def _init_table(save_file, trajectories_data):
tot_rows = len(trajectories_data)
trajectories_data.index = np.arange(tot_rows)
TABLE_FILTERS = tables.Filters(complevel=5,
complib='zlib',
shuffle=True,
fletcher32=True)
with tables.File(save_file, 'w') as fid:
rec_data = trajectories_data.to_records(index=False)
rec_data[
'skeleton_id'] = trajectories_data.index #this is only for the viewer
rec_data['frame_number'] = trajectories_data.index
fid.create_table('/',
'trajectories_data',
obj=rec_data,
filters=TABLE_FILTERS)
fid.create_carray('/',
'skeleton',
tables.Float32Atom(dflt=np.nan), (tot_rows, 49, 2),
filters=TABLE_FILTERS)
fid.create_carray('/',
'mask',
tables.Float32Atom(dflt=np.nan),
(tot_rows, roi_size, roi_size),
filters=TABLE_FILTERS)
def create_data_file(out_file, n_samples, image_shape, channels=4):
hdf5_file = tables.open_file(out_file, mode='w')
# complevel - compression level
# complib - the library for compression
filters = tables.Filters(complevel=5, complib='blosc')
data_shape = tuple([0, channels] + list(image_shape))
truth_shape = tuple([0, 1] + list(image_shape))
data_storage = hdf5_file.create_earray(hdf5_file.root,
'data',
tables.Float32Atom(),
shape=data_shape,
filters=filters,
expectedrows=n_samples)
truth_storage = hdf5_file.create_earray(hdf5_file.root,
'true',
tables.UInt8Atom(),
shape=truth_shape,
filters=filters,
expectedrows=n_samples)
affine_storage = hdf5_file.create_earray(hdf5_file.root,
'affine',
tables.Float32Atom(),
shape=(0, 4, 4),
filters=filters,
expectedrows=n_samples)
return hdf5_file, data_storage, truth_storage, affine_storage
def merge(out, fnames):
data = tables.openFile(out, mode='a')
for fname in fnames:
f = tables.openFile(fname, mode='r')
raw_targets = f.root.denseFeat
if 'denseFeat' in data.root:
prev_data = data.root.denseFeat
targets = data.createCArray(data.root,
'_y',
atom=tables.Float32Atom(),
shape=((raw_targets.shape[0] +
prev_data.shape[0], 436)))
targets[:prev_data.shape[0], :] = prev_data[:, :]
targets[prev_data.shape[0]:, :] = raw_targets[:, :]
data.flush()
data.removeNode(data.root, "denseFeat", 1)
else:
targets = data.createCArray(data.root,
'_y',
atom=tables.Float32Atom(),
shape=((raw_targets.shape[0], 436)))
targets[:, :] = raw_targets[:, :]
data.flush()
data.renameNode(data.root, "denseFeat", "_y")
data.flush()
f.close()
data.close()
def create_data_file(out_file, n_channels, n_samples, image_shape):
hdf5_file = tables.open_file(out_file, mode='w')
print("DEBUG: Opening HDF5 file")
filters = tables.Filters(complevel=5, complib='blosc')
data_shape = tuple([0, n_channels] + list(image_shape))
truth_shape = tuple([0, 1] + list(image_shape))
print("DEBUG: Writing data_storage to HDF5 file")
data_storage = hdf5_file.create_earray(hdf5_file.root,
'data',
tables.Float32Atom(),
shape=data_shape,
filters=filters,
expectedrows=n_samples)
print("DEBUG: Writing truth_storage to HDF5 file")
truth_storage = hdf5_file.create_earray(hdf5_file.root,
'truth',
tables.UInt8Atom(),
shape=truth_shape,
filters=filters,
expectedrows=n_samples)
print("DEBUG: Writing affine_storage to HDF5 file")
affine_storage = hdf5_file.create_earray(hdf5_file.root,
'affine',
tables.Float32Atom(),
shape=(0, 4, 4),
filters=filters,
expectedrows=n_samples)
return hdf5_file, data_storage, truth_storage, affine_storage
def setup_hdf5(h5_filename, expectedrows):
# Open file
h5file = tables.open_file(h5_filename, mode="w")
# A group for the normal data
table = h5file.create_table(h5file.root,
"summary",
WhiskerSeg,
"Summary data about each whisker segment",
expectedrows=expectedrows)
# Put the contour here
xpixels_vlarray = h5file.create_vlarray(
h5file.root,
'pixels_x',
tables.Float32Atom(shape=()),
