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member_calldef_t.has_static | (self) | describes, whether "callable" has static modifier or not | describes, whether "callable" has static modifier or not | def has_static(self):
"""describes, whether "callable" has static modifier or not"""
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member_calldef_t.function_type | (self) | returns function type. See :class:`type_t` hierarchy | returns function type. See :class:`type_t` hierarchy | def function_type(self):
"""returns function type. See :class:`type_t` hierarchy"""
if self.has_static:
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operator_t.symbol | (self) | operator's symbol. For example: operator+, symbol is equal to '+ | operator's symbol. For example: operator+, symbol is equal to '+ | def symbol(self):
"""operator's symbol. For example: operator+, symbol is equal to '+'"""
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constructor_t.explicit | (self) | True, if constructor has "explicit" keyword, False otherwise
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FilterSelect._filter_F_one_feature | (data, treatment_indicator, feature_name, y_name) |
Conduct F-test of the interaction between treatment and one feature.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
|
Conduct F-test of the interaction between treatment and one feature. | def _filter_F_one_feature(data, treatment_indicator, feature_name, y_name):
"""
Conduct F-test of the interaction between treatment and one feature.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
"""
Y = data[y_name]
X = data[[treatment_indicator, feature_name]]
X = sm.add_constant(X)
X['{}-{}'.format(treatment_indicator, feature_name)] = X[[treatment_indicator, feature_name]].product(axis=1)
model = sm.OLS(Y, X)
result = model.fit()
F_test = result.f_test(np.array([0, 0, 0, 1]))
F_test_result = pd.DataFrame({
'feature': feature_name, # for the interaction, not the main effect
'method': 'F-statistic',
'score': F_test.fvalue[0][0],
'p_value': F_test.pvalue,
'misc': 'df_num: {}, df_denom: {}'.format(F_test.df_num, F_test.df_denom),
}, index=[0]).reset_index(drop=True)
return F_test_result | [
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FilterSelect.filter_F | (self, data, treatment_indicator, features, y_name) |
Rank features based on the F-statistics of the interaction.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
features (list of string): list of feature names, that are columns in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
|
Rank features based on the F-statistics of the interaction. | def filter_F(self, data, treatment_indicator, features, y_name):
"""
Rank features based on the F-statistics of the interaction.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
features (list of string): list of feature names, that are columns in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
"""
all_result = pd.DataFrame()
for x_name_i in features:
one_result = self._filter_F_one_feature(data=data,
treatment_indicator=treatment_indicator, feature_name=x_name_i, y_name=y_name
)
all_result = pd.concat([all_result, one_result])
all_result = all_result.sort_values(by='score', ascending=False)
all_result['rank'] = all_result['score'].rank(ascending=False)
return all_result | [
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FilterSelect._filter_LR_one_feature | (data, treatment_indicator, feature_name, y_name, disp=True) |
Conduct LR (Likelihood Ratio) test of the interaction between treatment and one feature.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
Returns
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(pd.DataFrame): a data frame containing the feature importance statistics
|
Conduct LR (Likelihood Ratio) test of the interaction between treatment and one feature. | def _filter_LR_one_feature(data, treatment_indicator, feature_name, y_name, disp=True):
"""
Conduct LR (Likelihood Ratio) test of the interaction between treatment and one feature.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
"""
Y = data[y_name]
# Restricted model
X_r = data[[treatment_indicator, feature_name]]
X_r = sm.add_constant(X_r)
model_r = sm.Logit(Y, X_r)
result_r = model_r.fit(disp=disp)
# Full model (with interaction)
X_f = X_r.copy()
X_f['{}-{}'.format(treatment_indicator, feature_name)] = X_f[[treatment_indicator, feature_name]].product(axis=1)
model_f = sm.Logit(Y, X_f)
result_f = model_f.fit(disp=disp)
LR_stat = -2 * (result_r.llf - result_f.llf)
LR_df = len(result_f.params) - len(result_r.params)
LR_pvalue = 1 - stats.chi2.cdf(LR_stat, df=LR_df)
LR_test_result = pd.DataFrame({
'feature': feature_name, # for the interaction, not the main effect
'method': 'LRT-statistic',
'score': LR_stat,
'p_value': LR_pvalue,
'misc': 'df: {}'.format(LR_df),
}, index=[0]).reset_index(drop=True)
return LR_test_result | [
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FilterSelect.filter_LR | (self, data, treatment_indicator, features, y_name, disp=True) |
Rank features based on the LRT-statistics of the interaction.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
|
Rank features based on the LRT-statistics of the interaction. | def filter_LR(self, data, treatment_indicator, features, y_name, disp=True):
"""
Rank features based on the LRT-statistics of the interaction.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
"""
all_result = pd.DataFrame()
for x_name_i in features:
one_result = self._filter_LR_one_feature(data=data,
treatment_indicator=treatment_indicator, feature_name=x_name_i, y_name=y_name, disp=disp
)
all_result = pd.concat([all_result, one_result])
all_result = all_result.sort_values(by='score', ascending=False)
all_result['rank'] = all_result['score'].rank(ascending=False)
return all_result | [
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FilterSelect._GetNodeSummary | (data,
experiment_group_column='treatment_group_key',
y_name='conversion', smooth=True) |
To count the conversions and get the probabilities by treatment groups. This function comes from the uplift tree algorithm, that is used for tree node split evaluation.
Parameters
----------
data : DataFrame
The DataFrame that contains all the data (in the current "node").
experiment_group_column : str
Treatment indicator column name.
y_name : str
Label indicator column name.
smooth : bool
Smooth label count by adding 1 in case certain labels do not occur
naturally with a treatment. Prevents zero divisions.
Returns
-------
results : dict
Counts of conversions by treatment groups, of the form:
{'control': {0: 10, 1: 8}, 'treatment1': {0: 5, 1: 15}}
nodeSummary: dict
Probability of conversion and group size by treatment groups, of
the form:
{'control': [0.490, 500], 'treatment1': [0.584, 500]}
|
To count the conversions and get the probabilities by treatment groups. This function comes from the uplift tree algorithm, that is used for tree node split evaluation. | def _GetNodeSummary(data,
experiment_group_column='treatment_group_key',
y_name='conversion', smooth=True):
"""
To count the conversions and get the probabilities by treatment groups. This function comes from the uplift tree algorithm, that is used for tree node split evaluation.
Parameters
----------
data : DataFrame
The DataFrame that contains all the data (in the current "node").
experiment_group_column : str
Treatment indicator column name.
y_name : str
Label indicator column name.
smooth : bool
Smooth label count by adding 1 in case certain labels do not occur
naturally with a treatment. Prevents zero divisions.
Returns
-------
results : dict
Counts of conversions by treatment groups, of the form:
{'control': {0: 10, 1: 8}, 'treatment1': {0: 5, 1: 15}}
nodeSummary: dict
Probability of conversion and group size by treatment groups, of
the form:
{'control': [0.490, 500], 'treatment1': [0.584, 500]}
"""
# Note: results and nodeSummary are both dict with treatment_group_key
# as the key. So we can compute the treatment effect and/or
# divergence easily.
# Counts of conversions by treatment group
results_series = data.groupby([experiment_group_column, y_name]).size()
treatment_group_keys = results_series.index.levels[0].tolist()
y_name_keys = results_series.index.levels[1].tolist()
results = {}
for ti in treatment_group_keys:
results.update({ti: {}})
for ci in y_name_keys:
if smooth:
results[ti].update({ci: results_series[ti, ci]
if results_series.index.isin([(ti, ci)]).any()
else 1})
else:
results[ti].update({ci: results_series[ti, ci]})
# Probability of conversion and group size by treatment group
nodeSummary = {}
for treatment_group_key in results:
n_1 = results[treatment_group_key].get(1, 0)
n_total = (results[treatment_group_key].get(1, 0)
+ results[treatment_group_key].get(0, 0))
y_mean = 1.0 * n_1 / n_total
nodeSummary[treatment_group_key] = [y_mean, n_total]
return results, nodeSummary | [
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] | python | en | ['en', 'error', 'th'] | False |
FilterSelect._kl_divergence | (pk, qk) |
Calculate KL Divergence for binary classification.
Parameters
----------
pk (float): Probability of class 1 in treatment group
qk (float): Probability of class 1 in control group
|
Calculate KL Divergence for binary classification. | def _kl_divergence(pk, qk):
"""
Calculate KL Divergence for binary classification.
Parameters
----------
pk (float): Probability of class 1 in treatment group
qk (float): Probability of class 1 in control group
"""
if qk < 0.1**6:
qk = 0.1**6
elif qk > 1 - 0.1**6:
qk = 1 - 0.1**6
S = pk * np.log(pk / qk) + (1-pk) * np.log((1-pk) / (1-qk))
return S | [
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] | python | en | ['en', 'error', 'th'] | False |
FilterSelect._evaluate_KL | (self, nodeSummary, control_group='control') |
Calculate the multi-treatment unconditional D (one node)
with KL Divergence as split Evaluation function.
Parameters
----------
nodeSummary (dict): a dictionary containing the statistics for a tree node sample
control_group (string, optional, default='control'): the name for control group
Notes
-----
The function works for more than one non-control treatment groups.
|
Calculate the multi-treatment unconditional D (one node)
with KL Divergence as split Evaluation function. | def _evaluate_KL(self, nodeSummary, control_group='control'):
"""
Calculate the multi-treatment unconditional D (one node)
with KL Divergence as split Evaluation function.
Parameters
----------
nodeSummary (dict): a dictionary containing the statistics for a tree node sample
control_group (string, optional, default='control'): the name for control group
Notes
-----
The function works for more than one non-control treatment groups.
"""
if control_group not in nodeSummary:
return 0
pc = nodeSummary[control_group][0]
d_res = 0
for treatment_group in nodeSummary:
if treatment_group != control_group:
d_res += self._kl_divergence(nodeSummary[treatment_group][0], pc)
return d_res | [
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FilterSelect._evaluate_ED | (nodeSummary, control_group='control') |
Calculate the multi-treatment unconditional D (one node)
with Euclidean Distance as split Evaluation function.
