def run_script(cell_range):
# path_to_data = "/Users/stevecharczynski/workspace/data/warden/recall_trials/"
# save_dir = "/Users/stevecharczynski/workspace/data/warden/recall_trials/"
save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/warden/recall_trials_second_main_time/"
path_to_data = "/projectnb/ecog-eeg/stevechar/data/warden/recall_trials/"
# time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
#second_pres
data_processor = analysis.DataProcessor(
path_to_data, cell_range, window=[1250, 3000])
solver_params = {
"niter": 1500,
"stepsize": 100,
"interval": 20,
"method": "TNC",
"use_jac": True,
"T" : 100,
"disp":False
}
bounds_stim = {
"sigma": [1e-4, 1000.],
"mu": [1250, 3000.],
"a_1": [1e-10, 1/5.],
"a_2": [1e-10, 1/5.],
"a_3": [1e-10, 1/5.],
"a_4": [1e-10, 1/5.],
"a_0": [1e-10, 1/5.]
}
bounds_time = {
"sigma": [1e-4, 1000.],
"mu": [1250, 3000.],
"a_1": [1e-10, 1/2.],
"a_0": [1e-10, 1/2.]
}
pipeline = analysis.Pipeline(cell_range, data_processor, [
"Const","Gaussian", "GaussianStim"], save_dir=save_dir)
pipeline.set_model_bounds("Gaussian", bounds_time)
pipeline.set_model_bounds("GaussianStim", bounds_stim)
pipeline.set_model_bounds("Const", {"a_0":[10e-10, 1]})
# with open("/Users/stevecharczynski/workspace/data/warden/recall_trials/info.json") as f:
with open("/projectnb/ecog-eeg/stevechar/data/warden/recall_trials/info.json") as f:
stims = json.load(f)
stims = {int(k):v for k,v in stims.items()}
pipeline.set_model_info("GaussianStim", "stim_identity", stims, per_cell=True)
pipeline.set_model_x0("GaussianStim", [10, 1600, 1e-1, 1e-1,1e-1, 1e-1, 1e-1])
pipeline.set_model_x0("Gaussian", [10, 1600, 1e-1, 1e-1])
pipeline.set_model_x0("Const", [1e-1])
pipeline.fit_all_models(solver_params=solver_params)
pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "SigmaMuTau", 0.01)
# pipeline.compare_even_odd("SigmaMuTau", "SigmaMuTauStimWarden", 0.01)
pipeline.compare_models("Const", "Gaussian", 0.01, smoother_value=100)
pipeline.compare_models("Const", "GaussianStim", 0.01, smoother_value=100)
pipeline.compare_models("Gaussian", "GaussianStim", 0.01, smoother_value=100)
pipeline.compare_even_odd("Const", "Gaussian", 0.01)
pipeline.compare_even_odd("Const", "GaussianStim", 0.01)
pipeline.compare_even_odd("Gaussian", "GaussianStim", 0.01)
def run_script(cell_range):
path_to_data = "/Users/stevecharczynski/workspace/data/sheehan/lin_pos_set/s11"
data_processor = analysis.DataProcessor(path_to_data, cell_range)
n_t = 2.
solver_params = {
"niter": 300,
"stepsize": 500,
"interval": 10,
"method": "TNC",
"use_jac": True,
"T": 1,
"disp": False
}
bounds_pos = {
"a_1": [10**-10, 1 / n_t],
"ut": [0., 200.],
"st": [10., 2000.],
"a_0": [10**-10, 1 / n_t]
}
pipeline = analysis.Pipeline(
cell_range, data_processor,
["ConstVariable", "RelPosVariable", "AbsPosVariable"])
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
pipeline.set_model_bounds("AbsPosVariable", bounds_pos)
pipeline.set_model_bounds("RelPosVariable", bounds_pos)
pipeline.set_model_bounds("ConstVariable", {"a_0": [10**-10, 1]})
pipeline.set_model_x0("AbsPosVariable", [1e-5, 20, 10, 1e-5])
pipeline.set_model_x0("RelPosVariable", [1e-5, 20, 10, 1e-5])
pipeline.set_model_x0("ConstVariable", [1e-5])
# pipeline.show_rasters()
import numpy as np
import json
with open(path_to_data + "/abs_pos.json", 'r') as f:
abs_pos = np.array(json.load(f))
with open(path_to_data + "/rel_pos.json", 'r') as f:
rel_pos = np.array(json.load(f))
with open(path_to_data + "/velocity.json", 'r') as f:
velocity = np.array(json.load(f))
pipeline.set_model_info("AbsPosVariable", "abs_pos", abs_pos, True)
pipeline.set_model_info("RelPosVariable", "rel_pos", rel_pos, True)
pipeline.set_model_info("AbsPosVariable", "velocity", velocity, True)
pipeline.set_model_info("RelPosVariable", "velocity", velocity, True)
pipeline.fit_all_models(solver_params=solver_params)
pipeline.compare_models("ConstVariable",
"RelPosVariable",
0.01,
smoother_value=100)
pipeline.compare_models("ConstVariable",
"AbsPosVariable",
0.01,
smoother_value=100)
import maxlikespy.analysis as analysis
path_to_data = '/Users/stevecharczynski/workspace/data/jay/2nd_file'
cell_range = range(5, 10)
data_processor = analysis.DataProcessor(path_to_data, cell_range)
solver_params = {
"niter": 2,
"stepsize": 10000,
"interval": 10,
"method": "TNC",
"use_jac": True,
"T": 1,
"disp": False
}
bounds_t = {
"a_1": [0, 1 / 2],
"ut": [-1000, 10000],
"st": [100, 10000],
"a_0": [10**-10, 1 / 2]
}
bounds_c = {"a_0": [10**-10, 1 / 2]}
pipeline = analysis.Pipeline(cell_range, data_processor, ["Const", "Time"], 0)
pipeline.set_model_bounds("Time", bounds_t)
pipeline.set_model_bounds("Const", bounds_c)
pipeline.set_model_x0(["Const", "Time"], [[1e-5], [1e-5, 100, 100, 1e-5]])
pipeline.fit_all_models(solver_params=solver_params)
pipeline.compare_models("Const", "Time", 0.01)
def run_script(cell_range):
# path_to_data = "/Users/stevecharczynski/workspace/data/rossi_pool/a2/"
# save_dir = "/Users/stevecharczynski/workspace/data/rossi_pool/a2/"
save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/rossi_pool/a2/"
path_to_data = "/projectnb/ecog-eeg/stevechar/data/rossi_pool/a2/"
# time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
data_processor = analysis.DataProcessor(path_to_data,
cell_range,
window=[0, 3000])
solver_params = {
"niter": 500,
"stepsize": 100,
"interval": 10,
"method": "TNC",
"use_jac": True,
"T": 1,
"disp": False
}
bounds_smtstim = {
"sigma": [0, 1000.],
"mu": [0, 1000.],
"tau": [20, 10000.],
"a_1": [10**-10, 1 / 3.],
"a_2": [10**-10, 1 / 3.],
"a_0": [10**-10, 1 / 3.]
}
bounds_smtclass = {
"sigma": [0, 1000.],
"mu": [0, 1000.],
"tau": [20, 10000.],
"a_1": [10**-10, 1 / 5.],
"a_2": [10**-10, 1 / 5.],
"a_3": [10**-10, 1 / 5.],
"a_4": [10**-10, 1 / 5.],
"a_0": [10**-10, 1 / 5.]
}
bounds_smt = {
"sigma": [0, 1000.],
"mu": [0, 1000.],
"tau": [20, 10000.],
"a_1": [10**-10, 1 / 2.],
"a_0": [10**-10, 1 / 2.]
