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train | is_valid_superset | Returns True if the actual index is a valid superset of the expected index | bloop/session.py | def is_valid_superset(actual_projection, index):
"""Returns True if the actual index is a valid superset of the expected index"""
projection_type = actual_projection["ProjectionType"]
if projection_type == "ALL":
return True
meta = index.model.Meta
# all index types provide index keys and mo... | def is_valid_superset(actual_projection, index):
"""Returns True if the actual index is a valid superset of the expected index"""
projection_type = actual_projection["ProjectionType"]
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train | SessionWrapper.save_item | Save an object to DynamoDB.
:param item: Unpacked into kwargs for :func:`boto3.DynamoDB.Client.update_item`.
:raises bloop.exceptions.ConstraintViolation: if the condition (or atomic) is not met. | bloop/session.py | def save_item(self, item):
"""Save an object to DynamoDB.
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train | SessionWrapper.delete_item | Delete an object in DynamoDB.
:param item: Unpacked into kwargs for :func:`boto3.DynamoDB.Client.delete_item`.
:raises bloop.exceptions.ConstraintViolation: if the condition (or atomic) is not met. | bloop/session.py | def delete_item(self, item):
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train | SessionWrapper.load_items | Loads any number of items in chunks, handling continuation tokens.
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train | SessionWrapper.search_items | Invoke query/scan by name.
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:param str mode: "query" or "scan"
:param request: Unpacked into :func:`boto3.DynamoDB.Client.query` or :func:`boto3.DynamoDB.Client.scan` | bloop/session.py | def search_items(self, mode, request):
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train | SessionWrapper.create_table | Create the model's table. Returns True if the table is being created, False otherwise.
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... | def create_table(self, table_name, model):
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train | SessionWrapper.validate_table | Polls until a creating table is ready, then verifies the description against the model's requirements.
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train | SessionWrapper.enable_ttl | Calls UpdateTimeToLive on the table according to model.Meta["ttl"]
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train | SessionWrapper.enable_backups | Calls UpdateContinuousBackups on the table according to model.Meta["continuous_backups"]
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train | SessionWrapper.describe_stream | Wraps :func:`boto3.DynamoDBStreams.Client.describe_stream`, handling continuation tokens.
:param str stream_arn: Stream arn, usually from the model's ``Meta.stream["arn"]``.
:param str first_shard: *(Optional)* If provided, only shards after this shard id will be returned.
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train | SessionWrapper.get_shard_iterator | Wraps :func:`boto3.DynamoDBStreams.Client.get_shard_iterator`.
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:param str stream_arn: Stream arn. Usually :data:`Shard.stream_arn <bloop.stream.shard.Shard.stream_arn>`.
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train | SessionWrapper.get_stream_records | Wraps :func:`boto3.DynamoDBStreams.Client.get_records`.
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train | SessionWrapper.transaction_read | Wraps :func:`boto3.DynamoDB.Client.db.transact_get_items`.
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"""
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train | check_range_key | BeginsWith, Between, or any Comparison except '!=' against query_on.range_key | bloop/search.py | def check_range_key(query_on, key):
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train | Search.prepare | Constructs a :class:`~bloop.search.PreparedSearch`. | bloop/search.py | def prepare(self):
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train | PreparedSearch.prepare | Validates the search parameters and builds the base request dict for each Query/Scan call. | bloop/search.py | def prepare(
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train | SearchIterator.count | Number of items that have been loaded from DynamoDB so far, including buffered items. | bloop/search.py | def count(self):
"""Number of items that have been loaded from DynamoDB so far, including buffered items."""
if self.request["Select"] == "COUNT":
while not self.exhausted:
next(self, None)
return self._count | def count(self):
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train | SearchIterator.scanned | Number of items that DynamoDB evaluated, before any filter was applied. | bloop/search.py | def scanned(self):
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train | SearchIterator.reset | Reset to the initial state, clearing the buffer and zeroing count and scanned. | bloop/search.py | def reset(self):
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train | TxType.by_alias | get a type by the common bloop operation name: get/check/delete/save | bloop/transactions.py | def by_alias(cls, name: str) -> "TxType":
"""get a type by the common bloop operation name: get/check/delete/save"""
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train | Transaction.prepare | Create a new PreparedTransaction that can be committed.
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.. code-block:: python
>>> engine = Engine()
>>> tx = WriteTransaction(engine)
>>> prepared = tx.prepare()
>>> prepared.commi... | bloop/transactions.py | def prepare(self):
"""
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.. code-block:: python
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"""
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train | PreparedTransaction.prepare | Create a unique transaction id and dumps the items into a cached request object. | bloop/transactions.py | def prepare(self, engine, mode, items) -> None:
"""
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train | PreparedTransaction.commit | Commit the transaction with a fixed transaction id.
