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train | PatternGenerator._apply_mask | Create (if necessary) and apply the mask to the given matrix mat. | imagen/patterngenerator.py | def _apply_mask(self,p,mat):
"""Create (if necessary) and apply the mask to the given matrix mat."""
mask = p.mask
ms=p.mask_shape
if ms is not None:
mask = ms(x=p.x+p.size*(ms.x*np.cos(p.orientation)-ms.y*np.sin(p.orientation)),
y=p.y+p.size*(ms.x*np.si... | def _apply_mask(self,p,mat):
"""Create (if necessary) and apply the mask to the given matrix mat."""
mask = p.mask
ms=p.mask_shape
if ms is not None:
mask = ms(x=p.x+p.size*(ms.x*np.cos(p.orientation)-ms.y*np.sin(p.orientation)),
y=p.y+p.size*(ms.x*np.si... | [
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train | PatternGenerator.set_matrix_dimensions | Change the dimensions of the matrix into which the pattern
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rather than changing the bounds, xdensity, and ydensity
parameters directly. Subclasses can override this method to
update any internal data structures that may depend ... | imagen/patterngenerator.py | def set_matrix_dimensions(self, bounds, xdensity, ydensity):
"""
Change the dimensions of the matrix into which the pattern
will be drawn. Users of this class should call this method
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train | PatternGenerator.state_push | Save the state of the output functions, to be restored with state_pop. | imagen/patterngenerator.py | def state_push(self):
"Save the state of the output functions, to be restored with state_pop."
for of in self.output_fns:
if hasattr(of,'state_push'):
of.state_push()
super(PatternGenerator, self).state_push() | def state_push(self):
"Save the state of the output functions, to be restored with state_pop."
for of in self.output_fns:
if hasattr(of,'state_push'):
of.state_push()
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train | PatternGenerator.state_pop | Restore the state of the output functions saved by state_push. | imagen/patterngenerator.py | def state_pop(self):
"Restore the state of the output functions saved by state_push."
for of in self.output_fns:
if hasattr(of,'state_pop'):
of.state_pop()
super(PatternGenerator, self).state_pop() | def state_pop(self):
"Restore the state of the output functions saved by state_push."
for of in self.output_fns:
if hasattr(of,'state_pop'):
of.state_pop()
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train | PatternGenerator.anim | duration: The temporal duration to animate in the units
defined on the global time function.
offset: The temporal offset from which the animation is
generated given the supplied pattern
timestep: The time interval between successive frames. The
duration must be an exact multipl... | imagen/patterngenerator.py | def anim(self, duration, offset=0, timestep=1,
label=None, unit=None,
time_fn=param.Dynamic.time_fn):
"""
duration: The temporal duration to animate in the units
defined on the global time function.
offset: The temporal offset from which the animation is
... | def anim(self, duration, offset=0, timestep=1,
label=None, unit=None,
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"""
duration: The temporal duration to animate in the units
defined on the global time function.
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train | PatternGenerator.pil | Returns a PIL image for this pattern, overriding parameters if provided. | imagen/patterngenerator.py | def pil(self, **params_to_override):
"""Returns a PIL image for this pattern, overriding parameters if provided."""
from PIL.Image import fromarray
nchans = self.num_channels()
if nchans in [0, 1]:
mode, arr = None, self(**params_to_override)
arr = (255.0 / arr.m... | def pil(self, **params_to_override):
"""Returns a PIL image for this pattern, overriding parameters if provided."""
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train | Composite.state_push | Push the state of all generators | imagen/patterngenerator.py | def state_push(self):
"""
Push the state of all generators
"""
super(Composite,self).state_push()
for gen in self.generators:
gen.state_push() | def state_push(self):
"""
Push the state of all generators
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super(Composite,self).state_push()
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train | Composite.state_pop | Pop the state of all generators | imagen/patterngenerator.py | def state_pop(self):
"""
Pop the state of all generators
"""
super(Composite,self).state_pop()
for gen in self.generators:
gen.state_pop() | def state_pop(self):
"""
Pop the state of all generators
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super(Composite,self).state_pop()
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train | Composite.function | Constructs combined pattern out of the individual ones. | imagen/patterngenerator.py | def function(self,p):
"""Constructs combined pattern out of the individual ones."""
generators = self._advance_pattern_generators(p)
assert hasattr(p.operator,'reduce'),repr(p.operator)+" does not support 'reduce'."
# CEBALERT: mask gets applied by all PGs including the Composite itsel... | def function(self,p):
"""Constructs combined pattern out of the individual ones."""
generators = self._advance_pattern_generators(p)
assert hasattr(p.operator,'reduce'),repr(p.operator)+" does not support 'reduce'."
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train | compile_create_table | Postgresql Create Table statement formatter. | postpy/ddl.py | def compile_create_table(qualified_name: str, column_statement: str,
primary_key_statement: str) -> str:
"""Postgresql Create Table statement formatter."""
statement = """
CREATE TABLE {table} ({columns} {primary_keys});
""".format(table=qualified_name,
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"""Postgresql Create Table statement formatter."""
statement = """
CREATE TABLE {table} ({columns} {primary_keys});
""".format(table=qualified_name,
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train | compile_create_temporary_table | Postgresql Create Temporary Table statement formatter. | postpy/ddl.py | def compile_create_temporary_table(table_name: str,
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train | compile_column | Create column definition statement. | postpy/ddl.py | def compile_column(name: str, data_type: str, nullable: bool) -> str:
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null_str = 'NULL' if nullable else 'NOT NULL'
return '{name} {data_type} {null},'.format(name=name,
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train | MaterializedView.create | Declare materalized view. | postpy/ddl.py | def create(self, no_data=False):
"""Declare materalized view."""
if self.query:
ddl_statement = self.compile_create_as()
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ddl_statement = self.compile_create()
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train | predicted_effects_for_variant | For a given variant, return its set of predicted effects. Optionally
filter to transcripts where this variant results in a non-synonymous
change to the protein sequence.
