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ska-sa/montblanc | montblanc/util/__init__.py | redistribute_threads | def redistribute_threads(blockdimx, blockdimy, blockdimz,
dimx, dimy, dimz):
"""
Redistribute threads from the Z dimension towards the X dimension.
Also clamp number of threads to the problem dimension size,
if necessary
"""
# Shift threads from the z dimension
# into the y dimension
... | python | def redistribute_threads(blockdimx, blockdimy, blockdimz,
dimx, dimy, dimz):
"""
Redistribute threads from the Z dimension towards the X dimension.
Also clamp number of threads to the problem dimension size,
if necessary
"""
# Shift threads from the z dimension
# into the y dimension
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ska-sa/montblanc | montblanc/util/__init__.py | register_default_dimensions | def register_default_dimensions(cube, slvr_cfg):
""" Register the default dimensions for a RIME solver """
import montblanc.src_types as mbs
# Pull out the configuration options for the basics
autocor = slvr_cfg['auto_correlations']
ntime = 10
na = 7
nbands = 1
nchan = 16
npol = 4... | python | def register_default_dimensions(cube, slvr_cfg):
""" Register the default dimensions for a RIME solver """
import montblanc.src_types as mbs
# Pull out the configuration options for the basics
autocor = slvr_cfg['auto_correlations']
ntime = 10
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/helpers/cluster_gen.py | get_ip_address | def get_ip_address(ifname):
""" Hack to get IP address from the interface """
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
return socket.inet_ntoa(fcntl.ioctl(
s.fileno(),
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struct.pack('256s', ifname[:15])
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""" Hack to get IP address from the interface """
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
return socket.inet_ntoa(fcntl.ioctl(
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ska-sa/montblanc | install/cuda.py | nvcc_compiler_settings | 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')
cuda_path = os.environ.get('CUDA_PATH', default_cuda_path)
... | python | 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')
cuda_path = os.environ.get('CUDA_PATH', default_cuda_path)
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ska-sa/montblanc | install/cuda.py | inspect_cuda_version_and_devices | 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__ ... | python | 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, '''
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#include <stdio.h>
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ska-sa/montblanc | install/cuda.py | customize_compiler_for_nvcc | 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... | python | 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... | [
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ska-sa/montblanc | install/cuda.py | inspect_cuda | 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)
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ska-sa/montblanc | montblanc/solvers/rime_solver.py | RIMESolver.template_dict | 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... | python | 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
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ska-sa/montblanc | montblanc/factory.py | rime_solver | def rime_solver(slvr_cfg):
""" Factory function that produces a RIME solver """
from montblanc.impl.rime.tensorflow.RimeSolver import RimeSolver
return RimeSolver(slvr_cfg) | python | 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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/source_provider.py | find_sources | 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... | python | def find_sources(obj, argspec=None):
"""
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/source_provider.py | SourceProvider.sources | def sources(self):
"""
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):
...
... | python | def sources(self):
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Returns a dictionary of source methods found on this object,
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ska-sa/montblanc | montblanc/util/parallactic_angles.py | parallactic_angles | 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... | python | 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,),
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ska-sa/montblanc | montblanc/logsetup.py | setup_logging | 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... | python | 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'))
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/defaults_source_provider.py | constant_cache | 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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Instead, a single array of the first given shape is cre... | python | def constant_cache(method):
"""
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/defaults_source_provider.py | chunk_cache | def chunk_cache(method):
"""
Caches chunks of default data.
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avoid recomputing it on a subsequent queries to the
provider.
"""
@functools.wraps(method)
def wrapper(self, context):
# Defer to the method if no caching is enable... | python | def chunk_cache(method):
"""
Caches chunks of default data.
This decorator caches generated default data so as to
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provider.
"""
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _create_defaults_source_provider | 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
or the 'test' data source.
"""
from m... | python | 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
or the 'test' data source.
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _construct_tensorflow_expression | 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']
# ... | python | 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']
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _get_data | 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:
raise ValueError("'None' returned from "
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try:
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# Complain about None values
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _supply_data | 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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _apply_source_provider_dim_updates | def _apply_source_provider_dim_updates(cube, source_providers, budget_dims):
"""
Given a list of source_providers, apply the list of
suggested dimension updates given in provider.updated_dimensions()
to the supplied hypercube.
