add argument parser and option to read from file

kisli.py

@@ -24,6 +24,7 @@ import socket
 import struct
 import math
 import time
+import argparse
 
 import numpy as np
 from matplotlib import pyplot
@@ -125,49 +126,69 @@ def done():
     instrSend(s, "beeper.beep(0.150, 523)")
     instrSend(s, "beeper.beep(1.000, 440)")
 
+def calc_vector(x, y, arr):
+    pass
+
 """ ==============================================================================================================
         MAIN CODE STARTS HERE
 ============================================================================================================== """
-ip_address = "192.168.0.53"     # Place your instrument's IP address here.
-my_port = 5025
-
-output_data_path = time.strftime("data_%Y-%m-%d_%H-%M-%S.csv")   # This is the output file that is created which
-                                                                        # will hold your readings provided in ASCII
-                                                                        # format in a text file.
-
 
-s = socket.socket()                 # Establish a TCP/IP socket object
+parser = argparse.ArgumentParser()
-# Open the socket connection
+parser.add_argument('-fromfile')
-instrConnect(s, ip_address, my_port, 20000, 0, 0)
+parser.add_argument('-tofile')
+parser.add_argument('-ip', default="192.168.0.53")
+parser.add_argument('-port', default=5025)
+args = parser.parse_args()
 
-t1 = time.time()                    # Start the timer...
+ip_address = args.ip
+port = args.port
 
-Configure_Backplane(s)
+print(ip_address)
 
-a = np.zeros(shape=(90, 90))
+# reserve space for matrixes
-b = np.zeros(shape=(9, 10))
+full_matrix = np.zeros(shape=(90, 90))
+point_specific_matrix = np.zeros(shape=(9, 10))
 
 pyplot.ion()
 fig, ax = pyplot.subplots()
+axim = ax.imshow(full_matrix, interpolation='nearest', cmap='gray', vmin=0, vmax=30)
 
-axim = ax.imshow(a, interpolation='nearest', cmap='gray', vmin=0, vmax=30)
+t1 = time.time()                    # Start the timer...
 
-x = 0
+if args.fromfile:
-y = 0
+    #TODO check bounds of imported matrix
-print()
+    full_matrix = np.genfromtxt(args.fromfile, delimiter='\t')
-for ch1 in  [*range(1001, 1031)] + [*range(2001, 2031)] + [*range(3001, 3031)]:
+    for row in full_matrix:
+        point_specific_matrix=get_mapped(row)
+        axim.set_data(point_specific_matrix)
+        fig.canvas.flush_events()
+else:
+    s = socket.socket()                 # Establish a TCP/IP socket object
+    instrConnect(s, ip_address, port, 20000, 0, 0)
+
+    Configure_Backplane(s)
+
+    x = 0
+    y = 0
+    print()
+    for ch1 in  [*range(1001, 1031)] + [*range(2001, 2031)] + [*range(3001, 3031)]:
         for ch2 in [*range(1031, 1061)] + [*range(2031, 2061)] + [*range(3031, 3061)]:
-        a[x][y]=diff_4W_mess(s, ch1, ch2)
+            full_matrix[x][y]=diff_4W_mess(s, ch1, ch2)
-        b=get_mapped(a[x])
+            point_specific_matrix=get_mapped(full_matrix[x])
-        axim.set_data(b)
+            axim.set_data(point_specific_matrix)
             fig.canvas.flush_events()
             y+=1
         y=0
         x+=1
 
-done();
+    done()
-# Close the socket connection
+
-instrDisconnect(s)
+    # Close the socket connection
+    instrDisconnect(s)
+
+if args.tofile:
+    np.savetxt(args.tofile, full_matrix, delimiter="\t")
+
 t2 = time.time()
 
 # Notify the user of completion and the data streaming rate achieved.