sentence1 stringlengths 52 3.87M | sentence2 stringlengths 1 47.2k | label stringclasses 1
value |
|---|---|---|
def full_data(self):
"""
Returns all the info available for the user in the following format:
name [username] <id> (locale) bot_or_user
If any data is not available, it is not added.
"""
data = [
self.full_name,
self._username(),
se... | Returns all the info available for the user in the following format:
name [username] <id> (locale) bot_or_user
If any data is not available, it is not added. | entailment |
def full_data(self):
"""
Returns all the info available for the chat in the following format:
title [username] (type) <id>
If any data is not available, it is not added.
"""
data = [
self.chat.title,
self._username(),
self._type(),
... | Returns all the info available for the chat in the following format:
title [username] (type) <id>
If any data is not available, it is not added. | entailment |
def use_defaults(func):
"""
Decorator for functions that should automatically fall back to the Cohort-default filter_fn and
normalized_per_mb if not specified.
"""
@wraps(func)
def wrapper(row, cohort, filter_fn=None, normalized_per_mb=None, **kwargs):
filter_fn = first_not_none_param([f... | Decorator for functions that should automatically fall back to the Cohort-default filter_fn and
normalized_per_mb if not specified. | entailment |
def count_function(func):
"""
Decorator for functions that return a collection (technically a dict of collections) that should be
counted up. Also automatically falls back to the Cohort-default filter_fn and normalized_per_mb if
not specified.
"""
# Fall back to Cohort-level defaults.
@use_d... | Decorator for functions that return a collection (technically a dict of collections) that should be
counted up. Also automatically falls back to the Cohort-default filter_fn and normalized_per_mb if
not specified. | entailment |
def count_variants_function_builder(function_name, filterable_variant_function=None):
"""
Creates a function that counts variants that are filtered by the provided filterable_variant_function.
The filterable_variant_function is a function that takes a filterable_variant and returns True or False.
Users... | Creates a function that counts variants that are filtered by the provided filterable_variant_function.
The filterable_variant_function is a function that takes a filterable_variant and returns True or False.
Users of this builder need not worry about applying e.g. the Cohort's default `filter_fn`. That will be... | entailment |
def count_effects_function_builder(function_name, only_nonsynonymous, filterable_effect_function=None):
"""
Create a function that counts effects that are filtered by the provided filterable_effect_function.
The filterable_effect_function is a function that takes a filterable_effect and returns True or Fals... | Create a function that counts effects that are filtered by the provided filterable_effect_function.
The filterable_effect_function is a function that takes a filterable_effect and returns True or False.
Users of this builder need not worry about applying e.g. the Cohort's default `filter_fn`. That will be appl... | entailment |
def median_vaf_purity(row, cohort, **kwargs):
"""
Estimate purity based on 2 * median VAF.
Even if the Cohort has a default filter_fn, ignore it: we want to use all variants for
this estimate.
"""
patient_id = row["patient_id"]
patient = cohort.patient_from_id(patient_id)
variants = coh... | Estimate purity based on 2 * median VAF.
Even if the Cohort has a default filter_fn, ignore it: we want to use all variants for
this estimate. | entailment |
def bootstrap_auc(df, col, pred_col, n_bootstrap=1000):
"""
Calculate the boostrapped AUC for a given col trying to predict a pred_col.
Parameters
----------
df : pandas.DataFrame
col : str
column to retrieve the values from
pred_col : str
the column we're trying to predict
... | Calculate the boostrapped AUC for a given col trying to predict a pred_col.
Parameters
----------
df : pandas.DataFrame
col : str
column to retrieve the values from
pred_col : str
the column we're trying to predict
n_boostrap : int
the number of bootstrap samples
Re... | entailment |
def set_callbacks(self, worker_start_callback: callable, worker_end_callback: callable, are_async: bool = False):
"""
:param are_async: True if the callbacks execute asynchronously, posting any heavy work to another thread.
"""
# We are setting self.worker_start_callback and self.worker_... | :param are_async: True if the callbacks execute asynchronously, posting any heavy work to another thread. | entailment |
def _start_worker(self, worker: Worker):
"""
Can be safely called multiple times on the same worker (for workers that support it)
to start a new thread for it.
"""
# This function is called from main thread and from worker pools threads to start their children threads
wit... | Can be safely called multiple times on the same worker (for workers that support it)
to start a new thread for it. | entailment |
def new_worker(self, name: str):
"""Creates a new Worker and start a new Thread with it. Returns the Worker."""
if not self.running:
return self.immediate_worker
worker = self._new_worker(name)
self._start_worker(worker)
return worker | Creates a new Worker and start a new Thread with it. Returns the Worker. | entailment |
def new_worker_pool(self, name: str, min_workers: int = 0, max_workers: int = 1,
max_seconds_idle: int = DEFAULT_WORKER_POOL_MAX_SECONDS_IDLE):
"""
Creates a new worker pool and starts it.
Returns the Worker that schedules works to the pool.
"""
if not sel... | Creates a new worker pool and starts it.
