Merge remote-tracking branch 'origin/main'

Aswendt-Lab · Apr 16, 2024 · 93e2434 · 93e2434
2 parents cdeb101 + d61fc16
commit 93e2434
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diff --git a/code/FA_Mapping.py b/code/FA_Mapping.py
@@ -0,0 +1,52 @@
+import glob
+import os
+import pandas as pd
+import nibabel as nib
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import rcParams
+
+# Set font properties
+rcParams['font.family'] = 'times new roman'
+rcParams['font.size'] = 8
+
+# Define file paths
+path = r"C:\Users\arefk\Desktop\Projects\testData\proc_data"
+csv_voting_path = r"C:\Users\arefk\Desktop\Projects\testData\proc_data2\QC\votings.csv"
+
+# Read CSV file for voting
+df_voting = pd.read_csv(csv_voting_path, delimiter=";")
+
+# Find DWI files
+SearchP = os.path.join(path, "**", "dwi", "*dwi.nii.gz")
+BetFiles = glob.glob(SearchP, recursive=True)
+
+for bb in BetFiles:
+    temp = os.path.dirname(bb)
+    SearchFA = os.path.join(temp, "**", "fa_flipped.nii.gz")
+    FA = glob.glob(SearchFA, recursive=True)[0]
+
+    # Load DWI and FA images
+    dwi_img = nib.load(bb)
+    fa_img = nib.load(FA)
+
+    # Rotate images 90 degrees
+    dwi_data_rotated = np.rot90(dwi_img.get_fdata(), k=-1)
+    fa_data_rotated = np.rot90(fa_img.get_fdata(), k=-1)
+
+    # Plot images
+    fig, axes = plt.subplots(1, 2)
+    axes[0].imshow(dwi_data_rotated[:, :, dwi_data_rotated.shape[-2] // 2+3, dwi_data_rotated.shape[-1] // 2], cmap='gray')
+    axes[0].set_title('Original', fontweight='bold')
+    axes[1].imshow(fa_data_rotated[:, :, fa_data_rotated.shape[-1] // 2+3], cmap='gray')
+    axes[1].set_title('FA Map', fontweight='bold')
+
+    # Find corresponding entry in CSV_voting
+    df_bb_voting = df_voting[df_voting['Pathes'] == bb]
+    if not df_bb_voting.empty:
+        voting_value = df_bb_voting['Voting outliers (from 5)'].values[0]
+    else:
+        voting_value = 0
+    axes[0].text(10, 30, f'Voting Outliers: {voting_value}', color='blue', fontweight='bold')
+
+    plt.show()
diff --git a/code/ViolinPlots.py b/code/ViolinPlots.py
@@ -166,6 +166,6 @@ def compare_and_plot(data, column_name, group_column):
 
             ax.tick_params(axis='both', which='both', width=0.5,color='gray',length=2)
             plt.xticks(ha='right')
-            plt.savefig(os.path.join(out_path,feature+"_"+All_type[dd]+"withoutAbdominal.svg"), format='svg', bbox_inches='tight',transparent=False)
-            plt.savefig(os.path.join(out_path,feature+"_"+All_type[dd]+"withoutAbdominal.png"),dpi=300 ,format='png', bbox_inches='tight',transparent=False)
+            #plt.savefig(os.path.join(out_path,feature+"_"+All_type[dd]+"withoutAbdominal.svg"), format='svg', bbox_inches='tight',transparent=False)
+            #plt.savefig(os.path.join(out_path,feature+"_"+All_type[dd]+"withoutAbdominal.png"),dpi=300 ,format='png', bbox_inches='tight',transparent=False)
             plt.show()
diff --git a/code/ViolinPlots_abdominal.py b/code/ViolinPlots_abdominal.py
@@ -0,0 +1,169 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy.stats import ttest_ind, f_oneway
+import glob
+import os
+from scipy.stats import ttest_ind
+from statsmodels.stats.multitest import multipletests
+import re
+
+#% Function to read CSV files and store them in a dictionary
+script_dir = os.path.dirname(__file__)
+out_path = os.path.join(script_dir, '..', 'figures')
+cm = 1/2.54  # centimeters in inches
+def read_csv_files(files):
+    data_dict = {}
+    for ff,file in enumerate(files):
+        df = pd.read_csv(file)    
