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test | save | Export a numpy array to a png file.
Arguments:
filename (str): A filename to which to save the png data
numpy_data (numpy.ndarray OR str): The numpy array to save to png.
OR a string: If a string is provded, it should be a binary png str
Returns:
str. The expanded filename that now holds the png data
Raises:
ValueError: If the save fails; for instance if the binary string data
cannot be coerced into a png, or perhaps your numpy.ndarray is
ill-formed? | ndio/convert/png.py | def save(filename, numpy_data):
"""
Export a numpy array to a png file.
Arguments:
filename (str): A filename to which to save the png data
numpy_data (numpy.ndarray OR str): The numpy array to save to png.
OR a string: If a string is provded, it should be a binary png str
Returns:
str. The expanded filename that now holds the png data
Raises:
ValueError: If the save fails; for instance if the binary string data
cannot be coerced into a png, or perhaps your numpy.ndarray is
ill-formed?
"""
# Expand filename to be absolute
png_filename = os.path.expanduser(filename)
if type(numpy_data) is str:
fp = open(png_filename, "wb")
fp.write(numpy_data)
fp.close()
return png_filename
try:
if numpy_data.dtype.name != 'uint8':
m = 'I'
img = Image.fromarray(numpy_data, mode=m)
else:
img = Image.fromarray(numpy_data)
img.save(png_filename)
except Exception as e:
raise ValueError("Could not save png file {0}.".format(png_filename))
return png_filename | def save(filename, numpy_data):
"""
Export a numpy array to a png file.
Arguments:
filename (str): A filename to which to save the png data
numpy_data (numpy.ndarray OR str): The numpy array to save to png.
OR a string: If a string is provded, it should be a binary png str
Returns:
str. The expanded filename that now holds the png data
Raises:
ValueError: If the save fails; for instance if the binary string data
cannot be coerced into a png, or perhaps your numpy.ndarray is
ill-formed?
"""
# Expand filename to be absolute
png_filename = os.path.expanduser(filename)
if type(numpy_data) is str:
fp = open(png_filename, "wb")
fp.write(numpy_data)
fp.close()
return png_filename
try:
if numpy_data.dtype.name != 'uint8':
m = 'I'
img = Image.fromarray(numpy_data, mode=m)
else:
img = Image.fromarray(numpy_data)
img.save(png_filename)
except Exception as e:
raise ValueError("Could not save png file {0}.".format(png_filename))
return png_filename | [
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test | save_collection | Export a numpy array to a set of png files, with each Z-index 2D
array as its own 2D file.
Arguments:
png_filename_base: A filename template, such as "my-image-*.png"
which will lead to a collection of files named
"my-image-0.png", "my-image-1.png", etc.
numpy_data: The numpy array data to save to png.
Returns:
Array. A list of expanded filenames that hold png data. | ndio/convert/png.py | def save_collection(png_filename_base, numpy_data, start_layers_at=1):
"""
Export a numpy array to a set of png files, with each Z-index 2D
array as its own 2D file.
Arguments:
png_filename_base: A filename template, such as "my-image-*.png"
which will lead to a collection of files named
"my-image-0.png", "my-image-1.png", etc.
numpy_data: The numpy array data to save to png.
Returns:
Array. A list of expanded filenames that hold png data.
"""
file_ext = png_filename_base.split('.')[-1]
if file_ext in ['png']:
# Filename is "name*.ext", set file_base to "name*".
file_base = '.'.join(png_filename_base.split('.')[:-1])
else:
# Filename is "name*", set file_base to "name*".
# That is, extension wasn't included.
file_base = png_filename_base
file_ext = ".png"
file_base_array = file_base.split('*')
# The array of filenames to return
output_files = []
# Filename 0-padding
i = start_layers_at
for layer in numpy_data:
layer_filename = (str(i).zfill(6)).join(file_base_array) + file_ext
output_files.append(save(layer_filename, layer))
i += 1
return output_files | def save_collection(png_filename_base, numpy_data, start_layers_at=1):
"""
Export a numpy array to a set of png files, with each Z-index 2D
array as its own 2D file.
Arguments:
png_filename_base: A filename template, such as "my-image-*.png"
which will lead to a collection of files named
"my-image-0.png", "my-image-1.png", etc.
numpy_data: The numpy array data to save to png.
Returns:
Array. A list of expanded filenames that hold png data.
"""
file_ext = png_filename_base.split('.')[-1]
if file_ext in ['png']:
# Filename is "name*.ext", set file_base to "name*".
file_base = '.'.join(png_filename_base.split('.')[:-1])
else:
# Filename is "name*", set file_base to "name*".
# That is, extension wasn't included.
file_base = png_filename_base
file_ext = ".png"
file_base_array = file_base.split('*')
# The array of filenames to return
output_files = []
# Filename 0-padding
i = start_layers_at
for layer in numpy_data:
layer_filename = (str(i).zfill(6)).join(file_base_array) + file_ext
output_files.append(save(layer_filename, layer))
i += 1
return output_files | [
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test | load_collection | Import all files matching the filename base given with `png_filename_base`.
Images are ordered by alphabetical order, which means that you *MUST* 0-pad
your numbers if they span a power of ten (e.g. 0999-1000 or 09-10). This is
handled automatically by its complementary function, `png.save_collection`.
Also, look at how nicely these documentation lines are all the same length!
Arguments:
png_filename_base (str): An asterisk-wildcard string that should refer
to all PNGs in the stack. All *s are replaced according to regular
cmd-line expansion rules. See the 'glob' documentation for details
Returns:
A numpy array holding a 3D dataset | ndio/convert/png.py | def load_collection(png_filename_base):
"""
Import all files matching the filename base given with `png_filename_base`.
Images are ordered by alphabetical order, which means that you *MUST* 0-pad
your numbers if they span a power of ten (e.g. 0999-1000 or 09-10). This is
handled automatically by its complementary function, `png.save_collection`.
Also, look at how nicely these documentation lines are all the same length!
Arguments:
png_filename_base (str): An asterisk-wildcard string that should refer
to all PNGs in the stack. All *s are replaced according to regular
cmd-line expansion rules. See the 'glob' documentation for details
Returns:
A numpy array holding a 3D dataset
"""
# We expect images to be indexed by their alphabetical order.
files = glob.glob(png_filename_base)
files.sort()
numpy_data = []
for f in files:
numpy_data.append(load(f))
return numpy.concatenate(numpy_data) | def load_collection(png_filename_base):
"""
Import all files matching the filename base given with `png_filename_base`.
Images are ordered by alphabetical order, which means that you *MUST* 0-pad
your numbers if they span a power of ten (e.g. 0999-1000 or 09-10). This is
handled automatically by its complementary function, `png.save_collection`.
Also, look at how nicely these documentation lines are all the same length!
Arguments:
png_filename_base (str): An asterisk-wildcard string that should refer
to all PNGs in the stack. All *s are replaced according to regular
cmd-line expansion rules. See the 'glob' documentation for details
Returns:
A numpy array holding a 3D dataset
"""
# We expect images to be indexed by their alphabetical order.
files = glob.glob(png_filename_base)
files.sort()
numpy_data = []
for f in files:
numpy_data.append(load(f))
return numpy.concatenate(numpy_data) | [
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test | Status.print_workspace | Print workspace status. | yoda/subcommand/status.py | def print_workspace(self, name):
"""Print workspace status."""
path_list = find_path(name, self.config)
if len(path_list) == 0:
self.logger.error("No matches for `%s`" % name)
return False
for name, path in path_list.items():
self.print_status(name, path) | def print_workspace(self, name):
"""Print workspace status."""
path_list = find_path(name, self.config)
if len(path_list) == 0:
self.logger.error("No matches for `%s`" % name)
return False
for name, path in path_list.items():
self.print_status(name, path) | [
"Print",
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"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/subcommand/status.py#L56-L65 | [
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test | Status.print_status | Print repository status. | yoda/subcommand/status.py | def print_status(self, repo_name, repo_path):
"""Print repository status."""
color = Color()
self.logger.info(color.colored(
"=> [%s] %s" % (repo_name, repo_path), "green"))
try:
repo = Repository(repo_path)
repo.status()
except RepositoryError as e:
self.logger.error(e)
pass
print("\n") | def print_status(self, repo_name, repo_path):
"""Print repository status."""
color = Color()
self.logger.info(color.colored(
"=> [%s] %s" % (repo_name, repo_path), "green"))
try:
repo = Repository(repo_path)
repo.status()
except RepositoryError as e:
self.logger.error(e)
pass
print("\n") | [
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test | data.get_block_size | Gets the block-size for a given token at a given resolution.
Arguments:
token (str): The token to inspect
resolution (int : None): The resolution at which to inspect data.
If none is specified, uses the minimum available.
Returns:
int[3]: The xyz blocksize. | ndio/remote/data.py | def get_block_size(self, token, resolution=None):
"""
Gets the block-size for a given token at a given resolution.
Arguments:
token (str): The token to inspect
resolution (int : None): The resolution at which to inspect data.
If none is specified, uses the minimum available.
Returns:
int[3]: The xyz blocksize.
"""
cdims = self.get_metadata(token)['dataset']['cube_dimension']
if resolution is None:
resolution = min(cdims.keys())
return cdims[str(resolution)] | def get_block_size(self, token, resolution=None):
"""
Gets the block-size for a given token at a given resolution.
Arguments:
token (str): The token to inspect
resolution (int : None): The resolution at which to inspect data.
If none is specified, uses the minimum available.
Returns:
int[3]: The xyz blocksize.
"""
cdims = self.get_metadata(token)['dataset']['cube_dimension']
if resolution is None:
resolution = min(cdims.keys())
return cdims[str(resolution)] | [
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test | data.get_xy_slice | Return a binary-encoded, decompressed 2d image. You should
specify a 'token' and 'channel' pair. For image data, users
should use the channel 'image.'
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int):` The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
z_index (int): The z-slice to image
Returns:
str: binary image data | ndio/remote/data.py | def get_xy_slice(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_index,
resolution=0):
"""
Return a binary-encoded, decompressed 2d image. You should
specify a 'token' and 'channel' pair. For image data, users
should use the channel 'image.'
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int):` The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
z_index (int): The z-slice to image
Returns:
str: binary image data
"""
vol = self.get_cutout(token, channel, x_start, x_stop, y_start,
y_stop, z_index, z_index + 1, resolution)
vol = numpy.squeeze(vol) # 3D volume to 2D slice
return vol | def get_xy_slice(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_index,
resolution=0):
"""
Return a binary-encoded, decompressed 2d image. You should
specify a 'token' and 'channel' pair. For image data, users
should use the channel 'image.'
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int):` The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
z_index (int): The z-slice to image
Returns:
str: binary image data
"""
vol = self.get_cutout(token, channel, x_start, x_stop, y_start,
y_stop, z_index, z_index + 1, resolution)
vol = numpy.squeeze(vol) # 3D volume to 2D slice
return vol | [
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"vol"
] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | data.get_volume | Get a RAMONVolume volumetric cutout from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
ndio.ramon.RAMONVolume: Downloaded data. | ndio/remote/data.py | def get_volume(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get a RAMONVolume volumetric cutout from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
ndio.ramon.RAMONVolume: Downloaded data.
"""
size = (x_stop - x_start) * (y_stop - y_start) * (z_stop - z_start)
volume = ramon.RAMONVolume()
volume.xyz_offset = [x_start, y_start, z_start]
volume.resolution = resolution
volume.cutout = self.get_cutout(token, channel, x_start,
x_stop, y_start, y_stop,
z_start, z_stop,
resolution=resolution,
block_size=block_size,
neariso=neariso)
return volume | def get_volume(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get a RAMONVolume volumetric cutout from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
ndio.ramon.RAMONVolume: Downloaded data.
"""
size = (x_stop - x_start) * (y_stop - y_start) * (z_stop - z_start)
volume = ramon.RAMONVolume()
volume.xyz_offset = [x_start, y_start, z_start]
volume.resolution = resolution
volume.cutout = self.get_cutout(token, channel, x_start,
x_stop, y_start, y_stop,
z_start, z_stop,
resolution=resolution,
block_size=block_size,
neariso=neariso)
return volume | [
"Get",
"a",
"RAMONVolume",
"volumetric",
"cutout",
"from",
"the",
"neurodata",
"server",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/data.py#L167-L201 | [
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test | data.get_cutout | Get volumetric cutout data from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset. If not provided,
ndio uses the metadata of this tokenchannel to set. If you find
that your downloads are timing out or otherwise failing, it may
be wise to start off by making this smaller.
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
numpy.ndarray: Downloaded data. | ndio/remote/data.py | def get_cutout(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start=0, t_stop=1,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get volumetric cutout data from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset. If not provided,
ndio uses the metadata of this tokenchannel to set. If you find
that your downloads are timing out or otherwise failing, it may
be wise to start off by making this smaller.
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
numpy.ndarray: Downloaded data.
"""
if block_size is None:
# look up block size from metadata
block_size = self.get_block_size(token, resolution)
origin = self.get_image_offset(token, resolution)
# If z_stop - z_start is < 16, backend still pulls minimum 16 slices
if (z_stop - z_start) < 16:
z_slices = 16
else:
z_slices = z_stop - z_start
# Calculate size of the data to be downloaded.
size = (x_stop - x_start) * (y_stop - y_start) * z_slices * 4
# Switch which download function to use based on which libraries are
# available in this version of python.
if six.PY2:
dl_func = self._get_cutout_blosc_no_chunking
elif six.PY3:
dl_func = self._get_cutout_no_chunking
else:
raise ValueError("Invalid Python version.")
if size < self._chunk_threshold:
vol = dl_func(token, channel, resolution,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start, t_stop,
neariso=neariso)
vol = numpy.rollaxis(vol, 1)
vol = numpy.rollaxis(vol, 2)
return vol
else:
from ndio.utils.parallel import block_compute
blocks = block_compute(x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
origin, block_size)
vol = numpy.zeros(((z_stop - z_start),
(y_stop - y_start),
(x_stop - x_start)))
for b in blocks:
data = dl_func(token, channel, resolution,
b[0][0], b[0][1],
b[1][0], b[1][1],
b[2][0], b[2][1],
0, 1,
neariso=neariso)
if b == blocks[0]: # first block
vol = numpy.zeros(((z_stop - z_start),
(y_stop - y_start),
(x_stop - x_start)), dtype=data.dtype)
vol[b[2][0] - z_start: b[2][1] - z_start,
b[1][0] - y_start: b[1][1] - y_start,
b[0][0] - x_start: b[0][1] - x_start] = data
vol = numpy.rollaxis(vol, 1)
vol = numpy.rollaxis(vol, 2)
return vol | def get_cutout(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start=0, t_stop=1,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get volumetric cutout data from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset. If not provided,
ndio uses the metadata of this tokenchannel to set. If you find
that your downloads are timing out or otherwise failing, it may
be wise to start off by making this smaller.
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
numpy.ndarray: Downloaded data.
"""
if block_size is None:
# look up block size from metadata
block_size = self.get_block_size(token, resolution)
origin = self.get_image_offset(token, resolution)
# If z_stop - z_start is < 16, backend still pulls minimum 16 slices
if (z_stop - z_start) < 16:
z_slices = 16
else:
z_slices = z_stop - z_start
# Calculate size of the data to be downloaded.
size = (x_stop - x_start) * (y_stop - y_start) * z_slices * 4
# Switch which download function to use based on which libraries are
# available in this version of python.
if six.PY2:
dl_func = self._get_cutout_blosc_no_chunking
elif six.PY3:
dl_func = self._get_cutout_no_chunking
else:
raise ValueError("Invalid Python version.")
if size < self._chunk_threshold:
vol = dl_func(token, channel, resolution,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start, t_stop,
neariso=neariso)
vol = numpy.rollaxis(vol, 1)
vol = numpy.rollaxis(vol, 2)
return vol
else:
from ndio.utils.parallel import block_compute
blocks = block_compute(x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
origin, block_size)
vol = numpy.zeros(((z_stop - z_start),
(y_stop - y_start),
(x_stop - x_start)))
for b in blocks:
data = dl_func(token, channel, resolution,
b[0][0], b[0][1],
b[1][0], b[1][1],
b[2][0], b[2][1],
0, 1,
neariso=neariso)
if b == blocks[0]: # first block
vol = numpy.zeros(((z_stop - z_start),
(y_stop - y_start),
(x_stop - x_start)), dtype=data.dtype)
vol[b[2][0] - z_start: b[2][1] - z_start,
b[1][0] - y_start: b[1][1] - y_start,
b[0][0] - x_start: b[0][1] - x_start] = data
vol = numpy.rollaxis(vol, 1)
vol = numpy.rollaxis(vol, 2)
return vol | [
"Get",
"volumetric",
"cutout",
"data",
"from",
"the",
"neurodata",
"server",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/data.py#L203-L294 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | data.post_cutout | Post a cutout to the server.
Arguments:
token (str)
channel (str)
x_start (int)
y_start (int)
z_start (int)
data (numpy.ndarray): A numpy array of data. Pass in (x, y, z)
resolution (int : 0): Resolution at which to insert the data
Returns:
bool: True on success
Raises:
RemoteDataUploadError: if there's an issue during upload. | ndio/remote/data.py | def post_cutout(self, token, channel,
x_start,
y_start,
z_start,
data,
resolution=0):
"""
Post a cutout to the server.
Arguments:
token (str)
channel (str)
x_start (int)
y_start (int)
z_start (int)
data (numpy.ndarray): A numpy array of data. Pass in (x, y, z)
resolution (int : 0): Resolution at which to insert the data
Returns:
bool: True on success
Raises:
RemoteDataUploadError: if there's an issue during upload.
"""
datatype = self.get_proj_info(token)['channels'][channel]['datatype']
if data.dtype.name != datatype:
data = data.astype(datatype)
data = numpy.rollaxis(data, 1)
data = numpy.rollaxis(data, 2)
if six.PY3 or data.nbytes > 1.5e9:
ul_func = self._post_cutout_no_chunking_npz
else:
ul_func = self._post_cutout_no_chunking_blosc
if data.size < self._chunk_threshold:
return ul_func(token, channel, x_start,
y_start, z_start, data,
resolution)
return self._post_cutout_with_chunking(token, channel,
x_start, y_start, z_start, data,
resolution, ul_func) | def post_cutout(self, token, channel,
x_start,
y_start,
z_start,
data,
resolution=0):
"""
Post a cutout to the server.
Arguments:
token (str)
channel (str)
x_start (int)
y_start (int)
z_start (int)
data (numpy.ndarray): A numpy array of data. Pass in (x, y, z)
resolution (int : 0): Resolution at which to insert the data
Returns:
bool: True on success
Raises:
RemoteDataUploadError: if there's an issue during upload.
"""
datatype = self.get_proj_info(token)['channels'][channel]['datatype']
if data.dtype.name != datatype:
data = data.astype(datatype)
data = numpy.rollaxis(data, 1)
data = numpy.rollaxis(data, 2)
if six.PY3 or data.nbytes > 1.5e9:
ul_func = self._post_cutout_no_chunking_npz
else:
ul_func = self._post_cutout_no_chunking_blosc
if data.size < self._chunk_threshold:
return ul_func(token, channel, x_start,
y_start, z_start, data,
resolution)
return self._post_cutout_with_chunking(token, channel,
x_start, y_start, z_start, data,
resolution, ul_func) | [
"Post",
"a",
"cutout",
"to",
"the",
"server",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/data.py#L356-L399 | [
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test | data._post_cutout_no_chunking_blosc | Accepts data in zyx. !!! | ndio/remote/data.py | def _post_cutout_no_chunking_blosc(self, token, channel,
x_start, y_start, z_start,
data, resolution):
"""
Accepts data in zyx. !!!
"""
data = numpy.expand_dims(data, axis=0)
blosc_data = blosc.pack_array(data)
url = self.url("{}/{}/blosc/{}/{},{}/{},{}/{},{}/0,0/".format(
token, channel,
resolution,
x_start, x_start + data.shape[3],
y_start, y_start + data.shape[2],
z_start, z_start + data.shape[1]
))
req = self.remote_utils.post_url(url, data=blosc_data, headers={
'Content-Type': 'application/octet-stream'
})
if req.status_code is not 200:
raise RemoteDataUploadError(req.text)
else:
return True | def _post_cutout_no_chunking_blosc(self, token, channel,
x_start, y_start, z_start,
data, resolution):
"""
Accepts data in zyx. !!!
"""
data = numpy.expand_dims(data, axis=0)
blosc_data = blosc.pack_array(data)
url = self.url("{}/{}/blosc/{}/{},{}/{},{}/{},{}/0,0/".format(
token, channel,
resolution,
x_start, x_start + data.shape[3],
y_start, y_start + data.shape[2],
z_start, z_start + data.shape[1]
))
req = self.remote_utils.post_url(url, data=blosc_data, headers={
'Content-Type': 'application/octet-stream'
})
if req.status_code is not 200:
raise RemoteDataUploadError(req.text)
else:
return True | [
"Accepts",
"data",
"in",
"zyx",
".",
"!!!"
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/data.py#L447-L470 | [
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test | load | Import a TIFF file into a numpy array.
Arguments:
tiff_filename: A string filename of a TIFF datafile
Returns:
A numpy array with data from the TIFF file | ndio/convert/tiff.py | def load(tiff_filename):
"""
Import a TIFF file into a numpy array.
Arguments:
tiff_filename: A string filename of a TIFF datafile
Returns:
A numpy array with data from the TIFF file
"""
# Expand filename to be absolute
tiff_filename = os.path.expanduser(tiff_filename)
try:
img = tiff.imread(tiff_filename)
except Exception as e:
raise ValueError("Could not load file {0} for conversion."
.format(tiff_filename))
raise
return numpy.array(img) | def load(tiff_filename):
"""
Import a TIFF file into a numpy array.
Arguments:
tiff_filename: A string filename of a TIFF datafile
Returns:
A numpy array with data from the TIFF file
"""
# Expand filename to be absolute
tiff_filename = os.path.expanduser(tiff_filename)
try:
img = tiff.imread(tiff_filename)
except Exception as e:
raise ValueError("Could not load file {0} for conversion."
.format(tiff_filename))
raise
return numpy.array(img) | [
"Import",
"a",
"TIFF",
"file",
"into",
"a",
"numpy",
"array",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/tiff.py#L8-L28 | [
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test | save | Export a numpy array to a TIFF file.
Arguments:
tiff_filename: A filename to which to save the TIFF data
numpy_data: The numpy array to save to TIFF
Returns:
String. The expanded filename that now holds the TIFF data | ndio/convert/tiff.py | def save(tiff_filename, numpy_data):
"""
Export a numpy array to a TIFF file.
