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Merge pull request #91 from QuantStack/2020-pydata-budapest
2020 pydata budapest
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‎2020-06-30-PyData-Budapest/01-bqplot.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:37.710652Z",
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"start_time": "2020-06-30T14:44:36.873479Z"
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}
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},
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"outputs": [],
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"source": [
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"from bqplot import pyplot as plt\n",
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"import ipywidgets as widgets\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:37.783189Z",
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"start_time": "2020-06-30T14:44:37.771507Z"
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}
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},
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"outputs": [],
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"source": [
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"# generate some fake \n",
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"n = 2000\n",
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"x = np.linspace(0.0, 10.0, n)\n",
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"np.random.seed(0)\n",
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"y = np.cumsum(np.random.randn(n)*10).astype(int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:37.916067Z",
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"start_time": "2020-06-30T14:44:37.789416Z"
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}
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},
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"outputs": [],
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"source": [
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"fig_hist = plt.figure( title='Histogram in Voila')\n",
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"hist = plt.hist(y, bins=25, colors=['steelblue'])\n",
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"fig_hist"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:39.324769Z",
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"start_time": "2020-06-30T14:44:39.302431Z"
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}
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},
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"outputs": [],
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"source": [
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"hist.bins = 10;"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:39.967211Z",
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"start_time": "2020-06-30T14:44:39.947076Z"
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}
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},
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"outputs": [],
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"source": [
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"slider = widgets.IntSlider(min=1, max=100, v_model=30)\n",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:41.005101Z",
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"start_time": "2020-06-30T14:44:40.998667Z"
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}
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},
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"outputs": [],
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"source": [
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"slider.value = 20;"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:41.678387Z",
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"start_time": "2020-06-30T14:44:41.667654Z"
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}
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},
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"outputs": [],
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"source": [
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"widgets.link((hist, 'bins'), (slider, 'value'));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:42.384650Z",
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"start_time": "2020-06-30T14:44:42.280810Z"
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}
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},
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"outputs": [],
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"source": [
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"fig_lines = plt.figure( title='Line Chart')\n",
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"lines = plt.plot(x, y)\n",
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"fig_lines"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-30T14:44:43.494747Z",
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"start_time": "2020-06-30T14:44:43.473428Z"
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}
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},
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"outputs": [],
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"source": [
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"selector = plt.brush_int_selector()\n",
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"def update_range(*ignore):\n",
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" if selector.selected is not None and len(selector.selected) == 2:\n",
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" xmin, xmax = selector.selected\n",
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" mask = (x > xmin) & (x < xmax)\n",
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" hist.sample = y[mask]\n",
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"selector.observe(update_range, 'selected') "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎2020-06-30-PyData-Budapest/02-xplot.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"xeus-cling.svg\">"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#include <vector>\n",
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"#include <string>\n",
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"\n",
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"#include \"xtensor/xtensor.hpp\"\n",
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"#include \"xtensor/xrandom.hpp\"\n",
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"#include \"xtensor/xeval.hpp\"\n",
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"#include \"xtensor/xmath.hpp\"\n",
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"\n",
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"#include \"xwidgets/xslider.hpp\"\n",
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"#include \"xwidgets/xlink.hpp\"\n",
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"\n",
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"#include \"xplot/xfigure.hpp\"\n",
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"#include \"xplot/xmarks.hpp\"\n",
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"#include \"xplot/xaxes.hpp\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"std::size_t n = 200;\n",
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"\n",
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"auto x = xt::linspace(0.0, 10.0, n);\n",
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"\n",
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"xt::random::seed(0);\n",
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"auto cumsum = xt::eval(xt::cumsum(xt::random::randn<double>({n})));\n",
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"auto y = xt::eval(xt::floor(cumsum * 10.0));\n",
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"\n",
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"auto fig = xpl::figure().title(\"Histogram\").finalize();\n",
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"\n",
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"xpl::linear_scale xs, ys;\n",
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"xpl::hist hist(xs, ys);\n",
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"hist.sample = y;\n",
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"hist.bins = 25;\n",
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"hist.colors = std::vector<std::string>({\"#1f77b4\"});\n",
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"\n",
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"xpl::axis hx(xs), hy(ys);\n",
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"hy.orientation = \"vertical\";\n",
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"fig.add_axis(hx);\n",
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"fig.add_axis(hy);\n",
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"\n",
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"fig.add_mark(hist);\n",
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"fig"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"auto slider = xw::slider<std::size_t>()\n",
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" .min(1).max(100)\n",
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" .value(30).finalize();\n",
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"auto l = xw::link(slider, \"value\", hist, \"bins\");\n",
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"slider"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "C++14",
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"language": "C++14",
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"name": "xcpp14"
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},
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"language_info": {
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"codemirror_mode": "text/x-c++src",
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"file_extension": ".cpp",
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"mimetype": "text/x-c++src",
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"name": "c++",
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"version": "14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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