def get_bestd(wb, peak_idx, initial, Dguess=-12):
"""Returns best-fit initial and isotropic D """
x, y = wb.xy_picker(peak_idx=peak_idx, wholeblock=False, heights_instead=False)
L3 = []
D3 = []
for k in range(3):
L3.append(wb.profiles[k].len_microns)
D3.append(Dguess)
# best fit initial and corresponding D
params = diffusion.params_setup3D(L3, D3, wb.time_seconds, initial=initial,
isotropic=True, vinit=False)
minimize(func2min, params, args=(x, y), kws={'raypaths' : wb.raypaths, 'show_plot' : False})
bestD = params['log10Dx'].value
return bestD
def plotpeaks(wb_list, wbs_list, ilist, dlist, elist, dlist_slow=None,
slowb=[False]*6, legidx=13, sidelabels=True, ncol=5,
wholeblock=False, peak_idx_list = [0, 1, 2, 4, 5],
show_legend=True, dlabel='Isotropic\nDiffusion curve',
xtickgrid=[0.2]*6, ytickgrid=[5.]*6):
"""Takes wb = list of main whole block being plotted,
wbs = list of lists of whole block data being plotted in each panel,
ilist = list of initials, dlist = list of diffusivities,
elist = list of errors"""
#### legend ####
if show_legend is True:
styles = []
for wbs in wbs_list:
for wbtoplot in wbs:
styleToAdd = styledict[wbtoplot]
if styleToAdd not in styles:
styles.append(styleToAdd)
for sty in styles:
axes[legidx].plot(-100, -100, **sty)
axes[legidx].plot(-100, -100, '-k', label='"initial"', linewidth=1)
axes[legidx].plot(-100, -100, '-g', label=dlabel,
linewidth=3)
axes[legidx].plot(-100, -100, '--g', label='+/- error log10 D')
axes[legidx].legend(fancybox='on', loc=8) # 9 for top
axesranges = [range(3), range(3,6), range(6,9), range(9,12), range(12,15)]
### y axis limits and "initial"
for r in range(ncol):
for ax_idx in axesranges[r]:
ax = axes[ax_idx]
ax.plot(ax.get_xlim(), [ilist[r], ilist[r]], '-k', linewidth=1)
ax.set_ylim(0, tops[r])
xd = [] # x data to plot
yd = [] # y data to plot
yf = []
ys = []
ax_idx = 0
for idx in range(ncol): # loop through each row of data
# get basic data
peak_idx = peak_idx_list[idx]
initial = ilist[idx]
wb = wb_list[idx]
wbs = wbs_list[idx]
# diffusion curves
L3 = []
D3 = []
Dfast = []
Dslow = []
for k in range(3):
L3.append(wb.profiles[k].len_microns)
D3.append(dlist[idx])
if slowb[idx] is True:
D3[1] = dlist_slow[idx]
for k in range(3):
Dfast.append(D3[k] + elist[idx])
Dslow.append(D3[k] - elist[idx])
params = diffusion.params_setup3D(L3, D3, wb.time_seconds, initial)
xdiff, ydiff = diffusion.diffusion3Dwb_params(params,
raypaths=wb.raypaths, show_plot=False)
params = diffusion.params_setup3D(L3, Dfast, wb.time_seconds, initial)
xdiff, yfast = diffusion.diffusion3Dwb_params(params,
raypaths=wb.raypaths,
show_plot=False)
params = diffusion.params_setup3D(L3, Dslow, wb.time_seconds, initial)
xdiff, yslow = diffusion.diffusion3Dwb_params(params,
need_to_center_x_data=False,
raypaths=wb.raypaths,
show_plot=False)
for k in range(3):
m = max(xdiff[k])
xdiff[k] = xdiff[k] / m
xd.append(xdiff)
yd.append(ydiff)
yf.append(yfast)
ys.append(yslow)
### side labels ####
ylabelloc = 'left'
formatter = '{:.2f}'
if slowb[idx] is True:
subscript = '$_c$'
else:
subscript = ''
if sidelabels is True:
axes[15].set_ylabel(''.join(('3645\ncm$^{-1}$\n\nlog$_{10}$D', subscript,
'\n', formatter.format(peak_D[0]), '\n+/-', str(er[0]))),
rotation=0, ha=ylabelloc, va='center')
axes[16].set_ylabel(''.join(('3617\ncm$^{-1}$\n\nlog$_{10}$D', subscript, '\n',
formatter.format(peak_D[1]), '\n+/-', str(er[1]))),
rotation=0, ha=ylabelloc, va='center')
axes[17].set_ylabel(''.join(('3540\ncm$^{-1}$\n\nlog$_{10}$D', subscript, '\n',
formatter.format(peak_D[2]), '\n+/-', str(er[2]))),
rotation=0, ha=ylabelloc, va='center')
axes[18].set_ylabel(''.join(('3443\ncm$^{-1}$\n\nlog$_{10}$D', subscript, '\n',
formatter.format(peak_D[4]), '\n+/-', str(er[4]))),
rotation=0, ha=ylabelloc, va='center')
axes[19].set_ylabel(''.join(('3355\ncm$^{-1}$\n\nlog$_{10}$D', subscript, '\n',
formatter.format(peak_D[5]), '\n+/-', str(er[5]))),
rotation=0, ha=ylabelloc, va='center')
for k in range(3):
ax = axes[ax_idx]
# Set tick locations
xmajorLocator = MultipleLocator(xtickgrid[idx])
ymajorLocator = MultipleLocator(ytickgrid[idx])
ax.xaxis.set_major_locator(xmajorLocator)
ax.yaxis.set_major_locator(ymajorLocator)
if show_legend is True:
if ax_idx < legidx:
s = ''.join((string.ascii_uppercase[ax_idx],'.'))
elif ax_idx > legidx:
s = ''.join((string.ascii_uppercase[ax_idx-1],'.'))
else:
s = ''.join((string.ascii_uppercase[ax_idx],'.'))
ax.text(0.1, ax.get_ylim()[1] - 0.15*ax.get_ylim()[1], s,
backgroundcolor='w')
# Plot the diffusion curves
ax.plot(xd[idx][k], yd[idx][k], '-g', linewidth=3)
ax.plot(xd[idx][k], yf[idx][k], '--g')
ax.plot(xd[idx][k], ys[idx][k], '--g')
# Plot all wb data in wbs list of wholeblocks
for wb_idx in range(len(wbs)):
if wbs[wb_idx].raypaths[k] == wb.raypaths[k]:
xi3, yi3 = wbs[wb_idx].xy_picker(peak_idx=peak_idx,
wholeblock=wholeblock,
centered=False)
xi = xi3[k]
yi = yi3[k]
# Normalize x
L = wbs[wb_idx].profiles[k].len_microns
xi = np.array(xi) / L
ax.plot(xi, yi, **styledict[wbs[wb_idx]])
# label ray paths
if wb.profiles[k].raypath == 'a':
R = ''.join(('R || ', wb.profiles[k].raypath,'*'))
else:
R = ' '.join(('R ||', wb.profiles[k].raypath))
axes[ax_idx].text(0.65, ax.get_ylim()[1] - 0.15*ax.get_ylim()[1], R)
ax_idx = ax_idx + 1
