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246,600 | sebp/scikit-survival | sksurv/ensemble/survival_loss.py | CoxPH.update_terminal_regions | 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 | def update_terminal_regions(self, tree, X, y, residual, y_pred,
sample_weight, sample_mask,
learning_rate=1.0, k=0):
# update predictions
y_pred[:, k] += learning_rate * tree.predict(X).ravel() | [
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246,601 | sebp/scikit-survival | sksurv/setup.py | build_from_c_and_cpp_files | 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 | def build_from_c_and_cpp_files(extensions):
for extension in extensions:
sources = []
for sfile in extension.sources:
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if ext in ('.pyx', '.py'):
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246,602 | sebp/scikit-survival | sksurv/svm/survival_svm.py | SurvivalCounter._count_values | 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 | def _count_values(self):
indices = {yi: [i] for i, yi in enumerate(self.y) if self.status[i]}
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246,603 | sebp/scikit-survival | sksurv/svm/survival_svm.py | BaseSurvivalSVM._create_optimizer | 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':
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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=self.timeit)
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246,604 | sebp/scikit-survival | sksurv/svm/survival_svm.py | BaseSurvivalSVM._argsort_and_resolve_ties | 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
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inext = i + 1
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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 time[order[i]] == time[order[inext]]:
inext += 1
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246,605 | sebp/scikit-survival | sksurv/linear_model/aft.py | IPCRidge.fit | 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 | def fit(self, X, y):
X, event, time = check_arrays_survival(X, y)
weights = ipc_weights(event, time)
super().fit(X, numpy.log(time), sample_weight=weights)
return self | [
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246,606 | sebp/scikit-survival | sksurv/linear_model/coxph.py | BreslowEstimator.fit | 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 | def fit(self, linear_predictor, event, time):
risk_score = numpy.exp(linear_predictor)
order = numpy.argsort(time, kind="mergesort")
risk_score = risk_score[order]
uniq_times, n_events, n_at_risk = _compute_counts(event, time, order)
divisor = numpy.empty(n_at_risk.shape, dtype=... | [
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246,607 | sebp/scikit-survival | sksurv/linear_model/coxph.py | CoxPHOptimizer.nlog_likelihood | 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 | def nlog_likelihood(self, w):
time = self.time
n_samples = self.x.shape[0]
xw = numpy.dot(self.x, w)
loss = 0
risk_set = 0
k = 0
for i in range(n_samples):
ti = time[i]
while k < n_samples and ti == time[k]:
risk_set += num... | [
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246,608 | sebp/scikit-survival | sksurv/linear_model/coxph.py | CoxPHOptimizer.update | 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 | def update(self, w, offset=0):
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)
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246,609 | sebp/scikit-survival | sksurv/linear_model/coxph.py | CoxPHSurvivalAnalysis.fit | 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 | def fit(self, X, y):
X, event, time = check_arrays_survival(X, y)
if self.alpha < 0:
raise ValueError("alpha must be positive, but was %r" % self.alpha)
optimizer = CoxPHOptimizer(X, event, time, self.alpha)
verbose_reporter = VerboseReporter(self.verbose)
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246,610 | sebp/scikit-survival | sksurv/nonparametric.py | _compute_counts | 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 | def _compute_counts(event, time, order=None):
n_samples = event.shape[0]
if order is None:
order = numpy.argsort(time, kind="mergesort")
uniq_times = numpy.empty(n_samples, dtype=time.dtype)
uniq_events = numpy.empty(n_samples, dtype=numpy.int_)
uniq_counts = numpy.empty(n_samples, dtype=n... | [
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246,611 | sebp/scikit-survival | sksurv/nonparametric.py | _compute_counts_truncated | 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 | def _compute_counts_truncated(event, time_enter, time_exit):
if (time_enter > time_exit).any():
raise ValueError("exit time must be larger start time for all samples")
n_samples = event.shape[0]
uniq_times = numpy.sort(numpy.unique(numpy.concatenate((time_enter, time_exit))), kind="mergesort")
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246,612 | sebp/scikit-survival | sksurv/nonparametric.py | kaplan_meier_estimator | 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 | def kaplan_meier_estimator(event, time_exit, time_enter=None, time_min=None):
event, time_enter, time_exit = check_y_survival(event, time_enter, time_exit, allow_all_censored=True)
check_consistent_length(event, time_enter, time_exit)
if time_enter is None:
uniq_times, n_events, n_at_risk = _comput... | [
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246,613 | sebp/scikit-survival | sksurv/nonparametric.py | nelson_aalen_estimator | 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 | def nelson_aalen_estimator(event, time):
event, time = check_y_survival(event, time)
check_consistent_length(event, time)
uniq_times, n_events, n_at_risk = _compute_counts(event, time)
y = numpy.cumsum(n_events / n_at_risk)
return uniq_times, y | [
