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trodes2SS.py
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trodes2SS.py
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"""
Parse trodes data into SS data containers
# Written by AKG
# Edited 3-8-19 by MEC to accomodate tetrodes and tritrodes (line 94)
# Edited 3-22-19 by MEC to make a filter for marks with large negative channels because
# this crashes the decoder by going outside the bounds of the
# normal_pdf_int_lookup function
"""
import numpy as np
import scipy as sp
import scipy.stats
import scipy.io
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import holoviews as hv
import xarray as xr
import json
import functools
import dask
import dask.dataframe as dd
import dask.array as da
from spykshrk.franklab.data_containers import FlatLinearPosition, SpikeFeatures, \
EncodeSettings, pos_col_format, SpikeObservation, RippleTimes
def get_all_below_threshold(self, threshold):
ind = np.nonzero(np.all(self.values < threshold, axis=1))
return self.iloc[ind]
def get_any_above_threshold(self, threshold):
ind = np.nonzero(np.any(self.values >= threshold, axis=1))
return self.iloc[ind]
def get_all_above_threshold(self, threshold):
ind = np.nonzero(np.all(self.values > threshold, axis=1))
return self.iloc[ind]
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def threshold_marks_negative(marks, negthresh=-999):
pre_length = marks.shape
marks = get_all_above_threshold(marks, negthresh)
print(str(pre_length[0]-marks.shape[0])+' below '+str(negthresh)+'uV events removed')
return marks
def threshold_marks(marks, maxthresh=2000, minthresh=0):
pre_length = marks.shape
marks = get_all_below_threshold(marks, maxthresh)
print(str(pre_length[0]-marks.shape[0])+' above '+str(maxthresh)+'uV events removed')
pre_length = marks.shape
marks = get_any_above_threshold(marks, minthresh)
print(str(pre_length[0]-marks.shape[0])+' below '+str(minthresh)+'uV events removed')
return marks
class TrodesImport:
""" animalinfo - takes in animal d/e/t information; parses FFmat files into dataframes of each datatype
"""
def __init__(self, ff_dir, name, days, epochs, tetrodes, Fs=3e4):
""" init function
Args:
base_dir: root directory of animal data
name: name of animal
days: array of which days of data to process
tetrodes: array of which tetrodes to process
epochs: list of epochs for encoding
"""
self.ff_dir = ff_dir
self.name = name
self.days = days
self.epochs = epochs
self.tetrodes = tetrodes
self.Fs = Fs
def import_marks(self):
spk_amps = pd.DataFrame()
for day in self.days:
markname = self.ff_dir+self.name+'marks'+str(day)+'.mat'
markmat = scipy.io.loadmat(markname,squeeze_me=True,struct_as_record=False)
for ep in self.epochs:
de_amps = pd.DataFrame()
for tet in self.tetrodes:
marktimes = markmat['marks'][day-1][ep-1][tet-1].times*self.Fs
marktimes = marktimes.astype(np.int64,copy=False)
marks = markmat['marks'][day-1][ep-1][tet-1].marks
marks = marks.astype(np.int16,copy=False)
tet_marks = SpikeFeatures.from_numpy_single_epoch_elec(day ,ep, tet, marktimes,marks,sampling_rate=self.Fs)
if len(tet_marks.columns) == 4:
tet_marks.columns=['c00','c01','c02','c03']
if len(tet_marks.columns) == 3:
tet_marks.columns=['c00','c01','c02']
de_amps = de_amps.append(tet_marks)
de_amps.sort_index(level='timestamp', inplace=True)
print('duplicates found & removed: '+str(de_amps[de_amps.index.duplicated(keep='first')].size))
de_amps = de_amps[~de_amps.index.duplicated(keep='first')]
spk_amps = spk_amps.append(de_amps)
spk_amps.sampling_rate = self.Fs
return spk_amps
def import_pos(self, encode_settings, xy = 'x'):
allpos = pd.DataFrame()
for day in self.days:
for ep in self.epochs:
posname = self.ff_dir+self.name+'pos'+str(day)+'.mat'
posmat = scipy.io.loadmat(posname,squeeze_me=True,struct_as_record=False)
pos_time = self.Fs*posmat['pos'][day-1][ep-1].data[:,0]
pos_time = pos_time.astype(np.int64,copy=False)
