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DavidVFiumanoDavidVFiumano
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Add sizes to scatter plots (#289)
* Added a size feature. Not sure if it works yet, still need to add tests. * Added a few tests for the new sizes feature. * Forgot to add this file last commit. * Improved scatter_size.py example * Made some changes addressing Kushal's comments. * fixed an error caused by me forgetting to remove a cell in one of the notebooks * scattter plot added * updated to snake_case * updated to fixed some missing dependencies and remove unecessary code in the notebooks
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‎examples/desktop/scatter/scatter_size.py

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"""
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Scatter Plot
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============
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Example showing point size change for scatter plot.
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"""
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# test_example = true
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import numpy as np
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import fastplotlib as fpl
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# grid with 2 rows and 3 columns
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grid_shape = (2,1)
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# pan-zoom controllers for each view
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# views are synced if they have the
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# same controller ID
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controllers = [
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[0],
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[0]
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]
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# you can give string names for each subplot within the gridplot
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names = [
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["scalar_size"],
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["array_size"]
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]
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# Create the grid plot
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plot = fpl.GridPlot(
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shape=grid_shape,
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controllers=controllers,
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names=names,
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size=(1000, 1000)
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)
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# get y_values using sin function
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angles = np.arange(0, 20*np.pi+0.001, np.pi / 20)
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y_values = 30*np.sin(angles) # 1 thousand points
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x_values = np.array([x for x in range(len(y_values))], dtype=np.float32)
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data = np.column_stack([x_values, y_values])
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plot["scalar_size"].add_scatter(data=data, sizes=5, colors="blue") # add a set of scalar sizes
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non_scalar_sizes = np.abs((y_values / np.pi)) # ensure minimum size of 5
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plot["array_size"].add_scatter(data=data, sizes=non_scalar_sizes, colors="red")
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for graph in plot:
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graph.auto_scale(maintain_aspect=True)
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plot.show()
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if __name__ == "__main__":
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print(__doc__)
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fpl.run()

‎examples/desktop/screenshots/scatter_size.png

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‎examples/notebooks/scatter_sizes_animation.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from time import time\n",
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"\n",
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"import numpy as np\n",
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"import fastplotlib as fpl\n",
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"\n",
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"plot = fpl.Plot()\n",
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"\n",
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"points = np.array([[-1,0,1],[-1,0,1]], dtype=np.float32).swapaxes(0,1)\n",
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"size_delta_scales = np.array([10, 40, 100], dtype=np.float32)\n",
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"min_sizes = 6\n",
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"\n",
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"def update_positions():\n",
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" current_time = time()\n",
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" newPositions = points + np.sin(((current_time / 4) % 1)*np.pi)\n",
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" plot.graphics[0].data = newPositions\n",
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" plot.camera.width = 4*np.max(newPositions[0,:])\n",
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" plot.camera.height = 4*np.max(newPositions[1,:])\n",
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"\n",
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"def update_sizes():\n",
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" current_time = time()\n",
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" sin_sample = np.sin(((current_time / 4) % 1)*np.pi)\n",
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" size_delta = sin_sample*size_delta_scales\n",
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" plot.graphics[0].sizes = min_sizes + size_delta\n",
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"\n",
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"points = np.array([[0,0], \n",
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" [1,1], \n",
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" [2,2]])\n",
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"scatter = plot.add_scatter(points, colors=[\"red\", \"green\", \"blue\"], sizes=12)\n",
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"plot.add_animations(update_positions, update_sizes)\n",
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"plot.show(autoscale=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "fastplotlib-dev",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎examples/notebooks/scatter_sizes_grid.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"Scatter Plot\n",
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"============\n",
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"Example showing point size change for scatter plot.\n",
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"\"\"\"\n",
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"\n",
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"# test_example = true\n",
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"import numpy as np\n",
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"import fastplotlib as fpl\n",
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"\n",
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"# grid with 2 rows and 3 columns\n",
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"grid_shape = (2,1)\n",
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"\n",
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"# pan-zoom controllers for each view\n",
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"# views are synced if they have the \n",
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"# same controller ID\n",
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"controllers = [\n",
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" [0],\n",
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" [0]\n",
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"]\n",
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"\n",
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"\n",
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"# you can give string names for each subplot within the gridplot\n",
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"names = [\n",
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" [\"scalar_size\"],\n",
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" [\"array_size\"]\n",
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"]\n",
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"\n",
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"# Create the grid plot\n",
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"plot = fpl.GridPlot(\n",
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" shape=grid_shape,\n",
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" controllers=controllers,\n",
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" names=names,\n",
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" size=(1000, 1000)\n",
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")\n",
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"\n",
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"# get y_values using sin function\n",
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"angles = np.arange(0, 20*np.pi+0.001, np.pi / 20)\n",
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"y_values = 30*np.sin(angles) # 1 thousand points\n",
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"x_values = np.array([x for x in range(len(y_values))], dtype=np.float32)\n",
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"\n",
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"data = np.column_stack([x_values, y_values])\n",
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"\n",
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"plot[\"scalar_size\"].add_scatter(data=data, sizes=5, colors=\"blue\") # add a set of scalar sizes\n",
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"\n",
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"non_scalar_sizes = np.abs((y_values / np.pi)) # ensure minimum size of 5\n",
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"plot[\"array_size\"].add_scatter(data=data, sizes=non_scalar_sizes, colors=\"red\")\n",
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"\n",
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"for graph in plot:\n",
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" graph.auto_scale(maintain_aspect=True)\n",
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"\n",
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"plot.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "fastplotlib-dev",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎fastplotlib/graphics/_features/__init__.py