title='Every pixel of each whisker (x-coordinate)',
expectedrows=expectedrows)
ypixels_vlarray = h5file.create_vlarray(
h5file.root,
'pixels_y',
tables.Float32Atom(shape=()),
title='Every pixel of each whisker (y-coordinate)',
expectedrows=expectedrows)
h5file.close()
def initialize_file(save_name, experiments_data, roi_size, frac_train=0.99):
#divide data into train and test set
experiments_data = _add_is_train(experiments_data, frac_train)
#make sure the exp_data has the correct format for strings
exp_data_r = experiments_data.to_records(index=False)
dtypes = []
for col in experiments_data.columns:
ss = str(exp_data_r[col].dtype)
if ss == 'object':
ss = 'S{}'.format(
experiments_data[col].map(lambda x: len(x)).max())
dtypes.append((col, ss))
dtypes = np.dtype(dtypes)
exp_data_r = exp_data_r.astype(dtypes)
roi_data_dtypes = np.dtype([(x, np.int32) for x in ROI_DATA_COLS])
#create the new file
with tables.File(str(save_name), 'w') as fid_samples:
fid_samples.create_table('/',
"experiments_data",
exp_data_r,
filters=TABLE_FILTERS)
fid_samples.create_table('/',
"roi_data",
roi_data_dtypes,
filters=TABLE_FILTERS)
coords_g = fid_samples.create_group('/', 'coordinates')
fid_samples.create_earray(coords_g,
'skeletons',
atom=tables.Float32Atom(),
shape=(0, 49, 2),
chunkshape=(1, 49, 2),
filters=TABLE_FILTERS)
fid_samples.create_earray(coords_g,
'widths',
atom=tables.Float32Atom(),
shape=(0, 49),
chunkshape=(1, 49),
filters=TABLE_FILTERS)
fid_samples.create_earray('/',
'mask',
atom=tables.Float32Atom(),
shape=(0, roi_size, roi_size),
chunkshape=(1, roi_size, roi_size),
filters=TABLE_FILTERS)
fid_samples.create_earray('/',
'full_data',
atom=tables.Float32Atom(),
shape=(0, roi_size, roi_size),
chunkshape=(1, roi_size, roi_size),
filters=TABLE_FILTERS)
def addToH5File(h5file, clusters, freqs, store_intermediate=False):
#group into nrsolutions
poss_nr_sol = []
groups = []
for clusteridx, cluster in enumerate(clusters):
if not cluster['nrsol'] in poss_nr_sol:
poss_nr_sol.append(cluster['nrsol'])
groups.append([])
idx = poss_nr_sol.index(cluster['nrsol'])
groups[idx].append(clusteridx)
if 'sagefreqIdx' in h5file.root:
h5file.removeNode('/sagefreqIdx')
h5file.createArray(h5file.root, 'sagefreqIdx', freqs)
for igrp, grp in enumerate(groups):
# create arrays:
if store_intermediate:
cdata = np.load('tmp_store_cdata_%d.npy' % (grp[0]))
else:
cdata = clusters[grp[0]]['cdata']
arrayshape = cdata.shape[:-1] + (len(grp), 4)
for name in [
'sageradec%d' % igrp,
'sagephases%d' % igrp,
'sageamplitudes%d' % igrp
]:
if name in h5file.root:
h5file.removeNode('/' + name)
srcarray = h5file.createCArray(h5file.root,
'sageradec%d' % igrp,
tab.Float32Atom(),
shape=(len(grp), 2))
pharray = h5file.createCArray(h5file.root,
'sagephases%d' % igrp,
tab.Float32Atom(),
shape=arrayshape)
amparray = h5file.createCArray(h5file.root,
'sageamplitudes%d' % igrp,
tab.Float32Atom(),
shape=arrayshape)
for idx, clusteridx in enumerate(grp):
if store_intermediate:
cdata = np.load('tmp_store_cdata_%d.npy' % (clusteridx))
else:
cdata = clusters[clusteridx]['cdata']
pharray[:, :, :, idx, :] = np.angle(cdata)
amparray[:, :, :, idx, :] = np.absolute(cdata)
srcarray[idx, :] = np.array(
[clusters[clusteridx]['Ra'], clusters[clusteridx]['Dec']])
clusters[clusteridx]['cdata'] = []
if store_intermediate:
call("rm tmp_store_cdata_%d.npy" % (clusteridx), shell=True)
pharray.flush()
amparray.flush()
srcarray.flush()
def tables(docompute, dowrite, complib, verbose):
# Filenames
ifilename = os.path.join(OUT_DIR, "expression-inputs.h5")
ofilename = os.path.join(OUT_DIR, "expression-outputs.h5")
# Filters
shuffle = True
if complib == 'blosc':
filters = tb.Filters(complevel=1, complib='blosc', shuffle=shuffle)
