added sigma plots for intensity

plot.py

@@ -5,6 +5,8 @@ import matplotlib.pyplot as plt
 import h5py
 import glob
 import argparse
+import csv
+import re
 
 from scipy.signal import find_peaks
 
@@ -12,6 +14,8 @@ parser = argparse.ArgumentParser()
 parser.add_argument('--infile', type = str, required = True, help = 'file name of the .h5 file for names')
 parser.add_argument('--data', type = str, required = True, help = "write only the part of 1 file name before the _(nr-s).npy; files for creating the plots")
 parser.add_argument('--images', type = str, default = "mse_mssim", help = "naming the output images, default = mse_mssim")
+parser.add_argument('-q','--squared', action = argparse.BooleanOptionalAction, help = "squares the intensity values, otherwise normal values")
+parser.add_argument('-sp', '--sigma_plots', action = argparse.BooleanOptionalAction, help = 'shows the plots, where all intensity values of a sigma value are on one plot')
 
 args = parser.parse_args()
 
@@ -27,52 +31,105 @@ with h5py.File(args.infile, 'r') as hdf5_file:
                     datasets.append(ds_name) # appenditakse
             group_to_datasets[group_name] = datasets # iga grupile apenditakse tema oma ds
 
+group_names = list(group_to_datasets.keys()) # has all the groups which are different inensitires and 
+# all the datasets names are there with sigma and minimum but i only use sigma so the rest are not used at all 
+labels = group_to_datasets.get(group_names[0]) # gets all the groups labels in a list
 
 npy_files = sorted(glob.glob(f"{args.data}*.npy"))
-group_names = list(group_to_datasets.keys())
 
-for group_name, filename in zip(group_names, npy_files): #üle kõigi mürade
-    data = np.load(filename)
-    iterations = data[:,0]
+if args.sigma_plots:
+    column_accumulators = None
 
-    cols_rem = data.shape[1] - 1
-    cols = cols_rem // 2
-    mses = data[:, 1:1+cols]
-    mssim = data[:, 1+cols:1+2*cols]
+    for filename in npy_files:
+        data = np.load(filename)
+        iterations = data[:,0]
+        cols_rem = data.shape[1] - 1
+        cols = cols_rem // 2
+        mses = data[:, 1:1+cols]
+        #mssim = data[:, 1+cols:1+2*cols]
 
-    #print(f"keskmine mses {group_name}",np.mean(mses))
-    #print(f"keskmine mssim {group_name}",np.mean(mssim))
+        if column_accumulators is None:
+            column_accumulators = [[] for _ in range(mses.shape[1])]
 
-    #np.set_printoptions(threshold=np.inf)
-    #print("---",mssim)
-    fig, ax = plt.subplots(2, 1, figsize = (15,9))
-    ax0 = ax[0]
-    ax1 = ax[1]
+        for i in range(mses.shape[1]):
+            column_accumulators[i].append(mses[:, i])
 
-    labels = group_to_datasets[group_name]
-    #print("######################################################################")
-    for i in range(mses.shape[1]): #üle kõigi sigmade
-        ax0.plot(iterations, mses[:, i], label = labels[i])
-        #print(np.argmin(mses[:,i]))
-        #print(f"------{i}--------------------{i}-----------------")
+    column_vars = [np.column_stack(col) for col in column_accumulators]
 
-    ax0.set_xlabel('iterations')
-    ax0.set_ylabel('mse')
-    #ax0.set_yscale('log')
-    ax0.set_title(f'mse change - {group_name}')
-    ax0.legend()
-    ax0.grid(False)
 
-    for i in range(mssim.shape[1]):
-        ax1.plot(iterations, mssim[:, i], label = labels[i])
-    ax1.set_xlabel('iterations')
-    ax1.set_ylabel('mssim')
-    ax1.set_title(f'mssim - {group_name}')
-    ax1.legend()
-    ax1.grid(False)
+    flat_numbers = np.array([float(n) for s in group_names for n in re.findall(r"-?\d+\.?\d*", s)])
+    flat_numbers[flat_numbers < 1] = 1
+    flat_numbers = np.square(flat_numbers)
 
-    plt.tight_layout()
-    plt.savefig(f"{args.images}_{group_name}.png")
+    for i, col in enumerate(column_vars):
+        plt.figure(figsize=(15,9))
+        for j in range(col.shape[1]):
+            if args.squared:
+                plt.plot(col[:, j] * flat_numbers[j], label = group_names[j])
+            else:
+                plt.plot(col[:, j], label = group_names[j])
+        plt.title(f"{labels[i]}")
+        plt.legend()
+    #plt.show()
+
+
+with open('mse.csv', 'w', newline='') as csvfile, open('mssim.csv', 'w', newline='') as f2:
+    writer = csv.writer(csvfile)
+    writer2 = csv.writer(f2)
+
+    header = False
+    
+    for group_name, filename in zip(group_names, npy_files): #üle kõigi mürade
+        labels = group_to_datasets[group_name]
+
+        if not header:
+            writer.writerow([''] + labels)
+            writer2.writerow([''] + labels)
+            header = True
+        
+        row = [group_name]
+        row2 = [group_name]
+        
+        data = np.load(filename)
+        iterations = data[:,0]
+        
+        cols_rem = data.shape[1] - 1
+        cols = cols_rem // 2
+        mses = data[:, 1:1+cols]
+        mssim = data[:, 1+cols:1+2*cols]
+
+        #np.set_printoptions(threshold=np.inf)
+
+        fig, ax = plt.subplots(2, 1, figsize = (15,9))
+        ax0 = ax[0]
+        ax1 = ax[1]
+
+        for i in range(mses.shape[1]): #üle kõigi sigmade
+            ax0.plot(iterations, mses[:, i], label = labels[i])
+            row.append(np.min(mses[:,i]))
+
+        ax0.set_xlabel('iterations')
+        ax0.set_ylabel('mse')
+        #ax0.set_yscale('log')
+        ax0.set_title(f'mse change - {group_name}')
+        ax0.legend()
+        ax0.grid(False)
+
+        for i in range(mssim.shape[1]):
+            ax1.plot(iterations, mssim[:, i], label = labels[i])
+            row2.append(np.max(mssim[:,i]))
+
+        ax1.set_xlabel('iterations')
+        ax1.set_ylabel('mssim')
+        ax1.set_title(f'mssim - {group_name}')
+        ax1.legend()
+        ax1.grid(False)
+
+        writer.writerow(row)
+        writer2.writerow(row2)
+
+        plt.tight_layout()
+        plt.savefig(f"{args.images}_{group_name}.png")
 
 plt.show()