}
pipeline = analysis.Pipeline(
cell_range,
data_processor,
["Const", "SigmaMuTau", "SigmaMuTauStimRP", "SigmaMuTauStimClassRP"],
save_dir=save_dir)
pipeline.set_model_bounds("SigmaMuTau", bounds_smt)
pipeline.set_model_bounds("SigmaMuTauStimRP", bounds_smtstim)
pipeline.set_model_bounds("SigmaMuTauStimClassRP", bounds_smtclass)
pipeline.set_model_bounds("Const", {"a_0": [10e-10, 1]})
# with open("/Users/stevecharczynski/workspace/data/rossi_pool/a2/info.json") as f:
with open(
"/projectnb/ecog-eeg/stevechar/data/rossi_pool/a2/info.json") as f:
stims = json.load(f)
stims = {int(k): v for k, v in stims.items()}
pipeline.set_model_info("SigmaMuTauStimRP",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_info("SigmaMuTauStimClassRP",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_x0("SigmaMuTauStimRP",
[0.01, 300, 10, 1e-1, 1e-1, 1e-1])
pipeline.set_model_x0("SigmaMuTauStimClassRP",
[0.01, 300, 10, 1e-1, 1e-1, 1e-1, 1e-1, 1e-1])
pipeline.set_model_x0("SigmaMuTau", [0.01, 300, 10, 1e-1, 1e-1])
pipeline.set_model_x0("Const", [1e-1])
# pipeline.show_rasters()
pipeline.fit_all_models(solver_params=solver_params)
pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "SigmaMuTau", 0.01)
# pipeline.compare_even_odd("SigmaMuTau", "SigmaMuTauStimRP", 0.01)
pipeline.compare_models("Const", "SigmaMuTau", 0.01, smoother_value=100)
pipeline.compare_models("SigmaMuTau",
"SigmaMuTauStimRP",
0.01,
smoother_value=100)
pipeline.compare_models("SigmaMuTau",
"SigmaMuTauStimClassRP",
0.01,
smoother_value=100)
pipeline.compare_models("SigmaMuTauStimRP",
"SigmaMuTauStimClassRP",
0.01,
smoother_value=100)
pipeline.compare_even_odd("Const", "SigmaMuTau", 0.01)
pipeline.compare_even_odd("SigmaMuTau", "SigmaMuTauStimRP", 0.05)
pipeline.compare_even_odd("SigmaMuTau", "SigmaMuTauStimClassRP", 0.05)
pipeline.compare_even_odd("SigmaMuTauStimRP", "SigmaMuTauStimClassRP",
0.05)
def run_script(cell_range):
# save_dir = "/projectnb/ecog-eeg/stevechar/sheehan_runs/stable_lights_on_only/inbound/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/stable_lights_on_only/inbound/"
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range)
# n_t = 2.
# solver_params = {
# "niter": 500,
# "stepsize": 500,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# bounds_dual = {
# "ut_a": [-10., 100.],
# "st_a": [0.1, 90.],
# "a_0a": [10**-10, 1 / 3],
# "a_1a": [10**-10, 1 / 3],
# "ut_b": [-10., 100.],
# "st_b": [0.1, 90.],
# "a_0b": [10**-10, 1 / 3],
# "a_1b": [10**-10, 1 / 3],
# }
# bounds_norm = {
# "a_1": [10**-10, 1 / n_t],
# "ut": [-10., 100.],
# "st": [0.1, 90.],
# "a_0": [10**-10, 1 / n_t]
# }
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # # "ConstVariable", "RelPosVariable","DualPeakedRel", "AbsPosVariable"])
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "RelPosVariable", "AbsPosVariable", "DualPeakedRel", "DualPeakedAbs"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable", "AbsPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("AbsPosVariable", bounds_norm)
# pipeline.set_model_bounds("RelPosVariable", bounds_norm)
# pipeline.set_model_bounds("ConstVariable", {"a_0":[10**-10, 1]})
# pipeline.set_model_bounds("DualPeakedRel", bounds_dual)
# pipeline.set_model_bounds("DualPeakedAbs", bounds_dual)
# pipeline.set_model_x0("DualPeakedRel", [20, 1, 1e-5, 1e-5, 20, 1, 1e-5, 1e-5])
# pipeline.set_model_x0("DualPeakedAbs", [20, 1, 1e-5, 1e-5, 20, 1, 1e-5, 1e-5])
# pipeline.set_model_x0("AbsPosVariable", [1e-5, 20, 1, 1e-5])
# pipeline.set_model_x0("RelPosVariable", [1e-5, 20, 1, 1e-5])
# pipeline.set_model_x0("ConstVariable", [1e-5])
# # pipeline.show_rasters()
# with open(path_to_data+"/abs_pos.json", 'r') as f:
# abs_pos = np.array(json.load(f))
# with open(path_to_data+"/rel_pos.json", 'r') as f:
# rel_pos = np.array(json.load(f))
# pipeline.set_model_info("AbsPosVariable", "abs_pos", abs_pos, True)
# pipeline.set_model_info("RelPosVariable", "rel_pos", rel_pos, True)
# pipeline.set_model_info("DualPeakedRel", "rel_pos", rel_pos, True)
# pipeline.set_model_info("DualPeakedAbs", "abs_pos", abs_pos, True)
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_even_odd("RelPosVariable", "DualPeakedRel", 0.01)
# pipeline.compare_even_odd("AbsPosVariable", "DualPeakedAbs", 0.01)
# pipeline.compare_even_odd("ConstVariable", "RelPosVariable", 0.01)
# pipeline.compare_even_odd("ConstVariable", "AbsPosVariable", 0.01)
# pipeline.compare_models("AbsPosVariable", "DualPeakedAbs", 0.01, smoother_value=100)
# pipeline.compare_models("RelPosVariable", "DualPeakedRel", 0.01, smoother_value=100)
# pipeline.compare_models("ConstVariable", "RelPosVariable", 0.01, smoother_value=100)
# pipeline.compare_models("ConstVariable", "AbsPosVariable", 0.01, smoother_value=100)
save_dir = "/projectnb/ecog-eeg/stevechar/sheehan_runs/stable_lights_on_only/inbound/"
path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/stable_lights_on_only/inbound/"
data_processor = analysis.DataProcessor(
path_to_data, cell_range)
n_t = 2.
solver_params = {
"niter": 1000,
"stepsize": 50,
"interval": 10,
"method": "TNC",
"use_jac": True,
"T" : 1,
"disp":False
}
bounds_dual = {
"ut_a": [-10., 100.],
"st_a": [0.1, 90.],
"a_0a": [10**-10, 1 / 3],
"a_1a": [10**-10, 1 / 3],
"ut_b": [-10., 100.],
"st_b": [0.1, 90.],
"a_0b": [10**-10, 1 / 3],
"a_1b": [10**-10, 1 / 3],
}
bounds_norm = {
"a_1": [10**-10, 1 / n_t],
"ut": [-10., 100.],
"st": [0.1, 90.],
"a_0": [10**-10, 1 / n_t]
}
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable","DualPeakedRel", "AbsPosVariable"])
pipeline = analysis.Pipeline(cell_range, data_processor, [
"ConstVariable", "RelPosVariable", "AbsPosVariable"], save_dir=save_dir)
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "RelPosVariable", "AbsPosVariable"], save_dir=save_dir)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
pipeline.set_model_bounds("AbsPosVariable", bounds_norm)
pipeline.set_model_bounds("RelPosVariable", bounds_norm)
pipeline.set_model_bounds("ConstVariable", {"a_0":[10**-10, 1]})
pipeline.set_model_x0("AbsPosVariable", [1e-5, 20, 1, 1e-5])
pipeline.set_model_x0("RelPosVariable", [1e-5, 20, 1, 1e-5])
pipeline.set_model_x0("ConstVariable", [1e-5])
# pipeline.show_rasters()
with open(path_to_data+"/abs_pos.json", 'r') as f:
abs_pos = np.array(json.load(f))
with open(path_to_data+"/rel_pos.json", 'r') as f:
rel_pos = np.array(json.load(f))
pipeline.set_model_info("AbsPosVariable", "abs_pos", abs_pos, True)
pipeline.set_model_info("RelPosVariable", "rel_pos", rel_pos, True)
pipeline.fit_even_odd(solver_params=solver_params)
pipeline.fit_all_models(solver_params=solver_params)
pipeline.compare_even_odd("ConstVariable", "RelPosVariable", 0.01)
pipeline.compare_even_odd("ConstVariable", "AbsPosVariable", 0.01)
pipeline.compare_models("ConstVariable", "RelPosVariable", 0.01, smoother_value=100)
pipeline.compare_models("ConstVariable", "AbsPosVariable", 0.01, smoother_value=100)
def run_script(cell_range):
# path_to_data = "/Users/stevecharczynski/workspace/data/warden/recall_trials/"
# save_dir = "/Users/stevecharczynski/workspace/data/warden/recall_trials_both/"
save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/warden/recall_trials_both/"
path_to_data = "/projectnb/ecog-eeg/stevechar/data/warden/recall_trials/"
# time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
data_processor = analysis.DataProcessor(path_to_data,
cell_range,
window=[0, 3000])
solver_params = {
"niter": 1000,
"stepsize": 100,
"interval": 5,
"method": "TNC",
"use_jac": True,
"T": 1,
"disp": False
}
bounds_gaussianstim = {
"sigma1": [1e-5, 1000.],
"mu1": [0, 1700.],
"sigma2": [1e-5, 1000.],
"mu2": [1500, 3200.],
"a_1": [1e-10, 1 / 5.],
"a_2": [1e-10, 1 / 5.],
"a_3": [1e-10, 1 / 5.],
"a_4": [1e-10, 1 / 5.],
"a_0": [1e-10, 1 / 5.]
}
bounds_gaussianstim_pos = {
"sigma1": [1e-5, 1000.],
"mu1": [0, 1700.],
"sigma2": [1e-5, 1000.],
"mu2": [1500, 3200.],
"a_1": [1e-10, 1 / 5.],
"a_2": [1e-10, 1 / 5.],
"a_3": [1e-10, 1 / 5.],
"a_4": [1e-10, 1 / 5.],
"a_5": [1e-10, 1 / 5.],
"a_6": [1e-10, 1 / 5.],
"a_7": [1e-10, 1 / 5.],
"a_8": [1e-10, 1 / 5.],
"a_0": [1e-10, 1 / 5.]
