Added some features for CD diagram personalization

[msotocalvo]

Thank you for the great development you have done, this helps a lot! However, I made some modifications to the source code to give the user the ability to do some customizations to the CD diagram such as: Title, Font Size, Line Width and labels with the individual rank's values.

def cd_diagram(result, reverse, ax, width, fontsize=14, title="Critical Difference Diagram", linewidth=1.5):
"""
Creates a Critical Difference diagram with adjustable font size, title, and line thickness.
"""

def plot_line(line, color='k', **kwargs):
    kwargs.setdefault('linewidth', linewidth)
    ax.plot([pos[0] / width for pos in line], [pos[1] / height for pos in line], color=color, **kwargs)

def plot_text(x, y, s, *args, **kwargs):
    kwargs['fontsize'] = fontsize
    ax.text(x / width, y / height, s, *args, **kwargs)

result_copy = RankResult(**result._asdict())
result_copy = result_copy._replace(rankdf=result.rankdf.sort_values(by='meanrank'))
sorted_ranks, names, groups = get_sorted_rank_groups(result_copy, reverse)
cd = result.cd

lowv = min(1, int(math.floor(min(sorted_ranks))))
highv = max(len(sorted_ranks), int(math.ceil(max(sorted_ranks))))
cline = 0.4
textspace = 1
scalewidth = width - 2 * textspace

def rankpos(rank):
    if not reverse:
        relative_rank = rank - lowv
    else:
        relative_rank = highv - rank
    return textspace + scalewidth / (highv - lowv) * relative_rank

linesblank = 0.2 + 0.2 + (len(groups) - 1) * 0.1

# Add scale
distanceh = 0.25
cline += distanceh

# Calculate height needed for the image
minnotsignificant = max(2 * 0.2, linesblank)
height = cline + ((len(sorted_ranks) + 1) / 2) * 0.2 + minnotsignificant

if ax is None:
    fig, ax = plt.subplots(figsize=(width, height))
    fig.set_facecolor('white')
ax.set_axis_off()

# Upper left corner is (0,0)
ax.plot([0, 1], [0, 1], c="w")
ax.set_xlim(0, 1)
ax.set_ylim(1, 0)

plot_line([(textspace, cline), (width - textspace, cline)], linewidth=linewidth)

bigtick = 0.1
smalltick = 0.05

tick = None
for a in list(np.arange(lowv, highv, 0.5)) + [highv]:
    tick = smalltick
    if a == int(a):
        tick = bigtick
    plot_line([(rankpos(a), cline - tick / 2),
               (rankpos(a), cline)],
              linewidth=linewidth)

for a in range(lowv, highv + 1):
    plot_text(rankpos(a), cline - tick / 2 - 0.05, str(a),
              ha="center", va="bottom")

# Separate rank labels before population names
for i in range(math.ceil(len(sorted_ranks) / 2)):
    chei = cline + minnotsignificant + i * 0.2
    rank_label = f"{sorted_ranks[i]:.2f}"
    plot_line([(rankpos(sorted_ranks[i]), cline),
               (rankpos(sorted_ranks[i]), chei),
               (textspace - 0.1, chei)],
              linewidth=linewidth)
    plot_text(textspace + 0.2, chei - 0.06, rank_label, ha="right", va="center")
    plot_text(textspace - 0.2, chei, names[i], ha="right", va="center")

for i in range(math.ceil(len(sorted_ranks) / 2), len(sorted_ranks)):
    chei = cline + minnotsignificant + (len(sorted_ranks) - i - 1) * 0.2
    rank_label = f"{sorted_ranks[i]:.2f}"
    plot_line([(rankpos(sorted_ranks[i]), cline),
               (rankpos(sorted_ranks[i]), chei),
               (textspace + scalewidth + 0.1, chei)],
              linewidth=linewidth)
    plot_text(textspace + (scalewidth - 0.2), chei - 0.06, rank_label, ha="left", va="center")
    plot_text(textspace + scalewidth + 0.2, chei, names[i], ha="left", va="center")

# Upper scale
if not reverse:
    begin, end = rankpos(lowv), rankpos(lowv + cd)
else:
    begin, end = rankpos(highv), rankpos(highv - cd)

plot_line([(begin, distanceh), (end, distanceh)], linewidth=linewidth)
plot_line([(begin, distanceh + bigtick / 2),
           (begin, distanceh - bigtick / 2)],
          linewidth=linewidth)
plot_line([(end, distanceh + bigtick / 2),
           (end, distanceh - bigtick / 2)],
          linewidth=linewidth)
plot_text((begin + end) / 2, distanceh - 0.05, "CD",
          ha="center", va="bottom")

# No-significance lines
side = 0.05
no_sig_height = 0.1
start = cline + 0.2
for l, r in groups:
    plot_line([(rankpos(sorted_ranks[l]) - side, start),
               (rankpos(sorted_ranks[r]) + side, start)],
              linewidth=linewidth * 2)
    start += no_sig_height

# Add title to the diagram
ax.set_title(title, fontsize=fontsize + 2)

return ax

def plot_stats(result, *, allow_insignificant=False, ax=None, width=None, fontsize=18, title='', linewidth = 1.5):
"""
Creates a plot that supports the analysis of the results of the statistical test.

...

fontsize (int, default=14):
    Font size to be used in the plot.
"""
if not isinstance(result, RankResult):
    raise TypeError("result must be of type RankResult and should be the outcome of calling the autorank function.")

if result.omnibus == 'bayes':
    raise ValueError("plotting results of bayesian analysis not yet supported.")

if result.pvalue >= result.alpha and not allow_insignificant:
    raise ValueError(
        "result is not significant and results of the plot may be misleading. If you want to create the plot "
        "regardless, use the allow_insignificant parameter to suppress this exception.")

if ax is not None and width is not None:
    warnings.warn('width may be ignored because ax is defined.')
if width is None:
    width = 6

if result.omnibus == 'ttest':
    ax = ci_plot(result, True, ax, width, fontsize)
elif result.omnibus == 'wilcoxon':
    warnings.warn('No plot to visualize statistics for Wilcoxon test available. Doing nothing.')
elif result.posthoc == 'tukeyhsd':
    ax = ci_plot(result, True, ax, width, fontsize)
elif result.posthoc == 'nemenyi':
    ax = cd_diagram(result, True, ax, width, fontsize, title, linewidth)
return ax

Regards

[sherbold]

Looks interesting! Thanks for your contribution.

Could you create a PR for this? Then I can see the diff directly and review this easier.

[msotocalvo]

Sure, please to help. The PR has been created as 'Solution for issue #26'