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Merge pull request #51 from kushalkolar/high-level-widgets
ImageWidget
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‎examples/image_widget.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": 1,
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"id": "04f453ca-d0bc-411f-b2a6-d38294dd0a26",
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastplotlib.widgets import ImageWidget\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e933771b-f172-4fa9-b2f8-129723efb808",
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"metadata": {},
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"source": [
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"# Single image sequence"
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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": 2,
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"id": "ea87f9a6-437f-41f6-8739-c957fb04bdbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"a = np.random.rand(500, 512, 512)"
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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": 3,
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"id": "8b7a6066-ff69-4bee-bae6-160fb4038393",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6d575ba7671047ca88c36606344714fa",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"RFBOutputContext()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"iw = ImageWidget(\n",
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" data=a, \n",
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" slider_dims=[\"t\"],\n",
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" vmin_vmax_sliders=True,\n",
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" cmap=\"gnuplot2\"\n",
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")"
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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": 4,
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"id": "3d4cb44e-2c71-4bff-aeed-b2129f34d724",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8de187407b7746168c8d20a428d8712e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(JupyterWgpuCanvas(), IntSlider(value=0, description='dimension: t', max=499), FloatRangeSlider(…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"iw.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9908103c-c35c-4f33-ada1-0fc357c3fd5e",
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"metadata": {},
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"source": [
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"### Play with setting different window functions\n",
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"\n",
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"These can also be given as kwargs to `ImageWidget` during instantiation"
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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": 5,
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"id": "f278b26a-1b71-4e76-9cc7-efaddbd7b122",
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"metadata": {},
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"outputs": [],
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"source": [
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"# must be in the form of {dim: (func, window_size)}\n",
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"iw.window_funcs = {\"t\": (np.mean, 13)}"
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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": 6,
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"id": "cb4d4b7c-919f-41c0-b1cc-b4496473d760",
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"metadata": {},
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"outputs": [],
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"source": [
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"# change the winow size\n",
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"iw.window_funcs[\"t\"].window_size = 23"
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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": 7,
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"id": "2eea6432-4d38-4d42-ab75-f6aa1bab36f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# change the function\n",
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"iw.window_funcs[\"t\"].func = np.max"
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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": 8,
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"id": "afa2436f-2741-49d6-87f6-7a91a343fe0e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# or set it again\n",
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"iw.window_funcs = {\"t\": (np.min, 11)}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aca22179-1b1f-4c51-97bf-ce2d7044e451",
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"metadata": {},
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"source": [
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"# Gridplot of txy data"
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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": 10,
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"id": "882162eb-c873-42df-a945-d5e05ad141c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"dims = (100, 512, 512)\n",
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"data = [np.random.rand(*dims) for i in range(4)]"
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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": 12,
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"id": "bf9f92b6-38ad-4d78-b88c-a32d473b6462",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "005bcbc7755748cfaf0644e28beb3b0e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"RFBOutputContext()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"iw = ImageWidget(\n",
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" data=data, \n",
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" slider_dims=[\"t\"], \n",
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" # dims_order=\"txy\", # you can set this manually if dim order is not the usual\n",
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" vmin_vmax_sliders=True,\n",
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" names=[\"zero\", \"one\", \"two\", \"three\"],\n",
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" window_funcs={\"t\": (np.mean, 5)},\n",
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" cmap=\"gnuplot2\", \n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0721dc40-677e-431d-94c6-da59606199cb",
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"metadata": {},
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"source": [
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"### pan-zoom controllers are all synced in a `ImageWidget`"
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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": 13,
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"id": "403dde31-981a-46fb-b005-1bcef19c4f2c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2b0a10be5d5b43b5a08f51a9d8f9b1dc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(JupyterWgpuCanvas(), IntSlider(value=0, description='dimension: t', max=99), FloatRangeSlider(v…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"iw.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82545214-13c4-475e-87da-962117085834",
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"metadata": {},
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"source": [
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"### Index the subplots using the names given to `ImageWidget`"
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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": 14,
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"id": "b59d95e2-9092-4915-beef-01661d164781",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"two: Subplot @ 0x7f91486a7a00\n",
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" parent: None\n",
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" Graphics:\n",
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"\tfastplotlib.ImageGraphic @ 0x7f914881ceb0"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iw.plot[\"two\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dc727d1a-681e-4cbf-bfb2-898ceb31cbe0",
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"metadata": {},
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"source": [
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"### change window functions just like before"
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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": 15,
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"id": "a8f070db-da11-4062-95aa-f19b96351ee8",
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"metadata": {},
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"outputs": [],
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"source": [
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"iw.window_funcs[\"t\"].func = np.max"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3e89c10f-6e34-4d63-9805-88403d487432",
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"metadata": {},
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"source": [
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"## Gridplot of volumetric data"
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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": 16,
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"id": "b1587410-a08e-484c-8795-195a413d6374",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a2e4d723405345e0a7bd7b005330d018",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"RFBOutputContext()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"dims = (256, 256, 5, 100)\n",
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"data = [np.random.rand(*dims) for i in range(4)]\n",
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"\n",
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"iw = ImageWidget(\n",
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" data=data, \n",
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" slider_dims=[\"t\", \"z\"], \n",
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" dims_order=\"xyzt\", # example of how you can set this for non-standard orders\n",
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" vmin_vmax_sliders=True,\n",
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" names=[\"zero\", \"one\", \"two\", \"three\"],\n",
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" # window_funcs={\"t\": (np.mean, 5)}, # window functions can be slow when indexing multiple dims\n",
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" cmap=\"gnuplot2\", \n",
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")"
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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": 17,
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"id": "3ccea6c6-9580-4720-bce8-a5507cf867a3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "78a4ed0f59734124a7f3ee23e373e64a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(JupyterWgpuCanvas(), IntSlider(value=0, description='dimension: t', max=99), IntSlider(value=0,…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"iw.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2382809c-4c7d-4da4-9955-71d316dee46a",
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"metadata": {},
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"source": [
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"### window functions, can be slow when you have \"t\" and \"z\""
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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": 18,
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"id": "fd4433a9-2add-417c-a618-5891371efae0",
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"metadata": {},
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"outputs": [],
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"source": [
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"iw.window_funcs = {\"t\": (np.mean, 11)}"
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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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"id": "3090a7e2-558e-4975-82f4-6a67ae141900",
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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": "Python 3 (ipykernel)",
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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": "3.10.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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