elif complib == 'lzo':
filters = tb.Filters(complevel=1, complib='lzo', shuffle=shuffle)
elif complib == 'zlib':
filters = tb.Filters(complevel=1, complib='zlib', shuffle=shuffle)
else:
filters = tb.Filters(complevel=0, shuffle=False)
if verbose:
print("Will use filters:", filters)
if dowrite:
f = tb.open_file(ifilename, 'w')
# Build input arrays
t0 = time()
root = f.root
a = f.create_carray(root, 'a', tb.Float32Atom(),
shape, filters=filters)
b = f.create_carray(root, 'b', tb.Float32Atom(),
shape, filters=filters)
if verbose:
print("chunkshape:", a.chunkshape)
print("chunksize:", np.prod(a.chunkshape) * a.dtype.itemsize)
#row = np.linspace(0, 1, ncols)
row = np.arange(0, ncols, dtype='float32')
for i in range(nrows):
a[i] = row * (i + 1)
b[i] = row * (i + 1) * 2
f.close()
print("[tables.Expr] Time for creating inputs:", round(time() - t0, 3))
if docompute:
f = tb.open_file(ifilename, 'r')
fr = tb.open_file(ofilename, 'w')
a = f.root.a
b = f.root.b
r1 = f.create_carray(fr.root, 'r1', tb.Float32Atom(), shape,
filters=filters)
# The expression
e = tb.Expr(expr)
e.set_output(r1)
t0 = time()
e.eval()
if verbose:
print("First ten values:", r1[0, :10])
f.close()
fr.close()
print("[tables.Expr] Time for computing & save:",
round(time() - t0, 3))
def create_features(file_names, file_path):
flag = 0
columns = ['train_cat', 'train_dog']
w = tables.open_file('../window.h5', 'w')
atom1 = tables.Float32Atom()
array_w = w.create_earray(w.root, 'data', atom1, (0, 20))
l = tables.open_file('../label.h5', 'w')
atom2 = tables.Float32Atom()
array_l = l.create_earray(l.root, 'data', atom2, (0, 1))
temp = []
scaler = StandardScaler()
for col in columns:
count = 0
for file in file_names[col]:
if file == 0:
continue
filefp = file_path + file
data, fs = soundfile.read(filefp)
data = data.reshape(-1, 1)
wn = np.random.randn(len(data)).reshape(-1, 1)
data_wn = data + 0.0075 * wn
scaler.fit(data)
data = scaler.transform(data)
data_wn = scaler.transform(data_wn)
#Creating MFCC features for 50ms(800 data points) window and 50% overlap(400 hop_length)
mfcc = librosa.feature.mfcc(y=data.reshape(data.shape[0], ),
sr=fs,
n_fft=800,
hop_length=400)
mfcc_wn = librosa.feature.mfcc(y=data_wn.reshape(
data_wn.shape[0], ),
sr=fs,
n_fft=800,
hop_length=400)
#Finally for each of the windows 20 features are extracted
mfcc = mfcc.reshape(-1, 20)
mfcc_wn = mfcc_wn.reshape(-1, 20)
#Creating labels
if col == 'train_cat':
x = 0
else:
x = 1
label = np.array([[x]
for i in range(mfcc.shape[0])]).reshape(-1, 1)
label_wn = np.array([[x] for i in range(mfcc_wn.shape[0])
]).reshape(-1, 1)
array_w.append(mfcc)
array_w.append(mfcc_wn)
array_l.append(label)
array_l.append(label_wn)
count += mfcc.shape[0] + mfcc_wn.shape[0]
temp.append(count)
n_cat = temp[0] #number of windows with class label as cat
n_dog = temp[1] #number of windows with class label as dog
w.close()
l.close()
return n_cat, n_dog
def create_hdf5_file(self,
output_filepath,
data_group_labels=None,
case_list=None):
if data_group_labels is None:
data_group_labels = self.data_groups.keys()
hdf5_file = tables.open_file(output_filepath, mode='w')
filters = tables.Filters(complevel=5, complib='blosc')
for data_label, data_group in self.data_groups.iteritems():
num_cases = len(self.cases) * self.multiplier
if num_cases == 0:
raise FileNotFoundError(
'WARNING: No cases found. Cannot write to file.')
if data_group.output_shape is None:
output_shape = data_group.get_shape()
else:
output_shape = data_group.output_shape
# Add batch dimension
data_shape = tuple([0] + list(output_shape))
data_group.data_storage = hdf5_file.create_earray(
hdf5_file.root,
data_label,
tables.Float32Atom(),
shape=data_shape,
filters=filters,
expectedrows=num_cases)
