SIM and DATA mode now

infocalc.py

@@ -18,10 +18,24 @@ print("""Axiomatic Design Information Calculator by Joseph. T. Foley<foley AT ru
 From https://gitea.cs.ru.is/AxiomaticDesign/adcalc/""")
 parser = argparse.ArgumentParser(
     description="Axiomatic Design Information Calculator.")
-parser.add_argument('csvfile',
+subparsers = parser.add_subparsers(dest='mode')
+subparsers.required = True
+
+### MODE DATA
+parser_data = subparsers.add_parser("DATA")
+parser_data.add_argument('csvfile',
                     help="CSV file with data and headers")
-parser.add_argument('column',
+parser_data.add_argument('column',
                     help='Which column header to take data from')
+## MODE SIM
+parser_sim = subparsers.add_parser("SIM")
+parser_sim.add_argument('samplesize', type=int,
+                    help="sample size")
+parser_sim.add_argument('mean', type=float,
+                    help="mean(average) value")
+parser_sim.add_argument('stddev', type=float,
+                    help="sample standard deviation")
+## General Arguments
 parser.add_argument('minvalue', type=float,
                     help='Tolerance low limit')
 parser.add_argument('maxvalue', type=float,
@@ -32,6 +46,7 @@ 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
@@ -42,7 +57,7 @@ if not isinstance(numeric_level, int):
 logger = logging.getLogger("app")
 logger.setLevel(numeric_level)
 # log everything to file
-logpath = os.path.splitext(args.csvfile)[0]+".log"
+logpath = "infocalc.log"
 fh = logging.FileHandler(logpath)
 fh.setLevel(logging.DEBUG)
 # log to console
@@ -59,18 +74,28 @@ logger.addHandler(fh)
 
 logger.info("Creating infocalc log file %s", logpath)
 
+lowerbound = args.minvalue
+upperbound = args.maxvalue
+
+# seed values for variable scoping
+mean = 0
+stddev = 1
+samplesize =1
+
+if args.mode == "DATA":
     # 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)
+    samplesize = len(data)
+elif args.mode == "SIM":
+    mean = args.mean
+    stddev = args.stddev
+    samplesize = args.samplesize
+
 # 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)
@@ -79,7 +104,7 @@ info = -np.emath.log2(prob)
 ## 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'$n=%d$' % (samplesize),
     r'$\mu=%.2f$' % (mean, ),
     r'$\sigma=%.2f$' % (stddev, ),
     r'$P=%.2f$' % (prob, ),