def main(**kwargs):
p = Path(kwargs['folder_path'])
f_list = list(p.glob('**/*dat'))
outdir = p / 'results'
outdir.mkdir(exist_ok=True, parents=True)
for fi, f in enumerate(f_list):
print(f"[info] Analysing file {f.name}")
itr_no = 0
seg_no = 0
Vm_trail = []
f_path = f
f = str(f)
segments = nio.StimfitIO(f).read_block().segments
print(len(segments))
fig = plt.figure(figsize=(8, 5))
thresh_state = 0
ax = fig.add_subplot(111)
for si, segment in enumerate(segments):
seg_no += 1
fI = 10000 * fi + si
#print('analysing the segment ',segment)
analog_signals = segment.analogsignals
#print(analog_signals)
for ti, trace in enumerate(analog_signals):
itr_no += 1
v = trace
v = np.ravel(v)
if '1.0 pA' == str(v.units):
print('x 1pA')
continue
if np.isnan(v)[0] == True:
print('x Vnan')
continue
if itr_no != 1:
traces = 'traces compiled'
else:
traces = 'trace_alone'
print(f'trace {ti}', end=', ')
protocol_type = 'Current_clamp'
v = v.magnitude
tf = trace.t_stop
ti = trace.t_start
t = np.linspace(0, float(tf - ti), len(v))
ax.plot(t, v, label='trace numer = ' + str(itr_no))
try:
Trace_with_features = extractor(
t=t,
v=v,
filter=float(trace.sampling_rate) / 2500,
min_peak=-20.0,
dv_cutoff=20.0,
max_interval=0.005,
min_height=2.0,
thresh_frac=0.05,
baseline_interval=0.1,
baseline_detect_thresh=0.3,
id=None)
Trace_with_features.process_spikes()
neuron_threshold_v = Trace_with_features.spike_feature(
"threshold_v")
if thresh_state == 0 and len(neuron_threshold_v) >= 1:
neuron_threshold_v = Trace_with_features.spike_feature(
"threshold_v")[0]
neuron_threshold_t = Trace_with_features.spike_feature(
"threshold_t")[0]
Threshold_voltage = str(
'threshold voltage ' +
str(np.around(neuron_threshold_v, decimals=2)) +
"mV")
ax.plot(neuron_threshold_t,
neuron_threshold_v,
'o',
color='k',
label='Firing threshold')
plt.figtext(.05,
0.0,
Threshold_voltage + "mV",
fontsize=12,
va="top",
ha="left")
thresh_state = 1
except Exception as e:
print('Could not be plotted:', e)
ax.legend()
ax.set_ylabel('signal amplitude in ' + str(trace[0]).split()[1])
ax.set_xlabel('time (s)')
ax.set_ylim([-100, 60])
ax.set_title('recording type: ' + str(trace.name).split("-")[0] + ' ' +
str(seg_no) + ' of ' + 'traces')
outfile = str(outdir) + "/" + str(
f_path.stem) + " " + f'{fI}_compiled.svg'
plt.savefig(outfile, bbox_inches="tight")
print('---> Saved to %s' % outfile)
fig = plt.close()
itr_no = 0
seg_no = 0
thresh_state = 0
for si, segment in enumerate(segments):
seg_no += 1
fI = 10000 * fi + si
#print('analysing the segment ',segment)
analog_signals = segment.analogsignals
#print(analog_signals)
for ti, trace in enumerate(analog_signals):
fig1 = plt.figure(figsize=(8, 5))
ax1 = fig1.add_subplot(111)
TI = 100001 * fI + ti
itr_no += 1
v = trace
v = np.ravel(v)
if '1.0 pA' == str(v.units):
print('x 1pA')
continue
if np.isnan(v)[0] == True:
print('x Vnan')
continue
if itr_no != 1:
traces = 'traces compiled'
else:
traces = 'trace_alone'
print(f'trace {ti}', end=', ')
protocol_type = 'Current_clamp'
trace_unit = str(v.units).split('.')[1]
v = v.magnitude
tf = trace.t_stop
ti = trace.t_start
t = np.linspace(0, float(tf - ti), len(v))
Vm_i = np.mean(v[0:299])
Vm_f = np.mean(v[len(v) - 300:len(v)])
del_Vm = Vm_f - Vm_i
ax1.plot(t, v, label='trace numer = ' + str(itr_no))
plt.figtext(0.05,
0.00,
'sampling rate* total time = ' + str(
np.around(float(trace.sampling_rate) *
(float(tf - ti)),
decimals=2)),
fontsize=12,
va="top",
ha="left")
plt.figtext(0.05,
-0.05,
'sampling rate = ' + str(trace.sampling_rate),
fontsize=12,
va="top",
ha="left")
plt.figtext(0.05,
-0.10,
'total time of recording = ' +
str(np.around(tf - ti, decimals=2)),
fontsize=12,
va="top",
ha="left")
plt.figtext(0.05,
-0.15,
'Number of data points in the trace = ' +
str(len(v)),
fontsize=12,
va="top",
ha="left")
plt.figtext(0.05,
-0.20,
'membrane voltage variation : ' +
str(np.around(del_Vm, decimals=2)) + trace_unit,
fontsize=12,
va="top",
ha="left")
plt.figtext(0.05,
-0.25,
'membrane voltage after : ' +
str(np.around(Vm_f, decimals=1)) + trace_unit,
