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LinearSelector #168

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Merged
merged 18 commits into from
Apr 18, 2023
Merged

LinearSelector #168

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e11ab3f
better line slider, works
kushalkolar Apr 17, 2023
718eee7
lineslider events works
kushalkolar Apr 17, 2023
bed5a95
docstrings
kushalkolar Apr 17, 2023
4b0f7a9
LinearRegionSelector (#164)
kushalkolar Apr 18, 2023
4680b44
better line slider, works
kushalkolar Apr 17, 2023
d7861df
lineslider events works
kushalkolar Apr 17, 2023
c0a6103
docstrings
kushalkolar Apr 17, 2023
b9902d9
rename _linear to _linear_region
kushalkolar Apr 18, 2023
4cd3b35
rename
kushalkolar Apr 18, 2023
853acfe
move line_slider to selectors/_linear
kushalkolar Apr 18, 2023
03125dd
rename LineSlider -> LinearSelector
kushalkolar Apr 18, 2023
6728939
can sync selectors, made add_linear_selector()
kushalkolar Apr 18, 2023
bc9af00
merge conflicts
kushalkolar Apr 18, 2023
a04a687
can sync selectors, can remove them from synchronizer too
kushalkolar Apr 18, 2023
4dd9f9e
works for line collection
kushalkolar Apr 18, 2023
1c5ca1b
docstrings
kushalkolar Apr 18, 2023
e48cba8
update nb
kushalkolar Apr 18, 2023
3969ec0
update nb example
kushalkolar Apr 18, 2023
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301 changes: 301 additions & 0 deletions 301 examples/linear_region_selector.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "40bf515f-7ca3-4f16-8ec9-31076e8d4bde",
"metadata": {},
"source": [
"# `LinearRegionSelector` with single lines"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41f4e1d0-9ae9-4e59-9883-d9339d985afe",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import fastplotlib as fpl\n",
"import numpy as np\n",
"\n",
"\n",
"gp = fpl.GridPlot((2, 2))\n",
"\n",
"# preallocated size for zoomed data\n",
"zoomed_prealloc = 1_000\n",
"\n",
"# data to plot\n",
"xs = np.linspace(0, 100, 1_000)\n",
"sine = np.sin(xs) * 20\n",
"\n",
"# make sine along x axis\n",
"sine_graphic_x = gp[0, 0].add_line(sine)\n",
"\n",
"# just something that looks different for line along y-axis\n",
"sine_y = sine\n",
"sine_y[sine_y > 0] = 0\n",
"\n",
"# sine along y axis\n",
"sine_graphic_y = gp[0, 1].add_line(np.column_stack([sine_y, xs]))\n",
"\n",
"# offset the position of the graphic to demonstrate `get_selected_data()` later\n",
"sine_graphic_y.position.set_x(50)\n",
"sine_graphic_y.position.set_y(50)\n",
"\n",
"# add linear selectors\n",
"ls_x = sine_graphic_x.add_linear_region_selector() # default axis is \"x\"\n",
"ls_y = sine_graphic_y.add_linear_region_selector(axis=\"y\")\n",
"\n",
"# preallocate array for storing zoomed in data\n",
"zoomed_init = np.column_stack([np.arange(zoomed_prealloc), np.random.rand(zoomed_prealloc)])\n",
"\n",
"# make line graphics for displaying zoomed data\n",
"zoomed_x = gp[1, 0].add_line(zoomed_init)\n",
"zoomed_y = gp[1, 1].add_line(zoomed_init)\n",
"\n",
"\n",
"def interpolate(subdata: np.ndarray, axis: int):\n",
" \"\"\"1D interpolation to display within the preallocated data array\"\"\"\n",
" x = np.arange(0, zoomed_prealloc)\n",
" xp = np.linspace(0, zoomed_prealloc, subdata.shape[0])\n",
" \n",
" # interpolate to preallocated size\n",
" return np.interp(x, xp, fp=subdata[:, axis]) # use the y-values\n",
"\n",
"\n",
"def set_zoom_x(ev):\n",
" \"\"\"sets zoomed x selector data\"\"\"\n",
" selected_data = ev.pick_info[\"selected_data\"]\n",
" zoomed_x.data = interpolate(selected_data, axis=1) # use the y-values\n",
" gp[1, 0].auto_scale()\n",
"\n",
"\n",
"def set_zoom_y(ev):\n",
" \"\"\"sets zoomed y selector data\"\"\"\n",
" selected_data = ev.pick_info[\"selected_data\"]\n",
" zoomed_y.data = -interpolate(selected_data, axis=0) # use the x-values\n",
" gp[1, 1].auto_scale()\n",
"\n",
"\n",
"# update zoomed plots when bounds change\n",
"ls_x.bounds.add_event_handler(set_zoom_x)\n",
"ls_y.bounds.add_event_handler(set_zoom_y)\n",
"\n",
"gp.show()"
]
},
{
"cell_type": "markdown",
"id": "66b1c599-42c0-4223-b33e-37c1ef077204",
"metadata": {},
"source": [
