use assert_cmd::Command;
use std::num::NonZeroUsize;
use std::thread;

const HELP_STR: &str = "Creates a matplotlib script plotting the contents of the grid

Usage: pineappl plot [OPTIONS] <INPUT> <CONV_FUNS>...

Arguments:
  <INPUT>         Path to the input grid
  <CONV_FUNS>...  LHAPDF id(s) or name of the PDF set(s)

Options:
      --conv-fun-uncert-from <IDX>     Choose for which convolution function the uncertainty should be calculated [default: 0]
  -s, --scales <SCALES>                Set the number of scale variations [default: 7] [possible values: 1, 3, 7, 9]
      --subgrid-pull <ORDER,BIN,CHAN>  Show the pull for a specific grid three-dimensionally
      --asymmetry                      Plot the asymmetry
      --threads <THREADS>              Number of threads to utilize [default: {}]
      --no-conv-fun-unc                Disable the (time-consuming) calculation of PDF uncertainties
  -h, --help                           Print help
";

const DEFAULT_STR: &str = r#"#!/usr/bin/env python3

import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pickle

# color cycler for different PDF results
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
# color for the first PDF result with QCD-only predictions
colors0_qcd = "red"

# stylesheet for plot
stylesheet = {
    "axes.axisbelow": True,
    "axes.grid": True,
    "axes.labelsize": "small",
    "figure.constrained_layout.hspace": 0.0,
    "figure.constrained_layout.use": True,
    "figure.constrained_layout.wspace": 0.0,
    "font.family": "serif",
    "font.size": 14.0,
    "grid.linestyle": "dotted",
    "legend.borderpad": 0.0,
    "legend.fontsize": "x-small",
    "legend.frameon": False,
    "pdf.compression": 0,
    "text.usetex": True,
    "text.latex.preamble": r"\usepackage{siunitx}\usepackage{lmodern}\usepackage[T1]{fontenc}",
    "xtick.bottom": True,
    "xtick.top": True,
    "xtick.direction": "in",
    "xtick.major.width": 0.5,
    "xtick.minor.bottom": True,
    "xtick.minor.top": True,
    "xtick.minor.width": 0.5,
    "ytick.direction": "in",
    "ytick.left": True,
    "ytick.right": True,
    "ytick.major.width": 0.5,
    "ytick.minor.visible": True,
    "ytick.minor.width": 0.5,
}

# global plot labels
title  = r"LHCb differential W-boson production cross section at 7 TeV"
xlabel = r"$\eta_{\bar{\ell}}$"
ylabel = r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\eta_{\bar{\ell}}}$ [\si{pb}]"

# panel plot labels
ylabel_ratio_pdf   = "Ratio to {central_pdf}"
ylabel_double_ratio_pdf = "Ratio to NLO"
ylabel_rel_ewonoff = "NLO EW on/off [\si{\percent}]"
ylabel_rel_pdfunc  = "PDF uncertainty [\si{\percent}]"
ylabel_rel_pdfpull = "Pull [$\sigma$]"

label_rel_ewonoff_qcd       = "NLO QCD"
label_rel_ewonoff_ew        = "NLO QCD+EW"
label_rel_ewonoff_scale_unc = "7-p.\ scale var."
label_rel_ewonoff_pdf_unc   = "PDF uncertainty"

xlog = False
ylog = False


def main():
    panels = [
        # plot_int,
        plot_abs,
        plot_rel_ewonoff,
        plot_abs_pdfs,
        plot_ratio_pdf,
        plot_double_ratio_pdf,
        plot_rel_pdfunc,
        plot_rel_pdfpull,
    ]

    mpl.rcParams.update(stylesheet)
    plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
    # plt.rc("figure", figsize=(4.2, 2.6))

    data_slices = data()

    for index, kwargs in enumerate(data_slices):
        figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)

        if len(kwargs["x"]) > 2 and xlog:
            axes[0, 0].set_xscale("log")

        axes[0, 0].set_title(title)
        axes[-1, 0].set_xlabel(xlabel)

        for plot, axis in zip(panels, axes[:, 0]):
            plot(axis, **kwargs)

        if len(data_slices) == 1:
            figure.savefig("LHCB_WP_7TEV.pdf")
        else:
            figure.savefig("LHCB_WP_7TEV-{}.pdf".format(index))
        plt.close(figure)


def percent_diff(a, b):
    return (a / b - 1.0) * 100.0


def set_ylim(axis, save, load, filename):
    # extract the y limits *not* considering margins
    margins = axis.margins()
    axis.margins(y=0.0)
    ymin, ymax = axis.get_ylim()
    axis.margins(y=margins[1])

    inc = 1.0

    if (ymax - ymin) > 100.0:
        ymin = -50.0
        ymax = 50.0
        inc = 25.0
    elif (ymax - ymin) > 30.5:
        inc = 10.0
    elif (ymax - ymin) > 20.5:
        inc = 5.0
    elif (ymax - ymin) > 10.5:
        inc = 2.0
    elif (ymax - ymin) < 3.0:
        inc = 0.5

    ymin = math.floor(ymin / inc) * inc
    ymax = math.ceil(ymax / inc) * inc

    if save:
        with open(filename, "wb") as f:
            pickle.dump([ymin, ymax, inc], f)

    if load:
        resave = False

        with open(filename, "rb") as f:
            [saved_ymin, saved_ymax, saved_inc] = pickle.load(f)

        if saved_ymin < ymin:
            ymin = saved_ymin
            resave = True

        if saved_ymax > ymax:
            ymax = saved_ymax
            resave = True

        if saved_inc > inc:
            inc = saved_inc
            resave = True

        if resave:
            with open(filename, "wb") as f:
                pickle.dump([ymin, ymax, inc], f)

    axis.set_yticks(np.arange(ymin, ymax + inc, inc))
    space = 0.05 * (ymax - ymin)
    axis.set_ylim((ymin - space, ymax + space))


def plot_int(axis, **kwargs):
    xmin = np.array([])
    xmax = np.array([])
    x = np.array([])
    y = np.array([])

    for index, i in enumerate(kwargs["pdf_results"]):
        label, ycentral, ymin, ymax = i
        x = np.append(x, ycentral[:-1])
        xmin = np.append(xmin, ymin[:-1])
        xmax = np.append(xmax, ymax[:-1])
        y = np.append(y, label)

        # draw one- and two-sigma bands
        if label == "CENTRAL-PDF":
            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
            # TODO: this is only correct for MC PDF uncertainties
            axis.axvspan(x[-1] - 2.0 * (x[-1] - xmin[-1]), x[-1] + 2.0 * (xmax[-1] - x[-1]), alpha=0.1, color=colors[index], linewidth=0)

    axis.errorbar(x, y, xerr=(x - xmin, xmax - x), fmt=".", capsize=3, markersize=5, linewidth=1.5)
    axis.margins(x=0.1, y=0.1)


def plot_abs(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]

    axis.set_yscale("log" if ylog else "linear")
    axis.step(x, kwargs["y"], colors[0], linewidth=1.0, where="post", label=slice_label)
    axis.fill_between(x, kwargs["ymin"], kwargs["ymax"], alpha=0.4, color=colors[0], linewidth=0.5, step="post")
    axis.set_ylabel(ylabel)

    if slice_label != "":
        axis.legend()


def plot_ratio_pdf(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]

    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_uncertainties[0][0]))

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        y = y / pdf_uncertainties[0][1]
        ymin = ymin / pdf_uncertainties[0][1]
        ymax = ymax / pdf_uncertainties[0][1]

        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_double_ratio_pdf(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]

    axis.set_ylabel(ylabel_double_ratio_pdf)

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        if index == 0 or index == 2:
            y = y / pdf_uncertainties[0][1]
            ymin = ymin / pdf_uncertainties[0][1]
            ymax = ymax / pdf_uncertainties[0][1]
        else:
            y = y / pdf_uncertainties[1][1]
            ymin = ymin / pdf_uncertainties[1][1]
            ymax = ymax / pdf_uncertainties[1][1]
        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_abs_pdfs(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]
    channels = kwargs["channels"]

    axis.set_yscale("log" if ylog else "linear")
    axis.set_ylabel(ylabel)

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    linestyles = ["--", ":"]
    for index, ((label, y), linestyle) in enumerate(zip(channels, linestyles)):
        axis.step(x, y, color=colors[0], label=label, linestyle=linestyle, linewidth=1.0, where="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties) + len(linestyles)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_rel_ewonoff(axis, **kwargs):
    x = kwargs["x"]
    y = percent_diff(kwargs["y"], kwargs["qcd_y"])
    qcd_y = percent_diff(kwargs["qcd_y"], kwargs["qcd_y"])
    qcd_ymin = percent_diff(kwargs["qcd_min"], kwargs["qcd_y"])
    qcd_ymax = percent_diff(kwargs["qcd_max"], kwargs["qcd_y"])
    ymin = percent_diff(kwargs["ymin"], kwargs["qcd_y"])
    ymax = percent_diff(kwargs["ymax"], kwargs["qcd_y"])
    pdf_min = abs(percent_diff(kwargs["pdf_results"][0][2], kwargs["pdf_results"][0][1]))[:-1]
    pdf_max = abs(percent_diff(kwargs["pdf_results"][0][3], kwargs["pdf_results"][0][1]))[:-1]

    axis.step(x, qcd_y, colors0_qcd, label=label_rel_ewonoff_qcd, linewidth=1.0, where="post")
    # axis.fill_between(x, qcd_ymin, qcd_ymax, alpha=0.4, color=colors0_qcd, label=label_rel_ewonoff_scale_unc, linewidth=0.5, step="post")
    axis.step(x, y, colors[0], label=label_rel_ewonoff_ew, linewidth=1.0, where="post")
    axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[0], label=label_rel_ewonoff_scale_unc, linewidth=0.5, step="post")
    axis.errorbar(kwargs["mid"], y[:-1], yerr=(pdf_min, pdf_max), color=colors[0], label=label_rel_ewonoff_pdf_unc, fmt=".", capsize=1, markersize=0, linewidth=1)
    axis.set_ylabel(ylabel_rel_ewonoff)
    axis.legend(bbox_to_anchor=(0, 1.03, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=4)


def plot_rel_pdfunc(axis, **kwargs):
    x = kwargs["x"]
    pdf_uncertainties = kwargs["pdf_results"]

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        ymin = percent_diff(ymin, y)
        ymax = percent_diff(ymax, y)
        axis.step(x, ymax, color=colors[index], label=label, linewidth=1, where="post")
        axis.step(x, ymin, color=colors[index], linewidth=1, where="post")

    axis.set_ylabel(ylabel_rel_pdfunc)

    set_ylim(axis, False, False, "rel_pdfunc")


def plot_rel_pdfpull(axis, **kwargs):
    central_y = kwargs["pdf_results"][0][1]
    central_ymin = kwargs["pdf_results"][0][2]
    central_ymax = kwargs["pdf_results"][0][3]
    pdf_uncertainties = kwargs["pdf_results"]
    x = kwargs["x"]
    y = kwargs["y"]

