_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 75 19.8k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q228100 | once | train | def once(func):
"""Runs a thing once and once only."""
lock = threading.Lock()
def new_func(*args, **kwargs):
if new_func.called:
return
with lock:
if new_func.called:
return
rv = func(*args, **kwargs)
new_func.called = True
... | python | {
"resource": ""
} |
q228101 | get_host | train | def get_host(request):
"""
A reimplementation of Django's get_host, without the
SuspiciousOperation check.
"""
# We try three options, in order of decreasing preference.
if settings.USE_X_FORWARDED_HOST and (
'HTTP_X_FORWARDED_HOST' in request.META):
host = request.META['HTTP... | python | {
"resource": ""
} |
q228102 | install_middleware | train | def install_middleware(middleware_name, lookup_names=None):
"""
Install specified middleware
"""
if lookup_names is None:
lookup_names = (middleware_name,)
# default settings.MIDDLEWARE is None
middleware_attr = 'MIDDLEWARE' if getattr(settings,
... | python | {
"resource": ""
} |
q228103 | _fit_and_score | train | def _fit_and_score(est, x, y, scorer, train_index, test_index, parameters, fit_params, predict_params):
"""Train survival model on given data and return its score on test data"""
X_train, y_train = _safe_split(est, x, y, train_index)
train_params = fit_params.copy()
# Training
est.set_params(**para... | python | {
"resource": ""
} |
q228104 | CoxnetSurvivalAnalysis._interpolate_coefficients | train | def _interpolate_coefficients(self, alpha):
"""Interpolate coefficients by calculating the weighted average of coefficient vectors corresponding to
neighbors of alpha in the list of alphas constructed during training."""
exact = False
coef_idx = None
for i, val in enumerate(self.... | python | {
"resource": ""
} |
q228105 | CoxnetSurvivalAnalysis.predict | train | def predict(self, X, alpha=None):
"""The linear predictor of the model.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Test data of which to calculate log-likelihood from
alpha : float, optional
Constant that multiplies the penalty... | python | {
"resource": ""
} |
q228106 | BaseEnsembleSelection._create_base_ensemble | train | def _create_base_ensemble(self, out, n_estimators, n_folds):
"""For each base estimator collect models trained on each fold"""
ensemble_scores = numpy.empty((n_estimators, n_folds))
base_ensemble = numpy.empty_like(ensemble_scores, dtype=numpy.object)
for model, fold, score, est in out:
... | python | {
"resource": ""
} |
q228107 | BaseEnsembleSelection._create_cv_ensemble | train | def _create_cv_ensemble(self, base_ensemble, idx_models_included, model_names=None):
"""For each selected base estimator, average models trained on each fold"""
fitted_models = numpy.empty(len(idx_models_included), dtype=numpy.object)
for i, idx in enumerate(idx_models_included):
mod... | python | {
"resource": ""
} |
q228108 | BaseEnsembleSelection._get_base_estimators | train | def _get_base_estimators(self, X):
"""Takes special care of estimators using custom kernel function
Parameters
----------
X : array, shape = (n_samples, n_features)
Samples to pre-compute kernel matrix from.
Returns
-------
base_estimators : list
... | python | {
"resource": ""
} |
q228109 | BaseEnsembleSelection._restore_base_estimators | train | def _restore_base_estimators(self, kernel_cache, out, X, cv):
"""Restore custom kernel functions of estimators for predictions"""
train_folds = {fold: train_index for fold, (train_index, _) in enumerate(cv)}
for idx, fold, _, est in out:
if idx in kernel_cache:
if no... | python | {
"resource": ""
} |
q228110 | BaseEnsembleSelection._fit_and_score_ensemble | train | def _fit_and_score_ensemble(self, X, y, cv, **fit_params):
"""Create a cross-validated model by training a model for each fold with the same model parameters"""
fit_params_steps = self._split_fit_params(fit_params)
folds = list(cv.split(X, y))
# Take care of custom kernel functions
... | python | {
"resource": ""
} |
q228111 | BaseEnsembleSelection.fit | train | def fit(self, X, y=None, **fit_params):
"""Fit ensemble of models
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Training data.
y : array-like, optional
Target data if base estimators are supervised.
