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#vtb
def get_configuration_dict(self, secret_attrs=False):
cd = super(TaxonomicAmendmentsShard, self).get_configuration_dict(secret_attrs=secret_attrs)
cd[] = cd.pop()
cd[] = cd.pop()
if self._next_ott_id is not None:
cd[] = self._next_ott_id,
return cd
|
Overrides superclass method and renames some properties
|
### Input:
Overrides superclass method and renames some properties
### Response:
#vtb
def get_configuration_dict(self, secret_attrs=False):
cd = super(TaxonomicAmendmentsShard, self).get_configuration_dict(secret_attrs=secret_attrs)
cd[] = cd.pop()
cd[] = cd.pop()
if self._next_ott_id is not None:
cd[] = self._next_ott_id,
return cd
|
#vtb
def disconnect(cls):
app = AndroidApplication.instance()
f = app.create_future()
def on_permission_result(result):
if not result:
f.set_result(None)
return
def on_ready(mgr):
mgr.disconnect().then(f.set_result)
WifiManager.get().then(on_ready)
WifiManager.request_permission([
WifiManager.PERMISSION_CHANGE_WIFI_STATE
]).then(on_permission_result)
return f
|
Disconnect from the current network (if connected).
Returns
--------
result: future
A future that resolves to true if the disconnect was
successful. Will be set to None if the change network
permission is denied.
|
### Input:
Disconnect from the current network (if connected).
Returns
--------
result: future
A future that resolves to true if the disconnect was
successful. Will be set to None if the change network
permission is denied.
### Response:
#vtb
def disconnect(cls):
app = AndroidApplication.instance()
f = app.create_future()
def on_permission_result(result):
if not result:
f.set_result(None)
return
def on_ready(mgr):
mgr.disconnect().then(f.set_result)
WifiManager.get().then(on_ready)
WifiManager.request_permission([
WifiManager.PERMISSION_CHANGE_WIFI_STATE
]).then(on_permission_result)
return f
|
#vtb
def _wire_events(self):
self._device.on_open += self._on_open
self._device.on_close += self._on_close
self._device.on_read += self._on_read
self._device.on_write += self._on_write
self._zonetracker.on_fault += self._on_zone_fault
self._zonetracker.on_restore += self._on_zone_restore
|
Wires up the internal device events.
|
### Input:
Wires up the internal device events.
### Response:
#vtb
def _wire_events(self):
self._device.on_open += self._on_open
self._device.on_close += self._on_close
self._device.on_read += self._on_read
self._device.on_write += self._on_write
self._zonetracker.on_fault += self._on_zone_fault
self._zonetracker.on_restore += self._on_zone_restore
|
#vtb
def refetch_fields(self, missing_fields):
db_fields = self.mongokat_collection.find_one({"_id": self["_id"]}, fields={k: 1 for k in missing_fields})
self._fetched_fields += tuple(missing_fields)
if not db_fields:
return
for k, v in db_fields.items():
self[k] = v
|
Refetches a list of fields from the DB
|
### Input:
Refetches a list of fields from the DB
### Response:
#vtb
def refetch_fields(self, missing_fields):
db_fields = self.mongokat_collection.find_one({"_id": self["_id"]}, fields={k: 1 for k in missing_fields})
self._fetched_fields += tuple(missing_fields)
if not db_fields:
return
for k, v in db_fields.items():
self[k] = v
|
#vtb
def _force_https(self):
if self.session_cookie_secure:
if not self.app.debug:
self.app.config[] = True
criteria = [
self.app.debug,
flask.request.is_secure,
flask.request.headers.get(, ) == ,
]
local_options = self._get_local_options()
if local_options[] and not any(criteria):
if flask.request.url.startswith():
url = flask.request.url.replace(, , 1)
code = 302
if self.force_https_permanent:
code = 301
r = flask.redirect(url, code=code)
return r
|
Redirect any non-https requests to https.
Based largely on flask-sslify.
|
### Input:
Redirect any non-https requests to https.
Based largely on flask-sslify.
### Response:
#vtb
def _force_https(self):
if self.session_cookie_secure:
if not self.app.debug:
self.app.config[] = True
criteria = [
self.app.debug,
flask.request.is_secure,
flask.request.headers.get(, ) == ,
]
local_options = self._get_local_options()
if local_options[] and not any(criteria):
if flask.request.url.startswith():
url = flask.request.url.replace(, , 1)
code = 302
if self.force_https_permanent:
code = 301
r = flask.redirect(url, code=code)
return r
|
#vtb
def create(self, dataset_id):
from google.cloud.bigquery import Dataset
if self.exists(dataset_id):
raise DatasetCreationError(
"Dataset {0} already " "exists".format(dataset_id)
)
dataset = Dataset(self.client.dataset(dataset_id))
if self.location is not None:
dataset.location = self.location
try:
self.client.create_dataset(dataset)
except self.http_error as ex:
self.process_http_error(ex)
|
Create a dataset in Google BigQuery
Parameters
----------
dataset : str
Name of dataset to be written
|
### Input:
Create a dataset in Google BigQuery
Parameters
----------
dataset : str
Name of dataset to be written
### Response:
#vtb
def create(self, dataset_id):
from google.cloud.bigquery import Dataset
if self.exists(dataset_id):
raise DatasetCreationError(
"Dataset {0} already " "exists".format(dataset_id)
)
dataset = Dataset(self.client.dataset(dataset_id))
if self.location is not None:
dataset.location = self.location
try:
self.client.create_dataset(dataset)
except self.http_error as ex:
self.process_http_error(ex)
|
#vtb
def _create_session(self):
logger.debug("Create new phantomjs web driver")
self.driver = webdriver.PhantomJS(desired_capabilities=self.dcap,
**self.driver_args)
self.set_cookies(self.current_cookies)
self.driver.set_window_size(1920, 1080)
|
Creates a fresh session with no/default headers and proxies
|
### Input:
Creates a fresh session with no/default headers and proxies
### Response:
#vtb
def _create_session(self):
logger.debug("Create new phantomjs web driver")
self.driver = webdriver.PhantomJS(desired_capabilities=self.dcap,
**self.driver_args)
self.set_cookies(self.current_cookies)
self.driver.set_window_size(1920, 1080)
|
#vtb
def compact(self) -> str:
doc = "TX:{0}:{1}:{2}:{3}:{4}:{5}:{6}\n".format(self.version,
len(self.issuers),
len(self.inputs),
len(self.unlocks),
len(self.outputs),
if self.comment != "" else ,
self.locktime)
if self.version >= 3:
doc += "{0}\n".format(self.blockstamp)
for pubkey in self.issuers:
doc += "{0}\n".format(pubkey)
for i in self.inputs:
doc += "{0}\n".format(i.inline(self.version))
for u in self.unlocks:
doc += "{0}\n".format(u.inline())
for o in self.outputs:
doc += "{0}\n".format(o.inline())
if self.comment != "":
doc += "{0}\n".format(self.comment)
for s in self.signatures:
doc += "{0}\n".format(s)
return doc
|
Return a transaction in its compact format from the instance
:return:
|
### Input:
Return a transaction in its compact format from the instance
:return:
### Response:
#vtb
def compact(self) -> str:
doc = "TX:{0}:{1}:{2}:{3}:{4}:{5}:{6}\n".format(self.version,
len(self.issuers),
len(self.inputs),
len(self.unlocks),
len(self.outputs),
if self.comment != "" else ,
self.locktime)
if self.version >= 3:
doc += "{0}\n".format(self.blockstamp)
for pubkey in self.issuers:
doc += "{0}\n".format(pubkey)
for i in self.inputs:
doc += "{0}\n".format(i.inline(self.version))
for u in self.unlocks:
doc += "{0}\n".format(u.inline())
for o in self.outputs:
doc += "{0}\n".format(o.inline())
if self.comment != "":
doc += "{0}\n".format(self.comment)
for s in self.signatures:
doc += "{0}\n".format(s)
return doc
|
#vtb
def query(url, output=True, **kwargs):
key1=val1&key2=val2<xml>somecontent</xml>
if output is not True:
log.warning()
if not in kwargs:
kwargs[] =
opts = __opts__.copy()
if in kwargs:
opts.update(kwargs[])
del kwargs[]
ret = salt.utils.http.query(url=url, opts=opts, **kwargs)
return ret
|
Query a resource, and decode the return data
Passes through all the parameters described in the
:py:func:`utils.http.query function <salt.utils.http.query>`:
CLI Example:
.. code-block:: bash
salt-run http.query http://somelink.com/
salt-run http.query http://somelink.com/ method=POST \
params='key1=val1&key2=val2'
salt-run http.query http://somelink.com/ method=POST \
data='<xml>somecontent</xml>'
|
### Input:
Query a resource, and decode the return data
Passes through all the parameters described in the
:py:func:`utils.http.query function <salt.utils.http.query>`:
CLI Example:
.. code-block:: bash
salt-run http.query http://somelink.com/
salt-run http.query http://somelink.com/ method=POST \
params='key1=val1&key2=val2'
salt-run http.query http://somelink.com/ method=POST \
data='<xml>somecontent</xml>'
### Response:
#vtb
def query(url, output=True, **kwargs):
key1=val1&key2=val2<xml>somecontent</xml>
if output is not True:
log.warning()
if not in kwargs:
kwargs[] =
opts = __opts__.copy()
if in kwargs:
opts.update(kwargs[])
del kwargs[]
ret = salt.utils.http.query(url=url, opts=opts, **kwargs)
return ret
|
#vtb
def _parse_tree(self, node):
if in node.attrib:
self.kind = node.attrib[]
if in node.attrib:
self.width = int(node.attrib[])
if in node.attrib:
self.height = int(node.attrib[])
self.url = node.text
|
Parse a <image> object
|
### Input:
Parse a <image> object
### Response:
#vtb
def _parse_tree(self, node):
if in node.attrib:
self.kind = node.attrib[]
if in node.attrib:
self.width = int(node.attrib[])
if in node.attrib:
self.height = int(node.attrib[])
self.url = node.text
|
#vtb
def set_data(self, adjacency_mat=None, **kwargs):
if adjacency_mat is not None:
if adjacency_mat.shape[0] != adjacency_mat.shape[1]:
raise ValueError("Adjacency matrix should be square.")
self._adjacency_mat = adjacency_mat
for k in self._arrow_attributes:
if k in kwargs:
translated = (self._arrow_kw_trans[k] if k in
self._arrow_kw_trans else k)
setattr(self._edges, translated, kwargs.pop(k))
arrow_kwargs = {}
for k in self._arrow_kwargs:
if k in kwargs:
translated = (self._arrow_kw_trans[k] if k in
self._arrow_kw_trans else k)
arrow_kwargs[translated] = kwargs.pop(k)
node_kwargs = {}
for k in self._node_kwargs:
if k in kwargs:
translated = (self._node_kw_trans[k] if k in
self._node_kw_trans else k)
node_kwargs[translated] = kwargs.pop(k)
if len(kwargs) > 0:
raise TypeError("%s.set_data() got invalid keyword arguments: %S"
% (self.__class__.__name__, list(kwargs.keys())))
self._arrow_data = arrow_kwargs
self._node_data = node_kwargs
if not self._animate:
self.set_final_layout()
|
Set the data
Parameters
----------
adjacency_mat : ndarray | None
The adjacency matrix.
**kwargs : dict
Keyword arguments to pass to the arrows.
|
### Input:
Set the data
Parameters
----------
adjacency_mat : ndarray | None
The adjacency matrix.
**kwargs : dict
Keyword arguments to pass to the arrows.
### Response:
#vtb
def set_data(self, adjacency_mat=None, **kwargs):
if adjacency_mat is not None:
if adjacency_mat.shape[0] != adjacency_mat.shape[1]:
raise ValueError("Adjacency matrix should be square.")
self._adjacency_mat = adjacency_mat
for k in self._arrow_attributes:
if k in kwargs:
translated = (self._arrow_kw_trans[k] if k in
self._arrow_kw_trans else k)
setattr(self._edges, translated, kwargs.pop(k))
arrow_kwargs = {}
for k in self._arrow_kwargs:
if k in kwargs:
translated = (self._arrow_kw_trans[k] if k in
self._arrow_kw_trans else k)
arrow_kwargs[translated] = kwargs.pop(k)
node_kwargs = {}
for k in self._node_kwargs:
if k in kwargs:
translated = (self._node_kw_trans[k] if k in
self._node_kw_trans else k)
node_kwargs[translated] = kwargs.pop(k)
if len(kwargs) > 0:
raise TypeError("%s.set_data() got invalid keyword arguments: %S"
% (self.__class__.__name__, list(kwargs.keys())))
self._arrow_data = arrow_kwargs
self._node_data = node_kwargs
if not self._animate:
self.set_final_layout()
|
#vtb
def free(self):
data = WebDavXmlUtils.create_free_space_request_content()
response = self.execute_request(action=, path=, data=data)
return WebDavXmlUtils.parse_free_space_response(response.content, self.webdav.hostname)
|
Returns an amount of free space on remote WebDAV server.
More information you can find by link http://webdav.org/specs/rfc4918.html#METHOD_PROPFIND
:return: an amount of free space in bytes.
|
### Input:
Returns an amount of free space on remote WebDAV server.
More information you can find by link http://webdav.org/specs/rfc4918.html#METHOD_PROPFIND
:return: an amount of free space in bytes.
### Response:
#vtb
def free(self):
data = WebDavXmlUtils.create_free_space_request_content()
response = self.execute_request(action=, path=, data=data)
return WebDavXmlUtils.parse_free_space_response(response.content, self.webdav.hostname)
|
#vtb
def _on_message(channel, method, header, body):
print "Message:"
print "\t%r" % method
print "\t%r" % header
print "\t%r" % body
channel.basic_ack(method.delivery_tag)
channel.stop_consuming()
|
Invoked by pika when a message is delivered from RabbitMQ. The
channel is passed for your convenience. The basic_deliver object that
is passed in carries the exchange, routing key, delivery tag and
a redelivered flag for the message. The properties passed in is an
instance of BasicProperties with the message properties and the body
is the message that was sent.
:param pika.channel.Channel channel: The channel object
:param pika.Spec.Basic.Deliver method: The Deliver method
:param pika.Spec.BasicProperties properties: The client properties
:param str|unicode body: The message body
|
### Input:
Invoked by pika when a message is delivered from RabbitMQ. The
channel is passed for your convenience. The basic_deliver object that
is passed in carries the exchange, routing key, delivery tag and
a redelivered flag for the message. The properties passed in is an
instance of BasicProperties with the message properties and the body
is the message that was sent.
:param pika.channel.Channel channel: The channel object
:param pika.Spec.Basic.Deliver method: The Deliver method
:param pika.Spec.BasicProperties properties: The client properties
:param str|unicode body: The message body
### Response:
#vtb
def _on_message(channel, method, header, body):
print "Message:"
print "\t%r" % method
print "\t%r" % header
print "\t%r" % body
channel.basic_ack(method.delivery_tag)
channel.stop_consuming()
|
#vtb
def generate_screenshots(self):
headers = {: , : }
resp = requests.post(self.api_url, data=json.dumps(self.config), \
headers=headers, auth=self.auth)
resp = self._process_response(resp)
return resp.json()
|
Take a config file as input and generate screenshots
|
### Input:
Take a config file as input and generate screenshots
### Response:
#vtb
def generate_screenshots(self):
headers = {: , : }
resp = requests.post(self.api_url, data=json.dumps(self.config), \
headers=headers, auth=self.auth)
resp = self._process_response(resp)
return resp.json()
|
#vtb
def get_endpoint(self, session, **kwargs):
if self.endpoint is None:
try:
self._refresh_tokens(session)
self._fetch_credentials(session)
except:
raise AuthorizationFailure()
return self.endpoint
|
Get the HubiC storage endpoint uri.
If the current session has not been authenticated, this will trigger a
new authentication to the HubiC OAuth service.
:param keystoneclient.Session session: The session object to use for
queries.
:raises keystoneclient.exceptions.AuthorizationFailure: if something
goes wrong.
:returns: The uri to use for object-storage v1 requests.
:rtype: string
|
### Input:
Get the HubiC storage endpoint uri.
If the current session has not been authenticated, this will trigger a
new authentication to the HubiC OAuth service.
:param keystoneclient.Session session: The session object to use for
queries.
:raises keystoneclient.exceptions.AuthorizationFailure: if something
goes wrong.
:returns: The uri to use for object-storage v1 requests.
:rtype: string
### Response:
#vtb
def get_endpoint(self, session, **kwargs):
if self.endpoint is None:
try:
self._refresh_tokens(session)
self._fetch_credentials(session)
except:
raise AuthorizationFailure()
return self.endpoint
|
#vtb
def _create_variables_no_pretrain(self, n_features):
self.encoding_w_ = []
self.encoding_b_ = []
for l, layer in enumerate(self.layers):
w_name = .format(l)
b_name = .format(l)
if l == 0:
w_shape = [n_features, self.layers[l]]
else:
w_shape = [self.layers[l - 1], self.layers[l]]
w_init = tf.truncated_normal(shape=w_shape, stddev=0.1)
W = tf.Variable(w_init, name=w_name)
tf.summary.histogram(w_name, W)
self.encoding_w_.append(W)
b_init = tf.constant(0.1, shape=[self.layers[l]])
b = tf.Variable(b_init, name=b_name)
tf.summary.histogram(b_name, b)
self.encoding_b_.append(b)
|
Create model variables (no previous unsupervised pretraining).
:param n_features: number of features
:return: self
|
### Input:
Create model variables (no previous unsupervised pretraining).
:param n_features: number of features
:return: self
### Response:
#vtb
def _create_variables_no_pretrain(self, n_features):
self.encoding_w_ = []
self.encoding_b_ = []
for l, layer in enumerate(self.layers):
w_name = .format(l)
b_name = .format(l)
if l == 0:
w_shape = [n_features, self.layers[l]]
else:
w_shape = [self.layers[l - 1], self.layers[l]]
w_init = tf.truncated_normal(shape=w_shape, stddev=0.1)
W = tf.Variable(w_init, name=w_name)
tf.summary.histogram(w_name, W)
self.encoding_w_.append(W)
b_init = tf.constant(0.1, shape=[self.layers[l]])
b = tf.Variable(b_init, name=b_name)
tf.summary.histogram(b_name, b)
self.encoding_b_.append(b)
|
#vtb
def individual(self, ind_id=None):
for ind_obj in self.individuals:
if ind_obj.ind_id == ind_id:
return ind_obj
return None
|
Return a individual object
Args:
ind_id (str): A individual id
Returns:
individual (puzzle.models.individual)
|
### Input:
Return a individual object
Args:
ind_id (str): A individual id
Returns:
individual (puzzle.models.individual)
### Response:
#vtb
def individual(self, ind_id=None):
for ind_obj in self.individuals:
if ind_obj.ind_id == ind_id:
return ind_obj
return None
|
#vtb
async def request(
self,
method: str,
endpoint: str,
*,
headers: dict = None,
params: dict = None) -> dict:
url = .format(API_URL_SCAFFOLD, endpoint)
if not headers:
headers = {}
headers.update({: self._api_key})
if not params:
params = {}
params.update({
: self.latitude,
: self.longitude,
: self.altitude
})
async with self._websession.request(method, url, headers=headers,
params=params) as resp:
try:
resp.raise_for_status()
return await resp.json(content_type=None)
except client_exceptions.ClientError as err:
if any(code in str(err) for code in (, )):
raise InvalidApiKeyError()
raise RequestError(
.format(
endpoint, err)) from None
|
Make a request against air-matters.com.
|
### Input:
Make a request against air-matters.com.
