diff --git a/pineappl_cli/src/plot.py b/pineappl_cli/src/plot.py
index 7c1b926f..0f7b441c 100644
--- a/pineappl_cli/src/plot.py
+++ b/pineappl_cli/src/plot.py
@@ -1,18 +1,41 @@
 #!/usr/bin/env python3
 
-import math
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import pickle
 
+# global variables coming from the CLI
+# CLI_INSERT_CONFIG_BEGIN
+# add some placeholders meanwhile
+title = ""
+xlabel = ""
+ylabel = ""
+xlog = False
+ylog = False
+scales = 1
+plot_panels = {
+    "int": False,
+    "abs": False,
+    "rel_ewonoff": False,
+    "abs_pdfs": False,
+    "ratio_pdf": False,
+    "double_ratio_pdf": False,
+    "rel_pdfunc": False,
+    "rel_pdfpull": False,
+}
+output = ""
+data = {}
+metadata = {}
+# CLI_INSERT_CONFIG_END
+
 # 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 = {{
+stylesheet = {
     "axes.axisbelow": True,
     "axes.grid": True,
     "axes.labelsize": "small",
@@ -27,7 +50,7 @@
     "legend.frameon": False,
     "pdf.compression": 0,
     "text.usetex": True,
-    "text.latex.preamble": r"\usepackage{{siunitx}}\usepackage{{lmodern}}\usepackage[T1]{{fontenc}}",
+    "text.latex.preamble": r"\usepackage{siunitx}\usepackage{lmodern}\usepackage[T1]{fontenc}",
     "xtick.bottom": True,
     "xtick.top": True,
     "xtick.direction": "in",
@@ -41,27 +64,20 @@
     "ytick.major.width": 0.5,
     "ytick.minor.visible": True,
     "ytick.minor.width": 0.5,
-}}
-
-# global plot labels
-title  = r"{title}"
-xlabel = r"{xlabel}"
-ylabel = r"{ylabel}"
+}
 
 # panel plot labels
-ylabel_ratio_pdf        = r"Ratio to {{central_pdf}}"
+ylabel_ratio_pdf = r"Ratio to {{central_pdf}}"
 ylabel_double_ratio_pdf = r"Ratio to NLO"
-ylabel_rel_ewonoff      = r"NLO EW on/off [\si{{\percent}}]"
-ylabel_rel_pdfunc       = r"PDF uncertainty [\si{{\percent}}]"
-ylabel_rel_pdfpull      = r"Pull [$\sigma$]"
+ylabel_rel_ewonoff = r"NLO EW on/off [\si{\percent}]"
+ylabel_rel_pdfunc = r"PDF uncertainty [\si{\percent}]"
+ylabel_rel_pdfpull = r"Pull [$\sigma$]"
 
-label_rel_ewonoff_qcd       = r"NLO QCD"
-label_rel_ewonoff_ew        = r"NLO QCD+EW"
-label_rel_ewonoff_scale_unc = r"{scales}-p.\ scale var."
-label_rel_ewonoff_pdf_unc   = r"PDF uncertainty"
+label_rel_ewonoff_qcd = r"NLO QCD"
+label_rel_ewonoff_ew = r"NLO QCD+EW"
+label_rel_ewonoff_scale_unc = f"{scales}-p. scale var."
+label_rel_ewonoff_pdf_unc = r"PDF uncertainty"
 
-xlog = {xlog}
-ylog = {ylog}
 
 # linestyle for the channel breakdown shown in the panel `plot_abs_pdfs`. If the array
 # is empty, no channel breakdown will be shown, otherwise the most important channels,
@@ -70,25 +86,22 @@
 channel_breakdown_linestyles = []
 
 
-def main():
+def main(active_panels):
+    """Build a plot figure with various panels."""
+    # Find the active panels
     panels = [
-        {enable_int}plot_int,
-        {enable_abs}plot_abs,
-        {enable_rel_ewonoff}plot_rel_ewonoff,
-        {enable_abs_pdfs}plot_abs_pdfs,
-        {enable_ratio_pdf}plot_ratio_pdf,
-        {enable_double_ratio_pdf}plot_double_ratio_pdf,
-        {enable_rel_pdfunc}plot_rel_pdfunc,
-        {enable_rel_pdfpull}plot_rel_pdfpull,
+        PANEL_FNC_MAP[panel] for panel, enabled in active_panels.items() if enabled
     ]
 
+    # prepare the figure
     mpl.rcParams.update(stylesheet)
-    {enable_abs}plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
-    {enable_int}plt.rc("figure", figsize=(4.2, 2.6))
-
-    data_slices = data()
+    if len(panels) == 1:
+        plt.rc("figure", figsize=(4.2, 2.6))
+    else:
+        plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
 
-    for index, kwargs in enumerate(data_slices):
+    # Plot all data
+    for index, kwargs in enumerate(data):
         figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)
 
         if len(kwargs["x"]) > 2 and xlog:
@@ -100,10 +113,10 @@ def main():
         for plot, axis in zip(panels, axes[:, 0]):
             plot(axis, **kwargs)
 
-        if len(data_slices) == 1:
-            figure.savefig("{output}.pdf")
+        if len(data) == 1:
+            figure.savefig(f"{output}.pdf")
         else:
-            figure.savefig("{output}-{{}}.pdf".format(index))
+            figure.savefig(f"{output}-{index}.pdf")
         plt.close(figure)
 
 
@@ -133,8 +146,8 @@ def set_ylim(axis, save, load, filename):
     elif (ymax - ymin) < 3.0:
         inc = 0.5
 
-    ymin = math.floor(ymin / inc) * inc
-    ymax = math.ceil(ymax / inc) * inc
+    ymin = np.floor(ymin / inc) * inc
+    ymax = np.ceil(ymax / inc) * inc
 
     if save:
         with open(filename, "wb") as f:
@@ -167,14 +180,13 @@ def set_ylim(axis, save, load, filename):
     axis.set_ylim((ymin - space, ymax + space))
 
 
-def plot_int(axis, **kwargs):
+def plot_int(axis, /, pdf_results, **_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
+    for index, (label, ycentral, ymin, ymax) in enumerate(pdf_results):
         x = np.append(x, ycentral[:-1])
         xmin = np.append(xmin, ymin[:-1])
         xmax = np.append(xmax, ymax[:-1])
@@ -182,126 +194,251 @@ def plot_int(axis, **kwargs):
 
         # draw one- and two-sigma bands
         if label == "CENTRAL-PDF":
-            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
+            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.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"]
-
+def plot_abs(axis, /, x, y, ymin, ymax, slice_label="", **_kwargs):
     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.step(x, y, colors[0], linewidth=1.0, where="post", label=slice_label)
+    axis.fill_between(
+        x,
+        ymin,
+        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"]
+def plot_ratio_pdf(axis, /, x, pdf_results, slice_label="", **_kwargs):
+    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_results[0][0]))
 
-    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_uncertainties[0][0]))
-
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
-        y = y / pdf_uncertainties[0][1]
-        ymin = ymin / pdf_uncertainties[0][1]
-        ymax = ymax / pdf_uncertainties[0][1]
+        y = y / pdf_results[0][1]
+        ymin = ymin / pdf_results[0][1]
+        ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, slice_label="", **_kwargs):
     axis.set_ylabel(ylabel_double_ratio_pdf)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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]
+            y = y / pdf_results[0][1]
+            ymin = ymin / pdf_results[0][1]
+            ymax = ymax / pdf_results[0][1]
         else:
-            y = y / pdf_uncertainties[1][1]
-            ymin = ymin / pdf_uncertainties[1][1]
-            ymax = ymax / pdf_uncertainties[1][1]
+            y = y / pdf_results[1][1]
+            ymin = ymin / pdf_results[1][1]
+            ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, channels, slice_label="", **_kwargs):
     axis.set_yscale("log" if ylog else "linear")
     axis.set_ylabel(ylabel)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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")
-
-    for index, ((label, y), linestyle) in enumerate(zip(channels, channel_breakdown_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(channel_breakdown_linestyles)))
+        axis.fill_between(
+            x,
+            ymin,
+            ymax,
+            alpha=0.4,
+            color=colors[index],
+            label=label,
+            linewidth=0.5,
+            step="post",
+        )
+
+    for index, ((label, y), linestyle) in enumerate(
+        zip(channels, channel_breakdown_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_results) + len(channel_breakdown_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")
+        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, /, x, mid, y, ymin, ymax, qcd_y, pdf_results, **_kwargs):
+    y = percent_diff(y, qcd_y)
+    qcd_y = percent_diff(qcd_y, 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(ymin, qcd_y)
+    ymax = percent_diff(ymax, qcd_y)
+    pdf_min = abs(percent_diff(pdf_results[0][2], pdf_results[0][1]))[:-1]
+    pdf_max = abs(percent_diff(pdf_results[0][3], 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.fill_between(
+        x,
+        ymin,
+        ymax,
+        alpha=0.4,
+        color=colors[0],
+        label=label_rel_ewonoff_scale_unc,
+        linewidth=0.5,
+        step="post",
+    )
+    axis.errorbar(
+        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)
+    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):
+def plot_rel_pdfunc(axis, /, x, pdf_results, **_kwargs):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
         ymin = percent_diff(ymin, y)
         ymax = percent_diff(ymax, y)
@@ -313,38 +450,56 @@ def plot_rel_pdfunc(axis, **kwargs):
     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"]
+def plot_rel_pdfpull(axis, /, x, y, pdf_results, **_kwargs):
+    central_y = pdf_results[0][1]
+    central_ymin = pdf_results[0][2]
+    central_ymax = pdf_results[0][3]
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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.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_results)),
+    )  # rel_pdfpull
     axis.set_ylabel(ylabel_rel_pdfpull)
 
     set_ylim(axis, False, False, "rel_pdfpull")
 
