id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1 value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
32,200 | elastic/elasticsearch-py | elasticsearch/client/utils.py | query_params | def query_params(*es_query_params):
"""
Decorator that pops all accepted parameters from method's kwargs and puts
them in the params argument.
"""
def _wrapper(func):
@wraps(func)
def _wrapped(*args, **kwargs):
params = {}
if "params" in kwargs:
params = kwargs.pop("params").copy()
for p in es_query_params + GLOBAL_PARAMS:
if p in kwargs:
v = kwargs.pop(p)
if v is not None:
params[p] = _escape(v)
# don't treat ignore and request_timeout as other params to avoid escaping
for p in ("ignore", "request_timeout"):
if p in kwargs:
params[p] = kwargs.pop(p)
return func(*args, params=params, **kwargs)
return _wrapped
return _wrapper | python | def query_params(*es_query_params):
"""
Decorator that pops all accepted parameters from method's kwargs and puts
them in the params argument.
"""
def _wrapper(func):
@wraps(func)
def _wrapped(*args, **kwargs):
params = {}
if "params" in kwargs:
params = kwargs.pop("params").copy()
for p in es_query_params + GLOBAL_PARAMS:
if p in kwargs:
v = kwargs.pop(p)
if v is not None:
params[p] = _escape(v)
# don't treat ignore and request_timeout as other params to avoid escaping
for p in ("ignore", "request_timeout"):
if p in kwargs:
params[p] = kwargs.pop(p)
return func(*args, params=params, **kwargs)
return _wrapped
return _wrapper | [
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32,201 | elastic/elasticsearch-py | example/queries.py | print_hits | def print_hits(results):
" Simple utility function to print results of a search query. "
print_search_stats(results)
for hit in results['hits']['hits']:
# get created date for a repo and fallback to authored_date for a commit
created_at = parse_date(hit['_source'].get('created_at', hit['_source']['authored_date']))
print('/%s/%s/%s (%s): %s' % (
hit['_index'], hit['_type'], hit['_id'],
created_at.strftime('%Y-%m-%d'),
hit['_source']['description'].split('\n')[0]))
print('=' * 80)
print() | python | def print_hits(results):
" Simple utility function to print results of a search query. "
print_search_stats(results)
for hit in results['hits']['hits']:
# get created date for a repo and fallback to authored_date for a commit
created_at = parse_date(hit['_source'].get('created_at', hit['_source']['authored_date']))
print('/%s/%s/%s (%s): %s' % (
hit['_index'], hit['_type'], hit['_id'],
created_at.strftime('%Y-%m-%d'),
hit['_source']['description'].split('\n')[0]))
print('=' * 80)
print() | [
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32,202 | fmfn/BayesianOptimization | examples/async_optimization.py | BayesianOptimizationHandler.post | def post(self):
"""Deal with incoming requests."""
body = tornado.escape.json_decode(self.request.body)
try:
self._bo.register(
params=body["params"],
target=body["target"],
)
print("BO has registered: {} points.".format(len(self._bo.space)), end="\n\n")
except KeyError:
pass
finally:
suggested_params = self._bo.suggest(self._uf)
self.write(json.dumps(suggested_params)) | python | def post(self):
"""Deal with incoming requests."""
body = tornado.escape.json_decode(self.request.body)
try:
self._bo.register(
params=body["params"],
target=body["target"],
)
print("BO has registered: {} points.".format(len(self._bo.space)), end="\n\n")
except KeyError:
pass
finally:
suggested_params = self._bo.suggest(self._uf)
self.write(json.dumps(suggested_params)) | [
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32,203 | fmfn/BayesianOptimization | bayes_opt/bayesian_optimization.py | BayesianOptimization.register | def register(self, params, target):
"""Expect observation with known target"""
self._space.register(params, target)
self.dispatch(Events.OPTMIZATION_STEP) | python | def register(self, params, target):
"""Expect observation with known target"""
self._space.register(params, target)
self.dispatch(Events.OPTMIZATION_STEP) | [
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32,204 | fmfn/BayesianOptimization | bayes_opt/bayesian_optimization.py | BayesianOptimization.probe | def probe(self, params, lazy=True):
"""Probe target of x"""
if lazy:
self._queue.add(params)
else:
self._space.probe(params)
self.dispatch(Events.OPTMIZATION_STEP) | python | def probe(self, params, lazy=True):
"""Probe target of x"""
if lazy:
self._queue.add(params)
else:
self._space.probe(params)
self.dispatch(Events.OPTMIZATION_STEP) | [
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32,205 | fmfn/BayesianOptimization | bayes_opt/bayesian_optimization.py | BayesianOptimization.suggest | def suggest(self, utility_function):
"""Most promissing point to probe next"""
if len(self._space) == 0:
return self._space.array_to_params(self._space.random_sample())
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
# Finding argmax of the acquisition function.
suggestion = acq_max(
ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state
)
return self._space.array_to_params(suggestion) | python | def suggest(self, utility_function):
"""Most promissing point to probe next"""
if len(self._space) == 0:
return self._space.array_to_params(self._space.random_sample())
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
# Finding argmax of the acquisition function.
suggestion = acq_max(
ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state
)
return self._space.array_to_params(suggestion) | [
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32,206 | fmfn/BayesianOptimization | bayes_opt/bayesian_optimization.py | BayesianOptimization._prime_queue | def _prime_queue(self, init_points):
"""Make sure there's something in the queue at the very beginning."""
if self._queue.empty and self._space.empty:
init_points = max(init_points, 1)
for _ in range(init_points):
self._queue.add(self._space.random_sample()) | python | def _prime_queue(self, init_points):
"""Make sure there's something in the queue at the very beginning."""
if self._queue.empty and self._space.empty:
init_points = max(init_points, 1)
for _ in range(init_points):
self._queue.add(self._space.random_sample()) | [
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32,207 | fmfn/BayesianOptimization | bayes_opt/bayesian_optimization.py | BayesianOptimization.maximize | def maximize(self,
init_points=5,
n_iter=25,
acq='ucb',
kappa=2.576,
xi=0.0,
**gp_params):
"""Mazimize your function"""
self._prime_subscriptions()
self.dispatch(Events.OPTMIZATION_START)
self._prime_queue(init_points)
self.set_gp_params(**gp_params)
util = UtilityFunction(kind=acq, kappa=kappa, xi=xi)
iteration = 0
while not self._queue.empty or iteration < n_iter:
try:
x_probe = next(self._queue)
except StopIteration:
x_probe = self.suggest(util)
iteration += 1
self.probe(x_probe, lazy=False)
self.dispatch(Events.OPTMIZATION_END) | python | def maximize(self,
init_points=5,
n_iter=25,
acq='ucb',
kappa=2.576,
xi=0.0,
**gp_params):
"""Mazimize your function"""
self._prime_subscriptions()
self.dispatch(Events.OPTMIZATION_START)
self._prime_queue(init_points)
self.set_gp_params(**gp_params)
util = UtilityFunction(kind=acq, kappa=kappa, xi=xi)
iteration = 0
while not self._queue.empty or iteration < n_iter:
try:
x_probe = next(self._queue)
except StopIteration:
x_probe = self.suggest(util)
iteration += 1
self.probe(x_probe, lazy=False)
self.dispatch(Events.OPTMIZATION_END) | [
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32,208 | fmfn/BayesianOptimization | bayes_opt/target_space.py | TargetSpace.register | def register(self, params, target):
"""
Append a point and its target value to the known data.
Parameters
----------
x : ndarray
a single point, with len(x) == self.dim
y : float
target function value
Raises
------
KeyError:
if the point is not unique
Notes
-----
runs in ammortized constant time
Example
-------
>>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
>>> space = TargetSpace(lambda p1, p2: p1 + p2, pbounds)
>>> len(space)
0
>>> x = np.array([0, 0])
>>> y = 1
>>> space.add_observation(x, y)
>>> len(space)
1
"""
x = self._as_array(params)
if x in self:
raise KeyError('Data point {} is not unique'.format(x))
# Insert data into unique dictionary
self._cache[_hashable(x.ravel())] = target
self._params = np.concatenate([self._params, x.reshape(1, -1)])
self._target = np.concatenate([self._target, [target]]) | python | def register(self, params, target):
"""
Append a point and its target value to the known data.
Parameters
----------
x : ndarray
a single point, with len(x) == self.dim
y : float
target function value
Raises
------
KeyError:
if the point is not unique
Notes
-----
runs in ammortized constant time
Example
-------
>>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
>>> space = TargetSpace(lambda p1, p2: p1 + p2, pbounds)
>>> len(space)
0
>>> x = np.array([0, 0])
>>> y = 1
>>> space.add_observation(x, y)
>>> len(space)
1
"""
x = self._as_array(params)
if x in self:
raise KeyError('Data point {} is not unique'.format(x))
# Insert data into unique dictionary
self._cache[_hashable(x.ravel())] = target
self._params = np.concatenate([self._params, x.reshape(1, -1)])
self._target = np.concatenate([self._target, [target]]) | [
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y : float
target function value
Raises
------
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if the point is not unique
Notes
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Example
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>>> space = TargetSpace(lambda p1, p2: p1 + p2, pbounds)
>>> len(space)
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>>> x = np.array([0, 0])
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32,209 | fmfn/BayesianOptimization | bayes_opt/target_space.py | TargetSpace.probe | def probe(self, params):
"""
Evaulates a single point x, to obtain the value y and then records them
as observations.
Notes
-----
If x has been previously seen returns a cached value of y.
Parameters
----------
x : ndarray
a single point, with len(x) == self.dim
Returns
-------
y : float
target function value.
"""
x = self._as_array(params)
try:
target = self._cache[_hashable(x)]
except KeyError:
params = dict(zip(self._keys, x))
target = self.target_func(**params)
self.register(x, target)
return target | python | def probe(self, params):
"""
Evaulates a single point x, to obtain the value y and then records them
as observations.
Notes
-----
If x has been previously seen returns a cached value of y.
Parameters
----------
x : ndarray
a single point, with len(x) == self.dim
Returns
-------
y : float
target function value.
"""
x = self._as_array(params)
try:
target = self._cache[_hashable(x)]
except KeyError:
params = dict(zip(self._keys, x))
target = self.target_func(**params)
self.register(x, target)
return target | [
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Notes
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If x has been previously seen returns a cached value of y.
Parameters
----------
x : ndarray
a single point, with len(x) == self.dim
Returns
-------
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32,210 | fmfn/BayesianOptimization | bayes_opt/target_space.py | TargetSpace.random_sample | def random_sample(self):
"""
Creates random points within the bounds of the space.
Returns
----------
data: ndarray
[num x dim] array points with dimensions corresponding to `self._keys`
Example
-------
>>> target_func = lambda p1, p2: p1 + p2
>>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
>>> space = TargetSpace(target_func, pbounds, random_state=0)
>>> space.random_points(1)
array([[ 55.33253689, 0.54488318]])
"""
# TODO: support integer, category, and basic scipy.optimize constraints
data = np.empty((1, self.dim))
for col, (lower, upper) in enumerate(self._bounds):
data.T[col] = self.random_state.uniform(lower, upper, size=1)
return data.ravel() | python | def random_sample(self):
"""
Creates random points within the bounds of the space.
Returns
----------
data: ndarray
[num x dim] array points with dimensions corresponding to `self._keys`
Example
-------
>>> target_func = lambda p1, p2: p1 + p2
>>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
>>> space = TargetSpace(target_func, pbounds, random_state=0)
>>> space.random_points(1)
array([[ 55.33253689, 0.54488318]])
"""
# TODO: support integer, category, and basic scipy.optimize constraints
data = np.empty((1, self.dim))
for col, (lower, upper) in enumerate(self._bounds):
data.T[col] = self.random_state.uniform(lower, upper, size=1)
return data.ravel() | [
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Example
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>>> target_func = lambda p1, p2: p1 + p2
>>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
>>> space = TargetSpace(target_func, pbounds, random_state=0)
>>> space.random_points(1)
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32,211 | fmfn/BayesianOptimization | bayes_opt/target_space.py | TargetSpace.max | def max(self):
"""Get maximum target value found and corresponding parametes."""
try:
res = {
'target': self.target.max(),
'params': dict(
zip(self.keys, self.params[self.target.argmax()])
)
}
except ValueError:
res = {}
return res | python | def max(self):
"""Get maximum target value found and corresponding parametes."""
try:
res = {
'target': self.target.max(),
'params': dict(
zip(self.keys, self.params[self.target.argmax()])
)
}
except ValueError:
res = {}
return res | [
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32,212 | fmfn/BayesianOptimization | bayes_opt/target_space.py | TargetSpace.res | def res(self):
"""Get all target values found and corresponding parametes."""
params = [dict(zip(self.keys, p)) for p in self.params]
return [
{"target": target, "params": param}
for target, param in zip(self.target, params)
] | python | def res(self):
"""Get all target values found and corresponding parametes."""
params = [dict(zip(self.keys, p)) for p in self.params]
return [
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for target, param in zip(self.target, params)
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32,213 | fmfn/BayesianOptimization | bayes_opt/target_space.py | TargetSpace.set_bounds | def set_bounds(self, new_bounds):
"""
A method that allows changing the lower and upper searching bounds
Parameters
----------
new_bounds : dict
A dictionary with the parameter name and its new bounds
"""
for row, key in enumerate(self.keys):
if key in new_bounds:
self._bounds[row] = new_bounds[key] | python | def set_bounds(self, new_bounds):
"""
A method that allows changing the lower and upper searching bounds
Parameters
----------
new_bounds : dict
A dictionary with the parameter name and its new bounds
"""
for row, key in enumerate(self.keys):
if key in new_bounds:
self._bounds[row] = new_bounds[key] | [
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32,214 | fmfn/BayesianOptimization | examples/sklearn_example.py | get_data | def get_data():
"""Synthetic binary classification dataset."""
data, targets = make_classification(
n_samples=1000,
n_features=45,
n_informative=12,
n_redundant=7,
random_state=134985745,
)
return data, targets | python | def get_data():
"""Synthetic binary classification dataset."""
data, targets = make_classification(
n_samples=1000,
n_features=45,
n_informative=12,
n_redundant=7,
random_state=134985745,
)
return data, targets | [
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32,215 | fmfn/BayesianOptimization | examples/sklearn_example.py | svc_cv | def svc_cv(C, gamma, data, targets):
"""SVC cross validation.
This function will instantiate a SVC classifier with parameters C and
gamma. Combined with data and targets this will in turn be used to perform
cross validation. The result of cross validation is returned.
Our goal is to find combinations of C and gamma that maximizes the roc_auc
metric.
"""
estimator = SVC(C=C, gamma=gamma, random_state=2)
cval = cross_val_score(estimator, data, targets, scoring='roc_auc', cv=4)
return cval.mean() | python | def svc_cv(C, gamma, data, targets):
"""SVC cross validation.
This function will instantiate a SVC classifier with parameters C and
gamma. Combined with data and targets this will in turn be used to perform
cross validation. The result of cross validation is returned.
Our goal is to find combinations of C and gamma that maximizes the roc_auc
metric.
"""
estimator = SVC(C=C, gamma=gamma, random_state=2)
cval = cross_val_score(estimator, data, targets, scoring='roc_auc', cv=4)
return cval.mean() | [
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32,216 | fmfn/BayesianOptimization | examples/sklearn_example.py | rfc_cv | def rfc_cv(n_estimators, min_samples_split, max_features, data, targets):
"""Random Forest cross validation.
This function will instantiate a random forest classifier with parameters
n_estimators, min_samples_split, and max_features. Combined with data and
targets this will in turn be used to perform cross validation. The result
of cross validation is returned.