}
bounds_gaussian = {
"sigma1": [1e-5, 1000.],
"mu1": [0, 1700.],
"sigma2": [1e-5, 1000.],
"mu2": [1500, 3200.],
"a_1": [1e-10, 1 / 2.],
"a_0": [1e-10, 1 / 2.]
}
bounds_gaussian_pos = {
"sigma1": [1e-5, 1000.],
"mu1": [0, 1700.],
"sigma2": [1e-5, 1000.],
"mu2": [1500, 3200.],
"a_1": [1e-10, 1 / 2.],
"a_2": [1e-10, 1 / 2.],
"a_0": [1e-10, 1 / 2.]
}
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const","GaussianBoth", "GaussianStimBoth", "GaussianStimBoth1", \
# "GaussianStimBoth2", "GaussianStimBoth3", "GaussianStimBoth4"], save_dir=save_dir)
pipeline = analysis.Pipeline(
cell_range,
data_processor, [
"Const", "GaussianBoth", "GaussianStimBoth", "GaussianBothPos",
"GaussianStimBothPos"
],
save_dir=save_dir)
pipeline.set_model_bounds("GaussianBoth", bounds_gaussian)
# pipeline.set_model_bounds("GaussianStimBoth1", bounds_gaussian)
# pipeline.set_model_bounds("GaussianStimBoth2", bounds_gaussian)
# pipeline.set_model_bounds("GaussianStimBoth3", bounds_gaussian)
# pipeline.set_model_bounds("GaussianStimBoth4", bounds_gaussian)
pipeline.set_model_bounds("GaussianBothPos", bounds_gaussian_pos)
pipeline.set_model_bounds("GaussianStimBothPos", bounds_gaussianstim_pos)
pipeline.set_model_bounds("GaussianStimBoth", bounds_gaussianstim)
pipeline.set_model_bounds("Const", {"a_0": [10e-10, 1]})
# with open("/Users/stevecharczynski/workspace/data/warden/recall_trials/info.json") as f:
with open(
"/projectnb/ecog-eeg/stevechar/data/warden/recall_trials/info.json"
) as f:
stims = json.load(f)
stims = {int(k): v for k, v in stims.items()}
pipeline.set_model_info("GaussianStimBoth",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_info("GaussianStimBothPos",
"stim_identity",
stims,
per_cell=True)
# pipeline.set_model_info("GaussianStimBoth1", "stim_identity", stims, per_cell=True)
# pipeline.set_model_info("GaussianStimBoth2", "stim_identity", stims, per_cell=True)
# pipeline.set_model_info("GaussianStimBoth3", "stim_identity", stims, per_cell=True)
# pipeline.set_model_info("GaussianStimBoth4", "stim_identity", stims, per_cell=True)
pipeline.set_model_x0("GaussianStimBoth",
[10, 1000, 10, 2000, 1e-1, 1e-1, 1e-1, 1e-1, 1e-1])
pipeline.set_model_x0("GaussianStimBothPos", [
10, 1000, 10, 2000, 1e-1, 1e-1, 1e-1, 1e-1, 1e-1, 1e-1, 1e-1, 1e-1,
1e-1
])
pipeline.set_model_x0("GaussianBothPos",
[10, 1000, 10, 2000, 1e-1, 1e-1, 1e-1])
pipeline.set_model_x0("GaussianBoth", [10, 1000, 10, 2000, 1e-1, 1e-1])
# pipeline.set_model_x0("GaussianStimBoth1", [10, 1000,10, 2000, 1e-1, 1e-1])
# pipeline.set_model_x0("GaussianStimBoth2", [10, 1000,10, 2000, 1e-1, 1e-1])
# pipeline.set_model_x0("GaussianStimBoth3", [10, 1000,10, 2000, 1e-1, 1e-1])
# pipeline.set_model_x0("GaussianStimBoth4", [10, 1000,10, 2000, 1e-1, 1e-1])
pipeline.set_model_x0("Const", [1e-1])
pipeline.fit_all_models(solver_params=solver_params)
pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "GaussianBoth", 0.01)
# pipeline.compare_even_odd("GaussianBoth", "GaussianStimBoth", 0.01)
pipeline.compare_models("Const", "GaussianBoth", 0.01, smoother_value=100)
pipeline.compare_models("Const",
"GaussianStimBoth",
0.01,
smoother_value=100)
pipeline.compare_models("GaussianBoth",
"GaussianStimBoth",
0.01,
smoother_value=100)
pipeline.compare_models("Const",
"GaussianBothPos",
0.01,
smoother_value=100)
pipeline.compare_models("Const",
"GaussianStimBothPos",
0.01,
smoother_value=100)
pipeline.compare_models("GaussianBoth",
"GaussianBothPos",
0.01,
smoother_value=100)
pipeline.compare_models("GaussianBoth",
"GaussianStimBothPos",
0.01,
smoother_value=100)
pipeline.compare_models("GaussianBothPos",
"GaussianStimBoth",
0.01,
smoother_value=100)
pipeline.compare_models("GaussianBothPos",
"GaussianStimBothPos",
0.01,
smoother_value=100)
pipeline.compare_models("GaussianStimBoth",
"GaussianStimBothPos",
0.01,
smoother_value=100)
# pipeline.compare_models("Const", "GaussianStimBoth1", 0.01, smoother_value=100)
# pipeline.compare_models("Const", "GaussianStimBoth2", 0.01, smoother_value=100)
# pipeline.compare_models("Const", "GaussianStimBoth3", 0.01, smoother_value=100)
# pipeline.compare_models("Const", "GaussianStimBoth4", 0.01, smoother_value=100)
# pipeline.compare_models("GaussianStimBoth1", "GaussianStimBoth", 0.01, smoother_value=100)
# pipeline.compare_models("GaussianStimBoth2", "GaussianStimBoth", 0.01, smoother_value=100)
# pipeline.compare_models("GaussianStimBoth3", "GaussianStimBoth", 0.01, smoother_value=100)
# pipeline.compare_models("GaussianStimBoth4", "GaussianStimBoth", 0.01, smoother_value=100)
pipeline.compare_even_odd("Const", "GaussianBoth", 0.01)
pipeline.compare_even_odd("Const", "GaussianStimBoth", 0.01)
pipeline.compare_even_odd("GaussianBoth", "GaussianStimBoth", 0.01)
pipeline.compare_even_odd("Const", "GaussianBothPos", 0.01)
pipeline.compare_even_odd("Const", "GaussianStimBothPos", 0.01)
pipeline.compare_even_odd("GaussianBoth", "GaussianBothPos", 0.01)
pipeline.compare_even_odd("GaussianBoth", "GaussianStimBothPos", 0.01)
pipeline.compare_even_odd("GaussianBothPos", "GaussianStimBoth", 0.01)
pipeline.compare_even_odd("GaussianBothPos", "GaussianStimBothPos", 0.01)
pipeline.compare_even_odd("GaussianStimBoth", "GaussianStimBothPos", 0.01)
def run_script(cell_range):
# path_to_data = "/Users/stevecharczynski/workspace/data/jay/ca1_time_cell_data/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay/ca1_time_cells"
# save_dir = "/Users/stevecharczynski/Desktop/test/"
# # save_dir = "/projectnb/ecog-eeg/stevechar/ml_output/jay/ca1_time_cells/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/all_sessions/{0}".format(session)
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range)
# solver_params = {
# "niter": 1,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [0, 9000],
# "st": [10, 6000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_dual = {
# "a_1": [0, 1/3],
# "a_2": [0, 1/3],
# "ut_1": [0, 9000],
# "ut_2": [0, 9000],
# "st_1": [10, 6000],
# "st_2": [10, 6000],
# "a_0": [10**-10, 1/3]
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "TimeVariableLength", "DualVariableLength"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("ConstVariable", bounds_c)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("DualVariableLength", bounds_dual)
# pipeline.set_model_x0("TimeVariableLength", [1e-5, 1000, 300, 1e-5])
# pipeline.set_model_x0("ConstVariable", [1e-5])
# pipeline.set_model_x0("DualVariableLength", [1e-5, 1e-5, 1000, 3000, 300, 300, 1e-5])
# # pipeline.show_rasters()
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_even_odd("ConstVariable", "TimeVariableLength", 0.01)