Parameters
----------
nodeSummary (dict): a dictionary containing the statistics for a tree node sample
control_group (string, optional, default='control'): the name for control group
|
Calculate the multi-treatment unconditional D (one node)
with Euclidean Distance as split Evaluation function. | def _evaluate_ED(nodeSummary, control_group='control'):
"""
Calculate the multi-treatment unconditional D (one node)
with Euclidean Distance as split Evaluation function.
Parameters
----------
nodeSummary (dict): a dictionary containing the statistics for a tree node sample
control_group (string, optional, default='control'): the name for control group
"""
if control_group not in nodeSummary:
return 0
pc = nodeSummary[control_group][0]
d_res = 0
for treatment_group in nodeSummary:
if treatment_group != control_group:
d_res += 2 * (nodeSummary[treatment_group][0] - pc)**2
return d_res | [
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] | python | en | ['en', 'error', 'th'] | False |
FilterSelect._evaluate_Chi | (nodeSummary, control_group='control') |
Calculate the multi-treatment unconditional D (one node)
with Chi-Square as split Evaluation function.
Parameters
----------
nodeSummary (dict): a dictionary containing the statistics for a tree node sample
control_group (string, optional, default='control'): the name for control group
|
Calculate the multi-treatment unconditional D (one node)
with Chi-Square as split Evaluation function. | def _evaluate_Chi(nodeSummary, control_group='control'):
"""
Calculate the multi-treatment unconditional D (one node)
with Chi-Square as split Evaluation function.
Parameters
----------
nodeSummary (dict): a dictionary containing the statistics for a tree node sample
control_group (string, optional, default='control'): the name for control group
"""
if control_group not in nodeSummary:
return 0
pc = nodeSummary[control_group][0]
d_res = 0
for treatment_group in nodeSummary:
if treatment_group != control_group:
d_res += (
(nodeSummary[treatment_group][0] - pc)**2 / max(0.1**6, pc)
+ (nodeSummary[treatment_group][0] - pc)**2 / max(0.1**6, 1-pc)
)
return d_res | [
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FilterSelect._filter_D_one_feature | (self, data, feature_name, y_name,
n_bins=10, method='KL', control_group='control',
experiment_group_column='treatment_group_key',
null_impute=None) |
Calculate the chosen divergence measure for one feature.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
method (string, optional, default = 'KL'): taking one of the following values {'F', 'LR', 'KL', 'ED', 'Chi'}
The feature selection method to be used to rank the features.
'F' for F-test
'LR' for likelihood ratio test
'KL', 'ED', 'Chi' for bin-based uplift filter methods, KL divergence, Euclidean distance, Chi-Square respectively
experiment_group_column (string, optional, default = 'treatment_group_key'): the experiment column name in the DataFrame, which contains the treatment and control assignment label
control_group (string, optional, default = 'control'): name for control group, value in the experiment group column
n_bins (int, optional, default = 10): number of bins to be used for bin-based uplift filter methods
null_impute (str, optional, default=None): impute np.nan present in the data taking on of the following strategy values {'mean', 'median', 'most_frequent', None}. If Value is None and null is present then exception will be raised
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
|
Calculate the chosen divergence measure for one feature. | def _filter_D_one_feature(self, data, feature_name, y_name,
n_bins=10, method='KL', control_group='control',
experiment_group_column='treatment_group_key',
null_impute=None):
"""
Calculate the chosen divergence measure for one feature.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
feature_name (string): feature name, as one column in the data DataFrame
y_name (string): name of the outcome variable
method (string, optional, default = 'KL'): taking one of the following values {'F', 'LR', 'KL', 'ED', 'Chi'}
The feature selection method to be used to rank the features.
'F' for F-test
'LR' for likelihood ratio test
'KL', 'ED', 'Chi' for bin-based uplift filter methods, KL divergence, Euclidean distance, Chi-Square respectively
experiment_group_column (string, optional, default = 'treatment_group_key'): the experiment column name in the DataFrame, which contains the treatment and control assignment label
control_group (string, optional, default = 'control'): name for control group, value in the experiment group column
n_bins (int, optional, default = 10): number of bins to be used for bin-based uplift filter methods
null_impute (str, optional, default=None): impute np.nan present in the data taking on of the following strategy values {'mean', 'median', 'most_frequent', None}. If Value is None and null is present then exception will be raised
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
"""
# [TODO] Application to categorical features
if method == 'KL':
evaluationFunction = self._evaluate_KL
elif method == 'ED':
evaluationFunction = self._evaluate_ED
elif method == 'Chi':
evaluationFunction = self._evaluate_Chi
totalSize = len(data.index)
# impute null if enabled
if null_impute is not None:
data[feature_name] = SimpleImputer(missing_values=np.nan, strategy=null_impute).fit_transform(data[feature_name].values.reshape(-1, 1))
elif data[feature_name].isna().any():
raise Exception("Null value(s) present in column '{}'. Please impute the null value or use null_impute parameter provided!!!".format(feature_name))
# drop duplicate edges in pq.cut result to avoid issues
x_bin = pd.qcut(data[feature_name].values, n_bins, labels=False,
duplicates='drop')
d_children = 0
for i_bin in range(np.nanmax(x_bin).astype(int) + 1): # range(n_bins):
nodeSummary = self._GetNodeSummary(
data=data.loc[x_bin == i_bin],
experiment_group_column=experiment_group_column, y_name=y_name
)[1]
nodeScore = evaluationFunction(nodeSummary,
control_group=control_group)
nodeSize = sum([x[1] for x in list(nodeSummary.values())])
d_children += nodeScore * nodeSize / totalSize
parentNodeSummary = self._GetNodeSummary(
data=data, experiment_group_column=experiment_group_column, y_name=y_name
)[1]
d_parent = evaluationFunction(parentNodeSummary,
control_group=control_group)
d_res = d_children - d_parent
D_result = pd.DataFrame({
'feature': feature_name,
'method': method,
'score': d_res,
'p_value': None,
'misc': 'number_of_bins: {}'.format(min(n_bins, np.nanmax(x_bin).astype(int) + 1)),# format(n_bins),
}, index=[0]).reset_index(drop=True)
return(D_result) | [
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FilterSelect.filter_D | (self, data, features, y_name,
n_bins=10, method='KL', control_group='control',
experiment_group_column='treatment_group_key',
null_impute=None) |
Rank features based on the chosen divergence measure.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
features (list of string): list of feature names, that are columns in the data DataFrame
y_name (string): name of the outcome variable
method (string, optional, default = 'KL'): taking one of the following values {'F', 'LR', 'KL', 'ED', 'Chi'}
The feature selection method to be used to rank the features.
'F' for F-test
'LR' for likelihood ratio test
'KL', 'ED', 'Chi' for bin-based uplift filter methods, KL divergence, Euclidean distance, Chi-Square respectively
experiment_group_column (string, optional, default = 'treatment_group_key'): the experiment column name in the DataFrame, which contains the treatment and control assignment label
control_group (string, optional, default = 'control'): name for control group, value in the experiment group column
n_bins (int, optional, default = 10): number of bins to be used for bin-based uplift filter methods
null_impute (str, optional, default=None): impute np.nan present in the data taking on of the following strategy values {'mean', 'median', 'most_frequent', None}. If Value is None and null is present then exception will be raised
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
|
Rank features based on the chosen divergence measure. | def filter_D(self, data, features, y_name,
n_bins=10, method='KL', control_group='control',
experiment_group_column='treatment_group_key',
null_impute=None):
"""
Rank features based on the chosen divergence measure.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
treatment_indicator (string): the column name for binary indicator of treatment (value 1) or control (value 0)
features (list of string): list of feature names, that are columns in the data DataFrame
y_name (string): name of the outcome variable
method (string, optional, default = 'KL'): taking one of the following values {'F', 'LR', 'KL', 'ED', 'Chi'}
The feature selection method to be used to rank the features.
'F' for F-test
'LR' for likelihood ratio test
'KL', 'ED', 'Chi' for bin-based uplift filter methods, KL divergence, Euclidean distance, Chi-Square respectively
experiment_group_column (string, optional, default = 'treatment_group_key'): the experiment column name in the DataFrame, which contains the treatment and control assignment label
control_group (string, optional, default = 'control'): name for control group, value in the experiment group column
n_bins (int, optional, default = 10): number of bins to be used for bin-based uplift filter methods
null_impute (str, optional, default=None): impute np.nan present in the data taking on of the following strategy values {'mean', 'median', 'most_frequent', None}. If Value is None and null is present then exception will be raised
Returns
----------
(pd.DataFrame): a data frame containing the feature importance statistics
"""
all_result = pd.DataFrame()
for x_name_i in features:
one_result = self._filter_D_one_feature(
data=data, feature_name=x_name_i, y_name=y_name,
n_bins=n_bins, method=method, control_group=control_group,
experiment_group_column=experiment_group_column,
null_impute=null_impute
)
all_result = pd.concat([all_result, one_result])
all_result = all_result.sort_values(by='score', ascending=False)
all_result['rank'] = all_result['score'].rank(ascending=False)
return all_result | [
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FilterSelect.get_importance | (self, data, features, y_name, method,
experiment_group_column='treatment_group_key',
control_group = 'control',
treatment_group = 'treatment',
n_bins=5,
null_impute=None
) |
Rank features based on the chosen statistic of the interaction.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
features (list of string): list of feature names, that are columns in the data DataFrame
y_name (string): name of the outcome variable
method (string, optional, default = 'KL'): taking one of the following values {'F', 'LR', 'KL', 'ED', 'Chi'}
The feature selection method to be used to rank the features.
