def tay(e):
total = 0
for j in range(n):
mat = np.linalg.matrix_power(
np.matrix(ac.derivative(x[0], x[-1], n)), j) @ f
total = total + (mat[i] * ((x[e] - x[i])**j) / np.factorial(j))
return total
def __init__(self, n, xshift=0, yshift=0):
self.n = n
self.xshift = xshift
self.yshift = yshift
self.E = n + 0.5
self.coef = 1 / np.sqrt(2**n * np.factorial(n)) * (1 / np.pi)**(1/4)
self.hermite = hermite(n)
def factorialNumber(numbers):
result = list()
for num in numbers:
result.append(np.factorial(int(num)))
if len(result) == 1:
return result[0]
else:
return result
def annihilation_coefficients( self, x, stencil, max_order ):
num_dims = x.shape[0]
num_terms = int( numpy.round( scipy.misc.comb( max_order + num_dims,
num_dims ) ) )
num_stencil_pts = stencil.shape[0]
print num_terms, num_stencil_pts
I = multi_indices( num_dims, max_order )
print I
phi = numpy.empty( ( num_stencil_pts, num_terms ), numpy.float )
dy_dx = numpy.empty( ( num_stencil_pts, 1 ), numpy.float )
for i in range( num_terms + 1 ):
print stencil**( I[i] )
phi[:,i] = stencil**( I[i] )
if ( I[i] == order ):
dy_dx[i] = numpy.factorial( max_order )
else:
dy_dx[i] = 0.0
return numpy.linalg.solve( phi, dy_dx )
def Poisson_pmf(x, lambda1):
Poiss_pmf = (lambda1**x) * np.exp(-lambda1) / np.factorial(x)
return Poiss_pmf
def ejecutar_mat1(self, funcion):
if isinstance(funcion, mathfunctions):
if funcion.funcion == 'abs':
try:
return abs(self.aritexc.ejecutar_operacion(funcion.op1))
except:
print('error al convertir valores ')
return 0
if funcion.funcion == 'cbrt':
try:
return self.aritexc.ejecutar_operacion(funcion.op1)**(1. /
3.)
except:
print('error al convertir valores ')
return 0
if funcion.funcion == 'ceil':
try:
return np.ceil(self.aritexc.ejecutar_operacion(
funcion.op1))
except:
print('error al convertir valores ')
return 0
if funcion.funcion == 'celing':
try:
return np.ceil(self.aritexc.ejecutar_operacion(
funcion.op1))
except:
print('error al convertir valores ')
return 0
if funcion.funcion == 'degrees':
try:
return np.degrees(
self.aritexc.ejecutar_operacion(funcion.op1))
except:
print('error al convertir valores ')
return 0
if funcion.funcion == 'exp':
try:
return np.exp(self.aritexc.ejecutar_operacion(funcion.op1))
except:
print('error al convertir valores ')
return 0
if funcion.funcion == 'factorial':
try:
return np.factorial(
self.aritexc.ejecutar_operacion(funcion.op1))
except:
errorsem.append('error al convertir valores ')
return 0
if funcion.funcion == 'floor':
try:
return np.floor(
self.aritexc.ejecutar_operacion(funcion.op1))
except:
errorsem.append('error al convertir valores ')
return 0
if funcion.funcion == 'ln':
try:
return np.ln(self.aritexc.ejecutar_operacion(funcion.op1))
except:
errorsem.append('error al convertir valores ')
return 0
if funcion.funcion == 'log':
try:
return np.log10(
self.aritexc.ejecutar_operacion(funcion.op1))
except:
errorsem.append('error al convertir valores ')
return 0
if funcion.funcion == 'radians':
try:
return np.radians(
self.aritexc.ejecutar_operacion(funcion.op1))
except:
errorsem.append('error al convertir valores ')
return 0
if funcion.funcion == 'sign':
try:
valor = self.aritexc.ejecutar_operacion(funcion.op1)
return -1 if (valor < 0) else 1
except:
errorsem.append('error al convertir valores ')
return 0
import numpy as np
import matplotlib.pyplot as plt
x = -3
N = 20
S = 0
for n in range(0, N + 1):
S = S + x**n / np.factorial(n)
plt.plot(n, S)
def BPOcoef(self,order):
K = np.ones([order+1],int)
for ii in range(1,order):
K[ii] = np.factorial(order)/(np.factorial(ii)*np.factorial(order-ii))
return K
def Wigner3j(j1, j2, j3, m1, m2, m3):