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"""
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"""
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Commit the transaction with a fixed transaction id.
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:param objs: Objects to add to the set that are loaded in this transaction.
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train | CubeDimensionTranscoder.encode | Produces a numpy array of integers which encode
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train | dl_cub | Download cub archive from cub_url and store it in cub_archive_name | install/cub.py | def dl_cub(cub_url, cub_archive_name):
""" Download cub archive from cub_url and store it in cub_archive_name """
with open(cub_archive_name, 'wb') as f:
remote_file = urllib2.urlopen(cub_url)
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""" Download cub archive from cub_url and store it in cub_archive_name """
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train | sha_hash_file | Compute the SHA1 hash of filename | install/cub.py | def sha_hash_file(filename):
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hash_sha = hashlib.sha1()
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train | install_cub | Downloads and installs cub into mb_inc_path | install/cub.py | def install_cub(mb_inc_path):
""" Downloads and installs cub into mb_inc_path """
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train | cuda_architecture_flags | Emit a list of architecture flags for each CUDA device found
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"""
Emit a list of architecture flags for each CUDA device found
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# Figure out the necessary device architectures
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Emit a list of architecture flags for each CUDA device found
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train | create_tensorflow_extension | Create an extension that builds the custom tensorflow ops | install/tensorflow_ops_ext.py | def create_tensorflow_extension(nvcc_settings, device_info):
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train | CustomSourceProvider.updated_dimensions | Inform montblanc about dimension sizes | montblanc/examples/standalone.py | def updated_dimensions(self):
""" Inform montblanc about dimension sizes """
return [("ntime", args.ntime), # Timesteps
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("na", args.na), # Antenna
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train | CustomSourceProvider.point_lm | Supply point source lm coordinates to montblanc | montblanc/examples/standalone.py | def point_lm(self, context):
""" Supply point source lm coordinates to montblanc """
# Shape (npsrc, 2)
(ls, us), _ = context.array_extents(context.name)
return np.asarray(lm_coords[ls:us], dtype=context.dtype) | def point_lm(self, context):
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train | CustomSourceProvider.point_stokes | Supply point source stokes parameters to montblanc | montblanc/examples/standalone.py | def point_stokes(self, context):
""" Supply point source stokes parameters to montblanc """
# Shape (npsrc, ntime, 4)
(ls, us), (lt, ut), (l, u) = context.array_extents(context.name)
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train | CustomSourceProvider.uvw | Supply UVW antenna coordinates to montblanc | montblanc/examples/standalone.py | def uvw(self, context):
""" Supply UVW antenna coordinates to montblanc """
# Shape (ntime, na, 3)
(lt, ut), (la, ua), (l, u) = context.array_extents(context.name)
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data = np.empty(context.shape, context.dtype)
data[:,:,0] = np.arange(la+1,... | def uvw(self, context):
""" Supply UVW antenna coordinates to montblanc """
# Shape (ntime, na, 3)
(lt, ut), (la, ua), (l, u) = context.array_extents(context.name)
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data = np.empty(context.shape, context.dtype)
data[:,:,0] = np.arange(la+1,... | [
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train | reinitialize_command | Monkeypatch distutils.Distribution.reinitialize_command() to match behavior
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Monkeypatch distutils.Distribution.reinitialize_command() to match behavior
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train | nr_of_baselines | Compute the number of baselines for the
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"""
Compute the number of baselines for the
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auto-correlations should be taken into
account
"""
m = (na-1) if auto_correlations is False else (na+1)
return (na*m)//2 | def nr_of_baselines(na, auto_correlations=False):
"""
Compute the number of baselines for the
given number of antenna. Can specify whether
auto-correlations should be taken into
account
"""
m = (na-1) if auto_correlations is False else (na+1)
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"""
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"""
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train | array_bytes | Estimates the memory in bytes required for an array of the supplied shape and dtype | montblanc/util/__init__.py | def array_bytes(shape, dtype):
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train | random_like | Returns a random array of the same shape and type as the
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train | flatten | Return a flatten version of the nested argument | montblanc/util/__init__.py | def flatten(nested):
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train | dict_array_bytes | Return the number of bytes required by an array
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train | dict_array_bytes_required | Return the number of bytes required by
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train | viable_dim_config | Returns the number of timesteps possible, given the registered arrays
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Arguments
----------------
bytes_available : int
The memory budget, or available number of bytes
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arrays : list
List of dictionaries d... | montblanc/util/__init__.py | def viable_dim_config(bytes_available, arrays, template,
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"""
Returns the number of timesteps possible, given the registered arrays
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Arguments
----------------
bytes_available : int
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"""
Returns the number of timesteps possible, given the registered arrays
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train | shape_from_str_tuple | Substitutes string values in the supplied shape parameter
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sshape : tuple/string composed of integers and strings.
