--- a
+++ b/utils.py
@@ -0,0 +1,169 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 31 17:02:33 2021
+
+@author: m.beuque
+"""
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+from matplotlib import pyplot as plt
+import numpy as np
+import os
+import pandas as pd
+from xml.dom import minidom
+import xml.etree.ElementTree as ET
+
+
+
+def extract_transform_matrix(path_mis):
+    elmt_matrix = []
+    # parse an xml file by name
+    mydoc = minidom.parse(path_mis)
+    
+    items = mydoc.getElementsByTagName('TeachPoint')
+     
+    for elem in items:  
+        temp = str(elem.firstChild.data)
+        temp = temp.split(";")
+        val = []
+        val.append(list(temp[0].split(",")))
+        val.append(list(temp[1].split(",")))
+        elmt_matrix.append(val)
+    
+    elmt_matrix = np.array(elmt_matrix)
+    fixed_points_optical = elmt_matrix[:3,0]
+    moving_points_motor =  elmt_matrix[:3,1]
+    moving_points_optical = elmt_matrix[3:,0]
+    fixed_points_histo = elmt_matrix[3:,1]  
+    
+    return  fixed_points_optical, moving_points_motor,moving_points_optical,fixed_points_histo
+
+def process_spotlist(path_spotlist) :
+    f = open(path_spotlist) 
+    new_spotlist = []
+    for line in f:
+        if '#' not in line:
+            elmts = line.split()
+            temp_x = elmts[2][4:7]
+            temp_y = elmts[2][-3:]
+            elmts.remove(elmts[2])
+            elmts.insert(2,temp_y)
+            elmts.insert(2, temp_x)
+            new_spotlist.append(elmts)  
+    spotlist = np.array(new_spotlist, dtype = int)
+    f.close()
+    return spotlist
+
+def polygon_extraction(path_mis) :
+    ##get rois data
+    tree = ET.parse(path_mis)
+    root = tree.getroot()
+    
+    dict_roi = {}
+    for roi in root.iter('ROI'):
+        name_temp = roi.get('Name')
+        roi_list = []
+        for point in roi.getchildren() :
+            temp_xy = point.text.split(',')
+            roi_list.append(tuple([ int(x) for x in temp_xy ]))
+        #array_roi = np.array(roi_list)
+        #array_roi = array_roi.astype(int)
+        dict_roi[name_temp] = roi_list
+    return dict_roi
+
+def extract_center_pos(path_mis):
+    mydoc = minidom.parse(path_mis)
+    
+    items = mydoc.getElementsByTagName('View')
+    string_centerpos = items[0].attributes['CenterPos'].value
+    center_pos = list(string_centerpos.split(","))
+    return np.array(center_pos, dtype = int)
+
+def assemble_dataset_supervised_learning(full_labels,list_dataset,path_data, data_type):
+    frames = []
+    labels= []
+    for j,f in enumerate(list_dataset):
+        temp_df = pd.read_csv(os.path.join(path_data,f))
+        selected_labels=full_labels[full_labels["slide"] ==f[14:-4]]
+        unified_labels = list(selected_labels["unified_label"])
+        image_names = list(selected_labels["image_name"])
+        list_index = []
+        selected_names = []
+        if data_type == "stroma":
+            for i, elmt in enumerate(unified_labels):
+                if elmt[-1] == "g":
+                    labels.append("gland")
+                    list_index.append(i)
+                    selected_names.append(image_names[i])
+                elif elmt[-1] == "t":
+                    labels.append("epithelial tissue")
+                    list_index.append(i)
+                    selected_names.append(image_names[i])
+        if data_type == "grade":
+            for i, elmt in enumerate(unified_labels):
+                if elmt == "lowgrade_g":
+                    labels.append("low grade")
+                    list_index.append(i)
+                    selected_names.append(image_names[i])
+                elif elmt == "highgrade_g":
+                    labels.append("high grade")
+                    list_index.append(i)
+                    selected_names.append(image_names[i])
+                elif elmt == "healthy_g":
+                    labels.append("non-dysplasia")
+                    list_index.append(i)
+                    selected_names.append(image_names[i])
+
+        temp_df = temp_df.iloc[list_index]
+        if not temp_df.empty:
+            #temp_df.insert(loc=0, column='labels', value=np.array(labels))
+            #col_dataset = [j for i in range(len(temp_df.index))]
+            print(f[14:-4] + " has " + str(range(len(temp_df.index))) + " values")
+            col_dataset = [f[14:-4] for i in range(len(temp_df.index))]
+            #temp_df.insert(loc=1, column='dataset_name', value=col_dataset) 
+            temp_df.insert(loc=1, column='dataset_name', value=col_dataset) 
+            temp_df.insert(loc=2, column='image_name', value=selected_names) 
+            frames.append(temp_df)
+    print("size frames: ", len(frames))
+    full_dataset = pd.concat(frames)
+    del full_dataset['Unnamed: 0']
+    return full_dataset, labels
+
+def print_confusion_matrix(y_true, y_pred, class_names, figsize = (6,5), fontsize=14,normalize=False):
+    cm = confusion_matrix(y_true, y_pred,class_names)
+    # Only use the labels that appear in the data
+    #classes = classes[unique_labels(y_true, y_pred)]
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+        print("Normalized confusion matrix")
+    else:
+        print('Confusion matrix, without normalization')
+
+    sns.set(font_scale=1.4)
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+        print("Normalized confusion matrix")
+    else:
+        print('Confusion matrix, without normalization')
+    df_cm = pd.DataFrame(
+        cm, index=class_names, columns=class_names, 
+    )
+    fig = plt.figure(figsize=figsize)
+    try:
+        if normalize:
+            fmt = '.2f' 
+            heatmap = sns.heatmap(df_cm, annot=True, fmt=fmt,cmap="Blues",vmin=0, vmax=1)
+        else:
+            fmt = 'd' 
+            heatmap = sns.heatmap(df_cm, annot=True, fmt=fmt,cmap="Blues",vmax=max(np.sum((y_pred==class_names[0] )*(y_true==class_names[0])),np.sum((y_pred==class_names[1] )*(y_true==class_names[1]))),vmin=0)
+    except ValueError:
+        raise ValueError("Confusion matrix values must be integers.")
+    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
+    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+    fig.tight_layout()
+    return fig
+
+
+