made a 2D heatmap for inv_vref and noinv_vref parameters

Author: Andreas Pettersson

ana/inv_vref/heatmap.py

@@ -0,0 +1,94 @@
+"""heatmap of pedestal data separated by link_channel"""
+
+import pandas as pd
+import numpy as np
+import hist
+import matplotlib.pyplot as plt
+from pathlib import Path
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('pedestals', type=Path, help='decoded pedestal CSV file to summarize')
+parser.add_argument('-o','--output', type=Path, help='file to which to print, default is pedestal file with "-heatmap" suffix and extension changed to ".png"')
+parser.add_argument('--full-range', action='store_true', help='use full 10-bit range rather than dynamically change the binning to fit between the min/max of the data')
+args = parser.parse_args()
+
+if args.output is None:
+    args.output = args.pedestals.parent / (args.pedestals.stem + '-heatmap.png')
+
+samples = pd.read_csv(args.pedestals)
+
+fig, axes = plt.subplots(1, 2)
+
+# pedestal per link vs noinv
+
+link0_df = samples[(samples['ch'] < 32) & (samples['inv_vref'] == 600)]
+link1_df = samples[(samples['ch'] >= 32) & (samples['inv_vref'] == 600)]
+
+median_adc_0 = link0_df.groupby('noinv_vref')['adc'].median().sort_index()
+median_adc_1 = link1_df.groupby('noinv_vref')['adc'].median().sort_index()
+
+axes[0].plot(median_adc_0.index, median_adc_0.values)
+axes[1].plot(median_adc_1.index, median_adc_1.values)
+axes[0].set_ylabel('median pedestal [ADC]')
+axes[0].set_xlabel('noinv_vref')
+axes[1].set_xlabel('noinv_vref')
+axes[0].set_title('link 0')
+axes[1].set_title('link 1')
+
+plt.savefig('noinv_scan.png', dpi=400, bbox_inches='tight')
+plt.close()
+
+# pedestal per link vs inv
+
+fig, axes = plt.subplots(1, 2)
+
+link0_df = samples[(samples['ch'] < 32) & (samples['noinv_vref'] == 600)]
+link1_df = samples[(samples['ch'] >= 32) & (samples['noinv_vref'] == 600)]
+
+median_adc_0 = link0_df.groupby('inv_vref')['adc'].median().sort_index()
+median_adc_1 = link1_df.groupby('inv_vref')['adc'].median().sort_index()
+
+axes[0].plot(median_adc_0.index, median_adc_0.values)
+axes[1].plot(median_adc_1.index, median_adc_1.values)
+axes[0].set_ylabel('median pedestal [ADC]')
+axes[0].set_xlabel('inv_vref')
+axes[1].set_xlabel('inv_vref')
+axes[0].set_title('link 0')
+axes[1].set_title('link 1')
+
+plt.savefig('inv_scan.png', dpi=400, bbox_inches='tight')
+plt.close()
+
+# Heatmap
+
+channels = samples.groupby('ch')
+
+for ch_id, ch_df in channels:
+    print("channel ", ch_id)
+    fig = plt.figure()
+
+    heat = (
+        ch_df
+        .groupby(['noinv_vref', 'inv_vref'])['adc']
+        .median()
+        .unstack('inv_vref')
+        .sort_index()
+        .sort_index(axis=1)
+    )
+
+    plt.imshow(
+        heat.values,
+        extent=[
+            heat.columns.min(), heat.columns.max(),
+            heat.index.min(), heat.index.max()
+        ],
+        origin="lower",
+        aspect="auto",
+        #cmap="tab20"
+    )
+    plt.colorbar(label="median pedestal [ADC]")
+    plt.xlabel("inv_vref")
+    plt.ylabel("noinv_vref")
+    plt.savefig(f'heatmap_ch_{ch_id}.png', dpi=400, bbox_inches='tight')
+    plt.close()