Our goal is to find combinations of n_estimators, min_samples_split, and
max_features that minimzes the log loss.
"""
estimator = RFC(
n_estimators=n_estimators,
min_samples_split=min_samples_split,
max_features=max_features,
random_state=2
)
cval = cross_val_score(estimator, data, targets,
scoring='neg_log_loss', cv=4)
return cval.mean() | python | def rfc_cv(n_estimators, min_samples_split, max_features, data, targets):
"""Random Forest cross validation.
This function will instantiate a random forest classifier with parameters
n_estimators, min_samples_split, and max_features. Combined with data and
targets this will in turn be used to perform cross validation. The result
of cross validation is returned.
Our goal is to find combinations of n_estimators, min_samples_split, and
max_features that minimzes the log loss.
"""
estimator = RFC(
n_estimators=n_estimators,
min_samples_split=min_samples_split,
max_features=max_features,
random_state=2
)
cval = cross_val_score(estimator, data, targets,
scoring='neg_log_loss', cv=4)
return cval.mean() | [
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32,217 | fmfn/BayesianOptimization | examples/sklearn_example.py | optimize_svc | def optimize_svc(data, targets):
"""Apply Bayesian Optimization to SVC parameters."""
def svc_crossval(expC, expGamma):
"""Wrapper of SVC cross validation.
Notice how we transform between regular and log scale. While this
is not technically necessary, it greatly improves the performance
of the optimizer.
"""
C = 10 ** expC
gamma = 10 ** expGamma
return svc_cv(C=C, gamma=gamma, data=data, targets=targets)
optimizer = BayesianOptimization(
f=svc_crossval,
pbounds={"expC": (-3, 2), "expGamma": (-4, -1)},
random_state=1234,
verbose=2
)
optimizer.maximize(n_iter=10)
print("Final result:", optimizer.max) | python | def optimize_svc(data, targets):
"""Apply Bayesian Optimization to SVC parameters."""
def svc_crossval(expC, expGamma):
"""Wrapper of SVC cross validation.
Notice how we transform between regular and log scale. While this
is not technically necessary, it greatly improves the performance
of the optimizer.
"""
C = 10 ** expC
gamma = 10 ** expGamma
return svc_cv(C=C, gamma=gamma, data=data, targets=targets)
optimizer = BayesianOptimization(
f=svc_crossval,
pbounds={"expC": (-3, 2), "expGamma": (-4, -1)},
random_state=1234,
verbose=2
)
optimizer.maximize(n_iter=10)
print("Final result:", optimizer.max) | [
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32,218 | fmfn/BayesianOptimization | examples/sklearn_example.py | optimize_rfc | def optimize_rfc(data, targets):
"""Apply Bayesian Optimization to Random Forest parameters."""
def rfc_crossval(n_estimators, min_samples_split, max_features):
"""Wrapper of RandomForest cross validation.
Notice how we ensure n_estimators and min_samples_split are casted
to integer before we pass them along. Moreover, to avoid max_features
taking values outside the (0, 1) range, we also ensure it is capped
accordingly.
"""
return rfc_cv(
n_estimators=int(n_estimators),
min_samples_split=int(min_samples_split),
max_features=max(min(max_features, 0.999), 1e-3),
data=data,
targets=targets,
)
optimizer = BayesianOptimization(
f=rfc_crossval,
pbounds={
"n_estimators": (10, 250),
"min_samples_split": (2, 25),
"max_features": (0.1, 0.999),
},
random_state=1234,
verbose=2
)
optimizer.maximize(n_iter=10)
print("Final result:", optimizer.max) | python | def optimize_rfc(data, targets):
"""Apply Bayesian Optimization to Random Forest parameters."""
def rfc_crossval(n_estimators, min_samples_split, max_features):
"""Wrapper of RandomForest cross validation.
Notice how we ensure n_estimators and min_samples_split are casted
to integer before we pass them along. Moreover, to avoid max_features
taking values outside the (0, 1) range, we also ensure it is capped
accordingly.
"""
return rfc_cv(
n_estimators=int(n_estimators),
min_samples_split=int(min_samples_split),
max_features=max(min(max_features, 0.999), 1e-3),
data=data,
targets=targets,
)
optimizer = BayesianOptimization(
f=rfc_crossval,
pbounds={
"n_estimators": (10, 250),
"min_samples_split": (2, 25),
"max_features": (0.1, 0.999),
},
random_state=1234,
verbose=2
)
optimizer.maximize(n_iter=10)
print("Final result:", optimizer.max) | [
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32,219 | fmfn/BayesianOptimization | bayes_opt/util.py | load_logs | def load_logs(optimizer, logs):
"""Load previous ...
"""
import json
if isinstance(logs, str):
logs = [logs]
for log in logs:
with open(log, "r") as j:
while True:
try:
iteration = next(j)
except StopIteration:
break
iteration = json.loads(iteration)
try:
optimizer.register(
params=iteration["params"],
target=iteration["target"],
)
except KeyError:
pass
return optimizer | python | def load_logs(optimizer, logs):
"""Load previous ...
"""
import json
if isinstance(logs, str):
logs = [logs]
for log in logs:
with open(log, "r") as j:
while True:
try:
iteration = next(j)
except StopIteration:
break
iteration = json.loads(iteration)
try:
optimizer.register(
params=iteration["params"],
target=iteration["target"],
)
except KeyError:
pass
return optimizer | [
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32,220 | fmfn/BayesianOptimization | bayes_opt/util.py | ensure_rng | def ensure_rng(random_state=None):
"""
Creates a random number generator based on an optional seed. This can be
an integer or another random state for a seeded rng, or None for an
unseeded rng.
"""
if random_state is None:
random_state = np.random.RandomState()
elif isinstance(random_state, int):
random_state = np.random.RandomState(random_state)
else:
assert isinstance(random_state, np.random.RandomState)
return random_state | python | def ensure_rng(random_state=None):
"""
Creates a random number generator based on an optional seed. This can be
an integer or another random state for a seeded rng, or None for an
unseeded rng.
"""
if random_state is None:
random_state = np.random.RandomState()
elif isinstance(random_state, int):
random_state = np.random.RandomState(random_state)
else:
assert isinstance(random_state, np.random.RandomState)
return random_state | [
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32,221 | audreyr/cookiecutter | cookiecutter/repository.py | expand_abbreviations | def expand_abbreviations(template, abbreviations):
"""Expand abbreviations in a template name.
:param template: The project template name.
:param abbreviations: Abbreviation definitions.
"""
if template in abbreviations:
return abbreviations[template]
# Split on colon. If there is no colon, rest will be empty
# and prefix will be the whole template
prefix, sep, rest = template.partition(':')
if prefix in abbreviations:
return abbreviations[prefix].format(rest)
return template | python | def expand_abbreviations(template, abbreviations):
"""Expand abbreviations in a template name.
:param template: The project template name.
:param abbreviations: Abbreviation definitions.
"""
if template in abbreviations:
return abbreviations[template]
# Split on colon. If there is no colon, rest will be empty
# and prefix will be the whole template
prefix, sep, rest = template.partition(':')
if prefix in abbreviations:
return abbreviations[prefix].format(rest)
return template | [
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32,222 | audreyr/cookiecutter | cookiecutter/repository.py | repository_has_cookiecutter_json | def repository_has_cookiecutter_json(repo_directory):
"""Determine if `repo_directory` contains a `cookiecutter.json` file.
:param repo_directory: The candidate repository directory.
:return: True if the `repo_directory` is valid, else False.
"""
repo_directory_exists = os.path.isdir(repo_directory)
repo_config_exists = os.path.isfile(
os.path.join(repo_directory, 'cookiecutter.json')
)
return repo_directory_exists and repo_config_exists | python | def repository_has_cookiecutter_json(repo_directory):
"""Determine if `repo_directory` contains a `cookiecutter.json` file.
:param repo_directory: The candidate repository directory.
:return: True if the `repo_directory` is valid, else False.
"""
repo_directory_exists = os.path.isdir(repo_directory)
repo_config_exists = os.path.isfile(
os.path.join(repo_directory, 'cookiecutter.json')
)
return repo_directory_exists and repo_config_exists | [
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32,223 | audreyr/cookiecutter | cookiecutter/repository.py | determine_repo_dir | def determine_repo_dir(template, abbreviations, clone_to_dir, checkout,
no_input, password=None):
"""
Locate the repository directory from a template reference.
Applies repository abbreviations to the template reference.
If the template refers to a repository URL, clone it.
If the template is a path to a local repository, use it.
:param template: A directory containing a project template directory,
or a URL to a git repository.
:param abbreviations: A dictionary of repository abbreviation
definitions.
:param clone_to_dir: The directory to clone the repository into.
:param checkout: The branch, tag or commit ID to checkout after clone.
:param no_input: Prompt the user at command line for manual configuration?
:param password: The password to use when extracting the repository.
:return: A tuple containing the cookiecutter template directory, and
a boolean descriving whether that directory should be cleaned up
after the template has been instantiated.
:raises: `RepositoryNotFound` if a repository directory could not be found.
"""
template = expand_abbreviations(template, abbreviations)
if is_zip_file(template):
unzipped_dir = unzip(
zip_uri=template,
is_url=is_repo_url(template),
clone_to_dir=clone_to_dir,
no_input=no_input,
password=password
)
repository_candidates = [unzipped_dir]
cleanup = True
elif is_repo_url(template):
cloned_repo = clone(
repo_url=template,
checkout=checkout,
clone_to_dir=clone_to_dir,
no_input=no_input,
)
repository_candidates = [cloned_repo]
cleanup = False
else:
repository_candidates = [
template,
os.path.join(clone_to_dir, template)
]
cleanup = False
for repo_candidate in repository_candidates:
if repository_has_cookiecutter_json(repo_candidate):
return repo_candidate, cleanup
raise RepositoryNotFound(
'A valid repository for "{}" could not be found in the following '
'locations:\n{}'.format(
template,
'\n'.join(repository_candidates)
)
) | python | def determine_repo_dir(template, abbreviations, clone_to_dir, checkout,
no_input, password=None):
"""
Locate the repository directory from a template reference.
Applies repository abbreviations to the template reference.
If the template refers to a repository URL, clone it.
If the template is a path to a local repository, use it.
:param template: A directory containing a project template directory,
or a URL to a git repository.
:param abbreviations: A dictionary of repository abbreviation
definitions.
:param clone_to_dir: The directory to clone the repository into.
:param checkout: The branch, tag or commit ID to checkout after clone.
:param no_input: Prompt the user at command line for manual configuration?
:param password: The password to use when extracting the repository.
:return: A tuple containing the cookiecutter template directory, and
a boolean descriving whether that directory should be cleaned up
after the template has been instantiated.
:raises: `RepositoryNotFound` if a repository directory could not be found.
"""
template = expand_abbreviations(template, abbreviations)
if is_zip_file(template):
unzipped_dir = unzip(
zip_uri=template,
is_url=is_repo_url(template),
clone_to_dir=clone_to_dir,
no_input=no_input,
password=password
)
repository_candidates = [unzipped_dir]
cleanup = True
elif is_repo_url(template):
cloned_repo = clone(
repo_url=template,
checkout=checkout,
clone_to_dir=clone_to_dir,
no_input=no_input,
)
repository_candidates = [cloned_repo]
cleanup = False
else:
repository_candidates = [
template,
os.path.join(clone_to_dir, template)
]
cleanup = False
for repo_candidate in repository_candidates:
if repository_has_cookiecutter_json(repo_candidate):
return repo_candidate, cleanup
raise RepositoryNotFound(
'A valid repository for "{}" could not be found in the following '
'locations:\n{}'.format(
template,
'\n'.join(repository_candidates)
)
) | [
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:param template: A directory containing a project template directory,
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:param abbreviations: A dictionary of repository abbreviation
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:param clone_to_dir: The directory to clone the repository into.
:param checkout: The branch, tag or commit ID to checkout after clone.
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:param password: The password to use when extracting the repository.
:return: A tuple containing the cookiecutter template directory, and
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:raises: `RepositoryNotFound` if a repository directory could not be found. | [
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32,224 | audreyr/cookiecutter | cookiecutter/find.py | find_template | def find_template(repo_dir):
"""Determine which child directory of `repo_dir` is the project template.
:param repo_dir: Local directory of newly cloned repo.
:returns project_template: Relative path to project template.
"""
logger.debug('Searching {} for the project template.'.format(repo_dir))
repo_dir_contents = os.listdir(repo_dir)
project_template = None
for item in repo_dir_contents:
if 'cookiecutter' in item and '{{' in item and '}}' in item:
project_template = item
break
if project_template:
project_template = os.path.join(repo_dir, project_template)
logger.debug(
'The project template appears to be {}'.format(project_template)
)
return project_template
else:
raise NonTemplatedInputDirException | python | def find_template(repo_dir):
"""Determine which child directory of `repo_dir` is the project template.
:param repo_dir: Local directory of newly cloned repo.
:returns project_template: Relative path to project template.
"""
logger.debug('Searching {} for the project template.'.format(repo_dir))
repo_dir_contents = os.listdir(repo_dir)
project_template = None
for item in repo_dir_contents:
if 'cookiecutter' in item and '{{' in item and '}}' in item:
project_template = item
break
if project_template:
project_template = os.path.join(repo_dir, project_template)
logger.debug(
'The project template appears to be {}'.format(project_template)
)
return project_template
else:
raise NonTemplatedInputDirException | [
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32,225 | audreyr/cookiecutter | cookiecutter/generate.py | is_copy_only_path | def is_copy_only_path(path, context):
"""Check whether the given `path` should only be copied and not rendered.
Returns True if `path` matches a pattern in the given `context` dict,
otherwise False.
:param path: A file-system path referring to a file or dir that
should be rendered or just copied.
:param context: cookiecutter context.
"""
try:
for dont_render in context['cookiecutter']['_copy_without_render']:
if fnmatch.fnmatch(path, dont_render):
return True
except KeyError:
return False
return False | python | def is_copy_only_path(path, context):
"""Check whether the given `path` should only be copied and not rendered.
Returns True if `path` matches a pattern in the given `context` dict,
otherwise False.
:param path: A file-system path referring to a file or dir that
should be rendered or just copied.
:param context: cookiecutter context.
"""
try:
for dont_render in context['cookiecutter']['_copy_without_render']:
if fnmatch.fnmatch(path, dont_render):
return True
except KeyError:
return False
return False | [
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:param path: A file-system path referring to a file or dir that
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32,226 | audreyr/cookiecutter | cookiecutter/generate.py | apply_overwrites_to_context | def apply_overwrites_to_context(context, overwrite_context):
"""Modify the given context in place based on the overwrite_context."""
for variable, overwrite in overwrite_context.items():
if variable not in context:
# Do not include variables which are not used in the template
continue
context_value = context[variable]
if isinstance(context_value, list):
# We are dealing with a choice variable
if overwrite in context_value:
# This overwrite is actually valid for the given context
# Let's set it as default (by definition first item in list)
# see ``cookiecutter.prompt.prompt_choice_for_config``
context_value.remove(overwrite)
context_value.insert(0, overwrite)
else:
# Simply overwrite the value for this variable
context[variable] = overwrite | python | def apply_overwrites_to_context(context, overwrite_context):
"""Modify the given context in place based on the overwrite_context."""
for variable, overwrite in overwrite_context.items():
if variable not in context:
# Do not include variables which are not used in the template
continue
context_value = context[variable]
if isinstance(context_value, list):
# We are dealing with a choice variable
if overwrite in context_value:
# This overwrite is actually valid for the given context
# Let's set it as default (by definition first item in list)
# see ``cookiecutter.prompt.prompt_choice_for_config``
context_value.remove(overwrite)
context_value.insert(0, overwrite)
else:
# Simply overwrite the value for this variable
context[variable] = overwrite | [
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32,227 | audreyr/cookiecutter | cookiecutter/generate.py | generate_context | def generate_context(context_file='cookiecutter.json', default_context=None,
extra_context=None):
"""Generate the context for a Cookiecutter project template.