Returns
------... | python | {
"resource": ""
} |
q228112 | writearff | train | def writearff(data, filename, relation_name=None, index=True):
"""Write ARFF file
Parameters
----------
data : :class:`pandas.DataFrame`
DataFrame containing data
filename : string or file-like object
Path to ARFF file or file-like object. In the latter case,
the handle is ... | python | {
"resource": ""
} |
q228113 | _write_header | train | def _write_header(data, fp, relation_name, index):
"""Write header containing attribute names and types"""
fp.write("@relation {0}\n\n".format(relation_name))
if index:
data = data.reset_index()
attribute_names = _sanitize_column_names(data)
for column, series in data.iteritems():
... | python | {
"resource": ""
} |
q228114 | _sanitize_column_names | train | def _sanitize_column_names(data):
"""Replace illegal characters with underscore"""
new_names = {}
for name in data.columns:
new_names[name] = _ILLEGAL_CHARACTER_PAT.sub("_", name)
return new_names | python | {
"resource": ""
} |
q228115 | _write_data | train | def _write_data(data, fp):
"""Write the data section"""
fp.write("@data\n")
def to_str(x):
if pandas.isnull(x):
return '?'
else:
return str(x)
data = data.applymap(to_str)
n_rows = data.shape[0]
for i in range(n_rows):
str_values = list(data.iloc... | python | {
"resource": ""
} |
q228116 | Stacking.fit | train | def fit(self, X, y=None, **fit_params):
"""Fit base estimators.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Training data.
y : array-like, optional
Target data if base estimators are supervised.
Returns
-------
... | python | {
"resource": ""
} |
q228117 | standardize | train | def standardize(table, with_std=True):
"""
Perform Z-Normalization on each numeric column of the given table.
Parameters
----------
table : pandas.DataFrame or numpy.ndarray
Data to standardize.
with_std : bool, optional, default: True
If ``False`` data is only centered and not... | python | {
"resource": ""
} |
q228118 | encode_categorical | train | def encode_categorical(table, columns=None, **kwargs):
"""
Encode categorical columns with `M` categories into `M-1` columns according
to the one-hot scheme.
Parameters
----------
table : pandas.DataFrame
Table with categorical columns to encode.
columns : list-like, optional, defa... | python | {
"resource": ""
} |
q228119 | categorical_to_numeric | train | def categorical_to_numeric(table):
"""Encode categorical columns to numeric by converting each category to
an integer value.
Parameters
----------
table : pandas.DataFrame
Table with categorical columns to encode.
Returns
-------
encoded : pandas.DataFrame
Table with ca... | python | {
"resource": ""
} |
q228120 | check_y_survival | train | def check_y_survival(y_or_event, *args, allow_all_censored=False):
"""Check that array correctly represents an outcome for survival analysis.
Parameters
----------
y_or_event : structured array with two fields, or boolean array
If a structured array, it must contain the binary event indicator
... | python | {
"resource": ""
} |
q228121 | check_arrays_survival | train | def check_arrays_survival(X, y, **kwargs):
"""Check that all arrays have consistent first dimensions.
Parameters
----------
X : array-like
Data matrix containing feature vectors.
y : structured array with two fields
A structured array containing the binary event indicator
a... | python | {
"resource": ""
} |
q228122 | Surv.from_arrays | train | def from_arrays(event, time, name_event=None, name_time=None):
"""Create structured array.
Parameters
----------
event : array-like
Event indicator. A boolean array or array with values 0/1.
time : array-like
Observed time.
name_event : str|None
... | python | {
"resource": ""
} |
q228123 | Surv.from_dataframe | train | def from_dataframe(event, time, data):
"""Create structured array from data frame.
Parameters
----------
event : object
Identifier of column containing event indicator.
time : object
Identifier of column containing time.
data : pandas.DataFrame
... | python | {
"resource": ""
} |
q228124 | CoxPH.update_terminal_regions | train | def update_terminal_regions(self, tree, X, y, residual, y_pred,
sample_weight, sample_mask,
learning_rate=1.0, k=0):
"""Least squares does not need to update terminal regions.
But it has to update the predictions.