Parameters
----------
variant : varcode.Variant
transcript_id_whitelist : set
Filter effect predictions to only incl... | isovar/effect_prediction.py | def predicted_effects_for_variant(
variant,
transcript_id_whitelist=None,
only_coding_changes=True):
"""
For a given variant, return its set of predicted effects. Optionally
filter to transcripts where this variant results in a non-synonymous
change to the protein sequence.
... | def predicted_effects_for_variant(
variant,
transcript_id_whitelist=None,
only_coding_changes=True):
"""
For a given variant, return its set of predicted effects. Optionally
filter to transcripts where this variant results in a non-synonymous
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train | reference_transcripts_for_variant | For a given variant, find all the transcripts which overlap the
variant and for which it has a predictable effect on the amino acid
sequence of the protein. | isovar/effect_prediction.py | def reference_transcripts_for_variant(
variant,
transcript_id_whitelist=None,
only_coding_changes=True):
"""
For a given variant, find all the transcripts which overlap the
variant and for which it has a predictable effect on the amino acid
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transcript_id_whitelist=None,
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"""
For a given variant, find all the transcripts which overlap the
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train | pileup_reads_at_position | Returns a pileup column at the specified position. Unclear if a function
like this is hiding somewhere in pysam API. | isovar/locus_reads.py | def pileup_reads_at_position(samfile, chromosome, base0_position):
"""
Returns a pileup column at the specified position. Unclear if a function
like this is hiding somewhere in pysam API.
"""
# TODO: I want to pass truncate=True, stepper="all"
# but for some reason I get this error:
# ... | def pileup_reads_at_position(samfile, chromosome, base0_position):
"""
Returns a pileup column at the specified position. Unclear if a function
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"""
# TODO: I want to pass truncate=True, stepper="all"
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train | locus_read_generator | Generator that yields a sequence of ReadAtLocus records for reads which
contain the positions before and after a variant. The actual work to figure
out if what's between those positions matches a variant happens later in
the `variant_reads` module.
Parameters
----------
samfile : pysam.Alignmen... | isovar/locus_reads.py | def locus_read_generator(
samfile,
chromosome,
base1_position_before_variant,
base1_position_after_variant,
use_duplicate_reads=USE_DUPLICATE_READS,
use_secondary_alignments=USE_SECONDARY_ALIGNMENTS,
min_mapping_quality=MIN_READ_MAPPING_QUALITY):
"""
Gener... | def locus_read_generator(
samfile,
chromosome,
base1_position_before_variant,
base1_position_after_variant,
use_duplicate_reads=USE_DUPLICATE_READS,
use_secondary_alignments=USE_SECONDARY_ALIGNMENTS,
min_mapping_quality=MIN_READ_MAPPING_QUALITY):
"""
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train | locus_reads_dataframe | Traverse a BAM file to find all the reads overlapping a specified locus.
Parameters are the same as those for read_locus_generator. | isovar/locus_reads.py | def locus_reads_dataframe(*args, **kwargs):
"""
Traverse a BAM file to find all the reads overlapping a specified locus.
Parameters are the same as those for read_locus_generator.
"""
df_builder = DataFrameBuilder(
LocusRead,
variant_columns=False,
converters={
"... | def locus_reads_dataframe(*args, **kwargs):
"""
Traverse a BAM file to find all the reads overlapping a specified locus.
Parameters are the same as those for read_locus_generator.
"""
df_builder = DataFrameBuilder(
LocusRead,
variant_columns=False,
converters={
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train | LocusRead.from_pysam_pileup_element | Parameters
----------
pileup_element : pysam.PileupRead
base0_position_before_variant : int
base0_position_after_variant : int
use_secondary_alignments : bool
use_duplicate_reads : bool
min_mapping_quality : int
use_soft_clipped_bases : bool. Default... | isovar/locus_reads.py | def from_pysam_pileup_element(
cls,
pileup_element,
base0_position_before_variant,
base0_position_after_variant,
use_secondary_alignments,
use_duplicate_reads,
min_mapping_quality,
use_soft_clipped_bases=False):
"""
... | def from_pysam_pileup_element(
cls,
pileup_element,
base0_position_before_variant,
base0_position_after_variant,
use_secondary_alignments,
use_duplicate_reads,
min_mapping_quality,
use_soft_clipped_bases=False):
"""
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train | copy_from_csv_sql | Generate copy from csv statement. | postpy/dml_copy.py | def copy_from_csv_sql(qualified_name: str, delimiter=',', encoding='utf8',
null_str='', header=True, escape_str='\\', quote_char='"',
force_not_null=None, force_null=None):
"""Generate copy from csv statement."""
options = []
options.append("DELIMITER '%s'" % del... | def copy_from_csv_sql(qualified_name: str, delimiter=',', encoding='utf8',
null_str='', header=True, escape_str='\\', quote_char='"',
force_not_null=None, force_null=None):
"""Generate copy from csv statement."""
options = []
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train | sort_protein_sequences | Sort protein sequences in decreasing order of priority | isovar/protein_sequences.py | def sort_protein_sequences(protein_sequences):
"""
Sort protein sequences in decreasing order of priority
"""
return list(
sorted(
protein_sequences,
key=ProteinSequence.ascending_sort_key,
reverse=True)) | def sort_protein_sequences(protein_sequences):
"""
Sort protein sequences in decreasing order of priority
"""
return list(
sorted(
protein_sequences,
key=ProteinSequence.ascending_sort_key,
reverse=True)) | [
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train | reads_generator_to_protein_sequences_generator | Translates each coding variant in a collection to one or more
Translation objects, which are then aggregated into equivalent
ProteinSequence objects.
