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#vtb
def _augment_observation_files(self, e):
e.file_records = [self._augment_file(f) for f in e.file_records]
return e
|
Augment all the file records in an event
:internal:
|
### Input:
Augment all the file records in an event
:internal:
### Response:
#vtb
def _augment_observation_files(self, e):
e.file_records = [self._augment_file(f) for f in e.file_records]
return e
|
#vtb
def Backup(self, duration=0):
total = 0
duration_total = duration * 4
children = self.GetChildrenIndexes()
notes = 0
for voice in children:
v = self.GetChild(voice)
indexes = v.GetChildrenIndexes()
if len(indexes) > 1:
indexes.reverse()
for index in indexes:
notes += 1
note = v.GetChild(index)
if hasattr(note, "duration"):
total += note.duration
if total >= duration_total:
break
gap = [
v.GetChild(i).duration for i in range(
0,
self.index -
notes) if hasattr(
v.GetChild(i),
"duration")]
previous = 0
for item in gap:
if item == previous:
self.gap -= previous
item = item / 2
self.gap += item
previous = item
self.index -= notes
|
method to use when a backup tag is encountered in musicXML. Moves back in the bar by <duration>
:param duration:
:return:
|
### Input:
method to use when a backup tag is encountered in musicXML. Moves back in the bar by <duration>
:param duration:
:return:
### Response:
#vtb
def Backup(self, duration=0):
total = 0
duration_total = duration * 4
children = self.GetChildrenIndexes()
notes = 0
for voice in children:
v = self.GetChild(voice)
indexes = v.GetChildrenIndexes()
if len(indexes) > 1:
indexes.reverse()
for index in indexes:
notes += 1
note = v.GetChild(index)
if hasattr(note, "duration"):
total += note.duration
if total >= duration_total:
break
gap = [
v.GetChild(i).duration for i in range(
0,
self.index -
notes) if hasattr(
v.GetChild(i),
"duration")]
previous = 0
for item in gap:
if item == previous:
self.gap -= previous
item = item / 2
self.gap += item
previous = item
self.index -= notes
|
#vtb
def pickle_encode(session_dict):
"Returns the given session dictionary pickled and encoded as a string."
pickled = pickle.dumps(session_dict, pickle.HIGHEST_PROTOCOL)
return base64.encodestring(pickled + get_query_hash(pickled).encode())
|
Returns the given session dictionary pickled and encoded as a string.
|
### Input:
Returns the given session dictionary pickled and encoded as a string.
### Response:
#vtb
def pickle_encode(session_dict):
"Returns the given session dictionary pickled and encoded as a string."
pickled = pickle.dumps(session_dict, pickle.HIGHEST_PROTOCOL)
return base64.encodestring(pickled + get_query_hash(pickled).encode())
|
#vtb
def transform(self, y):
if isinstance(y, pd.DataFrame):
x = y.ix[:,0]
y = y.ix[:,1]
else:
x = y[:,0]
y = y[:,1]
if self.transform_type == :
return pd.DataFrame(np.add(x, y))
elif self.transform_type == :
return pd.DataFrame(np.subtract(x, y))
elif self.transform_type == :
return pd.DataFrame(np.multiply(x, y))
elif self.transform_type == :
return pd.DataFrame(np.divide(x, y))
elif self.transform_type == :
return pd.DataFrame(np.remainder(x, y))
elif self.transform_type == :
return pd.DataFrame(x**y)
|
Transform features per specified math function.
:param y:
:return:
|
### Input:
Transform features per specified math function.
:param y:
:return:
### Response:
#vtb
def transform(self, y):
if isinstance(y, pd.DataFrame):
x = y.ix[:,0]
y = y.ix[:,1]
else:
x = y[:,0]
y = y[:,1]
if self.transform_type == :
return pd.DataFrame(np.add(x, y))
elif self.transform_type == :
return pd.DataFrame(np.subtract(x, y))
elif self.transform_type == :
return pd.DataFrame(np.multiply(x, y))
elif self.transform_type == :
return pd.DataFrame(np.divide(x, y))
elif self.transform_type == :
return pd.DataFrame(np.remainder(x, y))
elif self.transform_type == :
return pd.DataFrame(x**y)
|
#vtb
def find_harpoon_options(self, configuration, args_dict):
d = lambda r: {} if r in (None, "", NotSpecified) else r
return MergedOptions.using(
dict(d(configuration.get()).items())
, dict(d(args_dict.get("harpoon")).items())
).as_dict()
|
Return us all the harpoon options
|
### Input:
Return us all the harpoon options
### Response:
#vtb
def find_harpoon_options(self, configuration, args_dict):
d = lambda r: {} if r in (None, "", NotSpecified) else r
return MergedOptions.using(
dict(d(configuration.get()).items())
, dict(d(args_dict.get("harpoon")).items())
).as_dict()
|
#vtb
def hash_file_contents(requirements_option: RequirementsOptions, path: Path) -> str:
return hashlib.sha256(path.read_bytes() + bytes(
requirements_option.name + arca.__version__, "utf-8"
)).hexdigest()
|
Returns a SHA256 hash of the contents of ``path`` combined with the Arca version.
|
### Input:
Returns a SHA256 hash of the contents of ``path`` combined with the Arca version.