 
-def data():
-    return {data}
+PANEL_FNC_MAP = {
+    "int": plot_int,
+    "abs": plot_abs,
+    "rel_ewonoff": plot_rel_ewonoff,
+    "abs_pdfs": plot_abs_pdfs,
+    "ratio_pdf": plot_ratio_pdf,
+    "double_ratio_pdf": plot_double_ratio_pdf,
+    "rel_pdfunc": plot_rel_pdfunc,
+    "rel_pdfpull": plot_rel_pdfpull,
+}
 
 
-def metadata():
-    return {{
-{metadata}
-    }}
+# CLI data variables
+# CLI_INSERT_DATA
+# end CLI data variables
 
 
 if __name__ == "__main__":
-    main()
+    main(plot_panels)
diff --git a/pineappl_cli/src/plot.rs b/pineappl_cli/src/plot.rs
index d7b27ff9..6320a9c4 100644
--- a/pineappl_cli/src/plot.rs
+++ b/pineappl_cli/src/plot.rs
@@ -16,6 +16,10 @@ use std::path::{Path, PathBuf};
 use std::process::ExitCode;
 use std::thread;
 
+const MARKER_CONFIG_BEGIN: &str = "# CLI_INSERT_CONFIG_BEGIN\n";
+const MARKER_CONFIG_END: &str = "# CLI_INSERT_CONFIG_END";
+const MARKER_DATA_INSERT: &str = "# CLI_INSERT_DATA";
+
 /// Creates a matplotlib script plotting the contents of the grid.
 #[derive(Parser)]
 pub struct Opts {
@@ -56,10 +60,12 @@ pub struct Opts {
     no_conv_fun_unc: bool,
 }
 
+/// Convert `slice` to (unformatted) Python list.
 fn map_format_join(slice: &[f64]) -> String {
     slice.iter().map(|x| format!("{x}")).join(", ")
 }
 
+/// Convert `slice` to Python list using `.7e`.
 fn map_format_e_join(slice: &[f64]) -> String {
     slice.iter().map(|x| format!("{x:.7e}")).join(", ")
 }
@@ -72,6 +78,7 @@ fn map_format_e_join_repeat_last(slice: &[f64]) -> String {
         .join(", ")
 }
 
+/// Convert a channel to a good Python string representation.
 fn map_format_channel(
     channel: &Channel,
     has_pdf1: bool,
@@ -99,12 +106,13 @@ fn map_format_channel(
         .join(" + ")
 }
 
+/// Convert channel contributions to Python tuples.
 fn map_format_channels(channels: &[(String, Vec<f64>)]) -> String {
     channels
         .iter()
         .map(|(label, bins)| {
             format!(
-                "                (r\"${}$\", np.array([{}]))",
+                "            (r\"${}$\", np.array([{}]))",
                 label,
                 map_format_e_join_repeat_last(bins)
             )
@@ -112,14 +120,15 @@ fn map_format_channels(channels: &[(String, Vec<f64>)]) -> String {
         .join(",\n")
 }
 
+/// Convert PDF results to a Python tuple.
 fn format_pdf_results(pdf_uncertainties: &[Vec<Vec<f64>>], conv_funs: &[ConvFuns]) -> String {
     pdf_uncertainties
         .iter()
         .zip(conv_funs.iter().map(|fun| &fun.label))
         .map(|(values, label)| {
             format!(
-                "                (
-                    \"{}\",
+                "            (
+                    r\"{}\",
                     np.array([{}]),
                     np.array([{}]),
                     np.array([{}]),
@@ -133,6 +142,7 @@ fn format_pdf_results(pdf_uncertainties: &[Vec<Vec<f64>>], conv_funs: &[ConvFuns
         .join("\n")
 }
 
+/// Convert metadata into a Python dict.
 fn format_metadata(metadata: &[(&String, &String)]) -> String {
     metadata
         .iter()
@@ -142,7 +152,7 @@ fn format_metadata(metadata: &[(&String, &String)]) -> String {
                 None
             } else {
                 Some(format!(
-                    "        \"{}\": r\"{}\",",
+                    "    \"{}\": r\"{}\",",
                     key,
                     if *key == "description" {
                         value.replace('\u{2013}', "--").replace('\u{2014}', "---")
@@ -160,6 +170,15 @@ fn format_metadata(metadata: &[(&String, &String)]) -> String {
         .join("\n")
 }
 
+/// Convert `b` into a Python bool literal.
+fn map_bool(b: bool) -> String {
+    if b {
+        "True".to_owned()
+    } else {
+        "False".to_owned()
+    }
+}
+
 impl Subcommand for Opts {
     fn run(&self, cfg: &GlobalConfiguration) -> Result<ExitCode> {
         ThreadPoolBuilder::new()
@@ -410,23 +429,23 @@ impl Subcommand for Opts {
 
                 writeln!(
                     &mut data_string,
-                    "        {{
-            \"slice_label\"    : r\"{slice_label}\",
-            \"x\"        : np.array([{x}]),
-            \"mid\"      : np.array([{mid}]),
-            \"pdf_results\" : [
+                    "    {{
+        \"slice_label\"    : r\"{slice_label}\",
+        \"x\"        : np.array([{x}]),
+        \"mid\"      : np.array([{mid}]),
+        \"pdf_results\" : [
 {pdf_results}
-            ],
-            \"qcd_y\"    : np.array([{qcd_y}]),
-            \"qcd_min\"  : np.array([{qcd_min}]),
-            \"qcd_max\"  : np.array([{qcd_max}]),
-            \"y\"        : np.array([{y}]),
-            \"ymin\"     : np.array([{ymin}]),
-            \"ymax\"     : np.array([{ymax}]),
-            \"channels\" : [
+        ],
+        \"qcd_y\"    : np.array([{qcd_y}]),
+        \"qcd_min\"  : np.array([{qcd_min}]),
+        \"qcd_max\"  : np.array([{qcd_max}]),
+        \"y\"        : np.array([{y}]),
+        \"ymin\"     : np.array([{ymin}]),
+        \"ymax\"     : np.array([{ymax}]),
+        \"channels\" : [
 {channels}
-            ],
-        }},",
+        ],
+    }},",
                     slice_label = label,
                     mid = map_format_join(&mid),
                     pdf_results = format_pdf_results(&conv_fun_uncertainties, &self.conv_funs),
@@ -442,7 +461,7 @@ impl Subcommand for Opts {
                 .unwrap_or_else(|_| unreachable!());
             }
 
-            data_string.push_str("    ]");
+            data_string.push_str("]");
 
             // prepare metadata
             let key_values = grid.key_values().cloned().unwrap_or_default();
@@ -489,46 +508,86 @@ impl Subcommand for Opts {
                     format!(" [\\si{{{yunit}}}]")
                 }
             );
-            let xlog = if xunit.is_empty() { "False" } else { "True" };
+            let xlog = !xunit.is_empty();
             let ylog = xlog;
             let title = key_values.get("description").map_or("", String::as_str);
             let bins = grid.bin_info().bins();
             let nconvs = self.conv_funs.len();
 