Parameters
----------
variant_and_overlapping_reads_generator : generator
Yields sequence of varcode.Variant objects paired with sequences
... | isovar/protein_sequences.py | def reads_generator_to_protein_sequences_generator(
variant_and_overlapping_reads_generator,
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,
mi... | def reads_generator_to_protein_sequences_generator(
variant_and_overlapping_reads_generator,
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,
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train | protein_sequences_generator_to_dataframe | Given a generator which yields (Variant, [ProteinSequence]) elements,
returns a pandas.DataFrame | isovar/protein_sequences.py | def protein_sequences_generator_to_dataframe(variant_and_protein_sequences_generator):
"""
Given a generator which yields (Variant, [ProteinSequence]) elements,
returns a pandas.DataFrame
"""
return dataframe_from_generator(
element_class=ProteinSequence,
variant_and_elements_generat... | def protein_sequences_generator_to_dataframe(variant_and_protein_sequences_generator):
"""
Given a generator which yields (Variant, [ProteinSequence]) elements,
returns a pandas.DataFrame
"""
return dataframe_from_generator(
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train | ProteinSequence._summarize_translations | Summarize a collection of Translation objects into three values:
1) List of unique reads supporting underlying variant sequences
2) Set of unique transcript names for establishing reading frames of the
translations.
3) Set of unique gene names for all transcripts used ... | isovar/protein_sequences.py | def _summarize_translations(cls, translations):
"""
Summarize a collection of Translation objects into three values:
1) List of unique reads supporting underlying variant sequences
2) Set of unique transcript names for establishing reading frames of the
translation... | def _summarize_translations(cls, translations):
"""
Summarize a collection of Translation objects into three values:
1) List of unique reads supporting underlying variant sequences
2) Set of unique transcript names for establishing reading frames of the
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train | ProteinSequence.from_translation_key | Create a ProteinSequence object from a TranslationKey, along with
all the extra fields a ProteinSequence requires. | isovar/protein_sequences.py | def from_translation_key(
cls,
translation_key,
translations,
overlapping_reads,
ref_reads,
alt_reads,
alt_reads_supporting_protein_sequence,
transcripts_overlapping_variant,
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train | ProteinSequence.ascending_sort_key | Sort protein sequences lexicographically by three criteria:
- number of unique supporting reads
- minimum mismatch versus a supporting reference transcript before variant
- minimum mismatch versus a supporting reference transcript after variant
- number of supporting refe... | isovar/protein_sequences.py | def ascending_sort_key(self):
"""
Sort protein sequences lexicographically by three criteria:
- number of unique supporting reads
- minimum mismatch versus a supporting reference transcript before variant
- minimum mismatch versus a supporting reference transcript aft... | def ascending_sort_key(self):
"""
Sort protein sequences lexicographically by three criteria:
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- minimum mismatch versus a supporting reference transcript before variant
- minimum mismatch versus a supporting reference transcript aft... | [
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train | make_delete_table | Table referencing a delete from using primary key join. | postpy/base.py | def make_delete_table(table: Table, delete_prefix='delete_from__') -> Table:
"""Table referencing a delete from using primary key join."""
name = delete_prefix + table.name
primary_key = table.primary_key
key_names = set(primary_key.column_names)
columns = [column for column in table.columns if col... | def make_delete_table(table: Table, delete_prefix='delete_from__') -> Table:
"""Table referencing a delete from using primary key join."""
name = delete_prefix + table.name
primary_key = table.primary_key
key_names = set(primary_key.column_names)
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train | order_table_columns | Record table column(s) and primary key columns by specified order. | postpy/base.py | def order_table_columns(table: Table, column_names: list) -> Table:
"""Record table column(s) and primary key columns by specified order."""
unordered_columns = table.column_names
index_order = (unordered_columns.index(name) for name in column_names)
ordered_columns = [table.columns[i] for i in index_o... | def order_table_columns(table: Table, column_names: list) -> Table:
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unordered_columns = table.column_names
index_order = (unordered_columns.index(name) for name in column_names)
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train | trim_variant_fields | Trims common prefixes from the ref and alt sequences
Parameters
----------
location : int
Position (starting from 1) on some chromosome
ref : str
Reference nucleotides
alt : str
Alternate (mutant) nucleotide
Returns adjusted triplet (location, ref, alt) | isovar/variant_helpers.py | def trim_variant_fields(location, ref, alt):
"""
Trims common prefixes from the ref and alt sequences
Parameters
----------
location : int
Position (starting from 1) on some chromosome
ref : str
Reference nucleotides
alt : str
Alternate (mutant) nucleotide
Ret... | def trim_variant_fields(location, ref, alt):
"""
Trims common prefixes from the ref and alt sequences
Parameters
----------
location : int
Position (starting from 1) on some chromosome
ref : str
Reference nucleotides
alt : str
Alternate (mutant) nucleotide
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train | base0_interval_for_variant_fields | Inteval of interbase offsets of the affected reference positions for a
particular variant's primary fields (pos, ref, alt).