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246,614 | sebp/scikit-survival | sksurv/nonparametric.py | ipc_weights | 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 | def ipc_weights(event, time):
if event.all():
return numpy.ones(time.shape[0])
unique_time, p = kaplan_meier_estimator(~event, time)
idx = numpy.searchsorted(unique_time, time[event])
Ghat = p[idx]
assert (Ghat > 0).all()
weights = numpy.zeros(time.shape[0])
weights[event] = 1.0 ... | [
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246,615 | sebp/scikit-survival | sksurv/nonparametric.py | SurvivalFunctionEstimator.fit | 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 | def fit(self, y):
event, time = check_y_survival(y, allow_all_censored=True)
unique_time, prob = kaplan_meier_estimator(event, time)
self.unique_time_ = numpy.concatenate(([-numpy.infty], unique_time))
self.prob_ = numpy.concatenate(([1.], prob))
return self | [
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246,616 | sebp/scikit-survival | sksurv/nonparametric.py | SurvivalFunctionEstimator.predict_proba | 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 | def predict_proba(self, time):
check_is_fitted(self, "unique_time_")
time = check_array(time, ensure_2d=False)
# K-M is undefined if estimate at last time point is non-zero
extends = time > self.unique_time_[-1]
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246,617 | sebp/scikit-survival | sksurv/nonparametric.py | CensoringDistributionEstimator.fit | 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 | def fit(self, y):
event, time = check_y_survival(y)
if event.all():
self.unique_time_ = numpy.unique(time)
self.prob_ = numpy.ones(self.unique_time_.shape[0])
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246,618 | sebp/scikit-survival | sksurv/nonparametric.py | CensoringDistributionEstimator.predict_ipcw | 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,)
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event, time = check_y_survival(y)
Ghat = self.predict_proba(time[event])
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raise ValueError("censoring survival function is zero at one or more time points")
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246,619 | sebp/scikit-survival | sksurv/metrics.py | concordance_index_censored | 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 | def concordance_index_censored(event_indicator, event_time, estimate, tied_tol=1e-8):
event_indicator, event_time, estimate = _check_inputs(
event_indicator, event_time, estimate)
w = numpy.ones_like(estimate)
return _estimate_concordance_index(event_indicator, event_time, estimate, w, tied_tol) | [
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246,620 | sebp/scikit-survival | sksurv/metrics.py | concordance_index_ipcw | 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`
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test_event, test_time = check_y_survival(survival_test)
if tau is not None:
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246,621 | sebp/scikit-survival | sksurv/kernels/clinical.py | _nominal_kernel | 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 | def _nominal_kernel(x, y, out):
for i in range(x.shape[0]):
for j in range(y.shape[0]):
out[i, j] += (x[i, :] == y[j, :]).sum()
return out | [
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246,622 | sebp/scikit-survival | sksurv/kernels/clinical.py | _get_continuous_and_ordinal_array | 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])
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nominal_columns = x.select_dtypes(include=['object', 'category']).columns
ordinal_columns = pandas.Index([v for v in nominal_columns if x[v].cat.ordered])
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246,623 | sebp/scikit-survival | sksurv/kernels/clinical.py | clinical_kernel | 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 | def clinical_kernel(x, y=None):
if y is not None:
if x.shape[1] != y.shape[1]:
raise ValueError('x and y have different number of features')
if not x.columns.equals(y.columns):
raise ValueError('columns do not match')
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y = x
mat = numpy.zeros((x.shape[0]... | [
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246,624 | sebp/scikit-survival | sksurv/kernels/clinical.py | ClinicalKernelTransform._prepare_by_column_dtype | 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 | def _prepare_by_column_dtype(self, X):
if not isinstance(X, pandas.DataFrame):
raise TypeError('X must be a pandas DataFrame')
numeric_columns = []
nominal_columns = []
numeric_ranges = []
fit_data = numpy.empty_like(X)
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246,625 | sebp/scikit-survival | sksurv/kernels/clinical.py | ClinicalKernelTransform.fit | 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 | def fit(self, X, y=None, **kwargs):
if X.ndim != 2:
raise ValueError("expected 2d array, but got %d" % X.ndim)
if self.fit_once:
self.X_fit_ = X
else:
self._prepare_by_column_dtype(X)
return self | [
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246,626 | sebp/scikit-survival | sksurv/kernels/clinical.py | ClinicalKernelTransform.transform | 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\_)
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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\_)
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246,627 | sebp/scikit-survival | sksurv/ensemble/boosting.py | _fit_stage_componentwise | 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 | def _fit_stage_componentwise(X, residuals, sample_weight, **fit_params):
n_features = X.shape[1]
base_learners = []
error = numpy.empty(n_features)
for component in range(n_features):
learner = ComponentwiseLeastSquares(component).fit(X, residuals, sample_weight)
