AG1mPTvczExb1rYpyOTjjz+O6XQaf/iHf9i4B9eeTCbx8uXL+OyzzxqZmpxlwF5yH2emzK5moMA1s831mjOowhbnzaDOlLQ9aITv229ksJ6DCQJJn7bkfnkuvVEY2UZE40hYbDOAyKV1zsS5tM4MvgMUvsf1nFFtA9Re422yoc9simZ8vnYmTrJ9sAyxo8w938+2hCAkA2r6DEFh4gOfAJtpWWfCzWPzOsiAmms6yLOviWgy1rYBng/67/Xn7IzZfPrJyV5sXKSPbXpvIiKvN/eZsg6foe3yIbIFECzIhvLT6+vrAvTpw8cffxzHx8eN52xMp9OSgdhu7/e8HR8fN+ZjuVyWuUS3nz17VvYUcP1e7/40kaOjo1IGmYmc923fngSqAVcog4E1DQOCEfARTyhv3mRiw2DmBoOIEmTAhdJyLb4Pg0F5AikVP6AFYMnCYMF6Yw3f9z3NqHNvIs+qqspJHPTFQYWZSAA17C4GwWdpGqRE3NcFu2bdAY0BS3ZCDkAYo9kLDAEnkJgtQT6MF0CK4WNuDJqyIc/gNwMimG6fmeyx8Pn1et2o1eZ9ZynYKW3WwPIBXOMoAKHMs1mlXMpCAEEfDVrRG4IrxsQZ6m427Abxlhl/028f9UVfCAzs7AyC8ykZBlSMm/Xo79MHg24DH9Yszo41yvr3sVZ2jsgyAxzkadDGtR2I5qCYPnruWdM8PfTs7KyMZzqdxvn5+QMwbYefg1OcUltA4H60BXheO1kebgawnc79prAXL17Ed7/73caYuQ56+KMf/ahhe31f2w/3xTbFLLjXL9/JmZIcwD0GpjO4xTFjA7MMMjlipjsH4oBzB4oGl/QVQsI65k3rkAPYr7yRFiaXhy65754Ll1vxWi7R8PcsS8vLAY71xQDG7xsE8rf9KuvbzTL2vg0HgNh/lx9koJf7xxyzf4hSCUAh92QdeV9KJhci9sw+2U/uw0/Evh4buaGD+DbrQybnHODYFnt+IB/aAk4HLZ77trlzRt0bq5GPfT/jYMM8pKTJHnBMXd8TTS9evHhQa41OwCxzVODr16/L51g7Bvz4d/94HtAXMv/vW3t7cvkHBsxCdvQGwATAmu0yI2UjYmPI5LIIXENq8Ma1ADE0XkMpiOxQ1Igoi98LJX8XhabvZh4A+3ym1+uVpzmaHQWY5ceTO33mBejFjvG3IaV21wvVDtDBSWZnDP6cDqW5ji4bfZwHxp80W67XNfiKeAggLGveMyvO6xlwRuyPvcNBIysHIh4PYzSLYP01wEAffSoMxtysm2WFAXIKms97M6vnHflh+HLQQP9yGY2ZDzsi35vrYKy9h4GAiXTvbrdrZdkJ1HIKHQOaPwOAMHuHgWYOcBo5mM7MkddVZoEZr9l/gwbkyskrfG6xWBTHVVVVfPbZZw05ee1ZT+k3wXaeo7x2aLZvbhmo0/I16OdyuYx/8S/+RcNe5O/6dBCvN9sQAxHfP7PtXjs5wDAYNwg3IWHddvBRVVVjg7DtvYNDB3vYFtauZYdfQMdtxzIwNCCgr84+4g/qui6EyHq9Llko6yU2yhvsKSs0k0tgji5aZ6w3OUtCAO655G+TA8iojQH2/oCsE94UzBxaR+zj+I2fMJHWFiRG7O0hfYV4oXyRcVr30Alnn3if8ToT7Pp/Nk9jq8fjcTn2jbHlDBf/ozvcN9tX45G2NWQ/ah/k9WnCxmSWWX1sMPc2aWMChDFRJw3Y/vjjj+P09LRxvdVqVeqjd7v7YwIXi0X5HjbY2I3+YuOXy2V5zgh4BoyDDNlU/761tyeDak9ixJ5loSwkovkYTwMuFoWftmTlxeBhNPzkxcyUePe+I1gW9Xa7jcVi0WAybMAj9obBT14ixeGNjT7ZAAae1AqlHiwgGwT6E7HfSZ4jcDsvFqrrPg0+GUOv12vUOzvoyLJGRj6TmbHwGgvezKv77mgf9hUdsGP1HFi+/tvzTeAV0SwpsKPMDtjA2k6A74zH47LoDYzcP6ddXdfswIu+AhqZc8qJcCQONJhbjLgfjAODYuYQ+ZkJMBOFYUZ/zCw73Wg2zEwSBt2sAn2mmQXGoDNHrEX3xTpo9tisn8GX+4JMHbAZQJnRcrDGnPv+PPb37OyscTymwYXXoB+NnGvhaQC6Nieb9doONbNoHrt1yesgA11nEPL7GVAb5MPKOfVtueeUt22oP+fr8j76QybCwb7Xk4NUZyKqqmqwyXkczghmm+O1mjOKnmv66pIA7CV1qb4e+sxJFbzvB2Q5MPT1fQ2CUq9X5srECHNqMM7a56eNobZd5X1sCGM0KMzkhUu87Kv9m/uYaKE5g0FgQsMW5Vplg1dnU7NuWu8N9M1eM16v+91uV0ofOp1OeWw2tpjgBNm4P9hi+17X1nsOvRZyGQ1ECpnV7J9gf72OsJH9fr9xbjX6hy80kZODAspJR6NRnJyclDOmCe4uLi5iPp+Xeel07k/9uLy8LD7B+CCvRWqpR6NRQ1bYzpubm/KQpMfIg/ftvj0ZVGcjgIHb7XZl8w4LwkYkYh9xOXXDZ22o+KzBO9e1MviBKmYLuAf3A6gaCHItMy/9fj/G43HDyThlyAHp1ER5EToK5TX+NmOZU5VOfTp17Wjei8CpLRsbG2UDFMbFtXyfNhk7lYusBoNB1HXdOI6QvjDPbc7fLb/v0hcvUAcHmaG1I7PxZVwATbOQBgeuPWcMZgFgqZzaw0DxUAA/ycvO0voGuOVhEn6gkY16RDTkSbNDQO/4rrMsBvDovOXgAM0Oh36jSziIzHp7bTkNi+zN1LUxFxmEGsAiL/SIubVMLc/MHkVEzGazuLu7KyVe/l6+n/vjMeb37XRyeRVjyZ/N4Aodymsry8FgxP3Kv90MaM12Zv3hs7YTuVnPcmDsIM6P5uY1+gYoY016A2rb8aqWGXYfPXOA4b8dQHJPBwZtsvcadubRus99eXgZfeW+tpEwfIAY5s7HcNpm5QARX8hr9g3I+7HAy2yq9ZzA0wEAfjRiv8a4js/e5kQr9xlQi92gMecOWsycZx1lM7aZzgzEsEv5YTHoo0vS+B7Ajmc7tIFh5jNngbDxEVH8meWb7ZeDvRx4AzLrum6QK4zFAbf7wZOfwSLGI/brkAbZ9hwdHZXaak5kOT09jcvLy4iIOD09jaurq5jP53F9fV1O/bD+0C9azrD4wWcAbQ4JAMu1ySu3d2GBf9vbk8+p9o/TiC7ZYLGilGYHzNDC/vFZlByDbIPkRcCCywoDQ0mqA+XA6aO8NloYCo6jof82iqTyVqtVY8HawUY00985zYWTtoNx2s3GygbXaSTk6KCBhixcioADQBaAawIDvu/Fi5G8u7srUTFnaJr1MFNs52AjkuWCseE6OCfm2ewnYMAlNgZVDlyoox4Ohw0WwAbET0tzgMMxhWRF+C7ggLM7XadtNsPfY55gFszk8no+UtIOwWyYga0Dz4hogD1nhfzAF7OMMBWw0YyReyLvXLOc+2wnYb3OLJ1ZUa9Dszd8LuuQZUvf7NRzn2mM9fDwsGww8hq1Tnptmcn19TxH6EEbq5gzNJkJeiyTZkBrG+cggntkls9rDH1vA2Xcz1kwbFPuH5/hfX4Aknn8tsPZXtoGI2uPg/Xl4I85MEikbxkQ8EN/HBRg49EJAIqzEASD2FDbW9tU6wNgFMIBFjYDWYNDfvBvzs5wz8eCJ2fk+Iz/Ro4OKPleHkdE82hZjlpD5vZDvjfg1n10UMI4nb0gY0U/AJ/4Xc+P99AgbxMpfA+iaTweNzIz6CGv0R82hXNPSlGwD64dtv5g/5hP26xczoNfhXhpOzCAbDdr3OvEzb6VdWZ/731X8/k8Iu4fKnNychK9Xi/evn0b19fXRQfBEOv1OlarVSMw8HGu+Bf2mTE/bM7meL7BYNB4UvZ2u21kdN+3Znvy6R8Rzc0RLDzq0jBaLJwM5HAqTCyG2enzfBavN1zlvqAMVVWVFImbU0kYCwAI95hMJkWhDg8PY7VaNR4H6hKBiOYGnAwEIvbH0W02mwdHeNmRexx8L7OzfN5sqMGlGRCzJKQKkQ/pIjNIBm7ck4WME379+nUBoIA+M+FtDJgBtlkyj+/k5KQYvevr63j58mVcX1/Hp59+Gr3e/W738Xgcv/qrvxo/+tGP4tWrV0UOgCxA92AwKJs4PRbPP2MFKNzd3cX19XXj3G+zQnd3d9Hv92M0GpWNGehgr9drlNFwH+uv2WgMUGan3LJzxbCzttB1PscYDFI8hzhvZOJ1UVV7lr0tG8Ra8ef4aWNBM0sMg+MNMV4jBtr0LRtprhURZbMY3/dnDMp5YAHvOXi2brIOOQWBbIHBhcdk2Wew62sbOHMPMztes9gez7mDE18Pu8V4cf7Mh/U2A1FACd9hbZsAYEOeSRIagDSDY+uI9c1HnuXgA1uObyB9jS7RL+uzgYt13ZuI+S4byx0AOCA3MEK3YB2rqiqnC9kO+imBlgXz1+v1Gk+dQ28NhpgT1opl/NicW++wuyah0AuOjMzBgNcb4zGJkcsycnCLzfe6tt1HhnzXBALX7na7jdIf9lhxggRjyOQDwQfXwJ5AiPA+WIJ7Ml8ObJEDmMSBAzKzrjEelz4gJ7AMbPXd3V1jwye+Pm/azuuUcXFSFAFbVVWNp1iyjuu6Lqdy3d7exmQyiel0Gre3t/H55583ThKpqiqOj4+j3++X5yIYO7HemDPYf+x0v98ve9A2m02cnJwUrDSfz0tQAgH5WMs47KvUnsxUZ6bN6TsWbq/XK5sU6npfNhARxahmQNgGqCPaU2EonGu9zFDSVy+SbNQxwixajOJsNouIfQoSZbMBshMz4PDCdnSe+wEYNJDyKRpOy5s9NYAx88Q1fA/Xpm82m8bC4/M4awwnKXi+x3FyzI9ZNwNnO2H/7fIEFvXh4WEcHx/Hr/7qr8Z3vvOdGAwG8cknn8R6vY6XL1/GBx98EB9//HHc3d3Ft771rdjtdvHHf/zHDYNuw3R0dFSiaOuIgYcB7263K+d4+tHrNuaHh4clOh+Px4UNov/MTcS+7t+sMvrKd5Ab6WezI2bwneHxMU7Wk7w+vDY933ZUrjN0mpFTOqzHMBVmWzN45JqsDXTOhtQyoS9cAx0D7HjtGryZmbaecn3rm9les8fIxk6W95El/TbDnmWbA1kD6HwPz7kBGPfGnmTgSUNu3MflC65bz4EK8rLsnRUw+LT+sSYcuHh8XkvoS2bXuU7WS/c/g7Jcy4vNdKmegzx0BLsPkwa4BKxjk9hwBTACZCM/AmuAGKCcfl9fXzeyN5Yj8uPz6I5ttlleBxrZbmd5ZUbb8mN8XJ/vIDeu6+Axs9r2KbYJvGYQzW/Xglv/bAtt7yL2Z/XnrAzPbjA7aiBsNhu9cDbO9ox1DFOMDaVkgXXjjDU23msPPaJsz2PBL7IJGjBqLFRVVWNTtDPG4Bz7TfuBvFaMJfgfwrCqqrJXDOBL8MKavLy8LOvXp4k5kOb9wWDQKJFypryqqiKLq6urEpjkwPt9a7Yng2oUkwkESOcjiTACGGszzvxPhJ9rPe28aAaz9IV7sjkgG+OIPTNFfzIbhYP3ZkMWiDduZYaDv+1w7Gxc/2tGzfe24TIzmI05kXEbELZhsHPFUDhSzdfgO8jOoNTjxlC55TRn1hHPpWVyeHgY19fX8U/+yT+Jjz/+OP7aX/tr8fbt21gul/GX/tJfir/8l/9yzGaz+I3f+I34B//gHzQ2fnAfnCyAGgNLQwYuN0LebCw1k2x9Ozo6Ksxdri13KQ16ZzBhoA2jYcdqAEGfbGSZBzbD2qla1yKisd7ynPFZ3wswxWtcK4NPrgl4aQOaznjYAJvJdOBtJgwgkAEgc2r7QSDO/V1i4EDD48yA2qUWDgDM7ll+th3ufwa/beCxTZ7um7+bAZXtBA7OY857RbJtzAEIT3PlfoACbAJMLEx9W98NCvK8WjctO1/HRIYzdAbj6I1LQtAjB+SAG67PZ8kGGpghv6Ojo1gul6UcyKzq7e1t8U32K9zX2SbGY5vR6dzvszBx4nIQr/nMSreVaLD2I+KBf3V5hTOEEAN+OixEgXWLOQJ4eU7zKRPuq4kK9x/bZv8TEQ3gaLvg+mvrPj4cO4peZtvGGJAN94qI8uCjtnJIz9nR0VE5BYOMhDOI3uCNPuIPuBfrwnqG/vBdy8Y2PffHemW9oN7buAKG3Mw9AL7X6xX9Bv9Y1x0sed4Zo4k65p/PYCuQLfejH+9be3vyRsUMDFgoFOAT8fkQfViBiH19os/e9OI0gI3YP2nKNVzZADnFaQVB8Q8PDxsnevA9WAaeVEgkSprFbGxmpjJDiSIaHPB9O6jMejk1aKPne5nppmWQi0FxWodFz7WRPffDALQxewYuZg7tED0eHFw2Bvzf7/fj+fPnMZvNygaWzWYTv/VbvxU///M/H3/rb/2t+It/8S/G9fV1vHr1Kt68eVOCL8sC+TrlihFnPs3AATD5DaDe7XYNA0FaEt3EwPn4IGRAShjnl8FvBjnZqVpPueZoNGqctmF2je/itNqAneusvZba+sFncnAG2+UxMW6vmwwg8z4JlyshizbQmdlr618ul7K98NjRTf8NqLHu8D0DSGe1HivvoNkZtbGL7nt+3/Lm//x95I9+53W6Wq3KNRw0Wdci9keaHh0dxWq1agA+WtZHB0Ueaw6OLBsDvbazi7HRZqfdbwNpAwDX/MPEUdJhGweYdflIRJRg4vb2Ni4vL8u6oC/Igiycj2BEtrlkyYQOsqZPzJFPoLE+ZYY6IsoYkalBjEENtshBiefPeo89Ym64p+cJe2K7QdCDn8hss/XAc+++WJ+xuYBj7oX8c/Yy+6BOp1M2yKEbNHy67SO10uiDySHu7QeuOJjhcw42zehnG+3XnAFoW++8t9nsz+vmPZ7O6FIPfrMRfbFYFHIGH4Tcr66uGocmZJzgecmAP9uDHKz1+/04OTmJ8/PzMjfD4bD0nQdnOYBpa1kmX6X2pTYqErGYNcGRVVVVjJWjn4j9AiayzoJ3JBSxZ3SchuJzGK6809kMcK/Xa5zagDL1er149uxZ3N3dxcXFRaN/LpPgGozdziiiucHQxtH1rpabx+lNFE5/I1c+53RSDj4ckJipseHBSHss2XByH+7NLmYAou/HvCEPOwQMc6/XK+eGYlhGo1FcXV3F5eVlkdWrV6+iqqpy1NCrV6/ie9/7Xvz2b/92/OQnP2noBH0fDocxGo2KA+HJfRz1k1ORdV0XffTTK51G5qg82GkcJsYMgI1+MAc4b1ouScjBiXUaYwaQMhttuZspzwGXAz3fJ+tBxJ6BsXPkf4wqffB3AAxe52a8Xe+KTJwhgR3ONqCN6bWeG4x4HdjZGyT4fgT4BjedTidOT0/LZmPricHldruN09PTmE6n8fbt2wd1uDnozwQA8+GW58+MPI2g0xkW+kRATMbKGQuABPegztPnONMfy9Znw2cwnP/297HzyMNMnRl9vuc5ycEPsojYP4yk292fTIF8fIwabBygczwelzWMPQVo8B1AAeATm4vteUynIqLYg1yHDpjnug46baNz5uMx/eF1gCSZMf9vUsVgj+Y1aIIHm+21wnziP21DPDYHg8yXQTtywYeZSd5utzEcDkugw+fwTRFRmGMauox+8jRkYwJnLihfcANsEzDNZrNGYI2PApQyJstwuVyWJ0NyTKvnE1134EL/kAN6Ahbw/EKSMA+eO2TDsb3L5bLh59sCWa9tdAc5GSsg/8w6s06eP38en376acNez+fzRpnTe6b63e1LgWqfoBHR3FzgR1bndMLBwUH0+/0G68x1I6IYPIMB1z5zL6embOD5jovpx+Nxg7maTqdxcHAQFxcXxej63gbyZkLpryP/zOxERCndsPEyaGUBm+lkEZipcc2Xmb58T4MkxuE+G7whJ7+Os4at4GB9A7uI/cLnPa7DRhn6xvVgBaqqihcvXsR8Po/5fN7QFeb+/Pw8fuM3fiNev37dAKlmxrfbbQwGgzg4OCj3M2Md0WRrkQU1kz4zmrnDgfPj0g/uz6Yan5mO/MyetkXuyCIDVe8s5zMZPDlwyeCHsXnu7WRZe3aIvg5yNbDN+sH9SZ8fHR3F6elpjMfj8lAV+sE17WAoVwDI0EhvMn9toNTsCa95Yy0/tgXM+8HBQTx//jyeP38eP/jBD8oxWrvd/YkszIfHim3abDbx4Ycfxt/4G38jvvOd7zQ2K7etuwySzBj5f9sHNhGdnZ01yiH8+HnskgFEVe2PRLO8yS74nOWsC1kHcnBl/WlrrBmntd2wbQYOrK9MfjCXyNxgzoF/p3NfwkJWCDvpEjYCPmR3c3MT8/m8rA2Px/tdHCQaEDFnXju+Nnpmm2OG2PbagZp1wS3PgYkRH9tn+cK6W8/a/EP2NdwL3+3+AEDRNWRtsJcDeMsNPbQdPjg4KL7WGVjq4O3nmAu+C5FRVfc1vdfX10U3sNn8n+fYD4BiTSMP1gKvu7TQp4LsdruYTqcxn8/L/hvW2Wg0avhmBxmeB5McHDFom57nLZMvzCVB5M3NTZEx6z2TNVzL+6lsk/K+M4+fa/74xz9ulCB2u93yhMyIKEGSbfr71mxf6vQPgA/slh0Ahic7SKJKpwIxPDgLA7kcoXsBG+BF7B0uxodTG2AxDIYPDg7i7OyspJjoi9MhOAM7IMaDAeBzEXumgpSimb+86JCfNxlgeJCrN6DYqZuxs+PIKT36g5xgPGz82QxHpI5hdVRr1iri4ZPY6rouzG02bgZEnCDyWFssFrFarQpT7MXuRe/om/vTrzx+giTqJpEJ13NNNtem3MPGh2sZ3JiBM7ii2Xii1xH7Tbpmiqx/rBVf29fM80tzoMf7jNNG1581AAAEMddeD4eHh/HRRx/Fr//6r8eLFy/i9evX8du//duxWq2KgYcJxPgShHjc7DEAVPvBCWaJDS5geTxW1lZbIMe6ev36dXz22WdlPRnYZJY3s8Hb7TZ+8zd/My4vLxvBjWWLA3ysZXDqMhOCM8A9Dh1WChvqbJ/Xn9cl93L6Pjt3g0SvgazLvh5Mnu17RJOpB9gSIMKSM0f0d7fbNbKEBhQ0QADXcsYLEOhg0WxZXd+nx+fz+YMHHzFfpMs9d3ncrAFnNihjtK6YFfcc0T/u4fKPrBdZ3gbE2KLsM5A/v9uIHQdQ2BGXruFXDLC8TjPj6j4wxuyL0V98kuXjPnPNm5ubWK1Whan1WjIRZ9KMIxDxZfgB1j2lEvw/HA4bpIXPXrbt4XhBmkm0qqri2bNn5fQkrwPGTeBi++IMdV5v9pvYQvT96uqqzI/PE6cUkfsYTxBQZv/v7xsfMEesOWdH7SeYm+FwWDJ7Nzc30e/3ywNgOBv7fXvYnlxTnYEOzSAqM0kw1BlQE+XDCOWnX9l54FCsDGUQUrgPP/wwJpNJuRfMMbVS1CZ6MwmGg2tiEDHeHquNi40nYwI0ofw4G5TcYNolBbxmg5WZ4pxqM/P02IKnr2Z7KHfAGFBT7sXntCBGrtfrlXOrvRAN0nLknQEc/bZjj9hnFxir9Q3jglOznDwujJg3rjh4c2BiVrrf7zdqqSP2xyIa1Ph/p9fN7nJP7kdNKIbejt3sl4FIG0C3XtiIZ6drIM3n7DTtIJGvT7fx/wDg7XYbv/u7vxuLxSIGg0E5K5W+LJfLB6UGdkb0ycGg57lNj9zsmNDliD0zFbGvU2V8DjyybvI3oN33f/v2basOmqEbDAYPyjD8WQe+1lUH8XkDGqclcWY6YzDoyuuHAHA2m7UGrXzOdi6ieQKTa1wNROgvWRuDS/fFc1vXdUwmk8IWGuSZbPFadHkCrw2Hw5LCJ+j3CQze6D6dTstRig6aaS5Z8Z4P5OO5zpkovoudB+Rj0x1oM8ZMOmS9o2XbaOKAz5uUyfXW2a6yti1n+pbXmQMbxsA1aDmItD+zPuCTmD9KOQiG6QsyxK50Op1GCRJlmdYZZGBwSZ/8YKFut1ueI+B1j/zwsTDeXM+66TlyLbdBtwEncnXgYkyCLGGanY1h/XW73bLHp6qq8vA5SCbPaVVVJbjwMX4ErS57sn0g4KYvjM1HCltXOp37/T3YBsY0Go3i5cuX0e/34+LiIs7Ozh7o9Pt2354Mqg2ed7tdQzmJIgEkVVUVZXcEzWT5LGdvRMmsClFm3vjixdfv9+Ply5cxHA7j+vo65vN59Hr7I3LoE+CaTQKOjGGIDA4w6IwX5cubBMxC2vj4KZOkovlsZvO4h8EAzh/QSP9sPDMQ90knZl8wBBg0AEWOVjEWXBM5RETM5/MGe2inTTOj3uZQ7BiYV9fkmyGs6+bTz7xB1fc1iEZHnVFxQEEZSa6fZu6sb5YTY27LCvAZMzsR98YIJ2KZupbPDAnvG0gjq8y285pBYS498pwiV4MAglrGbqPMa7PZrHx2Nps1ahANgnOdH3L0/dhAaxYlX4fxM8cu/bDcCVAtL/TUjKPHbzbVAN9BoPXUgBHnDaimXIq5IJDd7XbFgTMegjf6ijzRm6qqGg9MMeji3rZLBlpZfm0AOyIaG3C5P98F4HJtPs91/WAtZOPA20HK1dVVI7ixThi8mV31+5yVu91uS/YK+4lMkLVPaXK9KGsaPbKtRC8NuvLmRIPx3W5X/JjHYnKHeXMQaX329/LfXMu+1PpnoI8O8DmAPjrsfuQg3mwt96SGOdtv39clI37dtcy+Xx4v32FuI/YkGfc1O2/9mEwmMRwOo67rknVkbWHHaSbHuDagta7rQuw5q8C97UPpn4MPX597Y0+QCzX+LnVarVblODweUuZxREQBsWAgfN7p6Wn0er04Pz8v653Nx/Qb+90299hX2zyAts/f5imN6BbrDOwwGo1iOp1GVVUxm83i7Oysoae52U59FduTQTXNzJoL3gE2sBwwHRF7p2pA63MUIx7uSLezatvURbpnMplEp9Mpm1qcumNHPKAXMOyaz8xiYOTsGHEUPvIOw2TnUNd1AzBjsCP2hgg2yieXGCCXCdLnzcTaMNrIm52wYXBdOgvNBsHyZmFOJpMSkCAnf9ZAkX5kFjSDQF/fZ8JirCxvDCzzTzCT2TdnDAxYvPABNjhbSmJ8hinGFX3MT9C0jtqZ+gERDiBhF7y5sdvdH4/IOmpLSWc9wAmZyfFcEFQg48y4ZVmYaadkCmDn62cWxrWzXMvr9eTkpDCMXAMDndeH17eNMAGwHTSyojlYdBAQ8fAkFGwEtZ4GWRmMomcZaDCX1Btykg2v9Xq9woZZH5C/N2czfvTB94HZMktEnwAmfMeA2g4/ywz26c/9uT8Xb968aWzG5jMOaE9PT8v9kMnx8XG8evWq2PXNZlMeuMTDefwQHa7nch9KrPzAC7OkfgDNwcFBKd+jzAJg76wcc+nTmpCBmUNsnuecuTM5gjywDwZj1pMMdK1/bYFibnl95/XvAD0DGObJpAVrIPczEwE5a4WscmYmB6MuW7I94X+DO163P3DGwzY7Ym9TPGayEdvt/ZMBCbIGg0F5kiDsNqWN6AbEnksdTGzY1zvznbPfeR7JuA+Hw3J97Fiv1ysZckA0WAN9Hg6HhfRi8yGbcgHc5+fnUVX3+5AoCfMmQebRmVvWnMsjGStgHh1wsIsdoY8O6iA/ptNpbLfbODs7i/F4HMvlMvr9/s98+MtXuX0pUJ2BHROFwhiwYPD4cY2XUw4RzVpQmBOnCFkAvhfKamCBomHs7PhcGoBxNziJ2D+FjLHmaN8RPIqYQQbj8EaWiOZTqHBcdm78xqAbPCI7DJCZOQNTO0TLEjDlaxrEYQQj7o0i4ABG0MypU4p2opaFgwTmASNiwGzHkR0RYMQGj37aMFs3fUQS/XCWw3VqXBsGxQwk97QDdamEHbdTtwZJsMD01f2t67oR2NhJIk/rZt58gn6gw9YZM384CLOjXANAPR6PS12fHbzBltcqcqV/NtIeA7KyznrdE+gsl8tWYMF1zHpzXa8bg/PMjkZEYXhwuA4221hFvm92EDaJ+52cnMRsNivB12q1arClMEPMAfs4+D52iDKGg4ODOD8/j7u7u8J4zefzRmYO24Q9cPbNgbTBDBusXr9+3XjoEXNPMEDfCRY+/vjjePv2bQyHw3IU5uHh/WPg6fPNzU383M/9XPzgBz+I5XIZEVHAdkQ0mHzAEo6e9UhA58AxzweAgU1omZFENl6r1tNcmhHR3GTLvZCZAb9tWBuJ4GAbW+mWAXTWs/wZM8AORJGNj42zHLzeHFxlxpW/HeS5bhc5GYzRrxzE1nVdygcd1CA7EwZ3d3cNkqaq7jOPlO6g5wQxfIf1YBIGoGpWG/vrUiCXjUCsRDTr0rOsbaddUoKMOcGK+/I9bExd1zEajWI2mxXgb4yCrQMjkT3izO2Tk5MSfNruL5fLVvKKNZ8DHttdxsAThJ2NYs5cVsd7nc5+c/DFxUUj0HlXe1dA+W97+9LnVFvRmCSDFZSb9FwGb4CQzLy0sZZeHHaYXCOiWe+62WxKjaJBGUw5bDZjiWiWLOBovbgMbIkw7SCRA/dvOy0C4+8aP7OPXAsnY0dnoGTQ7siU+eEz3MtMkgGJr2mHUVVVYfsB1gb81Gll8EYzyGFRIy/XxntOAb44XpiwNrCdgY+ZElg4ZOyyHORhIOiTFm5ubkoWwmUUyMbZBMuK+XL/yVQ4NeusA31uc65tc23nTtrQDs6pVzt+xuUNkwbI9A9AlDMFDsose5cMub/UV7sZeOA8YY8jmudc53IoAx/WUJvu4YQNaBzkr9fr2O12JUtWVfep5ZOTk3jz5s2DB83k/rMm3r59W5hbmBseBwy4dSbC96fuEfvIpp+f//mfj4iIV69exWazKRtmYYtc1mRwSCDF5zM77z0Yq9WqYfOYZ+TigBx9+cEPfhB3d3el5Avbttvdp9U54Wc4HBbwj1PnhIDValXkYNICP+FHZ2MfCOhZ+9gvbAJj9ibBiH0A6/I3GGo/7GMwGBRWPGIfSLOGmR/ru3UhN+aCPrYFdfzfZsey77RtoeUSDBNZzCX3R5Yuz8l2IaK5f8OZSJqfiMj3DNixcQ68DeJyZsA20QCccaEPDm7434GUx0DfnQEkWLT/xbY4QLdNM6bBp9pX8OOjIQHy+cQu9NiZwIj9g2qQ6XA4jGfPnsVqtSpH+3Ks5s3NTVxeXsb19XWjLMR4odvdP1wPe4nuMp6MObChtiGUgxBIMT7AM/Jg3ujT+9bevjRTjeIT/eHAqXdySt6OGQPCjxU9R+WuDYpopqMnk0nDyQK8Wdje9LjZbMrROO5Xrj1iAdi5O9qOiLK5zZ/jN8DF5SGZEeh09o9nxTBgMAz+fMyRmS8z1JlJ8+fMzNZ1XeoOXSZA/wx6IvYbbnx0nR0mQZKNvo0vTp7xMk+Mm/HCRCBbl3BQ+5WDHq5hB8H4vCnVmQz6V1VV434YbowloMsshhlQg0Wu4YczoGePOQyXHdjxZmbOhs9jdArbbIT771RgxL70yHJnLWbgwLphTfn6fI6+58yBr2N9Z155neZginu7vtWBH3Nudtr3QpfMllkunnP6BXj89NNPG+vBc2BGiH7wfQAAMiMdTQDKOqU/HCXqa9D3zz//vDhc1j7OzKyf2Siz1+i1A0E+z++8cRB9zWlwZxp8esrBwUEcHx/H3d392f4R97Wg19fX8f3vfz9OT0/j+Pg4ttttfPzxx7HZbOIP//APY7u9P6vYZItBNWuXOer1euWcfEr+0Dv0YzAYNMAaNaCwgWaOsWMmGRyAuk7cmcfcrM8OLFkr9iv+TAbW1nvu48DfNpO/zRh7vaCjEft1jpwMCL0eLANnG73evB49HuyZT7hwEI48DLhdosVYnVW5ubkpZ02zzimFMDMOpuDvTCLZf5t8o295Pbq8g3FkeeV5p3mNwB6zth1QI2PPjbHQ0dFRzGazsv+LUgt0mmwpr6FrnU6nHO1Hpouyk1z65LGDOXq9XtkQCdlmvcKuDYfD4tOQw2azKWVc71t7+9I11Sgoi4o0X0Tz/EOXbhA9AzRhY5ymMFhAAaiHYvLtaG1wI/ZpHUfdZiTNhGHkAWIRTWbQzCRAGhCWGQnX9hk0Yyj5XMS+Dtv3oj/b7bakaHGyBiP8z3X947pN2GRY03yclRkKA40sA+rIzIzZqNIwuJxj6SfXGYQZUPO+AQyyNsNrltapURY8TBQG2UbEjTpOn9DikiJnS3gNOcAuZ0CP0XR5DDI2oDUT4mYGwgES16uqqqH3Bux8ziUprC9e49peJ2aKvN5gAclo8J7XkUFbLmuiv/TVa8pglNcZA30yuPLfGajQfG8HK543+k2fLZPMxNHskDOLi83zU846nfsd+QBG6w8OnvE40xVxv5GZ03QMSrKsnEbPY8klJ7Yzvp6DD8bG65RVcC/eHwwG5TQOGGrmgXIWn6QDiO31eoV1g/1yyQcgiSAW+8U1IvYbyGA3Yaq73W6xL/nkKOTLHPqkpKyPBBXYy3cBaq9XgzGzw9a7DMaOjo7i5OQklstlCQLsG90c7LfZMubJwJLxITPf23YHGfsYPGTAGjYrzI/tMeNGv9A5A1/+x/f4ODbKeBxMoK/YAkgBZ2tYb/SVvnjvFH2zPTCD7jl1YGISwWvO52gb4CM75sGZx6qqCuj0xl7LaTqdNjJJ+H/K8HxtbzScTCYRcX8qyXq9jk5n/+wNfIV9DT/MCf2bz+cN+bDO2GSMTOxvsUlgsXe1bKu/Su1LPfyFhvFyqi5izx75CBeXYkTs07s2AjAzpCHYaR8RJb0Ii+HD8dkdnJ1sxD66GwwGxZgB0jNAyc650+kUY0BUDetR13UJJFzqgeLRFysucnEdtIEQLLcfoMN4DH6y4cdx+ZgcUs05RZ+BRRujQj85Bg4Dd3BwEIvFovSfhcrcUgdKvRUA3A4fh5NBD7IwoDY4ySUFsLY4BYIUs8w4zszQkf7FiPM9Gy/ujf6waYR+5Qe/mBnytRwE2njlsdj4mTHy/FhO6JXliwzIMPA9HJVLBTCKgCICOjIafM8pXj5Pn81cOSgxk0MzSGftwazYiVHC42siR77rtdPGnNPsRJ3xcTDcBkDcZ45DpBSr273fwNvv9+PNmzcxn88LKJtMJsVOOEWaH6jkI8Uc/NAnbITH7sDLMt7t9hs23W+cvtlJ1jEZHb7jbCF6D6gDGGGbkDNrFbkCjsfjcdR1HbPZLNbrdUO/BoNBCT6wTzCSq9WqUd9J3whKsXF1XZdSJeuZU/0R+9NKMrliVo81mpsBEPK0nmFj7C8s+7bgjzHe3NwUQGsQyf/2kW0gMBMijMFZOb7HvDIWSC1snx+EQz+5V8T+ATLIFqDH+B2o2P856KuqKqbTadEBmFy+Z1nbR+SNvHyHftiX0XwUJePgO876sOYBw+6PZc788HlvrrVsKDdlPABxl6RiKznOFhB+fX0dg8EgPvroozg4OCglH2Qe6T/+mCcVsxaOj48L8B6NRjEcDuPi4qLoYfYlDlAdbBk/ZR0yQWgy531rb09++Iudu183o4qhwEjyf0SzHGI0GjXKJVxrOB6PIyLKIeOwkbvdrqQTd7v906WYdIOZXHPX7XYLQxTRPAUhYl9+wO5WFjdMIQAGh48hcY2n2RAaC8oAgtcZ83a7bX08uL+PrOmvAxfXLNb1Pu3tRYEDNuuWG33kuKLj4+MYjUbx5s2bWC6XxTFx3VzL5cAob4izQ/DfNmyZtcn6h2GwUXA6ENBkVoG/j46OCiCycd3tdrFcLhtlEoBFnxAQEcUhWm9t+HMa1eU2bWCZhqxyRsFOizHaUZqBcG2y9ct9dp0jG9hub28Lc5H7ZMbcTgqdcjmDwV9ENMZr8ONgKWJfa3l1dVVqj3NrYxAza2g9yfNruaAjeZ1FNJ0q69/OiCe0fv755yXYgmXt9XoFKPd6vRiNRg9Av21OBsuWkwMtB6HIn/45EPQc8JozYi4jIGAwWAAMsAY9/x4HAI4Nidig1WpVUtmZWKnr+w1eyP7q6qp8BgYOm4Gusw59goJPjfHc2vZyj3zCDZ/zGstgwnpgWbYFu21sHGskgxWIJz/IJKJ5YhOfz33DvtCHnBXjnP2Ih0eLWm9sg/2UWey47WnEfu8GwSh66UA012HzeUDXaDQq8jDwxM/AVLtW2esCmRuEe+65lm0BMvB3rGvYPmRsG80cjUajsv/KgbhtL1lCywdyAp2z3UYHVqtVXF9fx3A4jOPj4zg/P4/z8/NyP65DYIit4G++Tx/Z6E3Whu8ib+YWQjCXw9ke8z/rxYEp9uE9oH53e3L5RzZCLFgMKAsFUG1GkskDsK3X67Jz2Gz2ZrOJi4uLAoABczguM7z0ISIaTDITj8HAKOMgSSEaZPiR1bPZLC4vL8v3ABAsGDMj2ehZPmZduf9msylRJ4AdQG15GYBzDTtM6nnZmMHCMtDxdZi7DGzNmsBSHR4exunpaQyHwzg/Py+1Y64jtXMejUYNUMl7zKv1J6LJ4DP/XNtpPgxWZtmLAov94P7Iyic1DIfDsvGFa1NnCkOKI2eTi9NgXJvvoF+wfwRxLvnAKTiQ4XueCwdmGWRl0IhMMgPHmK1rzCmvcX8zSWyQccBsXbOscyBmg2sA4j753g4ADMyRP46V9Zqv4fGjvwSTOc2aWT5aW5YgXx/dsQ3b7XZl8x0Pv8FWARp5CAtZo4j9083sHN3s+DNgy6waesc9kKvL2LiWNwGjt2TWICPoI2MFoPs4wwxeCeA7nU4sFouSjsYmANAhPpAB2SS+R/8B184cYU+d8fMatb55n4B1z4DaMjKJgqxMdtiO+HOeD8bbBiixx8jRc+nyF/rdpgteJzlQNeDh78xeO1g0qcH1Xb/uAMh7eKxL2ApssQGrAaR9aw5svd5NMtmW5mt63GbJ+awJJuYEIikTTXwemdqPG6j3evebkCOiPDWWOch64eAWW+SjRNHhiCh+frVaFXw0GAxiuVwWMsH9sCwhsthcS9kVOv327duyjtAx1qJlaZBue9Nm9xmDyTKXhrQFou/bfftS5R9mThwZeQMKtXgonI+R6XQ6JT3o8g/YaK5zc3MT8/k8Dg8Py+aUiH1NMhNrVpZoLZd0oEiApMVi0XBEXO/s7KxEoN1ut0SDACUAUEQzBW7jZxAY0QT2EdF4gATXQ078bSNAM2vhzZ6TyaRsJnBaimvZ+OT+eQ5tYEl7E/hgaGxA5vN546gjl65E7Ov3YKAM7AyO7VRd38f97EiYz8zeY8xwYKQbDw7uj1z0wf9mc+p6/6h1dMYbPnyGJwbZG2NzWjg/hdEMtmvu6LePd/M8Z2DoNcg9fR07G4MM5sqpWssxs7g0y9cpUHTdjiaDbwNsG2CDCMZBaYSzPf7J3+E62I2c9rcz4mxqH7GF/TFb6bGRzXr27FlMp9OSJl8sFkXHsRkGamZ7u91u4+gqxuG5J4CLaG7Q5TM+/cTABJaNYAS7ZQeOfpp5NPtcVVV5EA3XhLgg6Mr6BWjKgRky55QPglJKQ8jooP/U7aODzsbYRhvYoVPD4bBxigf2ysDVusXfdb0v1bMNcvOactkM10A3rNN83rXkbRmDXHZhQJQzI7Z91mcaf/uBJbxucGqQiI4wZ7YFZr89ZjOu9rsR0bCFOfBiTPZl3MdlLvTZQWwGyXl+WE8wpjT7Y485k1OM3QGRg2nkio23bG3jLCMaWZ88T5kE4Hka9Hs+nzf03qC61+uVDA59v76+jmfPnhXdZz/GBx98UHAWZIkzfpRmAo6dtbSvoq/uk8tZnEV639rbl5IMixa2A4aR+jmiMBQElsIRdKdzf+xSp7Pf5EOEWVVV2ZkKIFgulzGZTBqlJDhUDK0dhplQ+gzwMvPoVL/P6XWJiRUqGxLGlMEA14nY7/iOiFJ6wG5/DBn9NbNNsyPGCQ0Gg5hMJuW4Ls6R5T4+C9RsL6DL7Bav89qzZ8/i5OSkLLzxeFxOW+l2u7FYLOLm5iZOTk4aD2xAPi478OsRTcBlB4Oh5nXmhiDCJSIEWPTP8s4BHqcu4LD8IBZk7nRxBiIYcjstHJRLA3KWwoy0ASh/Ww70zaDLRo17GswiE5wX12kDsa6fRc6MkXtl0MV7OELG6DTxY8C8DWTbocH229kwz9mBGcA47ep5zOsEx8ucApQp67q+vi4gEnYcBo1xXV1dlUdgv379upEKp/ShqqpS/2358z99sM3z/1yPvjuoQK6WHeslp+o9l3W9L+swq+ZArdPplECD+c9lPNZd3yP/Rr44WfQIBo51TJ+QIz4Bf5FtFcGPN347C2TW2DqUM2W8x09bDbHfz6wk+p+DPOSCTvF4e9s5ADc2yGsTv2EG12PJOo0c8BfYJb5n2+T75zWRWedud/9gF77PfXJQz9rMBxHYzsDI83n0yJiB/jHPXvu2ccgl+5XNZv/QFebJBIDlYB9t++KWgx9KAemHgxXrVGaj+ZzXTw6KLy8vC+mIz3Jmi70YsNmA2uvr6xLYcx/rAwEMZVgQQZ1Op0EoQVZyT7JoLglBbzNhksmVd7Us469SezKoZqIwiH7QS6fTaZyZ6jSJy0JYWABLFg4Gwo/4ZUHkp91hHG5ubspDF2CJcsQOYI5onldJ3SNpDTO1u92uRJFmTzPYzaUVTmGi8KSInaq2UfNxWlw3O1nkDKs7HA5jt9vFT3/605I6imie+Wt2AuNnAGHHjGE4PT2Nk5OTYgxGo1FD9ovFogRN/X6/pHbJEBBgAcTaDBJjR2f4vN/nHswb8w0A5XMYHTN8MHne9OP0u8EKzXrFdQ32rM8ZABpUtxldrtWWLmausgHjGnb03MfyzClI5prSKRg/rz07pKxzPgqRa5vNcHbFbD/j4LcBs9/DEbeN24CIz/p99C0DEbNW1M1zhGavd//gBUqZcJjcnw22rEtswfX1dTmn2frmbIeZXa8p5tsBM+MhUHPQ5TWR7Qk6nNcH42fvB+8DClx7bKDI3GQW0tf3fTyftkO2T+gdnyOYod7ax2OyH+Pu7q5kkCLubT5sd7/fL/XoGRSh+4zL5QSuNUaPkDlzw1iRtdeNG+sIXeWzeZ0uFotSJmgQa8be88OYXFudGVWzpB6/yyk9twBAE0iM2T9e6/gi+xjPvdeugS467WAIkOsMisdjYE9jzhiDbagDdQdt3W63+HLmz/bEmUDPkX9nJtzZkm63W46ytN63+XivpZwto69sboesoW02m5IpZ16wa71er8EqW2b0Je/hquv70jOO5Ot0OoUQ8CkkBMH2rei5Tz+hj8w5BFUb+/6+PWxPAtUsJlJ4jrCqqiqpDSbLjA2L7erqqjUSdLrJwBXQ5xrR7XZbrkGqHoPD8U+u49vtdkXBYb/pm6O+iD1r481EuRmE5kjWqT3eb6uzY7HwpEnkiyzNxAA+yQpERGGnAbPcw4DdQNpGyA7EAHQ8HsfJyUlhI7rdbnF8nU6n7Cquqv2RYCxwA1uMg2VnMIusGDNMMvpllgnAwyInIPPGH4NDBw7ogcGyP4MjREfYFU2Ah5H3/BLZGzDn5tRyBu4GyeiHf3v+AbY56rduMHZ+0BF0YbvdNh7R66eXMca82Y1A1Awd48/BSXZebQaX7xts2vG59tSAkcZ7lJAZ/BuAHBwcxF/4C38h1ut1XFxclLEjD56ERpkZcgCAdjqd8kAagIKBMX1G95zd4fvYFrOh6BE65/FbL7yx2/LnNe7rvST0A5KAjZ759BT6YibzMcfYxvph660TEAXYVoMM7B467EAIHwKhMZ/PywYtbySP2AdxzInHgO1CrozLBA32OQe8XMdlAgRLmYgw8HTmLdsBrmOyyTYXm2q9iWievuL55F7IzM0BjzejIhdsI7ri72QCIWK/tgFuZlczSWTGmDkxs2n5+l4eIyxtG9BlvvgcAJM164AAGdgW+z7+ncG+n87JOs1rwoEjMszXse2if5wYlB+Q42tG7DODR0dHMZ1Oi681YZAzC5vNJj788MOoqirOz8/j9va2VAagKwStZJzZU9HtdsuxfXVdx3Q6LSW21meDbHTQlQZ5znLLPuur1J4EqpkQQPJutyub454/f94AyqQjiCztkCP2YDkiyqSb1TIYAuC+ePEittttnJ+fN4rmrXC73a48MdHpV0ojXF9GhOd72Qlk588iN5vtSNb1mna2BAKOIjebTdnYFNEE1Fyf79sQ+5HCgAGYIc+TjTbOvky66vk6nfvswng8LmeI2tCu1+tYLBaNRUTZyWw2K47Xxga52xHZSdiJnZ6elprUTmdfGnB7e1sAIn3yZhgDakfX+Qg/ZGfdpEbcTtUPlDBodFoNcGHHyry6FtU6jp7bCWcAbWDu+XfZBs0g3c6eOcaJusbec2dmO+uvWbjM9phFMrDOzF0boM5jRMbYCe7nefM1uY6bWX/em0wmcX19HT/+8Y8LM4PDubi4iNlsVu7D9w4ODuLk5KSw1HZithPuO78BH/SHYAR7ZjlnQMf/Lk3K8+yUPOMxYDCo6XbvN15T8+17tsnf4+N9ryXbZkgN6xrrAZKEdW5d82fRM4Bep9MpaWrbODZYwqphS+mHwTHrCp0BkPT7/fIUSOba65LPows+ocRBuYEUv+mbH5hh1pWTODI7nO+dQYnnwvaHwCF/Nte0YivM6ubMbQby3pDK3HuvkksI7AN9P+TmvUm2UQ547OOYR4NqbAufM4nmQNvgN5e9OJtpQO21wP8OEj0n2QfSMhli++z38O3eX2OCxIEadfaj0SiqqipM8mq1KocDsPYj9sz95eVlscNsFjY5gg5QKrfd3p8uBnCngX1Y4856239jL5wNet/a25PLP1AIHlDix2hSG+00ltM4EfvaZpgXmhXaC4DrAPguLy8bz53HkAAqXC9EH22EYaEBYGYmvOAywDDrYKdIQ+Ei9gyP+xaxP9qG+9qxIlsMvFNnsAKcz8opJ3zGdVtmWLJzwOgDMmHVKSehD0dHR2VHMtfns5yLybnhgPvsJAwKHcl70w5gBlBqNgyGxPV+NKdld7tdYfspA+r3+8XQmGnm85z1iSxhCwx8rL/oPLqJsTSw8/92Pny37RivNkBNIGDD1gYwbawpxXLQgRFEVmZ/0AHX3aO/DnYZC06Ma9gh5r6Z/coA0g7Y10Am/M7MTpsMIu6f3Mlairg/y/78/LyAEXQD1ppr4qzH43F88MEHJetDCtvzzXxQ1gbrf3p6GlV1/xCFN2/eNFgcZAqQ8+tmvGlZDt4bwfjR5breb7rjPZgpM+T+nnXMmQAzkNZ9s36M1/bE+zGQaRsD73tSCjEej4v9pa/5KEtYMwJaWFrsnGUasU9tcw8eUIO8DZByBs3ZRZdw2CbTR2xp2/yQUqdW1ms9A9YM4vifOcbm+GQh36st8DGg9rz6Hiad2phdN66XAT39gThB53xSjkG0WXiu6TXu32bJHWC0AVPbJpMCtjkG1J57X89rw2O1HmcQaXvOfPi6EELUQptZ9zrrdDrlnHaCNAgz+zcTDugpa4iniG6324LFkInLOobDYfHxlCyZYKR/zi7lckUHl+/b4+1LnVPtR+hOJpNSt7NarWI6nZaNG4vFIk5PTxuRqNlnlMzp12xocGBHR0exXC4bAIlIGmBoxeVwf0eQnE7hlLGNlI1ERPO4QD7nhefPWfG5ro90A+j6GEGuyXtON3rh++lyPg7JtaER+zIb98n3cOkEhtBgjPpTgCyyhgFi8yZnhztoYaEBZgwkMkDhxBI2LWG82dBjdgpAj8E2A7Ferx+crzwej0sJUF3XJfInAPDcGfga6OHQzVhYP5wm83u5jMd/ZzbwMaNuRoLXHGhab7k+Bhh5MTaXSOBsuY5Bfl4PGGXf2xulrHOeewMvv8dY7TyZf2eJ7OTt1LJjRd45qDFTYyfH96m3JkCPiDg7OytMKwAS8FFVVbx8+TJ+6Zd+KabTaenfaDSKTz/9NH74wx82MgJHR0cPTnNx1sFyQ9bYUTOYBsYZhPjaBFReYwAIkwNtzbYm6xRzCItmHcXeen4zkLeuA25ysO5NVrBk/X6/nLiyWq0KyEZG7rfXL4AB2zGfzxvHwzE36BKvUcZIX00GuTSA9YDN8cO5sKHIKz/MzGSC7VcGu5an59X6zHjdV2TofS+sj7yeANN8x5uT+Q6fxz+hg9gCM8TMi08iyfpmcA477kd5M377VbOhJjXamGEz8bYz+TPWI9ub/Fn+bgsMkIvtsbMqjAWAm+3PeDyObrdbbAaA2OWGPm3Mp6BlGfV6vVgsFo1MLAG3j9UDq+EbAO0RezIPe+zPVdX+KFnLyRmK9629fakj9UjJk7YgdQ7Ifv36dZydncV4PI7tdn90ix0CihfRrDU1WMDoASA5HsvgjDpYNrngbKmJ9LnNmfniPhH7dDLGhD6ZMcawetFZuZxCRNmpd6M/KCzf4z7uAwYPcNfGbKPweRwYe4M5olZ/vtvtlicmbrf3NerUZVHykdns3W5XSkFg/GkYN5/YQr9s2Lrd+xIiACgbdkibevFTm5YDme32/gD8xWIRdb1/8AY6iBFzOh8H2QZkYXJ9xqjlyvs8PjazsWaoc+RvcOM5yXPP/xgzOxW+k4Mw2C+YPfTLIAL5cC2un1k/AyH+dxbHpQdtID83AwTG4k1/zF2bHPx9/uZzljuBtgG+GRc2xXFSzXQ6jbq+3yz39u3bwoRmUBNxD5z/+l//6/HRRx/FarWKuq7LAxpub2/jBz/4QbE5jNGPF85O2MGFmS3rYUQ8AAisB9sv39NsrIMYr/W27/IdlwQwXzxHIM+JN6KRLcsBmeeLtcVry+WygA3vPeEznO4Ea8fYbTMZp4MCGOrlchmz2axs1IyIRl0xNtvjQ4bcw/qKved+BIG+LqfARDRPR/A8Al7MQls2ZgUdXOUAysG49dWAywGoCShvcmwraXCm2IGU14QBl/XBpIltg+3fbrc/XYnx2J+2ATj662vYthqwZjaZ+2cg6qAZmwn5kLOtzKN9PaUTrIGIfd0/uuT1x/yQpXEwg18xPvIBENgxNjdfXV2VbLx1hs+bEOTBTByLSykJWQayxVQNVFVVfDzn8KNDrLs23X3fHrYnS4YURUSUs1wBLRFRTqNwmYGjPh5DTO2sJwhjAWOKkrIz3NEb36GOkAk3kHb5gRcr98BImV0yiDCQiWiWEmRwkw0cCu8NHUSzADWDhpzGcooXw4Fh5DgwG1mDfwcPND4HSwBj57ov7gPLicHcbDYlcCLypb+O7mmwPL4392chRzRLgSL2ZSaw6Dx+3kb9+vo65vN5rFarqKqqbKS0g2LObZSYW6cbzdw7+mZMAADYFQyLN6oxPubD7BpG3awU/bA8stMyELeD4fqusySLkFObrk30MWQu4QCQ42BwqFzDjKf16l0tB6wZZJgNdKbAcn8MtNvpEQha73q9XqlN/OY3vxmLxSLOz8/L2DmSirI1B6aMbzgcxvPnz2O73cZ8Po8//dM/LY8i9+OD/R3rHOPx/5YFuuI5dbkNnwVoZPDt63k9ZxtqMIJssy0D1EBaoI/opO0INs37OTLIgUBgffE3R5x64zpAhmweGTOeFkffDQxh7PiuA+Kzs7NyNi/MnzfIox9enyYFWFcuoQJw4cdubm4aJ3rkbI4DIJcLIn9e43UHXQ6i7Bfa1pznMM+xS/WsKxF7+xqxz1TZNnn9WS9pOTPr4An9NTi1DvO/dd+Em22ir5UDi7b1YP/iflkGrp/GhjsLl+/rYBhdY26RGz8OhHJjrs3w85qDZa57cXFRrndzcxPT6bTozXg8LsfZeo6yriJ3iBXsHk+wRi4w3fhk/Byyci03Y2HNtY2Vlm3fV609maleLpfFsJgtODs7K8wNx7DB8KBU6/W6nOvKIsg/MAiOEDGsk8mkALHlchmdzv6EEfqW0682OBHNI2OyQ8XBRDQdvRkGgLLrpOgnhtxshxcnBt3MHGw2joogBKPtdD5ODUDLojK4y2wsYzo8PCzBBo+O9VPoKMkgksXo2QiStgJ42whncGNWMWJ/HBAyNatDANHpdMqm1rxwyUZQ1kPkT8reDCEycmmL54I5xxBazxyN029eyzWrvO5mxicDTObbWQynCx3U2LEyny4T4tr0yQDEoJhxeSORay7Nkpi1rOv9ZuHsXDMTk9cL/+e11+/34+TkpNQ4s+58La/HP4thNvCbTqfFGX33u9+N29vbGI/H8cknn8TZ2VnM5/M4ODhonC0N43h0dBSXl5eFqbm+vo7Xr1/HdrttfTBFnlfrSX7PzUDJABSd8fscm8n1MmMNMM42gGZwgzyZe/pHHXAux3DfcdLog8FRHo8zGs5wZFuFPt7e3hbQbabfYNNMMPrL2t5sNvH27dtyLCnMMLoMAPJ6bgvaHFjaXhL0Yzd9XGybPTEZgwwZh+/l9cWa9pnpyNLXMviz/jtwta/gumaYsfvZH/p6yJp7ZZLAttn+0VkF+kOfIDcM5h10YH8sHzdsooPzTCx5LOiabTTrre1EDhNmvMcRnralOdA1IM6B62QyifF43NCviChlOMfHx3F9fR2z2axxahBjoaxpu73fZAjJg47CmhNIAo7BUWdnZ406fnSZWm/Ln3niFDBwHpnytrG+b+3tyUw1IIDyAJiFzWZTwLYfCIIiz+fzxqZBFrwjfiYYgAGDQb0cLCWLifIE+pEnPyIeKH+OonjdrJOjeS88Axpf2yDJIJT0Ev9zXZj4fr8fq9UqLi4uCgtvNpX7wxKTwsng36AnjydH9xxlRf/5LHWBEftD/AlgIqJs/mGs3kCYU368RmO+0B07VzsSwD0LmQABlp8on9NHnAWo6zoWi0WsVquiL8yNZYmxRy70PbOEfJf+u8aa932iioNH5oHrmcFz2Q3lTJn5N3thAMycmv2xI/F3SAeSNeDppozfY3R62uvD84Qc2gCJHb31gPnjb55waJbTsv+zgmgDWcsZ20AwikPh2DbrgG0Ox02Ril2v1w82qmHPAG0OOq3fef7sZD1fzkYwny61wsnCzqLngBoHIj5Szfd09iYHZtvttmy+pF8GUqw7bCpsWA4sPF6fjW3ga32yPaWxCRDwYHBGSZhtBPNBxoqNg2TYDKZZD1736B1jMztNxoexcH9kCXtnZjX7AzO6JmTwDwbTtiEu0bCNYU4MZtsCNushn0HPAFPZ3tEMmrO+2j4hW5ebAEgNVG3DbauzL82BYQa7Dkzy2P3bttoBFbrCHNJXZ1m9th0A2oc6eLAN4p70g+8eHh7Gs2fPCsFDgDadTkumAzxDCZGzh5y1D8vsUkwYaYgesAPj2e12cX5+Xl4fDAaF0Hz+/HlUVVWwBzYR2d7e3sZsNovDw8MYjUbliY0OuN+Xfry7PZmpduM59v1+v0RUm82mbCjEeVLugULCOPgsTxSTujWMDbVBnHxB7S2vrVarwvA6RdPGFplF8ft2/KSkiKzthK20fNfO1IyrjTSO2Mbu5uYmZrNZUWpSmjmyxlGs1+sGC42CY2zMVNnI43QNnG20Hbg4KiUKBpxdXFw0Ti8xO20wbieCrOq6Lqko+pfrkHMQkvvpIMIH2ru0htQVTtJGmob++TP0nfdtQKk/RjbWm6qqGkdr0fIjhPkbAwdL5eu1lQBE7J+A6JpP+mr94zU/KQu2HsdsoGEwncGy9c+OxlmXtpbHjAFGjhxp53vwd1t2Kc8Z77WBb4MJf+7i4qIw44zVpTK2F3zfZ7dHRCl3s7O2I7bj9/29TnnfusV7Pjnh7u6ubOYzG4jthCllzMyhbYdl4Wtk0OwTLfiM93D4+x4vDTmaLY1oBmO85r0l2DHWIPJeLBbFBnisfBYdWK1WBaS4zI51Z9bRAMmg12UaGTxlEoCxWmednbRO27fkTB99whbib7IOW47Z1yBzX9sA3Bu6PWbm26SAdZX7IWPrT/6ObbuZZ+bK50k7M2K9QdbUC3MvB1C85v456LAv9LUZE+wsPjCimY20/+Ha9MVBge0e97SP5f+qumeXbf/JfG02mzg+Pi6+3CfWsC6cEYOsPDg4iPF4XFhqMNPZ2Vn0+/2ybw1d4dQzruXs3cHBQVljZKXpp9e1CTX8rVlu5Piu9mchSP5tbV/qSD0rGIYPRpOJwPGQ0kNxmTA2GUZEAWkYOwC3FY40LQpH7TQMk/uFgmAkbCBdRxcRjfcMEs1IuRSF75i1xqjYcLr2lQXnaNvRv8GxF3u32y2PVSaK9b1xDBHNXdLMExGpWWgDBxYhx89R7+4nOm0292dubrfbcrqBT1ng3nZGmbnEIdoxYbAwggRK3W630V/ARVVVpWwF3WJD5Wg0Kmd/G6ia9UAnMEB1XZf7MVc4Cb4DYPd1zW74XvxmXsxgMXbYaesT+gf7775SSmR581nXJJthgjHEkVjnCd7snBhHdhgZSBto8T7f4ce60HY0ndkjPvuUZsPvQNepePrCfo/z8/NYr9elzAHbwVpwRswMJGwl90XnMmNjptkgxv01oGe+cbzsa8CG2obZ4bH2nUbOOo6OOUgxA4cd4bME0sjT65ixWk/5nNe358R6RT/Milpf+Y0MfSKIdYexYx+ur69L+hy7y/rFVzgLQKDSxoJal9oCOZMglgX3ALhk+SEL7PtjgY5PC+I6WQbWB+7Ba55Lb7x0sx7kcfl1Z2742/PkeWz7mx9ni9GDHHDlIMTBQQZ3OZBDhzw3fs8Bbt7gn30/92euPMeWHzoDwWG5sh5Go1HJoLKJnpIk/J79BWuRYDDvWSIjCwHj4MIgmjronB1BRhcXF6XvBDuMGwJqMBjEYrGI6+vrgmmWy2XJTrFXxWWvEA3v28P25CP1nL6i5gbDQT0sCw0lQqlYaDgwwAUghKjMTw10jRYT6XSTFyQKQ2TnReYjbtzM8jli5Z52pH4fg4ER4W8DnIj9xgQzU2awvaANSvk8x++ZbeS6dlZmsfnhiDqcgg0lgQtOHaaX9BR1dxgnxrfdbstpE8g9oln7S/+2220pc4Ex7fV6jY0WzC9y8pwhV4MF7omDpR72XUwn3zFr4WCKPthxwVRg9HEs2dF4bfB91zzbENmB2LGYPWTMBBx2ntzXgQtOwQCO1+g/TjKnYPNRWl5DDkIZZwYQBgy8b/bVABBdsmNw8zXMHjkLY0adgAOZYldOTk5KoH9xcdEoF6NkzYGdgQ5yxLGRLva8QQgYyFv3Dazos9duDgwMpDPLaf2wzfAc0ACVZmk9JzTbCbIs6Kef9mpQyP08TwTDZveYd3TNZUpOu/P9fr/fqLtFtqxvbBSM43K5jKurqzIWZOIMpQM3ZwVci2uG3TbTQaJ9Fn7ADUBi4Mv10VtS9raJXM+MbPY1+TMOTpAXfeWeeS0gE5+97rUKewlZwnqy3jqopq9e+4zJ9tOkj48xtCzRc5++w2c8b9ZZ+0UDQ+ue7bTXgQG4wXP20eildZ3TNiwTZHtzcxODwSBevnxZPg8xAllxenpa/kbnr66uYjQaldKO4XBYMvy73a7gI4JvjpeM2D+shYDBGW4OhzBRAnHpfhtfHR4exmq1isPDw5hOpzGbzUpWfLfbFSbc5EG2cW3tPVP9Z2y73a5EM0wgxhkh2pB2Op1yrBUKifGHbXDaDoBk5gZggoGACUdxI5qOmGv5O/TdDorv0QdH0xg4jIWBUY7yXcPnyDqDMcopiBD5HgvALBQPmfDRZ2Y3bahtxDAsjIk+GrxjxEhTUW5zdXVVzgN3RJ3P1DaLz3isHzDAZpCQGUbHD65BR7Ke5SCDRb1arQoDMB6PG+k+f88BVHauNzc3xXC5TpGggfEbuDqgsdG1kzLwRxfNkHFNdDqiafAJnuiTmTV01wycv+f7GpTDRnuTEK+5n4zfwMTOEkdu8JCZR/42q+25aZNfNr7IkceLf/DBB/H69etYLpdxfHwcFxcXjbNWM3NE7bTXC7oPc28ZYnMimk/kdJ0tNsD2I++VaAu+ASEGAAYnXvuMnb45YPHeAcuT77SBeZrXK0ehMkaC7ohmbaxBkwMBAycAl/Xz4OAgRqNRbDabB6y4szcwz9YhmsG1yRNOeTLgoo8OBsxUOhB2sEgwZf3LABdSJttZB3zWO//GR9n+ZSDsAJTmsi7k5cyc5T0YDMp7eR5Ya4BBB9r2C15z9N2vm+X1387aZWKHuWPcmR1m3pgv9NE+is9ke2q2m89hK7lPnq82G+PrITcDUthZ5EYJKvfm0d7b7TZOTk5iPp8Xv4k+8vnValWC/Yj7c/EhmCiZxZ7d3d01MrabzSYWi0XjKbnezMhm/eVy2cj8Uy/d7XZLUBVxH3ifnp6Wp2CzhlnPHE8KSYW+U7LIPLUFme/bvj25ppr6206nU5hkhJ0fDIDzIUoDmOZ6T1638aWsg2v5QSMYPxsAgOvx8XGDlTTg5nN2JIAYG66IPWhwCsupD4AK3zHAIPIFsIxGo+IQAAuAHJyPo2pvTkGZI9pr7gDTLGqPGefhtJ5ZSvpNRNrr9cqcIgeekpnZAvqGjJwu7nQ6BbTTMtPmkhCu5eABuTMOArH5fB69Xq9x1BDjyexxRLNcyUASEMGRdDgi17Zxrba0PHpkeVgmbZtzrIvoux0Lc8V3MvOUWbisE3acDvbsLB0woZ9m2vgOn/P3Pc6Ihw+YyLLnvcxK03q9Xnz44YdlwxmfY7PzdruNTz/9NGazWUREAdRtTrKu63LMogNNgy2Cdf43CCbz4eCbeXA2wWuDe2RWOYOxtrIkz62vndc4Qepjum199Dw44GPfC3MWsX9SItdBJj7hw2yeiYcMWBkndtuvRewzAi5f454+UtMpZXwH5YMGrbbnZndZ08jBusE4TX44Q2pg6d/WJ8vdsvH6Yv5gfg2afQ/WOt9BT7PcWYusNWwmOotd9biQi3XUTK77Q/Biu8K6Z54daOVSSOt+RPPhQPhB1iFrge8xTjbuWeYuFWxj5/m8g80MzK2D1gXGxWkq+GLvKYKhhaSbTqel9IjPkXVlIyD9wIdAQlpnttttwTK2DRCV1uu2AAdmGlDueebzkFb4c8Y9HA7LesL3sd45gcTXgfBk7sE++Lf3rb09CVRjxL1T3uDCCgywdYrHxh/H6Z3rgCkmnc0oTDQLPoPYiCjsFowGhigb1zb2gX7ZIeYoOaL5SOuIPUCxUeOag8EgxuNxXF1dFeDMmd5VVTWezJWdEwue8ZtlMbNptthHsjk1600hmc3AcQHKOYcXA+9yD2TsuTBLgcGgFt4pPYN/nKiZgczmuRaY39R+s0kROfEdOx4baAwgRgywa2CD4+p0OuVkFDMo2cBZbzLYRZYGx3aQnjt0HdDBqTkG+VzT4C2DBBtBgAuG1cEgBtTrgHswD9wnsxF5bXv9ZdaU1x8D015vZEkIUgG/2+22PFgqn12cgSTXzEyd32O+qa3mkb3U5cNk+x6ZwfMacrDKeiGIQj4O9MyM0afMBmcAQMusYM4S8J7l4fQxesba86Zhr08/HtxzzRqwzK3bLj/wuohoZsrQRY4Zw/5Yb2AcYQxNfDi4yPbMa96A3WvFY7J+OCuTAwLPNTJGl+w7+JxtL2ME2HIPb5qj/4zVsvD68f4cr03XdaMPzrA6cDdA5rPYbfcdu29ZmZSwv0a2rB/7b7IjfCdiX16BnXPgkfXOwb11Gtlktj3bHwdfjN++i9+Mpaqq4rfH43GcnZ3FZnO/wZDNsbvdrtgL7K19JMdDcrzd1dVVOcnj+vq6sV/o4OCgHDvsmmdnwWDJ8xwzvzDR2K5ut1vIBZNd6Jw31vt5IVW1P03NcuI+yItjPj1X71uzPXmjos9SNiOGg7YzJRJGIZxC8a71iD1gjdg/5ANmySkKpyX4m8cPuy6pjTUzGIrYP9YbQ4fBcWQf0TxSzYsWg4DSE9WywZJduAQGGJJcp4qR5jfOwxG4GS3+NztCn5CR2encHEHz/+XlZYNZuL29bRwtllOtNkpm9/1gh4gojzYnyKrrulEWYbbGIAPnah1D5gYnyMlz49fNfGMg6LdBnNlJ+uDTRWyAASroOP1Cb5hTp1oNZh2EontOPXJd2FquwevIy0EEY7ZBdqkV3zUI8PcMCvkbXeUzyCUDFLfMhGVm3e3m5ibevHnTAJ3IpKqqOD4+jjdv3hRdzcx4bu6XP2OQwWeYJzsd5F3XdeMBP23BuTNLDioz42fbQtAC++OyrzbgncfotezP5OCFeUavCBC9qRyw42yPH+1toEhzcIi+A8QJlP1kN5ewWMawoxyzylpBRqwD24qIe3Z9Pp+X7Jn7ZH2H6cfuuOUMgdes9c8kBgESss221YSMgwrrDv1hPhyk0cfHSuqyXjvj6GbQ7L4h11yaiSzqum4wquhUZufNhmedg5knKDP7jE75YALkyGecJfL1veb5LuOy7HiN316zvV6v4AMYZeaCDYYcGcv42R9GcIae0r+Tk5Po9/uNU7k4dYk1wH2dTQbboHv4HzYGZiJrt9vvXWOsPOCKqoHFYtEIcgD7Zqwh+JC9y4m4BzJzsAC5hn7a379v7e1JoHq73RYlyhtEDEqzwTCAdA0jwAU2EicHcM+MVF5gLh9gE1J2RJnR4W+zJxFR7o9x4PMG067J5DM5/QigBjzxXR+F1QbWuW7e7GhA7bEB0PPmJYMGb1BhcWVnZCCAwwdU0Oz4rQtZlhhJ9ALDCpiGvUAudhidTqewhmZYDIwYVw5CzKBkHQRMecyMxSUXpIMdyBlMe07pL3NkfbZuGvji5BibHRe641KRx+bfhtAGzmyON4Te3d01yq4sB68RZzT4jEGfP5uBCi1/juZrPhbsolOMZzwex09+8pOyMa2NqfHrGcB73g1uGc/Z2VnDgaED9INm1i4HtnkjFvtBmCP3lywOD1oxGPdmI9e1O3D2WslAK7O01Cxn9tiMlVk/7zuh37aB6DrjyCw0+mYZZTtt5jwiynF4AGf6enV11QAHjGc8HhcQ7nXBOnNQbABiP+V5NAHgYMbBZkTzIWG2S55b/8bGoWfoAvJznTPyxD+YMbZdtb3zI9cdDDvod2DvWmjrgtlGv+9x8XkTKOg+umm/AgnB9U2IOcBw8GDigtfcT9sMfJ7XloMU7jEYDEoWJCLKE3epg6b2HwKN+1MO4ZIKs8QHBwdlcx9rHT3abvfnvlN2gfxhubkvdhVbUVX3mWuvE2cOnOnAf3a73ZjP51FVVeOwATLg6DWf9THHXNfr1npjVtrrnODLWOax1kZ6fFXalzpSzwYCY4MxQ5EimoLFyeFMcgSNYufaWC8sG7Cc1vLnvfj5fGaqUTaMRU6vweiwCO1kuJaZchY14wRcRzQZJu+gNaB2cML1czqMvhvQ+Qe5YEQMsDODgiHmHn4suR/sYgbHrFh2nAagLH6z+K5dJhXN9Q8PD8vjWU9OTsoitsHj/oBms/3c3wybU/kO8MyG8Rl+GLf1h2t0u/uHtpjlNrizHprB9t8GJzT+bqtvNOPk8XpsXhPILJ+t7Cdt2VExBpw1ssxy4LNtzJlBMc1jdhbB4BU9bGO+OJfe139Xy4w4gMmOH33kB8aK7/ODvnkuzG62gSrStGb4bMv4DmfZ84MTdvkW1zXIN6hxfzw3AH2zXPTZ844N8OZrM3HYLp8AlG0O84I9Yn3n7Jl1gUCPzAmb2LkXm9fpHyCl07l/xDy19WaRnZrGtmK72MjlueZ6bYSE/QOvMR8AEAcUWQcyeHR5h4N95gb5DQaDVhbQ2QyDTBM5jBfdpeXMVtYXg3r0wyAf/TeARm5er1zPYLTX6zVKQwHR1hlK8nwt6zy65oAxEy0eIywvvg4AzHexh+AW9AGb7j73+/1y0kyv1yvH3R0cHMSHH35YcIofTNTr9Qp4pp/r9bpBvMAA478gPMyA80RDzrI+PDyM+Xze8EGU38K0Y89gtK+uruLy8rLgAOYfwojrWs6U2XlO0TV8B7rkjOr79rA9GVQjaLOfRDAGMAZiTn8MBoNSXsAxbrn+GUCYQVHEQwbMrFXb//6OU30RTcbHYAeQTxRqJTNTYFbGxxKxcLinx+ffBtMYkLwBwIyrAbd/GCP9RX4sKBxQG7DxUUosIIN5yzMDaYwAr7lOPiLKUUCnp6dlLtikwbW32/uNG376GilfjIVZqMxwWz52INYnnJMZZ4NdOzPPqZmg/ERCpy6RvR1gDgodiFmmBq7cz4AUnbbjySxBDi4y0wcjQx8ABnbUGHUHtxm4u5+0NpANwHkMDPM6TIh3lTPvjwXV1vl8z7ag0QDK4Is5ZC1Yn3xfZMprgCsf99ntdh+UU3FvWGiABHNBMOEaZubAMn+MIMBe+X+fKMTr6IDn2QQCjF5+WqLvaeDGHBm8W5ccFNAvgzGXJzFu2DXPCbrBk+HM4OesG+sj6wey2Gw2hV0E9NJnj9U/DqgMXtEr7kvjug5kAFwOSli/9B9dYJ1axtZfr1Gu4QDPezHcJ/qJXBlHJkf83bYsWM5q2M4ZjLfpz3a7L4WLiHKKE/fzfRgzumSyyjbBNm40GpXAjPVGAEWAiO7R5+12G8fHx9HpdMrTBindAAMAuK03rHke4kIGijW3Xq8bp2cAWAHSb9++bfh9nwON/npfk/dCRNxv1o6IsveHPtze3hbG2uVmkFr8tj7d3d01TgAyXquqqnEePI0HwbzfqPh4e/I51RH7FLY3k/C+0z2eJIxaVVXl0bJEqUwaxtIL3I4DI+iF51q87PBtJDPj5/RrRDQcHBG2gamPI+M+1AiSbjWQIzVEdEgzW0sfGY/TMRgvl6O0jctjMEhkQWZWjcWdWRencl2L2xaomMFwwOT+r9fruLy8jA8++CAWi0UJNKjpwhAQaDkdbRYfZoHXAbVOTWZQbEfDteyIHHVTL4deW08ohXFq0vNmGbQx+HbwBsaPgU0+F7F3iNaPNrDqvx3IsTbpI/rgAMnXh/l3yjaPw2PILTtzAA3/D4fDxlMCMfQ4zZcvX0an02k8nAV5WPfb5Mf7zigB2Lyecs0qY3LwwHyio206w9r296qqajxJzmUFDraYG7PS7hM6neVswNhmDwgarAMujchBuJvBLn15V6CKTbcduLq6ehBk+l6U8WQQd319XeYc+w9zz+sZ8OZrZ/2k78iRrIXXlOffsvV6Z9zMNddsa5atAx6AnvtOP1xOAsGEfPic69Ot0wSDtoX80Ff3i+tmsEzLx+SZNOIn25PHNquZsMglZGz2Y23lAMX2G1l6buyHKac4ODiI5XJZiJderxeXl5dlU/Jmsylsr3V4uVw2AseIPQvb6XTKwQK8v16v4/r6uhGYAbyR79XVVanHJuhcLBZF/x1I+Hudzv5oWfDM9fV1+V5d14XYgYgiaEdGV1dXjeyG66dp1kX6c3t7G6vVqsieEg/0wnYA1jrb5Nx+1vv/NrcnM9UAMKfU7GAQvkEvxhIAYxbMCz6fmZqNqQ0gxi63/FlHfDZsGGD6wT3N8nIfM2dmgGh8H+VtAyV8nntaUc0yG2wYxJhVMXg2U+B0OuPEgJmdNGBgIXu82+22MEc5IGE8AGkYZtLa9Pfi4iKOj49LWpyfu7u7GI1GMR6PSxQOyEf2rm124OQ+WNZ5/nFSltHBwf0jwu18GaNZSs+VU2ZmlzIDxFzZQDrA89rIINGAx+MziHPg4M9xzeycPP+8jgNkLcBmoC/Immu6FvMpgJrmvrKxJuuZx8Rc+FHmbYb5MUDtOnUzhhFRTppxeQG6BpgwS8rYPV7uS4rZhAHnwlbVfZkTR12hO1nGDh65t0kBy94t6xX9QgcNzB3ceX4cWAD6zJTzHvY4Z3CYLwCxA3Ea65XACgCU9QMgud3u65CRGzKwjXcQn7NVGUhiTylLyax0nlsDPdajgwbrGjYi60heg9hd1qqDc2f0AEO2f6695rdLI7zOc0mXgbQDLfSWeXWA67nnfWekbb8AbA6afX9vRPR8t9lazxnXykE5/WJ+ut37x3tTprHdbkspEUwzJBd11PapjMObkQkaKOvAVxweHpansh4cHMTx8XHjqZ70F/tiUs17FXjQUcQ9EKfk0TX7BAf4Q8vLD2pjQyRzwIlbl5eXpc8QmbPZrGFb6rpuPLyKkhGTRRH78rvhcBgHBwdFR9Fvr6f3rdmefE41E22j7Ug/osloe8c5xtLGIOIhQMqpPKe9aAblNKdzzcphsGzgnEbDoXpjoYF/Nl70EXaIMbg/dgD021F67rMZNTvVNsCMjHEuGCFAoOfFJS9OAVq+dvK73a5xyHw+EolFS4SOMaOOazKZlMfHP3v2rNRPEv2PRqPGyQNmrQ2cDTCcrnSwkfUQQ4EzRl6uMcdh8RnfZ7fblUP0qUtm3pGRGUgDsOyY0AFaduoGAQ64ACAGsgafBjZmDPI6sONwhsVgF3nYcbImzOZm0G5A09Yy+ObJYUdHR/Gtb30rPv/88zg7OyvXvbu7i/Pz86JXnO6QgSX9ZO1FRCMlbzmZEaSOF5uALhAsW3/yRkEDUgNKs4M8jIjr+Ix9+un+mfXN+vKzmFDLP4Ns/s5kBA095t6cueugz6QCa8hlV6x/1ghjYxy2V65N5zc6d3Bw0AhGaAYpZlRtI30/7LflRu2qg1jbXa9VZye5dpadwVOeKwNu2xID7zzHzIM3wRvc+amROeNisgg9t63gGvQXEst9t1y5hnXPR722fdbZWzPgJgxsbzyv+FYD7jaCzISFfT+leARL+CBO2/DmQcqKfB+ubdLPfeJ/H/WLH2DObB8oY3VZKI11ZB2F3NntdnFycvLguDyXbVBzjR/fbDYxHA4LAN9s7k87w89RCXBwcH/u9nK5bGQ8GLvtF36O87Y9BsZHGaezBu/b4+3JoJrmyNrAjM+wo5sD/FkYKLSPezKjyDVQBi9sGxEDEzsVFo6ZZDtNOyZHZ46cDXhsDA2s7djsyLmeI8OIaLBBjNsG0AAyovmkPRukPA8YQ4MlyjD8XQw8C9WLxEaC79zc3BTD5YwErJKdLq+dn5/HwcFBvHr1qtSrYRxOT08bJ3/gzJxuo8+AEgNpPuPsQgavdsh5XBHRANRcC5kzt7lm3vVmyCcDZBuZzBz7/vxvBs76aP3GmXi8dvb8bzCRdc2OyGxmVVUNQEkfXUvt+ch7Ht4FqHOgy3dI1XLeeF3X5emHDjpzkIeMcJ4unYCR4pG/ZrscgD5mP7wfwHW7NMs2z2meJ/SC9WtQwffQt6wr+W/uQcNOMH8G1LmMC/n4GCyuUVX7jIDlaGCIXc8brX1t205/30Cx2+2WDIVtuEEaJTkGKx4fgCIHhegj/xss5XXKfbzumCezhPzt9Z1JEvcfm2hGFuAEqDMb7OAfEMfr9glXV1cN++CThdqAJ+MzeDcJ4LViPwvYpAbZWQPIMLO76DxrxgG9AxbIKfTSBFEG6g5+nR3kfvaHVbXf7Me59axhnzJlcsZjpo/0zRiA8Thgns/nERHlKYNcDzv27NmzWCwW5X1vvOeHecCXOgh0uRUBweXlZZlX6p2n02kB1avVqpS5AJy73X25RvYHrEP65bJdEyfesE/JI3pAcGpdzpmp963ZnlxTjUKyIIi8aF60bF406LMRM2uDcTGgzWCbhcYCy2DDDtEpJINl+sL3XNSPc8ysHa+x8O2QbSwNyLIRq6qqHNnDIsEwYUhtdM1C5YCDz9hh5+DCThnZOx3qhgzot3cXs9Ai9o8Ux4ihA3xvs9mUjUWk4ebzedETnKcNLYbaOsFc5DR8PmcVg8HctGVPIvZnq1uOPgQ/G1X6gI75fe7LtezoDOpobeDa+s647cgNphmrGSq/ljM2BtK+hgG1fxzcwapk9stye1fzfACYuOfR0VH83M/9XLx58yZev35dnEvWa4/R68HAMrNcpHizczarbX21zSG7Y+ed1571CeBlvYzY733wiRl1XTc2DyKbDDTpL62NBbVDo584SebX93BwaZngJC0jPnd7e1vOled/sjcOttGbHGBiHwiADLpoyJHvA97spO0L2ogQZ4eyD/A6BZzYX0VEI92NbPP3kWlmfvl8Jj38cKm2IJp7Wd6u82admKwC/BjUZX1yYITMILH8OexMZuwjogFGTRTZXgCwPIcEPQQt2HJfw77W66mNpMgBlAPGiCglpLvdrmQ8eTIoOkfdM+P30bzdbrc86ZfjGbF7ea4i7vcFTafT4rdYP2bqua7Hxl4rbDB6GHEfJOX9Bdgg1p31mifNgrt8CMB4PC6fMdHEsX3WNQgTdN/lVpxSwpgYI3rCGsW2ZHzxvjXblzr9AyXGiFjou92uKAKLmO94QdmIGHCYBYpopuNwuDivDKK8GA1WzGi7D73e/hidDOo5wN3RdMTDExNcMmIDSJSOTCKiBBksHgNH+tNm9DLIQA5WbhsDy4rrIVP6YmNFCsjRcwaInc79cXc2FBgXPjOdTuPq6ipOT0+L8zs5OSmG3kDY7DPGivs6fWzG1nLAgLvGkmYjDrNFf7x7HWOIc7BczfAx5xiiNgYqs460NkDqrA5jzYGTHY2Bpsdo/TZQdMBlnfYa4xpOeVrvuH5b0Oax0QAxNrbWp06nE9/97ncbqe0/C1A3EKaUyPfOG2nMhHJfHL03vyJv/m5jxx/rJ8AHu4COoU98BqduUJwDpGznHJj4vl47rD2CXZ4g68/Z+aMHdpwRzVKmiCh1vpxmANsKEEf+9NmMOXMEyDEQNJtvXUTHvMkSnXSQzRg8Z7zWJiPrgteYgarn1aDKdplxwszZNmOzACbYVwcs1oNchufskcc2Ho8LyQFABPQYvHOfvLeH5ieqmsDiOga9ztQhB+tavg+y9W8DuGyPLAf7E+uPSYx8tBvfh9E9OjqK0WhUmP2I+30b6JKPqRuPxyU7A/MLCz0ejxsbvx1M7Xa74hPRz9FoVGqesQGTyaTUT1dVFcPhsAQpfI5yqeFwWGyQTxHCPq1Wq4YcCaKYL3QKdv7g4KABxsEbHMdnfQCj2A4wRvsJ9Nzr0nJnPdmO59bmr75K7cmg2o4bp8GCsXHHYKCoZhQyO2UAQZTOZOb382ZGFA7Dz/fssBw5e7K9szxi/+Q1ou88VkftZncyq0Kr67owwyywbJx93rE35NkxmNVgwWcGIOIhQ23mhc/ZoZmto1+r1aowzZzzWpRFaTNkcHt7W6L+0WgUL1++LO/5yYnUtWI4t9tt2bABwMbAI3unqT2XGBVvFnLAZPacmkIzrg6KPJdmrCw75pHrmGG0c6R/dia0nLq1/qIrBudeIzgovmenxWcAI9TVsXa4JsDP4+R9QEFm4nKg2NZyAGG21jKm3rFtg5eb9Tc7E+5B1gPQ7vQvn3FzajZiv4eD/j8GhHjfYA5g4PXJ53PwZ7BuVqotaLas7JQc3DoVi40y08q10Hs2NREIowMmAegjT0as67o8qIJ+5j0v2CrLkPtERHH2tqdZ7rnZ1vpevGcmjj7kAMj+BDkDotFLr28HzHyfeuKI/cOwXMoB+Mzr3kE88neGjv7mgMEsa7d7fzZ3LkVijbKJjrpa2xya9xrkazgI9HczA81aMtmTiQPmxXPK/5ZnXosG08yrNwDCHrfZULKk7NNarVZF7y4uLho+nycZA9B5siFHX/LwFhr6nn2qx0ONsnWNR5Z3u904PT2NxWLRyBAOBoNyr/l8XtZKJjH4LHOTyzD4Hn06ODgoJW8EYQTDPp1oNBqVB/Yxrk6nUx6ZbvxhIpB5Zxz4Yu8TeN/a25NBtRkIJrqu9ykIJt7pdRu7DB4MBDODy3sY1Bz1YpBYZAbCZtNRCDZW0KdsUO3Q6Q/v8XmM6eHhYYlkUWwbHUAOgNH9j4iGcc1Owekks0CZybQhz80Ow9GvQRMGwinh6+vrGI/Hpf41IhqlNgQRfPfq6ipWq1W8ePGigCfAdM4+uEwoYv+0Okf2TlFlAG+gzlgAjwYrEfuHETDvZmqscwYW6GeWVxvbY+eQWWvPnwFqXkN2cDZUeUOWAwc7qqy/fC8ft5QDk9wXM4ttwN5Opq1lR4vMqD/s9XqNI9cyk5HXHX3weqKcoKqq+NrXvhZ3d3fx6tWrODo6ihcvXpSyI+YvA1jfx+NyYJn7wGf9eWxUBuDIyMFqRLOEwP1pY/BsHxiHAR2ZCexG7rO/i2N2iYEDVxr35b3dbl/uRR94n3WELACMfpR4RBRGO2cY8lNluaf9iNdNzpS4vCWvGWTogNKZUjN1/LYuYNu4B3aM2lX7LRq2y0QMcgHstfkj+sv+gslkUrK9s9ms1M5ycgOfZ8N1BqsGPrYnZsl536SNZeGgweP12rTeGDi2ycZzYzuVwTmg/vr6umxoHo1GJWi2HcdPZFt5c3MT0+m0nL7BawTyEfvTLPBBVVU1QOxkMikPb+G62HHml9c4CcMPcrq7u4vPP/+80X/WqRvH7aHrnK6BH5pOp3F5eVnwEwDc5UUEL+iU7QOM+PX1dTnr3Y2+0ofr6+sSdOfSouvr68ZhEswvpZ3val9l4P2lnqiIkpGOymc7U9MU8fBMWC9UpyG4rssheM9pKzttpz0cYUXsi+/zkVG+l9knrmfWGONusHh8fBwR+5IJGzuMk1lFp6ed2s1RcQbkfI6+ZXbBY7Ws+N/BiME7Tox7A5xXq1UsFouYTCaNKJk0m8EuNVsG9AYHBvRmZ5hLnJfZ6Oy0nU7FmHgx59rKTme/+YjrmPFx1O1ABR2K2INZOz+Xu0TsnzxpHTagYJ7QA5yXjUwOsPxd/25jLP1d3scZ+pxkNsYYKBjIWe/toJHlY+9bz/jbYJm10+/3Sw2/j3UyUPK4PC+WEw1nWtd1/PCHPyx9uL29jVevXj0I1h9jhPM4c2OdGtzlvuHsHBDnQIU5aQucvC7oAwE7mT/0Km8o9fzRnN3DXpHNYXMnJ31wL9uFfEY1pR9kAEj3unSKpwACqvku6w3WjKPHAI0wuKw/z0u2DdZvPtfmI6ynmbXPwNA2IGe4zBwPBoMCSrjfY/aY+1CP3lYLDBN6eHgYL168iNPT0xiPx8Vfnp2dxatXr+Lzzz8v65g5sL5aHvgO5OVMk3XSoMv9IpjxA78cJNo35XWbm1lwruMsEJ9B1tlmwvQSlFxeXpasAdfy/a0jPFUQlpuTfpABgRFZV3y5QftisSgAfzKZxHa7LWyu5UA23r7UAZ33OWAfIKI4bhNbTfMpKm/fvo3r6+sC/p1x9NNXCdzAWj5DnHU6HA5jsVg0SlydPTPmMSZyUI2PZF3kjOv79rB9aVAdsWcacQgGSXYqLIYcyfp1L1wmNzsPsz8YjzaWjYlvA9sGezbKTtW5byyWo6OjePbsWWH+6rqOwWAQJycnEbHfqHR3d1dKHbJxshHOTtYtMymABPpvJqwNUPO9fN3tdtvYqGiHSiqNvlGasdlsSq1XRJRUG0aiqqr45JNPyj3aZEjjTGrew4ECJOgLTimzRcjMtZaMA6eRd3+TsrJMYXusOzaG1hOn9zGwngeDXOtaZmYyQPbcGHRlkMm9PKfMkdlrwLQ30eQyDLc8N15b6Bvr0oY9p2Wtm8w9AS1sG/LN7GK32y21kM4icc0MuixPv+f+8v22YKRtTfB5+mOH6ODB6xf55uAij899d3/y+sCZ0z+XefCdut5nZfguts41wegxZ+3W9f7BPg76AbnO3qDnrEUcOPaOddPr9cojxD1On5uLvJgbnD82O9t2ALk3FNsH+Iex2rZ6fukjLD/2wd9t8y8uWyMoaLMLWR8BGz4VwWt8MBjEixcvYjKZxMuXL8sTZnlA1o9//OP4oz/6oxJ89vv9cnIKfXW/7UecDUOOBkgODPFRZuN9Vr/Xlte4g7EcoDqj58DOzLyDIwdrMKYAQo7Bm81mjXrko6Ojoj+u+aVvAGLbBORBTTXlEswvpVEAT+bJ5R0EQn4EO+PMp0K5dNP20KdysMZ57/LysrGfgPmhFIY+UhrkgNLkUUSU503AcHc6nbi8vCzZIPfLaz1n1GzHHOD7JB4He+/bw/alTv8waDZLwEQ4guN7EQ+f4tbm4CIeMnRMpo9GMnjmOxH7iBnlsIHIxtfMgw0RfUXxTk5OCuPD9VE0sxgAOuq47FA93swUZueQU5P8bVDjz2fnYLlaTnyPhY5TxGjgTEjnEhx4w9JyuYyDg4PyOhsRHanTX4M+P/QH55lPAvFTN70RC2N1cLA/hN5Ag2sSRGE8bETMmjj4Qp8BhGxoidg/ZQzHivwN4rJTx+CbrXczM2lWuw1Qm3XNDKy/0+l0GrJqY4vQkTZAh34YBNEHgyD3P4NeO3zm1IFJTttyDcpC2oAWr8F8bzb357QOBoN4+/ZtI4gx25LBcF4XORCN2ANqB82PAWScm+XxGKDO5SfeWe+gzEDHfbNs3ZwRMiGALcHpE9wPBoOSAscpZ5tEH32SCX1y4HF8fBzD4bCMmRRy9gHYlTZG2uCLOWtjWDOgzgGi+833ut1uTCaT6HQ65WQY628uw0C+yJG17hIA+uNTFVyW5uDG+nR0dBQff/xxPH/+PMbjcfR6vVgul8X+XVxcxOvXrxuglHu5pJAxO/Nh/YuIhg4xx3zXAQb6gQ8nA2h5+15tvqeN+EGGtrW8x/V4GBjlHLY98/m8saZdvsb1PY+UQiBLAh77/6qqCkCnP2zGNfFwe3tb/B4HLXAf7s1DVxg/mYxcCsH65m9ntTxHPGDGsr27uyv3t57C2uM3XFrkfSoG9Ogw80nwxNxw0gfjxN/5bHOTWsxLzsS0tWyvvkrtyUx1Tkca9PqMW1o2ghlQo1C59sjKZrABgLMj4rO5PhAlzNeMaG5QQgENGCKipLBZmIBQR8swCzgXdsX6Xhl48RrNig0IwSAYaGR2HcPJYjLjxMJjsdnhWOE3m/tHuI5Go5Kack2kg4mbm5tYLpfxySefxLNnz0o/YDyIZpELRpw+Ykwzc4QzM9jPtWIRUR5Kg/GkZCSni5ETzo4+wTBgVJEZwJrAIn8/662NHQCB/jCP/s04DZiQBzqav2PgYUdrEGHDhuPIa8FOyO9zDWTpgCGvk7w27dwMztEn63QGyjT0JoPp/FnWBLJaLpcNp2cAlgG1583/+z52EjhwZyfa5i/f511jxMmb8TTL7SDUwS9jyuAeUJhBNbKk1AB9cIDsayE/b/z2vVlnruHlODLIhKra1y9T72q7bXvsAI/x2nf4t/XL8ve6Q+6dTqdsBqNMpdO5f4CKNx0ip6w3/G2Ze/1k2+ugFMDkdQSANcnQ7d7X4b558yaqqorJZFKegDefz2O32zXOje71esUWA66wYdZZXvMc0Q9sIsAeWbhvOZhmTswke/0gw0xWcF+TCe6v55XyCR8xFxHlRA7fi1MnbIPJpnLKDBsDkRU1xfgaNiZyDYP0TOpQy44cyMagW3l9sgYBo3Vdlw30bKokuMznUnvdMU7miDV8dXVVMEUG3z6nu9PplJNMKPsja2s9z3rCXDojY2IP/WbMrB2XC71vD9uXeviLI1UracQetNkAGZDkCDY7bF+/jYXITtSRM4vWzGc23GXgvf25j1yHpwPudruywYr6rH6/3zg4PmJ/ZBhHH5kJ9FjaHCOv2SEyrsxaWO5mo5A7xtP3sFHHUFOnxe7gzWYTV1dX0e/3Yzwex3a7LU+HgzFmXFVVxWKxiOPj4wIEXI4R8fC8axsuDEZmQzFaZn5w1jh2DJODJN87s7MYJYPviCgsvGUUsQeCw+GwoXN5DrPztV45hWq2JINkgg0DBXSCZjaclplpO/cczFpfkANOz59zJiczlznw9TrKrKzXJM2lClme9MfrIINSf573bRPcF46sytdyYGHH7nsQZDlAfYxlAUh6rjJLlZvXH44uByU5AMp9tQ3hfjh0Bz/oPf/j8GezWetRkg4MAYmeXwdobFxmDWNv0F/KTazjEftHcAN+vWbyEYcmZpAddm40GkW/34/5fN4YC/rth5e0ERi2h84gZf9gEOdAAACDLYIpNRHidcZa22w28cMf/rAAl5cvX5Ya6svLy7i8vIy3b99Gp9OJ09PTMm9eG+gets0Mcs7MunwGn2V2lM/abuf30etMSPG67WHOAqDTvG+gRrAHQKVUkr4iN1/f1+aZGNvtNgaDQSnb4PPsJUEXGOdgMGiwx/Q5B3smydhAyJGGPjYYfbNubbfbGI/HBfSv1+syH6vVquFPwRPIYTAYlI2Vu92uZH4I0NBF7mfizI+773a7pQ6cgJosLJ9HvtRjG08QFCCTbEvQS+b4Xe1nvf9vc3syU+2TIphEFGu32zWOYjLIoPF3diyZPTOgsGFrc76+JkrCdwzceZ1UqQEGgItI/e7urhyi3+v1CpPrNBmLyyxxdrI5/ctrdoI2zBHN9JGdm8GUWWBH8m7d7v3pAYAO7sH1F4tFjEaj8mjxn/70pxERJS1FpM8GxpcvX5aF6vsjPwyk33edJE4TNgFHzTWYE9eKwU5npwZAsQHgO/T/5uamMAfMkY01hhTjhcGxY7Ee5/k0AGnT0Zz5YD5538bO2YQclNrBATQsZ+u114ABIEyb+4g+5HHbgOe1+5ixNNPVtp75jA1zDh7ct8dAtu0GOvNY0GOHn8eR2UgDyTx3eV09xky7nwZithPuv1lmy93jR6+sH95cC7DElqAX2GbO1eU+6JsDSvTLc2hAhb2MuD/ZgO/BguYgkWu4FDAfCdoWDLt215mPqqrKWcBmyAiY20qLkA3Ari1I5r7c7+DgoJRoAIhYTw7GHYhFNPdEmESIiHICC+DyzZs38dOf/rRx3B3n+mcd93Wtr6xZEzKwh/ZveU4zM21dZd68aY6+8B0+a3ttm+JMQP4ObDy+CFaZjJ2BXQ6MI6L4C7Or9j1cDzBrko+14/0SbTYPANvr9Yr/p2/sH4qIRu17PjrR9ddXV1eFZb69vY1+vx/Hx8dxe3tbAK3BNfOKn4vYn3qEjhPQHR4exng8LgFDr9crGbzlchm73a48N8IPd0HO+E6XPXa73bi8vGxkeHu9XoPxJqPsjZbvW7M9CVRjXNjNCkvEucZEkY6AaPzt9Krfy0A4Ym9cnSLNTBOv8bnMfrn2EWXEifocZYz4YDAou4JZjABJUkm73a7sqqXvdnouXaBlgMVCzKxhduDII4MRb8iLiEZf7JTYcGSmMuI+1UWacbFYlDNCWSyA4fF4XDZosrhxgBhhg12zTxnYIPOTk5MHzplrudY6ZzEA0lzXzhpjg5EiBRexZ9ANJDudTgH4TnHhYPLRSjgnDI6PScwOCBbGgNEZDDNL6Ai67JKdLMOsXwZ36H0GwpmFIlhy3VwGMRlwc+8M1GlZ/63D/qzXp/uOwTfD5eZ1b7tB8/pFrv5ubjhY7svnHLjm9UvLgNrpcAcz3rCbMwkGcsjcbKrva6CCzXDNL6/TD/pC2hjH6TGjEw7iDdgBZvTRm5ut+76WZcu1sRUQFqxtbJcBY5s+mFjIp1NENHXLZIx1KLP5XI/rkM3sdrvl9A7WNTJwapwadeTNerMdgH3EbpuZdwDgzATrgv8BiegDfyMrxgHzSV/8JD/Wsu0vAM5+zwFNXsvcx/Ni+0DDr6IfOUCE2d9utwUoegNt1kd8ru91fn5edNXjZo7s97GljAlbSubVTzZkXXjTdN4YabYZf8emQDbmrtfrckY7+uBj7ZgvSiV5D7/KsXmsWcB9XdcN3IL99gkmzNdgMCiyYZO4141JutFoVOZpMBg0SmEI0rEpkAMR+6M537f29uTyD0AQ0QpOmR26FPwbwKHkBgdetETcKBQOwIuF1JeBNBNug5bTJN5kg8Ok/IHrmYkxaIvY1xw6bcsxUZaLnWtm+jD4LHT60QaguZ77G7E/ycJ9c9SeN9cgExyCmQAW3LNnz6Ku67i4uCgGdrVaRV3fH1N0enoa0+m0ARpxDO5DXe8ftc7nfAQaY2GDE0af99kQifOhLhIZZnYexwBYJpLm+D/Gb93xXOFInDqzDjlAMaOHrqDH7oPHabl4Tu2MMxBjfIzFLC5Or20cXMOsKwEBr9NnZO7simVrh4RjN4jJANVAIOswLTPWbcD5MeDrACHPoxv9j3hYjtEG0nNfMhDzuB3kZ0Cdmb4M5MxeGlQbfOe+GWhSMmLdYT8Am4eQEX0EFLCeWAfOLJm5y8DUrCpBa6fTKYwXbJsZQAcL2BeziJAQgJAsd9+f67bphPXCsrd+8n8O7BzI2h/A0EGweB3zvUxIoDtmbNvAIesLXXDAlzMByIJ54jpVVZXMG69xLcCrx8p3rHOZqPJ6b9P9vIZz4GOAxjh3u/uSScg2yx1G9/r6utT85uwc980lK8iOzf8OrhgfD8wxmOe+EXtiAf/LvTjPmeAJ4szH4DqDMJ/Piz+lNKTb7ZYHpgGUIRmdBXKWhXUGXiIrwvgZn19nTiOiyBAAj464nIYz4Y23HLShB94M61p6wHxVVeUMcMv9Z7XH1u9XoT0ZVCMs0hoYYBaNDQgTmg0Jk2UmwErj12y0YSB9j4gmC2cnbDBuFpMjiwBUKJCZudx3yhvyY2KdZmuTE2OxI83vcx3GyP926k5FYoQpjcDxedHgoNhsyfjMwrE5otPpxGg0iqurq/j6179ejiHyAnewkdlnjBURvccwGAxKmQeGwkADZptd4bPZ7EFaGf3KII/vumbVTtdOIxsUwD0N45cBBzoAm8373mhiPbXOZJ3k/sjL/WJu+DufZmKw5aAhB2foiHWUM4qn02kxmKSwM2PINTKwyaDWJwY8xlpYJl4X2eD6/TwW35NreV7tmPl+G/D1/2YZ3T/WGtfOa+oxR5FBpa/Ne22ZsiwXZxUceKKPsFdc03WerA1S1pYldZWsO3SHe7vv3JP6VQD1arWKqqqKTVgsFmWDoINCglyCAQJF+sS6z/Nku2YdyQDZet2mJw7GDSi9mYyAknHTF88VMgWkmCAyCeO0uj+TW97zgt5hj/9/7P1JjG1rlucJrdNYc1prbv+6CPd47pEZkZEhRZKIEDkCBohJTQpVCQmhUjFDKokho2JADZAQTJBAKdUAkFAKSkiUxABEwiwqE1VCZKY8PTzcX//ebexe605j7TlnMzj++85vL9v2IuyhTErx7pZMZnbObr5mNf/1X+v79u3tbcxmsxrgs69z5orAyXNswoR+eczykUkoxsY++r7P8hwgkxAynU4nxuNxRETBBMizd4HCP1xeXtbkBRzQREwAGCHGOp1OCdbINiCzERuSjCwz9/UCx+vr67KHtTHI1tZWzOfzGAwGZV/2iLU+zWazMocOVsEHlFxwDTafz1iHZVKw01kvaCXLen19Xbb58wJKiEv0kXISxhC75cDDc7lYLEpGnuCb8ibquU3UcB+f44WX74/68eAt9VBchA6lANzBnNo5mfEzA9DpdGqF+DaqvgZj5NQ493GaLiJqNU18RwTW7/drTDXnR9RTGgZJLi/AGGS2OYMm7oFzdJo2or5/to27HYkBGsCXFGBV1RffcXhMAKaMGdExfaUGCwOIYcHIk65lvl3OgGFn/lqtVtkmyo6JOjjmlcjeCzIA/WaCvBJ+tVrVdrbIAZZfPOE0uBkTB0gs2DSAJaXrLQbN4hqAMn/MQ25TZisZv8xUMg8ZyMG4GJjgsA2ubPCcxjWrYcN6cHAQv/d7vxe/+MUviox5qykzWFl3m5iJzLZbdvmd2+jjPkefQXUTKM/X8lnWxab7+28cnFk4A27u/X0H15oVpCwCeTDDTOo2g0YTDTBQTtWzNsKsqwGoAzBkMgM+y7jHLmLDqnrBNenwm5ub6PV6RW8uLi5qJSPIDbbXQN0lYU2ZSgN4ZN7jYXmKuD8T4gDZct90ZEaZMc/39xyZGaZvea/e7KcYVw7LGDpM3TuAtN/vl32JI+pvSbSP8v2x9/TbwVgegxz48ZnHOrPVzhBzvrOhtM2+j+xp9p22BZBczIH107YS3TJG4HrGsNVqFfn0S2BsP/BjPI96ZO+Mws9qtXkTKECUHTaqal0+wi4jHOAibKlfK46t7/f7MZvNCqA2yUN/WdDpjAhjb3DscwDbjD9z7ICf4BodNsETsfZHrBuzHaSsBX9hsuH9cfd48ELFiA3z0W63SwRohiBHsjaIGHZPPEAr14blBQUGtWZ+EQDYvYhNSYnLDczKWVAx5oBohAoBI/XkiNIpIx98bwDkw0EB7baxou1uI+k/s4cWakAy0St1wjC4BAGM0fn5eTx79qxEwxgMt8GlAmambQgYK5SZNvd6vdjb26ttqxaxMc5cz24AOIs8dmayub8Bg+ceI59lkbaz2JTvSJUhE7x4AEYCJsKMSjZCjDXPaZpn5przHHAyNrAz3iLQgBojaraScaf/1h0vguIe8/k8/vzP/7zICsyhZcMLxxhHUqSWc85vArOeH6eybYjNPNmhG2g1gWQzovyfx9wHbbFz8XdOzTNHpH4NLO5jT/nepVeMowEWa00i6vXPZgOxecwfNsg67HnnPAcFEXfLX2wjs5xS9oB+UOvJm2O5hl2DONARAlfLb2ZTc11vZh097q43zoDaWYqmw/PDnHHkbITHHFtuIG5Qb79EAIBdzNnUHLQxFpYV/81rsSkXAKgxn85YufyL5/glHM4G26/yne2BAbXbbltjXxRRf7EVvtXBHGPq+nPmlldzMy+00axw1lGPHzbc7cJuLZfLwk4vFotCoLkckbGyLrBd3nK5LMGjdczrwtBp9BFmnd+AW/qNL/IuWxzsxEP/GRdsNm9zpB/Ya693wiZQBkb5CT45l7Aw567Lv7i4qC3iR5+5zjtvmSxl+78mUuQ+XfyxHQ8G1Ts7O6XA3YyvmUbABn/bUNqpWHHMvCDYdrhOS5oFJGID2HHvbrcbo9GoBjicMorY1CnbUKBwCJgjbg4/H6CE8URQI+rgI7N5KF4GMSgCrKpZTa7NZQG+lut5RTHzcHl5Gefn56Uukjmj3wavXizIOHnM6CtzREDCM8fjcQGhnOPFFBEb0GkgxyIO0mA5KrYy2wC7bZlVYT5x3KQFO531CyLMhBC4LRaLePToUZGBJqZld3f3zhZiNiYuE2li3CwfNzc3xUBnPbKseNcanudtxBhXP4/5Zd4wok2AOjttA3TLF89ptzdv1YTtx7DzDJibfK2dNNch9z434m7pRXbCbnfWMcsHB9kn+t0kjzii7CCQEzOj1nd0x0GuSzSaympyG3NQzvPoE2PEXrjIrdd6MIb+38CW8h2n3WHv0BUAQ0QUW5IDIpd7oOu5JMgg1vMFSIFV/Os6YsuR2+LDWRI/3/JBWwhOkKnMPuJPAGK7u7slBW45YGxzHyM2viYiSqaCOfbaDo8pgQb39fybiXaQzucuT8vjk8ErsmVw7PHxYnffo9NZlxeyDSsBCjZzOp3Wnm1ygLGxvDC28/n8zpocB6AEeZeXl7GzsxOz2ayUVZl8GA6HtbKIra2t2qJbmFeenUs4ALYRUQJZyqs4h/EzQ+6xJjDCtuFHaCv4A5/SarVKwAab7bJGAgFk1RsqRETMZrMyn9y/qjYLHWkn840MIGeQYsgZuOX6+roAfmTi/dF8PLim2uUVRMNOy2C4zD4jtAaiGBuisIgNiwOTxmEGgXMQxLyAi8jT6QoDQLchYuNgUR76QRQNIHENYmYkaDu1UBxemJA/Q/kyK0JkS9RL2sjM3/X1dQGdBjwAEwAf11xfX8fr169jOBzGixcvasbRbOrV1VXM5/Ma62KF5V6r1eoOK06pB06efiAvjF92fnbAZmrZispAAJmxg8yO0uwGEX1ElH7BBHFP/8aQ25jSFy+QyS+x4XOMJ9d4nA3ICUJgHRkjs82WdZf9+FlmbPke52dW2AEd8sLfTeygn9UEcn2u20x2xKyVAZDnzMAd2fcc8mwD6hyQNLElyEjOBKEfALmIqAWOnp8so+4zY8d5PIMdDdAJgutchpP7gc7DkgGUcGhmOC0bgF/mAJl06YGD0lzqYNlfLBY1XeFaSAgTGVxj5sqZJMu4S5h8GPhEbPayzvP7fX9nO+xx4rdBWQa5nlOvO6GMwYE228DlF6f4t8G0A0T8k+2cS/M4aCs2AT1ldy3a4u3nIuovEmIMCZxz1iKPZZM8e1zRseFwWGQdhpa9m92GHLjnjJJLEbk/C/4iothCB3JkUviOAMzEDtiCF/5QqsfcUFrBOBIk2Y5HrAHl4eFh9Pv9EiiwFR5ED3O+v79fxhw/t7u7WwPN9s87OzulxMe2E/1inFiTlLODtpHYDEouHVgDxglWkQ0ToNgaB8LeKcukBeNjxpsykfdH8/EgUP3Hf/zH8erVqxKNDgaDGA6H0Wq1yuruiLjjFDHYdtAIJ0Jog2WjZiaHCbeTMuAGNJlxzGknPjNYsSM3yHBaF4PHc8yU4ajs+KwQWcGyYactBpEGuwbU7H2Jw3V6neh8f3+/KOh0Oo3FYv16ZxhCAADAJyLKQlOnqe0saY/reRmjwWBQ9hA1C0CtfWaY7Xhph40b/YctsIN0IMJ5dgwwMx5jyoCcfcjZBs6jvIJ5wIDzOc/xwhf6wVzRHqdu6bvH1rXTZjANzCzvOFWADDrE/DgL4zFrAjbIrwNcg0mnxQ08WZw2n8+LgUUnPEfci5XqsIFOhxr44yD5nz5yP4PoJjYwA3azlA46uR82zONhBsifc6DfOQhZLBZlYZKZOGybGSHfC3lz33gejpPvrKve3oqgxiUKlgfuCfBwqQNtxXYg58wNZWcGrN4NyQvO0D3vDuQSJjOd9AU9smzm+cufMxaWN8uwD85zYO77ALhYP2JfxTg788l8oNdZTgDTOejlcMYBn2LAbHl29tYL6p1dyhmPbH/os1lVxvb7glePVVVVJYtmP0gf6YsXQTP/lmX6BQh0QAvAA8QSTLDNG//3+/0CurM9WK1WMRwOy6JsfIC3NsTmGh/YHy+Xy9raIPrAblSU2pBZIFuBP4FRZq7oM9vHmkhzBgRdxd55AbJtG2PPnGYsRKCQcRS21eQKwSKZW68nwlbTH8bJfqcpYHt/rI8Hl39gCMbjcS3ai4hSg2yGFmNkpgoDAjjc3d2tGZvb29vazhMRmzeTcX/X6qGAPIcoLzNCZv8MjDOrx98cOFzabuCCY8ppeTsE3wuwYgeeU3W0x7VMOLFWa711HP3EMdB/gpzlcl0Lvr29Hfv7++U+GDCeF7F2zO/evYuLi4sYjUblMwwcbcxMA4YO1sxg2fs8+0DRMSpmWTBMBtR8Z4dshaavlsHhcFjqEzHyOb2enTaG0duVUZcH88/8DIfDEjBQC5lZGfroRbwGY/Tdzhf5g6WnzQR61MrZwDNWXoBmh2mm2GCK71zKgGGn9rzb7dZWyW9tbcX+/n4xxlW1eSOpAw4c/Hg8jp/97Gfx+vXrePv2bTx79iwuLy/Lywnos/sPUM+O3ixv7p/Tzjy/iXEGzFVVVd6UmuWSexq45P99DveNiNoYOJCyrDFW9IGxzc+2naF/nEM/rStmWB2Yo0e2UZnVdR8YX9so6ksN9OiXnbEJCfpgO42cAgpIb/u51tP7shFm6A0mLd/c16CbZ8DU+T7Yt5wdcDCA7QQ8ehcK7BBBqAEN404NNBk/Z3NyQJdlCvtomciyaLCDLcaGDwaDkhHzGHluOBw8GvjzP4EywA7CArmxXmHPGDczqN5RiUWAw+Ew2u12yV5ErMsCebX7zs5OnJ2dFXbV5I/tCD4gIsqOGtaVTNZRV00JFfpLWQZB6e3t7Z2tIZ3FYezANci55YkAzgG1/0eOkFfkAf/vIJDvmQvkF3lCfrF7tHe5XJY3CBtnmLi0vjvr1uls3uPw/rh7PAhU/4t/8S/ik08+KU7fbBnRjNmQiHq9m1OvCCvOhXMAfhY4OykOnk9bIqJEsUy8V3SjYHZm/I/RQyARKoTPjA5C7AWIGLDsADIzyfM8FmYoYJ7tXMzgopQYy9VqVd702O12Y29vL1qtetag1WoVAOQUJFF2u90uIGc4HBanYIcJ8ELxiL7ZKi9iA/x4hh2Zo2yMA4bLxsmMIgbToMppRtpFXwB8BwcH5VrY8uyMDGaRN79RE7aDVKb3ZMfImHVyWxg/xgEnxvgQ/OCYIzaOMzPUzL/rpumLZcqAwn8brPkaZ0C8PRhyhkPD+XLd7e1tHB0d1RhI75xgENFut+PRo0c1Vv/o6CiqalNyYtnIgZb1xXpnQO0A2SDa97QuOgBDlwyUzfDYgfh6jxUACbBI37L+OxD13CDzBgO2EbBWBnqkdmkzOowDZuyQT5MNri1lXA3eAJidTqcQE9RsYpM9fw7scPi0Pdfn9nq9GI/HsVgs4uzsrDDUZk+bWGUOz7/HFBnwugHu5TZZXrEPyDd6bx1xAMb8kFlCz9BLAFcO5BxsUhrFWBlMeZz4PzOBtqe0ETm27jMf3AfZIeOGzOV7czTJJ+1C1shOMf+uh3eAjo+jxBNbgi1Ff/i9Wq0zYaenp2VLvslkUtpxcXFRduQgs+st4byo12UjkEqTyeSOXhq4U+rBQnX3B732onrrnXUc+SJw53MvIF+tViWrjg3wjjAELSZMGFNnDiPqNfr0i8PsOD/YB5d1eH2Aa64B1NY53/++469zzt/U48Fb6nU6mz0TI6KALwTCgooQIRgGwBFR7pXT9p4411eZwTVoIWVhw4hhtZM3k0ybeJaNsw067cG52PFxTxtSO2gOhLqJCfcqchtDP9ORMLVlERGj0Sj29vaKYbq6uorJZFIz2qSleT7GmlXIw+GwKDcgw+eaKUHBxuNxYTP5HEfrrcPoE/1GUWGqzMwwdnYoKLediMEW3+3u7sazZ89qL6VgnAAWEZvtqczWABaQL5hlB4X0BaDNmHpcAYW03xkcnCuOxEGEDSaOAkNH7aJl1uDQmQ0HXP7MWQaCNs8V448M59pmByAecxtpAhMHXlVVxTfffBPffvvtHcDra3m2AxHrl3XF1zg4RkcAUrZVjLOzQQ7gDey5DtnARmTG26ySa6FZzW/Hme+dD+sb/XOwRnuwXezG4u3rzFjRdsp0PH5mVCM2xEAOqrCpAEsv3HaQxtxnJ++j2+3GcDiMvb29mM/ntd0P3GcDatvODP7dR9tqAyUYZQPkJv3iMz+DvjjAQgf8HcG2A0Dkizk08WLAm2WOw8yw/Zuzvfzmb+uVS4AMsJrkxDbC7cnzYn3I62IM0u3zkTl8GLXIgDcWJBIInJ2dlXFhm9eIDZAdj8flObDiJrEA767/5ZkRmwWJvV6vyLWDrG63W3AI/bN9B7ibYENPsDvM097eXtmDmgM/5GCFdV/4RgJOZBZfEbHBPva3VAlY5wyeDeadZbCdgPAgSGcMbSPASDzPMvL+aD4eXP7hGjgD14h6TaudtLd3s8HhVd82RBEbII0AUZIQsaknxPA7mnKpgQEsyoajxbiakUCIzZSgXJzXdO+Iu6wJRsgggQMAMZlMyqbyKNFyua7p8nZ9NuwIM+UIBwcHRTGWy2X0er148uRJGdezs7MyXt6Dk/Id+mEAyUGbIupbIvoV4wYPnGe2iPYy7zhmBycGcVdXVyVKJ3Kn/5nphyElsBgMBiVF5To10ubcA3mx8drd3S3ylPdbxXFy31xOkoMCUrs8w6yzj06nU1hExsgGej6f1xZw2RjaIQOglstlqY9z2x2kMjeMq9tsmXYAg0POzHwGvBGbN1E6fcgzDCwN2JkXM8aMD+cbOHGNz+d7t9PPJsjhmRnMIRP03SAtB36MFfKX60xt+zJwcXBgkGMiADnItbe2jZYLjzNtJ3PlbIXZbsbX4+09ohlTzyOBJmNFmt+BLnNp0D4cDmMwGMT5+XlMJpM7LJ/nLoM5tyNnXAwQGWvOdxBqdheQC3ihzXxmW8VnzKtT9iZ/PJ9mhgEjngPfzzJO+3lOXq8Bs+sgwvaiSX88JhnA53bnMec7/udZTvkbhOfsV0Q0BrfIKEw328FxzeXlZZGr09PTcs1kMolHjx6V69mEIL9vwvXS/X6/Fkx4txbkBx1xbfhqtSrMu8Eo44AOoAdkOcjaQsrAON/c3JQ3/UI+GiCbQKqqqvhA27vr6+vo9/uxtbV5BTqBxsnJSY11pm1VtVlwb33I5AvPwCcgS/Zfrj/P9vC+I8vij+l4MFMdsdm6DAMXsdlJwI7Cq1oRDjObTJDvw72corUhxvgTPWbwwPW5PRhMWBcMG8xqRP1te6T3vOE+Bt6Gw4DepS0eI8AJzwOYAQQZn+Pj49qb6twfhJyMwGAwKCzmaDQq193c3JStn7y4DsOGYmEEMQBmnhh3K1+32439/f0SNROZ0z4OajEBrAYd/HYAxTO5BiPlA2fF3xixR48e1ebTC0YwvmbnObzfJ/1hGyezUa5/5DqMI4cDSfrG94y1ZcDspl8egX5QFpIzI9lp+t7dbrcYYoMT3xudQMbN1hmcZdYY54f85/nOmYMmMJrXN9hRGSTzjMzaRmzqiC0DPNfnuzTCemuGrylQRQ4JsiyXZhD5nnn0wqw8Rg4kkNGmOaRNkA1mhxgPbJ5JDcbEQNMvfELeaC82JOut58DBSZPsIJMmDbjWmZnt7e2Swj89Pa3Za4PdpoDLAUD+7WDW19M+B2v5Xk6TW4/prxdpEkSYUMHWZLIkt9/lUHxvFjlis88yzwFQYWusL9Yjg2vbHsbC5/sz2/4cjGaQ5O+ZV8aOF7bkMicHyLQduaSem+CAsaHtjBeLHQ26q6qqLbI3acciRHbMsI9w6c/p6Wm02+1S4gcznglA/x2xIQksq+5XxGarybxbB/bYi70pg2Ec2+12qSVn7YrfI2AbQN8B1R5P+sk5tpm2ldhAZxUsH34pFfJjgolgxjLy/rh7PHhLPdc725iiJHxvNoxI0QYQtgdnYIPNBGZFIRp1iikvOgGM+DkAGBZSGlh4QZjTSkStrlmkLwYOERvWkSOniez4bm5u4vT0NPr9fgHvy+Uy3r59WyJkFM7tYbs6FGcymcTOzk6MRqNotVp3XvVKuyI2QB7lNJCwY7FBxIgQIQM08qtVaY/TVThvvmd+vb2YldKG1Yxh08F9eRtWDsxwAHYUZkuIykkZApq9ipsx8G4odkir1Wa3GgdLGNDsZNnqyYzGarUqDDkB4OXlZQHfNoh2cr4342S2J6L+RrfMmjqb4zQm89jkzM1k+b60D1nlXPrEPbJMcW0GGnbyWSft7DwGzmYgYz4MqOlfdqQGtoyH78lPr9cru9ww700LdjI7lGUeoAdAMlCi35yzWq1qr1xuWqTI+OdXLXNPy4EPO03O9YJPBzFma8noZWDJd8PhsKSSp9PpHaBokOJgyudkMOPgybbDtihiE+QCmpkLM+q2VVxL/zI76WDEZExua56z7Gdy4Egbyba6tAb7zLx4rJhzA6Am5jCDaINnyzpjmNlMBxZuu9ca2F87qGbuuLcXHWYCAmwAWCdwJTg20w/p0u12C5C2PwNkM9f9fr+U6vltpvgAr+mh/YyPAb+zPjD2zClvbXRmaTKZxM3NTW3RptuAHb69vY13796V5y0Wi7K4nv6YIWauB4NBIfyQBZNnTeSKmWnvTNIUSFdV/W2QjH+Tj3x/3D0eXP7BRDChgF0EC6BqtiTX/tgYmRHD6GGQMW5c38Ru45TMZNEuFIII1cCLvnCY0SQFE7FJV9mQ016cNYAK44NA2giyDdnr168L6xsRBWST4uEtg0S2Xpnr/Txhp902s8oGumwJZEdIbS/XoMieZ7abYi5thOg7c+A6d5cRYCxYge0UEuAW52L2EBlxcBIRZZ9p2uV64eVyUwpjRo5n2tgT4FAqQ5u8qjobD8bAJQZkOm5vb8sLUJAznrFarReB9nq94jyQbYy0X0SAszJLcB+4NqDNtbgOTpFDg1b607RwMAcGXGcdJeBEPvjJTtU6aafn52TbgP5n4JIBtfuX5Zd2GxT4bz/LjKgdDZ/z0iuPJXvicjiozSA2/4/8ExTYqdsRIhv8nYGAA2sDd8bHqXdsJc/HMTvraGaaH9sN2zlnNJBl6qcvLi7K+g7LUVP2IoPCHAjkbI1lwe126ptrGC+utb74Gdgh2ze3L9sBA2kfZhM5N/slB9Kujfcz/ewclNFXfE/WV48NftWf+2+z+zl49j0zy8n3+Ht8kTOYLtPgvm4T/XAWAhnBR5MtzAvvvTsTP+zGRE39bDYrfoeyGs7luQ6qMhHhTDPPBlQ2BTScyxh7zQbzhK0EY2BvaLsXcRIEMJ60x8CcgM8y72AOmbSd9UYSq9WmDMZkh4NB5s5kzV8FqrNd/zEdDy7/MNh1dI3CDIfDAhQAFE0Kj1GxcCCAXk3suijADGAK5gYmxFEzbK+ZaATPhsgOzJE2imTWjc8BkTiuiPom/BwW3Nvb27LAczwex2q1ijdv3kRVrRekHR4eRkTE2dlZaWe/36/tz0kKKwNAs3OMPelMR7qc5xIOO0yCEwchTSwL4A2wDrDhHqvVZrslWBiXe/jFBwYOXO+++H8ANGyvF/AgjxcXF7V6V8af/gOESLU6/Yoht8yYxcE4Yqx4BkED4wVo5br9/f0SDGQHe319XSs94Xluu42ex8NA0U6ceQIE87eBDACEZ/J5BjJ2GgaLAIdci0fffV8D6gyMDCYzu2sGyWNq1ss6aabX9atNKXKPle/FYTYHHSSwByg5aPOcNAUD7if3bQqC6A+2zftBe274oVwKOVwul2XNg8GBATVEB8EnAbUZVOuUgZ1ZTAf0BB2dTieOj49rb3f00RQ003+3gTHK7HD2N8hi08Jg7Jjf7geDbQB3H2Dmf8uV728QQ5szOMtyl2WX+bMOW1YcgLrPzurk+9t3fl+/kCkHO3zHPZquY264zts9umSJEhGDV0obaCclmTs7OzGdTsv801fqpJHT+XxesyeeI3w1do3vTOphTw04fViX7DMpp3T20ngDfUKnOId1M51Op+xSxX095mAVyEFsTcTmpTVsDlBVVSGxuOdisSifmazyHHv+rAvMp21rJnRarc3bdd8f33/8oPIPjDKAje2SeGUoW7Rl42kAmu/L5Del/QEzZh8pd+DeTl/irGz0nBaJqDMnZjy4FqexWGz2ML2+vo6dnZ3aSxHcXoAeNVIAJu5/dnZWXo39+vXr8jkOezQalUWZt7e35bk3Nzdl5xTalV8aAoiOqL+4ww4aI57LRJinbrdbmNTZbFb6iXOL2LxhkgUSMOa0GRCNITBzDUAB+HMeTAbnmTmDEWy1WiX9TlqX+TRA3draKqUhBmtmUWAAkB2zVZlJsfHFOWT2ibl34IdM5re1MfYwJ3lPZgdqdvh2oGbvndZ2u7zYFR2wE7Lzpd85nZ9/G7CbqbR8mOlEZwnGDbINEJk/Z6B8PqCQg+ubAhC+QxbNptlpNIEQB1YczKcD2EwEZNuVmdem+1k+LVez2azGuDoQM1PF384KMla2bSYRmAffD9tB6p3xYfyZWzvWHKCzhmOxWJTdPXxYt+3gfV+DSMtIE+jJ7LQXUhFojEaj2NraiouLi5jNZoVogKhAV5BT7m02js8y2Le8ZXBnUOJn+LAvpA/0mX5kubKO0a5MUtFWt4fzkAG3uSm4tZ7nZxj0W3+RB9t5B0Kc7zIzrgU4e7eO/IyIDWmVg3+v+4mImo1Fnhg79Jb+4yvwD870Mvar1TrzfHV1Vbbzc5AF+DWAZf62ttb7X3MtYNilLvQV/aGPgGTuQ0bUQb111bujoafMmw9jHpNr4APLEfbFOIN7/FXHjxl8/yCmGiXf3d2Ng4ODspMHxfZN6TyDADuyiLur5PNBlBgRtdeke/JxMr6HnTTKlyNzs2uslnV0hxGmz4AVQNL19XWJRgGAk8kkXr58WVbq7+/vFxCFwj569Ki0iYN2UqdlppG9kqfTaVxcXJQ6SxgZ2gaLH7EOPMzgwO5jEFAi+m5FBcR6H2r62mq1Yjwe39ldArCaa1J5RnZWgGqzGwZiyAaBDNsQ2REwh351rsEBTsmGz07Maa6clcgpTzOzzBfA1ePKdxjCqqrubLHkVD7Pc9mOQbBBBgaVMc/MogNEs2eZQXMfs6PKwYr1NGLD7jhgos8eL+s8QZYdj0vEckBhGXU7XAtJm/KuI01sWtN4fp/hp30ex1xawMG9/Tza5sDfQWU+l8DXuyG5jIx+4eQdvAH0Adi2iRwu1fAcRkQJbBkn5oLnu13u0+7ublmncnJyUpvHzDDfBwg9hv6eNvszwIWDYvwJ5A5B983NTRwdHdUW/HrbNcbf2QbbKMaa8/AHlocmv2WCyDqX7Y2DcgMXyw3XMKcOrJFfAhjAYA5SfDhTYT1k/A0+3e7cB89xxGbrU9qRF8Aho7ajnneDe9sGf8+P5Yq28rflgsM2iDHgOsY417Tbvmb7RBvsK/wsrsV38sKuiPUWuMvlstSDA+yx49RQA5LxSWTK0FGyuxAKsPjL5fLOS8FyJsNzaxLGfqDVapWMuPtJwOpS2/fH3ePBoBrwhYKenp7WjHVOT5lxMAtl52/hJD1iJwu7OJ/Pa04KI4exNbhGMXOkndN9nGcH5BShGUsMH+UObIvnOqlvv/02Li4u4vz8PFarVYlKV6v1QqfDw8PiiCI2b+li7FwGQaS6WCzi+Pi4sN8uU6CejbdNRWzAtOeFiNwsqsEhCobDoX6UHUYoq0DB6TPXXFxcxHQ6LU6iaWW92VPvwd3tdkvZEADYbMF4PC4b6iNf9A3Z8gtu6BsGClBgw+lsBufyfLPTOC0CO+YTGXHGBGNqWYTJ4FkEJ8yj9SY7YjubrIdmKzCqdvB2yv6c+fB9MnvEd7QhX49jYz6sUwAv+mqwRN8YH8s7986LTPnuvtRjzibQnlwaQJsymDaooL+AU+uPHQvPysF7BvYZoDFuPg8wjQ6yFRjySU2lg1+u9fO5Pzrmfhk08DnXuEzPJEVEHUybNTbjh012//wMfufvLFvfNxfMP/NKm/z9zs5OjMfjGI1GcX5+HicnJ6X8xKl+t9PBkXUCPaSPznA46MjBtgNcbCL3BhhnHcH2u1TJwCYDZOQXOb8vKMyyav0x6OfwmDQdtpOeH9uqnOVyP2Dfm8B5loMsu/x4N5amjKLnoulzjwc2nbnO5Aa4gzYj+/TLJVEw7OCadntdtsm+09RNE+iZBKAttANdZ0eR+Xxe5Ne19/y4hDJnBNFj7JT9Hf1wYOc5ttyj/9zPazr+i3K0Wq3/YUT89yOiioh/GRH/XkS8iIh/FBGPIuKfRcR/t6qqm1artRMR/9uI+HsRcRwR/05VVV/+9j7/o4j49yNiGRH/QVVV/9cf0p4Hgep2e71P8dnZWczn8xpD9NtG1f43ixkRNQDBuV7li9KgWJxHUT8CYtDc7a5fRuJVszn9lA1gZk3McgNGvHUM9bDc++bmJn71q18Vo+hggWsos9jd3S0/BwcHRUG8bRDRKOyz3wa1WCzi5OTkTi0Xgo+jWS6XtcWV/g24g9F2FEt7fH+2wsLY53Su771YrBeEsMo6s8BmrXCILMqjnpAN/anR3t/fLztmAKgxCoAElzXA+rr0odVq1XZLsFEkYDEz73P4zIARmWKMO51OqcvudrvR7/dLOxk3jCDXzWazOyDFjsptzOyXHaLnnDHJemr5bgLW+Ydzcew2mg6OM5AzWLWR9j19uF/exScbaTspnpEZeY8HB4DUgKVpwRH98XwD+CnlyQGFHbnH2kA098PkgHdN8i4HONGIKCVmvOK41+uVcfJuSdYx+gvQJVBCN7Bl3sIRe+F6bfoeEUX/cvaKvkRs3qLqQCvPiefbf2fHbLn0gWyhQ/mafr9fSurevHkT8/m8gGkH/pYzt4O2OGDn3vTR590XBDTJJudmFtBz5FIf5jgzus44IVPuk/XS1/Fsg/2mgCePve/ldjsT5Wc2ASz8oseOo6mUIINp9MzBhskEE2TZ/gE2YXuRbcuxx9DjwHjBvne73bJdHn6ZtiNnPJN7tlqtUh7JvfHtkGA5aEfHIS0pA0MnHYQB5Jv8BXoPGZkZZQeN2Z6jx/jprGv0LfuHfDTp8b+uo9VqfRgR/0FE/EFVVZetVuv/EBH/bkT8tyLif1FV1T9qtVr/61iD5f/Vb3+fVlX1aavV+ncj4n8aEf9Oq9X6g99e94cR8UFE/N9brdbPq6p6MCX/YKb6+Pi4cYcFR0EYqfKQbn1fVDOtERvjzLmOiGBZbBC8CGU0GtXAbcQm8vZvAyQbqywAAKSqquLNmzfx6tWrePr0aQ2sf/bZZ3F9fR0fffRRPHr0KNrtdszn83j16lV0u9149uxZeXnH4eFheU0qbUOYDf5h6GCqAN7sTesxo043Mxi03wwmfcxpZ/fZWx45fYQRZQwBigBnGwXaY4BgdsHnbm2tX9riPiyXyxK8YFCePHlSaw8A38w+Y0laisM1lpZf9xm22Ubecss40gZnNGDHMawAmIuLi9p+rBhoBwIZ2GbQa0fGPfw/48/8AAZpf2YwzEK6X+63QZCdT26vWUc7VcY7Z3z4PrNaBEkuobFMOjA0AM5HZjdd+kJwyzOyY/X+0sxvq1Vn9fL4YIcyqGWMzCaZJXJqHpkjKKZdgEayNnxHAM6cmaUk+PBn6KlljzQzOmJ5dCapqqpaJsXgh/7mHT1sx6xj9MF2t6rqC8eawHTWA4MA9GR7e7ssyGQLM5eS+XzLiOeoKctCuz3WJlw8/8i2252DY/wZ/2e9sG67DI7D11nvbKdMUvn+nO8sA8/xuGeQlcee8bQueIzzvAMAbUsy+2x752CTZ6CDTRmCVqtVtrD1pgYeL69L8X7WzDl228EUdgP58O446AZvLo5Yg3Zk2YQe8glhhg8x+L+5ualtW9fr9WI0GhW/hI/n3vTNgSVrPSzbkA/8eA4s19mWMs7+znPk2vWM7/4LcHQjotdqtW4joh8RryLivxYR/53ffv+/iYj/caxB9b/1278jIv6TiPhfttbC+29FxD+qquo6Ir5otVq/iYj/ckT8Zz+kMX/tg9q7iPoqXxuYJqXnu4h6pGSmAyWHybQRi9gosksQXNzPOQatOPimsoHMmnMPvzyC/vziF7+Ira2teP78eTHGP/vZz0opAAsRRqNRjMfjmM/ncXh4GJ1Op+xFbWBFe+xcAPMotZndiChBBD8cZu/9P0w5AUhEHZTxkhhKShhbp0sd3HBv2oVzYvEGpREAJKdAPd7UR/McDA5GGiM5Ho9r9bMOrojEUXIHEdzHz0QmHIXDEmJkDAjNJGXGDVDvhY5eIW5QBVByepfxcS0kxjA7ajtnyyNzk+UdcIRxNOjJgC8DGcs8v81AuyQhM03WUztldIuFZAaWHmvLr0FFrp12jXEGZVzHOYyzAwo7TPTAgIV756DTzJYDjczQu12MkR232VPvc46TJvsUUX+rLM6b5/FiCS9ksv7TVmwVoCC3DeCB/LPuA711gI7sca4JDIMFZxE5rHtmvnk2B3OQwZ7l3xk6iAeDvTwHzJODwTx/MIc5CPBYRUTtTXu5TMRghrYw9uiI0/58lsGNx5W+uz0ZoFvf8ph5vrjWWRsD1fvIB9s/HwbHbqfvZTtmPODgAHvlkop8NAXn8/k8IjaZKWdgGaPlclnL+uzt7RVwTWbx/Py8jA32Hr/j4Nw2o91ul0B3NBqVUiPvhATRYtsRsWG0+Z72j8fjWha03W6XjR94Pnrf7/cjIu74EMsi7bDMW66sA3keOc+ZAM9Bkzzko2ke/3UcVVV912q1/mcR8XVEXEbE/y3W5R5nVVWxKOPbiPjwt39/GBHf/PbaRavVOo91iciHEfFPdGtf86DjwftUR9xNZ/22geW7iA0b7cjROyRkYc0RtY0vhg7jNxqNyoszeIZ/EGoDKCJbs5lmVWG0qN3G2O7u7sbh4WG8ePEiRqNRnJ6ext7eXrmWhXsXFxeljMPCub29HdPptBYFZyDCi2mqqiqLG1EQttGh/74PbFdmcugnET3GHoOHw55Op8VZw/q7lhylHI1GteCFefJiSTs6z4sBL1sn2fC5ZALASv00UbuBKP9bThhTG2e+c/BEEOOX1DAmruHmc8sgQSDfuy922owLJS4G1D5wIFx/nxxb/u24MMY4b/pgJ+hFOs4wGNyaebCcuD3oAk4D+cQOwMyj+25X7jPnDQaDks1hDrw4J4OFvM0h3xk4EQTaibtvTSUzmenOIMG2zY7EsuMgzG1h3vICMj5n9xf0306OreC63W4ZX+ovHfxm1ggZRF6wb8ih9QEdIVBG/7AbAEEfDpY8fu5bBvi2GZyL3NnpW0fsG3LglYE0wYXT1pzvzFzeocIssoNe+oTNwS9QDuNAhuealaSPlk2Pm/vJs7ChDug41/KO3rn/jKWvzQDbNcLOxHhe/ZvrMrA3m2lADlh0NoD2eQ6Ybz63neNzt9/tox8moObzeQG5+Ex0Hf/p/rAFXavVivl8Hru7uyWQ9Hjv7u7WMriZ8ILcIjAyWMaWuTyQIBb5AhC3WusXguH7r6+vSxYGxnq1WpVSsEyi+P0QLBzmmax3sD+xD2FcmJNMLlmeHJxl0uFf8/G41Wr95/r/H1ZV9Q/5p9VqHcSaZf5JRJxFxP8xIv6b/6Ya13Q8uPzDkbKNqxUEYMPk8LeBUESdkbCi5mi/0+kUAHNwcFBSMBYS2uB722BFRM0BUUbBuW5XxPoFMG/evInBYBAfffRR7O7ulkV7vODAffB99vf3YzablS1wGCOnm3H8vGDF+9E2bdnniJe3aZkNMqikLSyOMEOGAXEKdDgclnGN2Lz+mKABAGdwxniT5iK6py12orDTfr03ToTz7DBcdrJcrhdJ5hfweFyZZ16+YgaO+85ms7IbB8Y719J7kYeBMP1hzNmTFyDu7Id3ccgsHvdiPLiGe2TmyuNhYJ3BtsGDx8SgHnkDhOD40L+8Yp+25swFC2+cEmXMva1T7pe3hLq6uorLy8vafvEETtYPxtfbd6LHdvjsCkNA58CI8UJGAUPUJRpM+BoDoCY2MNsaM+Jcz9h5kZAdUrvdLvvTMzf0D8dO4AxIcPDiHWec0ne/6Md8Pi9vrjPo9ctlvBDXwTGH++Q+0xeDV8v+fUGKgZoJAdtUgynsH/dDBtF3xs5zYuKBsWs6JyJq97KdBdRwDwJMZBY9y0FBky45g2T/aPnjfMaLOeFz+8mcseFv9J259pg2XcP8NmUHLNMGkNyHzz2uPNd648Psu3WOQG61WtX8mzNbEBc8D3tLgM/YsjYI+b66uqrtWIVddKkI2QgHaLav+Eayku6P7RIvqMmygQ8GLLdarVJm4jVq+G37SNZdGfcwz/RnNpvd0Q2/ZAZ7hK01iYBsmBRrOkwu/Bs43lVV9V/6nu//GxHxRVVVbyMiWq3W/yki/qsRsd9qtbrVmq3+KCK+++3530XExxHxbavV6kbEXqwXLPI5h6950PHgfaptsDEyTsvaoaCkCK8XlyEUeaGGwXCn06mxyWyX5B0ybGwj6hE/7DEglfuzWIDnR9TrT2Eanzx5UgQVUMfWNl5BvFgsysb1MOiMC7tm2OACGNhFIzNIThejkHYojDdg0M7M0TLzY/CHwqFgg8EgxuNx9Hq9mnNj/hhTwO1sNivAjCg5IkpqivnDaGCUbCBhG+gvRpL2seiR+WILIoBHrokkkMtgh3s4yDPobmJ2MCrMrw3u1tZWeW27gQVzm7d7tJPLbGJE/fXgBhr8bTBGP/J4mlmzjJt1zsyijWgG8gbxOEt+CAq5FjkiaMv19uiiHYadL2lTHKwdBVkZXqrDjhw4TebAZUfIkJ19xGYxq8fIQb/HINsUzyPj6DUD+VoH8nba6Jb7SyCAHTDAi9jsQkSfWMTkrbWosRwMBjEYDGr9w+bi4B2wsgsNujEcDss4WYb5nRlHZMjEiOXKh+UvAzuO+wA18m09w4Z5C0FkzJkD39PAjwDN4MQMJ3OI/jAeZC6tv5a3rIc8277F82u5z2NC+3N2xuPnQBvb5+fZLzpIdPBm0MzBmDgY8r0cQCFj2DiPncfCxJn7mINN38d+zEAQ8ghdhQwCEDuQ9FaqlgVkfzAY1Oax2+3WMpoG8ezsRKDvtzeSiWVMbc+zv7Es5Iw6Non2kKHyTjwGyKPRKKqqislkEuPxuNgcWOput1swCWNgvJEDnqy/9un+Lmch83EfIP/XcHwdEf+VVqvVj3X5x389Iv7ziPh/RsS/HesdQP57EfF//u35/+lv///Pfvv9/6OqqqrVav2nEfG/b7Va//NYL1T8WUT8v35Igx5c/mFwElHfIYEDoSW6j6gvQERwEBYOKzzAC4CMouBUEVaAJd+7LRiWs7OzWtqZa+iPWT1eW007YGpheM7Ozsp1RK5nZ2fR7XbLgsTr6+sYDocF0NuwYfCImHN6NSJqoCEiCmPCOBow2dkAds2sua9WBF6bzauz/V1WtMvLy5IZIGMQsQGrBuIssMA5UxbCfQ2WCVRwcpYLywQZBZxeZr7sjHAABtSMOfdwQERbcA78ziCIw4xuNiwAI3+XgQft9VZvDhCawIYZPe5JXzMbhTG2vDAm9MsOLIMLsx9OCeZ9ZpErZMsvI2q328XReQcJG3PLmtuIflMWErHZzYUgO+s7z2E8zUgyfqvVpubRY2xWmzHMaXzaaGDitKrvxeE66cy+e5tQgkTLmAE08sj/jA+L8yKi7J5DXxiH+Xx+pw6YRcDMB8wrDJoBkmXZgJpxyePmw/3JgM3f+x4eb+5pHTTAynKa65UZbw7sk0Fizmag22bzGAOvZTE4zEAVPc6LObPu2C54rLk3OmR7xHjZtnvcTHq5f9ilJqBjvc92yoDYNo/+2taYVQXMoUsQPsylbSO/AZdsDesXyAFeHeiTGb26uirEFngDPaPNw+GwnItOIS/YLfoEqzscDktQ6+yaCUFnOGiD/U5EFLIKQsDZLds91ghFbBb9ExCz5sk2yCREq7XZU96EH77Xtt4+wPJk2fm+zzlyFuv/X0dVVf+01Wr9JxHx/46IRUT8fyLiH0bE/yUi/lGr1fqf/Paz//i3l/zHEfG/a60XIp7EesePqKrqF631ziH/6rf3+R9UP2Dnj4gfWP6BMQOMZaeJEnHkSBcm0A7Nxgbmyz8oPAoTUV8VbtDklIgZ0oiNcURBbRicBoXNxulERHk7Ipu4LxabhXowPRFrRh1D7/51u91a+txRfnYAflkIffWLVeh3BlhWNgyNg4xud70n9HA4vLOnLfc08Do9PY3Ly8vo9/ulziu3q9Vq1erCva2VHaJTt9SJOpVqB2LGD4OF3NhRMN/02Wy1FZ+58P7ZNnIGrNzPNY7ud2YUSFma5WVsXDJjFsfg3AGUmWqzHTZ2OK0mgIA8E5jZGCIT+Tk+Bxm2/PmeZpBwmPzN2KM3Xtjs+cT55CAhg1lkBVDpzAfzhOOxI6Efthn0g3HNjt3jndk8OxXLlcG7zzcxwNwCprF5dsBmI9Fv3g6KLHrfeS/89Bs7Ya3ZNYhg3DKMDmIjaT/2OL9tsMnROvgzAOYwWPo+xqoJ6Pl+Tde7HMmBudvHfUww8JkJGu9OZLni3tY/dNgZNs6DaPC1meQx+cE52LmcjfROLE02kTbldnpM/b3nwnOagVK2Bw46DCQzcMy7eLkkhraa7PDYsDZltVrFbDYrGVHaQXDBGgN8GhsauL0El+APbCT39fZ0u7u7NYIGeTCZgC+jzMrsrgN1SkEA/Ng420lvbYltNp4xaeAtZ623lMyZ/QZH7OzslG2O2+12HBwcxGq1KtsRW39M7vjIupf9tp/3fXptWfo3cVRV9R9GxH+YPv481rt35HOvIuK/fc99/qOI+I/+f23Pg0G1X9wQsalnQhAzq8Dh2k4zWo6MERan9hDMzHCgvAgj/6PodprD4TAiopSD0H4c1e3tbXmJR6ezrjG28NDu8/PzaLfbtdW3q9WqbOuXHbmdH2PlICEialvB8ZnLZAwscs2yjSTstwG300qkyUajUUk3NwFRp1BPT0/LAgkCC8AoB0690+mUmnMAPvPQbm9qxLgHNcnuN2wjr2RnjJlbgEIO6uwE2u127S1qBmc8w0xLvjYDNw7G0Gwn7YqImpGzrGd21uyvx8hsVnZkfMacOu2NvFgO7PB5pmWHzwyS3FeX0tBOA2iup40OGuiH66CbgBnXeW4yyLJ8M9YeS/Q/AwkO2wwzcRlM+HwHoBlQ+/lOTTsgYi6Zh7y3tOWE53i+sCOUfRjIuNSk1+vVyseYs9vb27i4uKjNN+0yECbwQecAL4AhA1TbaM9PBnY+n3k3OP++ubasmyXL3zlYdLtMjjibRnv8fMYMXfR85CDa7cjlWBGbgNhtwR8hO01jx/h5jCI2LKX9JOfanuV5YH5zOZL1w0F4DnxsC7LddNDu8ebI/ot75MDVgbyDbxbnW9cgvhjr09PTQnRRNuVsmH0fPwbCzJ1JOTI/ttkeE3Rqa2urlJZBHrXbm9IL93k4HMZ0Oi0ZagJbsAagHj9H4My6iaurq1itVjEcDgtjz9jCerN2Bb8KA19Vmx19uA7bsVwuS/Y8B9v0Oc85NteVCdnGvj/uHj+YqTZrGhF3FNEHBiIDXxtlQBKgGuFAkWEHEQKDCN83GzCEBWHlO/YURlFYPAZgnkwmRaFsLG3cvfjOQIo+u/Ybx4US8VyUweCdZ3iVtg00IMbjBlj3vpkoBAvrqAm8vLysLZZz6iwiaiwXuxKwktkMl3dMsfF3PTa/vUgHMJzBLlsdsZKeA5AWEcX4YKDtRHLwwZjaAFlO3U7kzKwd44F8IG+0yf3jMCuR54/xMWB38Mm851IQ94la9swq2CEtl8va3sack9cf2MEaCLnPjKtfDsSzvILdQZdZa2Sf8TTA5XAw4zE1COR6s0t8h+z7Ws+1M1s5q5Hrh2mPwShzgL65Fps+Oai1nGT9dZBMetb9czmGx9og0MEo482PF/Ua4HN+u70pEYNkQA8A535uZm/5DlvGnNwXqHCYROHg3owttsXywPwhN71er1aKQvDmecmA0WNvUEh7DAIz0GgCimZDyQR6vJqCY+aKDJ79hseUz8yeZ+IgzwH9uS8Y9/meS+sb/TJosl/icNBpcOp5dcDuOUD2WE+0XC7j7Oysln1ttVqFcKHuP2Jj910KSX07mwhYHwi8YJK5Lmc27WvQs+3t7VJ+gm62Wuvt84bDYck244fIlnEvB3joK0Gy/SEBGbYDuaetXvNAPyiFxM9Q2rJarUo553Q6LXOBLXAJpQP0Jl1xaVrGU64EeH80Hw8C1avVquwPaSBgo+/Im4lA0JhgBJrzEDoAtSPc7OwMXEkxu14LQ8T129vbBRAixLxSe39/v0R9sEMYebbLiYiagmLEiaTt7HKUxzlms2zkHDHaoDNWPrx1jvsJoK6qqqzw53ksrAMAdbvdsgiw1+vF9fV12QeTI69CBmzTrsFgUFMo2g+b7cDDRozxxxA6OALYY1AwEA4gABsou9vs67lnZtxol+tz7cg5n/HlObx1jkUtlgcflm1nbGgfc47cNv1tdsX1tjhi3hrqexLEuB0s7iOL4HIFFq3ZmOZgmLnnWQa8pFEdPLE7gbdkoz95MR9txpE7EPbv7Pw9vwaGBj4RddaeNnquDE5cguN+43R4Pt8ZvNsG0Z7saHhW3g2JPjhQJqPkwIrnU/aUs17IWFVVtbfOZnDlrFZEFGfNc3DgTcGKS1Y8HuiYSQDLSWaiHczyv8exadwYMzJEBte2LR4HzzXjambb/c6pe2cUbLeZA2QfHTXBwLU5gAb4O7C1bjGWDqwMSq0Tlmvmzet/rA/OwuWgmzlw2YHnieflzJDvZ5nIwbufyd9bW1sxGAwKoXJ1dVXWOnm+VqtVWQyPb3PmwS978S4aDnC91zv22AeBPvXY2CH6wYtZ8KWQUvxMp9OChSDehsNhbZ0M40vbIOtoZ54fbI4JRfv5bF+xT4B/ZJn3TrCln8eKMpBsM7vd9U4pbP3ruch/R6yxyGg0im+++SbeH3ePH7RPtZXeAmtWhr/ztjKuJa6q9U4WML6AUIQmg02n7u0MDFowFF7I4OgUQ3hwcFAiMs53hM9nZqp5DsaaMcjRfA44LMA+DFTMmhiYWSn8P/WUKC7gLyIKmMYxEOHyWafTiel0WmrX7KAM8nEUXGOFZ5zZMxyDRAQdsQEcy+Wytq+z+292zuUnpNsMarhnRNRAuPtOn5mjdrt9p+aRvhlwMQak9SwH8/m8pMkHg0F5eQ33MsDy3zmN7sOBnx0ZBpf/CToJwhhns6kcOIrt7e34yU9+Ei9evIgvv/yyfHd8fFx7aY2df05BGwyYTbVuA0xyEO2gClmiT9YFFpH6XPrk+zkoQgZpu/cyN/uE/hu4cmB/LFc+qqqqsfoGwLkfOHH0wgGEs1VNb0GlDWStmDvKxGDEYM/oE7aM9kEa5D7ljISBh0GtbQpyl8tS8vgwpt6lhWd77mwL0XcDcw7PvQMi9NQA0DrF+Jjt9P28GIx+ZVbaTD335Pq8LsYgkLZxL9poWXEwSJ/trzwetlGcS7vtCz1O+EGTSnme8mEgjZ7TN9qWCTDm0rKd2+qDa1hkiE4yHpPJpOiOywmd6b29vS1b19qWur8Oyt0WywhBE7LCZ5lMQO+9aJJ5Zd6Ojo5qARLleJk4wT5WVVWIJLCIa66RkclkEsPhsPZ+AxNKJvBMKpEFXCwWta3z8psmLW854Fkul+UlOJabPP8ONN8z1fcfDwbVNjAG0REbgEC62kAN42V2miiU2iIzOY78I+rMUMSGqXHEb0Y5op4239raKukb+mHWGYHl3tzDKW6e6RW2EXe3BjKYNvtpViciSorL6c+I+otoPK42oA40HKDkSJd5YmEge13iuHOq3spEBgEWDqdhYFNVG4ac8pLhcBit1podPzs7KzuMZAAbsVFUSiZYZe09rc2auMzm9va2LCTJDJrnxP/jUHCGzAvAxmNsOWUFNmPcFHSYcTA77DZ47pBlZ1/y+FiWzcIYvKAHMGmj0SgODg7iL//yL+P4+Lj2fJfh4DzMZJp9Irh1wOI1Dk3BH9cYWGV9NhuXAUUGXw7oONcMs4MEl1NkJhc5Z2yzc6G9PBMnljMvDihoG+cyptgNgiACWs7NASIyQ9kH44oTdPbCMsWuPAZuBkyWE6513zz2jK9Bt69rsvWwtiYYHNRaJ7N8+8jz4MXL/kG2GAPGxQE/zwU0MXfoj+eMw7YoyyqHFxPbtnhtQwZ8tqu2/Q5oLGOW2fwsZ0msW/avmVU26HVGzNdxbsQmo8J3Ofiy7co6wT3sFyB3IH7YJYv3PFB+hO2lndgcXo5k9tTtpa2WD4+N+8e80DcCbggXMkX8jW922yKiMMDWMwN+bKqDuYyB+Ozy8rLI0HK5jOl0WrODyKptHEGQxz1nPjxGHHmukU0+x/64VAa94nzaulgsaiUmTUfW8R/T8YPfqJijbw6YExyrmQoDBVhWAwuDOrNxPszm2YjkhXeATZwtjGpElMU8GF5KE2DdDKwcOcOC0TbXzOYxaLfbtQicMbBQk4bNkTarlc2AcGAMqmqzQ4jBA8AZRWV+iGQBfCg3ikL7DA4x5GatuN9sNqvVtw0Gg8KE087pdFqAdgYvbpfrhV0+wBxjtJgP0uF5uzanqeiLjZ2dNYAFOQVQW75hCQCs3nrQjgQDCLh1ijmn0fjccp9BABkdjzcAxvLFsxxQLRaLGI/H8ebNm7i8vCwOjblmbtvtzZ7q6I8XtvK/z7fB917N1gHrZma5eE4urfDBeXYYZkYchHKOU/kO+AnsIjaBqbMABgwOuNAvj38GFAaXBju+H9kZAw0/JwN3xt5BBfczmHQQakBrvckA3rW3ZtUAx2bBsAkZWHr+mzKFHgOPYZPu+/B5Br1NQJAx8D0dKFgnmecmABlRX+eB3vk5PKspJe92GGRa9gzebW9pqwG5gwPbDIO37GvuA5B5/nwP943DfXKmJctz9sWAUr+hli3o5vN5yTyzLRxt8N7zZDHb7XVG8eTk5E5Q7XYbOHteM/vvQIe2MQ+eD6414YeOdbvrtzdzDpgCrLFcrncqcQaKIGC1WpXNDxifnZ2d8plZX5fIMVdeO2IyIMsB+st8833OEDF/liv/bf/FddgtFlVS075YLAqWen/cPR788peIjZHP7CmGvgloMrHe0s7n5KjOBsZRHmAqAw07KEAQO12QgiI6ZAsunA7A0Vvq+NXLeV9jVslzMB5WSCJwlCQzCgYzKKL37c77VGfGyY4FQ02ZB+2G+aKeGiDearVq2+OhPJl1cf2mN9Ln/H6/XxaX2ngtl8uyvQ9Ax/LD+OZ03mKxqL1BioNzmD+nxlxTb1liDAjgSL/zfcTGgXjBiB17xKYWzcwWTAdtg0GbzWY1xsAOnfZ4LGyoDaSp58ssCfLBQhzaube3F+PxOObzeUyn0wKseWkOY4u8YSjN0HU6nVLLCGPBXHs8LHM4BebBwQ3jmM/nHtaf+xgmZNoOz3OAHgComQt+aL/HwHMBwIS1MSvKYdDqrAPzxPhwDmOc70NbHShgD90GB4f0waCebA7z1zQmERvwbJDlbJzZPxa1GgTayTcRG1xvttrtcFvy3BsAZpDrdRlmjE0yeJwsTxkoZNmzrhqsuWzD4Mw6y73sb3IgxrjxPffx/DN+tDUTKpkY8PUGixlYuX05U+GMHPc0I839DMK3t7ej3+/HarUqL2lCdwgwYKIvLi6KrmLTePU2GcVWa52xmc/nZf6oacYuI6/YPNssdAq7aGKPNvd6veLj+MxjFBHFJw0Gg5Jxtv5hGylHxQ+6Hw6QyY7lANZvPca2u5SD+fS6Fc8DMmuf6cNBnNvDucZqTUx/zoTwTHCRs9jgKWcIKfd6f9w9HsxUmzXECJk1bLVaNQNrJomN3e14zRwh1IA0HAPKiXLDKmdlpMYpIuLg4KCsFCdqpPaQNtlImEWgPRgIFBbl5nOzI9wjG07OYcxQFgOSiM2m7YyBjbwdiMGfGc7d3d3yxknKNajDtqEFdANe6SdpN/oIs03baAesDYDafQW4kAUw2HbA5NIdjIK3NspAARkDQOeSmcz2eMs5mHJAlxknDCbGjuCL5xJMRESZf67LMgqApT8YIYBdk6NwGxzgoDe7u7txeHgYk8mkgOWqWtfoHRwcFBaIeT86Oioy/t1338Xu7u6dt4kaYMAstVqtWk0sY+0sjTM47kdTqRcHMm4G0ZkInpcBl20G9/E9zWIx1/QDGUS+DMrcDp7lAMxAykGwHVtm0AwS0U8DUwNp9y+zY4A9/uc+fAaj5QwcRIR3u3FbDKjz91wbEbW+53twnUGbwSQ2F3n2uPEct8e/DUTQW2w2cpcDNJMx1l/mx7JlwEnfkAPu5zUztIM2GvgaxPiH860v7q/HjnbeN1f3zUdTkNrURwN8s5puV1P2IYN73hyI3Y2I2jZujAdjz9iwcA9fEhGlxIGdtmxDOp31gnna5XIP7Df6bHufQSL3a9qFhnHhXjyf9SfuM2ODDHJfSA4+M0bA77MmwuCUPoAXsFPeacv2z21mfBwcOHiM2PgK17+DQbALtvdmrZuC8ZxBdCDLPCOPuc0+mmzIj+l4MFMNmIyIAnDMHJvhiNjsR0ldrZ2vAQhHfuGG05UwCt5WzBOP4KIUGAWDMLMKBkcGtGaH3CYbUIwoBsAg2MDLYNggnajPrAtKwljzDN/Db60iRdPr9WI8HtcWVqLgXq3OmHsnARy/0/ne8J5+ALYZW4wJ4+QFVJRnmMXjXNrhOmgDiIiozftisShAwvKWa8iqqqox9fTbTt3ji2z6O4OP1Wq9mIbSIMYQw0h/vCLbcggYQu7pl+UEOSYdmsEYzg2mnA39HXwsFos4PT2Nfr8f+/v75QVF+U2ZDjxYMQ7LQtCUgSJtMhNtcIF+ORAya+ZMUJPz5rAjNCtlEEtfM/PkxcsRGwdDf7Ks5euZe4Lv/Ezkn7nlWTkAo7+MmbMA1gHACiVoyJPlw8Ceg718mX+yZZSYefz9N/dy3XXEZm9h5svlRr5f/iyDb8bWgLrpQA5z0OTnW05sr/icg3Gxw7cMWKYMvByI5eDagNXBFPPPvTMLzLw62Gpikw1mmaOmAMhg3T4sy62BdA5WcnuaSAoDW2/31m63YzAYlIXZZDYBjtm3M/YwyKvVqvgImG7bM+t1xKbkDzk2OEVGXGLEfdx+3r6ag3VkxWAUv+v1M9g2+mNZMOHA4cwO+swYwfT6dfZcQxDtkjT/9vnZnu7s7JR702/LJ/JvH+RsivEMspYPy6v75PaYmHh/NB8/qKbagAdHZ8fBRMImG1jYuPDbtbtMJGluvqde1+wZwCBiHSXDCBpUWjgR6mwgYRMtbCgYL1JAIQF2Tq3eJ2Q51ebxQxl9HuPjiJE2euGDx5w3HQKg/BIbv2GKZxG9YkAB6RguGwantwlmcuCBHHh3AkAg7Br3IKhwZM29mtgk5Iu5NohzhI/s0AZ+2/DhEDAuNjgAG8aPueEtXnYae3t75VqYQ8+3gzfakQGSWQfuawCI7M7n8zg9PS2ZAZ6RgcLV1VWcnJyUFygwvzgLrygnEGN+s3xYDmlj3s6NuXCmg7Zxb4OGzI7dB74MwM382TH4PO4L6DcwZI4y2PKCL+QogyPanLNCfEYAlp0457ucJ8/9dDotThrb6DUOPMevXCb74MDbpT1NgMJANY+Jg4CI+qI/xshzlgMhs6AmEjzXmf3NDDOZS4MfZzY8bwAeP5vnV9Xd3Tt8rp/pIM9yZNnIssr9DFg5TALkMfVhoJIP7KR9D5+xs4PPtd+wHeB6B4aeD/rrzA02CtIIAI3dJXhk/E12ucaWdjvIXC6XhZ3udrtlrufzecn6OWi4uLgopBABbhM7jH5lX23/ZQKMfvMZhAfPioiaf3KQhcy6DHJ3d7fm/11DbYINRprPbDMdCDGv1ksDWu6dgThza3/gzAbXOnufQbxl0/JL/10qmMua/qrjvuD6x3A8mKk20PFk2GggSDCHTqd44Q6HAZXvjTI6GjRYaLc3NVaAKRwb0Sv3R2lom4ENggmw4jOnrPKOH6vVqvTFhoo2mhl0/arPtcHMgUYOTgxwMGqDwaC8/TFis4jLi+4c0dv5wpYxzhhXp99wtowh7Qc8GtRiNNjD08xuRNTqoN1Hv0YdEA7TjSzwLGrhzWDhGGiz64H5LDPUNmLIBs9i7qj7NwMPeB2Px9HtdgtziGwgDzZuTvl6rjHuNlbIBusO6B8LCskIZXmLWL+sCH0xe8T8GiC6/w5ykAvGwMEHZTRVVZXyHgdnrVarMFNmGi3jZpqRLYNyZ0vsIC0fBkF2JrQDFp+1CpZd62UGJNYV/nZqm+v9nQEAB0FlloPlcrMeAPk1EERWXBLFHJqpy4CW/uXA3qCBINXtdHbNi+gy25nHBQdtoGF9znNu5hY5chDroMA+JLfFZIgzCQasBl8cZnORjyYipEkGmj73mDCv3C+DGl9rgsSEQM6m5XG3jTWg5lo/L5fO0S7bCYM1xuLi4qJmuwgIDQQN/v2iJ0Am4Js2Aqo58C0AeN45cHV1VTuv01mv7XDA4rGxXeeARDIe4XzbPcYRIsl2hXFkHrmfn3V7e1sCYvrNnGL3YNG51jae82gPctBEGFgnuI45sC4zHoBnB1HYa8aVtjaVi1lHLFvOwlrP/7rg+sd4/KDyDwtuEzg2oGYrKYw4wmuF5d44lYgo1xmAesEgbaG0xIsWAciu3zQQs7OhLY5sAe4ABe5pp2EWknsBggAkLNwE5Ph6C7Pb0uls9my2IgGqANSHh4flVab0r6qqWirZzCLK6MCHtubFRlag7LgNhAEwLiUhNWZg4P560QdjTJthPpwN4IeyD9qMITG4BlAzjgBuGy8bj+ygHAAwT7u7u7V23tzclPIJG3PPN23jfmY9ALCujeUcBwiMhxcqEtwx15l58/1honMWwI6WuTHocUrTQTCyYVbObKzl2rJO36pq8wpdxt9G2gw1NsEOzg6F75xJiYiafcisoNknA5oMrgwSfT72iWuyE2+32zXWLu/xzc476ID1hT7CRnuBcd5JxoFGDsQdPFiOc2BnB0ufM7HR5HAtd5kRpR2WJ2Ta85cXcRpc+D7WU3TNsuZrbSvxF8588jwDAZ7nALOpJtd95Po87x4fy51tZ5Os5QDX82lW00GqgSDt49rsj5ABzstEAsSBX3qWg+uIzY49e3t75Y19ZCPH43GMRqOYTCaFbT44OIh3794V/SRD6SwymRbLpl9e5PUGyKwzM3mcLT9NjCzjSp8MbH2954PAAoIOAs07X/DSlOvr61LeYhvmLGQO5vjfO31kmaKttN+1zXxn8sZywfjaPxgke+yNk5APbJjXA/nZ74/m48Gg2uDEEws44zcvdMFJ2/FG1NkVOwuYV4NNHI9fEBIRpY4YA5rBKnW/GFkY3CbmxmwDAJ5zcQ6cx2GDx/P4H3DosXJZhwXTTsFRKs/yvYbDYezv75fXeXMfAMVqtXk7GyDNTKz7YnDj17hH1N8G5Tmyo8IIO5KPiFrAQfuaajox3AQLfqbZVq6DfWOumHv/dhvNhhmgdbvdO+UFtNW17p5nAizvE+26TAIBmEUYR7+Z046R+WZryZzStVEeDodlm8UMqgBf6CH9zWwejsEsinfM4FmeG+QA2UHO3EZvwWfHnRnIDNqcJfGizybHx72cKXJNc3a0/p2DdjtTg2aPWWZtLAfYMwMiO5r80gwCAYMerwchuKLmEqaLbIBlwcGZA5smkiP3x0y+wacZO1/v3w4ssB/WZz/fgNrbad4XCGQwndvhUhqDn8zOGrDYfpsJdtbLwTzyik5ahj0GjLt1Jo85//sn98/y4e+bGO78neWAw76NF2Hhh3wgg1tbWyVgY3y4t1/cRdZzuVwWO8VLw/r9fnnVONurtlqt8jImDmw5dp4+Z3IOWc+2LX+f/S+f5cCSzy1feX55lu2b5571NMgdJWYEFCY5DDyRJfv7prZGRC1oxiY0ZVJyVgudBnt8n93Na14i4s51lhFsbbvdjr29vdIXt/2vYqp/zMD7wTXV2VDgZLx5O+lrJhXnZ6dp5x6xqYs1MMC5E1G71ANWMjvefF8zdXYGjtK8SMbCaUbLoDo7AgMws6vcw9u+RdSV29F4/t+Rebe73jSf14STts/38b05x/XFDiqsUL7OgNnX2di7Nps+slOG70GfXTbkEh8viKyqqrYgy/IVEbUaTLIhzJ3fAhgRBWDTL4CfAwrADf3OQYKBJe31Qlobf84BSHjeAI63t7cFHNMmAxY7TuTz6uoq3r17V0AXDpR5WCwWpbzKzsFOqSmFGhG1OmQDTIAXpQzOLABgYMGdFch2wowLqd4c+NrJMW+MJXPW5GCRFa4xoHeZkeXa8+bx47f1Gp3gQMb8dk5+YLPQd2wgcu2shQEYwQUkRKu1WbdBwEU5lIkDfu5j4w12De5tuw0QPX/eMYB78jyut43iHM8p44RNzeypn5/HHFnM4MgBCnJnMsO6aPBP+7Nu2J5lxt1jmYGt22aQnAF3k7y5jU1/01ePlVlOdBxZYt4okZzNZo0Lh5FfdC9vf+jxgW2ldAxfe3OzfiMrQJs2AI6xCegS41VVVdmgAH+EDK1WqxpLHlHfMQd5tzzmDId1mD4yNsyzmVp+c23exz7Pm7MEzAH/Yw8dwDJ3GTtkfeI8EwP2m9meuv1uB0eec5MFebwspx6LPMYEEPTdMvn+uP/4QaC6XKyaZ5yJ2USUhglB0G10YE9wnhGb3TVarVZZEEFqCWB134KoiHqqFMWy4+XITAbtdFrfDgfHa/BvYGj2hHINFC/XtdHeJueE0hpo8CIJK0pmLHDmDhoMAiM2tdSOdgEMBuL03TXtVnaP2/X1dXktKgw6z0LZMfa0zWNCqol5p28EZJklN6DmepytQTFjbKcB8+J+GDzz+Wq12TmlqqryIpXFYhHn5+eFven3+wUMXV1dFVmmztAvBuh0NiviXaPvFJ7Tc/SduXIpDEwgemAA0KQP/J8dvp0YhtWOBhbVMpXZHubaaUp+8z3Xml3xGxDRK57jMq3cZs8PTg9myUDSbbXeu81NALGJNcpvOXWZlRfysLbDwSaH15LQbjJzZDoi1i9SevLkSUREvHr1qgRitiG5dMny7r4YUJulNNlggOTxYAwNvvO8Mic8x4vNPJbor4N/zwWHn+k+cK0zbi7xyIEEf+eAyfajCXj48L1om2XQNtp+zYFy7ovP83MyeOd5jC1BOddi/9hHf7FYlDVAjDvXYicpweBZrgnmfM4hcOcNtxFRMikE3BcXF3FxcVFIBPyKs4YRa90n6MTW0zeXC1puPQ70xzrg4MPn81wDaRhl+zAHJk04AjvsOet0OjUg7cyHZcABXZYX96/VWm+byraD9+kAbeL++KEmWXeZbZY16zX+15ioaQ68Xap1IAf0+XDbf2zHg0E1BgMA55d7oPxmn20IrCQ+j0lmIQ8TSl30YrF+mYUjVzsShMFRrVPlGIyIDTsVsamjNZjNrIVLMzLrvVwuy4ppAIgdsVmnrDQYpiYD7We5Lm0ymRQjgeEzyIdhJFXDGJsZ5uDeKL83waePg8EgIjZbCPE3bCljScoxIsp4MHakt32YIaiqzZshGQMDIS/OMStNEMbceNwwlMgCjCDX3MfYe3wM1HEWvV6vrF6vqnrKD5YHQ8ue7JmFYs78ogQcDvMKa2kj7dIHG39AXQ6erKtNTJzPa7oGfcjbFzKudjqZOTXrghwgc8wXcs3YORWdAyUHGNynaX4ceGErmGeeawfqTFAO9jMwR9YdDNAWHLQXwdEu9AF9oc3023pgNvL169eFefQ8uW0ZxOU5pr/oS0TUdlViXGmHnX622YAzB8XWUdtSt5U5MJDgeR4v66B11IDKcpUDLd/fffc1/DhDBCj6q+6ZZYA+NgEXB8Y+rwm853vRdvTIBAugyYQE8utt6LBDJkciotgV7ul3NqxWq2KvaA+2lmyKx2W5XJbdhrwGxlkufL8zC+gJ93Lgbx3nPPtHZwkcqDmLwfwScPB8gmAz/Tlgst2zH/fzvi8Ic2aNNiN/OUgkAMjfuf+2nw5cOd8+0DaGec1jDBh3wJeZfx85WLC9/jGD5r/qeDCoRnEAZCgObKrBiiM2R1kG06vV5sUsGAQOwNBgMKgBIg7ujyHLwBUBNuMXEbVdQtwOnF3EpqaM/hpM0zY7PIxaXmSXAR7PNaB222irr/GCR1h3X0c6zvuFG+DZmHFgSHOUjOIQ0DB/3McMPcwI9yCTUFVVjaE3A+laOuYts/hmoJEtv9DFc8wYedcEL15jfCxXdiwYMjtOHJLrvmGKACnX19dlT2j0Atkg/c1Y5rQabXb9vPtHkGPH7jINs0LoWXbAdmZ2DvTfxtvtM2jyrjVuv4NLZNABAAaegNhz7OCPMbZjpZ0GL9gMz7GBuh2CgQXzwxjwXDsV7oE8WmaRB8bErB/nMR5cz//0Db1Ah/je+s+1ZAnI+nj+3VfGgLF0gJPnyuPiDBVj6LRxZsgMcG1zXCrHdwZ+uY3WWWTUGRYHMAS/jDXXIEMZOHns6ZszAr63x8PBRQbkTQGo9QCAY/vt/kXcBYR5rg2k/Lfvh13I57DFpjOHjAU2lmtz4IafMNCczWZlnJhns7nIq+00xEq73S6+zwGQxxc7n+vi6YvJsIhNEOsxs+23vcEf2f+jE9wHGV0sFiWz6DG17GIz7bc5sAO03X1xQOkAEX+GT93a2iq12gQeWbYss9m+MR/+HhlAPyg3cxlcxGYRdlNQl2UMH559bMZZ74+7x4NBtVPOTsPxOvCIu7U/Vgb+J/0NS0ktKvclpTocDovy+4UidgBOYbjO2IYhC4LBoQF1Bilc55IFnosCRmwWeBjc5L4btKAAKJ+NtEGpo+ccITMOmYFlHBwgWFGXy/rbKc3g2NjyLBQJYHpzcxMnJyfFULbb7ZIep58YcAcFZiNpB+NlI4Yhou3uMwdzk8eF9tAXPy8zZu4j4355eVmMB4yyDZQX2K1Wq7JoBfnxTioYJT+7icXwmz8ZX2d4DJrQDRycg1TrnYOGJqdtcMu5Zr6z0/AY+cjMC+eY4TbrRdBFdgPZ93ZeAIImFrCJDXfwwrl5hxYHwwZBHh/rE0Gln2P2FLlzCQrncR26xbMzo29Zpn+WLTNUHHbUgFlKLgDjOUgxEHDQznjY0fpvZxXQacgUM7scZtLMMmfZNLBz+SB9NSvN+Bi8GHDne3tem+Y4j08+x+c5GHUwwrkmQwzy8vhxfwdpfo7bamCZmU8zzA5myeAZxALGCc6dSaQ9tG8wGNTANBkq22yzk8hAvrfJk9w3vyfAAXnEZp9o2xZ0whlt9M62ibIX2o/+5QwEY2YdYMzwxSbdHLAiC1xv/24bSfsZr3a7Xds9ivk18Ug/HVQ0jUP+HBm0PLo9mbwigOGeHpscRHJN/snk0Pcd2U/8mI4Hgep2u11qZomGAVTeHzjibp0f0aJZnMvLy3JtTslVVRXj8TgiNs7UBssOhwm08+h06qvsaZMjQbNQLlHIDI6NBP3x+ZxrJjqfbyOcAbt3P2AvZpwHRisb716vV+YiPzfPAd8zhrAMBvYYUhZCAkYAzhhR7ttqtWolKG6HjYlfWgFANfOeI39KIFD4xWIR8/k89vb2yvxhWPkNAGNs7ewZWztWxpGxBKQaTAMmYH48pwbPEVFqDi0DrJY3e0Pto5lqry43k+11CXbitNFA0aDfRtLzz3050DcyLoAUM7KwhpY9B2hmYA3gM2vTaq1rB6m9nE6nxREjF25bZqNpl4FfdsL+30GngV2urc1OhzmHRbLD82Js2w/akufD+ssc5GwCNsCy6DR1njPbNeaBa/wWT8+vnTWH59rjlZkq5o1+ezwyCDebndvLQTvI/mGHkGtknGCB9uUgARm3o8/g18GQxz+DWIPnDAQyyM6f5zR4U+DpjIoDwvvG3YSR/QQ+CZsBQwzRwSvC6QsLjPMz3VaTAgTSXoAcsXlFt7OWBMW+J7bE45vtF2ON/DlwykGUbZez4xH1Ol+3lQCTecaXUAbiAAUfbtvoXX3wNZ4nz9XW1lYMBoPaq86xAavVKkajUXQ6nVKj7q11TaxxXSYwPIYeH7AU55GhwI4wD5Y7k4vMEfoKCWk8kvvszGLT9++P+vEgUE20g3Bg4Fk8SFQbUV+EB2BB4K6uruLm5qa8RANDAuDe2dkp+0NbwJrYhwxSzQ7Z8GawyzUwzDgmhBGWEWNPX/L/tA8Dmxfa+aCtGBaUENY+729psG6DaSDiOk+zIFmJIjZMQrvdLmCX5y+Xy1IrTI2uo9SIjTPGkOAY/apoxhWmFueOAWTuOQfDgCxlgBIRsbe3Fx9++GG0Wq149epVzTm6Lo0+YBCzE85ACuNMyY5ZHxghDJR3M7DTB4gxjsgCLFzEmnmmFKFJLn0YuHAApG2EAb1ZVixrWQb52wwmskMAgQPNtXcYZ4+pGQyDGAw89+YtlLz23elY+snYMD4Osnm+Mxxc437lzzKgNng1y+PrmC+PNeU8HkdkygyqgTLfuwyKduQsFddjb2wj7PTNGjPngCs7vCYbzNgyN+6jx48xtk7iuE1q3MdE5bHNgZAZbjI6gKY8PmTNnG1i/psAMu3n/paXDCoNVmhflqMMEH34fg6sfOQxyn7MYD4HdjmQ5nmLxaKQDugU+gSg5BkGrH4u8mhfw/nUT0dE2bWjqjbrR2xTut3NC5aaSpD8fK5DpzMr67bZT6MH+Gpfu1qtSvbaTLUBqktf+MFOW04hSrDz7KPt8xgTghvjBY+ziToWedN37KozWzzDAVWeQ8uDbZgxggOGiKiNScTmFfARdYLRZVRN8tJEWNyn/x7rH+vxYFDNpFC3MxqNyqA3MbQR9ZXFgBde7sKBIA6Hw9pnEXFH8HjGfYpJe3L639ewiAhj6NXBHHzGvczsAA/FBPIAAQAASURBVOBh3x0F+3AEaOCJg2I/Wqfr3Xffk6zA7u5ubY9hAx2eZeXJjodrKGdotdbMNwwyZTi+N/d0vd5qtanJdiSbwR8LUHPwYFaANLDHDXYCEEHJifttNqTf75fyAmcXmD/kgTkjW7JcburQMLBOP/LZfD4v55Bec40tjodSKM7z68M9/941hz42BaMZWHJfZMKMjec6s98OfDHGOUtiZ0tbuUd21M4K5BIL2r21tVVKPWC3kHXmO4PrLAe0xWOETcFuZBDENdzfTFkTy2Kn577a0WXmm3aYeXPpBmPA/zn4sW7nlDky3AROyQAZFNv5Glg6WHHwn4OPiPpCS/QOcO022a7y24sYPVY8xy8RYtcIfIFflU3/GEvaTrbHwV7WCwM35iM7+DyWDoQsgx7PDMAc0Oegx/pLG7Is5iDBeubdgu4D9IvFotgUyipYk8QYeGeP7Dd97wzosRH2n4DUHEhynbNvTT6Qe5hh9djkMYqov6o7y6uP4XBYbAFttsxmDJIDPvuKVqtVso6Z6aW/yJ9ZeeMBng+Y9phYZmlT/rwp02/mnvEyAeH5yG0yg829M5Zh/Utum8lJVwbQ1/dH8/EgUI0w8WIXBBoBy+UXHniAVd7rlcm7vr6Ofr9fA8QG5zZ6FiR/zzVmj9weR+gWVN7W53IEnKANCYYUsGenhcPgea7P9nk2JN6POeLuW9NsTCzMACEbD5wg+0Xzna/HoAOeu91uYVMvLi5iPp/X6sncXmrXeFZ2Kiw2hcXNzo0XBPg75p7SoYi4YxgBJ69evaoZCteRwiTjXHIw4K36mHvmmL6aFXXaFMDAW8R4q6LLP+gTwIG+8ApegCJyfnt7W1biwwbaWDNuODcDN6+qz/N7HzvQBPx2d3dL4AVDbYdF37zoi3S9XxxhkIL+uJY/Yr3ewG9LY/4MVhlH5NROzX1EJ3mOGRYDHjNiGTTkw0EDv5l3B6+MOfLS6/VqDDF9cFDBuDSx02439/YCRtsNl5TwEg07SgNf+pznnXHIZRwEh9hfj4kXiCGbWdawkfeVrMB2n5+f115BjQ4RkJMxcgbU8+6tHpueZ/BIn/nccspneQzMIDaNp5/DXPo8s4+ZGW8C6bCjsJ/+Lge3WU6xMSZzPD7YTc9Xlm/sq0tE0E0DXxZGQgDd3t7W3guQ+waoxEeYtKLNyKyBWgaJDlTyostsQzyXfOYA3OfwTF41brtjzMHY8nzGuMlO9nq9oudgBe7b6/UK/nHQbSKNZ+GjvfA0ImqfuywqZ22a+s41yIX9dkSUDEAG5PaVEXe3BH1/NB8PXqjIzhJst9YEZBF4gCNgazwe15ybDSqHBY4JdGSfIzMDJAuG63LNsMCEcC3sa2Y/LDQYAZQYB+fnmjVBgA1scw02fTbwz4FCdgQYKd7sZ4cJcwljaoBRVVVJu3vlN3XVKPxgMKgBXBukq6urODs7K8/24gvLAG1i72pAJEGL623t5P3SFhswzstMlAEW/V2tNjuhtNvtUp/uMWX8sxNEjlwGgtOM2NQWXlxc1AIC95m5iVjXOBKEOCtgQ+c6Qc9zZiBpA7LG8w2+MjiwzBjMkhWA6WBeKMcgACATkbfTywwGTioDSYMxO1RKxcyq4dwJTs2WMV+M4XK5vLM7hmXdjtBMZWaoMvCmH2bTAEyZnWH+YFnt3LxewHJnksBzS5vRKX/POBKsoVcZyHMPZMTAoOme/p8+kgHLMrhareLi4qL0OwN5ywZzzufo4sXFRZydnZX77O7uxnA4rBEYZqdZq8GzMrPG+U3z5CAs207bWP+f5yX7msw82/84k2Od5MigGyDK9c6QESh4kZltgcscsN2cw9xdX18X246vc3+bmEs+oz+Xl5dFxre21q8dZ6/0iIj9/f3iM7gPc2777ppdxgKZ9DjTVz7H9zHe9Mu6TMCQSSjPv8efe+aA0mSLgwMTgrYnlikTeV6YvVwuS721d3HKsuV2Optme2H7z/fYSw5st891dgeijHl1nx1800eTTLanjJ8JzvfH3ePB5R8Ar+wknDpst9sFrO7u7pY9prPDitgABRa2ZeCQAQxHjuYdiTcpW0Q9Le06Vdrseuj8fDuQ+xyYHaGV2VvnABYM+p2+NwBgrHDe/X4/xuNxMaB2NjDEHhv+7nQ6ZbFEq9WK0WgUrVarpKhgi+28Ly4uCgC7uLiIy8vL6Pf78eGHH8bOzk68evWqlgaOWL+0gs3sSfMCbA0+AWGZ+QEc25FYXnBadoa9Xq8GVAnmyD7YoDWNMQYDA8RYeycLp7yRAwPLVmu9oIt6bl7I4D4AuJhHFufynZ/jwwAO+WwqNTKgyONnpwY49QtdMsNiB4ARdtDhwNGOBF3kORj7iM2r6wFfgGcHnS6tsZPkuQ6Mc0DB3w7IzUZajjw2zmhwGMha9jjQ4wzOvRiJec0A1HaJ+/q73A5KvbCp3lGFfjirYzBmZt7yarkiOM/jYkC7vb1d2DaySAa9BlHcA/DowNrPgm1fLpe1QN+MPIeBq/uG7bHfabL7mTm3zTYravnIc4GM2sZynf2LZYJnOVi1veFaZ4wyscJh/1dVVcxmszJW2APIAM+9+2t9cvmCn8N82h6cnJwUX0N7rYvOtCGHjDmAzoG95dFrQzIw9lzSZvvmrAd5nvM42m4QnDedb2LCzK//J2hZrVbR7/fj6uqq4CLsK76Xfvu17hFRIzKsmzzP2MM2LTPT2FjrMfPjLKTl975xwuYwXwb3Js/eg+r7jwfv/sEblRAUBIIJXi6XMZ1OC/OBocVhZuNISQisccTGAQH2rJQ4KjNBEXXwwjl58Z7v4UgRx2QQABA1k2YFz8/OKbrMWjldSEoM52JmKyt3q7WpFe73+3cYVDNKZtsBLzBplISg2Gaw/AatVqtVjOZoNCr3Ojg4iMFgEMPhME5OTgr46Xa7BdhGRCnzgFEjpWvj5NQSfcTh+HvLHRkNUsMeK1L07fZmAaZZMA4bA+ZntdrsTELA4fpQb02IY/TLZ1z3nY0eco+xYv2B05bIcgbUmaWgrf7erJsDlOzYef54PI7Ly8uynaLLEpB5/me9Ac8AFLtfDlIM1lwju1wuYzQa1eqnzZAwr2Q2aDt9MdNnPfL8Z0aaaz02TY6Y7zOw4BoTBQBDB7noMY7LIM3gtEkm7rs/84dzw2nS/0xIMKYGSdgWy4HtL+yoWUUfHju/bt3BnIM8s345gNna2orhcFhsitlzB0rMjwM2M4HMoQEEz/AzLbM5YHLAadtjXUGefBiYft/f/Hh+eAY2FruI3nue6CekAGQTtbr0yT7UbyrMIKgp8OZa27SmPjg7ZBm6ubmJy8vLGpjmPpkQyQRIHjPLkPUm+1na6ADK59n2+jsHFyZSPK8eW9sBywXzSYaBGmTm2i+ZgRCI2Ly8ziTbYrEoeg17nLMffn62zZZT+gUgNyZqIqaybtnOAtoXi83aIfsG28lWq3UnW5aPLHs/puPBoBqlZ0IQGFiJ1Wr95jnX39i4eSJxFIBL3w9DRD0QRiinAlEM3zunWHD+GPQMUHAwVgCejXC4D07V8FyzFbAzZh7MrOQAgOd7YVPTc20cIurlEWboiI6rqipzwsJQ10bntDKO8ebmJvb392MwGBQg2+l04vT0NL777rsCzquqKqzr6elpbWcUAGleUOe5y6+pdnrU4GlnZ6ds9edyGbPS3CfXmznCzqyg2SbGI2LjDDPzw0tk6IudVAZP9M9pNQdDzLXnzW1BLpmbzE7YQXgxpQ2m5eX29rYwTna+BrnWKcs0+gAjQ7rZ6U365izOzs5OHBwcFGfseeBvakpXq1Up63BWwHXaTe10cNIElu0Ys0PlyE4bJtZO3wEP21ki357fHNzQbuymg0LsUVOASc39fD4PH8xLE1BxAGF7A3g3GMtrAuij5dl1nvQD+8L/ZiLdR5hsg2ieZXLDcm4g5CCC7++roXe20Trk8jiXEzUFXE0g1G3OspL9mf2aAxzPi18KBmBm3CE/sp4jLwDZvL2qA0p0kednAIZfYP4Z04go5UfWZQc22SahW4zvzs5OLYti22qdzjJsmbTfzhkDZwT43LKemfCc1eQeOWCyP/K4EhCS0cYXDIfDskMK9o8MJSUy+AoCWJ6N3WQ+kW+2lQSsZn10eYZljbbmYJCxabVaBcDP5/OCtbIs54O2GudxPvfPGdX3x+Z4cE01KRgrKa/PjojazhSZQeA36WScqoGllSJiw2YYLBlMA4QygI7YGGmMsR074NlKDZB2uYlTKhk0eRcJ1xA7zWKloJyi3W6XEom8+IvDhp+xgc0aDAZ3BLvdbhfDTDsp2YBJdh0chu3y8rI4bp6zu7sbg8EgqmqdZux0OmUhIxmIZ8+excuXL2vlNFY65o3P/D1jYhbSaTNYmk6nE6PRqABqOz7kCMPFd4yTjWVemEHbYNkNOGzU6AvjPh6PizFFRhlLBx8Gc3bknJ/LLTgM+gE0OcAwQEReDHg8t7kcAFYeRoWD829vb2tgyCwN2Q7k16UInOf+MD+UEbncgDZQP8r4EKjxmR0a98xHZsrpDz8G09ZdMy84Y7JV7DHuQLmq1szc/v5+kQmcFA6YcUZeTAK4fQagODaDHd6Gmlm8+5ygD+wFWbZcK4nNQLacFTToop2AIe5lthhAja46aCDo8JZrlHlkEEx7IjZgyHbewQl9zMEL8u7vOR8blcGegSvPzMGKSSR/nsec75rYfwA1/XffMgPP+c6yQoIgk5Ypsl4uAwSkmbHP4JM2I7u2H5ZV5tRykYMlbBEEBnrB8/wMrvccY8Mh15C37N8MfLkONphsN7rqrFK2B4wN31sOPSf4LNsG5o+5IOtDux2UI/foGLaCRe8OeBgTg2eu4T4OEoytHFBYjgh2Z7PZnYDfgVvWIz4Ds9gGU0KbZfz9sTkeDKoRbnaZWC6Xha3EKdsRI0h27jgMA6UcUduQul4LweUcG0vXHSH8OWUcEQXcRtTT7P6J2CxMpC8IHIrJlmzz+bxmMD1WZopQmAxunfrNDCpjx+b+GQRGbMCw03XMS6/Xi8lkcmfPaLY4WyzW2xg+evQodnd34/DwsAQgp6enNePf6/VKVH56eloMQwYGNloeY/9wnrMI3oYIwMsr6hkHG1w7lfscYj5g1zxmjLWDGmQVVgKjT9vMAjlIw7Az107fub/IF8/24YAy73MKGKJ9HksDBX/nc13OQj8AsS4LQG/IQnU6nSIzjC9j7kAY2fT2mQBFxhDHn8EB7QO0etwyoMwyx0H/YdQ87xmcZ+divcIZ8v3e3l70er0Clt1vAxYHhsw188YPOpoPxom5z3bLsp1ZUICJa8Fzf/2mTAOGXK7gMeN7ys9ID2PDmCO3YzQala0tsRXn5+dFPkif2+4bMGRb4r4j4xkwcRhsOAPQdL3nrylwcYBoGcptznKGffHOEldXVwVsXV5eFl1DhzJpYHnhOuQLf2WGn/YSsJH9oYwrB8rIocse/XyDcvpi2TER5pIaxt3ZEZML1i92U/I2qPYnBLfYA9vSqtqsS1oul2VLQY+H9cOBt8fL/sQYhMCELfb6/X7J4gLg7T/cJsbCQQdjyk5I9jucb8CeMzIeQ7CRSTQTKrSNYM5y6rI9Ds+5SShIP66l/3+d7fT+quD/b/LxIFBtNgCDBXCzI0N5vdjL19th8pvJtIPiGoPdpkjaAmjGwbWGERvnl42lmdPMnjhdHrFhoi4vL8siPEf/NtjuDwKOEji9ZEcdUWdCnPZG0al5woHCsHvMGId3794V48EBUBmPx/HkyZN4/Phxaf/29nZ88803cXx8XIwmqceqqopDcLSeyyzsyDI7kAMo2uwUW6/XK29ys+GiPfTRb7+7urqqzQEGh/sCePneZQqAQI+/gyvf1wwjhx3V+fl52TIvgzfrhmUts5bOriDf97010uwCTLDffEkfM1PkvuC48os+cBAuA3CK2DqIka+qqpatMcBz+Qey69o9gDg6ldnvprFqYg0NYg2y/DuDLAdKtler1aqQBnlXD9sJMk0OkPmuqX41yw+Autvtlm0iM8DLfUVG/VZaB86ZkcsBp4En90M+bcNHo1H0+/2SjZtOp0VeHPBFbIgI7BYvPRoMBrW5MxA3q5gDBeSL55hUoY+ea4NTExW2+fYhjEG2v5YDzzPf2Qf5PtZNZN71pxBPtA/Zczvsg5gD6x1zlrNilm0AKoEsAIn2EMgOBoMia5Ql5L4BzBy4M+4ZZDkQtR/ORFCn04nBYFDabHuBHcMOeqF0p9MpshixXsNDPwkSXIZinck6AFniNy56vLC7jBtstNvCnDKvlgXPuUs1IYoYO3wX7c3YZ2trq+afkDHbU8s0+ucSL2dumwJ1P9+gn/FnLilde3/cfzx49w8UeT6fF4OeJ8rg2kZyuVzWtmfyYZBhI2hgboY7YuNgDRBw0BhZO9MMqA1uHJGR5rFSE1njPM2A2+FxLy8Miqi/WhXnZ+XxuY6ofQ3ROgCA81xDh0L5jYlOA1FOcXBwEHt7e7G3txez2SzOzs5iOp3eCQj8GlZHrP6M9FfEZuGdWaIMEDjPDCWyRKmHF3PSZ5cHcT1ALANFyyJtwFhnRqYpHW058vz63gByHwBLy6idf2ZwODjPrIrHzkc26g7YMkPtcc/3wtD6uRFRnIfLgvb392upvwwKGVf0gsDG7MlisShlRbDfjK9rp3OgRT8sR56XDDzzfHIO7B7jy1wYjJpljNiUhKFTtoEeVwdAzF1mXfM80jbvyeyXovx1jhxomgDgwF65pMVMvgNI+maAvVgs4u3btzWmdDgcFvbUQRC7ejiYYJ4p13KQz3WWJcbF7Kjnzg4/Zwt4tscg66izDLbBfNcUqNGPDJxzoMB4M+Z+vgM52sDfBsoO7tAN2uHFsVkXfBiAWy5oJ3PCYnOAH+12RiEH8cgsbeWcDLzQG48BukIQyDXYZnzJdDq9w6qCBQB9sPCMuQNJz53Hic8AwN4BBL9DO72miMwV8t7r9WqlcthQkwT0pQlzgJ8IoC1nzKdxCJ87sEGnLbf2DRApDqxdumG5wY8DxpEBbzSQCcfvO/669utv4vHg8g8MRkQU9iHi7k4eTYwv5SI26HbITATK5tRMZiMi7rJSTcyOU+yeaAw1BoBnGhBiYLxDh5kP98Ntz8pptpS0HIrhzeSbGDhHp3xuAG0AD1ixsl1cXJRFZru7u/GTn/wkWq1WnJ+fx/X1dXz77bc1IDQajWJnZydOTk5qiwyzUTQbwucAXLPSGQDlsT88PCxlKDs7O2W7PzNZWY7sgIiiYX8MariG7by8h7VBK/3BIDo7kAMmDuTTz4iIslrfbeHapkwE94GRx2FhbHE4jx49itPT07JPNs812wzLz1wgZ8gF82SnDYAjOCaI8qInaqOtR/Qh75dO/1yyQ1urarMdF322XnqcaacdZQ7SreccOajiXHTFn+Mk0UuzkJZ52ow9Yc5wsA40ckDJs7KToV1kZFarVa1O3ecxfm53ZnJzuYuBqwEpek5/fA87fYNvQBcZj/F4XGqmYaIBYLu7u2V7VEpFAEHe9cB1ztgCxglgMBwOawD45uam2DoDOtfROuXu+tTM5HpOMnOHXmX58pgYwGQyhDGHYfRhPWcOADL2Lfm5AEH7Eq5HZu0z2SM6yxH60el0ysuLWHzsNQAmoXJQ4jZzz6zDeQ1NHpccGNkWo/PeDQfyBUBeVWtmPb8q3MDTdt4+kz4BeC0rfDYej2s2B/3hue6j66bBC8hi3rGKfiK7nhO32XrIfCOztoP2MbaDJiSY7+yLsl3yGg7a6TJVnmHf9/64ezz4jYrUQXnxWKvVKrWnFoyIzQQiaN6SrCnlinDbAVixmg4ELaLuTHh+RH3RjaNs+uUFkSi0U9EZXBnA8xmO2yyMDb0dh7fay2yJSxM4smEHvKNYTl8ScQKEnj17FuPxOE5OToqzY89Z2oUC4wjn83mNyQckmI1D+Sm/aAKqbrsZMVJtvBiIlJgDBDu2JtYxImpGCwDIODFvvAG0KdqmjUTneQEf5zl4aGKoOd+Gz4ERQYNTm8iH27NarQpbyT1Wq1Wcnp4WAMP4ZQDp9jEXzDeLVemL2VBq6in9oW4wG236DUCiHbSVg3l2JsJZBX9GG2inmTHrRp6TzMyhC/4b2c3BuMeKsgtACqlYAKD7n68FkLj+2nYo2wg+b7c39c9mDHNN6X3yxfgAjEwyWGYBVXmsPUd+nrNgzG23243BYFAyDMPhMF6+fBnffPNN2aGE9xDgF1arVUyn0zg5OSmy5NdJY3sycLVMw9IBZnjjLmMfESV9z6Jv9CwiSqDiZ3gcPVfZjjeNpYGZfVIGcdyDANW+zTbEz0bWncXK7YqIIptuawbL/M84EPxYhpE/ZMi2nLFzCYRJJIJvZMsyZr+XAxqeg/wjVwD6nZ2dkhnL8ohtRp/NLiMHZEEof3PA5ADB/heAzDoG/D62wDv/GOugT8wHvoVx6Pf7RSaxL+hfXsCfA2Iz94yd/a5lw1nJHABxnf1Lvq/Lj8xoG0swpsYotv/vj7vHg8s/YFbMgjBZjjhtQFj5jDLzw/fcO6Je+2MhsXLbkNlZoNhNbUAgUaCI+lZLBpp27hmIRWwYQp6ZHTZt9C4n9AfWO9d9eRwxsu5/E5uHgrAIBkYKIzAej2N/fz9+/vOfx+vXr2O1WsXJyUmtPGQ4HEZEFCPAHORnG6xFRFmkSSBF+6krxdhkg06b86I12s5zPaa0A8fJPJrh2tnZKSwrBtbyhqG2c8WQICOufW067LTIMOCcMLTITkSUOtyIDaNLn3jjYqfTib29vbIIjJXh7XZ9QRdsn52f9cHP9wJH9KtpbHFST58+jU8++SS+/vrr2gp3ZCzrgtlGp/R9TgZwZls4347LAZkdTgY+TcFMZsLM/DMXOVDtdDplJ5t2e7M7Qw4EPMboNE7ZLxfy/uj3MaDICTuMIKte+OXrfeQALwMF+kQfbXeyHJhVY5GYy0j8siZsyNbWVpyfn8cXX3wRJycncXh4GIeHh1FVm73bl8tlvHv3rmTBer1e7O3txWg0ik5nvWCRBY7ICsw37Wm31y/JcGBlMLJarUq5DLrd6/Xi7OwsJpNJTT9ok4MUg9ycSUGWmsYjM3Yc+BWu8xwwRx57g5WIKK8oz+ywGUrLEL9tywBG1rFOp1OYfebfY2GbYsCEL3RAa1lC3/EzJroyc29mmHYi/x6L5XJZXoGe5XswGJTMK6UqtBX5gWygLx4n+0T0GZ/hRZLz+bwW/CMfJkbQndVqVfwl5CJywjygy4BXs+CMj4Na6y1Bi22vM/zMj8fX82h/a3k1CeMAxxgMOwyY5u+cDciBTT7u86E/huPBoNpRWcRmIZ0ZGjtVUtE2hE5F2PigTHa+TvdH1GuxszDltkZsamYBYIB8nDogBKY1os4Yk+5FWQ1IaL+NJs80E+10n9OeHPztvUodIGSmzMwyjp1XwEds3q4IczSbzWI6nRbjhEGjb5Qa5Os5cCreh9x1d91utzhSxsJyQV8wOMx1zloAZiLijgwx5rA/PhdHYGfJb4AjKSs7GQ6PswGdAx7fz/JhOTCbwOcuLwIkwLpsbW3F48ePS70yYwPL5j5hfL3wz0CAZzvj4nZynrdAXC6XBVR+9tlntXIfnp/HCT3GoZmBMQjPBjvPkXXDY29g7b77x2108MTnOG2eazuBXjD+zB2few2HU9PWa8aJ+XRpWAZpGVCQqbCNYKxsR32tD/qC7mRgaDnE5kRsWN2868rNzU30+/0ip4CFnZ2dsuPJ6elpTCaTODs7i8PDw/gH/+AflF2CTk5Oyu5Hp6ensVgsYn9/v8xxRNTWgsBkk2Ha29urBeD4BsZ/Op0We93tdssLsOhLq7Ve3+OyD3yUF4fZL2V7aoDqOXT5RA4UDT5MsLiEgX65dMA6wzNM0hgwf1+K3TLjYA/2EcYUn4NsXF5e3gmsGF+/VMuL76xb9tW8MTnLrWXX+u6sA/YiZ4Jc8gCQ9nazlvVud72I3uAy44RMEphlxR5G1DPZJora7XbJptIvdNYlboByL46lJpuMJG3mPvgxrz2wDHiBoGU3+yHGF7k0eM7BeJ4jB1nGXhA6vldT5vz9cfd4cE01h5kllxGYWeY8nDH70HIgxDh5nIQNBkABgfTkf180ZOfsus+rq6tauhAHY7DnaBqBs+GkjbSbPjqyN5iP2BgvCzF9weFlw21nYMODIbi+vo6tra04ODiIg4ODmM1mJZ1GHeRsNovJZFLSsHyOgro0hTbC8KE4jFG73Y79/f3Y29sre1TPZrPC8qHYBmOMp/uCPABimmqdGWcAInJ2fX1dQKcNLIvrfABgYLExblzr87PBwOA7GDCbzUKXzOwsl8tauoxnkqLs9XoxGo1iuVzGwcFBHB4elv3NeRslW7cBjDIz5TbSBvcjB5l2On5ZCf0/OjqqMc70iXs7eKKeEWDJfTIIcODJvZENM2oGOQ5e6aNlKJcr8Azk2qA764/PJ7DiO1ihzHTZBtnpMj/UQN8X1Dc5MZ7nFGvTubSZPthOeFGXZRe5Zn6z3FimABeAKTt/5nm5XMb5+XkBg3/7b//t+L3f+704ODiI8/Pz+Oabb+Lk5KQE2k+ePImIKIBpPB4XdpqM2Pb2dmGUDw4OotNZ79sbsdHXxWIRZ2dn5WVABwcHRYchQCj7aLc3O/cAsjOQcHkMY8t4ZQDCOZk1bbLLvleW4xx0Optj+c5glHvxvZ+Vr/fr3X0OhwErIJC3utoGUluNfgBi7Y8Af2TX8nhlXcdGezwYBwfP3MdBrm25dR47TCmdMzw54GbcHFDc3KxfagZgpM/4LgcPLmujnWQVJpNJeT7+CL/m8g4AOPejP4wDNiXbROTv9va2RkSiox57+6qMTWiDxz/PhzMnzmZi19vtdm37TNvT+w4Hpj/G48Gg2kAros6OeHKZhIgopQgRURMMJs1MJgdKQMrHUTRA3G3xtQZ1TgNfXFwUcIXjyUbP6W23H8WmXVwL62iW26DFNdUG0xx+XmbjMgDPbB4LECMi3r17V7a+8wtfGA/m5vT0tLDbT548qb0lz8yMo1OP1XQ6jXfv3hUjhJPzdoHMk/9mbCn1cDkEUblZAq4HvO3u7paU/c7OTozH47i6uorJZFLmiHEBKNhI2+CYRffinOzA8s4zjIdBpz/DAOPsqcvHobFl2s7OTpydncXV1VVh/5k72se4Mw4GAvTFrJ4DJAeDjGlVbdhks+idzuZVudwnl8F0u+sXArlEyMDB+gkQIhPiOceRAdwMonmWdQn9MKg2COJ/l/hkptcMlh0O8uIxyZklB20Ax06nU9YcGBhbzvnbOuxnZIDGgZ76fvxG7r0rDtfYWTpb4nkinUsqnCzFo0ePaqUsfi5z+MEHH8RwOIytra2YTqfx6tWreP36dXz77bexWq1K4MqBrJ+cnJRSF9L7vI2Sa7Dv1sV2ux3Pnz8vuntxcRGz2aww1O32ZocjB0DOengc3SdnR+jjfUFr/t8yabDpkjeDIEoemXffC5vne2abSZuyv/D/Lr/IbUa2fS7MKzLJYkp8FfsyA7Lsz02kmS1FX7z1HoSPbSfA3IE4fqTf79cCWgNJ5tMEFn+biGP8kRuTVtjs1WoV5+fnRf5domlf7r7gF9Gt4XAY29vbcXZ2VssMctBe7hdRL9vyy96YkxxcAXC51rvqGKfkoIbxtjwzj/x2UMV96Ctjy8H9kOMM0N8fd48Hg2oPPswGAkXkV27+20kkwnMaxEwLE2ZgxPl+2YmNpR2sGYEMplFMdh7Jzi4/34dZdwTVLLQVKSLuOGIAdQbSdpa000bU7EK+DiPWbq9rD1kAtFwuiwMbDAYRETVwBpCFKeUV0u/evSsGyAbP/XTwgRNnTADVtM/BBZ8ZBKLcACFnJnJQ1W63a6k6zru9vY2jo6OYTqeFceM+Tq/hqA3I+M7lORmMOcCjLIjaO0ftEXfZ1eycebX5YDAoz5xOp3dS1ldXV4XxcEqYdjM3jAFjZFBl4+h0oJ2zsyK00/XcmQkhcEO+LBceNzINlH8wFvSBufZzYZ+Q1QyukRMHvfTJ7Ln7zLmcY7DM8/KCMOsK92c8zXIj68jAfY6F62xvmC/6TTupW8xst/WHcTNYzkyq55rD9sVOmpez9Hq9uLy8jLdv38bl5WWMx+PykpfxeFy2uFwul/HZZ58VZp7xgPFbLtc1sX4Wuw1NJpM4Pj6Oq6urosudTif29/fj7Owszs/P4/LyspSbQBJMp9OYzWYF+O3u7taYPS/kzSRIU2DigDvPFffxvHNg+3ydx92lDF7wxTUGXGarmwJHy0hEfdvDiA0o9hsWAY3YEN/P9trkBW2jvIIxdOBjecH+mFhyva3tDXaeoMfZa9fqk80jY2kZZgco/Cfg0+ypySjsC+wxBAXnIJfsROPyMDK4jCHzSWDobCWyeHFxUSMibDMtP5ZJB1IENTmDwpzYD9tWmCW2D2BM8CcmW2yjvSOLx8f3cPBEm9wvM/fvj+bjB5V/YBRgnXFwEVEUAOEYDAYlvUjEx8sUDHIwlkyiWQkOR0r+DOFxihZnDNNhIJwNmtMeZlUNdOmPgYwjP9rAdxls2Sj7eX4rlJ2Gx8aCDXg8PDwsxod+YKBstImk86Kqs7Oz+O6776Lf7zeya67FY875rmm1PTsBsII734++ATLNzGP0UFoMSp4jQDyGkPpKjJ/BrtN+Dog4bGwyM+kAC2dudjci7oBZnmcHyXPQE7Y3jKi/WZR7UJ/oukjOtRHkyHJsUIBDB5Rl1muxWBQnvLOzU+rqcVQwQARCBmv8b6bFzDkHwAsn7TnCuTI+dtAESjhkgLxroAmkmNN2u13YVPqSmWFsFvWdLp8wcDJgwcmiD5Yn5tcH93CttMcsP8PP95EdMvPojIQBBm0BOJrNYm5arfX+0o8fP475fB7ffPNNeXnGeDyOwWAQz58/L5mUk5OT+Oyzz2I2m8X+/n70+/0ag+idkQAmAPKIiPPz83jz5k1Jl7NlZrfbjclkEtPptGSaDOrIbLCo3K99z4Gy7T9j5Tnx3FsXaHfONHAv/81Y22bA8JKJamK8DX5oQ/YH9jk5mMtyxRgZ1Js84LBOEAhz2K9Y1hgffLcDSrKIe3t7pb+dTqe816Df78fFxUWxX4yvgwyeg06jg970gLYToJlwgqnlXg5eHj9+HBFRdqyCcCJb4BJHrrUdsMxAINBe5Ha5XMZkMimkh0kOzxlYJjPUBtGMDfYLRjuDV+aJObY98WHcRP/MRjsYRyaxpXnDBLLPvo/tMpmFTCjm48fMZD8YVBugMEFsWYawEGVGbKJcvmPT9AxkDIhR2oh6TS7343MOp2hc7nF5eVljYTOLaKbUnxvcIdxm5W3QiLLtYDgcMfI8MyuAfht2hNWsFgeOeDwex3g8LmNhhxERd9I3gBfq4Vyr2O/3C3OK8mUmyO3J5SzMDYGL583GgSDJZR9mDTPTgQOjj5TuDAaDAtIAJTs7OzGbzcpzaJs33rfMNAFUHIiZT7+MAHknbWeDlZ2hQSzjQYqV4Ax5dGkKY2wmCGNsRwewsjxTUkGb7BzH43F5AyjPdIBJuQrMr+Wat4baOGeWlGe1Wut9zlngZJm3k0QGAalmsHEwfuNiRMRkMinrLgh07ES8oJi22PBbV+jHfWVZpEDzGx7zfDcFzDzD8pABcw6WMth2e90u7m3mycAD+Xd2gB1OANKXl5fx1VdfxfHxcSyXy9jb24vDw8Po9/vx9OnTWK1WBUij2x988EE8evSoVvoDIHEWkp0+JpNJDcwwh/YJ2L3t7e3avtcEUd6nneeZZfUY0Sau9eGshYM4X2c5slxbLrJNtQ1gzL1VZp5z27KmwNv+DRBlthL/Y/nmvoA8L8TPJTnIG3qTAWpVbbaGsy5itwBh+D92ymJu7Ldodw4kWRRLUJwDTso9XRrk8j/sMsA1IkpmA7u8u7sbw+Gw+HzG5OLiora4HYDP2AGcKUdyMI/seYGy5YT7ZVKGOae99oWWMZMRnh/jrFzmYzl3QObg2+NlnXEJGJ8h09lHGtCjAz7v/XH3eDCoBhQgOI6GEU6vJsbpOiWSnYSVF4Gy0DU5pWz0aBsG2wsGMtMG0DGwz5EV1wE8zA4ZGFs4M/i3EmGYzPLbSfjaDPL5jKAEQ2Kl9EI/R6YAETsS2KTHjx/Hd999VwCrHbYZd/pgoJLH1GPjuaV9lJ5Q752ZOkfhTTVd19fXcXBwcAdY3tzcFIOZ06UYyszS5hSWQZ3nlP7boTFffJdZI/qDfNjhAs4AzZRL8Wwcx3K5LFtJATqzo7bRhXH29fQVB7W1tVXOy/IKm7y9vV3OQUa9swXz32q1SrZhd3e3BEzICSxyZqA8bxGboNalH9yL6ykxoKSJTIyDHxhNZ3ocFBtQu9+WccsEsmhQd1+Qazn1Zzgv2zJ/b7YUOUGemFszfJ53+oK+GTQiT4DV58+fR6ezrpt//fp1nJ2dlRT/4eFhHBwcRKu1Lh347rvv4vb2Ng4ODuLDDz8s2QJvmbdarffnRS7Oz8/L1mIREWdnZxER8eTJkxgMBnFychKvXr2K+Xwek8mk5icoNSGAdAkO5xHgu++MB/LEGDYx1g5cmoCIgQefGzSZFLIM5WDaQMV21G1mXu8LwrJ+295aFgCgDu54fkQUfV+tVoW9BYwTDHFP+mi/vlpt9pTHftM35p1dXBgz+3X6aTmPWO+Hj120fWTuva6Guc8kjW045NxkMil2Y7lclvp72m0CItskAnKIBvuh5XJZy8pad/M8NcmlA2PrKfPB88FQZsHdb6/tsc8x+ed5sNxiQyFOHIxZ5n0PZ7s4z2V6TWx5Pt4z1Q84AL8AFpSXVLAZRpSbyTRwwXD5pTBOZzrVmwXHxtPpExv8HEGbJfDhycehGfxwfztJVjYDeOiPmRy+MzNrtsWCm5WU+zmF3G6vywOodaQuzeDKQQWGMSJqaX7mZjKZxMnJScxms5qhZx/y8/PzAvxpu9nZHIjkQMeGhtrMwWBQ2g4byNxivK3ogANAlBe0uEYfA+TSHgd9Bsg24B7vDKa5t+fL/bfxtSxigDx/ZjDY6SMiaozMarVeBMq5LqOhTTnzQZ+8KwTjQTtvbm7i7du3NZYVhpB6aRth6hLJymTjSLBs1t2g0LKwWq1KyQsOj8wRYNkOBKCIs6RNs9msBrwYY+wPO0Z4Dux4rJOMI8/O5WDu533MdJPtyMA57+hjAG8ZIjhhXABMztihLwYttmPO+mAXRqNR2UeavaVvbtYv3tnf369lFymR6/f78Xu/93tlV5DxeBxVVRVwDcBlEdrl5WXs7u7GaDQqfR2Px/Ho0aOykJh98W9ubsp9se9VVRVG3GAH3faidstHBjIZLDNGDi4zAeM5dkCXSRwHOtwXOXPGwCDagbwDQIMo7k3bmmSVNppRxK7wmX0pPoCxv7y8LOe7LJPzer1esUf0HxaSXX6wj/QZWfQY5QDD+rG/v19rm4EpP4vFogRcrtPnflxvBtiBNJkr/B9yw+JqfHfE5u2o9rNkSLB93nKXgM42BdBPXw1Um8gW+ktfzHg7MISlh3DJIN73JkOEfDmrYVAPDsCXRkR5Pvd1xhD9slyaJXdg8/5oPh4Eqv/gD/4gIjYChhLiyAEiq9WqMFg2gI4eMeaci4A7RWPwklmuiHpkb0NsVjYbgIi7NUscNk4ZgOCsMJQon++HkUCJMSIG+GZSDJC4D0KL8+fc3d3d2NvbqymQmQwbKNrXbrfLTgUff/xx9Pv9ePv2bTH6tMegpt/vx/n5ecxmsxrgwchx/+FwGIvF5q2NZhKYt4gob1MbDodlR5Imdt8RsYMw+sp4YpQy+44BM0OaA5aqWpepUAbj3RQMhDHKzBnttdHKzHFTpM8zzcgzT4DavCUbhsxGOTPVBmZeCMv3+/v7cX5+XtuFwPJpVpRn0CfXr5plQqYz62eQ4bEHqLku2jttcH1mQLxIarVal6a4thYZdT0mMskzrF+Ml8eY1LVBllkfbEYOYPKRbQcHwQoOzSVTtoWDwSBarVbZScRsGs7N7DO6YTnpdDqlThmZurm5iZ2dnXj37l28fPmyjPve3l7tTbidTif6/X4MBoN48uRJPH36NLrdbszn8xK8ekEWGSYYx/F4HJPJpLzkZX9/P0ajUWxvb5d66rdv38bt7W2Mx+PY3t4uusu8ITswhZn0oO/ZhrtczPpooGO7Zvm3rvl5ec4BXHznrTzN3rvMxcEgcnWfvtB+2o3eu7+2i8wbmS50x3Wwy+Wy2GTuyfnIJG08Pz8vZA0+ygExhAbXM0fINYERbQZ88QwAtd9f4DFm/HjZlUm2pswBfUVGHRx7DRU6GLFZU5SDqSwz6A33yLbBRFET2ZDJJEC+75OZYdv3iKgBahNKPJPPrCfY12yrMqnTRMhkJjoTRowNAQr/O4h8f9w9flD5B8LIRFvwESYvCojYpHojNsLVtEexo3cDR7OT3ANg6Lpkns+zHcFxHY7d13C+GWpAPgBstdqUbdy3MMTMp5lOG9ls5HGYmaW1M9vb26utknY5CvMBqDBrQKnH3/27fzfevXsXr169qqXxUBKMD3vI0qeq2rwZknYdHh7GeDyOb7/9tgaeO51OMQgwmoPBoGxBlJ0Hik3fs0PMbMZqtSovf8F4sqWVWUgApgMxg16AhRlqVtSbAfdiR35nOeV6t5+xN8OII7Khs9xaPg3Q7JB5LvJk9hUZaLVa8ebNm6IHXhiJPMPsRGxKZAwAPfc5CPQc4oyQS+7T6XRKUEYgZcDjtpJ6Xa3WL0vwFlPcyy/xcPlQxKaW0oums07gpPnbWQcDK89FDni/72gKzNkhJK/l8G9q3D031gHmj/HNmT7AztbWVo25WywWZVefra2t2NvbuyPnH3zwQXz88cdxcHBQdIjdTXjN8suXLyNinb168eJF2X7z5OQklstlKd3wnvO3t7fxzTffxLfffhuvX78ugRX2AXkkK+PgygEWOuSgzzrQ9Bl/A4I8xzlwRbaynHuO+Jw1AtTo8kpt70/M7263W2yg72sm3v/bVmdWmr74/tyv2+2Wl0a5v9zHWRLsJ3JDmRelFjkbk+XfREG2g01kRs6CsOOGdxpBJ6n/xp4D7hgDkwwAcYIGZyLa7XbpE/tue+0IYw6hQP/4mwWzBpkOvB3MGpBmf8A5bp/baBnDfrofERugbFnINdWMJW1jTF3ew9zYdmfS4D7M4Xbju/FdZrPfH3ePB4Hqv/iLv4if//znRSFyNGNG0UbEi6aycQFQ2ej5cy8mMiubwSWH02/5fhyZJebegGnYPBtKQBBtoH1WGBtGL3By2/xcg5NsNBH40WgU4/G4KJXHz5F2VVW1mlkzvjc3N/HP//k/L8oEiOl211tfvXv3rrYIoQlImfWcTCalltLKRaTP+LCVnBe/uW8Yfx/0JyutGQDPESU2VVXVWCWcIffht3eiAGCRJiXlyZx5hbSdDd/Z+WLMMe5mBxgXAh6nbH0P99OfO0vAGFgX6J/BCPPvbdAAPvSDTBPy79rmDEI85gA4s9ssQEZmkMG8gw9tBKjwHFbrG4DzPJxur9crW0gSTF9fX5e6UebegWyur24CDFxHX/PY5sMgmsPsv4MA5phrrL9Z1hycmLVnrGGHh8NhTKfTsuAYZo2Uf1VV8ejRo7Jl3uXlZQHCe3t78cEHH8RHH30Ujx49ina7XZh7WGh02y8mevfuXVncmEkA12ozPrSp1+uVt72ShnZq22NBUMv9veaAOQLwMM/WIR8GBzlgz4A3y47750VrpP0B1pYjB/25HWYcsx2xD/CWc01t5m8CExbiGwByDxMwlIREbBbcV1VVtkvMbG/TQjQDRYNo5HZ3dzdms1khyugP48T+18h/brP7yfw4+409YUE93+EDTBRYb7EVjLPlgsPBPrpqDMH/+GeDfJM1/tz3NwjPdh1bBTbw3uIO/OiryYjt7e0aoWAsRr/ob7vdLkCcvjmzwP0zQYCfQxcdUL0/mo8HgWoDLINX6rU4SA9GbPbWzOUTCGlmjAwsvVAFBwuo8hZm/DbgMqNtw8V9EUIMKUYRkAaY5vy8Lyr35DCr4XQvAIrDJQSk8BgTat8AQKPRqIANA/ZseAHxtCNiAz6vrq5Kmm88HpdV9dwHRtEBgtuaWVj6YwMSETV2hgh8MBiUMqBchpMjbwMv+tNqtUodJsYRdi5iAzS8byvA8fz8/M6Yc/DSFcYW5zIYDMq2XmaxLZ9mjszM3t6u3y5J+zCGrqPHUWQgnVkyBwIG9OhgE9jz55lx9dvNnLq03tI2OxIbT+aUuWQtRd7NBV3B0ZklYgxxtrSL2l1vT0ZJGSlrvy4ZRh0WjGuQHTNQyLrHyUdmJZvG1qC4CYw16YOv4W+ng+2QLQMEiN1ut7ZHu9ecEAQeHBwUVu7t27fFTh4eHsb29na8fv267H8+GAzi6dOn8dOf/jQ+/PDD6PV6cXZ2Fq9fv47ZbBZPnjyJvb29IqvPnj2L3d3d+Oyzz0qGq9vtxh/90R/F8+fPYzabxenpadE9SnKurq7i9PS0vLaZXT2wT96izLWcbLPKounz8/OS6civwmbssnxyZCLD9p95sv8A8HtfZc8nAAa763VEyDbPpV3YVAexlreIzavjYVVvbm5KZodnO+tqm7O7u1sL1k0yONggU8F18/m85iPwewTKrLFxxsiBKGOLfjO33W43hsNh3N7eFp2kXcYM8/m8Fpg7a+X++dm7u7uFdWa/c+wV7e/3+zVAuVhstgz1fFpGTNTgY3hmU6BmcM5cWp+zPc821Qdy6QDBY8x1XrPC/cgIeQ2XAbDH1ONKu90H6wA2yhUBlOZAOBFkZjIsH/cREj+G48HlHwwoDisP7t7eXo1VzQxNBiiZyclOC8eNAeB7/vaCMxsAMxzZKBh8GFC32+2y1Q/383ZqAC3X3zoN70VPmW2wopHWdU2oI3D2cwXE0Fb62ev1Sp8Wi/XWgQbesHgYoYiIFy9eRKfTibdv35b7GRR4fGknC5sMpDk371xi5tUL5zhgqumvAxZH1qSgqbO344RZd3bj+vq6vDzCADsDdRvQiCg7D/CCCsuVAbH3UHZgwT2RKdpIUEYASPmCWQrPp9sHC5VZVhw0cpazLP5tw+myJTPkjAH3Y1xog3ffYDy8S4MzON71A2CCk62qKubzeVnkxg8y3GqtMzFVtWZbR6NRcfzoh+dstVoV5jSvm7DTzHJsZhJ5agqI73N+nmvPlXfzyeslfD1Mnu0J+se5Lo8DILh/yCZ7cTPeb968KYtBGVMyap3Ouq6VNQ0vXryIjz76KG5vb+Pdu3cREYU9ZsFsq9WKvb29aLfbMZ1OY39/Pw4ODuL169dxc3MTjx8/roGck5OTsiXffD4vMuD9920XIVawFQ5AKZkxE2p2zkEm+uAxtB7ap2S/YHsD80bQZkBlnfH9YPaZI0oX0H3e1OfsD7aMhf7IpDOtvV6vVtdsH4bNJRt0fn5eSASCGcgY213eZmn9cd9ZhAopsFgsig9kLB3MMAeMxf7+fm1eGQ/sDCTD7e1tITKyLeZZ+AXmHXzBnKNv1lkWr9Mu+sj8uO7d8+h1Lpks41rkNGcAuS77PvuJ29vNdpfIf7YhJv7ok30eIN/lHBFxx/cy/ibWbONyhie32/piXeVgbNB7ZPP90Xz8oJe/mKHDSGHAs2FyKg1wilOOuFv3g3DmSAzhJpJGIImc8rY5GaRbGVF2fhA4DBaAMi+GyfWeGE0LOWNiMG3mBCANGCHSx0mzsA/j6Oib8cJhVlVVUsAoIE4MQ8K5t7e3MZ/PS4rJkbkZfpQMB0E7cJicZ+DqdBWGHjaZ+8FY4zRyRoF2t1qtAqi9cANZYtxxqNvb2/H48eNSu/3mzZtS8gBgcFszyzWdTss8TyaTWomFx/vq6qqUONAPHKbTZiwIynXoZkCyXFlX3M/MPjuwygaZNiN/WaccOGVWNzsEX++dONA37mn5Akw5HQnr58CEgPD6+roYaQOB5XKzA8Dl5eWdXSfyuNJeB9oes+w8DZyzXfB4ZjbK48h3uUwkOyrAASAlywtZFRMMjDVyTznCwcFBKaP47rvvyhs5AePMR7/fj36/X9qKLFHT+sUXXxSmEDC4v79f7DTztr29HZPJJI6OjuLZs2e1t7QCys/OzuLJkycFRM5mszg7Oyu1rQRh2U5Y/m2vADNm6gjssZcONqwT/G9dt75Y5i0TfM/8IM/YRUC/121QKgEzaH2NWLN79IF2mUFlrDPz63U67mcOCGBvLR9k25AfdPPi4qKAedoK2IN0cUmG5YW/CeCQWd7Ka/KJGvnLy8sSSEdEAbURcWdvauwT7ULvq2q9O9DV1VXZrYMxarXWC3tZG0Kgyvzw2wDZJaS2DYyVyzo8b5zrIMVEXyZiuCfnGY/Y7mQwS59djsH19IG/c/COvfeOHsZf/oy+5WAz+yXbPts67Dr+7/3RfDwIVAP8HIVWVVX2HzZDbbDN+a6xxJhyXzskALVBFAbAQMlGKxvb+5g8/rYx81ZDjsQAFL4fTsVMIvfhOgwm1xmUo6CZMUFRh8NhbZ9RA2raDjCeTqdlPBF6R9gECwCTN2/exOXlZc145gWPGBmvzMa5NbGhBtTuY2brGDuzdDw3op46ZS7Ozs4KM4fxtJPmd0QU0H91dRWTySRms1lh7uwY6cfOzk5xBHmrIeaQeWSMqXHFiJMSwzEgq+ybmo1gBoNZ9nGGTSyA2TY7RmTZjCcOF+YPmcvg2nNksEx7YIoYPxw/wRXjZBDiIMFg3FkXgyh2mWi1WjVgzoGeAVSQMW+5yRjnzJHHPeuQf+w8LN8e9/x5/tt64Hk1OCFIaQpW0FWzccPhsPx+9OhRHB8fx6tXr2pvHmT8DYzI9gAKd3d34/DwsDChBH2z2SwODg7i8ePHpb7+1atXMZlMYnt7O46Pj+Ply5dlHcX+/n4Jzrvdbjx9+rS8WOjo6Cgmk0ktYPeYUPLjMbeDZt4oHfKBrOVAM89pngtkEL1BnnOQBIjEBiFnjElmx4fDYRlnM8vWUWzB7e1tLUMH+PV2pQRR+CLsLiwtskPQhA7YXjPODpS5H1lQshncl4wGpRgGhNwXZt0lF5RkbW1tlZc4OQjGTjpQZB4d+GRyAdvGgliXQrZarTu1+IBYAi/7Bew0/ckBGG3LMmQ7R5tN1Jmcse21PBE4Yystj9lmID+2m2bHDWA9fnzHszPpkvUl62QG+ZZhgoQ81t6lhYxH02Gy4sd4/KA3KqJQKI5LGQykPbEYDRtaJtagCyDA547qUDDu2RQFcu+IesrfUdlqtbrzmnTXgsL+cF0TI0L7CBDMNFspUFKn/KyEbg+vdPezuQfPoS2TyaTch/QvygQzgeHGqAKw6K/HOWLDVETUFx1mAEbUbIXOQNvpWzssxgYj6cWBfusVTsDAMSKK4WacM6McsQZw4/E4nj59GtfX13F+fl7GEiOHcwNgm50ZjUaxXC5rbw0DxCEnOIC8iNElEXbIjK/lsoml9vhmJ8AzzfYYPKIrV1dXpY35xS8OAhjrDC4dUBJQIffIFOPM/QyEcWpXV1clA0RQdHBwUEp2OJhD3sppewAA557IGwu0aC/gIwML/s/A+vsO26X7xicDdjPeOQPBedgWAyFYz7ywjKzI5eVlYYwpbeKtohFrPT04OKi9an42m8VqtYpPPvkkfvrTn8b29nbs7e2VxYRVtd4fmkWLMITHx8fx+eefx8nJSa2G2HumsxBytVrF3t5eXF5exhdffBFHR0cFaBMUn5+fl0WkBmzOfDG+tl0ctvHWI8+RZc4Mm+9jZhZAy3ksggUkmHCgH/ZV3tUC8MFe48wP12I3AH60g8DKbB9kkllK5op+UWPPC68I+KfTafme8cQGEYA5I4KfY8ElegQ7TDuvr69L1pQAghePUfeOrzf4zQGmfR06wL7lMPcQGxGb7TgZC8aTQAadw69AInjuHQhlP4c+GsQaRJqBN2tthjdnQfif/mQMYx+ZCRSAs0Gw158RIDBO+D3bf8+DcYntkW0Uz8qsfAbfHmcOZOT90Xw8eGQMqNnb0UDCwhWxefkHhiIb0ojNwjTAQFOkY2abw6DKimCBMguCYlqQ7IzNJOdVuTyDz+gDbaI2DSU2m56ZQitnRJRtwlAM1xVmVt0gFiYFdnI2m9W26XJKzEbBgBljlwMYFN+ROOdbgYnKmX/aYmPttkdsWFEz8q1WqzBzbHm3XC7jyZMnhSmrqipGo1G5h+cIJ+2FkVVVFdYY9octBp8+fRrffPNNzOfzklZ32hDQh+NdLpclODGQZz4ILi2rHvfMEPAcO5ocJGTDbrDoLInH2PWHOEmcc7vdLgymFzXZ0QN6nP24r84O/eFtnFzPtWZ00BGz3DhuMhC2HYyh0+VmAu0skXHkwbqWA4+mw7rBgQPjHjkF7edbb5hL9MH2wIv0aDeA2ix+q9UqwILsU7/fj729vWIj2e3g8ePHMRqNYrFYxOHhYdm/98WLF/H8+fN4+vRp9Hq9ePv2bRwfH5cU/OHhYezv78dqtYqzs7M4Pj6OL774Ir799tvo9XrxwQcflAwki6an02lMJpN4+fJl9Pv9kv2azWbR6XTi8PCwOGB2GEKn2BqwqqryunSAHLtqMO/In7N56HzWEcsj45+JG+TNWQ0DG2eTuAdADVDt0gR0GzmHube+GqDxmXXXa3fou/tnH0kQCSB2f01iAHhhlwl0AaPIOtfDyPJs9AcfAtC1L4LMYdwiNsF1ngvax2e2IVdXV7UMGNfjswj4DUQZF79EyJ9HRClziljXqNteY49cDuK2Mdb208YpXMN43Besm2CwvWf+mWOTd8yhS5OMd7zmgrl1VtPtMMhnbF0Hj8x4bqwzma1G7sl48/33Hd9nb/+mHz9o9w+YZ6fz+CHFjqMGUMNyRWxKDaxIKDdO09GY/7ZTi4iag3IajLagFI7iIzaA3MLFfWzYbCS5FiAIIHIf/XYqCyuKmFNdBCduO89zSYQZDxTbQYHHEeYQp+b+AkhsMLyzAPcxGPL59MEsEobAQUkTy84BUKU0BSPNnFp5X716VRZi+m1b5+fnsbu7W3Y/4PXIbLl2dXUVv/71rwt4tNFqt9txdnYWNzc3ZbGi37TF38wxL8Awa2254SUujB19BYwzZzbQyLmZDA6PO795tkGBxwsGGZDGsb+/Xxb3Od28Wm3SeTyTNmEw7Tg9l8iMAQHMt8cC0GTWG7DllzPAqpsVZJ4I2Mx02fB7XDyGyKtZMo+tz/PfWaZ5hu+ZgwXGxpkZB8S9Xi+ePHlSdtxgh4uqqspbRgGieU/up0+fFvsCa027Pv7443jx4kW8efMmHj16FL/zO78Tb968KfM7n8/LVnp8/ujRo5K2Pzk5iePj4zg5OYmjo6NYLBYxGo1KWQf92Nvbi+vr63j58mVcXV3FJ598UvayfvToUVxeXsbZ2VmcnJzEwcFBqeVlHnkZDraVgMXg0WNvMO0xx17nAMiAGpmyDrn2H+DqINrPRMZsrwxQV6tVAcQAZNtpglOCVwBru92u7S6yXG7qtZEFFvGhc4BjBxqDwaDoOr4Cnbi9vS3rUWCimQsAOUGP9YayIAN/MiX0gbYSDNFXAloH1NazTBrAsnNP2mUb6C0ICdhzHTwlKJnNpY/ebs7+MvshDtptLIA88gzGjHFyAM/h+zsQbyJA7FORCeuEsY79mAmEPN7WGeQ1y4ltWCYh+I1993feFOIhmb8f2/FgpnpnZ6cwkcPhsDCkgElS5jCvBtwRdyMqM6p2qtlYIRTcw/9zDteYNbIhRQkdbdoR24FG3I3oLJyOfv3WJrNUsPKMhbcfM/vtNjm9w7O9mwR94LPValXKJsxSN7GePqw8GF/vTIAiG5R4zBxUADQxkjhSWCiz2lmpAUNeBMlBXd1oNCqsw2QyKa88HwwGcXBwEK9evYrxeBzPnj2Lx48fx9XVVfzFX/xFLJfrmt3RaFTAW1Wt68Gm02ns7u6WF44cHx+XMXO9IaUmODY7fpySDRzj5nHOxsvzzzORH5/LeJgNd40dcxIRZcy3trbi7OwsdnZ24pNPPomjo6PCuHkekA36lGtZPcecR//M+KxWq5JpQeZxck5hkrnwwjOCl1wigzw6o4SOWbZtU8zOIPM+L4PqJmBGsOCANYNwB7wGEgSUdoTtdjv29/fjww8/jO3t7Xj58mVMJpNSikM6H6Dc7/fjd3/3d2Nvby9OT0+j1WrFxcVFvHz5ssx7t9strwE/PDyMk5OTaLfb8fjx4wKqHj16FG/fvi07djDOn3zySQwGg1Ku8ebNmzg9PS3B2LNnz8pryi8vL+Pdu3dli7x3797FdDqNv/N3/k4B5ti18/PzeP36dUwmk5LNcfkItgu5Ra9dF4/emNkzEMnzYNl3VvC++UZ/YPgzCDGjTICD38lZGu7FDg/YXtf28jnyz7nojFlvgHheAEYQSnBCIJz139d4QaUX8DnLk20RoB5fs1qt68YZB9hfMiQu3ULezdTTDgB+xObNhgTatAVZidisacEXAOwYp62t9faABPL4doiLiIjRaBRbW+stVZmPiM2+2My3fThzbKY6IkoGxfJhefJhksPyBxBmrPkevGJMgp1kTrhvRBQ5idgEGshnDiKzrkTUyUeTFsYaHB7zTLrk4On9cfd4EKgmxeT0MI7OaWKiYW975VQ4k8g5mT3Nk5xBCMKZIyeMi8s8DNYjmhcYOY1j525wZBCCgMGALJfLAiLpH4GFtyej3Sg1fcwA3c7IhtPBC23FSFIvaRDMuBl0mGFGOXBybluTg6JvmaUD7FELiowAspl7A0PPj/tkYAfoBVTP5/N4/Phx2Qedeumtra1y3tnZWbx58yYiIg4ODuLRo0dxdnYW5+fnhfFrtVoxHo/L3rhHR0d3VojDCrHohkVYHO6P2el8cI7HDFnMcm8ghzNGvszQGXgTzLA7xmw2KwD517/+dbmeVG8G0+xAQXvsOLKjQC5pF2ASfYcZAvx5/1zYO+wD5TwcDhLprwE3wMT6nNkWtz3//r4D50Rfm7ZARF4ZRwJSZNmlTjyTkjCDWxwnurdareLw8DA+/vjjePLkSXS73Xj79m20WutdDiaTSezs7MTjx4/j+fPn8emnn0a/34/Xr1/H559/HkdHR/Hhhx/Gzc1NDIfDwjrO5/O4uLiIo6OjqKoqPvzww3jy5ElcXFzEZDIpW5Gx/d329nZ89NFH0ev1Csg/PT2t1YhSemW7RbD57NmzEryxYBiwjCzljIJtQNarTAo4w5IZOv7Pdpx5AIDxfPZRtt02UKNt1lXsFkDOawsgmfx82Ft0z6DTeyp3u91aqQnttkyRYVoul2UHJ+8og0wBWJfLZS3zlsGgg3+CYHbSoE/z+byMFeMBS+7xxU6aZLEPYfwhmACqJmxso7CFzAm6iRzY/29vb8d0Oi1jyzm0GSKr0+nUtqw1nrD8Wd4iNqSYgWoOYny+CUT6mo9s5y1jsOvOsBsTeY9xxg69yiA/g2QfHk+Pm8ku2yeCzWwPv+/469jdv6nHg0A1hiVi8wKGiGh8k5oBlbdE80TmUgmMkmsVc+kFhw1FBtMomY1cRNQMnFlG16l5IZENtlkLAAzC5rcG8lwziLnfgIZs7GAivI3dzc1NWTiHccUBYKxQUgcPzBWOPAPqiHrtlw0tcxFxd9cUxsl9o46alKHLWQBZsCBOszkNyrwbrDjNaGcIi7S1tRWz2aws1JxMJqXdw+EwxuNx3NzcxKtXr2rbJdqAM47ILuMDiwgrw5xn1sEADEYHY2cGBuOcwZoNXgbeZjIw/jbAVbWpe8QYowu8fMOBLP3zj1kzZN0LC91OlzQYLBEcw/BXVRXD4TD6/X4JgOjXeDwuW7BNp9OyJzvjRH8Xi80WTjwr17/akWQG7r7D7ea3Waym8hrX6FuWDZYYB9uL29vbUpYUEXfOf/z4cTx9+jT29/dLXf90Oi3Aq9VqlbKRTz/9NH72s59Fq9WKr776Kr744ov48ssv4/d///fjgw8+iIODg9je3o43b97E0dFRvH37trwp9W/9rb8V7XY7vvjii5jNZjGfz0vZxsHBQbRarfj222/ju+++i88//7zYbwDybDaL58+fx2g0itFoVOrgr6+vSzkL8r5YrHe/oeYYUAjjid7aaTN/OXvgg/lxYGe9MYAzOEBXeBZbsjKHVVXFdDotzDn6SPuRBexBVVVlQahLWMgUeX4Bj7SbYNcBAjaae/JCKgNA5I8SRspIqmqziBhA7jUUjKn9jnfQsC1Bfl2PbT1arVYFmJpZ5d7uE7qC7cfPegzRL15jzz3wb5Tk2XeZ+Mm+stNZLxTlBVzcD/vMMz1W1lnru20yiyqrarNPtv2g5TZ/bhk0seK5ccDI2DqrYOyTfTHBiJl0npUxE/OSg0bjK1/ndvh72HL7hvdH/Xhw+QcC5HIGJigDAgyvHRiRqtMQvtbsK+DKBrSJLcXhIxQ8n++ahM/lDq7f9DO43k4dBiZiw1AYTGOM/UNfMhtrgMBzvU3WeDyOxWJRHODr168L2ABwNTHRjCH9dtBxn7Mx2HYkbuXEUFhB2+12eZGKGTgbBS/GgkXJ9/b4O+AgYHDfcDi3t7dxdHRUrkXWYGtIAY7H4xiPx9HpdMo+urBNsFM4Rj8zgyQv3PF4M6dmOsg6uOY4z7WNMfNkI42Omf3ws+zUmrItHHYEjG/uh78zeGScebYZaowsjpoAhG0heekIYIYdO6bTaXnZi3c0oP0E7HbM9DEDajsqHwZa2fHZaaLr6C/jnBk32gCzb/bG7KntkB1dxIbt402p/X6/gNLLy8t49uxZHB4exk9/+tP49ttv41e/+lWMx+P49NNPY29vL16/fh3v3r2Lzz77LH7961/H48ePy84f29vb8fbt2/jVr34Vx8fHhal+/vx5XF1dxW9+85vY2dmJ4+PjaLfb8e7duwLOAMTt9nrbyIuLizg7OyslDE+ePIkPPvggbm5u4uTkJC4uLkr2qNfrxdOnT+P8/DxevXoVJycnZYxdeue5AEwyD+hVXnNj35AJCAMYl+ww7oB5AykDShMytp8EAIAH+mHAA7NLSYT7B9DDJlG2kJlZzvcWrrZ1XMP9/ZpvSqcI7JhDE132hX4jJeDfgJA2ZT+QwZ31g/tj962TEVEIIGy9y8OYD3RitVqVMjuAtN9s69In+wVk5/b2ttTw2w7YXtBm+3P8CN9jh9Htdrtd25bP97NNbgK9BqJ852DGNiKDX1+Hf+dosu20zZ9ZBnL7bPNsu9yuJoY6Eyjfd7xnqv+ahxkzShs8cRlMG8x6gYaNImAgp4+t2AZ5FmwMnlnlDOwdUWM8SudVA+lIOx8GPWbDaaNZVtqDwmAUfBgQWHCzI7q6uiq1ehg9L1JxXR79siHM4+SABSNmdsjAzM4Ho0qfMZgw1NQt48jcP/efuXcmwcrn0o9sDM3KOrDAuT979iyOj4/L4kP2oI6Isqe3wZEdAUbMTG9mMgHBDmboH+0zk+KdV2y8bUS5X9YJB1/oln+QtVyGlZ1KBpwZUHCYzXJ63gFFVVU1B+OsCY6T/Xu3trbi4OCggB0Anmtp2TOX9vHSiOzcaB+ykgOE+wIVxiYbf49Bln365pIW2kJQlWszmQ/bAwcg2AR2ndna2oqnT59GRMTXX38dNzfrtxQ+e/YsPvnkk6JTu7u7cXBwEE+ePClp7qOjo3j16lX85V/+ZQF0k8kkhsNhTKfT+OKLLwobXVVVeT01b088Pj4uQRHs697eXozH42i31wshT05O4vr6Ovb29mo26eTkJObzebx58yaqar2QjVeVUyfOwlcH3CYKKAthTKtqs52lbVgTy+bPDRpIsdvOA0SwPcggMut7WF5MspilI0A2u+hyMd4AS196vV4ZT9t2dCsHq7QFv7JarcriaAcG1GQDpE3W2KabcUZ3uAZdZjtT9+H29rbs7W9QaGBou+B24Jv80hdkDVafF82wmHm1WpUAJgdBkFM8j7G3bYqIWrBhHc52z6UYOajmfrY5zuo1gWaODKyzjTX5wVxwX+bawJnrvLOHMRY2xc/2wf8m9Gzf7U+yXuVxM3turOKA9/1x93jwyJhhcqrMzJ5rlJgIDCmfMzlO6wFqUAKcYo7sDGhyrQ/3bFIACxcClYFlRD0KtQP1/qJmNjjHiltV9bo4C7IV0CCadrFXN0YJB+pSBbO3OKOmZ0TcXa1LehfltmLbSDiNZgPN9ZR6UDuKged79k/lHnaG2Tg61ZqBkgOGyWQS0+k02u127O3tFVaarb38pjjGbblc1l6AkOfQJTWWV+9N7SDGbA6Gjs8BV5Qy0AfGIDMd7idz4XFgHsyIeYcPAoz7DL7H9r7vDb6zvCN/3W43njx5Em/fvi3OsaqqEqxkAD+bzcoCLoLlbrcbw+Gw5sgiomwbB+uUHUE29m47bfU5MOi5rtcHTKIDbQMg2oEOZzbOdsdMqdvkGlye1+v14rvvvqvt1b1YLOL09DSGw2H8/u//fuzu7hZw7XGhdr7dbseLFy/ik08+iRcvXsRgMIhXr17FL3/5y5hMJrFarWJ/f7+MQavVKmUBXqeBzgJOAUiAI8pK0Lkvv/wyTk9PY3t7u7xK3nXw6AX33N7eLn0A1Hr+MqgwoWHCwuyiGTUz2ZYXz3HEGnhdXl7WdmWhnbC7AFJfxzxGRMnIkGlzwEumwKwh2wYyBovFouxUZB9Jf7yupN1ul+DDY8ouLNivTOo4KwyoZS4yKIUFh/VutVqlfC9iUxPumm3bJc63zTQwRKbcVm8nyZjCQpuBdnCc2eH8mceAfmd55FraZXBrmTOR4naYWHIbffjeBr1uc5ZRxvj7QLLBLPPmDLeDnsws29/Yv9j/epy5Hwfzjqygb8jV+6P5eBCoxoCQyiEiptYUx+aoiIg2Iu5EWXm7m8w8AOCaQCmRLauqMwgx+I6or9jmsMKZLcygxnVxGEDONein/TZ4PN8sBePi/jKWjkgdNQPqvG0ebeJ3TnfVJvq3Dj6znDY27g/nOWXGuAI02AHAbIWZU66zw6FvBFYYAtdgRtTTl4DKyWQSnU4nDg4O4tmzZ2Ul+OvXr+P09LQ4vadPn5Y5IHgw4OF5zGF2dDw/X+OSIhtAg2oHjmZ6ssNnjmD0zNJxH8aK+8Ng0h/eMJplC+Dl+cuBVm6HA1zmmICJsTo+Pi4yYOON8cWIe1ysNyz65FyzHuxc4oCSeclHUzDC54y5U5RNOuF9wT0PgAA7ZMtPBnMeZ2fXkBlAVavVipOTk/j666+LLHQ6nQJk0CWygABXto68vb2N8/PzmE6nMRwO4+nTp6UW/ezsLF6+fFnGfLValdpSatiZF+ScYIkt0obDYezt7cWzZ8/i008/jf39/Wi1WvHmzZt4+fJlnJ+fx2KxiGfPnpV6V+SCl80ApF3CFrF54RI2g3H37/uIEx98bp32PDnrybxiL7HLBtDIx/7+fmkD8ugyP+SGnUNog+e43W7HZDIpiw5zmQN/W2fI7PA8An8yDfSNPcXJXGJbbXMhoqqqKm/M9BoW7ALZFtt3g1bKL/DxyLjZfzIvLkPzAcuKPcDeY+e8PSHXZpCYgbR13ODOQNjX+fNMHuEbmHOea9tlMM1xH7jOYNg2nWxCE3DGZhg/kO0h4AQjNTHd9i/03+1ot9u17R8B8W6DQXvub7vdLjv8cGCnmoiKPE4/1uPBTDU1kgbUTLJLPmwUzbLZmXKgrBi+iHopgMGljXFEvdbJwmXW1Yba93ZaJQN3hBVAiFBidHwgvN5CqEkJ8neAfANIb3rP/qIol1cGMwawFBloeGxg3DjHaWzuB5C5Lwr2eONIYKq9L7JZIxsmz7lZE+ae811Dn4OdwWAQ4/E4Hj16FIPBICaTSZyensb5+XntrZKr1ars+kG9KnOEQb4PsEVEDRC4HW5bZtfoj41yE8j0/06tGUjk+TXrh3N0Kt3Pt8MwKPg+tpr5gQml7aSQc2CaX0SAPJkRI5iK2CyI7Xa7ZWcYAiX01LKNbXH5lgGYx9dBKDbG9fU5oGIsGWOX79B+AyWz1b4X2Zi8c4oDEvr49u3boucAS9j0Fy9exOHhYYzH4+h2u6UU4+XLl/HVV1/F9vZ2zGazePXqVfzmN7+Jd+/exXK5jOFwGAcHB3F5eRnffPNN/OY3vynbJ7L3NYtFeSEM5QmDwaAAktFoFIeHh/HkyZM4ODgou5Scnp7Gt99+G0dHR7FarQrT2+/3S+0zgJq54KVNnhOPDZ/bhjNmtgHIlW2Js0rWTQNo7AeMvHfSMVgzI11VVZFzyyr2Km+D2mq1yu4d9jMw91VVldIz2zMAflVVRTYuLy8LeKU8qt3evO01YlOL/Pbt26IntB+ASSBmNp3yJXTFQYZfABWx2SZ3MpnUyB6Xs0Vs1hNkogAdYDywQfydSxtM5jSBZmdeLTPona9z8GHgbBLDYDh/Z//W1B7fI2OJJvvioMl20/ezzUfusX/5HsiQgwPkNJMbbh96gf2yb+Fc/AXzYb+LDUNeqqoqQdv37Xb1/vgBW+rB8jl9HnH3lcx2zAAnzssHRiKn+qwkCIgn3oJmQ55TGxH1xQEZ2Fjp7qt/dokHYJT2Uk+HobEDttOgTTbkBjMoAI4tYrMdkffpdcR9H2vnIIJ+YNBhXAxcrHB5zPgbhod7UFefzwfYmH1nzPxmNRtS1/hxncfRQQ5AxfurUn/a7/fLohXm1PX/BIKMowMd72nKCzIsJzjWbKAsj5llQQ88V3mcs5E18HAAxjPtGO0czaTTNwd097WB59JH5gv2DSPruQWcAA55mxt10eg0AQ21u8jB5eVlXFxc1OwCLJaD5yY5Z67sGNBds3A5ePccA6i5B2OOfONAAGgwvrw9bzQaFecF0OF5jMlqtSqvcs5rLnq9Xjx69CgODg7i4OAgfv7zn8fPfvazqKoqPv/88/jzP//z+Oabb2K5XMZ0Oo3T09MSLCCfx8fH8fbt2/j666/LNnj7+/slIxARRccjomQZAVCZBOGz6XQaV1dXZfeQ3/md3yn7VLM+wQEmxAOMdBMgsY3gN+NhgObzTUzwv3XRusM9vdsDn3uRHHNlm0dmB/sLo5ezpp1Op5RMIJvYOeTRBFJVVSUT8PTp0/ISpqpavxmW9vFKbu63tbUVh4eHsVqtyp766Bs6x5wul8uie2QMMpHRbrfLPv20k32ouReleowHfWYubecJ7tFL5AcSiPppA1XbQuYq+yf/ts4y/2a+sY2cY99P2wwy7Rfpp+2cD65xO22LsYcZi6D3Lsej/SZduLdLIh14OttpNtu6wTxmsgSsZMLAmQ0OY5PcZ4JnFovyucuJ/qqFij/m48ELFVEgnImV20wSzt6C6HNsqAx0mVzAJOdkQ2tBi9jUZFvAvEDOEbBBDPdEALkPRsmspdtkB+mFizla5Hkc3usY4eWZy+Uyzs7OSh9IgbMiHeVuAkcGqPTZhxk4AxiusXHkMxsW5tIMtctifODYMDIwJGY5LQde3BgRZdsvtgnDIcDOwljf3NzEu3fvitNcrVblDXGj0SiGw2FtzgBx/E37bOQYA7/gINfcei489tkYI1uWXQ6MLHPQZCQNZrmWhav5npxrts7PwciiNznzwHW+r/XO/WQszD7iRG3I2ULMgSlsJs7bGRI7aebBsk4b2RnB9Zw8hyDOWRoHmxzcAzlErh3EOBBfLpdxcnISOzs75eUr3BewQx1tv98vO26cnp4W1phdQ2CB0LE/+ZM/iZ/97GexWq3iV7/6Vfyzf/bP4p/8k38SFxcXpQ630+nUXvzxy1/+smbvAPmWIwfgMJkXFxdl/+nd3d3CUnc6nXjz5k0Bni9fvozLy8t48eJFWYwMg029PNk5arddioc9xHkj67ZTBjw5QLXDt/ybcUQ3zczCqCEP7AxBcI3cAZoJCNgRiDE0sKH9lC2Z2QOQR0SNGKF/LHplq0QICdrtYJrADcB2fn5e9ia2jYKccOC0Wq3K9m8eI9hi68xwOKwFHfyNvrlshHsQ0JGpcAlHrg32ugjPswFqE8Dz/HK4xMPX+J4ZtNr22ubaJ6KL3Nfj0eS/bWdNevAcziOIyiDa7WVMuJ5SIRM47ie/HZhg790O9wUZNfbyMznHxJJtmXdj4nz8o8f4viMTRz+m48E11YA/byfUxCwBus02eoI5OD+nmb2VVgY1ERvBy2DQCulzbKitxFY6jCtGwrV67CVqY4FgOz1jptEANTOjnU6nlNLAYJhlx/jzkxdzcNiYWXG5BwfpWSuWld8gkXExa095DoAgGwrOqaqqtqI7ImqOFmPm+YLVxNn4ZS47Ozvx8uXLOD09jV6vV96AOJlM4tWrV7FareLg4CD6/X7ZQg/HQ20fNaWsqGcMeA5ja5bJckYbGSuzxlk/mGODBbMHNs44cBawoVdmABlfshd5AWTOvmSGiTYip2ZtzNK4xpLrAG2AFOtEDqhw6mQDlstlAWMASvpydXVV2NCmDExmuJBHQJDZNAC85TcH0G43fSeoBFCzYwPzmwkAUuPME+VGbNdoYPUv/+W/jNPT04jYMMdu4/b2dnz88cfx/Pnz+Ht/7+/F7/zO78TNzU38+te/jn/8j/9x/OpXvypyyFgSsLiswbs9AJ5ubm5KHSXMOyANO8NbFz/88MP4+OOPY3d3Nz7//PP47LPPiq0D5HF/5IUFS2QdaCOlDMgF+gLAYsGq12N4vpinpgD0PjDGkQEO2zRix3mPAMCQHWiwNdgKgDe2zPpBgM5nDrhYiItts33k+6qqCpglA8R3e3t7sbu7G9PptLCD3DP7FWyRt/xDNxy00IfValWY5dlsdidrktlTgj3miuci78yl1+iYTTUQRXfcJj7LoN1y4Gs438E6/c6g10DTZJ3lCZ1oaoeBaJazbO9zsM5zbQcNgpFH5orrXX5hWea6PB7uH7aAQI7rOdf7rmdGnHvZf0Pwkel1XxkD9D2TFO+PzfFgUI0T9F6yBtQWVguy/8fIe0GfnaKjJDPG/J+PDAgjNgbFgNrXY/QQagwPqTSf523+zLyYNTOjnSM8In0cDQzpcrleJW5AbWDKj52KDRXANyt8TvV0u90CWDlgURwE5XtzH/rp1LgBXwZiZqMNZjDIBnY8DybaDDogod1ulzfKPX/+PF6/fh1ffvllVFUVP/3pT+Pv//2/HycnJ/HLX/6y1CYClFqt9c4H3iaN+en3++UzHA0ynvtGuQ7GEBluMuy+l8fURozrvKgL4LxcLsvnOFdnK3ieZczz6nHlM5hgnJPrUjkHQ45zwEm5hppzMPjICUzq7e1t9Pv9ODw8LIGc+3Z9fV3q3F2Kg96YyXF/GE8C26zv6EK+hxfkelz4YXs/O9LM/kRs3ia7tbV+DfxqtYrnz58Xlvf8/Dy++eabePnyZVxfX5dsCuMCW/y7v/u78Ud/9Efxh3/4h/H48ePodtdvUPzlL38Zf/Znfxb/6l/9q1odKvbIQSwLGWGUWLxWVVXRzyZn2ul0SvnVcDiM/f398nKko6OjAja2t7ej3+/HYrGIs7OzuLy8LC9WYvcW5BzyBIacABG7tFgsamUPOOY8dw7ucobPZEsGMzc3N3FxcVHqyFutVgGmsIYE1dg/dsaAISQIcXlXBg34CYMltwPAyRw5U2PZZQEoREOn0ylsND51uVyWxZM+N2IDqm5vb0sJB9sZYpORFebcvpZzuC/P43/sL9fgdwlUHOzg/xmTHNzYL1mfTDhlHaePBBzcKxMTts8G27a9zjJ5TQM+3KRHtvfZr/kwGLcfBlA7U+g1PIw/ffKaA8Ylor7mi2fcZ+/5PxOLXs9i2XVQ4vvgH7GvmenmGV58+X1HbuOP6XgwqHaNr8GqWUsEwOCOz12TaZbNzpXvslJloXe7fKBIfGcnm9NBOCLumUGPjYUNPgyoyweaIm4bAUAKBhiQgVHjHFZ5u26piUmG7bFh8LkGDZxnppjgJTsxjxPPoYaa7fOs5A5AvGgRgEZQwWcAVzMLfhMhW12dn5/H/v5+fPjhh3F7exvz+Ty+/PLLODs7K/v9fvLJJ8WRUt4xGAxif3+/lLrM5/PY398vtaRVVcXh4WF5KYKje/pucGbm2nNtht4BVXbMDjJwVhg6jBRyB3uOU8EhZEBt+aJdDiQ9L5mptVNysBaxcZStVqsANICpZZv2Iwuwfo8ePYq9vb1abfNisSiZHmTb2x3mdmQnwuFMjg+zPxmUZ3nGNuAIHSgDIppq1FmkRT0457G/9sXFRczn83jy5EnZHWMymZTvh8Nh/OEf/mH88R//cTx69Chubm7izZs3cX5+Hr/85S/jF7/4RXz22WdxdnZWgKBZYgCSQRJBScRmca1LobKt83jBmp6cnJSsAfODw6ROfDAY1BZRcS5AxYv5rAvolttiQI3+W1azbPK3gQznMQbYDMDxarWKs7OzwvQayG9vbxfZ89jgl8xu2n4jI7YFAFfsKrJlguT2dv0mweFwWOrV8QE+F13HfqIDtpvWYwgZ2hmxeXttzgZig/FVkCJczzluQw6aOeivF8zzubNdzJ3lzqCb34yhg3p0zz6IZ9iu2s9ynXXfwbrJDtvlJkbaffDz8m/Lou1XZnIB1JnE8rjZPjHfEVELvkw4dTqduLy8LLbM4+madzKGkDO2r8g0to2a/AzgjaGs3++P5uPBu39kxwcrQmqw6YAdM0OGQeFvK19EHQhwGIRz3fe10f/TDg6MCCtc/WILno/xgEFwWp8aRTsQt92/EUrvZ2owgcACLqmdROFy0GADznNoFz/L5bJEn9wnImpgziuIs8ExI9nr9UoKPy9MpH9m6vmOOTY7gAGClYYRMzgk6idwOT09jbOzs2IcYZ5evHgRz58/j6par74nA0DZCACOHUN4LXan0ykgyEAkL7wxo5EXnzIPtNkOgLpGxtdBBLKX2QXuZ2AM25Z3KsmAkZ+mbEbWoyaWOgfHOAAcvPuLw+Az5ndnZyeePXtWwDS2AOBj8Al7kkssIjZlRtYdxsJOlO+Q++zk+Q6dbXLyZABcS+vUcCYHyLI8e/Ysnjx5UlhHgPDPf/7z2N3djdlsFp9//nmxib1eL/70T/80/uRP/iRWq1V89dVXhf09OjqKX//61wWoP3r0qAb8M1sbEcVBwny5rIr+oUPME7LIYkXKXbBxx8fH5cUujx8/LnrUaq3LBgiE5vN5YUVhrR1ourYe8OpMhwEIjt9ybADn8jPbSGyEwTQ6TebJayLQYWqqkSUDCmQFuWEszfyaNMJmAloAza6BptSm3W6XkhpkHnBvXUL2mS/LurOMBvE8xxkD7tfv9+Py8rJ2fqvVKoTNfD4vto+F8WT4bBNWq80OEjs7O6UtjCVzb7vC3Lom2vPs+XZAxb1cUsmzID+QWfTZOp59LgcyY7/rMhHmnv5abjzull/3l37aJzKPPN+22brpeTYBZ7ln/LmedQJ5DL0zjPXQz0AOIRQ5p2n8TNKY+LNuvj/qx4NHBmDlaIsIFxBmBSCViGIjEGbe7AwBMIAqvue7iLuv7rTAcH4GIQbkOK3Val0/5PO4nu8NfGhnZqibmHAUnn6zDROfOfWN8YEFy+UY7pvBNOwKjAiGw33GsZkB4sgMO9fAwjW93MURPM/EqTUx7iyIok8Yce5tAGUj6sU6LCq0XHQ6nXj79m3s7e3F7e1teWscKWBWzJP2vLi4qK2AR/5cxmG5QV5wgjboGXDhQJgnHA+gp9Vq1RblOcAw4EDWAIlNGSHkMLM4WUYMTnKQZ3m20bRT5zwzhpQw0TevJ3j8+HGsVuuFoq7HhM2dzWZF7z3WHk+DJRyUA8LczyzTeQxc6mJ5dRkP4+++e25p087OTuzv75fXgh8cHJTFsiwu5q2D0+k0Wq1WjMfj+OlPfxp//Md/HJ9++mm8ffs2/vzP/zy+/vrrePXqVdk3eGtrK8bjcWk7MuLUsYETbbK+47zRZcAU/WG8t7a24tmzZzEcDmM+n8fLly/jiy++iK2trXj8+HF5Ecjt7W3t5U3dbrcAedsNxtLyRNDOvOWMI7JGu5xp8D2y/GJjciDE88y2ZiCPnHW73do2g7ZpgGhnj3JAZt1CHwjgp9NpIV8iIgaDQUTUd8RBLnmBFbLntxEa4Bt8EbAz5/ymjZQDdbvdGAwGtR2mGGvAM+tevF6JMUNu8PXUnCOr6D+7L3nRogNfZ3psOz2n7psBZCbQYOANqMEKto9+Lgc+wRlJgn9smIM867/7Y4CdmeVsx5Aj5NtBgNvHs7GJ2XYzr031/DzfjDpthST0+JnUwc5mLMX8NmGshzDUtsU/tuPBu3/k7YicSnMdD6wPjosJyqtKm468+IFn+7mZ8fi+CNWRp9vQJMwZSFtZzZJkQOq2e9EAwAoWmjo22G/qLf2Z02T0PY8BDoWxzQyAgZyDhIhNuQLPMZAGQLC5vw0/h8fNffX3AAHuyRhgRAC1TYoKiDXjCfj1+F9fX8cvfvGLmlGhZvf6+rrIH7syYJApNeHlC8xBlh0+ywyn55a/bXQdGDI3+f4ATi+Qc4Dlenru7RIOZDuPvQPI+4C1dQc5Y454FqwO8sV4m9kBCHU6nbIdGzJHMHZxcVG+8/qA/HyzRTb+HtfMHPEZMue+uozDdaNeRNRub8pXHDzwDAfD7ODR7a630xuNRtHtduPk5CTOz8+LLm5tbcWLFy/Ki1T+4A/+IA4ODuKrr76KP/uzP4t/+k//aczn8xiNRvH06dMCFMjcMO4AKANF5sn7GDMvrl1fLjcvEOl01nXUe3t7sbW1FcPhMCIivvrqq3jz5k1MJpNSoz0ajWIwGMR0Oo2XL1/G7e1tjEajAqYZs36/XzJuyLKZMQIhj6MP2+ssj7ncBTlwMBpR3/XD7QBYOjgwoYM8eCG1wZtLQWx7bCOZL+SLko7RaFTk3GVttikEmx4DdlGhn5QjAoCRSQc07gf19YwBPobsnRcxDgaDEqj7xz6Nkh+PP+N0cXFR7DNykYk05pP23gewmnwzJIvblktI8En2PbbTtiH4OZ6Bjjv4Q54i7hIW97Xd5zVl0SKi2FM/3wGfZY75zVjGz+D+DpJsPxkX7ufn5n7Zp/nlbV4nl++LnUSv3h/Nx4OZaow8KVwzPjhmR5+AGJQQw5iVz4wZk87fHDae2QGa5XPq01FjBtJWPKfmAPW00c7d+9Faicym0J4MEEgZwpoCor04JF9ncOTPMO6+jvEineOtjTyuAAuiVcAFqWS/zMPRrx0bAIKxNdih/bx8Yn9/v9QyAxZ4LiwOBox+eAwiNkaFMaQ97Hbgt8ZxjuWRcWGsud4LlhjbPK82ZgZkmTVgnv02NownumJ2qdfrlflhDpfLda2kAz8b3DwntAv9cFtov4Mig3untwEBPCdiw6LYuaxWqxiNRsWpAQYi1szc3t5eeTMcb4mjLx4vj7F1xP9nGfBxX9BpuUbnHdjlzAhzauDa6XTKFnUEYJRAvXjxIl68eBGXl5fxxRdfxHfffRcHBwcl/b+/vx/Pnz8vzNTnn38eq9UqXr58GZ999lm5NyCXYJN5xC6YDYV1pAwIlnMwGJQXhlCCcH5+XspUsGO07+LiIk5OTsr9eesiur+1tVXaNZvNiuOklAKwDHi1M8b+WtaRLc9/DpAsb8wJdt622L5htVoVwMs9HGib6HHmLiJqddZ8nu0q98evYfMJKikrI7DEnjizxPxjt9AlA1FkDSBt5pwMqnVma2urlIMxVwY8tsPOwHIMBoMyVyasDPAiotgvxgaSjM9tZ+gn9wHMNQXAOZhCNzkM3EyWMQYmrPwc/jchZRuZgTv3ZLy8mI8A0LYnLzqkHh6mn7Gnz8Y4DgIcnDb5d9tFB6TYhgysfW/a6DJWB3P2HYxFtruAfTLbJrLwTb7f9x332e0fw/HghYpmU62gGA4mjnpQA1kvZOB6M5WZmeaZ+X9HYBxZYZxqQSDsrGm3U0puE5G/jTO1iNzDyhtx9+1M1DpyPivV2SXBLDJ9aLqvlTTXLnvsYN3yrgq03+l6t80MtUGHnSNjyHVNhpQ+2BFeX1/HmzdvamPA2DpFlRnHbBD8LM8zq/5xOIAg2swznNpnjNkdoGm8Oddg1obKzoA5ZN4tiw4g7fjyVmWU/iAblicMpw+nVh3EMhdmFnlGZokNqG1wubcXnXJPtjSEAfVOCt7f1ttAMhZmPzzOnu8MsDzfzkj4ewcBOcjmPBbsuiSg09m8rt5zCfsJQNrf34+f/vSnZVHsb37zm1itVrG3txcffPBB7O/vx5s3b+Lo6Ci2ttYvZZlOp/Hdd9/F0dFRAWTUTPP8/f39AoRPT09jMpkU5vjZs2dlBxVqr5HXTqdTAsl+v19A8u3t+lXmVVXFs2fPSgA/m81iNpvF7u5ufPLJJ2UMLi8v4/Xr19Fut+ODDz6Ibne95Vyv14vDw8M4PT2t7U7EmBGIWJ7ok+e4iX2OqK+x8VoOB4SZobTM8BwD6qqqSgC3Wm22taPGmeDP7Kf10MClqqoCpBkrdlnhfpY7yskA4m4vukIw4kAB+4VdYGwob+G5Bj+0rdVqFUKCXY4AWdgTAkl+nLFhvnIQ44P7dDqdGsi3rUMmsPfWUdtaB0aeN9sF207+55lcn0GhwaEJiIwbDNRtwzMxgd+27cpkFLbl+vq6Ri5lf5af4+yw7RgYxODfBJT7BvsdsbHTXreADBpbZR/Hve0TsXmtVqsEC7aNtI+5/qt2//gxHw8G1WbhmAwUBOYCBpHJAyTk+icbUUfuEfWFGWYTOCfvz2hDacbZQNVMlGuKOM9K4Lo1Gw4DvIi7ryPNUT9A8vb2trA/Bjj8b9Cfo8Am1tRRrQEyAMdzhBMG9DhocB8jNqUXDgwA06PRqNZnA06znwa/uR4WQ5GjfxxLLnHIwCszsQREONTMyvIc0paWBRvS7AQMhDOz5LHncxgbvwyCe/nHb940u+ctBZvm344nsw4c/p/+eS5tGC3jzB9BLU4YIx0RpW7eTol2ue8E0yxIbCpnqqpN1iOzNj4vBzoZzFhu7aiyXXE5kw/rHs/DYbAW5NmzZ/Hpp5+W/ann83lh4j/88MOynzrXn5+fx2g0iu3t7Xj9+nWp47+8vIz9/f1YrdZb63388cexv78fp6encXp6Gm/evCkLx37yk5/ERx99FDc3N/Hll1/G27dvC1s8Ho9rpQs7OzsxmUxq9c87Ozsxn89LgLxareLJkyfx7NmzePr0aVRVFd9++21Z2OeXLE0mk5JhYuFdDiapu/R8InP5MGBFf32+dSHvEw8YdWBvcBSxSb0zl2Zu8Q8OiF2OAeNosMW5BEKAJZ7NDkj5uoj1mxvpJwE9gAn7DABrtVpl8bdBtNePoMPOdpkJRd55Vbx3/WAHCPpsPcpkjW0DY2BGn4yeQZv7Yv/HHFg2+N++s2m3HvvSrPtNNtWBs4G35TXjjWyHmmytZXtrayt6vV5ZHOzPeT6fm6lmjskA8Y4E220HAw4ScpBp3OJskA9wjxcsZ/KS++bxwOahg/g466gzF03Pf39sjh+0T3VE1EoFIjY1r6TGrBykbQDDVrom4bej9WGjjUA0Rdf8zgqEQDt6dfkE6WyMWE5Nctg52xA5EkXIUab8kosMrhmr+w6322PkxSPeO9zsKiv+HQAZGDJeOdghCjewdLTKHOD4MLy+r+fUhtP3MYOVlZ57+H6WDy+YZV7y3PMZP257Bu2MNeeYnTOL4tICzyPznucqYp1+5bmAaGQY8JWdisFIDjyyzlj2mtLqzKmDRAcqOXAk3c0Lcni+2TG/AAq2Pe+x7iDATtEONQNq5qPp84ioBcXZsXIdwST7Jjex+hngsYUji2spm/j666/j/Py8gNLhcFjeSNjpdGI2m8VkMom9vb34+c9/Hl999VXZWWg6nRYZcnr/4uIijo+Pyy4R29vb8fz58xiPx/Hu3bs4OTmJo6Ojkt3pdDpFjweDQezu7sbR0VFhk8k20c9Op1Pqnz/44IN48eJFdDqdODk5Kc96/Phx2dGDciSXR7H4jjFn32zrlcEBn1kXHdw5mEMOCESxu5Z7fvMZumeZcOrdvqXd3rwIxmy7WUJ0wnYLOQdcAqqpszazbJ1GZtEbdjaiVID5AXyTRXDWC3vrRc0eX+u8/VC73a69CCjbRNuuDGCz7SBIMZOc2U/rp/UwE2fejYL7U/PNkX0F/cHWNTGu2FzPefYxlpEM9D02+TwCIPqLb62qqkYCOdAxtrCNo74fssJBkXXEc8OP59B95zzLTdYJiAHGOa/L8Hg7qGWNkcfIRJkzRPcdnv8f4/HgmmrYaCI4wBSLwzIwiNg4LIMWO1ULVXayvgcHxteGwddaoRwdc44jZM6jvMMgyfc0SMlAHoUyQ849UTobOy9syOArAyHaB3tB+wHLMB04DRsuQDG7lQCarIz0k8DC8+xV02QfuC8givHw9dlYGMDZ4Lsvbk+WE4+Pf3sOM/uS72kjQfsNhu0gcnCTGTL6zgp6DA/9a2JehsPhHdCwXC5Lfb3T4JZ9Bx/3yR9jRf+sAxh8189zvuXAjgknAtAxK2HGw7WXq9WqlK+4/pd75lRiDh5oQx63+wJOBy45iKT/lEiYrXQ/6Q/X7+7uxrNnz6LX69XWJHz77bdxfHwcT548iV6vF6PRqACjN2/exGeffRa/+tWv4urqKv70T/80jo6O4quvvorXr18XZwp7PZvNImKzuA3w1e/3C/B69epV6TsvkbHOw5S+fv06JpNJ7O7ulp1IIjalN4PBIPr9foxGoxIkeG5Wq3V9Njt/0Marq6uyhzYAu9/vF/tz33zYmVp3kZ8MgLJM+b7IAOdzjxwYc27+3zKBvhhkOGjNZAuyhd5wbwekyIb39ebZLqEbDoe1IKfb7Za3c0ZsXubCAZC3zcl6Yh30uLp2PfsLs/74XI/1/5e9P4mRLM32/LBzzcwnG93cPYaMzKgqVb33qvOh0XpQs/EWDQhCExAkbbgRuCUpLSVCS5KCAAGSIHDbKwGEQIECBA3QRlwIEggBXDbRk/B6emNWZUVmxuCzTT6Z2dXC8/fZ756wqPciKbUazLhAwMPdrt37DWf4n/853/d5PKqqKlkm66bnKftfSCj7K99rUsvy8CHyxXOwLSBwCY3HKNtJA1xfPMdjaF/o9SSeK2SRz7DjEZudM7LsWw49nv7c7bL/zD5gb2+vQWQw5tkPUr7hoKeu60a5Cs/13uhee8Z8QKDSHrDAp2v79dGg2quXKWuI2KTanS4wcDTblhUyG+jMCtq4GnRwZQBOGwE/VlwEyREYBgiwZeBNe1B0t8MBgcFnNhROwbtfjNk2Jo5+RzRrahlD2BXSUgbUAGKctMtD3C+/hzH2ri3+3FGtAbRZDrMKGWBnZc3GmSs7mBz85LZjGLf9PTMYBgW01UFgNujbjLWNFyzozs5OXF9fN4y0xxlju1wuG9sjeX0C79n2Thy7WXnLR3YmjJ3Te1xklLYBGQC1t/aC/eL56ItlHXaTQ0EcWPr5vGMbUNg2tx+yD/kz2mT5I4Nje8VlO5Wvq6urWCwWRWdubm5iOp1Gt9uNX/ziFzEej+Py8jJevXoVy+Wy7O98f38fL1++jIeHh/iH//AfxqtXr8q2jsfHx9Fut8tuDrRrMBjE0dFRLJfLso8xckQfvCAQ5gtW6eLioswxta+MO2lgSs7YrWS1WsXV1VUB7mTnvNCP0h30FZlx3S96n7NCtlUmU0w8WC4+NLfYZphfQBQ1zbkWedvidNsFt30by4Zc0k/uOzo6il6vVwIMdBkWHPDiHTawtdhTyBXG3wDL5WIRza0QczbF4CgTAAaQ2CHYdct8xMY2GgTzfdfw8n4HK7TL9jWDQnyB5zsHLP6O73F/t80TNsc+3L4fQGyc4cDZ7HYeS/y/5xRfxz0ONJCVXBuOLHo+uD+PZw7svCsWpUPOivL/bPMdlHge3C4TCw4YrMNUH/gUWO/e5jrs33Z9CNP8GK4ftKUek2+gSGTrC6fMZSHbpgwG4BguO36UflvkifG3QQdQGwR6yx7uNwg3wDTbQX+yIbPRziA8Ox4DQ//9LwMN/KRvOUVoAEuKcXd3t1EHa9Dv6Bjj6uOyc0DBM+wYc9CTa4ltiBxN0xcb5rxYjCunGjO49/ttRLKjoB3ZSUVsmCIMKe+w0c11me7X3t5eASG0xSB+OBwWsOIAwwA0B4h2ZhjsD9Um+3ue07zrgWWAf8wtoIzvrlarAgYMUnIqGQdHihPwk4MnBy1uU+7Db7syuMjfN6imVIK/U1NdVY81zxywBDCK2JQ6HRwclKPHLy4u4vT0NG5vb+Ply5exXC7jL/7iL+L09DQuLi4K6xzxeNDGcDiMr776Kl6/ft3YNcRsZKfTiSdPnsTnn38eo9EoBoNBXF9fx2QyibOzs2JL+/1+AdToOrXq/H+5XJbTWS2zzB8lIU+fPo2f/exn0Wq1yhaAt7e30e/34/b2tpR0PDw8xGKxKIsmZ7NZkaVsJ/NC6Ay0mCcHZNbFbaxnvmwjmU+2hMvpaEAzTDFAgPaynsTZtw8RN/S1ruty2iQBEX+n7SY3VqtVOaXV6xW4aLfBnIEfY7UtSNmWLaLdWYf8XANxPsu2Mz97W8CaAWgOVLk/v5/vmCijzRlg+7354KmIDavr8iH/DvB0ecM2woIxtS33ONA+b7+KPm2zV7zHLDVrFDzeHj/rrOXXNh8Zxf8wZra/lnOPl+WLd3CoTw7OTN48PDyUA4uQZ5+xYPn4dG2/Ppqpdm1wRPNkREdgRFoR0WCzIzagKxsQ7nGE7e9lUBGxSWNbyXFMGVQZNEZsjCf9QtAz0KVfGAEbTC92coqMZ9mw0S6nv/wzgyT3z/XQBB0+dpS+ZZbaaSbXdON4SO3QxnzYiJkdR9EZ4OBsstNhrslqUC6RgxGX9OTgxXOw7fIc0yaD220OgL5x5e0eea5TwLkdsH5m1biHlL0X9jAWbDXH/Q6YcvDkOaNtdqSWOYMCy7yDSGTY+/bm9LyDwlz2hE4RtMIkOmWd58Y/MyDO8+z782d26Fx2CNgcA56f/vSncXx8HLe3t/GrX/0qzs7OSoBD+dTe3l7ZDpDdPHjGcDgszubVq1fx9ddfl10XeB/A4+uvv47FYtEIRCKiwVD/5Cc/iS+//LKUmUyn0zg/P4/T09O4vLyM9XrdqEOdzWaNHScAWDDTABBYNdZwcGrekydP4unTpzEej6PVasU333wTV1dXZT7ZmxpWnkWPbIFpMMx3sr237HBhRz1X3IPNwY5lIJD1FZtJvTjMuhk45N4LAXmO5TkHmZkNrarN7geUZjHWXmSMDpq0wfZ6uz+TTmYFHaA465OJA+tD1iG3wQApj30miWgX+upxMAvqNpjpd7sM3HiXbagzBvav2Ucg184uIRsOCpBz+wraxbMgR7y+yO2kP/479xkzeMyzrHk8kHePtUtxLNsZ3CMTJlxoH9818833kDez8MxdHv+6rsuajbquS3DgOXA//Fz2e3dgbGz36Xr/+uiFimYnMAg2gEQzBtSuh+PK0TYXAMuKvw1sEpHmdDSMOQqVlY72YiQdUX/I8ZvFQ8BdM2rwglBzPwY37/LxVwHUCDJ10e5ndmy0zXtM01+cF8Ycx+Hteeq6bgRMKJKjUwAiJQE5BQcw5fACB07e2o7FEE5V27hkJWa+HMjl9KONhOcxy1l2KsgcBs4L2uif5dFXTp8yV+w0waEcsBar1aocRINM5dIF95t+2phatixzjFmrtan9tLzyHAJWMzgEa2Zuc9rWDDT/rHPZYfoyW2LQncFPXpiV05tZv7gHWc6BxP39fbx58ybevn1bdGYwGBRmFgZ7OBzGaDQqezRTVkFwenFxEb/5zW/i8vKy6AC6xtgRLDl7Z0dVVVUcHh7GT37yk7ILyPn5ebx796481+sj2GFkuXw8kZQFcSxodVaQut3T09NSDsaiwvF4HA8PD/Hdd9/Fd999F//iX/yLxpZZo9EoIqLsf8zYwoDjRG0Hbfe2XVk3s85gV+if30OKG/nChnQ6nVgsFoVxJj1t2bf+IL8AFLff6wvMeNd1XdY+eJ9nZwz4bs7IsL4IEGKSIoMnSBmXJiLLGRA542TAbBCHfuYABl0ySbENTPNc2xY/y+UwOSixrc2MuMG0fT/y5UCAoIm6d8s4hAUk1od8QrY7HrccnOTfHQDlAMS2i/HyfHo+PCfbSIGMMWxnTR7y/KqqGgcNUZZiEJ6/QxsoGyN77c/29vbKc8kwel7su7E7jA27ofy2a9tc/Fiuj2aqbew8SRGb7atQApQjA8bM5ro8wHVD2Xmi2LlcA0fkvZ8xGjay/A2W1n1x5Eo7zRwA4i3sTiFFNBUkgw4LYXY0Zvjz33HsNnx2BmUiO50GS8O82FjCzKEoXkRhQI2xwOG5fQb22dDCfA+Hw5hMJoVha7fbZSHWkydPym4DzKHZactSDk58tLgdDN/x/WZfLH9Z2ZkzL+DIzI3BrOfP7AVj5JPIADvL5TLm83ksFouYz+cNWYC5Y46tLzlrQl8NwGkvz7FuIRdmzpxJABwQBLj+MsuRZcmybZnZZkiRNd5rp2IWJwcQbo//5mAIlpnx63a7JXAxo8ouDOgq9gPd2t3dLScN/upXvypbzU2n05IRInUPS2oZvbu7K9vSsU80u0b4Pev1utRid7vduL29jclkUuwk7LDT8dgd+oMDZDFlxKOj5Xh4xuTg4CC63W6MRqMYDoflHff393F9fR0REYeHhyXYR7co4/GFczU4ysyVgWYOhj3fsOjWJds2n26KjNqfuMwKnWVeaJv9g1PyLguwvcQWdbvdEtjc3t4WMJIDuRy8A17oO4wrc+FdcLK8Ww8YD4+vQWS2C1k3nN3N99leojv+Pv/Pgb3JhUxO5HfxmUG8/fiHgjDLDZkW5tH+2EE9fzNe2DY/H2oLcmE7j67aL7pm32Odx87kBuNhG8ocZDzioMj30R7aYN/v/tlHM4ZgHOwGwNlkm9dgeItOLuQjZ148x5+u7dcPOlHRgJqfTjnDjHpRAcJAKYAV1+lnP9ugGMdsR5a3qbNiW/H5zA7VDHIG7zyDNrhGc5txzPdn0GEhzMpmQ2lBtTNmXDxOZAPMRpiV9zuqqirPYs48Xmayab9TRHlMYWMAObCjOMzFYtFwiN1uNxaLRUyn0wJUqHHFGeB8uMzI4KgMGlz6YhYhYsOsbJtXxjkDOMuw5zrfy7j5bxFRdmUwKMeosfiLrc+sE94/HGePMzXY4F0udWLOAdQOFj1nHlPkAdmg7h7QB6A2kM7MWN5/OjvmzJLZIdvxoyfbnFMG0MzrNv0ARKInzLcXiXmec1u55/z8vGQSdnd3S5kEwSIMMLIKWHr27Fn89Kc/jdPT01itVjGdTsvuKa6P5Ohw9pGmdpm9r6nptp4aTGSwgQ07Pz8vwZ9ZRgKNVqtVDoCBeLC9QMfYDjWXKKB/jJkv5tXMLTILEFmvmyUtmTzgmballh2eyyJfgxP0x1kjr5vx8/h/zgZ5lxhsn8umDMYYM9tq20YDsk6nU8bT4DkzwlVVNYLpbe9Ffq1b1hcHPLZrfEa/mRfso/8ZdKHn9t3Zp7rvtsUGpNv8acSm3I53Oti3X9sGapl/99m+zv21jfFuGDwTuYPAytlVy6uxjftkW5lZbnTR7fUcWlbs60x6ZHLFpVMef8rUWE/l7R8hV5zZx0cZUFuOGRvuq+u6BJ7z+Tx+25XtxI/p+sHlH0yMQS+TCzBGQTCM/m5mIT0JdsKZOQQEs1DHym2Q6LSen0O7EUq+QzTOs+ygMAyud7Iw5zFiLHI6OzM3/q6NdQbTRIoABX8/l0w4AMiGDEcPQM87X7jtjujzs+xoDUANsgARHJZwc3NTjk8G7LRarcIKmuXh/dRgA+AMEnIGxGCCNvowooiN07a8YSjtJHMWxkyAQR5zQJv8D0dJFoWTEmkn7GpmQOzksjO3Ad7GqHPap+WFwDOXsFjGrYM5+0L7PLfbggqPif9vJ2bHsk13fBlAZ/mEmURHYPPQVdf3m2VzkEDfIiImk0lZALheP654v7+/L3o4GAxiPp+X3wmOmJu7u7uyoJH37e/vNwIxGGdKn+bzeUyn05jNZmVbNuSBtjIXtBvguFwuG2CNwMD3LpebxbAEAavV4wJGShsM5B0M4Yw9l4xNZkORvW3pYM81oNq2yfbaMmW5gVEG8DhzhCyYEECvmBeXWJhU4cpbh87n8wIGab+fb7m2bLZarQLG2ZnEa1AYh1wGYdAU0dy1x9/x+3Kdt31UBmtVtdlTnswJPsTvNrgymM0g0XPLWLhP2BxsUy6vyQGNyR2Xong8aKvnkDaYSMn+LoN9fy/vRU0bveNH9q87OzuNNTIeG8+Rx8p/97ts+yjZRIecRXewaJbb44m9Xq8ft+Dku657Xi6XhRjgfmwMsuFsPD6L9hh3cbLrp2v79dGg2sJgxUERciqGiJXvWTny746cEFY7ErPkmdU0A2wmhzbAXpgZwyjboWBgcooXhscOgDHhp43BtgU4eSzdfsaR/WAjNotCcRY2ONuCCDMNtN2pY8Y6t83t92Un7YwEys4YjkajcgQ7UXGr9XgE7y9/+cvo9/slrT4YDGI8Hsd6vS7bVHnLMBw0LK6ZDOaPgMqsiEGhnQJO0zt7ePx4vmWYyywIxtFlDMgVYIh6ZJjc2WwWd3d3MZvNGgdc+OAk5M5pTAB4do70jzZRtnFzc/NenboDD4y/mSsMK233nOYxRXbIFhhkWDYs21netwXP+crfsXO0PLuGmrExYxcRDQfB1ofIDgxUVW2yLma0W61WGUvKQgyIF4tFmbO6rks5AwAVx2Ww7914zBhXVVUyHHamHkv6bl00WDAYtW3j4BeCaXQaYMVe2xFR9Bc2y+9HXswAZxaV+TKwMSlhm047LFcmC/xd7vW7MlnhxaKtVqvYFMtQBsER0ZB9B8wsEDazmOXRpAvz7Lp+lxvlkhhfnjvGKQf9niPGAt3PjLnBrjNvBFL4EfvdzCZz8Au2nvsMxrjf/pT3uWTJ9taA0uNqu5btA22jnfgCr5kCC1i2/Cy3eZtPNgjG3mUgmzcKMMDFFvh+67D1x/OFLqILJrR8n/tFgGyZsay32+0S3HuO67pu2KO6rosNmEwmZb4zcQg4Ny7K+vjpal4fXf7BxHnimTxHnPwOa4aCZSYUJcwRbEQTkBt0eWsbC2FmzW1UDJq5bEx4hmvNEHZHhvSD57ldPD8DM9qVLxsj0vfcS2QI8+yIFOHOyoVicC+OmzZnlpW/ufwB5XPwkFe/40AODw+jruu4vLxsGF/GnNTs9fV1HBwcxHA4jNlsFhcXF3F0dBSr1aoAak5x8kIrtqtjXJyOzIy/HRJ/5+e2FKGNNM4jG4rsBMzgwlrmfb3NMK9Wq7JtHkETzJszOBgtL1BDfl0/brnlu7DQfJ825wU9ObXroIVneixpPwEC9bh2Srl0w7Ll8c1OLQOJrDPb9ASQ6CDIQNGOG7AU8Qgijo6Ootvtlnp2QAMBINsespiUOaO0ImJzBLX38WW82Qf68PAw7u/vS2aG99BGl4Mxjsi/GSmezzg6COQ5jDPjh7wCmtA32jydTuPq6qq0yTtcEPyZwTPT5gyPdeVD88bfsFNZVur6kQ2fzWaNDEvuL/8yW4we2nd4fLgAdmZ/3R4YOhMGDkbzO7PN4R7aFxFlK0SDEpc2bBsn6wj/chDgAMxBpEExsu0DgtBd20yCY97tANsnB3pNg/XevzPOXJmcol2eX+xhBuzImOfWhBnv9XH21ke/35ezXhnA2ycgl54DEwzYUeaC9kP4EBwC8o2DjAF4FhkVE1Ve6Opx8fzxTsYHHXX9swkCf9djD/HDM7Ad+F9nTCixIzj7y+z1jxl0f/Q+1Y5gDUSy4GR22v+P2KxszukhG5Ocrrcg21hmR7ttQi0ENkQR0UiTcFlY7UQy0+l3bRM0K7CDBbfDixHdN5jPDBZ5llP1Bifr9abm3O1zKsuOBYdu58dc8//hcBjD4TDevXtXnPHXX3/9nnNjLJGRP/3TPy0GG6PRarXizZs3hYkHxAwGg+I4+v1+oz7Yz261Wg3Z85hmEGeHnI2pn8FYZFbN9zBWgDsbRvpCEALjzFw6/c/uE+jMcrks6TYb0YhoMP8eZ+Y9BwQGaL6ff+v1upTkWNfcZ8uRATXOJLM5fgbPZL4Andle+Nr2HM9rp9MpbSaL4aDW98MGozswWev1umx3x44sVVWVwz1w1iwohTkCkLhcykx/v9+P3/u934unT59GVVUxnU7j9PQ0JpNJAySik7Y9DmjsiHKwYAIj7zqUL+51JgkwgMze3t6WPajzDiYGv9kmb5tDA5AMAGzXrcf8bWdnpyzszTLgcfZ402/eYbtqYOWFhpl5NxDL7WJePhS4OEPmUpKqqspBGbPZrMFUe0xyVsc2h/nj7/ggxoo58DuZE/TMO2hg03yoiRliLvvQuq4bZWmZWWW89/f3i14RGAEsCfSRVeTL/cvZmG0BmsGw7RrtdB1/HlMuy6uDPL/bWaA8ByYDHAQ4kOVzMjn2p/zd/gW76HJMZMoBmUGs7TO2a7XalIbyPfSfYA4dty9j3ngHvgEZ4X7OH0BGKFP7MQPmv8r10aCawc/GN7OemcHlwoDYiCB8BsUGjlxmwl07nMFGNvxmljGEfqcNK0CC9ztCd9mEDS1t+G2sDX1ivOirt2vKoNtAMBvknC6DnaTNZsUMlPwO2mDHSL0zF22t6zqurq7KPU5f03bYdjs5DCzgMJe2ADphctmu7/7+vtSNwubZUViWMhDeBgIsV7TNspxlDEMTEaVPvV6vGCHaC9gibb67u1tO4Vuv16Ve0ylqG2/AtMGS3817ACn8c1bDjFJd14WlIs3vFLzBFe33fPF/+sQ/B7gZ1G1j4fI8e+z/su+araKvBFjeh9q/1/XjdmiDwSCm02kB32zDho55a0oYvW63W2rfWUgLsGKccIB2XLu7u/Hs2bN49uxZ9Hq9mM/nDXBG8IQs8pNSEICTbUy2izhHMlOMnQE7MgPT1O124+XLl/HixYsYjUZlMSQBEgw7aWOzXrwTu23gaNuxbS4zWcH3s36he96+y2SN7bXtG0FsBlMGRG6XbSS2HhtCO/OR9LShqqoYjUYFhDNuWS9tN1utzYmPOZtiUOOgwDqfCRvkm/H2An+PJWPl7TAN6P27AROfGaAZhBvEAe7oE/qV55U5N3lmGclz5DncZkNyPbvBtGUu23W3hb/n4Ipn2g/RZssyNpS5wu9hk5zFwGYQpPN9g3kwite6+OI+srfME+MOueAMxN7eXsxms4bNdHbUpKczAMiVM/Qm99brddl6Fx/c6/XeW/OSr21Y6Mdy/aDdPzJAzCz1h/Zk5jJ7w/1mallkYzYAoQTUYAxQOt6fwaYnd1s6y8pkQJ6DBveTe92XzEDk92QnRE0toMCOJIMrA27Gc7XaLERwAOE0PYbBzKZZiRwgOMp2f+24AWyumXOws80B2+jk8bChM3jzvpgYD/dhmwPnQvl9n9tlhpi2bTvBywB+OBwWFhSjBBhm3GABYUQxmBg9MwiwAK4Px3h5ngyguADLEVGMK1sZsl3h/v5+IwPDM+zsMYwGM/zjuTkw8/O2/R/nhf58CEz7/55Ljzlj7YOMMvPkOmTGEodFeQ73mhUFXJyfn8evf/3rAqoIFr21XGYTeQYHwnQ6ncJU0362zMu1rOi8g/Qsx9gflzuZ+cRpm+Fz2/r9fhweHkav14u7u7u4uLiId+/exdnZWdze3pYtBAFhEZtFfW6T2UW/m3mKeB9gZ+Bg28FlmbBM5nu3MZx5zU4mPtAjZ+QoP8hAj7/l8eMUTIJensE9JmZsM7kXm2vAlJlrxtj+xGyo9Z++mU12wJH/73dYx5BPxiHPW35nxGZjAIIgfLv7b5/FZUDPuHJfXh/krJf9Auyr9dx9Ypw85tTDYxO3yYfHyASW226AbRnf398vdt9YyPPpjIcJAo+Fg4GqqmI2m70ne26D9QX7ZLlx/wjcwRgZr9FGdPng4KChG+12u3FKMPLLd9hw4NO1/fooUA0DipAY5BioZEYlA0UrJU44O3SMlVnBDCwMCiKiITj5QjAtrAiYFz0aDPBMpzBz2iqDxOwk84XRB/RYWXL/nELNQNusPO9lVfh6vdnwPYNKszHZueR7uQ/WO7MqHoPMpuAQM8Ngp5wNbg5wPB7IjOVtW5DjtKXHzwdX2OH73dQauo3UyjJvnn+YhNvb23J8MjVqvJ+aa2QceWPxotmobMw9NnbA7i9AjfSrTySzvDC2fk5mapijbQx1vjzuec7ydwz8DM6RD7PmfiaO3MDSTAptp39mgxk7njkcDuP4+LiU7iyXy3j9+nV8/fXXZd9manuRF7ct2wNfyBn6R/kIgSGBs+2OMzTb7BJggmCP532INAD4rdfrmM1mcXZ2Vlj3y8vLuLy8LOwrtdaMDXtf58yGd61xW8yQMvZ5XpAlZDKPl0EV/fNiqYgoepNZadtMg7BMrGQ5wIaRQUCeyEySicLPMS/UymbgRhsYE7fJ8uI2cyHP2E2DFvs6B0/0z+OW7ULWsRwMm2zIc5LJJmwT3/ce0r7cZ9ruNR0+ZI1j3HnOtv6hY5Btbjvvw84Dvnd3d0uWAFBJ8LztrAyDa9dEZx/sIAQdt8+tqqoQTbYX1FbDLpskMrhHNpkXxgE9NEFmuXHpGMRTxGb7T8accfOOUOv1urEfN3PtbSWxUdvGymPy6Xr/+ihQ/eWXXzactI1PVTUXfXBfruNisjKAimjuF4zAOUp1RJuNAleOBjFcXLQdofAz7ZTpk9lrR7gRTQNvIJANj79HfxxcWGjtGBBuAyiDY7NarhtGOfw8xi/XXTHGKDJp7DwWGEK/02PKvRiHXC9qo2AA4f4YgLvdBAlcfN8GIQcEEdEAhmZpbEhtKMyor1aPdYKDwaCxbzD/ACKk42CuMeTMtQOMiCjbqAHakAf6aqDPM/ibsz9ejAT4IK3v4IN+k+LP8umAkf7ARkW8vwMHbeb/nr+sz9vAhOXYsuf94gFIbJsXEQ35dkBpGYiIBivJYSoHBwfR6/ViOBzGYDBoHLqCQ96mS9bfrNPoOgBtNptFp9OJyWRSWG5W4aPvBkv0w/pNf1qtVgmSAH15cRTjnJ8DqH79+nV0Op3ClK9Wq+h2u+UQJh/6AqvHZwSH2AHYdY8HP61/rvs1cLKDNvi1PDNnlol8EJLf7yAFHTGxw/xYr7B3kCgGEMgxn+Hb7u7uyoJr7vtQTXYG/B4D6yN2FFnjud4dIgcs9M/ki8fTgXw+MAZ76rbZTnrO+Nx9MPiz/NtPRWyCIN6NfnkhttlP65hLEvLcOMC1T+b7ZP2MRdxWB/UeC57JPGeW3hiBcWbuGYMMhBlLlyq6n/Zd+B0HC5Zd/o/tpy+8h63tsAO9Xq/Mg8cHX8V9nkvXmfP/29vb4suczbKcbAuufG2z+z+W66PLPxz1e6Ii4r3/o0RWfLM3NrAGtnaeFjQbQBtRgz0bJ7PqZuOyocKQZVbBgpMZCa5t7C1XdsxmgvxMvs/fbARs/DK7kwObXA+dmWzuy2UwXnCSmfjcZ8Y4L+aJaLJNgAH6bYbNrKnlijaYSTH7isJnltpztFgsSnAHUGMsV6tN3SCHbJjFpd37+/sxGAwaQDZH5nxGPRr6QGpz25xu20UlYgO+HeSYJaHt6B7sSGYvd3Z2yv7IzjBkMOMLmSJD5ADSc2tw7WAkIhqO0s/Mf/N8ulbUIJyMgFOXvNtpY/piQLkN1NM3gh474G63Gz/96U9jMpmUo8LZco/34iwzgwVDRjq03++XDAROCXDsLa88B3kP5WwXvO7AoMrtICgwKFqvN6cWAkSQKxax0U9AM6CGenTk2ntY87yIZpkObctMup04MmJA7L77/w7S83y6HeiESRqzaw5SuDez4/YdZgvRHcYAhtt2FDCVL89RJkxM1gDWvD2kbfw24iL/blAPkDWg8n0mOzx29nsmAbx7VO6HiQD0C3IBG4dvIVg3E+waXtqRQX3OFjmbZjuU54TfKR/hPhNt6JKDdcugST/bKAcZ9ln4AINfs/05QGFdErsEmWSi/baZjK8Zb+ao1WoVYiBic04Bu7nYZmaQbt9kf2g9tF7s7e0VsuDTtf36KFD9x3/8x/Hll182QIaZ1rxIzWyn02mwc1k5+B4OG+F3epzJ9mLCiOaCBb/X7JOdU0Qz9cTv9MnMVQbbvteXnbrvRaG9bV5mVd0WK5OZKAM7GyADdTugiPdrELf1A0XF+DGWTsfiuGiPU0xmoQlm6I+NuB0HDCQKy/0Z+JlddSbEz8QQTafTsuOGAbWzJ8wvMohhWq8fa/1Go9F7J7fRn8ViUaL6uq4LQLGRzc6HPnp8smOjvwbT1hUWqVEPOxwOSxt6vV7pB8CAeXRfnQ7k3dYPHKOdJPLF95AxMzRmmO3kMig3YIQRMiMeEaXWt9frNbbye3h4KH30uDI3HkfGzvO/XC7jT/7kT+L4+Lg4NOZ4sVhEq9Uq2z0CRmknddnIGDoF+Ec/zs/PG4AcoOTsG/OeddKgl7HPAfM28N3pdGIwGMTOzk4JGCAyVqtVWQNgB+3tAgH72BaPmQNhs9G02585EDZjiuzg5BlD7He+sC3IlscuYlNSEtHcRSKDbu5jjni/ZdzgmyuXiBEU4ddWq1Up7WEuzB7bFvpym5hbgKdtnm24n+VnZgbQRFMO0ngW7fPWn3zPgBm7TAaDv2Vf7YCXHW1gXQ2mTby4TZ7HD135820HlRkU227RZvsgZyPtzx18ZJtrn2SZy2OLPG+TAbc5IhrzhE9hDvJ2vrwDQI1OuM9eV7VcLsv4M4boa963nXba98HEs3g3Z4rquv4rban3V/n8v8rXDz78xWlZFMBRokGpmSUcVI72uR/AFhEN4APYwJBZ6CKatWV27FmRslJxL9+z44t4H2DzLke2fJcx8O/ci9DiOHDumR3GGfH9zFLZADoC93McJdMGt9XKZsfAuOI8MZK8j7IS7vEYml1yOivPD+8lgjYDYybPz8yOMIO8h4eHxhHLALJsQBh3tmfDgSDL/P727dtYr9cxHA4bpTq0BRkx6wyQ8d7EBCsul/A/s/oOEHAWMIjo2Gg0Kqlo77XMkdkeKzPe1hPmNmduMK4ApCxDnhPm1Wlms1aWLS7e7yApB1pHR0fx8uXLuLy8LBkJavSdhTGgyoEVbfBWU1VVxc3NTZydnTWyNHd3d7G3txej0ajsie7nMX6j0SiePXtWQPirV6+K8+FgmdlsVvZ8vrm5KWy8x8FMm8cU/cigjzmxLiAf3mknzyfBAGNSVVWjNhPb47nKtiyzvgYH2aZybQMUvO9DhEQOFlzfamBO/5gTg1ODK78DkMD4ov8+MdLg3f3luV6Q5sVhua+2icyTdcikhLM0vAuwZd3x87b5F/4G65l1zrpptjKTLL68W4/9LxsHmOBYr9eN8Xemst1ul/FyyaEvfAlzZx1gziKambA8Hp6zbJ8YbwdZOYtmUgMZy9kw+w/bnryTCm3OGQb65mCY9rHwcTAYlL3wMz5C11nf4OyTQb/LSdhdyLY8BwqMPeM1mUxKv+g/fsVb5WI7P13brx9U/oGgZcY6s7cWLn9mxoPJtUI4TW7lyTWfZjLMcGZmHINmVoPLCgML6agcgc3MvK+sqPzN4MmRM06RMczPMOvGeDki9ftzQMI8ZEeGgmI0dnZ2isPIkX9mvjO77uDE76zrJkPoNhr42wjyjsxQ22lhqBxY8S4WhplB2gb06XO/3y97udJOANBsNisH2QC07RBzyQvyRIqcZ+EY8kIraryR77y7gvWk2+2W70REnJycFOaRNjkVmGXHjLiZD8Yjy4ZlE/mhvzl1nxlUj3N22JYjB70O0hw4vnv3LqbTaWmfg1HbHIMOxonv8DxqOVutVgM4X19fF0c2Go1KnXXe9hCQsFwu45tvvinvICB78uRJjEajeHh4iNPT07i+vm4wpPTXem6dzeNihomsjHUCO4JsuoSj03nc/5xt4JAFZC4iSr00gQpjZUYPXXPK1xkC7rf82ybYdmW2exuYNrAErHG/CQOXMWUwa8Jhm+zb7jiYYy64D3n1nsvIFsDQ82TQilzzd4OrnM2izbYBDnos1xlEGijx/rwQj4t7vUUelzOS3OO2eg4IcCKi7E/tTJhJGffZdg1AZ1nG19uGZRIm+5LsEyxHnk/LAlkp5g6fnLM1Bqvc61IJX4wXWSCDbcbF7eX/zJtlYDgcFh+SA1kHX7DVEBpeC+XsgvdLp195/HMQkuu6fTAY9swHy2zLNH26Hq8fdKKiJ8iRHP83+Mh1txHNhWgWHj6LaO4D6mg/opkmJ/IHELsoPwuw2+v+IFyZcc9pezPNNj7bQKSNe05jYhDsOBytZ4PPtc2ImCFxjbtXTfNdALLHxJkEnpvf86GaUgcxmbXiHke2tNMA0EYkywBg2HNC+6hbtYHPDtoGfW9vL7rdbgGmjD/brM3n82KUOC0SOcco0R4DjFar9V5KHR0xIwubnNkqxi7LGCCCxWUw1wbNXE7dE4AZqOU59tgAoByIIuPoEv0xi0i7HWD7b75szNEJs0HuB7unAMwo/aB9ODEHcn4fgaKdUcTjAlG2iWKejo+PYzweF0eEjDJmOzs7ZceQ09PTRh85odHsdq/XawSKpMCR9QzKDK6rqipHnFPC5Hnb29srByIB+JEr3u1Frci8a+8Xi0XRmaqqyqFTJijoC04U/XUGj/GxzHp+mR9qvQHKzLnLObg8tmYbDR632SjAhp/jNnnMGTfa4Pdgg7B1fj7zkOfN9o/nMJcZYDtY8zMyC2sgRbvtIxmHHEznQCP71YhNJg5bZtKGIN1+lu/aTmMjbRNtcx8eHsp8G+g6eIPQQL9NLphd9lj4H+0y6M6BDeNjG+4Agfa6NCgiSnYQuXXplOcuk2hcJh2RgzwfllFnXbJ+sBDdxKD7ho6xKwsytru7W9aNWE7ALPgRVxtg3+23Z7NZYcnxXeAkFkVuuzIW+LFdH334CwqEgtsZm8nyRvI5+sssjVMjfGYGxmnUzGzjGCj6N/jKk5tZADMaCBzg1GUtEZuaSDMQGaBHbAA1/88KmdNRZpQzW7ttvPJ8RLzPzJuNcZ/d3jw+fraNB+UNGeC7/34O/aFvGLVsjM0+2+FYrtx2ZIKFHYBpHL7ZReSD57J9oWtWq+rxlMiqqhq1tPv7+zEcDhspMPoEe2fDc3Nz02DnvAMHcwIA8jORuYhNvZvTv+12uwD+iCh11a67yyvkuY8+5kAvB1J81wwT8+Ljinmu2TvLQwYTlgWzIRhyywj3UqdJWpkgweUu2BTaaR2gD8hYxGbHEJhZAp66rsu+47PZLPb29uLm5ia+/fbbuLu7i4ODg+j3+0V+2K3F/SJL4lpvyASCSAfVzBU2it8jNttg0Zcc3HQ6nTg5OYlut9s4FImskhcrMhbr9bohH7ZlvV4vut1ug6TAYVLPzl60HnPXmnrsbQ8MbswwG6DRRgfZGTj7OchgxPvMus838H72fNe6gJ0w4UN78vOtC2biI97PhNmmMgbIMeOGTdhWyrCNKPFPPmPMuKzj1gP0jHexC0fOfDiLZvIrL0J00J+DDTO5DoSsp3ncbIdMnHHZlmNHCSYzS59lzuNWVZusN+/nXRlTWM6wPQDe7PN9ZQzAOBmMYwfpN+RWJjmYI97lOSH48AmoERGTyaTIFnYAlppxwHaRoUXX6R/gPWKzDzcZG4It5gJ/Z538dDWvjx4ZwIVrqrzqG4eMccIQI2g2FAg0zgMGGADiBV0RTRDuE4NwAnasFvRtQNJG0XtP2mhbYQzYfGVggCNzxJnHzwysjThtcsSa3+e+ZWNkkGQgbABkBoV+5cAjR8IGvGZkDRjtYJyy5x1mv5lXA2iDCKeNOc2LrcqIwM02eH64YIMxNGZ52u3Ho8arqioLvAAU7GNsEMq4cKAH/cYQ8n6AH4Ee9xmQmV3JjgFn5vbe3983Aku3CVYTRsULhS3ffMerzAF+AHI7PLNizAvzZNCfA1QcnjNLgGmcFgDUgSWOgH2VkVHLD7+bNTRT6mwFVwYEyAjzw7heXV3FN998E4vFIvb29uLo6Ci63W5MJpPSpgxyzXJa7hgjnu/5yOCSfjkoWiwWUVWPx6cD3Pv9flRVFdfX13Fzc1PsLTvYMB5mk4bDYSMLgXxRB75YLBr7pB8fH0en0yl1lXVdNw6usZ3Zxroa8Lg0wEGpbUG2jZYTQI9JA4Na7s9BA8+1bXXpRQbMZAbom+1ptsUEPf6+20Z/HEy4LQSkAGOeb70zQMyBqsdoG5h2NhT5xAZCEAFQXd7o59g2mHywz7B+eb9uy4fba5+UgwePmfvt91pWzODn59i3m0H3nPEu+zT33XPC+z7kizO4xzfC+uZgH9nm8nggm9hnTng17omIUpbBc1gTYjuzWq3KVpjMI4QCGR3ey/uQCRjpXq9XbGqn83hQWa/XKwTFarUqe/t/6Mq45cd0ffRCRQwDESBKxUIGgEV2ZBFNBtSLIGwUHV1hGLLCIkhMMqDC0bxBgEEMz8Dw4Lhs9DPw3GboDCAimtuZ2dAQAFiJeYaZAoNJFDEzTLzPjINZAZer8H4bCAN+L6IzaMAQ0u6ITd2YwSZOxswF76EfmQW1sXYaFiaHZ7PQq9PpxOXlZWGSPVZ+pseIdsKIcgHoANnUNpOi5+AHnolM+Mhvsx68yzLNPr/+zHNudpjxZuyWy8ct3gCYGXzYwNJnyz7tNojkftqQFxdjyC3zlsu8gMgyYjbY78o6Y2flhZkZkOWFq67jtaMzaOEe2m82mppRwCTzR9A3GAzi6OioBGp5nmBrCO4t49yTwSWsTsQm6HBtL23xeHpu+/1+9Pv98r7Ly8tSrnV2dlbe0+/34+TkJOr6kfkmsCLtfnJyEk+fPo3lcrO7iYMQAlPAMuzz5eVlzOfz0ibbaYMZgyGTBNmROgDLIMLrV/xM5I3vGxhYxvlH27ax8mbkDFIYhyyjGdB5lwb3wVsp8hljYZYzr2nIwadlCZlHP7yY0m20L8Seocsui+S5ZM7sDwFS2Jdc1sV3zWbabiHXLEQ0sMS2247grzzmzriRHYFAYB4Ya8gFg3LLAxcZLfqEDTCgNdD2OGRAzRggDx53ACjziz/BnzJm9MF2Kq/7sf3ifsq/TFpUVVVq/dvtx3JF5t82kdIu6xO/m7SkDyxyZf69DR+2inewuxbj+Onafv0gUB2xUQrqFBGoiA0TGbFhPlF6LoOrvLUWEbajwXa7XVLBCB/AKIOqzDBmYI6S2bAY6Pi7+cqfGcQRbGTw7pQLimRFN4hyOzPoAUxaYfM/G0G3w4DWl8E27XF2gXsciTtNhUJyQtOHZIY+MK+ubWYsyDisVquyz6+P8kbG3A/G1IacdsNIsP0f98xms0ag0O12y8prz4PZMuSc+8zy5GApYpP9YL7sdBhPj9dwOCzPovTCc2cmArnF8AKWLa+WeQe4OWBjTpAZ3rdNb/hbTtnmOfG88NMyZRbJIASbQnsBtBnkWLbcbgMWM8J89sUXX5QsxYsXLyLiMX16dXX1njOiTRlM+72MCXNM9o6gf5sOWI/cduwaQSUr/Unj06fj4+N4+vRpRDweOBOxSe8eHh7GkydPYjwel3e0Wq24urqKxWJRGLD5fF7aCBAncKWNyIXLUiKiobfYYMbJgUu2ow68zI4xR9k2mkCAvDGZ4GCHvkwmk/J8gqLlcllsid/lwNxyw/N5lwMm5hLAi4zyLPb2JkuVCRDaYL3GplHvzHz6ynrN3wjiAVgAJOuvM8m2V15EjRxm/bX/NTlDG3mut5z0mDogog+0yWSB/b/nIBNH/sw+3H/HljGGzIftcLYb27ImMLS5LQbOYBpnJhgb2z2TT7afjDfPZIy9boTAgqACufJ6CGfQwVT2CYxHq/VYzjmbzcr8uP795uYmFotFOacBZrzb7cZisWjgirxm69O1uX5QYQyCyUpyDD5/N+jIINx1pB9iylB4FBIBQom8sjizJAivWQErUMSGiXSqj7/z3Aw+G4OmFDaOACdh0OSUix2L22TwCsvlqJUxtEE0oHHf3S6z1DgSDLx/z4AQ4+zo3M/kM+YeEME823EwP/QdufA/DAesGmlpG3qYBxsPxtiMIG0zmEPuIqK8w4w9+zybofH4OD3c7XaLXGJEDXb5LgAAXXAtL3IGGGZHErOKsC12oGZFeRb955m0Hz3jc8sbOofhNtix8zZ75/HOjI9rbHO5gwGMsx6WD/QF1sR70mNjeD76wvhnPbf8eW4AIM+ePYujo6M4ODgoWRACGLM9jF3WA48lTsl1jwBAL0LN2Z/s/JBR3okDXK/XZZHkmzdvSh9o9+HhYZycnBR7ulwu4/nz59HtdksQWtd1XF1dxcXFRbTb7ZhOpwUItFqtsj7BYIhyJvpkfc6ZL8CZ6y3ph+s/rTMOxM3k2s44qPR4MQcEydyHjJggMaBFZiyzvNdZGf6GrlgXLE/2Jc7Ckj1xwGRdsAzzXZjaiGj40m0+i++ZzV4sFo0xRp5Wq8ft2pgT+wLLq/vk/hoU28+hmwR89s3O5KHbtgn8nbGkn8xbr9crGRcDXvSJoM9ttw20DWAcbIstRzwDXTCGcdDvgB1sgi5s+xydwI67PNKBJm2IiPd2HSIwIOiFOcbHuBQJMoDfbU+cWaadHO6Ez2au5/N5GW/wDEQm8kzZWCZIt12Wpx/b9dGgGqCB8cxGOCIaymLQweV6aRxUxIYhdfoEZwzbkAEjv9voIuS0IYN+M6e0LbO79MORLX/jvVwGvgBAQB7vzwYAh7rNKNMms5IZ7DMWbhNzkMsBPC6MdwZt7jNGz3/ne3lMvR9vHiP+5oCH/lE7SrQNI839Nujup427ZYA5IAgjq4EDZvw99pRb4Mh5rp0/88j7vCepa02ZYwdAZs0jNluaVVVVdnIwO2E5MZMNK5CP+eW92YHbyBtw8h6ct9O++fvuiz/L7zAbZQeJQbc8mYHJwbRZOxv2LPO8h3eYZUJnWJCDY4vYbElIEGj2C5CC7FimaSdHefMd2ymcIDtwcHBCVW12pqAkzu01OwVTPZ/Po67rsusL7ad+mnIlxoqUOSeAnp+fl5pKDgwysGE+0TUHnYx7u/1Yrw0Yc7Bv3UGPaTdy4P3snVWJiEbg66AkYhOE2/YYBBmcuqzFKX+DGPcX2eHvWW/NXmd5h4ThbwQNtqWw1CZV0AXsAGVGrnPmeT4Bk+/Rf/wJQAdAxGJSdMcBXkTEfD4vOoT8mdm1fWecMqGEzeZ+yur4aQDu4HTbnG0LRFqtx9p25GkymTRKvyi/4yQ/B3q0DT2yLhkAM262yyYljCHwkZY79Jx3mcE2KWCgTVkpcsmzmEf+b7KFMbD8Z/LEttW+yPLs0jhs1Wq1KjLmwA9bvL+/X+ay0+kUhtrvYc0JGwZ8urZfP6j8I+9aYKPjew10LUiZ8WLi7eTtqM2mmW3NdXIGEdyLovgZZg2sPDaqGIysiHzP4BfBt5NH0b2dFA4jMxA24nl7OH9vG7C3Id3GsDtwcETsObDSAtAcOWMgmB++Y5advzkQMai0ASM1y4b23tSed5qRRQYyYKS9yAPjb4aMPTupBzMQcmobQ+NdD+7u7mI8Hpd23dzclO+YEaV9MBOuEbdRwjBzoRcAXQNSDD0OmzmwTuDALD8GLa65pPYaVo/3mAW2s8266/n9UI0x4+ugzbLg7Ryt/9xvndoGpnPfGXs7nJ2dnfjpT38aX3zxRdzd3cV8Po93797F69evYzqdxosXL8r35/N5TCaTsiDPgJJtqSyHBPbIgAED8+AabcbeQSKyFhGllIo94wHwrM+4ubkpDBKOkR1HHh4eCoDG1p2fn8dsNitpeX4y1jDEBoP0g3vQF0Aa/fPOAlzetquqHnfTARTSF8t6zmBiu5GnXN4TEWVPcPahR4YJgPAr6/W6lENgTxhjQI+DT2TJ5YSZnMkBvWXeDLqZQHQfe8YzCYzb7XbxCSw6I+AEqHqPcvpmHwAzbDLj9va2yK1LmeiXAxnru3UeILqNjDLY8haX+GvmxbpgH2Y7wVhifyDqZrNZGUP7WMiL/f39km1hzKkNpqyB8aNdtMV67L65vbZ72d/4b3lcnenk78i1MQR6nv2ag3yTaZlY5L1m8plv+sN4gCXyIVHIj8fectpqPa4N4p0QBBxsBUllcmDblfX4x3R9NFMN+LUzBkxYAA10bMC9WMq1gggaTp+FAAZQEVEcM4rsyUWAbdBtDHME6on373ZEfoaNZ0SzVMDOFuG0opl1daCQjZ0Bh+vN/W4bey4bjVzTzjudLfC7+b+Nn1mLbcyD+2Yj6XcAcBkLgCfsGgx1XW/2wrbB43tupwMJZyW8cATDMR6PI+IxwvaewZTl+EhXxtOpYnYIASRYBjHavvI2TWbBHHB555q6rkstqPvJOOBgLR+WS8bB6V+DUJ80aSDjwBMZcyBpEOwxgNGwc0GPretZvml7Zh6ZOwCp9cGgj+9vY27sAHd2dmI+n8df/MVflL3Ju91uDIfD0l8Hg/THW7N5lxkDbdpAuw3wWq1WcebUHWe5JcihPMMXhz9gI9vtx8VIZBUACwC2q6urIic7Oztxc3NTDoNB1tA/2u1xo83eapLV/2zT1ev1Yr1el3KZqqoaQBrWs9VqxWAwKIEfz7cdcqbC8+XPrDuMAYw7MmpmlEVTZJIAq8hjXdeNU0npPxkj5AfiwG2iD7a11jHmlJ8AO2eTmEtsqWtjYfNh+k3osKtDVVVFvtBPvwO5ICvHdovYLWylfZb1y7aEezJRxXdgLtlmzfNrgEmpgkE7Y4YfAzSj7+v1uhz45OeyYI+6cfrrg3JydtC6CbPqsci2x0GUbRNjkHd9cb8IiiETjGcMZMFBJtgAr4zdarUqpTyQIMwZvpK+sHZjvV4XG5GJMsB1li3Gd3d3N66vrxvBRc60saCbthH8YHM+XduvH1RTbYAYEe8pWESUFKqjOUehuf7YRj5isxI6GzorME7d0a8Bl++1YCGQdtIZhBvkoHD8436DfvfL72acDDYdRDh6pt04LAycxzkbdCs8v/seg7G8oJT7bSgwXPyL2NQSb6t587wYKORoPGKz9/h6vS7powzCPDZ+to0P9yNTGC363uv14vDwsBhgA9OdnZ3SH+TSawNg97zYyGwN4xixSWNSIsKzAPGAoKOjo8IoREQxwtSQO4gzkGB/UACZsyD+DoyFA0ra6VIWs4o4Iz43q8Lv6/XmcAQMMWPOvbTRsp3lPKJ5qAjMEzLgUhDmM7PTyIEZT8sZMn53dxenp6fR6/Xi2bNnpY04iU6nE/P5vDDY0+m0yIHlyGwd73CAyy4Z6EVVPTK1sEMANxwgJ3kComgv8rper8vuQWQ3KNHodDqNoI7dceww3TYDBN6TbStslcdvtXo8DGk8HpcFjchh1jtnehhj5MblSs7CwMw6be15Jd28XC4LeLfts/7loMv2EHtBwFDXdaOG1EAKEGNA5UwLQMIAMwfA+D+XezDO2Im82xJ6TzADcFwul2U3DN4XESWwwU/khdWMnxev8V1KmtBn2o+fwn5hCxyYRzwGnoeHh2UMTVqZWXU9PXPv9SG0J++Qgs7QX3wo/WNhOTrF57ZDZAiddTA+sBxtCzLs/5FJ+2bu905BtIG+gAPoI/YOW4/MumSFMSTocnadgJaslUt46IczCAcHB2VBvWu/J5NJCTyxAdfX140Akaxaq9Uqe1EPh8O4ubmJi4uLxthkQuDT1bx+UE2163EimiDb4NAMnYXWKSwrh3/aqGV2i6icK0fdtJPvRERhUQweufJ3bTAR7pwWMjPK33i+o1Qrs5WPZzNmBjse021t8vN80S8bm/wcBxMGcvn5ZgIxKqTednd3o9frFSaBZ1XVY62wgSzv9HHJmVXA+HHl2kA7MsCdgbgZYMCLAxNKV2DuXIMHi0s/uM8sFP13mtoAkPd7UQlsGwzodDothqvb7TZO/rQM3d/fF/bBzI777/myw0Iu3R8cjt+VS4wYE77nVGHExikDwvjcwUjEBuxnGfLvMK4EGPTPzEceVwd5DmoBlLZHrdbjHs0vX76MVqsVk8mklHjUdV32nna96c7OTil72N3dLacvujzDMgkI8hia6c7OHEDECYxurwGzgxiPKVfO+llmkGXbTNsnnre3t1dYd1jziMeac4KCm5ubODs7i9vb2zJuOGX6ZkayqjY7UVAXen5+/h6wQg79PeQKnUX2vViUOlZnzaz/Bj2ZFECWIqLhM3i3SwQ8pgYcthe+j/ev15vSJ7OS9J9gw8ALHTIQMzkFiHSpkX0JINk6Vtd1AVXeTQT98s4gJiNYAOsSDmQNsAYT62wEQSyBjseRtQS0waVHEZtdOpBJAi4ymQDvVqtV/s/BSTmjxuX3exxd6uC5s8/xM5APB9e0A7DuOWHeyJaYWDB5SLuQZ4JT2zkCBvvt29vbRoa+3W43Fm2S1cG3M6+MEzpsAhG7x5jRHvbl393djbdv3xY/aiLg4ODgPezhK+OrH9v10TXVPvTF6T8LGYbk4OCgCKMNQUS8Z/Tb7eaqeQNZp/VxGrwbwTJblSc0M6YGlryfK0esZlzNbmIYze7aEPNeP3ObsH2oDQbkBrw50OBzXygMRgoDmscMZfb48Q5vm5cBTo6oqTWmrg1nAhibz+cFvNJWxtNBhg2fxwsZo62t1maBLO3a29srq+h5bgYSyBagPKJ5ep1Z6TxPyDHzvG1/UFJug8GgMCpmtm9vb8tBHswH/eQ53pMbWcMJ0GYMvw2yn8Ecw3jDZNohWf5cw50DMQC3ZaDV2ux17HnJQZrT/cwHi+hyZsJzayAZ0dy/107BF+3b29sr20Hd3NyUA1PMwvFMfgcQwiAyDsgJrJ/LeyxfyBWBghe9khVhL2mDPvrlQIb5JXg0ExaxIQfMugJueP9oNGrUYXpsIx6Z7tFoVILNk5OTGAwGcXt7G+fn53F1dVVKkobDYQkCqdcmEAN0jcfjwjI7GOn3+/Hw8FBkhZp0O3wCNQJ2jw/yTJkAfzebyDgYKJK1Qa4BIIwD44Kc58wBF/ppfTBQR455P/Jkeaa8iAWjOXNigqiu65KtIJPgDJT3nCZYweZ53UNVVWX+0CXbFGfqvP2jf+fdzAklJ671dRaDcXh4eIjZbNaQP3TFemTcsFo9HlhirJB1HP/t8XZ/zVijP5mRtq/hu8z5NgZ7G/6wbXIQ7fv5vjMcOQAzXmIsPIfMgf0RY01A7wATIgsbOJlMGj7LcwRwBicQ2Ds7tFwui+zax9C23waqf+zXRzPVVhBHhURfGDOU08yK03kRG4XIDoTPuPw32A4rlZ0lipIZabPq2y7fa6XF6Ob0MAKOQtohAKQyc2+F9RhyWckMqP15HkszD3wHEOUI2kAAZ4WiMWcRG4YCw08wlAGxo3c7ShQP0Htzc1OOec6OzQysA5eITYSPEcaZmqGnX4PBoMgP/WLcYYs9hjgy98kslcfagRH3mAm/vLyM1WoV/X6/OET2DzYwqKqqAHsHNvQH0IJhs1HNbL7lzu3LW5EhH2aseJ4ZFINxng07Cag2kOF7jKvn1Y7PLJKDi1wqZkC0bScZMzSMe2Y7zU7e398XphT57Ha7ZUsoL4odj8fF0bkchvkxGM3lEgYmjDXAlnth/7ANMHiMhbMC6/W6YTc9xwZWdnzs+uHyD94PI41Me87ZPYMa2fPz8zg/Py8lSTBS2HWcuUtI+v1+PH36tAC/iMeDaQANBBpe2I5s+FAP5BM74jEzm2t9sY004PIc8H7rttliQGL2E36H5TPbBdoHkMFuev0GNvT4+DgiNus7yI7gWyg7MxjMh/PY7hLQoGOUl5H6xwdERAFbgOnDw8PGUdSQL8gPMulSBxNRZFQI2M3cMnfIGjai3++XoJo2GlTzHPsk8AD+yQERMs/4mqAz8EZnXCpIP7OPdbBvv2R/hR5iv0wc8lyC6OzbLVfYLMYbOWTdig/1oewF30qGmHudmQJ8c1G3zfen02khRiFsXDJIv9DDTFDVdV22pf1t148ZdP+g3T9wJI6ouHy+fETTIDnCcXQMI5RBm40W7+Y7/pkBs8HxNiOMchmg8f3MkgIqzaoajJndQyEzy+yoPLPWfn9mR/PfbCx4Vq5vMvBCUQhyOJZ3Op027jUYci1tXdclnes2YBTYUYP6T1aO20C4DIR2O1CgHcwJbY/YyJKBhYM42HF+x1jwTAyv2Vae4Xrl3DcHB8gF8upaNdgVwAsOBdkiFcj4Wxb5yXdwthh2nuEMDu1BZxxMuQaT8XMAan3jyjLM5UyGwZ0zU9Y5s4eZ/fG42FFl2TPrznOyrBh88TvfxeY4GNzf34/RaBQRG6ZuMpkUufa2eU7H0y7rD2Cd+UTWacv9/X1xdjik1WpV5g55Xq1WJR0bsTlqmMDVOuggD4Bi9hAQti1Ydb2zGULuxT4sFouYTCaF+dzd3Y3hcBh1/cjwTiaTkl4GPFdVFePxuBygM5vNSrCCXtFP0soRm+3AHETbNgJKrZeWQfQ6XwbUthOZvUWX0BcDasbTPoX2WX+QU+8kxKJndBCgypwwt9fX12XBH2NPm8iceGElZRBmk/mMmn7sY7vdbpyEx1XXj/tgE/BvI2BMQpAZcCkFz0E/DagtcwQzZvPdHmTbvsnZXuwDOkSWKGeuTarx06WXzHcuW4BA8PfQc/t2LvpHRie/h89cYkIQga54nBlD9D0vfocdPjg4aGytyQLiTqdTaqsBvgDm4XBY+sfn3i6WOcA/oYu2684eozvMhQMd5vJflauqqsOI+N9GxF+PiDoi/gcR8ScR8X+OiJ9FxK8j4t+s6/qyeuzA342I/15ELCLi367r+h99/5x/KyL+Z98/9n9V1/V/8kPa81Gg2mwbqW4MOJPk1cMo+nK5LGkqO10D1Yhmuh9my+lLG2IDAiumWS9fFnB+pz02pLQdBbCxRrERWr/HgUEGKDktZOBpBtBgOUfRZgJ8P4qTAw6nIlF+jJjZEJwlRoIxYQ9ntsl6+/Zt6Q9pP/4B3mA2MnD0ePOMPE7ZWQKUIqKs8ieCR9ZY5d5ut8tWeDBvef4xhoAHnyTFZXn0fNipATQAW/v7+4UJYl5dp4mTNDi0Q2EvYYwkRhlnlEs8rDdmSKwfboPZGcuX6ww/JGuWXQMg2oKOuH1mDc2oZjbRTLCDVu7LTIf1F/YZIEZQxdxGbBZy+TROGNLMvngrM4M6xsA2xuCUoH+xWJQa7d3d3QJOe71eGS+co3VkMpkUW/Lw8FCYYcCz342s0A7kxLttIMMuabKjNHCKiPfIj4ODgwbT2G63YzAYNIB4t9uNly9flgD9u+++i7Ozs7i/vy9jjq54ThlnbJjljrFEjlyOxBwb5HJ9KNNnWUZ2MitneWVe0TnAFXKIfNk++nf6Y92x3SO1vlqtGqVy9kHsiFRVVZEl5NzstPsHUEIvycZQitNuPy6iXSwWcXp6Gvf39+VwIGSHthMQIWuMxTbf4l0uaB9bOxKAeAyQId5h4sJ6ZZvL5SAmB9qW8UyIoRfIlHGH+2KZcvaWi76YHOP7+Bnk3TubWd5MeNiPY7NoHwESC5F5HydmmmFmHtxHypxcmkgbzXCbFGE+rH/2n7Do+DFnP/4Vuv5uRPw/6rr+71dVtRsR3Yj4n0bE/6uu6/+wqqp/PyL+/Yj49yLivxsRv/v9vz+MiP9NRPxhVVVHEfE/j4h/LR6B+T+squo/rev68mMb89HlHzAYAA07VVLYnc7m4JOIzdG2NmAs9jEI4MLAYaQdSdMGmC872wyKfL/vwZj6XSi4U1kRTaVzwAAoQfCtXHaG28bPhsRME0DEDIDHJI8BbXe6Oo8hl1kZG2qPmRlaxuT6+rowbgBqK3MGWbQxBxPcQ9sYLwMyGxjAgdN/gFhSWrAe9/f3Jfo3mDagIACwU/W4mx228cTJsmUZu0WQdge0wdTRLmTcbC2GHUMFgEEOvBjMWYmIZh019/IMAzz/zWOLbBowet5y4MdP2m3GMI+bn4dc5HYz1wYsPG9biRLPt/7hpKxvODLqBA3kWNjkPvEZ8sRiPIM95IjxNADk7/yedyPg2dgumClS/dRuUyJE4MSuNN7WivpkjlGnBpd5BXjxjswwmUk30PDBSOw/a+aRBb9s1Ud/O53H+uuIiDdv3sTFxUVJ5/MeZNOBOgwsbYapdQ0nwSv6TLBfVVVhWQl6rLv837bOwIefJkXMWHpe+TsyCBgHBAMMOfyCPnnnn6qqSn07qXtkjdp09tFGJ7wrzGQyKePEhX1Zr9flECDIFMAYAfzz588bbZ1Op3F5eVnG1AEA7+Be+1MH7Nvag/45E4le2k+t1+tS/mc7wzijj3zuvdBpixld3uNysEzcGJz7GYyL/T3y4V2MkHV02jYOmwS4xZ5nVj0H4dsIJJMxtJVyJWrubV/Iitgfo88ELH6X/TQZZcaF8hK3y0ELf7ffwD7s7++/t4NPvjJJ9v+rq6qqUUT8NyPi3/7+vfcRcV9V1b8REf+t72/7TyLiP49HUP1vRMT/vn5s4N+rquqwqqrPvr/3P6vr+uL75/5nEfHfiYj/48e26aOZavZ8ZeBhVJhAnxrnlDtKbUBlQc5O2WxFxCZiysrPd7eliyPer/dE+G1wt7Eg21gRR34GBvTfBsjA2OycgbgNI2zytrSbAbUVHOX1/PhCwRlPApiIzbZaADnmyEwe70DJIzYgwp+ZmeA9GEn3gTa6xtlskR0lzA3OlbQYRoNFFGa5aMtyuSzpMrMQjImNaQbh6/XjIRJOHS4Wi7i+vo6IKO1Yr9dxcXHx3k4nzKXH1u9FNpBl5MTrD2zQ+a4Zl+wcvRqfvwFQLKd2GtnJeSw9p2ZqrBc5gOOn+2z9dABl0GPd5e/I7DbW0WwmoM2LJgEwyCCBV7v9WFPKfN3d3ZVdPtrtdmGJaUtmb9wWzyFjS9v8frNegCkyOrBZLARkgeVqtYrT09M4OzsrzJODSusaYI53sCjZ/fDhJrDlgJarq6u4vLws+s04oGfI9c7OTglm5/N5XF5eliDTNg+AGPEY5KzX6xgMBmXxJ+NhdizLfvYbnc7jrinebzjrrsGO7SZzQsAOQLAuRkTRd2dzXIOL3eQ72BYCJGwnz/OOJt4GDrnFxvOsh4eHresC2Lv85uam+F6TVS69PDg4iGfPnsVyuYzr6+tCILB3ufU/Ihpb/OUA20E4fcf+2y5lW+DAlc9brVYJWvkc3Y2Ixsl8kCb2bTlDxnOx7dgw5jnrgLGJF+0hDw64aKNZWo+JM2jWPS8S5m/og/UN7OJdRHiPiTiwgUt/8LnIB7rpsjeCVZOU3hrW+oaMORD3HFpWGRvG+F8yS31SVdU/0O//UV3X/5F+/69FxGlE/O+qqvqvR8Q/jIj/SUQ8q+v69ff3vImIZ9////OIeKXvf/P93z7094++PhpUU+7htCUCR21pxIaZQgHMSmFQmCwrjBU2YrPjSN6CzEqawaTBgo2wI0g+w0nayGewaoDtvtj5ZtDsPhh84nANdCOatW1ui/to9jffYyDj7IH3BcYJWMFQfDtw2oZCYVRhjrIBwWgRPXueaDtjhoPOdazIlRc1ZZbHtaIouB0RaUzXUcOuM3bbgizuMQjC0XEqH+xdxKMjuLq6KkwifXDQmJlmZB5nB6jGgbm/+TKwc2DkINPyzDMw7qSf6bdlLL8nl6tYHy23+btZbuyoABkGRTzTqUj/zf83M2Tdoj0sgvIuEQYMvMPlSowfAaUXtHHxPjt4B8gGB7C12EScp0/38+EPz549i+Pj48YuO2dnZ3F9fd1YnW+b4bHDDvFOgzbblYjNYt3BYFBSuF9//XUBX/SV9gAEYUZHo1EDtDrwns1mjcB7tVoV+YNVMxmBfjqDA5PvA3YIem5vb0v7mW+z2yY2LHOMM89D7rGjyLvZSMaBZ9IP5KPV2hwSBHtMsGbm2UwgfoR3wW5HRGM/YPpObT4LWjkkZrVaNRYVwhgynxzmwS4zyJ/tPe0zKMJuMkaMI4Ar+0+XGxEIWK895oA5/41n5SyQn4U92N/ff4+wQt59YW+xEfhEgChz4eALDOPsoW2n+5KZ623Bfi6VwTZgKy13yL9LNx3ImcQiiILsiYhSb81FFgs/bllkrquqahzc48yp++TggnnZ1l7b7Hxt8y3/Ja6zuq7/td/yeSci/hsR8e/Wdf1fVFX1d+Ox1MPtqauq+pe2x99HgWqck3e2MJMc0dwH1Q4york1jQfd4BEmg+cBqLcxZHzXTNG2y5Nsw5sdUgbJ3G9lM1A2IHafDKZol//Gs9xPv5d35nZsm48cxcOQsBr8/v7x+FfSw6R+W63HFemj0SguLi7KwpvhcFjuub6+biw+wUm6vWYwqBd1u5lbA2GnoTB6KLjZafYv9Sp3ZxXMjDjSh0kGmBjsG4zTPhg2jPx8Pi8gDEZjMBhExKNBOz8/L8bLMsjYmyUxU+3gkHnD+OIgLT+WAzspyxvPceoe+QRE8kzLLPcwFnaSed5saO2YshMxkxOxOY2Md+Ugw+wkY+S2uZyDvgMaGTOYPMAaz9vf32/USDrIx+GuVqsCxFlDkDMZlnX3geAjYrOrhhkds4mAGz776U9/Gp9//nksl8u4uLiI6+vruLi4iMvLyyJvBuzogwEb8+1SBubRdmA0GsXx8XGR33fv3sWrV6/i8vKyAMV+vx/Pnz+PJ0+elBP7FotFfPHFF40FWdSOEyww7g4iLQOweIBRExqWR3TDc13XdTkNM2cyDAI8X3ye9Qe2HoCGTeDCn7HgEPlg8aHBuFl3B26Z8TNwRb4AwzD+yD9jyu40tIW97b2QEdk+OjoqpUHZHuSsmMcqIhrBabYtPAN9QuchGGxz7bc9p9v8V14ky2f0jXFEtg3csFF7e3tl/YnJCz43QDUewRbYh3oOGVO+Z2AN0WMiCptqXAJ4JvCz3cNG8JP2OktGNgJiAr3BV7IegwtyxjubTKfTQjIgfzkD4+ASXWD8Pa7IbVVt9sG2XmYc9v/H65uI+Kau6//i+9//r/EIqt9WVfVZXdevq8fyjnfff/5tRLzU97/4/m/fxqZchL//5z+kQT/o8BcU0Oyfo3AUyilaBC5i+4puHJPZnszE+bKQcBlcOs3DZRBixskOykoa8X5ggHDZiGXgm0syHPHn/iHINn5ZYLlnG+g3oKNNODMW7/X7/Xj27FlJNR4fH5fyCuoiAc0+8pY2YiSurq7KIhocIXPPWJmNcP+Zb+SEdmLgLQs87+DgoLH7CIAcEO5dN/jc9XgYQBsj5pC24GAfHh5KnSu1uJ1Op+z5S39h9wyMbQzdV+aVFdsOQAh8zAwbPGSdsaxmEGF2DjDqBTI4b4MSmEMHs7TDc2iA5J9cyLOfY/l3cJEDIgfBmd0AiFkWABY4GuoEkV2nxr0XrGWFzIPBEo6dTILbaVnK5Txc6K6DdDsql4mMx+MYj8cxm83izZs3MZvN4vLysrBPyARy75X+nm8D+jynh4eHMRqNYjAYxGg0KjWQ3377bXz11VfFgR8cHMR4PI7f+Z3fKSw24HA8Hr+XPbu7u2tkaBgXGFSno2kT5SCLxaLooYMTfAmgDd3w3F9dXRV54J2tVqvsLmKbYVnvdDrR6/ViOp02Frc660DwQXsB2LZfBNyuQabfACCTS/Yjnn8DUUBcXddlAdlisSilKJeXl43jxlerx0WilAhRZ4/NzfqDrdimxzzPdpPv2N8xzttKCWwH7OPcb+sjumbf6UCe4IEyqfV6c6AO8+HdnUxoEPzQZ2cgsBPeDxwbCV5BLtBbMqk8n5/8H93DJoA1LIOuh87jhHxC/rAIExBtkO4gyfbFGVZk0Gx6p9NprIfI9pl2OTDhvZkYwIaTPQE7/Ktw1XX9pqqqV1VV/bKu6z+JiH89Iv759//+rYj4D7//+X/7/iv/aUT8j6uq+j/F40LF6++B9/8zIv7XVVWNv7/vvx0R/8EPadMP2lKPSQFQAZ698BAjEvH+FnPZoGLkuPx8hM1OzO0xoHDdNs/mnQcHBw3jZ/bJYNuBgt+TWaltINfvdbRn4GmgZDCdgbR/t7H05yj1aDQqtZoAT4PZh4eHePv2bWF8p9NpMea0BSW+urqK9fpxQdX19XVZqHN1ddVYWOR+2LDnYIc2ElzZgfX7/Vgul2Xh63A4LEwNTn4b6x+xOVYVI0CNKuMFCwkIy4woMmUGGbCAM7Zx5shmnKsBNTLKZVaMGl4DM4IXg7SsG/5n2cIBZfkgAKJOc5tcmU0kmMEZuR1m8XzZ+frZlm3LrseP7xl08B2Dbjse2gjTyLaD25gkGER/F9lfr9dxeXnZcLw8lxSz22yQwGcuecm2gbE1iLdT8hZr9/f38ed//ucFMFBmwe5I6B5gxk4SWxsRjXrcVqtVWN3BYFBA6e7ubtze3sbFxUW8fv06Tk9PizPt9Xrx9OnT+MUvfhEnJydxe3tbgutOp1NsA4EGP9HPXq9X+s5COwA56XX66/3T2eXEdoBxx34hlwAb9JlxAExgJ8zGISsAYbZQ9Lwhb+wtz3h620cvBKadzDGBuEv5AG9uI5k97JP3KKeWONflwwZzoA4HZ/V6vTg6OirlQSabCKIjNrtVMKaZNFqtVo11STm4dZsYe1h7wFvWacbENhF7FdHciQpdMXPN9yE1Ija7fvgZyKaDN3w284Q+mLyyL86LyZk/ZNbb3Tm7RLu9XZ2DfPQdWbXPxic745r3em6320WmfVorY0fgwK5C+DbbecbeZS20kTE1bnNQZTlm3M2+m9TZVv6Xr7/s8/8vX/9uRPwfqsedP76KiH8nIloR8X+pqup/GBFfR8S/+f29//d43E7vz+NxS71/5/v2XlRV9b+MiL///X3/i/r7RYsfe300qCbaw4DBKJjpidjUcwKMbFwAB5m5xpBSFuHnZrbaYJb3Wbm57JDMrvAMvpsDAJTOjF4GE3bQGA07drOMpNIy+2gjZHbBfcQIAZZhWGivF/Lw7ohNSp5xcLqVZ1gxeadTaThvl6oYMPMd2sV7eQbGEQfDe7vdbvT7/bKqnfkBIACKbHh4n/fgtSwiX7SZnTrsBGDceA4BHOk2ajttXJy6w/H6MrDmfsbZThMQl7dfzPPGlVlRxsP6AqAwQ+35dGYAB2hHxdjbsQM6kJdtY+h+R2ycmVlbt53n+v/Ivne0yCxmVVVlsZXrIpFtGFAAWb/fL6UOBEnsw4yOtFqtAnCQd/rbbrcbO4zQJhxcDqgNpB3AZJCH7CN7pKRpl698xDQsHraW/rKQiVpo5gSwPp/PYzabxenpaQF8yODx8XG8ePEixuNxPDw8xMXFRRwcHJQAGiDNGMKou00Ejjc3N8VeAu7YFYfxHQ6HjR0xMknC79gEWDYALPKOrLHmgWycj2o2mMy2ju/xTuQKu50DPgeM6JKBCrLBwulOp1OyUjDwmeU2yIH95X58Kmw07P5kMinrXty/7GvMSDN+7geBmP2riSueZSCFjAPGbUeyzUAGCQjJ9kFK4MsYU+QfvYvYlBriM21X0A/6YiabMUVGeBZZgIjNgtpMgqH/dV2Xe3kvxBK1y7bXPiXUmVv0HGDrhfU8m9068BvOIjjgYNtWYyQHR/QdOfNYMgbgMewSesz/XS5ncI3cMha04V8yaP6tV13X/+943AovX//6lnvriPgffeA5/3FE/Mf/Zdvzgzh8BIoUJwLi9Iyj0bwwwrWj6/W6kU5nEmE3zRpnsGx2zUqRWbzMFtohelscAxEMQ0Q0nCltz9E+798GJjL4t8Ez87htZxP66j4ZGPFeIle/20ocsdlVgYiZFJjZQtoBSEFB6TtR8DZAnTMQBBkYncPDwwYwpCbM0W+/32/UWOe5sjPLc0SUfnt7G7PZ7L0ACkNBijVi42TY0oxxcLBxf39fgF0OolwPjVGj/bAOpLS3bZlnecv/N0uA0UV/rBfokEsazAo562OGi+fxzKxTZgczUDbQ5nk82wfdGJzbadC+Xq9XSh3Y3s7tda0ldc88hxpfHM54PI6Tk5O4v78vIITMCyCaPtOOiGgEbtYx2wXawlzYseb5cnAJ68U8wbTjwKi5ZpyxpZ5rA0YyO3Vdx5MnTwpYXSwWcXh4GHt7e3F+fh6TyaSUujDOBwcHZbcR7Ot8Po93794VQM32a7YxHO7ivlBC4+0CYX9vb2/LYktsyHQ6jcFgEFVVFbYbHfSBKJRXOaNoe+HA24Esc5bZTGrE82El2D1AK4szDT4ZAy+ezowkbUD33Ccz98wz9oc5Zc0LsoAeoGf0B6BuH+Sg32OArHHRVssh95HRM3i2TtsnZOCHj8e3Mi4GZV48Z7uEDqEL+XPmwhmLiCg7osAW83zX/meWmHGxjzfx477e3t5Gv98vARf9Rx4oq2i324UQ8noc3kkwxfPJ5sBYW99tq71Yn7Gk3ev1ulHG5rpz7vcmAJYH+1EHqA4ufEIuvtV222WUXnC77fpXCXT/y74+evcPjAFGHueWSys4+MBstBlGs04oJawEF04kg8wMpp3esZLQlvx9/55ZcAyK34ExsRG38fJngDHalsG0BdoAy/dkhQCA2xiakTezwHNsRDDM/GTOAIE4AAwsTg+wZmPrlLkX8pnto027u7txcHAQ/X6/7N6BLACIMJ6tVuu9kgvAYwZwDliQI/4OwGBeeJ7BMGy1QaBTs2YZAdSsqnctGtvrMUdmEw1KDIgdVORgKQcrziyYubAB596cLjdDbUeTATXOyAERsmQA6zbY0DKmrnkG/DC3OA8Dqb29vRiPx6WMYDKZxOXlZdl+C6CFzSD4dTqXMUdWAIWAG3ZRYP6ZN+SG3w2wsXHIgsGz+2xmNi+yi3h02sPhsFGeAqBerVYxnU6j3+8XnUE3SC8jS4BHxh227Pnz59Fut+P6+jqur69LMPr27duywBgg/PLly7IAOSLK4UWXl5fx5s2beHh4KNux7e/vR6/XKzI/mUyKXHlHCMYe8FBVVVnoCDPu7dKWy2WpjWasCaaYf8C0dd4XvoLPzC7jKyIeAX8u67D/YSx5l+fAttjgBBBp9tu+Ddtl30E/0LXRaFQCDEodkFUHYOif32cm1mDKmbMMzvjMARmfG2wi54ynn+VnEhhgXyAc7Hf4vttHu73BgW0d8gMoZJ4NHGkfMu0+sWYgA0VnOhhr27ycSTToNI5wgAX4hCjs9XqFxSbDRV8NQpkH4xwHIg5mdnd3G2ssfMGiQ2Aidwb29jXo1MHBQSPT4znI9o25pD3YN8bW9vTT9f71g7bUQ2hy+pmUYMSmJgoQ5lpsJokInIncxtg60o9o1nUiPAYwvgzADHT5LH/HBsRGnXsNLAwozKRkxURZXK5hEINzzkyD24Zw+52+/F3+j+JjRPr9fqmVhLnf2dmJXq8Xg8EgptNpYZ8wZAYKTiWikIBRDASggfaRyTDIYRUzgHy9ftzLttvtFuPgIIfLRtgMggMzG2GPK3PJ5xiVnZ2dUuphpp8xXa02iwzzwjbX/OHEzPryDINY5mgbE+QAinlm3DIgZC74HAaE5xooWC7MuGZw7z7zHYPIzAa6P71eLw4PD2O5XJZdJWyI3W6+40VXk8mkpPzrui6MKg7W8+B+YODpqxe0RUQDWDEG3v+c8UEOXePOsz1P1Pljw6hDBpRQK+zFb6xDsBzTB/TFbL6fQ3vI0FCPzDHGV1dXcXNzE8fHx/H06dM4OzuLi4uLuLm5icViEbu7u/HkyZM4Pj4uDN9sNiuL3G5vb6PT6cR4PI7d3d0YDAZxe3sbl5eXZQ9vM7CrVbN0w1mEL774ItrtdpyenhZbwjoHgmPYY3bZALjOZrPGYq0MCmazWZEzZ7hsdyOa9p45R8+dGWKcc8AK8LHcWi9dUgCwMOP48PBQbB6yhh68ePEiHh4e4vT0tDCd2EbLJrKBLTRZZR8D2US7mIeIzXoT5M07Zvhnlkn7LOt8u/1YvgOjbttm22W75gAjZ3xs88yGGuCanLI9AuTxfcaNd1q/mBP7JUo5sJcuY+IzSppoQ15wyfyTofSCad4bsQmAIjYgeDgcFvlstR7LDfE9tJ/1Ia1WqwSmljFsAgE3xImzX14X4SyqfRP9Z2woR8FurtfrxuJi29pPoPrD1w86UZGoLKK5XypCnp0RUZvTmuv1ugiM2UkziFx8J4NH3ptZWxTeKXy+Z9CSGWIEB+PgRSp+r99l8JEBlRkdnoExBvzBBrqswoZqW7DguciAvNV6rBcFRKO4KARt3N3djX6/H+12uxy9bbCMU8eAexEPSk07HYGbwXNQ4qNxmVsWXfAswCpj5wDGDKiDGQBXTmHagZKGxYgDXCKau9k43UodqUER/WZczfLiFDKgzkAY422wz/P8Hs+n5dNAgEAlp+v5PoYPneB924Iys3YOVM2Y8xljSH04RyFTw0uA7VppdBwHuFo91oxi7KuqKiDbh/wwZgQ2OC8zagZMpPx5LkCFPvA5uoZcY6fQE/oHiB2NRqXe9+3bt6W8BIftQACdAXCj88y9x58sHdmcnAmk5hZm7ObmJs7PzwvYGo/H8fz587i4uCjjye4eZAvJstR1Hefn53F+fh6r1eNuEicnJ/H8+fPY2dmJ8/PzUsdLuVav14uqquL169dxfX1dGOWIKAv9+v1+mXuA18nJSZkTQGan04mjo6MYDodR149bIc5ms9jd3Y3T09MC3igpmM/nDefNe73bCAskzVZz3Do2GVkGwNleYe94D6CFn9zjLCtEBYCorjd1uBHROBQG4PPu3bvG+pGI5s4t24CnM0wOeLNuY4u4qL2mP7SZd/E8M7TYfj6zT3GJhhl7j4cBOM8jmNpWgwsQzCSZ/QbPs99iHmlbtnn4n1zDbhvhdV/YM8aBeczscSa9IGmYU+51uR+64LElyOI74B3a4f5gK0yg2Lc5sHMAYIzh+TYhR3aF73pXrfv7+1IO5fZQN25G+0PXNmLsx3J9NKhmUojemNQ8wUw+LJprplxgj9IQZdm5W1h4Z0Sz/ANn6QuFRZjNEm8TLv8/s0YZoDEGWen4vplqAxEbqoiNo/FqZY9jTmdl4JwZxAyGYJloOzWTeYFjXddlNwqyDxgwdhBg1T/zQC0bz8HoYhRpI+3Jih0RJbUNmDbLwLxxL23GUDoSd2BG3y0TgIyIaJSy0B5vjs8cA4a8OI5AhHnyHCADMGdcZvt5lsG358KG10wAY2CWl34yX1kOLKPum8EcumWWzu3OQarB5tHRUQwGgxLMcJQ9Rt1zjaPJQed6vS6nVOaxzelFWNU8JgYKnlfLKnoIW4nDzUydZafT6cTTp0/jyZMnpV2r1Spms1l89913hQ3mucPhMI6OjoojooQKcEoA53kwaOb7tJU+ulQE5+vtHgeDQbx48SImk0lMp9MCMuu6jtFoVILp6XQanU6nsdhwMBiU9y6Xyzg7Oyvz1+12Y39/PwaDQamXRpcuLi4awIrAjrF4/vx5CUSc3fLiY9sJ7AABFbJuxpZxtK5l3QDA2TfYpsAUApAZe+QPu+P6bmdV8VvI2TaAS5vZLxg7kfe0N+A3KZR9jYE+47tNr7O+OIgwoLbMW94dcDuQJrA0Q+7LpE4meHgnY8bldQP2sbY5fo+JGteUMx/+P33B93ixKFkS5s5ZAnRxtdrsl869yIsXktIul6bSFzIug8Eg2u32e1tjMvaWI/t//E0G8/g+L66nr3xOW7aVl2Iz6rpuLJQ36QNpRTbZQSljnGX+0/X+9dG7fzj1Dvg7ODhoGDOMIikUR4aAGCY8YhM55igsC6yNqo2RyxIc8Xtlba7NtmD7vbltvNeG3s+L2BgOg/8MvFxLaiOQWUH643dnAGCmgPtQdrfJgAlwaTaaccW5UJ+GcrMQidrcbMxhDV0ewTMB3nYSjDXspo0t85ZlwoyDQZmdsp23WUzSr2QBuBeA6xpKOx+DG8BvXqXNe2ywvEAPg4Uho30wAgaXZq8sy25HZpjpFxfy5nv8GW01O0W/LV+eY2Te3z06OoqTk5PC0g6Hw1JrOZ1OG2U9BEyww8ynWUgfuGFdAygguzjTDKxywOpAyCdU4vCcYneZhoOhp0+fxs9+9rNShzifz2OxWMS7d+9iNpsV50JZ1dHRUVRVVeqmLV/oEnNOYIVcsYCQtjN/MMyz2awEFdhOgpAnT57E/f19XFxclDFdLpcxGo2KfrEzx2QyKbsEkYXqdrul7KbT6cTJyUkpudnZ2YnxeFwOpmCxIvpImng8HsdwOCxbvnkBII6ZrIG3pHQNKnNpO4K8uyTQWQ7rIAuPGVOzpbYVtI17M+BrtR4XQLrcgq1YySxiS9Bp5pI+0Oder9dYK5SBOLpm5tx+wqRNRDTK4tDJHGRar60zXNgaP9uBCDrhd2CzDP5sgz2emQBwPwFzVbXZb9qyTpv4fg4ykBOTbryP9tEfEz2Uc9juoYuQLfbtBwcHZc0QABZ5ZCy8jgN55n7mlFN4LWMGyrZz7gvzanBseTDQpu0mIkyOuMzNQTpzhkxm/8DnfI9+mNza5mfyfP9Yrx+0TzWXI20MiHf6MFvEZSUxULaRMLhx9GuH70n3TxTuQ2DaCpxLSsxgAp54rgU2A323z0JMOwwG6RPG1MA79zELbo5gDUTManMvi52ITA3YDcpJeQ0Gg6JApIAMhpgfp+ydZnKaF6fFAjP6z+IzyxBz7wDMhsh10swpjICzIh4PjJazKa4tZNyRNRhWgI3ZeOTKQQrOxe+2ATZTA1OVHZgNKwbPDBXvc2DiRW9uh8FJBtd2XpZJ6xZBh8EP3yOo2N/fj8PDw/eMrcs3qqoqpUSeey+k4bPDw8OGs8KGOJXtYM3pTS7mFmADaEXOAPmAPPrJvNEean1PTk7i5cuXsb+/X8Dk1dVVOfmQNhAs7O/vl+3qkB9AhgES7UI2uP/h4SG63W5jESztgvX2DjnMN3u6U47Rbj/WeFI+QekL+9ve398XBtqs3e3tbfR6vXjy5EnRr/39/RiNRqWml0CC3UX29vbi5OSk7NziLABjxpZ/ZBiQdZh2gzQvHDNjiHw6e5iZXOwDtgT7xLi7hj4DN2y15ZJadJeWZZBvJtCZSNe/0lb6Tnt80R7Gw8QDfTSAzlko2wnGiLbk7Gy+DLK4sEMAKHTA5AhyZVIhEwe2gbSVMXaW2nNpnaaPyIYDa+YAGbHvdxuZF+bVhJX1zGUXzCclXd4WD8bamQuywZ3O47aBBJMOpk340CfayXj4s0z0Ib+M693d3XuH8NgPOBuErhm38blLXhgD22J8CWV4znaY1Ph0vX99NKhmUGFLqCE0Q4PTiWju5RzRVBYm1xNmBpr7Db65D1C8ja3jnoj3S0Uimml5lDUDb+5HCe2wDa4Mbg1aGCMricF5Bs1uJ6DVKX/ucTtzuz2GsIOua7WzMQj3dkG0w+UJNuB+d8TGaDvSxUHy3na7XQA+Y8Lf3W47WlKObk+r1Wowa3YIgAXKNgAkpE07nU4xFgAz17XTD9qHQYZRM5NA3RzGGCfsOVytVgUQebFWZqkNqJ39sCzD7gA6nUq2U/P4c3GPA1X0z7XCy+UyTk9P4+LiorFiH+Zyb28vDg8PY7V6XFgIaFqtVjEcDuPw8DDu7u5KSQfjuW33DRb8AVBms1nRLeq0AWTejotx3xakW2aRA74PaGO3DQCq5dBlCLC+8/m87NRA7aSZTOtARJMlJ5jINfa2H+yAsl6vS9lGdsaAeDPulJng9E9OTko5FG33ImWXdFVVFcPhsDCfJycnZV7ZLQVd4sAcgpGXL182CAPuQXb5G0EA6xKYM+9sErEpxyIrxp7TzCGlN+gxcoz9dCBqtg+7aLCW61E9lgZMzo7477TRPhAAxnupn+ZeZAOm3TLrNtp/YBf8E5nBrvIO5sH6xvuwZYyXL2zAtqDd4AqZx8b1er1SO+/Ltstkk5lxz4EBuckK+0YDyexjaSvglgwkeoedB1ADNk2m0C6DS+aFMWu1NnXtttk8n/IqglMH6s4c83cz37bXjLvbYhvjMYDkcpCFDXPwRkaF3/MuIj6xNKK5ENJkyzbw/AlQ//bro3f/wFBTl4YBYTJYEesoHSXCCBrQRjQXF0ZsryHOLAX38TfXfX0oMnd9EIKDAtm4Wcn5B7A2w2sQjjKYYbXQbwNLVhbanVmNHFD43WYbHP07pWwWxGDCwMvP5dAGnKwjaANLAhqPpy8vZCT44ru8zwe2cJndZq6RJx9+4HGC1QNoAKyRRwxRt9ttbLjPfcwxLIrl2iACkITB51keF/rAWgLANMDNhs56YLni4nuwx1w806m8zHLwfLYybLfbZdHlwcFBPH/+PI6Pj8s+x+/evSslGcw5YwfYJK3Jlm3r9Tr6/X5JkbOrBDrOLhbOWFlXAFkOZHAi3sqNMbdjyAE24+pUNQE+9xGkmUHj/wDbs7Oz6PV6RTbQATtlgy6zQKvVqsyx9drzZp2ntGI6nTYCKAMmnDljheyzkw6LCSkVYbGia00d0D558qQApuPj4+j3+6WvBFur1WMdt+1gXddlVw/+5n4zj87qwFITJKFP/X6/6AmBFwEac4ROG0jaBmITDICsQ2ZxsSFuJzaJgA+96Pf7jQwMi82qanNSHVkIM6CUCuV1MMxjDmoNApkvB5+2c/zNAQTjkIEpl32GSzOwUwBDE1TOGDgQWK8fd63g/ev1uoyddyex7clZPd6bGWn+bl9ucJ99I8Fjp9OJfr9fAC0BLwtXeZ7BPn20nTDgZW7QbcYDwAsBQ3bJQTBt530eP3TdjDuZUD+bvhNcIi+Woxy4II+WFc+H/aSxDX7FQQ8EKe/Fb7pkbpuv33Ztw2A/luujQTWABDYQw4pQRjSP84Sdyw4mp4O2vctRLd9BCCxcEdsPW8lA1gKR2YD8d//Oe/l7fjbv5MpGNINlsyG5rtd9t/HkWfx0qi07bsYjtxV2wOPAGJu5cNSelZK25wCF5+SaPJhHLis1ho1n4fhIW5tdur6+bqQ2Pd+7u7slGAA4wkazgwPtY/zydkOkyBlTpxtd5gEYz+PpOeEn4+FazyzfvJtnM0Y8x/WcyCGywzuQt1y73O12YzQaxXq9Lod6cIiKjTDjsLe3V1LYADGOS67rujC3lNEguyx+Y07IVHme7LzptwMF5JFt6ggAs0w7I2IHkhe+2T45Q4A9sbN3cGLwZX3A+VnXbBMIYHDKdmy01RkvDoMg1WznlsfJOwPRFo4Z73Q6BUyzXZvXQSCD+/v78eLFi6jrxx1AvMsG48TWp2RFPD43NzdxcXFRFkQ6Y4A9AIi6vzDXrNUAiHERDNtho5teJOpypGxnzMh6zU/WNfTEOoKMs4d0XddxdXUV7969a2TwkAdkDwDMnPmI9W1EA2PU7XYbKX/Lkf9GsJ7H2ADVeuOftNl+1bLlw3XsW7BtzmyQ8eBo9Lquy+I75DmDe+Qjg7Ds52wrwRZs/Ygc0SYz9SY2uJzBsK4QvPN+fmcsYbG52Ccf8A4Q7XQ6ZdtQAkbGaXd3t+FP0Bf7dvwx7fM2gPTLpUq016y2yx89tib/smxk+chy62wFNtQBlstnM0b4dG2/Pnr3D4y8DQg1Pgy4nQ/gJjMKZtpyxGwQaAGx4EQ096z2+7Mg+f4MtPm+DZDZgpz+MRDM7YDBr6qqHNLAnqHuI2PkyBjl9RZIgJ5tjDdj6ZQSDIyV0X11m51m4105uOF+npP7nT+P2KQ2WfBBX/N48/fM3q1WqxIx0zYMe2ZAkBcDAQPEXq/XGCun3sw0GdA4lck80C8A+Hq9OTUQA48Rx1gSSBCEZkDNuDlty2e0wQGrgztnVtARZC+n4ieTSRlLdu1YLpflABCeA/DhREKYitVqVcAfzCPzgoOm3ev1ugGoDUKQHeYAcGNmmd1ELFu5rCbLEH+j1hd9ol0wtJSC0BZnMJBZgCHzW9d1CbZcgmAGkL8BqHHYZh4pYeHdXIDLHBhn1skgbrV63AqPRYGTySTOzs7i/Pw8bm9vywmHds6j0ShevnwZDw8PZTs9TjzEUQLiCJo8LpT1sAc5tpBgjwB4Op0We4duI78+m4C/sUsG+g4Ad6CPDnEx3ugm8m6g7nFEz7Av9Knf7xd2k11ezs/P49WrV41SFN7d6Tzu5w34cTmHddEBNXNKsMo8kx1g60PbJoIJM4b8n3pn9M32g9/tX+g/MoS+O9hCTwi4TQQsFosie3zH7DHtcB9MuPhzt5kxQ7fpB+9frVaN7VwZX56NX3EbctDDGMAqm9hD940XuBefyPNZtJj7SdYTu8izvIgZVhtG2v2l/MJZGOz8NhtXVVUjGLONMM7x2Lvd1p2IKLJHW91n5txlTPaT27BCvn7MoPuja6o/BKhJfQKSUAxHp1YSp85caxXRPD4ZpQdw2gHYQcF6ex/YzBrY6OAwMQSO+pzWd/SbtxXiOwZOVvyjo6N49uxZ/PrXv475fP4eq4uim7018LOh9ueMO2wiW2Z5yyDGhjmxAm5TJn8HsJjHLBvUiM2+rhGbBW0cCmM2PQch/A3jcHd3VxY9waxhsFgwBavF9wAOgJa6rsuuBwQXLNawIYaZM9CndjRH8J4D775gB8348RkXjoyxdZ1dDgC5AAgYQMYC9pHt4tALnoWjN4B3KcZ4PI7BYFAOWgFYzefzGI1GcXh4GIeHhzGdTuPi4qLUrnNQBeMCcDe7SBu8gM/Am/aZcTVYpB+ZCXZNsrM2OE5vh+g+w5jivMwyWwfQVQNY5Dxiw6JyX2YHnYEB3NFn5t61uMy5S7B4txk+s5IGQxFRFhG2Wq0CpieTSSyXy7KNF/WvERHj8TjG43HZi36xWMRsNivA2/pIHbn1PeKRvaPm21ktgipkhMsEAn7BZVIEpw5KGUPKCvx92FHmh/nM+w0DEgHD24D6zs5OGT/qz1+/fh03Nzfx5s2b4rcyuEFudnZ2GvtAI8fIuwkS5M87l7B+gO9j75xlI9g0gDYTa3LIASdy4ywbIAmwaLuFzto+wxSz3SqA02CV95uUoG32v7Tbvt5tMaFibGH9ZIyZO5Mr9BX94R+ycHt7GwcHB6V0jT7s7Ow0yDH7A/SGOZ7P52W/d/Sb8iDWMKAT+GWAPEE2dhkZwYeRUV0ul2VRNX3GbmE7tmECy4ODAfvout4cmc4cm+lGT1zKCbnAegHrgYO8T9f266OZaiut07s5vW0jkNlBALKNi1MbPBfQG9EEOTBEZlp4BuwTaUAbHdrOd+1ouWxIEKK8PyiChQCalbLR47AEBNqGw4DLaRc7WiJXA1vayFihDFYOnF+u72QMDEzcf28JlFmAbOgiNg7FIN3HVTMeBleeS6J+ByY2yrBizAWAwSmzxWJRdmVgVwcCvclkUuQF2XOfSeGZRTIL6QDMRpk+mW2wwTVQN5DIc5Fln+cwF8yXA6ZOpxPX19dFhgkYGDe+x7jiOOr6Ma1t+YbJ7vV6hXWh9MKsbd5Oyf1iXvnHvBNYoXM8n77kANelGowzY2YGjGB4W6bIjsY7BBCQoRcACcu+QTDz4b1luWebPeH/DopdQ2yQSBsMMHCmsHewo85eEKizfdzbt29jsVjEdDqNiMfjkpkjxn48HsfOzk5jq0OIB+sEcsQ4AXhoE84VuQDEenElYNTBssEyNoH3Ms7YOBhas/PIEe2zfNX1Zpch2x9nUBx00WbqcN+8eVMAETKEvOW1ObbtljfkkjHgffZ93j7ONpy9gB0gMwYGtNh3+oEc8pP7AMj2Uy59ol2j0aj4xlx64+3fst8xiDXJkzNGyDfj48AH2wygZVxYGGtZNyvKc5ET/D7yzLNcC49sOUDiZ8YetJl/xg72WaxjiIhCOjibwU5AZGbsNx1EsBjawQDt8BqQ7IPRpw8RMlzWlaqqGrvSOFDjp3emItg0CM+ZGOb+07X9+kEnKmJ8HLli7GxUzUgjQBGbyN+/wzA4RWoWwNGSAbEF10Yvp/4AoU77R7yfOrKgWpBc18VlQG2DhDC79pZnRGyMj5kxnCbKanC9DewBTBhHLxih9IK0NYbLu1Q43ego2kA5G3OzbWazIx4dOac4cp8ZyYhNGpeDIfiMNjCeVfVYV2kQBbADXN7f35cT3NxnB2cux3H6HdCTT2J0n+gXRps5ApB4KzHXx/EPsGV9sfG0M8osDd+nH4CB+/v7skgQA2xgT91qDu7QhSdPnsTd3V1cXFxExOboXLaLc40gY+xMBHpLm60nZuhp82q1KvtZ0w5KosxCAYDMsNmx8rsBj4EV44QcARQdzLt9DnQIMMzq277QDrPqfM7OIgTz9MUyj1wADrxDUk7JA4psJxy4R0TM5/MyXoBC5gswTS09mRrGA+dK1oM+ez0BeuDghe/bDqB3vV6vjANB9cPDQ7x79y46nU5j31/rBYDZ5QXIEuUntMHv5u/ISZ4zxtgBFHPUaj3W/xMMIDsQEvzd5Si25cypdcBjxTu3sYoQB7TJNevb/CS2EvDI2CDP20gPnoVcOUOAzYIVxc9gI/NWh/QN37aNtUd2kVMHG1w8izHKfs0BC+95eHgo9f3gAuaE9zJvDviZe+acAAuijbbhi401DHrtS83MettPvo8t4xRP+uM5t2wSaBlQM+Zm4Z0dcDYtYxBkgHExIYguO+uSSTJkC4bduM5zypjZn/y2y/7tx3Z9NKhGWe1kHHn6PitPNhyOxDE0VjgulMK13I5iHeHaIWWBMAj3RVtyast/8zvseJyiNRvlseH9mQ3Pn+HcUNper1dKO3gHJQ0oCewTygxgYaUxgBDGMGKzoCtHnTBVEc3yBo9RRHN7JxstjAsAM88hq+1xKJ4vQAOnqlHD6ywFRsN11rPZLOq6LjVzDuq8Q00O5tgpIR9t7iCC95mZo5SGw44MiOzAHaxwYbAYV74PoHZQZhnnvsvLy7i+vm4wrrD2vAtjCsuIM93f34/j4+Not9sxm81KKQAGldpWWFKADPNtBjYHq858+KLGlr4wZtxnmbPcoUO2ETkzhHxlttlOnfbn+UUWc9kTjt067hIW5s4Bvx0ZNhBnbzCE3DkNbOYtO0DenW1czty5fIugnN1YAGXtdvu9LfYAwsvlsqFr7Xa7nHLKvPjYdNeFwtLh/B8eHkrGI+Kxjpv3u08w1d75B5mzHDDGfNc+hP67rMbzw7vY+Wa9Xpeg0fMasal9nc/n5fmAeHYlcVmPGc7M6nLxfwNJ2oHtciDBGAEEsWVkjXKGhHbzPcbQTKd1lrGYTqeNcs3RaNSonTbRQb9sO633nhcuZDgHhVzMmQMLAlo/l+AUfWeu6C+2yjoLk4y/8/Nub2/L4kqAN+1lvNxPz0v2gbzfxADPhiwi4DQBhSzwO7YAG+/AABuegbWDjEy2OeCgbx67TBaavEGukQ3PVw5kPjHUf/n1g5hqJgCjaoau3d6cOoWxNoD2cyKisJtMKMbNAkH0aGOVo+QMiCx4viyEXCiPQRLPda2RBZbnwLhgxNw/38PFM5xexRk4wiQqxth6/GwwaD+OFMNzf39f2Gk7JRs6p64MYLa1O4Np+kCtck61csGWYBjMpHghZ1VVxYkAMg3sPKeUKVTV44Ej3OP0txkjLrPTrl+zbOR5Bmgio/zfjKSZJRy9nYsNMvNuOfa70SvXVa/X68I4WoZt6AziM1Nmho76cxa0WZ5gMwEzZlMMoHMga4CEHOP4mBccKiU63MvWgwQITjluC3ZxNAbOHlPrGO/2YhvPMc90JsHgLDOSjKW3DWW885i41I02ZtuTd8twAJCDC5cvmcFGLwjomFcyVewrbJadOmjS57QP242cLhaLuLi4aLDqq9WqbNkJUED+Ly8vG8CO8cEPsAuP9SUiyqLJ1WqzNzULTwH3jKlLDxl72zfGhJIHH7zkxW+AO3bCwZaR/fJ4Mq+eUwfFfG6GDzlkngC3kFJOucPMe0u71WrVsM3OzNBeAlWyFwB1k0fU90ZEYagfHh5iPB5HVVVlfQXj5eCGuTKApm8wyXn8rTMGpy7hQc7sZ0yA2X8YbMJKm1Xm7wQrGfgxnuxaY73BrjrQRw74e13XZc0A76uqquzZjU55kbjZd9uZHITkYNrA3r6ZcfJpzCanjHXMiFs/6DMygg7YHlL6RTudtfD8WLe3XdvIyx/T9dGgejAYlNQ7xpYB5iAAtvG6ubkp23yhcERgEZudEVikBmPgaB0mzwoe0Ywybdhc3mBhthHM7JYj/mywuSezE7THrE0G9naKZvgwRk5JR2wicZwVY2t2xiwBQMSR7Hw+bwBAGzTGwQphoOH2+B4zQLQzP9vf436MC4s2uM91xxghtjJyKt4OCUW/u7srDOhoNHpvdTZtc0AQEQVI+IQ65g8wZ6OHY3cNMJ/RRmcQPHc8x3Jpo8a4A6qcauRdGFC/z33y85xZMLjD4Zyfn5f2mHXHALtWD2dvYO4699wGnBsBIbLOfr9OKyMzgIb5fF5KnSyDnof8bAd+lqMcADInLDqmj3zf89Vut0s7rWOMpwOnzGxZj5AzjyfOkWAxYrNQD+bX9+dylm16a1CH/nM4z3g8LoD6/v6+bIXmXVWwdciid5ep67qUhhBAMKZ8n8CPDIdrxw8ODsqiKz/HgR+yCBDy3DHeNzc3jQVkjC0ynANm3oHsEBwyvtkn0BdKyxyAYhtubm4K202QC7B1sGmSxfPiYBIdymCc8XctPe3BBjB3XiPjY+sB6iyo8xqTk5OTGI1GZf9yAlsv5kVPXUvuwMDBpmUUfWWObDsdsNq38FzX9FtHkBPGjCwG/hA5tT7wXWSa8bf9zf2ijZBVBE/sS+6yTuSSOe10OjGfz8tn6PK2NVCMDe1xRoC22p64X/6cANOEB1e2WcwPfbdP9hzxbLPUOaOf+5ODlk/X+9dHgep2ux2Xl5dxcXERg8EgXr58GXVdx9u3b+Ps7Cyqqoqf/OQn8fOf/zxev35dHClpHgTfwoWjoc4TA2ingsAY2GYQG7GpTeX/fGbGiL9byQyQzWZENJXPxpNnOIr33yOiUZ6R342irdfrhuPJCwnsOPm91WqVbbW83VeuiXJa2+wWIMfK4edn1p3PUUqMvQ0rETRG16wubTArl7cp490eU/ebazabxe7ubjlG2U7KAYkdKEbZTFNmDAz47eQYo+y0AawZdHqM+Zs/N5hz3a8dEGy8y0kYZ7MlzrAw3w4UML7MZQ7s2u12qcN2Kt3ZJ57F8228eYcdL89k8RGyho55XUNVVYUpdPsMRhx0OHBgPBwgZ7tgxtxybAfDGPmQGgcg1pWsY63Wpp7eAQkyi2O2E+QzgDBtcd0q38MGml1ar9eNxbqdzuNWby9evGhkD29vbwsDzC4uMOwmLeg3ay84hp5x5z0RUcgRgIcBpIN87/fu4Jq/WfbRJ88TjKQdPMGtfYbn1PLqxWv8sx6boPGcwVz7+c6i2CbaPyAn+T0GgJnNNemSWVMAFFlb7sWWZaCD/FH+AMimnpd1GBAcXJQJMvbX19cNQsTlClyUVrhcjYt25kAd4M5l+8yzbX+wbQDSbK/5m/UCosXrangeLLz3JzfRxPySwTSgxLYeHR2VMhnWJridDrIzAUgfGbeIzT7k6ATj5d/9+Ww2K7ptgor+2x7ybkghLzw0DnKAgWy7ZJJ282zb40/A+sPXR4FqHPyzZ8/ib//tvx2DwSD+yT/5J3F7exv9fj9+//d/P37nd34nptNpfP7557FcLuPrr7+Odrsdv/jFL8peqjAZOYJFaKxgXCgg9/E3G5xtTFMG4zlqxRAj5Hwnsw8GbxmkRjR3HoiIAuTsqFFghNL1q6zeh5UwWAeo8hMndnp62ojMbcgdUfJZjmJpl/tglsHMCm2m5tLfxyHf398Xpiq/B0OGUeFv9Il2Ou1pJoT6T47W9umElGLYUdFmQAUG0zst0GfmGBBtYEa7DKgNUg1U+V4OpDITh2PkGQYVXpiFo4UBZHxwkDgAxgKH7HcaaPN7xKYcwQdtHB4eRlVVZcGWF7pa/nmX68RpP+N0c3NTUs2uYXVmwyvzATXIDQEpDCFlAdY9gzRkyFmgbWyRA5ptc2CdcZkAABEgjLzTH+aU8gv2QXbNsoEhgGY2mzUcrbMeBmUEqF6PwPZ6n332WfR6vbIFJSD66uqqlOIxB3X9uCsR5WKz2awR/PHO9XpTorFcLkt70RkyAB4vyzA6RX8jNnv4OsBGR/hJv60zyJyzRg6MeJ7H0UEd88N7DGTta3JwzP9vb2+LnjhYQI/MnGbyBpLIIMYgxYRPlju+g59wKaIXqrv/dV2Xg2wWi0XpL3ZiZ2enLHqmxIfdRijpQLeZa/tIBxf2Ecy3g3BnFpwN4DnYNFh3xtfZm4jN4V6MW84oMH/D4bCw8LyL00aRKbfVAdDOzk5j5yOz1RFRgClzxhwg4wbMyHDGJ8wv4wSZwTts2+gfRIpxAPdiC3gnPsfBpQNTB6jGSsgt4+AF0ASYmWTMZFe+7Pt/bNdHgWrqs37605/G8fFx/IN/8A/iq6++iuFwWIzYn/3Zn8WXX34Zz549i/Pz8xiPx/HkyZN4+fJl3N/fxy9+8Yt4+/ZtnJ6eNtg5G6ttApkBdcSG2TKj7csRro2enakF3iyvBdyMr4U6M0j8bgNMOyI2xxSv1+vCRPAsA0wEmM9sZJzKxLjlLbgMDDFiMBRWUBz1NqeKceH/3jCf9xv02NEZFNjAuu7PhiNHzWaFF4tFXF5elvTaYDAowVfEJjthAGXnfHd3VwDeh8oIcNgGhYwj9cL8LctCDgptdD1PDuZ4thlgAAMAjH+dzuMCzsFgELe3tyW1S+oXFsbAOa9doH/IfwZuvGM4HDYCGYJo6i7pEwy6Zcr6hgMyS2eWFzmmDdZpnB964vpc5MvZLo8jzpQDbgAZAEvGncvBE3KLTfLiUWQts0c5cOb0TsCCAzieD3uEPDKmfBcWDBDlNLnbv7u7G8fHx/Hs2bOyh3Nd1+XYc4IEgBK6kOUEIAAz6f5yufbeY8/fkFOCQNLpzBU/mU++a/uADtkWYzcNPHOZQAZ2ljueaVbSzJ5BCEQAwZHvNZj+ECnhoI2go9PplMWbLpNAH2GWDdBoP3Lpcwboj8fPNoV58W4XrCMBxF5fX8fV1VUcHByUABw7NRqNytqLbNtoAzLE3xkr3otOmGCyzPEcwBnj62eih+iUgS72x4EapSIO4ChpcMaC77OtnwmR2WxWshLokueY/cWRI7O5DjIyKbcNc9A//K3JAS4ylR535IQAwPJqWeQyYWSfz72WJeZ6Op2+R1z4Gfw9l65+ujbXRx/+MpvN4o//+I9jMpnEZ599Fs+ePYtXr17FZDKJ09PTWC6X8d1335X66sFgEDc3N/GP/tE/ivv7+7i4uGiktg1a7TB5XzYmOUJCYM1AcT+KaPCCkgEmzR54wRVOH0XMrEpmvMwEbgPU/r6VM+J9JtlBhllAWAs+Z0EeC2yqzHiRAAEAAElEQVQABAamjup5dwaPViwbftrA7h4YCO9vinJhkHq9XiMdZcDvuTNQsRMweAJQr1arUvJhkOXAwvV5XPf394UpNDNpWaKd/j/GjIAkB2vMgxdwma1knjOjbfYcRhNjaYDKP+YQxhPDDwizU8DQ+zAC5BIZYrzN5nNxvDVsz3q9jul0GovFogGImW87dvqPTPl3z43Bh+8zCOKZZpLseM1muZ92bABTgybbFhyMdwFh/mCfAVAOPPkeTtH/pw8ssEO3IqKxUM5yCxDlaHXatlgsylqUiOZ2V7BvT58+jZOTk0Z53e3tbZyfn8disWgsDjYQYg55j+cSAEaf1+t1I52OHvIuAwGf0OZAxrLGfDHHzA129+DgIGazWUMuLXMEKi6bQhctA24HNh55M0ttUJ8DYPslyyPvMGg3eOT7XtCcgT3lHYyPwSlpesuHd2cxAcNlX4LeMgfYberqvY5pvV5Hr9drBM8ZGO7s7BR7b530O/kdm2WfaVtAIIou8X3v6Yy9hdxBhwHN6BaBvrOafiZ2gKASH5ozWPys681Ji8vlsrE2wnvou+/OJhjMcs82ttZyR1aa7D3z7bYhUzDakHb8tAzmoB+d2SazdV03tk4l8CITvVqtGoehWY+wpb/t+sRU/xUvhO/Fixfx5Zdfxnq9jn/8j/9xnJ+flxMEIyK+/vrruL+/j+Pj4zg6Oorz8/Ny8EO73Y6jo6Oi9E7R5ZQfl5XX99ip8pmBIu3NTh3Dsu0dBl42kI423WbaYeBuQI1TMJtG+5w6ZXwdURv81nVdVvV7Z4yIzelPBtQG364ho12k4DDoEZt0nWveMEQGEPP5vKQSI6IAOYAB/WaMcMCeO7OzOGoM583NTVxfX5eFUKSqPWeuZfWY8x5Oh3OKnnnNQQftYEzsABzRY0w6nU4Btd5O0g6Y8cr1vAbLOKper1cWhNE39wunZKbXKT6DzKw/ZiMcrDJOZCM4lRMn6wDN44vxJriy/hAAuNyB7Izb7FINLkCpWR4HvH6HHbsdhoPZbU6GzwA8sHWUS3AYEOAxZ61Wq8eyjW63W5xQVVUxmUxKAEYWYLFYlFKMiGgw2JTFIEur1apk7pAP+oxcAiAGg0E8efIkDg4OSuaCHVQuLi7KrjhsrwcjDjvHeACC8/66Ec1TQQFZXh9hJjUvnnKZSWbQbGuQL29Nhh0zSQEL2ev1GqyebYyPiYYYsBxgFwy6LB+0i37Z3yBb+AMu+wbbVWTF/sD9ALAZoNDuzHhii2kDz7GOuI+2t7QNu4EsDYfDxomQZJx8gqqf5XdYv/Cv/N1AnWAUnfGOInk887zc39+Xhe3U7gM4eZYDKHyZCQ0H05lVpYyQMeczZ5fQNQejDmIYH+bXjL6zHDm75MCWz7xIks9MhphIsN4a65jAoM8mxZAn7LYDOPTUmcyqqooM4cPs3xi7T9f266NGptPpxF/7a38txuNxnJ2dxZ/+6Z/GcDiM58+fx2QyKdsRPTw8xGAwiN3d3cJMM6mr1SouLy8LQwHg2xb5MJE468zoAip8/zYDZ1ZyW1RppxCxKafI7ciKlRlsv9+fI/xW/Bz1O82IgzbzgRM2I0SK27WNOUJkHHhm/oy2oGykhGHycBqOUA30efZ4PI6Dg4PGVnOwYZ5bM3zsWw2g2t3djaurq8K2wfzBlnF5HA1iI6KU1bBIK4NLfsKQE3wQtfMsAwdnLRhDBwoABAddvDOn45gHt4PAYj6fN1hZ5NVB4Ta5ZS6YD35uM3yeR8sr4JpdWADHEZvFSYw1Y8MWaYwDB724hplgy+DBxj4vFKMftN2g2dmV7AzYvcPvpa0EbMwzB6SQgp/P52XfbrIADlJob6/Xi36/XwCEF8D2+/3y2Ww2i8lkUhgoApX1+vHYY5hoTmAzmKQ/gE0cHKUjZDfIWjDe7LtO/+v68QTNh4fNQRrUSNrZE9g5Vc59ubQJYI5c5NS9798Gqg0YuAh6+X6n0yknOzrzRL1r1gWeTdqed9tGGBja/jhz4RIhgx70HpnkO07j81z7Ar7PmPJdZz0s55m0Qe89/rYrfN/rk7bZB/pjAoQ1GNg/6u8B1BFR5IwxMIjEtvjv9iX4qxyQ2y9ABtBm2s/80id0lXmgvj2PA1dVVUWW/XmWmxzAwOozv9aTbTJkLLEt6PK4GDcwZi4TxN/AxCOLJrciNmtDTJTwXsY1Bz/MCRjKRJv9FvPpXX8yy+0A6BOo/vD1USOD4frqq68iIuLnP/95jEaj+Oqrr4ozIYVkIzcajUp0GLE50auqqsb+uzbCEc0tuxAaBMaAGaGMaG7Zw+9msfgOz3JNpEEy7bBhRnn89xxpc1n4c/SYI3Uu3o1A397eNmrQMDZ+B6wAKXuUk3orDCqf393dlTrczNKQ/ibFzJzkaB2Dz3vYa9xbYAHQzQh7myvvVBDxaDDevn0b5+fnBbgfHBxEr9drGG+nxZAPjMRkMimgxaA0M44G1C794CcsC3MK22MZM4DzfVl2c4mCjarZGbMwzKkZCowi8+mMRmbxLYueM57hNjI2BGaw+zbmw+Gw6Il1BVnEiXqBC2y+2VfmL+uwx8ogBUeYwRF652DQnztABoyysA4nXdePx9hPp9PC7iLzZuiZc2pbcXbIw3K5jG63GycnJ6UmGofFQUwRm4VOe3t7MR6Po9vtlkNZ6CfyCFPHIk+cLTq2XjfLgWA/d3d3o9vtNmpbCYqp2WZhmrM1zn5gb82WRWxqVzN7h22yA/Z8GID6HfR5G1OJbWJ+yaI48PDWhs4k8h2/w7qP7QLAmV20vTPbmMkUs8RZN7cBK2euLFcGNX4H78aHmq00wIa5RGYB7D4Iy0Gw5Z+/sX9zXT8eOLZarUo5EhmQiEffD5FASYjlwEAbwOyxN+jPNpkxYhx7vV6Zl16vV4gZbCTjSkmE5xG/B77gHvSJsUQ30GnGknH2mJuQMkGUmWeTT9tIED8bGaBd2FPGkmdk+UOOHDwbW1huLYPIggko7qVfPJt2+5wME0uZuNt2/VXu+a/q9dE11efn52Wx2Js3b+JP//RPyyRh2A8ODuKzzz4r6SRYIJxZRPMkQ0+uDTl/M2tloUMxrQAIFwKLoUFh3RcDaoz9NkDC/TYSfqcFnT64H1xOA5mtMxOE0FMz5jbl5/FMACJOGOMIQ4eDoabOtePeEYMon7oqDIjHGhaNDAFAxP3lucxdZn4wBpPJpDgA0uUwZaTZudx3y03Eo/JfX19/EFA7u+F/gNEsg55716xaLvl+ZsLtPKuqKvuvk8rkQA7G5uHhIRaLxXuMNqdz5feaicipdjNlvj/LJj95Z2aycl2xaxlpk50q8sV7qqoq+1RTM27d5TuMGXLmYBW5y4yTAxjusfPzZzhZAAqnlML0sjcvOoT8O9hmPmGhI6LoB4wZzHZm6gmOYbXZN3g4HJaMind1ANjQRnQF29Ttdsu+7PP5vDDRyHO32y3fp8Skrje7PjBnrs/m/3X9eDKp24/zn06n5fvMEc9i7FwX65/Ma95KjNpVZI7AnvEClFiHmcuITTmFA1aDEewpz87A3peDOAfe2LBtOmZgbdtkucxBbl1vFvoZ4Ns2Ws5tNzOxkf0lzyIo6na7sbOzU06PZew5LAs9hrkeDAaNXUYuLi4a4Iv6WmTGPgG2nHnILCk/zXIjX7Sfud7f32/4rwwA6avZVZ5jEoNghYDLwaCzCiZpLEe2M1leuJgr74Lk51ne+TuBKcEPmVqTWyZgnB3J5IRtnjFFZqoZXwdpmWhyUImsO6A2bstB5qereX00qO73+yVivbq6ilarFS9fvox2ux3v3r0rkfCrV68KwOn3+/Gzn/0sbm9v4+3bt4XJwvBngG22wMzxh9i4/DczMBaWiE3NEYLteiaeldkKO3uzhxl0825H5vSBbXdclsA9gBoMlg2v+5+NuKN7wEFVVYWJYtEUDLEDh+VyWVhJ2kc6PyIaQId6XwzM7e1tw4g5g+CIl++TvnbdIO0l8KLm3myb+05/HcCsVo/7d15cXDRAhBnQDKZZYJhrkJlb1+MRgJASc7sJFrjssAEdsIuwmrSr2+0WGUOGMJiZUaavWd4YW2cDzJxYJpElg+ucXXGpjJlsZIj+wc6a7TEzQ7kOR84D0NlRgDFx+w1YDDA8ntgIAymPv9sAyOU7Pmp7tVqVOmSzrJ1OpxxsZfa43X5cA3J4eBgRUVLLs9ksut1u2TWF78E+M4/8G4/HcXx8XBjq6XRa9JxdGNxGB4XIIrp2dXUVl5eXZeywYzCPDtoYRwcRfMeHc93d3ZUTEWHXkSe26svyRbDhXSwMeniG5c/ZHuyQSxi4N9uSXBeLTGQ5NyCBWHHWAxn6EKNM3+iPGfic0UJ/eK7ZZMuw2cMPATrm1sDac4W9s+33Z4wlz0eGp9NpRETxP7DPZG/IXDiIYkxMUgHu/LtZTo8HfUZfDMrt4/F7tkmj0agAasqZDg4Oil/0gmn8pfXE72+1HssYptNpsVeAWdvzbaWn+ZmWjXzipOWdy4RffiZA3/puIIys8wzG2hkZ+0D7C2enI6LgMdp5f38f19fXDT9pXMU8EPwgs8hi9hsfuj4x1X/FixQ7bCJ1tIPBIF6/fh1VVcUXX3xRyhaIfj///POyQwV1S/kkLSsWv+e/5Z+AIJ5joGPgm4U1R4Z+X45mbXAx5hZGLgN4s6MGRV4xX1WP6TgWSJBezUoHM+UaR0eZsM42evSLd2JcnFEgTU1K0EbGkT/vc71jRJRDQzx+GAlH5U4FUjsMG3V5ednYVrCqHrdD86K9DKwtixxE5ENknM5jngBVjBftxChFNLchdGDFnBF8kf7cBqjtPJEFFr/BliL/ZAj8Ge228zXQRC7pa2YkaIPbZNm0Prm2k+c79Q4ARr54L6VDfMcpWbIL3gYMkGBwSPvNgJjNzPXtZrSyPFK2ZGaP/cwJoGgn4N57Pbfb7VIr7V0ZIh5ZpuFwGOPxuLyXIOHg4KCA5Lquy7aNZrcZv/F4XE6Zvby8LJkITo8lIPBiPM8lfV2tHrdSPD09LYdB8D76x0/mHwYd+2WiwouVfcJmxOYwES8SJqBCl6lJ904W2F2egSzmTGDeC5f5p+30C8YVJjyDvQzkbHddOsF9Ji+2BZgwoP6ddxnE8n+zryZiPH/IXx6XrKduiwmU3F6egQ/h2bZh9gHIJyAaG8j+1LQd28bzkUXq+9HHiM2C+wzs8RmuDfZ4EeCYAeY9LJpFTnkW24jyTvTLjDmEjDNbq9XqvWyQTwjNmWf+nwOmnMHO8+Wg3/4YwHt8fByXl5eNU0195Dhz6eDJMs3vzAPZqQyEXVK0t7cXT548iZ///OcxmUzi22+/LXhgvV4XPLCNdc5yyrMZN9uQT9f710eDaqJcBPvu7i7evn0bw+GwHELwJ3/yJ4V5vLu7i1//+tcNY4RCeXERl50KimGQakNodg1DwTNQNICC2QEbG5w+z+TKLJiFivbbAVmgt7XXCwD5vmunvVG+FTqnrexoHWUavNIPgPjBwUG5FwYAxzgYDIpy81w+Q6Fh56htY9cE2sScIRfcT/sZK/o2nU7LQi6Dzf39/WIYt0X6OEnS99fX10WGDKjNPmJsuWy8cFh2hAcHB8WYIzuk8DDMBD52MsiTWS2esVqt3tu5ZblcNmrMHShFbDIqlvEsm5m5twz7O9zj9nIPsuh6eINWxr2uN4dDAHCYX+aQ+nf0zGNBCUKeV5yD9cJsIECcdnCoigMfrxfg3YvFIvr9fsMZV9UjO+uAglrjTqdTaqwBMCcnJ9Hv9wvYxAZAGODkWCQIUcBYDIfDGA6HBTiy1y01rQS86I53n8lzyUK9xWJRmDdsCKUpLOICJDhQATgDoAgQ0H3GhcXGyAK7nGB7mB8CfbI+nlMHXmZvARu0CZvoNR8OwPi+bRO6YZbeNsZrLABOPijF7J/ZdF/oZNYjB3pmayESDIQdyNpuO2B2wGl7xHMJPF1a47ZgMx00oXcur4mIssgVn311dVXGgb7SbpM7ZkPR5cy4536YZGCubN8Ihn0PtoWdpRwgEgQz1vQnExI8r67rxjkQ/J1Dw5xZyXPMODuY5/v0lXc722hCwNjh4eEhzs/P39ukwGNoAog22KYzrt7wwcErIBs/RXbtxYsXJVuIDSEzRNlJJuwcgHrseCd+/UNBxqfrI0F1RMTPfvazIigwDRjfX//61zGZTMqAu8arvPB7h0btZo5WHT0ZiOFsDLqdqscoWSj8XoThQ8A5osmO23g6iqWt/G5wbkDn+zEQsCBmbQDU9JF32rFmxtvjYlDHT8BYp/O4iBDwTt0yzPVgMCiBB3MCi8BnGDaMBmwkbTGDS9u9mBK2jgDr6uqqAJdWq1XaZ2acvmeGFiBKitvOxjLgMXadHU7C8+jxZnspQFCv1ytywK4YyDClMhglwDPjbkCK87OTJFPg4Ip+5gyKQbTlj1IUM7mZaeHvzjqgC5ZBnsW4eV4NtnyYDvPDyX7UUdMPxsMABzkxm4SR9ncBgTBxbLW1u7vb2F/ZdYIcjw4zBUvF+LBeoNVqlaC/1+uVEh+zocilF4/iUFmYC5Bn0SCgnv2+YbiRVTJ1EVHS8thQwD07gqCLzDMXLDckQrfbjdFo1Ai+mTfkjcuOfXd3c4ImMp0DLmwDDKZrXO34beMJDAxgCV4A8swX84c9Q+5dcmGmGftthtG1/QQIzj55dyGXGtBnL7Y1Q4d94jP/5Nm8l+dvC1rtI3i+/QPyjYwxhthk5Ny20HM8m83KmDvYbbVahfmFVHHg4n20AVoEYWbPYWEN+h2MGFgjywbckAhme102iG/z4nnGxvYUeev1ekWOwQ+wrrZz6Dfthhlm9yyCkJxVyEQZY8u42d+YqAPcYyPQGWQpZyeYIwecZLUo24EQwB/t7+/HeDwuQTCEErIAgXRychIvXryI4XAY33zzTcxmszLe2ED8D/NhnaKN+HJjD9b6bJNHy30mgn5M10eBapznV1999V4dEgbXTECn87h45vb29r3SCdLgsDYcl2vAg0IBygyC1+t1icxwHNtYPd/PZ07nGYRbGMw8WtHMPuTdLWwseaa3zjHYMgDFuBpswES5lpBxdRTrtlpB2cgecHR5eVkMLe0AUJESx8iNx+MGmGEeMGD02Wyk22qGnXtvb2/j8vKyLNjrdDrloBEcHsYTBoJxx7iSvqQukHtgQKmBpm95C0E7BbP1lAmYXYbVc+DmlCtzyBgxDt1utwBQaliRf54NuwpzRB+zobKjdhoVJoa550IubATNcHk8Ld+AS4wr/7xoyNkFmCPkaDgcFhBqYOLAFB3lp+u0zUaj12x5hy4A7GHNALQEY5Z15hY5YeyoXbYstFqtIlNZltkpx/taA8IBC/P5vJQxeQFgXT8uxGWMeb4P/AH0eHcP1jkAGqzn6CTz66wHpSrIo9dmOFUMoEJ+vB+vAarBjm2WASzfAfQBIJhj20YCU8sqYwGz6gCG3Sawg5QTQBjgT2y/eRcLf703NzaffvDTYDFn/bz2xX6JfjHf3W43+v1+IQssxw4CrMNZT11mZVsREaWm2O2xr0IX7N8YH/QC3eaQsDyeyAVzRRtMOLg8DL004QMrTYBLVoI5xYdja5EHFqQbUxAQEtzhdzKQN5GCHAI2DW4ZE/tly7oDLuSKvxE0mQjwOHidCDrAeGRMAf7xs1qtVoxGo3j27Fns7OzE2dlZWTQMYbO/vx/Pnz+P58+fx3w+j1evXjWObsem/s2/+TdjMBjE06dP45tvvomjo6PY2dkpa44oA8rEIPJvLMS8EeCYkc+E5Kdrc330MeVXV1dxfHwc19fXRcAzo2ils2GP2DBvGDuvNjdAwNk/efKkgDTe47RhNqwGETZQEU2DyGcAI9rGZbbEkSpKDODBGGY2m/e7VMQK6YUzPN/v4PlcgAeDbt5B33LtF6kzjAJMGH2HgTYrMx6Pi4PDMGCUYO5gl1A2l1fYKVHWsVgs4u3bt2XOAQBmqcx0e+ELv8MIwuxHRAEjtAUA5KwBsoRRBQSYxfY9ZumRD94dsXEA3m7K7OZwOCx7D9/e3sZkMinz4wVSsBoAWGcDAHVmqD3vllfLip0MQQ/PM8A10EB+GQdS8czd/f19XF1dlYNF+A61towXcsWzI6Iw2sgeeoucorfejYbsCk6fMbfDZvwZF4J96uzJZHhnDxys20dQCTtqRtKMPeQA+6oDWOfzeVkka1tR13XJbGQZRI7M1hK0GNwwd9gJB444R9tLgnfAF86Yy3uNG4xzDDSXyzlIKVs3zS5mJtWMMuMIoWAGGtknQDWDawDKAivGi7YgJ9YFZJGAOCIauywYCHE/z6Gt6Kl9hgE1F3rRbrcLMQD4s05zQE9Ot7MepaqqskiQsX14eCgAkt2aeDZjiY9wGt4sOXPkQBlmEtvHrh/tdrscTY380AcDQ/xBLrl0hsyZRpMEzA3zzhjzfhNU6Chzy8J1+oEO4TsZQ5ekAobxrTDyjHEuTWNMaDffo5/eaYbLJJqJM2QIbOLsjtl7nmEi6OzsrJGtw3ZBXEQ81pdfXFw0yp/QBQL+drsd3377bfzxH/9x8f9svZgzEQ7wzHrTjwy0kSEz79uuT0z1X/GC7bm8vIyTk5NYrVbx9u3b9yaJizTsarWK4XBYok+iJoQOocRwY0BHo1F0u904OzsrQMtOwY6My0LgVKeZcv5vkMtlx5CDBpQAI2pHwwWThKIybqvVqrHbhplzK7hLFGyscND+nr+L4cYoAAYjooACACjfgd0g9YxCU2OF8/TBEK5vdfsx/k7DYbzZ6QB2jnIHM/d810wEhpXUOXLjcg6eYRBKG3hWfocZYIxKr9eLw8PDYjwBLmxJBRh01sXPY1sq0rDUzMJkGHg4qKNdgAjkwM+3jCEH9BeDmHUQ/eC9yDpgivn3eOAkcVBnZ2dl7nAssMj9fr/U1lMDyU4C1N4/PDyUz3GgdkzeQrKqqsaJnN4Zo91ul+cin3t7e3F4eFgWHk0mk7i4uIjr6+uGzvT7/ej1eiUws13AluQdRXBYvIOAbDqdxtu3b2M6nTbKY2gD40zN88HBwXtzxxw4+5IZVJ7FPHn80CnaTHsB+cwfpAT6D4gBnFj++DzLI3qQGVsTAbTVMhYRZX0EcpDZYMrQzCLa9gIUbA8ALowFuwoBUh08O9ikP7YJzAfzw9haHzLDyHcYs05nsxif/lBGdHh4GHVdl5M0mfPValWCBbaapQ0ORugbLDWsfQb4BjwGgy7dw4YSyDDPvI+Al/FmLHie/RPPNoAnYDJgYy6YD+8IhB3EvjPfDq6wHZQaEiC6T+6/A0T8DIQRfdpGoiE33taUZ9guIu9eXA1w5mLcHMhVVdUoUzTx5bUHd3d3cXR0VAiExWIRl5eXRYcXi0WxMxAJZALYy/vh4SH+6T/9p8X+4ouQf9rDz05ns4gZ/bWe57l0mcgnpvrD10fXVJ+enkZVVXF5eRmTyaTBqkQ0FwmuVo8nIHHq4uvXr8tiHUfbCCpGD4N1fn5eardz7W5Ec9ubzBCjPGak+T7AN2LDQhus8ly+nwGmmWmEy06KXT1w5DZYgGZHsQi9gaHBkyPozMSb7WGREv3A4FvBYFgA97CCGHUz605rOW1F2t1sMGyDx4Wjxu/u7qLX6zW27CPFmwMYwI1B7Xw+b5z2ZaaZNtrx+j6ej1Phe8gBLOrR0VFhhwA6GFsvgHMKMYMgtiSD+SeAMKjP2QfGAvmw0c/ZD+bWWQvk2OVXGXibCbeTWa/XcXNzUww5Qcp6vS4Hi9gYw6r0+/0Yj8flXgDddDptHH7CM2HKDNiRBcaGWv3JZBLX19flb5Qn+bnHx8fx7NmzqOvHEovz8/NSgwjo8laZLLYDbPrADAeS3rKr2+3G8fFx7O3txenpaZydnZXaaNh0ZBVdxu64LM0lSNhD1xUz13a23qcZGUeOKb9xEMX48x0vzma80TeCewJEynsodUH3XH+L/QHUoevMHfYbUEPfcfiA9syCOgODfvA+17czPpA0rpM+PDyM+/v7xsEk2Fe32+wpAM5yyvyQKbBuG4wD4JFtWFPAMqVU6C+BMqUWsJJeNIbtruu6sTe/swsOADLo4e/5c7IleTtE2sF36bcDD1+7u7tlUTWyjjwyXvZbVVUV2e90OjEej6Pf78fDw0PxB8ijwT72GB0cjUZF1mjrw8NDAcyWc8+pM5Dr9bqUMDI22+SOftsfIxuMFXqEzDPvECr4IkovWX/CoWSWZWw2eg9R4XU9Xp/Gd3Z3d+PJkyfx7t27ksmYz+cxGo1iMpmUEj0CE8bVgYQJqbxzlu0RcufxgljxWo1PV/P66H2q8x6uBgAIL84AAzYYDApjuc1YGXREbOqpUC4YDzsu7rPQWJH8PBuaiGisauZ+HLxrrQCYCBh1o2zx49Qlhqrb7ZbFUO/evStjgiHiHR7TDyk6Cg5YNyBzEMFYwVIwZjZ80+m0GF3SSQBM+k80j8Fz2YBZRm9R57Hm/QAXjEm/34/BYFA+410ZPHJRY8/iL0f69A3l9lZ09C0HWDaOBAXML4vKMDCwfXVdNzILPDOzNmY72EqS+kOnIW2gPJeMSa5zs0zky8FHTi8aINBWZxVwOH4+ZQ0AUafq2bqt3+83ZHM2m5W0MQ4NOWFsfRS4ZRZAurOzUw6S4hm9Xq9ROz+dTsuiPhxWu92O09PTUoKzXq8bu/8gU8gsYBIbgUPBOZhF5P5erxcnJydxcXFRAgxOXKuqqgBzzy/2AmBLMEO7cLK2RbCWyAPtgeFjrO30zOBzP/NCewBs7IICm8qe8NxrsOGyKkpcLE+MK0GHwf/h4WFh0QFLkArYu36/X0AeKXmea7bYZAPjyd/QKaf1sUkEXpSvOVi3/3BgnPXa+mJ7jJwThHAGg9lWQAdtIavrmnnLAcED3yUAB4DzvE6nUwJNy3VmqU3cYFfQPV+0g/c60LFP4PsuZbAdwQ7hL7BDy+Xm0LHRaBS7u7txeXlZsk+3t7dlVx2z57Zn9AFdYNwcpJIp29nZKWQddgQ5pOQsE1Gea5NoBp+Ma16/Q1aCdpK1gyygHG69Xsc333zTmAPIFjKZq9VmF67hcFjm4urqqgSkXnh9dXVV5MOgfj6fx3r9WALCHKF39M9ZOUoaLTsOMHkG4wE5QsD0l4Fqvv9jvD4aVNsB+XJdkf+1Wq2YTqdlAjEICBNgnL8DZOzAzWRHbEosIjYL/nKEjVGxEXIk7ppmFIj3UAMG68U+tiwSYFFlRPNAGhwRC3AMNJ0udSRsB5vZiAy0uRxRcy+gEWfHewAdgH7SkVxu//39fWEMMU65HRhRHHkOBCKiUfJBIGIDbgaVy4zyZDIp25R5sQpGjZpfzyvjEhHFCbs222lhfu92u/H8+fMS7a/X67K1E+UmBqyMO/OFw7dTyLrhQJF+ZmbM2RcuGzczumbkc6qW9zvrwvMdFAFCYc0YT0Dg7e1tWZgYsalp5tmLxSImk0lJTzvwA5iSuiX4BPDzDOYRBpzxpVTp5uYmzs/Pi92ww5/NZg2m3lumbQNHLoVhHMyG5rHnWV999VVZ2EUgikNBjpxZw46ZNQT0VlXV2I3EuyYh2wTGjBO1/ZmZxilGRNnGMG99B3iNiAKmTQQAfJBJgJPfv40J5R3UAlv+0Bfqgg0QkWlIBpMyOdtoWwGh4kNELHf0z3rqRXueT/6W2XzuQW+s49g3p+HRCQA7QTmljcyLd3hx8AVQhK1ut9sFULkkCJlxptOBM5d9gTNQdb3ZqhEm3O3gewB7ZAPZouSN4I3xYNyc1bQ/wDbD5L9586ZBgLE4lvehNzDQtBlMwViznscg1UEZmyLQVmy65zKTGQbiyKJBN5/jJz0mo9GokeliGzt21WEdCqWPjBnfBwSfnJzEaDQqmVJs3vX1dSFnCMSMdyIes9EHBwdlUTRbJVr23X4Hgthck432c8yzyR4HOZ+u7ddH11Rj/GGhLYAuVUDYcSh2+k7TAYpID8Eemt1CqW0YDSpskO0AbGy4bDQB9QbUBv0I0Xw+j/l8Hu/evSt/j9gYHSJ1apMjNgvZXAOMQ4qIxv/9E+CKY8SYZ6bTBtZBDMwCbA0GmMWBZkphvgzIie7tTAyiM8gjrQuLcXNzU464ZbcE2sBcWnGd2mS7PJ82R5/pk0s9+DwHGDYkTrEz5zzr2bNnBfCPx+OYzWZlh5Jcb4wBtrxn4JuNjQF0ZpzNCuTPzVoaCGbW2zV5vCsvJLFsIHMwbt4Nw7WXsIyU7EREWfhGChynCaCFyQWkM2YEL/Qrp3Kd/aAdNzc3ZQ7YRx3nen9/X0C1x8/AyUGUQQhgGNAB+DfIwnm/efOmgBJk3bLHO5Ellxa4/hyHSHmKmUAc9nq92b3Bc+60PPe5VpNxoezLhybZ7tFXs8DIjd/D+LsmmTFxFozvs3MTshKxKV1BZgg66vqRcUdXvQbDwIT5cEAEq3l7e1tKAhgrggne4SDJ7c4kiPXPZIftL+2ijfZpzBGBKYQKqX/GnKwL44PfArgyb8wv4BI59JZ/zg5mIsn6Tj9prxfjIsvYCmwLfXcAiuwjk8iE9YDxIjOCDBAwQp7V9aY80fYtYlObjL+CaKP9yBQBLYE3f4MMwYcbaJtE8d9NdjCOYAkuxhI9Jeu+u7sbn3/+eTx9+jTG43Hs7u7GmzdvSlbm9evXsVqt4vXr141abWwm+vXixYt4/vx5yZpxMBQ6OxgMYjablTI4fBH2FhDOeDM+lm3mHJ/BPOHnYc0ZE9sWxgxdte5/uj58fRSoXq1WpTYI8GtH6TRmxMb4MDGAqNFoFCcnJ3F/f19OaKRkACYMhUOIXD6BcGAQvLI6omlY7EDM4iFsABUMKGkrhM91pnwfIMa7bawcUTpFxP9hIXLkj5PNz+PdDhYANrm/Ozs7jRpGADZMo++zMfXvvJP/Y+Q8r8y5gSDOHXZmNBqVxY/5+9vk6vb2Nq6vrxvMDPPtlH1mEpgH2uVdNWw82asVuR0OhwXsIdfv3r0r8uhxRXbs2AzWsmPDOGOIqVt/9+7de/Pu9CMXz3GtogGW++4A1kab3w3G0UMcA6AMdsyOZzwex3g8LjpKHX2v1yuBEuPMZ2R10HvLkQMxAwvqbquqKoAZR/TFF18UBg/b8Zvf/KYE3C5lMRu1LaVrRsrzxbOQE4MWQIJ11GNuMAe49DZobHHnBcN2dt7hxNsBMkbsQczaCNZEcK+ZfcBG3r/d/6wT6Iq3BUWnLdfIEO2kv3xeVVUB194eNCIa7cBXOBAh+EAWHBQ4EwV7+cUXX8RisYjT09PGOh7L9zZG14CAsciLGy37ltmqqhpbSDImDmSwGT5cpdPplNIbsg34Le9n7qCC9/Z6vVJy5HYY0GQmHf2zDDIHyBv9Ze6wf7bPfu6HQDsBPTq2Xq+LTF5fXxe/yrhmZt3lnjzL2TTYcrO6jBvEFf1wAJd34eAd9B3gHbHJrBMY8XeyK5QVIifj8TgGg0EcHx+XcsbRaFRKc9jNCpKJvaFNdLAQez6fR7fbjd///d8v70MXyHKjj8fHx/Htt982MBbzzTgZG9BnywaBB7rHmLqUN4Nrj13W9+yHtl0mMn9s10cvVGSybVhwKDhZBt4AxGl8ot/Ly8vG9/wOp8xc0G+2zVv64MQjNpEoRsGA20yrgSOsFIytU98YVoQTI0L7DJJx1CgsLAQOkTZEvG+4cEj0HzafdFBur8eGQymcqhuPxw0jaQNXVVXMZrPCTheBEPCxo3H7+L8BCgdgrNfrODo6KgvRsnIBTDxu1Hd571zeQZsMqF3PZyZ3W628xwvDOhwOy4r31WpVFsR60UYGzMiXwTBttDOmrQ5iYKvcdz8r/+S9li9/DoDYlnLFSHpnFP5ux59ZG5+k1263CxggnU1gAMDDoLM372g0Kos90R1SmTgnQE9mVQgmHbSOx+OyNoF2np+fl3Gmn8grF/rgcfJ4+zvOYiBbTgFb5532ZW7NOhtgEwwRjNjxwdwBks3MjUajiNjUjvKT7fYATnyPACpnPQAdBm30BYYPvUBODKgzI+qsEjbfgboXSNN3diAATHuRInPugJGsGbqHHWRhbLfbjTdv3hRbyL3Mq1PWJl+410QFBI2BJiAF/4W8Iv+eR3To5uYm3r59W07qg4g5ODiIq6urUhZS13WDKEIfyfhgc7D1ZFKyzhvQ2J+Z3aRPzggaTNEHE2KMPf7IbH3OZFivkGMyldT+MxaWyQyaeRf6Y3uYyz9Xq1VMJpOib+gF44ndsEw5O+HyqlxXjy1hrNvtzTaJEY/B3c9+9rN49uxZKf/i4BXqnAlQut1unJycxMPDQ1xfX8eTJ0/iiy++iHfv3pX5ZLvEZ8+exXg8jrOzs7IryvX1dfzqV78qZzm02+24uroqWVPslW2efaxlxb/nsjSAvzMexjaMWQbnxlX2VZ+u5vXR5R8RzQVz3k3A9/j/3ou6qjbHVCPERPER8d5hAzYuBjrch9KZSbSRAnxgKHCMADocEmk7FlXgEIgi7+/vi1L52XY6Tu24ray2xyHmeyI2hyGYtSBgyWMeEYXdwyGgrDhMgwiDShuvbrfbKMOhHRHNbbIMNDFgrVarBAqsOgZ0eS9sZMNOwAwiUT2BiJl5jLDT0AaK/me2CZnAIZLGZIxZSMW2RRzSklNbDmTM+NJ/nBH3GpRggGDh+L+jfq7swMzCAR4tLwaKDiyQzRyUwEZy8A8BoefTC9NYuMix3Zz0lXcMYN4AQDg5dAvdxYFnkIqOAf4AXvQLgIyMAKgiNs6J+TFIpV84RmeHXKLm8bfMu8wHEOASKH/m7e1YxMY8sTMEcsmznNY3M8UiJLajs/N04Av7bRbYtbnoHiAH/WM8aIPBGWNT13Vjka1JEe7vdDqlZheA6NIS5o7SHew7YJUACkBngoLvQ2ZERMliAeqRE8aEdjG+DgYyKeCL9gLIeBa1wLZZXJYjkwKMM8TD4eFh+f7FxUVpL7Jo209ZkgHsNtDCO/gcUIwemMyiv8gkMmQAhX1kTulfJlacbWS+GHv0mGCY7/E3+obdtU+LiFKCRmBEphfbQA098oPc+x3MO//sbxg3193zHe9ww30E9GTjyNjB5J6dnZUtVvnuaDQq805gA3P9m9/8pmSQsZOM03Q6jdFoFPf39/HNN9/En//5n8d0Om1kjQDtZAF3d3fj6uqq4Q+MQciAee2HbR7juM3+OVthTGfwbT340OVA78d4fTRTzYVyAIQMzCy8GDYm06AjorlCFfZ5b2+vHPTh6AmFx+lEbE5hMjtoIXHanHZ6xbyBWUSUBTi9Xi+Gw2FJO1VVFZPJpDzHhoFUEs7HAuuSDsA9fcW50O/MXvokPjPXjthxVBHNMgVKKAyavQAzIhoAnu9zL47KBsoGbLl83Jf47OysbJvY7/cjYpNytOJZNgiWYDJdLw+gYTyYF7NqzlqYsUbWzOphnGES9vf3y64SpFjpN0b/Q232ZVbY7DmOLbMGZvscIGEEzfgQWFBL6DGnr7kUxtkYxgJnH7EpL8AhMreug+T7sNYsyIWlBljxmXcGoOyn1WqVXX7I+qAjgBdqovf29uLJkydlUZdLKQBa8/m87CID+0kwCOuNw0BenNFg/F3eMp/PC3iHtcnySl8yM8T96BKBoYMq7rOzI3hgbNA9AjrmxrXGdvY802AMWbu7uyuyju55YThlJNyLLtGmq6urImfUvyKDZq681oJ2WEepIadcgy3dsEHeVYQxRS6QS+5n55Krq6vo9/tFZ2k388UY2ceYoaaN9gsmDLKuYkOyL7NM8MzRaFTmqa432+Ghi3d3d431Gc4+0TYYcW9r6uASVpmyKvwuWzO6hIg5MClFNhemneCPbBL9NpB2lpjvuM3IrYNpxgCdcoDkYMzBL8EXaxKur68bNo8MCLp+e3tb7LgPPzJpxlwyL9gt9Aubhf5QsoHM3d/fx9nZWdzc3MTPf/7zGI1G0W4/HpBzdnYWV1dXZX/xXq9XDks7PDwsGaKjo6PY3d2Nv/iLv4jT09NCAJCNfXjYnLJLf6+urhr11/v7+zGdTovfptyT0hGXjFgmKR3Kcuv1Kw4g0QWTas4E5Xl38PXp2n59NKjGeDmiNmMX8f4xpt5ux8YqA2IMEUaJtBVKaYUyy2CgabDrz1FigyiDt8wQPDw8vHdkMMaNLWwAAPf39yUSderKNZwOLgwKfeqVmUj6SElHNpbUXrou2IwJ7GBm86wwROrMGcbJNZv83c4TYHJ6ehoPDw9l03p/h/uspPxkX9kMqCOaC3LMShswOQhyqYcj6rquS4DW7/fLtkdsS4Rxg+mzXNpgMD4uLbGhMVNultTPcKkIgNh9BZzzNy7+D7Dje55Xy5ezNow9GRg7aTskwK3LJmCZ7ZTYomo+n8ft7W2DMXRa/e7ucc9k2LrRaFSexxZj9JV6aden4hBwMoBEt9VMi1k7Owb0xTtu8Bk2AVtgIM54ms2GIXPgDCuFfWPcsBvYQOaOhZ5m0pw5g51yEOCAC7vmeXGghr5ERNnpoa7rkmnAqQJguCaTSWP3JQc0jAWlCtm+Ir+MI4DJuoHuMG4O3pzxYwz4nEDKGQxsjEkDdMBsLbbWWUnG3EDcoNr2h+xNvsyIWnYIRNjNA9BGGt8BF4EHte/r9WO53Gw2i8lkUtoNcCIYvLq6Ku/G1iDvkCq2tTwbVh0dAKQyNwa9EZtadvTbYxqx8T2MkwPJnAFjPPEr6IgDCBhz+k7QxFog7AbjslwuS9bC664c5LlttqcOnrEZgH3uHQwG0e124yc/+Uk5Hffi4iJOT09jtVo1dheBwIE95/RMTj1kJ466flyky+mrYAZ0ZblcliAFUsIBbs7E40OzzyJ4om/ONEdEYx68XsS+3z7OusK92MZcdvfp2lwfvaUeK/zZFcATC/AA8BoEcq8jJrODBtgRUYyVDZyBG/cYYFiRzVBj5PgOoK3dbsfR0VFhXfkugsaKW8AJAMh7lCLcMFg4JRwhbTIDQHsxILTd7d7f338vDYwi3tzclM9pKyAStgAjwwE9/X6/PJtVvzBYZqUdveYggH5cXl4Ww8Y+pDg4A8v8PTszp+48/9yP4hp05CDB4J2LftiYMsaUM3h/Xcsw/0fm7Axx1IxXZrJs/PyZDT6XWTPLpcfH4B4naNab5zh4zUB/f3+/BHswYNwDKEC+MMZmFakdZY5IbZ6cnBSgwt8vLy+LXHQ6j9vPoTN1XRem7eDgoOx5fXNzE2dnZyXtihzheKjHRp+QU/TaOsW4GYxS90jmiTbA7BnM4bi96xCOxtuRIU+MEeAWMsBAmVIOQE5ENGyiQSGAh9IkkxWsWdm2qHG5XJYjjOkDgaKDMduXrKODwaCMhxd72Y543YwvZN5OGRmKiPdsOPJk54ytZwcZnumsjmUe/aGNg8GgAB1AjksDs93JQJLnWvfoh30KrCzMKf7A4BV21wCYNptFZH92Z7bM5sJEO/CgHbQP2SHAqOu6rGWpqqqUezkDyTOs+54ffDRtdwBhu0emjACPZ/LdiCjsKmPqrSEt92AK9Alswd+RU28pl7MMkALMuTM+Zq9tj1utVozH4xLcdzqdRlbu/Pw8JpNJLBaLUo6EPYI8mM1mcXp6Gt99913Joi8Wi2LnKIUEZDOvYAXm0/aeLJEzT7zfmX1sFO2y3tm28FxnasBhzuYxjjwb24p88GzL44euHzOT/dHhxs7O5vjwi4uLxmIb2CZvjh6xYaXNIBlsAUjrui6Lk2xQUQ5qBD25gB3uM+Ngo+WUH5+vVquy367fw+f8o/0cWuC0CG3BANoRoYCO+Hm2U2f00+wLzBDCjZPO7CNsOo7JAIzFHdSDmcXeNq8Gk1w2QtSUsUUUDAnzZWBOmw36qCVlu0E7hmysczoyX2bRbFwZX9qFA7y6uirvt5FxEMX7DAB4t/tGkGZG0+yH32/Hbbbb4ID+oAvUl2dnmN/lOXJGptvtlppAMyIwHPQzs6KkKQnQWq3HVDEZHp4PaEF+0XeAeLfbbdT12eAznrSJU/3Qbxt7anI9pmZ/rVtO6ToLgdOg3QS6dV0XmSczhAxT5ww4xXYxPwB/am9da057cIaM+Wq1Kov7sFn0i2dbXtAdFo+yfzp6TgkMTDw2FHBAoA8zanCTwbaBtEEKcmfWi3nH+aJP9MVzRf9Xq9V7DLkZYsiJ+XweEVECi226iF2ELfVe54Bbg2nrTy7JQV6yvTMwwf7jW2BQOWSDuUXW+Rx5JBhqtTantXY6nbKmCFlCd9B/Lq9Zoh1mgi0ztqcAMOSQZ8Nau488k/Yy3uh9Zit5R8QGOCMjPJ+Fzu6/58egejAYFLmlTIXSpOvr68KcI9eMiUE+soscb7ORBAD9fj96vV4cHh6WtSLoJlu6sgUm60m8mD7Lz2QyiZubm+j3+3F+fl4W/19fX5e2XF1dla1CCVDxzYeHh3F+fl7Gxz7NoDYiyhjZr67X6/jss8/i8vLyvT3bHbwzd8wx/hPbbH0xgWObgp5ty+R8uh6vjwbVADLYpIho1FFxXC6TgxFHoHH0Ec0FVzC8L168iLOzs1KjabbQTovfrTxmJLgQYDPX/NsGbBAup8ts2Gm3HSztcakCTq7VapV9R305fWcFdRqWPhoUsUE+Rvru7q5xWAv9bbVahZ2OeIx0AcM54MmpHBy808CLxSLevn1bask4HY1nMafOBDCmOAGi+/V6s2jKdclsKeS+MC6ec6f9uGxk+B7AkMCDK7M2Oc3ly0w0LFJmsc1uZ9bYbDuXy00MAjDaTts5q5IZdRteA2oOS/AODPxuYOVAjLSv96CGefEcuySDsQZ8wzpdX1+XwA8QRB8ZH+9H7B0dYKvIYGUWh/FzStsMJIE5zNdyuSzHp7sfACLLxHr9uNgKoASoj4jG0eboH/3xuoltgBnZjmhuM+dgGgCDzTH7i0Nz0Nxqtcq6E4gI2urAh5Knh4eHAlYBKXaStJ32encc5iQiSiABUOb7jD/995qX9XrdAIqZxHDJiIG6g1fGwul25sJMvn2C3+OgMBM2mQDI9sU2AntMoIMvofQPoM87AP/T6bQsAOXv7hs+w/bGANFBMAGNgTUX2UePL0AMBpeMEePiLBj2yDX0yL0DjNVqVTKlMKtkG9wvM5yUfbgEqN/vF7uB3JJ5MBtLxsPBH2Njm2z7aF1ptx9P/Tw+Pi4+xu3FR1xfX0er1SrZn9vb23J6qxelQjwwNiZuKIPCt9HvfET7/v5+PH/+vGyZ5w0bkO28dgw9RO4oFULvTNxlhho5M6PNXOWxMymRCVCTYB+6PjHVH3EZ7LAohQUpDDpCY2C1Xq9L+hUHFbEBkqy6HY/H8ebNm3JyEkrpFAd1yJeXl+8xK2abUSaDYhy6HfE2dtpsqwXVRh1DYmYShgBlhdHiH2ACBozIdb1elwUJdgoYNxwqinR1dVWifDP7KFNOQ93f3zecr8spctmAU6nr9ePiiLdv30bE4zZ9HEmMkcuZAs877SEAoE8RGzDBeBtoeQ5dGwnIyixxZlzoB32xvGVWnfudps+yMxwOYzgcFsaCPrvEx31Dbpi3iA1Ah6FFTuiDgz4D5Rx85f/zHTPTyKZPQ4Thc/0042Dmn6AJ2caJIp+0me861QrQW602R5wDPnAANrgE4Th92wbmMLNOOGaeyS40HqfBYNAACnk7S3SG39FJGGEDKe8rzF65ACPGBlkzgQA4cIbCrG3eF9pybFYau8OzDw8P31vsRJvQJeSOQIjA2KQEKWWXhDA21mnGzow1Ms3c7+7uFmDksUXuKDlD/hlbfIZ1E2CKfjGngHOAIvJqBjwDLsBGxGbtTi5xoU3bMkNm5eg/fsP1q/QZm87YwOqT8redNvCxn8MOuJbeQbbnJ2e/er1eIzvFmPCT9hP4ORiwXfFiXJc/GSBHbBYsMs6AbffLBAIB6MHBQSwWi1Lug41qt9ulFJOAnP+TVcYWORi0DCP/bJ86GAzKxgPD4TCqqir7nZs5pl+tVquc5spiUwc49O3u7q7INAx7u92OyWTSIOWurq4aekqG7MmTJ3F/fx/T6bT4CgfrvMdlX0+fPi3nOazX67JQ8ptvvmkEhein3+f1KQ7sTQaBFezfLZNu36dr+/XRNdWkYfjdTgFj6fqmiI2hA4zwPRQcJV+v13FxcVHesbe3F5999ln86le/itvb21LjiUPh2bTFdc0AHCIzs0owUrTBRs794sKYo8A4Ob6PwHmHARgYQAXvsyHH0D48bPb5RSkQWqfmCUgAZGYRIzaBRXYK7O8LaIjYGGMAlRXFkel0Oo3T09MS6Y/H4+JI7TSdPvL4wUT6wA76ldvhFCdO0+2DGfX/eTfzbnCV04NcOBLaA3BiYVDEZmtH2Erq7kgLAjZYeMO4mm101gJZdIaAfhlYMG9mNw3a+a5l1vXGEVGCTt53d7c5Nh5AxHgbXJtVtzNH1mGIDUwxzMvlshwq0+12C1NDapQ9eyOi7JVsx0tAAAAmyKT/mQXOQVXOYLCS3o4hgxRfBsHef58xBIDSXztIaoEBJ8zHYrEo9sfyiM4z1gRZbhPPMUOH/bq6uipy7bIXZKqu68apfIAQgzqAjckIZBanmtnmnG0gk0HffK9lmeCA+WVMABqeTzPHmYkl84CcEuT4HtrIXNBWZwDdPsu3mTlnvHyvbZxlybXJ2xhll9MYpHsRp+fPQIa/WZa4Jy/WI6ClL3zX5R1+vxfvYbeQSzOWZlBt1xy0EFy59CXXunuxPzuXUP6CDDiAJBD37kJZtmhft9stdmZvb68A6IODg1LmMZ1O4/LysoBm7IlPBD0/Py+MMqWK2EfeSUDlGnvei95ZjlhgTBDCtn11XcdgMHgvcMG2MrbYAHYPwh6zY8rOzk4hfY6OjmJnZydms1l8+eWXcX19Hd9++21ZbFlVVQl8sFOMLe0bDAZxdnbW8A/IvBnxT9f710fvU22mKmID3Pi/gSesTGYZzUbCUJrBQSjv7+/jV7/6VUwmk8apaxHvbwOHM8SB2BhFRMNRGwQ6kkZwaKdZKhsVp41pC0bN0d/e3l5RHrMpt7e3Zes+jt19+vRpDAaDmM/nMZ1Oi4LloIC/YwRwIjAHMIdmDAHljCv3Oi1vJ4YTuLy8jKurq9jd3Y2jo6NimPiOAyp/n7E0mwZI4O+WFwM5QABOPbNmtJWxMZhkHuysaIt/x+BhHAlSDIZ5n+fMi9FgUClzAGTnNGUGrsvlsqz85z7mNGc9+A6Xxw2jCtAlOAFQs4jGc5Br6dFndpgxUwTrgg5wH+1wMOpxZ5s8mBuMMG3PrD0OH4Dm8gg+B7CwcJHxzADZQZfrEskSWZ74Lm3e398vASv6hI0AzPEdapfpO3NoWUCGkQvuY94BfASbXpCMrgPobU/Rv7quiw1g+zrIBGp3eT7fta0D5ACgGWProPXNQQ0XwYBBsGXBASTzSdkB70FfYBYzc+uFp8isWWv+Zt22TBmceqGu7bRB92q1OV3OlzNJljn65UwIz3SAb7lnXL2QFzn1c7zYnTZCzhi8mxyJ2NQ5dzqdcrKf/SJAEP1FHvw7QaqDVmwM/tZ2GPuJfX94eGicu+CtapkjxocglVpz2GzsODvUmCzBLjDPHBCE7gLYuZfg/vb2tqyfuLu7KzpEPX9VVWUtErbamRl8Dm1BZy1/DnzoB9vxPn36NNrtdvzu7/5uvH79uthI17kz5sikA14CAUpRsOt7e3vx7NmzWK0es32np6fxB3/wB3F0dFRkzXJh4g5fy65O7AuPDNinZbD/oSuTFj+m6wdtqWfAwODBRvB5u92O4+Pj2N/fj+vr6wZQzNFyRJRtn8wOwTZg8En94SSpoeOz+/v7sggB4bMBgo2KiKIsZk5zCi5HjjhqR60Y7hy5YYBwOrTZKX9SsN1ut+yIwDsimunKbQxEfhdzcHNzU9LqNobUWLpOymCOuaGODDD1/PnzEvWbXc3MGmPN53l7LZSUwIo2k0onPQ1gxMl5bLNRg5UyO+RyBBs7p2jJuiCLOC0DamciACeMPXW/LLBijslMuI4asHJ7e1v2d+ZyRoX3btMzdIV7YXCodwXMUdPsrIaDEtgU5MYLTp2idh06DAYZJDOnEY/MOHXMdszMB+M+n8/L57wrM8nUdkc0gbe3fUMOPU5m+fgusu05Q1bJ/ACeHVR54SHjT1CATeJdLIrk2d4JB3DB/dxLEIRNIV2dA330yVkuB7TMMSlss4vY4oeHh8ZBKsgbMs2zkG/28UX3PF/MC3NEG8zEZsaagMTOHL3CDrlmmzmnLtU6mQExQMCfIa98D5/CszPxYVlFHnMwALDZdmW58+WMHrIE8Fsul+UchPv7+1IOSL/MGtMeZATfYXkjoNsWUPpZDhacIUJ+zBabxOF+l7dxH3aUMajrzZ7dEVF2q6LNEZuDkZgjZB2iiUwLR8DbXznTgg+1PcYWssc98uj5qKrH7OPNzU0sFotGHTxjyE/XIFsmTaT5ImvFotXT09Pi0548eRI/+clP4u3bt/Hdd98V34UdAhtkhph5t51EJ9Hb3/zmN8Wu/vW//tcbWZ3nz5/HxcVFAeu5msCsfc40YZNoTw44P13N66PLPzA6VgIzYDgkjDGb+HPhmGFaLOg2zAcHBzEajeKrr74qi4xIa8CCwSKjlOPxOJ4+fRp7e3vx6tWrsiUPbBTRLIxXBl84YKJA2pWZVe8BbYXC+ZvdwbGx1RERMQCfmldqy1AawBysIYAKNiBic2AOf6+qqjhZbweIE/T2Vs4wmG24vLyMs7OzqKoqnj17Vo7gjXh/+yw7ObNPsBVm/PlpsImhuL+/L84Rh45ym701YxbR3Js6YhMY2VjQJoyJAZl/J/hAXpya517POYGcZSMiCiOAPMCEm7m1Ifbz7MB4JsbV/cdYU26BkSS1CUsGe2lmFwCJ/BOAYSzNxjJOBLzItVlXg3wDWi7LRg5+vDCH+bZcmZn1vPJ8Bwt+V8RmT+scgNpWAW6pQfd9zpYwt8w1zod+8J77+/u4uroqgMBlDtyP88fm5EM9cp0uc2QWm201HZCaTUOOzdAyr8wxdpt0PbqEbeN+ggpk0eSC5wO9xCYjdwaNyHq2AdZpdo4wOUMGiEASG+uyDWTMthd9si+CjDHwZOwtg7TT8mxwmYM3/51n8k5ndlhX42fyfebPuud7eD4ZF8bZxAEyhu47eOA9XuyGbPBZzpbZxucMLzYjByKwowR92FHKdZh/5owxWS6XJcDCJh4cHJSShIhNaRuBMXMX8ZgF2d/fj8vLy8J44097vV4JIukzdpksuH0+/cSWe/7ZWQm9v76+LuNEYHN8fBw7Ozvxm9/8ptiSX/7yl/F3/s7fia+++qocCmMSCgbdc+lgotvtxtHRUdzd3ZVgw0QlfrTdbsdXX30V4/E4vvvuu7L+invN4DtYRlbMtpuYdHv+Mib6E1P9V7wwgAg8xgpDzal6sLFs4G+2wWmIs7OzaLcfD0BhGyuzhj7mGABK6YSBEgLdarXKoSTr9bpsm0M7Li4u4vr6uizEQiEcjbLIA+PlCDcDaEfmAHEHHE4vuoyEmiUWbQGoAMdOibGvKYoc8X76GcPGZ6T6cEQYd2/thUJh4G5vb+Py8jKm02lJJfkodIykmRkUzalkg0QHYBHNUy8xyDg4HIWNiEs2crTueaANyCRjCqC2/GUg7WcSeHDhEPx9Z0fo/8HBQTH2zBusMc4tp/Tol9lYjC/3bNM9lzLwPeRkOp3G9fV1I0ChxjkzHi4HcFrfwQ/zYbCa59V7Sxvc5gAwpzgdXPh9fgfj7QwRz7fjQ/edmeC7XmRooIYjZq6QT+tuRDTqDcmGwcyjawQpBOYE8QSMnntAthlnBxHIsDMojJdrVJl717wyPsiPa1vNmFKSB3towMeYUIqRs5LUb3J9iLliPEg9E9CbCTPQMntGQM5YESAB/i2nPIe2OiD3+NF3b53oPviZBuK2E/kygCaA4feDg4MYDofx8PDQWIzH5WCP9gMy0St+j9hk2hgPgq2IzWI0ACilf55bjwE+zTJmPXW/CXhsuw3CGTOTIfhRTkkFAK9Wq+j1egVY7+/vl8yNg7fZbNbYqWY4HBaiyXvEgyP4STlFq9Uqh33d3d01MuXoGn3H9mHHrIMEmTDfgE0vUGQ8Ga+7u7v4gz/4g/jlL38Zf+/v/b1SSvTZZ5/F8fFx/P2///fjq6++KvXaYAZn79G57AMo6/T3sIvWyXb7sayRbDPjPxgMSkkLbcZf8hzmNmeiIzaA2nr46Xr/+ihQbUODgXDpAQeBTCaTRkqDyJzJpLj+8PAwXr58GavVqqyAPTw8jNPT07i4uIinT5/G3/gbfyO+/vrraLVa5aja+XwenU4nDg8PI2IDOL744otYLpfl/VVVlQ3Xq+ox3dPr9eLJkyfx8PAQ3333XQFfERuGwODOrLGNYGZXvOWW2U7S5ThhjCMAlEVj6/W6AAjG2M7a7AVOBkaMtLIZ7czwksonLXdzc1OChvl8HpeXl3F/fx+Hh4eFAWBxBW0x45yBq9NJOXUIy4XM8B1vneVykIj3t8GyQ0IWmQvahSHMK+0NIGxYDYRog1N/2fnCNHMvrApBF30CSBtkmvG0IzZo3Hbl4IK/2bFNJpOynRNjXVWPp4PBoHrhjNOcedwBZJZ3ADQMKOMDkEM2zBrxE2AJwKXOknmg//zdwAQ9iYgCBJkDLht/Ayq3H0BHwGinSemGWTiz8XwXW8B4XF1dNeSK91P6g3NGxyyzDhB4njNI/rsBuNmrzMB7LBi3DArX68ddUcwK0h7mBD3OzCRzi+xzr8tr+Gk2Ez10bTcy2ul0GrX7zDGHa1nmCdojNnba76Lf7Xa7ZAWxjYAOv7uuNyVdfM+2weUkHmfLFXrCXAJ2YTFhAT2O+AL6g0+w/mXbaf3E9mNvkLXpdNpY94H8UcNMKR42DmBrWeMzBxGsoUKn7QOxfegwJyCen58X++A1ODDk9J9AAJBoEotSCfwGpYHr9To+//zzODg4KEeHUycNgfDkyZPY2dmJi4uLsnMHMk9WhzHnndYFghdvTer68MFgUPBMVVVFHnd2duIP//AP44svvoivv/46Op1OPHnyJMbjcazX6/ijP/qjsuUhRBRjbpIjEz/IF32xfTMWw/8jl/ko98vLy2K/nbnAptjX0iYTHLyLOeHEyE9X8/ooUI0gYZjNAgLOJpNJzGazBrO6s7NTADVGpN/vx3g8jvv7+/j222/j4eHxuGtA4WeffRa/93u/Vwxzv9+Pp0+fxtdffx2r1SqePXsWL1++jO+++66kdTg2e7lcxuHhYRweHpaFGqQNF4tFvHv3rgA6GLODg4MGeEaYXOtqY+NV02Zz7HgxVDgojDlsAquAcWgRmwUvjB2ROEpvNgpHS9mDP3fJCcwZbcHYLZfLsq3R7u5ujEaj8l6AB+NpQG0wZMABEDGzxeUaNUAWAReXnajZewNtfvr9Bn9+Fg4/A3AznBhwjI23HfIzqBvc398vTgHjvFgsSlkPRo1+fQgsezwYa/d1vV43WGYz2wDcuq7j7du3MZ/PS03c7u5uOc3Q/Qa4ebcEgL7rNukTvwNyB4NBHB0dlQCNshbAkLNNs9msISdOuaJT6ITbhi563BgTQFF2Br7HwLTX6zVq3gEzPJugEV1CJwCPBsYAcZftoBPopZlIs9+MIQDZoI4+k33wd2kjfWY+YZ7QR/+fOQOg4FyxASwSXCwWDbDkdjsgR4edkWH3G/qBTJrlcgofGwvYMwC1fYyIxqJJZMtyaTBrW2122KQBDKtLb9Az1tRENE/g9btMnHA58+bvU8pDnwjU67oux07Dzt7d3RV7YZbdmTJsumv16SMyulqtyqI6bDWyhWwagKHz21jGzBYjG+zY4WCjrutSasE4O2jAnh0eHha9ZI7McFMS52wOu3b0er0YjUYF1LJ1HECcrCoy2O124+DgoABiZB+Qiq4hnxHROLMBAMqce7EhukQ/WThp3et0OvG3/tbfiuPj4/jn//yfx9dff11OVFyv1/Htt9+WdSrOEAOEHUBhDx1IWZey7PG7sQn3eT6RTdtY5MM4Dr/LPDHX19fX5ZnbMjdc1tUf4/XRCxW73W5jlfZqtSoLBlarVVxdXUW32y2s8du3b4uRNPhjz+S3b98WZ3h+fl7KQ0gXff3112UbGYDD/v5+XF1dxfn5eUnBOo3MkcQYg+fPn8erV6/KPpcAe7fFexB7uzUcOultgzKU1HWGLj+gz4eHhwUYUQ/JjgN2sGYQeJdXomOY2SIN4XZfAGNONRus+T18j23jcp+snBEbMEstMf01GPZiGgwlwZTTXBg3O3AbqczwWEmZP899xCZjATBwQJRLB3z5b6T1uN9pRxZ84RQJzJhbAhcbchsuOzPm3AbN7CPvA5iasVqvH08fvLm5KfMDqGWevdMB4+DMCXJsNo95NFvBxRZ1rjHEOZEOnM1m7zHJZvIZ64go7NBgMCiMB2wUi66c4ncAZ6eAw3NtO3pm1o7vsNjZgQ/Pt4PECeOgmRtACWBgMpnEcDgswYgXTNEeAxIOYGKtCTrKc50JIHBkXlyTznyalaZfgFpsnftmmTTLDuBHb5lndJBgGLDsrGUuj8NOAMoZF9piEIFu5zpiz6/XZ9BPdImsGofVcFIhY88Yef0M8socm/VDRnkPv5s93gYYCFYAd9x/dXVVdH1nZ6fYTs+D2embm5sGuMU+A7ix+y5rimhmkbnMgmPXCOL4jhfT2k7hP7IMQp7Q1oiNzWThaF3X5bhvAJ3BIPoOiGadE+tEnj592jjkjK0uWQt1eXlZSkIvLi4aZScsxOcAOWQAv4Vs8n4HhA6q0EcHNtiP+XzeILl+8YtfxBdffBGdTifevn0bl5eX8eWXX5YtNd++fVuew7gRGKKveVcz/o4NwnYzh4wN12q1iuFw2MhKQr7gc9Ft5B/dPzo6amT3KY9ttVpxdXVV7JaJsm2B2afr8frohYqUV5i9wOhS0vG7v/u7cXBwEP/sn/2zePbsWbx48SL+6I/+KG5ubuKzzz6Lfr8fR0dHcXt7W+qqh8NhqUMjVfFnf/ZnpRZqPp+Xzc9x3tRvc6rY559/Hq1WK169elWEltPUjo+PGyxPdh5XV1eNbakMUr36F+Ngw+tIPSIa217hgHH+o9GoOFUcKPd4pwDeYTDs9wPkMRr8HdYVcBHRTCne3z+eGgVrYuBJH3HUdiKkEL04z86RtGceO1hNjxHMnN/trECupTZr5y2IDHh8P2Pn1JV3V7DTxajzbgNdxhGQs1gs4uzsrNTh019qGp26/20supkjBxRuEw6N+mjehzNFfmBneIfXGvD3DFg8VgAOAgFnn25ubuLm5qZ8nyPF2V8Vw+wgB2AIiPHCXRgo0tSr1aoslKQvgFKz7GY4zb7lYIUxNMvidLnr/iM2e8C7bMFHRd/f35d6eZwSwAiZW61Wpf2wXQRedV03jn4nkwCYQR5dsoIcYYscfP5/2PuvGEnTbD0Xe8OkDZ8Z6SqzqrK6q7raje2ZxvaWIEDpgIcAzSFFCCKgSwESIAoSeKcLnQvdyAAiJBzgXFAkBEIiSIgEsTfBvbln9sz09Jie7mlb3qQ3EZEZJn0YXeQ8K974O2c2a5MQD9D9A4Wqyoz4/+//vmXe9a71rc/ZSNYXIO3lDQTagF+/+JkHU95dhOefnp6OdJNweQGEMLeu38gz2Qnv5Y3sAeYJJryNooObZBs9B7rScH+OByQ8m7KdZLBQLBYDbPnPk0GBv68D7iSgdruVTqeDKYeggZFOAmpnUrkv90ImPSsGYUXAlM/n4+hrfLDbSc80sWbIB0DN2Uaej/1JZjcB5gSrExMTKpVKOjs708HBwYhtoqMV8szhJ9lsNg6VcptHFimXy2l+fl7FYjHIum63Gx2NvJNWp9OJ8guXZ/Zb4cO63W7sdxkbGwu94Pm8J8Dby9YYP36P7AqEIM/9+te/rldeeUVbW1t69uyZrl27FsEnpCAdQVh79w1ejoE9Y8xehoKvY10dY4CDwCqetQK3ScNAc2lpSalUStvb23Ha5/j4uBYXF7W0tKT79++PEIbuY5iXX3V9yVS/wIVioci0ggNojY+Pa3NzU5ubm5qentb169f16NEj1et1Xbt2LRjstbU17e3t6caNG9Er8tq1azo7Owt2m1Y0pNJIP5+dnYXybW9vh3N+/Pix+v2+5ufnI6Lt9Xq6ceOGWq1WpKskjbTQaTabYezckCA8KB7MIGywM6TSKBj0DW/S6Al9RPaUrQDMAS+MQRqCKTbNoSD5fD6eR20W30vWdHJh5GHzk6k5FAHggRFxBp85wfjjiHlvjCCG20sbHOQkU4HSMIXuBotxOTjHeXidcpL1BbBKn++iwfcx/j7fzna7M/d6fmQd+XFHn3TU3NPLgjyzwkYYPoMTJc3OXI6Pjyufz6tQKIwAaeQBZwcodkbRgxxnr/kcIN7rH2kJ5YwmDs2BFs/2kgA36si2gylPfboTRJ6QX4A6upWsTXXWyeUXGfF6TAfQXDjaVGrYMo75QU8By74xCoABewvITKfTUSZEsIVzlxQZPgAG68c8khpmneiO5IwjABobAkuNjel2uxFEAAiS8ujdfNwGMI/cF90FaDswxqmTvk5uNOdzAEfPyHm/cdbb64oB88gOf1ibVCo1IpN+IQe+MZz5ptyNsflz3Db43PjP+Lfbp7GxsQgcuB+tI5k/Lt974LZBGp4vwH2RMYJRiCOCz3q9Hs+RFB18/F2cCAKAsda8Azrn88aGvNPT0/Az7JXis5QDEDRMTU0FyOU9eF8ybvg49IINnPl8PkoPuSe6KSlsIGNtNBrB/HqQ1+l0RogQALRnrLic3UYW8FmsMXYW/0r5E2x6v3/Z6eT8/FydTkf7+/va2NiI8rsnT55ob28v5Ml1MHmxbviw8/PzyGhLo3u7kplG9+EE/p6R5P1mZmaUz+fVarV0cHAwUqZGUDM5OamdnZ0gThqNRpAFXsLqc/nlNXq9EKgmWr5+/bqmpqa0tbUV4NnZJCLK6elpra+vq1arBVim5dfZ2Zm++tWv6u2331a9Xtfp6amuX78eu4YfPnwYClkqlUKZULj9/X3t7++PsJYcTkK6BcV78OBBAEU3+jg4HDcsA++Bw8FRwsR5ehVjgeK64PkmHmd7ACUYHQeg7vQdvGAsfPOeG+dOpxMMhjPcXMfHx2EUYecBjMkoFLDqpR7MB0yip4B5L8A393E2VlI4IC53OHwuWR/LnHs5AwaRi+cBAGARHVg580l5iPfsdnkhAOh2L/cJsPmGDInfkzV25gvDyMU6OcBgPL6bPZu9PKyBY24xloVCIVgrwAROC9DkDBjgCgOblCd34AB5n1OcrbekY96YL4IcACuyj8x4Cps1ooSKewEAcQi0jnN9YKzejYL19o176ANBHfImDftv+7t1u91gOr37BADfZYPghzGmUqlgspNMGboKOwsL5xkraRgsU94Ak4UD9DpOMjtkBQh2koHvYDAIIOZZCtaGNfbA1oG3zyHjBPAy7y4jydpQ9Jy0NySCO2B00pl7CAPYZWQGZ+6ZFmdjCZIoL8SvnJ+fq1AoBJuJ7vT7/ThC/JexftgCt1s8F1lDl3xMbF6r1+vRIcYJALddPIv5YW2YczImzIMf4kUXKz+dmMAGX0GWyO0oF8SMP9NZbgfcFxcXUbfM57DHnOrJ/fCLMNSFQiHAG88lQC8UCpHFqVaroU/tdjtAN0SbExowuhBsZM0J5pgHZMdtALLs+0DALP6dZCmTl8KQif97f+/vxV6mpaUlvffee/rxj3+szc3NmI+HDx+G/cYuYIPc9iHDvpcH+SCDBHmA7DCXjlO4kmA3lUppdnZWxWJRh4eHWl9fH8n6pVKpaHpA+dRgMFClUlG9Xo9yIUhIbMkXmYn+i64Xbqn3la98RdevX9f29rZOT09VLBZ1586dYFXy+bw++OCDAC1HR0fK5XLa29uLPtHUGH/7298OZhvjcnx8rA8++ECVSkWvvPKKqtWqjo6OdP/+/WDP6J08MTGhdrut4+Njvf7661pdXdXDhw+1v78fRg52FSOOM4A582gftgmDisIhyAARGD1AAODBU8nSsOsGjgVAiTI7C4Ui48h5PuN3ZpyyDZyIgyfAC3V9kmKzFnXcKKGnBRkvzzg5Obly016yEwVALbnBAZYAuQE4SsNoHAcOyMKg8U5eC+rzw3tyL4y7g1wfo9eJ44S93APH486v3+8HmG42mwGg+K4f8OIMqTPwHihgGJ1FcFkAsLOW6JKDJNaYueEezCfv7Kw7Y3BQjBEFvHEPBxEuz64bnKp4dnamRqPxuW4DDgRh63xueEccG5frGg4DIOTzhC57mQvggXE4a+Nz52VcU1NTAVwJIvkujp1aR2+j6O/APDkzzXtRf8y74VDJPlDG4eVMHnRhO3Bq/B8wDND1DdPMnwfF1Kc7WM7n8xFoOODmfZOpZn8XvuMbufw8AGf9HKwlCQ3ANOCQe2Wz2ZEevOiWkw3MI99hLgCVXs7F8yAHWH+6TXHxLAeXrtf87frOfCMHZHgIkviMB/3+jKt0zuutkTNkkHXFL3igyBy5/UmWE2IL3I4RCJPdcH+HzLPW1NV6iRiB3Pn5uUqlkk5OTqKEk6wboJySETItrBkbram9985WBEYQW2TIvVWpM6nIo6Twr+i9Z06wD2SIaP3LRT0znzs7O9Pi4qL+wT/4B7p27ZpqtZqePXumR48e6ZNPPolAMpvNamdnRzs7OxFYu9wzDubP25165shlivVyH8X/fd8TcwBG8qDj+fPnI7pUKpWCYe/3+0FWZrNZXb9+XW+99ZaePXumn/70p1EGyHf/Y5jqLzLofiFQPTk5qYWFBR0cHOj58+daXV3Vq6++qpWVFdXrdZ2dnenHP/6xarWavvrVr0Z3i88++0zZbFZvvvmmCoVCpB52d3fDWe/v78cJQzdv3lSlUtH169eVz+fV6XRUKpXi5LCpqSldv35dmUwmAHyxWNTu7q7W1tYCPLZarTDebJTBMLrTJa0L40UKzJVQ0ucYahgjaWg8EXQ2tZH6khROkO95HbUDPgA0jiqTyUTQApsO4OR+09PTEU1jmKThLnJnOHlPd1ruSBgvhoWyGE8ZOnvsJQVEs9LQ0Tt7zxq4syFI8dZN7tid7eS+fI419KCH50saOaIbZw9TwIYRZIJ/c282pSD7/i58HhDoMsDcAcwANN6BhmAGwOaAl3mmjZMb+6QjZl48UGFMpFgpbWA3OvrB/cjGuLPFsTlA8A2HOHZnnXH8DoCSMuU/Q+eYJ1hGDyI9wGW8Lj/OhnpGJSlHHsQ5u+vj9QyWOyBOX/Qd/9gNyku4CoVCBBewaj72JFvvNiGfzwdAh6ni/ZCp5OWBHbLPOnj5Fe8H2E4GeJ5lkUY7xlzVjtLXATYbNstLIaRhr/FkiQNrnU5fds4A3Lh8uy4zftYZ3YbhhvmWLssh6NvsAY3bcwe3HrQ7wHaQnczqOXilpeVgMKw3dfKC77i98vtgbxifB1LJGn7fp+ABvBMF/lz+Te053/GAkPXgs9gEGgZIiuwNa0hZFN0hmCvG1+/3VSwWo2QF39Xr9bS9vR22Fbmg7SxgEKIhmTmjPK7f749smqcECd+OHYH0YX8KOkNA4Ueo0+rPa/Kxmd/61rd0//59fe9735MkHRwcaG5uLjAK9ydrhO91uclkMgFoIcCwFQRS6D66xL4ydJNgAezEngvuPxgMVK/Xw9+6/HAPgnkIgN/93d/VK6+8oh/84Aeq1Wp67733As+4PuJrPPvx5TV6vRCoPj8/1/e//33lcjndvXtXt2/f1vz8vE5OTvT8+XMdHh7q7t27+mt/7a9pYmJCOzs7evTokcbGxjQ7OxuLODc3p729PaXTac3Pz+vg4CCMJx06cOAHBwe6ceOGMplM9Lfu94ctaiYnJ1Uul/X48WMdHR3p5s2barVa2tvbi9QqBirJ8PA7Tx96KyOMfzZ7WYNcLBbjuG7aJO3v74+kwAaDYckK96MeCkOIAfO6QMbHZ0m/wSh6hNjtXm7kBCxjLJ2Zw6F6atdZlSTbLA2BOqAbUOI1xyivn/6UBOnOjCSdPnMEQLmqxRPrDyAZDIblMxhYZ7KlYWcLP13OgxbmnGDDN27AShMQOAtMWtoDiiSodWYYtgiw4se/YsQdRPN5ny+/pzt21sKBOpkWN37IAiUKfMfBdxJcOFjiXanrg5liPgeDQcxzsVgM44uMM7ekhMkMeQ28j8UvjD/A3jucJMsIpNHWlugycuvz6gATB+2lXzg2gh/W2YM0HJ8HvxyDzDrh1D1bQoDOmiRZ+unp6Sjb8vdDNrzTAxs7PXOFjeIzbscABQQBkkba1VHC4Q4U3UdPHAjzHPQaO+2OHv1EtzwQY73Pzs5iPMnMC3PtIJ9xIGOesQAIOSPMZjYHzW4v/DnIO2NJssh8h8NKPCOFXgJcJiYmIkD1vQKAUGSH+fQsJf6IDXvU6fr40DPS9owZuXedAtwTSAIUWePk3Pt+iXQ6HWxnr9fT3NxcsMfYNUlaXl6OskLkivGMj48HGYSN5fAS6qnxp51OJ+ZKugyK2FCK7vDOBFY8g+Dj+Ph4JJuJTgPykwE8ZTWUrXiAgYwRLNy9e1ebm5v64IMPVC6XVa1WNTY2FmWoyBuBicsX810ul0f2QiRlGb3F1jIWfFkmk9Frr72m6elptVotNRoNVSqVsMu0CvaN5NIw6+gyPTk5qWKxqPHxcW1vbyuVSkU5L+dWYMuKxWKcGukdn37V9SVT/QLXYDCIGs+DgwNtbm7qo48+UiqV0m//9m+rXC5rZWUljsZ87bXXtLCwEEKUz+e1vr6ucrms2dlZPX/+PDZZZLNZvf7665qbm1Mul4t+tzA8hUJBhUIhotSZmZkwPru7u9GaZ3t7O8Azi18qlQK8A165N8aAyBZ2ipRQPp/XtWvXVKlUlM/n9fjxYzWbzegcgYLTLUAalg/gbABnsHHSsK4SI+vOS9IIc4XCYdTdiVOGg+Hms7ynR+ZuTF2pnS13FtqBEEwU8+rAHEDCe/EzSSPAJQlY/dkYH08Xw3jwfd4NoOFAnZIV7gWLQoQO68NcO4CFyXAgzu8YUzabDbaF9/Nghf8DnmBC3YlJo4yjA3J35gAWGFVn0fg8DhRWMMlie6CHHLFODpBg+/wZrVZrpKSDdcQZAsK8QwMy5vdkDT317GvqAZ0zl8iKA2rkEtlzXXE20uWUOcJBMSZAULKzBgFsOp0e2XjneoHTg2nzzzjAYZ4BKZKiV7SXArAu6LcDCd4fhs6dNoAOWXAdGhsbGwkcAUKsGz/3wCEZYMAg8y7+e+bIN+0SCPBZemHDunnJAu8DsIah89KXJMD1wJK1JSMIgUI6mzIBBy1XOXrPvBEIuP7wGTIQ1H/7OMfGxjQ/P69WqxVtEj0T4wAb/fQMBTrnjOb6+noEBcg564B8MP8+fj6H7WA/AjbOsyOUfAwGg2Bp0dWZmRkVCoXIfOLzj46OVKvVQnd2dnZULBYlKU4JRracsOj1LvcW0DOe8jnkxdej0+lEqYk02qVHGu7Zwl7gE50th0hjHTKZTDDtHlgwlzDeBJ/IwsTEhG7duqWlpSV997vfVb9/WZZ27do1PX78WGtra8pkMqpUKtGu0+0odhZ5cbLDddMzP9IlaF1cXFS321WpVJKkKCuhS8ezZ8/UaDRCzlOpVJyaiE4wv8hDr9fT/Py8vv3tb+u73/2uWq2W0um03n///ZFsCfPd7XZHggXkGFb+y+vz1wuB6nw+r9/+7d+O46tpp3P37t04/YjT3VC8TCajRqMh6dJQZDIZzczMaG5uTo8fP9a7776ro6MjTU9P6/bt28rn8yOR6NHRkba3t0dqqKrVakRORLT0fd3e3g4llIbMG6c8Ec2T9pA0UhaA8zw+PtbExGUj+rt370Zt6/j4uJaWllStVuNY6Ha7rWq1Gt1GANqvvPKKZmdn9eDBA9XrdUlDJgSjD1BLsijMAUb49PQ0BB7jh5MvFApKp9OxmcPrKBmzpw9hlJgPdyTeTYH1Q0GZT78w+s5SuiPjffmMp+O9xjX5DB+jG+ijo6MAMzTvd9ZH0sipfc7Ys9YOLs/OzkaAIrKBcWNnvzTcgY1RpDaQewLEMEJJdp9/e8s4AB5jIHXuYMI7eThYYw5xFswb6w7jwNyyMS/JUMMie7rQn+H1jegh7+LOmjIAfkcwgKwj/w4AvDQCOcDBECABhnEOzrIyft+854GbM7oEZDD36Bw1mc4e+0ENzsoA2NFRD2Ic1PKudFbBYfseD76D3HBPfkaWAFvlWRh3woAnB4YO4mBYyRjwBz3FNngwgmwm2Xmehb77vhAcsbfj4/eAC/oR8ywAEkAG5tlLEZx9hMhgHScmJuLQMWw9Ou1MI8y935M/bu8cvPPukkbKSHxOM5lMtInkeG3XXQf0HlRC4GDP3Z76gSpJph2ZJijzdDysN/f172Kb0Tnuh245M1upVLS8vBxBSqVSCUDdbrfjNNFu97JdHQfQ8GwALfIvDTcLswERO0WAXCgURg6xyufz6vcvy0f8hE334S7nrN1gMAhiy+cVm44v8PuxJgDudDodR6PDqv/pn/6pWq1W2I6trS2tra2FTTo6OvpcCYvLOGuHXHPK4sXFRZCKfsBTt9vVxsaG0ul0ZN35HKdQenlbLpeLUlPO3SiXy3r99deVyWT053/+52q32zo7O9POzo7+9b/+1yNBCKc1MqfINrrDHPvvvryuvl4IVE9MTOjVV1/V7u6uNjc3lU6n9dJLLymdTkeE/vLLL0f66+TkRA8fPhxhdYnovvvd7+rRo0fRyH1ubk6DwUD7+/tRSH9ycqJ2u62dnZ2RlCebAObm5iRdgqv9/f1gx32DEM7Xo3RAhbfRkYbMDzuYZ2ZmtLCwoE6no+fPn+v09FTVajU2QXa7XR0eHgbrIo2mNTc3N7W7uxv9IxmTl1OguCiE19JJCgYcow6jg7MF1BGdYgycSfRWaSiFswP+b2dhHFhLo22BPFXrLApzLX1+EyEA3kElgJnLQZUz+WQUYAMAdK70gKskuz09PR3pOhg7r9njc85cIq9ec9vpdKJHNQwTRh1mHyAJ48m9vEsIcsg68kx3yLAkBK/+3mz2AcABiAhCGR9g24Ek84+MMfdJxs6DHGebHbTi4AgMGTfBijNMXluMbDoI4N0Anb75zcu3vESBekSAnoMHwE632w2AhTyiBx5IOeB14Mg4+Rly61kOvgtwBKjRpqrXu+yadHBwEPJCahzwgX75nA8Gg2hp5sAFvUSGsHdeAsB7sCEsnU4HY4yeuTyiR5JCJh04O4vKurE+qVQqwARzwncB4jyLzyVBXq/XGwG+rBU2DJlOpVIBsvg3AI25d3vubDdrhZw7857M8vAdDhHD/qBrAELs9+np6edKcph3B2z8njlPbrJLXg6C+ZyznMwDMoW+cCWzQugR/2bOer2eZmZm9PLLL2tiYkLb29s6OzuLkww3NjZG3tPLugDo6DLPQL6xEWxoBDTPzMzE/I+Pj+vRo0c6Pz+PbkRkPGC/Dw8PI8DE52JbsOME48mSDydYeB4ZBPfF5XJZq6urWl1d1c9//nP1epcH0LEvK5/Pa3Nz83ObiDOZjBYWFsKWTUxMaH9/P9r1QlBIl+Tk9evXo4QDX8I6UH9O5xRvGoCcUGedy+VUrVY1Pj6uSqUSm3Q3NjbU7/f18ccf61vf+pb+4A/+QLu7u/rJT34SASxZA7AD+87Qc/QP3UXWklnqq66rZPmLcr0QqHaHef36dd24cUOS9OjRo2iRV6lUtL+/r8XFxdgEsLGxoVQqpRs3bkR6YmdnR7VaTdeuXdPXv/51LS8vq91uR7ugfr+vcrmsxcVF1Wo17e3tRQSfyWS0tLSkl156SYeHhyP9Mon8YXr8gjXj3xhgDCUghCh5YmIi2HhqyDqdTrB/pH1grKnto1/m1NRUgCF3gDDTOBIAiaQRJ4ajw0gg+BhXaqf4LrWgGFHvagDgwEg7QMJIO9i9ClB7vTFOm5/7rnUHxs6IwfZ7Sg5wIo1u6HFgivMBfJDS7Ha70ekEdprP81l6b2JsMYCM0zfEATi98wPrDgD27ikABz/i1gMm/o/zBXziJGEmuI+3JETPeA/mA5bPAaezaoBsZzDodJHc+OaG0cEIQEgaBly+wZL54k8SSCQDK/7tzBD/T6atHdwA7JK/Q27RQWfBPTPirf34Ls6Ed+bifozLSz6QOZ7L2nrZmDPjrJGXmvhcuQMF3FA61u/31Wq1Rpw1ZQLUgDqgZ70IJGCIPa3O95Mt/dBvAkHuR4kKc4ps8DPmBICHfrOpCh1wIAM4Yg6wu9lsNtoUOjPL9xm/g23G6uQA74KP4nuAAN4b+QRc8Uy3ezyLutOJiYkA73R0IJD2oGxyclJTU1OR0eFeyDmyBZjju9gsACr2kfd1HcU/UN7D/fics+Ku4zyHtfJgXJIWFxf16quvqtfr6cGDB7q4uFCpVNLh4WGcgFgsFqNV3uTkpBYXF0M+Li4uor0mbfPQQewUPemXlpaifW6n09HU1JSePn0ard/YN1AoFCRddh2hN7XbPPcjPmfosuumM9DJ+ZKkmzdv6u7du6pWq3r+/Lk+++yzIK6QeSeCkCfmkLaNlF8eHR2p0+mMZDCZj3a7HW1+sTG8B+/Cnin33e4rC4WCisWibt26JUnR5YMzPhYXF5XNZlWv17Wzs6M333xT6+vrEaBD1iD/jB05SRIp2FJ0+i8C1V/k64X7VC8tLWlxcVGzs7PBIk9PT+vu3buanZ2VpEi/YaSpU+10OlECUiqV9Id/+Icju2e73W603WOzX6PRULlc1vT0dJzotri4qGKxqKdPn2pjY0PSZbrWHSeAmlo7Z/6kIbsKm0hq3AFvq9UKRwY7DROIEAKmpeEmK8pjstmsGo1G1GjzLO7vzBMCTBuiZErPgZo7ZYwWjLU0NNz8mwtnRLQPaMMh+biSTgqnioFypo+fuZwwZpgCP5Idg4gzS6ZL3cHzTIwfcwiYn5ub0/LycqSdO53OSE0e352ZmYmSBICygwNSyZ6uhNHg/QhoYDQdSFGKwfgcdALIvKwp2Rrt9PR0JM1J6tDT0xhcZ+b4HQFnsrTEy4wYvzO7lDgA6knxusx5Kp7AEHnwoMkzIcytywNjTbK90rCTC2DSgzfk3mWEz3ppAePywBE959nILO+CjDFfAD2cCFdyTKw9Mo6usO8CWaTml/ui56yDOzdq1rvd7kgGyrMCrG8yIEin07GOyVItnpsMOjwgZn3Re2clAf6M2+XQZY1noXcEgpACAH/eCXn0Q7n8vXy9PTPlwRj65wDbA3zGmQRenlEAWPAeZCtLpZLGx8fVaDQi28P6+H4HD+zYS4G+JzMHPn6XawdtrIXrvwfKzG8SsAP2HJCzXrwX785aZrNZzc7O6vbt28pkMnr69GlkKuhUQ1CBzbp+/brm5uZiTU9OTlSr1aLWGKZ8MLg8krxYLOrg4EDn5+eqVCpaWVnR+fm5arWapGH9+NjYmGq1WsgJmxrJsqAnuVwuGF5qfp15Z96cwMB/ZzKZAPK93mVt97Vr1/S1r31NX/nKV/TTn/40upBRlpeUPw/EWIfx8XHNzs6q2Wyq2WxG1nxubk67u7tB1BHEIhd81wNIJ6eQbyeqJicn9frrr0tSsPWTk5M6Pj6OwHliYiIIwF6vp/X19ciWITM8s1Qq6e2339aPf/zjKO0BSPsYkfe/6HI9+yJeLwSq+/1+tM7b2toKAahWq1pcXNTExITu3bun999/X7lcLtp5wZSR/nzjjTdUrVZjc0Cj0VCpVNLS0pL29va0u7sbJxFhGEqlkrLZrNrttmq1mtbW1qJWCWWjWbo7LS8NwPm4ceL0I2nYQxaDncvlos82zdBhnh3cukFEGUhZwVRms1lVKpWI6HFWGAacsrPrjB9DwXtgJJk/2BRJIyDOQQljc+eD8l2Vrndw6SUysGo4NDfW0udBEwyygxCYeGcz3UE4S8aYHRAAomZnZ7WwsKCJiYko56CnNMFNtVpVuVwO9oCsA+9aKBRULpfj+PizszO1Wq1IozmAYj6kYQcFr/sGRHiGgPfzACOVSo3U1lK/m8lcbooFbAJ4cNJkInx9ALJ8hmyLlwq5Q2ddkB9ANQCb+5EZYc0YF+sLEHPWD70BxAP+3PEzZoIPQCWZBwCtyxPfQb4Zh29sBDj6Rj4cnsskcoZj9HH5vZ3pgmnyMThjyOd8AzeBD2sL2OedKPfyzU0AGOaAsfqmY/QfG5AEqMwLIB058Zpnz1jhLB2o46Q9Ze33cMbewZkHWcgNc+pMNwFjkuV3/ede6I8HV8m59M2PXsrG7wFDnvXx9XfAwhoA4ur1eoBMDwzy+XyUZfEzdAE5xq/gB7mSMsc8+5h43yRoJvjFF7Dmrm/8wS7xTNh05iCXy+nWrVsaHx9Xs9mMMkvs1MXFhWZmZmLD5OTkpK5fv65CoaBUangoC2VyZIHR616vp1KppFqtpsHgslPQ4uKipqen42RCNkI2Gg09e/Ys/AOkT7d7eagb75LJZD7XU5oNscgW32csExMTkUFJpS47d7F3ZX5+XgsLCzo6OtL+/n7Uk5+fn+uzzz4LggEighKWTCajlZUVNZtNHR8fK5fLqd1uq91uxwFDZOU5xZD1RHdZP9htL7Hw9We9kM1r166F3q+ururi4kL3799XvV7XycmJpqen1Ww2I0A5Pj7W/v7+SOko906n06pUKtrc3Ize7WAEB8eedfBxfXl9/nphpnp3d1elUknT09Oq1+uq1+va3d0N1vbhw4fa3NzUxMSE9vb2dHx8rMnJSd24cUPValWTk5PB5ADWZmZmNDs7q+3tbX366adqtVpqt9vhXKvVqt58802tra2p0Wio2WyOpGD4N4wFQoqgugD0+8OaZoyRR4akSZ2RIoiASQSUHB4eBpvD89ggIF3u1uUi9QWYwMF5RIrR59mABpSBz/lmMgyaNAT0KJyziUkmEaPC++GYYQVwTv57Vyiek4xIHYjlcrkAiHTh8BSdO+0ko+6MJCCA+0qXpS5TU1MBkp01n5qaUj6fj3pUatIODg7CSE5NTenatWsqlUojh/Jks9k4vMCNKUACthBZg9EsFovRNtLfEQPsDC9gyNk2Py4dmWUOCMQkhf4ALpB9agt5F99wmkzXwdZfVU5ALR+bf50NRxYIwpBf0q2AV39PADjvhHNErpFB5t5rI5Fn9Ia1d8Y4lUoF0+N1qdzTwaIDOwdq/jfjTsqg65eXRHQ6HU1OTqpUKmlqaipqKGHQcVDcd2zs8mAG3p/1Bsx74OP6ABAl24HtIvMlDcEqa4/8AlScaUq2Y3PW3O0n8+p7MRgPa4cs+twgTwDKwWAQB2SgO8kWeQ56+dtBxVUlEZ4RIghNZng8KHbSxUG9g5hut6tmszmSTfBU+OTkpGZmZqLsj8/4/BSLRc3MzEQ9roMm1twDN798/rDfycxjcm64PMiEsEHOXH7T6bRu3bql6enp8KsEe8jI0tJS2KXZ2VktLi5qfHw87Gk+nw+Zm5ycHDkToNlshv9nz9DNmzf16quvqtlsRomTZ0SQ27OzsxjDYDBQrVZToVDQ1NRU6LA3FyCgZH4o05MUGcBut6vl5WVlMpeNE2B2KVXp9Xr67LPPdHh4qEajocPDwxEfhW2kI5mk2Ct2fn6ug4ODsKe+0Z0NjB50Ii/ojQfzns1yuR4MBgHqkVdO14RQYtMupSMAfLLdvA/jQB53d3eDuPKsp5Njrlfo75fX1dcLgeqxsbHobYhBmZiYUC6X0+7urj7++GM9fPgwNji89NJLsZmwWq2qUqmEcfW66enp6WgG/9Zbb+no6Eg/+tGP1G63lcvl9Morr+j69esqFouhcESybELAOHpfXU/fE8ljbKRhFIsgexcG0k9uzCmdIMUnXdbswsqfnJzEUerSpUFgYw6MLUDIU47c2+uGccixUFaiQTkNGQDeHbYkCTowCoBq7o3jxnBc1TXD2WSUM5mW5/8AKv525kvSyL2SbI00GlQ4mwZL4uUZkj6n/LzD1NSUCoVC1Hy2220dHBxEh4BcLhelIGxWZEwwzdR/A3yRDb7jhpFuNs4KOSvn8+4gwoM7Z/UogXBGls9w0IKDVhhTbw2XzCD4u1Cz6Ie64LgJApBVHBcb5SSNGFaeg5H2Eh+CB/SL39NRw+tK2aSWBLouHzCv3Bu99w2XADgH+cmUpQMM/z9r5ywSv2ccrB8Xh1TR6hN2mlMBvRwFp4kDGxsbi+BP0sipmugBwZTbCu9vzxyidx58wIKxvt5mD+cOuPWNmdzHGSvkw8vCGJ+fjkewAegha+TzhvP28g3exefbSzR8TRkbPyPrQgDA2JzN9IwFz+U+zIWvPXPuABw/Qs15s9kMIoZ390219Pz1gMnX0eUi+Z5++f95hmdh+LnLMzaHOXGANjk5qfn5ee3v70fWjndLpVJaWFhQoVCIzOq1a9e0tLSk8/PzOFCNo8YhwJAVfFS73Y6NcOl0WtevX9fExIQ++eSTAHuUmnknCy8d8i5jvK/LmDQkI7DhlOak08ODr6jnpxRicnIyzpdoNptaXl5Wt3vZdIBSloODgyCFCCIhMJAf9pkMBoNo0YkeeMaE9/PGCH7gHOtXLpejTLbXuyzjImOFDLLBk2Cj17ssU+12Lzux9PvDDlleOpZkvKXhhmOXe3TQSQX0w4MF3wx71ZUMFL9I1wuXfwCuiAbPz8+1tramZrOpk5MTTU1N6ZVXXtHXvvY1ra6uBhhyEIPhYWMLGxlfeeWV2Fj16quvanFxUdeuXdONGzc0OTmpTz/9NDZrETGijNRUS8P0KhG018cS2ZL6dDY4CXJdmAE13iTea1FRKhghd+60XCoWixEMAA65P+k2lBKj6EfuSkMW21u5OWh1pmgwGO54dxBGcICBSJ7cB3BnLh1E8PykQcARS8Nj1H3e/PvSEMghVw7CnG3hb47s5h6kxb1LCsbend7+/n7URDIW1p7uDL7unrZ1ZpCNSoChbDYb3Wy81Ia5Szr1ZPcP5uqqiJ93ptyDINEBBfPEz3zNXa64AJ50knGm3LMvrgc8F8PMevA7ADMy4M/3zUQ+F8yf6xbzzP8BDQ4SkwFZMnPDPPb7oz2KCUJoR8XaIZ/e8YPyH+7lzyQ4cmczPT2tlZUVzc7Ohk4il9gUOv9gG5ypYi9HuVz+HOADwHjgApjG4SLH2FbvcuBZFk6aY58B9k8aMta+vwKQ6GAQnaTEhvI25AGwA/hmnLCLnpVydo6a3KtAJ6xfKpWK1HO5XI56XycQAI3cn/f3DA3j591dPp25YxxJHeIZvV5vJJ3uesj6A3g8wEF2kM2rgEeSdWZOnbwgkPDWdegNwSr+yDMazCc2hYBKGgYY+Xw+SiKRW8aFXrGWu7u7wTRDOlDm6e+ezWa1tbWl3d3dyNhy+WmGyawHB8FRMue66K33vBwnm80GqPT9AIBrgncA8vHxsZ4/fx5ye3FxEdlMSZqbmwtgy/qSzfaN7G7fsaFf+cpXVCgU9OMf/ziyKJQSLS4uRvMFyBJKLAaDgZaWluIEx3v37o3I3/PnzyOYBdB7Z6xMJhNZfsdsyJHLPPIL2YF8UqKZJB78/19eV18vBKoxULVaTdvb22o0GuE8pqentby8rF/7tV/T22+/HakiFuHk5CROTSJt2mq1okd1tVrVzMyMms2mtre3Iz2/tLSker2u73//+wF4Zmdn9dFHH8XCklb0FLMLUKFQCGbWmWdabmHker3LE55gmpPtraSh83FjLg3TkwAvr6HDsRIZSwqn5DvGqXElpTw9PR0AFdZnMBjE0a84OmkIqKUh8MUhAQYwCBw/7ewhwQHgDefvDJOzeSgjCspzkuApmdbGeCXZGE93cT/qg1lbjAbAFVaCMg8MW6PRUL1ej77dkiIjMTExoVKpFDX6jNkdHc4SeQIwwTDk8/mR0haP9NmUSaDHruxM5rJ+v1QqjRgkQAxAxMuDPPCgvyvP87ElSxYw4M7qIPM4FebQAZMHN8wLqVWXD+QdJwsA83pdl0XYGfYHcD+/PHCURg/dccCJzpF29c4rzInrK6AN3QHQEUz53MDqAmBhodDDwWAw0gu+Uqkok8nEAQq8r9sAxgaY8VQu9Y6Hh4dhawAXjUYj5hu7Bxhxdo55B0DwTA/EsDmAeDIF2DPPlHBfdMGBJrab7JhnN7ArbHTyDISDbWyBB3AepLMmDigZN6fzJb/nIDlJKric+Vo7cHagy+/4HL8rlUoxv91uV/Pz87ExzDcVu+wzDmwDf5IbeX0MruPOzHs2EHvEM3hn9lGwngRT2GHKA7LZbDDR6PXExES0Y8WW4s8WFxdVKpVG9lq02+3I4HFQ1GAwCLALEQHL2+12g5nu9Xoql8sjYyHIdLCKHCEPzAPte2lqwDtCfADwsYPgEN/HVKlUlEpd1oXv7e3FHgfqv5l35tiJC+qjvY4bX0VnDXTy/Pxc+Xw+9lGdn59H+RcZbrJp7JGiFnt7e1tLS0the3Z3d4OB96wd+ortJePtgabrAJ9LEiB++X4MJ8/AIn9R+ceXTPV/5HV6eqpPP/1UjUZDGxsbGgwGWl5e1ssvv6ybN2/qzp07un37dggNi4fwwSzu7Oxoc3Mzji0dHx/X4eGhnj59qsePH6tcLke/6/feey+aoEuXC7u5uRn1l7Ar/MEpSENhn5ycVLPZHEkNwn4gMChNKpUaOYHKDSUK7cYasOytrAAPXi5ycXGhzc1NSZeOs1Ao6Pj4WJ1OJ5w+DosjzgGCpNqvagnn8ywNe5HiUHGMGGScKU6UC6OVdER8xhlF/z1RLf+GASeo4R7OVCUvZ8mREXp14sipV3O2h76igNjj42PVarXYQOMpWW+Vh/EnC5CsWUz25cV54KxgJ1gf5A5Qzwang4ODaKvkmQXmizV248XzvfwgyV4BUHG8bhQBVOicZ00kBcD1zZ++vvztaX56tOIUMK7JLI4Hfn7P5Du4/MC4AZ5SqVQETMwRQCQJ2r222EtNSFUSoKIDPBdZGR8fj135sEiME4dPxivJtlLLXK/XRzZpUSLkzDWZFg9cuJwZZg63t7eD7cNZEyCzNt6LF/n2AJLPY1N4FnbG1w4w4wwoQAVABACB7WS93BZhG1l/1gvAx9p7+RLr7bLN5aByMLisIb2qzMX3D6BDXj/vYJ7rquAeEOe/94CLgJuzCiqVinK5XLCgnt1z0O/gxokY12nGndQZ9Jn/I98EMvzMW+xxsX6Li4vhaylXZB5TqeGmWc6FqFQq8bzl5eXYAyWNZq+Ye4ILZIHPoI+np6exdthAMkqUf2Jj0Vv3VQSx6HGpVFK5XNb6+noEiZQCAjiZSwgQJzfcB7AeZDNdvyFF+A52AV0l2B0fvzwQbmZmRteuXdPHH3+s7e1t/eAHP4h5gnEvFApR+tLv92NPFDX81J7/2q/9mu7cuRNnfdAb+zd+4zf0zjvv6N69e9GNBvnxDLMTLs5Ie+aDOabk05sYsH7ICH/7+3x5XX29EKg+OzvTxsaGpqamdOfOHb300ku6e/du1DvDoCYjbhiZRqMRRfHXr1+P2s5isagHDx7oww8/DAPRaDT06NEj9Xo9VSoV3b59O2pjM5lMsNmVSiUiYgwStdcIDU3UkwJB6gWmHYOPUcPZOHvHfWBJUTCUG8X1jhfOcOB8OXESQQdAYhBwZDgTDpnhFEFnFTHagBv/WxoFN54W5vKyE2l0E0wy4gS0+889zQywdOOeTNczfq/lpFbea7kJTpx59/o5/k9gAtCSNNK9wFsSuTMHILJODuxIq52cnERv0bOzs+gPSnqUTALve3Jyonq9rqOjI6XTl8f9UtLkwJtxulFzNouxetDEHPJ/3oP1Y328ZMDLfrxsBD1g3gG2Xh7E5xhzEgz72jqLDJvB/3k/1hWHxPNIWZOmTRp2QISz5slA0td6ZmZG6fTlKarOjjvYR84JQNwmMM/OAqXT6TguGGADGCcgRV7Oz8+D9aO0AcZsbOyyjz0gm+AePajVarFxi3IzAA3Bp5eJHB0dhawzHmywAznAa683bLmHnWGvAex8Op2OPQV8H9uYrG8l6PfMGDLjARB2J5/Ph22jDMEZXQe63I8xARwymUzoo2fW/F6srwd93N+JiSRD53aL9/YNlazLzs6O2u32yBHsBDxud/3dJI0EX0lQnwxIeAd+lyzv43MePGWz2ZCva9euaXl5WY1GQ7VaTalUKvaTMGfYr0ajMVKuUSqVouMPmVtJQW70+5cb9Xd2diI7gQ5QlsTmRTI5yBB2p1KpaHV1ValUSrVabaRvP9mcg4OD0K9SqRTzSPcxMAGf4dneweXw8DDYevw2GzN5P7cv+AbWm7XgGaxjqVQKsu7WrVtKpVL66KOPtLa2Fthkd3c3/L7PAeCXTBqyeffuXf3Df/gPlcvl9N577+nBgwd68uSJms2m5ufn9f3vf1+dTkflcln9fj/6dwOKnWxzHfDxY0OQI3wRQQv2El3jd66fyUzjl9fw+ksdU76ysqKVlRVVq9UAeg4KJYWzol/r/v6+dnd3Q8BXVlaCsdjf39fKykocr1mtVrW/v6/p6WmNjV2enFitVnVycqKFhQWVy2XV63VNTU1FCgWFkoZsXCaTUb1eH4nOUBhJKpfLUVZAnTapYdrnScONHx6tYfABBqS3vFa21+uFwsIaJE/pQ2iZLy9HIKVFKqxcLodhcGbRU1Xc0xXD646TCgUL7I7O2WpXTq/tk4aOgvfyUobk+NyRe7mD90aG6YJBlIZ9OAnYAE/FYlHpdDoOJyBLAENInao0BJrOXDNHno4HmDjDRNkH7Du12JTneImNp4MBGgQbDiKdhWYNcOaAFf7gYJg7Zw7c0bLuHjzBoHFfB+MOgvk5QNLZS/7vRhWZTYJc5gvWzkEFMo3jZINzOp2O8i3mHjAJIOYdAX+eHUC+qbvEnlAXT62l14o7+OPnbjMYH7JD71dkjFabOEZkmO8WCoV4BvLDHJ6cnCifz0fHAWTG34vvIBukYpkTdJl15vsEaAStyDzywTu5XfOMluu3B84OzicmJkaYSIIeZMrb5SHX2D9K3txGuh3hQsacKOBzbMqShodVJUttuIfLdDJj4gQCVzLwYj4gTfjO/v5+BCHJWnIHHM56cz/klft6YMiasH5upx2AewmJZ2sAtJVKRTMzM5qfn9fu7q42NjaizNDHiG9hTbPZrKrVqmZnZ+PwFg/Me72e6vW6+v1+7IWidplg0oGXy7a/28nJicrlsu7evavx8fHocS1Je3t7AeK9Drjf72tnZyeIMOS0UqmoUChofX19JEPc6/XUbrejzpsArtfrRcs79A0Zw4bhD/HZHmjl83nNzc3p5s2bSqfT+vGPf6x2u62f/vSn2t/fH8kQ4gvK5XLsaQArHR8fR/eUvb290HFKW09OTnT//n298847sUn02bNnI6eygjcYu2eTk1m9ZKCNzPF/D6DxHY4hXEekLzcq/qrrhUB1uVzW3/ybfzNOqQMUSUOHjrIDMlutVtRg7+7uanz88jjNjY0NffTRR5HymZmZ0euvvx6dRGipNzk5qXq9rr29vTDGtNRDUbwjBGlRSSMK4xHX+Pi45ufno4tHt9sNBhxg7Y4uKXykk/wZDhwR6r29vWBkaQEHS4kyoCBEmQC3bDYbqeSpqakAlb7Rgjn3dI40Wt+NISHtllQqOkAka1rdUXiNozTsXQ1ocFbTHSDfT9YNs1709Wy1WrFDmneiJo7UOg6BLECr1QqGAnYU5poNfu6QYbacsSMoSzpwZ1JJIfJO5+fnI8yt14xSjsL84+S9I4A7qX7/smuBpxcxYg6qk+k3DCWgGTn10g4HzozFDR3zxs/5rMtI8m//LuAp2REG5+plSD63rgdsxmONAHsc1ODv7EExa8N7IlsEAXwOeXKw5wy3gzBJUUbhLKsHBXT3YK6QTy+R4N0I5Kj5ZJMyz2aDFlkyxknqm/UEkALEnE2DSeMkRt7XN18yLml0MyHrgx2ippOg02smAQdJ4sLZwW63G3rNSbKsFS1GCVAApPyMy8sksF1u25yhRwfZ2OW6npRzJwau+r0Da9cNAhRKEqhJvri47N9M0IK8+/iQtSRT7e/EOwNaeK6PkTVi/ARArBFlchxGdnZ2pjt37oy0PMSn+PtCImAvaS3Kxj+CP7p9nJ6exj4VSimo1Wec2B6egT6zVmyI56RKQP3k5KRarVYcTsJ8eoaE8gjYdk5nPj4+1tbW1khWy+2el5Tl8/kou/CyLz7PPhlKTVgfnsuR7dVqNfR2dnZWJycnqlQqOjk5iYCLVou+ydIDh1Tq8hAd2nGyR+Ps7Ez/6l/9Kx0fH8f8cjYHOu/y4hlJ3h95caLGATX/d5vo90O3PavpWCqZtfzyGr1e+JjycrkcUYozkRhBdmU3m03t7u5qf39ftVotGJx0Oq2dnR19//vf1/HxsV577TXNzc3p2rVrOj091cOHD/Xs2bNoifbw4cOIoBcWFrSxsRH1Yb3e5e5f2BpPHXu9H5GabxbCMEijp1WdnZ1pf39/hBlAkNPpdNSb0fZG0khNHenC+fl57ezsjDhqB66uBJ4Gx+Dye+oxcdx81pXB05nScIMORp1SBX8nZ0z8SjoBaZRZ4ufj4+MjwRWOD+Xzkgpv2QYzeXx8HO2YvBaUezmzPxgMVK/XwygdHR1FShggBbiHPQbIAla4Hyl50nasL++E3ACAGT/Gi7lH/vw7ADwYNFLjDg69XtiZceY3WfbB+mLcPVii3MhbbCGHns3wQIHAANlHRmDWXQ5w3Bha5MaZEd7L06bJNCPAhLXk2GNki9Zb/N/fARkCqJHm9PfC/vBdB87oUCaTiUyIg3yOLQbwsg/DwR19uynLaLfb8XscExkJ9I75oKYeG4XMAS4BVe12e6RFpm82orabziRedoFOIzvog59S6+U3AF0HoB5cIFtsmMTmjI2NBYOHLYU19LIL1sfLtpwtRzYGg0EE0gBFBzLMa/K71ANLl2AD/eB7DgRclh08e0DJ/5kXdIZAh04T1LHTMYM188uDMJ6LnLDxz4NCZ7KRZ/epSfbQ7z0+Pq5isRg2cXp6WoVCQSsrK8rlctre3tb5+blu376tu3fvxsFYtVpNN2/eVKlUCkaYDfsQDADPg4MDjY2NRflhs9mM0jbqd72W38kXr5NnjQC5sKzSJfO7vb0dNgH9ZY2QPXxVsVjUSy+9pEKhoK2tLW1ubqrT6QTZQd0ynUymp6f15MmTqOtGZ3q9XpRdEUSjP/grr1kvFArRRvDi4kKHh4fa3d2NEiT2iI2Pj2tubi46nbGGuVxOBwcHI9nHjz76SLlcTr/3e7+nv/23/7b29/f13e9+V+fn51HjLUnb29sjcoxMuO5h85xB5lluh8EczsA76eLZYOwKNgZsgo78sitJ4HzRrhcG1Z5G8IgJ1vfk5ERra2uhfKenp1paWorTkx4+fKidnR3duXMn6ra63a6+//3vR99MDlDZ2dlRt9vVysqKDg8PozVWOj08XQ5BwtnD2jj7y2ZFLxFwpaW1DuwMRp2fe1TGMZ9JdpzPEa2vr6+PGFWcqAs094ApcqZGGt1VTAQKKHKlh3H2DX+SRrpv8F4oirP5/NzBnTOFzkIwz7lcLmruWAcH955WdkYHQOdlF97lw9keUvmnp6dRVsC7wOQ5O82mES+1cJlll7mkOCXMWWQfqwcDnkZ3ltJbtzFf0hCQAqAwWg4GeQbrBRhKlmIwn86QAjauCoq4PGiDpSLo9PpPD/YcxPrF+FmXZMqcoMUDKWm0fSK2gYt5xWlRY8zYXJ6QG09xeuBIUEMmgvXHFiDTBG0wjv3+ZV0ous77UA6WyWTihE5p2A3InQyZNt9s6uuMrAJwsZPOLNPZxWtXCVZZe69vBLA68Paa+IuLi2D2WHcYM97BA0kPnGCwCAAJUpEn5BK760EUG4yZG9bKAavrhDPyvAefd7mi7pf1BLB4ls7lNymfzCF/u531oJb7wB6SQWNdPDXOXDAfSRImqUfJbJH7H/ySB68+Xrd13kqR/6dSKc3Pz6vVaunx48cxLy+99FLUfKfT6TjeGlmhLhefe35+rsPDwwDLmcxlaeTm5qaazWaUYLnvwBZ6hxFk30kB5Ig9AoDfJ0+eRPcb5tK7bkGCZDIZLS0t6ZVXXlGn09H7778/UnIyNzenO3fu6Pj4OOwMoJMyCTJHzG25XNbCwoIkRfkhAeH09LRmZma0uroaWdFOpxOnLz5+/DjGxUE/W1tbcTrv3t5ekDH4a8YjSfPz8/rmN7+phYUFLS0t6Tvf+Y42NjZGNqCiZ/5vJ6dczjz7jc1ElgmqscV8jzIu5gYdwmbyXMcQrPeXTPUvv164pZ5H3Cg8Br5er2t9fV3b29ux0Ol0Wmtra3r48KHq9bqOj481Nzen1dVVzczM6OjoSE+ePNHR0ZEqlYrGx8e1srKi09NTzc7Oql6va39/P1I91ILhcDCukqLvMMwXaXUMBIYbJ8Xl7Ik7bkCLt87ihCQYJe/akU6nR3btLywsxIlObNTEwMCcems2BwrpdFr5fH4EEAO6UBCPFnlHLoAi33XQiLNyIMhzua87BIAYzyFN6I7J2VSAX7/f/1ytHY4FMMfPHEA6qMbIkJLEEXu9G6DGgTnv4PWzzLF0eWgHxsaZYTpd0I/cawK9RAeAxtzhAGCLAWe+bklWjn8jQ8icrzfv65sRYVSQYWSbP3zWMweeGiUT4IDEjSTvC/BHNwiw3GEijxhonLgztg6uAV0wQtgRsjw4B3QMR8ezXUaxP/yNLSoWizEvpKuxWzCPvvZk2GBy+T6brJAXnBVjQAawSegj8trr9UIuYXbZIIX9KBaLIT9eSkRNI+uALYVAACi4AwbgOmkAmEY+AUqwdMgFzB7fZWMl68PceEkBIIESFOwgn/fyHC/tIsjyTA02ytlkbLeXNMEYJ+14ks11AOtZPAfs+AdJIwHY+fl5sJqw89zHbaMH3S6XyQCbv5N2lefzboPBYAQE8dxcLheykslkNDs7GzaqXq9rc3NTGxsbmpi4PMJ6enpaCwsLmp+fV7/f1/r6ugqFQpwZ4bqB7Wk0GiGHExMTcXhRs9kMn8beBOYesAiJQiaJeSPgzefzeumll6KEgW4d9GkuFAox7872s/6+Z6fZbGprayvqogeDQQQXT548UbVajcPkOOUQsIm+sN7Xr19Xp9PR2tqaCoXCSMaVIIPTCTudjqanp3V0dKStra0o36pWq3rttdfChiwuLsaGZbelXl9eKpX09//+39fv/d7vaX19Xf/u3/27CBIoASET7mRKUpbxC9hMl1GIG/6PzyeD4CWMrKfjC9aU53lQj63/8rr6eiFQ7ZG+1292Oh1tbm7q+fPn2traCjbhk08+0cOHD6NR+fLysl5//XWVy2WdnJzohz/8oba2tlSpVFQul7W8vKxmsxmbpFAmHPHe3l44HtL/GCYAmLfkceAmDVkZabghjPdKMnEYv0KhECkqNh06+0XdGGkgIm2MBw4FNmFubk69Xk9bW1tRH4kwSwqnTR2is03JlLuXShC5YjwcqKIwDk64eBcMqoMr5iGVSgWblk6nIxDwFE+v14tWQYwbJwJr6M9Lpo8wzmQheFdXbMYD4GJNMbjOODsL5NfY2FikB5EZno2BhhnC0Xn6zEtZSCF6XTxA1EsQHOQ4o+hgCHnk+85ae0oPucLBMTZJAd74nLO6zJ0DZACZA27/7lVAm987WHCGhM8Ccr1MgecRrGIn0GE2TcF2IVfMFz9LpjKR81wuN9JOkeDGU52np6cjpQ0eYLos01cfYEDpBt8DCNBZCNAO44jceJYAx8wGbn7uwSn66WwqNi2VSkWGhaycBzfuSL30wjvheB9jGFLAP8CIeaUMBicKY82zyKQhv55FIQt1dnYWR94zTj9Mw0Exa+s2i0CarhXYYA9QAR/8cXl3ttdT594NgkM0JI0ct00G0nXC5d5JGNaPuUnqCWvrG7ld38mk0t0IuafEY2xsLDrKlEolTUxMqF6vK5PJaG5uTrdv3w6Z4HTXVCoVJUt0tel2u5qenlaz2YyTED0gIGiia0W3243A1n/P+LBrAGpsq3R5zPns7GzoOfap1+vFxl6wAMCQ3trIAXtrCFwpjUBGscN7e3sqFAqRIadUxzcY44unp6e1tLSkUqkURBctVtGTWq2mqakpbW1t6Y033oijwNvttsbGxpTL5TQ/P6+/8Tf+hkqlku7fvx/63Gg0VKlUYl/BwsKCPv3001iTb33rW3rjjTf07rvv6vnz57p3716889nZWdSVgyWc+GCesHvuZ7g/dgDQja55MMzvsKP4Rt4BMsr3zPEsvv+rri/LP17g8nQZRv7Jkyd68OCBnj9/HtHnxcWFTk5OtLy8HI7slVde0dTUlPb29vTs2TN1u11Vq1X1ej1tbm6qUqmoWq2GMTg5OdHs7GwYAd/wd3BwEAAulUpFOyjfWU3ET7s7DJjXXpO2dMcGozM9PR0bzxBS/p3sSTo7O6vJyUkdHBzo13/91zU7O6t79+7p/v37YXiuXbumbDardrutUqkUHQAwatR8zszMxGYJB9IOEjESABoH2XweA5ZM1yTTpg6mnB0DBLLBBCDghtUZDNaDcSTbEfm7+P+9hMTHOTZ22eDfSzrciPD+OCqeg9Jj9H3zjIMaZ7O4Hw7a59xZcGSKg3oAD8wFoMKZT+YZlt9T0A4svTTJU8sAKpyI7x/wbg+SIt3J85gz7omTSY6XdfNMjc8pep9MsbtxdRl1ZoU/vjHY74Wz4zscEOFg2OXYU6CAO+YO4AggkIanSfJ5HDxj944V6Icz7XzeT/QkyOXevd5ouzCcnp/8hw3p9/uh357+p3sHc4lcwUZ50MY4GQ/6zr4BTpilswN2q1QqjdS7cq9utxubtCm1wg6STfEUPWAGWwIQ9Iwga+v3YJ6TQbcD1qTMEdA3m82RzJE02kefv5ENvs840UXkhTkBuCHPdGdwGfbLgxKAjI/jKvDtuoJcsQ+EMh7PxAwGg+gUBYve6/XixFhKehYWFpTNZqMsYWlpKdaD+Zek3d3dsNXr6+sjARNrQ2cJyq2wPa7zXjftBAnlKb1eT6VSSTMzM7p7924wutQ9+0l/XovN3LgPxzZCIvR6PT169GhkvnO5nObm5qJMB7n38jo6k2Wzl5tmy+Wy0ul01MRTBgO47vV6WlhYCD1ot9s6PDzU8vKyVlZWtLe3p9dff12FQkE3btzQ9PS05ufnde/ePf3Zn/3ZSKC1t7en7e3tyKx2u1398Ic/1P3793VxcaFisRh2pV6vR3kNthq75dnOpKy7zuAT8Ce0bnRZBEg7cYIM8Ht/nvtwzy5/eV19/aVANUbq4OBAP/nJT/Tee++p1WrFqYq0iJmfn1c6nY50DSnfwWCgubm56AwCi81mAGfCB4NB1DOx4AAZNmvQlgcH6KkedikjNFxX1SRJw2jPWQ0iY2e8cVYoL8zY6uqqjo+PtbGxEW1zcAywmjCrzqLg+Lwxv9e/OnMFMAM4SvocOw0L463EPCXp6+gOHEfkbCFRKzv2j46Oon8zJQ4YL5hSB2EO2q+SI69lZ10BCGQpWA/fqAWoJgpPpVIRAABKkSPuS1qSd/FNprAfABPGAIsHICKI8BIMHAEg3llqT8PBDmC4nC31VB9rhbzzXQyhp+LcwLGeV2Ve/OcObhy4XrVWvAcBpxtpL1NhHpBHB1Ne3w3zm2QQ+b6n+b3Om7F6OteZPcZEBshZOAfh7vT4HEzz9PR0ZN+wBQTzPkceuHhdP2lf5IRnkML3NQBM05qNsjaYU9/87LYTwOX67DWRrAH1o267OPqcdaRsw0EY9tVtJAGaNNwDgp64s6aLCAAc0sJTy67/LrOeXfFsDmDMZZLPOxBNyiRXMuuF3aDOnv0oyeAcYOM/c2bdAyDXNy9l8YwAGRHAiZffeJBC0MUekZmZGZVKpZCtWq2ml156SaurqwE4KVfzlofHx8c6ODiIEw+ZR8iKsbEx7e3tqdvtamdnZ6S8g3XxIMHLVRxoezZscXFRL730kvr9fnT4OD09VbPZHMn+keU9OTkZqZVH/lOpVGQlkGvsCr6qUqnozTff1MzMjN5///0A8JR4uX3r9XrB+FPuwpkDAH4+UyqVtLS0pK2trchWTE9P68aNGzo4OND169ejzCadTuvRo0fa39/XRx99FBs6Dw4OAoCm08NSTnDQxsZGtPvEB7k8IP/ggGTw6PPv7DM6zGfoN889mHvWF13x7EqSAGPtkU3Xk192fZFB9wuXf9D7cWtrS++8847eeecddTqd2Ll+enqqw8PDWDgAIgI+GAzraelPCVt9fn6u3d3dUA6AJn2IcRgoFmUCpOj4v6RwFIAq7o/xwtHAggDUaJ/GZhW6mJBGxbA4y4ehAFjBqACmJicntby8HA7i4uIiggAHd84eS0N2Thqm9r1mShqtfwaMECQ4k3LVBWBIMnMejQKu2SAqKQ7tkRRHBzvLRvqU8RMcpdPD1k7uZHG+lArAJDmT4vXSyCKODUWnZtAZSeSQ7wAOHOzi9P1ZtPHjnZgfAgl6a7OGHiQ5AHG5ZSNXcuMH88EYnZ0g4wPYkIYHSBBEugx4AOI/R8Z9na+SH+7rv/dgzQM0TzV6CYmXpABSPY2I/nt2gjF5wODMtNcYk3FIdglhzJ4GdYCDw/W0sKSob2afAIDa2VYv/XJQ5BkMQAG6R6eker0ecsh6YXvGxy9PqaSG1dlxLgfeAFavcUxmB5gf7kWWifptadhjnOcQhHc6nXgnd7ru3AkMHdA6iMV2O1vszJiDMNbJ5dv/7YxmEixzP5df1hvdSo4LfeRzSTIF9pLgjYCcjIHbK9cVf5a/V3I86D5ZDGwSgJmuSswrgVGj0Yi697feeisICDb+Xbt2TfPz8xoMLrvrwHju7u6q0+kEWbW6uqpSqRTnN/T7/bi3l5ShE9gjLtdj9KBarYacLS8vq1gsqlarRTaatn8c1IKsT05OBhOMX+h2uyHfnoljTfj3tWvXYu/DJ598Eu/AvPr6Ma/YfW+F12q1tLKyonw+r1qtFlkL6rH7/b6q1aqWlpa0uroamyEvLi475Hz88cf60Y9+pJ2dHU1MTOitt97SwcGBfvCDH8R7ebDiQRYbHT1zDHB1/5G0xegrv/MNq4BsLyOEKJKGbUuRL8goB/M8y/GEl9R+kQHzf8z1QqD66OhI3/ve99RoNPTxxx/r/v37Oj8/1+zsrC4uLvTw4UP1+30tLi7GDvRyuayVlRXdvHlT0mV7GIw2P6MvbblcDtByeHiofr8fkRagzdlbT3fizCSFQUk6XNgvaQiinUXwzWenp6daX18PJUQpYA/W19ejvsl3z3PyIb2lqbnc2dmJlDaGE+CPIS8UCiMbfRBqxpZknF0BHLh6Wgil4XPOELkzhhHDmUpDRwtIJmWdSqVUKpVGuiH0+/1ggVF2B2eAKNbCGRAMnpd54KhwNoAhd5wAOhrq8444fkC1Az0CG2ecGAMG3Nnvq9gVetWenZ0Fo4RB9HdC/mgB6eUwXrYjjaaY+bn/4dQtxgkg4PvIvgN2v1h7Z25YZ2cxnClEVjD4yYDGn5P8HV0x2MDHPQeDQcyfg2DkiPQ8v/OggPWShv3ovQ8s8w5T58DZS4JwEr7REMYbRphUtoN+5gm59414tOgkIBwbuzw5kWeQVuedmA/S1oAXbBRyhH1jYxaO052iB0vIAN1NIDoogZOG5TbIha8B8o+csU7u0OnY5Kwm42I+mB9nGh004/xd3vw0W2dw/W/Wj7G4bfPf+XiZV88W+Km7MPbIHrpEi7RCoRCBD/Yewif5PoAXgu1MJhObXT375+uQTqfjgBB0enJyUnNzcyqVSmGfr1+/rtdff12lUilkZ2FhQRcXF9rb29NgcFlS0mg0dHh4GEzrxMSEDg8PVSqVdOPGDb333nu6f/++er1edDjhcjvpOs07OvCanJzUm2++GTYlnb5slzsYDMvjer2e9vf3Azz7npuxsbGQD+yGlzd4uZH7pLm5Oc3NzanRaOjZs2cjwRbjQOcosaF+u1AoxCFqFxcXunv3bhB3xWIxslUEzHNzc7EhtF6va3V1VVNTU2o2m/rggw/0gx/8QLVaTUtLS/qrf/Wvql6vq9vtanl5Wffv34/sExiFdpyeneBv7+3OnKCPbrtdhui3DYZgj5HbLuwpZbmuOw7kIWQcF/FZ7Cxj+PL65dcLgepWq6V//I//sSRpYWFhZOe619sWi0UtLi4Gg33z5k21Wi1tbm6GE2NTAHXFHGGezWbVarXCYHkDeRxXNptVpVLR3NxcOCQAUafTiZ22AIJkmQGC4kYXJUJgAJF+YiNtd3AgKDbGDKcAs4SBJT3n6UB2C5P+KhaLAZxhvWFiMc7OQrozSdZrJpkrvkPazsfB953hRxG5Bwrn7bOo98K5+kZKLgcKnlby58EoYwgxhozTGSVPP/uufUpsyER4DSsHIpyfn4eB82e5sSZww8AhWzzXU3VsQvVyF7IhgGkHpKVSKYwlz3SWzVlFBwSp1HB/gZf0kKZnPpBJ/g+w8CAnmV73cfu4XL6SbJsDCS9ZwTAjT8mMEDLs88IzAX/IF7rr7BVjcdbfuyQAknAc6XR6pG6UC5CRLJ/xMbnjSTLGHJXsezeoWwQAS0PgTDAEs+WdiKgf9Tp77kc9JxkCwIKPDceH3PPHa/x7vd5In3tkyB01+oee8jvk3E8O9Oe6TcKm8ftkMO8BNu96VWaETCEtCLFV/h0+7+CaC3lGBrxuHjllgycygp0lcGbN8Wv4Agcf/t6MA3lgbmCgCab9GHieB5gnICQoo5yj3W4rl8tpdXVVt27dClYaRpljqnd2dqK84PDwUCcnJ3HqKwes9Pt9/fmf/7n29/fjmG9khwtZc9viGRTfyE3QzKY9fECv14tNkNh3SgZZE2dRvXzP94i4v2CMuVxOi4uLevLkSZS1MCbsBTLmmca5uTnNzMxoampKhUJBm5ubyufzmpycDH1DDl555RW98cYbajQacSbC1772tWhZ2G63NT8/r2KxqNXVVX3ta19Ts9nUz372M21vb0dtNDYM2cNmScP2pthRiB/emc+5DXcZR6bpGe6ZAycx0ulhSVbSN+MT0Atsou8XQ3b5vNviX3V9kdnsFwLVzWYzaqepg2q1Wsrn81pcXIxjyzEu5XJZN27c0Pr6erC+lDGw25kG6iyw90X13eaAawr7Ly4u1Gq1okZMUjjmpANyYMT/vSb7+vXrYWA44Y1UMA3bKW/huZR5SIr0FuNFoWlx5GlZwB3GOp/PRytBnIGnpQEAbvgQbk+Nesoc4OROL8kYA0J97nEQpIOc0fZx8DeGwh1LknnFOP4yFptnU0eKcXFWVxo6apwWTsrbMQG+AV3IG+lUGF82DGJMnJ2DvcEoOnMN6PHAgjHijL0PMUdyO3vCfCZBnc+ZZyIwqrA2zpDxHb8/cu8ZGv7NevgzHFQ7O8W9PQB11hB58E4MSUPrDBZywFwCjgeDwQh45v0cZPvc4ICREYJsd8DOnhJcMb5kWptg2jM2fg+YabIzU1NT0SYS5tOZfdhO9k8AUmdnZwM4ALZp90UwTV01/2cMzWYzfu97DADUzA3vBsMMYPPAiHXkPdEFnC/ryztnMplg3AHTztAzZwQUPJu2ptgJzzygM26npdEDcbAx2CECKtbV5Yt781ln+HgPAKHPQT6fHzlPwTNNyQ40AELXBUDgxMSEOp1OsJHIgJfuefYGe5jL5UJXnYQhA3txcaG5uTmVy2UdHBzo8PBQr7zyinZ3d9VsNlUoFLS/v69MJqPDw0PlcjltbW2p1+tpdnY2TkScn59Xs9nU48ePJUnPnj37HFBzYMVJswRcvDt+dmJiQvl8XsfHx9rd3R059Mj3FOA/KZ1ykoWxeWbBgwvICw+i6Mzy5MkTHR8fR0kLvpTv44uRe45ov3btWpx1sbS0pFwup7W1tZj769ev65vf/KZ2dnaUTqdj3hYXF3V4eKjDw0Otr6/ryZMn+va3v63l5WXdvXs32v4+ffpUkuJ9Ke1MknfoGDrp5BlyiG5iv7FhvuHRyxWRedYBwsP3ojmpwv3cH3iQSKDCWvIOBEFfZND8F10vBKpTqZSWlpbimNHp6Wm9+eabKpVKGgwGUTtITVS/39fa2pq2t7dHoqfFxUUtLi6q3W7HcZ44JQxrr9cLQ+XGmz8ubIBPUlrJejdKNmCHAVrsUOakxkajEYbX2xBJCmBzdHQUdbsueET0OGkAqwMeF07p0jjBXjlQuqp+GIPhKUJnHJO1w/5vUmwYNGn0wJokAHawjjHFiXg0yzrxN/92xpO5cwDuqd1MJhMHtzjjwBywaYyNHBzpyu95N4wUIAiggAPDcWJYcMB8D1B/dnYWm7mQSZ4By+i7srk/c8l8shk1mcZm/pgL5jjJfvF5mBivTed9PehgrWF5k4GRBzMOWnxMv+xy0I18+uYzrmTgx9/IE0af9DnjQKadXfGd6L6J1scKo0ePfBwoz0wGeTCPzmpls9mRDbf+LJwYwQP2g7G22+3IzAFS2aNBDbiXC8BQY7PQOS8xcn3xEjUYVh87OuhzJQ0PuvHPsTEU2+UywOUBMGuB3vFOjA/H6muGPHiHjmRmwwNyB9gABGfd3C840eBy7AE9dge7ksvlwr4iY9gASjqSNabcg5/7+JNMHZ05KAODZSW7xHyit8ijM+Hj4+OqVCoqFApqt9tROkgHKHRtcnJS5XJZ4+PjOjw81NjYmHZ2diLY2N7eVrFYjLrrs7Mzra2tKZvN6s6dO1pfX9fa2toI2YRP5t3HxsYi+PPNrOVyOUpInE1Fds/Pz7WxsfG5LIn7ai8JGgwGsXGRYE66ZJMBqQ8fPvycD0JWIM4IKN2PFIvFyGTncrmYYzDJ1taWdnd3NTExod3dXY2Pj+vGjRsqlUq6detWzP3jx4+1s7Ojvb09pdOXNcrr6+vqdDphYx4+fKh2u621tbWQF8o/mVf3+8iry5DbVoAuPilZMoXPwa57RtBtnJMoTuqAH9x3uJ/wTIHbKy7/9190fZFB9wuB6mq1qr/7d//uSPonl8spnU5H0f3+/r42NjbU6/U0NzcXjg8DmsvldH5+ru3t7TilydkDQDlO6uzsLI4SBhQ6E9HtdlWv1+OYU4wsNZP9/uWRvQcHBwGwiWar1arGxsZ079692IApKU4KLJfLIbzuJBFKBJCxOyuEwLuxh6EiWCiXy5HuRAgxPnweR4nCOAvnAMMZRn4/GAyiphWmvd/vx2E0ODCPVpP39E2IyWiWucGIerqaOXFGnBQUaz41NaVSqaRcLjdyNLQbFtY0m708Khbg7eP2mjVqR+lXTH0+h7pg9L3GO51OR3oSufOSFa+3d/aVsTnLwj0xdowN5+pdRpIdB5hPZMGDIeQJB+EMHGNwRykN61JJ5XMvZzoxnA6CuQCTLk/c10GPM4hJpsQNvKSRcbiMu+z5/gCYHQeY0rDenxKf5Hx4wICcT0xMjHR78f7EjM0dhztCLzmB5RwbG4s6TO4DeJucnBwJPMlu4bBITTMm+t9CUFBmBjBGn3hnZ3C5CAyouYUUgKWFUUUGPHCXFHtHpGF7SWQeBlNSlP05GGMtYeg9mPP0PXKCLfFMB/eA7eb7yGpSprlXJpMJsIYs0WqNdaEskAyqpPA/MKMOfpLj9CDBgaizkqw1Qc5gMIhAyNcN2wLwp6/03NycqtVqbP5uNBqxRqlUKjbxUzrZ6XTCfxCcViqVaGsL+N7Y2ND+/r6kYbtK3o2MmtfhMu+AVuwia8JeImdX8bce/PuGYIJX1op7DAaD2F/0la98RWNjY/rkk08+d2gL80vGBNvAIV6Mhzpo9nkRWO3u7ur58+dqNBq6ffu2Go2GlpeXo8QTff7www+1urqq27dvq9frBRZoNBra2toKgu6HP/xhZJ79IBrWiyPfU6lU6Db6TPke/hB5S9pRSBLXjaSNd0Dtth1Cz7MnZM68hInnOOHmWMwzmh4YfpFB8190vRCo7vf7US+9t7en9fV17e/v686dOwFcpWF9KsYFhgSn6KlBThDirPuzs+FpQpJG0kb8cbaVY6z9s87K9fv9MCgIyNTUlG7cuKF0Oh3PnZmZUbfbjc2CtMCD2aZBO2DQHb40eoIhxgnlx7FTn0Vwwc89kkwyPKRbAKzMi4MaB3MIv7MEGAKcHsrs3/GgxQGip4H5POlv1svT3O5cAJGUZGBcMWScgMnYnKkDzEiXTjuXy0V2xBl8ABLzQAnP/v5+BEqkWr00ie9jPNxxeuofWQYUu+Fhvhinp+hgsweDy415bAgi+KR1ogeKvnakhR2U+tjZXMT/3SEiJ3zXMwdJ48uV/D/39BIQSr588yEgzI0v7D5zkQS7zBft3Bg7LCTBq78LY2Sd0RsCZHc2BDLIDoED9oH6buwGgROOBmdCtgEwhuz5O9PBQRoGDD4OGB/WwutzvfMOl2ec3BEi6y6b/PEA0IMyJxfoToJeOUgG2AKkuRf670EK78XPHUgmu2Pwe18zbADBBTbM/YSvO88D/HqGw4E7sjE+Ph4b0rznLww1B6jQLYIxYOO4l8uaZzw82Pe6euSROaRMx4MAbB9BnuvX7/7u7+rOnTs6PT3VBx98EHW5bKqTpNnZWUmXwQB2ZGVlJcog+/1+HDPebDZjs32/39crr7yicrmsd999NwCupMjYUrJDqdFgMAi7jEx6YI8tBVD7BmrXD+SfU0273e7IWuBH0um0fvrTn0ZHJYJJNt6xzmRqYKVpiZdOX272XF1djZOYyUzgt4vFot5++211u13Nzc2p3+/HQS1/8Ad/EPujMpmM9vb29Lu/+7t69OiRfvjDHwaGQIYvLi6iNJS58IYCyBYy474D3XA54n2xLx40eiaGUhwnb7Bhzux7IIyddrLCM7AQDvgf9Ayd5OL/Xpb35fX564Vb6mWzWT19+lQnJyfRr1G6bC5/fHwcO6SpFeTi+HCc8vT0tA4ODkY2FxHZYjBIryG05XI5HABG1xkMjDWOjhQWzJAk3blzR1/72tdUrVa1vr6unZ0dzczMqNe7bFpPtHtycqL9/X3lcrkoKwEEAYSSINidDEqDEFJHDbsFU0WqyDtS+K5nnKCzwn45SzsYDIJVAJj5ZjLYGFd+ALsrGAAYRXMGvtfrxWEFkoJtZT4cvHganfpOSbGp01sB4Xj4HmOfmprS/Px8AHAMAgwlLDRZBhwOa5LJZCK96qyTB0AOkFlTjA/vnYzYATt8BkfjG3koO8F5ukM+OzsL+faUqm+sdOCYHLOn55NMmgMWBwQAff7w+6vkOJmeR8aojfc1Y178ciYMJ+Kb5XwsOG6MOyCo1xv2E2djlpe8ALSk4cFJzszwHjh63odneire52FyclLValXn5+dqNpvhnLAlBETOoKPD6CPvAyMF4KMWUhq2tWOtGJOzswAO5hTQBkgmIENfvJMDAYekYAk9K9VutzU9PR2y6QwzINudpwNkZ6s8MODf/j2CJbddjMODLmQqyUZjlxg3+sUz2KODHCA7nKzHd+m2QcvTJAvOnPGu2D5/f+7DmLLZbIAgT9XzewKodrutVGrYwjWTudyTc3FxoZs3b+rw8FA/+9nPopxkc3Mz/FGv14vTQmu1WszxzMxMtM07PT1Vq9XS7Oysrl+/rtnZWX344YfR+aVWq42c4osNpnsEAJE5dvZfGrZ09XMAmD98NPrkJRLMOXLowTKdQKamplStVqOW2bMNHpyNj49reXlZs7OzOjw8VCp1WTNcKpV0+/btOCyHQGp3d1erq6v6/d///SAyzs/P9fDhQ21tbWlzczP2iD19+lS1Wk1vvfVW1Hzv7OwE0J+ZmYkM/OnpaRzrjh1iXcExSZsMsPYmDMgB2T78E2tAUOzBKYF4kt32jJPrHgSY27erska+V4f5c+DuRJSTiVdd/u5fxOuFQTW7smFrcUpHR0eq1+uRcsSYEd0CnAEatBkCSMDgeJTrwBoHDbDFKOJE2DiB8uBkcUTSpfIuLCxoenpaT5480e7urtrttlZXV3V6eqpSqaTV1VVtb2/r/v370aAdo0AJhDQUNAehSQFnHqjDzOfzsePYU++eTpeGKXEPGBxQu0PyFC6srdfrOmAirYiSeTocxaXDCfd1Roj7wGAwNme3aJeGIfHLWXU3JsiBs0YwIpSGAMBh29vttvb391Wv10MuHRQRAPgBFTgjZ3cdiDnLhuzBThJ4OGvgAY0bwU6nEwEjc5DJXG5+JbjzUiLG4v29uT9Oxdkt1tVBsxvZJEvtOuBssQMILubEU43oHhkkB0fOMnowguzwnj52gFwSwDmjOzExEa02GQdBum9IBoTzXTagMhZ+7qlkZ/zp8ILMcYoqre64cD7I1eHhYdgi2DoPnJhj3o11YIz8+6oLmUR2yfjw/gDpJDtFhxj/XFJepGHGwTdWe9re7ayDYWdlufzfyayC63xSxjxj4iw74NRtoIN+JxkkRQkCXRCwuciG29DBYBDMKJdnYtwGeHrdWUPafxLweVkH4/SOGoyfeXY2e2JiQm+++abu3r0bnWCQO+qYGXe9Xo+WdDy/1WppZ2dHtVotvl+tVjU3N6fvf//72t7ejkBqZ2cn/BcyQp9n+kYT+KFjXgcNweFlWF5egC7x7h7w8jz3hfjp+fl5VSoVPXnyJGyF2x/WuVKp6Pr16/r6178e51YQWI6Pj0eZRjZ72U5ya2tLt27d0t/5O39HR0dH2t3d1dzcXJQhbm5uRteew8NDvf/++1paWtL29raeP3+uer2uBw8e6PDwUOVyOVrWnZ6eam9vb8Suo3tua72jFZ8lYHTgjEwz9x4Ucz/smJM4fN83ebvvdN30bLmPxcGy23F+ntwUj83xksUvr89fLwSqe72e9vb2lM/n45RB+j7W63U1m001Gg21221VKpUwUtRBAbpgM0mtTk1NjbTgwaBT6wUzVKvVwhlTmoEwwQQ4a0AES50ZDNHa2lqwBrdu3dLCwkKcqsUO393d3QAqPF9SHBErDTcBAgilIaMyGAwi3S9dgv7p6enYlOeMLE4GYXU21iNQLo+APQ2PUfPIl02cziYwh5zuhOPnoiTDN1A6MOEdYSG9DyYX5QvO7pBCv4pNdGcKmPXPApxhODY3N1Wr1cJQ9Pv9+A4GCIcKuPFymSQb7eCPNC0pblKIrFE6nQ5mjDHDQHIojNemMrcYK56J4fIexA5MYCClYf9pHz8BLP/HEfEzN8TJ8hpna3m2B3ZXpRU9+HLg7IxlKjXcDJcES/551sSdD7aCexBAef0lgaADPM+sJIE0zh/599IxHCEsGKepNRqNqHv0khRkk9+hd+7cmF/Gwj14D2l4amS/f9lS9KoSGWewWS9knXcnTcvvkkDV19RLRZgz5oJg0AEAa+/f5fKglHVnvT2jkwTUSQfO773Uwxlf9NBLELBFLs9k5dwuQgrwvkmb6uCDMbteJLM36G0+nw8758y+34Mx8s4eQLK5NZfL6ebNm1pdXY1NiLVaTY8ePQr2l6wVsidJS0tLUd7BviQnUWq1mj799FM9e/YsDkgC6GGH0JF6vR52qtfrhf3sdrsj7L+kyLKwLs6gsoGfe5G1I1iDzUee2Ry4vLysdDqttbW1AMfM7fT0tAqFgiYnJ/Xyyy/r9u3barVa+uCDD6J7x/n5uYrFon7jN35Dc3Nz+g//4T9oZ2dHpVJJ3/rWt3R2dqZ33303emfXajUdHR0pk8no9ddf1+HhoXZ2dnRycqL19XU1m009f/5czWZT9+7di0Dt8PAwyAp8kGfVkpmmJFmBzGHDWAs/TZJgz7NRrItvanZSj+e5bnA5Kw0eQIbRHQgI9NsDdeyX224nb/4iUP0lU/0feQGis9msXn755UjH/9mf/Zk2NjZ0eHgYkRaGS7pMyVOWgSDABmHEPP0OMKQVWip1Wex/7dq1OE6VjTwsNg4GA4RjIxouFArB5GQyGR0dHalQKMQmBhQMAa1UKnFgBw4fA83fDmK8dIAOEny2VCoF04oiSsNaJml0Z22SGUoyzygezpTUnzsFHAD9sd1R8X9YMAdhDkTcSTIv7mhQOsov6LDBe3oU7eAboy6NHj7h3TX8tDlSfUT2MLqwRoAa/u8gz4GeA2AMGHWz/OE+MEFJY+mA140mDJMzMdRj864OfJBb75SAXAN4uAfAjnmXRk/g9MAH4AWoSAIVZ1td1lwGkkDbWVXm11l2Z5ORP8AmTD8lEWSlnKmHkUXOvZQCZ0tPXDacIitkVXxekS/vtc68OKB3Fp45Zc0rlUpkdOjFizzncrmROlbve8xcuI4yb0kmCwDDd7xNIbXQPIMA12WBuU9uZuT9vbTFnabXyGI7fa74DmuB3kkKe5WcP89wSMOSDw/S/J7eIcrlBxuF7CPT2AivC+X3dDtxYiPJaPMMAgnYTemSYc1ms2o2myNzhXxjc9PpdBzeQSCCzCeBjzPCyLATGZzq95u/+ZsqlUpqt9uq1WoR/FHWgB+kNRrvcHh4qCdPnoy87/n5eTQK8BS+A37GQSaN2u9isRj7fJh7CArfrOxZJ4IKSAd+l5QlSiLQkUKhoDfeeENTU1P69NNPVavVRspc5ufnI1MzMzOjW7duhe6vrKzopZdeknRZ6kMr352dHWWzWV27di1AfalU0r1797S1taVaraYPP/xQ2ezlCZDLy8sRcBweHo6ck1Gr1UaCYIhAsuPpdFrFYjFsmb+nB/uw0OgjMsVceOYVuYSo88ttt2dxWBPXLw9+WS/34/hsx2cOvNFN1s9Zct/o+MuybF9ef4nyj+3tbd26dUudTida31ALhRGfnp6OesSVlRW12+0RhrNQKCiVSkUkCGDh94AKjDkCvLm5GYuNo0fAnH2hdzHlERhNAJl0aYjY0PD8+fNwoOxEBrQ5U4oz8wiOe8E6+glpGCqvt8UgJUESYMTvSYDB+6IYGPFu93Lz48HBwQgzRE0YLA+KTds3+oxKCuYPoOGK6ZukJI3cH5DU6XQiy4CDSTpnxuC7tDHcdCUAfHU6nTjAgLlMp9PRFYFn+Po6g4sh80jaN1vybtlsNvqL833AnW9g8vVl3R0c846053LG24