Parameters
----------
base1_location : int
First reference nucleotide of variant or, for insertions, the base
before the insertion.
ref : str
Referenc... | isovar/variant_helpers.py | def base0_interval_for_variant_fields(base1_location, ref, alt):
"""
Inteval of interbase offsets of the affected reference positions for a
particular variant's primary fields (pos, ref, alt).
Parameters
----------
base1_location : int
First reference nucleotide of variant or, for inser... | def base0_interval_for_variant_fields(base1_location, ref, alt):
"""
Inteval of interbase offsets of the affected reference positions for a
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----------
base1_location : int
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train | base0_interval_for_variant | Inteval of interbase offsets of the affected reference positions for a
particular variant.
Parameters
----------
variant : varcode.Variant
Returns triplet of (base1_location, ref, alt) | isovar/variant_helpers.py | def base0_interval_for_variant(variant):
"""
Inteval of interbase offsets of the affected reference positions for a
particular variant.
Parameters
----------
variant : varcode.Variant
Returns triplet of (base1_location, ref, alt)
"""
base1_location, ref, alt = trim_variant(variant)... | def base0_interval_for_variant(variant):
"""
Inteval of interbase offsets of the affected reference positions for a
particular variant.
Parameters
----------
variant : varcode.Variant
Returns triplet of (base1_location, ref, alt)
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train | interbase_range_affected_by_variant_on_transcript | Convert from a variant's position in global genomic coordinates on the
forward strand to an interval of interbase offsets on a particular
transcript's mRNA.
Parameters
----------
variant : varcode.Variant
transcript : pyensembl.Transcript
Assumes that the transcript overlaps the variant.
... | isovar/variant_helpers.py | def interbase_range_affected_by_variant_on_transcript(variant, transcript):
"""
Convert from a variant's position in global genomic coordinates on the
forward strand to an interval of interbase offsets on a particular
transcript's mRNA.
Parameters
----------
variant : varcode.Variant
t... | def interbase_range_affected_by_variant_on_transcript(variant, transcript):
"""
Convert from a variant's position in global genomic coordinates on the
forward strand to an interval of interbase offsets on a particular
transcript's mRNA.
Parameters
----------
variant : varcode.Variant
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train | insert | Insert a collection of namedtuple records. | postpy/dml.py | def insert(conn, qualified_name: str, column_names, records):
"""Insert a collection of namedtuple records."""
query = create_insert_statement(qualified_name, column_names)
with conn:
with conn.cursor(cursor_factory=NamedTupleCursor) as cursor:
for record in records:
cu... | def insert(conn, qualified_name: str, column_names, records):
"""Insert a collection of namedtuple records."""
query = create_insert_statement(qualified_name, column_names)
with conn:
with conn.cursor(cursor_factory=NamedTupleCursor) as cursor:
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train | insert_many | Insert many records by chunking data into insert statements.
Notes
-----
records should be Iterable collection of namedtuples or tuples. | postpy/dml.py | def insert_many(conn, tablename, column_names, records, chunksize=2500):
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Notes
-----
records should be Iterable collection of namedtuples or tuples.
"""
groups = chunks(records, chunksize)
column_str = ','.join(column_names)
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"""Insert many records by chunking data into insert statements.
Notes
-----
records should be Iterable collection of namedtuples or tuples.
"""
groups = chunks(records, chunksize)
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train | upsert_records | Upsert records. | postpy/dml.py | def upsert_records(conn, records, upsert_statement):
"""Upsert records."""
with conn:
with conn.cursor() as cursor:
for record in records:
cursor.execute(upsert_statement, record) | def upsert_records(conn, records, upsert_statement):
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train | delete_joined_table_sql | SQL statement for a joined delete from.
Generate SQL statement for deleting the intersection of rows between
both tables from table referenced by tablename. | postpy/dml.py | def delete_joined_table_sql(qualified_name, removing_qualified_name, primary_key):
"""SQL statement for a joined delete from.
Generate SQL statement for deleting the intersection of rows between
both tables from table referenced by tablename.
"""
condition_template = 't.{}=d.{}'
where_clause = ... | def delete_joined_table_sql(qualified_name, removing_qualified_name, primary_key):
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Generate SQL statement for deleting the intersection of rows between
both tables from table referenced by tablename.
"""
condition_template = 't.{}=d.{}'
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train | copy_from_csv | Copy file-like object to database table.
Notes
-----
Implementation defaults to postgres standard except for encoding.
Postgres falls back on client encoding, while function defaults to utf-8.
References
----------
https://www.postgresql.org/docs/current/static/sql-copy.html | postpy/dml.py | def copy_from_csv(conn, file, qualified_name: str, delimiter=',', encoding='utf8',
null_str='', header=True, escape_str='\\', quote_char='"',
force_not_null=None, force_null=None):
"""Copy file-like object to database table.