"""
# upd... | python | {
"resource": ""
} |
q228125 | build_from_c_and_cpp_files | train | def build_from_c_and_cpp_files(extensions):
"""Modify the extensions to build from the .c and .cpp files.
This is useful for releases, this way cython is not required to
run python setup.py install.
"""
for extension in extensions:
sources = []
for sfile in extension.sources:
... | python | {
"resource": ""
} |
q228126 | SurvivalCounter._count_values | train | def _count_values(self):
"""Return dict mapping relevance level to sample index"""
indices = {yi: [i] for i, yi in enumerate(self.y) if self.status[i]}
return indices | python | {
"resource": ""
} |
q228127 | BaseSurvivalSVM._create_optimizer | train | def _create_optimizer(self, X, y, status):
"""Samples are ordered by relevance"""
if self.optimizer is None:
self.optimizer = 'avltree'
times, ranks = y
if self.optimizer == 'simple':
optimizer = SimpleOptimizer(X, status, self.alpha, self.rank_ratio, timeit=sel... | python | {
"resource": ""
} |
q228128 | BaseSurvivalSVM._argsort_and_resolve_ties | train | def _argsort_and_resolve_ties(time, random_state):
"""Like numpy.argsort, but resolves ties uniformly at random"""
n_samples = len(time)
order = numpy.argsort(time, kind="mergesort")
i = 0
while i < n_samples - 1:
inext = i + 1
while inext < n_samples and... | python | {
"resource": ""
} |
q228129 | IPCRidge.fit | train | def fit(self, X, y):
"""Build an accelerated failure time model.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data matrix.
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicator
... | python | {
"resource": ""
} |
q228130 | BreslowEstimator.fit | train | def fit(self, linear_predictor, event, time):
"""Compute baseline cumulative hazard function.
Parameters
----------
linear_predictor : array-like, shape = (n_samples,)
Linear predictor of risk: `X @ coef`.
event : array-like, shape = (n_samples,)
Contain... | python | {
"resource": ""
} |
q228131 | CoxPHOptimizer.nlog_likelihood | train | def nlog_likelihood(self, w):
"""Compute negative partial log-likelihood
Parameters
----------
w : array, shape = (n_features,)
Estimate of coefficients
Returns
-------
loss : float
Average negative partial log-likelihood
"""
... | python | {
"resource": ""
} |
q228132 | CoxPHOptimizer.update | train | def update(self, w, offset=0):
"""Compute gradient and Hessian matrix with respect to `w`."""
time = self.time
x = self.x
exp_xw = numpy.exp(offset + numpy.dot(x, w))
n_samples, n_features = x.shape
gradient = numpy.zeros((1, n_features), dtype=float)
hessian = n... | python | {
"resource": ""
} |
q228133 | CoxPHSurvivalAnalysis.fit | train | def fit(self, X, y):
"""Minimize negative partial log-likelihood for provided data.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data matrix
y : structured array, shape = (n_samples,)
A structured array containing the binary even... | python | {
"resource": ""
} |
q228134 | _compute_counts | train | def _compute_counts(event, time, order=None):
"""Count right censored and uncensored samples at each unique time point.
Parameters
----------
event : array
Boolean event indicator.
time : array
Survival time or time of censoring.
order : array or None
Indices to order ... | python | {
"resource": ""
} |
q228135 | _compute_counts_truncated | train | def _compute_counts_truncated(event, time_enter, time_exit):
"""Compute counts for left truncated and right censored survival data.
Parameters
----------
event : array
Boolean event indicator.
time_start : array
Time when a subject entered the study.
time_exit : array
... | python | {
"resource": ""
} |
q228136 | kaplan_meier_estimator | train | def kaplan_meier_estimator(event, time_exit, time_enter=None, time_min=None):
"""Kaplan-Meier estimator of survival function.
Parameters
----------
event : array-like, shape = (n_samples,)
Contains binary event indicators.
time_exit : array-like, shape = (n_samples,)
Contains event... | python | {
"resource": ""
} |
q228137 | nelson_aalen_estimator | train | def nelson_aalen_estimator(event, time):
"""Nelson-Aalen estimator of cumulative hazard function.
Parameters
----------
event : array-like, shape = (n_samples,)
Contains binary event indicators.
time : array-like, shape = (n_samples,)
Contains event/censoring times.
