2023_Kalantari_AIDAqc/code/Code4Figures/CSVplot.py

#%% SNR Chang vs Standard plot for RT and Cryp

p_address = r"C:\Users\aswen\Documents\Data\2023_Kalantari_AIDAqc\outputs\validation\QC_Standard\94_m_As_allslice\calculated_features\caculated_features_structural.csv"
p_address2= r"C:\Users\aswen\Documents\Data\2023_Kalantari_AIDAqc\outputs\validation\QC_Standard\94c_m_As_allslices\calculated_features\caculated_features_structural.csv"
#p_save = r"\\10.209.5.114\Publications\2023_Kalantari_AIDAqc\outputs\QC_Final\validation\ChangVSStandard"
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

cm = 1/2.54  # centimeters in inches
# Set the color palette to 'Set2'

plt.figure(figsize=(18*cm/3,7.30*cm),dpi=300)
# Load the CSV data into a pandas DataFrame
data = pd.read_csv(p_address)
data2 = pd.read_csv(p_address2)



chang = data["SNR Chang"].to_frame().rename(columns={"SNR Chang": "SNR"})
normal = data["SNR Normal"].to_frame().rename(columns={"SNR Normal": "SNR"})
chang["type"] = "chang_RT"
normal["type"] = "normal_RT"



chang2 = data2["SNR Chang"].to_frame().rename(columns={"SNR Chang": "SNR"})
normal2 = data2["SNR Normal"].to_frame().rename(columns={"SNR Normal": "SNR"})
chang2["type"] = "chang_Cryo"
normal2["type"] = "normal_Cryo"




Data_merged = data[["SNR Chang","SNR Normal"]]
Data_merged.rename(columns={"SNR Normal":"SNR-standard"})
#Data_merged[["SNR Chang","SNR Normal"]] = Data_merged[["SNR Chang","SNR Normal"]].apply(lambda x: np.power((x/20),10))

Data_merged2 = data2[["SNR Chang","SNR Normal"]]
Data_merged2.rename(columns={"SNR Normal":"SNR-standard"})
#Data_merged2[["SNR Chang","SNR Normal"]] = Data_merged2[["SNR Chang","SNR Normal"]].apply(lambda x: np.power((x/20),10))


SNR = pd.concat([chang, normal,chang2,normal2])
#SNR["SNR"] = SNR["SNR"].apply(lambda x: np.power(x/20,10))

tips = SNR
x = "type"
y = "SNR"
sns.set_style('ticks')
# Create circular marker style for left side
circle_marker = {"marker": "o", "markerfacecolor": "black", "markersize": 3}


#tips = sns.load_dataset("tips")
plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams['font.size'] = 8
plt.title("(c) Chang vs Standard SNR",weight='bold')
ax=sns.barplot(x="type", y="SNR", data=tips, capsize=0 ,palette="pastel",errorbar="se",ci=None)
#sns.swarmplot(x="type", y="SNR", data=tips, color="0", alpha=.5)
for i in range(len(Data_merged)):
    plt.plot(["chang_RT", "normal_RT"], Data_merged.iloc[i], color="black", **circle_marker,linewidth=0.5)

for i in range(len(Data_merged2)):
    plt.plot(["chang_Cryo", "normal_Cryo"], Data_merged2.iloc[i], color="black", **circle_marker,linewidth=0.5)

ax.spines['top'].set_linewidth(0)  # Top border
ax.spines['right'].set_linewidth(0)  # Right border
ax.spines['bottom'].set_linewidth(0.5)  # Bottom border
ax.spines['left'].set_linewidth(0.5)  # Left border
# Set the font to Times New Roman and font size to 8 points
#ax.set_ylim(bottom=0, top=45)
ax.set_xticklabels(ax.get_xticklabels(), rotation=20,fontsize=8)
# Rest of your code...
ax.set_xlabel('')
ax.tick_params(axis='both', which='both', width=0.5,color='gray',length=2)
plt.show()


