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alvarogzp/telegram-bot-framework | bot/action/util/format.py | UserFormatter.full_data | 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... | python | def full_data(self):
"""
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name [username] <id> (locale) bot_or_user
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alvarogzp/telegram-bot-framework | bot/action/util/format.py | ChatFormatter.full_data | def full_data(self):
"""
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title [username] (type) <id>
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"""
data = [
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title [username] (type) <id>
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hammerlab/cohorts | cohorts/functions.py | use_defaults | 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... | python | 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... | [
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hammerlab/cohorts | cohorts/functions.py | count_function | def count_function(func):
"""
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hammerlab/cohorts | cohorts/functions.py | count_variants_function_builder | 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... | python | 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.
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hammerlab/cohorts | cohorts/functions.py | count_effects_function_builder | def count_effects_function_builder(function_name, only_nonsynonymous, filterable_effect_function=None):
"""
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hammerlab/cohorts | cohorts/functions.py | median_vaf_purity | 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... | python | 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
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"""
patient_id = row["patient_id"]
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hammerlab/cohorts | cohorts/model.py | bootstrap_auc | 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
... | python | def bootstrap_auc(df, col, pred_col, n_bootstrap=1000):
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Calculate the boostrapped AUC for a given col trying to predict a pred_col.
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df : pandas.DataFrame
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column to retrieve the values from
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alvarogzp/telegram-bot-framework | bot/multithreading/scheduler.py | SchedulerApi.set_callbacks | 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.
"""
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alvarogzp/telegram-bot-framework | bot/multithreading/scheduler.py | SchedulerApi._start_worker | def _start_worker(self, worker: Worker):
"""
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"""
# This function is called from main thread and from worker pools threads to start their children threads
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alvarogzp/telegram-bot-framework | bot/multithreading/scheduler.py | SchedulerApi.new_worker | def new_worker(self, name: str):
"""Creates a new Worker and start a new Thread with it. Returns the Worker."""
if not self.running:
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worker = self._new_worker(name)
self._start_worker(worker)
return worker | python | def new_worker(self, name: str):
"""Creates a new Worker and start a new Thread with it. Returns the Worker."""
if not self.running:
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worker = self._new_worker(name)
self._start_worker(worker)
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alvarogzp/telegram-bot-framework | bot/multithreading/scheduler.py | SchedulerApi.new_worker_pool | def new_worker_pool(self, name: str, min_workers: int = 0, max_workers: int = 1,
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hammerlab/cohorts | cohorts/cohort.py | Cohort.as_dataframe | def as_dataframe(self, on=None, join_with=None, join_how=None,
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Return this Cohort as a DataFrame, and optionally include additional columns
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hammerlab/cohorts | cohorts/cohort.py | Cohort.load_dataframe | def load_dataframe(self, df_loader_name):
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hammerlab/cohorts | cohorts/cohort.py | Cohort._get_function_name | 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 | python | 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__
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hammerlab/cohorts | cohorts/cohort.py | Cohort.load_variants | 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
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Filter to a subset of patients
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hammerlab/cohorts | cohorts/cohort.py | Cohort._hash_filter_fn | def _hash_filter_fn(self, filter_fn, **kwargs):
""" Construct string representing state of filter_fn
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"""
filter_fn_name = self._get_function_name(filter_fn, default="filter-none")
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hammerlab/cohorts | cohorts/cohort.py | Cohort._load_single_patient_variants | def _load_single_patient_variants(self, patient, filter_fn, use_cache=True, **kwargs):
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Note that filtered variants are first merged before filtering, and
each step is cached independently. Turn on debug ... | python | def _load_single_patient_variants(self, patient, filter_fn, use_cache=True, **kwargs):
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hammerlab/cohorts | cohorts/cohort.py | Cohort._load_single_patient_merged_variants | def _load_single_patient_merged_variants(self, patient, use_cache=True):
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Note that merged variants are not filtered.
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hammerlab/cohorts | cohorts/cohort.py | Cohort.load_polyphen_annotations | 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.
... | python | def load_polyphen_annotations(self, as_dataframe=False,
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"""Load a dataframe containing polyphen2 annotations for all variants
Parameters
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database_file : string, sqlite
Path to the WHESS/Polyphen2 SQLite database.
