def edit_spike_array_params(self, shell=False):
'''Edit spike array parameters and adjust dig_in_mapping accordingly
Parameters
----------
shell : bool, whether to use CLI or GUI
'''
if not hasattr(self, 'dig_in_mapping'):
self.spike_array_params = None
return
sa = deepcopy(self.spike_array_params)
tmp = userIO.fill_dict(sa,
'Spike Array Parameters\n(Times in ms)',
shell=shell)
if tmp is None:
return
dim = self.dig_in_mapping
dim['spike_array'] = False
if tmp['dig_ins_to_use'] != ['']:
tmp['dig_ins_to_use'] = [int(x) for x in tmp['dig_ins_to_use']]
dim.loc[[x in tmp['dig_ins_to_use'] for x in dim.channel],
'spike_array'] = True
dim['laser_channels'] = False
if tmp['laser_channels'] != ['']:
tmp['laser_channels'] = [int(x) for x in tmp['laser_channels']]
dim.loc[[x in tmp['laser_channels'] for x in dim.channel],
'laser'] = True
self.spike_array_params = tmp.copy()
wt.write_params_to_json('spike_array_params', self.root_dir, tmp)
def _setup_digital_mapping(self, dig_type, dig_in_names=None, shell=False):
'''sets up dig_in_mapping dataframe and queries user to fill in columns
Parameters
----------
dig_in_names : list of str (optional)
shell : bool (optional)
True for command-line interface
False (default) for GUI
'''
rec_info = self.rec_info
df = pd.DataFrame()
df['channel'] = rec_info.get('dig_%s' % dig_type)
n_dig_in = len(df)
# Names
if dig_in_names:
df['name'] = dig_in_names
else:
df['name'] = ''
# Parameters to query
if dig_type == 'in':
df['palatability_rank'] = 0
df['laser'] = False
df['spike_array'] = True
df['exclude'] = False
# Re-format for query
idx = df.index
df.index = ['dig_%s_%i' % (dig_type, x) for x in df.channel]
dig_str = dig_type + 'put'
# Query for user input
prompt = ('Digital %s Parameters\nSet palatability ranks from 1 to %i'
'\nor blank to exclude from pal_id analysis') % (dig_str,
len(df))
tmp = userIO.fill_dict(df.to_dict(), prompt=prompt, shell=shell)
# Reformat for storage
df2 = pd.DataFrame.from_dict(tmp)
df2 = df2.sort_values(by=['channel'])
df2.index = idx
if dig_type == 'in':
df2['palatability_rank'] = df2['palatability_rank'].fillna(
-1).astype('int')
if dig_type == 'in':
self.dig_in_mapping = df2.copy()
else:
self.dig_out_mapping = df2.copy()
if os.path.isfile(self.h5_file):
dio.h5io.write_digital_map_to_h5(self.h5_file, self.dig_in_mapping,
dig_type)
def edit_psth_params(self, shell=False):
'''Allows user interface for editing psth parameters
Parameters
----------
shell : bool (optional)
True if you want command-line interface, False for GUI (default)
'''
tmp = userIO.fill_dict(self.psth_params,
'PSTH Parameters\n(Times in ms)',
shell=shell)
if tmp:
self.psth_params = tmp
wt.write_params_to_json('psth_params', self.root_dir, tmp)
def _order_dirs(self, shell=False, order_dict=None):
'''set order of redcording directories
'''
if 'SSH_CONNECTION' in os.environ:
shell = True
if self.recording_dirs == []:
return
if order_dict is None:
self.recording_dirs = [
x[:-1] if x.endswith(os.sep) else x
for x in self.recording_dirs
]
top_dirs = {
os.path.basename(x): os.path.dirname(x)
for x in self.recording_dirs
}
file_dirs = list(top_dirs.keys())
order_dict = dict.fromkeys(file_dirs, 0)
tmp = userIO.dictIO(order_dict, shell=shell)
order_dict = userIO.fill_dict(order_dict,
('Set order of recordings (1-%i)\n'
'Leave blank to delete directory'
' from list') % len(file_dirs),
shell)
if order_dict is None:
return
file_dirs = [
k for k, v in order_dict.items() if v is not None and v != 0
]
file_dirs = sorted(file_dirs, key=order_dict.get)
file_dirs = [os.path.join(top_dirs.get(x), x) for x in file_dirs]
else:
file_dirs = sorted(self.recording_dirs, key=order_dict.get)
self.recording_dirs = file_dirs
def _set_CAR_groups(self,
group_keyword=None,
shell=False,
group_areas=None):
'''Sets that electrode groups for common average referencing and
