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clewis7clewis7
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add jupyter-sidecar as a dependency, update simple notebook (#300)
* add jupyter-sidecar as a dependency, update simple notebook * Update setup.py * add sidecar for plots * sidecar updates * add sidecar to gridplot * add close method to image widget, add sidecar to image widget * fix notebook errors * fix linear region selector * add vbox as kwarg for additional ipywidgets when showing plot and gridplot * fix notebooks
1 parent 0f5655c commit e910e14

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-147
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‎examples/notebooks/gridplot_simple.ipynb

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‎examples/notebooks/linear_region_selector.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "40bf515f-7ca3-4f16-8ec9-31076e8d4bde",
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"id": "1db50ec4-8754-4421-9f5e-6ba8ca6b81e3",
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"metadata": {},
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"source": [
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"# `LinearRegionSelector` with single lines"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "41f4e1d0-9ae9-4e59-9883-d9339d985afe",
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"metadata": {
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"tags": []
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},
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"id": "b7bbfeb4-1ad0-47db-9a82-3d3f642a1f63",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fastplotlib as fpl\n",
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"ls_x.selection.add_event_handler(set_zoom_x)\n",
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"ls_y.selection.add_event_handler(set_zoom_y)\n",
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"\n",
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"# make some ipywidget sliders too\n",
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"# these are not necessary, it's just to show how they can be connected\n",
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"x_range_slider = IntRangeSlider(\n",
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" value=ls_x.selection(),\n",
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" min=ls_x.limits[0],\n",
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" max=ls_x.limits[1],\n",
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" description=\"x\"\n",
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")\n",
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"\n",
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"y_range_slider = FloatRangeSlider(\n",
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" value=ls_y.selection(),\n",
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" min=ls_y.limits[0],\n",
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" max=ls_y.limits[1],\n",
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" description=\"x\"\n",
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")\n",
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"\n",
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"# connect the region selector to the ipywidget slider\n",
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"ls_x.add_ipywidget_handler(x_range_slider, step=5)\n",
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"ls_y.add_ipywidget_handler(y_range_slider, step=0.1)\n",
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"\n",
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"VBox([gp.show(), x_range_slider, y_range_slider])"
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"gp.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": "66b1c599-42c0-4223-b33e-37c1ef077204",
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"id": "0bad4a35-f860-4f85-9061-920154ab682b",
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"metadata": {},
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"source": [
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"### 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."
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b26a37d-aa1d-478e-ad77-99f68a2b7d0c",
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"metadata": {
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"tags": []
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},
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"id": "2c96a3ff-c2e7-4683-8097-8491e97dd6d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"ls_x.selection()"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c2be060c-8f87-4b5c-8262-619768f6e6af",
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"metadata": {
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"tags": []
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},
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"id": "3ec71e3f-291c-43c6-a954-0a082ba5981c",
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"metadata": {},
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"outputs": [],
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"source": [
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"ls_x.get_selected_indices()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1bef432-d764-4841-bd6d-9b9e4c86ff62",
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"id": "1588a89e-1da4-4ada-92e2-7437ba942065",
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"metadata": {},
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"source": [
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"### 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."
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c370d6d7-d92a-4680-8bf0-2f9d541028be",
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"metadata": {
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"tags": []
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},
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"id": "18e10277-6d5d-42fe-8715-1733efabefa0",
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"metadata": {},
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"outputs": [],
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"source": [
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"ls_y.selection()"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cdf351e1-63a2-4f5a-8199-8ac3f70909c1",
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"metadata": {
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"tags": []
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},
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"id": "8e9c42b9-60d2-4544-96c5-c8c6832b79e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"ls_y.get_selected_indices()"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fd608ad-9732-4f50-9d43-8630603c86d0",
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"metadata": {
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"tags": []
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},
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"id": "a9583d2e-ec52-405c-a875-f3fec5e3aa16",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fastplotlib as fpl\n",
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},
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{
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"cell_type": "markdown",
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"id": "63acd2b6-958e-458d-bf01-903037644cfe",
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"id": "0fa051b5-d6bc-4e4e-8f12-44f638a00c88",
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"metadata": {},
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"source": [
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"# Large line stack with selector"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "20e53223-6ccd-4145-bf67-32eb409d3b0a",
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"metadata": {
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"tags": []
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},
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"id": "d5ffb678-c989-49ee-85a9-4fd7822f033c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fastplotlib as fpl\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "80e276ba-23b3-43d0-9e0c-86acab79ac67",
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"id": "cbcd6309-fb47-4941-9fd1-2b091feb3ae7",
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"metadata": {},
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"outputs": [],
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"source": []
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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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"version": "3.11.3"
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}
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},
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"nbformat": 4,

‎examples/notebooks/linear_selector.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "e0354810-f942-4e4a-b4b9-bb8c083a314e",
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"id": "a06e1fd9-47df-42a3-a76c-19e23d7b89fd",
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"metadata": {},
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"source": [
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"## `LinearSelector`, draggable selector that can optionally associated with an ipywidget."
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d79bb7e0-90af-4459-8dcb-a7a21a89ef64",
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"metadata": {
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"tags": []
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},
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"id": "eb95ba19-14b5-4bf4-93d9-05182fa500cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fastplotlib as fpl\n",
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"selector3.add_ipywidget_handler(ipywidget_slider3, step=0.1)\n",
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"\n",
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"plot.auto_scale()\n",
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"plot.show()\n",
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"VBox([plot.show(), ipywidget_slider, ipywidget_slider2, ipywidget_slider3])"
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"plot.show(vbox=[ipywidget_slider])"
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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": "7ab9f141-f92f-4c4c-808b-97dafd64ca25",
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"metadata": {},
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"outputs": [],
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"source": [
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"selector.step = 0.1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2c49cdc2-0555-410c-ae2e-da36c3bf3bf0",
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"id": "3b0f448f-bbe4-4b87-98e3-093f561c216c",
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"metadata": {},
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"source": [
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"### Drag linear selectors with the mouse, hold \"Shift\" to synchronize movement of all the selectors"
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]
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},
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{
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"cell_type": "markdown",
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"id": "69057edd-7e23-41e7-a284-ac55df1df5d9",
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"id": "c6f041b7-8779-46f1-8454-13cec66f53fd",
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"metadata": {},
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"source": [
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"## Also works for line collections"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1a3b98bd-7139-48d9-bd70-66c500cd260d",
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"metadata": {
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"tags": []
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},
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"id": "e36da217-f82a-4dfa-9556-1f4a2c7c4f1c",
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"metadata": {},
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"outputs": [],
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"sines = [sine] * 10\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b6c2d9d6-ffe0-484c-a550-cafb44fa8465",
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"id": "71ae4fca-f644-4d4f-8f32-f9d069bbc2f1",
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"metadata": {},
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"outputs": [],
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"source": []
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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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"version": "3.11.3"
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}
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},
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"nbformat": 4,

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