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Interactivity #84

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Closed
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Interactivity #84

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67cc06c
beginning base logic for interactivity impl
clewis7 Dec 12, 2022
68a89d7
further changes and ideas
clewis7 Dec 12, 2022
24cf116
wip
clewis7 Dec 14, 2022
761712f
grrrrr
clewis7 Dec 15, 2022
1cc4da6
oops
clewis7 Dec 15, 2022
663db25
reorg
clewis7 Dec 15, 2022
d4d877c
small changes and reorg
clewis7 Dec 15, 2022
99c580a
updates to line, works w previous example
clewis7 Dec 17, 2022
ffcc534
small changes
clewis7 Dec 18, 2022
959eb62
git is so agitating sometimes
clewis7 Dec 18, 2022
7a5a986
fixing z_position
clewis7 Dec 19, 2022
7454ae0
implementing reset_feature
clewis7 Dec 19, 2022
fdf6685
blah
clewis7 Dec 19, 2022
82bfce1
accidentally deleted heatmap file
clewis7 Dec 19, 2022
8330756
Delete event_linecollection.ipynb
clewis7 Dec 19, 2022
16ad0df
Delete event_single_contour.ipynb
clewis7 Dec 19, 2022
6338031
ugly but functional
clewis7 Dec 20, 2022
e78ec24
LineCollection has @world_object, fix float64 bug in LineGraphic
kushalkolar Dec 21, 2022
e51c2f2
new interactivity using graphic_attr
clewis7 Dec 24, 2022
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beginning base logic for interactivity impl
  • Loading branch information
@clewis7
clewis7 committed Dec 23, 2022
commit 67cc06c312b9d9e0449f47d36a091e5713ef4643
391 changes: 391 additions & 0 deletions 391 examples/event_handler.ipynb
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{
"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
}
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