def responses(expt, pre, post, thresh, filter=None):
key = (expt.nwb_file, pre, post)
result_cache = load_cache(result_cache_file)
if key in result_cache:
res = result_cache[key]
if 'avg_amp' not in res:
return None, None, None
avg_amp = res['avg_amp']
avg_trace = Trace(data=res['data'], dt=res['dt'])
n_sweeps = res['n_sweeps']
return avg_amp, avg_trace, n_sweeps
if filter is not None:
responses, artifact = get_response(expt, pre, post, type='pulse')
response_subset = response_filter(responses, freq_range=filter[0], holding_range=filter[1], pulse=True)
if len(response_subset) > 0:
avg_trace, avg_amp, _, _ = get_amplitude(response_subset)
n_sweeps = len(response_subset)
else:
analyzer = DynamicsAnalyzer(expt, pre, post, align_to='spike')
avg_amp, _, avg_trace, _, n_sweeps = analyzer.estimate_amplitude(plot=False)
artifact = analyzer.cross_talk()
if n_sweeps == 0 or artifact > thresh:
result_cache[key] = {}
ret = None, None, n_sweeps
else:
result_cache[key] = {'avg_amp': avg_amp, 'data': avg_trace.data, 'dt': avg_trace.dt, 'n_sweeps': n_sweeps}
ret = avg_amp, avg_trace, n_sweeps
data = pickle.dumps(result_cache)
open(result_cache_file, 'wb').write(data)
print (key)
return ret
def train_response_plot(expt_list, name=None, summary_plots=[None, None], color=None):
grand_train = [[], []]
train_plots = pg.plot()
train_plots.setLabels(left=('Vm', 'V'))
tau =15e-3
lp = 1000
for expt in expt_list:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]:
print ('Processing experiment: %s' % (expt.nwb_file))
train_responses, artifact = get_response(expt, pre, post, analysis_type='train')
if artifact > 0.03e-3:
continue
train_filter = response_filter(train_responses['responses'], freq_range=[50, 50], train=0, delta_t=250)
pulse_offsets = response_filter(train_responses['pulse_offsets'], freq_range=[50, 50], train=0, delta_t=250)
if len(train_filter[0]) > 5:
ind_avg = TraceList(train_filter[0]).mean()
rec_avg = TraceList(train_filter[1]).mean()
rec_avg.t0 = 0.3
grand_train[0].append(ind_avg)
grand_train[1].append(rec_avg)
train_plots.plot(ind_avg.time_values, ind_avg.data)
train_plots.plot(rec_avg.time_values, rec_avg.data)
app.processEvents()
if len(grand_train[0]) != 0:
print (name + ' n = %d' % len(grand_train[0]))
ind_grand_mean = TraceList(grand_train[0]).mean()
rec_grand_mean = TraceList(grand_train[1]).mean()
ind_grand_mean_dec = bessel_filter(exp_deconvolve(ind_grand_mean, tau), lp)
train_plots.addLegend()
train_plots.plot(ind_grand_mean.time_values, ind_grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
train_plots.plot(rec_grand_mean.time_values, rec_grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
train_amps = train_amp([grand_train[0], grand_train[1]], pulse_offsets, '+')
if ind_grand_mean is not None:
train_plots = summary_plot_train(ind_grand_mean, plot=summary_plots[0], color=color,
name=(legend + ' 50 Hz induction'))
train_plots = summary_plot_train(rec_grand_mean, plot=summary_plots[0], color=color)
train_plots2 = summary_plot_train(ind_grand_mean_dec, plot=summary_plots[1], color=color,
name=(legend + ' 50 Hz induction'))
return train_plots, train_plots2, train_amps
else:
print ("No Traces")
return None
def save_fit_psp_test_set():
"""NOTE THIS CODE DOES NOT WORK BUT IS HERE FOR DOCUMENTATION PURPOSES SO
THAT WE CAN TRACE BACK HOW THE TEST DATA WAS CREATED IF NEEDED.
Create a test set of data for testing the fit_psp function. Uses Steph's
original first_puls_feature.py code to filter out error causing data.