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        diff = y - central_y
        yerr = np.where(diff > 0.0, y - ymin, ymax - y)
        cerr = np.where(diff > 0.0, central_ymax - central_y, central_y - central_ymin)
        pull = diff / np.sqrt(np.power(yerr, 2) + np.power(cerr, 2))

        axis.step(x, pull, color=colors[index], label=label, linewidth=1, where="post", zorder=2 * index + 1)

    axis.legend(bbox_to_anchor=(0, 1.03, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties))) #rel_pdfpull
    axis.set_ylabel(ylabel_rel_pdfpull)

    set_ylim(axis, False, False, "rel_pdfpull")


def data():
    return [
        {
            "slice_label"    : r"",
            "x"        : np.array([2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 4, 4.5]),
            "mid"      : np.array([2.125, 2.375, 2.625, 2.875, 3.125, 3.375, 3.75, 4.25]),
            "pdf_results" : [
                (
                    "NNPDF31\_nlo\_as\_0118\_luxqed",
                    np.array([7.5461655e2, 6.9027941e2, 6.0022595e2, 4.8548211e2, 3.6191001e2, 2.4582640e2, 1.1584074e2, 2.7504644e1, 2.7504644e1]),
                    np.array([7.4602445e2, 6.8228693e2, 5.9312383e2, 4.7957251e2, 3.5732461e2, 2.4250580e2, 1.1409722e2, 2.6743047e1, 2.6743047e1]),
                    np.array([7.6320866e2, 6.9827189e2, 6.0732807e2, 4.9139170e2, 3.6649540e2, 2.4914699e2, 1.1758425e2, 2.8266240e1, 2.8266240e1]),
                ),
                (
                    "NNPDF4.0",
                    np.array([7.8845642e2, 7.2060104e2, 6.2525179e2, 5.0384928e2, 3.7399422e2, 2.5300320e2, 1.1909100e2, 2.9002507e1, 2.9002507e1]),
                    np.array([7.8450378e2, 7.1690933e2, 6.2191897e2, 5.0097415e2, 3.7162070e2, 2.5109709e2, 1.1772232e2, 2.8044400e1, 2.8044400e1]),
                    np.array([7.9240905e2, 7.2429275e2, 6.2858461e2, 5.0672441e2, 3.7636773e2, 2.5490931e2, 1.2045969e2, 2.9960613e1, 2.9960613e1]),
                ),
            ],
            "qcd_y"    : np.array([7.6246034e2, 6.9684577e2, 6.0548681e2, 4.8928139e2, 3.6454175e2, 2.4754316e2, 1.1667878e2, 2.7737493e1, 2.7737493e1]),
            "qcd_min"  : np.array([7.3365413e2, 6.7075857e2, 5.8286230e2, 4.7122879e2, 3.5126111e2, 2.3864984e2, 1.1258836e2, 2.6810991e1, 2.6810991e1]),
            "qcd_max"  : np.array([7.8308860e2, 7.1589472e2, 6.2240294e2, 5.0304405e2, 3.7486018e2, 2.5457732e2, 1.1997317e2, 2.8492180e1, 2.8492180e1]),
            "y"        : np.array([7.5459110e2, 6.9028342e2, 6.0025198e2, 4.8552235e2, 3.6195456e2, 2.4586691e2, 1.1586851e2, 2.7517266e1, 2.7517266e1]),
            "ymin"     : np.array([7.2595107e2, 6.6417441e2, 5.7747295e2, 4.6723687e2, 3.4843600e2, 2.3677625e2, 1.1166942e2, 2.6562471e1, 2.6562471e1]),
            "ymax"     : np.array([7.7529764e2, 7.0957480e2, 6.1750966e2, 4.9966237e2, 3.7261436e2, 2.5316566e2, 1.1930157e2, 2.8306905e1, 2.8306905e1]),
            "channels" : [
                (r"$\mathrm{u}\bar{\mathrm{d}} + \mathrm{c}\bar{\mathrm{s}}$", np.array([8.4002759e2, 7.7448295e2, 6.7891182e2, 5.5341626e2, 4.1562095e2, 2.8427837e2, 1.3470473e2, 3.1886258e1, 3.1886258e1])),
                (r"$\mathrm{u}\mathrm{g} + \mathrm{c}\mathrm{g}$", np.array([-6.0727462e1, -6.1109036e1, -5.7385834e1, -4.9385114e1, -3.8287410e1, -2.6578788e1, -1.2142190e1, -2.3686722e0, -2.3686722e0])),
                (r"$\mathrm{g}\bar{\mathrm{s}} + \mathrm{g}\bar{\mathrm{d}}$", np.array([-2.4969360e1, -2.3319483e1, -2.1436419e1, -1.8639887e1, -1.5462782e1, -1.1889878e1, -6.7199873e0, -2.0056686e0, -2.0056686e0])),
                (r"$\mathrm{u}\gamma + \mathrm{c}\gamma$", np.array([1.7176328e-1, 1.4518685e-1, 1.1534278e-1, 7.2943823e-2, 4.9352954e-2, 3.8564621e-2, 1.2734974e-2, 3.4154203e-3, 3.4154203e-3])),
                (r"$\gamma\bar{\mathrm{s}} + \gamma\bar{\mathrm{d}}$", np.array([8.8565923e-2, 8.3802762e-2, 4.7074109e-2, 5.8147927e-2, 3.4452663e-2, 1.8643688e-2, 1.3223117e-2, 1.9334685e-3, 1.9334685e-3]))
            ],
        },
    ]


def metadata():
    return {
        "arxiv": r"1505.07024",
        "description": r"LHCb differential W-boson production cross section at 7 TeV",
        "hepdata": r"10.17182/hepdata.2114.v1/t4",
        "initial_state_1": r"2212",
        "initial_state_2": r"2212",
        "lumi_id_types": r"pdg_mc_ids",
        "mg5amc_repo": r"http://bazaar.launchpad.net/~maddevelopers/mg5amcnlo/3.1.2/",
        "mg5amc_revno": r"983",
        "nnpdf_id": r"LHCBWZMU7TEV",
        "pineappl_gitversion": r"v0.4.1-114-gdce19e0",
        "runcard_gitversion": r"82de4ad",
        "x1_label": r"etal",
        "x1_label_tex": r"$\eta_{\bar{\ell}}$",
        "x1_unit": r"",
        "y_label": r"dsig/detal",
        "y_label_tex": r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\eta_{\bar{\ell}}}$",
        "y_unit": r"\pico\barn",
    }


if __name__ == "__main__":
    main()
"#;

const SUBGRID_PULL_STR: &str = r"#!/usr/bin/env python3

import matplotlib.pyplot as plt
import numpy as np
from math import fabs, log10
from scipy.interpolate import griddata

x1 = np.array([1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 1.0000000e0, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 9.3094408e-1, 8.6278393e-1, 8.6278393e-1, 8.6278393e-1, 8.6278393e-1, 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x2 = np.array([1.0000000e0, 9.3094408e-1, 8.6278393e-1, 7.9562425e-1, 7.2958684e-1, 6.6481395e-1, 6.0147220e-1, 5.3975723e-1, 4.7989890e-1, 4.2216678e-1, 3.6687532e-1, 3.1438740e-1, 2.6511370e-1, 2.1950413e-1, 1.7802566e-1, 1.4112081e-1, 1.0914376e-1, 8.2281221e-2, 6.0480029e-2, 4.3414917e-2, 3.0521584e-2, 2.1089187e-2, 1.4375069e-2, 9.6991596e-3, 6.4962062e-3, 4.3285006e-3, 2.8738676e-3, 1.9034634e-3, 1.2586797e-3, 8.3140688e-4, 5.4877953e-4, 3.6205450e-4, 2.3878783e-4, 1.5745606e-4, 1.0000000e0, 9.3094408e-1, 8.6278393e-1, 7.9562425e-1, 7.2958684e-1, 6.6481395e-1, 6.0147220e-1, 5.3975723e-1, 4.7989890e-1, 4.2216678e-1, 3.6687532e-1, 3.1438740e-1, 2.6511370e-1, 2.1950413e-1, 1.7802566e-1, 1.4112081e-1, 1.0914376e-1, 8.2281221e-2, 6.0480029e-2, 4.3414917e-2, 3.0521584e-2, 2.1089187e-2, 1.4375069e-2, 9.6991596e-3, 6.4962062e-3, 4.3285006e-3, 2.8738676e-3, 1.9034634e-3, 1.2586797e-3, 8.3140688e-4, 5.4877953e-4, 3.6205450e-4, 2.3878783e-4, 1.5745606e-4, 1.0000000e0, 9.3094408e-1, 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3.3277622e-4, -1.6067416e-4, -4.1602217e-4, 1.2018187e-4, 1.1339273e-4, -6.8148408e-6, -8.5193388e-6, 7.4974305e-7, 2.6756500e-26, 6.5646229e-11, -6.6713562e-10, -6.6920930e-9, -1.8938415e-8, -3.8361839e-8, 1.3944163e-8, 1.3740334e-7, -2.0724964e-7, 3.4016518e-6, 6.9656523e-5, 1.6236569e-4, 1.0456041e-4, -3.0321707e-5, -3.3108491e-5, -5.0377425e-6, 7.7321367e-7, -4.8441446e-6, -1.3836940e-5, -4.3280496e-6, 4.5264510e-7, 1.3206784e-9, 9.0789578e-28, 6.2126304e-12, 1.5798654e-11, 2.3170290e-10, 3.5435462e-10, 1.7267708e-9, -4.3101794e-10, -5.7085448e-9, -9.3355721e-8, 1.1083124e-6, -3.2317539e-7, -1.3572595e-5, -1.2485768e-5, -1.4067410e-6, -2.1245218e-7, -2.9071409e-7, -6.8490342e-7, -7.4999602e-7, -7.8875047e-8, 1.8591121e-8, -3.0724506e-28, -1.3992807e-12, 3.1558144e-12, 3.0973499e-11, 8.3814135e-11, 9.4465101e-11, -7.9526084e-11, -4.4493691e-10, 8.7757349e-9, -1.3457356e-7, -2.0511661e-7, -1.1824214e-8, -2.0322501e-9, -6.7921281e-10, -3.9270519e-11, 6.1648977e-12, 3.4897604e-14])
x = 0.5 * np.log(x1 / x2)
y = np.sqrt(x1 * x2)

nrap = 50
nmass = 50

sym_min = -max(fabs(np.min(x)), fabs(np.max(x)))
sym_max =  max(fabs(np.min(x)), fabs(np.max(x)))

xi = np.linspace(sym_min, sym_max, (nrap // 2) * 2 + 1)
yi = np.logspace(log10(np.min(y)), log10(np.max(y)), nmass)
zi = griddata((x, y), z, (xi[None, :], yi[:, None]), method='linear', rescale=True)

#print(xi.shape)
#print(yi.shape)
#print(zi.shape)

# mask impossible kinematic values
for iy, ix in np.ndindex(zi.shape):
    #print(ix, iy)
    x1v = yi[iy] * np.exp(xi[ix])
    x2v = yi[iy] / np.exp(xi[ix])

