Feature/import

PyEIS/PyEIS.py

@@ -9,27 +9,23 @@
 from __future__ import division
 import pandas as pd
 import numpy as np
+import mpmath as mp
+import matplotlib as mpl
+import seaborn as sns
 from scipy.constants import codata
 from pylab import *
-from scipy.optimize import curve_fit
-import mpmath as mp
-from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit
+from lmfit import minimize, report_fit
+
 #from scipy.optimize import leastsq
 pd.options.mode.chained_assignment = None
 
 #Plotting
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-from matplotlib.ticker import LinearLocator, FormatStrFormatter
-import seaborn as sns
-import matplotlib.ticker as mtick
 mpl.rc('mathtext', fontset='stixsans', default='regular')
 mpl.rcParams.update({'axes.labelsize':22})
 mpl.rc('xtick', labelsize=16) 
 mpl.rc('ytick', labelsize=16)
 mpl.rc('legend',fontsize=14)
 
-from scipy.constants import codata
 F = codata.physical_constants['Faraday constant'][0]
 Rg = codata.physical_constants['molar gas constant'][0]
 
@@ -57,7 +53,7 @@ def freq_gen(f_start, f_stop, pts_decade=7):
     [1] = Angular frequency range [1/s]
     '''
     f_decades = np.log10(f_start) - np.log10(f_stop)
-    f_range = np.logspace(np.log10(f_start), np.log10(f_stop), num=np.around(pts_decade*f_decades), endpoint=True)
+    f_range = np.logspace(np.log10(f_start), np.log10(f_stop), num=np.around(pts_decade*f_decades).astype(int), endpoint=True)
     w_range = 2 * np.pi * f_range
     return f_range, w_range
 
@@ -2230,22 +2226,22 @@ class EIS_exp:
     def __init__(self, path, data, cycle='off', mask=['none','none']):
         self.df_raw0 = []
         self.cycleno = []
-        for j in range(len(data)):
-            if data[j].find(".mpt") != -1: #file is a .mpt file
-                self.df_raw0.append(extract_mpt(path=path, EIS_name=data[j])) #reads all datafiles
-            elif data[j].find(".DTA") != -1: #file is a .dta file
-                self.df_raw0.append(extract_dta(path=path, EIS_name=data[j])) #reads all datafiles
-            elif data[j].find(".z") != -1: #file is a .z file
-                self.df_raw0.append(extract_solar(path=path, EIS_name=data[j])) #reads all datafiles
+        for j, f in enumerate(data, start=0):
+            if f.endswith('mpt'): #file is a .mpt file
+                self.df_raw0.append(extract_mpt(path=path, EIS_name=f)) #reads all datafiles
+            elif f.endswith('DTA'): #file is a .dta file
+                self.df_raw0.append(extract_dta(path=path, EIS_name=f)) #reads all datafiles
+            elif f.endswith('z'): #file is a .z file
+                self.df_raw0.append(extract_solar(path=path, EIS_name=f)) #reads all datafiles
+            elif f.endswith('txt'):
+                self.df_raw0.append(extract_csv(path=path,  EIS_name=f))
             else:
                 print('Data file(s) could not be identified')
-
             self.cycleno.append(self.df_raw0[j].cycle_number)
             if np.min(self.cycleno[j]) <= np.max(self.cycleno[j-1]):
                 if j > 0: #corrects cycle_number except for the first data file
                     self.df_raw0[j].update({'cycle_number': self.cycleno[j]+np.max(self.cycleno[j-1])}) #corrects cycle number
-#            else:
-#                print('__init__ Error (#1)')
+
 
         #currently need to append a cycle_number coloumn to gamry files
 
@@ -4024,7 +4020,7 @@ def EIS_fit(self, params, circuit, weight_func='modulus', nan_policy='raise'):
         self.circuit_fit = []
         self.fit_E = []
         for i in range(len(self.df)):
-            self.Fit.append(minimize(leastsq_errorfunc, params, method='leastsq', args=(self.df[i].w.values, self.df[i].re.values, self.df[i].im.values, circuit, weight_func), nan_policy=nan_policy, maxfev=9999990))
+            self.Fit.append(minimize(leastsq_errorfunc, params, method='leastsq', args=(self.df[i].w.values, self.df[i].re.values, self.df[i].im.values, circuit, weight_func), nan_policy=nan_policy, max_nfev=9999990))
             print(report_fit(self.Fit[i]))
 
