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Merge pull request #3 from ganglix/update-to-formal-format
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fig label unit change
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@ganglix
ganglix authored Aug 11, 2018
2 parents afe0dd7 + 2632e19 commit 17c2ef7
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3,372 changes: 3,372 additions & 0 deletions .ipynb_checkpoints/Test_drive-checkpoint.ipynb
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66 changes: 33 additions & 33 deletions Tafel_LPR_fit_notebook_IRcomp_IFilm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def BVeq(E,Eeq,i0,Ba,Bc):
i0: exchange current density
Ba: Tafel slope, anodic
Bc: Tafel slope, cathodic
NOTE: B-V equation has the same mathmatic form when used in single and mixed
NOTE: B-V equation has the same mathematic form when used in single and mixed
electrode process; however Eeq and i0 may have different terminology
"""
ia = i0*10**((E-Eeq)/Ba)
Expand Down Expand Up @@ -57,13 +57,13 @@ class Info:
filename:.xlsx or .csv
scantype: 'one_step' or 'two_step', default is 'one step'
"""
def __init__(self,filename,scantype='one_step',area=1,two_step_drift_offset=True, pd_dfIE = None,use_pd_df=False):
def __init__(self,filename,scantype='one_step',two_step_drift_offset=True, pd_dfIE = None,use_pd_df=False,area=1):
self.filename = filename
self.scantype = scantype
self.area = area

if use_pd_df:
df = pd_dfIE
df = pd_dfIE.copy()
df.columns = ['I','E']
df['i_density']=df['I']/self.area
df['i_density_abs'] = df.i_density.abs()
Expand Down Expand Up @@ -134,13 +134,13 @@ def __init__(self, info):

#output attributes can be added dynamically
self.Ecorr = None
self.icorr = None
self.Icorr = None
self.Ba = None
self.Bc = None

self.B = None
self.Rp = None
self.icorr_LPR = None
self.Icorr_LPR = None
# initilize figure
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1, 1, 1)
Expand Down Expand Up @@ -192,24 +192,24 @@ def BV_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=T
E_select_c = E_select[I_select<0]
Bc_scan,intercept_c = np.polyfit(np.log10(I_select_c)[0:taf_init], E_select_c[0:taf_init], 1) # quick fit slope

icorr = 10**(-(intercept_a - intercept_c)*1.0/(Ba_scan-Bc_scan))
Icorr = 10**(-(intercept_a - intercept_c)*1.0/(Ba_scan-Bc_scan))

############## end of guess parameters from scan###############

# bound based on guess
bound = ([OCP-0.001,icorr*0.01,Ba_scan-0.20,Bc_scan-0.20],
[OCP+0.001,icorr*100.0,Ba_scan+0.20,Bc_scan+0.20])
bound = ([OCP-0.001,Icorr*0.01,Ba_scan-0.20,Bc_scan-0.20],
[OCP+0.001,Icorr*100.0,Ba_scan+0.20,Bc_scan+0.20])

p_guess = [OCP,icorr,Ba_scan,Bc_scan]
p_guess = [OCP,Icorr,Ba_scan,Bc_scan]

popt, pcov = curve_fit(BVeq,E_select, I_select, p_guess, bounds=bound) # popt is optimal parameter array
############## end of Fitting parameters from scan#############

# out put
taf_series = pd.Series(popt ,index=['Ecorr','icorr','Ba','Bc'])
taf_series = pd.Series(popt ,index=['Ecorr','Icorr','Ba','Bc'])

