Merge pull request #19 from snizzleorg/main

Additional demo to illustrate how to use snapi to calculate G2 correlation from a ptu file

demos/g2_from_file.py

@@ -0,0 +1,42 @@
+from snAPI.Main import *
+from matplotlib import pyplot as plt
+import click
+from pathlib import Path
+import pandas as pd
+
+
+@click.command()
+@click.argument('filename',type=click.Path(exists=True), metavar='FILENAME.ptu')
+@click.option('--A', '-a', type=int, default=1, help='Correlation channel A (default: 1)')
+@click.option('--B', '-b', type=int, default=2, help='Correlation channel B (default: 2)')
+@click.option('--windowsize', '-ws', type=int, default=10000, help='size of the correlation window in ps (default: 100000)')
+@click.option('--binsize', '-bs', type=int, default=10, help='size of the correlation bin size in ps (default: 10)')
+def g2correlation(filename, a=1, b=2, windowsize=10000, binsize=10):
+    sn = snAPI()
+    sn.getFileDevice(filename)
+    sn.correlation.setG2Parameters(a, b, windowsize, binsize)
+    sn.correlation.measure(waitFinished=True)
+    g2, lagtimes = sn.correlation.getG2Data()
+
+    df = pd.DataFrame([{'lagtime': x, 'g2': y} for x, y in zip(lagtimes, g2)])
+    # Drop rows where either 'X' or 'Y' value is zero
+    df = df[(df != 0).all(1)]
+
+    # Plot the DataFrame
+    df.plot(x='lagtime', y='g2', label=f'{filename}')
+    plt.xlabel('lagtime (s)')
+    plt.ylabel('g(2) amplitude')
+    plt.title("g(2) correlation")
+
+    # Save the plot to a PDF file   
+    plt.savefig(Path(filename).with_suffix('.' + 'pdf'), format='pdf')
+    # Save the plot to a PNG file   
+    plt.savefig(Path(filename).with_suffix('.' + 'png'), format='png')
+    # Save the g2-correlation to csv
+    df.to_csv(Path(filename).with_suffix('.' + 'csv'))
+    # Save the g2-correlation to Excel
+    df.to_excel(Path(filename).with_suffix('.' + 'xlsx'))
+    # Save the g2-correlation to json
+    df.to_json(Path(filename).with_suffix('.' + 'json'))
+if(__name__ == "__main__"):
+    g2correlation()