# Naming convention is bad here, TODO, think about this.
data_group.casename_storage = hdf5_file.create_earray(
hdf5_file.root,
'_'.join([data_label, 'casenames']),
tables.StringAtom(256),
shape=(0, 1),
filters=filters,
expectedrows=num_cases)
data_group.affine_storage = hdf5_file.create_earray(
hdf5_file.root,
'_'.join([data_label, 'affines']),
tables.Float32Atom(),
shape=(0, 4, 4),
filters=filters,
expectedrows=num_cases)
return hdf5_file
def create_data_file(out_file,
n_channels,
n_samples,
image_shape,
storage_names=('data', 'truth', 'affine'),
affine_shape=(0, 4, 4),
normalize=True,
affine_dtype=tables.Float32Atom()):
hdf5_file = tables.open_file(out_file, mode='w')
filters = tables.Filters(
complevel=5
) #, complib='blosc') # suggested remove in https://github.com/ellisdg/3DUnetCNN/issues/58
data_shape = tuple([0, n_channels] + list(image_shape))
truth_shape = tuple([0, 1] + list(image_shape))
if not normalize:
data_storage = hdf5_file.create_earray(hdf5_file.root,
storage_names[0],
tables.Int8Atom(),
shape=data_shape,
filters=filters,
expectedrows=n_samples)
else:
data_storage = hdf5_file.create_earray(hdf5_file.root,
storage_names[0],
tables.Float32Atom(),
shape=data_shape,
filters=filters,
expectedrows=n_samples)
truth_storage = hdf5_file.create_earray(hdf5_file.root,
storage_names[1],
tables.UInt8Atom(),
shape=truth_shape,
filters=filters,
expectedrows=n_samples)
affine_storage = hdf5_file.create_earray(hdf5_file.root,
storage_names[2],
affine_dtype,
shape=affine_shape,
filters=filters,
expectedrows=n_samples)
if len(storage_names) == 4:
normalization_storage = hdf5_file.create_earray(hdf5_file.root,
storage_names[3],
tables.Float32Atom(),
shape=(0, 2),
filters=filters,
expectedrows=n_samples)
# will hold mean and std of this case for later normalization
return hdf5_file, data_storage, truth_storage, affine_storage, normalization_storage
return hdf5_file, data_storage, truth_storage, affine_storage
def save(self, filename: str, mode: str = 'h5', **kwargs):
"""
:param filename:
:param mode:
:param kwargs:
:return:
"""
if mode == 'h5':
compression = tables.Filters(complib='zlib',
shuffle=True,
complevel=1)
h5handle = tables.open_file(filename,
mode="w",
title="Test file",
filters=compression)
h5handle.create_array('/',
'topology',
np.array(json.dumps(
self.dye.dye_definition)).reshape(1),
shape=(1, ))
h5handle.create_earray(where='/',
name='coordinates',
atom=tables.Float32Atom(),
shape=(0, self.dye.n_atoms, 3))
h5handle.create_earray(where='/',
name='time',
atom=tables.Float32Atom(),
shape=(0, ))
h5handle.create_group(where='/', name='fluorescence')
h5handle.create_earray(where='/fluorescence/',
name='quencher_distance',
atom=tables.Float32Atom(),
shape=(0, ))
# set units
h5handle.root.time.set_attr('units', 'picoseconds')
h5handle.root.xyz.set_attr('units', 'angstroms')
h5handle.root.fluorescence.quencher_distance.set_attr(
'units', 'angstroms')
h5handle.root.xyz.append(self.xyz)
h5handle.root.time.append(self.time_axis)
h5handle.close()
elif mode == 'xyz':
skip = kwargs.get('skip', 1)
coordinates = self.xyz[::skip]
n_frames = coordinates.shape[0]
coordinates = coordinates.reshape(n_frames, 3)
chisurf.fio.coordinates.write_xyz(filename, coordinates)
elif mode == 'npy':
np.save(filename, self.xyz)
def createHDF5File(self):
out_file_path = os.path.join(self._output_path, self._output_file_name)
try:
hdf5_file = tables.open_file(out_file_path, mode='w')
filters = tables.Filters(complevel=5, complib='blosc')
data_shape = tuple([0, self.num_modalities] +
list(self._image_shape))
data_storage = hdf5_file.create_earray(
hdf5_file.root,
'data',
tables.Float32Atom(),
shape=data_shape,
filters=filters,
expectedrows=self.num_modalities)
if self.label_format == "nii":
truth_shape = tuple([0, 1] + list(self._image_shape))
truth_storage = hdf5_file.create_earray(
hdf5_file.root,
'truth',
tables.UInt8Atom(),
shape=truth_shape,
filters=filters,
expectedrows=self.num_modalities)
elif self.label_format == 'csv':
truth_shape = tuple([0, self._image_shape[-1]])
truth_storage = hdf5_file.create_earray(
hdf5_file.root,
'truth',
tables.UInt32Atom(),
shape=truth_shape,
filters=filters,
expectedrows=self.num_modalities)
else:
raise ValueError("Fail to recognize label format: %s" %
self.label_format)
affine_storage = hdf5_file.create_earray(
hdf5_file.root,
'affine',
tables.Float32Atom(),
shape=(0, 4, 4),
filters=filters,
expectedrows=self.num_modalities)
return hdf5_file, data_storage, truth_storage, affine_storage
except Exception as e:
# If something goes wrong, delete the incomplete data file
os.remove(out_file_path)
raise e
def main():
here = os.path.abspath(os.path.dirname(__file__))
data_dir = os.path.abspath(os.path.join(here, '..', 'data'))
file_path = os.path.join(data_dir, 'pytables-earray.h5')
