Skip to content

Interactivity attempt 4 #93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
kushalkolar merged 13 commits into from
Dec 29, 2022
Merged

Interactivity attempt 4 #93

Show file tree
Hide file tree
Changes from 9 commits
Commits
Show all changes
13 commits
Select commit Hold shift + click to select a range
6f63f64
beginning base logic for interactivity impl
Dec 12, 2022
2ccb19b
further changes and ideas
Dec 12, 2022
835197a
oops
Dec 15, 2022
0dd74aa
accidentally deleted heatmap file
Dec 19, 2022
cfa0aa9
rebase
Dec 28, 2022
a39ceaa
further rebase
Dec 28, 2022
557c611
single contour example
Dec 28, 2022
76a82f1
interactivity modification
Dec 28, 2022
af8cd2e
refactoring classes
Dec 28, 2022
0517d5c
single contour updates/fixes
Dec 29, 2022
e2eb1c8
line collection, fixing feature event names
Dec 29, 2022
fa208ca
minor changes
Dec 29, 2022
118b3a8
interactivity works, image -> lines -> lines with colors
kushalkolar Dec 29, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
391 changes: 391 additions & 0 deletions examples/event_handler.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,391 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a0b458c1-53c6-43d9-a72a-41f51bfe493d",
"metadata": {},
"source": [
"### notebook for learning event handler system"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "638b6a65-6d78-459c-ac88-35312233d22a",
"metadata": {},
"outputs": [],
"source": [
"from mesmerize_core import *\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"from fastplotlib import GridPlot, Image, Plot, Line, Heatmap\n",
"from scipy.spatial import distance\n",
"from ipywidgets.widgets import IntSlider, VBox\n",
"import pygfx"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "80938adc-1775-4751-94dd-89fb94ed673a",
"metadata": {},
"outputs": [],
"source": [
"class Contour_Selection():\n",
" def __init__(\n",
" self,\n",
" gp: GridPlot,\n",
" coms,\n",
" ):\n",
" self.gp = gp\n",
" self.heatmap = self.gp.subplots[0, 1].scene.children[0]\n",
" self.image = None\n",
" self._contour_index = None \n",
" \n",
" for child in self.gp.subplots[0, 0].scene.children:\n",
" if isinstance(child, pygfx.Image):\n",
" self.image = child\n",
" break;\n",
" if self.image == None:\n",
" raise ValueError(\"No image found!\")\n",
" self.coms = np.array(coms)\n",
" \n",
" self.image.add_event_handler(self.event_handler, \"click\")\n",
" \n",
" # first need to add event handler for when contour is clicked on\n",
" # should also trigger highlighting in heatmap\n",
" def event_handler(self, event):\n",
" if self._contour_index is not None:\n",
" self.remove_highlight()\n",
" self.add_highlight(event)\n",
" else:\n",
" self.add_highlight(event)\n",
" \n",
" def add_highlight(self, event):\n",
" click_location = np.array(event.pick_info[\"index\"])\n",
" self._contour_index = np.linalg.norm((self.coms - click_location), axis=1).argsort()[0] + 1\n",
" line = self.gp.subplots[0, 0].scene.children[self._contour_index]\n",
" line.geometry.colors.data[:] = np.array([1.0, 1.0, 1.0, 1.0]) \n",
" line.geometry.colors.update_range()\n",
" #self.heatmap.add_highlight(self._contour_index)\n",
" \n",
" def remove_highlight(self):\n",
" # change color of highlighted index back to normal\n",
" line = self.gp.subplots[0, 0].scene.children[self._contour_index]\n",
" line.geometry.colors.data[:] = np.array([1., 0., 0., 0.7]) \n",
" line.geometry.colors.update_range()\n",
" # for h in self.heatmap._highlights:\n",
" # self.heatmap.remove_highlight(h)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7b3129e6-5d82-4f39-b887-e584421c3a74",
"metadata": {},
"outputs": [],
"source": [
"set_parent_raw_data_path(\"/home/kushal/caiman_data/\")\n",
"\n",
"batch_path = \"/home/clewis7/caiman_data/cnmf_practice/batch.pickle\"\n",
"\n",
"movie_path = \"/home/kushal/caiman_data/example_movies/Sue_2x_3000_40_-46.tif\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "776c6dd8-cf07-47ee-bcc4-5fe84bc030f5",
"metadata": {},
"outputs": [],
"source": [
"df = load_batch(batch_path)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b22fd79b-5a7a-48fb-898c-4d3f3a34c118",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>algo</th>\n",
" <th>item_name</th>\n",
" <th>input_movie_path</th>\n",
" <th>params</th>\n",
" <th>outputs</th>\n",
" <th>comments</th>\n",
" <th>uuid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>mcorr</td>\n",
