diff --git a/Analysis/AQMeshData b/Analysis/AQMeshData new file mode 120000 index 0000000..369ff64 --- /dev/null +++ b/Analysis/AQMeshData @@ -0,0 +1 @@ +/nfs/see-fs-01_users/earhbu/UNRESP_AQtools/ \ No newline at end of file diff --git a/Analysis/Stats_our_obs.py b/Analysis/Stats_our_obs.py new file mode 100644 index 0000000..9a5ebba --- /dev/null +++ b/Analysis/Stats_our_obs.py @@ -0,0 +1,366 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +"""Timeseries Analysis +.. module:: Stats + :platform: Unix + :synopis: +.. moduleauther: CEMAC (UoL) +.. description: This module was developed by CEMAC as part of the UNRESP + Project. This script takes CALPUFF concrec data from 2 models and compares + with observations. + From the output of timeseries.py normalise the data + :copyright: © 2019 University of Leeds. + :license: BSD-2 Clause. +Example: + To use:: + coming soon +.. CEMAC_UNRESPForcastingSystem: + https://github.com/cemac/UNRESPForcastingSystem +""" +import os +import glob +import matplotlib as mpl +import pandas as pd +import warnings +import numpy as np +import matplotlib.pyplot as plt +from datetime import datetime +from sklearn import preprocessing +warnings.filterwarnings("ignore") +# University System python may be broken +# If some one insists on using it... +BACKEND = mpl.get_backend() +if BACKEND == 'Qt4Agg' and sys.version_info[0] == 2: + # Fix the backend + print('swapping to Agg Backend') + mpl.pyplot.switch_backend('Agg') + +Towns = ['ElPanama', 'Pacaya'] +NormalisePlots = False +CompositePlots = False +ScatterPlots = False +BinScatterPlots = True +# https://www2.dmu.dk/AtmosphericEnvironment/Expost/database/docs/PPM_conversion.pdf +# my data for SO2 is ppb +# 1 ppb = 2.62 μg/m3 +# must divide by 2.6 +# 0 get our data + + +def getdatamarch(StationName, var1='PM2.5', var2='SO2'): + pname = 'AQMeshData'+'/data/' + StationName + '/AQMeshData*_2017*.csv' + for rw in glob.iglob(pname): + day_data = pd.read_csv(rw, index_col=0, parse_dates=True, ) + data = day_data[day_data.SensorLabel == var1] + alldata = data + #alldata = data[data.Status == 'Valid'] + if var2 is not None: + data2 = day_data[day_data.SensorLabel == var2] + alldata2 = data2 + #alldata2 = data2[data2.Status == 'Valid'] + alldata = alldata.loc['2017-03-01':'2017-04-01'] + alldata2 = alldata2.loc['2017-03-01':'2017-04-01'] + return alldata, alldata2 + + +def dateparse(x): return pd.datetime.strptime(x, '%d/%m/%Y %H:%M') + + +def getdatamarch_origin(): + obs = '/scratch/Projects/UNRESP_Data/OBS' + station = 'ElPanama' + def dateparse(x): return pd.datetime.strptime(x, '%d/%m/%Y %H:%M') + df = pd.read_csv(glob.glob(obs + '/' + station + '*.csv')[0], index_col=0, + parse_dates=True, date_parser=dateparse) + pm25 = pd.DataFrame() + so2 = pd.DataFrame() + SO2 = df['1733_SO2_calibrated'] + PM25 = df['1733150_PM2.5_Scaled'] + SO2 = SO2.loc['2017-03-01':'2017-04-01'] + PM25 = PM25.loc['2017-03-01':'2017-04-01'] + so2['Scaled'] = SO2 + pm25['Scaled'] = PM25 + return so2, pm25 +# -------------------------------------------------------------------------- # +# Take data out put from timeseries.py and nomalise # +# # +# # +# ------------------------ 1. Normalise ------------------------------------ # + + +def gen_normalised_plots1(town, plot=None): + """gen_normalised_plots + ..description: generate normalised plots from data sets, rescale to maxium + = 1? + ..args: + town(str) + dataset(str) + """ + PM25, SO2 = getdatamarch(town, var1='PM2.5', var2='SO2') + # Turn to numpy array + pm25so2 = pd.DataFrame() + pm25so2['PM25'] = PM25['Scaled'] + pm25so2['SO2'] = SO2['Scaled']*2.62 + x = pm25so2.values + # scikit learn makes this quick + min_max_scaler = preprocessing.MinMaxScaler() + x_scaled = min_max_scaler.fit_transform(x) + # Pop back into a dataframe + datanorm = pd.DataFrame(x_scaled, columns=['PM25', 'SO2'], + index=pm25so2.index) + return pm25so2, datanorm + + +def gen_normalised_plots(): + """gen_normalised_plots + ..description: generate normalised plots from data sets, rescale