def plot_all_events_and_fits(self):
P3 = PH.regular_grid(1,
5,
order='columns',
figsize=(12, 8.),
showgrid=False,
verticalspacing=0.1,
horizontalspacing=0.02,
margins={
'leftmargin': 0.07,
'rightmargin': 0.05,
'topmargin': 0.03,
'bottommargin': 0.05
},
labelposition=(-0.12, 0.95))
idx = [a for a in P3.axdict.keys()]
ncol = 5
offset2 = 0.
k = 0
for itr in range(len(all_evok)): # for each trace
for evok in all_evok[itr]: # for each ok event in that trace
P3.axdict[idx[k]].plot([
self.cell_summary['indiv_tb'][itr][0],
self.cell_summary['indiv_tb'][itr][-1]
],
np.zeros(2) + offset2,
'b--',
linewidth=0.3)
P3.axdict[idx[k]].plot(
self.cell_summary['indiv_tb'][itr],
self.cell_summary['allevents'][itr][evok] + offset2,
'k--',
linewidth=0.5)
P3.axdict[idx[k]].plot(
self.cell_summary['indiv_tb'][itr],
-self.cell_summary['best_fit'][itr][evok] + offset2,
'r--',
linewidth=0.5)
if k == 4:
k = 0
offset2 += 20.
else:
k += 1
mpl.show()
def summarize_inputs(self):
P = PH.regular_grid(2, 3, order='columns', figsize=(10., 8), showgrid=False)
ax = [P.axdict[x] for x in P.axdict.keys()]
# PH.nice_plot(ax)
allendings = []
cellendings = {}
for cell, v in self.VCN_Inputs.items():
cellendings[cell] = []
for s in v[1]:
if isinstance(s, list) or isinstance(s, tuple):
allendings.append(s[0])
cellendings[cell].append(s[0])
else:
continue
# print('not list or tuple: ', cell, s)
# allendings.append(s)
# cellendings[cell].append(s)
ax[0].set_title('All ending areas')
ax[0].hist(allendings, bins=20)
ax[0].set_ylim((0, 25))
ax[0].set_ylabel('N')
ax[0].set_xlim((0,350))
ax[0].set_xlabel('Area ($um^2$)')
normd = []
ratio1 = []
ratio2 = []
meansize = []
maxsize = []
convergence = []
for cell in cellendings.keys():
normd.extend(cellendings[cell]/np.max(cellendings[cell]))
ratio1.append(cellendings[cell][1]/cellendings[cell][0])
ratio2.append(np.mean(cellendings[cell])/cellendings[cell][0])
meansize.append(np.mean(cellendings[cell]))
maxsize.append(np.max(cellendings[cell]))
convergence.append(len(cellendings[cell]))
print('convergence: ', convergence)
ax[1].set_title('Normalized by largest')
ax[1].hist(normd, bins=20, range=(0,1.0), align='mid')
ax[1].set_xlabel('Area ($um^2$)')
ax[1].set_ylabel('N')
ax[2].set_title('Ratio largest to next largest')
ax[2].hist(ratio1, bins=10, range=(0,1.0), align='mid')
ax[2].set_xlabel('Area ($um^2$)')
ax[3].set_title('Ratio of mean to largest')
ax[3].hist(ratio2, bins=10, range=(0,1.0), align='mid')
ax[3].set_xlabel('Area ($um^2$)')
ax[4].set_title('Convergence vs. mean size')
ax[4].set_xlim((0., 200.))
ax[4].set_xlabel('Area ($um^2$)')
ax[4].set_ylim((0., 15.))
ax[4].set_ylabel('Convergence')
fit = np.polyfit(meansize, convergence, 1)
fit_fn = np.poly1d(fit)
r, p = scipy.stats.pearsonr(meansize, convergence)
ax[4].text(x=0.05, y=0.95, s=f"r: {r:.3f}, p={p:.3e}", fontsize=8)
ax[4].scatter(meansize, convergence)
ax[4].plot(meansize, fit_fn(meansize), '--k')
ax[5].set_title('Convergence vs max size')
fit = np.polyfit(maxsize, convergence, 1)
fit_fn = np.poly1d(fit)
r, p = scipy.stats.pearsonr(maxsize, convergence)
ax[5].text(x=0.05, y=0.95, s=f"r: {r:.3f}, p={p:.3f}", fontsize=8)
ax[5].scatter(maxsize, convergence)
ax[5].plot(maxsize, fit_fn(maxsize), '--k')
ax[5].set_xlim((0., 350.))
ax[5].set_xlabel('Area ($um^2$)')
ax[5].set_ylim((0., 15.))