Returns the Worker that schedules works to the pool. | entailment |
def as_dataframe(self, on=None, join_with=None, join_how=None,
return_cols=False, rename_cols=False,
keep_paren_contents=True, **kwargs):
"""
Return this Cohort as a DataFrame, and optionally include additional columns
using `on`.
on : str or fu... | Return this Cohort as a DataFrame, and optionally include additional columns
using `on`.
on : str or function or list or dict, optional
- A column name.
- Or a function that creates a new column for comparison, e.g. count.snv_count.
- Or a list of column-generating f... | entailment |
def load_dataframe(self, df_loader_name):
"""
Instead of joining a DataFrameJoiner with the Cohort in `as_dataframe`, sometimes
we may want to just directly load a particular DataFrame.
"""
logger.debug("loading dataframe: {}".format(df_loader_name))
# Get the DataFrameLo... | Instead of joining a DataFrameJoiner with the Cohort in `as_dataframe`, sometimes
we may want to just directly load a particular DataFrame. | entailment |
def _get_function_name(self, fn, default="None"):
""" Return name of function, using default value if function not defined
"""
if fn is None:
fn_name = default
else:
fn_name = fn.__name__
return fn_name | Return name of function, using default value if function not defined | entailment |
def load_variants(self, patients=None, filter_fn=None, **kwargs):
"""Load a dictionary of patient_id to varcode.VariantCollection
Parameters
----------
patients : str, optional
Filter to a subset of patients
filter_fn : function
Takes a FilterableVariant ... | Load a dictionary of patient_id to varcode.VariantCollection
Parameters
----------
patients : str, optional
Filter to a subset of patients
filter_fn : function
Takes a FilterableVariant and returns a boolean. Only variants returning True are preserved.
... | entailment |
def _hash_filter_fn(self, filter_fn, **kwargs):
""" Construct string representing state of filter_fn
Used to cache filtered variants or effects uniquely depending on filter fn values
"""
filter_fn_name = self._get_function_name(filter_fn, default="filter-none")
logger.debug("... | Construct string representing state of filter_fn
Used to cache filtered variants or effects uniquely depending on filter fn values | entailment |
def _load_single_patient_variants(self, patient, filter_fn, use_cache=True, **kwargs):
""" Load filtered, merged variants for a single patient, optionally using cache
Note that filtered variants are first merged before filtering, and
each step is cached independently. Turn on debug ... | Load filtered, merged variants for a single patient, optionally using cache
Note that filtered variants are first merged before filtering, and
each step is cached independently. Turn on debug statements for more
details about cached files.
Use `_load_single_pati... | entailment |
def _load_single_patient_merged_variants(self, patient, use_cache=True):
""" Load merged variants for a single patient, optionally using cache
Note that merged variants are not filtered.
Use `_load_single_patient_variants` to get filtered variants
"""
logger.debug("loadi... | Load merged variants for a single patient, optionally using cache
Note that merged variants are not filtered.
Use `_load_single_patient_variants` to get filtered variants | entailment |
def load_polyphen_annotations(self, as_dataframe=False,
filter_fn=None):
"""Load a dataframe containing polyphen2 annotations for all variants
Parameters
----------
database_file : string, sqlite
Path to the WHESS/Polyphen2 SQLite database.
... | Load a dataframe containing polyphen2 annotations for all variants
Parameters
----------
database_file : string, sqlite
Path to the WHESS/Polyphen2 SQLite database.
Can be downloaded and bunzip2"ed from http://bit.ly/208mlIU
filter_fn : function
Takes... | entailment |
def load_effects(self, patients=None, only_nonsynonymous=False,
all_effects=False, filter_fn=None, **kwargs):
"""Load a dictionary of patient_id to varcode.EffectCollection
Note that this only loads one effect per variant.
Parameters
----------
patients : s... | Load a dictionary of patient_id to varcode.EffectCollection
Note that this only loads one effect per variant.