Arguments:
tiff_filename: A filename to which to save the TIFF data
numpy_data: The numpy array to save to TIFF
Returns:
String. The expanded filename that now holds the TIFF data
"""
# Expand filename to be absolute
tiff_filename = os.path.expanduser(tiff_filename)
if type(numpy_data) is str:
fp = open(png_filename, "wb")
fp.write(numpy_data)
fp.close()
return png_filename
try:
img = tiff.imsave(tiff_filename, numpy_data)
except Exception as e:
raise ValueError("Could not save TIFF file {0}.".format(tiff_filename))
return tiff_filename | def save(tiff_filename, numpy_data):
"""
Export a numpy array to a TIFF file.
Arguments:
tiff_filename: A filename to which to save the TIFF data
numpy_data: The numpy array to save to TIFF
Returns:
String. The expanded filename that now holds the TIFF data
"""
# Expand filename to be absolute
tiff_filename = os.path.expanduser(tiff_filename)
if type(numpy_data) is str:
fp = open(png_filename, "wb")
fp.write(numpy_data)
fp.close()
return png_filename
try:
img = tiff.imsave(tiff_filename, numpy_data)
except Exception as e:
raise ValueError("Could not save TIFF file {0}.".format(tiff_filename))
return tiff_filename | [
"Export",
"a",
"numpy",
"array",
"to",
"a",
"TIFF",
"file",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/tiff.py#L31-L56 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | load_tiff_multipage | Load a multipage tiff into a single variable in x,y,z format.
Arguments:
tiff_filename: Filename of source data
dtype: data type to use for the returned tensor
Returns:
Array containing contents from input tiff file in xyz order | ndio/convert/tiff.py | def load_tiff_multipage(tiff_filename, dtype='float32'):
"""
Load a multipage tiff into a single variable in x,y,z format.
Arguments:
tiff_filename: Filename of source data
dtype: data type to use for the returned tensor
Returns:
Array containing contents from input tiff file in xyz order
"""
if not os.path.isfile(tiff_filename):
raise RuntimeError('could not find file "%s"' % tiff_filename)
# load the data from multi-layer TIF files
data = tiff.imread(tiff_filename)
im = []
while True:
Xi = numpy.array(data, dtype=dtype)
if Xi.ndim == 2:
Xi = Xi[numpy.newaxis, ...] # add slice dimension
im.append(Xi)
try:
data.seek(data.tell()+1)
except EOFError:
break # this just means hit end of file (not really an error)
im = numpy.concatenate(im, axis=0) # list of 2d -> tensor
im = numpy.rollaxis(im, 1)
im = numpy.rollaxis(im, 2)
return im | def load_tiff_multipage(tiff_filename, dtype='float32'):
"""
Load a multipage tiff into a single variable in x,y,z format.
Arguments:
tiff_filename: Filename of source data
dtype: data type to use for the returned tensor
Returns:
Array containing contents from input tiff file in xyz order
"""
if not os.path.isfile(tiff_filename):
raise RuntimeError('could not find file "%s"' % tiff_filename)
# load the data from multi-layer TIF files
data = tiff.imread(tiff_filename)
im = []
while True:
Xi = numpy.array(data, dtype=dtype)
if Xi.ndim == 2:
Xi = Xi[numpy.newaxis, ...] # add slice dimension
im.append(Xi)
try:
data.seek(data.tell()+1)
except EOFError:
break # this just means hit end of file (not really an error)
im = numpy.concatenate(im, axis=0) # list of 2d -> tensor
im = numpy.rollaxis(im, 1)
im = numpy.rollaxis(im, 2)
return im | [
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"format",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/tiff.py#L98-L133 | [
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test | Config.write | Write config in configuration file.
Data must me a dict. | yoda/config.py | def write(self):
"""
Write config in configuration file.
Data must me a dict.
"""
file = open(self.config_file, "w+")
file.write(yaml.dump(dict(self), default_flow_style=False))
file.close() | def write(self):
"""
Write config in configuration file.
Data must me a dict.
"""
file = open(self.config_file, "w+")
file.write(yaml.dump(dict(self), default_flow_style=False))
file.close() | [
"Write",
"config",
"in",
"configuration",
"file",
".",
"Data",
"must",
"me",
"a",
"dict",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/config.py#L50-L57 | [
"def",
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"close",
"(",
")"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | Bzr.clone | Clone repository from url. | yoda/adapter/bzr.py | def clone(self, url):
"""Clone repository from url."""
return self.execute("%s branch %s %s" % (self.executable,
url, self.path)) | def clone(self, url):
"""Clone repository from url."""
return self.execute("%s branch %s %s" % (self.executable,
url, self.path)) | [
"Clone",
"repository",
"from",
"url",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/adapter/bzr.py#L31-L34 | [
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] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | get_version | Get version from package resources. | yoda/version.py | def get_version():
"""Get version from package resources."""
requirement = pkg_resources.Requirement.parse("yoda")
provider = pkg_resources.get_provider(requirement)
return provider.version | def get_version():
"""Get version from package resources."""
requirement = pkg_resources.Requirement.parse("yoda")
provider = pkg_resources.get_provider(requirement)
return provider.version | [
"Get",
"version",
"from",
"package",
"resources",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/version.py#L20-L24 | [
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"version"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | mix_and_match | Mixing and matching positional args and keyword options. | examples/complex_example.py | def mix_and_match(name, greeting='Hello', yell=False):
'''Mixing and matching positional args and keyword options.'''
say = '%s, %s' % (greeting, name)
if yell:
print '%s!' % say.upper()
else:
print '%s.' % say | def mix_and_match(name, greeting='Hello', yell=False):
'''Mixing and matching positional args and keyword options.'''
say = '%s, %s' % (greeting, name)
if yell:
print '%s!' % say.upper()
else:
print '%s.' % say | [
"Mixing",
"and",
"matching",
"positional",
"args",
"and",
"keyword",
"options",
"."
] | jmohr/compago | python | https://github.com/jmohr/compago/blob/8cd6a2894f7b69844b1e4f367344f51a8ef07baf/examples/complex_example.py#L33-L39 | [
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test | option_decorator | Same as mix_and_match, but using the @option decorator. | examples/complex_example.py | def option_decorator(name, greeting, yell):
'''Same as mix_and_match, but using the @option decorator.'''
# Use the @option decorator when you need more control over the
# command line options.
say = '%s, %s' % (greeting, name)
if yell:
print '%s!' % say.upper()
else:
print '%s.' % say | def option_decorator(name, greeting, yell):
'''Same as mix_and_match, but using the @option decorator.'''
# Use the @option decorator when you need more control over the
# command line options.
say = '%s, %s' % (greeting, name)
if yell:
print '%s!' % say.upper()
else:
print '%s.' % say | [
"Same",
"as",
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"but",
"using",
"the"
] | jmohr/compago | python | https://github.com/jmohr/compago/blob/8cd6a2894f7b69844b1e4f367344f51a8ef07baf/examples/complex_example.py#L43-L51 | [
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test | load | Import a nifti file into a numpy array. TODO: Currently only
transfers raw data for compatibility with annotation and ND formats
Arguments:
nifti_filename (str): A string filename of a nifti datafile
Returns:
A numpy array with data from the nifti file | ndio/convert/nifti.py | def load(nifti_filename):
"""
Import a nifti file into a numpy array. TODO: Currently only
transfers raw data for compatibility with annotation and ND formats
Arguments:
nifti_filename (str): A string filename of a nifti datafile
Returns:
A numpy array with data from the nifti file
"""
# Expand filename to be absolute
nifti_filename = os.path.expanduser(nifti_filename)
try:
data = nib.load(nifti_filename)
img = data.get_data()
except Exception as e:
raise ValueError("Could not load file {0} for conversion."
.format(nifti_filename))
raise
return img | def load(nifti_filename):
"""
Import a nifti file into a numpy array. TODO: Currently only
transfers raw data for compatibility with annotation and ND formats
Arguments:
nifti_filename (str): A string filename of a nifti datafile
Returns:
A numpy array with data from the nifti file
"""
# Expand filename to be absolute
nifti_filename = os.path.expanduser(nifti_filename)
try:
data = nib.load(nifti_filename)
img = data.get_data()
except Exception as e:
raise ValueError("Could not load file {0} for conversion."
.format(nifti_filename))
raise
return img | [
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"for",
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"with",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/nifti.py#L8-L31 | [
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test | save | Export a numpy array to a nifti file. TODO: currently using dummy
headers and identity matrix affine transform. This can be expanded.
Arguments:
nifti_filename (str): A filename to which to save the nifti data
numpy_data (numpy.ndarray): The numpy array to save to nifti
Returns:
String. The expanded filename that now holds the nifti data | ndio/convert/nifti.py | def save(nifti_filename, numpy_data):
"""
Export a numpy array to a nifti file. TODO: currently using dummy
headers and identity matrix affine transform. This can be expanded.
Arguments:
nifti_filename (str): A filename to which to save the nifti data
numpy_data (numpy.ndarray): The numpy array to save to nifti
Returns:
String. The expanded filename that now holds the nifti data
"""
# Expand filename to be absolute
nifti_filename = os.path.expanduser(nifti_filename)
try:
nifti_img = nib.Nifti1Image(numpy_data, numpy.eye(4))
nib.save(nifti_img, nifti_filename)
except Exception as e:
raise ValueError("Could not save file {0}.".format(nifti_filename))
return nifti_filename | def save(nifti_filename, numpy_data):
"""
Export a numpy array to a nifti file. TODO: currently using dummy
headers and identity matrix affine transform. This can be expanded.
Arguments:
nifti_filename (str): A filename to which to save the nifti data
numpy_data (numpy.ndarray): The numpy array to save to nifti
Returns:
String. The expanded filename that now holds the nifti data
"""
# Expand filename to be absolute
nifti_filename = os.path.expanduser(nifti_filename)
try:
nifti_img = nib.Nifti1Image(numpy_data, numpy.eye(4))
nib.save(nifti_img, nifti_filename)
except Exception as e:
raise ValueError("Could not save file {0}.".format(nifti_filename))
return nifti_filename | [
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test | neuroRemote.ping | Return the status-code of the API (estimated using the public-tokens
lookup page).
Arguments:
suffix (str : 'public_tokens/'): The url endpoint to check
Returns:
int: status code | ndio/remote/neuroRemote.py | def ping(self, suffix='public_tokens/'):
"""
Return the status-code of the API (estimated using the public-tokens
lookup page).
Arguments:
suffix (str : 'public_tokens/'): The url endpoint to check
Returns:
int: status code
"""
return self.remote_utils.ping(super(neuroRemote, self).url(), suffix) | def ping(self, suffix='public_tokens/'):
"""
Return the status-code of the API (estimated using the public-tokens
lookup page).
Arguments:
suffix (str : 'public_tokens/'): The url endpoint to check
Returns:
int: status code
"""
return self.remote_utils.ping(super(neuroRemote, self).url(), suffix) | [
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test | neuroRemote.url | Return a constructed URL, appending an optional suffix (uri path).
Arguments:
suffix (str : ""): The suffix to append to the end of the URL
Returns:
str: The complete URL | ndio/remote/neuroRemote.py | def url(self, suffix=""):
"""
Return a constructed URL, appending an optional suffix (uri path).
Arguments:
suffix (str : ""): The suffix to append to the end of the URL
Returns:
str: The complete URL
"""
return super(neuroRemote,
self).url('{}/'.format(self._ext) + suffix) | def url(self, suffix=""):
"""
Return a constructed URL, appending an optional suffix (uri path).
Arguments:
suffix (str : ""): The suffix to append to the end of the URL
Returns:
str: The complete URL
"""
return super(neuroRemote,
self).url('{}/'.format(self._ext) + suffix) | [
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test | neuroRemote.reserve_ids | Requests a list of next-available-IDs from the server.
Arguments:
quantity (int): The number of IDs to reserve
Returns:
int[quantity]: List of IDs you've been granted | ndio/remote/neuroRemote.py | def reserve_ids(self, token, channel, quantity):
"""
Requests a list of next-available-IDs from the server.
Arguments:
quantity (int): The number of IDs to reserve
Returns:
int[quantity]: List of IDs you've been granted
"""
quantity = str(quantity)
url = self.url("{}/{}/reserve/{}/".format(token, channel, quantity))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Invalid req: ' + req.status_code)
out = req.json()
return [out[0] + i for i in range(out[1])] | def reserve_ids(self, token, channel, quantity):
"""
Requests a list of next-available-IDs from the server.
Arguments:
quantity (int): The number of IDs to reserve
Returns:
int[quantity]: List of IDs you've been granted
"""
quantity = str(quantity)
url = self.url("{}/{}/reserve/{}/".format(token, channel, quantity))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Invalid req: ' + req.status_code)
out = req.json()
return [out[0] + i for i in range(out[1])] | [
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test | neuroRemote.merge_ids | Call the restful endpoint to merge two RAMON objects into one.
Arguments:
token (str): The token to inspect
channel (str): The channel to inspect
ids (int[]): the list of the IDs to merge
delete (bool : False): Whether to delete after merging.
Returns:
json: The ID as returned by ndstore | ndio/remote/neuroRemote.py | def merge_ids(self, token, channel, ids, delete=False):
"""
Call the restful endpoint to merge two RAMON objects into one.
Arguments:
token (str): The token to inspect
channel (str): The channel to inspect
ids (int[]): the list of the IDs to merge
delete (bool : False): Whether to delete after merging.
Returns:
json: The ID as returned by ndstore
"""
url = self.url() + "/merge/{}/".format(','.join([str(i) for i in ids]))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataUploadError('Could not merge ids {}'.format(
','.join([str(i) for i in ids])))
if delete:
self.delete_ramon(token, channel, ids[1:])
return True | def merge_ids(self, token, channel, ids, delete=False):
"""
Call the restful endpoint to merge two RAMON objects into one.
Arguments:
token (str): The token to inspect
channel (str): The channel to inspect
ids (int[]): the list of the IDs to merge
delete (bool : False): Whether to delete after merging.
Returns:
json: The ID as returned by ndstore
"""
url = self.url() + "/merge/{}/".format(','.join([str(i) for i in ids]))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataUploadError('Could not merge ids {}'.format(
','.join([str(i) for i in ids])))
if delete:
self.delete_ramon(token, channel, ids[1:])
return True | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neuroRemote.py#L201-L221 | [
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test | neuroRemote.create_channels | Creates channels given a dictionary in 'new_channels_data'
, 'dataset' name, and 'token' (project) name.
Arguments:
token (str): Token to identify project
dataset (str): Dataset name to identify dataset to download from
new_channels_data (dict): New channel data to upload into new
channels
Returns:
bool: Process completed succesfully or not | ndio/remote/neuroRemote.py | def create_channels(self, dataset, token, new_channels_data):
"""
Creates channels given a dictionary in 'new_channels_data'
, 'dataset' name, and 'token' (project) name.
Arguments:
token (str): Token to identify project
dataset (str): Dataset name to identify dataset to download from
new_channels_data (dict): New channel data to upload into new
channels
Returns:
bool: Process completed succesfully or not
"""
channels = {}
for channel_new in new_channels_data:
self._check_channel(channel_new.name)
if channel_new.channel_type not in ['image', 'annotation']:
raise ValueError('Channel type must be ' +
'neuroRemote.IMAGE or ' +
'neuroRemote.ANNOTATION.')
if channel_new.readonly * 1 not in [0, 1]:
raise ValueError("readonly must be 0 (False) or 1 (True).")
channels[channel_new.name] = {
"channel_name": channel_new.name,
"channel_type": channel_new.channel_type,
"datatype": channel_new.dtype,
"readonly": channel_new.readonly * 1
}
req = requests.post(self.url("/{}/project/".format(dataset) +
"{}".format(token)),
json={"channels": {channels}}, verify=False)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req.text))
else:
return True | def create_channels(self, dataset, token, new_channels_data):
"""
Creates channels given a dictionary in 'new_channels_data'
, 'dataset' name, and 'token' (project) name.
Arguments:
token (str): Token to identify project
dataset (str): Dataset name to identify dataset to download from
new_channels_data (dict): New channel data to upload into new
channels
Returns:
bool: Process completed succesfully or not
"""
channels = {}
for channel_new in new_channels_data:
self._check_channel(channel_new.name)
if channel_new.channel_type not in ['image', 'annotation']:
raise ValueError('Channel type must be ' +
'neuroRemote.IMAGE or ' +
'neuroRemote.ANNOTATION.')
if channel_new.readonly * 1 not in [0, 1]:
raise ValueError("readonly must be 0 (False) or 1 (True).")
channels[channel_new.name] = {
"channel_name": channel_new.name,
"channel_type": channel_new.channel_type,
"datatype": channel_new.dtype,
"readonly": channel_new.readonly * 1
}
req = requests.post(self.url("/{}/project/".format(dataset) +
"{}".format(token)),
json={"channels": {channels}}, verify=False)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req.text))
else:
return True | [
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test | neuroRemote.propagate | Kick off the propagate function on the remote server.
Arguments:
token (str): The token to propagate
channel (str): The channel to propagate
Returns:
boolean: Success | ndio/remote/neuroRemote.py | def propagate(self, token, channel):
"""
Kick off the propagate function on the remote server.
Arguments:
token (str): The token to propagate
channel (str): The channel to propagate
Returns:
boolean: Success
"""
if self.get_propagate_status(token, channel) != u'0':
return
url = self.url('sd/{}/{}/setPropagate/1/'.format(token, channel))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataUploadError('Propagate fail: {}'.format(req.text))
return True | def propagate(self, token, channel):
"""
Kick off the propagate function on the remote server.
Arguments:
token (str): The token to propagate
channel (str): The channel to propagate
Returns:
boolean: Success
"""
if self.get_propagate_status(token, channel) != u'0':
return
url = self.url('sd/{}/{}/setPropagate/1/'.format(token, channel))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataUploadError('Propagate fail: {}'.format(req.text))
return True | [
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"function",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neuroRemote.py#L269-L286 | [
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test | neuroRemote.get_propagate_status | Get the propagate status for a token/channel pair.
Arguments:
token (str): The token to check
channel (str): The channel to check
Returns:
str: The status code | ndio/remote/neuroRemote.py | def get_propagate_status(self, token, channel):
"""
Get the propagate status for a token/channel pair.
Arguments:
token (str): The token to check
channel (str): The channel to check
Returns:
str: The status code
"""
url = self.url('sd/{}/{}/getPropagate/'.format(token, channel))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise ValueError('Bad pair: {}/{}'.format(token, channel))
return req.text | def get_propagate_status(self, token, channel):
"""
Get the propagate status for a token/channel pair.
Arguments:
token (str): The token to check
channel (str): The channel to check
Returns:
str: The status code
"""
url = self.url('sd/{}/{}/getPropagate/'.format(token, channel))
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise ValueError('Bad pair: {}/{}'.format(token, channel))
return req.text | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neuroRemote.py#L289-L304 | [
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test | resources.create_project | Creates a project with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
hostname (str): Hostname
s3backend (str): S3 region to save the data in
is_public (int): 1 is public. 0 is not public.
kvserver (str): Server to store key value pairs in
kvengine (str): Database to store key value pairs in
mdengine (str): ???
description (str): Description for your project
Returns:
bool: True if project created, false if not created. | ndio/remote/resources.py | def create_project(self,
project_name,
dataset_name,
hostname,
is_public,
s3backend=0,
kvserver='localhost',
kvengine='MySQL',
mdengine='MySQL',
description=''):
"""
Creates a project with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
hostname (str): Hostname
s3backend (str): S3 region to save the data in
is_public (int): 1 is public. 0 is not public.
kvserver (str): Server to store key value pairs in
kvengine (str): Database to store key value pairs in
mdengine (str): ???
description (str): Description for your project
Returns:
bool: True if project created, false if not created.
"""
url = self.url() + "/resource/dataset/{}".format(
dataset_name) + "/project/{}/".format(project_name)
json = {
"project_name": project_name,
"host": hostname,
"s3backend": s3backend,
"public": is_public,
"kvserver": kvserver,
"kvengine": kvengine,
"mdengine": mdengine,
"project_description": description
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req))
if req.content == "" or req.content == b'':
return True
else:
return False | def create_project(self,
project_name,
dataset_name,
hostname,
is_public,
s3backend=0,
kvserver='localhost',
kvengine='MySQL',
mdengine='MySQL',
description=''):
"""
Creates a project with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
hostname (str): Hostname
s3backend (str): S3 region to save the data in
is_public (int): 1 is public. 0 is not public.
kvserver (str): Server to store key value pairs in
kvengine (str): Database to store key value pairs in
mdengine (str): ???
description (str): Description for your project
Returns:
bool: True if project created, false if not created.
"""
url = self.url() + "/resource/dataset/{}".format(
dataset_name) + "/project/{}/".format(project_name)
json = {
"project_name": project_name,
"host": hostname,
"s3backend": s3backend,
"public": is_public,
"kvserver": kvserver,
"kvengine": kvengine,
"mdengine": mdengine,
"project_description": description
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req))
if req.content == "" or req.content == b'':
return True
else:
return False | [
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test | resources.list_projects | Lists a set of projects related to a dataset.
Arguments:
dataset_name (str): Dataset name to search projects for
Returns:
dict: Projects found based on dataset query | ndio/remote/resources.py | def list_projects(self, dataset_name):
"""
Lists a set of projects related to a dataset.
Arguments:
dataset_name (str): Dataset name to search projects for
Returns:
dict: Projects found based on dataset query
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/"
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | def list_projects(self, dataset_name):
"""
Lists a set of projects related to a dataset.
Arguments:
dataset_name (str): Dataset name to search projects for
Returns:
dict: Projects found based on dataset query
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/"
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/resources.py#L164-L182 | [
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test | resources.create_token | Creates a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
is_public (int): 1 is public. 0 is not public
Returns:
bool: True if project created, false if not created. | ndio/remote/resources.py | def create_token(self,
token_name,
project_name,
dataset_name,
is_public):
"""
Creates a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
is_public (int): 1 is public. 0 is not public
Returns:
bool: True if project created, false if not created.
"""
url = self.url() + '/nd/resource/dataset/{}'.format(
dataset_name) + '/project/{}'.format(project_name) + \
'/token/{}/'.format(token_name)
json = {
"token_name": token_name,
"public": is_public
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Cout not upload {}:'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | def create_token(self,
token_name,
project_name,
dataset_name,
is_public):
"""
Creates a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
is_public (int): 1 is public. 0 is not public
Returns:
bool: True if project created, false if not created.
"""
url = self.url() + '/nd/resource/dataset/{}'.format(
dataset_name) + '/project/{}'.format(project_name) + \
'/token/{}/'.format(token_name)
json = {
"token_name": token_name,
"public": is_public
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Cout not upload {}:'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | [
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test | resources.get_token | Get a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
Returns:
dict: Token info | ndio/remote/resources.py | def get_token(self,
token_name,
project_name,
dataset_name):
"""
Get a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
Returns:
dict: Token info
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name)\
+ "/token/{}/".format(token_name)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataUploadError('Could not find {}'.format(req.text))
else:
return req.json() | def get_token(self,
token_name,
project_name,
dataset_name):
"""
Get a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
Returns:
dict: Token info
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name)\
+ "/token/{}/".format(token_name)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataUploadError('Could not find {}'.format(req.text))
else:
return req.json() | [
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test | resources.delete_token | Delete a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
channel_name (str): Channel name project is based on
Returns:
bool: True if project deleted, false if not deleted. | ndio/remote/resources.py | def delete_token(self,
token_name,
project_name,
dataset_name):
"""
Delete a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
channel_name (str): Channel name project is based on
Returns:
bool: True if project deleted, false if not deleted.
