Fixed some code quality issues from sonar

src/ctapipe/irf/vis_utils.py

@@ -0,0 +1,64 @@
+import numpy as np
+import scipy.stats as st
+
+
+def find_columnwise_stats(table, col_bins, percentiles, density=False):
+    tab = np.squeeze(table)
+    out = np.ones((tab.shape[1], 5)) * -1
+    # This loop over the columns seems unavoidable,
+    # so having a reasonable number of bins in that
+    # direction is good
+    for idx, col in enumerate(tab.T):
+        if (col > 0).sum() == 0:
+            continue
+        col_est = st.rv_histogram((col, col_bins), density=density)
+        out[idx, 0] = col_est.mean()
+        out[idx, 1] = col_est.median()
+        out[idx, 2] = col_est.std()
+        out[idx, 3] = col_est.ppf(percentiles[0])
+        out[idx, 4] = col_est.ppf(percentiles[1])
+    return out
+
+
+def rebin_x_2d_hist(hist, xbins, x_cent, num_bins_merge=3):
+    num_y, num_x = hist.shape
+    if (num_x) % num_bins_merge == 0:
+        rebin_x = xbins[::num_bins_merge]
+        rebin_xcent = x_cent.reshape(-1, num_bins_merge).mean(axis=1)
+        rebin_hist = hist.reshape(num_y, -1, num_bins_merge).sum(axis=2)
+        return rebin_x, rebin_xcent, rebin_hist
+    else:
+        raise ValueError(
+            f"Could not merge {num_bins_merge} along axis of dimension {num_x}"
+        )
+
+
+def get_2d_hist_from_table(x_prefix, y_prefix, table, column):
+    x_lo_name, x_hi_name = f"{x_prefix}_LO", f"{x_prefix}_HI"
+    y_lo_name, y_hi_name = f"{y_prefix}_LO", f"{y_prefix}_HI"
+
+    xbins = np.hstack((table[x_lo_name][0], table[x_hi_name][0][-1]))
+    ybins = np.hstack((table[y_lo_name][0], table[y_hi_name][0][-1]))
+
+    if isinstance(column, str):
+        mat_vals = np.squeeze(table[column])
+    else:
+        mat_vals = column
+
+    return mat_vals, xbins, ybins
+
+
+def get_bin_centers(bins):
+    return np.convolve(bins, kernel=[0.5, 0.5], mode="valid")
+
+
+def get_x_bin_values_with_rebinning(num_rebin, xbins, xcent, mat_vals, density):
+    if num_rebin > 1:
+        rebin_x, rebin_xcent, rebin_hist = rebin_x_2d_hist(
+            mat_vals, xbins, xcent, num_bins_merge=num_rebin
+        )
+        density = False
+    else:
+        rebin_x, rebin_xcent, rebin_hist = xbins, xcent, mat_vals
+
+    return rebin_x, rebin_xcent, rebin_hist, density

src/ctapipe/irf/visualisation.py

@@ -1,71 +1,37 @@
 import astropy.units as u
 import matplotlib.pyplot as plt
 import numpy as np
-import scipy.stats as st
 from astropy.visualization import quantity_support
 from matplotlib.colors import LogNorm
 from pyirf.binning import join_bin_lo_hi
 
+from .vis_utils import (
+    find_columnwise_stats,
+    get_2d_hist_from_table,
+    get_bin_centers,
+    get_x_bin_values_with_rebinning,
+)
+
 quantity_support()
 
 
-def plot_2D_irf_table(
+def plot_2d_irf_table(
     ax, table, column, x_prefix, y_prefix, x_label=None, y_label=None, **mpl_args
 ):
-    x_lo_name, x_hi_name = f"{x_prefix}_LO", f"{x_prefix}_HI"
-    y_lo_name, y_hi_name = f"{y_prefix}_LO", f"{y_prefix}_HI"
-
-    xbins = np.hstack((table[x_lo_name][0], table[x_hi_name][0][-1]))
+    mat_vals, xbins, ybins = get_2d_hist_from_table(x_prefix, y_prefix, table, column)
 