Parameters
----------
patients : str, optional
Filter to a subset of patients
only_nonsynonymous : bool, optional
If true, load only nonsynonymo... | entailment |
def load_kallisto(self):
"""
Load Kallisto transcript quantification data for a cohort
Parameters
----------
Returns
-------
kallisto_data : Pandas dataframe
Pandas dataframe with Kallisto data for all patients
columns include patient_id,... | Load Kallisto transcript quantification data for a cohort
Parameters
----------
Returns
-------
kallisto_data : Pandas dataframe
Pandas dataframe with Kallisto data for all patients
columns include patient_id, gene_name, est_counts | entailment |
def _load_single_patient_kallisto(self, patient):
"""
Load Kallisto gene quantification given a patient
Parameters
----------
patient : Patient
Returns
-------
data: Pandas dataframe
Pandas dataframe of sample's Kallisto data
colu... | Load Kallisto gene quantification given a patient
Parameters
----------
patient : Patient
Returns
-------
data: Pandas dataframe
Pandas dataframe of sample's Kallisto data
columns include patient_id, target_id, length, eff_length, est_counts, tpm | entailment |
def load_cufflinks(self, filter_ok=True):
"""
Load a Cufflinks gene expression data for a cohort
Parameters
----------
filter_ok : bool, optional
If true, filter Cufflinks data to row with FPKM_status == "OK"
Returns
-------
cufflinks_data : ... | Load a Cufflinks gene expression data for a cohort
Parameters
----------
filter_ok : bool, optional
If true, filter Cufflinks data to row with FPKM_status == "OK"
Returns
-------
cufflinks_data : Pandas dataframe
Pandas dataframe with Cufflinks d... | entailment |
def _load_single_patient_cufflinks(self, patient, filter_ok):
"""
Load Cufflinks gene quantification given a patient
Parameters
----------
patient : Patient
filter_ok : bool, optional
If true, filter Cufflinks data to row with FPKM_status == "OK"
Ret... | Load Cufflinks gene quantification given a patient
Parameters
----------
patient : Patient
filter_ok : bool, optional
If true, filter Cufflinks data to row with FPKM_status == "OK"
Returns
-------
data: Pandas dataframe
Pandas dataframe o... | entailment |
def get_filtered_isovar_epitopes(self, epitopes, ic50_cutoff):
"""
Mostly replicates topiary.build_epitope_collection_from_binding_predictions
Note: topiary needs to do fancy stuff like subsequence_protein_offset + binding_prediction.offset
in order to figure out whether a variant is in... | Mostly replicates topiary.build_epitope_collection_from_binding_predictions
Note: topiary needs to do fancy stuff like subsequence_protein_offset + binding_prediction.offset
in order to figure out whether a variant is in the peptide because it only has the variant's
offset into the full protein... | entailment |
def plot_roc_curve(self, on, bootstrap_samples=100, ax=None, **kwargs):
"""Plot an ROC curve for benefit and a given variable
Parameters
----------
on : str or function or list or dict
See `cohort.load.as_dataframe`
bootstrap_samples : int, optional
Numbe... | Plot an ROC curve for benefit and a given variable
Parameters
----------
on : str or function or list or dict
See `cohort.load.as_dataframe`
bootstrap_samples : int, optional
Number of boostrap samples to use to compute the AUC
ax : Axes, default None
... | entailment |
def plot_benefit(self, on, benefit_col="benefit", label="Response", ax=None,
alternative="two-sided", boolean_value_map={},
order=None, **kwargs):
"""Plot a comparison of benefit/response in the cohort on a given variable
"""
no_benefit_plot_name = "No %... | Plot a comparison of benefit/response in the cohort on a given variable | entailment |
def plot_boolean(self,
on,
boolean_col,
plot_col=None,
boolean_label=None,
boolean_value_map={},
order=None,
ax=None,
alternative="two-sided",
... | Plot a comparison of `boolean_col` in the cohort on a given variable via
`on` or `col`.
If the variable (through `on` or `col`) is binary this will compare
odds-ratios and perform a Fisher's exact test.
If the variable is numeric, this will compare the distributions through
a M... | entailment |
def plot_survival(self,
on,
how="os",
survival_units="Days",
strata=None,
ax=None,
ci_show=False,
with_condition_color="#B38600",
no_condition_c... | Plot a Kaplan Meier survival curve by splitting the cohort into two groups
Parameters
----------
on : str or function or list or dict
See `cohort.load.as_dataframe`
how : {"os", "pfs"}, optional
Whether to plot OS (overall survival) or PFS (progression free surviv... | entailment |
def plot_correlation(self, on, x_col=None, plot_type="jointplot", stat_func=pearsonr, show_stat_func=True, plot_kwargs={}, **kwargs):
"""Plot the correlation between two variables.
Parameters
----------
on : list or dict of functions or strings
See `cohort.load.as_dataframe`... | Plot the correlation between two variables.
Parameters
----------
on : list or dict of functions or strings
See `cohort.load.as_dataframe`
x_col : str, optional
If `on` is a dict, this guarantees we have the expected ordering.
plot_type : str, optional
... | entailment |
def _list_patient_ids(self):
""" Utility function to return a list of patient ids in the Cohort
"""
results = []
for patient in self:
results.append(patient.id)
return(results) | Utility function to return a list of patient ids in the Cohort | entailment |
def summarize_provenance_per_cache(self):
"""Utility function to summarize provenance files for cached items used by a Cohort,
for each cache_dir that exists. Only existing cache_dirs are summarized.
This is a summary of provenance files because the function checks to see whether all
pa... | Utility function to summarize provenance files for cached items used by a Cohort,
for each cache_dir that exists. Only existing cache_dirs are summarized.
This is a summary of provenance files because the function checks to see whether all
patients data have the same provenance within the cache... | entailment |
def summarize_dataframe(self):
"""Summarize default dataframe for this cohort using a hash function.
Useful for confirming the version of data used in various reports, e.g. ipynbs
"""
if self.dataframe_hash:
return(self.dataframe_hash)
else:
df = self._as_... | Summarize default dataframe for this cohort using a hash function.
Useful for confirming the version of data used in various reports, e.g. ipynbs | entailment |
def summarize_provenance(self):
"""Utility function to summarize provenance files for cached items used by a Cohort.
At the moment, most PROVENANCE files contain details about packages used to
generate files. However, this function is generic & so it summarizes the contents
of those fil... | Utility function to summarize provenance files for cached items used by a Cohort.
At the moment, most PROVENANCE files contain details about packages used to
generate files. However, this function is generic & so it summarizes the contents
of those files irrespective of their contents.
