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train | greedy_merge | Greedily merge overlapping sequences into longer sequences.
Accepts a collection of VariantSequence objects and returns another
collection of elongated variant sequences. The reads field of the
returned VariantSequence object will contain reads which
only partially overlap the full sequence. | isovar/assembly.py | def greedy_merge(
variant_sequences,
min_overlap_size=MIN_VARIANT_SEQUENCE_ASSEMBLY_OVERLAP_SIZE):
"""
Greedily merge overlapping sequences into longer sequences.
Accepts a collection of VariantSequence objects and returns another
collection of elongated variant sequences. The reads fie... | def greedy_merge(
variant_sequences,
min_overlap_size=MIN_VARIANT_SEQUENCE_ASSEMBLY_OVERLAP_SIZE):
"""
Greedily merge overlapping sequences into longer sequences.
Accepts a collection of VariantSequence objects and returns another
collection of elongated variant sequences. The reads fie... | [
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train | collapse_substrings | Combine shorter sequences which are fully contained in longer sequences.
Parameters
----------
variant_sequences : list
List of VariantSequence objects
Returns a (potentially shorter) list without any contained subsequences. | isovar/assembly.py | def collapse_substrings(variant_sequences):
"""
Combine shorter sequences which are fully contained in longer sequences.
Parameters
----------
variant_sequences : list
List of VariantSequence objects
Returns a (potentially shorter) list without any contained subsequences.
"""
if... | def collapse_substrings(variant_sequences):
"""
Combine shorter sequences which are fully contained in longer sequences.
Parameters
----------
variant_sequences : list
List of VariantSequence objects
Returns a (potentially shorter) list without any contained subsequences.
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train | iterative_overlap_assembly | Assembles longer sequences from reads centered on a variant by
between merging all pairs of overlapping sequences and collapsing
shorter sequences onto every longer sequence which contains them.
Returns a list of variant sequences, sorted by decreasing read support. | isovar/assembly.py | def iterative_overlap_assembly(
variant_sequences,
min_overlap_size=MIN_VARIANT_SEQUENCE_ASSEMBLY_OVERLAP_SIZE):
"""
Assembles longer sequences from reads centered on a variant by
between merging all pairs of overlapping sequences and collapsing
shorter sequences onto every longer sequen... | def iterative_overlap_assembly(
variant_sequences,
min_overlap_size=MIN_VARIANT_SEQUENCE_ASSEMBLY_OVERLAP_SIZE):
"""
Assembles longer sequences from reads centered on a variant by
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train | groupby | Group elements of the list `xs` by keys generated from calling `key_fn`.
Returns a dictionary which maps keys to sub-lists of `xs`. | isovar/common.py | def groupby(xs, key_fn):
"""
Group elements of the list `xs` by keys generated from calling `key_fn`.
Returns a dictionary which maps keys to sub-lists of `xs`.
"""
result = defaultdict(list)
for x in xs:
key = key_fn(x)
result[key].append(x)
return result | def groupby(xs, key_fn):
"""
Group elements of the list `xs` by keys generated from calling `key_fn`.
Returns a dictionary which maps keys to sub-lists of `xs`.
"""
result = defaultdict(list)
for x in xs:
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result[key].append(x)
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train | ortho_basis | Generates an orthonormal basis in the plane perpendicular to `normal`
The orthonormal basis generated spans the plane defined with `normal` as
its normal vector. The handedness of `on1` and `on2` in the returned
basis is such that:
.. math::
\\mathsf{on1} \\times \\mathsf{on2} =
... | opan/utils/vector.py | def ortho_basis(normal, ref_vec=None):
"""Generates an orthonormal basis in the plane perpendicular to `normal`
The orthonormal basis generated spans the plane defined with `normal` as
its normal vector. The handedness of `on1` and `on2` in the returned
basis is such that:
.. math::
... | def ortho_basis(normal, ref_vec=None):
"""Generates an orthonormal basis in the plane perpendicular to `normal`
The orthonormal basis generated spans the plane defined with `normal` as
its normal vector. The handedness of `on1` and `on2` in the returned
basis is such that:
.. math::
... | [
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train | orthonorm_check | Checks orthonormality of the column vectors of a matrix.
If a one-dimensional |nparray| is passed to `a`, it is treated as a single
column vector, rather than a row matrix of length-one column vectors.
The matrix `a` does not need to be square, though it must have at least
as many rows as columns, sin... | opan/utils/vector.py | def orthonorm_check(a, tol=_DEF.ORTHONORM_TOL, report=False):
"""Checks orthonormality of the column vectors of a matrix.
If a one-dimensional |nparray| is passed to `a`, it is treated as a single
column vector, rather than a row matrix of length-one column vectors.
The matrix `a` does not need to be ... | def orthonorm_check(a, tol=_DEF.ORTHONORM_TOL, report=False):
"""Checks orthonormality of the column vectors of a matrix.
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train | parallel_check | Checks whether two vectors are parallel OR anti-parallel.
Vectors must be of the same dimension.
Parameters
----------
vec1
length-R |npfloat_| --
First vector to compare
vec2
length-R |npfloat_| --
Second vector to compare
Returns
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par
... | opan/utils/vector.py | def parallel_check(vec1, vec2):
"""Checks whether two vectors are parallel OR anti-parallel.
Vectors must be of the same dimension.