-    ybins = np.hstack((table[y_lo_name][0], table[y_hi_name][0][-1]))
     if not x_label:
         x_label = x_prefix
     if not y_label:
         y_label = y_prefix
-    if isinstance(column, str):
-        mat_vals = np.squeeze(table[column])
-    else:
-        mat_vals = column
-
-    plot = plot_hist2D(
+    plot = plot_hist2d(
         ax, mat_vals, xbins, ybins, xlabel=x_label, ylabel=y_label, **mpl_args
     )
     plt.colorbar(plot)
     return ax
 
 
-def rebin_x_2D_hist(hist, xbins, x_cent, num_bins_merge=3):
-    num_y, num_x = hist.shape
-    if (num_x) % num_bins_merge == 0:
-        rebin_x = xbins[::num_bins_merge]
-        rebin_xcent = x_cent.reshape(-1, num_bins_merge).mean(axis=1)
-        rebin_hist = hist.reshape(num_y, -1, num_bins_merge).sum(axis=2)
-        return rebin_x, rebin_xcent, rebin_hist
-    else:
-        raise ValueError(
-            f"Could not merge {num_bins_merge} along axis of dimension {num_x}"
-        )
-
-
-def find_columnwise_stats(table, col_bins, percentiles, density=False):
-    tab = np.squeeze(table)
-    out = np.ones((tab.shape[1], 5)) * -1
-    # This loop over the columns seems unavoidable,
-    # so having a reasonable number of bins in that
-    # direction is good
-    for idx, col in enumerate(tab.T):
-        if (col > 0).sum() == 0:
-            continue
-        col_est = st.rv_histogram((col, col_bins), density=density)
-        out[idx, 0] = col_est.mean()
-        out[idx, 1] = col_est.median()
-        out[idx, 2] = col_est.std()
-        out[idx, 3] = col_est.ppf(percentiles[0])
-        out[idx, 4] = col_est.ppf(percentiles[1])
-    return out
-
-
-def plot_2D_table_with_col_stats(
+def plot_2d_table_with_col_stats(
     ax,
     table,
     column,
@@ -88,35 +54,14 @@ def plot_2D_table_with_col_stats(
         1 -> median + standard deviation
         2 -> median + user specified quantiles around median (default 0.1 to 0.9)
     """
-    x_lo_name, x_hi_name = f"{x_prefix}_LO", f"{x_prefix}_HI"
-    y_lo_name, y_hi_name = f"{y_prefix}_LO", f"{y_prefix}_HI"
-
-    xbins = np.hstack((table[x_lo_name][0], table[x_hi_name][0][-1]))
-
-    ybins = np.hstack((table[y_lo_name][0], table[y_hi_name][0][-1]))
 
-    xcent = np.convolve(
-        [0.5, 0.5], np.hstack((table[x_lo_name][0], table[x_hi_name][0][-1])), "valid"
+    mat_vals, xbins, ybins = get_2d_hist_from_table(x_prefix, y_prefix, table, column)
+    xcent = get_bin_centers(xbins)
+    rebin_x, rebin_xcent, rebin_hist = get_x_bin_values_with_rebinning(
+        num_rebin, xbins, xcent, mat_vals, density
     )
-    if not x_label:
-        x_label = x_prefix
-    if not y_label:
-        y_label = y_prefix
-    if isinstance(column, str):
-        mat_vals = np.squeeze(table[column])
-    else:
-        mat_vals = column
 
-    if num_rebin > 1:
-        rebin_x, rebin_xcent, rebin_hist = rebin_x_2D_hist(
-            mat_vals, xbins, xcent, num_bins_merge=num_rebin
-        )
-        density = False
-    else:
-        rebin_x, rebin_xcent, rebin_hist = xbins, xcent, mat_vals
-
-    stats = find_columnwise_stats(rebin_hist, ybins, quantiles, density)
-    plot = plot_hist2D(
+    plot = plot_hist2d(
         ax,
         rebin_hist,
         rebin_x,
@@ -127,36 +72,25 @@ def plot_2D_table_with_col_stats(
     )
     plt.colorbar(plot)
 
