add baseline and no-other responses
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@ -3,7 +3,7 @@ import nixio as nix
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import os
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from numpy.core.fromnumeric import repeat
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from traitlets.traitlets import Instance
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from chirp_ams import get_signals, other_freq, other_signal, self_freq
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from chirp_ams import get_signals
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from model import simulate, load_models
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from IPython import embed
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import matplotlib.pyplot as plt
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@ -39,83 +39,176 @@ def save(filename, name, stimulus_settings, model_settings, self_signal, other_s
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b.metadata = mdata
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# save stimulus
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stim_da = b.create_data_array("complete_stimulus", "nix.timeseries.sampled", dtype=nix.DataType.Float,
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data=complete_stimulus)
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stim_da.label = "voltage"
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stim_da.label = "mv/cm"
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stim_da.label = "mV/cm"
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dim = stim_da.append_sampled_dimension(model_settings["deltat"])
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dim.label = "time"
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dim.unit = "s"
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self_freq_da = None
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if self_freq is not None:
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self_freq_da = b.create_data_array("self frequency", "nix.timeseries.sampled", dtype=nix.DataType.Float,
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data=self_freq)
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self_freq_da.label = "frequency"
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self_freq_da.label = "Hz"
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dim = self_freq_da.append_sampled_dimension(model_settings["deltat"])
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dim.label = "time"
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dim.unit = "s"
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other_freq_da = None
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if other_freq is not None:
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other_freq_da = b.create_data_array("other frequency", "nix.timeseries.sampled", dtype=nix.DataType.Float,
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data=self_freq)
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self_freq_da.label = "frequency"
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self_freq_da.label = "Hz"
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dim = other_freq_da.append_sampled_dimension(model_settings["deltat"])
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dim.label = "time"
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dim.unit = "s"
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# save responses
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for i in range(len(responses)):
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da = b.create_data_array("response_%i" %i, "nix.timeseries.events.spike_times",
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dtype=nix.DataType.Float, data=responses[i])
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da.label = "time"
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da.unit = "s"
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dim = da.append_range_dimension()
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dim.link_data_array(da, [-1])
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dim = da.append_alias_range_dimension()
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# bind it all together
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tag = b.create_tag("chirp stimulation", "nix.stimulus", [0.0])
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tag.extent = [stim_da.shape[0] * stim_da.dimensions[0].sampling_interval]
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for da in b.data_arrays:
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if "response" in da.name.lower():
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tag.references.append(da)
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tag.create_feature(stim_da, nix.LinkType.Untagged)
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if self_freq_da is not None:
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tag.create_feature(self_freq_da, nix.LinkType.Untagged)
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if other_freq_da is not None:
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tag.create_feature(other_freq_da, nix.LinkType.Untagged)
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nf.close()
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def save_baseline_response(filename, block_name, spikes, model_settings, overwrite=False):
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if os.path.exists(filename) and not overwrite:
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nf = nix.File.open(filename, nix.FileMode.ReadWrite)
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else:
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nf = nix.File.open(filename, mode=nix.FileMode.Overwrite,
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compression=nix.Compression.DeflateNormal)
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if block_name in nf.blocks:
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print("Data with this name is already stored! ", block_name)
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nf.close()
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return
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mdata = nf.create_section(block_name, "nix.simulation")
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append_settings(mdata, "model parameter", "nix.model.settings", model_settings)
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b = nf.create_block(block_name, "nix.simulation")
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b.metadata = mdata
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da = b.create_data_array("baseline_response", "nix.timeseries.events.spike_times",
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dtype=nix.DataType.Float, data=spikes)
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da.label = "time"
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da.unit = "s"
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dim = da.append_alias_range_dimension()
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nf.close()
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def get_baseline_response(model_params, duration=10):
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eodf = model_params["EODf"]
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dt = model_params["deltat"]
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cell_params = model_params.copy()
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del cell_params["cell"]
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del cell_params["EODf"]
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time, pre_stim = get_pre_stimulus(eodf)
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baseline_time = np.arange(0.0, duration, dt)
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eod = np.sin(baseline_time * eodf * 2 * np.pi)
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stim = np.hstack((pre_stim, eod))
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v_0 = np.random.rand(1)[0]
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cell_params["v_zero"] = v_0
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spikes = simulate(stim, **cell_params)
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spikes = spikes[spikes > time[-1]] - time[-1]
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return spikes
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def get_pre_stimulus(eodf, duration=2, df=20, contrast=20, dt=1./20000):
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time = np.arange(0.0, duration, dt)
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eod = np.sin(time * eodf * 2 * np.pi)
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eod_2 = np.sin(time * (eodf + df) * 2 * np.pi) * contrast/100
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eod_2 *= np.hanning(len(eod_2))
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stim = eod + eod_2
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return time, stim
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def simulate_responses(stimulus_params, model_params, repeats=10, deltaf=20):
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cell_params = model_params.copy()
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cell = cell_params["cell"]
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filename = "cell_%s.nix" % cell
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del cell_params["cell"]
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del cell_params["EODf"]
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for c in stimulus_params["contrasts"]:
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print("\t\tcreating stimuli ... ")
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conditions = ["other", "self"]
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pre_time, pre_stim = get_pre_stimulus(stimulus_params["eodfs"]["self"], dt=model_params["deltat"])
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for contrast in stimulus_params["contrasts"]:
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params = stimulus_params.copy()
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params["contrast"] = contrast
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del params["contrasts"]
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del params["chirp_frequency"]
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params["contrast"] = c
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time, self_signal, self_freq, other_signal, other_freq = get_signals(**params)
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signal = (self_signal + other_signal)
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signal /= np.max(signal)
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print("\t\tcreating p-unit responses ...")