### Response:
#vtb
def hash_file_contents(requirements_option: RequirementsOptions, path: Path) -> str:
return hashlib.sha256(path.read_bytes() + bytes(
requirements_option.name + arca.__version__, "utf-8"
)).hexdigest()
|
#vtb
def is_valid_camel(cls, input_string, strcmp=None, ignore=):
if not input_string:
return False
input_string = .join([c for c in input_string if c.isalpha()])
matches = cls._get_regex_search(input_string,
cls.REGEX_CAMEL.format(SEP=cls.REGEX_SEPARATORS),
match_index=0,
ignore=ignore)
if matches or input_string == strcmp:
if strcmp:
index = input_string.find(strcmp) - 1
is_camel = strcmp[0].isupper() and input_string[index].islower()
is_input = strcmp == input_string
is_start = index + 1 == 0
return is_camel or is_input or is_start
return True
elif len(input_string) == 1:
return True
return False
|
Checks to see if an input string is valid for use in camel casing
This assumes that all lowercase strings are not valid camel case situations and no camel string
can just be a capitalized word. Took ideas from here:
http://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
:param input_string: str, input word
:param strcmp: str, force detection on a substring just in case its undetectable
(e.g. part of a section of text that's all lowercase)
:param ignore: str, what kind of string to ignore in the regex search
:return: bool, whether it is valid or not
|
### Input:
Checks to see if an input string is valid for use in camel casing
This assumes that all lowercase strings are not valid camel case situations and no camel string
can just be a capitalized word. Took ideas from here:
http://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
:param input_string: str, input word
:param strcmp: str, force detection on a substring just in case its undetectable
(e.g. part of a section of text that's all lowercase)
:param ignore: str, what kind of string to ignore in the regex search
:return: bool, whether it is valid or not
### Response:
#vtb
def is_valid_camel(cls, input_string, strcmp=None, ignore=):
if not input_string:
return False
input_string = .join([c for c in input_string if c.isalpha()])
matches = cls._get_regex_search(input_string,
cls.REGEX_CAMEL.format(SEP=cls.REGEX_SEPARATORS),
match_index=0,
ignore=ignore)
if matches or input_string == strcmp:
if strcmp:
index = input_string.find(strcmp) - 1
is_camel = strcmp[0].isupper() and input_string[index].islower()
is_input = strcmp == input_string
is_start = index + 1 == 0
return is_camel or is_input or is_start
return True
elif len(input_string) == 1:
return True
return False
|
#vtb
def ring_is_planar(ring, r_atoms):
normals = []
for a in r_atoms:
adj = pybel.ob.OBAtomAtomIter(a.OBAtom)
n_coords = [pybel.Atom(neigh).coords for neigh in adj if ring.IsMember(neigh)]
vec1, vec2 = vector(a.coords, n_coords[0]), vector(a.coords, n_coords[1])
normals.append(np.cross(vec1, vec2))
for n1, n2 in itertools.product(normals, repeat=2):
arom_angle = vecangle(n1, n2)
if all([arom_angle > config.AROMATIC_PLANARITY, arom_angle < 180.0 - config.AROMATIC_PLANARITY]):
return False
return True
|
Given a set of ring atoms, check if the ring is sufficiently planar
to be considered aromatic
|
### Input:
Given a set of ring atoms, check if the ring is sufficiently planar
to be considered aromatic
### Response:
#vtb
def ring_is_planar(ring, r_atoms):
normals = []
for a in r_atoms:
adj = pybel.ob.OBAtomAtomIter(a.OBAtom)
n_coords = [pybel.Atom(neigh).coords for neigh in adj if ring.IsMember(neigh)]
vec1, vec2 = vector(a.coords, n_coords[0]), vector(a.coords, n_coords[1])
normals.append(np.cross(vec1, vec2))
for n1, n2 in itertools.product(normals, repeat=2):
arom_angle = vecangle(n1, n2)
if all([arom_angle > config.AROMATIC_PLANARITY, arom_angle < 180.0 - config.AROMATIC_PLANARITY]):
return False
return True
|
#vtb
def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
w_data, w_supp_data = separate_signals(data, w_idx)
fft_w = np.fft.fftshift(fft.fft(w_data))
fft_w_supp = np.fft.fftshift(fft.fft(w_supp_data))
freqs_w = np.linspace(-sampling_rate/2.0,
sampling_rate/2.0,
w_data.shape[-1])
bounds = [(None,None),
(0,None),
(0,None),
(-np.pi, np.pi),
(None,None),
(None, None)]
n_params = len(bounds)
params = np.zeros(fft_w.shape[:-1] + (n_params,))
for repeat in range(w_data.shape[0]):
for echo in range(w_data.shape[1]):
for coil in range(w_data.shape[2]):
sig = fft_w[repeat, echo, coil]
params[repeat, echo, coil] = _do_lorentzian_fit(freqs_w,
sig, bounds)
area_w = params[..., 1]
s = np.mean(area_w.reshape(-1, area_w.shape[-1]), 0)
n = np.var(area_w.reshape(-1, area_w.shape[-1]), 0)
amp_weight = s/n
amp_weight = amp_weight / np.sum(amp_weight)
phase_param = params[..., 3]
zero_phi_w = np.mean(phase_param.reshape(-1, phase_param.shape[-1]),0)
weight = amp_weight * np.exp(-1j * zero_phi_w)
na = np.newaxis
weighted_w_data = np.mean(np.fft.ifft(np.fft.fftshift(
weight[na, na, :, na] * fft_w)), coil_dim)
weighted_w_supp_data = np.mean(np.fft.ifft(np.fft.fftshift(
weight[na, na, : ,na] * fft_w_supp)) , coil_dim)
def normalize_this(x):
return x * (x.shape[-1] / (np.sum(np.abs(x))))
weighted_w_data = normalize_this(weighted_w_data)
weighted_w_supp_data = normalize_this(weighted_w_supp_data)
return weighted_w_data.squeeze(), weighted_w_supp_data.squeeze()
|
Combine data across coils based on the amplitude of the water peak,
according to:
.. math::
X = \sum_{i}{w_i S_i}
Where X is the resulting combined signal, $S_i$ are the individual coil
signals and $w_i$ are calculated as:
.. math::
w_i = mean(S_i) / var (S_i)
following [Hall2013]_. In addition, we apply a phase-correction, so that
all the phases of the signals from each coil are 0
Parameters
----------
data : float array
The data as it comes from the scanner, with shape (transients, echos,
coils, time points)
w_idx : list
The indices to the non-water-suppressed transients. Per default we take
the 2nd-4th transients. We dump the first one, because it seems to be
quite different than the rest of them...
coil_dim : int
The dimension on which the coils are represented. Default: 2
sampling rate : float
The sampling rate in Hz. Default : 5000.
References
----------
.. [Hall2013] Emma L. Hall, Mary C. Stephenson, Darren Price, Peter
G. Morris (2013). Methodology for improved detection of low
concentration metabolites in MRS: Optimised combination of signals from
multi-element coil arrays. Neuroimage 86: 35-42.
.. [Wald1997] Wald, L. and Wright, S. (1997). Theory and application of
array coils in MR spectroscopy. NMR in Biomedicine, 10: 394-410.
.. [Keeler2005] Keeler, J (2005). Understanding NMR spectroscopy, second
edition. Wiley (West Sussex, UK).
|
### Input:
Combine data across coils based on the amplitude of the water peak,
according to:
.. math::
X = \sum_{i}{w_i S_i}
Where X is the resulting combined signal, $S_i$ are the individual coil
signals and $w_i$ are calculated as:
.. math::
w_i = mean(S_i) / var (S_i)
following [Hall2013]_. In addition, we apply a phase-correction, so that
all the phases of the signals from each coil are 0
Parameters
----------
data : float array
The data as it comes from the scanner, with shape (transients, echos,
coils, time points)
w_idx : list
The indices to the non-water-suppressed transients. Per default we take
the 2nd-4th transients. We dump the first one, because it seems to be
quite different than the rest of them...
coil_dim : int
The dimension on which the coils are represented. Default: 2
sampling rate : float
The sampling rate in Hz. Default : 5000.