# One (and only one) of the shape dimensions *must* be 0. The dimension
# being 0 means that the resulting EArray object can be extended along it.
# Multiple enlargeable dimensions are not supported right now.
shape = (0, 300)
# An EArray contains homogeneous data. Every atomic object (i.e. every
# single element) has the same type and shape.
atom = tb.Float32Atom()
# An EArray supports compression
filters = tb.Filters(complevel=5, complib='zlib')
with tb.open_file(file_path, 'w') as f:
# create an empty EArray
earray = f.create_earray(where='/',
name='Array0',
atom=atom,
shape=shape,
title='My EArray',
filters=filters)
# number of times that we need to write some data
num = 100
for i in range(num):
rows = np.random.randint(low=10, high=100)
cols = shape[1]
# define some data
sequence = np.random.random((rows, cols)).astype('float32')
# append the data to the EArray
earray.append(sequence=sequence)
def insert_embeddings_pytables(self):
try:
self.get_model()
self.model.init_model()
self.model.load()
embeds_file = tables.open_file(os.path.join(
cnt.DATA_FOLDER, cnt.SIAMESE_EMBEDDINGS_FILE),
mode='w')
atom = tables.Float32Atom()
embeds_arr = embeds_file.create_earray(
embeds_file.root, 'data', atom,
(0, cnt.SIAMESE_EMBEDDING_SIZE))
sent_tokens_file = tables.open_file(os.path.join(
cnt.DATA_FOLDER, cnt.SENT_TOKENS_FILE),
mode='r')
sent_tokens = sent_tokens_file.root.data
n, batch_size = len(sent_tokens), cnt.PYTABLES_INSERT_BATCH_SIZE
num_batches = int(math.ceil(float(n) / batch_size))
for m in range(num_batches):
start, end = m * batch_size, min((m + 1) * batch_size, n)
tokens_arr_input = gutils.get_wv_siamese(
self.wv_model, sent_tokens[start:end, :])
embeds = self.model.get_embeddings(tokens_arr_input)
embeds_arr.append(embeds)
finally:
sent_tokens_file.close()
embeds_file.close()
def add_group_hdf5(save_path, group, expected_shapes, where='/', names=None):
"""
Adds a new group to an HDF5 archive, with the x train, y train, x test, and y test separated. The expected_shapes
should be a list of length 4, one shape that matches to each of these data subsets, unless you override the name
parameters.
Parameters
----------
save_path <string> : The physical path to the archive file
group <string> : The name for the group you are adding
expected_shapes <list> : A list of expected shapes for the data that will be added here. It doesn't have to be exact
where <string> : A the root hierarchical path that you want to add the group to
names <list> : A list of strings for the names of the subsets of the groups
Returns
-------
hdf5_file, [data] : The HDF5 file that was opened and a list of arrays for each of the datasets that were added.
"""
names = names if names else ["x_train", "y_train", "x_test", "y_test"]
hdf5_file = tables.open_file(save_path, mode='a')
h_comp = tables.Filters(complevel=5, complib='blosc')
h_group = hdf5_file.create_group(where, group, group)
h_data = []
for k, shape in zip(names, expected_shapes):
h_data.append(hdf5_file.create_earray(h_group, k,
tables.Float32Atom(),
shape=(0, shape[1]),
filters=h_comp,
expectedrows=shape[0]))
return hdf5_file, h_data
def add_transformed(save_path, group, buffer=1000, where='/'):
"""
Takes a group and normalizes it for training. This is done quietly and only transformed groups are used for
training networks.
Parameters
----------
save_path <string> : The physical path to the archive file
group <string> : The name for the group you are adding
buffer <int> : an integer defining the number of data points to load into memory at a time.
where <string> : A the root hierarchical path that you want to add the group to
"""
hdf5_file = tables.open_file(save_path, mode='a')
parent = hdf5_file.get_node(where+group)
data = (parent.x_train, parent.y_train, parent.x_test, parent.y_test)
shapes = map(lambda x: x.shape, data)
h_comp = tables.Filters(complevel=5, complib='blosc')
h_group = hdf5_file.create_group(where+group, 'transformed', 'Data scaled to Gaussian distribution')
h_data = []
for k, shape in zip(["x_train", "y_train", "x_test", "y_test"], shapes):
h_data.append(hdf5_file.create_carray(h_group, k,
tables.Float32Atom(),
shape=shape,
filters=h_comp))
scale = tr.get_transform(data[0])
for i, d in enumerate(data):