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from ._colors import ColorFeature, CmapFeature, ImageCmapFeature, HeatmapCmapFeature
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from ._data import PointsDataFeature, ImageDataFeature, HeatmapDataFeature
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from ._sizes import PointsSizesFeature
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from ._present import PresentFeature
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from ._thickness import ThicknessFeature
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from ._base import GraphicFeature, GraphicFeatureIndexable, FeatureEvent, to_gpu_supported_dtype
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"ImageCmapFeature",
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"HeatmapCmapFeature",
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"PointsDataFeature",
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"PointsSizesFeature",
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"ImageDataFeature",
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"HeatmapDataFeature",
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"PresentFeature",

‎fastplotlib/graphics/_features/_sizes.py

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from typing import Any
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import numpy as np
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import pygfx
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from ._base import (
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GraphicFeatureIndexable,
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cleanup_slice,
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FeatureEvent,
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to_gpu_supported_dtype,
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cleanup_array_slice,
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)
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class PointsSizesFeature(GraphicFeatureIndexable):
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"""
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Access to the vertex buffer data shown in the graphic.
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Supports fancy indexing if the data array also supports it.
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"""
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def __init__(self, parent, sizes: Any, collection_index: int = None):
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sizes = self._fix_sizes(sizes, parent)
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super(PointsSizesFeature, self).__init__(
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parent, sizes, collection_index=collection_index
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)
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@property
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def buffer(self) -> pygfx.Buffer:
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return self._parent.world_object.geometry.sizes
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def __getitem__(self, item):
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return self.buffer.data[item]
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def _fix_sizes(self, sizes, parent):
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graphic_type = parent.__class__.__name__
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n_datapoints = parent.data().shape[0]
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if not isinstance(sizes, (list, tuple, np.ndarray)):
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sizes = np.full(n_datapoints, sizes, dtype=np.float32) # force it into a float to avoid weird gpu errors
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elif not isinstance(sizes, np.ndarray): # if it's not a ndarray already, make it one
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sizes = np.array(sizes, dtype=np.float32) # read it in as a numpy.float32
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if (sizes.ndim != 1) or (sizes.size != parent.data().shape[0]):
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raise ValueError(
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f"sequence of `sizes` must be 1 dimensional with "
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f"the same length as the number of datapoints"
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)
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sizes = to_gpu_supported_dtype(sizes)
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if any(s < 0 for s in sizes):
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raise ValueError("All sizes must be positive numbers greater than or equal to 0.0.")
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if sizes.ndim == 1:
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if graphic_type == "ScatterGraphic":
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sizes = np.array(sizes)
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else:
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raise ValueError(f"Sizes must be an array of shape (n,) where n == the number of data points provided.\
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Received shape={sizes.shape}.")
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return np.array(sizes)
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def __setitem__(self, key, value):
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if isinstance(key, np.ndarray):
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# make sure 1D array of int or boolean
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key = cleanup_array_slice(key, self._upper_bound)
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# put sizes into right shape if they're only indexing datapoints
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if isinstance(key, (slice, int, np.ndarray, np.integer)):
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value = self._fix_sizes(value, self._parent)