Notes
-----
Implementation defaults to pos... | def copy_from_csv(conn, file, qualified_name: str, delimiter=',', encoding='utf8',
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force_not_null=None, force_null=None):
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train | get_user_tables | Retrieve all user tables. | postpy/admin.py | def get_user_tables(conn):
"""Retrieve all user tables."""
query_string = "select schemaname, relname from pg_stat_user_tables;"
with conn.cursor() as cursor:
cursor.execute(query_string)
tables = cursor.fetchall()
return tables | def get_user_tables(conn):
"""Retrieve all user tables."""
query_string = "select schemaname, relname from pg_stat_user_tables;"
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cursor.execute(query_string)
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train | get_primary_keys | Returns primary key columns for a specific table. | postpy/admin.py | def get_primary_keys(conn, table: str, schema='public'):
"""Returns primary key columns for a specific table."""
query = """\
SELECT
c.constraint_name AS pkey_constraint_name,
c.column_name AS column_name
FROM
information_schema.key_column_usage AS c
JOIN information_schema.table_constraints AS t
... | def get_primary_keys(conn, table: str, schema='public'):
"""Returns primary key columns for a specific table."""
query = """\
SELECT
c.constraint_name AS pkey_constraint_name,
c.column_name AS column_name
FROM
information_schema.key_column_usage AS c
JOIN information_schema.table_constraints AS t
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train | get_column_metadata | Returns column data following db.Column parameter specification. | postpy/admin.py | def get_column_metadata(conn, table: str, schema='public'):
"""Returns column data following db.Column parameter specification."""
query = """\
SELECT
attname as name,
format_type(atttypid, atttypmod) AS data_type,
NOT attnotnull AS nullable
FROM pg_catalog.pg_attribute
WHERE attrelid=%s::regclass
AND a... | def get_column_metadata(conn, table: str, schema='public'):
"""Returns column data following db.Column parameter specification."""
query = """\
SELECT
attname as name,
format_type(atttypid, atttypmod) AS data_type,
NOT attnotnull AS nullable
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train | reflect_table | Reflect basic table attributes. | postpy/admin.py | def reflect_table(conn, table_name, schema='public'):
"""Reflect basic table attributes."""
column_meta = list(get_column_metadata(conn, table_name, schema=schema))
primary_key_columns = list(get_primary_keys(conn, table_name, schema=schema))
columns = [Column(**column_data) for column_data in column_... | def reflect_table(conn, table_name, schema='public'):
"""Reflect basic table attributes."""
column_meta = list(get_column_metadata(conn, table_name, schema=schema))
primary_key_columns = list(get_primary_keys(conn, table_name, schema=schema))
columns = [Column(**column_data) for column_data in column_... | [
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train | reset | Reset database. | postpy/admin.py | def reset(db_name):
"""Reset database."""
conn = psycopg2.connect(database='postgres')
db = Database(db_name)
conn.autocommit = True
with conn.cursor() as cursor:
cursor.execute(db.drop_statement())
cursor.execute(db.create_statement())
conn.close() | def reset(db_name):
"""Reset database."""
conn = psycopg2.connect(database='postgres')
db = Database(db_name)
conn.autocommit = True
with conn.cursor() as cursor:
cursor.execute(db.drop_statement())
cursor.execute(db.create_statement())
conn.close() | [
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train | install_extensions | Install Postgres extension if available.
Notes
-----
- superuser is generally required for installing extensions.
- Currently does not support specific schema. | postpy/admin.py | def install_extensions(extensions, **connection_parameters):
"""Install Postgres extension if available.
Notes
-----
- superuser is generally required for installing extensions.
- Currently does not support specific schema.
"""
from postpy.connections import connect
conn = connect(**c... | def install_extensions(extensions, **connection_parameters):
"""Install Postgres extension if available.
Notes
-----
- superuser is generally required for installing extensions.
- Currently does not support specific schema.
"""
from postpy.connections import connect
conn = connect(**c... | [
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train | ExecutorDriverProxy.update | Sends a status update to the framework scheduler.
Retrying as necessary until an acknowledgement has been received or the
executor is terminated (in which case, a TASK_LOST status update will be
sent).
See Scheduler.statusUpdate for more information about status update
acknowled... | mentor/proxies/executor.py | def update(self, status):
"""Sends a status update to the framework scheduler.
Retrying as necessary until an acknowledgement has been received or the
executor is terminated (in which case, a TASK_LOST status update will be
sent).
See Scheduler.statusUpdate for more information ... | def update(self, status):
"""Sends a status update to the framework scheduler.
Retrying as necessary until an acknowledgement has been received or the
executor is terminated (in which case, a TASK_LOST status update will be
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See Scheduler.statusUpdate for more information ... | [
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train | ExecutorDriverProxy.message | Sends a message to the framework scheduler.
These messages are best effort; do not expect a framework message to be
retransmitted in any reliable fashion. | mentor/proxies/executor.py | def message(self, data):
"""Sends a message to the framework scheduler.
These messages are best effort; do not expect a framework message to be
retransmitted in any reliable fashion.
"""
logging.info('Driver sends framework message {}'.format(data))
return self.driver.se... | def message(self, data):
"""Sends a message to the framework scheduler.
These messages are best effort; do not expect a framework message to be
retransmitted in any reliable fashion.
"""
logging.info('Driver sends framework message {}'.format(data))
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train | RailDriver.get_controller_list | Returns an iterable of tuples containing (index, controller_name) pairs.
Controller indexes start at 0.
You may easily transform this to a {name: index} mapping by using:
>>> controllers = {name: index for index, name in raildriver.get_controller_list()}
:return enumerate | raildriver/library.py | def get_controller_list(self):
"""
Returns an iterable of tuples containing (index, controller_name) pairs.
Controller indexes start at 0.