for j in xrange(int(math.ceil(d.shape[0] / buffer))):
if i%2 == 1:
h_data[i][j*buffer:(j+1)*buffer] = d[j*buffer:(j+1)*buffer]
else:
h_data[i][j * buffer:(j + 1) * buffer] = scale.transform(d[j * buffer:(j + 1) * buffer])
hdf5_file.flush()
hdf5_file.close()
def open_h5_files() -> (list, list):
float_atom = tables.Float32Atom()
int_atom = tables.Int32Atom()
fd_m = tables.open_file(os.path.join(MATRIX_DATASET_FOLDER, "all.h5"),
mode="w")
data_m = fd_m.create_earray(fd_m.root,
"data",
float_atom,
(0, MATRIX_DIMENSION, MATRIX_DIMENSION),
expectedrows=600000)
label_m = fd_m.create_earray(fd_m.root,
"labels",
int_atom, (0, 1),
expectedrows=600000)
fd_t = tables.open_file(os.path.join(TENSOR_DATASET_FOLDER, "all.h5"),
mode="w")
data_t = fd_t.create_earray(
fd_t.root,
"data",
float_atom, (0, TENSOR_DIMENSION, TENSOR_DIMENSION, TENSOR_DIMENSION),
expectedrows=600000)
label_t = fd_t.create_earray(fd_t.root,
"labels",
int_atom, (0, 1),
expectedrows=600000)
fd_m_test = tables.open_file(os.path.join(MATRIX_DATASET_FOLDER,
"all_test.h5"),
mode="w")
data_m_test = fd_m.create_earray(fd_m_test.root,
"data",
float_atom,
(0, MATRIX_DIMENSION, MATRIX_DIMENSION),
expectedrows=60000)
label_m_test = fd_m.create_earray(fd_m_test.root,
"labels",
int_atom, (0, 1),
expectedrows=60000)
fd_t_test = tables.open_file(os.path.join(TENSOR_DATASET_FOLDER,
"all_test.h5"),
mode="w")
data_t_test = fd_m.create_earray(
fd_t_test.root,
"data",
float_atom, (0, TENSOR_DIMENSION, TENSOR_DIMENSION, TENSOR_DIMENSION),
expectedrows=60000)
label_t_test = fd_m.create_earray(fd_t_test.root,
"labels",
int_atom, (0, 1),
expectedrows=60000)
return (fd_m,data_m,label_m),\
(fd_t,data_t,label_t),\
(fd_m_test,data_m_test,label_m_test),\
(fd_t_test,data_t_test,label_t_test)
def init_hdf5(path, shapes):
"""
Initialize hdf5 file to be used ba dataset
"""
x_shape, y_shape = shapes
# make pytables
ensure_tables()
h5file = tables.openFile(path, mode="w", title="SVHN Dataset")
gcolumns = h5file.createGroup(h5file.root, "Data", "Data")
atom = tables.Float32Atom(
) if config.floatX == 'float32' else tables.Float64Atom()
filters = DenseDesignMatrixPyTables.filters
h5file.createCArray(gcolumns,
'X',
atom=atom,
shape=x_shape,
title="Data values",
filters=filters)
h5file.createCArray(gcolumns,
'y',
atom=atom,
shape=y_shape,
title="Data targets",
filters=filters)
return h5file, gcolumns
def create_data_file(out_file, n_samples, image_shape, modality_names):
# pdb.set_trace()
hdf5_file = tables.open_file(out_file, mode='w')
filters = tables.Filters(complevel=5, complib='blosc')
modality_shape = tuple([0, 1] + list(image_shape))
brain_width_shape = (0, 2, 3)
modality_storage_list = [
hdf5_file.create_earray(hdf5_file.root,
modality_name,
tables.Float32Atom(),
shape=modality_shape,
filters=filters,
expectedrows=n_samples)
for modality_name in modality_names
]
brain_width_storage = hdf5_file.create_earray(hdf5_file.root,
'brain_width',
tables.UInt8Atom(),
shape=brain_width_shape,
filters=filters,
expectedrows=n_samples)
return hdf5_file, modality_storage_list, brain_width_storage
def init_hdf5(self, path, shapes):
"""
.. todo::
WRITEME properly
Initialize hdf5 file to be used ba dataset
"""
x_shape, y_shape = shapes
# make pytables
ensure_tables()
h5file = tables.openFile(path, mode="w", title="SVHN Dataset")
gcolumns = h5file.createGroup(h5file.root, "Data", "Data")
atom = (tables.Float32Atom()
if config.floatX == 'float32' else tables.Float64Atom())
h5file.createCArray(gcolumns,
'X',
atom=atom,
shape=x_shape,
title="Data values",
filters=self.filters)
h5file.createCArray(gcolumns,
'y',
atom=atom,
shape=y_shape,
title="Data targets",
filters=self.filters)
return h5file, gcolumns
def create_VLFloatArray(self, name, array, group):
"""Stores a homogenous variable length float array in a group"""
self.h5file.create_vlarray(group,
name,
tables.Float32Atom(),
"ragged array of floats",
chunkshape = 512)
def create_image_data():
try:
img_arr_file = tables.open_file(cnt.IMAGE_ARRAY_PATH, mode='w')
atom = tables.Float32Atom()
img_arr = img_arr_file.create_earray(
img_arr_file.root, 'data', atom,
(0, cnt.IMAGE_SIZE, cnt.IMAGE_SIZE, 3))
chunk_size, labels = 5000, []
for df_chunk in pd.read_csv(cnt.OUTPUT_FILE_PATH,
chunksize=chunk_size):
df = df_chunk[list(