# pipeline.compare_even_odd("TimeVariableLength", "DualVariableLength", 0.01)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("ConstVariable", "TimeVariableLength", 0.01, smoother_value=100)
# # pipeline.compare_models("TimeVariableLength", "DualVariableLength", 0.01, smoother_value=100)
# # path_to_data = "/Users/stevecharczynski/workspace/data/jay/lec/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay/lec/"
# # save_dir = "/Users/stevecharczynski/workspace/data/jay/lec/"
# save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/jay/lec/"
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range)
# solver_params = {
# "niter": 500,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [0, 9000],
# "st": [10, 6000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_dual = {
# "a_1": [0, 1/3],
# "a_2": [0, 1/3],
# "ut_1": [0, 9000],
# "ut_2": [0, 9000],
# "st_1": [10, 6000],
# "st_2": [10, 6000],
# "a_0": [10**-10, 1/3]
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n]
# }
# bounds_smt = {
# "sigma": [0, 6000.],
# "mu": [0, 9000.],
# "tau": [20, 20000.],
# "a_1": [10**-10, 1/2.],
# "a_0": [10**-10, 1/2.]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "TimeVariableLength", "DualVariableLength", "SigmaMuTauVariableLength"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("ConstVariable", bounds_c)
# pipeline.set_model_bounds("SigmaMuTauVariableLength", bounds_smt)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("DualVariableLength", bounds_dual)
# # pipeline.set_model_bounds("LogNormalVariableLength", bounds_t)
# pipeline.set_model_x0("SigmaMuTauVariableLength", [100, 1000, 300, 1e-5, 1e-5])
# pipeline.set_model_x0("ConstVariable", [1e-5])
# # pipeline.set_model_x0("LogNormalVariableLength", [1e-5, 1000, 100, 1e-5])
# pipeline.set_model_x0("TimeVariableLength", [1e-5, 1000, 100, 1e-5])
# pipeline.set_model_x0("DualVariableLength", [1e-5, 1e-5, 1000, 3000, 300, 300, 1e-5])
# # pipeline.show_rasters()
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_even_odd("ConstVariable", "SigmaMuTauVariableLength", 0.01)
# pipeline.compare_even_odd("ConstVariable", "TimeVariableLength", 0.01)
# pipeline.compare_even_odd("TimeVariableLength", "SigmaMuTauVariableLength", 0.01)
# pipeline.compare_even_odd("TimeVariableLength", "DualVariableLength", 0.01)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("ConstVariable", "TimeVariableLength", 0.01, smoother_value=100)
# pipeline.compare_models("TimeVariableLength", "SigmaMuTauVariableLength", 0.01, smoother_value=100)
# pipeline.compare_models("ConstVariable", "SigmaMuTauVariableLength", 0.01, smoother_value=100)
# pipeline.compare_models("TimeVariableLength", "DualVariableLength", 0.01, smoother_value=100)
# pipeline.compare_models("TimeVariableLength", "DualVariableLength", 0.01, smoother_value=100)
# path_to_data = "/Users/stevecharczynski/workspace/data/jay/lec/"
path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay/lec_pyr"
# save_dir = "/Users/stevecharczynski/workspace/data/jay/lec/"
save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/jay/lec/"
# time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
data_processor = analysis.DataProcessor(path_to_data, cell_range)
solver_params = {
"niter": 500,
"stepsize": 1000,
"interval": 10,
"method": "TNC",
"use_jac": True,
"T": 1,
"disp": False
}
n = 2
bounds_t = {
"a_1": [0, 1 / n],
"ut": [0, 9000],
"st": [10, 6000],
"a_0": [10**-10, 1 / n]
}
bounds_inhib = {
"a_1": [10e-10, 1 / n],
"ut": [0, 9000],
"st": [10, 6000],
"a_0": [10**-10, 1 / n]
}
bounds_dual = {
"a_1": [0, 1 / 3],
"a_2": [0, 1 / 3],
"ut_1": [0, 9000],
"ut_2": [0, 9000],
"st_1": [10, 6000],
"st_2": [10, 6000],
"a_0": [10**-10, 1 / 3]
}
bounds_c = {"a_0": [10**-10, 1 / n]}
bounds_smt = {
"sigma": [0, 6000.],
"mu": [0, 9000.],
"tau": [20, 20000.],
"a_1": [10**-10, 1 / 2.],
"a_0": [10**-10, 1 / 2.]
}
# bounds_smt_inhib = {
# "sigma": [0, 6000.],
# "mu": [0, 9000.],
# "tau": [20, 20000.],
# "a_1": [10**-10, 1/2.],
# "a_0": [10**-10, 1/2.]
# }
pipeline = analysis.Pipeline(
cell_range,
data_processor, [
"ConstVariable", "SigmaMuTauVariableLength",
"InhibitSigmaMuTauJayLEC", "TimeVariableLength",
"TimeInhibitVariableLength"
],
save_dir=save_dir)
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
pipeline.set_model_bounds("ConstVariable", bounds_c)
pipeline.set_model_bounds("TimeVariableLength", bounds_t)
pipeline.set_model_bounds("TimeInhibitVariableLength", bounds_inhib)
pipeline.set_model_bounds("InhibitSigmaMuTauJayLEC", bounds_smt)
pipeline.set_model_bounds("SigmaMuTauVariableLength", bounds_smt)
# pipeline.set_model_bounds("LogNormalVariableLength", bounds_t)
pipeline.set_model_x0("ConstVariable", [1e-5])
pipeline.set_model_x0("SigmaMuTauVariableLength",
[100, 1000, 300, 1e-5, 1e-5])
pipeline.set_model_x0("InhibitSigmaMuTauJayLEC",
[100, 1000, 300, 1e-5, 1e-4])
# pipeline.set_model_x0("LogNormalVariableLength", [1e-5, 1000, 100, 1e-5])
pipeline.set_model_x0("TimeVariableLength", [1e-5, 1000, 100, 1e-5])
pipeline.set_model_x0("TimeInhibitVariableLength", [1e-5, 1000, 100, 1e-4])
# pipeline.show_rasters()
pipeline.fit_even_odd(solver_params=solver_params)
pipeline.fit_all_models(solver_params=solver_params)
pipeline.compare_even_odd("ConstVariable", "TimeInhibitVariableLength",
0.01)
pipeline.compare_even_odd("ConstVariable", "SigmaMuTauVariableLength",
0.01)
pipeline.compare_even_odd("ConstVariable", "InhibitSigmaMuTauJayLEC", 0.01)
pipeline.compare_even_odd("ConstVariable", "TimeVariableLength", 0.01)
# pipeline.compare_even_odd("Const", "Time", 0.01)
pipeline.compare_models("ConstVariable",
"TimeVariableLength",
0.01,
smoother_value=100)
pipeline.compare_models("ConstVariable",
"TimeInhibitVariableLength",
0.01,
smoother_value=100)
pipeline.compare_models("ConstVariable",
"SigmaMuTauVariableLength",
0.01,
smoother_value=100)
pipeline.compare_models("ConstVariable",
"InhibitSigmaMuTauJayLEC",
0.01,
smoother_value=100)
def run_script(cell_range):
# path_to_data = "/Users/stevecharczynski/workspace/data/warden/recog_trials/"
# save_dir = "/Users/stevecharczynski/workspace/data/warden/recog_trials_first_stim_time/"
save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/warden/recog_trials_first_stim_time/"
path_to_data = "/projectnb/ecog-eeg/stevechar/data/warden/recog_trials/"
data_processor = analysis.DataProcessor(path_to_data,
cell_range,
window=[-500, 1500])
solver_params = {
"niter": 1500,
"stepsize": 100,
"interval": 20,
"method": "TNC",
"use_jac": True,
"T": 100,
"disp": False
}
bounds_stim = {
"sigma": [1e-4, 1000.],
"mu": [0, 1500.],
"a_1": [1e-10, 1 / 2.],
"a_0": [1e-10, 1 / 2.]