-            let enable_int = if bins == 1 { "" } else { "# " };
-            let enable_abs = if bins == 1 { "# " } else { "" };
+            let enable_int = bins == 1;
+            let enable_abs = !enable_int;
             // TODO: only enable if there are EW corrections
             let enable_rel_ewonoff = enable_abs;
-            let enable_abs_pdfs = if nconvs == 1 || bins == 1 { "# " } else { "" };
+            let enable_abs_pdfs = !(nconvs == 1 || bins == 1);
             let enable_ratio_pdf = enable_abs_pdfs;
             let enable_double_ratio_pdf = enable_abs_pdfs;
-            let enable_rel_pdfunc = if nconvs == 1 || bins == 1 || self.no_conv_fun_unc {
-                "# "
-            } else {
-                ""
-            };
+            let enable_rel_pdfunc = !(nconvs == 1 || bins == 1 || self.no_conv_fun_unc);
             let enable_rel_pdfpull = enable_rel_pdfunc;
 
-            print!(
-                include_str!("plot.py"),
-                enable_int = enable_int,
-                enable_abs = enable_abs,
-                enable_rel_ewonoff = enable_rel_ewonoff,
-                enable_abs_pdfs = enable_abs_pdfs,
-                enable_ratio_pdf = enable_ratio_pdf,
-                enable_double_ratio_pdf = enable_double_ratio_pdf,
-                enable_rel_pdfunc = enable_rel_pdfunc,
-                enable_rel_pdfpull = enable_rel_pdfpull,
+            let config = format!(
+                "title = r\"{title}\"
+xlabel = r\"{xlabel}\"
+ylabel = r\"{ylabel}\"
+xlog = {xlog}
+ylog = {ylog}
+scales = {scales}
+plot_panels = {{
+    \"int\": {enable_int},
+    \"abs\": {enable_abs},
+    \"rel_ewonoff\": {enable_rel_ewonoff},
+    \"abs_pdfs\": {enable_abs_pdfs},
+    \"ratio_pdf\": {enable_ratio_pdf},
+    \"double_ratio_pdf\": {enable_double_ratio_pdf},
+    \"rel_pdfunc\": {enable_rel_pdfunc},
+    \"rel_pdfpull\": {enable_rel_pdfpull},
+}}
+output = r\"{output}\"",
+                enable_int = map_bool(enable_int),
+                enable_abs = map_bool(enable_abs),
+                enable_rel_ewonoff = map_bool(enable_rel_ewonoff),
+                enable_abs_pdfs = map_bool(enable_abs_pdfs),
+                enable_ratio_pdf = map_bool(enable_ratio_pdf),
+                enable_double_ratio_pdf = map_bool(enable_double_ratio_pdf),
+                enable_rel_pdfunc = map_bool(enable_rel_pdfunc),
+                enable_rel_pdfpull = map_bool(enable_rel_pdfpull),
                 xlabel = xlabel,
                 ylabel = ylabel,
-                xlog = xlog,
-                ylog = ylog,
+                xlog = map_bool(xlog),
+                ylog = map_bool(ylog),
                 title = title,
                 scales = self.scales,
                 output = output.to_str().unwrap(),
-                data = data_string,
+            );
+
+            let data = format!(
+                "data = {data_string}
+metadata = {{
+{metadata}
+}}",
+                data_string = data_string,
                 metadata = format_metadata(&vector),
             );
+            let template = include_str!("plot.py");
+            // UNWRAP: if there are no markers the template is wrong
+            let config_marker_begin = template.find(MARKER_CONFIG_BEGIN).unwrap();
+            let config_marker_end = template.find(MARKER_CONFIG_END).unwrap();
+            let data_marker = template.find(MARKER_DATA_INSERT).unwrap();
+            // echo template and dynamic content
+            print!("{}", template.get(0..config_marker_begin).unwrap());
+            print!("{}", config);
+            print!(
+                "{}",
+                template
+                    .get((config_marker_end + MARKER_CONFIG_END.len())..data_marker)
+                    .unwrap()
+            );
+            print!("{}", data);
+            print!(
+                "{}",
+                template
+                    .get((data_marker + MARKER_DATA_INSERT.len())..)
+                    .unwrap()
+            );
         } else {
             // TODO: enforce two arguments with clap
             assert_eq!(self.conv_funs.len(), 2);
diff --git a/pineappl_cli/tests/plot.rs b/pineappl_cli/tests/plot.rs
index 7309ec2f..8c158c2e 100644
--- a/pineappl_cli/tests/plot.rs
+++ b/pineappl_cli/tests/plot.rs
@@ -23,12 +23,30 @@ Options:
 
 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
 
+# global variables coming from the CLI
+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}]"
+xlog = False
+ylog = False
+scales = 7
+plot_panels = {
+    "int": False,
+    "abs": True,
+    "rel_ewonoff": True,
+    "abs_pdfs": True,
+    "ratio_pdf": True,
+    "double_ratio_pdf": True,
+    "rel_pdfunc": True,
+    "rel_pdfpull": True,
+}
+output = r"LHCB_WP_7TEV_opt"
+
 # 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
@@ -66,25 +84,18 @@ stylesheet = {
     "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        = r"Ratio to {central_pdf}"
+ylabel_ratio_pdf = r"Ratio to {{central_pdf}}"
 ylabel_double_ratio_pdf = r"Ratio to NLO"
-ylabel_rel_ewonoff      = r"NLO EW on/off [\si{\percent}]"
-ylabel_rel_pdfunc       = r"PDF uncertainty [\si{\percent}]"
-ylabel_rel_pdfpull      = r"Pull [$\sigma$]"
+ylabel_rel_ewonoff = r"NLO EW on/off [\si{\percent}]"
+ylabel_rel_pdfunc = r"PDF uncertainty [\si{\percent}]"
+ylabel_rel_pdfpull = r"Pull [$\sigma$]"
 
-label_rel_ewonoff_qcd       = r"NLO QCD"
-label_rel_ewonoff_ew        = r"NLO QCD+EW"
-label_rel_ewonoff_scale_unc = r"7-p.\ scale var."
-label_rel_ewonoff_pdf_unc   = r"PDF uncertainty"
+label_rel_ewonoff_qcd = r"NLO QCD"
+label_rel_ewonoff_ew = r"NLO QCD+EW"
+label_rel_ewonoff_scale_unc = f"{scales}-p. scale var."
+label_rel_ewonoff_pdf_unc = r"PDF uncertainty"
 
-xlog = False
-ylog = False
 
 # linestyle for the channel breakdown shown in the panel `plot_abs_pdfs`. If the array
 # is empty, no channel breakdown will be shown, otherwise the most important channels,
@@ -93,25 +104,22 @@ ylog = False
 channel_breakdown_linestyles = []
 
 
-def main():
+def main(active_panels):
+    """Build a plot figure with various panels."""
+    # Find the active panels
     panels = [
-        # plot_int,
-        plot_abs,
-        plot_rel_ewonoff,
-        plot_abs_pdfs,
-        plot_ratio_pdf,
-        plot_double_ratio_pdf,
-        plot_rel_pdfunc,
-        plot_rel_pdfpull,
+        PANEL_FNC_MAP[panel] for panel, enabled in active_panels.items() if enabled
     ]
 
+    # prepare the figure
     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()
+    if len(panels) == 1:
+        plt.rc("figure", figsize=(4.2, 2.6))
+    else:
+        plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
 
-    for index, kwargs in enumerate(data_slices):
+    # Plot all data
+    for index, kwargs in enumerate(data):
         figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)
 
         if len(kwargs["x"]) > 2 and xlog:
@@ -123,10 +131,10 @@ def main():
         for plot, axis in zip(panels, axes[:, 0]):
             plot(axis, **kwargs)
 
-        if len(data_slices) == 1:
-            figure.savefig("LHCB_WP_7TEV_opt.pdf")
+        if len(data) == 1:
+            figure.savefig(f"{output}.pdf")
         else:
-            figure.savefig("LHCB_WP_7TEV_opt-{}.pdf".format(index))
+            figure.savefig(f"{output}-{index}.pdf")
         plt.close(figure)
 
 
@@ -156,8 +164,8 @@ def set_ylim(axis, save, load, filename):
     elif (ymax - ymin) < 3.0:
         inc = 0.5
 
-    ymin = math.floor(ymin / inc) * inc
-    ymax = math.ceil(ymax / inc) * inc
+    ymin = np.floor(ymin / inc) * inc
+    ymax = np.ceil(ymax / inc) * inc
 
     if save:
         with open(filename, "wb") as f:
@@ -190,14 +198,13 @@ def set_ylim(axis, save, load, filename):
     axis.set_ylim((ymin - space, ymax + space))
 
 
-def plot_int(axis, **kwargs):
+def plot_int(axis, /, pdf_results, **_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
+    for index, (label, ycentral, ymin, ymax) in enumerate(pdf_results):
         x = np.append(x, ycentral[:-1])
         xmin = np.append(xmin, ymin[:-1])
         xmax = np.append(xmax, ymax[:-1])
@@ -205,126 +212,251 @@ def plot_int(axis, **kwargs):
 
