def plot_sweep_set_summary(nwb_file,
highlight_sweep_number,
sweep_numbers,
highlight_color='#0779BE',
background_color='#dddddd'):
fig = plt.figure(frameon=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_ylabel('')
for sn in sweep_numbers:
v, i, t, r, dt = load_experiment(nwb_file, sn)
ax.plot(t, v, linewidth=0.5, color=background_color)
v, i, t, r, dt = load_experiment(nwb_file, highlight_sweep_number)
plt.plot(t, v, linewidth=1, color=highlight_color)
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
tstart = stim_start - 0.05
tend = stim_start + stim_dur + 0.25
ax.set_ylim(AXIS_Y_RANGE[0], AXIS_Y_RANGE[1])
ax.set_xlim(tstart, tend)
return fig
def plot_sag_figures(nwb_file, cell_features, lims_features, sweep_features,
image_dir, sizes, cell_image_files):
fig = plt.figure()
for d in cell_features["long_squares"]["subthreshold_sweeps"]:
if d['peak_deflect'][0] == lims_features["vm_for_sag"]:
v, i, t, r, dt = load_experiment(nwb_file, int(d['id']))
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
plt.plot(t, v, color='black')
plt.scatter(d['peak_deflect'][1],
d['peak_deflect'][0],
color='red',
zorder=10)
#plt.plot([stim_start + stim_dur - 0.1, stim_start + stim_dur], [d['steady'], d['steady']], color='red', zorder=10)
plt.xlim(stim_start - 0.25, stim_start + stim_dur + 0.25)
plt.title("sag = {:.3g}".format(lims_features['sag']))
plt.tight_layout()
save_figure(fig,
'sag',
'sag',
image_dir,
sizes,
cell_image_files,
scalew=2)
def plot_fi_curve_figures(nwb_file, cell_features, lims_features,
sweep_features, image_dir, sizes, cell_image_files):
fig = plt.figure()
fi_sorted = sorted(cell_features["long_squares"]["spiking_sweeps"],
key=lambda s: s['stim_amp'])
x = [d['stim_amp'] for d in fi_sorted]
y = [d['avg_rate'] for d in fi_sorted]
last_zero_idx = np.nonzero(y)[0][0] - 1
plt.scatter(x, y, color='black')
plt.plot(x[last_zero_idx:],
cell_features["long_squares"]["fi_fit_slope"] *
(np.array(x[last_zero_idx:]) - x[last_zero_idx]),
color='red')
plt.xlabel("pA")
plt.ylabel("spikes/sec")
plt.title("slope = {:.3g}".format(lims_features["f_i_curve_slope"]))
rheo_hero_sweeps = [
int(lims_features["rheobase_sweep_num"]),
int(lims_features["thumbnail_sweep_num"])
]
rheo_hero_x = []
for s in rheo_hero_sweeps:
v, i, t, r, dt = load_experiment(nwb_file, s)
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
rheo_hero_x.append(stim_amp)
rheo_hero_y = [
len(get_spikes(sweep_features, s)) for s in rheo_hero_sweeps
]
plt.scatter(rheo_hero_x, rheo_hero_y, zorder=20)
plt.tight_layout()
save_figure(fig,
'fi_curve',
'fi_curve',
image_dir,
sizes,
cell_image_files,
scalew=2)
def plot_instantaneous_threshold_thumbnail(nwb_file,
sweep_numbers,
cell_features,
lims_features,
sweep_features,
color='red'):
min_sweep_number = None
for sn in sorted(sweep_numbers):
spikes = get_spikes(sweep_features, sn)
if len(spikes) > 0:
min_sweep_number = sn if min_sweep_number is None else min(
min_sweep_number, sn)
fig = plt.figure(frameon=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xlabel('')
ax.set_ylabel('')
v, i, t, r, dt = load_experiment(nwb_file, sn)
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
tstart = stim_start - 0.002
tend = stim_start + stim_dur + 0.005
tscale = 0.005
plt.plot(t, v, linewidth=1, color=color)
plt.ylim(AXIS_Y_RANGE[0], AXIS_Y_RANGE[1])
plt.xlim(tstart, tend)
return fig
def plot_single_ap_values(nwb_file, sweep_numbers, lims_features,
