np.random.seed(sd)
curr_date = datetime.datetime.now().strftime('%Y_%m_%d') + '_'
gen_fn_dir = os.path.abspath('..') + '/shared_scripts'
sys.path.append(gen_fn_dir)
import general_file_fns as gff
gen_params = gff.load_pickle_file('../general_params/general_params.p')
from binned_spikes_class import spike_counts
from dim_red_fns import run_dim_red
from scipy.spatial.distance import pdist
from sklearn import neighbors
save_dir = gff.return_dir(gen_params['results_dir'] + '2019_03_22_tda/')
plot_barcode = True
cmd_line = False
# if thrsh is True then we threshold out low density points (nt-TDA in the
# paper)
if cmd_line:
session = sys.argv[1]
state = sys.argv[2]
thrsh = sys.argv[3] # threshold out low density pts
else:
session = 'Mouse28-140313'
state = 'Wake'
thrsh = False
area = 'ADn'
sd = int((time.time() % 1) * (2**31))
np.random.seed(sd)
curr_date = datetime.datetime.now().strftime('%Y_%m_%d') + '_'
gen_fn_dir = os.path.abspath('..') + '/shared_scripts'
sys.path.append(gen_fn_dir)
import general_file_fns as gff
gen_params = gff.load_pickle_file('../general_params/general_params.p')
from binned_spikes_class import spike_counts
from dim_red_fns import run_dim_red
cols = gen_params['cols']
dir_to_save = gff.return_dir(gen_params['results_dir'] + '2019_06_03_dim_red/')
command_line = False
if command_line:
session = sys.argv[1]
state = sys.argv[2]
# If condition is 'joint' should unpack state into first and second
condition = sys.argv[3]
target_dim = int(sys.argv[4])
desired_nSamples = int(sys.argv[5])
else:
session = 'Mouse28-140313'
state = 'Wake'
#state2 = 'REM'
condition = 'solo'
target_dim = 3
session = 'Mouse28-140313'
make_processed_files = True
make_rates = True
if make_processed_files:
data_path = gen_params['raw_data_dir'] + session + '/'
params = {
'session': 'Mouse28-140313',
'data_path': data_path,
'eeg_sampling_rate': 1250.,
'spike_sampling_interval': 1.0 / (20e3)
}
data = drf.gather_session_spike_info(params)
save_dir = gff.return_dir(gen_params['processed_data_dir'])
gff.save_pickle_file(data, save_dir + '%s.p' % session)
if make_rates:
print 'Getting kernel rates'
t0 = time.time()
sigma = 0.1
params = {'dt': 0.05, 'method': 'gaussian', 'sigma': sigma}
inp_data = gff.load_pickle_file(gen_params['processed_data_dir'] +
'%s.p' % session)
rates = rf.get_rates_and_angles_by_interval(inp_data,
params,
smooth_type='kernel',
just_wake=True)
save_dir = gff.return_dir(gen_params['kernel_rates_dir'] +
'%0.0fms_sigma/' % (sigma * 1000))
sd = int((time.time() % 1) * (2**31))
np.random.seed(sd)
curr_date = datetime.datetime.now().strftime('%Y_%m_%d') + '_'
gen_fn_dir = os.path.abspath('..') + '/shared_scripts'
sys.path.append(gen_fn_dir)
import general_file_fns as gff
gen_params = gff.load_pickle_file('../general_params/general_params.p')
from binned_spikes_class import spike_counts
from dim_red_fns import run_dim_red
import manifold_fit_and_decode_fns as mff
dir_to_save = gff.return_dir(gen_params['results_dir'] +
'2019_06_03_curve_fits/')
cmd_line = False
if cmd_line:
session = sys.argv[1]
fit_dim = int(sys.argv[2])
nKnots = int(sys.argv[3])
knot_order = sys.argv[4]
penalty_type = sys.argv[5]
nTests = int(sys.argv[6])
train_frac = float(sys.argv[7])
else:
session = 'Mouse28-140313'
fit_dim = 3
nKnots = 15
knot_order = 'wt_per_len'