Parameters
----------
vec1
length-R |npfloat_| --
First vector to compare
vec2
length-R |npfloat_| --
Second vector to compare
... | def parallel_check(vec1, vec2):
"""Checks whether two vectors are parallel OR anti-parallel.
Vectors must be of the same dimension.
Parameters
----------
vec1
length-R |npfloat_| --
First vector to compare
vec2
length-R |npfloat_| --
Second vector to compare
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train | proj | Vector projection.
Calculated as:
.. math::
\\mathsf{vec\\_onto} * \\frac{\\mathsf{vec}\\cdot\\mathsf{vec\\_onto}}
{\\mathsf{vec\\_onto}\\cdot\\mathsf{vec\\_onto}}
Parameters
----------
vec
length-R |npfloat_| --
Vector to project
vec_onto
length-R ... | opan/utils/vector.py | def proj(vec, vec_onto):
""" Vector projection.
Calculated as:
.. math::
\\mathsf{vec\\_onto} * \\frac{\\mathsf{vec}\\cdot\\mathsf{vec\\_onto}}
{\\mathsf{vec\\_onto}\\cdot\\mathsf{vec\\_onto}}
Parameters
----------
vec
length-R |npfloat_| --
Vector to projec... | def proj(vec, vec_onto):
""" Vector projection.
Calculated as:
.. math::
\\mathsf{vec\\_onto} * \\frac{\\mathsf{vec}\\cdot\\mathsf{vec\\_onto}}
{\\mathsf{vec\\_onto}\\cdot\\mathsf{vec\\_onto}}
Parameters
----------
vec
length-R |npfloat_| --
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train | rej | Vector rejection.
Calculated by subtracting from `vec` the projection of `vec` onto
`vec_onto`:
.. math::
\\mathsf{vec} - \\mathrm{proj}\\left(\\mathsf{vec},
\\ \\mathsf{vec\\_onto}\\right)
Parameters
----------
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length-R |npfloat_| --
Vector to reject
... | opan/utils/vector.py | def rej(vec, vec_onto):
""" Vector rejection.
Calculated by subtracting from `vec` the projection of `vec` onto
`vec_onto`:
.. math::
\\mathsf{vec} - \\mathrm{proj}\\left(\\mathsf{vec},
\\ \\mathsf{vec\\_onto}\\right)
Parameters
----------
vec
length-R |npfloat_| ... | def rej(vec, vec_onto):
""" Vector rejection.
Calculated by subtracting from `vec` the projection of `vec` onto
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.. math::
\\mathsf{vec} - \\mathrm{proj}\\left(\\mathsf{vec},
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Parameters
----------
vec
length-R |npfloat_| ... | [
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train | vec_angle | Angle between two R-dimensional vectors.
Angle calculated as:
.. math::
\\arccos\\left[
\\frac{\\mathsf{vec1}\cdot\\mathsf{vec2}}
{\\left\\|\\mathsf{vec1}\\right\\|
\\left\\|\\mathsf{vec2}\\right\\|}
\\right]
Parameters
----------
vec1
length-R... | opan/utils/vector.py | def vec_angle(vec1, vec2):
""" Angle between two R-dimensional vectors.
Angle calculated as:
.. math::
\\arccos\\left[
\\frac{\\mathsf{vec1}\cdot\\mathsf{vec2}}
{\\left\\|\\mathsf{vec1}\\right\\|
\\left\\|\\mathsf{vec2}\\right\\|}
\\right]
Parameters
-... | def vec_angle(vec1, vec2):
""" Angle between two R-dimensional vectors.
Angle calculated as:
.. math::
\\arccos\\left[
\\frac{\\mathsf{vec1}\cdot\\mathsf{vec2}}
{\\left\\|\\mathsf{vec1}\\right\\|
\\left\\|\\mathsf{vec2}\\right\\|}
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Parameters
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train | new_module | Do all of the gruntwork associated with creating a new module. | cbsettings/importers.py | def new_module(name):
"""
Do all of the gruntwork associated with creating a new module.
"""
parent = None
if '.' in name:
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sys.modules[name] = module
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"""
Do all of the gruntwork associated with creating a new module.
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parent = None
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train | allele_counts_dataframe | Creates a DataFrame containing number of reads supporting the
ref vs. alt alleles for each variant. | isovar/allele_counts.py | def allele_counts_dataframe(variant_and_allele_reads_generator):
"""
Creates a DataFrame containing number of reads supporting the
ref vs. alt alleles for each variant.
"""
df_builder = DataFrameBuilder(
AlleleCount,
extra_column_fns={
"gene": lambda variant, _: ";".join(... | def allele_counts_dataframe(variant_and_allele_reads_generator):
"""
Creates a DataFrame containing number of reads supporting the
ref vs. alt alleles for each variant.