Loads the JSON file as a Python object, with key being the JSON filename.
:param context_file: JSON file containing key/value pairs for populating
the cookiecutter's variables.
:param default_context: Dictionary containing config to take into account.
:param extra_context: Dictionary containing configuration overrides
"""
context = OrderedDict([])
try:
with open(context_file) as file_handle:
obj = json.load(file_handle, object_pairs_hook=OrderedDict)
except ValueError as e:
# JSON decoding error. Let's throw a new exception that is more
# friendly for the developer or user.
full_fpath = os.path.abspath(context_file)
json_exc_message = str(e)
our_exc_message = (
'JSON decoding error while loading "{0}". Decoding'
' error details: "{1}"'.format(full_fpath, json_exc_message))
raise ContextDecodingException(our_exc_message)
# Add the Python object to the context dictionary
file_name = os.path.split(context_file)[1]
file_stem = file_name.split('.')[0]
context[file_stem] = obj
# Overwrite context variable defaults with the default context from the
# user's global config, if available
if default_context:
apply_overwrites_to_context(obj, default_context)
if extra_context:
apply_overwrites_to_context(obj, extra_context)
logger.debug('Context generated is {}'.format(context))
return context | python | def generate_context(context_file='cookiecutter.json', default_context=None,
extra_context=None):
"""Generate the context for a Cookiecutter project template.
Loads the JSON file as a Python object, with key being the JSON filename.
:param context_file: JSON file containing key/value pairs for populating
the cookiecutter's variables.
:param default_context: Dictionary containing config to take into account.
:param extra_context: Dictionary containing configuration overrides
"""
context = OrderedDict([])
try:
with open(context_file) as file_handle:
obj = json.load(file_handle, object_pairs_hook=OrderedDict)
except ValueError as e:
# JSON decoding error. Let's throw a new exception that is more
# friendly for the developer or user.
full_fpath = os.path.abspath(context_file)
json_exc_message = str(e)
our_exc_message = (
'JSON decoding error while loading "{0}". Decoding'
' error details: "{1}"'.format(full_fpath, json_exc_message))
raise ContextDecodingException(our_exc_message)
# Add the Python object to the context dictionary
file_name = os.path.split(context_file)[1]
file_stem = file_name.split('.')[0]
context[file_stem] = obj
# Overwrite context variable defaults with the default context from the
# user's global config, if available
if default_context:
apply_overwrites_to_context(obj, default_context)
if extra_context:
apply_overwrites_to_context(obj, extra_context)
logger.debug('Context generated is {}'.format(context))
return context | [
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:param context_file: JSON file containing key/value pairs for populating
the cookiecutter's variables.
:param default_context: Dictionary containing config to take into account.
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32,228 | audreyr/cookiecutter | cookiecutter/generate.py | generate_file | def generate_file(project_dir, infile, context, env):
"""Render filename of infile as name of outfile, handle infile correctly.
Dealing with infile appropriately:
a. If infile is a binary file, copy it over without rendering.
b. If infile is a text file, render its contents and write the
rendered infile to outfile.
Precondition:
When calling `generate_file()`, the root template dir must be the
current working directory. Using `utils.work_in()` is the recommended
way to perform this directory change.
:param project_dir: Absolute path to the resulting generated project.
:param infile: Input file to generate the file from. Relative to the root
template dir.
:param context: Dict for populating the cookiecutter's variables.
:param env: Jinja2 template execution environment.
"""
logger.debug('Processing file {}'.format(infile))
# Render the path to the output file (not including the root project dir)
outfile_tmpl = env.from_string(infile)
outfile = os.path.join(project_dir, outfile_tmpl.render(**context))
file_name_is_empty = os.path.isdir(outfile)
if file_name_is_empty:
logger.debug('The resulting file name is empty: {0}'.format(outfile))
return
logger.debug('Created file at {0}'.format(outfile))
# Just copy over binary files. Don't render.
logger.debug("Check {} to see if it's a binary".format(infile))
if is_binary(infile):
logger.debug(
'Copying binary {} to {} without rendering'
''.format(infile, outfile)
)
shutil.copyfile(infile, outfile)
else:
# Force fwd slashes on Windows for get_template
# This is a by-design Jinja issue
infile_fwd_slashes = infile.replace(os.path.sep, '/')
# Render the file
try:
tmpl = env.get_template(infile_fwd_slashes)
except TemplateSyntaxError as exception:
# Disable translated so that printed exception contains verbose
# information about syntax error location
exception.translated = False
raise
rendered_file = tmpl.render(**context)
logger.debug('Writing contents to file {}'.format(outfile))
with io.open(outfile, 'w', encoding='utf-8') as fh:
fh.write(rendered_file)
# Apply file permissions to output file
shutil.copymode(infile, outfile) | python | def generate_file(project_dir, infile, context, env):
"""Render filename of infile as name of outfile, handle infile correctly.
Dealing with infile appropriately:
a. If infile is a binary file, copy it over without rendering.
b. If infile is a text file, render its contents and write the
rendered infile to outfile.
Precondition:
When calling `generate_file()`, the root template dir must be the
current working directory. Using `utils.work_in()` is the recommended
way to perform this directory change.
:param project_dir: Absolute path to the resulting generated project.
:param infile: Input file to generate the file from. Relative to the root
template dir.
:param context: Dict for populating the cookiecutter's variables.
:param env: Jinja2 template execution environment.
"""
logger.debug('Processing file {}'.format(infile))
# Render the path to the output file (not including the root project dir)
outfile_tmpl = env.from_string(infile)
outfile = os.path.join(project_dir, outfile_tmpl.render(**context))
file_name_is_empty = os.path.isdir(outfile)
if file_name_is_empty:
logger.debug('The resulting file name is empty: {0}'.format(outfile))
return
logger.debug('Created file at {0}'.format(outfile))
# Just copy over binary files. Don't render.
logger.debug("Check {} to see if it's a binary".format(infile))
if is_binary(infile):
logger.debug(
'Copying binary {} to {} without rendering'
''.format(infile, outfile)
)
shutil.copyfile(infile, outfile)
else:
# Force fwd slashes on Windows for get_template
# This is a by-design Jinja issue
infile_fwd_slashes = infile.replace(os.path.sep, '/')
# Render the file
try:
tmpl = env.get_template(infile_fwd_slashes)
except TemplateSyntaxError as exception:
# Disable translated so that printed exception contains verbose
# information about syntax error location
exception.translated = False
raise
rendered_file = tmpl.render(**context)
logger.debug('Writing contents to file {}'.format(outfile))
with io.open(outfile, 'w', encoding='utf-8') as fh:
fh.write(rendered_file)
# Apply file permissions to output file
shutil.copymode(infile, outfile) | [
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:param infile: Input file to generate the file from. Relative to the root
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32,229 | audreyr/cookiecutter | cookiecutter/generate.py | render_and_create_dir | def render_and_create_dir(dirname, context, output_dir, environment,
overwrite_if_exists=False):
"""Render name of a directory, create the directory, return its path."""
name_tmpl = environment.from_string(dirname)
rendered_dirname = name_tmpl.render(**context)
dir_to_create = os.path.normpath(
os.path.join(output_dir, rendered_dirname)
)
logger.debug('Rendered dir {} must exist in output_dir {}'.format(
dir_to_create,
output_dir
))
output_dir_exists = os.path.exists(dir_to_create)
if output_dir_exists:
if overwrite_if_exists:
logger.debug(
'Output directory {} already exists,'
'overwriting it'.format(dir_to_create)
)
else:
msg = 'Error: "{}" directory already exists'.format(dir_to_create)
raise OutputDirExistsException(msg)
else:
make_sure_path_exists(dir_to_create)
return dir_to_create, not output_dir_exists | python | def render_and_create_dir(dirname, context, output_dir, environment,
overwrite_if_exists=False):
"""Render name of a directory, create the directory, return its path."""
name_tmpl = environment.from_string(dirname)
rendered_dirname = name_tmpl.render(**context)
dir_to_create = os.path.normpath(
os.path.join(output_dir, rendered_dirname)
)
logger.debug('Rendered dir {} must exist in output_dir {}'.format(
dir_to_create,
output_dir
))
output_dir_exists = os.path.exists(dir_to_create)
if output_dir_exists:
if overwrite_if_exists:
logger.debug(
'Output directory {} already exists,'
'overwriting it'.format(dir_to_create)
)
else:
msg = 'Error: "{}" directory already exists'.format(dir_to_create)
raise OutputDirExistsException(msg)
else:
make_sure_path_exists(dir_to_create)
return dir_to_create, not output_dir_exists | [
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32,230 | audreyr/cookiecutter | cookiecutter/generate.py | _run_hook_from_repo_dir | def _run_hook_from_repo_dir(repo_dir, hook_name, project_dir, context,
delete_project_on_failure):
"""Run hook from repo directory, clean project directory if hook fails.
:param repo_dir: Project template input directory.
:param hook_name: The hook to execute.
:param project_dir: The directory to execute the script from.
:param context: Cookiecutter project context.
:param delete_project_on_failure: Delete the project directory on hook
failure?
"""
with work_in(repo_dir):
try:
run_hook(hook_name, project_dir, context)
except FailedHookException:
if delete_project_on_failure:
rmtree(project_dir)
logger.error(
"Stopping generation because {} hook "
"script didn't exit successfully".format(hook_name)
)
raise | python | def _run_hook_from_repo_dir(repo_dir, hook_name, project_dir, context,
delete_project_on_failure):
"""Run hook from repo directory, clean project directory if hook fails.
:param repo_dir: Project template input directory.
:param hook_name: The hook to execute.
:param project_dir: The directory to execute the script from.
:param context: Cookiecutter project context.
:param delete_project_on_failure: Delete the project directory on hook
failure?
"""
with work_in(repo_dir):
try:
run_hook(hook_name, project_dir, context)
except FailedHookException:
if delete_project_on_failure:
rmtree(project_dir)
logger.error(
"Stopping generation because {} hook "
"script didn't exit successfully".format(hook_name)
)
raise | [
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:param repo_dir: Project template input directory.
:param hook_name: The hook to execute.
:param project_dir: The directory to execute the script from.
:param context: Cookiecutter project context.
:param delete_project_on_failure: Delete the project directory on hook
failure? | [
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32,231 | audreyr/cookiecutter | cookiecutter/config.py | _expand_path | def _expand_path(path):
"""Expand both environment variables and user home in the given path."""
path = os.path.expandvars(path)
path = os.path.expanduser(path)
return path | python | def _expand_path(path):
"""Expand both environment variables and user home in the given path."""
path = os.path.expandvars(path)
path = os.path.expanduser(path)
return path | [
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32,232 | audreyr/cookiecutter | cookiecutter/config.py | get_config | def get_config(config_path):
"""Retrieve the config from the specified path, returning a config dict."""
if not os.path.exists(config_path):
raise ConfigDoesNotExistException
logger.debug('config_path is {0}'.format(config_path))
with io.open(config_path, encoding='utf-8') as file_handle:
try:
yaml_dict = poyo.parse_string(file_handle.read())
except poyo.exceptions.PoyoException as e:
raise InvalidConfiguration(
'Unable to parse YAML file {}. Error: {}'
''.format(config_path, e)
)
config_dict = merge_configs(DEFAULT_CONFIG, yaml_dict)
raw_replay_dir = config_dict['replay_dir']
config_dict['replay_dir'] = _expand_path(raw_replay_dir)
raw_cookies_dir = config_dict['cookiecutters_dir']
config_dict['cookiecutters_dir'] = _expand_path(raw_cookies_dir)
return config_dict | python | def get_config(config_path):
"""Retrieve the config from the specified path, returning a config dict."""
if not os.path.exists(config_path):
raise ConfigDoesNotExistException
logger.debug('config_path is {0}'.format(config_path))
with io.open(config_path, encoding='utf-8') as file_handle:
try:
yaml_dict = poyo.parse_string(file_handle.read())
except poyo.exceptions.PoyoException as e:
raise InvalidConfiguration(
'Unable to parse YAML file {}. Error: {}'
''.format(config_path, e)
)
config_dict = merge_configs(DEFAULT_CONFIG, yaml_dict)
raw_replay_dir = config_dict['replay_dir']
config_dict['replay_dir'] = _expand_path(raw_replay_dir)
raw_cookies_dir = config_dict['cookiecutters_dir']
config_dict['cookiecutters_dir'] = _expand_path(raw_cookies_dir)
return config_dict | [
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32,233 | audreyr/cookiecutter | cookiecutter/config.py | get_user_config | def get_user_config(config_file=None, default_config=False):
"""Return the user config as a dict.
If ``default_config`` is True, ignore ``config_file`` and return default
values for the config parameters.
If a path to a ``config_file`` is given, that is different from the default
location, load the user config from that.
Otherwise look up the config file path in the ``COOKIECUTTER_CONFIG``
environment variable. If set, load the config from this path. This will
raise an error if the specified path is not valid.
If the environment variable is not set, try the default config file path
before falling back to the default config values.
"""
# Do NOT load a config. Return defaults instead.
if default_config:
return copy.copy(DEFAULT_CONFIG)
# Load the given config file
if config_file and config_file is not USER_CONFIG_PATH:
return get_config(config_file)
try:
# Does the user set up a config environment variable?
env_config_file = os.environ['COOKIECUTTER_CONFIG']
except KeyError:
# Load an optional user config if it exists
# otherwise return the defaults
if os.path.exists(USER_CONFIG_PATH):
return get_config(USER_CONFIG_PATH)
else:
return copy.copy(DEFAULT_CONFIG)
else:
# There is a config environment variable. Try to load it.
# Do not check for existence, so invalid file paths raise an error.
return get_config(env_config_file) | python | def get_user_config(config_file=None, default_config=False):
"""Return the user config as a dict.
If ``default_config`` is True, ignore ``config_file`` and return default
values for the config parameters.
If a path to a ``config_file`` is given, that is different from the default
location, load the user config from that.
Otherwise look up the config file path in the ``COOKIECUTTER_CONFIG``
environment variable. If set, load the config from this path. This will
raise an error if the specified path is not valid.
If the environment variable is not set, try the default config file path
before falling back to the default config values.
"""
# Do NOT load a config. Return defaults instead.
if default_config:
return copy.copy(DEFAULT_CONFIG)
# Load the given config file
if config_file and config_file is not USER_CONFIG_PATH:
return get_config(config_file)
try:
# Does the user set up a config environment variable?
env_config_file = os.environ['COOKIECUTTER_CONFIG']
except KeyError:
# Load an optional user config if it exists
# otherwise return the defaults
if os.path.exists(USER_CONFIG_PATH):
return get_config(USER_CONFIG_PATH)
else:
return copy.copy(DEFAULT_CONFIG)
else:
# There is a config environment variable. Try to load it.
# Do not check for existence, so invalid file paths raise an error.
return get_config(env_config_file) | [
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32,234 | audreyr/cookiecutter | cookiecutter/utils.py | make_sure_path_exists | def make_sure_path_exists(path):
"""Ensure that a directory exists.
:param path: A directory path.