}
pipeline = analysis.Pipeline(cell_range, data_processor, [
"Const","GaussianStim1", \
"GaussianStim2", "GaussianStim3", "GaussianStim4"], save_dir=save_dir)
pipeline.set_model_bounds("GaussianStim1", bounds_stim)
pipeline.set_model_bounds("GaussianStim2", bounds_stim)
pipeline.set_model_bounds("GaussianStim3", bounds_stim)
pipeline.set_model_bounds("GaussianStim4", bounds_stim)
pipeline.set_model_bounds("Const", {"a_0": [10e-10, 1]})
# with open("/Users/stevecharczynski/workspace/data/warden/recog_trials/info.json") as f:
with open(
"/projectnb/ecog-eeg/stevechar/data/warden/recog_trials/info.json"
) as f:
stims = json.load(f)
stims = {int(k): v for k, v in stims.items()}
pipeline.set_model_info("GaussianStim1",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_info("GaussianStim2",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_info("GaussianStim3",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_info("GaussianStim4",
"stim_identity",
stims,
per_cell=True)
pipeline.set_model_x0("GaussianStim1", [10, 10, 1e-1, 1e-1])
pipeline.set_model_x0("GaussianStim2", [10, 10, 1e-1, 1e-1])
pipeline.set_model_x0("GaussianStim3", [10, 10, 1e-1, 1e-1])
pipeline.set_model_x0("GaussianStim4", [10, 10, 1e-1, 1e-1])
pipeline.set_model_x0("Const", [1e-1])
pipeline.fit_all_models(solver_params=solver_params)
pipeline.fit_even_odd(solver_params=solver_params)
pipeline.compare_models("Const", "GaussianStim1", 0.01, smoother_value=100)
pipeline.compare_models("Const", "GaussianStim2", 0.01, smoother_value=100)
pipeline.compare_models("Const", "GaussianStim3", 0.01, smoother_value=100)
pipeline.compare_models("Const", "GaussianStim4", 0.01, smoother_value=100)
pipeline.compare_even_odd("Const", "GaussianStim1", 0.01)
pipeline.compare_even_odd("Const", "GaussianStim2", 0.01)
pipeline.compare_even_odd("Const", "GaussianStim3", 0.01)
pipeline.compare_even_odd("Const", "GaussianStim4", 0.01)
def run_script(cell_range, session):
# path_to_data = '/Users/stevecharczynski/workspace/data/jay/2nd_file'
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay"
# # x = np.full(95, 0)
# # y = np.full(95, 8000)
# # trial_lengths = np.array(list(zip(x,y)))
# # with open('/Users/stevecharczynski/workspace/data/jay/2nd_file/trial_lengths.json', 'w') as f:
# # json.dump(trial_lengths.tolist(), f)
# data_processor = DataProcessor(
# path_to_data, cell_range)
# n = 2
# solver_params = {
# "niter": 2,
# "stepsize": 10000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# }
# bounds_smt = {
# "sigma": [10, 10000],
# "mu": [-1000, 10000],
# "tau": [0.0002, 0.10],
# "a_1": [10**-7, 0.5],
# "a_0": [10**-7, 0.5]
# }
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [-1000,10000],
# "st": [100, 10000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n]
# }
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "Const", "Time"], 0)
# # pipeline.set_model_bounds("Time", bounds_t)
# # pipeline.set_model_bounds("Const", bounds_c)
# pipeline.set_model_bounds(["Time", "Const"], [bounds_t, bounds_c])
# pipeline.set_model_x0(["Const", "Time"], [[1e-5], [1e-5, 100, 100, 1e-5]])
# # pipeline.set_model_bounds("SigmaMuTau", bounds_smt)
# pipeline.fit_even_odd(solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Const", "Time", 0.01)
# pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("Time", "SigmaMuTau", 0.01)
# path_to_data = '/Users/stevecharczynski/workspace/data/brincat_miller'
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/brincat_miller/"
# time_info = [500, 1750]
# data_processor = DataProcessor(
# path_to_data, cell_range, time_info=time_info)
# n = 2
# mean_delta = 0.10 * (time_info[1] - time_info[0])
# mean_bounds = (
# (time_info[0] - mean_delta),
# (time_info[1] + mean_delta))
# solver_params = {
# "niter": 100,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# }
# bounds_smt = {
# "sigma": [10, 1000],
# "mu": [100, 2300],
# "tau": [10, 5000],
# "a_1": [10**-7, 0.5],
# "a_0": [10**-7, 0.5]
# }
# bounds = {
# "a_1": [0, 1 / n],
# "ut": [mean_bounds[0], mean_bounds[1]],
# "st": [10, 1000],
# "a_0": [10**-10, 1 / n]
# }
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "SigmaMuTau", "Time"], 0)
# # pipeline = AnalysisPipeline(cell_range, data_processor, [
# # "Time","SigmaMuTau"], 0)
# pipeline.set_model_bounds("Time", bounds)
# pipeline.set_model_bounds("SigmaMuTau", bounds_smt)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Time", "SigmaMuTau", 0.01)
# path_to_data = '/Users/stevecharczynski/workspace/data/brincat_miller'
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/brincat_miller/"
# time_info = [500, 1750]
# data_processor = DataProcessor(
# path_to_data, cell_range, time_info=time_info)
# n = 2
# mean_delta = 0.10 * (time_info[1] - time_info[0])
# mean_bounds = (
# (time_info[0] - mean_delta),
# (time_info[1] + mean_delta))
# solver_params = {
# "niter": 5,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# }
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [mean_bounds[0], mean_bounds[1]],
# "st": [10, 1000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n]
# }
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "Const", "Time"], 0)
# # pipeline = AnalysisPipeline(cell_range, data_processor, [
# # "Time","SigmaMuTau"], 0)
# pipeline.set_model_bounds("Time", bounds_t)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Const", "Time", 0.01)
# path_to_data = "/Users/stevecharczynski/workspace/data/salz"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/salz"
# time_info = [1000, 21000]
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=time_info)
# solver_params = {
# "niter": 2,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# }
# bounds = {
# "a_1": [0, 1 / 2],
# "ut": [0, 42000,],
# "st": [10, 50000],
# "a_0": [10**-10, 1 / 2]
# }
# bounds_c = {"a_0": [10**-10, 0.999]}
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const", "Time"], 0)
# pipeline.set_model_bounds("Time", bounds)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.set_model_x0("Time", [1e-5, 500, 500, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# pipeline.fit_even_odd(solver_params)
# pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.fit_all_models(solver_params)
# pipeline.compare_models("Const", "Time", 0.01)
# path_to_data = "/Users/stevecharczynski/workspace/data/cromer"
# # path_to_data = '/projectnb/ecog-eeg/stevechar/data/cromer'
# # path_to_data = "/usr3/bustaff/scharcz/workspace/cromer/"
# time_info = [400, 2000]
# data_processor = DataProcessor(
# path_to_data, cell_range, time_info=time_info)
# n_c = 5
# solver_params = {
# "niter": 1,
# "stepsize": 100,
# "interval": 10,
# "method": "TNC",
# "use_jac": False,
# }
# bounds_cat = {
# "ut": [0, 2400],
# "st": [10, 5000],
# "a_0": [10**-10, 1 / n_c],
# "a_1": [10**-10, 1 / n_c],
# "a_2": [10**-10, 1 / n_c],
# "a_3": [10**-10, 1 / n_c],
# "a_4": [10**-10, 1 / n_c]
# }
# n_cs = 3
# bounds_cs = {
# "ut": [0, 2400],
# "st": [10, 5000],
# "a_0": [10**-10, 1 / n_cs],
# "a_1": [10**-10, 1 / n_cs],
# "a_2": [10**-10, 1 / n_cs],
# }
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "CatSetTime","CatTime"], 0.01)
# pipeline.set_model_bounds("CatSetTime", bounds_cs)
# pipeline.set_model_bounds("CatTime", bounds_cat)
# pipeline.set_model_info("CatSetTime", "pairs", [(1,2), (3,4)])
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("CatSetTime", "CatTime", 0.01)
# pipeline.show_condition_fit("CatTime")
# pipeline.show_condition_fit("CatSetTime")
# path_to_data = "/Users/stevecharczynski/workspace/data/kim"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/kim"
# time_info = [0, 4784]
# data_processor = DataProcessor(
# path_to_data, cell_range, time_info=time_info)
# n = 2
# swarm_params = {
# "phip": 0.5,
# "phig": 0.5,
# "omega": 0.5,
# "minstep": 1e-10,
# "minfunc": 1e-10,
# "maxiter": 1000
# }
# bounds = {
# "a_1": [0, 1 / n],
# "ut": [-200, 5200],
# "st": [10, 10000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_c = {"a_0": [10**-10, 0.999]}
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "Const", "Time"], 0, swarm_params)
# pipeline.set_model_bounds("Time", bounds)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.fit_all_models(1)
# pipeline.compare_models("Const", "Time", 0.01)
# path_to_data = "/Users/stevecharczynski/workspace/data/cromer"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/cromer"
# # with open(path_to_data+'/number_of_trials.json', 'r') as f:
# # num = json.load(f)
# # x = np.full(max(num), 400)
# # y = np.full(max(num), 2000)
# # trial_lengths = np.array(list(zip(x,y)))
# # with open(path_to_data+'/trial_lengths.json', 'w') as f:
# # json.dump(trial_lengths.tolist(), f)
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[400,2000])