"""
df_builder = DataFrameBuilder(
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train | install_extension | Install Postgres extension. | postpy/extensions.py | def install_extension(conn, extension: str):
"""Install Postgres extension."""
query = 'CREATE EXTENSION IF NOT EXISTS "%s";'
with conn.cursor() as cursor:
cursor.execute(query, (AsIs(extension),))
installed = check_extension(conn, extension)
if not installed:
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"""Install Postgres extension."""
query = 'CREATE EXTENSION IF NOT EXISTS "%s";'
with conn.cursor() as cursor:
cursor.execute(query, (AsIs(extension),))
installed = check_extension(conn, extension)
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train | check_extension | Check to see if an extension is installed. | postpy/extensions.py | def check_extension(conn, extension: str) -> bool:
"""Check to see if an extension is installed."""
query = 'SELECT installed_version FROM pg_available_extensions WHERE name=%s;'
with conn.cursor() as cursor:
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train | xrange.count | rangeobject.count(value) -> integer -- return number of occurrences
of value | picklable_itertools/range.py | def count(self, i):
"""rangeobject.count(value) -> integer -- return number of occurrences
of value
"""
if self._stop > self._start and self._step > 0:
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train | make_iterable | Ensure obj is iterable. | opencorpora/reader.py | def make_iterable(obj, default=None):
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train | CorpusReader.iter_documents | Return an iterator over corpus documents. | opencorpora/reader.py | def iter_documents(self, fileids=None, categories=None, _destroy=False):
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train | CorpusReader.catalog | Return information about documents in corpora:
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train | CorpusReader._create_meta_cache | Try to dump metadata to a file. | opencorpora/reader.py | def _create_meta_cache(self):
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train | CorpusReader._load_meta_cache | Try to load metadata from file. | opencorpora/reader.py | def _load_meta_cache(self):
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train | CorpusReader._compute_document_meta | Return documents meta information that can
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Return documents meta information that can
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meta = OrderedDict()
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train | CorpusReader._document_xml | Return xml Element for the document document_id. | opencorpora/reader.py | def _document_xml(self, doc_id):
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doc_str = self._get_doc_by_raw_offset(str(doc_id))
return compat.ElementTree.XML(doc_str.encode('utf8')) | def _document_xml(self, doc_id):
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train | CorpusReader._get_doc_by_raw_offset | Load document from xml using bytes offset information.
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train | CorpusReader._get_doc_by_line_offset | Load document from xml using line offset information.
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bounds = self._get_meta()[str(doc_id)].bounds
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train | _threeDdot_simple | Return Ma, where M is a 3x3 transformation matrix, for each pixel | imagen/colorspaces.py | def _threeDdot_simple(M,a):
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result = np.empty(a.shape,dtype=a.dtype)
for i in range(a.shape[0]):
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... | def _threeDdot_simple(M,a):
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train | _swaplch | Reverse the order of an LCH numpy dstack or tuple for analysis. | imagen/colorspaces.py | def _swaplch(LCH):
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try: # Numpy array
L,C,H = np.dsplit(LCH,3)
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return H,C,L | def _swaplch(LCH):
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train | ColorSpace.convert | Convert image or color "what" from "from_" colorpace to "to"
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numpy dstack or a color tuple. | imagen/colorspaces.py | def convert(self, from_, to, what):
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Convert image or color "what" from "from_" colorpace to "to"
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"""
if(from_.lower()==to.lower()):
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# Check i... | def convert(self, from_, to, what):
"""
Convert image or color "what" from "from_" colorpace to "to"
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numpy dstack or a color tuple.
"""
if(from_.lower()==to.lower()):
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train | ColorSpace.rgb_to_hsv | linear rgb to hsv | imagen/colorspaces.py | def rgb_to_hsv(self,RGB):
"linear rgb to hsv"
gammaRGB = self._gamma_rgb(RGB)
return self._ABC_to_DEF_by_fn(gammaRGB,rgb_to_hsv) | def rgb_to_hsv(self,RGB):
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gammaRGB = self._gamma_rgb(RGB)
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train | ColorSpace.hsv_to_rgb | hsv to linear rgb | imagen/colorspaces.py | def hsv_to_rgb(self,HSV):
"hsv to linear rgb"
gammaRGB = self._ABC_to_DEF_by_fn(HSV,hsv_to_rgb)
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gammaRGB = self._ABC_to_DEF_by_fn(HSV,hsv_to_rgb)
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train | ColorConverter.image2working | Transform images i provided into the specified working
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return self.colorspace.convert(self.image_space,
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train | ColorConverter.working2analysis | Transform working space inputs to the analysis color space. | imagen/colorspaces.py | def working2analysis(self,r):
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train | ColorConverter.analysis2working | Convert back from the analysis color space to the working space. | imagen/colorspaces.py | def analysis2working(self,a):
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train | ColorConverter.analysis2display | Utility conversion function that transforms data from the
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"""
Utility conversion function that transforms data from the
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train | iterparse | iterparse variant that supports 'tag' parameter (like lxml),
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iterparse variant that supports 'tag' parameter (like lxml),
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train | bounds | Compute chunk bounds from text file according to start_re and end_re:
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train | load_chunk | Load a chunk from file using Bounds info.
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train | add_reference_context_args | Extends an ArgumentParser instance with the following commandline arguments:
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"""
Extends an ArgumentParser instance with the following commandline arguments:
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parser.add_argument(
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Extends an ArgumentParser instance with the following commandline arguments:
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train | generate_numeric_range | Generate postgresql numeric range and label for insertion.
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----------
items: iterable labels for ranges.
lower_bound: numeric lower bound
upper_bound: numeric upper bound | postpy/data_types.py | def generate_numeric_range(items, lower_bound, upper_bound):
"""Generate postgresql numeric range and label for insertion.
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----------
items: iterable labels for ranges.
lower_bound: numeric lower bound
upper_bound: numeric upper bound
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items: iterable labels for ranges.