#%% SNR Chang vs Standard plot for RT and Cryp Version 2

p_address = r"C:\Users\aswen\Documents\Data\2023_Kalantari_AIDAqc\outputs\validation\QC_Standard\94_m_As\calculated_features\caculated_features_anatomical.csv"
p_address2= r"C:\Users\aswen\Documents\Data\2023_Kalantari_AIDAqc\outputs\validation\QC_Standard\94c_m_As\calculated_features\caculated_features_anatomical.csv"
#p_save = r"\\10.209.5.114\Publications\2023_Kalantari_AIDAqc\outputs\QC_Final\validation\ChangVSStandard"
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

cm = 1/2.54  # centimeters in inches
# Set the color palette to 'Set2'

plt.figure(figsize=(18*cm/3,7.30*cm),dpi=300)
# Load the CSV data into a pandas DataFrame
data = pd.read_csv(p_address)
data2 = pd.read_csv(p_address2)



chang = data["SNR Chang"].to_frame().rename(columns={"SNR Chang": "SNR"})
normal = data["SNR Normal"].to_frame().rename(columns={"SNR Normal": "SNR"})
chang["type"] = "chang_rt"
normal["type"] = "normal_rt"



chang2 = data2["SNR Chang"].to_frame().rename(columns={"SNR Chang": "SNR"})
normal2 = data2["SNR Normal"].to_frame().rename(columns={"SNR Normal": "SNR"})
chang2["type"] = "chang_cryo"
normal2["type"] = "normal_cryo"




SNR = pd.concat([chang, normal,chang2,normal2])
SNR["SNR"] = SNR["SNR"].apply(lambda x: np.power(10,x/20))


SNR = SNR.sort_values('type')




#Data_of_selected_feature = Data_of_selected_feature.sort_values("Dataset",ascending=False)
# creating boxplots
#plt.figure(figsize=(6*cm,6*cm*np.e),dpi=300)
sns.set_style('ticks')
sns.set(font='Times New Roman', font_scale=0.9,style=None)  # Set font to Times New Roman and font size to 9
palette = 'Set2'
ax = sns.violinplot(x="type", y="SNR", data=SNR, hue="type", dodge=False,
                    palette=palette,
                    scale="width", inner=None,linewidth=1)
patches = ax.patches
#legend_colors = [patch.get_facecolor() for patch in patches[:]]

xlim = ax.get_xlim()
ylim = ax.get_ylim()
for violin in ax.collections:
    bbox = violin.get_paths()[0].get_extents()
    x0, y0, width, height = bbox.bounds
    violin.set_clip_path(plt.Rectangle((x0, y0), width / 2, height, transform=ax.transData))

sns.boxplot(x="type", y="SNR", data=SNR, saturation=1, showfliers=False,
            width=0.3, boxprops={'zorder': 3, 'facecolor': 'none'}, ax=ax, linewidth=1)
old_len_collections = len(ax.collections)
sns.stripplot(x="type", y="SNR", data=SNR, size=1.1, hue="type", palette=palette, dodge=False, ax=ax)
for dots in ax.collections[old_len_collections:]:
    dots.set_offsets(dots.get_offsets() + np.array([0.12, 0]))
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.legend_.remove()
ax
ax.set_xlabel('')
ax.set_xticklabels(ax.get_xticklabels(), rotation=20,fontsize=8)
#ax.set_ylim(bottom=15, top=40)

#%% Plot all chang vs normal

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats
import numpy as np
import os


cm = 1/2.54  # centimeters in inches
# Specify the path to your Excel file
excel_file_path = r"C:\Users\arefk\OneDrive\Desktop\Projects\2023_Kalantari_AIDAqc\outputs\files_4figs\combined_data_anat.csv"
plt.figure(figsize=(10*cm,10*cm),dpi=300)
# Read the data into a pandas DataFrame
df = pd.read_csv(excel_file_path)