to maxium + = 1? + ..args: + town(str) + dataset(str) + """ + PM25, SO2 = getdatamarch_origin() + # Turn to numpy array + pm25so2 = pd.DataFrame() + pm25so2['PM25'] = PM25['Scaled'] + pm25so2['SO2'] = SO2['Scaled'] + x = pm25so2.values + # scikit learn makes this quick + min_max_scaler = preprocessing.MinMaxScaler() + x_scaled = min_max_scaler.fit_transform(x) + # Pop back into a dataframe + datanorm = pd.DataFrame(x_scaled, columns=['PM25', 'SO2'], + index=pm25so2.index) + return pm25so2, datanorm + + +# Notes ElPanama +# ECMWF Looked Crazy at the end of March and we had no data for end of March +# so i removed that data? +""" +pm25so2, datanorm = gen_normalised_plots('ElPanama') +datanorm.to_csv('NormalisedPM25SO_ElPanama_Mar2017.csv') +pm25so2.to_csv('PM25SO_ElPanama_Mar2017.csv') +test = datanorm.reset_index() +fig, ax = plt.subplots(figsize=(10, 5)) +test.plot.scatter(x='TETimestamp',y='SO2',style='.',ax=ax, s=1) +test.plot.scatter(x='TETimestamp',y='PM25',style='.',ax=ax, s=1, color='r') +plt.legend(['PM25','SO2']) +plt.show() +fig, ax = plt.subplots(figsize=(10, 5)) +test[['SO2','PM25']].plot(ax=ax) +plt.show() +test = pm25so2.reset_index() +fig, ax = plt.subplots(figsize=(10, 5)) +test.plot.scatter(x='TETimestamp',y='SO2',style='.',ax=ax, s=1) +test.plot.scatter(x='TETimestamp',y='PM25',style='.',ax=ax, s=1, color='r') +plt.legend(['PM25','SO2']) +plt.show() +fig, ax = plt.subplots(figsize=(10, 5)) +test[['SO2','PM25']].plot(ax=ax) +plt.show() +pm25so2, datanorm = gNormalisedPen_normalised_plots('Pacaya') +datanorm.to_csv('NormalisedPM25SO_Pacaya_Mar2017.csv') +pm25so2.to_csv('PM25SO_Pacaya_Mar2017.csv') +fig, ax = plt.subplots(figsize=(10, 5)) +pm25so2.plot(ax=ax) +""" + + +if NormalisePlots is True: + for town in Towns: + df = gen_normalised_plots(town, plot='Y')[0] + plt.clf() + # profile = pandas_profiling.ProfileReport(df) + # profile.to_file(town + 'normalised_stats.html') + +# ------------------------ 2. Composite days (normalised)-------------------- # +if CompositePlots is True: + for town in Towns: + for i in np.arange(2): + fig, ax = plt.subplots(figsize=(10, 5)) + df = gen_normalised_plots(town)[i] + df = df.groupby(df.index.hour).mean() + if i == 1: + x = df.values + # scikit learn makes this quick + min_max_scaler = preprocessing.MinMaxScaler() + x_scaled = min_max_scaler.fit_transform(x) + # Pop back into a dataframe + df = pd.DataFrame(x_scaled, columns=['Observations', 'NAM', + 'ECMWF'], index=df.index) + df.plot(ax=ax) + plt.title('Composite mean hourly concentrations (Normalised)') + plt.ylabel('Normalised concentration') + plt.xlabel(' Hour in day') + plt.savefig(town + 'Composite_day' + str(i) + '.png') + plt.clf() + +if ScatterPlots is True: + for town in Towns: + pm25so2, df = gen_normalised_plots(town) + # add an hour of day column + th = 0.2 + df['hour'] = df.index.hour + df['minute'] = df.index.minute + df['minod'] = df.hour*60+df.minute + # Plot as scatter plot + fig, ax = plt.subplots(figsize=(10, 5)) + plt.scatter(df.minod[df.PM25.values>=th], df.PM25[df.PM25.values>=th], marker='*', alpha=0.4) + plt.scatter(df.minod[df.SO2.values>=th], df.SO2[df.SO2.values>=th], marker='d', alpha=0.2) + plt.title('Scatter plot of Normalised concentrations grouped by min' + + ' of day \n' + town) + plt.ylabel('Normalised concentrations') + plt.xlabel('min of day') + plt.legend(['PM25', 'SO2']) + plt.savefig(town+'scatterbymin_newgrpbymin_threshold_'+str(th)+'.png') + plt.clf() + # Plot as rose/ clock thing.. + times = [6, 3, 0, 21, 18, 15, 12, 9] + fig = plt.figure(figsize=(12, 10)) + ax = fig.add_subplot(221, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod / (24*60), df.PM25, + alpha=0.40, marker='d', label='PM25') + ax.set_xticklabels(times) + ax.set_title('\n PM25') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + ax = fig.add_subplot(222, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod[df.SO2.values>=th] / (24*60), df.SO2[df.SO2.values>=th], + alpha=0.50, marker='x', color='orange', label='SO2') + times = [6, 3, 