Returns
... | python | {
"resource": ""
} |
q228138 | ipc_weights | train | def ipc_weights(event, time):
"""Compute inverse probability of censoring weights
Parameters
----------
event : array, shape = (n_samples,)
Boolean event indicator.
time : array, shape = (n_samples,)
Time when a subject experienced an event or was censored.
Returns
-------... | python | {
"resource": ""
} |
q228139 | SurvivalFunctionEstimator.fit | train | def fit(self, y):
"""Estimate survival distribution from training data.
Parameters
----------
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicator
as first field, and time of event or time of censoring as
... | python | {
"resource": ""
} |
q228140 | SurvivalFunctionEstimator.predict_proba | train | def predict_proba(self, time):
"""Return probability of an event after given time point.
:math:`\\hat{S}(t) = P(T > t)`
Parameters
----------
time : array, shape = (n_samples,)
Time to estimate probability at.
Returns
-------
prob : array, s... | python | {
"resource": ""
} |
q228141 | CensoringDistributionEstimator.fit | train | def fit(self, y):
"""Estimate censoring distribution from training data.
Parameters
----------
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicator
as first field, and time of event or time of censoring as
... | python | {
"resource": ""
} |
q228142 | CensoringDistributionEstimator.predict_ipcw | train | def predict_ipcw(self, y):
"""Return inverse probability of censoring weights at given time points.
:math:`\\omega_i = \\delta_i / \\hat{G}(y_i)`
Parameters
----------
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicato... | python | {
"resource": ""
} |
q228143 | concordance_index_censored | train | def concordance_index_censored(event_indicator, event_time, estimate, tied_tol=1e-8):
"""Concordance index for right-censored data
The concordance index is defined as the proportion of all comparable pairs
in which the predictions and outcomes are concordant.
Samples are comparable if for at least one... | python | {
"resource": ""
} |
q228144 | concordance_index_ipcw | train | def concordance_index_ipcw(survival_train, survival_test, estimate, tau=None, tied_tol=1e-8):
"""Concordance index for right-censored data based on inverse probability of censoring weights.
This is an alternative to the estimator in :func:`concordance_index_censored`
that does not depend on the distributio... | python | {
"resource": ""
} |
q228145 | _nominal_kernel | train | def _nominal_kernel(x, y, out):
"""Number of features that match exactly"""
for i in range(x.shape[0]):
for j in range(y.shape[0]):
out[i, j] += (x[i, :] == y[j, :]).sum()
return out | python | {
"resource": ""
} |
q228146 | _get_continuous_and_ordinal_array | train | def _get_continuous_and_ordinal_array(x):
"""Convert array from continuous and ordered categorical columns"""
nominal_columns = x.select_dtypes(include=['object', 'category']).columns
ordinal_columns = pandas.Index([v for v in nominal_columns if x[v].cat.ordered])
continuous_columns = x.select_dtypes(in... | python | {
"resource": ""
} |
q228147 | clinical_kernel | train | def clinical_kernel(x, y=None):
"""Computes clinical kernel
The clinical kernel distinguishes between continuous
ordinal,and nominal variables.
Parameters
----------
x : pandas.DataFrame, shape = (n_samples_x, n_features)
Training data
y : pandas.DataFrame, shape = (n_samples_y, n... | python | {
"resource": ""
} |
q228148 | ClinicalKernelTransform._prepare_by_column_dtype | train | def _prepare_by_column_dtype(self, X):
"""Get distance functions for each column's dtype"""
if not isinstance(X, pandas.DataFrame):
raise TypeError('X must be a pandas DataFrame')
numeric_columns = []
nominal_columns = []
numeric_ranges = []
fit_data = numpy... | python | {
"resource": ""
} |
q228149 | ClinicalKernelTransform.fit | train | def fit(self, X, y=None, **kwargs):
"""Determine transformation parameters from data in X.
Subsequent calls to `transform(Y)` compute the pairwise
distance to `X`.
Parameters of the clinical kernel are only updated
if `fit_once` is `False`, otherwise you have to
explicit... | python | {
"resource": ""
} |
q228150 | ClinicalKernelTransform.transform | train | def transform(self, Y):
r"""Compute all pairwise distances between `self.X_fit_` and `Y`.