References
----------
.. [Hall2013] Emma L. Hall, Mary C. Stephenson, Darren Price, Peter
G. Morris (2013). Methodology for improved detection of low
concentration metabolites in MRS: Optimised combination of signals from
multi-element coil arrays. Neuroimage 86: 35-42.
.. [Wald1997] Wald, L. and Wright, S. (1997). Theory and application of
array coils in MR spectroscopy. NMR in Biomedicine, 10: 394-410.
.. [Keeler2005] Keeler, J (2005). Understanding NMR spectroscopy, second
edition. Wiley (West Sussex, UK).
### Response:
#vtb
def coil_combine(data, w_idx=[1,2,3], coil_dim=2, sampling_rate=5000.):
w_data, w_supp_data = separate_signals(data, w_idx)
fft_w = np.fft.fftshift(fft.fft(w_data))
fft_w_supp = np.fft.fftshift(fft.fft(w_supp_data))
freqs_w = np.linspace(-sampling_rate/2.0,
sampling_rate/2.0,
w_data.shape[-1])
bounds = [(None,None),
(0,None),
(0,None),
(-np.pi, np.pi),
(None,None),
(None, None)]
n_params = len(bounds)
params = np.zeros(fft_w.shape[:-1] + (n_params,))
for repeat in range(w_data.shape[0]):
for echo in range(w_data.shape[1]):
for coil in range(w_data.shape[2]):
sig = fft_w[repeat, echo, coil]
params[repeat, echo, coil] = _do_lorentzian_fit(freqs_w,
sig, bounds)
area_w = params[..., 1]
s = np.mean(area_w.reshape(-1, area_w.shape[-1]), 0)
n = np.var(area_w.reshape(-1, area_w.shape[-1]), 0)
amp_weight = s/n
amp_weight = amp_weight / np.sum(amp_weight)
phase_param = params[..., 3]
zero_phi_w = np.mean(phase_param.reshape(-1, phase_param.shape[-1]),0)
weight = amp_weight * np.exp(-1j * zero_phi_w)
na = np.newaxis
weighted_w_data = np.mean(np.fft.ifft(np.fft.fftshift(
weight[na, na, :, na] * fft_w)), coil_dim)
weighted_w_supp_data = np.mean(np.fft.ifft(np.fft.fftshift(
weight[na, na, : ,na] * fft_w_supp)) , coil_dim)
def normalize_this(x):
return x * (x.shape[-1] / (np.sum(np.abs(x))))
weighted_w_data = normalize_this(weighted_w_data)
weighted_w_supp_data = normalize_this(weighted_w_supp_data)
return weighted_w_data.squeeze(), weighted_w_supp_data.squeeze()
|
#vtb
def GetSOAPHeaders(self, create_method):
header = create_method(self._SOAP_HEADER_CLASS)
header.networkCode = self._ad_manager_client.network_code
header.applicationName = .join([
self._ad_manager_client.application_name,
googleads.common.GenerateLibSig(self._PRODUCT_SIG)])
return header
|
Returns the SOAP headers required for request authorization.
Args:
create_method: The SOAP library specific method used to instantiate SOAP
objects.
Returns:
A SOAP object containing the headers.
|
### Input:
Returns the SOAP headers required for request authorization.
Args:
create_method: The SOAP library specific method used to instantiate SOAP
objects.
Returns:
A SOAP object containing the headers.
### Response:
#vtb
def GetSOAPHeaders(self, create_method):
header = create_method(self._SOAP_HEADER_CLASS)
header.networkCode = self._ad_manager_client.network_code
header.applicationName = .join([
self._ad_manager_client.application_name,
googleads.common.GenerateLibSig(self._PRODUCT_SIG)])
return header
|
#vtb
def immutable_worker(worker, state, pre_state, created):
return WorkerData._make(chain(
(getattr(worker, f) for f in WORKER_OWN_FIELDS),
(state, pre_state, created),
(worker.heartbeats[-1] if worker.heartbeats else None,),
))
|
Converts to an immutable slots class to handle internally.
|
### Input:
Converts to an immutable slots class to handle internally.
### Response:
#vtb
def immutable_worker(worker, state, pre_state, created):
return WorkerData._make(chain(
(getattr(worker, f) for f in WORKER_OWN_FIELDS),
(state, pre_state, created),
(worker.heartbeats[-1] if worker.heartbeats else None,),
))
|
#vtb
def _get_asam_configuration(driver_url=):
asam_config = __opts__[] if in __opts__ else None
if asam_config:
try:
for asam_server, service_config in six.iteritems(asam_config):
username = service_config.get(, None)
password = service_config.get(, None)
protocol = service_config.get(, )
port = service_config.get(, 3451)
if not username or not password:
log.error(
, asam_server
)
return False
ret = {
: "{0}://{1}:{2}/config/PlatformEdit.html".format(protocol, asam_server, port),
: "{0}://{1}:{2}/config/PlatformConfig.html".format(protocol, asam_server, port),
: "{0}://{1}:{2}/config/PlatformSetEdit.html".format(protocol, asam_server, port),
: "{0}://{1}:{2}/config/PlatformSetConfig.html".format(protocol, asam_server, port),
: username,
: password
}
if (not driver_url) or (driver_url == asam_server):
return ret
except Exception as exc:
log.error(, exc)
return False
if driver_url:
log.error(
, driver_url
)
return False
return False
|
Return the configuration read from the master configuration
file or directory
|
### Input:
Return the configuration read from the master configuration
file or directory
### Response:
#vtb
def _get_asam_configuration(driver_url=):
asam_config = __opts__[] if in __opts__ else None
if asam_config:
try:
for asam_server, service_config in six.iteritems(asam_config):
username = service_config.get(, None)
password = service_config.get(, None)
protocol = service_config.get(, )
port = service_config.get(, 3451)
if not username or not password:
log.error(
, asam_server
)
return False
ret = {
: "{0}://{1}:{2}/config/PlatformEdit.html".format(protocol, asam_server, port),
: "{0}://{1}:{2}/config/PlatformConfig.html".format(protocol, asam_server, port),
: "{0}://{1}:{2}/config/PlatformSetEdit.html".format(protocol, asam_server, port),
: "{0}://{1}:{2}/config/PlatformSetConfig.html".format(protocol, asam_server, port),
: username,
: password
}
if (not driver_url) or (driver_url == asam_server):
return ret
except Exception as exc:
log.error(, exc)
return False
if driver_url:
log.error(
, driver_url
)
return False
return False
|
#vtb
def _increment(self, n=1):
if self._cur_position >= self.num_tokens-1:
self._cur_positon = self.num_tokens - 1
self._finished = True
else:
self._cur_position += n
|
Move forward n tokens in the stream.
|
### Input:
Move forward n tokens in the stream.