"""
logger.debug('Making sure path exists: {}'.format(path))
try:
os.makedirs(path)
logger.debug('Created directory at: {}'.format(path))
except OSError as exception:
if exception.errno != errno.EEXIST:
return False
return True | python | def make_sure_path_exists(path):
"""Ensure that a directory exists.
:param path: A directory path.
"""
logger.debug('Making sure path exists: {}'.format(path))
try:
os.makedirs(path)
logger.debug('Created directory at: {}'.format(path))
except OSError as exception:
if exception.errno != errno.EEXIST:
return False
return True | [
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32,235 | audreyr/cookiecutter | cookiecutter/utils.py | work_in | def work_in(dirname=None):
"""Context manager version of os.chdir.
When exited, returns to the working directory prior to entering.
"""
curdir = os.getcwd()
try:
if dirname is not None:
os.chdir(dirname)
yield
finally:
os.chdir(curdir) | python | def work_in(dirname=None):
"""Context manager version of os.chdir.
When exited, returns to the working directory prior to entering.
"""
curdir = os.getcwd()
try:
if dirname is not None:
os.chdir(dirname)
yield
finally:
os.chdir(curdir) | [
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32,236 | audreyr/cookiecutter | cookiecutter/utils.py | make_executable | def make_executable(script_path):
"""Make `script_path` executable.
:param script_path: The file to change
"""
status = os.stat(script_path)
os.chmod(script_path, status.st_mode | stat.S_IEXEC) | python | def make_executable(script_path):
"""Make `script_path` executable.
:param script_path: The file to change
"""
status = os.stat(script_path)
os.chmod(script_path, status.st_mode | stat.S_IEXEC) | [
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32,237 | audreyr/cookiecutter | cookiecutter/zipfile.py | unzip | def unzip(zip_uri, is_url, clone_to_dir='.', no_input=False, password=None):
"""Download and unpack a zipfile at a given URI.
This will download the zipfile to the cookiecutter repository,
and unpack into a temporary directory.
:param zip_uri: The URI for the zipfile.
:param is_url: Is the zip URI a URL or a file?
:param clone_to_dir: The cookiecutter repository directory
to put the archive into.
:param no_input: Supress any prompts
:param password: The password to use when unpacking the repository.
"""
# Ensure that clone_to_dir exists
clone_to_dir = os.path.expanduser(clone_to_dir)
make_sure_path_exists(clone_to_dir)
if is_url:
# Build the name of the cached zipfile,
# and prompt to delete if it already exists.
identifier = zip_uri.rsplit('/', 1)[1]
zip_path = os.path.join(clone_to_dir, identifier)
if os.path.exists(zip_path):
download = prompt_and_delete(zip_path, no_input=no_input)
else:
download = True
if download:
# (Re) download the zipfile
r = requests.get(zip_uri, stream=True)
with open(zip_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
else:
# Just use the local zipfile as-is.
zip_path = os.path.abspath(zip_uri)
# Now unpack the repository. The zipfile will be unpacked
# into a temporary directory
try:
zip_file = ZipFile(zip_path)
if len(zip_file.namelist()) == 0:
raise InvalidZipRepository(
'Zip repository {} is empty'.format(zip_uri)
)
# The first record in the zipfile should be the directory entry for
# the archive. If it isn't a directory, there's a problem.
first_filename = zip_file.namelist()[0]
if not first_filename.endswith('/'):
raise InvalidZipRepository(
'Zip repository {} does not include '
'a top-level directory'.format(zip_uri)
)
# Construct the final target directory
project_name = first_filename[:-1]
unzip_base = tempfile.mkdtemp()
unzip_path = os.path.join(unzip_base, project_name)
# Extract the zip file into the temporary directory
try:
zip_file.extractall(path=unzip_base)
except RuntimeError:
# File is password protected; try to get a password from the
# environment; if that doesn't work, ask the user.
if password is not None:
try:
zip_file.extractall(
path=unzip_base,
pwd=password.encode('utf-8')
)
except RuntimeError:
raise InvalidZipRepository(
'Invalid password provided for protected repository'
)
elif no_input:
raise InvalidZipRepository(
'Unable to unlock password protected repository'
)
else:
retry = 0
while retry is not None:
try:
password = read_repo_password('Repo password')
zip_file.extractall(
path=unzip_base,
pwd=password.encode('utf-8')
)
retry = None
except RuntimeError:
retry += 1
if retry == 3:
raise InvalidZipRepository(
'Invalid password provided '
'for protected repository'
)
except BadZipFile:
raise InvalidZipRepository(
'Zip repository {} is not a valid zip archive:'.format(zip_uri)
)
return unzip_path | python | def unzip(zip_uri, is_url, clone_to_dir='.', no_input=False, password=None):
"""Download and unpack a zipfile at a given URI.
This will download the zipfile to the cookiecutter repository,
and unpack into a temporary directory.
:param zip_uri: The URI for the zipfile.
:param is_url: Is the zip URI a URL or a file?
:param clone_to_dir: The cookiecutter repository directory
to put the archive into.
:param no_input: Supress any prompts
:param password: The password to use when unpacking the repository.
"""
# Ensure that clone_to_dir exists
clone_to_dir = os.path.expanduser(clone_to_dir)
make_sure_path_exists(clone_to_dir)
if is_url:
# Build the name of the cached zipfile,
# and prompt to delete if it already exists.
identifier = zip_uri.rsplit('/', 1)[1]
zip_path = os.path.join(clone_to_dir, identifier)
if os.path.exists(zip_path):
download = prompt_and_delete(zip_path, no_input=no_input)
else:
download = True
if download:
# (Re) download the zipfile
r = requests.get(zip_uri, stream=True)
with open(zip_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
else:
# Just use the local zipfile as-is.
zip_path = os.path.abspath(zip_uri)
# Now unpack the repository. The zipfile will be unpacked
# into a temporary directory
try:
zip_file = ZipFile(zip_path)
if len(zip_file.namelist()) == 0:
raise InvalidZipRepository(
'Zip repository {} is empty'.format(zip_uri)
)
# The first record in the zipfile should be the directory entry for
# the archive. If it isn't a directory, there's a problem.
first_filename = zip_file.namelist()[0]
if not first_filename.endswith('/'):
raise InvalidZipRepository(
'Zip repository {} does not include '
'a top-level directory'.format(zip_uri)
)
# Construct the final target directory
project_name = first_filename[:-1]
unzip_base = tempfile.mkdtemp()
unzip_path = os.path.join(unzip_base, project_name)
# Extract the zip file into the temporary directory
try:
zip_file.extractall(path=unzip_base)
except RuntimeError:
# File is password protected; try to get a password from the
# environment; if that doesn't work, ask the user.
if password is not None:
try:
zip_file.extractall(
path=unzip_base,
pwd=password.encode('utf-8')
)
except RuntimeError:
raise InvalidZipRepository(
'Invalid password provided for protected repository'
)
elif no_input:
raise InvalidZipRepository(
'Unable to unlock password protected repository'
)
else:
retry = 0
while retry is not None:
try:
password = read_repo_password('Repo password')
zip_file.extractall(
path=unzip_base,
pwd=password.encode('utf-8')
)
retry = None
except RuntimeError:
retry += 1
if retry == 3:
raise InvalidZipRepository(
'Invalid password provided '
'for protected repository'
)
except BadZipFile:
raise InvalidZipRepository(
'Zip repository {} is not a valid zip archive:'.format(zip_uri)
)
return unzip_path | [
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:param is_url: Is the zip URI a URL or a file?
:param clone_to_dir: The cookiecutter repository directory
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32,238 | audreyr/cookiecutter | cookiecutter/main.py | cookiecutter | def cookiecutter(
template, checkout=None, no_input=False, extra_context=None,
replay=False, overwrite_if_exists=False, output_dir='.',
config_file=None, default_config=False, password=None):
"""
Run Cookiecutter just as if using it from the command line.
:param template: A directory containing a project template directory,
or a URL to a git repository.
:param checkout: The branch, tag or commit ID to checkout after clone.
:param no_input: Prompt the user at command line for manual configuration?
:param extra_context: A dictionary of context that overrides default
and user configuration.
:param: overwrite_if_exists: Overwrite the contents of output directory
if it exists
:param output_dir: Where to output the generated project dir into.
:param config_file: User configuration file path.
:param default_config: Use default values rather than a config file.
:param password: The password to use when extracting the repository.
"""
if replay and ((no_input is not False) or (extra_context is not None)):
err_msg = (
"You can not use both replay and no_input or extra_context "
"at the same time."
)
raise InvalidModeException(err_msg)
config_dict = get_user_config(
config_file=config_file,
default_config=default_config,
)
repo_dir, cleanup = determine_repo_dir(
template=template,
abbreviations=config_dict['abbreviations'],
clone_to_dir=config_dict['cookiecutters_dir'],
checkout=checkout,
no_input=no_input,
password=password
)
template_name = os.path.basename(os.path.abspath(repo_dir))
if replay:
context = load(config_dict['replay_dir'], template_name)
else:
context_file = os.path.join(repo_dir, 'cookiecutter.json')
logger.debug('context_file is {}'.format(context_file))
context = generate_context(
context_file=context_file,
default_context=config_dict['default_context'],
extra_context=extra_context,
)
# prompt the user to manually configure at the command line.
# except when 'no-input' flag is set
context['cookiecutter'] = prompt_for_config(context, no_input)
# include template dir or url in the context dict
context['cookiecutter']['_template'] = template
dump(config_dict['replay_dir'], template_name, context)
# Create project from local context and project template.
result = generate_files(
repo_dir=repo_dir,
context=context,
overwrite_if_exists=overwrite_if_exists,
output_dir=output_dir
)
# Cleanup (if required)
if cleanup:
rmtree(repo_dir)
return result | python | def cookiecutter(
template, checkout=None, no_input=False, extra_context=None,
replay=False, overwrite_if_exists=False, output_dir='.',
config_file=None, default_config=False, password=None):
"""
Run Cookiecutter just as if using it from the command line.
:param template: A directory containing a project template directory,
or a URL to a git repository.
:param checkout: The branch, tag or commit ID to checkout after clone.
:param no_input: Prompt the user at command line for manual configuration?
:param extra_context: A dictionary of context that overrides default
and user configuration.
:param: overwrite_if_exists: Overwrite the contents of output directory
if it exists
:param output_dir: Where to output the generated project dir into.
:param config_file: User configuration file path.
:param default_config: Use default values rather than a config file.
:param password: The password to use when extracting the repository.
"""
if replay and ((no_input is not False) or (extra_context is not None)):
err_msg = (
"You can not use both replay and no_input or extra_context "
"at the same time."
)
raise InvalidModeException(err_msg)
config_dict = get_user_config(
config_file=config_file,
default_config=default_config,
)
repo_dir, cleanup = determine_repo_dir(
template=template,
abbreviations=config_dict['abbreviations'],
clone_to_dir=config_dict['cookiecutters_dir'],
checkout=checkout,
no_input=no_input,
password=password
)
template_name = os.path.basename(os.path.abspath(repo_dir))
if replay:
context = load(config_dict['replay_dir'], template_name)
else:
context_file = os.path.join(repo_dir, 'cookiecutter.json')
logger.debug('context_file is {}'.format(context_file))
context = generate_context(
context_file=context_file,
default_context=config_dict['default_context'],
extra_context=extra_context,
)
# prompt the user to manually configure at the command line.
# except when 'no-input' flag is set
context['cookiecutter'] = prompt_for_config(context, no_input)
# include template dir or url in the context dict
context['cookiecutter']['_template'] = template
dump(config_dict['replay_dir'], template_name, context)
# Create project from local context and project template.
result = generate_files(
repo_dir=repo_dir,
context=context,
overwrite_if_exists=overwrite_if_exists,
output_dir=output_dir
)
# Cleanup (if required)
if cleanup:
rmtree(repo_dir)
return result | [
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:param no_input: Prompt the user at command line for manual configuration?
:param extra_context: A dictionary of context that overrides default
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:param: overwrite_if_exists: Overwrite the contents of output directory
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:param output_dir: Where to output the generated project dir into.
:param config_file: User configuration file path.
:param default_config: Use default values rather than a config file.
:param password: The password to use when extracting the repository. | [
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32,239 | audreyr/cookiecutter | cookiecutter/prompt.py | read_user_choice | def read_user_choice(var_name, options):
"""Prompt the user to choose from several options for the given variable.
The first item will be returned if no input happens.
:param str var_name: Variable as specified in the context
:param list options: Sequence of options that are available to select from
:return: Exactly one item of ``options`` that has been chosen by the user
"""
# Please see http://click.pocoo.org/4/api/#click.prompt
if not isinstance(options, list):
raise TypeError
if not options:
raise ValueError
choice_map = OrderedDict(
(u'{}'.format(i), value) for i, value in enumerate(options, 1)
)
choices = choice_map.keys()
default = u'1'
choice_lines = [u'{} - {}'.format(*c) for c in choice_map.items()]
prompt = u'\n'.join((
u'Select {}:'.format(var_name),
u'\n'.join(choice_lines),
u'Choose from {}'.format(u', '.join(choices))
))
user_choice = click.prompt(
prompt, type=click.Choice(choices), default=default
)
return choice_map[user_choice] | python | def read_user_choice(var_name, options):
"""Prompt the user to choose from several options for the given variable.
The first item will be returned if no input happens.
:param str var_name: Variable as specified in the context
:param list options: Sequence of options that are available to select from
:return: Exactly one item of ``options`` that has been chosen by the user
"""
# Please see http://click.pocoo.org/4/api/#click.prompt
if not isinstance(options, list):
raise TypeError
if not options:
raise ValueError
choice_map = OrderedDict(
(u'{}'.format(i), value) for i, value in enumerate(options, 1)
)
choices = choice_map.keys()
default = u'1'
choice_lines = [u'{} - {}'.format(*c) for c in choice_map.items()]
prompt = u'\n'.join((
u'Select {}:'.format(var_name),
u'\n'.join(choice_lines),
u'Choose from {}'.format(u', '.join(choices))
))
user_choice = click.prompt(
prompt, type=click.Choice(choices), default=default
)
return choice_map[user_choice] | [
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32,240 | audreyr/cookiecutter | cookiecutter/prompt.py | read_user_dict | def read_user_dict(var_name, default_value):
"""Prompt the user to provide a dictionary of data.
:param str var_name: Variable as specified in the context
:param default_value: Value that will be returned if no input is provided
:return: A Python dictionary to use in the context.
"""
# Please see http://click.pocoo.org/4/api/#click.prompt
if not isinstance(default_value, dict):
raise TypeError
default_display = 'default'
user_value = click.prompt(
var_name,
default=default_display,
type=click.STRING,
value_proc=process_json,
)
if user_value == default_display:
# Return the given default w/o any processing
return default_value
return user_value | python | def read_user_dict(var_name, default_value):
"""Prompt the user to provide a dictionary of data.
:param str var_name: Variable as specified in the context
:param default_value: Value that will be returned if no input is provided
:return: A Python dictionary to use in the context.
"""
# Please see http://click.pocoo.org/4/api/#click.prompt
if not isinstance(default_value, dict):
raise TypeError
default_display = 'default'
user_value = click.prompt(
var_name,
default=default_display,
type=click.STRING,
value_proc=process_json,
)
if user_value == default_display:
# Return the given default w/o any processing
return default_value
return user_value | [
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32,241 | audreyr/cookiecutter | cookiecutter/prompt.py | prompt_choice_for_config | def prompt_choice_for_config(cookiecutter_dict, env, key, options, no_input):
"""Prompt the user which option to choose from the given. Each of the
possible choices is rendered beforehand.