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# otherwise assume that they have the right shape
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# numpy will throw errors if it can't broadcast
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if value.size != self.buffer.data[key].size:
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raise ValueError(f"{value.size} is not equal to buffer size {self.buffer.data[key].size}.\
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If you want to set size to a non-scalar value, make sure it's the right length!")
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self.buffer.data[key] = value
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self._update_range(key)
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# avoid creating dicts constantly if there are no events to handle
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if len(self._event_handlers) > 0:
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self._feature_changed(key, value)
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def _update_range(self, key):
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self._update_range_indices(key)
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def _feature_changed(self, key, new_data):
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if key is not None:
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key = cleanup_slice(key, self._upper_bound)
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if isinstance(key, (int, np.integer)):
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indices = [key]
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elif isinstance(key, slice):
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indices = range(key.start, key.stop, key.step)
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elif isinstance(key, np.ndarray):
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indices = key
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elif key is None:
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indices = None
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pick_info = {
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"index": indices,
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"collection-index": self._collection_index,
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"world_object": self._parent.world_object,
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"new_data": new_data,
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}
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event_data = FeatureEvent(type="sizes", pick_info=pick_info)
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self._call_event_handlers(event_data)

‎fastplotlib/graphics/scatter.py

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from ..utils import parse_cmap_values
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from ._base import Graphic
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from ._features import PointsDataFeature, ColorFeature, CmapFeature
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from ._features import PointsDataFeature, ColorFeature, CmapFeature, PointsSizesFeature
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class ScatterGraphic(Graphic):
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feature_events = ("data", "colors", "cmap", "present")
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feature_events = ("data", "sizes", "colors", "cmap", "present")
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def __init__(
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self,
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data: np.ndarray,
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sizes: Union[int, np.ndarray, list] = 1,
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sizes: Union[int, float, np.ndarray, list] = 1,
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colors: np.ndarray = "w",
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alpha: float = 1.0,
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cmap: str = None,
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self, self.colors(), cmap_name=cmap, cmap_values=cmap_values
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)
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89-
if isinstance(sizes, int):
90-
sizes = np.full(self.data().shape[0], sizes, dtype=np.float32)
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elif isinstance(sizes, np.ndarray):
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if (sizes.ndim != 1) or (sizes.size != self.data().shape[0]):
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raise ValueError(
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f"numpy array of `sizes` must be 1 dimensional with "
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f"the same length as the number of datapoints"
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)
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elif isinstance(sizes, list):
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if len(sizes) != self.data().shape[0]:
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raise ValueError(
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"list of `sizes` must have the same length as the number of datapoints"
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)
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self.sizes = PointsSizesFeature(self, sizes)
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super(ScatterGraphic, self).__init__(*args, **kwargs)
10491

10592
world_object = pygfx.Points(
106-
pygfx.Geometry(positions=self.data(), sizes=sizes, colors=self.colors()),
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pygfx.Geometry(positions=self.data(), sizes=self.sizes(), colors=self.colors()),
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material=pygfx.PointsMaterial(vertex_colors=True, vertex_sizes=True),
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)
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