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246,628 | sebp/scikit-survival | sksurv/ensemble/boosting.py | ComponentwiseGradientBoostingSurvivalAnalysis.coef_ | 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 | def coef_(self):
coef = numpy.zeros(self.n_features_ + 1, dtype=float)
for estimator in self.estimators_:
coef[estimator.component] += self.learning_rate * estimator.coef_
return coef | [
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246,629 | sebp/scikit-survival | sksurv/ensemble/boosting.py | GradientBoostingSurvivalAnalysis._fit_stage | def _fit_stage(self, i, X, y, y_pred, sample_weight, sample_mask,
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246,630 | sebp/scikit-survival | sksurv/ensemble/boosting.py | GradientBoostingSurvivalAnalysis._fit_stages | def _fit_stages(self, X, y, y_pred, sample_weight, random_state,
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begin_at_stage=0, monitor=None, X_idx_sorted=None):
n_samples = X.shape[0]
do_oob = self.subsample < 1.0
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246,631 | sebp/scikit-survival | sksurv/ensemble/boosting.py | GradientBoostingSurvivalAnalysis.fit | 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,)
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random_state = check_random_state(self.random_state)
X, event, time = check_arrays_survival(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
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246,632 | sebp/scikit-survival | sksurv/ensemble/boosting.py | GradientBoostingSurvivalAnalysis.staged_predict | 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 | def staged_predict(self, X):
check_is_fitted(self, 'estimators_')
# if dropout wasn't used during training, proceed as usual,
# otherwise consider scaling factor of individual trees
if not hasattr(self, "scale_"):
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246,633 | sebp/scikit-survival | sksurv/svm/minlip.py | MinlipSurvivalAnalysis.fit | 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 | def fit(self, X, y):
X, event, time = check_arrays_survival(X, y)
self._fit(X, event, time)
return self | [
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246,634 | sebp/scikit-survival | sksurv/svm/minlip.py | MinlipSurvivalAnalysis.predict | 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 | def predict(self, X):
K = self._get_kernel(X, self.X_fit_)
pred = -numpy.dot(self.coef_, K.T)
return pred.ravel() | [
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246,635 | sebp/scikit-survival | sksurv/datasets/base.py | get_x_y | 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 | def get_x_y(data_frame, attr_labels, pos_label=None, survival=True):
if survival:
if len(attr_labels) != 2:
raise ValueError("expected sequence of length two for attr_labels, but got %d" % len(attr_labels))
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246,636 | sebp/scikit-survival | sksurv/datasets/base.py | load_arff_files_standardized | 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 | def load_arff_files_standardized(path_training, attr_labels, pos_label=None, path_testing=None, survival=True,
standardize_numeric=True, to_numeric=True):
dataset = loadarff(path_training)
if "index" in dataset.columns:
dataset.index = dataset["index"].astype(object)
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246,637 | sebp/scikit-survival | sksurv/datasets/base.py | load_aids | 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 | def load_aids(endpoint="aids"):
labels_aids = ['censor', 'time']
labels_death = ['censor_d', 'time_d']
if endpoint == "aids":
attr_labels = labels_aids
drop_columns = labels_death
elif endpoint == "death":
attr_labels = labels_death
drop_columns = labels_aids
else:
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246,638 | seemethere/nba_py | nba_py/__init__.py | _api_scrape | 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 | def _api_scrape(json_inp, ndx):
try:
headers = json_inp['resultSets'][ndx]['headers']
values = json_inp['resultSets'][ndx]['rowSet']
except KeyError:
# This is so ugly but this is what you get when your data comes out
# in not a standard format
try:
headers = ... | [
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246,639 | seemethere/nba_py | nba_py/player.py | get_player | 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 | def get_player(first_name,
last_name=None,
season=constants.CURRENT_SEASON,
only_current=0,
just_id=True):
if last_name is None:
name = first_name.lower()
else:
name = '{}, {}'.format(last_name, first_name).lower()
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246,640 | ishikota/PyPokerEngine | pypokerengine/players.py | BasePokerPlayer.respond_to_ask | 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 | def respond_to_ask(self, message):
valid_actions, hole_card, round_state = self.__parse_ask_message(message)
return self.declare_action(valid_actions, hole_card, round_state) | [
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246,641 | ishikota/PyPokerEngine | pypokerengine/players.py | BasePokerPlayer.receive_notification | 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)
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msg_type = message["message_type"]
if msg_type == "game_start_message":
info = self.__parse_game_start_message(message)
self.receive_game_start_message(info)
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246,642 | alex-sherman/unsync | examples/mixing_methods.py | result_continuation | 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 | async def result_continuation(task):
await asyncio.sleep(0.1)
num, res = task.result()
return num, res * 2 | [