defines which brain region electrodes eneded up in
Parameters
----------
group_keyword : str or int
Keyword corresponding to a preset electrode grouping in CAR_params.json
Or integer indicating number of CAR groups
shell : bool
True for command-line interface, False (default) for GUI
'''
if not hasattr(self, 'electrode_mapping'):
raise ValueError('Set electrode mapping before setting CAR groups')
em = self.electrode_mapping.copy()
car_param_file = os.path.join(self.root_dir, 'analysis_params',
'CAR_params.json')
if os.path.isfile(car_param_file):
tmp = dio.params.load_params('CAR_params', self.root_dir)
if tmp is not None:
group_electrodes = tmp
else:
raise ValueError(
'CAR_params file exists in recording dir, but is empty')
else:
if group_keyword is None:
group_keyword = userIO.get_user_input(
'Input keyword for CAR parameters or number of CAR groups',
shell=shell)
if group_keyword is None:
ValueError('Must provide a keyword or number of groups')
if group_keyword.isnumeric():
num_groups = int(group_keyword)
group_electrodes = dio.params.select_CAR_groups(num_groups,
em,
shell=shell)
else:
group_electrodes = dio.params.load_params(
'CAR_params', self.root_dir, default_keyword=group_keyword)
num_groups = len(group_electrodes)
if group_areas is not None and len(group_areas) == num_groups:
for i, x in enumerate(zip(group_electrodes, group_areas)):
em.loc[x[0], 'area'] = x[1]
em.loc[x[0], 'CAR_group'] = i
else:
group_names = ['Group %i' % i for i in range(num_groups)]
area_dict = dict.fromkeys(group_names, '')
area_dict = userIO.fill_dict(area_dict,
'Set Areas for CAR groups',
shell=shell)
for k, v in area_dict.items():
i = int(k.replace('Group', ''))
em.loc[group_electrodes[i], 'area'] = v
em.loc[group_electrodes[i], 'CAR_group'] = i
self.CAR_electrodes = group_electrodes
self.electrode_mapping = em.copy()
def port_in_dataset(rec_dir=None, shell=False):
'''Import an existing dataset into this framework
'''
if rec_dir is None:
rec_dir = userIO.get_filedirs('Select recording directory',
shell=shell)
if rec_dir is None:
return None
dat = dataset(rec_dir, shell=shell)
# Check files that will be overwritten: log_file, save_file
if os.path.isfile(dat.save_file):
prompt = '%s already exists. Continuing will overwrite this. Continue?' % dat.save_file
q = userIO.ask_user(prompt, shell=shell)
if q == 0:
print('Aborted')
return None
# if os.path.isfile(dat.h5_file):
# prompt = '%s already exists. Continuinlg will overwrite this. Continue?' % dat.h5_file
# q = userIO.ask_user(prompt, shell=shell)
# if q == 0:
# print('Aborted')
# return None
if os.path.isfile(dat.log_file):
prompt = '%s already exists. Continuing will append to this. Continue?' % dat.log_file
q = userIO.ask_user(prompt, shell=shell)
if q == 0:
print('Aborted')
return None
with open(dat.log_file, 'a') as f:
print(
'\n==========\nPorting dataset into blechpy format\n==========\n',
file=f)
print(dat, file=f)
status = dat.process_status
dat.initParams(shell=shell)
user_status = status.copy()
user_status = userIO.fill_dict(user_status,
'Which processes have already been '
'done to the data?',
shell=shell)
status.update(user_status)
# if h5 exists data must have been extracted
if not os.path.isfile(dat.h5_file) or status['extract_data'] == False:
dat.save()
return dat
# write eletrode map and digital input & output maps to hf5
dio.h5io.write_electrode_map_to_h5(dat.h5_file, dat.electrode_mapping)
if dat.rec_info.get('dig_in') is not None:
dio.h5io.write_digital_map_to_h5(dat.h5_file, dat.dig_in_mapping, 'in')
if dat.rec_info.get('dig_out') is not None:
dio.h5io.write_digital_map_to_h5(dat.h5_file, dat.dig_out_mapping,
'out')
node_list = dio.h5io.get_node_list(dat.h5_file)
if (status['create_trial_list'] == False) and ('digital_in' in node_list):
dat.create_trial_list()
dat.save()
return dat