Add code with non-LE test

code/main.py

@@ -0,0 +1,121 @@
+import scipy.integrate as sitg
+import scipy.interpolate as sitp
+import scipy.optimize as sopt
+import scipy.linalg as salg
+import math as m
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+
+def create_subplot():
+   return plt.subplots(layout='constrained')[1]
+
+
+class NonLinear:
+   bisect_exp = "x**2 * np.sin(x)"
+   newton_exp = "np.sin(x) * np.sqrt(np.abs(x))"
+
+   @staticmethod
+   def generate_array(min, max):
+      point_count = int(m.fabs(max-min))*10
+      x = np.linspace(min, max, point_count)
+      return list(x.tolist())
+
+   @staticmethod
+   def slice_array(range: list[float], val_min, val_max):
+      def index_search(range: list[float], val):
+         i = 0
+         for v in range:
+            if v >= val:
+               return i
+            i += 1
+         return -1
+
+      index_l = index_search(
+          range, val_min) if val_min is not None else range.index(min(range))
+      index_r = index_search(
+          range, val_max) if val_max is not None else range.index(max(range))
+      return range[index_l:index_r+1]
+
+   @staticmethod
+   def plt_append(sp, x: list[float], y: list[float], label: str, format: str):
+      sp.plot(x, y, format, label=label)
+
+   @staticmethod
+   def bisect(x, x_min, x_max):
+      def f(x): return eval(NonLinear.bisect_exp)
+      y = f(np.array(x))
+      root = sopt.bisect(f, x_min, x_max)
+      solution = root[0] if root is tuple else root
+      return list(y), (float(solution), float(f(solution)))
+
+   @staticmethod
+   def plot_bisect():
+      bounds = 0, 6
+      split_val = 1
+      x1 = NonLinear.generate_array(bounds[0], bounds[1])
+      x2 = NonLinear.slice_array(x1, split_val, None)
+
+      sp = create_subplot()
+
+      sol1 = NonLinear.bisect(x1, bounds[0], bounds[1])
+      sol2 = NonLinear.bisect(x2, split_val, bounds[1])
+
+      NonLinear.plt_append(
+          sp, x1, sol1[0], f"Исходные данные (y={NonLinear.bisect_exp})", "-b")
+      NonLinear.plt_append(
+          sp, *(sol1[1]), f"bisect at [{bounds[0]},{bounds[1]}]", "or")
+      NonLinear.plt_append(
+          sp, *(sol2[1]), f"bisect at [{split_val},{bounds[1]}]", "og")
+
+      sp.set_title("scipy.optimize.bisect")
+      sp.legend(loc='lower left')
+
+   @staticmethod
+   def newton(x, x0):
+      def f(x): return eval(NonLinear.bisect_exp)
+      y = f(np.array(x))
+      root = sopt.newton(f, x0)
+      solution = root[0] if root is tuple else root
+      return list(y), (float(solution), float(f(solution)))
+
+   @staticmethod
+   def plot_newton():
+      bounds = -2, 7
+      split_l, split_r = 2, 5
+      x1 = NonLinear.generate_array(bounds[0], bounds[1])
+      x2 = NonLinear.slice_array(x1, split_l, split_r)
+      x0_1, x0_2 = 1/100, 4
+      sp = create_subplot()
+
+      sol1 = NonLinear.newton(x1, x0_1)
+      sol2 = NonLinear.newton(x2, x0_2)
+
+      NonLinear.plt_append(
+          sp, x1, sol1[0], f"Исходные данные (y={NonLinear.newton_exp})", "-b")
+      NonLinear.plt_append(
+          sp, *(sol1[1]), f"newton at [{bounds[0]},{bounds[1]}]", "or")
+      NonLinear.plt_append(
+          sp, *(sol2[1]), f"newton at [{split_l},{bounds[1]}]", "og")
+
+      sp.set_title("scipy.optimize.newton")
+      sp.legend(loc='lower left')
+
+   @staticmethod
+   def plot(method: str = "all"):
+      if method in ["bisect", "all"]:
+         NonLinear.plot_bisect()
+      if method in ["newton", "all"]:
+         NonLinear.plot_newton()
+      plt.ylabel("y")
+      plt.xlabel("x")
+      plt.show()
+
+
+def main():
+   NonLinear.plot()
+
+
+if __name__ == "__main__":
+   main()