first potentially useful version

commands.py

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+from datetime import datetime
+import pandas as pd
+import epics
+
+from utils.df import drop_col, compare_dfs, count_true
+from utils.epics import DataGetter
+from utils.execute import parallel
+#from utils.execute import serial as parallel
+from utils.fileio import load_config, load_csv, store_csv
+from utils.printing import print_good, print_bad
+
+
+def run(clargs):
+    commands = {
+        "check": run_check,
+        "compare": run_compare
+    }
+    commands[clargs.command](clargs)
+
+
+def run_check(clargs):
+    filename = clargs.filename
+    chans = load_config(filename)
+    pvs = (epics.PV(ch) for ch in chans) # putting PV constructors into ThreadPoolExecutor has weird effects
+
+    get_data = DataGetter(clargs.timeout, clargs.silent)
+    data = parallel(get_data, pvs, chans)
+
+    df = pd.DataFrame(data).T
+    df = df.infer_objects() #TODO: why is this needed?
+#    print(df)
+#    print(df.dtypes)
+
+    connected = df["connected"]
+    if connected.all():
+        print_good("all connections OK")
+    else:
+        ntotal = len(connected)
+        ngood = count_true(connected)
+        print_bad(f"only {ngood}/{ntotal} connections OK")
+
+    output = clargs.output
+    if not output:
+        return
+
+    timestamp = datetime.now()
+    meta = f"{filename} / {timestamp}"
+    store_csv(df, output, meta)
+
+
+def run_compare(clargs):
+    fn1, fn2 = clargs.filenames
+    df1 = load_csv(fn1)
+    df2 = load_csv(fn2)
+
+    if clargs.ignore_values:
+        drop_col(df1, "value")
+        drop_col(df2, "value")
+
+    diff = compare_dfs(df1, df2)
+    if diff.empty:
+        print_good(f'"{fn1}" and "{fn2}" are identical')
+    else:
+        print_bad(f'"{fn1}" and "{fn2}" differ:')
+        print(diff)
+
+
+