         # draw one- and two-sigma bands
         if label == "CENTRAL-PDF":
-            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
+            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.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"]
-
+def plot_abs(axis, /, x, y, ymin, ymax, slice_label="", **_kwargs):
     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.step(x, y, colors[0], linewidth=1.0, where="post", label=slice_label)
+    axis.fill_between(
+        x,
+        ymin,
+        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]))
+def plot_ratio_pdf(axis, /, x, pdf_results, slice_label="", **_kwargs):
+    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_results[0][0]))
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
-        y = y / pdf_uncertainties[0][1]
-        ymin = ymin / pdf_uncertainties[0][1]
-        ymax = ymax / pdf_uncertainties[0][1]
+        y = y / pdf_results[0][1]
+        ymin = ymin / pdf_results[0][1]
+        ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, slice_label="", **_kwargs):
     axis.set_ylabel(ylabel_double_ratio_pdf)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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]
+            y = y / pdf_results[0][1]
+            ymin = ymin / pdf_results[0][1]
+            ymax = ymax / pdf_results[0][1]
         else:
-            y = y / pdf_uncertainties[1][1]
-            ymin = ymin / pdf_uncertainties[1][1]
-            ymax = ymax / pdf_uncertainties[1][1]
+            y = y / pdf_results[1][1]
+            ymin = ymin / pdf_results[1][1]
+            ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, channels, slice_label="", **_kwargs):
     axis.set_yscale("log" if ylog else "linear")
     axis.set_ylabel(ylabel)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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")
-
-    for index, ((label, y), linestyle) in enumerate(zip(channels, channel_breakdown_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(channel_breakdown_linestyles)))
+        axis.fill_between(
+            x,
+            ymin,
+            ymax,
+            alpha=0.4,
+            color=colors[index],
+            label=label,
+            linewidth=0.5,
+            step="post",
+        )
+
+    for index, ((label, y), linestyle) in enumerate(
+        zip(channels, channel_breakdown_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_results) + len(channel_breakdown_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")
+        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, /, x, mid, y, ymin, ymax, qcd_y, pdf_results, **_kwargs):
+    y = percent_diff(y, qcd_y)
+    qcd_y = percent_diff(qcd_y, 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(ymin, qcd_y)
+    ymax = percent_diff(ymax, qcd_y)
+    pdf_min = abs(percent_diff(pdf_results[0][2], pdf_results[0][1]))[:-1]
+    pdf_max = abs(percent_diff(pdf_results[0][3], 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.fill_between(
+        x,
+        ymin,
+        ymax,
+        alpha=0.4,
+        color=colors[0],
+        label=label_rel_ewonoff_scale_unc,
+        linewidth=0.5,
+        step="post",
+    )
+    axis.errorbar(
+        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)
-
+    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):
+def plot_rel_pdfunc(axis, /, x, pdf_results, **_kwargs):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
         ymin = percent_diff(ymin, y)
         ymax = percent_diff(ymax, y)
@@ -336,90 +468,111 @@ def plot_rel_pdfunc(axis, **kwargs):
     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"]
+def plot_rel_pdfpull(axis, /, x, y, pdf_results, **_kwargs):
+    central_y = pdf_results[0][1]
+    central_ymin = pdf_results[0][2]
+    central_ymax = pdf_results[0][3]
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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.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_results)),
+    )  # 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",
+PANEL_FNC_MAP = {
+    "int": plot_int,
+    "abs": plot_abs,
+    "rel_ewonoff": plot_rel_ewonoff,
+    "abs_pdfs": plot_abs_pdfs,
+    "ratio_pdf": plot_ratio_pdf,
+    "double_ratio_pdf": plot_double_ratio_pdf,
+    "rel_pdfunc": plot_rel_pdfunc,
+    "rel_pdfpull": plot_rel_pdfpull,
+}
+
+
+# CLI data variables
+data = [
+    {
+        "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" : [
+            (
+                    r"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",
+            (
+                    r"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",
-    }
+        ],
+        "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]))
+        ],
+    },
+]
+metadata = {
+    "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",
+}
+# end CLI data variables
 
 
 if __name__ == "__main__":
-    main()
+    main(plot_panels)
 "#;
 
 const SUBGRID_PULL_STR: &str = r"#!/usr/bin/env python3
@@ -488,12 +641,30 @@ 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
 
+# global variables coming from the CLI
+title = r""
+xlabel = r"$\cos \theta^*$"
+ylabel = r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\cos \theta^*}$ [\si{pb}]"
+xlog = False
+ylog = False
+scales = 7
+plot_panels = {
+    "int": False,
+    "abs": True,
+    "rel_ewonoff": True,
+    "abs_pdfs": False,
+    "ratio_pdf": False,
+    "double_ratio_pdf": False,
+    "rel_pdfunc": False,
+    "rel_pdfpull": False,
+}
+output = r"CMS_DY_14TEV_MLL_6000_COSTH"
+
 # 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
@@ -531,25 +702,18 @@ stylesheet = {
     "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        = r"Ratio to {central_pdf}"
+ylabel_ratio_pdf = r"Ratio to {{central_pdf}}"
 ylabel_double_ratio_pdf = r"Ratio to NLO"
-ylabel_rel_ewonoff      = r"NLO EW on/off [\si{\percent}]"
-ylabel_rel_pdfunc       = r"PDF uncertainty [\si{\percent}]"
-ylabel_rel_pdfpull      = r"Pull [$\sigma$]"
+ylabel_rel_ewonoff = r"NLO EW on/off [\si{\percent}]"
+ylabel_rel_pdfunc = r"PDF uncertainty [\si{\percent}]"
+ylabel_rel_pdfpull = r"Pull [$\sigma$]"
 
-label_rel_ewonoff_qcd       = r"NLO QCD"
-label_rel_ewonoff_ew        = r"NLO QCD+EW"
-label_rel_ewonoff_scale_unc = r"7-p.\ scale var."
-label_rel_ewonoff_pdf_unc   = r"PDF uncertainty"
+label_rel_ewonoff_qcd = r"NLO QCD"
+label_rel_ewonoff_ew = r"NLO QCD+EW"
+label_rel_ewonoff_scale_unc = f"{scales}-p. scale var."
+label_rel_ewonoff_pdf_unc = r"PDF uncertainty"
 
-xlog = False
-ylog = False
 
 # linestyle for the channel breakdown shown in the panel `plot_abs_pdfs`. If the array
 # is empty, no channel breakdown will be shown, otherwise the most important channels,
@@ -558,25 +722,22 @@ ylog = False
 channel_breakdown_linestyles = []
 
 
-def main():
+def main(active_panels):
+    """Build a plot figure with various panels."""
+    # Find the active panels
     panels = [
-        # plot_int,
-        plot_abs,
-        plot_rel_ewonoff,
-        # plot_abs_pdfs,
-        # plot_ratio_pdf,
-        # plot_double_ratio_pdf,
-        # plot_rel_pdfunc,
-        # plot_rel_pdfpull,
+        PANEL_FNC_MAP[panel] for panel, enabled in active_panels.items() if enabled
     ]
 
+    # prepare the figure
     mpl.rcParams.update(stylesheet)
-    plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
-    # plt.rc("figure", figsize=(4.2, 2.6))
+    if len(panels) == 1:
+        plt.rc("figure", figsize=(4.2, 2.6))
+    else:
+        plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
 
-    data_slices = data()
-
-    for index, kwargs in enumerate(data_slices):
+    # Plot all data
+    for index, kwargs in enumerate(data):
         figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)
 
         if len(kwargs["x"]) > 2 and xlog:
@@ -588,10 +749,10 @@ def main():
         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")
+        if len(data) == 1:
+            figure.savefig(f"{output}.pdf")
         else:
-            figure.savefig("CMS_DY_14TEV_MLL_6000_COSTH-{}.pdf".format(index))
+            figure.savefig(f"{output}-{index}.pdf")
         plt.close(figure)
 
 
@@ -621,8 +782,8 @@ def set_ylim(axis, save, load, filename):
     elif (ymax - ymin) < 3.0:
         inc = 0.5
 
-    ymin = math.floor(ymin / inc) * inc
-    ymax = math.ceil(ymax / inc) * inc
+    ymin = np.floor(ymin / inc) * inc
+    ymax = np.ceil(ymax / inc) * inc
 
     if save:
         with open(filename, "wb") as f:
@@ -655,14 +816,13 @@ def set_ylim(axis, save, load, filename):
     axis.set_ylim((ymin - space, ymax + space))
 