"""
rendered_options = [
render_variable(env, raw, cookiecutter_dict) for raw in options
]
if no_input:
return rendered_options[0]
return read_user_choice(key, rendered_options) | python | def prompt_choice_for_config(cookiecutter_dict, env, key, options, no_input):
"""Prompt the user which option to choose from the given. Each of the
possible choices is rendered beforehand.
"""
rendered_options = [
render_variable(env, raw, cookiecutter_dict) for raw in options
]
if no_input:
return rendered_options[0]
return read_user_choice(key, rendered_options) | [
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32,242 | audreyr/cookiecutter | cookiecutter/prompt.py | prompt_for_config | def prompt_for_config(context, no_input=False):
"""
Prompts the user to enter new config, using context as a source for the
field names and sample values.
:param no_input: Prompt the user at command line for manual configuration?
"""
cookiecutter_dict = OrderedDict([])
env = StrictEnvironment(context=context)
# First pass: Handle simple and raw variables, plus choices.
# These must be done first because the dictionaries keys and
# values might refer to them.
for key, raw in iteritems(context[u'cookiecutter']):
if key.startswith(u'_'):
cookiecutter_dict[key] = raw
continue
try:
if isinstance(raw, list):
# We are dealing with a choice variable
val = prompt_choice_for_config(
cookiecutter_dict, env, key, raw, no_input
)
cookiecutter_dict[key] = val
elif not isinstance(raw, dict):
# We are dealing with a regular variable
val = render_variable(env, raw, cookiecutter_dict)
if not no_input:
val = read_user_variable(key, val)
cookiecutter_dict[key] = val
except UndefinedError as err:
msg = "Unable to render variable '{}'".format(key)
raise UndefinedVariableInTemplate(msg, err, context)
# Second pass; handle the dictionaries.
for key, raw in iteritems(context[u'cookiecutter']):
try:
if isinstance(raw, dict):
# We are dealing with a dict variable
val = render_variable(env, raw, cookiecutter_dict)
if not no_input:
val = read_user_dict(key, val)
cookiecutter_dict[key] = val
except UndefinedError as err:
msg = "Unable to render variable '{}'".format(key)
raise UndefinedVariableInTemplate(msg, err, context)
return cookiecutter_dict | python | def prompt_for_config(context, no_input=False):
"""
Prompts the user to enter new config, using context as a source for the
field names and sample values.
:param no_input: Prompt the user at command line for manual configuration?
"""
cookiecutter_dict = OrderedDict([])
env = StrictEnvironment(context=context)
# First pass: Handle simple and raw variables, plus choices.
# These must be done first because the dictionaries keys and
# values might refer to them.
for key, raw in iteritems(context[u'cookiecutter']):
if key.startswith(u'_'):
cookiecutter_dict[key] = raw
continue
try:
if isinstance(raw, list):
# We are dealing with a choice variable
val = prompt_choice_for_config(
cookiecutter_dict, env, key, raw, no_input
)
cookiecutter_dict[key] = val
elif not isinstance(raw, dict):
# We are dealing with a regular variable
val = render_variable(env, raw, cookiecutter_dict)
if not no_input:
val = read_user_variable(key, val)
cookiecutter_dict[key] = val
except UndefinedError as err:
msg = "Unable to render variable '{}'".format(key)
raise UndefinedVariableInTemplate(msg, err, context)
# Second pass; handle the dictionaries.
for key, raw in iteritems(context[u'cookiecutter']):
try:
if isinstance(raw, dict):
# We are dealing with a dict variable
val = render_variable(env, raw, cookiecutter_dict)
if not no_input:
val = read_user_dict(key, val)
cookiecutter_dict[key] = val
except UndefinedError as err:
msg = "Unable to render variable '{}'".format(key)
raise UndefinedVariableInTemplate(msg, err, context)
return cookiecutter_dict | [
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32,243 | audreyr/cookiecutter | cookiecutter/environment.py | ExtensionLoaderMixin._read_extensions | def _read_extensions(self, context):
"""Return list of extensions as str to be passed on to the Jinja2 env.
If context does not contain the relevant info, return an empty
list instead.
"""
try:
extensions = context['cookiecutter']['_extensions']
except KeyError:
return []
else:
return [str(ext) for ext in extensions] | python | def _read_extensions(self, context):
"""Return list of extensions as str to be passed on to the Jinja2 env.
If context does not contain the relevant info, return an empty
list instead.
"""
try:
extensions = context['cookiecutter']['_extensions']
except KeyError:
return []
else:
return [str(ext) for ext in extensions] | [
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32,244 | audreyr/cookiecutter | cookiecutter/log.py | configure_logger | def configure_logger(stream_level='DEBUG', debug_file=None):
"""Configure logging for cookiecutter.
Set up logging to stdout with given level. If ``debug_file`` is given set
up logging to file with DEBUG level.
"""
# Set up 'cookiecutter' logger
logger = logging.getLogger('cookiecutter')
logger.setLevel(logging.DEBUG)
# Remove all attached handlers, in case there was
# a logger with using the name 'cookiecutter'
del logger.handlers[:]
# Create a file handler if a log file is provided
if debug_file is not None:
debug_formatter = logging.Formatter(LOG_FORMATS['DEBUG'])
file_handler = logging.FileHandler(debug_file)
file_handler.setLevel(LOG_LEVELS['DEBUG'])
file_handler.setFormatter(debug_formatter)
logger.addHandler(file_handler)
# Get settings based on the given stream_level
log_formatter = logging.Formatter(LOG_FORMATS[stream_level])
log_level = LOG_LEVELS[stream_level]
# Create a stream handler
stream_handler = logging.StreamHandler(stream=sys.stdout)
stream_handler.setLevel(log_level)
stream_handler.setFormatter(log_formatter)
logger.addHandler(stream_handler)
return logger | python | def configure_logger(stream_level='DEBUG', debug_file=None):
"""Configure logging for cookiecutter.
Set up logging to stdout with given level. If ``debug_file`` is given set
up logging to file with DEBUG level.
"""
# Set up 'cookiecutter' logger
logger = logging.getLogger('cookiecutter')
logger.setLevel(logging.DEBUG)
# Remove all attached handlers, in case there was
# a logger with using the name 'cookiecutter'
del logger.handlers[:]
# Create a file handler if a log file is provided
if debug_file is not None:
debug_formatter = logging.Formatter(LOG_FORMATS['DEBUG'])
file_handler = logging.FileHandler(debug_file)
file_handler.setLevel(LOG_LEVELS['DEBUG'])
file_handler.setFormatter(debug_formatter)
logger.addHandler(file_handler)
# Get settings based on the given stream_level
log_formatter = logging.Formatter(LOG_FORMATS[stream_level])
log_level = LOG_LEVELS[stream_level]
# Create a stream handler
stream_handler = logging.StreamHandler(stream=sys.stdout)
stream_handler.setLevel(log_level)
stream_handler.setFormatter(log_formatter)
logger.addHandler(stream_handler)
return logger | [
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32,245 | audreyr/cookiecutter | cookiecutter/vcs.py | identify_repo | def identify_repo(repo_url):
"""Determine if `repo_url` should be treated as a URL to a git or hg repo.
Repos can be identified by prepending "hg+" or "git+" to the repo URL.
:param repo_url: Repo URL of unknown type.
:returns: ('git', repo_url), ('hg', repo_url), or None.
"""
repo_url_values = repo_url.split('+')
if len(repo_url_values) == 2:
repo_type = repo_url_values[0]
if repo_type in ["git", "hg"]:
return repo_type, repo_url_values[1]
else:
raise UnknownRepoType
else:
if 'git' in repo_url:
return 'git', repo_url
elif 'bitbucket' in repo_url:
return 'hg', repo_url
else:
raise UnknownRepoType | python | def identify_repo(repo_url):
"""Determine if `repo_url` should be treated as a URL to a git or hg repo.
Repos can be identified by prepending "hg+" or "git+" to the repo URL.
:param repo_url: Repo URL of unknown type.
:returns: ('git', repo_url), ('hg', repo_url), or None.
"""
repo_url_values = repo_url.split('+')
if len(repo_url_values) == 2:
repo_type = repo_url_values[0]
if repo_type in ["git", "hg"]:
return repo_type, repo_url_values[1]
else:
raise UnknownRepoType
else:
if 'git' in repo_url:
return 'git', repo_url
elif 'bitbucket' in repo_url:
return 'hg', repo_url
else:
raise UnknownRepoType | [
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32,246 | audreyr/cookiecutter | cookiecutter/vcs.py | clone | def clone(repo_url, checkout=None, clone_to_dir='.', no_input=False):
"""Clone a repo to the current directory.
:param repo_url: Repo URL of unknown type.
:param checkout: The branch, tag or commit ID to checkout after clone.
:param clone_to_dir: The directory to clone to.
Defaults to the current directory.
:param no_input: Suppress all user prompts when calling via API.
"""
# Ensure that clone_to_dir exists
clone_to_dir = os.path.expanduser(clone_to_dir)
make_sure_path_exists(clone_to_dir)
# identify the repo_type
repo_type, repo_url = identify_repo(repo_url)
# check that the appropriate VCS for the repo_type is installed
if not is_vcs_installed(repo_type):
msg = "'{0}' is not installed.".format(repo_type)
raise VCSNotInstalled(msg)
repo_url = repo_url.rstrip('/')
tail = os.path.split(repo_url)[1]
if repo_type == 'git':
repo_dir = os.path.normpath(os.path.join(clone_to_dir,
tail.rsplit('.git')[0]))
elif repo_type == 'hg':
repo_dir = os.path.normpath(os.path.join(clone_to_dir, tail))
logger.debug('repo_dir is {0}'.format(repo_dir))
if os.path.isdir(repo_dir):
clone = prompt_and_delete(repo_dir, no_input=no_input)
else:
clone = True
if clone:
try:
subprocess.check_output(
[repo_type, 'clone', repo_url],
cwd=clone_to_dir,
stderr=subprocess.STDOUT,
)
if checkout is not None:
subprocess.check_output(
[repo_type, 'checkout', checkout],
cwd=repo_dir,
stderr=subprocess.STDOUT,
)
except subprocess.CalledProcessError as clone_error:
output = clone_error.output.decode('utf-8')
if 'not found' in output.lower():
raise RepositoryNotFound(
'The repository {} could not be found, '
'have you made a typo?'.format(repo_url)
)
if any(error in output for error in BRANCH_ERRORS):
raise RepositoryCloneFailed(
'The {} branch of repository {} could not found, '
'have you made a typo?'.format(checkout, repo_url)
)
raise
return repo_dir | python | def clone(repo_url, checkout=None, clone_to_dir='.', no_input=False):
"""Clone a repo to the current directory.
:param repo_url: Repo URL of unknown type.
:param checkout: The branch, tag or commit ID to checkout after clone.
:param clone_to_dir: The directory to clone to.
Defaults to the current directory.
:param no_input: Suppress all user prompts when calling via API.
"""
# Ensure that clone_to_dir exists
clone_to_dir = os.path.expanduser(clone_to_dir)
make_sure_path_exists(clone_to_dir)
# identify the repo_type
repo_type, repo_url = identify_repo(repo_url)
# check that the appropriate VCS for the repo_type is installed
if not is_vcs_installed(repo_type):
msg = "'{0}' is not installed.".format(repo_type)
raise VCSNotInstalled(msg)
repo_url = repo_url.rstrip('/')
tail = os.path.split(repo_url)[1]
if repo_type == 'git':
repo_dir = os.path.normpath(os.path.join(clone_to_dir,
tail.rsplit('.git')[0]))
elif repo_type == 'hg':
repo_dir = os.path.normpath(os.path.join(clone_to_dir, tail))
logger.debug('repo_dir is {0}'.format(repo_dir))
if os.path.isdir(repo_dir):
clone = prompt_and_delete(repo_dir, no_input=no_input)
else:
clone = True
if clone:
try:
subprocess.check_output(
[repo_type, 'clone', repo_url],
cwd=clone_to_dir,
stderr=subprocess.STDOUT,
)
if checkout is not None:
subprocess.check_output(
[repo_type, 'checkout', checkout],
cwd=repo_dir,
stderr=subprocess.STDOUT,
)
except subprocess.CalledProcessError as clone_error:
output = clone_error.output.decode('utf-8')
if 'not found' in output.lower():
raise RepositoryNotFound(
'The repository {} could not be found, '
'have you made a typo?'.format(repo_url)
)
if any(error in output for error in BRANCH_ERRORS):
raise RepositoryCloneFailed(
'The {} branch of repository {} could not found, '
'have you made a typo?'.format(checkout, repo_url)
)
raise
return repo_dir | [
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32,247 | audreyr/cookiecutter | cookiecutter/hooks.py | valid_hook | def valid_hook(hook_file, hook_name):
"""Determine if a hook file is valid.
:param hook_file: The hook file to consider for validity
:param hook_name: The hook to find
:return: The hook file validity
"""
filename = os.path.basename(hook_file)
basename = os.path.splitext(filename)[0]
matching_hook = basename == hook_name
supported_hook = basename in _HOOKS
backup_file = filename.endswith('~')
return matching_hook and supported_hook and not backup_file | python | def valid_hook(hook_file, hook_name):
"""Determine if a hook file is valid.
:param hook_file: The hook file to consider for validity
:param hook_name: The hook to find
:return: The hook file validity
"""
filename = os.path.basename(hook_file)
basename = os.path.splitext(filename)[0]
matching_hook = basename == hook_name
supported_hook = basename in _HOOKS
backup_file = filename.endswith('~')
return matching_hook and supported_hook and not backup_file | [
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32,248 | audreyr/cookiecutter | cookiecutter/hooks.py | find_hook | def find_hook(hook_name, hooks_dir='hooks'):
"""Return a dict of all hook scripts provided.
Must be called with the project template as the current working directory.
Dict's key will be the hook/script's name, without extension, while values
will be the absolute path to the script. Missing scripts will not be
included in the returned dict.
:param hook_name: The hook to find
:param hooks_dir: The hook directory in the template
:return: The absolute path to the hook script or None
"""
logger.debug('hooks_dir is {}'.format(os.path.abspath(hooks_dir)))
if not os.path.isdir(hooks_dir):
logger.debug('No hooks/ dir in template_dir')
return None
for hook_file in os.listdir(hooks_dir):
if valid_hook(hook_file, hook_name):
return os.path.abspath(os.path.join(hooks_dir, hook_file))
return None | python | def find_hook(hook_name, hooks_dir='hooks'):
"""Return a dict of all hook scripts provided.
Must be called with the project template as the current working directory.
Dict's key will be the hook/script's name, without extension, while values
will be the absolute path to the script. Missing scripts will not be
included in the returned dict.
:param hook_name: The hook to find
:param hooks_dir: The hook directory in the template
:return: The absolute path to the hook script or None
"""
logger.debug('hooks_dir is {}'.format(os.path.abspath(hooks_dir)))
if not os.path.isdir(hooks_dir):
logger.debug('No hooks/ dir in template_dir')
return None
for hook_file in os.listdir(hooks_dir):
if valid_hook(hook_file, hook_name):
return os.path.abspath(os.path.join(hooks_dir, hook_file))
return None | [
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32,249 | audreyr/cookiecutter | cookiecutter/hooks.py | run_script | def run_script(script_path, cwd='.'):
"""Execute a script from a working directory.
:param script_path: Absolute path to the script to run.
:param cwd: The directory to run the script from.