'F' for F-test
'LR' for likelihood ratio test
'KL', 'ED', 'Chi' for bin-based uplift filter methods, KL divergence, Euclidean distance, Chi-Square respectively
experiment_group_column (string): the experiment column name in the DataFrame, which contains the treatment and control assignment label
control_group (string): name for control group, value in the experiment group column
treatment_group (string): name for treatment group, value in the experiment group column
n_bins (int, optional): number of bins to be used for bin-based uplift filter methods
null_impute (str, optional, default=None): impute np.nan present in the data taking on of the following strategy values {'mean', 'median', 'most_frequent', None}. If value is None and null is present then exception will be raised
Returns
----------
(pd.DataFrame): a data frame with following columns: ['method', 'feature', 'rank', 'score', 'p_value', 'misc']
|
Rank features based on the chosen statistic of the interaction. | def get_importance(self, data, features, y_name, method,
experiment_group_column='treatment_group_key',
control_group = 'control',
treatment_group = 'treatment',
n_bins=5,
null_impute=None
):
"""
Rank features based on the chosen statistic of the interaction.
Parameters
----------
data (pd.Dataframe): DataFrame containing outcome, features, and experiment group
features (list of string): list of feature names, that are columns in the data DataFrame
y_name (string): name of the outcome variable
method (string, optional, default = 'KL'): taking one of the following values {'F', 'LR', 'KL', 'ED', 'Chi'}
The feature selection method to be used to rank the features.
'F' for F-test
'LR' for likelihood ratio test
'KL', 'ED', 'Chi' for bin-based uplift filter methods, KL divergence, Euclidean distance, Chi-Square respectively
experiment_group_column (string): the experiment column name in the DataFrame, which contains the treatment and control assignment label
control_group (string): name for control group, value in the experiment group column
treatment_group (string): name for treatment group, value in the experiment group column
n_bins (int, optional): number of bins to be used for bin-based uplift filter methods
null_impute (str, optional, default=None): impute np.nan present in the data taking on of the following strategy values {'mean', 'median', 'most_frequent', None}. If value is None and null is present then exception will be raised
Returns
----------
(pd.DataFrame): a data frame with following columns: ['method', 'feature', 'rank', 'score', 'p_value', 'misc']
"""
if method == 'F':
data = data[data[experiment_group_column].isin([control_group, treatment_group])]
data['treatment_indicator'] = 0
data.loc[data[experiment_group_column]==treatment_group,'treatment_indicator'] = 1
all_result = self.filter_F(data=data,
treatment_indicator='treatment_indicator', features=features, y_name=y_name
)
elif method == 'LR':
data = data[data[experiment_group_column].isin([control_group, treatment_group])]
data['treatment_indicator'] = 0
data.loc[data[experiment_group_column]==treatment_group,'treatment_indicator'] = 1
all_result = self.filter_LR(data=data, disp=True,
treatment_indicator='treatment_indicator', features=features, y_name=y_name
)
else:
all_result = self.filter_D(data=data, method=method,
features=features, y_name=y_name,
n_bins=n_bins, control_group=control_group,
experiment_group_column=experiment_group_column,
null_impute=null_impute
)
all_result['method'] = method + ' filter'
return all_result[['method', 'feature', 'rank', 'score', 'p_value', 'misc']] | [
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calibrate | (ps, treatment) | Calibrate propensity scores with logistic GAM.
Ref: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
Args:
ps (numpy.array): a propensity score vector
treatment (numpy.array): a binary treatment vector (0: control, 1: treated)
Returns:
(numpy.array): a calibrated propensity score vector
| Calibrate propensity scores with logistic GAM. | def calibrate(ps, treatment):
"""Calibrate propensity scores with logistic GAM.
Ref: https://pygam.readthedocs.io/en/latest/api/logisticgam.html
Args:
ps (numpy.array): a propensity score vector
treatment (numpy.array): a binary treatment vector (0: control, 1: treated)
Returns:
(numpy.array): a calibrated propensity score vector
"""
gam = LogisticGAM(s(0)).fit(ps, treatment)
return gam.predict_proba(ps) | [
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compute_propensity_score | (X, treatment, p_model=None, X_pred=None, treatment_pred=None, calibrate_p=True) | Generate propensity score if user didn't provide
Args:
X (np.matrix): features for training
treatment (np.array or pd.Series): a treatment vector for training
p_model (propensity model object, optional):
ElasticNetPropensityModel (default) / GradientBoostedPropensityModel
X_pred (np.matrix, optional): features for prediction
treatment_pred (np.array or pd.Series, optional): a treatment vector for prediciton
calibrate_p (bool, optional): whether calibrate the propensity score
Returns:
(tuple)
- p (numpy.ndarray): propensity score
- p_model (PropensityModel): a trained PropensityModel object
| Generate propensity score if user didn't provide | def compute_propensity_score(X, treatment, p_model=None, X_pred=None, treatment_pred=None, calibrate_p=True):
"""Generate propensity score if user didn't provide
Args:
X (np.matrix): features for training
treatment (np.array or pd.Series): a treatment vector for training
p_model (propensity model object, optional):
ElasticNetPropensityModel (default) / GradientBoostedPropensityModel
X_pred (np.matrix, optional): features for prediction
treatment_pred (np.array or pd.Series, optional): a treatment vector for prediciton
calibrate_p (bool, optional): whether calibrate the propensity score
Returns:
(tuple)
- p (numpy.ndarray): propensity score
- p_model (PropensityModel): a trained PropensityModel object
"""
if treatment_pred is None:
treatment_pred = treatment.copy()
if p_model is None:
p_model = ElasticNetPropensityModel()
p_model.fit(X, treatment)
if X_pred is None:
p = p_model.predict(X)
else:
p = p_model.predict(X_pred)
if calibrate_p:
logger.info('Calibrating propensity scores.')
p = calibrate(p, treatment_pred)
# force the p values within the range
eps = np.finfo(float).eps
p = np.where(p < 0 + eps, 0 + eps*1.001, p)
p = np.where(p > 1 - eps, 1 - eps*1.001, p)
return p, p_model | [
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PropensityModel.__init__ | (self, clip_bounds=(1e-3, 1 - 1e-3), **model_kwargs) |
Args:
clip_bounds (tuple): lower and upper bounds for clipping propensity scores. Bounds should be implemented
such that: 0 < lower < upper < 1, to avoid division by zero in BaseRLearner.fit_predict() step.
model_kwargs: Keyword arguments to be passed to the underlying classification model.
|
Args:
clip_bounds (tuple): lower and upper bounds for clipping propensity scores. Bounds should be implemented
such that: 0 < lower < upper < 1, to avoid division by zero in BaseRLearner.fit_predict() step.
model_kwargs: Keyword arguments to be passed to the underlying classification model.
| def __init__(self, clip_bounds=(1e-3, 1 - 1e-3), **model_kwargs):
"""
Args:
clip_bounds (tuple): lower and upper bounds for clipping propensity scores. Bounds should be implemented
such that: 0 < lower < upper < 1, to avoid division by zero in BaseRLearner.fit_predict() step.
model_kwargs: Keyword arguments to be passed to the underlying classification model.
"""
self.clip_bounds = clip_bounds
self.model_kwargs = model_kwargs
self.model = self._model | [
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PropensityModel.fit | (self, X, y) |
Fit a propensity model.
Args:
X (numpy.ndarray): a feature matrix
y (numpy.ndarray): a binary target vector
|
Fit a propensity model. | def fit(self, X, y):
"""
Fit a propensity model.
Args:
X (numpy.ndarray): a feature matrix
y (numpy.ndarray): a binary target vector
"""
self.model.fit(X, y) | [
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PropensityModel.predict | (self, X) |
Predict propensity scores.
Args:
X (numpy.ndarray): a feature matrix
Returns:
(numpy.ndarray): Propensity scores between 0 and 1.
|
Predict propensity scores. | def predict(self, X):
"""
Predict propensity scores.
Args:
X (numpy.ndarray): a feature matrix
Returns:
(numpy.ndarray): Propensity scores between 0 and 1.
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PropensityModel.fit_predict | (self, X, y) |
Fit a propensity model and predict propensity scores.
Args:
X (numpy.ndarray): a feature matrix
y (numpy.ndarray): a binary target vector
Returns:
(numpy.ndarray): Propensity scores between 0 and 1.
|
Fit a propensity model and predict propensity scores. | def fit_predict(self, X, y):
"""
Fit a propensity model and predict propensity scores.
Args:
X (numpy.ndarray): a feature matrix
y (numpy.ndarray): a binary target vector
Returns:
(numpy.ndarray): Propensity scores between 0 and 1.
"""
self.fit(X, y)
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logger.info('AUC score: {:.6f}'.format(auc(y, propensity_scores)))
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GradientBoostedPropensityModel.fit | (self, X, y, early_stopping_rounds=10, stop_val_size=0.2) |
Fit a propensity model.
Args:
X (numpy.ndarray): a feature matrix
y (numpy.ndarray): a binary target vector
|
Fit a propensity model. | def fit(self, X, y, early_stopping_rounds=10, stop_val_size=0.2):
"""
Fit a propensity model.
Args:
X (numpy.ndarray): a feature matrix
y (numpy.ndarray): a binary target vector
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self.model.fit(
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super(GradientBoostedPropensityModel, self).fit(X, y) | [
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GradientBoostedPropensityModel.predict | (self, X) |
Predict propensity scores.
Args:
X (numpy.ndarray): a feature matrix
Returns:
(numpy.ndarray): Propensity scores between 0 and 1.
|
Predict propensity scores. | def predict(self, X):
"""
Predict propensity scores.
Args:
X (numpy.ndarray): a feature matrix
Returns:
(numpy.ndarray): Propensity scores between 0 and 1.
"""
if self.early_stop:
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get_root | () | Get the project root directory.
We require that all commands are run from the project root, i.e. the
directory that contains setup.py, setup.cfg, and versioneer.py .
| Get the project root directory. | def get_root():
"""Get the project root directory.
We require that all commands are run from the project root, i.e. the
directory that contains setup.py, setup.cfg, and versioneer.py .
