From 910a222fa60ce6ea0831f2956470b8a0b9f62670 Mon Sep 17 00:00:00 2001 From: leshe4ka46 Date: Sat, 18 Oct 2025 12:25:53 +0300 Subject: nvidia2 --- .../1-06_data_visualization.ipynb | 2487 ++++++++++++++++++++ 1 file changed, 2487 insertions(+) create mode 100644 Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb (limited to 'Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb') diff --git a/Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb new file mode 100644 index 0000000..4db2cfe --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb @@ -0,0 +1,2487 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53a7b12-538d-4459-b82a-a35c8c417849", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "ae497b71-bc43-471e-8970-88a1878e7cf9", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "a149b6d1-1880-4a5d-9d71-f963d3097aa4", + "metadata": {}, + "source": [ + "## 06 - Data Visualization ##\n", + "\n", + "**Table of Contents**\n", + "
\n", + "This notebook demonstrates the basics of data visualization for large datasets. This notebook covers the below sections: \n", + "1. [Data Visualization](#Data-Visualization)\n", + "2. [Bar Chart](#Bar-Chart)\n", + " * [Histogram](#Histogram)\n", + " * [Exercise #1 - Bar Chart](#Exercise-#1---Bar-Chart)\n", + "3. [Scatter Plot](#Scatter-Plot)\n", + "4. [Line Chart](#Line-Chart)\n", + "5. [Datashader](#Datashader)\n", + " * [Datashader Accelerated by GPU](#Datashader-Accelerated-by-GPU)\n", + "6. [Interactive Visualization](#Interactive-Visualization)\n", + " * [cuxfilter and Dashboard](#cuxfilter-and-Dashboard)\n", + "6. [Other Libraries](#Other-Libraries)" + ] + }, + { + "cell_type": "markdown", + "id": "39f0f08f-92a2-4bfc-b8bc-5904aa70b5fc", + "metadata": {}, + "source": [ + "## Data Visualization ##\n", + "Data visualization is an important part of data science for several reasons: \n", + "* **Data exploration**: enables data scientists to explore data and quickly identify patterns, trends, and outliers that may not be apparent when looking at raw data in tabular format\n", + "* **Interpretation**: transforms large and complex datasets into more digestible visual formats, making it easier to comprehend vast amounts of information\n", + "* **Communication**: helps data scientists communicate complex insights to stakeholders in an easy-to-understand visual format, making data more accessible to non-technical audiences\n", + "\n", + "Below is the simple dashboard we will create in this notebook: \n", + "\n", + "

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3300e580-f39d-4147-8ad8-dfbf611ad323", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexcountylatlongname
00mDARLINGTON54.533638-1.524400FRANCIS
10mDARLINGTON54.426254-1.465314EDWARD
20mDARLINGTON54.555199-1.496417TEDDY
30mDARLINGTON54.547909-1.572342ANGUS
40mDARLINGTON54.477638-1.605995CHARLIE
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" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext cudf.pandas\n", + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "db58461b-5877-4768-8586-a46765381b6b", + "metadata": {}, + "source": [ + "## Bar Chart ##\n", + "Bar charts are used to show and compare categorical data. It represent numercial values with rectangular bars where the length or height of each bar corresponds to the value it represents. \n", + "\n", + "Below we show the top 5 counties with the most people. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a0acbf39-b10c-4998-96d7-dde142d844e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df.groupby('county').size().sort_values(ascending=False).head().