-    sel = stats[:, 0] > 0
-    if stat_kind == 1:
-        y_idx = 0
-        err = stats[sel, 2]
-        label = "mean + std"
-    if stat_kind == 2:
-        y_idx = 1
-        err = stats[sel, 2]
-        label = "median + std"
-    if stat_kind == 3:
-        y_idx = 1
-        err = np.zeros_like(stats[:, 3:])
-        err[sel, 0] = stats[sel, 1] - stats[sel, 3]
-        err[sel, 1] = stats[sel, 4] - stats[sel, 1]
-        err = err[sel, :].T
-        label = f"median + IRQ[{quantiles[0]:.2f},{quantiles[1]:.2f}]"
-
-    ax.errorbar(
-        x=rebin_xcent[sel],
-        y=stats[sel, y_idx],
-        yerr=err,
-        label=label,
-        **mpl_args["stats"],
+    ax = plot_2d_table_col_stats(
+        ax,
+        table,
+        column,
+        x_prefix,
+        y_prefix,
+        num_rebin,
+        stat_kind,
+        quantiles,
+        x_label,
+        y_label,
+        density,
+        mpl_args,
+        lbl_prefix="",
     )
-    ax.legend(loc="best")
-
     return ax
 
 
-def plot_2D_table_col_stats(
+def plot_2d_table_col_stats(
     ax,
     table,
     column,
@@ -171,38 +105,18 @@ def plot_2D_table_col_stats(
     lbl_prefix="",
     mpl_args={"xscale": "log"},
 ):
-    """Function to draw columnwise statistics of 2D hist
+    """Function to draw columnwise statistics of 2d hist
     the content values shown depending on stat_kind:
         0 -> mean + standard deviation
         1 -> median + standard deviation
         2 -> median + user specified quantiles around median (default 0.1 to 0.9)
     """
-    x_lo_name, x_hi_name = f"{x_prefix}_LO", f"{x_prefix}_HI"
-    y_lo_name, y_hi_name = f"{y_prefix}_LO", f"{y_prefix}_HI"
 
-    xbins = np.hstack((table[x_lo_name][0], table[x_hi_name][0][-1]))
-
-    ybins = np.hstack((table[y_lo_name][0], table[y_hi_name][0][-1]))
-
-    xcent = np.convolve(
-        [0.5, 0.5], np.hstack((table[x_lo_name][0], table[x_hi_name][0][-1])), "valid"
+    mat_vals, xbins, ybins = get_2d_hist_from_table(x_prefix, y_prefix, table, column)
+    xcent = get_bin_centers(xbins)
+    rebin_x, rebin_xcent, rebin_hist = get_x_bin_values_with_rebinning(
+        num_rebin, xbins, xcent, mat_vals, density
     )
-    if not x_label:
-        x_label = x_prefix
-    if not y_label:
-        y_label = y_prefix
-    if isinstance(column, str):
-        mat_vals = np.squeeze(table[column])
-    else:
-        mat_vals = column
-
-    if num_rebin > 1:
-        rebin_x, rebin_xcent, rebin_hist = rebin_x_2D_hist(
-            mat_vals, xbins, xcent, num_bins_merge=num_rebin
-        )
-        density = False
-    else:
-        rebin_xcent, rebin_hist = xcent, mat_vals
 
     stats = find_columnwise_stats(rebin_hist, ybins, quantiles, density)
 
@@ -262,7 +176,7 @@ def plot_irf_table(
     ax.stairs(vals, bins, label=label, **mpl_args)
 
 
-def plot_hist2D_as_contour(
+def plot_hist2d_as_contour(
     ax,
     hist,
     xedges,
@@ -283,7 +197,7 @@ def plot_hist2D_as_contour(
     return out
 
 
-def plot_hist2D(
+def plot_hist2d(
     ax,
     hist,
     xedges,