### Response:
#vtb
def _increment(self, n=1):
if self._cur_position >= self.num_tokens-1:
self._cur_positon = self.num_tokens - 1
self._finished = True
else:
self._cur_position += n
|
#vtb
def _get_result_constructor(self):
if not self._values_list:
return self.model._construct_instance
elif self._flat_values_list:
key = self._only_fields[0]
return lambda row: row[key]
else:
return lambda row: [row[f] for f in self._only_fields]
|
Returns a function that will be used to instantiate query results
|
### Input:
Returns a function that will be used to instantiate query results
### Response:
#vtb
def _get_result_constructor(self):
if not self._values_list:
return self.model._construct_instance
elif self._flat_values_list:
key = self._only_fields[0]
return lambda row: row[key]
else:
return lambda row: [row[f] for f in self._only_fields]
|
#vtb
def save_diskspace(fname, reason, config):
if config["algorithm"].get("save_diskspace", False):
for ext in ["", ".bai"]:
if os.path.exists(fname + ext):
with open(fname + ext, "w") as out_handle:
out_handle.write("File removed to save disk space: %s" % reason)
|
Overwrite a file in place with a short message to save disk.
This keeps files as a sanity check on processes working, but saves
disk by replacing them with a short message.
|
### Input:
Overwrite a file in place with a short message to save disk.
This keeps files as a sanity check on processes working, but saves
disk by replacing them with a short message.
### Response:
#vtb
def save_diskspace(fname, reason, config):
if config["algorithm"].get("save_diskspace", False):
for ext in ["", ".bai"]:
if os.path.exists(fname + ext):
with open(fname + ext, "w") as out_handle:
out_handle.write("File removed to save disk space: %s" % reason)
|
#vtb
def cast(self, method, args={}, declare=None, retry=None,
retry_policy=None, type=None, exchange=None, **props):
retry = self.retry if retry is None else retry
body = {: self.name, : method, : args}
_retry_policy = self.retry_policy
if retry_policy:
_retry_policy = dict(_retry_policy, **retry_policy)
if type and type not in self.types:
raise ValueError(.format(type))
elif not type:
type = ACTOR_TYPE.DIRECT
props.setdefault(, self.routing_key)
props.setdefault(, self.serializer)
exchange = exchange or self.type_to_exchange[type]()
declare = (maybe_list(declare) or []) + [exchange]
with producers[self._connection].acquire(block=True) as producer:
return producer.publish(body, exchange=exchange, declare=declare,
retry=retry, retry_policy=retry_policy,
**props)
|
Send message to actor. Discarding replies.
|
### Input:
Send message to actor. Discarding replies.
### Response:
#vtb
def cast(self, method, args={}, declare=None, retry=None,
retry_policy=None, type=None, exchange=None, **props):
retry = self.retry if retry is None else retry
body = {: self.name, : method, : args}
_retry_policy = self.retry_policy
if retry_policy:
_retry_policy = dict(_retry_policy, **retry_policy)
if type and type not in self.types:
raise ValueError(.format(type))
elif not type:
type = ACTOR_TYPE.DIRECT
props.setdefault(, self.routing_key)
props.setdefault(, self.serializer)
exchange = exchange or self.type_to_exchange[type]()
declare = (maybe_list(declare) or []) + [exchange]
with producers[self._connection].acquire(block=True) as producer:
return producer.publish(body, exchange=exchange, declare=declare,
retry=retry, retry_policy=retry_policy,
**props)
|
#vtb
def pass_to_pipeline_if_article(
self,
response,
source_domain,
original_url,
rss_title=None
):
if self.helper.heuristics.is_article(response, original_url):
return self.pass_to_pipeline(
response, source_domain, rss_title=None)
|
Responsible for passing a NewscrawlerItem to the pipeline if the
response contains an article.
:param obj response: the scrapy response to work on
:param str source_domain: the response's domain as set for the crawler
:param str original_url: the url set in the json file
:param str rss_title: the title extracted by an rssCrawler
:return NewscrawlerItem: NewscrawlerItem to pass to the pipeline
|
### Input:
Responsible for passing a NewscrawlerItem to the pipeline if the
response contains an article.
:param obj response: the scrapy response to work on
:param str source_domain: the response's domain as set for the crawler
:param str original_url: the url set in the json file
:param str rss_title: the title extracted by an rssCrawler
:return NewscrawlerItem: NewscrawlerItem to pass to the pipeline
### Response:
#vtb
def pass_to_pipeline_if_article(
self,
response,
source_domain,
original_url,
rss_title=None
):
if self.helper.heuristics.is_article(response, original_url):
return self.pass_to_pipeline(
response, source_domain, rss_title=None)
|
#vtb
def update_service(self, service_id, **kwargs):
body = self._formdata(kwargs, FastlyService.FIELDS)
content = self._fetch("/service/%s" % service_id, method="PUT", body=body)
return FastlyService(self, content)
|
Update a service.
|
### Input:
Update a service.
### Response:
#vtb
def update_service(self, service_id, **kwargs):
body = self._formdata(kwargs, FastlyService.FIELDS)
content = self._fetch("/service/%s" % service_id, method="PUT", body=body)
return FastlyService(self, content)
|
#vtb
def get_template_name(self):
template = self.get_template()
page_templates = settings.get_page_templates()
for t in page_templates:
if t[0] == template:
return t[1]
return template
|
Get the template name of this page if defined or if a closer
parent has a defined template or
:data:`pages.settings.PAGE_DEFAULT_TEMPLATE` otherwise.
|
### Input:
Get the template name of this page if defined or if a closer
parent has a defined template or
:data:`pages.settings.PAGE_DEFAULT_TEMPLATE` otherwise.