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variables : dict... | montblanc/util/__init__.py | def shape_from_str_tuple(sshape, variables, ignore=None):
"""
Substitutes string values in the supplied shape parameter
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----------
sshape : tuple/string composed of integers and strings.
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train | shape_list | Shape a list of lists into the appropriate shape and data type | montblanc/util/__init__.py | def shape_list(l,shape,dtype):
""" Shape a list of lists into the appropriate shape and data type """
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train | array_convert_function | Return a function defining the conversion process between two NumPy
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""" Return a function defining the conversion process between two NumPy
arrays of different shapes """
if not isinstance(sshape_one, tuple): sshape_one = (sshape_one,)
if not isinstance(sshape_two, tuple): sshape_two = (sshape_two,)
s_o... | def array_convert_function(sshape_one, sshape_two, variables):
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train | redistribute_threads | Redistribute threads from the Z dimension towards the X dimension.
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"""
Redistribute threads from the Z dimension towards the X dimension.
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train | register_default_dimensions | Register the default dimensions for a RIME solver | montblanc/util/__init__.py | def register_default_dimensions(cube, slvr_cfg):
""" Register the default dimensions for a RIME solver """
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train | get_ip_address | Hack to get IP address from the interface | montblanc/impl/rime/tensorflow/helpers/cluster_gen.py | def get_ip_address(ifname):
""" Hack to get IP address from the interface """
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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train | nvcc_compiler_settings | Find nvcc and the CUDA installation | install/cuda.py | def nvcc_compiler_settings():
""" Find nvcc and the CUDA installation """
search_paths = os.environ.get('PATH', '').split(os.pathsep)
nvcc_path = find_in_path('nvcc', search_paths)
default_cuda_path = os.path.join('usr', 'local', 'cuda')
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""" Find nvcc and the CUDA installation """
search_paths = os.environ.get('PATH', '').split(os.pathsep)
nvcc_path = find_in_path('nvcc', search_paths)
default_cuda_path = os.path.join('usr', 'local', 'cuda')
cuda_path = os.environ.get('CUDA_PATH', default_cuda_path)
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train | inspect_cuda_version_and_devices | Poor mans deviceQuery. Returns CUDA_VERSION information and
CUDA device information in JSON format | install/cuda.py | def inspect_cuda_version_and_devices(compiler, settings):
"""
Poor mans deviceQuery. Returns CUDA_VERSION information and
CUDA device information in JSON format
"""
try:
output = build_and_run(compiler, '''
#include <cuda.h>
#include <stdio.h>
__device__ ... | def inspect_cuda_version_and_devices(compiler, settings):
"""
Poor mans deviceQuery. Returns CUDA_VERSION information and
CUDA device information in JSON format
"""
try:
output = build_and_run(compiler, '''
#include <cuda.h>
#include <stdio.h>
__device__ ... | [
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train | customize_compiler_for_nvcc | inject deep into distutils to customize gcc/nvcc dispatch | install/cuda.py | def customize_compiler_for_nvcc(compiler, nvcc_settings):
"""inject deep into distutils to customize gcc/nvcc dispatch """
# tell the compiler it can process .cu files
compiler.src_extensions.append('.cu')
# save references to the default compiler_so and _compile methods
default_compiler_so = comp... | def customize_compiler_for_nvcc(compiler, nvcc_settings):
"""inject deep into distutils to customize gcc/nvcc dispatch """
# tell the compiler it can process .cu files
compiler.src_extensions.append('.cu')
# save references to the default compiler_so and _compile methods
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train | inspect_cuda | Return cuda device information and nvcc/cuda setup | install/cuda.py | def inspect_cuda():
""" Return cuda device information and nvcc/cuda setup """
nvcc_settings = nvcc_compiler_settings()
sysconfig.get_config_vars()
nvcc_compiler = ccompiler.new_compiler()
sysconfig.customize_compiler(nvcc_compiler)
customize_compiler_for_nvcc(nvcc_compiler, nvcc_settings)
... | def inspect_cuda():
""" Return cuda device information and nvcc/cuda setup """
nvcc_settings = nvcc_compiler_settings()
sysconfig.get_config_vars()
nvcc_compiler = ccompiler.new_compiler()
sysconfig.customize_compiler(nvcc_compiler)
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train | RIMESolver.template_dict | Returns a dictionary suitable for templating strings with
properties and dimensions related to this Solver object.
