acbalingit

import numpy as np
import time
import matplotlib.pyplot as plt

#function
def f(x):
	y = x**2 - 4*x*np.sin(x) + (2*np.sin(x))**2
	return y
	
#routine

acbalingit

## a second order differential equation
## D2 y + 2 r w D y + w^2 y = 0
# D is the differential operator
# r is the damping ratio (r = c/(2mw))
# w is the undamped frequency
# c is the viscous damping coefficent
# m is the mass
#////////////////////////////////////////////////////////////////////////////////
#//                                                                            //
#//  Description:                                                              //

acbalingit

# vanilla RK4 written in R (07Dec2011)
#	evaluates a 2nd order ODE by splitting into 2- 1st order ODE 
#	and solved by Runge-Kutta Method
# Concepts and algorithm from:
#  http://en.wikipedia.org/wiki/Runge-Kutta_methods
#  http://www.cms.livjm.ac.uk/etchells/notes/ma200/2ndodes.pdf
#
#	f1(t,y) : substituted function from dz(y')/dt = F(z,y,t), z = y'
#	
#	rk4(f, h, tinit, yinit, limit): main RK4 routine

acbalingit

# vanilla RK4 written in R (09Dec2011) v1.1
#	evaluates a 2nd order ODE by splitting into 2- 1st order ODE 
#	and solved by Runge-Kutta Method
# Concepts and algorithm from:
#  http://en.wikipedia.org/wiki/Runge-Kutta_methods
#  http://www.cms.livjm.ac.uk/etchells/notes/ma200/2ndodes.pdf
#
#	f1(t,y) : substituted function from dz(y')/dt = F(z,y,t), z = y'
#	
#	rk4(f, h, tinit, yinit, limit): main RK4 routine

acbalingit

def cat_n_times(s, n):
	print n*str(s)
	
	
def is_divisible_by_3(n):
	if n%3 == 0:	print "This number is divisible by 3."
	else:	print "This number is not divisible by 3."
		
		

acbalingit

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import *
import time

def func(x):
	return x*x - 4*x*np.sin(x) +(2*np.sin(x))**2
	

acbalingit

__author__ = "Chester Balingit"

import numpy as np
from scipy import optimize
import matplotlib.pyplot as plt


## Part 1 - Find all the zeros of the function: 
## f(x) = cos(x) - xsin(x) using the optimize.brentq
## function of the scipy library

acbalingit

import scipy.optimize as optimize
import numpy as np
import collections
import matplotlib.pyplot as plt
from numpy import random

def sampler(points,mu,sigma):
	return np.linspace(0, 2*np.pi, points), \
	np.sin(np.linspace(0, 2*np.pi, points)) \
	+ random.normal(mu,sigma,size=(points))

acbalingit

import numpy as np
import matplotlib.pyplot as plt

func = lambda x: np.sin(x)
d1func = lambda x: np.cos(x)
d3func = lambda x: -1*np.cos(x)

def trapezoidal(func, n, dfunc='null',dddfunc='null', x0=0, xn=np.pi):
	
	x = np.linspace(x0, xn, n)

acbalingit

import random
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from plots import *
import pickle
#bump

def coord_solver(trigs, rad):
    """