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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "discrete-retention", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import json\n", | ||
| "import math\n", | ||
| "import numpy as np" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "ethical-posting", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "with open('./data.json') as f:\n", | ||
| " data = json.load(f)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "emerging-marking", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "values = np.array(data['values'], dtype='float32')\n", | ||
| "values = values.reshape((data['height'], data['width']))[:10,]\n", | ||
| "values.shape" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "twelve-cabinet", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "from bqplot import Figure, LinearScale, Axis, ColorScale\n", | ||
| "from bqplot_image_gl import ImageGL, Contour\n", | ||
| "import ipywidgets as widgets\n", | ||
| "scale_x = LinearScale(min=-1, max=4, allow_padding=False)\n", | ||
| "scale_y = LinearScale(min=-1, max=4, allow_padding=False)\n", | ||
| "scales = {'x': scale_x, 'y': scale_y}\n", | ||
| "axis_x = Axis(scale=scale_x, label='x')\n", | ||
| "axis_y = Axis(scale=scale_y, label='y', orientation='vertical')\n", | ||
| "scales_image = {'x': scale_x, 'y': scale_y, 'image': ColorScale(min=np.min(values).item(), max=np.max(values).item())}\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "appreciated-transformation", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "figure = Figure(scales=scales, axes=[axis_x, axis_y])\n", | ||
| "image = ImageGL(image=values, scales=scales_image, x=[0, 2], y=[0, 2])\n", | ||
| "figure.marks = (image, )" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "buried-cooperation", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "figure" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "sufficient-shirt", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from bqplot_image_gl.interacts import MouseInteraction" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "empirical-model", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "widget_label = widgets.Label(value=\"move cursor for information\")\n", | ||
| "widget_label" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "known-possession", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "interaction = MouseInteraction(x_scale=scales_image['x'], y_scale=scales_image['y'], move_throttle=70)\n", | ||
| "figure.interaction = interaction\n", | ||
| "def on_mouse_msg(interaction, data, buffers):\n", | ||
| " # it might be a good idea to throttle on the Python side as well, for instance when many computations\n", | ||
| " # happen, we can effectively ignore the queue of messages\n", | ||
| " if data['event'] == 'mousemove':\n", | ||
| " domain_x = data['domain']['x']\n", | ||
| " domain_y = data['domain']['y']\n", | ||
| " normalized_x = (domain_x - image.x[0]) / (image.x[1] - image.x[0])\n", | ||
| " normalized_y = (domain_y - image.y[0]) / (image.y[1] - image.y[0])\n", | ||
| " # TODO: think about +/-1 and pixel edges\n", | ||
| " pixel_x = int(math.floor(normalized_x * image.image.shape[1]))\n", | ||
| " pixel_y = int(math.floor(normalized_y * image.image.shape[0]))\n", | ||
| " if pixel_x >= 0 and pixel_x < image.image.shape[1] and pixel_y >= 0 and pixel_y < image.image.shape[0]:\n", | ||
| " value = str(image.image[pixel_y, pixel_x])\n", | ||
| " else:\n", | ||
| " value = \"out of range\"\n", | ||
| " msg = f\"x={pixel_x} y={pixel_y} value={value} (nx={normalized_x} ny={normalized_y})\"\n", | ||
| " widget_label.value = msg\n", | ||
| " elif data['event'] == 'mouseleave':\n", | ||
| " widget_label.value = \"Bye!\"\n", | ||
| " elif data['event'] == 'mouseenter':\n", | ||
| " widget_label.value = \"Almost there...\" # this is is not visible because mousemove overwrites the msg\n", | ||
| " \n", | ||
| "interaction.on_msg(on_mouse_msg)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "federal-domestic", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.8.6" | ||
| }, | ||
| "widgets": { | ||
| "application/vnd.jupyter.widget-state+json": { | ||
| "state": {}, | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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