ax[5].set_xlabel('Convergence')
mpl.show()
# 'AN_Result_VCN_c09_XM13nacncoop_II_soma=1.489_all_multisite_020_tonepip_030dB_16000.0_MS.p'
patterns = fn.keys()
print ('patterns: ', patterns)
p = 'c09'
gbcs.append('Summary')
# fig, ax = plt.subplots(2, 4, figsize=(10,6))
lmar = 0.1
rmar = -0.01
tmar = 0.01
bmar = 0.08
hspace = 0.05
vspace = 0.1
nplts = len(gbcs)
nrows, ncols = PH.getLayoutDimensions(nplts, pref='height')
P = PH.regular_grid(nrows, ncols, figsize=(10, 6), panel_labels=gbcs)
#PH.Plotter(rcshape=sizer, label=False, figsize=(10 , 6))
P.figure_handle.suptitle('Reverse Correlations, AN types={0:2s}'.format(SR))
ax2 = {}
for i, g in enumerate(P.axdict.keys()):
if i >= len(gbcs):
continue
P.axdict[g].set_xlim(-5., 0.)
# P.axdict[g].set_axis_bgcolor('gainsboro')
P.axdict[g].tick_params(labelsize=6)
ax2[g] = twinax(P.figure_handle, P.axdict[g], pos=1.0)
for l in P.axlabels:
l.set_text(' ')
# plt.show()
# exit()
sTC = {}
def plot_vciv(self):
P = PH.regular_grid(2,
2,
order='columns',
figsize=(8., 6.),
showgrid=False,
verticalspacing=0.1,
horizontalspacing=0.12,
margins={
'leftmargin': 0.12,
'rightmargin': 0.12,
'topmargin': 0.08,
'bottommargin': 0.1
},
labelposition=(-0.12, 0.95))
(date, sliceid, cell, proto,
p3) = self.file_cell_protocol(self.datapath)
P.figure_handle.suptitle(os.path.join(date, sliceid, cell,
proto).replace('_', '\_'),
fontsize=12)
for i in range(self.AR.traces.shape[0]):
P.axdict['A'].plot(self.AR.time_base * 1e3,
self.AR.traces[i, :] * 1e12,
'k-',
linewidth=0.5)
PH.talbotTicks(P.axdict['A'],
tickPlacesAdd={
'x': 0,
'y': 0
},
floatAdd={
'x': 0,
'y': 0
})
P.axdict['A'].set_xlabel('T (ms)')
P.axdict['A'].set_ylabel('I (pA)')
PH.crossAxes(P.axdict['C'], xyzero=(-60., 0.))
P.axdict['C'].plot(self.vcss_vcmd * 1e3,
self.vcss_Im * 1e12,
'ks-',
linewidth=1,
markersize=4)
P.axdict['C'].set_xlabel('V (mV)')
P.axdict['C'].set_ylabel('I (pA)')
PH.talbotTicks(P.axdict['C'],
tickPlacesAdd={
'x': 0,
'y': 0
},
floatAdd={
'x': 0,
'y': 0
})
P.axdict['B'].set_xlabel('I (nA)')
P.axdict['B'].set_ylabel('V (mV)')
PH.talbotTicks(P.axdict['B'],
tickPlacesAdd={
'x': 1,
'y': 0
},
floatAdd={
'x': 2,
'y': 0
})
P.axdict['D'].set_xlabel('I (pA)')
P.axdict['D'].set_ylabel('Latency (ms)')
self.IVFigure = P.figure_handle
if self.plot:
mpl.show()
def plot_individual_events(self,
fit_err_limit=1000.,
tau2_range=2.5,
title='',
pdf=None):
P = PH.regular_grid(3,
3,
order='columns',
figsize=(8., 8.),
showgrid=False,
verticalspacing=0.1,
horizontalspacing=0.12,
margins={
'leftmargin': 0.12,
'rightmargin': 0.12,
'topmargin': 0.03,
'bottommargin': 0.1
},
labelposition=(-0.12, 0.95))
P.figure_handle.suptitle(title)
all_evok = self.cell_summary[
'indiv_evok'] # this is the list of ok events - a 2d list by
all_notok = self.cell_summary['indiv_notok']
# print('all evok: ', all_evok)
# print('len allevok: ', len(all_evok))
#
# # print('all_notok: ', all_notok)
# # print('indiv tau1: ', self.cell_summary['indiv_tau1'])
# exit(1)
trdat = []