0, 21, 18, 15, 12, 9] + ax.set_xticklabels(times) + ax.set_title('SO2') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + ax = fig.add_subplot(224, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod / (24*60), df.PM25, + alpha=0.40, marker='d', label='PM25') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod[df.SO2.values>=th] / (24*60), df.SO2[df.SO2.values>=th], + alpha=0.50, marker='x', label='SO2') + ax.set_xticklabels(times) + ax.set_title('both') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + plt.suptitle('Scatter plots in polar co-ordinates of normalised ' + + 'concentrations \n for ' + town + '\n') + plt.tight_layout() + plt.savefig(town + 'normalised_clock_scatter_newdata_grpbymin_threshold_'+str(th)+'.png') + plt.clf() + +if ScatterPlots is True: + for town in Towns: + #pm25so2, df = gen_normalised_plots(town) + # add an hour of day column + th = 0.1 + df['hour'] = df.index.hour + df['minute'] = df.index.minute + df['minod'] = df.hour + # Plot as scatter plot + fig, ax = plt.subplots(figsize=(10, 5)) + plt.scatter(df.minod[df.PM25.values>=th], df.PM25[df.PM25.values>=th], marker='*', alpha=0.4) + plt.scatter(df.minod[df.SO2.values>=th], df.SO2[df.SO2.values>=th], marker='d', alpha=0.2) + plt.title('Scatter plot of Normalised concentrations grouped by hour' + + ' of day \n' + town) + plt.ylabel('Normalised concentrations') + plt.xlabel('min of day') + plt.legend(['PM25', 'SO2']) + plt.savefig(town+'scatterbyhour_new_grphour_threshold_'+str(th)+'.png') + plt.clf() + # Plot as rose/ clock thing.. + times = [6, 3, 0, 21, 18, 15, 12, 9] + fig = plt.figure(figsize=(12, 10)) + ax = fig.add_subplot(221, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod / (24), df.PM25, + alpha=0.40, marker='d', label='PM25') + ax.set_xticklabels(times) + ax.set_title('\n PM25') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + ax = fig.add_subplot(222, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod[df.SO2.values>=th] / (24), df.SO2[df.SO2.values>=th], + alpha=0.50, marker='x', color='orange', label='SO2') + times = [6, 3, 0, 21, 18, 15, 12, 9] + ax.set_xticklabels(times) + ax.set_title('SO2') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + ax = fig.add_subplot(224, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod / (24), df.PM25, + alpha=0.40, marker='d', label='PM25') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod[df.SO2.values>=th] / (24), df.SO2[df.SO2.values>=th], + alpha=0.50, marker='x', label='SO2') + ax.set_xticklabels(times) + ax.set_title('both') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + plt.suptitle('Scatter plots in polar co-ordinates of normalised ' + + 'concentrations \n for ' + town + '\n') + plt.tight_layout() + plt.savefig(town + 'normalised_clock_scatter_newdata_grphour_threshold_' + str(th)+'.png') + plt.clf() + +if BinScatterPlots is True: + # pm25so2, datanorm = gen_normalised_plots('ElPanama') + # pm25so2.plot.scatter(x='TETimestamp', y='SO2', style='.', ax=ax, s=1) + # pm25so2.plot.scatter(x='TETimestamp', y='PM25', style='.', ax=ax, s=1, color='r') + binLims = [0*2.62, 10*2.62, 20*2.62, 40*2.62, 2000*2.62] + binLims = [0, 10, 20, 40, 2000] + bin_lables = ["vlow", "low", "mod", "high"] + for town in Towns: + df, norm = gen_normalised_plots1(town) + SO2bins = pd.cut(df.SO2, binLims, labels=bin_lables) + PM25bins = pd.cut(df.PM25, binLims, labels=bin_lables) + df.PM25[PM25bins == 'vlow'] = 0 + df.PM25[PM25bins == 'low'] = binLims[1] + df.PM25[PM25bins == 'mod'] = binLims[2] + df.PM25[PM25bins == 'high'] = binLims[3] + df.SO2[SO2bins == 'vlow'] = binLims[0] + df.SO2[SO2bins == 'low'] = binLims[1] + df.SO2[SO2bins == 'mod'] = binLims[2] + df.SO2[SO2bins == 'high'] = binLims[3] + fig, ax = plt.subplots(figsize=(10, 5)) + print(df.columns) + df[['PM25', 'SO2']].plot() + plt.show() + # add an hour of day column + th = 1 + df['hour'] = df.index.hour + df['minute'] = df.index.minute + df['minod'] = df.hour + # Plot as scatter plot + fig, ax = plt.subplots(figsize=(10, 