    #print('y = {} m/s = {} -> x1 = {} x2 = {}'.format(xi[ix], yi[iy], x1v, x2v))

    if x1v > 1.0 or x2v > 1.0:
        zi[iy, ix] = np.nan

figure, axes = plt.subplots(1, 2, constrained_layout=True)
figure.set_size_inches(10, 5)

mesh = axes[0].pcolormesh(xi, yi, zi, shading='nearest', linewidth=0, snap=True)
axes[0].scatter(x, y, marker='*', s=5)
axes[0].set_yscale('log')
axes[0].set_xlabel(r'$y = 1/2 \log (x_1/x_2)$')
axes[0].set_ylabel(r'$M/\sqrt{s} = \sqrt{x_1 x_2}$')
#axes[0].set_aspect('equal', share=True)

x1i = np.logspace(log10(np.min(x1)), log10(np.max(x1)), 50)
x2i = np.logspace(log10(np.min(x2)), log10(np.max(x2)), 50)
z12i = griddata((x1, x2), z, (x1i[None, :], x2i[:, None]), method='linear', rescale=True)

mesh = axes[1].pcolormesh(x1i, x2i, z12i, shading='nearest', linewidth=0, snap=True)
axes[1].set_xscale('log')
axes[1].set_yscale('log')
axes[1].scatter(x1, x2, marker='*', s=5)
axes[1].set_aspect('equal', share=True)
axes[1].set_xlabel(r'$x_1$')
axes[1].set_ylabel(r'$x_2$')

figure.colorbar(mesh, ax=axes, extend='min')
figure.savefig('plot.pdf')
";

const DRELL_YAN_AFB_STR: &str = r#"#!/usr/bin/env python3

import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pickle

# color cycler for different PDF results
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
# color for the first PDF result with QCD-only predictions
colors0_qcd = "red"

# stylesheet for plot
stylesheet = {
    "axes.axisbelow": True,
    "axes.grid": True,
    "axes.labelsize": "small",
    "figure.constrained_layout.hspace": 0.0,
    "figure.constrained_layout.use": True,
    "figure.constrained_layout.wspace": 0.0,
    "font.family": "serif",
    "font.size": 14.0,
    "grid.linestyle": "dotted",
    "legend.borderpad": 0.0,
    "legend.fontsize": "x-small",
    "legend.frameon": False,
    "pdf.compression": 0,
    "text.usetex": True,
    "text.latex.preamble": r"\usepackage{siunitx}\usepackage{lmodern}\usepackage[T1]{fontenc}",
    "xtick.bottom": True,
    "xtick.top": True,
    "xtick.direction": "in",
    "xtick.major.width": 0.5,
    "xtick.minor.bottom": True,
    "xtick.minor.top": True,
    "xtick.minor.width": 0.5,
    "ytick.direction": "in",
    "ytick.left": True,
    "ytick.right": True,
    "ytick.major.width": 0.5,
    "ytick.minor.visible": True,
    "ytick.minor.width": 0.5,
}

# global plot labels
title  = r""
xlabel = r"$\cos \theta^*$"
ylabel = r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\cos \theta^*}$ [\si{pb}]"

# panel plot labels
ylabel_ratio_pdf   = "Ratio to {central_pdf}"
ylabel_double_ratio_pdf = "Ratio to NLO"
ylabel_rel_ewonoff = "NLO EW on/off [\si{\percent}]"
ylabel_rel_pdfunc  = "PDF uncertainty [\si{\percent}]"
ylabel_rel_pdfpull = "Pull [$\sigma$]"

label_rel_ewonoff_qcd       = "NLO QCD"
label_rel_ewonoff_ew        = "NLO QCD+EW"
label_rel_ewonoff_scale_unc = "7-p.\ scale var."
label_rel_ewonoff_pdf_unc   = "PDF uncertainty"

xlog = False
ylog = False


def main():
    panels = [
        # plot_int,
        plot_abs,
        plot_rel_ewonoff,
        # plot_abs_pdfs,
        # plot_ratio_pdf,
        # plot_double_ratio_pdf,
        # plot_rel_pdfunc,
        # plot_rel_pdfpull,
    ]

    mpl.rcParams.update(stylesheet)
    plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
    # plt.rc("figure", figsize=(4.2, 2.6))

    data_slices = data()

    for index, kwargs in enumerate(data_slices):
        figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)

        if len(kwargs["x"]) > 2 and xlog:
            axes[0, 0].set_xscale("log")

        axes[0, 0].set_title(title)
        axes[-1, 0].set_xlabel(xlabel)

        for plot, axis in zip(panels, axes[:, 0]):
            plot(axis, **kwargs)

        if len(data_slices) == 1:
            figure.savefig("CMS_DY_14TEV_MLL_6000_COSTH.pdf")
        else:
            figure.savefig("CMS_DY_14TEV_MLL_6000_COSTH-{}.pdf".format(index))
        plt.close(figure)


def percent_diff(a, b):
    return (a / b - 1.0) * 100.0


def set_ylim(axis, save, load, filename):
    # extract the y limits *not* considering margins
    margins = axis.margins()
    axis.margins(y=0.0)
    ymin, ymax = axis.get_ylim()
    axis.margins(y=margins[1])

    inc = 1.0

    if (ymax - ymin) > 100.0:
        ymin = -50.0
        ymax = 50.0
        inc = 25.0
    elif (ymax - ymin) > 30.5:
        inc = 10.0
    elif (ymax - ymin) > 20.5:
        inc = 5.0
    elif (ymax - ymin) > 10.5:
        inc = 2.0
    elif (ymax - ymin) < 3.0:
        inc = 0.5

    ymin = math.floor(ymin / inc) * inc
    ymax = math.ceil(ymax / inc) * inc

    if save:
        with open(filename, "wb") as f:
            pickle.dump([ymin, ymax, inc], f)

    if load:
        resave = False

        with open(filename, "rb") as f:
            [saved_ymin, saved_ymax, saved_inc] = pickle.load(f)

        if saved_ymin < ymin:
            ymin = saved_ymin
            resave = True

        if saved_ymax > ymax:
            ymax = saved_ymax
            resave = True

        if saved_inc > inc:
            inc = saved_inc
            resave = True

        if resave:
            with open(filename, "wb") as f:
                pickle.dump([ymin, ymax, inc], f)

    axis.set_yticks(np.arange(ymin, ymax + inc, inc))
    space = 0.05 * (ymax - ymin)
    axis.set_ylim((ymin - space, ymax + space))


def plot_int(axis, **kwargs):
    xmin = np.array([])
    xmax = np.array([])
    x = np.array([])
    y = np.array([])

    for index, i in enumerate(kwargs["pdf_results"]):
        label, ycentral, ymin, ymax = i
        x = np.append(x, ycentral[:-1])
        xmin = np.append(xmin, ymin[:-1])
        xmax = np.append(xmax, ymax[:-1])
        y = np.append(y, label)

        # draw one- and two-sigma bands
        if label == "CENTRAL-PDF":
            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
            # TODO: this is only correct for MC PDF uncertainties
            axis.axvspan(x[-1] - 2.0 * (x[-1] - xmin[-1]), x[-1] + 2.0 * (xmax[-1] - x[-1]), alpha=0.1, color=colors[index], linewidth=0)

    axis.errorbar(x, y, xerr=(x - xmin, xmax - x), fmt=".", capsize=3, markersize=5, linewidth=1.5)
    axis.margins(x=0.1, y=0.1)


def plot_abs(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]

    axis.set_yscale("log" if ylog else "linear")
    axis.step(x, kwargs["y"], colors[0], linewidth=1.0, where="post", label=slice_label)
    axis.fill_between(x, kwargs["ymin"], kwargs["ymax"], alpha=0.4, color=colors[0], linewidth=0.5, step="post")
    axis.set_ylabel(ylabel)

    if slice_label != "":
        axis.legend()


def plot_ratio_pdf(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]

    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_uncertainties[0][0]))

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        y = y / pdf_uncertainties[0][1]
        ymin = ymin / pdf_uncertainties[0][1]
        ymax = ymax / pdf_uncertainties[0][1]

        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_double_ratio_pdf(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]

    axis.set_ylabel(ylabel_double_ratio_pdf)

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        if index == 0 or index == 2:
            y = y / pdf_uncertainties[0][1]
            ymin = ymin / pdf_uncertainties[0][1]
            ymax = ymax / pdf_uncertainties[0][1]
        else:
            y = y / pdf_uncertainties[1][1]
            ymin = ymin / pdf_uncertainties[1][1]
            ymax = ymax / pdf_uncertainties[1][1]
        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_abs_pdfs(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]
    channels = kwargs["channels"]

    axis.set_yscale("log" if ylog else "linear")
    axis.set_ylabel(ylabel)

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    linestyles = ["--", ":"]
    for index, ((label, y), linestyle) in enumerate(zip(channels, linestyles)):
        axis.step(x, y, color=colors[0], label=label, linestyle=linestyle, linewidth=1.0, where="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties) + len(linestyles)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_rel_ewonoff(axis, **kwargs):
    x = kwargs["x"]
    y = percent_diff(kwargs["y"], kwargs["qcd_y"])
    qcd_y = percent_diff(kwargs["qcd_y"], kwargs["qcd_y"])
    qcd_ymin = percent_diff(kwargs["qcd_min"], kwargs["qcd_y"])
    qcd_ymax = percent_diff(kwargs["qcd_max"], kwargs["qcd_y"])
    ymin = percent_diff(kwargs["ymin"], kwargs["qcd_y"])
    ymax = percent_diff(kwargs["ymax"], kwargs["qcd_y"])
    pdf_min = abs(percent_diff(kwargs["pdf_results"][0][2], kwargs["pdf_results"][0][1]))[:-1]
    pdf_max = abs(percent_diff(kwargs["pdf_results"][0][3], kwargs["pdf_results"][0][1]))[:-1]

    axis.step(x, qcd_y, colors0_qcd, label=label_rel_ewonoff_qcd, linewidth=1.0, where="post")
    # axis.fill_between(x, qcd_ymin, qcd_ymax, alpha=0.4, color=colors0_qcd, label=label_rel_ewonoff_scale_unc, linewidth=0.5, step="post")
    axis.step(x, y, colors[0], label=label_rel_ewonoff_ew, linewidth=1.0, where="post")
    axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[0], label=label_rel_ewonoff_scale_unc, linewidth=0.5, step="post")
    axis.errorbar(kwargs["mid"], y[:-1], yerr=(pdf_min, pdf_max), color=colors[0], label=label_rel_ewonoff_pdf_unc, fmt=".", capsize=1, markersize=0, linewidth=1)
    axis.set_ylabel(ylabel_rel_ewonoff)
    axis.legend(bbox_to_anchor=(0, 1.03, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=4)


def plot_rel_pdfunc(axis, **kwargs):
    x = kwargs["x"]
    pdf_uncertainties = kwargs["pdf_results"]

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        ymin = percent_diff(ymin, y)
        ymax = percent_diff(ymax, y)
        axis.step(x, ymax, color=colors[index], label=label, linewidth=1, where="post")
        axis.step(x, ymin, color=colors[index], linewidth=1, where="post")

    axis.set_ylabel(ylabel_rel_pdfunc)

    set_ylim(axis, False, False, "rel_pdfunc")


def plot_rel_pdfpull(axis, **kwargs):
    central_y = kwargs["pdf_results"][0][1]
    central_ymin = kwargs["pdf_results"][0][2]
    central_ymax = kwargs["pdf_results"][0][3]
    pdf_uncertainties = kwargs["pdf_results"]
    x = kwargs["x"]
    y = kwargs["y"]

