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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... |
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(),
... |
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... |
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... |
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... |
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... |
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... |
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
... |
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_... |
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... |
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 |
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... |
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... |
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... |
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 |
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 ... |
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("... |
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 ... |
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... |
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.
... |
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... |
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,... |
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... |
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 : ... |
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... |
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... |
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... |
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 %... |
def plot_boolean(self,
on,
boolean_col,
plot_col=None,
boolean_label=None,
boolean_value_map={},
order=None,
ax=None,
alternative="two-sided",
... |
def plot_survival(self,
on,
how="os",
survival_units="Days",
strata=None,
ax=None,
ci_show=False,
with_condition_color="#B38600",
no_condition_c... |
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`... |
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) |
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... |
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_... |
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... |
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... |
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... |
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... |
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... |
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... |
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... |
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... |
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 ... |
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
... |
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(... |
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)
... |
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 `... |
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) |
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... |
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... |
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... |
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... |
def mann_whitney_plot(data,
condition,
distribution,
ax=None,
condition_value=None,
alternative="two-sided",
skip_plot=False,
**kwargs):
"""
Create a box plot... |
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... |
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... |
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
... |
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... |
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):
... |
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) |
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
----------... |
def _plot_kmf_single(df,
condition_col,
survival_col,
censor_col,
threshold,
title,
xlabel,
ylabel,
ax,
with_condition_color,
... |
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... |
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 |
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... |
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 ... |
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.
... |
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... |
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... |
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... |
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 ... |
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... |
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 |
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... |
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... |
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 ... |
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... |
def get_instance(cls, state):
""":rtype: UserStorageHandler"""
if cls.instance is None:
cls.instance = UserStorageHandler(state)
return cls.instance |
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()
... |
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... |
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... |
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... |
def safe_log_error(self, error: Exception, *info: str):
"""Log error failing silently on error"""
self.__do_safe(lambda: self.logger.error(error, *info)) |
def safe_log_info(self, *info: str):
"""Log info failing silently on error"""
self.__do_safe(lambda: self.logger.info(*info)) |
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]
... |
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... |
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... |
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 |
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`]
... |
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... |
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... |
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... |
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... |
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 |
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... |
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`]
... |
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... |
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 ... |
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... |
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`]
"""
... |
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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