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hammerlab/cohorts | cohorts/cohort.py | Cohort.load_effects | 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... | python | def load_effects(self, patients=None, only_nonsynonymous=False,
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hammerlab/cohorts | cohorts/cohort.py | Cohort.load_kallisto | 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,... | python | 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
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hammerlab/cohorts | cohorts/cohort.py | Cohort._load_single_patient_kallisto | def _load_single_patient_kallisto(self, patient):
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Load Kallisto gene quantification given a patient
Parameters
----------
patient : Patient
Returns
-------
data: Pandas dataframe
Pandas dataframe of sample's Kallisto data
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patient : Patient
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hammerlab/cohorts | cohorts/cohort.py | Cohort.load_cufflinks | 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 : ... | python | def load_cufflinks(self, filter_ok=True):
"""
Load a Cufflinks gene expression data for a cohort
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----------
filter_ok : bool, optional
If true, filter Cufflinks data to row with FPKM_status == "OK"
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hammerlab/cohorts | cohorts/cohort.py | Cohort._load_single_patient_cufflinks | def _load_single_patient_cufflinks(self, patient, filter_ok):
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patient : Patient
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patient : Patient
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hammerlab/cohorts | cohorts/cohort.py | Cohort.get_filtered_isovar_epitopes | def get_filtered_isovar_epitopes(self, epitopes, ic50_cutoff):
"""
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Note: topiary needs to do fancy stuff like subsequence_protein_offset + binding_prediction.offset
in order to figure out whether a variant is in... | python | def get_filtered_isovar_epitopes(self, epitopes, ic50_cutoff):
"""
Mostly replicates topiary.build_epitope_collection_from_binding_predictions
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hammerlab/cohorts | cohorts/cohort.py | Cohort.plot_roc_curve | 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... | python | def plot_roc_curve(self, on, bootstrap_samples=100, ax=None, **kwargs):
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Parameters
----------
on : str or function or list or dict
See `cohort.load.as_dataframe`
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hammerlab/cohorts | cohorts/cohort.py | Cohort.plot_benefit | 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 %... | python | 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
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hammerlab/cohorts | cohorts/cohort.py | Cohort.plot_boolean | def plot_boolean(self,
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hammerlab/cohorts | cohorts/cohort.py | Cohort.plot_survival | def plot_survival(self,
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how="os",
survival_units="Days",
strata=None,
ax=None,
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hammerlab/cohorts | cohorts/cohort.py | Cohort.plot_correlation | def plot_correlation(self, on, x_col=None, plot_type="jointplot", stat_func=pearsonr, show_stat_func=True, plot_kwargs={}, **kwargs):
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hammerlab/cohorts | cohorts/cohort.py | Cohort._list_patient_ids | def _list_patient_ids(self):
""" Utility function to return a list of patient ids in the Cohort
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results.append(patient.id)
return(results) | python | def _list_patient_ids(self):
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results = []
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results.append(patient.id)
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hammerlab/cohorts | cohorts/cohort.py | Cohort.summarize_provenance_per_cache | def summarize_provenance_per_cache(self):
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hammerlab/cohorts | cohorts/cohort.py | Cohort.summarize_dataframe | 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_... | python | 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
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hammerlab/cohorts | cohorts/cohort.py | Cohort.summarize_provenance | def summarize_provenance(self):
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hammerlab/cohorts | cohorts/cohort.py | Cohort.summarize_data_sources | 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... | python | def summarize_data_sources(self):
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Dictionary with summary of data sources