Used in templated GPU kernels. | montblanc/solvers/rime_solver.py | def template_dict(self):
"""
Returns a dictionary suitable for templating strings with
properties and dimensions related to this Solver object.
Used in templated GPU kernels.
"""
slvr = self
D = {
# Constants
'LIGHTSPEED': montblanc.const... | def template_dict(self):
"""
Returns a dictionary suitable for templating strings with
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"""
slvr = self
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train | rime_solver | Factory function that produces a RIME solver | montblanc/factory.py | def rime_solver(slvr_cfg):
""" Factory function that produces a RIME solver """
from montblanc.impl.rime.tensorflow.RimeSolver import RimeSolver
return RimeSolver(slvr_cfg) | def rime_solver(slvr_cfg):
""" Factory function that produces a RIME solver """
from montblanc.impl.rime.tensorflow.RimeSolver import RimeSolver
return RimeSolver(slvr_cfg) | [
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train | find_sources | Returns a dictionary of source methods found on this object,
keyed on method name. Source methods are identified.by argspec,
a list of argument specifiers. So for e.g. an argpsec of
:code:`[['self', 'context'], ['s', 'c']]` would match
methods looking like:
.. code-block:: python
def f(sel... | montblanc/impl/rime/tensorflow/sources/source_provider.py | def find_sources(obj, argspec=None):
"""
Returns a dictionary of source methods found on this object,
keyed on method name. Source methods are identified.by argspec,
a list of argument specifiers. So for e.g. an argpsec of
:code:`[['self', 'context'], ['s', 'c']]` would match
methods looking lik... | def find_sources(obj, argspec=None):
"""
Returns a dictionary of source methods found on this object,
keyed on method name. Source methods are identified.by argspec,
a list of argument specifiers. So for e.g. an argpsec of
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train | SourceProvider.sources | Returns a dictionary of source methods found on this object,
keyed on method name. Source methods are identified by
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.. code-block:: python
def f(self, context):
...
is a source method, but
... | montblanc/impl/rime/tensorflow/sources/source_provider.py | def sources(self):
"""
Returns a dictionary of source methods found on this object,
keyed on method name. Source methods are identified by
(self, context) arguments on this object. For example:
.. code-block:: python
def f(self, context):
...
... | def sources(self):
"""
Returns a dictionary of source methods found on this object,
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.. code-block:: python
def f(self, context):
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train | parallactic_angles | Computes parallactic angles per timestep for the given
reference antenna position and field centre.
Arguments:
times: ndarray
Array of unique times with shape (ntime,),
obtained from TIME column of MS table
antenna_positions: ndarray of shape (na, 3)
Antenna ... | montblanc/util/parallactic_angles.py | def parallactic_angles(times, antenna_positions, field_centre):
"""
Computes parallactic angles per timestep for the given
reference antenna position and field centre.
Arguments:
times: ndarray
Array of unique times with shape (ntime,),
obtained from TIME column of MS ta... | def parallactic_angles(times, antenna_positions, field_centre):
"""
Computes parallactic angles per timestep for the given
reference antenna position and field centre.
Arguments:
times: ndarray
Array of unique times with shape (ntime,),
obtained from TIME column of MS ta... | [
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train | setup_logging | Setup logging configuration | montblanc/logsetup.py | def setup_logging():
""" Setup logging configuration """
# Console formatter, mention name
cfmt = logging.Formatter(('%(name)s - %(levelname)s - %(message)s'))
# File formatter, mention time
ffmt = logging.Formatter(('%(asctime)s - %(levelname)s - %(message)s'))
# Console handler
ch = log... | def setup_logging():
""" Setup logging configuration """
# Console formatter, mention name
cfmt = logging.Formatter(('%(name)s - %(levelname)s - %(message)s'))
# File formatter, mention time
ffmt = logging.Formatter(('%(asctime)s - %(levelname)s - %(message)s'))
# Console handler
ch = log... | [
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train | constant_cache | Caches constant arrays associated with an array name.
The intent of this decorator is to avoid the cost
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especially data created by np.zeros or np.ones.
Instead, a single array of the first given shape is created
and any further requests for co... | montblanc/impl/rime/tensorflow/sources/defaults_source_provider.py | def constant_cache(method):
"""
Caches constant arrays associated with an array name.
The intent of this decorator is to avoid the cost
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especially data created by np.zeros or np.ones.
Instead, a single array of the first given shape is cre... | def constant_cache(method):
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train | chunk_cache | Caches chunks of default data.