df_chunk['image_path'].apply(lambda x: os.path.exists(x)))]
print(df.shape)
labels += list(df['age_group'])
file_paths = list(df['image_path'])
img_arr.append([
img_to_array(
load_img(image).convert('RGB').resize(
(cnt.IMAGE_SIZE, cnt.IMAGE_SIZE))) / 255.0
for image in file_paths
])
shutils.save_data_pkl(labels, cnt.LABELS_PATH)
finally:
img_arr_file.close()
def interpolate( self, facetlistfile ) :
"""
"""
#facetdbname = os.path.join(self.globaldb, 'facets')
#os.system( 'makesourcedb in=%s out=%s append=False' % (facetlistfile, facetdbname) )
#patch_table = pt.table( os.path.join(facetdbname, 'SOURCES', 'PATCHES' ) )
#if 'facets' in self.hdf5.root: self.hdf5.root.facets.remove()
#description = {'name': tables.StringCol(40), 'position':tables.Float64Col(2)}
#self.facets = self.hdf5.createTable(self.hdf5.root, 'facets', description)
#facet = self.facets.row
#for patch in patch_table :
#facet['name'] = patch['PATCHNAME']
#facet['position'] = array([patch['RA'], patch['DEC']])
#facet.append()
#self.facets.flush()
self.N_facets = len(self.facets)
self.facet_names = self.facets[:]['name']
self.facet_positions = self.facets[:]['position']
print self.n_list
if 'STEC_facets' in self.hdf5.root: self.hdf5.root.STEC_facets.remove()
self.STEC_facets = self.hdf5.createCArray(self.hdf5.root, 'STEC_facets', tables.Float32Atom(), shape = (self.N_pol, self.n_list[:].shape[0], self.N_facets, self.N_stations))
#if 'facet_piercepoints' in self.hdf5.root: self.hdf5.root.facet_piercepoints.remove()
#description = {'positions':tables.Float64Col((self.N_facets, self.N_stations,2)), \
#'positions_xyz':tables.Float64Col((self.N_facets, self.N_stations,3)), \
#'zenith_angles':tables.Float64Col((self.N_facets, self.N_stations))}
#self.facet_piercepoints = self.hdf5.createTable(self.hdf5.root, 'facet_piercepoints', description)
#height = self.piercepoints.attrs.height
#facet_piercepoints_row = self.facet_piercepoints.row
#print "Calculating facet piercepoints..."
#for n in self.n_list:
#piercepoints = PiercePoints( self.times[ n ], self.pointing, self.array_center, self.facet_positions, self.station_positions, height = height )
#facet_piercepoints_row['positions'] = piercepoints.positions
#facet_piercepoints_row['positions_xyz'] = piercepoints.positions_xyz
#facet_piercepoints_row['zenith_angles'] = piercepoints.zenith_angles
#facet_piercepoints_row.append()
#self.facet_piercepoints.flush()
r_0 = self.TECfit_white.attrs.r_0
beta = self.TECfit_white.attrs.beta
for facet_idx in range(self.N_facets) :
for station_idx in range(self.N_stations):
for pol_idx in range(self.N_pol) :
TEC_list = []
for n in range(len(self.n_list)):
p = self.facet_piercepoints[n]['positions_xyz'][facet_idx, station_idx,:]
za = self.facet_piercepoints[n]['zenith_angles'][facet_idx, station_idx]
Xp_table = reshape(self.piercepoints[n]['positions_xyz'], (self.N_piercepoints, 3) )
v = self.TECfit_white[ pol_idx, n, :, : ].reshape((self.N_piercepoints,1))
D2 = sum((Xp_table - p)**2,1)
C = (D2 / ( r_0**2 ) )**( beta / 2. ) / -2.
self.STEC_facets[pol_idx, n, facet_idx, station_idx] = dot(C, v)/cos(za)
def get_cnn_features(image_list, split, batch_size, relu=False):
hdf5_path = "%s-%s" % (split, "cnn_features.hdf5")
hdf5_file = tables.open_file(hdf5_path, mode='w')
filters = tables.Filters(complevel=5, complib='blosc')
data_storage = hdf5_file.createEArray(hdf5_file.root,
'feats',
tables.Float32Atom(),
shape=(0, 100352),
filters=filters,
expectedrows=len(image_list))
for start, end in zip(
range(0,
len(image_list) + batch_size, batch_size),
range(batch_size,
len(image_list) + batch_size, batch_size)):
print("Processing %s images %d-%d / %d" %
(split, start, end, len(image_list)))
batch_list = image_list[start:end]
feats = cnn.get_features(batch_list,
layers='conv5_4',
layer_sizes=[512, 14, 14])
# transpose and flatten feats to prepare for reshape(14*14, 512)
feats = np.array(map(lambda x: x.T.flatten(), feats))
if relu:
feats = np.clip(feats, a_min=0., a_max=np.inf, out=feats) # RELU
data_storage.append(feats)
print("Finished processing %d images" % len(data_storage))
hdf5_file.close()
def create_hdf5_file(output_filepath, data_groups, data_collection):
# Investigate hdf5 files.
hdf5_file = tables.open_file(output_filepath, mode='w')