+        # Extract the data type from the file name (assuming the file name contains "anat", "diff", or "func")
+        data_type = "anat" if "anat" in file else ("diff" if "diff" in file else "func")
+        data_dict[ff] = {data_type:df} 
+
+    return data_dict
+
+
+# Function for statistical comparison and plotting
+def compare_and_plot(data, column_name, group_column):
+    sns.set_palette("Set2")  # Use colors for color-blind people
+    plt.figure(figsize=(9*cm, 6*cm))
+    sns.boxplot(x=group_column, y=column_name, data=data)
+    plt.xlabel(group_column)
+    plt.ylabel(column_name)
+    plt.title(f"Statistical Comparison of {column_name} by {group_column}")
+    plt.tight_layout()
+    #plt.savefig(f"{column_name}_by_{group_column}_boxplot.png")
+    plt.show()
+
+Path = r"C:\Users\arefk\Desktop\Projects\AIDAqcOutput_of_all_Datasets"
+
+anat_files = [file for file in glob.glob(os.path.join(Path, "*/*/*caculated_features_anat.csv"), recursive=True) if "m_Rei" in file or "7_m_Lo" in file]
+diff_files = [file for file in glob.glob(os.path.join(Path, "*/*/*caculated_features_diff.csv"), recursive=True) if "m_Rei" in file or "7_m_Lo" in file]
+func_files = [file for file in glob.glob(os.path.join(Path, "*/*/*caculated_features_func.csv"), recursive=True) if "m_Rei" in file or "7_m_Lo" in file]
+
+All_files = [anat_files,diff_files,func_files]
+
+# Read the CSV files and store them in dictionaries
+anat_data = read_csv_files(anat_files)
+diff_data = read_csv_files(diff_files)
+func_data = read_csv_files(func_files)
+All_Data = [anat_data,diff_data,func_data]
+All_type = ["anat","diff","func"]
+#% data statistisc figure 7
+BINS = [8,8,8]
+features_to_compare = ["SNR Chang", "SNR Normal", "tSNR (Averaged Brain ROI)", "Displacement factor (std of Mutual information)"]
+
+#features_to_compare = ["SpatRx", "SpatRy", "Slicethick"]
+
+
+Data_of_selected_feature2 = pd.DataFrame()
+for dd,data in enumerate(All_Data):
+    for feature in features_to_compare:
+        cc = 0
+        temp = pd.DataFrame()
+        Data_of_selected_feature = pd.DataFrame()
+        temp_data = pd.DataFrame()
+
+        for key in data:
+            try:
+                temp_data[feature] = data[key][All_type[dd]][feature]
+            except KeyError:
+                continue
+            temp_data["Dataset"] = All_files[dd][cc].split(os.sep)[-3]
+            cc = cc +1
+            Data_of_selected_feature = pd.concat([Data_of_selected_feature, temp_data], ignore_index=True)
+            #Data_of_selected_feature2 = pd.concat([Data_of_selected_feature2, temp_data], ignore_index=True)
+
+
+
+        if not Data_of_selected_feature.empty:
+            Data_of_selected_feature['sort'] = Data_of_selected_feature['Dataset'].str.extract('(\d+)', expand=True).astype(int)
+            Data_of_selected_feature = Data_of_selected_feature.sort_values('sort')
+
+            if feature == "SNR Normal":
+                Data_of_selected_feature.rename(columns={"SNR Normal": "SNR-Standard (dB)"}, inplace=True)
+                feature = "SNR-Standard (dB)"
+            if feature == "SNR Chang":
+                Data_of_selected_feature.rename(columns={"SNR Chang": "SNR-Chang (dB)"}, inplace=True)
+                feature = "SNR-Chang (dB)"    
+            elif feature == "tSNR (Averaged Brain ROI)":
+                Data_of_selected_feature.rename(columns={"tSNR (Averaged Brain ROI)": "tSNR (dB)"}, inplace=True)
+                feature = "tSNR (dB)"
+            elif feature == "Displacement factor (std of Mutual information)":
