import os
import re
import numpy as np
import scipy.io as sio
from scipy.fftpack import fft
import pandas as pd
from .movie import Movie, FullFieldFlashMovie
pd.set_option('display.width', 1000)
pd.set_option('display.max_columns', 100)
#################################################
[docs]def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
##################################################
[docs]def compute_FFT_OneCycle(FR, TF, downsample):
one_cyc = np.int(((1000. / downsample) / TF))
FR_cyc = list(chunks(FR, one_cyc))
if (TF == 15. or TF == 8.):
FR_cyc = FR_cyc[:-1]
FR_cyc_avg = np.mean(FR_cyc, axis=0)
y = FR_cyc_avg
AMP = 2 * np.abs(fft(y) / len(y))
F0 = 0.5 * AMP[0]
assert (F0 - np.mean(y) < 1.e-4)
F1 = AMP[1]
return F0, F1
##################################################
[docs]def create_ff_mov(frame_rate, tst, tend, xrng, yrng):
ff_mov_on = FullFieldFlashMovie(np.arange(xrng), np.arange(yrng), tst, tend, frame_rate=frame_rate,
max_intensity=1).full(t_max=tend) # +0.5)
ff_mov_off = FullFieldFlashMovie(np.arange(xrng), np.arange(yrng), tst, tend, frame_rate=frame_rate,
max_intensity=-1).full(t_max=tend) # +0.5)
return ff_mov_on, ff_mov_off
##################################################
[docs]def create_grating_movie_list(gr_dir_name):
gr_fnames = os.listdir(gr_dir_name)
gr_fnames_ord = sorted(gr_fnames, key=lambda x: (int(re.sub('\D', '', x)), x))
gr_mov_list = []
for fname in gr_fnames_ord[:5]:
movie_file = os.path.join(gr_dir_name, fname)
m_file = sio.loadmat(movie_file)
m_data_raw = m_file['mov'].T
swid = np.shape(m_data_raw)[1]
res = int(np.sqrt(swid / (8 * 16)))
m_data = np.reshape(m_data_raw, (3000, 8 * res, 16 * res))
m1 = Movie(m_data[:500, :, :], row_range=np.linspace(0, 120, m_data.shape[1], endpoint=True), col_range=np.linspace(0, 120, m_data.shape[2], endpoint=True), frame_rate=1000.)
gr_mov_list.append(m1)
return gr_mov_list
"""
##################################################
metrics_dir = os.path.join(os.path.dirname(__file__), 'cell_metrics')
def get_data_metrics_for_each_subclass(ctype):
# Load csv file into dataframe
if ctype.find('_sus') >= 0:
prs_fn = os.path.join(metrics_dir, '{}_cells_v3.csv'.format(ctype))
else:
prs_fn = os.path.join(metrics_dir, '{}_cell_data.csv'.format(ctype))
prs_df = pd.read_csv(prs_fn)
N_class, nmet = np.shape(prs_df)
# Group data by subclasses based on max F0 vals
exp_df = prs_df.iloc[:, [13, 14, 17, 18, 28, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54]].copy() # Bl_lat,Wh_lat,Bl_si, wh_si, spont, 5 F0s, 5 F1s
sub_df = exp_df.iloc[:, [5, 6, 7, 8, 9]]
exp_df['max_tf'] = sub_df.idxmax(axis=1).values # sub_df.idxmax(axis=1)
exp_means = exp_df.groupby(['max_tf']).mean()
exp_std = exp_df.groupby(['max_tf']).std()
exp_nsub = exp_df.groupby(['max_tf']).size()
max_ind_arr = np.where(exp_nsub == np.max(exp_nsub))
max_nsub_ind = max_ind_arr[0][0]
# Get means and std dev for subclasses
exp_prs_dict = {}
for scn in np.arange(len(exp_nsub)):
f0_exp = exp_means.iloc[scn, 5:10].values
f1_exp = exp_means.iloc[scn, 10:].values
spont_exp = exp_means.iloc[scn, 4:5].values
if ctype.find('OFF') >= 0:
si_exp = exp_means.iloc[scn, 2:3].values