"### On the x-axis we have a 1-1 mapping from the data that we have passed and the line geometry positions. So the `bounds` min max corresponds directly to the data indices."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b26a37d-aa1d-478e-ad77-99f68a2b7d0c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ls_x.bounds()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2be060c-8f87-4b5c-8262-619768f6e6af",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ls_x.get_selected_indices()"
]
},
{
"cell_type": "markdown",
"id": "d1bef432-d764-4841-bd6d-9b9e4c86ff62",
"metadata": {},
"source": [
"### However, for the y-axis line we have passed a 2D array where we've used a linspace, so there is not a 1-1 mapping from the data to the line geometry positions. Use `get_selected_indices()` to get the indices of the data bounded by the current selection. In addition the position of the Graphic is not `(0, 0)`. You must use `get_selected_indices()` whenever you want the indices of the selected data."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c370d6d7-d92a-4680-8bf0-2f9d541028be",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ls_y.bounds()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cdf351e1-63a2-4f5a-8199-8ac3f70909c1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ls_y.get_selected_indices()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fd608ad-9732-4f50-9d43-8630603c86d0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import fastplotlib as fpl\n",
"import numpy as np\n",
"\n",
"# data to plot\n",
"xs = np.linspace(0, 100, 1_000)\n",
"sine = np.sin(xs) * 20\n",
"cosine = np.cos(xs) * 20\n",
"\n",
"plot = fpl.GridPlot((5, 1))\n",
"\n",
"# sines and cosines\n",
"sines = [sine] * 2\n",
"cosines = [cosine] * 2\n",
"\n",
"# make line stack\n",
"line_stack = plot[0, 0].add_line_stack(sines + cosines, separation=50)\n",
"\n",
"# make selector\n",
"selector = line_stack.add_linear_region_selector()\n",
"\n",
"# populate subplots with preallocated graphics\n",
"for i, subplot in enumerate(plot):\n",
" if i == 0:\n",
" # skip the first one\n",
" continue\n",
" # make line graphics for displaying zoomed data\n",
" subplot.add_line(zoomed_init, name=\"zoomed\")\n",
"\n",
"\n",
"def update_zoomed_subplots(ev):\n",
" \"\"\"update the zoomed subplots\"\"\"\n",
" zoomed_data = selector.get_selected_data()\n",
" \n",
" for i in range(len(zoomed_data)):\n",
" data = interpolate(zoomed_data[i], axis=1)\n",
" plot[i + 1, 0][\"zoomed\"].data = data\n",
" plot[i + 1, 0].auto_scale()\n",
"\n",
"\n",
"selector.bounds.add_event_handler(update_zoomed_subplots)\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"id": "63acd2b6-958e-458d-bf01-903037644cfe",
"metadata": {},
"source": [
"# Large line stack with selector"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20e53223-6ccd-4145-bf67-32eb409d3b0a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import fastplotlib as fpl\n",
"import numpy as np\n",
"\n",
"# data to plot\n",
"xs = np.linspace(0, 250, 10_000)\n",
"sine = np.sin(xs) * 20\n",
"cosine = np.cos(xs) * 20\n",
"\n",
"plot = fpl.GridPlot((1, 2))\n",
"\n",
"# sines and cosines\n",
"sines = [sine] * 1_00\n",
"cosines = [cosine] * 1_00\n",
"\n",
"# make line stack\n",
"line_stack = plot[0, 0].add_line_stack(sines + cosines, separation=50)\n",
"\n",
"# make selector\n",
"stack_selector = line_stack.add_linear_region_selector(padding=200)\n",
"\n",
"zoomed_line_stack = plot[0, 1].add_line_stack([zoomed_init] * 2_000, separation=50, name=\"zoomed\")\n",
" \n",
"def update_zoomed_stack(ev):\n",
" \"\"\"update the zoomed subplots\"\"\"\n",
" zoomed_data = stack_selector.get_selected_data()\n",
" \n",
" for i in range(len(zoomed_data)):\n",
" data = interpolate(zoomed_data[i], axis=1)\n",
" zoomed_line_stack.graphics[i].data = data\n",
" \n",
" plot[0, 1].auto_scale()\n",
"\n",
"\n",
"stack_selector.bounds.add_event_handler(update_zoomed_stack)\n",
"plot.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3fa61ffd-43d5-42d0-b3e1-5541f58185cd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"plot[0, 0].auto_scale()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80e276ba-23b3-43d0-9e0c-86acab79ac67",
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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