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.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: a71338dbc41bde02cdd4e7b6cbe6ae59
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/464b65b24fdedc416eded46199f2935d/export_charts.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Export Accelerator Timeline\n\nThis is an example script to generate static plots of the accelerator data via \nmatplotlib.\nTo run the script, make sure your environment has the requirements \nof `requirements_export_charts.txt` installed.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\nfrom pathlib import Path\n\nimport matplotlib as mpl\nimport matplotlib.ticker as plticker\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom matplotlib.figure import Figure\n\nfrom utilities.csv_reader import Column, import_collider_data\nfrom utilities.plot_helper import (PARTICLE_TYPES, PLOTLY_MPL_SYMBOL_MAP, EnergyConfiguration,\n                                   LuminosityConfiguration, LuminosityOverEnergyConfiguration,\n                                   PlotConfiguration, assign_textposition)\nfrom utilities.sphinx_helper import get_gallery_dir, is_sphinx_build\n\n\ndef plot(data: pd.DataFrame, configuration: PlotConfiguration) -> Figure:\n    \"\"\"Generate interactive plots with matplotlib, based on the given configuration, \n    which defines the columns to use, labels and the text positions.\n\n    Args:\n        data (pd.DataFrame): DataFrame containing the (modified) accelerator timeline data\n        configuration (PlotConfiguration): See :class:`utilities.plot_helper.PlotConfiguration`\n\n    Returns:\n        Figure: Matplotlib figure \n    \"\"\"\n    fig, ax = plt.subplots()\n            \n    pad = mpl.rcParams[\"lines.markersize\"]/3\n    vmap = {\"top\": pad, \"middle\": 0, \"bottom\": -pad}\n    hmap = {\"left\": -pad*2, \"center\": 0, \"right\": pad*2}\n    alignment_map = {\n        \"left\": \"right\", \"center\": \"center\", \"right\": \"left\", \n        \"top\": \"bottom\", \"middle\": \"center\", \"bottom\": \"top\"\n    }\n\n    for particle_type in PARTICLE_TYPES:\n        mask = data[Column.TYPE] == particle_type.shorthand\n        marker = PLOTLY_MPL_SYMBOL_MAP[particle_type.symbol]\n\n        for has_been_built in (True, False):\n            if has_been_built:\n                builtmask, fillstyle, legend_prefix = data[Column.BUILT], \"full\", \"\"\n            else:\n                builtmask, fillstyle, legend_prefix = ~data[Column.BUILT], \"none\", \"_\"\n\n            ax.plot(\n                data.loc[mask & builtmask, configuration.xcolumn], \n                data.loc[mask & builtmask, configuration.ycolumn],\n                linestyle=\"none\",\n                marker=marker, fillstyle=fillstyle,\n                color=particle_type.color,\n                label=f\"{legend_prefix}{particle_type.latex}\",\n            )\n\n        for x, y, text, textposition in zip(data.loc[mask, configuration.xcolumn], \n                                            data.loc[mask, configuration.ycolumn], \n                                            data.loc[mask, Column.NAME], \n                                            data.loc[mask, configuration.textposition]):\n            v, h = textposition.split(\" \")\n            ax.annotate(text, xy=(x, y),  \n                xytext=(hmap[h], vmap[v]), \n                textcoords=\"offset pixels\", \n                ha=alignment_map[h], va=alignment_map[v]\n            )\n\n    ax.set_xlabel(configuration.xlabel)\n    ax.set_ylabel(configuration.ylabel)\n    for axis in (\"x\", \"y\"):\n        if axis in configuration.logscale:\n            getattr(ax, f\"set_{axis}scale\")(\"log\")\n            lim = getattr(ax, f\"get_{axis}lim\")()\n            numticks = int(np.log10(lim[1]/lim[0])) + 1\n            getattr(ax, f\"{axis}axis\").set_major_locator(plticker.LogLocator(base=10.0, numticks=numticks))\n            getattr(ax, f\"{axis}axis\").set_minor_locator(plticker.LogLocator(base=10.0, subs=np.arange(2, 10)))\n            getattr(ax, f\"{axis}axis\").set_minor_formatter(plticker.NullFormatter())\n        else:\n            getattr(ax, f\"set_{axis}scale\")(\"linear\")\n            getattr(ax, f\"{axis}axis\").set_major_locator(plticker.MultipleLocator(base=10.0))\n            getattr(ax, f\"{axis}axis\").set_minor_locator(plticker.MultipleLocator(base=1.0))\n\n\n    ax.legend(loc='upper left', bbox_to_anchor=(1, 1), borderaxespad=0., title='Particles', ncol=1)\n    return fig \n\n\nif __name__ == \"__main__\":\n    if is_sphinx_build():\n        MAIN_DIR = Path()\n        output_dir = get_gallery_dir()\n    else:\n        MAIN_DIR = Path(__file__).parent\n        output_dir = MAIN_DIR / \"images\"\n\n    plt.style.use(MAIN_DIR / \"utilities\" / \"chart.mplstyle\")\n\n    data = import_collider_data()\n    data = assign_textposition(data)\n    \n    fig_com = plot(data, EnergyConfiguration)\n    fig_com.savefig(output_dir / \"energy.pdf\")\n    fig_com.savefig(output_dir / \"energy.png\")\n\n    fig_lumi = plot(data, LuminosityConfiguration)\n    fig_lumi.savefig(output_dir / \"luminosity.pdf\")\n    fig_lumi.savefig(output_dir / \"luminosity.png\")\n\n    fig_lumi_vs_com = plot(data, LuminosityOverEnergyConfiguration)\n    fig_lumi_vs_com.savefig(output_dir / \"luminosity-vs-energy.pdf\")\n    fig_lumi_vs_com.savefig(output_dir / \"luminosity-vs-energy.png\")\n    \n    # plt.show()"
+      ]
+    }
+  ],
+  "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": "3.9.17"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/4b4f2203ddc9d489e59223854d32d17e/interactive_charts.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Interactive Accelerator Timeline\n\nThis script allows you to interactively explore the accelerator data,\neither by running the script and viewing the plots in a browser,\nby running the script in interactive-mode e.g. in vscode \nor by checking the from this script generated gallery.\n\nTo run the script, make sure your environment has the requirements \nof `requirements_interactive_charts.txt` installed.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preparations \n\nImport modules and define plotting function.\nThis code is omitted in the interactive gallery, so that you can immediately enjoy the interactive plots below.\nCheck [interactive.py](https://github.com/pylhc/accelerator_timeline/blob/master/interactive.py) \nfor the full example code.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# No code to see here in the interactive gallery or the generated jupyter notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Energy Timeline\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig_com = plot(data, EnergyConfiguration)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Luminosity timeline\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig_lumi = plot(data, LuminosityConfiguration)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Luminosity vs. Energy \n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig_lumi_energy = plot(data, LuminosityOverEnergyConfiguration)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Save plots\n\nSave the plots as PDF and PNG.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_dir = Path(\"images\")\n\nplotly.io.write_image(fig_com, output_dir / \"energy-plotly.pdf\", format=\"pdf\")\nplotly.io.write_image(fig_com, output_dir / \"energy-plotly.png\", format=\"png\")\nplotly.io.write_image(fig_lumi, output_dir / \"luminosity-plotly.pdf\", format=\"pdf\")\nplotly.io.write_image(fig_lumi, output_dir / \"luminosity-plotly.png\", format=\"png\")\nplotly.io.write_image(fig_lumi_energy, output_dir / \"luminosity-vs-energy-plotly.pdf\", format=\"pdf\")\nplotly.io.write_image(fig_lumi_energy, output_dir / \"luminosity-vs-energy-plotly.png\", format=\"png\")"
+      ]
+    }
+  ],
+  "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": "3.9.17"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/a3e0be0bf8d99a8bc1746d8344c38a82/export_charts.py

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+""" 
+Export Accelerator Timeline
+***************************
+
+This is an example script to generate static plots of the accelerator data via 
+matplotlib.
+To run the script, make sure your environment has the requirements 
+of `requirements_export_charts.txt` installed.
+"""
+import os
+from pathlib import Path
+
+import matplotlib as mpl
+import matplotlib.ticker as plticker
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+from matplotlib.figure import Figure
+
+from utilities.csv_reader import Column, import_collider_data
+from utilities.plot_helper import (PARTICLE_TYPES, PLOTLY_MPL_SYMBOL_MAP, EnergyConfiguration,
+                                   LuminosityConfiguration, LuminosityOverEnergyConfiguration,
+                                   PlotConfiguration, assign_textposition)
+from utilities.sphinx_helper import get_gallery_dir, is_sphinx_build
+
+
+def plot(data: pd.DataFrame, configuration: PlotConfiguration) -> Figure:
+    """Generate interactive plots with matplotlib, based on the given configuration, 
+    which defines the columns to use, labels and the text positions.