pos_runx = posmat['pos'][day-1][ep-1].data[:,5]
pos_runy = posmat['pos'][day-1][ep-1].data[:,6]
pos_vel = posmat['pos'][day-1][ep-1].data[:,8]
if 'x' in xy:
pos_obj = FlatLinearPosition.from_numpy_single_epoch(day, ep, pos_time, pos_runx, pos_vel, self.Fs,
encode_settings.arm_coordinates)
if 'y' in xy:
pos_obj = FlatLinearPosition.from_numpy_single_epoch(day, ep, pos_time, pos_runy, pos_vel, self.Fs,
encode_settings.arm_coordinates)
allpos = allpos.append(pos_obj)
allpos.sampling_rate = self.Fs
return allpos
def import_rips(self,pos_obj=None, velthresh=4):
allrips = pd.DataFrame()
for day in self.days:
for ep in self.epochs:
ripname = self.ff_dir+self.name+'ca1rippleskons'+str(day)+'.mat'
ripmat = scipy.io.loadmat(ripname,squeeze_me=True,struct_as_record=False)
#generate a pandas table with starttime, endtime, and maxthresh columns, then instantiate RippleTimes
ripdata = {'starttime':ripmat['ca1rippleskons'][day-1][ep-1].starttime,
'endtime':ripmat['ca1rippleskons'][day-1][ep-1].endtime,
'maxthresh':ripmat['ca1rippleskons'][day-1][ep-1].maxthresh}
rippd = pd.DataFrame(ripdata,pd.MultiIndex.from_product([[day],[ep],
range(len(ripmat['ca1rippleskons'][day-1][ep-1].maxthresh))],
names=['day','epoch','event']))
#reorder the fields
rippd = rippd[['starttime','endtime','maxthresh']]
#rip_obj = RippleTimes.create_default(rippd, 1)
#if pos_obj is not None:
#add an additional field for velocity and filter out events exceeding velthresh
#veltmp = pos_obj.get_irregular_resampled_old(self.Fs*rip_obj['starttime'])
#rip_obj['vels'] = veltmp['linvel_flat'].values
#rip_obj = rip_obj.iloc[rip_obj['vels'].values < 4]
#allrips = allrips.append(rip_obj)
allrips = allrips.append(rippd)
return allrips
def convert_dan_posterior_to_xarray(posterior_df, tetrode_dictionary, velocity_filter, encode_settings, decode_settings, transition_matrix, shuffle_offset, trial_order, marks_time_shift_amount, position_bin_centers=None):
'''Converts pandas dataframe from Dan's 1D decoder to xarray Dataset
Parameters
----------
posterior_df : pandas.DataFrame, shape (n_time, n_columns)
position_bin_centers : None or ndarray, shape (n_position_bins,), optional
Returns
-------
results : xarray.Dataset
'''
is_position_bin = posterior_df.columns.str.startswith('x')
if position_bin_centers is None:
n_position_bins = is_position_bin.sum()
position_bin_centers = np.arange(n_position_bins)
coords = dict(
day=posterior_df.loc[:, 'day'].values,
epoch=posterior_df.loc[:, 'epoch'].values,
timestamp=posterior_df.loc[:, 'timestamp'].values,
time=posterior_df.loc[:, 'time'].values,
position=position_bin_centers,
num_spikes=posterior_df.loc[:, 'num_spikes'].values,
dec_bin=posterior_df.loc[:, 'dec_bin'].values,
ripple_grp=posterior_df.loc[:, 'ripple_grp'].values,
)
return xr.Dataset(
{'posterior': (('time','position'), posterior_df.loc[:, is_position_bin].values),
'velocity filter encode': velocity_filter,
'velocity filter decode': velocity_filter,
'tetrodes': tetrode_dictionary,
'shuffle offset': shuffle_offset,
'marks_time_shift_amount': marks_time_shift_amount,
'trial_order': trial_order,
'sampling_rate': encode_settings['sampling_rate'],
'pos_bins': encode_settings['pos_bins'],
'pos_bin_edges': encode_settings['pos_bin_edges'],
'pos_bin_delta': encode_settings['pos_bin_delta'],
'pos_kernel': encode_settings['pos_kernel'],
'pos_kernel_std': encode_settings['pos_kernel_std'],
'mark_kernel_std': encode_settings['mark_kernel_std'],
'pos_num_bins': encode_settings['pos_num_bins'],
'pos_col_names': encode_settings['pos_col_names'],
'arm_coordinates': (encode_settings['arm_coordinates'][0]),
'trans_smooth_std': decode_settings['trans_smooth_std'],
'trans_uniform_gain': decode_settings['trans_uniform_gain'],
'time_bin_size': decode_settings['time_bin_size'],
'transition_matrix_name': 'flat powered',
'multiindex': ['day','epoch','timestamp','time'],
'transition_matrix': (('position','position'), transition_matrix)},
coords=coords)