### Response:
#vtb
async def request(
self,
method: str,
endpoint: str,
*,
headers: dict = None,
params: dict = None) -> dict:
url = .format(API_URL_SCAFFOLD, endpoint)
if not headers:
headers = {}
headers.update({: self._api_key})
if not params:
params = {}
params.update({
: self.latitude,
: self.longitude,
: self.altitude
})
async with self._websession.request(method, url, headers=headers,
params=params) as resp:
try:
resp.raise_for_status()
return await resp.json(content_type=None)
except client_exceptions.ClientError as err:
if any(code in str(err) for code in (, )):
raise InvalidApiKeyError()
raise RequestError(
.format(
endpoint, err)) from None
|
#vtb
def p_expr_BAND_expr(p):
p[0] = make_binary(p.lineno(2), , p[1], p[3], lambda x, y: x & y)
|
expr : expr BAND expr
|
### Input:
expr : expr BAND expr
### Response:
#vtb
def p_expr_BAND_expr(p):
p[0] = make_binary(p.lineno(2), , p[1], p[3], lambda x, y: x & y)
|
#vtb
def attribute_rewrite_map(self):
rewrite_map = dict()
token_rewrite_map = self.generate_attribute_token_rewrite_map()
for attribute_name, type_instance in self.getmembers():
if isinstance(type_instance, DataType):
attribute_tokens = attribute_name.split()
rewritten_attribute_name =
for token in attribute_tokens:
rewritten_attribute_name += token_rewrite_map[token] + "_"
rewritten_attribute_name = rewritten_attribute_name[:-1]
rewrite_map[attribute_name] = rewritten_attribute_name
return rewrite_map
|
Example: long_name -> a_b
:return: the rewrite map
:rtype: dict
|
### Input:
Example: long_name -> a_b
:return: the rewrite map
:rtype: dict
### Response:
#vtb
def attribute_rewrite_map(self):
rewrite_map = dict()
token_rewrite_map = self.generate_attribute_token_rewrite_map()
for attribute_name, type_instance in self.getmembers():
if isinstance(type_instance, DataType):
attribute_tokens = attribute_name.split()
rewritten_attribute_name =
for token in attribute_tokens:
rewritten_attribute_name += token_rewrite_map[token] + "_"
rewritten_attribute_name = rewritten_attribute_name[:-1]
rewrite_map[attribute_name] = rewritten_attribute_name
return rewrite_map
|
#vtb
def delete(self, request, key):
request.DELETE = http.QueryDict(request.body)
email_addr = request.DELETE.get()
user_id = request.DELETE.get()
if not email_addr:
return http.HttpResponseBadRequest()
try:
email = EmailAddressValidation.objects.get(address=email_addr,
user_id=user_id)
except EmailAddressValidation.DoesNotExist:
pass
else:
email.delete()
return http.HttpResponse(status=204)
try:
email = EmailAddress.objects.get(address=email_addr,
user_id=user_id)
except EmailAddress.DoesNotExist:
raise http.Http404
email.user = None
email.save()
return http.HttpResponse(status=204)
|
Remove an email address, validated or not.
|
### Input:
Remove an email address, validated or not.
### Response:
#vtb
def delete(self, request, key):
request.DELETE = http.QueryDict(request.body)
email_addr = request.DELETE.get()
user_id = request.DELETE.get()
if not email_addr:
return http.HttpResponseBadRequest()
try:
email = EmailAddressValidation.objects.get(address=email_addr,
user_id=user_id)
except EmailAddressValidation.DoesNotExist:
pass
else:
email.delete()
return http.HttpResponse(status=204)
try:
email = EmailAddress.objects.get(address=email_addr,
user_id=user_id)
except EmailAddress.DoesNotExist:
raise http.Http404
email.user = None
email.save()
return http.HttpResponse(status=204)
|
#vtb
def join_room(self, room_id_or_alias):
if not room_id_or_alias:
raise MatrixError("No alias or room ID to join.")
path = "/join/%s" % quote(room_id_or_alias)
return self._send("POST", path)
|
Performs /join/$room_id
Args:
room_id_or_alias (str): The room ID or room alias to join.
|
### Input:
Performs /join/$room_id
Args:
room_id_or_alias (str): The room ID or room alias to join.
### Response:
#vtb
def join_room(self, room_id_or_alias):
if not room_id_or_alias:
raise MatrixError("No alias or room ID to join.")
path = "/join/%s" % quote(room_id_or_alias)
return self._send("POST", path)
|
#vtb
def private_download_url(self, url, expires=3600):
deadline = int(time.time()) + expires
if in url:
url +=
else:
url +=
url = .format(url, str(deadline))
token = self.token(url)
return .format(url, token)
|
生成私有资源下载链接
Args:
url: 私有空间资源的原始URL
expires: 下载凭证有效期，默认为3600s
Returns:
私有资源的下载链接
|
### Input:
生成私有资源下载链接
Args:
url: 私有空间资源的原始URL
expires: 下载凭证有效期，默认为3600s
Returns:
私有资源的下载链接
### Response:
#vtb
def private_download_url(self, url, expires=3600):
deadline = int(time.time()) + expires
if in url:
url +=
else:
url +=
url = .format(url, str(deadline))
token = self.token(url)
return .format(url, token)
|
#vtb
def read(self):
line = self.trace_file.readline()
if line == :
if self.loop:
self._reopen_file()
else:
self.trace_file.close()
self.trace_file = None
raise DataSourceError()
message = JsonFormatter.deserialize(line)
timestamp = message.get(, None)
if self.realtime and timestamp is not None:
self._store_timestamp(timestamp)
self._wait(self.starting_time, self.first_timestamp, timestamp)
return line + "\x00"
|
Read a line of data from the input source at a time.
|
### Input:
Read a line of data from the input source at a time.
### Response:
#vtb
def read(self):
line = self.trace_file.readline()
if line == :
if self.loop:
self._reopen_file()
else:
self.trace_file.close()
self.trace_file = None
raise DataSourceError()
message = JsonFormatter.deserialize(line)
timestamp = message.get(, None)
if self.realtime and timestamp is not None:
self._store_timestamp(timestamp)
self._wait(self.starting_time, self.first_timestamp, timestamp)
return line + "\x00"
|
#vtb
def create(self, req, **kwargs):
response = requests.post(self.url, json=req, **self.req_args())
return self.parse_response(response)
|
Uses POST to send a first metadata statement signing request to
a signing service.
:param req: The metadata statement that the entity wants signed
:return: returns a dictionary with 'sms' and 'loc' as keys.
|
### Input:
Uses POST to send a first metadata statement signing request to
a signing service.
:param req: The metadata statement that the entity wants signed
:return: returns a dictionary with 'sms' and 'loc' as keys.
### Response:
#vtb
def create(self, req, **kwargs):
response = requests.post(self.url, json=req, **self.req_args())
return self.parse_response(response)
|
#vtb
def _to_graph(self, contexts):
prev = None
for context in contexts:
if prev is None:
prev = context
continue
yield prev[0], context[1], context[0]
prev = context
|
This is an iterator that returns each edge of our graph
with its two nodes
|
### Input:
This is an iterator that returns each edge of our graph
with its two nodes
### Response:
#vtb
def _to_graph(self, contexts):
prev = None
for context in contexts:
if prev is None:
prev = context
continue
yield prev[0], context[1], context[0]
prev = context
|
#vtb
def to_igraph(self, weighted=None):
return ig
|
Converts this Graph object to an igraph-compatible object.
Requires the python-igraph library.
|
### Input:
Converts this Graph object to an igraph-compatible object.
Requires the python-igraph library.
### Response:
#vtb
def to_igraph(self, weighted=None):
return ig
|
#vtb
def doi(self, doi, only_message=True):
request_url = build_url_endpoint(
.join([self.ENDPOINT, doi])
)
request_params = {}
result = self.do_http_request(
,
request_url,
data=request_params,
custom_header=str(self.etiquette)
)
if result.status_code == 404:
return
result = result.json()
return result[] if only_message is True else result
|
This method retrieve the DOI metadata related to a given DOI
number.
args: Crossref DOI id (String)
return: JSON
Example:
>>> from crossref.restful import Works
>>> works = Works()
>>> works.doi('10.1590/S0004-28032013005000001')
{'is-referenced-by-count': 6, 'reference-count': 216, 'DOI': '10.1590/s0004-28032013005000001',
'subtitle': [], 'issued': {'date-parts': [[2013, 4, 19]]}, 'source': 'Crossref',
'short-container-title': ['Arq. Gastroenterol.'], 'references-count': 216, 'short-title': [],
'deposited': {'timestamp': 1495911725000, 'date-time': '2017-05-27T19:02:05Z',
'date-parts': [[2017, 5, 27]]}, 'ISSN': ['0004-2803'], 'type': 'journal-article',
'URL': 'http://dx.doi.org/10.1590/s0004-28032013005000001',
'indexed': {'timestamp': 1496034748592, 'date-time': '2017-05-29T05:12:28Z',
'date-parts': [[2017, 5, 29]]}, 'content-domain': {'crossmark-restriction': False, 'domain': []},
'created': {'timestamp': 1374613284000, 'date-time': '2013-07-23T21:01:24Z',
'date-parts': [[2013, 7, 23]]}, 'issn-type': [{'value': '0004-2803', 'type': 'electronic'}],
'page': '81-96', 'volume': '50', 'original-title': [], 'subject': ['Gastroenterology'],
'relation': {}, 'container-title': ['Arquivos de Gastroenterologia'], 'member': '530',
'prefix': '10.1590', 'published-print': {'date-parts': [[2013, 4, 19]]},
'title': ['3rd BRAZILIAN CONSENSUS ON Helicobacter pylori'],
'publisher': 'FapUNIFESP (SciELO)', 'alternative-id': ['S0004-28032013000200081'],
'abstract': '<jats:p>Significant abstract data..... .</jats:p>',
'author': [{'affiliation': [{'name': 'Universidade Federal de Minas Gerais, BRAZIL'}],
'family': 'Coelho', 'given': 'Luiz Gonzaga'}, {'affiliation': [
{'name': 'Universidade Federal do Rio Grande do Sul, Brazil'}], 'family': 'Maguinilk',
'given': 'Ismael'}, {'affiliation': [
{'name': 'Presidente de Honra do Núcleo Brasileiro para Estudo do Helicobacter, Brazil'}],
'family': 'Zaterka', 'given': 'Schlioma'}, {'affiliation': [
{'name': 'Universidade Federal do Piauí, Brasil'}], 'family': 'Parente', 'given': 'José Miguel'},
{'affiliation': [{'name': 'Universidade Federal de Minas Gerais, BRAZIL'}],
'family': 'Passos', 'given': 'Maria do Carmo Friche'}, {'affiliation': [
{'name': 'Universidade de São Paulo, Brasil'}], 'family': 'Moraes-Filho',
'given': 'Joaquim Prado P.'}], 'score': 1.0, 'issue': '2'}
|
### Input:
This method retrieve the DOI metadata related to a given DOI
number.
args: Crossref DOI id (String)
return: JSON
Example:
>>> from crossref.restful import Works
>>> works = Works()
>>> works.doi('10.1590/S0004-28032013005000001')
{'is-referenced-by-count': 6, 'reference-count': 216, 'DOI': '10.1590/s0004-28032013005000001',
'subtitle': [], 'issued': {'date-parts': [[2013, 4, 19]]}, 'source': 'Crossref',
'short-container-title': ['Arq. Gastroenterol.'], 'references-count': 216, 'short-title': [],
'deposited': {'timestamp': 1495911725000, 'date-time': '2017-05-27T19:02:05Z',
'date-parts': [[2017, 5, 27]]}, 'ISSN': ['0004-2803'], 'type': 'journal-article',
'URL': 'http://dx.doi.org/10.1590/s0004-28032013005000001',
'indexed': {'timestamp': 1496034748592, 'date-time': '2017-05-29T05:12:28Z',
'date-parts': [[2017, 5, 29]]}, 'content-domain': {'crossmark-restriction': False, 'domain': []},
'created': {'timestamp': 1374613284000, 'date-time': '2013-07-23T21:01:24Z',
'date-parts': [[2013, 7, 23]]}, 'issn-type': [{'value': '0004-2803', 'type': 'electronic'}],
'page': '81-96', 'volume': '50', 'original-title': [], 'subject': ['Gastroenterology'],
'relation': {}, 'container-title': ['Arquivos de Gastroenterologia'], 'member': '530',
'prefix': '10.1590', 'published-print': {'date-parts': [[2013, 4, 19]]},
'title': ['3rd BRAZILIAN CONSENSUS ON Helicobacter pylori'],
'publisher': 'FapUNIFESP (SciELO)', 'alternative-id': ['S0004-28032013000200081'],
'abstract': '<jats:p>Significant abstract data..... .</jats:p>',
'author': [{'affiliation': [{'name': 'Universidade Federal de Minas Gerais, BRAZIL'}],
'family': 'Coelho', 'given': 'Luiz Gonzaga'}, {'affiliation': [
{'name': 'Universidade Federal do Rio Grande do Sul, Brazil'}], 'family': 'Maguinilk',
'given': 'Ismael'}, {'affiliation': [
{'name': 'Presidente de Honra do Núcleo Brasileiro para Estudo do Helicobacter, Brazil'}],
'family': 'Zaterka', 'given': 'Schlioma'}, {'affiliation': [
{'name': 'Universidade Federal do Piauí, Brasil'}], 'family': 'Parente', 'given': 'José Miguel'},
{'affiliation': [{'name': 'Universidade Federal de Minas Gerais, BRAZIL'}],
'family': 'Passos', 'given': 'Maria do Carmo Friche'}, {'affiliation': [
{'name': 'Universidade de São Paulo, Brasil'}], 'family': 'Moraes-Filho',
'given': 'Joaquim Prado P.'}], 'score': 1.0, 'issue': '2'}
### Response:
#vtb
def doi(self, doi, only_message=True):
request_url = build_url_endpoint(
.join([self.ENDPOINT, doi])
)
request_params = {}
result = self.do_http_request(
,
request_url,
data=request_params,
custom_header=str(self.etiquette)
)
if result.status_code == 404:
return
result = result.json()
return result[] if only_message is True else result
|
#vtb
def zonal_stats(raster, vector):
output_layer_name = zonal_stats_steps[]
exposure = raster.keywords[]
if raster.crs().authid() != vector.crs().authid():
layer = reproject(vector, raster.crs())
output_layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, output_layer)
else:
layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, layer)
input_band = layer.keywords.get(, 1)
analysis = QgsZonalStatistics(
layer,
raster,
,
input_band,
QgsZonalStatistics.Sum)
result = analysis.calculateStatistics(None)
LOGGER.debug(tr( % (raster.source(), result)))
output_field = exposure_count_field[] % exposure
if raster.crs().authid() != vector.crs().authid():
output_layer.startEditing()
field = create_field_from_definition(
exposure_count_field, exposure)
output_layer.addAttribute(field)
new_index = output_layer.fields().lookupField(field.name())
old_index = layer.fields().lookupField()
for feature_input, feature_output in zip(
layer.getFeatures(), output_layer.getFeatures()):
output_layer.changeAttributeValue(
feature_input.id(), new_index, feature_input[old_index])
output_layer.commitChanges()
layer = output_layer
else:
fields_to_rename = {
: output_field
}
if qgis_version() >= 21600:
rename_fields(layer, fields_to_rename)
else:
copy_fields(layer, fields_to_rename)
remove_fields(layer, list(fields_to_rename.keys()))
layer.commitChanges()
layer.startEditing()
request = QgsFeatureRequest()
expression = % output_field
request.setFilterExpression(expression)
request.setFlags(QgsFeatureRequest.NoGeometry)
index = layer.fields().lookupField(output_field)
for feature in layer.getFeatures():
if feature[output_field] is None:
layer.changeAttributeValue(feature.id(), index, 0)
layer.commitChanges()
layer.keywords = raster.keywords.copy()
layer.keywords[] = vector.keywords[].copy()
layer.keywords[] = (
raster.keywords[].copy())
key = exposure_count_field[] % raster.keywords[]
layer.keywords[][key] = output_field
layer.keywords[][total_field[]] = output_field
layer.keywords[] = raster.keywords.copy()
layer.keywords[] = vector.keywords[
].copy()
layer.keywords[] = (
vector.keywords[])
layer.keywords[] = (
layer_purpose_aggregate_hazard_impacted[])
layer.keywords[] = output_layer_name
check_layer(layer)
return layer
|
Reclassify a continuous raster layer.
Issue https://github.com/inasafe/inasafe/issues/3190
The algorithm will take care about projections.
We don't want to reproject the raster layer.
So if CRS are different, we reproject the vector layer and then we do a
lookup from the reprojected layer to the original vector layer.
:param raster: The raster layer.
:type raster: QgsRasterLayer
:param vector: The vector layer.
:type vector: QgsVectorLayer
:return: The output of the zonal stats.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
|
### Input:
Reclassify a continuous raster layer.
Issue https://github.com/inasafe/inasafe/issues/3190
The algorithm will take care about projections.
We don't want to reproject the raster layer.
So if CRS are different, we reproject the vector layer and then we do a
lookup from the reprojected layer to the original vector layer.
:param raster: The raster layer.
:type raster: QgsRasterLayer
:param vector: The vector layer.
:type vector: QgsVectorLayer
:return: The output of the zonal stats.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
### Response:
#vtb
def zonal_stats(raster, vector):
output_layer_name = zonal_stats_steps[]
exposure = raster.keywords[]
if raster.crs().authid() != vector.crs().authid():
layer = reproject(vector, raster.crs())
output_layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, output_layer)
else:
layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, layer)
input_band = layer.keywords.get(, 1)
analysis = QgsZonalStatistics(
layer,
raster,
,
input_band,
QgsZonalStatistics.Sum)
result = analysis.calculateStatistics(None)
LOGGER.debug(tr( % (raster.source(), result)))
output_field = exposure_count_field[] % exposure
if raster.crs().authid() != vector.crs().authid():
output_layer.startEditing()
field = create_field_from_definition(
exposure_count_field, exposure)
output_layer.addAttribute(field)
new_index = output_layer.fields().lookupField(field.name())
old_index = layer.fields().lookupField()
for feature_input, feature_output in zip(
layer.getFeatures(), output_layer.getFeatures()):
output_layer.changeAttributeValue(
feature_input.id(), new_index, feature_input[old_index])
output_layer.commitChanges()
layer = output_layer
else:
fields_to_rename = {
: output_field
}
if qgis_version() >= 21600:
rename_fields(layer, fields_to_rename)
else:
copy_fields(layer, fields_to_rename)
remove_fields(layer, list(fields_to_rename.keys()))
layer.commitChanges()
layer.startEditing()
request = QgsFeatureRequest()
expression = % output_field
request.setFilterExpression(expression)
request.setFlags(QgsFeatureRequest.NoGeometry)
index = layer.fields().lookupField(output_field)
for feature in layer.getFeatures():
if feature[output_field] is None:
layer.changeAttributeValue(feature.id(), index, 0)
layer.commitChanges()
layer.keywords = raster.keywords.copy()
layer.keywords[] = vector.keywords[].copy()
layer.keywords[] = (
raster.keywords[].copy())
key = exposure_count_field[] % raster.keywords[]
layer.keywords[][key] = output_field
layer.keywords[][total_field[]] = output_field
layer.keywords[] = raster.keywords.copy()
layer.keywords[] = vector.keywords[
].copy()
layer.keywords[] = (
vector.keywords[])
layer.keywords[] = (
layer_purpose_aggregate_hazard_impacted[])
layer.keywords[] = output_layer_name
check_layer(layer)
return layer
|
#vtb
def guard_submit(analysis):
if not analysis.getResult():
return False
for interim in analysis.getInterimFields():
if not interim.get("value", ""):
return False
if not analysis.getAttachment():
if analysis.getAttachmentOption() == :
return False
if IRequestAnalysis.providedBy(analysis):
point_of_capture = analysis.getPointOfCapture()
if point_of_capture == "lab" and not analysis.isSampleReceived():
return False
if point_of_capture == "field" and not analysis.isSampleSampled():
return False
if not analysis.bika_setup.getAllowToSubmitNotAssigned():
if not user_has_super_roles():
if not analysis.getAnalyst():
return False
if analysis.getAnalyst() != api.get_current_user().getId():
return False
for dependency in analysis.getDependencies():
if not is_submitted_or_submittable(dependency):
return False
return True
|
Return whether the transition "submit" can be performed or not
|
### Input:
Return whether the transition "submit" can be performed or not
### Response:
#vtb
def guard_submit(analysis):
if not analysis.getResult():
return False
for interim in analysis.getInterimFields():
if not interim.get("value", ""):
return False
if not analysis.getAttachment():
if analysis.getAttachmentOption() == :
return False
if IRequestAnalysis.providedBy(analysis):
point_of_capture = analysis.getPointOfCapture()
if point_of_capture == "lab" and not analysis.isSampleReceived():
return False
if point_of_capture == "field" and not analysis.isSampleSampled():
return False
if not analysis.bika_setup.getAllowToSubmitNotAssigned():
if not user_has_super_roles():
if not analysis.getAnalyst():
return False
if analysis.getAnalyst() != api.get_current_user().getId():
return False
for dependency in analysis.getDependencies():
if not is_submitted_or_submittable(dependency):
return False
return True
|
#vtb
def get_argument_parser():
desc =
parser = cli.get_argument_parser(desc=desc)
g = parser.add_argument_group()
g.add_argument(
, , type=cli.str_type, required=True,
metavar=cli.file_mv, help=
)
g.add_argument(
, , type=cli.str_type,
required=True,
metavar=cli.file_mv, help=
)
g.add_argument(
, , type=cli.str_type,
metavar=cli.str_mv, default=,
help=
)
cli.add_reporting_args(parser)
return parser
|
Create the argument parser for the script.