Example run statement
python save save_fit_psp_test_set.py --organism mouse --connection ee
Comment in the code that does the saving at the bottom
"""
import pyqtgraph as pg
import numpy as np
import csv
import sys
import argparse
from multipatch_analysis.experiment_list import cached_experiments
from manuscript_figures import get_response, get_amplitude, response_filter, feature_anova, write_cache, trace_plot, \
colors_human, colors_mouse, fail_rate, pulse_qc, feature_kw
from synapse_comparison import load_cache, summary_plot_pulse
from neuroanalysis.data import TraceList, Trace
from neuroanalysis.ui.plot_grid import PlotGrid
from multipatch_analysis.connection_detection import fit_psp
from rep_connections import ee_connections, human_connections, no_include, all_connections, ie_connections, ii_connections, ei_connections
from multipatch_analysis.synaptic_dynamics import DynamicsAnalyzer
from scipy import stats
import time
import pandas as pd
import json
import os
app = pg.mkQApp()
pg.dbg()
pg.setConfigOption('background', 'w')
pg.setConfigOption('foreground', 'k')
parser = argparse.ArgumentParser(description='Enter organism and type of connection you"d like to analyze ex: mouse ee (all mouse excitatory-'
'excitatory). Alternatively enter a cre-type connection ex: sim1-sim1')
parser.add_argument('--organism', dest='organism', help='Select mouse or human')
parser.add_argument('--connection', dest='connection', help='Specify connections to analyze')
args = vars(parser.parse_args(sys.argv[1:]))
all_expts = cached_experiments()
manifest = {'Type': [], 'Connection': [], 'amp': [], 'latency': [],'rise':[], 'rise2080': [], 'rise1090': [], 'rise1080': [],
'decay': [], 'nrmse': [], 'CV': []}
fit_qc = {'nrmse': 8, 'decay': 499e-3}
if args['organism'] == 'mouse':
color_palette = colors_mouse
calcium = 'high'
age = '40-60'
sweep_threshold = 3
threshold = 0.03e-3
connection = args['connection']
if connection == 'ee':
connection_types = ee_connections.keys()
elif connection == 'ii':
connection_types = ii_connections.keys()
elif connection == 'ei':
connection_types = ei_connections.keys()
elif connection == 'ie':
connection_types == ie_connections.keys()
elif connection == 'all':
connection_types = all_connections.keys()
elif len(connection.split('-')) == 2:
c_type = connection.split('-')
if c_type[0] == '2/3':
pre_type = ('2/3', 'unknown')
else:
pre_type = (None, c_type[0])
if c_type[1] == '2/3':
post_type = ('2/3', 'unknown')
else:
post_type = (None, c_type[0])
connection_types = [(pre_type, post_type)]
elif args['organism'] == 'human':
color_palette = colors_human
calcium = None
age = None
sweep_threshold = 5
threshold = None
connection = args['connection']
if connection == 'ee':
connection_types = human_connections.keys()
else:
c_type = connection.split('-')
connection_types = [((c_type[0], 'unknown'), (c_type[1], 'unknown'))]
plt = pg.plot()
scale_offset = (-20, -20)
scale_anchor = (0.4, 1)
holding = [-65, -75]
qc_plot = pg.plot()
grand_response = {}
expt_ids = {}
feature_plot = None
feature2_plot = PlotGrid()
feature2_plot.set_shape(5,1)
feature2_plot.show()
feature3_plot = PlotGrid()
feature3_plot.set_shape(1, 3)
feature3_plot.show()
amp_plot = pg.plot()
synapse_plot = PlotGrid()
synapse_plot.set_shape(len(connection_types), 1)
synapse_plot.show()
for c in range(len(connection_types)):
cre_type = (connection_types[c][0][1], connection_types[c][1][1])
target_layer = (connection_types[c][0][0], connection_types[c][1][0])
conn_type = connection_types[c]
expt_list = all_expts.select(cre_type=cre_type, target_layer=target_layer, calcium=calcium, age=age)
color = color_palette[c]
grand_response[conn_type[0]] = {'trace': [], 'amp': [], 'latency': [], 'rise': [], 'dist': [], 'decay':[], 'CV': [], 'amp_measured': []}
