Add script to visualize TRG scalers

bin/trg_scalers.py

@@ -0,0 +1,76 @@
+import argparse
+from matplotlib.lines import Line2D
+import matplotlib.pyplot as plt
+import numpy as np
+import polars as pl
+
+parser = argparse.ArgumentParser(
+ description="Visualize the TRG scalers for a single run.",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+)
+parser.add_argument("infile", help="path to the TRG scalers CSV file")
+parser.add_argument("--t-bins", type=int, default=100, help="number of bins along t")
+parser.add_argument(
+ "--t-max", type=float, default=float("inf"), help="maximum time in seconds"
+)
+parser.add_argument("--t-min", type=float, default=0.0, help="minimum time in seconds")
+parser.add_argument("--include-drift-veto-counter", action="store_true")
+parser.add_argument("--include-pulser-counter", action="store_true")
+parser.add_argument("--include-scaledown-counter", action="store_true")
+parser.add_argument("--remove-input-counter", action="store_true")
+parser.add_argument("--remove-output-counter", action="store_true")
+args = parser.parse_args()
+
+columns = {
+ "input": not args.remove_input_counter,
+ "drift_veto": args.include_drift_veto_counter,
+ "scaledown": args.include_scaledown_counter,
+ "pulser": args.include_pulser_counter,
+ "output": not args.remove_output_counter,
+}
+
+df = pl.read_csv(args.infile, comment_prefix="#").filter(
+ pl.col("trg_time").is_between(args.t_min, args.t_max)
+)
+# All histograms should have the same binning
+_, t_edges = np.histogram(df["trg_time"], bins=args.t_bins)
+
+for name, included in columns.items():
+ if not included:
+ continue
+ """
+ We only know the time of the output counters. For all the other ones we just
+ know by how much they were incremented. The best we can do is assume that
+ those counts are evenly spread out over the time interval.
+ """
+ times = np.array(
+ df.filter(pl.col(name).is_not_null())
+ .rename({"trg_time": "t_right"})
+ .with_columns(
+ t_left=pl.col("t_right") - pl.col("t_right").diff(),
+ counts=pl.col(name).diff(),
+ )
+ .filter(pl.col("counts") > 0)
+ .select(
+ "t_left",
+ step=((pl.col("t_right") - pl.col("t_left")) / pl.col("counts")),
+ i=pl.int_ranges(1, pl.col("counts") + 1),
+ )
+ .explode("i")
+ .select(times=pl.col("t_left") + pl.col("step") * pl.col("i"))
+ )
+ if df[name][0] > 0:
+ times = np.append(df["trg_time"][0], times)
+
+ plt.hist(
+ times, bins=t_edges, histtype="step", label=f"{name} ({times.size} counts)"
+ )
+
+plt.xlabel("TRG time [s]")
+plt.ylabel("Counts")
+
+handles, labels = plt.gca().get_legend_handles_labels()
+new_handles = [Line2D([], [], c=h.get_edgecolor()) for h in handles]
+plt.legend(handles=new_handles, labels=labels)
+
+plt.show()