Shrodprob stuff

mas/shrodequ_superpos/main.py

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+#!/usr/bin/python3
+import matplotlib.pyplot as plt
+import numpy as np
+from phys import *
+
+x = np.arange(0, BOX_LENGTH, 0.00001)
+n = np.arange(1, 10, 1)
+
+X, N = np.meshgrid(x, n)
+P = prob(X, N)
+
+fig = plt.figure()
+ax = plt.axes(projection='3d')
+ax.plot_surface(X, N, P, rstride=1, cstride=1, cmap='viridis', edgecolor='none')
+ax.set_xlabel('x [m]')
+ax.set_ylabel('n [int]')
+ax.set_zlabel('probability [frac]');
+
+plt.show()
+
+

mas/shrodequ_superpos/phys/__init__.py

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+from phys.func import *
+from phys.const import *
+from phys.box import box
+from phys.particle import particle

mas/shrodequ_superpos/phys/box.py

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+from phys.particle import particle
+
+class box:
+    def __init__(self, length: float):
+        self.length = length
+
+    def add_particle(pobj: particle):
+        pass
+

mas/shrodequ_superpos/phys/const.py

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+import numpy as np
+from scipy.constants import Planck, electron_mass 
+
+# constants
+BOX_LENGTH = 0.01 # box length (meters)
+h = Planck # plancks constant
+
+D = np.sqrt(2/BOX_LENGTH) # norm factor
+m = electron_mass # mass
+
+# total energy function
+def energy(n):
+    return (((h*n)/BOX_LENGTH)**2)*(1/(8*m))
+
+# Wave func inner coef
+def k(n):
+    return np.sqrt( (8 * ((np.pi)**2) * m * energy(n))/(h**2) )
+
+# pre calculate energy states
+energy_states = []
+for n in range(0, 11):
+    energy_states.append( energy(n) )
+
+psi_k = []
+for n in range(0, 11):
+    psi_k.append( k(n) )

mas/shrodequ_superpos/phys/func.py

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+import cmath
+import numpy as np
+from phys.const import *
+
+# wave function
+def psi(x, n) -> float:
+    return D * np.sin( k(n) * x ) 
+
+def psi_opt(x, n) -> float: # optimized version
+    return D * np.sin( psi_k[n] * x )
+
+# probability density function
+def prob(x, n) -> float:
+    return (psi(x, n))**2
+
+def prob_opt(x, n) -> float: # optimized version
+    return abs(psi_opt(x,n)) ** 2

mas/shrodequ_superpos/phys/particle.py

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+from phys.func import psi, prob, E
+
+class particle:
+    def __init__(self, mass: float, n_state: int):
+        self.mass = mass
+        self.n_state = n_state
+        
+        self.energy = 
+        
+
+