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upper_bound: numeric upper bound
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train | edge_average | Return the mean value around the edge of an array. | imagen/image.py | def edge_average(a):
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train | GenericImage._reduced_call | Simplified version of PatternGenerator's __call__ method. | imagen/image.py | def _reduced_call(self, **params_to_override):
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Simplified version of PatternGenerator's __call__ method.
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Simplified version of PatternGenerator's __call__ method.
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train | GenericImage._process_channels | Add the channel information to the channel_data attribute. | imagen/image.py | def _process_channels(self,p,**params_to_override):
"""
Add the channel information to the channel_data attribute.
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orig_image = self._image
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train | FileImage.set_matrix_dimensions | Subclassed to delete the cached image when matrix dimensions
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"""
Subclassed to delete the cached image when matrix dimensions
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self._image = None
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train | FileImage._load_pil_image | Load image using PIL. | imagen/image.py | def _load_pil_image(self, filename):
"""
Load image using PIL.
"""
self._channel_data = []
self._original_channel_data = []
im = Image.open(filename)
self._image = ImageOps.grayscale(im)
im.load()
file_data = np.asarray(im, float)
file_da... | def _load_pil_image(self, filename):
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Load image using PIL.
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self._channel_data = []
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train | FileImage._load_npy | Load image using Numpy. | imagen/image.py | def _load_npy(self, filename):
"""
Load image using Numpy.
"""
self._channel_data = []
self._original_channel_data = []
file_channel_data = np.load(filename)
file_channel_data = file_channel_data / file_channel_data.max()
for i in range(file_channel_data.... | def _load_npy(self, filename):
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Load image using Numpy.
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self._channel_data = []
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train | add_protein_sequence_args | Extends an ArgumentParser instance with the following args:
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"""
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--max-protein-sequences-per-variant
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--protein-sequence-length
"""
protein_sequence_group = parser.add_argument_group(
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train | kwargfetch.ok_kwarg | Helper method for screening keyword arguments | opan/utils/decorate.py | def ok_kwarg(val):
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train | run | Run bulk record indexing. | invenio_indexer/cli.py | def run(delayed, concurrency, version_type=None, queue=None,
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:param pid_type: Pid type.
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train | process_actions | Process queue actions. | invenio_indexer/cli.py | def process_actions(actions):
"""Process queue actions."""
queue = current_app.config['INDEXER_MQ_QUEUE']
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for action in actions:
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train | init_queue | Initialize indexing queue. | invenio_indexer/cli.py | def init_queue():
"""Initialize indexing queue."""
def action(queue):
queue.declare()
click.secho('Indexing queue has been initialized.', fg='green')
return queue
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train | purge_queue | Purge indexing queue. | invenio_indexer/cli.py | def purge_queue():
"""Purge indexing queue."""
def action(queue):
queue.purge()
click.secho('Indexing queue has been purged.', fg='green')
return queue
return action | def purge_queue():
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train | delete_queue | Delete indexing queue. | invenio_indexer/cli.py | def delete_queue():
"""Delete indexing queue."""
def action(queue):
queue.delete()
click.secho('Indexing queue has been deleted.', fg='green')
return queue
return action | def delete_queue():
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click.secho('Indexing queue has been deleted.', fg='green')
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train | variant_matches_reference_sequence | Make sure that reference nucleotides we expect to see on the reference
transcript from a variant are the same ones we encounter. | isovar/reference_sequence_key.py | def variant_matches_reference_sequence(variant, ref_seq_on_transcript, strand):
"""
Make sure that reference nucleotides we expect to see on the reference
transcript from a variant are the same ones we encounter.
"""
if strand == "-":
ref_seq_on_transcript = reverse_complement_dna(ref_seq_on... | def variant_matches_reference_sequence(variant, ref_seq_on_transcript, strand):
"""
Make sure that reference nucleotides we expect to see on the reference
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"""
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train | ReferenceSequenceKey.from_variant_and_transcript | Extracts the reference sequence around a variant locus on a particular
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Parameters
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variant : varcode.Variant
transcript : pyensembl.Transcript
context_size : int
Returns SequenceKey object.
Can also return None if Transcript lack... | isovar/reference_sequence_key.py | def from_variant_and_transcript(
cls, variant, transcript, context_size):
"""
Extracts the reference sequence around a variant locus on a particular
transcript.
Parameters
----------
variant : varcode.Variant
transcript : pyensembl.Transcript
... | def from_variant_and_transcript(
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"""
Extracts the reference sequence around a variant locus on a particular
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----------
variant : varcode.Variant
transcript : pyensembl.Transcript
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train | ellipsize | >>> print(ellipsize(u'lorem ipsum dolor sit amet', 40))
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>>> print(ellipsize(u'lorem ipsum dolor sit amet', 20))
lorem ipsum dolor... | turq/util/text.py | def ellipsize(s, max_length=60):
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train | wrap | Circularly alias the numeric value x into the range [lower,upper).
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Circularly alias the numeric value x into the range [lower,upper).
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train | SineGrating.function | Return a sine grating pattern (two-dimensional sine wave). | imagen/__init__.py | def function(self,p):
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train | Line._pixelsize | Calculate line width necessary to cover at least one pixel on all axes. | imagen/__init__.py | def _pixelsize(self, p):
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train | Line._count_pixels_on_line | Count the number of pixels rendered on this line. | imagen/__init__.py | def _count_pixels_on_line(self, y, p):
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train | Line._minimal_y | For the specified y and one offset by half a pixel, return the
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extra pixels are needlessly included (which would cause
double-width lines). | imagen/__init__.py | def _minimal_y(self, p):
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For the specified y and one offset by half a pixel, return the
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train | SquareGrating.function | Return a square-wave grating (alternating black and white bars). | imagen/__init__.py | def function(self,p):
"""
Return a square-wave grating (alternating black and white bars).