Dimension global_sizes are always updated with the supplied sizes and
... | python | def _apply_source_provider_dim_updates(cube, source_providers, budget_dims):
"""
Given a list of source_providers, apply the list of
suggested dimension updates given in provider.updated_dimensions()
to the supplied hypercube.
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _setup_hypercube | 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... | python | 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')
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | _partition | def _partition(iter_dims, data_sources):
"""
Partition data sources into
1. Dictionary of data sources associated with radio sources.
2. List of data sources to feed multiple times.
3. List of data sources to feed once.
"""
src_nr_vars = set(source_var_types().values())
iter_dims = set... | python | def _partition(iter_dims, data_sources):
"""
Partition data sources into
1. Dictionary of data sources associated with radio sources.
2. List of data sources to feed multiple times.
3. List of data sources to feed once.
"""
src_nr_vars = set(source_var_types().values())
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | RimeSolver._feed | 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 | python | 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:
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | RimeSolver._feed_impl | 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
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""" Implementation of staging_area feeding """
session = self._tf_session
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | RimeSolver._compute | 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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montblanc.log.exception("Compute Exception")
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""" Call the tensorflow compute """
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descriptor, enq = self._tfrun(self._tf_expr[shard], feed_dict=feed_dict)
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | RimeSolver._consume | 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] | python | def _consume(self, data_sinks, cube, global_iter_args):
""" Consume stub """
try:
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/RimeSolver.py | RimeSolver._consume_impl | 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
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""" Consume """
LSA = self._tf_feed_data.local
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ska-sa/montblanc | montblanc/__init__.py | rime_solver_cfg | 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
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"""
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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | _create_filenames | def _create_filenames(filename_schema, feed_type):
"""
Returns a dictionary of beam filename pairs,
keyed on correlation,from the cartesian product
of correlations and real, imaginary pairs
Given 'beam_$(corr)_$(reim).fits' returns:
{
'xx' : ('beam_xx_re.fits', 'beam_xx_im.fits'),
'... | python | def _create_filenames(filename_schema, feed_type):
"""
Returns a dictionary of beam filename pairs,
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | _open_fits_files | def _open_fits_files(filenames):
"""
Given a {correlation: filename} mapping for filenames
returns a {correlation: file handle} mapping
"""
kw = { 'mode' : 'update', 'memmap' : False }
def _fh(fn):
""" Returns a filehandle or None if file does not exist """
return fits.open(fn, ... | python | def _open_fits_files(filenames):
"""
Given a {correlation: filename} mapping for filenames
returns a {correlation: file handle} mapping
"""
kw = { 'mode' : 'update', 'memmap' : False }
def _fh(fn):
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | _create_axes | 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... | python | 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()
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | FitsBeamSourceProvider._initialise | def _initialise(self, feed_type="linear"):
"""
Initialise the object by generating appropriate filenames,
opening associated file handles and inspecting the FITS axes
of these files.
"""
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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/fits_beam_source_provider.py | FitsBeamSourceProvider.ebeam | def ebeam(self, context):
""" ebeam cube data source """
if context.shape != self.shape:
raise ValueError("Partial feeding of the "
"beam cube is not yet supported %s %s." % (context.shape, self.shape))
ebeam = np.empty(context.shape, context.dtype)
# Iterat... | python | 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)
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sinks/ms_sink_provider.py | MSSinkProvider.model_vis | 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... | python | 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]
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | _cache | def _cache(method):
"""
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returning tuples containing the lower and upper extents
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"""
Decorator for caching data source return values
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | _proxy | def _proxy(method):
"""
Decorator returning a method that proxies a data source.
"""
@functools.wraps(method)
def memoizer(self, context):
return method(context)
return memoizer | python | def _proxy(method):
"""
Decorator returning a method that proxies a data source.