# Investigate this line.
# Compression levels = complevel. No compression = 0
# Compression library = Method of compresion.
filters = tables.Filters(complevel=5, complib='blosc')
data_storages = []
for data_group_label in data_groups:
data_group = data_collection.data_groups[data_group_label]
num_cases, output_shape = data_group.get_augment_num_shape()
modalities = data_group.get_modalities()
# Input data has multiple 'channels' i.e. modalities.
data_shape = tuple([0, modalities] + list(output_shape))
print data_group.label, data_shape
data_group.data_storage = hdf5_file.create_earray(
hdf5_file.root,
data_group.label,
tables.Float32Atom(),
shape=data_shape,
filters=filters,
expectedrows=num_cases)
return hdf5_file
def insert_embeddings_pytables(self, batch_size=25000):
try:
self.load()
embeds_file = tables.open_file('data/w2v_embeddings.h5', mode='w')
atom = tables.Float32Atom()
embeds_arr = embeds_file.create_earray(embeds_file.root, 'data',
atom,
(0, self.embedding_size))
tokens_file = tables.open_file('data/sent_tokens.h5', mode='r')
sent_tokens = tokens_file.root.data
sent_tokens = [[w.decode('utf-8') for w in tokens]
for tokens in sent_tokens]
n = len(sent_tokens)
num_batches = int(math.ceil(float(n) / batch_size))
vocabulary = [
word
for word, index in self.tfidf_vectorizer.vocabulary_.items()
]
for m in range(num_batches):
start, end = m * batch_size, min((m + 1) * batch_size, n)
matrix = self.tfidf_vectorizer.transform(
sent_tokens[start:end, :])
embeds = self.get_weighted_sentence_vectors(
matrix, vocabulary, end - start)
embeds_arr.append(embeds)
finally:
tokens_file.close()
embeds_file.close()
def create_hdf5_file(output_filepath,
num_cases,
output_sizes,
preloaded=False):
""" Creates a multi-tiered HDF5 file at each resolution provided in 'output_sizes'.
Also stores string filepaths associated with the data.
Big credit to https://github.com/ellisdg/3DUnetCNN for bringing HDF5 into
my life.
"""
hdf5_file = tables.open_file(output_filepath, mode='w')
filters = tables.Filters(complevel=5, complib='blosc')
hdf5_file.create_earray(hdf5_file.root,
'imagenames',
tables.StringAtom(256),
shape=(0, 1),
filters=filters,
expectedrows=num_cases)
for output_size in output_sizes:
hdf5_file.create_earray(hdf5_file.root,
'data_' + str(output_size[0]),
tables.Float32Atom(),
shape=(0, ) + output_size,
filters=filters,
expectedrows=num_cases)
return hdf5_file
def dump_test_set(self, h5filepath, nframes, framesize):
# set rng to a hardcoded state, so we always have the same test set!
self.numpy_rng.seed(1)
with tables.openFile(h5filepath, 'w') as h5file:
h5file.createArray(h5file.root, 'test_targets',
self.partitions['test']['targets'])
vids = h5file.createCArray(h5file.root,
'test_images',
tables.Float32Atom(),
shape=(10000, nframes, framesize,
framesize),
filters=tables.Filters(complevel=5,
complib='zlib'))
pos = h5file.createCArray(h5file.root,
'test_pos',
tables.UInt16Atom(),
shape=(10000, nframes, 2),
filters=tables.Filters(complevel=5,
complib='zlib'))
for i in range(100):
print i
(vids[i * 100:(i + 1) * 100], pos[i * 100:(i + 1) * 100],
_) = self.get_batch('test',
100,
nframes,
framesize,
idx=np.arange(i * 100, (i + 1) * 100))
h5file.flush()
def resize(h5file, start, stop):
ensure_tables()
# TODO is there any smarter and more efficient way to this?
data = h5file.getNode('/', "Data")
try:
gcolumns = h5file.createGroup('/', "Data_", "Data")
except tables.exceptions.NodeError:
h5file.removeNode('/', "Data_", 1)
gcolumns = h5file.createGroup('/', "Data_", "Data")
start = 0 if start is None else start
stop = gcolumns.X.nrows if stop is None else stop
atom = tables.Float32Atom() if config.floatX == 'float32' else tables.Float64Atom()
filters = DenseDesignMatrixPyTables.filters
x = h5file.createCArray(gcolumns, 'X', atom = atom, shape = ((stop - start, data.X.shape[1])),
title = "Data values", filters = filters)
y = h5file.createCArray(gcolumns, 'y', atom = atom, shape = ((stop - start, 10)),
title = "Data targets", filters = filters)
x[:] = data.X[start:stop]
y[:] = data.y[start:stop]
h5file.removeNode('/', "Data", 1)
h5file.renameNode('/', "Data", "Data_")
h5file.flush()
return h5file, gcolumns
