#plt.rc('text', usetex=True)
name = 'CDM'
numb = '095'
bs = 600
nmesh = 1024
num = 200
#mesh = BigFileMesh('ethos4_mesh_%s_%s_h07.bigfile' % (nmesh,bs),'Field')
mesh1 = BigFileMesh(
'/n/dvorkin_lab/anadr/%s_%s_1024_600_h0.6909.bigfile' % (name, numb),
'Field')
#mesh2 = BigFileMesh('/n/dvorkin_lab/anadr/%s_%s_1024_600_hbugfix.bigfile' % (name,numb),'Field')
mesh1 = np.fft.fftshift(mesh1.preview(Nmesh=nmesh))
print 'done'
rot = rotate(mesh1, 30, reshape=False)
sys.exit()
nnx = np.random.uniform(0, 10)
nny = np.random.uniform(0, 10)
nnz = np.random.uniform(0, 10)
theta = np.random.uniform(0, 2 * np.pi)
nx = 1 / np.sqrt(nnx**2 + nny**2 + nnz**2) * nnx
ny = 1 / np.sqrt(nnx**2 + nny**2 + nnz**2) * nny
nz = 1 / np.sqrt(nnx**2 + nny**2 + nnz**2) * nnz
## 128 x 128 x 128 pixels, with 60K items in train and 10K in validation.
X = np.zeros((nsplit, 128, 128, 128, 1))
for iid in np.arange(nsplit):
mid = zero + iid
print('Loading %d' % mid)
fpath = '/global/cscratch1/sd/mjwilson/MLRSD/fastpm/fpm-%d-1.0000' % mid
mesh = BigFileMesh(fpath,
dataset='1/Field',
mode='real',
header='Header')
X[iid, :, :, :, 0] = mesh.preview()[:, :, sslice]
X_train = X[nvalid:, :, :, :, :]
X_test = X[:nvalid, :, :, :, :]
_labels = labels[zero:zero + nsplit]
_y_train = np.digitize(_labels[:, 2][nvalid:], bins)
_y_test = np.digitize(_labels[:, 2][:nvalid], bins)
## One-hot encode target column.
y_train = to_categorical(_y_train, num_classes=nhot)
y_test = to_categorical(_y_test, num_classes=nhot)
##
model = prep_model3D()
def generator(fpath,
batch_size=32,
nmesh=128,
nslice=16,
mode='train',
regress=True,
nhot=10,
_print=False,
Field='kField'):
f = open(fpath, 'r')
while True:
images = []
labels = []
jump = np.floor(nmesh / nslice).astype(np.int)
while len(images) < batch_size:
line = f.readline()
line = line.replace('\n', '')
if line == '':
## https://www.pyimagesearch.com/2018/12/24/how-to-use-keras-fit-and-fit_generator-a-hands-on-tutorial/
f.seek(0)
line = f.readline()
if mode == 'eval':
break
if Field == 'Field':
_file = BigFileMesh(line,
dataset='1/Field',
mode='real',
header='Header')
attrs = _file.attrs
## (1. + delta) on preview.
mesh = _file.preview() - 1.0
if _print:
print(
'Loading mock: (h = %.3lf, Om = %.3lf, f = %.3lf).' %
(attrs['h'], attrs['Om0'], attrs['Om0']**0.545))
for ii, sslice in enumerate(np.arange(0, nmesh, jump)):
## Split 3D sim into _nslice_ 2D (x, z) slices; Mesh returns (1 + delta).
images.append(mesh[:, sslice, :])
labels.append(
[attrs['h'], attrs['Om0'], attrs['Om0']**0.545])
## Bin sims in f and use bin index as a supervised label.
elif Field == 'kField':
_file = BigFileMesh(line,
dataset='1/kField',
mode='complex',
header='Header')
attrs = _file.attrs
if _print:
print(
'Loading mock: (h = %.3lf, Om = %.3lf, f = %.3lf).' %
(attrs['h'], attrs['Om0'], attrs['Om0']**0.545))
field = _file.to_complex_field()
shape = field.shape
## Complex field (convolution?); Arbitrary slice.
images.append(_file.to_complex_field()[:, :, 0])
labels.append([attrs['h'], attrs['Om0'], attrs['Om0']**0.545])
else:
raise ValueError('\n\nRequested mode is not available.\n\n')
images = np.array(images).reshape(batch_size, nmesh, nmesh, 1)
labels = np.array(labels)
## Use f as label.
labels = labels[:, 2]
if not regress:
fmin = .25**0.545
fmax = .35**0.545
## Number of one-hot encodings == number of bins.
bins = np.linspace(fmin, fmax, nhot)
labels = np.digitize(labels, bins)
## One-hot encode target column.
labels = to_categorical(labels, num_classes=nhot)
##
yield (images, labels)
nny = np.random.uniform(0, 10)
nnz = np.random.uniform(0, 10)
theta = np.random.uniform(0, 2 * np.pi)
nx = 1 / np.sqrt(nnx**2 + nny**2 + nnz**2) * nnx
ny = 1 / np.sqrt(nnx**2 + nny**2 + nnz**2) * nny
nz = 1 / np.sqrt(nnx**2 + nny**2 + nnz**2) * nnz
R = rotation(nx, ny, nz, theta)
subcat['Position'] = da.transpose(
da.dot(R, da.transpose(subcat['Position'])))
mesh = subcat.to_mesh(Nmesh=nmesh, BoxSize=bs)
proj1 = np.fft.fftshift(mesh.preview(axes=[0, 1], Nmesh=nmesh))
proj2 = np.fft.fftshift(mesh.preview(axes=[0, 2], Nmesh=nmesh))
proj3 = np.fft.fftshift(mesh.preview(axes=[1, 2], Nmesh=nmesh))
np.save(
'/n/dvorkin_lab/anadr/%s_proj/%s/hbugfix/%s_%s_projxy_%s_%s' %
(name, numb, name, numb, count, item), proj1)
np.save(
'/n/dvorkin_lab/anadr/%s_proj/%s/hbugfix/%s_%s_projxz_%s_%s' %
(name, numb, name, numb, count, item), proj2)
np.save(
'/n/dvorkin_lab/anadr/%s_proj/%s/hbugfix/%s_%s_projyz_%s_%s' %
(name, numb, name, numb, count, item), proj3)
print("%s seconds" % (time.time() - start_time))