"""
@functools.wraps(method)
def memoizer(self, context):
return method(context)
return memoizer | [
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | CachedSourceProvider.start | 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() | python | 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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/cached_source_provider.py | CachedSourceProvider.stop | 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() | python | 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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/config.py | default_base_ant_pairs | 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
... | python | 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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/config.py | default_antenna1 | 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 | python | 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 | [
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/config.py | default_antenna2 | 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 | python | 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 | [
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/config.py | identity_on_pols | 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 | python | 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]]]
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/config.py | default_stokes | def default_stokes(self, context):
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Returns [[1, 0], tiled up to other dimensions
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"""
A = np.empty(context.shape, context.dtype)
A[:,:,:] = [[[1,0,0,0]]]
return A | python | 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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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.frequency | 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) | python | 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) | [
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.ref_frequency | 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... | python | 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)
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.uvw | 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,
... | python | 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 | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.antenna1 | def antenna1(self, context):
""" antenna1 data source """
lrow, urow = MS.uvw_row_extents(context)
antenna1 = self._manager.ordered_uvw_table.getcol(
MS.ANTENNA1, startrow=lrow, nrow=urow-lrow)
return antenna1.reshape(context.shape).astype(context.dtype) | python | def antenna1(self, context):
""" antenna1 data source """
lrow, urow = MS.uvw_row_extents(context)
antenna1 = self._manager.ordered_uvw_table.getcol(
MS.ANTENNA1, startrow=lrow, nrow=urow-lrow)
return antenna1.reshape(context.shape).astype(context.dtype) | [
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.antenna2 | def antenna2(self, context):
""" antenna2 data source """
lrow, urow = MS.uvw_row_extents(context)
antenna2 = self._manager.ordered_uvw_table.getcol(
MS.ANTENNA2, startrow=lrow, nrow=urow-lrow)
return antenna2.reshape(context.shape).astype(context.dtype) | python | def antenna2(self, context):
""" antenna2 data source """
lrow, urow = MS.uvw_row_extents(context)
antenna2 = self._manager.ordered_uvw_table.getcol(
MS.ANTENNA2, startrow=lrow, nrow=urow-lrow)
return antenna2.reshape(context.shape).astype(context.dtype) | [
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.parallactic_angles | def parallactic_angles(self, context):
""" parallactic angle data source """
# Time and antenna extents
(lt, ut), (la, ua) = context.dim_extents('ntime', 'na')
return (mbu.parallactic_angles(self._times[lt:ut],
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""" parallactic angle data source """
# Time and antenna extents
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.observed_vis | def observed_vis(self, context):
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lrow, urow = MS.row_extents(context)
data = self._manager.ordered_main_table.getcol(
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.flag | def flag(self, context):
""" Flag data source """
lrow, urow = MS.row_extents(context)
flag = self._manager.ordered_main_table.getcol(
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""" Flag data source """
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sources/ms_source_provider.py | MSSourceProvider.weight | def weight(self, context):
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weight = self._manager.ordered_main_table.getcol(
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# WEIGHT is applied across all channels
weight = np.repeat(weight, self._manager.ch... | python | def weight(self, context):
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weight = self._manager.ordered_main_table.getcol(
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/__init__.py | load_tf_lib | def load_tf_lib():
""" Load the tensorflow library """
from os.path import join as pjoin
import pkg_resources
import tensorflow as tf
path = pjoin('ext', 'rime.so')
rime_lib_path = pkg_resources.resource_filename("montblanc", path)
return tf.load_op_library(rime_lib_path) | python | def load_tf_lib():
""" Load the tensorflow library """
from os.path import join as pjoin
import pkg_resources
import tensorflow as tf
path = pjoin('ext', 'rime.so')
rime_lib_path = pkg_resources.resource_filename("montblanc", path)
return tf.load_op_library(rime_lib_path) | [
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ska-sa/montblanc | montblanc/configuration.py | raise_validator_errors | def raise_validator_errors(validator):
"""
Raise any errors associated with the validator.
Parameters
----------
validator : :class:`cerberus.Validator`
Validator
Raises
------
ValueError
Raised if errors existed on `validator`.
Message describing each error and... | python | def raise_validator_errors(validator):
"""
Raise any errors associated with the validator.
