def __init__(self, t_on, t_off, n_summary=6, seed=None):
"""See SummaryStats.py for docstring"""
super(DAPSummaryStats, self).__init__(seed=seed)
self.t_on = t_on
self.t_off = t_off
self.n_summary = n_summary
data_dir = '/home/ateska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat'
I, v, t, t_on, t_off, dt = load_current(data_dir,
protocol='rampIV',
ramp_amp=3.1)
self.t = t
self.v0 = v
from dap import DAPcython
# General Settings Pick
n_rounds = 1
n_summary = 17
n_samples = 5
n_hiddens = [15]
n_components = 1
dt = 0.01
reg_lambda = 0.01
n_params = 2
# Load the current
data_dir = '/home/alteska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat' # best cell
I, v, t, t_on, t_off, dt = load_current(data_dir, protocol='IV', ramp_amp=1)
params, labels = obs_params(reduced_model=True)
print(params)
print(labels)
# Set up themodel
dap = DAPcython(-75, params)
U = dap.simulate(dt, t, I)
# generate data format for SNPE / OBSERVABLE
x_o = {'data': U.reshape(-1),
'time': t,
'dt': dt,
'I': I}
# General Settings Pick
n_rounds = 1
n_summary = 17
n_samples = 1000
n_hiddens = [10]
n_components = 1
dt = 0.01
reg_lambda = 0.01
n_params = 2
# Load the current
data_dir = '/home/alteska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat' # best cell
protocol = 'rampIV' # 'IV' # 'rampIV' # 'Zap20'
ramp_amp = 3.1
I, v, t, t_on, t_off, dt = load_current(data_dir,
protocol=protocol,
ramp_amp=ramp_amp)
I_step, v_step, t_step, t_on_step, t_off_step, dt_step = load_current(
data_dir, protocol='IV', ramp_amp=1)
tstep = t_step[0:16200]
Istep = I_step[2500:18700]
vstep = v_step[2500:18700]
I_all = [I, Istep]
dt_all = [dt, dt_step]
params, labels = obs_params(reduced_model=True)
params = params[:n_params]
print(params)
print(labels)
from dap.utils import load_current
from dap.analyze_APs import (get_spike_characteristics,
get_spike_characteristics_dict, check_measures)
data_dir = '/home/ateska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat' # best cell
# data_dir = '/home/ateska/Desktop/LFI_DAP/data/rawData/2015_08_11d.dat' # second best cell
# load the data
i_inj, v, t, t_on, t_off, dt = load_current(data_dir,
protocol='rampIV',
ramp_amp=3.1)
# extract AP/DAP characteristics
return_characteristics = [
'AP_amp', 'AP_width', 'DAP_amp', 'DAP_width', 'DAP_deflection', 'DAP_time'
]
get_spike_characteristics_dict = get_spike_characteristics_dict(
for_data=True) # standard parameters to use
# AP_amp, AP_width, DAP_amp, DAP_width, DAP_deflection, DAP_time = get_spike_characteristics(v, t, return_characteristics,
# v_rest=v[0], std_idx_times=(0, 1),
# check=True, **get_spike_characteristics_dict)
characteristics = get_spike_characteristics(v,
t,
return_characteristics,
v_rest=v[0],
std_idx_times=(0, 1),
check=True,
**get_spike_characteristics_dict)
check_measures(v, t, characteristics)
# load the file
directory = './parameters/'
dir = glob.glob(directory + '*')
fname_start = dir[0].find('dap_')
fname_stop = dir[0].find('n_')
fname = dir[0][fname_start:fname_stop]
df_param = pd.read_csv(fname + '.csv')
df_param.set_index('Unnamed: 0', inplace=True)
# calculate DAP
# load the input data
Ir, vr, tr, t_onr, t_offr, dtr = load_current(data_dir,
protocol='rampIV',
ramp_amp=3.1)
Is, vs, ts, t_ons, t_offs, dts = load_current(data_dir,
protocol='IV',
ramp_amp=1)
# get traces for both currents
U_steps, U_ramps = [], []
for i, j in tqdm(df_param.iterrows()):
# get parameters
par_temp = j.values
# define a model
dap = DAPcython(-75, j)
reg_lambda = 0.01
# picking experiments observables
observables = {
'loss.lprobs', 'imputation_values', 'h1.mW', 'h1.mb', 'h2.mW', 'h2.mb',
'weights.mW', 'weights.mb', 'means.mW0', 'means.mW1', 'means.mb0',
'means.mb1', 'precisions.mW0', 'precisions.mW1', 'precisions.mb0',
'precisions.mb1'
}
# Load the current
data_dir = '/home/alteska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat' # best cell
protocol = 'IV' # 'IV' # 'rampIV' # 'Zap20'
ramp_amp = 1
I, v, t, t_on, t_off, dt = load_current(data_dir,
protocol=protocol,
ramp_amp=ramp_amp)
I_ramp, v_ramp, t_ramp, t_on_ramp, t_off_ramp, dt_ramp = load_current(
data_dir, protocol='rampIV', ramp_amp=3.1)
# Set up themodel
params, labels = obs_params(reduced_model=True)
dap = DAPcython(-75, params)
U_ramp = dap.simulate(dt_ramp, t_ramp, I_ramp)
U = dap.simulate(dt, t, I)
# generate data format for SNPE / OBSERVABLE
x_o = {'data': v.reshape(-1), 'time': t, 'dt': dt, 'I': I}
# Setup Priors
prior_min, prior_max, labels = load_prior_ranges(n_params)
import timeit
import matplotlib.pyplot as plt
from dap import DAPcython
from dap.utils import load_current, obs_params_gbar
params, labels = obs_params_gbar()
data_dir = '/home/ateska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat' # best cell
# data_dir = '/home/ateska/Desktop/LFI_DAP/data/rawData/2015_08_11d.dat' # second best cell
# load the data
I, v, t, t_on, t_off, dt = load_current(data_dir, protocol='rampIV', ramp_amp=3.1)
I_iv, v_iv, t_iv, t_on_iv, t_off_iv, dt_iv = load_current(data_dir, protocol='IV', ramp_amp=1)
# define and run the model
dap = DAPcython(-75, params)
U = dap.simulate(dt, t, I)
U_iv = dap.simulate(dt_iv, t_iv, I_iv)
print("cython:", timeit.timeit(lambda: dap.simulate(dt, t, I), number=int(1)))
print("cython:", timeit.timeit(lambda: dap.simulate(dt_iv, t_iv, I_iv), number=int(1)))
print(dt, dt_iv)
fig, ax = plt.subplots(2, 1, figsize=(10,20))
ax[0].plot(t, I)
ax[0].plot(t, v)
ax[0].plot(t, U)
ax[1].plot(t_iv, I_iv)
ax[1].plot(t_iv, v_iv)