EE9/GUKkZaGh7tTgYFp+yGz8EfuuYyxO9dRvl9krn2FDUAne/juD0V6wy1A3b0AuPrQR3yOxgM+5SjV8788lz69npZTrFYDJkCOOGAnG0ldU4a2N/b6zgZm6flkQcCYuYDx04JmDRkM9FlXw9kiGcxx8iZp19hApkXZJn3dHAM282zHWxi+3q94f4SxoTsswYut3zH2XHYXg+8PQPigUPS7hIwsK4ETm63PBB2+fTLs0HcB7nl+4PBYGRNCoWCut3LetVkBop58OzQ7Oyspqen9c1vflNTU1Pa2dnRD3/4w5FyGdct1tv3Ofg9GavXu/I3h7AgW5OTk1peXtabb76pt99+O5hvsnIE7sViMcoVmJe9vb0A5awpLV7RF8+GEHhyDzK5zA91ztPT05qbm4tSQMpOKDninfEDdEpyeZEUXW88W4F8sFbFYlHj4+P68MMPI0hhfTlFEv+dy+X06quvRq30q6++GiB+fn4+7Euz2dRLL72ka9eu6ZNPPtH6+ro++eSTKCdF/9bW1jQYXJbS7O3tqVKpaDAYxKbHR48ejTDGvBMZ1unpad29e1cfffSR2u22CoXCSBbTiaIk+0sw6XYSuwgz7PJFFhhc4SSW4xDW2O226y5yiu3CJhB0uj2FZHP74Rkv14cvr19+vRCozmQyIcydTkc//OEP1Wg0wphS+wwDKI3WT8Iun52dRX3vyclJ9JOEwZmbm4tdvd1uN/pxkkZCEBEw0k9cbCY6Pj7Wp59+qmKxGGwmwr20tKRHjx7FUa7FYjFSsV6GgpNwttgZHFc++htTLuKbLFEiHHYSKHv60p1OMkrlPUhLUYbjbDpOySNj7z1LgOEg2a90Oj1yzKyk2DQiDVObPA9HB1hjbbzLBxsTASu+6dDTaO12e2TzIXIBSwdo8c2WPk/IqYNqz4Sk0+mREyoxfsnUmKeHcQzOpgGC2BSaTK85APJAis+en5+PHBjkAJ7v8H/kg7F48JIELQ6MPb3t7ygNmQ2csaf4uCdyz/3cUXjgxDNcNnzDpjTKPjrw5N8w1g6iKMGhiwPggU13AGHuDUBHZng33hXQwHzyLMYNw8Z4kDeeA+AFcPBcAnWeTe2p6zA1rMfHx9HWjpIzxp3MEGEvfcO3M+/IIQ6YeWYd2SDprK0Hnu6MXXaSGxSRNcC4B1UeKDIuPosMOjjG/vg8O5kAk84zCMABLvxsbGwsyhuSrCBjRDaQ7ampKZXLZU1PT2tpaUnf/OY3Qx+Xl5djA7YTKMnMDuvlupm0NawDc4It4Zjw+fl5vfHGG9ra2tLz58+jJzUHtficsk+pXq/r2bNnUeLH3iQHwtgjsm1eluBgC/1ibqkN9xaQgCfKLN0OeR0wxA73Qed7vd7nNslls1ndvHlT5+fnOjg4GMm0TUxMqFqtKp/Px7hv3bqli4sLFQoFvfrqq6pUKmo0GlGqha/49NNPNT09rYWFhZFMw8HBgdbW1jQ9Pa1Go6F8Pq/19XVJisNwwCe7u7tBTnGNjY1pZmZGL7/8subm5tRsNvX2228HyO31evrss88+B2S93MNLMzwYYwMzwTAEi3fCYiwe2DjewG65P0eHkH/+ECA5wOY9wB60H/TzHJCHTCajQqEQtsDL3a66fB6/aNcLl398/PHHESmvr6+HA8AQEx0SlT99+jQcoRtq31UPizM5Oak7d+6oUqlEOQmRKClfDIGzZN6oH+OJEYC5zuVy2tnZCfY2lUppeXlZ09PTIz2u2fBGNOdA1nf2OpPuhofNaIBNxuVOk+dLo7vlk0BOGrIhGEMc2+HhoXZ3dyMAQDlxWB4d8w4YXk8ROZMqKWqzYfdLpVK0GWS8MPIoJYa12+0G2JQu68hnZmYiyve6POYbEH52dqZ2u61OpxNgnjF7lxBP5zIfKD2OnAt5cObYDQ+gy1kGnLQHFHze03LOqDpIwfC5M8WhOKtH0Ikj86DKx888eRDpzKuvic+rM0vOcLNezoSzDp7mZnMSgA4j7+2UkFkPkHk2QAiw6mDKy0PIJLEeAFDS7LC4rIezxHQJYp0I7J0l5h273a46nU504GHdWSdnWCcmJjQ7Oxvf8wMTcIToJKlhSbGpGmDNmqCD2Cn6DCMrSXvGPKDPvD8ghns6i5wM3JkPB9MeeHN/7oH8MRYPUFhrbI+nn3kueuxZROyXZzggNrwcx2XVdcCzbM56k+lotVojLKFnFH1TYzKLIknz8/Oan5/Xj370o2D7Xn75ZX388cfhZ5yxBLzwb3/HQqEQdhiCAdBUrVYjeO73L8uKyJAeHR3Fuuzt7QW5dHJyomKxqPv37+vo6EgbGxt69uxZACD0Shp2ImG9CDA8K4JtQ275t2dEfJ8A79pqtUbYS+SB+3B/9ykAadYeWZudndX4+LjW1tZUqVRUKpXid2yC5B38bArA4NjYZRtEjgE/PDyMMwneffddVSoVvfbaa5qZmdH29naU07Chvd1uh//Bz8/Pz2swGOj58+fR2CCdTqtarcbhdJSQZjIZ/eEf/mGckLyxsaE//dM/HckQeAba9c7JAmScdeSzDoR9zTxY5H5+OWHi/sdtv+MO1h3763jEGxSQRUN3vLsJNvnL6+rrhUD11NSUFhcX9fOf/1yHh4cR3fT7lwescCKUt2Kj76V0KQCc+kTNJAw26f9qtarj42NtbW3p6OhoZLOcM64XFxcqlUqxmQPQ5/Vc2WxWBwcHEY2OjY3p4OAgwH6tVovNDowPYIHxl4agxdP4kqJ9UL/fD3Yah+2MoPT5E7t4XiyEGSZP5fq9AAudTidSnb4T18ECf/x3Di4BVufn53EgAEDo6OhIxWJRpVJpZOMfBokabmrkAVd8luf5KYWSRo7whnUkw+AOACcHKPF5cdZDGtYpO3PmDh8HLA378cJyes2r94ZORtmeKnenlCzj8TX0NDDvcXx8rHT6skbS5xNA4xkfT536c3BUHgwl2TSez3gByT5WZ+U988B30RdANVmjpAwDZLyURdII63d+fh7ZAd9QC8ghsGYO6HfPugwGg5GA0NPfPIOAjrS0dyvwXuoEy8wD6WocPMEfdol5QO795EVsHZut+/1+tOiibMlLVJAl5IdyBmSO93e2H7mFqSfljVPLZrMjAQdzkVxjfo59TNonns17AKB9oyyyxu88i4Esu5P2dDj/d/DtgNoza8wPRI2D/W63G3t00BfKCQFk3e6wD/Ha2lroG1nOfD6vUqmkhw8fhgyNj4/r8ePHYRuQIXyUv5eXzHFoCnOEbhBgzc/PRwvVk5MTVatVZbPZONgF8Mq+EfYkffjhhzFXsKvIeK/XG/F5zpQSeCUDdD6HXaVE0LMY3q3Ig0m3C9hw5p85Qc9dBhnP9evX9eu//uu6d+9egDpKudBTxs173bhxI0gqgDJHojOXhUJBT548iUYH3/nOd1QsFqMnNrJTqVS0uLiojY2N6Cp0cHAQQVm321WpVFK5XNaNGzd08+bNCNo/++yzODkRv/jJJ5/o4OBgJEOFbkBSILvMr2fi3d7iW5nDZNDsWUvm1P0f38Meub7zb8bG57HV3NcZdD7DOzA2SB3GRzeUX3ZdJX9flOuFQDWRpkcv5XJZ6fSwPuv8/DyOgMVpSMM0GG1snIXicy+99FIwStlsNjYqSkNGlUjYI2mEam5uTjdu3IiNBpy0tru7q8FgoJdeeikUnciUU8RwFs7Q8McBiTsQNxBe38vl0SaCjnNxhs1T1ChN8mcYvVarFQLNplGAGeCAFGVyLA4+cdQeYROwcHiC12PCZlKiwcYZB/+Aan6OkUR2nBnhuWz28BOouKdnG6TRoMej+yQLDeiBIWUNWU8H7t3ucKMlgQaAJQnmpdGNIZ7iRjaQL9YXB4eDItAAsGBkYfHYOMS9mAf/v5deuGPzMfnPHOBj6HkGBj8JuP0PAZRnUpifZBbK7+trRTDldgE2infiPmNjY8rn8yHXnnmCqfaactoqwgB7KzUCeEkBgPy9AZFkRa4CGP4dzwaxBtR9k61hV7+XHvB95hR5gXkFNFLa5qwu9bEunw5kftkGInecXnoBIIRhY23p9uBjRY5hr10n0Cdsm78XrCpjwzF7tskBW1KmnIhxfffgc2JiQnfu3IlM55MnT3R8fKyZmRkVi0U9fPhwhHQgoC4Wi2o2m9rb24u53tnZiaADHWJ+PJBIlqJhW7LZbJyUi2zOz89HQLu8vKzNzU3t7OxodnZW5XI5xnXv3j1NTk4GSQFwffLkyUhaf2lpKXQEFp35SWa6rtJVaRjsE8QSNPHZJGCWFB1vkjXl7hNZf8/mjY+PR9nfxx9/rHq9rkwmE+95fn4ee5h4rvufGzduaGJiQg8ePNDm5qamp6cjAzQ5OakHDx7o2bNnqlQqevTokWq1miqVSgQx6A0tFyHQyIyhw5zqu7CwoJs3byqVSunWrVs6ODjQH/3RH8VepHfeeWfExicvB8ge4CD3YA5fJyc+WB/PwrIWrvfoOdiJzfbSpR+G6EPHnaVGfxiHYxLPPPBv5I3v/zK9/fIaXi8EqpvNZpRiwBCUSqVoJH92dhYbzKiHxJH5QQUoFQIwMzOju3fvRm00zBSbMHxTnredSafTUV9ZLpf12muvBeC+e/eudnZ2ouXQ6uqqMpmMlpaWlM/n9cknn4RAuxGXhk7FFY/OBdSPweLyHs7K+JVkGb3e2RlHZzpdwTCgftCJs3YooDQEzslyEa/rdoWAFen3+7HznT7KgEEHwaQm/VAFD0ZgQVBA3v0qlhxQ74cwMI++sVAabZ7vV5KNZ449ePAUNiwT4MFZM+7na8Z7Iyf+HrwD6wfD4jXmHt0DxlymeB8OVACgcn+fQ19bT7/BzPp8IcPIDxulOHzJga8bWl9r3o9+8sjsVQyIMzVcSQYUthhmCkCSzWYjoCHt7vrEoT2k11kPfxYAJZmZYVwOplkzgj7XHwCn1416yYCn1Pkd8wGYl4aHunhJCeUbPIM1ZszuTF1O+EwyK5FKpYIp4zkwrsiwgy5kFP1G78kEIH+eYmYtfP+FZwoYnwezfJ+sBGuMHCB/yIg7dh+vpJEuBAAUnjM1NaU33nhDd+7cUbPZ1NbWVtRKd7tdPXz4MOTNAQRZhPfffz+Y5FarpXq9HjJIwOAAke+yPsjO6elpBGPIB8RMq9XS1NSUbt68qSdPnmhjYyM23UuX+1Ta7bb6/b4eP36sSqWira2tKCf08jpOiDw4OBjJCLl/4fJMndtw/kZevMyOjA1g0ANAPu8svtsDt88EnvR7R3YJfJnT09PTyE4nSxKWl5f1h3/4h1Higc85ODgIP//s2TM1m8040IV5IevKZsp6vS5peCQ9NhB97vf7UeZRrVa1u7urlZUV/dN/+k+1t7cXmVSfS3T3Kr/j8o2MY6u9nIi5dV/lAJu/0U9AMyVv3j4UOUin0yNtbXm+A2pkGZ30wBkyjnf1DDZz4H71y+vq64VANaUC09PTwQbAMGIcut2uKpVKpEr7/X4AbgAhhvratWuamJiIVncLCwvRcJ3jQ1dWVjQxMaH19XUtLy9rbW1Nx8fH8TyMcLvdjvq4druthw8fqtvtKp/Pa2lpSRcXF3FC4vb2dgg3Su4O2RUDdhgWitQhjCJAR9IIQJNG288BuBz4wEIBxJzR8g4UdFphg6e34cOJwqxhBB2g+KYZxkPHjVarFYCaCB9FQ9kAn5SeEPgwVygbAMPrrcbGxoL1xvGcnp6q0+mMsFgAOQAURodTv+ju4Bey54CE9+N93VH7Bjl+D8vk4An22HtN+zs6a887wZz5ewIIANQYcU/Pcl83ejw/6RAx5hjJq8Asv3Pw5al6N4w+Vw4mfD+Bj4F3Qj+cqXSWg/0HDmbQVf44++GB5FXZHe9owPt64JkMWLgP4NV1kfpG/s/zU6lUnMbpu+PJrjD33sHn+Pg4ABXsNUCFkgTf0AhrzucBMs4kMYdeTuGyIo2mgRkXY3PAmgRczDnrDQPHPHqw4nYMJ+v66yDSgTt2lH+7bLr9cT10IO2AxGXA33dyclKrq6u6deuWvv71r2tnZ0fHx8dxcIfXgjI+f89msxmyW6/X40RC3xDqe2IImHkvak89+GeOqNknqEilUnr+/Hn01C8UCpqdnQ3fSDu5hYUFbW1tBWPOe2J7KbdENr2EhnlzhjmTGR5RzcYzr5fFLuAvkCuAL8CYn0uXwSmgDdbUZZJsz8zMjJaXl9XtdrW5uTkSiCNH6Cz6S8ZkYWFBqVQq/F0+n1ehUNDKyorW19f19OlTNZtNHR0dqdPpxGmMN2/e1Pb2ts7Pz7W5uRnBS6lUijUjM8icgkWKxaKmp6f1zjvvRNbGfZADaicUXIb9d0mQzGeS5RTJzI6vJfYSggb5JTPOs525dkbbn+3/Ri+T9o+MC0GMd/Rxf4G+/iqmOsnGf9GuFwLVkiJqpNCfAxPYkQyLSm0TDr1YLIbTOTs7U6lU0sLCgprNprLZrL7xjW/EaX4YgV6vF0Zmbm5OtVotmGdSHouLi8pkMlGHSW9pDEs+n4+aOnbfe+oYEAhoQJjPz8+js8nMzIzy+XykkjwtIw3T8VdFqQ5yUVIcEsbTlcTnCHaenqJs+IPtohaVd+33+9EeyB0EpQYu7NSDkz6iW4m3OOMdACgAX5wN93RFdWMpDdkrejD7McLuxJkrZ/AwIGwQ8/ZBSRDtbHwycHHQx7/JGJDq8nkZDIbHveOYkywwTChG2hkDPgOjlkoNWyM688zYeR8MICCd53JPZ+p9fp115mfOAiLTnspPZmd8Ay5rmQQjjNfLD7gH3Vx8A4xvnnR58nIpSVEnyr0BRb5xiSCRz6EfpOE9/Z3ciOdOknkiY4HzYJ29ZMP3hjBuwIzrAfPFnLmzTAI15tplBEYX54XD9TX0tWDNnUFy3XCnyeeZD/4PyPDMhX+ezAy2EpkBDLJWzK/LKGPjcAm3qz4exspcIB8+Jn4+Pj4eHYkgFqiL5oQ9ZOXo6GhERgEcZNooC0ulUtra2tL29nbIGmvDuzFH6Dh/E0QjW57dyWQuW7C9/fbbUYtL6zgOPHny5Elsik+nL1vLUdZHuYXLEGPxGndkEdJGGoIkwBEAGluErU7OP3pB7T7lV24jYC7Zd5AEgqnU5b6aGzduaHZ2VpubmzEnyB+6hMxMTU1FG1XKPev1un784x/r9ddfV6PR0PHxcdSdn52daWNjQ41GQ4uLi9E15eTkRM1mM8qntre3NT09Hb4Tu+QBNfLgTHe/f7k/yrM2yLB3ucGOJ/XKbSubXZ3N9znDnnuA4iQHdg4blAwU3aexpmQCIXySmU78jGcaYNBh7d3PoLPY3mRQ8eX1+euFQTWOh5olnEilUtHc3JwajYaazaakYf0jPZFRrnQ6rcPDQ33yySdKpVKqVqt699134zTDZG/P8fHxANT9/uWmyEqlEoZ1enpa9Xpd9Xo90khE2kTbbDqiq4BHn9LogSsnJyfa29tTv9/XwsJCgGmUKpn+cGfO/xF85sz/z8+8htFbQ1EacXx8HB1QqCfz1LgfSEENJ0xakqlCeTBQBB+VSiUYX7833/HI3vudAiydUfbDAjBKdEcgncT7eQSMwrOejBXnjzP192O+k/OaHCcBEA6QFFeyHR8MHLX4nnIDtFCqQhDCPdLpy/7a1NLi5JKMrzTKKjPHvk44bOb4qvSu38cZPAc3ySwJjuGXySfPdvlMMkz+XAw1GRgAjaQAXnyeeZ+cnAw9orRkbGwsyrwKhUK8r+sSc+rykqw9Z1zIiYMSL+Vw9oZnURYBIAGMI4MEWtLw5Dlkx7NA2EaC4eQOeVKs7jy5N3Pvz2dNkmvgLLIzvOieO0Xu620v+dtlwAECwIv58TIxtycOIJKAgnd02fXsl7Pb/M6ZdJ7BWNEL9s5861vfUqPR0I9+9KPYXwMw8mCN0kHf8Mr9zs/Po86XIJDnY+Npe4icsD6AWwJvgsSpqSkVCgW99NJLOjk50f379yVdHgV+8+ZNnZ2d6enTp2q1WiPA3DMh2CNfPw+6+b/rqQNs79wwGAzCz3hWxJlSLzEiIPDsGhk9fodv5j6s6/j4eATX6+vr2tzcDCDu7CsMbD6fVyqVijp4AmdJcRIyQUo+n9err76qw8NDffbZZ5qamtKdO3diMyHdVCCr8D3O3F/FvnrQhV1iv5CTGl7PjKwwh8nMHRf35Xesmz8/WQ7qgBc/yv99f0CS7PAs4OHhYYDgZKbIgXjycv1j3Fys9X8sC/1FBt4vBKpTqZRu374drKN0uZizs7NaWVlRt9vV1taWnjx5EhEfERfgqN+/rDWbmZlRt9vVjRs3NDk5qd3dXdVqtRGjKGmkjERSsFy1Wi2E/vnz51E7BSApFovRn/ri4mKEVcIhShoRPAwYTCqdTRBg70bhipEEdq5Azp6iNCgwgorzwtFLChBKa8LJycnY+SwNN/6R0ktGuozJ73d0dBSnRmL8nXXBscH6AcI9FQ0jRrB0cXERGyNYN5wOhh2j4q2eHHRWq1Xdvn07+s4+efIkPocz8/dLgn9fRzcKbuhhSdg40+v1gtUASDM+DB334LswGLwj78kYmQdnwpIgmrEhg14PDchgTZMAyVnkpGOECUc+Mc7MvYNM/mauAHXJoA8nAVj03/F9xpwEesg8m4VKpVJ8zzuJ8DPve5pKpUZOaSMw9mAKeSLdyYlrDk79fQCqsNr+bplMJoJugnIcGnJM0EhP32RWivdlzDCYyYyK11U680QZkvT544iT886YHWQnAyJP6fd6wzIxZ+sdBLOOtNJkDZLMFiCK8QMGksEW9sOzJIA0gEIy8Erqb/Je2I1KpaJyuayZmRmtra2FPD179ixY7G63q6mpqShV8/rwg4MDzc3NaXl5OTpruI3OZDJRKsCYpWF7Mx8Lz2MDPqUIjx8/jg1y2KHBYKBHjx5FzTS+jGAdmSVTC1jHPyQzUUlSApvEvTzQY84LhYLq9fqIT3PbncvlIgNar9dHDifCrrHW3INyyHa7HawvPaQdyGUyGS0sLMQR4+VyOfpVk529du2aXn755dAvdL/X62l3dzcyFul0Osplnj17Fj9DNqnXRobZx+U+GJlnrjzIwGa43UkyuEkQjKwCfpMdl1hDt1HoO/dn/TzDytqxDk4keKDihBZMddIf+vcc+CZtP/dj7XwzNHbgy+vq64VANZG4G5rx8XEdHh7q4uJCjUYj0kyuqKTAYHn4PnWH3p4NIfIoGQdK1I1z6na7WltbG2HMqMGFpUiWZXitqzss6kD9mGIM8VXO042+Cy8CDuC5iq1GuagRhUlxMI6Bmp6eVqFQCBAM64DBdYDpSgIzScqQFFgmk4n7eJ23p5MAmigo7+xpKkA4G9AkBatA/TxzjGHzFFW1WtW1a9e0sLCgr3/968rn83rw4IF+9rOfBYsDqwko4HABxojR8XpajNHY2Fg0s3cGxNl30rI+Z9yP53hwAuBmzdGHfr+vQqEwUq+bTNuxrm4oYUL5nbN3V9VMciVZbK/lRzd87D5XSTnxzzE+rw9G73ESXC7nyXfj94APSiCQK0CpBwh8//T0NIJJB3zMDd1TyCZ4Shw9YIMa60amjDIk9BWA6MFgKpVSPp+PMgLAE84dWWbuCW55Lp9PjgmHBJPudeBJloqWbb9srZzFZx6RG7cFOGlniHu94WYuB+98h/+zedTlDbaS9U2uTdL+9Pv9KAFx+fHvYQ+v0g+3n3SUOj8/19OnT/XP/tk/izWYm5vT0dFR1CtPTU0FIKZMcWxsLA7Kmpqa0srKSugZtgxZoJYXeXHWjvn0jFmr1VImk9HNmzdjz06lUtHz5891/fp1DQYDbW5uRhCOHEM2eTDP/ZPywZwzh4zdyQPPAtDq0TM1PA/A5GU5g8Egukn1+/3w6S7Dvk69Xk+Tk5O6e/eu3nrrLZ2dnenDDz/U48ePtba2FuPFDk9MTOirX/2qbt++Hf2m2XiPbi0vL2tubk4LCwuR/Rsbu2yDOzU1pYODgziC/Y033lCr1dLGxkZsJsQmui1xXXWb7lkQZ5fBHS6DSRbabboHwi7fTnI4G54kg7hPMrvsZZjYWTIGyeCbZ0MegpWcQXe23Mk2xoNv5vcEgR5UYz/AHX7g3pfX8HohUA3IobtHq9WKjRb8PTs7G4INkCFCJTJ/9OhR3OvJkydhvDxVCeN3fn4+Ep1lMpkAQjMzMyM1cEmwWKlUYrcvG3O81ggjhAMBtLuRdcPOvx2EoLRJUITQTkxcnqRGHbR/HmDFYSv+czYlwrLCFPBu/PHnOQjxtCksLGULMEs4eZTWa8thr6Vhf1LqjAG5yVpWgI63WPOUqqdwLy4uT8r6nd/5HeXzeT179iyyHLTng51PgmIHD/ztoJLWdHSlkTQC/knrUrKBU8KpeN0s6+olDqxVuVxWLpcLmWFfAawVcu2g39N61NozLx4Y8DtpuEHPDwtgTBhr/76DrqvYLZ8rZ0vRsWQwhQN2phTQykUQQeAFGAGkOPh3eZAUQHl3d3cEyHjfc3QYOfN9F86oIPfIJvfjHr4GZEuSzo860MFgEGUhJycnoTOk6FkLZIX5oEzAZQ/H5oy8A0iXsyT7y1oxZ8isrw3/JljwUh9kt9vtRrDr6++g8ap1Yhy0NsS5exDFs309sCeAtiRA4bkO2hwoAMbT6XTor/+70WjEM772ta8pnU6rVqvp3r17IZ8LCwu6e/eu1tfXIxuRTqf16NEjnZ1dnt7rekSJQ7Je2bMv2DaAE//f29sLu3hwcKBMJqN6vR77cGBaXV7xS17u5iwha4V/wnZ6sM2YeAcO6sCPsv/GA3UPEvwsAmwc4J/1xEaxNmNjY/qt3/ot/fW//teVSqX0R3/0R2o0Gjo6OtJrr70WtvT4+FivvfZaHMNOze/29nbUlGcyGf3ar/2abty4EZ1Yrl+/rlQqpc8++yzsyf7+vg4PD7W8vKxcLqd79+7FxkJ0FhKGtQM8M0ce+HlA6JkW1wXIQcbptsGzmXyfLmHYhuTz+FmSTHG76sQitoVnJIF98j0JpHieM9Ssseum638yMGbe/P++R+RXXUky4It0vRCo7vf7ccQujr3VamlhYSFOLWQhjo+PVa/Xo7VOPp+PXdqTk5OxW5e6St9ZTORaKpX0+uuv6+joSK1WS5VKRWdnZ9rc3NTR0ZGOj49VKBQkXQLspIN1Bji5yYt0E+Ol64UbNFcUDxQAtB7dwWBi3DAepKEcULtys+sZBou6rmz2sjk/BpmAAaDiYNKjaGkYafIzQJ8bCwAy4ASgkE5fHk5CacjJyYl2dnZi06mnvnkvnulOQBoCXN6V98aBHB4eRqnH3t6eHj16FKlE2q4lN8QkjRRGA2PEGjIeABhzhNPgHVh35MJb1AHmCXI4YppnY2wBStRvOqAmSHLjhHPCUCI3ns6FlUJmnBFmvr1mz4EU9/dyJTf8zBmZBQdXrBH/R1f4ntcEA9BZEwI+Z4VgjcmaYDeSdem0wOL9PYgCbMIAeVaHzIiPn/djHXlPD/TRHfTH5Rb2cnp6OmQHYMozfe59syrr7Fk611VkhTny8SIr2Cu6g7C+XDCxLlOMn78BisgJdaKMG0fu7+Zr70Adtj+fz+vw8DB0B6ePc/d1Ql5cljxLhzwyTp/bTCYz0q9bUtgkOhWdnV2euvfmm2/qm9/8piTpxz/+se7fvx/g6pvf/KZmZmb07rvv6smTJ0G4IAOzs7MjYB3bS028EzTIBX970Ml32KiP/YdZZ/MtR0C7/fBN89h9AmPWgM96Bx/fJJoEdcgG4+Kz7KPxe3qQia1gjLxfUl4ymUy0//sX/+JfaGNjI8ixubk5/a2/9bdi4+Bbb72lYrGodrutwWCgTz/9VD/5yU+0vr6uhYUFzczMBLB/+PChstmsXn75ZVWrVT169EjNZlOdTkcbGxsRqFSrVW1sbGh3d3fErsDK8x7Mc5JpTpY2oC/YN/TzKpvoOuegnPXjSvo8/7cD4aRtZnweGLB+3IN3BbcgQ4B9Hxt67Z1HeAZy4j6VslTu4Zkat1H/Q7tSqVRG0k8lbQ4Gg/8qlUrdkvTPJc1Kek/S/3QwGJynUqkJSf9PSW9Jqkv6bwaDwbNf3OMfSfqfS+pJ+l8OBoN/95cZywuB6nT6sn56e3tbR0dHuri4PNVwdnY2QFkulwsA5t0i0un0SK9J+oKWSqWRo39R8F6vp2vXrumll16Kxd/b29PKyopef/11ffe73400HHVpuVwuhGAwGOjg4GAkEsTBHBwcBHPLqVLS5wv5MezO+rBxhe+hEK6gRHOk+9wYe0SM0eU7GDlKHWCGvFsCwNuBpgPapMHgu7wfZSD8cSaTe2PQ2+22ms3mSG9UB6Aos9f6MgZXPgcbpI/ovLC2tqZUKqVarRZgmO8y97AkvJdnEAgYGJMbLLqNAFI8O+FpUpyb7+L3HrmMmzVhLZPgGflymfG1cbYLYOKMr4PRq5xFUs54T4wp883YuNzQ+hxwJQM9v7f/309C87Hxe59j1o93Q6cJ+AgW+TzMPOlN3pn6d5wy88WaOsvodorNhx6cAMaQcZ8P79/sQZLfFzvkpW/Mt5dK8DfBo8+FA2kPAgD8jNODYu6DbHh2wzeLo4vYKQIiz2S5PnobTuTE9z3wO+8C4nLtYNnf2y9nwTyVzPN83wC2i77i2exlFynsLYdO0dZ0ZWVFb731lmZmZvTgwQNtbGyoWq1qdXVVMzMzunnzpv78z/9cz549C6JmYmIiyhGwvczzxcVFbGR3P+AbXZPvBlhmI3Or1Qq7UigUdHx8rP39fU1PT8e+Im9px/ry/t6m0bsseX26l6AxpqQN9IAJYoNNhH4B4JJBuwNvmhJIUj6f1+zsrI6Pj/XkyRPdv39/xKYsLi7q+fPnMV/9fl8fffRRHM7y2WefaXx8XN/85jfjsLfNzU1Vq1WNj49rY2NDmUxGT58+1fb2th49eqSTk5Moh5yamtLW1pb29/cjaM7n8+p0OmFjsSEOXFlP3g9dQO+SGehk7TXf8+/7PQk2kGm+yxzzHccjno3y+7tf9yDX/SI2wPUHlp7187IX5sIzbNJwfwrz5H7GA3kPKJIkxFXXfwHg/b+S9Jmk4i/+/3+U9H8eDAb/PJVK/T90CZb/77/4+2AwGNxOpVJ/9xef+29SqdTrkv6upDckXZP0J6lU6pXBYPCrX/SK64VA9enpqX7yk58olUrFhoZCoRBs9NnZmer1uvb39wO0ARQpDahWqwFk2AhHu7hCoRCtsqamplQqlVQoFHRxcaFnz57p6dOnI2wpxkka9m/0JvKSRgSq0+lof39fqdTlhhJnrRA2T5FhbNzxJJ0Gn3MWGwVzoAm7IQ0jSnfGBB7SsEyC8VOKQcmMOyh3zIwtWUPL872tHevhLJ93xGg2m8Gge6TO+LjYvMK7eGTt5SwOqJCbdDqt/f39eAbOAmOMIfee2/yechgYSWeFHUS7EclkMpGSpz6UFCUbtJAhXyv/rDsx5hdnjDFlIyhr4ela/qbmH8PJ73DibsQATj7vzPlVbetcNvnDPbz1mhtZB9GAf96PwK7XG/Zh9iyNvxcGFxAIyHN2E2fCkdEAHA9icG7eKQbmxDfL8rePHXmjxhAWGQfjICL5M4KGdPqyzRljBRRzb97DnRPjJ+DudrsR1LHXI8nIku3x73N5AJlKDY8Q90CPueazLnPIEbKDDCcDPt4tl8sFOGE+fWMmIIZ1dmY6mepmLG6XHFCzToyF96QcgnIBaqlPT0/1G7/xG5qenlalUpEkVSoV9ft9vf/++/r3//7f69VXX9U3vvENffvb39b4+Lj+yT/5J3r33XdH2Eb0BeKn3++H7qMjDogod8SvYCeZR8Dy8fFx2Chkjvt6jTctXsnUuT9LZkTome4ZHHQik8lEVg+doMYVneF8ANoIOrmBj0CGPOh3f4DMjY+Pa2VlRZOTk9ra2lKr1YoAjP0ki4uLERA1m00tLCzo3/7bf6utrS0tLi6q2Wxqenpav/3bv61cLqf9/X2dnp5qdXVVi4uLwXY/ffpUtVpNa2trqtVqmp6e1srKiq5du6b19XWtr69H6zdsh89PMmvq4BZ59WwrQYez09zHy2VcxvHPXlpBaYjrsAefBMvYD8bj4+Re3r4T3eFvxkApl8vs1NRUkJSM04GzZ/NdRx3IO1ZxvfWMZTLA/C95pVKpFUn/Y0n/raT/depywf9A0v/kFx/5J5L+97oE1f/1L/4tSf9C0v/tF5//ryX988FgcCbpaSqVeiTpbUk/fNHxvPDhL4PBQPl8PvptetoM5w3AgdnzFjwAWnpOAoA9pdrr9dTpdLS5ualOp6MHDx6o3W4rlUqp0WhE9O0sbDLd7YLCju9ms6l0+rL9nzNGOFSv/5VG29/wZ3p6+nOHjqRSqUhnS6OpWEAZ/0cJGCMgDnDowkwKmvpiQLsrt0ewnuLCSRHQdDqdANOsD2k+jKI7upOTk2BWvIsBnwM8www4U8d9kilznI4HVBgWNrA6KJKGNX+kYjEIsETOgAIQfDc96+gBi4NE5gVj4+UmyDLriCGk8wlrxpwCYCj58S4SjEMadm5BNh2gIou8v7O/zC/gIMkUJ40da8O/eTf/LM9mvpIbc1lnxsI7omdcjIWNSTChExMTarVaESSwxhcXF1HeQFCDU0M//Fk+f9IloIKN8RS+j4Wg0WtBvbyG9QME+ZHlzI+vBUwQTFE2m40uJa4fvqGX9eb5zIkzTt6PGxnFxnkw4nXdV60XOnhVBgrw4SVvvqES0kFS1N9iU5lPCA/0ELlI2q3kGnrw5jLljKITF6VSKY5+5z1u3Lih1dVVvfbaa7q4uNC9e/f05MkTPX36VJ999pmWl5f11ltv6Rvf+IbGxsb0ne98R5999lkESQDgfr8/knljHlgnT6dTHgfQYD1ZG+ZJUvi4bDYbB5Nh37FZgPFMJjNS7sQ68ztAVyaTGWnv6cElnUeS+y0YJwEJ/8eXHBwcfG59XHeSfgkZW1paUiqVim4r2WxW169f1+zsrL7+9a9rdXU1asbZP/Unf/In2t3d1csvv6yLiwtdv35dX/va19Tv9/XBBx9ocnJSr732mm7duqWf/vSnajQaKpVKmpycjE2JkuIgmb29PW1tbcUGYjI7Xs7n9sjZY+SOy8vUnNlnHcAFqdRw4zf3cACK7/UN03zOfQ56QaYWnXbih7XHJvJzJybwBfj95DMI0pJ2HD10UO+/uyrDzOVZMWzpfwEm+ldd/xdJ/1tJhV/8f1bS4WAwgGrfkLT8i38vS1qXpMFg0E2lUs1ffH5Z0rt2T//OC10vBKozmYyuX7+uGzduqNu9POVpd3dXZ2dnmp+fV7FY1P7+fjjISqUSBpLWbJ1OJ1I6CBjMptcl9no9bWxs6PHjx+r3+5qamopNe+fn5+GYKAHAkXgkhrBzYAysum88AHQ6u8s9MDz8G8DgqVlYe98ti5Di7FEGd8bJrh3O6sJuYGAAsp6mceUG9HH1er2RntYwLIwZpaBmmXpFyiA6nU4cxMPzAAuu+J6OYi482k+lUpFN8PVwoMTvHTg6AGPdAWC8n4NP2i5iqDA2Dlb5mxZXpM68wwVGju8xDg/2mK/BYBDHtbNZBkMKS+o1y+iPz1NyLnwTrddHe7tDD0RZS89WeIqYuXTm2YNQHJIHYDgi5hmZ9lpZX98ke5PP54O9m5iYiN6xzo6QipcUXQbY3Do5OamZmZmYJ3SI4InSEZd5nGG/f1l+xvP4mWcnPIvEmD24YB6S74UckCFJllCh1wBzyqc8m1Gv1wMQUWuO40Y+AM/e8zyZLiYoAUj4OgEEPdCEVXVGkjEh26wpz5yamhrpyARx4sGCdyhJBneMO7mpLgkE3ImPjY1pfn5e4+PjKpfLWlxcVLvdVq1W087Ojr73ve/po48+UqVS0c7OjjY3NzUxMaFKpaLf//3f19tvv61nz57pvffe08cff6xmsxlrVigUoibcT81EzhkffqlcLofdZW0BJATvvV4vGHXKOjh+OxkIowteZsace7AOiMcfOtAm00Gg5NlVmETGCtOJb4PJTgJPABaZVt7Ny6qWl5c1GAy0tramdrutcrmsr371q8pkMnrrrbf06quvhg7g31OpVHRk+St/5a+MHA//s5/9TJJiv9Qf//EfxzodHh7qwYMH2tnZiTF+/PHHocvINmPG1iJP6IEH8O6T8Au8O++NHFIK4kRI8h5O1qF/LucuV2AIxk+gwrpxOaHg7LiXXrlP80DL7bnrFXbddThp33kHt/9J3eS5yCmf/VXXf0bQXU2lUj+1//93g8Hgv+M/qVTqv5K0NxgM3kulUr/3n+uh/ynXC4Hqqakp3b59W81mU8+fP4/TEFEg6mLpq0z6ifpGNh04iyhdCkSn0wl2wqNpaVi7mExReeqKzzo4wqhOTk7GvaVh+xscoj/LAQhj4/fU9znYRtgASSg6rA73SKfTsfnONzW6AuHQSBHyWQyslwUAJtmwCRPhaWVShQ5+MRyASoAan+d0KwemgG9XQmnUKScNPQ7dnSYOhLIeNnE5owogAnx5wEIaGMcE0+4MAM91tpHPJBlNXx8PcpwdwpC4EfEaXmfsYN/duMJaw1wjq56K9820zkB4cMjc887ORLouOfD1sTGnrAnr5LXEyK6DSXcI3B9ZckaYZzDHyZZsnjXwWnxKJCiZIej19SHI8Hphd5KMG7DtGSbk1DcMIws+Btezs7OzkQMzXEclRctIfk8Wiftjc2g/6lkHyAVPpQLGCSI4/Arg7U49mfXwmkr01oMe1x3PNPgc8TMAYqvVirHB5vtGYJd/D+4ceDDvLjPoFODRbUMul9PS0pKWl5ejbpf7tNttFYtFlcvl0NObN28qn89rfX1d3/72t/Xaa6/pnXfe0Q9+8AOtra1FSeLR0VG8e6PRUCZzuenb/Ucmk4n9K51OZ6SFIvKH72FMgKnDw8Mo86A9HHPizL7bh2TAg+64zYEJx244u8/6kanDXvg+EBhWwD5yl8xqskacwudMJ4FdrVYLv726uqobN24E2dFoNPS9731PR0dHmpqa0snJiRqNRszR66+/Hh10tra29ODBA52dnWlxcVFPnz4dsTf9fl/r6+sjWexut6uZmRnVarXwU4BD3gf5cyLEWeAkY+u2nPv4BfB0ootnuE9BX/FZDsZZY9YXe87vINZ8vwR+0G00dtZLHx3zJANT7BHrypx5sItOQ5YkGWonHZ3Jdz3+i0D1f8arNhgMvvUrfv+bkv56KpX6H0ma1GVN9f9VUjmVSmV/wVavSNr8xec3JV2XtJFKpbKSSrrcsMjPufw7L3S9EKjudrv69NNP1el0RtiQfv+yr/L4+Liq1WqkvVFyOiqQ7ncgS0RNVE06TBoeFYuQY8xbrVb0I3VAhtE/OzuL0xcB9tIQNHgtqgsa/0+Cdhg437gEmOH9YRkvLi7iMAKMI07T2VaPNvk3oBjDikADxmDsfE5I03kXC4AaLCpt4DAesPfHx8cxRsoykuyJG3BpyLYC8v2zzvi5g+VdpGHt6eTkpGZnZyUpTo2EJcHIeO09wUMyze8dJ1hTvouRS6WGh4k4Q8lnWGfu6YbN68symUxssEPGnS31FlvUwANCPEDkZ1eBDk+TJxkQl01pCJw9Tc6aOfDF4DvjzmedOUQ3SIk7++FsKEbef45M4ND9PVze6cZC6pogBPk4Pz9Xo9EIQOisPeN3oEgZDXPtDscDAcblQQJ2AQd3enoauu6Ow+uQmX+CEQKXq5w6Y04GCfRhx9lRAoLdcmeN3XGwzL3YLI4N88+whp7Bc/YoGexKwx7c3grQg5AkU+c2k58jj0mg4s/mOczNwsKCbty4oUqlomKxGHX22HreH8BL+V69Xtdv/dZvqVwu64//+I917969ONjEg0lsAzbOgQbyTllOqVTS+fnlASYEeMghmSfY5E6nM1IPz3u7XDF3BJN8FpvL3CPnkCOePUjKD5cDJD5HVrdYLKrT6ejo6EjpdDp8K2uf1Pdutxs2njIZMhXsLaApwczMjPL5vNbW1tRsNpXL5SIbQG05c1Qul3Xv3j3VajV99NFHOjg4ULd7eb7EN77xDVWrVe3v72t3d1f7+/uq1WqRJZUuSxNrtVrYQWeWpdF+6Q4sPXvl9iVJQHBP/71nCVyvk+QAfzzz5Xrmn0UW8XM+Bj7rthr94zOMy+/vYBcfg+x7eRWynwww3F97pt59iNsOn4ekfU/q+n9GpvpXXoPB4B9J+keS9Aum+n8zGAz+fiqV+v9I+lu67ADyP5P0//3FV/71L/7/w1/8/j8MBoNBKpX615L+X6lU6v+ky42KdyT9+C8zphcG1Y1GY6SlFQoMwKtWq9rd3dXz58/DiKCwOHCiapwZmx5ZWA5fwEg785hOpzU3N/e5ukWApDMBOAfGmux44SlpUuwOYvmMG0BXJO6Ncer1egGoMVSDwSCcpqccpdH2OC7I3BfHmmQ4MaAIL3MDkKJjB2y8s1k4Y1JR0nATH0qHgXF22+faU5fODjuL4lEvTLc0NDRHR0fRipGDCmBX6NBAHR0KzdoiO/yhryxMJ+vjiu9dTdwgY4RwdrATDnwdODFfBIkOqnHggG8vRfE18hKO5IYUz5j4/wGNpKedzb7KUTiQRPZdZpOsigMsN7yuJ8gg2QM2C/FMasVpI5bccMk8Mk/U8DpzT2kE8+SpeYItT286gPU1SKY/3UF5+RbvDwjgMzippF76WiQ3VyJfjM8DNWTQN57huGH16W9O5wjGwdxjZxx0o6c+R/7OnsL19fSAA5vGZ5Jgm5/xxwND/5zX/bIO7ow944jDL5fLseGQU3FZi7Ozy/aprVZLKysr6vf7AbhbrVaUD/ybf/NvRjpOFIvFKEEkcIX08HalFxcXKhaLMZ+np6chq8h4NpsNgOfAqV6vhy54RslJHi8PoOwMUMjvyWgwn7lcLsbjNpm/qd32YJu1HgwGKpVKmpub0/HxcQBYavEJkNwm8F7451KpFDXhnuVLp9NxZsDCwoKePn2q69ev64033tDe3p7S6bTef//9mJfJyUmtrKwE87y+vq5Go6GDgwPlcjn95m/+pnK5nJ48eaLd3V1tbW1pb28vCBzsJTaf8eIjGBe/c/mFjMP+OjHitpHLMzf4MGfzfc6cwfYMObrqOuIEBGNyVhrCLkmm4KMcnziY5fMekLn8JYG7E1yMk0ACoI+sea21Zwo9wPv/I0v9n3L97yT981Qq9X+Q9L6k//4XP//vJf3T1OVGxIYuO35oMBh8kkql/t+SPpXUlfS/GPwlOn9ILwiqB4OBZmZmtLe3FwahWq3q7t27SqfTcZLh0dFRKCkMAA4OQO27kenvm06nAwh6BMoOdI6xpq4RwTs9PVWr1YoTmCg/8bQ86Q5YXeoZnX0BRA0Gg3gGbEW/3w9A44InDTeT0YJPGh74kM/ng6WmfhhmGyVzB+QgiV3lztrxGVhrNsnBoLZarYj0Ua7BYHggi9efOUh3hZVGD5twoOzGnPXzz7Bu/N5bWLlTlaR6va5MJhObztilzpp5mtlLDQAzyXIad26+aYTvYUQYm6cQATRswkLmvKTAAYLPDc6anwHSmUcMo58myGddNh14exDFJk137oxzcXFR3W43Ogm4TErDlknS0PH4unk2xgGWA1r/brFYVKlUisAMfea9S6WSms1mABRniAmCMplM9Bv2zIxveGbNMeSweA7uneF3nSQgA0h4QIhuJlknSSN2yu0Cc+QBAXLlG635N3LL+xOYScOSiG73cn8JpXEXFxdRRoAewxTCzHvwyzOSAZAz9C7nvoYeVDo44H3IEDmowImPjY2NHP/twV8SgLj+EVyjv5nMZXlXsVjU5uam+v1+bKTLZDJx6mOhUNDi4mLo/+TkZADH+/fv67333tP29naQIUmWj7XBBmFnXBbK5XKw5gTtzJ37iF6vp4ODgwgaqa1Htz1zApkBiEEXkSk2Snt21rsv8QdiiPXgGdgDdJS9HtVqVQcHB9rb24s58HV32XBgxdx6HX25XP7cibHNZlM///nPlc/n1e129e67l3u7nj59qgcPHmh8fFyLi4v69re/LUn67LPPtLOzo729PfV6Pa2srOirX/2q8vm8fv7zn2ttbU0HBweRhUBek6WOyaybB3UeIAJekUHfW+IgHJl2UIz/8N97QOpjw74wRh8f/ofgAmyBPCIfngHzbITbF7fBSaLF38vtDYE99sE3PCfBuNt+95/8zkuKeN+kvf0fyjUYDL4j6Tu/+PcTXXbvSH7mVNLf/iXf/2912UHkP+l64T7V4+PjmpmZ0dLSUkShT548UafT0ezsbBzTmmzlg3FzQJ1KpaJ2DaeJwpAe9ogZo4OBQJg7nU6wDt7TGQFLp4f1gkl2C2PsIK5YLEYpBvfwLhPOjntqxFOFlHx4OpNShuS9nOkBCPgpW5SUJEFrt9tVq9UKpougAEVl4w8b+ZKGCYMKaEShWANpdOMRfxg37C1z6kEC4ySIAfS7Uz87O1Oj0dDMzIxyuVykPZvNZtTD+/HDrVYrmFCcrzsdgA0AF4ME8zcYDKLnKcEI8+AbfFw+2Fzku+2pG2YueX4SfDvo4/MeIAGw+Y4HbL1eb0RPcHa8p7PcbIgjxedglDX07IMbeX5HcMDauKHm39S4N5vNAAOwr8hbs9mMEycxwugV7CMbpnzuPSPja8eaegtDfk+gCnDFgTmDhGx6GtgzA653jBXQ7fMC6CK9zdyweQw5cNuEjjJu7/TCPAOsmHvflMkF04jcuGPEAedyuQBZrsMuD54C9vkgCHFnK2lEH9loBuvqa5p0/p4tYE6SY5mYmIg2rK4D6BGOnjW9efOmisWiXn75ZXW7Xf3Jn/yJPvroo9APzyygx5Ii8M7lcjo8PFStVot2nsViMcaHjej1etGrGhtBq0Fq+dk8dnp6GnNH/TJrwnjQWfSOMolMJjMCimkjeHBwEGteLBbjfaTRTXPISJKAWltbi3aBDpi9XSAX84V+IWOLi4u6ffu2VldXR460r1ar4ZfGx8f12WefqdFoKJ1O6/nz5zo+PlalUlG1WtWDBw+0v7+v58+f6+joSHNzc1peXtb5+eUx841GQ5ubm8Gmp9PpKNFzGXSZSPoh7Bf2GrkmmCEY5XLyKBlceBbCfTL2gbkmuPI1drtPJoQyIICpZ615hpdb4iOcqEGX3G549sA/k9RpPsP3nEBIzh8/90DDmftkBpDP/6rL5/2Ldr3wRsWlpaVIEZ2cnIQxIfLOZrPBiiLo1FwlWV5PfQK82JkNq9Hv9+N4WYC3dCn8frgHJSjONCFUAEBnIBgbETH12W6EcL5XgRSUyhVYuhS2ycnJaAPnB4Y4eEfoSA0ReR8fH2tvb2+k/pk/BAwwifycdUg6216vF03yUQxndJ1F5R2dCWONeGfPMmCwfR4BXs6OAVY9mHGwORgMwlnzXU7JpNXawsKClpeXNTc3p6mpKdXrdR0fH0fa9Pj4WMViUcViMfqnkh7nCGyMDsCNdyQTwZo503R6ehoONpPJxDivmit3fp5t4PPoA+sC8Hfwl5RbN67Mp5ehUBvPfXiWA2MHx57+TTKWzmrwPL7vYB4ZRk+o6U+lUtEnl/lCzj1lnWw75cyhj5cLcJBs/8b68F1n+fz9WHeex3wyh+yTIJhFpz2Y4LPoGL93O8a9fL3dGQEkWX9/z+SYPCPl7C+g1G0QABqbi11xxhMw599xmeIdHRzkcrnIvrm8siasQ/IezvD5OyGvg8Eg9Ax9JDBB92iDB1NK4Dw7O6taraY/+qM/0vPnz+MAqeQGNpcJ/FKj0VC73Y69DpQn0bHDgzkOJGNzHhkifg8AJlB2W8nP+L/LHDpYr9dVKpVG5okzGvArbO5j8yO+k8CLwIbAmsCbICUpSwBo5Nw7OVG3v7S0pN/5nd/Ryy+/rJ2dHU1PT6tWq4UOTk9Px16l58+f6/Hjxzo8PFSv11OxWFSlUtHk5KR2d3dVq9UigzAzM6P9/f2Q6Xq9rnq9PlKzj3/l34wR+9rtDjcTA4qTmRc+yz0cODrjjOxy8XMvd3Ab7muPLuEDnFjjAvN49sb9qJNOro/+DLeF6BW/5z7IlL8jvh2m3MfGvXxuPFvrLHXyfRiPy/KX19XXCx/+0uv19LOf/exzLDIKmkxnO4hzAYBl5N+VSkWdTicEiDIPDOZgcFmDyWEAGDxvUN/rDeuXeA7OzJ2KNEx9YWRRVu8K4iCUd0KhAFiZTGakZAGWnQ2SMM04a3dCzAfsNWMYGxsL9t3Zq9PT05E+ysw7DoX3wgBh5Ekj0nowGe06KyBJxWLxcx0Ostls1Bkyx6TtcSwwmTCHpDVJ63EvT687K+7GhPkaGxtTvV6PtC+Op1qtxsFA3kkBA8HueV9nUm6AcYwH68m4kFv6vLbbbbVarQD+Xn/PXDsTh/xiuBzw8rwkM+KskrOYXJ4BSBpJ1p05dcba59MZM59vvu/BAjLW7/fj2HgPnJwhc9lxRomAM1lK4oAracD9M6wF95KGeyf43fn5ecgdJQTSsMwC/QHEXcXewELhVAF1BBkOzFkHvs89kkGNp2ABdughcuplZR6UeeDmz/GAwcESwIoxegoah847uQ7iSNnQiOzwzjCpzu6zts7UeTbAZddBEmvhNcjIFmvttfQrKyuanZ2NGt1araZms6l79+5pa2srQC2ECwEb8omMtlqtCIKKxWLYK69r5r0kRceWg4MDnZ2djZT/9fv9OCcB8EbmBfLAQZ2XYzEn+EzKn1ivs7MztdvtsCWsL/PFfCfviV1mn8ZVma8kK0kQMzY2psXFRaXTaa2uruorX/mKms2m/uW//Jc6Pj5WtVpVLpeLU4Q7nY4eP36sTCajzc3NkQzR/Py89vb2tLm5qefPn4esMc/FYlH1el1HR0exwZax+hi5nNH1jePIETYC/UiWY/D55Lu7PCYDeL7jgJqgCBvmWUYHo9hvSnPISnvJGj4DHXRdSQbZySDDA4VkkOB+i/lJMsnJgALZS5al+HOSgYfP1V8Eqq+y6V+U64VAda/X06NHj3RwcBCsSxIcYnwnJydHBBQlX1xclKSRTRm04nF2zA8gwCkhnG48MXhJ1oz/s6nIwWzyckDtB4H45wGuMBWUtlCvBFil3CPZyioZ0XrwIGnEEHIwgAMxhNo3aqGgHCOL08UxDgaXtdSlUknz8/OfS7HT25lUJ0GKM4zUn7MOBArOOJOmhP3wYIs1kYZMFYCC9XQABXPptZ0XFxdx+pYzlv1+P/rCejcJr92Vhmykl1IwD4AePusMCBkA5IPxYHQAQR5IEmy4vJGqdbb+l5U9OODxi2e6w00CZL7P/3leMhXsNYG8L8/34JP1Yj0cwPqcdLvDzj3JloKe0nR9c3lwPUsysMgIDo314nPZ7GVXAt/LkHQIyJCXbXBPQBnr0usNW2Ghj0nmm7X0sXoq2B0yc+7ZG3SfuWJt0RvukWSw/N+smWfhSOW7PWEekU+e5YwXoB7981phxpt8PvrOujmYZF3IeqCv+Xw+/u9ZRbr7LC4uam5uTrlcTt/4xjf02Wef6fHjx/E+ZCfQQ7JSZJrcDwBwCSicYUtumvYggtKJTGbY0pOONcw3c+1lMF4y4Iwg8u4y4QE4Y0qlhmWHbDIvFAojJIUzqhcXF8rlcnFSoRMjScBDMMMaUH5169YtnZ+f6+bNm+r3+/rRj36kTqcT3Xck6Z133olsAn4pqbP9fl+ffvrpSNkSYLnb7erg4CDKWvxQE2TOmVPezy/sqQf6vn4eSCQZarcFrk9+H9bH1wObQeAEieCAWhpugEcHnVhMZuWdaHH7niQ5kBECCt7Fg1cHw3yPOYDocfaasbt/Rg99Xphn94eexUnO3ZfX568XBtUoh6e8nSV1A4JjYhGpbWu1WsEsYRww+lwspoMYHFE6nQ7DyDjoLerAJxnZ+mYAhNEBr7dkgymlrtLTJNRLIaipVCqYVA5R4b4wvhhqBz2AN8AvYA+w60wZn8ehXiXsbsS9r3W329X6+rrS6cvacuaODhXujEulktLpdIANnGMSjDC26enpKJ1x5hQgDphjPLTFYy68bpjNrbzP9PT0iIEql8saDAbBKjsbjrF2EO/3AxzCqHt9KiCXUhq/eHcPIjzF6+ygN/bnd4AN5tnrtr2215lrd1puYP33Sbab+3udJkbYgTTrRtDqgB95Rm7447vx/d1ctwlwkgba60HRPWeSGLsHFcmgBWaReaScirlJHsvrjonP0Yc4GeQ4Q8R8eScQnJLbCIC9gyPkkbXxjAyb2ggO6j8uTgABAABJREFU/bAgd5Y4bA+skkG4/x99Rj5g8JNyyVz7u/L3xcVFMGsAaTZAM9/oCXaCsblD9vGR2fBMlwfWMIscxpXL5ZTP57W6uqpUKqWVlRW9++67evDgQcz19vZ2yJUDBy9vYex8Jp1Ox7rDNsIeYuvZLyEpTv70Q1ba7XbMA5kXTpll/nnvpA/juUl2kc9jI09OTsJ+OFHiB3LxzjyjVCqpUqmoVqup1WqNZCyRSdcpSVFGs7CwEIHV6uqqms2mnj17Jklx+u75+bkePnwYY6cEhnfwoM9biXpQyJ4e9lqQXXa7hj56/bPbPz7jcp3UBfQeW5K8R5KZxmd4Jsc/6zaIMkvW1cssybZ7Nt6f7Xt2uCe+24ME10e/B7LtWS3kDJ1PBhPYXPQTIhDs4t/3NUQe8KH+fAfsjM3n/str9HohUI2Bw/B7hw3/fa/Xi9Q8SkAZwMHBweccdL1eD0EnAsM4orAYTZympzL5OZ9HgHi2dwG5SikdHKRSw1pEwBPjxTFgOACcgEtqxwFTtPjrdDphcABwOF/+8ExnV7hgAnHqLuAoQrJrgqfXYG9pPYcxhxmBgSa6bTabUTJD6QZGk1R6Pp8fmVvm5aoShpOTEzWbzWjBxvcxSMgOBm5sbCyyAQA/egdzDwA3a8mBFQ7gT05OlM/nwynUajX1+/2od/eNRZ7+D+XIDjdyeucHZATQ7p/1gMdlHCdKu0iclGcwkobVAZfL6VWfcUMNSGD+/XOAMLoVoBOABk+ZMp/Ixfj4uPL5fBxTjA7RZ/wqlj3J5jrgRRf9mTgh5oXfeZ90gND4+HicOOqskweggHBKyQD1vpYOpqXRunVneLg/7wQr7rqKHQLoT0xMaGFhQe12W/fv35ekkc9zeZDvAYezRm6LJIXOIv8e+LEenlp35p95LZVKGh8fV71eH7GhHlTRppLDvviMr6G/h2d5sDMw4sxvsVhULpeL8wYoCzg8PNRHH32k4+NjlctlZbPZ2BeCnBMAe4023XqYexhrr3fGvrLedFvBxhQKhZCTJOgBXAGkvZWmZxkdlPFsfu8y5yVxDpzPz89VLBajJNHtkgeOExMTajQaUZrBGF0PUqnUCIDCbrJ+S0tLOjs702effRYb2emClAzcPTDw9/W198ABW3d0dKRmsznCtDtARI59vj275vrm2Reex+VZGAeZXuaD7PNdt63MkRMsXE5kcE98vzO6yTVCN5Lj9Hf3jBP4B/lgzOg4n0+SEjzPddyzGow7uWbO+LtuOCPt42aOpC/LP37V9cIt9YhkMNYogJce0BpLGgJtabQvNJ/n78FgoP39/ehRnM/nQ9A8dY1x81IDN5zSaDsxHK4zvIBbZ5pg8q5SoCQLy/d5H4IGHD6MCJtdPLjwDYaeQoLtZf4wAN4phM8zFzyD3wOuHbzweYACxps+rawr7QM7nU6ALxhd5oU1J5BxFoH59cj+4uIiGHgUliPHYZAGg2F/Y0pfOJiGtYdV3d7e1uHhoaamppTP5yOdjDNBLpABjNHZ2eWhGoeHh5KGwI0TKQHzzIFvOEulUtE5ZjAYRD2ggz7WhnlxIARAmZycVD6fDxbQ0+ke1PHvq9hpLncIgGEvCbnKiML89vv9kW4xOKMkQ86/kQUArJdZ0BHC+6LiFP29AD+e6UgC8EwmE50aOp1OjAnwDPBxdhZ5T9azevmPgyGfS2ewfD2ZWw+4cXrO5ntbR8ZP7a6vfzab1cbGhlqtVuigA1YuD/YdqDo44nf8nnFgR5lfnHFSHnh/ZI+5pYsFbKkHlwTXdHkYGxsb6efswARbxlidyT04OIjAfnZ2VuVyWb1eL4K73d3dOPhGUsgctc3Ig+uVZ+p6vV5k+gqFgiqVSqwrtn5sbCzKxAjO8VOeMUWfkVnvpoEtctnwQDc538yHs/v8zll1SvggOdLpdNh3AJwHS7DqyDP2iZ8BSKXL7CPsPPJx584ddTodffDBB1GG5DLG+zC/BDPogAcMbvecLaVsBjuTzNReJffOrqOHfJafwex7JgAyi88ldYtxOfB2Mg59YN4JcL1siudTUkow4Jk97ulki7+r67QHux7Ee+mJr4d/1+2z6zrv4TrMevBuSUadd0uy31xJe+bj/fL6/PXCTLU0WpfG/zHq/ntnAbz3KUat1+upXC6P1GxSm0kHCJytNOztiHPyDX6ZTCaMBoIPmEfgEB6vY0XoAaOSRsbMZ1BgZ1vYpMEGPowgYA1jzD0x+s54VCoVzczMjJQjwGRikBi3dzigvs0jamfpfa14ZwdujD2VSo3MLwws35U0cgxzEuD5OzNun2vkhh7mrEkyRQkAajQa4WjpvDE2NqbZ2VldXFxEHT3vB2CXFBkDxgEz32q1YsMSaw8bn06nA5wTGHnNMcaPnzkIpL0fDtdBtjS68x4n4yUfyCaA18G1A2pJn3NG7hwwhs6UMccOeNAX1+cku+O/4x35DLo2MzMT2RUPANE93oc5494+d15PzT2Qd3rgOiCibAw9oWwBmXLH7KwRnRN4FvOMLXFg5GwMc+i6wBzwM2/zl0y7Tk5OqtVqRX2sb7a6isVxmUFnGaODx2QmwdctyYC50+YCfDEnlA6go5AAnoHiGaSSAR/IHvYDoEvQRO39+fl5bA6mtndjYyPqhrvdrvb395VKpaKfNHbAmXXew0EggJHgjzZ5fJ4yQy8F8y41sMTcF78lKexrNnt5WAzdZlhjB0lcDtySZQXoGb7Q5Y/xe2Dd6/WiRz1ZJDYMSwoflk6nR0rS3FZDTjWbTS0uLmpxcTHm6/333x9h3F1PPABwptiJMIAsdpI5YUO81/1KGrGTDuSYR3SIdyJQdSDpG6Tdvnng7P4H0s2JMQfp/i7YHPQdwsdZYy/jA1D7s9yWevka7+cAPUlkoHNkWhhzMrhwvWQOveSG90Au8JfImcut+x2fIyfc0HcPFBzof3mNXi/cwdsdTtIZUS5AXSufQ3GTDB2LT2SVTqe1vLwchsMjTC6EjBRJ0nFgsLzOm+9hiC8uLkY6EzhjIY12MsAxZ7PZYAcwvJR8wHpSP310dKR2ux09OAFlgML5+fnoEOKdQ/g+bDLgyt+f3ebeASHJnEqjh7v43LnDgEWmthpww3s5I898eFvAZIkMLQ4lRVADw+KygHHlnoPBIDpsICMYN5gbb4UHQ+AnGkrDFlZ8FubJyzxchlhbLwfAiCdBotcCowNu2JNBhLMMrFES9CSZK97BMwCsJXPluoPh85Rm8rt+by7PuHhA7KyLM0X0Wz86Ooo+u6wltbMEkICuQqEQoIhsCo7CGUd3GABkxuEblH0nfTabDbaIexDwkCUDIMFsObB0AgBn5qw+zhbg46AAUOSbJX3eCZ5df3H4zvg5kHCHxbN8oxPgxQFGUm5+WWDEuKTLTWpzc3N68uRJvAvAHeDFHDoDlkwLk/5Gz9jP4HMMGHFmjNKD/f39yEak05edPSBijo6OorwIJh55dIax17tsfVcqlUb02cfJGLHxgAjkihIx7ul9zqVhBo91dTaS6ypg7cxtMuvgrdbQBT7nZUEENp6BQeekq9swOgBD3wgCc7mcVlZWND4+rlqtph/96EfhQ7BTSdDGfPrz8WeezXGwzdgdwLmeuswjo76uTmT4fhpfF4Cxyygk11Vr48/3eWJNvGSTMbkPdlIg+a58HnljHh1o8zP3zTzb7axn1JENriS77UAYHWEOnP13LOObHj0w8CyE2xf8Fs9kftHLX3VdRRx8Ua4XAtUeXTvYhcE4PDxUsVgc2UiUXADSgYC6TqcTTntmZkaSQjC8vqnX60VZCMKK4eH/AHuEnvvwb5gUnL87Y4+KHYzhFBEoSgSI9vv9ftRMw/K2223VarUAiLA5i4uLqlarMV/SkGnF6UrDk90ArihHp9OJTTLulJMsgztSB1fMA0rD/ML4AULoN8q8Ejmzlp62LBQKWl5eVrfb1YcffqhutxvrycYY5tx7qxKIAI7pSY2y811niQE8vhGRNQJQw0TDiBGkXFxcjJSceAod58PcOcPqTDrv4vPqXXD4PkEAv/eAwAMkXyd/Br8D4HiwxHiTYMyNON/HMCadixtrnzccBfWu/J9AiWCmVqtFsESgxTuiG4wZQOYlGdRt+pj4DGl4139fXz4LYEOnnbVCl5NlMV737uCMTjMOFNAtXyNkM8kMIQ/YCWSS7IuXv3AvZy/JGKFnniaXFGUljM9L0pxF9MDNM3oEDtlsVs1mM4AuAQrAzbMk2HDvekFmcWZmZqQsYzAYjPRtzmSGZV7IDgAXAO5AmZIfD5i9rts3JEpDxjObzardbgcLztryWbIZrI+DIbKOHhwDOsbGxoIh9jR88vueFfR1xU45w+pBI+/gsuqMtcsxctbrDU/s9dao2Bmvq85kMiNlOOjO/v6+dnZ2ore9M6v4G+Tc5c9tH2vhGRUCYGzGycnJSDDqNsrvlwz+kwSajwMZdplnTE7scX9k2+168n0gyPDdyKSDaT4H7nGSBWLPf+4lYEkyh3G4PHEvz/zzXv6uTtq4/Xb75vsokB3mgHuhE8n3wM940Oj2zctV/yJA/UW//lIbFTEE3qtYGkb2/B+DykEdhUJB/X4/amAnJyd1dHQULeicYfC0Tr/fj9MSpdEIFEV1p4gxQUmdrXBjyOUCxX1OT0+DwWWDDs+HxUwaifPzc21tbcXx29QjVqtVVSqVAGA4Fa9VxijQGorf4fRhp3k+9/D7oThuQJwhdOaTw05wftRyw8J5Com1d3DCsxYWFpTL5bS2tiZJqlarKpfLYWAwGIAu+oqfnZ3FbvCLi4voiEAtJXMLkHdn7/3N8/l8OFcMGc6eFDoAndIdnL6zwoDPZEoOw45jc4biqkxCr9eLjgEwujzX2Wnuz32QB36O03JZxujzHdcX9M/Xyx2D64obapcldIGDJzCoyAUbSHk2qXRqQT2IcsfB+CjFgAl1B5NOp6N+1LMCnjpFNx1seOAAMEDf2aTqJR98D4fInABCkgyg97dOOhWycgSG3MdLvrB12Env0IFt87IKX1OfR+7N2Hx+fb0988DPOBRpb29vZO6Rw3K5rH6/Hwd5UP9fr9ejxCedvqwhnZ+f19TUVJS1ML+URbhM7+/vRxDtdcj9fj/8ASVRjIs5J0DxFpr4HK87dlYPufCyMZ8jMgiUiHhXFgArmzK9bMDl3QMYv7fLMfLKukASYOez2ayKxWKUCTIWL5fxbIA0LH0kwMSG4oewsx6skHHBHj59+nQkwwlI9XXhvZ2ocobVyw25sDNHR0efC/J9zpDdZPDrY0iWFjh4xtY608xzmGf3d87YOlD0d+P+2B3uyRpyLw8mGJfbIscErKFnpng2z+V5yB+bwA8ODuKdkvqdZH+dVU4SALwLgarjhOS7g9l4N2wkcoJMeIbwVzHRbrO+iNcLt9RjUwt1rgBN2uQhfHt7e5IuDfa1a9diIQ8PD5VOpzU3NzciDM4qwagOBpcbvGC/3WAB+nAg7jQlBaBqt9vBkEoa6V0NYMMheqqvVCppbGwsom5PQXm6HcN0cnKiRqOhTqcTUTsbZpydwHDBsrvDwQhiCEnded2wNJpGc2OeBCoOuh1oo4gcCMMmNEAG4AKgwoEIsF9uqDk56/z8XJVKJTabcRw4BgUg4j1mYQe8bACngWFy45BkOGgNhxE6ODiImlA+44YCOSGgSAZgfJ75dJbaAxLm3QMrjPj5+WUbxsXFRR0eHkYLSC8tYK1/GfsgjR517Gw26+vstQdUyXswZndcbihdrihpYrzItveedqDb7Xajy4/PE2tOm0lJcax8KpUKRovyERwPDCGlHtwT+cD2UHN9cXGhg4ODOBDKMwGsE0DU1xJWyFPBAFwcPSDe914AHpFRP4DE9ZEgkn0j7vxYA5h2ZDCdTkcpAkSA67s7Kbd7SQYOcIE8MP/eGcL3SPAdynUA1dQTY8cymYwODw/VarVGWhh6cMb/Ly5G+8qfnp4G8+0gpt/vR9cegnnfxE0tJzJfLpdHesrzXtjxYrEYcwtby/yj58g0c+wpcdaev9E9siuMHTueTLGzBh60Acxpj8e7JjMsuVwuAD02rdFohC4Q8EuKoB25gd2XFKQDcsRcFYtFLSws6P79+wFk/Z68F+/Cd7FbXB6sY6N9DpAFlwnWHV+STqdVKpV0eHg4Ul6Q1FP0jrEC7PzeTjY4s+z6KI0y1fwfjICM8K4Opvmcywl/GKMHjABYDy4ZP/aIkjn8LYQC2XSfa+YyGeAwN/4+jCnpM933YP94J8/UeubC54R3hzRJjvPLa/R6YabamQ4/RINFPz4+jtOiisWi5ubmRlJtAFzAMs6VBYQxyGazKhQKcTIhrKKXOwC6AOWMUdKIcQYEIlAIGjW5Y2Nj4XwkRQ017CfPBzDArCOkCN75+blmZ2ej1zNsg7OBDiYAEA5SGDP11X7kuzswZ9sxNEmw5RcKBniBefc+2CgW9wS0wmYDRnzDKGswMzMT0TcKPDExESmw2dnZMHz04kb5vQTFAcRVqVVOYIPxJt2IA5KGLcuSPUZhCGCAnOUgWPJ59XQtcs56I+84TZ/3crkcNfLS5wFzUqf8fry3p+L9Hhg4ZzwcZPBdZ/p5x6SxxNH9/9j7kyfJsuM8H/YYcs6Ycqrqqq7qEQ00yKZIUCBhMplE+5mMC620007/lXYy04rSRtrJpIWMoJGQCCMBYmA3wB7RVV1jzhlDzhkR3yK/x+O5p7JBFbdd16ysMiPj3nsGP+6vv+7HD/PCPMD04mBxL2XbzHTiHJaAjVSlhYWFBE7D4TDnoV6vpwPG2OPUuj2ABuamXq9nqsBwOMz17FB0mf9qB5P1aGcCMOPKFgsLC5nfe3V1lRUrGo1GOrl24jF8ZvcjqhtRbfh8oYMAR7Tf991k5CxDjJmZMXTU3t5ejMfjrOjTbDZjNBol4xsROUekvjlNq1ar5Tgwp0SWcAYA6TjSVGVhLXvD53g826AO0GWO0SfIXr1ezwiHIwUOl9NWR2mQfa8lQLZJATP7jJ83v9N2M6/Mr0EL73UaAI7Z2tpabGxsJEnC3xqNRoJtbAZ2ZDAYZFQIgM9Y016ICLOprtmP/PzxH/9x3L9/P7744ovo9Xp5RDjyiV5jfrxGDXjRxWWEjM/tmPqZ0+k0D6ohfZL0yHJcbeNtBxxJ5Hu8l3HzBlp0gu1aOVdOwWEssU12PNHzdsIN+oni2SlBzxikR8z0NzJu+TS5hiza/jHvPN9zUxJ+XCYqIyIdNkcUsOd+vxl4RwGYs99m077p10tvVIRxiIgMmyFo5MYS6qrValm0H8/s6uoqer1eNBqNzO8rQxWcSmil7vCFPVIrBRSmQaxDHAgQisnAJGIWziUUDKsBgAMou5SbjQDsO22LqJZDQymgSGknC/zy8jIXtcPGZhlRuizOEoh5XJgXKxr6yPvH43HmGvtCScOgGHDWarX0WhmbVqsV0+k05xnj3e/3486dO/HWW2/FdDqNL7/8Mn784x/HkydPYmFhIXq9XiwvL0er1ap45bTXRgylx3dQRM7x9qZLxhT22p/Tv9Lh4bn1ej03awKQeQ8GGsPKHDNXw+Gw0ibG078jew61IQcGXmY6b7psWOxYWulZKZdhQsbB9wF2vMGQsaTCAusdWQLYAJIZI4ABfQfcAVxxcnjPZDKpyJAjAjjKOOdm9ZaWlhLsYnxwHhlz6qWzdiOunT07HDDcTrdaWlqKW7duxebmZp4o2+l0cu7tZNIH9CJ6hfFkHOzAWo+xpvmsjEY43cVz6P8BduQ0c6oqKU+sB/YZIGfNZjOOj48r+unq6ipP2GPOqBvNxkPkD8a+bKfTCXBQ0HXInsso4khR6YH1z7xERObxEqVAxlzNgnlANpEH9CfvRv87HaFk3w2Q6Jt1L3aEI8MdBXz99dcrZFG5HhnH0WiUEQLnTNNm5I37cLhg9ZFFrrm5ufjTP/3TeOedd+L+/fvR6/Xi7Owstre3s19ms51G5kiZ9Y+Za9sDyyi6G+dlfn4+90mwgf/o6Cjl5qZnQGaV/WVewQo49iW5wPxZv5ROF78bkDL/rCGTJegURwAhjxg77z+yQ897sTmMkx1i6x/Lit9Pe8vLRJrlkjYYz9ju4Iy6TbSVeSE6VEb7IEluum5q4zflemlQ7UR8lBXpA+SKUe8YpjdiBlhhrl37E0FaXV3N3Glvsom4FhADSlhI2Fd7sbQNw2jQYS+WNABO9oqIzPWFDYFNo00I2Gg0yg2KvBMFh7JyaB/Dv7y8nEyGywgSijHQBqzgKJjJNmtdXjcJNOOMUsDo0z+HSpkXlBphbMabcXFaDt8DgKDo3n333eh2uxER8cUXX8Tf/M3fxNOnT2NpaSnW1tZy3G3ACBPCGrHAYTM5zMPlg2q1WTkucjTtUDFeDi3zbMKAJZtfhgVtXAxQ8eyRU4eOPT+eV6cTmQXHoBhk33Q5fQSl6TE0023Q5fC0HYHz8/PM02Tu7Bh40xeGGxDiNVxGkKga4rW6srJSYSKRqWazWWHZWLu8CwPQaDSSXabP+/v70e1244033oiPP/44AQ0OakRUDqmh//1+P40z80kN41rtOn3i9ddfj8XFxfj8889jOr3OlSdky+mLABzPx3Q6y2/ld8bTrFbJMJvZMrC2nkF+rGdgfdGd0+k09vf3U9+02+3MY8YpnkwmmWfL2JsIsIMEIIetdsqNN8zB3vE3M4nr6+uV/GB0slk45J6TFp1KgwxcXl5m3XfWI7JONIx3s7kdWfImYgMkxhA9bAeY/82Qo++RSWwg6X/T6TR6vV4eZMWYX1xcpG0COBP1pf92sOmf1wvz64iAnepGoxF//Md/HG+//XZu1v7ss8/iV7/6VUUn2Xkv2WnLniOYpXNuphxbZTKAdAfWyk0ntPqCcTbbTXuQdf7OGHntOOLjaGJJpJglBh8wjx5j3ss7sC3MRa1Wy/1BzBV9Jv8dhwKdOz8/n+SF13+5/tCFtkMGv/6ux8ff4TIRVco9f8f2s0aRd+bcke1X183XS6d/uCYpBgtgFBHJJqAACR/funUrOp1OKg+MJB4QHhFC4ecBPjHWKHPACexWxCyX+vj4uFJZg0WFE7C0tBTdbjcPQUFor66uMrSJgfACdB6kNyCgBK18cAToqzdyMYYofxvOiGo1gVJRucLJTfeaRUE5ABQZB9JvyIMvw5b0DbDm0NDi4mLcunUra08zJ+12O8FYvV6P1157Lb7zne/ERx99FD/84Q/jo48+ikajEbdu3UqgXfY5IhIcY2SILMC2keMJCDw7O6ucFEYpQxsPQJaVE+NzE6tvw2+P3lGCiGvDjHEvw2dcZjsARSh7xg5QZnm6iV1xxAYF53Cs2Q/WlZlds3KM2eHhYSVHzuXiIqJinE5PT5ORbDSuK8VwseZIpaJ/vJt67oRaHXGo1WrJqrrMJD8TBfHfcMCm02luuqX+cb/fj/39/QoI4b0RkWkejkbQHt67vr6e8kQaAs406xrHvNQT7peBPEay3IwLqC8jG/Qd9taMEvLG/Wtraymf3W43nj17FpeXlxkNajabsbOzk+sKQ48h5YRBDmEhBE606vDwMJ4/f57MHPrLeyGwEThEgDA2bDOPOLb0l1Qy+kZ/PW+kSbA+uWDhvBmdE1idXkeOeLvdTt1M1ARgWwJCr2mnjuG8Ex0jpZF9ARGRJ0nisGLLsIHoBJ9HALtrkgXgRbQOHWAmFjnwxbN2d3fj5z//efz0pz+tnCPAWCOryJ/z1hlrA0ZHUMv9FE6jAYSxVtibg62m79aJdoZtT2kveo62WU/RfuwVNnQymVQObEEvsRY9/qxZ1jNtdgUf61jk01EmMMbl5WU64K4HbweIy+QHEZwSTJukK50NEzwmUZgzvseF/JQ57Tgkbo8jMZaZ33Z9k0H3S4Nq51Ht7u7mQvdxqRHVTTObm5uVo2gBt6XxtrdpRtqhlIjZIR0Rs3rLvBOjj9AgkIBoV7ogBWU4HKYypE0c6oJSRLjx5spxKUNCCJ03I5SbIljMLEiE3NVKvg6o+T4Wk9kDL6x6fVZDnLSalZWVNJZ43lb0LDbuX15ejtXV1VhaWoqNjY1MgTFYp1+wWOfn5/GjH/0o/u7v/i7+4R/+ISaTSdbmRoFFzNihiMh5QemVCuvq6io3g47H4wRy/X4/Dg8P02AtLCxUThIz8+yUAELMznk1WKE/pAQ5BQFwgbw5LIwhNKD2HDK/OGfePOdn3cQ2MO6OzKBwXZmCC0cJ1g6jD2sUEXlKpXP6XW0CA86Y1GqzzVdlGJv9E41GI8skdrvdF8qb0R8DB95hxxEwCYPkQzna7fYLOYGAWGSk1+ulU2456/V6yZzSh6urq0w/Iz+YfH8ANREnmE6MOJGBk5OTSq65//Fu+o6hNutu58oREpg0ZATjVqvV8tCj4XAYjcas8kan04nV1dXodDpxcXF9oJJzoJF50nQAjRABOG2j0Sh2dnZyPBjLRqMRnU4nx8+OMfOE/kGPIovMF7qAdWnADVgdj8cZFYPx85rC4WMtA2DtADOOVA+iTdzv/QrWgU4FoM/oX8rAAticXsdGd+wj4+NIj9lW1p3ZUQA/ToUZbAAQjCjtZtx2d3fjL//yL+Px48eZaslaQ+bKsXEOP587jcDyyXqFfCBKRT9JzXJUBfsPILeTjw1xWyKiYm+tW2mDiTjmyUzu4uJibqjmuYB51i/PZK2jQ5gv+kebeSdVhnDMkREqnjGfToEq5wn7Swlep9TZpnvubBes++gza876xuShnSE7vyVAL2XEe2xeXTdfL31MOSGL4+PjTGXAu2QxmRUmlwr2MCIqSgpD5Em0gDi9w6Gdy8vrwv0wVhGRitXKlI0RsJ/2LFFoS0tLmcJC9QMEHuVA20qFZIOJkqHdEVHJOUegqTjiGrC0HVBjsMH4oMgdcmNeImYnaUVUDxHBQLJYGQMDKsB8OUcOa19cXCQgI6yFs4As+Hjhx48fx8OHD+Pw8DDfj2LlXvrgjR4YTQNEOwCwlaenp3nwCCk7DgdiGOg3TkNpGAF7HhvmAyaK8Sd6wXPd/psYxIgZOHKKCJcZWwCBjcbXKTCANPfRZoCcQ8kAYhwLjATv2tzcrBjP4+PjCptnZgRmLyKi1+ulgSpD5uQWAnTsxDitCScKeWOzoB0G2CYcGochYf4MKuv1em6uxdkCZLufCwsLWY2IU00pI4dxg8W1s+Y9BqxHxsT7SrwHoJxHh93Recw7zJqjFciMow/MvfWHN5pyYuudO3diNBrF/v5+gnDLCs8gh93f4Z0HBwcxmUwSiNIe1iLPgRAxgONzNpg6tczAgXdbt3jMytQdnHf+YfTRsfQPUO514soZjUYj9TRrkbXJODlKWK/Xcz8BUQ0cSsr1QQrRFu4/Pj5+wf4ZUDH2BqK0EQbX8sIaYWwiZqkwT548yTQTs+/IniOXtm9mR5E36yjsHfNoQsYkFn0gAuGqUgantMtttE11+6wX3DbmxmDb/XUddFIIsR98D2INIGw5Y8yQv7m560OwvOne7bXD5FQwyBnGzLnYtVot8ZLJNK8Dy47nP2JGSjpywRyaMDLbzuVxLYlBR9luInpeXdXrpUA1XjFhGgYbwGUQQ34ZxpPFhJeE5+3d+xh6GxyOhnWoHOCEkCK8/I2FgLEg/xeGBQEmHw32mlq7GAbeQXvMrES8mO/otvO50xIQdufPsfhKNj5ixsyWedR+rxcx7aKyCIYdJ4AxIr2l0WhkPrnvxXDAbuPpUipvNBrFxsZG5rVSxeXq6iodqfF4HF988UX85Cc/ibOzs9ja2qqANwN8FCj9tMcfcc16bW5uxsbGRoK44XCYyg9QjLJjHgAbhBxRbISuYQ/MZDMnTrvBWSrllLmi/Q6hmrW7KVcWg8D7aKuZAl+W+dJA2nkqDQ/9Z81GXFd6IB0ANom+IuNs6DUIRd6QBUddLMM4WkQmHEliYxnzZvaO+50rT9/9LhwvgBcANCLi9u3bCWS9SZL3w/bRRqIwl5eX6fy53B1tGQwG+TfmkjkxkweYY+0aFJS6w6yRwcBNLKL/LyNROH6rq6uxt7eXY4uTW6/XY3d3Nw10p9OJ09PTrCdvQAWTuLy8nJV2AOOEss3+mmVFd7DpEB0G2OV3wBzsHvMP08taYy0DWCipiH5mjG0THInwWKNnfc90en2gFuPoMD9jgl6t16/3B+CUkjpnJhLZNgvKuF5dXVWAO+/3d5AHoj7IP6QSlbOsRwx8DYImk+u64/TNBIYv3oudM5lUbtrku7bTOLmMX/k31p1BIesHOTdJxPyWttZOqdcUutPvQY8wlj6fAH1Y6necbMaW6CSOFbqLKjnoD5wu1gDfhYACGxmkwoQz9yZrPCb01aQGfbR9MGj2hUwwp/yd9zE/peyUDknEbGPw8vJynrz6265vMvB+6fSP9fX1FGSADQPM52ZZEFqXNjOLCMhhg4e9W1goTl7Es0PgeSdKCsO8sLAQ7XY7Op1Otgk2wB6tUxpYfCy2iMgjZc0c0jZCsQbSCK/zcTEye3t7+RlGrwSQZqtKlpPfyxAxgg+A9ljCvBNCHA6HyUjXarU8nIT2oryJPnS73VhfX8/wFsYP5bG+vp758xgN5GFvby8mk0mCHFg/jEKzOTsQ4eLiIvb39/PwCZhDTkXsdrvR6/ViZWUld49Tf5wxgCGPqObb8rMVFoxnqfy5F2DKOCJnXGZX6Q+XQS/32XDaODB3/o4jNlZu3Ee7MBqlsUIGeY7zcGu1WpbFY2z7/X6lkgklvzBIrBWMBfNWr9cTgFneMVisJU7WZJ0iA+gA+sG9Zei5Vqtlrj4h7JOTk0zfIC8WuTw7O4vBYJCsFBVIImaHrZD6xDtI/4LJRmYwQufn59Hr9XJ94iRwf0RUgJrr4JdRCeTPzoh1S8lMGewgL4CBpaWl6HQ6MR5fVxR69uxZVidCP56dncUXX3wRw+Ew91GMx+NYX1+Pzc3NrBVsWet0OrG+vh6np6fx7NmzaDQamTZE6ggyUq/XMz8duXXUyQbfzoVZUutth7udV+w1TY4uctRqtXIzLN+HiaZ9rDfWD7oUnWBAaWeXdRFxXZ2DtYKsoCMcLaCf2DKqJDA22Co+M6Dx+geo8n3LRKkfGGf+7rVk+aKvrLuImdNv+8N8Wqcwho4oG8ghv6whvl/eh/6njwbVyEx5n4FlSTz5/SYGDEwZE7CCyQ++6+gFAD+iWjmM+XBKHXPPd3G8GE9SmMr2oTNtB2yLLON8nzlz1IDvWH5L552+85kdectfOZcmSxx5c3rhq6t6vfQx5a1WKz2bjY2NZHEiZmH2iKgYDAMKMzsIJEoJow7wY9c0gkZIlwWJUBBqA0xTQYQFfnR0lF471UkWFxdjbW0t6+hymhvKgA1MgACnRsC0lrmRKAzAw2RyHYbHs8O429NmIVDZxMAzYsZK3bRoeBdeLooDdpowsHOqImZstKswwNoxjzD8KDJYOnLWv/vd76aBBsQBNufm5jI96M0334zd3d08mIX3GihgND02pHogRwcHB9Hv9zPcRpidjYLc12w2Y29vL0NyPNdVWwA+NjIoTQMmUlYiZpvurJCsAJGBm8KsJVvgqIINFmDCrBDfKceLzbswtWbOy7AeQHhubi4dSN7hPOe5uet67aRAwEpOp9PMI2Y+yFUnUgFzQ7vMIHkMaBPzQrvNZrFuGBNObcTora+vR7vdTsBPXw4PD3Ou0C2we4wDYzwYDBJ022BzYAnjjJ7CUYiIFwwKYMBgxClovBNDzD1OFXN0gat0nti4Byv8zjvvxPb2djSb1xv4Hjx4kJG25eXl2N/fj4ODg4pj2O12YzgcRqfTiYODgzTMJycnlXKmp6ensbe3l/dS6cQgkL6y9vk7usYMKfOB48972RwJM26Shgilw++WpYuLi2R0bUNsdwzUDNbt2JnQYL7QY5YD9CFjgP42QOZ+dCVg2HaR99AOTjC1XfA6oG08oyRdSibadsX6hXuti+y0ceGI04abZNNg2Mxp+b/ZWfpTOo7WdcxTSTDYkeAeIkL+LjrRugsH204IJJoB/uXlZUZ5zVr7+cg97cC2OprCXJmgMTguSYOSjMOpsHNhJ9/5846I+fu8hzn2ePM8zxMEgPWXySicQUctf9vld33TrpcuqYdSiojKIkHhR1Q9RofMXQc6olpVwP+YYIwozA//HMYAiMJMw2pdXV1XHdjb28s8Nt69srISGxsbMT8/XylCz0IlHcL5UgAJg3XaibcIA1qyUPZoWVCMH3nhZlgs4CVL6XtZWAAUxoxT/FB29GcymSSjbXaSe6fTaQL/W7duRbN5XUKr3W7Ha6+9lsAaQFOv1+Ptt9/OOcQ4f/zxx3F1dRVvvfVW7O3txW9+85vo9/uZF43SZkMXY0vRfWpew0jBgCM/nJwGK/rs2bOsn8zGOMaMaAPOGUqUsXStU0BhrXZdwcCpESVgtdL4OnakZA0Mogy0uQ9lXYJy5gewyrtddcHpEsiK02Jg7YlqMEawp6wz0hcwLP1+P9c3wJZ0KsqFITO8izQBUhGQufF4nBvpXNccA0J/xuPrzYXj8bhSuxdg0+v1UvfwXBuZ1dXVDFMyHjDbXjfoF0K1V1dXFUBtoGz9hKFhh79zwMm/tkEzO8g7DWjKi/x3ZAtZMZPWbrfj008/jaurq9jc3IzHjx/nAUz9fj8ODg6ydBmbBm/fvh2tVisWFxdjOBzmhl9XN2k0GrG3t5dj603a6BDabQfe1ZfQ+waei4uLmU7ifOpWq5XOuB0p+j6dTlM38GxABYc/sWYBMAZjBpTl2kAOXBHGDKpTTyKiUumK9wDizOhFREbRsI9Ea2gjVThg9U2e8HzsgdlMyy19ZMwYb+aq1C/l//xDxiBorM+xIdZrjiaUBI8jUgbjTrVgDOxAuG0G9DzbwNYMM/+jo4kGoOuRARN46DVHKMyaM8480ye4lnuuSB3sdDpZNYd+0GbnjjMGrB33l3Gy3oB0YLxckhfHoXR2vIZu0i/IGfJqp4j3o4P8PObC9vDV9eL10seU493ZKCMogG0GHBCGAPEz3j1KIyIypBIx8/4cFnEY2iEN2HFCut6RT9gMRgNgMh7PqkZghBF63kHaCYuUFIhy4XMhnChMcs/tZRsAkXsHYABI4SFjzJw2UxphG7eIKjvk9oxGoywj6DrPGMSLi4sM5c/Pz8e9e/fi/v37cXV1Fffu3Ytvf/vblSoajBfeOEbz2bNn8Xd/93dxdnYW3/nOdyIi4s///M9jOp3GvXv3ciPUaDTKSjBmURljlCcAHOPVaFxvpgLE4FEjg7u7uxERabAI8fNdFAnPJ0UG5hfwXjocJYtaAiZklQtD5Xmy8fCFfNDOkq3iO7SD//mZPpGvyTPoJ+uB3FGMOEqTdQKYANB64x+hf0DwyclJDAaDCnDBAMB41uv1zDudTqeZ2mPgDGClL6RqAAz4GSeQTakwk2ZpXCcW4MNa4nNA3HA4zHJnAHXIAtZtrVZLkEXkhTSmiFmtYzPSrvYBSHcJxpJgMOnguWdOGR8DWcZme3s76vV6spwY2ePj49jd3U1d+53vfCfLFPJ3jlqfm5vLOtbNZrNSVg5HAnCGPBJG5u8AEuswZJbPy3S9o6OjuLq6PgQMIGHHzONh48148bPLLZrEcXgaUImMoi9pt51l2sI1Go0q+hUAbzLFepa54V3uv8kZA10zpWZjiVzi3HrTY5l2wRzxP+Nv5tJEA/f4M/6VLL+f7XFi3E1w8ZltFM80oeC5NCC3znN/zGKXEQRjEGwRueA4CEQMDApdjaVWq2VpWcYeosf2oVarpZyOx+NK5I4IO22PmG1Ct6y5vZZn5giZoq/cD4aC7HPk2g5RSWaWZFAZtSgjA3Y2ywtMQirnq+vm66WZah/osrq6WlncJcgze1MCasJaJSvtjUQsIAtQRPVEOxQ8v7OL+uTkJA4ODrK0Ta/XS9aGnG8rBwTf+dKcmka7uMxeWDHbaHqXr5kq7gfEuXoJbWFRm7W0g8GYsXHAzoZD3NxLSAtgDRvM2I7H4wS0PPPi4iJ+8IMfxLvvvhudTifOzs6S8Y+IHN/Dw8O4detW/P3f/3388pe/jD/4gz+If/Wv/lWcnJzET3/603j06FHOCX2C1eKdlh/+YbT5HCAAUAEEsAOfzZeMuVMiUETch4yZzTGD7bQXxhuFb5mxw4MsGOyWbIoBB5f773k2e2R2yKwsz4KJYawwCBgRWDPAEWUVneqCsoeNPTo6itFolMCcXemWwcFgEMfHxwl6kWPAMIwuwJNwt6sqOATP8+fn5zNtgxxFHCnK+MEA4gRtbm7mngHY8DIyhPzA1hoUsDGTNvlUTAAha2swGKQB87wY7DAWjB8OBWNJfriPDedZyCPPNUOHYSsdNVgrxhiD+uTJk3Sinz59GpeXl7G1tRVLS0t52i3jZGDvDXtmYZFRnFnWAOOMPLOGiYy4cgvtiYiMEFFFw+Cr0+mkY+fxRAfYweY93Ev1CdrKO5ybXDo0BqQOhXvdehOix9uMrVnZiKhU/+B7PNcAqnRUeD913pEr329Z89/Ly2QQY0w/SxbYTDf3lqSAx8360wCYvpYg2JGaUpbLdvhdJr5YT2a+S+ePdhBlIzJcElle34wLBE69Xk9CgrmcTqfpuGOjzIyX0QXncMMy0387PhGzCl5mr8fjcWWDLhVKrPc9Z8iS55bvlPPJ8/n9plxrLmwaevHrrtIZ/qZdLw2qT09Pc4NMOfmeTAAe+csoam8oYuMgGwYRZufQ2XPjPaVSAgRMp9PMv8Rob2xsRKvVys07BumAFjPmtBvjjnFCeeC12nlgsaBcEWQDawMHQuYw4Qi0WT8bEcbRmyAiquV1GBuUm0+4vH37dgVsTyaTTJewgcfbPz09je9///vxve99L9lDABRzTH50q9WKP//zP4+jo6P47ne/G7//+78fu7u78X/+z/+JDz/8MJ48eRLz8/PR7XaTNceZ8OZCszfICtd0Oq2MFQAQhm0ymR0yANsPW+oQMKwtTK2du4jI0zXNGJiBwfGzsilTSZhHGyQzBjzLBrZkdwyebXDMkLGmDNyROadb2LjzLBhJcupJlcJ4AHxWVlayQgRRBYAPjpLzkhkbANfKykqF3fM8AGoPDw/j7OwsoyjINSX9qJSDgVtcXIxOpxM7OzsZ3WC8XerOrDmOIhEW8nb5GUcCuaYNJgL4zCkipWFm/XvDHffzrMnkehOvmU1k+yYGkHlj/iMi2WnPZURk9QlHro6OjhLE9vv9mE6neey4n13qHIfVb5pnM7LWm+g60rRI32HdtlqtCuPGGmINI7N2SgEMOCTHx8dZJx+b4jGhKkwpe+WGTINK60/mGgecNBiPkR0J+sCGTd7F2KIz0VcGPNgponHWDzikbjNAm3tN1pTOdnnRXgNWfsduMAYlg8n8AuQMumkTsmOWGcDqjYvoKZebLOXOsmlCy04I7y/nlPZcXV1lxIh2YOtoC2OHbPHz4uJiHB8fV1K7nPaAnrP8O3JmQsBOk/U+9zKunlvv2yiZbtaTnXiTK9ZF3O/UQc+35dQ2D1vBe03y3OS0vbpm10uB6qurqwRjFmoDXC4MGaCDMGjJBvuUNefoAYy9Ac8gh/efnJzE9vZ25jNxz8bGRnS73WRmaQvfYROMSwEC1gDeKHYEi3fCgiCYGB4E3awE3jRjYEBt48l3vOgAx/fv348333wzptPrneSPHz/OcDrjARsAmCHvcmtrKysE2LjADAHCcGYePnwYvV4vFhYW4sMPP4ytra1YW1urzMl4PI69vb34/PPP4/Hjx3Hv3r1466234r333ov9/f341a9+Fb/+9a/j2bNncfv27bh161acnZ3F06dP4+joKJlE2E07OhGRY29WqmT7ray4h98JdTPXZX4f93Nh5JhfxtTMiuvAWvkZBDhUz9/M7jHPnmOeFzEDxm6j+2jFyndg8J0ewHcj4gVWATCI4wq7CyjFGBg8kz7g/gIUiXhQTaXZbGaKASXqAHo4hsgdxqNkNTAK9fr1pkTaTQTi8vL6xECMghW/+4vzSz6vme16vV453dXglPZgiHk/fWd8J5PJjZulYDA9T+g9s1g2zAYjyLP1DmuANDqMnDdpk6IC8FxcXIzXXnstK5c4UmijyxjQT77LmiAE7nC1NxFjbO1k1Gq1ZJgbjUaCYN5vtpcTUWHxCK3jTM3Pz2fFF9pGJIOIhdc+beOkUNpjIENbDBBK1pT+eIOpZc5r3VHJiJlNdDjdzDjPQL9gD9wujy/P9MV6R2cxN9YxjiDRNz73HhP0Me2ws1HqJ/Sy14YBmMkb6yyPrTcUlljCDLZtHPJBhNLz5Kgm40F1K+TCGwEjrkmQTqcTtVot64t7jwdphPQBUo5Nu9bf6EYcTWTVVcIMnG3jbUM8d7aHHk/2IDgqwHihB826s2a4vF65eEbpODLuJaAuZbG8XjHV/48Xm30wyAgMytcKyjvVAbBcsA4Ybnt3Vg6wRd7lz/tOTk7SoMB4c+/q6mq0Wq3cuEjKB/dxMpqVGkYf44ugO+QUUT3C2Uw9AJ82ksLBIqNvPMNsAONJCJpF0e1249vf/nZ897vfjbW1tTg5OYmHDx9mdQan0OCNoxDa7XbcuXMnarXZMaz9fj8Pu3jjjTei3+/H48ePY2dnJ5XG5eVlDAaD+M//+T/HvXv34t/9u38XCwsL8eTJk3j06FEeKLC9vR3Pnj3LqhBLS0vx+eefx6effhq//OUvYzQa5cZEyuCx2YgKLTADBqH2kMvaxS5LRDiXVAKe7fm0w2TFbmNk5weHwyyllXoZSkPJGegbCDmSUSos1kvJ+jAOZq9ZH85L5bsYeZRsmXIBgOHCKSzLl/G3jY2NZCZJqQGA8jljsLKy8kK6Ed9hjsbjcTqhtJF8bNhjoir0o9/vR7PZjK2trWg2mzEYDDKNizUHWLJR4nc7RrwXA3J8fFxJMyBiVhoiXwBNz0npQKEPaI9ZM7PQDqE6vF2yj5ZZwD6l3M7Pz2NxcTHXMuuWaALv39zczFMyYVFNaHhceH7JiqFPcFYMzktSBflj7jH+MNOsETvQyDBjEnHNxEdEZVMzenI8Hke/38/1zmdu12AwiOFwmPcBeA0saG/JHHqt0HeDHPQ681NGJA0mcCDM4rImPW4+N4AxYS1wlc6y1y36yw4fa8E11w1iudBT9Xo9GVA/3+OBvJcOAf02CWFHl3ci34B6t9Xpi3Yu2MdjlpU2oQN4r9vdaDQqMoI8AZQNqLGN4BKisLTPOtvpQ7TFm4/J1bazzLrhO8yT1yrOueXI0RCcMeYfOWRsmVOz+7TVsu3/WXu0wf0wA2+9x/0mpV5d1eulQHWjMctVg+WBnSoNAawqgLrRaCRThecVEQlkUDawlgaIZlkJl+KdotTq9XqytL1eL7rdbpaHwmAPh8NKLiMnKcJuOD8OgUWxeBc8SogFd3l5mWFOK1aH+MbjcS5UFnZEVGrHRkQavHa7HR988EG89957cfv27YiY5bOzKRBlzJhZ2Mfj681MPA/WB9Bzfn4eT58+jSdPnsTCwkK0Wq38Gz8PBoP4+OOP4913343nz5/Hl19+GR9//HEcHx/H2tpavPbaaxERsb29HY8fP46Tk5N49uxZMpMXFxexs7MTETNHyqy4GRtSC/judDrNE+Loow2889xQkmaYUUjO0bzJ88bQuGQbc+eog1mWiNkGtTLnH0V/E6vO2gBEleHOiK+vc21WBAVYsqUeQxwuSui5egbPMvPnUDdpAijsw8PDNBCspYhIOSlD4xGzcDjRLdYM4+++2omYTKo54AA/5AYdwYloHGTD3gLWaMSsdCQGClYfvUM7GVdktjRqpTMFiEFOXX2HZ0W8mJ9o9o934zCadOCZBq9sLJ5Op+lgG4Qgu/SffRFfffVV9Hq93BiM/LlfBpWsC/KTcVgNmABANqweH9sA+muWn357gyz50bYlyDIEymAwqOS5u70RkZE0M5elg1C2ywSJf2fenJph54nv8jc7xuWF/TOrX7KzZpQZ35vYX4Nc0tnopwExfUBGnMZ2k76lbXYaLbfWiwaVlmePXTkWdkCxM5Yh7EG5zkhDY30+ffo0WWQ7rugq18TH9iFzsPDdbjffX+byc8CQnQSqJQ2Hw+wH+h3GHR27tLSURQiINHs9lAQJ4+fNi2XqhwlLnoHTRLUm5p25c1obf3c7uN/PZc4sZ8grc83PjuK9uqrXS+dUj8fjPIgFI4uCcLgaUIRCgbW04seTr9VmJ3ixeAAO3u1chop5D+z0+vp6lkKDCYXZwLPDIFPf2Pnb1D/GsJYLIWIm1AAuQB79sfEwM0BlEt8La7u+vh61Wi2ePHmSAHJtbS3u3r0bq6ur2a7T09M4OjqKhw8f5nHMJeMPG//48eOo1WrR6/Uyr/ry8vrUuEajEY8ePYrz8/MsjUbov9PpZL78D37wgzg/P4//+3//b3z22WcxGo2i2+3GH/zBH8Ti4mLWwUVR7e3tZf51u91OxsmAkPknfIYDhSJBSbHZzRtLAVGclEfImO+fnJxUmI5y93XEiyyk5xZgyff8XXvuOAMOVd7EMLNeSkBtg4Wc8LvlCEOMckfeWRO0e2lpKcvIEWnA0aANKHjnqhK9oR/IF0oTQAXbxe8rKysJqOm72UrYH96Ngid1gzGAyb64uMicWEDmYDBIRwyjSnWWXq8XGxsb8ezZszzp0I7UeDzbqOnLm+rMDANaAE4Ya7PhgHsznjhR7jtEgx0w/2zGNiKyJrRZKYAL88TcsVcAJ4LUGiqi8C7kHjBM/e7JZHbkNXMMoHKKVL1er5AIBsbuP/1Bx5ntMpixEwVLC6EREcm4O/+ZMTw7O4ujo6MEN0tLS5WNe8iuCRGAVgmm/Tc7CdbZBv7W/XaqLfNlxQe3y06Wwapr3zOmBt4l6HTKAONp2S51DPPi3P+yLczheDzOSA1jYUBl8sGyybPsnJg9p//ox7IdJRNvJ4i/1+vXeyjQ67S72+1mupZZeebFGMFpWJeXlxldc7SB9rlkIrgE+wqLbwcffYF9Yj1eXFxU3uOxQ45Kthc9iR7y92mnHWLG7KZNltYdrMubPrPDxjhaftBJOLDIIuP6266vczC/CddLH1O+u7tbKW82Ho9zcyBCzwKHOXbtSzN09Xq9UgHCC4K0DnI1vQmMWtQsrGazGb1eLw1Pq9VKAbcnbEWPgeJdKDhALQoDQxQxYycBr/b4WGi08erqKpX/ZDLJUyEjrhfQ+vp6dLvdiIhUapeX18e337p1K77zne/EwsJC7O3tRb/fjy+++CJ2d3cTVAI2uVi8ZtkwVICgk5OTuH//fvT7/djd3Y2FhYW4d+9efOtb30qm9/33348PP/ww/uRP/iTW1tbir/7qr+InP/lJXF1dxcbGRvzhH/5hbG1txeHhYTx+/Di63W7883/+z+P8/Dx+9atfJVOEsgGQwayV5cUwJgbcBkbOqeTfeDzONCTC97CbKCzSFFD2bAaz8bAT6EhLGWK1EwioRekg0362gRPyZ1a8ZBtsfMr1Vjpz/Mwzl5aWsiIGYX5YEjsLOJhs6jJTSLUNnu12GujhKLHhj530o9EoAXtE5Lw3Go1cn6Q2TafTTD1xf2B0KI1HRIzxjbiunR4Rsbe3Fx9//HGMx+PY2NjI+aXNrHVkq6yIwdoeDAaVHEqHoJlfgw+cYjP7tN+6g/cBBEo5MSBCR/BuM54Gg9Rmh0BgDT1//rwCFHEGAG7IOnoMIwwDyGZdHIaSyTfwLBlVnrewsJD7RCBaeI/zSHnmyclJ5u7bqJPacnx8HNPpdUlMmENyXknrKHNG7TR73dIHg3su3lc6hqwJ9JWdx/K51hkGlAai3ENK2dzcXDqp3hNjsoW1XzrhdoasL/jM82jAaB2L41qr1VIv2/FD3pFrly50/wzcDR4Bsr7HTsdN6ZzWw8gZUeThcJj6ZGlpKQG59TifeWzYC9BoNHKtYzP5HAKANcNY0Vbkzk5vRGTqITJEJKxkuh15ZA0iG+iJm/YnIDes65Ko4VmWMdslRwTsHHkekA2Pv21XOXfYFM/1q+vF66VANUoG5RAxK27vcJxTQgiLsJi9YDxJCIpzhSNmBhpBBUyyEQrB4dAP1yZG6C0UCCpKgQWJgivZDdgnDmlBOClP55xwsxOnp6f5Pg45YfEsLCzExsZGLC8vJ0gGmHz729+Ou3fvxsLCQuzu7sbOzk58/vnn8fz582Q5cBrsbTLmZk35n8XOhs2FhYW4e/du9Hq9ePPNN+P111+P4XAYX375ZfzsZz+LBw8e5KKv1Wrxr//1v868zvn5+ej1enH//v147733ot/vx09+8pP4+OOP47PPPotarRa3b9+OXq+XDpbzlB2eRQGVudW8CwV4cnISR0dHmfZDOA7jDJCKiEqpRS4reLNBBhEATGQCWUWWMHhWQA69R1QZKd5rpQfQ84YuZNHMiZkInmsmykaSseDeiOtTQ5FX5yPagUGGmdPxeFYiD4CL44wTyjgCRJrNZjKONhzIGu89ODjIdjGfgAsqyMzPz8fh4WFlEyXyN5lMotfrRaPRiGfPnsXh4WHMz8+nfNBvUk3MuDF3zCPPYzMlc2MjU4IvRywYP/SCn+sye2Z0/Rnttb5Ahs00MjbT6TQB9fHxcbTb7Qxj7+zsZOSJWsboXwOzst2TyXXO52g0qsgeepbKDDeVHcNxMfPqVBrYxfF4nCUbATgASXQLutCEhx0b7AZMIeNI38poktcZfWecDSgoD+nN4vSprFbB+3CC/R474MiN17u/C0in8hGOAPchS4xXaat4Fw5pCboBOnbOS3bazjwbP+1EO8KDHqIdjCH3M18wmaWOKh0D2mNQ6fXFxkOq/lBb+vz8vFJaMSLSuTUAnEwmaY/X1tZy3wZOWqPRiDt37lTqzQPYzZoDwAHLJycnGU3iex5Dxg297hx/5ooxKasJ0XZX80IuTNJZZ1hv4wB63XAB1nkmc2lny2RQKTMl6UNbfe9vu14x1f+PF+DF4T7AhZUJP8MUIxwWKrM2JeuAABBCQdkRBu73+7G/v5+ggXALIXDYE55rEIKAwDgaEJtlGo+vw9Kw8BgLh19RSqSpoGBqtVocHBykEsAZYFF0u93o9XqZj8tpg/fu3Ys7d+5EvV6P3d3dePLkSeZFd7vdHFdvLmDs3T8+J9Xl/v378f/9f/9fDAaD6Pf7yf5SHWAwGMSvfvWrqNVq8fDhw+j3+3F8fBzf//734+23347JZBI/+clPYjqdZjUPTmX76U9/Gn/xF38Rjx49yrQVFr0Nldkds6540sjFaDRKjxsH6/j4OOfF+d9mMZ0fWHrbzrH0YrehtUFiXpBhlDuOGpGXkpn6uov54F3cb3DgUmi8t3TwaD+G3rmpKGcALmkbyIGdXJ7farUqaUEYHxxAHzyBY8geAObQzBNVJwyGIyLLWmGsWGuEWiMijShM7MXFRezv76eRPDs7y5SmVquVBpoUB/pYhtEN9mmry0MasDCmHJdtQIljjRH1XKC7DGjskDm32jJhhtVzzHqmX8fHxzEejzPtqdG4rtrCBmMiZSYCeKbfZzaWNnPh9Jq5sxNgVtFsKWAZYA9bal3IJkrmnT4COmD6IDZg4JBtOwv+3+N30xgCcEqgjTx7DOzIYQ8YE5wDy7R/Zox4L20oHQ9AGSCQv9n58X28B5lz1RfPb5n6Qb/MiPMe1nPELB3KYJm5tu4pmWSPtcG228xVRrwAZREz5pMxiYiUAXRQr9dLW+r3RczYeXQzzg/pqdjx09PT6Ha7GQGBjGC+PX+kLkZERta8f4zxNgFHPylRCki+ySFlbgxOGQfG2uvIc4xMMk6ea+YMWXF06CY55V38HVvhNLBSnmg/jjcn1r66qtdLgWoDZkKOTIYNdkRVyfAPAO5QHEw2YeuI60lbX1/PkjgseDYEHhwcZI4mB5lQY5gjtK3YnBZB2xBCs+XOuWTzJPVWydWGHeO7eNAnJyeZ4zscDuPZs2cxHo/j1q1bCf5oMyXvAHu9Xi9WV1fTI6eyxt7eXkREbi7DiDsvlTH0HOFQkEbyJ3/yJ1kz9s0330wDPB5f51v++te/jqdPn0ar1Yp333037t+/H//sn/2zTLHo9/uxtbUV0+k0tra20rH5+c9/Hp9++mm88cYbcevWrTg8PIzf/OY3Ua/XY3NzM9ODImZ5vSxmDCmsqHPvHbpCQXEPrBVy4rAVY4JSNovgUKEVi5UWoJ120i6HaSNmx4ObwUbWLPNWnihvO6JlWNmhX/phhoL2cz9AHIePSitUuPA6RcZdynJ9fT1TCer1ehweHka/3095Z9MigKjb7b6wIZDIFafjwVKfn18fbtTr9SIiMoQLkGLufDAQDCd5wvV6PTY2NmI0GsX+/n7U6/XodDo5Lw7XYyBhq8t5aTavq4gMBoM0kAYRjIHZSmTWJdXoM3rQEQ30FHNlgGm21ACD95rZjqiWHIOZQ44Gg0EcHh5mHrqBIO+gHThw5+fncfv27Tg+Ps7N2si+0zUAHnbc3VZS+hhzwD4gCEfebGO9Xs/9FZAZ4/E4U194X7N5XVef/rK+WB/l3hWzzLZNyKeZYHQCzykZ7lIvOITOHPkePudv3GcGz5u5IKQiZvmo3gdjcI1Dwn38veyzxw6nmrWODPF96147gnaYkGH3t0wb4L6b+o3DY13HWrB8s2a4j/U4mUxST+Co7+zsvFB+1mMHGxsRsbW1Fevr6/Ho0aO0kbVaLdbW1rKt1M7HWWLd0D9suE+Mpb12EObm5hKv1Ov1THO0zHmMAKUQgcii9bujBqVdcI6/n21MY0fPhKJ/N+nA34nmIz+W83JPAWPiyMqrq3q9NFNt5WOQ7FBmRHVzDkIBYEFgMKZ4SmxW5KAWwDaVQTDwEbM62GxK7Ha7sbq6moCnZPcsvLSdz1BseImtVqvCgsLCwMByzLY31mAkjo6O4smTJ3FxcRHtdjuWl5fTg7W3PxqNYm9vL6bTaayurqYhOjo6it3d3dxcScpEqVC88CKulcHv/u7vxvvvvx9Pnz6NX/ziF7mQDg4OotFoxFtvvZXtWllZicvLy/j5z38eT58+ja2trfjDP/zDuHfvXhpGpwVMp9O4detWLC8vx+7ubvz4xz+Op0+fZhrH3t5e7O3txcLCQoIvAEWtVktlxg5pQC7KFcdhOBym80FN3tFolOwS4TjC/A5HwxI4591steefubRBsnGwvEfMwDBHqnsTLevAl9kkgxw7Q1aW/M7acfiZ+cfRsBLmM3acM6aAQVhW2EVYZvrnyAwOTb1ej36/n2WmkE+AvktAoqBZCyjc6fS6PB/GEcYVo0XoFLBlJ4X23b59O2WcNBTGBMabfjKWEdXQvdtnwEj/zX4xxjAwAH/ky9EOxr2sYkBqQzmfZnoMkCy/fpfZKwNs9poA/tGhXMgYgBrQ3Wq14vz8PAaDQcXZcFUVvltG+WCkG41GzhdRJOaRigpcyLjBR5mHit5ibrmHPRiMN3snzCoa5JWhaOt7g0gDl/IZZmX5m22VdQdz5HXI5waiOC5EUSBWIqoby+gDcmhgZufeIJY15b54zkwmOCLD3+1s21bSNjPpdv7Q55ZpP9u2CmfGqQcmV9jn41QDy49ZeYAfURkqdmFL79+/n3qTPl9dXW+8X11dzcj21dVVdLvdGA6HuQHWJAmRyVImiCbSBtsyojXMg/OY6YNJEtYal50t6wCeh4Pl9pi0sdyXn6EP+Dv/I0u2ocyV90M4C8F67h+73IZv2vXSTDWGGLDI5GOcmSQuDCchPbN2CEyn00nWJeJa0bDxhd205B5PJpPodruxtrYWq6ur0e12k/m1R2kvkHfZQFhhIswojJK95Du12vXJcvv7+zGdVjdXPX/+PE+lg4FlkSOsAMGTk5PY29vLerMI/tnZWRweHma5IMALOdQAS0ALHnK73Y633347/v2///cxGAziZz/7WVbqeP78eWxubsbrr7+ex657A+m/+lf/Kj744INkIA8ODjL6sLe3F6PRKK6uruLdd9+N7e3t+LM/+7OYTqfx7rvvRr/fzzJ7p6encefOncyJMziyMcFgk8IREQnYXFPZIUoqsozH44ozZUYwYrabnncayPJ+szY2PAACP8PGihy8iKgcL+8cQRtdPqN9PIu2OLxugzKdzpi8iJlRwyFzDjWsgnODSV9A3jBsLmdIGgHOksPLgCVyGTudTsoe/2CWnGd9cXGRzCRlLCOu86fZyFaGKbkXcM44IR98jvHE8JktAWgCXLzxzGWrXIOXcbZeoz3IhJk7G0fmEFYWveGUBPpilq/c5OPUAHRNRKTjA9hg02kJinAguadkqbwmvDkLQIAuJnLBOllbW3thHRAJ9Lgz5hsbG5VxNNCEiABQkbaDA4dOpb04W4AO/sZ685r0OBgQAjhKptXrxfN/U3SVMeR+g0evb/fXbSvbxZwjW9xnsG4d4eogXwfYHf21zEMS1OuzvUvlu3F0zTa6TTgDljnLmEE197AOaQPjbdbauvDq6iojHOgSnuH3R0RGdZkXdPXa2lr2HduPjPNc6rlTp92VjEhBszNPf7C/rmBm8E/6EDLFmJcA1LrhJscD2ef96DnLrefIcsr3SyfMtgXHq3SArIOQUUf1cTasV15d/2/XS5fUw3PBc0RAmCQUKEqaibFAoGjxNhE6GwkMKLmh1J3mmOK1tbWsAuJFZUXG+xAoNhfCHJNyUnpVhKXJ54uIDIUDhr1T/eDgINmwfr+fRncymWS5KwzK3t5e1rskzBlxDSwODw9TSXjzG94w38GDXFxcjG63G+vr69FoNOKHP/xhDAaD2N3djXfeeSfef//9+PLLL+OXv/xlZUPY1tZW3Lt3L5Xc66+/nnm1r7/+erTb7Xjy5El8+umnMRwO46233oqdnZ34H//jf8TJyUn8y3/5L6PRuK488NFHH8VgMMiTG5EJxj2iWvnCzhVzjFJhPIfDYSwtLeVOf+RuZWUlSxPa4Dl0yvV1RsKKFaeOuYR5w2Fi/FEwjkpEzA4CskFEqTL/bg+yxTNoN98DtBp4+b1LS0vZR8YOBn55eTkajUYagZWVlcozzIawVpD9RqOR7CAGCwbR5aNgMXgGgM86gP74wKednZ38zunpabbFu+0B0LCWDmUbIAKWT05OMlQMw45TZsA0nU4ToNqI2IAyHhADzCntt8HjYu0yBqSc3MQgoetY9xEzp89rhP5T8x6gYUPPOBugG2CRqoSeZmzm5+cz9cWyyPOcHgBgtuyR8uJNVZ57A1bPQSmvrB2DLb4HKKNsY71ej6Ojo0qOvgG155KLvtBu3h0xi2BQStKsIHJgNtA6zKDEhAzftzNvmcXR8ufIWum8A3iZGwM2OxOea9aZ+827eZeddDscfN+fM8/uA5eJCcsebSSiG3EdzWVsXaXKOrNWq6VtQxcBiB1x8+mFOAP1er2SHsraghw4PT1NfQl77SgU5BrPhICIiHwf+rXdbicQ59nj8TixBGl4kA3Ml/UWMui0D4+vCUEDfGxK6VQxf2atkVmXyqR/Jg34DD1x01wzRuWas9P8dZdl9Zt4vTRTzUKAGbahQbliDMwAMenz8/MJ7iJmHp1Dumbm+Iyc0W63G51OJ5/jHfdOJ3FIfmFhIVMOAFnUfQZc2SASauSZFxcXCaat+G0kECJACAYJj7lWq1XyU9lE0mg04vj4OPM9fcABoOb09DSBe71ez7QSnJJ6vZ6br0iRuHPnTtbjpl8/+clP4quvvorvfve78R/+w3+Izc3NGI1GWSeWE6Y+//zz+N//+3/ncx48eBAPHz6Mb33rW7G4uBiff/55fPLJJ/HJJ59ERMSdO3fyKORyY4QjB1yMa8RsswqHU3ByHkCXMYSRx7AjHyxw5oJ5QA6c9uGyjDh7MPQRszQfQITZdZ5Pu13OyMqPz8w0O/TP9+0EovB5rg0dzoZzqF19g3UTES/IjdnqiEgZxhi5Xzi6bPKx0WWs3baISKcWdml3dzc3JfIMG3SMBWkAnkM2RtZqtYz20DcANs/o9/sZzWG9OWzP5Rza0iCUYUw7S+gyyxohXQOaiKiw8sgya9ZMpKMJyMdNwBDd5PYbaNFWnkU7SUNxqJcxpb2ASebBG1gN+HxghdfYYDDIdD8iSDD2yC5zMRwOs6wpx5AzfjyD9ToYDCoMHWsShtHX14Fpjwc6CFsSUS1PyfqJqOb38j3bNH5mLOws2Jnw3xx1MmDhXZbHMrKLs8HFfNnpQRbMdpcsKM9mHdomsi4jqtVcbgJXfGZSwhEYrxnsmqsyeQMc7+If9gxbyvcYq1qtlg6e+3hychJra2u56ZiorPc5sR9od3c3154ZWgiIWq0WnU4nNjY24uTkJLa3t1N2mCf2h9Rq17nWc3Nzsbm5GePxOCO5JQ6wPEK4OSpF/0q5vmlTs+eFMcSmWxeV41s+46YLJ8rMuO+3jvG8W2ZfXdXrpZlqPMsyPIrRLcuZRVQ3W9jLj6jWS4yYhShRQC67RW6nN/uhEB3G5x4bAIe1yYdmMbsvDj1OJtdpKFQAob3cy+JjQZHrFXHNVHa73fydhU25M0LytVota1EfHx+nAuX4b9por7/b7WaIfzqdxuHhYVxcXOSRxJeXl7GzsxO1Wi3W19djfX09Dg8Pcwf1e++9V2H7CDlPJpP47LPP4n/+z/8ZX375ZVYsOTo6iqdPn8bq6mqMRqMsfba+vp5KE/BloIkS9gLEiWGeYXQ5NMasgVkt0h7M8jof2XLG+wyoYcoiZmASZe3qGTa4yB9tMVAujWFElaEziDN7bZkvWYOImUE2iEeWnabEdylHyLiabeN9yHyz2czKGsj5eDyOVquV4zOZTDI9ajweZzUPqs/wDNihs7OzWFtbi4ODg8yjh808OTnJNQHTg4O0tLRUyc/G2LERD9Yc+QKskxbGWphOr09u5eAGlwQzwGKckRGvKbOadpbM/tixwCnB4GKAcRYILZfvNVuLrmIuuR8g7XSX0kklemXnksihwUlZBtFsnoEg8kekib0stM//YA4d1kYHs36I1LFWWD+0CycFEASQJE0IOTfRYaBr/eIx4T4DBEeCDIw9piWIcDjfa9K6xjLC/6XcuH12ZryJzpGLklC6SZ/ZaaevrEtHN9EBjsjxfOcdu5/0leeUzgJj4H0E3Ivd4oCmcn5YV0tLS1lS09Fp7Kjnj74xXmy2Z3wODg7SLrOXBlaYPRwAZ6durKysvHAi73g8zpNjIbxYz0TEHEmnxF4pi1z0g/l1VNZyw7tviu7YKSztiR1Ezzn6xLa3nAvPrdtoZ6fcI1Suq28yE/2PXS8Fqm3EHXrC0HqyyX1yOJSFzOTbk3dIClZodXU12USDaXJwuQ9wdlMYczQaZWjJoAQBAZhdXc0Oa8FDJgzre/D8uTjlbzqdxmuvvZbjQ1pBxKx8GQxOxLWQHhwcJEDFY37ttddSWaEwYGsXFhay/wg/IIbFtLq6Gh988EF88MEH8YMf/CDW1tbi888/j//4H/9jPHnyJP70T/80hsNhfPLJJ/HGG29kCOv09DSePXsWP/vZz+LRo0dxcHAQjx8/jp2dnQz/22EAOERERZlzOYpho+moBPMEWHKOLPmY4/G4Mo4seObNm2ocHQBURFQNn5268XhWfQKFbECDYeJeh9kBEmY//S4rL+QUEGFAzTpgjMrnkltsBoq+utoMY8HzYNxHo1ECVMAwjPfFxUWlvCKyRFvOz88reYcGgBwBHhHx/PnzCkCyMcYAml33Rp+FhYWsQDKdzmrCz83N5UZF+s3zmZuTk5PKkepEIiyHLtdmp4jLa5m5LtlOHJRm8/o0N7N0k8kkDb2dbK8J+u1xsCPvdhlMlGCm0bjOS/a7W61WZbMh8uUNRkTCAFMRszQ+3ueDMnB0WHusD28CJ+zNNR7PKssQFcRok5OPbi9L6DG+jDljQltL59XsKXNoYFACD+bUBIj7zt8NPspa6aWdos9uH048jrjZXWSeMboptakERW6/5dTpVvTJ5IHBEoDNawg7yXwjrzeNN2sLG1k6ArTZIL5er2c6GqVn2TSNzYVh9bjacWWckINGo5FO4eLiYkY3IiI3Y+PIUonI6wF84EOw+CziujqRNxtCqDCG3uyNA27dZPDqKDeX90UwX95LULLLHmcuO1TGOAbApex7rbi9/O89IP4+P1uGImaR19LJLa9vMuj+Jx1TTi4hBs5pCPxzbnXEjE3gGUyMQxYY3Hq9nhurUOxsjISRqdVqL5QNu7i4SA/UoNNGju/TBvpSpoIQBuc5/CsFv9VqxZ07d+LWrVvJvrgKA2N0cXGRtYCfPXsWw+Ewut1uesbN5nU93rfeeivzt3d3d+PNN9+MR48exdHRUZYNxDBRCYCF8d5778V7770XnU4nPv3007hz507Mzc3Fj370o5hOp/Hee+/F0tJSdLvd2NzczDD19vZ2hoYphh9xDdDv3LkT9+/fjwcPHsRvfvObeP78eW5cYoGZtYqInHuPE8re7BtjjgIkVYWx45k4WRGzndL8bAVhsIJsIRsodoD0+fl5AkOeDdPK/cjRTYCav3GZLTs/P0/g6ooxbh8K0UYUJ5S5pWSknYjDw8PsM9+D0WWt4XThbHHq4uXlZRwdHaWBY535+0RlcGYNJDCEjUYjDg8Pc41Qgo/TLs2AA6hcJo2xIxfZbCWHz2xublaM+/7+fjqkjHUZCWAc+R/m1MbNjnxpSJgbSmNhrPxdmFgDKdI1zPSwDgCVsHjIFvtGCCsj724Pxhpdh1NkJ8mbJV3r/PLyMp4/f54RHjOekBCOgnCwDGwxUSH+XlYgAQhDWkREblwH4HttoCNoq0EjBIR1D239OkBQziXfJV2oBMB2RAyASicW+4OeIhqCHvA/5tKRDs+lnSQTAmWqCAASUO6NbQZi5f4F7JEjYSXjiSNE+9E93IcjYzBZMpt2ZiOqh4AwBqwJxhidhY0gOjWdTjPK6uootAtixM5sxCx1zJVvrDcY+8lkEgcHB1nAgAOOaEsZda7X6zEcDrOfdmppI5fxBmQiY0RbiUjTHi7rHK9tf/cmtrq8nEbDONnpKckcns37PfeWfzs4lls7ALwDGXh13Xy9dEk9FM7i4mKCEntcKBQz1BhT/kepcy8b0Jw7Za894lpIqK5ByTcYIr6LUNEG2DDCqhYsgC/sM4uNSiMwsxgR/hkM9Hq9uH37dqytrb0QMmRBYgQfPHgQOzs7GQ5/++23Y25uLh49ehTHx8cxPz8f77//fqyvr2dFjclkEs+ePUvwg1FiswWLOyLid3/3d+MHP/hBPHz4MP7mb/4mJpNJ3L59O37605/G3/7t38a/+Bf/Iu7fvx+Hh4fRarXykBZCsXjmW1tb8W//7b+NZrMZn376abRarfjxj38cX3zxRdTr9TS8sBLMe8naMC8GyXyP0oPecEm+NJUpHD4GjHpjDRvaUKw2jiXr69AiMgLgcyjbURTk12yloyP0kTYC8Al/lkwl6wclxdjhMBhcO0fXBnt5eTk6nU4cHx9nLi9AwkzPeDzOiim0+eLiItM1AMh37tzJUoeDwSCOjo6SuSf1h3GDGcc5vLy8rgD0e7/3ezGdXteTHQwG+Ww2OtqAAa69CdTRAebh1q1bKVecNkrdWBtgxouL/kZEHl0PqF1YWKg4JJ4Xr1uMkYEI8wEQBYh4Y6P1lB07dCQsm8fUrBIEgvNn+Rupbna2YPdpkytAsK8CB5+1UgIRxmU6nUan08loBbJHdLDf7+f3Tk9PK44icuk5LDcI8jNH2qP7FxcXM72I6jHluNwENEpmzyAQ+WZdlYwv9oGxwEYgz06/Kt9rhpK1xs+833atZJHRMXasAdOO8HChi+gHwId7SyKBv2GzALZmT81wG9C7brmZaPSpmVZfdiQZB4NsyAv2AR0eHuame6Kx9Xq9ku9PHjVzQWRsdXU1jo6OKo4b76M6DSw3OgDSiu/zXjvGXr/YeHAB64S22NlxtBZd5XGhbQandm4MdrExvt+XATR/N9A1gemIJe/zsz1vdjxLxtr9cLstc193vWKq/x8vQAADzyJyDjQCgjABMgANVjiEY1qtVoJg52OxoE9PT+P09DRr7FJv1V4a7UAJRUQuUEoy0QYWBkoR1h1DTKjTqQIoagBut9uNra2t2NzcjIjITXYoBPpPyG9paSnu3r0bGxsbmb/85Zdf5kJkk+Du7m58+eWXydowXuyIpzRYq9XKti4uLsbe3l789V//dTx48CCurq5rcf7FX/xFfv+rr76Ko6OjODo6yrzUs7Oz2NjYSAP65ptvxp/8yZ/E6elpfPjhh/Hll1/G3/7t38bJyUmsr6/H1tZWzhNjyHhGzEJDPnQAmYDBZWMHMsG7MdTMMSwLwN/vQrHgpDE/KBiMkwElgITQJCyNGUr3KWLmIJhl53sYVdjoRuP6QByqFbiNdn5syKwUqcvNz3zfxpBUDFKtXF+1Vqslk3l4eJiVHmBX2GvQ6XRy7SDzR0dHlVMGOUBpPB7HcDjMFJHDw8Ps37179+L111+PWq0WH3/8cdY/dvjZY+s8eUdwarVanvJp/YLjwf+ATrNtJdOME2vDx5i5RrXDowYCBm20wWkgLusGeCyZJ7Po1DR3JRR0IO+xXNN/ZA85Jv8c3eOwN+8iXerq6ioriJhhN+NMPrYjD2XOOs4uB225bRAO5JYDqBhj2mknAtmPiATPnFgHqKTfJlYMnv27nV0uO99l+J370SfMicuP2T7w3DI/mL8bVDDvZiLNoCLzPN8ONKdl8hyv5/Iz2uWIBv1FXnCwrFvs+HlsLbt+Dn20LjSw9hhBUrEu5+bmKk6g0/Bwxpl/9KRTL3FiAa3oTu9Pmk6vN7Z7LFjrlKm1o2RZQw4dkTC54tQNouTowhIUG2i75KtBqJlh4yX6YQxjtp/3Ied2rPxug2sunK+bgG3pOJWXZd2kEWOCTf+6+19d/wRQXbI1DDiXwSiAmu/ClniBHx8fVyppOLcMxddqtbJQPArb+Vk3GUqAmwFVuTD4ncUEQ2pHgdwrgMZ0ep1Xtb6+XjktjtA3zBQ5wuQz1uv1WF9fj+l0Gru7u3F4eJgbLebmrg9IePDgQQwGgzg4OEiFg9Fst9uxtbUVz58/z5CrPfSjo6PciGjHB+Cyu7sb29vbuSAePnwYm5ub8Ud/9Edx69atPHUKUPvo0aN48OBBbkjc2NjIA2rMACED5bjSduZ5Or2u5418cMEYl4AVkAW4LhlC55sbwBuskupgRQFjZoVVXm43jA4MVMlMoVDr9XqG/gyumFvkxgwa96MsG41G9Hq9ZBsx1IBnZJq2MCbkLUZEpmSwnlgvnU4nGo1GysjKykqWEGSMqEk9NzcXh4eHudkWZgnmp91ux927d6Pf78fDhw+z7WasAAKkBLBOWF/1+vWufxthmJ/x+Hq/wWg0qjzXDFoZpuZngwHGjzExg/Z18uvQKvNcRikwoAY+lnkfgsL4OlJTyh3tLtlyb0It2fKIWaku+kzayunpaaZ9sB7QsUQmkEV0NmQFALvMe6edgCuAkAGaIxNnZ2fRarXSgWIsSoBn1tggzUyw552rzCO1LJQOFxcAnvXoChiscYN5Rz5KUMHFvJH6B6g1eIqobsD3fAGA/Ew7XcxzqVe4iCjBrMLM+r200X2JqG7kNkPvzaTYfJNQjCltB/g2Go3KUd3cgw1eWVlJ5xsdhp6g/8ip11Cr1cpn3hQdgn024CRC5bzqer2eqXD0EecPewow5zkue1tepbOFbiHlDYfGa9uOtMeYe00KMHdl5B/7ZqfIsm5n2vPlyCvvtz72M7jPTrHBv53GV1f1eilQPZlMKkfLOtRb5lA7DOj0BZ7DhNkzRKBYUCx2Ng0SrmRCMQY8Y2lpKQWQZ/H+kuXy32u1WmUh2LN0fjDGCCNweHiYgNzPJ9QO+CX01e/3Y3t7O8OmzsM7Pz+PBw8eVHZr4zBcXV1XWoB1ZqNmxMzrRtgjqjV0I6qhnvH4epfzyspKbG5uxpMnTzKN5eTkJH7xi1/Ehx9+GL/5zW9iZWUlOp1O9gnlxTMxMl7MjLflgXYBKGETOp1OZUMn/XRVDt5lZ8tVW1w/2ZEKHxjhUnrIptkG51VbSSJ/5B1HzHZbM+e8g1A5jJ0dTYCNq44AsgDA0+k05Zc0DTahTafTnHuzfs1mMyMc/X4/9vf3c444XRSD5L4SAZhMrsuXkaeI0by8vIxer5fOC+XRzOx/+umnmf+MHLfb7UoddYwUTgJVc8rNvDaIV1dXsbe3l6w5oMyKn58NbJljh2/NoHkuzPyVDCOyVoZbDZq5z/cSCWCc7bAbqHl90FbuYRwYZ4Nxgz7awHy5CtJoNIqLi4vodDrRbDbj6OgoWWucfdqAY+WoizcUjsfjWF9fT0cCHcK6s0wtLS3F6elpApper5d7FzDCrqwEmGE9ed1xGVA7ksD3b2Jc/R3fU69XS3ECVmi/9Tzyhqw7NcK6rmxbKaN8HyBUyjmyYfIE2UB2/W7sHf8MANHPpR40YOP3r3PofJ/H0euAC9vIz2AAqmKwWZVUL9a+CQHy6OmjmV0qbmxtbcVgMIjt7e2cG68hwLyPDGczv8E+76OqFudHoO+YEzsZpJIwdmZo7QCj0/0865+bgC/t5xlcvoexcB4zwNbROM8Tl+fPuMe6q9SZ1m38b3tjXfWPgeqbWPJvyvXSOdXOFWUBI7xe8BGRm5AsiPy9DJHwD9DEdwAAeHQuB4Qw+LQ42GGnfNBmv9PAikUOcDErFBGVUKEVMO8E7NHvi4uLDJv6SHDC7C4nxaJwKTd7ml4EVvwoS9In6vV65QhiXzyPRcczDg8P4/3334/33nsvIiK2t7fjk08+ic8//zwePXoUX331VbRaraxHHFENkUZUHQMbBy4Myfn59cE1jNudO3cqBoj6vFZMGAyYWmQQg29ACrNYhtbMXFuOYcj4vVQm/i4sHkyJZdosC/Nj5qhWq1U2ixnIMS6eQwMRjL5LObXb7QxZklqwv7+fFWhgf8xwI1/n59eHE2HYqMtK3uP5+XlGDvb29tJYuHYyYIicXtYYB8xQFYKj5HFWAV6ACZzxiFke7HQ6jb29vUoKFTLkUKTlz/N8enqahtvyaABEm29ifco14zxSjJsjbnYa0YPUz3Y+KxegBWfFDqpTSSAgmHszeowzJRIZf2SOEmJEFkwe2Bg3Go2sA02kgMgADpQdqbOzs5RhSo15jQ8Gg+wraXTWYx5z7ABryZfthwGhUyZKZppnMs+sQ6d2IFMcKQ2zbjYZ++HIRAmqLB/0HT1g8OQIC30tv4MOKmWvJK3MUJs1tC42Q40OhQTyeDFOtNFy5z75e24XcmydB1g1M8vaYn6wkeyZqdfrebgX78HBxtatrq7G7u5unJ9fn+7Kveh5Suyi22gPhAxAHhYaPU6bcLCRdSKY2BiwBSDSTDh9IsLN88woW78wn5Y3orYucWqZAqjb+bJc2PY4soYj4XaZrTbBZue8JJ38TrPaxjqvrhevlwLVsKRlqMD5WM6Rvbq6qtSVdK4bQlmGjf1cDEvEjCUmd7A0RJzEh4DyHHvdVtYW+svLy9wBzAEUgKKjo6MKm0JaAsbFoW4Y1OFwGM3mdfmti4uL2N7eztxVs+8GPQbDDlMbGNAHfuZZ3/3ud6Pf78dnn30WS0tLyaoyH7wPAMe4b2xsRKvVit3d3TQeu7u78Vd/9VdxcXERb7zxRh4nbkVhZW6l6Hlz+TyHRCOispkEJtLhMoMEgxenfZCKwEX7HW5jDJFFR09wygzWuQywabtDcnyXvptxo3+MA7vea7VapXqFWRS+Ayhyabh+v5/OGSlIjG+/34/nz5/nBjw2B7KpzIqa/6mgc3l5WTntc3FxMZ4/f57pJFdXVxml4P7JZBLdbjeazevNxdQtJ70EmaSEZL1ej9FolICC9W9jhpMVcb0vgfxt5sbMv8GNHTIcYtai14nBh3+2LgBATKfXKTBlFSH+h0hAD+DE2Iih58wiQy5Mp9M08AA+Rwf8M+8EKFElxHJqYwtY52efJWBjjp5F/uk/lXg8xhh8RxPQu9yL7JPmx/us363zTZ5wWX94TZt8MRC0PDOvsJZm/8roKakf6AT0J2sVp4W1SRsY41IeeHdpT0pwwj+nbTj6ia6ijegtZJv+AKIMdvnMpQ9tlz1GpdNNm0gvcmUlrx/G1p/XateHwNFe8qUjIqOQEbP66zibdn4mk1m1j4jI8q603Y480bRerxe7u7s51qT4MXeQErbFRJHQQ44QocOdWsL+FmTC8oM828bYGWH92pFAxolkcpH+w1oqZStiRuyV68e6xzJh4sygGF1jMsyOFP10RMP2HfvLmlpeXq5szPRVruNv2vVPPqbc7DPMF+wZCxWQy4Wg+RkwyghJrVbLBcRC5x4ALd5nxGyzR0RkDjVtKz3tiKoX5lxVgM/l5WUeDnN+fp6heDxWNiTu7+9HxPUGQzzwiGthxoNmsyHAkYVDexkTdj7D0DtFgMvMmx2F8Xgcv/71r/NUKdi/MszHXHGhtJi3g4OD+C//5b/EX/zFX8RkMol33nknlpaWcm6siErWG/BbOjBEAJABwB6bR1jsKF4rE4MTnovyNHA2g+Oc5ohIB8kG35dZuzL3rQSj9tCRL0AWgJ0LuSIUyX4Ag/tarVbJ3zeIj4isGHF1dRVra2u5SXQ0GuUc+wTOTqeTJdYAYBhVmDlSAk5OTuLp06cxPz8fr732Wr6r1+tFu92OwWAQKysrsb+/nwBsYWEh2u12rK2t5XhwuEK73Y5arZZ54M7J5QAYHCKcY7fNeoFNy+xRcCqGmbqSFXIEgPYxx15DTs1w5Id1yJrnWTzHBzFh+OkzYPr09DRWVlYyXcZpFLxnaWkp56cEY/X6ddrOwcFBpkhh/CaTSWUzJ22u12en5vFMACN6jXV1dnaWRthO9tHR0QsRFN45nU5zXm3EIyIdMMbBcu31Tejd6Wg4WdYXpSE2EDUzij4xIODdvAudxbgif/zOvciXT260HkLHoCfsVKOjLZfcb3aS5zDPjKOdEL+r1NusZfRUSWQw7zgLriTC3/wz3zFQ5necfMBns9nMY8SJULCOYHLZoA/pBiB1SUvaj41FXhkT5oV1fXJykvLHoWYR16fCIoPoJ+5Dh/NeIsuuNoTT6TVJFHoymWT7kWF0MnKFPTLZZ+fYKSSWY9pgMoD3mkk2EeB1hjyxLi03fh/zXjqnNzHoBuzMjeXb7eIedMMrpvrrr3/SMeX8bG/Wnp1BkheLjRjfcb6QwziUDCMHFONBmoQZR7xTFjmTj2IgJQQPi4VGaBtBRbGwIHk+C5WFBEgbj8exs7OTABIWfTKZVHZ187xyw0nE7IRGA7UyJMm42euMqB5QgEKjLza8ZtJYNHjwl5fXdXb/1//6X/HrX/86Li4uotvtZrsNLm9i/+hPRFQ2XTgnDbBCrq6ZChSWlY1/johksum766Ijh5PJ7LhulFwKuVKUStY/YpbCUm4Q4ju0z3LHfTZeKH6iAcgiMu1UGeZvZWWlUiPcObURkTWgz87OMod5MpnE/v5+XFxcZO71xsZGjimOBONIu6i8ApOMk8i6uHPnTuzs7MRoNEon9uLiosJcE7YdjUaxurqatdaRVdhCQArOFgwtbSM/2OFa5oc1v7u7W8n3Y0x5l+XGDp0v56diJAyobagABzjzzBclwZAHWFuiCugm1gy/OzJHP4mwoKMMiqbT68216CVAa8RsQxpt8imV6BDGCh3EvHvfSslAsf7sALCeLK/z8/O58RwgjdMNSWBHkX4x/45M2Xn9OgN9EyFgfV0ye9ZLzq/lXfQXksZtNGsP2LFusYNggG1iw86GdS3Psc7nmXzXaYx8B4KFy2SQbajtgtlKPsfJxtllHlwNyzLBxlRX2yGVhz7AipsgciWi8Xic1YcioqJ3y30CtNtOxsrKSoVwIy2QdYmM8SxHBIjgQd7YOWScGE/mh7QP0vR4tqMEjPvc3PXR6Ohhy5dtpMGv02UM6MvohYGv5ZpxNVg2DvB64m/ls/keMsJ8GCT7vWasjfvAeDeRVK+u6+ulmWomjMn2Bh1AKRPP5waKTAiAjcUMqxUxC5+zCE5OTl7wxmgLJXoIU1nQbGgQbPKzzV5gGMyMoZxPTk4S/ABiUc6whISsp9NplqMCTDlkSh/slVpYLahWjCXQNJhmsQNcASw8g77AfntneMS18v7bv/3b+Oijj+L4+DgZ0ZKVLRc8bUIOrAD4nc1pHDsNoC43dvJ8X7zLOcx2ypzfVYJ4ZNC5un6u++PwNM4I/beS5LJSshxaYfF85AzZthLmn5lMjCoGh3kmBQTD9fHHH+d6It0DwIPMmHlDZugTwA/WlRNLSaHymu31etHv99PgHBwcRETkpsrBYFAZLwBUo9HIvQQlw8XY8bnXKGHa/f39G8ERbaNfnjvmGV3g+Y54McRpNtHzGjE7EhpZPjo6yvcAKM020XYMMWFdHASYXNKgSkYLOXfIGQZ4fn4+92isra1VQNzx8XECG2+4RX/xPMu/5RbZKwGEGV1HgWgr82qZNnMMoKvVai8ARINLy2jJXFs/olv9efn3m5hudC8OSukc2xH12Jcgg/8tg7yTMUR2GSPAJPsQTNR4/JFXiKiSfKGfpB/QPtaUmWD0PG2gnURTsb12AmgLtqTRmJ386agOetWpA/zNMmY55++k3qDjSIliDqgCZBljzEg3o5pHuTZNLJl4KZ21cjxYR7DxjIcjISalcIoB9ugvnmPAbgeTZ9lJQw6sh5An29ISBDNWfg56iTGxHUbXmJQyLijtlttx08VzSntdXjc5yt+U659UpxqlwEaPq6urF04GM3vHBDrkBNBAkbBYyO+EUYiIzOOGVWUhodAwkmaBEAznaKLo+RsKwqwewgS4Nuhwzhl5W1RYoA8Ys5vYFC4EnlSRyWSS+a0A3zJMaUfGi9GMCsaeex0W9bPo38HBQXz00Ufx2muvRcS1MvEx9FbgETMnyUbUi5d3XlxcVCpH1Ov12NzcTNDBgR5Uc0GeAHIYwTLUieFCmdB/AxWUWwmKzWZFzNI67ATSt7IigZl5xt6GrxxfABa57dyLkWS8SucFGWO8lpeXYzgcxsOHD9PIcbBHt9uNXq+XLDJt85iVoUYAN8x0r9dL4MXnGxsbMT8/n3nMACiqhNRqtQTLEZEbE9kAyYalVqtVqd5CKgOGplarVcKyjAGRJeQCwFiGOvm+N+oZZJmV9djzM6DChrhenx1jvLy8nGPudzGnbLKKuI6quTpGRGQJw8lkkiX9eHcZmnW/PHeActrK5lBIBDYaGki52gpsGuk3ZvbpN7JMTj9ggHVoYoL2GfzTb/4RJkcf0uavu0qAXBp0kyR2msbj2aFVZToI9xnklM4z3/G64LIzwTwwXvxcssMRs1Qm2mtwa/DkVDPGCn1FPzyeyHEZxSz1mnUlOoH2OppkR8pOCWQQ7+Vddtac5re6uhqDwSDOzs6ySlGtNiunF3GNEdiXhG7D9npvDUAeHY4NRZ9AHpBuxbuYP56HI06kyDaltMfoXC47lzwbeWAsp9NpOk9mbUtwigx4PP2eErTyGW1knNyeiGoVDnCNn2E7hKzxDEfwjRtKeSI9z7LjsfGYvLpevF4aVLOZAIWN92sBACSZKcBTvbqaFYMn5xpA3Gq1shzO4uJirKysJOvGAS1WIP5n4TAri/LDGaCd9i6t2MwOdLvdTOvwxjtYaNq/u7tb6XdEFWyVYIq/uUYm3ru9SwNWxp+Fg3d9cnKS4TnuZwwMxCNeBPb1ej36/X70+/04Pz+P9957L/r9fuawkbMGK+e2mwm0MTHwRrmura3FyspKzqEPyTHjgCEDKNH2y8vrEk0bGxvJ+BncWvYYp5uYJoPj0phYZmkbfTYw4x7fj/Lxu8ya0x/ajXMHcCRaAqChBOPZ2Vlsb2/nIT8AFlIR+v1+GmdAkwG2HUTGcnl5OTY3NysG2oqT6iIcuDQej2NtbS0Bv51kHBzawZg1GteVJSzP8/PzWSOb9jEejHtEpGyU7aadyIyZlhIE+Pk813+LmB1GA3vFuwD+gHnnM9JO5Ji5pA59o9FINtWbkpxHW7JgrFXLJOkbfFamO7EmeD/vhrkkzWc8HucGV0rt4RSxZkrAV6vNKvFY1m2IabPZ5clkkqAJecc+GCh4jbjPfF6CDes+s8wGZyW4ZYx4Hk4j95REAWuYe0vWlec4P9eOhNlPUn6YE9sZO38AavSNnUXrcMaX5+EQ0m9sBG13njR9Zp05EmpbVDoxnh/GOyIqzhnlUHmHT5RlnpvN6xJ7RGtYP61WK1OH2FxYq832ZMBo49Q5j54UFkdkGGO+Sxvpixlqb5hHB7D2vXnYjg9jbmCOfDO3JmvMUntdcTHnJcnjNWZ5x96zpt135A0ZLcGu7Wi5Rtwu5It1YCfMTmbptH3d9YqpfokLxU0eM2WbEBILGQuWNArC00wQQu6wnhkrjj02S+FQB585b5J3G0i5LQabeOb+HODAqXU2VvSFnFLGwMwFF8qcPjkvGiEtFSN98GVBbrfbcfv27ZhMJvHpp5/Gs2fPsnSZc0RtaEplzpgwB48fP45ms5nHj+/t7cXBwUE6IfTBiwunyHPBu2A2caB6vV7lxCqcGDZzliEwDACyxCJnk58984jI+TFjGTELJxsw+qjl0ogaMBiQ2hB7HJ3OFBEvKE+MgJkCQp5EE6bT2eZL0iVgWz777LNkTN98883cF3B+fn0qJYYsYnayniuimP1H0VLhgbEA7BoAYKAuLy8z5EqbcJQuLy+j0+nkXHc6nWSnSRPgRDXScmBXKQXnWuv8f3Z2Fv1+P9eGcxHNqpZRKj+DOfVasgPE7/TXLBJtgg2DRKCfjoY53M740W8DGQys33+TAeRnt7vValX2cgAoptNpls9kfNm3QY48YAgm0fouIvJQHqoz1Wqz0o+Hh4cRMWNYy3Qytxu5B5yiy+2EmIW2rvD3bPAZO+tTM+uls+v7DUgAQ9xv58zMsGXMoMXsL9FS3l+CeYNTM9K0qYxW8VmZBuT2o3+RU+4zU2r9aSDJ/Y4MWwYN/CNmewHskAAi0VUA5tXV1SSVDAwBw8hbOVewyZPJJA4ODpJIgnyYTqeZJhcRSRyh51z/3w43Nqlev97vgU01ELTzCSFgGXKxBDtVxgaAW+MU4wnm144VbS3XDN935MOA2veXzib3W674jnGHIxKQBTzHzqUJFuMI2zTsZel0vbpevF66TvXR0VEuUDNjLEK8TxYr4Vy8xfn5+djb20uAfX5+HvPz88lQR8zYIIdvEBgEwZUEDGidsmCAUbKReKIsDINrAB0gAjBzcXERh4eHuaEsYla3l/c5xF7W2mXB0UYzyFbyN4Vn6vV6lip69OhRnJycJDhB0Xrh4ZiYxeCdVhAo3qOjo4iITCcgbcObD1FOZRg5YrZ5k5KGCwsLeUqjc+IXFhaSIXBIyce7np+fZwQkItKIcLgExtIGojTkEbNQGUxhCa64YHoNzLhuSvNgTGw8baCYe555dnYWvV4vIxwwArSP59Xr9TRWx8fHMTc3l9GbiGtQ9atf/SoODw+TrWY+nL8fERn+59kYzWazmSeA+jQ2craJUiwuLiagfPjwYZ4eisOFMcDo8z+pI2ykQ2njFAHsWCekTI1GozSIzDljz9HshIVpL2NWgjQzviWAQo5dgYD1gcO1uLiYJfNgxSjrhVNiI+Q9IF4vBoc4Vw4V2wm2wSZScnx8HCsrK3lKHU4r7fB6RwYWFxdzfwVAj4gR+q3dbmeKltthYGbdyTgbADKuzK/H2PejHwy+PEf8fBOZwNpFv1sXcg9y5b+hA9DH9Iu/4cj6u5Y3+m2Qw9whv+4P7eV/jwV2hWcSHfA7ymiK+8s6Y88O0Qbajb2y7HtOTHqUOpfxj4hkju1E8TxSnVqtVjpwHnuzw9iNkixhLtCBvMeVu5BH0o/Qj+TCGwMwZoB05oPPXWLQssLY+SwAkyVes34nfTJILR0h5s7AtXRwmC/eYwLJ84Qs8TzbrDJFp3TYIqJCPlnn+fn+2brQqWLGd3aSbrKVr67r66WZaoTM+dH27hE6duZeXFwku8PmPgBFRGQVjohZzjYKm8/MZqBoEEIWrQ0DEw+Y8uZEs20YODN9hOJtvDDmGH0Wp9tjw1ivz8LUDjsDJhByK2YDFDOlBsHj8Tg++eSTODo6im63WwH0KF/njjNfPMcgxGDYzoDZZBYO42hm3UCBkD1zgoOEEeDyO8wwoiR4BnlxKFb6AktJKKzsn0NdjCm/o/jpb8lc+GczTBh2noOsGHyUgNogCwcDmWLcYHn4PmUkYd3b7XYsLi5Gu92Od999Nz744IP40Y9+FE+ePMkUEG+WhV0GEBJK5W+E4WE+SU9YWFiIVqtVWd/I6fPnzxOokQ7SaDRe2PNgR3E0GqVRZBOP9YMjXbB/5+fnlRQSokSHh4cpLxxMYia3ZHD8GW2znJTMTunYkmbUbF6X9MPJpzTj8fHxCyDRbDjrEXl2ySpSMpAL3ler1fL5yGG73U7ARJWW0WiUnxFNs14kncggwHKMUad6AXoKFvDk5CSZyEajkaXFYMWtZ01aOB2HNWZ9Y11WzpMvxsTpS7zPrHDJQHq+ATCMufeWuPQlOtzyUoIQolp2DN2uksUz2LM+sX7yfdYZjqi5P3wH2+XNx4wZMu6xQvbLsePye9wWxtCOhPW6ZR8d2Gw2syQrsre0tFSxY9gT5wPDKhMF4rnoDeaEddRszs598FjZCfbhMJ4Tvse64H+ID363U1LKnElCz6FlzTnOJfC07DE3vv+m+fJa9rwwfiWZANng9YiMW1dwf0lueG6sxwzakbN/DFSX6/ubdL00U43xxpthUhB8JhbmhEVJzhOT7XwswDRKuWQy+E42WguPf3yPaiS8AwWJwJKyYraC9jov1YdJlAJij9rhFi6UgxewWXe+YzbEgAGnpFzw/C1ixohweWFGVNlaAwAAtMGAGRPe5xPTysWOsWHDIQdEsFGNNIcy78refWkQzPAwrg6b8TsKAmBtx8oneNZqtVTuBlPIGnNaRgh4tseP8XDoD5BixWhHJSJShhhDWPrxeLYBls03w+EwDg4OEjDT/nv37sXv/d7vxZdffhkfffRRNBqNeO2119JpoT/IFiXYyEn0qXqNRiNrWbdarTg+Pk6HE+ALo09OLSXzGDOYKMC52b96fVa2i7lC7onuEKVgLiaT6418Bi4ur4eMOHpjEOT+mwku5dXvQwdhpKg171KZ4/E4Sxg2Go0YDAYREXm0t42KQ8BmqCeT64gbBr0EN+gQ7if9wiU9XfoScOC1xBy4EocNto0/RhLZo21ECgFtyAlOG1UXGH/ncV5dXVXazbtuAv427hGzCJSdIzuszJf7QPTEOoXLTCLv8XdLRrgE+yXRYefbgIZ+l06ZZcDPN2OL82WGEcBvUMP3kUezx8xxmabAvFu38jxsa6mTLY9E0KiExTOJltwE/E1UmOnk/ax/iAW+h93wuiayxtjyt729vYoNYn15zubn5zNtCd3sMcJ5ZE79fANT7Aq/o/eYG//M/JY6kHZZvrBDtn8eR9Ymsm7cA0imvUQEGQvbbgNgA2uwhB0bO9/0m3Gx08+Y2nF8dX399dKg2gbEYXAvZCfU+3cE3Yu/2bw+8IHnINA28L6sxCKqx3VjXGGn2ViCl08+qnNP3c6IayBE+Sp2HpMTbi/Ti8MLEwCG8PFd2mOlmJPQnFX6QPFaOfJ86gdTWcHeqxcpz3YEwGFQQv1ra2txdHSUrJzZ7MlkEhsbG7G4uBhPnz6N/f39BGSUyKO8EKwqQNzPMGAv5xM5cq40wJPcWi7LlBnzxcXFWF9fj7Ozs6yyACAxADGoQZGgaMx8ctnZ4B9KCLnB2FlxTybXdV2JeADGGXtC+r1eL6Mjz58/j+FwmPKKMVhaWoqdnZ34sz/7s3j69Gmcn59nTv1gMMh5Ho/HmZvIRlA+tyMHC7y6uho7OzsJdiOuK1gArkejURwdHUWv18v1c3p6mic3kuvLOMGeGhhgKGgjbDyGFKPglA7mh81EjnqVbBFsqsO3ZlGQPVcXYE4smwAa+uO5vbq6qhw+1Wg0KtVSWJsOyTMOGKTJZHakO9WOvEZou+fJwJxUKPfJlZacv8oY4LT5ORGREQmiS+iBiJkjCHuNHLFJ24d4XF5eptywea10GugTa8PrjP47T9Njb2Didcjcl6Fpvks/vEkMgFVWeDK4skNHe8pQuYG+x5nnISN8l3VhB8vsM9dkMsn9BiVzacaYZ0bM9lCYjGDu6FdJTjldiu87ilq2zc+CRGH8kVEcKfQyfSAS7Ygd40e0i3Hmnvn5+eh2uymHJlc8ZyZDGIvJZJJlR/kuttCOuOWhTMvhZ/rpSC/3lc6559Xzjx6k3/5b2R8/r5RN3smYMyfIuVPQyj4bq7kfJkINqO2Q88+6ynJcluW96XrFVL/EVS5gBhvjBIt9eXmZ+bV8v1zgVBLpdrtxdXUVx8fHKRjkCpeKzBeGiPdbSdTr9QSKp6encXJyEkdHRzEcDtOz9sadiKjsem82m1l8HqFGmSHsZiZpm1MVcpD//yEyhNhKn/8dlmaTX0R1A477b/bEHq0VdunN8z5YOjx7cta459atW7lp7unTp/Hw4cMMQ47H46zGQv4moeKb5IT3w4iaLbGTQrgV5ha5IoLAODYa1xUqms1mAr+5ublMHzAzZXDk+fF42PCVIS07NxgKs9rIXK1Wy019BwcHlSOfyVtGlijxCBsLUO31ejl/tGd7ezseP36ceYew3MwVcmDli5MSEZmLbcBItRfqv7ZarRgOh3F6ehq9Xi9OTk6yFnVEJKCCsTLryaYiSkqenZ3lGgHkE62CNaEtNnC0oXQ26Z9TgMg3dqoUc+x7AOyAXICVa+F3Op2YTCbJUGNcPA+kgZVgDoeOVBoYSDN2tVot54yKIfTdjBDfxQljQ5adQQyac7INcLyWWJOOtDWbzSQTeA+HfACKfXgScm6gglNYAoWbDGiZ+2s2mjnyhT42mKPt6PlarVbRjcgK7fBhIMy/5dXA33JmZtDsbilLJQg3+KcPjIftg9lv98+628wwV+nkmzzxngxKPaKjHaVw9K0E4CayDNgjbgb2rEV+Z014vJALSCQwAe/AmTZrurCwEEtLS0mIuFJXRLyQ9sLYTiaTWFtby9QlR8q9Bu3oRczOwYBEMlGIjTHgtZ3g59IhiXjx5MMyRdF2x3Jm2+8xLBlyjzNzZCaZe6mh7X0XJXbAVpiUKEkwnB0cNDvor9jqr79eClQTInUqA4IM8wHD4aoGzjOOiNwhjNfjHfXerIEQMqlmhF2SjFQSP8NGGJaNeplWrDaeZtLNzNhD5GeEnneNRqN8HykQzWYzQblPkTPjWS4KFIuVlRfETaDZ48VnKAyz0yhFG00MF4podXU1NjY2Ynd3N3Z3d2M4HMbKykrONYC30+lUDvSwjETMQkvMF8AmYlYvszQuKEQra5QEMudw/Hg8jufPnycob7VaCV7N3NMehxBLVpXxMqg18+RnMY48t91uR71ej+fPn8f+/n5cXV3F6upqAm3AJutgf3//hbxj8qFR2AYZgPCzs7OMntB+OxhsXo2IeP3116NWq8Xu7m6Gm3FkqebhfQIbGxsVdms6ncbu7m7mVNPX8/PzNODLy8sZLibtBEaQnG7kkUgHKTqMMRuX0QXIhP9nDZpRtJNpZs7vNHMUEdlGHAFkCtl37qjXtkP91mPMG2OK/jCTQ+TCGwdvAiLI/NnZWZ76ii4wQDAzZvAEEFlbW8sUN4Nq9BspQmUpOSIJk8ns9EuvZy6PC/33s8p2GYiUOstMahntQxbMQPs51jGOVAA07BjxXeaQteX9HvSV9Qf5UZI5TlGkjTcBjBJYc5WMJvNTjhttRib5Hv+s15yTD0A0y2tdxRiUKVV+PxfvRXfws4Gy22zGlb09yBbvN6F1eXlZSYXxYSroY8bJLL4dQAgMy5vH2kDQ/bN+YTypdOQoMJedqnKecd4ZXwNrOwBm6EtH2PNO+0ogbZ2HnPs5ZTTCeMBto392NG+K/hCVslNWOiivrhevl2aqHfqMmG10g50ejUZZVonFAzuHZ7mzsxO1Wi3W19fzxL1ut1vZALi0tJS7gwnjwyR3u90Eel7ULHpvQrm8vIx+vx+DwaCS/9tqtdIwcBIhABgBM0vBIpmbm0ujdHR0lMzN8vJyrK2tRavVyvq8/X4/Hj9+nCwczK4rBJh1IbfWuUwGl6VysEFCCd3EmrDYygVho8diPDo6ik8++ST+6I/+KL797W/H559/Hj/96U+TLZhMJtl/3m1Wy8wvht4Kn4WL4reyZh7cpohZyBelfOvWrbh161Z8+eWXEXEdgj47O4uDg4MXDgMxGwioQLFERDpkXLApZvYJhcFGtlqtrEYxGo1id3c3jo+PE6C4ZB4KaTqdVjZJwv46jMn7YYcBz64iQ23y8XhWP9w5u+fn53nM/GAwiFarFVtbW3F+fh4HBwdxfn4eGxsbCSLZzQ9wJg2FNU3qRcQsnxiZRa4c2t7f349ms5kbaefnr08TZJwBbBgD5hQZWFhYSKBPn3AsmEvq5AOm9/f3K6AA2VpcXEzWF13U7XZTJg0yAG0GTXY4mSfSKgDLzAlroXSa6RcMqwGYw8/oVvQcpIMjL5ZVwJ+dgWazmTXrAQfWA41GI+uNI4sRUcmlpq+Mj+9H/5ESU6/XM5xPyo71sYEfss2aZLzK9B30LuPhNYSzbOBi2QO4mOmjPwYQ6BTGzilcTpWy/rD+ZGxg+HGI0WHeLI48lYDIOtjOA++m7SWQM2Cjvd6/wDxApOAA0SbG1s6iQbwZdY8JoHcymeQ+Dc+p57nT6WQ/cSJrtdoLKWMcmuQ9Csg7Y+Q599zzXtKfuDg/w6UdDQBN0oETStafPptt9nzZntTr9UqExzajBPisO4NYxsKOU/k+E1SOqpaOAWNY9sHOScSLJ9Iif6VzWBJvnqMyouirHPNv2vVSoLr0mDD6KBeH6iOuJ5VSPVdXV9Fut2NnZyciIu7evZsKp9vtVgTA+YuAYIxvq9VKds4eOEoTgAEbt7e3F/1+PwWXI8edymGvuPRQESYUw9LSUlZeWFxcjMePH0dExO/+7u/mgj44OIhnz55VKp3Aqvufw5wA6psAsFkyKym31SwqcxRRPZqZ73nxlGzWdDrN0/o6nU7s7u5W2GnSNHg/jKXZ3PPz8zxExKyeQ6ZOpcAg9Hq9ODw8rKQImO1hzPb29rJPyBDKisVM/wGIJfPmOcfo0o+rq6v05plT2HCcPaIzHM7C2CNXZgCcs+vDI4hecMgQeeRsBqzVapUNY6Rt0I7JZJKMPcAIuRyNRnmIzHg8joODgxgOh7G8vBxPnjyJ09PTuH37dm7AOzw8zDkx48EaX11drYwTbCjOxXA4zHXWbDbj8PAwWq1Wylqv18sxMevh8pSELM2IIWuudc0YwpatrKxkpMxRNJw05ICcYgwc+dze9Mjc0B7X2ua9rsUN08baArjwLACywYsNDmuGDXg3sZ6OCKIfx+NxsuNey4wHzBuGn3fQTpxcUu5YR6xR5AanhDbQTh/QxNo322zmEj3hqJtBBO0z2YCe4h/fK4GAmTk/H7ktI2cGxiUw8BwxngaWRJL4ntvhFALvHbEMl7aF9lrfWyYsj74X+wGRRQSPd+H8eq6dWoOu9+V+8GwDLcaZNYdOLOfbwJ4+o5tZn4yRy4DyfffRJEiZolBGoZgvQLPn3KDZY48MO7Jke8jYef7slNme8cwyqul5Lm0UclU6CqwX5Mvt8ZwYaDvKZyKO59mRK21hOcZeB77f9vybDJr/seulNyo61M/FQoD9QMG5pBYMETvNy9AcF4vWlS8wIoDviEjAjWCfnp5WvE8207lKwXQ6TaYR4GBWzofT8By885WVlbh//340m80EOs+ePYsnT57E+++/H2+++Wb0+/149uxZPHjwIEuAweraCTDgQ4GV1T6cxgFQsNH6ustszU2smNkKg1raU6vV4uDgIP78z/88er1ePHz4MBYXF/NIbG8kAfR1u93Y29uLy8vLjEJ4rqyo7HiZoSPf1SCHuWcxoyBhx2q1WjpspRxhQDAGKCXYWY6jZgNrrTareIHzZSXkUnDMESk/KBo7Pig05IxIDP2cTqfR6XSi3W7H0tJS7O/vZ341EQ3aPTc3O7reodFGo5EHEcE2v/POOzE3NxeHh4dxdXUV29vbFWYEBrRev05XQYGzTsjHY/4A7zCtKG2cDGqHb29vx+XlZaZ4OK8RpW8DBnvryh8cChQxO4rb9eFrtVo+GwPgdCmvFRwjywM18Z2zCRhyGNxOkaNAhOs9x4w7OZ2eH/aJoFdI3fFG6dKIed0jrzCXrGnCzT4PgAtZhohgzpxShZ5m7A0ScQydHuBoI+vXLBzrz58ZPNupp29+p0GE32GCwGsYuXIk0Wx1RFR0aakbSsDi+acPvB/AahBmJ92EB+9Bzg2EsZ2lc2XHwADGe3NuAt7j8ay0IiwyY8JBW+hf30/7absBHfJuFhrHzGC8nA/raV84lHZ0SU/y+PIz/UMPmVWmjfTZDDa62WcbcDlK4OeDMczCOtoLJihtLuPgfVH8K8Gu72HOkFHabvb4JofT8+Ior51LVz+aTCaV9cvnJufswNh2GpPRhzJS4nX5265vMuh+6WPKEQqMFqAZgWYilpaWYnV1NQaDQWxubsbW1lblyFIWAwIecS10g8GgAspKL8shPIyF2QyzQrwDoabCgXOwEFIWPCkmsD4RERsbG/H6669XwrdPnz6NL774Ivu7u7sb+/v78eDBg2TUS1aXMQRAEJbr9/uZImI2hp9hygyIbXzL0JDZGBQrQMPtcN6dAdB0Os2KIBcXFwn+MebT6TRZRYADB5aMx9fHWmOsGGfucXj56uq6wsrS0lLs7e3ldxzi4nsAKC7LRMkwcZmBpx3Ly8vRarXi8nJ26qNzvMfj68MxvPs7IiqMZdlOswM2jpwwduvWrVwL5D2vrq5mWsbz58/TGel0Oqnkx+NxhpVhHlk7AE2cSWSb2s6wtICqUsEzHhg7ymZxZP3JyUm02+087Onq6irW1tZS3pERy53lhc2LZbULzxfrDmPD3gQcKOSEdnv9wvoAWLmHDXpsosShdh7ocDisHDaETCPjZtT5G8/HmNMX2GunZgAGGGunmyHn1lE27AB065rJZJLpRzhYgAszroyX5R7QcnV1ldU80G0GQdzHeBlgOoUCooK2W7Y8rwYcBtH+no02fUNevEEKm1Oypn6+++LQetk+AKDtCGDXKSFlaiFpaSUARhaQV6+30rHgZ88Veoo2c/k5pY5GF/BzCcCYK34v0454FnNuZtPjRb8th+U8G1CXoJ31x3Pn5+ezrr6JI48X7fKBYd4fZaBrgs1EDfgE2WB+bmJ67axA0jGPdvq4x7Lmv7sfln8+LxltOzQ8i/5i2+wIgEVYI4wpZEvJaJe4oxxnz0/pFHs9uv8et/+XCiDf1OuflP4BW0hY22AJwNjr9WI8HsfW1la89dZbsbi4mJURIq6FY2VlJXfWTybXOYr7+/vR6XRe8MpZ3OSrsnC8ACKqNaIRTqqMUIkBZo9/hMw5qMbAj9Pcnj9/HvV6PdbW1jJX+uDgIFZXV6Pf76fB+va3vx37+/uxvb0du7u7CaB83KqB2GAwqJR7svIFxAGorYRwOFASVtKMr8evVPIR1d3GDnXCsvT7/Qx7O28TowMgbLVa0Wg0YmNjI0vFsRABQIAY5seLnYNJSiOJMiJXlDbYiaD9ZocNMqyc6AttIpUCkEqb6B+yHhG5GQ2l7nxVwJeNDakBa2tr0e124+TkJPr9flxeXmYJwoODg0r1D5xM8gEvLi5iMBjEo0eP8lh3NhjCSMLsYvQ5ppo5tVyZPXYYlY2WlKVyhRLuRYZRwPX69XHFjUYjDg8Ps551u91O5sRRIqcKmO0xK+86rwBH5p42u4RXxOwkTlcV4BATZLwcB9ahy8khK4w9YBo54B5Ago9Tnk6nGYWgnTg2AGrGA0AE2KENsJPeeOW0EowcKRzed4HBQ49NJpOMurgcISCpNJLOKabtNuK1Wi1TCr4uV9X6hHFGTsyMOTpm579Wq5ZSNENbRpzscDvUz3NvcujLsYLRQ+5YGzzbRI0jHsy/WXCzhCXgdh/9N9qGk8M/t9fEiZ+BvDI/TvHBSZ2bm0vGunTgDMI9H3aQkUnk9CbHiIpWjoiUMsDaBKBhPzxHtlvYDNYsbcAGcq9Lczr65lQ7O0a2DzicyAptZiztgCCHjm4ZSJf6lDnyuPL9Uub9DGw4a4Z7GS87umabrTd5hnP6b5JJ+uDxdPTFc2G587h4zl5d1eul0z9gsbyxD/CJIrai2NzcrJRtQmGORqNMuUD4AaAABQT/9PQ0qw4ADhB+FhQsnr1awtj37t1LFpqNPIQ05+bm4uTkJHZ3dzO/FGMPII+IrKNZq12nRwCov/Od7+SiGQwGmRN8eXkZGxsblRrcXCyKwWAQBwcHlZxOg2n/b4/dAJhxMLvnfOWI6sEpZu5YhF74jOH9+/ejXq/Hz3/+89jf34/Nzc3KSVeEHQFg3iXsDaAYpBL0c/Ece/j8c5g/YlZSzw5VxIuF7pHVkomAcWf8MMo4gp4fGFCcGsaUfyhvZM81k1dWVhKEHh8fx+eff54MMGPDnDYajczRpx46OdUAGedDAqovLi6SxSZnkH6wTn2YArWz6UtEVE4iIzXCoBHnh5MPB4NBjgeyd35+nuuV+1nr1gPIF2PHGnSpSSt8DGe/30+59CYm5skRHKd7Md+uNgDLz5rAmHnzEOkW7LvwvNfr9TzZkMN3YIHZQ2B5p1QlgPHo6CiNK88g2oaOYI7QcfSd+zCg7Xa7EvZHX5lZdJqIU07K/GkTERhpHC6iSaPRqJK3bqBQpsvY4FvfGUzW6/VMLfAat3NkMFhGWBwGL9lH+mUnkjkHbNB2xsvPYT2VsoaONsjhOyYsDNQYb+bTwNbkgAEX82AADPgBVDFGrFdHdWu1WqVYgAE6ssb3zZyX6QKMNTrdOr50EAzQ0cdmsUkDcQSHPvM8nu82o6MN+lydAwDNWDJ3nh87j9g83sHc2iGwXcQp4PNyntwHyyZttfPKd0s8gDwRVWIMDM7thNjZ9jvcL+MFO9J2Vj3fXieWC/ebz8vyuTddpcP9TbpeClTjRToEAggmDB0RmTeIsR2NRimkCPtoNIrnz5/H/Px89Hq9iLgG45ubmxExEwQMKTnZfHZ+fh7D4TBrdFLCjsXI4l9bW4vl5eUMvcNGTSaT3GTFZh1v5Lm6uspNkYSdT05OMmd6PB7H66+/nsJ/enqaZc0iItbW1vLgBJS3xzAi8rtmoK10rRScjlB6tf6+Bd7sSDmP9nIxjHzv8vIy9vb24u7du/HGG29kvi/sBxdAjNxMDBYL1mFi3osXbWXhjT1uqx0DfscI8zMKxgfuGJjzDkAVsknbmCMzuzzDIN4RGTYsmaGBVd3a2qocOPTkyZPY3d2NyWSSUY+Ia8PQarXS6RoOhwlaqBG+sLCQsunaqi4xSB8s91QjQGESDSLVxhvCUOKMK04OTNT6+nqG/SkN6NzFtbW16HQ6+W47ezjQlkNSM5gTZLtWu059YY0CmDHmNvrIUrN5XXuZ/HjysJlHAybWCUYaWSZyxnzOz88nGEaG6T9j5tJbgGfS4Rg3r3PGwQ4tbSJ0bUfXVRJKgNZozDYGsxbtXAEqiHiwaZXvMXakmhgo0E5Yd/o2Ho9jOBy+0B4Mr/