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "8b6d94bc-7006-4e73-accb-2649d7dec596", + "metadata": {}, + "source": [ + "### Histogram ###\n", + "Bar charts can also be used to show the distribution of data points across different subgroups. This is referred to as a historgram, which is done by counting the number of occurrences (frequency distribution) of each unique value in a dataset. It is used to visualize the shape, center, and spread of a dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b804a51e-63b7-4389-8dd5-3beea5a5950b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AAABrCAsAAGANYQEAAKwhLAAAgDWEBQAAsIawAAAA1hAWAADAGsICAABYQ1gAAABrCAsAAGANYQEAAKwhLAAAgDWEBQAAsIawAAAA1hAWAADAGsICAABYQ1gAAABrCAsAAGANYQEAAKwhLAAAgDVBhUVBQYEyMjIUHx+v+Ph4de3aVQsXLgzXbAAAIMoEFRbNmzfXpEmTVFRUpKKiIp133nm69NJLtWnTpnDNBwAAoogzmJ1zcnIC1h988EEVFBSosLBQZ5xxhtXBAABA9AkqLH6qoqJCL730kg4fPqyuXbvanAkAAESpoMNiw4YN6tq1q77//ns1bNhQ8+bNU/v27Svd3+v1yuv1+tfLy8tDmxQAANR4QX8q5PTTT9e6detUWFioESNGaOjQodq8eXOl++fn5yshIcG/uN3uKg0MAABqrqDDol69emrdurWysrKUn5+vM888U4899lil++fl5amsrMy/eDyeKg0MAABqrpCfsfiRMSbgrY6fc7lccrlcVT0NAACIAkGFxR133KHs7Gy53W4dPHhQc+bM0fLly7Vo0aJwzQcAAKJIUGHxxRdf6JprrtHevXuVkJCgjIwMLVq0SH369AnXfAAAIIoEFRbPPvtsuOYAAAC1AN8VAgAArCEsAACANYQFAACwhrAAAADWEBYAAMAawgIAAFhDWAAAAGsICwAAYA1hAQAArCEsAACANYQFAACwhrAAAADWEBYAAMCaoL7dFAAA2Jd2+4JqPd+uSf3C9rO5YwEAAKwhLAAAgDWEBQAAsIawAAAA1hAWAADAGsICAABYQ1gAAABrCAsAAGANYQEAAKwhLAAAgDWEBQAAsIawAAAA1hAWAADAGsICAABYQ1gAAABrCAsAAGANYQEAAKwhLAAAgDWEBQAAsIawAAAA1gQVFvn5+erSpYsaNWqkpk2b6rLLLtPWrVvDNRsAAIgyQYXFu+++q9zcXBUWFmrJkiU6evSo+vbtq8OHD4drPgAAEEWcwey8aNGigPVp06apadOmKi4u1rnnnmt1MAAAEH2CCoufKysrkyQlJiZWuo/X65XX6/Wvl5eXV+WUAACgBgv54U1jjMaOHavu3burQ4cOle6Xn5+vhIQE/+J2u0M9JQAAqOFCDotRo0Zp/fr1mj179i/ul5eXp7KyMv/i8XhCPSUAAKjhQnor5KabbtJrr72m9957T82bN//FfV0ul1wuV0jDAQCA6BJUWBhjdNNNN2nevHlavny50tPTwzUXAACIQkGFRW5url544QW9+uqratSokfbt2ydJSkhIUFxcXFgGBAAA0SOoZywKCgpUVlamXr16KSUlxb/MnTs3XPMBAIAoEvRbIQAAAJXhu0IAAIA1hAUAALCGsAAAANYQFgAAwBrCAgAAWENYAAAAawgLAABgDWEBAACsISwAAIA1hAUAALCGsAAAANYQFgAAwBrCAgAAWENYAAAAawgLAABgDWEBAACsISwAAIA1hAUAALCGsAAAANYQFgAAwBrCAgAAWENYAAAAawgLAABgDWEBAACsISwAAIA1hAUAALCGsAAAANYQFgAAwBrCAgAAWENYAAAAawgLAABgDWEBAACsISwAAIA1hAUAALAm6LB47733lJOTo9TUVDkcDs2fPz8MYwEAgGgUdFgcPnxYZ555pp588slwzAMAAKKYM9gDsrOzlZ2dHY5ZAABAlAs6LILl9Xrl9Xr96+Xl5eE+JQAAiJCwP7yZn5+vhIQE/+J2u8N9SgAAECFhD4u8vDyVlZX5F4/HE+5TAgCACAn7WyEul0sulyvcpwEAADUAv8cCAABYE/Qdi0OHDmn79u3+9U8//VTr1q1TYmKiWrRoYXU4AAAQXYIOi6KiIvXu3du/PnbsWEnS0KFDNX36dGuDAQCA6BN0WPTq1UvGmHDMAgAAohzPWAAAAGsICwAAYA1hAQAArCEsAACANYQFAACwhrAAAADWEBYAAMAawgIAAFhDWAAAAGsICwAAYA1hAQAArCEsAACANYQFAACwhrAAAADWEBYAAMAawgIAAFhDWAAAAGsICwAAYA1hAQAArCEsAACANYQFAACwhrAAAADWEBYAAMAawgIAAFhDWAAAAGsICwAAYA1hAQAArCEsAACANYQFAACwhrAAAADWEBYAAMAawgIAAFhDWAAAAGtCCot//vOfSk9PV2xsrDp37qz333/f9lwAACAKBR0Wc+fO1ZgxY3TnnXdq7dq16tGjh7Kzs1VaWhqO+QAAQBQJOiymTJmi66+/XjfccIPatWunRx99VG63WwUFBeGYDwAARJGgwuLIkSMqLi5W3759A17v27evVq5caXUwAAAQfZzB7HzgwAFVVFQoOTk54PXk5GTt27fvuMd4vV55vV7/ellZmSSpvLw8qEF93m+D2r+qgp2vKmrztUnVe31cmz1cmx21+dok/l1pSzRc24/HGGN+eUcThN27dxtJZuXKlQGvT5gwwZx++unHPWb8+PFGEgsLCwsLC0stWDwezy+2QlB3LE4++WTFxMQcc3di//79x9zF+FFeXp7Gjh3rX/f5fPr666+VlJQkh8MRzOmDVl5eLrfbLY/Ho/j4+LCeq7pxbdGpNl+bVLuvj2uLTlybPcYYHTx4UKmpqb+4X1BhUa9ePXXu3FlLlizRgAED/K8vWbJEl1566XGPcblccrlcAa81btw4mNNWWXx8fK37P9SPuLboVJuvTard18e1RSeuzY6EhIRf3SeosJCksWPH6pprrlFWVpa6du2qZ555RqWlpRo+fHhIQwIAgNoj6LC48sor9dVXX+n+++/X3r171aFDB7355ptq2bJlOOYDAABRJOiwkKSRI0dq5MiRtmexzuVyafz48ce8FVMbcG3RqTZfm1S7r49ri05cW/VzmF/93AgAAMCJ4UvIAACANYQFAACwhrAAAADWEBYArOGRLQAhfSqkpvr8889VUFCglStXat++fXI4HEpOTtY555yj4cOHy+12R3pEoFZzuVz6+OOP1a5du0iPAiBCas2nQj744ANlZ2fL7Xarb9++Sk5OljFG+/fv15IlS+TxeLRw4UJ169Yt0qOGhcfj0fjx4/Xcc89FepSgfffddyouLlZiYqLat28fsO3777/Xiy++qGuvvTZC01Xdli1bVFhYqK5du6pt27b65JNP9Nhjj8nr9erqq6/WeeedF+kRg/bTX9P/U4899piuvvpqJSUlSZKmTJlSnWOFzTfffKMZM2aopKREKSkpGjp0aNT+RWXt2rVq3Lix0tPTJUmzZs1SQUGBSktL1bJlS40aNUqDBw+O8JShuemmmzRo0CD16NEj0qOExRNPPKGioiL169dPgwYN0r///W/l5+fL5/PpD3/4g+6//345nTXgfkEwX0JWk2VlZZkxY8ZUun3MmDEmKyurGieqXuvWrTN16tSJ9BhB27p1q2nZsqVxOBymTp06pmfPnmbPnj3+7fv27YvK6/rRwoULTb169UxiYqKJjY01CxcuNE2aNDEXXHCBOf/8843T6TTLli2L9JhBczgcplOnTqZXr14Bi8PhMF26dDG9evUyvXv3jvSYIUtJSTEHDhwwxhizc+dO06xZM9OsWTPTp08f07x5c5OQkGC2bNkS4SlDk5mZad5++21jjDFTp041cXFx5uabbzYFBQVmzJgxpmHDhubZZ5+N8JSh+fHfI6eddpqZNGmS2bt3b6RHsub+++83jRo1Mpdffrlp1qyZmTRpkklKSjITJkwwEydONE2aNDH33HNPpMc0xhhTa8IiNjbWfPLJJ5Vu37Jli4mNja3Giex69dVXf3F55JFHovIP4Msuu8z079/ffPnll6akpMTk5OSY9PR089lnnxljoj8sunbtau68805jjDGzZ882J510krnjjjv82++44w7Tp0+fSI0XsokTJ5r09PRjosjpdJpNmzZFaCp7HA6H+eKLL4wxxgwePNj06tXLHD582BhjzPfff2/69+9vrrjiikiOGLL69ev7//nKzMw0Tz/9dMD2559/3rRv3z4So1WZw+EwS5cuNaNHjzYnn3yyqVu3rrnkkkvM66+/bioqKiI9XpW0atXKvPzyy8aY//1FMiYmxsyaNcu//ZVXXjGtW7eO1HgBak1YpKenm+eee67S7c8995xJT0+vxons+rHEHQ5HpUs0/gHctGlTs379+oDXRo4caVq0aGF27NgR9WERHx9vSkpKjDHGVFRUGKfTaYqLi/3bN2zYYJKTkyM1XpV89NFHpk2bNuavf/2rOXLkiDGmdobF8QKqsLDQNG/ePBKjVVlSUpIpKioyxvzvn79169YFbN++fbuJi4uLxGhV9tP/3Y4cOWLmzp1rLrzwQhMTE2NSU1PNHXfc4f/nMdrExcX5g9AYY+rWrWs2btzoX9+1a5epX79+JEY7Rq35VMitt96q4cOHa9SoUXr11VdVWFioVatW6dVXX9WoUaM0YsQI3XbbbZEeM2QpKSl6+eWX5fP5jrusWbMm0iOG5LvvvjvmPcF//OMfuuSSS9SzZ09t27YtQpPZV6dOHcXGxgZ8u2+jRo1UVlYWuaGqoEuXLiouLtaXX36prKwsbdiwQQ6HI9JjWfPjtXi9XiUnJwdsS05O1pdffhmJsaosOztbBQUFkqSePXvqP//5T8D2F198Ua1bt47EaFbVrVtXgwYN0qJFi7Rz507deOONev7553X66adHerSQNGvWTJs3b5YklZSUqKKiwr8uSZs2bVLTpk0jNV6gSJeNTXPmzDFnnXWWcTqd/r/FO51Oc9ZZZ5m5c+dGerwqycnJMXfffXel29etW2ccDkc1TmRHly5dzMyZM4+7LTc31zRu3Diq71hkZGSYhQsX+tc3bNhgfvjhB//6+++/H9V30n40e/Zsk5ycbOrUqVNr7lh07NjRZGZmmoYNG5pXXnklYPu7775rTjnllAhNVzW7d+82aWlp5txzzzVjx441cXFxpnv37ubGG2805557rqlXr55ZsGBBpMcMyU/vWByPz+czixcvrsaJ7LnzzjtNkyZNzA033GDS09NNXl6eadGihSkoKDBPPfWUcbvd5pZbbon0mMYYY2rA46P2XHnllbryyiv1ww8/6MCBA5Kkk08+WXXr1o3wZFX3t7/9TYcPH650e+vWrfXOO+9U40R2DBgwQLNnz9Y111xzzLYnn3xSPp9PTz31VAQms2PEiBGqqKjwr3fo0CFg+8KFC6PyUyE/N3jwYHXv3l3FxcW14puOx48fH7Bev379gPXXX389aj95kJqaqrVr12rSpEl6/fXXZYzRRx99JI/Ho27dumnFihXKysqK9JghadmypWJiYird7nA41KdPn2qcyJ777rtPcXFxKiws1F/+8heNGzdOGRkZuu222/Ttt98qJydHDzzwQKTHlFSLPm4KAAAir9Y8YwEAACKPsAAAANYQFgAAwBrCAgAAWENYAAhJr169NGbMmLCe495771WnTp3Ceg4AdhEWAGqt6dOnB/xCMgDhR1gAAABrCAsgSi1atEjdu3dX48aNlZSUpP79+2vHjh3+7StXrlSnTp0UGxurrKwszZ8/Xw6HQ+vWrfPvs3nzZl188cVq2LChkpOTdc011/h/udyJOHr0qEaNGuWf4a677tJPfzWOw+HQ/PnzA45p3Lixpk+f7l///PPPNXjwYCUmJqpBgwbKysrSqlWrjnu+Tz/9VK1bt9aIESPk8/l05MgR3XbbbTrllFPUoEEDnXXWWVq+fLkkafny5Ro2bJjKysrkcDjkcDh07733nvC1AQgNYQFEqcOHD2vs2LFavXq1li1bpjp16mjAgAHy+Xw6ePCgcnJy1LFjR61Zs0YPPPCAxo0bF3D83r171bNnT3Xq1ElFRUVatGiRvvjiCw0aNOiEZ5gxY4acTqdWrVqlxx9/XI888oj+9a9/nfDxhw4dUs+ePbVnzx699tpr+vjjj3XbbbfJ5/Mds+/GjRvVrVs3DRw4UAUFBapTp46GDRumFStWaM6cOVq/fr0GDhyoiy66SCUlJTrnnHP06KOPKj4+Xnv37tXevXt16623nvBsAEIU2d8oDsCW/fv3G0lmw4YNpqCgwCQlJZnvvvvOv33q1KlGklm7dq0xxpi7777b9O3bN+BneDweI8ls3br1V8/Xs2dP065dO+Pz+fyvjRs3zrRr186/LsnMmzcv4LiEhAQzbdo0Y4wxTz/9tGnUqJH56quvjnuO8ePHmzPPPNOsXLnSJCYmmr///e/+bdu3bzcOh8Ps3r074Jjzzz/f5OXlGWOMmTZtmklISPjVawFgT636rhDgt2THjh26++67VVhYqAMHDvj/ll9aWqqtW7cqIyNDsbGx/v1///vfBxxfXFysd955Rw0bNjzuz27Tps2vznD22WcHfKNp165d9fDDD6uiouIXv7PhR+vWrVNmZqYSExMr3ae0tFQXXHCBJkyYoFtuucX/+po1a2SMOWZOr9erpKSkXz03gPAgLIAolZOTI7fbralTpyo1NVU+n08dOnTQkSNHZIw55ivMzc++Fsjn8yknJ0eTJ08+5menpKRYmdHhcBxz3h9++MH/n+Pi4n71ZzRp0kSpqamaM2eOrr/+esXHx0v63/wxMTEqLi4+JmKOF0sAqgfPWABR6KuvvtKWLVt011136fzzz1e7du30zTff+Le3bdtW69evl9fr9b9WVFQU8DN+97vfadOmTUpLS1Pr1q0DlgYNGpzQHIWFhcesn3baaf4/6Js0aaK9e/f6t5eUlOjbb7/1r2dkZGjdunX6+uuvKz1HXFyc3njjDcXGxurCCy/UwYMHJUmZmZmqqKjQ/v37j5m/WbNmkqR69eoFfLssgPAjLIAodNJJJykpKUnPPPOMtm/frrfffltjx471b//jH/8on8+nP//5z9qyZYveeustPfTQQ5Lkv5ORm5urr7/+WkOGDNFHH32knTt3avHixbruuutO+A9jj8ejsWPHauvWrZo9e7aeeOIJjR492r/9vPPO05NPPqk1a9aoqKhIw4cPV926df3bhwwZombNmumyyy7TihUrtHPnTr388sv68MMPA87ToEEDLViwQE6nU9nZ2Tp06JDatGmjq666Stdee61eeeUVffrpp1q9erUmT56sN998U5KUlpamQ4cOadmyZTpw4EBA1AAIk8g+4gEgVEuWLDHt2rUzLpfLZGRkmOXLlwc8LLlixQqTkZFh6tWrZzp37mxeeOEFI8l88skn/p+xbds2M2DAANO4cWMTFxdn2rZta8aMGRPwQGZlevbsaUaOHGmGDx9u4uPjzUknnWRuv/32gGN3795t+vbtaxo0aGBOO+008+abbwY8vGmMMbt27TKXX365iY+PN/Xr1zdZWVlm1apVxpj/f3jzRwcPHjTnnHOO6dGjhzl06JA5cuSIueeee0xaWpqpW7euadasmRkwYIBZv369/5jhw4ebpKQkI8mMHz8+tP+yAZwwhzE/ewMUQK30/PPP+3+vw4k82wAAoeDhTaCWmjlzplq1aqVTTjlFH3/8scaNG6dBgwYRFQDCirAAaql9+/bpnnvu0b59+5SSkqKBAwfqwQcfPKFjS0tL1b59+0q3b968WS1atLA1KoBahLdCABzj6NGj2rVrV6Xb09LS5HTy9xIAxyIsAACANXzcFAAAWENYAAAAawgLAABgDWEBAACsISwAAIA1hAUAALCGsAAAANYQFgAAwJr/A1je/r7Do28GAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", + "df['age_bucket']=pd.cut(df['age'], bins=bins, right=True, include_lowest=True, labels=False)\n", + "df.groupby('age_bucket').size().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "88341062-8ecc-4264-8c42-bee7ae173c05", + "metadata": {}, + "source": [ + "### Exercise #1 - Bar Chart ###\n", + "We would like to find the distribution of sex in the population. \n", + "\n", + "**Instructions**:
\n", + "* Modify the `` only and execute the below cell plot the number of each sex in our dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d0d37093-e329-498d-b748-7fa3fb792303", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.groupby('sex').size().plot(kind='bar')" + ] + }, + { + "cell_type": "raw", + "id": "b52410a7-777f-4b0a-862f-9fed419d9c79", + "metadata": {}, + "source": [ + "\n", + "df.groupby('sex').size().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "5e49a376-90f6-4ae1-9a47-fae469a5d1da", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "676bd687-b998-4724-bda5-e1d68307bb24", + "metadata": {}, + "source": [ + "## Scatter Plot ##\n", + "The scatter plot is used to show the relationship between two variables in a dataset. It can also be used to display coordinates of each data point to help identify outliers or clusters. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e170e8ba-5dbf-428d-b00a-0880d8bdcfad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# sample a very small percentage of the data\n", + "small_df=df.sample(1000)\n", + "\n", + "small_df.plot(kind='scatter', x='lat', y='long')" + ] + }, + { + "cell_type": "markdown", + "id": "8e0dbf2c-9bbf-4621-bce6-661ede296af9", + "metadata": {}, + "source": [ + "## Line Chart ##\n", + "Line charts are good for connecting individual data points to show trends. It's useful for visualizing changes, trends, and patterns over time. \n", + "\n", + "The scatter plot doesn't scale well with the number of data points. When the data becomes large, the scatter plot take a long time to complete. Below is line chart of the compute time for different data sizes. \n", + "\n", + "

\n", + "\n", + "**Note**: Below is the code used to produce this image. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ce18692d-b36f-4275-b056-fd1ec1170abc", + "metadata": {}, + "outputs": [], + "source": [ + "# import time\n", + "# import matplotlib.pyplot as plt\n", + "\n", + "# fig, ax=plt.subplots()\n", + "# exec_times={}\n", + "\n", + "# for size in (5*(10**i) for i in range(1, 8)): \n", + "# start=time.time()\n", + "# df.sample(size).plot(kind='scatter', x='long', y='lat', ax=ax)\n", + "# duration=time.time()-start\n", + "# exec_times[size]=duration\n", + "# ax.clear()\n", + "\n", + "# ax.plot(exec_times.keys(), exec_times.values(), marker='o')\n", + "# ax.set_xscale('log')\n", + "# ax.set_xlabel('Data Size')\n", + "# ax.set_ylabel('Execution Time')\n", + "# ax.set_title(\"Scatter Plot Doesn't Scale Well With Data Size\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "44e65ae7-f223-455f-a463-10f1e193ef64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "b26686d7-fb05-49a0-9006-036810d86160", + "metadata": {}, + "source": [ + "## Datashader ##\n", + "[Datashader](https://datashader.org/#) is an open-source Python library for analyzing and visualizing large datasets. Specifically, Datashader is designed to \"rasterize\" or \"aggregate\" datasets into regular grids that can be