import numpy as np import torch import os import matplotlib.pyplot as plt #from torchdiffeq import odeint from torchdiffeq import odeint_adjoint as odeint from linear_memory.linear_memory import LinearMemory import linear_memory.utils as ut from import_utils import add_path from scipy.interpolate import interp1d add_path('pyssa') import pyssa.ssa as ssa import pyssa.models.standard_models as sm add_path('pymbvi') from pymbvi.models.mjp.autograd_partition_specific_models import SimpleGeneExpression from pymbvi.util import num_derivative, autograd_jacobian torch.manual_seed(2007301620) # get simulation model pre, post, rates = sm.get_standard_model("simple_gene_expression") # prepare initial conditions initial = np.array([0.0, 1.0, 0.0, 0.0]) tspan = np.array([0.0, 3e3]) # set up gene expression model moment_initial = np.zeros(9) moment_initial[0:3] = initial[1:4]
import sys import os from pathlib import Path import numpy as np import torch import matplotlib.pyplot as plt from torchdiffeq import odeint_adjoint as odeint workdir = Path(__file__).resolve().parent.parent.parent sys.path.append(str(workdir)) from import_utils import add_path add_path('pyssa') #pyssa_path = '/Users/christian/Documents/Code/pyssa' #sys.path.append(pyssa_path) import pyssa.util as ut torch.set_default_dtype(torch.float64) torch.manual_seed(2008181715) # load data load_path = os.path.dirname(os.path.realpath(__file__)) + '/data.npz' data = np.load(load_path) moment_initial = torch.from_numpy(data['moment_initial']) rates = data['rates'] A_true = data['A_true'] b_true = data['b_true'] num_samples = data['num_samples'] tspan = data['tspan'] t_plot = data['t_plot'] delta_t = data['delta_t']