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hammerlab/cohorts | cohorts/variant_stats.py | strelka_somatic_variant_stats | 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... | python | def strelka_somatic_variant_stats(variant, variant_metadata):
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hammerlab/cohorts | cohorts/variant_stats.py | _strelka_variant_stats | 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
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Dictionary of Strelka-specific variant calling fields
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hammerlab/cohorts | cohorts/variant_stats.py | mutect_somatic_variant_stats | def mutect_somatic_variant_stats(variant, variant_metadata):
"""Parse out the variant calling statistics for a given variant from a Mutect VCF
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----------
variant : varcode.Variant
sample_info : dict
Dictionary of sample to variant calling statistics, corresponds to the sample col... | python | def mutect_somatic_variant_stats(variant, variant_metadata):
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hammerlab/cohorts | cohorts/variant_stats.py | _mutect_variant_stats | def _mutect_variant_stats(variant, sample_info):
"""Parse a single sample"s variant calling statistics based on Mutect"s (v1) VCF output
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variant : varcode.Variant
sample_info : dict
Dictionary of Mutect-specific variant calling fields
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hammerlab/cohorts | cohorts/variant_stats.py | maf_somatic_variant_stats | def maf_somatic_variant_stats(variant, variant_metadata):
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variant_metadata : dic... | python | def maf_somatic_variant_stats(variant, variant_metadata):
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hammerlab/cohorts | cohorts/variant_stats.py | _vcf_is_strelka | def _vcf_is_strelka(variant_file, variant_metadata):
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"""
if "strelka" in variant_file.lower():
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hammerlab/cohorts | cohorts/variant_stats.py | variant_stats_from_variant | def variant_stats_from_variant(variant,
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alvarogzp/telegram-bot-framework | bot/multithreading/worker/pool/workers/limited_lifespan.py | LimitedLifespanQueueWorker._get_and_execute | def _get_and_execute(self):
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alvarogzp/telegram-bot-framework | bot/action/standard/info/formatter/chat.py | ChatInfoFormatter.format | def format(self, full_info: bool = False):
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alvarogzp/telegram-bot-framework | bot/action/standard/chatsettings/__init__.py | ChatSettings.list | def list(self):
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hammerlab/cohorts | cohorts/variant_filters.py | load_ensembl_coverage | def load_ensembl_coverage(cohort, coverage_path, min_tumor_depth, min_normal_depth=0,
pageant_dir_fn=None):
"""
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coverage_path is a path to Pageant CoverageDepth output directory, with
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pageant_dir_fn=None):
"""
Load in Pageant CoverageDepth results with Ensembl loci.
coverage_path is a path to Pageant CoverageDepth output directory, with
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hammerlab/cohorts | cohorts/plot.py | vertical_percent | 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) | python | def vertical_percent(plot, percent=0.1):
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Using the size of the y axis, return a fraction of that size.
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hammerlab/cohorts | cohorts/plot.py | hide_ticks | def hide_ticks(plot, min_tick_value=None, max_tick_value=None):
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for tick, tick_value in zip(plot.get_yticklabels(), plot.get_yticks()):
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hammerlab/cohorts | cohorts/plot.py | add_significance_indicator | def add_significance_indicator(plot, col_a=0, col_b=1, significant=False):
"""
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"""
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"""
Add a p-value significance indicator.
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# Give the plot a little room for the significance indicator
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hammerlab/cohorts | cohorts/plot.py | stripboxplot | def stripboxplot(x, y, data, ax=None, significant=None, **kwargs):
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Overlay a stripplot on top of a boxplot.
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Overlay a stripplot on top of a boxplot.
