68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
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from Baseline import get_baseline_class
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from CellData import CellData
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from models.LIFACnoise import LifacNoiseModel
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from Baseline import BaselineCellData, BaselineModel
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from os import listdir
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from IPython import embed
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import pyrelacs.DataLoader as Dl
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for meep in Dl.load("invivo_data/2011-10-25-aa-invivo-1/info.dat"):
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print(meep)
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quit()
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def icelldata_of_dir(base_path):
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global COUNT
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for item in sorted(listdir(base_path)):
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item_path = base_path + item
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try:
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data = CellData(item_path)
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yield data
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except TypeError as e:
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print(str(e))
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except IndexError as e:
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print(str(e), "\n")
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except ValueError as e:
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print(str(e), "\n")
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print("Currently throw errors: {}".format(COUNT))
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for data in icelldata_of_dir("invivo_data/"):
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v1 = data.get_base_traces(data.V1)
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if len(v1) == 0:
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embed()
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quit()
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quit()
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parameter_bursty_model = {'step_size': 5e-05, 'mem_tau': 0.0066693150193490695, 'v_base': 0, 'v_zero': 0,
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'threshold': 1, 'v_offset': -45.703125, 'input_scaling': 172.13861987237314,
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'delta_a': 0.06148215166012024, 'tau_a': 0.03391674075000068, 'a_zero': 2,
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'noise_strength': 0.0684136549210377, 'dend_tau': 0.0013694103932013805,
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'refractory_period': 0.001}
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eod = 752
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model = LifacNoiseModel(parameter_bursty_model)
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baseline_model = get_baseline_class(model, 752, trials=2)
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baseline_model.get_burstiness()
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quit()
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for cell_data in icelldata_of_dir("data/"):
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baseline = get_baseline_class(cell_data)
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baseline.get_burstiness() |