Parameters
----------
y : array-like, shape = (n_samples_y, n_features)
Returns
-------
kernel : ndarray, shape = (n_samples_y, n_samples_X_fit\_)
Kernel matrix... | python | {
"resource": ""
} |
q228151 | _fit_stage_componentwise | train | def _fit_stage_componentwise(X, residuals, sample_weight, **fit_params):
"""Fit component-wise weighted least squares model"""
n_features = X.shape[1]
base_learners = []
error = numpy.empty(n_features)
for component in range(n_features):
learner = ComponentwiseLeastSquares(component).fit(X,... | python | {
"resource": ""
} |
q228152 | ComponentwiseGradientBoostingSurvivalAnalysis.coef_ | train | def coef_(self):
"""Return the aggregated coefficients.
Returns
-------
coef_ : ndarray, shape = (n_features + 1,)
Coefficients of features. The first element denotes the intercept.
"""
coef = numpy.zeros(self.n_features_ + 1, dtype=float)
for estima... | python | {
"resource": ""
} |
q228153 | GradientBoostingSurvivalAnalysis._fit_stage | train | def _fit_stage(self, i, X, y, y_pred, sample_weight, sample_mask,
random_state, scale, X_idx_sorted, X_csc=None, X_csr=None):
"""Fit another stage of ``n_classes_`` trees to the boosting model. """
assert sample_mask.dtype == numpy.bool
loss = self.loss_
# whether to... | python | {
"resource": ""
} |
q228154 | GradientBoostingSurvivalAnalysis._fit_stages | train | def _fit_stages(self, X, y, y_pred, sample_weight, random_state,
begin_at_stage=0, monitor=None, X_idx_sorted=None):
"""Iteratively fits the stages.
For each stage it computes the progress (OOB, train score)
and delegates to ``_fit_stage``.
Returns the number of stag... | python | {
"resource": ""
} |
q228155 | GradientBoostingSurvivalAnalysis.fit | train | def fit(self, X, y, sample_weight=None, monitor=None):
"""Fit the gradient boosting model.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data matrix
y : structured array, shape = (n_samples,)
A structured array containing the bina... | python | {
"resource": ""
} |
q228156 | GradientBoostingSurvivalAnalysis.staged_predict | train | def staged_predict(self, X):
"""Predict hazard at each stage for X.
This method allows monitoring (i.e. determine error on testing set)
after each stage.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
The input samples.
Return... | python | {
"resource": ""
} |
q228157 | MinlipSurvivalAnalysis.fit | train | def fit(self, X, y):
"""Build a MINLIP survival model from training data.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data matrix.
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicat... | python | {
"resource": ""
} |
q228158 | MinlipSurvivalAnalysis.predict | train | def predict(self, X):
"""Predict risk score of experiencing an event.
Higher scores indicate shorter survival (high risk),
lower scores longer survival (low risk).
Parameters
----------
X : array-like, shape = (n_samples, n_features)
The input samples.
... | python | {
"resource": ""
} |
q228159 | get_x_y | train | def get_x_y(data_frame, attr_labels, pos_label=None, survival=True):
"""Split data frame into features and labels.
Parameters
----------
data_frame : pandas.DataFrame, shape = (n_samples, n_columns)
A data frame.
attr_labels : sequence of str or None
A list of one or more columns t... | python | {
"resource": ""
} |
q228160 | load_arff_files_standardized | train | def load_arff_files_standardized(path_training, attr_labels, pos_label=None, path_testing=None, survival=True,
standardize_numeric=True, to_numeric=True):
"""Load dataset in ARFF format.
Parameters
----------
path_training : str
Path to ARFF file containing data... | python | {
"resource": ""
} |
q228161 | load_aids | train | def load_aids(endpoint="aids"):
"""Load and return the AIDS Clinical Trial dataset
The dataset has 1,151 samples and 11 features.