tThrbNihpokztt1ph5JNpitpLLIJQ2+LmMmwG1wXkJfpB/A09/xxFBgAlyZKcN1pTvGaD5Qkd5zTGv/M2RSO8v8eesxZJsod3WYaWDUjLVtBeH3+1nPPgcAMo4lBEBZBzHhcggfWFOeTdjZrDNOmdcDeDdDmTPc8yJrfQPJ4KxdeTMdpdn2VH0c3G2/Syzzzex0MwD7zB5Zvl19MMy4P4hk15HREttM62jbH8d3XOk3OurZPNNUtH+0tl8db14vRSo/ta3vpU5n+fn57G2tha1Wi12dnaSAY6YKf52u53siieyVqtlSTy8RwAaLCEhXys7DCyKeHl5OQGIwRWLkHDq8+fPK+Ep8j5hBc0GISydTifu3LkT6+vrsby8HDs7O/H48eP44osvYjKZxP3796Pb7WYO5N7eXh6m0uv1sn/kR8IkI9gO4TE2KFaHYbzQvFhLMG0jYmbTC4bxpJ9OnzDrUqvVYmNjI9bX1+Ojjz6Kk5OT2NrayoUJuIRpdKQBMMPfea7H1kCHvzuFx96+FULJeNo4eGxtHAGK9I0QMyDV5ZC4bjJg3MuGzYhq+ScY6uPj4zg6OspNeKSYEBlpNpsxGo0SmB0dHWW41s4qfaL6Qq/Xq+RxM2bMJ8AKINRoNOLZs2fJiBLlocY04JZx5RhhG3HLI+PkedrY2Iitra3UB8gpc+R0DrOn1LeGsQfMOr8dJwZn12CPfgMqYeFtrMzI8H4bBNgzLssQ4JSa2hzlDTttmaCqj+XK5APj4bKHZo1YbzbQNpR2Jpw3yth0Op2UV2Se3FVAO32zU0rb+JwcbYAiwNzAz7rHQMIgCtkxs1qyc0Rc+J22sJfFax5dQOSjdJxLXeEcUY8n8hwRlXMAyvWG7FgvMl7WWbzb4057reOsW7BJ5ea7mwC79Tnj6xrK3ihn+WY9m1hAl5WpMrTN6886kGciE2bNb2KCvXF2eXk5ptPrfRfYbMaLZ7fb7UzFo50AWjtOjmiUqSmMf+lkEcWxXreTwncdLfB6hLTgPQaYdiR4n8fUcumxtOzw7NK+O+qDvFhWkE87iCUTjr6xI+mxtIwYFyA/rAvbVGwUZA347Osuj/s37XrpOtULCwsxGo2ygsfu7m4aTwwZm8FsiDEuCA+Ti5AbgCE0VjYISkQ1pxjhdYhqbm4uDXe/349Wq5W5vjBDLCKeR551vV6PXq8Xb731Vqyvr8fl5WU8fPgwfv3rX8eTJ0+i0bg+cRA26/LyMmtL1+v1PIIadosQLM4DCtMhLbM8BlYG2xEvnlLIooT1s3dvFog+8m7nyvJsK/WVlZUst7azs1MJ07Pgm81mAjnvPvazuUrGpfy7Fy6Lmzn2WPl59BlvHHYLmavVaunJ00aeT2oQm9o8FgAWAwMMPuB4OBxGu92OVqsVjx8/Tsft6Ogo85MxfPQZZcQaGo/HcXR0lL/DElmpkW8ccQ3gKZ8HG2MjD2hHtjnG3KAhIio1YZlPgCopAYwZbAfAixCuwXmj0chUEW8k8hpmzogS4GACFGmHgTf3MebeoMn4eoztiBrII68A3/F4nKe3lSFxA5BGY3bAEGOL/qD6DM4V3ykdCYz66enpCweqGOiZLTVYMQvmcPRgMMg9GhgvdCP3whDawcSJ89qKuAZDOG+uaez3I59lHwAWZuHs+JZzUzK3/B2gZsfb4M0OCjqTebVT4nYY+DtagIw5OlfKYAmcrXPor79Lm90WHCz0peeR7ztd0eAHQFWuXYNj5qHU4QZL6GUDZvqNI1qrzdIozK5aBn24kW2M8/GXlpbi9PQ0NjY20p7aDmHjXH2JqBW6h/5ad7E+aE/5P3Nr+fOaMiAt2XUYX+7H5pZrrnQimWMz6ZYH5NRjiJyZ1LI9tH1nrNAttMXgHCfL8olcRVQxFT+XbQOzGUQzp7VaLaP8RLVqtVrqllfXzddLgepHjx7Fd7/73QSLgA2UB0ZieXk5QQTCZ0WCgXUdaoQd0OvwtpWd2TMr7aurqzzFLWKWKrC8vBx3796NlZWVGI1GqViPj4+TqXb+7NraWty/fz/eeuutuLy8jK+++ip+85vfRL/fj263mxuxImbHPzebzdzdT24tuZbs1qcftKtkFhy+jJgtsvJzGDuYRcbfHq6fwRja6zSYnEwmyZwy5mtra3FwcBDb29vRbDZjY2MjgWBENW+RsbZCweO3kSFlgZrCZtC4J6LKOLhOsEEnYIHoAvNgJoW+2FFBsZDfSO57o9HIFCLmySUbGb+rq+tjxdfX16Pdbqc82aFCwUVEVkNh/m00zSpTV/jy8jKOjo5ieXk53nvvvbi4uIjd3d1ot9tx+/bt3LjoSju9Xi+63W60Wq3Y2dmJZ8+epXzxHq8XcnZh3hkLAEzErKazjw8+OTmJlZWVBJedTic6nU5lkxBjaRYUA0JOMf10mhKMakRUdv2jF5ALAxLmE/krWU+zbAYJ5GY77xEmvFa7jqCxPpB3nBVSxWBxkXcbRBu809PTrFVvg8i8ABzpG3PlNVD+bAbUp8oZFBE1ZA1FRG4K91rg8qZdHAHnrVqfILdebzbc/E40kEhRadyd+2p5KB136wjAQMmqeh5oF3LBmBh8GFC7fwb2pZNGX9FBLnnHO9wWdI8jhiXDzv9mCA14nD7EmDhlkHULQLUz5o3qlinPoW0yF8+xk4GcYsv6/X6FUUc/kmZ0eHiY82sG1s92BIS1x+9lakHJQru9jDMyQaTUuoc1R5tLcsrzgHxA+pX4xfNsm1qyycx9mYZkx9EOm/+ZgHMEpJRLj2epH3k378F+I1+WLZOTHmeulZWVdMAgFfz3V1f1euljyp8/f14J3XljCSFsAJon6yYFzclorhVtrwkAanAIS8ZiwMtG2ZiB6XQ6sbGxkUwMAODo6Cg3jrEBknrBr732Wty9ezf7yoEcnU4nWV5YRBQXAkfImEXMZicMtIXeXq8Vrr1jFhAbPX1QBgqtZO0ZYz/HrFHJFMHc8a75+flotVpxfn4eT58+TebajG5EJPNm48x7AKx8H5as1+sl++dIRckIWLlZOVkZAq4josJOY4iRA9pohgpZgTGt1+vJ/rpkGOCTZzD2FxcXsbOzU0m1wPjgoPEMyyoyYJbt9u3bsbS0FE+ePMnnra2tZX7+9773vUxDarVasb29HScnJ/HgwYOYTqexubkZFxcX8fHHH+d8oYQBpQYPsEMl8Pb4eGMmTg2MBZsoT05OKptUOfmUvQ8AOuuD8fg6N5cqLOgM8tRxQlg/LhlnpsZsmtcT4MdA2gbJz/Z80n+cK3SKS36Suw2DDwhkDTO2EZGMtgG1ATLj7PYZgNlBZX177QJu2LTKXJfAHTDXbDYzKsJ70NWkAJkV5fkuYWlSoFyvZQlE2mMHgHHxvHpM0AWWVYfyDa7LeQRQ0cYScFtWnBpxE4vrdroNXPxMFIw543klsPc6s6NgeaE/1jl2SmmH+8i8muxwG9Fbln/3hXXiCKKBpQE69o3qWIypwarf5RMXnV6HXoA4s20yg0x73B8DfdtOz6/17tfNHzbB1agcJWBt+P0GvCb6bgLaHsMykmHgyvPsaHnuvKme+S4/w9FhDAysyzFw1IrxhuxxmVBsmIk6Y62bHJvyct+/iddLgWo2B3Y6nTSC9qpPT0+j1+tVQE5EdddrxMwbLxkFBBkFAkDhHyFgNrXU6/UYDocVIUPoeMZgMMgSW2dnZwmoacPGxkasrq5Gq9VK9o20kaOjozg9Pc38cE6Mo/JCRCRTWavVYmtrK/vDzn7YIR9qYIEzq8HPOBIYbhgAjHzJbBloc9kAObTl95a5cYCRx48fJxig5jSsOKDLSseMA+NBSTQWL2Xl2PBFu7gXj9yhVYwUv5dMRul8AEAMmADY5JiRxlHmUgNOALa0hfmGSa3Vavms6fQ6l7ndbketVss0IOSPaA3G0CkHGEQODCJUShTl7OwsIyYYNarTvPbaa1Gr1eLjjz+On/3sZ7G9vZ3zyxr4OlbGxoHPVlZW4s6dO7G/vx/Hx8fRbrczZzgict8CRovnIA/eLGxWA4M/nU5zIyYl5pA7mHPkZGVlJQaDQYLL0hkvDWnJ/hhMm9nh6GRKM66trSXr4jSIWq2W6402woQR0r6JpXGUZDgcxunpaQVk21G00XP4F8fDTKY3R3GfyQfu9b4V7oXFjJixnqwfzgOwMedv5boyGWAAaoPrsnQls8znduJoE98xsARcAhhYmx4H3o9zfXV1vbEch5k+WY9EVAGQwauZPY8rcxBRTUPgfo+fgR/j7/VmnWlnye/3mJZOlu+nJjwAxlFPpwKa9beDzVrmudhPA3LmjjmzPNN/zxmRyL29vRwTmFfLBnbQMm4Z5GczqyWYNgHF97GXpXPm9eD5KseO59pxKS/a5GcyVtZBNzn+/Gx5Ly+vM8YeHWA9x5hGRB7CdZPDiFxBOKCb6bOdGzsK6CzrO9sTpyC9uqrXS5fUw7BMJpOst4sxwqg6ZGvBLj2oiGp5OBYYk014EPDCvZeXl3liIYqIlAgrEgA1IHA8HifDiiAtLS1VNp/B2sBGsRGx0bgufcZ35ufnY29vLzdV9nq9BIkcHkK/nA9aVsTgHvJqLy4uMnTqEmhW0hHVskUOI+Npo6hYxPZwy4sFx/cJH9NfgGJpbPg+z0RZmkXa2NiIxcXFGA6HuSnPCshtMwuAgkMRWn6s/KxMKZkIE+EQJcaXsXHoDoOHgq3ValkJZjAYJMsHYHf+X7N5XWGGcdzf38/3wwQxJmazUHaws+4zpSBrtVp89tlneUw5UaGPPvootre349mzZ3F8fFwpS2cmzWCuNFg29J1OJ9mKjY2NdCyI4sBm4CwC8AEw0+n1Pgo2FPFsnknZPQ6EoS0YVjsbPrmROfHmZMbZOskVbDDusJeA/Hq9noC6VqulDsMokr+OkxARCUhhge08lmtnMpnkpiyDHfQFMlMCamTfAKtk5fleCbT9O6lMyG5phJFz9A3MF8+wHraMGrw7aminxhvRALJOrymf542CfJ/nQT64PYwPY1QyzPzPOjUYscyU7KHnx5dJHsaI+avVaplmCFBFDgGAZirtnJh9Lln8Ur7RBbzfoXnPRVlRgjbZVljvABZJsyrHAHkr7bTnnfaUUcrJZBL9fr8SNYmY5Ysje7TF6ZNeG+W7b2I+vR4AiKxp9Lj7c9NYe43ZqUUfM0cGwIyD9SffgUyynaINli3kwKQRNtXOtyNo5RrxnDrSHTFLVyv1jMfT8uFIw3g8q8hCeqB1M/NF9P/rrnIOv0nXS4FqmLfnz59HxLXhvn37diwsLGSusVmEcpFYMFH4GBCD44jZASQRUdkkh/FqNq+PUPZOaowz4DpitiBdY9OLm2s0GsXi4mKWOFtZWYm7d+8mIwB7fXh4mDWFKe1nxwKFApCygLpeJIqLHbUOvUREpmTQVhYMi9jAzoCaZ1nZA4oZe48NYAEHBkVB28l/Lpli2m8lSZ9hI9nU6tMWYS/NgpRer4GF32nwQX9v374d7XY7TyWs1+vJHGNoqLyAsrWx5yKUxua8w8PDZMBIU3J7AO2bm5sxmUziyZMncXJyEq1WK5rNZsqMlRIyyvpgvBk/qlvQjul0midt7uzsxMnJSe4FwPlCtshZB7SYnfs61hp2+PLy+nTJ1dXVmEyuq+rAIvq0QaINjAvtb7VauUkJgMWc0hczoLQJNvLg4CAuLi6yOsrGxka+x/LhSJYZI3+OzsDAEikiooUOoNKE5YnNnTgVZqzMqDNXgGfmyHIJ6OGfjSL6Dzn4OoMPcCvZS76HbHkMzHZ7zSPj3Gv2Cz1gxtD6uwRoEdcOB9EMG9CS9XK7iRZa/5VhfgME/keuAWfOmS3HnffRLusp6yq3sbRPyLYJHld58Xq2bo6IdOCsR/13M7bMBZflmt9L2TfQYx3hYLCe/Uy34+LiogIcDYyRI6eTGHA7Osj36QcyW37HY4uuwvHAvpTyHhG5tvweQB8yTB88Rowtsm2yhzFgLJl/voPOLuXZ42zHHabY/XaUgnbYwbZdhuzxnLosLM9zmplTG+kLuMKRWZw+Ry2QU84+wM4RWcZ2l0RUvX59Si1R0k6nU1kDr64Xr5cC1eRPOjzU6XRiZWUlF7on18CGy5+xkKxIbHTtQTHJ0+k0Wq1Wxdj5kBEMswEsm+KazWZlsyLPPzw8jOXl5bh161Y0m81YX1+PiIiDg4PY29uLvb29XGyNRiPzysinjbgG5d1uN42QqxIAvstwFgsPpglWHSfCC9wsUBmOBSh4w4sNiVlSe+IAhYgX2erx+Dr/lT6URfbNVnvu5ubm4u7du3Hr1q3Y39/P3GAWbRmGiqgW5ed5XtSWP8vTG2+8Eevr6/HgwYP48ssvY3V1NTY2NrLNlLgbDAZZczkiYn9/P66uZgd2WOna+QFEGUwbBEdcs2NEb+bn56PdbufmVyvbkimG0WKezf4RneA47eFwGHt7e3FxcZHVG0oQwWdmeX1IC98pKxLMz89OJJtOZ3VecY5gzr2GuJcc44iIJ0+e5JhS/ssbM1HEyDqbA3knwIVcYRwZM8p2AgE9XgNmyZeXl9OZso4xc++6vOgu7ue54/E4GRn/DQfC7F25Lsy2W27NPCPvXudeZ3aELi8vK7WlvYmJe5xq4YiQdYgjCu4X7LXBiP9ukIicGXh5g5X1O86B2dYS+DJm5RgauNo2sD5LUIw8ewwNdAwG7FTxN7eFPnlM3D47B24D6wfdbz1mosl9py2np6cVUihilhrncofYYYMt2wePl3WPIwBfl1pU9vMmmXSev4kPl8hl7PxM6zrGyRtZy/HkPsbDNozI4002iTHwGrBT77FFnkswbZtIexl/wLKfyVjZaSrHn8v2jfWIHBgMe56JuqF3rq6ukhxoNBqxurqaANlsPWvPJVPdpjKPn/0zPJe0Pb6Hnnl13Xy9FKi+uLiIdrud3g0G1CWYOAgExjeiuvuYq/QUSR1hYTcajWSJd3d3c9KZaAyiN6kAph2ucb5UCdz5G6z0rVu3skzcaDTKjUaUSUNZw15sb2+nwe12u7NBLcKCZq0xgix0s8hsRLCiinjxVC9+ZgGX7Iu/D7AqgeFkMkmGnbnxO/09wD7Kx+E6ogvT6fWx5uS6l/nrZta8CcJMXalMrZjYGAr7C0P94MGD+Pzzz6PdbidrPJlMotVqJbv8+7//+3mc/N///d9njXWHSplTxs8MLgCyZDxQfuvr6zEajeL4+DiePHlSGXtHYpANZBHGw4Cj3W7nu8jthWUdjUZ5PwrYbZmbm8sNtWdnZzEcDjMVgLkipSEiotVqZQ7w8vJyxSE1KwiYA3jiyJ6cnESn08l63GZEVldXs18wIowD6Sx8H4DqlCnmxRuPDEwYB4w338dZMftlJs8RHgNeLowUuf9Ov7LRQxeUoBW5BrDQb+vCciOT90nwHOQYoOfyfF/HcvI/DgP9L8G1ZZH2MS6MJ0bZVUUYR9aAHXQYUOtaA09AEO0zM8m4lECOcTE7SruZG0ehxuNxsth2uOi/n8fvJStv4M2+C9pcMqqAIfeB7xtI2nmibURQuI+oinPfndbFM9rtdp6HwNw6KuD1YQDNer6pRjcXOrrc/OZ/ttm8yw4L3zEhVjr96AjLAr+7dFvEDNyWc8RYcr/l0HLFuBno0g7e4fQlvusIVmkfSzxDn62/kEU7a14bjCGMt4GtN0p6LCHz0HXeR1FGgUjfs2zybPK1GcuSkOPsDq9z5gWi4x8D1ZaRb9r1UqC60WjEO++8E/Pz89Hv9+P09DQePnyYDCHMNROBUNkj9uJC4a2vr0ev14ulpaU4Pj6Ofr+faQwPHjzIjQ/1ej03SbKgyMG0MUUIbQgiZpvDEKSlpaVotVqxsbERm5ubMR6P4/DwMI6PjzNdBJBydHQUT58+jWfPniXbA7O2sLCQVT9oF7mVCD2HKxDWthds5p/F6lxi/o7w37RpJKJajsmACzDgUCLK3caR58EkY6DYqIbiAQTVarV47bXXYnNzM9n45eXlGI1GcXBwkLnoDmcBbFjQBp58x6yEL8YdwPDw4cM4Pj6O+/fvx+3bt3NjKSXqrq6u4t13343XX3899vb24vPPP89T5uiPmQDGDZABE0K+veuuwyCPRqPKRhwMGmxTozE7ZISIDkCxnPeFhYU8nZBNdCcnJ9HtduPJkyeVMlUeK+RrcXExarVaglwz+2aIABP0lU2GJZsdEdl/DAdpTwD04+PjGA6H0Ww2kx3u9Xpxenqahzv0+/0EQOPxdVUdQJzXGYofR5mNwRGzQ50AdTDoyJQZdBtIKvIQgeD5NloYeh8ItLS0FJPJJFPMBoNBAh7u4bIBZs4B1MitnTGMqo0Z84MuK5lk2F7fj071ujZjzByWuay0mVJ2BnKsOaKSXAZCrA/rVS6zw/Sd5zvcTz/sIPF+s+12Chhb/u5TBQ3k7ZzguHpjlwGZ+817TIhYJyAbzL+BmR3jm8CXnZ5yU5nnkn6xHqmcgzwwloBfg1r3HX1M+pbTleibSYKbmHaeSV/tDLpiRMkAW9bLaAFyhzxwuZKW669zlePodDdHDEpiyE6aoyg8E0KlbLvn3HPssTKBVeaXl58BrnFcjYMgpczYM8foVUchIiI3y6NDfIYH42gHxLnetBH7Szv4Du/AaYTciIjETL1eLw4ODuLV9eL1UqC6Xr/Or7l7924sLCzE4eFhRUFi3CJmk1qWp0GRXl5eH5t8+/btPEocVohnPHv2LHZ2dpIt7Ha7aezM1vnEOoSVXGCEDTCN9z8/Px+rq6vR7XZzU9jR0VFsb2/nfc1mMz2zvb29+Oqrr7KU2NraWqWcIILLJkpC2DZEsJf0P6Ka72WD4wXtBWlFZPYgIhJUsUDMHpWMGQDLm8CosQ3bD1Bzeo3BOv2iMgjgkxPubFz4vhlrwHvJoEfMyt3RNi92KxGOuKfEHf2/f/9+vPPOO/H222/H/Px8fPbZZ/HFF19ErXZdHhGgxT2MJYAWkLW0tJSGu9lsZiWC4+PjVI5mGcgJnkwmyTAD7IjweD34hMBOpxNLS0vJkrIODg4OEgwa3DB/zDPsBYwP4NThRGSatbi0tJQMB4aX+b66usqUGeZkMpnE8fFx7q/whpizs7Not9txcnISS0tLMRqNUkfwbOp/oy84SKBWq0W/349msxn7+/spDwAc2mwdUhp+1omPK4+ITMfBqLIHxGFk9BWOAnOLY1PmT9qxsUPFGmGsAb3IMW0gPYnxRKcZoJs1ZW0YxHh9fx1LapDBmJhRhJSwngI0leweespAxGDYTLXXLv1g7Vv+aYuBSslkIyslQQJAM8lAWz1GgAbPTSlHgHXbKwOfsq20h376/dYnfoZZZ55FBMgOAGmFyI5P/nTtdhwyOzD0y6Dd37NMMR7MmZ/J8zx3dtqYC0fOPP6WN+aCHGMizciDnYGFhYXKeRPlnJURXMYYHef+lGDac8O8lHa2XDN27Gxr+Rtywb2WLTvJtIvvMI52hrAlvt9yyRyWRJVPbUYnu68R1RQ5R/d5Pu1bWFjIg/LYq4RObLVa0e124+jo6LdW//C4fhOvlz5RsdfrpfJfW1uL3d3d9GjIAWVnqBccChpPbWFhIVZXVxNIr66uVrz/wWCQjBbl7ljw9swBcktLS8l2cUoTz3ZOLznL7XY7gfHx8XHs7+9nhYqDg4Nk2bvdbvaBnwEsMC94crCWKEunaEREhS2z0S+Vsg0YTLdDSFyl8TEbbabM36/X65Vc6ojqaUuUGOOZLDjm3Mz21dVV7O7uViIJEZF5XbTPTB0KsmTIfNko06+rq6tYWlpKxh/5MtifTCaxuroaH3zwQbz//vvx9ttvR7fbjb/+67+Ojz76KKbTaZZ8dG6ax4d2dzqdWF1dzcgF+WwoMEcFqDJTq13nvTEHMC6wZI6eMC58d3V1NabTaRwdHcXu7m7+HRDPHCHvo9Eo28+cwqCTXkMNYjYEIq8nJyeZ5mTQTY4eTh9OD84CQJO0muFwmEaXuR+NRmk8cK6azWYcHh5WQvBzc3N5sAx9vbi4iNFolFEUGzgzdMgIBoQ5M4Nm5tcgFzl2DjxzWKvVElADhMs1bEPFWi0/c8TKa8kGGjnGqUKv0T4AY7mGLKvIvN9LO5g79Cnz68ohgIQyJ5eLNrkCBU6U1yhyiUNmXWCg53QHAx6e50N4DFgdoTCLzHdgYp3mgxPJ/ciP383PN5UILdnKm1IleL774Oca0Fl+kCnk12CLueD9BsjYTkfZeC7jYdKESBXtvYl9tbwSiaNNnI/glIbSgSkv2m5bbgIAG1zmJCOHsPjoCM8J/fB9PH95eTmdD/SRN99xWe5L5tZ6o7StrvPM390+2k/f+cxOj8fdBBv21PoOuUTO0MPT6TRJRtrFd5m7ku1mDLCXl5eXudGYE27BcK1WK9bX1+Pg4CAePXqUOnpraysWFxfj2bNnsbKy8iqn+rdcLw2qMdStVis3+GGkUCCwiGV+EwvLeXnkyu7t7cX+/n7m7x4eHib4RtkCGlCYCBtGw8eCk9MZMQOGgOHV1dVYW1vLsmlXV1eZ4sA7h8NhNBqzo4pR2BxPDfMIy4cSKvtdq13nl15cXCRo91HWNt7leHGV3zVjZGWDQQagoKhL44ExsAHEWPJ9DHsJmqwgms1mBRzD+rHgbHz5u8FkyRBj+BlPOwiUZJufn4/nz5/HcDiM1dXVfE+tVov19fX4/ve/H9///vczTeiHP/xh/Kf/9J/i4uIi7t27l5EO2kj1FtoHCF1eXo7Dw8Pc5GjjwjtpH2MFy8W4ms1BziKq1SwYw/H4unYwawLWlndhiB3mKyu9YJzM6AGwnT9KRKfMGz4+Po7RaBTj8TgNBtVuer1eRkIwrDiryIXZUjb0EOkwo4mMHR0dpYGzgwAYhoU028jJjjbuyCiOL7mpNrxmutiAS375eDyubCw1O0mbzV4yh86FtrHkZ0AFv+NcMXb0ExnCeSL6Yzaefvo5XM79NaPGXDHvrDtAMnrHgNprkv5YZg02zGgSJTGb7rYDBgw4/Q4DlDIP2g7FTeSDnRq/n/6xSbbMXff44Izxd0daGVuzztzH3DmlDfBTkgl2zmirnQ0Y4NIGWK83Go3Y2tqKhw8f5vPt2PFds7sGsJ5Ttx8Wmfchg+gZExwlcLMzZwaVd5aygOza4StlzM8vx8sOHesJ55z5KqOklrnSqaS9jE05B5778hmWPXSYZQwi0MSYo5VEapkTbI2jMN58yJxjw3kPWMZpObTHbZ2bm4vNzc0X7B/4jagepGKtVos7d+5Eq9WKzz77LO3g5uZmPHz48IU5enW9JKgmt7hWq1UOqyBnDYBrwTQg5FCLRqORLDEbnsjRbjab0e/3Y3t7Ow0ziw+wDMuGMF5eXubGD0ATeb/NZjM2NzdTUBwSZlMlQH80GsXR0VHle1Z61OCm/yghFrMNo8ONR0dHyazevn07JpNJHB4eJiuIk8DFwmEsS08XQA3AwpFwfWorFSvlEuDSTnIFDfRcRxSjQdjK4Unmx7uLv85BsAHnmWbAI2YHG9Ce5eXluHPnTmxtbcX5+fVx16RWIAtLS0vx+7//+/Fv/s2/iVu3bsXR0VH89//+3+NHP/pR1Gq12NjYqJx2CZBEGTMOyA9H8a6vr8dkMklmmLHBcNL/xcXFSg4asukawhHVDab881paW1vLuR8MBhWGm3UF0wkou7i4SIfRckveP44CckKIFcaav5GGwPxgUCnv2O/3Y29vL4ExzmZENXfSjgXGD+MByEf5IyvOP+YqAZPBFmOGHNNHHCPGzOsQI4xRwqmwHBogGxzRBnQc7I6dnBLc4ZywVszeleAOPUJY1rJC30t2Cjlm7ZQsrMFtycR6cznGHr1iPcD7zMQbBDrMzTjwHdpi1p130EbWiMEhfWLMaJ/nhnbzM+Nt8GZm0Q6OZcDf9Tv4m/theXA6ROk8ePxtv/x3/qH7ShDnvFuew2eDwSBtDm1iPpAr7IaBo9vktcrvyK3lyGyr+26ATP9Lxt3zbEbYGMGXn+GotFMaTBC4CoblxtE3dIQJCv4vgb7BrWXPVYIYI4+7HQe32WPP5476ug60cRJOs51rmHCXC+Q5zDXjYnKKkr3GCvv7+2mrcJiIZuL89nq9mE6nGdX97LPPkt2m5Oxvu4xZvmnXS4FqjCNhC+d6YZgIOQFaIiIZNlhfDoGgXB3AhKoAbBRE0ZQn4JkRPD09zRJdlPfDYB4eHmaeMwC22+1mmIlUB05wQ+hpP/m0LDgbwkajkYyZFxT5uBhwe9Aw7nt7e9keK3cvIit03seC93NZTIAVAEtEdaMEF/mjGEOUhvsWEelwGNSbaQFYYuCdegLzi7fsPjikGVFlS6zUUKxra2sJqKk/fffu3fj000/j17/+dbTb7VhfX4979+7F97///Zifn49PPvkk/tt/+2/xs5/9LNbW1uLevXspMzhnGKelpaXM+6VShsNu4/H4hVP33D5y1AzWMc5EAwyMyrmxsRiPrzfxlTmrZmBop3PCDbLdNtYCgJla0Kw31jElAQG6bGDz8eIcj25dYCCzvLwcS0tLybLDFgOubbzKsDDjg85gnACZdgbRA5Yfh9+Hw2HKZJlWUYJNh2aRYQwM95vVpa+MKc92KhWGnvZhHG1gSza5BDi+Bz1AnwExzvXnM7+nHDePg4EjOsFzgWFmXmijHf9y/dq5sYPMGHicMei0jbaW4MXvsp4CiDDOliUcGdLvvKka0HITe2mnBL1L+wwmve6xUQ7Bey6QB9/vcXB6JAwgYBJbQD9h+c/OzuLk5CQBtefdDpl1rMeo7LMBPJcrRJRhfjsIZl4NRml7KYNm+umX7Y6ZaK9HLt7nKAH2z8C0jJp5PjyfJShmcy7v9PxA2HEwmnUAbbOMmBww8+17GWvrFmSUz9BHzWYz0wlpK3tbJpPrlD4q9ziC7TlzidTLy8vodruxvr4ejx49iul0mgd9Ec1rtVrRbrdje3s7Wq1WjEajCq57dd18vdTo1Gq1PMZ7OBxmioQ3/xkIIuA2rvPz81kybDAYZF4mgLPZbOYmIow4oLrRaCSDygaik5OTWFtbi3a7HRsbG5lk/+zZs4iYGfFutxsrKyvR6/Xi4uIiWfP9/f3M++Q9LHwMixkFe3UoVJeIA5y4Fiz5rOPxOAEH42klYCVmpYAQoxzLPDyOROc7DrFbcaDo8EB5jxk/+sS4Y6RY4DDyETMGy2kOjJm96YhZnrWVCxfsQsTs0I56vR6tViveeOONuHfvXvR6vWi327G8vBw7OzvxV3/1V/HGG29kn1dXV6PRaMTjx4/jRz/6Ufzyl7+sbCg1803FCoyvD6RBQdMncprJZTb7VhpiDBTyhLxgMBlHgxt/xwqZfxhqM8/uD2DDgHo8vj75kNw3ThJEqVpROx+cecUxwxEEdNfr9XyXmehWqxWtVivOzs6yhGJElfXiKo2OmRvk0OxMyfwZSCGT/I3KJG43awEwBWgpQ89eO6wrOzIRs70HJUtKe+yo2PCz5jCmZSoAY4Qs2NGgXWaFCfWaGXM+LODMzL71A/fyu0ENF0DJn5kB9ZyWToCf7w2PJXttvTKdTlPGTHCUDCD6lDllTEo2lT7wTOeNe43RfoMy1oCdA4+hZdPrmbm0rDN+BjnWDSWwA1Q5QkWbnVLAmAP6SueL93ojsdtH/3h36fSaqfcz6avTivz5TWNoeUAHO0XQLKwjMtxnwO93Wv/ybG+gY30TKSnZU+YXR8iMN7YAmUeP+ndkj3ehY0oHs9xk7TROYw1kopQR+gduANwTmcbJsjxax2B70U2NRiN6vV689tpr8cUXX8Ti4mKMRqM4PT3NaLDT65aXl7Pwg0mP33aVjvE36Xrp6h8R14qSDQWuK2kBt+JpNBpZ0/r8/Dz6/X5+pzwCGEHn2aQ1WMgI/0+n01hfX49ut5ubGTc2NqLf78fq6mqyee+8805cXV1lnrZZRIAmYfBms5khDoBM6W1b4aLUyJlms6a9bZQibD0KxcwPysyKEuNjxt+sDm1xaoeVutkxMw7cFxGV0lgsQINjWDu+P5lMspwP84ZyKT1Y/m4lb0NlVoiLTXCLi4vxO7/zO/HBBx/E6upqHt5ydXUV//AP/xBvv/12DIfD+OSTT+LevXtx69at6PV68fOf/zz29vai2+1m7WU2ZrARjrwxmNi5ubmsa91sNiunQJolNihAYZs9QNk3m83KgSYAPcaI75qxLFlJjAngzmk9KKxyI4+ZOsA4gBmnFZmmYouZQ/qGUSFU6HCuwT/AHeaDNBezeIBYHA36DNiyjGPgHNJFTpB5fmccGDcreZ4PEDWwYIxYZzzP65GxZ0wNPvi7wZDnD51gBtFt9rtK0FiCUrfZhvjs7Kxyyiz/OypiptzPszNgneCIhwEPkT7rDv/s8D/vxCaQl2lghNzjvKPXLX9uN23z3Pvv6BznrqLHWCO0h/0vBmGsMc897/Q+CkcfSobY/TdjaYbc4Ivv21Hgc+SYNV86F+hU5/SzhjyPEbNNa+iVxcXFF5hn+gbwdDtLEGod77nyz4zNTcDZ82mdj07werG8My8eS9sTr8GScceBLh1VngUZyMX4OiXTqWq8y+lT9NeEgx3h8XhcsRPcCwljogoCx5GwiOp6IG0PmUYWbL+xZWysxx7V6/VYX1+Pi4vrg8TW1tYyot9oNDL9dW1tLRYWFuLZs2eZOgsx6JKrr64Xr5dO/yAPiOoXBlf8DxDDEC4uLsbq6mocHx/HF198EU+ePEnGmmoJLCxvmHPIHpBwdXVdv3ZhYSE2NzfTQ5ufn49Wq5VKcn19PQ+3OD09jS+//DJ2dnYyxE8YjXbQdhhsKpPY4zO75hxRQMN0Os1NiKScYEBOTk5iOBxWALUNvJUCi5nvADT4zIbZjFjp0PAsG1qAJPOGsYD9NKjh/YCUEvgBIl1RAKNZGhkMf1lCr2SvlpaWotvtxv379+N73/te3Lt3L0HX3NxcfPjhh3F4eBirq6uxv78f77//fnzve9+LP/qjP0oj+Nprr8Xp6WksLy8nwAR4UZ2CiMZkMsmqIjgApOYsLS29AE7cH+8lMMOKMuMgkYioGKOIKnNjA8F3kRtHaCgVB9ApmUXPjU/npA3tdrtiWJaXl7PPlPI7Pz+P1dXVWF1drTiBJaveaDQyvcQpMe6Pwa4ZdsLaZjB5B+DKMmTG2Kc/2tAyN9ZHjAttM/ArmboyRGwmqnSgrQ8NKg3SDfDMYrktBuYG1hhcg9myjRhLv9N6xJ8ZOKBjDUxYv+hSvkffTZoAiIhmALxpi8E0z6EtJcBG5p1G4L76O9a/vMe6AwfIjj362/smGIeyL3bQWBNuV8kI2zFEDyPXZcqTAWoJ6Omr73cfLXsGSawzdJsZ45tAJM91frttiW0FcgS49DojJYo1wjMtJwbnBst2IHin58LpZ75MPkXMKgERrbB8cWFrsDG24zcx35ZTEx88y06+dTq6GNxCZNPz53Qanm/bSYUNxm4wGMTq6mqO19zcXB4v7nXlyDORSMB0Oe5XV1dZMpfTn+/evRsff/xxgn++Z2bae4bMjNsReXVVr39SnWoWwMHBQfR6vajVZsd3whgjBICzk5OT+PLLL+PBgwepwJl855c6l5NFPz8/O3p2YWEhbt++XWE879y5E+12OwaDQR7Osr6+nuWxOIgEADQajWJ/fz9qtVq2k4UHGIGZ42JBkMvovKdywyELCADPAmfxcl9EVD4jN9qbD8wko1hLQGBjxEK1YYCB4jKjamNKRMGhSgOqElDzbObJVS+4zBKaZQXMrq+vx9HRUTx//jyrqrz11lvxwQcfxL179/IwocFgEP/1v/7X+Lu/+7vY3NyMjY2NuH37dszPz8c777wT9Xo9dnZ2EsguLS1lrjxVHgaDQTSb1+Ub2ZAICIDRZZwxjih6NqYxzkQySiOEXAK0b3JsIqKi+G6qTeqx5n2sJVfcKJlSHBM2KMJqIBM8A7ButhC2uF6vR7/fz5rSvAsQApPCYT/z8/OZRmUgwBph3Oy0mZ22QxIRFSfNYNvAxMA14sWKBgAhj72NqYFiCai5nO7B+rYjXFY+AYR4Xpg/AwqMF9/1RZudVsDzndLhZ7uijZ+HU8N9OCglM46eMDPLmKKT0AW0zRuSDR7tLDKXtNOEAX9jHK0neIbz2WkzbUVflYQE44ZMsx+iVpvllNIe32+nwONn54LPDOCxh2a4y7Xu8UTW7ARFzNhQt8/OITqAfsAcWp6th0tdxnf4R6SD9sKuloCTNvh5Xivug+fda5TPcHKs125aM049KKNETqWyjpybuz6l0m3mO06nuamSk9trZ902wX/z+AGi6afln7HnXgi4Uvd4LwHRecgDdHS73a6UkQUXQbYQbSU11+w3Opr1QKokWOmNN96Izz77LNvtaH4ZoaE6VKvVit923aRPvynXP6mk3uHhYRwcHGSO6+XlZayurkan04m7d+9GrVaLnZ2d+M1vfpM5loQX7t69mwYP0ImQs/EvYlamDIEtN2JxPPgbb7wR8/Pz8fTp0wRUlLDj/qOjo9jZ2Yl+v5+Gfnl5ubIDNiKi3+9nSDtiBigREDZjIWSAcJef4TILgNEow9D2Jg2mDDAQbELyfAa4dkjdws/FwsZw2CNn3Hk3Rqtk5wFc9MtGzkbWisTpAlZgVrKtVitu3bqVXvHCwkKsrKxkkXlqCP/iF7+IP/uzP4uf/vSneYLjZDKJ58+fx9raWjQajfjVr34VP/7xj+Ps7CwODw9jZ2enUuGDkxRv3bpVGa/z8/NMcbAxBDQvLy+nfCDDsK1nZ2fphHnscKrKdBjm3MDCOehmCS07/Ox2m0X0/1zT6TTTdJh7IhWkbzGvZj1Q8KS+REQ6S41GI9mO+fn5PB0U54+2leDOwKCUJ8uNgQ3PKuWxZD5pfxlqvgm4mGnyXBgoeKw9Z3YUvG7LdWYDe9PzzJb7XveFC4eX+UNeYIzsEPI+UghY0x4T99XsHf1Er5hF5Rjwcpw8BgAGz5XBMOPHxT4UZNsyzEX7mVuDkZIJRNdgZ9BtrFM7sIwrP3scTF5EzJhYnkm4nnZERI7PTRvOfBksIwPl+BnAWp+XrDX20HOHvXTKDevZJEvpxHl8Wbusza9zWNE/ZW6323JT+51GwXNoA98p0xRvum5aK3b+HT1wdaqIqGy2xmkroxO0zTrEesl1wl2GEJnh3cgm7fXaB0wzT3yGQ7GyspI6utFoxP7+fh4WRRqtSRCv67W1tRgOh1lNbWVlJUajUY4L47G7uxtLS0vx1VdfpTO2tLSUdqPRaMS9e/diZ2cn5wZZ2t3dvXFuXl3/hJJ6u7u7cXp6mqCHkANpFA8ePMiTEBcXF7OONR5dRCQbHRGV0+tggwC8XuRmiUjY39jYiMXFxdjZ2clT5whjYwiGw2EC6vPz80w5gS2CcSF8UnrwKN1Go5FhHMIiLESz6lYKKBYYPxsxFiwL0B66801JX+CCqXC1D5Qlz2McAZVeeBEzoGDnoDRsk8msRBm/837ABf0tgYBB9k3MA2O9trYW4/F1xYt6vR7dbjfW1tby2Prz8/P4/PPP4y//8i/jF7/4RaysrMTGxkZubgVQfPjhh/Hpp5/G3t5eLCwsxGg0iq2trRgMBnFychKdTicBO+NAu3d3d7M8kEGjc9bpx3Q6raQxYIxcMs9sWUS1uocBNXJipwaHys4SMuP8aUK+OKeeI9hpWA36xHxiZGA8HNrm+6enp7n58PDwMBmgpaWlWF1dTQYf+XAFmPF4nKck3pQXiyEDyDOuNizMDX9jvSBfXluAawMls5q+vP5K1trAwM6kgTnfc6620wkMlm66vL6sKwxa6B+pOYAks6WTyXUaTAneaC+yy7MBroyb7/F8cI/ZTe4vo1XIpkEB32PM7PC7nzDGjgbyfjvzjuoAJsoNUrQDp5ETCgEAZuLNnhpAuZ/IXkkq8Df3DycQWbS8um3YIubO8wz4YtwNtnmvnwtDSXSVddloNGI0GlX0Cmyl587rpIxC0ifupb3IDnPJ2kVPlU4t7zeTTN/43E6gc4wBrNgIl1ctdQAXY4Z+4zu2P/Tdck3/yvaa6OI7yHPp7HutOB3LkQOz5RA1jDfvBChTGpgUEPAGYBj9A6ZirtiYDWnCHraLi4sYDAa5Z6jf71f2qg2Hw9TjbICu1Wq5t2l3dzcWFxdjMBjkZ6VeLS8TGd/E66VzqieTSVYVWFpaquwo3dnZieFwGCcnJxWmmdQNG17y3Bxah5mMiEoIguR7BJbSauPxOB4+fBhHR0cpmDDHw+Ewlef8/HymNTjsHTFTfFaA9BUhwrC5LnVEdSe9wyTuJ6yl89wMpvnHd+z5AvC9aYKQYxmqwkO20SsVqHPdGo1GLhbGhYVv5UZ/SiXDZbbEgIj7zFRZka6srMTJyUn8wz/8Q1xeXsbW1laWz/ud3/mdGI/H8eGHH8ZHH30UP/7xj6PT6SQIX19fj7fffjs+/PDDGI/H8emnn8bp6Wmsr6/HkydPIiIq9dCRQ069pC3D4TB2d3dTlkmb4LIRZS54thl4zz8sQOmYMe/IjcfCc2UgX4aEbfycDsLfGHPkhL0MGFYz6LynXq8n44xDi7NMuJC1g4LmGY3G7Eh3Gz3GjXZhoCnZ5GO/DXJvYvDKv5thNhNrVonLY1c6vL4MOLkHY0/fnBNvkMF8ls6ynxURFQfVaR8G1DgdpLs5BxOjTh/QbWaFPeY2fgaX3HtTe5F1R6EYa55Zsr03gVD3vXR+iPR5DVjX2KFqNpuZ6lDqD68d1onTswAhHmM7Uqwxs7pmChl3kxXuB/eXDpSdcL4PCCq/X5Icdl4YYzOQnn/IHNjLwWBQAae80+QH7zTRwVgbHNJnM+98hkNpME17bQt4l22HwbDnk2d6zVvncS+RAsbVe54g2/we73coHQpHObzHwClOvN+sOONiUMxaIXrgyEFEtaIVgLZWq1VOgFxfX4+vvvoqIq6jH+iAs7OzJHrsXDAGbBQnHQjQjY2AmT89Pc3nRUSWLSbVllQ+24VutxuPHz/OcSXNtdPpxGRyXTDi1fXi9VKgGm8QkAELy8LH6FLihUUKowPQcChzdXU11tfXo16vZ0gdJtkKnQWCge/3+7G7u5tn0vd6vWQN+/1+nrzW7XZzkXnBIUgocbOL9mhtUM3slqkTAAnniTJmpWfLv1qtlrlJgBXnflG6z2F/K1r3xawLY24HAebaAMAbVkrvsqynjOKk75YJLodFbdTLMBz5vgcHB7GyspI5vxsbG3H37t340Y9+FF9++WX8/d//fezv78fa2lpW8uj1erG5uRkPHjzIFCTGbjQaxfHxcbTb7TTUJRiIuK7BjZwS3SBXHoVK3jzjg8MzmUy+9sAPPisZMhsuxs/Knvk2QLG8AoIBolb6pF+VIA0D7CO3kTv+RnWcWq2WzOjx8XEqZx8+g/xadsg5dHiVKBShd4MdjAtrhTbZ4NlhLdku5Mu5zmbISibL0RYzbqU+4G92bmDfDTK5vLHHsm+Q77VTRimQOVdS8fyWmxD5mfFnHhkf7jMTa+fdbCly476il8xOIrOAeT/XTlM53owNnzviQn/dN+5jrgzoqVbhKItlvFzjbke5Hu102BnxxXgY4APkPKbos7L/tN3rl7+b0KC/dpjMegLwcOxYgyZcmFuc2lIOnctesoaW5xLIIlvuA3rQdrjU+/TTzrEdUe61HjFgxWmIiEp0xqU7iSBCnPG55591UV5merlKLOL3OOJGX0r9gm4B8PrZ3mvgNrBeTZj50C2eC3FBusXt27fj0aNHlTEnzZXvw1ADqFdWVqLT6SQeu7q6io2NjSyRSrSYv+O8vv322/Hw4cN4+vRpfg+iDYegxBuvrtn10nWqfbQ3SmB+fj6Gw2Fsb2/HYDBIr2c6nW3mIVwE0EBgbt26FSsrK3F0dBSXl5d5MAxAHE+sVrs+grher8fBwUEC6vn5+VhcXIzhcFgxNL1er5JW4dJQZsnMXtEXhzxtnGiXlTILE4G0AcNIwNzjcXK8dLvdjkajEc+fP0/2MyIqpd5slObm5qLdbucmPMr32ZDZGKI8+R/DYiaUftmrNxNoQ0PUgLHifQb7jC/foa/cRzoQ/Xj48GFcXFzE1tZW3LlzJwaDQRwdHcXTp0/j+Pg4vvvd78bFxUXs7u7G1tZWXFxcxE9+8pM4PT2NXq8XEde58JyASJt9AIidIBy3Wq2WbbMxQDlxaASKj36hmO2keC3wj3sdGjfrZUaA+fbfzSx6/giJ0hdy6/g7sk25PJfoAjxjrCiDh8GBGWw0GnF8fBzb29sV5qzT6aQjjTwaRNBmQAjyBsD2uikVs5kqs5iWMzO/zuV0hMjfxckudVjJSvEes7d81wwif0MOXGrRwCgiUrZ8nw1zKVNmTZEdr2O+z7oFaJpx9Bh4PdoRN+vIur6JieY+5Jb1430FOHs3OZXIN31jPssycwYZJQgh9F3KA2MZEclk85kP2/K6dhvNYltHfl0kg3cyfjeNU7kOPO44nf67Iy92iAA/yCc5/LQRBxX9w8YxOxcR8UKtfGSSuTRje3U1O8AK3cXYwAjbcbADaDDrNpS6gc8tx/TFshExO8sCXcw70FElScUz7Iz4so3yPCCDtJOKR+hE7yfwPDnyiGNkG2inAplpt9sJZp0Gwnifnp5mJTQz+KTRPXr0KKtZLS4uxvHxcQwGg2zP5eVlbGxsZFT++fPnCcgZjzL/n7k/Pj5OMqrT6cQXX3wR4/E4Op1O2gzIpOl0mnb4t12lw/pNul66+gdKlTyu8fj6GO4HDx7E3t5egt+IGdvpAZ5OpxliZiHv7e3FkydP8hATM6soFtjNo6OjDImgDHy4RaPRSOBmoOlFiKEvjY6ZbIynF5EBdGlkS3ajLNO2tLSUIDriWilScL3f78d4PE5lAvB2uTZyN32YAZ/5gIWIqDDSNvb8M7jGiJeMPGymw3/O6TIYQZHRVnJ4zZDSdzO4R0dH0W6341vf+lYsLS3FG2+8EWdnZ/H8+fN49uxZ/MEf/EH0er3467/+61SGh4eHsb29nUwfpyG6NjjeNSktw+EwC9fjbZv9MqtuQ0b/PLYRkfsImHf/jf4BlgyizUTwXaIRKHj+hpwyP7DZjUYjc+fM8nIh21dXV9lGmAv6RPoF40GJpGbzunoPz5hOp+m0ra6u5r1meAB4jFVZ0xWZNbCi7waNZqpLcOLIjhlsOypet6WT5M+9rt0GM8C8hzbTH+aDZ7LWDcam0+sd/IuLixmSddnP8XicUQkAI3LgaIjXiYED65K2uoSXAYzXbJmqYEDNmuf3Up7QFawP5hBZtJMO6OZZdtRNDDCWzAOOAt9FbzAunIlASJ1xJFoSEZVxoSqCqxExDiZSABd2Wu3ElQ4VY+z5tqwbdJVsufUJ72LebHeYO9Yg7fGeCj5DVkoWnTm13Ht9lPPMz2aGuZy2xP2ljvTatt6y84acOz9+YWEhT141RnDKGm1jPWJjSvtjGTTopVwdzyrtOuPNz61WK3Uc68P6ibZbTtHdzBM6oNG4zo2OqJbAxM7zHsaMqh3ILP3udrtxfHwcnU6nUpI0YqaDANpHR0exu7sbzWYzDg4OYmFhIXq9Xuzu7uZ6mk6n0ev1suxvvV6PjY2NOD09jb29vRiPrzdKDgaDrDqyuLgYKysrsbCwkIUFXl03Xy+d/mHDcn5+nmXsjo6OYnFxMVqtVio05+hSQYFTDUejUfT7/WS/2GB2cnIS7XY7ut1uKsPp9HoX+tHRUeaRnZycxMLCQh4CQ5UGQoL2Rr2pkAVVsrtmWVFyrrjBTt/SQbDXbAaOjZMRkQJI6RvX367X6+kRwnLOzc3Fa6+9FkdHR3F4eJinArLQYSFL4BtRPVzFxoDfWai8H8fHbMRNTI6VIgayXp/luPkoeRvhiOqJdIzb3t5enJ+fx927d6PT6cS9e/diNBrFz372s2g2m7G5uRlLS0vx5MmTGAwGWSXk6OgoywfxfsYNhUo7qLvcaDSyogtMGXnptVqtUjrOALpklmk741bmEzJ+Bp0obS7aynjAZvBexojTu1ZXVyvMEaFR5HpxcTH7RP5co9HItCLaST+Oj49jbW0tczEvLi6SiaBGOz9HRG4UQkEzVjgnZRURZBJD5yiInTbLpo077fd4AUocNvb8cJ+ZspvYawNryzbfc/sMGgwMkDOXmTPDZ1DOuLK2LCM29FQGop0ly1rKFPf5MJOSZQfU0paSWTNrbeBbsrJe+34PYIO5BEww7vQHnQzxYWYdPevnmvErgSXz7jrQpcMD8CnBIZeZRc89l2WE55iQcOSJebdcWrZKR9HgkH6X+gagjB2y/Ht+sUX+5++7T+hsg07rZ4CuS8g5Sslc8B4TBuX8sfYdlbET4r1UYITSpmLfDAK9MZzxZ53YsfdYlalfto0RUYm6gzFI5cAOM2bIGN9nfSP7OH+en/n5+djc3IyDg4M4Pj5O2zQej5MYPDw8zHs4pG40GqXDvby8nPtaDg4O4vLyMpaXl6PT6cRgMKhE86lwRQRzfX09gXLE9V60VqsVCwsLsbOzE1dXV3lw3t7eXhIAbHgF483Pz2e5vrfffjuOj4/j4OCgko7y6ppdLw2qWfhnZ2cZNri4uIher5eGHDYZsIVihen25jgWUcRs8ZM6QjI9G5zm5+dzR+xoNIrHjx/H5eVlHlGOofMmCoePImbVM2zEzFgBdGg3CwkFYCbOuaFWemwa4X3uW7kz38oOFuby8jK2t7djYWEh7t69GxFRMZ4O95a5W7zHyp+2klKDUrVhsbdN6JBwIYCTe8xsk8rDO620XBO32WzG3bt34/j4OH7605/G3NxcfOtb34r33nsvJpNJ/PCHP4xHjx5FrVaLtbW1mEwm8eWXX2aZQyIiERG9Xi+jEW67vX4bcUBNvV7PHHxAkI0cDDusO+yt8x5L5V0y1TAJZmCZBz/HqTGlo8PzYam52DCCw+lcZU7mWlhYiNXV1ajX6zEajVK+NzY24vj4OFmg0WiU7ez1ejEej+Pg4OCFVITDw8OKI83cO62kZJgjZgbNRtdyi/yXANmX/26QbOfVn3lObnpO6ejcxN5iYN1O33MTeGJdG+ATXTDzfHFxEa1Wq6KDzNBah9iZZ42Xa7ocD8aJOUJ3AoBLptZsIu8zc++2852bgBS6vSz/iGyajbczZbbRQNpzRTibmvOAGDPwfoaBF/rVG90Bg6xFM8alLTCYtOxadpzaYWe3BNVc1tuObtoh5HPeYZaaObejjd1irSB7NzllNzmVXp+uEV+Caca3/Mw5947imCwwMPW80f7SiYMs8/4J2sr/jkJZ9zNepfNlRh+wXjrrl5eXlbVs59TE1erqarK89Xo9ScXJ5DpN4vbt27G6upqg2ex0q9WKyeR6TxHjsbKyEs+fP4/BYJAY6OrqKl577bUsfUffFhcX4+nTp9FsVk/uJdIwHo/j7t27FQeZPu7v71dOqYYYmUwmcfv27Xj8+HGmUbXb7cRbS0tLmZ+9vb2d+4q+7ip18Dfpeun0j+l0moAZBrbX66XXd3h4mB4TJxqywB3WOTs7y3J3bEIjvIDRZyMbAIpNCs+ePYsnT56kp7W1tVUJK0VE5ouWRtYLowTaeJK01fmOGEBvOCkNAO+0QXD5nJIpN1BYWFiIjY2NqNfr8fz58wr76jrBNggRUVFIzE+Zv4qSd11OgwmeXRoOV7gwgOV355SaSbOiJQ2m2+3G3NxcfPTRRzGZTLJe9JMnT2J3dzfzw+bn5/MYe94BYGecSIu5CegCKFBWDmlyDzn/PAt5pGII4+zwILLA2DsM6VCcnTiAA8DczCgbUWE3DJScLuETMGGgeT+y7Co8rC9C4LQPBToeXx9WxCbNdrtdOTkR486c8rudN+SXOYJtwjmgDRHVcnVf5/yVTpEjMGZSS+eY8Sw3jd3EzhoAIMd2KHmu5Zv2lyC2ZBm5h42f7h+OJU5PRCQRgfxYP/AzRgv5LB2JEmCX7acNfofzme0IRlQrmJTglL74Pvpdsnz01d9hDHmHN0TawTFgox1O47AuNnvNGLl/9ImUKlg3+kPb6Q/v8qY869gSOJdOkL8HGLO8+HKKicEafeR356wbYCIbpYNUbqClTzjDpcPjfpRpUsxP2T8ukwkG9XbKPGfu401Oq8fV42eW22vSqUXoDaJjtslc6ETrpYio6K5yTBgXnmvwPRqNkqHGLp2fnyexeHh4mKD71q1bsb+/n2casF+n1+vl+QxU5yAF7+rqKlMvlpeX4+zsLLrdbmxvbye+WllZieFwmMeONxqNPA26Xq/HV199FRsbGwm8sf8QIqSMoMO/+OKLWFtbSz0JOWWb1e/3Y3V1tUKevbqq10sf/gK4OD4+zgobBibj8Th6vV7cunUrIq5Z69FolArL3jTCurS0lKf+2NCurKzE2tpaRFynUGD8h8NhbG1txdzcXHQ6nfR88TBRzp54FqCVOwtkZWUlPUEAA4qIPhOiRcB4hlmFer2e9SIBClYEVjg2zLCpPG9ra6sSYrO3Tl9Ko4pSsaEqw4quW3qTsuJ5sFuw82YMUdQ3gXCH7W0YUQBffvllnJ2dxa1bt7K++M7OTtTrs5yu58+fx/Lycty6dSsuLi7i+Pg4wQTtZF4MuphfhzbNcpZsM2kPGC0ciJt2OBOZYaxIa4qoHiXvXFCPlQ2XwRZhOmQXp4Bxp90GxKwFHICIa+VYq9Uqx6KjuIkG8W7SNejr1dVVAmrWg42uZQt23WOLzGEIHYVBznEAPEesK76L82T9wGVgzjMs72agSvDu+b0p3aCUX7cfHWJg5LWLTC0sLKThioiKDDWbzcoekKOjo3SWbnJG0QEGpAYgAAjLngGJ2XT666vUITyrZOj9Pc8Ba4jv4KTSVhMYyByyY1BOHy0zZVSC9Q6ZQRusZ5AvR1f+f+z9SYxkWZamif1PBhVRGVV0VjNTM/Nw8znCMyKjMgpZmRnICQTJXPSqekd0EwRqQy4IcMEmQYAASRC9ItArAgVUA90AwSqimEDVorJYmaycImN0D/fwyeZRTU1nURlVdBIRLsS/K/+7Jp4Jy0Z1Nej+AIOpisp77w7nnvOf/5x7rr/P9Tafe38dmMUMrLeXz+O/+1p3R4t7HJz732mLpxe4gxEzfQ4k/W/j8Ths1uc9MPNO2viaRgd6bj5tQSZdbhxw83dskvcvZsZdN3A5a+3jOYvZ976ylpjrWHawDz6eHpnxNc8zSXfBKUDmHD9AtHmkF11FCgZyNj8/H/RyLpcLqaz+PPQ9ucleEccj44whbYYEob1e4vXo6EilUkndbjfsyer3+1pYWEiRIhQ2WFhYCFXSSNsEvLNhkVTVYrGowWAQzi5IkmkpwK+6ZjlNX6frlat/UPvw/Pw8hJ8x1IVCQfV6XePxWN1uV61WS0mShE1O8/PzQXDId8Xwk/MKi1Mul0M+aafTCYze2dmZisViqB4hTfPwMGrOVMYelecJSRNhHQwGWl5eDovFFybhHEIkfMaFwPpiRNl6zWNpms/LJgYPe6IQ8HgxSJ4Ty/s8lIahiQ+hkKYKFGUImESJYQyZS0/lcAbYlZQzsPydPHGY2svLyxRg63Q62tra0snJSYgq7OzshPBVLpdTq9XSyclJYLaRMfrs4x4zJhhFNsDCxKJQs9lsSDHyShuDwUC9Xi+cNlWtVlPHlzOOGBUH5oyTt8fZXHemuId347hlMpkQ7nNwI02rpADKyXcG1PMOmHjWDYwVaVG0sd1uhzxpnNFerxcO0mFuYfw88oFxAtDFjrEbO+TA54d15dEXByY4KZ4DiYy6fNMWd1IAR8hJzPryfwzkvX/MoTvJzko5iHO9cHExOUnWqxh4RY7Ly2mFEC+ZiJOJ4+PMKevMxzt2HLw/tD0Gi8gh65B5igEZcgoxEkd+fEx8zhx4xewe4+RpCw4w3dDHoXfuZ004YHZAy7N8ftHbyIvrOo8qeTTFx9XBb/xOH1PmxMGi3+v/uxPkz+JvACqe68/3vhIF8UgYtgRA7ey9OygefUIWXN49EuZ6nfXjv3taozsBcb/cyXbm2eWFyx2S2Jnz77NWHFg7wGcsXff6eEAKeAUvd3SGw2HqFFru8SPDAfeVSkWtVivYfNKMhsOharVa2C9WrVYD8Pb9MdgnyEjHKq4TSNOj5B3RFgBwpVLR3NycWq2WKpVKYK9JA8GOwKJz0iLrvd/vh2dyhDmR4kKhoPX1dTWbTfV6Pe3t7UlSwHXfXLOvVwLVMHZ4N4BcvDZyhtrttvb29pQkia5cuRKYYNI8PBlfUvCSyAn1zQnOvpGgD5jwTSzxoo6VBELvDB9tv3Lliur1etgc5jlU9BnD76VkvF6msyv0E3aRdhGmd0+d/CnSRADnDuI93cNBszOhvijjEJcrK8ZjPJ6EbMkxprwP98eHi3i+NwYQRwpAzoE7DlqlKaBwMMT4EFmANQGAOaNDH6SpwXEwdXl5qXK5rOvXr6tQKITcYBQecsZYMZco0UwmExiIOFzp4XdkclZaioMcB6iMJ31HuaEgYZlYB5IC24CTxSZeSaESAuvRmUJkr1arBeaC58bj3263A+PhzoMbIFec/i5Au7M57jxwAawcYNDWWQY0Hi83eIy/y7YbNAcu/h1ntp25c6Maf+ZgxiNcjAv9npubC04YG0jZzAloBFhDINBOKv7AbnE58HQHgjXI+MRsqYNdzxd2oIruog9+dkAMPj0q5GPnYI/xdp3E+gDg0g8HjA6wkUt33D2Nwc9BgFChbQ5++Z/1TalJD9/DztMP/o9lDd3icuhyEI854+BOlI8N76A9sQPnF7rV2+zl6eJ1gx2GGBmNRmGzmn/Px0yapp+54yEpzAeOXGxLYsBPf9wJATjGaT7u5M4aex/LOELjY+bRQV/zyK73k3mhv56a5G3wI8EhKkgBxY77fqijo6PQVycZkiRRp9PRwsJCSJfI5/OpkyHPzs60sLAQqnZVKpXACBO9ZEN4sVjU6uqqkiQJzPbS0pKOj48D+GYcCoVCANcXF5PyxJVKRXt7e2ED4mAwCFiLjY5uP7CX169fV7lc1tbWVqj+kclkAgH1d+VUf52vV96oOD8/r2q1qnK5rOFwGIwDIQR27tbrdS0tLaler4f8ThbZyclJYOhQkihugCKMGUza4uJiMGC+2GiXe8OAHFhTPLZKpaJaraajoyOdnJyoVCppeXk5sEbcK01Lk5HSAgDFeLLI2J3rRp/x4XmAUw+ZwWCQR47ydOPIOxy00F8AuRtEv1CKKNRYQQGsAAb0mftiJsYZOGdjGQs2EsHakz+GQcDQYuzcUMBmkp7CQkeBOsjG6XFDMTc3p2vXrkmaAMijo6PgqeOYSQpMr4Ng+u4ggHZ4f2k/93tIL44k+OV/Q9YxVEQXPHcdB4W1MT8/r8FgEDaJ+MlZrthgwcn9R/Yd1DqDTFQJltvbHoMcmBpklL47M+8OBnIbpwnFDpKDMeTS5ZfPWNux8XdDHq8Pf74DAXfU/Hs43cyLg3P6Gpe4pKwj44HhJNLmYBlH0nN2AVAuT7OYQtctTiJ4RIn+xalcMTssTavO8F5kEBkAWMYpU/HcAJRg1vnnoNTf7xWXAHQuPzEoc1sAWPTNzy4vDpKcjeVy8IEcMNY+NjETHANpl8nY8WccXOYdrDgAjcfP240zjR6grThF7nDhNI3Hk439gGxfNz62fM7cUC3LHaDRaBTmxh0Kd5hjksMZcebZa+jTLtcfLp9xDnTsQMQ2X5oSHj7+3AMQ9lQY8o0hG1hLThCgs9ABvifCU00YS48KZLNZra+vh1Ol0Q31el3NZlPValVHR0eq1+saDAbh0DE2M3r1FWwcZBeMdbFYDD878y4pECXtdjtVMQw9AziuVCrq9Xqh7Gc2mw2ZAaVSSc1mU9vb22HNEtUkRZb87L/tmjVfX5frlUB1sVjUrVu3wsJoNpupI7PxsPL5vK5fv661tTVVq9XgdQ2Hk7SPra2tkOhfq9XUaDTCM1iEgL5cLhdqWjuw4n0oAkkpJcTCbTabgdnme+Rv12o1FYvFl4QZcADjwUl93W43dfCCg2OUCZeXdHJlxEJwlheAuLCwoHa7HY4GXV9fD6DPN0A64JHSOdHS1NP3cKDnypHb2e12w9HfpA542CsISS6XYk8xcDBhhLJQaoyRPyNJJukVrVYrKFuUk6dEOAjP5XKpOqNSenMT/eYUKRwf8rAx0IARgJ6Pk6Qgm5LCnHgajBtJjCN9Ykyd3XODPhxOan4uLS1pNBoFuUceB4OBkiQJhx0g+zDaHKYkTZlsl69sdlq/Gtn1yjtEZ2IAyjN8bDGeMOnc+1VpA25wfe0yHj7/HiL2e/wz/5115fIzK4WD/z0iFDNnMSD36IMzzzj33sZY1pBTWCxqfCO3XpLQo3k4GgAWADZ6yh2SGPRj3B28zBoHZ2b53HUU7XHHhPc748u9fs6AAxz+7qCUOXbniu+wQYr1ICnU5EVfO/MY6w3WFkDPgaEDRJ7vzoADvDilwHWJ98OdvngeXFbps+tX5siBN3rS+xW3Axl03evOiM+7Ey/+HvbCeK48thDdHIPhuE98RsUtxjTe0OfjwuUOfJIkqTr5OGke/fG17m1Cjvi795l2IGfIvc8JDgb6COyQy00KIDBnThzF5IOTH5LCGm+326ka/h5VoOLGwsKCtre3wztbrZbG40nljcvLy6DH2dxLvjN9ajQagUzM5/M6OjrSaDRSvV6XpLBHg7mFaEHe5+fndXBwEMYZfAODjW7yyJU0PdWZNEzmDieEdV0qlf7Ow1++ztcrgWpAwGAw0MnJSTjREOWKUC4sLGh5eTl4UR7mgb1BAZCPiHLEAyPMzCYVPDZncVGUsKgxmwk4y2YndaN5BmFy8rtZfH4YA4q43+9rMBiEPCUUFt/hfvKkXeGhQDwkxSEw+Xw+pHigwB4/fqzt7W1J0traWkrwvaIHcxGH0mJFyzudsWDjDvMAK+usCWw8c8uY+jNQTHEVDAcwzAftx7C7rDibjlPiBtzLwiEfpEww574pjMNgSqVSSB1CqbqsOlBArmJZAvQ4w8WcM1Zs3gCEOigBNIzH46BY3fEi3QhAjczyfE8ZIIWD9tMnB2XMA+PM3OA4uvFCuSO/l5fpQ5YAMAAU1iKK1mUbozkrmkK7GG/uixmomJnG6LnDGANevxc5BwAw7zzTHUwHUB5J8PbExtzzt4lCuWzl83mdnJykHEXkhV32gGvWUAyKuMfXuFez8Tb5/x7hoo8YaJhwjC56aVb0gLYAhONaz64L+Mf4oxtcxlz3ORvLmmXtO8BCh6Jn3ZFizXu7fCyccWacnQ2dBQodOPncOyh1p4H++7v9ObHTQltcN0tp5twZ1Xhu+Tuy4yCYqB7fh9CgHbEjxNx6n3zssW9OmMyKUPCz233Gmt+p4cxaQAZou0dXfJzjdcCYj8fjVEqLR22QRddF3ldkyZ0XX9e0sVKphDUHOeh7cI6Pj1/CF5JCyt3BwUHoC7WjwRGAYuxA7OSj69CXfH5ychLkntMzs9ls2ACJDSI1ihQP5IM25nK5QOAgb6Rl8l4izEQ83Q4gTzgSMZEXXy4rX7frlUA1Au75xd1uV7VaTYVCQSsrK7p582ZYlDC7TEa73Vav11M2m9XKykoqf5jyZhzBTXHySqWSKtnX6/XCUdaj0SgcVe2bfc7Pz0PBczzOpaWlALBhLFmgbuyk6UEvnILkB6QUCpNjop1tRBFgIPAec7mcFhcXg/cHe5jLTXbnsrj29/fV7/eVz+e1srISgB1tiw+dcYXrAMWNJt/zkCTAgHI/GxsboYQOCoeQE+MRVylgPACdrlBjhc5nnnfmYU3mx0GAgyKYNUnByDvo9HxV2InhcBi8bN+46nPl4+hhS04JdWPnYA8AirLy9B/a4ICIewqFQiiFlyRJSJOi3zEDjeOHYavX60HpuSHCqXRDjaFzoO2pKu7sjEajUAKKFKaLi4sUm0ObHVi4sxSzpTzDx5q2x+y0Ax7ehyMeAyCezbtiAO8AgrmHXaJt3DOLkeQzWFWMHrLGdzzNBsc4l8uFU8aIdCAftJUwuzsiDs74zIGh95V2xA5FzDyyZlmryD3zwv3xOvU14WlVXkfbmU3uc3mPN0U6mJWm5cvQ1S4TyC/PdeDFRi1/Bn11g++MdwyyGSOXK+ac8aH/PiYxQeH9j2UkdgKcPJDS+24AfNhSbJfLI6AOgIOuw546oAbAupMYAxsnpHg/+tnnAL3DmMWkWRwhdQfNbY2TPi4HvtZ8rfua5h53APxndKRHmnwcmGufG+wTzghOMUSeyzkyWqvVtL+/H3QtzyP1y0sJM1/usDPWzBVRfrBBu93WycmJGo1GilBkDNl4T1SxVCppc3MzlC720rJEBpBf5HpjY0P7+/uq1WpBfnK5XNiYz4FqYBbfOImTBQGJXi0UCgFjfXOlr79XST0HboBhTkpE+cHObG1tpUAHOTooJWcRAd67u7u6uJicZU8FA8La3W5X3W43sNdeRk+a5gaicMnD4zNP+XD2CU+d/9mZ754eJwu5scBg0n/3dAFqW1tbarVaunHjRhDs8Xgc+sOmCFJpnMXysGEMNFACrtRi8OjA0r+7trYW5kGaKjgHR7GBcqPoDL+D+DhXGq8WpeXsLcqOsWWTRq/XC6fR8T2UGUDl5OQksNss9uPj47CZ0sfQq24wP+7FO3vjYxSHYV1Z0WdkG8bfQcV4PA4njFLv0/ORPRTIHgWAG5GTubm51LG5tIdIAwYFB8RlwZkijyYw7tRR99A4c+2hVTd0cUoHz4zD0/wMkPDwL5eDandonTmJ2ao4EuNrwFlir0zi7eCdDrQBDj4GDqR4LnqOvsJEoUt80ylj6GkjPMejANJ0AyFywXjFbYmZN3cuXGZhNp2hdp1B+91JiR0l5sDXjD/DAVucWkD/6ZM7P/E4Ayxj4AZw5LPYoXLG2N/h/cJB8veh7729jGksuz4mzqzGz/Nxi9lTn39Pf/DPXJYAMYwpfUCvsN48qsd4xDnqOETuSLo+9f7EgNcdONrpcsSzXM5x5pyY4Pkx4HVdKE3BfLy2vU20Eb2F3qf9zC399zXvZT15dpIkoUDA5eV0jxFgezgc6sWLF6nKRlTnQr5Y+0QG3d5cuXJFOzs7KhQK6nQ6ISWFikHNZjNgEk4xpL2AWNJNmAc2OIJTRqPJWQPStHIaDjynTlO2dmFhQa1WS51ORzdv3lSSJGo2my9VBMMmez46lUFwwmJZ+OaaXq/MVLMR5/z8PORDs8tUmiiWxcVFVSoV3b9/X+PxOGwIlBSOJ+cse0DzcDjUwcFBYL7X1tZCuJ8JJu1Emhw4Q2kqFiNgCuaZhcEGRY5E94MzUH7O5gG2eR7sJGkeGEaU2crKSmDTG41GqLf8+PFjHRwcqFAoaG1tLdSN7HQ6ev78eVCglUpFo9Ek3xa2jv7gdKDUYyXvICWeKynNYAHyXDGgLNxY+GfO5ErT3fkoMA/vYvw8tEmJRQw0z4zBlTRlqqrValjMsbFmzgAM5XJZ4/FYzWYzAGxPowA84okDQJNkeqy3p54gwzB+0oQRbrVaYQzpI0rIlXmSJCF6Qmm/s7OzUCaQ9zH+sOq0mQ02Hlrn55hxdPbTjRHy48bYHRvGlDEhTQSjyO/MlzuWyBQOFtEKSam2uXF30OfjBJBygx0zbQ7WZ8k6oIaxQIYwlh6Wj9ky7nO5x8DSPhix09PTcNIredMACfKqIRjcOUcG45QF1hdOVQxuXNYdlLqj4I6zb4IFzKHr3BFhvmY5Ds6u+s8OqB34+BpjTbGOHDi6k+dsdOzIuzFHdqRpeosDfeYyDkU7YPax9L7ybGcU6RfPj50xl1v/3UFdzLzHjK63Ma4qRN+ROa9URKgeve39dv1On5xgidnlGIDOAry+/mi7O1H0z5/BM4lWYNMZD/8dWfD7AXbc7+PMvPN3t8HuqHmfXU6c0Jmbmwv5/Pl8XsfHx2GevY+DwSD0h3Gfn58P6RgbGxt6+PBhmF/0EKl83W5X7XY7kD3ML5EHiBLYYfQXpJOTNVROY2/X4eGhrly5oiRJQqSMuSLa2u/3gxMBEUW1snq9rs3NTT148CCkwTabzRQGQFawgWAfChJ8Fe6IZeTreL1y9Q9Ksqyvr2t9fT2wnScnJ+r1elpcXNTm5qZarZaGw2Ewbnh17XZbx8fHIQ2ABUZ4g2ohnkRPFRAMUKVSCflP7nnjNaJweMfi4qIWFhaCYmFDJcCURcepRgAGhAqPUFLw/qhGQoWGYrGoRqOhubk5HR4e6vHjx3r69Knm5+e1tLSkWq0WHIcnT55oOByGUoOeG8w4u6fvSh4PP1ZUMYPhjGXMKqGo+B4gCSMKQ8i4wgq54nZDzf3SdEMHABLAGLNNXM5o+eY7+s7vfo+zTWdnZ6EOqJeIAuxWKpWgfKRp5QPyzpz9dmPuoIRoBAwSIBzALCmwGEmShEgK6Sl+qiaOU7lcTu3OZlwwFl6XmTGEuUOuMR6Mn7PG9IMxcsaSzbH87hs1PdUIMOPzATBi7Dzk746VA5U4gsJ9ABL6MEtZu/y6E8e4xZujWEfIrqeiONvt8xxvJOQ9GDqqBjA+cToNFzrDHXcHwnzmkTcHa4ybO/weOXLHyR2SOHLm40V//F7WgY+9V9xgjWHknZl29tZlALlzUMZ7ndViPgBSPLdarQagRfucheXe2EF0do95Qw7oEzLi/eFvtI9xd9DPc9GLDlp5HwSDg2J+5xmzGF70AxeVrbwNyC8/s4cJW+rrI2bSaaOnR7ied8colkEfR+bZiQrkz22Tp7DEoNadNB9zX4NOjLn8wAC7fonL2Pr7kE/vd8y4xrJ2eTk5Djybzerw8DC1J8NtnVcEoRydH6YF8+u6Jo7MeKlaj0LT73gdUm53OBxqfX1d+/v7qlarqRzu/f39oPcYr3K5HM7z2N/fVy6XU6fTCX978uRJiNiDB5BJJ5SQsfF4HMoIQr58c82+Xnmj4muvvaarV69qY2ND8/Pzarfb2t/fT7FwzWYzCBdeJYq5UChoY2MjLILhcBjKtSwtLUma7G7d398PAL5arQbA6DWyHfiNx+NQxsqBByXrXPG7UfQQjoNnZwrx+LLZrCqVijY2NlSv10PKhjRRPK1WS9vb2/r88881GAy0tramxcXF4Dnu7Oyo2+2qUqloYWEhhIpdSQNweN8skOLKOgacs5guSWGReEjy/Pw81Kju9/va398PnrF7uFzO/MXG0xm9brer8Xgccrxm3e/ltQhVxfPpxtQNAvd7nXBpEgVxo8G9KHwcLVd6XlWGfF6eSViN/sBUslk3k8mEQ4i63a6Oj49ToIP3ka9MzhwgO2YRh8NhKvca+XCw4uw09yAHDiZGo1FQyJSPpMwT7AhjzzjEQMbXgs8d93mNa5czjFIM9hgb5hJ2yMGeAwKfR+QM+UXu3elwMOIOGQbZncu4Moe3NQ7Pw2YTqeNv9Jt3s5eEdnuUh77jhGDMYgaVC13l7KG3C4NMpAZmy9uNjDmLGjNyzsQ6CHOQwhw5kORnj5R4n5lLnomxdseIUDNEADqBZ7sOBhDH7YkdEAfE7mD6eyFe+NnHxx0Gxp/7YrYWmUAeAYUx44dD6cDJ+w2goW/ktGK3PD8ZUsX76WuKn2fpUXQFY+WREdcfs9atr0/WkusInhePqetsZIN3uk32z52B9/x7nhvLF2PD1Wg0Upv6PKWK0qQeBWs0Gqlazeh27qlWq0GHUnLOD9JaWlpSu90O53QwP+VyWe12O/zc6XQCLkHnolPY8N7pdEI/iCqzefDo6Cj87fnz52EjO+tidXVVOzs74TyM0WgUNu4zTtns5DyJXC6XOiANvQ35whgwxh618HU16/I5+g99JUmyKem/lbQmaSzpn47H4/8qSZJFSf9C0k1JTyT9p+Px+DiZCMp/Jel/KulE0n8+Ho9/+eWz/jNJ/4cvH/1/GY/H/83fp02vXFLvO9/5jlZWVpTJZHR0dKS9vb0UC9jv99VsNoPg4lnDRFKazcNs7GQlNaTT6YTwCKcEDYfDANBdcSMssAMwgyg3cpNOT09DXUYEGaPqG+BssoKHzmKr1Wp66623VK/XVSgUAvs9GAx0584d/eQnP1G73Q7hmnK5HMIuMPe1Wk0LCwspkBIbaICDOw+uxBB0N8rOPvO7AxJnCmCJVldX9e677+rFixd6+PChut2u1tfXU0dRz2LKUKpxmSpyhDOZaX1N5h3DwuUH93BojJfewvgMh8MQLQC8APYZK2cmUDIYQx8vjB/gg9KG7rDkcjltbGyEiMPOzk5IaXIm0UOKbPyESfaUIWdDAMXSlHUjvOjMJjKLfPAsT2/B6PN8/50IUalU0unpadgU6yw07/ccZje6HvHAaMGGAJBoD+sf9p3QKawbGy3ZZ4D8xfKFfDt4iJWz3+OsrMs+7QMo0AdkmPH08XBDjmzwfTYu00aOt2dcC4VCkCVn/X0tJkkS5sN1n/fPf44P5XBGno17XtbT9aGkFAvu64M++rz6usZByWQyoS4tutDlmXfG0TIHbe6kxc6DkxX+XOQS4EH7mQtnUv0ZzsRyjz+TsQTcOstKu+iDt4+/+fhig/iZPvhGbe6N+8Fc+rNo8/z8fCgHihy7jLvN4B0+py5rjBH/e1SA9riz6Y457eZdMbuNPvUIU7w/gH65HnMZYL3Fes6dGvZD+VrA+fDIAOuOfpGf7Gz65eWl1tbWlM1mUwcKNRqN0H9STH1d8bxyuaxsNqvd3d2gi3Fam82mJIUURMaffQ3j8VidTic1Ru5kkf6JnYGcyuUmFT9gmSF12u12IICSZJIuif2sVCqpsyAKhYJqtZp2dnYCoZPJZHR8fBwcBuwA+4KwP4wfNhhbTqWR/4Fcl5L+N+Px+JdJklQlfZgkyZ9K+s8l/X/H4/F/mSTJfyHpv5D0v5X0P5H0xpf//qGk/7ukf/glCP8/SvoHmoDzD5Mk+dfj8fj4VRv0SqA6l8vp2rVrYbLPz8+1sLAQQK80Ta0AQAAMj46OgqBQ5xBQcn5+rr29Pe3u7ur8/FyVSkUrKythI6IbNb/cu8Uj9Y0GeLnueXrlD3LDAdluEABQCNTy8rLW19e1srKiWq2mWq2mTCaj7e1t3b17Vx988IEGg4EWFhZSeeKUhANkFovFsMhjJQe4d+YjBtQoiZipADS4EmbOWNjOiDIP+/v7evjwoU5PT7W4uBiqQXD5wqJdhMCZP9JzACykSaCoOPzHGQnC/5T54e+wSDDIbhAwoIS1Dg8Pw5yhlNxQ+/tgCNgM6fnSjCMVbDKZjJ49e6bnz59LklZWVkLOaqlUCmxCsVgMaUmezsQ4UQ2CuXKgCuiLnSgHZIwvDKjnPhP+dTnAOFNthLkAtDCXOHqAOn+Wyw/5fciBs1TD4TC1MYf3I+/u+AG6+cwP2HDg4kDF17YDXwfPHqlxZ8yrIvAcB1G0wUO7/m435DCFLu8ANgdkRE287e6oZTKZcGIa9zCuOJAAIAeytId7aJMTBIwDziTz6ew89zKeDvSQQXc2ud//d90IIHOniOfhYMSOPePOHHAfUUIHgsihy6zrZx9j+uipJawpj1rwvjg3Gz3DWnJ21QGrg2jkAF3Dd50FjiMt7uTRV8AM63DEV1AAAQAASURBVJvnAUp9v4sTJ75WsX20n8+RO6IxtMfn08kOb7Oz2z4W6HvXx4AyX5fuaHk6IevYn+060xn8y8vpycI+ljGwj2UsZlLZc8BptOiIer2u4+PjoIfJYXbbxTMLhYK2trZC6hx9BtAjW/SBYgXSNHrnsko7YIyRj7m5Oa2vr2swGOjw8DDIATrN5bRWq6WYavAYm+IlBTm9cuWK9vf39dZbb+nRo0dBluj7aDQKOA6G3Tc7j0ajcGaEO3f/sa/xeLwjaefLn7tJktyWdFXSfyLpd7/82n8j6S80AdX/iaT/djwRmp8mSbKQJMnGl9/90/F43JSkL4H5/1jS//NV2/RKoNrzZc/Pz9VoNAIr3O12gwdIjUf+ceAHB66Qn/j8+XM9fPgwJL+TKsF7pHSo7svOSkpvjIPxw8OKFT7gAWBBHiWGNWYyMDIIc6lU0srKilZWVkJ6xOnpqX71q1/pxz/+cdh0yUZEKZ1HR3/caKJYUTAoPgQWJerghH5fXl6+5Cm6snZmyu/DyNLf3d3d0JbV1dXgqUpTpgY2AqPtuYDMEUqFcKUzYX757nQcGmea+D4AVUofT+uK1DdWeP1wP9wllpk4HAqDRj7jxcWFnj17Ft7XaDRCCsjZ2ZlqtZpyucnu7ePj4yC3yKkzNyhiZM8ZKxxNj454rqc7FC4vAA8AgRt85oc2eK43YBz5537SfgAjbuhpH4wHa4bnurPmLLDPG2PBzyhlBwkOerlvFrvFWPj3nNlyBxU557kALAffDjCcHXc2zB22mCnGmMf9mMX8Jsk0D9T75N+l7b5fInacAYMYaQcVvua5kDue7cSCtxVnhf6jf9zRiMctZvYd0Plpicg4a3dWDrw7TYyljxNz6OMXOwYxEORz2uh62SNmLlv+vXhevb0uk/4+X8/+fvrAd5G3WGcAcriX77r9cFDL/MYMNm3jOz4e/O/rw9cUzqIfCgX4Yx5mseJcPmZuo/nbLLKIf+g00uUYa5wj9qh4tKjb7Qa9H2/s57mkVpyfnwfml4pMzshCBvkJs/wNdhbc4Pn6jUZDR0dHwW75pmYi2USXGAsv6evyXCqVtL29rXK5HE49JJf+/Pw82KrhcKjFxcXgjCETRAsB6pS9Q5d99NFHIZrv+pyzMjKZTOg/Nmw0GoU0RpyKuIZ9fLlM/He8lpMk+cB+/6fj8fifzvpikiQ3JX1P0s8krX0JuCVpV5P0EGkCuLfstudffvZVn7/y9col9WAaABjunQFqEZjRaBQOPWAi8/l8yEnd3t4Ok76wsKDFxcWUovZ83JiVdoPhioV78PAoU0f+EaCQTWZ+wh/tRpmUSiVtbGyo0WioXq+H/PHPPvtMDx48CCdKLi0tBTDnih2vm38sdGlaqxSw5CE0r0QhpTfhcC/Kj/HycBL9531edpAxCALwJUMaOyJLS0saDod68uSJLi4uUvWsnS0EgLHrGHBKm13RuBIBXLFwPS/W0xHccHp6AYCc73F5CJt3MR4uG8PhMBVeBCB7dRRYCGejer1eqEnszPDc3FxQNJ6fjOwyF86Qe5ud/QKQ0U5nznifG6skScIpXJJCpAT58pQMX0vtdjs8j2d5uTcHy7AytMvTAJBLZ8yZW8AJc+4RDwAhsop8IZcOsEg1QRZcadMflwOffw9rI9/IDU42fWFeWFcAQVIhnFEFbDOffB8Z43JQ4YAMI+YAyceBttM2zxv353vEj345KcH9DmoA0QA15pQx8Xx+xtHD9D7f9M31jY+/AyevPOQywjzGgNrlPpYnvn95eZmq4kIf6J9HsFwGaIODYn+Pv5u/u4wiy4w98+V7DXz+mQMnQJDrGIA6E45OzWazL40d7XfGdNacAMR8n4zbb/8dQC/ppbrL7hAxJp76gu7wv3s74jXt4+7txwFHx7M+stnJvqbxeLLpG13HfhbAH/OMjm+1Wjo/Pw/6t9frpZxK5MRtaJzz7hE9dz6Pjo6CbgRLOHnlJTN5X6PRCFVAzs7OtLy8rNPT00DqsD8IMIxDMRqNQjrlaDSJMnrxBE5+9vMHiLbxP/p3YWFBvV5P9Xo9kKKeN40dRu4B4q7H/nu4Dsfj8T/4u76UJElF0v9b0v96PB53ovU3TpLkv7dyJK8EqhlY32DFoBOq6XQ6ajabKXDtwGl/f1+tVisIzcrKSip5H3YEJtM3aQA4XBG7wfTFj1C4AsnlcgFcnJ+fh8WHd+usVrVa1cbGRii+/uGHHwbBbjaboVIJ93kBeRQV7/eSUO6Be1gZZQMrGANK+uEMJn0Nk5lL141GYaMg3Wjy/ZgRJpwE4GEhsmmUMaftfjgEY+mMKyXHSAlyAImBcLDlixq5Sr4MQTEuftAKLIXLgOdV8j6fX4BSPp9P5d/xbk4vhCWi3Thm9NNrrLsBpm0OAp0NQ7Fyka/Hc93IuwPEcxgXWHPGOQZshDR7vZ4khU21KOtZoJ92+rg5O+dAnv9jw8mzGXOez3g5aHSG13+mXT53vg54t89rzJy5YgXIOMvmlRQ8taBSqQSmhytmmlg/yHcMwGKm3QEdz3Bw5myrg3N/LqQFTJ6PCc6yA1RnBWcxmMihR85iQDmLjXQm22UE3UKIONY3OFYO0H2e3elzp4Ox8LSCWD/SVgewrJNYVnxN8V3GAn3jLL3/77qbyx1DgBi/z5JF7BrlYPm+/49+jSM/PtYOlmfJjbeBNe3tiN+NLgcM4jAwj25bfA0jVx4l5vm0jXfHaVPeBtqxsrKi8Xiszc1NbW1tBXCNU8a+hPF4HMgN1iIAN5/Pq1arhf1ZMNaAUtqOHshmJ5U/sL/Md7VaTdWiZo0VCgX1er2Q4ueAnLnDpoGbKH9HhHA8nqTGsdkYW0LBBRhh+oP+hpjc399XoVAI39/e3k5F+ZFtd3LJBBgOh6F+tc+bO1kuk6SUxGv3qy7XF/+hryRJ8poA6v/HeDz+4y8/3kuSZGM8Hu8kk/SO/S8/35a0abdf+/KzbU3TRfj8L/4+7XllpprT4ABbMNEnJydqtVra399Xr9cLm/hQoqenpzo4OAg7ZwuFQqosnjS7/JqUzqFioXt4R5qGyzCGHLrhpdDa7XbYuIXnDyACIBYKBV25ckXXrl1TPp/X7du39cknn+jy8lIrKyuSJgw2tYi73W7IsaW0nrMzHu5EcbjH732IQZSDXsYB5YQSc4XvbIezOgBE/z7j7oaAMA9jyfvZ+eyRARap53rRRhQTRpRwEgbc0y0Ab86SE+LkZ9iJJElC+TzG050Lr+ThbUJWPE0FgELbULh83wGi5+H6vDCmziT73LtyZr5iQ1cqlVKhVZyzWcbfHUqiRZ5PFzsTKHiXq3gO3XG5uLhIseDOlDHe/Bw7Kg4yXBYYR9aZG+ZZQNjXgo8nPzvodhbPATgA3oEkIIF+OinghgKWCF2DISESgdzgDHsEhnll7GijAyLXda7XmG83bg4EY/aX99BfBzPObjvo4bsOWOkzz/W8fgf9DmQ9pYk+u8PnTgLf5Xn+HNrPGnPZxW64czcr4uCOG1FU120OhAGM3m6ezzpwu+OyGcupg0gfYyeHfM0zFh6ZcFaUMXKHONb9sePmjmg8Z7Pah95w59LXOPdI05RF1x+x/HLFTmHspMFmS+momDs99KdUKoVUBErAefqmH5IDyOXyU2q9OEI+Pzm0idQNStTBPFNP2vf1oCuI/knTKlRJMikhzHpxxp/xcnxSLBYDmwxmYm7r9brefPNNPX78OJyqTNlV3yNxdnYWNjpjh+bn57W2tqaVlRV99tlnKhQKqtfr4QwRZ843Nze1t7en9fV1HR4eBnvpjh8kzdHRUcr+8XdkICaR/mNeyUS4/5mk2+Px+P9mf/rXkv4zSf/ll///K/v8f5UkyT/XZKNi+0vg/f+R9H9NkqTx5ff+R5L+d3+fNr0SqL68vNTe3l4QZAD2+fl5AJcUSQewDIeTzXrHx8eBwS6VSqn6ts6gcbG4XVE78JKm4SKUrecHoVzZtMWxnoD/4XBSWcRBQ7lc1vLycgij3L9/X48fP9ZwOEwVQefAm4uLCx0eHqrb7YaDaGgPi8+B2Xg8PQXJjVwMClD8KEApHbJ0IIfXPD8/r8FgoBcvXuji4iKcHMnCZjHEO6NxUji4AoV+cXERNvStrKyEOXIA5SwXbfHFBsPtCktSYPYcfEoKOVsAeZT8/Py8KpWKOp3OSwyghwzjTR/S9KCF2Pjl8/mwudINO993YO99dkOHIfE5glH377j8eqgUh8SdHgeuKPZZoAYZc2aJeyjRxHPr9boODw9TTgYOFNEaBz4+JjyTdgImGVtXuvQROUIJu/H3fjjocUDK+vDNV/F3XE5ZAx6F8LGKQTN9gvUCDODAYYjd+Pd6vbBOMKgx0HC2n8tBH+/1cDafx6luXD5nsVNNKhtgjj56agGG3mUfuaHdDtiZozh9ytuCw+HsIoCU73t0jDbHBIg7+D4e7gz42kLevF+x7PFOZIb2sk6c7aZPcdscaPi4OrhGp7ose/qg9482MG+8h7XknzHXPtZc/m7m3h1g/z7j6M6pA3N3ANy2sF5gSZ0EQLfEpJfLbBwZYB27vvfobMyKnp2daXd3N3zf2+dj4jWjidIA3rEV5DZns9kU28z69nHKZrOB0HASIu6rR4M8HcP1IjKEbRsMBilHmTQKnINf/epXwUaOx+OQCsI+NFhr7B/zw8FhW1tbIX31+PhYuVxOT58+VZJM9gtdu3ZN+/v72tjY0MHBQWDMaStzOhxOztHIZrOhhrUTj9VqNZQIXFlZCQfn/Ee+fkvS/0zSp0mSfPzlZ/97TcD0/ytJkv+FpKeS/tMv//ZvNCmn90CTknr/c0kaj8fNJEn+z5J+8eX3/k/jLzctvur1yqCa3BvyojudTjjp8OTkJADqi4sLVSoVnZ6eqtls6vnz50qSSe6nh/EBM3G+lv/spV6cHSdNg8XEM6l7zZn1zWZT29vboYoIedYYDxY35f6ePn2qra1Jzvry8rIuLy/V6XQCI728vKyLiwvdu3dPh4eHmp+fD6VwHFD7IR2Mn5Q2EDEjHzMzbpDdiJCmMT8/H0r07e3t6fDwMLWJ0RWQpFTdU995LikoUYwCisfTY+J2o3Cr1WoqXcePM0WBSy8fqw1YYGwuLyfHxY7H09rJw+EwOEVeSghgJSkoTlgTFJtX5ahUKhoOh4F1RIZom7NjgC5pCh5Rms54OtAhMuKK2oEHiswrwPjmTZSuG1k36ERdeCYA0I0Oa4pNs7lcLqRa4awh+8y312H2deebZ7kwig7yAHjuRPIMZIR7nf3g+8iQg9J4TGIGFFny7/OzgxrPKY6jW1QWoK3Mhcu65+qSskQbHDD5OMVOM/2gj24YcXAckHpf3Mnw8cUJcADocuBOrjt5tMnBlaebuaNDu30undl1kEnqludcOiCe5VwAhIi2MG+Moa8tdIOkIMvOHsftpa2uc3kX8huTAA6gfR36PPIej0S5Y+Pfc7lmLnHiHNDyLk8hjG2g98vHhmf4XPg6d/LK38vFenbHgnF0YImNZh68jT7+yKeveVhRJ4kcyLp88G4nMngP/eRwLB8bJ6V6vd5MUiqXy4U0BmwT72PssbGMBeudKIM7q7OewdpmPCCmyIPGMaGqE9WS0N3kklNdjVxpGGTXHdnsJPqO/W+1WilZY3/QgwcP9Ou//ushUk//SA+kiogTYJwe6baTqO5wOFSr1dJXXb7O/0Nf4/H4R5K+KsH7D2Z8fyzpf/kVz/qvJf3X/13b9Eqgejgc6ujoKDDS/X5frVYreFV4NZTzOj09VafT0fb2tjqdTmA7URwk08P8eFiORY3AOYuKcoT9dMajUqmoVCqpUCio3+9rd3c3tHU0GgXP1Y0TZdZGo1EAdOvr6+r1eqEUYJJM6kmurq4qm81qf38/LBhSJlCsznp5eNkFzRckAu4sJMaXC0XKZ7wXj3J3d1edTkdLS0shJEWbGDPamclkXipwD5vg73SmEsVABKLb7QZHZH5+PjDb5HvCkqPg4wvWE6DCs702Nw4XyhCQ5CFP5GaWQXbFXyqVArOJAmQHOfcgD3j9/E7OHt/j/W6EmFvaEQMi8p6ddeZvzjS6knbZhiVFmXuFCtqMfLdareDkckoo4+hOE+vJw9K0nTXmTJPnYjPHzmQ5qPBxlV5mbJHN+HdnwTE+jKuvHwCDM+wO6J3NczlA/v2gFAdIyDprxMEVusujM/5ub5vLVCwf3s94fNxg8hzmx50E5BPg43PLdx04+njTJuZ31smO3hcnIHgPc+Rg1tlqB2Iuc+g337Pgee1cjE/sHPF8dyhip4Tv+Zw5MQHYcnlwh9bXhtskxt11N2sQveWAyp0++u+MOnPMu2ICJHbKHLD6PLlsIcPef9IkYkKEZ7i+ghTxeXcgHs8RfWaecUTdaeO7njPPPPg88l4qZ1xeXgYdDQDFaWP9+gZJj1BRqcNTFkgdcXlyZ5E2M14eBfUKYciR90lSIP0gYzxd0B3BSqWSShnEtkKGUdkKIrFer2t3dzflABUKhcBKQ2JJCnvecDoYpy+++CK0GQJsNBoFEhQSDLu9v78f2uebNFnTcWriN9f0+nulf/T7fQ0GA+3u7mowGIQdtuVyOZwwd3Z2psPDQz169CjkEy0sLAQh4H82hblClKYF5wGOl5eTMmow4M6++CIjR9VZaT9whu/7gnGGlb48f/48FGznmUmSaH9/P1QsIY2FzQIO5KTp7nAWoytnV2goE1IwpCkbivH0GsXkhA0Gg7CrOZvNamVlJbCfbpRwSlwpwz5zqhK7vB3UZTKZsDlxMBgEMN3tdkPuG8+kjQ46HeBI6RChp5B4HqSH0bLZrDY2NpTP5/Xo0aMwBowlffCKJnHqhcsHtYedsQTQEuryuYtD6yg67ws/O2ilPy7TzBnhw1nhcgd6zD0G6uLiIhwAgAOLwQN0Aaaz2ckhBSj4UqkUnC+fc9rgjH02mw3hSjdSjIeDBq837Uysgyn+7uCZZ7isOevljrWDUmcOnbHmM3d2kEP6wXswnsi7O+iSUs6Ps1HuTNAedzr9cuDBd2eBFAd4jI0DeB8TB4FJkqQqHPgYOmPq4Iv3FQoFXbt2TXt7eym94vNInxz0MR5xlMHnztl5nkPJVfSbOx9839nkWcDYP3PGEMfJ5SDOA/bIjDs7Ps4OENwZpm30fW1tTZ1OJ5XvzPtjOQdk00bkJWZ10Z+0D93kqRbuSLO+cEx8HwXj4454HMXydRinOHmb3NnwMyJ8jhzMM14+jpAR7rT7nNCHeL0wpqPRKOiibreb0g9+CBV4wx1OHGra7M4EcoI+cILDyR7mzPUIepS+MfexzvP3SAr7ukajUdDzl5eXWl5e1vn5eeosAreTzWYzzIPX0c7lclpcXNTZ2ZmOjo50cnKiarWqTqcTztBgHxLkhOeHFwoFra6uBp1z+/btQHp6BI/5qNfrKX0R47X48jH4ul2vzFQThoeRhG2G/SFs0e/3tbW1pdPTU1UqFdXr9ZQAo3BiYCEpJagw3p6cPxqNQu6sGynfFJjNZtVoNHR+fh7YdXb5OujzEDeMNW2hb7QbBUlIDbaTcnKej+ZGUJqeouSGGIMNQPQcUh9zvl8oFMIGya2tLe3t7QVnxXdpu1GlzyyImEHycnsO5rw0UL/fD/nzZ2dnqlarqR3LPNsNO4yC57TCRgHi4xB27IzcuHFDCwsLIZLBOAC8CTc7+8t88b87Es4awZa498894/E4VV4I5Ruzz25oAGa8EwPCGACouZ/nu1Hkfc6CnpycKJebnKhVr9dVLpfDbu+Li4sA1gntObPcaDQC+4FBPT09DayDs0lxX1HwbiTom7OfMVvoP3MxtrEiZgwZGweEDqYxyvF9PM9TARhrDCN9on+5XE7lcjmsde87htgrALhB9jUNiHCgD3CljQ6m3Qngef4c2uDMr7PXXLTHx4v7YuYzdkqSJAlHKROhw8lzI8r7vf+8m78hBz6+tMXlhbEEAMWbetFTPs7eD+SJ57ncxm32z2ifj6f3zecAneApKX4xdzs7Oy+x8LTd85udKY6dQeaB96If44NJnBl1/eXOznA4fInZdmDudtb77uDU123s1DAebpPcYeU5DsJ97XsVCqIh8by6vspkMkGvwaD6sxgfconRR8gxEVrftM1awM4xd75WycvGdtImT2Wi/9iiJEm0uLgYcrfL5bIajYb29/dD5GU8Hqter2s0GoWjwXEQpEnZWiLoOPL9fj/YVsaE6DlliQeDgarVqnq9XnjfcDhUp9MJR6qzp+rk5EQXF5NzRChFSAovqR9UAcFRp38w6HNzc1pbW1Or1QqgPXaEvrmm1yuB6ouLC+3t7aUAmZdZQ2n2+321221dXl6qVqsF4OkLDkDrx1+6MpAUDB8LiZDO2dmZOp1O8Bxhnbz6BoJGXtLS0lLq0AYMt4f6pDQAQGlhpDOZTMhfYhPm3NxcEFZSKvxEQRSBhzxRsrSH0D7K2T1BB1so4Ha7rb29PUkKOerO/PI+Z5G9soB78h4KBbx4JIE8+n6/H8YSJUi7YMadvWEuUaIe3mUjIE4LNaJ9HpaXl1Uul8PYELLiO9lsNjCu0jTn2NkixsPBdsxueRqMGyHCYDwLOYgNO3JPOg7t89JhzhQCbDGeDpxgCrrdbopxkxRKT3qd2lwuF/YzDIdDra+vp8KR0jQPlYo3jCF99r57X5lXB9tcGB1k1Q20O7g+fowBsuVhVO7zUDH/x6DIAbU/k/FgXQPmXOfASrM+fL4I2cJg8Vxnu3hGDDLjkDrt9vfHQM2de9KNGENnSR28uhPCu1xnzgrNA1Jd3n3jLm10ppHPHXz5OxwUe5/dsYYQ8bmLnVHGztcVn8VsLnPmcho70Q6oZzHe3k+Alc+Xy90spg3ZRbYZI2yKrwcHzF6ilOe7kwvriQ2TphvixuNpjXjmLY5yeVpO3O5ZDhly43PpjoHrSU+vQF59Ezcywhz4uLqD53rZ/4aOdKKE/jO3STLdv1Iul1NMKmPrxBHj4DLEOgPoQ8oxZ5TgI63R58vHED1NFTHIrsvLyaZ8NgMyJu12W8PhUEtLSzo6OgrpTuQsI5fggGKxqEajEUoBjsfjVLQH3LOzsxN0HONEKmWxWNSNGzd0cHAQSLeLiwttbW2FMo5zc3PqdDohD9txh+u6fD6vGzduqN1u6+joKIzf33X4y9f5emVQzQKpVqsBFLEgPGTiOUSAXvdkWVgoVWdU4rALygCGJZfLBcaWBVev10OJMfKnSC2hPfzM/9SqZsMe7UeApWn6BLWE2SRZqVRUqVRCCkS32w2l5+g7i90ZNcaL/rHQ/X0OPPBucTxwVlZWVpTL5cLmOw/NYnxQxg7uMeDUAef7KHY3tByyAqNHfhtz4oDDjTKGGKAC6KOdgDpYZv7HOOAEJcm0pFG5XE6Bb2dJ3DnAmDEWfnIj93B5ekd8QiVMCVcMqJ25huWAKfYNjB7aRd6TZFqv10Oh2Ww21D6l/jSGttvtam9vT+VyObTVHQrPGcco4AgxtsgFfWBMnHVy+YznkzYSIo2VsDNyvtZjUEXUJ87f9Lmhbf65s5ku1zHzRbqYg2gPuTqDhpPpjCpOYQwq3Tmin7MMeMxMxxdywNpnXN3xdNbRZYfL95MA7GK5dALDI2b0j7CwA3z6wHzRd54DOHGg4m1zAoFx8M2dPJdxdGAVO/Y+Xt4ufmftxA4LIMmBG+PjzrID1DgFMWblkRv6QJje5UFSeK+PEfoAufMN1byLdT4eTytExUDUGWtfX87ixhESxjuWAf5OG32efd5ogwO/mIDy+zwa4brBnRfAH21zAOsy6yQNOs/TnobDYdj3wFyzlonwMX/oRdrmzlWSTPfcYDN8/lxn8jn6lHWWz08OANrZ2QnRMHQJVTOoNsSYXl5epqpiuc2n3dVqNXxG+t9oNAo1uMFdAPBKpaLl5WXt7u7q+Pg4gOpOpxP0IpHKwWCger0e2sQ8MQfM/+7ubvjs2rVr4fRIzj+YdcVr8ut0vfIx5devXw+lakjoR2EBVD0Hy/OMWPye+uBGyT1D9/RhlQB7bFhgsXl+L6fjoYBokwNpB9ruZfM3KV3r1T3XYrEYjlPnH32qVqtB0cNmuAJydo93eKhZSleaiNNZstlsUOYoDwwp7UNxcDlwc+PIAsPgOAstTZUdjgltKBaLAbQ6C+n/A9wLhYK63W4qrBmnEAConbEjvabZbOr4+Firq6uhZqmPJf11gEa7YyYwNtRu4BkTHAZAGA6NAywUN2PukQ4iAChfoirOKDn4yGQmZZHOz8/VbrfDBmCiL8zv/v5+OGSAyjX0kfqlrVYrsB9sVGEdergzZgABpm683UFyVpb3xaDSoyR87mvbL+bMAQ+XM3pu0NxRxIHCMLrRZM3x/kwmE/Zk4GA6K+jAz6sWEUGDqfOf4/A7epG2uoGPHXRnndERgK74EKUYCMW60ceTdeGOozvtzL/LZVytBbbN55XnuwPr8+bpNb7WPMrlfYkNt8uGs9DORiMHsfMXVz5xWUIOeAfj7pVckFd/nsucO5SuN6QpOeJstc8R74377HPj7/J58koR7sS70xg7S7PWi8sHfXVgHDs4vg6x4ejsOLrlgJlUwVlOAPMaky/Yr/F4Gt31+1zWXX/zXfrCmmcdO9FGWynfyjrD5mIzsLN+ZLmPN30goj4ajVIMNekg1Wo1EG7Z7HRfyrVr11QsFvX06dMgm+gi+rW2thZ0Dkw5duD09FSLi4vheZBVVOK4fv26jo+PValU1G63NT8/r7m5ObVaLRUKBb322mu6f/9+YMWxUefn51peXla1WtXjx49DdN0JKlJqSFkZDoeB1Ptmo+JXX68EqguFgjY2NrS1taVutxtKwCRJkjqVjNzbJElCWTtC4ihxX/zO6jkTKqXzsnq9XmCEc7lcEKBcLhfqUMJ8sqi9WLwDOFd6LDhn77jfQ9WAOQdG0hSYeVud9XHj75eXknJHgr/59zFMsIQoBT73sJ7f56E3vouyBiwkSZLadHh6eqp2ux3mq1wupzYZkXbg4NjnFFAaV1hwOaLvKBe8Xhjyg4ODYAD29vbCnM5azIw/zC4Gied7+1zu+C4g3hXncDgMso2CdWbWHTDSODxHjzH2vDwUuqeMkMZCu+hDv9/X4uKipKlDVavVUmCXNJHDw0OdnZ2FNTAajQJrHq83HNuYRZ4VRXJDS19iwOFgLQY/PDdmMnl/DKjdKDuLzZpDP/AdAO5gMND8/Hwor3l6ehpOJuOwFowvazSTyYRKQJ5Lyz90BQyy5x4zNgCqJEle2rQZgwNnn1xWfewdTLpBj1nvmFH2+XB22VlrPid3l/vdwZ/ldPq65VkORpFFXy+834ESa9fZR3fgeXfMeEN8sMacgIgdZW97zNy6M8/fIHY8dcIBqoNkB6+MhdsI+uS2BTng74yT989TfNw54r20gXGj/Z5TzLsYyxj8etscqAOg0MFxlAS76PLgTorf42XmXB7jPiGDrDnWn4+/f5/IAvbBx8adOlIEd3d3w7rl3egW0tokBX1POgnPAczGBA7sLv1mTAuFgs7Pz4O9IjrLWoEoyWQyunbtmg4PD9VqtTQeTyuMNZvN4OiMRqNw7gXzyuZY8rN7vV4oCsFGxrm5OdXrdb377rt68OBByLve2toKssI9z58/D6c69no9FYvFcIZFoVDQ3NxcyAGXJumZjqdwaP+2KyZTvk7XK4FqQAGTTeji+Pg4NfB4a+VyWaVS6SUD7UyPh1Awkh4O6vV6wQOUJotsaWkpgHU3xn6oiJQ+etvL6bjXL6XL20npEla0iVBtzJC6gXJQ4SEw+sl7nD329sCEeejJGTppCpJRAM5u0WfGgLbFebZceOmZzGSncD6fD3MJU8vJTLDstIV749w6FqJ7vM4AO2tGYXvagkKmLBDP83QKFITnznleGUqNe3z+CH05UwbAA1QgEyg5ZJEC/LGx8XFOkiTkgftpYC5XHm52FscBC2wErClpPplMJmza5ZhbcqXpA5tTfE58PbmRjMFzzB47WzMLCNP/mM330LtHqngO/cU4O+im/175gPudpeMZyBfMF9EO5twjTZ6O5IYGwMn6RH/RfmccGRcfL3dqfU1DKrhso5s8pE3fmRt33mKnnLbyHsbAgTz3xXI2a/48H5W+xYCYeaZNPgeAFXfueYY/01Pf/Hk+51wxSHR9GPeHNe8pScgKZM94PA5lP2dFBeO+8163IzExEH+fZ3olB+5j4x12iLkbDochfQ+Zo3oP9pXn0z/0AXrNc699/HydSelTaklDoN2A07jPLvf8jfXu5JWnDbIefX5iBhtQxrw60cH9Hl1DH/rvPHM8nqa+UZXL5ZC+OcjGlg+Hw5D+6XPs5ePcCZMUSLy5ubmQ1nh+fh7OqRiNRlpdXdXKyoo+/fRTlcvlALjv3r2rUqmkzc3NcFgeuntlZUVJkujo6Cg4//l8PlTdYk8aeyLG47EODg505cqVQDwlSaKHDx/q8PAw4INOpxMKHBwdHeni4kIbGxuBnERXHx8fh7knGoledyLo4uJCJycnun79+t9aq/rrfL3yMeWdTkfZbDbkf+3u7oacm0ajoVqtpmq1mlokgAmMnR8M4grw4mJSgJzDKqTp6YgOap0JZLE6IIkVDELigEFK7z5HaHiXGxMApCtkvufhMWdbXHF7WNzZVIzgrPQMvGDa6OyEA/lYqfN3b0PMQjrrBhMNEMR5yeWmeevkiHMB5mEufcME4MTHAcUW7wanIguKVppukqOtbiBQ6M4iomD8oBYcBcaGY+WfP3+ecjgYe89rc6fEw4Yuz57uABuD/PR6vRRAc8OH7OdykzKNpVJJBwcHodwfbQYwAxjq9bokhdKUGFacGyJBGGMHnzGj4GvDQQGy4+spvjdmRn2ePPUCEOlOSMyeYSjJlffne7nLOH/T1yZ/81KUyAtAiwhLfGy5g3mPKjD2vu4ADDHw8764DMfOexw9Go/HgQ2L9RUy4POJ3kCHObHh0Tbuj8E13431o/cHZ32Wo4Vc8CzXvci399H7TpTA9RZ983xs3ucOBbYjJi+cAGG+HdS7DmHdMVa0nXc4k+8OgZR2QNFT/M6z3IFBB9BHdyZ8fPz7yAA6n3cAPPnnzohvNPf0B9rm68R1MeuCqLKTEW7D3BYxNh6dANgCfumP2z8H24xpnHtOHjLzwZjyz3Wyp4V5qqOTXKREcI9vKPX14nKEDYsjHoBYxoWa/I4nANbValXr6+s6Pj7W1atX1ev19OjRI0mTmtNUsKrX6yGl0d9LKTx04vn5eYi2samRlBwqP7FG9/b2go44OztTt9tNyT/rodvtBvLs6Ogo2Hk2fSLL9I05paAA0dOLiws1Go0QRf3mevl65TrVZ2dnqtfr6vf7evHihXZ2djQcDkO5r/Dg3LSqBIsegXSPkb/7wSEsWsK37t3joeMxAmpYkM7esMkOpsuNCIsTAXI22hUtxtLD284+ekqJA1np5ZzTWayPfy6ldz2Px+NUWo30clgWdsafOctwouydZZMUUh445AbFy6ZPIgCASa9ry0KMFS5z606Mf+6eMG2KgRRA2dMnXLa4l77RPgfUw+EkJN3pdHR8fJwKP0sKCsuBkY81lwNqxg6lg8HxOXfQ4TLhpY4uLy91cHCg09PTYDQZB8YfhxJHk5NCYbW8EogbJQ83e0TGGSNnJN2Zc/YVmXKHjLXC9/13X2fOMHtkwOdQUgiF4qCSs+f3OmD3dRyvtbm5uWAkLi8vQ5RMSq8rGEGMJeWlkAH0AjqH8QFA4GQ5mIyBPW13Ft/ZVjZd87wY+DKusXPn8+FjzvN9rH3tucPEePncODDyEzZ9vTjo9DkAOMfRG6JVs9aSy4nLK2uZsXNW19daDHDpszO4DtCwKbTf1wKfA0Zog1fBIX+Xdnk7sQ/YKWQlZkG9drWXZUUmPHITl3PDnkIizLId9IeSc76O3GF1pzFJkrDZz+WIn9GpfgS7k0KuW4rFYljP6KdZdoe5J6WD/Qyuu52UcF3jsu8HvMRy6TrGN4V7dNpZd5dP2GrXL5eXlyqXy4FgQhbfeecd7e3t6enTp8pkMnry5ImGw0m1D/oDaRDLaqw72+12qD1N+8fjcUjFuHLlip49e6ZqtaqbN2/q9u3b4ZThxcVFHRwcBPb98vJSxWJRCwsLIT0uSZJU1N8dDE8FRVYvLy/1zjvv6OzsTLdv3w5yMTc3p3v37umrrlmEzNfpeiVQjZB1u13t7Oyo1WqFjYJ4u64kYzAJcHK21gWUmrtsAkC5uOeEgSAPiFC3sxEoJg/7s1hdkfpCBVCzEGIA7mEpFLcbWH+vAylXiA7CvI0YPHJi+T6AizY4q+PsFP2RpoaPxersiM8DYUZnwPFInaVgfmBSpPQuZcCQn4jIfMDCwGx7WJANj9K0hilzLCnMv7MbjDty4c4PBoQNF75ZD8djNBoFVhp5c2afOeU7MZh3eeBz7uGCcUa+3aGk7TBNgOyzs7MAshqNRgCDJycnOj4+Djl/CwsLYTOLO5qupD2a8lWHs3huMXPsjogre94R58DGYxM7a76unAlyB4U+4MhS497f69UakME4ahQzyrB4rVZL+Xw+AF5kyRm1VquVYiS9Xz7PDjA9lMx4+f4IB8XMvwMv9J/nZtJ21wvMh6c/AYac+edywO3sVQxGfB78OQ4ueB+VGlwuYqaY9/pakpSSMcaNd/KZRzT8u7Tf02bQk04UuK5F1hhbd9ZnPZ+2OmProNF1Zwwmfe3Fjid2CvkmT9VTkyCY4jS14XBSq5j16GPo4+xtddvjqRi+npljxoGxZdwceLtz7d+nHawp5LDX6wVZ4zunp6epjbg8m/FDv+PgJsn0JEWYWNerzJc7AxA8Ho3hvrm5udSBVzgxrAf65OvTnUawCfMMa01OMaVsd3Z2UrKHXX3x4kXIi2bOqNbBOFQqFe3s7IT1CbDl7143ejgc6vHjx0GGWq1W0I8w0ThStIeqIB7lAhRXq1Xt7++nZIk+k4UwHA61vb2d2u8kKWyO/+aafb0SqGah9/t9HR8fhxJyhCnicBiGhNxQ9/5ZCNJUmcXepOczItTS1CC7EFKfGgWBwsxkMqEMDc8ASPJsKlWw4J0hoJ0sUto7K8Qeh0C9j67Y4nFyFtiBLs9zQ+rsU6zIeS/tc1aHdgCqqKuMUslms2GzB2XsOCjEGSiewf0AV97D4nWmhQXuTgghLmnK9seOS9xHFDGhMUCiOyyw6hhgH1MPc+LAIU8+djH4QLadXScnlnYjR/zO2HouP2N3enoa8noPDg7Cu3BIDg4OlMvlgoOQyUxrn6M83dkC/GDEXf6Z73i90TeMiLNTswwqbYtZV2eI+AynuFqt6vDwMESg/P3uIHIlyXTD7Hg8DjIG4+csqjQ1ZP5saVpFhHWUJJPa7ugJHHXPNef9PJd559nOpvpmHQeaPjY8DyPI2nCA7A6wM4HO5sWRHx+zWK48esK6AuB5H+mzb45yUObrHCfRQR3PcdDkjKwTHN4+Bz3xenPw5/Lk4NYdAV/3DgjpL+1Dzny+uAcSxQEmlztqMHmetuKkCmwhOoCDssbjsWq1WpB51jBRWXf0RqNRqH8MYPG+MfaxMxnbBfqCnCF7XjnDySkuHxeew/gD1NH76HcH0ow3a6pSqaTSTJxY4iJqKU1PoKQtPg/0D1l13c74uD3AEWEOMplMCoC7bov1B7oQuS+Xy8G5OTg40NzcnFZXV1UqldRut9Vut7WwsBBSO5gjdCDHiS8vLwd7gD48ODgIVdSwhdjhdrsd5s/XLvWlmbtisRhOfvaxyWQyqeib2yVSTCDDSHXp9/tKkkTf+ta39PDhQ0kKdpp1v76+rlqtFv7+VZfP89fteiVQPRqN1Gq11Gq1wqJCKDASrjAlBfYVcMNiddYSxTKL7cKDJJXDQ8vO6iAkUjqnGXDmoD5m2tzAs7hjVoJ3wSS7MXEw7YDHDyNBAUtKecCMm+8cdybGDZyHXB340DYfDx9nnss4YlBcwQA6KZ3j88n4YkxY+CxOxtbBRrwpCDCJcZEUSiNizLlgGekrY46cwF4CoH0ceE8cdqYvsXy6jLhx9wiCjxUXf2MekZ9qtaqLiwv1er1QBsnZK69eQ6Qll8uFmu8APmeZiQY1m81Uu91xok9eosqZK+QiHhNnYrxfvj48jxkngfFwMOMOH0wcCtnXJAoeuUTWnJWmsk+/3w+RDMaJU8j8SHE2b/p80kbKQmaz6bxLB3gwsqwhchdpHwAAkACIjh1pLnfGPbXJ2X7ei7OIUeNZHtZ2hs7n3/WF61gHSA5SvJ3MPT/zPMaOvFHWjQNNfmYMfP25rkIePPLhsuLOiPfZ80IdKPMO+oWsMkY831MJHKSj23wsACseyQBAetpFNptN1RkGbMNgkmqG00+pV0qwsSa4PHLG/MWMqc8zn/nc8jNy6pvhZzlmLnvj8XQDp3/O2LOvx2014z0ajYKTgJ7hc3Qw88DGTW8XpALzGNtRfx7fYa4cTNM+Txdxneb9p8/uLMc6HXl1ggTAWygU9N5776nf7+vZs2ep8T8+Pg7kBg5YrVYLVYhevHihQqGgt956S9vb2zo6OtJbb70V3uenRjebzXDYmeuk8XgcADmne45GI1UqFZ2fnweiCIcjSRK9/fbbun//fnDch8NhytkgOuDy88UXX4T1c3x8HMafg4kePXqk1157TV988YW+uV6+XglUUzoGIOCsMhPpi4BrFhuGsmYxSUopO4AZ/1CInh4Bm+dMo4fHYIZQBNI0X5eL76KwEUr6QNgOxSJNQYEvZFfuKHEvcM9nGGgWq4fBnLkAkLsHjWKi3ShJZ04YBz53MOqAH3YFUJLNZkNpNjYuutFNCU1uukHTmQYWPrnQOEG+iRCF68+iL4AxlB1gLga4zia7s+NjMx6nT3F0+XKD4w6JpNSmS9qM4Zw1z85k5fP5EJZjrr1djJkDN0J0jLPXqXY2EyCJQXAAGpcGg4X1ExsZL0/1iMEafXHQ40wnjiSXM7Cef4pj4saMuXYwzXvJ/2PN4JghXxiISqWSchRIp8hkJhtwPFcQIOcMqkfDmGvXU15uEDIAGeNevoPjVi6XU2MV//PqNrTZ94yQX808ua5zZ9NzvJFld9SRNy7aGjs73haABXIa3xdHbZgv16Vcrntcf9JuZBdQxHhxD7LDffTNx80d9Fiu6L/nHPM/Y+kOuOsPfvYNlcil60v64AD94uIiAHI27OdyOdXr9VSeues2QLo7BbHT64TLrLWJnDKHvkmW+133ufNLG30OXX7Y8+FOkl/ICHZgfn5eh4eHqUgjY8yaZ25Zl9hh+oQOwQlxEA/JQV8hGty5Zz2AK1hLtMntmEc23UlhPMfj6eZzZGN5eVmNRkO7u7upw1Jwxt1ZrFQqOjw8DMB6d3dXV65c0enpqe7cuaMkSXTt2rXw/U6no2vXrml3dzeAZvRfqVQKgNsjLrlcTsvLy1pZWdHjx4+1sLCgXC6nbrerXq8XWOg7d+7o8vJS1Wo1nPdADr2kkOrhbDfrz1MsIcLOz8+1tLQUnIpvrpevV67+gQcOYALosQgcILEIYg8SZeE1U6V0TdZOp6NmsxlyfFic0rRcmzRVzOShOrNBPq0zHy6gblA9JYP2kf8mvXyKnof9/Rmu8LiX/hPWg6kGiLrC8+dI6dxU2uxGyVlQB3h8H+XIZpv4oAqvJDEej7WwsCBJL7FrjA1lDH33ecxKeeqHRw+c3aJvyEts7Og/peRQxg4aMXTxPcxB7IDANsIQebuYWwdetInfGTMULmMCA+XMOQ4d40oaFG2FiaXWKADSc735zFkMGANnYWLDhWzQXpcfdwhchuiPG2XGjnFCFnyeGGcHtHGEhH+uB/wQmUqlEtazG8Z+v69+vx9+dwDOO1lHzK802fwI6+dt8TC2A7SY+aMfvrYZN+aE1AE3+r4O4nsx/A5sXQ959M3HyefELwd8Pk8OvBzAcsVz48AhBuL+bl+fDoacgXT9zfNiYO5tZU2zXh2Yxyyi3xv33efRZTOeC+aeqJvrf352eSBNLiaKWNs4WJTKw7kjeusEAeuHtjloc5voJI//Q0e5XqNNMckAoMxmsyGlxBlsKV3ZBD3B8x008kx0F7rfyaRmsxna5DrHIxneRnSiyxP9RA68P556hZ1j/DxnOt6j48w4Y1osFkM01uWbvgBGcbDOzs7UaDRUKBTU6XQ0Pz+v69ev6+HDh1pcXFS73VY+n9fp6alWVlZULpd1fHysd999VwsLC2GtME75fF69Xk9PnjwJbZqfn9ejR4+CfR2NJqXziGT63CVJopWVFa2vrytJEm1vb2s8HocUOaJpfsLuxsZGSFXp9/uhoMT5+bkWFha0vr6un/70pyqVSvre976nX/7yl7q4uNDNmzeVyWTUbDZ1djY5RfrXf/3X9fz5czWbzZfWpl+z1u7X5XolUE3oIEkSLSwshFOAWJjOXEtT1kqasqZ4Q55D5GwkYVxqKKLkEU7p5bxAnsEC8ZCiK2E38CwiV6x+OatJO6Q0043i4dluJOM8XN/o5e+Ic8DdY+YdGABnnf1+Z2BoD6ADReRs43A4DCfNSdNjc0ejkY6Pj1NgJD6umwXvDLqPOSDe5cDHzMPTfMc3QtFGnJAbN27o/PxczWYzHBqQyWRCmx1MOLMSRwXG43Fgz90hY3w9/Et/+Ltv6pSmJ+gBJD0vHebYWRY3UMjp6elp6vAinnNxcaFWqxWYLcDgrLC+yylj4IbMjRX9dXY2NtouQ37FDqIbf2eUeGa8pnxsnBGF+WODrAMONsfAhrMhB0A+HA5DbXUPHbP5q1QqpTYCErocDAYp9jhmCWcBmpgh83e6M0IfZwFCPgeIMZawq4S00VvOJscAxB0C70ecN+zz5usPfeSy6/3k3YSMcQ5j2UDevI/eDgc1tGFW/2YxyOhdfy/yE8vprDUBQIJIgAGMQby3DZl0R5Q2e1RHUsj9B/DUarXACI5Go8D8eQUQj7SxDrALjAntivUrthG58f674+O6SJoUFaD9rJ8YFPNuJ8Z4v++LQl6IEM/NzWl/fz+lX2JGnO8DhF03eLu4GCvXE5A3cb+xsz6vbsu4X1LYg8Xf2VsRM95OONA25GdpaUm5XE4PHz4MB9zNz8/rW9/6lobDYSgv/O6774Y0oN3dXVWr1ZmVrLDp/X5fb7/9to6Pj7WzsxMyAdC1kAqU5rt582aoBtXtdlWtVsOR46VSSYeHhzo9PQ2VpiBhDg8Ptba2pnK5rKOjI+VyOf3u7/6u/vRP/zTsjaOqx/e//33lcjl9/PHHIW0zn8/rs88+08nJSYgofHO9fL1yTjXlXbzqh2/gQBBQfnzuGxykKRggzD0aTRLl2+12MDB4+w6qAbilUimUHXPDg4E7OzsL+d/u7dIulD5tcaDBZwiSA2q+4/f4IoZVRxm4InYP2L1kZ/rpoxt8z+/C0AGafRMIbeSdrigAZg5Ee71eMLCE3kjVcMfAWZaYcXFnhfGPAS7jRL98B7YDasbDN3rs7e0FNsjBCI4Bc+WGhXfGaScxg8Y1Ho9DPqvnckrTSjUodQckDoYYM97r4dPxeBxO/Ds+Pg4M2NzcnHZ3d0ObKpVKYFK63W5YP26IHLC5gXGHC8PoIMU3mzqQ9jbGQIX3uYPJHDtLxHtiZjIec8AC62N+fj4VIfBUGQAoc4gRZC16LuV4PNkQhvInJQPAgsNEpSBvjwNjQrl+4SShv+LIm4+LV1PgXt4BIOBeLzdGFClm0JFrwJ2DDY+SOBCKAWw8dzHwcoAZs9i8m7HxNeZjAVESA0PXfbGTwPdZJ3GfsSmxDvN0HtruIJ3+0qY4hYefZ4H72PlFznmXs7REhkajUcpOET3hsCjXPS4vsJvIsjumsd50p8LXNn/3Eo8ut95f1gRrAHvggJWx4D7GBTDIui0Wi2GDJqSAj0cceWSd8kz64frE2+3RYOQFQE7fvewjrDWl5GKnGJtPGhlyxHO87jS6Hh3daDSUz+fDZsQnT54EXQugv3PnTnj++++/r7OzM+3v74c5OTw8VL1e19nZmWq1msbjSTpFs9nU/Py8SqWSnj59GtpPn9gzgr0cDAZ677339PDhQ2UyGbXb7UAwsT729/cDU//222/r5OREe3t7GgwGKpVKoYJUr9fT/v6+/tk/+2dhbXMOwg9/+EP1+3198sknKZvfbrdD29bX18NhO/GFzH9dr1eu/sFuZiYRRYZR5G/OWiN8vmGiVCrpypUrqtfrarVaevHihVqtVqqmYjY72VjEYlhaWlK5XA4LHAa2VqtpeXlZ0gSk7e7uBkFywO8K3pl1DAdKnM9YwCxKB1P+rNjQOzPrhs1D3A6EUYCASdrhhtgBkyvpGGSVy+UA/AApjKkrf1dGkgIby/uZLzfYDrD8MxZe7CygOFGynlPMODlTAdBwpersxHg8DikfccgTA4rc+OY6DDVyw+YMj5KgYJ2Voa+MFfdgBF2JS1PnitArF/Kyv78f5KRUKuno6CgYuWq1GgxDXLHEQYePtRtNN4Rxio87qbEzwfy5U+zyyLs9jQC2GAcChyRmz2KGlbaRVoVMkzeNAR6Px2HjJnm4MI8Oovg7ToyzfkTUMJgOhN2pYO0yf3Gb6a8DZNhq2E/P92X8mHMHEcgK7B2ywnsx5g5unVlmXTn7DJPo65C/0XaPMng/uOL1EjvzDvh8bl03uIw62PVnuqPPP0898/U8q4yg7+VAftxZcAfC9ZuDM9qILuTy6FXMiHo0kecyxxAPPNdrVaND3Uagi0gVwzF32+CEFM/x1DHmy6MbjA/zHF/xeQrYKq+dzXhir+lTHI04OTkJY+sb02MnPXYqnGjhAoy6XorLIDrZ5HLEmhqP0xsfXXfSZ7cztLVer6eqW/nGZzYq53KT02y3t7e1s7OjK1eu6MGDB2HsOVKcakf37t1Lbc4eDidnJbRarVBf+sWLF9rf39eNGzeUz+fDGQqMCwfN4HwTebt69WqqxCpOHkTA9va2RqORqtWqTk5O9Pnnn4dqXqVSSUtLS3r06JG+853vBNJKmp7WWa/Xtba2pp/97GfK5XJaXFzU3t5esJlJkmhxcVEvXrzQixcvXpKxb67J9UqgOkmSUPXBGQcpfZiK5yLHgDqfz2txcVGLi4vKZrOhDiK1OVEMhLtgUWu1msrlcvD4stls2LjUbrdDjg8stTPgDv4cFAKQXKHxL2YIWZgYDGeZUXL87pUbXBlcXl4Gj5ELw+RlCWlfrKykaS6lM9oOgJzpgc1GQXrpukKhoLW1NY1GkyL8sSGRpgfDuDF3hYbR9U0o9Il+OVvMmLshBmTEYV6eh8HEsYCd53f67ePt7L2zmd5mgK1XaEFW3Ej66Y2Ud3N5csfG+0GbeT99yuUmJ1V2Oh31+31VKhVdXFyEjYgO6JFhGC0AhM/DcDjdNIdDwJj4znEHhp4KxJh5bW6MpcuzM0tc/g4HSi7bfk82mw1pGIBT38xK+SryVQeDQZBBgA1GhsoDMNAYbmSxVCqFGtQADndAaY+PDfLh+sH3L+Cgwx4xjlR3cEc9XhOsGeYJJ4B5RX84QHcGHPlxsM5zXb5jMO2kgIMjd4rdsfDLWW/vg0eGaLMDX9qBzvQ17Zcz1T436CJ/bgzkfcxcPvibs7asX78/ZnvpW1xTmYsxpLpObB+4vIwZY+N56my894gic4EuYWM049Lv94Nzydgjfw4IPVLM5WwuZf9YL4yfO3rIHGkRcalBd5Yc1NJn5pzx5nffBOyRAf/ngLlQKASih3HwueHkU5dn3oX+c7tFm7CHfBYTMYBz3n39+vWgYxcXF7Wzs6NyuaybN2+GVI18Pq+VlZXAErNHpN1uq1QqqVqtqtls6sqVK6HG9NLSkg4ODgKO8XXSaDRSZRnX19fD9/v9frALbtt93AHhEDa0f3t7O2ycfPHihUqlUpCzGzduaHl5Wb/61a/0ve99T++8847+3b/7d5KkjY2NUEJwa2srEDSA+2+u9PXKGxVR4igy/xwDLqUL//vBIKRr7OzshJJq0jTn6bXXXpMk3b9/X81mU+VyOeQGjcfjsHGJ5wwGgxBSL5VKIYcoSZJw7LYDUQd2zkqjtKR03q8zu7HRcqAoTXd4Mx4whs4UYnS8RN14PA4KjzGLgSYXAIRrOBwGgBHPD9/Fy2QO2u12AHyj0Sh41hgqFDPP8EotrvgA7bzX2W0cK2dGaK+zoW4EPHKAkeS0O58nxkhSSuljbEgr8AoatNnZUq/0gTLi2d4uAJKnELlM8W4UG2AT2ef5+Xw+1Bll/mOHgfud8cIAOnDyNrqDhdF1x8LBR7xTHSPkTGosz/5dN4DOivrn/AxLw9hSsYT5GI/H4QhejxD4PPE5GwQxQg54nJmUpnnYlAsDNNA3wrywjfPz8+r1eqk9HMwJ/cdQk1/p8898eOoQ8+XA2Q25bzyNGXQ+970QDlK4eHfMTKJXnCHnc9rha86fy1jC8vucOkhifbl+8/7SPl+ryBRtAfSgd2KABuBG9mKnABmKo36uh5wscP1OqpDrBdaSRwfi56KnHVji3DlxhIwjNwAbxsnJAGeGJaXa5sSBy5Ez+c6IO6NNWwA/jC81pD365ONMJA7bAtB0J8ntEvPlNtL1jq8VB7vMuzPwjImfgujkhaQQCUQmGT/0PYDT9y/4M2kTpz9i34bDycnQ5+fn6na7Wl1dDUAZImQ0GunKlStBP2xsbATiARLg/Pxcx8fHqlQqoU416R5PnjzRO++8o8PDQ7XbbeVyObVaLQ2Hkz0ii4uL6nQ6WlxcVKVSCZvYHz16pCRJwkZYQL/bW3eS4ipU9LPX6+nw8FDlcjnYynfffVc7Ozv68MMPJU2w18cff6yzs7OAYTjmvFarKZPJaHFxMZS9nXW5DH7drlc+UZFQTVwSD0UBcFheXlY+n9fh4WHYbZskibrdro6Pj18CqIVCQQsLCyqXy0EJ8B5yuIbDYcjj7nQ6wXOF/cIQjMfjEKJykOvGK1ZSHv5kgWFQY4/M742BljNEHnKWpuCMfFppWgnEQRmLBePvLAYLxIHS5eWl+v2+stlsGCveQ5jQr0qlEsAMjgFK03Oqea8zm9K0vJSXbAPUxH2WpvXNUf6MGwpUmpbpc+BF9RaUrZdac9kB/DiLniRJKFfnaSZJMqmt7o6GzzcyB5DzZ5JS4OHfGIi604Ax8L/5XFDflu/Fmwh93WFcnRF244wsYUg8zcafFRv7GCDznThKg1MQA23a6kCaOXewkcvlUlUFKEHo7Kc7QT5umUwmHDRF2whpxuuYezudTjDsvHNubi4caER/OPYd8M/GMkAtbfN9FTgLbHrCIc9kMi+VOmRdObPnY+1zyXz4YRDS1CHjCHbWg+sEnuXzEDO7DowdKLtjQD9ittXb66DXnTzmist1g+s4n1ePDtFu19GeCuF6he95H2cxtC5HfB/g6UDSIwzO0MYlKGmT98H1KPOFnGIz0aHer3gd0v5Z+sSjA3GpQY8WuRw4WMfR8vz3Wc4y80QUCfAdEy4u4wAvlw90FuPsANftL2vUCw6wuZL3ebucaHLATSUtnJckScIa8hxqxtH3uMzPz6tWq+no6Cism6WlJW1ubmp/f1/r6+t6+vSpbt68qY2NDW1tbenk5ESvvfaaHj9+HOyerwVwECVql5eXdXBwoPX1df3yl78MY0BfFxcXw/wiH2Cbi4sLLS4uqlAohJJ+lON0edvY2FAmk9H9+/fD/KNvfB8W7frud7+r9fV1/dVf/VU4Sj1JEvV6vTDOFxcXqlQqIUL73e9+V/1+X8ViUb/61a/0zfXy9crpHyggaVo1whUpLCAlxi4uLtRoNCRNc8M8jI4yciAFO31ycqJOpxM2DbAxEeUGawAAYtGenp4GA+ThPd7l/SAn0/N98bgxHChcdwSk6cltHuJ1YOTfc4U4HA5Ttb55lxs0xtvZXACfh6rcMAIUeac0VVrMjYem+bs0XZieg+5pFu7hcw/9dkbJS1Ch1GH2UIC0w1lWd9QIa7pBY04YB2ct3DjgdGFs+B4sJLvW49CuV6NxIOWsPEaE+UJZYTCQaZcfctcYH0J6AFlfT27ouJz1dDlyGaG9jA9MTswUutw7SHDDxRhgHF02fa9ADL4cvLNOuJ855b75+XnV6/WwKTNOh/I5xhBSOxenBkfaL1+zMDfdbjcYfTdC5EoCoogUACCQXV8zzDWOJ/1ljhqNhjKZyYYiImTuLHkaj4+hlHbUaQNjjdw5MRCDZgfGPkc4q7HRj/OEuWKA7TrPQXsMzr29fNdl1NlI1/fxO/mZd0vTk0a9LXGfAU3OrnskkItoB/qRtevOokepXD8hH9yLrHnKFXMTs+1EWmOwGRM9rm98bD31IbYTPt7IGzrg4uJC5XI52AGXD9ejSZIE+0q+NylpmcykVCAA0J0XiBvSXhiz2Ab6e4hK8DOXpznRZ5dL/oYMuZzQX9rB2Dpzz7h6dHJlZUWnp6fqdDqqVCrq9XqqVqt6/fXXdefOHZ2fn+vw8FCZTEbXrl3T3t6eTk5OtL+/r+fPn4fNzUtLS8pmpyfess+jXC6rUChoe3tb2Ww2nERIO/juYDBQtVoNbWw2myl5ILoyGo20sLAQZIhnZDIZbW9vh+ckSRKOQS8Wi6rX6yF3ezwe69atW2q1Wvr5z38eIn/YKvbIkOZ2cXGhW7du6e7du2o0GhoOh38noHbZ/Lpdr5z+weVKB9CAxwlo5DukduTz+bDAz8/Pg3dE2gY7op88eRI2GnpJLb7DhYJy5tIZRgcnDl5QtixqlAvK2XPV6B+LF2Fxg8R7HHzxfGdanNV2wMzi8GfzMwABRcoivby81PHxser1uhYWFoJT4YBNSh9ogOEAJPgmLme/YVDds4eRiRmiGEy5fHjfqcCAIwTAmZ+fDwbHgY3LlzQFu87aOwACzPOu0Wj00mmNfObygJPD9xwQ4gC4ESESQN9RPLQZBeehdh8bBzw+r25MPAwOkEZ5OjACVDgj7U5qDHIcsPvfHKjFBt3zWvm7g2gH3eyBiOWW56+srITvzs/P6/JysjmGNYssEpHimfQJlsWdYJxaIlS1Wk29Xi+EY5HnWMnXarWXIiHODjsAJgIAOwRoZozQF8Ph8KXT45gHZxO95BzjD0jgmQ7sYofe17ezt66fXN8BOH3eHFw5yHJdFpMIsa7jfS7j8bP9e05kMCcx+I71trePdzk45b5yuRx0GheyC5AgMsGFTnP55rmsKfQ2z+DvcSlZbys2APmX0iVHOXjLGWIH0L4OJYWSaHyGDiWq4mPEunAdRn8Gg0F4L5frqG63G47adkCXyWTU6XRC+7wSEjbToyW020Gf6znWhpMr6FS3YbSPi/c5YeAOOzqF57merFarwR6sr69rb29Po9FIb7/9tiTpzp07wXYsLS3ppz/9qbLZrOr1emjjL37xC41Go1Dys1KpaDweq9Fo6OzsLByqUiqVQnm7+fl5PXjwQMViMYBzHCSOpCeSt7m5qXa7HXQPwDuTyejo6EhJkmh1dTWMB9FuonLYc444H41GgdDsdDo6OTnRrVu39N577+nDDz/UwcFBmFPWT6fTCeklEJyDwUB3796VNClJ+vnnn79UJemba3q9Mqj2kBI7XguFgjY2NrS0tBRqLe7v72tnZ0e1Wk1LS0taX19XPp/XwcGB9vb2dHh4GFjscrmsYrGofr+vw8PDcHgDdXxhnRx8sNgdUKO0PPQmpWs9xyF2z6VGMHl+/N44zOrA0kG0b2JCgdA+SSlwi3KJARAKF2ae1Ag3vFevXtVgMAhlDlFuABJyywGgzgJL03rLzjY6Q0hb+d8VAtEIgCXtdiWPcgAcwmq4l+2MOjuZvfRenFPoDgJt9oN/vPSZRyc8DQBWGSDubJCDL9oZs/VsngNgkbpAn0h1oL3Inx+37SzceJw+wcuBLe+JlRjPwFg72xw7dDFIYe1g9HFMMZYx2HLWPQZjzsRTDcMZeBwX1jlj1+/3A0BwRpd5BLSy7gAyvubz+by63W5qs2On0wnrPGbVXB5IAwG4O/OFA4iBZB5JY/Mx4vkcmkR5KwDjLKDI35BpByF+L2M4C0T6vPr65fJ0Adcn/Ow6yMG0OxmzQLIDaXf83Nlm/h3wMP68wyM+cRt9rNAprmeZAyJprBeAFHrNwa47OLS5UqmETXvx8fU+3sgXoMv1JzqX99EPgC9t86gLZVeJfrgT6+lSPr/+GfqAeWOe0Z/MKWsPuZSUIpyItMH0ukwxV67T48il22J+j9MIPYrrdje2L8iNy5rPh9/HHI7H4+BwJEkSotrx/ZnMJH0sk5mkUwAm+/2+Hj16pPX1dX3729/WRx99pJOTE33xxRdqNBq6du2aut1ucARfvHgR2oJTXq1Wgy3EPpfLZbXbbV1cXKjX6+ni4iKU9kWO6Q99vnLliprNZiAaeSbfKxaLunr1qg4ODjQajUK/OfmVNLaVlRVls1kdHR2Fah6fffaZzs7O9J3vfEdnZ2f6sz/7s3BOwtWrV7W9vZ2K+rAWyuWyKpWKjo6OVCwW9e1vf1t//ud/rl6vp8XFRX1zzb5eGVQ7g5vLTUqt3Lp1S2+88UZY1JzYUyqV9M4772hzczOEYh89eqSdnR0NBgM1Go1Q/eDx48ehTiKbDQFDlINz1kxSSnGS0gAgI5eW8Kf/72kF0jQfGk8WIM8ClqbltmC2EUJnlAAnfrlidHDu3rYbKpSbgxXeRX9IHSFvi/d4aoiniMCieVqFG3tPKwA88dw4XxkZGA6H4f2zQtEOZngexhEAxFxgtFyxYyzZ+OEgBTAwGo1CXi1jxngiHz4HyC3/8z5AmxsSxsbZbSm9y96NjINb/jk4g42Nw5heIoyxon3Ik8shf3NW0o1bzBTF4W8MOwDWIwIeYWFsAFmww179BHlbWFgIO9mRE0A/OZflclkHBwcpBhpDwPt98+ZoNErpAwdhyNfh4WGoBsNhLxgEz/9EbyDnMHz8jmz5JlieyfzAwjtT5qlC2Ww2VIZwhsydYL9i0OpRLv7ujDX38053cBhznsOz3KlyhwLw44Dd2+nyFrPDyI8/18EQf48JEJdX5ND/OdvtzoHrINcrDkhcd/i73cEHxPKcXC6XSkH0dyIP7sA7+cL44EDxPfrIuCRJkoquuGwyH6w1X7sAJs/rRp87WYFuqNVqarfbAaDmcjktLS2p2WyqWq2GylquK5hXSAHmib95qhWfs1Z9IyZ2wNM/0HuMu8sSl68RnyPfTBj/L6XZaAg+0m/QKTj3zo6ztnu9XgCl0qSKyMXFhZ49e6ZOp6NCoaDNzc1wL8QO4wA2oBrG+vq6Hj16pIWFhVTeNg4+Dh77RcAVsNqXl5e6cuWKCoVCqG3NnPv+qJs3b6ZSTTnIDNAvSTdu3NDp6WmohDYcDvXpp59qOBzqH/2jf6SFhQX9+Mc/fgmk0xbHUO+9955KpZI++OADffe739WzZ8/0ox/9SNIEbK+trYUzFmZdsWPzdbr+Xukf7s0uLCzo+vXrAcCNRqMw0e+++66+9a1vSZIODg7U6XR0fHysk5MTVSoVlctlXVxc6OjoKFUqxkPnXkXC87BiY+SsDkoQ1gJQh/KLmSwANSAOBerhdmmqDCWFcA/j4QaFy8PqznC5EeR7w+EwVYPZQ8IYSmcnpSkrJilldGhzfLoUoStnRWi/K1ZPU6F9kkIJHpcD75sbJq+JzXjRLubCw30YRfL6kmSyYcXZbpc7B3U4G55CFIfnpWm4LJPJBKaU8FtsFBkDZ8lwVrgYCwfTDszcacNo4BAwrs6I8UycFuaKtmDonRGMHQf64evEZc2jAw66XUbcqPJ3mF3aj8Eh+kRIG+fZQ8eStLOzE+QVo4Q8AfA8KtJutwMYJ/Ujm82GnHRSPHwzr28oxsAxBoAXabrfIHZWeDdGB7mQFHQbERfy8505BQg5exm/xyMjHn1xGXD2ljlwJ8/n0/UTz40jK+gSn1+vxuPv5Xv87CBWSp+g6u+M2XOXTwdFzmizHnyM/PmAUmTGgbs7j7FDQQoffed5rDWvUe1AJZvNpjZb8313UogcIrP0wfUT84R8ugOPPKI7vMoPz/Q5pC3YB77PGMzNzaVqXo/HY5VKpbCXAD0YO9boXN7tuc3IiDP7DoI9xctTY3wNud6K15k7uj6n3rfYzjPPOCuMCbqGvgAQwQ4ub27vcrmc3njjDT19+lRbW1tqtVqam5vTb/3Wb+n8/Fzb29tqNptqNpthTHO5XDgnA8fi/v37wWHCRrRarVQU0UmBer0e8A/57pRElRSqexweHobxwEa12+2wqbrdbodIPqkaksKhYUmSaG9vT0tLS/qt3/ot9ft9PXnyJJRnXFlZ0fb2dkjNPT8/D+kn4/FYnU5Hu7u7+if/5J/oyZMn+vDDD0Nb1tbWUnrjmyt9vTKohhHN5/Pa2NjQ5uamRqORtra29OzZM+3t7YX8Q8rDPH78WE+ePAmF1glRoDTIv0RJAYJdIcdpCdI0p9oVGmFAVxK0W0p74x5iB5RL6YMueA8KxnO/ALlfxQLQHge2zrLybMAAi9I9ZK+jTY45ebxxf7wGJ4oEVhJF6UaY98djytzwP20ll5SL/gOyXAm6A+CgzccFpex5yMvLyzo5OVG/3w8H/3BUOgoYYOjAxSs5eI6rM/fMhYe7yRV0xp/2xWCTzzGkni5CO2A1HQjG/XcGjHv5nTFzYO8yyOe+iYg5dMbVIyGMWyyTGB0vHeZGLV4DMdPEHF9eXmpxcVH1el07OzvB2HtYGAPCfONQn5+fq1araTgcBqPBHLEhBzaJPntImegOazaWZd7vgIXLdQdAyZ1X0oMwNPl8PuRJIgMnJyepjZAACoCHA2gH1D5fHoVJkiTsmeA7zLPrOQeX8TwhZx59Yq0xbrNAsIenXV4d1LAW3Cnw5/C/9/+rDHDcnxiEu6PO/Pm+DqKSMSspTU9Cpd3036N/HtHxvjtwg1lE73kbaQf/A+z4HTbUdSj95DsuB7Ct/A75w3d8HJ2kAXTxPIAT30P+nTCBTcVZRK+5c4fMu91weeX5Upq9drLLxzeOBCIf/mw+c3vNeMdRS29ntVoNLDSb0Xu9XqoqCM8plUq6efOmHj16FJyf5eVldbtdffDBB4F5T5Ik6Cb0M6Cdcr1XrlzR8+fPtbCwEHRXLpcLdqVSqejw8FDZbFa1Wi3oso2NDc3Pz2t3d1f9fl/z8/MhDZYIHc8qFova399Xt9vVxcVFqD/NBsdisajT09NQ4o553tjY0Nramn7xi1/o8PAwVE/K5/Pa2dkJOiyfn5wdcvXqVeXzee3u7mptbU3ZbFZ//Md/HE5+zGQmuemXl5e6du3azDXNfMR66et0vTKoxlivrq5qeXlZx8fHIXXj6tWrevfdd0OYd29vT/fv39fe3p7G40muEYYV5eIVRADUntuJgnPWDgPmBjIGKnzGMx008zzfXSwpeIwoVP8+7UNBYrRceXmY0z1V+oBS8ZJIGAre4wbZ011Y/L673RUxJfXi2pwoLxQ+yg2Q5UwIi1ia5gu6sWKuaDeGDnDmoWlpWrWANvi8BwE0NkOSjo6ONB5Pc2qdUXOm2FkiaQJmSfmBqfZn48A5mAD4UjHEHQQuxoV3SgrMAOOCPPp3YxDq7LwzRIQW3WkAKNJGxiwGLDgSDrA8mgNjh6xJ6bQXcop9ndH2GEj5GNAPZBzj3+l0wgljzD+yBqBhXo+Pj8O41Gq1YDSKxaIqlUpgYJhT32xHu3x/h69Jrq9S7vH6cZDEs9jl3uv1lM1O9ylg+AgxA4CdnYqveE3yDtdf9AUD6c4iY+xRE28/8+OsKJenQ8RsYexsccVjyHg7YJzFfDI/PgYebRuNRimQ6rLleluaRrricnX+bO5xJ9IZ+RhYnp+fp0oeOkuKXvQUCge9nrJBnxmfuCqP2zLWLu/zPUCAROwKBAL2oFQqpewj7WU8nTnd3NzU6empjo+Pw7jFcu52FXDka8QjX56Owmcub55Xzv8chOJ/Z37oE9+PCR6qM/k96FA+p6LXLNsP2JWmJ7QWi8VUWVSOHT87O9Pdu3dDHnSlUgltuXbtmnq9XpAJ1qI/G11eLpfV6/X03e9+N+wFoxRhNpvV+vq6Tk9PdevWLe3s7ARS4PXXX9fy8rKePXumwWCgxcXFoEscR6ysrCiXy+nw8PCl3HRPZQLMO86gMtmDBw/U6/W0tLSkubk5tdvtcFgVOIQ6/Z988onOz8/1/e9/XysrK/riiy/CPhicyvfffz8w6d9cs69XAtW5XC7kQc/Pz6vZbCqbzWptbU2bm5taXFzU7u6u2u22Hj16pF/96le6uLhQtVpVo9FIVVLAoz4+Pg5GyxkEQBRAAUXC7yg5BAOlxAJy8AdD5UbPWV1X3hjnWQwQisH/Tlt9MTjLiUJ2Y8N7+bsbNQwPDLUbfebAfwfcVavV0D5vO2MTgwZvH44CBo8QtjQ9CtVTKBzcjkaTExnpO8aNMQX8Mve0BRbQFTWsJO3H2Di4Zy7jU6WSJAlK1wG4M6U+d5eXk0okCwsL4e+eSx6PI3Ptm6w8HcjHB7mDGXDnCGYAI08/mRue5+PjIMflmmc40HXHLAYks1hDgIMbTncA+Ww8Hofd7h4Spz2np6ch7cuZQQwza9uZZpwnIhLlclmNRiM4Qd1uN7TZw+wxQ+qVGOgT+sJD+8yZr7sYbBK14GCq8Xh6QA1sFH2C4SfSFAMSnof+illfB9jMCyW5aH9cLScGsA7iGCtn5JFX2hc7uPHcIysOXOI0AJcbrjhFxe9zAsHf48CKtY08Oes8Sz69bjh/iwkVdDZtZZ04yHdnlTY7O8u98Rr19vF+Jyn8+9LUGWAuCN17dC12vkiZY/2w7hzIJskkVanVagWg54wl48ezYfdhdXHgkUO3LTyDz30jsUc7WPO0cZY8xWPmkRe368iy6w23r65PIXVoO2X/kEEiR1Qcu7i4SB16k8/ng2OyuLio09NTPXjwIMwXumhtbS2krfo5Dp1OR9evXw8lY5kT5u7o6CiAWfLM19fXlSSJbt++HUrXeXU0MFGtVlM2m9WLFy8CkYaukxTy6NHNtVotjAe2t9VqhT1u/Mz4FgoF/e7v/m44IfHzzz/X22+/rdFopHv37unhw4eq1+sBVzUaDTUaDZ2cnOiXv/yl6vW6vrlmX68Equfm5vT9739fa2trGo/Hev78eWDKPvnkEw0GA927d09bW1saDicnBHGUuDOMkkKIn2PP2QiFQEhTtiEO9ztLJk3ZPleu7rWhwN2zI+UE5RyH/1jUtIPLw1qAsThsybtdCcEOOkDwzTUoOMYCxcyzKJvjTAALBNZYUgBZLDD6AxB0dsYdBcKljCEbCGEdAb9sGoU1bzabYeGy4B10egkrDA5z5OPoho95Ozk5CYwzipQqKChe2nV5eRnybbmYT3JcHQDDZDCO5XJZ1Wo1bGLzsKmzV0RXYqDKfDPuzpri5cOKD4fTKjO0nTmZBaTdocTAMA6zqly44fLx9U3GHsXxXGRkOGYjkTucON6HQ9TpdIIse46lr0GcNzeujDPtQc6dZXe21QG/R5NmgWN38HiOrxXG1h2L0WgUjB3Pw2k8PDxMyTD3Mz+0yYEvugq5iwE/gM11mjuVtNf75s4/OtFBL89nHB0o8lzkyfvpzKY0PUwo/o7LmcssACEG0LTN2V3GyuWUZzowQPZwDpyY8bQb9IADOh8rbMgsII0cuuMbOw843cwTuoG2el9pKyFz2gBwnp+fD/bMU/+QEXQjuoFxJRJ5cXGRqg/NcdgLCws6OjoKz4ojbu5oOAkUs/TxPLst9TGK9eksQMzv7gTxTNL6/MRcdKw77IwprDwMNG08PT0NkdV6vR4iX8PhZM8Mcu+bRZEzIpxsLLx+/bparZaOj4+VyWS0ubmpZrMZnrW6uqq9vb0gf3t7e8HJYT8PdmV1dTU4MEmSqFqtamVlRY8ePVKv11Mulwt7yzxFiH6+ePEiRDJ4dqFQCAAZ4mJxcVGLi4t6/vx5AMyc2pvJZFKpgr/5m7+pUqmko6MjffDBB+F7lUpFw+FQDx48COkgz549C4RdkiSBnSbj4G+73H593a5XAtV4yaVSKZSc2dvb0wcffKCTkxMdHx/r+fPnIc/Qj9OG4by8vAxeHwLAph/fYIbip8KEg1YPic9S9ix2T0NA4aFcUHhchJtihskZIg9XuvdIm9zYOpCH4fQwrgN/B/fj8Vjtdvulo9kJ2XgfUVi+2xkg4W2bxTDBcgLQJIVcrWKxGHIPnX1xhUpfC4WCyuVyavzdUXFQDrOMcfQcPpQ6OXAOJmATUC6eI50kSTj5yyMEbChz5c7lBoa5yuVyoZSj99vTRthIyriPRtMNX6QB0JfRKJ0ug2FgjP2zWTV7YwcSw07bnXEjBSgGL4AIl+sYeHhtZgeHs0AqMouCdqbV0xAcACOXrBnu43OPDBC2ddDv0SvG1uXbx8jl2/vBfc7K4UQ68+1j4GwmzyBi4pEy3unv8NQKxtxBm4+TzyttZ95ikJrJZFLMK+keONued4xsehSINQiz7+PDO1wGcWTj1BvXJbTT54d1zuWpGy47LjOARfpDuo1HyrAD6FBk3McNcELkzTfq+Vy7w+P6xEGzM/7oJ9roa5RxZA7RYUmShAoj0mQTmo9Rv99/KZLgTL0782w+JEWR6g+5XC4clLa1tZWKALgepo2uT3y9MDb+OXLDWgSQOwPtFZtcJ/A+d9y4j+/ydx9nXzeQUdyP7mTDM+MFGcKa7vV6qtfroSJKNptVtVoNR20vLCyEvGJORr1586ba7baOj4/DiYWLi4t68OBBWFNOlJCiViqVgtO9vLwcmN8bN25oZ2cnjB0658GDBxoMBiEyT7oI6WZEODudjorFot5++23t7e1pb29Pw+FQrVYrjHk+n9e7776r69ev66OPPko57/wMS+6nVNfrdW1vb6vVagVQ3mw2tb+/r9/5nd+RpJDCm81Oyo/ijFy7dk1zc3PfnKb4t1yvfKLiysqKVlZWdHh4qF/+8pe6f/++dnZ21Gq1ggCzERHgJE3CFblcTsfHx6FOZL1eT5W58vQNhMsZFilt+AElviBRjO4ZOxPqzIWz0dJ0F76DYRa9s2XZbDZ4ywBBBw8espKm7HbMnLGoYC1o3+rqagD51N6sVqthMXl9YdoIq4QCQDH5u11R+i5336hGG3FyAMCuBIfD6eE+MOteVYQ5qlQqYZwBfm5U3Xni+TA0hPMooQRLgzOA4aZOcTyPPheMi8sMssnYuUKiH8iWs5u0bTgcqlarBcPt5Rj5PmOCMWINuay4wafvAGUHdHGOOTLNRiOejWw7E+pz4mvJS3l52/jZgZTvJ3DDx3dpExdOWezQ8Xucs4pcEZ52Vvqr3sd8wvbGzCt9dvBOGwm7u6xwH86DgwXe66xd/D7a7dGHGCQUCoUUUHAwzbMd2DoYJIRPmwFXRIOQO77rYIn1EUeTfJ5pizvQPgb+u8uGyxjjx3tdjt1Bct0bO3/opLm5uVCHNx7LmPSIWXqcbWSN+XenyR1z1p/X7nfgFkc3HCyRpsE4OOBkDbIeieix0Y3++5z7GJJrfHZ2Fg4wwebQ536/H9rAeOOAsS6cJPA1HwNuZGSW7CBjsc6IIx8uF85m+1pkHnwzN/PpcozcQapQ/3k8nqRlbWxsqNPp6NatWzo4OFC73Q5y544ShCBsOKxvNptVpVLRJ598kkoJPDw81IMHD0LaBpsZr1+/HlK02HS4trambrerTqejpaUllUolPXnyRJK0sbGher2uZ8+e6eDgIJTKu3r1amrekK9Go6F+v69qtapyuRwANfaDuWg0Gtrc3NTKyop2d3d1cHCg8XhCyLmeJJVzPJ6kiFDtZG9vT6+99pqWlpb0wQcfKJfLaWVlRXfu3Am2bTQaBef27OxMy8vLkib7nuJiDvHl+vrrdr0SqC6VSlpeXta9e/f0ox/9SPfv3w+hBQyG13hGUSMA5+fnev78eSj3U61WJaVZMJgUQmTSlCXwxY2CcEBN5QV+516Uik+053XyPZ7rOcQsQt+MxwJvNpvq9XphlzEltmL2x739MPBfsvTOzHo7vDYu3uzl5WU4jALA5mFSB01eQjBOT/E8U5QcbfT7crlcYMsdCMAokhPoGyuZR0oQAfI9hO0sDG3zMmcoaZ8zjIwbN/LjUJyMI+OMco3lBmXNGJJKQeqHO230nbbxrCRJwvG2sYH2/npomM0h/ixncYjK0EdAlJda9HZ5ehBG2Z/na8OBoRtw7w9j7SH2GOBiZAEhvhnMx1eapt84aOGZvt74rkdrXH94+oYD8TgNw9ebR8qIjgBQYDRZx8gXa5+2OkiLQYSHqZ3h9SgGY4csZDLTQyh8bFw/+b1cPjdERBxQUtqQ/iMzPheMD32LmUkHMPxzEOvOUcxourw50cH3mWPXubzPI4SzQDmAgL+RhhGvLdYj7fPxYK48wsH3vCSjM7ysVfSBO8s+Z7Gc+HijQ112hsOhjo+Pg6wgc9zjLLk7UgCbJJlUpKB0Gs92sMsadUeN/vFd14suf9zja5U59L0hjIV/hh13NtrHxlP2iFzFssW9VBAbjyeRWGrhe6R5fn5eR0dHevfdd7W3t6d2ux0IOtbZxsaGdnZ2gsMEEObAuouLi5DOce3atcDM0j8Im3w+r4WFBW1vb6cO9lpYWND6+rquXbumX/7yl9rc3NTnn38ecMDR0ZGazWZg14vFoqrVajj8TpoAZOSu1WoFOYCopFY+Y1itVkNa7dbWVkgTkaakj+sqj/5JExzH2P70pz9VkkxOarxy5YoODw9VKBR0dHQUUkCvXLkSyKzT09Nw9sU31+zrlUD1eDzWw4cP9emnn+revXt68eKF5ufnQ+gCMJEkSciXIg8JL+nZs2chNAmb5QwJn3tOsKdOOBhwJcGk813+ByhKU/DuysZDkwBZZ21RsLQRJo2NYA7SXOEAehz0ooRgKzDOAFWUtNe5ZaOSNGUQ6BdGHTCCgYJZd/bBASGGPpPJhJrQOEWMhaRwAI/nafvfAJCMjaSQ7+xMMACA73g7XKH7XLg8+dw5iGFuCaEyDij40Wh6VHnMHgOm2AXvtT55BilCKFZ3yrzv/C5NAZx/j4iLR2McHLocOiPhbCaOKnNK9CBmn7nI33SHwoGaA6GYbfUxjyML/n1APTJPO9y4x+wm/YyZRe9f7AQ4C03bYybZZR2HKZYJHEHCyK5T3OFxgO4gAH3gBopx43sOHON9DA5QY2AaX04GYCj5nbbRX4/c8HwHOdI0Fc4rz8Tssd/nbfJxj/vrY+Nz4u3wcWJeISBov6SUTnM96PraK+XQthgEeoqYfwdA7v3xsXbHj8vn2XPckVUuUtOcIEIfA4zQJc7i+9rwPOHxeBxsKHM7Hk/rJXMvffeN2J6i4f3yEoSkYrlTxHhQFcjXla8j3ud98Kge7XBZQIadOWVt+/i4PiBXem5uTvv7+zo/n5z62mg0ArgrFAq6d+9eALo8M0kSNRoNHRwchE185+fn+ta3vqX9/X2tr6+r0+kEwM1hLrdu3dKDBw/CXCdJEmpQ7+zshEomnCZN5a1ut6vV1VV98sknKRtMRJtUkaWlJW1vb4fD72q1WthACaly48YNffbZZykHHvLr5OQkRJDr9bqSZHrE+Gg0CgQcfUIONzc3lc1m9Tu/8zu6f/++Hj58qMPDwzDnCwsL+tWvfqXV1dVQ5m9hYUFvvvmmjo+PValU1G639dZbb+nnP/+5bty48U0FkK+4XglU9/t93bt3T0dHR5IUBOLk5CTFZKEcYW+73a4Gg4Hu3Lmjs7Mz1Wo1ra6uhu9Jk0VHrUYMHGAsZksAVx4KBYRLU/YTQSPfCsXHszBMHnIC+NImZxpQMjCapE3Mz8+rUqmEsKWzGA6wUBAx4IzbPRwOw0YGGFT6i8KmD+4c8DOK1o1PbPicsUuSJOSIobxRmCjFOJ8X5UqbcBIKhYJKpVIAFRhGAIwrX0AvThYMKQo3ZltoFwqSZ/h8udOBc1YqlQK77gYC5tBDyIwdjIWklwyuG0v+zv8+3kmSaHFxMQV4eK+zUhhj2iC9zE466HGwyuVMoa+XWYDbDaYDez53Bo7L2c2YZeUeZB+ZjtvC3MSRkti4e38YF99M4yFc1rGniXn7mXOeySZEHBUiLb6m+L6DEIyeO9QwSIypO40uGz7efJe/uTMeA1BkxMtDDofDUOUGw0/bKX0VO20u2zw3jijEQNrlmXXmzpW3kzbEsuEy5mkIXHHqhkfLfF5Z6zzz7OwshO4ZE2nKtqOH/PkQIvHYcp+nI7mz6H1BbtCbl5eXoeQaYwAgpf3uHJOqxVz6JkXkFCcKh5gNeJyyiHNIH9CvbsvQ774+fT5dVzB/6CHWMHrW73cnLCaS3Anmu57Ggf50ZwgZqFQqYQz4nDSGmGTIZqcHQF2/fl3Hx8cpTJAkiTY3N9VqtbS3txfmiXl79OiRhsOhOp1OcHqz2aw6nY7y+by2trbUbrcDdun1eiqXy2F8SIPo9/s6PT3VYDAIG/lIF1lZWVEmk1Gz2Qxr9eJiUqf/6Ogo5EufnZ1pZ2cnpJ1Atj1//lzSJG+bze1zc3OBuc7n81peXg5MMnK1vLwc0k8++uijkKvNdXp6qn/xL/6Fzs/P9e1vf1sPHjwIJzlubW3p7OxMW1tbury81PLyciot6fT0NDgnP/zhD/WTn/xEf9vltuPrdr0yU02uGyEojNTCwkJgKVn84/E4bEok7NJoNEIuNawJSsFDaQ5wAGIxSMTAojRRQM5AAWovLy9Dfi4AjvfwP+1AKXEf+Wsw2byvWCyqVqulAJIboZOTk6AkeS5A03P8uLwN9LfX66WMPsrQFT1GJJvNhnrVMVuP0SC0Sv+lKRvjudiSQsqJs0coFwwszhAeMdVgCBUBRhg/NwL0i/JBXuEERUm7Y+DmaQeMgQNm/saYeEQB2YhruWJIPMeTeUUeMXbOvEtTp4M5Zb4ob8XcuYFxYz5LFpkbb587UXFKVMwcxgYMueVnZ4s8EsDfuddBC1EBz4t0wEM7XE75zEGvj0fMHPq4MlZ+mIc/LwbqRId8PTq7zv+EkDOZTABg8RpmX4HrIq+vTIiZZ3pKBv1wZ4X2u6PufeQdMdPLeHl1G38nIM3nIP6ddziYitvp4+lz4I58fA9zQdu9fXFbaAfr2EEb0S0Hojj5rFUvV8nvyLs/2x0p7qENvJvxRnaRS3SX62HeQZuQN76HXPm+CmfPJaVqjztx4HMBEI7XmzQ96CNOT3J553/mxplq5pHLIzROkvA3jxLHNtllk/fFOsCju0768HzmvVqtBocDZwaQ6FEtdDLtKRaLYWMm8jMaTXKvDw4OUkzxxcVFOMSFMsAuq5eXl6rX66E6COPV7/dDmT5pYg+XlpZCyt/FxYXW19fV7/e1u7sbxqnZbIY2eTQTULy2thbIK+YT29vv98PBWPV6XaenpyFXnLFjfDOZaWnE1dVVvf322/rOd76jP/3TPw0g+8aNG3rttdfUbrf17//9v9doNNLS0pIePnyoq1evhtriEKPgnX6/rytXroQUy5OTE62vr+vp06fa3d0NBQi+uV6+XglUI8BnZ2ehHvX8/LyWlpaUz09PGwPItNttbW9vh4LpbFaE7YkVNYwek4syY8EDDl3BxSDYgZWDCw9xIvyAN9917iCOxUU4hfcCrGIP3vsAYHMwJ6VZItojTfP74sUeK8eYveTZ4/FkUyMeP88ZjUYBcGMMfEcwypB2OLMcgxv6hzEg35r2EGFwcO4g11kzjAolhciXw4Fhlzv5zv5cN3SMqTRNV0FRwQ4TBgOEeFqJh9d9vt2JceAXRx9idi9mkJm7mPlF6TJGXm/bnTBkIAat/rkbbwwibXPwE8u1rycMtM9NzOph1Fif7vzxvbidPu+0xQGwj18MxJBXN8ge4cHQOoBx4ECfPLXDWWh+d0DhfcbAutyyVtAp7sjEa8bHEQLBQbg7dDzfgQTOL5sbeb+DMwdizrw54+jgxnPIfW6RHdrl69zfG7c3ZkFd5zKuvlfAmWDmklSQbrcb5mFubi4cQe36EL2Ns8N7AbKAKXQpa4nxmKXXaBM6BpDpa5d7vBQm0UnYSCIKPtbMPWB/Fuhknj2FAxuJvvbN1PzsoNvl1Nelz23swDJHsb52J8vlgHe7c+Jrxr/rBAVODoQLEZ5CoRDyl5nvarUa+sZa4FmkUAwGg1TVItbHt7/9bd27d0/vv/++vvjiixDRqNVqajabwc77emYjXrFY1PLysm7fvq1KpRKcGN8fU6lUAsBkPNlE6ERKHPkajyeH7VSrVTWbTZ2cnOjNN9/U9vZ2YK2Xl5d1dHQUNirW6/UU+ebHj1er1WAziLYRmf2TP/kTPX78WOVyWZ1OR3fv3tXdu3dDVGR1dTXojhcvXgRStFqtamFhQf1+X2+++aZ2d3fVbDYD2fD+++/rRz/6kVqtljKZzN9aUs91xNfxeiVQTdjk9u3bOjo6Coc1SNPyXNVqVaurqzo/P1e73Vaz2VQulwv5Q0ySV7zw0k+AQlciGCwMBIoT5edsFADSjb8/R0qf5gSgcBbHlYSzCrQPQ4/BRmHCVAGo49AhKQUoeC4PLZIHGrdZmpY3cpYZpQij44wCYwrLB0hNkiSUyKFfPt7kJAKiCMNyeQiXfvkJaChzlAuOCIqS9gHKd3Z2wtwxfozrcDgMKRqwcjHTTBvoh6dbEBFxJseZZ5cHZ5pmORTOiDqw94iBzxky6g6iz7czgxhS5tBBc8xw+Tw4iHTWbRaz5CHUmMVC7uMNTL7+fENq3F7a6ZuRfOz8HePxNOfQgX58H0bZUwL4G233KhcxM+tzliRJ6rQ65JB7fWxJLcF4sm687Ju3gffFfYkdBmSX390xRy58nhlnT/dg7HwsiTAwP65P6avLA/JGe+m3EwY+Zz7mTjz482PZpP2uhwAivlYXFxdTZSmHw2E4DMjTEngfABNm2FlVDlRxp4dDKnq9XmpDN/fEepo2MK7Ig0eYaO94PA7RA3QWNgcd7s7yrMsdY4+6Sen0mFiPuEPn8+Rj60QPOs6dNp93byO2DTscR6CQUWeeWacxyD85OUnl/LK3xHP8AdSSwv+MOXYHe+TpNqSekof86NEjSQqHzr3//vva2dnR/v5+Sp/i4NVqNS0uLmpnZycwvoVCQZubm+r1ejo5OVGv1wvkGydcIi+sO5dr16dupyUFAL60tKTd3d3Qr8vLS+3u7gYsxTp3BxOGfGVlRYuLi2FzMgf/XFxc6Ec/+lFw3Ch33G63QzrRwsJCanM/aSQ4oMViUb/3e7+nv/mbvwn6bGtrK+Sbe5T+76r+8XW+XglUn56e6vnz56F+IR5dzArm83nt7++r1WppeXk5CHO321WpVAr3c43H6Y0TAEQPgYcGf8koOLvK8z1XNgYTKD6egVGXlGJAY7Dh72cBZLPZ0Id4cyTGgIVIu7g33jULQ80i8pMePe3FFRsKHAPJ92KDSZudYWQeGTdnAuk/3+fZznh4WS8HiZ4fiIF3Vo6FzDjAUOEpMw9u2HgvippxInLgpQwBEr1eT51OJwD9WU4DbJczCwAOQIADyxhIxSwjv9MvlyNnBGeFPx0sYFRxLJ1F5XOeS7vj9eEGks/d6XTD6E6lG4oYFHoUxz/37znL6X30NUpfUPIOVOI153OFs8Za8nH2PRf0I3aEeT9rC+aUzcHO1M3PzwcZpR8OcmIH3GUijgI4S0gbvupzZ+D5W+w0xbLE990AO6jzcwLI9fWUMECL61P0rjusvNP1Cu9ypyCWA5dZjwzRBioYHB8fB/3k1T4c3Hv/0BHslalUKqEqUpIkKV00Go0Cs0mbvG3ubMTsrKRU2UU2z9E2yv25zHrkkDQGX/f8jJ0gb9dtBYQB4DYuN8s8cs3aU+LOi8uOR3W8rThBfO6VT3imrwNvq8+x2+XxeKzFxcUwf+hybB7nK8SHLZVKpVTEdHFxMVT2gN1GlmBMq9WqCoWCjo+PJUnf/e53Q4k43ul2jpxh6kozpoVCQbdv3w6gmPQUaXLAVaVSUT6fV6/Xe4kY4B7WJJHjzc1Nlctl3b59W9LEcfj+97+vXC6nn//850qSCVO8vr6ubrcbMM5wONTh4WFIXWHu0GOHh4fK5XKhmoivt+3t7eBwIJtnZ2d6//33dXh4qL29PWUyGbXbbQ0GA125ckW//du/rX/zb/5NsCu1Wk2dTkfHx8f6+c9/HuZkbm4ujPM318vXK+dUIzCkc+CF7u/vhw0Hd+7c0e7urkqlkiqVio6Pj3VxcRFC/QjeYDAIJ/Ktra2pWCymGI8YGMz630PdXAiXGzln0fgdhteZRQR3FnsoTevvIty+ucI3QkpTo8mGAv+MewiPSdOKDQ6waAvt80XMZygEB+yez+V9oi/OLkvp8KEbX++LM3FewB