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Merge pull request #2 from v1j4y/vj_as
Added files for generating data.
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generateData.py
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generateData.py
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#!/usr/bin/python
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import numpy as np
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import sys, getopt
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from jastlib import getCoefList, get_sphere_distribution, generateElsAroundPoints
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def main(argv):
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Natom = 2
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Ratio = 5
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inputfile = ''
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outputfile = ''
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try:
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opts, args = getopt.getopt(argv,"ha:r:",["atom=","ratio="])
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except getopt.GetoptError:
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print('test.py -a <natoms> -r <ratio>')
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sys.exit(2)
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for opt, arg in opts:
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if opt == '-h':
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print('test.py -a <inputfile> -r <outputfile>')
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sys.exit()
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elif opt in ("-a", "--atom"):
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Natom = int(arg)
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elif opt in ("-r", "--ratio"):
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Ratio = int(arg)
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Nelec = Ratio
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Nord = 5
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L1 = 20.0
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n = Natom # number of points
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dmin = 0.1 # min dist between points
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Ls = np.array([L1,L1,L1]) # lengths of the box
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shift = -10.0
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kappa = 2.0
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filename_atom = "geometry.txt"
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filename_coeffsa = "jast_coeffs.txt"
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(coeffsa, coeffsb, coeffsc) = getCoefList(Nord,n)
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coeffsall = np.concatenate((coeffsa,coeffsb,coeffsc))
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atomList,_,_ = get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True)
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L1 = 15.0
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n = Nelec # number of points
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dmin = 0.1 # min dist between points
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Ls = np.array([L1,L1,L1]) # lengths of the box
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shift = -10.0
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kappa = 2.0
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filename_elec = "elec_coord.txt"
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rlist = generateElsAroundPoints(n,atomList,dmin)
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# Save file
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np.savetxt(filename_elec,rlist)
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np.savetxt(filename_atom,atomList)
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np.savetxt(filename_coeffsa,coeffsall)
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if __name__ == "__main__":
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main(sys.argv[1:])
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271
jastlib.py
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271
jastlib.py
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import numpy as np
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import matplotlib.pyplot as plt
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def get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True):
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"""Get random points in a box with given dimensions and minimum separation.
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Parameters:
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- n: number of points
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- dmin: minimum distance
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- Ls: dimensions of box, shape (3,) array
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- maxiter: maximum number of iterations.
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- allow_wall: whether to allow points on wall;
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(if False: points need to keep distance dmin/2 from the walls.)
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Return:
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- ps: array (n, 3) of point positions,
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with 0 <= ps[:, i] < Ls[i]
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- n_iter: number of iterations
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- dratio: average nearest-neighbor distance, divided by dmin.
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Note: with a fill density (sphere volume divided by box volume) above about
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0.53, it takes very long. (Random close-packed spheres have a fill density
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of 0.64).
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Author: Han-Kwang Nienhuys (2020)
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Copying: BSD, GPL, LGPL, CC-BY, CC-BY-SA
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See Stackoverflow: https://stackoverflow.com/a/62895898/6228891
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"""
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Ls = np.array(Ls).reshape(3)
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if not allow_wall:
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Ls -= dmin
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# filling factor; 0.64 is for random close-packed spheres
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# This is an estimate because close packing is complicated near the walls.
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# It doesn't work well for small L/dmin ratios.
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sphere_vol = np.pi/6*dmin**3
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box_vol = np.prod(Ls + 0.5*dmin)
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fill_dens = n*sphere_vol/box_vol
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if fill_dens > 0.64:
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msg = f'Too many to fit in the volume, density {fill_dens:.3g}>0.64'
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raise ValueError(msg)
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# initial try
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ps = np.random.uniform(size=(n, 3)) * Ls
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# distance-squared matrix (diagonal is self-distance, don't count)
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dsq = ((ps - ps.reshape(n, 1, 3))**2).sum(axis=2)
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dsq[np.arange(n), np.arange(n)] = np.infty
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for iter_no in range(int(maxiter)):
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# find points that have too close neighbors
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close_counts = np.sum(dsq < dmin**2, axis=1) # shape (n,)
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n_close = np.count_nonzero(close_counts)
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if n_close == 0:
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break
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# Move the one with the largest number of too-close neighbors
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imv = np.argmax(close_counts)
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# new positions
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newp = np.random.uniform(size=3)*Ls
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ps[imv]= newp
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# update distance matrix
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new_dsq_row = ((ps - newp.reshape(1, 3))**2).sum(axis=-1)
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dsq[imv, :] = dsq[:, imv] = new_dsq_row
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dsq[imv, imv] = np.inf
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else:
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raise RuntimeError(f'Failed after {iter_no+1} iterations.')