You may easily transform this to a {name: index} mapping by using:
>>> controllers = {name: index for index, name in raildriver.get_contro... | def get_controller_list(self):
"""
Returns an iterable of tuples containing (index, controller_name) pairs.
Controller indexes start at 0.
You may easily transform this to a {name: index} mapping by using:
>>> controllers = {name: index for index, name in raildriver.get_contro... | [
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train | InvenioIndexer.init_app | Flask application initialization.
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train | OpanAnharmRepo.close | .. todo:: REPO.close docstring | opan/vpt2/repo.py | def close(self):
""" .. todo:: REPO.close docstring
"""
# Imports
import h5py as h5
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"""
# Imports
import h5py as h5
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train | OpanAnharmRepo.load | .. todo:: REPO.load docstring | opan/vpt2/repo.py | def load(self, fname):
""" .. todo:: REPO.load docstring
"""
# Imports
import h5py as h5
from ..error import RepoError
# If repo not None, complain
if not self._repo == None:
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"""
# Imports
import h5py as h5
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train | OpanAnharmRepo.is_open | .. todo:: is_open docstring | opan/vpt2/repo.py | def is_open(self):
""" .. todo:: is_open docstring
"""
# Imports
import h5py as h5
# Return whether repo is bound; depends on the 'None' assignment in
# 'close'
retval = isinstance(self._repo, h5.File)
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""" .. todo:: is_open docstring
"""
# Imports
import h5py as h5
# Return whether repo is bound; depends on the 'None' assignment in
# 'close'
retval = isinstance(self._repo, h5.File)
return retval | [
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train | OpanAnharmRepo.store_data | .. todo:: store_data docstring | opan/vpt2/repo.py | def store_data(self, data, datatype, mode, disp, clobber=False):
""" .. todo:: store_data docstring
"""
# Imports
import h5py as h5
from ..error import RepoError as RErr
from ..const import EnumDispDirection as _E_DD
from ..const import EnumAnharmRepoData
... | def store_data(self, data, datatype, mode, disp, clobber=False):
""" .. todo:: store_data docstring
"""
# Imports
import h5py as h5
from ..error import RepoError as RErr
from ..const import EnumDispDirection as _E_DD
from ..const import EnumAnharmRepoData
... | [
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train | OpanAnharmRepo.get_data | .. todo:: docstring for get_data | opan/vpt2/repo.py | def get_data(self, datatype, mode, disp):
""" .. todo:: docstring for get_data
"""
# Imports
import os
from ..const import EnumDispDirection as _E_DD
from ..const import EnumAnharmRepoData
from ..error import RepoError as RErr
# Must be valid mode
... | def get_data(self, datatype, mode, disp):
""" .. todo:: docstring for get_data
"""
# Imports
import os
from ..const import EnumDispDirection as _E_DD
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# Must be valid mode
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train | OpanAnharmRepo.has_data | .. todo:: has_data docstring | opan/vpt2/repo.py | def has_data(self, datatype, mode, disp):
""" .. todo:: has_data docstring
"""
# Imports
from ..error import RepoError
# Just try to get the data. Simply pass up all exceptions except for
# those if the group or data doesn't exist. DO re-raise a non-bound
# re... | def has_data(self, datatype, mode, disp):
""" .. todo:: has_data docstring
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train | OpanAnharmRepo.store_param | .. todo:: store_param docstring | opan/vpt2/repo.py | def store_param(self, value, param, clobber=False):
""" .. todo:: store_param docstring
"""
# Imports
from ..const import EnumAnharmRepoParam
from ..error import RepoError as RErr
# Must be a valid parameter name
if not param in EnumAnharmRepoParam:
... | def store_param(self, value, param, clobber=False):
""" .. todo:: store_param docstring
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# Imports
from ..const import EnumAnharmRepoParam
from ..error import RepoError as RErr
# Must be a valid parameter name
if not param in EnumAnharmRepoParam:
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train | OpanAnharmRepo.get_param | .. todo:: docstring for get_param | opan/vpt2/repo.py | def get_param(self, param):
""" .. todo:: docstring for get_param
"""
# Imports
import os
from ..const import EnumAnharmRepoParam
from ..error import RepoError as RErr
# Must be a valid parameter name
if not param in EnumAnharmRepoParam:
rais... | def get_param(self, param):
""" .. todo:: docstring for get_param
"""
# Imports
import os
from ..const import EnumAnharmRepoParam
from ..error import RepoError as RErr
# Must be a valid parameter name
if not param in EnumAnharmRepoParam:
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train | OpanAnharmRepo.has_param | .. todo:: has_param docstring | opan/vpt2/repo.py | def has_param(self, param):
""" .. todo:: has_param docstring
"""
# Imports
from ..error import RepoError
# Try to get the param; pass along all errors, except 'data' error
# from RepoError
retval = True
try:
self.get_param(param)
ex... | def has_param(self, param):
""" .. todo:: has_param docstring
"""
# Imports
from ..error import RepoError
# Try to get the param; pass along all errors, except 'data' error
# from RepoError
retval = True
try:
self.get_param(param)
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train | OpanAnharmRepo.is_dirty | .. todo:: docstring for is_dirty | opan/vpt2/repo.py | def is_dirty(self):
""" .. todo:: docstring for is_dirty
"""
# Imports
from ..error import RepoError as RErr
# Get the return value from the dataset, complaining if repo not
# bound. Using 'require_dataset' since any repo w/o a defined
# 'dirty' value is just ... | def is_dirty(self):
""" .. todo:: docstring for is_dirty
"""
# Imports
from ..error import RepoError as RErr
# Get the return value from the dataset, complaining if repo not
# bound. Using 'require_dataset' since any repo w/o a defined
# 'dirty' value is just ... | [
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train | OpanAnharmRepo.set_dirty | .. todo:: set_clean docstring | opan/vpt2/repo.py | def set_dirty(self, dirty):
""" .. todo:: set_clean docstring
"""
# Complain if 'dirty' isn't boolean
if not isinstance(dirty, bool):
raise ValueError("'dirty' must be Boolean")