sweep_features, cell_features, type_name):
figs = [plt.figure() for f in range(3 + len(sweep_numbers))]
v, i, t, r, dt = load_experiment(nwb_file, sweep_numbers[0])
if type_name == "short_square" or type_name == "long_square":
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
elif type_name == "ramp":
stim_start, start_idx = get_ramp_stim_characteristics(i, t)
gen_features = [
"threshold", "peak", "trough", "fast_trough", "slow_trough"
]
voltage_features = [
"threshold_v", "peak_v", "trough_v", "fast_trough_v", "slow_trough_v"
]
time_features = [
"threshold_t", "peak_t", "trough_t", "fast_trough_t", "slow_trough_t"
]
for sn in sweep_numbers:
spikes = get_spikes(sweep_features, sn)
if (len(spikes) < 1):
logging.warning("no spikes in sweep %d" % sn)
continue
if type_name != "long_square":
voltages = [spikes[0][f] for f in voltage_features]
times = [spikes[0][f] for f in time_features]
else:
rheo_sn = cell_features["long_squares"]["rheobase_sweep"]["id"]
rheo_spike = get_spikes(sweep_features, rheo_sn)[0]
voltages = [rheo_spike[f] for f in voltage_features]
times = [rheo_spike[f] for f in time_features]
plt.figure(figs[0].number)
plt.scatter(range(len(voltages)), voltages, color='gray')
plt.tight_layout()
plt.figure(figs[1].number)
plt.scatter(range(len(times)), times, color='gray')
plt.tight_layout()
plt.figure(figs[2].number)
plt.scatter([0], [spikes[0]['upstroke'] / (-spikes[0]['downstroke'])],
color='gray')
plt.tight_layout()
plt.figure(figs[0].number)
yvals = [
float(lims_features[k + "_v_" + type_name]) for k in gen_features
if lims_features[k + "_v_" + type_name] is not None
]
xvals = range(len(yvals))
plt.scatter(xvals, yvals, color='blue', marker='_', s=40, zorder=100)
plt.xticks(xvals, ['thr', 'pk', 'tr', 'ftr', 'str'])
plt.title(type_name + ": voltages")
plt.figure(figs[1].number)
yvals = [
float(lims_features[k + "_t_" + type_name]) for k in gen_features
if lims_features[k + "_t_" + type_name] is not None
]
xvals = range(len(yvals))
plt.scatter(xvals, yvals, color='blue', marker='_', s=40, zorder=100)
plt.xticks(xvals, ['thr', 'pk', 'tr', 'ftr', 'str'])
plt.title(type_name + ": times")
plt.figure(figs[2].number)
if lims_features["upstroke_downstroke_ratio_" + type_name] is not None:
plt.scatter(
[0],
[float(lims_features["upstroke_downstroke_ratio_" + type_name])],
color='blue',
marker='_',
s=40,
zorder=100)
plt.xticks([])
plt.title(type_name + ": up/down")
for index, sn in enumerate(sweep_numbers):
plt.figure(figs[3 + index].number)
v, i, t, r, dt = load_experiment(nwb_file, sn)
plt.plot(t, v, color='black')
plt.title(str(sn))
spikes = get_spikes(sweep_features, sn)
nspikes = len(spikes)
if type_name != "long_square" and nspikes:
if nspikes == 0:
logging.warning("no spikes in sweep %d" % sn)
continue
voltages = [spikes[0][f] for f in voltage_features]
times = [spikes[0][f] for f in time_features]
else:
rheo_sn = cell_features["long_squares"]["rheobase_sweep"]["id"]
rheo_spike = get_spikes(sweep_features, rheo_sn)[0]
voltages = [rheo_spike[f] for f in voltage_features]
times = [rheo_spike[f] for f in time_features]
plt.scatter(times, voltages, color='red', zorder=20)
delta_v = 5.0
if nspikes:
plt.plot([
spikes[0]['upstroke_t'] - 1e-3 *
(delta_v / spikes[0]['upstroke']), spikes[0]['upstroke_t'] +
1e-3 * (delta_v / spikes[0]['upstroke'])
], [
spikes[0]['upstroke_v'] - delta_v,