"""
root = os.path.realpath(os.path.abspath(os.getcwd()))
setup_py = os.path.join(root, "setup.py")
versioneer_py = os.path.join(root, "versioneer.py")
if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
# allow 'python path/to/setup.py COMMAND'
root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
setup_py = os.path.join(root, "setup.py")
versioneer_py = os.path.join(root, "versioneer.py")
if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
err = (
"Versioneer was unable to run the project root directory. "
"Versioneer requires setup.py to be executed from "
"its immediate directory (like 'python setup.py COMMAND'), "
"or in a way that lets it use sys.argv[0] to find the root "
"(like 'python path/to/setup.py COMMAND')."
)
raise VersioneerBadRootError(err)
try:
# Certain runtime workflows (setup.py install/develop in a setuptools
# tree) execute all dependencies in a single python process, so
# "versioneer" may be imported multiple times, and python's shared
# module-import table will cache the first one. So we can't use
# os.path.dirname(__file__), as that will find whichever
# versioneer.py was first imported, even in later projects.
me = os.path.realpath(os.path.abspath(__file__))
me_dir = os.path.normcase(os.path.splitext(me)[0])
vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
if me_dir != vsr_dir:
print(
"Warning: build in %s is using versioneer.py from %s"
% (os.path.dirname(me), versioneer_py)
)
except NameError:
pass
return root | [
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] | [
293,
0
] | [
333,
15
] | python | en | ['en', 'en', 'en'] | True |
get_config_from_root | (root) | Read the project setup.cfg file to determine Versioneer config. | Read the project setup.cfg file to determine Versioneer config. | def get_config_from_root(root):
"""Read the project setup.cfg file to determine Versioneer config."""
# This might raise EnvironmentError (if setup.cfg is missing), or
# configparser.NoSectionError (if it lacks a [versioneer] section), or
# configparser.NoOptionError (if it lacks "VCS="). See the docstring at
# the top of versioneer.py for instructions on writing your setup.cfg .
setup_cfg = os.path.join(root, "setup.cfg")
parser = configparser.SafeConfigParser()
with open(setup_cfg) as f:
parser.readfp(f)
VCS = parser.get("versioneer", "VCS") # mandatory
def get(parser, name):
if parser.has_option("versioneer", name):
return parser.get("versioneer", name)
return None
cfg = VersioneerConfig()
cfg.VCS = VCS
cfg.style = get(parser, "style") or ""
cfg.versionfile_source = get(parser, "versionfile_source")
cfg.versionfile_build = get(parser, "versionfile_build")
cfg.tag_prefix = get(parser, "tag_prefix")
if cfg.tag_prefix in ("''", '""'):
cfg.tag_prefix = ""
cfg.parentdir_prefix = get(parser, "parentdir_prefix")
cfg.verbose = get(parser, "verbose")
return cfg | [
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] | [
336,
0
] | [
363,
14
] | python | en | ['en', 'en', 'en'] | True |
register_vcs_handler | (vcs, method) | Decorator to mark a method as the handler for a particular VCS. | Decorator to mark a method as the handler for a particular VCS. | def register_vcs_handler(vcs, method): # decorator
"""Decorator to mark a method as the handler for a particular VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate | [
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375,
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385,
19
] | python | en | ['en', 'en', 'en'] | True |
run_command | (commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) | Call the given command(s). | Call the given command(s). | def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
p = None
for c in commands:
try:
dispcmd = str([c] + args)
# remember shell=False, so use git.cmd on windows, not just git
p = subprocess.Popen(
[c] + args,
cwd=cwd,
env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr else None),
)
break
except OSError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %s" % dispcmd)
print(e)
return None, None
else:
if verbose:
print("unable to find command, tried {}".format(commands))
return None, None
stdout = p.communicate()[0].strip()
if sys.version_info[0] >= 3:
stdout = stdout.decode()
if p.returncode != 0:
if verbose:
print("unable to run %s (error)" % dispcmd)
print("stdout was %s" % stdout)
return None, p.returncode
return stdout, p.returncode | [
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388,
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] | [
424,
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] | python | en | ['en', 'en', 'en'] | True |
git_get_keywords | (versionfile_abs) | Extract version information from the given file. | Extract version information from the given file. | def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
f = open(versionfile_abs)
for line in f.readlines():
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
f.close()
except OSError:
pass
return keywords | [
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953,
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978,
19
] | python | en | ['en', 'en', 'en'] | True |
git_versions_from_keywords | (keywords, tag_prefix, verbose) | Get version information from git keywords. | Get version information from git keywords. | def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if not keywords:
raise NotThisMethod("no keywords at all, weird")
date = keywords.get("date")
if date is not None:
# git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = {r.strip() for r in refnames.strip("()").split(",")}
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = {r for r in refs if re.search(r"\d", r)}
if verbose:
print("discarding '%s', no digits" % ",".join(refs - tags))
if verbose:
print("likely tags: %s" % ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix) :]
if verbose:
print("picking %s" % r)
return {
"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False,
"error": None,
"date": date,
}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {
"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False,
"error": "no suitable tags",
"date": None,
} | [
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] | [
982,
0
] | [
1040,
5
] | python | en | ['en', 'da', 'en'] | True |
git_pieces_from_vcs | (tag_prefix, root, verbose, run_command=run_command) | Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
| Get version from 'git describe' in the root of the source tree. | def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %s not under git control" % root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = run_command(
GITS,
[
"describe",
"--tags",
"--dirty",
"--always",
"--long",
"--match",
"%s*" % tag_prefix,
],
cwd=root,
)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[: git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
if not mo:
# unparsable. Maybe git-describe is misbehaving?
pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%s' doesn't start with prefix '%s'"
print(fmt % (full_tag, tag_prefix))
pieces["error"] = "tag '{}' doesn't start with prefix '{}'".format(
full_tag,
tag_prefix,
)
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix) :]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[
0
].strip()
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces | [
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] | [
1044,
0
] | [
1141,
17
] | python | en | ['en', 'en', 'en'] | True |
do_vcs_install | (manifest_in, versionfile_source, ipy) | Git-specific installation logic for Versioneer.
For Git, this means creating/changing .gitattributes to mark _version.py
for export-subst keyword substitution.
| Git-specific installation logic for Versioneer. | def do_vcs_install(manifest_in, versionfile_source, ipy):
"""Git-specific installation logic for Versioneer.
For Git, this means creating/changing .gitattributes to mark _version.py
for export-subst keyword substitution.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
files = [manifest_in, versionfile_source]
if ipy:
files.append(ipy)
try:
me = __file__
if me.endswith(".pyc") or me.endswith(".pyo"):
me = os.path.splitext(me)[0] + ".py"
versioneer_file = os.path.relpath(me)
except NameError:
versioneer_file = "versioneer.py"
files.append(versioneer_file)
present = False
try:
f = open(".gitattributes")
for line in f.readlines():
if line.strip().startswith(versionfile_source):
if "export-subst" in line.strip().split()[1:]:
present = True
f.close()
except OSError:
pass
if not present:
f = open(".gitattributes", "a+")
f.write("%s export-subst\n" % versionfile_source)
f.close()
files.append(".gitattributes")
run_command(GITS, ["add", "--"] + files) | [
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] | [
1144,
0
] | [
1179,
44
] | python | en | ['en', 'en', 'en'] | True |
versions_from_parentdir | (parentdir_prefix, root, verbose) | Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
| Try to determine the version from the parent directory name. | def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for i in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {
"version": dirname[len(parentdir_prefix) :],
"full-revisionid": None,
"dirty": False,
"error": None,
"date": None,
}
else:
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print(
"Tried directories %s but none started with prefix %s"
% (str(rootdirs), parentdir_prefix)
)
raise NotThisMethod("rootdir doesn't start with parentdir_prefix") | [
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] | [
1210,
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] | python | en | ['en', 'en', 'en'] | True |
versions_from_file | (filename) | Try to determine the version from _version.py if present. | Try to determine the version from _version.py if present. | def versions_from_file(filename):
"""Try to determine the version from _version.py if present."""
try:
with open(filename) as f:
contents = f.read()
except OSError:
raise NotThisMethod("unable to read _version.py")
mo = re.search(
r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S
)
if not mo:
mo = re.search(
r"version_json = '''\r\n(.*)''' # END VERSION_JSON", contents, re.M | re.S
)
if not mo:
raise NotThisMethod("no version_json in _version.py")
return json.loads(mo.group(1)) | [
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] | [
1231,
0
] | [
1247,
34
] | python | en | ['en', 'en', 'en'] | True |
write_to_version_file | (filename, versions) | Write the given version number to the given _version.py file. | Write the given version number to the given _version.py file. | def write_to_version_file(filename, versions):
"""Write the given version number to the given _version.py file."""
os.unlink(filename)
contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": "))
with open(filename, "w") as f:
f.write(SHORT_VERSION_PY % contents)
print("set {} to '{}'".format(filename, versions["version"])) | [
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1250,
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1257,
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] | python | en | ['en', 'en', 'en'] | True |
plus_or_dot | (pieces) | Return a + if we don't already have one, else return a . | Return a + if we don't already have one, else return a . | def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+" | [
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] | [
1260,
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] | [
1264,
14
] | python | en | ['en', 'en', 'en'] | True |
render_pep440 | (pieces) | Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
| Build up version string, with post-release "local version identifier". | def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered | [
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1288,
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] | python | en | ['en', 'en', 'en'] | True |
render_pep440_pre | (pieces) | TAG[.post.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post.devDISTANCE
| TAG[.post.devDISTANCE] -- No -dirty. | def render_pep440_pre(pieces):
"""TAG[.post.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post.dev%d" % pieces["distance"]
return rendered | [
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1304,
19
] | python | en | ['en', 'en', 'pt'] | True |
render_pep440_post | (pieces) | TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
| TAG[.postDISTANCE[.dev0]+gHEX] . | def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
return rendered | [
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1307,
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] | [
1331,
19
] | python | cy | ['en', 'cy', 'hi'] | False |
render_pep440_old | (pieces) | TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Eexceptions:
1: no tags. 0.postDISTANCE[.dev0]
| TAG[.postDISTANCE[.dev0]] . | def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Eexceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered | [
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] | [
1334,
0
] | [
1353,
19
] | python | en | ['en', 'mt', 'hi'] | False |
render_git_describe | (pieces) | TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
| TAG[-DISTANCE-gHEX][-dirty]. | def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered | [
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1373,
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] | python | en | ['en', 'en', 'en'] | False |
render_git_describe_long | (pieces) | TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
| TAG-DISTANCE-gHEX[-dirty]. | def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered | [
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] | python | en | ['en', 'en', 'pt'] | False |
render | (pieces, style) | Render the given version pieces into the requested style. | Render the given version pieces into the requested style. | def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {
"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None,
}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%s'" % style)
return {
"version": rendered,
"full-revisionid": pieces["long"],
"dirty": pieces["dirty"],
"error": None,
"date": pieces.get("date"),
} | [
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] | python | en | ['en', 'en', 'en'] | True |
get_versions | (verbose=False) | Get the project version from whatever source is available.
