Deploying to gh-pages from @ 2851030 🚀

.buildinfo

@@ -1,4 +1,4 @@
 # 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: b3fef996bce6e2164f4694e9f57bc2e3
+config: 503564ba66c0cf62f00def5dbb8f5a47
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/464b65b24fdedc416eded46199f2935d/export_charts.ipynb

@@ -15,7 +15,7 @@
       },
       "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()"
+        "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, check_all_types_accounted_for)\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    check_all_types_accounted_for(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()"
       ]
     }
   ],

_downloads/4b4f2203ddc9d489e59223854d32d17e/interactive_charts.ipynb

@@ -11,7 +11,7 @@
       "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"
+        "## 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_charts.py) \nfor the full example code.\n\n\n"
       ]
     },
     {

_downloads/a3e0be0bf8d99a8bc1746d8344c38a82/export_charts.py

@@ -20,7 +20,7 @@
 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)
+                                   PlotConfiguration, assign_textposition, check_all_types_accounted_for)
 from utilities.sphinx_helper import get_gallery_dir, is_sphinx_build
 
 
@@ -107,6 +107,7 @@ def plot(data: pd.DataFrame, configuration: PlotConfiguration) -> Figure:
 
     data = import_collider_data()
     data = assign_textposition(data)
+    check_all_types_accounted_for(data)
 
     fig_com = plot(data, EnergyConfiguration)
     fig_com.savefig(output_dir / "energy.pdf")

_downloads/b760214149b3052618b0077a3658b14f/interactive_charts.py

@@ -16,7 +16,7 @@
 # 
 # Import modules and define plotting function.
 # This code is omitted in the interactive gallery, so that you can immediately enjoy the interactive plots below.
-# Check `interactive.py <https://github.com/pylhc/accelerator_timeline/blob/master/interactive.py>`_ 
+# Check `interactive.py <https://github.com/pylhc/accelerator_timeline/blob/master/interactive_charts.py>`_ 
 # for the full example code.
 #
 
@@ -32,7 +32,7 @@
 from utilities.csv_reader import Column, import_collider_data
 from utilities.plot_helper import (PARTICLE_TYPES, EnergyConfiguration, LuminosityConfiguration,
                                    LuminosityOverEnergyConfiguration, PlotConfiguration,
-                                   assign_textposition)
+                                   assign_textposition, check_all_types_accounted_for)
 from utilities.sphinx_helper import get_gallery_dir, is_interactive, is_sphinx_build
 
 # Hack for rendering LaTeX in VSCode 
@@ -45,6 +45,7 @@
 # Import Data ---
 data = import_collider_data()
 data = assign_textposition(data)
+check_all_types_accounted_for(data)
 
 # Plotting Function ---
 # This is the definition of the actual plotting function,