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246,643 | alex-sherman/unsync | examples/mixing_methods.py | result_processor | 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 | async def result_processor(tasks):
output = {}
for task in tasks:
num, res = await task
output[num] = res
return output | [
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246,644 | fastavro/fastavro | fastavro/_read_py.py | read_union | 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 | def read_union(fo, writer_schema, reader_schema=None):
# schema resolution
index = read_long(fo)
if reader_schema:
# Handle case where the reader schema is just a single type (not union)
if not isinstance(reader_schema, list):
if match_types(writer_schema[index], reader_schema):
... | [
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246,645 | fastavro/fastavro | fastavro/_read_py.py | read_data | 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 | def read_data(fo, writer_schema, reader_schema=None):
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 reader schema to None so that no
# schema resoluti... | [
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246,646 | fastavro/fastavro | fastavro/_read_py.py | _iter_avro_records | 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 | def _iter_avro_records(fo, header, codec, writer_schema, reader_schema):
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:
try:
block_count = read_long(fo)... | [
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246,647 | fastavro/fastavro | fastavro/_read_py.py | _iter_avro_blocks | 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 | def _iter_avro_blocks(fo, header, codec, writer_schema, reader_schema):
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()
try:
num_block_records = r... | [
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246,648 | fastavro/fastavro | fastavro/_write_py.py | prepare_timestamp_millis | 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 | def prepare_timestamp_millis(data, schema):
if isinstance(data, datetime.datetime):
if data.tzinfo is not None:
delta = (data - epoch)
return int(delta.total_seconds() * MLS_PER_SECOND)
t = int(time.mktime(data.timetuple())) * MLS_PER_SECOND + int(
data.microsecon... | [
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246,649 | fastavro/fastavro | fastavro/_write_py.py | prepare_timestamp_micros | 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 | def prepare_timestamp_micros(data, schema):
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.mktime(data.timetuple())) * MCS_PER_SECOND + \
data.microsecond
... | [
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246,650 | fastavro/fastavro | fastavro/_write_py.py | prepare_date | 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 | def prepare_date(data, schema):
if isinstance(data, datetime.date):
return data.toordinal() - DAYS_SHIFT
else:
return data | [
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246,651 | fastavro/fastavro | fastavro/_write_py.py | prepare_uuid | 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 | def prepare_uuid(data, schema):
if isinstance(data, uuid.UUID):
return str(data)
else:
return data | [
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246,652 | fastavro/fastavro | fastavro/_write_py.py | prepare_time_millis | 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 | def prepare_time_millis(data, schema):
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:
return data | [
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246,653 | fastavro/fastavro | fastavro/_write_py.py | prepare_time_micros | def prepare_time_micros(data, schema):
"""Convert datetime.time to int timestamp with microseconds"""
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else:
return da... | python | def prepare_time_micros(data, schema):
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246,654 | fastavro/fastavro | fastavro/_write_py.py | prepare_bytes_decimal | 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 | def prepare_bytes_decimal(data, schema):
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 ValueError(
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246,655 | fastavro/fastavro | fastavro/_write_py.py | prepare_fixed_decimal | 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 | def prepare_fixed_decimal(data, schema):
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_tuple()
if -exp > scale:
raise ValueError(
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246,656 | fastavro/fastavro | fastavro/_write_py.py | write_crc32 | def write_crc32(fo, bytes):
"""A 4-byte, big-endian CRC32 checksum"""
data = crc32(bytes) & 0xFFFFFFFF
fo.write(pack('>I', data)) | python | def write_crc32(fo, bytes):
data = crc32(bytes) & 0xFFFFFFFF
fo.write(pack('>I', data)) | [
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246,657 | fastavro/fastavro | fastavro/_write_py.py | write_union | 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 | def write_union(fo, datum, schema):
if isinstance(datum, tuple):
(name, datum) = datum
for index, candidate in enumerate(schema):
if extract_record_type(candidate) == 'record':
schema_name = candidate['name']
else:
schema_name = candidate
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246,658 | fastavro/fastavro | fastavro/_write_py.py | write_data | 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 | def write_data(fo, datum, schema):
record_type = extract_record_type(schema)
logical_type = extract_logical_type(schema)
fn = WRITERS.get(record_type)
if fn:
if logical_type:
prepare = LOGICAL_WRITERS.get(logical_type)
if prepare:
datum = prepare(datum, s... | [
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246,659 | fastavro/fastavro | fastavro/_write_py.py | null_write_block | def null_write_block(fo, block_bytes):
"""Write block in "null" codec."""
