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Utilities/Norm_image.py

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+import os
+from PIL import Image
+import numpy as np
+import torch
+
+
+
+# Set the input and output directories
+input_dir = './'
+output_dir = './'
+
+# Create the output directory if it doesn't already exist
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Loop through each file in the input directory
+for filename in os.listdir(input_dir):
+    # Open the image file using PIL
+    img_path = os.path.join(input_dir, filename)
+    img = Image.open(img_path)
+    img_gray = img.convert('L')
+
+    # Convert the image to a NumPy array
+    img_np = np.array(img_gray)
+
+    # Convert the NumPy array to a PyTorch tensor
+    img_tensor = torch.from_numpy(img_np)
+
+    # Replace values below 200 with 0, and set all other values to 1
+    img_tensor[img_tensor <= 100] = 3
+    img_tensor[img_tensor > 100] = 1
+
+    # Reshape the tensor from 64 x 64 to 4096 x 1
+    # img_tensor = img_tensor.reshape((64, 64))
+    img_tensor = img_tensor.t()
+    img_tensor = img_tensor.reshape((4096,1))
+    img = torch.flatten(img_tensor, 0, -1)
+
+    # Save the altered tensor as a text file
+    tensor_name = os.path.splitext(filename)[0] + '.txt'
+    tensor_path = os.path.join(output_dir, tensor_name)
+    np.savetxt(tensor_path, img_tensor.numpy(), fmt='%d')

Utilities/convert_array2fig.py

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+from PIL import Image
+import os
+
+file_dir = './'
+
+
+def get_file_list(file_dir):
+    for root, dirs, files in os.walk(file_dir):
+        pass
+    return files
+
+
+data_list = get_file_list(file_dir)
+
+
+def convert_array2fig(filename: str):
+    with open("2000test/" + filename) as f:
+        img = Image.new("RGB", (64, 64), (255, 255, 255))
+        data = f.readlines()
+        for i in range(len(data)):
+            if int((data[i].strip())) == 3:
+                row = i // 64
+                col = i % 64
+                img.putpixel((col,row), (0, 0, 0))
+
+        img.rotate(90).transpose(Image.FLIP_TOP_BOTTOM).save("./" + filename.split(".")[0] + ".png", dpi=(600, 600))
+        # img.show()
+
+
+
+
+for name in data_list:
+    convert_array2fig(name)

Utilities/convert_arry2colormap.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+from PIL import Image
+
+dataset = pd.read_csv('.csv',header=None)
+
+
+num_columns = dataset.shape[1]
+submatrix_width = 5
+num_submatrices = num_columns // submatrix_width
+
+for i in range(num_submatrices):
+    start_col = i * submatrix_width
+    end_col = start_col + submatrix_width
+
+    submatrix = dataset[:, start_col:end_col]
+
+    np.savetxt(f'submatrix_{i + 1}.txt', submatrix, delimiter='\t', fmt='%.6f')  # Save as .txt
+
+    print(f'Saved submatrix_{i + 1}.txt')
+
+
+
+# fig, ax1 = plt.subplots()
+# c = ax1.pcolor(dataset1,cmap='viridis')
+# fig.tight_layout()
+# plt.axis('off')
+# plt.show()