def Main():
args = parser.parse_args()
cty_type = args.cty
cty_list = args.cty_list
data_path = os.path.join(os.path.dirname(man_opt.__file__), os.pardir,
'assets', 'aggregated_data')
mouse_data_filename = os.path.join(data_path, 'Mouse_class_data.csv')
mouse_datatype_filename = os.path.join(data_path,
'Mouse_class_datatype.csv')
me_ttype_map_path = os.path.join(data_path, 'me_ttype.pkl')
sdk_data_filename = os.path.join(data_path, 'sdk.csv')
sdk_datatype_filename = os.path.join(data_path, 'sdk_datatype.csv')
sdk_data = man_utils.read_csv_with_dtype(sdk_data_filename,
sdk_datatype_filename)
if cty_type == 'ttype':
mouse_data = man_utils.read_csv_with_dtype(mouse_data_filename,
mouse_datatype_filename)
me_ttype_map = utility.load_pickle(me_ttype_map_path)
metype_cluster = mouse_data.loc[
mouse_data.hof_index == 0, ['Cell_id', 'Dendrite_type', 'me_type']]
sdk_me = pd.merge(sdk_data, metype_cluster, how='left',
on='Cell_id').dropna(how='any', subset=['me_type'])
sdk_me['ttype'] = sdk_me['me_type'].apply(lambda x: me_ttype_map[x])
cell_df = sdk_me.loc[sdk_me.ttype.isin(cty_list), ]
elif cty_type == 'Cre_line':
cell_df = sdk_data.loc[sdk_data.line_name.isin(cty_list), ]
cell_ids = cell_df.Cell_id.unique().tolist()
rc = Client(profile=os.getenv('IPYTHON_PROFILE'))
logger.debug('Using ipyparallel with %d engines', len(rc))
lview = rc.load_balanced_view()
lview.map_sync(get_efeatures, cell_ids)
data_path = os.path.join(os.getcwd(), os.pardir,
os.pardir, 'assets', 'aggregated_data')
mouse_data_filename = os.path.join(data_path, 'Mouse_class_data.csv')
mouse_datatype_filename = os.path.join(data_path, 'Mouse_class_datatype.csv')
train_ephys_max_amp_filename = os.path.join(
data_path, 'train_ephys_max_amp.csv')
train_ephys_max_amp_dtype_filename = os.path.join(
data_path, 'train_ephys_max_amp_dtype.csv')
train_ephys_max_amp_fields_filename = os.path.join(
data_path, 'train_ephys_max_amp_fields.json')
hof_model_ephys_max_amp_filename = os.path.join(
data_path, 'hof_model_ephys_max_amp.csv')
hof_model_ephys_max_amp_dtype_filename = os.path.join(data_path,
'hof_model_ephys_max_amp_datatype.csv')
mouse_data_df = man_utils.read_csv_with_dtype(
mouse_data_filename, mouse_datatype_filename)
ephys_data = man_utils.read_csv_with_dtype(train_ephys_max_amp_filename,
train_ephys_max_amp_dtype_filename)
model_ephys_data = man_utils.read_csv_with_dtype(hof_model_ephys_max_amp_filename,
hof_model_ephys_max_amp_dtype_filename)
# %% Fix the cell-type label for coloring
layer_cty = 1
me_cty = 0
if layer_cty:
cty_cluster = mouse_data_df.loc[mouse_data_df.hof_index == 0, ['Cell_id', 'Layer',
'Broad_Cre_line', ]]
cty_cluster.dropna(axis=0, inplace=True, how='any')
cty_cluster['cty_layer'] = cty_cluster.apply(
# %% Data path
data_path = os.path.join(os.getcwd(), os.pardir, os.pardir, 'assets',
'aggregated_data')
mouse_data_filename = os.path.join(data_path, 'Mouse_class_data.csv')
mouse_datatype_filename = os.path.join(data_path, 'Mouse_class_datatype.csv')
param_data_filename = os.path.join(data_path, 'allactive_params.csv')
param_datatype_filename = os.path.join(data_path,
'allactive_params_datatype.csv')
broad_subclass_colors_filename = os.path.join(data_path,
'broad_subclass_colors.pkl')
me_ttype_map_path = os.path.join(data_path, 'me_ttype.pkl')
# %% Loading data
mouse_data_df = man_utils.read_csv_with_dtype(mouse_data_filename,
mouse_datatype_filename)
mouse_data_df = man_utils.add_transcriptomic_subclass(mouse_data_df,
me_ttype_map_path)
mouse_data_df = man_utils.add_broad_subclass(mouse_data_df)
hof_param_data = man_utils.read_csv_with_dtype(param_data_filename,
param_datatype_filename)
subclass_cluster = mouse_data_df.loc[mouse_data_df.hof_index == 0,
['Cell_id', 'Broad_subclass']]
hof_param_subclass = pd.merge(hof_param_data,
subclass_cluster,
how='left',
on='Cell_id')
broad_subclass_color_dict = utility.load_pickle(broad_subclass_colors_filename)
