Simulate Data from the SceneWalk Model

import os
import shutil
import sys
from collections import OrderedDict
import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import norm as normal, truncnorm as truncated_normal
import seaborn as sns
from scenewalk.scenewalk_model_object import scenewalk as scenewalk_model
from scenewalk.utils import loadData
from scenewalk.simulation import simulate_dataset

Get some basis data

dataDict = loadData.load_data("spst_all")
dataDict = loadData.shorten_set(dataDict, 2)
x_dat, y_dat, dur_dat, im_dat, densities_dat, d_range = loadData.dataDict2vars(dataDict)

shortening from 35 to 2

Scenewalk Model

Here you define the setup of the model.

sw = scenewalk_model("subtractive", "zero", "off", "off", "off", {'x': d_range[0], 'y': d_range[1]}, {"logged_z":True, "coupled_oms":True})

sw.whoami()

'I am a subtractive scenewalk model, initialized with zero activation, in 2 exponents mode, with om_i as a fraction, with logged z'

It will tell you which parameters it needs and the order if you’re passing them in as a list:

sw.get_param_list_order()

['omegaAttention',
 'omfrac',
 'sigmaAttention',
 'sigmaInhib',
 'gamma',
 'lamb',
 'inhibStrength',
 'zeta']

param_dict = OrderedDict({
    "omegaAttention": 18.5,
    "omfrac": 10,
    "sigmaAttention": 6,
    "sigmaInhib": 12,
    "gamma" : 8,
    "lamb" : 0.6,
    "inhibStrength" :0.7,
    "zeta" : -2,
})

sw.update_params(param_dict)
#print(sw.get_params())
print("have we provided all relevant parameters?:", sw.check_params_for_config())
print("are the parameters within the defined bounds?:", sw.check_params_in_bounds())

have we provided all relevant parameters?: True
are the parameters within the defined bounds?: True

Now Simulate

sim_id = simulate_dataset.simulate(dur_dat, im_dat, densities_dat, sw, params=None, start_loc="center", x_path=None, y_path=None, resample_durs=False, verbose = True)

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Load simulated data

sim_dat_dict = loadData.load_sim_data(os.path.abspath(sim_id))

More settings for simulations

Advanced settings include instead of params=None you can give it a dictionary of all parameters the model needs by subject. i.e.

param_subj_dict = {
    0 : param_dict,
    1 : param_dict,
    2 : param_dict,
    3 : param_dict,
    4 : param_dict
}

sim_id = simulate_dataset.simulate(dur_dat, im_dat, densities_dat, sw, params=param_subj_dict, start_loc="center", x_path=None, y_path=None, resample_durs=False, verbose=True)

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sw.get_params()

OrderedDict([('omegaAttention', 18.5),
             ('omegaInhib', 1.85),
             ('sigmaAttention', 6),
             ('sigmaInhib', 12),
             ('gamma', 8),
             ('lamb', 0.6),
             ('inhibStrength', 0.7),
             ('zeta', 0.01)])