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        diff = y - central_y
        yerr = np.where(diff > 0.0, y - ymin, ymax - y)
        cerr = np.where(diff > 0.0, central_ymax - central_y, central_y - central_ymin)
        pull = diff / np.sqrt(np.power(yerr, 2) + np.power(cerr, 2))

        axis.step(x, pull, color=colors[index], label=label, linewidth=1, where="post", zorder=2 * index + 1)

    axis.legend(bbox_to_anchor=(0, 1.03, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties))) #rel_pdfpull
    axis.set_ylabel(ylabel_rel_pdfpull)

    set_ylim(axis, False, False, "rel_pdfpull")


def data():
    return [
        {
            "slice_label"    : r"",
            "x"        : np.array([0, 0.040000000000000036, 0.08000000000000007, 0.1200000000000001, 0.15999999999999992, 0.19999999999999996, 0.24, 0.28, 0.32000000000000006, 0.3600000000000001, 0.40000000000000013, 0.43999999999999995, 0.48, 0.52, 0.56, 0.6000000000000001, 0.6400000000000001, 0.6799999999999999, 0.72, 0.76, 0.8, 0.8400000000000001, 0.8800000000000001, 0.9199999999999999, 0.96, 1]),
            "mid"      : np.array([0.020000000000000018, 0.06000000000000005, 0.10000000000000009, 0.14, 0.17999999999999994, 0.21999999999999997, 0.26, 0.30000000000000004, 0.3400000000000001, 0.3800000000000001, 0.42000000000000004, 0.45999999999999996, 0.5, 0.54, 0.5800000000000001, 0.6200000000000001, 0.66, 0.7, 0.74, 0.78, 0.8200000000000001, 0.8600000000000001, 0.9, 0.94, 0.98]),
            "pdf_results" : [
                (
                    "NNPDF40\_nnlo\_as\_01180",
                    np.array([-8.9073097e-3, -2.5258011e-2, -4.2074993e-2, -5.7119354e-2, -7.2072246e-2, -8.8769233e-2, -1.0168073e-1, -1.1291620e-1, -1.2694004e-1, -1.3981641e-1, -1.4822989e-1, -1.5828910e-1, -1.6656074e-1, -1.7672691e-1, -1.8106409e-1, -1.8604958e-1, -1.9264779e-1, -1.9711216e-1, -1.9947876e-1, -2.0333702e-1, -2.0602952e-1, -2.0751500e-1, -2.0920588e-1, -2.0522087e-1, -1.5755961e-1, -1.5755961e-1]),
                    np.array([-1.3416731e-2, -3.8485931e-2, -6.4022917e-2, -8.7525834e-2, -1.1059996e-1, -1.3506164e-1, -1.5518895e-1, -1.7370393e-1, -1.9408882e-1, -2.1338899e-1, -2.2702283e-1, -2.4212719e-1, -2.5490585e-1, -2.6928706e-1, -2.7696185e-1, -2.8511886e-1, -2.9435668e-1, -3.0107075e-1, -3.0528544e-1, -3.1066117e-1, -3.1459806e-1, -3.1687573e-1, -3.1925830e-1, -3.1465864e-1, -2.4456638e-1, -2.4456638e-1]),
                    np.array([-4.3978881e-3, -1.2030092e-2, -2.0127068e-2, -2.6712874e-2, -3.3544535e-2, -4.2476821e-2, -4.8172510e-2, -5.2128475e-2, -5.9791257e-2, -6.6243833e-2, -6.9436948e-2, -7.4451017e-2, -7.8215644e-2, -8.4166747e-2, -8.5166318e-2, -8.6980291e-2, -9.0938899e-2, -9.3153568e-2, -9.3672084e-2, -9.6012876e-2, -9.7460983e-2, -9.8154272e-2, -9.9153450e-2, -9.5783090e-2, -7.0552833e-2, -7.0552833e-2]),
                ),
            ],
            "qcd_y"    : np.array([-9.7774455e-3, -2.7803070e-2, -4.6322000e-2, -6.2975151e-2, -7.9511210e-2, -9.7716412e-2, -1.1201228e-1, -1.2465352e-1, -1.3992382e-1, -1.5403592e-1, -1.6342388e-1, -1.7449431e-1, -1.8365024e-1, -1.9465371e-1, -1.9962541e-1, -2.0517482e-1, -2.1229826e-1, -2.1722257e-1, -2.1993325e-1, -2.2407089e-1, -2.2699239e-1, -2.2865013e-1, -2.3047026e-1, -2.2613492e-1, -1.7065560e-1, -1.7065560e-1]),
            "qcd_min"  : np.array([-9.9495415e-3, -2.8039568e-2, -4.6641585e-2, -6.3405979e-2, -8.0058809e-2, -9.8477267e-2, -1.1292865e-1, -1.2565850e-1, -1.4105077e-1, -1.5520442e-1, -1.6480071e-1, -1.7585102e-1, -1.8515335e-1, -1.9618737e-1, -2.0119655e-1, -2.0679841e-1, -2.1382807e-1, -2.1888069e-1, -2.2163057e-1, -2.2580492e-1, -2.2871629e-1, -2.3041863e-1, -2.3219166e-1, -2.2801534e-1, -1.7200834e-1, -1.7200834e-1]),
            "qcd_max"  : np.array([-9.6561309e-3, -2.7599708e-2, -4.6069879e-2, -6.2637658e-2, -7.9083183e-2, -9.7073087e-2, -1.1122072e-1, -1.2380361e-1, -1.3896519e-1, -1.5306307e-1, -1.6222176e-1, -1.7335756e-1, -1.8235507e-1, -1.9334313e-1, -1.9829474e-1, -2.0380214e-1, -2.1106506e-1, -2.1583806e-1, -2.1851565e-1, -2.2261390e-1, -2.2555043e-1, -2.2716600e-1, -2.2905247e-1, -2.2452090e-1, -1.6964796e-1, -1.6964796e-1]),
            "y"        : np.array([-9.7774455e-3, -2.7803070e-2, -4.6322000e-2, -6.2975151e-2, -7.9511210e-2, -9.7716412e-2, -1.1201228e-1, -1.2465352e-1, -1.3992382e-1, -1.5403592e-1, -1.6342388e-1, -1.7449431e-1, -1.8365024e-1, -1.9465371e-1, -1.9962541e-1, -2.0517482e-1, -2.1229826e-1, -2.1722257e-1, -2.1993325e-1, -2.2407089e-1, -2.2699239e-1, -2.2865013e-1, -2.3047026e-1, -2.2613492e-1, -1.7065560e-1, -1.7065560e-1]),
            "ymin"     : np.array([-9.9495415e-3, -2.8039568e-2, -4.6641585e-2, -6.3405979e-2, -8.0058809e-2, -9.8477267e-2, -1.1292865e-1, -1.2565850e-1, -1.4105077e-1, -1.5520442e-1, -1.6480071e-1, -1.7585102e-1, -1.8515335e-1, -1.9618737e-1, -2.0119655e-1, -2.0679841e-1, -2.1382807e-1, -2.1888069e-1, -2.2163057e-1, -2.2580492e-1, -2.2871629e-1, -2.3041863e-1, -2.3219166e-1, -2.2801534e-1, -1.7200834e-1, -1.7200834e-1]),
            "ymax"     : np.array([-9.6561309e-3, -2.7599708e-2, -4.6069879e-2, -6.2637658e-2, -7.9083183e-2, -9.7073087e-2, -1.1122072e-1, -1.2380361e-1, -1.3896519e-1, -1.5306307e-1, -1.6222176e-1, -1.7335756e-1, -1.8235507e-1, -1.9334313e-1, -1.9829474e-1, -2.0380214e-1, -2.1106506e-1, -2.1583806e-1, -2.1851565e-1, -2.2261390e-1, -2.2555043e-1, -2.2716600e-1, -2.2905247e-1, -2.2452090e-1, -1.6964796e-1, -1.6964796e-1]),
            "channels" : [

            ],
        },
    ]


def metadata():
    return {
        "arxiv": r"",
        "description": r"",
        "hepdata": r"",
        "initial_state_1": r"2212",
        "initial_state_2": r"2212",
        "lumi_id_types": r"pdg_mc_ids",
        "mg5amc_repo": r"http://bazaar.launchpad.net/~maddevelopers/mg5amcnlo/3.3.1/",
        "mg5amc_revno": r"981",
        "patches": r"",
        "pineappl_gitversion": r"v0.5.4-49-g04650d9",
        "results_pdf": r"MSHT20nnlo_as118",
        "runcard_gitversion": r"7b5180d",
        "tau_min": r"",
        "user_cuts": r"",
        "x1_label": r"costh",
        "x1_label_tex": r"$\cos \theta^*$",
        "x1_unit": r"",
        "y_label": r"dsig/dcosth",
        "y_label_tex": r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\cos \theta^*}$",
        "y_unit": r"\pico\barn",
    }


if __name__ == "__main__":
    main()
"#;

const DRELL_YAN_MASS_SLICES_STR: &str = r#"#!/usr/bin/env python3

import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pickle

# color cycler for different PDF results
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
# color for the first PDF result with QCD-only predictions
colors0_qcd = "red"

# stylesheet for plot
stylesheet = {
    "axes.axisbelow": True,
    "axes.grid": True,
    "axes.labelsize": "small",
    "figure.constrained_layout.hspace": 0.0,
    "figure.constrained_layout.use": True,
    "figure.constrained_layout.wspace": 0.0,
    "font.family": "serif",
    "font.size": 14.0,
    "grid.linestyle": "dotted",
    "legend.borderpad": 0.0,
    "legend.fontsize": "x-small",
    "legend.frameon": False,
    "pdf.compression": 0,
    "text.usetex": True,
    "text.latex.preamble": r"\usepackage{siunitx}\usepackage{lmodern}\usepackage[T1]{fontenc}",
    "xtick.bottom": True,
    "xtick.top": True,
    "xtick.direction": "in",
    "xtick.major.width": 0.5,
    "xtick.minor.bottom": True,
    "xtick.minor.top": True,
    "xtick.minor.width": 0.5,
    "ytick.direction": "in",
    "ytick.left": True,
    "ytick.right": True,
    "ytick.major.width": 0.5,
    "ytick.minor.visible": True,
    "ytick.minor.width": 0.5,
}

# global plot labels
title  = r""
xlabel = r"$\cos \theta^*$"
ylabel = r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\cos \theta^*}$ [\si{pb}]"