5lG48n48xzYZthLFw5cn/8Tmd63Dt2tgwjyTjzfM9zjuUjHsOY3Wfc4ohGbIRhaDz6EYNOZyt8LB2gudPjgA0j7wAgBl/MZyxbzoTHwJq/eXv45/1g7ojKMJ5xGD0G7XHffb0y3y577qDwWezsEfJ0htnlTEqn7rgj4+yll9pjLHh+LpcL1UF8jtEXMbvN7zHA9PtdZp1l5vs+rowRbWZsARi+3nmH6zi+72k98TqA+Y0dIZ7h742dyHjOmccYSPP9TGYa2bu4uAgpZwAncjrdUUFm5+fndevWLR0fH4fqDzCbcS457cBBICWACgWeWucEDu31tAZ/lutFyAzIBnSQb+7mPXGZU3eIXR+QMsIadmd+fn5e/X5f4/E4EAS+Ed71Pv2I9bk7cMgCz0dvuVy6g8v/vsETmfeShfwdR5mTcLGRsaM1NzeXIj78uVT/GgwGKhaLeu2117S9va1MJqO1tTW1Wq3gTF25ckWdTkenp6f65JNPdHp6qvn5eW1ubur+/fv66KOPUukR6PTr169rf39fJycnoRBAJjMpA9tqtULqBA4Udg4bxL4ldD/YwmXG+3rz5k3t7+/r2rVr2t7eliTdvn07MOFEV5Ik0VtvvaV+v6+DgwP1ej2trKwEJ39tbS3oYUA9QPwf/+N/rE8++UT9fl9bW1shR7pQKKhSqajf7+vq1as6Pz8PxBPrrFgsqtFo6K//+q9148YNffHFFyEqkMlkdOvWLT1//jyk2/rJlF91xTbv63S9Eqgm54yjP8/Pz0O9Q2niyf3iF78ICoe/AbYrlYrq9boymUmCfbPZVL/f1+LiohYWFgLTK03L2cWhWcCHCzKGwpk7z3t24O31nT0/zlmHGBihRAHUKDZnpXgHfXADOxxOduzXarVwUIqHGGPj5ODXn+/sNG3jciaZfjqT7crSmRhSHRzE+/tQEnEepJetgpWJ2+snOaHgYQ0A+W7sMXgoDvrjeW2wOxgTH3evRxyzUHFkRJpGKBxE8Cw+9/JPfMcZbf/MlalvfMP4OiPo34/bFYMpN/7Iox/I4213cMp9kl4ybP5+B2POKPpzPHUB+aRt7hy4THrfPPLkbXQA79/3kL+zhrTNoyf+TgcxMdspTdY6h/V4hMfD1z6mrHf/btxH1xWMqc9b3DfaQb+dXXdHYlbEIAZlDsCZJ2l66lzs/DgrGesad7ZiJ4l74/xu/x9giZwQDRgMBgEUkkrjutMdWQApz3r+/HkqV5l1BwOHvfAa1d52d0iZS+aGtRnPN6DOHdKLi4sAbjzK5RsoGUuXj7iPgGVsG+Bmfn4+ACHsGmk/mUy6Mkbs8HLFrLjLj0dgXe5oG7oZmWBNuPPobK/Ph8uLp+1IExIKx4kjvEmzA0OUSqXURsxOpxP2/Tx9+lSnp6f6wQ9+oJ/+9KepqCG1lJnHmzdvqlar6eHDh2q32yoWiyFf/3vf+54+/vhjfetb39L5+Xl4B7n52ClJoX2SAtHHfrDt7W1ls1k1Go1UZZJyuRxOyGRN5vN53bhxQx988IFarZaWlpZCJRfSkTKZjFZXVzUYDHR0dKStra2wP80jB/5zp9MJvz99+lTf/e539ZOf/CQ4KuiXzc1NFQoF7ezs6J133tE//If/UP/qX/2r4KBKk9rc9LlYLOr58+epVMuFhYUgMxSnaDab+ub66uuVNyouLCyE/COMUL/fV6fTUbfbVblcTikthDqXy2l5eVmFQkHtdltPnz7V+fm5FhcXtbKyIml6AhWgLDZevnHPFQxKLWZTURQOrmmzG2gvUeSLwpUyCsbBtzNisNYYLNqHgkSpUUHENxjxd9rjQBMww3f7/X5wapxJoC2eQzxL6WKQHBD7GANay+WyMplJWSBnJhwgwND4BiQ/SQ9P3XO++buk1IZBcuExlmwy8fQWZIr+eaoCytEBNZeDFr8cgPG7A0QMcQxukDF+xug6OPU2YGwYIwc5sFuMRQy+Znn8zJ2Dgb8NXMfXLGbT+83vzsg7iJXS5eZwlFyOvB202b8TO6XOhs5qt+e3wryyTjzndRbw9XHhXgcJvi8ilgf6zndjIB87Dqwh7vfLIyL87n2bNXbxz+4Y41g62KGPPsYebaBdsbPh73dd5PMEKPV73VkCUPM9/iVJEvSJpBQBQlu4Bz0DwPNKHc4EA/A9h5e2A5KYW2dg4wgXoBFg6Y5irB9chnDOnOxxAO8yRj+YW4AVh4JQf9ijeE5AuPPiLDX6xG1ZnLrmzLo0ZexpazynjA9rkHWB7SyXy+r1eimb5fqPOZLSkSBYfn52QgHWEzsOc0qUoVwu66c//Wk47bDb7apWq4VqHK1WK1QVe/78eQC5kgJ7/dlnn6nb7epXv/pVqFKWy+V0/fp1tVot9Xq91J4l+sXYElEnb5+/oTOxk6TDJkmia9euBaeiUqlod3dXCwsLYc0uLi7q8PBQzWYzyMXh4aE2NjYCi57JTKK19Xo9sOT7+/thTTQaDf3yl7/UcDjUlStX9Prrr+vTTz9VoVDQwsKCdnZ21Gg09PHHH+vDDz+UNHECNjY2UvJ269YtffHFFxqPx+FExySZ7L36gz/4A/3VX/1VKP/Hevvbrr/r7///fL3yMeUIGQtiMBiE/GlCLQwouWyZzKRs2vn5uQ4PD9Xr9VQsFkNoYzweh7qRngrgqQ4AQow5CscBLwbe0wQ8ZYLfpZdZEWeIhsNhaiOlKw1XWChkNxAYaO7jmHEPWVYqFUnTTYZujLzd8c/+HQ8He1oL/Xc2lO8RWvMxcHCQJEn4TrPZDOMSK5mYAWSs3BnCQXIDWCgUUrVQaQugG1DuIXw3lvFc+ngxd573h+LxPFLvK/d5GJ/nONBx+ed/vwc5go2LGUgPz8bPvby8DDvgmRPa4AbUIxcYSu+T98sNcOwMODDDCMYA2NmqGBjG76E93BeDer7P2vAcaK6YsZ813jzbmeTxeJxap7Rp1tx5v5x9R7b43Bl9BxY+BzFwd1mNoyLOks5iDj2dxp3p+Pn8436MuQNq3zfAM+M5o50xqPbxcHDpcusRBQejzobzOX27uLgIRAtrJt4zw9zAbNM2QE4M6gGL2Ah0EvWEGSd3gADsPi/Ik8+R79mBsQPcuNx6jjbOjTuO9M/30vjmxWazqeFwGMLz3W43sKLIictd7DjjLDFWDt7dyUXWfO37GvUIjMuwyynfdfsPKCVy4fbH+8V5C95e+latVsOhW8jxysqKCoWCHj9+HE5NPDg40MLCQoh6zM/PS1IggCiO8OzZM7VarQBkFxcXQz426afHx8eBHMT+np9PDoUByPb7fc3Pz4dcdmS5WCy+VBWEdjAm165d05MnTzQej/X48WPVajUtLCzo+PhYS0tLoWKHNI38D4dDXbt2TRcXF3rjjTf0wQcfSJJu3LihlZUVdbtdLS8vh2o4v//7v69ms6knT55oMBgElhnn4LXXXtPnn3+ux48fp+y6R5BWV1e1t7enK1euSJL+4i/+Iji/2Ww2pMB973vf0+eff66nT5++RLZ8c82+Xjmn2tMDBoOBDg4OUmVWnH3yBTQcTo9bJl9OUurIa1eCbtCy2Wn1CQ/h8VnMzKAgXSH4ou73+6lT8jAmbHYj7IaC8/CvGzw3iih/V15sxAA8Uh8bb56NBA6oeJYbe1JV+NxBbrFYDJ6zs50wWYQr2WCBcfYx9nmlZJCkUMcX5eMgKZvNppSpsxowH86ge5jVnQ826VxeTo6NBjT581HeKHD6GJcgdGPhn7tcebicMfe5dBlyufd7uPj94uIitSnSx9WVjzO//p4YsHE5IGcdMFYOcuJ3cLnj4I5A3Cdk1mWGOfP+8KyvGhsHrN5PvhenVjhQ8OciH86y8izWA3KBQxk7EPEVs4fuiMYs/Ff11+fJ+8fYMqfef2dPvS+eYjRrLmNgy2cATwfZsZPrrD3z6OAsnhsnJNyBp12x8+Vz5k47fXBwj24CEPB+dwI8fYL1Hcunp5/xPYA1utv1vK8BB4nufLuzy7t8DOijPwv5QD/FjpWzsz4+kAWSArhOkiTUQqc9kA4ekWHtw3C6w+BjHMtBnJ4WO0Z+Mf70z9efrz36yjgSuc3n86rVauEI9kajoaOjIyVJEoAxBJgkXb16VTdv3tTe3l6IWt+6dUvb29va2dkJpfD8pGFK6hFtff311/XZZ5/p8vJST5480f7+vqrVaipnnooXZ2dnWllZ0dramu7evRvIoqWlJS0tLSmXy6XqQTPuCwsLgaRbXl7W3t5eao6Rd/LKHz58mCIbWq2WSqVSOCwGu+WpPRzy0u/39fOf/1xnZ2daXFzUxsaGqtWqNjY29Itf/ELn5+f69V//dUkK7DebYdEpH3/8sU5PT4PDm8lkAr4iHeb8/FxHR0e6vLwM6R6np6daX18POKLRaKhQKOjDDz9MOVo4r36Y2TdX+nolUC1NGGTKTWF82eGLYgb0SUopMHJ+CAUiDHzfFTeKxNkyV46AXWd68JJ5Jt/1Ch1soiCnEqFGkQKEuRxce2jLQ2ooHU+pIEcLYIkyoX8ODHife7wsBvpDPU1pykaTo40h4vuwN7CngOOYEfcwOjnotItamBgOZ9+dhXGjxP2M2cnJSVCCKBocK/KfR6NROPEQBR2n0eCIECb0tJUYUDvjGIep48/cUHjfkDV3EF2JOjiSpptXHYw5m+Qbw1yWnIn153q/3IDRDz6PvxtfDupd0c9KhWFuuAA6MYDmH3P8Vc+PwXI8Zg7uY7bd++tj6evE383zaFMMlLz9gFyfX28DoCoen5jBc7bGGT0HjbTZgbgDP0+zcGeccfM5RE7J93TZcblzMsPbBaDC+Ps68P4CXugzY0ObuTeWI2erZ0XZcJA5ettlyQ/CwDH355Huxr0AEi6iFR7lYY7odzyeEDFePxtbwbi6s8TfYrAcs+3MB/p0PB6HWuPoWQd9vV4vHEoGQAMguTy7jDJGPr6MJb8zh/QfW8XeoFhuvdqLO6A4AG6LvYoWaTZO/HBKH7av1+ulmGvY4J/+9KchFfGHP/yhRqOR7t69q2q1qkwmE8B1kkz3THHs+LVr1/TFF1/o6OgobJybm5sLB6bAOj969EiDwUDvvvuu9vf3tbe3F8BoqVTS0dGRDg8Pg9MCg04/qVVfq9V0cHDw0vywmZbx29vb02g00sbGhq5evap+v68nT56oVqulqsdQJW0wGOidd97R7u6uRqNJNP7KlSv6jd/4DX3wwQc6Pj7WjRs3gs382c9+FtJbxuOxrl69GtrNdx88eKDLy0tVq9VQpQNcBrl569Yt/dqv/Zr++q//Wg8fPtStW7fC3CwtLel3f/d3dfv2bR0cHIToOoSZ5/d/1fVVNunrcL1y+geLB6GifisL14EfCwymU1KKqUVZeyjdWRE8benlagOwFb7pz/MLeb970wBxL6HmYNEBNZ87+0u74tAPz/RyXw4y+Nz7wRhgrHg2YBW2gtwm2sVCh/W+vLwMzDLvxOhUq9UQugTw0wbf6OeMmaSQc8b4MiexYyDpJfDC88gbBxgTGRiNRsFb5lmFQiGEAj10T1jQmXlX/M4CxbLhzhbtdGDrBtP79FVgHfl0WWd+XX651x0R/5vLAm3hnxu5mFH2z+I54Fm+htwBiIGGM7qx8xB/xvN8HJyJi+XAf/e2xMzqrDH39/kYOQDjilMy/JnIIGvJAbnLK6F5l4d4bGMnyYGNy5aPIY5yPFYOtPmupzfEjgeAzJ8TOx0O+mKA/VVj62MROz+ePjUr2uDryu/1iEHstKETqUvv8u77IxxYAkxZX/n85NQ3j+ah/2BhfYxn5X77vLp+87XjrJ+nb/BM5oF2z8/Pv+TgsBndZZT6y5eXlyqVSgGgYKu8rbVaLUR/Y2cb++NOO3MTExzx58wv+c2MgzsKjJXLgsum5/FfXExOFAT8or/JGedE3Uwmo9dff107OzuqVCoaDAapWuTXr1/XnTt3tLOzE5x5wPrl5WVImyCH+tatWzo6OgoOJqmDc3NzoSZ1sVhUt9sNthA2HHJmOJycruh7gEhPdGd5NBppcXExlQpCykmz2dSNGzfUaDS0tbUVcuQLhYKePXumnZ2dINv5fD7ICiTX8+fPlSSJbt++rd///d+XJL377rva29vT3Nycrl69qmazqfPz8zDOnOnQ6XTC/HI42vvvv6/BYBCY5GazGXLg6/W65ubm1Ov1Amv+x3/8x2q329rY2FC329Vbb72lhw8fSpL+5E/+JABxCC9PM/vmRMWvvl6ZqSaV4fj4OOxgzWazqtVqYcGjECWFxSFNjTYeE3+DWY0ZXzfcgDMWrbOzsTGSpsCK7/gGJQcw+Xw+tNWBEu/y0ki+kRKFA8D2+pTUiMUIw4rEBfJ9Ew4OCO8lrSNmudzYXF5epnapO5sEi+1AkpANud3k8A2H09zyarUans2Y8n5XzCiiOHSNMi8Wi8GZoo88h2cDpkulkg4ODjQYDDQcDkMIj7YC0CUF5Yo8YbhoD5/5fDrA8s0lDmIZB2ftmBdPfUHeYOsc8MS5xQ6OuN/DZm7Q+Z4bw1im+Z17GFPpZdAwi8mMQXXMgMXvjsPmjJEbnVkA+e/6uzODzOOstrs8831vt//PGqAvvqadtY3ZbQfh/p04L5vvUA9Zmhwowrg5GUAf3Rlgbumbs8E+5vHcOavrsul6gftjx8/nnvXr/YqjNj4/zp67bnFZ4e/xs51U4DusDfKpkWEAHuQD+sXHOnac0H+ZzPSIed47K5WHjcA+7wBl5Jp7nThgDDxtCV0JGQA492gq7/WffXxxEGgL76Ht5CHHaV4ua95Gv3ivM/fMM+OAnAJgGQfPjwfwu9PG327cuKHd3d2AA2AxkR1km2omsM7OsJ+enqpSqWhpaUnb29vhQJjxeBxAr6RQ2ID9Wvl8Xp988ony+bza7baWlpbCCYTSxKaenJzo5s2b2tnZUblcDmAfG+gyAX7h3fV6PaSCwNA6gZbP57W2tqa9vT3lcjk9e/ZMu7u7unHjRnAUmG9SWLa2tnRxcaGjoyPV63U1m83gZBWLRX3nO98JdbNLpZKuXr0aKoYkSaK/+Iu/SB3KlM/nwwZNgHqz2dTW1pZ+8IMf6PHjxxoOh/qDP/gDbW1t6fHjx7px44Y+//xzlUolNRoN3b17Nxwg81u/9Vva3t5WvV5Xt9tVq9XSeDzW0tJSKnIMRolPtIyv2LH/ul2vfKIihfozmYwWFhZS5V8Ag3FOsqTgSTrw5u+cMsikOYONwmYRsGgGg0FYKHwnVjAIBPeh7Alr+eWAGTDu4AKDjaIETGcyk/qWnhPuCkmaKAYOi4G1AJTRDgwFC97zmRFSqq5UKpVgkBxA8r5GoxEMmTsigCJKGKEsHCTyffoLIxSDNN9QhMF0pg9DhJL3UChzgTPFbubRaBTqmPv4nZychEiAVwTBGPlcOksds6cxGyxNc+EdXEovV06IP3MWyuUNIxI7Jtwfj08Mhr3dMYiMnxH/70xbzHLFgMPTFbx/rCV+x6B6/qg/09MAYnYsjsJ4O+mnM8C8OwZGMQvrYM7n24EQbfaUAObFc1MdgPAOQrz0hTWArGBQHMi6rMQOhMsW7Yhlg3s9dY5xiJ0U1wk+Xw7AffzoYwzuYhmKP+c9jOEsZtR1YuwkxevJ2+cAwduJTvR1ARD3i/l1x8+dMMYNIsN1lNsX7BFjBGNJNJbPsA/IKvaMNR+vM1h5UvAkhfrHnr6Sy+VS8hTPJePJ5078+DplTGbJiMuVrwOe4/LikZTYWYTF3d/fD3PLiYTeLg5HKZVKAVhTbYJNfJS2o4Qc7/VN/6RPdDqdEBVgPtrtthqNRsAD2PpKpRJyu1utlhYXF3X9+nXt7u4G4q5Wq4Uyf7VaLYw/hNLOzo5KpZLK5fJLc5XL5fT666+r2+2q2+2q0WhoOByq1WppYWEh2KeVlRVlMpPqWfV6XYPBQL/2a7+mL774Qu12O8xVtVrV6emp/vIv/1Kbm5t6+PChVlZW1O/3tbm5qZ/85CcajUba3NzUkydPlCSJ3n33XS0uLuoXv/hFKCv4e7/3ezo4ONCf/umf6saNG7p27Zp+9rOfaTSapI18/PHH+u3f/m31ej19/vnnL5Uu3tjY0F/+5V+GnPJMJqPj42Pl8/nAhEuTg2tYV38bsP46X68Eqt9++21Jkxw3Th7zC0UlpQ2Cs6J+eTghm82GyiHOJkoKnn2/3095Sf4u0kGSJAnPwdN3wytNFZIbcRQLQgZY4Pm0BUCIY1AulwMj7EYKwO1pFzFgyWQyoeSggzsUPAoXD5ud9NL0IAL6SL+q1WrqWV6+iMWBIXHFjcKIAQ1tA3hwH3Mbh0g9nOkHscDqei51Ljc9aIP3O3sCEwTr7ywSCpgQrm/KcWMeg2BnEz1yQV8dGDlAdaclDovzmYdNeU4c8nd22QGBP5dnzwK3fm/cBv+b1/F1UOyhbr/H5TMG7r52aBNjOgvExX3yFCsUsqeK+d/dGfM15cY9br87H6w95pdnAx4ASi5H9CUGpcheJpMJsjwajULok3a4XDEmPmboufhvyJu3PZNJ76WgPzCFPh+sq1mb4+hHfFRyPO/xWLgD4MDT+xM7er5RfJbcuOzOcgicSXWZwnn2d7oc+t6L2BnwiBV/o152PEYOWp1AGQ6HKeDgenA8Hr+U1sf9npaCjoKB5X/6iTzSJtYpskCaHzbRAbGPRZwr79+NnT0H2C6H7higx8l5Zi3AbmYyk3JurVYrjBF5vufn51pdXQ2nA5bL5XDYSpIk4eRDQG69Xg8yTNm69957Ty9evAh9pfQgzg22rNlsKpudbCgEfHc6nRDxLBaL2tvbCyk55LSzJ8mJqXa7HYAk+c6MRZJMK5/cvXs3fM4mSIA1h6Jls1nt7u6mDqv5+c9/niIZ6vV6SG25ceNG6CfVSj7//HN1Oh2Vy2U9e/YsOJGffvqpNjc3dXx8rCRJ9Prrr+vevXtqt9v6oz/6I925c0e3b9/WH/3RH+nf/tt/q7m5Ob322mtaWlrSRx99FMYnl8vpvffe09bWln7xi18EW8x6WV1dVbFY1O7ubugv8h9juW+u6fX3Sv9g44UbVkL3UnonsRscFKunOThz5koU1pSNhSx6Z5CldMUPN0rSNFTnFRRi5exGxp/rXrqDbv8OBer9Xgcl+Xw+5JCTtoADQXkeLvrnmx391KJMJhNK8TEeGF/Gy8vloaD8mR7a453eVk9FcMPoLBP98zCyjw0F8AGsXt2ENp+fn4ewlbP53reYzaNtvB9DlSRJyGPz9Jp4Ttyx8Wd66DbO4/Vx4H8MjLOU9I3nsBmT+xy4xIybpznFoD8GzRhgxs6BMjLuLK33i7nDOXG5jvvK77G8xxegC4Ubs2uMpTsPDqJcJ3zVe2LQ7HM4K+/aoywOPhxMOwBCJ8xitTHEnrLDWva2+Ny7g+GOgI8r7cWwMZaSUhEgvu/A36MvzuyR0uVg0ceXPRquB/gZfYM8831+d9AVR0BcRmJQ6/PnTruHyF1/+Rx6Kp3Py8XF9ETFy8vLwAwyDrHseF+xKa7PeL/nZMdA2Z8dOwKkuTFmXl6UU+9802LsTDB2tLNUKoVN7j6ejJ/bQuQBW8p7Ywfao5kuP9KUqfRUDXcwmINMZnJSIrJQLpf14sWLkMI5Pz+vtbW1kCeMgwBTfPfu3QDQWVfIQ7fbDc9dW1vTcDgMG/g++eSToNv6/b7K5XIArd1uV/l8XouLi/rBD36gu3fvamlpSXfv3lWz2VSj0VCj0dDl5WXIU15bW9N4PNbe3l6QCepFM4bkfUsKKRydTkfFYlHf+ta39OzZs1DJg370+/2wxvL5vO7fvx82OTLvyH2hUNA777yja9eu6c6dO1pcXNT6+rpyuZz+5b/8l7p+/bouLy/17NmzsH4A+tVqVfv7+6FEH/XOm82mcrmc/vW//teSpD/8wz/Uw4cPdXp6qt/7vd/Thx9+qH/+z/95KhoOnnr06JFOT0+1uLgYShx63jjrz+U/tmOz1t7X9XolUH3nzh39xm/8hiSFfB9SEVxBubF1hUsVBDc6fq8rGWmSj+0HzTgodiDNs6S0cqrVatrf3w+Lz5Uqz+D7KCrACMraWV+U22g0CqdE+judBaSEDQZLmlZXiDdDoCwxjK60uR8nJj5aGcUEeAdosxBge33nuoNSr/LgbEU8xoToJKXegYHz8noYAGcfAdnj8TiE8Rgn7ueib7Gz5AwSc+HgMnawnB2NARngyJ0D/46nxrjjx3w7+y0ppaQc/DCf7gTwDn9u3D7GLWaEiYwQCWA+uJhfHw/vG/KAgfX16SA+BmbIJmPi6z1mXGNnxj/jXo8m+Br277LWHLTGjHj8M+uJ7zoDHPdVUgqEY7iQbdYrzh9rInZ2YoDsY4cR4hlxG+KIhjtDfjkQj8cBefOImjszPqesGcZ1Vl98zXu7HeR5v+mP62cH8s7S+4EpzKvLPGPuYJByeXE+ONVAWP+eShITAcwF7XC97HM1K/faIx7OqnsKl5cFdGLAnfx8Ph9Inlh+3SGnzdxLSTS3ccyjz6uvLWxDDH7cuQbo8U7Xlx7ZGY1GYaMakZputxucCA5E2dnZUbFYVD6fD+kLHCG+sLCQGmf2/LDJcH9/X2+++aaKxaKePn2qs7Mz3b59O1QOQc4BvLTjzTff1M7Ojv7sz/4ssNerq6vqdDo6OjrS3t6e8vm86vW6Go2GpAkjTRpYPp/X1tZWIAqZA0mpdMPl5eUAOFnn1Wo1lITFrmUymbAR33UvUY1CoaA/+qM/0tbWlj788EMVi0WdnJzo6OhIT58+1Wg0OSkydrhg/Ml37nQ6evPNN3Xnzh2dnZ2FEx0LhYLefPNNHR8f6+c//7mq1aru3LkT7qOKGHux7t27F6IPhUIh9P309FQPHjzQ8vJy2PiIY/F3Aeqv+/XK1T8khWoaeJzOmEjT0JM0BVh87orADQQKEeDIP5SEGzYpHVZH+Fh0V69eVb1eV7/fD96aK1xnvtxYuSLD4MSb8gi1OdhyEI4yjRlaDKZvBKQvMNiMkxsP/9mBd5wy4oYOFgePnkXtm+x8rjAK/O7MBuMKY+dAwQ2INA3fem7xyclJMABeisfZcn52MApg93BmsVgM5ZAclLp8OaBkbgBK/rmz/H4hC87EITMuIxhl+uMVYXgv33dAF6ck+HpwYASw4+/SpG44rInvjudens/7fX3QVt/86p+7DNFfl8E45OzpNM5g0hd3qpBTNxQxoImfzRw4gHBn2Ndu7LwDCnC4fHxcbhyw47Qy7g7IHdTPcgBcT/AOB0yzgCvPdpnlXi5nWWc5vfQhTs3w/notZu+Lzz19jfvnjL+PMXowbnNMLvg7fWOggz8IBp6PbgTQ834/dMTlB93G/a6/+Hu86dqjK/6d2PHlb+7UAvBpn/eP+sPS9NRI2o0e8PKxfj6DR5vQgx4t8AOxIFAgFpCD2IEaDAYv6b9GoxE+R3YAZPQFfb+wsKBGo6Ht7e1go0mLwBYQIex2u2EjIXoPwP7GG2/o+vXr+pu/+RtduXIl4AcvNTgcDvX06VPNzc2p3++HlANSF6jlnc1m1el0dPXqVR0fH4eSeUtLS+p2u/rDP/xDffrpp6n60vPz83rttdd09+5dDYeTPUUrKyva3t7WwsJCSOOIo6borYWFhTDOpVJJh4eHuri4CHupvFb0xcWFut1uCmPgTCbJpG72Z599pl6vp8FgEFJMfvzjH2t1dVXvvvuudnd3g+NBlY/z88np08zD2dlZOB2StJv19XW98847unPnTnjG4uKiarVaSJ3NZDJaWlrS4eGhMplJ3jRHxw8Gg2BfkyTRysqK8vl8KMuXyWRCFZG5uckR8l91ub77ul2vfPgLOU2E8bhcsfJ33/jm4FhKb6xjAbKovcJDDIa53Ehj1FB4u7u7ev78uTqdjsbjSSUMwCYKy9vrYJh2OsDmO3GuIQvJU2GcpfHQPsqLutJJkoQ0AUCA99U3yfA7BoS5cAPMOAMWKYvHuMK+8jtOkTQFMYTnHATEYVwHnc408b1ZYNVDvp7m4ykDgBIH3siQMyYYbowP84FR4Rn8DBtCWxlHTx3AAMaAj2d7/2PW1Z0DHw8HKg7o47QIf5//Hzuac3NzQeHBGnnusF/c7zLO+1CY7nT4fcgS/zvgjpl1wAbrxgEdf3cm0ZlZngc48RA5zxmPx0Hh0xZnWlkXMcDzMYnBL585iGOcvQybt8HHJHZk+J6fkuZj+VXOvzt4MVB3Z4T5c1nku8xrDOAZH58bl0su18c8378TOxTeFtrqOiqWJ8aD+UUOPDLg78DJADRKU5Ya/coa9j0vMVim725raANt9c2oHhKnb+6M8DPsZkyKeBSU+8nVZQ5fe+01bW1tpSp7+Dy43MQ2z/Wb6zzXLw6I3alBhzorzPeJirj+ID1vaWlJSZLo+fPnymYnOctEb7DNlHvt9XrhrAcYUErsVatVvf766/roo49S5EkuN6ms0Ww2tbOzI0n6zne+o2fPnoXKGJlMRjdu3NDy8rIePnyo4+NjDQaDUBzh8vIy2PjDw0NdvXpV9+7d04sXL5TNTooefOtb39KjR490584dnZ6eqlQqhePM6/V60DGUa2RcmIO5uTl1Op3AAP/Zn/1ZWEukZzJ+zGm8jnEc19fXdXx8rN3d3SAzgNZyuazd3V1tbGzojTfe0PPnzwMWGo/H2tjYUJIkIf0jk8lofn5ev/M7v6OnT59qc3NT+/v7+uSTTwKhdnZ2FjaCEhlYXV1Vu90O41atVoNTsri4qIcPH4b2U92IPSSDwUCdTke5XE61Wk3fXLOv5FU8ikqlMn7vvfcCK0foB0GHuT45OQkGi38IqYMuFCnPw0N2pR4bRC6EDcXu96GQUUQOoBxUe65g/PdZ75HSObX0g+onHm70dBVJ4TQogK0zyA5i4vnwXbrOwEiTxYsC8rbwfdrPxWcAfle+vN/BvBtY+ss/rvhEyPgIYpwIN4CAEjd+8/PzqbQSby/vQYa8H4ylhzN9LlHksN5u2H1cPA/bQYIzyzHb6M+IN0s56MJ48dy48owzdbHs0y/uQcYc6CLP3BPf71csX7MAXcxe4gQhr/4u2ufODs91gMt4eJv5jreLsXbQ4vLqz3fm28Pf3jefNwdf6A2PcnhkRkqnjzgo9md5Xx38MK+z1nQMZB38+jzERpqLtrqeYkydfXVHYJaT4PozTvFwdtcjV95X+uj9GI/HajQawbmlSpOn1Xg73Sng+cwLINbbgvPrYWhfh/E8uS7xNrje4/ukRo3H45TeZ2yQm9i2YWN8QynfgSWUFHJs3elErj0tJp5X74engZCCMivi5n2j+gTy7NE/2uKl5iqVSqgvje72dZzP59VoNMIGxGq1mrITr732mp49exbGiw2Cc3NzWlxcDJsRl5eX9fnnn+vo6EhvvfWWhsOhvvjii2A/lpeXJU0qTgDac7lcaNv8/LxWVlZ0fn4e0j2KxaJu3rypZ8+e6ebNm7p371541vr6ur744ovU0d7lcllJkuj4+DhEDQqFQjh9sFQqBbvR6/VC/rfnvc/PzwdQ7rK0srKiw8PDcFhNp9PRwcFBALSQCpxEvbCwoPfee08/+clPdHp6qmq1qqtXr+rw8FCdTieseZjxarUadAGpLzgIGxsbOj091erqqu7fvx/WJZsgnZV34gS8QAQCx6jb7apYLOqHP/xhcFyazeaH4/H4H8SyNzc3N15dXX1JJv8+1/b29sx3/A/5euWNiihP3xhGmgGgBiYB757F7zWL3WADxFx5SlMFSZjLWU5ngOJNKnH43gGCP9eBnpROwnfgx8Xf4lqfLozeZ2maOsHzWIRu1BxcuCNAuIYd684ODofDkG/GrnbYXt+VTmjU++6hS1fgAE5AoAOOGCBwHyFMKb1xjPvY0MG98UZTaZqP7ODc583LLPJsjLbv3o6f6xt2vio1xMGAG07G0AEAhhM5cTDkoWNvI2OHLPg4xuPqoNb748DcP4fF4z2sDwc+XwUIZ4GK+Dt8hixkMtMDi9wwz7p8rhlDmDZ3lmibf8cBHmvMf6dftN9ZyxiA+vOldF1g2h8DbWf9stlsABxf5Qg4MGXe+T8Gqf4c3uvOvQNrZMbTP5yAmAXY3SmLHS1fl36fg2Xej/52WYlBOuvU23h5eRlSFaiygPySPoB+5dnodicmAL+sYcAOQI/oBaAWx9jnxtc/a8TXt88hAN7lKGZx/ZCpOPqQJMlLh1oNh0MdHR2lnBdf4/TfHfpZugB7yudevtXn1Z1cyvfBqEJsefUq5tI3W1ar1fA3ADug29eVR6k5vVCasNeHh4daWVnR1tZWat6Ojo60v7+vWq0WTv4bDodaXFzU48eP1e12U33ktMQ33ngj1aZSqRRSNwaDgfb29rS6uqrXX39dT5480aeffqrXX39de3t7SpJJOT9J+uKLL1JEEqRLp9NRrVbT5eVlyA93uwGgptyckzCrq6tKkiSkJBIdvn79ujY3N1UoFPT+++9re3s7VEphrJGXs7OzgKc+/fRTdbtdFQoFFQoFPXz4MBXZwEFinN566y19/PHHev311zUcDrW1taXLy8mx47/5m7+pH//4x9rY2AgbKalEQkoV5QWTJAkR0OXlZc3Pz+vw8FCj0aRu+tramk5OTrS3t6d+v/93HlMe66Wv0/XK6R+AMmma0O9pEix2Qk4sWP6O4MEGuPfrQJdJ8bAXipKFzvcckHNPzNDxXTcgbpgIK3pI3Q2tP8MXJe8C2AOYSa/A8UCxeJ88F9DbDGAGIMe71jOZTMgBI2dZernKBYDcxzquECC9XCsX0M3fAQwO2NxAu4Hk7+7ouIMSM5Exw+gAgv7Qv/jZDqYcKDjAccPF5aDTZY576Z/309vEP6/84gxV7ND5s33+GXcPRTsYducxZgd5Ns8kQsEhCp7j7Y6TX87MAqhiABUDMr9iJ8L76n/ztvMsd7Li9/tY+bjyHU85iMFS7Ow4OHHd4nPmDmjchxik+nO9HfGcsnaltOMRRyLQhW7E48gZc4XM8UzAqPfTQbs7VIybt8NZYr9woGJZjtNFmHd3+EajUSo/NXYy3Wn38XTHincB7tH5pC/4oVZ8x/uFTmF9kkLkc+wODWst1iFeESl2dnz++v1+yoHm2eh8j9yi8+O5YbyYU3feGQva5cQLbcHRRfdxFgK/0393ppFxSC1SBkmr8APNaBfl/RYXF7W/vx/60el0VKlUAtBk3PP5ySEtp6engYBZX1/X2dmZSqWSfvCDH+iv//qvQ04384FDtLGxoU6no/Pzc7VarTDGFxcXITVlY2MjgPKVlZXw/kePHqlcLmtra0snJye6deuWkmRyGiFrH0IKAotxJW+5VqsFO0vUhPGqVCrqdDqpmuZEJyqVivL5vN5//3198skn2t3dTUUyNjY2dO3aNVUqleA8cNrh7//+72t5eVl/8zd/o1u3bun27ds6Pz9Xo9EINcFh0Xd2dsKBMuR6r6ys6Dvf+Y4++ugjSQrseq/XUz6fD+UH3333XT18+FCDwSAco44jfHl5GUo/4rxwpgQ5499cs69XBtUYbMIffO5Kl7J7lUolfL9Wq4UJQ2kAHjwfcRZYdi8qBnvu6XPfV3lJHs7lvTAqruBQUDwnZgF5litivku401kNZzA87YNwpLcfBU86TZxDKkmdTke9Xk8XFxeq1+uptIiYhXRw638DlKG4/QAE2u3lmxzYOoPn6TIx2IvnkOdz8IwbNx9r30znBg8HAQWYy+VC+pGDBgf3zhDGDOUshteZ1DjvEcYmZu352Teaer/cMfOoiRtWZxYdGMagjbb74RKcvsl4w4b4Jlu/YgfB2TGfN8bGU4XcqXXQxf3xGvNn0+fYiWOsYiAYy70zcb6mfe37uMbfcdDjRmFWWpW3Ie6LO/wO6uOxxEB7Px3wurPK+LqOAxw6MIVxGo0mZc2YLweYsfPucuOAnc9c5uIxi8fU5RGHQJqWQPVnOpHgrGccPQOQ8H+pVAprF73PhcOP7sG++EW6UOx0+Jpgs5zbm3gzLrbBGUhY40KhoHK5HMAHY+L7iSQF8EibYIsBzRADjCVMabvdTskhcsOa9DnBhmSzWVWr1QAaa7WaisWi2u12OC2Pua5UKgEMXl5eamVlRcfHx9rf3w/vww7BNheLxVC+tNlsBjmUpqV2s9lsOD3xypUrKdBGdYnRaKQ33nhD+XxeP/vZz0KeudsmGPQXL16kZOqtt97S9773Pf3Jn/yJut2uNjY2Als8HA7DUeeDwUC3bt3SvXv3QkWQVqsV8pnL5bI2Nzf1+PFjNRoNPX/+POQhM9eFQkFHR0dqNpupvufzed28eTMw365XRqORbt68qY2NDV2/fl0ffvihHj16FKKtzMGVK1fUaDR0//798E7muNlsam9vT+VyWY8fPw7ycnh4GGweaZUXF5ODX/78z/9cl5eXqtfrunnzpp4/f67V1VV99tlnwd4yr+VyWblcLhRx4L2j/197f9Yc+XVdiaMrJ+Q8AUjMQKHmiVVksUiaFCXZoiWLtmS727YcPUf3i1/6PvdnuK/34b44bnSH/WDJ7bbD7bZoybIpkuJcM2tEFQrzjETOiQQSOfwf0mtj5amkOih1t/taOBEIAJm/4Qz77LP22vvs0+ocyZ7L5bpIL4b3ptNpVCoVm3ufV1RP/CKWLwSqKThkXwnGuEAwbpUToNVqmduIyoYLCZkE4DAEBOiO/1MmQYEAlbIygcoaAd0LrP64wIWg02U2uaioMlPmiO10WVZl5tT12W63TfEoi6WLtS6wytxTQR8cHNhmDabB4biwTpwIutjzPeqC530cGw2hUGOA4IoMBtvLxcZdaJUBNCHzHx4qo9lCqNABmNJhu6jEms3uTY76bPatstkav6zMv/azFgVXCn41NpvP4yLjuql13HUsfxrAVGCpxozKngIblT2OkxqcjO+kbKii7MWKKeBQUKbzxt20SjnTjaA6d9S44t8KWpRRU0Zc+4Tfax8piHXb0stAYd04B9Sg02fovXyPygfvU2+FtsP1HvQC1PxOGWodQxegs997yQnnA9tE411BtM5jGkSuJ4nP7OVBILhR+VbwrLJDWVXjp5cxwve7hiYBMkMHdIyoG3jghzLHui6QDKDeVM8HmVceksE6MN8y30/wTMKHOprsqRo31OV+v99iY6mrtF+53iUSCdvwpV45jo3OBcqMtonrBtsKHBrt1BE0YKLRqIFBjiFT33IMuGGR4Qper9cOS+MBK81mJ10hjYVoNGr5obPZLABYXC/Joddeew2PHj1CrVazzW/ZbLbrFEM+j6cVLi8vo91um9FBT4ySGWx/Op3G9vY2nnvuOTx48MByUIdCIXz22WeYmJjAzs4OwuEwzp49i3fffReFQsHqWKvVcHBwYPNldHQUuVwOwWCwKxUgmevR0VHLwx0Ohy2ue3x8HOl0Gjdv3rTxo8wmk0nE43EEg0Fks1nLPhYIBEwGgA5OYNo8pukjOXRwcICtrS1kMhlbcyORCMrlMgYHB1EsFrv65+DgAH//93+PcrmMRCKBkZERrKysWLYPympfXx+OHTuGzc1NlMtlbG9vd+kCyoNuEqVscm4ztlrJqqPybPnCKfVUYVSrVVOynJi6iPF/VUT24n8QGC4SLmCiYqFbm89QZc9BV6UPdCt21ltBpQI/F0gomNYFEsAzC5D7m/1AZUWLkBNbWVDdea0uaI/HY4pNF/VWq2VxXa1WC5FIxI6J14XdBf3KcumiS3eQspBun7n1Yt+6oFj7S69xN3SqIcRns31qdLXbhydB8l49Kl37UUEO66kggte7oLoXK6fjz3cwblsBUq+DJjiWGlutAIp14xxQOVOZVfaMheBWGU2yBby+Xq8jl8tZe13Wjc9UUKT90Ks+2r/6v8uUqWeEv1kP1QlqMLgGgs5tLSqT/F/Z/s8Dq5R1Neh66Qi39NIbbvvV2NbPXFZdQX2vd/On1yZXFrKBtVqti8DgBit9PkGfa6TpGKmu1PAi1p3vcL1HHFd9rupU1iOZTFrdqcPd+c2iYBKApf3kexSoKqvPNrLdbC+JCMp3pVLpGkuSQKoD2+3D3NjqOaT+SSaTFrpAJphGq+oCV/c1m03b5EZdwvRr7pqiOp/1IotM/a7sMt/J/qeBQlafIF8zr+g5BdT5Ho/HAFupVLK83+1223IeAzCAzfjwcDhsvxOJBM6fP4979+7h4ODAchrHYjGsrq6i2ezETFOXDg4OotFoYH193bJe0DsZDoctTDGZTFpML+OLA4EA/tt/+28IhUJ44403LBdzMpm0ZAH9/f2YmZnpOoiFQJqe30wmg62tLfT19VmfjYyMWPq6iYkJY8inpqawtbWFYDCI4eFh7O3t4c6dOxbXz3EcHR1FLBZDNBpFOBzG0tKSsc3K6Pt8Phw/fhy/9mu/hu3tbUQiEVy9ehXr6+v46KOPjH2uVCp2lDrvy2azXcZoKpXCxsaGbUysVquWSYUyyvnIA2bm5+dRLBbNUFNDStdyvhOAnYa9s7ODRqPRFVFwVJ4tXzj7x4kTJyzrAsFiKBTqYnSp2DjpqYAUACqg5mcUKAKFSCRiqXlUCbn36YKmCwRwuDC67Jnr7mRdFWgrIOQ9utBSkTM0gRNfrUPgMLcqGWRuQqTC5XV8D0GHy/LR8k+n0xZryGu4kCiABg6zXmhIgxo77Hfg8Ihdj8djzA0/V9aJ/aYLnY6D9rMCQZepcicm+48yoOBIN0PSy8E2MeSGfaCZYAB0sf5Ad6YH1lOv5SKrbJG2QwGVjhnrpsaNXkd5cTPcqMdAlZoqYl2s1WBQVpGsET+nHClQViNLgWUvw8itvxqkP81wUMau1zylbOt8cw0jF9iqnGkqNp2PrsuR/caQlc8LVXINT30mr1WdoGCO46bt135UZtz9m3V05zmf02wexsZSJ4bD4Z7HpKvO0I2Mqo/5XJcJdOWQ8q7PcMNMVHdyXFKplNWLxqUa0S4Q1Q3TLJzbrVbL9L7qOAWUSnzo+LrtptueIF8NLP7N/tE6E+yyLZof2gWnPPpZ+5B1VmaPRo3qXAI+zX6k8qGGOOvYbncY8kajgbGxMfNgck3mc/g+vof9xk150WgUuVzOQhB4lDozew0MDCAYDGJhYcHax/5SPVir1RCNRjExMQGfz4eFhQVjT1XG0+m0hUEwawgNRgCoVCpdut/j8eDUqVOWei+TySAYDCIWi+HJkyeIx+O4fPkylpaWsL+/b6ELPp/PjAK/349yuWwHn0SjUfPoXb58GYuLixYWQjnnicfhcNjApB7Rvr+/j1/6pV9CIBDAo0eP8O1vfxvXrl3D1tYWYrEYyuUyyuWyHaxD+Uyn00gkEpbTm54Mr9eLubk5IyLcTbblchnRaLQrDS8PsymXyzhz5owdBc99Vtx8mUqlcObMGXz00UfGSAOH4baaRYY6hnVuNjvHvadSKaytrSEcDuPYsWMolUp48OBBz8wcgUCgPTAw4H78M5XNzc1/+tk/WDRp+8HBgQkJgTHT6Ghsm7pMCUJcQEY2j/foznsdaAoAi8sUqYLTBUOvV9aJ97gLtMsiucYBFSKVLMFdL+VDpoQuFmZNUWBBNkkXWgVIzB/KCa5tVlBLdkTBC/ub4E/fTbZA85Dr2LB/evW1y66yru59urhSHjSloC5o7DMN/9F600XM+pKRVJni+LlxjsChFc6FkUCaOdLVNayA3PV2uHUHng05UqDE0An9TEGKC5gJTPkds5JQRpTZczfGad20XloUHH1e/fU79XZwHF3AqEBHmVLKhdtOBSHsc32fjpk7P1k/LsS9gBX7TYE479fPKS/UCcqI9+oz/tb6uvPA7VeOD2XbBfPaN5RFLnSa4Ye6RRdsPksBorZJZcNl2lknnZ8KIFW38HOGZ3g8Hfc+ZZv1UT1Cna56jnLMwqwE6onivGw2mzYvqW90ozDfp2sMw9doaPf19RlwVVlRTxWf4Ya4sF3qAWE/KumjXiV9h5vPXmWIMa9sp4a0uQYDcBgzTqKBrCoJAXobYrFY14nEkUgE7XbbUtolk0ksLCwgkUgYUCSgDAQCGB0dRaPRwMLCgvUd2V0ywLlcDu1221Lerays2PrPPVWXLl3C3t4enj592pUvm/1HuSY5QpZ5cHAQr776qoFVjg9P90smk2g2m7h9+zYmJyexvr6OiYkJ5HI5W9vIso6Pj+Pg4ADFYhE7Ozu2LvBEwoGBAXs/vQyFQsFIQ+aGJq75/d//fXzrW9/CD3/4Q4TDYfz1X/+1GW9bW1s4e/asbZBkO+PxOP7Df/gPuHv3Lu7evYutrS0AsJR8nGdDQ0NdufMrlYp57BnyFYlEzAABgO3tbcsbTjw2MDCARqOBkZERa9f58+ftFEWmseWc8Hg6ebC58ZN6p1qtIpfLYWxsDC+++CJ2dnYwPj6OBw8ePKMbjwrQOy3A5138D0wmY4NpyXES6sLJ2FN3o50L7hQAUZnzHj1GmqBKNzhS0fE5LivCBVjj1xTgAYcMr36mizHdaXwe8yXrPcqesG+UXdvf38fe3p7tIA6FQgiHw/YuLRoeQoaEsdS8h25B7TcW1ls3zfFHDZJ2u901mZQJ1j7TsAu2UVkn1lk9BOp2pzuPizDr6zK+XByVweEx7+qOpfLXVFt8nqbE4m+N23ZddpRPukrV5UoFx3er8UXDR69xgawacvxRBko/U3lzZcEFnm7mEzVClWHj+KlsuABffxRcu4BaDQYFbm792GatL4BnxlqBJJ+tRosycloXZWJZBz7HZcQpo2qg6rP4ncqetl/f3cuIYlFAxh/KhhrTauTSy6I6RsEk30FAHYvFEIvFug5F6SVnGlrRK+yll0GldVADW9lxbZcyWplMBoODgwaQ9XAPsqU81pk/rt7gWJJg0LzUrueG81z1u55QSNDO+cq5yzHUDY2a2s9lrxU003hRcKvGDUEe+1HXwEgkYmyym5te5Zlp1bRf2N+6f0nrwHGKxWKIRCIYGxvD6OhoVz0KhYL1IZ9FQFur1bC0tAQAlnSAYDKTySAejyOfz2NlZcXGe39/35hsn8+HQqGAvr4+DAwMGKBjPmXK7be+9S2cO3cO5XIZFy5ceCYMSMdaDR6PpxOa8NFHH5nhQQY1FothYmIC/f39ttFubm4OiUQCtVoNZ8+eNT3BfuRhJRpSNDg4aCA2FotZKrn+/n7LohGNRu30QcrR1atX8bWvfQ3r6+s4efIk1tfXsbW1Zbmyo9Eonj59io2NDRvrQCCA06dPY2NjAzdu3IDH0wnfiEajdobFxMSEeQ6y2SxyuRxarRaOHTuGZrNpMemUk3g8jkgkgkAggC9/+cvGTE9MTCAYDGJzcxPtdhupVAp3795FOBzGysqKzQOud2SsuflxY2MDqVQKZ8+eRbVaRb1ex9jYGH7zN38TH330EcrlMi5evIij0rt8YaaaizdwqKSp7JWBaTabXXlJlYVUtlQZAFVsLMpq8B6ge0EGns0ZC3Rv4HMXXCpKKiBdgHRxcoEUGXoqe2VAyQLwGmVrGA6iDLsCKd1cqIt5rVbriq0iS+EuLmw7x0eVBwG6AiwFc9zYwsnl8/kMwNN9pf1OJeHz+bpi93SRI0ji/9rPVPIqQy6oVKCsLJvGeLvsGcM86OZVBo4ypMrW5+sk8WdfuaESXJBYL75L+wE4DINR9lvv0fFUmdMFj/KvbKwLdvls7cdewE83GPJH2+Xeo/Vl4f+usaBgUMdVDRVlNjmWCiLcwjqrd0PvY73JIKqHhjLh6hltO3CYY7jXJi+2ywVXvdqpfaEMtT5P38t5QoCp7aMMeDweY9Ta7Y7rnenDuPFJ66TvJQCk7mi1Wl3hZAoGFBizL109ynZShnQstV0EwJVKxdgs1dNkmJliVOujIJEywfpTp+zu7nalgeOCrzIAwLJ48Do9gVPZYfWo0Dhl37lrD5ltrbMaaAwnALrj0nXuESRzPNw4aOpdrhmMveXmyt3dXRw7dgyrq6vweDqMITOCEKyTqWw2myiVSsZ2EjwTKAWDQSQSCWxvb9shLPV6HfF43MCjZjCpVCoYGhqynMcMu6H+YwiH1+u1HM/T09OYnZ3Fc889h5WVFWxvb6Ner2NtbQ2VSgWnTp1CNpu10Axd9/f29pDP5209Y1gE28V5RNb44sWLljUEgP0ul8vGvpPAajY7Md0rKytmLDx9+hStVssycdDwSiQSmJychM/nw+PHj5FIJHD//v2uvT+JRAJf/epXcffuXdy4cQOlUgmPHj0yPcq4dBoqwWAQqVQKHo8Hs7Oz2N7exvnz51GpVDA3N2fr4NDQEMrlsun2ZDKJWCyGjY0N80REo1GMjo5ic3MTAwMDWF5eRrPZxMDAAJ48eWKbGEulEorFIlKpFFZWVrC7u4tKpWJrOfUIdUk4HO4yQLiO8iCeVCqFq1evYmFhAW+++SYymQxu376Nn1bcdesXqfxMoJqLE900/F9BB4VFmVgqM5dpdIsLQLgYcSKqYOhvXQD1WbxGF0O+g+42Xqusml5Dw0EXIMadcdEkC6D5TXnoAf8n8OICou1X9yLjrqn4lXVRFomLNhemVqvVtSBwMadiALrTkPF/Ps8F3mr8cBIqM6qsroIm7W8XnOj48v1qbPAe18CgXHHRU4CtbVOAoYWfk0Wja04zsfDdLnh3GU99pmtgKbBShezKVC8WTFl8fb4rz+68UrCh/er2AeugzLACCy29DAL97QJXNXQUpLENCvxdI6oXC81nqVdIjSPXKFTZddur80Y/d+uh7dFQB46xK7cqm2yDy17rePF7BVfUi/yMbutwOGxz3+1LBdWM0+c79AASbY9bCMjcflNdpca0GjmNRsMO/aBRSh1Altw1ktRI1XnMmE72l9aL1yYSCWNB+QzqLfWcKXGjwJ0GtM45PsPVhyQc9BqVR7aX4EM3BPJ6PV6deoBrGT2+Pp/PdBDZao6x3+/HwsICfL5OnmAy/rpJk6nj9vf3LSxDAXA0GkU8HkexWLR4WJ/Ph2q1ir6+PqRSKWxtbdlYEbC1220sLy9bXZm9oq+vD1evXrVN0YxtHhwcxIcffoixsTEkEgkUCgU7cOb27dt4+eWXUSgUsLm5ae/SMNFGo2FxyD6fD7FYDKFQCOvr63ZMOXNOB4NBLC8vWz7+g4MD86YODQ0hEokYW5zL5YxBBzoG3uPHj00G+vv7jdHNZDKYnJzE66+/jpmZGTx9+tSAo+IFxvuvra2hWq1iZWXFdPzp06ext7dnn4XDYbz66qvY39/H/Pw8RkdH4ff7sbm5aQw7vdi5XM6OTVeSkQbyyMgINjc3bS7Ozs4iEAhYiM/MzIwZWhMTExgeHrbTHCmf1Dv8TYY8Go0CAPL5vB1cs7CwYKz3l770JYyMjODSpUvwer34oz/6o6PQj59SvjCoJnigEqPy0UWaixeVpoIUBYTuwqyMGtDNFvJ/oNuN3WvR6PU/FyMNR3EZH+AQYLJ9dDvxvdp2LvAaJ+vxeEx5q6JX9y4XHwUxvJ/9R0BN5ofXq9FCZaKAmcqKfU1r3e0DLnzaFu1bbqQkC+YWMjFcRFl/ZV/d0BaNvW61Wl3hLPqb40GQ4rrK9bnqRlRDRhWHuo0Zk8kx0HGlLLuMHBc6BV/KwipQUNlTGVB55TgqoHZl96eBUle+ufApSFMw6T6b9VIjSOezzgn92wWgWkcFOtofn3ettoH16GVEhcPhrpAANRhVNvQzfQ+Lm7Gl1/i58urqHV6v76IOUONHDQf1tLjP0udRLjhfNbxAjS3tX85/N4uPvkv1hhoLBM3u3HYNf76LMkZ9xM+YzYIyTv2p3iDtJ76f863VanXlmW+3D48gZ4qzcDiMUqlk7LIaVGrca3iXrhWaT1+NBOoE1yBT74PKkaaMbbfbqFQqBrTIRnPcGP/thuAAMCKCgNrj6bDq09PTWFxctJBDrhtM3UdQrpv6+vr6kE6nUS6XDWQHg0Ekk0mEw2EUCgV4vV6kUinrf9ZreXkZfr/fQGg+n4fX6zWATw9QrVaDx9Nhy2dmZlAqldBqtfDyyy+jUqng5s2b8Ps7J/T94Ac/sI30XF8WFxetLSMjI5ibm0OtVrPsLOyvZrOJS5cuYXh4GO+//z6Gh4cxOjqK4eFhNBoN3Lp1y3QE4+/pwdjd3cXKyooB0b29PaTTafT19dnpkjs7O0in08bmjo6OYmFhAdVqFa+88gqOHz+Ozc1NzM7OIpvNmrHOteTEiRP49re/jfn5eXz00UfI5XLmkWm321haWjKD6+zZs/id3/kd1Ot1/PjHP8b09DR8Ph/u379vzHUkEjFDg/qYALher5uXgrnGJyYm0G4fxsSfPXsWc3NzCIVCdnYFN4bm83mk02msrq52bbznPNNwRwX4fr8fc3NzljucWVXOnTuH69ev4yc/+QlyuZx5aj6vfJ4h/4tQvhCopoC5C4gCOU1sz+85YaisFTgokOF3qnCB7gXSXQhc5swFE/q3vp8LAN/Pdqkrj4UKm0qcYNcNM9F3UqnQCteFitcrm8L3Mhct26nADzgMtVEXGvuKLi9llpUh0/cSYDI/ND9TdoeLiIIp/u3GCHI8tS+8Xm9X2ImybAqGFUzz2WSBOLZMt0R2g6C03T6MlVRWkXGPVCDsBzLveoy8GnoqSypnZKEUnKmM8D6OnSsL+m4+T2VWgbPb18p46Xtd4K9j7QJobY8ygixuvGyvOaPzww0LUWDdSy60Pu5cVUPDlSEF1GoI8f2fZ2irvCvI6mXg9AKkLG64lvts1xDrxVL3eq477mwrwaur07Swvsxi0MuwVGOXgNaNvee72b98LttBuWC/694BbavqKnrTuImQoJ6hHNRV6uHSOlAXs2ieajVUVI8Gg0E7/U3XDY4N55rX67UwGwJ514h15YBtUZlXbyl1qMor1wmCXF3H2C/M+KAGxMbGBiKRCJLJJCqViuV75r4PAF3MK/UK8xwfHBwgnU5barvd3V00Gp08x4yFjkajSCaTlg6XG+/K5bIdKMUc1KxbPB6H3+9HNpu10IRjx45ZfmfuM9re3ra+CgQC1o5MJmP6dnNzsyuOnp7VZrOJyclJ+P1+XL9+HYlEAh6PB5OTkwA6uILp/8i4T05O2tHnuo9JY4ZpdLbbbWQyGfMO+P1+3Lx5E61WCydPnkQ4HLa46NXVVctpzfXp9OnTuHz5Mv7mb/7G4pIpg8QLDEWKxWL49//+32Nvbw//+T//Z4TDYUxMTOD27ds4duyYZQxhtisNg2HYCMG5xsKvr68blkokElhaWrK2tdttJBIJxONxnDhxAs1mE48fPzbDRfUqGW4abF6v17K3RKNRbG1t2Z44xo0/evQI77zzTpeRfFR6l58pTzVdNtykoQCT1+jCSJCkAEoXUdea1wUK6I5rdhe4Xosqi8umazv0Gv2cyleZRx7Zyo1zPMWO3ytLH41G0Wq1LP6NmUEUkLDtvWJD9Vm66LNfmVeTz1UGT1lzje/VNnPhodsY6GbxlK1Tly/HiYu1LmB6BDDvY311wVfWrhfLz/4lwOe9bBvHmG0nm+7Kmt/vtw1CVEJ8NxlwWuUE3ewPZdr1mWTgFOi4YJZy1gv4KeOhBoPKnTJ++r8aeXymgh4tCsY43ryXc4H36zXsB51jep/OVxc4av3cOav1cuun4N+9n892U6ixv3sZzSzaBjJoCmD1Po5XL4PGrZvWUdlW7Qf1/vR6prK8Or/JTqsR4xqLqlupY1gUCKoxoWBd5UeNYvX+qFxp2B2Bks5ftleNRs4xjq22QQ081oX6Kx6PG2BmqdVqBtJU5twQumq1anVziRs+k/pGmWp+75I0fIa2s1c/0TvEbAvAIcBVrwPBNMG4qwfC4bDFpnu9XjsMhScRMkQjmUyi1WphaGgI8/Pz9mye2MtwiEAgYKnluOnODZNhzuyzZ89ic3PT4ripa5vNTjhjMpnExYsXcefOHYTDYRw/fhwzMzOYn59HIpGw2N1ms9mVbeTcuXNYWlpCMpnE6Ogotra2zJDZ3d1FuVyG3++3ECeGKczPz5v8DA0NYWtry47b3tvbs5j5aDSK9fV1pFIpRCIR7O3t2eeuAUaCa2VlBWNjYyiXyxZKkUgkbM/Su+++aycVezwdtjiVSuH48eO4dOkSFhcXsbq6imQyiUKhYGNP3UA2fmxsDH/xF3+BmZkZi7/nplCOG70BNEhJQHE/Vn9/P3Z2dnD58mVUq1U8fvzYUhDyEJqVlRUL/eJzcrkcrl27Bo/Hg3K5bKTZ2NgYFhYWzDhTDwjQyWPNLB8eTyeNIY9g/+STTxAOhzE6OoqPPvoIIyMjz3j1jsph+cKgmgtJvV7vAipqvbgLKgXGBXtUOr3YIPc5OlE+D1C7i5/eq23QRcpd9NSioyIni6cMCa1l91kEckCHZeFGI3WHKdMBoEvZsr4E0C7DxroRLCqLpH3sLuo6RrqQa1hOL1abC5GCfRZtKzeCsU5aby68BLcan0xAxz7S44ep3BQk8JkcJ7aD7WY7uGvZBTpqHHDR5/OV5XdlxXUJK0jh9QqKlLHmM9XYpEGmLJyCJmVA+Z0aEG49WFxATSZE2+TOHbJFCkjUCNBxdOVAwZ7OA/6o3Corw/b1Yr75uYYLude5QF/7SdlOV4ZcHaEeHwX0vX67c/bzPGnadu0jlSEdHzKRfI/rDVGZdBlVNaxV3nSxZ/1Uz6pBTa8VwwX4uYba6Cl9Gi/sGkNufL7KPPuFrCJlgv1JcobMr0vUqHGsoVMKvLVvdOzYdp0Lqu8JZBU0U7+QHFHSg/dr/DoAO8yE+ktjsal3qa9ZR6ao47U8Kpxt5Tvz+Tz6+vowPz+PVCqFSqViMe3qoaNOGRwctPzZ7GfGX9MoePLkCQBYmE0ikUC5XDavx9DQEG7duoVGo4F4PG5jdHBwgO3tbYyMjGBoaAgTExN48OABotEo1tbW0Gq1cObMGUQiETsEJpVK2bHtNBgYAsMNiS+99BLq9Tp2d3extraGzz77rCsDSqPRwODgoNW5WCzaZnmVF4J1GtSpVAonTpzAxx9/jN3dXTvEh/Hlf/M3f2Pss57qODg4iNHRUZTLZSwsLFgGkqGhIdRqNQOhHk8nHZ3X67WsKX6/3wyJWCxmJBI9pdQhlEueHJlMJrG8vIzJyUkcHBzgyZMnpkdDoZAB/VKpZMCY2U0qlQqSyaSRndFoFKdPn7YTHLlRlf3NdZnGBOOs6cmo1WpYWFjA2NgY3nvvPfT19WFjY6NLN/Qqqit/0coXAtVUIqpclDnoxb65yolFF2jeq6wI0A0k1U3qMkh8vgJifg4cKnwqLr7bBT6fB+7d692E9sFgELVazTZn8DOmu+GzqdCpUJXtJRPK/uTEc8Ei66YbQ7nYKGtGxa1ggru+lT12gRrDPvi9LvAKPmlpA4epscgIuUwt28JFVFl4BRKcqFRGLGSVyZzpRiOP53DTEz0hmkaL72CfqhGogIPfKUh0F2TXYNEFWllrl61TgKV97Y6xGq06l1xDTDfzqbwqcGMdaaS4Mq39oUao1tcdR30v+5vv0mcQOLjGBGVJGUvKsIIHj+cwNl/HwAWuCqj5XBY1snWR1I1cCuhcgOwaK8pS6jjrGLvGPGWJ40I5dA0IZYT1mQQ/vI8gQQ0qneO694J9S/exegTZv7rTnyF8Oh7MSMGiepaFISVsr3pDdOyU1fZ6Oye9MZ6TIM2tK3DopSKobbVaFqZAIKLEAOvE4nqXdKwpM3yWjillrNFodGVmcY0Hxva6a52CZo65m5O/3W6Dh2Tk83kLiSkWi5bpYmpqymJYqdf6+/sNLGYyGWxsbKCvrw+nTp3CgwcPbMNiNps1EJdIJIykYRgFAV8ikbAMIQz9aLc7qdh4Ql8qlcL4+LjFfDcaDSQSCUSjUTx48MCya5RKJfh8PgwNDeHRo0doNBo4fvy45ZbmeHMNpQ6jUXnnzh3E43FcvHgRy8vLxtxevHgRT548MUPj2LFjAIBr166ZHAAdo4ahLidPnsSDBw8sFvknP/mJreGatYpGDOdUMplEOp3G8vIySqWSxVgXi0Wk02l84xvfwM7ODvb29oxNX1tbw/nz53Ht2jWsrq4C6Kxpw8PDyOfzqFariMVilqeb80TJg2aziVwuh0KhgFdeeQVPnjzB/Py85aje29tDPB5Hf38/Njc3USgUMD09jXq9bjHVwWDQDq4JBoN488038fbbb6NYLNrcU4xGoM4wEnoDMpkMcrkcpqamsL+/j08//dS8McwUc1R6ly8cU61KB+jtflXwQhDighfeq/eosuN9vM4FOa6LFehO56ULNUERgateD3QzRwS76qZUcMHAfca6cSMHQ0KoODVbiC7CapBoyicXaGtb3UKQSZCsCyhBp7ZPWVodC10o3AXABY3af1yU1bBg/blIq0uY36ncsN+13Rq7qIwkgYcuVGTGqZwqlcozRpHH47GFygUyLtBmO9lXyri5ss46KhjmO122Vpk6HQd1SbOvuGAoQOOYquLVucPnsJ7aNtaJbVPApJ+7YI8Lnc51NzRE55GCR+0jnVO9DFZep3KpbXSZXgU7rq6gTnCNH84X1Rd8di9mWg0tZUOVbXbHyAXDbh8o2Of9XNy58YzGIe/XECuyt+l0GnNzc9ZflFO+qxfgpYGscqzyx3d5PJ6ujXatVqsrNahrBOn4qLEPHO5Bob6gXDLffzQaRSQSwfr6ehehEI1GTdYpX/ytm4/Vo0QdqqSNAmcF/BwXGiKsP8MHOI7cAKchYjpmPP2QskEWl/MiHA53eRlVpnlfIBDA0NAQRkZGsLGxYSCIuZ/pNWAWDpIOPp/PdF0wGEQ2m8XU1BTK5bIBPe0vykg+nzdDKZfLWT+lUinL5MFjtf1+P9LptJ0kODU1ZbmmmX6NJ/syjR83S25vbyOTyWBtbc3Yb8bFk/jhEdynTp3Czs6OhVIAHcBGBli9m48fPzYvCkNXVlZW4PN1soXUajUMDQ1hb2/P9hrMzc3hzJkz2N3dxf3791Gv15FIJJBOp5HNZruMfAA4ffo0hoaGLITT7/dja2sLuVwOOzs7mJ6exje+8Q288sor+KM/+iNLe8fDfz7++GNsbm4inU4bE0zZTiaTSKVSSKVSyGaz2N7e7lovNPWh3+/HtWvXLI6cJ6gytGhoaMhSLl66dAnXrl3D+vo6zp07h+XlZRQKBVy6dAnT09O4deuWGWmVSsXis6kPzp49i3A4jGvXrsHr7YQFfelLX8KdO3dw6dIllEolzM3N2fpD4kt1mlt0bflFLF8YVKtCUjc1cAgSufjpgqSLGXDI/iqrokrAZYoIoLhgAt0bHgngNc6Z9QXwzD26wPIZfLemqSOQBg4tNLJ/BGwKhqh4+S4FIVz4ydhQIbNoTltlX/QaTnYuDvqZmxNaYyB1DPhcrR/Hk/8r0FeAwQWTi7ECVl3M1JjQRd9l9Diu7FcFY+w3NeZ0bLh47O7uWr+rQaXvobuNIJbypvKgwI2LkjL3HGfWwwWsvUAa+8L11GhfKJglAFUQ6Hp0XPZWgRTfqyCPpRf7yvfqZ/zcZZbVuORz3X5WfaDgS0M5FNQrEFLjhb9doNtLdnqBW9aR80L7SI0eracaQmpwE2QqoHbHRo1xXk+5VyNW/3f1pbJlZKX57EajYfs0FEjze1cW1FDSz3RvAtCZF4xJVvDMee7z+SyOtd0+PKqb40UWtFgsPkOMeL1eiwNtNptdTBcBEoGzGph0XXPc2Hf83p2HrLMa3TpH9DPVn5T9er1uYRNAB2QTKCkxAxwa87y32ezEEpOJbrfbXTmfqXcIRtln6XQaQ0NDWFxctFzGxWIRPp8Pw8PDFhJCnUgjx+vthAVyEyBjqPP5PCYnJ20vCfVzJBLB9va25X0+ODjA1NQUCoUCarUadnZ2EIlEbE1iJhMeXMJc1k+fPsXIyIiB3vHxcSQSCTtdj23t7++Hx+PpMpDIUjNkMZ1OY3Jy0u7JZDLY3983NlW9I5xr5XLZ5jo3VabTabRaLQvl2NrawokTJyxe+qWXXsLa2poZDT5fJ4/0+vp617ylB/XYsWOYnJxEs9nERx99ZKExPp8Pp0+fxoULFwAAb7/9NmZmZlCtVhEKhRCNRrGxsWGbFE+cOIGlpSU0Gg1UKhVcuXIF2WwWw8PDyOVylsNbDdShoSFUq1W0Wi3kcjkLw+nv77dY9TfffBMzMzM4OOjkh19fX8f29jbW1tYQCoWws7NjWWC2trbw2WefwevtpPp78OBBF0YKBAI4efIkJiYm8JOf/MT6ur+/H8ViEdvb2/joo49QqVTQ39/fRaj1IvqOymHxfBGLIhwOt3mSjgoE0J2HVYGWgg4WTjSNC1ZWzAUjyrzpQmKN+AcFogeP6KZIZUe1vlxcOImpUJSd4GJJIEk2mmyGtoWf6amLXEBZFw3baLUON/lR0FWBU5lqWIQyLCzqDWDbXLClzKK2X9lAfW4vRo73cUFlPylDqAsegK5NjFzU9V2u96CXt4LXqPtK+9VNl6bjrQBJWTPWg+8jaGQmARolLgutbJZuvPxpIJbyqf3KomPA+/UaLuqsszKHarDpZ72AJuvjeioITJQt5jPZNzpXVCYIEHmfGj98N0GEMrHKOur1Lph2+1QNPraXgEzbqddTd+g4q0GqY6GgVvtO2+3Wm+3WOaEkgdZbDQjgMOMK9YnX6+3aVMlxpZwT7Oq73ZzUHEcNGaNcK8nA93ODGxfLZrMTUzo6Omqb3bxeb9d16s0C0FUnlVntdwKu7e3trrAx7XdtAzfX6Xf0OOh7dLzUEGY9deO2Ow46tuxnN4yLoR8afsN3EIiSjaVOIKtMbwBlR8mYkZERFAoFS3Xn8/mwvb0Nr7ezX2dnZ8cYa65ngUAAAwMDyOfzCAaDtimQYQZjY2MWcsEfGkODg4NotVoWnsFNfQC6vK6hUMiYbGbj2NzcxNe//nULgyBgTqfT8Hg8doR3tVpFo9HA9PQ02u02FhcX8eUvfxmVSgWrq6tmvDDfONnlVquF+fl5DA8PW4x1s9m0HNs05jgO3IjJXO5M+UemdW9vD6dOnUIoFMLGxgYKhYKt32TyXe/z7/7u7+L06dMoFAp47733cOPGjS6dweO7f/M3fxN/8id/go2NDTMQKJunT5/G9vY2isWixa4DsP1CDH/xeDyWzYqyrXNoZGQE2WwWtVoNJ0+eRCKRQL1ex87OjunRjY0NG8Pd3V2TR+Zz10Ljb3BwEJubm/B4PDh+/DjK5bIZV7p+qN5KpVKoVqumaxRQ7+3t3Wi32y/BKX6/v51KpdyPf6ays7PT8x3/N5cvxFQToKirlUVDPbjIKLjTxVzde/yMC4EqP6B7oVWwoQumThAF1BRAgnwXYPCZVJiMEeOEZ7t4JDvvIXOtWSmYuYKAWlksDYEgo+Gy6GS8NbbRTSun/aRMHD/TjTZAd+5rZb51Mepleeo9ZHYAdLFXBEdUVtwYYoIlbLC6PjnuKitaT2W9yay44BE43H2vgE2BjGZWYV+QtVLjQevQbDYt5k9TeamMU37JNLjA0P3bBZx8Dhdk9dzouCj7yLbr4s+6a19yTigQdQG1yjvHWr0PBJw6p1zQqnNW56HKoRYai+p14HM4bq4B5xrEqhO0f1yAq+Ot/a3gyzX4+Xz1slD+XYDcq+hzFLTyfwWtWk/Wmxv3tB1kat3wKfa76g+2lXOSY6oGZTAYxN7engE1bm6jjtF3ZDIZnDt3DrOzs8bAsQ8HBwftBDtmy3EBPGWb4I3yxo12CsZpSBwcHFgWC+oxhmDopiiuPzQwNHxMCQe2ifKlBhnllKEYuoZof7KuBEcKmglyKe9k/Ck3HBuCPoa+UAaWlpZskyHT6/FZTInH03mTySSWlpYsX3epVEIsFrNQAh4Ao7G7rANT3nm93q7Naa1Wy/IfA0AoFLL0fySXgsEgNjY20N/fj7fffhuJRAL9/f3IZDKYnZ1FpVLBzs6O3c97i8Uidnd38eqrr2JzcxPJZBLnz5/Hj3/8466xYfq2Wq2GdDptGxlTqRR2d3dRKBQQCoUwODiItbU17O7uWjw4cHjM9sDAgOVqp6xvbm7i+eefx2effWZhM5zPigeSyST+4A/+ACdOnMBnn32GDz/8EE+ePLG1lXICAK+99href//9rhMOL1++jIcPHxpY171DJ0+exOrqKi5cuIC9vT1cu3YNAAz4s0QiEYuT39raQjgcxqlTp4yBvnfvHvr6+pBIJJDL5UzOz58/j/n5ecsUouc+xGIxADAvLlMGcs7TuKXRTP1EPdff34++vj4LU+H/TNNI2f288tP05T/18oVA9fnz57sWLqDbxUt2hQw0iyptMovqJuZmF/dZXJypcFzWiULPRVk3u7mAWt21CnS42GosocfTObKUwMxlUWmx0SXnLogEg8pIsR9YX12w9T4bGMcYYNvZl2wj29RrN64aMZ8HXJWpUbcy36sAVNkjAg2yA2RBdDzpbmP+Ti5WLnDWRU372gWlruy5rC9jzMkwq8eEY6zv1752WWs1iFSO1cjTMBM35IR9DRwanJQZGiqUWQBdsvZ5jC//VuCgxhGv17ml84Dv4hwg66NhCNp+lT2N8dWMKawX66Dt1Hrq3HIBsDu+lD9107tspv6vfcDFUOvO+rjzRsdMn0eDip/pvWqcuMwO+1X7UuVUjR2Vex6+wPdHIhEz3NQAURZV+1pDybTOrIPP57OQBGYtUi8aDX2CWI/Hg+XlZcs73G63beM1T4DT9lGnU07ViA4EAohGo/B4PMaSkqGn8cCNdeVyuYtQ4DpC4M7Y0l6Gnhphfr/fjAb2vytzNFpUnsjSMxRExxqAMXoqkwT/3FNDRhqAgdZIJGLHjPMaeidyuRxefPFFPHr0CF5vJ2+16tlisWhjR5BDr8PKygo8Ho9lmFpZWcH+/r5tiNvb28PIyAg8Ho/Vo1AoGDhutVqWEo8p7sh8A7AMFqx3JpPB1taWjQVjdAkmp6am8PDhQ7RaLUxNTeH9999HvV7H6OiohfvQk8uQo93dXYTDYZTLZRSLRcRiMezs7Bip5fV2Nm5yzGu1mo1Jq9UJo4nH48ZGc15NTk6iXC6bPFOf6fwMhUIWU3zr1i388R//8TMMM8NpvvOd78Dj8eDhw4dotzubO1977TU8efIElUoF9XodT58+tdzio6OjKJVKyOfz+Oijj0xHMd6aa1Sz2TQwTS+Fz9c5vIXHqI+MjFg2FD7nueees4NfWNfp6WnMzc2ZbJVKJYyMjACA5RTnWsw0gqoHgc5ayLCQd99913QG5eTkyZN4+vTpTwXUv+jlZzpRkUpRF1Kgd7onVWwKanVB40LG53BBocWn1+sxr1TK+kOWxgUkdN0pQFdhUraNbdC2aFgGnxuJRLqAjAsWeb/maqUL3wVjer3me9bisp16jS7WbA8LFxyNuVYwynYTgLvuYzcDAMdWQRMXMfatZgdRoMj3K0OkYElDErSOQDfwJPvJdnIxZp8ow6l9wf5RY0wBtX6m/ap/q6HB/2nw8F71vKicKbhyDReVAf7vGjYusFTw5PavMpZq1HBOsA4EMdqP2gcKXAk2+D4Ny2m1Wl0GNeejAm1Nq6fjQr2iKdPYb+qN0PFVsKq6R08Qozyzvpqf2S3sVzc8RcdAjQm+VxlIlSFlT9knmq+fP1y8aWxx4VOmVWWWxSULWAfVl8riUh7JHrP+7Ndjx46hWq1ic3MT1WrV9Lvf77csBmQE+R4ysXyf5mtmX5F9po5nnUhOqJeNMk+doxkpOJbKrOm4uXLB67Q/NJ+1GmfsI2aRIJhnXDJjnGlgcFNhMpm0zXEMyVDvRDwex/DwMGZnZy3EglmiCJru3buHK1euYHFx0UIvlBgYGxvDw4cPjbkeHBxEoVCw+U3wXiwWLQaarLnf78fi4iJCoRB2d3dx+vRp+Hw+bGxsIB6P4/z581hYWIDX60UsFkM4HDaGmt7XY8eOYXR0FEtLS7h48SLm5+ct5pwAjYDT6+3E8M7PzxugJRvNEE1uGGQdvd7O5tW9vT2Uy2U714IhEtFoFFNTU5brmXMsGAxie3sbuVwO4XDYUgj29/cjm83i/v37RvAQpFNWhoeH8eKLL+LixYt4/Pgx/v7v/x4HBweIx+MWTlGv1/Hbv/3btgHyu9/9rmUAabfbeP/997vmY6vVQjabNQJibW3Njl8nZmGITF9fH8bGxrC5uWmeAYYWzc3NmWHNrDA8IdHv92NoaMhCf/L5PDKZDGKxGJ4+fWqk5sHBgR06c+/ePcTjcUxNTeHRo0cAOix7KpXC4uKi6Uav14tjx47h3Llz+Lu/+7uutSYej+PKlSsmh+qV7lXctfsXqXwhUD0zM4MrV648wx4B3XG8nChUTFT+dA/prl5V9q6r3V3IuCjzfzLTytC4u73VItRFXAESGVVlIRSYqJXLSUSAqtk3qPgVtOrCz75wQxBc5o0gXvtY+0BBgv5W9pjFZezU9a+gygVSbhgOr1cAxsJ+ZthErVaz/Jm6kcgF027oDvtOvQvuATl8n7qqdGxZB62bbmRUY8EFQXqP9r3KuPY3i3o8gMOsC+oK1vpqndl3CuTUg6BGaC/5VTl16+9eq4aGy8K1Woex+y4Drs/RH71WTwLVQsBENovjDOCZOrg5f/U9CqJU7+hc0Hmkxg3bp2PNeebOIa2PPofgU/UV56my5GwzgZsCYX7PXLHtdtvSYBFIajiMhjdRFtRQUVkm4GQ9lUSgIUNArTqNrnGfz4fHjx+b+5rXEDirkcy2KIvMfmIMbCKRMNaWWQwAWOou1Ts6N9Q4UmaN/clx1/fyc93UxzYxxIN6SwG1G7oCwA6U8vl8iEQiXURDu93ZhDgwMGB9US6X4fP5kEwmkc/nLQyAQHNwcNA2xvl8PmN3KZvhcBjnzp2zw1RyuZyFQDDEJJfLYWxsDNvb22i1WpZ2jXOtUChYn8fjcdTrdctSRX3IkINGo4GlpSWLp15fX0e73caZM2dsc+L+/r4ZeZOTk8jn88Z0TkxM2KmNw8PD8Pl8BmwTiYQdDDI4OGhpACnLHo/HNtJxPeShasxvHY1Gcf78eczNzRkozeVy2NraMrnn3iUac16vF1NTU6jX67YfhiEQoVAI2Wy2i/zr7+9HKBRCuVzG/Pw8PvjgAwP9oVDIcjRPTk4iGAxiZ2cHS0tLqNfrmJycNI9NOBzG2tqaEQm1Wg0XLlyAz+ezg1mGh4ctzEIxz9jYGLa2thCNRjE5OYnl5WW0Wi1UKhW8/vrr8Pv9Fn7FEJ1SqYQrV67gypUr+OSTT9BsNjE8PIxLly7h7t275t1h//h8Ply/ft3kf2lpyeZptVq1sKNWq2UpE/P5PN5++22LxQc6WOd3fud3AAAff/wxIpEIXn75ZfzoRz/CUXm2fGGmWkGPKjhlSagoNYYJQNfiQ4tfrSE+3+PxWHA/lZq6QgkA+F6gG4QrS8HvdQFVwBaJREzhK+DkdVzENBaQ32nWD+AwvRn7hYw+3+eCJW2z/lbmUz9X97UCD+1zfs93KIByGWE+U/uVi4cCYtf9rc/lMxn/Wa1Wu2KRFair4eW2g/3JRVHbTENMj4ZlXQgiOP7hcNhOmQJg7moF8Mq6uqy2Kys63i5rzPfrtQT0lFsCBle+OcZ8n76fn3E8dAwpjyorLsDV+nEsXLabsk4WkYWGAK8h6OJcUKOLY6nMsPaXGnLsD+1/zXai93P8+Vtl3WVuKc/KxKtxqoYMP9N3AOhKJae6iGPugngFpXwvQYOOpeafJrhxWVIyUpxDrseLbaDhxdhj15DiO6m/OO4MFeCeEC62jEuljuX9ZDQJJA8ODmwjNtuvYJj3qQEAwJg0jpN7+qPmkie4pT6lHKih5nr01FvK+cW2U/b4XLLGHo+nK3ORrh+uXPAess5ML0iQxIwTBwcH4KasYrFo61YwGEQ4HEY+nzfWXg0FZupYX1/HrVu3EI1GUSgULG6Yhkgg0DlCnLLIw1OCwSCi0SgGBwfx6NEjpFIp27Dn8/lsPJjWLh6Po1QqoVgsYnJyEtFoFMvLy/B6vTh58iRKpZIdGlKtVnHy5Ek0m03k83ljrGOxGObn57GwsICJiQk0Gp1UcZ999hnGx8cxOTmJ2dlZAB3Dkf+rLCWTSVy9ehUffvgharWaxYbz9L+vfOUrlsKO46mehpGREZTLZSNt2Nfb29vY29szOR8eHkapVLJTOcngMm3d3t4elpeX8dlnn9kegVOnThnwD4fDGBgYwF/+5V9a6sPnnnvOQLbH4+kyiGkIZTIZrK+vI5/PAwC2trZMrpvNTgpDn8+H5eVlS8M4PDyM7e1tbG1t4dy5c3jjjTfwve99D/v7+0gkEqjVamYkPHnyBNlsFjs7O/i93/s9LC4u4saNG9jf3zeGnbqHfce5Rf3LfWPhcBivvfaa5UFnXm6GKHH+RCIRlEol3L9/H+l0GlevXsWVK1eOQPXnlJ/pmHIFPy47zc90cVDlyv9dt78uEuqadhka142tRdkmBUj8TK8BYMqJjADrQGXrsp4uQNdwEAU3uii67LS6wt1CBgtA14Kgz+zFoqrxwsXg4OCgy5XptkH/VtDtjiGVtAuuWRfWT61xt57uOPF9BOjt9uGhQlyg+RyyGgQUOr5kP8nysY7qSqbb1t24qCmN9HOOo8oLwaSOscotx6lXzDa/72V0ah8B3fND5VZZFvY5jQutm2tA6bM1zliNOy28RtutjKXKC6/VjW6UKfYfx57ypPJMwK7hGToG7rztBSJdWdM2qRHoenfcdnPOaZ31GpVzHRs13lUe+JmGK+iYK4Cjce/qDlcm2a80MLUObn3Y3wQTNDLJPqo+Uj3d19dnAEMN7Z/Wjyr/ZMlcMkPZdr5fZYYyqeBJ2WkdT60XDUC22yV7KKd8hrJvQHd+cwK0ZvNwkyXbwKwVwWDQXPnMsc0wEG4g83g85tZnmkHGG5ONZLxrvV63/uZBIl6v1zaU6l4H9i89AH6/H9Vq1VK1NZtN7O7u2tpM8Lizs4NkMmlAkOCLubEp+7lcDmfOnMHAwACuX7+OcDiM5eVl9Pf3o9lsYmJiwpjuwcFBA3Ycg729Pdy9e7eL5X/y5AlisRj29vYs/KLZbOKDDz4wWWH2isHBQWxtbeHhw4fY2tqyd1A+4/G4tRcATpw4gXw+j2g0iuPHjyObzaJcLiOXy2F0dBRDQ0OYm5vDxsaG6UkSK8lkEslkEjMzMyY758+fRzAYRD6fx9WrV7GysoLV1VW0Wi2sr6/D6/VifHzc8mUTrI+NjZm8XL161VLt0dDmushSrVYRDAbt0J9MJoPbt29jbW0NXq/XMogcO3bMUh4CncN3rly5gkqlgrW1NRwcHOBv//Zv7YyMVquF4eFhRKNRLC4udq2L7GuSKK1W56j7g4MDJBIJ3L171+7RdcTn82FkZASvvfYaPvnkE0QiEfz6r/86Tp8+jfv37+OnFVfH/iKVL3yiIhlFN84W6F4IdGFSRaysncbmKRChMCjrQ9ely8Ypi6FgoheL4y7WtOaYNkm/1/g/1pcggJ8zBpSLiDJYypwok8hk+SwELmQDerHRbKcuMtrXuigCh4uHC6IUdLMP+H4qBtcdqqwl26wbfNRjoKwdF3K9X8eBoEPrz/qxLtwwpLF3bLu23wXH3DilAM9lyymL2heUIwWurqzqos7r+Dw9cEENTR0nlU8NHVAG1HV36+KvbXCNMxokali57nze43qSenmW2E72FT/XjBJkHWlw6b0uWFJQTfZF5VzBq4Ji/lYDUuvHZ/Qy0t3P3OIa4C7braBL26T6xNUrqg/c52rb+B3HlM/ReisjrDLFdyiw5d98D3MfR6NRFItF6z8yZwR8zWbTNsjFYjFLodVqtWyjMXPyul4mjheBJzcxupu8+TzKCvuKMgAcGtD6DB0nlU89HVaNafaDpvBUueG4kXAgqOU85nf0CDC/czgctpR3DKFhfLzqe6/Xa5sLGdfabDaxvb3dNY7K/vt8PjtmmqEE+XzewhEYe51MJm3T4tDQEDyeTrxypVJBrVbD8PCwgf5EImGb9yqVCuLxuKWvKxQKdu3e3h7W1taQSCSwurpqjPH29jYajYaxmKurqxaK0Wh0TknkuNMIevXVV3Hjxg1Uq1UsLCzYqYmlUgmZTAbxeBzz8/Not9v2P0NV2GYAuHr1Kq5fv27PZppT6sRoNIpoNIpkMolcLoeHDx9aP546dQqNRgPlchlbW1td63EoFMIv//Iv47nnnsN7771n4zwxMYFyuYzNzU0cHBwYqNdQkL29PfzkJz9BvV43wLq3t4fV1VW8+OKLuHDhAu7du2cxyul0Gs1ms+v0Qa+3s1HxN37jN2yz3927d22jMvtncXERS0tLGBwcNG9NMpk0hvmNN97AW2+9hd3dXYtFf/nll3FwcIBbt26ZLqeeUeyQSCRsPtRqNfzpn/5pFyG3t7dnBt7e3h7OnTuHBw8eoNVq4eLFi2i1Wvjud79rHomj8mz5woe/8GhToPsAC+CQPeUpYbqYUolzwdfFjNatukkUKLbbbXPdUGkpIPd4PLZBhvc0m80uEK5MpYKESCRigI9KnEpaAQtBgLI8vVLY6f1kEZWhUbc+FTHDYNhugg9lthXYKhhT4EeFrGwjcAi2CIJoCChAVBZN36sgXseMz2c72Fcuy8N6K3AHug914bv5Pd/BOE4F/ATq7GfKId/NZzO+0pUz1luBGdur462uaNZJ5YdtIwjS9ioTrM/kPHABtCtzBCxsHxcXZW3JVirbGAqFnmEk2C4FJMoW6o+OJT9TYM9nuN4JBStUztoOAhwaoTqHWBSgaXo/Mq4q567hrAaSbmBV4OeOvd6rn7uA3QWqKm8qP/q9LmaspxoY/Iyfq5yRoVQdQraZ9dG5o88PBAKWCQEANjc3bUMcQ0ZUDxOoUm5YN8aMsi3UA6qTY7GYzRHd+KghLOxjzfak88g1aLhmqHHsziM1rF2jRfuBdVDmXmWmUql0GdSUOaajYzsYJsNY8Hb78Ph29iPnG0M/XE8AvaFsP9PraTYRbjSkjgiHw4hEIraxbWhoyEBaMBjE0NAQVlZWjD0eHh5GOp1GtVrF5OSkGSaUKWbJyGazGBoaQrlcRrlcxvDwsB0Bvr29jUgkgnPnzuHWrVsW9+vz+WyTWrPZxKuvvgqPx2OAmznI7927h1KpZPOh2WyiVCohHA7jhRdewIMHD0yWotEoms3DvMpbW1t23Pann35qaxl1frvdCf0IhULY3Ny0kBiO27lz55DJZLC6uor5+XnTm9RJzPFNsEzj8eLFi6hWq1hfX8f09DSePHkCr9eL1dVVY5NJuimhRjn7gz/4A9TrdXz3u9+1+Hqg43lguCJxS7PZxEsvvYSzZ8/i8ePHlu4vEolgfHwcjUYDs7Oz8Ho7GzcZix+NRjEwMIBwOIzx8XF88sknqFar6O/vRzKZxNTUFHZ3d7G8vNzlXeTGWACWS31sbAypVAr379+3EBp3XVH9ODk5iXQ6jaWlJSwuLuLdd9/F7u6upezrVVw9+otWvtDhL9FotP3cc8+Z8tLDLxQscRIoOFBGWZWkupb5N1lw3sPJA3QGnqmfeL0eharMkaZ5UgBK9xTjb3Xx00ms4IH5UjmZgGeznHCR0pRIGpvHBYqbM3QxUhaQjA3wbIo5FsY7cyyYY5T3uQyxAhZdhKhwFLgoiFA2lOOhqZdqtdozIN2Ey3OY4sxlt/kud7OqtrVXH1AONM6RdVK5UwXIe1lUBtkHvcATFbIaRq5cu89kP7mGI+VY68F66Xs5Zqy767Hhs9RQcBfrVqvVtajy2WTWWBcCJS4+2i4dF36mRh//1ucBhwcoaUo6NRo19aOOD+vKWFIaRpQJNQhYT7af36vRzuv0N6/p5d36vIVAQayrx2gEKTDW0AyVo5+mZ12gyH7j/3Sv0wtBOWo0Orl+A4EAarUaAoGAxbdTR9FrRBDBcWFMMABjY6mfYrGYgSN6Jdk+6mLqXJclZn/yEA8at8oW67zhGFLm1ROhBjz7R/sZONRzuhdG9YoSBdRFqkP4XJVztoN5edVrxk3PvI8x6plMBpubmzhx4gQ2Njbg8/mws7NjTGIkEjFmneOSz+fNEIlGo6hUKsaOx+NxbG5uIpvN2vubzabF1rNt5XIZx48fx+DgIB48eIBMJoNgMIj19XUDSP39/cbIM2d2LpfD9PS05YAul8u4evUqwuEw3nnnHUuhpwd+NJudDYjLy8smJwzR4LzWQ37a7balG2QISrt9uKFyamoK6XQab7/9tnlKuGHQ6/Vamj+Oy7FjxywDja4FZGAZ7+yC33a7c1DN66+/bh6KRqPRlQN8fX0ds7Oz2N/fNxKA84aHtnDecDNrLBbDyZMn8fDhQzsciTLPPmMWk2q1iueffx6//uu/jgcPHuAHP/gB6vU6JiYm8Eu/9Eu4fv26bfDnBkwmTwgGg3jllVeQTCZx8+ZNZDIZbGxsYGtrC5OTk3j69Kmlr9Q1kPMzGAxiamoKm5ubuHjxIj777DOTUZ1n3JBMjHLlyhVcvnwZADA3N4cPP/yw67mrq6s9D2bx+Xztnwa6v0gplUr/tA9/oXC7iz2VpLKkwLMZCviZgl0XULtgRNk83kO3jAIb182smQyUDeThLAQgnLScfHpUNllRpovj+12gq8yK5lZVi53sM5/B+tCiZJJ+FgUbZBY52ZV95IYbZdbVzc7+VqWnICgYDHbtildmSutKEMz3sr0KRjmeGvfOOhD8uSE8aiW7jBLr0CsUyN1gRUXKcWP9lNV0AYvKmdZXga3L6LrPcsGSgjSdA+xDfZ8yWir3BKDK4LusvM4hslH0zvAe11jk2DKFGz1KfDfnDu/h3FFDggBEn6ueFNZd+0kNaH6mcqZtZtuU7VdA5wIuBWk6N10vi/7vGo/KIvJZvYwrl8nX57gMvcq42xdady5SuglXZZb1YHy09mc0GoXXe3h4ip68yvnA9hEYAEAymTRwTlCoG7nU40Ug2Gq1LL0dmVf2K3PlaiYkuq1do4X9S3BIvabjon1PWXYzw1BWuKlK1wE+n6BXPUDUy+q1I/jTE349Ho+lDyQBQr3HvuOYEMydPn0aT58+NfDO5/j9fjvKnEdx53I5G59UKoXt7W0EAgGcO3cOc3NztllU2W9uvtPQy7GxMSQSCWSzWUxOTiIWi2FxcdH6NxAIPHPCXq1WQyaTQTqdRj6fx8HBAb75zW9ia2sL77//vmUKWVpawsjICBqNzoZEjROvVCp2ZDnHi0d87+zsGEhOJpPW5zQeTp06hePHj+PGjRuYnZ1Fu9029pMyRfn1eDyWtvHp06fw+zsnWHJMqUMZtkBdSD2oem9+fh6BQACvv/468vk87t+/jzNnzsDn62TrUO9ZMpmE1+tFLpfrSpJQrVbR19eHwcFBAMCjR4/M4PX5fAa49eC2arWKRCKBX/3VX8Wnn36K999/H/v7+xgaGsKbb76Jd955B6urq3bsut/vt9AYsurcUJlKpXDnzh3LQrO6umqAnyUajSIej6PRaJiHhYc4vfvuu119yww3mn+e+buj0ShqtRo+/fRTrK+vmzcmEAhgYmICq6urOCrPli8cU00WloOvrjYF1MCz2SyUcXYVZ6/FREG7gqFSqWQMAJkxTgaCOo1NVtcwwSvBiBoK+m6XcSZg5POB7k1/+s5Wq2WTTN2mwOGpXMpmcRMMn6OLkIJ5tlGBPxcGdRkrm0gWWQEnrV/NmsEFh/cr2NWQDNZJFyAdRy6QCnJdwKxAkrunFVSpoaMeCvaDG//Ncfd6vc+4kNXQ4PUu86sLOWXVZSG5cPdiNvkcBW0Eudo36s3h8/gZ66ZGid7LoguJAjouXFoXHR/Kgeat1jAFrQvHh4BbwbACa/X8qBHUbh9mjuECScNAx4t1VzlXUOt6AvQe1Q00TD0ejxnCnPN8n4Zs9DKOdbMg79c5rLLAe1zvCuukz2Hf9vK+sH3sZzWqeb+SCnwO5Z+HZ/C9w8PDtmFOs2v09/cb88zxo4zt7+8bM8bwAp/P98yR4HwX2budnR2rm+4rUXCu46993263n9nPomBWDQfqYgXNOh/IEKq+53W8l/qQ9QTQlSnC4+nsP2BIgt/vt9AMbghUNpuyzz4iu7m1tYVGo2EM48svv4y5uTnLY830a7u7u7Z2ErhxM+P8/Dyy2SzGx8exv7+PQqFgc4SAjt7Bl19+GU+fPrWDXSqVCorFosX9Dg8PY3Nz0wwir9eLZDKJ6elpVCoV3Lx5Ex6PB5cuXcLs7CwePXpk8cu1Wg0DAwPI5XI4deqUxW0zGwiNQALpXC6HUqlk7DLDPth/V65cwY0bNyzcaH5+3ljz7e1tALDDWqgreNLgxsaGeZUZtqLzzOv12njSUx4Oh3H58mU0Gg3cu3cPp0+fxtjYGMrlMmq1GlKpFEZGRnD37l0Ui0X09fVZOAT/ZjYVFq/Xi/7+frzwwgt4/Pgxtra27HAY4DBElGtaMpk0+Xz99dfx0Ucf4fbt26jX68hkMvjmN7+Jt956C6urq13GD/NRp9NpnD592jZvBgIBrKysYGBgAB6Pxza7Mo865wi9Iu12G1/96ldRrVYxMzNjekqZ6Ugkgnw+j2QyaQZIKBTCmTNnkE6nUalU7JRHJj/w+/3/05hql2z6RSpfOKWeGwqgFqMqPrX6VEm6yo3FFV7dTKOsH1kGWoPqBlRm2GWDmAtTXYdcdBSI6cKgizXbonHVfL6CP75PwZ8uGLyXCpqKlSySsrPsCz7X3RxKIAzA0gIqy8QFmHVkO1g3rQ+ALgCuz1bXLJkJjg2BpgtiXABF2eH7qHiUWVSQqgpTGaZerl+CHh0nBdwKqLzew9zXlDP1criAmfJEBqRXKIgaPayTu8grs6n95n6nXhnKs97D/uP/Ksvs3179qqdn0QhzwQr7jHNU6+H2C/tMgZDbXgI7/u+GbiiTpPOf8841gDi+WldljZUhVoNZ66v9zmcqwFa543PdMWJROdXP+LlmhGH7XRlywb6CSradoU48eZD9MzIyguXlZfP+kHX0+/22sO7t7SGbzSIcDpt8Erik02kUCoWucQJgYSF6IBBwuL+lUCh0hYuxj8hsE6xrH30ee69pSdWYdtcH9X60223bQKlrEOWLc8KdL2rINptNZDIZFAoFpFIpeDwebG5uWr/REAQOjUyCSeobAlz2DWNqw+EwnnvuOdy5c8c+W1hYMEPI5+vEFLdara4NeEwL12q1DPwNDAygr6/PgDe9tN/85jdRrVYxMTGB4eFhzMzM2PPZXoJSn8+H3d1d5HI5nD59GrVaDffv30csFsMLL7yAer2O1dVVC2EhMGw2OxlFdnZ2sLW1ZUww+zmZTGJ8fNwyQTAsg6y61+vFysoKRkZG8ODBA/T19WF0dBT37t0zw4BhAmSXKXcElPV63fqFRADnAAk9Ekp9fX04deoUZmdn8Z3vfAehUAg//OEPkUqlsLOzg5GREeRyOczNzSEYDNpBMdzw5/P5MDg4iDNnzuDatWs2d6mTU6kUpqam8NFHH5nhTSNU1+tWq4V4PG7jMTU1hadPn2J5edlSMJ49exZ/+7d/a+n7wuGwvYeZWiYnJ62vaOzE43GkUilEIhF4vV7Mzc11kVHc2JpIJHD16lWUy2WL3SZg5pwbGBhAKBRCIBBAoVCwenBOr66u4oMPPsDu7m5XOKGGjhyVZ8sXBtVAN0hSoKMuWl00uKBpnKUyPED35kECH/dgFQqsKnngMGG/HrihzCLd3cokejwecxGRUeK9etIX66ZgWsEQwa4uDLTmAHS58TQzA/tRFwXXiFCmjswZcJgPm/2uHgC2Q5+lrLgLmBWc6ZiwvbohTjemAd2HiLjMpysrwLObGLVoPQkedKxc4KwWN6/RTXDKart9wLq77ns+i/XRPqNsqLwqe8s2KxgkOFKmkn2ihqaCZ+1btotAQL9j2+iC1pzI2idqzBH4u+y+9i3HX2WfnykwdWP2WS9tN+/TOGO+n+9SQ1EBJQ1Dl81W+dIxYH1UN2lfaVy0+71+pvKgLKk+R5lrBdZuvRiHq/3tkgTUc9r32l8E5ryeOgvoHPrBw0FIGNBI5yEjLASgfB4zCXCxDIfDxn6xTcyAwTGiPqDMUedT/7JtOnfVe8EwF85NlQfWiwu/HjNOuaP8alahSCRiwJX1cvWnGswE/gCQz+exv7+Pzc1Nay8NFBqh1LUci4GBAeTzeZOFWCyGZDJpWTdqtRr6+/vh9XpRKpUsJ3IqlTKjZ2JiwvIX81Reut/Hxsawvr6OWq2GEydOoFgsYmtrC9PT04hGo8hms4jFYvjwww8Rj8cxODhoadwSiYQxipOTkxbry1CQsbExVCoVPHr0qOvwodu3b2NoaAjZbNaYdjLoDHOoVCqWHo/vCgaD+OSTT6x/mMFkb28Pr776Kt577z2kUikcHBygXC4jk8kgn8+jUCggHo9jb28PyWQSu7u72N/fRyaTgdfrRaFQwN7eHlZWVuxAGp/P1+U5o1wAhzmxs9msMbI//OEPAQAXL17EwsKCnRZZKBSws7ODSCRiRtHJkycxMzODV155BefPn8fDhw9NvmlEcW/CrVu3uk4/ZWgK/y4UChgYGLDNvjwJcn9/HyMjIxgeHgYA3L5928A458CpU6ewvr6OarWKqakpmwMDAwPwer1YXFxEtVo1lpiAOJ1OI5vNGoPODCPXrl0zr0Oj0cDo6Kidlkl9uLy8jNHRUZw/fx6Tk5OYm5vD9PQ0Tp06hQ8//LCLiGDYUigUsvSSn1d+kZnqL7RRMRaLtZlWhUUXXRbNB6rP10VRF39+54YiuJkCdHDJFAB4RlEDh5tQuBi6h8kosKWLpt1u20YUTamndXTdiy4jri5FKoJGo7ObnHF8blHAoCCU71cAw79ppRPocpK7R5GrsaJ1V0DFTYTaJu0nAmzdka3jqOCd73HBtAI1BdUEaC7Tr54BXRSVyVeQ6LLu7APtRxdQUQZ1k6vKqsta8h79XIEM73NDnBR865irTBFEUr4VCCuo6tXXWle37cqaut4lyji9MwQcbJcCJDWYetWhlx7RuruHN6lhpIZbr1AByo7ObxewucYucMh4FQoFY5S0H11vgWvoaPtc2VJmVQE67+XfLphnv7Devd6j853joJuB6/U6BgcHbQM1wze4IVTDwQKBQNdJe6VSCT6fz9KYAbAjySl/KjvaPzTIAdgmPuoMuvz5nYZl8N5IJIJisdgVFqNGqYa+uV4utgU4XF/UINQ1RceCz1Iyge/q6+szhpD1oMtfvamUYyVnGo1ORg4AWF5eRrvdSbEWj8cRCARQqVSMTSXpwphU6gieTvj06VMcHBzg4sWLFi4yOTmJzc1N2+SYTCZRqVRw8uRJLC4uYnh42AwqjgPPXcjn8yiVSjaXotGoZf9YWFiA3+83b0MikbB82aFQCOfPnwfQOT250Wjg2LFjuHv3rgFJxvmqd5ehLFeuXMH169cxMDCAg4MDO7KbeaFXVlYQCoWQz+cRiUQstptZSagDJyYmUCqVkEqlbFOkZqPReUGGmnKha8vVq1fxa7/2a7h16xbW1tbg9/vx0ksvYXFxEXNzc5iYmEC1WsX9+/fNc9FsdmLCh4aGsL+/j8nJSWxvb6Ner6NWqyGdTtshOjROm82m5eKmx5gH7oTDYSwuLtoYAMDKyoqx9KVSCfF43NIETk5OYm1tzQyax48fWyrEbDZrJ2GWy2XzqnCMI5EIdnZ28Nprr1mebU1hmUgkzLgYHx83r/Po6KgdROPxeHDhwgU8ffoU3/ve97C3t4dYLIaBgQEUCgXDRvv7+9ja2vrcjYrsh5+3VCqVf9obFV2WWXeacmGgoHNx0IVIFxbgcGMjlTInKdCdyon36cKqClZZMQJOMoZa6Pokc6yMrWb40MVDWWDgMN5Xs1mwnppJhEBPU/2pgaDAXtlIXQwIXhTQceFjXfm/GhjsL32u9jfHwgUVytYq08nr6fJVwKALmdsG7UMFdJrDmuOmrCPv0TqxLZQLfacufuwj1l29HFr4PrJbLgOt9e/FcrtssW6Mpez1YnJZ+E412pSNY9uVbdb6qNHE9/IZGppBMMK+4ZhR/jUGWQsXS627gh3VBb3qw/ryHpVlZSnVKNBxZd8oA+0any7rzqI6Y2dnxzZsueDYNXS0v7XPtU9dWdJ5Sh2mRphr5Kl86li63iQaV7VazfaOtNudExhTqRQqlYrpMTJuZLF5XbPZya/c399vRsXw8DDy+bwBcQI8joue+qjhQew7r7cTqkZ92W53QjEYJ0z2jc9V79fu7q71k0tIUAbV2KAud+vAz9UAA2CZJtjHGrrHd42OjtqmTbYFQFf6Ol7LehJE6nwheTI3N4eDgwPbhMc2RCIRRKNRbGxsoFqtmp7gO3moSj6fN0C8ubmJarWKoaGhLk9Bq9VJZfv6668bQ31wcIDl5WUbs+HhYas3mUjd98LnE2Rxoz6fPzo6ip2dHQSDQdy6dQvT09Oo1+uWECAUCqFWq1meY4YQ7e/vI5lMIhQKGRjN5XI2z1iHhYUFTE9PG9PJo87z+Tzy+bx5KFqtFmZnZ+H3+1EsFm2ce3n1WC8y+Eq6fOMb38Abb7wBn8+Hf/Wv/hX+8i//Euvr67h79y42Njbw4osvIpvNYnZ21jyjPFTF5/NZuj0eEtNqdTZD0mDlnC2XyzYfaaS9+OKLWFpaQqFQQLFYRCqVQqlUQiAQsKPm+/v7kc/nEQ6H7cjz3//938f+/j4ePnwIn8+HpaUly6RCOS6Xy6a/GRKSTqfNaGq327h161bXiaacO41GA1evXgUAfPLJJ0ilUtjd3cWTJ0/Q19eH8+fP4/Lly3jvvfewvr6OaDSKvr4+vPzyy1heXrZn7e7uduXfPird5QuHf1CY6/V6l9JUZlEBtbKBCvA05lQ36WmaPl3UWXTiqHuYykPBgy7GbhypAnrgMB5Qgaa6YNX1qawHGQ9NcUegoWwYWQs+l2CAz9K2KttJBooAQ+tMI0MBpPYEHFKnAABMkklEQVQTfytQ0BAU3sOxIPjSTBK6SCnDxXsI0pSlU3CtoIogVLM7KGPJ+xWEaT1d4KSMuQKAXmEmlAGXUdVsJPytjL0LsPU336Px924ohcvisj8UnLqGiAJWl53lsxVQ6vNdY4h10mfq9W7YCOcLZUFlVZlVjqfr3VCgxLYqE8g2qXdBDXMFo+xTBd8cb7ZV9YkCXzWo3DFQkOXKg85dPkM3ASujrCSAK3OuV6CXPPSSDbaXfcD88tyQ5Pf7jQVVDxDv4yluBOUEO61Wq2sjF8dG6xIKhSxMgePFea4eB5Uxyj/BOzeraV/zOrdvFRzxWgVPqqc4f12vjBtyRgadhi6BWiAQsPSDfE6j0clqUSwWsbu7a8CZ+oTtZnt5eh1TkD5+/NhOBCTIZh7pYrFoKeJ8vs4pfu12Z7NhJBJBq9Wyay5evIiHDx8aexwMBlGpVCzbSiQSwfT0NN577z2k02kDkalUCo1Gw/rd5+vEu3o8HjsYpa+vD/F4HBMTE4hEIlhfXzciql6vY2VlBalUCq1W53CPp0+folarmUHAE/0o24zTV12zt7eHcrlsJw16PB47Qj2fz8Pr9drR60y1yPAX1ZdqPDKbDd9FnafhOP39/Th58iQeP37c5f2amJjAb/3Wb2F7ext+vx8/+clPsL29DZ/Ph8ePH2N/fx8/+MEPbG6rnHFD5PT0NBqNBj755BML6wA664WGGTUaDWxubppeGB4ethMHn3/+efh8Prz77rtoNpsGjvv6+mxj6fHjx7GxsYFvfetbWFlZwY9//GPL8MJwC7ZNPZdkzuPxOL7yla9gbm7OPBuU73Q6bd6pcDiMqakpzM3NYXZ2FpFIBNvb2yiVSujr68Pv/d7voVKp4M///M/tSPtoNIqpqSncunXLQsX0dFaOTa/ikjS/SOVn2qioLm9dGJThVDAAdLOmXDyUxVAGkoKj4I3PVGDlMlVU0ARtukBpLGIvxgo4BP6M79PFV+O3FPTSHajPZ/s0FEMXFL6LwEEZaO1PTlrN6wwcspwKxsiUcyFUA4P110VaQy4I+JX5cQGJAhmyNQqy9D5dqNkmLhYcC44Z6+56Fdh+BSQKajQ2nSwx5YEAQF3KOt6UQ76TsuYu9K4Ma7s4DurFUPCpY6XGnTLwbJsaIK5Xht/RKNT+UkDCvtX4fpctVsNN28sxU4NIQa8CSnoLFOi63ig1lnTcOOd7bWDUuUbZ1r5Q0K0y4cb4u23XvtWxc+WTxj3nnJICrIcCatZTDQrewzmo79Ux4+LI3wostH5MVcdsGgAMPBFQcz4CHZczU8PRKKK7PBgMWuyztpssPseW8bJ9fX2Wa1jllum7GN7BzXOMgVX5cOcOP1dZU33B34xFbzQaFtvMPucGOsayqjy7Gyw1Aw/7mcdWt1otTE5OYnV11cBau314tDv1P4F3tVq1+HMAdsjKxsYG/P5OyrxisWiHsvj9fmOoh4eHkclk8OjRIwCddG08PTCdTuPJkydoNjt5j9vttoUHkFE+ffo0ZmdnLY6ZQNXj8Vi2jWq1inK5jHg8bjKRTCYtM0S5XEY2m0UoFLK8ycwHz/X35s2biMVi6O/vx2effWbG2enTp7G1tQWv12ugWOU5HA5bmBXHi4YGY/cZcsI1ZnBwsGvd3NrasjVR12nqEJ5MybEbHx/Hl7/8Zayvr9tBMATz/+7f/Tt88MEH2NzcRLPZRDabxf7+Po4dO4bf+I3fwP/4H//DDK/p6WlsbGwgEAjg1KlT9p7Hjx9bTLPq6kAggNHRUWO1BwYG0G63kcvlMDY2hl/7tV+zvNt+vx8fffRR114yxthzHq+treHNN99ENpvFe++9Z3H93JishAP1IgALPTl//jy8Xq9lZ6HBTH1RrVZt4+z+/j4ePXqERCKBRCKBxcVFBINB/LN/9s9w5coVfO9730O73dm3cfHiReTzeTx58gT9/f1YWloyPaV7xo7Ks+VnOvxFGT93sXABFtDNvik45uKjoFGZLGVmuTgS0NNi5aKqgNrrPcxHrWwL60uFr0w7rT8qbF5HoOYyqVoXgmyyRAcHB5ZOyGUR9TONM+WzWF++Q4GJgjYNP3HBkzJ4fDZ/1EjQ8VEjQtlCr/cwZRQXrl7snwIjZUQVRCpzxz5nP1BhAs/GoaqSVXaak9yVQw0rYXHZWF6vLIACLwV9ChAU/Lh9z+v5W2VN2Tn9n/2iSkrfpyBOY23VgFVvBhfjVusw1pGyo/3L9+gYugCU9ewVPsS6ue3Qv3UcXPlSY5l103azr2js8V2UbT7HlUVep4YMn8fvFDTxO93z4ILqXsYjwaqOmWukK/jWVJO8hu1zwbSyuxwXprMjE8i6M3OBx+MxfRIKhRCJRBCLxezY6WAwiFwuZ/1N2We4G0/joyer3W4bI84UbQTpnDNMh0edx3axbwnKdK65IFnHj6CF/USQpeyk6mIyfkw7x012HEO+T4/3Hh4exvr6uq0VBAlMF8Z4aBonnFvsG8pqJBKxg1qWlpYsJR/XIwJnnl7I7BWxWMziimOxmIXtjI2NodFoYH19HYlEwuKOx8bGsLGxgaGhIcuYkclkjFnPZDJYWVkxmaAhwVRplPXd3V0MDg4ay91qtTA8PGxHqtPgTSQSWF5eNqMpGo1aX5RKJTSbhye3MlTly1/+sp2WyJABpnYkGFPyieEi/I6ePjL4DK/QTCv0NGQyGezt7eGrX/0qlpaWcP36ddtE+fWvfx3xeByXL1/GX/zFX8Dn6xzAU6vVUCgUcOHCBSwuLqJer1sqykQiYSkTn3/+ecvHzLAxndPUX0zNGwqFcO7cOdy8eRNf/vKX8cILL+C///f/bhldVldXu9Yxekp2dnYQi8UQiUTwK7/yK7h+/brlfD44OLCxBmBGE2Prd3d3MTo6iueffx6zs7Mol8s2T1W30Jg6ODjAsWPHrE7nzp3DzMyM7W341re+hd/4jd/A97//fdy+fRuNRsMyodDwY19xbnMOfF5MtdfrbZMQ+HlLrVb7px1TDaArdzPQvVgpaNRFXBdaBQW8V+Of1T3Ie/k5QQqF3Q0T0ANO9ECBXqyYpnxS1pKxpgQCdL3yWVxg+HxlNT0ejzEB6o5WwMD3KDgAul3SLmgj460hIWSiyMore80fBU+ua9X9TBlLLew/Ag1lwTku7tjyOz09TwGtMn4uq+saYlyU+bnbb8q2kr1TgOKyzy7zpsBQwYrLbCq41e9ZJ7dvdaMsP2d/af+xaCwnryc4YJ9SDnp5Azg2QHfYlBpiGjvLPnXBLmVIwbIaa9pOPte9V0GTjqW2XQ1GvkPnujLRKmt8tvYrjSL1Tqm8ubKndWcbtf/4nRr+rieFnhI3rtfVNSq3lGVtP/9WfalGJ/Pq7+/v22l2bAuBT7PZNC8bY3tp4DOzAgkE94CYdrttYGZ3d9fkzu/3Y3Bw0HIIq0HFjCDcIKfyrmyizje+U/UlcOj5I+tNdlCNBPanyiDlnqEqOu/0+G9u7GK9lKmOxWKoVCo2LgTPDFFjoSFDsEwm9mtf+xrW1tawvr6O559/Hvfu3TOGmOEChULB6kIAB8AAda1WQzgcxsmTJ/Hpp58CgB0602g0sLGxYYfKBINBTExM2OmIL7zwgsVPt9udfOS1Wg2Dg4PGKE9PT9uBNMViEbVazeKABwYG7NCUZrNzJHsul0N/fz/K5bLJEBlQrm/UJdFoFKOjo5Z/+vnnnzfAmMvl8PjxY/j9fgtf6e/vtywnPKCGpBo9rcVisUsPUy/G43HbgHnx4kW89957WFxctPCX1157Df/iX/wL1Ot1fPDBB1hfX0csFrOju4HDg1o4twOBADKZjDHf4+Pj8Pv9WFxcxOjoKJaXl20uMuQnGAwilUqZMfLuu+8ikUhYaMXq6irOnTuHlZUVY73VOKhUKojH4/jt3/5tHBwc4K233jI5r9frGB0dtXnQbDYtYwqziwCwcc7lctjf30c0GkUulzO5CgaD2NraQjAYNCNyf38fly5dwvLyssVyf+Mb38Bzzz2HH/7wh7h27ZqFc/B0ZmItxV1uBpaj8mz5whsV2+22KWSg252qilXZSmUvdLF2QYSyzroo8RlaBy0KimlFctFS9lTBm7aJu7I1XpohIK5xwJirdrvdtduYbVWgoAaDAmwFH+wvjdvUhUT7zWW1yKIoUNcjztXAIYikW4j38NlaR44dQZ2CXx03BajsA2XWFZyx3ho2QvlRK1tZSDVWFIxxklPOtB91UXflxAXW7uf6HRW7awC69+t7uDBov7J/ODcIItx+0PooyKUMueOh/cB7KGvqsnTr6PajgmDXw6HGkfaBGqd6vWusqIxrPRTkuoy/ssE6X/QaZbRVDsmGuYw65UwBrM5VZZN0LPR7Bbqqj9y6uMaEjo+CyXa7bQCYrmAePxyLxUw+6A4eHh629GLKhpZKJasbF316lgqFAtrtdlcM7O7urulHj8djadII5FhXxopyDLkpjTGVlEHqH/X46IZJ1esEw0pI8NnUw+129ymeCoD4bs4D5u+l5zKZTNpx7dTpbkwzwxII7uhRbDQa5ir3er3GylJnUrZOnTqFBw8e4NKlS3j69CkWFhaQTCYta0e73bZNoIFAwA5sOXXqFB4/fmwyzc2e8XgcyWQSN27csFj0ZrNpDLvP18lxTYN6Y2MDxWIRoVAI169ft1Agjj1PHqTRQMOCzCe9V81mEydPnsTy8jKq1SqazaaBfgJyHhhEtltTIg4PD6Ovrw9Pnz5FNBrFa6+9hk8//RRnz57FxsYG5ubmLKyov78fL774Im7fvm3sKfMic8NoX1+fAWrOP47NwMAAgsGgZQi5ffu29VNfXx9OnjyJwcFB7O3t4a233sJPfvIT7O/vG8vL9lNuuZa+9tpruHr1Kt5++2309/djcXERd+/etTlJwyoSiSAcDtshMeFw2NIeAh0Q+v3vfx87OzsIBAJYWFhAtVo1GWTf0Wv0ta99DcPDw/jjP/5j80ARrO7u7mJ8fNw2A9LD4fV2Qm+Gh4exu7uL27dvo9k8DBniBtJ2uxOKwhSZlGmfz2dGSCAQwJtvvomTJ0/i7/7u7/Do0aOudTgSiZhcUd+4J5Melc8vP1P4h7LE6oKmsgPQNdlZdHEnAGCsKBlwXcB6LcJcnNRFSjcmAIsZVpBBsOC6+BXguhspCTgInBXYcYHhO5VFZNuA7rRbLjBVQ0DBCNln1k1BsoJJjU/8PNaP97lHg38e8FCgwWcpk806qvHkAnEqYPU+cFLSEGEfeTwec38SOBJE8vlaF/3OZUxZR7IC+rlucHKv1zFhccGbekTcPlF5Yv15L+VGDykC0JX5REEpZZHPBmBggm5RBdR8L+uhp+ipQtS6ckFhH7EtyohrmznHWVedw9pnCn7VKNGiQEn7WRV1L3erGsxcFHTuaJ15L5+loFZ1ixpIKsMq/3y3GgrsB46vW3i/GhRqQPJwBbLM7Htu6mLGB9bv+PHjmJ+ft5CEer1u9wYCAcTjcTvyenh4GOVy2dzn3NTo9Xot5lf3GNBFy8wg7IdYLGYuXy6meugTdaCGg1Af9fKKqf5VmdcxabcPQ33UgKFBqoaQhu1p7DgAY93pLteTGxlOwry9AOyYdub15gYxAnvGGNPwOTg4wPDwMAqFAlqtloVZlMtlez7QcfUzQ0oymbQNjUxLRoNGwxXpBQU6bv/Lly9jZWXFsnD09/ej0WgY+GSe62PHjmFvbw/pdBp37txBMBg0kLm9vW0yPDExYR6ZRCJhWToI8t05zr6m3Pp8PhSLRYyPj+PSpUu4ceMGxsbGcPr0ady9excejwcrKytdJ2ru7+/jlVdeMQ9EX18fZmZmjMkHYMelU7/6/X7L/Z1IJLC/v49EIoFms2npCz0eD1KpFKLRqKXwO3bsGB4/foyVlRXLNHLq1CnkcjkLkeB8iEQiuHjxop02SOOVG12DwaClvBsaGrIDT/x+P1KpFIaGhrC0tGT5yV2vFsdXM7CkUil87Wtfw8jICL773e+afhofH8fq6qqNTSwWg9frRTabNeN7aGjIUjYy3Gd4eLhLbhuNzrHkQ0NDGB8fx4MHD8zbVS6XbdzfeOMNnDhxAt/73vfME6Ue53g8bsYovVyqw+jByOVynxv+QfLu5y17e3v/tMM/uOi4MYdU0lSQCrKo3F32h4uZLtyqiJW1ZlHWTxdwlzWnQlBAoS4mF1CoC5wKlawOQSILGRJ+TtCjQIL9o6wAXZzsEzIZwCFTzb4gI92rsF7KfFPgqRD5LAXYyohqv7hAiJ8raOjF+Log1e0/ZSCVHefCyT7mwq19zHFmm/jbZYk5wdlmDY9RLwGfqYBOZVG9Gfyfz1dZ7CW/vJ4Kl58RICs7qoadyxZ/HlDXVHBqwLItHEsNH9G6KWDlsxUkqlyokcy5QtZRAac+z322CyS1LXqdyqZex2e6IFt33GvOZs6rXkBOjU7W3wXaOo9UB2l7CZD5DpUTd365xg4L79HUi8wkEQqFUCgUntn03Gg0MD8/31VXgnK632OxmDGCzJag84KyQ+aNLC2fr4dPsL1kx/he3eBI+aHM6jynHmQ/c06rftU+pOzz3UxdR8OJqc50nBmmwTbQJe7qLM5FzjMydzTkydBzPFqtlhn3lDcaHczEUSwWjcVk5gUeZMJYVGbF4mZCpqKLRqOWeo/giGMdCHSO356enkYgEMDs7KzFvnKM1IBiRo9Go4ELFy5YnvFCoWDhFl6v1wwsAHZYx8HBAeLxuMXwNhoNqwvTt9E4pzwEg0HLJZ1MJnHixAl8/PHHFmfOGGymcRseHsbVq1exsLCAYDCIO3fu2JiQPeU8ZKYUFvV2ZDIZC8np6+uz0Ke+vj4MDg7i+eefRzQaxaNHj7C/v4+BgQEAHYOGJyiWSiWsrKzYejM6OmrA+u7du7hy5Yqtl9yDoIYhGXIaO16vF+fOncPQ0BC2t7fh9XoRjUaxvr7+jO7me5gP/IUXXsDOzg5u3rxphsLe3h62trYwPj6Ozc1NyzRSr9ctuwuzcQDA+vq6GVdAh81n39PQ8Pl8ZmhzTh4/fhxbW1v4l//yXyKVSuGDDz7A1tYW4vG4EYTMZV+tVq1PXIJC9dZR6V2+EKhWwMbChb0XM0WFS8WtixyFjwqVStZlp11wqWwRXYF8JpW0sr1UKnSP9QovUKADwJhoWrTafgCmHKkkFBDQheee7uiy9mwLY8oINAkGFdhpCAavc78HurNgcAFyc8UqAHCZPS5eLpvLa1x3vrL3Cio+D7jqEc3KFLnsM12MnLgKrJUd5ARn36ingW1ScKBsOsEUr9UFX4ubWeTz5NgtrIsyrexf7U9+r2PAvuD3VHrqimUdFCDrRiodX+0PNUwUzGs8Opl1Beq93H4qGzpnlbFXMKb95jK9Lgus81KNT+3/drttjBiBpbL5fB71Fv9n1gMW1zvTq82uAfB5Rpj2jeoZ6hTX2GXbND++EhW7u7uIx+MIhULY39/HxMQEHjx4YO1lzmU+JxqN2rHMND5ozBJocxMTwxSY3svj8dimPjLUdNeTjNA8+G54lxrbHC8lVbRtanRSL6fTaWxvb1sIhh744fV2wgiKxaL93Ww2kUwmkc1mu7walB/2ZzKZND1MBp4xq5R7jiMBPefnyMgI9vf3zVMwODiIwcFBfPLJJyiVSrYHh6ngUqmUHUnOuNyxsTEkEgm0223bJDkwMIDFxUXbtJhIJBCNRvHgwQMMDg5iaWkJzeZhpqVIJGJxs6x/JpPBw4cP0W63La5X8z7TUKFschzz+TxOnTqFJ0+eWJ9Eo1HLHsOUaRzPcDiMXC6HdruNiYkJzM7O2gbGfD6PwcFBFItFG9M33ngDx48fx5kzZ/DBBx/YRkmudVwPFbgrMUFGe29vD0tLS5ZEgOM+MjKCF154AZubm3j33XdRKpVw9uxZ09WaJSUUCln4CL0Uw8PDiEajmJubsywnzPVOTwd1Ig0Fn8+HeDxuceiPHj3C9vY2EomEGSLca0UdHQ6HMTAwgFwuh3PnzuHDDz9ErVaz9IaUqUqlYnOYh/a0221Eo1GcOnUKfX19uHHjBqrVKkZGRuz48kqlYvO23e6Egx0/fhyNRgMXL17EX/3VXxmjHQqF8O1vfxvRaBR/+Zd/aRlqiHM0EQGNUV3XVWfX63VcvnwZm5ub+LzyeeviL0L5whsVdXF2O50/VJQEKQR4QHfogW56VAbDtZBcwAR0u9D5Xm4spJLzeDxd8UEEb1rnXi5cxsYxfpr15M5q3TxFAKvMNuugrIkCcLpwFeApGKJBoO3VWEbgWaFlm9VToN8pKOD9CiQ5kZQFVdBEIKfhNC4I199ULApy9V41HPQAHwXI7oR2Aa8qZCo+FhoUyjJr+1SO+bcaVzQGXSClfUtwpAuC9rkCDhZtkxo7QHeoBr+jC1mvV1ZZGW7dXKXMoM4pZfEVvOo8IOuvz+7VVypbygbrs9w2at/oPdo3vfqK84yfEyi6IVoEEgSUalSp/KpXg7KuC4frLdDr2YcaK0wZZJ3dsVeDgAtZKBSyzYFkUBn6wVM+KVdkppeWluDxeOzwDDLTu7u7BkzJ5JLlox4jkOSYafoz6kodb9ZbPTA6D8lssQ9cOdPrlfRgqAPljGzz0tKSGdycq/xhtgkdPxoEBETa5wzna7fb2NnZ6SI0aKDwwBfKDFnjaDSKM2fOwOPxWAaEVquFCxcuIJvN4vr167bprr+/38Zof38fi4uL8Pv96O/vR6VSQTKZxOjoKLa2trC+vo54PG6sK8efwO/OnTsWNsLNfgAsVjoajdpJent7e8jlcnjxxRdx//59bG5uYn193Vz8BGo0jgcHBzEwMID79+8jGAxiYWEB6XTaDu1hBhQaMplMxkgi5jmmPmLMf6FQgMfjwcbGhs1HbpItFou4ceMGnjx5YnpUQ/+UMOCawLzeAwMDXQAvGAzijTfewN7eHubn520TZDabBdDZuOf3d3I6FwoFvP7665idncXAwABCoZBlDLlz544ZkjxtkHHR+/v78Pl8Fhbj8XhsT0IwGMRv//Zv4/bt29je3jZZA2BhK9QV1Mle72FIzeuvv463337b0vjt7e0hFAqZ7FF30+ilXMdiMWxtbZk8+3w+21zr8XjsmPepqSmsrKyY4ZVMJvHOO+9Y2CeNkPv37+NHP/qReVH29/eRTqcxOTmJ3d1d25jJ9Y86i4QgjZtms9lFNh6V7vKFQbUumAqa1VWqzIS6TegWVPe/MnPKcgDdWUPcOvQCCVzoVAgopC5I4/NZD83dTCXDhY2poxj2oa5BMmNUHG5WENZDWSi2U/M9KyAkq6u73/lMrT/rznsUFJBVcllq7UN1kSsY4PP4GVkxBQkuqFdm0W2Pawgou8q+c4Eu+8utr/ajvlf7nKdmalH5U/Cjz+J7KV9uexVQ81o1YpSNc5lgl4V02ViOpxsyQvDFaynjCvhZP9fzoAdd8J2sl4ZMKNBSgKRgU/92QbBrwGmf/jTGwjVY1LDU8eWC3KseXDg4XmyXpm5TWeTCwGdzMWfRTXL8XmWJfcb66dgDvU/fZJsIjvgMsltkRwk6CPCi0agxYWTfgMPUXJS93d1dC2/w+/2Ym5szWdK6kR3T3f0EXdRnBOfMOELWmDGpbBOZP17PouCVR1GTeVT9n0ql7HS5drttMdsaasM2KLGhYwN0smswZSA3vQWDQeu3VquFVCoFv9+PfD5vpxH6/X4zMng/40+VVdbQvbm5OdsYWavVMDIygr29PcuDXC6XceHCBSOVbt68CY/Hg88++wxer9fCLOiaZ8xws9nJvMHQHmaWaLVaNu4cp+HhYfNcXb161XI0ExwSqPM+eh3i8TgePXoEr9drYQVMRej1ei2bBMHd1taWAb9vfOMb+OSTT1AsFpHL5RAMBlEoFGzuAR1dMzg4iLNnz2Jubg6tVudYbeof/qaBxg2u9EKQ4R0cHMS//bf/FtevX8eHH36IU6dO4T/+x/+I4eFh/Pmf/7mdMOr3+xGPx3H+/HncunULCwsLePr0Ka5cuWJyXyqVsLq6iu3tbRQKBZNVbsTUsyKi0ah5b6anp7G6ump9cebMGQuJSaVSli6QukjnEdPR7e/vI5VKIRQK4dq1axbGMTQ0ZCkNaShSF1F/TE1N4eTJk8jlclhZWUE8Hsf09DR2d3exvr5u7+rv78epU6dMP504cQLz8/NdRvCLL76Is2fP4p133sHS0pLNR7/fj8nJSYyMjGBxcdFOfAyFQl1kAY1qAvfp6WnbQ3BUepefKfuHgllddFzQoWyzAkp1A1GYqLxccK3PJAhWsK2uZlpZVI6sG2MJCTR1ESYTxQWaSrzZbGJvb8/uIQDm91zgyADpxOKCpcCQQq4CC3SfeKcLHdujfcFFXplFfYdufuPfyvxyDPQ6F5gp68j26cZBBYsKxJU1VdaZ19DA0Vh5BWN8v7aTcsQFVvuRsqG/AXSNL8dAgTR/XPCjcugaKvxb30PjRdvrjr1ey3oo0NFFRQE1x1zZ716GijKvvYxRN8+q/qYc6JgpINbx0bFWeeJzdD6q8aIAnde6hoWCUe0n9YYwDEUXHmU/CcjdfR7ap+7JkXy/ptbkOGjIVi+jwJ2THEsdI44NFzAaBuoiZk5apjHjJi+CVgDY3NxEPB5HpVIxdzbfwbAQAicCbOotft5sNp9JFUdAoUCyVCohmUx2HTrBe3XMNNOE7idROSJQYx8FAgFjRqvVKra3t82rx3qprtA5p/3abreNqY1EIojH4xYrTAMF6MRBt9ttiz+lbNRqNbz00ku4du2aGTVDQ0Oo1+u2GS+fz2NlZQVTU1OYmJjA/Py8bTCkLPb19WF9fR2Tk5OWP/rll1+2zWHRaLSrXZFIxNKbMRtFNBrF9PS0bRYjyFf5Z8q7ZDKJdDptbPTY2JgRCNywmEwmsbOzA5/PZ/m7BwYG7Dl6sA2PH+cR2mTrfT6f5T3PZrOIRqN4+PChgfx6vW6HuHDu+Xw+jI+P480338S7776LjY2NLiNI5xjlgcYl2eGDgwO88MILuHTpEmq1Gu7du4fx8XH8p//0n9BoNPDDH/7Q7icrnc/nsb6+Dp/PhwsXLqBer+PBgwfY3d1Ff38/xsbGcPnyZZRKJRQKBcvzvLe3Z5sbC4UCXn31VZRKJczMzMDr9dqmxHg8jvHxcUtPNz09jZs3b5oHiR6jZrNp2XPYf+fPn8fa2hpmZmZw4sQJ1Go1XLp0Caurq6hWqzh//jzy+TxmZmYQCoVsHpw6dQq7u7vY3t7G2tqaebmZZYbpHem5vn//PhqNBnZ3d3H//n28+eabCAaDuH//Pn7lV34F0WgU//W//lcbY2KLkZERVKtVPHz40GK+G42GpVIkIUd9vLKygtHRUaysrCAYDJph8nnlp5Ep/9TLz3yiInC4SHLRUHcBAGMEdOGp1+tdk0wBJoGkC7j4OUECwYYy4VRCzBNNd0mrdXjwggvINMuGhlzowuAy6tx5rwcbUFlRITL0RGM7eS3BJdM00QJmn1Chun3MftDnKdBQQ0fHitfqIk/Fqn3PcaVLnc9VIKfP1LHRZ+viqwyfgh6XlWZRxpZjy/oqE6nPZPt4nT7L4zk8XZOLM8dZGUStO9usLLMaXwqkFRDquBAE6HhofmAFwzo+ChR13NWQUhacil3fzznVi8nWuamH+KhRqmOqIJTvdOVa5UKNDgXofJ479pQ/lWGVfzc0S/veNVbZPq0jCxd3ypU7lgoIXRl166GHnPB6HTOOJze2kUAgCwkcGuJk09rtNtbW1gAAqVQKGxsbFmPJDBbKTJPZrNfrtqjzfTs7O11GuXp36I7m3OT41ut1rK2tIZlMmlxQ/jU/MXUc3eaa05+FngA1vvl7Z2eny4CkPGp4E7/npkH2PT13Cq455mTZ+/v7sbW11RW+x3HhpsRAIIBr165ZxpNIJIL9/X1LJUeZ7evrQzqdxt27dy3jB8NTGGPt9XrtmOeBgQHMzs6iUql0nVVAI6Ber2N+fh4DAwPY2dlBMplEf38/ZmZmEIvF7IhyMpCabSUSiaC/v9+MgL6+PkxOTsLr9WJzcxOtVssMAw19AjpZRMic86hwhk1o5hSy2UwFxw17BJCMtWYITiAQsDRuNOj+5E/+xLJGcF5Qt/JQnkgkgkgkYiEjSnoAwL179zA6OoqXX34Z//yf/3MUCgX8+Mc/Ri6Xs+PWiQN4qmF/fz92d3e7DilpNjspA+v1uqUYZGhMrVbD9vY2hoaGzCuxsLCAZrOJYrGIYDCIsbExAMDi4iJOnTqFhYUFLCwsmKGi6w3DMXw+Hy5evIjLly/jo48+shMmyTbPzs6i1Wrh/PnziEQiuH37th3UNDIygsHBQRQKBQs/CQaDxiQfHBxgc3MToVAIyWTSPC+JRMLGqq+vD9VqFYuLi/jmN7+JSqWCzz77zOSQrDPzaDPvNAE1T9okqKauqNfrFu7RaDSQTqextbWFo9K7fKGUeqFQqH3ixIln2DP+32txYSETrEqbiyonieuyB7oZTi4qBNJ8tz6Llq8CMk1rwzpqjmkFalTECnrIQFOpk/XmYqWgUhUE7+WCqAuNMkEUXF0wXCNC3THsWxdUM+bbZS6VyVYXqv5mXbkgE6CQzWN9FGSpp6IXc043HcffZUD1/cqQu+EyfCfdcXyeuuI1JEg9I7xG+1aBnRYXbLO9fI4LqjnOujgpuGRf8lkcI4J9HRvWjZ81m00LHdJQHf1bgaq+m+OvMsf6sy6uV0H7TQ0plWXt71795xpLarTpva5sqizyM9Ud2jfavwoc1WjQtms99Lm9jAY1RhjypLLjMussHCOdz4yd5PdkLVlnr9eLTCaDbDaLgYEBy87Bhc7r9WJsbMw+Y+YHLox+v99isNk+DcNj7CPZYYauKdMbCoWs/zhPyWAynEJlnO9xdQjHQjfF6Xgzxpjxp83m4aEW+iz1GCohEwwGMTAwgLW1NRu/Y8eOmffQ7/cbuE0mk3bCYSAQsFzI1EXsfxIbHG96EIaHh7G5uWkZOSYnJ00+nj59anLBvgkGgzh58iSePn2KWq32zKZrxh8zu0KxWEQkEsHk5CTm5uZsnKLRqMmgZm4BYDGsmUwGIyMjmJubw7Fjx3D37l20251T8shs8sh4glgy+AwZYb82m00D/swmwnasr6+b3MTjcUxOTmJ2dtY2IwKdMJN0Om36hhtIqYuo49RryvtoMLBfvV4vvv3tbyOZTCIcDuPcuXPIZDJ455138Gd/9meo1+u4cuUKVlZWcOzYMczPz9ux6c899xzm5uYsZKJer2NychITExP467/+a9y5c8fW+kAgYAfbMAUfD89hO3w+H1KplJ2YeezYMWxtbeHGjRt2DcOvgMP9Vz5fJ3vKiRMn8OTJE7RandAoGlEejwejo6MWfrS0tGSG9blz53Dx4kXcvn0b+XzeTr8sl8tGtvHdqVTKnj00NGTM+O7uLp48eYLh4WG8+eabmJ2dtTHTHN30MsViMVSrVdMfSthQvzC1oG7W9Pl8FhI2Pz//uSn1FFP9POXg4OCffko9BUSMPVZgx8HRrBNkQ7jg0LXJSa5AQdlBBSo62ABM2VJBkO3QTYNUJJwsXHR0seBCSQWujCpwmAlEGWu2i5NCc0m7eVF1sWUf9XIv828XtPIZ7AMCLQVYHAMNw9F+5KRxmVwu7gpk2a9ceNgXfB77iLKgYJbvZgykttsFzuwLl/lWI0lZTm0vQaJrQHAsXcZeAZT7uf7vGlNuUZlmP7l9y75XUEgZp9xTibtMMvtVGVeyfuxLGkaUcx1Dn89ni6YCahY94VIBEf93vSP8TMGr9p2Oqz6LbVODU0OOegF5/QEOM0coQ67Gksocn6ULuguWtZ/5LO1XlVOVS22LGi3a7wCeuZ7AmPVnHRiHmkqlLHXd1tYWDg4OMDExgVwuZ8dAt9ttS+emh0ToZqe+vj5UKhVkMhlsb2/D5/PZoR2xWMw+03R67XbbcieTPe7lbWm1Wl2bEdvt7oNZVCewD3T+BwIBxGIxy9fLehAEKCCPRCIWp6n91Wx2wvAImhnGMDc3h/7+fvT19VnKMxIonMcKaJl72+PxYHJyEouLi7ZxjEB5fHzcvAzMQ72/v4+NjQ20Wh0P5JUrV7CxsWFMa19fH+bn59Futy2elm3z+/0Wn6tZShqNBlZXVxGPx7G5uYlkMgmv12shEZQv9pcaJhsbGyiVSrh37x58Ph/efPNNvPPOOxYTq2n9qLvJnLtrMkMWJicnsb29bafqeTweYz/D4bAdOBMIBHDixAl4vV6cPXsWxWIRH3/8cRehop5BDcHkhkgabTrXLl++jC9/+cvw+TqH9Hg8Hly/fh23b9+2TCg3b960TZz5fB6jo6OYmprChQsX4PV6LWvM5cuX0dfXhzt37mB+ft72KoyNjeHkyZNYX1+3cM21tTUzRtvtNgYGBmw+crPqjRs37NAXZdZp0CWTSXz9619HLpfD4uIiPvnkE/j9ncwfDK1hm3hc+JMnT2wenjlzBidPnsSdO3e6DH4SWdSdo6Oj5lnZ2NiwrDHVahW/9Vu/ha2tLcufffPmTczPz2N0dLTLkAU6+zrC4TD29/dx+fJlHBwc4PHjx6ZvqRcp78RRNAp8Pl9Xkoij8mz5mc0JAjEdCAIcZcqA7qOZ1VVPC1I3CukCqAsVAR8teQXBnBi8hs+lgqCw6sYKNQT4DgIVzcNIwVaAofV0GVDdmKkLNXejq2tOWSVlfggQ6HLR07C0rvxNVoLPJGjTvuf9yiABeKZtysrRkFCA6Hof1LpVwEQWh2kP2ddu/+nzNGRH5cEFuRwTZR97gXbtY32Xy1hqnRQUs30KrLgJljJOkKvyRDlXo5CKW0GfC/LVum+32wYQFLj7fD6LyeW7+G4COQX1amDxc16vxhFwCGa0fm6/ugyy2/8uiNVxUqCs7XXHTFNwcqxdeVZQ7461fs97e3kEFPSyDjQgtW4K/JUVZ39p+k6tK8eHgIIlkUgA6Ljm4/E4Wq1OaEQ+n0er1cmXyzA5zafMcAuyjwBM1zHml14IMq3sR5/PZyfMeb1eFItFA3/8nqCO+jgej3fpcbKa7E9u/FIjnyAmEAhgamoKi4uL1q7BwUFUKhWTa84N5nDWsaBuV4OKrB1l1es99PDRkOB7qP/pLSAIaLfb2N7e7tLfHMONjQ2kUilks1k7jZAH8pCde/jwoTHSzADCvNfVatXCVs6dO4dHjx4ZeA8GgxarzNjparWKqakpDAwMYHl52dIeRqPRrkwnIyMjeOWVV+yI6Xa7c3oogSHzUzP1HT0lzWYT8Xgc/f39ePr0qbXXJUlKpZLptv7+fhuTWq1mINTj6RxE9MYbb+C5557Dn/7pn2JmZuaZMCudfwTZ7D+OIeWHhg/DWy5cuID333/f0uQ1Gg1MTk5ibGwM9XodS0tLePr0KS5evIjvfOc7+OCDD/DBBx8gn88jGo3izp076O/vRzweRyaTgc/XiSUPBoNIp9NYXFzEzs6O6WzqhHA4bJ6g/f19XLx4Efl8Hjdv3jTZppGo+4u8Xi8uXbqEU6dO4S/+4i8sLzfB+YULF1Cr1VAsFjEzM4O+vj7k83kziKemplCr1XDt2jX4fJ0TVQcGBjA/P2/eKW5+HR4exvXr1y2XtRpen3zyCWKxGH7zN38T3/ve97C2tmbpJzVMloY0Q1hWVlYsZIZGKY0BpgnUjY3c+8GQs7m5ORyVZ8sX3qjIyci8nLqBzAXBGmfMBYwWNL9TplEXL76PTAK/Z0wZ/1eWhM9nUUZcF2PNl0zAwlhpZfbIuNBlohuO2C4FVJxoZD7YDn0uJygXP2XJeI278ZP9wHdoeIQLStlevpdgjO1URkrZRC5k7DPX0HEZRB1vNWho1CgwU1bWBV0K6DQ05vNYYy4IHHu2sRcbqZ/xXQoqXfDOz3oxsi54VIODYIf9TLnhcygPAMzlrCEcyr4SGLhhNWqMaX/xGe6mPK075yr7VWVf5ZLv7dUv7v9sp3oj1PjQceAYqRGuc0JlQmWSxhq/V/3g6gzXQFE5Z99pSBH7DfjpcdlqKPA7ZXUURLM99OBRV/A7xjwyHy1jbeld4Ka5drttrl8y1GSp6Iql4czvGMvM/iZDy3oGAgHb6LS3t2expZQburV5yARwuPFW5ZxAVuWX7Cjv4eZI5mRWbwXnLgkOr9dr8c3hcLgrxaAaImTaqRvJKLIPPR6Pnb6XzWaNyaNcMJsKw3DY3+zTUChkjKHf7zfwz3hShn4xbWooFEI2m0UwGMTo6Cg2NzdNRl999VU7+Y8butgnpVIJw8PD9jcBa6vViTuempqyUJnFxUUMDg4iFAoZKItEIpiYmLAQhnv37lkYB40exum2251Y8tXVVTtoREF0JpMx4MVcxktLSxZnnMvlTM58Ph+ee+45TE1N4Z133sGdO3dsU5uGtCkzXqvVLNRD1wvO/1gshpdeegn/5t/8G7zyyiu4efMmnj59ilAohIcPHyIWi2FxcRFer9dCMvL5vAHVaDSK7e1teDwenDhxArlczkI1GGbz2muvYWFhAcvLy13hiwTL6XQa7Xbbrn/99ddRr9cth7fOXXqQms1OtpVEIgGfz4e/+qu/MkMsmUwin89jenoaMzMz8Pv91k+Li4vw+Tr7vq5cuYJisYhUKoV8Po9cLmeeFGKE3d1dZDIZeDwefPDBBzh27Bh8Ph+ePn2KVCqFZDKJ7e1tS9X4Z3/2Z5bxZmtrC61Wy+QgkUjgypUryGazyGQy2N3dtbSLipuY+5psPL1MDDsZHBy0w6A+ryhO/D9RPB7PmwD+PwB8AP5/7Xb7//1/7OU9yhcG1WQjgG4W1AV4GkKhiycnGRVyL8ZJFzEKMl3eZEr4bIZmuOBa6wh0s9260FMACEZokXKxYuGioYutPo/tZ5wilT8XcwUByuwrKOBirYCZixnBKtlcWp8Entr/7CO6FHuBSK0bx0jdjQC6QIWCUW2bji/bQ4YJ6N6Ypy4jZVJZdKIquGXbtO/UINJTK/X5VN7ud65CUGDEv9XN7brFlBFm+9k2AmgCFX7Wy7PCvylLqsTVsGIfuCyJx9N9cl67fXhyqPYD20CgpyCe79X26dzt5V3Qeqn8KWh1F1EdQwW7fIeCapeldo0zBdnaHgXVfAYZGG2DGjDKzrtsNd9HzwDb69aBbWRfUycxdIz3E4xVq1VjZxn/6vV6MTg4iJWVFcuG4PV2Qjh4/DhT65VKJQvx4clsPCab7VTjwePxGHPFcSiXy8aeks3VtHmUg1QqhWKxiHa7bQC00egcpcxMBGw7Q5xCoZABMoJY9xAtlVn2s2YU0P0LurGRQFDllHLHTXnRaBSVSsWedXBwgIGBAYtDb7fbOHXqFDY2Nuz45zNnzmBra8tCTAg0qHfJMnMdYDhEIBDA+vq6xcIHg0HMzMwgl8uZEdloNLqOiC8WiyZH3CRJGSGLmM1mkU6nTd8AMA9Gs9nEwMCAseLb29vw+/0WAtRsNg2kMfvHzs4OYrGYgX9unOW4HRwcoFqt4syZM/D5fLh582YX2cNn/PjHP8b9+/eN4Gq1Ws/MY8qlGkWcy5S/RCKB559/Hr/6q7+Kl19+Gevr6/jRj36EaDSKp0+f4syZM9jY2ECtVkO5XO46qIjGiN/vx8WLFwF02Obz589btpJsNovh4WE8ffrUsqso0cGUg9xw+vLLL6O/vx+zs7MG5Pv6+iw/N3V4KBTCiy++iLGxMXz/+9/H9evXEY1G4fV2Mu/s7OwglUrZKYnz8/MGUrk29PX1meeg0Wjg7NmzqNVqFnJEAzqdTmNoaAiNRgNf+9rXcPPmTWSzWRw7dgzxeBx+fyfLzNraGra3t1EsFo1A8Pv9GBsbQygUQrFYxLFjx1AsFjE2Noa1tTWTQRpwSqgo6bO/v4+RkRHEYjGcPHkS6XQa169fRzKZxP8NxePx+AD8fwF8A8AKgGsej+ev2u32g3+sOv1M2T8AdC3wCqpc5kuZAj1eWwGETkbeywWPzJ6yXCx0i3LhICPN4HsWzZ1KAOMyr2RXCUi5CNJVqQH9wGGuVnX7E+RycaAyUmbeBSwu+GdRg0P7lowAFx2yVNomKko3ywr73V3UlDVUA0XropvclEXuxWwqc6tuXhf0KHhh/5D95LMI1vQZbAuvUVbdrTflgNe7YROut0DlVvue96uM8jveS4NH2RD9TYab7eXYAIdGm96vbddYc227bvpRGejFMLOe2lb93/1OgaoaYCrD7H/1dqhS1vt1TFWJa0iMAltez7oo4HPbpqy7O55uW9QQVpCs79V608BSb4Gy8DrvqLN4EiGNSo/HY+5cZmKg25rZCyiLlUoFBwcHGBwcRCaTwerqKorFooEsbn7mASY7OzsIBoNIpVIYGBiwGEnmifb5fLb5jkZGMpnsYr6pB9PptDG0wWAQOzs7xqIyUwnTAB4/fhzr6+vGmIVCIftOXfzsn0ql0pX2jnLvjgtwaHBxXKkfmIqPcsH0aEz5RuOB7wiFQhbrTIDKttTrdczNzcHv91tohettYIhLPp+3OUhgFI1G7XTOWq2GqakplMtlmx8852B4eBgbGxt2Wl+j0clLnslksLOzY3Gy9BLeunULZ8+etQ2C3LhZq9VsY161WsWxY8csJp6b89vtNoaHh1GtVk3/7u/vGxNMXcSsJz6fz46qbjQamJmZsbRzAMwQD4VC+Oyzz3BwcGCGohq+SgJx/mm4DnV3NBpFX18fvvWtb+Hy5cu4ePEirl+/jvn5ecTjcdy+fRubm5solUq2aY/ejLNnz6LVamF1dRU3btxAMpm0LB2xWAw7Ozu2P+H111+38AbKGcuJEydw5coV/OAHP0BfXx++9KUv4eDgAG+99VbXhvJwOIyRkRHk83mMj4+j3W7j5MmTyOfz+Ku/+ivk83nDHewDGjv7+/u4fv26eei4NlA/Ure8/PLLSCQSuH37to0fx6ZUKuG1115DPp/Hxx9/bAYAs4Uws8j29jaef/557O7uYmlpyXQD92OcOXMGfr8fq6ur2NnZQS6Xs7zi2WzWwl01Fp/6mPMuk8ng+eefx9tvv42tra0uj1av4q4//xvLKwBm2+32HAB4PJ7vAfhtAP//A6oJ4uiiALrjiznRGo3DAyqUzSI44338AZ5l5SKRiAFRr/cwfzMnrD6f4EzT2VGYaTmzjm4uY74TOIwFbrVa5pakUlbLXWOIqTB0MyPbx0XaBTL8W8GCLioKEJTFZj9wgeTmTfa3HoWs7WMbVRES2CjI4LMJimhhk3VzQawCMj4L6A4fUXbZNRaUvdR68D4Fma5bSSeusp36v/aFglX2WS/2Xp/fC2RRQVIO1RtBhoZjy4VV+0Fd5wrSKUNaBx13NVr5HeWD/eb2gbZPGT9eq2BU41pdRlf7Uw1S1l0Zc53nagSoQa2MsD6LDKzOSzWOtG1uX+hvbSfroXqGi5vKPRdIZfGVHafxQNlxw0nIPpN1VNaOadPC4TAGBwftWRo/Wy6XkU6nLWyhWCxa7HMoFEKhULCxoauYrB1BZLFYNABB8MM6Mg63UChYCjkdDz21lWQGWWbqW5/PZ0z7+vq6AR6fr3OaI3M2uyQJDYxKpWKMH3Wym+2C9/EwCgDWR9Fo1EApAAP+AMwLQDmip0712OTkpMXODgwM2Il17D8yoCMjI9jY2LAT7Mi001gZGRnBwcEBtre37Z6NjQ0Dql5v57AXjisBs9frtRjejY0NDA4OYmFhwVL4kZC4cOECrl27hmKxiHK5bONDgN9ud8IzpqenzVjjkd7z8/NmZPl8Ppw/fx6ffvqpGViJRMKycPh8PgsP4WZKjvPg4CDC4bAdDsIQFpJQ7A/XE6u/eY3H09kg+sILL2BgYABf/epXMTU1hU8++QR/+Id/iFAohAsXLuCXf/mX8ejRIywsLNhBM61WC9PT00gkErh375554+LxOLa3t3Hu3Dk8efIEKysrNuZra2vY29vrOm0VgBlCP/nJT+DzdTZGzs3NWUpL9cgyU8fk5CRisRimp6fx6aefYnV11bAGZT4Wi2FjY8NCMTSjGWPVaaCyH3/pl34JQ0ND+P73v2/z48KFC1hYWMBXvvIV7OzsYHFxESsrK/B4PAiHwxgfH8fOzk5XCsbR0VGUSiXs7OxgYGAAfX19Ft/Ng1oSiYSFzhD0s6+UwKDu5YZG7h/o6+vD97//fUulx31e/xeUcQDL8v8KgF/6R6oLgJ8h+wcVFNkMTnZlruv1ujEvqrA1tRxZZXWh8zdPraIypvAQcHweG6cLoIJrZVVdFlCfQ4Ei+60uewXvTC+lMa8askEQxfdxodZcsyy62CvjpaCYQIcKmfVS44YThe1k/BzfSUDDOrmTgvUnoNZ2keHRBYPfk+nWZxBY8l1q/bOfFbzp+CujSHli3+v4u+PoAnc+n+BJlbyCMQ1/+J+BN8qEyjoLQZS6PJWVYH0UUKuxpH/zXdpXWlxvgxpjrDOfp+91QzUoG6w7fzjOOgbaf5TzXuPPuvPZXKCV+XA3KPbyYmg79VqVOy3KkAGH3gFlhdjvfD5j3xUIuH2rCw6fQyOfYQZkj7nIq3HNBarZbJoni3NjcnLSwEoymcTKygpqtZoZy+l02nLWer3eroXZ4zlMWUZdp94PGsGMdyZI59jQW8LxY/s0/Shd3+xHjhGBJvNoZ7NZc0dvbW0ZqO/r67NDTpgxgPmmWU/+JhtOQ1eNMd7Lwzs43prdAOhO4UlZcE+m5UaxoaEhO4K8r6/PThPkHCf7zzh0GlGjo6MmQ/V63XIWM4yDm73I+sViMfT19aFYLJruZHiMz9fZHJpKpcw4KZfLGBsbw8cff4x8Pm9z0e/34+TJkwiFQiiXyxaGs7q6ikwmY7Hs9Fyl02lkMhkEAgHcu3fPQgcnJiYMKHGOcr1g/mNu9Dt+/Dju3LljXlqe0NhrLWa/axy/Eibj4+P48pe/jG984xuIx+OIxWIAgMePH+Pg4ACVSgVbW1t4/Pgx9vb2LM6dmbqYSWZ4eBitViel3K1bt9BsNrG5uWkZO9LptIU87OzsdJ0nwWwkq6urSKfTOH/+PB48eGAhMTTCVS9Go1FcunQJ+/v7+PDDD5HNZpFKpdDf349SqWQx6QydYjYe3ePy1a9+FWtra3Y8eCAQwK/8yq8gm83i+9//PiqVioH9YrGIF198EXNzc1heXjZSJhgM4ktf+hIeP36M9fV1jI+PY3d314hHxpZns1kEAgF8/etfR6FQwPz8PHZ3d1EqlUw/M+SLhjnP9eD8p8Hg93dOUnzuuefw6aefolqtYnh4GKVSqcsL8b+5DHo8nuvy/x+22+0//D/18p+lfGFQDRweHEEAp9kICKJcQM2FXcMg+D2fARwuqFyI9OAWLi5kTRSA8DvdFMb7NKRBFSfQO/abCoobcwiEFKC6wM4FBVzYdKFXQK07pZUVUwOEyot9yQ2TwGEKPgXHCrAUtLlue/1cARldffosLtwEZ72MEY49QT7r5vV6u46EVVaVbJ8bMsTfXHg5fqyTGiGsj4JCd6F2Q0MIJlhP9aCQddaxZN0ItBRkKYPO69QTw/bSFcyNVpQJ/q0gmHXk5wowFJiyHhpaQjli4TXqYuf4qfGn8qNGiTLM2kZlrFlnbTevISOpOkFBshuuwWeqZ0ENaNZX5UzBu17POruhUewjBV8ukHb7g+PsGtWM7WV9s9ks/H6/xeSOjY1he3vbDgdh1gcC75MnTyIQCGB1dbUr1R4PgeBBLjwtb3t72/7mMcr0pBF8eL3erkNc+E4FP+x3jgGBmIaiqZywzQQNDK9g5gugs1DzUJFms5N1AgAmJyctvzTDYjhnOQ/ZDpVTyomGG7A+BH7ZbBa7u7tIpVIWj6pjFw6HMTk5aaweWfZGo4GNjQ0AQCaTMaafssf0fkzBpgYcjRXGzhJo09VPgE0mmOkQCY7C4bClaNM1SE+xZKz98ePHsbKyYowhUyBms1n4fD5Lb/fkyRMDc0DnEJxsNosLFy5YLmtuqDx79ixu3LiBTCaDra0txONxJBIJ+P2d7A6jo6N46aWXkE6ncefOHXg8HruWfUcjkuuCepUIvDmnaBx+5Stfwa//+q8jFArhzp07CIfDeOutt7C+vo5KpWKp+/x+PwYHB229IMibmZkxgyiZTFruda/Xi+XlZZOLixcvYm9vDx9//LGlX/T5fHjxxRdRKBSwvLyMr3/968hms3jw4EEXgcAsIfQGXL58GadOnQIAvP3222bwJhIJI5k8Ho/tOWD6SBpM/f39OHPmjIUbcSNjMplEoVDAhx9+aGFeXH8o66urq/B4PBZvz30T3MdQLBYxNTWF1dVVNJtNuw6A1aFarZphy+PVaXjSwFA9Tr0YCoUwOTmJwcFBHD9+HPV6HclkEs1m0/LpT0xMoFAo4POKS3r8HCXb/ul5qlcBTMr/E//w2T9a+cKHv0xNTT0TCqFgSEEDcHgcri6qqvxcIMdYNY/HY2yrMtBU5Ora5QKgYRmc/BQ0HjWuwNYFkHRzsE6aI1SPOVdADHQf/sHvlJ3T8BR3g6QCcIJpTdzPsI5ms5N+SOOV2c9uaIACNv1MATVdvIwh57U6dmyL3+83cEwwrACdzL6CY8oC3V1c3BX0sV58rttnCiA5/m5ogDLNXBQJjJVRV4BES1wVv7IvGsbggjuOlb6X/2vbFPiSnSVAoRHghp8oY+rKSi/Djc9jUaCqgJ3tcOMg+Zxe71dmmG3U75SlVwZY76VRSiNY66ngTkG1ei9cYE/5UQNOCzeLKYjW8eRC4vP5DHRqe/h+ZaxpIOpc46l1HCc9xY4uVcZkqouYG+mAzsmJ6XTaTlCr1+uWzoyLaSKRwNraGjwejx0iwfAMDZGIRCKW/5cglUCMJy2y7wgamVWE80KNU/WE8bhvzmHtHzWACBo5zpxf3ByoIJJ9S/3mGuLtdidUJZVKWXwv0xISLBNwqfdD20GPEg2abDaLsbExbG5uwuvtZH3g39ybMzo6agd1BAIBMxiYOYVsXj6ftyPiaWDoYTAvvvgiHj16hO3tbTOmIpGI9dXBwQFGRkYMEHKtSSQSFiPs9XZOa+TJmYlEwk7l6+/vh8/nw/T0NB48eGDzfWJiwk4WBA7XPM4J5timLHJeUZa+/e1vo1Qq4e7du9jc3DSA1Wh0jq9WY0t1grvuUE6SySSOHz+OL33pS7hw4QJOnz6NO3fuYGlpCQ8fPsTc3JylWazVatjd3UUymcTly5cRCoUwNzeH2dlZRKNRZDIZLC4udq13sVjMZDQQCODMmTMIBoNYX19Hs9m0LDCjo6MIhUKYnZ3F7/7u7yKZTOK//Jf/Yt9Tdum5SqfTOHv2LJ5//nmsrKzg+9//PoAOViGgpkwCHZIxHA4jk8mg1WqhUqng5ZdfRi6Xw+PHj21t5BjSsKaRzKPQCZi5n4H/x2Ix279AJpqy3dfXh5GREczOzsLv92NgYAC5XA7RaNRkk/qh1eqk6+SaTa9ZMBi0bDI+nw8DAwN46aWXcOHCBQQCAfzpn/4pWq0WRkZGrH75fB63bt3qeTCLx+Np61r185R2u/1TD3/xeDx+AI8B/Co6YPoagH/Vbrfv/y+pwM9QvnD2Dy5SrnsHOMzEoSCAnwHdYEGfpSwKwxsYQ0fFzfuBww1hQLcLUV27CrD1ObpQK0NNtpwCp8aGstX8nwuxhmCo0uH1CvYAdAHQXgCBCksXfQWUBH4uqNVrtK7KqCtLq94Bl9EkkOeYky1QFzvHkApbWWwXbHLx5Lu42Oo46DPoWlI3ugtu2R7WUcdFx47/s41kzHifGkIsXLzZBy6IJ1hkHJyGqPC5Cqh1PDiGeq1er8Bf54bKt7LGykLyf5U5fR7TRnJuqqzq5i8W/q/zRkMF1GDh9WrY6Dj1mof8Tj0nuiGWY0HAyvlJGVNgz3pwbHRs2QadJy6zz+dxXqtHSY1vyo66pglolbVnOqpms4mFhQUD3pFIBFNTU9jd3bW0V5Qhn8+HWCyG1dVVyxPMZzFVlub5ZR0ZxsC+ikajXQYKAQPHR/eZsL/D4TDK5bK1dX9/345KJ8inTDLLxMTEBObn502O1HB1iQNNuxcIBOzgEQCWMYThKdRt1WrVgEEoFLLDa+iW19RelC8y7kz9NT4+jlKphHg8jnw+j5GREQMh7AP2XbPZOWXw5MmTuH//vskDgcvDhw/tGgIjjfHu7+9HIpHAzMwMstmspfjjXGDO5Ha7k+quUCiYHPE0PnoN1MgiI1gsFhGLxTAyMoKlpSUsLCwgk8lgfX3ddNj4+Diy2SzK5bLlzeYG0sePH5th12w2La692WxiaGgIMzMzWF1dNSOImayYz1z1EseR4UeVSsXi2Zme8Pz58/j6179ux4Lfv38f8/PzePr0qeUQ1zHc3d3F+fPnkUgkMDc3h3Q6jb6+PttUyrzgZGXHxsawvLxsfTg/P2+pIovFIgBgYGDAYoq/853voFqt4vHjx9ZGgnnqDQL4iYkJvPXWW3Z4Es9cYPx+s3l4smK1WsXk5KT197Fjx1AqlcwIGBoawvT0NABgeXkZ9XrdDoWhXKqBGI/HbY0YHx9HvV7HgwcPLESEfUWjk0es9/f3I5fLYXBwEMeOHcOPf/xjBAKH+eobjYax6ho2xk2pXq8Xp0+fxq/+6q9iY2MD2WwWa2trCAQC2NraQjgctj0O6+vr+GnlfyFT/T97T8Pj8fy/APwQnZR6//kfE1ADP2NMtQsEyRooCHQXJXXr6aJOpU3BJSOigEpjcnUTmBYF2cqEKausoRHWAf/AaJCt5DOoZNk+BT36vzLyuti7i76yNPyfSpthAQRqCvS4IdE9DU/ro58pYODiqewC2Uq/329xf5xwuklR26rjR5a31WoZCCeDw3coSKabjPey7TpeNKQI4LXPFSQrE+t6KlhcdlNZTz5L48FcFljBKp+l4QL8ns9Qg0TjqXURUtDMMaesuGy1MofKoqohR1nTuEaVNXXL6kLIzynfCvBdgKqssPaRtoPMJ40t7WPOW36nRo0av2RHdGMj+9htI+utYI1112vUY6a5tNkfruHFcaC88x38UfnSzXw8mYxAtlar2WeU80KhgMnJSYtjJStHQEYWslQqYXd3146aJlOpDLjm12fbuBjz8BHVcdyopPtTMpmMxQgzXpiAKBQKYXh42MJOms2mhUJwHpw4cQLz8/Po7+9HtVpFf38/Dg4ODKARZJD909AxMnDceKfEjOoIgj6y3MxswUWd9afObLfbxgi3Wi1cvHgR2WwWg4ODqNfrXRsoAdiR02o0sy+LxSIePHhg/5Ode/DgAer1uqUze/ToUZfu5qbKUqlkgIlyPz4+juXlZVy9ehWFQsEYTs4RpivT03/JhlarVUQiEZMDjpnP50OhULDDabxeL5aWlmzTIUMCUqkUpqamMDs7a+MBwGJymbO6v78fyWQSjx8/NmDHPtANiq1WJ7uWGlI0wICOB+Z3f/d3cerUKSSTSQQCAfzoRz/CzMyMeZmTySQuXryImZkZbG5umt7z+zu5q2dnZy3jDQ1+n89nRmgmkzFZ4/NoqNAL0Gh0Dl+h/A8PD2N9fR1bW1tYXFy0d+VyOdMNQ0NDuHTpEvL5PN5//33zWlC21RAlkG82mxgeHrZNvxMTE9ZO1vVf/+t/DZ/Ph7/5m7+xcSmXy/YTCoWMYabMsx3NZtPitalPotEonnvuOezv7yOXy1n2lu3tbXi9XtvgSP2u5IF6Alutlh1EQ0P07NmzmJ2dxYMHD1AqlfDyyy9bus7l5eUuwu//ltJut98C8NY/dj1YvlD4h8fj2Qaw+L+vOkflqByVo3JUjspROSpH5f/ycqzdbmfcDz0ezw8ADP4veke23W6/+b/oWf9HyhcC1UflqByVo3JUjspROSpH5agclWeL939+yVE5KkflqByVo3JUjspROSpH5aeVI1B9VI7KUTkqR+WoHJWjclSOys9ZjkD1UTkqR+WoHJWjclSOylE5Kj9nOQLVR+WoHJWjclSOylE5KkflqPyc5QhUH5WjclSOylE5KkflqByVo/JzliNQfVSOylE5KkflqByVo3JUjsrPWY5A9VE5KkflqByVo3JUjspROSo/ZzkC1UflqByVo3JUjspROSpH5aj8nOUIVB+Vo3JUjspROSpH5agclaPyc5b/B0QFvaJ+nwYIAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Note that you can also call the data variable by name\n", + "ep.plot_bands(modis_pre_bands[\"sur_refl_b01_1\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8895ff83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xarray.core.dataset.Dataset\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
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+       "    ...                                  ...\n",
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+       "    band         int64 1\n",
+       "    spatial_ref  int64 0\n",
+       "  * variable     (variable) <U14 'sur_refl_b01_1' ... 'sur_refl_b04_1'\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "array([[[1902., 1949., 1947., ..., 1327., 1327., 1181.],\n", + " [1949., 2160., 2095., ..., 1327., 1273., 1273.],\n", + " [2054., 2156., 2187., ..., 1139., 1101., 1206.],\n", + " ...,\n", + " [1387., 1469., 1469., ..., 343., 499., 1006.],\n", + " [1298., 1316., 1469., ..., 905., 499., 436.],\n", + " [1316., 1316., 1454., ..., 905., 578., 351.]],\n", + "\n", + " [[1056., 1072., 1012., ..., 745., 745., 581.],\n", + " [1051., 1150., 1039., ..., 745., 595., 595.],\n", + " [1073., 1220., 1152., ..., 588., 564., 563.],\n", + " ...,\n", + " [ 759., 813., 813., ..., 150., 218., 852.],\n", + " [ 703., 735., 813., ..., 585., 218., 199.],\n", + " [ 735., 735., 798., ..., 585., 338., 109.]],\n", + "\n", + " [[1534., 1567., 1527., ..., 1107., 1107., 1069.],\n", + " [1548., 1776., 1665., ..., 1107., 1121., 1121.],\n", + " [1696., 1813., 1820., ..., 1029., 1018., 1077.],\n", + " ...,\n", + " [1209., 1267., 1267., ..., 597., 788., 1153.],\n", + " [1165., 1154., 1267., ..., 1089., 788., 603.],\n", + " [1154., 1154., 1231., ..., 1089., 759., 516.]]])\n", + "Coordinates:\n", + " * y (y) float64 4.448e+06 4.447e+06 ... 3.337e+06 3.336e+06\n", + " * x (x) float64 -1.001e+07 -1.001e+07 ... -8.896e+06 -8.896e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + " * variable (variable) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot MODIS RGB numpy image array\n", + "ep.plot_rgb(modis_arr,\n", + " rgb=[0, 2, 1],\n", + " title='RGB Image of MODIS Data')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "23521b38", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.\n", + "- bounds: (167.65071105957, 14.928194130078, -47.743430543984, 86.453745196084)\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open fire boundary\n", + "fire_boundary_path = os.path.join(\"cold-springs-fire\",\n", + " \"vector_layers\",\n", + " \"fire-boundary-geomac\",\n", + " \"co_cold_springs_20160711_2200_dd83.shp\")\n", + "\n", + "fire_boundary = gpd.read_file(fire_boundary_path)\n", + "\n", + "# Check the CRS of your study area extent\n", + "fire_boundary.crs" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "44f43ab8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: unnamed\n", + "Axis Info [cartesian]:\n", + "- [east]: Easting (Meter)\n", + "- [north]: Northing (Meter)\n", + "Area of Use:\n", + "- undefined\n", + "Coordinate Operation:\n", + "- name: unnamed\n", + "- method: Sinusoidal\n", + "Datum: Not specified (based on custom spheroid)\n", + "- Ellipsoid: Custom spheroid\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check CRS\n", + "if not fire_boundary.crs == modis_rgb_xr.rio.crs:\n", + " # If the crs is not equal reproject the data\n", + " fire_bound_sin = fire_boundary.to_crs(modis_rgb_xr.rio.crs)\n", + "\n", + "fire_bound_sin.crs" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "1bb02948", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Notice this is a box - representing the spatial extent\n", + "# of your study area\n", + "crop_bound_box = [box(*fire_boundary.total_bounds)]\n", + "crop_bound_box[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "0fc03e97", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Notice that this is the actual shape of the fire boundary\n", + "# First you need to decide whether you want to clip to the\n", + "# Box / extent (above) or the shape that you see here.\n", + "fire_boundary.geometry[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "66d3d073", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Dimensions:         (x: 12, y: 3)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 4.446e+06 4.446e+06 4.445e+06\n",