"""
run_thru_shell = sys.platform.startswith('win')
if script_path.endswith('.py'):
script_command = [sys.executable, script_path]
else:
script_command = [script_path]
utils.make_executable(script_path)
try:
proc = subprocess.Popen(
script_command,
shell=run_thru_shell,
cwd=cwd
)
exit_status = proc.wait()
if exit_status != EXIT_SUCCESS:
raise FailedHookException(
'Hook script failed (exit status: {})'.format(exit_status)
)
except OSError as os_error:
if os_error.errno == errno.ENOEXEC:
raise FailedHookException(
'Hook script failed, might be an '
'empty file or missing a shebang'
)
raise FailedHookException(
'Hook script failed (error: {})'.format(os_error)
) | python | def run_script(script_path, cwd='.'):
"""Execute a script from a working directory.
:param script_path: Absolute path to the script to run.
:param cwd: The directory to run the script from.
"""
run_thru_shell = sys.platform.startswith('win')
if script_path.endswith('.py'):
script_command = [sys.executable, script_path]
else:
script_command = [script_path]
utils.make_executable(script_path)
try:
proc = subprocess.Popen(
script_command,
shell=run_thru_shell,
cwd=cwd
)
exit_status = proc.wait()
if exit_status != EXIT_SUCCESS:
raise FailedHookException(
'Hook script failed (exit status: {})'.format(exit_status)
)
except OSError as os_error:
if os_error.errno == errno.ENOEXEC:
raise FailedHookException(
'Hook script failed, might be an '
'empty file or missing a shebang'
)
raise FailedHookException(
'Hook script failed (error: {})'.format(os_error)
) | [
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32,250 | audreyr/cookiecutter | cookiecutter/hooks.py | run_script_with_context | def run_script_with_context(script_path, cwd, context):
"""Execute a script after rendering it with Jinja.
:param script_path: Absolute path to the script to run.
:param cwd: The directory to run the script from.
:param context: Cookiecutter project template context.
"""
_, extension = os.path.splitext(script_path)
contents = io.open(script_path, 'r', encoding='utf-8').read()
with tempfile.NamedTemporaryFile(
delete=False,
mode='wb',
suffix=extension
) as temp:
env = StrictEnvironment(
context=context,
keep_trailing_newline=True,
)
template = env.from_string(contents)
output = template.render(**context)
temp.write(output.encode('utf-8'))
run_script(temp.name, cwd) | python | def run_script_with_context(script_path, cwd, context):
"""Execute a script after rendering it with Jinja.
:param script_path: Absolute path to the script to run.
:param cwd: The directory to run the script from.
:param context: Cookiecutter project template context.
"""
_, extension = os.path.splitext(script_path)
contents = io.open(script_path, 'r', encoding='utf-8').read()
with tempfile.NamedTemporaryFile(
delete=False,
mode='wb',
suffix=extension
) as temp:
env = StrictEnvironment(
context=context,
keep_trailing_newline=True,
)
template = env.from_string(contents)
output = template.render(**context)
temp.write(output.encode('utf-8'))
run_script(temp.name, cwd) | [
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32,251 | audreyr/cookiecutter | cookiecutter/hooks.py | run_hook | def run_hook(hook_name, project_dir, context):
"""
Try to find and execute a hook from the specified project directory.
:param hook_name: The hook to execute.
:param project_dir: The directory to execute the script from.
:param context: Cookiecutter project context.
"""
script = find_hook(hook_name)
if script is None:
logger.debug('No {} hook found'.format(hook_name))
return
logger.debug('Running hook {}'.format(hook_name))
run_script_with_context(script, project_dir, context) | python | def run_hook(hook_name, project_dir, context):
"""
Try to find and execute a hook from the specified project directory.
:param hook_name: The hook to execute.
:param project_dir: The directory to execute the script from.
:param context: Cookiecutter project context.
"""
script = find_hook(hook_name)
if script is None:
logger.debug('No {} hook found'.format(hook_name))
return
logger.debug('Running hook {}'.format(hook_name))
run_script_with_context(script, project_dir, context) | [
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32,252 | audreyr/cookiecutter | cookiecutter/cli.py | version_msg | def version_msg():
"""Return the Cookiecutter version, location and Python powering it."""
python_version = sys.version[:3]
location = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
message = u'Cookiecutter %(version)s from {} (Python {})'
return message.format(location, python_version) | python | def version_msg():
"""Return the Cookiecutter version, location and Python powering it."""
python_version = sys.version[:3]
location = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
message = u'Cookiecutter %(version)s from {} (Python {})'
return message.format(location, python_version) | [
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32,253 | audreyr/cookiecutter | cookiecutter/cli.py | validate_extra_context | def validate_extra_context(ctx, param, value):
"""Validate extra context."""
for s in value:
if '=' not in s:
raise click.BadParameter(
'EXTRA_CONTEXT should contain items of the form key=value; '
"'{}' doesn't match that form".format(s)
)
# Convert tuple -- e.g.: (u'program_name=foobar', u'startsecs=66')
# to dict -- e.g.: {'program_name': 'foobar', 'startsecs': '66'}
return collections.OrderedDict(s.split('=', 1) for s in value) or None | python | def validate_extra_context(ctx, param, value):
"""Validate extra context."""
for s in value:
if '=' not in s:
raise click.BadParameter(
'EXTRA_CONTEXT should contain items of the form key=value; '
"'{}' doesn't match that form".format(s)
)
# Convert tuple -- e.g.: (u'program_name=foobar', u'startsecs=66')
# to dict -- e.g.: {'program_name': 'foobar', 'startsecs': '66'}
return collections.OrderedDict(s.split('=', 1) for s in value) or None | [
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32,254 | graphql-python/graphene | graphene/types/mountedtype.py | MountedType.mounted | def mounted(cls, unmounted): # noqa: N802
"""
Mount the UnmountedType instance
"""
assert isinstance(unmounted, UnmountedType), ("{} can't mount {}").format(
cls.__name__, repr(unmounted)
)
return cls(
unmounted.get_type(),
*unmounted.args,
_creation_counter=unmounted.creation_counter,
**unmounted.kwargs
) | python | def mounted(cls, unmounted): # noqa: N802
"""
Mount the UnmountedType instance
"""
assert isinstance(unmounted, UnmountedType), ("{} can't mount {}").format(
cls.__name__, repr(unmounted)
)
return cls(
unmounted.get_type(),
*unmounted.args,
_creation_counter=unmounted.creation_counter,
**unmounted.kwargs
) | [
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32,255 | graphql-python/graphene | graphene/pyutils/signature.py | Parameter.replace | def replace(
self,
name=_void,
kind=_void,
annotation=_void,
default=_void,
_partial_kwarg=_void,
):
"""Creates a customized copy of the Parameter."""
if name is _void:
name = self._name
if kind is _void:
kind = self._kind
if annotation is _void:
annotation = self._annotation
if default is _void:
default = self._default
if _partial_kwarg is _void:
_partial_kwarg = self._partial_kwarg
return type(self)(
name,
kind,
default=default,
annotation=annotation,
_partial_kwarg=_partial_kwarg,
) | python | def replace(
self,
name=_void,
kind=_void,
annotation=_void,
default=_void,
_partial_kwarg=_void,
):
"""Creates a customized copy of the Parameter."""
if name is _void:
name = self._name
if kind is _void:
kind = self._kind
if annotation is _void:
annotation = self._annotation
if default is _void:
default = self._default
if _partial_kwarg is _void:
_partial_kwarg = self._partial_kwarg
return type(self)(
name,
kind,
default=default,
annotation=annotation,
_partial_kwarg=_partial_kwarg,
) | [
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32,256 | graphql-python/graphene | graphene/pyutils/signature.py | Signature.from_function | def from_function(cls, func):
"""Constructs Signature for the given python function"""
if not isinstance(func, types.FunctionType):
raise TypeError("{!r} is not a Python function".format(func))
Parameter = cls._parameter_cls
# Parameter information.
func_code = func.__code__
pos_count = func_code.co_argcount
arg_names = func_code.co_varnames
positional = tuple(arg_names[:pos_count])
keyword_only_count = getattr(func_code, "co_kwonlyargcount", 0)
keyword_only = arg_names[pos_count : (pos_count + keyword_only_count)]
annotations = getattr(func, "__annotations__", {})
defaults = func.__defaults__
kwdefaults = getattr(func, "__kwdefaults__", None)
if defaults:
pos_default_count = len(defaults)
else:
pos_default_count = 0
parameters = []
# Non-keyword-only parameters w/o defaults.
non_default_count = pos_count - pos_default_count
for name in positional[:non_default_count]:
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(name, annotation=annotation, kind=_POSITIONAL_OR_KEYWORD)
)
# ... w/ defaults.
for offset, name in enumerate(positional[non_default_count:]):
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(
name,
annotation=annotation,
kind=_POSITIONAL_OR_KEYWORD,
default=defaults[offset],
)
)
# *args
if func_code.co_flags & 0x04:
name = arg_names[pos_count + keyword_only_count]
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(name, annotation=annotation, kind=_VAR_POSITIONAL)
)
# Keyword-only parameters.
for name in keyword_only:
default = _empty
if kwdefaults is not None:
default = kwdefaults.get(name, _empty)
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(
name, annotation=annotation, kind=_KEYWORD_ONLY, default=default
)
)
# **kwargs
if func_code.co_flags & 0x08:
index = pos_count + keyword_only_count
if func_code.co_flags & 0x04:
index += 1
name = arg_names[index]
annotation = annotations.get(name, _empty)
parameters.append(Parameter(name, annotation=annotation, kind=_VAR_KEYWORD))
return cls(
parameters,
return_annotation=annotations.get("return", _empty),
__validate_parameters__=False,
) | python | def from_function(cls, func):
"""Constructs Signature for the given python function"""
if not isinstance(func, types.FunctionType):
raise TypeError("{!r} is not a Python function".format(func))
Parameter = cls._parameter_cls
# Parameter information.
func_code = func.__code__
pos_count = func_code.co_argcount
arg_names = func_code.co_varnames
positional = tuple(arg_names[:pos_count])
keyword_only_count = getattr(func_code, "co_kwonlyargcount", 0)
keyword_only = arg_names[pos_count : (pos_count + keyword_only_count)]
annotations = getattr(func, "__annotations__", {})
defaults = func.__defaults__
kwdefaults = getattr(func, "__kwdefaults__", None)
if defaults:
pos_default_count = len(defaults)
else:
pos_default_count = 0
parameters = []
# Non-keyword-only parameters w/o defaults.
non_default_count = pos_count - pos_default_count
for name in positional[:non_default_count]:
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(name, annotation=annotation, kind=_POSITIONAL_OR_KEYWORD)
)
# ... w/ defaults.
for offset, name in enumerate(positional[non_default_count:]):
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(
name,
annotation=annotation,
kind=_POSITIONAL_OR_KEYWORD,
default=defaults[offset],
)
)
# *args
if func_code.co_flags & 0x04:
name = arg_names[pos_count + keyword_only_count]
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(name, annotation=annotation, kind=_VAR_POSITIONAL)
)
# Keyword-only parameters.
for name in keyword_only:
default = _empty
if kwdefaults is not None:
default = kwdefaults.get(name, _empty)
annotation = annotations.get(name, _empty)
parameters.append(
Parameter(
name, annotation=annotation, kind=_KEYWORD_ONLY, default=default
)
)
# **kwargs
if func_code.co_flags & 0x08:
index = pos_count + keyword_only_count
if func_code.co_flags & 0x04:
index += 1
name = arg_names[index]
annotation = annotations.get(name, _empty)
parameters.append(Parameter(name, annotation=annotation, kind=_VAR_KEYWORD))
return cls(
parameters,
return_annotation=annotations.get("return", _empty),
__validate_parameters__=False,
) | [
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32,257 | graphql-python/graphene | graphene/pyutils/signature.py | Signature._bind | def _bind(self, args, kwargs, partial=False):
"""Private method. Don't use directly."""
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parameters_ex = ()
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msg = msg.format(arg=param.name)
raise TypeError(msg)
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# parameter. So, lets start parsing `kwargs`, starting
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parameters_ex = (param,)
break
else:
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break
else:
msg = "{arg!r} parameter lacking default value"
msg = msg.format(arg=param.name)
raise TypeError(msg)
else:
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param = next(parameters)
except StopIteration:
raise TypeError("too many positional arguments")
else:
if param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY):
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raise TypeError("too many positional arguments")
if param.kind == _VAR_POSITIONAL:
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# all positional arguments we have left and move on to
# the next phase
values = [arg_val]
values.extend(arg_vals)
arguments[param.name] = tuple(values)
break
if param.name in kwargs:
raise TypeError(
"multiple values for argument "
"{arg!r}".format(arg=param.name)
)
arguments[param.name] = arg_val
# Now, we iterate through the remaining parameters to process
# keyword arguments
kwargs_param = None
for param in itertools.chain(parameters_ex, parameters):
if param.kind == _POSITIONAL_ONLY:
# This should never happen in case of a properly built
# Signature object (but let's have this check here
# to ensure correct behaviour just in case)
raise TypeError(
"{arg!r} parameter is positional only, "
"but was passed as a keyword".format(arg=param.name)
)
if param.kind == _VAR_KEYWORD:
# Memorize that we have a '**kwargs'-like parameter
kwargs_param = param
continue
param_name = param.name
try:
arg_val = kwargs.pop(param_name)
except KeyError:
# We have no value for this parameter. It's fine though,
# if it has a default value, or it is an '*args'-like
# parameter, left alone by the processing of positional
# arguments.
if (
not partial
and param.kind != _VAR_POSITIONAL
and param.default is _empty
):
raise TypeError(
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)
else:
arguments[param_name] = arg_val
if kwargs:
if kwargs_param is not None:
# Process our '**kwargs'-like parameter
arguments[kwargs_param.name] = kwargs
else:
raise TypeError("too many keyword arguments")
return self._bound_arguments_cls(self, arguments) | python | def _bind(self, args, kwargs, partial=False):
"""Private method. Don't use directly."""
arguments = OrderedDict()
parameters = iter(self.parameters.values())
parameters_ex = ()
arg_vals = iter(args)
if partial:
# Support for binding arguments to 'functools.partial' objects.
# See 'functools.partial' case in 'signature()' implementation
# for details.
for param_name, param in self.parameters.items():
if param._partial_kwarg and param_name not in kwargs:
# Simulating 'functools.partial' behavior
kwargs[param_name] = param.default
while True:
# Let's iterate through the positional arguments and corresponding
# parameters
try:
arg_val = next(arg_vals)
except StopIteration:
# No more positional arguments
try:
param = next(parameters)
except StopIteration:
# No more parameters. That's it. Just need to check that
# we have no `kwargs` after this while loop
break
else:
if param.kind == _VAR_POSITIONAL:
# That's OK, just empty *args. Let's start parsing
# kwargs
break
elif param.name in kwargs:
if param.kind == _POSITIONAL_ONLY:
msg = (
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"but was passed as a keyword"
)
msg = msg.format(arg=param.name)
raise TypeError(msg)
parameters_ex = (param,)
break
elif param.kind == _VAR_KEYWORD or param.default is not _empty:
# That's fine too - we have a default value for this
# parameter. So, lets start parsing `kwargs`, starting
# with the current parameter
parameters_ex = (param,)
break
else:
if partial:
parameters_ex = (param,)
break
else:
msg = "{arg!r} parameter lacking default value"
msg = msg.format(arg=param.name)
raise TypeError(msg)
else:
# We have a positional argument to process
try:
param = next(parameters)
except StopIteration:
raise TypeError("too many positional arguments")
else:
if param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY):
# Looks like we have no parameter for this positional
# argument
raise TypeError("too many positional arguments")
if param.kind == _VAR_POSITIONAL:
# We have an '*args'-like argument, let's fill it with
# all positional arguments we have left and move on to
# the next phase
values = [arg_val]
values.extend(arg_vals)
arguments[param.name] = tuple(values)
break
if param.name in kwargs:
raise TypeError(
"multiple values for argument "
"{arg!r}".format(arg=param.name)
)
arguments[param.name] = arg_val
# Now, we iterate through the remaining parameters to process
# keyword arguments
kwargs_param = None
for param in itertools.chain(parameters_ex, parameters):
if param.kind == _POSITIONAL_ONLY:
# This should never happen in case of a properly built
# Signature object (but let's have this check here
# to ensure correct behaviour just in case)
raise TypeError(
"{arg!r} parameter is positional only, "
"but was passed as a keyword".format(arg=param.name)
)
if param.kind == _VAR_KEYWORD:
# Memorize that we have a '**kwargs'-like parameter
kwargs_param = param
continue
param_name = param.name
try:
arg_val = kwargs.pop(param_name)
except KeyError:
# We have no value for this parameter. It's fine though,
# if it has a default value, or it is an '*args'-like
# parameter, left alone by the processing of positional
# arguments.
if (
not partial
and param.kind != _VAR_POSITIONAL
and param.default is _empty
):
raise TypeError(
"{arg!r} parameter lacking default value".format(arg=param_name)
)
else:
arguments[param_name] = arg_val
if kwargs:
if kwargs_param is not None:
# Process our '**kwargs'-like parameter
arguments[kwargs_param.name] = kwargs
else:
raise TypeError("too many keyword arguments")
return self._bound_arguments_cls(self, arguments) | [
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32,258 | graphql-python/graphene | graphene/pyutils/signature.py | Signature.bind_partial | def bind_partial(self, *args, **kwargs):
"""Get a BoundArguments object, that partially maps the
passed `args` and `kwargs` to the function's signature.