... | entailment |
def summarize_data_sources(self):
"""Utility function to summarize data source status for this Cohort, useful for confirming
the state of data used for an analysis
Returns
----------
Dictionary with summary of data sources
Currently contains
- dataframe_hash: ha... | Utility function to summarize data source status for this Cohort, useful for confirming
the state of data used for an analysis
Returns
----------
Dictionary with summary of data sources
Currently contains
- dataframe_hash: hash of the dataframe (see `?cohorts.Cohort.sum... | entailment |
def strelka_somatic_variant_stats(variant, variant_metadata):
"""Parse out the variant calling statistics for a given variant from a Strelka VCF
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of sample to variant calling statistics, corresponds to the sample c... | Parse out the variant calling statistics for a given variant from a Strelka VCF
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of sample to variant calling statistics, corresponds to the sample columns
in a Strelka VCF
Returns
-------
SomaticV... | entailment |
def _strelka_variant_stats(variant, sample_info):
"""Parse a single sample"s variant calling statistics based on Strelka VCF output
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of Strelka-specific variant calling fields
Returns
-------
VariantSt... | Parse a single sample"s variant calling statistics based on Strelka VCF output
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of Strelka-specific variant calling fields
Returns
-------
VariantStats | entailment |
def mutect_somatic_variant_stats(variant, variant_metadata):
"""Parse out the variant calling statistics for a given variant from a Mutect VCF
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of sample to variant calling statistics, corresponds to the sample col... | Parse out the variant calling statistics for a given variant from a Mutect VCF
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of sample to variant calling statistics, corresponds to the sample columns
in a Mutect VCF
Returns
-------
SomaticVar... | entailment |
def _mutect_variant_stats(variant, sample_info):
"""Parse a single sample"s variant calling statistics based on Mutect"s (v1) VCF output
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of Mutect-specific variant calling fields
Returns
-------
Varia... | Parse a single sample"s variant calling statistics based on Mutect"s (v1) VCF output
Parameters
----------
variant : varcode.Variant
sample_info : dict
Dictionary of Mutect-specific variant calling fields
Returns
-------
VariantStats | entailment |
def maf_somatic_variant_stats(variant, variant_metadata):
"""
Parse out the variant calling statistics for a given variant from a MAF file
Assumes the MAF format described here: https://www.biostars.org/p/161298/#161777
Parameters
----------
variant : varcode.Variant
variant_metadata : dic... | Parse out the variant calling statistics for a given variant from a MAF file
Assumes the MAF format described here: https://www.biostars.org/p/161298/#161777
Parameters
----------
variant : varcode.Variant
variant_metadata : dict
Dictionary of metadata for this variant
Returns
---... | entailment |
def _vcf_is_strelka(variant_file, variant_metadata):
"""Return True if variant_file given is in strelka format
"""
if "strelka" in variant_file.lower():
return True
elif "NORMAL" in variant_metadata["sample_info"].keys():
return True
else:
vcf_reader = vcf.Reader(open(variant... | Return True if variant_file given is in strelka format | entailment |
def variant_stats_from_variant(variant,
metadata,
merge_fn=(lambda all_stats: \
max(all_stats, key=(lambda stats: stats.tumor_stats.depth)))):
"""Parse the variant calling stats from a variant called from multiple variant ... | Parse the variant calling stats from a variant called from multiple variant files. The stats are merged
based on `merge_fn`
Parameters
----------
variant : varcode.Variant
metadata : dict
Dictionary of variant file to variant calling metadata from that file
merge_fn : function
F... | entailment |
def _get_and_execute(self):
"""
:return: True if it should continue running, False if it should end its execution.
"""
try:
work = self.queue.get(timeout=self.max_seconds_idle)
except queue.Empty:
# max_seconds_idle has been exhausted, exiting
... | :return: True if it should continue running, False if it should end its execution. | entailment |
def format(self, full_info: bool = False):
"""
:param full_info: If True, adds more info about the chat. Please, note that this additional info requires
to make up to THREE synchronous api calls.
"""
chat = self.api_object
if full_info:
self.__format_full(... | :param full_info: If True, adds more info about the chat. Please, note that this additional info requires
to make up to THREE synchronous api calls. | entailment |
def list(self):
"""
:rtype: list(setting_name, value, default_value, is_set, is_supported)
"""
settings = []
for setting in _SETTINGS:
value = self.get(setting)
is_set = self.is_set(setting)
default_value = self.get_default_value(setting)
... | :rtype: list(setting_name, value, default_value, is_set, is_supported) | entailment |
def load_ensembl_coverage(cohort, coverage_path, min_tumor_depth, min_normal_depth=0,
pageant_dir_fn=None):
"""
Load in Pageant CoverageDepth results with Ensembl loci.
coverage_path is a path to Pageant CoverageDepth output directory, with
one subdirectory per patient and a `... | Load in Pageant CoverageDepth results with Ensembl loci.
coverage_path is a path to Pageant CoverageDepth output directory, with
one subdirectory per patient and a `cdf.csv` file inside each patient subdir.
If min_normal_depth is 0, calculate tumor coverage. Otherwise, calculate
join tumor/normal cove... | entailment |
def vertical_percent(plot, percent=0.1):
"""
Using the size of the y axis, return a fraction of that size.
