def load_pca(save_path):
res = defaultdict(list)
experiment_dirs = sorted(
[os.path.join(save_path, d) for d in os.listdir(save_path)])
for d in experiment_dirs:
temp_res = fio.load_pickle(d)
reduce.chain_defaultdicts(res, temp_res)
return res
def analyze_data(save_path,
condition_config,
m_threshold=None,
excitatory=True):
name = 'dict'
if not excitatory:
name += '_I'
res = fio.load_pickle(os.path.join(save_path, name + '.pkl'))
list_odor_on = res['DAQ_O_ON_F']
list_water_on = res['DAQ_W_ON_F']
res['ssig'] = []
res['msig'] = []
res['sig'] = []
for i in range(len(list_odor_on)):
p_list = res['p'][i]
dff_list = res['dff'][i]
ssig_list = []
msig_list = []
sig_list = []
onset_list = []
amplitude_list = []
duration_list = []
odor = res['odor'][i]
for p, dff in zip(p_list, dff_list):
ssig = np.array(p) < condition_config.p_threshold
reached_ssig = [
np.all(x)
for x in _rolling_window(ssig, condition_config.p_window)
]
if odor == 'water':
s, e = list_water_on[i], list_water_on[i] + 10
else:
s, e = list_odor_on[i], list_water_on[i]
if np.any(reached_ssig[s:e]):
statistical_significance = True
else:
statistical_significance = False
if m_threshold == None:
magnitude_threshold = condition_config.m_threshold
else:
magnitude_threshold = m_threshold
if excitatory:
msig = dff > magnitude_threshold
else:
msig = dff < magnitude_threshold
reached_msig = np.array([
np.all(x)
for x in _rolling_window(msig, condition_config.m_window)
])
if np.any(reached_msig[s:e]):
mag_significance = True
else:
mag_significance = False
if statistical_significance and mag_significance:
reached_ssig = [
np.all(x)
for x in _rolling_window(ssig, condition_config.p_window)
]
onset = np.where(reached_ssig[s:e])[0][0]
onset_list.append(onset)
duration = np.sum(reached_msig[s:e])
duration_list.append(duration)
if excitatory:
amplitude = np.max(dff[s:e])
else:
amplitude = np.min(dff[s:e])
amplitude_list.append(amplitude)
else:
onset_list.append(-1)
duration_list.append(-1)
amplitude_list.append(-1)
ssig_list.append(statistical_significance)
msig_list.append(mag_significance)
sig_list.append(statistical_significance and mag_significance)
res['ssig'].append(np.array(ssig_list).astype(int))
res['msig'].append(np.array(msig_list).astype(int))
res['sig'].append(np.array(sig_list).astype(int))
res['onset'].append(np.array(onset_list).astype(int))
res['duration'].append(np.array(duration_list).astype(int))
res['amplitude'].append(np.array(amplitude_list))
for key, val in res.items():
res[key] = np.array(val)
return res
plot_bool=True,
odor_end=odor_end)
ixa = temp['odor_valence'] == 'CS+'
ixb = temp['odor_valence'] == 'CS-'
a = temp['corrcoef'][ixa]
b = temp['corrcoef'][ixb]
print(ranksums(a, b))
if condition.name == 'PIR':
naive_config = statistics.analyze.PIR_NAIVE_Config()
data_path_ = os.path.join(Config.LOCAL_DATA_PATH,
Config.LOCAL_DATA_TIMEPOINT_FOLDER,
naive_config.condition.name)
save_path_ = os.path.join(Config.LOCAL_EXPERIMENT_PATH, 'COUNTING',
naive_config.condition.name)
res_naive = fio.load_pickle(os.path.join(save_path_, 'dict.pkl'))
learned_day_per_mouse_, last_day_per_mouse_ = get_days_per_mouse(
data_path_, naive_config.condition)
#
res = statistics.analyze.analyze_data(save_path,
condition_config,
m_threshold=.1)
