command line args and option to normalize y

.gitignore

@@ -1 +1,2 @@
 *~
+*.log

infocalc.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+## Axiomatic Design Information Calculator (and plotter)
+## Author: Joseph Timothy foley <foley AT RU.IS>
+## Start Date:  2026-02-27
+## Input:  data in csv file
+## Output:  information calculation and PDF for report/presentation
+import os
+import logging
+import argparse
+from pathlib import PurePath##https://docs.python.org/3/library/pathlib.html#module-pathlib
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import norm
+import pandas as pd
+
+#Main program loop
+print("""Axiomatic Design Information Calculator by Joseph. T. Foley<foley AT ru DOT is>
+From https://gitea.cs.ru.is/AxiomaticDesign/adcalc/""")
+parser = argparse.ArgumentParser(
+    description="Axiomatic Design Information Calculator.")
+parser.add_argument('csvfile',
+                    help="CSV file with data and headers")
+parser.add_argument('column',
+                    help='Which column header to take data from')
+parser.add_argument('minvalue', type=float,
+                    help='Tolerance low limit')
+parser.add_argument('maxvalue', type=float,
+                    help='Tolerance high limit')
+parser.add_argument('--normalizey', action="store_true",
+                    help='Set y-axis to normalized probability density')
+parser.add_argument('--log', default="INFO",
+                    help='Console log level:  Number or DEBUG, INFO, WARNING, ERROR')
+parser.add_argument('--graphinfo',
+                    help='Put information on the PDF graph')
+args = parser.parse_args()
+
+## Set up logging
+numeric_level = getattr(logging, args.log.upper(), None)
+if not isinstance(numeric_level, int):
+    raise ValueError(f'Invalid log level: {args.log}')
+#print(f"Log level:  {numeric_level}")
+logger = logging.getLogger("app")
+logger.setLevel(numeric_level)
+# log everything to file
+logpath = os.path.splitext(args.csvfile)[0]+".log"
+fh = logging.FileHandler(logpath)
+fh.setLevel(logging.DEBUG)
+# log to console
+ch = logging.StreamHandler()
+ch.setLevel(numeric_level)
+# create formatter and add to handlers
+consoleformatter = logging.Formatter('%(message)s')
+ch.setFormatter(consoleformatter)
+spamformatter = logging.Formatter('%(asctime)s %(name)s[%(levelname)s] %(message)s')
+fh.setFormatter(spamformatter)
+# add the handlers to logger
+logger.addHandler(ch)
+logger.addHandler(fh)
+
+logger.info("Creating infocalc log file %s", logpath)
+
+# filename pre-processing for output
+inpath = PurePath(args.csvfile)
+print(f"Input: {inpath}")
+
+# grab the data and process
+data = np.array(pd.read_csv(inpath)[args.column])
+lowerbound = args.minvalue
+upperbound = args.maxvalue
+logger.debug(f"data:{data}, lower:{lowerbound}, upper:{upperbound}")
+
+mean = data.mean()
+stddev = data.std(ddof=1)
+# Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
+prob = norm.cdf(upperbound, mean, stddev) - norm.cdf(lowerbound, mean, stddev)
+#print("probability: %f", prob)
+info = -np.emath.log2(prob)
+#print("information content: %f bits", info)
+## place text on plot:  https://matplotlib.org/3.3.4/gallery/recipes/placing_text_boxes.html
+fig, ax = plt.subplots()
+textstr = '\n'.join((
+    r'$n=%d$' % (len(data)),
+    r'$\mu=%.2f$' % (mean, ),
+    r'$\sigma=%.2f$' % (stddev, ),
+    r'$P=%.2f$' % (prob, ),
+    r'$I=%.2f$ bits' % (info, )))
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+
+# place a text box in upper left in axes coords
+ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+
+x = np.linspace(mean-3*stddev, mean+3*stddev, 500)
+y = norm.pdf(x, loc=mean, scale=stddev)
+if args.normalizey:
+    y = y * stddev#rescale back to unity area
+plt.axvline(x=mean, color="green", linestyle="dashed", label="mean")
+plt.axvline(lowerbound, color="red")
+plt.axvline(upperbound, color="red")
+
+plt.plot(x, y, 'b-', label='Normal distribution')
+#yt = scipy.stats.t.pdf(x, len(data)-1, mean, stddev)
+#plt.plot(x, yt, 'g-', label='T Distribution')
+coloredregion = (x >= lowerbound) & ( x <= upperbound ) #select x values
+plt.fill_between(x, 0, y, where=coloredregion, color="grey", alpha=0.5, label="Design range")
+plt.xlabel('X')
+plt.ylabel('Probability density')
+plt.legend()
+plt.grid(True)
+
+top = plt.ylim()[1]
+plt.show()

testdata.csv

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+data1,data2,
+1.0,1.1,
+1.1,1.2,
+0.9,1.3,
+1.0,1.4,
+1.0,1.5,
+0.9,1.6,
+0.9,1.7,