"""
return np.around(
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train | Selector.function | Selects and returns one of the patterns in the list. | imagen/__init__.py | def function(self,p):
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pg=p.generators[int_index]
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train | Selector.get_current_generator | Return the current generator (as specified by self.index). | imagen/__init__.py | def get_current_generator(self):
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int_index=int(len(self.generators)*wrap(0,1.0,self.inspect_value('index')))
return self.generators[int_index] | def get_current_generator(self):
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train | Selector.channels | Get channel data from the current generator. use_cached is
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True in the current_generator in order to avoid generating the
same data twice (the first time by self() and the second with
current_generator.channels() ). | imagen/__init__.py | def channels(self, use_cached=False, **params_to_override):
"""
Get channel data from the current generator. use_cached is
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train | Selector.num_channels | Get the number of channels in the input generators. | imagen/__init__.py | def num_channels(self):
"""
Get the number of channels in the input generators.
"""
if(self.inspect_value('index') is None):
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Get the number of channels in the input generators.
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train | LogGaussian._setup_xy | Produce pattern coordinate matrices from the bounds and
density (or rows and cols), and transforms them according to
x, y, and orientation. | imagen/__init__.py | def _setup_xy(self, p):
"""
Produce pattern coordinate matrices from the bounds and
density (or rows and cols), and transforms them according to
x, y, and orientation.
"""
self.debug("bounds=%s, xdensity=%s, ydensity=%s, x=%s, y=%s, orientation=%s",p.bounds, p.xdensity, p... | def _setup_xy(self, p):
"""
Produce pattern coordinate matrices from the bounds and
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"""
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train | LogGaussian._create_and_rotate_coordinate_arrays | Create pattern matrices from x and y vectors, and rotate
them to the specified orientation. | imagen/__init__.py | def _create_and_rotate_coordinate_arrays(self, x, y, p):
"""
Create pattern matrices from x and y vectors, and rotate
them to the specified orientation.
"""
if p.aspect_ratio == 0 or p.size == 0:
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x = (x*10... | def _create_and_rotate_coordinate_arrays(self, x, y, p):
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train | TimeSeries.extract_specific_interval | Overload if special behaviour is required when a series ends. | imagen/__init__.py | def extract_specific_interval(self, interval_start, interval_end):
"""
Overload if special behaviour is required when a series ends.
"""
interval_start = int(interval_start)
interval_end = int(interval_end)
if interval_start >= interval_end:
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Overload if special behaviour is required when a series ends.
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train | PowerSpectrum._set_frequency_spacing | Frequency spacing to use, i.e. how to map the available
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NOTE: We're calculating the spacing of a range between the
highest and lowest frequencies, the actual segmentation and
averaging of the frequencies to fit this spacing occurs in
_... | imagen/__init__.py | def _set_frequency_spacing(self, min_freq, max_freq):
"""
Frequency spacing to use, i.e. how to map the available
frequency range to the discrete sheet rows.
NOTE: We're calculating the spacing of a range between the
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... | def _set_frequency_spacing(self, min_freq, max_freq):
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Frequency spacing to use, i.e. how to map the available
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train | PowerSpectrum._get_row_amplitudes | Perform a real Discrete Fourier Transform (DFT; implemented
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sample from the signal multiplied by the smoothing window.
See numpy.rfft for information about the Fourier transform. | imagen/__init__.py | def _get_row_amplitudes(self):
"""
Perform a real Discrete Fourier Transform (DFT; implemented
using a Fast Fourier Transform algorithm, FFT) of the current
sample from the signal multiplied by the smoothing window.
See numpy.rfft for information about the Fourier transform.
... | def _get_row_amplitudes(self):
"""
Perform a real Discrete Fourier Transform (DFT; implemented
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sample from the signal multiplied by the smoothing window.
See numpy.rfft for information about the Fourier transform.
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train | create_graph | This function creates a graph of vertices and edges from segments returned by SLIC.
:param array grid: A grid of segments as returned by the slic function defined in skimage library
:return: A graph as [vertices, edges] | segraph/segraph.py | def create_graph(grid):
"""
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:param array grid: A grid of segments as returned by the slic function defined in skimage library
:return: A graph as [vertices, edges]
"""
try:
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except I... | def create_graph(grid):
"""
This function creates a graph of vertices and edges from segments returned by SLIC.
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:return: A graph as [vertices, edges]
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train | default_record_to_index | Get index/doc_type given a record.
It tries to extract from `record['$schema']` the index and doc_type.
If it fails, return the default values.
:param record: The record object.
:returns: Tuple (index, doc_type). | invenio_indexer/utils.py | def default_record_to_index(record):
"""Get index/doc_type given a record.
It tries to extract from `record['$schema']` the index and doc_type.
If it fails, return the default values.
:param record: The record object.
:returns: Tuple (index, doc_type).
"""
index_names = current_search.mapp... | def default_record_to_index(record):
"""Get index/doc_type given a record.
It tries to extract from `record['$schema']` the index and doc_type.
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:param record: The record object.
:returns: Tuple (index, doc_type).