Parameters
----------
Returns
-------
`argparse.ArgumentParser`
The arguemnt parser.
|
### Input:
Create the argument parser for the script.
Parameters
----------
Returns
-------
`argparse.ArgumentParser`
The arguemnt parser.
### Response:
#vtb
def get_argument_parser():
desc =
parser = cli.get_argument_parser(desc=desc)
g = parser.add_argument_group()
g.add_argument(
, , type=cli.str_type, required=True,
metavar=cli.file_mv, help=
)
g.add_argument(
, , type=cli.str_type,
required=True,
metavar=cli.file_mv, help=
)
g.add_argument(
, , type=cli.str_type,
metavar=cli.str_mv, default=,
help=
)
cli.add_reporting_args(parser)
return parser
|
#vtb
def deferred(timeout=None):
reactor, reactor_thread = threaded_reactor()
if reactor is None:
raise ImportError("twisted is not available or could not be imported")
try:
timeout is None or timeout + 0
except TypeError:
raise TypeError(" argument must be a number or None")
def decorate(func):
def wrapper(*args, **kargs):
q = Queue()
def callback(value):
q.put(None)
def errback(failure):
try:
failure.raiseException()
except:
q.put(sys.exc_info())
def g():
try:
d = func(*args, **kargs)
try:
d.addCallbacks(callback, errback)
except AttributeError:
raise TypeError("you must return a twisted Deferred "
"from your test case!")
except:
q.put(sys.exc_info())
reactor.callFromThread(g)
try:
error = q.get(timeout=timeout)
except Empty:
raise TimeExpired("timeout expired before end of test (%f s.)"
% timeout)
if error is not None:
exc_type, exc_value, tb = error
raise exc_type, exc_value, tb
wrapper = make_decorator(func)(wrapper)
return wrapper
return decorate
|
By wrapping a test function with this decorator, you can return a
twisted Deferred and the test will wait for the deferred to be triggered.
The whole test function will run inside the Twisted event loop.
The optional timeout parameter specifies the maximum duration of the test.
The difference with timed() is that timed() will still wait for the test
to end, while deferred() will stop the test when its timeout has expired.
The latter is more desireable when dealing with network tests, because
the result may actually never arrive.
If the callback is triggered, the test has passed.
If the errback is triggered or the timeout expires, the test has failed.
Example::
@deferred(timeout=5.0)
def test_resolve():
return reactor.resolve("www.python.org")
Attention! If you combine this decorator with other decorators (like
"raises"), deferred() must be called *first*!
In other words, this is good::
@raises(DNSLookupError)
@deferred()
def test_error():
return reactor.resolve("xxxjhjhj.biz")
and this is bad::
@deferred()
@raises(DNSLookupError)
def test_error():
return reactor.resolve("xxxjhjhj.biz")
|
### Input:
By wrapping a test function with this decorator, you can return a
twisted Deferred and the test will wait for the deferred to be triggered.
The whole test function will run inside the Twisted event loop.
The optional timeout parameter specifies the maximum duration of the test.
The difference with timed() is that timed() will still wait for the test
to end, while deferred() will stop the test when its timeout has expired.
The latter is more desireable when dealing with network tests, because
the result may actually never arrive.
If the callback is triggered, the test has passed.
If the errback is triggered or the timeout expires, the test has failed.
Example::
@deferred(timeout=5.0)
def test_resolve():
return reactor.resolve("www.python.org")
Attention! If you combine this decorator with other decorators (like
"raises"), deferred() must be called *first*!
In other words, this is good::
@raises(DNSLookupError)
@deferred()
def test_error():
return reactor.resolve("xxxjhjhj.biz")
and this is bad::
@deferred()
@raises(DNSLookupError)
def test_error():
return reactor.resolve("xxxjhjhj.biz")
### Response:
#vtb
def deferred(timeout=None):
reactor, reactor_thread = threaded_reactor()
if reactor is None:
raise ImportError("twisted is not available or could not be imported")
try:
timeout is None or timeout + 0
except TypeError:
raise TypeError(" argument must be a number or None")
def decorate(func):
def wrapper(*args, **kargs):
q = Queue()
def callback(value):
q.put(None)
def errback(failure):
try:
failure.raiseException()
except:
q.put(sys.exc_info())
def g():
try:
d = func(*args, **kargs)
try:
d.addCallbacks(callback, errback)
except AttributeError:
raise TypeError("you must return a twisted Deferred "
"from your test case!")
except:
q.put(sys.exc_info())
reactor.callFromThread(g)
try:
error = q.get(timeout=timeout)
except Empty:
raise TimeExpired("timeout expired before end of test (%f s.)"
% timeout)
if error is not None:
exc_type, exc_value, tb = error
raise exc_type, exc_value, tb
wrapper = make_decorator(func)(wrapper)
return wrapper
return decorate
|
#vtb
def invert(interval):
interval.reverse()
res = list(interval)
interval.reverse()
return res
|
Invert an interval.
Example:
>>> invert(['C', 'E'])
['E', 'C']
|
### Input:
Invert an interval.
Example:
>>> invert(['C', 'E'])
['E', 'C']
### Response:
#vtb
def invert(interval):
interval.reverse()
res = list(interval)
interval.reverse()
return res
|
#vtb
def pos_tag_sents(
sentences: List[List[str]], engine: str = "perceptron", corpus: str = "orchid"
) -> List[List[Tuple[str, str]]]:
if not sentences:
return []
return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]
|
Part of Speech tagging Sentence function.
:param list sentences: a list of lists of tokenized words
:param str engine:
* unigram - unigram tagger
* perceptron - perceptron tagger (default)
* artagger - RDR POS tagger
:param str corpus:
* orchid - annotated Thai academic articles (default)
* orchid_ud - annotated Thai academic articles using Universal Dependencies Tags
* pud - Parallel Universal Dependencies (PUD) treebanks
:return: returns a list of labels regarding which part of speech it is
|
### Input:
Part of Speech tagging Sentence function.
:param list sentences: a list of lists of tokenized words
:param str engine:
* unigram - unigram tagger
* perceptron - perceptron tagger (default)
* artagger - RDR POS tagger
:param str corpus:
* orchid - annotated Thai academic articles (default)
* orchid_ud - annotated Thai academic articles using Universal Dependencies Tags
* pud - Parallel Universal Dependencies (PUD) treebanks
:return: returns a list of labels regarding which part of speech it is
### Response:
#vtb
def pos_tag_sents(
sentences: List[List[str]], engine: str = "perceptron", corpus: str = "orchid"
) -> List[List[Tuple[str, str]]]:
if not sentences:
return []
return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]
|
#vtb
def can_unsubscribe_from_topic(self, topic, user):
return (
user.is_authenticated and
topic.has_subscriber(user) and
self._perform_basic_permission_check(topic.forum, user, )
)
|
Given a topic, checks whether the user can remove it from their subscription list.
|
### Input:
Given a topic, checks whether the user can remove it from their subscription list.
### Response:
#vtb
def can_unsubscribe_from_topic(self, topic, user):
return (
user.is_authenticated and
topic.has_subscriber(user) and
self._perform_basic_permission_check(topic.forum, user, )
)
|
#vtb
def discover_setup_packages():
logger = logging.getLogger(__name__)
import eups
eups_client = eups.Eups()
products = eups_client.getSetupProducts()
packages = {}
for package in products:
name = package.name
info = {
: package.dir,
: package.version
}
packages[name] = info
logger.debug(.format(
name=name, **info))
return packages
|
Summarize packages currently set up by EUPS, listing their
set up directories and EUPS version names.
Returns
-------
packages : `dict`
Dictionary with keys that are EUPS package names. Values are
dictionaries with fields:
- ``'dir'``: absolute directory path of the set up package.
- ``'version'``: EUPS version string for package.
Notes
-----
This function imports the ``eups`` Python package, which is assumed to
be available in the build environmen. This function is designed to
encapsulate all direct EUPS interactions need by the stack documentation
build process.
|
### Input:
Summarize packages currently set up by EUPS, listing their
set up directories and EUPS version names.
Returns
-------
packages : `dict`
Dictionary with keys that are EUPS package names. Values are
dictionaries with fields:
- ``'dir'``: absolute directory path of the set up package.
- ``'version'``: EUPS version string for package.
Notes
-----
This function imports the ``eups`` Python package, which is assumed to
be available in the build environmen. This function is designed to
encapsulate all direct EUPS interactions need by the stack documentation
build process.
### Response:
#vtb
def discover_setup_packages():
logger = logging.getLogger(__name__)
import eups
eups_client = eups.Eups()
products = eups_client.getSetupProducts()
packages = {}
for package in products:
name = package.name
info = {
: package.dir,
: package.version
}
packages[name] = info
logger.debug(.format(
name=name, **info))
return packages
|
#vtb
def show_help(name):
print(.format(name))
print()
print()
print()
print()
print()
print()
print(DEADBEEF DEADBEEF DEADBEEF DEADBEEF\)
print(ABABABAB CDCDCDCD EFEFEFEF AEAEAEAE\)
print()
|
Show help and basic usage
|
### Input:
Show help and basic usage
### Response:
#vtb
def show_help(name):
print(.format(name))
print()
print()
print()
print()
print()
print()
print(DEADBEEF DEADBEEF DEADBEEF DEADBEEF\)
print(ABABABAB CDCDCDCD EFEFEFEF AEAEAEAE\)
print()
|
#vtb
def _back_compatible_gemini(conf_files, data):
if vcfanno.is_human(data, builds=["37"]):
for f in conf_files:
if f and os.path.basename(f) == "gemini.conf" and os.path.exists(f):
with open(f) as in_handle:
for line in in_handle:
if line.startswith("file"):
fname = line.strip().split("=")[-1].replace(, ).strip()
if fname.find(".tidy.") > 0:
return install.get_gemini_dir(data)
return None
|
Provide old install directory for configuration with GEMINI supplied tidy VCFs.
Handles new style (bcbio installed) and old style (GEMINI installed)
configuration and data locations.
|
### Input:
Provide old install directory for configuration with GEMINI supplied tidy VCFs.
Handles new style (bcbio installed) and old style (GEMINI installed)
configuration and data locations.
### Response:
#vtb
def _back_compatible_gemini(conf_files, data):
if vcfanno.is_human(data, builds=["37"]):
for f in conf_files:
if f and os.path.basename(f) == "gemini.conf" and os.path.exists(f):
with open(f) as in_handle:
for line in in_handle:
if line.startswith("file"):
fname = line.strip().split("=")[-1].replace(, ).strip()
if fname.find(".tidy.") > 0:
return install.get_gemini_dir(data)
return None
|
#vtb
def fit(self, X, *args, **kwargs):
self.constant_value = self._get_constant_value(X)
if self.constant_value is None:
if self.unfittable_model:
self.model = getattr(scipy.stats, self.model_class)(*args, **kwargs)
else:
self.model = getattr(scipy.stats, self.model_class)(X, *args, **kwargs)
for name in self.METHOD_NAMES:
attribute = getattr(self.__class__, name)
if isinstance(attribute, str):
setattr(self, name, getattr(self.model, attribute))
elif attribute is None:
setattr(self, name, missing_method_scipy_wrapper(lambda x: x))
else:
self._replace_constant_methods()
self.fitted = True
|
Fit scipy model to an array of values.
Args:
X(`np.ndarray` or `pd.DataFrame`): Datapoints to be estimated from. Must be 1-d
Returns:
None
|
### Input:
Fit scipy model to an array of values.
Args:
X(`np.ndarray` or `pd.DataFrame`): Datapoints to be estimated from. Must be 1-d
Returns:
None
### Response:
#vtb
def fit(self, X, *args, **kwargs):
self.constant_value = self._get_constant_value(X)
if self.constant_value is None:
if self.unfittable_model:
self.model = getattr(scipy.stats, self.model_class)(*args, **kwargs)
else:
self.model = getattr(scipy.stats, self.model_class)(X, *args, **kwargs)
for name in self.METHOD_NAMES:
attribute = getattr(self.__class__, name)
if isinstance(attribute, str):
setattr(self, name, getattr(self.model, attribute))
elif attribute is None:
setattr(self, name, missing_method_scipy_wrapper(lambda x: x))
else:
self._replace_constant_methods()
self.fitted = True
|
#vtb
def set(self, data=None):
self.__data = data
self.__exception = None
self.__event.set()
|
Sets the event
|
### Input:
Sets the event
### Response:
#vtb
def set(self, data=None):
self.__data = data
self.__exception = None
self.__event.set()
|
#vtb
def get_permissions_for_registration(self):
qs = Permission.objects.none()
for instance in self.modeladmin_instances:
qs = qs | instance.get_permissions_for_registration()
return qs
|
Utilised by Wagtail's 'register_permissions' hook to allow permissions
for a all models grouped by this class to be assigned to Groups in
settings.
|
### Input:
Utilised by Wagtail's 'register_permissions' hook to allow permissions
for a all models grouped by this class to be assigned to Groups in
settings.
### Response:
#vtb
def get_permissions_for_registration(self):
qs = Permission.objects.none()
for instance in self.modeladmin_instances:
qs = qs | instance.get_permissions_for_registration()
return qs
|
#vtb
def _pre_analysis(self):
for item in self._starts:
callstack = None
if isinstance(item, tuple):
ip = item[0]
state = self._create_initial_state(item[0], item[1])
elif isinstance(item, SimState):
state = item.copy()
ip = state.solver.eval_one(state.ip)
self._reset_state_mode(state, )
else:
raise AngrCFGError( % str(type(item)))
self._symbolic_function_initial_state[ip] = state
path_wrapper = CFGJob(ip, state, self._context_sensitivity_level, None, None, call_stack=callstack)
key = path_wrapper.block_id
if key not in self._start_keys:
self._start_keys.append(key)
self._insert_job(path_wrapper)
self._register_analysis_job(path_wrapper.func_addr, path_wrapper)
|
Initialization work. Executed prior to the analysis.
:return: None
|
### Input:
Initialization work. Executed prior to the analysis.
:return: None
### Response:
#vtb
def _pre_analysis(self):
for item in self._starts:
callstack = None
if isinstance(item, tuple):
ip = item[0]
state = self._create_initial_state(item[0], item[1])
elif isinstance(item, SimState):
state = item.copy()
ip = state.solver.eval_one(state.ip)
self._reset_state_mode(state, )
else:
raise AngrCFGError( % str(type(item)))
self._symbolic_function_initial_state[ip] = state
path_wrapper = CFGJob(ip, state, self._context_sensitivity_level, None, None, call_stack=callstack)
key = path_wrapper.block_id
if key not in self._start_keys:
self._start_keys.append(key)
self._insert_job(path_wrapper)
self._register_analysis_job(path_wrapper.func_addr, path_wrapper)
|
#vtb
def timeseries(self):
if self._timeseries is None:
if isinstance(self.grid.network.timeseries.generation_fluctuating.
columns, pd.MultiIndex):
if self.weather_cell_id:
try:
timeseries = self.grid.network.timeseries.\
generation_fluctuating[
self.type, self.weather_cell_id].to_frame()
except KeyError:
logger.exception("No time series for type {} and "
"weather cell ID {} given.".format(
self.type, self.weather_cell_id))
raise
else:
logger.exception("No weather cell ID provided for "
"fluctuating generator {}.".format(
repr(self)))
raise KeyError
else:
try:
timeseries = self.grid.network.timeseries.\
generation_fluctuating[self.type].to_frame()
except KeyError:
logger.exception("No time series for type {} "
"given.".format(self.type))
raise
timeseries = timeseries * self.nominal_capacity
if self.curtailment is not None:
timeseries = timeseries.join(
self.curtailment.to_frame(), how=)
timeseries.p = timeseries.p - timeseries.curtailment.fillna(0)
if self.timeseries_reactive is not None:
timeseries[] = self.timeseries_reactive
else:
timeseries[] = timeseries[] * self.q_sign * tan(acos(
self.power_factor))
return timeseries
else:
return self._timeseries.loc[
self.grid.network.timeseries.timeindex, :]
|
Feed-in time series of generator
It returns the actual time series used in power flow analysis. If
:attr:`_timeseries` is not :obj:`None`, it is returned. Otherwise,
:meth:`timeseries` looks for generation and curtailment time series
of the according type of technology (and weather cell) in
:class:`~.grid.network.TimeSeries`.
Returns
-------
:pandas:`pandas.DataFrame<dataframe>`
DataFrame containing active power in kW in column 'p' and
reactive power in kVA in column 'q'.
|
### Input:
Feed-in time series of generator
It returns the actual time series used in power flow analysis. If
:attr:`_timeseries` is not :obj:`None`, it is returned. Otherwise,
:meth:`timeseries` looks for generation and curtailment time series
of the according type of technology (and weather cell) in
:class:`~.grid.network.TimeSeries`.
Returns
-------
:pandas:`pandas.DataFrame<dataframe>`
DataFrame containing active power in kW in column 'p' and
reactive power in kVA in column 'q'.
### Response:
#vtb
def timeseries(self):
if self._timeseries is None:
if isinstance(self.grid.network.timeseries.generation_fluctuating.
columns, pd.MultiIndex):
if self.weather_cell_id:
try:
timeseries = self.grid.network.timeseries.\
generation_fluctuating[
self.type, self.weather_cell_id].to_frame()
except KeyError:
logger.exception("No time series for type {} and "
"weather cell ID {} given.".format(
self.type, self.weather_cell_id))
raise
else:
logger.exception("No weather cell ID provided for "
"fluctuating generator {}.".format(
repr(self)))
raise KeyError
else:
try:
timeseries = self.grid.network.timeseries.\
generation_fluctuating[self.type].to_frame()
except KeyError:
logger.exception("No time series for type {} "
"given.".format(self.type))
raise
timeseries = timeseries * self.nominal_capacity
if self.curtailment is not None:
timeseries = timeseries.join(
self.curtailment.to_frame(), how=)
timeseries.p = timeseries.p - timeseries.curtailment.fillna(0)
if self.timeseries_reactive is not None:
timeseries[] = self.timeseries_reactive
else:
timeseries[] = timeseries[] * self.q_sign * tan(acos(
self.power_factor))
return timeseries
else:
return self._timeseries.loc[
self.grid.network.timeseries.timeindex, :]
|
#vtb
def from_schema(self, schema_node):
params = []
for param_schema in schema_node.children:
location = param_schema.name
if location is :
name = param_schema.__class__.__name__
if name == :
name = schema_node.__class__.__name__ +
param = self.parameter_converter(location,
param_schema)
param[] = name
if self.ref:
param = self._ref(param)
params.append(param)
elif location in ((, , , , )):
for node_schema in param_schema.children:
param = self.parameter_converter(location, node_schema)
if self.ref:
param = self._ref(param)
params.append(param)
return params
|
Creates a list of Swagger params from a colander request schema.
:param schema_node:
Request schema to be transformed into Swagger.
:param validators:
Validators used in colander with the schema.
:rtype: list
:returns: List of Swagger parameters.
|
### Input:
Creates a list of Swagger params from a colander request schema.
:param schema_node:
Request schema to be transformed into Swagger.
:param validators:
Validators used in colander with the schema.
:rtype: list
:returns: List of Swagger parameters.
