acbalingit

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acbalingit

#!/usr/bin/env python

from pylab import *
from scipy.integrate import quad

def argument(k):
	return lambda y: y*exp(-k*sin(y))
x = linspace(0, 30)
y = map(lambda x: quad(argument(x), 0, pi)[0], x)

acbalingit

def function(param, **kwargs):

    """(one-line description of the function/class specified, like in Twitter!)

    (a more definitive description of the function. Also add necessary details
     on how the function was built, and how it works. When developing code, you
     may add dev notes and TODOs to be easily readable when scrolling through
     functions/class)

    Parameters

acbalingit

#!/usr/bin/env python

import numpy as np
from scipy.integrate import dblquad

### integration routine

def distance(coord1,coord2):
	cdef double length
	length = (coord1[0] - coord2[0])**2

acbalingit

import numpy as np
#import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt

from n1 import *



"""

acbalingit

func = lambda x, y: y**2 + 1
def OR1solver(func, bound, h):
	x_vals, y_vals = [], []
	x, y = 0, 0

	for i in range(int(bound/h)):
		k = np.zeros(4)
		k[0] = h*func(x, y)
		k[1] = h*func(x + 0.5*h, y + 0.5*k[0])
		k[2] = h*func(x + 0.5*h, y + 0.5*k[1])

acbalingit

if __name__ == "__main__":
    work_queue = Queue()
    for i in [runif, lattice, clustering, partclust, radial, walker]:
        work_queue.put(i)
        
    processes = [Process(target=operation,args=(all,)) for all in [runif, lattice, clustering, partclust, radial, walker] ]
    for p in processes:
        p.start()
    for p in processes:
        p.join()

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):
    """

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 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))