This decorator caches generated default data so as to
avoid recomputing it on a subsequent queries to the
provider. | montblanc/impl/rime/tensorflow/sources/defaults_source_provider.py | def chunk_cache(method):
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train | _create_defaults_source_provider | Create a DefaultsSourceProvider object. This provides default
data sources for each array defined on the hypercube. The data sources
may either by obtained from the arrays 'default' data source
or the 'test' data source. | montblanc/impl/rime/tensorflow/RimeSolver.py | def _create_defaults_source_provider(cube, data_source):
"""
Create a DefaultsSourceProvider object. This provides default
data sources for each array defined on the hypercube. The data sources
may either by obtained from the arrays 'default' data source
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"""
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"""
Create a DefaultsSourceProvider object. This provides default
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may either by obtained from the arrays 'default' data source
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train | _construct_tensorflow_expression | Constructs a tensorflow expression for computing the RIME | montblanc/impl/rime/tensorflow/RimeSolver.py | def _construct_tensorflow_expression(slvr_cfg, feed_data, device, shard):
""" Constructs a tensorflow expression for computing the RIME """
zero = tf.constant(0)
src_count = zero
src_ph_vars = feed_data.src_ph_vars
LSA = feed_data.local
polarisation_type = slvr_cfg['polarisation_type']
# ... | def _construct_tensorflow_expression(slvr_cfg, feed_data, device, shard):
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zero = tf.constant(0)
src_count = zero
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train | _get_data | Get data from the data source, checking the return values | montblanc/impl/rime/tensorflow/RimeSolver.py | def _get_data(data_source, context):
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try:
# Get data from the data source
data = data_source.source(context)
# Complain about None values
if data is None:
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try:
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# Complain about None values
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train | _supply_data | Supply data to the data sink | montblanc/impl/rime/tensorflow/RimeSolver.py | def _supply_data(data_sink, context):
""" Supply data to the data sink """
try:
data_sink.sink(context)
except Exception as e:
ex = ValueError("An exception occurred while "
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""" Supply data to the data sink """
try:
data_sink.sink(context)
except Exception as e:
ex = ValueError("An exception occurred while "
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train | _apply_source_provider_dim_updates | Given a list of source_providers, apply the list of
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Dimension global_sizes are always updated with the supplied sizes and
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"""
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suggested dimension updates given in provider.updated_dimensions()
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"""
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train | _setup_hypercube | Sets up the hypercube given a solver configuration | montblanc/impl/rime/tensorflow/RimeSolver.py | def _setup_hypercube(cube, slvr_cfg):
""" Sets up the hypercube given a solver configuration """
mbu.register_default_dimensions(cube, slvr_cfg)
# Configure the dimensions of the beam cube
cube.register_dimension('beam_lw', 2,
description='E Beam cube l width')
cube.reg... | def _setup_hypercube(cube, slvr_cfg):
""" Sets up the hypercube given a solver configuration """
mbu.register_default_dimensions(cube, slvr_cfg)
# Configure the dimensions of the beam cube
cube.register_dimension('beam_lw', 2,
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train | _partition | Partition data sources into
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"""
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2. List of data sources to feed multiple times.
3. List of data sources to feed once.
"""
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iter_dims = set... | def _partition(iter_dims, data_sources):
"""
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3. List of data sources to feed once.
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train | RimeSolver._feed | Feed stub | montblanc/impl/rime/tensorflow/RimeSolver.py | def _feed(self, cube, data_sources, data_sinks, global_iter_args):
""" Feed stub """
try:
self._feed_impl(cube, data_sources, data_sinks, global_iter_args)
except Exception as e:
montblanc.log.exception("Feed Exception")
raise | def _feed(self, cube, data_sources, data_sinks, global_iter_args):
""" Feed stub """
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except Exception as e:
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train | RimeSolver._feed_impl | Implementation of staging_area feeding | montblanc/impl/rime/tensorflow/RimeSolver.py | def _feed_impl(self, cube, data_sources, data_sinks, global_iter_args):
""" Implementation of staging_area feeding """
session = self._tf_session
FD = self._tf_feed_data
LSA = FD.local
# Get source strides out before the local sizes are modified during
# the source loops... | def _feed_impl(self, cube, data_sources, data_sinks, global_iter_args):
""" Implementation of staging_area feeding """
session = self._tf_session
FD = self._tf_feed_data
LSA = FD.local
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train | RimeSolver._compute | Call the tensorflow compute | montblanc/impl/rime/tensorflow/RimeSolver.py | def _compute(self, feed_dict, shard):
""" Call the tensorflow compute """
try:
descriptor, enq = self._tfrun(self._tf_expr[shard], feed_dict=feed_dict)
self._inputs_waiting.decrement(shard)
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... | def _compute(self, feed_dict, shard):
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self._inputs_waiting.decrement(shard)
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montblanc.log.exception("Compute Exception")