# Drop rows with NaN values in the specified columns
#df = df.dropna(subset=['SNR-Chang (dB)', 'SNR-Standard (dB)'])

df['SNR Chang'] = df['SNR Chang'].apply(lambda x: np.power(10,x/20))
df['SNR Normal'] = df['SNR Normal'].apply(lambda x: np.power(10,x/20))
df['names'] = df['FileAddress'].apply(lambda x:x.split("mri")[1].split(os.path.sep)[1])



plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams['font.size'] = 8
plt.title("All anatomical data",weight='bold', fontsize=10)

# Calculate the correlation and p-value between 'SNR-Chang' and 'SNR-Standard'
#correlation, p_value = stats.pearsonr(df['SNR-Chang (dB)'], df['SNR-Standard (dB)'])
correlation, p_value = stats.spearmanr(df['SNR Chang'], df['SNR Normal'], nan_policy='omit',alternative='two-sided')

# Set seaborn style
sns.set_style('whitegrid')


# Create a scatter plot
#ax=sns.scatterplot(data=df, x='SNR-Chang', y='SNR-Standard',palette="gray_r",s=7)
ax=sns.scatterplot(data=df, x='SNR Chang', y='SNR Normal',hue="names",palette="Spectral_r",s=7)
ax.set_title("All anatomical data", weight='bold', fontsize=11)




# Set title and labels including the correlation and p-value
#plt.title(f'Correlation: {correlation:.8f}, P-value: {p_value:.8f}')
plt.xlabel('SNR-Chang')
plt.ylabel('SNR-Standard')

ax.set_xlim(0,200)
ax.set_ylim(0,140)

ax.spines['top'].set_linewidth(0)  # Top border
ax.spines['right'].set_linewidth(0)  # Right border
ax.spines['bottom'].set_linewidth(0.5)  # Bottom border
ax.spines['left'].set_linewidth(0.5)  # Left border
# Show the plot
# Move the legend outside the plot to the right side
legend =plt.legend(title="Dataset", loc='center left', bbox_to_anchor=(1, 0.5), fontsize=5,handlelength=0.5)
legend.get_title().set_fontfamily('Times New Roman')
for text in legend.get_texts():
    text.set_fontfamily('Times New Roman')

#ax.legend_.remove()
plt.show()

#%% Plot all chang vs normal and corrolate 0 to 50 and 50 to end

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats
import numpy as np


cm = 1/2.54  # centimeters in inches
# Specify the path to your Excel file
excel_file_path = r"Y:\2023_Kalantari_AIDAqc\outputs\QC_Final\validation\Chang&normal_all.xlsx"
plt.figure(figsize=(8*cm,8*cm),dpi=300)
# Read the data into a pandas DataFrame
df = pd.read_excel(excel_file_path, engine='openpyxl')

# Drop rows with NaN values in the specified columns
#df = df.dropna(subset=['SNR-Chang (dB)', 'SNR-Standard (dB)'])

df['SNR-Chang'] = df['SNR-Chang'].apply(lambda x: np.power(10,x/20))
df['SNR-Standard'] = df['SNR-Standard'].apply(lambda x: np.power(10,x/20))

df = df.sort_values('sort')


filtered_df = df[df['Dataset'].isin(['94_m_We', '94_m_Va'])]

plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams['font.size'] = 8
plt.title("All anatomical data",weight='bold')

# Create two dataframes, one for values smaller than 50 and another for values 50 or greater
df_smaller_than_50 = df[df['SNR-Chang'] < 60 & df['SNR-Standard'] < 75]

# Calculate the correlation and p-value for the group where 'SNR-Chang' < 50
correlation_smaller_than_50, p_value_smaller_than_50 = stats.spearmanr(df_smaller_than_50['SNR-Chang'], df_smaller_than_50['SNR-Standard'], nan_policy='omit', alternative='two-sided')
print("Correlation:", correlation_smaller_than_50)
print("P-value:", p_value_smaller_than_50)