# n_t = 2.
# solver_params = {
# "niter": 1,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp": False,
# }
# bounds = {
# "a_1": [10**-10, 1 / n_t],
# "ut": [0., 6000.],
# "st": [10., 5000.],
# "a_0": [10**-10, 1 / n_t]
# }
# bounds_c = {"a_0": [10**-10, 0.999]}
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Time", "Const"], 0)
# # pipeline.show_rasters()
# pipeline.set_model_bounds("Time", bounds)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.set_model_x0("Time", [1e-5, 400, 100, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# # pipeline.fit_even_odd(solver_params)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Const", "Time", p_value=0.01, smoother_value=1)
# path_to_data = '/Users/stevecharczynski/workspace/data/cromer'
# # path_to_data = "/usr3/bustaff/scharcz/workspace/cromer/"
# time_info = [400, 2000]
# data_processor = DataProcessor(
# path_to_data, cell_range, time_info=time_info)
# n_c = 3
# swarm_params = {
# "phip": 0.5,
# "phig": 0.7,
# "omega": 0.7,
# "minstep": 1e-10,
# "minfunc": 1e-10,
# "maxiter": 1000
# }
# bounds_cat = {
# "ut": [0, 2400],
# "st": [10, 5000],
# "a_0": [10**-10, 1 / n_c],
# "a_1": [10**-10, 1 / n_c],
# "a_2": [10**-10, 1 / n_c],
# }
# n_t = 2
# bounds_t = {
# "a_1": [0, 1 / n_t],
# "ut": [0, 2400],
# "st": [10, 5000],
# "a_0": [10**-10, 1 / n_t]
# }
# # bounds_cat = ((0,2400), (10, 5000), (10**-10, 1 / n), (0, 1 / n),(0, 1 / n), (0, 1 / n), (0, 1 / n))
# # bounds= ((0, 1 / n), (0,2400), (10, 5000), (10**-10, 1 / n))
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "CatSetTime", "Time"], 0, swarm_params)
# pipeline.set_model_bounds("Time", bounds_t)
# pipeline.set_model_bounds("CatSetTime", bounds_cat)
# pipeline.set_model_info("CatSetTime", "pairs", [(1,2), (3,4)])
# pipeline.fit_all_models(1)
# pipeline.compare_models("Time", "CatSetTime", 0.01)
# pipeline.show_condition_fit("CatSetTime")
# path_to_data = '/Users/stevecharczynski/workspace/rui_fake_cells/mixed_firing'
# time_info = RegionInfo(0, 2000)
# data_processor = DataProcessor(
# path_to_data, cell_range, time_info=time_info)
# n = 2
# bounds = {
# "a_1": [0, 1 / n],
# "ut": [-500, 2500],
# "st": [0.01, 5000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_c = {"a_0": [10**-10, 0.99]}
# swarm_params = {
# "phip": 0.5,
# "phig": 0.5,
# "omega": 0.5,
# "minstep": 1e-10,
# "minfunc": 1e-10,
# "maxiter": 1000
# }
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "Const", "Time"], 0, swarm_params)
# pipeline.set_model_bounds("Time", bounds)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.fit_all_models(3)
# pipeline.compare_models("Const", "Time", 0.01)
# util.collect_data(cell_range, "log_likelihoods")
# util.collect_data(cell_range, "model_comparisons")
# util.collect_data(cell_range, "cell_fits")
# path_to_data = "/Users/stevecharczynski/workspace/data/sheehan/lin_track_s1"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/cromer"
# time_info = [0, 3993]
# data_processor = DataProcessor(
# path_to_data, cell_range)
# n_t = 2.
# solver_params = {
# "niter": 10,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n_t]
# }
# bounds_t = {
# "a_1": [10**-10, 1 / n_t],
# "ut": [0., 2400.],
# "st": [10., 5000.],
# "a_0": [10**-10, 1 / n_t]
# }
# pipeline = AnalysisPipeline(cell_range, data_processor, [
# "Const", "TimeVariableLength"], 0)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("Const", {"a_0":[10**-10, 1]})
# # with open("/Users/stevecharczynski/workspace/data/sheehan/lin_track_s1/trial_length.json", 'rb') as f:
# # trial_length = json.load(f)
# # pipeline.set_model_info("TimeVariableLength", "trial_length", trial_length)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Const", "TimeVariableLength", 0.01)
# path_to_data = "/Users/stevecharczynski/workspace/data/crcns/hc-2/hc2/ec013.527"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/s25"
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 1076686])
# n_t = 2.
# solver_params = {
# "niter": 20,
# "stepsize": 50,
# "interval": 4,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":True
# }
# bounds_pos = {
# "ut_x": [20, 200],
# "st_x": [1., 30.],
# "ut_y": [50., 250.],
# "st_y": [1., 30.],
# "a_0": [10**-10, 1 / n_t],
# "a_1": [10**-10, 1 / n_t]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "PlaceField", "Const"])
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("PlaceField", bounds_pos)
# pipeline.set_model_bounds("Const", {"a_0":[10**-10, 0.5]})
# pipeline.set_model_x0("PlaceField", [100,10,100,10,1e-5, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# import numpy as np
# import json
# with open(path_to_data+"/pos_minus_removed.json", 'rb') as f:
# pos = np.array(json.load(f))
# pipeline.set_model_info("PlaceField", "pos", pos, False)
# model_dict = pipeline.fit_all_models(solver_params=solver_params)
# plotting.plot_summed_2d(data_processor.spikes_binned[8], [300,300], pos, model_dict["PlaceField"][8].fit)
# pipeline.compare_models("Const", "PlaceField", 0.01, smoother_value=1000)
# path_to_data = "/Users/stevecharczynski/workspace/data/bulkin/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/bolkan/clusters_files/"
# data_processor = analysis.DataProcessor(path_to_data, cell_range, [0, 60000])
# solver_params = {
# "niter": 300,
# "stepsize": 5000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# bounds_vel = {
# "a_1": [10**-10, 1 / 2],
# "ut": [0., 70000.],
# "st": [100, 80000.],
# "a_0": [10**-10, 1 / 2]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const", "Time"])
# pipeline.set_model_bounds("Time", bounds_vel)
# pipeline.set_model_bounds("Const", {"a_0":[10**-10, 1]})
# pipeline.set_model_x0("Time", [1e-5, 20000, 1000, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# # pipeline.show_rasters()
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Const", "Time", 0.001, smoother_value=1000)
# pipeline.compare_even_odd("Const", "Time", 0.001)
# path_to_data = '/Users/stevecharczynski/workspace/data/brincat_miller'
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/brincat_miller/"
# time_info = [500, 1750]
# save_dir = "/Users/stevecharczynski/Desktop/test/"
# save_dir = "/projectnb/ecog-eeg/stevechar/ml_output/brincat_miller/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/all_sessions/{0}".format(session)
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=time_info)
# n_t = 2.
# solver_params = {
# "niter": 5,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# mean_delta = 0.10 * (time_info[1] - time_info[0])
# mean_bounds = (
# (time_info[0] - mean_delta),
# (time_info[1] + mean_delta))
# n = 2
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [mean_bounds[0], mean_bounds[1]],
# "st": [10, 1000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const", "Time"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.set_model_bounds("Time", bounds_t)
# pipeline.set_model_x0("Time", [1e-5, 700, 300, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# # pipeline.show_rasters()
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "Time", 0.01)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("Const", "Time", 0.01, smoother_value=100)
# # path_to_data = "/Users/stevecharczynski/workspace/data/jay/ca1_time_cell_data/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay/ca1_time_cells"
# # save_dir = "/Users/stevecharczynski/Desktop/test/"
# save_dir = "/projectnb/ecog-eeg/stevechar/ml_output/jay/ca1_time_cells/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/all_sessions/{0}".format(session)
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range)
# solver_params = {
# "niter": 500,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [0, 9000],
# "st": [10, 6000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_dual = {
# "a_1": [0, 1/3],
# "a_2": [0, 1/3],
# "ut_1": [0, 9000],
# "ut_2": [0, 9000],
# "st_1": [10, 6000],
# "st_2": [10, 6000],
# "a_0": [10**-10, 1/3]
# }
# bounds_c = {
# "a_0": [10**-10, 1 / n]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "TimeVariableLength", "DualVariableLength"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("ConstVariable", bounds_c)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("DualVariableLength", bounds_dual)
# pipeline.set_model_x0("TimeVariableLength", [1e-5, 1000, 300, 1e-5])
# pipeline.set_model_x0("ConstVariable", [1e-5])
# pipeline.set_model_x0("DualVariableLength", [1e-5, 1e-5, 1000, 3000, 300, 300, 1e-5])
# # pipeline.show_rasters()
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_even_odd("ConstVariable", "TimeVariableLength", 0.01)
# pipeline.compare_even_odd("TimeVariableLength", "DualVariableLength", 0.01)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("ConstVariable", "TimeVariableLength", 0.01, smoother_value=100)
# pipeline.compare_models("TimeVariableLength", "DualVariableLength", 0.01, smoother_value=100)
# # path_to_data = "/Users/stevecharczynski/workspace/data/warden/recog_trials/"
# # save_dir = "/Users/stevecharczynski/workspace/data/warden/recog_trials/"
# save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/warden/recog_trials/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/warden/recog_trials/"
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 1500])