#======================================================================
# Wigner3j.m by David Terr, Raytheon, 6-17-04
#
# Compute the Wigner 3j symbol using the Racah formula [1].
#
# Usage:
# from wigner import Wigner3j
# wigner = Wigner3j(j1,j2,j3,m1,m2,m3)
#
# / j1 j2 j3 \
# | |
# \ m1 m2 m3 /
#
# Reference: Wigner 3j-Symbol entry of Eric Weinstein's Mathworld:
# http://mathworld.wolfram.com/Wigner3j-Symbol.html
#======================================================================
# Error checking
if 2*j1 != np.floor(2*j1) or 2*j2 != np.floor(2*j2) or \
2*j3 != np.floor(2*j3) or 2*m1 != np.floor(2*m1) or \
2*m2 != np.floor(2*m2) or 2*m3 != np.floor(2*m3):
print('All arguments must be integers or half-integers.')
return -1
# Additional check if the sum of the second row equals zero
if m1 + m2 + m3 != 0:
print('3j-Symbol unphysical')
return 0
if j1 - m1 != np.floor(j1 - m1):
print('2*j1 and 2*m1 must have the same parity')
return 0
if j2 - m2 != np.floor(j2 - m2):
print('2*j2 and 2*m2 must have the same parity')
return
0
if j3 - m3 != np.floor(j3 - m3):
print('2*j3 and 2*m3 must have the same parity')
return 0
if j3 > j1 + j2 or j3 < np.abs(j1 - j2):
print('j3 is out of bounds')
return 0
if np.abs(m1) > j1:
print('m1 is out of bounds')
return 0
if np.abs(m2) > j2:
print('m2 is out of bounds')
return 0
if np.abs(m3) > j3:
print('m3 is out of bounds')
return 0
t1 = j2 - m1 - j3
t2 = j1 + m2 - j3
t3 = j1 + j2 - j3
t4 = j1 - m1
t5 = j2 + m2
tmin = max(0, max(t1, t2))
tmax = min(t3, min(t4, t5))
tvec = np.arange(tmin, tmax + 1, 1)
wigner = 0
for t in tvec:
wigner += (-1)**t / (np.factorial(t) * np.factorial(t - t1) *
np.factorial(t - t2) * np.factorial(t3 - t) *
np.factorial(t4 - t) * np.factorial(t5 - t))
return wigner * (-1)**(j1 - j2 - m3) * np.sqrt(
np.factorial(j1 + j2 - j3) * np.factorial(j1 - j2 + j3) *
np.factorial(-j1 + j2 + j3) / np.factorial(j1 + j2 + j3 + 1) *
np.factorial(j1 + m1) * np.factorial(j1 - m1) * np.factorial(j2 + m2) *
np.factorial(j2 - m2) * np.factorial(j3 + m3) * np.factorial(j3 - m3))
def hornersrule(A, t, k):
I = np.identity(len(A)) # Create I
p = np.identity(len(A))
for i in range(k, -1, -1)[:-1]:
p = np.matmul(np.add(I, t * (1 / np.factorial(k))), p)
return p
def find_factorial_sum(n):
digits = str(n).split()
return sum([np.factorial(int(d)) for d in digits])
def find_factorial_sum(n):
digits = str(n).split()
return sum([np.factorial(int(d)) for d in digits])
