Gallery bokeh

Test_2.py

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+#MultiGlyph
+from bokeh.plotting import figure, show
+
+# prepare some data
+x = [1, 2, 3, 4, 5]
+y1 = [6, 7, 8, 9, 10]
+y2 = [11, 3, 4, 5, 6]
+y3 = [4, 5, 15, 7, 2]
+
+# create a new plot with a title and axis labels
+p = figure(title="Multiple glyphs example", x_axis_label="x", y_axis_label="y")
+
+# add multiple renderers
+p.line(x, y1, legend_label="Temp.", color="blue", line_width=2)
+p.vbar(x=x, top=y2, legend_label="Rate", width=0.5, bottom=0, color="red")
+p.circle(x, y3, legend_label="Objects", color="yellow", size=12)
+
+# show the results
+show(p)

histogram.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from bokeh.layouts import column\n",
+    "from bokeh.models import Div, TeX\n",
+    "from bokeh.plotting import figure, show\n",
+    "\n",
+    "p = figure(width=670, height=400, toolbar_location=None,\n",
+    "           title=\"Normal (Gaussian) Distribution\")\n",
+    "\n",
+    "n = 1000\n",
+    "rng = np.random.default_rng(825914)\n",
+    "x = rng.normal(loc=4.7, scale=12.3, size=n)\n",
+    "\n",
+    "# Scale random data so that it has mean of 0 and standard deviation of 1\n",
+    "xbar = x.mean()\n",
+    "sigma = x.std()\n",
+    "scaled = (x - xbar) / sigma\n",
+    "\n",
+    "# Histogram\n",
+    "bins = np.linspace(-3, 3, 40)\n",
+    "hist, edges = np.histogram(scaled, density=True, bins=bins)\n",
+    "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
+    "         fill_color=\"pink\", line_color=\t\"purple\",\n",
+    "         legend_label=f\"{n} random samples\")\n",
+    "\n",
+    "# Probability density function\n",
+    "x = np.linspace(-3.0, 3.0, 100)\n",
+    "pdf = np.exp(-0.5*x**2) / np.sqrt(2.0*np.pi)\n",
+    "p.line(x, pdf, line_width=2, line_color=\"purple\",\n",
+    "       legend_label=\"Probability Density Function\")\n",
+    "\n",
+    "p.y_range.start = 0\n",
+    "p.xaxis.axis_label = \"x\"\n",
+    "p.yaxis.axis_label = \"PDF(x)\"\n",
+    "\n",
+    "p.xaxis.ticker = [-3, -2, -1, 0, 1, 2, 3]\n",
+    "p.xaxis.major_label_overrides = {\n",
+    "    -3: TeX(r\"\\overline{x} - 3\\sigma\"),\n",
+    "    -2: TeX(r\"\\overline{x} - 2\\sigma\"),\n",
+    "    -1: TeX(r\"\\overline{x} - \\sigma\"),\n",
+    "     0: TeX(r\"\\overline{x}\"),\n",
+    "     1: TeX(r\"\\overline{x} + \\sigma\"),\n",
+    "     2: TeX(r\"\\overline{x} + 2\\sigma\"),\n",
+    "     3: TeX(r\"\\overline{x} + 3\\sigma\"),\n",
+    "}\n",
+    "\n",
+    "p.yaxis.ticker = [0, 0.1, 0.2, 0.3, 0.4]\n",
+    "p.yaxis.major_label_overrides = {\n",
+    "    0: TeX(r\"0\"),\n",
+    "    0.1: TeX(r\"0.1/\\sigma\"),\n",
+    "    0.2: TeX(r\"0.2/\\sigma\"),\n",
+    "    0.3: TeX(r\"0.3/\\sigma\"),\n",
+    "    0.4: TeX(r\"0.4/\\sigma\"),\n",
+    "}\n",
+    "\n",
+    "div = Div(text=r\"\"\"\n",
+    "A histogram of a samples from a Normal (Gaussian) distribution, together with\n",
+    "the ideal probability density function, given by the equation:\n",
+    "<p />\n",
+    "$$\n",
+    "\\qquad PDF(x) = \\frac{1}{\\sigma\\sqrt{2\\pi}} \\exp\\left[-\\frac{1}{2}\n",
+    "\\left(\\frac{x-\\overline{x}}{\\sigma}\\right)^2 \\right]\n",
+    "$$\n",
+    "\"\"\")\n",
+    "\n",
+    "show(column(p, div))"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

jitter_plot.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bokeh.layouts import column\n",
+    "from bokeh.plotting import figure, show\n",
+    "from bokeh.sampledata.autompg import autompg\n",
+    "from bokeh.transform import jitter,factor_cmap\n",
+    "from bokeh.io import curdoc\n",
+    "\n",
+    "curdoc().theme = 'dark_minimal'\n",
+    "years = sorted(autompg.yr.unique())\n",
+    "\n",
+    "p1 = figure(width=600, height=300, title=\"Years vs mpg without jittering\")\n",
+    "p1.xgrid.grid_line_color = None\n",
+    "p1.xaxis.ticker = years\n",
+    "\n",
+    "p1.scatter(x='yr', y='mpg', size=9, alpha=0.4,fill_color=\"green\", source=autompg)\n",
+    "\n",
+    "p2 = figure(width=600, height=300, title=\"Years vs mpg with jittering\")\n",
+    "p2.xgrid.grid_line_color = None\n",
+    "p2.xaxis.ticker = years\n",
+    "\n",
+    "p2.scatter(x=jitter('yr', 0.4), y='mpg', size=9, alpha=0.4,fill_color=\"red\", source=autompg)\n",
+    "\n",
+    "show(column(p1, p2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

slope.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from bokeh.palettes import Turbo256\n",
+    "from bokeh.models import Slope\n",
+    "from bokeh.plotting import figure, show\n",
+    "from bokeh.transform import linear_cmap\n",
+    "\n",
+    "# linear equation parameters\n",
+    "slope, intercept = 2, 10\n",
+    "\n",
+    "x = list(range(0, 8))\n",
+    "y = [i**2 for i in x]\n",
+    "# create linear color mapper\n",
+    "mapper = linear_cmap(field_name=\"y\", palette=Turbo256, low=min(y), high=max(y))\n",
+    "\n",
+    "xpts = np.arange(0, 20, 0.2)\n",
+    "ypts = slope * xpts + intercept + np.random.normal(0, 4, 100)\n",
+    "\n",
+    "p = figure(width=450, height=450, x_axis_label='x', y_axis_label='y',\n",
+    "           background_fill_color=\"#fafafa\")\n",
+    "p.y_range.start = 0\n",
+    "\n",
+    "p.circle(xpts, ypts, size=6, alpha=0.6 ,fill_color=mapper)\n",
+    "\n",
+    "slope = Slope(gradient=slope, y_intercept=intercept,\n",
+    "              line_color=\"deeppink\", line_dash='dashed', line_width=4)\n",
+    "\n",
+    "p.add_layout(slope)\n",
+    "\n",
+    "show(p)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}