### Response:
#vtb
def from_schema(self, schema_node):
params = []
for param_schema in schema_node.children:
location = param_schema.name
if location is :
name = param_schema.__class__.__name__
if name == :
name = schema_node.__class__.__name__ +
param = self.parameter_converter(location,
param_schema)
param[] = name
if self.ref:
param = self._ref(param)
params.append(param)
elif location in ((, , , , )):
for node_schema in param_schema.children:
param = self.parameter_converter(location, node_schema)
if self.ref:
param = self._ref(param)
params.append(param)
return params
|
#vtb
def select_candidates(config):
download_candidates = []
for group in config.group:
summary_file = get_summary(config.section, group, config.uri, config.use_cache)
entries = parse_summary(summary_file)
for entry in filter_entries(entries, config):
download_candidates.append((entry, group))
return download_candidates
|
Select candidates to download.
Parameters
----------
config: NgdConfig
Runtime configuration object
Returns
-------
list of (<candidate entry>, <taxonomic group>)
|
### Input:
Select candidates to download.
Parameters
----------
config: NgdConfig
Runtime configuration object
Returns
-------
list of (<candidate entry>, <taxonomic group>)
### Response:
#vtb
def select_candidates(config):
download_candidates = []
for group in config.group:
summary_file = get_summary(config.section, group, config.uri, config.use_cache)
entries = parse_summary(summary_file)
for entry in filter_entries(entries, config):
download_candidates.append((entry, group))
return download_candidates
|
#vtb
def process_dividends(self, next_session, asset_finder, adjustment_reader):
position_tracker = self.position_tracker
held_sids = set(position_tracker.positions)
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
stock_dividends = (
adjustment_reader.get_stock_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
)
position_tracker.earn_dividends(
cash_dividends,
stock_dividends,
)
self._cash_flow(
position_tracker.pay_dividends(
next_session,
),
)
|
Process dividends for the next session.
This will earn us any dividends whose ex-date is the next session as
well as paying out any dividends whose pay-date is the next session
|
### Input:
Process dividends for the next session.
This will earn us any dividends whose ex-date is the next session as
well as paying out any dividends whose pay-date is the next session
### Response:
#vtb
def process_dividends(self, next_session, asset_finder, adjustment_reader):
position_tracker = self.position_tracker
held_sids = set(position_tracker.positions)
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
stock_dividends = (
adjustment_reader.get_stock_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
)
position_tracker.earn_dividends(
cash_dividends,
stock_dividends,
)
self._cash_flow(
position_tracker.pay_dividends(
next_session,
),
)
|
#vtb
def reset_trial(self, trial, new_config, new_experiment_tag):
trial.experiment_tag = new_experiment_tag
trial.config = new_config
trainable = trial.runner
with warn_if_slow("reset_config"):
reset_val = ray.get(trainable.reset_config.remote(new_config))
return reset_val
|
Tries to invoke `Trainable.reset_config()` to reset trial.
Args:
trial (Trial): Trial to be reset.
new_config (dict): New configuration for Trial
trainable.
new_experiment_tag (str): New experiment name
for trial.
Returns:
True if `reset_config` is successful else False.
|
### Input:
Tries to invoke `Trainable.reset_config()` to reset trial.
Args:
trial (Trial): Trial to be reset.
new_config (dict): New configuration for Trial
trainable.
new_experiment_tag (str): New experiment name
for trial.
Returns:
True if `reset_config` is successful else False.
### Response:
#vtb
def reset_trial(self, trial, new_config, new_experiment_tag):
trial.experiment_tag = new_experiment_tag
trial.config = new_config
trainable = trial.runner
with warn_if_slow("reset_config"):
reset_val = ray.get(trainable.reset_config.remote(new_config))
return reset_val
|
#vtb
def RegisterTextKey(cls, key, atomid):
def getter(tags, key):
return tags[atomid]
def setter(tags, key, value):
tags[atomid] = value
def deleter(tags, key):
del(tags[atomid])
cls.RegisterKey(key, getter, setter, deleter)
|
Register a text key.
If the key you need to register is a simple one-to-one mapping
of MP4 atom name to EasyMP4Tags key, then you can use this
function::
EasyMP4Tags.RegisterTextKey("artist", "\xa9ART")
|
### Input:
Register a text key.
If the key you need to register is a simple one-to-one mapping
of MP4 atom name to EasyMP4Tags key, then you can use this
function::
EasyMP4Tags.RegisterTextKey("artist", "\xa9ART")
### Response:
#vtb
def RegisterTextKey(cls, key, atomid):
def getter(tags, key):
return tags[atomid]
def setter(tags, key, value):
tags[atomid] = value
def deleter(tags, key):
del(tags[atomid])
cls.RegisterKey(key, getter, setter, deleter)
|
#vtb
def _to_dict(self):
_dict = {}
if hasattr(self, ) and self.class_name is not None:
_dict[] = self.class_name
if hasattr(self, ) and self.score is not None:
_dict[] = self.score
if hasattr(self, ) and self.type_hierarchy is not None:
_dict[] = self.type_hierarchy
return _dict
|
Return a json dictionary representing this model.
|
### Input:
Return a json dictionary representing this model.
### Response:
#vtb
def _to_dict(self):
_dict = {}
if hasattr(self, ) and self.class_name is not None:
_dict[] = self.class_name
if hasattr(self, ) and self.score is not None:
_dict[] = self.score
if hasattr(self, ) and self.type_hierarchy is not None:
_dict[] = self.type_hierarchy
return _dict
|
#vtb
def ticker(ctx, market):
market = Market(market, bitshares_instance=ctx.bitshares)
ticker = market.ticker()
t = [["key", "value"]]
for key in ticker:
t.append([key, str(ticker[key])])
print_table(t)
|
Show ticker of a market
|
### Input:
Show ticker of a market
### Response:
#vtb
def ticker(ctx, market):
market = Market(market, bitshares_instance=ctx.bitshares)
ticker = market.ticker()
t = [["key", "value"]]
for key in ticker:
t.append([key, str(ticker[key])])
print_table(t)
|
#vtb
def worker_collectionfinish(self, node, ids):
if self.shuttingdown:
return
self.config.hook.pytest_xdist_node_collection_finished(node=node, ids=ids)
self._session.testscollected = len(ids)
self.sched.add_node_collection(node, ids)
if self.terminal:
self.trdist.setstatus(node.gateway.spec, "[%d]" % (len(ids)))
if self.sched.collection_is_completed:
if self.terminal and not self.sched.has_pending:
self.trdist.ensure_show_status()
self.terminal.write_line("")
if self.config.option.verbose > 0:
self.terminal.write_line(
"scheduling tests via %s" % (self.sched.__class__.__name__)
)
self.sched.schedule()
|
worker has finished test collection.
This adds the collection for this node to the scheduler. If
the scheduler indicates collection is finished (i.e. all
initial nodes have submitted their collections), then tells the
scheduler to schedule the collected items. When initiating
scheduling the first time it logs which scheduler is in use.
|
### Input:
worker has finished test collection.
This adds the collection for this node to the scheduler. If
the scheduler indicates collection is finished (i.e. all
initial nodes have submitted their collections), then tells the
scheduler to schedule the collected items. When initiating
scheduling the first time it logs which scheduler is in use.
### Response:
#vtb
def worker_collectionfinish(self, node, ids):
if self.shuttingdown:
return
self.config.hook.pytest_xdist_node_collection_finished(node=node, ids=ids)
self._session.testscollected = len(ids)
self.sched.add_node_collection(node, ids)
if self.terminal:
self.trdist.setstatus(node.gateway.spec, "[%d]" % (len(ids)))
if self.sched.collection_is_completed:
if self.terminal and not self.sched.has_pending:
self.trdist.ensure_show_status()
self.terminal.write_line("")
if self.config.option.verbose > 0:
self.terminal.write_line(
"scheduling tests via %s" % (self.sched.__class__.__name__)
)
self.sched.schedule()
|
#vtb
def rounding_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
outputs = tf.squeeze(tf.to_int32(predictions))
labels = tf.squeeze(labels)
weights = weights_fn(labels)
labels = tf.to_int32(labels)
return tf.to_float(tf.equal(outputs, labels)), weights
|
Rounding accuracy for L1/L2 losses: round down the predictions to ints.
|
### Input:
Rounding accuracy for L1/L2 losses: round down the predictions to ints.
### Response:
#vtb
def rounding_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
outputs = tf.squeeze(tf.to_int32(predictions))
labels = tf.squeeze(labels)
weights = weights_fn(labels)
labels = tf.to_int32(labels)
return tf.to_float(tf.equal(outputs, labels)), weights
|
#vtb
def _repr_png_(self):
app.process_events()
QApplication.processEvents()
img = read_pixels()
return bytes(_make_png(img))
|
This is used by ipython to plot inline.
|
### Input:
This is used by ipython to plot inline.
### Response:
#vtb
def _repr_png_(self):
app.process_events()
QApplication.processEvents()
img = read_pixels()
return bytes(_make_png(img))
|
#vtb
def bots_create(self, bot):
self.client.bots(_method="POST", _json=bot.to_json(), _params=dict(userToken=self.token))
|
Save new bot
:param bot: bot object to save
:type bot: Bot
|
### Input:
Save new bot
:param bot: bot object to save
:type bot: Bot
### Response:
#vtb
def bots_create(self, bot):
self.client.bots(_method="POST", _json=bot.to_json(), _params=dict(userToken=self.token))
|
#vtb
def raise_for_execution_errors(nb, output_path):
error = None
for cell in nb.cells:
if cell.get("outputs") is None:
continue
for output in cell.outputs:
if output.output_type == "error":
error = PapermillExecutionError(
exec_count=cell.execution_count,
source=cell.source,
ename=output.ename,
evalue=output.evalue,
traceback=output.traceback,
)
break
if error:
error_msg = ERROR_MESSAGE_TEMPLATE % str(error.exec_count)
error_msg_cell = nbformat.v4.new_code_cell(
source="%%html\n" + error_msg,
outputs=[
nbformat.v4.new_output(output_type="display_data", data={"text/html": error_msg})
],
metadata={"inputHidden": True, "hide_input": True},
)
nb.cells = [error_msg_cell] + nb.cells
write_ipynb(nb, output_path)
raise error
|
Assigned parameters into the appropriate place in the input notebook
Parameters
----------
nb : NotebookNode
Executable notebook object
output_path : str
Path to write executed notebook
|
### Input:
Assigned parameters into the appropriate place in the input notebook
Parameters
----------
nb : NotebookNode
Executable notebook object
output_path : str
Path to write executed notebook
### Response:
#vtb
def raise_for_execution_errors(nb, output_path):
error = None
for cell in nb.cells:
if cell.get("outputs") is None:
continue
for output in cell.outputs:
if output.output_type == "error":
error = PapermillExecutionError(
exec_count=cell.execution_count,
source=cell.source,
ename=output.ename,
evalue=output.evalue,
traceback=output.traceback,
)
break
if error:
error_msg = ERROR_MESSAGE_TEMPLATE % str(error.exec_count)
error_msg_cell = nbformat.v4.new_code_cell(
source="%%html\n" + error_msg,
outputs=[
nbformat.v4.new_output(output_type="display_data", data={"text/html": error_msg})
],
metadata={"inputHidden": True, "hide_input": True},
)
nb.cells = [error_msg_cell] + nb.cells
write_ipynb(nb, output_path)
raise error
|
#vtb
def read(self, pos, size, **kwargs):
short_reads = kwargs.pop(, None)
if self.write_mode is None:
self.write_mode = False
elif self.write_mode is True:
raise SimFileError("Cannot read and write to the same SimPackets")
if pos is None:
pos = len(self.content)
if pos < 0:
raise SimFileError("SimPacket.read(%d): Negative packet number?" % pos)
elif pos > len(self.content):
raise SimFileError("SimPacket.read(%d): Packet number is past frontier of %d?" % (pos, len(self.content)))
elif pos != len(self.content):
_, realsize = self.content[pos]
self.state.solver.add(size <= realsize)
if not self.state.solver.satisfiable():
raise SimFileError("Packet read size constraint made state unsatisfiable???")
return self.content[pos] + (pos+1,)
if type(size) is int:
size = self.state.solver.BVV(size, self.state.arch.bits)
packet = (data, size)
self.content.append(packet)
return packet + (pos+1,)
|
Read a packet from the stream.
:param int pos: The packet number to read from the sequence of the stream. May be None to append to the stream.
:param size: The size to read. May be symbolic.
:param short_reads: Whether to replace the size with a symbolic value constrained to less than or equal to the original size. If unspecified, will be chosen based on the state option.
:return: A tuple of the data read (a bitvector of the length that is the maximum length of the read) and the actual size of the read.
|
### Input:
Read a packet from the stream.
:param int pos: The packet number to read from the sequence of the stream. May be None to append to the stream.
:param size: The size to read. May be symbolic.
:param short_reads: Whether to replace the size with a symbolic value constrained to less than or equal to the original size. If unspecified, will be chosen based on the state option.
:return: A tuple of the data read (a bitvector of the length that is the maximum length of the read) and the actual size of the read.
### Response:
#vtb
def read(self, pos, size, **kwargs):
short_reads = kwargs.pop(, None)
if self.write_mode is None:
self.write_mode = False
elif self.write_mode is True:
raise SimFileError("Cannot read and write to the same SimPackets")
if pos is None:
pos = len(self.content)
if pos < 0:
raise SimFileError("SimPacket.read(%d): Negative packet number?" % pos)
elif pos > len(self.content):
raise SimFileError("SimPacket.read(%d): Packet number is past frontier of %d?" % (pos, len(self.content)))
elif pos != len(self.content):
_, realsize = self.content[pos]
self.state.solver.add(size <= realsize)
if not self.state.solver.satisfiable():
raise SimFileError("Packet read size constraint made state unsatisfiable???")
return self.content[pos] + (pos+1,)
if type(size) is int:
size = self.state.solver.BVV(size, self.state.arch.bits)
packet = (data, size)
self.content.append(packet)
return packet + (pos+1,)
|
#vtb
def simxGetDistanceHandle(clientID, distanceObjectName, operationMode):
handle = ct.c_int()
if (sys.version_info[0] == 3) and (type(distanceObjectName) is str):
distanceObjectName=distanceObjectName.encode()
return c_GetDistanceHandle(clientID, distanceObjectName, ct.byref(handle), operationMode), handle.value
|
Please have a look at the function description/documentation in the V-REP user manual
|
### Input:
Please have a look at the function description/documentation in the V-REP user manual
### Response:
#vtb
def simxGetDistanceHandle(clientID, distanceObjectName, operationMode):
handle = ct.c_int()
if (sys.version_info[0] == 3) and (type(distanceObjectName) is str):
distanceObjectName=distanceObjectName.encode()
return c_GetDistanceHandle(clientID, distanceObjectName, ct.byref(handle), operationMode), handle.value
|
#vtb
def _new_masterpassword(self, password):
if self.config_key in self.config and self.config[self.config_key]:
raise Exception("Storage already has a masterpassword!")
self.decrypted_master = hexlify(os.urandom(32)).decode("ascii")
self.password = password
self._save_encrypted_masterpassword()
return self.masterkey
|
Generate a new random masterkey, encrypt it with the password and
store it in the store.
:param str password: Password to use for en-/de-cryption
|
### Input:
Generate a new random masterkey, encrypt it with the password and
store it in the store.
:param str password: Password to use for en-/de-cryption
### Response:
#vtb
def _new_masterpassword(self, password):
if self.config_key in self.config and self.config[self.config_key]:
raise Exception("Storage already has a masterpassword!")
self.decrypted_master = hexlify(os.urandom(32)).decode("ascii")
self.password = password
self._save_encrypted_masterpassword()
return self.masterkey
|
#vtb
def has_ocsp_must_staple_extension(certificate: cryptography.x509.Certificate) -> bool:
has_ocsp_must_staple = False
try:
tls_feature_ext = certificate.extensions.get_extension_for_oid(ExtensionOID.TLS_FEATURE)
for feature_type in tls_feature_ext.value:
if feature_type == cryptography.x509.TLSFeatureType.status_request:
has_ocsp_must_staple = True
break
except ExtensionNotFound:
pass
return has_ocsp_must_staple
|
Return True if the certificate has the OCSP Must-Staple extension defined in RFC 6066.
|
### Input:
Return True if the certificate has the OCSP Must-Staple extension defined in RFC 6066.
### Response:
#vtb
def has_ocsp_must_staple_extension(certificate: cryptography.x509.Certificate) -> bool:
has_ocsp_must_staple = False
try:
tls_feature_ext = certificate.extensions.get_extension_for_oid(ExtensionOID.TLS_FEATURE)
for feature_type in tls_feature_ext.value:
if feature_type == cryptography.x509.TLSFeatureType.status_request:
has_ocsp_must_staple = True
break
except ExtensionNotFound:
pass
return has_ocsp_must_staple
|
#vtb
def _fix_set_options(cls, options):
optional_set_options = (, )
mandatory_set_options = (, )
def _get_set(value_str):
return cls._expand_error_codes(set(value_str.split()) - {})
for opt in optional_set_options:
value = getattr(options, opt)
if value is not None:
setattr(options, opt, _get_set(value))
for opt in mandatory_set_options:
value = getattr(options, opt)
if value is None:
value =
if not isinstance(value, Set):
value = _get_set(value)
setattr(options, opt, value)
return options
|
Alter the set options from None/strings to sets in place.
|
### Input:
Alter the set options from None/strings to sets in place.
### Response:
#vtb
def _fix_set_options(cls, options):
optional_set_options = (, )
mandatory_set_options = (, )
def _get_set(value_str):
return cls._expand_error_codes(set(value_str.split()) - {})
for opt in optional_set_options:
value = getattr(options, opt)
if value is not None:
setattr(options, opt, _get_set(value))
for opt in mandatory_set_options:
value = getattr(options, opt)
if value is None:
value =
if not isinstance(value, Set):
value = _get_set(value)
setattr(options, opt, value)
return options
|
#vtb
def update_md5(filenames):
import re
for name in filenames:
base = os.path.basename(name)
f = open(name,)
md5_data[base] = md5(f.read()).hexdigest()
f.close()
data = [" %r: %r,\n" % it for it in md5_data.items()]
data.sort()
repl = "".join(data)
import inspect
srcfile = inspect.getsourcefile(sys.modules[__name__])
f = open(srcfile, ); src = f.read(); f.close()
match = re.search("\nmd5_data = {\n([^}]+)}", src)
if not match:
print >>sys.stderr, "Internal error!"
sys.exit(2)
src = src[:match.start(1)] + repl + src[match.end(1):]
f = open(srcfile,)
f.write(src)
f.close()
|
Update our built-in md5 registry
|
### Input:
Update our built-in md5 registry
### Response:
#vtb
def update_md5(filenames):
import re
for name in filenames:
base = os.path.basename(name)
f = open(name,)
md5_data[base] = md5(f.read()).hexdigest()
f.close()
data = [" %r: %r,\n" % it for it in md5_data.items()]
data.sort()
repl = "".join(data)
import inspect
srcfile = inspect.getsourcefile(sys.modules[__name__])
f = open(srcfile, ); src = f.read(); f.close()
match = re.search("\nmd5_data = {\n([^}]+)}", src)
if not match:
print >>sys.stderr, "Internal error!"
