jgrewe/chirp_probing
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Jan Grewe 2020-09-14 11:06:07 +02:00
parent 65fe9db570
commit efc79f9d07
2 changed files with 52 additions and 41 deletions
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84
punit_responses.py
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@ -3,7 +3,7 @@ import nixio as nix
import os import os
from numpy.core.fromnumeric import repeat from numpy.core.fromnumeric import repeat
from traitlets.traitlets import Instance from traitlets.traitlets import Instance
from chirp_ams import get_signals from chirp_ams import get_signals, other_freq, other_signal, self_freq
from model import simulate, load_models from model import simulate, load_models
from IPython import embed from IPython import embed
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -21,8 +21,7 @@ def append_settings(section, sec_name, sec_type, settings):
section[k] = settings[k] section[k] = settings[k]
def save(filename, name, stimulus_settings, model_settings, stimulus, responses, overwrite=False): def save(filename, name, stimulus_settings, model_settings, self_signal, other_signal, self_freq, other_freq, complete_stimulus, responses, overwrite=False):
print("saving! ", filename, name)
if os.path.exists(filename) and not overwrite: if os.path.exists(filename) and not overwrite:
nf = nix.File.open(filename, nix.FileMode.ReadWrite) nf = nix.File.open(filename, nix.FileMode.ReadWrite)
else: else:
@ -40,8 +39,8 @@ def save(filename, name, stimulus_settings, model_settings, stimulus, responses,
b.metadata = mdata b.metadata = mdata
# save stimulus # save stimulus
stim_da = b.create_data_array("stimulus", "nix.timeseries.sampled", dtype=nix.DataType.Float, stim_da = b.create_data_array("complete_stimulus", "nix.timeseries.sampled", dtype=nix.DataType.Float,
data=stimulus) data=complete_stimulus)
stim_da.label = "voltage" stim_da.label = "voltage"
stim_da.label = "mv/cm" stim_da.label = "mv/cm"
dim = stim_da.append_sampled_dimension(model_settings["deltat"]) dim = stim_da.append_sampled_dimension(model_settings["deltat"])
@ -56,22 +55,18 @@ def save(filename, name, stimulus_settings, model_settings, stimulus, responses,
da.unit = "s" da.unit = "s"
dim = da.append_range_dimension() dim = da.append_range_dimension()
dim.link_data_array(da, [-1]) dim.link_data_array(da, [-1])
nf.close() nf.close()
pass
def plot_responses():
pass
def simulate_responses(stimulus_params, model_params, repeats=10, deltaf=20):
def simulate_responses(stimulus_params, model_params, repeats=10):
cell_params = model_params.copy() cell_params = model_params.copy()
cell = cell_params["cell"] cell = cell_params["cell"]
del cell_params["cell"] del cell_params["cell"]
del cell_params["EODf"] del cell_params["EODf"]
for c in stimulus_params["contrasts"]: for c in stimulus_params["contrasts"]:
print("creating stimuli\n\tcontrast: ", str(c), "\t condition: ", print("\t\tcreating stimuli ... ")
stimulus_params["condition"])
params = stimulus_params.copy() params = stimulus_params.copy()
del params["contrasts"] del params["contrasts"]
del params["chirp_frequency"] del params["chirp_frequency"]
@ -79,41 +74,48 @@ def simulate_responses(stimulus_params, model_params, repeats=10):
time, self_signal, self_freq, other_signal, other_freq = get_signals(**params) time, self_signal, self_freq, other_signal, other_freq = get_signals(**params)
signal = (self_signal + other_signal) signal = (self_signal + other_signal)
signal /= np.max(signal) signal /= np.max(signal)
print("create p-unit responses for cell: ", cell) print("\t\tcreating p-unit responses ...")
spikes = [] spikes = []
no_other_spikes = []
for r in range(repeats): for r in range(repeats):
spikes.append(simulate(signal, **cell_params)) spikes.append(simulate(signal, **cell_params))
save("test.nix", "contrast_%.3f_condition_%s" %(c, stimulus_params["condition"]), params, no_other_spikes.append(simulate, self_signal))
cell_params, signal, spikes) print("\t\tsaving ...")
save("cell_%s.nix" % cell, "contrast_%.3f_condition_%s_deltaf_%i" %(c, stimulus_params["condition"], deltaf), params,
cell_params, self_signal, other_signal, self_freq, other_freq, signal, spikes)
def main(): def main():
cell_id = 20
models = load_models("models.csv") models = load_models("models.csv")
deltaf = 20. # Hz, difference frequency between self and other deltafs = [-200, -100, -20, 20, 100, 200] # Hz, difference frequency between self and other
model_params = models[cell_id]
stimulus_params = {"eodfs": {"self": model_params["EODf"], for cell_id in range(len(models)):
"other": model_params["EODf"] + deltaf}, model_params = models[cell_id]
"contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125], print("Cell: %s" % model_params["cell"])
"chirp_size": 100, # Hz, frequency excursion for deltaf in deltafs:
"chirp_duration": 0.015, # s, chirp duration print("\t Deltaf: %i" % deltaf)
"chirp_amplitude_dip": 0.05, # %, amplitude drop during chirp stimulus_params = {"eodfs": {"self": model_params["EODf"],
"chirp_frequency": 5, # Hz, how often does the fish chirp "other": model_params["EODf"] + deltaf},
"duration": 5., # s, total duration of simulation "contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
"dt": model_params["deltat"], # s, stepsize of the simulation "chirp_size": 100, # Hz, frequency excursion
} "chirp_duration": 0.015, # s, chirp duration
chirp_times = np.arange(stimulus_params["chirp_duration"], "chirp_amplitude_dip": 0.05, # %, amplitude drop during chirp
stimulus_params["duration"] - stimulus_params["chirp_duration"], "chirp_frequency": 5, # Hz, how often does the fish chirp
1./stimulus_params["chirp_frequency"]) "duration": 5., # s, total duration of simulation
stimulus_params["chirp_times"] = chirp_times "dt": model_params["deltat"], # s, stepsize of the simulation
conditions = ["other", "self"] }
chirp_times = np.arange(stimulus_params["chirp_duration"],
for c in conditions: stimulus_params["duration"] - stimulus_params["chirp_duration"],
stimulus_params["condition"] = c 1./stimulus_params["chirp_frequency"])
simulate_responses(stimulus_params, model_params, repeats=25) stimulus_params["chirp_times"] = chirp_times
pass conditions = ["other", "self"]
for c in conditions:
print("\t\tcondition: %s" % c)
stimulus_params["condition"] = c
simulate_responses(stimulus_params, model_params, repeats=25,
deltaf=deltaf)
if __name__ == "__main__": if __name__ == "__main__":
main() main()

9
response_discriminability.py Normal file
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@ -0,0 +1,9 @@
import nixio as nix
import matplotlib.pyplot as plt
def main():
pass
if __name__ == "__main__":
main()