The dataset has 2 endpoints:
1. AIDS defining event, which occurred for 96 patients (8.3%)
2. Death, which occurred for 26 patients (2.3%)
Parameters
---------... | python | {
"resource": ""
} |
q228162 | _api_scrape | train | def _api_scrape(json_inp, ndx):
"""
Internal method to streamline the getting of data from the json
Args:
json_inp (json): json input from our caller
ndx (int): index where the data is located in the api
Returns:
If pandas is present:
DataFrame (pandas.DataFrame): d... | python | {
"resource": ""
} |
q228163 | get_player | train | def get_player(first_name,
last_name=None,
season=constants.CURRENT_SEASON,
only_current=0,
just_id=True):
"""
Calls our PlayerList class to get a full list of players and then returns
just an id if specified or the full row of player information
... | python | {
"resource": ""
} |
q228164 | BasePokerPlayer.respond_to_ask | train | def respond_to_ask(self, message):
"""Called from Dealer when ask message received from RoundManager"""
valid_actions, hole_card, round_state = self.__parse_ask_message(message)
return self.declare_action(valid_actions, hole_card, round_state) | python | {
"resource": ""
} |
q228165 | BasePokerPlayer.receive_notification | train | def receive_notification(self, message):
"""Called from Dealer when notification received from RoundManager"""
msg_type = message["message_type"]
if msg_type == "game_start_message":
info = self.__parse_game_start_message(message)
self.receive_game_start_message(info)
elif msg_type == "rou... | python | {
"resource": ""
} |
q228166 | result_continuation | train | async def result_continuation(task):
"""A preliminary result processor we'll chain on to the original task
This will get executed wherever the source task was executed, in this
case one of the threads in the ThreadPoolExecutor"""
await asyncio.sleep(0.1)
num, res = task.result()
return num... | python | {
"resource": ""
} |
q228167 | result_processor | train | async def result_processor(tasks):
"""An async result aggregator that combines all the results
This gets executed in unsync.loop and unsync.thread"""
output = {}
for task in tasks:
num, res = await task
output[num] = res
return output | python | {
"resource": ""
} |
q228168 | read_union | train | def read_union(fo, writer_schema, reader_schema=None):
"""A union is encoded by first writing a long value indicating the
zero-based position within the union of the schema of its value.
The value is then encoded per the indicated schema within the union.
"""
# schema resolution
index = read_lo... | python | {
"resource": ""
} |
q228169 | read_data | train | def read_data(fo, writer_schema, reader_schema=None):
"""Read data from file object according to schema."""
record_type = extract_record_type(writer_schema)
logical_type = extract_logical_type(writer_schema)
if reader_schema and record_type in AVRO_TYPES:
# If the schemas are the same, set the... | python | {
"resource": ""
} |
q228170 | _iter_avro_records | train | def _iter_avro_records(fo, header, codec, writer_schema, reader_schema):
"""Return iterator over avro records."""
sync_marker = header['sync']
read_block = BLOCK_READERS.get(codec)
if not read_block:
raise ValueError('Unrecognized codec: %r' % codec)
block_count = 0
while True:
... | python | {
"resource": ""
} |
q228171 | _iter_avro_blocks | train | def _iter_avro_blocks(fo, header, codec, writer_schema, reader_schema):
"""Return iterator over avro blocks."""
