def find_padding(sample=default_sample):
unpadded = CremiFile(cremi_path(sample=sample), "r")
unpadded_raw = unpadded.read_raw()
unpadded_shape_px = Coordinate(unpadded_raw.data.shape)
padded = CremiFile(cremi_path(sample=sample, padded=True), "r")
padded_raw = padded.read_raw()
padded_shape_px = Coordinate(padded_raw.data.shape)
fafb_res = Coordinate(unpadded_raw.resolution)
data_shape_nm = unpadded_shape_px * fafb_res
padding_px = math.ceil((padded_shape_px - unpadded_shape_px) / 2)
padding_nm = padding_px * fafb_res
print("shape (nm): {}".format(data_shape_nm))
print("padding (nm): {}".format(padding_nm))
print("l1 shape (px): {}".format(math.ceil(data_shape_nm / L1_RES)))
print("l1 padding (px): {}".format(math.ceil(padding_nm / L1_RES)))
# Check the content of the datafile
print "Has raw: " + str(file.has_raw())
print "Has neuron ids: " + str(file.has_neuron_ids())
print "Has clefts: " + str(file.has_clefts())
print "Has annotations: " + str(file.has_annotations())
# Read everything there is.
#
# If you are using the padded versions of the datasets (where raw is larger to
# provide more context), the offsets of neuron_ids, clefts, and annotations tell
# you where they are placed in nm relative to (0,0,0) of the raw volume.
#
# In other words, neuron_ids, clefts, and annotations are exactly the same
# between the padded and unpadded versions, except for the offset attribute.
raw = file.read_raw()
neuron_ids = file.read_neuron_ids()
clefts = file.read_clefts()
annotations = file.read_annotations()
print "Read raw: " + str(raw) + \
", resolution " + str(raw.resolution) + \
", offset " + str(raw.offset) + \
("" if raw.comment == None else ", comment \"" + raw.comment + "\"")
print "Read neuron_ids: " + str(neuron_ids) + \
", resolution " + str(neuron_ids.resolution) + \
", offset " + str(neuron_ids.offset) + \
("" if neuron_ids.comment == None else ", comment \"" + neuron_ids.comment + "\"")
print "Read clefts: " + str(clefts) + \
def Reading(filename, isTest=False):
# # Read the data into dataset
# print "Filename: ", filename
# # with h5py.File('sample_A_20160501.hdf', 'r') as f:
# with h5py.File(filename, 'r') as f:
# print f["volumes"]
# imageDataSet = f["volumes/raw"][:]
# labelDataSet = f["volumes/labels/neuron_ids"][:]
# imageDataSet = imageDataSet.astype(np.float32)
# labelDataSet = labelDataSet.astype(np.float32)
# return imageDataSet, labelDataSet
file = CremiFile(filename, "r")
print filename
# Check the content of the datafile
print "Has raw : " + str(file.has_raw())
print "Has neuron ids : " + str(file.has_neuron_ids())
print "Has clefts : " + str(file.has_clefts())
print "Has annotations : " + str(file.has_annotations())
# Read everything there is.
#
# If you are using the padded versions of the datasets (where raw is larger to
# provide more context), the offsets of neuron_ids, clefts, and annotations tell
# you where they are placed in nm relative to (0,0,0) of the raw volume.
#
# In other words, neuron_ids, clefts, and annotations are exactly the same
# between the padded and unpadded versions, except for the offset attribute.
raw = file.read_raw()
if not isTest:
neuron_ids = file.read_neuron_ids()
clefts = file.read_clefts()
annotations = file.read_annotations()
print "Read raw: " + str(raw) + \
", resolution " + str(raw.resolution) + \
", offset " + str(raw.offset) + \
("" if raw.comment == None else ", comment \"" + raw.comment + "\"")
if not isTest:
print "Read neuron_ids: " + str(neuron_ids) + \
", resolution " + str(neuron_ids.resolution) + \
", offset " + str(neuron_ids.offset) + \
("" if neuron_ids.comment == None else ", comment \"" + neuron_ids.comment + "\"")
# neuron_ids.offset will contain the starting point of neuron_ids inside the raw volume.
# Note that these numbers are given in nm.
# print "Read clefts: " + str(clefts) + \
# ", resolution " + str(clefts.resolution) + \
# ", offset " + str(clefts.offset) + \
# ("" if clefts.comment == None else ", comment \"" + clefts.comment + "\"")
# print "Read annotations:"
# for (id, type, location) in zip(annotations.ids(), annotations.types(), annotations.locations()):
# print str(id) + " of type " + type + " at " + str(np.array(location)+np.array(annotations.offset))
# print "Pre- and post-synaptic partners:"
# for (pre, post) in annotations.pre_post_partners:
# print str(pre) + " -> " + str(post)
with h5py.File(filename, 'r') as f:
print f["volumes"]
imageDataSet = f["volumes/raw"][:]
if not isTest:
labelDataSet = f["volumes/labels/neuron_ids"][:]
imageDataSet = imageDataSet.astype(np.float32)
if not isTest:
labelDataSet = labelDataSet.astype(np.float32)
if not isTest:
return imageDataSet, labelDataSet
return imageDataSet
# Check the content of the datafile
print "Has raw: " + str(file.has_raw())
print "Has neuron ids: " + str(file.has_neuron_ids())
print "Has clefts: " + str(file.has_clefts())
print "Has annotations: " + str(file.has_annotations())
# Read everything there is.
#
# If you are using the padded versions of the datasets (where raw is larger to
# provide more context), the offsets of neuron_ids, clefts, and annotations tell
# you where they are placed in nm relative to (0,0,0) of the raw volume.
#
# In other words, neuron_ids, clefts, and annotations are exactly the same
# between the padded and unpadded versions, except for the offset attribute.
raw = file.read_raw()
neuron_ids = file.read_neuron_ids()
clefts = file.read_clefts()
annotations = file.read_annotations()
print "Read raw: " + str(raw) + \
", resolution " + str(raw.resolution) + \
", offset " + str(raw.offset) + \
("" if raw.comment == None else ", comment \"" + raw.comment + "\"")
print "Read neuron_ids: " + str(neuron_ids) + \
", resolution " + str(neuron_ids.resolution) + \
", offset " + str(neuron_ids.offset) + \
("" if neuron_ids.comment == None else ", comment \"" + neuron_ids.comment + "\"")
print "Read clefts: " + str(clefts) + \