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hammerlab/cohorts | cohorts/plot.py | fishers_exact_plot | 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
----------
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Dataframe to ret... | python | def fishers_exact_plot(data, condition1, condition2, ax=None,
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Perform a Fisher's exact test to compare to binary columns
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hammerlab/cohorts | cohorts/plot.py | mann_whitney_plot | def mann_whitney_plot(data,
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**kwargs):
"""
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hammerlab/cohorts | cohorts/plot.py | roc_curve_plot | def roc_curve_plot(data, value_column, outcome_column, bootstrap_samples=100, ax=None):
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Parameters
----------
data : Pandas dataframe
Dataframe to retrieve information from
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hammerlab/cohorts | cohorts/utils.py | get_cache_dir | def get_cache_dir(cache_dir, cache_root_dir=None, *args, **kwargs):
"""
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hammerlab/cohorts | cohorts/utils.py | _strip_column_name | def _strip_column_name(col_name, keep_paren_contents=True):
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hammerlab/cohorts | cohorts/utils.py | DataFrameHolder.return_obj | 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)
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hammerlab/cohorts | cohorts/provenance.py | compare_provenance | def compare_provenance(
this_provenance, other_provenance,
left_outer_diff = "In current but not comparison",
right_outer_diff = "In comparison but not current"):
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----------... | python | def compare_provenance(
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Parameters
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this_provenance: provenance dict (to be compared to "other_provenance")
other_provenance: comparison provenance dict
(optional)
left_outer_diff: description/prefix used when pr... | [
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hammerlab/cohorts | cohorts/survival.py | _plot_kmf_single | def _plot_kmf_single(df,
condition_col,
survival_col,
censor_col,
threshold,
title,
xlabel,
ylabel,
ax,
with_condition_color,
... | python | def _plot_kmf_single(df,
condition_col,
survival_col,
censor_col,
threshold,
title,
xlabel,
ylabel,
ax,
with_condition_color,
... | [
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hammerlab/cohorts | cohorts/survival.py | plot_kmf | def plot_kmf(df,
condition_col,
censor_col,
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strata_col=None,
threshold=None,
title=None,
xlabel=None,
ylabel=None,
ax=None,
with_condition_color="#B38600",
no_cond... | python | def plot_kmf(df,
condition_col,
censor_col,
survival_col,
strata_col=None,
threshold=None,
title=None,
xlabel=None,
ylabel=None,
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alvarogzp/telegram-bot-framework | bot/action/util/textformat.py | FormattedText.concat | 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 | python | 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 | [
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alvarogzp/telegram-bot-framework | bot/action/util/textformat.py | FormattedText.join | def join(self, formatted_texts):
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for formatted_text in formatted_texts:
assert self._is_compatible(formatted_text), "Cannot... | python | def join(self, formatted_texts):
""":type formatted_texts: list[FormattedText]"""
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alvarogzp/telegram-bot-framework | bot/action/util/textformat.py | FormattedTextStringFormat.concat | def concat(self, *args, **kwargs):
"""
:type args: FormattedText
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assert self.formatted_text._is_compatible(arg), "Cannot concat text with different modes"
self.format_args.append(arg.text)
for kwarg ... | python | def concat(self, *args, **kwargs):
"""
:type args: FormattedText
:type kwargs: FormattedText
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hammerlab/cohorts | cohorts/random.py | random_cohort | 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.
... | python | 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.
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hammerlab/cohorts | cohorts/random.py | generate_random_missense_variants | 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 = []
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random_ref = choice(ba... | python | 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 = []
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hammerlab/cohorts | cohorts/random.py | generate_simple_vcf | 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... | python | 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)
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carletes/mock-ssh-server | mockssh/sftp.py | SFTPServerInterface.list_folder | 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... | python | def list_folder(self, path):
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# Inspired by https://github.com/rspivak/sftpserver/blob/0.3/src/sftpserver/stub_sftp.py#L70
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hammerlab/cohorts | cohorts/varcode_utils.py | filter_variants | 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 ... | python | 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
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hammerlab/cohorts | cohorts/varcode_utils.py | filter_effects | 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... | python | def filter_effects(effect_collection, variant_collection, patient, filter_fn, all_effects, **kwargs):
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Parameters
----------
effect_collection : varcode.EffectCollection
variant_collection : varcode.VariantCollection
patient : cohorts.Patient
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garethr/django-timelog | src/timelog/lib.py | count_lines_in | 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 | python | 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 | [
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garethr/django-timelog | src/timelog/lib.py | view_name_from | def view_name_from(path):
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try:
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except KeyError:
view = resolve(path)
module = path
name = ''
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"Resolve a path to the full python module name of the related view function"
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garethr/django-timelog | src/timelog/lib.py | generate_table_from | def generate_table_from(data):
"Output a nicely formatted ascii table"
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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... | python | 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"])
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garethr/django-timelog | src/timelog/lib.py | analyze_log_file | def analyze_log_file(logfile, pattern, reverse_paths=True, progress=True):
