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# cython: language_level=3
import cython
import numpy as np
from cython.parallel import prange, parallel

from libc.math cimport sqrt
from libc.math cimport nearbyint

@cython.boundscheck(False)
@cython.wraparound(False)

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//
//  This is a lattice random walk config generator using pivot algorithm.
//  can be self-avoid or not self-avoid
//  lattice_SAW(chain array, number of steps(monomers), step length(bond), self-avoid or no-self-avoid)
//
//  Created by guangshi on 8/25/14.
//  Copyright (c) 2014 Guang Shi. All rights reserved.
//

#include <math.h>

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// dynamically load the script on demand
class ScriptLoader {
  constructor(script) {
    this.script = script;
    this.scriptElement = document.createElement('script');
    this.head = document.querySelector('head');
  }

  load () {
    return new Promise((resolve, reject) => {

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import numpy as np

def walk(n):
    # check if the number of steps is an integer
    if int(n) != n:
        print('number of steps should be an integer')
        return None
    # the initial position is (0,0)
    xy_0 = np.array([0.0, 0.0])
    # generate displacements of each step

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import numpy as np

x = np.random.rand(1000)
y = np.random.rand(1000)

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def next_batch_nonlinear_map(bs, h, w, anisotropy=True):
    # ... same code ...
        y.append(np.dot(item, item)) # only changes here
    # ... same code ...

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anisotropy = False
learning_rate = 0.005
batch_size = 200
h = 10
w = 10
channels = 1
x = tf.placeholder(tf.float32, [batch_size, h, w, channels])
y = tf.placeholder(tf.float32, [batch_size, h, w, channels])

linear_map = np.random.rand(h,w)

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import numpy as np
import timeit
from scipy.spatial.distance import cdist

# define a dot product function used for the rotate operation
def v_dot(a):return lambda b: np.dot(a,b)

class lattice_SAW:
    def __init__(self,N,l0):
        self.N = N

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ps aux | grep name | grep -v grep

ps aux | grep name | grep -v grep | awk '{print $2}'