Create models.py

models.py

@@ -0,0 +1,57 @@
+# 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