# panel plot labels
ylabel_ratio_pdf   = "Ratio to {central_pdf}"
ylabel_double_ratio_pdf = "Ratio to NLO"
ylabel_rel_ewonoff = "NLO EW on/off [\si{\percent}]"
ylabel_rel_pdfunc  = "PDF uncertainty [\si{\percent}]"
ylabel_rel_pdfpull = "Pull [$\sigma$]"

label_rel_ewonoff_qcd       = "NLO QCD"
label_rel_ewonoff_ew        = "NLO QCD+EW"
label_rel_ewonoff_scale_unc = "7-p.\ scale var."
label_rel_ewonoff_pdf_unc   = "PDF uncertainty"

xlog = False
ylog = False


def main():
    panels = [
        # plot_int,
        plot_abs,
        plot_rel_ewonoff,
        # plot_abs_pdfs,
        # plot_ratio_pdf,
        # plot_double_ratio_pdf,
        # plot_rel_pdfunc,
        # plot_rel_pdfpull,
    ]

    mpl.rcParams.update(stylesheet)
    plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
    # plt.rc("figure", figsize=(4.2, 2.6))

    data_slices = data()

    for index, kwargs in enumerate(data_slices):
        figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)

        if len(kwargs["x"]) > 2 and xlog:
            axes[0, 0].set_xscale("log")

        axes[0, 0].set_title(title)
        axes[-1, 0].set_xlabel(xlabel)

        for plot, axis in zip(panels, axes[:, 0]):
            plot(axis, **kwargs)

        if len(data_slices) == 1:
            figure.savefig("NNPDF_DY_14TEV_BSM_AFB.pdf")
        else:
            figure.savefig("NNPDF_DY_14TEV_BSM_AFB-{}.pdf".format(index))
        plt.close(figure)


def percent_diff(a, b):
    return (a / b - 1.0) * 100.0


def set_ylim(axis, save, load, filename):
    # extract the y limits *not* considering margins
    margins = axis.margins()
    axis.margins(y=0.0)
    ymin, ymax = axis.get_ylim()
    axis.margins(y=margins[1])

    inc = 1.0

    if (ymax - ymin) > 100.0:
        ymin = -50.0
        ymax = 50.0
        inc = 25.0
    elif (ymax - ymin) > 30.5:
        inc = 10.0
    elif (ymax - ymin) > 20.5:
        inc = 5.0
    elif (ymax - ymin) > 10.5:
        inc = 2.0
    elif (ymax - ymin) < 3.0:
        inc = 0.5

    ymin = math.floor(ymin / inc) * inc
    ymax = math.ceil(ymax / inc) * inc

    if save:
        with open(filename, "wb") as f:
            pickle.dump([ymin, ymax, inc], f)

    if load:
        resave = False

        with open(filename, "rb") as f:
            [saved_ymin, saved_ymax, saved_inc] = pickle.load(f)

        if saved_ymin < ymin:
            ymin = saved_ymin
            resave = True

        if saved_ymax > ymax:
            ymax = saved_ymax
            resave = True

        if saved_inc > inc:
            inc = saved_inc
            resave = True

        if resave:
            with open(filename, "wb") as f:
                pickle.dump([ymin, ymax, inc], f)

    axis.set_yticks(np.arange(ymin, ymax + inc, inc))
    space = 0.05 * (ymax - ymin)
    axis.set_ylim((ymin - space, ymax + space))


def plot_int(axis, **kwargs):
    xmin = np.array([])
    xmax = np.array([])
    x = np.array([])
    y = np.array([])

    for index, i in enumerate(kwargs["pdf_results"]):
        label, ycentral, ymin, ymax = i
        x = np.append(x, ycentral[:-1])
        xmin = np.append(xmin, ymin[:-1])
        xmax = np.append(xmax, ymax[:-1])
        y = np.append(y, label)

        # draw one- and two-sigma bands
        if label == "CENTRAL-PDF":
            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
            # TODO: this is only correct for MC PDF uncertainties
            axis.axvspan(x[-1] - 2.0 * (x[-1] - xmin[-1]), x[-1] + 2.0 * (xmax[-1] - x[-1]), alpha=0.1, color=colors[index], linewidth=0)

    axis.errorbar(x, y, xerr=(x - xmin, xmax - x), fmt=".", capsize=3, markersize=5, linewidth=1.5)
    axis.margins(x=0.1, y=0.1)


def plot_abs(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]

    axis.set_yscale("log" if ylog else "linear")
    axis.step(x, kwargs["y"], colors[0], linewidth=1.0, where="post", label=slice_label)
    axis.fill_between(x, kwargs["ymin"], kwargs["ymax"], alpha=0.4, color=colors[0], linewidth=0.5, step="post")
    axis.set_ylabel(ylabel)

    if slice_label != "":
        axis.legend()


def plot_ratio_pdf(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]

    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_uncertainties[0][0]))

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        y = y / pdf_uncertainties[0][1]
        ymin = ymin / pdf_uncertainties[0][1]
        ymax = ymax / pdf_uncertainties[0][1]

        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_double_ratio_pdf(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]

    axis.set_ylabel(ylabel_double_ratio_pdf)

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        if index == 0 or index == 2:
            y = y / pdf_uncertainties[0][1]
            ymin = ymin / pdf_uncertainties[0][1]
            ymax = ymax / pdf_uncertainties[0][1]
        else:
            y = y / pdf_uncertainties[1][1]
            ymin = ymin / pdf_uncertainties[1][1]
            ymax = ymax / pdf_uncertainties[1][1]
        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_abs_pdfs(axis, **kwargs):
    x = kwargs["x"]
    slice_label = kwargs["slice_label"]
    pdf_uncertainties = kwargs["pdf_results"]
    channels = kwargs["channels"]

    axis.set_yscale("log" if ylog else "linear")
    axis.set_ylabel(ylabel)

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        axis.step(x, y, color=colors[index], linewidth=1.0, where="post")
        axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[index], label=label, linewidth=0.5, step="post")

    linestyles = ["--", ":"]
    for index, ((label, y), linestyle) in enumerate(zip(channels, linestyles)):
        axis.step(x, y, color=colors[0], label=label, linestyle=linestyle, linewidth=1.0, where="post")

    axis.legend(bbox_to_anchor=(0, -0.24, 1, 0.2), loc="upper left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties) + len(linestyles)))

    if slice_label != "":
        t = axis.text(0.98, 0.98, slice_label, horizontalalignment="right", verticalalignment="top", transform=axis.transAxes, fontsize="x-small")
        t.set_bbox({ "alpha": 0.7, "boxstyle": "square, pad=0.0", "edgecolor": "white", "facecolor": "white" })


def plot_rel_ewonoff(axis, **kwargs):
    x = kwargs["x"]
    y = percent_diff(kwargs["y"], kwargs["qcd_y"])
    qcd_y = percent_diff(kwargs["qcd_y"], kwargs["qcd_y"])
    qcd_ymin = percent_diff(kwargs["qcd_min"], kwargs["qcd_y"])
    qcd_ymax = percent_diff(kwargs["qcd_max"], kwargs["qcd_y"])
    ymin = percent_diff(kwargs["ymin"], kwargs["qcd_y"])
    ymax = percent_diff(kwargs["ymax"], kwargs["qcd_y"])
    pdf_min = abs(percent_diff(kwargs["pdf_results"][0][2], kwargs["pdf_results"][0][1]))[:-1]
    pdf_max = abs(percent_diff(kwargs["pdf_results"][0][3], kwargs["pdf_results"][0][1]))[:-1]

    axis.step(x, qcd_y, colors0_qcd, label=label_rel_ewonoff_qcd, linewidth=1.0, where="post")
    # axis.fill_between(x, qcd_ymin, qcd_ymax, alpha=0.4, color=colors0_qcd, label=label_rel_ewonoff_scale_unc, linewidth=0.5, step="post")
    axis.step(x, y, colors[0], label=label_rel_ewonoff_ew, linewidth=1.0, where="post")
    axis.fill_between(x, ymin, ymax, alpha=0.4, color=colors[0], label=label_rel_ewonoff_scale_unc, linewidth=0.5, step="post")
    axis.errorbar(kwargs["mid"], y[:-1], yerr=(pdf_min, pdf_max), color=colors[0], label=label_rel_ewonoff_pdf_unc, fmt=".", capsize=1, markersize=0, linewidth=1)
    axis.set_ylabel(ylabel_rel_ewonoff)
    axis.legend(bbox_to_anchor=(0, 1.03, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=4)


def plot_rel_pdfunc(axis, **kwargs):
    x = kwargs["x"]
    pdf_uncertainties = kwargs["pdf_results"]

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        ymin = percent_diff(ymin, y)
        ymax = percent_diff(ymax, y)
        axis.step(x, ymax, color=colors[index], label=label, linewidth=1, where="post")
        axis.step(x, ymin, color=colors[index], linewidth=1, where="post")

    axis.set_ylabel(ylabel_rel_pdfunc)

    set_ylim(axis, False, False, "rel_pdfunc")


def plot_rel_pdfpull(axis, **kwargs):
    central_y = kwargs["pdf_results"][0][1]
    central_ymin = kwargs["pdf_results"][0][2]
    central_ymax = kwargs["pdf_results"][0][3]
    pdf_uncertainties = kwargs["pdf_results"]
    x = kwargs["x"]
    y = kwargs["y"]

    for index, i in enumerate(pdf_uncertainties):
        label, y, ymin, ymax = i
        diff = y - central_y
        yerr = np.where(diff > 0.0, y - ymin, ymax - y)
        cerr = np.where(diff > 0.0, central_ymax - central_y, central_y - central_ymin)
        pull = diff / np.sqrt(np.power(yerr, 2) + np.power(cerr, 2))

        axis.step(x, pull, color=colors[index], label=label, linewidth=1, where="post", zorder=2 * index + 1)

    axis.legend(bbox_to_anchor=(0, 1.03, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=min(4, len(pdf_uncertainties))) #rel_pdfpull
    axis.set_ylabel(ylabel_rel_pdfpull)

    set_ylim(axis, False, False, "rel_pdfpull")