 
-def plot_int(axis, **kwargs):
+def plot_int(axis, /, pdf_results, **_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
+    for index, (label, ycentral, ymin, ymax) in enumerate(pdf_results):
         x = np.append(x, ycentral[:-1])
         xmin = np.append(xmin, ymin[:-1])
         xmax = np.append(xmax, ymax[:-1])
@@ -670,126 +830,251 @@ def plot_int(axis, **kwargs):
 
         # draw one- and two-sigma bands
         if label == "CENTRAL-PDF":
-            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
+            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.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"]
-
+def plot_abs(axis, /, x, y, ymin, ymax, slice_label="", **_kwargs):
     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.step(x, y, colors[0], linewidth=1.0, where="post", label=slice_label)
+    axis.fill_between(
+        x,
+        ymin,
+        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"]
+def plot_ratio_pdf(axis, /, x, pdf_results, slice_label="", **_kwargs):
+    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_results[0][0]))
 
-    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_uncertainties[0][0]))
-
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
-        y = y / pdf_uncertainties[0][1]
-        ymin = ymin / pdf_uncertainties[0][1]
-        ymax = ymax / pdf_uncertainties[0][1]
+        y = y / pdf_results[0][1]
+        ymin = ymin / pdf_results[0][1]
+        ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, slice_label="", **_kwargs):
     axis.set_ylabel(ylabel_double_ratio_pdf)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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]
+            y = y / pdf_results[0][1]
+            ymin = ymin / pdf_results[0][1]
+            ymax = ymax / pdf_results[0][1]
         else:
-            y = y / pdf_uncertainties[1][1]
-            ymin = ymin / pdf_uncertainties[1][1]
-            ymax = ymax / pdf_uncertainties[1][1]
+            y = y / pdf_results[1][1]
+            ymin = ymin / pdf_results[1][1]
+            ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, channels, slice_label="", **_kwargs):
     axis.set_yscale("log" if ylog else "linear")
     axis.set_ylabel(ylabel)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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")
-
-    for index, ((label, y), linestyle) in enumerate(zip(channels, channel_breakdown_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(channel_breakdown_linestyles)))
+        axis.fill_between(
+            x,
+            ymin,
+            ymax,
+            alpha=0.4,
+            color=colors[index],
+            label=label,
+            linewidth=0.5,
+            step="post",
+        )
+
+    for index, ((label, y), linestyle) in enumerate(
+        zip(channels, channel_breakdown_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_results) + len(channel_breakdown_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")
+        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, /, x, mid, y, ymin, ymax, qcd_y, pdf_results, **_kwargs):
+    y = percent_diff(y, qcd_y)
+    qcd_y = percent_diff(qcd_y, 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(ymin, qcd_y)
+    ymax = percent_diff(ymax, qcd_y)
+    pdf_min = abs(percent_diff(pdf_results[0][2], pdf_results[0][1]))[:-1]
+    pdf_max = abs(percent_diff(pdf_results[0][3], 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.fill_between(
+        x,
+        ymin,
+        ymax,
+        alpha=0.4,
+        color=colors[0],
+        label=label_rel_ewonoff_scale_unc,
+        linewidth=0.5,
+        step="post",
+    )
+    axis.errorbar(
+        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)
-
+    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):
+def plot_rel_pdfunc(axis, /, x, pdf_results, **_kwargs):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
         ymin = percent_diff(ymin, y)
         ymax = percent_diff(ymax, y)
@@ -801,93 +1086,132 @@ def plot_rel_pdfunc(axis, **kwargs):
     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"]
+def plot_rel_pdfpull(axis, /, x, y, pdf_results, **_kwargs):
+    central_y = pdf_results[0][1]
+    central_ymin = pdf_results[0][2]
+    central_ymax = pdf_results[0][3]
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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.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_results)),
+    )  # 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",
+PANEL_FNC_MAP = {
+    "int": plot_int,
+    "abs": plot_abs,
+    "rel_ewonoff": plot_rel_ewonoff,
+    "abs_pdfs": plot_abs_pdfs,
+    "ratio_pdf": plot_ratio_pdf,
+    "double_ratio_pdf": plot_double_ratio_pdf,
+    "rel_pdfunc": plot_rel_pdfunc,
+    "rel_pdfpull": plot_rel_pdfpull,
+}
+
+
+# CLI data variables
+data = [
+    {
+        "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" : [
+            (
+                    r"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",
-    }
+        ],
+        "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" : [
+
+        ],
+    },
+]
+metadata = {
+    "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",
+}
+# end CLI data variables
 
 
 if __name__ == "__main__":
-    main()
+    main(plot_panels)
 "#;
 
 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
 
+# global variables coming from the CLI
+title = r""
+xlabel = r"$\cos \theta^*$"
+ylabel = r"$\frac{\mathrm{d}\sigma}{\mathrm{d}\cos \theta^*}$ [\si{pb}]"
+xlog = False
+ylog = False
+scales = 7
+plot_panels = {
+    "int": False,
+    "abs": True,
+    "rel_ewonoff": True,
+    "abs_pdfs": False,
+    "ratio_pdf": False,
+    "double_ratio_pdf": False,
+    "rel_pdfunc": False,
+    "rel_pdfpull": False,
+}
+output = r"NNPDF_DY_14TEV_BSM_AFB"
+
 # 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
@@ -925,25 +1249,18 @@ stylesheet = {
     "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        = r"Ratio to {central_pdf}"
+ylabel_ratio_pdf = r"Ratio to {{central_pdf}}"
 ylabel_double_ratio_pdf = r"Ratio to NLO"
-ylabel_rel_ewonoff      = r"NLO EW on/off [\si{\percent}]"
-ylabel_rel_pdfunc       = r"PDF uncertainty [\si{\percent}]"
-ylabel_rel_pdfpull      = r"Pull [$\sigma$]"
+ylabel_rel_ewonoff = r"NLO EW on/off [\si{\percent}]"
+ylabel_rel_pdfunc = r"PDF uncertainty [\si{\percent}]"
+ylabel_rel_pdfpull = r"Pull [$\sigma$]"
 
-label_rel_ewonoff_qcd       = r"NLO QCD"
-label_rel_ewonoff_ew        = r"NLO QCD+EW"
-label_rel_ewonoff_scale_unc = r"7-p.\ scale var."
-label_rel_ewonoff_pdf_unc   = r"PDF uncertainty"
+label_rel_ewonoff_qcd = r"NLO QCD"
+label_rel_ewonoff_ew = r"NLO QCD+EW"
+label_rel_ewonoff_scale_unc = f"{scales}-p. scale var."
+label_rel_ewonoff_pdf_unc = r"PDF uncertainty"
 
-xlog = False
-ylog = False
 
 # linestyle for the channel breakdown shown in the panel `plot_abs_pdfs`. If the array
 # is empty, no channel breakdown will be shown, otherwise the most important channels,
@@ -952,25 +1269,22 @@ ylog = False
 channel_breakdown_linestyles = []
 
 
-def main():
+def main(active_panels):
+    """Build a plot figure with various panels."""
+    # Find the active panels
     panels = [
-        # plot_int,
-        plot_abs,
-        plot_rel_ewonoff,
-        # plot_abs_pdfs,
-        # plot_ratio_pdf,
-        # plot_double_ratio_pdf,
-        # plot_rel_pdfunc,
-        # plot_rel_pdfpull,
+        PANEL_FNC_MAP[panel] for panel, enabled in active_panels.items() if enabled
     ]
 
+    # prepare the figure
     mpl.rcParams.update(stylesheet)
-    plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
-    # plt.rc("figure", figsize=(4.2, 2.6))
+    if len(panels) == 1:
+        plt.rc("figure", figsize=(4.2, 2.6))
+    else:
+        plt.rc("figure", figsize=(6.4, 2.4 * len(panels)))
 
-    data_slices = data()
-
-    for index, kwargs in enumerate(data_slices):
+    # Plot all data
+    for index, kwargs in enumerate(data):
         figure, axes = plt.subplots(len(panels), 1, sharex=True, squeeze=False)
 
         if len(kwargs["x"]) > 2 and xlog:
@@ -982,10 +1296,10 @@ def main():
         for plot, axis in zip(panels, axes[:, 0]):
             plot(axis, **kwargs)
 
-        if len(data_slices) == 1:
-            figure.savefig("NNPDF_DY_14TEV_BSM_AFB.pdf")
+        if len(data) == 1:
+            figure.savefig(f"{output}.pdf")
         else:
-            figure.savefig("NNPDF_DY_14TEV_BSM_AFB-{}.pdf".format(index))
+            figure.savefig(f"{output}-{index}.pdf")
         plt.close(figure)
 