Returns dict with two keys: 'version' and 'full'.
| Get the project version from whatever source is available. | def get_versions(verbose=False):
"""Get the project version from whatever source is available.
Returns dict with two keys: 'version' and 'full'.
"""
if "versioneer" in sys.modules:
# see the discussion in cmdclass.py:get_cmdclass()
del sys.modules["versioneer"]
root = get_root()
cfg = get_config_from_root(root)
assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
handlers = HANDLERS.get(cfg.VCS)
assert handlers, "unrecognized VCS '%s'" % cfg.VCS
verbose = verbose or cfg.verbose
assert (
cfg.versionfile_source is not None
), "please set versioneer.versionfile_source"
assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
versionfile_abs = os.path.join(root, cfg.versionfile_source)
# extract version from first of: _version.py, VCS command (e.g. 'git
# describe'), parentdir. This is meant to work for developers using a
# source checkout, for users of a tarball created by 'setup.py sdist',
# and for users of a tarball/zipball created by 'git archive' or github's
# download-from-tag feature or the equivalent in other VCSes.
get_keywords_f = handlers.get("get_keywords")
from_keywords_f = handlers.get("keywords")
if get_keywords_f and from_keywords_f:
try:
keywords = get_keywords_f(versionfile_abs)
ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
if verbose:
print("got version from expanded keyword %s" % ver)
return ver
except NotThisMethod:
pass
try:
ver = versions_from_file(versionfile_abs)
if verbose:
print("got version from file {} {}".format(versionfile_abs, ver))
return ver
except NotThisMethod:
pass
from_vcs_f = handlers.get("pieces_from_vcs")
if from_vcs_f:
try:
pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
ver = render(pieces, cfg.style)
if verbose:
print("got version from VCS %s" % ver)
return ver
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
if verbose:
print("got version from parentdir %s" % ver)
return ver
except NotThisMethod:
pass
if verbose:
print("unable to compute version")
return {
"version": "0+unknown",
"full-revisionid": None,
"dirty": None,
"error": "unable to compute version",
"date": None,
} | [
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1438,
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] | [
1516,
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get_version | () | Get the short version string for this project. | Get the short version string for this project. | def get_version():
"""Get the short version string for this project."""
return get_versions()["version"] | [
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] | [
1521,
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] | python | en | ['en', 'en', 'en'] | True |
get_cmdclass | () | Get the custom setuptools/distutils subclasses used by Versioneer. | Get the custom setuptools/distutils subclasses used by Versioneer. | def get_cmdclass():
"""Get the custom setuptools/distutils subclasses used by Versioneer."""
if "versioneer" in sys.modules:
del sys.modules["versioneer"]
# this fixes the "python setup.py develop" case (also 'install' and
# 'easy_install .'), in which subdependencies of the main project are
# built (using setup.py bdist_egg) in the same python process. Assume
# a main project A and a dependency B, which use different versions
# of Versioneer. A's setup.py imports A's Versioneer, leaving it in
# sys.modules by the time B's setup.py is executed, causing B to run
# with the wrong versioneer. Setuptools wraps the sub-dep builds in a
# sandbox that restores sys.modules to it's pre-build state, so the
# parent is protected against the child's "import versioneer". By
# removing ourselves from sys.modules here, before the child build
# happens, we protect the child from the parent's versioneer too.
# Also see https://github.com/warner/python-versioneer/issues/52
cmds = {}
# we add "version" to both distutils and setuptools
from distutils.core import Command
class cmd_version(Command):
description = "report generated version string"
user_options = []
boolean_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
vers = get_versions(verbose=True)
print("Version: %s" % vers["version"])
print(" full-revisionid: %s" % vers.get("full-revisionid"))
print(" dirty: %s" % vers.get("dirty"))
print(" date: %s" % vers.get("date"))
if vers["error"]:
print(" error: %s" % vers["error"])
cmds["version"] = cmd_version
# we override "build_py" in both distutils and setuptools
#
# most invocation pathways end up running build_py:
# distutils/build -> build_py
# distutils/install -> distutils/build ->..
# setuptools/bdist_wheel -> distutils/install ->..
# setuptools/bdist_egg -> distutils/install_lib -> build_py
# setuptools/install -> bdist_egg ->..
# setuptools/develop -> ?
# pip install:
# copies source tree to a tempdir before running egg_info/etc
# if .git isn't copied too, 'git describe' will fail
# then does setup.py bdist_wheel, or sometimes setup.py install
# setup.py egg_info -> ?
# we override different "build_py" commands for both environments
if "setuptools" in sys.modules:
from setuptools.command.build_py import build_py as _build_py
else:
from distutils.command.build_py import build_py as _build_py
class cmd_build_py(_build_py):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
_build_py.run(self)
# now locate _version.py in the new build/ directory and replace
# it with an updated value
if cfg.versionfile_build:
target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
cmds["build_py"] = cmd_build_py
if "cx_Freeze" in sys.modules: # cx_freeze enabled?
from cx_Freeze.dist import build_exe as _build_exe
# nczeczulin reports that py2exe won't like the pep440-style string
# as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
# setup(console=[{
# "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
# "product_version": versioneer.get_version(),
# ...
class cmd_build_exe(_build_exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_build_exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
cmds["build_exe"] = cmd_build_exe
del cmds["build_py"]
if "py2exe" in sys.modules: # py2exe enabled?
from py2exe.distutils_buildexe import py2exe as _py2exe # py3
class cmd_py2exe(_py2exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_py2exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
cmds["py2exe"] = cmd_py2exe
# we override different "sdist" commands for both environments
if "setuptools" in sys.modules:
from setuptools.command.sdist import sdist as _sdist
else:
from distutils.command.sdist import sdist as _sdist
class cmd_sdist(_sdist):
def run(self):
versions = get_versions()
self._versioneer_generated_versions = versions
# unless we update this, the command will keep using the old
# version
self.distribution.metadata.version = versions["version"]
return _sdist.run(self)
def make_release_tree(self, base_dir, files):
root = get_root()
cfg = get_config_from_root(root)
_sdist.make_release_tree(self, base_dir, files)
# now locate _version.py in the new base_dir directory
# (remembering that it may be a hardlink) and replace it with an
# updated value
target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
print("UPDATING %s" % target_versionfile)
write_to_version_file(
target_versionfile, self._versioneer_generated_versions
)
cmds["sdist"] = cmd_sdist
return cmds | [
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] | [
1524,
0
] | [
1700,
15
] | python | en | ['en', 'et', 'en'] | True |
do_setup | () | Main VCS-independent setup function for installing Versioneer. | Main VCS-independent setup function for installing Versioneer. | def do_setup():
"""Main VCS-independent setup function for installing Versioneer."""
root = get_root()
try:
cfg = get_config_from_root(root)
except (OSError, configparser.NoSectionError, configparser.NoOptionError) as e:
if isinstance(e, (EnvironmentError, configparser.NoSectionError)):
print("Adding sample versioneer config to setup.cfg", file=sys.stderr)
with open(os.path.join(root, "setup.cfg"), "a") as f:
f.write(SAMPLE_CONFIG)
print(CONFIG_ERROR, file=sys.stderr)
return 1
print(" creating %s" % cfg.versionfile_source)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py")
if os.path.exists(ipy):
try:
with open(ipy) as f:
old = f.read()
except OSError:
old = ""
if INIT_PY_SNIPPET not in old:
print(" appending to %s" % ipy)
with open(ipy, "a") as f:
f.write(INIT_PY_SNIPPET)
else:
print(" %s unmodified" % ipy)
else:
print(" %s doesn't exist, ok" % ipy)
ipy = None
# Make sure both the top-level "versioneer.py" and versionfile_source
# (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
# they'll be copied into source distributions. Pip won't be able to
# install the package without this.
manifest_in = os.path.join(root, "MANIFEST.in")
simple_includes = set()
try:
with open(manifest_in) as f:
for line in f:
if line.startswith("include "):
for include in line.split()[1:]:
simple_includes.add(include)
except OSError:
pass
# That doesn't cover everything MANIFEST.in can do
# (http://docs.python.org/2/distutils/sourcedist.html#commands), so
# it might give some false negatives. Appending redundant 'include'
# lines is safe, though.
if "versioneer.py" not in simple_includes:
print(" appending 'versioneer.py' to MANIFEST.in")
with open(manifest_in, "a") as f:
f.write("include versioneer.py\n")
else:
print(" 'versioneer.py' already in MANIFEST.in")
if cfg.versionfile_source not in simple_includes:
print(
" appending versionfile_source ('%s') to MANIFEST.in"
% cfg.versionfile_source
)
with open(manifest_in, "a") as f:
f.write("include %s\n" % cfg.versionfile_source)
else:
print(" versionfile_source already in MANIFEST.in")
# Make VCS-specific changes. For git, this means creating/changing
# .gitattributes to mark _version.py for export-subst keyword
# substitution.
do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
return 0 | [
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1747,
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] | [
1829,
12
] | python | en | ['en', 'en', 'en'] | True |
scan_setup_py | () | Validate the contents of setup.py against Versioneer's expectations. | Validate the contents of setup.py against Versioneer's expectations. | def scan_setup_py():
"""Validate the contents of setup.py against Versioneer's expectations."""
found = set()
setters = False
errors = 0
with open("setup.py") as f:
for line in f.readlines():
if "import versioneer" in line:
found.add("import")
if "versioneer.get_cmdclass()" in line:
found.add("cmdclass")
if "versioneer.get_version()" in line:
found.add("get_version")
if "versioneer.VCS" in line:
setters = True
if "versioneer.versionfile_source" in line:
setters = True
if len(found) != 3:
print("")
print("Your setup.py appears to be missing some important items")
print("(but I might be wrong). Please make sure it has something")
print("roughly like the following:")
print("")
print(" import versioneer")
print(" setup( version=versioneer.get_version(),")
print(" cmdclass=versioneer.get_cmdclass(), ...)")
print("")
errors += 1
if setters:
print("You should remove lines like 'versioneer.VCS = ' and")
print("'versioneer.versionfile_source = ' . This configuration")
print("now lives in setup.cfg, and should be removed from setup.py")
print("")
errors += 1
return errors | [
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1832,
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1866,
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] | python | en | ['en', 'en', 'en'] | True |
make_flatten | (decl_or_decls) |
Converts tree representation of declarations to flatten one.