5)) + plt.scatter(df.minod[df.PM25.values >= th], + df.PM25[df.PM25.values >= th], marker='*', alpha=0.4) + plt.scatter(df.minod[df.SO2.values >= th], + df.SO2[df.SO2.values >= th], marker='d', alpha=0.2) + plt.title('Scatter plot of Normalised concentrations grouped by hour' + + ' of day \n' + town) + plt.ylabel('Normalised concentrations') + plt.xlabel('min of day') + plt.legend(['PM25', 'SO2']) + plt.savefig('Binned' + town+'scatterbyhour_new_grphour_orig.png') + plt.clf() + # Plot as rose/ clock thing.. + times = [6, 3, 0, 21, 18, 15, 12, 9] + fig = plt.figure(figsize=(12, 10)) + ax = fig.add_subplot(221, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod / (24), df.PM25, + alpha=0.40, marker='d', label='PM25') + ax.set_xticklabels(times) + ax.set_title('\n PM25') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + ax = fig.add_subplot(222, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod[df.SO2.values>=th] / (24), df.SO2[df.SO2.values>=th], + alpha=0.50, marker='x', color='orange', label='SO2') + times = [6, 3, 0, 21, 18, 15, 12, 9] + ax.set_xticklabels(times) + ax.set_title('SO2') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + ax = fig.add_subplot(224, projection='polar') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod / (24), df.PM25, + alpha=0.40, marker='d', label='PM25') + plt.scatter(np.pi / 2.0 - 2 * np.pi * df.minod[df.SO2.values>=th] / (24), df.SO2[df.SO2.values>=th], + alpha=0.50, marker='x', label='SO2') + ax.set_xticklabels(times) + ax.set_title('both') + plt.legend(scatterpoints=5, loc="upper left", bbox_to_anchor=(1.04, 1)) + plt.suptitle('Scatter plots in polar co-ordinates of normalised ' + + 'concentrations \n for ' + town + '\n') + plt.tight_layout() + plt.savefig('Binned' + town + 'normalised_clock_scatter_newdata_grphour_orig.png') + plt.clf() diff --git a/Analysis/data_change/NormalisedPM25SO_ELPanama_Mar2017.csv b/Analysis/data_change/NormalisedPM25SO_ELPanama_Mar2017.csv new file mode 100644 index 0000000..96493b3 --- /dev/null +++ b/Analysis/data_change/NormalisedPM25SO_ELPanama_Mar2017.csv @@ -0,0 +1,3045 @@ +TETimestamp,PM25,SO2 +2017-03-01 00:00:00+00:00,0.06554440242272586,0.2425880636718582 +2017-03-01 00:15:00+00:00,0.046464014559103706,0.47766765349465823 +2017-03-01 00:30:00+00:00,0.06554440242272586,0.3613935727463852 +2017-03-01 00:45:00+00:00,0.04686211505104217,0.2628966977016919 +2017-03-01 01:00:00+00:00,0.06366764296073024,0.48662061469372575 +2017-03-01 01:15:00+00:00,0.02630306821736287,0.3667281646429541 +2017-03-01 01:30:00+00:00,0.010379048539824267,0.04383518542354943 +2017-03-01 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+2017-04-01 20:46:00+00:00,2.84,13.258 +2017-04-01 21:01:00+00:00,2.67,13.251 +2017-04-01 21:16:00+00:00,2.8,10.937000000000001 +2017-04-01 21:31:00+00:00,2.58,14.436 +2017-04-01 21:46:00+00:00,4.18,12.626 +2017-04-01 22:01:00+00:00,3.23,20.335 +2017-04-01 22:16:00+00:00,3.67,7.124 +2017-04-01 22:31:00+00:00,2.28,17.188 +2017-04-01 22:46:00+00:00,2.37,6.61 +2017-04-01 23:01:00+00:00,2.22,5.821000000000001 +2017-04-01 23:16:00+00:00,2.21,5.442 +2017-04-01 23:31:00+00:00,2.06,5.012 +2017-04-01 23:46:00+00:00,2.21,4.671 diff --git a/Run.sh b/Run.sh index c2aed64..34af24e 100755 --- a/Run.sh +++ b/Run.sh @@ -649,7 +649,7 @@ if [ "$runCALPUFF" = true ]; then mv concrec0200${i2}.dat concrec0200${j2}.dat done mv concrec*.dat ./CALPUFF_OUT/CALPUFF/${rundate}/. - rm -f *.con *.lst *.dat *.clr *.bna *.grd + #rm -f *.con *.lst *.dat *.clr *.bna *.grd echo " ---> FINISHED ###" fi fi diff --git a/calmettest.py b/calmettest.py new file mode 100644 index 0000000..a85d93d --- /dev/null +++ b/calmettest.py @@ -0,0 +1,358 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# https://github.com/yeyuguo/metamet/tree/master/metlib/calmet +# calmet6.py + +import os +import sys +#import re +from datetime import datetime, timedelta +import numpy as np +#import scipy as sp +#from scipy.io.numpyio import fread as sp_fread +#import matplotlib.pyplot as plt +#from mpl_toolkits.basemap import