self.Ecorr = taf_series.Ecorr
self.icorr = taf_series.icorr
self.Icorr = taf_series.Icorr
self.Ba = taf_series.Ba
self.Bc = taf_series.Bc
self.B = self.Ba*abs(self.Bc)/(2.303*(self.Ba+abs(self.Bc)))
Expand All @@ -222,26 +222,26 @@ def BV_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=T
self.line_data_sel.set_data(np.abs(I_select),E_select) #selceted data
self.line__data_dis1.set_data(np.abs(I[0:start]),E[0:start]) #disgarded data
self.line__data_dis2.set_data(np.abs(I[stop:-1]),E[stop:-1]) #disgarded data
self.line_guess.set_data(np.abs(BVeq(_,OCP,icorr,Ba_scan,Bc_scan)),_) #Initial Guess
self.line_guess.set_data(np.abs(BVeq(_,OCP,Icorr,Ba_scan,Bc_scan)),_) #Initial Guess
self.line_bv.set_data(np.abs(BVeq(_,*popt)),_)#Fitted from observation
self.ax.semilogx()

self.line_tan1.set_data(self.icorr*10**((_-self.Ecorr)/self.Ba),_) # tangent line
self.line_tan2.set_data(self.icorr*10**((_-self.Ecorr)/self.Bc),_)
self.line_tan1.set_data(self.Icorr*10**((_-self.Ecorr)/self.Ba),_) # tangent line
self.line_tan2.set_data(self.Icorr*10**((_-self.Ecorr)/self.Bc),_)

else:
self.line_data_sel.set_data((I_select),E_select) #selceted data
self.line__data_dis1.set_data((I[0:start]),E[0:start]) #disgarded data
self.line__data_dis2.set_data((I[stop:-1]),E[stop:-1]) #disgarded data
self.line_guess.set_data((BVeq(_,OCP,icorr,Ba_scan,Bc_scan)),_) #Initial Guess
self.line_guess.set_data((BVeq(_,OCP,Icorr,Ba_scan,Bc_scan)),_) #Initial Guess
self.line_bv.set_data((BVeq(_,*popt)),_)#Fitted from observation
self.ax.set_xscale('linear')

self.line_tan1.set_data([],[])
self.line_tan2.set_data([],[])


plt.xlabel('Averaged logi [$A/m^2$]')
plt.xlabel('I_select [A]')
plt.ylabel('E_select [V]')
plt.title(str(fname))
plt.legend(loc='best')
Expand All @@ -267,7 +267,7 @@ def BV_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=T
)

print '\r'
print 'Unit:V, A/m^2\n',taf_series
print 'Unit:V, A\n',taf_series
print 'guess', p_guess
print 'bound', bound

Expand All @@ -280,8 +280,8 @@ def BV_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=T
Rp1,_ = np.polyfit(df_LPR1.I,df_LPR1.E, 1) # quick fit slope
Rp2,_ = np.polyfit(df_LPR2.I,df_LPR2.E, 1) # quick fit slope
self.Rp = np.mean([Rp1, Rp2])
self.icorr_LPR = self.B/self.Rp/self.area
LPR_series = pd.Series([self.B, self.Rp, self.icorr_LPR] ,index=['B','Rp','icorr_LPR'])
self.Icorr_LPR = self.B/self.Rp/self.area
LPR_series = pd.Series([self.B, self.Rp, self.Icorr_LPR] ,index=['B','Rp','Icorr_LPR'])

if False:
fig1,ax1 = plt.subplots(figsize=(8,6))
Expand All @@ -292,7 +292,7 @@ def BV_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=T
ax1.set_ylabel('E(V)')
plt.show()

print 'Unit:V, Ohm, A/m^2'
print 'Unit:V, Ohm, A'
print LPR_series
self.result = taf_series.append(LPR_series)
return self
Expand Down Expand Up @@ -361,7 +361,7 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=
E_select_c = E_select[I_select<0]
Bc_scan,intercept_c = np.polyfit(np.log10(I_select_c)[0:taf_init], E_select_c[0:taf_init], 1) # quick fit slope

icorr = 10**(-(intercept_a - intercept_c)*1.0/(Ba_scan-Bc_scan))
Icorr = 10**(-(intercept_a - intercept_c)*1.0/(Ba_scan-Bc_scan))