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name)\
+ "/token/{}/".format(token_name)
req = self.remote_utils.delete_url(url)
if req.status_code is not 204:
raise RemoteDataUploadError("Could not delete {}".format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | def delete_token(self,
token_name,
project_name,
dataset_name):
"""
Delete a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
channel_name (str): Channel name project is based on
Returns:
bool: True if project deleted, false if not deleted.
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name)\
+ "/token/{}/".format(token_name)
req = self.remote_utils.delete_url(url)
if req.status_code is not 204:
raise RemoteDataUploadError("Could not delete {}".format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | [
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test | resources.list_tokens | Lists a set of tokens that are public in Neurodata.
Arguments:
Returns:
dict: Public tokens found in Neurodata | ndio/remote/resources.py | def list_tokens(self):
"""
Lists a set of tokens that are public in Neurodata.
Arguments:
Returns:
dict: Public tokens found in Neurodata
"""
url = self.url() + "/nd/resource/public/token/"
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Coud not find {}'.format(req.text))
else:
return req.json() | def list_tokens(self):
"""
Lists a set of tokens that are public in Neurodata.
Arguments:
Returns:
dict: Public tokens found in Neurodata
"""
url = self.url() + "/nd/resource/public/token/"
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Coud not find {}'.format(req.text))
else:
return req.json() | [
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test | resources.create_dataset | Creates a dataset.
Arguments:
name (str): Name of dataset
x_img_size (int): max x coordinate of image size
y_img_size (int): max y coordinate of image size
z_img_size (int): max z coordinate of image size
x_vox_res (float): x voxel resolution
y_vox_res (float): y voxel resolution
z_vox_res (float): z voxel resolution
x_offset (int): x offset amount
y_offset (int): y offset amount
z_offset (int): z offset amount
scaling_levels (int): Level of resolution scaling
scaling_option (int): Z slices is 0 or Isotropic is 1
dataset_description (str): Your description of the dataset
is_public (int): 1 'true' or 0 'false' for viewability of data set
in public
Returns:
bool: True if dataset created, False if not | ndio/remote/resources.py | def create_dataset(self,
name,
x_img_size,
y_img_size,
z_img_size,
x_vox_res,
y_vox_res,
z_vox_res,
x_offset=0,
y_offset=0,
z_offset=0,
scaling_levels=0,
scaling_option=0,
dataset_description="",
is_public=0):
"""
Creates a dataset.
Arguments:
name (str): Name of dataset
x_img_size (int): max x coordinate of image size
y_img_size (int): max y coordinate of image size
z_img_size (int): max z coordinate of image size
x_vox_res (float): x voxel resolution
y_vox_res (float): y voxel resolution
z_vox_res (float): z voxel resolution
x_offset (int): x offset amount
y_offset (int): y offset amount
z_offset (int): z offset amount
scaling_levels (int): Level of resolution scaling
scaling_option (int): Z slices is 0 or Isotropic is 1
dataset_description (str): Your description of the dataset
is_public (int): 1 'true' or 0 'false' for viewability of data set
in public
Returns:
bool: True if dataset created, False if not
"""
url = self.url() + "/resource/dataset/{}".format(name)
json = {
"dataset_name": name,
"ximagesize": x_img_size,
"yimagesize": y_img_size,
"zimagesize": z_img_size,
"xvoxelres": x_vox_res,
"yvoxelres": y_vox_res,
"zvoxelres": z_vox_res,
"xoffset": x_offset,
"yoffset": y_offset,
"zoffset": z_offset,
"scalinglevels": scaling_levels,
"scalingoption": scaling_option,
"dataset_description": dataset_description,
"public": is_public
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | def create_dataset(self,
name,
x_img_size,
y_img_size,
z_img_size,
x_vox_res,
y_vox_res,
z_vox_res,
x_offset=0,
y_offset=0,
z_offset=0,
scaling_levels=0,
scaling_option=0,
dataset_description="",
is_public=0):
"""
Creates a dataset.
Arguments:
name (str): Name of dataset
x_img_size (int): max x coordinate of image size
y_img_size (int): max y coordinate of image size
z_img_size (int): max z coordinate of image size
x_vox_res (float): x voxel resolution
y_vox_res (float): y voxel resolution
z_vox_res (float): z voxel resolution
x_offset (int): x offset amount
y_offset (int): y offset amount
z_offset (int): z offset amount
scaling_levels (int): Level of resolution scaling
scaling_option (int): Z slices is 0 or Isotropic is 1
dataset_description (str): Your description of the dataset
is_public (int): 1 'true' or 0 'false' for viewability of data set
in public
Returns:
bool: True if dataset created, False if not
"""
url = self.url() + "/resource/dataset/{}".format(name)
json = {
"dataset_name": name,
"ximagesize": x_img_size,
"yimagesize": y_img_size,
"zimagesize": z_img_size,
"xvoxelres": x_vox_res,
"yvoxelres": y_vox_res,
"zvoxelres": z_vox_res,
"xoffset": x_offset,
"yoffset": y_offset,
"zoffset": z_offset,
"scalinglevels": scaling_levels,
"scalingoption": scaling_option,
"dataset_description": dataset_description,
"public": is_public
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | [
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"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/resources.py#L281-L343 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | resources.get_dataset | Returns info regarding a particular dataset.
Arugments:
name (str): Dataset name
Returns:
dict: Dataset information | ndio/remote/resources.py | def get_dataset(self, name):
"""
Returns info regarding a particular dataset.
Arugments:
name (str): Dataset name
Returns:
dict: Dataset information
"""
url = self.url() + "/resource/dataset/{}".format(name)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | def get_dataset(self, name):
"""
Returns info regarding a particular dataset.
Arugments:
name (str): Dataset name
Returns:
dict: Dataset information
"""
url = self.url() + "/resource/dataset/{}".format(name)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | [
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"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/resources.py#L345-L361 | [
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test | resources.list_datasets | Lists datasets in resources. Setting 'get_global_public' to 'True'
will retrieve all public datasets in cloud. 'False' will get user's
public datasets.
Arguments:
get_global_public (bool): True if user wants all public datasets in
cloud. False if user wants only their
public datasets.
Returns:
dict: Returns datasets in JSON format | ndio/remote/resources.py | def list_datasets(self, get_global_public):
"""
Lists datasets in resources. Setting 'get_global_public' to 'True'
will retrieve all public datasets in cloud. 'False' will get user's
public datasets.
Arguments:
get_global_public (bool): True if user wants all public datasets in
cloud. False if user wants only their
public datasets.
Returns:
dict: Returns datasets in JSON format
"""
appending = ""
if get_global_public:
appending = "public"
url = self.url() + "/resource/{}dataset/".format(appending)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | def list_datasets(self, get_global_public):
"""
Lists datasets in resources. Setting 'get_global_public' to 'True'
will retrieve all public datasets in cloud. 'False' will get user's
public datasets.
Arguments:
get_global_public (bool): True if user wants all public datasets in
cloud. False if user wants only their
public datasets.
Returns:
dict: Returns datasets in JSON format
"""
appending = ""
if get_global_public:
appending = "public"
url = self.url() + "/resource/{}dataset/".format(appending)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/resources.py#L363-L387 | [
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test | resources.delete_dataset | Arguments:
name (str): Name of dataset to delete
Returns:
bool: True if dataset deleted, False if not | ndio/remote/resources.py | def delete_dataset(self, name):
"""
Arguments:
name (str): Name of dataset to delete
Returns:
bool: True if dataset deleted, False if not
"""
url = self.url() + "/resource/dataset/{}".format(name)
req = self.remote_utils.delete_url(url)
if req.status_code is not 204:
raise RemoteDataUploadError('Could not delete {}'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | def delete_dataset(self, name):
"""
Arguments:
name (str): Name of dataset to delete
Returns:
bool: True if dataset deleted, False if not
"""
url = self.url() + "/resource/dataset/{}".format(name)
req = self.remote_utils.delete_url(url)
if req.status_code is not 204:
raise RemoteDataUploadError('Could not delete {}'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/resources.py#L389-L405 | [
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test | resources.create_channel | Create a new channel on the Remote, using channel_data.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
channel_type (str): Type of the channel (e.g. `neurodata.IMAGE`)
dtype (str): The datatype of the channel's data (e.g. `uint8`)
startwindow (int): Window to start in
endwindow (int): Window to end in
readonly (int): Can others write to this channel?
propagate (int): Allow propogation? 1 is True, 0 is False
resolution (int): Resolution scaling
channel_description (str): Your description of the channel
Returns:
bool: `True` if successful, `False` otherwise.
Raises:
ValueError: If your args were bad :(
RemoteDataUploadError: If the channel data is valid but upload
fails for some other reason. | ndio/remote/resources.py | def create_channel(self,
channel_name,
project_name,
dataset_name,
channel_type,
dtype,
startwindow,
endwindow,
readonly=0,
start_time=0,
end_time=0,
propagate=0,
resolution=0,
channel_description=''):
"""
Create a new channel on the Remote, using channel_data.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
channel_type (str): Type of the channel (e.g. `neurodata.IMAGE`)
dtype (str): The datatype of the channel's data (e.g. `uint8`)
startwindow (int): Window to start in
endwindow (int): Window to end in
readonly (int): Can others write to this channel?
propagate (int): Allow propogation? 1 is True, 0 is False
resolution (int): Resolution scaling
channel_description (str): Your description of the channel
Returns:
bool: `True` if successful, `False` otherwise.
Raises:
ValueError: If your args were bad :(
RemoteDataUploadError: If the channel data is valid but upload
fails for some other reason.
"""
self._check_channel(channel_name)
if channel_type not in ['image', 'annotation', 'timeseries']:
raise ValueError('Channel type must be ' +
'neurodata.IMAGE or neurodata.ANNOTATION.')
if readonly * 1 not in [0, 1]:
raise ValueError("readonly must be 0 (False) or 1 (True).")
# Good job! You supplied very nice arguments.
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name) + \
"/channel/{}/".format(channel_name)
json = {
"channel_name": channel_name,
"channel_type": channel_type,
"channel_datatype": dtype,
"startwindow": startwindow,
"endwindow": endwindow,
'starttime': start_time,
'endtime': end_time,
'readonly': readonly,
'propagate': propagate,
'resolution': resolution,
'channel_description': channel_description
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | def create_channel(self,
channel_name,
project_name,
dataset_name,
channel_type,
dtype,
startwindow,
endwindow,
readonly=0,
start_time=0,
end_time=0,
propagate=0,
resolution=0,
channel_description=''):
"""
Create a new channel on the Remote, using channel_data.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
channel_type (str): Type of the channel (e.g. `neurodata.IMAGE`)
dtype (str): The datatype of the channel's data (e.g. `uint8`)
startwindow (int): Window to start in
endwindow (int): Window to end in
readonly (int): Can others write to this channel?
propagate (int): Allow propogation? 1 is True, 0 is False
resolution (int): Resolution scaling
channel_description (str): Your description of the channel
Returns:
bool: `True` if successful, `False` otherwise.
Raises:
ValueError: If your args were bad :(
RemoteDataUploadError: If the channel data is valid but upload
fails for some other reason.
"""
self._check_channel(channel_name)
if channel_type not in ['image', 'annotation', 'timeseries']:
raise ValueError('Channel type must be ' +
'neurodata.IMAGE or neurodata.ANNOTATION.')
if readonly * 1 not in [0, 1]:
raise ValueError("readonly must be 0 (False) or 1 (True).")
# Good job! You supplied very nice arguments.
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name) + \
"/channel/{}/".format(channel_name)
json = {
"channel_name": channel_name,
"channel_type": channel_type,
"channel_datatype": dtype,
"startwindow": startwindow,
"endwindow": endwindow,
'starttime': start_time,
'endtime': end_time,
'readonly': readonly,
'propagate': propagate,
'resolution': resolution,
'channel_description': channel_description
}
req = self.remote_utils.post_url(url, json=json)
if req.status_code is not 201:
raise RemoteDataUploadError('Could not upload {}'.format(req.text))
if req.content == "" or req.content == b'':
return True
else:
return False | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/resources.py#L415-L487 | [
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test | resources.get_channel | Gets info about a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
dict: Channel info | ndio/remote/resources.py | def get_channel(self, channel_name, project_name, dataset_name):
"""
Gets info about a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
dict: Channel info
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name) + \
"/channel/{}/".format(channel_name)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | def get_channel(self, channel_name, project_name, dataset_name):
"""
Gets info about a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
dict: Channel info
"""
url = self.url() + "/nd/resource/dataset/{}".format(dataset_name)\
+ "/project/{}".format(project_name) + \
"/channel/{}/".format(channel_name)
req = self.remote_utils.get_url(url)
if req.status_code is not 200:
raise RemoteDataNotFoundError('Could not find {}'.format(req.text))
else:
return req.json() | [
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"json",
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")"
] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | Show.parse | Parse show subcommand. | yoda/subcommand/show.py | def parse(self):
"""Parse show subcommand."""
parser = self.subparser.add_parser(
"show",
help="Show workspace details",
description="Show workspace details.")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--all', action='store_true', help="All workspaces")
group.add_argument('name', type=str, help="Workspace name", nargs='?') | def parse(self):
"""Parse show subcommand."""
parser = self.subparser.add_parser(
"show",
help="Show workspace details",
description="Show workspace details.")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--all', action='store_true', help="All workspaces")
group.add_argument('name', type=str, help="Workspace name", nargs='?') | [
"Parse",
"show",
"subcommand",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/subcommand/show.py#L40-L49 | [
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",",
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")"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | Show.execute | Execute show subcommand. | yoda/subcommand/show.py | def execute(self, args):
"""Execute show subcommand."""
if args.name is not None:
self.show_workspace(slashes2dash(args.name))
elif args.all is not None:
self.show_all() | def execute(self, args):
"""Execute show subcommand."""
if args.name is not None:
self.show_workspace(slashes2dash(args.name))
elif args.all is not None:
self.show_all() | [
"Execute",
"show",
"subcommand",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/subcommand/show.py#L51-L56 | [
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")"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | Show.show_workspace | Show specific workspace. | yoda/subcommand/show.py | def show_workspace(self, name):
"""Show specific workspace."""
if not self.workspace.exists(name):
raise ValueError("Workspace `%s` doesn't exists." % name)
color = Color()
workspaces = self.workspace.list()
self.logger.info("<== %s workspace ==>" % color.colored(name, "green"))
self.logger.info("\tPath: %s" % workspaces[name]["path"])
self.logger.info("\tNumber of repositories: %s"
% color.colored(
len(workspaces[name]["repositories"]),
"yellow"))
repo_colored = color.colored("Repositories", "blue")
path_colored = color.colored("Path", "blue")
trepositories = PrettyTable(
[repo_colored, path_colored, color.colored("+", "blue")])
trepositories.align[repo_colored] = "l"
trepositories.align[path_colored] = "l"
for repo_name in workspaces[name]["repositories"]:
fullname = "%s/%s" % (name, repo_name)
fullpath = find_path(fullname, self.config)[fullname]
try:
repo = Repository(fullpath)
repo_scm = repo.get_scm()
except RepositoryAdapterNotFound:
repo_scm = None
trepositories.add_row(
[color.colored(repo_name, "cyan"), fullpath, repo_scm])
self.logger.info(trepositories) | def show_workspace(self, name):
"""Show specific workspace."""
if not self.workspace.exists(name):
raise ValueError("Workspace `%s` doesn't exists." % name)
color = Color()
workspaces = self.workspace.list()
self.logger.info("<== %s workspace ==>" % color.colored(name, "green"))
self.logger.info("\tPath: %s" % workspaces[name]["path"])
self.logger.info("\tNumber of repositories: %s"
% color.colored(
len(workspaces[name]["repositories"]),
"yellow"))
repo_colored = color.colored("Repositories", "blue")
path_colored = color.colored("Path", "blue")
trepositories = PrettyTable(
[repo_colored, path_colored, color.colored("+", "blue")])
trepositories.align[repo_colored] = "l"
trepositories.align[path_colored] = "l"
for repo_name in workspaces[name]["repositories"]:
fullname = "%s/%s" % (name, repo_name)
fullpath = find_path(fullname, self.config)[fullname]
try:
repo = Repository(fullpath)
repo_scm = repo.get_scm()
except RepositoryAdapterNotFound:
repo_scm = None
trepositories.add_row(
[color.colored(repo_name, "cyan"), fullpath, repo_scm])
self.logger.info(trepositories) | [
"Show",
"specific",
"workspace",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/subcommand/show.py#L58-L91 | [
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"logger",
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"info",
"(",
"trepositories",
")"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | Show.show_all | Show details for all workspaces. | yoda/subcommand/show.py | def show_all(self):
"""Show details for all workspaces."""
for ws in self.workspace.list().keys():
self.show_workspace(ws)
print("\n\n") | def show_all(self):
"""Show details for all workspaces."""
for ws in self.workspace.list().keys():
self.show_workspace(ws)
print("\n\n") | [
"Show",
"details",
"for",
"all",
"workspaces",
"."
] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/subcommand/show.py#L93-L97 | [
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"show_workspace",
"(",
"ws",
")",
"print",
"(",
"\"\\n\\n\"",
")"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | Remote.url | Get the base URL of the Remote.
Arguments:
None
Returns:
`str` base URL | ndio/remote/Remote.py | def url(self, endpoint=''):
"""
Get the base URL of the Remote.
Arguments:
None
Returns:
`str` base URL
"""
if not endpoint.startswith('/'):
endpoint = "/" + endpoint
return self.protocol + "://" + self.hostname + endpoint | def url(self, endpoint=''):
"""
Get the base URL of the Remote.
Arguments:
None
Returns:
`str` base URL
"""
if not endpoint.startswith('/'):
endpoint = "/" + endpoint
return self.protocol + "://" + self.hostname + endpoint | [
"Get",
"the",
"base",
"URL",
"of",
"the",
"Remote",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/Remote.py#L21-L32 | [
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"hostname",
"+",
"endpoint"
] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | Remote.ping | Ping the server to make sure that you can access the base URL.
Arguments:
None
Returns:
`boolean` Successful access of server (or status code) | ndio/remote/Remote.py | def ping(self, endpoint=''):
"""
Ping the server to make sure that you can access the base URL.
Arguments:
None
Returns:
`boolean` Successful access of server (or status code)
"""
r = requests.get(self.url() + "/" + endpoint)
return r.status_code | def ping(self, endpoint=''):
"""
Ping the server to make sure that you can access the base URL.
Arguments:
None
Returns:
`boolean` Successful access of server (or status code)
"""
r = requests.get(self.url() + "/" + endpoint)
return r.status_code | [
"Ping",
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"server",
"to",
"make",
"sure",
"that",
"you",
"can",
"access",
"the",
"base",
"URL",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/Remote.py#L34-L44 | [
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test | export_dae | Converts a dense annotation to a DAE, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success | ndio/utils/mesh.py | def export_dae(filename, cutout, level=0):
"""
Converts a dense annotation to a DAE, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success
"""
if ".dae" not in filename:
filename = filename + ".dae"
vs, fs = mcubes.marching_cubes(cutout, level)
mcubes.export_mesh(vs, fs, filename, "ndioexport") | def export_dae(filename, cutout, level=0):
"""
Converts a dense annotation to a DAE, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success
"""
if ".dae" not in filename:
filename = filename + ".dae"
vs, fs = mcubes.marching_cubes(cutout, level)
mcubes.export_mesh(vs, fs, filename, "ndioexport") | [
"Converts",
"a",
"dense",
"annotation",
"to",
"a",
"DAE",
"using",
"Marching",
"Cubes",
"(",
"PyMCubes",
")",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/utils/mesh.py#L5-L21 | [
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")"
] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | export_obj | Converts a dense annotation to a obj, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success | ndio/utils/mesh.py | def export_obj(filename, cutout, level=0):
"""
Converts a dense annotation to a obj, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success
"""
if ".obj" not in filename:
filename = filename + ".obj"
vs, fs = mcubes.marching_cubes(cutout, level)
mcubes.export_obj(vs, fs, filename) | def export_obj(filename, cutout, level=0):
"""
Converts a dense annotation to a obj, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success
"""
if ".obj" not in filename:
filename = filename + ".obj"
vs, fs = mcubes.marching_cubes(cutout, level)
mcubes.export_obj(vs, fs, filename) | [
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"a",
"dense",
"annotation",
"to",
"a",
"obj",
"using",
"Marching",
"Cubes",
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")",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/utils/mesh.py#L24-L40 | [
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test | export_ply | Converts a dense annotation to a .PLY, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success | ndio/utils/mesh.py | def export_ply(filename, cutout, level=0):
"""
Converts a dense annotation to a .PLY, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success
"""
if ".ply" not in filename:
filename = filename + ".ply"
vs, fs = mcubes.marching_cubes(cutout, level)
with open(filename, 'w') as fh:
lines = [
"ply"
"format ascii 1.0",
"comment generated by ndio",
"element vertex " + str(len(vs)),
"property float32 x",
"property float32 y",
"property float32 z",
"element face " + str(len(fs)),
"property list uint8 int32 vertex_index",
"end_header"
]
fh.writelines(lines)
for v in vs:
fh.write("{} {} {}".format(v[0], v[1], v[2]))
for f in fs:
fh.write("3 {} {} {}".format(f[0], f[1], f[2])) | def export_ply(filename, cutout, level=0):
"""
Converts a dense annotation to a .PLY, using Marching Cubes (PyMCubes).
Arguments:
filename (str): The filename to write out to
cutout (numpy.ndarray): The dense annotation
level (int): The level at which to run mcubes
Returns:
boolean success
"""
if ".ply" not in filename:
filename = filename + ".ply"
vs, fs = mcubes.marching_cubes(cutout, level)
with open(filename, 'w') as fh:
lines = [
"ply"
"format ascii 1.0",
"comment generated by ndio",
"element vertex " + str(len(vs)),
"property float32 x",
"property float32 y",
"property float32 z",
"element face " + str(len(fs)),
"property list uint8 int32 vertex_index",
"end_header"
]
fh.writelines(lines)
for v in vs:
fh.write("{} {} {}".format(v[0], v[1], v[2]))
for f in fs:
fh.write("3 {} {} {}".format(f[0], f[1], f[2])) | [
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"using",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/utils/mesh.py#L43-L77 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | _guess_format_from_extension | Guess the appropriate data type from file extension.