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train | RimeSolver._consume | Consume stub | montblanc/impl/rime/tensorflow/RimeSolver.py | def _consume(self, data_sinks, cube, global_iter_args):
""" Consume stub """
try:
return self._consume_impl(data_sinks, cube, global_iter_args)
except Exception as e:
montblanc.log.exception("Consumer Exception")
raise e, None, sys.exc_info()[2] | def _consume(self, data_sinks, cube, global_iter_args):
""" Consume stub """
try:
return self._consume_impl(data_sinks, cube, global_iter_args)
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montblanc.log.exception("Consumer Exception")
raise e, None, sys.exc_info()[2] | [
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train | RimeSolver._consume_impl | Consume | montblanc/impl/rime/tensorflow/RimeSolver.py | def _consume_impl(self, data_sinks, cube, global_iter_args):
""" Consume """
LSA = self._tf_feed_data.local
output = self._tfrun(LSA.output.get_op)
# Expect the descriptor in the first tuple position
assert len(output) > 0
assert LSA.output.fed_arrays[0] == 'descriptor'... | def _consume_impl(self, data_sinks, cube, global_iter_args):
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LSA = self._tf_feed_data.local
output = self._tfrun(LSA.output.get_op)
# Expect the descriptor in the first tuple position
assert len(output) > 0
assert LSA.output.fed_arrays[0] == 'descriptor'... | [
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train | rime_solver_cfg | Produces a SolverConfiguration object, inherited from
a simple python dict, and containing the options required
to configure the RIME Solver.
Keyword arguments
-----------------
Any keyword arguments are inserted into the
returned dict.
Returns
-------
A SolverConfiguration object. | montblanc/__init__.py | def rime_solver_cfg(**kwargs):
"""
Produces a SolverConfiguration object, inherited from
a simple python dict, and containing the options required
to configure the RIME Solver.
Keyword arguments
-----------------
Any keyword arguments are inserted into the
returned dict.
Returns
... | def rime_solver_cfg(**kwargs):
"""
Produces a SolverConfiguration object, inherited from
a simple python dict, and containing the options required
to configure the RIME Solver.
Keyword arguments
-----------------
Any keyword arguments are inserted into the
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train | _create_filenames | Returns a dictionary of beam filename pairs,
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Given 'beam_$(corr)_$(reim).fits' returns:
{
'xx' : ('beam_xx_re.fits', 'beam_xx_im.fits'),
'xy' : ('beam_xy_re.fits', 'beam_xy_im.fits'),
...
'... | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | def _create_filenames(filename_schema, feed_type):
"""
Returns a dictionary of beam filename pairs,
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of correlations and real, imaginary pairs
Given 'beam_$(corr)_$(reim).fits' returns:
{
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"""
Returns a dictionary of beam filename pairs,
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Given 'beam_$(corr)_$(reim).fits' returns:
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train | _open_fits_files | Given a {correlation: filename} mapping for filenames
returns a {correlation: file handle} mapping | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | def _open_fits_files(filenames):
"""
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"""
kw = { 'mode' : 'update', 'memmap' : False }
def _fh(fn):
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return fits.open(fn, ... | def _open_fits_files(filenames):
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Given a {correlation: filename} mapping for filenames
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kw = { 'mode' : 'update', 'memmap' : False }
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train | _create_axes | Create a FitsAxes object | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | def _create_axes(filenames, file_dict):
""" Create a FitsAxes object """
try:
# Loop through the file_dictionary, finding the
# first open FITS file.
f = iter(f for tup in file_dict.itervalues()
for f in tup if f is not None).next()
except StopIteration as e:
rai... | def _create_axes(filenames, file_dict):
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try:
# Loop through the file_dictionary, finding the
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f = iter(f for tup in file_dict.itervalues()
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train | FitsBeamSourceProvider._initialise | Initialise the object by generating appropriate filenames,
opening associated file handles and inspecting the FITS axes
of these files. | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | def _initialise(self, feed_type="linear"):
"""
Initialise the object by generating appropriate filenames,
opening associated file handles and inspecting the FITS axes
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"""
self._filenames = filenames = _create_filenames(self._filename_schema,
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Initialise the object by generating appropriate filenames,
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self._filenames = filenames = _create_filenames(self._filename_schema,
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train | FitsBeamSourceProvider.ebeam | ebeam cube data source | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | def ebeam(self, context):
""" ebeam cube data source """
if context.shape != self.shape:
raise ValueError("Partial feeding of the "
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ebeam = np.empty(context.shape, context.dtype)
# Iterat... | def ebeam(self, context):
""" ebeam cube data source """
if context.shape != self.shape:
raise ValueError("Partial feeding of the "
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ebeam = np.empty(context.shape, context.dtype)
# Iterat... | [
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train | MSSinkProvider.model_vis | model visibility data sink | montblanc/impl/rime/tensorflow/sinks/ms_sink_provider.py | def model_vis(self, context):
""" model visibility data sink """