+
+    Args:
+        data (pd.DataFrame): DataFrame containing the (modified) accelerator timeline data
+        configuration (PlotConfiguration): See :class:`utilities.plot_helper.PlotConfiguration`
+
+    Returns:
+        Figure: Matplotlib figure 
+    """
+    fig, ax = plt.subplots()
+
+    pad = mpl.rcParams["lines.markersize"]/3
+    vmap = {"top": pad, "middle": 0, "bottom": -pad}
+    hmap = {"left": -pad*2, "center": 0, "right": pad*2}
+    alignment_map = {
+        "left": "right", "center": "center", "right": "left", 
+        "top": "bottom", "middle": "center", "bottom": "top"
+    }
+
+    for particle_type in PARTICLE_TYPES:
+        mask = data[Column.TYPE] == particle_type.shorthand
+        marker = PLOTLY_MPL_SYMBOL_MAP[particle_type.symbol]
+
+        for has_been_built in (True, False):
+            if has_been_built:
+                builtmask, fillstyle, legend_prefix = data[Column.BUILT], "full", ""
+            else:
+                builtmask, fillstyle, legend_prefix = ~data[Column.BUILT], "none", "_"
+
+            ax.plot(
+                data.loc[mask & builtmask, configuration.xcolumn], 
+                data.loc[mask & builtmask, configuration.ycolumn],
+                linestyle="none",
+                marker=marker, fillstyle=fillstyle,
+                color=particle_type.color,
+                label=f"{legend_prefix}{particle_type.latex}",
+            )
+
+        for x, y, text, textposition in zip(data.loc[mask, configuration.xcolumn], 
+                                            data.loc[mask, configuration.ycolumn], 
+                                            data.loc[mask, Column.NAME], 
+                                            data.loc[mask, configuration.textposition]):
+            v, h = textposition.split(" ")
+            ax.annotate(text, xy=(x, y),  
+                xytext=(hmap[h], vmap[v]), 
+                textcoords="offset pixels", 
+                ha=alignment_map[h], va=alignment_map[v]
+            )
+
+    ax.set_xlabel(configuration.xlabel)
+    ax.set_ylabel(configuration.ylabel)
+    for axis in ("x", "y"):
+        if axis in configuration.logscale:
+            getattr(ax, f"set_{axis}scale")("log")
+            lim = getattr(ax, f"get_{axis}lim")()
+            numticks = int(np.log10(lim[1]/lim[0])) + 1
+            getattr(ax, f"{axis}axis").set_major_locator(plticker.LogLocator(base=10.0, numticks=numticks))
+            getattr(ax, f"{axis}axis").set_minor_locator(plticker.LogLocator(base=10.0, subs=np.arange(2, 10)))
+            getattr(ax, f"{axis}axis").set_minor_formatter(plticker.NullFormatter())
+        else:
+            getattr(ax, f"set_{axis}scale")("linear")
+            getattr(ax, f"{axis}axis").set_major_locator(plticker.MultipleLocator(base=10.0))
+            getattr(ax, f"{axis}axis").set_minor_locator(plticker.MultipleLocator(base=1.0))
+
+
+    ax.legend(loc='upper left', bbox_to_anchor=(1, 1), borderaxespad=0., title='Particles', ncol=1)
+    return fig 
+
+
+if __name__ == "__main__":
+    if is_sphinx_build():
+        MAIN_DIR = Path()
+        output_dir = get_gallery_dir()
+    else:
+        MAIN_DIR = Path(__file__).parent
+        output_dir = MAIN_DIR / "images"
+
+    plt.style.use(MAIN_DIR / "utilities" / "chart.mplstyle")
+
+    data = import_collider_data()
+    data = assign_textposition(data)
+
+    fig_com = plot(data, EnergyConfiguration)
+    fig_com.savefig(output_dir / "energy.pdf")
+    fig_com.savefig(output_dir / "energy.png")
+
+    fig_lumi = plot(data, LuminosityConfiguration)
+    fig_lumi.savefig(output_dir / "luminosity.pdf")
+    fig_lumi.savefig(output_dir / "luminosity.png")
+
+    fig_lumi_vs_com = plot(data, LuminosityOverEnergyConfiguration)
+    fig_lumi_vs_com.savefig(output_dir / "luminosity-vs-energy.pdf")
+    fig_lumi_vs_com.savefig(output_dir / "luminosity-vs-energy.png")
+
+    # plt.show()
+
+# sphinx_gallery_thumbnail_path = 'gallery/luminosity-vs-energy.png'