" <td>my_movie</td>\n",
" <td>example_movies/Sue_2x_3000_40_-46.tif</td>\n",
" <td>{'main': {'max_shifts': (24, 24), 'strides': (...</td>\n",
" <td>{'mean-projection-path': 1ed8feb3-9fc8-4a78-8f...</td>\n",
" <td>None</td>\n",
" <td>1ed8feb3-9fc8-4a78-8f6d-164620822016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>cnmf</td>\n",
" <td>my_movie</td>\n",
" <td>1ed8feb3-9fc8-4a78-8f6d-164620822016/1ed8feb3-...</td>\n",
" <td>{'main': {'fr': 30, 'p': 1, 'nb': 2, 'merge_th...</td>\n",
" <td>{'mean-projection-path': 5f4e3e27-ac1f-4ede-90...</td>\n",
" <td>None</td>\n",
" <td>5f4e3e27-ac1f-4ede-903b-be43bd81fddc</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" algo item_name input_movie_path \\\n",
"0 mcorr my_movie example_movies/Sue_2x_3000_40_-46.tif \n",
"1 cnmf my_movie 1ed8feb3-9fc8-4a78-8f6d-164620822016/1ed8feb3-... \n",
"\n",
" params \\\n",
"0 {'main': {'max_shifts': (24, 24), 'strides': (... \n",
"1 {'main': {'fr': 30, 'p': 1, 'nb': 2, 'merge_th... \n",
"\n",
" outputs comments \\\n",
"0 {'mean-projection-path': 1ed8feb3-9fc8-4a78-8f... None \n",
"1 {'mean-projection-path': 5f4e3e27-ac1f-4ede-90... None \n",
"\n",
" uuid \n",
"0 1ed8feb3-9fc8-4a78-8f6d-164620822016 \n",
"1 5f4e3e27-ac1f-4ede-903b-be43bd81fddc "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "03045957-837e-49e3-83aa-3e069af977a1",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b283ef0f3084d828f80fd3a6cb25413",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"RFBOutputContext()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8b104ac5f10e4d53866953a4401593df",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(JupyterWgpuCanvas(), IntSlider(value=0, max=2999)))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gp = GridPlot(shape=(1,2))\n",
"\n",
"contours, coms = df.iloc[-1].cnmf.get_contours()\n",
"movie = df.iloc[-1].cnmf.get_input_memmap()\n",
"temporal = df.iloc[-1].cnmf.get_temporal()\n",
"\n",
"contour_graphic = Image(movie[0].T, cmap=\"gnuplot2\")\n",
"heatmap = Heatmap(data=temporal[:,0:1000], cmap=\"jet\")\n",
"\n",
"slider = IntSlider(value=0, min=0, max=movie.shape[0] - 1, step=1)\n",
"\n",
"gp.subplots[0,0].add_graphic(contour_graphic)\n",
"gp.subplots[0,1].add_graphic(heatmap)\n",
"\n",
"for coor in contours:\n",
" # line data has to be 3D\n",
" zs = np.ones(coor.shape[0]) # this will place it above the image graphic\n",
" c3d = [coor[:, 0], coor[:, 1], zs]\n",
" coors_3d = np.dstack(c3d)[0]\n",
"\n",
" # make all the lines red, [R, G, B, A] array\n",
" colors = np.vstack([[1., 0., 0., 0.7]] * coors_3d.shape[0])\n",
" line_graphic = Line(data=coors_3d, colors=colors, zlevel=1)\n",
" gp.subplots[0, 0].add_graphic(line_graphic)\n",
"\n",
"previous_slider_value = 0\n",
"def update_frame(): \n",
" if slider.value == previous_slider_value:\n",
" return\n",
" contour_graphic.update_data(data=movie[slider.value].T)\n",
"\n",
"gp.add_animations([update_frame])\n",
"\n",
"VBox([gp.show(), slider])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2bf4a1ea-3559-4846-bc32-4dddaca2d470",
"metadata": {},
"outputs": [],
"source": [
"contour_selection = Contour_Selection(gp=gp, coms=coms)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5ecdbdea-4e77-462f-a18d-5ed9b45faada",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(155, 3000)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temporal.shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "51b3feea-af91-4158-97a2-8dbadd2480b5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(coms[0])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3b2011f4-046b-4396-a592-81a5128d2482",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([7.12861818, 9.84114483])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coms[0]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c5791bf4-a116-4093-bce1-eee7bc25221c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<fastplotlib.subplot.Subplot at 0x7f5b5d218430>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gp.subplots[0, 1]"
]
},
{
"cell_type": "markdown",
"id": "ae9cce16-370f-44d2-b532-dc442cce379d",
"metadata": {},
"source": [
"next steps:\n",
" clicking on a contour should highlight it and the heatmap row"
]
}
],
"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": "3.9.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Loading