### Response:
#vtb
def get_template_name(self):
template = self.get_template()
page_templates = settings.get_page_templates()
for t in page_templates:
if t[0] == template:
return t[1]
return template
|
#vtb
def score(package_path):
python_files = find_files(package_path, )
total_counter = Counter()
for python_file in python_files:
output = run_pylint(python_file)
counter = parse_pylint_output(output)
total_counter += counter
score_value = 0
for count, stat in enumerate(total_counter):
score_value += SCORING_VALUES[stat] * count
return score_value / 5
|
Runs pylint on a package and returns a score
Lower score is better
:param package_path: path of the package to score
:return: number of score
|
### Input:
Runs pylint on a package and returns a score
Lower score is better
:param package_path: path of the package to score
:return: number of score
### Response:
#vtb
def score(package_path):
python_files = find_files(package_path, )
total_counter = Counter()
for python_file in python_files:
output = run_pylint(python_file)
counter = parse_pylint_output(output)
total_counter += counter
score_value = 0
for count, stat in enumerate(total_counter):
score_value += SCORING_VALUES[stat] * count
return score_value / 5
|
#vtb
def replace_vcf_info(keyword, annotation, variant_line=None, variant_dict=None):
new_info = .format(keyword, annotation)
logger.debug("Replacing the variant information {0}".format(new_info))
fixed_variant = None
new_info_list = []
if variant_line:
logger.debug("Adding information to a variant line")
splitted_variant = variant_line.rstrip().split()
logger.debug("Adding information to splitted variant line")
old_info = splitted_variant[7]
if old_info == :
new_info_string = new_info
else:
splitted_info_string = old_info.split()
for info in splitted_info_string:
splitted_info_entry = info.split()
if splitted_info_entry[0] == keyword:
new_info_list.append(new_info)
else:
new_info_list.append(info)
new_info_string = .join(new_info_list)
splitted_variant[7] = new_info_string
fixed_variant = .join(splitted_variant)
elif variant_dict:
logger.debug("Adding information to a variant dict")
old_info = variant_dict[]
if old_info == :
variant_dict[] = new_info
else:
for info in old_info.split():
splitted_info_entry = info.split()
if splitted_info_entry[0] == keyword:
new_info_list.append(new_info)
else:
new_info_list.append(info)
new_info_string = .join(new_info_list)
variant_dict[] = new_info_string
fixed_variant = variant_dict
return fixed_variant
|
Replace the information of a info field of a vcf variant line or a
variant dict.
Arguments:
variant_line (str): A vcf formatted variant line
variant_dict (dict): A variant dictionary
keyword (str): The info field key
annotation (str): If the annotation is a key, value pair
this is the string that represents the value
Returns:
variant_line (str): A annotated variant line
|
### Input:
Replace the information of a info field of a vcf variant line or a
variant dict.
Arguments:
variant_line (str): A vcf formatted variant line
variant_dict (dict): A variant dictionary
keyword (str): The info field key
annotation (str): If the annotation is a key, value pair
this is the string that represents the value
Returns:
variant_line (str): A annotated variant line
### Response:
#vtb
def replace_vcf_info(keyword, annotation, variant_line=None, variant_dict=None):
new_info = .format(keyword, annotation)
logger.debug("Replacing the variant information {0}".format(new_info))
fixed_variant = None
new_info_list = []
if variant_line:
logger.debug("Adding information to a variant line")
splitted_variant = variant_line.rstrip().split()
logger.debug("Adding information to splitted variant line")
old_info = splitted_variant[7]
if old_info == :
new_info_string = new_info
else:
splitted_info_string = old_info.split()
for info in splitted_info_string:
splitted_info_entry = info.split()
if splitted_info_entry[0] == keyword:
new_info_list.append(new_info)
else:
new_info_list.append(info)
new_info_string = .join(new_info_list)
splitted_variant[7] = new_info_string
fixed_variant = .join(splitted_variant)
elif variant_dict:
logger.debug("Adding information to a variant dict")
old_info = variant_dict[]
if old_info == :
variant_dict[] = new_info
else:
for info in old_info.split():
splitted_info_entry = info.split()
if splitted_info_entry[0] == keyword:
new_info_list.append(new_info)
else:
new_info_list.append(info)
new_info_string = .join(new_info_list)
variant_dict[] = new_info_string
fixed_variant = variant_dict
return fixed_variant
|
#vtb
def primitive(self, primitive):
self.entry_number = primitive[]
self.item_hash = primitive[]
self.timestamp = primitive[]
|
Entry from Python primitive.
|
### Input:
Entry from Python primitive.
### Response:
#vtb
def primitive(self, primitive):
self.entry_number = primitive[]
self.item_hash = primitive[]
self.timestamp = primitive[]
|
#vtb
def interpolate_exe(self, testString):
testString = testString.strip()
if not (testString.startswith() and testString.endswith()):
return testString
newString = testString
testString = testString[2:-1]
testList = testString.split()
if len(testList) == 2:
if testList[0] == :
newString = distutils.spawn.find_executable(testList[1])
if not newString:
errmsg = "Cannot find exe %s in your path " %(testList[1])
errmsg += "and you specified ${which:%s}." %(testList[1])
raise ValueError(errmsg)
return newString
|
Replace testString with a path to an executable based on the format.
If this looks like
${which:lalapps_tmpltbank}
it will return the equivalent of which(lalapps_tmpltbank)
Otherwise it will return an unchanged string.
Parameters
-----------
testString : string
The input string
Returns
--------
newString : string
The output string.
|
### Input:
Replace testString with a path to an executable based on the format.
If this looks like
${which:lalapps_tmpltbank}
it will return the equivalent of which(lalapps_tmpltbank)
Otherwise it will return an unchanged string.
Parameters
-----------
testString : string
The input string
Returns
--------
newString : string
The output string.
### Response:
#vtb
def interpolate_exe(self, testString):
testString = testString.strip()
if not (testString.startswith() and testString.endswith()):
return testString
newString = testString
testString = testString[2:-1]
testList = testString.split()
if len(testList) == 2:
if testList[0] == :
newString = distutils.spawn.find_executable(testList[1])
if not newString:
errmsg = "Cannot find exe %s in your path " %(testList[1])
errmsg += "and you specified ${which:%s}." %(testList[1])
raise ValueError(errmsg)
return newString
|
#vtb
def client_get(self, url, **kwargs):
response = requests.get(self.make_url(url), headers=self.headers)
if not response.ok:
raise Exception(
.format(
status=response.status_code, reason=response.reason))
return response.json()
|
Send GET request with given url.
|
### Input:
Send GET request with given url.