## end if
# Try to retrieve the dataset; complain if repo not bound.
try... | def set_dirty(self, dirty):
""" .. todo:: set_clean docstring
"""
# Complain if 'dirty' isn't boolean
if not isinstance(dirty, bool):
raise ValueError("'dirty' must be Boolean")
## end if
# Try to retrieve the dataset; complain if repo not bound.
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train | OpanAnharmRepo.get_XYZ | .. todo:: docstring for get_xyz | opan/vpt2/repo.py | def get_XYZ(self, mode, disp):
""" .. todo:: docstring for get_xyz
"""
# Imports
from ..xyz import OpanXYZ as XYZ
from ..const import EnumAnharmRepoParam, EnumAnharmRepoData
# Generate XYZ and return
out_XYZ = XYZ(atom_syms=self.get_param(EnumAnharmRepoParam.ato... | def get_XYZ(self, mode, disp):
""" .. todo:: docstring for get_xyz
"""
# Imports
from ..xyz import OpanXYZ as XYZ
from ..const import EnumAnharmRepoParam, EnumAnharmRepoData
# Generate XYZ and return
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"""Stops the scheduler driver.
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"""Requests resources from Mesos.
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train | SchedulerDriverProxy.accept | Accepts the given offers and performs a sequence of operations
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See Offer.Operation in mesos.proto for the set of available operations.
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"""Accepts the given offers and performs a sequence of operations
on those accepted offers.
See Offer.Operation in mesos.proto for the set of available operations.
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train | SchedulerDriverProxy.acknowledge | Acknowledges the status update.
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Not that explicit acknowledgements must be requested via the constructor
argument, otherwise a call to this method will cause the driver to
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"""Acknowledges the status update.
This should only be called once the status update is processed durably
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Not that explicit acknowledgements must be requested via the constructor
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train | SchedulerDriverProxy.message | Sends a message from the framework to one of its executors.
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retransmitted in any reliable fashion. | mentor/proxies/scheduler.py | def message(self, executor_id, slave_id, message):
"""Sends a message from the framework to one of its executors.
These messages are best effort; do not expect a framework message to be
retransmitted in any reliable fashion.
"""
logging.info('Sends message `{}` to executor `{}` ... | def message(self, executor_id, slave_id, message):
"""Sends a message from the framework to one of its executors.
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train | _connect_func | Handles GtkBuilder signal connect events | gi_composites.py | def _connect_func(builder, obj, signal_name, handler_name,
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if connect_object is None:
extra = ()
else:
extra = (connect_object,)
# The handler name refers to an attribute on the template insta... | def _connect_func(builder, obj, signal_name, handler_name,
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train | _init_template | This would be better as an override for Gtk.Widget | gi_composites.py | def _init_template(self, cls, base_init_template):
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# .. if you disagree, feel free to fix it and issue a PR :)
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train | extract_haml | babel translation token extract function for haml files | haml/util.py | def extract_haml(fileobj, keywords, comment_tags, options):
""" babel translation token extract function for haml files """
import haml
from mako import lexer, parsetree
from mako.ext.babelplugin import extract_nodes
encoding = options.get('input_encoding', options.get('encoding', None))
temp... | def extract_haml(fileobj, keywords, comment_tags, options):
""" babel translation token extract function for haml files """
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--drop-secondary-alignments | isovar/cli/rna_args.py | def add_rna_args(
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--min-mapping-quality
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train | get_single_allele_from_reads | Given a sequence of AlleleRead objects, which are expected to all have
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train | setup | Setup integration
Register plug-ins and integrate into the host
Arguments:
console (bool): DEPRECATED
port (int, optional): DEPRECATED | pyblish_houdini/lib.py | def setup(console=False, port=None):
"""Setup integration
Register plug-ins and integrate into the host
Arguments:
console (bool): DEPRECATED
port (int, optional): DEPRECATED
"""
if self._has_been_setup:
teardown()
register_plugins()
register_host()
self._ha... | def setup(console=False, port=None):
"""Setup integration
Register plug-ins and integrate into the host
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console (bool): DEPRECATED
port (int, optional): DEPRECATED
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if self._has_been_setup:
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train | register_host | Register supported hosts | pyblish_houdini/lib.py | def register_host():
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pyblish.api.register_host("hython")
pyblish.api.register_host("hpython")
pyblish.api.register_host("houdini") | def register_host():
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train | maintained_selection | Maintain selection during context
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>>> with maintained_selection():
... # Modify selection
... node.setSelected(on=False, clear_all_selected=True)
>>> # Selection restored | pyblish_houdini/lib.py | def maintained_selection():
"""Maintain selection during context
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>>> with maintained_selection():
... # Modify selection
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>>> # Selection restored
"""
previous_selection = hou.selectedNodes()
t... | def maintained_selection():
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train | execute_transaction | Execute several statements in single DB transaction. | postpy/sql.py | def execute_transaction(conn, statements: Iterable):
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with conn:
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for statement in statements:
cursor.execute(statement)
conn.commit() | def execute_transaction(conn, statements: Iterable):
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train | execute_transactions | Execute several statements each as a single DB transaction. | postpy/sql.py | def execute_transactions(conn, statements: Iterable):
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try:
cursor.execute(statement)
conn.commit()
except psycopg2.Program... | def execute_transactions(conn, statements: Iterable):
"""Execute several statements each as a single DB transaction."""