sys.exit(2)
src = src[:match.start(1)] + repl + src[match.end(1):]
f = open(srcfile,)
f.write(src)
f.close()
|
#vtb
def auth(name, nodes, pcsuser=, pcspasswd=, extra_args=None):
pcs cluster auth\
ret = {: name, : True, : , : {}}
auth_required = False
authorized = __salt__[](nodes=nodes)
log.trace(, authorized)
authorized_dict = {}
for line in authorized[].splitlines():
node = line.split()[0].strip()
auth_state = line.split()[1].strip()
if node in nodes:
authorized_dict.update({node: auth_state})
log.trace(, authorized_dict)
for node in nodes:
if node in authorized_dict and authorized_dict[node] == :
ret[] += .format(node)
else:
auth_required = True
if __opts__[]:
ret[] += .format(node)
if not auth_required:
return ret
if __opts__[]:
ret[] = None
return ret
if not isinstance(extra_args, (list, tuple)):
extra_args = []
if not in extra_args:
extra_args += []
authorize = __salt__[](nodes=nodes, pcsuser=pcsuser, pcspasswd=pcspasswd, extra_args=extra_args)
log.trace(, authorize)
authorize_dict = {}
for line in authorize[].splitlines():
node = line.split()[0].strip()
auth_state = line.split()[1].strip()
if node in nodes:
authorize_dict.update({node: auth_state})
log.trace(, authorize_dict)
for node in nodes:
if node in authorize_dict and authorize_dict[node] == :
ret[] += .format(node)
ret[].update({node: {: , : }})
else:
ret[] = False
if node in authorized_dict:
ret[] += .format(node, authorized_dict[node])
if node in authorize_dict:
ret[] += .format(node, authorize_dict[node])
return ret
|
Ensure all nodes are authorized to the cluster
name
Irrelevant, not used (recommended: pcs_auth__auth)
nodes
a list of nodes which should be authorized to the cluster
pcsuser
user for communication with pcs (default: hacluster)
pcspasswd
password for pcsuser (default: hacluster)
extra_args
list of extra args for the \'pcs cluster auth\' command
Example:
.. code-block:: yaml
pcs_auth__auth:
pcs.auth:
- nodes:
- node1.example.com
- node2.example.com
- pcsuser: hacluster
- pcspasswd: hoonetorg
- extra_args: []
|
### Input:
Ensure all nodes are authorized to the cluster
name
Irrelevant, not used (recommended: pcs_auth__auth)
nodes
a list of nodes which should be authorized to the cluster
pcsuser
user for communication with pcs (default: hacluster)
pcspasswd
password for pcsuser (default: hacluster)
extra_args
list of extra args for the \'pcs cluster auth\' command
Example:
.. code-block:: yaml
pcs_auth__auth:
pcs.auth:
- nodes:
- node1.example.com
- node2.example.com
- pcsuser: hacluster
- pcspasswd: hoonetorg
- extra_args: []
### Response:
#vtb
def auth(name, nodes, pcsuser=, pcspasswd=, extra_args=None):
pcs cluster auth\
ret = {: name, : True, : , : {}}
auth_required = False
authorized = __salt__[](nodes=nodes)
log.trace(, authorized)
authorized_dict = {}
for line in authorized[].splitlines():
node = line.split()[0].strip()
auth_state = line.split()[1].strip()
if node in nodes:
authorized_dict.update({node: auth_state})
log.trace(, authorized_dict)
for node in nodes:
if node in authorized_dict and authorized_dict[node] == :
ret[] += .format(node)
else:
auth_required = True
if __opts__[]:
ret[] += .format(node)
if not auth_required:
return ret
if __opts__[]:
ret[] = None
return ret
if not isinstance(extra_args, (list, tuple)):
extra_args = []
if not in extra_args:
extra_args += []
authorize = __salt__[](nodes=nodes, pcsuser=pcsuser, pcspasswd=pcspasswd, extra_args=extra_args)
log.trace(, authorize)
authorize_dict = {}
for line in authorize[].splitlines():
node = line.split()[0].strip()
auth_state = line.split()[1].strip()
if node in nodes:
authorize_dict.update({node: auth_state})
log.trace(, authorize_dict)
for node in nodes:
if node in authorize_dict and authorize_dict[node] == :
ret[] += .format(node)
ret[].update({node: {: , : }})
else:
ret[] = False
if node in authorized_dict:
ret[] += .format(node, authorized_dict[node])
if node in authorize_dict:
ret[] += .format(node, authorize_dict[node])
return ret
|
#vtb
def reaction_charge(reaction, compound_charge):
charge_sum = 0.0
for compound, value in reaction.compounds:
charge = compound_charge.get(compound.name, float())
charge_sum += charge * float(value)
return charge_sum
|
Calculate the overall charge for the specified reaction.
Args:
reaction: :class:`psamm.reaction.Reaction`.
compound_charge: a map from each compound to charge values.
|
### Input:
Calculate the overall charge for the specified reaction.
Args:
reaction: :class:`psamm.reaction.Reaction`.
compound_charge: a map from each compound to charge values.
### Response:
#vtb
def reaction_charge(reaction, compound_charge):
charge_sum = 0.0
for compound, value in reaction.compounds:
charge = compound_charge.get(compound.name, float())
charge_sum += charge * float(value)
return charge_sum
|
#vtb
def gen_mapname():
filepath = None
while (filepath is None) or (os.path.exists(os.path.join(config[], filepath))):
filepath = % _gen_string()
return filepath
|
Generate a uniq mapfile pathname.
|
### Input:
Generate a uniq mapfile pathname.
### Response:
#vtb
def gen_mapname():
filepath = None
while (filepath is None) or (os.path.exists(os.path.join(config[], filepath))):
filepath = % _gen_string()
return filepath
|
#vtb
def backpropagate_3d(uSin, angles, res, nm, lD=0, coords=None,
weight_angles=True, onlyreal=False,
padding=(True, True), padfac=1.75, padval=None,
intp_order=2, dtype=None,
num_cores=ncores,
save_memory=False,
copy=True,
count=None, max_count=None,
verbose=0):
r
A = angles.size
if len(uSin.shape) != 3:
raise ValueError("Input data `uSin` must have shape (A,Ny,Nx).")
if len(uSin) != A:
raise ValueError("`len(angles)` must be equal to `len(uSin)`.")
if len(list(padding)) != 2:
raise ValueError("`padding` must be boolean tuple of length 2!")
if np.array(padding).dtype is not np.dtype(bool):
raise ValueError("Parameter `padding` must be boolean tuple.")
if coords is not None:
raise NotImplementedError("Setting coordinates is not yet supported.")
if num_cores > ncores:
raise ValueError("`num_cores` must not exceed number "
+ "of physical cores: {}".format(ncores))
if dtype is None:
dtype = np.float_
dtype = np.dtype(dtype)
if dtype.name not in ["float32", "float64"]:
raise ValueError("dtype must be float32 or float64!")
dtype_complex = np.dtype("complex{}".format(
2 * np.int(dtype.name.strip("float"))))
ct_dt_map = {np.dtype(np.float32): ctypes.c_float,
np.dtype(np.float64): ctypes.c_double
}
if max_count is not None:
max_count.value += A + 2
ne.set_num_threads(num_cores)
uSin = np.array(uSin, copy=copy)
lny, lnx = uSin.shape[1], uSin.shape[2]
ln = lnx
orderx = np.int(max(64., 2**np.ceil(np.log(lnx * padfac) / np.log(2))))
ordery = np.int(max(64., 2**np.ceil(np.log(lny * padfac) / np.log(2))))
if padding[0]:
padx = orderx - lnx
else:
padx = 0
if padding[1]:
pady = ordery - lny
else:
pady = 0
padyl = np.int(np.ceil(pady / 2))
padyr = pady - padyl
padxl = np.int(np.ceil(padx / 2))
padxr = padx - padxl
lNx, lNy = lnx + padx, lny + pady
lNz = ln
if verbose > 0:
print("......Image size (x,y): {}x{}, padded: {}x{}".format(
lnx, lny, lNx, lNy))
if weight_angles:
weights = util.compute_angle_weights_1d(angles).reshape(-1, 1, 1)
uSin *= weights
km = (2 * np.pi * nm) / res
fx = np.fft.fftfreq(lNx)
fy = np.fft.fftfreq(lNy)
kx = 2 * np.pi * fx
ky = 2 * np.pi * fy
dphi0 = 2 * np.pi / A
kx = kx.reshape(1, -1)
ky = ky.reshape(-1, 1)
filter_klp = (kx**2 + ky**2 < km**2)
M = 1. / km * np.sqrt((km**2 - kx**2 - ky**2) * filter_klp)
prefactor = -1j * km / (2 * np.pi)
prefactor *= dphi0
prefactor *= np.abs(kx) * filter_klp
prefactor *= np.exp(-1j * km * (M-1) * lD)
if count is not None:
count.value += 1
center = lNz / 2.0
z = np.linspace(-center, center, lNz, endpoint=False)
zv = z.reshape(-1, 1, 1)
Mp = M.reshape(lNy, lNx)
f2_exp_fac = 1j * km * (Mp - 1)
if save_memory:
pass
else:
filter2 = ne.evaluate("exp(factor * zv)",
local_dict={"factor": f2_exp_fac,
"zv": zv})
if count is not None:
count.value += 1
if onlyreal:
outarr = np.zeros((ln, lny, lnx), dtype=dtype)
else:
outarr = np.zeros((ln, lny, lnx), dtype=dtype_complex)
oneslice = pyfftw.empty_aligned((lNy, lNx), dtype_complex)
myfftw_plan = pyfftw.FFTW(oneslice, oneslice, threads=num_cores,
flags=["FFTW_ESTIMATE"], axes=(0, 1))
inarr = pyfftw.empty_aligned((lNy, lNx), dtype_complex)
myifftw_plan = pyfftw.FFTW(inarr, inarr, threads=num_cores,
axes=(0, 1),
direction="FFTW_BACKWARD",
flags=["FFTW_MEASURE"])
shared_array = mp.RawArray(ct_dt_map[dtype], ln * lny * lnx)
arr = np.frombuffer(shared_array, dtype=dtype).reshape(ln, lny, lnx)
pool4loop = mp.Pool(processes=num_cores,
initializer=_init_worker,
initargs=(shared_array, (ln, lny, lnx), dtype))
filtered_proj = np.zeros((ln, lny, lnx), dtype=dtype_complex)
for aa in np.arange(A):
if padval is None:
oneslice[:] = np.pad(uSin[aa],
((padyl, padyr), (padxl, padxr)),
mode="edge")
else:
oneslice[:] = np.pad(uSin[aa],
((padyl, padyr), (padxl, padxr)),
mode="linear_ramp",
end_values=(padval,))
myfftw_plan.execute()
oneslice *= prefactor / (lNx * lNy)
for p in range(len(zv)):
if save_memory:
ne.evaluate("exp(factor * zvp) * projectioni",
local_dict={"zvp": zv[p],
"projectioni": oneslice,
"factor": f2_exp_fac},
out=inarr)
else:
np.multiply(filter2[p], oneslice, out=inarr)
myifftw_plan.execute()
filtered_proj[p, :, :] = inarr[padyl:lny+padyl, padxl:lnx+padxl]
arr[:] = filtered_proj.real
phi0 = np.rad2deg(angles[aa])
if not onlyreal:
filtered_proj_imag = filtered_proj.imag
_mprotate(phi0, lny, pool4loop, intp_order)
outarr.real += arr
if not onlyreal:
arr[:] = filtered_proj_imag
_mprotate(phi0, lny, pool4loop, intp_order)
outarr.imag += arr
if count is not None:
count.value += 1
pool4loop.terminate()
pool4loop.join()
_cleanup_worker()
return outarr
|
r"""3D backpropagation
Three-dimensional diffraction tomography reconstruction
algorithm for scattering of a plane wave
:math:`u_0(\mathbf{r}) = u_0(x,y,z)`
by a dielectric object with refractive index
:math:`n(x,y,z)`.
This method implements the 3D backpropagation algorithm
:cite:`Mueller2015arxiv`.
.. math::
f(\mathbf{r}) =
-\frac{i k_\mathrm{m}}{2\pi}
\sum_{j=1}^{N} \! \Delta \phi_0 D_{-\phi_j} \!\!
\left \{
\text{FFT}^{-1}_{\mathrm{2D}}
\left \{
\left| k_\mathrm{Dx} \right|
\frac{\text{FFT}_{\mathrm{2D}} \left \{
u_{\mathrm{B},\phi_j}(x_\mathrm{D}, y_\mathrm{D}) \right \}}
{u_0(l_\mathrm{D})}
\exp \! \left[i k_\mathrm{m}(M - 1) \cdot
(z_{\phi_j}-l_\mathrm{D}) \right]
\right \}
\right \}
with the forward :math:`\text{FFT}_{\mathrm{2D}}` and inverse
:math:`\text{FFT}^{-1}_{\mathrm{2D}}` 2D fast Fourier transform, the
rotational operator :math:`D_{-\phi_j}`, the angular distance between the
projections :math:`\Delta \phi_0`, the ramp filter in Fourier space
:math:`|k_\mathrm{Dx}|`, and the propagation distance
:math:`(z_{\phi_j}-l_\mathrm{D})`.
Parameters
----------
uSin: (A, Ny, Nx) ndarray
Three-dimensional sinogram of plane recordings
:math:`u_{\mathrm{B}, \phi_j}(x_\mathrm{D}, y_\mathrm{D})`
divided by the incident plane wave :math:`u_0(l_\mathrm{D})`
measured at the detector.
angles: (A,) ndarray
Angular positions :math:`\phi_j` of `uSin` in radians.
res: float
Vacuum wavelength of the light :math:`\lambda` in pixels.
nm: float
Refractive index of the surrounding medium :math:`n_\mathrm{m}`.
lD: float
Distance from center of rotation to detector plane
:math:`l_\mathrm{D}` in pixels.
coords: None [(3, M) ndarray]
Only compute the output image at these coordinates. This
keyword is reserved for future versions and is not
implemented yet.
weight_angles: bool
If `True`, weights each backpropagated projection with a factor
proportional to the angular distance between the neighboring
projections.
.. math::
\Delta \phi_0 \longmapsto \Delta \phi_j =
\frac{\phi_{j+1} - \phi_{j-1}}{2}
.. versionadded:: 0.1.1
onlyreal: bool
If `True`, only the real part of the reconstructed image
will be returned. This saves computation time.
padding: tuple of bool
Pad the input data to the second next power of 2 before
Fourier transforming. This reduces artifacts and speeds up
the process for input image sizes that are not powers of 2.
The default is padding in x and y: `padding=(True, True)`.
For padding only in x-direction (e.g. for cylindrical
symmetries), set `padding` to `(True, False)`. To turn off
padding, set it to `(False, False)`.
padfac: float
Increase padding size of the input data. A value greater
than one will trigger padding to the second-next power of
two. For example, a value of 1.75 will lead to a padded
size of 256 for an initial size of 144, whereas it will
lead to a padded size of 512 for an initial size of 150.
Values geater than 2 are allowed. This parameter may
greatly increase memory usage!
padval: float
The value used for padding. This is important for the Rytov
approximation, where an approximat zero in the phase might
translate to 2πi due to the unwrapping algorithm. In that
case, this value should be a multiple of 2πi.
If `padval` is `None`, then the edge values are used for
padding (see documentation of :func:`numpy.pad`).
intp_order: int between 0 and 5
Order of the interpolation for rotation.
See :func:`scipy.ndimage.interpolation.rotate` for details.
dtype: dtype object or argument for :func:`numpy.dtype`
The data type that is used for calculations (float or double).
Defaults to `numpy.float_`.
num_cores: int
The number of cores to use for parallel operations. This value
defaults to the number of cores on the system.
save_memory: bool
Saves memory at the cost of longer computation time.
.. versionadded:: 0.1.5
copy: bool
Copy input sinogram `uSin` for data processing. If `copy`
is set to `False`, then `uSin` will be overridden.
.. versionadded:: 0.1.5
count, max_count: multiprocessing.Value or `None`
Can be used to monitor the progress of the algorithm.
Initially, the value of `max_count.value` is incremented
by the total number of steps. At each step, the value
of `count.value` is incremented.
verbose: int
Increment to increase verbosity.
Returns
-------
f: ndarray of shape (Nx, Ny, Nx), complex if `onlyreal==False`
Reconstructed object function :math:`f(\mathbf{r})` as defined
by the Helmholtz equation.
:math:`f(x,z) =
k_m^2 \left(\left(\frac{n(x,z)}{n_m}\right)^2 -1\right)`
See Also
--------
odt_to_ri: conversion of the object function :math:`f(\mathbf{r})`
to refractive index :math:`n(\mathbf{r})`
Notes
-----
Do not use the parameter `lD` in combination with the Rytov
approximation - the propagation is not correctly described.
Instead, numerically refocus the sinogram prior to converting
it to Rytov data (using e.g. :func:`odtbrain.sinogram_as_rytov`)
with a numerical focusing algorithm (available in the Python
package :py:mod:`nrefocus`).
|
### Input:
r"""3D backpropagation
Three-dimensional diffraction tomography reconstruction
algorithm for scattering of a plane wave
:math:`u_0(\mathbf{r}) = u_0(x,y,z)`
by a dielectric object with refractive index
:math:`n(x,y,z)`.
This method implements the 3D backpropagation algorithm
:cite:`Mueller2015arxiv`.
.. math::
f(\mathbf{r}) =
-\frac{i k_\mathrm{m}}{2\pi}
\sum_{j=1}^{N} \! \Delta \phi_0 D_{-\phi_j} \!\!
\left \{
\text{FFT}^{-1}_{\mathrm{2D}}
\left \{
\left| k_\mathrm{Dx} \right|
\frac{\text{FFT}_{\mathrm{2D}} \left \{
u_{\mathrm{B},\phi_j}(x_\mathrm{D}, y_\mathrm{D}) \right \}}
{u_0(l_\mathrm{D})}
\exp \! \left[i k_\mathrm{m}(M - 1) \cdot
(z_{\phi_j}-l_\mathrm{D}) \right]
\right \}
\right \}
with the forward :math:`\text{FFT}_{\mathrm{2D}}` and inverse
:math:`\text{FFT}^{-1}_{\mathrm{2D}}` 2D fast Fourier transform, the
rotational operator :math:`D_{-\phi_j}`, the angular distance between the
projections :math:`\Delta \phi_0`, the ramp filter in Fourier space
:math:`|k_\mathrm{Dx}|`, and the propagation distance
:math:`(z_{\phi_j}-l_\mathrm{D})`.
Parameters
----------
uSin: (A, Ny, Nx) ndarray
Three-dimensional sinogram of plane recordings
:math:`u_{\mathrm{B}, \phi_j}(x_\mathrm{D}, y_\mathrm{D})`
divided by the incident plane wave :math:`u_0(l_\mathrm{D})`
measured at the detector.
angles: (A,) ndarray
Angular positions :math:`\phi_j` of `uSin` in radians.
res: float
Vacuum wavelength of the light :math:`\lambda` in pixels.
nm: float
Refractive index of the surrounding medium :math:`n_\mathrm{m}`.
lD: float
Distance from center of rotation to detector plane
:math:`l_\mathrm{D}` in pixels.
coords: None [(3, M) ndarray]
Only compute the output image at these coordinates. This
keyword is reserved for future versions and is not
implemented yet.
weight_angles: bool
If `True`, weights each backpropagated projection with a factor
proportional to the angular distance between the neighboring
projections.
.. math::
\Delta \phi_0 \longmapsto \Delta \phi_j =
\frac{\phi_{j+1} - \phi_{j-1}}{2}
.. versionadded:: 0.1.1
onlyreal: bool
If `True`, only the real part of the reconstructed image
will be returned. This saves computation time.
padding: tuple of bool
Pad the input data to the second next power of 2 before
Fourier transforming. This reduces artifacts and speeds up
the process for input image sizes that are not powers of 2.
The default is padding in x and y: `padding=(True, True)`.
For padding only in x-direction (e.g. for cylindrical
symmetries), set `padding` to `(True, False)`. To turn off
padding, set it to `(False, False)`.
padfac: float
Increase padding size of the input data. A value greater
than one will trigger padding to the second-next power of
two. For example, a value of 1.75 will lead to a padded
size of 256 for an initial size of 144, whereas it will
lead to a padded size of 512 for an initial size of 150.
Values geater than 2 are allowed. This parameter may
greatly increase memory usage!
padval: float
The value used for padding. This is important for the Rytov
approximation, where an approximat zero in the phase might
translate to 2πi due to the unwrapping algorithm. In that
case, this value should be a multiple of 2πi.
If `padval` is `None`, then the edge values are used for
padding (see documentation of :func:`numpy.pad`).
intp_order: int between 0 and 5
Order of the interpolation for rotation.
See :func:`scipy.ndimage.interpolation.rotate` for details.
dtype: dtype object or argument for :func:`numpy.dtype`
The data type that is used for calculations (float or double).
Defaults to `numpy.float_`.
num_cores: int
The number of cores to use for parallel operations. This value
defaults to the number of cores on the system.
save_memory: bool
Saves memory at the cost of longer computation time.
.. versionadded:: 0.1.5
copy: bool
Copy input sinogram `uSin` for data processing. If `copy`
is set to `False`, then `uSin` will be overridden.
.. versionadded:: 0.1.5
count, max_count: multiprocessing.Value or `None`
Can be used to monitor the progress of the algorithm.
Initially, the value of `max_count.value` is incremented
by the total number of steps. At each step, the value
of `count.value` is incremented.
verbose: int
Increment to increase verbosity.