sync_marker = header['sync']
read_block = BLOCK_READERS.get(codec)
if not read_block:
raise ValueError('Unrecognized codec: %r' % codec)
while True:
offset = fo.tell()
... | python | {
"resource": ""
} |
q228172 | prepare_timestamp_millis | train | def prepare_timestamp_millis(data, schema):
"""Converts datetime.datetime object to int timestamp with milliseconds
"""
if isinstance(data, datetime.datetime):
if data.tzinfo is not None:
delta = (data - epoch)
return int(delta.total_seconds() * MLS_PER_SECOND)
t = in... | python | {
"resource": ""
} |
q228173 | prepare_timestamp_micros | train | def prepare_timestamp_micros(data, schema):
"""Converts datetime.datetime to int timestamp with microseconds"""
if isinstance(data, datetime.datetime):
if data.tzinfo is not None:
delta = (data - epoch)
return int(delta.total_seconds() * MCS_PER_SECOND)
t = int(time.mktim... | python | {
"resource": ""
} |
q228174 | prepare_date | train | def prepare_date(data, schema):
"""Converts datetime.date to int timestamp"""
if isinstance(data, datetime.date):
return data.toordinal() - DAYS_SHIFT
else:
return data | python | {
"resource": ""
} |
q228175 | prepare_uuid | train | def prepare_uuid(data, schema):
"""Converts uuid.UUID to
string formatted UUID xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
"""
if isinstance(data, uuid.UUID):
return str(data)
else:
return data | python | {
"resource": ""
} |
q228176 | prepare_time_millis | train | def prepare_time_millis(data, schema):
"""Convert datetime.time to int timestamp with milliseconds"""
if isinstance(data, datetime.time):
return int(
data.hour * MLS_PER_HOUR + data.minute * MLS_PER_MINUTE
+ data.second * MLS_PER_SECOND + int(data.microsecond / 1000))
else:
... | python | {
"resource": ""
} |
q228177 | prepare_time_micros | train | def prepare_time_micros(data, schema):
"""Convert datetime.time to int timestamp with microseconds"""
if isinstance(data, datetime.time):
return long(data.hour * MCS_PER_HOUR + data.minute * MCS_PER_MINUTE
+ data.second * MCS_PER_SECOND + data.microsecond)
else:
return da... | python | {
"resource": ""
} |
q228178 | prepare_bytes_decimal | train | def prepare_bytes_decimal(data, schema):
"""Convert decimal.Decimal to bytes"""
if not isinstance(data, decimal.Decimal):
return data
scale = schema.get('scale', 0)
# based on https://github.com/apache/avro/pull/82/
sign, digits, exp = data.as_tuple()
if -exp > scale:
raise Va... | python | {
"resource": ""
} |
q228179 | prepare_fixed_decimal | train | def prepare_fixed_decimal(data, schema):
"""Converts decimal.Decimal to fixed length bytes array"""
if not isinstance(data, decimal.Decimal):
return data
scale = schema.get('scale', 0)
size = schema['size']
# based on https://github.com/apache/avro/pull/82/
sign, digits, exp = data.as_... | python | {
"resource": ""
} |
q228180 | write_crc32 | train | def write_crc32(fo, bytes):
"""A 4-byte, big-endian CRC32 checksum"""
data = crc32(bytes) & 0xFFFFFFFF
fo.write(pack('>I', data)) | python | {
"resource": ""
} |
q228181 | write_union | train | def write_union(fo, datum, schema):
"""A union is encoded by first writing a long value indicating the
zero-based position within the union of the schema of its value. The value
is then encoded per the indicated schema within the union."""
if isinstance(datum, tuple):
(name, datum) = datum
... | python | {
"resource": ""
} |
q228182 | write_data | train | def write_data(fo, datum, schema):
"""Write a datum of data to output stream.
Paramaters
----------
fo: file-like
Output file
datum: object
Data to write
schema: dict
Schemda to use
"""
record_type = extract_record_type(schema)
logical_type = extract_logical... | python | {
"resource": ""
} |
q228183 | null_write_block | train | def null_write_block(fo, block_bytes):
"""Write block in "null" codec."""
write_long(fo, len(block_bytes))
fo.write(block_bytes) | python | {
"resource": ""
} |
q228184 | deflate_write_block | train | def deflate_write_block(fo, block_bytes):
"""Write block in "deflate" codec."""
# The first two characters and last character are zlib
# wrappers around deflate data.
data = compress(block_bytes)[2:-1]
write_long(fo, len(data))
fo.write(data) | python | {
"resource": ""
} |
q228185 | schemaless_writer | train | def schemaless_writer(fo, schema, record):
"""Write a single record without the schema or header information
Parameters
----------
fo: file-like
Output file
schema: dict
Schema
record: dict
Record to write
Example::
parsed_schema = fastavro.parse_schema(sc... | python | {
"resource": ""
} |
q228186 | validate_int | train | def validate_int(datum, **kwargs):
"""
Check that the data value is a non floating
point number with size less that Int32.
Also support for logicalType timestamp validation with datetime.
