anyuzx

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

anyuzx

ps aux | grep name | grep -v grep

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

anyuzx

from scipy import sparse, io

m = sparse.csr_matrix([[0,0,0],[1,0,0],[0,1,0]])
m              # <3x3 sparse matrix of type '<type 'numpy.int64'>' with 2 stored elements in Compressed Sparse Row format>

io.mmwrite("test.mtx", m)
del m

newm = io.mmread("test.mtx")
newm           # <3x3 sparse matrix of type '<type 'numpy.int32'>' with 2 stored elements in COOrdinate format>

anyuzx

# Since there is no real autocorrelation compute function in python and numpy
# I write my own one

import numpy as np

# Suppose data array represent a time series data
# each element represent data at given time
# Assume time are equally spaced
def acf(data):
	mean = np.mean(data)

anyuzx

Notes about LAMMPS source codes, compute_gyration.h and compute_gyration.cpp.

compute_gyration.h

#ifdef COMPUTE_CLASS

ComputeStyle(gyration,ComputeGyration)

#else

anyuzx

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

anyuzx

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

anyuzx

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)

anyuzx

import numpy as np

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

anyuzx

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