spikes[0]['upstroke_v'] + delta_v
],
color='red')
if 'downstroke_t' in spikes[0]:
plt.plot([
spikes[0]['downstroke_t'] - 1e-3 *
(delta_v / spikes[0]['downstroke']),
spikes[0]['downstroke_t'] + 1e-3 *
(delta_v / spikes[0]['downstroke'])
], [
spikes[0]['downstroke_v'] - delta_v,
spikes[0]['downstroke_v'] + delta_v
],
color='red')
else:
logging.warning("spike has no downstroke time, clipped")
if type_name == "ramp":
if nspikes:
plt.xlim(spikes[0]["threshold_t"] - 0.002,
spikes[0]["fast_trough_t"] + 0.01)
elif type_name == "short_square":
plt.xlim(stim_start - 0.002, stim_start + stim_dur + 0.01)
elif type_name == "long_square":
plt.xlim(times[0] - 0.002, times[-2] + 0.002)
plt.tight_layout()
return figs
def plot_hero_figures(nwb_file, cell_features, lims_features, sweep_features,
image_dir, sizes, cell_image_files):
fig = plt.figure()
v, i, t, r, dt = load_experiment(nwb_file,
int(lims_features["thumbnail_sweep_num"]))
plt.plot(t, v, color='black')
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
plt.xlim(stim_start - 0.05, stim_start + stim_dur + 0.05)
plt.ylim(-110, 50)
spike_times = [
spk['threshold_t'] for spk in get_spikes(
sweep_features, lims_features["thumbnail_sweep_num"])
]
isis = np.diff(np.array(spike_times))
plt.title("thumbnail {:d}, amp = {:.1f}".format(
lims_features["thumbnail_sweep_num"], stim_amp))
plt.tight_layout()
save_figure(fig,
'thumbnail_0',
'thumbnail',
image_dir,
sizes,
cell_image_files,
scalew=2)
fig = plt.figure()
plt.plot(range(len(isis)), isis)
plt.ylabel("ISI (ms)")
if lims_features.get("adaptation", None) is not None:
plt.title("adapt = {:.3g}".format(lims_features["adaptation"]))
else:
plt.title("adapt = not defined")
for k in ["has_delay", "has_burst", "has_pause"]:
if lims_features.get(k, None) is None:
lims_features[k] = False
plt.tight_layout()
save_figure(fig, 'thumbnail_1', 'thumbnail', image_dir, sizes,
cell_image_files)
yvals = [
float(lims_features["has_delay"]),
float(lims_features["has_burst"]),
float(lims_features["has_pause"]),
]
xvals = range(len(yvals))
fig = plt.figure()
plt.scatter(xvals, yvals, color='red')
plt.xticks(xvals, ['Delay', 'Burst', 'Pause'])
plt.title("flags")
plt.tight_layout()
save_figure(fig, 'thumbnail_2', 'thumbnail', image_dir, sizes,
cell_image_files)
summary_fig = plot_long_square_summary(nwb_file, cell_features,
lims_features, sweep_features)
save_figure(summary_fig,
'ephys_summary',
'thumbnail',
image_dir,
sizes,
cell_image_files,
scalew=2)
def plot_subthreshold_long_square_figures(nwb_file, cell_features,
lims_features, sweep_features,
image_dir, sizes, cell_image_files):
lsq_sweeps = cell_features["long_squares"]["sweeps"]
sub_sweeps = cell_features["long_squares"]["subthreshold_sweeps"]
tau_sweeps = cell_features["long_squares"][
"subthreshold_membrane_property_sweeps"]
# 0a - Plot VI curve and linear fit, along with vrest
x = np.array([s['stim_amp'] for s in sub_sweeps])
y = np.array([s['peak_deflect'][0] for s in sub_sweeps])
i = np.array([s['stim_amp'] for s in tau_sweeps])
fig = plt.figure()
plt.scatter(x, y, color='black')
plt.plot([x.min(), x.max()],
[lims_features["vrest"], lims_features["vrest"]],
color="blue",
linewidth=2)
plt.plot(i,
i * 1e-3 * lims_features["ri"] + lims_features["vrest"],
color="red",
linewidth=2)
plt.xlabel("pA")
plt.ylabel("mV")
plt.title("ri = {:.1f}, vrest = {:.1f}".format(lims_features["ri"],
lims_features["vrest"]))
