from Baseline import get_baseline_class
from CellData import CellData
from models.LIFACnoise import LifacNoiseModel
from Baseline import BaselineCellData, BaselineModel
from os import listdir
from IPython import embed
import pyrelacs.DataLoader as Dl


for meep in Dl.load("invivo_data/2011-10-25-aa-invivo-1/info.dat"):
    print(meep)



quit()



def icelldata_of_dir(base_path):
    global COUNT
    for item in sorted(listdir(base_path)):
        item_path = base_path + item

        try:
            data = CellData(item_path)

            yield data

        except TypeError as e:
            print(str(e))

        except IndexError as e:
            print(str(e), "\n")

        except ValueError as e:
            print(str(e), "\n")

    print("Currently throw errors: {}".format(COUNT))


for data in icelldata_of_dir("invivo_data/"):
    v1 = data.get_base_traces(data.V1)

    if len(v1) == 0:
        embed()
        quit()


quit()

parameter_bursty_model = {'step_size': 5e-05, 'mem_tau': 0.0066693150193490695, 'v_base': 0, 'v_zero': 0,
                          'threshold': 1, 'v_offset': -45.703125, 'input_scaling': 172.13861987237314,
                          'delta_a': 0.06148215166012024, 'tau_a': 0.03391674075000068, 'a_zero': 2,
                          'noise_strength': 0.0684136549210377, 'dend_tau': 0.0013694103932013805,
                          'refractory_period': 0.001}
eod = 752
model = LifacNoiseModel(parameter_bursty_model)
baseline_model = get_baseline_class(model, 752, trials=2)
baseline_model.get_burstiness()

quit()


for cell_data in icelldata_of_dir("data/"):
    baseline = get_baseline_class(cell_data)

    baseline.get_burstiness()