"""
plot_bottom, plot_top = plot.get_ylim()
return percent * (plot_top - plot_bottom) | Using the size of the y axis, return a fraction of that size. | entailment |
def hide_ticks(plot, min_tick_value=None, max_tick_value=None):
"""Hide tick values that are outside of [min_tick_value, max_tick_value]"""
for tick, tick_value in zip(plot.get_yticklabels(), plot.get_yticks()):
tick_label = as_numeric(tick_value)
if tick_label:
if (min_tick_value is... | Hide tick values that are outside of [min_tick_value, max_tick_value] | entailment |
def add_significance_indicator(plot, col_a=0, col_b=1, significant=False):
"""
Add a p-value significance indicator.
"""
plot_bottom, plot_top = plot.get_ylim()
# Give the plot a little room for the significance indicator
line_height = vertical_percent(plot, 0.1)
# Add some extra spacing bel... | Add a p-value significance indicator. | entailment |
def stripboxplot(x, y, data, ax=None, significant=None, **kwargs):
"""
Overlay a stripplot on top of a boxplot.
"""
ax = sb.boxplot(
x=x,
y=y,
data=data,
ax=ax,
fliersize=0,
**kwargs
)
plot = sb.stripplot(
x=x,
y=y,
data=da... | Overlay a stripplot on top of a boxplot. | entailment |
def fishers_exact_plot(data, condition1, condition2, ax=None,
condition1_value=None,
alternative="two-sided", **kwargs):
"""
Perform a Fisher's exact test to compare to binary columns
Parameters
----------
data: Pandas dataframe
Dataframe to ret... | Perform a Fisher's exact test to compare to binary columns
Parameters
----------
data: Pandas dataframe
Dataframe to retrieve information from
condition1: str
First binary column to compare (and used for test sidedness)
condition2: str
Second binary column to compare
... | entailment |
def mann_whitney_plot(data,
condition,
distribution,
ax=None,
condition_value=None,
alternative="two-sided",
skip_plot=False,
**kwargs):
"""
Create a box plot... | Create a box plot comparing a condition and perform a
Mann Whitney test to compare the distribution in condition A v B
Parameters
----------
data: Pandas dataframe
Dataframe to retrieve information from
condition: str
Column to use as the splitting criteria
distribution: str
... | entailment |
def roc_curve_plot(data, value_column, outcome_column, bootstrap_samples=100, ax=None):
"""Create a ROC curve and compute the bootstrap AUC for the given variable and outcome
Parameters
----------
data : Pandas dataframe
Dataframe to retrieve information from
value_column : str
Colu... | Create a ROC curve and compute the bootstrap AUC for the given variable and outcome
Parameters
----------
data : Pandas dataframe
Dataframe to retrieve information from
value_column : str
Column to retrieve the values from
outcome_column : str
Column to use as the outcome va... | entailment |
def get_cache_dir(cache_dir, cache_root_dir=None, *args, **kwargs):
"""
Return full cache_dir, according to following logic:
- if cache_dir is a full path (per path.isabs), return that value
- if not and if cache_root_dir is not None, join two paths
- otherwise, log warnings and return N... | Return full cache_dir, according to following logic:
- if cache_dir is a full path (per path.isabs), return that value
- if not and if cache_root_dir is not None, join two paths
- otherwise, log warnings and return None
Separately, if args or kwargs are given, format cache_dir using kwargs | entailment |
def _strip_column_name(col_name, keep_paren_contents=True):
"""
Utility script applying several regexs to a string.
Intended to be used by `strip_column_names`.
This function will:
1. replace informative punctuation components with text
2. (optionally) remove text within parentheses
... | Utility script applying several regexs to a string.
Intended to be used by `strip_column_names`.
This function will:
1. replace informative punctuation components with text
2. (optionally) remove text within parentheses
3. replace remaining punctuation/whitespace with _
4. strip... | entailment |
def strip_column_names(cols, keep_paren_contents=True):
"""
Utility script for renaming pandas columns to patsy-friendly names.
Revised names have been:
- stripped of all punctuation and whitespace (converted to text or `_`)
- converted to lower case
Takes a list of column names, retur... | Utility script for renaming pandas columns to patsy-friendly names.
Revised names have been:
- stripped of all punctuation and whitespace (converted to text or `_`)
- converted to lower case
Takes a list of column names, returns a dict mapping
names to revised names.
If there are any ... | entailment |
def set_attributes(obj, additional_data):
"""
Given an object and a dictionary, give the object new attributes from that dictionary.
Uses _strip_column_name to git rid of whitespace/uppercase/special characters.
"""
for key, value in additional_data.items():
if hasattr(obj, key):
... | Given an object and a dictionary, give the object new attributes from that dictionary.
Uses _strip_column_name to git rid of whitespace/uppercase/special characters. | entailment |
def return_obj(cols, df, return_cols=False):
"""Construct a DataFrameHolder and then return either that or the DataFrame."""
df_holder = DataFrameHolder(cols=cols, df=df)
return df_holder.return_self(return_cols=return_cols) | Construct a DataFrameHolder and then return either that or the DataFrame. | entailment |
def compare_provenance(
this_provenance, other_provenance,
left_outer_diff = "In current but not comparison",
right_outer_diff = "In comparison but not current"):
"""Utility function to compare two abritrary provenance dicts
returns number of discrepancies.