:param decl_or_decls: reference to list of declaration's or single
declaration
:type decl_or_decls: :class:`declaration_t` or [ :class:`declaration_t` ]
:rtype: [ all internal declarations ]
|
Converts tree representation of declarations to flatten one. | def make_flatten(decl_or_decls):
"""
Converts tree representation of declarations to flatten one.
:param decl_or_decls: reference to list of declaration's or single
declaration
:type decl_or_decls: :class:`declaration_t` or [ :class:`declaration_t` ]
:rtype: [ all internal declarations ]
"""
def proceed_single(decl):
answer = [decl]
if not isinstance(decl, scopedef_t):
return answer
for elem in decl.declarations:
if isinstance(elem, scopedef_t):
answer.extend(proceed_single(elem))
else:
answer.append(elem)
return answer
decls = []
if isinstance(decl_or_decls, list):
decls.extend(decl_or_decls)
else:
decls.append(decl_or_decls)
answer = []
for decl in decls:
answer.extend(proceed_single(decl))
return answer | [
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1251,
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find_all_declarations | (
declarations,
decl_type=None,
name=None,
parent=None,
recursive=True,
fullname=None) |
Returns a list of all declarations that match criteria, defined by
developer.
For more information about arguments see :class:`match_declaration_t`
class.
:rtype: [ matched declarations ]
|
Returns a list of all declarations that match criteria, defined by
developer. | def find_all_declarations(
declarations,
decl_type=None,
name=None,
parent=None,
recursive=True,
fullname=None):
"""
Returns a list of all declarations that match criteria, defined by
developer.
For more information about arguments see :class:`match_declaration_t`
class.
:rtype: [ matched declarations ]
"""
if recursive:
decls = make_flatten(declarations)
else:
decls = declarations
return list(
filter(
algorithm.match_declaration_t(
decl_type=decl_type,
name=name,
fullname=fullname,
parent=parent),
decls)) | [
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1284,
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] | python | en | ['en', 'error', 'th'] | False |
find_declaration | (
declarations,
decl_type=None,
name=None,
parent=None,
recursive=True,
fullname=None) |
Returns single declaration that match criteria, defined by developer.
If more the one declaration was found None will be returned.
For more information about arguments see :class:`match_declaration_t`
class.
:rtype: matched declaration :class:`declaration_t` or None
|
Returns single declaration that match criteria, defined by developer.
If more the one declaration was found None will be returned. | def find_declaration(
declarations,
decl_type=None,
name=None,
parent=None,
recursive=True,
fullname=None):
"""
Returns single declaration that match criteria, defined by developer.
If more the one declaration was found None will be returned.
For more information about arguments see :class:`match_declaration_t`
class.
:rtype: matched declaration :class:`declaration_t` or None
"""
decl = find_all_declarations(
declarations,
decl_type=decl_type,
name=name,
parent=parent,
recursive=recursive,
fullname=fullname)
if len(decl) == 1:
return decl[0] | [
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1313,
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] | python | en | ['en', 'error', 'th'] | False |
find_first_declaration | (
declarations,
decl_type=None,
name=None,
parent=None,
recursive=True,
fullname=None) |
Returns first declaration that match criteria, defined by developer.
For more information about arguments see :class:`match_declaration_t`
class.
:rtype: matched declaration :class:`declaration_t` or None
|
Returns first declaration that match criteria, defined by developer. | def find_first_declaration(
declarations,
decl_type=None,
name=None,
parent=None,
recursive=True,
fullname=None):
"""
Returns first declaration that match criteria, defined by developer.
For more information about arguments see :class:`match_declaration_t`
class.
:rtype: matched declaration :class:`declaration_t` or None
"""
decl_matcher = algorithm.match_declaration_t(
decl_type=decl_type,
name=name,
fullname=fullname,
parent=parent)
if recursive:
decls = make_flatten(declarations)
else:
decls = declarations
for decl in decls:
if decl_matcher(decl):
return decl
return None | [
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1316,
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1345,
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] | python | en | ['en', 'error', 'th'] | False |
declaration_files | (decl_or_decls) |
Returns set of files
Every declaration is declared in some file. This function returns set, that
contains all file names of declarations.
:param decl_or_decls: reference to list of declaration's or single
declaration
:type decl_or_decls: :class:`declaration_t` or [:class:`declaration_t`]
:rtype: set(declaration file names)
|
Returns set of files | def declaration_files(decl_or_decls):
"""
Returns set of files
Every declaration is declared in some file. This function returns set, that
contains all file names of declarations.
:param decl_or_decls: reference to list of declaration's or single
declaration
:type decl_or_decls: :class:`declaration_t` or [:class:`declaration_t`]
:rtype: set(declaration file names)
"""
files = set()
decls = make_flatten(decl_or_decls)
for decl in decls:
if decl.location:
files.add(decl.location.file_name)
return files | [
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matcher.find | (decl_matcher, decls, recursive=True) |
Returns a list of declarations that match `decl_matcher` defined
criteria or None
:param decl_matcher: Python callable object, that takes one argument -
reference to a declaration
:param decls: the search scope, :class:declaration_t object or
:class:declaration_t objects list t
:param recursive: boolean, if True, the method will run `decl_matcher`
on the internal declarations too
|
Returns a list of declarations that match `decl_matcher` defined
criteria or None | def find(decl_matcher, decls, recursive=True):
"""
Returns a list of declarations that match `decl_matcher` defined
criteria or None
:param decl_matcher: Python callable object, that takes one argument -
reference to a declaration
:param decls: the search scope, :class:declaration_t object or
:class:declaration_t objects list t
:param recursive: boolean, if True, the method will run `decl_matcher`
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"""
where = []
if isinstance(decls, list):
where.extend(decls)
else:
where.append(decls)
if recursive:
where = make_flatten(where)
return list(filter(decl_matcher, where)) | [
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matcher.find_single | (decl_matcher, decls, recursive=True) |
Returns a reference to the declaration, that match `decl_matcher`
defined criteria.
if a unique declaration could not be found the method will return None.
:param decl_matcher: Python callable object, that takes one argument -
reference to a declaration
:param decls: the search scope, :class:declaration_t object or
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:param recursive: boolean, if True, the method will run `decl_matcher`
on the internal declarations too
|
Returns a reference to the declaration, that match `decl_matcher`
defined criteria. | def find_single(decl_matcher, decls, recursive=True):
"""
Returns a reference to the declaration, that match `decl_matcher`
defined criteria.
if a unique declaration could not be found the method will return None.
:param decl_matcher: Python callable object, that takes one argument -
reference to a declaration
:param decls: the search scope, :class:declaration_t object or
:class:declaration_t objects list t
:param recursive: boolean, if True, the method will run `decl_matcher`
on the internal declarations too
"""
answer = matcher.find(decl_matcher, decls, recursive)
if len(answer) == 1:
return answer[0] | [
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matcher.get_single | (decl_matcher, decls, recursive=True) |
Returns a reference to declaration, that match `decl_matcher` defined
criteria.
If a unique declaration could not be found, an appropriate exception
will be raised.
:param decl_matcher: Python callable object, that takes one argument -
reference to a declaration
:param decls: the search scope, :class:declaration_t object or
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:param recursive: boolean, if True, the method will run `decl_matcher`
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|
Returns a reference to declaration, that match `decl_matcher` defined
criteria. | def get_single(decl_matcher, decls, recursive=True):
"""
Returns a reference to declaration, that match `decl_matcher` defined
criteria.
If a unique declaration could not be found, an appropriate exception
will be raised.
:param decl_matcher: Python callable object, that takes one argument -
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:param decls: the search scope, :class:declaration_t object or
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:param recursive: boolean, if True, the method will run `decl_matcher`
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"""
answer = matcher.find(decl_matcher, decls, recursive)
if len(answer) == 1:
return answer[0]
elif not answer:
raise runtime_errors.declaration_not_found_t(decl_matcher)
else:
raise runtime_errors.multiple_declarations_found_t(decl_matcher) | [
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scopedef_t._logger | (self) | reference to :attr:`pygccxml.utils.loggers.queries_engine` logger | reference to :attr:`pygccxml.utils.loggers.queries_engine` logger | def _logger(self):
"""reference to :attr:`pygccxml.utils.loggers.queries_engine` logger"""
return utils.loggers.queries_engine | [
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scopedef_t._get__cmp__scope_items | (self) | implementation details | implementation details | def _get__cmp__scope_items(self):
"""implementation details"""
raise NotImplementedError() | [
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scopedef_t._get__cmp__items | (self) | implementation details | implementation details | def _get__cmp__items(self):
"""implementation details"""
items = []
if self._optimized:
# in this case we don't need to build class internal declarations
# list
items.append(self._all_decls_not_recursive.sort())
else:
items.append(self.declarations.sort())
items.extend(self._get__cmp__scope_items())
return items | [
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scopedef_t.declarations | (self) |
List of children declarations.