### Response:
#vtb
def client_get(self, url, **kwargs):
response = requests.get(self.make_url(url), headers=self.headers)
if not response.ok:
raise Exception(
.format(
status=response.status_code, reason=response.reason))
return response.json()
|
#vtb
def fstab_present(name, fs_file, fs_vfstype, fs_mntops=,
fs_freq=0, fs_passno=0, mount_by=None,
config=, mount=True, match_on=):
UUID=xxx
ret = {
: name,
: False,
: {},
: [],
}
if fs_mntops == :
if __grains__[] in [, ]:
fs_mntops =
elif __grains__[] == :
fs_mntops =
if config == :
if __grains__[] in [, ]:
config =
elif __grains__[] == :
config =
if not fs_file == :
fs_file = fs_file.rstrip()
fs_spec = _convert_to(name, mount_by)
if not fs_spec:
msg =
ret[].append(msg.format(name, mount_by))
return ret
if __opts__[]:
if __grains__[] in [, ]:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
config=config,
test=True)
elif __grains__[] == :
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
mount=mount,
config=config,
test=True,
match_on=match_on)
else:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
dump=fs_freq,
pass_num=fs_passno,
config=config,
test=True,
match_on=match_on)
ret[] = None
if out == :
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
msg =
ret[].append(msg.format(fs_file, config))
else:
ret[] = False
msg =
ret[].append(msg.format(fs_file, config, out))
return ret
if __grains__[] in [, ]:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
config=config)
elif __grains__[] == :
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
mount=mount,
config=config,
match_on=match_on)
else:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
dump=fs_freq,
pass_num=fs_passno,
config=config,
match_on=match_on)
ret[] = True
if out == :
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
ret[][] = out
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
ret[][] = out
msg =
ret[].append(msg.format(fs_file, config))
else:
ret[] = False
msg =
ret[].append(msg.format(fs_file, config, out))
return ret
|
Makes sure that a fstab mount point is pressent.
name
The name of block device. Can be any valid fs_spec value.
fs_file
Mount point (target) for the filesystem.
fs_vfstype
The type of the filesystem (e.g. ext4, xfs, btrfs, ...)
fs_mntops
The mount options associated with the filesystem. Default is
``defaults``.
fs_freq
Field is used by dump to determine which fs need to be
dumped. Default is ``0``
fs_passno
Field is used by fsck to determine the order in which
filesystem checks are done at boot time. Default is ``0``
mount_by
Select the final value for fs_spec. Can be [``None``,
``device``, ``label``, ``uuid``, ``partlabel``,
``partuuid``]. If ``None``, the value for fs_spect will be the
parameter ``name``, in other case will search the correct
value based on the device name. For example, for ``uuid``, the
value for fs_spec will be of type 'UUID=xxx' instead of the
device name set in ``name``.
config
Place where the fstab file lives. Default is ``/etc/fstab``
mount
Set if the mount should be mounted immediately. Default is
``True``
match_on
A name or list of fstab properties on which this state should
be applied. Default is ``auto``, a special value indicating
to guess based on fstype. In general, ``auto`` matches on
name for recognized special devices and device otherwise.
|
### Input:
Makes sure that a fstab mount point is pressent.
name
The name of block device. Can be any valid fs_spec value.
fs_file
Mount point (target) for the filesystem.
fs_vfstype
The type of the filesystem (e.g. ext4, xfs, btrfs, ...)
fs_mntops
The mount options associated with the filesystem. Default is
``defaults``.
fs_freq
Field is used by dump to determine which fs need to be
dumped. Default is ``0``
fs_passno
Field is used by fsck to determine the order in which
filesystem checks are done at boot time. Default is ``0``
mount_by
Select the final value for fs_spec. Can be [``None``,
``device``, ``label``, ``uuid``, ``partlabel``,
``partuuid``]. If ``None``, the value for fs_spect will be the
parameter ``name``, in other case will search the correct
value based on the device name. For example, for ``uuid``, the
value for fs_spec will be of type 'UUID=xxx' instead of the
device name set in ``name``.
config
Place where the fstab file lives. Default is ``/etc/fstab``
mount
Set if the mount should be mounted immediately. Default is
``True``
match_on
A name or list of fstab properties on which this state should
be applied. Default is ``auto``, a special value indicating
to guess based on fstype. In general, ``auto`` matches on
name for recognized special devices and device otherwise.
### Response:
#vtb
def fstab_present(name, fs_file, fs_vfstype, fs_mntops=,
fs_freq=0, fs_passno=0, mount_by=None,
config=, mount=True, match_on=):
UUID=xxx
ret = {
: name,
: False,
: {},
: [],
}
if fs_mntops == :
if __grains__[] in [, ]:
fs_mntops =
elif __grains__[] == :
fs_mntops =
if config == :
if __grains__[] in [, ]:
config =
elif __grains__[] == :
config =
if not fs_file == :
fs_file = fs_file.rstrip()
fs_spec = _convert_to(name, mount_by)
if not fs_spec:
msg =
ret[].append(msg.format(name, mount_by))
return ret
if __opts__[]:
if __grains__[] in [, ]:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
config=config,
test=True)
elif __grains__[] == :
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
mount=mount,
config=config,
test=True,
match_on=match_on)
else:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
dump=fs_freq,
pass_num=fs_passno,
config=config,
test=True,
match_on=match_on)
ret[] = None
if out == :
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
msg =
ret[].append(msg.format(fs_file, config))
else:
ret[] = False
msg =
ret[].append(msg.format(fs_file, config, out))
return ret
if __grains__[] in [, ]:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
config=config)
elif __grains__[] == :
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
mount=mount,
config=config,
match_on=match_on)
else:
out = __salt__[](name=fs_file,
device=fs_spec,
fstype=fs_vfstype,
opts=fs_mntops,
dump=fs_freq,
pass_num=fs_passno,
config=config,
match_on=match_on)
ret[] = True
if out == :
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
ret[][] = out
msg =
ret[].append(msg.format(fs_file, config))
elif out == :
ret[][] = out
msg =
ret[].append(msg.format(fs_file, config))
else:
ret[] = False
msg =
ret[].append(msg.format(fs_file, config, out))
return ret
|
#vtb
def do_PROPPATCH(self, environ, start_response):
path = environ["PATH_INFO"]
res = self._davProvider.get_resource_inst(path, environ)
environ.setdefault("HTTP_DEPTH", "0")
if environ["HTTP_DEPTH"] != "0":
self._fail(HTTP_BAD_REQUEST, "Depth must be .")
if res is None:
self._fail(HTTP_NOT_FOUND)
self._evaluate_if_headers(res, environ)
self._check_write_permission(res, "0", environ)
requestEL = util.parse_xml_body(environ)
if requestEL.tag != "{DAV:}propertyupdate":
self._fail(HTTP_BAD_REQUEST)
propupdatelist = []
for ppnode in requestEL:
propupdatemethod = None
if ppnode.tag == "{DAV:}remove":
propupdatemethod = "remove"
elif ppnode.tag == "{DAV:}set":
propupdatemethod = "set"
else:
self._fail(
HTTP_BAD_REQUEST, "Unknown tag (expected or )."
