NUCE 2113 lab 5

NUCE_2113/lab5/.~lock.labviz.ods#

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+,danesabo,danesabo-laptop,24.02.2025 15:04,file:///home/danesabo/snap/libreoffice/336/.config/libreoffice/4;

NUCE_2113/lab5/viz.py

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+import glob
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+def load_spectrum_data(filename):
+    """
+    Load spectrum data from a text file in the provided format.
+    
+    This function:
+      - Reads the file line by line.
+      - Finds the "$DATA:" section.
+      - Skips the first line after "$DATA:" (assumed to be the channel range header).
+      - Collects all subsequent lines (until the next section indicated by a line starting with '$').
+      - Parses these lines into a NumPy array.
+      
+    Parameters:
+      filename (str): Path to the spectrum file.
+      
+    Returns:
+      numpy.ndarray: Array of count values.
+    """
+    with open(filename, 'r') as f:
+        lines = f.readlines()
+
+    data_lines = []
+    in_data_section = False
+    skip_first_data_line = True  # flag to skip the channel-range header
+
+    for line in lines:
+        # Look for the beginning of the data section
+        if line.strip().startswith("$DATA:"):
+            in_data_section = True
+            continue
+        if in_data_section:
+            # If we hit a new section header, exit the data section
+            if line.strip().startswith("$"):
+                break
+            # Skip the first line after "$DATA:" if it contains exactly two numbers (channel range header)
+            if skip_first_data_line:
+                parts = line.strip().split()
+                if len(parts) == 2:
+                    skip_first_data_line = False
+                    continue
+                skip_first_data_line = False  # even if not two numbers, do not skip subsequent lines
+            # Append non-empty lines
+            if line.strip():
+                data_lines.append(line.strip())
+
+    # Combine the collected lines into one string and parse numbers
+    data_str = " ".join(data_lines)
+    # Convert the string of numbers into a NumPy array
+    data = np.fromstring(data_str, sep=' ')
+    return data
+
+# Get a list of all .Spe files in the current directory
+spe_files = glob.glob("*.Spe")
+
+# To store data for the combined plot
+combined_data = []
+
+for file in spe_files:
+    # Load the spectrum data
+    spectrum_data = load_spectrum_data(file)
+    # Create an array for channel numbers (one per data point)
+    channels = np.arange(len(spectrum_data))
+    
+    # Store the data for the combined plot (use file name without extension for legend)
+    base_name = os.path.splitext(os.path.basename(file))[0]
+    combined_data.append((base_name, channels, spectrum_data))
+    
+    # Plot individual spectrum
+    plt.figure(figsize=(10, 6))
+    plt.step(channels, spectrum_data, where='mid', color='blue')
+    plt.xlabel('Channel')
+    plt.ylabel('Counts')
+    plt.title(f"Spectrum: {base_name}")
+    plt.grid(True)
+    plt.tight_layout()
+    
+    # Save individual figure as PNG
+    output_filename = base_name + '.png'
+    plt.savefig(output_filename)
+    plt.close()  # Close the figure to free memory
+
+# Now create a combined plot with all spectra
+plt.figure(figsize=(12, 8))
+for name, channels, spectrum_data in combined_data:
+    # Do not specify a color so that the default cycle gives different colors for each curve
+    plt.step(channels, spectrum_data, where='mid', label=name)
+    
+plt.xlabel('Channel')
+plt.ylabel('Counts')
+plt.title("Combined Spectrum Data")
+plt.grid(True)
+plt.legend()
+plt.tight_layout()
+
+# Save and display the combined plot
+plt.savefig('combined_spectrum.png')
+plt.show()