update

Source/main_poincare_sections_linear.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+"""
+Main: Computes Poincaré Sections (Linear Algorithm)
+
+Computes the Poincaré Sections with a linear algorithm 
+(i.e. no parallel computing).
+
+@ Author: Moussouni, Yaël (MSc student) & Bhat, Junaid Ramzan (MSc student)
+@ Institution:  Université de Strasbourg, CNRS, Observatoire astronomique
+                de Strasbourg, UMR 7550, F-67000 Strasbourg, France
+@ Date: 2024-11-29
+"""
+import numpy as np
+
+import potentials as pot
+import integrator as itg
+import initial_conditions as init
+import poincare_sections as pcs
+
+# Parameters
+OUT_DIR = "./Output/"
+FILENAME_PREFIX = "poincare_sections_linear_"
+EXTENSION = ".csv"
+DEFAULT_N_iter = 30000
+DEFAULT_N_part = 100
+DEFAULT_h = 0.01
+E_all = np.array([1/100, 1/12, 1/10, 1/8, 1/6])
+
+text_E = ["1/100", "1/12", "1/10", "1/8", "1/6"]
+
+def compute_poincare_sections_linear(E: float,
+                                    N_iter: int = DEFAULT_N_iter,
+                                    N_part: int = DEFAULT_N_part,
+                                    h: float = DEFAULT_h) -> tuple:
+    """
+    Computes the Poincaré sections for a given energy E.
+    @params:
+        - E: the total energy of each particles
+        - N_iter: the number of iteration
+        - N_part: the number of particles
+        - h: integration steps
+    @returns:
+        - y_section, v_section: arrays containing the y and v coordinates of 
+          the Poincaré sections
+    """
+    W_all_part = init.n_energy_part(pot.hh_potential, N_part, E)
+    y_section = []
+    v_section = []
+    for i in range(N_part):
+        W_part = W_all_part[:,:,i]
+        
+        # Perform integration
+        t_part, coord_part = itg.rk4(0, W_part, h, N_inter, pot.hh_evolution)
+
+        # Extract positions and velocities
+        x_part = coord_part[:, 0, 0]
+        y_part = coord_part[:, 0, 1]
+        u_part = coord_part[:, 1, 0]
+        v_part = coord_part[:, 1, 1]
+
+        # Find Poincaré section points for the current initial condition
+        y_pcs, v_pcs = pcs.pcs_find_legacy(x_part, y_part, u_part, v_part)
+        # The legacy is important here, the algorithm is the same but the 
+        # data format is different...
+
+        # Append the current Poincaré section points to the overall lists
+        y_section += y_pcs
+        v_section += v_pcs
+    return y_section, v_section
+
+if __name__ == "__main__":
+    y_section_all = []
+    v_section_all = []
+    for i in range(len(E_all)):
+        y_section, v_section = compute_poincare_sections_linear(E_all[i])
+        section = np.array([y_section, v_section])
+        filename = OUT_DIR + FILENAME_PREFIX\
+                + str(text_E[i][2:]) + EXTENSION
+        np.savetxt(filename, section)
+
+
+
+
+
+    
+