Notes about LAMMPS
source codes, compute_gyration.h
and compute_gyration.cpp
.
#ifdef COMPUTE_CLASS
ComputeStyle(gyration,ComputeGyration)
#else
// Use Gists to store code you would like to remember later on | |
console.log(window); // log the "window" object to the console |
# 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) |
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> |
ps aux | grep name | grep -v grep
ps aux | grep name | grep -v grep | awk '{print $2}'
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 |
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) |
def next_batch_nonlinear_map(bs, h, w, anisotropy=True): | |
# ... same code ... | |
y.append(np.dot(item, item)) # only changes here | |
# ... same code ... |
import numpy as np | |
x = np.random.rand(1000) | |
y = np.random.rand(1000) |
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 |