def organizer_decode_day(condition, decodeConfig, data_path, save_path):
#TODO: obsolete right now. need to fix before using
'''
Run decoding experiments with labels based on the day of presentation. Data is contained
:param condition: experimental condition. For example, condition OFC.
Must contain fields: name, paths, odors, csp
:param decodeConfig: class config, contains as fields relevant parameters to run decoding experiment
:param data_path:
:param save_path:
:return:
'''
data_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.mat_ext)))
config_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.cons_ext)))
list_of_all_data = np.array([Config.load_mat_f(d) for d in data_pathnames])
list_of_all_cons = np.array(
[Config.load_cons_f(d) for d in config_pathnames])
mouse_names_per_file = np.array(
[cons.NAME_MOUSE for cons in list_of_all_cons])
mouse_names, list_of_mouse_ix = np.unique(mouse_names_per_file,
return_inverse=True)
if mouse_names.size != len(condition.paths):
raise ValueError(
"res has {0:d} mice, but filter has {1:d} mice".format(
mouse_names.size, len(condition.paths)))
for i, mouse_name in enumerate(mouse_names):
start_time = time.time()
ix = mouse_name == mouse_names_per_file
list_of_cons = list_of_all_cons[ix]
list_of_data = list_of_all_data[ix]
for cons in list_of_cons:
assert cons.NAME_MOUSE == mouse_name, 'Wrong mouse file!'
cons = list_of_cons[0]
cons_dict = cons.__dict__
for key, value in cons_dict.items():
if isinstance(value, list) or isinstance(value, np.ndarray):
pass
else:
setattr(decodeConfig, key, value)
odor = condition.odors[i]
if decodeConfig.decode_style == 'identity':
csp = None
else:
csp = condition.csp[i]
scores = decoding.decode_day_labels(list_of_cons, list_of_data, odor,
csp, decodeConfig)
name = cons.NAME_MOUSE
fio.save_json(save_path=save_path, save_name=name, config=decodeConfig)
fio.save_numpy(save_path=save_path, save_name=name, data=scores)
print("Analyzed: {0:s} in {1:.2f} seconds".format(
name,
time.time() - start_time))
def do_PCA(condition, PCAConfig, data_path, save_path):
'''
Run PCA experiment. For each mouse, data should be in format of (trials * time) X neurons,
stacked for all odor conditions
:param condition:
:param PCAConfig:
:param data_path:
:param save_path:
:return:
'''
data_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.mat_ext)))
config_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.cons_ext)))
list_of_all_data = np.array([Config.load_mat_f(d) for d in data_pathnames])
list_of_all_cons = np.array(
[Config.load_cons_f(d) for d in config_pathnames])
mouse_names_per_file = np.array(
[cons.NAME_MOUSE for cons in list_of_all_cons])
mouse_names, list_of_mouse_ix = np.unique(mouse_names_per_file,
return_inverse=True)
if mouse_names.size != len(condition.paths):
raise ValueError(
"res has {0:d} mice, but filter has {1:d} mice".format(
mouse_names.size, len(condition.paths)))
for i, mouse_name in enumerate(mouse_names):
ix = mouse_name == mouse_names_per_file
start_day = 0
list_of_cons = list_of_all_cons[ix][start_day:]
list_of_data = list_of_all_data[ix][start_day:]
for cons in list_of_cons:
assert cons.NAME_MOUSE == mouse_name, 'Wrong mouse file!'
if hasattr(condition, 'odors'):
odor = condition.odors[i]
csp = condition.csp[i]
else:
odor = condition.dt_odors[i] + condition.pt_csp[i]
csp = condition.dt_csp[i] + condition.pt_csp[i]
name = list_of_cons[0].NAME_MOUSE
out, variance_explained = PCA(list_of_cons, list_of_data, odor, csp,
PCAConfig)
fio.save_pickle(save_path=save_path, save_name=name, data=out)
print("Analyzed: {}. Variance explained: {}".format(
name, variance_explained))
def load_all_cons(data_path):
res = defaultdict(list)
config_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.cons_ext)))
for i, config_pn in enumerate(config_pathnames):
cons = Config.load_cons_f(config_pn)
for key, val in cons.__dict__.items():
if isinstance(val, list):
res[key].append(np.array(val))
else:
res[key].append(val)
for key, val in res.items():
if key == 'DAQ_DATA':
arr = np.empty(len(val), dtype='object')
for i, v in enumerate(val):
arr[i] = v
res[key] = arr
else:
res[key] = np.array(val)
return res
# condition = experimental_conditions.BEHAVIOR_OFC_MUSH_HALO
# data_path = os.path.join(Config.LOCAL_DATA_PATH, Config.LOCAL_DATA_TIMEPOINT_FOLDER, condition.name)
condition = experimental_conditions.BEHAVIOR_OFC_HALO_PRETRAINING
data_path = os.path.join(Config.LOCAL_DATA_PATH,
Config.LOCAL_DATA_BEHAVIOR_FOLDER, condition.name)
# load cons
# paths = condition.paths
# import matlab.engine
# eng = matlab.engine.start_matlab()
# for path in paths:
# data_path = os.path.join(path, 'data')
# data_wildcard = os.path.join(data_path, '*.mat')
# matfile_paths = glob.glob(data_wildcard)
# list_of_mats, list_of_cons = [],[]
# for i, p in enumerate(matfile_paths):
# mat, obj_name = init.load_matlab.load_calcium_traces_from_matlab(p, eng)
# dir = eng.eval(obj_name + ".constants.DIR")
# cons = Cons(dir, condition.timing_override[i])
# _look_at_timing(cons)
#look at cons once loaded