 
@@ -1015,8 +1329,8 @@ def set_ylim(axis, save, load, filename):
     elif (ymax - ymin) < 3.0:
         inc = 0.5
 
-    ymin = math.floor(ymin / inc) * inc
-    ymax = math.ceil(ymax / inc) * inc
+    ymin = np.floor(ymin / inc) * inc
+    ymax = np.ceil(ymax / inc) * inc
 
     if save:
         with open(filename, "wb") as f:
@@ -1049,14 +1363,13 @@ def set_ylim(axis, save, load, filename):
     axis.set_ylim((ymin - space, ymax + space))
 
 
-def plot_int(axis, **kwargs):
+def plot_int(axis, /, pdf_results, **_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
+    for index, (label, ycentral, ymin, ymax) in enumerate(pdf_results):
         x = np.append(x, ycentral[:-1])
         xmin = np.append(xmin, ymin[:-1])
         xmax = np.append(xmax, ymax[:-1])
@@ -1064,126 +1377,251 @@ def plot_int(axis, **kwargs):
 
         # draw one- and two-sigma bands
         if label == "CENTRAL-PDF":
-            axis.axvspan(xmin[-1], xmax[-1], alpha=0.3, color=colors[index], linewidth=0)
+            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.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"]
-
+def plot_abs(axis, /, x, y, ymin, ymax, slice_label="", **_kwargs):
     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.step(x, y, colors[0], linewidth=1.0, where="post", label=slice_label)
+    axis.fill_between(
+        x,
+        ymin,
+        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]))
+def plot_ratio_pdf(axis, /, x, pdf_results, slice_label="", **_kwargs):
+    axis.set_ylabel(ylabel_ratio_pdf.format(central_pdf=pdf_results[0][0]))
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
-        y = y / pdf_uncertainties[0][1]
-        ymin = ymin / pdf_uncertainties[0][1]
-        ymax = ymax / pdf_uncertainties[0][1]
+        y = y / pdf_results[0][1]
+        ymin = ymin / pdf_results[0][1]
+        ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, slice_label="", **_kwargs):
     axis.set_ylabel(ylabel_double_ratio_pdf)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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]
+            y = y / pdf_results[0][1]
+            ymin = ymin / pdf_results[0][1]
+            ymax = ymax / pdf_results[0][1]
         else:
-            y = y / pdf_uncertainties[1][1]
-            ymin = ymin / pdf_uncertainties[1][1]
-            ymax = ymax / pdf_uncertainties[1][1]
+            y = y / pdf_results[1][1]
+            ymin = ymin / pdf_results[1][1]
+            ymax = ymax / pdf_results[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)))
+        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_results)),
+    )
 
     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"]
-
+        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, /, x, pdf_results, channels, slice_label="", **_kwargs):
     axis.set_yscale("log" if ylog else "linear")
     axis.set_ylabel(ylabel)
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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")
-
-    for index, ((label, y), linestyle) in enumerate(zip(channels, channel_breakdown_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(channel_breakdown_linestyles)))
+        axis.fill_between(
+            x,
+            ymin,
+            ymax,
+            alpha=0.4,
+            color=colors[index],
+            label=label,
+            linewidth=0.5,
+            step="post",
+        )
+
+    for index, ((label, y), linestyle) in enumerate(
+        zip(channels, channel_breakdown_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_results) + len(channel_breakdown_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")
+        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, /, x, mid, y, ymin, ymax, qcd_y, pdf_results, **_kwargs):
+    y = percent_diff(y, qcd_y)
+    qcd_y = percent_diff(qcd_y, 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(ymin, qcd_y)
+    ymax = percent_diff(ymax, qcd_y)
+    pdf_min = abs(percent_diff(pdf_results[0][2], pdf_results[0][1]))[:-1]
+    pdf_max = abs(percent_diff(pdf_results[0][3], 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.fill_between(
+        x,
+        ymin,
+        ymax,
+        alpha=0.4,
+        color=colors[0],
+        label=label_rel_ewonoff_scale_unc,
+        linewidth=0.5,
+        step="post",
+    )
+    axis.errorbar(
+        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)
+    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):
+def plot_rel_pdfunc(axis, /, x, pdf_results, **_kwargs):
+    for index, i in enumerate(pdf_results):
         label, y, ymin, ymax = i
         ymin = percent_diff(ymin, y)
         ymax = percent_diff(ymax, y)
@@ -1195,225 +1633,246 @@ def plot_rel_pdfunc(axis, **kwargs):
     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"]
+def plot_rel_pdfpull(axis, /, x, y, pdf_results, **_kwargs):
+    central_y = pdf_results[0][1]
+    central_ymin = pdf_results[0][2]
+    central_ymax = pdf_results[0][3]
 