Int32 = -2147483648<=datum<=2147483647
conditional python types
(int, long, numbers.Integral,
date... | python | {
"resource": ""
} |
q228187 | validate_float | train | def validate_float(datum, **kwargs):
"""
Check that the data value is a floating
point number or double precision.
conditional python types
(int, long, float, numbers.Real)
Parameters
----------
datum: Any
Data being validated
kwargs: Any
Unused kwargs
"""
r... | python | {
"resource": ""
} |
q228188 | validate_record | train | def validate_record(datum, schema, parent_ns=None, raise_errors=True):
"""
Check that the data is a Mapping type with all schema defined fields
validated as True.
Parameters
----------
datum: Any
Data being validated
schema: dict
Schema
parent_ns: str
parent name... | python | {
"resource": ""
} |
q228189 | validate_union | train | def validate_union(datum, schema, parent_ns=None, raise_errors=True):
"""
Check that the data is a list type with possible options to
validate as True.
Parameters
----------
datum: Any
Data being validated
schema: dict
Schema
parent_ns: str
parent namespace
r... | python | {
"resource": ""
} |
q228190 | validate_many | train | def validate_many(records, schema, raise_errors=True):
"""
Validate a list of data!
Parameters
----------
records: iterable
List of records to validate
schema: dict
Schema
raise_errors: bool, optional
If true, errors are raised for invalid data. If false, a simple
... | python | {
"resource": ""
} |
q228191 | parse_schema | train | def parse_schema(schema, _write_hint=True, _force=False):
"""Returns a parsed avro schema
It is not necessary to call parse_schema but doing so and saving the parsed
schema for use later will make future operations faster as the schema will
not need to be reparsed.
Parameters
----------
sc... | python | {
"resource": ""
} |
q228192 | load_schema | train | def load_schema(schema_path):
'''
Returns a schema loaded from the file at `schema_path`.
Will recursively load referenced schemas assuming they can be found in
files in the same directory and named with the convention
`<type_name>.avsc`.
'''
with open(schema_path) as fd:
schema = j... | python | {
"resource": ""
} |
q228193 | ToolTip.showtip | train | def showtip(self, text):
"Display text in tooltip window"
self.text = text
if self.tipwindow or not self.text:
return
x, y, cx, cy = self.widget.bbox("insert")
x = x + self.widget.winfo_rootx() + 27
y = y + cy + self.widget.winfo_rooty() +27
self.tipwi... | python | {
"resource": ""
} |
q228194 | TkApplication.run | train | def run(self):
"""Ejecute the main loop."""
self.toplevel.protocol("WM_DELETE_WINDOW", self.__on_window_close)
self.toplevel.mainloop() | python | {
"resource": ""
} |
q228195 | MyApplication.create_regpoly | train | def create_regpoly(self, x0, y0, x1, y1, sides=0, start=90, extent=360, **kw):
"""Create a regular polygon"""
coords = self.__regpoly_coords(x0, y0, x1, y1, sides, start, extent)
return self.canvas.create_polygon(*coords, **kw) | python | {
"resource": ""
} |
q228196 | MyApplication.__regpoly_coords | train | def __regpoly_coords(self, x0, y0, x1, y1, sides, start, extent):
"""Create the coordinates of the regular polygon specified"""
coords = []
if extent == 0:
return coords
xm = (x0 + x1) / 2.
ym = (y0 + y1) / 2.
rx = xm - x0
ry = ym - y0
n = s... | python | {
"resource": ""
} |
q228197 | Builder.get_image | train | def get_image(self, path):
"""Return tk image corresponding to name which is taken form path."""
image = ''
name = os.path.basename(path)
if not StockImage.is_registered(name):
ipath = self.__find_image(path)
if ipath is not None:
StockImage.regist... | python | {
"resource": ""
} |
q228198 | Builder.import_variables | train | def import_variables(self, container, varnames=None):
"""Helper method to avoid call get_variable for every variable."""
if varnames is None:
for keyword in self.tkvariables:
setattr(container, keyword, self.tkvariables[keyword])
else:
for keyword in varna... | python | {
"resource": ""
} |
q228199 | Builder.create_variable | train | def create_variable(self, varname, vtype=None):
"""Create a tk variable.
If the variable was created previously return that instance.
"""
var_types = ('string', 'int', 'boolean', 'double')
vname = varname
var = None
type_from_name = 'string' # default type
... | python | {
"resource": ""
} |
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