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train | get_postgres_encoding | Python to postgres encoding map. | postpy/pg_encodings.py | def get_postgres_encoding(python_encoding: str) -> str:
"""Python to postgres encoding map."""
encoding = normalize_encoding(python_encoding.lower())
encoding_ = aliases.aliases[encoding.replace('_', '', 1)].upper()
pg_encoding = PG_ENCODING_MAP[encoding_.replace('_', '')]
return pg_encoding | def get_postgres_encoding(python_encoding: str) -> str:
"""Python to postgres encoding map."""
encoding = normalize_encoding(python_encoding.lower())
encoding_ = aliases.aliases[encoding.replace('_', '', 1)].upper()
pg_encoding = PG_ENCODING_MAP[encoding_.replace('_', '')]
return pg_encoding | [
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train | _init | Try loading each binding in turn
Please note: the entire Qt module is replaced with this code:
sys.modules["Qt"] = binding()
This means no functions or variables can be called after
this has executed. | pyblish_houdini/vendor/Qt.py | def _init():
"""Try loading each binding in turn
Please note: the entire Qt module is replaced with this code:
sys.modules["Qt"] = binding()
This means no functions or variables can be called after
this has executed.
"""
preferred = os.getenv("QT_PREFERRED_BINDING")
verbose = os.... | def _init():
"""Try loading each binding in turn
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preferred = os.getenv("QT_PREFERRED_BINDING")
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train | OrcaOutput.en_last | Report the energies from the last SCF present in the output.
Returns a |dict| providing the various energy values from the
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:attr:`~opan.output.OrcaOutput.p_en`.
Any energy value not relevant to the parsed
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""" Report the energies from the last SCF present in the output.
Returns a |dict| providing the various energy values from the
last SCF cycle performed in the output. Keys are those of
:attr:`~opan.output.OrcaOutput.p_en`.
Any energy value not relevant to the ... | def en_last(self):
""" Report the energies from the last SCF present in the output.
Returns a |dict| providing the various energy values from the
last SCF cycle performed in the output. Keys are those of
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train | connect | Create a database connection. | postpy/connections.py | def connect(host=None, database=None, user=None, password=None, **kwargs):
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host = host or os.environ['PGHOST']
database = database or os.environ['PGDATABASE']
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train | execute_orca | Executes |orca| on a dynamically constructed input file.
.. warning:: Function is still under active development! Execution with
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**Execution**
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train | _setup | Set up module.
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""" Set up module.
Open a UDP socket, and listen in a thread.
"""
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train | discover | Discover devices on the local network.
:param timeout: Optional timeout in seconds.
:returns: Set of discovered host addresses. | orvibo/s20.py | def discover(timeout=DISCOVERY_TIMEOUT):
""" Discover devices on the local network.
:param timeout: Optional timeout in seconds.
:returns: Set of discovered host addresses.
"""
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""" Discover devices on the local network.
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Discovery of multiple switches must be done synchronously.
:returns: Tuple of MAC address and reversed MAC... | orvibo/s20.py | def _discover_mac(self):
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Discovery is done by sending a UDP broadcast.
All configured devices reply. The response contains
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Discovery of multiple switches must be done synchronously.
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train | S20._subscribe | Subscribe to the device.
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- Enables state changes on the device
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train | S20._control | Control device state.
Possible states are ON or OFF.
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""" Control device state.
Possible states are ON or OFF.
:param state: Switch to this state.
"""
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train | S20._discovery_resp | Handle a discovery response.
:param data: Payload.
:param addr: Address tuple.
:returns: MAC and reversed MAC. | orvibo/s20.py | def _discovery_resp(self, data):
""" Handle a discovery response.
:param data: Payload.
:param addr: Address tuple.
:returns: MAC and reversed MAC.
"""
if _is_discovery_response(data):
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train | S20._subscribe_resp | Handle a subscribe response.
:param data: Payload.
:returns: State (ON/OFF) | orvibo/s20.py | def _subscribe_resp(self, data):
""" Handle a subscribe response.
:param data: Payload.
:returns: State (ON/OFF)
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train | S20._control_resp | Handle a control response.
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:param state: Requested state.
:returns: Acknowledged state. | orvibo/s20.py | def _control_resp(self, data, state):
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train | S20._udp_transact | Complete a UDP transaction.
UDP is stateless and not guaranteed, so we have to
take some mitigation steps:
- Send payload multiple times.
- Wait for awhile to receive response.
:param payload: Payload to send.
:param handler: Response handler.
:param args: Argum... | orvibo/s20.py | def _udp_transact(self, payload, handler, *args,
broadcast=False, timeout=TIMEOUT):
""" Complete a UDP transaction.
UDP is stateless and not guaranteed, so we have to
take some mitigation steps:
- Send payload multiple times.
- Wait for awhile to receive re... | def _udp_transact(self, payload, handler, *args,
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train | load | Load OpenCorpora corpus.
The ``source`` can be any of the following:
- a file name/path
- a file object
- a file-like object
- a URL using the HTTP or FTP protocol | opencorpora/reader_lxml.py | def load(source):
"""
Load OpenCorpora corpus.
The ``source`` can be any of the following:
- a file name/path
- a file object
- a file-like object
- a URL using the HTTP or FTP protocol
"""
parser = get_xml_parser()
return etree.parse(source, parser=parser).getroot() | def load(source):
"""
Load OpenCorpora corpus.