-    for index, i in enumerate(pdf_uncertainties):
+    for index, i in enumerate(pdf_results):
         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.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_results)),
+    )  # 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",
+PANEL_FNC_MAP = {
+    "int": plot_int,
+    "abs": plot_abs,
+    "rel_ewonoff": plot_rel_ewonoff,
+    "abs_pdfs": plot_abs_pdfs,
+    "ratio_pdf": plot_ratio_pdf,
+    "double_ratio_pdf": plot_double_ratio_pdf,
+    "rel_pdfunc": plot_rel_pdfunc,
+    "rel_pdfpull": plot_rel_pdfpull,
+}
+
+
+# CLI data variables
+data = [
+    {
+        "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" : [
+            (
+                    r"NNPDF40\_nnlo\_as\_01180",
                     np.array([1.2030676e0, 1.1565560e0, 1.1130457e0, 1.0725099e0, 1.0387691e0, 1.0063348e0, 9.7581283e-1, 9.5075401e-1, 9.2789806e-1, 9.0985382e-1, 8.9310798e-1, 8.8180179e-1, 8.7261653e-1, 8.6606981e-1, 8.6360260e-1, 8.6373393e-1, 8.6791957e-1, 8.7409325e-1, 8.8588921e-1, 8.9893349e-1, 9.1509988e-1, 9.3716398e-1, 9.6097839e-1, 9.8834317e-1, 1.0180896e0, 1.0535762e0, 1.0895800e0, 1.1288776e0, 1.1737558e0, 1.2197631e0, 1.2689601e0, 1.3247334e0, 1.3811064e0, 1.4397822e0, 1.5032413e0, 1.5720274e0, 1.6419769e0, 1.7134034e0, 1.7913589e0, 1.8745908e0, 1.9576793e0, 2.0405341e0, 2.1340674e0, 2.2269273e0, 2.3239393e0, 2.4234584e0, 2.5266015e0, 2.6334832e0, 2.7449504e0, 2.8548557e0, 2.8548557e0]),
                     np.array([1.2030676e0, 1.1565560e0, 1.1130457e0, 1.0725099e0, 1.0387691e0, 1.0063348e0, 9.7581283e-1, 9.5075401e-1, 9.2789806e-1, 9.0985382e-1, 8.9310798e-1, 8.8180179e-1, 8.7261653e-1, 8.6606981e-1, 8.6360260e-1, 8.6373393e-1, 8.6791957e-1, 8.7409325e-1, 8.8588921e-1, 8.9893349e-1, 9.1509988e-1, 9.3716398e-1, 9.6097839e-1, 9.8834317e-1, 1.0180896e0, 1.0535762e0, 1.0895800e0, 1.1288776e0, 1.1737558e0, 1.2197631e0, 1.2689601e0, 1.3247334e0, 1.3811064e0, 1.4397822e0, 1.5032413e0, 1.5720274e0, 1.6419769e0, 1.7134034e0, 1.7913589e0, 1.8745908e0, 1.9576793e0, 2.0405341e0, 2.1340674e0, 2.2269273e0, 2.3239393e0, 2.4234584e0, 2.5266015e0, 2.6334832e0, 2.7449504e0, 2.8548557e0, 2.8548557e0]),
                     np.array([1.2030676e0, 1.1565560e0, 1.1130457e0, 1.0725099e0, 1.0387691e0, 1.0063348e0, 9.7581283e-1, 9.5075401e-1, 9.2789806e-1, 9.0985382e-1, 8.9310798e-1, 8.8180179e-1, 8.7261653e-1, 8.6606981e-1, 8.6360260e-1, 8.6373393e-1, 8.6791957e-1, 8.7409325e-1, 8.8588921e-1, 8.9893349e-1, 9.1509988e-1, 9.3716398e-1, 9.6097839e-1, 9.8834317e-1, 1.0180896e0, 1.0535762e0, 1.0895800e0, 1.1288776e0, 1.1737558e0, 1.2197631e0, 1.2689601e0, 1.3247334e0, 1.3811064e0, 1.4397822e0, 1.5032413e0, 1.5720274e0, 1.6419769e0, 1.7134034e0, 1.7913589e0, 1.8745908e0, 1.9576793e0, 2.0405341e0, 2.1340674e0, 2.2269273e0, 2.3239393e0, 2.4234584e0, 2.5266015e0, 2.6334832e0, 2.7449504e0, 2.8548557e0, 2.8548557e0]),
                 ),
-            ],
-            "qcd_y"    : np.array([1.2030676e0, 1.1565560e0, 1.1130457e0, 1.0725099e0, 1.0387691e0, 1.0063348e0, 9.7581283e-1, 9.5075401e-1, 9.2789806e-1, 9.0985382e-1, 8.9310798e-1, 8.8180179e-1, 8.7261653e-1, 8.6606981e-1, 8.6360260e-1, 8.6373393e-1, 8.6791957e-1, 8.7409325e-1, 8.8588921e-1, 8.9893349e-1, 9.1509988e-1, 9.3716398e-1, 9.6097839e-1, 9.8834317e-1, 1.0180896e0, 1.0535762e0, 1.0895800e0, 1.1288776e0, 1.1737558e0, 1.2197631e0, 1.2689601e0, 1.3247334e0, 1.3811064e0, 1.4397822e0, 1.5032413e0, 1.5720274e0, 1.6419769e0, 1.7134034e0, 1.7913589e0, 1.8745908e0, 1.9576793e0, 2.0405341e0, 2.1340674e0, 2.2269273e0, 2.3239393e0, 2.4234584e0, 2.5266015e0, 2.6334832e0, 2.7449504e0, 2.8548557e0, 2.8548557e0]),
-            "qcd_min"  : np.array([1.1476341e0, 1.1032579e0, 1.0617385e0, 1.0231508e0, 9.9097579e-1, 9.6014207e-1, 9.3113469e-1, 9.0728416e-1, 8.8560674e-1, 8.6851930e-1, 8.5267109e-1, 8.4203435e-1, 8.3342689e-1, 8.2736426e-1, 8.2520004e-1, 8.2555967e-1, 8.2975676e-1, 8.3587859e-1, 8.4733087e-1, 8.6002996e-1, 8.7572987e-1, 8.9705641e-1, 9.2007984e-1, 9.4649062e-1, 9.7517487e-1, 1.0093523e0, 1.0440650e0, 1.0819022e0, 1.1251039e0, 1.1693745e0, 1.2166754e0, 1.2702912e0, 1.3245299e0, 1.3809292e0, 1.4419071e0, 1.5079840e0, 1.5752031e0, 1.6438283e0, 1.7186871e0, 1.7987148e0, 1.8783985e0, 1.9580267e0, 2.0477772e0, 2.1369454e0, 2.2300739e0, 2.3256051e0, 2.4246078e0, 2.5271474e0, 2.6341671e0, 2.7396171e0, 2.7396171e0]),
-            "qcd_max"  : np.array([1.2482967e0, 1.2000452e0, 1.1549126e0, 1.1127812e0, 1.0777638e0, 1.0440155e0, 1.0122494e0, 9.8620414e-1, 9.6237779e-1, 9.4354507e-1, 9.2605954e-1, 9.1419910e-1, 9.0453198e-1, 8.9757973e-1, 8.9485425e-1, 8.9478263e-1, 8.9894680e-1, 9.0514901e-1, 9.1721599e-1, 9.3052688e-1, 9.4705558e-1, 9.6970487e-1, 9.9414526e-1, 1.0222704e0, 1.0528665e0, 1.0894022e0, 1.1264355e0, 1.1669048e0, 1.2131264e0, 1.2605276e0, 1.3112501e0, 1.3687598e0, 1.4268454e0, 1.4873516e0, 1.5528115e0, 1.6237809e0, 1.6959271e0, 1.7696100e0, 1.8500634e0, 1.9358676e0, 2.0217144e0, 2.1071620e0, 2.2037519e0, 2.2995896e0, 2.3997365e0, 2.5024724e0, 2.6089569e0, 2.7193485e0, 2.8344013e0, 2.9479075e0, 2.9479075e0]),
-            "y"        : np.array([1.2030676e0, 1.1565560e0, 1.1130457e0, 1.0725099e0, 1.0387691e0, 1.0063348e0, 9.7581283e-1, 9.5075401e-1, 9.2789806e-1, 9.0985382e-1, 8.9310798e-1, 8.8180179e-1, 8.7261653e-1, 8.6606981e-1, 8.6360260e-1, 8.6373393e-1, 8.6791957e-1, 8.7409325e-1, 8.8588921e-1, 8.9893349e-1, 9.1509988e-1, 9.3716398e-1, 9.6097839e-1, 9.8834317e-1, 1.0180896e0, 1.0535762e0, 1.0895800e0, 1.1288776e0, 1.1737558e0, 1.2197631e0, 1.2689601e0, 1.3247334e0, 1.3811064e0, 1.4397822e0, 1.5032413e0, 1.5720274e0, 1.6419769e0, 1.7134034e0, 1.7913589e0, 1.8745908e0, 1.9576793e0, 2.0405341e0, 2.1340674e0, 2.2269273e0, 2.3239393e0, 2.4234584e0, 2.5266015e0, 2.6334832e0, 2.7449504e0, 2.8548557e0, 2.8548557e0]),
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+        ],
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                 ),
-            ],
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-            "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]),
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-            "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" : [
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-                (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"$\SI{1000}{GeV} < M_{\ell\bar{\ell}} < \SI{3000}{GeV}$",
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-            "pdf_results" : [
-                (
-                    "NNPDF40\_nnlo\_as\_01180",
+        ],
+        "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" : [
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+        ],
+    },
+    {
+        "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]),
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+        "pdf_results" : [
+            (
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                 ),
-            ],
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-            "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]),
-            "ymin"     : 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]),
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-        },
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-                (
-                    "NNPDF40\_nnlo\_as\_01180",
+        ],