Returns:
List[declarations.declaration_t]
|
List of children declarations. | def declarations(self):
"""
List of children declarations.
Returns:
List[declarations.declaration_t]
"""
if self._optimized:
return self._all_decls_not_recursive
else:
return self._get_declarations_impl() | [
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scopedef_t.declarations | (self, declarations) |
Set list of all declarations defined in the namespace.
Args:
List[declarations.declaration_t]: list of declarations
Not implemented.
|
Set list of all declarations defined in the namespace. | def declarations(self, declarations):
"""
Set list of all declarations defined in the namespace.
Args:
List[declarations.declaration_t]: list of declarations
Not implemented.
"""
raise NotImplementedError() | [
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scopedef_t.__decl_types | (decl) | implementation details | implementation details | def __decl_types(decl):
"""implementation details"""
types = []
bases = list(decl.__class__.__bases__)
if 'pygccxml' in decl.__class__.__module__:
types.append(decl.__class__)
while bases:
base = bases.pop()
if base is declaration.declaration_t:
continue
if base is byte_info.byte_info:
continue
if base is elaborated_info.elaborated_info:
continue
if 'pygccxml' not in base.__module__:
continue
types.append(base)
bases.extend(base.__bases__)
return types | [
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scopedef_t.clear_optimizer | (self) | Cleans query optimizer state | Cleans query optimizer state | def clear_optimizer(self):
"""Cleans query optimizer state"""
self._optimized = False
self._type2decls = {}
self._type2name2decls = {}
self._type2decls_nr = {}
self._type2name2decls_nr = {}
self._all_decls = None
self._all_decls_not_recursive = None
for decl in self.declarations:
if isinstance(decl, scopedef_t):
decl.clear_optimizer() | [
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scopedef_t.init_optimizer | (self) |
Initializes query optimizer state.
There are 4 internals hash tables:
1. from type to declarations
2. from type to declarations for non-recursive queries
3. from type to name to declarations
4. from type to name to declarations for non-recursive queries
Almost every query includes declaration type information. Also very
common query is to search some declaration(s) by name or full name.
Those hash tables allows to search declaration very quick.
|
Initializes query optimizer state. | def init_optimizer(self):
"""
Initializes query optimizer state.
There are 4 internals hash tables:
1. from type to declarations
2. from type to declarations for non-recursive queries
3. from type to name to declarations
4. from type to name to declarations for non-recursive queries
Almost every query includes declaration type information. Also very
common query is to search some declaration(s) by name or full name.
Those hash tables allows to search declaration very quick.
"""
if self.name == '::':
self._logger.debug(
"preparing data structures for query optimizer - started")
start_time = time.clock()
self.clear_optimizer()
for dtype in scopedef_t._impl_all_decl_types:
self._type2decls[dtype] = []
self._type2decls_nr[dtype] = []
self._type2name2decls[dtype] = {}
self._type2name2decls_nr[dtype] = {}
self._all_decls_not_recursive = self.declarations
self._all_decls = make_flatten(
self._all_decls_not_recursive)
for decl in self._all_decls:
types = self.__decl_types(decl)
for type_ in types:
self._type2decls[type_].append(decl)
name2decls = self._type2name2decls[type_]
if decl.name not in name2decls:
name2decls[decl.name] = []
name2decls[decl.name].append(decl)
if self is decl.parent:
self._type2decls_nr[type_].append(decl)
name2decls_nr = self._type2name2decls_nr[type_]
if decl.name not in name2decls_nr:
name2decls_nr[decl.name] = []
name2decls_nr[decl.name].append(decl)
for decl in self._all_decls_not_recursive:
if isinstance(decl, scopedef_t):
decl.init_optimizer()
if self.name == '::':
self._logger.debug((
"preparing data structures for query optimizer - " +
"done( %f seconds ). "), (time.clock() - start_time))
self._optimized = True | [
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330,
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scopedef_t._build_operator_name | (name, function, symbol) | implementation details | implementation details | def _build_operator_name(name, function, symbol):
"""implementation details"""
def add_operator(sym):
if 'new' in sym or 'delete' in sym:
return 'operator ' + sym
else:
return 'operator' + sym
if isinstance(name, collections.Callable) and None is function:
name = None
if name:
if 'operator' not in name:
name = add_operator(name)
return name
elif symbol:
return add_operator(symbol)
return name | [
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scopedef_t.__normalize_args | (**keywds) | implementation details | implementation details | def __normalize_args(**keywds):
"""implementation details"""
if isinstance(keywds['name'], collections.Callable) and \
None is keywds['function']:
keywds['function'] = keywds['name']
keywds['name'] = None
return keywds | [
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scopedef_t.__findout_recursive | (self, **keywds) | implementation details | implementation details | def __findout_recursive(self, **keywds):
"""implementation details"""
if None is keywds['recursive']:
return self.RECURSIVE_DEFAULT
else:
return keywds['recursive'] | [
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scopedef_t.__findout_allow_empty | (self, **keywds) | implementation details | implementation details | def __findout_allow_empty(self, **keywds):
"""implementation details"""
if None is keywds['allow_empty']:
return self.ALLOW_EMPTY_MDECL_WRAPPER
else:
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scopedef_t.__findout_decl_type | (match_class, **keywds) | implementation details | implementation details | def __findout_decl_type(match_class, **keywds):
"""implementation details"""
if 'decl_type' in keywds:
return keywds['decl_type']
matcher_args = keywds.copy()
del matcher_args['function']
del matcher_args['recursive']
if 'allow_empty' in matcher_args:
del matcher_args['allow_empty']
decl_matcher = match_class(**matcher_args)
if decl_matcher.decl_type:
return decl_matcher.decl_type
return None | [
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scopedef_t.__create_matcher | (self, match_class, **keywds) | implementation details | implementation details | def __create_matcher(self, match_class, **keywds):
"""implementation details"""
matcher_args = keywds.copy()
del matcher_args['function']
del matcher_args['recursive']
if 'allow_empty' in matcher_args:
del matcher_args['allow_empty']
decl_matcher = decl_matcher = match_class(**matcher_args)
if keywds['function']:
self._logger.debug(
'running query: %s and <user defined function>',
str(decl_matcher))
return lambda decl: decl_matcher(decl) and keywds['function'](decl)
else:
self._logger.debug('running query: %s', str(decl_matcher))
return decl_matcher | [
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scopedef_t.__findout_range | (self, name, decl_type, recursive) | implementation details | implementation details | def __findout_range(self, name, decl_type, recursive):
"""implementation details"""
if not self._optimized:
self._logger.debug(
'running non optimized query - optimization has not been done')
decls = self.declarations
if recursive:
decls = make_flatten(self.declarations)
if decl_type:
decls = [d for d in decls if isinstance(d, decl_type)]
return decls
if name and templates.is_instantiation(name):
# templates has tricky mode to compare them, so lets check the
# whole range
name = None
if name and decl_type:
impl_match = scopedef_t._impl_matchers[scopedef_t.decl](name=name)
if impl_match.is_full_name():
name = impl_match.decl_name_only
if recursive:
self._logger.debug(
'query has been optimized on type and name')
return self._type2name2decls[decl_type].get(name, [])
else:
self._logger.debug(
'non recursive query has been optimized on type and name')
return self._type2name2decls_nr[decl_type].get(name, [])
elif decl_type:
if recursive:
self._logger.debug('query has been optimized on type')
return self._type2decls[decl_type]
else:
self._logger.debug(
'non recursive query has been optimized on type')
return self._type2decls_nr[decl_type]
else:
if recursive:
self._logger.debug((
'query has not been optimized ( hint: query does not ' +
'contain type and/or name )'))
return self._all_decls
else:
self._logger.debug((
'non recursive query has not been optimized ( hint: ' +
'query does not contain type and/or name )'))
return self._all_decls_not_recursive | [
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scopedef_t._find_single | (self, match_class, **keywds) | implementation details | implementation details | def _find_single(self, match_class, **keywds):
"""implementation details"""
self._logger.debug('find single query execution - started')
start_time = time.clock()
norm_keywds = self.__normalize_args(**keywds)
decl_matcher = self.__create_matcher(match_class, **norm_keywds)
dtype = self.__findout_decl_type(match_class, **norm_keywds)
recursive_ = self.__findout_recursive(**norm_keywds)
decls = self.__findout_range(norm_keywds['name'], dtype, recursive_)
found = matcher.get_single(decl_matcher, decls, False)
self._logger.debug(
'find single query execution - done( %f seconds )',
(time.clock() - start_time))
return found | [
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scopedef_t._find_multiple | (self, match_class, **keywds) | implementation details | implementation details | def _find_multiple(self, match_class, **keywds):
"""implementation details"""
self._logger.debug('find all query execution - started')
start_time = time.clock()
norm_keywds = self.__normalize_args(**keywds)
decl_matcher = self.__create_matcher(match_class, **norm_keywds)
dtype = self.__findout_decl_type(match_class, **norm_keywds)
recursive_ = self.__findout_recursive(**norm_keywds)
allow_empty = self.__findout_allow_empty(**norm_keywds)
decls = self.__findout_range(norm_keywds['name'], dtype, recursive_)
found = matcher.find(decl_matcher, decls, False)
mfound = mdecl_wrapper.mdecl_wrapper_t(found)
self._logger.debug('%d declaration(s) that match query', len(mfound))
self._logger.debug(
'find single query execution - done( %f seconds )',
(time.clock() - start_time))
if not mfound and not allow_empty:
raise RuntimeError(
"Multi declaration query returned 0 declarations.")