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spikes = []
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no_other_spikes = []
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for r in range(repeats):
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spikes.append(simulate(signal, **cell_params))
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no_other_spikes.append(simulate, self_signal))
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print("\t\tsaving ...")
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save("cell_%s.nix" % cell, "contrast_%.3f_condition_%s_deltaf_%i" %(c, stimulus_params["condition"], deltaf), params,
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cell_params, self_signal, other_signal, self_freq, other_freq, signal, spikes)
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for condition in conditions:
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print("\tcontrast: %s, condition: %s" %(contrast, condition), " "*10, end="\r")
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block_name = "contrast_%.3f_condition_%s_deltaf_%i" %(contrast, condition, deltaf)
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params["condition"] = condition
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time, self_signal, self_freq, other_signal, other_freq = get_signals(**params)
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full_signal = (self_signal + other_signal)
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spikes = []
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no_other_spikes = []
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for _ in range(repeats):
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v_0 = np.random.rand(1)[0]
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cell_params["v_zero"] = v_0
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sp = simulate(np.hstack((pre_stim, full_signal)), **cell_params)
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spikes.append(sp[sp > pre_time[-1]] - pre_time[-1])
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if condition == "self":
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v_0 = np.random.rand(1)[0]
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cell_params["v_zero"] = v_0
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no_other_spikes.append(simulate(np.hstack((pre_stim, self_signal)), **cell_params))
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if condition == "self":
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name = "contrast_%.3f_condition_no_other_deltaf_%i" %(contrast, deltaf)
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save(filename, name, params, cell_params, self_signal, None, self_freq, None, self_signal, no_other_spikes)
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save(filename, block_name, params, cell_params, self_signal, other_signal, self_freq, other_freq, full_signal, spikes)
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print("\n")
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def main():
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models = load_models("models.csv")
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deltafs = [-200, -100, -20, 20, 100, 200] # Hz, difference frequency between self and other
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stimulus_params = { "eodfs": {"self": 0.0, "other": 0.0},
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"contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
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"chirp_size": 100, # Hz, frequency excursion
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"chirp_duration": 0.015, # s, chirp duration
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"chirp_amplitude_dip": 0.05, # %, amplitude drop during chirp
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"chirp_frequency": 5, # Hz, how often does the fish chirp
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"duration": 5., # s, total duration of simulation
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"dt": 1, # s, stepsize of the simulation
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}
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for cell_id in range(len(models)):
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model_params = models[cell_id]
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baseline_spikes = get_baseline_response(model_params, duration=30)
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save_baseline_response( "cell_%s.nix" % model_params["cell"], "baseline response", baseline_spikes, model_params)
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print("Cell: %s" % model_params["cell"])
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for deltaf in deltafs:
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stimulus_params["eodfs"] = {"self": model_params["EODf"], "other": model_params["EODf"] + deltaf}
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stimulus_params["dt"] = model_params["deltat"]
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print("\t Deltaf: %i" % deltaf)
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stimulus_params = {"eodfs": {"self": model_params["EODf"],
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"other": model_params["EODf"] + deltaf},
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"contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
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"chirp_size": 100, # Hz, frequency excursion
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"chirp_duration": 0.015, # s, chirp duration
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"chirp_amplitude_dip": 0.05, # %, amplitude drop during chirp
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"chirp_frequency": 5, # Hz, how often does the fish chirp
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"duration": 5., # s, total duration of simulation
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"dt": model_params["deltat"], # s, stepsize of the simulation
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}
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chirp_times = np.arange(stimulus_params["chirp_duration"],
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stimulus_params["duration"] - stimulus_params["chirp_duration"],
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1./stimulus_params["chirp_frequency"])
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stimulus_params["chirp_times"] = chirp_times
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conditions = ["other", "self"]
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for c in conditions:
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print("\t\tcondition: %s" % c)
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stimulus_params["condition"] = c
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simulate_responses(stimulus_params, model_params, repeats=25,
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deltaf=deltaf)
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stimulus_params["chirp_times"] = chirp_times
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simulate_responses(stimulus_params, model_params, repeats=25, deltaf=deltaf)
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exit()
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if __name__ == "__main__":
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main()
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