def data():
    return [
        {
            "slice_label"    : r"$\SI{200}{GeV} < M_{\ell\bar{\ell}} < \SI{500}{GeV}$",
            "x"        : np.array([-1, -0.96, -0.92, -0.88, -0.84, -0.8, -0.76, -0.72, -0.68, -0.64, -0.6, -0.56, -0.52, -0.48, -0.44, -0.4, -0.36, -0.32, -0.28, -0.24, -0.2, -0.16, -0.12, -0.08, -0.04, 0, 0.04, 0.08, 0.12, 0.16, 0.2, 0.24, 0.28, 0.32, 0.36, 0.4, 0.44, 0.48, 0.52, 0.56, 0.6, 0.64, 0.68, 0.72, 0.76, 0.8, 0.84, 0.88, 0.92, 0.96, 1]),
            "mid"      : np.array([-0.98, -0.94, -0.9, -0.86, -0.8200000000000001, -0.78, -0.74, -0.7, -0.66, -0.62, -0.5800000000000001, -0.54, -0.5, -0.45999999999999996, -0.42000000000000004, -0.38, -0.33999999999999997, -0.30000000000000004, -0.26, -0.22, -0.18, -0.14, -0.1, -0.06, -0.02, 0.02, 0.06, 0.1, 0.14, 0.18, 0.22, 0.26, 0.30000000000000004, 0.33999999999999997, 0.38, 0.42000000000000004, 0.45999999999999996, 0.5, 0.54, 0.5800000000000001, 0.62, 0.66, 0.7, 0.74, 0.78, 0.8200000000000001, 0.86, 0.9, 0.94, 0.98]),
            "pdf_results" : [
                (
                    "NNPDF40\_nnlo\_as\_01180",
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        {
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            "qcd_y"    : np.array([4.0594871e-2, 3.8970516e-2, 3.7589145e-2, 3.6287278e-2, 3.5138659e-2, 3.4083575e-2, 3.3138203e-2, 3.2369514e-2, 3.1687339e-2, 3.1130680e-2, 3.0694113e-2, 3.0348044e-2, 3.0193553e-2, 3.0118871e-2, 3.0174121e-2, 3.0339279e-2, 3.0573898e-2, 3.1016543e-2, 3.1542002e-2, 3.2184767e-2, 3.2964449e-2, 3.3870335e-2, 3.4866769e-2, 3.6057960e-2, 3.7346103e-2, 3.8648244e-2, 4.0227527e-2, 4.1803650e-2, 4.3536855e-2, 4.5451532e-2, 4.7387640e-2, 4.9457643e-2, 5.1733512e-2, 5.4105127e-2, 5.6588818e-2, 5.9238183e-2, 6.1866112e-2, 6.4704350e-2, 6.7748017e-2, 7.0789065e-2, 7.4060501e-2, 7.7264134e-2, 8.0877170e-2, 8.4406261e-2, 8.8038767e-2, 9.1920534e-2, 9.5741259e-2, 9.9856666e-2, 1.0403280e-1, 1.0829409e-1, 1.0829409e-1]),
            "qcd_min"  : np.array([4.0037777e-2, 3.8435726e-2, 3.7073393e-2, 3.5789220e-2, 3.4655883e-2, 3.3613703e-2, 3.2680468e-2, 3.1921166e-2, 3.1247155e-2, 3.0695910e-2, 3.0263715e-2, 2.9921326e-2, 2.9765787e-2, 2.9690226e-2, 2.9743185e-2, 2.9903344e-2, 3.0131781e-2, 3.0565874e-2, 3.1081207e-2, 3.1711632e-2, 3.2477115e-2, 3.3366364e-2, 3.4346364e-2, 3.5517091e-2, 3.6783757e-2, 3.8063466e-2, 3.9616935e-2, 4.1166189e-2, 4.2871525e-2, 4.4755097e-2, 4.6659767e-2, 4.8697082e-2, 5.0934848e-2, 5.3268112e-2, 5.5712599e-2, 5.8320365e-2, 6.0905504e-2, 6.3698651e-2, 6.6694216e-2, 6.9687107e-2, 7.2907813e-2, 7.6059137e-2, 7.9616205e-2, 8.3090171e-2, 8.6665976e-2, 9.0484669e-2, 9.4247104e-2, 9.8297870e-2, 1.0240852e-1, 1.0660338e-1, 1.0660338e-1]),
            "qcd_max"  : np.array([4.1061145e-2, 3.9418149e-2, 3.8020807e-2, 3.6704197e-2, 3.5542954e-2, 3.4477622e-2, 3.3522380e-2, 3.2746246e-2, 3.2057661e-2, 3.1497253e-2, 3.1057591e-2, 3.0708825e-2, 3.0556281e-2, 3.0483016e-2, 3.0540730e-2, 3.0711032e-2, 3.0951879e-2, 3.1402556e-2, 3.1937511e-2, 3.2591833e-2, 3.3384624e-2, 3.4305895e-2, 3.5317075e-2, 3.6526854e-2, 3.7834301e-2, 3.9156804e-2, 4.0759176e-2, 4.2359581e-2, 4.4117603e-2, 4.6059981e-2, 4.8024100e-2, 5.0123011e-2, 5.2433124e-2, 5.4838902e-2, 5.7357234e-2, 6.0043264e-2, 6.2709323e-2, 6.5587507e-2, 6.8673601e-2, 7.1757263e-2, 7.5073207e-2, 7.8323545e-2, 8.1985664e-2, 8.5563285e-2, 8.9245686e-2, 9.3183548e-2, 9.7055206e-2, 1.0122759e-1, 1.0546145e-1, 1.0978108e-1, 1.0978108e-1]),
            "y"        : np.array([4.0594871e-2, 3.8970516e-2, 3.7589145e-2, 3.6287278e-2, 3.5138659e-2, 3.4083575e-2, 3.3138203e-2, 3.2369514e-2, 3.1687339e-2, 3.1130680e-2, 3.0694113e-2, 3.0348044e-2, 3.0193553e-2, 3.0118871e-2, 3.0174121e-2, 3.0339279e-2, 3.0573898e-2, 3.1016543e-2, 3.1542002e-2, 3.2184767e-2, 3.2964449e-2, 3.3870335e-2, 3.4866769e-2, 3.6057960e-2, 3.7346103e-2, 3.8648244e-2, 4.0227527e-2, 4.1803650e-2, 4.3536855e-2, 4.5451532e-2, 4.7387640e-2, 4.9457643e-2, 5.1733512e-2, 5.4105127e-2, 5.6588818e-2, 5.9238183e-2, 6.1866112e-2, 6.4704350e-2, 6.7748017e-2, 7.0789065e-2, 7.4060501e-2, 7.7264134e-2, 8.0877170e-2, 8.4406261e-2, 8.8038767e-2, 9.1920534e-2, 9.5741259e-2, 9.9856666e-2, 1.0403280e-1, 1.0829409e-1, 1.0829409e-1]),
            "ymin"     : np.array([4.0037777e-2, 3.8435726e-2, 3.7073393e-2, 3.5789220e-2, 3.4655883e-2, 3.3613703e-2, 3.2680468e-2, 3.1921166e-2, 3.1247155e-2, 3.0695910e-2, 3.0263715e-2, 2.9921326e-2, 2.9765787e-2, 2.9690226e-2, 2.9743185e-2, 2.9903344e-2, 3.0131781e-2, 3.0565874e-2, 3.1081207e-2, 3.1711632e-2, 3.2477115e-2, 3.3366364e-2, 3.4346364e-2, 3.5517091e-2, 3.6783757e-2, 3.8063466e-2, 3.9616935e-2, 4.1166189e-2, 4.2871525e-2, 4.4755097e-2, 4.6659767e-2, 4.8697082e-2, 5.0934848e-2, 5.3268112e-2, 5.5712599e-2, 5.8320365e-2, 6.0905504e-2, 6.3698651e-2, 6.6694216e-2, 6.9687107e-2, 7.2907813e-2, 7.6059137e-2, 7.9616205e-2, 8.3090171e-2, 8.6665976e-2, 9.0484669e-2, 9.4247104e-2, 9.8297870e-2, 1.0240852e-1, 1.0660338e-1, 1.0660338e-1]),
            "ymax"     : np.array([4.1061145e-2, 3.9418149e-2, 3.8020807e-2, 3.6704197e-2, 3.5542954e-2, 3.4477622e-2, 3.3522380e-2, 3.2746246e-2, 3.2057661e-2, 3.1497253e-2, 3.1057591e-2, 3.0708825e-2, 3.0556281e-2, 3.0483016e-2, 3.0540730e-2, 3.0711032e-2, 3.0951879e-2, 3.1402556e-2, 3.1937511e-2, 3.2591833e-2, 3.3384624e-2, 3.4305895e-2, 3.5317075e-2, 3.6526854e-2, 3.7834301e-2, 3.9156804e-2, 4.0759176e-2, 4.2359581e-2, 4.4117603e-2, 4.6059981e-2, 4.8024100e-2, 5.0123011e-2, 5.2433124e-2, 5.4838902e-2, 5.7357234e-2, 6.0043264e-2, 6.2709323e-2, 6.5587507e-2, 6.8673601e-2, 7.1757263e-2, 7.5073207e-2, 7.8323545e-2, 8.1985664e-2, 8.5563285e-2, 8.9245686e-2, 9.3183548e-2, 9.7055206e-2, 1.0122759e-1, 1.0546145e-1, 1.0978108e-1, 1.0978108e-1]),
            "channels" : [
                (r"$\mathrm{u}\bar{\mathrm{u}} + \mathrm{c}\bar{\mathrm{c}}$", np.array([2.6199757e-2, 2.5141774e-2, 2.4301385e-2, 2.3475409e-2, 2.2742818e-2, 2.2111053e-2, 2.1562477e-2, 2.1150586e-2, 2.0753029e-2, 2.0527176e-2, 2.0286675e-2, 2.0198805e-2, 2.0196503e-2, 2.0277685e-2, 2.0455607e-2, 2.0654565e-2, 2.0963610e-2, 2.1393063e-2, 2.1909943e-2, 2.2517162e-2, 2.3181602e-2, 2.3967128e-2, 2.4795910e-2, 2.5775243e-2, 2.6848306e-2, 2.7911025e-2, 2.9189817e-2, 3.0427277e-2, 3.1802230e-2, 3.3315181e-2, 3.4815501e-2, 3.6458797e-2, 3.8188509e-2, 4.0036615e-2, 4.1945442e-2, 4.3957344e-2, 4.6030617e-2, 4.8180061e-2, 5.0492953e-2, 5.2808985e-2, 5.5264729e-2, 5.7718158e-2, 6.0458116e-2, 6.3119505e-2, 6.5844881e-2, 6.8772294e-2, 7.1647003e-2, 7.4759927e-2, 7.7907487e-2, 8.1060130e-2, 8.1060130e-2])),
                (r"$\mathrm{d}\bar{\mathrm{d}} + \mathrm{s}\bar{\mathrm{s}} + \mathrm{b}\bar{\mathrm{b}}$", np.array([1.4395113e-2, 1.3828743e-2, 1.3287760e-2, 1.2811869e-2, 1.2395841e-2, 1.1972522e-2, 1.1575727e-2, 1.1218927e-2, 1.0934310e-2, 1.0603503e-2, 1.0407438e-2, 1.0149239e-2, 9.9970507e-3, 9.8411857e-3, 9.7185140e-3, 9.6847141e-3, 9.6102880e-3, 9.6234798e-3, 9.6320596e-3, 9.6676052e-3, 9.7828473e-3, 9.9032066e-3, 1.0070859e-2, 1.0282716e-2, 1.0497797e-2, 1.0737219e-2, 1.1037711e-2, 1.1376372e-2, 1.1734625e-2, 1.2136350e-2, 1.2572139e-2, 1.2998846e-2, 1.3545003e-2, 1.4068512e-2, 1.4643376e-2, 1.5280839e-2, 1.5835494e-2, 1.6524288e-2, 1.7255064e-2, 1.7980080e-2, 1.8795773e-2, 1.9545976e-2, 2.0419054e-2, 2.1286756e-2, 2.2193887e-2, 2.3148239e-2, 2.4094256e-2, 2.5096739e-2, 2.6125312e-2, 2.7233960e-2, 2.7233960e-2]))
            ],
        },
        {
            "slice_label"    : r"$\SI{1000}{GeV} < M_{\ell\bar{\ell}} < \SI{3000}{GeV}$",
            "x"        : np.array([-1, -0.96, -0.92, -0.88, -0.84, -0.8, -0.76, -0.72, -0.68, -0.64, -0.6, -0.56, -0.52, -0.48, -0.44, -0.4, -0.36, -0.32, -0.28, -0.24, -0.2, -0.16, -0.12, -0.08, -0.04, 0, 0.04, 0.08, 0.12, 0.16, 0.2, 0.24, 0.28, 0.32, 0.36, 0.4, 0.44, 0.48, 0.52, 0.56, 0.6, 0.64, 0.68, 0.72, 0.76, 0.8, 0.84, 0.88, 0.92, 0.96, 1]),
            "mid"      : np.array([-0.98, -0.94, -0.9, -0.86, -0.8200000000000001, -0.78, -0.74, -0.7, -0.66, -0.62, -0.5800000000000001, -0.54, -0.5, -0.45999999999999996, -0.42000000000000004, -0.38, -0.33999999999999997, -0.30000000000000004, -0.26, -0.22, -0.18, -0.14, -0.1, -0.06, -0.02, 0.02, 0.06, 0.1, 0.14, 0.18, 0.22, 0.26, 0.30000000000000004, 0.33999999999999997, 0.38, 0.42000000000000004, 0.45999999999999996, 0.5, 0.54, 0.5800000000000001, 0.62, 0.66, 0.7, 0.74, 0.78, 0.8200000000000001, 0.86, 0.9, 0.94, 0.98]),
            "pdf_results" : [
                (
                    "NNPDF40\_nnlo\_as\_01180",
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                    np.array([2.5617082e-3, 2.4598979e-3, 2.3739593e-3, 2.2904004e-3, 2.2169883e-3, 2.1545803e-3, 2.0954206e-3, 2.0498675e-3, 2.0070737e-3, 1.9754803e-3, 1.9463467e-3, 1.9278404e-3, 1.9236438e-3, 1.9198349e-3, 1.9306588e-3, 1.9387108e-3, 1.9583331e-3, 1.9907351e-3, 2.0286806e-3, 2.0733607e-3, 2.1270186e-3, 2.1863925e-3, 2.2577539e-3, 2.3390006e-3, 2.4212326e-3, 2.5112892e-3, 2.6161915e-3, 2.7238882e-3, 2.8385163e-3, 2.9647013e-3, 3.0959433e-3, 3.2331088e-3, 3.3817486e-3, 3.5385707e-3, 3.7031757e-3, 3.8780884e-3, 4.0550706e-3, 4.2388498e-3, 4.4433747e-3, 4.6413012e-3, 4.8539670e-3, 5.0680268e-3, 5.3044183e-3, 5.5388017e-3, 5.7755439e-3, 6.0326566e-3, 6.2848452e-3, 6.5558742e-3, 6.8328348e-3, 7.1072149e-3, 7.1072149e-3]),
                    np.array([2.5617082e-3, 2.4598979e-3, 2.3739593e-3, 2.2904004e-3, 2.2169883e-3, 2.1545803e-3, 2.0954206e-3, 2.0498675e-3, 2.0070737e-3, 1.9754803e-3, 1.9463467e-3, 1.9278404e-3, 1.9236438e-3, 1.9198349e-3, 1.9306588e-3, 1.9387108e-3, 1.9583331e-3, 1.9907351e-3, 2.0286806e-3, 2.0733607e-3, 2.1270186e-3, 2.1863925e-3, 2.2577539e-3, 2.3390006e-3, 2.4212326e-3, 2.5112892e-3, 2.6161915e-3, 2.7238882e-3, 2.8385163e-3, 2.9647013e-3, 3.0959433e-3, 3.2331088e-3, 3.3817486e-3, 3.5385707e-3, 3.7031757e-3, 3.8780884e-3, 4.0550706e-3, 4.2388498e-3, 4.4433747e-3, 4.6413012e-3, 4.8539670e-3, 5.0680268e-3, 5.3044183e-3, 5.5388017e-3, 5.7755439e-3, 6.0326566e-3, 6.2848452e-3, 6.5558742e-3, 6.8328348e-3, 7.1072149e-3, 7.1072149e-3]),
                ),
            ],
