options to control the legend and font

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
@@ -44,8 +45,16 @@ 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()
 
@@ -101,9 +110,10 @@ 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)
+## 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),
@@ -115,7 +125,7 @@ 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)
 
 x = np.linspace(mean-3*stddev, mean+3*stddev, 500)
@@ -130,11 +140,16 @@ 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.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)
-
+#plt.grid(True)
 top = plt.ylim()[1]
+
+if args.outfile:
+    plt.savefig(args.outfile,bbox_inches='tight')
+else:
     plt.show()