# solver_params = {
# "niter": 1000,
# "stepsize": 100,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# bounds_smtstim = {
# "sigma": [0, 1000.],
# "mu": [0, 1500.],
# "tau": [20, 20000.],
# "a_1": [10**-10, 1/5.],
# "a_2": [10**-10, 1/5.],
# "a_3": [10**-10, 1/5.],
# "a_4": [10**-10, 1/5.],
# "a_0": [10**-10, 1/5.]
# }
# bounds_smt = {
# "sigma": [0, 1000.],
# "mu": [0, 1500.],
# "tau": [20, 20000.],
# "a_1": [10**-10, 1/2.],
# "a_0": [10**-10, 1/2.]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const","SigmaMuTau", "SigmaMuTauStim"], save_dir=save_dir)
# pipeline.set_model_bounds("SigmaMuTau", bounds_smt)
# pipeline.set_model_bounds("SigmaMuTauStim", bounds_smtstim)
# pipeline.set_model_bounds("Const", {"a_0":[10e-10, 1]})
# # with open("/Users/stevecharczynski/workspace/data/warden/recog_trials/info.json") as f:
# with open("/projectnb/ecog-eeg/stevechar/data/warden/recog_trials/info.json") as f:
# stims = json.load(f)
# stims = {int(k):v for k,v in stims.items()}
# pipeline.set_model_info("SigmaMuTauStim", "stim_identity", stims, per_cell=True)
# pipeline.set_model_x0("SigmaMuTauStim", [0.01, 1000, 100, 1e-1, 1e-1,1e-1, 1e-1, 1e-1])
# pipeline.set_model_x0("SigmaMuTau", [0.01, 1000, 100, 1e-1, 1e-1])
# pipeline.set_model_x0("Const", [1e-1])
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "SigmaMuTau", 0.01)
# pipeline.compare_even_odd("SigmaMuTau", "SigmaMuTauStim", 0.01)
# pipeline.compare_models("Const", "SigmaMuTau", 0.01, smoother_value=100)
# pipeline.compare_models("SigmaMuTau", "SigmaMuTauStim", 0.01, smoother_value=100)
# # path_to_data = "/Users/stevecharczynski/workspace/data/rossi_pool/a2/"
# # save_dir = "/Users/stevecharczynski/workspace/data/rossi_pool/a2/"
# save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/rossi_pool/a2/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/rossi_pool/a2/"
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 3000])
# solver_params = {
# "niter": 1000,
# "stepsize": 100,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# bounds_smtstim = {
# "sigma": [0, 1000.],
# "mu": [0, 3000.],
# "tau": [20, 20000.],
# "a_1": [10**-10, 1/5.],
# "a_2": [10**-10, 1/5.],
# "a_0": [10**-10, 1/5.]
# }
# bounds_smt = {
# "sigma": [0, 1000.],
# "mu": [0, 3000.],
# "tau": [20, 20000.],
# "a_1": [10**-10, 1/2.],
# "a_0": [10**-10, 1/2.]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const","SigmaMuTau", "SigmaMuTauStimRP"], save_dir=save_dir)
# pipeline.set_model_bounds("SigmaMuTau", bounds_smt)
# pipeline.set_model_bounds("SigmaMuTauStimRP", bounds_smtstim)
# pipeline.set_model_bounds("Const", {"a_0":[10e-10, 1]})
# # with open("/Users/stevecharczynski/workspace/data/rossi_pool/a2/info.json") as f:
# with open("/projectnb/ecog-eeg/stevechar/data/rossi_pool/a2/info.json") as f:
# stims = json.load(f)
# stims = {int(k):v for k,v in stims.items()}
# pipeline.set_model_info("SigmaMuTauStimRP", "stim_identity", stims, per_cell=True)
# pipeline.set_model_x0("SigmaMuTauStimRP", [0.01, 1000, 100, 1e-1, 1e-1, 1e-1])
# pipeline.set_model_x0("SigmaMuTau", [0.01, 1000, 100, 1e-1, 1e-1])
# pipeline.set_model_x0("Const", [1e-1])
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "SigmaMuTau", 0.01)
# pipeline.compare_even_odd("SigmaMuTau", "SigmaMuTauStimRP", 0.01)
# pipeline.compare_models("Const", "SigmaMuTau", 0.01, smoother_value=100)
# pipeline.compare_models("SigmaMuTau", "SigmaMuTauStimRP", 0.01, smoother_value=100)
# # path_to_data = "/Users/stevecharczynski/workspace/data/warden/recall_trials/"
# # save_dir = "/Users/stevecharczynski/workspace/data/warden/recall_trials/"
# save_dir = "/projectnb/ecog-eeg/stevechar/ml_runs/warden/recall_trials"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/warden/recall_trials/"
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 3000])
# n_t = 2.
# solver_params = {
# "niter": 100,
# "stepsize": 100,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_smtstim = {
# "sigma1": [0, 1000.],
# "sigma2": [0, 1000.],
# # "sigma": [0.1, 0.15],
# "mu1": [0, 3000.],
# "mu2": [0, 3000.],
# "tau1": [20, 20000.],
# "tau2": [20, 20000.],
# "a_1": [10**-10, 1/5.],
# "a_2": [10**-10, 1/5.],
# "a_3": [10**-10, 1/5.],
# "a_4": [10**-10, 1/5.],
# "a_0": [10**-10, 1/5.]
# }
# bounds_smt = {
# "sigma1": [0, 1000.],
# "sigma2": [0, 1000.],
# # "sigma": [0.1, 0.15],
# "mu1": [0, 3000.],
# "mu2": [0, 3000.],
# "tau1": [20, 20000.],
# "tau2": [20, 20000.],
# "a_1": [10**-10, 1/5.],
# "a_0": [10**-10, 1/5.]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "SigmaMuTauDual", "SigmaMuTauDualStim"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("SigmaMuTauDual", bounds_smt)
# pipeline.set_model_bounds("SigmaMuTauDualStim", bounds_smtstim)
# # with open("/Users/stevecharczynski/workspace/data/warden/recall_trials/info.json") as f:
# with open("/projectnb/ecog-eeg/stevechar/data/warden/recall_trials/info.json") as f:
# stims = json.load(f)
# stims = {int(k):v for k,v in stims.items()}
# pipeline.set_model_info("SigmaMuTauDualStim", "stim_identity", stims, per_cell=True)
# pipeline.set_model_x0("SigmaMuTauDualStim", [0.01,0.01, 1000,2000, 100, 100, 1e-1, 1e-1,1e-1, 1e-1, 1e-1])
# pipeline.set_model_x0("SigmaMuTauDual", [0.01,0.01, 1000,2000, 100, 100, 1e-1, 1e-1])
# pipeline.fit_all_models(solver_params=solver_params)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("SigmaMuTauDual", "SigmaMuTauDualStim", 0.01, smoother_value=100)
# # path_to_data = "/Users/stevecharczynski/workspace/data/sheehan/iti/sep_pos/1/"
# # save_dir = "/Users/stevecharczynski/workspace/data/sheehan/iti/sep_pos/1/"
# save_dir = "/projectnb/ecog-eeg/stevechar/sheehan_runs/iti/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/iti/"
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 10500])
# n_t = 2.
# solver_params = {
# "niter": 300,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_t = {
# "a_1": [0, 1 / n],
# "ut": [-1000, 11000],
# "st": [10, 6000],
# "a_0": [10**-10, 1 / n]
# }
# bounds_c = {
# "a_0": [10**-10, 1]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const", "Time"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("Const", bounds_c)
# pipeline.set_model_bounds("Time", bounds_t)
# pipeline.set_model_x0("Time", [1e-5, 1000, 300, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# # pipeline.show_rasters(show=False)
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_even_odd("Const", "Time", 0.01)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("Const", "Time", 0.01, smoother_value=100)
# path_to_data = "/Users/stevecharczynski/workspace/data/rossi_pool/a1/"
# save_dir = "/Users/stevecharczynski/workspace/data/rossi_pool/a1/results/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay/ca1_time_cells"
# # save_dir = "/Users/stevecharczynski/Desktop/test/"
# # save_dir = "/projectnb/ecog-eeg/stevechar/ml_output/jay/ca1_time_cells/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/all_sessions/{0}".format(session)
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 5000])
# n_t = 2.
# solver_params = {
# "niter": 10,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": False,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_smt = {
# "sigma": [1e-4, 1.],
# "mu1": [0, 5000.],
# "mu2": [0, 5000.],
# "tau": [10, 5000.],
# "a_1": [10**-2, 1/3.],
# "a_2": [10**-2, 1/3.],
# "a_0": [10**-4, 1/3.]
# }
# bounds_c = {
# "a_0": [10**-10, 1]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const", "SigmaMuTauDual"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("SigmaMuTauDual", bounds_smt)
# pipeline.set_model_bounds("Const", bounds_c)
# with open("/Users/stevecharczynski/workspace/data/rossi_pool/a1/stim_identity.json") as f:
# stims = json.load(f)
# pipeline.set_model_info("SigmaMuTauDual", "stim_identity", stims)
# pipeline.set_model_x0("SigmaMuTauDual", [0.01, 1000, 4000, 300, 1e-1, 1e-1, 1e-1])
# pipeline.set_model_x0("Const", [1e-5])
# # pipeline.show_rasters(save=True)
# pipeline.fit_all_models(solver_params=solver_params)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("Const", "SigmaMuTauDual", 0.01, smoother_value=100)
# path_to_data = "/Users/stevecharczynski/workspace/data/rossi_pool/a1/"
# save_dir = "/Users/stevecharczynski/workspace/data/rossi_pool/a1/results/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/jay/ca1_time_cells"