expt_ids[conn_type[0]] = []
synapse_plot[c, 0].addLegend()
for expt in expt_list:
for pre, post in expt.connections:
if [expt.uid, pre, post] in no_include:
continue
cre_check = expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]
layer_check = expt.cells[pre].target_layer == target_layer[0] and expt.cells[post].target_layer == target_layer[1]
if cre_check is True and layer_check is True:
pulse_response, artifact = get_response(expt, pre, post, analysis_type='pulse')
if threshold is not None and artifact > threshold:
continue
response_subset, hold = response_filter(pulse_response, freq_range=[0, 50], holding_range=holding, pulse=True)
if len(response_subset) >= sweep_threshold:
qc_plot.clear()
qc_list = pulse_qc(response_subset, baseline=1.5, pulse=None, plot=qc_plot)
if len(qc_list) >= sweep_threshold:
avg_trace, avg_amp, amp_sign, peak_t = get_amplitude(qc_list)
# if amp_sign is '-':
# continue
# #print ('%s, %0.0f' %((expt.uid, pre, post), hold, ))
# all_amps = fail_rate(response_subset, '+', peak_t)
# cv = np.std(all_amps)/np.mean(all_amps)
#
# # weight parts of the trace during fitting
dt = avg_trace.dt
weight = np.ones(len(avg_trace.data))*10. #set everything to ten initially
weight[int(10e-3/dt):int(12e-3/dt)] = 0. #area around stim artifact
weight[int(12e-3/dt):int(19e-3/dt)] = 30. #area around steep PSP rise
# check if the test data dir is there and if not create it
test_data_dir='test_psp_fit'
if not os.path.isdir(test_data_dir):
os.mkdir(test_data_dir)
save_dict={}
save_dict['input']={'data': avg_trace.data.tolist(),
'dtype': str(avg_trace.data.dtype),
'dt': float(avg_trace.dt),
'amp_sign': amp_sign,
'yoffset': 0,
'xoffset': 14e-3,
'avg_amp': float(avg_amp),
'method': 'leastsq',
'stacked': False,
'rise_time_mult_factor': 10.,
'weight': weight.tolist()}
# need to remake trace because different output is created
avg_trace_simple=Trace(data=np.array(save_dict['input']['data']), dt=save_dict['input']['dt']) # create Trace object
psp_fits_original = fit_psp(avg_trace,
sign=save_dict['input']['amp_sign'],
yoffset=save_dict['input']['yoffset'],
xoffset=save_dict['input']['xoffset'],
amp=save_dict['input']['avg_amp'],
method=save_dict['input']['method'],
stacked=save_dict['input']['stacked'],
rise_time_mult_factor=save_dict['input']['rise_time_mult_factor'],
fit_kws={'weights': save_dict['input']['weight']})
psp_fits_simple = fit_psp(avg_trace_simple,
sign=save_dict['input']['amp_sign'],
yoffset=save_dict['input']['yoffset'],
xoffset=save_dict['input']['xoffset'],
amp=save_dict['input']['avg_amp'],
method=save_dict['input']['method'],
stacked=save_dict['input']['stacked'],
rise_time_mult_factor=save_dict['input']['rise_time_mult_factor'],
fit_kws={'weights': save_dict['input']['weight']})
print expt.uid, pre, post
if psp_fits_original.nrmse()!=psp_fits_simple.nrmse():
print ' the nrmse values dont match'
print '\toriginal', psp_fits_original.nrmse()
print '\tsimple', psp_fits_simple.nrmse()
expt_list = all_expts.select(cre_type=cre_type, target_layer=target_layer, calcium=calcium, age=age)
color = color_palette[c]
grand_response[type[0]] = {'trace': [], 'amp': [], 'latency': [], 'rise': [], 'dist': [], 'decay':[], 'CV': [], 'amp_measured': []}
expt_ids[type[0]] = []
synapse_plot[c, 0].addLegend()
for expt in expt_list:
for pre, post in expt.connections:
if [expt.uid, pre, post] in no_include:
continue
cre_check = expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]
layer_check = expt.cells[pre].target_layer == target_layer[0] and expt.cells[post].target_layer == target_layer[1]
if cre_check is True and layer_check is True:
pulse_response, artifact = get_response(expt, pre, post, type='pulse')
if threshold is not None and artifact > threshold:
continue
