Single limit/tolerance option now working

infocalc.py

@@ -9,6 +9,7 @@ import logging
 import argparse
 from pathlib import PurePath##https://docs.python.org/3/library/pathlib.html#module-pathlib
 import numpy as np
+import matplotlib
 import matplotlib.pyplot as plt
 from scipy.stats import norm
 import pandas as pd
@@ -36,16 +37,24 @@ parser_sim.add_argument('mean', type=float,
 parser_sim.add_argument('stddev', type=float,
                     help="sample standard deviation")
 ## General Arguments
-parser.add_argument('minvalue', type=float,
+parser.add_argument('--lowerbound', type=float,
                     help='Tolerance low limit')
-parser.add_argument('maxvalue', type=float,
+parser.add_argument('--upperbound', 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('--legend', action="store_true",
+                    help='Put legend on the PDF graph')
 parser.add_argument('--graphinfo', action="store_true",
                     help='Put information on the PDF graph')
+parser.add_argument('--xlabel',
+                    help='X-axis label, if needed')
+parser.add_argument('--outfile',
+                    help="output graph to PDF file")
+parser.add_argument('--fontsize', default=14, type=int,
+                    help="Adjust font size")
 
 args = parser.parse_args()
 
@@ -72,10 +81,7 @@ fh.setFormatter(spamformatter)
 logger.addHandler(ch)
 logger.addHandler(fh)
 
-logger.info("Creating infocalc log file %s", logpath)
-
-lowerbound = args.minvalue
-upperbound = args.maxvalue
+logger.debug("Creating infocalc log file %s", logpath)
 
 # seed values for variable scoping
 mean = 0
@@ -96,14 +102,26 @@ elif args.mode == "SIM":
     stddev = args.stddev
     samplesize = args.samplesize
 
+# time to deal with the bounds
+    
 # 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)
+prob = 0
+if args.upperbound and args.lowerbound:
+    prob = norm.cdf(args.upperbound, mean, stddev) - norm.cdf(args.lowerbound, mean, stddev)
+elif args.upperbound:
+    prob = norm.cdf(args.upperbound, mean, stddev)
+elif args.lowerbound:
+    prob = 1 - norm.cdf(args.lowerbound, mean, stddev)
+else:
+    prob = 1# no bounds set!
+    ##TODO!!!!
 #print("probability: %f", prob)
 info = -np.emath.log2(prob)
 #print("information content: %f bits", info)
+## set default fontsize
+matplotlib.rcParams['font.size']=args.fontsize
 ## place text on plot:  https://matplotlib.org/3.3.4/gallery/recipes/placing_text_boxes.html
 fig, ax = plt.subplots()
-
 if args.graphinfo:#put info on corner of graph
     textstr = '\n'.join((
         r'$n=%d$' % (samplesize),
@@ -115,26 +133,49 @@ if args.graphinfo:#put info on corner of graph
     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,
+    ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=args.fontsize,
             verticalalignment='top', bbox=props)
+xgraphlimits = {"min": mean-3*stddev, "max": mean+3*stddev}
+if args.lowerbound and xgraphlimits["min"] > args.lowerbound:
+    xgraphlimits["min"] = args.lowerbound
+if args.upperbound and xgraphlimits["max"] < args.upperbound:
+    xgraphlimits["max"] = args.upperbound
 
-x = np.linspace(mean-3*stddev, mean+3*stddev, 500)
+x = np.linspace(xgraphlimits["min"], xgraphlimits["max"], 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")
+if args.lowerbound:
+    plt.axvline(args.lowerbound, color="red")
+if args.upperbound:
+    plt.axvline(args.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)
 
+# Filter for which region to fill
+coloredregion = x#default fill all
+if args.lowerbound and args.upperbound:
+    coloredregion = (x >= args.lowerbound) & ( x <= args.upperbound )
+elif args.upperbound:
+    coloredregion =  x <= args.upperbound 
+elif args.lowerbound:
+    coloredregion = x >= args.lowerbound
+    
+plt.fill_between(x, 0, y, where=coloredregion, color="grey", alpha=0.5, label="Design range",)
+    
+if args.xlabel:
+    plt.xlabel(args.xlabel)
+plt.ylabel('Probability density')
+if args.legend:
+    plt.legend()
+#plt.grid(True)
 top = plt.ylim()[1]
+
+if args.outfile:
+    logger.info(f"Graph output to {args.outfile}")
+    plt.savefig(args.outfile,bbox_inches='tight')
+else:
     plt.show()

tests.sh

@@ -1,4 +1,5 @@
 #!/bin/bash
+# Get infocalc.py from https://gitea.cs.ru.is/AxiomaticDesign/adcalc
 echo "Loading data from file"
 ./infocalc.py DATA testdata.csv data1 0.9 1.1 --graphinfo
 echo "Creating simulated curve from parameters"