The ``source`` can be any of the following:
- a file name/path
- a file object
- a file-like object
- a URL using the HTTP or FTP protocol
"""
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return etree.parse(source, parser=parser).getroot() | [
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train | find_mutant_amino_acid_interval | Parameters
----------
cdna_sequence : skbio.DNA or str
cDNA sequence found in RNAseq data
cdna_first_codon_offset : int
Offset into cDNA sequence to first complete codon, lets us skip
past UTR region and incomplete codons.
cdna_variant_start_offset : int
Interbase start... | isovar/translation.py | def find_mutant_amino_acid_interval(
cdna_sequence,
cdna_first_codon_offset,
cdna_variant_start_offset,
cdna_variant_end_offset,
n_ref,
n_amino_acids):
"""
Parameters
----------
cdna_sequence : skbio.DNA or str
cDNA sequence found in RNAseq data
... | def find_mutant_amino_acid_interval(
cdna_sequence,
cdna_first_codon_offset,
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cdna_variant_end_offset,
n_ref,
n_amino_acids):
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cdna_sequence : skbio.DNA or str
cDNA sequence found in RNAseq data
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train | translation_generator | Given all detected VariantSequence objects for a particular variant
and all the ReferenceContext objects for that locus, translate
multiple protein sequences, up to the number specified by the argument
max_protein_sequences_per_variant.
Parameters
----------
variant_sequences : list of VariantS... | isovar/translation.py | def translation_generator(
variant_sequences,
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min_transcript_prefix_length,
max_transcript_mismatches,
include_mismatches_after_variant,
protein_sequence_length=None):
"""
Given all detected VariantSequence objects for a particular variant
... | def translation_generator(
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train | translate_variant_reads | Given a variant and its associated alt reads, construct variant sequences
and translate them into Translation objects.
Returns 0 or more Translation objects.
Parameters
----------
variant : varcode.Variant
variant_reads : sequence or generator
AlleleRead objects supporting the variant... | isovar/translation.py | def translate_variant_reads(
variant,
variant_reads,
protein_sequence_length,
transcript_id_whitelist=None,
min_alt_rna_reads=MIN_ALT_RNA_READS,
min_variant_sequence_coverage=MIN_VARIANT_SEQUENCE_COVERAGE,
min_transcript_prefix_length=MIN_TRANSCRIPT_PREFIX_LENGTH,... | def translate_variant_reads(
variant,
variant_reads,
protein_sequence_length,
transcript_id_whitelist=None,
min_alt_rna_reads=MIN_ALT_RNA_READS,
min_variant_sequence_coverage=MIN_VARIANT_SEQUENCE_COVERAGE,
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train | translate_variants | Translates each coding variant in a collection to one or more protein
fragment sequences (if the variant is not filtered and its spanning RNA
sequences can be given a reading frame).
Parameters
----------
variants_with_reads : sequence or generator
Each item of this sequence should be a pai... | isovar/translation.py | def translate_variants(
variants_with_supporting_reads,
transcript_id_whitelist=None,
protein_sequence_length=PROTEIN_SEQUENCE_LENGTH,
min_alt_rna_reads=MIN_ALT_RNA_READS,
min_variant_sequence_coverage=MIN_VARIANT_SEQUENCE_COVERAGE,
min_transcript_prefix_length=MIN_TRANSC... | def translate_variants(
variants_with_supporting_reads,
transcript_id_whitelist=None,
protein_sequence_length=PROTEIN_SEQUENCE_LENGTH,
min_alt_rna_reads=MIN_ALT_RNA_READS,
min_variant_sequence_coverage=MIN_VARIANT_SEQUENCE_COVERAGE,
min_transcript_prefix_length=MIN_TRANSC... | [
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train | translations_generator_to_dataframe | Given a generator of (Variant, [Translation]) pairs,
returns a DataFrame of translated protein fragments with columns
for each field of a Translation object (and chr/pos/ref/alt per variant). | isovar/translation.py | def translations_generator_to_dataframe(translations_generator):
"""
Given a generator of (Variant, [Translation]) pairs,
returns a DataFrame of translated protein fragments with columns
for each field of a Translation object (and chr/pos/ref/alt per variant).
"""
return dataframe_from_generator... | def translations_generator_to_dataframe(translations_generator):
"""
Given a generator of (Variant, [Translation]) pairs,
returns a DataFrame of translated protein fragments with columns
for each field of a Translation object (and chr/pos/ref/alt per variant).
"""
return dataframe_from_generator... | [
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train | Translation.as_translation_key | Project Translation object or any other derived class into just a
TranslationKey, which has fewer fields and can be used as a
dictionary key. | isovar/translation.py | def as_translation_key(self):
"""
Project Translation object or any other derived class into just a
TranslationKey, which has fewer fields and can be used as a
dictionary key.
"""
return TranslationKey(**{
name: getattr(self, name)
for name in Tran... | def as_translation_key(self):
"""
Project Translation object or any other derived class into just a
TranslationKey, which has fewer fields and can be used as a
dictionary key.
"""
return TranslationKey(**{
name: getattr(self, name)
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train | Translation.from_variant_sequence_and_reference_context | Attempt to translate a single VariantSequence using the reading frame
from a single ReferenceContext.
