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# models.py
import numpy as np
from scipy.optimize import curve_fit
from sympy import symbols, sympify, lambdify
import warnings
class BioprocessModel:
def __init__(self):
self.params = {}
self.models = {}
self.r2 = {}
self.rmse = {}
def set_model(self, model_type, equation_str, param_str):
equation_str = equation_str.strip()
if '=' in equation_str:
equation_str = equation_str.split('=', 1)[1].strip()
params = [param.strip() for param in param_str.split(',')]
self.models[model_type] = {
'equation_str': equation_str,
'params': params
}
t = symbols('t')
param_symbols = symbols(params)
expr = sympify(equation_str)
func = lambdify((t, *param_symbols), expr, 'numpy')
self.models[model_type]['function'] = func
def fit_model(self, model_type, time, data, bounds):
func = self.models[model_type]['function']
params = self.models[model_type]['params']
p0 = np.ones(len(params))
lower_bounds, upper_bounds = bounds
lower_bounds = np.array(lower_bounds)
upper_bounds = np.array(upper_bounds)
if len(lower_bounds) != len(params):
lower_bounds = np.full(len(params), -np.inf)
if len(upper_bounds) != len(params):
upper_bounds = np.full(len(params), np.inf)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
popt, _ = curve_fit(func, time, data, p0=p0, bounds=(lower_bounds, upper_bounds), maxfev=10000)
self.params[model_type] = dict(zip(params, popt))
y_pred = func(time, *popt)
ss_res = np.sum((data - y_pred) ** 2)
ss_tot = np.sum((data - np.mean(data)) ** 2)
self.r2[model_type] = 1 - (ss_res / ss_tot)
self.rmse[model_type] = np.sqrt(np.mean((data - y_pred) ** 2))
return y_pred
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