def interpolate_KDTree(x, y, v, grid, logger):
logger.info(datetime.datetime.now())
logger.info(x.shape)
known = zip(x, y)
# print type(known)
known = numpy.array(known)
# print type(known), type(v), known.shape, v.shape
lats = numpy.arange(15, 60, 0.01)
lons = numpy.arange(70, 140, 0.01)
# print lats.shape, lons.shape
ask = []
for i in range(len(lats)):
for j in range(len(lons)):
ask.append((lats[i], lons[j]))
ask = numpy.array(ask)
# print '-------------'
# print type(ask),ask.shape
leafsize = 10
Nnear = 8
eps = 0.005
p = 2
invdisttree = Invdisttree(known, v, leafsize=leafsize, stat=1)
interpol = invdisttree(ask, nnear=Nnear, eps=eps, p=p)
grid = interpol.reshape(4500, 7000)
logger.info(datetime.datetime.now())
del interpol
return grid
def __init__(self, **kw):
# the path to files containing wave functions (to fit with)
self._path = kw.get('p', '/data/users/mklymenko/science/H2_100/programing/dis/v2/')
self._sn = kw.get('sn', 10) # id of the system
self._qn = kw.get('qn', 0) # id of the wave function
print '\n--------------------------------------------------------------'
print 'The wave function is associated with the file'
print self._path+'ff_'+str(self._sn)+'.dat'
print ' the number of the quantum state is {}'.format(self._qn)
self.flag = kw.get('flag', 'none')
if self.flag == 'int':
print("Reading from file and creating the interpolant..."),
stdout.flush()
X, F = self.read_from_file(self._sn, self._qn, self._path)
self.invdisttree = Invdisttree(X.T, F)
print("Done!")
print '--------------------------------------------------------------\n'
]
for j, g in enumerate(geom):
for j1 in range(geom.orbitals[j]):
ldos[:, j] += pdos[ind, :]
ind += 1
args = geom[:, 0].argsort()
ldos = ldos[:, args]
from invdisttree import Invdisttree
xnew, ynew, znew = np.mgrid[-coords_min[0]:2 * coords_max[0]:300j,
-coords_min[1]:2 * coords_max[1]:10j,
-coords_min[2]:2 * coords_max[2]:10j]
size = xnew.shape
ldos_grid = np.zeros((len(energy), size[0], size[1], size[2]))
for j in range(len(energy)):
print(j)
interp = Invdisttree(
np.vstack((geom.xyz[:, 0], geom.xyz[:, 1], geom.xyz[:, 2])).T,
ldos[j, :])
ldos_grid[j, :, :, :] = interp(np.vstack(
(xnew.flatten(), ynew.flatten(), znew.flatten())).T,
nnear=11,
eps=0,
p=1).reshape(xnew.shape)
print(ldos_grid)
def do_fit(self):
""" The function does the fitting procedure"""
if (self._flag == 1):
self._gf = [0.2]
self._gf = self.par*(self._num_fu*len(self._sites)*2)
x, F = self.read_from_file(
self._sn, self._qn, self._path) # read data from the file
# ,ftol=1.0e-7,xtol=1.0e-8)
popt, pcov = curve_fit(
self.modelfun, x, F, p0=self._gf, maxfev=5000)
self._gf = popt
elif (self._flag == 2):
# par=[0.0]*(self._num_fu*5)
# for j in xrange(self._num_fu):
# par[j*5]=0.0*math.copysign(1,(pow(-1,j)))
# self._gf[j*5]=0.1
# par[j*5+1]=6.45
# par[j*5+2]=0.0
# par[j*5+3]=0.05
# par[j*5+4]=1.0
X, F = self.read_from_file(self._sn, self._qn, self._path) # read data from the file
# height, xx, width=self.moments(F)
# Tracer()()
# par=[0.0]*(self._num_fu*5)
# for j in xrange(self._num_fu):
# par[j*5]=x[0,xx]
# par[j*5]=X[0,xx]*math.copysign(1,(pow(-1,j)))
# par[j*5+1]=X[1,xx]
# par[j*5+2]=X[2,xx]
# par[j*5+3]=0.007
# par[j*5+4]=height*math.copysign(1,(pow(-1,j)))
xi, yi, zi = np.mgrid[-6.5:6.5:160j, 4.0:8.9:160j, -7.5:7.5:160j]
x, y, z = xi.flatten(), yi.flatten(), zi.flatten()
XX = np.vstack((x, y, z))
invdisttree = Invdisttree(X.T, F, leafsize=10, stat=1)
AA = invdisttree(XX.T, nnear=130, eps=0, p=1)
# aaa1,bbb1=self.detect_local_minima(-AA.reshape(xi.shape))
# aaa2,bbb2=self.detect_local_maxima(-AA.reshape(xi.shape))
if self.peaks==[]:
print '\n---------------------------------------------------------------------'
print 'Detecting maxima and minima of target function...',
peaks_min, min_coord, peaks_max, max_coord = self.detect_min_max(AA.reshape(xi.shape))
print 'done'
print 'Number of the min peaks: {}'.format(len(peaks_min))
print 'Number of the max peaks: {}'.format(len(peaks_max))
print '---------------------------------------------------------------------\n'
# fig=plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.plot_surface(xi[:,:,60],yi[:,:,60],bbb2[:,:,60], cmap=cm.jet, linewidth=0.2)
# plt.hold(True)
# plt.show()
if peaks_max==[]:
peaks=np.insert(peaks_min, np.arange(len(peaks_max)), peaks_max)
coords=np.insert(min_coord, np.arange(max_coord.shape[1]), max_coord, axis=1)
else:
peaks = np.insert(peaks_max, np.arange(len(peaks_min)), peaks_min)
coords = np.insert(max_coord, np.arange(min_coord.shape[1]), min_coord, axis=1)
self.peaks=peaks
self.coords=coords
par = [0.0]*(self._num_fu*5)
j1 = 0
aaaa = 1
for j in xrange(self._num_fu):
if (j > aaaa*self.coords.shape[1]-1):
j1 = 0
aaaa += 1
par[j*5] = xi[self.coords[0, j1], self.coords[0, j1], self.coords[0, j1]]
par[j*5+1] = yi[self.coords[1, j1], self.coords[1, j1], self.coords[1, j1]]
par[j*5+2] = zi[self.coords[2, j1], self.coords[2, j1], self.coords[2, j1]]
# par[j*5+3] = 0.1003+0.1000*math.copysign(1, (pow(-1, j)))
par[j*5+3] = 0.0001
# if j < 15:
# par[j*5+3] = 0.00001
# else:
# par[j*5+3] = 0.0005
par[j*5+4] = self.peaks[j1]
# print(coords[0, j1], coords[1, j1], coords[2, j1])
j1 += 1
# popt, pcov = curve_fit(self.modelfun1, x[:,1:20000], F[1:20000],p0=par,maxfev=150000,xtol=1e-8,ftol=1e-8)
popt, pcov = curve_fit(
self.modelfun1, X, F, p0=par, maxfev=150000, xtol=1e-6,
ftol=1e-8)
# popt, pcov = curve_fit(self.modelfun1, XX, AA, p0=par)
self._gf = popt
# self.error=np.diagonal(pcov, offset=0)
# print(pcov)
else:
# pass
sys.exit("Wrong flag in do_fit")
def get_value(self,x):
X, F = self.read_from_file(self._sn, self._qn, self._path)
invdisttree = Invdisttree(X.T, F, leafsize=10, stat=1)
return invdisttree(x, nnear=130, eps=0, p=1)