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+        "qcd_min"  : np.array([1.1796105e-7, 1.1367206e-7, 1.0940640e-7, 1.0473022e-7, 1.0094995e-7, 9.6817367e-8, 9.3368040e-8, 8.9841440e-8, 8.6438729e-8, 8.3389429e-8, 8.0592033e-8, 7.7501048e-8, 7.5100969e-8, 7.2799905e-8, 7.0734091e-8, 6.8678517e-8, 6.6600127e-8, 6.5343484e-8, 6.3861008e-8, 6.2611336e-8, 6.1492668e-8, 6.0680109e-8, 5.9926419e-8, 5.9534539e-8, 5.9300399e-8, 5.9178721e-8, 5.9281434e-8, 5.9570740e-8, 5.9942127e-8, 6.0724158e-8, 6.1428979e-8, 6.2599969e-8, 6.3904432e-8, 6.5297162e-8, 6.6843778e-8, 6.8632843e-8, 7.0630564e-8, 7.2883947e-8, 7.5177425e-8, 7.7737465e-8, 8.0492081e-8, 8.3474223e-8, 8.6500375e-8, 8.9868353e-8, 9.3354036e-8, 9.7186902e-8, 1.0107609e-7, 1.0497339e-7, 1.0928445e-7, 1.1383240e-7, 1.1383240e-7]),
+        "qcd_max"  : np.array([1.5031713e-7, 1.4486644e-7, 1.3943889e-7, 1.3346815e-7, 1.2867540e-7, 1.2342279e-7, 1.1905669e-7, 1.1458942e-7, 1.1027138e-7, 1.0641247e-7, 1.0289371e-7, 9.8977949e-8, 9.5964016e-8, 9.3079140e-8, 9.0482540e-8, 8.7917761e-8, 8.5317122e-8, 8.3776037e-8, 8.1946300e-8, 8.0412453e-8, 7.9048531e-8, 7.8082511e-8, 7.7200627e-8, 7.6780539e-8, 7.6555496e-8, 7.6492081e-8, 7.6709085e-8, 7.7159748e-8, 7.7728575e-8, 7.8810410e-8, 7.9813916e-8, 8.1414088e-8, 8.3184083e-8, 8.5064404e-8, 8.7145967e-8, 8.9541837e-8, 9.2203014e-8, 9.5197361e-8, 9.8246621e-8, 1.0163432e-7, 1.0528027e-7, 1.0921464e-7, 1.1321161e-7, 1.1764575e-7, 1.2223338e-7, 1.2726711e-7, 1.3237650e-7, 1.3748886e-7, 1.4314700e-7, 1.4910383e-7, 1.4910383e-7]),
+        "y"        : np.array([1.3268262e-7, 1.2786453e-7, 1.2307005e-7, 1.1780526e-7, 1.1356356e-7, 1.0892100e-7, 1.0505353e-7, 1.0109804e-7, 9.7278368e-8, 9.3859898e-8, 9.0732778e-8, 8.7265819e-8, 8.4585006e-8, 8.2016912e-8, 7.9708423e-8, 7.7419509e-8, 7.5101943e-8, 7.3713994e-8, 7.2071670e-8, 7.0690848e-8, 6.9458697e-8, 6.8574148e-8, 6.7759607e-8, 6.7352353e-8, 6.7120091e-8, 6.7021996e-8, 6.7173994e-8, 6.7534167e-8, 6.7992384e-8, 6.8908159e-8, 6.9745535e-8, 7.1108396e-8, 7.2621295e-8, 7.4232542e-8, 7.6019061e-8, 7.8080590e-8, 8.0376525e-8, 8.2963179e-8, 8.5596531e-8, 8.8529203e-8, 9.1685084e-8, 9.5096261e-8, 9.8559654e-8, 1.0240826e-7, 1.0639069e-7, 1.1076525e-7, 1.1520479e-7, 1.1965034e-7, 1.2456911e-7, 1.2975301e-7, 1.2975301e-7]),
+        "ymin"     : np.array([1.1796105e-7, 1.1367206e-7, 1.0940640e-7, 1.0473022e-7, 1.0094995e-7, 9.6817367e-8, 9.3368040e-8, 8.9841440e-8, 8.6438729e-8, 8.3389429e-8, 8.0592033e-8, 7.7501048e-8, 7.5100969e-8, 7.2799905e-8, 7.0734091e-8, 6.8678517e-8, 6.6600127e-8, 6.5343484e-8, 6.3861008e-8, 6.2611336e-8, 6.1492668e-8, 6.0680109e-8, 5.9926419e-8, 5.9534539e-8, 5.9300399e-8, 5.9178721e-8, 5.9281434e-8, 5.9570740e-8, 5.9942127e-8, 6.0724158e-8, 6.1428979e-8, 6.2599969e-8, 6.3904432e-8, 6.5297162e-8, 6.6843778e-8, 6.8632843e-8, 7.0630564e-8, 7.2883947e-8, 7.5177425e-8, 7.7737465e-8, 8.0492081e-8, 8.3474223e-8, 8.6500375e-8, 8.9868353e-8, 9.3354036e-8, 9.7186902e-8, 1.0107609e-7, 1.0497339e-7, 1.0928445e-7, 1.1383240e-7, 1.1383240e-7]),
+        "ymax"     : np.array([1.5031713e-7, 1.4486644e-7, 1.3943889e-7, 1.3346815e-7, 1.2867540e-7, 1.2342279e-7, 1.1905669e-7, 1.1458942e-7, 1.1027138e-7, 1.0641247e-7, 1.0289371e-7, 9.8977949e-8, 9.5964016e-8, 9.3079140e-8, 9.0482540e-8, 8.7917761e-8, 8.5317122e-8, 8.3776037e-8, 8.1946300e-8, 8.0412453e-8, 7.9048531e-8, 7.8082511e-8, 7.7200627e-8, 7.6780539e-8, 7.6555496e-8, 7.6492081e-8, 7.6709085e-8, 7.7159748e-8, 7.7728575e-8, 7.8810410e-8, 7.9813916e-8, 8.1414088e-8, 8.3184083e-8, 8.5064404e-8, 8.7145967e-8, 8.9541837e-8, 9.2203014e-8, 9.5197361e-8, 9.8246621e-8, 1.0163432e-7, 1.0528027e-7, 1.0921464e-7, 1.1321161e-7, 1.1764575e-7, 1.2223338e-7, 1.2726711e-7, 1.3237650e-7, 1.3748886e-7, 1.4314700e-7, 1.4910383e-7, 1.4910383e-7]),
+        "channels" : [
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+        ],
+    },
+    {
+        "slice_label"    : r"$\SI{8000}{GeV} < M_{\ell\bar{\ell}} < \SI{10000}{GeV}$",
+        "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]),
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+        "pdf_results" : [
+            (
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                     np.array([7.1861026e-10, 6.9052982e-10, 6.6277085e-10, 6.3573468e-10, 6.1024606e-10, 5.8475908e-10, 5.6096402e-10, 5.3833955e-10, 5.1558216e-10, 4.9404513e-10, 4.7398312e-10, 4.5273929e-10, 4.3405736e-10, 4.1643201e-10, 3.9946960e-10, 3.8248920e-10, 3.6635833e-10, 3.5323838e-10, 3.3921398e-10, 3.2684546e-10, 3.1412226e-10, 3.0313435e-10, 2.9192112e-10, 2.8290167e-10, 2.7525858e-10, 2.6743219e-10, 2.5992966e-10, 2.5408868e-10, 2.4832543e-10, 2.4538556e-10, 2.4100708e-10, 2.3910018e-10, 2.3747322e-10, 2.3728921e-10, 2.3669027e-10, 2.3798116e-10, 2.3947283e-10, 2.4222947e-10, 2.4587694e-10, 2.5017261e-10, 2.5579842e-10, 2.6167570e-10, 2.6866230e-10, 2.7632565e-10, 2.8548191e-10, 2.9544050e-10, 3.0598398e-10, 3.1671560e-10, 3.2904259e-10, 3.4234846e-10, 3.4234846e-10]),
                 ),
-            ],
-            "qcd_y"    : np.array([7.1861026e-10, 6.9052982e-10, 6.6277085e-10, 6.3573468e-10, 6.1024606e-10, 5.8475908e-10, 5.6096402e-10, 5.3833955e-10, 5.1558216e-10, 4.9404513e-10, 4.7398312e-10, 4.5273929e-10, 4.3405736e-10, 4.1643201e-10, 3.9946960e-10, 3.8248920e-10, 3.6635833e-10, 3.5323838e-10, 3.3921398e-10, 3.2684546e-10, 3.1412226e-10, 3.0313435e-10, 2.9192112e-10, 2.8290167e-10, 2.7525858e-10, 2.6743219e-10, 2.5992966e-10, 2.5408868e-10, 2.4832543e-10, 2.4538556e-10, 2.4100708e-10, 2.3910018e-10, 2.3747322e-10, 2.3728921e-10, 2.3669027e-10, 2.3798116e-10, 2.3947283e-10, 2.4222947e-10, 2.4587694e-10, 2.5017261e-10, 2.5579842e-10, 2.6167570e-10, 2.6866230e-10, 2.7632565e-10, 2.8548191e-10, 2.9544050e-10, 3.0598398e-10, 3.1671560e-10, 3.2904259e-10, 3.4234846e-10, 3.4234846e-10]),
-            "qcd_min"  : np.array([6.0733393e-10, 5.8359975e-10, 5.6013663e-10, 5.3731953e-10, 5.1577550e-10, 4.9425471e-10, 4.7413668e-10, 4.5502613e-10, 4.3579683e-10, 4.1761844e-10, 4.0067486e-10, 3.8275700e-10, 3.6698642e-10, 3.5210349e-10, 3.3780462e-10, 3.2347174e-10, 3.0987916e-10, 2.9880336e-10, 2.8698310e-10, 2.7656554e-10, 2.6585543e-10, 2.5660126e-10, 2.4717194e-10, 2.3959131e-10, 2.3316588e-10, 2.2659350e-10, 2.2029631e-10, 2.1541466e-10, 2.1059049e-10, 2.0816229e-10, 2.0451475e-10, 2.0294117e-10, 2.0162673e-10, 2.0153357e-10, 2.0108215e-10, 2.0224072e-10, 2.0356183e-10, 2.0595804e-10, 2.0910726e-10, 2.1281117e-10, 2.1763190e-10, 2.2268082e-10, 2.2865687e-10, 2.3520471e-10, 2.4302664e-10, 2.5151226e-10, 2.6051000e-10, 2.6966237e-10, 2.8016281e-10, 2.9149775e-10, 2.9149775e-10]),
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+            (
+                    r"NNPDF40\_nnlo\_as\_01180",
                     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]),
                     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]),
                     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_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",
-    }
+        ],
+        "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]))
+        ],
+    },
+]
+metadata = {
+    "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",
+}
+# end CLI data variables
 
 
 if __name__ == "__main__":
-    main()
+    main(plot_panels)
 "#;
 
 const THREE_PDF_ERROR_STR: &str = "convolutions with 3 convolution functions is not supported