+       "  * x               (x) float64 -8.989e+06 -8.989e+06 ... -8.985e+06 -8.984e+06\n",
+       "    band            int64 1\n",
+       "    spatial_ref     int64 0\n",
+       "Data variables:\n",
+       "    sur_refl_b01_1  (y, x) float64 nan nan 428.0 450.0 ... 458.0 nan nan nan\n",
+       "    sur_refl_b02_1  (y, x) float64 nan nan 3.013e+03 2.809e+03 ... nan nan nan\n",
+       "    sur_refl_b03_1  (y, x) float64 nan nan 259.0 235.0 ... 265.0 nan nan nan\n",
+       "    sur_refl_b04_1  (y, x) float64 nan nan 563.0 541.0 ... 518.0 nan nan nan\n",
+       "    sur_refl_b07_1  (y, x) float64 nan nan 832.0 820.0 ... 804.0 nan nan nan\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 12, y: 3)\n", + "Coordinates:\n", + " * y (y) float64 4.446e+06 4.446e+06 4.445e+06\n", + " * x (x) float64 -8.989e+06 -8.989e+06 ... -8.985e+06 -8.984e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + "Data variables:\n", + " sur_refl_b01_1 (y, x) float64 nan nan 428.0 450.0 ... 458.0 nan nan nan\n", + " sur_refl_b02_1 (y, x) float64 nan nan 3.013e+03 2.809e+03 ... nan nan nan\n", + " sur_refl_b03_1 (y, x) float64 nan nan 259.0 235.0 ... 265.0 nan nan nan\n", + " sur_refl_b04_1 (y, x) float64 nan nan 563.0 541.0 ... 518.0 nan nan nan\n", + " sur_refl_b07_1 (y, x) float64 nan nan 832.0 820.0 ... 804.0 nan nan nan\n", + "Attributes: (12/136)\n", + " ADDITIONALLAYERS1KM: 11\n", + " ADDITIONALLAYERS500M: 1\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open just the bands that you want to process\n", + "desired_bands = [\"sur_refl_b01_1\",\n", + " \"sur_refl_b02_1\",\n", + " \"sur_refl_b03_1\",\n", + " \"sur_refl_b04_1\",\n", + " \"sur_refl_b07_1\"]\n", + "\n", + "# Create a box representing the spatial extent of your data\n", + "crop_bound_box = [box(*fire_boundary.total_bounds)]\n", + "# Clip the data by chaining together rio.clip with rio.open_rasterio\n", + "# from_disk=True allows you to only open the data that you wish to work with\n", + "modis_pre_clip = rxr.open_rasterio(modis_pre_path,\n", + " masked=True,\n", + " variable=desired_bands).rio.clip(crop_bound_box,\n", + " crs=fire_boundary.crs,\n", + " # Include all pixels even partial pixels\n", + " all_touched=True,\n", + " from_disk=True).squeeze()\n", + "# The final clipped data\n", + "modis_pre_clip" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "57c3902b", + "metadata": {}, + "outputs": [], + "source": [ + "# For demonstration purpposes i'm creating a plotting extent\n", + "from rasterio.plot import plotting_extent\n", + "\n", + "modis_ext = plotting_extent(modis_pre_clip.to_array().values[0],\n", + " modis_pre_clip.rio.transform())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "bde30d32", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# View cropped data\n", + "f, ax = plt.subplots()\n", + "ep.plot_bands(modis_pre_clip.to_array().values[0],\n", + " ax=ax,\n", + " extent=modis_ext,\n", + " title=\"Plot of data clipped to the crop box (extent)\")\n", + "fire_bound_sin.plot(ax=ax,\n", + " color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "b95bea9a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:         (x: 8, y: 3)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 4.446e+06 4.446e+06 4.445e+06\n",
+       "  * x               (x) float64 -8.988e+06 -8.988e+06 ... -8.986e+06 -8.985e+06\n",
+       "    band            int64 1\n",
+       "    spatial_ref     int64 0\n",
+       "Data variables:\n",
+       "    sur_refl_b01_1  (y, x) float64 428.0 450.0 450.0 438.0 ... 442.0 458.0 nan\n",
+       "    sur_refl_b02_1  (y, x) float64 3.013e+03 2.809e+03 ... 2.496e+03 nan\n",
+       "    sur_refl_b03_1  (y, x) float64 259.0 235.0 235.0 231.0 ... 230.0 265.0 nan\n",
+       "    sur_refl_b04_1  (y, x) float64 563.0 541.0 541.0 528.0 ... 476.0 518.0 nan\n",
+       "    sur_refl_b07_1  (y, x) float64 832.0 820.0 820.0 820.0 ... 789.0 804.0 nan\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 8, y: 3)\n", + "Coordinates:\n", + " * y (y) float64 4.446e+06 4.446e+06 4.445e+06\n", + " * x (x) float64 -8.988e+06 -8.988e+06 ... -8.986e+06 -8.985e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + "Data variables:\n", + " sur_refl_b01_1 (y, x) float64 428.0 450.0 450.0 438.0 ... 442.0 458.0 nan\n", + " sur_refl_b02_1 (y, x) float64 3.013e+03 2.809e+03 ... 2.496e+03 nan\n", + " sur_refl_b03_1 (y, x) float64 259.0 235.0 235.0 231.0 ... 230.0 265.0 nan\n", + " sur_refl_b04_1 (y, x) float64 563.0 541.0 541.0 528.0 ... 476.0 518.0 nan\n", + " sur_refl_b07_1 (y, x) float64 832.0 820.0 820.0 820.0 ... 789.0 804.0 nan\n", + "Attributes: (12/136)\n", + " ADDITIONALLAYERS1KM: 11\n", + " ADDITIONALLAYERS500M: 1\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open just the bands that you want to process\n", + "desired_bands = [\"sur_refl_b01_1\",\n", + " \"sur_refl_b02_1\",\n", + " \"sur_refl_b03_1\",\n", + " \"sur_refl_b04_1\",\n", + " \"sur_refl_b07_1\"]\n", + "\n", + "# Create a box representing the spatial extent of your data\n", + "crop_bound_box = [box(*fire_boundary.total_bounds)]\n", + "# Clip the data by chaining together rio.clip with rio.open_rasterio\n", + "# from_disk=True allows you to only open the data that you wish to work with\n", + "modis_pre_clip_geom = rxr.open_rasterio(modis_pre_path,\n", + " masked=True,\n", + " variable=desired_bands).rio.clip(fire_boundary.geometry.apply(mapping),\n", + " crs=fire_boundary.crs,\n", + " # Include all pixels even partial pixels\n", + " all_touched=True,\n", + " from_disk=True).squeeze()\n", + "# The final clipped data - Notice there are fewer pixels\n", + "# In the output object\n", + "modis_pre_clip_geom" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d55732a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# View cropped data - note the different b etew\n", + "f, ax = plt.subplots()\n", + "ep.plot_bands(modis_pre_clip_geom.to_array().values[0],\n", + " ax=ax,\n", + " extent=modis_ext,\n", + " title=\"Plot of the data clipped to the geometry\")\n", + "fire_bound_sin.plot(ax=ax,\n", + " color=\"green\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c128ed84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:         (x: 8, y: 3)\n",
+       "Coordinates:\n",
+       "  * y               (y) float64 4.446e+06 4.446e+06 4.445e+06\n",
+       "  * x               (x) float64 -8.988e+06 -8.988e+06 ... -8.986e+06 -8.985e+06\n",
+       "    band            int64 1\n",
+       "    spatial_ref     int64 0\n",
+       "Data variables:\n",
+       "    sur_refl_b01_1  (y, x) float64 428.0 450.0 450.0 438.0 ... 442.0 458.0 458.0\n",
+       "    sur_refl_b02_1  (y, x) float64 3.013e+03 2.809e+03 ... 2.496e+03 2.496e+03\n",
+       "    sur_refl_b03_1  (y, x) float64 259.0 235.0 235.0 231.0 ... 230.0 265.0 265.0\n",
+       "    sur_refl_b04_1  (y, x) float64 563.0 541.0 541.0 528.0 ... 476.0 518.0 518.0\n",
+       "    sur_refl_b07_1  (y, x) float64 832.0 820.0 820.0 820.0 ... 789.0 804.0 804.0\n",
+       "Attributes: (12/136)\n",
+       "    ADDITIONALLAYERS1KM:                 11\n",
+       "    ADDITIONALLAYERS500M:                1\n",
+       "    ASSOCIATEDINSTRUMENTSHORTNAME.1:     MODIS\n",
+       "    ASSOCIATEDPLATFORMSHORTNAME.1:       Terra\n",
+       "    ASSOCIATEDSENSORSHORTNAME.1:         MODIS\n",
+       "    AUTOMATICQUALITYFLAG.1:              Passed\n",
+       "    ...                                  ...\n",
+       "    total_additional_observations_1km:   2705510\n",
+       "    total_additional_observations_500m:  660129\n",
+       "    VERSIONID:                           6\n",
+       "    VERTICALTILENUMBER:                  5\n",
+       "    WESTBOUNDINGCOORDINATE:              -117.486656023174\n",
+       "    ZONEIDENTIFIER:                      Universal Transverse Mercator UTM
" + ], + "text/plain": [ + "\n", + "Dimensions: (x: 8, y: 3)\n", + "Coordinates:\n", + " * y (y) float64 4.446e+06 4.446e+06 4.445e+06\n", + " * x (x) float64 -8.988e+06 -8.988e+06 ... -8.986e+06 -8.985e+06\n", + " band int64 1\n", + " spatial_ref int64 0\n", + "Data variables:\n", + " sur_refl_b01_1 (y, x) float64 428.0 450.0 450.0 438.0 ... 442.0 458.0 458.0\n", + " sur_refl_b02_1 (y, x) float64 3.013e+03 2.809e+03 ... 2.496e+03 2.496e+03\n", + " sur_refl_b03_1 (y, x) float64 259.0 235.0 235.0 231.0 ... 230.0 265.0 265.0\n", + " sur_refl_b04_1 (y, x) float64 563.0 541.0 541.0 528.0 ... 476.0 518.0 518.0\n", + " sur_refl_b07_1 (y, x) float64 832.0 820.0 820.0 820.0 ... 789.0 804.0 804.0\n", + "Attributes: (12/136)\n", + " ADDITIONALLAYERS1KM: 11\n", + " ADDITIONALLAYERS500M: 1\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ASSOCIATEDSENSORSHORTNAME.1: MODIS\n", + " AUTOMATICQUALITYFLAG.1: Passed\n", + " ... ...\n", + " total_additional_observations_1km: 2705510\n", + " total_additional_observations_500m: 660129\n", + " VERSIONID: 6\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174\n", + " ZONEIDENTIFIER: Universal Transverse Mercator UTM" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open the data but don't clip just yet\n", + "modis_bands = rxr.open_rasterio(modis_pre_path,\n", + " masked=True,\n", + " variable=desired_bands)\n", + "\n", + "# Reproject the fire boundary\n", + "fire_boundary_sin = fire_boundary.to_crs(modis_bands.rio.crs)\n", + "\n", + "modis_bands_clip = modis_bands.rio.clip([box(*fire_bound_sin.total_bounds)],\n", + " all_touched=True,\n", + " from_disk=True).squeeze()\n", + "modis_bands_clip" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "fa89dcb2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# View cropped data\n", + "f, ax = plt.subplots()\n", + "ep.plot_bands(modis_bands_clip.to_array().values[0],\n", + " ax=ax,\n", + " extent=modis_ext,\n", + " title=\"Plot of the data clipped to the geometry\")\n", + "fire_bound_sin.plot(ax=ax,\n", + " color=\"purple\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "50416e3f", + "metadata": {}, + "outputs": [], + "source": [ + "def open_clean_bands(band_path,\n", + " crop_bound,\n", + " valid_range=None,\n", + " variable=None):\n", + " \"\"\"Open, subset and crop a MODIS h4 file.\n", + "\n", + " Parameters\n", + " -----------\n", + " band_path : string \n", + " A path to the array to be opened.\n", + " crop_bound : geopandas GeoDataFrame\n", + " A geopandas dataframe to be used to crop the raster data using rioxarray clip().\n", + " valid_range:tuple (optional)\n", + " A tuple of min and max range of values for the data. Default = None.\n", + " variable : List\n", + " A list of variables to be opened from the raster.\n", + "\n", + " Returns\n", + " -----------\n", + " band : xarray DataArray\n", + " Cropped xarray DataArray\n", + " \"\"\"\n", + "\n", + " crop_bound_box = [box(*crop_bound.total_bounds)]\n", + "\n", + " try:\n", + " band = rxr.open_rasterio(band_path,\n", + " masked=True,\n", + " variable=variable).rio.clip(crop_bound_box,\n", + " crs=crop_bound.crs,\n", + " all_touched=True,\n", + " from_disk=True).squeeze()\n", + " except:\n", + " raise ValueError(\n", + " \"Oops - I couldn't clip your data. This may be due to a crs error.\")\n", + "\n", + " # Only mask the data to the valid range if a valid range tuple is provided\n", + " if valid_range is not None:\n", + " mask = ((band < valid_range[0]) | (band > valid_range[1]))\n", + " band = band.where(~xr.where(mask, True, False))\n", + "\n", + " return band" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "6b0f9f2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Open bands with function\n", + "clean_bands = open_clean_bands(band_path=modis_pre_path,\n", + " crop_bound=fire_boundary,\n", + " valid_range=(0, 10000),\n", + " variable=desired_bands)\n", + "\n", + "# Plot bands opened with function\n", + "ep.plot_bands(clean_bands.to_array().values,\n", + " title=desired_bands,\n", + " figsize=(10, 4))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "dc44bcf7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory to save path: cold-springs-modis-h4/07_july_2016/final_output\n", + "The directory cold-springs-modis-h4/07_july_2016/final_output does not exist - creating it now.\n" + ] + } + ], + "source": [ + "# Define path and file name for new tif file\n", + "stacked_file_path = os.path.join(os.path.dirname(modis_pre_path),\n", + " \"final_output\",\n", + " \"modis_band_1.tif\")\n", + "\n", + "# Get the directory needed for the defined path\n", + "modis_dir_path = os.path.dirname(stacked_file_path)\n", + "print(\"Directory to save path:\", modis_dir_path)\n", + "\n", + "# Create the directory if it does not exist\n", + "if not os.path.exists(modis_dir_path):\n", + " os.mkdir(modis_dir_path)\n", + " print(\"The directory\", modis_dir_path, \"does not exist - creating it now.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "3f570970", + "metadata": {}, + "outputs": [], + "source": [ + "# Here you decide how much of the data you want to export.\n", + "# A single layer vs a stacked / array\n", + "# Export a single band to a geotiff\n", + "clean_bands.rio.to_raster(stacked_file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "df789fe8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(CRS.from_wkt('PROJCS[\"unnamed\",GEOGCS[\"Unknown datum based upon the custom spheroid\",DATUM[\"Not_specified_based_on_custom_spheroid\",SPHEROID[\"Custom spheroid\",6371007.181,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]'),\n", + " None)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Open the file to make sure it looks ok\n", + "modis_b1_xr = rxr.open_rasterio(stacked_file_path,\n", + " masked=True)\n", + "\n", + "modis_b1_xr.rio.crs, modis_b1_xr.rio.nodata" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "b05a87d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "ep.plot_bands(modis_b1_xr,\n", + " figsize=(10, 4))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51d674fa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 56f5e31582a0fc916cefd7b891c21e82033eefea Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 6 Oct 2021 17:21:13 -0500 Subject: [PATCH 117/171] Add files via upload --- download_data/download_daily_modis_gee.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/download_data/download_daily_modis_gee.py b/download_data/download_daily_modis_gee.py index 99e3172..efa9497 100644 --- a/download_data/download_daily_modis_gee.py +++ b/download_data/download_daily_modis_gee.py @@ -4,8 +4,10 @@ import xarray as xr #data_dir = '/home/cccr/msingh/data/merra_aod_global_1980_2020/MERRA2_100.tavg1_2d_aer_Nx.1981_monmean.nc4' import sys -dates = sys.argv[1]# '2020-03-23' -datee = sys.argv[2]# '2020-04-24' +#dates = sys.argv[1]# '2020-03-23' +#datee = sys.argv[2]# '2020-04-24' +dates = '2020-03-23' +datee = '2020-04-24' #ds_merra2 = xr.open_dataset(data_dir) collection_evi = ee.ImageCollection('MODIS/MYD09GA_006_EVI').select('EVI').filterDate(dates,datee ).mean() collection_ndsi = ee.ImageCollection('MODIS/MYD09GA_006_NDSI').select('NDSI').filterDate(dates,datee ).mean() @@ -18,7 +20,7 @@ lons = np.arange(70,90,0.25) #print(lats.shape, lons.shape, ds_merra2.TOTEXTTAU.values.shape) evi = np.zeros((1, lats.shape[0], lons.shape[0])) -ndsi = np.zeros(1, (lats.shape[0], lons.shape[0])) +ndsi = np.zeros((1, lats.shape[0], lons.shape[0])) ndwi = np.zeros((1, lats.shape[0], lons.shape[0])) ndvi = np.zeros((1, lats.shape[0], lons.shape[0])) @@ -37,7 +39,7 @@ ndwi[0, i_lat, j_lon] = ee.Number(data).getInfo() data = collection_ndvi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDVI")# 0.5 degree = 50km =50000 ndvi[0, i_lat, j_lon] = ee.Number(data).getInfo() - #print(lats[i_lat], lons[j_lon], ndvi[i_lat, j_lon]) + print(lats[i_lat], lons[j_lon], ndvi[i_lat, j_lon]) times = pd.date_range(dates, periods=1) ds = xr.DataArray(evi, coords=[times, lats, lons], dims=["time", "lat", "lon"]) From b3862b68351b3226fd7dabf8d7cc8d383e4e2bd1 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 6 Oct 2021 17:22:42 -0500 Subject: [PATCH 118/171] updated - fixed bugs --- download_data/download_daily_modis_gee.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/download_data/download_daily_modis_gee.py b/download_data/download_daily_modis_gee.py index efa9497..b2fcc4c 100644 --- a/download_data/download_daily_modis_gee.py +++ b/download_data/download_daily_modis_gee.py @@ -39,7 +39,7 @@ ndwi[0, i_lat, j_lon] = ee.Number(data).getInfo() data = collection_ndvi.reduceRegion(ee.Reducer.mean(),p,25000).get("NDVI")# 0.5 degree = 50km =50000 ndvi[0, i_lat, j_lon] = ee.Number(data).getInfo() - print(lats[i_lat], lons[j_lon], ndvi[i_lat, j_lon]) + print(lats[i_lat], lons[j_lon], ndvi[0,i_lat, j_lon]) times = pd.date_range(dates, periods=1) ds = xr.DataArray(evi, coords=[times, lats, lons], dims=["time", "lat", "lon"]) From cb004cac6def46c5accdb54e28241003717e9f6e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 12 Oct 2021 14:20:14 -0500 Subject: [PATCH 119/171] Create coupling_indices.py --- xarray_tutorial/coupling_indices.py | 270 ++++++++++++++++++++++++++++ 1 file changed, 270 insertions(+) create mode 100644 xarray_tutorial/coupling_indices.py diff --git a/xarray_tutorial/coupling_indices.py b/xarray_tutorial/coupling_indices.py new file mode 100644 index 0000000..f975b6a --- /dev/null +++ b/xarray_tutorial/coupling_indices.py @@ -0,0 +1,270 @@ +def compute_distribution(v): + """ + v: vector de valores enteros + devuelve un diccionario con la probabilidad de cada valor + computado como la frecuencia de ocurrencia + """ + d= defaultdict(int) + for e in v: d[e]+=1 + s= float(sum(d.values())) + return dict((k, v/s) for k, v in d.items()) + +def entropy(y): + """ + Computa la entropia de un vector discreto + """ + # P(Y) + Py= compute_distribution(y) + res=0.0 + for k, v in Py.items(): + res+=v*log2(v) + return -res + + def conditional_entropy(x, y): + """ + x: vector de numeros reales + y: vector de numeros enteros + devuelve H(Y|X) + """ + # discretizacion de X + #print(int(x.size/10)) + # https://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information#:~:text=3%20Answers&text=There%20is%20no%20best%20number,on%20histograms%20have%20been%20proposed. + # Choosing number of bins + #hx, bx= histogram(x, bins=int(x.size/10), density=True) + hx, bx= histogram(x, bins=int(np.sqrt(x.size/5)),density=True) + + Py= compute_distribution(y) + Px= compute_distribution(digitize(x,bx)) + + res= 0 + for ey in set(y): + # P(X | Y) + x1= x[y==ey] + condPxy= compute_distribution(digitize(x1,bx)) + + for k, v in condPxy.items(): + res+= (v*Py[ey]*(log2(Px[k]) - log2(v*Py[ey]))) + +def mutual_information(x,y): + return entropy(y) - conditional_entropy(x,y) + +#https://course.ccs.neu.edu/cs6140sp15/7_locality_cluster/Assignment-6/NMI.pdf +def normalized_mutual_information(x,y): + return (2* mutual_information(x,y))/(entropy(x)+entropy(y)) + +from copent import transent +from pandas import read_csv +import numpy as np + +url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00381/PRSA_data_2010.1.1-2014.12.31.csv" +prsa2010 = read_csv(url) +# index: 5(PM2.5),6(Dew Point),7(Temperature),8(Pressure),10(Cumulative Wind Speed) +data = prsa2010.iloc[2200:2700,[5,8]].values + +te = np.zeros(24) +for lag in range(1,2): + te[lag-1] = transent(data[:,0],data[:,1],1) + print(data[:,0].shape) + str_ = "TE from pressure to PM2.5 at %d hours lag : %f" %(lag,te[lag-1]) + print(str_) + +# TE from y to x +def transfer_entropy(x,y): + return transent(x,y,40) + +def multivariate_mutual_information(xs1,xs2,xtar): + # https://stackoverflow.com/questions/20332750/python-joint-distribution-of-n-variables + numBins =int(np.sqrt(xs1.shape[0]/5)) # number of bins in each dimension + #print(xtar) + xs1 = xs1[np.isfinite(xs1)] + xs2 = xs2[np.isfinite(xs1)] + xtar = xtar[np.isfinite(xs1)] + + xs2 = xs2[np.isfinite(xs2)] + xs2 = xs2[np.isfinite(xtar)] + + xtar = xtar[np.isfinite(xs2)] + xtar = xtar[np.isfinite(xtar)] + #print(xtar) + data = np.stack((xs1, xs2, xtar)).T + #data = np.random.randn(100000, 3) # generate 100000 3-d random data points + jointProbs3, edges = np.histogramdd(data, bins=numBins) + jointProbs3 /= jointProbs3.sum() + jointProbs2, edges = np.histogramdd(data[:,:2], bins=numBins) + jointProbs2 /= jointProbs2.sum() + jointProbs1, edges = np.histogramdd(data[:,2:], bins=numBins) + jointProbs1 /= jointProbs1.sum() + jointProbs3_ = jointProbs3.copy() + for i in range(jointProbs1.shape[0]): + jointProbs3_[i,:,:] = jointProbs1[i]*jointProbs2[:,:] + arr = jointProbs3*(np.log2(jointProbs3)-np.log2(jointProbs3_)) + return np.mean(arr[np.isfinite(arr)]) + +# TCI for control +# mrsos_control[0].shape +# mrsos_std = np.zeros((mrsos_control[0].shape[1], mrsos_control[0].shape[2])) +# for i_lat in range(mrsos_control[0].shape[1]): +# for j_lon in range(mrsos_control) +# https://stackoverflow.com/questions/58546999/calculate-correlation-in-xarray-with-missing-data +def linear_trend(x, y): + #print(x.shape) +# if np.sum(np.isnan(x))>0: +# pf = np.empty((x.shape[0])) +# else: +# try: + idx = np.isfinite(x) & np.isfinite(y) + #print(x.shape) + pf = np.polyfit(x[idx], y[idx], 1) +# except: +# pf = np.empty((x.shape[0])) + return xr.DataArray(pf[0]) + +def compute_tci(sm, lh): + print(sm.shape, lh.shape) + sm_std = sm.std(dim='time') + slopes = np.zeros_like(sm_std.values) + + x = sm + y = lh + n = y.notnull().sum(dim='time') + xmean = x.mean(axis=0) + ymean = y.mean(axis=0) + xstd = x.std(axis=0) + ystd = y.std(axis=0) + cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n) + slopes = cov/(xstd**2) + intercept = ymean - xmean*slopes + sm_std['slopes'] = (('lat', 'lon'), slopes) + tci = sm_std.slopes*sm_std + return tci +#mrsos_control[0].std(dim='time').plot() + +def compute_aci(tas, hfss): + sm = tas + lh = hfss + + print(sm.shape, lh.shape) + sm_std = sm.std(dim='time') + slopes = np.zeros_like(sm_std.values) + + x = sm + y = lh + n = y.notnull().sum(dim='time') + xmean = x.mean(axis=0) + ymean = y.mean(axis=0) + xstd = x.std(axis=0) + ystd = y.std(axis=0) + cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n) + slopes = cov/(xstd**2) + intercept = ymean - xmean*slopes + sm_std['slopes'] = (('lat', 'lon'), slopes) + tci = sm_std.slopes*sm_std + return tci +#mrsos_control[0].std(dim='time').plot() + +def soilm_memory(ds_soilm): + threshold = 1./np.exp(1.) + soilm = ds_soilm.values + smemory = np.zeros_like((ds_soilm.values[0,:,:])) + ntim = 50 # ds_piClim_control_MPIESM_r1i1p1f1_mrso.mrso.values.shape[0] + correlation_ = np.zeros((ntim, \ + ds_soilm.shape[1], \ + ds_soilm.shape[2])) + for tt in range(2,ntim): + soilm_lagged = soilm[tt:,:,:] + times = ds_soilm.time.values[tt:] + lats = ds_soilm.lat.values + lons = ds_soilm.lon.values + ds = xr.Dataset({ + 'soilm': xr.DataArray( + data = soilm[:-tt], # enter data here + dims = ['time', 'lat', 'lon'], + coords = {'time': times, 'lat':lats, 'lon':lons}, + ), + 'soilm_lagged': xr.DataArray( + data = soilm_lagged, # enter data here + dims = ['time', 'lat', 'lon'], + coords = {'time': times, 'lat':lats, 'lon':lons}, + + ) + }, + ) + #print('lag = ', tt) + x = ds['soilm'] + y = ds['soilm_lagged'] + n = y.notnull().sum(dim='time') + xmean = x.mean(axis=0) + ymean = y.mean(axis=0) + xstd = x.std(axis=0) + ystd = y.std(axis=0) + + #4. Compute covariance along time axis + cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n) + + #5. Compute correlation along time axis + cor = cov/(xstd*ystd) + correlation_[tt-2,:,:] = cor.values + for i_lat in range(correlation_.shape[1]): + for j_lon in range(correlation_.shape[2]): + idx = np.where(correlation_[:,i_lat, j_lon] < 1/np.exp(1.))[0] + #print(idx) + #print(len(idx)) + #print(np.sum(np.isnan(soilm[:,i_lat,j_lon]))) + if len(idx)==0: + smemory[i_lat, j_lon] = np.nan + elif len(idx)==2: + smemory[i_lat, j_lon] = np.nan + else: + print(idx) + smemory[i_lat, j_lon] = idx[0]+1 + ds = xr.Dataset({ + 'smemory': xr.DataArray( + data = smemory, # enter data here + dims = [ 'lat', 'lon'], + coords = {'lat':lats, 'lon':lons}, + ) + }, + ) + + return ds + + def notaro_feedback_parameter(x,y): + tau = 20 # daily data + stptau = x.shift(time=tau) + st = x + atptau = y.shift(time=tau) + n = atptau.notnull().sum(dim='time') + xmean = st.mean(axis=0, skipna =True) + ymean = atptau.mean(axis=0, skipna =True) + xstd = st.std(axis=0, skipna =True) + ystd = atptau.std(axis=0, skipna =True) + cov_num = np.sum((st - xmean)*(atptau - ymean), axis=0)/(n) + + n = stptau.notnull().sum(dim='time') + xmean = st.mean(axis=0, skipna =True) + ymean = stptau.mean(axis=0, skipna =True) + xstd = st.std(axis=0, skipna =True) + ystd = stptau.std(axis=0, skipna =True) + cov_den = np.sum((st - xmean)*(stptau - ymean), axis=0)/(n) + + nfp = cov_num/cov_den + + return nfp + + def zengs_gamma(x,y): + n = y.notnull().sum(dim='time') + xmean = x.mean(axis=0) + ymean = y.mean(axis=0) + xstd = x.std(axis=0) + ystd = y.std(axis=0) + + #4. Compute covariance along time axis + cov = np.sum((x - xmean)*(y - ymean), axis=0)/(n) + + #5. Compute correlation along time axis + cor = cov/(xstd*ystd) + + cor = cor * (xstd/ystd) + return cor + + From af226fd6d7d7200092018635d4b70d641184e791 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 21 Oct 2021 00:57:31 -0500 Subject: [PATCH 120/171] Add files via upload --- deep_learning/patchify.py | 183 ++++++++++ ...rcnn_keras_austin_evaluation_playground.py | 333 ++++++++++++++++++ 2 files changed, 516 insertions(+) create mode 100644 deep_learning/patchify.py create mode 100644 deep_learning/srcnn_keras_austin_evaluation_playground.py diff --git a/deep_learning/patchify.py b/deep_learning/patchify.py new file mode 100644 index 0000000..6a2a7ce --- /dev/null +++ b/deep_learning/patchify.py @@ -0,0 +1,183 @@ +""" +Patchify.py +""" + +from typing import Tuple, Union, cast + +import numpy as np +from view_as_windows import * + + +Imsize = Union[Tuple[int, int], Tuple[int, int, int]] + + +def patchify(image: np.ndarray, patch_size: Imsize, step: int = 1) -> np.ndarray: + """ + Split a 2D or 3D image into small patches given the patch size. + + Parameters + ---------- + image: the image to be split. It can be 2d (m, n) or 3d (k, m, n) + patch_size: the size of a single patch + step: the step size between patches + + Examples + -------- + >>> image = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) + >>> patches = patchify(image, (2, 2), step=1) # split image into 2*3 small 2*2 patches. + >>> assert patches.shape == (2, 3, 2, 2) + >>> reconstructed_image = unpatchify(patches, image.shape) + >>> assert (reconstructed_image == image).all() + """ + return view_as_windows(image, patch_size, step) + + +def unpatchify(patches: np.ndarray, imsize: Imsize) -> np.ndarray: + """ + Merge patches into the orignal image + + Parameters + ---------- + patches: the patches to merge. It can be patches for a 2d image (k, l, m, n) + or 3d volume (i, j, k, l, m, n) + imsize: the size of the original image or volume + + Examples + -------- + >>> image = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) + >>> patches = patchify(image, (2, 2), step=1) # split image into 2*3 small 2*2 patches. + >>> assert patches.shape == (2, 3, 2, 2) + >>> reconstructed_image = unpatchify(patches, image.shape) + >>> assert (reconstructed_image == image).all() + """ + + assert len(patches.shape) / 2 == len( + imsize + ), "The patches dimension is not equal to the original image size" + + if len(patches.shape) == 4: + return _unpatchify2d(patches, cast(Tuple[int, int], imsize)) + elif len(patches.shape) == 6: + return _unpatchify3d(patches, cast(Tuple[int, int, int], imsize)) + else: + raise NotImplementedError( + "Unpatchify only supports a matrix of 2D patches (k, l, m, n)" + f"or 3D volumes (i, j, k, l, m, n), but got: {patches.shape}" + ) + + +def _unpatchify2d( # pylint: disable=too-many-locals + patches: np.ndarray, imsize: Tuple[int, int] +) -> np.ndarray: + + assert len(patches.shape) == 4 + + i_h, i_w = imsize + image = np.zeros(imsize, dtype=patches.dtype) + + n_h, n_w, p_h, p_w = patches.shape + + s_w = 0 if n_w <= 1 else (i_w - p_w) / (n_w - 1) + s_h = 0 if n_h <= 1 else (i_h - p_h) / (n_h - 1) + + # The step size should be same for all patches, otherwise the patches are unable + # to reconstruct into a image + if int(s_w) != s_w: + raise NonUniformStepSizeError(i_w, n_w, p_w, s_w) + if int(s_h) != s_h: + raise NonUniformStepSizeError(i_h, n_h, p_h, s_h) + s_w = int(s_w) + s_h = int(s_h) + + i, j = 0, 0 + + while True: + i_o, j_o = i * s_h, j * s_w + + image[i_o : i_o + p_h, j_o : j_o + p_w] = patches[i, j] + + if j < n_w - 1: + j = min((j_o + p_w) // s_w, n_w - 1) + elif i < n_h - 1 and j >= n_w - 1: + # Go to next row + i = min((i_o + p_h) // s_h, n_h - 1) + j = 0 + elif i >= n_h - 1 and j >= n_w - 1: + # Finished + break + else: + raise RuntimeError("Unreachable") + + return image + + +def _unpatchify3d( # pylint: disable=too-many-locals + patches: np.ndarray, imsize: Tuple[int, int, int] +) -> np.ndarray: + + assert len(patches.shape) == 6 + + i_h, i_w, i_c = imsize + image = np.zeros(imsize, dtype=patches.dtype) + + n_h, n_w, n_c, p_h, p_w, p_c = patches.shape + + s_w = 0 if n_w <= 1 else (i_w - p_w) / (n_w - 1) + s_h = 0 if n_h <= 1 else (i_h - p_h) / (n_h - 1) + s_c = 0 if n_c <= 1 else (i_c - p_c) / (n_c - 1) + + # The step size should be same for all patches, otherwise the patches are unable + # to reconstruct into a image + if int(s_w) != s_w: + raise NonUniformStepSizeError(i_w, n_w, p_w, s_w) + if int(s_h) != s_h: + raise NonUniformStepSizeError(i_h, n_h, p_h, s_h) + if int(s_c) != s_c: + raise NonUniformStepSizeError(i_c, n_c, p_c, s_c) + + s_w = int(s_w) + s_h = int(s_h) + s_c = int(s_c) + + i, j, k = 0, 0, 0 + + while True: + + i_o, j_o, k_o = i * s_h, j * s_w, k * s_c + + image[i_o : i_o + p_h, j_o : j_o + p_w, k_o : k_o + p_c] = patches[i, j, k] + + if k < n_c - 1: + k = min((k_o + p_c) // s_c, n_c - 1) + elif j < n_w - 1 and k >= n_c - 1: + j = min((j_o + p_w) // s_w, n_w - 1) + k = 0 + elif i < n_h - 1 and j >= n_w - 1 and k >= n_c - 1: + i = min((i_o + p_h) // s_h, n_h - 1) + j = 0 + k = 0 + elif i >= n_h - 1 and j >= n_w - 1 and k >= n_c - 1: + # Finished + break + else: + raise RuntimeError("Unreachable") + + return image + + +class NonUniformStepSizeError(RuntimeError): + def __init__( + self, imsize: int, n_patches: int, patch_size: int, step_size: float + ) -> None: + super().__init__(imsize, n_patches, patch_size, step_size) + self.n_patches = n_patches + self.patch_size = patch_size + self.imsize = imsize + self.step_size = step_size + + def __repr__(self) -> str: + return f"Unpatchify only supports reconstructing image with a uniform step size for all patches. \ +However, reconstructing {self.n_patches} x {self.patch_size}px patches to an {self.imsize} image requires {self.step_size} as step size, which is not an integer." + + def __str__(self) -> str: + return self.__repr__() diff --git a/deep_learning/srcnn_keras_austin_evaluation_playground.py b/deep_learning/srcnn_keras_austin_evaluation_playground.py new file mode 100644 index 0000000..9885664 --- /dev/null +++ b/deep_learning/srcnn_keras_austin_evaluation_playground.py @@ -0,0 +1,333 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +import numpy as np +import matplotlib.pyplot as plt +import xarray as xr +import tensorflow as tf +import tensorflow.keras as keras +from tensorflow.keras.layers import * +import tensorflow.keras.backend as K +import seaborn as sns +import pickle +#from src.score import * # load_test_data defined here +from collections import OrderedDict + +from tensorflow.keras.layers import Input, UpSampling3D, AveragePooling3D, concatenate, ReLU, Reshape, Concatenate, Permute +#from DLWP.custom import CubeSpherePadding2D, CubeSphereConv2D +from tensorflow.keras.layers import Reshape, Concatenate, Permute +from tensorflow.keras.models import Model +import glob +from tensorflow.keras.models import load_model + +import os +import numpy as np + +from tensorflow.keras.models import Sequential, model_from_json +from tensorflow.keras.layers import Conv2D +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.layers import Activation +import cv2 +from patchify import * +#import tensorly as tl + + +# In[2]: + + +def mse(y, t): + return np.mean(np.square(y - t)) + +def psnr(y, t, max_=200.0): + return 20 * np.log10(max_) - 10 * np.log10(mse(y, t)) + +def ssim(x, y, max_=200.0): + mu_x = np.mean(x) + mu_y = np.mean(y) + var_x = np.var(x) + var_y = np.var(y) + cov = np.mean((x - mu_x) * (y - mu_y)) + c1 = np.square(0.01 * max_) + c2 = np.square(0.03 * max_) + return ((2 * mu_x * mu_y + c1) * (2 * cov + c2)) / ((mu_x**2 + mu_y**2 + c1) * (var_x + var_y + c2)) + + +# In[3]: + + +data_dir='/lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/austin/' +file_ = glob.glob(data_dir+'GSMaP.v7_0.10deg_????-*_austin.nc') + + +# In[4]: + + +ds = xr.open_mfdataset(file_, combine='by_coords') + + +# In[5]: + + +ds_test = ds.sel(time=slice('2010', '2020')) +min_ = 0.0 # min_max.values[0] +max_ = 200.0 #min_max.values[1] + + +# In[6]: + + +weight_filename = 'srcnn_weight_2008.hdf5' +model = Sequential() +model.add(Conv2D(64,9,padding='same',input_shape=(20,20,1))) +model.add(Activation('relu')) +model.add(Conv2D(32,1,padding='same')) +model.add(Activation('relu')) +model.add(Conv2D(1,5,padding='same')) +# optimizer = Adam(lr=0.001) +# model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['accuracy']) +model.load_weights(os.path.join('./model/',weight_filename)) + + +# In[7]: + + +ds_test = ds.sel(time=slice('2010', '2020')) +year = 2010 +data_ = ds_test.pr.sel(time=slice(str(year), str(year))).values + + +# In[8]: + + +data_n = 2*((data_[:,:,:]-min_)/(max_-min_)) +print(data_.shape, data_n.shape) + +X = [] +y = [] +for i_ in range(data_n.shape[0]): + img = data_n[i_,:,:] + if True:#np.sum(img)>0: + scale = 2 + res = cv2.resize(img, dsize=(img.shape[0]*scale, img.shape[1]*scale), interpolation=cv2.INTER_CUBIC) + res_ = cv2.resize(res, dsize=(int(res.shape[0]*(1/scale)), int(res.shape[1]*(1/scale))), interpolation=cv2.INTER_CUBIC) + #data_n_[i_,:,:] = res_ + X.append(img) + y.append(res_) +X = np.asarray(X) +y = np.asarray(y) + + +# In[134]: + + +sub_images_ = patchify(X[0], (20,20), step=10) +sub_labels_ = patchify(X[0], (20,20), step=10) +fig,ax = plt.subplots(ncols=2,nrows=1, figsize=(11.69,8.27)) +ax[0].imshow(sub_images_[0,0]) +sub_pred1 = model.predict(sub_images_[0,0,np.newaxis,:,:,np.newaxis])[0] +sub_pred1[sub_pred1<=1e-4] = 0.0 +ax[1].imshow(sub_pred1) +print(np.sum(sub_images_[0,0])) +print(np.sum(model.predict(sub_images_[0,0,np.newaxis,:,:,np.newaxis])[0])) + + +# In[110]: + + +fig,ax = plt.subplots(ncols=2,nrows=1, figsize=(11.69,8.27)) +ax[0].imshow(sub_images_[0,1]) +sub_pred2 = model.predict(sub_images_[0,1,np.newaxis,:,:,np.newaxis])[0] +sub_pred2[sub_pred2<=1e-4] = 0.0 +ax[1].imshow(sub_pred2) +print(np.sum(sub_images_[0,1])) +print(np.sum(sub_labels_[0,1])) +print(np.sum(model.predict(sub_images_[0,1,np.newaxis,:,:,np.newaxis])[0])) +print(np.min(sub_pred2), np.max(sub_pred2), np.min(sub_images_[0,1]), np.max(sub_images_[0,1])) + + +# In[105]: + + +fig,ax = plt.subplots(ncols=2,nrows=1, figsize=(11.69,8.27)) +ax[0].imshow(sub_images_[1,0]) +sub_pred3 = model.predict(sub_images_[1,0,np.newaxis,:,:,np.newaxis])[0] +sub_pred3[sub_pred3<=1e-4] = 0.0 +ax[1].imshow(sub_pred3) +print(np.sum(sub_images_[1,0])) +print(np.sum(sub_labels_[1,0])) +print(np.sum(model.predict(sub_images_[1,0,np.newaxis,:,:,np.newaxis])[0])) +print(np.min(sub_pred3), np.max(sub_pred3), np.min(sub_images_[1,0]), np.max(sub_images_[1,0])) + + +# In[139]: + + +fig,ax = plt.subplots(ncols=2,nrows=1, figsize=(11.69,8.27)) +ax[0].imshow(sub_images_[1,1]) +sub_pred4 = model.predict(sub_images_[1,1,np.newaxis,:,:,np.newaxis])[0] +sub_pred4[sub_pred4<=1e-4] = 0.0 +ax[1].imshow(sub_pred4) +print(np.sum(sub_images_[1,1])) +print(np.sum(sub_labels_[1,1])) +print(np.sum(model.predict(sub_images_[1,1,np.newaxis,:,:,np.newaxis])[0])) +print(np.min(sub_pred4), np.max(sub_pred4), np.min(sub_images_[1,1]), np.max(sub_images_[1,1])) +print(sub_pred4.shape) + + +# In[159]: + + +sub_images_ = patchify(X[0], (20,20), step=10) +patches =np.copy(sub_images_) +sub_pred = model.predict(sub_images_[0,0,np.newaxis,:,:,np.newaxis])[0] +patches[0,0] = sub_pred[:,:,0] + +sub_pred = model.predict(sub_images_[0,1,np.newaxis,:,:,np.newaxis])[0] +patches[0,1] = sub_pred[:,:,0] + +sub_pred = model.predict(sub_images_[1,0,np.newaxis,:,:,np.newaxis])[0] +patches[1,0] = sub_pred[:,:,0] + +sub_pred = model.predict(sub_images_[1,1,np.newaxis,:,:,np.newaxis])[0] +patches[1,1] = sub_pred[:,:,0] +#patches[sub_images_<=1e-5]=0.0 +patches[sub_images_==0.0] = 0.0 +reconstructed_image = unpatchify(patches, image.shape) + +#print(np.min(patches[0,0]), + + +# In[160]: + + +plt.imshow(reconstructed_image) + + +# In[161]: + + +print(psnr(reconstructed_image, y[0]), psnr(X[0], y[0])) + + +# In[144]: + + +plt.imshow(sub_images_[0,1]) + + +# In[145]: + + +#print(X[0].shape) +X_pred = np.copy(X) +for i_x in range(X.shape[0]): + for i in range(0,X[i_x].shape[0]-10,10): + for j in range(0,X[i_x].shape[1]-10,10): + print(i, i+20,j, j+20) + if np.sum(X[i_x:i_x+1,i:i+20,,,,,,,j:j+20])==0.0: + continue + X_pred[i_x,i:i+20, j:j+20] = model.predict(X[i_x:i_x+1,i:i+20,j:j+20,np.newaxis])[:,:,0] + + +# In[ ]: + + +psnr(y, X_pred), psnr(X,y) + + +# In[ ]: + + + + + +# In[96]: + + +# sub_pred4[sub_pred4<=1e-4] = 0.0 +# if np.sum(sub_images_) +X_pred = np.copy(X) +sub_pred_list = [] +# Transform to sub images and predict +for i_ in range(1):#X.shape[0]): + print(i_) + image = X[i_] + sub_images_ = patchify(image, (20,20), step=10) + + image = y[i_] + sub_labels_ = patchify(image, (20,20), step=10) + sub_pred_ = np.zeros_like((sub_images_)) + print(sub_images_.shape) + #print(sub_images_.shape) + for j_ in range(sub_images_.shape[0]): + for k_ in range(sub_images_.shape[1]): + print(j_,k_) + #if np.sum(sub_images_[j_,k_])>0.0: + sub_pred_[j_,k_,:,:] = model.predict(sub_images_[j_,k_,np.newaxis,:,:,np.newaxis])[:,:,0] + sub_pred_list.append(sub_pred_[j_,k_,:,:]) + #sub_pred_[sub_pred_<=1e-4] = 0.0 + #sub_pred_[j_,k_,:,:][sub_pred_[j_,k_,:,:]<=1e-5]=0.0 + #sub_pred_[j_,k_,:,:][sub_images_[j_,k_,:,:]==0.0]=0.0 + + #print(sub_pred_.shape) + pred_reconstructed = unpatchify(sub_pred_, image.shape) + print(pred_reconstructed.shape) + X_pred[i_] = pred_reconstructed + + +# In[99]: + + +plt.imshow(sub_pred_list[4]) + + +# In[98]: + + +import matplotlib.pyplot as plt +fig,ax = plt.subplots(ncols=3,nrows=1, figsize=(11.69,8.27)) +data_ = [y[0], X[0], X_pred[0]] +tits_ = ['Ground Truth', 'Cubic', 'SRCNN'] +for i in range(3): + ax[i].imshow(data_[i]) + ax[i].set_title(tits_[i]) + + +# In[ ]: + + + + + +# In[ ]: + + + + + +# In[ ]: + + + + + +# In[ ]: + + + + + +# In[ ]: + + + + + +# In[ ]: + + + + From ec54cbeb94d159c7c0aa38b3eb1d3c2709186ac5 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 18 Nov 2021 19:08:36 +0530 Subject: [PATCH 121/171] Created using Colaboratory --- xarray_tutorial/three_d_plot.ipynb | 241 +++++++++++++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 xarray_tutorial/three_d_plot.ipynb diff --git a/xarray_tutorial/three_d_plot.ipynb b/xarray_tutorial/three_d_plot.ipynb new file mode 100644 index 0000000..620c138 --- /dev/null +++ b/xarray_tutorial/three_d_plot.ipynb @@ -0,0 +1,241 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "three_d_plot.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyPCauGvcaQ7bYCt552YBnwm", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0DzKR2vNKAOn" + }, + "source": [ + "" + ], + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517 + }, + "id": "eJTMQrwHKDDL", + "outputId": "a626e58e-d044-4faf-e0cf-851aea1a9647" + }, + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objects as go\n", + "\n", + "\n", + "def get_arrow(axisname=\"x\"):\n", + "\n", + " # Create arrow body\n", + " body = go.Scatter3d(\n", + " marker=dict(size=1, color=colorscale[0][1]),\n", + " line=dict(color=colorscale[0][1], width=3),\n", + " showlegend=False, # hide the legend\n", + " )\n", + "\n", + " head = go.Cone(\n", + " sizeref=0.1,\n", + " autocolorscale=None,\n", + " colorscale=colorscale,\n", + " showscale=False, # disable additional colorscale for arrowheads\n", + " hovertext=axisname,\n", + " )\n", + " for ax, direction in zip((\"x\", \"y\", \"z\"), (\"u\", \"v\", \"w\")):\n", + " if ax == axisname:\n", + " body[ax] = data[ax][\"min\"], data[ax][\"max\"]\n", + " head[ax] = [data[ax][\"max\"]]\n", + " head[direction] = [1]\n", + " else:\n", + " body[ax] = data[ax][\"mid\"], data[ax][\"mid\"]\n", + " head[ax] = [data[ax][\"mid\"]]\n", + " head[direction] = [0]\n", + "\n", + " return [body, head]\n", + "\n", + "\n", + "def add_axis_arrows(fig):\n", + " for ax in (\"x\", \"y\", \"z\"):\n", + " for item in get_arrow(ax):\n", + " fig.add_trace(item)\n", + "\n", + "\n", + "def get_annotation_for_ax(ax):\n", + " d = dict(showarrow=False, text=ax, xanchor=\"left\", font=dict(color=\"#1f1f1f\"))\n", + " for ax_ in (\"x\", \"y\", \"z\"):\n", + " if ax_ == ax:\n", + " d[ax_] = data[ax][\"max\"] - data[ax][\"range\"] * 0.05\n", + " else:\n", + " d[ax_] = data[ax_][\"mid\"]\n", + "\n", + " if ax in {\"x\", \"y\"}:\n", + " d[\"xshift\"] = 15\n", + "\n", + " return d\n", + "\n", + "\n", + "def get_axis_names():\n", + " return [get_annotation_for_ax(ax) for ax in (\"x\", \"y\", \"z\")]\n", + "\n", + "\n", + "def get_scene_axis(axisname=\"x\"):\n", + "\n", + " return dict(\n", + " title=\"\", # remove axis label (x,y,z)\n", + " showbackground=False,\n", + " visible=True,\n", + " showticklabels=False, # hide numeric values of axes\n", + " showgrid=True, # Show box around plot\n", + " gridcolor=\"grey\", # Box color\n", + " tickvals=[data[axisname][\"min\"], data[axisname][\"max\"]], # Set box limits\n", + " range=[\n", + " data[axisname][\"min\"],\n", + " data[axisname][\"max\"],\n", + " ], # Prevent extra lines around box\n", + " )\n", + "\n", + "\n", + "fig = go.Figure(\n", + " \n", + " layout=dict(\n", + " title=\"surface\",\n", + " autosize=True,\n", + " width=700,\n", + " height=500,\n", + " margin=dict(l=20, r=20, b=25, t=25),\n", + " scene=dict(\n", + " xaxis=get_scene_axis(\"x\"),\n", + " yaxis=get_scene_axis(\"y\"),\n", + " zaxis=get_scene_axis(\"z\"),\n", + " annotations=get_axis_names(),\n", + " ),\n", + " ),\n", + ")\n", + "\n", + "add_axis_arrows(fig)\n", + "\n", + "N = 1000\n", + "t = np.linspace(0, 1, 100)\n", + "y = np.sin(t)\n", + "t = np.linspace(0, 10, 50)\n", + "x, y, z = np.cos(t), np.sin(t), t\n", + "t = np.zeros_like((t))+1.0\n", + "z = t\n", + "\n", + "fig.add_trace(go.Scatter3d(x=x, y=y, z=z))\n", + "fig.show()" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
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\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3PK5uAA0LhSq" + }, + "source": [ + "" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "bb8QqvgLPwH1" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From d6779e5e9c7291c5ca52b768f9bd660081dbe20d Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 21 Nov 2021 00:02:48 -0600 Subject: [PATCH 122/171] Add files via upload --- xarray_tutorial/alka-eol-data.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 xarray_tutorial/alka-eol-data.ipynb diff --git a/xarray_tutorial/alka-eol-data.ipynb b/xarray_tutorial/alka-eol-data.ipynb new file mode 100644 index 0000000..b53efb1 --- /dev/null +++ b/xarray_tutorial/alka-eol-data.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"#!wget -rc -nH --cut-dirs=3 --ftp-user=anonymous ftp://data.eol.ucar.edu/pub/download/data/TIWAR299318/ \n#wget ftp://data.eol.ucar.edu/pub/download/data/TIWAR299318/ST4.2012060312.24h.Z\n#gunzip ST4.2012060312.24h.Z\n#https://data.eol.ucar.edu/dataset/21.093","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-21T05:06:54.407149Z","iopub.execute_input":"2021-11-21T05:06:54.407717Z","iopub.status.idle":"2021-11-21T05:06:54.411686Z","shell.execute_reply.started":"2021-11-21T05:06:54.407675Z","shell.execute_reply":"2021-11-21T05:06:54.410847Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"#!tar -xvf ./TIWAR299318/katz_stageIV_readme.txt.gz","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:06:59.113714Z","iopub.execute_input":"2021-11-21T05:06:59.113999Z","iopub.status.idle":"2021-11-21T05:06:59.118026Z","shell.execute_reply.started":"2021-11-21T05:06:59.113970Z","shell.execute_reply":"2021-11-21T05:06:59.117217Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"#!ls TIWAR299318","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:07:01.900991Z","iopub.execute_input":"2021-11-21T05:07:01.901584Z","iopub.status.idle":"2021-11-21T05:07:01.906315Z","shell.execute_reply.started":"2021-11-21T05:07:01.901531Z","shell.execute_reply":"2021-11-21T05:07:01.905406Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"!conda install -y -c conda-forge pygrib","metadata":{"execution":{"iopub.status.busy":"2021-11-21T04:48:21.270875Z","iopub.execute_input":"2021-11-21T04:48:21.271649Z","iopub.status.idle":"2021-11-21T04:55:49.642119Z","shell.execute_reply.started":"2021-11-21T04:48:21.271604Z","shell.execute_reply":"2021-11-21T04:55:49.641059Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"#from basemap import Basemap # import Basemap matplotlib toolkit\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pygrib # import pygrib interface to grib_api","metadata":{"execution":{"iopub.status.busy":"2021-11-21T06:00:21.588111Z","iopub.execute_input":"2021-11-21T06:00:21.589029Z","iopub.status.idle":"2021-11-21T06:00:21.593517Z","shell.execute_reply.started":"2021-11-21T06:00:21.588988Z","shell.execute_reply":"2021-11-21T06:00:21.592733Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"grbs = pygrib.open('../input/st4-sample-file/ST4.2012060312.24h')","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:05:52.418689Z","iopub.execute_input":"2021-11-21T05:05:52.419422Z","iopub.status.idle":"2021-11-21T05:05:52.460057Z","shell.execute_reply.started":"2021-11-21T05:05:52.419372Z","shell.execute_reply":"2021-11-21T05:05:52.458874Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"for grb in grbs:\n print(grb)","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:05:54.234728Z","iopub.execute_input":"2021-11-21T05:05:54.235602Z","iopub.status.idle":"2021-11-21T05:05:54.256260Z","shell.execute_reply.started":"2021-11-21T05:05:54.235541Z","shell.execute_reply":"2021-11-21T05:05:54.255187Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"#https://nbviewer.org/gist/jswhit/8635665","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:07:12.414279Z","iopub.execute_input":"2021-11-21T05:07:12.414879Z","iopub.status.idle":"2021-11-21T05:07:12.418006Z","shell.execute_reply.started":"2021-11-21T05:07:12.414839Z","shell.execute_reply":"2021-11-21T05:07:12.417480Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"grb.keys()","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:07:27.903175Z","iopub.execute_input":"2021-11-21T05:07:27.903503Z","iopub.status.idle":"2021-11-21T05:07:27.912766Z","shell.execute_reply.started":"2021-11-21T05:07:27.903469Z","shell.execute_reply":"2021-11-21T05:07:27.911773Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"pr = np.array(grb.values)","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:08:28.403681Z","iopub.execute_input":"2021-11-21T05:08:28.404100Z","iopub.status.idle":"2021-11-21T05:08:28.431741Z","shell.execute_reply.started":"2021-11-21T05:08:28.404070Z","shell.execute_reply":"2021-11-21T05:08:28.430816Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"lats, lons = grb.latlons() ","metadata":{"execution":{"iopub.status.busy":"2021-11-21T05:08:43.792329Z","iopub.execute_input":"2021-11-21T05:08:43.792634Z","iopub.status.idle":"2021-11-21T05:08:44.051593Z","shell.execute_reply.started":"2021-11-21T05:08:43.792604Z","shell.execute_reply":"2021-11-21T05:08:44.050629Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"pr[pr==9999] = np.nan","metadata":{"execution":{"iopub.status.busy":"2021-11-21T06:01:56.119685Z","iopub.execute_input":"2021-11-21T06:01:56.120316Z","iopub.status.idle":"2021-11-21T06:01:56.129208Z","shell.execute_reply.started":"2021-11-21T06:01:56.120255Z","shell.execute_reply":"2021-11-21T06:01:56.128220Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"plt.imshow(pr)","metadata":{"execution":{"iopub.status.busy":"2021-11-21T06:02:01.048556Z","iopub.execute_input":"2021-11-21T06:02:01.048831Z","iopub.status.idle":"2021-11-21T06:02:01.427619Z","shell.execute_reply.started":"2021-11-21T06:02:01.048803Z","shell.execute_reply":"2021-11-21T06:02:01.426651Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file From 3dc29a8b9fa58343a87fa0b55315e3f6d01d2807 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 1 Dec 2021 23:45:34 +0530 Subject: [PATCH 123/171] Create quiver.py --- xarray_tutorial/quiver.py | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 xarray_tutorial/quiver.py diff --git a/xarray_tutorial/quiver.py b/xarray_tutorial/quiver.py new file mode 100644 index 0000000..616078a --- /dev/null +++ b/xarray_tutorial/quiver.py @@ -0,0 +1,4 @@ +ds = u.to_dataset(name = 'uas') +ds['vas'] = v + +ds.plot.quiver('lon', 'lat', 'uas', 'vas', ax=ax[0,0]) From 0b21ef98d68c6622e7a24ee1eeb52e86136180bc Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 7 Dec 2021 12:56:30 +0530 Subject: [PATCH 124/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 52c83d8..80c0d80 100644 --- a/README.md +++ b/README.md @@ -177,6 +177,8 @@ Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 73. Lower tropospheric stability (LTS = θ700hPa − θ1000hPa; Kelvin), which is defined as the difference in potential temperature (θ) between the 700-hPa level and the surface +74. Shallow copy and deep copy in python https://stackoverflow.com/questions/41125834/trying-to-do-a-shallow-copy-on-list-in-python + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From dbfaa23a96a2a3e865bce30f7a44b67704db454e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 21 Dec 2021 19:37:57 +0530 Subject: [PATCH 125/171] Update coupling_indices.py --- xarray_tutorial/coupling_indices.py | 1 + 1 file changed, 1 insertion(+) diff --git a/xarray_tutorial/coupling_indices.py b/xarray_tutorial/coupling_indices.py index f975b6a..3c820e2 100644 --- a/xarray_tutorial/coupling_indices.py +++ b/xarray_tutorial/coupling_indices.py @@ -44,6 +44,7 @@ def conditional_entropy(x, y): for k, v in condPxy.items(): res+= (v*Py[ey]*(log2(Px[k]) - log2(v*Py[ey]))) + return res def mutual_information(x,y): return entropy(y) - conditional_entropy(x,y) From f6bcf1a1db7cff2a848214e4ea5df992d4633474 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 21 Dec 2021 19:47:36 +0530 Subject: [PATCH 126/171] Create dask_parallel_xarray.py --- xarray_tutorial/dask_parallel_xarray.py | 62 +++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 xarray_tutorial/dask_parallel_xarray.py diff --git a/xarray_tutorial/dask_parallel_xarray.py b/xarray_tutorial/dask_parallel_xarray.py new file mode 100644 index 0000000..eb28418 --- /dev/null +++ b/xarray_tutorial/dask_parallel_xarray.py @@ -0,0 +1,62 @@ +from collections import defaultdict +def compute_distribution(v): + """ + v: vector de valores enteros + devuelve un diccionario con la probabilidad de cada valor + computado como la frecuencia de ocurrencia + """ + d= defaultdict(int) + for e in v: d[e]+=1 + s= float(sum(d.values())) + return dict((k, v/s) for k, v in d.items()) +def entropy(y): + """ + Computa la entropia de un vector discreto + """ + # P(Y) + Py= compute_distribution(y) + res=0.0 + for k, v in Py.items(): + res+=v*np.log2(v) + return -res + +def conditional_entropy(x, y): + """ + x: vector de numeros reales + y: vector de numeros enteros + devuelve H(Y|X) + """ + # discretizacion de X + #print(int(x.size/10)) + # https://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information#:~:text=3%20Answers&text=There%20is%20no%20best%20number,on%20histograms%20have%20been%20proposed. + # Choosing number of bins + #hx, bx= histogram(x, bins=int(x.size/10), density=True) + hx, bx= np.histogram(x, bins=int(np.sqrt(x.size/5)),density=True) + + Py= compute_distribution(y) + Px= compute_distribution(np.digitize(x,bx)) + + res= 0 + for ey in set(y): + # P(X | Y) + x1= x[y==ey] + condPxy= compute_distribution(np.digitize(x1,bx)) + + for k, v in condPxy.items(): + res+= (v*Py[ey]*(np.log2(Px[k]) - np.log2(v*Py[ey]))) + return res + +def mutual_information(x,y): + return entropy(y) - conditional_entropy(x,y) + +def mutual_information_ufunc(x, y, dim='time'): + return xr.apply_ufunc( + mutual_information, + x, + y, + input_core_dims=[[dim], [dim]], + dask="parallelized", + output_dtypes=[float], + ) + + mi = mutual_information_ufunc(x.chunk({'time': -1}),y.chunk({'time': -1})) From 374671344c9a598f281210d605e8a9c62bc436fb Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 26 Dec 2021 09:53:22 -0600 Subject: [PATCH 127/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index bf6c208..0957ebc 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -21,3 +21,5 @@ 10. Mask R-CNN: https://arxiv.org/pdf/1703.06870.pdf 11. Anchor boxes: https://towardsdatascience.com/anchor-boxes-the-key-to-quality-object-detection-ddf9d612d4f9 + +12. https://developers.arcgis.com/python/sample-notebooks/tabular-data-supervised-learning-using-automl/ From 955002d9a1092e972a985ad9ac9a3e725f159708 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 26 Dec 2021 10:02:09 -0600 Subject: [PATCH 128/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index 0957ebc..f46b71f 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -23,3 +23,5 @@ 11. Anchor boxes: https://towardsdatascience.com/anchor-boxes-the-key-to-quality-object-detection-ddf9d612d4f9 12. https://developers.arcgis.com/python/sample-notebooks/tabular-data-supervised-learning-using-automl/ + +13. https://www.fast.ai/2019/05/03/decrappify/ From a6d9bc66e2932298754405dabc40de9abc8cacd2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 26 Dec 2021 10:05:17 -0600 Subject: [PATCH 129/171] Update README.md --- deep_learning/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deep_learning/README.md b/deep_learning/README.md index f46b71f..3d564a8 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -22,6 +22,6 @@ 11. Anchor boxes: https://towardsdatascience.com/anchor-boxes-the-key-to-quality-object-detection-ddf9d612d4f9 -12. https://developers.arcgis.com/python/sample-notebooks/tabular-data-supervised-learning-using-automl/ +12. Road extraction using ArcGIS API https://developers.arcgis.com/python/sample-notebooks/tabular-data-supervised-learning-using-automl/ -13. https://www.fast.ai/2019/05/03/decrappify/ +13. Awesome advice on how to train GANs https://www.fast.ai/2019/05/03/decrappify/ From 4115a6266d339e83dc48e84fb9ea66da63a637a1 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 26 Dec 2021 10:12:45 -0600 Subject: [PATCH 130/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index 3d564a8..36e1629 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -25,3 +25,5 @@ 12. Road extraction using ArcGIS API https://developers.arcgis.com/python/sample-notebooks/tabular-data-supervised-learning-using-automl/ 13. Awesome advice on how to train GANs https://www.fast.ai/2019/05/03/decrappify/ + +14. AutoML: https://github.com/windmaple/awesome-AutoML From de2a2045249210126092301750f798c7d134f7ff Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 26 Dec 2021 10:30:05 -0600 Subject: [PATCH 131/171] Update README.md --- deep_learning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_learning/README.md b/deep_learning/README.md index 36e1629..c59e841 100644 --- a/deep_learning/README.md +++ b/deep_learning/README.md @@ -27,3 +27,5 @@ 13. Awesome advice on how to train GANs https://www.fast.ai/2019/05/03/decrappify/ 14. AutoML: https://github.com/windmaple/awesome-AutoML + +15. AutoDL: https://github.com/D-X-Y/Awesome-AutoDL From a27443ecd50f320316513a5b760f001517d03d3c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Wed, 19 Jan 2022 12:09:02 -0600 Subject: [PATCH 132/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 80c0d80..cd8a7cf 100644 --- a/README.md +++ b/README.md @@ -236,6 +236,8 @@ For the weights, the following can be followed: https://stackoverflow.com/questi 15. Create xarray dataset from dataarrays 16. Use tmux to run a process in the background: https://medium.com/@praveendhawan/tmux-run-commands-in-the-background-bad007810318 + + 17. Indentation error when transferring from jupyter notebook to python script https://stackoverflow.com/questions/1024435/how-to-fix-python-indentation From 1c8758105d5cf4c926eb9bdbb4d871e7586abb0e Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 20 Jan 2022 14:32:52 -0600 Subject: [PATCH 133/171] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index cd8a7cf..769d4e4 100644 --- a/README.md +++ b/README.md @@ -237,8 +237,9 @@ For the weights, the following can be followed: https://stackoverflow.com/questi 16. Use tmux to run a process in the background: https://medium.com/@praveendhawan/tmux-run-commands-in-the-background-bad007810318 - 17. Indentation error when transferring from jupyter notebook to python script https://stackoverflow.com/questions/1024435/how-to-fix-python-indentation +17. Indentation error when transferring from jupyter notebook to python script https://stackoverflow.com/questions/1024435/how-to-fix-python-indentation +18. Correcting aspect ratio in cartopy: ax[i,j].set_aspect('auto') From c0bd3bb3463d1ecce12dd8edb83da0e0e2ac2cc5 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 10:01:43 -0600 Subject: [PATCH 134/171] Create README.md --- web/README.md | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 web/README.md diff --git a/web/README.md b/web/README.md new file mode 100644 index 0000000..341b5ac --- /dev/null +++ b/web/README.md @@ -0,0 +1,3 @@ +# Database + +sudo -u postgres psql postgres From 1257baa40ce927f141c347f5aa7790135bff0dec Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 10:08:50 -0600 Subject: [PATCH 135/171] Update README.md --- web/README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/web/README.md b/web/README.md index 341b5ac..825d690 100644 --- a/web/README.md +++ b/web/README.md @@ -1,3 +1,8 @@ # Database -sudo -u postgres psql postgres +Start postrgresql: sudo -u postgres psql postgres + +List users: \du + +Exit: \q + From a2295c7199c8e51a5176b7167c6ad040f42a0f80 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 10:12:12 -0600 Subject: [PATCH 136/171] Update README.md --- web/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/web/README.md b/web/README.md index 825d690..c1a70bc 100644 --- a/web/README.md +++ b/web/README.md @@ -6,3 +6,5 @@ List users: \du Exit: \q +Change password: \password + From 3b3453aa3543786efdad7a4824826a25bb461182 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 10:13:36 -0600 Subject: [PATCH 137/171] Update README.md --- web/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/web/README.md b/web/README.md index c1a70bc..eeb3d5e 100644 --- a/web/README.md +++ b/web/README.md @@ -8,3 +8,4 @@ Exit: \q Change password: \password +List databases: \l From c2f940acb55d3055aed69e857277281badc52476 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 11:57:01 -0600 Subject: [PATCH 138/171] Update README.md --- web/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/web/README.md b/web/README.md index eeb3d5e..1d35123 100644 --- a/web/README.md +++ b/web/README.md @@ -9,3 +9,5 @@ Exit: \q Change password: \password List databases: \l + +Basic pgadmin commands: https://www.youtube.com/watch?v=Dd2ej-QKrWY&t=1201s From 34c89ee9db0e3377588770b289d0d9875fa1f36f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 14:21:19 -0600 Subject: [PATCH 139/171] Update README.md --- web/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/web/README.md b/web/README.md index 1d35123..f51d5e1 100644 --- a/web/README.md +++ b/web/README.md @@ -11,3 +11,5 @@ Change password: \password List databases: \l Basic pgadmin commands: https://www.youtube.com/watch?v=Dd2ej-QKrWY&t=1201s + +Django with postgresql: https://stackpython.medium.com/how-to-start-django-project-with-a-database-postgresql-aaa1d74659d8 From e6aa0aa245aefc038f3193997fe2ddd99cf70145 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sat, 5 Feb 2022 14:47:47 -0600 Subject: [PATCH 140/171] Update README.md --- web/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/web/README.md b/web/README.md index f51d5e1..ca5575e 100644 --- a/web/README.md +++ b/web/README.md @@ -13,3 +13,5 @@ List databases: \l Basic pgadmin commands: https://www.youtube.com/watch?v=Dd2ej-QKrWY&t=1201s Django with postgresql: https://stackpython.medium.com/how-to-start-django-project-with-a-database-postgresql-aaa1d74659d8 + +Django CRUD application: https://medium.com/swlh/django-crud-application-postgresql-97c62d42eb38 From 2cc54338770eaac5be319952ae4e1fb0fd53e945 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 6 Feb 2022 03:55:06 -0600 Subject: [PATCH 141/171] Update README.md --- web/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/web/README.md b/web/README.md index ca5575e..82b1e3f 100644 --- a/web/README.md +++ b/web/README.md @@ -15,3 +15,5 @@ Basic pgadmin commands: https://www.youtube.com/watch?v=Dd2ej-QKrWY&t=1201s Django with postgresql: https://stackpython.medium.com/how-to-start-django-project-with-a-database-postgresql-aaa1d74659d8 Django CRUD application: https://medium.com/swlh/django-crud-application-postgresql-97c62d42eb38 + +Django CRUD application 2: https://medium.com/@john.bagiliko/django-crud-web-application-77ef05af1f00 From 69d032c7098d7c4ae94e1d0383eb8b1dae3a91fa Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 6 Feb 2022 04:16:59 -0600 Subject: [PATCH 142/171] Update README.md --- web/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/web/README.md b/web/README.md index 82b1e3f..cb9cfa4 100644 --- a/web/README.md +++ b/web/README.md @@ -12,8 +12,12 @@ List databases: \l Basic pgadmin commands: https://www.youtube.com/watch?v=Dd2ej-QKrWY&t=1201s +# Django apps + Django with postgresql: https://stackpython.medium.com/how-to-start-django-project-with-a-database-postgresql-aaa1d74659d8 Django CRUD application: https://medium.com/swlh/django-crud-application-postgresql-97c62d42eb38 Django CRUD application 2: https://medium.com/@john.bagiliko/django-crud-web-application-77ef05af1f00 + +Curly braces with percent html: https://stackoverflow.com/questions/14887579/what-dose-the-curly-brace-and-percent-sign-mean-in-html From 239a140ec0e0ecc4947a4b4a8193b651d0a734c2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 6 Feb 2022 08:40:18 -0600 Subject: [PATCH 143/171] Update README.md --- web/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/web/README.md b/web/README.md index cb9cfa4..f234bc3 100644 --- a/web/README.md +++ b/web/README.md @@ -21,3 +21,5 @@ Django CRUD application: https://medium.com/swlh/django-crud-application-postgre Django CRUD application 2: https://medium.com/@john.bagiliko/django-crud-web-application-77ef05af1f00 Curly braces with percent html: https://stackoverflow.com/questions/14887579/what-dose-the-curly-brace-and-percent-sign-mean-in-html + +Django template language: https://docs.djangoproject.com/en/4.0/ref/templates/language/ From dccef92f7992badc87a9e1fbbb4bc537c6fe2b7d Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 17 Feb 2022 15:00:01 +0530 Subject: [PATCH 144/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 769d4e4..29ae7c3 100644 --- a/README.md +++ b/README.md @@ -179,6 +179,8 @@ Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 74. Shallow copy and deep copy in python https://stackoverflow.com/questions/41125834/trying-to-do-a-shallow-copy-on-list-in-python +75. Climate indices https://climate-indices.readthedocs.io/en/latest/ + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From b48975b61ef33a3f01dc7453c9df1f2d62cbf059 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 18 Feb 2022 11:08:06 +0530 Subject: [PATCH 145/171] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 29ae7c3..c151726 100644 --- a/README.md +++ b/README.md @@ -181,6 +181,8 @@ Now start the notebook by noting the link from NOTEBOOK_LOGFILE1 75. Climate indices https://climate-indices.readthedocs.io/en/latest/ +76. Copying to box using lftp mirror -R folder + Troubleshooting 1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python From d73253f02172683aaf183623972695719338841c Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 24 Feb 2022 14:52:30 +0530 Subject: [PATCH 146/171] Created using Colaboratory --- xarray_tutorial/three_d_plot.ipynb | 95 +++++++++++++++++++----------- 1 file changed, 62 insertions(+), 33 deletions(-) diff --git a/xarray_tutorial/three_d_plot.ipynb b/xarray_tutorial/three_d_plot.ipynb index 620c138..2e59640 100644 --- a/xarray_tutorial/three_d_plot.ipynb +++ b/xarray_tutorial/three_d_plot.ipynb @@ -5,7 +5,8 @@ "colab": { "name": "three_d_plot.ipynb", "provenance": [], - "authorship_tag": "ABX9TyPCauGvcaQ7bYCt552YBnwm", + "collapsed_sections": [], + "authorship_tag": "ABX9TyM+fVOqn1BZGxcX2KBPGGIE", "include_colab_link": true }, "kernelspec": { @@ -33,9 +34,60 @@ "id": "0DzKR2vNKAOn" }, "source": [ - "" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objects as go\n", + "\n", + "\n", + "def get_data():\n", + " x = np.linspace(-1, 1, 10)\n", + " y = np.linspace(-1, 1, 10)\n", + "\n", + " X, Y = np.meshgrid(x, y)\n", + "\n", + " Z = np.array([[2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291],\n", + " [2.58677481, 3.22528864, 3.65334814, 3.86669336, 3.86399048,\n", + " 3.64525411, 3.21186215, 2.56819809, 1.72989472, 0.78569291]])\n", + " return X, Y, Z\n", + "\n", + "\n", + "# One-color arrows & arrowheads\n", + "colorscale = [\n", + " [0, \"rgb(84,48,5)\"],\n", + " [1, \"rgb(84,48,5)\"],\n", + "]\n", + "\n", + "X, Y, Z = get_data()\n", + "\n", + "data = {\n", + " key: {\n", + " \"min\": v.min(),\n", + " \"max\": v.max(),\n", + " \"mid\": (v.max() + v.min()) / 2,\n", + " \"range\": v.max() - v.min(),\n", + " }\n", + " for (key, v) in dict(x=X, y=Y, z=Z).items()\n", + "}\n", + "\n" ], - "execution_count": 23, + "execution_count": 2, "outputs": [] }, { @@ -46,14 +98,9 @@ "height": 517 }, "id": "eJTMQrwHKDDL", - "outputId": "a626e58e-d044-4faf-e0cf-851aea1a9647" + "outputId": "8d7e0fcb-3a7c-4e80-a7e2-f16c0e04dac1" }, "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import plotly.graph_objects as go\n", - "\n", - "\n", "def get_arrow(axisname=\"x\"):\n", "\n", " # Create arrow body\n", @@ -154,7 +201,7 @@ "fig.add_trace(go.Scatter3d(x=x, y=y, z=z))\n", "fig.show()" ], - "execution_count": 24, + "execution_count": 3, "outputs": [ { "output_type": "display_data", @@ -163,24 +210,10 @@ "\n", "\n", "\n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - " \n", + "
\n", - "