Parameters
----------... | Utility function to compare two abritrary provenance dicts
returns number of discrepancies.
Parameters
----------
this_provenance: provenance dict (to be compared to "other_provenance")
other_provenance: comparison provenance dict
(optional)
left_outer_diff: description/prefix used when pr... | entailment |
def _plot_kmf_single(df,
condition_col,
survival_col,
censor_col,
threshold,
title,
xlabel,
ylabel,
ax,
with_condition_color,
... | Helper function to produce a single KM survival plot, among observations in df by groups defined by condition_col.
All inputs are required - this function is intended to be called by `plot_kmf`. | entailment |
def plot_kmf(df,
condition_col,
censor_col,
survival_col,
strata_col=None,
threshold=None,
title=None,
xlabel=None,
ylabel=None,
ax=None,
with_condition_color="#B38600",
no_cond... | Plot survival curves by splitting the dataset into two groups based on
condition_col. Report results for a log-rank test (if two groups are plotted)
or CoxPH survival analysis (if >2 groups) for association with survival.
Regarding definition of groups:
If condition_col is numeric, values are split... | entailment |
def concat(self, formatted_text):
""":type formatted_text: FormattedText"""
assert self._is_compatible(formatted_text), "Cannot concat text with different modes"
self.text += formatted_text.text
return self | :type formatted_text: FormattedText | entailment |
def join(self, formatted_texts):
""":type formatted_texts: list[FormattedText]"""
formatted_texts = list(formatted_texts) # so that after the first iteration elements are not lost if generator
for formatted_text in formatted_texts:
assert self._is_compatible(formatted_text), "Cannot... | :type formatted_texts: list[FormattedText] | entailment |
def concat(self, *args, **kwargs):
"""
:type args: FormattedText
:type kwargs: FormattedText
"""
for arg in args:
assert self.formatted_text._is_compatible(arg), "Cannot concat text with different modes"
self.format_args.append(arg.text)
for kwarg ... | :type args: FormattedText
:type kwargs: FormattedText | entailment |
def random_cohort(size, cache_dir, data_dir=None,
min_random_variants=None,
max_random_variants=None,
seed_val=1234):
"""
Parameters
----------
min_random_variants: optional, int
Minimum number of random variants to be generated per patient.
... | Parameters
----------
min_random_variants: optional, int
Minimum number of random variants to be generated per patient.
max_random_variants: optional, int
Maximum number of random variants to be generated per patient. | entailment |
def generate_random_missense_variants(num_variants=10, max_search=100000, reference="GRCh37"):
"""
Generate a random collection of missense variants by trying random variants repeatedly.
"""
variants = []
for i in range(max_search):
bases = ["A", "C", "T", "G"]
random_ref = choice(ba... | Generate a random collection of missense variants by trying random variants repeatedly. | entailment |
def generate_simple_vcf(filename, variant_collection):
"""
Output a very simple metadata-free VCF for each variant in a variant_collection.
"""
contigs = []
positions = []
refs = []
alts = []
for variant in variant_collection:
contigs.append("chr" + variant.contig)
positi... | Output a very simple metadata-free VCF for each variant in a variant_collection. | entailment |
def list_folder(self, path):
"""Looks up folder contents of `path.`"""
# Inspired by https://github.com/rspivak/sftpserver/blob/0.3/src/sftpserver/stub_sftp.py#L70
try:
folder_contents = []
for f in os.listdir(path):
attr = paramiko.SFTPAttributes.from_sta... | Looks up folder contents of `path.` | entailment |
def filter_variants(variant_collection, patient, filter_fn, **kwargs):
"""Filter variants from the Variant Collection
Parameters
----------
variant_collection : varcode.VariantCollection
patient : cohorts.Patient
filter_fn: function
Takes a FilterableVariant and returns a boolean. Only ... | Filter variants from the Variant Collection
Parameters
----------
variant_collection : varcode.VariantCollection
patient : cohorts.Patient
filter_fn: function
Takes a FilterableVariant and returns a boolean. Only variants returning True are preserved.
Returns
-------
varcode.Va... | entailment |
def filter_effects(effect_collection, variant_collection, patient, filter_fn, all_effects, **kwargs):
"""Filter variants from the Effect Collection
Parameters
----------
effect_collection : varcode.EffectCollection
variant_collection : varcode.VariantCollection
patient : cohorts.Patient
fil... | Filter variants from the Effect Collection
Parameters
----------
effect_collection : varcode.EffectCollection
variant_collection : varcode.VariantCollection
patient : cohorts.Patient
filter_fn : function
Takes a FilterableEffect and returns a boolean. Only effects returning True are pre... | entailment |
def count_lines_in(filename):
"Count lines in a file"
f = open(filename)
lines = 0
buf_size = 1024 * 1024
read_f = f.read # loop optimization
buf = read_f(buf_size)
while buf:
lines += buf.count('\n')
buf = read_f(buf_size)
return lines | Count lines in a file | entailment |
def view_name_from(path):
"Resolve a path to the full python module name of the related view function"
try:
return CACHED_VIEWS[path]
except KeyError:
view = resolve(path)
module = path
name = ''
if hasattr(view.func, '__module__'):
module = resol... | Resolve a path to the full python module name of the related view function | entailment |
def generate_table_from(data):
"Output a nicely formatted ascii table"
table = Texttable(max_width=120)
table.add_row(["view", "method", "status", "count", "minimum", "maximum", "mean", "stdev", "queries", "querytime"])
table.set_cols_align(["l", "l", "l", "r", "r", "r", "r", "r", "r", "r"])
for i... | Output a nicely formatted ascii table | entailment |
def analyze_log_file(logfile, pattern, reverse_paths=True, progress=True):
"Given a log file and regex group and extract the performance data"
if progress:
lines = count_lines_in(logfile)
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=lines+1).start()
counter = 0
data ... | Given a log file and regex group and extract the performance data | entailment |
def to_string(self, limit=None):
"""
Create a string representation of this collection, showing up to
`limit` items.