Returns
-------
f: ndarray of shape (Nx, Ny, Nx), complex if `onlyreal==False`
Reconstructed object function :math:`f(\mathbf{r})` as defined
by the Helmholtz equation.
:math:`f(x,z) =
k_m^2 \left(\left(\frac{n(x,z)}{n_m}\right)^2 -1\right)`
See Also
--------
odt_to_ri: conversion of the object function :math:`f(\mathbf{r})`
to refractive index :math:`n(\mathbf{r})`
Notes
-----
Do not use the parameter `lD` in combination with the Rytov
approximation - the propagation is not correctly described.
Instead, numerically refocus the sinogram prior to converting
it to Rytov data (using e.g. :func:`odtbrain.sinogram_as_rytov`)
with a numerical focusing algorithm (available in the Python
package :py:mod:`nrefocus`).
### Response:
#vtb
def backpropagate_3d(uSin, angles, res, nm, lD=0, coords=None,
weight_angles=True, onlyreal=False,
padding=(True, True), padfac=1.75, padval=None,
intp_order=2, dtype=None,
num_cores=ncores,
save_memory=False,
copy=True,
count=None, max_count=None,
verbose=0):
r
A = angles.size
if len(uSin.shape) != 3:
raise ValueError("Input data `uSin` must have shape (A,Ny,Nx).")
if len(uSin) != A:
raise ValueError("`len(angles)` must be equal to `len(uSin)`.")
if len(list(padding)) != 2:
raise ValueError("`padding` must be boolean tuple of length 2!")
if np.array(padding).dtype is not np.dtype(bool):
raise ValueError("Parameter `padding` must be boolean tuple.")
if coords is not None:
raise NotImplementedError("Setting coordinates is not yet supported.")
if num_cores > ncores:
raise ValueError("`num_cores` must not exceed number "
+ "of physical cores: {}".format(ncores))
if dtype is None:
dtype = np.float_
dtype = np.dtype(dtype)
if dtype.name not in ["float32", "float64"]:
raise ValueError("dtype must be float32 or float64!")
dtype_complex = np.dtype("complex{}".format(
2 * np.int(dtype.name.strip("float"))))
ct_dt_map = {np.dtype(np.float32): ctypes.c_float,
np.dtype(np.float64): ctypes.c_double
}
if max_count is not None:
max_count.value += A + 2
ne.set_num_threads(num_cores)
uSin = np.array(uSin, copy=copy)
lny, lnx = uSin.shape[1], uSin.shape[2]
ln = lnx
orderx = np.int(max(64., 2**np.ceil(np.log(lnx * padfac) / np.log(2))))
ordery = np.int(max(64., 2**np.ceil(np.log(lny * padfac) / np.log(2))))
if padding[0]:
padx = orderx - lnx
else:
padx = 0
if padding[1]:
pady = ordery - lny
else:
pady = 0
padyl = np.int(np.ceil(pady / 2))
padyr = pady - padyl
padxl = np.int(np.ceil(padx / 2))
padxr = padx - padxl
lNx, lNy = lnx + padx, lny + pady
lNz = ln
if verbose > 0:
print("......Image size (x,y): {}x{}, padded: {}x{}".format(
lnx, lny, lNx, lNy))
if weight_angles:
weights = util.compute_angle_weights_1d(angles).reshape(-1, 1, 1)
uSin *= weights
km = (2 * np.pi * nm) / res
fx = np.fft.fftfreq(lNx)
fy = np.fft.fftfreq(lNy)
kx = 2 * np.pi * fx
ky = 2 * np.pi * fy
dphi0 = 2 * np.pi / A
kx = kx.reshape(1, -1)
ky = ky.reshape(-1, 1)
filter_klp = (kx**2 + ky**2 < km**2)
M = 1. / km * np.sqrt((km**2 - kx**2 - ky**2) * filter_klp)
prefactor = -1j * km / (2 * np.pi)
prefactor *= dphi0
prefactor *= np.abs(kx) * filter_klp
prefactor *= np.exp(-1j * km * (M-1) * lD)
if count is not None:
count.value += 1
center = lNz / 2.0
z = np.linspace(-center, center, lNz, endpoint=False)
zv = z.reshape(-1, 1, 1)
Mp = M.reshape(lNy, lNx)
f2_exp_fac = 1j * km * (Mp - 1)
if save_memory:
pass
else:
filter2 = ne.evaluate("exp(factor * zv)",
local_dict={"factor": f2_exp_fac,
"zv": zv})
if count is not None:
count.value += 1
if onlyreal:
outarr = np.zeros((ln, lny, lnx), dtype=dtype)
else:
outarr = np.zeros((ln, lny, lnx), dtype=dtype_complex)
oneslice = pyfftw.empty_aligned((lNy, lNx), dtype_complex)
myfftw_plan = pyfftw.FFTW(oneslice, oneslice, threads=num_cores,
flags=["FFTW_ESTIMATE"], axes=(0, 1))
inarr = pyfftw.empty_aligned((lNy, lNx), dtype_complex)
myifftw_plan = pyfftw.FFTW(inarr, inarr, threads=num_cores,
axes=(0, 1),
direction="FFTW_BACKWARD",
flags=["FFTW_MEASURE"])
shared_array = mp.RawArray(ct_dt_map[dtype], ln * lny * lnx)
arr = np.frombuffer(shared_array, dtype=dtype).reshape(ln, lny, lnx)
pool4loop = mp.Pool(processes=num_cores,
initializer=_init_worker,
initargs=(shared_array, (ln, lny, lnx), dtype))
filtered_proj = np.zeros((ln, lny, lnx), dtype=dtype_complex)
for aa in np.arange(A):
if padval is None:
oneslice[:] = np.pad(uSin[aa],
((padyl, padyr), (padxl, padxr)),
mode="edge")
else:
oneslice[:] = np.pad(uSin[aa],
((padyl, padyr), (padxl, padxr)),
mode="linear_ramp",
end_values=(padval,))
myfftw_plan.execute()
oneslice *= prefactor / (lNx * lNy)
for p in range(len(zv)):
if save_memory:
ne.evaluate("exp(factor * zvp) * projectioni",
local_dict={"zvp": zv[p],
"projectioni": oneslice,
"factor": f2_exp_fac},
out=inarr)
else:
np.multiply(filter2[p], oneslice, out=inarr)
myifftw_plan.execute()
filtered_proj[p, :, :] = inarr[padyl:lny+padyl, padxl:lnx+padxl]
arr[:] = filtered_proj.real
phi0 = np.rad2deg(angles[aa])
if not onlyreal:
filtered_proj_imag = filtered_proj.imag
_mprotate(phi0, lny, pool4loop, intp_order)
outarr.real += arr
if not onlyreal:
arr[:] = filtered_proj_imag
_mprotate(phi0, lny, pool4loop, intp_order)
outarr.imag += arr
if count is not None:
count.value += 1
pool4loop.terminate()
pool4loop.join()
_cleanup_worker()
return outarr
|
#vtb
def replace_config(config, name):
global static_stages
if static_stages is None:
static_stages = PipelineStages()
stages = static_stages
if in config:
path = config[]
if not os.path.isabs(path) and config.get():
path = os.path.join(config[], path)
try:
stages.load_external_stages(config[])
except IOError:
return streamcorpus_pipeline
if in config:
for mod in config[]:
try:
stages.load_module_stages(mod)
except ImportError:
return streamcorpus_pipeline
else:
stages = static_stages
new_sub_modules = set(stage
for stage in stages.itervalues()
if hasattr(stage, ))
return NewSubModules(streamcorpus_pipeline, new_sub_modules)
|
Replace the top-level pipeline configurable object.
This investigates a number of sources, including
`external_stages_path` and `external_stages_modules` configuration
and `streamcorpus_pipeline.stages` entry points, and uses these to
find the actual :data:`sub_modules` for
:mod:`streamcorpus_pipeline`.
|
### Input:
Replace the top-level pipeline configurable object.
This investigates a number of sources, including
`external_stages_path` and `external_stages_modules` configuration
and `streamcorpus_pipeline.stages` entry points, and uses these to
find the actual :data:`sub_modules` for
:mod:`streamcorpus_pipeline`.
### Response:
#vtb
def replace_config(config, name):
global static_stages
if static_stages is None:
static_stages = PipelineStages()
stages = static_stages
if in config:
path = config[]
if not os.path.isabs(path) and config.get():
path = os.path.join(config[], path)
try:
stages.load_external_stages(config[])
except IOError:
return streamcorpus_pipeline
if in config:
for mod in config[]:
try:
stages.load_module_stages(mod)
except ImportError:
return streamcorpus_pipeline
else:
stages = static_stages
new_sub_modules = set(stage
for stage in stages.itervalues()
if hasattr(stage, ))
return NewSubModules(streamcorpus_pipeline, new_sub_modules)
|
#vtb
def _ret8(ins):
output = _8bit_oper(ins.quad[1])
output.append()
output.append( % str(ins.quad[2]))
return output
|
Returns from a procedure / function an 8bits value
|
### Input:
Returns from a procedure / function an 8bits value
### Response:
#vtb
def _ret8(ins):
output = _8bit_oper(ins.quad[1])
output.append()
output.append( % str(ins.quad[2]))
return output
|
#vtb
def children(self, p_todo, p_only_direct=False):
children = \
self._depgraph.outgoing_neighbors(hash(p_todo), not p_only_direct)
return [self._tododict[child] for child in children]
|
Returns a list of child todos that the given todo (in)directly depends
on.
|
### Input:
Returns a list of child todos that the given todo (in)directly depends
on.
### Response:
#vtb
def children(self, p_todo, p_only_direct=False):
children = \
self._depgraph.outgoing_neighbors(hash(p_todo), not p_only_direct)
return [self._tododict[child] for child in children]
|
#vtb
def is_businessdate(in_date):
if not isinstance(in_date, BaseDate):
try:
in_date = BusinessDate(in_date)
except:
return False
y, m, d, = in_date.to_ymd()
return is_valid_ymd(y, m, d)
|
checks whether the provided date is a date
:param BusinessDate, int or float in_date:
:return bool:
|
### Input:
checks whether the provided date is a date
:param BusinessDate, int or float in_date:
:return bool:
### Response:
#vtb
def is_businessdate(in_date):
if not isinstance(in_date, BaseDate):
try:
in_date = BusinessDate(in_date)
except:
return False
y, m, d, = in_date.to_ymd()
return is_valid_ymd(y, m, d)
|
#vtb
def convert(self, vroot, entry_variables):
self.graph_info = GraphInfo(vroot)
self.entry_variables = entry_variables
cnt = 0
with nn.parameter_scope(self.name):
for t, func in enumerate(self.graph_info.funcs):
if func.name == "BatchNormalization":
bn_func = func
if bn_func.info.args["batch_stat"] == False:
o = self._bn_linear_conversion(bn_func, cnt)
cnt += 1
continue
o = self._identity_conversion(func)
self.end_variable = o
return self.end_variable
|
All functions are replaced with the same `new` function.
Args:
vroot (:obj:`Variable`): NNabla Variable
entry_variables (:obj:`Variable`): Entry variable from which the conversion starts.
|
### Input:
All functions are replaced with the same `new` function.
Args:
vroot (:obj:`Variable`): NNabla Variable
entry_variables (:obj:`Variable`): Entry variable from which the conversion starts.
### Response:
#vtb
def convert(self, vroot, entry_variables):
self.graph_info = GraphInfo(vroot)
self.entry_variables = entry_variables
cnt = 0
with nn.parameter_scope(self.name):
for t, func in enumerate(self.graph_info.funcs):
if func.name == "BatchNormalization":
bn_func = func
if bn_func.info.args["batch_stat"] == False:
o = self._bn_linear_conversion(bn_func, cnt)
cnt += 1
continue
o = self._identity_conversion(func)
self.end_variable = o
return self.end_variable
|
#vtb
def get_init_kwargs(self):
init_kwargs = {}
for k in self.init_kwargs:
if k in self.core_property_set:
init_kwargs[k] = getattr(self, k)
elif k in self:
init_kwargs[k] = self[k]
return init_kwargs
|
Generates keyword arguments for creating a new Docker client instance.
:return: Keyword arguments as defined through this configuration.
:rtype: dict
|
### Input:
Generates keyword arguments for creating a new Docker client instance.
:return: Keyword arguments as defined through this configuration.
:rtype: dict
### Response:
#vtb
def get_init_kwargs(self):
init_kwargs = {}
for k in self.init_kwargs:
if k in self.core_property_set:
init_kwargs[k] = getattr(self, k)
elif k in self:
init_kwargs[k] = self[k]
return init_kwargs
|
#vtb
def current(self, value):
current = min(max(self._min, value), self._max)
self._current = current
if current > self._stop :
self._stop = current
self._start = current-self._width
elif current < self._start :
self._start = current
self._stop = current + self._width
if abs(self._start - self._min) <= self._sticky_lenght :
self._start = self._min
if abs(self._stop - self._max) <= self._sticky_lenght :
self._stop = self._max
|
set current cursor position
|
### Input:
set current cursor position
### Response:
#vtb
def current(self, value):
current = min(max(self._min, value), self._max)
self._current = current
if current > self._stop :
self._stop = current
self._start = current-self._width
elif current < self._start :
self._start = current
self._stop = current + self._width
if abs(self._start - self._min) <= self._sticky_lenght :
self._start = self._min
if abs(self._stop - self._max) <= self._sticky_lenght :
self._stop = self._max
|
#vtb
def _release_lock(self):
if not self._has_lock():
return
lfp = self._lock_file_path()
try:
if os.name == :
os.chmod(lfp, 0777)
os.remove(lfp)
except OSError:
pass
self._owns_lock = False
|
Release our lock if we have one
|
### Input:
Release our lock if we have one
### Response:
#vtb
def _release_lock(self):
if not self._has_lock():
return
lfp = self._lock_file_path()
try:
if os.name == :
os.chmod(lfp, 0777)
os.remove(lfp)
except OSError:
pass
self._owns_lock = False
|
#vtb
def trace(msg):
if os.environ.get() == :
print(, msg, file=sys.stderr)
|
Print a trace message to stderr if environment variable is set.
|
### Input:
Print a trace message to stderr if environment variable is set.
### Response:
#vtb
def trace(msg):
if os.environ.get() == :
print(, msg, file=sys.stderr)
|
#vtb
def random_variants(
count,
genome_name="GRCh38",
deletions=True,
insertions=True,
random_seed=None):
rng = random.Random(random_seed)
ensembl = genome_for_reference_name(genome_name)
if ensembl in _transcript_ids_cache:
transcript_ids = _transcript_ids_cache[ensembl]
else:
transcript_ids = ensembl.transcript_ids()
_transcript_ids_cache[ensembl] = transcript_ids
variants = []
for _ in range(count * 100):
if len(variants) < count:
transcript_id = rng.choice(transcript_ids)
transcript = ensembl.transcript_by_id(transcript_id)
if not transcript.complete:
continue
exon = rng.choice(transcript.exons)
base1_genomic_position = rng.randint(exon.start, exon.end)
transcript_offset = transcript.spliced_offset(base1_genomic_position)
seq = transcript.sequence
ref = str(seq[transcript_offset])
if transcript.on_backward_strand:
ref = reverse_complement(ref)
alt_nucleotides = [x for x in STANDARD_NUCLEOTIDES if x != ref]
if insertions:
nucleotide_pairs = [
x + y
for x in STANDARD_NUCLEOTIDES
for y in STANDARD_NUCLEOTIDES
]
alt_nucleotides.extend(nucleotide_pairs)
if deletions:
alt_nucleotides.append("")
alt = rng.choice(alt_nucleotides)
variant = Variant(
transcript.contig,
base1_genomic_position,
ref=ref,
alt=alt,
ensembl=ensembl)
variants.append(variant)
else:
return VariantCollection(variants)
raise ValueError(
("Unable to generate %d random variants, "
"there may be a problem with PyEnsembl") % count)
|
Generate a VariantCollection with random variants that overlap
at least one complete coding transcript.
|
### Input:
Generate a VariantCollection with random variants that overlap
at least one complete coding transcript.
### Response:
#vtb
def random_variants(
count,
genome_name="GRCh38",
deletions=True,
insertions=True,
random_seed=None):
rng = random.Random(random_seed)
ensembl = genome_for_reference_name(genome_name)
if ensembl in _transcript_ids_cache:
transcript_ids = _transcript_ids_cache[ensembl]
else:
transcript_ids = ensembl.transcript_ids()
_transcript_ids_cache[ensembl] = transcript_ids
variants = []
for _ in range(count * 100):
if len(variants) < count:
transcript_id = rng.choice(transcript_ids)
transcript = ensembl.transcript_by_id(transcript_id)
if not transcript.complete:
continue
exon = rng.choice(transcript.exons)
base1_genomic_position = rng.randint(exon.start, exon.end)
transcript_offset = transcript.spliced_offset(base1_genomic_position)
seq = transcript.sequence
ref = str(seq[transcript_offset])
if transcript.on_backward_strand:
ref = reverse_complement(ref)
alt_nucleotides = [x for x in STANDARD_NUCLEOTIDES if x != ref]
if insertions:
nucleotide_pairs = [
x + y
for x in STANDARD_NUCLEOTIDES
for y in STANDARD_NUCLEOTIDES
]
alt_nucleotides.extend(nucleotide_pairs)
if deletions:
alt_nucleotides.append("")
alt = rng.choice(alt_nucleotides)
variant = Variant(
transcript.contig,
base1_genomic_position,
ref=ref,
alt=alt,
ensembl=ensembl)
variants.append(variant)
else:
return VariantCollection(variants)
raise ValueError(
("Unable to generate %d random variants, "
"there may be a problem with PyEnsembl") % count)
|
#vtb
def check_crystal_equivalence(crystal_a, crystal_b):
cryst_a = spglib.get_symmetry_dataset(ase_to_spgcell(crystal_a), symprec=1e-5, angle_tolerance=-1.0, hall_number=0)
cryst_b = spglib.get_symmetry_dataset(ase_to_spgcell(crystal_b), symprec=1e-5, angle_tolerance=-1.0, hall_number=0)
samecell = np.allclose(cryst_a[], cryst_b[], atol=1e-5)
samenatoms = len(cryst_a[]) == len(cryst_b[])
samespg = cryst_a[] == cryst_b[]
def test_rotations_translations(cryst_a, cryst_b, repeat):
cell = cryst_a[]
pristine = crystal(, [(0, 0., 0.)],
spacegroup=int(cryst_a[]),
cellpar=[cell[0]/repeat[0], cell[1]/repeat[1], cell[2]/repeat[2]]).repeat(repeat)
sym_set_p = spglib.get_symmetry_dataset(ase_to_spgcell(pristine), symprec=1e-5,
angle_tolerance=-1.0, hall_number=0)
for _,trans in enumerate(zip(sym_set_p[], sym_set_p[])):
pnew=(np.matmul(trans[0],cryst_a[].T).T + trans[1]) % 1.0
fulln = np.concatenate([cryst_a[][:, None], pnew], axis=1)
fullb = np.concatenate([cryst_b[][:, None], cryst_b[]], axis=1)
sorted_n = np.array(sorted([ list(row) for row in list(fulln) ]))
sorted_b = np.array(sorted([ list(row) for row in list(fullb) ]))
if np.allclose(sorted_n, sorted_b, atol=1e-5):
return True
return False
if samecell and samenatoms and samespg:
cell = cryst_a[]
rng1 = range(1, int(norm(cell[0])/2.))
rng2 = range(1, int(norm(cell[1])/2.))
rng3 = range(1, int(norm(cell[2])/2.))
for repeat in itertools.product(rng1, rng2, rng3):
if test_rotations_translations(cryst_a, cryst_b, repeat):
return True
return False
|
Function that identifies whether two crystals are equivalent
|
### Input:
Function that identifies whether two crystals are equivalent
### Response:
#vtb
def check_crystal_equivalence(crystal_a, crystal_b):
cryst_a = spglib.get_symmetry_dataset(ase_to_spgcell(crystal_a), symprec=1e-5, angle_tolerance=-1.0, hall_number=0)
cryst_b = spglib.get_symmetry_dataset(ase_to_spgcell(crystal_b), symprec=1e-5, angle_tolerance=-1.0, hall_number=0)
samecell = np.allclose(cryst_a[], cryst_b[], atol=1e-5)
samenatoms = len(cryst_a[]) == len(cryst_b[])
samespg = cryst_a[] == cryst_b[]
def test_rotations_translations(cryst_a, cryst_b, repeat):
cell = cryst_a[]
pristine = crystal(, [(0, 0., 0.)],
spacegroup=int(cryst_a[]),
cellpar=[cell[0]/repeat[0], cell[1]/repeat[1], cell[2]/repeat[2]]).repeat(repeat)
sym_set_p = spglib.get_symmetry_dataset(ase_to_spgcell(pristine), symprec=1e-5,
angle_tolerance=-1.0, hall_number=0)
for _,trans in enumerate(zip(sym_set_p[], sym_set_p[])):
pnew=(np.matmul(trans[0],cryst_a[].T).T + trans[1]) % 1.0
fulln = np.concatenate([cryst_a[][:, None], pnew], axis=1)
fullb = np.concatenate([cryst_b[][:, None], cryst_b[]], axis=1)
sorted_n = np.array(sorted([ list(row) for row in list(fulln) ]))
sorted_b = np.array(sorted([ list(row) for row in list(fullb) ]))
if np.allclose(sorted_n, sorted_b, atol=1e-5):
return True
return False
if samecell and samenatoms and samespg:
cell = cryst_a[]
rng1 = range(1, int(norm(cell[0])/2.))
rng2 = range(1, int(norm(cell[1])/2.))
rng3 = range(1, int(norm(cell[2])/2.))
for repeat in itertools.product(rng1, rng2, rng3):
if test_rotations_translations(cryst_a, cryst_b, repeat):
return True
return False
|
#vtb
def simple_moving_matrix(x, n=10):
if x.ndim > 1 and len(x[0]) > 1:
x = np.average(x, axis=1)
h = n / 2
o = 0 if h * 2 == n else 1
xx = []
for i in range(h, len(x) - h):
xx.append(x[i-h:i+h+o])
return np.array(xx)
|
Create simple moving matrix.