fontsize=12,
va="top",
ha="left")
plt.figtext(0.05,
-0.30,
'membrane voltage before : ' +
str(np.around(Vm_i, decimals=1)) + trace_unit,
fontsize=12,
va="top",
ha="left")
try:
Trace_with_features = extractor(
t=t,
v=v,
filter=float(trace.sampling_rate) / 2500,
min_peak=-20.0,
dv_cutoff=20.0,
max_interval=0.005,
min_height=2.0,
thresh_frac=0.05,
baseline_interval=0.1,
baseline_detect_thresh=0.3,
id=None)
Trace_with_features.process_spikes()
neuron_threshold_v = Trace_with_features.spike_feature(
"threshold_v")
if thresh_state == 0 and len(neuron_threshold_v) >= 1:
neuron_threshold_v = Trace_with_features.spike_feature(
"threshold_v")[0]
neuron_threshold_t = Trace_with_features.spike_feature(
"threshold_t")[0]
Threshold_voltage = str(
'threshold voltage = ' +
str(np.around(neuron_threshold_v, decimals=2)) +
"mV")
ax1.plot(neuron_threshold_t,
neuron_threshold_v,
'o',
color='r',
label='Firing Threshold')
plt.figtext(.05,
-0.35,
Threshold_voltage + "mV",
fontsize=12,
va="top",
ha="left")
thresh_state = 1
except Exception as e:
print('Could not be plotted:', e)
ax1.legend()
ax1.set_ylabel('signal amplitude in ' +
str(trace[0]).split()[1])
ax1.set_xlabel('time (s)')
ax1.set_ylim([-100, 60])
ax1.set_title('recording type: ' +
str(trace.name).split("-")[0] + ': single trace')
outfile = str(outdir) + "/" + str(
f_path.stem) + " " + f'{TI}.svg'
plt.savefig(outfile, bbox_inches="tight")
print('---> Saved to %s' % outfile)
fig1 = plt.close()
def main(**kwargs):
p = Path(kwargs['folder_path'])
f_list = list(p.glob('**/*.dat'))
outdir = p / 'results'
outdir.mkdir(exist_ok=True, parents=True)
for fi, f in enumerate(f_list):
print(f"[info] Analysing file {f.name}")
itr_no = 0
seg_no = 0
Vm_trail = []
thresh_state = 0
f_path = f
f = str(f)
segments = nio.StimfitIO(f).read_block().segments
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111)
for si, segment in enumerate(segments):
seg_no += 1
fI = 10000 * fi + si
#print('analysing the segment ',segment)
analog_signals = segment.analogsignals
#print(analog_signals)
for ti, trace in enumerate(analog_signals):
itr_no += 1
v = trace
v = np.ravel(v)
if '1.0 pA' == str(v.units):
print('x 1pA')
continue
if np.isnan(v)[0] == True:
print('x Vnan')
continue
if itr_no != 1:
traces = 'traces compiled'
else:
traces = 'trace_alone'
print(f'trace {ti}', end=', ')
protocol_type = 'Current_clamp'
v = v.magnitude
Vm_trace = np.mean(v[len(v) - 300:len(v)])
tf = trace.t_stop
ti = trace.t_start
t = np.linspace(0, float(tf - ti), len(v))
ax.plot(t, v, label='trace numer = ' + str(itr_no))
try:
Trace_with_features = extractor(
t=t,
v=v,
filter=float(trace.sampling_rate) / 2500,
min_peak=-20.0,
dv_cutoff=20.0,
max_interval=0.005,
min_height=2.0,
thresh_frac=0.05,
baseline_interval=0.1,
baseline_detect_thresh=0.3,
id=None)
Trace_with_features.process_spikes()
neuron_threshold_v = Trace_with_features.spike_feature(
"threshold_v")
if thresh_state == 0 and len(neuron_threshold_v) >= 1:
neuron_threshold_v = Trace_with_features.spike_feature(
"threshold_v")[0]
neuron_threshold_t = Trace_with_features.spike_feature(
"threshold_t")[0]
trough_v = Trace_with_features.spike_feature(
"trough_v")[0]
trough_t = Trace_with_features.spike_feature(
"trough_t")[0]
ax.plot(neuron_threshold_t,
neuron_threshold_v,
'o',
color='k',
label='threshold voltage')
ax.plot(trough_t,
trough_v,
'o',
color='r',
label='trough_v')
ax.figtext(
1, 0.20, "trough_v = " +
str(np.around(trough_v, decimals=2)) + "mV")
ax.figtext(
1, 0.15, "trough_t = " +
str(np.around(trough_t, decimals=2)) + 's')
threshold_state = 1
except Exception as e:
print('Could not be plotted:', e)
ax.legend()
ax.set_ylabel('signal amplitude in ' + str(trace[0]).split()[1])
ax.set_xlabel('time (s)')
ax.set_title('recording type: ' + str(trace.name).split("-")[0] + ' ' +
str(len(segments)) + ' ' + traces +
' of segment number ' + str(seg_no))
outfile = str(outdir) + "/" + str(
f_path.stem) + " " + f'{fI}_compiled.png'
plt.savefig(outfile)
print('---> Saved to %s' % outfile)
fig = plt.close()