"""
header = self.short_string()
if len(self) == 0:
return header
contents = ""
element_lines = [
" -- %s" % (elem... | Create a string representation of this collection, showing up to
`limit` items. | entailment |
def get_instance(cls, state):
""":rtype: UserStorageHandler"""
if cls.instance is None:
cls.instance = UserStorageHandler(state)
return cls.instance | :rtype: UserStorageHandler | entailment |
def _get_active_threads_names():
"""May contain sensitive info (like user ids). Use with care."""
active_threads = threading.enumerate()
return FormattedText().join(
[
FormattedText().newline().normal(" - {name}").start_format().bold(name=thread.name).end_format()
... | May contain sensitive info (like user ids). Use with care. | entailment |
def _get_running_workers_names(running_workers: list):
"""May contain sensitive info (like user ids). Use with care."""
return FormattedText().join(
[
FormattedText().newline().normal(" - {name}").start_format().bold(name=worker.name).end_format()
for worker i... | May contain sensitive info (like user ids). Use with care. | entailment |
def _get_worker_pools_names(worker_pools: list):
"""May contain sensitive info (like user ids). Use with care."""
return FormattedText().join(
[
FormattedText().newline().normal(" - {name}").start_format().bold(name=worker.name).end_format()
for worker in work... | May contain sensitive info (like user ids). Use with care. | entailment |
def format(self, member_info: bool = False):
"""
:param member_info: If True, adds also chat member info. Please, note that this additional info requires
to make ONE api call.
"""
user = self.api_object
self.__format_user(user)
if member_info and self.chat.typ... | :param member_info: If True, adds also chat member info. Please, note that this additional info requires
to make ONE api call. | entailment |
def safe_log_error(self, error: Exception, *info: str):
"""Log error failing silently on error"""
self.__do_safe(lambda: self.logger.error(error, *info)) | Log error failing silently on error | entailment |
def safe_log_info(self, *info: str):
"""Log info failing silently on error"""
self.__do_safe(lambda: self.logger.info(*info)) | Log info failing silently on error | entailment |
def wald_wolfowitz(sequence):
"""
implements the wald-wolfowitz runs test:
http://en.wikipedia.org/wiki/Wald-Wolfowitz_runs_test
http://support.sas.com/kb/33/092.html
:param sequence: any iterable with at most 2 values. e.g.
'1001001'
[1, 0, 1, 0, 1]
... | implements the wald-wolfowitz runs test:
http://en.wikipedia.org/wiki/Wald-Wolfowitz_runs_test
http://support.sas.com/kb/33/092.html
:param sequence: any iterable with at most 2 values. e.g.
'1001001'
[1, 0, 1, 0, 1]
'abaaabbba'
:rtype: a ... | entailment |
def auto_correlation(sequence):
"""
test for the autocorrelation of a sequence between t and t - 1
as the 'auto_correlation' it is less likely that the sequence is
generated randomly.
:param sequence: any iterable with at most 2 values that can be turned
into a float via np.floa... | test for the autocorrelation of a sequence between t and t - 1
as the 'auto_correlation' it is less likely that the sequence is
generated randomly.
:param sequence: any iterable with at most 2 values that can be turned
into a float via np.float . e.g.
'1001001'
... | entailment |
def _parse_header_links(response):
"""
Parse the links from a Link: header field.
.. todo:: Links with the same relation collide at the moment.
:param bytes value: The header value.
:rtype: `dict`
:return: A dictionary of parsed links, keyed by ``rel`` or ``url``.
"""
values = respon... | Parse the links from a Link: header field.
.. todo:: Links with the same relation collide at the moment.
:param bytes value: The header value.
:rtype: `dict`
:return: A dictionary of parsed links, keyed by ``rel`` or ``url``. | entailment |
def _default_client(jws_client, reactor, key, alg):
"""
Make a client if we didn't get one.
"""
if jws_client is None:
pool = HTTPConnectionPool(reactor)
agent = Agent(reactor, pool=pool)
jws_client = JWSClient(HTTPClient(agent=agent), key, alg)
return jws_client | Make a client if we didn't get one. | entailment |
def _find_supported_challenge(authzr, responders):
"""
Find a challenge combination that consists of a single challenge that the
responder can satisfy.
:param ~acme.messages.AuthorizationResource auth: The authorization to
examine.
:type responder: List[`~txacme.interfaces.IResponder`]
... | Find a challenge combination that consists of a single challenge that the
responder can satisfy.