Basemap +#from matplotlib import mlab + +__all__ = ['Calmet6Dataset'] + + +class CalmetStopIteration(Exception): + pass + + +_Calmet2DLabVar = [ + ('SC', 'IPGT'), ('US', 'USTAR'), ('ZI', 'ZI'), + ('L', 'EL'), ('WS', 'WSTAR'), + ('RMM', 'RMM'), + ('TK', 'TEMPK'), ('D', 'RHO'), ('Q', 'QSW'), + ('RH', 'IRH'), + ('PC', 'IPCODE') +] +_Calmet2DLabVarCALGRD = [ + ('SC', 'IPGT'), ('US', 'USTAR'), ('ZI', 'ZI'), + ('L', 'EL'), ('WS', 'WSTAR'), + #('RMM', 'RMM'), + ('TK', 'TEMPK'), ('D', 'RHO'), ('Q', 'QSW'), + ('RH', 'IRH'), + #('PC', 'IPCODE') +] +_CalmetFileDecl = [ + ('DATASET', 'S16'), ('DATAVER', 'S16'), ('DATAMOD', 'S64') +] +_CalmetControlParas = [ + ('IBYR', '%(INTTYPE)s'), ('IBMO', '%(INTTYPE)s'), ('IBDY', '%(INTTYPE)s'), + ('IBHR', '%(INTTYPE)s'), ('IBSEC', '%(INTTYPE)s'), + ('IEYR', '%(INTTYPE)s'), ('IEMO', '%(INTTYPE)s'), ('IEDY', '%(INTTYPE)s'), + ('IEHR', '%(INTTYPE)s'), ('IESEC', '%(INTTYPE)s'), ('AXTZ', 'S8'), + ('IRLG', '%(INTTYPE)s'), ('IRTYPE', '%(INTTYPE)s'), + ('NX', '%(INTTYPE)s'), ('NY', '%(INTTYPE)s'), ('NZ', '%(INTTYPE)s'), + ('DGRID', '%(REALTYPE)s'), ('XORIGR', + '%(REALTYPE)s'), ('YORIGR', '%(REALTYPE)s'), + ('IWFCOD', '%(INTTYPE)s'), ('NSSTA', '%(INTTYPE)s'), + ('NUSTA', '%(INTTYPE)s'), ('NPSTA', '%(INTTYPE)s'), ('NOWSTA', + '%(INTTYPE)s'), ('NLU', '%(INTTYPE)s'), + ('IWAT1', '%(INTTYPE)s'), ('IWAT2', + '%(INTTYPE)s'), ('LCALGRD', '%(INTTYPE)s'), + ('PMAP', 'S8'), ('DATUM', 'S8'), ('DATEN', 'S12'), + ('FEAST', '%(REALTYPE)s'), ('FNORTH', + '%(REALTYPE)s'), ('UTMHEM', 'S4'), ('IUTMZN', '%(INTTYPE)s'), + ('RNLAT0', '%(REALTYPE)s'), ('RELON0', '%(REALTYPE)s'), ('XLAT1', + '%(REALTYPE)s'), ('XLAT2', '%(REALTYPE)s') +] +_CalmetCellFaceHeights = [ + ('CLAB1', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['ZFACEM', '(%(NCELLFACE)d,)%(REALTYPE)s'] +] +_CalmetXYSurfStations1 = [ + ('CLAB2', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['XSSTA', '(%(NSSTA)d,)%(REALTYPE)s'] +] +_CalmetXYSurfStations2 = [ + ('CLAB3', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['YSSTA', '(%(NSSTA)d,)%(REALTYPE)s'] +] +_CalmetXYUpperStations1 = [ + ('CLAB4', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['XUSTA', '(%(NUSTA)d,)%(REALTYPE)s'], +] +_CalmetXYUpperStations2 = [ + ('CLAB5', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['YUSTA', '(%(NUSTA)d,)%(REALTYPE)s'] +] +_CalmetXYPrecStations1 = [ + ('CLAB6', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['XPSTA', '(%(NPSTA)d,)%(REALTYPE)s'] +] +_CalmetXYPrecStations2 = [ + ('CLAB7', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['YPSTA', '(%(NPSTA)d,)%(REALTYPE)s'] +] +_CalmetSurfRough = [ + ('CLAB8', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['Z0', '(%(NY)d, %(NX)d)%(REALTYPE)s'] +] +_CalmetLanduse = [ + ('CLAB9', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['ILANDU', '(%(NY)d, %(NX)d)%(INTTYPE)s'] +] +_CalmetElev = [ + ('CLAB10', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['ELEV', '(%(NY)d, %(NX)d)%(REALTYPE)s'] +] +_CalmetLeafAreaIndex = [ + ('CLAB11', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['XLAI', '(%(NY)d, %(NX)d)%(REALTYPE)s'] +] +_CalmetNearestSurfStationNo = [ + ('CLAB12', 'S8'), ('IDUM', '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ('IDUM', + '%(INTTYPE)s'), ('IDUM', '%(INTTYPE)s'), ['NEARS', '(%(NY)d, %(NX)d)%(INTTYPE)s'] +] +_CalmetData = [ + ('LAB', 'S8'), ('NDATHRB', '%(INTTYPE)s'), ('IBSEC', '%(INTTYPE)s'), ('NDATHRE', + '%(INTTYPE)s'), ('IESEC', '%(INTTYPE)s'), ('DATA', '(%(NY)d, %(NX)d)%(REALTYPE)s') +] +_CalmetDataI = [ + ('LAB', 'S8'), ('NDATHRB', '%(INTTYPE)s'), ('IBSEC', '%(INTTYPE)s'), ('NDATHRE', + '%(INTTYPE)s'), ('IESEC', '%(INTTYPE)s'), ('DATA', '(%(NY)d, %(NX)d)%(INTTYPE)s') +] +# TODO: sub NY, NX!!! + + +class Calmet6Dataset(object): + """Calmet6Dataset represents Calmet version 6 datafile. + Usage: + data = Calmet6Dataset(filename) + + each variable in the usermanual can be accessed via data.VARNAME or data.