Arguments:
ext: The file extension (period optional)
Returns:
String. The format (without leading period),
or False if none was found or couldn't be guessed | ndio/convert/convert.py | def _guess_format_from_extension(ext):
"""
Guess the appropriate data type from file extension.
Arguments:
ext: The file extension (period optional)
Returns:
String. The format (without leading period),
or False if none was found or couldn't be guessed
"""
ext = ext.strip('.')
# We look through FILE_FORMATS for this extension.
# - If it appears zero times, return False. We can't guess.
# - If it appears once, we can simply return that format.
# - If it appears more than once, we can't guess (it's ambiguous,
# e.g .m = RAMON or MATLAB)
formats = []
for fmt in FILE_FORMATS:
if ext in FILE_FORMATS[fmt]:
formats.append(fmt)
if formats == [] or len(formats) > 1:
return False
return formats[0] | def _guess_format_from_extension(ext):
"""
Guess the appropriate data type from file extension.
Arguments:
ext: The file extension (period optional)
Returns:
String. The format (without leading period),
or False if none was found or couldn't be guessed
"""
ext = ext.strip('.')
# We look through FILE_FORMATS for this extension.
# - If it appears zero times, return False. We can't guess.
# - If it appears once, we can simply return that format.
# - If it appears more than once, we can't guess (it's ambiguous,
# e.g .m = RAMON or MATLAB)
formats = []
for fmt in FILE_FORMATS:
if ext in FILE_FORMATS[fmt]:
formats.append(fmt)
if formats == [] or len(formats) > 1:
return False
return formats[0] | [
"Guess",
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"appropriate",
"data",
"type",
"from",
"file",
"extension",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/convert.py#L44-L71 | [
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test | open | Reads in a file from disk.
Arguments:
in_file: The name of the file to read in
in_fmt: The format of in_file, if you want to be explicit
Returns:
numpy.ndarray | ndio/convert/convert.py | def open(in_file, in_fmt=None):
"""
Reads in a file from disk.
Arguments:
in_file: The name of the file to read in
in_fmt: The format of in_file, if you want to be explicit
Returns:
numpy.ndarray
"""
fmt = in_file.split('.')[-1]
if in_fmt:
fmt = in_fmt
fmt = fmt.lower()
if fmt in ['png', 'jpg', 'tiff', 'tif', 'jpeg']:
return Image.open(in_file)
else:
raise NotImplementedError("Cannot open file of type {fmt}".format(fmt)) | def open(in_file, in_fmt=None):
"""
Reads in a file from disk.
Arguments:
in_file: The name of the file to read in
in_fmt: The format of in_file, if you want to be explicit
Returns:
numpy.ndarray
"""
fmt = in_file.split('.')[-1]
if in_fmt:
fmt = in_fmt
fmt = fmt.lower()
if fmt in ['png', 'jpg', 'tiff', 'tif', 'jpeg']:
return Image.open(in_file)
else:
raise NotImplementedError("Cannot open file of type {fmt}".format(fmt)) | [
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"from",
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"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/convert.py#L74-L93 | [
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test | convert | Converts in_file to out_file, guessing datatype in the absence of
in_fmt and out_fmt.
Arguments:
in_file: The name of the (existing) datafile to read
out_file: The name of the file to create with converted data
in_fmt: Optional. The format of incoming data, if not guessable
out_fmt: Optional. The format of outgoing data, if not guessable
Returns:
String. Output filename | ndio/convert/convert.py | def convert(in_file, out_file, in_fmt="", out_fmt=""):
"""
Converts in_file to out_file, guessing datatype in the absence of
in_fmt and out_fmt.
Arguments:
in_file: The name of the (existing) datafile to read
out_file: The name of the file to create with converted data
in_fmt: Optional. The format of incoming data, if not guessable
out_fmt: Optional. The format of outgoing data, if not guessable
Returns:
String. Output filename
"""
# First verify that in_file exists and out_file doesn't.
in_file = os.path.expanduser(in_file)
out_file = os.path.expanduser(out_file)
if not os.path.exists(in_file):
raise IOError("Input file {0} does not exist, stopping..."
.format(in_file))
# Get formats, either by explicitly naming them or by guessing.
# TODO: It'd be neat to check here if an explicit fmt matches the guess.
in_fmt = in_fmt.lower() or _guess_format_from_extension(
in_file.split('.')[-1].lower())
out_fmt = out_fmt.lower() or _guess_format_from_extension(
out_file.split('.')[-1].lower())
if not in_fmt or not out_fmt:
raise ValueError("Cannot determine conversion formats.")
return False
if in_fmt is out_fmt:
# This is the case when this module (intended for LONI) is used
# indescriminately to 'funnel' data into one format.
shutil.copyfileobj(in_file, out_file)
return out_file
# Import
if in_fmt == 'hdf5':
from . import hdf5
data = hdf5.load(in_file)
elif in_fmt == 'tiff':
from . import tiff
data = tiff.load(in_file)
elif in_fmt == 'png':
from . import png
data = png.load(in_file)
else:
return _fail_pair_conversion(in_fmt, out_fmt)
# Export
if out_fmt == 'hdf5':
from . import hdf5
return hdf5.save(out_file, data)
elif out_fmt == 'tiff':
from . import tiff
return tiff.save(out_file, data)
elif out_fmt == 'png':
from . import png
return png.export_png(out_file, data)
else:
return _fail_pair_conversion(in_fmt, out_fmt)
return _fail_pair_conversion(in_fmt, out_fmt) | def convert(in_file, out_file, in_fmt="", out_fmt=""):
"""
Converts in_file to out_file, guessing datatype in the absence of
in_fmt and out_fmt.
Arguments:
in_file: The name of the (existing) datafile to read
out_file: The name of the file to create with converted data
in_fmt: Optional. The format of incoming data, if not guessable
out_fmt: Optional. The format of outgoing data, if not guessable
Returns:
String. Output filename
"""
# First verify that in_file exists and out_file doesn't.
in_file = os.path.expanduser(in_file)
out_file = os.path.expanduser(out_file)
if not os.path.exists(in_file):
raise IOError("Input file {0} does not exist, stopping..."
.format(in_file))
# Get formats, either by explicitly naming them or by guessing.
# TODO: It'd be neat to check here if an explicit fmt matches the guess.
in_fmt = in_fmt.lower() or _guess_format_from_extension(
in_file.split('.')[-1].lower())
out_fmt = out_fmt.lower() or _guess_format_from_extension(
out_file.split('.')[-1].lower())
if not in_fmt or not out_fmt:
raise ValueError("Cannot determine conversion formats.")
return False
if in_fmt is out_fmt:
# This is the case when this module (intended for LONI) is used
# indescriminately to 'funnel' data into one format.
shutil.copyfileobj(in_file, out_file)
return out_file
# Import
if in_fmt == 'hdf5':
from . import hdf5
data = hdf5.load(in_file)
elif in_fmt == 'tiff':
from . import tiff
data = tiff.load(in_file)
elif in_fmt == 'png':
from . import png
data = png.load(in_file)
else:
return _fail_pair_conversion(in_fmt, out_fmt)
# Export
if out_fmt == 'hdf5':
from . import hdf5
return hdf5.save(out_file, data)
elif out_fmt == 'tiff':
from . import tiff
return tiff.save(out_file, data)
elif out_fmt == 'png':
from . import png
return png.export_png(out_file, data)
else:
return _fail_pair_conversion(in_fmt, out_fmt)
return _fail_pair_conversion(in_fmt, out_fmt) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/convert.py#L96-L161 | [
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test | grute.build_graph | Builds a graph using the graph-services endpoint.
Arguments:
project (str): The project to use
site (str): The site in question
subject (str): The subject's identifier
session (str): The session (per subject)
scan (str): The scan identifier
size (str): Whether to return a big (grute.BIG) or small
(grute.SMALL) graph. For a better explanation, see m2g.io.
email (str : self.email)*: An email to notify
invariants (str[]: Invariants.ALL)*: An array of invariants to
compute. You can use the grute.Invariants class to construct a
list, or simply pass grute.Invariants.ALL to compute them all.
fiber_file (str: DEFAULT_FIBER_FILE)*: A local filename of an
MRI Studio .dat file
atlas_file (str: None)*: A local atlas file, in NIFTI .nii format.
If none is specified, the Desikan atlas is used by default.
use_threads (bool: False)*: Whether to run the download in a Python
thread. If set to True, the call to `build_graph` will end
quickly, and the `callback` will be called with the returned
status-code of the restful call as its only argument.
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads is False. Otherwise, None
Raises:
ValueError: When the supplied values are invalid (contain invalid
characters, bad email address supplied, etc.)
RemoteDataNotFoundError: When the data cannot be processed due to
a server error. | ndio/remote/grute.py | def build_graph(self, project, site, subject, session, scan,
size, email=None, invariants=Invariants.ALL,
fiber_file=DEFAULT_FIBER_FILE, atlas_file=None,
use_threads=False, callback=None):
"""
Builds a graph using the graph-services endpoint.
Arguments:
project (str): The project to use
site (str): The site in question
subject (str): The subject's identifier
session (str): The session (per subject)
scan (str): The scan identifier
size (str): Whether to return a big (grute.BIG) or small
(grute.SMALL) graph. For a better explanation, see m2g.io.
email (str : self.email)*: An email to notify
invariants (str[]: Invariants.ALL)*: An array of invariants to
compute. You can use the grute.Invariants class to construct a
list, or simply pass grute.Invariants.ALL to compute them all.
fiber_file (str: DEFAULT_FIBER_FILE)*: A local filename of an
MRI Studio .dat file
atlas_file (str: None)*: A local atlas file, in NIFTI .nii format.
If none is specified, the Desikan atlas is used by default.
use_threads (bool: False)*: Whether to run the download in a Python
thread. If set to True, the call to `build_graph` will end
quickly, and the `callback` will be called with the returned
status-code of the restful call as its only argument.
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads is False. Otherwise, None
Raises:
ValueError: When the supplied values are invalid (contain invalid
characters, bad email address supplied, etc.)
RemoteDataNotFoundError: When the data cannot be processed due to
a server error.
"""
if email is None:
email = self.email
if not set(invariants) <= set(Invariants.ALL):
raise ValueError("Invariants must be a subset of Invariants.ALL.")
if use_threads and callback is not None:
if not hasattr(callback, '__call__'):
raise ValueError("callback must be a function.")
if len(inspect.getargspec(callback).args) != 1:
raise ValueError("callback must take exactly 1 argument.")
# Once we get here, we know the callback is
if size not in [self.BIG, self.SMALL]:
raise ValueError("size must be either grute.BIG or grute.SMALL.")
url = "buildgraph/{}/{}/{}/{}/{}/{}/{}/{}/".format(
project,
site,
subject,
session,
scan,
size,
email,
"/".join(invariants)
)
if " " in url:
raise ValueError("Arguments must not contain spaces.")
if use_threads:
# Run in the background.
download_thread = threading.Thread(
target=self._run_build_graph,
args=[url, fiber_file, atlas_file, callback]
)
download_thread.start()
else:
# Run in the foreground.
return self._run_build_graph(url, fiber_file, atlas_file)
return | def build_graph(self, project, site, subject, session, scan,
size, email=None, invariants=Invariants.ALL,
fiber_file=DEFAULT_FIBER_FILE, atlas_file=None,
use_threads=False, callback=None):
"""
Builds a graph using the graph-services endpoint.
Arguments:
project (str): The project to use
site (str): The site in question
subject (str): The subject's identifier
session (str): The session (per subject)
scan (str): The scan identifier
size (str): Whether to return a big (grute.BIG) or small
(grute.SMALL) graph. For a better explanation, see m2g.io.
email (str : self.email)*: An email to notify
invariants (str[]: Invariants.ALL)*: An array of invariants to
compute. You can use the grute.Invariants class to construct a
list, or simply pass grute.Invariants.ALL to compute them all.
fiber_file (str: DEFAULT_FIBER_FILE)*: A local filename of an
MRI Studio .dat file
atlas_file (str: None)*: A local atlas file, in NIFTI .nii format.
If none is specified, the Desikan atlas is used by default.
use_threads (bool: False)*: Whether to run the download in a Python
thread. If set to True, the call to `build_graph` will end
quickly, and the `callback` will be called with the returned
status-code of the restful call as its only argument.
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads is False. Otherwise, None
Raises:
ValueError: When the supplied values are invalid (contain invalid
characters, bad email address supplied, etc.)
RemoteDataNotFoundError: When the data cannot be processed due to
a server error.
"""
if email is None:
email = self.email
if not set(invariants) <= set(Invariants.ALL):
raise ValueError("Invariants must be a subset of Invariants.ALL.")
if use_threads and callback is not None:
if not hasattr(callback, '__call__'):
raise ValueError("callback must be a function.")
if len(inspect.getargspec(callback).args) != 1:
raise ValueError("callback must take exactly 1 argument.")
# Once we get here, we know the callback is
if size not in [self.BIG, self.SMALL]:
raise ValueError("size must be either grute.BIG or grute.SMALL.")
url = "buildgraph/{}/{}/{}/{}/{}/{}/{}/{}/".format(
project,
site,
subject,
session,
scan,
size,
email,
"/".join(invariants)
)
if " " in url:
raise ValueError("Arguments must not contain spaces.")
if use_threads:
# Run in the background.
download_thread = threading.Thread(
target=self._run_build_graph,
args=[url, fiber_file, atlas_file, callback]
)
download_thread.start()
else:
# Run in the foreground.
return self._run_build_graph(url, fiber_file, atlas_file)
return | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/grute.py#L151-L231 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | grute.compute_invariants | Compute invariants from an existing GraphML file using the remote
grute graph services.
Arguments:
graph_file (str): The filename of the graphml file
input_format (str): One of grute.GraphFormats
invariants (str[]: Invariants.ALL)*: An array of grute.Invariants
to compute on the graph
email (str: self.email)*: The email to notify upon completion
use_threads (bool: False)*: Whether to use Python threads to run
computation in the background when waiting for the server to
return the invariants
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads is False. Otherwise, None
Raises:
ValueError: If the graph file does not exist, or if there are
issues with the passed arguments
RemoteDataUploadError: If there is an issue packing the file
RemoteError: If the server experiences difficulty computing invs | ndio/remote/grute.py | def compute_invariants(self, graph_file, input_format,
invariants=Invariants.ALL, email=None,
use_threads=False, callback=None):
"""
Compute invariants from an existing GraphML file using the remote
grute graph services.
Arguments:
graph_file (str): The filename of the graphml file
input_format (str): One of grute.GraphFormats
invariants (str[]: Invariants.ALL)*: An array of grute.Invariants
to compute on the graph
email (str: self.email)*: The email to notify upon completion
use_threads (bool: False)*: Whether to use Python threads to run
computation in the background when waiting for the server to
return the invariants
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads is False. Otherwise, None
Raises:
ValueError: If the graph file does not exist, or if there are
issues with the passed arguments
RemoteDataUploadError: If there is an issue packing the file
RemoteError: If the server experiences difficulty computing invs
"""
if email is None:
email = self.email
if input_format not in GraphFormats._any:
raise ValueError("Invalid input format, {}.".format(input_format))
if not set(invariants) <= set(Invariants.ALL):
raise ValueError("Invariants must be a subset of Invariants.ALL.")
if use_threads and callback is not None:
if not hasattr(callback, '__call__'):
raise ValueError("callback must be a function.")
if len(inspect.getargspec(callback).args) != 1:
raise ValueError("callback must take exactly 1 argument.")
url = "graphupload/{}/{}/{}/".format(
email,
input_format,
"/".join(invariants)
)
if " " in url:
raise ValueError("Arguments cannot have spaces in them.")
if not (os.path.exists(graph_file)):
raise ValueError("File {} does not exist.".format(graph_file))
if use_threads:
# Run in the background.
upload_thread = threading.Thread(
target=self._run_compute_invariants,
args=[url, graph_file, callback]
)
upload_thread.start()
else:
# Run in the foreground.
return self._run_compute_invariants(url, graph_file)
return | def compute_invariants(self, graph_file, input_format,
invariants=Invariants.ALL, email=None,
use_threads=False, callback=None):
"""
Compute invariants from an existing GraphML file using the remote
grute graph services.
Arguments:
graph_file (str): The filename of the graphml file
input_format (str): One of grute.GraphFormats
invariants (str[]: Invariants.ALL)*: An array of grute.Invariants
to compute on the graph
email (str: self.email)*: The email to notify upon completion
use_threads (bool: False)*: Whether to use Python threads to run
computation in the background when waiting for the server to
return the invariants
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads is False. Otherwise, None
Raises:
ValueError: If the graph file does not exist, or if there are
issues with the passed arguments
RemoteDataUploadError: If there is an issue packing the file
RemoteError: If the server experiences difficulty computing invs
"""
if email is None:
email = self.email
if input_format not in GraphFormats._any:
raise ValueError("Invalid input format, {}.".format(input_format))
if not set(invariants) <= set(Invariants.ALL):
raise ValueError("Invariants must be a subset of Invariants.ALL.")
if use_threads and callback is not None:
if not hasattr(callback, '__call__'):
raise ValueError("callback must be a function.")
if len(inspect.getargspec(callback).args) != 1:
raise ValueError("callback must take exactly 1 argument.")
url = "graphupload/{}/{}/{}/".format(
email,
input_format,
"/".join(invariants)
)
if " " in url:
raise ValueError("Arguments cannot have spaces in them.")
if not (os.path.exists(graph_file)):
raise ValueError("File {} does not exist.".format(graph_file))
if use_threads:
# Run in the background.
upload_thread = threading.Thread(
target=self._run_compute_invariants,
args=[url, graph_file, callback]
)
upload_thread.start()
else:
# Run in the foreground.
return self._run_compute_invariants(url, graph_file)
return | [
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"services",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/grute.py#L262-L329 | [
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test | grute.convert_graph | Convert a graph from one GraphFormat to another.
Arguments:
graph_file (str): Filename of the file to convert
input_format (str): A grute.GraphFormats
output_formats (str[]): A grute.GraphFormats
email (str: self.email)*: The email to notify
use_threads (bool: False)*: Whether to use Python threads to run
computation in the background when waiting for the server
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads=False. Else, no return value.
Raises:
RemoteDataUploadError: If there's an issue uploading the data
RemoteError: If there's a server-side issue
ValueError: If there's a problem with the supplied arguments | ndio/remote/grute.py | def convert_graph(self, graph_file, input_format, output_formats,
email=None, use_threads=False, callback=None):
"""
Convert a graph from one GraphFormat to another.
Arguments:
graph_file (str): Filename of the file to convert
input_format (str): A grute.GraphFormats
output_formats (str[]): A grute.GraphFormats
email (str: self.email)*: The email to notify
use_threads (bool: False)*: Whether to use Python threads to run
computation in the background when waiting for the server
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads=False. Else, no return value.
Raises:
RemoteDataUploadError: If there's an issue uploading the data
RemoteError: If there's a server-side issue
ValueError: If there's a problem with the supplied arguments
"""
if email is None:
email = self.email
if input_format not in GraphFormats._any:
raise ValueError("Invalid input format {}.".format(input_format))
if not set(output_formats) <= set(GraphFormats._any):
raise ValueError("Output formats must be a GraphFormats.")
if use_threads and callback is not None:
if not hasattr(callback, '__call__'):
raise ValueError("callback must be a function.")
if len(inspect.getargspec(callback).args) != 1:
raise ValueError("callback must take exactly 1 argument.")
if not (os.path.exists(graph_file)):
raise ValueError("No such file, {}!".format(graph_file))
url = "convert/{}/{}/{}/l".format(
email,
input_format,
','.join(output_formats)
)
if " " in url:
raise ValueError("Spaces are not permitted in arguments.")
if use_threads:
# Run in the background.
convert_thread = threading.Thread(
target=self._run_convert_graph,
args=[url, graph_file, callback]
)
convert_thread.start()
else:
# Run in the foreground.
return self._run_convert_graph(url, graph_file)
return | def convert_graph(self, graph_file, input_format, output_formats,
email=None, use_threads=False, callback=None):
"""
Convert a graph from one GraphFormat to another.
Arguments:
graph_file (str): Filename of the file to convert
input_format (str): A grute.GraphFormats
output_formats (str[]): A grute.GraphFormats
email (str: self.email)*: The email to notify
use_threads (bool: False)*: Whether to use Python threads to run
computation in the background when waiting for the server
callback (function: None)*: The function to run upon completion of
the call, if using threads. (Will not be called if use_threads
is set to False.)
Returns:
HTTP Response if use_threads=False. Else, no return value.
Raises:
RemoteDataUploadError: If there's an issue uploading the data
RemoteError: If there's a server-side issue
ValueError: If there's a problem with the supplied arguments
"""
if email is None:
email = self.email
if input_format not in GraphFormats._any:
raise ValueError("Invalid input format {}.".format(input_format))
if not set(output_formats) <= set(GraphFormats._any):
raise ValueError("Output formats must be a GraphFormats.")
if use_threads and callback is not None:
if not hasattr(callback, '__call__'):
raise ValueError("callback must be a function.")
if len(inspect.getargspec(callback).args) != 1:
raise ValueError("callback must take exactly 1 argument.")
if not (os.path.exists(graph_file)):
raise ValueError("No such file, {}!".format(graph_file))
url = "convert/{}/{}/{}/l".format(
email,
input_format,
','.join(output_formats)
)
if " " in url:
raise ValueError("Spaces are not permitted in arguments.")
if use_threads:
# Run in the background.
convert_thread = threading.Thread(
target=self._run_convert_graph,
args=[url, graph_file, callback]
)
convert_thread.start()
else:
# Run in the foreground.
return self._run_convert_graph(url, graph_file)
return | [
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"to",
"another",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/grute.py#L356-L417 | [
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test | to_dict | Converts a RAMON object list to a JSON-style dictionary. Useful for going
from an array of RAMONs to a dictionary, indexed by ID.
Arguments:
ramons (RAMON[]): A list of RAMON objects
flatten (boolean: False): Not implemented
Returns:
dict: A python dictionary of RAMON objects. | ndio/ramon/__init__.py | def to_dict(ramons, flatten=False):
"""
Converts a RAMON object list to a JSON-style dictionary. Useful for going
from an array of RAMONs to a dictionary, indexed by ID.
Arguments:
ramons (RAMON[]): A list of RAMON objects
flatten (boolean: False): Not implemented
Returns:
dict: A python dictionary of RAMON objects.
"""
if type(ramons) is not list:
ramons = [ramons]
out_ramons = {}
for r in ramons:
out_ramons[r.id] = {
"id": r.id,
"type": _reverse_ramon_types[type(r)],
"metadata": vars(r)
}
return out_ramons | def to_dict(ramons, flatten=False):
"""
Converts a RAMON object list to a JSON-style dictionary. Useful for going
from an array of RAMONs to a dictionary, indexed by ID.
Arguments:
ramons (RAMON[]): A list of RAMON objects
flatten (boolean: False): Not implemented
Returns:
dict: A python dictionary of RAMON objects.