\n", + " }) }; \n", "\n", "" ] @@ -223,7 +252,7 @@ "source": [ "" ], - "execution_count": 24, + "execution_count": null, "outputs": [] }, { From 3295957cd9f124bf31ddeef15736119fb39fde9a Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Sun, 27 Feb 2022 17:06:01 +0530 Subject: [PATCH 147/171] Add files via upload --- geopandas-tutorial/geopandas-sownal.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 geopandas-tutorial/geopandas-sownal.ipynb diff --git a/geopandas-tutorial/geopandas-sownal.ipynb b/geopandas-tutorial/geopandas-sownal.ipynb new file mode 100644 index 0000000..ede3ca6 --- /dev/null +++ b/geopandas-tutorial/geopandas-sownal.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nfrom shapely.geometry import Point, Polygon\ndf = pd.read_csv('../input/kc-house-data/kc_house_data.csv')\ndf = df[['id', 'price', 'sqft_living', 'waterfront', 'zipcode', 'long', 'lat']]\ndf.head()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-02-27T11:16:55.940315Z","iopub.execute_input":"2022-02-27T11:16:55.940875Z","iopub.status.idle":"2022-02-27T11:16:56.880747Z","shell.execute_reply.started":"2022-02-27T11:16:55.940752Z","shell.execute_reply":"2022-02-27T11:16:56.879842Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"# kings_county_map = gpd.read_file('kc_tract_10.shp')\n# kings_county_map.plot()","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:16:57.788812Z","iopub.execute_input":"2022-02-27T11:16:57.790027Z","iopub.status.idle":"2022-02-27T11:16:57.794891Z","shell.execute_reply.started":"2022-02-27T11:16:57.789964Z","shell.execute_reply":"2022-02-27T11:16:57.794155Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"# kings_county_map.to_crs(epsg=4326).plot()","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:16:58.652989Z","iopub.execute_input":"2022-02-27T11:16:58.653702Z","iopub.status.idle":"2022-02-27T11:16:58.660495Z","shell.execute_reply.started":"2022-02-27T11:16:58.653646Z","shell.execute_reply":"2022-02-27T11:16:58.659228Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"crs = {'init':'EPSG:4326'}\ngeometry = [Point(xy) for xy in zip(df['long'], df['lat'])]\ngeo_df = gpd.GeoDataFrame(df, \n crs = crs, \n geometry = geometry)","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:16:59.610526Z","iopub.execute_input":"2022-02-27T11:16:59.611798Z","iopub.status.idle":"2022-02-27T11:17:00.754162Z","shell.execute_reply.started":"2022-02-27T11:16:59.611745Z","shell.execute_reply":"2022-02-27T11:17:00.753193Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"geo_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:00.756017Z","iopub.execute_input":"2022-02-27T11:17:00.756344Z","iopub.status.idle":"2022-02-27T11:17:00.779411Z","shell.execute_reply.started":"2022-02-27T11:17:00.756300Z","shell.execute_reply":"2022-02-27T11:17:00.778300Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize = (10,10))\n#kings_county_map.to_crs(epsg=4326).plot(ax=ax, color='lightgrey')\ngeo_df.plot(ax=ax)\nax.set_title('Kings County Real Estate')","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:03.360917Z","iopub.execute_input":"2022-02-27T11:17:03.361234Z","iopub.status.idle":"2022-02-27T11:17:07.695678Z","shell.execute_reply.started":"2022-02-27T11:17:03.361204Z","shell.execute_reply":"2022-02-27T11:17:07.694737Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize = (10,10))\n#kings_county_map.to_crs(epsg=4326).plot(ax=ax, color='lightgrey')\ngeo_df.plot(ax=ax, alpha = .1 )\nax.set_title('Kings County Real Estate')\nplt.savefig('Property Map')","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:07.697816Z","iopub.execute_input":"2022-02-27T11:17:07.698395Z","iopub.status.idle":"2022-02-27T11:17:12.137370Z","shell.execute_reply.started":"2022-02-27T11:17:07.698341Z","shell.execute_reply":"2022-02-27T11:17:12.136677Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"geo_df['price_log'] = np.log(geo_df['price'])\nfig, ax = plt.subplots(figsize = (10,10))\n#kings_county_map.to_crs(epsg=4326).plot(ax=ax, color='lightgrey')\ngeo_df.plot(column = 'price_log', ax=ax, cmap = 'rainbow',\n legend = True, legend_kwds={'shrink': 0.3}, \n markersize = 10)\nax.set_title('Kings County Price Heatmap')\nplt.savefig('Heat Map')","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:12.138617Z","iopub.execute_input":"2022-02-27T11:17:12.139183Z","iopub.status.idle":"2022-02-27T11:17:17.508212Z","shell.execute_reply.started":"2022-02-27T11:17:12.139151Z","shell.execute_reply":"2022-02-27T11:17:17.507613Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"geo_df['sqft_log'] = np.log(geo_df['sqft_living'])\nfig, ax = plt.subplots(figsize = (10,10))\n#kings_county_map.to_crs(epsg=4326).plot(ax=ax, color='lightgrey')\ngeo_df.plot(column = 'sqft_log', ax=ax, cmap = 'winter',\n legend = True, legend_kwds={'shrink': 0.3},\n alpha = .5)\nax.set_title('Sqft Heatmap')\n","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:17.509263Z","iopub.execute_input":"2022-02-27T11:17:17.509611Z","iopub.status.idle":"2022-02-27T11:17:22.934239Z","shell.execute_reply.started":"2022-02-27T11:17:17.509583Z","shell.execute_reply":"2022-02-27T11:17:22.933371Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"df_fiji = pd.read_csv('../input/sownalfiji/53YearsTemp.csv')","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:22.936558Z","iopub.execute_input":"2022-02-27T11:17:22.937084Z","iopub.status.idle":"2022-02-27T11:17:22.954552Z","shell.execute_reply.started":"2022-02-27T11:17:22.937044Z","shell.execute_reply":"2022-02-27T11:17:22.953789Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"df_fiji","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:05:42.200815Z","iopub.execute_input":"2022-02-27T11:05:42.201283Z","iopub.status.idle":"2022-02-27T11:05:42.236686Z","shell.execute_reply.started":"2022-02-27T11:05:42.201252Z","shell.execute_reply":"2022-02-27T11:05:42.236267Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# fiji_county_map = gpd.read_file('../input/fjishape2/FJI_adm2.shp')\n# fiji_county_map.plot()","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:22.955650Z","iopub.execute_input":"2022-02-27T11:17:22.955905Z","iopub.status.idle":"2022-02-27T11:17:22.959696Z","shell.execute_reply.started":"2022-02-27T11:17:22.955876Z","shell.execute_reply":"2022-02-27T11:17:22.958928Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# fiji_county_map.to_crs(epsg=3460).to_crs(epsg=4326).plot()","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:22.960776Z","iopub.execute_input":"2022-02-27T11:17:22.961380Z","iopub.status.idle":"2022-02-27T11:17:22.971161Z","shell.execute_reply.started":"2022-02-27T11:17:22.961350Z","shell.execute_reply":"2022-02-27T11:17:22.970579Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"#crs = {'init':'EPSG:3460'}\ngeometry = [Point(xy) for xy in zip(df_fiji.iloc[1:-1,:]['long'], df_fiji.iloc[1:-1,:]['lat'])]\ngeo_df = gpd.GeoDataFrame(df_fiji.iloc[1:-1,:], \n #crs = crs, \n geometry = geometry)","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:45.056786Z","iopub.execute_input":"2022-02-27T11:17:45.057077Z","iopub.status.idle":"2022-02-27T11:17:45.159315Z","shell.execute_reply.started":"2022-02-27T11:17:45.057042Z","shell.execute_reply":"2022-02-27T11:17:45.158266Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"geo_df","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:17:47.690222Z","iopub.execute_input":"2022-02-27T11:17:47.690537Z","iopub.status.idle":"2022-02-27T11:17:47.742911Z","shell.execute_reply.started":"2022-02-27T11:17:47.690507Z","shell.execute_reply":"2022-02-27T11:17:47.742299Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"import cartopy.crs as ccrs\nimport cartopy\nimport cartopy.feature as cfeature\nimport matplotlib.path as mpath","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:22:09.260989Z","iopub.execute_input":"2022-02-27T11:22:09.262457Z","iopub.status.idle":"2022-02-27T11:22:09.267801Z","shell.execute_reply.started":"2022-02-27T11:22:09.262400Z","shell.execute_reply":"2022-02-27T11:22:09.266540Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"geo_df['Year'] = geo_df['Year']\nfig, ax = plt.subplots(figsize = (10,10), subplot_kw={'projection': ccrs.PlateCarree()})\n#fiji_county_map.to_crs(epsg=3460).plot(ax=ax, color='lightgrey')\n\nax.add_feature(cartopy.feature.LAND, edgecolor='black',zorder=1)\ngeo_df.plot(column = 'Year', ax=ax, cmap = 'rainbow',\n legend = True, legend_kwds={'shrink': 0.2}, \n markersize = 100)\nax.set_title('Fiji 1965')\nax.coastlines(resolution='10m', alpha=1)\n\nxlim = [-20, -16]\nylim = [176.0, 210]\nlower_space = 10 # this needs to be manually increased if the lower arched is cut off by changing lon and lat lims\n\nrect = mpath.Path([[xlim[0], ylim[0]],\n [xlim[1], ylim[0]],\n [xlim[1], ylim[1]],\n [xlim[0], ylim[1]],\n [xlim[0], ylim[0]],\n ]).interpolated(20)\n\nproj_to_data = ccrs.PlateCarree()._as_mpl_transform(ax) - ax.transData\nrect_in_target = proj_to_data.transform_path(rect)\n\nax.set_boundary(rect_in_target)\n\n# resol = '50m' # use data at this scale\n# land = cartopy.feature.NaturalEarthFeature('physical', 'land', \\\n# scale=resol, edgecolor='k', facecolor=cfeature.COLORS['land'])\n# ax.add_feature(land, facecolor='beige')\n# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n# linewidth=2, color='gray', alpha=0.5, linestyle='--')\n\n\n\nplt.savefig('Heat Map')","metadata":{"execution":{"iopub.status.busy":"2022-02-27T11:33:42.176403Z","iopub.execute_input":"2022-02-27T11:33:42.176761Z","iopub.status.idle":"2022-02-27T11:33:43.161130Z","shell.execute_reply.started":"2022-02-27T11:33:42.176711Z","shell.execute_reply":"2022-02-27T11:33:43.159939Z"},"trusted":true},"execution_count":43,"outputs":[]}]} \ No newline at end of file From dc8b0a999c3316e958710872ecc7336f84e810c0 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 24 Mar 2022 14:40:28 -0500 Subject: [PATCH 148/171] Create common_dates.py --- xarray_tutorial/common_dates.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 xarray_tutorial/common_dates.py diff --git a/xarray_tutorial/common_dates.py b/xarray_tutorial/common_dates.py new file mode 100644 index 0000000..74739e1 --- /dev/null +++ b/xarray_tutorial/common_dates.py @@ -0,0 +1,21 @@ +ds_obs = ds_observed[i_var] + +file_ = base_dir+'historical_v1/'+var+'_historical_'+df_corr_['model'].iloc[cnt]+'_ens_'+str(df_corr_['ensemble'].iloc[cnt])+'.nc' +print(file_) +ds_historical = xr.open_dataset(file_).sel(lat=location_[0], lon= 360-location_[1], method='nearest') +datetimeindex = ds_historical.indexes['time'].to_datetimeindex() +ds_historical['time'] = datetimeindex + +common_dates = np.intersect1d(np.asarray(ds_historical.time.values.astype('datetime64[D]').tolist()), \ + np.asarray(ds_obs.time.values.astype('datetime64[D]').tolist())) +ds_historical['time'] = ('time', ds_historical.time.values.astype('datetime64[D]')) +ds_historical_ = ds_historical.sel(time=common_dates) +ds_obs_ = ds_obs.sel(time=common_dates) + +file_ = base_dir+'ssp126_v1/'+var+'_ssp126_'+df_corr_['model'].iloc[cnt]+'_ens_'+str(df_corr_['ensemble'].iloc[cnt])+'.nc' +print(file_) +ds_ssp126 = xr.open_dataset(file_).sel(lat=location_[0], lon= 360-location_[1], method='nearest') + +file_ = base_dir+'ssp585_v1/'+var+'_ssp585_'+df_corr_['model'].iloc[cnt]+'_ens_'+str(df_corr_['ensemble'].iloc[cnt])+'.nc' +print(file_) +ds_ssp585 = xr.open_dataset(file_).sel(lat=location_[0], lon= 360-location_[1], method='nearest') From 65cd9aac5d6c68f52fb9ab6e0f82618be8d3320f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 29 Mar 2022 17:37:18 +0530 Subject: [PATCH 149/171] Create agera5_download.py --- download_data/agera5_download.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 download_data/agera5_download.py diff --git a/download_data/agera5_download.py b/download_data/agera5_download.py new file mode 100644 index 0000000..659f1e8 --- /dev/null +++ b/download_data/agera5_download.py @@ -0,0 +1,29 @@ +import cdsapi + +c = cdsapi.Client() +for year in range(1979,2020): + for i_mon in range(1,13,3): + c.retrieve( + 'sis-agrometeorological-indicators', + { + 'format': 'tgz', + 'variable': 'solar_radiation_flux', + 'year': str(year), + 'month': [ + '0'+str(i_mon), '0'+str(i_mon+1), '0'+str(i_mon+2), + ], + 'day': [ + '01', '02', '03', + '04', '05', '06', + '07', '08', '09', + '10', '11', '12', + '13', '14', '15', + '16', '17', '18', + '19', '20', '21', + '22', '23', '24', + '25', '26', '27', + '28', '29', '30', + '31', + ], + }, + 'agera5_'+str(year)+'_'+str(i_mon)+'_'+str(i_mon+1)+'.tar.gz') From b7b2f41c1378daa636b3765ef68d6fde6a292307 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Thu, 31 Mar 2022 20:26:30 +0530 Subject: [PATCH 150/171] Create mixed_input_dl.py --- deep_learning/mixed_input_dl.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 deep_learning/mixed_input_dl.py diff --git a/deep_learning/mixed_input_dl.py b/deep_learning/mixed_input_dl.py new file mode 100644 index 0000000..42b8ee5 --- /dev/null +++ b/deep_learning/mixed_input_dl.py @@ -0,0 +1,16 @@ +def model_sealevel(input_shape): + + input1 = tf.keras.Input(shape=input_shape, name='meteorological_input') + location_input = tf.keras.Input(shape=(2,), name='location_input') + x = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu")(input1) + x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x) + x = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu")(x) + x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x) + x = tf.keras.layers.Flatten()(x) + x = tf.keras.layers.Dense(4096)(x) + location = tf.keras.layers.Dense(16, activation='relu')( location_input ) + x = tf.keras.layers.Concatenate()([x, location]) + x = tf.keras.layers.Dense(512)(x) + output = tf.keras.layers.Dense(1)(x) + return tf.keras.Model(inputs=[input1, location_input], outputs=output) +model = model_sealevel(input_shape) From 31a8d1a40ee8ed2e809a5fea1818bb46fdce6389 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Mon, 4 Apr 2022 14:03:07 -0500 Subject: [PATCH 151/171] Create replace_value_based_on_coordinates.py --- xarray_tutorial/replace_value_based_on_coordinates.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 xarray_tutorial/replace_value_based_on_coordinates.py diff --git a/xarray_tutorial/replace_value_based_on_coordinates.py b/xarray_tutorial/replace_value_based_on_coordinates.py new file mode 100644 index 0000000..3ab8c0d --- /dev/null +++ b/xarray_tutorial/replace_value_based_on_coordinates.py @@ -0,0 +1 @@ +ds_.loc[dict(lat=10.05, lon=2.05, time=ds_.time.values[0])] = 1 From 887704f886f5034ab6c0dd2c72f8df19ef2d07c2 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 12 Apr 2022 19:08:27 +0530 Subject: [PATCH 152/171] Create README.md --- download_data/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 download_data/README.md diff --git a/download_data/README.md b/download_data/README.md new file mode 100644 index 0000000..832a7e8 --- /dev/null +++ b/download_data/README.md @@ -0,0 +1 @@ +Download MODIS NDVI and temperature: https://colab.research.google.com/drive/1UxpEnEweM0m_44ICDJhAG2RJxNCqI4qi?usp=sharing From 165e9536317d86067941fef57d966cb15700d04f Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 15 Apr 2022 13:45:41 -0500 Subject: [PATCH 153/171] Create README.md --- download_data/CMIP6/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 download_data/CMIP6/README.md diff --git a/download_data/CMIP6/README.md b/download_data/CMIP6/README.md new file mode 100644 index 0000000..5f5b371 --- /dev/null +++ b/download_data/CMIP6/README.md @@ -0,0 +1 @@ +CMIP6 data From 4dceedb25ec7578548718502e8d47f4d7337115b Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Fri, 15 Apr 2022 13:47:09 -0500 Subject: [PATCH 154/171] Add files via upload --- .../CMIP6/CMIP6_data_extraction_Nigeria.ipynb | 1007 ++++++++ .../ClimateModel_selection_indices.ipynb | 2044 +++++++++++++++++ download_data/CMIP6/extract_hist_ens.py | 216 ++ 3 files changed, 3267 insertions(+) create mode 100644 download_data/CMIP6/CMIP6_data_extraction_Nigeria.ipynb create mode 100644 download_data/CMIP6/ClimateModel_selection_indices.ipynb create mode 100644 download_data/CMIP6/extract_hist_ens.py diff --git a/download_data/CMIP6/CMIP6_data_extraction_Nigeria.ipynb b/download_data/CMIP6/CMIP6_data_extraction_Nigeria.ipynb new file mode 100644 index 0000000..7995c70 --- /dev/null +++ b/download_data/CMIP6/CMIP6_data_extraction_Nigeria.ipynb @@ -0,0 +1,1007 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "abebc3eb", + "metadata": {}, + "outputs": [], + "source": [ + "#conda install -c conda-forge gcsfs\n", + "#conda install -c conda-forge zarr\n", + "# ! pip install xarray\n", + "# conda update pandas\n", + "# conda install -c conda-forge cftime" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8548c675", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr\n", + "import zarr\n", + "import gcsfs\n", + "import seaborn as sns\n", + "import os \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9d2a0009", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " activity_id institution_id source_id experiment_id \\\n", + "15618 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 \n", + "54784 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "66614 ScenarioMIP BCC BCC-CSM2-MR ssp126 \n", + "68936 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 \n", + "72721 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "... ... ... ... ... \n", + "499906 ScenarioMIP EC-Earth-Consortium EC-Earth3-Veg-LR ssp126 \n", + "500008 ScenarioMIP EC-Earth-Consortium EC-Earth3-Veg-LR ssp126 \n", + "502254 ScenarioMIP IPSL IPSL-CM5A2-INCA ssp126 \n", + "513845 ScenarioMIP CMCC CMCC-ESM2 ssp126 \n", + "514457 ScenarioMIP NCAR CESM2-WACCM ssp126 \n", + "\n", + " member_id table_id variable_id grid_label \\\n", + "15618 r1i1p1f1 day tas gr1 \n", + "54784 r1i1p1f2 day tas gr \n", + "66614 r1i1p1f1 day tas gn \n", + "68936 r1i1p1f2 day tas gr \n", + "72721 r4i1p1f2 day tas gr \n", + "... ... ... ... ... \n", + "499906 r2i1p1f1 day tas gr \n", + "500008 r3i1p1f1 day tas gr \n", + "502254 r1i1p1f1 day tas gr \n", + "513845 r1i1p1f1 day tas gn \n", + "514457 r1i1p1f1 day tas gn \n", + "\n", + " zstore dcpp_init_year \\\n", + "15618 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "54784 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "66614 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "68936 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "72721 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "... ... ... \n", + "499906 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "500008 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "502254 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM5A2-I... NaN \n", + "513845 gs://cmip6/CMIP6/ScenarioMIP/CMCC/CMCC-ESM2/ss... NaN \n", + "514457 gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/... NaN \n", + "\n", + " version \n", + "15618 20180701 \n", + "54784 20190219 \n", + "66614 20190315 \n", + "68936 20190328 \n", + "72721 20190410 \n", + "... ... \n", + "499906 20201201 \n", + "500008 20201201 \n", + "502254 20201218 \n", + "513845 20210126 \n", + "514457 20210211 \n", + "\n", + "[199 rows x 11 columns]\n", + " activity_id institution_id source_id experiment_id member_id \\\n", + "15563 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 r1i1p1f1 \n", + "54679 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 r1i1p1f2 \n", + "66636 ScenarioMIP BCC BCC-CSM2-MR ssp126 r1i1p1f1 \n", + "68944 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 r1i1p1f2 \n", + "73240 ScenarioMIP IPSL IPSL-CM6A-LR ssp126 r3i1p1f1 \n", + "... ... ... ... ... ... \n", + "516324 ScenarioMIP CSIRO-ARCCSS ACCESS-CM2 ssp126 r1i1p1f1 \n", + "523369 ScenarioMIP MRI MRI-ESM2-0 ssp126 r4i1p1f1 \n", + "523404 ScenarioMIP MRI MRI-ESM2-0 ssp126 r3i1p1f1 \n", + "523418 ScenarioMIP MRI MRI-ESM2-0 ssp126 r5i1p1f1 \n", + "523432 ScenarioMIP MRI MRI-ESM2-0 ssp126 r2i1p1f1 \n", + "\n", + " table_id variable_id grid_label \\\n", + "15563 day pr gr1 \n", + "54679 day pr gr \n", + "66636 day pr gn \n", + "68944 day pr gr \n", + "73240 day pr gr \n", + "... ... ... ... \n", + "516324 day pr gn \n", + "523369 day pr gn \n", + "523404 day pr gn \n", + "523418 day pr gn \n", + "523432 day pr gn \n", + "\n", + " zstore dcpp_init_year \\\n", + "15563 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "54679 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "66636 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "68944 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "73240 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR... NaN \n", + "... ... ... \n", + "516324 gs://cmip6/CMIP6/ScenarioMIP/CSIRO-ARCCSS/ACCE... NaN \n", + "523369 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523404 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523418 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523432 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "\n", + " version \n", + "15563 20180701 \n", + "54679 20190219 \n", + "66636 20190315 \n", + "68944 20190328 \n", + "73240 20190410 \n", + "... ... \n", + "516324 20210317 \n", + "523369 20210907 \n", + "523404 20210907 \n", + "523418 20210907 \n", + "523432 20210907 \n", + "\n", + "[188 rows x 11 columns]\n", + " activity_id institution_id source_id experiment_id member_id \\\n", + "15617 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 r1i1p1f1 \n", + "53796 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 r1i1p1f2 \n", + "66620 ScenarioMIP BCC BCC-CSM2-MR ssp126 r1i1p1f1 \n", + "68946 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 r1i1p1f2 \n", + "120175 ScenarioMIP CCCma CanESM5 ssp126 r7i1p1f1 \n", + "... ... ... ... ... ... \n", + "516323 ScenarioMIP CSIRO-ARCCSS ACCESS-CM2 ssp126 r1i1p1f1 \n", + "523371 ScenarioMIP MRI MRI-ESM2-0 ssp126 r4i1p1f1 \n", + "523396 ScenarioMIP MRI MRI-ESM2-0 ssp126 r3i1p1f1 \n", + "523420 ScenarioMIP MRI MRI-ESM2-0 ssp126 r5i1p1f1 \n", + "523433 ScenarioMIP MRI MRI-ESM2-0 ssp126 r2i1p1f1 \n", + "\n", + " table_id variable_id grid_label \\\n", + "15617 day tasmax gr1 \n", + "53796 day tasmax gr \n", + "66620 day tasmax gn \n", + "68946 day tasmax gr \n", + "120175 day tasmax gn \n", + "... ... ... ... \n", + "516323 day tasmax gn \n", + "523371 day tasmax gn \n", + "523396 day tasmax gn \n", + "523420 day tasmax gn \n", + "523433 day tasmax gn \n", + "\n", + " zstore dcpp_init_year \\\n", + "15617 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "53796 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "66620 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "68946 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "120175 gs://cmip6/CMIP6/ScenarioMIP/CCCma/CanESM5/ssp... NaN \n", + "... ... ... \n", + "516323 gs://cmip6/CMIP6/ScenarioMIP/CSIRO-ARCCSS/ACCE... NaN \n", + "523371 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523396 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523420 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523433 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "\n", + " version \n", + "15617 20180701 \n", + "53796 20190219 \n", + "66620 20190315 \n", + "68946 20190328 \n", + "120175 20190429 \n", + "... ... \n", + "516323 20210317 \n", + "523371 20210907 \n", + "523396 20210907 \n", + "523420 20210907 \n", + "523433 20210907 \n", + "\n", + "[173 rows x 11 columns]\n", + " activity_id institution_id source_id experiment_id member_id \\\n", + "15616 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 r1i1p1f1 \n", + "53776 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 r1i1p1f2 \n", + "66627 ScenarioMIP BCC BCC-CSM2-MR ssp126 r1i1p1f1 \n", + "68960 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 r1i1p1f2 \n", + "73116 ScenarioMIP IPSL IPSL-CM6A-LR ssp126 r3i1p1f1 \n", + "... ... ... ... ... ... \n", + "516322 ScenarioMIP CSIRO-ARCCSS ACCESS-CM2 ssp126 r1i1p1f1 \n", + "523350 ScenarioMIP MRI MRI-ESM2-0 ssp126 r3i1p1f1 \n", + "523393 ScenarioMIP MRI MRI-ESM2-0 ssp126 r4i1p1f1 \n", + "523421 ScenarioMIP MRI MRI-ESM2-0 ssp126 r5i1p1f1 \n", + "523436 ScenarioMIP MRI MRI-ESM2-0 ssp126 r2i1p1f1 \n", + "\n", + " table_id variable_id grid_label \\\n", + "15616 day tasmin gr1 \n", + "53776 day tasmin gr \n", + "66627 day tasmin gn \n", + "68960 day tasmin gr \n", + "73116 day tasmin gr \n", + "... ... ... ... \n", + "516322 day tasmin gn \n", + "523350 day tasmin gn \n", + "523393 day tasmin gn \n", + "523421 day tasmin gn \n", + "523436 day tasmin gn \n", + "\n", + " zstore dcpp_init_year \\\n", + "15616 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "53776 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "66627 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "68960 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "73116 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR... NaN \n", + "... ... ... \n", + "516322 gs://cmip6/CMIP6/ScenarioMIP/CSIRO-ARCCSS/ACCE... NaN \n", + "523350 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523393 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523421 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "523436 gs://cmip6/CMIP6/ScenarioMIP/MRI/MRI-ESM2-0/ss... NaN \n", + "\n", + " version \n", + "15616 20180701 \n", + "53776 20190219 \n", + "66627 20190315 \n", + "68960 20190328 \n", + "73116 20190410 \n", + "... ... \n", + "516322 20210317 \n", + "523350 20210907 \n", + "523393 20210907 \n", + "523421 20210907 \n", + "523436 20210907 \n", + "\n", + "[189 rows x 11 columns]\n", + " activity_id institution_id source_id experiment_id \\\n", + "15614 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 \n", + "53786 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "66624 ScenarioMIP BCC BCC-CSM2-MR ssp126 \n", + "68972 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 \n", + "72700 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "... ... ... ... ... \n", + "521801 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521814 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521819 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521886 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521897 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "\n", + " member_id table_id variable_id grid_label \\\n", + "15614 r1i1p1f1 day uas gr1 \n", + "53786 r1i1p1f2 day uas gr \n", + "66624 r1i1p1f1 day uas gn \n", + "68972 r1i1p1f2 day uas gr \n", + "72700 r4i1p1f2 day uas gr \n", + "... ... ... ... ... \n", + "521801 r108i1p1f1 day uas gr \n", + "521814 r132i1p1f1 day uas gr \n", + "521819 r145i1p1f1 day uas gr \n", + "521886 r107i1p1f1 day uas gr \n", + "521897 r109i1p1f1 day uas gr \n", + "\n", + " zstore dcpp_init_year \\\n", + "15614 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "53786 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "66624 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "68972 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "72700 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "... ... ... \n", + "521801 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521814 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521819 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521886 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521897 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "\n", + " version \n", + "15614 20180701 \n", + "53786 20190219 \n", + "66624 20190315 \n", + "68972 20190328 \n", + "72700 20190410 \n", + "... ... \n", + "521801 20210615 \n", + "521814 20210615 \n", + "521819 20210615 \n", + "521886 20210615 \n", + "521897 20210615 \n", + "\n", + "[218 rows x 11 columns]\n", + " activity_id institution_id source_id experiment_id \\\n", + "15613 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 \n", + "53775 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "66617 ScenarioMIP BCC BCC-CSM2-MR ssp126 \n", + "68961 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 \n", + "72701 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "... ... ... ... ... \n", + "521825 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521831 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521885 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521896 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "521909 ScenarioMIP EC-Earth-Consortium EC-Earth3 ssp126 \n", + "\n", + " member_id table_id variable_id grid_label \\\n", + "15613 r1i1p1f1 day vas gr1 \n", + "53775 r1i1p1f2 day vas gr \n", + "66617 r1i1p1f1 day vas gn \n", + "68961 r1i1p1f2 day vas gr \n", + "72701 r4i1p1f2 day vas gr \n", + "... ... ... ... ... \n", + "521825 r133i1p1f1 day vas gr \n", + "521831 r106i1p1f1 day vas gr \n", + "521885 r107i1p1f1 day vas gr \n", + "521896 r109i1p1f1 day vas gr \n", + "521909 r118i1p1f1 day vas gr \n", + "\n", + " zstore dcpp_init_year \\\n", + "15613 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "53775 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "66617 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "68961 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "72701 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "... ... ... \n", + "521825 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521831 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521885 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521896 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "521909 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "\n", + " version \n", + "15613 20180701 \n", + "53775 20190219 \n", + "66617 20190315 \n", + "68961 20190328 \n", + "72701 20190410 \n", + "... ... \n", + "521825 20210615 \n", + "521831 20210615 \n", + "521885 20210615 \n", + "521896 20210615 \n", + "521909 20210615 \n", + "\n", + "[221 rows x 11 columns]\n", + " activity_id institution_id source_id experiment_id \\\n", + "15484 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 \n", + "54695 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "68976 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 \n", + "72010 ScenarioMIP CNRM-CERFACS CNRM-ESM2-1 ssp126 \n", + "72625 ScenarioMIP CNRM-CERFACS CNRM-CM6-1 ssp126 \n", + "... ... ... ... ... \n", + "499951 ScenarioMIP EC-Earth-Consortium EC-Earth3-Veg-LR ssp126 \n", + "499968 ScenarioMIP EC-Earth-Consortium EC-Earth3-Veg-LR ssp126 \n", + "502282 ScenarioMIP IPSL IPSL-CM5A2-INCA ssp126 \n", + "513797 ScenarioMIP CMCC CMCC-ESM2 ssp126 \n", + "514453 ScenarioMIP NCAR CESM2-WACCM ssp126 \n", + "\n", + " member_id table_id variable_id grid_label \\\n", + "15484 r1i1p1f1 day hurs gr1 \n", + "54695 r1i1p1f2 day hurs gr \n", + "68976 r1i1p1f2 day hurs gr \n", + "72010 r3i1p1f2 day hurs gr \n", + "72625 r5i1p1f2 day hurs gr \n", + "... ... ... ... ... \n", + "499951 r2i1p1f1 day hurs gr \n", + "499968 r3i1p1f1 day hurs gr \n", + "502282 r1i1p1f1 day hurs gr \n", + "513797 r1i1p1f1 day hurs gn \n", + "514453 r1i1p1f1 day hurs gn \n", + "\n", + " zstore dcpp_init_year \\\n", + "15484 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "54695 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "68976 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "72010 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "72625 gs://cmip6/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM... NaN \n", + "... ... ... \n", + "499951 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "499968 gs://cmip6/CMIP6/ScenarioMIP/EC-Earth-Consorti... NaN \n", + "502282 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM5A2-I... NaN \n", + "513797 gs://cmip6/CMIP6/ScenarioMIP/CMCC/CMCC-ESM2/ss... NaN \n", + "514453 gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/... NaN \n", + "\n", + " version \n", + "15484 20180701 \n", + "54695 20190219 \n", + "68976 20190328 \n", + "72010 20190410 \n", + "72625 20190410 \n", + "... ... \n", + "499951 20201201 \n", + "499968 20201201 \n", + "502282 20201218 \n", + "513797 20210126 \n", + "514453 20210211 \n", + "\n", + "[132 rows x 11 columns]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " activity_id institution_id source_id experiment_id member_id table_id \\\n", + "15622 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 r1i1p1f1 day \n", + "\n", + " variable_id grid_label \\\n", + "15622 rsds gr1 \n", + "\n", + " zstore dcpp_init_year \\\n", + "15622 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "\n", + " version \n", + "15622 20180701 \n", + " activity_id institution_id source_id experiment_id member_id \\\n", + "15621 ScenarioMIP NOAA-GFDL GFDL-ESM4 ssp126 r1i1p1f1 \n", + "66616 ScenarioMIP BCC BCC-CSM2-MR ssp126 r1i1p1f1 \n", + "73111 ScenarioMIP IPSL IPSL-CM6A-LR ssp126 r3i1p1f1 \n", + "74830 ScenarioMIP IPSL IPSL-CM6A-LR ssp126 r2i1p1f1 \n", + "120179 ScenarioMIP CCCma CanESM5 ssp126 r7i1p1f1 \n", + "... ... ... ... ... ... \n", + "496487 ScenarioMIP MOHC UKESM1-0-LL ssp126 r5i1p1f2 \n", + "498289 ScenarioMIP CCCR-IITM IITM-ESM ssp126 r1i1p1f1 \n", + "502252 ScenarioMIP IPSL IPSL-CM5A2-INCA ssp126 r1i1p1f1 \n", + "513777 ScenarioMIP CMCC CMCC-ESM2 ssp126 r1i1p1f1 \n", + "514458 ScenarioMIP NCAR CESM2-WACCM ssp126 r1i1p1f1 \n", + "\n", + " table_id variable_id grid_label \\\n", + "15621 day sfcWind gr1 \n", + "66616 day sfcWind gn \n", + "73111 day sfcWind gr \n", + "74830 day sfcWind gr \n", + "120179 day sfcWind gn \n", + "... ... ... ... \n", + "496487 day sfcWind gn \n", + "498289 day sfcWind gn \n", + "502252 day sfcWind gr \n", + "513777 day sfcWind gn \n", + "514458 day sfcWind gn \n", + "\n", + " zstore dcpp_init_year \\\n", + "15621 gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ES... NaN \n", + "66616 gs://cmip6/CMIP6/ScenarioMIP/BCC/BCC-CSM2-MR/s... NaN \n", + "73111 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR... NaN \n", + "74830 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR... NaN \n", + "120179 gs://cmip6/CMIP6/ScenarioMIP/CCCma/CanESM5/ssp... NaN \n", + "... ... ... \n", + "496487 gs://cmip6/CMIP6/ScenarioMIP/MOHC/UKESM1-0-LL/... NaN \n", + "498289 gs://cmip6/CMIP6/ScenarioMIP/CCCR-IITM/IITM-ES... NaN \n", + "502252 gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM5A2-I... NaN \n", + "513777 gs://cmip6/CMIP6/ScenarioMIP/CMCC/CMCC-ESM2/ss... NaN \n", + "514458 gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/... NaN \n", + "\n", + " version \n", + "15621 20180701 \n", + "66616 20190315 \n", + "73111 20190410 \n", + "74830 20190410 \n", + "120179 20190429 \n", + "... ... \n", + "496487 20201015 \n", + "498289 20201112 \n", + "502252 20201218 \n", + "513777 20210126 \n", + "514458 20210211 \n", + "\n", + "[115 rows x 11 columns]\n" + ] + } + ], + "source": [ + "xr.set_options(display_style='html')\n", + "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')\n", + "\n", + "df_ta = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'tas' & experiment_id == 'ssp126'\")\n", + "print(df_ta)\n", + "\n", + "df_pr = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'pr' & experiment_id == 'ssp126'\")\n", + "print(df_pr)\n", + "\n", + "df_tasmax = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'tasmax' & experiment_id == 'ssp126'\")\n", + "print(df_tasmax)\n", + "\n", + "df_tasmin = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'tasmin' & experiment_id == 'ssp126'\")\n", + "print(df_tasmin)\n", + "\n", + "df_uas = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'uas' & experiment_id == 'ssp126'\")\n", + "print(df_uas)\n", + "\n", + "df_vas = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'vas' & experiment_id == 'ssp126'\")\n", + "print(df_vas)\n", + "\n", + "df_hurs = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'hurs' & experiment_id == 'ssp126'\")\n", + "print(df_hurs)\n", + "\n", + "df_rsds = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'rsds' & experiment_id == 'ssp126'\")\n", + "print(df_rsds)\n", + "\n", + "df_sfcWind = df.query(\"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == 'sfcWind' & experiment_id == 'ssp126'\")\n", + "print(df_sfcWind)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ae589f73", + "metadata": {}, + "outputs": [], + "source": [ + "df_pr['institution_id'].value_counts().to_csv('pr_ssp126_data_available_unique_models.csv')\n", + "df_ta['institution_id'].value_counts().to_csv('tas_ssp126_data_available_unique_models.csv')\n", + "df_tasmax['institution_id'].value_counts().to_csv('tasmax_ssp126_data_available_unique_models.csv')\n", + "df_tasmin['institution_id'].value_counts().to_csv('tasmin_ssp126_data_available_unique_models.csv')\n", + "df_uas['institution_id'].value_counts().to_csv('uas_ssp126_data_available_unique_models.csv')\n", + "df_vas['institution_id'].value_counts().to_csv('vas_ssp126_data_available_unique_models.csv')\n", + "df_hurs['institution_id'].value_counts().to_csv('hurs_ssp126_data_available_unique_models.csv')\n", + "df_rsds['institution_id'].value_counts().to_csv('rsds_ssp126_data_available_unique_models.csv')\n", + "df_sfcWind['institution_id'].value_counts().to_csv('sfcWind_ssp126_data_available_unique_models.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6ff08862", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing pr_ssp126_CCCma_ens_1.nc\n", + "Writing pr_ssp126_MOHC_ens_1.nc\n", + "Writing pr_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing pr_ssp126_MPI-M_ens_1.nc\n", + "Writing pr_ssp126_IPSL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing pr_ssp126_MRI_ens_1.nc\n", + "Writing pr_ssp126_NCAR_ens_1.nc\n", + "Writing pr_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing pr_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing pr_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing pr_ssp126_NUIST_ens_1.nc\n", + "Writing pr_ssp126_CMCC_ens_1.nc\n", + "Writing pr_ssp126_INM_ens_1.nc\n", + "Writing pr_ssp126_NCC_ens_1.nc\n", + "Writing pr_ssp126_KIOST_ens_1.nc\n", + "Writing pr_ssp126_NOAA-GFDL_ens_1.nc\n", + "Writing pr_ssp126_CAMS_ens_1.nc\n", + "Writing pr_ssp126_DWD_ens_1.nc\n", + "Writing pr_ssp126_DKRZ_ens_1.nc\n", + "Writing pr_ssp126_AWI_ens_1.nc\n", + "Writing pr_ssp126_BCC_ens_1.nc\n", + "Writing pr_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing tas_ssp126_MIROC_ens_1.nc\n", + "Writing tas_ssp126_CCCma_ens_1.nc\n", + "Writing tas_ssp126_MOHC_ens_1.nc\n", + "Writing tas_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing tas_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing tas_ssp126_MPI-M_ens_1.nc\n", + "Writing tas_ssp126_IPSL_ens_1.nc\n", + "Writing tas_ssp126_CAS_ens_1.nc\n", + "Writing tas_ssp126_NCAR_ens_1.nc\n", + "Writing tas_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing tas_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing tas_ssp126_INM_ens_1.nc\n", + "Writing tas_ssp126_NASA-GISS_ens_1.nc\n", + "Writing tas_ssp126_NUIST_ens_1.nc\n", + "Writing tas_ssp126_CMCC_ens_1.nc\n", + "Writing tas_ssp126_KIOST_ens_1.nc\n", + "Writing tas_ssp126_NCC_ens_1.nc\n", + "Writing tas_ssp126_NOAA-GFDL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing tas_ssp126_MRI_ens_1.nc\n", + "Writing tas_ssp126_CAMS_ens_1.nc\n", + "Writing tas_ssp126_DWD_ens_1.nc\n", + "Writing tas_ssp126_DKRZ_ens_1.nc\n", + "Writing tas_ssp126_AWI_ens_1.nc\n", + "Writing tas_ssp126_BCC_ens_1.nc\n", + "Writing tas_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing tasmax_ssp126_MIROC_ens_1.nc\n", + "Writing tasmax_ssp126_CCCma_ens_1.nc\n", + "Writing tasmax_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing tasmax_ssp126_MPI-M_ens_1.nc\n", + "Writing tasmax_ssp126_IPSL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing tasmax_ssp126_MRI_ens_1.nc\n", + "Writing tasmax_ssp126_CAS_ens_1.nc\n", + "Writing tasmax_ssp126_NCAR_ens_1.nc\n", + "Writing tasmax_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing tasmax_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing tasmax_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing tasmax_ssp126_CMCC_ens_1.nc\n", + "Writing tasmax_ssp126_INM_ens_1.nc\n", + "Writing tasmax_ssp126_NUIST_ens_1.nc\n", + "Writing tasmax_ssp126_KIOST_ens_1.nc\n", + "Writing tasmax_ssp126_NOAA-GFDL_ens_1.nc\n", + "Writing tasmax_ssp126_NCC_ens_1.nc\n", + "Writing tasmax_ssp126_CAMS_ens_1.nc\n", + "Writing tasmax_ssp126_DWD_ens_1.nc\n", + "Writing tasmax_ssp126_DKRZ_ens_1.nc\n", + "Writing tasmax_ssp126_AWI_ens_1.nc\n", + "Writing tasmax_ssp126_BCC_ens_1.nc\n", + "Writing tasmax_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing tasmin_ssp126_MIROC_ens_1.nc\n", + "Writing tasmin_ssp126_CCCma_ens_1.nc\n", + "Writing tasmin_ssp126_MOHC_ens_1.nc\n", + "Writing tasmin_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing tasmin_ssp126_MPI-M_ens_1.nc\n", + "Writing tasmin_ssp126_IPSL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing tasmin_ssp126_MRI_ens_1.nc\n", + "Writing tasmin_ssp126_NCAR_ens_1.nc\n", + "Writing tasmin_ssp126_CAS_ens_1.nc\n", + "Writing tasmin_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing tasmin_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing tasmin_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing tasmin_ssp126_NUIST_ens_1.nc\n", + "Writing tasmin_ssp126_CMCC_ens_1.nc\n", + "Writing tasmin_ssp126_INM_ens_1.nc\n", + "Writing tasmin_ssp126_KIOST_ens_1.nc\n", + "Writing tasmin_ssp126_NOAA-GFDL_ens_1.nc\n", + "Writing tasmin_ssp126_NCC_ens_1.nc\n", + "Writing tasmin_ssp126_DWD_ens_1.nc\n", + "Writing tasmin_ssp126_DKRZ_ens_1.nc\n", + "Writing tasmin_ssp126_AWI_ens_1.nc\n", + "Writing tasmin_ssp126_BCC_ens_1.nc\n", + "Writing tasmin_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing uas_ssp126_MIROC_ens_1.nc\n", + "Writing uas_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing uas_ssp126_CCCma_ens_1.nc\n", + "Writing uas_ssp126_MOHC_ens_1.nc\n", + "Writing uas_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing uas_ssp126_MPI-M_ens_1.nc\n", + "Writing uas_ssp126_IPSL_ens_1.nc\n", + "Writing uas_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing uas_ssp126_INM_ens_1.nc\n", + "Writing uas_ssp126_NUIST_ens_1.nc\n", + "Writing uas_ssp126_NOAA-GFDL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing uas_ssp126_MRI_ens_1.nc\n", + "Writing uas_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing uas_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing uas_ssp126_DKRZ_ens_1.nc\n", + "Writing uas_ssp126_DWD_ens_1.nc\n", + "Writing uas_ssp126_AWI_ens_1.nc\n", + "Writing uas_ssp126_BCC_ens_1.nc\n", + "Writing uas_ssp126_CMCC_ens_1.nc\n", + "Writing vas_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing vas_ssp126_MIROC_ens_1.nc\n", + "Writing vas_ssp126_CCCma_ens_1.nc\n", + "Writing vas_ssp126_MOHC_ens_1.nc\n", + "Writing vas_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing vas_ssp126_MPI-M_ens_1.nc\n", + "Writing vas_ssp126_IPSL_ens_1.nc\n", + "Writing vas_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing vas_ssp126_CMCC_ens_1.nc\n", + "Writing vas_ssp126_INM_ens_1.nc\n", + "Writing vas_ssp126_NUIST_ens_1.nc\n", + "Writing vas_ssp126_NOAA-GFDL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing vas_ssp126_MRI_ens_1.nc\n", + "Writing vas_ssp126_KIOST_ens_1.nc\n", + "Writing vas_ssp126_DKRZ_ens_1.nc\n", + "Writing vas_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing vas_ssp126_AWI_ens_1.nc\n", + "Writing vas_ssp126_BCC_ens_1.nc\n", + "Writing vas_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing hurs_ssp126_CCCma_ens_1.nc\n", + "Writing hurs_ssp126_MOHC_ens_1.nc\n", + "Writing hurs_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing hurs_ssp126_CNRM-CERFACS_ens_1.nc\n", + "Writing hurs_ssp126_MPI-M_ens_1.nc\n", + "Writing hurs_ssp126_IPSL_ens_1.nc\n", + "Writing hurs_ssp126_NCAR_ens_1.nc\n", + "Writing hurs_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing hurs_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing hurs_ssp126_CMCC_ens_1.nc\n", + "Writing hurs_ssp126_INM_ens_1.nc\n", + "Writing hurs_ssp126_NCC_ens_1.nc\n", + "Writing hurs_ssp126_NOAA-GFDL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing hurs_ssp126_MRI_ens_1.nc\n", + "Writing hurs_ssp126_DWD_ens_1.nc\n", + "Writing hurs_ssp126_DKRZ_ens_1.nc\n", + "Writing hurs_ssp126_AWI_ens_1.nc\n", + "Writing hurs_ssp126_CCCR-IITM_ens_1.nc\n", + "Writing rsds_ssp126_NOAA-GFDL_ens_1.nc\n", + "Writing sfcWind_ssp126_CCCma_ens_1.nc\n", + "Writing sfcWind_ssp126_MOHC_ens_1.nc\n", + "Writing sfcWind_ssp126_MPI-M_ens_1.nc\n", + "Writing sfcWind_ssp126_IPSL_ens_1.nc\n", + "Writing sfcWind_ssp126_MIROC_ens_1.nc\n", + "Writing sfcWind_ssp126_NCAR_ens_1.nc\n", + "Writing sfcWind_ssp126_NIMS-KMA_ens_1.nc\n", + "Writing sfcWind_ssp126_CSIRO-ARCCSS_ens_1.nc\n", + "Writing sfcWind_ssp126_CMCC_ens_1.nc\n", + "Writing sfcWind_ssp126_INM_ens_1.nc\n", + "Writing sfcWind_ssp126_EC-Earth-Consortium_ens_1.nc\n", + "Writing sfcWind_ssp126_NOAA-GFDL_ens_1.nc\n", + "Writing sfcWind_ssp126_NCC_ens_1.nc\n", + "Writing sfcWind_ssp126_DWD_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xarray\\coding\\times.py:673: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing sfcWind_ssp126_MRI_ens_1.nc\n", + "Writing sfcWind_ssp126_BCC_ens_1.nc\n", + "Writing sfcWind_ssp126_DKRZ_ens_1.nc\n", + "Writing sfcWind_ssp126_AWI_ens_1.nc\n", + "Writing sfcWind_ssp126_CCCR-IITM_ens_1.nc\n" + ] + } + ], + "source": [ + "var_ = ['pr', 'tas', 'tasmax', 'tasmin', 'uas', 'vas', 'hurs', 'rsds', 'sfcWind']\n", + "#var_ = ['uas','vas', 'hurs']\n", + "for var in var_:\n", + " #print(var, type(var))\n", + " unique_models = pd.read_csv(var+'_ssp126_data_available_unique_models.csv')\n", + " #unique_models.iloc[:,0]\n", + " for i_model,model in enumerate(unique_models.iloc[:,0]):\n", + " #tas_model_ = []\n", + " #print('test')\n", + " #print(type(model))\n", + " #print(\"table_id == 'day' & variable_id == 'tas' & experiment_id == 'historical' & institution_id =='\"+model+\"'\")\n", + " query_ = \"activity_id=='ScenarioMIP' & table_id == 'day' & variable_id == '\"+var+\"' & experiment_id == 'ssp126' & institution_id =='\"+model+\"'\"\n", + " df_ta = df.query(query_)\n", + " #print('test')\n", + " filename_ = var+'_ssp126_'+str(model)+\".nc\"\n", + "# filename_ = filename_[:-3]+'_ens_'+str(ens+1)+'.nc'\n", + " #for ens in range(unique_models.iloc[i_model,1]):\n", + " for ens in range(1):\n", + " filename_ = var+'_ssp126_'+str(model)+'_ens_'+str(ens+1)+'.nc'\n", + " count = []\n", + " #print(unique_models.iloc[i_model,1])\n", + " if not os.path.isfile(filename_):\n", + " #for ens in range(1):\n", + " # this only needs to be created once\n", + " \n", + " gcs = gcsfs.GCSFileSystem(token='anon')\n", + " #print('test')\n", + " # get the path to a specific zarr store (the first one from the dataframe above)\n", + " zstore = df_ta.zstore.values[ens] # The index here controls everything, the number\n", + "\n", + " # create a mutable-mapping-style interface to the store\n", + " mapper = gcs.get_mapper(zstore)\n", + "\n", + " # open it using xarray and zarr\n", + " ds_ta = xr.open_zarr(mapper, consolidated=True)\n", + " #print(ds_ta.sel(lat=slice(29,32)).sel(lon=slice(261,264)))\n", + " \n", + " try:\n", + " #tas_hist_ = ds_ta[var].sel(lat=30.3134, lon= 360-97.7666, method='nearest').sel(time=slice('2021','2098'))\n", + " #print('test 1') #tas_hist_ = ds_ta[var].sel(lat=30.3134, lon= 360-97.7666, method='nearest').sel(time=slice('2021','2098'))\n", + " tas_hist_ = ds_ta[var].sel(lat=slice(4,14)).sel(lon=slice(2,16)).sel(time=slice('2021','2098'))\n", + " #print(tas_hist_.values.shape[0]>0) \n", + "\n", + " if tas_hist_.values.shape[0]>0:\n", + " count.append(ens)\n", + " #print('After ', tas_hist_.values.shape) \n", + "\n", + " except:\n", + " continue\n", + " \n", + " #print(tas_hist_.time.values.shape[0])\n", + " #print(type(tas_hist_.time.values[0]).__name__)\n", + " #no_of_days=5475\n", + " #print(count)\n", + " \n", + " print(\"Writing \", filename_)\n", + " out_data = tas_hist_\n", + " out_data[var+'_'] = (('time', 'lat', 'lon'), np.asarray(tas_hist_))\n", + " out_data[var+'_'].to_netcdf(filename_)\n", + "# if tas_hist_.values.shape[0]>0:\n", + "# if ens==count[0]:\n", + "# if type(tas_hist_.time.values[0]).__name__=='DatetimeNoLeap':\n", + "# no_of_days=28470\n", + "# elif type(tas_hist_.time.values[0]).__name__=='Datetime360Day':\n", + "# no_of_days=28080\n", + "# else: \n", + "# no_of_days=28489\n", + "# out_data = tas_hist_\n", + "# else:\n", + "# continue\n", + "# print('test', no_of_days, tas_hist_.shape)\n", + "# if tas_hist_.values.shape[0]==no_of_days:\n", + "#\t\t # There is a difference of resolution within the same model for some cases\n", + "# if ens==0 and tas_hist_.shape[1]*tas_hist_.shape[2]<5: \n", + "# tas_model_.append(tas_hist_.values)\n", + "#\t\t\t\n", + "# if ens>0: \n", + "# if tas_hist_.shape[1]*tas_hist_.shape[2]<5: \n", + "## if tas_model_[0].shape==tas_hist_.shape: \n", + "## tas_model_.append(tas_hist_.values) if tas_model_[0].shape==tas_hist_.shape else continue\n", + "# if tas_model_[0].shape==tas_hist_.shape: tas_model_.append(tas_hist_.values)\n", + "# if tas_hist_.shape[1]*tas_hist_.shape[2]>4 and not os.path.isfile(filename_[:-3]+'_ens_'+str(ens+1)+'.nc'): \n", + "# out_data[var+'_'] = (('time', 'lat', 'lon'), np.asarray(tas_hist_))\n", + "# out_data[var+'_'].to_netcdf(filename_[:-3]+'_ens_'+str(ens+1)+'.nc')\n", + "# print('Writing '+filename_[:-3]+'_ens_'+str(ens+1)+'.nc')\n", + "# print('ensemble number: ',ens)\n", + "# ##############################################################################\n", + "# #print('test ', len(tas_model_))\n", + "# if len(tas_model_)>0:\n", + "#\n", + "## print(tas_model_[0].shape[0], tas_model_[0].shape[1],tas_model_[0].shape[2])\n", + "# #tas_model__ = np.zeros((tas_model_[0].shape[0], tas_model_[0].shape[1],tas_model_[0].shape[2]))\n", + "# #print(np.nanmean(np.asarray(tas_model_),axis=0).shape)\n", + "# #tas_model__ = np.nanmean(np.asarray(tas_model_),axis=0)\n", + "# #print(tas_model__.shape)\n", + "#\n", + "# if tas_hist_.shape[1]*tas_hist_.shape[2]<5:\n", + "# out_data[var+'_'] = (('time', 'lat', 'lon'), np.nanmean(np.asarray(tas_model_),axis=0))\n", + "# out_data[var+'_'].to_netcdf(filename_)\n", + "# else:\n", + "# continue\n", + "# #out_data[var+'_'] = (('time', 'lat', 'lon'), np.asarray(tas_model_[i_ens]))\n", + "# #out_data[var+'_'].to_netcdf(filename_[:-3]+'_ens_'+str(i_ens+1)+'.nc')\n", + "# \n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9aa84ed", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/download_data/CMIP6/ClimateModel_selection_indices.ipynb b/download_data/CMIP6/ClimateModel_selection_indices.ipynb new file mode 100644 index 0000000..3c05cde --- /dev/null +++ b/download_data/CMIP6/ClimateModel_selection_indices.ipynb @@ -0,0 +1,2044 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c3c23bad", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr\n", + "import zarr\n", + "import gcsfs\n", + "import seaborn as sns\n", + "import glob\n", + "from typing import Any, Callable, Optional, Tuple, Union\n", + "from pint.definitions import UnitDefinition\n", + "import pint.converters\n", + "import pint.unit\n", + "import pint\n", + "from packaging import version\n", + "import xesmf as xe\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from statsmodels.distributions.empirical_distribution import ECDF\n", + "from scipy.stats import gamma, norm\n", + "from scipy.signal import detrend\n", + "import xarray as xr\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "716c024f", + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install statsmodels" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6fec6c98", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/bin/python\r\n" + ] + } + ], + "source": [ + "!which python" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e4839381", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_EC-Earth-Consortium_ens_1.nc\n", + "0.991884806179612\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_MIROC_ens_1.nc\n", + "0.9819538618214154\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC historical 1 0.981954\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_CCCma_ens_1.nc\n", + "0.9514585930928066\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC historical 1 0.981954\n", + "2 CCCma historical 1 0.951459\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_CNRM-CERFACS_ens_1.nc\n", + "0.9639472778924861\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC 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E3SM-Project historical 1 0.981294\n", + "19 NIMS-KMA historical 1 0.971084\n", + "20 HAMMOZ-Consortium historical 1 0.990021\n", + "21 KIOST historical 1 0.943528\n", + "22 CCCR-IITM historical 1 0.991833\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_AWI_ens_1.nc\n", + "0.9915367175088512\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC historical 1 0.981954\n", + "2 CCCma historical 1 0.951459\n", + "3 CNRM-CERFACS historical 1 0.963947\n", + "4 CSIRO historical 1 0.972026\n", + "5 NCC historical 1 0.978054\n", + "6 IPSL historical 1 0.995376\n", + "7 MOHC historical 1 0.977746\n", + "8 MPI-M historical 1 0.994259\n", + "9 NCAR historical 1 0.976775\n", + "10 INM historical 1 0.994114\n", + "11 MRI historical 1 0.901858\n", + "12 CAS historical 1 0.790723\n", + "13 BCC historical 1 0.975067\n", + "14 CMCC historical 1 0.983843\n", + "15 CSIRO-ARCCSS historical 1 0.982055\n", + "16 NOAA-GFDL historical 1 0.987099\n", + "17 NUIST historical 1 -0.853698\n", + "18 E3SM-Project historical 1 0.981294\n", + "19 NIMS-KMA historical 1 0.971084\n", + "20 HAMMOZ-Consortium historical 1 0.990021\n", + "21 KIOST historical 1 0.943528\n", + "22 CCCR-IITM historical 1 0.991833\n", + "23 AWI historical 1 0.991537\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_SNU_ens_1.nc\n", + "0.9890313709987582\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC historical 1 0.981954\n", + "2 CCCma historical 1 0.951459\n", + "3 CNRM-CERFACS historical 1 0.963947\n", + "4 CSIRO historical 1 0.972026\n", + "5 NCC historical 1 0.978054\n", + "6 IPSL historical 1 0.995376\n", + "7 MOHC historical 1 0.977746\n", + "8 MPI-M historical 1 0.994259\n", + "9 NCAR historical 1 0.976775\n", + "10 INM historical 1 0.994114\n", + "11 MRI historical 1 0.901858\n", + "12 CAS historical 1 0.790723\n", + "13 BCC historical 1 0.975067\n", + "14 CMCC historical 1 0.983843\n", + "15 CSIRO-ARCCSS historical 1 0.982055\n", + "16 NOAA-GFDL historical 1 0.987099\n", + "17 NUIST historical 1 -0.853698\n", + "18 E3SM-Project historical 1 0.981294\n", + "19 NIMS-KMA historical 1 0.971084\n", + "20 HAMMOZ-Consortium historical 1 0.990021\n", + "21 KIOST historical 1 0.943528\n", + "22 CCCR-IITM historical 1 0.991833\n", + "23 AWI historical 1 0.991537\n", + "24 SNU historical 1 0.989031\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_AS-RCEC_ens_1.nc\n", + "0.9093570284894291\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC historical 1 0.981954\n", + "2 CCCma historical 1 0.951459\n", + "3 CNRM-CERFACS historical 1 0.963947\n", + "4 CSIRO historical 1 0.972026\n", + "5 NCC historical 1 0.978054\n", + "6 IPSL historical 1 0.995376\n", + "7 MOHC historical 1 0.977746\n", + "8 MPI-M historical 1 0.994259\n", + "9 NCAR historical 1 0.976775\n", + "10 INM historical 1 0.994114\n", + "11 MRI historical 1 0.901858\n", + "12 CAS historical 1 0.790723\n", + "13 BCC historical 1 0.975067\n", + "14 CMCC historical 1 0.983843\n", + "15 CSIRO-ARCCSS historical 1 0.982055\n", + "16 NOAA-GFDL historical 1 0.987099\n", + "17 NUIST historical 1 -0.853698\n", + "18 E3SM-Project historical 1 0.981294\n", + "19 NIMS-KMA historical 1 0.971084\n", + "20 HAMMOZ-Consortium historical 1 0.990021\n", + "21 KIOST historical 1 0.943528\n", + "22 CCCR-IITM historical 1 0.991833\n", + "23 AWI historical 1 0.991537\n", + "24 SNU historical 1 0.989031\n", + "25 AS-RCEC historical 1 0.909357\n", + "/mnt/d/CMIP6_data_extraction_hist/pr_historical_CAMS_ens_1.nc\n", + "0.997073453146687\n", + " model experiment ensemble corr\n", + "0 EC-Earth-Consortium historical 1 0.991885\n", + "1 MIROC historical 1 0.981954\n", + "2 CCCma historical 1 0.951459\n", + "3 CNRM-CERFACS historical 1 0.963947\n", + "4 CSIRO historical 1 0.972026\n", + "5 NCC historical 1 0.978054\n", + "6 IPSL historical 1 0.995376\n", + "7 MOHC historical 1 0.977746\n", + "8 MPI-M historical 1 0.994259\n", + "9 NCAR historical 1 0.976775\n", + "10 INM historical 1 0.994114\n", + "11 MRI historical 1 0.901858\n", + "12 CAS historical 1 0.790723\n", + "13 BCC historical 1 0.975067\n", + "14 CMCC historical 1 0.983843\n", + "15 CSIRO-ARCCSS historical 1 0.982055\n", + "16 NOAA-GFDL historical 1 0.987099\n", + "17 NUIST historical 1 -0.853698\n", + "18 E3SM-Project historical 1 0.981294\n", + "19 NIMS-KMA historical 1 0.971084\n", + "20 HAMMOZ-Consortium historical 1 0.990021\n", + "21 KIOST historical 1 0.943528\n", + "22 CCCR-IITM historical 1 0.991833\n", + "23 AWI historical 1 0.991537\n", + "24 SNU historical 1 0.989031\n", + "25 AS-RCEC historical 1 0.909357\n", + "26 CAMS historical 1 0.997073\n" + ] + } + ], + "source": [ + "xr.set_options(display_style='html')\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "lats,late, lons,lone = [4,14,2,16]\n", + "#ds_obs = xr.open_mfdataset('D:/Observed_Climatedata/AirTemp/air*.nc').air.sel(time=slice('1979', '2014')).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon')\n", + "ds_obs = xr.open_mfdataset('/mnt/d/Observed_Climatedata/Precipitation/precip*.nc').precip.sel(time=slice('1979', '2014')).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon')\n", + "y = ds_obs.groupby(\"time.month\").mean(\"time\")\n", + "\n", + "# Computing distributions of correlation between seasonal cycle of precipitation\n", + "\n", + "#corr_ = []\n", + "df_corr = pd.DataFrame()\n", + "\n", + "cnt = 0\n", + "base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + "vars_ = ['pr']#,'pr', 'tas', 'tasmax', 'tasmin', 'uas', 'vas', 'hurs']\n", + "exps = ['historical']#, 'ssp126', 'ssp585']\n", + "\n", + "for i_var,var in enumerate(vars_):\n", + " for i_exp,exp in enumerate(exps):\n", + " df_data_availability = pd.read_csv(base_dir+var+'_'+exp+'_data_available_unique_models.csv')\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + " # add model loop here\n", + " for i_model in range(df_data_availability.shape[0]):\n", + " for i_ens in range(1):\n", + " file_ = data_base_dir+var+'_'+exp+'_'+df_data_availability.iloc[i_model,0]+'_ens_'+str(i_ens+1)+'.nc'\n", + " #print('Reading ')\n", + " print(file_)\n", + " try:\n", + " ds_hist = xr.open_dataset(file_)[var+'_'].sel(time=slice('1979', '2014')). \\\n", + " sel(lat=slice(lats,late)).sel(lon=slice(lons,lone)).mean(dim='lat').mean(dim='lon')*86400\n", + " x = ds_hist.groupby(\"time.month\").mean(\"time\")\n", + " #y = ds_obs.groupby(\"time.month\").mean(\"time\")\n", + " #corr_.append(np.corrcoef(x,y)[0,1])\n", + " #print(x,y)\n", + "\n", + " print(np.corrcoef(x,y)[0,1])\n", + " df_corr = df_corr.append(pd.DataFrame({'model':df_data_availability.iloc[i_model,0], \\\n", + " 'experiment':exp, 'ensemble':i_ens+1, 'corr':np.corrcoef(x,y)[0,1]}, index=[cnt]))\n", + " print(df_corr)\n", + " cnt = cnt + 1\n", + " #print('Successfully read ', file_)\n", + " except:\n", + " continue\n", + "\n", + "df_corr.to_csv('pr.csv') " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7c623770", + "metadata": {}, + "outputs": [], + "source": [ + "df_corr_tas = pd.read_csv('tas.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7036cbf9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0modelexperimentensemblecorr
11MIROChistorical10.967822
33CNRM-CERFACShistorical10.985947
88MPI-Mhistorical10.810287
1010INMhistorical10.804853
1313AWIhistorical10.954570
1414BCChistorical10.832696
1616CMCChistorical10.827532
1717NIMS-KMAhistorical10.831156
1818NOAA-GFDLhistorical10.815695
2222CCCR-IITMhistorical10.868073
2323AS-RCEChistorical10.824656
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 model experiment ensemble corr\n", + "1 1 MIROC historical 1 0.967822\n", + "3 3 CNRM-CERFACS historical 1 0.985947\n", + "8 8 MPI-M historical 1 0.810287\n", + "10 10 INM historical 1 0.804853\n", + "13 13 AWI historical 1 0.954570\n", + "14 14 BCC historical 1 0.832696\n", + "16 16 CMCC historical 1 0.827532\n", + "17 17 NIMS-KMA historical 1 0.831156\n", + "18 18 NOAA-GFDL historical 1 0.815695\n", + "22 22 CCCR-IITM historical 1 0.868073\n", + "23 23 AS-RCEC historical 1 0.824656" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_corr_tas[df_corr_tas['corr']>0.8]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "64c92619", + "metadata": {}, + "outputs": [], + "source": [ + "def quantile_correction(obs_data, mod_data, sce_data, modified=True):\n", + " cdf = ECDF(mod_data)\n", + " p = cdf(sce_data) * 100\n", + " cor = np.subtract(*[np.nanpercentile(x, p) for x in [obs_data, mod_data]])\n", + " if modified:\n", + " mid = np.subtract(*[np.nanpercentile(x, 50) for x in [obs_data, mod_data]])\n", + " g = np.true_divide(*[np.nanpercentile(x, 50) for x in [obs_data, mod_data]])\n", + "\n", + " iqr_obs_data = np.subtract(*np.nanpercentile(obs_data, [75, 25]))\n", + " iqr_mod_data = np.subtract(*np.nanpercentile(mod_data, [75, 25]))\n", + "\n", + " f = np.true_divide(iqr_obs_data, iqr_mod_data)\n", + " cor = g * mid + f * (cor - mid)\n", + " return sce_data + cor\n", + " else:\n", + " return sce_data + cor\n", + " \n", + "class XBiasCorrection(object):\n", + " def __init__(self, obs_data, mod_data, sce_data, dim=\"time\"):\n", + " self.obs_data = obs_data\n", + " self.mod_data = mod_data\n", + " self.sce_data = sce_data\n", + " self.dim = dim\n", + "\n", + " def correct(\n", + " self,\n", + " method=\"modified_quantile\",\n", + " lower_limit=0.1,\n", + " cdf_threshold=0.9999999,\n", + " vectorize=True,\n", + " dask=\"parallelized\",\n", + " **apply_ufunc_kwargs\n", + " ):\n", + " dtype = self._set_dtype()\n", + " dim = self.dim\n", + "\n", + " corrected = xr.apply_ufunc(\n", + " quantile_correction,\n", + " self.obs_data,\n", + " self.mod_data,\n", + " self.sce_data,\n", + " vectorize=vectorize,\n", + " dask=dask,\n", + " input_core_dims=[['time'], ['time'], ['time']],\n", + " output_core_dims=[['time']],\n", + " kwargs={\"modified\": False},\n", + " **apply_ufunc_kwargs\n", + " )\n", + " self.corrected = corrected\n", + " return self.corrected\n", + "\n", + " def _set_dtype(self):\n", + " aa = self.mod_data\n", + " if isinstance(aa, xr.Dataset):\n", + " dtype = aa[list(aa.data_vars)[0]].dtype\n", + " elif isinstance(aa, xr.DataArray):\n", + " dtype = aa.dtype\n", + " return dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2c542bf9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "image/png": { + "height": 262, + "width": 393 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ds_obs = xr.open_mfdataset('/mnt/d/Observed_Climatedata/AirTemp_Min_Max/tmin*.nc').tmin\n", + "ds_obs.mean(dim='time').plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "19ccd5b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 277, + "width": 383 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + "file_= data_base_dir+'tasmin_historical_EC-Earth-Consortium_ens_1.nc'\n", + "ds_model_hist = xr.open_dataset(file_).tasmin_\n", + "ds_model_hist.mean(dim='time').plot()\n" + ] + }, + { + "cell_type": "markdown", + "id": "30ddb0ed", + "metadata": {}, + "source": [ + "# Bias correction for Max. Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4aa2008d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmax_historical_MIROC_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmax_historical_CCCma_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmax_historical_CNRM-CERFACS_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmax_historical_NCC_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmax_historical_IPSL_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; 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returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmax_historical_CMCC_ens_1.nc\n" + ] + } + ], + "source": [ + "lats,late, lons,lone = [4,14,2,16]\n", + "ds_obs = xr.open_mfdataset('/mnt/d/Observed_Climatedata/AirTemp_Min_Max/tmax*.nc').tmax.sel(time=slice('1979', '2014')).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone))+273.15 #.mean(dim='lat').mean(dim='lon')\n", + "#ds_obs = xr.open_mfdataset('/mnt/d/Observed_Climatedata/Precipitation/precip*.nc').precip.sel(time=slice('1979', '2014')).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone))#.mean(dim='lat').mean(dim='lon')\n", + "\n", + "############################################################\n", + "vars_ = ['tasmax']\n", + "exps = ['historical']\n", + "cnt = 0\n", + "base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + "for i_var,var in enumerate(vars_):\n", + " for i_exp,exp in enumerate(exps):\n", + " df_data_availability = pd.read_csv(base_dir+var+'_'+exp+'_data_available_unique_models.csv')\n", + " \n", + " # add model loop here\n", + " for i_model in range(1,df_data_availability.shape[0]):\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + " for i_ens in range(1):\n", + " file_ = data_base_dir+var+'_'+exp+'_'+df_data_availability.iloc[i_model,0]+'_ens_'+str(i_ens+1)+'.nc'\n", + " #print('Reading ')\n", + " print(file_)\n", + " try:\n", + " ds_model_hist = xr.open_dataset(file_)[var+'_']\n", + "#######################################################\n", + "\n", + "\n", + "\n", + "# data_base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + "# file_ = data_base_dir+'pr_historical_MIROC_ens_1.nc'\n", + "# ds_model_hist = xr.open_dataset(file_)\n", + "#ds_model_hist.sel(time=slice('1979','2014'))\n", + "\n", + " ds_historical = ds_model_hist.copy()\n", + " datetimeindex = ds_historical.indexes['time']#.to_datetimeindex()\n", + " ds_historical['time'] = datetimeindex\n", + "\n", + " common_dates = np.intersect1d(np.asarray(ds_historical.time.values.astype('datetime64[D]').tolist()), \\\n", + " np.asarray(ds_obs.time.values.astype('datetime64[D]').tolist()))\n", + " ds_historical['time'] = ('time', ds_historical.time.values.astype('datetime64[D]'))\n", + " ds_historical_ = ds_historical.sel(time=common_dates)\n", + " ds_obs_ = ds_obs.sel(time=common_dates)\n", + "\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_ssp126/'\n", + " file_ = data_base_dir+'tasmax_ssp126_'+df_data_availability.iloc[i_model,0]+'_ens_1.nc'\n", + " ds_ssp126 = xr.open_dataset(file_)\n", + "\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_ssp585/'\n", + " file_ = data_base_dir+'tasmax_ssp585_'+df_data_availability.iloc[i_model,0]+'_ens_1.nc'\n", + " ds_ssp585 = xr.open_dataset(file_)\n", + "\n", + " ds_out = xr.Dataset({'lat': (['lat'], ds_historical_.lat.values),\n", + " 'lon': (['lon'], ds_historical_.lon.values),\n", + " }\n", + " )\n", + " regridder = xe.Regridder(ds_obs_, ds_out, 'bilinear')\n", + " #regridder.clean_weight_file()\n", + " ds_obs_o = regridder(ds_obs_)\n", + " \n", + " \n", + " # Multiply model data with 86400 to convert to mm/day\n", + " bias_corrected = np.zeros_like(ds_ssp126.tasmax_.values)\n", + " for i_ in range(bias_corrected.shape[1]):\n", + " for j_ in range(bias_corrected.shape[2]):\n", + " bias_corrected[:,i_,j_] = quantile_correction(ds_obs_o.values[:,i_,j_], \\\n", + " ds_historical_.tasmax_.values[:,i_,j_]*86400, \\\n", + " ds_ssp126.tasmax_.values[:,i_,j_]*86400, modified=True)\n", + " ds_ssp126['tasmax_bc'] = (('time', 'lat', 'lon'), bias_corrected)\n", + " ds_ssp126.to_netcdf('tasmax_ssp126_'+df_data_availability.iloc[i_model,0]+'_ens_bias_corrected.nc')\n", + " \n", + "\n", + " # Multiply model data with 86400 to convert to mm/day\n", + " bias_corrected = np.zeros_like(ds_ssp585.tasmax_.values)\n", + " for i_ in range(bias_corrected.shape[1]):\n", + " for j_ in range(bias_corrected.shape[2]):\n", + " bias_corrected[:,i_,j_] = quantile_correction(ds_obs_o.values[:,i_,j_], \\\n", + " ds_historical_.tasmax_.values[:,i_,j_]*86400, \\\n", + " ds_ssp585.tasmax_.values[:,i_,j_]*86400, modified=True)\n", + " ds_ssp585['tasmax_bc'] = (('time', 'lat', 'lon'), bias_corrected)\n", + " ds_ssp585.to_netcdf('tasmax_ssp585_'+df_data_availability.iloc[i_model,0]+'_ens_bias_corrected.nc')\n", + " \n", + " except:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0b910ffc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'tmax' (time: 13149, lat: 20, lon: 28)>\n",
+       "dask.array<getitem, shape=(13149, 20, 28), dtype=float32, chunksize=(366, 20, 28), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 13.75 13.25 12.75 12.25 11.75 ... 5.75 5.25 4.75 4.25\n",
+       "  * lon      (lon) float32 2.25 2.75 3.25 3.75 4.25 ... 14.25 14.75 15.25 15.75\n",
+       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2014-12-31\n",
+       "Attributes:\n",
+       "    level_desc:    Surface\n",
+       "    parent_stat:   Other\n",
+       "    long_name:     Daily Maximum Temperature\n",
+       "    cell_methods:  time: mean\n",
+       "    valid_range:   [-90.  50.]\n",
+       "    avg_period:    0000-00-01 00:00:00\n",
+       "    dataset:       CPC Global Temperature\n",
+       "    comments:      GTS data and is gridded using the Shepard Algorithm\n",
+       "    max_period:    6z to 6z\n",
+       "    actual_range:  [-59.656933  22.819536]\n",
+       "    units:         degC\n",
+       "    var_desc:      Maximum Temperature\n",
+       "    statistic:     Maximum
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * lat (lat) float32 13.75 13.25 12.75 12.25 11.75 ... 5.75 5.25 4.75 4.25\n", + " * lon (lon) float32 2.25 2.75 3.25 3.75 4.25 ... 14.25 14.75 15.25 15.75\n", + " * time (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2014-12-31\n", + "Attributes:\n", + " level_desc: Surface\n", + " parent_stat: Other\n", + " long_name: Daily Maximum Temperature\n", + " cell_methods: time: mean\n", + " valid_range: [-90. 50.]\n", + " avg_period: 0000-00-01 00:00:00\n", + " dataset: CPC Global Temperature\n", + " comments: GTS data and is gridded using the Shepard Algorithm\n", + " max_period: 6z to 6z\n", + " actual_range: [-59.656933 22.819536]\n", + " units: degC\n", + " var_desc: Maximum Temperature\n", + " statistic: Maximum" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_obs" + ] + }, + { + "cell_type": "markdown", + "id": "64076c89", + "metadata": {}, + "source": [ + "# Bias correction for Min. Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ba8d83ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_MIROC_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_CCCma_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_CNRM-CERFACS_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": 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{ + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_INM_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_MRI_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_BCC_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_AWI_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_CAS_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_NOAA-GFDL_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_NUIST_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_HAMMOZ-Consortium_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_CSIRO-ARCCSS_ens_1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/dask/array/core.py:450: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " o = func(*args, **kwargs)\n", + "/root/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n", + " overwrite_input=overwrite_input, interpolation=interpolation\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_NIMS-KMA_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_NASA-GISS_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_SNU_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_KIOST_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_AS-RCEC_ens_1.nc\n", + "/mnt/d/CMIP6_data_extraction_hist/tasmin_historical_CMCC_ens_1.nc\n" + ] + } + ], + "source": [ + "lats,late, lons,lone = [4,14,2,16]\n", + "ds_obs = xr.open_mfdataset('/mnt/d/Observed_Climatedata/AirTemp_Min_Max/tmin*.nc').tmin.sel(time=slice('1979', '2014')).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone))+273.15 #.mean(dim='lat').mean(dim='lon')\n", + "#ds_obs = xr.open_mfdataset('/mnt/d/Observed_Climatedata/Precipitation/precip*.nc').precip.sel(time=slice('1979', '2014')).sel(lat=slice(late,lats)).sel(lon=slice(lons,lone))#.mean(dim='lat').mean(dim='lon')\n", + "\n", + "############################################################\n", + "vars_ = ['tasmin']\n", + "exps = ['historical']\n", + "cnt = 0\n", + "base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + "for i_var,var in enumerate(vars_):\n", + " for i_exp,exp in enumerate(exps):\n", + " df_data_availability = pd.read_csv(base_dir+var+'_'+exp+'_data_available_unique_models.csv')\n", + " \n", + " # add model loop here\n", + " for i_model in range(1,df_data_availability.shape[0]):\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + " for i_ens in range(1):\n", + " file_ = data_base_dir+var+'_'+exp+'_'+df_data_availability.iloc[i_model,0]+'_ens_'+str(i_ens+1)+'.nc'\n", + " #print('Reading ')\n", + " print(file_)\n", + " try:\n", + " ds_model_hist = xr.open_dataset(file_)[var+'_']\n", + " #######################################################\n", + "\n", + "\n", + "\n", + " # data_base_dir = '/mnt/d/CMIP6_data_extraction_hist/'\n", + " # file_ = data_base_dir+'pr_historical_MIROC_ens_1.nc'\n", + " # ds_model_hist = xr.open_dataset(file_)\n", + " #ds_model_hist.sel(time=slice('1979','2014'))\n", + "\n", + " ds_historical = ds_model_hist.copy()\n", + " datetimeindex = ds_historical.indexes['time']#.to_datetimeindex()\n", + " ds_historical['time'] = datetimeindex\n", + "\n", + " common_dates = np.intersect1d(np.asarray(ds_historical.time.values.astype('datetime64[D]').tolist()), \\\n", + " np.asarray(ds_obs.time.values.astype('datetime64[D]').tolist()))\n", + " ds_historical['time'] = ('time', ds_historical.time.values.astype('datetime64[D]'))\n", + " ds_historical_ = ds_historical.sel(time=common_dates)\n", + " ds_obs_ = ds_obs.sel(time=common_dates)\n", + "\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_ssp126/'\n", + " file_ = data_base_dir+'tasmin_ssp126_'+df_data_availability.iloc[i_model,0]+'_ens_1.nc'\n", + " ds_ssp126 = xr.open_dataset(file_)\n", + "\n", + " data_base_dir = '/mnt/d/CMIP6_data_extraction_ssp585/'\n", + " file_ = data_base_dir+'tasmin_ssp585_'+df_data_availability.iloc[i_model,0]+'_ens_1.nc'\n", + " ds_ssp585 = xr.open_dataset(file_)\n", + "\n", + " ds_out = xr.Dataset({'lat': (['lat'], ds_historical_.lat.values),\n", + " 'lon': (['lon'], ds_historical_.lon.values),\n", + " }\n", + " )\n", + " regridder = xe.Regridder(ds_obs_, ds_out, 'bilinear')\n", + " #regridder.clean_weight_file()\n", + " ds_obs_o = regridder(ds_obs_)\n", + " regridder = xe.Regridder(ds_ssp126, ds_out, 'bilinear')\n", + " ds_ssp126_ = regridder(ds_ssp126.tasmin_)\n", + " regridder = xe.Regridder(ds_ssp585, ds_out, 'bilinear')\n", + " ds_ssp585_ = regridder(ds_ssp585.tasmin_)\n", + "\n", + "\n", + " # Multiply model data with 86400 to convert to mm/day\n", + " bias_corrected = np.zeros_like(ds_ssp126_.values)\n", + " for i_ in range(bias_corrected.shape[1]):\n", + " for j_ in range(bias_corrected.shape[2]):\n", + " bias_corrected[:,i_,j_] = quantile_correction(ds_obs_o.values[:,i_,j_], \\\n", + " ds_historical_.values[:,i_,j_]*86400, \\\n", + " ds_ssp126_.values[:,i_,j_]*86400, modified=True)\n", + " ds_ssp126_['tasmin_bc'] = (('time', 'lat', 'lon'), bias_corrected)\n", + " ds_ssp126_.to_netcdf('tasmin_ssp126_'+df_data_availability.iloc[i_model,0]+'_ens_bias_corrected.nc')\n", + "\n", + "\n", + " # Multiply model data with 86400 to convert to mm/day\n", + " bias_corrected = np.zeros_like(ds_ssp585_.values)\n", + " for i_ in range(bias_corrected.shape[1]):\n", + " for j_ in range(bias_corrected.shape[2]):\n", + " bias_corrected[:,i_,j_] = quantile_correction(ds_obs_o.values[:,i_,j_], \\\n", + " ds_historical_.values[:,i_,j_]*86400, \\\n", + " ds_ssp585_.values[:,i_,j_]*86400, modified=True)\n", + " ds_ssp585_['tasmin_bc'] = (('time', 'lat', 'lon'), bias_corrected)\n", + " ds_ssp585_.to_netcdf('tasmin_ssp585_'+df_data_availability.iloc[i_model,0]+'_ens_bias_corrected.nc')\n", + "\n", + " except:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4f16746", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bc73d02", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "321981e7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/download_data/CMIP6/extract_hist_ens.py b/download_data/CMIP6/extract_hist_ens.py new file mode 100644 index 0000000..e87ffbf --- /dev/null +++ b/download_data/CMIP6/extract_hist_ens.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +from matplotlib import pyplot as plt +import numpy as np +import pandas as pd +import xarray as xr +import zarr +import gcsfs +import seaborn as sns +import os + +xr.set_options(display_style='html') +#get_ipython().run_line_magic('matplotlib', 'inline') +#get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'") + + +# In[2]: + + +df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv') +print(df.head()) +# +# +## In[3]: +# +# +#df_ta = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'tas' & experiment_id == 'historical'") +#print(df_ta) +# +# +## In[4]: +# +# +#df_pr = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'pr' & experiment_id == 'historical'") +#print(df_pr) +# +# +## In[5]: +# +# +#df_tasmax = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'tasmax' & experiment_id == 'historical'") +#print(df_tasmax) +# +# +## In[6]: +# +# +#df_tasmin = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'tasmin' & experiment_id == 'historical'") +#print(df_tasmin) +# + +df_uas = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'uas' & experiment_id == 'historical'") +print(df_uas) +df_vas = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'vas' & experiment_id == 'historical'") +print(df_vas) +df_hurs = df.query("activity_id=='CMIP' & table_id == 'day' & variable_id == 'hurs' & experiment_id == 'historical'") +print(df_vas) +# In[7]: + + +#df_pr['institution_id'].value_counts().to_csv('pr_historical_data_available_unique_models.csv') +#df_ta['institution_id'].value_counts().to_csv('tas_historical_data_available_unique_models.csv') +#df_tasmax['institution_id'].value_counts().to_csv('tasmax_historical_data_available_unique_models.csv') +#df_tasmin['institution_id'].value_counts().to_csv('tasmin_historical_data_available_unique_models.csv') +df_uas['institution_id'].value_counts().to_csv('uas_historical_data_available_unique_models.csv') +df_vas['institution_id'].value_counts().to_csv('vas_historical_data_available_unique_models.csv') +df_hurs['institution_id'].value_counts().to_csv('hurs_historical_data_available_unique_models.csv') + + +# In[20]: + + +var_ = ['pr', 'tas', 'tasmax', 'tasmin', 'uas', 'vas', 'hurs'] +#var_ = ['uas', 'vas'] +#var_ = ['hurs'] +for var in var_: + #print(var, type(var)) + unique_models = pd.read_csv(var+'_historical_data_available_unique_models.csv') + #unique_models.iloc[:,0] + for i_model,model in enumerate(unique_models.iloc[:,0]): + #tas_model_ = [] + #print('test') + #print(type(model)) + #print("table_id == 'day' & variable_id == 'tas' & experiment_id == 'historical' & institution_id =='"+model+"'") + query_ = "activity_id=='CMIP' & table_id == 'day' & variable_id == '"+var+"' & experiment_id == 'historical' & institution_id =='"+model+"'" + df_ta = df.query(query_) + #print('test') + filename_ = var+'_historical_'+str(model)+".nc" +# filename_ = filename_[:-3]+'_ens_'+str(ens+1)+'.nc' + for ens in range(unique_models.iloc[i_model,1]): + filename_ = var+'_historical_'+str(model)+'_ens_'+str(ens+1)+'.nc' + count = [] + #print(unique_models.iloc[i_model,1]) + if not os.path.isfile(filename_): + #for ens in range(1): + # this only needs to be created once + + gcs = gcsfs.GCSFileSystem(token='anon') + #print('test') + # get the path to a specific zarr store (the first one from the dataframe above) + zstore = df_ta.zstore.values[ens] # The index here controls everything, the number + + # create a mutable-mapping-style interface to the store + mapper = gcs.get_mapper(zstore) + + # open it using xarray and zarr + ds_ta = xr.open_zarr(mapper, consolidated=True) + #print(ds_ta.sel(lat=slice(29,32)).sel(lon=slice(261,264))) + + try: + #tas_hist_ = ds_ta[var].sel(lat=30.3134, lon= 360-97.7666, method='nearest').sel(time=slice('2021','2098')) + #print('test 1') #tas_hist_ = ds_ta[var].sel(lat=30.3134, lon= 360-97.7666, method='nearest').sel(time=slice('2021','2098')) + tas_hist_ = ds_ta[var].sel(lat=slice(29,32)).sel(lon=slice(261,264)).sel(time=slice('1950','2014')) + #print(tas_hist_.values.shape[0]>0) + + if tas_hist_.values.shape[0]>0: + count.append(ens) + #print('After ', tas_hist_.values.shape) + + except: + continue + + #print(tas_hist_.time.values.shape[0]) + #print(type(tas_hist_.time.values[0]).__name__) + #no_of_days=5475 + #print(count) + + print("Writing ", filename_) + out_data = tas_hist_ + out_data[var+'_'] = (('time', 'lat', 'lon'), np.asarray(tas_hist_)) + out_data[var+'_'].to_netcdf(filename_) +# if tas_hist_.values.shape[0]>0: +# if ens==count[0]: +# if type(tas_hist_.time.values[0]).__name__=='DatetimeNoLeap': +# no_of_days=28470 +# elif type(tas_hist_.time.values[0]).__name__=='Datetime360Day': +# no_of_days=28080 +# else: +# no_of_days=28489 +# out_data = tas_hist_ +# else: +# continue +# print('test', no_of_days, tas_hist_.shape) +# if tas_hist_.values.shape[0]==no_of_days: +# # There is a difference of resolution within the same model for some cases +# if ens==0 and tas_hist_.shape[1]*tas_hist_.shape[2]<5: +# tas_model_.append(tas_hist_.values) +# +# if ens>0: +# if tas_hist_.shape[1]*tas_hist_.shape[2]<5: +## if tas_model_[0].shape==tas_hist_.shape: +## tas_model_.append(tas_hist_.values) if tas_model_[0].shape==tas_hist_.shape else continue +# if tas_model_[0].shape==tas_hist_.shape: tas_model_.append(tas_hist_.values) +# if tas_hist_.shape[1]*tas_hist_.shape[2]>4 and not os.path.isfile(filename_[:-3]+'_ens_'+str(ens+1)+'.nc'): +# out_data[var+'_'] = (('time', 'lat', 'lon'), np.asarray(tas_hist_)) +# out_data[var+'_'].to_netcdf(filename_[:-3]+'_ens_'+str(ens+1)+'.nc') +# print('Writing '+filename_[:-3]+'_ens_'+str(ens+1)+'.nc') +# print('ensemble number: ',ens) +# ############################################################################## +# #print('test ', len(tas_model_)) +# if len(tas_model_)>0: +# +## print(tas_model_[0].shape[0], tas_model_[0].shape[1],tas_model_[0].shape[2]) +# #tas_model__ = np.zeros((tas_model_[0].shape[0], tas_model_[0].shape[1],tas_model_[0].shape[2])) +# #print(np.nanmean(np.asarray(tas_model_),axis=0).shape) +# #tas_model__ = np.nanmean(np.asarray(tas_model_),axis=0) +# #print(tas_model__.shape) +# +# if tas_hist_.shape[1]*tas_hist_.shape[2]<5: +# out_data[var+'_'] = (('time', 'lat', 'lon'), np.nanmean(np.asarray(tas_model_),axis=0)) +# out_data[var+'_'].to_netcdf(filename_) +# else: +# continue +# #out_data[var+'_'] = (('time', 'lat', 'lon'), np.asarray(tas_model_[i_ens])) +# #out_data[var+'_'].to_netcdf(filename_[:-3]+'_ens_'+str(i_ens+1)+'.nc') +# + + + + #print(df_ta) +# unique_models.iloc[:,0] + + +# In[ ]: + +# +#count[0] +# +# +## In[ ]: +# +# +#tas_model_.shape +# + +# In[ ]: + + + + + +# In[ ]: + + + + + +# In[ ]: + + + + From 8fea38046a015a958bc3fcf52800ef5fc706ca13 Mon Sep 17 00:00:00 2001 From: Manmeet Singh Date: Tue, 19 Apr 2022 11:50:55 -0500 Subject: [PATCH 155/171] Add files via upload --- ...ricts-Census-2011-20220411T082910Z-001.zip | Bin 0 -> 7851016 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 xarray_tutorial/Districts-Census-2011-20220411T082910Z-001.zip 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