# res_naive = statistics.analyze.analyze_data(save_path_, naive_config, m_threshold= .1)
# res_naive['odor_valence'] = np.array(['Naive'] * len(res_naive['day']))
excitatory = [True, False]
thresholds = [0.1, -0.05]
for i, sign in enumerate(excitatory):
res = statistics.analyze.analyze_data(save_path,
condition_config,
m_threshold=thresholds[i],
import seaborn as sns
conditions = [
conditions.PIR, conditions.OFC, conditions.OFC_LONGTERM,
conditions.OFC_COMPOSITE, conditions.MPFC_COMPOSITE
]
save_path = os.path.join(Config.LOCAL_FIGURE_PATH, 'MISC', 'SNR')
out = defaultdict(list)
psth = False
for condition in conditions:
data_path = os.path.join(Config.LOCAL_EXPERIMENT_PATH, 'COUNTING',
condition.name)
res = fio.load_pickle(os.path.join(data_path, 'dict.pkl'))
mice = np.unique(res['mouse'])
res = filter.filter_days_per_mouse(res, len(mice) * [0])
if psth:
for i, d in enumerate(res['data']):
res['data'][i] = np.mean(d, axis=1)
for mouse in mice:
temp = filter.filter(res, {'mouse': mouse})
data = np.concatenate(temp['data'], axis=1)
out['condition'].append(condition.name)
out['mouse'].append(mouse)
out['data'].append(data)
from collections import defaultdict
from tools import file_io
import filter
import reduce
import plot
data_directory = Config().LOCAL_DATA_PATH
data_directory = os.path.join(data_directory, 'registration','motion_metrics')
conditions = glob.glob(os.path.join(data_directory, '*/'))
res = defaultdict(list)
for c in conditions:
mouse_pickles = glob.glob(os.path.join(c, '*.pkl'))
for pickle in mouse_pickles:
temp = file_io.load_pickle(pickle)
reduce.chain_defaultdicts(res, temp)
res['within_day_crisp_average'] = []
for v in res['within_day_crisp']:
res['within_day_crisp_average'].append(np.mean(v))
for k, v in res.items():
res[k] = np.array(v)
save_path = os.path.join(Config.LOCAL_FIGURE_PATH, 'MISC', 'MOTION_METRICS')
mouse = 'M4_OFC'
vmin, vmax = 0.6, 1
from scipy.stats import ranksums, wilcoxon, kruskal
plt.style.use('default')
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
mpl.rcParams['font.size'] = 6
mpl.rcParams['font.family'] = 'arial'
condition_config = statistics.analyze.OFC_Reversal_Config()
condition = condition_config.condition
data_path = os.path.join(Config.LOCAL_DATA_PATH, Config.LOCAL_DATA_TIMEPOINT_FOLDER, condition.name)
save_path = os.path.join(Config.LOCAL_EXPERIMENT_PATH, 'COUNTING', condition.name)
figure_path = os.path.join(Config.LOCAL_FIGURE_PATH, 'OTHER', 'COUNTING', condition.name)
#analysis
res = fio.load_pickle(os.path.join(save_path, 'dict.pkl'))
# res.pop('data')
# retrieving relevant days
learned_day_per_mouse, last_day_per_mouse = get_days_per_mouse(data_path, condition)
if condition_config.start_at_training and hasattr(condition, 'training_start_day'):
start_days_per_mouse = condition.training_start_day
else:
start_days_per_mouse = [0] * len(np.unique(res['mouse']))
training_start_day_per_mouse = condition.training_start_day
lick_res = behavior.behavior_analysis.get_licks_per_day(data_path, condition)
analysis.add_odor_value(lick_res, condition)
lick_res = filter.filter(lick_res, {'odor_valence': ['CS+', 'CS-', 'PT CS+']})
lick_res = reduce.new_filter_reduce(lick_res, ['odor_valence', 'day', 'mouse'], reduce_key='lick_boolean')