"""
if type(ramons) is not list:
ramons = [ramons]
out_ramons = {}
for r in ramons:
out_ramons[r.id] = {
"id": r.id,
"type": _reverse_ramon_types[type(r)],
"metadata": vars(r)
}
return out_ramons | [
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"of",
"RAMONs",
"to",
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"dictionary",
"indexed",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/ramon/__init__.py#L152-L174 | [
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test | to_json | Converts RAMON objects into a JSON string which can be directly written out
to a .json file. You can pass either a single RAMON or a list. If you pass
a single RAMON, it will still be exported with the ID as the key. In other
words:
type(from_json(to_json(ramon))) # ALWAYS returns a list
...even if `type(ramon)` is a RAMON, not a list.
Arguments:
ramons (RAMON or list): The RAMON object(s) to convert to JSON.
flatten (bool : False): If ID should be used as a key. If not, then
a single JSON document is returned.
Returns:
str: The JSON representation of the RAMON objects, in the schema:
```
{
<id>: {
type: . . . ,
metadata: {
. . .
}
},
}
```
Raises:
ValueError: If an invalid RAMON is passed. | ndio/ramon/__init__.py | def to_json(ramons, flatten=False):
"""
Converts RAMON objects into a JSON string which can be directly written out
to a .json file. You can pass either a single RAMON or a list. If you pass
a single RAMON, it will still be exported with the ID as the key. In other
words:
type(from_json(to_json(ramon))) # ALWAYS returns a list
...even if `type(ramon)` is a RAMON, not a list.
Arguments:
ramons (RAMON or list): The RAMON object(s) to convert to JSON.
flatten (bool : False): If ID should be used as a key. If not, then
a single JSON document is returned.
Returns:
str: The JSON representation of the RAMON objects, in the schema:
```
{
<id>: {
type: . . . ,
metadata: {
. . .
}
},
}
```
Raises:
ValueError: If an invalid RAMON is passed.
"""
if type(ramons) is not list:
ramons = [ramons]
out_ramons = {}
for r in ramons:
out_ramons[r.id] = {
"id": r.id,
"type": _reverse_ramon_types[type(r)],
"metadata": vars(r)
}
if flatten:
return jsonlib.dumps(out_ramons.values()[0])
return jsonlib.dumps(out_ramons) | def to_json(ramons, flatten=False):
"""
Converts RAMON objects into a JSON string which can be directly written out
to a .json file. You can pass either a single RAMON or a list. If you pass
a single RAMON, it will still be exported with the ID as the key. In other
words:
type(from_json(to_json(ramon))) # ALWAYS returns a list
...even if `type(ramon)` is a RAMON, not a list.
Arguments:
ramons (RAMON or list): The RAMON object(s) to convert to JSON.
flatten (bool : False): If ID should be used as a key. If not, then
a single JSON document is returned.
Returns:
str: The JSON representation of the RAMON objects, in the schema:
```
{
<id>: {
type: . . . ,
metadata: {
. . .
}
},
}
```
Raises:
ValueError: If an invalid RAMON is passed.
"""
if type(ramons) is not list:
ramons = [ramons]
out_ramons = {}
for r in ramons:
out_ramons[r.id] = {
"id": r.id,
"type": _reverse_ramon_types[type(r)],
"metadata": vars(r)
}
if flatten:
return jsonlib.dumps(out_ramons.values()[0])
return jsonlib.dumps(out_ramons) | [
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test | from_json | Converts JSON to a python list of RAMON objects. if `cutout` is provided,
the `cutout` attribute of the RAMON object is populated. Otherwise, it's
left empty. `json` should be an ID-level dictionary, like so:
{
16: {
type: "segment",
metadata: {
. . .
}
},
}
NOTE: If more than one item is in the dictionary, then a Python list of
RAMON objects is returned instead of a single RAMON.
Arguments:
json (str or dict): The JSON to import to RAMON objects
cutout: Currently not supported.
Returns:
[RAMON] | ndio/ramon/__init__.py | def from_json(json, cutout=None):
"""
Converts JSON to a python list of RAMON objects. if `cutout` is provided,
the `cutout` attribute of the RAMON object is populated. Otherwise, it's
left empty. `json` should be an ID-level dictionary, like so:
{
16: {
type: "segment",
metadata: {
. . .
}
},
}
NOTE: If more than one item is in the dictionary, then a Python list of
RAMON objects is returned instead of a single RAMON.
Arguments:
json (str or dict): The JSON to import to RAMON objects
cutout: Currently not supported.
Returns:
[RAMON]
"""
if type(json) is str:
json = jsonlib.loads(json)
out_ramons = []
for (rid, rdata) in six.iteritems(json):
_md = rdata['metadata']
r = AnnotationType.RAMON(rdata['type'])(
id=rid,
author=_md['author'],
status=_md['status'],
confidence=_md['confidence'],
kvpairs=copy.deepcopy(_md['kvpairs'])
)
if rdata['type'] == 'segment':
r.segmentclass = _md.get('segmentclass')
r.neuron = _md.get('neuron')
if 'synapses' in _md:
r.synapses = _md['synapses'][:]
if 'organelles' in _md:
r.organelles = _md['organelles'][:]
elif rdata['type'] in ['neuron', 'synapse']:
if 'segments' in _md:
r.segments = _md['segments'][:]
elif rdata['type'] == 'organelle':
r.organelle_class = _md['organelleclass'][:]
elif rdata['type'] == 'synapse':
r.synapse_type = _md.get('synapse_type')
r.weight = _md.get('weight')
out_ramons.append(r)
return out_ramons | def from_json(json, cutout=None):
"""
Converts JSON to a python list of RAMON objects. if `cutout` is provided,
the `cutout` attribute of the RAMON object is populated. Otherwise, it's
left empty. `json` should be an ID-level dictionary, like so:
{
16: {
type: "segment",
metadata: {
. . .
}
},
}
NOTE: If more than one item is in the dictionary, then a Python list of
RAMON objects is returned instead of a single RAMON.
Arguments:
json (str or dict): The JSON to import to RAMON objects
cutout: Currently not supported.
Returns:
[RAMON]
"""
if type(json) is str:
json = jsonlib.loads(json)
out_ramons = []
for (rid, rdata) in six.iteritems(json):
_md = rdata['metadata']
r = AnnotationType.RAMON(rdata['type'])(
id=rid,
author=_md['author'],
status=_md['status'],
confidence=_md['confidence'],
kvpairs=copy.deepcopy(_md['kvpairs'])
)
if rdata['type'] == 'segment':
r.segmentclass = _md.get('segmentclass')
r.neuron = _md.get('neuron')
if 'synapses' in _md:
r.synapses = _md['synapses'][:]
if 'organelles' in _md:
r.organelles = _md['organelles'][:]
elif rdata['type'] in ['neuron', 'synapse']:
if 'segments' in _md:
r.segments = _md['segments'][:]
elif rdata['type'] == 'organelle':
r.organelle_class = _md['organelleclass'][:]
elif rdata['type'] == 'synapse':
r.synapse_type = _md.get('synapse_type')
r.weight = _md.get('weight')
out_ramons.append(r)
return out_ramons | [
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test | from_hdf5 | Converts an HDF5 file to a RAMON object. Returns an object that is a child-
-class of RAMON (though it's determined at run-time what type is returned).
Accessing multiple IDs from the same file is not supported, because it's
not dramatically faster to access each item in the hdf5 file at the same
time It's semantically and computationally easier to run this function
several times on the same file.
Arguments:
hdf5 (h5py.File): A h5py File object that holds RAMON data
anno_id (int): The ID of the RAMON obj to extract from the file. This
defaults to the first one (sorted) if none is specified.
Returns:
ndio.RAMON object | ndio/ramon/__init__.py | def from_hdf5(hdf5, anno_id=None):
"""
Converts an HDF5 file to a RAMON object. Returns an object that is a child-
-class of RAMON (though it's determined at run-time what type is returned).
Accessing multiple IDs from the same file is not supported, because it's
not dramatically faster to access each item in the hdf5 file at the same
time It's semantically and computationally easier to run this function
several times on the same file.
Arguments:
hdf5 (h5py.File): A h5py File object that holds RAMON data
anno_id (int): The ID of the RAMON obj to extract from the file. This
defaults to the first one (sorted) if none is specified.
Returns:
ndio.RAMON object
"""
if anno_id is None:
# The user just wants the first item we find, so... Yeah.
return from_hdf5(hdf5, list(hdf5.keys())[0])
# First, get the actual object we're going to download.
anno_id = str(anno_id)
if anno_id not in list(hdf5.keys()):
raise ValueError("ID {} is not in this file. Options are: {}".format(
anno_id,
", ".join(list(hdf5.keys()))
))
anno = hdf5[anno_id]
# anno now holds just the RAMON of interest
# This is the most complicated line in here: It creates an object whose
# type is conditional on the ANNOTATION_TYPE of the hdf5 object.
try:
r = AnnotationType.get_class(anno['ANNOTATION_TYPE'][0])()
except:
raise InvalidRAMONError("This is not a valid RAMON type.")
# All RAMON types definitely have these attributes:
metadata = anno['METADATA']
r.author = metadata['AUTHOR'][0]
r.confidence = metadata['CONFIDENCE'][0]
r.status = metadata['STATUS'][0]
r.id = anno_id
# These are a little tougher, some RAMON types have special attributes:
if type(r) in [RAMONNeuron, RAMONSynapse]:
r.segments = metadata['SEGMENTS'][()]
if 'KVPAIRS' in metadata:
kvs = metadata['KVPAIRS'][()][0].split()
if len(kvs) != 0:
for i in kvs:
k, v = str(i).split(',')
r.kvpairs[str(k)] = str(v)
else:
r.kvpairs = {}
if issubclass(type(r), RAMONVolume):
if 'CUTOUT' in anno:
r.cutout = anno['CUTOUT'][()]
if 'XYZOFFSET' in anno:
r.cutout = anno['XYZOFFSET'][()]
if 'RESOLUTION' in anno:
r.cutout = anno['RESOLUTION'][()]
if type(r) is RAMONSynapse:
r.synapse_type = metadata['SYNAPSE_TYPE'][0]
r.weight = metadata['WEIGHT'][0]
if type(r) is RAMONSegment:
if 'NEURON' in metadata:
r.neuron = metadata['NEURON'][0]
if 'PARENTSEED' in metadata:
r.parent_seed = metadata['PARENTSEED'][0]
if 'SEGMENTCLASS' in metadata:
r.segmentclass = metadata['SEGMENTCLASS'][0]
if 'SYNAPSES' in metadata:
r.synapses = metadata['SYNAPSES'][()]
if 'ORGANELLES' in metadata:
r.organelles = metadata['ORGANELLES'][()]
if type(r) is RAMONOrganelle:
r.organelle_class = metadata['ORGANELLECLASS'][0]
return r | def from_hdf5(hdf5, anno_id=None):
"""
Converts an HDF5 file to a RAMON object. Returns an object that is a child-
-class of RAMON (though it's determined at run-time what type is returned).
Accessing multiple IDs from the same file is not supported, because it's
not dramatically faster to access each item in the hdf5 file at the same
time It's semantically and computationally easier to run this function
several times on the same file.
Arguments:
hdf5 (h5py.File): A h5py File object that holds RAMON data
anno_id (int): The ID of the RAMON obj to extract from the file. This
defaults to the first one (sorted) if none is specified.
Returns:
ndio.RAMON object
"""
if anno_id is None:
# The user just wants the first item we find, so... Yeah.
return from_hdf5(hdf5, list(hdf5.keys())[0])
# First, get the actual object we're going to download.
anno_id = str(anno_id)
if anno_id not in list(hdf5.keys()):
raise ValueError("ID {} is not in this file. Options are: {}".format(
anno_id,
", ".join(list(hdf5.keys()))
))
anno = hdf5[anno_id]
# anno now holds just the RAMON of interest
# This is the most complicated line in here: It creates an object whose
# type is conditional on the ANNOTATION_TYPE of the hdf5 object.
try:
r = AnnotationType.get_class(anno['ANNOTATION_TYPE'][0])()
except:
raise InvalidRAMONError("This is not a valid RAMON type.")
# All RAMON types definitely have these attributes:
metadata = anno['METADATA']
r.author = metadata['AUTHOR'][0]
r.confidence = metadata['CONFIDENCE'][0]
r.status = metadata['STATUS'][0]
r.id = anno_id
# These are a little tougher, some RAMON types have special attributes:
if type(r) in [RAMONNeuron, RAMONSynapse]:
r.segments = metadata['SEGMENTS'][()]
if 'KVPAIRS' in metadata:
kvs = metadata['KVPAIRS'][()][0].split()
if len(kvs) != 0:
for i in kvs:
k, v = str(i).split(',')
r.kvpairs[str(k)] = str(v)
else:
r.kvpairs = {}
if issubclass(type(r), RAMONVolume):
if 'CUTOUT' in anno:
r.cutout = anno['CUTOUT'][()]
if 'XYZOFFSET' in anno:
r.cutout = anno['XYZOFFSET'][()]
if 'RESOLUTION' in anno:
r.cutout = anno['RESOLUTION'][()]
if type(r) is RAMONSynapse:
r.synapse_type = metadata['SYNAPSE_TYPE'][0]
r.weight = metadata['WEIGHT'][0]
if type(r) is RAMONSegment:
if 'NEURON' in metadata:
r.neuron = metadata['NEURON'][0]
if 'PARENTSEED' in metadata:
r.parent_seed = metadata['PARENTSEED'][0]
if 'SEGMENTCLASS' in metadata:
r.segmentclass = metadata['SEGMENTCLASS'][0]
if 'SYNAPSES' in metadata:
r.synapses = metadata['SYNAPSES'][()]
if 'ORGANELLES' in metadata:
r.organelles = metadata['ORGANELLES'][()]
if type(r) is RAMONOrganelle:
r.organelle_class = metadata['ORGANELLECLASS'][0]
return r | [
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test | to_hdf5 | Exports a RAMON object to an HDF5 file object.
Arguments:
ramon (RAMON): A subclass of RAMONBase
hdf5 (str): Export filename
Returns:
hdf5.File
Raises:
InvalidRAMONError: if you pass a non-RAMON object | ndio/ramon/__init__.py | def to_hdf5(ramon, hdf5=None):
"""
Exports a RAMON object to an HDF5 file object.
Arguments:
ramon (RAMON): A subclass of RAMONBase
hdf5 (str): Export filename
Returns:
hdf5.File
Raises:
InvalidRAMONError: if you pass a non-RAMON object
"""
if issubclass(type(ramon), RAMONBase) is False:
raise InvalidRAMONError("Invalid RAMON supplied to ramon.to_hdf5.")
import h5py
import numpy
if hdf5 is None:
tmpfile = tempfile.NamedTemporaryFile(delete=False)
else:
tmpfile = hdf5
with h5py.File(tmpfile.name, "a") as hdf5:
# First we'll export things that all RAMON objects have in
# common, starting with the Group that encompasses each ID:
grp = hdf5.create_group(str(ramon.id))
grp.create_dataset("ANNOTATION_TYPE", (1,),
numpy.uint32,
data=AnnotationType.get_int(type(ramon)))
if hasattr(ramon, 'cutout'):
if ramon.cutout is not None:
grp.create_dataset('CUTOUT', ramon.cutout.shape,
ramon.cutout.dtype, data=ramon.cutout)
grp.create_dataset('RESOLUTION', (1,),
numpy.uint32, data=ramon.resolution)
grp.create_dataset('XYZOFFSET', (3,),
numpy.uint32, data=ramon.xyz_offset)
# Next, add general metadata.
metadata = grp.create_group('METADATA')
metadata.create_dataset('AUTHOR', (1,),
dtype=h5py.special_dtype(vlen=str),
data=ramon.author)
fstring = StringIO()
csvw = csv.writer(fstring, delimiter=',')
csvw.writerows([r for r in six.iteritems(ramon.kvpairs)])
metadata.create_dataset('KVPAIRS', (1,),
dtype=h5py.special_dtype(vlen=str),
data=fstring.getvalue())
metadata.create_dataset('CONFIDENCE', (1,), numpy.float,
data=ramon.confidence)
metadata.create_dataset('STATUS', (1,), numpy.uint32,
data=ramon.status)
# Finally, add type-specific metadata:
if hasattr(ramon, 'segments'):
metadata.create_dataset('SEGMENTS',
data=numpy.asarray(ramon.segments,
dtype=numpy.uint32))
if hasattr(ramon, 'synapse_type'):
metadata.create_dataset('SYNAPSE_TYPE', (1,), numpy.uint32,
data=ramon.synapse_type)
if hasattr(ramon, 'weight'):
metadata.create_dataset('WEIGHT', (1,),
numpy.float, data=ramon.weight)
if hasattr(ramon, 'neuron'):
metadata.create_dataset('NEURON', (1,),
numpy.uint32, data=ramon.neuron)
if hasattr(ramon, 'segmentclass'):
metadata.create_dataset('SEGMENTCLASS', (1,), numpy.uint32,
data=ramon.segmentclass)
if hasattr(ramon, 'synapses'):
metadata.create_dataset('SYNAPSES', (len(ramon.synapses),),
numpy.uint32, data=ramon.synapses)
if hasattr(ramon, 'organelles'):
metadata.create_dataset('ORGANELLES',
(len(ramon.organelles),),
numpy.uint32,
data=ramon.organelles)
if hasattr(ramon, 'organelle_class'):
metadata.create_dataset('ORGANELLECLASS', (1,),
numpy.uint32,
data=ramon.organelle_class)
hdf5.flush()
tmpfile.seek(0)
return tmpfile
return False | def to_hdf5(ramon, hdf5=None):
"""
Exports a RAMON object to an HDF5 file object.
Arguments:
ramon (RAMON): A subclass of RAMONBase
hdf5 (str): Export filename
Returns:
hdf5.File
Raises:
InvalidRAMONError: if you pass a non-RAMON object
"""
if issubclass(type(ramon), RAMONBase) is False:
raise InvalidRAMONError("Invalid RAMON supplied to ramon.to_hdf5.")
import h5py
import numpy
if hdf5 is None:
tmpfile = tempfile.NamedTemporaryFile(delete=False)
else:
tmpfile = hdf5
with h5py.File(tmpfile.name, "a") as hdf5:
# First we'll export things that all RAMON objects have in
# common, starting with the Group that encompasses each ID:
grp = hdf5.create_group(str(ramon.id))
grp.create_dataset("ANNOTATION_TYPE", (1,),
numpy.uint32,
data=AnnotationType.get_int(type(ramon)))
if hasattr(ramon, 'cutout'):
if ramon.cutout is not None:
grp.create_dataset('CUTOUT', ramon.cutout.shape,
ramon.cutout.dtype, data=ramon.cutout)
grp.create_dataset('RESOLUTION', (1,),
numpy.uint32, data=ramon.resolution)
grp.create_dataset('XYZOFFSET', (3,),
numpy.uint32, data=ramon.xyz_offset)
# Next, add general metadata.
metadata = grp.create_group('METADATA')
metadata.create_dataset('AUTHOR', (1,),
dtype=h5py.special_dtype(vlen=str),
data=ramon.author)
fstring = StringIO()
csvw = csv.writer(fstring, delimiter=',')
csvw.writerows([r for r in six.iteritems(ramon.kvpairs)])
metadata.create_dataset('KVPAIRS', (1,),
dtype=h5py.special_dtype(vlen=str),
data=fstring.getvalue())
metadata.create_dataset('CONFIDENCE', (1,), numpy.float,
data=ramon.confidence)
metadata.create_dataset('STATUS', (1,), numpy.uint32,
data=ramon.status)
# Finally, add type-specific metadata:
if hasattr(ramon, 'segments'):
metadata.create_dataset('SEGMENTS',
data=numpy.asarray(ramon.segments,
dtype=numpy.uint32))
if hasattr(ramon, 'synapse_type'):
metadata.create_dataset('SYNAPSE_TYPE', (1,), numpy.uint32,
data=ramon.synapse_type)
if hasattr(ramon, 'weight'):
metadata.create_dataset('WEIGHT', (1,),
numpy.float, data=ramon.weight)
if hasattr(ramon, 'neuron'):
metadata.create_dataset('NEURON', (1,),
numpy.uint32, data=ramon.neuron)
if hasattr(ramon, 'segmentclass'):
metadata.create_dataset('SEGMENTCLASS', (1,), numpy.uint32,
data=ramon.segmentclass)
if hasattr(ramon, 'synapses'):
metadata.create_dataset('SYNAPSES', (len(ramon.synapses),),
numpy.uint32, data=ramon.synapses)
if hasattr(ramon, 'organelles'):
metadata.create_dataset('ORGANELLES',
(len(ramon.organelles),),
numpy.uint32,
data=ramon.organelles)
if hasattr(ramon, 'organelle_class'):
metadata.create_dataset('ORGANELLECLASS', (1,),
numpy.uint32,
data=ramon.organelle_class)
hdf5.flush()
tmpfile.seek(0)
return tmpfile
return False | [
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test | AnnotationType.RAMON | Takes str or int, returns class type | ndio/ramon/__init__.py | def RAMON(typ):
"""
Takes str or int, returns class type
"""
if six.PY2:
lookup = [str, unicode]
elif six.PY3:
lookup = [str]
if type(typ) is int:
return _ramon_types[typ]
elif type(typ) in lookup:
return _ramon_types[_types[typ]] | def RAMON(typ):
"""
Takes str or int, returns class type
"""
if six.PY2:
lookup = [str, unicode]
elif six.PY3:
lookup = [str]
if type(typ) is int:
return _ramon_types[typ]
elif type(typ) in lookup:
return _ramon_types[_types[typ]] | [
"Takes",
"str",
"or",
"int",
"returns",
"class",
"type"
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/ramon/__init__.py#L137-L149 | [
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test | neurodata.get_xy_slice | Return a binary-encoded, decompressed 2d image. You should
specify a 'token' and 'channel' pair. For image data, users
should use the channel 'image.'
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int):` The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
z_index (int): The z-slice to image
Returns:
str: binary image data | ndio/remote/neurodata.py | def get_xy_slice(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_index,
resolution=0):
"""
Return a binary-encoded, decompressed 2d image. You should
specify a 'token' and 'channel' pair. For image data, users
should use the channel 'image.'
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int):` The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
z_index (int): The z-slice to image
Returns:
str: binary image data
"""
return self.data.get_xy_slice(token, channel,
x_start, x_stop,
y_start, y_stop,
z_index,
resolution) | def get_xy_slice(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_index,
resolution=0):
"""
Return a binary-encoded, decompressed 2d image. You should
specify a 'token' and 'channel' pair. For image data, users
should use the channel 'image.'
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int):` The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
z_index (int): The z-slice to image
Returns:
str: binary image data
"""
return self.data.get_xy_slice(token, channel,
x_start, x_stop,
y_start, y_stop,
z_index,
resolution) | [
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test | neurodata.get_volume | Get a RAMONVolume volumetric cutout from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
ndio.ramon.RAMONVolume: Downloaded data. | ndio/remote/neurodata.py | def get_volume(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get a RAMONVolume volumetric cutout from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
ndio.ramon.RAMONVolume: Downloaded data.