plt.tight_layout()
save_figure(fig, 'VI_curve', 'subthreshold_long_squares', image_dir, sizes,
cell_image_files)
# 0b - Plot tau curve and average
fig = plt.figure()
x = np.array([s['stim_amp'] for s in tau_sweeps])
y = np.array([s['tau'] for s in tau_sweeps])
plt.scatter(x, y, color='black')
i = np.array([s['stim_amp'] for s in tau_sweeps])
plt.plot([i.min(), i.max()], [
cell_features["long_squares"]["tau"],
cell_features["long_squares"]["tau"]
],
color="red",
linewidth=2)
plt.xlabel("pA")
ylim = plt.ylim()
plt.ylim(0, ylim[1])
plt.ylabel("tau (s)")
plt.tight_layout()
save_figure(fig, 'tau_curve', 'subthreshold_long_squares', image_dir,
sizes, cell_image_files)
subthresh_dict = {s['id']: s for s in tau_sweeps}
# 0c - Plot the subthreshold squares
tau_sweeps = [s['id'] for s in tau_sweeps]
tau_figs = [plt.figure() for i in range(len(tau_sweeps))]
for index, s in enumerate(tau_sweeps):
v, i, t, r, dt = load_experiment(nwb_file, s)
plt.figure(tau_figs[index].number)
plt.plot(t, v, color="black")
if index == 0:
min_y, max_y = plt.ylim()
else:
ylims = plt.ylim()
if min_y > ylims[0]:
min_y = ylims[0]
if max_y < ylims[1]:
max_y = ylims[1]
stim_start, stim_dur, stim_amp, start_idx, end_idx = get_square_stim_characteristics(
i, t)
plt.xlim(stim_start - 0.05, stim_start + stim_dur + 0.05)
peak_idx = subthresh_dict[s]['peak_deflect'][1]
peak_t = peak_idx * dt
plt.scatter([peak_t], [subthresh_dict[s]['peak_deflect'][0]],
color='red',
zorder=10)
popt = ft.fit_membrane_time_constant(v, t, stim_start, peak_t)
plt.title(str(s))
plt.plot(t[start_idx:peak_idx],
exp_curve(t[start_idx:peak_idx] - t[start_idx], *popt),
color='blue')
for index, s in enumerate(tau_sweeps):
plt.figure(tau_figs[index].number)
plt.ylim(min_y, max_y)
plt.tight_layout()
for index, tau_fig in enumerate(tau_figs):
save_figure(tau_figs[index], 'tau_%d' % index,
'subthreshold_long_squares', image_dir, sizes,
cell_image_files)
def MLIN(voltage, current, res, cap, dt, MAKE_PLOT=False, SHOW_PLOT=False, BLOCK=False, PUBLICATION_PLOT=False):
'''voltage, current
input:
voltage: numpy array of voltage with test pulse cut out
current: numpy array of stimulus with test pulse cut out '''
t = np.arange(0, len(current)) * dt
(_, _, _, start_idx, end_idx) = get_square_stim_characteristics(current, t, no_test_pulse=True)
stim_len = end_idx - start_idx
distribution_start_ind=start_idx + int(.5/dt)
distribution_end_ind=start_idx + stim_len
v_section=voltage[distribution_start_ind:distribution_end_ind]
if MAKE_PLOT:
times=np.arange(0, len(voltage))*dt
plt.figure(figsize=(15, 11))
plt.subplot2grid((7,2), (0,0), colspan=2)
plt.plot(times[distribution_start_ind:distribution_end_ind], v_section)
plt.title('voltage for histogram')
print(v_section)
v_section=v_section-np.mean(v_section)
var_of_section=np.var(v_section)
sv_for_expsymm=np.std(v_section)/np.sqrt(2)
subthreshold_long_square_voltage_distribution=stats.norm(loc=0, scale=np.sqrt(var_of_section))
#--autocorrelation
tau_4AC=res*cap
AC=autocorr(v_section-np.mean(v_section))
ACtime=np.arange(0,len(AC))*dt
#--fit autocorrelation with decaying exponential
(popt, pcov)= curve_fit(exp_decay, ACtime, AC, p0=[AC[0],tau_4AC])
tau_from_AC=popt[1]
if MAKE_PLOT:
plt.subplot2grid((7,2), (1,0), rowspan=3)
plt.hist(v_section, bins=50, normed=True, label='data')
data_grid=np.arange(min(v_section), max(v_section), abs(min(v_section)-max(v_section))/100.)