__dict__[varname] . + Some helper attrs: + data.parameters: list of parameters like 'NX', 'NY', etc, + invar: list of other header invariables + var2d: list of 2d vars like 'USTAR', 'RHO', 'ZI', etc + var3d: list of 3d vars like 'U', 'V', 'W', 'ZTEMP' + vartime: list of time vars: 'BEGTIME', 'ENDTIME' + TOTAL_BEGTIME: begin time as a python datetime object + TOTAL_ENDTIME: end time as a python datetime object + BEGTIME: array of each timestep's begin time + ENDTIME: array of each timestep's end time + """ + + def __init__(self, fname, bit=32): + self.INT4TYPE = 'i4' + self.INT8TYPE = 'i8' + self.REAL4TYPE = 'f4' + self.REAL8TYPE = 'f8' + + self.parameters = [] + self.invar = ['ZFACEM', 'Z0', 'ILANDU', 'ELEV', 'XLAI'] + self.var3d = [] + self.var2d = [] + + if bit == 64: + self._recheadlen = 8 + self.INTTYPE = 'i4' + self.REALTYPE = 'f8' + else: + self._recheadlen = 4 + self.INTTYPE = 'i4' + self.REALTYPE = 'f4' + + self.fname = fname + self._f = open(fname, 'rb') + # Get file size + self._f.seek(0, 2) + self._filesize = self._f.tell() + + now_pos = 0 + + # # Read Header + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetFileDecl, add_to_dataset=True) + self.NCOM, now_pos = self._read_record(now_pos, 'i4') + self.parameters.append('NCOM') + self.COMMENT = [] + for i in range(self.NCOM): + comment, now_pos = self._read_record(now_pos, 'S') + self.COMMENT.append(comment) + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetControlParas, add_to_dataset=True) + self.NCELLFACE = self.NZ + 1 + self.TOTAL_BEGTIME = datetime( + self.IBYR, self.IBMO, self.IBDY, self.IBHR) + timedelta(seconds=int(self.IBSEC)) + self.TOTAL_ENDTIME = datetime( + self.IEYR, self.IEMO, self.IEDY, self.IEHR) + timedelta(seconds=int(self.IESEC)) + self.parameters.extend([n for n, t in _CalmetControlParas]) + self.parameters.extend(['NCELLFACE', 'TOTAL_BEGTIME', 'TOTAL_ENDTIME']) + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetCellFaceHeights, add_to_dataset=True) + + if self.NSSTA >= 1: + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetXYSurfStations1, add_to_dataset=True) + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetXYSurfStations2, add_to_dataset=True) + self.invar.extend(['XSSTA', 'YSSTA']) + if self.NUSTA >= 1: + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetXYUpperStations1, add_to_dataset=True) + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetXYUpperStations2, add_to_dataset=True) + self.invar.extend(['XUSTA', 'YUSTA']) + if self.NPSTA >= 1: + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetXYPrecStations1, add_to_dataset=True) + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetXYPrecStations2, add_to_dataset=True) + self.invar.extend(['XPSTA', 'YPSTA']) + + for rec in [_CalmetSurfRough, _CalmetLanduse, _CalmetElev, _CalmetLeafAreaIndex]: + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=rec, add_to_dataset=True) + + if self.NSSTA >= 1: + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetNearestSurfStationNo, add_to_dataset=True) + self.invar.append('NEARS') + + # # Read Data + # # Option LCALGRD + MET2D_LIST = _Calmet2DLabVarCALGRD if self.LCALGRD else _Calmet2DLabVar + # # Create arrays + UVW_jumpsize = 8 + 4 * \ + np.dtype(self.INTTYPE).itemsize + self.NX * \ + self.NY * np.dtype(self.REALTYPE).itemsize + # # Calc total record length (IRLG is only the hour number) + pos_mem = now_pos + tmpU, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetData, jumpsize=UVW_jumpsize) + if tmpU is not None: + NDATHRB, IBSEC, NDATHRE, IESEC = list(tmpU)[1:5] + first_begt = datetime.strptime( + str(NDATHRB), "%Y%j%H") + timedelta(seconds=int(IBSEC)) + first_endt = datetime.strptime( + str(NDATHRE), "%Y%j%H") + timedelta(seconds=int(IESEC)) + tdelta_sec = (first_endt - first_begt).total_seconds() + ttotal_sec = (self.TOTAL_ENDTIME - + self.TOTAL_BEGTIME).total_seconds() + self.rec_num = int(np.ceil(ttotal_sec / tdelta_sec)) + else: + self.rec_num = self.IRLG + now_pos = pos_mem + + self.U = np.zeros((self.rec_num, self.NZ, self.NY, + self.NX), dtype=self.REALTYPE) + self.V = np.zeros((self.rec_num, self.NZ, self.NY, + self.NX), dtype=self.REALTYPE) + self.var3d.extend(['U', 'V']) + if self.LCALGRD: + self.W = np.zeros((self.rec_num, self.NZ, self.NY, + self.NX), dtype=self.REALTYPE) + self.var3d.append('W') + if self.LCALGRD and self.IRTYPE == 1: + self.ZTEMP = np.zeros( + (self.rec_num, self.NZ, self.NY, self.NX), dtype=self.REALTYPE) + self.var3d.append('ZTEMP') + if self.IRTYPE == 1: + for labelname, varname in MET2D_LIST: + dt = 'i4' if varname.startswith('I') else 'f4' + self.