analyzed further or viewed as images, making it simple and quick to see the properties and patterns of data. \n", + "\n", + "Plotting for big data is challenging because rendering a large number of points takes a long time. Datashader shifts the burden of visualization from rendering to computing. Underneath the hood, it turns a long list of (x, y) points into a 2D histogram instead of plotting each point individually. Furthermore, this aggregation can be accelerated through parallel computing. The resulting gridded data structure is then turn into an image, using color to show the magnitude, before being embedding into a plotting program. \n", + "\n", + "Datashader generates a plot using a five-step [pipeline](https://datashader.org/getting_started/Pipeline.html): \n", + "\n", + "

\n", + "\n", + "Below we demonstrate how Datashader is used. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a7bd466f-0e40-4b40-bd50-82c9fdc17e2f", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import time\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import datashader as ds\n", + "import datashader.transfer_functions as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "813eb18b-5234-4a1f-ae05-58bcf8750e9f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexcountylatlongname
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10mDARLINGTON54.426254-1.465314EDWARD
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" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533646 -1.524401 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547905 -1.572341 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605994 CHARLIE" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5f0085a4-97fa-494f-a525-792f2b65593e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (lat: 600, long: 600)> Size: 1MB\n",
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n",
+       "Coordinates:\n",
+       "  * long     (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n",
+       "  * lat      (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n",
+       "Attributes:\n",
+       "    x_range:  (-6.368374347686768, 2.7000913619995117)\n",
+       "    y_range:  (49.519046783447266, 56.261409759521484)
" + ], + "text/plain": [ + " Size: 1MB\n", + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n", + "Coordinates:\n", + " * long (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n", + " * lat (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n", + "Attributes:\n", + " x_range: (-6.368374347686768, 2.7000913619995117)\n", + " y_range: (49.519046783447266, 56.261409759521484)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 1.25 seconds\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "\n", + "# get points\n", + "ds_points_pandas=ds.Canvas().points(df,'long','lat')\n", + "display(ds_points_pandas)\n", + "\n", + "# plot points\n", + "plt.imshow(tf.shade(ds_points_pandas))\n", + "\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "09268b18-7e81-46e7-978b-964fe56cda2e", + "metadata": {}, + "source": [ + "### Datashader Accelerated by GPU ###\n", + "Datashader can be accelerated by assigning the computation to a GPU. As previously mentioned, the GPU typically has far more (though individually less powerful) cores available than a CPU does, and for highly parallelizable computations like those in Datashader a GPU can typically achieve much faster performance at a given price point than a CPU or distributed set of CPUs can. The DataFrame from cuDF can be used as a replacement for rasterization. The performance benefits are significant since the entire data-processing pipeline is executed on the GPU and there is no bottleneck from data transfer. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "557e70e8-9eaf-4d6b-9048-8a1b433943bd", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexcountylatlongname
00mDARLINGTON54.533638-1.524400FRANCIS
10mDARLINGTON54.426254-1.465314EDWARD
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30mDARLINGTON54.547909-1.572342ANGUS
40mDARLINGTON54.477638-1.605995CHARLIE
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" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import cudf\n", + "\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "gdf=cudf.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6a5b3eb3-40f2-46fb-a45b-5450f45ff398", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (lat: 600, long: 600)> Size: 1MB\n",
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n",
+       "Coordinates:\n",
+       "  * long     (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n",
+       "  * lat      (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n",
+       "Attributes:\n",
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" + ], + "text/plain": [ + " Size: 1MB\n", + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n", + "Coordinates:\n", + " * long (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n", + " * lat (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n", + "Attributes:\n", + " x_range: (-6.368374, 2.7000911)\n", + " y_range: (49.51904, 56.26141)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 14.53 seconds\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "\n", + "# get points\n", + "ds_points_cudf=ds.Canvas().points(gdf,'long','lat')\n", + "display(ds_points_cudf)\n", + "\n", + "# plot points\n", + "plt.imshow(tf.shade(ds_points_cudf))\n", + "\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "98b1c4a7-9b60-4970-859f-b9ffedd4315c", + "metadata": {}, + "source": [ + "**Note**: Please re-execute the above cell if it took more than a few seconds for the more accurate compute time. " + ] + }, + { + "cell_type": "markdown", + "id": "2e664fb6-7482-49d0-bc60-efaefd2184d3", + "metadata": {}, + "source": [ + "## Interactive Visualization ##\n", + "Data visualization is crucial in data science as it bridges the gap between complex data and human understanding, making insights more accessible, actionable, and impactful throughout the data science process. Bringing interactivity in data visualization further enables: \n", + "* **Discovery**: enables discovery of hidden patterns, trends, and outliers that may not be apparant in static visualizations\n", + "* **Enhanced understanding**: allows users to view data from multiple perspective and levels of detail\n", + "* **Customization**: provides the ability to rapidly filter, sort, and aggregate data, leading to a more impactful presentation" + ] + }, + { + "cell_type": "markdown", + "id": "084911da-fb99-43e8-bff6-e11f032b4c9e", + "metadata": {}, + "source": [ + "### cuxfilter and Dashboard ###\n", + "cuxfilter enables GPU accelerated cross-filtering dashboards, which is ideal for multi-chart exploratory data analysis. Cross-filtering lets users interact with one chart and apply that interaction as a filter to other charts in the dashboard. \n", + "\n", + "cuxfilter acts as a connector library, which provides the connections between different visualization libraries and a GPU DataFrame without much hassle. This also allows users to use charts from different libraries in a single dashboard, while also providing the interaction. Currently, cuxfilter supports: \n", + "* [Bokeh](https://bokeh.org/) Charts\n", + " * Bar chart\n", + " * Line chart\n", + " * Choropleth\n", + "* [Datashader](https://datashader.org/) Charts\n", + " * Line\n", + " * Scatter\n", + "* [Panel Widgets](https://panel.holoviz.org/api/panel.widgets.html)\n", + " * Range\n", + " * Float\n", + " * Int\n", + " * Dropdown\n", + " * Multiselect" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f1777e6a-a717-49d4-9b38-e855d987ca92", + "metadata": {}, + "outputs": [], + "source": [ + "import cuxfilter as cxf\n", + "\n", + "# factorize county for multiselect widget\n", + "gdf['county'], county_names = gdf['county'].factorize()\n", + "county_map = dict(zip(list(range(len(county_names))), county_names.to_arrow()))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "680c06ed-f8c6-4de5-b366-78f0c1092aeb", + "metadata": {}, + "outputs": [], + "source": [ + "# create cuxfilter DataFrame\n", + "cxf_data = cxf.DataFrame.from_dataframe(gdf)\n", + "\n", + "# create Datashader scatter plot\n", + "scatter_chart = cxf.charts.scatter(x='long', y='lat')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c2d8a372-6e14-442c-a61d-f5ea44fc7f6c", + "metadata": {}, + "outputs": [], + "source": [ + "# create Bokeh bar charts\n", + "chart_3=cxf.charts.bar('age')\n", + "chart_2=cxf.charts.bar('sex')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fb700802-45b8-4558-b58b-c2a96cfda78b", + "metadata": {}, + "outputs": [], + "source": [ + "# define layout\n", + "layout_array=[[1, 2, 2], \n", + " [3, 2, 2]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7964e08-1e77-4431-83b1-ef0e798d32f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n", + " require([\"h3\"], function(h3) {\n", + " window.h3 = h3\n", + " on_load()\n", + " })\n", + " require([\"deck-gl\"], function(deck) {\n", + " window.deck = deck\n", + " on_load()\n", + " })\n", + " require([\"deck-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-csv\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-tiles\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"mapbox-gl\"], function(mapboxgl) {\n", + " window.mapboxgl = mapboxgl\n", + " on_load()\n", + " })\n", + " require([\"carto\"], function() {\n", + " on_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 8;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.4\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.4\"];\n", + " const inline_js = [ function(Bokeh) {\n", + " inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " (function(root, factory) {\n", + " factory(root[\"Bokeh\"]);\n", + " })(this, function(Bokeh) {\n", + " let define;\n", + " return (function outer(modules, entry) {\n", + " if (Bokeh != null) {\n", + " return Bokeh.register_plugin(modules, entry);\n", + " } else {\n", + " throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n", + " }\n", + " })\n", + " ({\n", + " \"custom/main\": function(require, module, exports) {\n", + " const models = {\n", + " \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n", + " };\n", + " require(\"base\").register_models(models);\n", + " module.exports = models;\n", + " },\n", + " \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n", + " \"use strict\";\n", + " var _a;\n", + " Object.defineProperty(exports, \"__esModule\", { value: true });\n", + " exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n", + " const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n", + " class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n", + " connect_signals() {\n", + " super.connect_signals();\n", + " this.on_change([this.model.properties.active], () => {\n", + " this.model._active = this.model.active;\n", + " });\n", + " }\n", + " }\n", + " exports.CustomInspectToolView = CustomInspectToolView;\n", + " CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n", + " class CustomInspectTool extends inspect_tool_1.InspectTool {\n", + " constructor(attrs) {\n", + " super(attrs);\n", + " }\n", + " }\n", + " exports.CustomInspectTool = CustomInspectTool;\n", + " _a = CustomInspectTool;\n", + " CustomInspectTool.