plt.plot(data_grid, subthreshold_long_square_voltage_distribution.pdf(data_grid), 'r', label='gauss with\nmeasured var')
plt.plot(data_grid, expsymm_pdf(data_grid, sv_for_expsymm), 'm', lw=3, label='expsymm function')
plt.xlabel('voltage (mV)')
plt.title('Mean subtracted voltage hist')
plt.legend()
#--cumulative density function
(h, edges)=np.histogram(v_section, bins=50)
centers=find_bin_center(edges)
CDFx=centers
CDFy=np.cumsum(h)/float(len(v_section))
plt.subplot2grid((7,2), (4,0), rowspan=3)
plt.plot(CDFx, CDFy, label='data')
# plt.plot(CDFx, sig(CDFx, popt[0], popt[1]), label='fit')
plt.plot(data_grid, subthreshold_long_square_voltage_distribution.cdf(data_grid), 'r', label='gauss with\nmeasured var')
plt.plot(data_grid, expsymm_cdf(data_grid, sv_for_expsymm), 'm', lw=3, label='expsymm func')
plt.title('Normalized cumulative sum')
plt.xlabel('v-mean(v)')
plt.legend()
plt.subplot2grid((7,2), (1,1), rowspan=3)
plt.plot(ACtime, AC, label='data')
plt.xlabel('shift (s)')
plt.title('Auto correlation')
plt.plot(ACtime, exp_decay(ACtime, AC[0], tau_4AC), label='RC')
plt.plot(ACtime, exp_decay(ACtime, popt[0], tau_from_AC), label='fit')
plt.legend()
plt.tight_layout()
if SHOW_PLOT:
plt.show(block=BLOCK)
if PUBLICATION_PLOT:
times=np.arange(0, len(voltage))*dt
plt.figure(figsize=(14, 7))
plt.subplot2grid((3,3), (0,0), colspan=3)
plt.xlabel('time (s)', fontsize=14)
plt.ylabel('(mV)', fontsize=14)
plt.plot(times[distribution_start_ind:distribution_end_ind], v_section*1.e3)
plt.title('Voltage for histogram', fontsize=16)
plt.subplot2grid((3,3), (1,0), rowspan=2)
plt.hist(v_section*1.e3, bins=50, normed=True, label='data')
data_grid=np.arange(min(v_section), max(v_section), abs(min(v_section)-max(v_section))/100.)
# plt.plot(data_grid, subthreshold_long_square_voltage_distribution.pdf(data_grid), 'r', label='gauss with\nmeasured var')
plt.plot(data_grid*1.e3, 1.e-3*expsymm_pdf(data_grid, sv_for_expsymm), 'm', lw=3, label='expsymm ')
plt.xlabel('voltage (mV)', fontsize=14)
plt.title('Mean subtracted voltage hist', fontsize=16)
plt.legend(loc=1)
#--cumulative density function
(h, edges)=np.histogram(v_section, bins=50)
centers=find_bin_center(edges)
CDFx=centers
CDFy=np.cumsum(h)/float(len(v_section))
plt.subplot2grid((3,3), (1,1), rowspan=2)
plt.plot(CDFx*1e3, CDFy, label='data')
# plt.plot(CDFx, sig(CDFx, popt[0], popt[1]), label='fit')
# plt.plot(data_grid, subthreshold_long_square_voltage_distribution.cdf(data_grid), 'r', label='gauss with\nmeasured var')
plt.plot(data_grid*1.e3, expsymm_cdf(data_grid, sv_for_expsymm), 'm', lw=3, label='expsymm')
plt.title('Normalized cumulative sum', fontsize=16)
plt.xlabel('V-mean(V) (mV)', fontsize=16)
plt.legend(loc=2, fontsize=14)
plt.subplot2grid((3,3), (1,2), rowspan=2)
plt.plot(ACtime, AC*1.e3, label='data')
plt.xlabel('shift (s)', fontsize=14)
plt.title('Auto correlation', fontsize=16)
# plt.plot(ACtime, exp_decay(ACtime, AC[0], tau_4AC), label='RC')
plt.plot(ACtime, exp_decay(ACtime, popt[0]*1.e3, tau_from_AC), lw=3, label='fit')
plt.legend(loc=1)
plt.tight_layout()
return var_of_section, sv_for_expsymm, tau_from_AC