write_long(fo, len(block_bytes))
fo.write(block_bytes) | python | def null_write_block(fo, block_bytes):
write_long(fo, len(block_bytes))
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246,660 | fastavro/fastavro | fastavro/_write_py.py | deflate_write_block | 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 | def deflate_write_block(fo, block_bytes):
# The first two characters and last character are zlib
# wrappers around deflate data.
data = compress(block_bytes)[2:-1]
write_long(fo, len(data))
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246,661 | fastavro/fastavro | fastavro/_write_py.py | schemaless_writer | 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 | def schemaless_writer(fo, schema, record):
schema = parse_schema(schema)
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parsed_schema = fastavro.parse_schema(schema)
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246,662 | fastavro/fastavro | fastavro/_validation_py.py | validate_int | 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 | def validate_int(datum, **kwargs):
return (
(isinstance(datum, (int, long, numbers.Integral))
and INT_MIN_VALUE <= datum <= INT_MAX_VALUE
and not isinstance(datum, bool))
or isinstance(
datum, (datetime.time, datetime.datetime, datetime.date)
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246,663 | fastavro/fastavro | fastavro/_validation_py.py | validate_float | 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 | def validate_float(datum, **kwargs):
return (
isinstance(datum, (int, long, float, numbers.Real))
and not isinstance(datum, bool)
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246,664 | fastavro/fastavro | fastavro/_validation_py.py | validate_record | 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 | def validate_record(datum, schema, parent_ns=None, raise_errors=True):
_, namespace = schema_name(schema, parent_ns)
return (
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all(validate(datum=datum.get(f['name'], f.get('default', no_value)),
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246,665 | fastavro/fastavro | fastavro/_validation_py.py | validate_union | 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 | def validate_union(datum, schema, parent_ns=None, raise_errors=True):
if isinstance(datum, tuple):
(name, datum) = datum
for candidate in schema:
if extract_record_type(candidate) == 'record':
if name == candidate["name"]:
return validate(datum, schema... | [
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246,666 | fastavro/fastavro | fastavro/_validation_py.py | validate_many | 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 | def validate_many(records, schema, raise_errors=True):
errors = []
results = []
for record in records:
try:
results.append(validate(record, schema, raise_errors=raise_errors))
except ValidationError as e:
errors.extend(e.errors)
if raise_errors and errors:
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246,667 | fastavro/fastavro | fastavro/_schema_py.py | parse_schema | 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 | def parse_schema(schema, _write_hint=True, _force=False):
if _force:
return _parse_schema(schema, "", _write_hint)
elif isinstance(schema, dict) and "__fastavro_parsed" in schema:
return schema
else:
return _parse_schema(schema, "", _write_hint) | [
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246,668 | fastavro/fastavro | fastavro/_schema_py.py | load_schema | 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 | 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`.
'''
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246,669 | alejandroautalan/pygubu | pygubu/widgets/simpletooltip.py | ToolTip.showtip | 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 | 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
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246,670 | alejandroautalan/pygubu | pygubu/__init__.py | TkApplication.run | def run(self):
"""Ejecute the main loop."""
self.toplevel.protocol("WM_DELETE_WINDOW", self.__on_window_close)
self.toplevel.mainloop() | python | def run(self):
self.toplevel.protocol("WM_DELETE_WINDOW", self.__on_window_close)
self.toplevel.mainloop() | [
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246,671 | alejandroautalan/pygubu | examples/py2exe/myapp.py | MyApplication.create_regpoly | 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 | def create_regpoly(self, x0, y0, x1, y1, sides=0, start=90, extent=360, **kw):
coords = self.__regpoly_coords(x0, y0, x1, y1, sides, start, extent)
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246,672 | alejandroautalan/pygubu | examples/py2exe/myapp.py | MyApplication.__regpoly_coords | def __regpoly_coords(self, x0, y0, x1, y1, sides, start, extent):
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coords = []
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246,673 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.get_image | 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 | def get_image(self, path):
image = ''
name = os.path.basename(path)
if not StockImage.is_registered(name):
ipath = self.__find_image(path)
if ipath is not None:
StockImage.register(name, ipath)
else:
msg = "Image '{0}' not found... | [
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246,674 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.import_variables | 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 | def import_variables(self, container, varnames=None):
if varnames is None:
for keyword in self.tkvariables:
setattr(container, keyword, self.tkvariables[keyword])
else:
for keyword in varnames:
if keyword in self.tkvariables:
se... | [
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246,675 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.create_variable | 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 | def create_variable(self, varname, vtype=None):
var_types = ('string', 'int', 'boolean', 'double')
vname = varname
var = None
type_from_name = 'string' # default type
if ':' in varname:
type_from_name, vname = varname.split(':')
# Fix incorrect order bug... | [
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246,676 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.add_from_file | def add_from_file(self, fpath):
"""Load ui definition from file."""