__dict__[varname] = np.zeros( + (self.rec_num, self.NY, self.NX), dtype=dt) + self.var2d.append(varname) + + # # BEGTIME and ENDTIME are datetime arrays, with shape (rec_num, ) + self.BEGTIME = np.zeros((self.rec_num,), dtype='O') + self.ENDTIME = np.zeros((self.rec_num,), dtype='O') + self.vartime = ['BEGTIME', 'ENDTIME'] + + # # UVW + for t in range(self.rec_num): + try: + for k in range(self.NZ): + tmpU, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetData, jumpsize=UVW_jumpsize) + tmpV, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetData, jumpsize=UVW_jumpsize) + if tmpU is not None: + self.U[t, k] = tmpU[5] + if tmpV is not None: + self.V[t, k] = tmpV[5] + if self.LCALGRD: + tmpW, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetData, jumpsize=UVW_jumpsize) + if tmpW is not None: + self.W[t, k] = tmpW[5] + if tmpU is not None: + NDATHRB, IBSEC, NDATHRE, IESEC = list(tmpU)[1:5] + self.BEGTIME[t] = datetime.strptime( + str(NDATHRB), "%Y%j%H") + timedelta(seconds=int(IBSEC)) + self.ENDTIME[t] = datetime.strptime( + str(NDATHRE), "%Y%j%H") + timedelta(seconds=int(IESEC)) + # print self.BEGTIME[t], self.ENDTIME[t], now_pos + # # Temperature + if self.LCALGRD and self.IRTYPE == 1: + for k in range(self.NZ): + tmpZTEMP, now_pos = self._read_record( + now_pos, 'S', convert_type=_CalmetData, jumpsize=UVW_jumpsize) + if tmpZTEMP is not None: + self.ZTEMP[t, k] = tmpZTEMP[5] + # # 2D Met Fields + if self.IRTYPE == 1: + for labelname, varname in MET2D_LIST: + dt = _CalmetDataI if varname.startswith( + 'I') else _CalmetData + tmp, now_pos = self._read_record( + now_pos, 'S', convert_type=dt) + # self.__dict__['CLAB%s' % labelname] = tmp[0] + self.__dict__[varname][t] = tmp[5] + # # Useless Labels + # if tmpU is not None: + # self.CLABU = tmpU[0] + # if tmpV is not None: + # self.CLABV = tmpV[0] + # if self.LCALGRD: + # if tmpW is not None: + # self.CLABW = tmpW[0] + # if self.LCALGRD and self.IRTYPE == 1: + # if tmpZTEMP is not None: + # self.CLABT = tmpZTEMP[0] + except CalmetStopIteration: + sys.stderr.write( + "Warning: Calmet output file %s may be incomplete!\n" % self.fname) + break + + def _read_record(self, pos, dtype, shape=1, convert_type=None, add_to_dataset=False, jumpsize=0): + try: + recsize = np.memmap(self._f, dtype='i%d' % + self._recheadlen, mode='c', offset=pos, shape=(1,))[0] + except ValueError: + raise CalmetStopIteration + + if recsize == 0: + return None, pos + jumpsize + self._recheadlen * 2 + if convert_type is None: + if dtype == 'S': + dtype = 'S%d' % recsize + data = np.memmap(self._f, dtype=dtype, mode='c', + offset=pos + self._recheadlen, shape=shape) + if shape == 1: + data = data[0] + else: + c = 0 + new_cv_type = [] + for cp in convert_type: + name = cp[0] + tp = cp[1] + if name == 'IDUM': + name = 'IDUM%d' % c + c += 1 + if '%' in tp: + tp = tp % self.__dict__ + new_cv_type.append((name, tp)) + self._f.seek(pos + self._recheadlen) + data = np.fromfile(self._f, dtype=new_cv_type, count=1)[0] + if add_to_dataset is True: + for i, cp in enumerate(new_cv_type): + if not cp[0].startswith('IDUM') and not cp[0].startswith('CLAB'): + self.