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if not allow_wall:
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ps += dmin/2
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dratio = (np.sqrt(dsq.min(axis=1))/dmin).mean()
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return ps, iter_no+1, dratio
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def generateElsAroundPoints(n,LS,dmin):
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"""
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Parameters:
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- n: number of points
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- LS: list of position of all atoms
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- dmin: minimum intra block distance
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- shift: inter block distance
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Return:
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- r: array (n, 3) of point positions,
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"""
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xs = None
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for Ls in LS:
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# Get list of random points around Ls
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distrib,a,b = get_sphere_distribution(n,dmin,Ls)
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if xs is None:
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xs = distrib[:,0]
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ys = distrib[:,1]
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zs = distrib[:,2]
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else:
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xs = np.concatenate((xs,distrib[:,0]))
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ys = np.concatenate((ys,distrib[:,1]))
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zs = np.concatenate((zs,distrib[:,2]))
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return((np.array((xs,ys,zs))).T)
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def getCoefList(Nord,Natom):
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assert(Nord < 11)
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dict = {
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0 : lambda x,y:x-y-2,
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1 : lambda x,y:x-y,
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2 : lambda x,y:x-y,
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3 : lambda x,y:x-y,
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4 : lambda x,y:x-y,
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5 : lambda x,y:x-y,
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6 : lambda x,y:x-y,
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7 : lambda x,y:x-y,
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8 : lambda x,y:x-y,
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9 : lambda x,y:x-y,
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10 : lambda x,y:x-y,
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11 : lambda x,y:x-y,
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}
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count = 0
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for p in range(2,Nord+1):
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for k in range(p-1,-1,-1):
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lmax = dict[k](p,k)
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for l in range(lmax,-1,-1):
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if (p-k-l) & 1 is 0:
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count += 1
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coeflista = np.random.rand(Nord+1,Natom)
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coeflistb = np.random.rand(Nord+1)
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coeflistc = np.random.rand(count,Natom)
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return (coeflista.reshape((Nord+1)*Natom),coeflistb,coeflistc.reshape(count*Natom))
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#return (coeflista,coeflistb,coeflistc)
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def get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True):
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"""Get random points in a box with given dimensions and minimum separation.
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Parameters:
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- n: number of points
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- dmin: minimum distance
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- Ls: dimensions of box, shape (3,) array
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- maxiter: maximum number of iterations.
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- allow_wall: whether to allow points on wall;
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(if False: points need to keep distance dmin/2 from the walls.)
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Return:
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- ps: array (n, 3) of point positions,
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with 0 <= ps[:, i] < Ls[i]
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- n_iter: number of iterations
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- dratio: average nearest-neighbor distance, divided by dmin.
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Note: with a fill density (sphere volume divided by box volume) above about
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0.53, it takes very long. (Random close-packed spheres have a fill density
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of 0.64).
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Author: Han-Kwang Nienhuys (2020)
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Copying: BSD, GPL, LGPL, CC-BY, CC-BY-SA
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See Stackoverflow: https://stackoverflow.com/a/62895898/6228891
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"""
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Ls = np.array(Ls).reshape(3)
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if not allow_wall:
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Ls -= dmin
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# filling factor; 0.64 is for random close-packed spheres
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# This is an estimate because close packing is complicated near the walls.
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# It doesn't work well for small L/dmin ratios.
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sphere_vol = np.pi/6*dmin**3
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box_vol = np.prod(Ls + 0.5*dmin)
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fill_dens = n*sphere_vol/box_vol
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if fill_dens > 0.64:
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msg = f'Too many to fit in the volume, density {fill_dens:.3g}>0.64'
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raise ValueError(msg)
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# initial try
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ps = np.random.uniform(size=(n, 3)) * Ls
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# distance-squared matrix (diagonal is self-distance, don't count)
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dsq = ((ps - ps.reshape(n, 1, 3))**2).sum(axis=2)
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dsq[np.arange(n), np.arange(n)] = np.infty
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for iter_no in range(int(maxiter)):
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# find points that have too close neighbors
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close_counts = np.sum(dsq < dmin**2, axis=1) # shape (n,)
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n_close = np.count_nonzero(close_counts)
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if n_close == 0:
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break
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# Move the one with the largest number of too-close neighbors
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imv = np.argmax(close_counts)
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# new positions
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newp = np.random.uniform(size=3)*Ls
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ps[imv]= newp
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# update distance matrix
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new_dsq_row = ((ps - newp.reshape(1, 3))**2).sum(axis=-1)
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dsq[imv, :] = dsq[:, imv] = new_dsq_row
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dsq[imv, imv] = np.inf
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else:
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raise RuntimeError(f'Failed after {iter_no+1} iterations.')
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if not allow_wall:
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ps += dmin/2
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dratio = (np.sqrt(dsq.min(axis=1))/dmin).mean()
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return ps, iter_no+1, dratio
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def scalingee(r,kappa=1.0):
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return (numpy.ones_like(r) - numpy.exp(-kappa*r))/kappa
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def scalingen(r,kappa=1.0):
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return numpy.exp(-kappa*r)
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if False:
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Nord = 5
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L1 = 2.0
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n = 2 # number of points
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dmin = 0.1 # min dist between points
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Ls = np.array([L1,L1,L1]) # lengths of the box
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shift = -10.0
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kappa = 2.0
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filename_atom = str(n) + "_geometry.txt"
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filename_elec = str(n)
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filename_coeffs = str(n) + "_jast_coeffs.txt"
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(coeffsa, coeffsb, coeffsc) = getCoefList(Nord,n)
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coeffsall = np.concatenate((coeffsa,coeffsb,coeffsc))
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print(coeffsa.shape,coeffsb.shape,coeffsc.shape)
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atomList,_,_ = get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True)
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#print(atomList)
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L1 = 5.0
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n = 5 # number of points
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dmin = 0.1 # min dist between points
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Ls = np.array([L1,L1,L1]) # lengths of the box
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shift = -10.0
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kappa = 2.0
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filename_elec = filename_elec + "_" + str(n) + "_elec_coord.txt"
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#rlist = generateBlockRandomPointsAtShftApart(n,L1,dmin,shift)
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rlist = generateElsAroundPoints(n,atomList,dmin)
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# Save file
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np.savetxt(filename_elec,rlist)
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np.savetxt(filename_atom,atomList)
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np.savetxt(filename_coeffs,coeffsall)
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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xs = rlist.T[0]
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ys = rlist.T[1]
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zs = rlist.T[2]
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ax.scatter(xs, ys, zs, marker='o')
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plt.show()
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rijScaled = np.array([[(lambda xval, yval: np.linalg.norm(xval-yval))(xval,yval) for yval in rlist] for xval in rlist])
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plt.imshow(rijScaled)
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plt.colorbar()
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plt.show()
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