dez 2025

script/CPython/k_fit.py

@@ -0,0 +1,25 @@
+import numpy as np
+from scipy.optimize import least_squares
+
+def k_fit(y_dat):
+    """
+    Fit linear and quadratic to K total
+    """
+    def optimize_func(a, y, n):
+        result = []
+        i, j = 0, 0
+        for u in range(len(y)):
+            val = y[0] + a[0] * i + a[1] * j**2 - y[u]
+            i = i + 1
+            if u >= n: j = j + 1
+            result.append(val)
+        return result
+    mini = None
+    for n in range(len(y_dat)):
+        a_init = (0.0001, 0.0001)
+        result = least_squares(optimize_func, a_init, args=(y_dat, n))
+        if mini is None or result["cost"] < mini:
+            mini = result["cost"]
+            n_fit = n
+            a_fit = result["x"]
+    return (a_fit[0], a_fit[1], n_fit)