)
for propnode in ppnode:
if propnode.tag != "{DAV:}prop":
self._fail(HTTP_BAD_REQUEST, "Unknown tag (expected ).")
for propertynode in propnode:
propvalue = None
if propupdatemethod == "remove":
propvalue = None
if len(propertynode) > 0:
self._fail(
HTTP_BAD_REQUEST,
"prop element must be empty for .",
)
else:
propvalue = propertynode
propupdatelist.append((propertynode.tag, propvalue))
successflag = True
writeresultlist = []
for (name, propvalue) in propupdatelist:
try:
res.set_property_value(name, propvalue, dry_run=True)
except Exception as e:
writeresult = as_DAVError(e)
else:
writeresult = "200 OK"
writeresultlist.append((name, writeresult))
successflag = successflag and writeresult == "200 OK"
propResponseList = []
responsedescription = []
if not successflag:
for (name, result) in writeresultlist:
if result == "200 OK":
result = DAVError(HTTP_FAILED_DEPENDENCY)
elif isinstance(result, DAVError):
responsedescription.append(result.get_user_info())
propResponseList.append((name, result))
else:
for (name, propvalue) in propupdatelist:
try:
res.set_property_value(name, propvalue, dry_run=False)
propResponseList.append((name, None))
except Exception as e:
e = as_DAVError(e)
propResponseList.append((name, e))
responsedescription.append(e.get_user_info())
multistatusEL = xml_tools.make_multistatus_el()
href = res.get_href()
util.add_property_response(multistatusEL, href, propResponseList)
if responsedescription:
etree.SubElement(
multistatusEL, "{DAV:}responsedescription"
).text = "\n".join(responsedescription)
return util.send_multi_status_response(environ, start_response, multistatusEL)
|
Handle PROPPATCH request to set or remove a property.
@see http://www.webdav.org/specs/rfc4918.html#METHOD_PROPPATCH
|
### Input:
Handle PROPPATCH request to set or remove a property.
@see http://www.webdav.org/specs/rfc4918.html#METHOD_PROPPATCH
### Response:
#vtb
def do_PROPPATCH(self, environ, start_response):
path = environ["PATH_INFO"]
res = self._davProvider.get_resource_inst(path, environ)
environ.setdefault("HTTP_DEPTH", "0")
if environ["HTTP_DEPTH"] != "0":
self._fail(HTTP_BAD_REQUEST, "Depth must be .")
if res is None:
self._fail(HTTP_NOT_FOUND)
self._evaluate_if_headers(res, environ)
self._check_write_permission(res, "0", environ)
requestEL = util.parse_xml_body(environ)
if requestEL.tag != "{DAV:}propertyupdate":
self._fail(HTTP_BAD_REQUEST)
propupdatelist = []
for ppnode in requestEL:
propupdatemethod = None
if ppnode.tag == "{DAV:}remove":
propupdatemethod = "remove"
elif ppnode.tag == "{DAV:}set":
propupdatemethod = "set"
else:
self._fail(
HTTP_BAD_REQUEST, "Unknown tag (expected or )."
)
for propnode in ppnode:
if propnode.tag != "{DAV:}prop":
self._fail(HTTP_BAD_REQUEST, "Unknown tag (expected ).")
for propertynode in propnode:
propvalue = None
if propupdatemethod == "remove":
propvalue = None
if len(propertynode) > 0:
self._fail(
HTTP_BAD_REQUEST,
"prop element must be empty for .",
)
else:
propvalue = propertynode
propupdatelist.append((propertynode.tag, propvalue))
successflag = True
writeresultlist = []
for (name, propvalue) in propupdatelist:
try:
res.set_property_value(name, propvalue, dry_run=True)
except Exception as e:
writeresult = as_DAVError(e)
else:
writeresult = "200 OK"
writeresultlist.append((name, writeresult))
successflag = successflag and writeresult == "200 OK"
propResponseList = []
responsedescription = []
if not successflag:
for (name, result) in writeresultlist:
if result == "200 OK":
result = DAVError(HTTP_FAILED_DEPENDENCY)
elif isinstance(result, DAVError):
responsedescription.append(result.get_user_info())
propResponseList.append((name, result))
else:
for (name, propvalue) in propupdatelist:
try:
res.set_property_value(name, propvalue, dry_run=False)
propResponseList.append((name, None))
except Exception as e:
e = as_DAVError(e)
propResponseList.append((name, e))
responsedescription.append(e.get_user_info())
multistatusEL = xml_tools.make_multistatus_el()
href = res.get_href()
util.add_property_response(multistatusEL, href, propResponseList)
if responsedescription:
etree.SubElement(
multistatusEL, "{DAV:}responsedescription"
).text = "\n".join(responsedescription)
return util.send_multi_status_response(environ, start_response, multistatusEL)
|
#vtb
def _add_image_part(self, image):
partname = self._next_image_partname(image.ext)
image_part = ImagePart.from_image(image, partname)
self.append(image_part)
return image_part
|
Return an |ImagePart| instance newly created from image and appended
to the collection.
|
### Input:
Return an |ImagePart| instance newly created from image and appended
to the collection.
### Response:
#vtb
def _add_image_part(self, image):
partname = self._next_image_partname(image.ext)
image_part = ImagePart.from_image(image, partname)
self.append(image_part)
return image_part
|
#vtb
def generate_ucsm_handle(hostname, username, password):
ucs_handle = UcsHandle()
try:
success = ucs_handle.Login(hostname, username, password)
except UcsException as e:
print("Cisco client exception %(msg)s" % (e.message))
raise exception.UcsConnectionError(message=e.message)
return success, ucs_handle
|
Creates UCS Manager handle object and establishes a session with
UCS Manager.
:param hostname: UCS Manager hostname or IP-address
:param username: Username to login to UCS Manager
:param password: Login user password
:raises UcsConnectionError: In case of error.
|
### Input:
Creates UCS Manager handle object and establishes a session with
UCS Manager.
:param hostname: UCS Manager hostname or IP-address
:param username: Username to login to UCS Manager
:param password: Login user password
:raises UcsConnectionError: In case of error.
