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 export_charts installed, e.g. via uv pip install -r pyproject.toml --extra export_charts. This is automatically resolved when running this script via uv run export_charts.py.

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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,
    check_all_types_accounted_for,
)
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)
    check_all_types_accounted_for(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()