:param ~acme.messages.AuthorizationResource auth: The authorization to
examine.
:type responder: List[`~txacme.interfaces.IResponder`]
:param responder: The possible responders to use.
:raises... | entailment |
def answer_challenge(authzr, client, responders):
"""
Complete an authorization using a responder.
:param ~acme.messages.AuthorizationResource auth: The authorization to
complete.
:param .Client client: The ACME client.
:type responders: List[`~txacme.interfaces.IResponder`]
:param res... | Complete an authorization using a responder.
:param ~acme.messages.AuthorizationResource auth: The authorization to
complete.
:param .Client client: The ACME client.
:type responders: List[`~txacme.interfaces.IResponder`]
:param responders: A list of responders that can be used to complete the... | entailment |
def poll_until_valid(authzr, clock, client, timeout=300.0):
"""
Poll an authorization until it is in a state other than pending or
processing.
:param ~acme.messages.AuthorizationResource auth: The authorization to
complete.
:param clock: The ``IReactorTime`` implementation to use; usually t... | Poll an authorization until it is in a state other than pending or
processing.
:param ~acme.messages.AuthorizationResource auth: The authorization to
complete.
:param clock: The ``IReactorTime`` implementation to use; usually the
reactor, when not testing.
:param .Client client: The ACM... | entailment |
def from_url(cls, reactor, url, key, alg=RS256, jws_client=None):
"""
Construct a client from an ACME directory at a given URL.
:param url: The ``twisted.python.url.URL`` to fetch the directory from.
See `txacme.urls` for constants for various well-known public
directori... | Construct a client from an ACME directory at a given URL.
:param url: The ``twisted.python.url.URL`` to fetch the directory from.
See `txacme.urls` for constants for various well-known public
directories.
:param reactor: The Twisted reactor to use.
:param ~josepy.jwk.JWK... | entailment |
def register(self, new_reg=None):
"""
Create a new registration with the ACME server.
:param ~acme.messages.NewRegistration new_reg: The registration message
to use, or ``None`` to construct one.
:return: The registration resource.
:rtype: Deferred[`~acme.messages.R... | Create a new registration with the ACME server.
:param ~acme.messages.NewRegistration new_reg: The registration message
to use, or ``None`` to construct one.
:return: The registration resource.
:rtype: Deferred[`~acme.messages.RegistrationResource`] | entailment |
def _maybe_location(cls, response, uri=None):
"""
Get the Location: if there is one.
"""
location = response.headers.getRawHeaders(b'location', [None])[0]
if location is not None:
return location.decode('ascii')
return uri | Get the Location: if there is one. | entailment |
def _maybe_registered(self, failure, new_reg):
"""
If the registration already exists, we should just load it.
"""
failure.trap(ServerError)
response = failure.value.response
if response.code == http.CONFLICT:
reg = new_reg.update(
resource=mes... | If the registration already exists, we should just load it. | entailment |
def agree_to_tos(self, regr):
"""
Accept the terms-of-service for a registration.
:param ~acme.messages.RegistrationResource regr: The registration to
update.
:return: The updated registration resource.
:rtype: Deferred[`~acme.messages.RegistrationResource`]
... | Accept the terms-of-service for a registration.
:param ~acme.messages.RegistrationResource regr: The registration to
update.
:return: The updated registration resource.
:rtype: Deferred[`~acme.messages.RegistrationResource`] | entailment |
def update_registration(self, regr, uri=None):
"""
Submit a registration to the server to update it.
:param ~acme.messages.RegistrationResource regr: The registration to
update. Can be a :class:`~acme.messages.NewRegistration` instead,
in order to create a new registrat... | Submit a registration to the server to update it.
:param ~acme.messages.RegistrationResource regr: The registration to
update. Can be a :class:`~acme.messages.NewRegistration` instead,
in order to create a new registration.
:param str uri: The url to submit to. Must be
... | entailment |
def _parse_regr_response(self, response, uri=None, new_authzr_uri=None,
terms_of_service=None):
"""
Parse a registration response from the server.
"""
links = _parse_header_links(response)
if u'terms-of-service' in links:
terms_of_service ... | Parse a registration response from the server. | entailment |
def _check_regr(self, regr, new_reg):
"""
Check that a registration response contains the registration we were
expecting.
"""
body = getattr(new_reg, 'body', new_reg)
for k, v in body.items():
if k == 'resource' or not v:
continue
i... | Check that a registration response contains the registration we were
expecting. | entailment |
def request_challenges(self, identifier):
"""
Create a new authorization.
:param ~acme.messages.Identifier identifier: The identifier to
authorize.
:return: The new authorization resource.
:rtype: Deferred[`~acme.messages.AuthorizationResource`]
"""
... | Create a new authorization.
:param ~acme.messages.Identifier identifier: The identifier to
authorize.
:return: The new authorization resource.
:rtype: Deferred[`~acme.messages.AuthorizationResource`] | entailment |
def _expect_response(cls, response, code):
"""
Ensure we got the expected response code.
"""
if response.code != code:
raise errors.ClientError(
'Expected {!r} response but got {!r}'.format(
code, response.code))
return response | Ensure we got the expected response code. | entailment |
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