updated readme with infocalc info

.gitignore

@@ -1,2 +1,5 @@
 *~
 *.log
+flycheck_*.py
+\#*#
+test.pdf

README.md

@@ -1,3 +1,5 @@
 # ad-calc
 
 Tools to help calculating values for Axiomatic Design analysis
+
+`infocalc.py` calculates information content based upon a csv file or statistical parameters and upper/lower limits

infocalc.py

@@ -9,8 +9,10 @@ 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
+from scipy.stats import norm,t
+import scipy.stats
 import pandas as pd
 
 #Main program loop
@@ -36,16 +38,24 @@ 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 +82,7 @@ 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
@@ -90,20 +97,30 @@ if args.mode == "DATA":
     data = np.array(pd.read_csv(inpath)[args.column])
     mean = data.mean()
     stddev = data.std(ddof=1)
+    # Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
     samplesize = len(data)
 elif args.mode == "SIM":
     mean = args.mean
     stddev = args.stddev
     samplesize = args.samplesize
+df = samplesize - 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)
+prob = 0
+if args.upperbound and args.lowerbound:
+    prob = t.cdf(df,args.upperbound, mean, stddev) - t.cdf(df,args.lowerbound, mean, stddev)
+elif args.upperbound:
+    prob = t.cdf(df,args.upperbound, mean, stddev)
+elif args.lowerbound:
+    prob = 1 - t.cdf(df,args.lowerbound, mean, stddev)
+else:
+    prob = 1# no bounds set!
 #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 +132,49 @@ 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]
-plt.show()
+
+if args.outfile:
+    logger.info(f"Graph output to {args.outfile}")
+    plt.savefig(args.outfile,bbox_inches='tight')
+else:
+    plt.show()

normdist.py

@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.stats import norm
-
-## Data goes here for now --foley
-data = np.array([1, 1.1, 0.9, 1, 1, 0.9, 0.9])
-lowerbound = 0.9
-upperbound = 1.0
-
-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) * 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()
-# annotate values on X after drawing the graphs
-
-
-
-

pandas-test.py

@@ -1,23 +0,0 @@
-#!/usr/bin/env python
-import pandas as pd
-
-df = pd.DataFrame(
-    {
-        "Name": [
-            "Braund, Mr. Owen Harris",
-            "Allen, Mr. William Hentry",
-            "Bonnell, Miss. Elizabeth",
-            ],
-        "Age": [22, 35, 58],
-        "Sex": ["male", "male", "female"],
-    }       
-)
-
-print(df.describe())
-
-titanic = pd.read_csv("titanic.csv")
-print(titanic.head(8))
-titanic.to_excel("titanic.xlsx", sheet_name="passengers", index=False)
-titanic_xltest = pd.read_excel("titanic.xlsx", sheet_name="passengers")
-print("INFO")
-print(titanic.info())

tests.sh

@@ -1,5 +1,6 @@
 #!/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
+./infocalc.py --lowerbound 0.9 --upperbound 1.1 --graphinfo DATA testdata.csv data1
 echo "Creating simulated curve from parameters"
-./infocalc.py SIM 8 1.0 0.5 0.9 1.1
+./infocalc.py --lowerbound 0.9 --upperbound 1.1 SIM 8 1.0 0.5