"""
return self.data.get_volume(token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution, block_size, neariso) | def get_volume(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get a RAMONVolume volumetric cutout from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
ndio.ramon.RAMONVolume: Downloaded data.
"""
return self.data.get_volume(token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution, block_size, neariso) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neurodata.py#L162-L189 | [
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test | neurodata.get_cutout | Get volumetric cutout data from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset. If not provided,
ndio uses the metadata of this tokenchannel to set. If you find
that your downloads are timing out or otherwise failing, it may
be wise to start off by making this smaller.
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
numpy.ndarray: Downloaded data. | ndio/remote/neurodata.py | def get_cutout(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start=0, t_stop=1,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get volumetric cutout data from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset. If not provided,
ndio uses the metadata of this tokenchannel to set. If you find
that your downloads are timing out or otherwise failing, it may
be wise to start off by making this smaller.
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
numpy.ndarray: Downloaded data.
"""
return self.data.get_cutout(token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start, t_stop,
resolution,
block_size,
neariso) | def get_cutout(self, token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start=0, t_stop=1,
resolution=1,
block_size=DEFAULT_BLOCK_SIZE,
neariso=False):
"""
Get volumetric cutout data from the neurodata server.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
block_size (int[3]): Block size of this dataset. If not provided,
ndio uses the metadata of this tokenchannel to set. If you find
that your downloads are timing out or otherwise failing, it may
be wise to start off by making this smaller.
neariso (bool : False): Passes the 'neariso' param to the cutout.
If you don't know what this means, ignore it!
Returns:
numpy.ndarray: Downloaded data.
"""
return self.data.get_cutout(token, channel,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
t_start, t_stop,
resolution,
block_size,
neariso) | [
"Get",
"volumetric",
"cutout",
"data",
"from",
"the",
"neurodata",
"server",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neurodata.py#L191-L225 | [
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test | neurodata.post_cutout | Post a cutout to the server.
Arguments:
token (str)
channel (str)
x_start (int)
y_start (int)
z_start (int)
data (numpy.ndarray): A numpy array of data. Pass in (x, y, z)
resolution (int : 0): Resolution at which to insert the data
Returns:
bool: True on success
Raises:
RemoteDataUploadError: if there's an issue during upload. | ndio/remote/neurodata.py | def post_cutout(self, token, channel,
x_start,
y_start,
z_start,
data,
resolution=0):
"""
Post a cutout to the server.
Arguments:
token (str)
channel (str)
x_start (int)
y_start (int)
z_start (int)
data (numpy.ndarray): A numpy array of data. Pass in (x, y, z)
resolution (int : 0): Resolution at which to insert the data
Returns:
bool: True on success
Raises:
RemoteDataUploadError: if there's an issue during upload.
"""
return self.data.post_cutout(token, channel,
x_start,
y_start,
z_start,
data,
resolution) | def post_cutout(self, token, channel,
x_start,
y_start,
z_start,
data,
resolution=0):
"""
Post a cutout to the server.
Arguments:
token (str)
channel (str)
x_start (int)
y_start (int)
z_start (int)
data (numpy.ndarray): A numpy array of data. Pass in (x, y, z)
resolution (int : 0): Resolution at which to insert the data
Returns:
bool: True on success
Raises:
RemoteDataUploadError: if there's an issue during upload.
"""
return self.data.post_cutout(token, channel,
x_start,
y_start,
z_start,
data,
resolution) | [
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"a",
"cutout",
"to",
"the",
"server",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neurodata.py#L230-L259 | [
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test | neurodata.create_project | Creates a project with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
hostname (str): Hostname
s3backend (str): S3 region to save the data in
is_public (int): 1 is public. 0 is not public.
kvserver (str): Server to store key value pairs in
kvengine (str): Database to store key value pairs in
mdengine (str): ???
description (str): Description for your project
Returns:
bool: True if project created, false if not created. | ndio/remote/neurodata.py | def create_project(self,
project_name,
dataset_name,
hostname,
is_public,
s3backend=0,
kvserver='localhost',
kvengine='MySQL',
mdengine='MySQL',
description=''):
"""
Creates a project with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
hostname (str): Hostname
s3backend (str): S3 region to save the data in
is_public (int): 1 is public. 0 is not public.
kvserver (str): Server to store key value pairs in
kvengine (str): Database to store key value pairs in
mdengine (str): ???
description (str): Description for your project
Returns:
bool: True if project created, false if not created.
"""
return self.resources.create_project(project_name,
dataset_name,
hostname,
is_public,
s3backend,
kvserver,
kvengine,
mdengine,
description) | def create_project(self,
project_name,
dataset_name,
hostname,
is_public,
s3backend=0,
kvserver='localhost',
kvengine='MySQL',
mdengine='MySQL',
description=''):
"""
Creates a project with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
hostname (str): Hostname
s3backend (str): S3 region to save the data in
is_public (int): 1 is public. 0 is not public.
kvserver (str): Server to store key value pairs in
kvengine (str): Database to store key value pairs in
mdengine (str): ???
description (str): Description for your project
Returns:
bool: True if project created, false if not created.
"""
return self.resources.create_project(project_name,
dataset_name,
hostname,
is_public,
s3backend,
kvserver,
kvengine,
mdengine,
description) | [
"Creates",
"a",
"project",
"with",
"the",
"given",
"parameters",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neurodata.py#L406-L441 | [
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test | neurodata.create_token | Creates a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
is_public (int): 1 is public. 0 is not public
Returns:
bool: True if project created, false if not created. | ndio/remote/neurodata.py | def create_token(self,
token_name,
project_name,
dataset_name,
is_public):
"""
Creates a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
is_public (int): 1 is public. 0 is not public
Returns:
bool: True if project created, false if not created.
"""
return self.resources.create_token(token_name,
project_name,
dataset_name,
is_public) | def create_token(self,
token_name,
project_name,
dataset_name,
is_public):
"""
Creates a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
is_public (int): 1 is public. 0 is not public
Returns:
bool: True if project created, false if not created.
"""
return self.resources.create_token(token_name,
project_name,
dataset_name,
is_public) | [
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")"
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test | neurodata.get_token | Get a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
Returns:
dict: Token info | ndio/remote/neurodata.py | def get_token(self,
token_name,
project_name,
dataset_name):
"""
Get a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
Returns:
dict: Token info
"""
return self.resources.get_token(token_name,
project_name,
dataset_name) | def get_token(self,
token_name,
project_name,
dataset_name):
"""
Get a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
Returns:
dict: Token info
"""
return self.resources.get_token(token_name,
project_name,
dataset_name) | [
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")"
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test | neurodata.delete_token | Delete a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
channel_name (str): Channel name project is based on
Returns:
bool: True if project deleted, false if not deleted. | ndio/remote/neurodata.py | def delete_token(self,
token_name,
project_name,
dataset_name):
"""
Delete a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
channel_name (str): Channel name project is based on
Returns:
bool: True if project deleted, false if not deleted.
"""
return self.resources.delete_token(token_name,
project_name,
dataset_name) | def delete_token(self,
token_name,
project_name,
dataset_name):
"""
Delete a token with the given parameters.
Arguments:
project_name (str): Project name
dataset_name (str): Dataset name project is based on
token_name (str): Token name
channel_name (str): Channel name project is based on
Returns:
bool: True if project deleted, false if not deleted.
"""
return self.resources.delete_token(token_name,
project_name,
dataset_name) | [
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test | neurodata.create_dataset | Creates a dataset.
Arguments:
name (str): Name of dataset
x_img_size (int): max x coordinate of image size
y_img_size (int): max y coordinate of image size
z_img_size (int): max z coordinate of image size
x_vox_res (float): x voxel resolution
y_vox_res (float): y voxel resolution
z_vox_res (float): z voxel resolution
x_offset (int): x offset amount
y_offset (int): y offset amount
z_offset (int): z offset amount
scaling_levels (int): Level of resolution scaling
scaling_option (int): Z slices is 0 or Isotropic is 1
dataset_description (str): Your description of the dataset
is_public (int): 1 'true' or 0 'false' for viewability of data set
in public
Returns:
bool: True if dataset created, False if not | ndio/remote/neurodata.py | def create_dataset(self,
name,
x_img_size,
y_img_size,
z_img_size,
x_vox_res,
y_vox_res,
z_vox_res,
x_offset=0,
y_offset=0,
z_offset=0,
scaling_levels=0,
scaling_option=0,
dataset_description="",
is_public=0):
"""
Creates a dataset.
Arguments:
name (str): Name of dataset
x_img_size (int): max x coordinate of image size
y_img_size (int): max y coordinate of image size
z_img_size (int): max z coordinate of image size
x_vox_res (float): x voxel resolution
y_vox_res (float): y voxel resolution
z_vox_res (float): z voxel resolution
x_offset (int): x offset amount
y_offset (int): y offset amount
z_offset (int): z offset amount
scaling_levels (int): Level of resolution scaling
scaling_option (int): Z slices is 0 or Isotropic is 1
dataset_description (str): Your description of the dataset
is_public (int): 1 'true' or 0 'false' for viewability of data set
in public
Returns:
bool: True if dataset created, False if not
"""
return self.resources.create_dataset(name,
x_img_size,
y_img_size,
z_img_size,
x_vox_res,
y_vox_res,
z_vox_res,
x_offset,
y_offset,
z_offset,
scaling_levels,
scaling_option,
dataset_description,
is_public) | def create_dataset(self,
name,
x_img_size,
y_img_size,
z_img_size,
x_vox_res,
y_vox_res,
z_vox_res,
x_offset=0,
y_offset=0,
z_offset=0,
scaling_levels=0,
scaling_option=0,
dataset_description="",
is_public=0):
"""
Creates a dataset.
Arguments:
name (str): Name of dataset
x_img_size (int): max x coordinate of image size
y_img_size (int): max y coordinate of image size
z_img_size (int): max z coordinate of image size
x_vox_res (float): x voxel resolution
y_vox_res (float): y voxel resolution
z_vox_res (float): z voxel resolution
x_offset (int): x offset amount
y_offset (int): y offset amount
z_offset (int): z offset amount
scaling_levels (int): Level of resolution scaling
scaling_option (int): Z slices is 0 or Isotropic is 1
dataset_description (str): Your description of the dataset
is_public (int): 1 'true' or 0 'false' for viewability of data set
in public
Returns:
bool: True if dataset created, False if not
"""
return self.resources.create_dataset(name,
x_img_size,
y_img_size,
z_img_size,
x_vox_res,
y_vox_res,
z_vox_res,
x_offset,
y_offset,
z_offset,
scaling_levels,
scaling_option,
dataset_description,
is_public) | [
"Creates",
"a",
"dataset",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neurodata.py#L549-L600 | [
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test | neurodata.create_channel | Create a new channel on the Remote, using channel_data.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
channel_type (str): Type of the channel (e.g. `neurodata.IMAGE`)
dtype (str): The datatype of the channel's data (e.g. `uint8`)
startwindow (int): Window to start in
endwindow (int): Window to end in
readonly (int): Can others write to this channel?
propagate (int): Allow propogation? 1 is True, 0 is False
resolution (int): Resolution scaling
channel_description (str): Your description of the channel
Returns:
bool: `True` if successful, `False` otherwise.
Raises:
ValueError: If your args were bad :(
RemoteDataUploadError: If the channel data is valid but upload
fails for some other reason. | ndio/remote/neurodata.py | def create_channel(self,
channel_name,
project_name,
dataset_name,
channel_type,
dtype,
startwindow,
endwindow,
readonly=0,
start_time=0,
end_time=0,
propagate=0,
resolution=0,
channel_description=''):
"""
Create a new channel on the Remote, using channel_data.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
channel_type (str): Type of the channel (e.g. `neurodata.IMAGE`)
dtype (str): The datatype of the channel's data (e.g. `uint8`)
startwindow (int): Window to start in
endwindow (int): Window to end in
readonly (int): Can others write to this channel?
propagate (int): Allow propogation? 1 is True, 0 is False
resolution (int): Resolution scaling
channel_description (str): Your description of the channel
Returns:
bool: `True` if successful, `False` otherwise.
Raises:
ValueError: If your args were bad :(
RemoteDataUploadError: If the channel data is valid but upload
fails for some other reason.
"""
return self.resources.create_channel(channel_name,
project_name,
dataset_name,
channel_type,
dtype,
startwindow,
endwindow,
readonly,
start_time,
end_time,
propagate,
resolution,
channel_description) | def create_channel(self,
channel_name,
project_name,
dataset_name,
channel_type,
dtype,
startwindow,
endwindow,
readonly=0,
start_time=0,
end_time=0,
propagate=0,
resolution=0,
channel_description=''):
"""
Create a new channel on the Remote, using channel_data.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
channel_type (str): Type of the channel (e.g. `neurodata.IMAGE`)
dtype (str): The datatype of the channel's data (e.g. `uint8`)
startwindow (int): Window to start in
endwindow (int): Window to end in
readonly (int): Can others write to this channel?
propagate (int): Allow propogation? 1 is True, 0 is False
resolution (int): Resolution scaling
channel_description (str): Your description of the channel
Returns:
bool: `True` if successful, `False` otherwise.
Raises:
ValueError: If your args were bad :(
RemoteDataUploadError: If the channel data is valid but upload
fails for some other reason.
"""
return self.resources.create_channel(channel_name,
project_name,
dataset_name,
channel_type,
dtype,
startwindow,
endwindow,
readonly,
start_time,
end_time,
propagate,
resolution,
channel_description) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/neurodata.py#L644-L694 | [
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test | neurodata.get_channel | Gets info about a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
dict: Channel info | ndio/remote/neurodata.py | def get_channel(self, channel_name, project_name, dataset_name):
"""
Gets info about a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
dict: Channel info
"""
return self.resources.get_channel(channel_name, project_name,
dataset_name) | def get_channel(self, channel_name, project_name, dataset_name):
"""
Gets info about a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
dict: Channel info
"""
return self.resources.get_channel(channel_name, project_name,
dataset_name) | [
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test | neurodata.delete_channel | Deletes a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
bool: True if channel deleted, False if not | ndio/remote/neurodata.py | def delete_channel(self, channel_name, project_name, dataset_name):
"""
Deletes a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
bool: True if channel deleted, False if not
"""
return self.resources.delete_channel(channel_name, project_name,
dataset_name) | def delete_channel(self, channel_name, project_name, dataset_name):
"""
Deletes a channel given its name, name of its project
, and name of its dataset.
Arguments:
channel_name (str): Channel name
project_name (str): Project name
dataset_name (str): Dataset name
Returns:
bool: True if channel deleted, False if not
"""
return self.resources.delete_channel(channel_name, project_name,
dataset_name) | [
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test | NDIngest.add_channel | Arguments:
channel_name (str): Channel Name is the specific name of a
specific series of data. Standard naming convention is to do
ImageTypeIterationNumber or NameSubProjectName.
datatype (str): The data type is the storage method of data in
the channel. It can be uint8, uint16, uint32, uint64, or
float32.
channel_type (str): The channel type is the kind of data being
stored in the channel. It can be image, annotation, or
timeseries.
data_url (str): This url points to the root directory of the
files. Dropbox (or any data requiring authentication to
download such as private s3) is not an acceptable HTTP
Server. See additional instructions in documentation online
to format s3 properly so it is http accessible.
file_format (str): File format refers to the overarching kind
of data, as in slices (normal image data) or catmaid
(tile-based).
file_type (str): File type refers to the specific type of file
that the data is stored in, as in, tiff, png, or tif.
exceptions (int): Exceptions is an option to enable the
possibility for annotations to contradict each other (assign
different values to the same point). 1 corresponds to True,
0 corresponds to False.
resolution (int): Resolution is the starting resolution of the
data being uploaded to the channel.
windowrange (int, int): Window range is the maximum and minimum
pixel values for a particular image. This is used so that the
image can be displayed in a readable way for viewing through
RESTful calls
readonly (int): This option allows the user to control if,
after the initial data commit, the channel is read-only.
Generally this is suggested with data that will be publicly
viewable.
Returns:
None | ndio/remote/ndingest.py | def add_channel(self, channel_name, datatype, channel_type,
data_url, file_format, file_type, exceptions=None,
resolution=None, windowrange=None, readonly=None):
"""
Arguments:
channel_name (str): Channel Name is the specific name of a
specific series of data. Standard naming convention is to do
ImageTypeIterationNumber or NameSubProjectName.
datatype (str): The data type is the storage method of data in
the channel. It can be uint8, uint16, uint32, uint64, or
float32.
channel_type (str): The channel type is the kind of data being
stored in the channel. It can be image, annotation, or
timeseries.
data_url (str): This url points to the root directory of the
files. Dropbox (or any data requiring authentication to
download such as private s3) is not an acceptable HTTP
Server. See additional instructions in documentation online
to format s3 properly so it is http accessible.
file_format (str): File format refers to the overarching kind
of data, as in slices (normal image data) or catmaid
(tile-based).
file_type (str): File type refers to the specific type of file
that the data is stored in, as in, tiff, png, or tif.
exceptions (int): Exceptions is an option to enable the
possibility for annotations to contradict each other (assign
different values to the same point). 1 corresponds to True,
0 corresponds to False.
resolution (int): Resolution is the starting resolution of the
data being uploaded to the channel.
windowrange (int, int): Window range is the maximum and minimum
pixel values for a particular image. This is used so that the
image can be displayed in a readable way for viewing through
RESTful calls
readonly (int): This option allows the user to control if,
after the initial data commit, the channel is read-only.
Generally this is suggested with data that will be publicly
viewable.
Returns:
None
"""
self.channels[channel_name] = [
channel_name.strip().replace(" ", ""), datatype,
channel_type.lower(), data_url,
file_format, file_type, exceptions, resolution,
windowrange, readonly
] | def add_channel(self, channel_name, datatype, channel_type,
data_url, file_format, file_type, exceptions=None,
resolution=None, windowrange=None, readonly=None):
"""
Arguments:
channel_name (str): Channel Name is the specific name of a
specific series of data. Standard naming convention is to do
ImageTypeIterationNumber or NameSubProjectName.
datatype (str): The data type is the storage method of data in
the channel. It can be uint8, uint16, uint32, uint64, or
float32.
channel_type (str): The channel type is the kind of data being
stored in the channel. It can be image, annotation, or
timeseries.
data_url (str): This url points to the root directory of the
files. Dropbox (or any data requiring authentication to
download such as private s3) is not an acceptable HTTP
Server. See additional instructions in documentation online
to format s3 properly so it is http accessible.
file_format (str): File format refers to the overarching kind
of data, as in slices (normal image data) or catmaid
(tile-based).
file_type (str): File type refers to the specific type of file
that the data is stored in, as in, tiff, png, or tif.
exceptions (int): Exceptions is an option to enable the
possibility for annotations to contradict each other (assign
different values to the same point). 1 corresponds to True,
0 corresponds to False.
resolution (int): Resolution is the starting resolution of the
data being uploaded to the channel.
windowrange (int, int): Window range is the maximum and minimum
pixel values for a particular image. This is used so that the
image can be displayed in a readable way for viewing through
RESTful calls
readonly (int): This option allows the user to control if,
after the initial data commit, the channel is read-only.
Generally this is suggested with data that will be publicly
viewable.
Returns:
None
"""
self.channels[channel_name] = [
channel_name.strip().replace(" ", ""), datatype,
channel_type.lower(), data_url,
file_format, file_type, exceptions, resolution,
windowrange, readonly
] | [
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test | NDIngest.add_project | Arguments:
project_name (str): Project name is the specific project within
a dataset's name. If there is only one project associated
with a dataset then standard convention is to name the
project the same as its associated dataset.
token_name (str): The token name is the default token. If you
do not wish to specify one, a default one will be created for
you with the same name as the project name. However, if the
project is private you must specify a token.
public (int): This option allows users to specify if they want
the project/channels to be publicly viewable/search-able.
(1, 0) = (TRUE, FALSE)
Returns:
None | ndio/remote/ndingest.py | def add_project(self, project_name, token_name=None, public=None):
"""
Arguments:
project_name (str): Project name is the specific project within
a dataset's name. If there is only one project associated
with a dataset then standard convention is to name the
project the same as its associated dataset.
token_name (str): The token name is the default token. If you
do not wish to specify one, a default one will be created for
you with the same name as the project name. However, if the
project is private you must specify a token.
public (int): This option allows users to specify if they want
the project/channels to be publicly viewable/search-able.
(1, 0) = (TRUE, FALSE)
Returns:
None
"""
self.project = (project_name.strip().replace(" ", ""),
token_name.strip().replace(" ", ""), public) | def add_project(self, project_name, token_name=None, public=None):
"""
Arguments:
project_name (str): Project name is the specific project within
a dataset's name. If there is only one project associated
with a dataset then standard convention is to name the
project the same as its associated dataset.
token_name (str): The token name is the default token. If you
do not wish to specify one, a default one will be created for
you with the same name as the project name. However, if the
project is private you must specify a token.
public (int): This option allows users to specify if they want
the project/channels to be publicly viewable/search-able.
(1, 0) = (TRUE, FALSE)
Returns:
None
"""
self.project = (project_name.strip().replace(" ", ""),
token_name.strip().replace(" ", ""), public) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L115-L134 | [
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test | NDIngest.add_dataset | Add a new dataset to the ingest.
Arguments:
dataset_name (str): Dataset Name is the overarching name of the
research effort. Standard naming convention is to do
LabNamePublicationYear or LeadResearcherCurrentYear.
imagesize (int, int, int): Image size is the pixel count
dimensions of the data. For example is the data is stored
as a series of 100 slices each 2100x2000 pixel TIFF images,
the X,Y,Z dimensions are (2100, 2000, 100).
voxelres (float, float, float): Voxel Resolution is the number
of voxels per unit pixel. We store X,Y,Z voxel resolution
separately.
offset (int, int, int): If your data is not well aligned and
there is "excess" image data you do not wish to examine, but
are present in your images, offset is how you specify where
your actual image starts. Offset is provided a pixel
coordinate offset from origin which specifies the "actual"
origin of the image. The offset is for X,Y,Z dimensions.
timerange (int, int): Time Range is a parameter to support
storage of Time Series data, so the value of the tuple is a
0 to X range of how many images over time were taken. It
takes 2 inputs timeStepStart and timeStepStop.
scalinglevels (int): Scaling levels is the number of levels the
data is scalable to (how many zoom levels are present in the
data). The highest resolution of the data is at scaling level
0, and for each level up the data is down sampled by 2x2
(per slice). To learn more about the sampling service used,
visit the the propagation service page.
scaling (int): Scaling is the scaling method of the data being
stored. 0 corresponds to a Z-slice orientation (as in a
collection of tiff images in which each tiff is a slice on
the z plane) where data will be scaled only on the xy plane,
not the z plane. 1 corresponds to an isotropic orientation
(in which each tiff is a slice on the y plane) where data
is scaled along all axis.
