140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
'''plotting the MSE and SSIM against the number of iterations'''
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import numpy as np
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import matplotlib.pyplot as plt
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import h5py
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import glob
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import argparse
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import csv
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import re
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from scipy.signal import find_peaks
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parser = argparse.ArgumentParser()
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parser.add_argument('--infile', type = str, required = True, help = 'file name of the .h5 file for names')
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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")
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parser.add_argument('--images', type = str, default = "mse_mssim", help = "naming the output images, default = mse_mssim")
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parser.add_argument('-q','--squared', action = argparse.BooleanOptionalAction, help = "squares the intensity values, otherwise normal values")
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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')
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args = parser.parse_args()
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group_to_datasets = {}
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with h5py.File(args.infile, 'r') as hdf5_file:
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for group_name in hdf5_file: # loop üle grupi nimede
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group = hdf5_file[group_name] #salvestab grupi nime
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if isinstance(group, h5py.Group):
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datasets = [] #kui grupi nimi on h5py.Group nimi siis
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for ds_name in group: #vaatab üle kõik datasetid grupi sees
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if isinstance(group[ds_name], h5py.Dataset): # kui vastab ds nimele
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datasets.append(ds_name) # appenditakse
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group_to_datasets[group_name] = datasets # iga grupile apenditakse tema oma ds
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group_names = list(group_to_datasets.keys()) # has all the groups which are different inensitires and
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# all the datasets names are there with sigma and minimum but i only use sigma so the rest are not used at all
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labels = group_to_datasets.get(group_names[0]) # gets all the groups labels in a list
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npy_files = sorted(glob.glob(f"{args.data}*.npy"))
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if args.sigma_plots:
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column_accumulators = None
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for filename in npy_files:
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data = np.load(filename)
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iterations = data[:,0]
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cols_rem = data.shape[1] - 1
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cols = cols_rem // 2
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mses = data[:, 1:1+cols]
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#mssim = data[:, 1+cols:1+2*cols]
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if column_accumulators is None:
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column_accumulators = [[] for _ in range(mses.shape[1])]
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for i in range(mses.shape[1]):
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column_accumulators[i].append(mses[:, i])
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column_vars = [np.column_stack(col) for col in column_accumulators]
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flat_numbers = np.array([float(n) for s in group_names for n in re.findall(r"-?\d+\.?\d*", s)])
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flat_numbers[flat_numbers < 1] = 1
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flat_numbers = np.square(flat_numbers)
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for i, col in enumerate(column_vars):
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plt.figure(figsize=(15,9))
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for j in range(col.shape[1]):
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if args.squared:
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plt.plot(col[:, j] * flat_numbers[j], label = group_names[j])
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else:
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plt.plot(col[:, j], label = group_names[j])
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plt.title(f"{labels[i]}")
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plt.legend()
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#plt.show()
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with open('mse.csv', 'w', newline='') as csvfile, open('mssim.csv', 'w', newline='') as f2:
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writer = csv.writer(csvfile)
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writer2 = csv.writer(f2)
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header = False
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for group_name, filename in zip(group_names, npy_files): #üle kõigi mürade
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labels = group_to_datasets[group_name]
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if not header:
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writer.writerow([''] + labels)
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writer2.writerow([''] + labels)
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header = True
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row = [group_name]
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row2 = [group_name]
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data = np.load(filename)
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iterations = data[:,0]
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cols_rem = data.shape[1] - 1
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cols = cols_rem // 2
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mses = data[:, 1:1+cols]
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mssim = data[:, 1+cols:1+2*cols]
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#np.set_printoptions(threshold=np.inf)
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fig, ax = plt.subplots(2, 1, figsize = (15,9))
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ax0 = ax[0]
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ax1 = ax[1]
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for i in range(mses.shape[1]): #üle kõigi sigmade
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ax0.plot(iterations, mses[:, i], label = labels[i])
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row.append(np.min(mses[:,i]))
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ax0.set_xlabel('iterations')
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ax0.set_ylabel('mse')
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#ax0.set_yscale('log')
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ax0.set_title(f'mse change - {group_name}')
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ax0.legend()
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ax0.grid(False)
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for i in range(mssim.shape[1]):
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ax1.plot(iterations, mssim[:, i], label = labels[i])
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row2.append(np.max(mssim[:,i]))
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ax1.set_xlabel('iterations')
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ax1.set_ylabel('mssim')
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ax1.set_title(f'mssim - {group_name}')
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ax1.legend()
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ax1.grid(False)
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writer.writerow(row)
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writer2.writerow(row2)
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plt.tight_layout()
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plt.savefig(f"{args.images}_{group_name}.png")
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plt.show()
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