def TafelSlopeVPerDec(rawd, interd, var='I(A)', vk='Ewe(V)_E0', boolDevFrac=0.5, boolDevNOut=3,
dyDevFrac=0.2, dyDevNOut=5, dyDevAbs = 0.,
dx=1., maxFracOutliers=0.5, critSegVRange=0.04, critSegIEnd=3.e-5,
critSegVEnd=0.36, SavGolPts=10, inverty=False):
# initialize the arrays to hold Tafel values (considered both
# intermediate data and figures of merit)
interd['TafelSlope'] = []
interd['TafelEstart'] = []
interd['TafelFitErange'] = []
interd['TafelLogIex'] = []
booldn_segstart = 3 * boolDevNOut
dn_segstart = 3 * dyDevNOut
inter.calcsegind(rawd, interd, SGpts=SavGolPts) # breaks experiment into segments
inter.calccurvregions(rawd, interd, SGpts=SavGolPts, inverty=inverty) # runs on all segments
linsub = inter.calcLinSub(rawd, interd, var=var, inverty=inverty) # returns 1 if successful, 0 if not
if inverty:
yinv=-1.
else:
yinv=1.
if not linsub:
nanlist=[float('nan')]*len(interd['segprops_dlist'])
interd['TafelSlope'] = nanlist
interd['TafelEstart'] = nanlist
interd['TafelFitErange'] = nanlist
interd['TafelLogIex'] = nanlist
return float('nan')
inter.SegSG(rawd, interd, SGpts=SGpts, order=1, k=var+'_LinSub')
for seg in range(len(interd['segprops_dlist'])):
inds=interd['segprops_dlist'][seg]['inds']
i=interd['I(A)_LinSub_SG'][inds]*yinv
# if i.sum()==0.:#LinSub attempted on all segments, here each segment is tried until 1 works
# continue
if vk in rawd.keys():
v = rawd[vk]
else:
v = interd[vk]
v=v[inds]
posinds=numpy.where(i>zero_thresh)
invboolarr=numpy.float32(i<=zero_thresh)
istart_segs, len_segs, fitdy_segs, fitinterc_segs=inter.findzerosegs(
invboolarr, boolDevFrac, boolDevNOut, booldn_segstart, SGnpts=SavGolPts,
dx=dx, maxfracoutliers=maxFracOutliers)
if len(istart_segs)==0:
# no Tafel segments
interd['TafelSlope'].append(float('nan'))
interd['TafelEstart'].append(float('nan'))
interd['TafelFitVrange'].append(float('nan'))
interd['TafelLogIex'].append(float('nan'))
continue
ind=numpy.argmax(len_segs)
i0=istart_segs[ind]
i1=i0+len_segs[ind]
taffitinds=numpy.arange(i0, i1)
interd['segprops_dlist'][seg]['TafelFitInds']=inds[taffitinds]
i=i[i0:i1]
i[i<zero_thresh]=zero_thresh #needed due to outliers
v=v[i0:i1]
il=numpy.log10(i)
try:
istart_segs, len_segs, fitdy_segs, fitinterc_segs, dy=inter.findlinearsegs(
il, dyDevFrac, dyDevNOut, dn_segstart, dydev_abs=dyDevAbs, dx=dx, critdy_fracmaxdy=None)
except:
interd['TafelSlope'].append(float('nan'))
interd['TafelEstart'].append(float('nan'))
interd['TafelFitVrange'].append(float('nan'))
interd['Tafel_logExCurrent'].append(float('nan'))
continue
if len(istart_segs)==0:
# no Tafel segments
interd['TafelSlope'].append(float('nan'))
interd['TafelEstart'].append(float('nan'))
interd['TafelFitVrange'].append(float('nan'))
interd['TafelLogIex'].append(float('nan'))
continue
#only take those segments covering a certain V range and with a min current for the top 10th of the V range
# in the segment and positive slope for there on out and then take the steepest one.
ind=None
maxdy=0
npts=critSegVRange/dx
npts2=max(2, npts//10+1)
for count2, (it0, slen, dyv) in enumerate(zip(istart_segs, len_segs, fitdy_segs)):
if slen<npts:
continue
it1=it0+slen
if numpy.mean(i[it1-npts2:it1])<critSegIEnd:
continue
if numpy.mean(v[it1-npts2:it1])<critSegVEnd:
continue
if numpy.any(dy[it1:]<0.):
continue
if dyv>maxdy:
maxdy=dyv
ind=count2
if ind is None:
# no Tafel segments
interd['TafelSlope'].append(float('nan'))
interd['TafelEstart'].append(float('nan'))
interd['TafelFitVrange'].append(float('nan'))
interd['TafelLogIex'].append(float('nan'))
continue
i0=istart_segs[ind]
i1=i0+len_segs[ind]
tafinds=numpy.arange(i0, i1)
it=il[tafinds]
vt=v[tafinds]
fitdy, fitint=numpy.polyfit(vt, it, 1)
interd['TafelSlope'].append(1./fitdy)
interd['TafelEstart'].append(v[0])
interd['TafelFitVrange'].append(vt.max()-vt.min())
interd['TafelLogIex'].append(fitint)
interd['segprops_dlist'][seg]['TafelInds']=inds[taffitinds][tafinds]
#FOMs (the entire list):
fomarr=numpy.array(interd['TafelSlope']) #in interd there is a fom for each segment but save the first not NaN one as scalar FOM
goodinds=numpy.where(numpy.logical_not(numpy.isnan(fomarr)))[0]
if len(goodinds)==0:
return float('nan')
return fomarr[goodinds[0]]
def TafelLogIex(rawd, interd):
fomarr=numpy.array(interd['TafelLogIex'])
goodinds=numpy.where(numpy.logical_not(numpy.isnan(fomarr)))[0]
if len(goodinds)==0:
return float('nan')
return fomarr[goodinds[0]]