            "qcd_y"    : np.array([2.5617082e-3, 2.4598979e-3, 2.3739593e-3, 2.2904004e-3, 2.2169883e-3, 2.1545803e-3, 2.0954206e-3, 2.0498675e-3, 2.0070737e-3, 1.9754803e-3, 1.9463467e-3, 1.9278404e-3, 1.9236438e-3, 1.9198349e-3, 1.9306588e-3, 1.9387108e-3, 1.9583331e-3, 1.9907351e-3, 2.0286806e-3, 2.0733607e-3, 2.1270186e-3, 2.1863925e-3, 2.2577539e-3, 2.3390006e-3, 2.4212326e-3, 2.5112892e-3, 2.6161915e-3, 2.7238882e-3, 2.8385163e-3, 2.9647013e-3, 3.0959433e-3, 3.2331088e-3, 3.3817486e-3, 3.5385707e-3, 3.7031757e-3, 3.8780884e-3, 4.0550706e-3, 4.2388498e-3, 4.4433747e-3, 4.6413012e-3, 4.8539670e-3, 5.0680268e-3, 5.3044183e-3, 5.5388017e-3, 5.7755439e-3, 6.0326566e-3, 6.2848452e-3, 6.5558742e-3, 6.8328348e-3, 7.1072149e-3, 7.1072149e-3]),
            "qcd_min"  : np.array([2.4433990e-3, 2.3462555e-3, 2.2643201e-3, 2.1845478e-3, 2.1145236e-3, 2.0548881e-3, 1.9983845e-3, 1.9548476e-3, 1.9139322e-3, 1.8836686e-3, 1.8557506e-3, 1.8379680e-3, 1.8337991e-3, 1.8299901e-3, 1.8401805e-3, 1.8476284e-3, 1.8662068e-3, 1.8968667e-3, 1.9328299e-3, 1.9751895e-3, 2.0260910e-3, 2.0824733e-3, 2.1502855e-3, 2.2275197e-3, 2.3055794e-3, 2.3911361e-3, 2.4909493e-3, 2.5933063e-3, 2.7022640e-3, 2.8222590e-3, 2.9470724e-3, 3.0775451e-3, 3.2189071e-3, 3.3680276e-3, 3.5246274e-3, 3.6910487e-3, 3.8593518e-3, 4.0342136e-3, 4.2287372e-3, 4.4170780e-3, 4.6194516e-3, 4.8230030e-3, 5.0480045e-3, 5.2710327e-3, 5.4962622e-3, 5.7409237e-3, 5.9809253e-3, 6.2388890e-3, 6.5023785e-3, 6.7635515e-3, 6.7635515e-3]),
            "qcd_max"  : np.array([2.6900604e-3, 2.5831920e-3, 2.4929066e-3, 2.4052452e-3, 2.3281584e-3, 2.2627536e-3, 2.2007167e-3, 2.1529838e-3, 2.1081610e-3, 2.0751367e-3, 2.0446966e-3, 2.0254163e-3, 2.0212045e-3, 2.0174111e-3, 2.0289322e-3, 2.0376618e-3, 2.0584262e-3, 2.0927416e-3, 2.1328564e-3, 2.1800780e-3, 2.2367509e-3, 2.2993910e-3, 2.3746249e-3, 2.4602457e-3, 2.5470394e-3, 2.6420082e-3, 2.7524493e-3, 2.8659720e-3, 2.9867879e-3, 3.1197183e-3, 3.2579655e-3, 3.4024228e-3, 3.5589936e-3, 3.7242136e-3, 3.8975378e-3, 4.0816963e-3, 4.2681397e-3, 4.4616317e-3, 4.6770565e-3, 4.8854207e-3, 5.1092919e-3, 5.3348103e-3, 5.5835965e-3, 5.8303478e-3, 6.0796332e-3, 6.3502993e-3, 6.6157573e-3, 6.9010042e-3, 7.1926442e-3, 7.4813962e-3, 7.4813962e-3]),
            "y"        : np.array([2.5617082e-3, 2.4598979e-3, 2.3739593e-3, 2.2904004e-3, 2.2169883e-3, 2.1545803e-3, 2.0954206e-3, 2.0498675e-3, 2.0070737e-3, 1.9754803e-3, 1.9463467e-3, 1.9278404e-3, 1.9236438e-3, 1.9198349e-3, 1.9306588e-3, 1.9387108e-3, 1.9583331e-3, 1.9907351e-3, 2.0286806e-3, 2.0733607e-3, 2.1270186e-3, 2.1863925e-3, 2.2577539e-3, 2.3390006e-3, 2.4212326e-3, 2.5112892e-3, 2.6161915e-3, 2.7238882e-3, 2.8385163e-3, 2.9647013e-3, 3.0959433e-3, 3.2331088e-3, 3.3817486e-3, 3.5385707e-3, 3.7031757e-3, 3.8780884e-3, 4.0550706e-3, 4.2388498e-3, 4.4433747e-3, 4.6413012e-3, 4.8539670e-3, 5.0680268e-3, 5.3044183e-3, 5.5388017e-3, 5.7755439e-3, 6.0326566e-3, 6.2848452e-3, 6.5558742e-3, 6.8328348e-3, 7.1072149e-3, 7.1072149e-3]),
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            ],
        },
        {
            "slice_label"    : r"$\SI{3000}{GeV} < M_{\ell\bar{\ell}} < \SI{5000}{GeV}$",
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                ),
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            ],
        },
        {
            "slice_label"    : r"$\SI{10000}{GeV} < M_{\ell\bar{\ell}} < \SI{14000}{GeV}$",
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            ],
            "qcd_y"    : np.array([5.9784203e-12, 5.7334000e-12, 5.5158405e-12, 5.2821781e-12, 5.0757322e-12, 4.8721048e-12, 4.6751045e-12, 4.4854392e-12, 4.3023311e-12, 4.1272521e-12, 3.9585369e-12, 3.8041358e-12, 3.6380746e-12, 3.5024271e-12, 3.3710447e-12, 3.2510444e-12, 3.1260172e-12, 3.0105698e-12, 2.9080579e-12, 2.8034115e-12, 2.7170918e-12, 2.6321534e-12, 2.5586050e-12, 2.4978602e-12, 2.4378311e-12, 2.3877748e-12, 2.3427105e-12, 2.3080580e-12, 2.2782058e-12, 2.2586106e-12, 2.2397268e-12, 2.2391422e-12, 2.2396377e-12, 2.2520626e-12, 2.2723190e-12, 2.2981583e-12, 2.3297045e-12, 2.3715628e-12, 2.4196963e-12, 2.4771198e-12, 2.5408710e-12, 2.6118028e-12, 2.6948071e-12, 2.7819795e-12, 2.8750032e-12, 2.9793716e-12, 3.0914562e-12, 3.2108107e-12, 3.3326464e-12, 3.4695631e-12, 3.4695631e-12]),
            "qcd_min"  : np.array([4.8320667e-12, 4.6340168e-12, 4.4581332e-12, 4.2694034e-12, 4.1024778e-12, 3.9380551e-12, 3.7788147e-12, 3.6256865e-12, 3.4778027e-12, 3.3363436e-12, 3.2001398e-12, 3.0753736e-12, 2.9412981e-12, 2.8318282e-12, 2.7257422e-12, 2.6288731e-12, 2.5280116e-12, 2.4349649e-12, 2.3522879e-12, 2.2679037e-12, 2.1983550e-12, 2.1299028e-12, 2.0707980e-12, 2.0218582e-12, 1.9736388e-12, 1.9333927e-12, 1.8971892e-12, 1.8695481e-12, 1.8457255e-12, 1.8302086e-12, 1.8152970e-12, 1.8151223e-12, 1.8158688e-12, 1.8262736e-12, 1.8429841e-12, 1.8642947e-12, 1.8901548e-12, 1.9243658e-12, 1.9637075e-12, 2.0105556e-12, 2.0624878e-12, 2.1202545e-12, 2.1877949e-12, 2.2587192e-12, 2.3343787e-12, 2.4192297e-12, 2.5103115e-12, 2.6072867e-12, 2.7062549e-12, 2.8174517e-12, 2.8174517e-12]),
            "qcd_max"  : np.array([7.4982907e-12, 7.1910016e-12, 6.9182003e-12, 6.6249200e-12, 6.3661005e-12, 6.1104359e-12, 5.8633804e-12, 5.6252128e-12, 5.3953612e-12, 5.1756951e-12, 4.9638200e-12, 4.7701171e-12, 4.5616013e-12, 4.3911866e-12, 4.2262299e-12, 4.0755193e-12, 3.9183857e-12, 3.7731478e-12, 3.6442761e-12, 3.5126965e-12, 3.4040659e-12, 3.2972015e-12, 3.2043850e-12, 3.1279352e-12, 3.0521448e-12, 2.9890066e-12, 2.9321201e-12, 2.8880447e-12, 2.8500918e-12, 2.8249788e-12, 2.8007072e-12, 2.7994768e-12, 2.7995189e-12, 2.8144977e-12, 2.8393388e-12, 2.8710337e-12, 2.9099934e-12, 2.9618605e-12, 3.0214992e-12, 3.0927929e-12, 3.1720739e-12, 3.2603093e-12, 3.3636599e-12, 3.4722126e-12, 3.5880952e-12, 3.7181683e-12, 3.8579312e-12, 4.0067802e-12, 4.1587643e-12, 4.3295963e-12, 4.3295963e-12]),
            "y"        : np.array([5.9784203e-12, 5.7334000e-12, 5.5158405e-12, 5.2821781e-12, 5.0757322e-12, 4.8721048e-12, 4.6751045e-12, 4.4854392e-12, 4.3023311e-12, 4.1272521e-12, 3.9585369e-12, 3.8041358e-12, 3.6380746e-12, 3.5024271e-12, 3.3710447e-12, 3.2510444e-12, 3.1260172e-12, 3.0105698e-12, 2.9080579e-12, 2.8034115e-12, 2.7170918e-12, 2.6321534e-12, 2.5586050e-12, 2.4978602e-12, 2.4378311e-12, 2.3877748e-12, 2.3427105e-12, 2.3080580e-12, 2.2782058e-12, 2.2586106e-12, 2.2397268e-12, 2.2391422e-12, 2.2396377e-12, 2.2520626e-12, 2.2723190e-12, 2.2981583e-12, 2.3297045e-12, 2.3715628e-12, 2.4196963e-12, 2.4771198e-12, 2.5408710e-12, 2.6118028e-12, 2.6948071e-12, 2.7819795e-12, 2.8750032e-12, 2.9793716e-12, 3.0914562e-12, 3.2108107e-12, 3.3326464e-12, 3.4695631e-12, 3.4695631e-12]),
            "ymin"     : np.array([4.8320667e-12, 4.6340168e-12, 4.4581332e-12, 4.2694034e-12, 4.1024778e-12, 3.9380551e-12, 3.7788147e-12, 3.6256865e-12, 3.4778027e-12, 3.3363436e-12, 3.2001398e-12, 3.0753736e-12, 2.9412981e-12, 2.8318282e-12, 2.7257422e-12, 2.6288731e-12, 2.5280116e-12, 2.4349649e-12, 2.3522879e-12, 2.2679037e-12, 2.1983550e-12, 2.1299028e-12, 2.0707980e-12, 2.0218582e-12, 1.9736388e-12, 1.9333927e-12, 1.8971892e-12, 1.8695481e-12, 1.8457255e-12, 1.8302086e-12, 1.8152970e-12, 1.8151223e-12, 1.8158688e-12, 1.8262736e-12, 1.8429841e-12, 1.8642947e-12, 1.8901548e-12, 1.9243658e-12, 1.9637075e-12, 2.0105556e-12, 2.0624878e-12, 2.1202545e-12, 2.1877949e-12, 2.2587192e-12, 2.3343787e-12, 2.4192297e-12, 2.5103115e-12, 2.6072867e-12, 2.7062549e-12, 2.8174517e-12, 2.8174517e-12]),
            "ymax"     : np.array([7.4982907e-12, 7.1910016e-12, 6.9182003e-12, 6.6249200e-12, 6.3661005e-12, 6.1104359e-12, 5.8633804e-12, 5.6252128e-12, 5.3953612e-12, 5.1756951e-12, 4.9638200e-12, 4.7701171e-12, 4.5616013e-12, 4.3911866e-12, 4.2262299e-12, 4.0755193e-12, 3.9183857e-12, 3.7731478e-12, 3.6442761e-12, 3.5126965e-12, 3.4040659e-12, 3.2972015e-12, 3.2043850e-12, 3.1279352e-12, 3.0521448e-12, 2.9890066e-12, 2.9321201e-12, 2.8880447e-12, 2.8500918e-12, 2.8249788e-12, 2.8007072e-12, 2.7994768e-12, 2.7995189e-12, 2.8144977e-12, 2.8393388e-12, 2.8710337e-12, 2.9099934e-12, 2.9618605e-12, 3.0214992e-12, 3.0927929e-12, 3.1720739e-12, 3.2603093e-12, 3.3636599e-12, 3.4722126e-12, 3.5880952e-12, 3.7181683e-12, 3.8579312e-12, 4.0067802e-12, 4.1587643e-12, 4.3295963e-12, 4.3295963e-12]),
            "channels" : [
                (r"$\mathrm{u}\bar{\mathrm{u}} + \mathrm{c}\bar{\mathrm{c}}$", np.array([5.7928985e-12, 5.5545492e-12, 5.3436676e-12, 5.1170724e-12, 4.9162902e-12, 4.7204604e-12, 4.5290894e-12, 4.3461366e-12, 4.1703059e-12, 3.9987049e-12, 3.8352353e-12, 3.6854207e-12, 3.5246703e-12, 3.3934168e-12, 3.2662258e-12, 3.1498699e-12, 3.0290848e-12, 2.9170428e-12, 2.8178193e-12, 2.7171531e-12, 2.6338379e-12, 2.5506545e-12, 2.4799421e-12, 2.4205546e-12, 2.3628084e-12, 2.3149553e-12, 2.2710462e-12, 2.2372451e-12, 2.2081619e-12, 2.1896656e-12, 2.1712047e-12, 2.1709859e-12, 2.1712641e-12, 2.1831538e-12, 2.2025217e-12, 2.2284757e-12, 2.2590442e-12, 2.2993731e-12, 2.3460771e-12, 2.4020975e-12, 2.4638450e-12, 2.5323895e-12, 2.6127955e-12, 2.6978311e-12, 2.7877094e-12, 2.8894223e-12, 2.9977314e-12, 3.1142142e-12, 3.2318652e-12, 3.3649215e-12, 3.3649215e-12])),
                (r"$\mathrm{d}\bar{\mathrm{d}} + \mathrm{s}\bar{\mathrm{s}} + \mathrm{b}\bar{\mathrm{b}}$", np.array([1.8552176e-13, 1.7885086e-13, 1.7217283e-13, 1.6510577e-13, 1.5944207e-13, 1.5164441e-13, 1.4601514e-13, 1.3930258e-13, 1.3202519e-13, 1.2854728e-13, 1.2330167e-13, 1.1871509e-13, 1.1340433e-13, 1.0901027e-13, 1.0481896e-13, 1.0117454e-13, 9.6932317e-14, 9.3526940e-14, 9.0238533e-14, 8.6258414e-14, 8.3253847e-14, 8.1498874e-14, 7.8662857e-14, 7.7305575e-14, 7.5022648e-14, 7.2819518e-14, 7.1664335e-14, 7.0812969e-14, 7.0043876e-14, 6.8944971e-14, 6.8522087e-14, 6.8156305e-14, 6.8373568e-14, 6.8908752e-14, 6.9797327e-14, 6.9682621e-14, 7.0660333e-14, 7.2189766e-14, 7.3619284e-14, 7.5022326e-14, 7.7026006e-14, 7.9413264e-14, 8.2011611e-14, 8.4148408e-14, 8.7293834e-14, 8.9949315e-14, 9.3724817e-14, 9.6596463e-14, 1.0078114e-13, 1.0464156e-13, 1.0464156e-13]))
            ],
        },
    ]