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pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=lines+1).start()
counter = 0
data ... | python | 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
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hammerlab/cohorts | cohorts/collection.py | Collection.to_string | def to_string(self, limit=None):
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Create a string representation of this collection, showing up to
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alvarogzp/telegram-bot-framework | bot/action/standard/userinfo.py | UserStorageHandler.get_instance | def get_instance(cls, state):
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alvarogzp/telegram-bot-framework | bot/action/standard/benchmark.py | WorkersAction._get_running_workers_names | def _get_running_workers_names(running_workers: list):
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alvarogzp/telegram-bot-framework | bot/action/standard/benchmark.py | WorkersAction._get_worker_pools_names | def _get_worker_pools_names(worker_pools: list):
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alvarogzp/telegram-bot-framework | bot/action/standard/info/formatter/user.py | UserInfoFormatter.format | def format(self, member_info: bool = False):
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"""
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alvarogzp/telegram-bot-framework | bot/bot.py | UpdatesProcessor.safe_log_error | def safe_log_error(self, error: Exception, *info: str):
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brentp/skidmarks | skidmarks.py | wald_wolfowitz | def wald_wolfowitz(sequence):
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implements the wald-wolfowitz runs test:
http://en.wikipedia.org/wiki/Wald-Wolfowitz_runs_test
http://support.sas.com/kb/33/092.html
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'1001001'
[1, 0, 1, 0, 1]
... | python | def wald_wolfowitz(sequence):
"""
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http://support.sas.com/kb/33/092.html
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twisted/txacme | src/txacme/client.py | _parse_header_links | def _parse_header_links(response):
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.. todo:: Links with the same relation collide at the moment.
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:rtype: `dict`
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twisted/txacme | src/txacme/client.py | _default_client | def _default_client(jws_client, reactor, key, alg):
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twisted/txacme | src/txacme/client.py | _find_supported_challenge | def _find_supported_challenge(authzr, responders):
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:param ~acme.messages.AuthorizationResource auth: The authorization to
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:type responder: List[`~txacme.interfaces.IResponder`]
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twisted/txacme | src/txacme/client.py | answer_challenge | def answer_challenge(authzr, client, responders):
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:param .Client client: The ACME client.
:type responders: List[`~txacme.interfaces.IResponder`]
:param res... | python | def answer_challenge(authzr, client, responders):
"""
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twisted/txacme | src/txacme/client.py | poll_until_valid | def poll_until_valid(authzr, clock, client, timeout=300.0):
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processing.
:param ~acme.messages.AuthorizationResource auth: The authorization to
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"""
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twisted/txacme | src/txacme/client.py | Client.from_url | 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.
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directori... | python | def from_url(cls, reactor, url, key, alg=RS256, jws_client=None):
"""
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:param reactor: The Twisted reactor to use.
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twisted/txacme | src/txacme/client.py | Client.register | 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... | python | 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.
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twisted/txacme | src/txacme/client.py | Client._maybe_location | 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 | python | 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 | [
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twisted/txacme | src/txacme/client.py | Client._maybe_registered | 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(
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"""
If the registration already exists, we should just load it.
"""
failure.trap(ServerError)
response = failure.value.response
if response.code == http.CONFLICT:
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twisted/txacme | src/txacme/client.py | Client.agree_to_tos | 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`]
... | python | 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`]
... | [
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:param ~acme.messages.RegistrationResource regr: The registration to
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:return: The updated registration resource.
:rtype: Deferred[`~acme.messages.RegistrationResource`] | [
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twisted/txacme | src/txacme/client.py | Client.update_registration | 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... | python | 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,
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twisted/txacme | src/txacme/client.py | Client._parse_regr_response | 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 ... | python | 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 ... | [
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twisted/txacme | src/txacme/client.py | Client._check_regr | 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... | python | 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():
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twisted/txacme | src/txacme/client.py | Client.request_challenges | 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`]
"""
... | python | 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`]
"""
... | [
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:param ~acme.messages.Identifier identifier: The identifier to
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:return: The new authorization resource.
:rtype: Deferred[`~acme.messages.AuthorizationResource`] | [
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] | train | https://github.com/twisted/txacme/blob/9478381cc63c6d53d14bf8db8407c923f472989a/src/txacme/client.py#L268-L289 |
twisted/txacme | src/txacme/client.py | Client._expect_response | 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 | python | 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 | [
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