Raises `TypeError` if the passed arguments can not be bound.
"""
return self._bind(args, kwargs, partial=True) | python | def bind_partial(self, *args, **kwargs):
"""Get a BoundArguments object, that partially maps the
passed `args` and `kwargs` to the function's signature.
Raises `TypeError` if the passed arguments can not be bound.
"""
return self._bind(args, kwargs, partial=True) | [
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32,259 | graphql-python/graphene | graphene/relay/node.py | is_node | def is_node(objecttype):
"""
Check if the given objecttype has Node as an interface
"""
if not isclass(objecttype):
return False
if not issubclass(objecttype, ObjectType):
return False
for i in objecttype._meta.interfaces:
if issubclass(i, Node):
return True
return False | python | def is_node(objecttype):
"""
Check if the given objecttype has Node as an interface
"""
if not isclass(objecttype):
return False
if not issubclass(objecttype, ObjectType):
return False
for i in objecttype._meta.interfaces:
if issubclass(i, Node):
return True
return False | [
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32,260 | graphql-python/graphene | graphene/pyutils/version.py | get_complete_version | def get_complete_version(version=None):
"""Returns a tuple of the graphene version. If version argument is non-empty,
then checks for correctness of the tuple provided.
"""
if version is None:
from graphene import VERSION as version
else:
assert len(version) == 5
assert version[3] in ("alpha", "beta", "rc", "final")
return version | python | def get_complete_version(version=None):
"""Returns a tuple of the graphene version. If version argument is non-empty,
then checks for correctness of the tuple provided.
"""
if version is None:
from graphene import VERSION as version
else:
assert len(version) == 5
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32,261 | graphql-python/graphene | graphene/types/utils.py | get_field_as | def get_field_as(value, _as=None):
"""
Get type mounted
"""
if isinstance(value, MountedType):
return value
elif isinstance(value, UnmountedType):
if _as is None:
return value
return _as.mounted(value) | python | def get_field_as(value, _as=None):
"""
Get type mounted
"""
if isinstance(value, MountedType):
return value
elif isinstance(value, UnmountedType):
if _as is None:
return value
return _as.mounted(value) | [
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32,262 | elastic/elasticsearch-dsl-py | examples/composite_agg.py | scan_aggs | def scan_aggs(search, source_aggs, inner_aggs={}, size=10):
"""
Helper function used to iterate over all possible bucket combinations of
``source_aggs``, returning results of ``inner_aggs`` for each. Uses the
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"""
def run_search(**kwargs):
s = search[:0]
s.aggs.bucket('comp', 'composite', sources=source_aggs, size=size, **kwargs)
for agg_name, agg in inner_aggs.items():
s.aggs['comp'][agg_name] = agg
return s.execute()
response = run_search()
while response.aggregations.comp.buckets:
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yield b
if 'after_key' in response.aggregations.comp:
after = response.aggregations.comp.after_key
else:
after= response.aggregations.comp.buckets[-1].key
response = run_search(after=after) | python | def scan_aggs(search, source_aggs, inner_aggs={}, size=10):
"""
Helper function used to iterate over all possible bucket combinations of
``source_aggs``, returning results of ``inner_aggs`` for each. Uses the
``composite`` aggregation under the hood to perform this.
"""
def run_search(**kwargs):
s = search[:0]
s.aggs.bucket('comp', 'composite', sources=source_aggs, size=size, **kwargs)
for agg_name, agg in inner_aggs.items():
s.aggs['comp'][agg_name] = agg
return s.execute()
response = run_search()
while response.aggregations.comp.buckets:
for b in response.aggregations.comp.buckets:
yield b
if 'after_key' in response.aggregations.comp:
after = response.aggregations.comp.after_key
else:
after= response.aggregations.comp.buckets[-1].key
response = run_search(after=after) | [
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32,263 | elastic/elasticsearch-dsl-py | examples/completion.py | Person.clean | def clean(self):
"""
Automatically construct the suggestion input and weight by taking all
possible permutation of Person's name as ``input`` and taking their
popularity as ``weight``.
"""
self.suggest = {
'input': [' '.join(p) for p in permutations(self.name.split())],
'weight': self.popularity
} | python | def clean(self):
"""
Automatically construct the suggestion input and weight by taking all
possible permutation of Person's name as ``input`` and taking their
popularity as ``weight``.
"""
self.suggest = {
'input': [' '.join(p) for p in permutations(self.name.split())],
'weight': self.popularity
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32,264 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/utils.py | ObjectBase.__list_fields | def __list_fields(cls):
"""
Get all the fields defined for our class, if we have an Index, try
looking at the index mappings as well, mark the fields from Index as
optional.
"""
for name in cls._doc_type.mapping:
field = cls._doc_type.mapping[name]
yield name, field, False
if hasattr(cls.__class__, '_index'):
if not cls._index._mapping:
return
for name in cls._index._mapping:
# don't return fields that are in _doc_type
if name in cls._doc_type.mapping:
continue
field = cls._index._mapping[name]
yield name, field, True | python | def __list_fields(cls):
"""
Get all the fields defined for our class, if we have an Index, try
looking at the index mappings as well, mark the fields from Index as
optional.
"""
for name in cls._doc_type.mapping:
field = cls._doc_type.mapping[name]
yield name, field, False
if hasattr(cls.__class__, '_index'):
if not cls._index._mapping:
return
for name in cls._index._mapping:
# don't return fields that are in _doc_type
if name in cls._doc_type.mapping:
continue
field = cls._index._mapping[name]
yield name, field, True | [
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32,265 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/update_by_query.py | UpdateByQuery.to_dict | def to_dict(self, **kwargs):
"""
Serialize the search into the dictionary that will be sent over as the
request'ubq body.
All additional keyword arguments will be included into the dictionary.
"""
d = {}
if self.query:
d["query"] = self.query.to_dict()
if self._script:
d['script'] = self._script
d.update(self._extra)
d.update(kwargs)
return d | python | def to_dict(self, **kwargs):
"""
Serialize the search into the dictionary that will be sent over as the
request'ubq body.
All additional keyword arguments will be included into the dictionary.
"""
d = {}
if self.query:
d["query"] = self.query.to_dict()
if self._script:
d['script'] = self._script
d.update(self._extra)
d.update(kwargs)
return d | [
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32,266 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/mapping.py | Properties._collect_fields | def _collect_fields(self):
""" Iterate over all Field objects within, including multi fields. """
for f in itervalues(self.properties.to_dict()):
yield f
# multi fields
if hasattr(f, 'fields'):
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yield inner_f
# nested and inner objects
if hasattr(f, '_collect_fields'):
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yield inner_f | python | def _collect_fields(self):
""" Iterate over all Field objects within, including multi fields. """
for f in itervalues(self.properties.to_dict()):
yield f
# multi fields
if hasattr(f, 'fields'):
for inner_f in itervalues(f.fields.to_dict()):
yield inner_f
# nested and inner objects
if hasattr(f, '_collect_fields'):
for inner_f in f._collect_fields():
yield inner_f | [
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32,267 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.analyzer | def analyzer(self, *args, **kwargs):
"""
Explicitly add an analyzer to an index. Note that all custom analyzers
defined in mappings will also be created. This is useful for search analyzers.
Example::
from elasticsearch_dsl import analyzer, tokenizer
my_analyzer = analyzer('my_analyzer',
tokenizer=tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
filter=['lowercase']
)
i = Index('blog')
i.analyzer(my_analyzer)
"""
analyzer = analysis.analyzer(*args, **kwargs)
d = analyzer.get_analysis_definition()
# empty custom analyzer, probably already defined out of our control
if not d:
return
# merge the definition
merge(self._analysis, d, True) | python | def analyzer(self, *args, **kwargs):
"""
Explicitly add an analyzer to an index. Note that all custom analyzers
defined in mappings will also be created. This is useful for search analyzers.
Example::
from elasticsearch_dsl import analyzer, tokenizer
my_analyzer = analyzer('my_analyzer',
tokenizer=tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
filter=['lowercase']
)
i = Index('blog')
i.analyzer(my_analyzer)
"""
analyzer = analysis.analyzer(*args, **kwargs)
d = analyzer.get_analysis_definition()
# empty custom analyzer, probably already defined out of our control
if not d:
return
# merge the definition
merge(self._analysis, d, True) | [
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32,268 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.create | def create(self, using=None, **kwargs):
"""
Creates the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.create`` unchanged.
"""
self._get_connection(using).indices.create(index=self._name, body=self.to_dict(), **kwargs) | python | def create(self, using=None, **kwargs):
"""
Creates the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.create`` unchanged.
"""
self._get_connection(using).indices.create(index=self._name, body=self.to_dict(), **kwargs) | [
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32,269 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.save | def save(self, using=None):
"""
Sync the index definition with elasticsearch, creating the index if it
doesn't exist and updating its settings and mappings if it does.
Note some settings and mapping changes cannot be done on an open
index (or at all on an existing index) and for those this method will
fail with the underlying exception.
"""
if not self.exists(using=using):
return self.create(using=using)
body = self.to_dict()
settings = body.pop('settings', {})
analysis = settings.pop('analysis', None)
current_settings = self.get_settings(using=using)[self._name]['settings']['index']
if analysis:
if self.is_closed(using=using):
# closed index, update away
settings['analysis'] = analysis
else:
# compare analysis definition, if all analysis objects are
# already defined as requested, skip analysis update and
# proceed, otherwise raise IllegalOperation
existing_analysis = current_settings.get('analysis', {})
if any(
existing_analysis.get(section, {}).get(k, None) != analysis[section][k]
for section in analysis
for k in analysis[section]
):
raise IllegalOperation(
'You cannot update analysis configuration on an open index, you need to close index %s first.' % self._name)
# try and update the settings
if settings:
settings = settings.copy()
for k, v in list(settings.items()):
if k in current_settings and current_settings[k] == str(v):
del settings[k]
if settings:
self.put_settings(using=using, body=settings)
# update the mappings, any conflict in the mappings will result in an
# exception
mappings = body.pop('mappings', {})
if mappings:
self.put_mapping(using=using, body=mappings) | python | def save(self, using=None):
"""
Sync the index definition with elasticsearch, creating the index if it
doesn't exist and updating its settings and mappings if it does.
Note some settings and mapping changes cannot be done on an open
index (or at all on an existing index) and for those this method will
fail with the underlying exception.
"""
if not self.exists(using=using):
return self.create(using=using)
body = self.to_dict()
settings = body.pop('settings', {})
analysis = settings.pop('analysis', None)
current_settings = self.get_settings(using=using)[self._name]['settings']['index']
if analysis:
if self.is_closed(using=using):
# closed index, update away
settings['analysis'] = analysis
else:
# compare analysis definition, if all analysis objects are
# already defined as requested, skip analysis update and
# proceed, otherwise raise IllegalOperation
existing_analysis = current_settings.get('analysis', {})
if any(
existing_analysis.get(section, {}).get(k, None) != analysis[section][k]
for section in analysis
for k in analysis[section]
):
raise IllegalOperation(
'You cannot update analysis configuration on an open index, you need to close index %s first.' % self._name)
# try and update the settings
if settings:
settings = settings.copy()
for k, v in list(settings.items()):
if k in current_settings and current_settings[k] == str(v):
del settings[k]
if settings:
self.put_settings(using=using, body=settings)
# update the mappings, any conflict in the mappings will result in an
# exception
mappings = body.pop('mappings', {})
if mappings:
self.put_mapping(using=using, body=mappings) | [
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32,270 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.analyze | def analyze(self, using=None, **kwargs):
"""
Perform the analysis process on a text and return the tokens breakdown
of the text.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.analyze`` unchanged.
"""
return self._get_connection(using).indices.analyze(index=self._name, **kwargs) | python | def analyze(self, using=None, **kwargs):
"""
Perform the analysis process on a text and return the tokens breakdown
of the text.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.analyze`` unchanged.
"""
return self._get_connection(using).indices.analyze(index=self._name, **kwargs) | [
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32,271 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.refresh | def refresh(self, using=None, **kwargs):
"""
Preforms a refresh operation on the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.refresh`` unchanged.
"""
return self._get_connection(using).indices.refresh(index=self._name, **kwargs) | python | def refresh(self, using=None, **kwargs):
"""
Preforms a refresh operation on the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.refresh`` unchanged.
"""
return self._get_connection(using).indices.refresh(index=self._name, **kwargs) | [
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32,272 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.flush | def flush(self, using=None, **kwargs):
"""
Preforms a flush operation on the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.flush`` unchanged.
"""
return self._get_connection(using).indices.flush(index=self._name, **kwargs) | python | def flush(self, using=None, **kwargs):
"""
Preforms a flush operation on the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.flush`` unchanged.
"""
return self._get_connection(using).indices.flush(index=self._name, **kwargs) | [
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32,273 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.get | def get(self, using=None, **kwargs):
"""
The get index API allows to retrieve information about the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get`` unchanged.
"""
return self._get_connection(using).indices.get(index=self._name, **kwargs) | python | def get(self, using=None, **kwargs):
"""
The get index API allows to retrieve information about the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get`` unchanged.
"""
return self._get_connection(using).indices.get(index=self._name, **kwargs) | [
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32,274 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.open | def open(self, using=None, **kwargs):
"""
Opens the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.open`` unchanged.
"""
return self._get_connection(using).indices.open(index=self._name, **kwargs) | python | def open(self, using=None, **kwargs):
"""
Opens the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.open`` unchanged.
"""
return self._get_connection(using).indices.open(index=self._name, **kwargs) | [
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32,275 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.close | def close(self, using=None, **kwargs):
"""
Closes the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.close`` unchanged.
"""
return self._get_connection(using).indices.close(index=self._name, **kwargs) | python | def close(self, using=None, **kwargs):
"""
Closes the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.close`` unchanged.