__name__ = \"CustomInspectTool\";\n", + " (() => {\n", + " _a.prototype.default_view = CustomInspectToolView;\n", + " _a.define(({ Boolean }) => ({\n", + " _active: [Boolean, true]\n", + " }));\n", + " _a.register_alias(\"customInspect\", () => new _a());\n", + " })();\n", + " //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n", + " }\n", + " }, \"custom/main\");\n", + " ;\n", + " });\n", + "\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n require([\"h3\"], function(h3) {\n window.h3 = h3\n on_load()\n })\n require([\"deck-gl\"], function(deck) {\n window.deck = deck\n on_load()\n })\n require([\"deck-json\"], function() {\n on_load()\n })\n require([\"loader-csv\"], function() {\n on_load()\n })\n require([\"loader-json\"], function() {\n on_load()\n })\n require([\"loader-tiles\"], function() {\n on_load()\n })\n require([\"mapbox-gl\"], function(mapboxgl) {\n window.mapboxgl = mapboxgl\n on_load()\n })\n require([\"carto\"], function() {\n on_load()\n })\n root._bokeh_is_loading = css_urls.length + 8;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 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document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.4\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.4\"];\n const inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n (function(root, factory) {\n factory(root[\"Bokeh\"]);\n })(this, function(Bokeh) {\n let define;\n return (function outer(modules, entry) {\n if (Bokeh != null) {\n return Bokeh.register_plugin(modules, entry);\n } else {\n throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n }\n })\n ({\n \"custom/main\": function(require, module, exports) {\n const models = {\n \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n };\n require(\"base\").register_models(models);\n module.exports = models;\n },\n \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n \"use strict\";\n var _a;\n Object.defineProperty(exports, \"__esModule\", { value: true });\n exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n connect_signals() {\n super.connect_signals();\n this.on_change([this.model.properties.active], () => {\n this.model._active = this.model.active;\n });\n }\n }\n exports.CustomInspectToolView = CustomInspectToolView;\n CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n class CustomInspectTool extends inspect_tool_1.InspectTool {\n constructor(attrs) {\n super(attrs);\n }\n }\n exports.CustomInspectTool = CustomInspectTool;\n _a = CustomInspectTool;\n CustomInspectTool.__name__ = \"CustomInspectTool\";\n (() => {\n _a.prototype.default_view = CustomInspectToolView;\n _a.define(({ Boolean }) => ({\n _active: [Boolean, true]\n }));\n _a.register_alias(\"customInspect\", () => new _a());\n })();\n //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n }\n }, \"custom/main\");\n ;\n });\n\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create multiselect widget\n", + "county_widget = cxf.charts.panel_widgets.multi_select('county', label_map=county_map)\n", + "\n", + "# define layout\n", + "dash = cxf_data.dashboard(charts=[chart_2, scatter_chart, chart_3],sidebar=[county_widget], theme=cxf.themes.dark, data_size_widget=True, layout_array=layout_array)\n", + "\n", + "dash.app()" + ] + }, + { + "cell_type": "markdown", + "id": "08b9ca80-2469-4f49-8781-1910bdce41bd", + "metadata": {}, + "source": [ + "## Other Libraries ##\n", + "* Plotly:\n", + " * https://dash.plotly.com/holoviews#gpu-accelerating-datashader-and-linked-selections-with-rapids\n", + " * https://developer.nvidia.com/blog/making-a-plotly-dash-census-viz-powered-by-rapids/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e9d7f104-879d-4784-bf47-541fa4cda445", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "9dc3b5a1-051f-4e4c-b830-1dbf066eb622", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-07_etl.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "89ffb0bd-9cbf-4f75-affd-0f614e4074e3", + "metadata": {}, + "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": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- cgit v1.2.3