response_subset, hold = response_filter(pulse_response, freq_range=[0, 50], holding_range=holding, pulse=True)
if len(response_subset) >= sweep_threshold:
qc_plot.clear()
qc_list = pulse_qc(response_subset, baseline=1.5, pulse=None, plot=qc_plot)
if len(qc_list) >= sweep_threshold:
avg_trace, avg_amp, amp_sign, peak_t = get_amplitude(qc_list)
if amp_sign is '-':
continue
#print ('%s, %0.0f' %((expt.uid, pre, post), hold, ))
all_amps = fail_rate(response_subset, '+', peak_t)
cv = np.std(all_amps)/np.mean(all_amps)
# weight parts of the trace during fitting
dt = avg_trace.dt
weight = np.ones(len(avg_trace.data))*10. #set everything to ten initially
weight[int(10e-3/dt):int(12e-3/dt)] = 0. #area around stim artifact
def first_pulse_plot(expt_list,
name=None,
summary_plot=None,
color=None,
scatter=0,
features=False):
amp_plots = pg.plot()
amp_plots.setLabels(left=('Vm', 'V'))
grand_response = []
avg_amps = {'amp': [], 'latency': [], 'rise': []}
for expt in expt_list:
if expt.connections is not None:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[
post].cre_type == cre_type[1]:
all_responses, artifact = get_response(
expt, pre, post, analysis_type='pulse')
if artifact > 0.03e-3:
continue
filtered_responses = response_filter(
all_responses,
freq_range=[0, 50],
holding_range=[-68, -72],
pulse=True)
n_sweeps = len(filtered_responses)
if n_sweeps >= 10:
avg_trace, avg_amp, amp_sign, _ = get_amplitude(
filtered_responses)
if expt.cells[
pre].cre_type in EXCITATORY_CRE_TYPES and avg_amp < 0:
continue
elif expt.cells[
pre].cre_type in INHIBITORY_CRE_TYPES and avg_amp > 0:
continue
avg_trace.t0 = 0
avg_amps['amp'].append(avg_amp)
grand_response.append(avg_trace)
if features is True:
psp_fits = fit_psp(avg_trace,
sign=amp_sign,
yoffset=0,
amp=avg_amp,
method='leastsq',
fit_kws={})
avg_amps['latency'].append(
psp_fits.best_values['xoffset'] - 10e-3)
avg_amps['rise'].append(
psp_fits.best_values['rise_time'])
current_connection_HS = post, pre
if len(expt.connections) > 1 and args.recip is True:
for i, x in enumerate(expt.connections):
if x == current_connection_HS: # determine if a reciprocal connection
amp_plots.plot(avg_trace.time_values,
avg_trace.data,
pen={
'color': 'r',
'width': 1
})
break
elif x != current_connection_HS and i == len(
expt.connections
) - 1: # reciprocal connection was not found
amp_plots.plot(avg_trace.time_values,
avg_trace.data)
else:
amp_plots.plot(avg_trace.time_values,
avg_trace.data)
app.processEvents()
if len(grand_response) != 0:
print(name + ' n = %d' % len(grand_response))
grand_mean = TraceList(grand_response).mean()
grand_amp = np.mean(np.array(avg_amps['amp']))
grand_amp_sem = stats.sem(np.array(avg_amps['amp']))
amp_plots.addLegend()
amp_plots.plot(grand_mean.time_values,
grand_mean.data,
pen={
'color': 'g',
'width': 3
},
name=name)
amp_plots.addLine(y=grand_amp, pen={'color': 'g'})
if grand_mean is not None:
print(legend + ' Grand mean amplitude = %f +- %f' %
(grand_amp, grand_amp_sem))
if features is True:
feature_list = (avg_amps['amp'], avg_amps['latency'],
avg_amps['rise'])
labels = (['Vm', 'V'], ['t', 's'], ['t', 's'])
titles = ('Amplitude', 'Latency', 'Rise time')
else:
feature_list = [avg_amps['amp']]
labels = (['Vm', 'V'])
titles = 'Amplitude'
summary_plots = summary_plot_pulse(feature_list[0],
labels=labels,
titles=titles,
i=scatter,
grand_trace=grand_mean,
plot=summary_plot,
color=color,
name=legend)
return avg_amps, summary_plots
else:
print("No Traces")
return avg_amps, None
def train_response_plot(expt_list,
name=None,
summary_plots=[None, None],
color=None):
grand_train = [[], []]
train_plots = pg.plot()
train_plots.setLabels(left=('Vm', 'V'))
tau = 15e-3
lp = 1000
for expt in expt_list:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[
post].cre_type == cre_type[1]:
print('Processing experiment: %s' % (expt.nwb_file))
train_responses, artifact = get_response(expt,
pre,
post,
analysis_type='train')
if artifact > 0.03e-3:
continue
train_filter = response_filter(train_responses['responses'],
freq_range=[50, 50],
train=0,
delta_t=250)
pulse_offsets = response_filter(
train_responses['pulse_offsets'],
freq_range=[50, 50],
train=0,
delta_t=250)
if len(train_filter[0]) > 5:
ind_avg = TraceList(train_filter[0]).mean()
rec_avg = TraceList(train_filter[1]).mean()
rec_avg.t0 = 0.3
grand_train[0].append(ind_avg)
grand_train[1].append(rec_avg)
train_plots.plot(ind_avg.time_values, ind_avg.data)
train_plots.plot(rec_avg.time_values, rec_avg.data)
app.processEvents()
if len(grand_train[0]) != 0:
print(name + ' n = %d' % len(grand_train[0]))
ind_grand_mean = TraceList(grand_train[0]).mean()
rec_grand_mean = TraceList(grand_train[1]).mean()
ind_grand_mean_dec = bessel_filter(exp_deconvolve(ind_grand_mean, tau),
lp)
train_plots.addLegend()
train_plots.plot(ind_grand_mean.time_values,
ind_grand_mean.data,
pen={
'color': 'g',
'width': 3
},
name=name)
train_plots.plot(rec_grand_mean.time_values,
rec_grand_mean.data,
pen={
'color': 'g',
'width': 3
},
name=name)
train_amps = train_amp([grand_train[0], grand_train[1]], pulse_offsets,
'+')
if ind_grand_mean is not None:
train_plots = summary_plot_train(ind_grand_mean,
plot=summary_plots[0],
color=color,
name=(legend +
' 50 Hz induction'))
train_plots = summary_plot_train(rec_grand_mean,
plot=summary_plots[0],
color=color)
train_plots2 = summary_plot_train(ind_grand_mean_dec,
plot=summary_plots[1],
color=color,
name=(legend +
' 50 Hz induction'))
return train_plots, train_plots2, train_amps
else:
print("No Traces")
return None
connection_type = tuple(connection_type.split('-'))
expt_id, pre_cell, post_cell = connection_types[
connection_type] #all_connections[connection_type]
expt = all_expts[expt_id]
if expt.cells[pre_cell].cre_type in EXCITATORY_CRE_TYPES:
holding = holding_e
sign = '+'
elif expt.cells[pre_cell].cre_type in INHIBITORY_CRE_TYPES:
holding = holding_i
sign = '-'
pulse_response, artifact = get_response(expt,
pre_cell,
post_cell,
type='pulse')
sweep_list = response_filter(pulse_response,
freq_range=[0, 50],
holding_range=holding,
pulse=True)
n_sweeps = len(sweep_list)
if n_sweeps > sweep_threshold:
qc_list = pulse_qc(sweep_list,
baseline=2.5,
pulse=None,
plot=grid[row, 1])
qc_sweeps = len(qc_list)
if qc_sweeps > sweep_threshold:
avg_first_pulse = trace_avg(qc_list)
avg_first_pulse.t0 = 0
if plot_sweeps is True:
for current_sweep in qc_list:
current_sweep.t0 = 0
bsub_trace = bsub(current_sweep)
for pre, post in expt.connections:
if [expt.uid, pre, post] in no_include:
continue
cre_check = expt.cells[pre].cre_type == cre_type[0] and expt.cells[
post].cre_type == cre_type[1]
layer_check = expt.cells[pre].target_layer == target_layer[
0] and expt.cells[post].target_layer == target_layer[1]
if cre_check is True and layer_check is True:
pulse_response, artifact = get_response(expt,
pre,
post,
type='pulse')
if threshold is not None and artifact > threshold:
continue
response_subset = response_filter(pulse_response,
freq_range=[0, 50],
holding_range=[-68, -72],
pulse=True)
if len(response_subset) >= sweep_threshold:
qc_plot.clear()
qc_list = pulse_qc(response_subset,
baseline=2.5,
pulse=None,
plot=qc_plot)
if len(qc_list) >= sweep_threshold:
avg_trace, avg_amp, amp_sign, peak_t = get_amplitude(
qc_list)
if amp_sign is '-':
continue
#grand_response[cre_type[0]]['fail_rate'].append(fail_rate(response_subset, '+', peak_t))
psp_fits = fit_psp(avg_trace,
sign=amp_sign,
def save_fit_psp_test_set():
"""NOTE THIS CODE DOES NOT WORK BUT IS HERE FOR DOCUMENTATION PURPOSES SO
THAT WE CAN TRACE BACK HOW THE TEST DATA WAS CREATED IF NEEDED.