ttp_exp = exp_means.iloc[scn, 0:1].values
elif ctype.find('ON') >= 0:
si_exp = exp_means.iloc[scn, 3:4].values
ttp_exp = exp_means.iloc[scn, 1:2].values
else:
si_exp = np.NaN * np.ones((1, 5))
ttp_exp = np.NaN * np.ones((1, 2))
nsub = exp_nsub.iloc[scn]
if nsub == 1:
f0_std = np.mean(exp_std.iloc[max_nsub_ind, 5:10].values) * np.ones((1, 5))
f1_std = np.mean(exp_std.iloc[max_nsub_ind, 10:].values) * np.ones((1, 5))
spont_std = np.mean(exp_std.iloc[max_nsub_ind, 4:5].values) * np.ones((1, 5))
if ctype.find('OFF') >= 0:
si_std = np.mean(exp_std.iloc[max_nsub_ind, 2:3].values) * np.ones((1, 5))
elif ctype.find('ON') >= 0:
si_std = np.mean(exp_std.iloc[max_nsub_ind, 3:4].values) * np.ones((1, 5))
else:
si_std = np.NaN * np.ones((1, 5))
else:
f0_std = exp_std.iloc[scn, 5:10].values
f1_std = exp_std.iloc[scn, 10:].values
spont_std = exp_std.iloc[scn, 4:5].values
if ctype.find('OFF') >= 0:
si_std = exp_std.iloc[scn, 2:3].values
elif ctype.find('ON') >= 0:
si_std = exp_std.iloc[scn, 3:4].values
else:
si_std = np.NaN * np.ones((1, 5))
if ctype.find('t') >= 0:
tcross = 40.
si_inf_exp = (si_exp - tcross / 200.) * (200. / (200. - tcross - 40.))
elif ctype.find('s') >= 0:
tcross = 60.
si_inf_exp = (si_exp - tcross / 200.) * (200. / (200. - tcross - 40.))
else:
si_inf_exp = np.nan
dict_key = exp_means.iloc[scn].name[3:]
exp_prs_dict[dict_key] = {}
exp_prs_dict[dict_key]['f0_exp'] = f0_exp
exp_prs_dict[dict_key]['f1_exp'] = f1_exp
exp_prs_dict[dict_key]['spont_exp'] = spont_exp
exp_prs_dict[dict_key]['si_exp'] = si_exp
exp_prs_dict[dict_key]['si_inf_exp'] = si_inf_exp
exp_prs_dict[dict_key]['ttp_exp'] = ttp_exp
exp_prs_dict[dict_key]['f0_std'] = f0_std
exp_prs_dict[dict_key]['f1_std'] = f1_std
exp_prs_dict[dict_key]['spont_std'] = spont_std
exp_prs_dict[dict_key]['si_std'] = si_std
exp_prs_dict[dict_key]['nsub'] = nsub
exp_prs_dict[dict_key]['N_class'] = N_class
return exp_prs_dict
"""
##################################################
[docs]def check_optim_results_against_bounds(bounds, opt_wts, opt_kpeaks):
bds_wts0 = bounds[0]
bds_wts1 = bounds[1]
bds_kp0 = bounds[2]
bds_kp1 = bounds[3]
opt_wts0 = opt_wts[0]
opt_wts1 = opt_wts[1]
opt_kp0 = opt_kpeaks[0]
opt_kp1 = opt_kpeaks[1]
if (opt_wts0 == bds_wts0[0] or opt_wts0 == bds_wts0[1]):
prm_on_bds = 'w0'
elif (opt_wts1 == bds_wts1[0] or opt_wts1 == bds_wts1[1]):
prm_on_bds = 'w1'
elif (opt_kp0 == bds_kp0[0] or opt_kp0 == bds_kp0[1]):
prm_on_bds = 'kp0'
elif (opt_kp1 == bds_kp1[0] or opt_kp1 == bds_kp1[1]):
prm_on_bds = 'kp1'
else:
prm_on_bds = 'None'
return prm_on_bds
[docs]def cross_from_above(x, threshold):
"""Return the indices into *x* where *x* crosses some threshold from above."""
x = np.asarray(x)
ind = np.nonzero((x[:-1] >= threshold) & (x[1:] < threshold))[0]
if len(ind):
return ind+1
else:
return ind
#######################################################
[docs]def get_tcross_from_temporal_kernel(temporal_kernel):
max_ind = np.argmax(temporal_kernel)
min_ind = np.argmin(temporal_kernel)
temp_tcross_ind = cross_from_above(temporal_kernel[max_ind:min_ind], 0.0)
tcross_ind = max_ind + temp_tcross_ind[0]
return tcross_ind
