#!/usr/bin/env python

# coding: utf-8

# In[ ]:

import numpy as np

import math

import scipy.stats as stats

import matplotlib.pyplot as plt

import matplotlib.patches as patches

import pandas as pd

plt.style.use('seaborn')

# Adjusting settings for visual enhancements

label_size = 18 # Increased label size

legend_size = 19 # Increased legend size

title_size = 17 # Increased title size

line_width = 1 # Bolder lines

marker_size = 8 # Larger dots

y_axis_value_label_size = label_size # Increased y-axis value label size for better visibility

# process length to ratio

def process_ratio(df):

num_row = len(df["ModSelc"])

df_ratio = df

# process to ratio

# Columns to divide

columns_to_divide = ["ModSell", "ModSelLOOl", "YKbaselinel", "YKsplitl", "YK_adjl"]

for cln in columns_to_divide:

df_ratio[cln] = df_ratio[cln]/df_ratio["Min_Length"]

df_ratio["Min_Length"] = 1

# process YK-adj

if df_ratio["YK_adjc"].max() == 0: # if every YK-adj return cal{Y}

df_ratio["YK_adjc"] = 1

df_ratio["YK_adjl"] = 1.35

else:

mr = df_ratio["YK_adjl"].max()

for i in range(num_row):

if df_ratio.loc[i, "YK_adjc"] == 0:

df_ratio.loc[i, "YK_adjc"] = 1

df_ratio.loc[i, "YK_adjl"] = mr+0.1

return df_ratio

# Functions to plot data

def plot_coverage(ax, df):

M_cat_indices_equal = range(len(df["ModSelc"]))

M_values_str = df.iloc[:,0].astype(str).tolist()

for method, color, marker in zip(["ModSelc", "ModSelLOOc", "YKbaselinec", "YKsplitc", "YK_adjc"],

['blue', 'green', 'red', 'purple', 'orange'],

['o','v','h','p','>']):

new_label = "ModSel-CP" if "ModSelc" in method else "ModSel-CP-LOO" if "ModSelLOOc" in method else "YK-baseline" if "YKbaselinec" in method else "YK-adjust" if "YK_adjc" in method else "YK-split" if "YKsplitc" in method else method.split('_')[0]

ax.plot(M_cat_indices_equal, df[method], label=new_label, marker=marker, linestyle='-',

color=color, linewidth=line_width, markersize=marker_size)

ax.axhline(y=0.9, color='black', linestyle='--', linewidth=2)

ax.set_xticks(M_cat_indices_equal)

ax.set_xticklabels(M_values_str, rotation=45, fontsize=label_size)

ax.margins(x=0.05, y=0.05)

ax.tick_params(axis='y', labelsize=y_axis_value_label_size)

def plot_length(ax, df):

M_cat_indices_equal = range(len(df["ModSelc"]))

M_values_str = df.iloc[:,0].astype(str).tolist()

for method, color, marker in zip(["ModSell", "ModSelLOOl", "YKbaselinel", "YKsplitl", "YK_adjl", "Min_Length"],

['blue', 'green', 'red', 'purple','orange', 'brown'],

['o','v','h','p','>','s']):

new_label = "ModSel-CP" if "ModSell" in method else "ModSel-CP-LOO" if "ModSelLOOl" in method else "YK-baseline" if "YKbaselinel" in method else "YK-adjust" if "YK_adjl" in method else "YK-split" if "YKsplitl" in method else "$\min_{\lambda \in \Lambda} |\widehat{C}^{\lambda}_{\hat{q}(\lambda)}(X_{n+1})|$" if "Min_Length" in method else method.split('_')[0]

ax.plot(M_cat_indices_equal, df[method], label=new_label, marker = marker, linestyle='-',

color=color, linewidth=line_width, markersize=marker_size)

ax.set_xticks(M_cat_indices_equal)

ax.set_xticklabels(M_values_str, rotation=45, fontsize=label_size)

ax.margins(x=0.05, y=0.05)

# Customize y-axis to show 'Infinity'

max_y_val = df["YK_adjl"].max()

ticks = ax.get_yticks() # Get current ticks

new_ticks = np.unique(np.append(ticks, max_y_val)) # Append to the list of ticks

if np.max(new_ticks)>max_y_val:

new_ticks = new_ticks[new_ticks<=max_y_val]

new_ticklabels = [f"{item:.1f}" if item < max_y_val else r'$\infty$' for item in new_ticks]

ax.set_yticks(new_ticks) # Set new ticks

ax.set_yticklabels(new_ticklabels) # Set new tick labels

ax.tick_params(axis='y', labelsize=y_axis_value_label_size)

def plot4by2(dfs, titles):

# Create a figure with subplots

fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10), sharex=True)

# Fill each subplot

for i in range(4):

axes[0, i].set_title(titles[i], fontsize=title_size, fontweight='bold')

plot_coverage(axes[0, i], dfs[i])

plot_length(axes[1, i], dfs[i])

if dfs[i].columns[0] == "M":

axes[1, i].set_xlabel('$|\Lambda|$', fontsize=label_size)

else:

axes[1, i].set_xlabel('$n$', fontsize=label_size)

# Set row titles

for ax, row in zip(axes[:,0], ['Coverage', 'Length ratio']):

ax.set_ylabel(row, fontsize=label_size)

# set the universal legend

handles, labels = ax.get_legend_handles_labels()

fig.legend(handles, labels, loc='lower center', bbox_to_anchor=(0.5, -0.13), ncol=3, fontsize=legend_size)

# Adjust layout and show plot

plt.tight_layout(rect=[0, 0, 1, 0.95])

if dfs[0].columns[0] == "M":

filename = f"R_{titles[0]}_n.pdf"

else:

filename = f"R_{titles[0]}_M.pdf"

plt.savefig(filename, bbox_inches='tight')

plt.show()