__dict__[cp[0]] = data[i] +# print pos + recsize + self._recheadlen * 2 + return data, pos + recsize + self._recheadlen * 2 diff --git a/calmetvas.md b/calmetvas.md new file mode 100644 index 0000000..77ccb29 --- /dev/null +++ b/calmetvas.md @@ -0,0 +1,22 @@ +## CALMET output + +Varname | type | description +--------|-------|-------- +ELEV | real array |Gridded field of terrain elevations for each grid cell +ILANDU | integer array | Gridded field of land use category for each grid cell +NDATHR | integer | Year, Julian day and hour in the form YYYYJJJHH (or YYJJJHH) +U | xyz | U-component (m/s) of the winds at each grid point +V | xyz | V-component (m/s) of the winds at each grid point +W | xyz | W-component (m/s) of the winds at each grid point +RHO |xyz | air density +TEMPK |xyz | temp (K) +QSW| xyz| Short-wave solar radiation +IRH |xyz | relative humidity +IPCODE | xyz | precipitation code +USTAR | xyz | Friction velocity (m/s) +WSTAR | xyz | Convective velocity scale (m/s) +RMM | xyz | Hourly precipitation rate (mm/hr) +IPGT | xyz | PGT stability class (integer array) +Zi | xyz | layer height above surfaced +El | xyz | ??? +ZFACEM | xyz | heights of cell faces diff --git a/maptoolkit.py b/maptoolkit.py new file mode 120000 index 0000000..9aad104 --- /dev/null +++ b/maptoolkit.py @@ -0,0 +1 @@ +Python/maptoolkit.py \ No newline at end of file diff --git a/plot_calmet.py b/plot_calmet.py new file mode 100644 index 0000000..a65168c --- /dev/null +++ b/plot_calmet.py @@ -0,0 +1,2 @@ +from calmettest import * +data = Calmet6Dataset('CALPUFF_OUT/CALMET/20191015/calmet.dat') diff --git a/unresp_analysis.yml b/unresp_analysis.yml index 346339d..8f8adbe 100644 --- a/unresp_analysis.yml +++ b/unresp_analysis.yml @@ -2,136 +2,14 @@ name: unresp_analysis channels: - conda-forge - defaults + - anaconda dependencies: - - _libgcc_mutex=0.1 - - astroid=2.3.* - - backcall=0.1.* - - basemap=1.2.* - - blas=1.0 - - bzip2=1.0.8 - - ca-certificates=2019.8.* - - certifi=2019.9.* - - cftime=1.0.3.4 - - curl=7.65.* - - cycler=0.10.* - - dbus=1.13.6 - - decorator=4.4.* - - eccodes=2.10.0 - - expat=2.2.* - - fontconfig=2.13.* - - freetype=2.9.* - - geos=3.6.* - - glib=2.56.2 - - gst-plugins-base=1.14* - - gstreamer=1.14.0 - - hdf4=4.2.* - - hdf5=1.10.* - - icu=58.2 - - intel-openmp=2019.4 - - ipython=7.8.* - - ipython_genutils=0.2.* - - isort=4.3.* - - jasper=1.900.* - - jedi=0.15.* - - jinja2=2.10.* - - jpeg=9c - - kiwisolver=1.1.* - - krb5=1.16.1 - - lazy-object-proxy=1.4.* - - libaec=1.0.* - - libcurl=7.65.* - - libedit=3.1.20181209 - - libffi=3.2.* - - libgcc-ng=9.1.* - - libgfortran=3.0.* - - libgfortran-ng=7.3.* - - libnetcdf=4.6.* - - libpng=1.6.* - - libssh2=1.8.* - - libstdcxx-ng=9.1.* - - libuuid=1.0.* - - libxcb=1.13 - - libxml2=2.9.* - - markupsafe=1.1.* - - matplotlib=3.1.* - - mccabe=0.6.* - - mkl=2019.4 - - mkl-service=2.3.* - - mkl_fft=1.0.14 - - mkl_random=1.1.* - - ncurses=6.1 - - netcdf4=1.4.2 - - numpy=1.17.* - - numpy-base=1.17.* - - openssl=1.1.* - - pandas=0.5.* - - pandas-profiling=1.4.* - - parso=0.5.* - - pcre=8.43 - - pexpect=4.7.* - - pickleshare=0.7.* - - pip=19.2.3 - - proj4=5.2.0 - - prompt_toolkit=2.0.10 - - ptyprocess=0.6.* - - pygments=2.4.* - - pylint=2.4.* - - pyparsing=2.4.* - - pyproj=1.9.* - - pyqt=5.9.* - - pyshp=2.1.* - - python=3.6.* - - python-dateutil=2.8.* - - python-eccodes=2.10.* - - pytz=2019.3 - - qt=5.9.* - - readline=7.0 - - scipy=1.3.* - - setuptools=41.4.0 - - sip=4.19.* - - six=1.12.* - - sqlite=3.30.* - - tk=8.6.* - - tornado=6.0.* - - traitlets=4.3.* - - typed-ast=1.4.* - - utm=0.4.* - - wcwidth=0.1.* - - wheel=0.33.* - - wrapt=1.11.* - - xz=5.2.* - - zlib=1.2.11 + - ipython + - matplotlib + - netcdf4 + - numpy + - pandas + - python=3.8 + - pip - pip: - - absl-py==0.7.* - - astor==0.8.* - - chardet==3.0.* - - cvxpy==1.0.* - - dill==0.3.* - - ecos==2.0.7.post1 - - fancyimpute==0.5.* - - future==0.17.* - - gast==0.2.* - - gmplot==1.2.* - - google-pasta==0.1.* - - grpcio==1.2.* - - h5py==2.9.* - - idna==2.8 - - joblib==0.13.* - - keras==2.2.* - - keras-applications==1.0.* - - keras-preprocessing==1.1.* - - knnimpute==0.1.* - - markdown==3.1.* - - multiprocess==0.70.* - - osqp==0.5.* - - protobuf==3.9.* - - pyyaml==5.1.* - - requests==2.1.* - - scikit-learn==0.1.* - - scs==2.1.1-2 - - tensorboard==1.14.* - - tensorflow==1.14.* - - tensorflow-estimator==1.14.* - - termcolor==1.1.* - - urllib3==1.4.* - - werkzeug==0.15.* + - scikit-learn