return mfound | [
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scopedef_t.decl | (
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to declaration, that is matched defined
criteria | returns reference to declaration, that is matched defined
criteria | def decl(
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to declaration, that is matched defined
criteria"""
return (
self._find_single(
self._impl_matchers[
scopedef_t.decl],
name=name,
function=function,
decl_type=decl_type,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.decls | (
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of declarations, that are matched defined criteria | returns a set of declarations, that are matched defined criteria | def decls(
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of declarations, that are matched defined criteria"""
return (
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self._impl_matchers[
scopedef_t.decl],
name=name,
function=function,
decl_type=decl_type,
header_dir=header_dir,
header_file=header_file,
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.class_ | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to class declaration, that is matched defined
criteria | returns reference to class declaration, that is matched defined
criteria | def class_(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to class declaration, that is matched defined
criteria"""
return (
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self._impl_matchers[scopedef_t.class_],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.class_],
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recursive=recursive)
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.classes | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of class declarations, that are matched defined
criteria | returns a set of class declarations, that are matched defined
criteria | def classes(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of class declarations, that are matched defined
criteria"""
return (
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self._impl_matchers[scopedef_t.class_],
name=name,
function=function,
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recursive=recursive,
allow_empty=allow_empty)
) | [
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.variable | (
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to variable declaration, that is matched defined
criteria | returns reference to variable declaration, that is matched defined
criteria | def variable(
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to variable declaration, that is matched defined
criteria"""
return (
self._find_single(
self._impl_matchers[
scopedef_t.variable],
name=name,
function=function,
decl_type=decl_type,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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")",
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] | [
599,
4
] | [
620,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.variables | (
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of variable declarations, that are matched defined
criteria | returns a set of variable declarations, that are matched defined
criteria | def variables(
self,
name=None,
function=None,
decl_type=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of variable declarations, that are matched defined
criteria"""
return (
self._find_multiple(
self._impl_matchers[
scopedef_t.variable],
name=name,
function=function,
decl_type=decl_type,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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622,
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645,
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.calldef | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to "calldef" declaration, that is matched defined
criteria | returns reference to "calldef" declaration, that is matched defined
criteria | def calldef(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to "calldef" declaration, that is matched defined
criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.calldef],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.calldef],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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647,
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] | [
670,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.calldefs | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of :class:`calldef_t` declarations, that are matched
defined criteria | returns a set of :class:`calldef_t` declarations, that are matched
defined criteria | def calldefs(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of :class:`calldef_t` declarations, that are matched
defined criteria"""
return (
self._find_multiple(
self._impl_matchers[scopedef_t.calldef],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.calldef],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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672,
4
] | [
697,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.operator | (
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to operator declaration, that is matched
defined criteria | returns reference to operator declaration, that is matched
defined criteria | def operator(
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to operator declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.operator],
name=self._build_operator_name(name,
function,
symbol),
symbol=symbol,
function=self._build_operator_function(name,
function),
decl_type=self._impl_decl_types[scopedef_t.operator],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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699,
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726,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.operators | (
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of operator declarations, that are matched
defined criteria | returns a set of operator declarations, that are matched
defined criteria | def operators(
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of operator declarations, that are matched
defined criteria"""
return (
self._find_multiple(
self._impl_matchers[scopedef_t.operator],
name=self._build_operator_name(name,
function,
symbol),
symbol=symbol,
function=self._build_operator_function(name,
function),
decl_type=self._impl_decl_types[scopedef_t.operator],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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728,
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757,
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scopedef_t.member_function | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to member declaration, that is matched
defined criteria | returns reference to member declaration, that is matched
defined criteria | def member_function(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to member declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.member_function],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.member_function],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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759,
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] | [
782,
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.mem_fun | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) |
Deprecated method. Use the member_function() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
|
Deprecated method. Use the member_function() method instead. | def mem_fun(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""
Deprecated method. Use the member_function() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
"""
warnings.warn(
"The mem_fun() method is deprecated. \n" +
"Please use the member_function() method instead.",
DeprecationWarning)
return self.member_function(
name, function,
return_type, arg_types,
header_dir, header_file,
recursive) | [
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784,
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] | [
807,
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] | python | en | ['en', 'error', 'th'] | False |
scopedef_t.member_functions | (
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name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of member function declarations, that are matched
defined criteria | returns a set of member function declarations, that are matched
defined criteria | def member_functions(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of member function declarations, that are matched
defined criteria"""
return (
self._find_multiple(
self._impl_matchers[scopedef_t.member_function],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.member_function],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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809,
4
] | [
834,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.mem_funs | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) |
Deprecated method. Use the member_functions() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
|
Deprecated method. Use the member_functions() method instead. | def mem_funs(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""
Deprecated method. Use the member_functions() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
"""
warnings.warn(
"The mem_funs() method is deprecated. \n" +
"Please use the member_functions() method instead.",
DeprecationWarning)
return self.member_functions(
name, function,
return_type, arg_types,
header_dir, header_file,
recursive, allow_empty) | [
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scopedef_t.constructor | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to constructor declaration, that is matched
defined criteria | returns reference to constructor declaration, that is matched
defined criteria | def constructor(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to constructor declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.constructor],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.constructor],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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862,
4
] | [
885,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.constructors | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of constructor declarations, that are matched
defined criteria | returns a set of constructor declarations, that are matched
defined criteria | def constructors(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of constructor declarations, that are matched
defined criteria"""
return (
self._find_multiple(
self._impl_matchers[scopedef_t.constructor],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.constructor],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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887,
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] | [
912,
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] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.member_operator | (
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to member operator declaration, that is matched
defined criteria | returns reference to member operator declaration, that is matched
defined criteria | def member_operator(
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to member operator declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.member_operator],
name=self._build_operator_name(name,
function,
symbol),
symbol=symbol,
function=self._build_operator_function(name,
function),
decl_type=self._impl_decl_types[scopedef_t.member_operator],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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914,
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941,
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scopedef_t.mem_oper | (
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) |
Deprecated method. Use the member_operator() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
|
Deprecated method. Use the member_operator() method instead. | def mem_oper(
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""
Deprecated method. Use the member_operator() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
"""
warnings.warn(
"The mem_oper() method is deprecated. \n" +
"Please use the member_operator() method instead.",
DeprecationWarning)
return self.member_operator(
name, function,
symbol, return_type,
arg_types, header_dir,
header_file, recursive) | [
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943,
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967,
35
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scopedef_t.member_operators | (
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of member operator declarations, that are matched
defined criteria | returns a set of member operator declarations, that are matched
defined criteria | def member_operators(
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of member operator declarations, that are matched
defined criteria"""
return (
self._find_multiple(
self._impl_matchers[scopedef_t.member_operator],
name=self._build_operator_name(name,
function,
symbol),
symbol=symbol,
function=self._build_operator_function(name,
function),
decl_type=self._impl_decl_types[scopedef_t.member_operator],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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969,
4
] | [
998,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.mem_opers | (
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) |
Deprecated method. Use the member_operators() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
|
Deprecated method. Use the member_operators() method instead. | def mem_opers(
self,
name=None,
function=None,
symbol=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""
Deprecated method. Use the member_operators() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
"""
warnings.warn(
"The mem_opers() method is deprecated. \n" +
"Please use the member_operators() method instead.",
DeprecationWarning)
return self.member_operators(
name, function,
symbol, return_type,
arg_types, header_dir,
header_file, recursive,
allow_empty) | [
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1000,
4
] | [
1026,
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] | python | en | ['en', 'error', 'th'] | False |
scopedef_t.casting_operator | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to casting operator declaration, that is matched
defined criteria | returns reference to casting operator declaration, that is matched
defined criteria | def casting_operator(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to casting operator declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.casting_operator],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.casting_operator],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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1028,
4
] | [
1051,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.casting_operators | (
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of casting operator declarations, that are matched
defined criteria | returns a set of casting operator declarations, that are matched
defined criteria | def casting_operators(
self,
name=None,
function=None,
return_type=None,
arg_types=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of casting operator declarations, that are matched
defined criteria"""
return (
self._find_multiple(
self._impl_matchers[scopedef_t.casting_operator],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.casting_operator],
return_type=return_type,
arg_types=arg_types,
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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1053,
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] | [
1078,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.enumeration | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to enumeration declaration, that is matched
defined criteria | returns reference to enumeration declaration, that is matched
defined criteria | def enumeration(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to enumeration declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.enumeration],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.enumeration],
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
) | [
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1080,
4
] | [
1099,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.enum | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None) |
Deprecated method. Use the enumeration() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
|
Deprecated method. Use the enumeration() method instead. | def enum(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None):
"""
Deprecated method. Use the enumeration() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
"""
warnings.warn(
"The enum() method is deprecated. \n" +
"Please use the enumeration() method instead.",
DeprecationWarning)
return self.enumeration(
name, function, header_dir, header_file, recursive) | [
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1101,
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1119,
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scopedef_t.enumerations | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) | returns a set of enumeration declarations, that are matched
defined criteria | returns a set of enumeration declarations, that are matched
defined criteria | def enumerations(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""returns a set of enumeration declarations, that are matched
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return (
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name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.enumeration],
header_dir=header_dir,
header_file=header_file,
recursive=recursive,
allow_empty=allow_empty)
) | [
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")",
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1121,
4
] | [
1142,
9
] | python | en | ['en', 'en', 'en'] | True |
scopedef_t.enums | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None) |
Deprecated method. Use the enumerations() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
|
Deprecated method. Use the enumerations() method instead. | def enums(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None,
allow_empty=None):
"""
Deprecated method. Use the enumerations() method instead.
Deprecated since v1.9.0. Will be removed in v2.0.0
"""
warnings.warn(
"The enums() method is deprecated. \n" +
"Please use the enumerations() method instead.",
DeprecationWarning)
return self.enumerations(
name, function, header_dir, header_file, recursive, allow_empty) | [
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1163,
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] | python | en | ['en', 'error', 'th'] | False |
scopedef_t.typedef | (
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None) | returns reference to typedef declaration, that is matched
defined criteria | returns reference to typedef declaration, that is matched
defined criteria | def typedef(
self,
name=None,
function=None,
header_dir=None,
header_file=None,
recursive=None):
"""returns reference to typedef declaration, that is matched
defined criteria"""
return (
self._find_single(
self._impl_matchers[scopedef_t.typedef],
name=name,
function=function,
decl_type=self._impl_decl_types[
scopedef_t.typedef],
header_dir=header_dir,
header_file=header_file,
recursive=recursive)
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] | python | en | ['en', 'en', 'en'] | True |
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