# # save_dir = "/Users/stevecharczynski/Desktop/test/"
# # save_dir = "/projectnb/ecog-eeg/stevechar/ml_output/jay/ca1_time_cells/"
# # path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/all_sessions/{0}".format(session)
# # time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
# data_processor = analysis.DataProcessor(
# path_to_data, cell_range, window=[0, 5000])
# n_t = 2.
# solver_params = {
# "niter": 5,
# "stepsize": 1000,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# n = 2
# bounds_smtstim = {
# "sigma1": [0, 1000.],
# "sigma2": [0, 1000.],
# # "sigma": [0.1, 0.15],
# "mu1": [0, 5000.],
# "mu2": [0, 5000.],
# "tau1": [20, 20000.],
# "tau2": [20, 20000.],
# "a_1": [10**-5, 1/3.],
# "a_2": [10**-5, 1/3.],
# "a_0": [10**-5, 1/3.]
# }
# bounds_smt = {
# "sigma1": [0, 1000.],
# "sigma2": [0, 1000.],
# # "sigma": [0.1, 0.15],
# "mu1": [0, 5000.],
# "mu2": [0, 5000.],
# "tau1": [20, 20000.],
# "tau2": [20, 20000.],
# "a_1": [10**-5, 1/3.],
# "a_0": [10**-5, 1/3.]
# }
# bounds_c = {
# "a_0": [10**-10, 1]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "SigmaMuTauDual", "SigmaMuTauDualStim"], save_dir=save_dir)
# # pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable"], save_dir=save_dir)
# # pipeline.set_model_bounds("TimeVariableLength", bounds_t)
# pipeline.set_model_bounds("SigmaMuTauDualStim", bounds_smtstim)
# pipeline.set_model_bounds("SigmaMuTauDual", bounds_smt)
# with open("/Users/stevecharczynski/workspace/data/rossi_pool/a1/stim_identity.json") as f:
# stims = json.load(f)
# pipeline.set_model_info("SigmaMuTauDualStim", "stim_identity", stims)
# pipeline.set_model_x0("SigmaMuTauDualStim", [0.01,0.01, 1000,4000, 100, 100, 1e-1, 1e-1, 1e-1])
# pipeline.set_model_x0("SigmaMuTauDual", [0.01,0.01, 1000,4000, 100, 100, 1e-1, 1e-1])
# # pipeline.set_model_x0("Const", [1e-5])
# # pipeline.show_rasters(save=True)
# pipeline.fit_all_models(solver_params=solver_params)
# # pipeline.compare_even_odd("Const", "Time", 0.01)
# pipeline.compare_models("SigmaMuTauDual", "SigmaMuTauDualStim", 0.01, smoother_value=100)
# path_to_data = "/Users/stevecharczynski/workspace/maxlikespy/examples/input_data/"
# save_dir = "/Users/stevecharczynski/Desktop/test/"
# data_processor = analysis.DataProcessor(path_to_data, cell_range, [0, 10000])
# solver_params = {
# "niter": 5,
# "stepsize": 200,
# "interval": 10,
# "method": "TNC",
# "use_jac": True,
# "T" : 1,
# "disp":False
# }
# bounds_vel = {
# "a_1": [10e-10, 1 / 2],
# "ut": [-1000, 12000.],
# "st": [100, 20000.],
# "a_0": [10e-10, 1 / 2]
# }
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "Const", "Time"], save_dir=save_dir)
# pipeline.set_model_bounds("Time", bounds_vel)
# pipeline.set_model_bounds("Const", {"a_0":[10**-10, 1]})
# pipeline.set_model_x0("Time", [1e-5, 2000, 200, 1e-5])
# pipeline.set_model_x0("Const", [1e-5])
# # pipeline.show_rasters()
# pipeline.fit_even_odd(solver_params=solver_params)
# pipeline.fit_all_models(solver_params=solver_params)
# pipeline.compare_models("Const", "Time", 0.001, smoother_value=100)
# pipeline.compare_even_odd("Const", "Time", 0.001)
path_to_data = "/Users/stevecharczynski/workspace/data/sheehan/random_box_lights_on/lights1_move1/inbound/"
save_dir = "/Users/stevecharczynski/workspace/data/sheehan/random_box_lights_on/lights1_move1/inbound/"
# save_dir = "/projectnb/ecog-eeg/stevechar/sheehan_runs/random_lights_on_only/inbound/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/random_lights_on_only/inbound/"
# save_dir = "/projectnb/ecog-eeg/stevechar/sheehan_runs/stable_lights_on_only/outbound/"
# path_to_data = "/projectnb/ecog-eeg/stevechar/data/sheehan/stable_lights_on_only/outbound/"
# time_info = list(zip(np.zeros(len(trial_length), dtype=int), trial_length))
data_processor = analysis.DataProcessor(path_to_data, cell_range)
n_t = 2.
solver_params = {
"niter": 1,
"stepsize": 500,
"interval": 10,
"method": "TNC",
"use_jac": True,
"T": 1,
"disp": False
}
bounds_dual = {
"ut_a": [0., 100.],
"st_a": [0.1, 100.],
"a_0a": [10**-10, 1 / 3],
"a_1a": [10**-10, 1 / 3],
"ut_b": [0., 100.],
"st_b": [0.1, 100.],
"a_0b": [10**-10, 1 / 3],
"a_1b": [10**-10, 1 / 3],
}
bounds_norm = {
"a_1": [10**-10, 1 / n_t],
"ut": [0., 100.],
"st": [0.1, 100.],
"a_0": [10**-10, 1 / n_t]
}
bounds_t = {
"a_1": [10**-10, 1 / n_t],
"ut": [0., 5000.],
"st": [10., 5000.],
"a_0": [10**-10, 1 / n_t]
}
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# # "ConstVariable", "RelPosVariable","DualPeakedRel", "AbsPosVariable"])
pipeline = analysis.Pipeline(
cell_range,
data_processor, [
"ConstVariable", "RelPosVariable", "AbsPosVariable",
"DualPeakedRel", "DualPeakedAbs"
],
save_dir=save_dir)
# pipeline = analysis.Pipeline(cell_range, data_processor, [
# "ConstVariable", "RelPosVariable", "AbsPosVariable"], save_dir=save_dir)
# pipeline.set_model_bounds("TimeVariableLength", bounds_t)
pipeline.set_model_bounds("AbsPosVariable", bounds_norm)
pipeline.set_model_bounds("RelPosVariable", bounds_norm)
pipeline.set_model_bounds("ConstVariable", {"a_0": [10**-10, 1]})
pipeline.set_model_bounds("DualPeakedRel", bounds_dual)
pipeline.set_model_bounds("DualPeakedAbs", bounds_dual)
pipeline.set_model_x0("DualPeakedRel",
[20, 1, 1e-5, 1e-5, 20, 1, 1e-5, 1e-5])
pipeline.set_model_x0("DualPeakedAbs",
[20, 1, 1e-5, 1e-5, 20, 1, 1e-5, 1e-5])
pipeline.set_model_x0("AbsPosVariable", [1e-5, 20, 1, 1e-5])
pipeline.set_model_x0("RelPosVariable", [1e-5, 20, 1, 1e-5])
pipeline.set_model_x0("ConstVariable", [1e-5])
# pipeline.show_rasters()
import numpy as np
import json
with open(path_to_data + "/abs_pos.json", 'r') as f:
abs_pos = np.array(json.load(f))
with open(path_to_data + "/rel_pos.json", 'r') as f:
rel_pos = np.array(json.load(f))
pipeline.set_model_info("AbsPosVariable", "abs_pos", abs_pos, True)
pipeline.set_model_info("RelPosVariable", "rel_pos", rel_pos, True)
pipeline.set_model_info("DualPeakedRel", "rel_pos", rel_pos, True)
pipeline.set_model_info("DualPeakedAbs", "abs_pos", abs_pos, True)
pipeline.fit_even_odd(solver_params=solver_params)
pipeline.fit_all_models(solver_params=solver_params)
pipeline.compare_even_odd("RelPosVariable", "DualPeakedRel", 0.01)
pipeline.compare_even_odd("AbsPosVariable", "DualPeakedAbs", 0.01)
pipeline.compare_even_odd("ConstVariable", "RelPosVariable", 0.01)
pipeline.compare_even_odd("ConstVariable", "AbsPosVariable", 0.01)
pipeline.compare_models("AbsPosVariable",
"DualPeakedAbs",
0.01,
smoother_value=100)
pipeline.compare_models("RelPosVariable",
"DualPeakedRel",
0.01,
smoother_value=100)
pipeline.compare_models("ConstVariable",
"RelPosVariable",
0.01,
smoother_value=100)
pipeline.compare_models("ConstVariable",
"AbsPosVariable",
0.01,
smoother_value=100)