Parameters
----------
x : ndarray
A numpy array
n : integer
The number of sample points used to make average
Returns
-------
ndarray
A n x n numpy array which will be useful for calculating confidentail
interval of simple moving average
|
### Input:
Create simple moving matrix.
Parameters
----------
x : ndarray
A numpy array
n : integer
The number of sample points used to make average
Returns
-------
ndarray
A n x n numpy array which will be useful for calculating confidentail
interval of simple moving average
### Response:
#vtb
def simple_moving_matrix(x, n=10):
if x.ndim > 1 and len(x[0]) > 1:
x = np.average(x, axis=1)
h = n / 2
o = 0 if h * 2 == n else 1
xx = []
for i in range(h, len(x) - h):
xx.append(x[i-h:i+h+o])
return np.array(xx)
|
#vtb
def char(i):
i = ord(i)
if i not in font:
return [(0,)] * 8
return [(ord(row),) for row in font[i].decode()]
|
Get image data for the character `i` (a one character string).
Returned as a list of rows. Each row is a tuple containing the
packed pixels.
|
### Input:
Get image data for the character `i` (a one character string).
Returned as a list of rows. Each row is a tuple containing the
packed pixels.
### Response:
#vtb
def char(i):
i = ord(i)
if i not in font:
return [(0,)] * 8
return [(ord(row),) for row in font[i].decode()]
|
#vtb
def casefold_with_i_dots(text):
text = unicodedata.normalize(, text).replace(, ).replace(, )
return text.casefold()
|
Convert capital I's and capital dotted İ's to lowercase in the way
that's appropriate for Turkish and related languages, then case-fold
the rest of the letters.
|
### Input:
Convert capital I's and capital dotted İ's to lowercase in the way
that's appropriate for Turkish and related languages, then case-fold
the rest of the letters.
### Response:
#vtb
def casefold_with_i_dots(text):
text = unicodedata.normalize(, text).replace(, ).replace(, )
return text.casefold()
|
#vtb
def query_publishers(self, publisher_query):
content = self._serialize.body(publisher_query, )
response = self._send(http_method=,
location_id=,
version=,
content=content)
return self._deserialize(, response)
|
QueryPublishers.
[Preview API]
:param :class:`<PublisherQuery> <azure.devops.v5_0.gallery.models.PublisherQuery>` publisher_query:
:rtype: :class:`<PublisherQueryResult> <azure.devops.v5_0.gallery.models.PublisherQueryResult>`
|
### Input:
QueryPublishers.
[Preview API]
:param :class:`<PublisherQuery> <azure.devops.v5_0.gallery.models.PublisherQuery>` publisher_query:
:rtype: :class:`<PublisherQueryResult> <azure.devops.v5_0.gallery.models.PublisherQueryResult>`
### Response:
#vtb
def query_publishers(self, publisher_query):
content = self._serialize.body(publisher_query, )
response = self._send(http_method=,
location_id=,
version=,
content=content)
return self._deserialize(, response)
|
#vtb
def addFilter(self, *lstFilters, **dctFilters) :
"add a new filter to the query"
dstF = {}
if len(lstFilters) > 0 :
if type(lstFilters[0]) is types.DictType :
dstF = lstFilters[0]
lstFilters = lstFilters[1:]
if len(dctFilters) > 0 :
dstF = dict(dstF, **dctFilters)
filts = {}
for k, v in dstF.iteritems() :
sk = k.split()
if len(sk) == 2 :
operator = sk[-1].strip().upper()
if operator not in self.operators :
raise ValueError( % operator)
kk = % (self.rabaClass.__name__, k)
elif len(sk) == 1 :
operator = "="
kk = % (self.rabaClass.__name__, k)
else :
raise ValueError( % k)
if isRabaObject(v) :
vv = v.getJsonEncoding()
else :
vv = v
if sk[0].find() > -1 :
kk = self._parseJoint(sk[0], operator)
filts[kk] = vv
for lt in lstFilters :
for l in lt :
match = self.fieldPattern.match(l)
if match == None :
raise ValueError("RabaQuery Error: Invalid filter " % l)
field = match.group(1)
operator = match.group(2)
value = match.group(4)
if field.find() > -1 :
joink = self._parseJoint(field, operator, value)
filts[joink] = value
else :
filts[ %(self.rabaClass.__name__, field, operator)] = value
self.filters.append(filts)
|
add a new filter to the query
|
### Input:
add a new filter to the query
### Response:
#vtb
def addFilter(self, *lstFilters, **dctFilters) :
"add a new filter to the query"
dstF = {}
if len(lstFilters) > 0 :
if type(lstFilters[0]) is types.DictType :
dstF = lstFilters[0]
lstFilters = lstFilters[1:]
if len(dctFilters) > 0 :
dstF = dict(dstF, **dctFilters)
filts = {}
for k, v in dstF.iteritems() :
sk = k.split()
if len(sk) == 2 :
operator = sk[-1].strip().upper()
if operator not in self.operators :
raise ValueError( % operator)
kk = % (self.rabaClass.__name__, k)
elif len(sk) == 1 :
operator = "="
kk = % (self.rabaClass.__name__, k)
else :
raise ValueError( % k)
if isRabaObject(v) :
vv = v.getJsonEncoding()
else :
vv = v
if sk[0].find() > -1 :
kk = self._parseJoint(sk[0], operator)
filts[kk] = vv
for lt in lstFilters :
for l in lt :
match = self.fieldPattern.match(l)
if match == None :
raise ValueError("RabaQuery Error: Invalid filter " % l)
field = match.group(1)
operator = match.group(2)
value = match.group(4)
if field.find() > -1 :
joink = self._parseJoint(field, operator, value)
filts[joink] = value
else :
filts[ %(self.rabaClass.__name__, field, operator)] = value
self.filters.append(filts)
|
#vtb
def trigger_actions(self, subsystem):
for py3_module, trigger_action in self.udev_consumers[subsystem]:
if trigger_action in ON_TRIGGER_ACTIONS:
self.py3_wrapper.log(
"%s udev event, refresh consumer %s"
% (subsystem, py3_module.module_full_name)
)
py3_module.force_update()
|
Refresh all modules which subscribed to the given subsystem.
|
### Input:
Refresh all modules which subscribed to the given subsystem.
### Response:
#vtb
def trigger_actions(self, subsystem):
for py3_module, trigger_action in self.udev_consumers[subsystem]:
if trigger_action in ON_TRIGGER_ACTIONS:
self.py3_wrapper.log(
"%s udev event, refresh consumer %s"
% (subsystem, py3_module.module_full_name)
)
py3_module.force_update()
|
#vtb
def post_status(self, body="", id="", parentid="", stashid=""):
if self.standard_grant_type is not "authorization_code":
raise DeviantartError("Authentication through Authorization Code (Grant Type) is required in order to connect to this endpoint.")
response = self._req(, post_data={
"body":body,
"id":id,
"parentid":parentid,
"stashid":stashid
})
return response[]
|
Post a status
:param username: The body of the status
:param id: The id of the object you wish to share
:param parentid: The parentid of the object you wish to share
:param stashid: The stashid of the object you wish to add to the status
|
### Input:
Post a status
:param username: The body of the status
:param id: The id of the object you wish to share
:param parentid: The parentid of the object you wish to share
:param stashid: The stashid of the object you wish to add to the status
### Response:
#vtb
def post_status(self, body="", id="", parentid="", stashid=""):
if self.standard_grant_type is not "authorization_code":
raise DeviantartError("Authentication through Authorization Code (Grant Type) is required in order to connect to this endpoint.")
response = self._req(, post_data={
"body":body,
"id":id,
"parentid":parentid,
"stashid":stashid
})
return response[]
|
#vtb
def make_clusters(span_tree, cut_value):
iv0, iv1 = span_tree.nonzero()
match_dict = {}
for i0, i1 in zip(iv0, iv1):
d = span_tree[i0, i1]
if d > cut_value:
continue
imin = int(min(i0, i1))
imax = int(max(i0, i1))
if imin in match_dict:
match_dict[imin][imax] = True
else:
match_dict[imin] = {imax: True}
working = True
while working:
working = False
rev_dict = make_rev_dict_unique(match_dict)
k_sort = rev_dict.keys()
k_sort.sort()
for k in k_sort:
v = rev_dict[k]
if len(v) > 1:
working = True
v_sort = v.keys()
v_sort.sort()
cluster_idx = v_sort[0]
for vv in v_sort[1:]:
try:
to_merge = match_dict.pop(vv)
except:
continue
try:
match_dict[cluster_idx].update(to_merge)
match_dict[cluster_idx][vv] = True
except:
continue
try:
match_dict[cluster_idx].pop(cluster_idx)
except:
pass
cdict = {}
for k, v in match_dict.items():
cdict[k] = v.keys()
rdict = make_reverse_dict(cdict)
return cdict, rdict
|
Find clusters from the spanning tree
Parameters
----------
span_tree : a sparse nsrcs x nsrcs array
Filled with zeros except for the active edges, which are filled with the
edge measures (either distances or sigmas
cut_value : float
Value used to cluster group. All links with measures above this calue will be cut.
returns dict(int:[int,...])
A dictionary of clusters. Each cluster is a source index and the list of other sources in the cluster.
|
### Input:
Find clusters from the spanning tree
Parameters
----------
span_tree : a sparse nsrcs x nsrcs array
Filled with zeros except for the active edges, which are filled with the
edge measures (either distances or sigmas
cut_value : float
Value used to cluster group. All links with measures above this calue will be cut.
returns dict(int:[int,...])
A dictionary of clusters. Each cluster is a source index and the list of other sources in the cluster.
### Response:
#vtb
def make_clusters(span_tree, cut_value):
iv0, iv1 = span_tree.nonzero()
match_dict = {}
for i0, i1 in zip(iv0, iv1):
d = span_tree[i0, i1]
if d > cut_value:
continue
imin = int(min(i0, i1))
imax = int(max(i0, i1))
if imin in match_dict:
match_dict[imin][imax] = True
else:
match_dict[imin] = {imax: True}
working = True
while working:
working = False
rev_dict = make_rev_dict_unique(match_dict)
k_sort = rev_dict.keys()
k_sort.sort()
for k in k_sort:
v = rev_dict[k]
if len(v) > 1:
working = True
v_sort = v.keys()
v_sort.sort()
cluster_idx = v_sort[0]
for vv in v_sort[1:]:
try:
to_merge = match_dict.pop(vv)
except:
continue
try:
match_dict[cluster_idx].update(to_merge)
match_dict[cluster_idx][vv] = True
except:
continue
try:
match_dict[cluster_idx].pop(cluster_idx)
except:
pass
cdict = {}
for k, v in match_dict.items():
cdict[k] = v.keys()
rdict = make_reverse_dict(cdict)
return cdict, rdict
|
#vtb
def to_netcdf(dataset, path_or_file=None, mode=, format=None, group=None,
engine=None, encoding=None, unlimited_dims=None, compute=True,
multifile=False):
if isinstance(path_or_file, Path):
path_or_file = str(path_or_file)
if encoding is None:
encoding = {}
if path_or_file is None:
if engine is None:
engine =
elif engine != :
raise ValueError(
"or engine= is supported" % engine)
if not compute:
raise NotImplementedError(
)
elif isinstance(path_or_file, str):
if engine is None:
engine = _get_default_engine(path_or_file)
path_or_file = _normalize_path(path_or_file)
else:
engine =
_validate_dataset_names(dataset)
_validate_attrs(dataset)
try:
store_open = WRITEABLE_STORES[engine]
except KeyError:
raise ValueError( % engine)
if format is not None:
format = format.upper()
scheduler = _get_scheduler()
have_chunks = any(v.chunks for v in dataset.variables.values())
autoclose = have_chunks and scheduler in [, ]
if autoclose and engine == :
raise NotImplementedError("Writing netCDF files with the %s backend "
"is not currently supported with daskunlimited_dims', None)
if isinstance(unlimited_dims, str):
unlimited_dims = [unlimited_dims]
writer = ArrayWriter()
try:
dump_to_store(dataset, store, writer, encoding=encoding,
unlimited_dims=unlimited_dims)
if autoclose:
store.close()
if multifile:
return writer, store
writes = writer.sync(compute=compute)
if path_or_file is None:
store.sync()
return target.getvalue()
finally:
if not multifile and compute:
store.close()
if not compute:
import dask
return dask.delayed(_finalize_store)(writes, store)
|
This function creates an appropriate datastore for writing a dataset to
disk as a netCDF file
See `Dataset.to_netcdf` for full API docs.
The ``multifile`` argument is only for the private use of save_mfdataset.
|
### Input:
This function creates an appropriate datastore for writing a dataset to
disk as a netCDF file
See `Dataset.to_netcdf` for full API docs.
The ``multifile`` argument is only for the private use of save_mfdataset.
### Response:
#vtb
def to_netcdf(dataset, path_or_file=None, mode=, format=None, group=None,
engine=None, encoding=None, unlimited_dims=None, compute=True,
multifile=False):
if isinstance(path_or_file, Path):
path_or_file = str(path_or_file)
if encoding is None:
encoding = {}
if path_or_file is None:
if engine is None:
engine =
elif engine != :
raise ValueError(
"or engine= is supported" % engine)
if not compute:
raise NotImplementedError(
)
elif isinstance(path_or_file, str):
if engine is None:
engine = _get_default_engine(path_or_file)
path_or_file = _normalize_path(path_or_file)
else:
engine =
_validate_dataset_names(dataset)
_validate_attrs(dataset)
try:
store_open = WRITEABLE_STORES[engine]
except KeyError:
raise ValueError( % engine)
if format is not None:
format = format.upper()
scheduler = _get_scheduler()
have_chunks = any(v.chunks for v in dataset.variables.values())
autoclose = have_chunks and scheduler in [, ]
if autoclose and engine == :
raise NotImplementedError("Writing netCDF files with the %s backend "
"is not currently supported with daskunlimited_dims', None)
if isinstance(unlimited_dims, str):
unlimited_dims = [unlimited_dims]
writer = ArrayWriter()
try:
dump_to_store(dataset, store, writer, encoding=encoding,
unlimited_dims=unlimited_dims)
if autoclose:
store.close()
if multifile:
return writer, store
writes = writer.sync(compute=compute)
if path_or_file is None:
store.sync()
return target.getvalue()
finally:
if not multifile and compute:
store.close()
if not compute:
import dask
return dask.delayed(_finalize_store)(writes, store)
|
#vtb
def interpret_header(self):
if in self.header:
self.date = self.header[]
elif in self.header:
self.date = self.header[]
else:
raise Exception("Image does not have a DATE_OBS or DATE-OBS field")
self.cy, self.cx = self.header[], self.header[]
sun_radius_angular = sun.solar_semidiameter_angular_size(t=time.parse_time(self.date)).arcsec
arcsec_per_pixel = self.header[]
self.sun_radius_pixel = (sun_radius_angular / arcsec_per_pixel)
|
Read pertinent information from the image headers,
especially location and radius of the Sun to calculate the default thematic map
:return: setes self.date, self.cy, self.cx, and self.sun_radius_pixel
|
### Input:
Read pertinent information from the image headers,
especially location and radius of the Sun to calculate the default thematic map
:return: setes self.date, self.cy, self.cx, and self.sun_radius_pixel
### Response:
#vtb
def interpret_header(self):
if in self.header:
self.date = self.header[]
elif in self.header:
self.date = self.header[]
else:
raise Exception("Image does not have a DATE_OBS or DATE-OBS field")
self.cy, self.cx = self.header[], self.header[]
sun_radius_angular = sun.solar_semidiameter_angular_size(t=time.parse_time(self.date)).arcsec
arcsec_per_pixel = self.header[]
self.sun_radius_pixel = (sun_radius_angular / arcsec_per_pixel)
|
#vtb
def _computeStatus(self, dfile, service):
if service:
if not dfile[].has_key(service):
return self.ST_UNTRACKED
else:
return dfile[][service][]
first_service_key=dfile[].keys()[0]
first_status=dfile[][first_service_key][]
all_status_match=True
for service in dfile[]:
if dfile[][service][]!=first_status:
return self.ST_COMPLICATED
return first_status
|
Computes status for file, basically this means if
more than one service handles the file, it will place
a 'C' (for complicated) otherwise if status matches
between all services, will place that status
|
### Input:
Computes status for file, basically this means if
more than one service handles the file, it will place
a 'C' (for complicated) otherwise if status matches
between all services, will place that status
### Response:
#vtb
def _computeStatus(self, dfile, service):
if service:
if not dfile[].has_key(service):
return self.ST_UNTRACKED
else:
return dfile[][service][]
first_service_key=dfile[].keys()[0]
first_status=dfile[][first_service_key][]
all_status_match=True
for service in dfile[]:
if dfile[][service][]!=first_status:
return self.ST_COMPLICATED
return first_status
|
#vtb
def _handle_state_change_msg(self, new_helper):
assert self.my_pplan_helper is not None
assert self.my_instance is not None and self.my_instance.py_class is not None
if self.my_pplan_helper.get_topology_state() != new_helper.get_topology_state():
self.my_pplan_helper = new_helper
if new_helper.is_topology_running():
if not self.is_instance_started:
self.start_instance_if_possible()
self.my_instance.py_class.invoke_activate()
elif new_helper.is_topology_paused():
self.my_instance.py_class.invoke_deactivate()
else:
raise RuntimeError("Unexpected TopologyState update: %s" % new_helper.get_topology_state())
else:
Log.info("Topology state remains the same.")
|
Called when state change is commanded by stream manager
|
### Input:
Called when state change is commanded by stream manager
### Response:
#vtb
def _handle_state_change_msg(self, new_helper):
assert self.my_pplan_helper is not None
assert self.my_instance is not None and self.my_instance.py_class is not None
if self.my_pplan_helper.get_topology_state() != new_helper.get_topology_state():
self.my_pplan_helper = new_helper
if new_helper.is_topology_running():
if not self.is_instance_started:
self.start_instance_if_possible()
self.my_instance.py_class.invoke_activate()
elif new_helper.is_topology_paused():
self.my_instance.py_class.invoke_deactivate()
else:
raise RuntimeError("Unexpected TopologyState update: %s" % new_helper.get_topology_state())
else:
Log.info("Topology state remains the same.")
|
#vtb
def is_valid_group(group_name, nova_creds):
valid_groups = []
for key, value in nova_creds.items():
supernova_groups = value.get(, [])
if hasattr(supernova_groups, ):
supernova_groups = [supernova_groups]
valid_groups.extend(supernova_groups)
valid_groups.append()
if group_name in valid_groups:
return True
else:
return False
|
Checks to see if the configuration file contains a SUPERNOVA_GROUP
configuration option.
|
### Input:
Checks to see if the configuration file contains a SUPERNOVA_GROUP
configuration option.