trfit = []
trdecfit = []
for itr in range(len(all_evok)): # for each trace
for evok in all_evok[itr]: # for each ok event in that trace
P.axdict['A'].plot(self.cell_summary['indiv_tau1'][itr][evok],
self.cell_summary['indiv_amp'][itr][evok],
'ko',
markersize=3)
P.axdict['B'].plot(self.cell_summary['indiv_tau2'][itr][evok],
self.cell_summary['indiv_amp'][itr][evok],
'ko',
markersize=3)
P.axdict['C'].plot(self.cell_summary['indiv_tau1'][itr][evok],
self.cell_summary['indiv_tau2'][itr][evok],
'ko',
markersize=3)
P.axdict['D'].plot(
self.cell_summary['indiv_amp'][itr][evok],
self.cell_summary['indiv_fiterr'][itr][evok],
'ko',
markersize=3)
P.axdict['H'].plot(
self.cell_summary['indiv_tau1'][itr][evok],
self.cell_summary['indiv_Qtotal'][itr][evok],
'ko',
markersize=3)
trdat.append(
np.column_stack([
self.cell_summary['indiv_tb'][itr],
self.cell_summary['allevents'][itr][evok]
]))
#idl = len(self.cell_summary['best_decay_fit'][itr][evok])
trfit.append(
np.column_stack([
self.cell_summary['indiv_tb'][itr],
-self.cell_summary['best_fit'][itr][evok]
]))
trdecfit.append(
np.column_stack([
self.cell_summary['indiv_tb'][itr],
-self.cell_summary['best_decay_fit'][itr][evok]
]))
dat_coll = collections.LineCollection(trdat,
colors='k',
linewidths=0.5)
fit_coll = collections.LineCollection(trfit,
colors='r',
linewidths=0.25)
# decay_fit_coll = collections.LineCollection(trdecfit, colors='c', linewidths=0.3)
P.axdict['G'].add_collection(dat_coll)
P.axdict['G'].add_collection(fit_coll)
# P.axdict['G'].add_collection(decay_fit_coll)
n_trdat = []
n_trfit = []
for itr in range(len(all_notok)):
for notok in all_notok[itr]:
n_trdat.append(
np.column_stack([
self.cell_summary['indiv_tb'][itr],
self.cell_summary['allevents'][itr][notok]
]))
n_trfit.append(
np.column_stack([
self.cell_summary['indiv_tb'][itr],
-self.cell_summary['best_fit'][itr][notok]
]))
P.axdict['D'].plot(
self.cell_summary['indiv_amp'][itr][notok],
self.cell_summary['indiv_fiterr'][itr][notok],
'ro',
markersize=3)
n_dat_coll = collections.LineCollection(n_trdat,
colors='b',
linewidths=0.35)
n_fit_coll = collections.LineCollection(n_trfit,
colors='y',
linewidths=0.25)
P.axdict['E'].add_collection(n_dat_coll)
P.axdict['E'].add_collection(n_fit_coll)
P.axdict['A'].set_xlabel(r'$tau_1$ (ms)')
P.axdict['A'].set_ylabel(r'Amp (pA)')
P.axdict['B'].set_xlabel(r'$tau_2$ (ms)')
P.axdict['B'].set_ylabel(r'Amp (pA)')
P.axdict['C'].set_xlabel(r'$\tau_1$ (ms)')
P.axdict['C'].set_ylabel(r'$\tau_2$ (ms)')
P.axdict['D'].set_xlabel(r'Amp (pA)')
P.axdict['D'].set_ylabel(r'Fit Error (cost)')
P.axdict['H'].set_xlabel(r'$\tau_1$ (ms)')
P.axdict['H'].set_ylabel(r'Qtotal')
P.axdict['G'].set_ylim((-100., 20.))
P.axdict['G'].set_xlim((-2., 25.))
P.axdict['E'].set_ylim((-100., 20.))
P.axdict['E'].set_xlim((-2., 25.))