Returns:
None | ndio/remote/ndingest.py | def add_dataset(self, dataset_name, imagesize, voxelres, offset=None,
timerange=None, scalinglevels=None, scaling=None):
"""
Add a new dataset to the ingest.
Arguments:
dataset_name (str): Dataset Name is the overarching name of the
research effort. Standard naming convention is to do
LabNamePublicationYear or LeadResearcherCurrentYear.
imagesize (int, int, int): Image size is the pixel count
dimensions of the data. For example is the data is stored
as a series of 100 slices each 2100x2000 pixel TIFF images,
the X,Y,Z dimensions are (2100, 2000, 100).
voxelres (float, float, float): Voxel Resolution is the number
of voxels per unit pixel. We store X,Y,Z voxel resolution
separately.
offset (int, int, int): If your data is not well aligned and
there is "excess" image data you do not wish to examine, but
are present in your images, offset is how you specify where
your actual image starts. Offset is provided a pixel
coordinate offset from origin which specifies the "actual"
origin of the image. The offset is for X,Y,Z dimensions.
timerange (int, int): Time Range is a parameter to support
storage of Time Series data, so the value of the tuple is a
0 to X range of how many images over time were taken. It
takes 2 inputs timeStepStart and timeStepStop.
scalinglevels (int): Scaling levels is the number of levels the
data is scalable to (how many zoom levels are present in the
data). The highest resolution of the data is at scaling level
0, and for each level up the data is down sampled by 2x2
(per slice). To learn more about the sampling service used,
visit the the propagation service page.
scaling (int): Scaling is the scaling method of the data being
stored. 0 corresponds to a Z-slice orientation (as in a
collection of tiff images in which each tiff is a slice on
the z plane) where data will be scaled only on the xy plane,
not the z plane. 1 corresponds to an isotropic orientation
(in which each tiff is a slice on the y plane) where data
is scaled along all axis.
Returns:
None
"""
self.dataset = (dataset_name.strip().replace(" ", ""), imagesize,
voxelres, offset, timerange, scalinglevels, scaling) | def add_dataset(self, dataset_name, imagesize, voxelres, offset=None,
timerange=None, scalinglevels=None, scaling=None):
"""
Add a new dataset to the ingest.
Arguments:
dataset_name (str): Dataset Name is the overarching name of the
research effort. Standard naming convention is to do
LabNamePublicationYear or LeadResearcherCurrentYear.
imagesize (int, int, int): Image size is the pixel count
dimensions of the data. For example is the data is stored
as a series of 100 slices each 2100x2000 pixel TIFF images,
the X,Y,Z dimensions are (2100, 2000, 100).
voxelres (float, float, float): Voxel Resolution is the number
of voxels per unit pixel. We store X,Y,Z voxel resolution
separately.
offset (int, int, int): If your data is not well aligned and
there is "excess" image data you do not wish to examine, but
are present in your images, offset is how you specify where
your actual image starts. Offset is provided a pixel
coordinate offset from origin which specifies the "actual"
origin of the image. The offset is for X,Y,Z dimensions.
timerange (int, int): Time Range is a parameter to support
storage of Time Series data, so the value of the tuple is a
0 to X range of how many images over time were taken. It
takes 2 inputs timeStepStart and timeStepStop.
scalinglevels (int): Scaling levels is the number of levels the
data is scalable to (how many zoom levels are present in the
data). The highest resolution of the data is at scaling level
0, and for each level up the data is down sampled by 2x2
(per slice). To learn more about the sampling service used,
visit the the propagation service page.
scaling (int): Scaling is the scaling method of the data being
stored. 0 corresponds to a Z-slice orientation (as in a
collection of tiff images in which each tiff is a slice on
the z plane) where data will be scaled only on the xy plane,
not the z plane. 1 corresponds to an isotropic orientation
(in which each tiff is a slice on the y plane) where data
is scaled along all axis.
Returns:
None
"""
self.dataset = (dataset_name.strip().replace(" ", ""), imagesize,
voxelres, offset, timerange, scalinglevels, scaling) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L136-L180 | [
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test | NDIngest.nd_json | Genarate ND json object. | ndio/remote/ndingest.py | def nd_json(self, dataset, project, channel_list, metadata):
"""
Genarate ND json object.
"""
nd_dict = {}
nd_dict['dataset'] = self.dataset_dict(*dataset)
nd_dict['project'] = self.project_dict(*project)
nd_dict['metadata'] = metadata
nd_dict['channels'] = {}
for channel_name, value in channel_list.items():
nd_dict['channels'][channel_name] = self.channel_dict(*value)
return json.dumps(nd_dict, sort_keys=True, indent=4) | def nd_json(self, dataset, project, channel_list, metadata):
"""
Genarate ND json object.
"""
nd_dict = {}
nd_dict['dataset'] = self.dataset_dict(*dataset)
nd_dict['project'] = self.project_dict(*project)
nd_dict['metadata'] = metadata
nd_dict['channels'] = {}
for channel_name, value in channel_list.items():
nd_dict['channels'][channel_name] = self.channel_dict(*value)
return json.dumps(nd_dict, sort_keys=True, indent=4) | [
"Genarate",
"ND",
"json",
"object",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L192-L204 | [
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test | NDIngest.dataset_dict | Generate the dataset dictionary | ndio/remote/ndingest.py | def dataset_dict(
self, dataset_name, imagesize, voxelres,
offset, timerange, scalinglevels, scaling):
"""Generate the dataset dictionary"""
dataset_dict = {}
dataset_dict['dataset_name'] = dataset_name
dataset_dict['imagesize'] = imagesize
dataset_dict['voxelres'] = voxelres
if offset is not None:
dataset_dict['offset'] = offset
if timerange is not None:
dataset_dict['timerange'] = timerange
if scalinglevels is not None:
dataset_dict['scalinglevels'] = scalinglevels
if scaling is not None:
dataset_dict['scaling'] = scaling
return dataset_dict | def dataset_dict(
self, dataset_name, imagesize, voxelres,
offset, timerange, scalinglevels, scaling):
"""Generate the dataset dictionary"""
dataset_dict = {}
dataset_dict['dataset_name'] = dataset_name
dataset_dict['imagesize'] = imagesize
dataset_dict['voxelres'] = voxelres
if offset is not None:
dataset_dict['offset'] = offset
if timerange is not None:
dataset_dict['timerange'] = timerange
if scalinglevels is not None:
dataset_dict['scalinglevels'] = scalinglevels
if scaling is not None:
dataset_dict['scaling'] = scaling
return dataset_dict | [
"Generate",
"the",
"dataset",
"dictionary"
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L220-L236 | [
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test | NDIngest.channel_dict | Generate the project dictionary. | ndio/remote/ndingest.py | def channel_dict(self, channel_name, datatype, channel_type, data_url,
file_format, file_type, exceptions, resolution,
windowrange, readonly):
"""
Generate the project dictionary.
"""
channel_dict = {}
channel_dict['channel_name'] = channel_name
channel_dict['datatype'] = datatype
channel_dict['channel_type'] = channel_type
if exceptions is not None:
channel_dict['exceptions'] = exceptions
if resolution is not None:
channel_dict['resolution'] = resolution
if windowrange is not None:
channel_dict['windowrange'] = windowrange
if readonly is not None:
channel_dict['readonly'] = readonly
channel_dict['data_url'] = data_url
channel_dict['file_format'] = file_format
channel_dict['file_type'] = file_type
return channel_dict | def channel_dict(self, channel_name, datatype, channel_type, data_url,
file_format, file_type, exceptions, resolution,
windowrange, readonly):
"""
Generate the project dictionary.
"""
channel_dict = {}
channel_dict['channel_name'] = channel_name
channel_dict['datatype'] = datatype
channel_dict['channel_type'] = channel_type
if exceptions is not None:
channel_dict['exceptions'] = exceptions
if resolution is not None:
channel_dict['resolution'] = resolution
if windowrange is not None:
channel_dict['windowrange'] = windowrange
if readonly is not None:
channel_dict['readonly'] = readonly
channel_dict['data_url'] = data_url
channel_dict['file_format'] = file_format
channel_dict['file_type'] = file_type
return channel_dict | [
"Generate",
"the",
"project",
"dictionary",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L238-L259 | [
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test | NDIngest.project_dict | Genarate the project dictionary. | ndio/remote/ndingest.py | def project_dict(self, project_name, token_name, public):
"""
Genarate the project dictionary.
"""
project_dict = {}
project_dict['project_name'] = project_name
if token_name is not None:
if token_name == '':
project_dict['token_name'] = project_name
else:
project_dict['token_name'] = token_name
else:
project_dict['token_name'] = project_name
if public is not None:
project_dict['public'] = public
return project_dict | def project_dict(self, project_name, token_name, public):
"""
Genarate the project dictionary.
"""
project_dict = {}
project_dict['project_name'] = project_name
if token_name is not None:
if token_name == '':
project_dict['token_name'] = project_name
else:
project_dict['token_name'] = token_name
else:
project_dict['token_name'] = project_name
if public is not None:
project_dict['public'] = public
return project_dict | [
"Genarate",
"the",
"project",
"dictionary",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L261-L277 | [
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test | NDIngest.identify_imagesize | Identify the image size using the data location and other parameters | ndio/remote/ndingest.py | def identify_imagesize(self, image_type, image_path='/tmp/img.'):
"""
Identify the image size using the data location and other parameters
"""
dims = ()
try:
if (image_type.lower() == 'png'):
dims = np.shape(ndpng.load('{}{}'.format(
image_path, image_type
)))
elif (image_type.lower() == 'tif' or image_type.lower() == 'tiff'):
dims = np.shape(ndtiff.load('{}{}'.format(
image_path, image_type
)))
else:
raise ValueError("Unsupported image type.")
except:
raise OSError('The file was not accessible at {}{}'.format(
image_path,
image_type
))
return dims[::-1] | def identify_imagesize(self, image_type, image_path='/tmp/img.'):
"""
Identify the image size using the data location and other parameters
"""
dims = ()
try:
if (image_type.lower() == 'png'):
dims = np.shape(ndpng.load('{}{}'.format(
image_path, image_type
)))
elif (image_type.lower() == 'tif' or image_type.lower() == 'tiff'):
dims = np.shape(ndtiff.load('{}{}'.format(
image_path, image_type
)))
else:
raise ValueError("Unsupported image type.")
except:
raise OSError('The file was not accessible at {}{}'.format(
image_path,
image_type
))
return dims[::-1] | [
"Identify",
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"size",
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"data",
"location",
"and",
"other",
"parameters"
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L279-L300 | [
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test | NDIngest.verify_path | Verify the path supplied. | ndio/remote/ndingest.py | def verify_path(self, data, verifytype):
"""
Verify the path supplied.
"""
# Insert try and catch blocks
try:
token_name = data["project"]["token_name"]
except:
token_name = data["project"]["project_name"]
channel_names = list(data["channels"].copy().keys())
imgsz = data['dataset']['imagesize']
for i in range(0, len(channel_names)):
channel_type = data["channels"][
channel_names[i]]["channel_type"]
path = data["channels"][channel_names[i]]["data_url"]
aws_pattern = re.compile("^(http:\/\/)(.+)(\.s3\.amazonaws\.com)")
file_type = data["channels"][channel_names[i]]["file_type"]
if "offset" in data["dataset"]:
offset = data["dataset"]["offset"][0]
else:
offset = 0
if (aws_pattern.match(path)):
verifytype = VERIFY_BY_SLICE
if (channel_type == "timeseries"):
timerange = data["dataset"]["timerange"]
try:
assert(timerange[0] != timerange[1])
except AssertionError:
raise ValueError('Timeseries values are the same, did you\
specify the time steps?')
for j in range(timerange[0], timerange[1] + 1):
# Test for tifs or such? Currently test for just not
# empty
if (verifytype == VERIFY_BY_FOLDER):
work_path = "{}/{}/{}/time{}/".format(
path, token_name, channel_names[i], ("%04d" % j))
elif (verifytype == VERIFY_BY_SLICE):
work_path = "{}/{}/{}/time{}/{}.{}".format(
path, token_name, channel_names[i], ("%04d" % j),
("%04d" % offset), file_type)
else:
raise TypeError('Incorrect verify method')
# Check for accessibility
try:
if (verifytype == VERIFY_BY_FOLDER):
resp = requests.head(work_path)
assert(resp.status_code == 200)
elif (verifytype == VERIFY_BY_SLICE):
resp = requests.get(
work_path, stream=True, verify=False)
with open('/tmp/img.{}'.format(file_type),
'wb') as out_file:
shutil.copyfileobj(resp.raw, out_file)
out_file.close()
assert(resp.status_code == 200)
resp.close()
except AssertionError:
raise OSError('Files are not http accessible: \
Error: {}, Path: {}'.format(resp.status_code,
work_path))
# Attempt to Verify imagesize here
try:
if (verifytype == VERIFY_BY_SLICE):
assert(list(self.identify_imagesize(file_type)) ==
imgsz[0:2])
except:
raise ValueError('File image size does not match\
provided image size.')
else:
# Test for tifs or such? Currently test for just not empty
if (verifytype == VERIFY_BY_FOLDER):
work_path = "{}/{}/{}/".format(
path, token_name, channel_names[i])
elif (verifytype == VERIFY_BY_SLICE):
work_path = "{}/{}/{}/{}.{}".format(
path, token_name, channel_names[i],
("%04d" % offset), file_type)
else:
raise TypeError('Incorrect verify method')
# Check for accessibility
if (verifytype == VERIFY_BY_FOLDER):
resp = requests.head(work_path)
elif (verifytype == VERIFY_BY_SLICE):
resp = requests.get(work_path, stream=True, verify=False)
with open('/tmp/img.{}'.format(file_type),
'wb') as out_file:
shutil.copyfileobj(resp.raw, out_file)
out_file.close()
resp.close()
if (resp.status_code >= 300):
raise OSError('Files are not http accessible: \
Error: {}, Path: {}'.format(resp.status_code,
work_path))
# Attempt to Verify imagesize here
try:
if (verifytype == VERIFY_BY_SLICE):
assert(list(self.identify_imagesize(file_type)) ==
imgsz[0:2])
except:
raise ValueError('File image size does not match\
provided image size.') | def verify_path(self, data, verifytype):
"""
Verify the path supplied.
"""
# Insert try and catch blocks
try:
token_name = data["project"]["token_name"]
except:
token_name = data["project"]["project_name"]
channel_names = list(data["channels"].copy().keys())
imgsz = data['dataset']['imagesize']
for i in range(0, len(channel_names)):
channel_type = data["channels"][
channel_names[i]]["channel_type"]
path = data["channels"][channel_names[i]]["data_url"]
aws_pattern = re.compile("^(http:\/\/)(.+)(\.s3\.amazonaws\.com)")
file_type = data["channels"][channel_names[i]]["file_type"]
if "offset" in data["dataset"]:
offset = data["dataset"]["offset"][0]
else:
offset = 0
if (aws_pattern.match(path)):
verifytype = VERIFY_BY_SLICE
if (channel_type == "timeseries"):
timerange = data["dataset"]["timerange"]
try:
assert(timerange[0] != timerange[1])
except AssertionError:
raise ValueError('Timeseries values are the same, did you\
specify the time steps?')
for j in range(timerange[0], timerange[1] + 1):
# Test for tifs or such? Currently test for just not
# empty
if (verifytype == VERIFY_BY_FOLDER):
work_path = "{}/{}/{}/time{}/".format(
path, token_name, channel_names[i], ("%04d" % j))
elif (verifytype == VERIFY_BY_SLICE):
work_path = "{}/{}/{}/time{}/{}.{}".format(
path, token_name, channel_names[i], ("%04d" % j),
("%04d" % offset), file_type)
else:
raise TypeError('Incorrect verify method')
# Check for accessibility
try:
if (verifytype == VERIFY_BY_FOLDER):
resp = requests.head(work_path)
assert(resp.status_code == 200)
elif (verifytype == VERIFY_BY_SLICE):
resp = requests.get(
work_path, stream=True, verify=False)
with open('/tmp/img.{}'.format(file_type),
'wb') as out_file:
shutil.copyfileobj(resp.raw, out_file)
out_file.close()
assert(resp.status_code == 200)
resp.close()
except AssertionError:
raise OSError('Files are not http accessible: \
Error: {}, Path: {}'.format(resp.status_code,
work_path))
# Attempt to Verify imagesize here
try:
if (verifytype == VERIFY_BY_SLICE):
assert(list(self.identify_imagesize(file_type)) ==
imgsz[0:2])
except:
raise ValueError('File image size does not match\
provided image size.')
else:
# Test for tifs or such? Currently test for just not empty
if (verifytype == VERIFY_BY_FOLDER):
work_path = "{}/{}/{}/".format(
path, token_name, channel_names[i])
elif (verifytype == VERIFY_BY_SLICE):
work_path = "{}/{}/{}/{}.{}".format(
path, token_name, channel_names[i],
("%04d" % offset), file_type)
else:
raise TypeError('Incorrect verify method')
# Check for accessibility
if (verifytype == VERIFY_BY_FOLDER):
resp = requests.head(work_path)
elif (verifytype == VERIFY_BY_SLICE):
resp = requests.get(work_path, stream=True, verify=False)
with open('/tmp/img.{}'.format(file_type),
'wb') as out_file:
shutil.copyfileobj(resp.raw, out_file)
out_file.close()
resp.close()
if (resp.status_code >= 300):
raise OSError('Files are not http accessible: \
Error: {}, Path: {}'.format(resp.status_code,
work_path))
# Attempt to Verify imagesize here
try:
if (verifytype == VERIFY_BY_SLICE):
assert(list(self.identify_imagesize(file_type)) ==
imgsz[0:2])
except:
raise ValueError('File image size does not match\
provided image size.') | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L302-L409 | [
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test | NDIngest.put_data | Try to post data to the server. | ndio/remote/ndingest.py | def put_data(self, data):
"""
Try to post data to the server.
"""
URLPath = self.oo.url("autoIngest/")
# URLPath = 'https://{}/ca/autoIngest/'.format(self.oo.site_host)
try:
response = requests.post(URLPath, data=json.dumps(data),
verify=False)
assert(response.status_code == 200)
print("From ndio: {}".format(response.content))
except:
raise OSError("Error in posting JSON file {}\
".format(response.status_code)) | def put_data(self, data):
"""
Try to post data to the server.
"""
URLPath = self.oo.url("autoIngest/")
# URLPath = 'https://{}/ca/autoIngest/'.format(self.oo.site_host)
try:
response = requests.post(URLPath, data=json.dumps(data),
verify=False)
assert(response.status_code == 200)
print("From ndio: {}".format(response.content))
except:
raise OSError("Error in posting JSON file {}\
".format(response.status_code)) | [
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"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/ndingest.py#L452-L465 | [
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test | NDIngest.post_data | Arguments:
file_name (str): The file name of the json file to post (optional).
If this is left unspecified it is assumed the data is in the
AutoIngest object.
dev (bool): If pushing to a microns dev branch server set this
to True, if not leave False.
verifytype (enum): Set http verification type, by checking the
first slice is accessible or by checking channel folder.
NOTE: If verification occurs by folder there is NO image size
or type verification. Enum: [Folder, Slice]
Returns:
None | ndio/remote/ndingest.py | def post_data(self, file_name=None, legacy=False,
verifytype=VERIFY_BY_SLICE):
"""
Arguments:
file_name (str): The file name of the json file to post (optional).
If this is left unspecified it is assumed the data is in the
AutoIngest object.
dev (bool): If pushing to a microns dev branch server set this
to True, if not leave False.
verifytype (enum): Set http verification type, by checking the
first slice is accessible or by checking channel folder.
NOTE: If verification occurs by folder there is NO image size
or type verification. Enum: [Folder, Slice]
Returns:
None
"""
if (file_name is None):
complete_example = (
self.dataset, self.project, self.channels, self.metadata)
data = json.loads(self.nd_json(*complete_example))
else:
try:
with open(file_name) as data_file:
data = json.load(data_file)
except:
raise OSError("Error opening file")
# self.verify_path(data, verifytype)
# self.verify_json(data)
self.put_data(data) | def post_data(self, file_name=None, legacy=False,
verifytype=VERIFY_BY_SLICE):
"""
Arguments:
file_name (str): The file name of the json file to post (optional).
If this is left unspecified it is assumed the data is in the
AutoIngest object.
dev (bool): If pushing to a microns dev branch server set this
to True, if not leave False.
verifytype (enum): Set http verification type, by checking the
first slice is accessible or by checking channel folder.
NOTE: If verification occurs by folder there is NO image size
or type verification. Enum: [Folder, Slice]
Returns:
None
"""
if (file_name is None):
complete_example = (
self.dataset, self.project, self.channels, self.metadata)
data = json.loads(self.nd_json(*complete_example))
else:
try:
with open(file_name) as data_file:
data = json.load(data_file)
except:
raise OSError("Error opening file")
# self.verify_path(data, verifytype)
# self.verify_json(data)
self.put_data(data) | [
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")"
] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | NDIngest.output_json | Arguments:
file_name(str : '/tmp/ND.json'): The file name to store the json to
Returns:
None | ndio/remote/ndingest.py | def output_json(self, file_name='/tmp/ND.json'):
"""
Arguments:
file_name(str : '/tmp/ND.json'): The file name to store the json to
Returns:
None
"""
complete_example = (
self.dataset, self.project, self.channels, self.metadata)
data = json.loads(self.nd_json(*complete_example))
# self.verify_json(data)
self.verify_path(data, VERIFY_BY_SLICE)
f = open(file_name, 'w')
f.write(str(data))
f.close() | def output_json(self, file_name='/tmp/ND.json'):
"""
Arguments:
file_name(str : '/tmp/ND.json'): The file name to store the json to
Returns:
None
"""
complete_example = (
self.dataset, self.project, self.channels, self.metadata)
data = json.loads(self.nd_json(*complete_example))
# self.verify_json(data)
self.verify_path(data, VERIFY_BY_SLICE)
f = open(file_name, 'w')
f.write(str(data))
f.close() | [
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test | find_path | Find path for given workspace and|or repository. | yoda/__init__.py | def find_path(name, config, wsonly=False):
"""Find path for given workspace and|or repository."""
workspace = Workspace(config)
config = config["workspaces"]
path_list = {}
if name.find('/') != -1:
wsonly = False
try:
ws, repo = name.split('/')
except ValueError:
raise ValueError("There is too many / in `name` argument. "
"Argument syntax: `workspace/repository`.")
if (workspace.exists(ws)):
if (repo in config[ws]["repositories"]):
path_name = "%s/%s" % (ws, repo)
path_list[path_name] = config[ws]["repositories"][repo]
for ws_name, ws in sorted(config.items()):
if (name == ws_name):
if wsonly is True:
return {ws_name: ws["path"]}
repositories = sorted(config[ws_name]["repositories"].items())
for name, path in repositories:
path_list["%s/%s" % (ws_name, name)] = path
break
for repo_name, repo_path in sorted(ws["repositories"].items()):
if (repo_name == name):
path_list["%s/%s" % (ws_name, repo_name)] = repo_path
return path_list | def find_path(name, config, wsonly=False):
"""Find path for given workspace and|or repository."""
workspace = Workspace(config)
config = config["workspaces"]
path_list = {}
if name.find('/') != -1:
wsonly = False
try:
ws, repo = name.split('/')
except ValueError:
raise ValueError("There is too many / in `name` argument. "
"Argument syntax: `workspace/repository`.")
if (workspace.exists(ws)):
if (repo in config[ws]["repositories"]):
path_name = "%s/%s" % (ws, repo)
path_list[path_name] = config[ws]["repositories"][repo]
for ws_name, ws in sorted(config.items()):
if (name == ws_name):
if wsonly is True:
return {ws_name: ws["path"]}
repositories = sorted(config[ws_name]["repositories"].items())
for name, path in repositories:
path_list["%s/%s" % (ws_name, name)] = path
break
for repo_name, repo_path in sorted(ws["repositories"].items()):
if (repo_name == name):
path_list["%s/%s" % (ws_name, repo_name)] = repo_path
return path_list | [
"Find",
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] | Numergy/yoda | python | https://github.com/Numergy/yoda/blob/109f0e9441130488b0155f05883ef6531cf46ee9/yoda/__init__.py#L17-L49 | [
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"return",
"path_list"
] | 109f0e9441130488b0155f05883ef6531cf46ee9 |
test | metadata.get_public_tokens | Get a list of public tokens available on this server.