def metadata():
    return {
        "arxiv": r"",
        "description": r"",
        "hepdata": r"",
        "initial_state_1": r"2212",
        "initial_state_2": r"2212",
        "lumi_id_types": r"pdg_mc_ids",
        "mg5amc_repo": r"http://bazaar.launchpad.net/~maddevelopers/mg5amcnlo/3.3.1/",
        "mg5amc_revno": r"981",
        "patches": r"",
        "pineappl_gitversion": r"v0.5.2-64-g402587e-dirty",
        "results_pdf": r"MSHT20nnlo_as118",
        "runcard_gitversion": r"9b81106-dirty",
        "tau_min": r"",
        "user_cuts": r"mmllmax=500.0",
        "x1_label": r"Mll",
        "x1_label_tex": r"$M_{\ell\bar{\ell}}$",
        "x1_unit": r"\giga\electronvolt",
        "x2_label": r"costh",
        "x2_label_tex": r"$\cos \theta^*$",
        "x2_unit": r"",
        "y_label": r"dsig/dcosth",
        "y_label_tex": r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\cos \theta^*}$",
        "y_unit": r"\pico\barn",
    }


if __name__ == "__main__":
    main()
"#;

#[test]
fn help() {
    Command::cargo_bin("pineappl")
        .unwrap()
        .args(["plot", "--help"])
        .assert()
        .success()
        .stdout(
            HELP_STR.replace(
                "{}",
                &thread::available_parallelism()
                    .map_or(1, NonZeroUsize::get)
                    .to_string(),
            ),
        );
}

#[test]
fn default() {
    Command::cargo_bin("pineappl")
        .unwrap()
        .args([
            "plot",
            "--threads=1",
            "../test-data/LHCB_WP_7TEV.pineappl.lz4",
            "NNPDF31_nlo_as_0118_luxqed",
            "NNPDF40_nnlo_as_01180=NNPDF4.0",
        ])
        .assert()
        .success()
        .stdout(DEFAULT_STR);
}

#[test]
fn subgrid_pull() {
    Command::cargo_bin("pineappl")
        .unwrap()
        .args([
            "plot",
            "--subgrid-pull=0,0,0",
            "--threads=1",
            "../test-data/LHCB_WP_7TEV.pineappl.lz4",
            "NNPDF31_nlo_as_0118_luxqed",
            "NNPDF40_nnlo_as_01180",
        ])
        .assert()
        .success()
        .stdout(SUBGRID_PULL_STR);
}

#[test]
fn drell_yan_afb() {
    Command::cargo_bin("pineappl")
        .unwrap()
        .args([
            "plot",
            "--asymmetry",
            "--threads=1",
            "../test-data/CMS_DY_14TEV_MLL_6000_COSTH.pineappl.lz4",
            "NNPDF40_nnlo_as_01180",
        ])
        .assert()
        .success()
        .stdout(DRELL_YAN_AFB_STR);
}

#[test]
fn drell_yan_mass_slices() {
    Command::cargo_bin("pineappl")
        .unwrap()
        .args([
            "plot",
            "--no-conv-fun-unc",
            "--threads=1",
            "../test-data/NNPDF_DY_14TEV_BSM_AFB.pineappl.lz4",
            "NNPDF40_nnlo_as_01180",
        ])
        .assert()
        .success()
        .stdout(DRELL_YAN_MASS_SLICES_STR);
}