Create a test set of data for testing the fit_psp function. Uses Steph's
original first_puls_feature.py code to filter out error causing data.
Example run statement
python save save_fit_psp_test_set.py --organism mouse --connection ee
Comment in the code that does the saving at the bottom
"""
import pyqtgraph as pg
import numpy as np
import csv
import sys
import argparse
from multipatch_analysis.experiment_list import cached_experiments
from manuscript_figures import get_response, get_amplitude, response_filter, feature_anova, write_cache, trace_plot, \
colors_human, colors_mouse, fail_rate, pulse_qc, feature_kw
from synapse_comparison import load_cache, summary_plot_pulse
from neuroanalysis.data import TraceList, Trace
from neuroanalysis.ui.plot_grid import PlotGrid
from multipatch_analysis.connection_detection import fit_psp
from rep_connections import ee_connections, human_connections, no_include, all_connections, ie_connections, ii_connections, ei_connections
from multipatch_analysis.synaptic_dynamics import DynamicsAnalyzer
from scipy import stats
import time
import pandas as pd
import json
import os
app = pg.mkQApp()
pg.dbg()
pg.setConfigOption('background', 'w')
pg.setConfigOption('foreground', 'k')
parser = argparse.ArgumentParser(
description=
'Enter organism and type of connection you"d like to analyze ex: mouse ee (all mouse excitatory-'
'excitatory). Alternatively enter a cre-type connection ex: sim1-sim1')
parser.add_argument('--organism',
dest='organism',
help='Select mouse or human')
parser.add_argument('--connection',
dest='connection',
help='Specify connections to analyze')
args = vars(parser.parse_args(sys.argv[1:]))
all_expts = cached_experiments()
manifest = {
'Type': [],
'Connection': [],
'amp': [],
'latency': [],
'rise': [],
'rise2080': [],
'rise1090': [],
'rise1080': [],
'decay': [],
'nrmse': [],
'CV': []
}
fit_qc = {'nrmse': 8, 'decay': 499e-3}
if args['organism'] == 'mouse':
color_palette = colors_mouse
calcium = 'high'
age = '40-60'
sweep_threshold = 3
threshold = 0.03e-3
connection = args['connection']
if connection == 'ee':
connection_types = ee_connections.keys()
elif connection == 'ii':
connection_types = ii_connections.keys()
elif connection == 'ei':
connection_types = ei_connections.keys()
elif connection == 'ie':
connection_types == ie_connections.keys()
elif connection == 'all':
connection_types = all_connections.keys()
elif len(connection.split('-')) == 2:
c_type = connection.split('-')
if c_type[0] == '2/3':
pre_type = ('2/3', 'unknown')
else:
pre_type = (None, c_type[0])
if c_type[1] == '2/3':
post_type = ('2/3', 'unknown')
else:
post_type = (None, c_type[0])
connection_types = [(pre_type, post_type)]
elif args['organism'] == 'human':
color_palette = colors_human
calcium = None
age = None
sweep_threshold = 5
threshold = None
connection = args['connection']
if connection == 'ee':
connection_types = human_connections.keys()
else:
c_type = connection.split('-')
connection_types = [((c_type[0], 'unknown'), (c_type[1],
'unknown'))]
plt = pg.plot()
scale_offset = (-20, -20)
scale_anchor = (0.4, 1)
holding = [-65, -75]
qc_plot = pg.plot()
grand_response = {}
expt_ids = {}
feature_plot = None
feature2_plot = PlotGrid()
feature2_plot.set_shape(5, 1)
feature2_plot.show()
feature3_plot = PlotGrid()
feature3_plot.set_shape(1, 3)
feature3_plot.show()
amp_plot = pg.plot()
synapse_plot = PlotGrid()
synapse_plot.set_shape(len(connection_types), 1)
synapse_plot.show()
for c in range(len(connection_types)):
cre_type = (connection_types[c][0][1], connection_types[c][1][1])
target_layer = (connection_types[c][0][0], connection_types[c][1][0])
conn_type = connection_types[c]