def shorten_channel_name(param_name):
sec = param_name.split('.')[-1]
shortened_name = replace_channel_name(param_name)
if sec in shortened_name:
return shortened_name
else:
return shortened_name + f'.{sec}'
# %% Model paths
data_path = Path(
man_opt.__file__).parent.parent.joinpath('assets/aggregated_data')
mouse_data_filename = Path(data_path / 'Mouse_class_data.csv')
mouse_datatype_filename = Path(data_path / 'Mouse_class_datatype.csv')
mouse_data_df = man_utils.read_csv_with_dtype(mouse_data_filename,
mouse_datatype_filename)
cell_list = [
"327962063", "473564515", "478793814", "481001895", "483101699",
"484635029", "485058595", "486560376", "584682764"
]
cell_type_df = mouse_data_df.loc[(mouse_data_df.hof_index == 0) &
(mouse_data_df.Cell_id.isin(cell_list)),
["Cell_id", "dendrite_type"]].reset_index(
drop=True)
original_model_path = "/allen/aibs/mat/ateam_shared/Mouse_Model_Fit_Metrics"
model_refit_path = "/allen/programs/celltypes/workgroups/humancolumn_ephysmodeling/"\
"anin/Optimizations_HPC/Mouse_Benchmark"
# %% Load Models
return diff_ephys_df
# %% Data paths
data_path = os.path.join(os.path.dirname(man_opt.__file__), os.pardir, 'assets', 'aggregated_data')
mouse_data_filename = os.path.join(data_path, 'Mouse_class_data.csv')
mouse_datatype_filename = os.path.join(data_path, 'Mouse_class_datatype.csv')
broad_subclass_colors_filename = os.path.join(
data_path, 'broad_subclass_colors.pkl')
sdk_data_filename = os.path.join(data_path, 'sdk.csv')
sdk_datatype_filename = os.path.join(data_path, 'sdk_datatype.csv')
me_ttype_map_path = os.path.join(data_path, 'me_ttype.pkl')
mouse_data = man_utils.read_csv_with_dtype(mouse_data_filename, mouse_datatype_filename)
mouse_data = man_utils.add_transcriptomic_subclass(
mouse_data, me_ttype_map_path)
broad_subclass_color_dict = utility.load_pickle(broad_subclass_colors_filename)
transcriptomic_subclass = mouse_data.loc[mouse_data.hof_index == 0, ['Cell_id', 'ttype']]
sdk_data = man_utils.read_csv_with_dtype(sdk_data_filename, sdk_datatype_filename)
inh_subclasses = ["Vip", "Sst", "Pvalb"]
palette = {inh_subclass: broad_subclass_color_dict[inh_subclass]
for inh_subclass in inh_subclasses}
sdk_data = pd.merge(sdk_data, transcriptomic_subclass, on='Cell_id', how='left')
sdk_data = sdk_data.rename(columns={'ef__threshold_i_long_square': 'rheobase',
'ef__f_i_curve_slope': 'fi_slope'})
sdk_data = sdk_data.loc[sdk_data.ttype.isin(inh_subclasses), ['Cell_id', 'ttype', 'rheobase',
'fi_slope']]
train_ephys_max_amp_fname = os.path.join(data_path, 'train_ephys_max_amp.csv')
train_ephys_max_amp_dtype_fname = os.path.join(
data_path, 'train_ephys_max_amp_dtype.csv')
train_ephys_max_amp_fields_fname = os.path.join(
data_path, 'train_ephys_max_amp_fields.json')
cre_coloring_filename = os.path.join(data_path, 'cre_color_tasic16.pkl')
bcre_coloring_filename = os.path.join(data_path, 'bcre_color_tasic16.pkl')
cre_ttype_filename = os.path.join(data_path, 'cre_ttype_map.pkl')
filtered_me_inh_cells_filename = os.path.join(
data_path, 'filtered_me_inh_cells.pkl')
filtered_me_exc_cells_filename = os.path.join(
data_path, 'filtered_me_exc_cells.pkl')
mouse_data_df = man_utils.read_csv_with_dtype(
mouse_data_filename, mouse_datatype_filename)
morph_data = man_utils.read_csv_with_dtype(
morph_data_filename, morph_datatype_filename)
# morph_data = morph_data.loc[:,[morph_feature for morph_feature in morph_data.columns
# if not any(sec in morph_feature for sec in['apical','axon'])]]
morph_fields = man_utils.get_data_fields(morph_data)
ephys_data = man_utils.read_csv_with_dtype(train_ephys_max_amp_fname,
train_ephys_max_amp_dtype_fname)
ephys_fields = utility.load_json(train_ephys_max_amp_fields_fname)
hof_param_data = man_utils.read_csv_with_dtype(
param_data_filename, param_datatype_filename)
cre_color_dict = utility.load_pickle(cre_coloring_filename)
bcre_color_dict = utility.load_pickle(bcre_coloring_filename)