Arguments:
None
Returns:
str[]: list of public tokens | ndio/remote/metadata.py | def get_public_tokens(self):
"""
Get a list of public tokens available on this server.
Arguments:
None
Returns:
str[]: list of public tokens
"""
r = self.remote_utils.get_url(self.url() + "public_tokens/")
return r.json() | def get_public_tokens(self):
"""
Get a list of public tokens available on this server.
Arguments:
None
Returns:
str[]: list of public tokens
"""
r = self.remote_utils.get_url(self.url() + "public_tokens/")
return r.json() | [
"Get",
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"list",
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"public",
"tokens",
"available",
"on",
"this",
"server",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/metadata.py#L44-L55 | [
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test | metadata.get_public_datasets_and_tokens | NOTE: VERY SLOW!
Get a dictionary relating key:dataset to value:[tokens] that rely
on that dataset.
Arguments:
None
Returns:
dict: relating key:dataset to value:[tokens] | ndio/remote/metadata.py | def get_public_datasets_and_tokens(self):
"""
NOTE: VERY SLOW!
Get a dictionary relating key:dataset to value:[tokens] that rely
on that dataset.
Arguments:
None
Returns:
dict: relating key:dataset to value:[tokens]
"""
datasets = {}
tokens = self.get_public_tokens()
for t in tokens:
dataset = self.get_token_dataset(t)
if dataset in datasets:
datasets[dataset].append(t)
else:
datasets[dataset] = [t]
return datasets | def get_public_datasets_and_tokens(self):
"""
NOTE: VERY SLOW!
Get a dictionary relating key:dataset to value:[tokens] that rely
on that dataset.
Arguments:
None
Returns:
dict: relating key:dataset to value:[tokens]
"""
datasets = {}
tokens = self.get_public_tokens()
for t in tokens:
dataset = self.get_token_dataset(t)
if dataset in datasets:
datasets[dataset].append(t)
else:
datasets[dataset] = [t]
return datasets | [
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":",
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"that",
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test | metadata.get_proj_info | Return the project info for a given token.
Arguments:
token (str): Token to return information for
Returns:
JSON: representation of proj_info | ndio/remote/metadata.py | def get_proj_info(self, token):
"""
Return the project info for a given token.
Arguments:
token (str): Token to return information for
Returns:
JSON: representation of proj_info
"""
r = self.remote_utils.get_url(self.url() + "{}/info/".format(token))
return r.json() | def get_proj_info(self, token):
"""
Return the project info for a given token.
Arguments:
token (str): Token to return information for
Returns:
JSON: representation of proj_info
"""
r = self.remote_utils.get_url(self.url() + "{}/info/".format(token))
return r.json() | [
"Return",
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"info",
"for",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/metadata.py#L104-L115 | [
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test | metadata.get_image_size | Return the size of the volume (3D). Convenient for when you want
to download the entirety of a dataset.
Arguments:
token (str): The token for which to find the dataset image bounds
resolution (int : 0): The resolution at which to get image bounds.
Defaults to 0, to get the largest area available.
Returns:
int[3]: The size of the bounds. Should == get_volume.shape
Raises:
RemoteDataNotFoundError: If the token is invalid, or if the
metadata at that resolution is unavailable in projinfo. | ndio/remote/metadata.py | def get_image_size(self, token, resolution=0):
"""
Return the size of the volume (3D). Convenient for when you want
to download the entirety of a dataset.
Arguments:
token (str): The token for which to find the dataset image bounds
resolution (int : 0): The resolution at which to get image bounds.
Defaults to 0, to get the largest area available.
Returns:
int[3]: The size of the bounds. Should == get_volume.shape
Raises:
RemoteDataNotFoundError: If the token is invalid, or if the
metadata at that resolution is unavailable in projinfo.
"""
info = self.get_proj_info(token)
res = str(resolution)
if res not in info['dataset']['imagesize']:
raise RemoteDataNotFoundError("Resolution " + res +
" is not available.")
return info['dataset']['imagesize'][str(resolution)] | def get_image_size(self, token, resolution=0):
"""
Return the size of the volume (3D). Convenient for when you want
to download the entirety of a dataset.
Arguments:
token (str): The token for which to find the dataset image bounds
resolution (int : 0): The resolution at which to get image bounds.
Defaults to 0, to get the largest area available.
Returns:
int[3]: The size of the bounds. Should == get_volume.shape
Raises:
RemoteDataNotFoundError: If the token is invalid, or if the
metadata at that resolution is unavailable in projinfo.
"""
info = self.get_proj_info(token)
res = str(resolution)
if res not in info['dataset']['imagesize']:
raise RemoteDataNotFoundError("Resolution " + res +
" is not available.")
return info['dataset']['imagesize'][str(resolution)] | [
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test | metadata.set_metadata | Insert new metadata into the OCP metadata database.
Arguments:
token (str): Token of the datum to set
data (str): A dictionary to insert as metadata. Include `secret`.
Returns:
json: Info of the inserted ID (convenience) or an error message.
Throws:
RemoteDataUploadError: If the token is already populated, or if
there is an issue with your specified `secret` key. | ndio/remote/metadata.py | def set_metadata(self, token, data):
"""
Insert new metadata into the OCP metadata database.
Arguments:
token (str): Token of the datum to set
data (str): A dictionary to insert as metadata. Include `secret`.
Returns:
json: Info of the inserted ID (convenience) or an error message.
Throws:
RemoteDataUploadError: If the token is already populated, or if
there is an issue with your specified `secret` key.
"""
req = requests.post(self.meta_url("metadata/ocp/set/" + token),
json=data, verify=False)
if req.status_code != 200:
raise RemoteDataUploadError(
"Could not upload metadata: " + req.json()['message']
)
return req.json() | def set_metadata(self, token, data):
"""
Insert new metadata into the OCP metadata database.
Arguments:
token (str): Token of the datum to set
data (str): A dictionary to insert as metadata. Include `secret`.
Returns:
json: Info of the inserted ID (convenience) or an error message.
Throws:
RemoteDataUploadError: If the token is already populated, or if
there is an issue with your specified `secret` key.
"""
req = requests.post(self.meta_url("metadata/ocp/set/" + token),
json=data, verify=False)
if req.status_code != 200:
raise RemoteDataUploadError(
"Could not upload metadata: " + req.json()['message']
)
return req.json() | [
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"metadata",
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test | metadata.add_subvolume | Adds a new subvolume to a token/channel.
Arguments:
token (str): The token to write to in LIMS
channel (str): Channel to add in the subvolume. Can be `None`
x_start (int): Start in x dimension
x_stop (int): Stop in x dimension
y_start (int): Start in y dimension
y_stop (int): Stop in y dimension
z_start (int): Start in z dimension
z_stop (int): Stop in z dimension
resolution (int): The resolution at which this subvolume is seen
title (str): The title to set for the subvolume
notes (str): Optional extra thoughts on the subvolume
Returns:
boolean: success | ndio/remote/metadata.py | def add_subvolume(self, token, channel, secret,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution, title, notes):
"""
Adds a new subvolume to a token/channel.
Arguments:
token (str): The token to write to in LIMS
channel (str): Channel to add in the subvolume. Can be `None`
x_start (int): Start in x dimension
x_stop (int): Stop in x dimension
y_start (int): Start in y dimension
y_stop (int): Stop in y dimension
z_start (int): Start in z dimension
z_stop (int): Stop in z dimension
resolution (int): The resolution at which this subvolume is seen
title (str): The title to set for the subvolume
notes (str): Optional extra thoughts on the subvolume
Returns:
boolean: success
"""
md = self.get_metadata(token)['metadata']
if 'subvolumes' in md:
subvols = md['subvolumes']
else:
subvols = []
subvols.append({
'token': token,
'channel': channel,
'x_start': x_start,
'x_stop': x_stop,
'y_start': y_start,
'y_stop': y_stop,
'z_start': z_start,
'z_stop': z_stop,
'resolution': resolution,
'title': title,
'notes': notes
})
return self.set_metadata(token, {
'secret': secret,
'subvolumes': subvols
}) | def add_subvolume(self, token, channel, secret,
x_start, x_stop,
y_start, y_stop,
z_start, z_stop,
resolution, title, notes):
"""
Adds a new subvolume to a token/channel.
Arguments:
token (str): The token to write to in LIMS
channel (str): Channel to add in the subvolume. Can be `None`
x_start (int): Start in x dimension
x_stop (int): Stop in x dimension
y_start (int): Start in y dimension
y_stop (int): Stop in y dimension
z_start (int): Start in z dimension
z_stop (int): Stop in z dimension
resolution (int): The resolution at which this subvolume is seen
title (str): The title to set for the subvolume
notes (str): Optional extra thoughts on the subvolume
Returns:
boolean: success
"""
md = self.get_metadata(token)['metadata']
if 'subvolumes' in md:
subvols = md['subvolumes']
else:
subvols = []
subvols.append({
'token': token,
'channel': channel,
'x_start': x_start,
'x_stop': x_stop,
'y_start': y_start,
'y_stop': y_stop,
'z_start': z_start,
'z_stop': z_stop,
'resolution': resolution,
'title': title,
'notes': notes
})
return self.set_metadata(token, {
'secret': secret,
'subvolumes': subvols
}) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/metadata.py#L200-L247 | [
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":",
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")"
] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | remote_utils.get_url | Get a response object for a given url.
Arguments:
url (str): The url make a get to
token (str): The authentication token
Returns:
obj: The response object | ndio/remote/remote_utils.py | def get_url(self, url):
"""
Get a response object for a given url.
Arguments:
url (str): The url make a get to
token (str): The authentication token
Returns:
obj: The response object
"""
try:
req = requests.get(url, headers={
'Authorization': 'Token {}'.format(self._user_token)
}, verify=False)
if req.status_code is 403:
raise ValueError("Access Denied")
else:
return req
except requests.exceptions.ConnectionError as e:
if str(e) == '403 Client Error: Forbidden':
raise ValueError('Access Denied')
else:
raise e | def get_url(self, url):
"""
Get a response object for a given url.
Arguments:
url (str): The url make a get to
token (str): The authentication token
Returns:
obj: The response object
"""
try:
req = requests.get(url, headers={
'Authorization': 'Token {}'.format(self._user_token)
}, verify=False)
if req.status_code is 403:
raise ValueError("Access Denied")
else:
return req
except requests.exceptions.ConnectionError as e:
if str(e) == '403 Client Error: Forbidden':
raise ValueError('Access Denied')
else:
raise e | [
"Get",
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"object",
"for",
"a",
"given",
"url",
"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/remote_utils.py#L19-L42 | [
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test | remote_utils.post_url | Returns a post resquest object taking in a url, user token, and
possible json information.
Arguments:
url (str): The url to make post to
token (str): The authentication token
json (dict): json info to send
Returns:
obj: Post request object | ndio/remote/remote_utils.py | def post_url(self, url, token='', json=None, data=None, headers=None):
"""
Returns a post resquest object taking in a url, user token, and
possible json information.
Arguments:
url (str): The url to make post to
token (str): The authentication token
json (dict): json info to send
Returns:
obj: Post request object
"""
if (token == ''):
token = self._user_token
if headers:
headers.update({'Authorization': 'Token {}'.format(token)})
else:
headers = {'Authorization': 'Token {}'.format(token)}
if json:
return requests.post(url,
headers=headers,
json=json,
verify=False)
if data:
return requests.post(url,
headers=headers,
data=data,
verify=False)
return requests.post(url,
headers=headers,
verify=False) | def post_url(self, url, token='', json=None, data=None, headers=None):
"""
Returns a post resquest object taking in a url, user token, and
possible json information.
Arguments:
url (str): The url to make post to
token (str): The authentication token
json (dict): json info to send
Returns:
obj: Post request object
"""
if (token == ''):
token = self._user_token
if headers:
headers.update({'Authorization': 'Token {}'.format(token)})
else:
headers = {'Authorization': 'Token {}'.format(token)}
if json:
return requests.post(url,
headers=headers,
json=json,
verify=False)
if data:
return requests.post(url,
headers=headers,
data=data,
verify=False)
return requests.post(url,
headers=headers,
verify=False) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/remote_utils.py#L44-L78 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | remote_utils.delete_url | Returns a delete resquest object taking in a url and user token.
Arguments:
url (str): The url to make post to
token (str): The authentication token
Returns:
obj: Delete request object | ndio/remote/remote_utils.py | def delete_url(self, url, token=''):
"""
Returns a delete resquest object taking in a url and user token.
Arguments:
url (str): The url to make post to
token (str): The authentication token
Returns:
obj: Delete request object
"""
if (token == ''):
token = self._user_token
return requests.delete(url,
headers={
'Authorization': 'Token {}'.format(token)},
verify=False,) | def delete_url(self, url, token=''):
"""
Returns a delete resquest object taking in a url and user token.
Arguments:
url (str): The url to make post to
token (str): The authentication token
Returns:
obj: Delete request object
"""
if (token == ''):
token = self._user_token
return requests.delete(url,
headers={
'Authorization': 'Token {}'.format(token)},
verify=False,) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/remote_utils.py#L80-L97 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | remote_utils.ping | Ping the server to make sure that you can access the base URL.
Arguments:
None
Returns:
`boolean` Successful access of server (or status code) | ndio/remote/remote_utils.py | def ping(self, url, endpoint=''):
"""
Ping the server to make sure that you can access the base URL.
Arguments:
None
Returns:
`boolean` Successful access of server (or status code)
"""
r = self.get_url(url + "/" + endpoint)
return r.status_code | def ping(self, url, endpoint=''):
"""
Ping the server to make sure that you can access the base URL.
Arguments:
None
Returns:
`boolean` Successful access of server (or status code)
"""
r = self.get_url(url + "/" + endpoint)
return r.status_code | [
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"."
] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/remote/remote_utils.py#L99-L109 | [
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test | load | Import a HDF5 file into a numpy array.
Arguments:
hdf5_filename: A string filename of a HDF5 datafile
Returns:
A numpy array with data from the HDF5 file | ndio/convert/hdf5.py | def load(hdf5_filename):
"""
Import a HDF5 file into a numpy array.
Arguments:
hdf5_filename: A string filename of a HDF5 datafile
Returns:
A numpy array with data from the HDF5 file
"""
# Expand filename to be absolute
hdf5_filename = os.path.expanduser(hdf5_filename)
try:
f = h5py.File(hdf5_filename, "r")
# neurodata stores data inside the 'cutout' h5 dataset
data_layers = f.get('image').get('CUTOUT')
except Exception as e:
raise ValueError("Could not load file {0} for conversion. {}".format(
hdf5_filename, e))
raise
return numpy.array(data_layers) | def load(hdf5_filename):
"""
Import a HDF5 file into a numpy array.
Arguments:
hdf5_filename: A string filename of a HDF5 datafile
Returns:
A numpy array with data from the HDF5 file
"""
# Expand filename to be absolute
hdf5_filename = os.path.expanduser(hdf5_filename)
try:
f = h5py.File(hdf5_filename, "r")
# neurodata stores data inside the 'cutout' h5 dataset
data_layers = f.get('image').get('CUTOUT')
except Exception as e:
raise ValueError("Could not load file {0} for conversion. {}".format(
hdf5_filename, e))
raise
return numpy.array(data_layers) | [
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/hdf5.py#L7-L29 | [
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] | 792dd5816bc770b05a3db2f4327da42ff6253531 |
test | save | Export a numpy array to a HDF5 file.
Arguments:
hdf5_filename (str): A filename to which to save the HDF5 data
array (numpy.ndarray): The numpy array to save to HDF5
Returns:
String. The expanded filename that now holds the HDF5 data | ndio/convert/hdf5.py | def save(hdf5_filename, array):
"""
Export a numpy array to a HDF5 file.
Arguments:
hdf5_filename (str): A filename to which to save the HDF5 data
array (numpy.ndarray): The numpy array to save to HDF5
Returns:
String. The expanded filename that now holds the HDF5 data
"""
# Expand filename to be absolute
hdf5_filename = os.path.expanduser(hdf5_filename)
try:
h = h5py.File(hdf5_filename, "w")
h.create_dataset('CUTOUT', data=array)
h.close()
except Exception as e:
raise ValueError("Could not save HDF5 file {0}.".format(hdf5_filename))
return hdf5_filename | def save(hdf5_filename, array):
"""
Export a numpy array to a HDF5 file.
Arguments:
hdf5_filename (str): A filename to which to save the HDF5 data
array (numpy.ndarray): The numpy array to save to HDF5
Returns:
String. The expanded filename that now holds the HDF5 data
"""
# Expand filename to be absolute
hdf5_filename = os.path.expanduser(hdf5_filename)
try:
h = h5py.File(hdf5_filename, "w")
h.create_dataset('CUTOUT', data=array)
h.close()
except Exception as e:
raise ValueError("Could not save HDF5 file {0}.".format(hdf5_filename))
return hdf5_filename | [
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"HDF5",
"file",
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] | neurodata/ndio | python | https://github.com/neurodata/ndio/blob/792dd5816bc770b05a3db2f4327da42ff6253531/ndio/convert/hdf5.py#L32-L53 | [
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test | ProcessExecutor.run | return values of execute are set as result of the task
returned by ensure_future(), obtainable via task.result() | ribosome/process.py | def run(self, job: Job) -> Future[Result]:
''' return values of execute are set as result of the task
returned by ensure_future(), obtainable via task.result()
'''
if not self.watcher_ready:
self.log.error(f'child watcher unattached when executing {job}')
job.cancel('unattached watcher')
elif not self.can_execute(job):
self.log.error('invalid execution job: {}'.format(job))
job.cancel('invalid')
else:
self.log.debug('executing {}'.format(job))
task = asyncio.ensure_future(self._execute(job), loop=self.loop)
task.add_done_callback(job.finish)
task.add_done_callback(L(self.job_done)(job, _))
self.current[job.client] = job
return job.status | def run(self, job: Job) -> Future[Result]:
''' return values of execute are set as result of the task
returned by ensure_future(), obtainable via task.result()
'''
if not self.watcher_ready:
self.log.error(f'child watcher unattached when executing {job}')
job.cancel('unattached watcher')
elif not self.can_execute(job):
self.log.error('invalid execution job: {}'.format(job))
job.cancel('invalid')
else:
self.log.debug('executing {}'.format(job))
task = asyncio.ensure_future(self._execute(job), loop=self.loop)
task.add_done_callback(job.finish)
task.add_done_callback(L(self.job_done)(job, _))
self.current[job.client] = job
return job.status | [
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"returned",
"by",
"ensure_future",
"()",
"obtainable",
"via",
"task",
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] | tek/ribosome | python | https://github.com/tek/ribosome/blob/b2ce9e118faa46d93506cbbb5f27ecfbd4e8a1cc/ribosome/process.py#L170-L186 | [
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test | infer_gaps_in_tree | Adds a character matrix to DendroPy tree and infers gaps using
Fitch's algorithm.
Infer gaps in sequences at ancestral nodes. | pyasr/gaps.py | def infer_gaps_in_tree(df_seq, tree, id_col='id', sequence_col='sequence'):
"""Adds a character matrix to DendroPy tree and infers gaps using
Fitch's algorithm.
Infer gaps in sequences at ancestral nodes.
"""
taxa = tree.taxon_namespace
# Get alignment as fasta
alignment = df_seq.phylo.to_fasta(id_col=id_col, id_only=True,
sequence_col=sequence_col)
# Build a Sequence data matrix from Dendropy
data = dendropy.ProteinCharacterMatrix.get(
data=alignment,
schema="fasta",
taxon_namespace=taxa)
# Construct a map object between sequence data and tree data.
taxon_state_sets_map = data.taxon_state_sets_map(gaps_as_missing=False)
# Fitch algorithm to determine placement of gaps
dendropy.model.parsimony.fitch_down_pass(tree.postorder_node_iter(),
taxon_state_sets_map=taxon_state_sets_map)
dendropy.model.parsimony.fitch_up_pass(tree.preorder_node_iter())
return tree | def infer_gaps_in_tree(df_seq, tree, id_col='id', sequence_col='sequence'):
"""Adds a character matrix to DendroPy tree and infers gaps using
Fitch's algorithm.
Infer gaps in sequences at ancestral nodes.
"""
taxa = tree.taxon_namespace
# Get alignment as fasta
alignment = df_seq.phylo.to_fasta(id_col=id_col, id_only=True,
sequence_col=sequence_col)
# Build a Sequence data matrix from Dendropy
data = dendropy.ProteinCharacterMatrix.get(
data=alignment,
schema="fasta",
taxon_namespace=taxa)
# Construct a map object between sequence data and tree data.
taxon_state_sets_map = data.taxon_state_sets_map(gaps_as_missing=False)
# Fitch algorithm to determine placement of gaps
dendropy.model.parsimony.fitch_down_pass(tree.postorder_node_iter(),
taxon_state_sets_map=taxon_state_sets_map)
dendropy.model.parsimony.fitch_up_pass(tree.preorder_node_iter())
return tree | [
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] | Zsailer/pyasr | python | https://github.com/Zsailer/pyasr/blob/f9a05912ae2409a4cb2d8bac878bb5230c8824b4/pyasr/gaps.py#L3-L28 | [
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] | f9a05912ae2409a4cb2d8bac878bb5230c8824b4 |
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