added a python file for parsing qucs data file

qucs/python/parse_result.py

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+import re
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def parse_file(name):
+
+    file = open(name)
+
+    # the dict this function returns
+    data = {}
+    numpoints = 1
+    ind = 0
+    shape = []
+    variables = {}
+    
+    for line in file:
+        #print line
+        if line.startswith('<'):
+            if line.startswith('<indep'):
+                #print line
+                r = re.match(r'\<(\w+) (\w+) (\d+)\>', line)
+                g = r.groups()
+                # there can be several independent variables -> numpoints keeps 
+                # the total number of points
+                numpoints = numpoints * int(g[2])
+                name = g[1]
+                # reserve an array for the values
+                data[name] = np.zeros(int(g[2]))
+                ind = 0
+                # save the simulation points in an array
+                shape = np.append(shape, int(g[2]))
+                # save that this variable is independent
+                variables[name] = 'indep'
+     
+            if line.startswith('<dep'):
+                #print line
+                r = re.match(r'\<dep (\S+)', line)
+                g = r.groups()
+                name = g[0]
+                # reserve a complex matrix to be on the safe side 
+                data[name] = np.zeros(int(numpoints), np.complex128)
+                ind = 0
+                # store that this is a dependent variable
+                variables[name] = 'dep'
+    
+        else:
+            jind = line.find('j')
+            
+            if(jind == -1):
+                # real number -> just parse it            
+                val = float(line)
+            else:
+                # complex number -> break into re/im part
+                val_re = line[0:jind-1]
+                sign = line[jind-1]
+                val_im = sign + line[jind+1:-1]
+                # and convert it into a complex number
+                val = complex(float(val_re), float(val_im))
+    
+            # store the extracted datapoint
+            data[name][ind] = val
+            ind = ind + 1
+    
+    
+    data['variables'] = variables
+    # reverse the shape variable in order to get the reshape operation (see below)
+    # correct
+    shape = shape[::-1]
+
+    # here comes the clever trick :-)
+    # if a dependent variable depends on N > 1 (independent) variables,
+    # we reshape the vector we have obtained so far into an N-dimensional
+    # matrix
+    for key in data['variables']:
+        temp = data['variables'][key]
+        if temp == 'dep':
+            temp_data = data[key]
+            data[key] = np.reshape(temp_data, shape)
+    
+    return data

qucs/python/parse_result_example.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+import parse_result as pr
+
+
+# create the dat file to load with:
+# qucsator < rc_ac_sweep.net > rc_ac_sweep.dat
+
+
+
+data = pr.parse_file('rc_ac_sweep.dat')
+
+x = data['acfrequency']
+y = np.abs(data['out.v'])
+c = data['Cx']
+
+plt.loglog(x,y[0,:],'-rx')
+plt.loglog(x,y[4,:],'-go')
+
+plt.legend(['Cx=' + str(c[0]), 'Cx=' + str(c[4])])
+
+plt.xlabel('acfrequency')
+plt.ylabel('abs(out.v)')
+plt.grid()
+plt.show()

qucs/python/rc_ac_sweep.net

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+# Qucs 0.0.16  /home/cnovak/src/qucs/rc_ac.sch
+# you can use this file from octave with rc_ac_octave.m...
+
+Vac:V1 in gnd U="1 V" f="1 kHz"
+R:R1 out in R="1 kOhm"
+C:C1 out gnd C="Cx"
+
+.SW:SW1 Sim="AC1" Type="lin" Param="Cx" Start="10 nF" Stop="100 nF" Points="5"
+
+.AC:AC1 Start="1 Hz" Stop="10 MHz" Points="100" Type="log" Noise="no"