+                Data_of_selected_feature.rename(columns={"Displacement factor (std of Mutual information)": "Motion severity (a.u)"}, inplace=True)
+                BINS[dd] = 10
+                feature = "Motion severity (a.u)"
+
+
+            #Data_of_selected_feature2["Vol"] = Data_of_selected_feature2["SpatRx"]*Data_of_selected_feature2["SpatRy"]*Data_of_selected_feature2["Slicethick"]
+
+            #Data_of_selected_feature = Data_of_selected_feature.sort_values("Dataset",ascending=False)
+            # creating boxplots
+            if All_type[dd] == "anat":
+                plt.figure(figsize=(6*cm,3*cm),dpi=600)
+            else:
+                plt.figure(figsize=(6*cm,3*cm),dpi=600)
+
+            sns.set_style('ticks')
+            sns.set(font='Times New Roman',style=None)  # Set font to Times New Roman and font size to 9
+            palette = 'Set2'
+            ax = sns.violinplot(x="Dataset", y=feature, data=Data_of_selected_feature, hue="Dataset", 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="Dataset", y=feature, data=Data_of_selected_feature, 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="Dataset", y=feature, data=Data_of_selected_feature,size=1.1, hue="Dataset", 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.locator_params(axis='y', nbins=BINS[dd])  # Set the number of ticks for the y-axis
+
+            ax
+            ax.set_xticklabels(ax.get_xticklabels(), rotation=45,fontsize=8)
+            ax.set_yticklabels(ax.get_yticklabels(),fontsize=8)
+
+
+            #ax.set_yticks(np.linspace(ax.get_ylim()[0], ax.get_ylim()[1], BINS[dd]))
+
+
+# =============================================================================
+#             for label, color in zip(ax.get_xticklabels(), legend_colors):
+#                 label.set_color(color)
+# =============================================================================
+            ax.set_xlabel('')
+            ax.set_title(All_type[dd].capitalize(),weight='bold',fontsize=10)
+            y_label = ax.set_ylabel(ax.get_ylabel(),fontsize=8)
+
+# =============================================================================
+            ax.xaxis.grid(True, linestyle='-', which='major', color='gray', linewidth=0)
+            ax.xaxis.grid(True, linestyle='--', which='minor', color='gray', linewidth=0)
+# 
+            ax.yaxis.grid(True, linestyle='-', which='major', color='gray', linewidth=0)
+            ax.yaxis.grid(True, linestyle='--', which='minor', color='gray', linewidth=0)        
+# =============================================================================
+            ax.spines['top'].set_linewidth(0.5)  # Top border
+            ax.spines['right'].set_linewidth(0.5)  # Right border
+            ax.spines['bottom'].set_linewidth(0.5)  # Bottom border
+            ax.spines['left'].set_linewidth(0.5)  # Left border
+                        # Set axis ticks font and number of ticks
+            ax.tick_params(axis='both', which='both', width=0.5, color='gray', length=2)
+
+            ax.tick_params(axis='both', which='both', width=0.5,color='gray',length=2)
+            plt.xticks(ha='right')
+            plt.savefig(os.path.join(out_path,feature+"_"+All_type[dd]+"Abdominal.svg"), format='svg', bbox_inches='tight',transparent=False)
+            plt.savefig(os.path.join(out_path,feature+"_"+All_type[dd]+"Abdominal.png"),dpi=300 ,format='png', bbox_inches='tight',transparent=False)
+            plt.show()
diff --git a/figures/Final_figs.pptx b/figures/Final_figs.pptx
diff --git a/figures/Motion severity (a.u)_diffAbdominal.png b/figures/Motion severity (a.u)_diffAbdominal.png