Parameters
----------
validator : :class:`cerberus.Validator`
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Raises
------
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tjguk/winshell | winshell.py | indented | def indented(text, level, indent=2):
"""Take a multiline text and indent it as a block"""
return "\n".join("%s%s" % (level * indent * " ", s) for s in text.splitlines()) | python | def indented(text, level, indent=2):
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"""Put curly brackets round an indented text"""
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tjguk/winshell | winshell.py | copy_file | def copy_file(
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no_confirm=False,
rename_on_collision=True,
silent=False,
extra_flags=0,
hWnd=None
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no_confirm=False,
rename_on_collision=True,
silent=False,
extra_flags=0,
hWnd=None
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tjguk/winshell | winshell.py | move_file | def move_file(
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no_confirm=False,
rename_on_collision=True,
silent=False,
extra_flags=0,
hWnd=None
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source_path,
target_path,
allow_undo=True,
no_confirm=False,
rename_on_collision=True,
silent=False,
extra_flags=0,
hWnd=None
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tjguk/winshell | winshell.py | rename_file | def rename_file(
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silent=False,
extra_flags=0,
hWnd=None
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source_path,
target_path,
allow_undo=True,
no_confirm=False,
rename_on_collision=True,
silent=False,
extra_flags=0,
hWnd=None
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tjguk/winshell | winshell.py | delete_file | def delete_file(
source_path,
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no_confirm=False,
silent=False,
extra_flags=0,
hWnd=None
):
"""Perform a shell-based file delete. Deleting in
this way uses the system recycle bin, allows the
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animation during the de... | python | def delete_file(
source_path,
allow_undo=True,
no_confirm=False,
silent=False,
extra_flags=0,
hWnd=None
):
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tjguk/winshell | winshell.py | structured_storage | def structured_storage(filename):
"""Pick out info from MS documents with embedded
structured storage(typically MS Word docs etc.)
Returns a dictionary of information found
"""
if not pythoncom.StgIsStorageFile(filename):
return {}
flags = storagecon.STGM_READ | storagecon.STGM_SHARE... | python | def structured_storage(filename):
"""Pick out info from MS documents with embedded
structured storage(typically MS Word docs etc.)
Returns a dictionary of information found
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tjguk/winshell | winshell.py | CreateShortcut | def CreateShortcut(Path, Target, Arguments="", StartIn="", Icon=("", 0), Description=""):
"""Create a Windows shortcut:
Path - As what file should the shortcut be created?
Target - What command should the desktop use?
Arguments - What arguments should be supplied to the command?
StartIn - What fold... | python | def CreateShortcut(Path, Target, Arguments="", StartIn="", Icon=("", 0), Description=""):
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tjguk/winshell | winshell.py | ShellRecycleBin.undelete | def undelete(self, original_filepath):
"""Restore the most recent version of a filepath, returning
the filepath it was restored to(as rename-on-collision will
apply if a file already exists at that path).
"""
candidates = self.versions(original_filepath)
if not candidates... | python | def undelete(self, original_filepath):
"""Restore the most recent version of a filepath, returning
the filepath it was restored to(as rename-on-collision will
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/queue_wrapper.py | _get_queue_types | def _get_queue_types(fed_arrays, data_sources):
"""
Given a list of arrays to feed in fed_arrays, return
a list of associated queue types, obtained from tuples
in the data_sources dictionary
"""
try:
return [data_sources[n].dtype for n in fed_arrays]
except KeyError as e:
rai... | python | def _get_queue_types(fed_arrays, data_sources):
"""
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a list of associated queue types, obtained from tuples
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"""
try:
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/queue_wrapper.py | create_queue_wrapper | def create_queue_wrapper(name, queue_size, fed_arrays, data_sources, *args, **kwargs):
"""
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name: string
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queue_size: integer
Size of the queue
fed_arrays: list
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ska-sa/montblanc | montblanc/util/parsing.py | parse_python_assigns | def parse_python_assigns(assign_str):
"""
Parses a string, containing assign statements
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.. code-block:: python
h5 = katdal.open('123456789.h5')
kwargs = parse_python_assigns("spw=3; scans=[1,2];"
"targets='bpcal,radec';"
... | python | def parse_python_assigns(assign_str):
"""
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.. code-block:: python
h5 = katdal.open('123456789.h5')
kwargs = parse_python_assigns("spw=3; scans=[1,2];"
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sinks/sink_provider.py | find_sinks | def find_sinks(obj):
"""
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...