expt_list = all_expts.select(cre_type=cre_type,
target_layer=target_layer,
calcium=calcium,
age=age)
color = color_palette[c]
grand_response[conn_type[0]] = {
'trace': [],
'amp': [],
'latency': [],
'rise': [],
'dist': [],
'decay': [],
'CV': [],
'amp_measured': []
}
expt_ids[conn_type[0]] = []
synapse_plot[c, 0].addLegend()
for expt in expt_list:
for pre, post in expt.connections:
if [expt.uid, pre, post] in no_include:
continue
cre_check = expt.cells[pre].cre_type == cre_type[
0] and expt.cells[post].cre_type == cre_type[1]
layer_check = expt.cells[pre].target_layer == target_layer[
0] and expt.cells[post].target_layer == target_layer[1]
if cre_check is True and layer_check is True:
pulse_response, artifact = get_response(
expt, pre, post, analysis_type='pulse')
if threshold is not None and artifact > threshold:
continue
response_subset, hold = response_filter(
pulse_response,
freq_range=[0, 50],
holding_range=holding,
pulse=True)
if len(response_subset) >= sweep_threshold:
qc_plot.clear()
qc_list = pulse_qc(response_subset,
baseline=1.5,
pulse=None,
plot=qc_plot)
if len(qc_list) >= sweep_threshold:
avg_trace, avg_amp, amp_sign, peak_t = get_amplitude(
qc_list)
# if amp_sign is '-':
# continue
# #print ('%s, %0.0f' %((expt.uid, pre, post), hold, ))
# all_amps = fail_rate(response_subset, '+', peak_t)
# cv = np.std(all_amps)/np.mean(all_amps)
#
# # weight parts of the trace during fitting
dt = avg_trace.dt
weight = np.ones(
len(avg_trace.data
)) * 10. #set everything to ten initially
weight[int(10e-3 / dt):int(
12e-3 / dt)] = 0. #area around stim artifact
weight[int(12e-3 / dt):int(
19e-3 / dt)] = 30. #area around steep PSP rise
# check if the test data dir is there and if not create it
test_data_dir = 'test_psp_fit'
if not os.path.isdir(test_data_dir):
os.mkdir(test_data_dir)
save_dict = {}
save_dict['input'] = {
'data': avg_trace.data.tolist(),
'dtype': str(avg_trace.data.dtype),
'dt': float(avg_trace.dt),
'amp_sign': amp_sign,
'yoffset': 0,
'xoffset': 14e-3,
'avg_amp': float(avg_amp),
'method': 'leastsq',
'stacked': False,
'rise_time_mult_factor': 10.,
'weight': weight.tolist()
}
# need to remake trace because different output is created
avg_trace_simple = Trace(
data=np.array(save_dict['input']['data']),
dt=save_dict['input']
['dt']) # create Trace object
psp_fits_original = fit_psp(
avg_trace,
sign=save_dict['input']['amp_sign'],
yoffset=save_dict['input']['yoffset'],
xoffset=save_dict['input']['xoffset'],
amp=save_dict['input']['avg_amp'],
method=save_dict['input']['method'],
stacked=save_dict['input']['stacked'],
rise_time_mult_factor=save_dict['input']
['rise_time_mult_factor'],
fit_kws={
'weights': save_dict['input']['weight']
})
psp_fits_simple = fit_psp(
avg_trace_simple,
sign=save_dict['input']['amp_sign'],
yoffset=save_dict['input']['yoffset'],
xoffset=save_dict['input']['xoffset'],
amp=save_dict['input']['avg_amp'],
method=save_dict['input']['method'],
stacked=save_dict['input']['stacked'],
rise_time_mult_factor=save_dict['input']
['rise_time_mult_factor'],
fit_kws={
'weights': save_dict['input']['weight']
})
print expt.uid, pre, post
if psp_fits_original.nrmse(
) != psp_fits_simple.nrmse():
print ' the nrmse values dont match'
print '\toriginal', psp_fits_original.nrmse()
print '\tsimple', psp_fits_simple.nrmse()
def first_pulse_plot(expt_list, name=None, summary_plot=None, color=None, scatter=0, features=False):
amp_plots = pg.plot()
amp_plots.setLabels(left=('Vm', 'V'))
grand_response = []
avg_amps = {'amp': [], 'latency': [], 'rise': []}
for expt in expt_list:
if expt.connections is not None:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]:
all_responses, artifact = get_response(expt, pre, post, analysis_type='pulse')
if artifact > 0.03e-3:
continue
filtered_responses = response_filter(all_responses, freq_range=[0, 50], holding_range=[-68, -72], pulse=True)
n_sweeps = len(filtered_responses)
if n_sweeps >= 10:
avg_trace, avg_amp, amp_sign, _ = get_amplitude(filtered_responses)
if expt.cells[pre].cre_type in EXCITATORY_CRE_TYPES and avg_amp < 0:
continue
elif expt.cells[pre].cre_type in INHIBITORY_CRE_TYPES and avg_amp > 0:
continue
avg_trace.t0 = 0
avg_amps['amp'].append(avg_amp)
grand_response.append(avg_trace)
if features is True:
psp_fits = fit_psp(avg_trace, sign=amp_sign, yoffset=0, amp=avg_amp, method='leastsq',
fit_kws={})
avg_amps['latency'].append(psp_fits.best_values['xoffset'] - 10e-3)
avg_amps['rise'].append(psp_fits.best_values['rise_time'])
current_connection_HS = post, pre
if len(expt.connections) > 1 and args.recip is True:
for i,x in enumerate(expt.connections):
if x == current_connection_HS: # determine if a reciprocal connection
amp_plots.plot(avg_trace.time_values, avg_trace.data, pen={'color': 'r', 'width': 1})
break
elif x != current_connection_HS and i == len(expt.connections) - 1: # reciprocal connection was not found
amp_plots.plot(avg_trace.time_values, avg_trace.data)
else:
amp_plots.plot(avg_trace.time_values, avg_trace.data)
app.processEvents()
if len(grand_response) != 0:
print(name + ' n = %d' % len(grand_response))
grand_mean = TraceList(grand_response).mean()
grand_amp = np.mean(np.array(avg_amps['amp']))
grand_amp_sem = stats.sem(np.array(avg_amps['amp']))
amp_plots.addLegend()
amp_plots.plot(grand_mean.time_values, grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
amp_plots.addLine(y=grand_amp, pen={'color': 'g'})
if grand_mean is not None:
print(legend + ' Grand mean amplitude = %f +- %f' % (grand_amp, grand_amp_sem))
if features is True:
feature_list = (avg_amps['amp'], avg_amps['latency'], avg_amps['rise'])
labels = (['Vm', 'V'], ['t', 's'], ['t', 's'])
titles = ('Amplitude', 'Latency', 'Rise time')
else:
feature_list = [avg_amps['amp']]
labels = (['Vm', 'V'])
titles = 'Amplitude'
summary_plots = summary_plot_pulse(feature_list[0], labels=labels, titles=titles, i=scatter,
grand_trace=grand_mean, plot=summary_plot, color=color, name=legend)
return avg_amps, summary_plots
else:
print ("No Traces")
return avg_amps, None
maxYtrain = []
maxYdec = []
for row in range(len(connection_types)):
connection_type = connection_types.keys()[row]
if type(connection_type) is not tuple:
connection_type = tuple(connection_type.split('-'))
expt_id, pre_cell, post_cell = connection_types[connection_type] #all_connections[connection_type]
expt = all_expts[expt_id]
if expt.cells[pre_cell].cre_type in EXCITATORY_CRE_TYPES:
holding = holding_e
sign = '+'
elif expt.cells[pre_cell].cre_type in INHIBITORY_CRE_TYPES:
holding = holding_i
sign = '-'
pulse_response, artifact = get_response(expt, pre_cell, post_cell, analysis_type='pulse')
sweep_list = response_filter(pulse_response, freq_range=[0, 50], holding_range=holding, pulse=True)
n_sweeps = len(sweep_list[0])
if n_sweeps > sweep_threshold:
qc_list = pulse_qc(sweep_list, baseline=2, pulse=None, plot=pg.plot())
qc_sweeps = len(qc_list)
if qc_sweeps > sweep_threshold:
avg_first_pulse = trace_avg(qc_list)
avg_first_pulse.t0 = 0
if plot_sweeps is True:
for current_sweep in qc_list:
current_sweep.t0 = 0
bsub_trace = bsub(current_sweep)
trace_plot(bsub_trace, sweep_color, plot=grid[row, 0], x_range=[-2e-3, 27e-3])
trace_plot(avg_first_pulse, avg_color, plot=grid[row, 0], x_range=[-2e-3, 27e-3])
label = pg.LabelItem('%s, n = %d' % (connection_type, qc_sweeps))
label.setParentItem(grid[row, 0].vb)