if self.tree is None:
base, name = os.path.split(fpath)
self.add_resource_path(base)
self.tree = tree = ET.parse(fpath)
self.root = tree.getroot()
self.objects = {}
els... | python | def add_from_file(self, fpath):
if self.tree is None:
base, name = os.path.split(fpath)
self.add_resource_path(base)
self.tree = tree = ET.parse(fpath)
self.root = tree.getroot()
self.objects = {}
else:
# TODO: append to current tre... | [
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246,677 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.add_from_string | def add_from_string(self, strdata):
"""Load ui definition from string."""
if self.tree is None:
self.tree = tree = ET.ElementTree(ET.fromstring(strdata))
self.root = tree.getroot()
self.objects = {}
else:
# TODO: append to current tree
... | python | def add_from_string(self, strdata):
if self.tree is None:
self.tree = tree = ET.ElementTree(ET.fromstring(strdata))
self.root = tree.getroot()
self.objects = {}
else:
# TODO: append to current tree
pass | [
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246,678 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.add_from_xmlnode | def add_from_xmlnode(self, element):
"""Load ui definition from xml.etree.Element node."""
if self.tree is None:
root = ET.Element('interface')
root.append(element)
self.tree = tree = ET.ElementTree(root)
self.root = tree.getroot()
self.objects... | python | def add_from_xmlnode(self, element):
if self.tree is None:
root = ET.Element('interface')
root.append(element)
self.tree = tree = ET.ElementTree(root)
self.root = tree.getroot()
self.objects = {}
# ET.dump(tree)
else:
# ... | [
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246,679 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.get_object | def get_object(self, name, master=None):
"""Find and create the widget named name.
Use master as parent. If widget was already created, return
that instance."""
widget = None
if name in self.objects:
widget = self.objects[name].widget
else:
xpath =... | python | def get_object(self, name, master=None):
widget = None
if name in self.objects:
widget = self.objects[name].widget
else:
xpath = ".//object[@id='{0}']".format(name)
node = self.tree.find(xpath)
if node is not None:
root = BuilderObj... | [
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246,680 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder._realize | def _realize(self, master, element):
"""Builds a widget from xml element using master as parent."""
data = data_xmlnode_to_dict(element, self.translator)
cname = data['class']
uniqueid = data['id']
if cname not in CLASS_MAP:
self._import_class(cname)
if cna... | python | def _realize(self, master, element):
data = data_xmlnode_to_dict(element, self.translator)
cname = data['class']
uniqueid = data['id']
if cname not in CLASS_MAP:
self._import_class(cname)
if cname in CLASS_MAP:
self._pre_process_data(data)
pa... | [
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246,681 | alejandroautalan/pygubu | pygubu/builder/__init__.py | Builder.connect_callbacks | def connect_callbacks(self, callbacks_bag):
"""Connect callbacks specified in callbacks_bag with callbacks
defined in the ui definition.
Return a list with the name of the callbacks not connected.
"""
notconnected = []
for wname, builderobj in self.objects.items():
... | python | def connect_callbacks(self, callbacks_bag):
notconnected = []
for wname, builderobj in self.objects.items():
missing = builderobj.connect_commands(callbacks_bag)
if missing is not None:
notconnected.extend(missing)
missing = builderobj.connect_bindings... | [
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246,682 | alejandroautalan/pygubu | pygubudesigner/util/selecttool.py | SelectTool._start_selecting | def _start_selecting(self, event):
"""Comienza con el proceso de seleccion."""
self._selecting = True
canvas = self._canvas
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
self._sstart = (x, y)
if not self._sobject:
self._sobject = canvas.creat... | python | def _start_selecting(self, event):
self._selecting = True
canvas = self._canvas
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
self._sstart = (x, y)
if not self._sobject:
self._sobject = canvas.create_rectangle(
self._sstart[0], self._... | [
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246,683 | alejandroautalan/pygubu | pygubudesigner/util/selecttool.py | SelectTool._keep_selecting | def _keep_selecting(self, event):
"""Continua con el proceso de seleccion.
Crea o redimensiona el cuadro de seleccion de acuerdo con
la posicion del raton."""
canvas = self._canvas
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
canvas.coords(self._sobject... | python | def _keep_selecting(self, event):
canvas = self._canvas
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
canvas.coords(self._sobject,
self._sstart[0], self._sstart[1], x, y) | [
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246,684 | alejandroautalan/pygubu | pygubudesigner/util/selecttool.py | SelectTool._finish_selecting | def _finish_selecting(self, event):
"""Finaliza la seleccion.