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...
is not... | python | def find_sinks(obj):
"""
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ska-sa/montblanc | montblanc/impl/rime/tensorflow/sinks/sink_provider.py | SinkProvider.sinks | def sinks(self):
"""
Returns a dictionary of sink methods found on this object,
keyed on method name. Sink methods are identified by
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def f(self, context):
...
is a sink method, but
def f(self, c... | python | def sinks(self):
"""
Returns a dictionary of sink methods found on this object,
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ska-sa/montblanc | montblanc/util/ant_uvw.py | _antenna_uvw | def _antenna_uvw(uvw, antenna1, antenna2, chunks, nr_of_antenna):
""" numba implementation of antenna_uvw """
if antenna1.ndim != 1:
raise ValueError("antenna1 shape should be (row,)")
if antenna2.ndim != 1:
raise ValueError("antenna2 shape should be (row,)")
if uvw.ndim != 2 or uvw.s... | python | def _antenna_uvw(uvw, antenna1, antenna2, chunks, nr_of_antenna):
""" numba implementation of antenna_uvw """
if antenna1.ndim != 1:
raise ValueError("antenna1 shape should be (row,)")
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raise ValueError("antenna2 shape should be (row,)")
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ska-sa/montblanc | montblanc/util/ant_uvw.py | _raise_decomposition_errors | def _raise_decomposition_errors(uvw, antenna1, antenna2,
chunks, ant_uvw, max_err):
""" Raises informative exception for an invalid decomposition """
start = 0
problem_str = []
for ci, chunk in enumerate(chunks):
end = start + chunk
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chunks, ant_uvw, max_err):
""" Raises informative exception for an invalid decomposition """
start = 0
problem_str = []
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ska-sa/montblanc | montblanc/util/ant_uvw.py | _raise_missing_antenna_errors | def _raise_missing_antenna_errors(ant_uvw, max_err):
""" Raises an informative error for missing antenna """
# Find antenna uvw coordinates where any UVW component was nan
# nan + real == nan
problems = np.nonzero(np.add.reduce(np.isnan(ant_uvw), axis=2))
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for c, a in zip(*prob... | python | def _raise_missing_antenna_errors(ant_uvw, max_err):
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problems = np.nonzero(np.add.reduce(np.isnan(ant_uvw), axis=2))
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ska-sa/montblanc | montblanc/util/ant_uvw.py | antenna_uvw | def antenna_uvw(uvw, antenna1, antenna2, chunks,
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check_decomposition=False, max_err=100):
"""
Computes per-antenna UVW coordinates from baseline ``uvw``,
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Computes per-antenna UVW coordinates from baseline ``uvw``,
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ska-sa/montblanc | montblanc/src_types.py | default_sources | def default_sources(**kwargs):
"""
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ska-sa/montblanc | montblanc/src_types.py | sources_to_nr_vars | def sources_to_nr_vars(sources):
"""
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a source numbering variable to number of sources mapping.
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sources_to_nr_vars({'po... | python | def sources_to_nr_vars(sources):
"""
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ska-sa/montblanc | montblanc/src_types.py | source_range_tuple | def source_range_tuple(start, end, nr_var_dict):
"""
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containing the numbers of each source, returns a dictionary
containing tuples of the start and end index
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"""
starts = np.array([0 for nr_var in SOURCE... | python | def source_range_tuple(start, end, nr_var_dict):
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kislyuk/aegea | aegea/packages/github3/auths.py | Authorization.update | def update(self, scopes=[], add_scopes=[], rm_scopes=[], note='',
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bwohlberg/jonga | jonga.py | current_function | def current_function(frame):
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Get reference to currently running function from inspect/trace stack frame.