             self.fit_E.append(np.average(self.df[i].E_avg))
@@ -4698,7 +4694,7 @@ def EIS_plot(self, bode='off', fitting='off', rr='off', nyq_xlim='none', nyq_yli
         for i in range(len(self.df)):
             ax.plot(self.df[i].re, self.df[i].im, marker='o', ms=4, lw=2, color=colors[i], ls='-', label=self.label_cycleno[i])
             if fitting == 'on':
-                ax.plot(self.circuit_fit[i].real, -self.circuit_fit[i].imag, lw=0, marker='o', ms=8, mec='r', mew=1, mfc='none', label='')
+                ax.plot([i.real for i in self.circuit_fit[i]], [-i.imag for i in self.circuit_fit[i]], lw=0, marker='o', ms=8, mec='r', mew=1, mfc='none', label='')
 
         ### Bode Plot
         if bode=='on':
@@ -6023,4 +6019,4 @@ def EIS_sim_fit(self, params, circuit, weight_func='modulus', nan_policy='raise'
 
 #print()
 #print('---> PyEIS Core Loaded (v. 0.5.7 - 02/01/19)')
-#print()
+#print()

PyEIS/PyEIS_Data_extraction.py

@@ -13,7 +13,6 @@
 from __future__ import division
 import pandas as pd
 import numpy as np
-from scipy.constants import codata
 
 #### Extracting .mpt files with PEIS or GEIS data
 def correct_text_EIS(text_header):
@@ -142,6 +141,23 @@ def extract_solar(path, EIS_name):
     data = data.assign(cycle_number = 1.0)
     return data
 
-#
-#print()
-#print('---> Data Extraction Script Loaded (v. 0.0.2 - 06/27/18)')
+
+def extract_csv(path,  EIS_name,  sep='\t'):
+    '''
+    Extract data from simple csv style file. 
+
+    The standard seperator is TAB and one row with column names. We want the columns 
+    to named in a unified way: f, re, im, Z_mag, Y_phase.
+    '''
+
+    raw = pd.read_csv(path+EIS_name,  sep='\t')
+    data = raw.rename(columns={'Frequency':  'f', 
+                                            'Z_re':  're', 
+                                            'Z_img':  'im', 
+                                            'Z_E': 'Z_mag', 
+                                            'Phase_E': 'Y_phase'})
+
+    data = data.assign(cycle_number = 1.0)
+
+    return data
+

Tutorials/data/ex3.txt

@@ -0,0 +1,21 @@
+Frequency	Z_E	Phase_E	Z_re	Z_img
+9.99999974737875E-5	100583.6953125	-0.279304325580597	27.0720300383251	7.764292820412
+2.64000002061948E-4	94564.09375	-0.30045759677887	25.2917649727993	7.83634239799212
+6.94999995175749E-4	74765.84375	-0.277011215686798	20.1363550823005	5.72519214566868
+0.00182999996468425	60787.3515625	-0.291797459125519	16.3009754118168	4.89634619343949
+0.00482999999076128	50841.2578125	-0.292887330055237	13.6293204344493	4.11005724755008
+0.012699999846518	41034.671875	-0.373376429080963	10.6980794680164	4.19100089402257
+0.0335999988019466	32260.626953125	-0.488816767930985	7.97511565518254	4.24171869846458
+0.0886000022292137	23141.171875	-0.673698782920837	5.06387704288688	4.04245395981071
+0.233999997377396	14394.5966796875	-0.830590903759003	2.71832007079357	2.97582963502232
+0.61599999666214	8027.01171875	-0.968166828155518	1.27394300153653	1.85165059664597
+1.62000000476837	4135.037109375	-1.06667029857636	0.559271484191949	1.01377527470784
+4.28000020980835	2047.56225585938	-1.12656533718109	0.246390984067987	0.517672058733144
+11.3000001907349	1010.5595703125	-1.14149475097656	0.117776716105174	0.257280251327722
+29.7999992370605	493.506958007813	-1.1841938495636	0.0521006506280274	0.127983487310445
+78.5544738769531	229.480880737305	-1.19812071323395	0.0233956747070571	0.0598439806122616
+207.120727539063	104.836708068848	-1.12022435665131	0.012783215532553	0.0264246675054082
+546.105041503906	52.2368049621582	-0.889707863330841	0.00920929245798327	0.0113629988017006
+1439.88830566406	33.1943054199219	-0.550724923610687	0.00792018481248082	0.00486380987101006
+3796.48291015625	27.5287494659424	-0.273702025413513	0.00742113193798794	0.0020834666438922
+10010	25.8860626220703	-0.116779141128063	0.00719873118873418	8.44504077744677E-4