Va_guess = 1000.0 # Va>0
Vc_guess = -1000.0 # Vc<0
Expand All @@ -370,19 +370,19 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=

# bound based on guess

bound = ([OCP-0.001,icorr*0.01,Ba_scan-0.20,Bc_scan-0.20,0.0001,Vc_guess],
[OCP+0.001,icorr*100.0,Ba_scan+0.20,Bc_scan+0.20,Va_guess,-0.0001])
bound = ([OCP-0.001,Icorr*0.01,Ba_scan-0.20,Bc_scan-0.20,0.0001,Vc_guess],
[OCP+0.001,Icorr*100.0,Ba_scan+0.20,Bc_scan+0.20,Va_guess,-0.0001])

p_guess = [OCP,icorr,Ba_scan,Bc_scan,Va_guess,Vc_guess]
p_guess = [OCP,Icorr,Ba_scan,Bc_scan,Va_guess,Vc_guess]

popt, pcov = curve_fit(BVFeq,E_select, I_select, p_guess, bounds=bound) # popt is optimal parameter array
############## end of Fitting parameters from scan#############

# out put
taf_series = pd.Series(popt ,index=['Ecorr','icorr','Ba','Bc','Va','Vc'])
taf_series = pd.Series(popt ,index=['Ecorr','Icorr','Ba','Bc','Va','Vc'])

self.Ecorr = taf_series.Ecorr
self.icorr = taf_series.icorr
self.Icorr = taf_series.Icorr
self.Ba = taf_series.Ba
self.Bc = taf_series.Bc
self.Va = taf_series.Va
Expand All @@ -404,8 +404,8 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=
self.line_bvf.set_data(np.abs(BVFeq(_,*popt)),_)#BVF Fitted from observation
self.ax.semilogx()

# self.line_tan1.set_data(self.icorr*10**((_-self.Ecorr)/self.Ba),_)
# self.line_tan2.set_data(self.icorr*10**((_-self.Ecorr)/self.Bc),_)
# self.line_tan1.set_data(self.Icorr*10**((_-self.Ecorr)/self.Ba),_)
# self.line_tan2.set_data(self.Icorr*10**((_-self.Ecorr)/self.Bc),_)

else:
self.line_data_sel.set_data((I_select),E_select) #selceted data
Expand All @@ -420,7 +420,7 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=
# self.line_tan2.set_data([],[])


plt.xlabel('Averaged logi [$A/m^2$]')
plt.xlabel('I_select [A]')
plt.ylabel('E_select [V]')
plt.title(str(fname))
plt.legend(loc='best')
Expand All @@ -446,7 +446,7 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=
)

print '\r'
print 'Unit:V, A/m^2\n',taf_series
print 'Unit:V, A\n',taf_series
print 'guess', p_guess
print 'bound', bound

Expand All @@ -458,8 +458,8 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=
Rp1,_ = np.polyfit(df_LPR1.I,df_LPR1.E, 1) # quick fit slope
Rp2,_ = np.polyfit(df_LPR2.I,df_LPR2.E, 1) # quick fit slope
self.Rp = np.mean([Rp1, Rp2])
self.icorr_LPR = self.B/self.Rp/self.area
LPR_series = pd.Series([self.B, self.Rp, self.icorr_LPR] ,index=['B','Rp','icorr_LPR'])
self.Icorr_LPR = self.B/self.Rp/self.area
LPR_series = pd.Series([self.B, self.Rp, self.Icorr_LPR] ,index=['B','Rp','Icorr_LPR'])

if False:
fig1,ax1 = plt.subplots(figsize=(8,6))
Expand All @@ -470,7 +470,7 @@ def BVF_LPR_manual(self,data_range,df_IE,fname = '',taf_init=200,R=0, auto_zoom=
ax1.set_ylabel('E(V)')
plt.show()

print 'Unit:V, Ohm, A/m^2'
print 'Unit:V, Ohm, A'
print LPR_series
self.result = taf_series.append(LPR_series)
return self
Expand Down
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