def extract_ccw_ipsi_clipped_avgs(self):
avgs = []
for trial in self.raw_clipped_data:
segments = mat_utils.cutAndGetMultiple(self.cmd, trial)
for segment in segments:
avgs.append(self.extract_ccw_ipsi_from_plot_segment(segment).mean())
return avgs
def n_segments(self):
"""
Number of cycles cut from trial
:return:
"""
return len(mat_utils.cutAndGetMultiple(self.cmd, self.cmd))
def extract_clipped_avgs(self):
"""
To be used by clipped_avgs_per_trial_table
:return: list of pairs of clockwise/counter_clockwise averages per trial
"""
avgs_c_wise = []
avgs_c_c_wise = []
for trial in self.raw_clipped_data:
segments = mat_utils.cutAndGetMultiple(self.cmd, trial)
for segment in segments:
avgs_c_wise.append(segment[:self.data_plot_segment_half].mean())
avgs_c_c_wise.append(segment[self.data_plot_segment_half:].mean())
return avgs_c_wise, avgs_c_c_wise
def get_pooled_vms_bsl_cw_ccw(self):
bsls = []
cws = []
ccws = []
for w in self.raw_clipped_baselined:
bsl = self.extract_bsl(w)
bsls.extend(bsl)
cycles = mat_utils.cutAndGetMultiple(self.cmd, w)
for c in cycles:
mid = int(len(c)/2)
cw = c[:mid]
cws.extend(cw)
ccw = c[mid:]
ccws.extend(ccw)
bsls = np.array(bsls, dtype=np.float64)
cws = np.array(cws, dtype=np.float64)
ccws = np.array(ccws, dtype=np.float64)
return bsls, cws, ccws
def get_clipped_cycles(self):
"""
.. warning::
Will only work with 2 cycles (cutInHalf)
:return columns: t1bsl1+t1cycle1, t1bsl2+t1cycle2, t2bsl1+t2cycle1, t2bsl2+t2cycle2
:rtype: list
"""
columns = []
for trial in self.raw_clipped_data:
bsl = self.extract_bsl(trial)
bsls = mat_utils.cutInHalf(bsl) # WARNING: works only with 2 cycles
cycles = mat_utils.cutAndGetMultiple(self.cmd, trial) # WARNING: works only with 2 cycles
for segment_id in range(self.n_segments):
column = np.hstack((bsls[segment_id], cycles[segment_id]))
columns.append(column)
return columns