Marca como seleccionados todos los objetos que se encuentran
dentro del recuadro de seleccion."""
self._selecting = False
canvas = self._canvas
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
... | python | def _finish_selecting(self, event):
self._selecting = False
canvas = self._canvas
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
canvas.coords(self._sobject, -1, -1, -1, -1)
canvas.itemconfigure(self._sobject, state=tk.HIDDEN)
sel_region = self.... | [
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246,685 | alejandroautalan/pygubu | pygubu/widgets/calendarframe.py | matrix_coords | def matrix_coords(rows, cols, rowh, colw, ox=0, oy=0):
"Generate coords for a matrix of rects"
for i, f, c in rowmajor(rows, cols):
x = ox + c * colw
y = oy + f * rowh
x1 = x + colw
y1 = y + rowh
yield (i, x, y, x1, y1) | python | def matrix_coords(rows, cols, rowh, colw, ox=0, oy=0):
"Generate coords for a matrix of rects"
for i, f, c in rowmajor(rows, cols):
x = ox + c * colw
y = oy + f * rowh
x1 = x + colw
y1 = y + rowh
yield (i, x, y, x1, y1) | [
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246,686 | alejandroautalan/pygubu | pygubudesigner/util/__init__.py | ArrayVar.get | def get(self):
'''Return a dictionary that represents the Tcl array'''
value = {}
for (elementname, elementvar) in self._elementvars.items():
value[elementname] = elementvar.get()
return value | python | def get(self):
'''Return a dictionary that represents the Tcl array'''
value = {}
for (elementname, elementvar) in self._elementvars.items():
value[elementname] = elementvar.get()
return value | [
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246,687 | alejandroautalan/pygubu | pygubu/widgets/editabletreeview.py | EditableTreeview.yview | def yview(self, *args):
"""Update inplace widgets position when doing vertical scroll"""
self.after_idle(self.__updateWnds)
ttk.Treeview.yview(self, *args) | python | def yview(self, *args):
self.after_idle(self.__updateWnds)
ttk.Treeview.yview(self, *args) | [
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246,688 | alejandroautalan/pygubu | pygubu/widgets/editabletreeview.py | EditableTreeview.xview | def xview(self, *args):
"""Update inplace widgets position when doing horizontal scroll"""
self.after_idle(self.__updateWnds)
ttk.Treeview.xview(self, *args) | python | def xview(self, *args):
self.after_idle(self.__updateWnds)
ttk.Treeview.xview(self, *args) | [
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246,689 | alejandroautalan/pygubu | pygubu/widgets/editabletreeview.py | EditableTreeview.__check_focus | def __check_focus(self, event):
"""Checks if the focus has changed"""
#print('Event:', event.type, event.x, event.y)
changed = False
if not self._curfocus:
changed = True
elif self._curfocus != self.focus():
self.__clear_inplace_widgets()
chang... | python | def __check_focus(self, event):
#print('Event:', event.type, event.x, event.y)
changed = False
if not self._curfocus:
changed = True
elif self._curfocus != self.focus():
self.__clear_inplace_widgets()
changed = True
newfocus = self.focus()
... | [
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246,690 | alejandroautalan/pygubu | pygubu/widgets/editabletreeview.py | EditableTreeview.__focus | def __focus(self, item):
"""Called when focus item has changed"""
cols = self.__get_display_columns()
for col in cols:
self.__event_info =(col,item)
self.event_generate('<<TreeviewInplaceEdit>>')
if col in self._inplace_widgets:
w = self._inpla... | python | def __focus(self, item):
cols = self.__get_display_columns()
for col in cols:
self.__event_info =(col,item)
self.event_generate('<<TreeviewInplaceEdit>>')
if col in self._inplace_widgets:
w = self._inplace_widgets[col]
w.bind('<Key-Tab>... | [
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246,691 | alejandroautalan/pygubu | pygubu/widgets/editabletreeview.py | EditableTreeview.__clear_inplace_widgets | def __clear_inplace_widgets(self):
"""Remove all inplace edit widgets."""
cols = self.__get_display_columns()
#print('Clear:', cols)
for c in cols:
if c in self._inplace_widgets:
widget = self._inplace_widgets[c]
widget.place_forget()
... | python | def __clear_inplace_widgets(self):
cols = self.__get_display_columns()
#print('Clear:', cols)
for c in cols:
if c in self._inplace_widgets:
widget = self._inplace_widgets[c]
widget.place_forget()
self._inplace_widgets_show.pop(c, None) | [
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246,692 | alejandroautalan/pygubu | setup.py | CustomInstall.run | def run(self):
"""Run parent install, and then save the install dir in the script."""