column = self._vis_column
msshape = None
# Do we have a column descriptor for the supplied column?
try:
coldesc = self._manager.column_descriptors[column]
except KeyError as e:
colde... | def model_vis(self, context):
""" model visibility data sink """
column = self._vis_column
msshape = None
# Do we have a column descriptor for the supplied column?
try:
coldesc = self._manager.column_descriptors[column]
except KeyError as e:
colde... | [
"model",
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train | _cache | Decorator for caching data source return values
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dimensions. | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | def _cache(method):
"""
Decorator for caching data source return values
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Iterate over the array shape descriptor e.g. (ntime, nbl, 3)
returning tuples containing the lower and upper extents
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Decorator for caching data source return values
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returning tuples containing the lower and upper extents
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train | _proxy | Decorator returning a method that proxies a data source. | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | def _proxy(method):
"""
Decorator returning a method that proxies a data source.
"""
@functools.wraps(method)
def memoizer(self, context):
return method(context)
return memoizer | def _proxy(method):
"""
Decorator returning a method that proxies a data source.
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@functools.wraps(method)
def memoizer(self, context):
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train | CachedSourceProvider.start | Perform any logic on solution start | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | def start(self, start_context):
""" Perform any logic on solution start """
for p in self._providers:
p.start(start_context)
if self._clear_start:
self.clear_cache() | def start(self, start_context):
""" Perform any logic on solution start """
for p in self._providers:
p.start(start_context)
if self._clear_start:
self.clear_cache() | [
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train | CachedSourceProvider.stop | Perform any logic on solution stop | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | def stop(self, stop_context):
""" Perform any logic on solution stop """
for p in self._providers:
p.stop(stop_context)
if self._clear_stop:
self.clear_cache() | def stop(self, stop_context):
""" Perform any logic on solution stop """
for p in self._providers:
p.stop(stop_context)
if self._clear_stop:
self.clear_cache() | [
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train | default_base_ant_pairs | Compute base antenna pairs | montblanc/impl/rime/tensorflow/config.py | def default_base_ant_pairs(self, context):
""" Compute base antenna pairs """
k = 0 if context.cfg['auto_correlations'] == True else 1
na = context.dim_global_size('na')
gen = (i.astype(context.dtype) for i in np.triu_indices(na, k))
# Cache np.triu_indices(na, k) as its likely that (na, k) will
... | def default_base_ant_pairs(self, context):
""" Compute base antenna pairs """
k = 0 if context.cfg['auto_correlations'] == True else 1
na = context.dim_global_size('na')
gen = (i.astype(context.dtype) for i in np.triu_indices(na, k))
# Cache np.triu_indices(na, k) as its likely that (na, k) will
... | [
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train | default_antenna1 | Default antenna1 values | montblanc/impl/rime/tensorflow/config.py | def default_antenna1(self, context):
""" Default antenna1 values """
ant1, ant2 = default_base_ant_pairs(self, context)
(tl, tu), (bl, bu) = context.dim_extents('ntime', 'nbl')
ant1_result = np.empty(context.shape, context.dtype)
ant1_result[:,:] = ant1[np.newaxis,bl:bu]
return ant1_result | def default_antenna1(self, context):
""" Default antenna1 values """
ant1, ant2 = default_base_ant_pairs(self, context)
(tl, tu), (bl, bu) = context.dim_extents('ntime', 'nbl')
ant1_result = np.empty(context.shape, context.dtype)
ant1_result[:,:] = ant1[np.newaxis,bl:bu]
return ant1_result | [
"Default",
"antenna1",
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] | ska-sa/montblanc | python | https://github.com/ska-sa/montblanc/blob/8a2e742e7500bcc6196489b735f87b233075dd2d/montblanc/impl/rime/tensorflow/config.py#L89-L95 | [
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train | default_antenna2 | Default antenna2 values | montblanc/impl/rime/tensorflow/config.py | def default_antenna2(self, context):