config_pathnames = glob.glob(os.path.join(data_path, '*' + Config.cons_ext))
for i, config_pn in enumerate(config_pathnames):
cons = Config.load_cons_f(config_pn)
_look_at_timing(cons)
# print(cons.DIR + '__' + str(cons.TRIAL_FRAMES))
# print(cons.DIR + '__' + str(cons.ODOR_TRIALS))
def organizer_test_split(condition, decodeConfig, data_path, save_path):
'''
:param condition:
:param decodeConfig:
:param data_path:
:param save_path:
:return:
'''
data_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.mat_ext)))
config_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.cons_ext)))
list_of_all_data = np.array([Config.load_mat_f(d) for d in data_pathnames])
list_of_all_cons = np.array(
[Config.load_cons_f(d) for d in config_pathnames])
mouse_names_per_file = np.array(
[cons.NAME_MOUSE for cons in list_of_all_cons])
mouse_names, list_of_mouse_ix = np.unique(mouse_names_per_file,
return_inverse=True)
if mouse_names.size != len(condition.paths):
raise ValueError(
"res has {0:d} mice, but filter has {1:d} mice".format(
mouse_names.size, len(condition.paths)))
start_days = decodeConfig.start_day
end_days = decodeConfig.end_day
days_per_mouse = []
for x, y in zip(start_days, end_days):
days_per_mouse.append(np.arange(x, y + 1))
print(days_per_mouse)
for i, mouse_name in enumerate(mouse_names):
start_time = time.time()
ix = mouse_name == mouse_names_per_file
list_of_cons_ = list_of_all_cons[ix]
list_of_data_ = list_of_all_data[ix]
for cons in list_of_cons_:
assert cons.NAME_MOUSE == mouse_name, 'Wrong mouse file!'
if len(days_per_mouse[i]):
list_of_cons = list_of_cons_[days_per_mouse[i]]
list_of_data = list_of_data_[days_per_mouse[i]]
cons = list_of_cons[0]
cons_dict = cons.__dict__
for key, value in cons_dict.items():
if isinstance(value, list) or isinstance(value, np.ndarray):
pass
else:
setattr(decodeConfig, key, value)
if hasattr(condition, 'odors'):
odor = condition.odors[i]
csp = condition.csp[i]
else:
odor = condition.dt_odors[i]
csp = condition.dt_csp[i]
cons_odors = [cons.ODOR_UNIQUE for cons in list_of_cons]
ix = [
i for i, unique_odors in enumerate(cons_odors)
if odor[0] in unique_odors
]
list_of_cons = [list_of_cons[i] for i in ix]
list_of_data = [list_of_data[i] for i in ix]
scores_res = decoding.test_split(list_of_cons, list_of_data, odor,
csp, decodeConfig)
name = cons.NAME_MOUSE
fio.save_json(save_path=save_path,
save_name=name,
config=decodeConfig)
fio.save_pickle(save_path=save_path,
save_name=name,
data=scores_res)
print("Analyzed: {0:s} in {1:.2f} seconds".format(
name,
time.time() - start_time))
def organizer_decode_odor_within_day(condition, decodeConfig, data_path,
save_path):
'''
Run decoding experiments with labels based on the odor that was presented. Data is contained
:param condition: experimental condition. For example, condition OFC.
Must contain fields: name, paths, odors, csp
:param decodeConfig: class config, contains as fields relevant parameters to run decoding experiment
:param data_path:
:param save_path:
:return:
'''
#TODO: see if it works when odors are different for each animal
data_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.mat_ext)))
config_pathnames = sorted(
glob.glob(os.path.join(data_path, '*' + Config.cons_ext)))
mouse_names_per_file = [
Config.load_cons_f(d).NAME_MOUSE for d in config_pathnames
]
mouse_names, list_of_mouse_ix = np.unique(mouse_names_per_file,
return_inverse=True)
if mouse_names.size != len(condition.paths):
raise ValueError(
"res has {0:d} mice, but filter has {1:d} mice".format(
mouse_names.size, len(condition.paths)))
for i, data_pn in enumerate(data_pathnames):
start_time = time.time()
cons = Config.load_cons_f(config_pathnames[i])
data = Config.load_mat_f(data_pn)
cons_dict = cons.__dict__
for key, value in cons_dict.items():
if isinstance(value, list) or isinstance(value, np.ndarray):
pass
else:
setattr(decodeConfig, key, value)
if hasattr(condition, 'odors'):
odor = condition.odors[list_of_mouse_ix[i]]
else:
odor = condition.dt_odors[list_of_mouse_ix[i]]
if decodeConfig.decode_style == 'identity':
csp = None
else:
if hasattr(condition, 'odors'):
csp = condition.csp[list_of_mouse_ix[i]]
else:
csp = condition.dt_csp[list_of_mouse_ix[i]]
if odor[0] in cons.ODOR_UNIQUE:
scores = decoding.decode_odor_labels(cons, data, odor, csp,
decodeConfig)
mouse = cons.NAME_MOUSE
date = cons.NAME_DATE
plane = cons.NAME_PLANE
name = mouse + '__' + date + '__' + plane
fio.save_json(save_path=save_path,
save_name=name,
config=decodeConfig)
fio.save_numpy(save_path=save_path, save_name=name, data=scores)
print("Analyzed: {0:s} in {1:.2f} seconds".format(
data_pn,
time.time() - start_time))