install.run(self)
#
# Remove old pygubu.py from scripts path if exists
spath = os.path.join(self.install_scripts, 'pygubu')
for ext in ('.py', '.pyw'):
filename = spath + ex... | python | def run(self):
install.run(self)
#
# Remove old pygubu.py from scripts path if exists
spath = os.path.join(self.install_scripts, 'pygubu')
for ext in ('.py', '.pyw'):
filename = spath + ext
if os.path.exists(filename):
os.remove(filename)
... | [
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246,693 | alejandroautalan/pygubu | pygubudesigner/propertieseditor.py | PropertiesEditor.hide_all | def hide_all(self):
"""Hide all properties from property editor."""
self.current = None
for _v, (label, widget) in self._propbag.items():
label.grid_remove()
widget.grid_remove() | python | def hide_all(self):
self.current = None
for _v, (label, widget) in self._propbag.items():
label.grid_remove()
widget.grid_remove() | [
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246,694 | alejandroautalan/pygubu | pygubu/builder/builderobject.py | BuilderObject._get_init_args | def _get_init_args(self):
"""Creates dict with properties marked as readonly"""
args = {}
for rop in self.ro_properties:
if rop in self.properties:
args[rop] = self.properties[rop]
return args | python | def _get_init_args(self):
args = {}
for rop in self.ro_properties:
if rop in self.properties:
args[rop] = self.properties[rop]
return args | [
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246,695 | alejandroautalan/pygubu | pygubudesigner/previewer.py | OnCanvasMenuPreview._calculate_menu_wh | def _calculate_menu_wh(self):
""" Calculate menu widht and height."""
w = iw = 50
h = ih = 0
# menu.index returns None if there are no choices
index = self._menu.index(tk.END)
index = index if index is not None else 0
count = index + 1
# First calculate u... | python | def _calculate_menu_wh(self):
w = iw = 50
h = ih = 0
# menu.index returns None if there are no choices
index = self._menu.index(tk.END)
index = index if index is not None else 0
count = index + 1
# First calculate using the font paramters of root menu:
fo... | [
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246,696 | alejandroautalan/pygubu | pygubudesigner/previewer.py | PreviewHelper._over_resizer | def _over_resizer(self, x, y):
"Returns True if mouse is over a resizer"
over_resizer = False
c = self.canvas
ids = c.find_overlapping(x, y, x, y)
if ids:
o = ids[0]
tags = c.gettags(o)
if 'resizer' in tags:
over_resizer = True... | python | def _over_resizer(self, x, y):
"Returns True if mouse is over a resizer"
over_resizer = False
c = self.canvas
ids = c.find_overlapping(x, y, x, y)
if ids:
o = ids[0]
tags = c.gettags(o)
if 'resizer' in tags:
over_resizer = True... | [
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246,697 | alejandroautalan/pygubu | pygubudesigner/previewer.py | PreviewHelper.resize_preview | def resize_preview(self, dw, dh):
"Resizes preview that is currently dragged"
# identify preview
if self._objects_moving:
id_ = self._objects_moving[0]
tags = self.canvas.gettags(id_)
for tag in tags:
if tag.startswith('preview_'):
... | python | def resize_preview(self, dw, dh):
"Resizes preview that is currently dragged"
# identify preview
if self._objects_moving:
id_ = self._objects_moving[0]
tags = self.canvas.gettags(id_)
for tag in tags:
if tag.startswith('preview_'):
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246,698 | alejandroautalan/pygubu | pygubudesigner/previewer.py | PreviewHelper.move_previews | def move_previews(self):
"Move previews after a resize event"
# calculate new positions
min_y = self._calc_preview_ypos()
for idx, (key, p) in enumerate(self.previews.items()):
new_dy = min_y[idx] - p.y
self.previews[key].move_by(0, new_dy)
self._update_c... | python | def move_previews(self):
"Move previews after a resize event"
# calculate new positions
min_y = self._calc_preview_ypos()
for idx, (key, p) in enumerate(self.previews.items()):
new_dy = min_y[idx] - p.y
self.previews[key].move_by(0, new_dy)
self._update_c... | [
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246,699 | alejandroautalan/pygubu | pygubudesigner/previewer.py | PreviewHelper._calc_preview_ypos | def _calc_preview_ypos(self):
"Calculates the previews positions on canvas"
y = 10
min_y = [y]
for k, p in self.previews.items():
y += p.height() + self.padding
min_y.append(y)
return min_y | python | def _calc_preview_ypos(self):
"Calculates the previews positions on canvas"
y = 10
min_y = [y]
for k, p in self.previews.items():
y += p.height() + self.padding
min_y.append(y)
return min_y | [
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