""" Default antenna2 values """
ant1, ant2 = default_base_ant_pairs(self, context)
(tl, tu), (bl, bu) = context.dim_extents('ntime', 'nbl')
ant2_result = np.empty(context.shape, context.dtype)
ant2_result[:,:] = ant2[np.newaxis,bl:bu]
return ant2_result | def default_antenna2(self, context):
""" Default antenna2 values """
ant1, ant2 = default_base_ant_pairs(self, context)
(tl, tu), (bl, bu) = context.dim_extents('ntime', 'nbl')
ant2_result = np.empty(context.shape, context.dtype)
ant2_result[:,:] = ant2[np.newaxis,bl:bu]
return ant2_result | [
"Default",
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] | ska-sa/montblanc | python | https://github.com/ska-sa/montblanc/blob/8a2e742e7500bcc6196489b735f87b233075dd2d/montblanc/impl/rime/tensorflow/config.py#L97-L103 | [
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train | identity_on_pols | Returns [[1, 0], tiled up to other dimensions
[0, 1]] | montblanc/impl/rime/tensorflow/config.py | def identity_on_pols(self, context):
"""
Returns [[1, 0], tiled up to other dimensions
[0, 1]]
"""
A = np.empty(context.shape, context.dtype)
A[:,:,:] = [[[1,0,0,1]]]
return A | def identity_on_pols(self, context):
"""
Returns [[1, 0], tiled up to other dimensions
[0, 1]]
"""
A = np.empty(context.shape, context.dtype)
A[:,:,:] = [[[1,0,0,1]]]
return A | [
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train | default_stokes | Returns [[1, 0], tiled up to other dimensions
[0, 0]] | montblanc/impl/rime/tensorflow/config.py | def default_stokes(self, context):
"""
Returns [[1, 0], tiled up to other dimensions
[0, 0]]
"""
A = np.empty(context.shape, context.dtype)
A[:,:,:] = [[[1,0,0,0]]]
return A | def default_stokes(self, context):
"""
Returns [[1, 0], tiled up to other dimensions
[0, 0]]
"""
A = np.empty(context.shape, context.dtype)
A[:,:,:] = [[[1,0,0,0]]]
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train | MSSourceProvider.frequency | Frequency data source | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | def frequency(self, context):
""" Frequency data source """
channels = self._manager.spectral_window_table.getcol(MS.CHAN_FREQ)
return channels.reshape(context.shape).astype(context.dtype) | def frequency(self, context):
""" Frequency data source """
channels = self._manager.spectral_window_table.getcol(MS.CHAN_FREQ)
return channels.reshape(context.shape).astype(context.dtype) | [
"Frequency",
"data",
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] | ska-sa/montblanc | python | https://github.com/ska-sa/montblanc/blob/8a2e742e7500bcc6196489b735f87b233075dd2d/montblanc/impl/rime/tensorflow/sources/ms_source_provider.py#L93-L96 | [
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train | MSSourceProvider.ref_frequency | Reference frequency data source | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | def ref_frequency(self, context):
""" Reference frequency data source """
num_chans = self._manager.spectral_window_table.getcol(MS.NUM_CHAN)
ref_freqs = self._manager.spectral_window_table.getcol(MS.REF_FREQUENCY)
data = np.hstack((np.repeat(rf, bs) for bs, rf in zip(num_chans, ref_fre... | def ref_frequency(self, context):
""" Reference frequency data source """
num_chans = self._manager.spectral_window_table.getcol(MS.NUM_CHAN)
ref_freqs = self._manager.spectral_window_table.getcol(MS.REF_FREQUENCY)
data = np.hstack((np.repeat(rf, bs) for bs, rf in zip(num_chans, ref_fre... | [
"Reference",
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] | ska-sa/montblanc | python | https://github.com/ska-sa/montblanc/blob/8a2e742e7500bcc6196489b735f87b233075dd2d/montblanc/impl/rime/tensorflow/sources/ms_source_provider.py#L98-L104 | [
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train | MSSourceProvider.uvw | Per-antenna UVW coordinate data source | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | def uvw(self, context):
""" Per-antenna UVW coordinate data source """
# Hacky access of private member
cube = context._cube
# Create antenna1 source context
a1_actual = cube.array("antenna1", reify=True)
a1_ctx = SourceContext("antenna1", cube, context.cfg,
... | def uvw(self, context):
""" Per-antenna UVW coordinate data source """
# Hacky access of private member
cube = context._cube
# Create antenna1 source context
a1_actual = cube.array("antenna1", reify=True)
a1_ctx = SourceContext("antenna1", cube, context.cfg,
... | [
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] | ska-sa/montblanc | python | https://github.com/ska-sa/montblanc/blob/8a2e742e7500bcc6196489b735f87b233075dd2d/montblanc/impl/rime/tensorflow/sources/ms_source_provider.py#L106-L141 | [
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