mattwthompson

dummy gro file for velocity error
1
    1FOO    foo 9328   3.064   2.394   6.775-10.3772 -0.2281  2.8391
   5.70000   3.41000  34.06000

mattwthompson

d = np.linspace(0, 2, num=10000)
A = 1 - 2 /np.pi * ((1 - d/2)*np.sqrt(d - d**2/4) + np.arctan((1-d/2)/(np.sqrt(d-d**2/4))))

fig, axes = plt.subplots(1, 3, figsize = (16, 3))
axes[0].plot(d/2, A)
axes[0].grid()
axes[1].plot(d/2, np.gradient(A, d/2))
axes[2].plot(d/2, np.gradient(np.gradient(A, d/2), d/2))
for ax in axes:
    ax.set_xlim(0, 1)

mattwthompson

import numpy as np
import matplotlib.pyplot as plt


histo = np.loadtxt('histo.xvg', comments=['#', '@'])

x_values = histo[:, 0]

plt.figure(figsize=(10, 4))

mattwthompson

import numpy as np                                                                                                  


def find_nearest(distances, val):
    idx = (np.abs(distances[:, 1] - val)).argmin()
    return distances[idx, 0]


spacing = 0.05
distances = np.loadtxt('distance_summary.dat')

mattwthompson

#                                                                                                                                            
#@setup_pdf
#
read
   stru frozen_500ps.stru
# pound is a comment before and ! for comments after
#can use this section to change B_iso values
#b[1]     = 1.5000 !r[203] !H 
#b[2]     = 0.9000 !r[204] !O 
#b[3]     = 0.4000 !r[205] !Ti

mattwthompson

mattwthompson

import numpy as np
import mdtraj as md
import pdb
import matplotlib.pyplot as plt
import seaborn as sns
from mdtraj.geometry.order import _compute_director
from matplotlib.colors import normalize

def cat_angle(A, B, C):
    a = (B[:,:,1]-A[:,:,1])*(C[:,:,2]-A[:,:,2])-(C[:,:,1]-A[:,:,1])*(B[:,:,2]-A[:,:,2])

mattwthompson

1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0                                                                                       
0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 
0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 
0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 
0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 
1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 
0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 
0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 
1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 
0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 

mattwthompson

import re

def _atoi(text):
    return int(text) if text.isdigit() else text

def natural_sort(text):
    return [_atoi(a) for a in re.split(r'(\d+)', text)]


lst = ['mxene_001g', 'mxene_002', 'mxene_003', 'mxene_004', 'mxene_005', 'opls_753', 'opls_754', 'opls_755', 'opls_759']

mattwthompson

import random

import numpy as np


composition = [1/3, 1/3, 1/3]

sites = np.zeros(shape=(300,))

val_1 = composition[2] + composition[1]