def main(**kwargs):
p = Path(kwargs['folder_path'])
f_list = list(p.glob('**/*dat'))
outdir = p / 'results_animated'
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
for si, segment in enumerate(segments):
#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
v = ssig.resample(v, 5000)
tf = trace.t_stop
ti = trace.t_start
t = np.linspace(0, float(tf - ti), len(v))
t = ssig.resample(t, 5000)
data = np.array([t, v])
fig1 = plt.figure()
l, = plt.plot([], [], 'r-')
plt.xlim(0, 1.2)
plt.ylim(-100, 60)
plt.xlabel('time(s)')
plt.ylabel('signal amplitude (mV)')
plt.title('training the slice')
line_ani = animation.FuncAnimation(fig1,
update_line,
5000,
fargs=(data, l),
interval=1,
blit=True)
line_ani.save(f'{outdir}/{si}_{ti}_lines.mp4', writer=writer)
plt.close()
"""
Created on Thu Sep 19 11:58:52 2019
@author: anzal
"""
import os
import neo.io as nio
import numpy as np
import matplotlib.pyplot as plt
from allensdk.ephys.ephys_extractor import EphysSweepFeatureExtractor
#Trace_with_features.spike_feature_keys()
file_to_read = nio.StimfitIO('/mnt/5D4B-FA71/Data/190822/trace_alone.dat')
segments = file_to_read.read_block().segments
iteration_number = 0
for segment in segments:
#print(segment)
analog_signals = segment.analogsignals
#print(analog_signals)
for trace in analog_signals:
iteration_number += 1
#print(trace)
sampling_rate = trace.sampling_rate
#print(sampling_rate)
v = trace
v = np.ravel(v)
#pick only traces with current clamp data
if '1.0 pA' == str(v.units):
pass
try:
os.mkdir(folder_to_read + '/Results/Current_clamp')
except:
pass
#make the file path
results_folder = str(folder_to_read + '/Results/Current_clamp/')
#list out all the files with .dat extension for plotting
for root, dirs, files in os.walk(folder_to_read):
for file in files:
if file.endswith(".dat"):
print(file)
file_name = str(file).split(".")[0]
print(file_name)
#import the file of interest
file_to_read = nio.StimfitIO(root + file)
segments = file_to_read.read_block().segments
#segments = ()
iteration_number = 0
segment_number = 0
Vm_trail = []
threshold_state = 0
for segment in segments:
segment_number += 1
# print(segment)
analog_signals = segment.analogsignals
# print(analog_signals)
for trace in analog_signals:
iteration_number += 1
# print(trace)
v = trace
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()
"""
import os
import numpy as np
import neo.io as nio
import matplotlib.pyplot as plt
#set the directory path with files to read
folder_to_read = "/home/anzal/Documents/trial_sulu"
#list out all the files with .mat extension for plotting
for root, dirs, files in os.walk(folder_to_read):
for file in files:
if file.endswith(".abf"):
print(file)
#import the file of interest
file_to_read = nio.StimfitIO(root + file)
segments = ()
for trace in file_to_read.read_block().segments:
segments += (trace, )
print(segments)
for segment in segments:
for trace in segment.analogsignals:
print(trace.sampling_rate)
v = trace
print(trace)
ti = trace.t_start
print(trace.t_start)
tf = trace.t_stop
print(trace.t_stop)
tp = float(tf - ti)
print(float(tf - ti))
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()
def main(**kwargs):
p = Path(kwargs['folder_path'])
f_list = list(p.glob('**/*abf'))
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
# 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 mV' == str(v.units):
# print('x 1mV')
# 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 = 'Voltage_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([0,10])
# ax.set_xlim([2,5])
# ax.set_title('recording type: '+str(trace.name).split("-")[0]+' '+str(len(segments))+' '+str(trace)+ ' of segment number '+ str(seg_no))
# outfile = str(outdir)+"/"+str(f_path.stem)+" "+f'{fI}_compiled.png'
# plt.savefig(outfile, bbox_inches = "tight")
# print('---> Saved to %s' % outfile)
# fig = plt.close()
itr_no = 0
seg_no = 0
thresh_state = 0
fig1 = plt.figure(figsize=(16, 12))
sub_plt_no = 0
for si, segment in enumerate(segments):
seg_no += 1
fI = 10000 * fi + si
#print('analysing the segment ',segment)
analog_signals = segment.analogsignals
subplot_index = np.arange(
1,
len(segments) * len(analog_signals) + 1).reshape(
[len(segments), len(analog_signals)]).transpose().ravel()
#print(analog_signals)
for ti, trace in enumerate(analog_signals):
ax1 = fig1.add_subplot(len(analog_signals), len(segments),
subplot_index[sub_plt_no])
sub_plt_no += 1
TI = 100001 * fI + ti
itr_no += 1
v = trace
v = np.ravel(v)
if '1.0 mV' == str(v.units):
print('x 1mV')
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 = 'Voltage_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.figeext(.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([0,10])
ax1.set_xlim([2, 5])
ax1.set_title(str(trace.name).split("-")[0])
plt.suptitle(
"Readings from 3 different channels(subplot titlle:channel Id)",
size=16)
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=0.9,
wspace=0.3,
hspace=0.5)
# plt.tight_layout()
outfile = str(outdir) + "/" + str(f_path.stem) + " " + f'{TI}.png'
plt.savefig(outfile, bbox_inches="tight")
print('---> Saved to %s' % outfile)
plt.close()
except:
pass
#make the file path
results_folder = str(folder_to_read + '/Results/Current_clamp/')
#list out all the files with .dat extension for plotting
files = glob.glob(folder_to_read + '/*.dat', recursive=True)
#for root, dirs, files in os.walk(folder_to_read):
for file in files:
# if file.endswith(".dat"):
# print(file)
file_name = str(file).split('/')[-1].split(".")[0]
# print (file_name)
#import the file of interest
# file_to_read = nio.StimfitIO(file)
segments = nio.StimfitIO(file).read_block().segments
# segments = ()
iteration_number = 0
segment_number = 0
Vm_trail = []
threshold_state = 0
for segment in segments:
segment_number += 1
# print(segment)
analog_signals = segment.analogsignals
# print(analog_signals)
for trace in analog_signals:
iteration_number += 1
# print(trace)
v = trace
v = np.ravel(v)