"""
return self._get_connection(using).indices.close(index=self._name, **kwargs) | [
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32,276 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.delete | def delete(self, using=None, **kwargs):
"""
Deletes the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.delete`` unchanged.
"""
return self._get_connection(using).indices.delete(index=self._name, **kwargs) | python | def delete(self, using=None, **kwargs):
"""
Deletes the index in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.delete`` unchanged.
"""
return self._get_connection(using).indices.delete(index=self._name, **kwargs) | [
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32,277 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.exists | def exists(self, using=None, **kwargs):
"""
Returns ``True`` if the index already exists in elasticsearch.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.exists`` unchanged.
"""
return self._get_connection(using).indices.exists(index=self._name, **kwargs) | python | def exists(self, using=None, **kwargs):
"""
Returns ``True`` if the index already exists in elasticsearch.
Any additional keyword arguments will be passed to
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"""
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32,278 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.put_mapping | def put_mapping(self, using=None, **kwargs):
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"""
return self._get_connection(using).indices.put_mapping(index=self._name, **kwargs) | python | def put_mapping(self, using=None, **kwargs):
"""
Register specific mapping definition for a specific type.
Any additional keyword arguments will be passed to
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"""
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32,279 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.get_mapping | def get_mapping(self, using=None, **kwargs):
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"""
return self._get_connection(using).indices.get_mapping(index=self._name, **kwargs) | python | def get_mapping(self, using=None, **kwargs):
"""
Retrieve specific mapping definition for a specific type.
Any additional keyword arguments will be passed to
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"""
return self._get_connection(using).indices.get_mapping(index=self._name, **kwargs) | [
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32,280 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.get_field_mapping | def get_field_mapping(self, using=None, **kwargs):
"""
Retrieve mapping definition of a specific field.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_field_mapping`` unchanged.
"""
return self._get_connection(using).indices.get_field_mapping(index=self._name, **kwargs) | python | def get_field_mapping(self, using=None, **kwargs):
"""
Retrieve mapping definition of a specific field.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_field_mapping`` unchanged.
"""
return self._get_connection(using).indices.get_field_mapping(index=self._name, **kwargs) | [
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32,281 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.put_alias | def put_alias(self, using=None, **kwargs):
"""
Create an alias for the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.put_alias`` unchanged.
"""
return self._get_connection(using).indices.put_alias(index=self._name, **kwargs) | python | def put_alias(self, using=None, **kwargs):
"""
Create an alias for the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.put_alias`` unchanged.
"""
return self._get_connection(using).indices.put_alias(index=self._name, **kwargs) | [
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32,282 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.exists_alias | def exists_alias(self, using=None, **kwargs):
"""
Return a boolean indicating whether given alias exists for this index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.exists_alias`` unchanged.
"""
return self._get_connection(using).indices.exists_alias(index=self._name, **kwargs) | python | def exists_alias(self, using=None, **kwargs):
"""
Return a boolean indicating whether given alias exists for this index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.exists_alias`` unchanged.
"""
return self._get_connection(using).indices.exists_alias(index=self._name, **kwargs) | [
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32,283 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.get_alias | def get_alias(self, using=None, **kwargs):
"""
Retrieve a specified alias.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_alias`` unchanged.
"""
return self._get_connection(using).indices.get_alias(index=self._name, **kwargs) | python | def get_alias(self, using=None, **kwargs):
"""
Retrieve a specified alias.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_alias`` unchanged.
"""
return self._get_connection(using).indices.get_alias(index=self._name, **kwargs) | [
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32,284 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.delete_alias | def delete_alias(self, using=None, **kwargs):
"""
Delete specific alias.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.delete_alias`` unchanged.
"""
return self._get_connection(using).indices.delete_alias(index=self._name, **kwargs) | python | def delete_alias(self, using=None, **kwargs):
"""
Delete specific alias.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.delete_alias`` unchanged.
"""
return self._get_connection(using).indices.delete_alias(index=self._name, **kwargs) | [
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32,285 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.get_settings | def get_settings(self, using=None, **kwargs):
"""
Retrieve settings for the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_settings`` unchanged.
"""
return self._get_connection(using).indices.get_settings(index=self._name, **kwargs) | python | def get_settings(self, using=None, **kwargs):
"""
Retrieve settings for the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_settings`` unchanged.
"""
return self._get_connection(using).indices.get_settings(index=self._name, **kwargs) | [
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32,286 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.put_settings | def put_settings(self, using=None, **kwargs):
"""
Change specific index level settings in real time.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.put_settings`` unchanged.
"""
return self._get_connection(using).indices.put_settings(index=self._name, **kwargs) | python | def put_settings(self, using=None, **kwargs):
"""
Change specific index level settings in real time.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.put_settings`` unchanged.
"""
return self._get_connection(using).indices.put_settings(index=self._name, **kwargs) | [
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32,287 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.stats | def stats(self, using=None, **kwargs):
"""
Retrieve statistics on different operations happening on the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.stats`` unchanged.
"""
return self._get_connection(using).indices.stats(index=self._name, **kwargs) | python | def stats(self, using=None, **kwargs):
"""
Retrieve statistics on different operations happening on the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.stats`` unchanged.
"""
return self._get_connection(using).indices.stats(index=self._name, **kwargs) | [
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32,288 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.validate_query | def validate_query(self, using=None, **kwargs):
"""
Validate a potentially expensive query without executing it.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.validate_query`` unchanged.
"""
return self._get_connection(using).indices.validate_query(index=self._name, **kwargs) | python | def validate_query(self, using=None, **kwargs):
"""
Validate a potentially expensive query without executing it.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.validate_query`` unchanged.
"""
return self._get_connection(using).indices.validate_query(index=self._name, **kwargs) | [
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32,289 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.clear_cache | def clear_cache(self, using=None, **kwargs):
"""
Clear all caches or specific cached associated with the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.clear_cache`` unchanged.
"""
return self._get_connection(using).indices.clear_cache(index=self._name, **kwargs) | python | def clear_cache(self, using=None, **kwargs):
"""
Clear all caches or specific cached associated with the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.clear_cache`` unchanged.
"""
return self._get_connection(using).indices.clear_cache(index=self._name, **kwargs) | [
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32,290 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.recovery | def recovery(self, using=None, **kwargs):
"""
The indices recovery API provides insight into on-going shard
recoveries for the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.recovery`` unchanged.
"""
return self._get_connection(using).indices.recovery(index=self._name, **kwargs) | python | def recovery(self, using=None, **kwargs):
"""
The indices recovery API provides insight into on-going shard
recoveries for the index.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.recovery`` unchanged.
"""
return self._get_connection(using).indices.recovery(index=self._name, **kwargs) | [
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32,291 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.upgrade | def upgrade(self, using=None, **kwargs):
"""
Upgrade the index to the latest format.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.upgrade`` unchanged.
"""
return self._get_connection(using).indices.upgrade(index=self._name, **kwargs) | python | def upgrade(self, using=None, **kwargs):
"""
Upgrade the index to the latest format.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.upgrade`` unchanged.
"""
return self._get_connection(using).indices.upgrade(index=self._name, **kwargs) | [
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32,292 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.get_upgrade | def get_upgrade(self, using=None, **kwargs):
"""
Monitor how much of the index is upgraded.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_upgrade`` unchanged.
"""
return self._get_connection(using).indices.get_upgrade(index=self._name, **kwargs) | python | def get_upgrade(self, using=None, **kwargs):
"""
Monitor how much of the index is upgraded.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.get_upgrade`` unchanged.
"""
return self._get_connection(using).indices.get_upgrade(index=self._name, **kwargs) | [
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... | Monitor how much of the index is upgraded.
Any additional keyword arguments will be passed to
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] | 874b52472fc47b601de0e5fa0e4300e21aff0085 | https://github.com/elastic/elasticsearch-dsl-py/blob/874b52472fc47b601de0e5fa0e4300e21aff0085/elasticsearch_dsl/index.py#L528-L535 |
32,293 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.shard_stores | def shard_stores(self, using=None, **kwargs):
"""
Provides store information for shard copies of the index. Store
information reports on which nodes shard copies exist, the shard copy
version, indicating how recent they are, and any exceptions encountered
while opening the shard index or from earlier engine failure.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.shard_stores`` unchanged.
"""
return self._get_connection(using).indices.shard_stores(index=self._name, **kwargs) | python | def shard_stores(self, using=None, **kwargs):
"""
Provides store information for shard copies of the index. Store
information reports on which nodes shard copies exist, the shard copy
version, indicating how recent they are, and any exceptions encountered
while opening the shard index or from earlier engine failure.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.shard_stores`` unchanged.
"""
return self._get_connection(using).indices.shard_stores(index=self._name, **kwargs) | [
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32,294 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/index.py | Index.forcemerge | def forcemerge(self, using=None, **kwargs):
"""
The force merge API allows to force merging of the index through an
API. The merge relates to the number of segments a Lucene index holds
within each shard. The force merge operation allows to reduce the
number of segments by merging them.
This call will block until the merge is complete. If the http
connection is lost, the request will continue in the background, and
any new requests will block until the previous force merge is complete.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.forcemerge`` unchanged.
"""
return self._get_connection(using).indices.forcemerge(index=self._name, **kwargs) | python | def forcemerge(self, using=None, **kwargs):
"""
The force merge API allows to force merging of the index through an
API. The merge relates to the number of segments a Lucene index holds
within each shard. The force merge operation allows to reduce the
number of segments by merging them.
This call will block until the merge is complete. If the http
connection is lost, the request will continue in the background, and
any new requests will block until the previous force merge is complete.
Any additional keyword arguments will be passed to
``Elasticsearch.indices.forcemerge`` unchanged.
"""
return self._get_connection(using).indices.forcemerge(index=self._name, **kwargs) | [
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32,295 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/connections.py | Connections.configure | def configure(self, **kwargs):
"""
Configure multiple connections at once, useful for passing in config
dictionaries obtained from other sources, like Django's settings or a
configuration management tool.
Example::
connections.configure(
default={'hosts': 'localhost'},
dev={'hosts': ['esdev1.example.com:9200'], sniff_on_start=True}
)
Connections will only be constructed lazily when requested through
``get_connection``.
"""
for k in list(self._conns):
# try and preserve existing client to keep the persistent connections alive
if k in self._kwargs and kwargs.get(k, None) == self._kwargs[k]:
continue
del self._conns[k]
self._kwargs = kwargs | python | def configure(self, **kwargs):
"""
Configure multiple connections at once, useful for passing in config
dictionaries obtained from other sources, like Django's settings or a
configuration management tool.
Example::
connections.configure(
default={'hosts': 'localhost'},
dev={'hosts': ['esdev1.example.com:9200'], sniff_on_start=True}
)
Connections will only be constructed lazily when requested through
``get_connection``.
"""
for k in list(self._conns):
# try and preserve existing client to keep the persistent connections alive
if k in self._kwargs and kwargs.get(k, None) == self._kwargs[k]:
continue
del self._conns[k]
self._kwargs = kwargs | [
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32,296 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/connections.py | Connections.remove_connection | def remove_connection(self, alias):
"""
Remove connection from the registry. Raises ``KeyError`` if connection
wasn't found.
"""
errors = 0
for d in (self._conns, self._kwargs):
try:
del d[alias]
except KeyError:
errors += 1
if errors == 2:
raise KeyError('There is no connection with alias %r.' % alias) | python | def remove_connection(self, alias):
"""
Remove connection from the registry. Raises ``KeyError`` if connection
wasn't found.
"""
errors = 0
for d in (self._conns, self._kwargs):
try:
del d[alias]
except KeyError:
errors += 1
if errors == 2:
raise KeyError('There is no connection with alias %r.' % alias) | [
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32,297 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/connections.py | Connections.create_connection | def create_connection(self, alias='default', **kwargs):
"""
Construct an instance of ``elasticsearch.Elasticsearch`` and register
it under given alias.
"""
kwargs.setdefault('serializer', serializer)
conn = self._conns[alias] = Elasticsearch(**kwargs)
return conn | python | def create_connection(self, alias='default', **kwargs):
"""
Construct an instance of ``elasticsearch.Elasticsearch`` and register
it under given alias.
"""
kwargs.setdefault('serializer', serializer)
conn = self._conns[alias] = Elasticsearch(**kwargs)
return conn | [
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32,298 | elastic/elasticsearch-dsl-py | examples/alias_migration.py | setup | def setup():
"""
Create the index template in elasticsearch specifying the mappings and any
settings to be used. This can be run at any time, ideally at every new code
deploy.
"""
# create an index template
index_template = BlogPost._index.as_template(ALIAS, PATTERN)
# upload the template into elasticsearch
# potentially overriding the one already there
index_template.save()
# create the first index if it doesn't exist
if not BlogPost._index.exists():
migrate(move_data=False) | python | def setup():
"""
Create the index template in elasticsearch specifying the mappings and any
settings to be used. This can be run at any time, ideally at every new code
deploy.
"""
# create an index template
index_template = BlogPost._index.as_template(ALIAS, PATTERN)
# upload the template into elasticsearch
# potentially overriding the one already there
index_template.save()
# create the first index if it doesn't exist
if not BlogPost._index.exists():
migrate(move_data=False) | [
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32,299 | elastic/elasticsearch-dsl-py | elasticsearch_dsl/analysis.py | CustomAnalyzer.simulate | def simulate(self, text, using='default', explain=False, attributes=None):
"""
Use the Analyze API of elasticsearch to test the outcome of this analyzer.
:arg text: Text to be analyzed
:arg using: connection alias to use, defaults to ``'default'``
:arg explain: will output all token attributes for each token. You can
filter token attributes you want to output by setting ``attributes``
option.
:arg attributes: if ``explain`` is specified, filter the token
attributes to return.
"""
es = connections.get_connection(using)
body = {'text': text, 'explain': explain}
if attributes:
body['attributes'] = attributes
definition = self.get_analysis_definition()
analyzer_def = self.get_definition()
for section in ('tokenizer', 'char_filter', 'filter'):
if section not in analyzer_def:
continue
sec_def = definition.get(section, {})
sec_names = analyzer_def[section]
if isinstance(sec_names, six.string_types):
body[section] = sec_def.get(sec_names, sec_names)
else:
body[section] = [sec_def.get(sec_name, sec_name) for sec_name in sec_names]
if self._builtin_type != 'custom':
body['analyzer'] = self._builtin_type
return AttrDict(es.indices.analyze(body=body)) | python | def simulate(self, text, using='default', explain=False, attributes=None):
"""
Use the Analyze API of elasticsearch to test the outcome of this analyzer.
:arg text: Text to be analyzed
:arg using: connection alias to use, defaults to ``'default'``
:arg explain: will output all token attributes for each token. You can
filter token attributes you want to output by setting ``attributes``
option.
:arg attributes: if ``explain`` is specified, filter the token
attributes to return.
"""
es = connections.get_connection(using)
body = {'text': text, 'explain': explain}
if attributes:
body['attributes'] = attributes
definition = self.get_analysis_definition()
analyzer_def = self.get_definition()
for section in ('tokenizer', 'char_filter', 'filter'):
if section not in analyzer_def:
continue
sec_def = definition.get(section, {})
sec_names = analyzer_def[section]
if isinstance(sec_names, six.string_types):
body[section] = sec_def.get(sec_names, sec_names)
else:
body[section] = [sec_def.get(sec_name, sec_name) for sec_name in sec_names]
if self._builtin_type != 'custom':
body['analyzer'] = self._builtin_type
return AttrDict(es.indices.analyze(body=body)) | [
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:arg explain: will output all token attributes for each token. You can
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