Addition of spatialResolution_StdDevDistribution_creator.py to create the files for SpatialResolution 2D distributions

Author: granadogmarc

spatialResolution_StdDevDistribution_creator.py

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+import os
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import argparse
+
+# Function to pool the matrix
+def pool_matrix(df, N_prime, M_prime):
+    # Get the original dimensions
+    N, M = df.shape
+
+    # Calculate block sizes
+    A = N // N_prime
+    B = M // M_prime
+
+    if N % N_prime != 0 or M % M_prime != 0:
+        raise ValueError("N and M must be divisible by N' and M' respectively.")
+
+    # Reshape into blocks and calculate mean for each block
+    pooled = (
+        df.values.reshape(N_prime, A, M_prime, B)  # Reshape into blocks
+        .mean(axis=(1, 3))  # Average over the blocks
+    )
+
+    # Convert back to DataFrame for better readability
+    return pd.DataFrame(pooled)
+
+# Function to write matrix with coordinates
+def write_matrix_with_coordinates(df, lenX, lenY, outputfile="output.txt"):
+    # Get number of rows and columns from the matrix shape
+    rows, cols = df.shape
+    
+    # Generate coordinate arrays for X and Y
+    x_coords = np.linspace(-lenX/2, lenX/2, cols)
+    y_coords = np.linspace(-lenY/2, lenY/2, rows)
+
+    # Open file to write the output
+    with open(outputfile, "w") as file:
+        # Write header row with x coordinates
+        file.write(" ".join([f"{x:.2f}" for x in x_coords]) + "\n")
+        
+        # Write each row with y coordinates and corresponding matrix data
+        for y, row_data in zip(y_coords, df.values):
+            file.write(f"{y:.2f}" + " " + " ".join(map(str, row_data)) + "\n")
+    
+    return y_coords, df
+
+# Main function that will be executed when the script runs
+def main():
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Process matrix with coordinate output.")
+    
+    # Define the flags and their corresponding arguments
+    parser.add_argument('-i', '--inputfile', type=str, required=True, help="Input file path")
+    parser.add_argument('-o', '--outputfile', type=str, required=True, help="Output file path")
+    parser.add_argument('-n', '--NumRows', type=int, required=True, help="Number of rows of the final matrix.")
+    parser.add_argument('-m', '--NumColumns', type=int, required=True, help="Number of columns of the final matrix.")
+    parser.add_argument('-x', '--lenX', type=float, required=True, help="Length of X axis")
+    parser.add_argument('-y', '--lenY', type=float, required=True, help="Length of Y axis")
+
+    args = parser.parse_args()
+    
+    # Read the input file into a DataFrame
+    df = pd.read_csv(args.inputfile, sep='\s+', header=None, index_col=None)
+
+    # Pool the matrix if necessary
+    pooled_df = pool_matrix(df, args.N_prime, args.M_prime)
+
+    # Write the matrix with coordinates to the output file
+    write_matrix_with_coordinates(pooled_df, lenX=args.lenX, lenY=args.lenY, outputfile=args.outputfile)
+
+if __name__ == "__main__":
+    main()