# put in averaged event too
# self.cell_summary['averaged'].extend([{'tb': aj.avgeventtb, 'avg': aj.avgevent, 'fit': {'amplitude': aj.Amplitude,
# 'tau1': aj.tau1, 'tau2': aj.tau2, 'risepower': aj.risepower}, 'best_fit': aj.avg_best_fit,
# 'risetenninety': aj.risetenninety, 'decaythirtyseven': aj.decaythirtyseven}])
aev = self.cell_summary['averaged']
for i in range(len(aev)):
P.axdict['F'].plot(aev[i]['tb'],
aev[i]['avg'],
'k-',
linewidth=0.8)
P.axdict['F'].plot(aev[i]['tb'],
aev[i]['best_fit'],
'r--',
linewidth=0.4)
if pdf is None:
mpl.show()
else:
pdf.savefig(dpi=300)
mpl.close()
def summarize_inputs():
import matplotlib.pyplot as mpl
import pylibrary.PlotHelpers as PH
P = PH.regular_grid(2, 3, order='columns', figsize=(10., 8), showgrid=False)
ax = [P.axdict[x] for x in P.axdict.keys()]
print(ax)
# PH.nice_plot(ax)
allendings = []
cellendings = {}
for cell, v in iter(VCN_Inputs_Clean.items()):
cellendings[cell] = []
for s in v[1]:
# print cell, s[0]
allendings.append(s[0])
cellendings[cell].append(s[0])
ax[0].hist(allendings, bins=20)
ax[0].set_title('All ending areas')
ax[0].set_ylim((0, 25))
ax[0].set_ylabel('N')
ax[0].set_xlim((0,350))
ax[0].set_xlabel('Area (um^2)')
normd = []
ratio1 = []
ratio2 = []
meansize = []
maxsize = []
convergence = []
for cell in cellendings.keys():
normd.extend(cellendings[cell]/np.max(cellendings[cell]))
ratio1.append(cellendings[cell][1]/cellendings[cell][0])
ratio2.append(np.mean(cellendings[cell])/cellendings[cell][0])
meansize.append(np.mean(cellendings[cell]))
maxsize.append(np.max(cellendings[cell]))
convergence.append(len(cellendings[cell]))
print('convergence: ', convergence)
ax[1].set_title('Normaized by largest')
ax[1].hist(normd, bins=20, range=(0,1.0), align='mid')
ax[1].set_xlabel('Area (um^2)')
ax[1].set_ylabel('N')
ax[2].set_title('ratio largest to next largest')
ax[2].hist(ratio1, bins=10, range=(0,1.0), align='mid')
ax[2].set_xlabel('Area (um^2)')
ax[2].set_title('ratio mean to largest')
ax[3].set_title('Ratio of mean to largest')
ax[3].hist(ratio2, bins=10, range=(0,1.0), align='mid')
ax[3].set_xlabel('Area (um^2)')
ax[4].set_xlim((0., 200.))
ax[4].set_xlabel('Area (um^2)')
ax[4].set_ylim((0., 15.))
ax[4].set_ylabel('Convergence')
ax[4].set_title('Convergence vs. mean size')
fit = np.polyfit(meansize, convergence, 1)
fit_fn = np.poly1d(fit)
ax[4].scatter(meansize, convergence)
ax[4].plot(meansize, fit_fn(meansize), '--k')
ax[5].set_title('Convergence vs max size')
fit = np.polyfit(maxsize, convergence, 1)
fit_fn = np.poly1d(fit)
ax[5].scatter(maxsize, convergence)
ax[5].plot(maxsize, fit_fn(maxsize), '--k')
ax[5].set_xlim((0., 350.))
ax[5].set_xlabel('Area (um^2)')
ax[5].set_ylim((0., 15.))
ax[5].set_xlabel('Convergence')
mpl.show()
('VCN_c14', {'pos': [l1, wid, yp[3], ht]}),
('VCN_c16', {'pos': [l1, wid, yp[4], ht]}),
('VCN_c17', {'pos': [l2, wid, yp[5], ht]}),
('VCN_c18', {'pos': [l2, wid, yp[0], ht]}),
('VCN_c19', {'pos': [l2, wid, yp[1], ht]}),
('VCN_c20', {'pos': [l2, wid, yp[3], ht]}),
('VCN_c21', {'pos': [l1, wid, yp[4], ht]}),
('VCN_c22', {'pos': [l2, wid, yp[5], ht]}),
('None', {}),
]) # dict elements are [left, width, bottom, height] for the axes in the plot.
# gr = [(a, a+1, 0, 1) for a in range(0, 8)] # just generate subplots - shape does not matter
gbc_names = [s[-2:] for s in sizer.keys()]
gbc_names = ['09']
P = PH.regular_grid(6, 2, figsize=(6,8), panel_labels=list(sizer.keys()), labelposition=(0.05, 0.95))
# P = PH.Plotter(rcshape=sizer, label=False, figsize=(6, 8), labeloffset=[0.6, 0.])
inputPattern = "VCN_c10" # None # 'VCN_c10'
for gbc in gbc_names:
if gbc == 'ne':
continue
print ('='*32)
cell = 'VCN_c{0:s}'.format(gbc)
# an_result_file = 'AN_Result_VCN_c{0:s}_delays_N{1:03d}_040dB_4000.0_{2:2s}.p'.format(gbc, nrep, SR)
# andatafile = Path(baseDirectory, cell, simDirectory, 'AN', an_result_file)
# print(' an result file: {0:s}'.format(str(andatafile)))
# print (andatafile.is_file() )
# if not andatafile.is_file() or forcerun: # only run if no evidence we have run this already