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Stack frame obtained via trace or inspect
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Currently running function
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bwohlberg/jonga | jonga.py | CallTracer._trace | def _trace(self, frame, event, arg):
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Initial H value in HSV colour specification
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.create_review_comment | def create_review_comment(self, body, commit_id, path, position):
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.iter_comments | def iter_comments(self, number=-1, etag=None):
"""Iterate over the comments on this pull request.
:param int number: (optional), number of comments to return. Default:
-1 returns all available comments.
:param str etag: (optional), ETag from a previous request to the same
... | python | def iter_comments(self, number=-1, etag=None):
"""Iterate over the comments on this pull request.
:param int number: (optional), number of comments to return. Default:
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.iter_files | def iter_files(self, number=-1, etag=None):
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.iter_issue_comments | def iter_issue_comments(self, number=-1, etag=None):
"""Iterate over the issue comments on this pull request.
:param int number: (optional), number of comments to return. Default:
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:param str etag: (optional), ETag from a previous request to the sam... | python | def iter_issue_comments(self, number=-1, etag=None):
"""Iterate over the issue comments on this pull request.
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.merge | def merge(self, commit_message='', sha=None):
"""Merge this pull request.
:param str commit_message: (optional), message to be used for the
merge commit
:returns: bool
"""
parameters = {'commit_message': commit_message}
if sha:
parameters['sha'] =... | python | def merge(self, commit_message='', sha=None):
"""Merge this pull request.
:param str commit_message: (optional), message to be used for the
merge commit
:returns: bool
"""
parameters = {'commit_message': commit_message}
if sha:
parameters['sha'] =... | [
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.patch | def patch(self):
"""Return the patch"""
resp = self._get(self._api,
headers={'Accept': 'application/vnd.github.patch'})
return resp.content if self._boolean(resp, 200, 404) else None | python | def patch(self):
"""Return the patch"""
resp = self._get(self._api,
headers={'Accept': 'application/vnd.github.patch'})
return resp.content if self._boolean(resp, 200, 404) else None | [
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kislyuk/aegea | aegea/packages/github3/pulls.py | PullRequest.update | def update(self, title=None, body=None, state=None):
"""Update this pull request.
:param str title: (optional), title of the pull
:param str body: (optional), body of the pull request
:param str state: (optional), ('open', 'closed')
:returns: bool
"""
data = {'ti... | python | def update(self, title=None, body=None, state=None):
"""Update this pull request.
:param str title: (optional), title of the pull
:param str body: (optional), body of the pull request
:param str state: (optional), ('open', 'closed')
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kislyuk/aegea | aegea/packages/github3/pulls.py | ReviewComment.reply | def reply(self, body):
"""Reply to this review comment with a new review comment.
:param str body: The text of the comment.
:returns: The created review comment.
:rtype: :class:`~github3.pulls.ReviewComment`
"""
url = self._build_url('comments', base_url=self.pull_reques... | python | def reply(self, body):
"""Reply to this review comment with a new review comment.
:param str body: The text of the comment.
:returns: The created review comment.
:rtype: :class:`~github3.pulls.ReviewComment`
"""
url = self._build_url('comments', base_url=self.pull_reques... | [
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kislyuk/aegea | aegea/packages/github3/orgs.py | Team.add_member | def add_member(self, login):
"""Add ``login`` to this team.
:returns: bool
"""
warnings.warn(
'This is no longer supported by the GitHub API, see '
'https://developer.github.com/changes/2014-09-23-one-more-week'
'-before-the-add-team-member-api-breaki... | python | def add_member(self, login):
"""Add ``login`` to this team.
:returns: bool
"""
warnings.warn(
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'https://developer.github.com/changes/2014-09-23-one-more-week'
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kislyuk/aegea | aegea/packages/github3/orgs.py | Team.add_repo | def add_repo(self, repo):
"""Add ``repo`` to this team.
:param str repo: (required), form: 'user/repo'
:returns: bool
"""
url = self._build_url('repos', repo, base_url=self._api)
return self._boolean(self._put(url), 204, 404) | python | def add_repo(self, repo):
"""Add ``repo`` to this team.
:param str repo: (required), form: 'user/repo'
:returns: bool
"""
url = self._build_url('repos', repo, base_url=self._api)
return self._boolean(self._put(url), 204, 404) | [
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