with conn.cursor() as cursor:
for statement in statements:
try:
cursor.execute(statement)
conn.commit()
except psycopg2.Program... | [
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train | execute_closing_transaction | Open a connection, commit a transaction, and close it. | postpy/sql.py | def execute_closing_transaction(statements: Iterable):
"""Open a connection, commit a transaction, and close it."""
with closing(connect()) as conn:
with conn.cursor() as cursor:
for statement in statements:
cursor.execute(statement) | def execute_closing_transaction(statements: Iterable):
"""Open a connection, commit a transaction, and close it."""
with closing(connect()) as conn:
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train | select | Return a select statement's results as a namedtuple.
Parameters
----------
conn : database connection
query : select query string
params : query parameters.
name : server side cursor name. defaults to client side.
itersize : number of records fetched by server. | postpy/sql.py | def select(conn, query: str, params=None, name=None, itersize=5000):
"""Return a select statement's results as a namedtuple.
Parameters
----------
conn : database connection
query : select query string
params : query parameters.
name : server side cursor name. defaults to client side.
i... | def select(conn, query: str, params=None, name=None, itersize=5000):
"""Return a select statement's results as a namedtuple.
Parameters
----------
conn : database connection
query : select query string
params : query parameters.
name : server side cursor name. defaults to client side.
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train | select_dict | Return a select statement's results as dictionary.
Parameters
----------
conn : database connection
query : select query string
params : query parameters.
name : server side cursor name. defaults to client side.
itersize : number of records fetched by server. | postpy/sql.py | def select_dict(conn, query: str, params=None, name=None, itersize=5000):
"""Return a select statement's results as dictionary.
Parameters
----------
conn : database connection
query : select query string
params : query parameters.
name : server side cursor name. defaults to client side.
... | def select_dict(conn, query: str, params=None, name=None, itersize=5000):
"""Return a select statement's results as dictionary.
Parameters
----------
conn : database connection
query : select query string
params : query parameters.
name : server side cursor name. defaults to client side.
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train | select_each | Run select query for each parameter set in single transaction. | postpy/sql.py | def select_each(conn, query: str, parameter_groups, name=None):
"""Run select query for each parameter set in single transaction."""
with conn:
with conn.cursor(name=name) as cursor:
for parameters in parameter_groups:
cursor.execute(query, parameters)
yield ... | def select_each(conn, query: str, parameter_groups, name=None):
"""Run select query for each parameter set in single transaction."""
with conn:
with conn.cursor(name=name) as cursor:
for parameters in parameter_groups:
cursor.execute(query, parameters)
yield ... | [
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train | query_columns | Lightweight query to retrieve column list of select query.
Notes
-----
Strongly urged to specify a cursor name for performance. | postpy/sql.py | def query_columns(conn, query, name=None):
"""Lightweight query to retrieve column list of select query.
Notes
-----
Strongly urged to specify a cursor name for performance.
"""
with conn.cursor(name) as cursor:
cursor.itersize = 1
cursor.execute(query)
cursor.fetchmany... | def query_columns(conn, query, name=None):
"""Lightweight query to retrieve column list of select query.
Notes
-----
Strongly urged to specify a cursor name for performance.
"""
with conn.cursor(name) as cursor:
cursor.itersize = 1
cursor.execute(query)
cursor.fetchmany... | [
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] | portfoliome/postpy | python | https://github.com/portfoliome/postpy/blob/fe26199131b15295fc5f669a0ad2a7f47bf490ee/postpy/sql.py#L91-L105 | [
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train | ReferenceCodingSequenceKey.from_variant_and_transcript_and_sequence_key | Assuming that the transcript has a coding sequence, take a
ReferenceSequenceKey (region of the transcript around the variant) and
return a ReferenceCodingSequenceKey (or None). | isovar/reference_coding_sequence_key.py | def from_variant_and_transcript_and_sequence_key(
cls, variant, transcript, sequence_key):
"""
Assuming that the transcript has a coding sequence, take a
ReferenceSequenceKey (region of the transcript around the variant) and
return a ReferenceCodingSequenceKey (or None).
... | def from_variant_and_transcript_and_sequence_key(
cls, variant, transcript, sequence_key):
"""
Assuming that the transcript has a coding sequence, take a
ReferenceSequenceKey (region of the transcript around the variant) and
return a ReferenceCodingSequenceKey (or None).
... | [
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train | ReferenceCodingSequenceKey.from_variant_and_transcript | Extracts the reference sequence around a variant locus on a particular
transcript and determines the reading frame at the start of that
sequence context.
Parameters
----------
variant : varcode.Variant
transcript : pyensembl.Transcript
context_size : int
... | isovar/reference_coding_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 and determines the reading frame at the start of that
sequence context.
... | def from_variant_and_transcript(
cls,
variant,
transcript,
context_size):
"""
Extracts the reference sequence around a variant locus on a particular
transcript and determines the reading frame at the start of that
sequence context.
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"\"Expected transcript %s for variant %s to have start codon\"",
... | b39b684920e3f6b344851d6598a1a1c67bce913b |
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