### Response:
#vtb
def is_valid_group(group_name, nova_creds):
valid_groups = []
for key, value in nova_creds.items():
supernova_groups = value.get(, [])
if hasattr(supernova_groups, ):
supernova_groups = [supernova_groups]
valid_groups.extend(supernova_groups)
valid_groups.append()
if group_name in valid_groups:
return True
else:
return False
|
#vtb
def _draw_fold_indicator(self, top, mouse_over, collapsed, painter):
rect = QtCore.QRect(0, top, self.sizeHint().width(),
self.sizeHint().height())
if self._native:
if os.environ[].lower() not in PYQT5_API:
opt = QtGui.QStyleOptionViewItemV2()
else:
opt = QtWidgets.QStyleOptionViewItem()
opt.rect = rect
opt.state = (QtWidgets.QStyle.State_Active |
QtWidgets.QStyle.State_Item |
QtWidgets.QStyle.State_Children)
if not collapsed:
opt.state |= QtWidgets.QStyle.State_Open
if mouse_over:
opt.state |= (QtWidgets.QStyle.State_MouseOver |
QtWidgets.QStyle.State_Enabled |
QtWidgets.QStyle.State_Selected)
opt.palette.setBrush(QtGui.QPalette.Window,
self.palette().highlight())
opt.rect.translate(-2, 0)
self.style().drawPrimitive(QtWidgets.QStyle.PE_IndicatorBranch,
opt, painter, self)
else:
index = 0
if not collapsed:
index = 2
if mouse_over:
index += 1
QtGui.QIcon(self._custom_indicators[index]).paint(painter, rect)
|
Draw the fold indicator/trigger (arrow).
:param top: Top position
:param mouse_over: Whether the mouse is over the indicator
:param collapsed: Whether the trigger is collapsed or not.
:param painter: QPainter
|
### Input:
Draw the fold indicator/trigger (arrow).
:param top: Top position
:param mouse_over: Whether the mouse is over the indicator
:param collapsed: Whether the trigger is collapsed or not.
:param painter: QPainter
### Response:
#vtb
def _draw_fold_indicator(self, top, mouse_over, collapsed, painter):
rect = QtCore.QRect(0, top, self.sizeHint().width(),
self.sizeHint().height())
if self._native:
if os.environ[].lower() not in PYQT5_API:
opt = QtGui.QStyleOptionViewItemV2()
else:
opt = QtWidgets.QStyleOptionViewItem()
opt.rect = rect
opt.state = (QtWidgets.QStyle.State_Active |
QtWidgets.QStyle.State_Item |
QtWidgets.QStyle.State_Children)
if not collapsed:
opt.state |= QtWidgets.QStyle.State_Open
if mouse_over:
opt.state |= (QtWidgets.QStyle.State_MouseOver |
QtWidgets.QStyle.State_Enabled |
QtWidgets.QStyle.State_Selected)
opt.palette.setBrush(QtGui.QPalette.Window,
self.palette().highlight())
opt.rect.translate(-2, 0)
self.style().drawPrimitive(QtWidgets.QStyle.PE_IndicatorBranch,
opt, painter, self)
else:
index = 0
if not collapsed:
index = 2
if mouse_over:
index += 1
QtGui.QIcon(self._custom_indicators[index]).paint(painter, rect)
|
#vtb
def get_spark_context(conf=None):
if hasattr(SparkContext, "getOrCreate"):
with SparkContext._lock:
if SparkContext._active_spark_context is None:
spark_conf = create_spark_conf() if conf is None else conf
return SparkContext.getOrCreate(spark_conf)
else:
return SparkContext.getOrCreate()
else:
if SparkContext._active_spark_context is None:
spark_conf = create_spark_conf() if conf is None else conf
return SparkContext(conf=spark_conf)
else:
return SparkContext._active_spark_context
|
Get the current active spark context and create one if no active instance
:param conf: combining bigdl configs into spark conf
:return: SparkContext
|
### Input:
Get the current active spark context and create one if no active instance
:param conf: combining bigdl configs into spark conf
:return: SparkContext
### Response:
#vtb
def get_spark_context(conf=None):
if hasattr(SparkContext, "getOrCreate"):
with SparkContext._lock:
if SparkContext._active_spark_context is None:
spark_conf = create_spark_conf() if conf is None else conf
return SparkContext.getOrCreate(spark_conf)
else:
return SparkContext.getOrCreate()
else:
if SparkContext._active_spark_context is None:
spark_conf = create_spark_conf() if conf is None else conf
return SparkContext(conf=spark_conf)
else:
return SparkContext._active_spark_context
|
#vtb
def get_distutils_display_options():
short_display_opts = set( + o[1] for o in Distribution.display_options
if o[1])
long_display_opts = set( + o[0] for o in Distribution.display_options)
short_display_opts.add()
long_display_opts.add()
display_commands = set([
, , , , ,
])
return short_display_opts.union(long_display_opts.union(display_commands))
|
Returns a set of all the distutils display options in their long and
short forms. These are the setup.py arguments such as --name or --version
which print the project's metadata and then exit.
Returns
-------
opts : set
The long and short form display option arguments, including the - or --
|
### Input:
Returns a set of all the distutils display options in their long and
short forms. These are the setup.py arguments such as --name or --version
which print the project's metadata and then exit.
Returns
-------
opts : set
The long and short form display option arguments, including the - or --
### Response:
#vtb
def get_distutils_display_options():
short_display_opts = set( + o[1] for o in Distribution.display_options
if o[1])
long_display_opts = set( + o[0] for o in Distribution.display_options)
short_display_opts.add()
long_display_opts.add()
display_commands = set([
, , , , ,
])
return short_display_opts.union(long_display_opts.union(display_commands))
|
#vtb
def cluster(x, cluster=, n_clusters=3, ndims=None, format_data=True):
if cluster == None:
return x
elif (isinstance(cluster, six.string_types) and cluster==) or \
(isinstance(cluster, dict) and cluster[]==):
if not _has_hdbscan:
raise ImportError()
if ndims != None:
warnings.warn()
if format_data:
x = formatter(x, ppca=True)
if isinstance(cluster, six.string_types):
model = models[cluster]
if cluster != :
model_params = {
: n_clusters
}
else:
model_params = {}
elif type(cluster) is dict:
if isinstance(cluster[], six.string_types):
model = models[cluster[]]
model_params = cluster[]
model = model(**model_params)
model.fit(np.vstack(x))
return list(model.labels_)
|
Performs clustering analysis and returns a list of cluster labels
Parameters
----------
x : A Numpy array, Pandas Dataframe or list of arrays/dfs
The data to be clustered. You can pass a single array/df or a list.
If a list is passed, the arrays will be stacked and the clustering
will be performed across all lists (i.e. not within each list).
cluster : str or dict
Model to use to discover clusters. Support algorithms are: KMeans,
MiniBatchKMeans, AgglomerativeClustering, Birch, FeatureAgglomeration,
SpectralClustering and HDBSCAN (default: KMeans). Can be passed as a
string, but for finer control of the model parameters, pass as a
dictionary, e.g. reduce={'model' : 'KMeans', 'params' : {'max_iter' : 100}}.
See scikit-learn specific model docs for details on parameters supported for
each model.
n_clusters : int
Number of clusters to discover. Not required for HDBSCAN.
format_data : bool
Whether or not to first call the format_data function (default: True).
ndims : None
Deprecated argument. Please use new analyze function to perform
combinations of transformations
Returns
----------
cluster_labels : list
An list of cluster labels
|
### Input:
Performs clustering analysis and returns a list of cluster labels
Parameters
----------
x : A Numpy array, Pandas Dataframe or list of arrays/dfs
The data to be clustered. You can pass a single array/df or a list.
If a list is passed, the arrays will be stacked and the clustering
will be performed across all lists (i.e. not within each list).
cluster : str or dict
Model to use to discover clusters. Support algorithms are: KMeans,
MiniBatchKMeans, AgglomerativeClustering, Birch, FeatureAgglomeration,
SpectralClustering and HDBSCAN (default: KMeans). Can be passed as a
string, but for finer control of the model parameters, pass as a
dictionary, e.g. reduce={'model' : 'KMeans', 'params' : {'max_iter' : 100}}.
See scikit-learn specific model docs for details on parameters supported for
each model.
n_clusters : int
Number of clusters to discover. Not required for HDBSCAN.
format_data : bool
Whether or not to first call the format_data function (default: True).
ndims : None
Deprecated argument. Please use new analyze function to perform
combinations of transformations
Returns
----------
cluster_labels : list
An list of cluster labels
### Response:
#vtb
def cluster(x, cluster=, n_clusters=3, ndims=None, format_data=True):
if cluster == None:
return x
elif (isinstance(cluster, six.string_types) and cluster==) or \
(isinstance(cluster, dict) and cluster[]==):
if not _has_hdbscan:
raise ImportError()
if ndims != None:
warnings.warn()
if format_data:
x = formatter(x, ppca=True)
if isinstance(cluster, six.string_types):
model = models[cluster]
if cluster != :
model_params = {
: n_clusters
}
else:
model_params = {}
elif type(cluster) is dict:
if isinstance(cluster[], six.string_types):
model = models[cluster[]]
model_params = cluster[]
model = model(**model_params)
model.fit(np.vstack(x))
return list(model.labels_)
|
#vtb
def gemini_query(self, query_id):
logger.debug("Looking for query with id {0}".format(query_id))
return self.query(GeminiQuery).filter_by(id=query_id).first()
|
Return a gemini query
Args:
name (str)
|
### Input:
Return a gemini query
Args:
name (str)
### Response:
#vtb
def gemini_query(self, query_id):
logger.debug("Looking for query with id {0}".format(query_id))
return self.query(GeminiQuery).filter_by(id=query_id).first()
|
#vtb
def deprecated(msg=):
def wrapper(func):
@functools.wraps(func)
def new_func(*args, **kwargs):
warning_string = "Call to deprecated function or property `%s`." % func.__name__
warning_string = warning_string + + msg
warnings.warn(
warning_string,
category=DeprecationWarning,
)
return func(*args, **kwargs)
return new_func
return wrapper
|
This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used.
:param msg:
Additional message added to the warning.
|
### Input:
This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used.
:param msg:
Additional message added to the warning.
### Response:
#vtb
def deprecated(msg=):
def wrapper(func):
@functools.wraps(func)
def new_func(*args, **kwargs):
warning_string = "Call to deprecated function or property `%s`." % func.__name__
warning_string = warning_string + + msg
warnings.warn(
warning_string,
category=DeprecationWarning,
)
return func(*args, **kwargs)
return new_func
return wrapper
|
#vtb
def template_sphere_shell(outer_radius, inner_radius=0):
r
img = _template_sphere_disc(dim=3, outer_radius=outer_radius,
inner_radius=inner_radius)
return img
|
r"""
This method generates an image array of a sphere-shell. It is useful for
passing to Cubic networks as a ``template`` to make spherical shaped
networks.
Parameters
----------
outer_radius : int
Number of nodes in the outer radius of the sphere.
inner_radius : int
Number of nodes in the inner radius of the shell. a value of 0 will
result in a solid sphere.
Returns
-------
A Numpy array containing 1's to demarcate the sphere-shell, and 0's
elsewhere.
|
### Input:
r"""
This method generates an image array of a sphere-shell. It is useful for
passing to Cubic networks as a ``template`` to make spherical shaped
networks.
Parameters
----------
outer_radius : int
Number of nodes in the outer radius of the sphere.
inner_radius : int
Number of nodes in the inner radius of the shell. a value of 0 will
result in a solid sphere.
Returns
-------
A Numpy array containing 1's to demarcate the sphere-shell, and 0's
elsewhere.
### Response:
#vtb
def template_sphere_shell(outer_radius, inner_radius=0):
r
img = _template_sphere_disc(dim=3, outer_radius=outer_radius,
inner_radius=inner_radius)
return img
|
#vtb
def get_PolyFromPolyFileObj(PolyFileObj, SavePathInp=None, units=, comments=, skiprows=0, shape0=2):
assert type(PolyFileObj) in [list,str] or hasattr(PolyFileObj,"Poly") or np.asarray(PolyFileObj).ndim==2, "Arg PolyFileObj must be str (PathFileExt), a ToFu object with attribute Poly or an iterable convertible to 2d np.ndarray !"
addInfo = {}
if type(PolyFileObj) in [list,str]:
PathFileExt = get_FileFromInfos(Path=SavePathInp, Name=PolyFileObj)
addInfo = {:PathFileExt}
PolyFileObj = np.loadtxt(PathFileExt, dtype=float, comments=comments, delimiter=None, converters=None, skiprows=skiprows, usecols=None, unpack=False, ndmin=2)
elif hasattr(PolyFileObj,"Poly"):
addInfo = {:PolyFileObj.Id.SaveName}
PolyFileObj = PolyFileObj.Poly
Poly = np.asarray(PolyFileObj)
assert Poly.ndim==2 and shape0 in Poly.shape and max(Poly.shape)>=3 and not np.any(np.isnan(Poly)), "Arg np.asarray(PolyFileObj) must be a (2,N) or (N,2) np.ndarray with non NaNs !"
Poly = Poly if Poly.shape[0]==shape0 else Poly.T
Poly = convert_units(Poly, In=units, Out=)
return Poly, addInfo
|
Return a polygon as a np.ndarray, extracted from a txt file or from a ToFu object, with appropriate units
Useful for :meth:`tofu.plugins.AUG.Ves._create()`
Parameters
----------
PolyFileObj : str / :mod:`tofu.geom` object / np.ndarray
The source where the polygon is to be found, either:
- str: the name of a file containing the coorindates of a polygon to be loaded with :meth:`numpy.loadtxt()`
- A :mod:`tofu.geom` object: with attribute 'Poly'
- np.ndarray: an 2-dimensional array containing the 2D cartesian coordinates of a polygon
SavePathInp : str / None
The absolute path where the input file is stored
units : str
Flag indicating in which units the polygon coordinates is expressed in the input file / object / array (will be converted to meters)
comments : str
Parameter to be fed to :meth:`numpy.loadtxt()` if PolyFileObj is a file name
skiprows : int
Parameter to be fed to :meth:`numpy.loadtxt()` if PolyFileObj is a file name
shape0 : int
Specifies whether the loaded array is a (2,N) or (3,N) array (transposed it if necessary)
Returns
-------
Poly : np.ndarray
(2,N) np.ndarray containing the 2D cartesian coordinates of the polygon, where N is the number of points
addInfo : dict
Dictionaryb containing information on the origin of the polygon, for the record (e.g.: the name and absolute path of the file from which it was extracted)
|
### Input:
Return a polygon as a np.ndarray, extracted from a txt file or from a ToFu object, with appropriate units
Useful for :meth:`tofu.plugins.AUG.Ves._create()`
Parameters
----------
PolyFileObj : str / :mod:`tofu.geom` object / np.ndarray
The source where the polygon is to be found, either:
- str: the name of a file containing the coorindates of a polygon to be loaded with :meth:`numpy.loadtxt()`
- A :mod:`tofu.geom` object: with attribute 'Poly'
- np.ndarray: an 2-dimensional array containing the 2D cartesian coordinates of a polygon
SavePathInp : str / None
The absolute path where the input file is stored
units : str
Flag indicating in which units the polygon coordinates is expressed in the input file / object / array (will be converted to meters)
comments : str
Parameter to be fed to :meth:`numpy.loadtxt()` if PolyFileObj is a file name
skiprows : int
Parameter to be fed to :meth:`numpy.loadtxt()` if PolyFileObj is a file name
shape0 : int
Specifies whether the loaded array is a (2,N) or (3,N) array (transposed it if necessary)
Returns
-------
Poly : np.ndarray
(2,N) np.ndarray containing the 2D cartesian coordinates of the polygon, where N is the number of points
addInfo : dict
Dictionaryb containing information on the origin of the polygon, for the record (e.g.: the name and absolute path of the file from which it was extracted)
### Response:
#vtb
def get_PolyFromPolyFileObj(PolyFileObj, SavePathInp=None, units=, comments=, skiprows=0, shape0=2):
assert type(PolyFileObj) in [list,str] or hasattr(PolyFileObj,"Poly") or np.asarray(PolyFileObj).ndim==2, "Arg PolyFileObj must be str (PathFileExt), a ToFu object with attribute Poly or an iterable convertible to 2d np.ndarray !"
addInfo = {}
if type(PolyFileObj) in [list,str]:
PathFileExt = get_FileFromInfos(Path=SavePathInp, Name=PolyFileObj)
addInfo = {:PathFileExt}
PolyFileObj = np.loadtxt(PathFileExt, dtype=float, comments=comments, delimiter=None, converters=None, skiprows=skiprows, usecols=None, unpack=False, ndmin=2)
elif hasattr(PolyFileObj,"Poly"):
addInfo = {:PolyFileObj.Id.SaveName}
PolyFileObj = PolyFileObj.Poly
Poly = np.asarray(PolyFileObj)
assert Poly.ndim==2 and shape0 in Poly.shape and max(Poly.shape)>=3 and not np.any(np.isnan(Poly)), "Arg np.asarray(PolyFileObj) must be a (2,N) or (N,2) np.ndarray with non NaNs !"
Poly = Poly if Poly.shape[0]==shape0 else Poly.T
Poly = convert_units(Poly, In=units, Out=)
return Poly, addInfo
|
#vtb
def get_upgrades(self, remove_applied=True):
if self.upgrades is None:
plugins = self._load_upgrades(remove_applied=remove_applied)
self.upgrades = self.order_upgrades(plugins, self.history)
return self.upgrades
|
Get upgrades (ordered according to their dependencies).
:param remove_applied: Set to false to return all upgrades, otherwise
already applied upgrades are removed from their graph (incl. all
their dependencies.
|
### Input:
Get upgrades (ordered according to their dependencies).
:param remove_applied: Set to false to return all upgrades, otherwise
already applied upgrades are removed from their graph (incl. all
their dependencies.
### Response:
#vtb
def get_upgrades(self, remove_applied=True):
if self.upgrades is None:
plugins = self._load_upgrades(remove_applied=remove_applied)
self.upgrades = self.order_upgrades(plugins, self.history)
return self.upgrades
|
#vtb
def _upload_file(compute, project_id, file_path, path):
path = "/projects/{}/files/{}".format(project_id, path.replace("\\", "/"))
with open(file_path, "rb") as f:
yield from compute.http_query("POST", path, f, timeout=None)
|
Upload a file to a remote project
:param file_path: File path on the controller file system
:param path: File path on the remote system relative to project directory
|
### Input:
Upload a file to a remote project
:param file_path: File path on the controller file system
:param path: File path on the remote system relative to project directory
### Response:
#vtb
def _upload_file(compute, project_id, file_path, path):
path = "/projects/{}/files/{}".format(project_id, path.replace("\\", "/"))
with open(file_path, "rb") as f:
yield from compute.http_query("POST", path, f, timeout=None)
|
#vtb
def admeig(classname, f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
args = f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau
A = getattr(adm, + classname)(*args)
perm_keys = get_permissible_wcs(classname, f)
if perm_keys != :
A = A[perm_keys][:, perm_keys]
w, v = np.linalg.eig(A.T)
return w, v
|
Compute the eigenvalues and eigenvectors for a QCD anomalous dimension
matrix that is defined in `adm.adm_s_X` where X is the name of the sector.
Supports memoization. Output analogous to `np.linalg.eig`.
|
### Input:
Compute the eigenvalues and eigenvectors for a QCD anomalous dimension
matrix that is defined in `adm.adm_s_X` where X is the name of the sector.
Supports memoization. Output analogous to `np.linalg.eig`.
### Response:
#vtb
def admeig(classname, f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau):
args = f, m_u, m_d, m_s, m_c, m_b, m_e, m_mu, m_tau
A = getattr(adm, + classname)(*args)
perm_keys = get_permissible_wcs(classname, f)
if perm_keys != :
A = A[perm_keys][:, perm_keys]
w, v = np.linalg.eig(A.T)
return w, v
|
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