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model_params.py
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from classes import Model
import numpy as np
from bead_params import bead_params_play, bead_params_all_exps, bead_params_cell_pellet, bead_params_activin_ant, bead_params_bmp4_ant, bead_params_threshold, bead_params_run2_modelA, bead_params_run2_modelB
def load_models(select_exp):
""" load models with correct parameters based upon experiment """
# define models
modelN = 2
models = [Model() for i in range(modelN)]
# parameters which stay the same regardless of experiment
models[0].name = 'inducer_SMAD'
models[0].label = 'Without\nnbhd'
models[0].index_string = 'A'
models[0].plot_color = 'C8'
models[1].name = 'inducer_SMAD_nbhd'
models[1].label = 'With\nnbhd'
models[1].index_string = 'B'
models[1].plot_color = 'C1'
if select_exp == 'testing':
models[0].bead_params = bead_params_play
models[0].threshold = 0.427922233370511
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (2.28635614897028)
models[0].inducer_scaling = 10.0 ** (2.40585923229449)
models[1].bead_params = bead_params_play
models[1].threshold = 0.454828043213113
models[1].nbhd_size = 2*np.floor(62.8098261951711) + 1
models[1].inhibitor_scaling = 10.0 ** 1.57491873987476
models[1].inducer_scaling = 10.0 ** (-1.79339961627464)
elif select_exp == 'all_exps':
models[0].bead_params = bead_params_all_exps
models[0].threshold = 0.427922233370511
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (2.28635614897028)
models[0].inducer_scaling = 10.0 ** (2.40585923229449)
models[1].bead_params = bead_params_all_exps
models[1].threshold = 0.454828043213113
models[1].nbhd_size = 2*np.floor(62.8098261951711) + 1
models[1].inhibitor_scaling = 10.0 ** 1.57491873987476
models[1].inducer_scaling = 10.0 ** (-1.79339961627464)
elif select_exp == 'cell_pellet':
models[0].bead_params = bead_params_cell_pellet
models[0].threshold = 0.497687138515397
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (1.45030857599371)
models[0].inducer_scaling = 10.0 ** (1.29838186230979)
models[1].bead_params = bead_params_cell_pellet
models[1].threshold = 0.421573076609568
models[1].nbhd_size = 2*np.floor(56.5325206131255) + 1
models[1].inhibitor_scaling = 10.0 ** -0.329085126426299
models[1].inducer_scaling = 10.0 ** (-2.61174561686766)
elif select_exp == 'activin_ant':
models[0].bead_params = bead_params_activin_ant
models[0].threshold = 0.496121824802409
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (0.826017091134685)
models[0].inducer_scaling = 10.0 ** (1.57477776175289)
models[1].bead_params = bead_params_activin_ant
models[1].threshold = 0.431647005869817
models[1].nbhd_size = 2*np.floor(130.477438526608) + 1
models[1].inhibitor_scaling = 10.0 ** 2.22004529783025
models[1].inducer_scaling = 10.0 ** (-1.62424826505605)
elif select_exp == 'bmp4_ant':
models[0].bead_params = bead_params_bmp4_ant
models[0].threshold = 0.485598910878967
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (2.22676213806672)
models[0].inducer_scaling = 10.0 ** (2.67031533897687)
models[1].bead_params = bead_params_bmp4_ant
models[1].threshold = 0.245747297043042
models[1].nbhd_size = 2*np.floor(33.4112433863738) + 1
models[1].inhibitor_scaling = 10.0 ** 2.39830585243343
models[1].inducer_scaling = 10.0 ** (-2.70035400851161)
elif select_exp == 'threshold':
models[0].bead_params = bead_params_threshold
models[0].threshold = 0.456084933585625
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (-2.87675720588008)
models[0].inducer_scaling = 10.0 ** (0.912230470757642)
models[1].bead_params = bead_params_threshold
models[1].threshold = 0.450426293687721
models[1].nbhd_size = 2*np.floor(62.0953642599517) + 1
models[1].inhibitor_scaling = 10.0 ** 1.43501518320614
models[1].inducer_scaling = 10.0 ** (1.30491072217825)
elif select_exp == 'run2_A_B':
models[0].bead_params = bead_params_run2_modelA
models[0].threshold = 0.493950265800207
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (2.02042081653826)
models[0].inducer_scaling = 10.0 ** (2.55881683637734)
models[1].bead_params = bead_params_run2_modelB
models[1].threshold = 0.246273569161305
models[1].nbhd_size = 2*np.floor(97.0223908634862) + 1
models[1].inhibitor_scaling = 10.0 ** (1.80372902814812)
models[1].inducer_scaling = 10.0 ** (-0.160334345526756)
else:
print("Unexpected input for 'select_exp'.\nPlease choose valid choice from 'experiment_options'.\nLoaded 'testing' parameter values.")
models[0].bead_params = bead_params_play
models[0].threshold = 0.497687138515397
models[0].nbhd_size = None
models[0].inhibitor_scaling = 10.0 ** (1.45030857599371)
models[0].inducer_scaling = 10.0 ** (1.29838186230979)
models[1].bead_params = bead_params_play
models[1].threshold = 0.421573076609568
models[1].nbhd_size = 2*np.floor(56.5325206131255) + 1
models[1].inhibitor_scaling = 10.0 ** -0.329085126426299
models[1].inducer_scaling = 10.0 ** (-2.61174561686766)
return models