Parameters
----------
variant_sequence : VariantSequence
reference_context : ReferenceContext
min_transcript_prefix_length : int
Minimum number of nucleoti... | isovar/translation.py | def from_variant_sequence_and_reference_context(
cls,
variant_sequence,
reference_context,
min_transcript_prefix_length,
max_transcript_mismatches,
include_mismatches_after_variant,
protein_sequence_length=None):
"""
Att... | def from_variant_sequence_and_reference_context(
cls,
variant_sequence,
reference_context,
min_transcript_prefix_length,
max_transcript_mismatches,
include_mismatches_after_variant,
protein_sequence_length=None):
"""
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"VariantSequence",
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"the",
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"ReferenceContext",
"."
] | openvax/isovar | python | https://github.com/openvax/isovar/blob/b39b684920e3f6b344851d6598a1a1c67bce913b/isovar/translation.py#L161-L258 | [
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train | Cachet.postComponents | Create a new component.
:param name: Name of the component
:param status: Status of the component; 1-4
:param description: (optional) Description of the component
:param link: (optional) A hyperlink to the component
:param order: (optional) Order of the component
:param ... | cachet.py | def postComponents(self, name, status, **kwargs):
'''Create a new component.
:param name: Name of the component
:param status: Status of the component; 1-4
:param description: (optional) Description of the component
:param link: (optional) A hyperlink to the component
:p... | def postComponents(self, name, status, **kwargs):
'''Create a new component.
:param name: Name of the component
:param status: Status of the component; 1-4
:param description: (optional) Description of the component
:param link: (optional) A hyperlink to the component
:p... | [
"Create",
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"new",
"component",
"."
] | imlonghao/cachet.python | python | https://github.com/imlonghao/cachet.python/blob/624b0d8e09b551a3be45dec207da6aa89f1e56e8/cachet.py#L51-L67 | [
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train | Cachet.postIncidents | Create a new incident.
:param name: Name of the incident
:param message: A message (supporting Markdown) to explain more.
:param status: Status of the incident.
:param visible: Whether the incident is publicly visible.
:param component_id: (optional) Component to update.
... | cachet.py | def postIncidents(self, name, message, status, visible, **kwargs):
'''Create a new incident.
:param name: Name of the incident
:param message: A message (supporting Markdown) to explain more.
:param status: Status of the incident.
:param visible: Whether the incident is publicly... | def postIncidents(self, name, message, status, visible, **kwargs):
'''Create a new incident.
:param name: Name of the incident
:param message: A message (supporting Markdown) to explain more.
:param status: Status of the incident.
:param visible: Whether the incident is publicly... | [
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] | imlonghao/cachet.python | python | https://github.com/imlonghao/cachet.python/blob/624b0d8e09b551a3be45dec207da6aa89f1e56e8/cachet.py#L168-L186 | [
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train | Cachet.postMetrics | Create a new metric.
:param name: Name of metric
:param suffix: Measurments in
:param description: Description of what the metric is measuring
:param default_value: The default value to use when a point is added
:param display_chart: (optional) Whether to display the chart on th... | cachet.py | def postMetrics(self, name, suffix, description, default_value, **kwargs):
'''Create a new metric.
:param name: Name of metric
:param suffix: Measurments in
:param description: Description of what the metric is measuring
:param default_value: The default value to use when a poin... | def postMetrics(self, name, suffix, description, default_value, **kwargs):
'''Create a new metric.
:param name: Name of metric
:param suffix: Measurments in
:param description: Description of what the metric is measuring
:param default_value: The default value to use when a poin... | [
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] | imlonghao/cachet.python | python | https://github.com/imlonghao/cachet.python/blob/624b0d8e09b551a3be45dec207da6aa89f1e56e8/cachet.py#L223-L239 | [
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train | Cachet.postMetricsPointsByID | Add a metric point to a given metric.
:param id: Metric ID
:param value: Value to plot on the metric graph
:param timestamp: Unix timestamp of the point was measured
:return: :class:`Response <Response>` object
:rtype: requests.Response | cachet.py | def postMetricsPointsByID(self, id, value, **kwargs):
'''Add a metric point to a given metric.
:param id: Metric ID
:param value: Value to plot on the metric graph
:param timestamp: Unix timestamp of the point was measured
:return: :class:`Response <Response>` object
:rt... | def postMetricsPointsByID(self, id, value, **kwargs):
'''Add a metric point to a given metric.
:param id: Metric ID
:param value: Value to plot on the metric graph
:param timestamp: Unix timestamp of the point was measured
:return: :class:`Response <Response>` object
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train | ctr_mass | Calculate the center of mass of the indicated geometry.
Take a geometry and atom masses and compute the location of
the center of mass.
Parameters
----------
geom
length-3N |npfloat_| --
Coordinates of the atoms
masses
length-N OR length-3N |npfloat_| --
Atomic... | opan/utils/inertia.py | def ctr_mass(geom, masses):
"""Calculate the center of mass of the indicated geometry.
Take a geometry and atom masses and compute the location of
the center of mass.
Parameters
----------
geom
length-3N |npfloat_| --
Coordinates of the atoms
masses
length-N OR len... | def ctr_mass(geom, masses):
"""Calculate the center of mass of the indicated geometry.
Take a geometry and atom masses and compute the location of
the center of mass.
Parameters
----------
geom
length-3N |npfloat_| --
Coordinates of the atoms
masses
length-N OR len... | [
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"the",
"indicated",
"geometry",
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] | bskinn/opan | python | https://github.com/bskinn/opan/blob/0b1b21662df6abc971407a9386db21a8796fbfe5/opan/utils/inertia.py#L52-L113 | [
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