### Response:
#vtb
def generate_ucsm_handle(hostname, username, password):
ucs_handle = UcsHandle()
try:
success = ucs_handle.Login(hostname, username, password)
except UcsException as e:
print("Cisco client exception %(msg)s" % (e.message))
raise exception.UcsConnectionError(message=e.message)
return success, ucs_handle
|
#vtb
def recursive_cov(self, cov, length, mean, chain, scaling=1, epsilon=0):
r
n = length + len(chain)
k = length
new_mean = self.recursive_mean(mean, length, chain)
t0 = k * np.outer(mean, mean)
t1 = np.dot(chain.T, chain)
t2 = n * np.outer(new_mean, new_mean)
t3 = epsilon * np.eye(cov.shape[0])
new_cov = (
k - 1) / (
n - 1.) * cov + scaling / (
n - 1.) * (
t0 + t1 - t2 + t3)
return new_cov, new_mean
|
r"""Compute the covariance recursively.
Return the new covariance and the new mean.
.. math::
C_k & = \frac{1}{k-1} (\sum_{i=1}^k x_i x_i^T - k\bar{x_k}\bar{x_k}^T)
C_n & = \frac{1}{n-1} (\sum_{i=1}^k x_i x_i^T + \sum_{i=k+1}^n x_i x_i^T - n\bar{x_n}\bar{x_n}^T)
& = \frac{1}{n-1} ((k-1)C_k + k\bar{x_k}\bar{x_k}^T + \sum_{i=k+1}^n x_i x_i^T - n\bar{x_n}\bar{x_n}^T)
:Parameters:
- cov : matrix
Previous covariance matrix.
- length : int
Length of chain used to compute the previous covariance.
- mean : array
Previous mean.
- chain : array
Sample used to update covariance.
- scaling : float
Scaling parameter
- epsilon : float
Set to a small value to avoid singular matrices.
|
### Input:
r"""Compute the covariance recursively.
Return the new covariance and the new mean.
.. math::
C_k & = \frac{1}{k-1} (\sum_{i=1}^k x_i x_i^T - k\bar{x_k}\bar{x_k}^T)
C_n & = \frac{1}{n-1} (\sum_{i=1}^k x_i x_i^T + \sum_{i=k+1}^n x_i x_i^T - n\bar{x_n}\bar{x_n}^T)
& = \frac{1}{n-1} ((k-1)C_k + k\bar{x_k}\bar{x_k}^T + \sum_{i=k+1}^n x_i x_i^T - n\bar{x_n}\bar{x_n}^T)
:Parameters:
- cov : matrix
Previous covariance matrix.
- length : int
Length of chain used to compute the previous covariance.
- mean : array
Previous mean.
- chain : array
Sample used to update covariance.
- scaling : float
Scaling parameter
- epsilon : float
Set to a small value to avoid singular matrices.
### Response:
#vtb
def recursive_cov(self, cov, length, mean, chain, scaling=1, epsilon=0):
r
n = length + len(chain)
k = length
new_mean = self.recursive_mean(mean, length, chain)
t0 = k * np.outer(mean, mean)
t1 = np.dot(chain.T, chain)
t2 = n * np.outer(new_mean, new_mean)
t3 = epsilon * np.eye(cov.shape[0])
new_cov = (
k - 1) / (
n - 1.) * cov + scaling / (
n - 1.) * (
t0 + t1 - t2 + t3)
return new_cov, new_mean
|
#vtb
def get_next_step(self):
if self.parent.is_selected_layer_keywordless:
self.parent.parent_step = self
self.parent.existing_keywords = None
self.parent.set_mode_label_to_keywords_creation()
new_step = self.parent.step_kw_purpose
else:
if layers_intersect(self.parent.hazard_layer,
self.parent.exposure_layer):
new_step = self.parent.step_fc_agglayer_origin
else:
new_step = self.parent.step_fc_disjoint_layers
return new_step
|
Find the proper step when user clicks the Next button.
:returns: The step to be switched to
:rtype: WizardStep instance or None
|
### Input:
Find the proper step when user clicks the Next button.
:returns: The step to be switched to
:rtype: WizardStep instance or None
### Response:
#vtb
def get_next_step(self):
if self.parent.is_selected_layer_keywordless:
self.parent.parent_step = self
self.parent.existing_keywords = None
self.parent.set_mode_label_to_keywords_creation()
new_step = self.parent.step_kw_purpose
else:
if layers_intersect(self.parent.hazard_layer,
self.parent.exposure_layer):
new_step = self.parent.step_fc_agglayer_origin
else:
new_step = self.parent.step_fc_disjoint_layers
return new_step
|
#vtb
def printer(self, message, color_level=):
if self.job_args.get():
print(cloud_utils.return_colorized(msg=message, color=color_level))
else:
print(message)
|
Print Messages and Log it.
:param message: item to print to screen
|
### Input:
Print Messages and Log it.
:param message: item to print to screen
### Response:
#vtb
def printer(self, message, color_level=):
if self.job_args.get():
print(cloud_utils.return_colorized(msg=message, color=color_level))
else:
print(message)
|
#vtb
def _get_col_dimstr(tdim, is_string=False):
dimstr =
if tdim is None:
dimstr =
else:
if is_string:
if len(tdim) > 1:
dimstr = [str(d) for d in tdim[1:]]
else:
if len(tdim) > 1 or tdim[0] > 1:
dimstr = [str(d) for d in tdim]
if dimstr != :
dimstr = .join(dimstr)
dimstr = % dimstr
return dimstr
|
not for variable length
|
### Input:
not for variable length
### Response:
#vtb
def _get_col_dimstr(tdim, is_string=False):
dimstr =
if tdim is None:
dimstr =
else:
if is_string:
if len(tdim) > 1:
dimstr = [str(d) for d in tdim[1:]]
else:
if len(tdim) > 1 or tdim[0] > 1:
dimstr = [str(d) for d in tdim]
if dimstr != :
dimstr = .join(dimstr)
dimstr = % dimstr
return dimstr
|
#vtb
def show_disk(name=None, kwargs=None, call=None):
if not kwargs or not in kwargs:
log.error(
)
return False
conn = get_conn()
return _expand_disk(conn.ex_get_volume(kwargs[]))
|
Show the details of an existing disk.
CLI Example:
.. code-block:: bash
salt-cloud -a show_disk myinstance disk_name=mydisk
salt-cloud -f show_disk gce disk_name=mydisk
|
### Input:
Show the details of an existing disk.
CLI Example:
.. code-block:: bash
salt-cloud -a show_disk myinstance disk_name=mydisk
salt-cloud -f show_disk gce disk_name=mydisk
### Response:
#vtb
def show_disk(name=None, kwargs=None, call=None):
if not kwargs or not in kwargs:
log.error(
)
return False
conn = get_conn()
return _expand_disk(conn.ex_get_volume(kwargs[]))
|
#vtb
def get_environment(self):
environment = {}
cpu_cmd =
mem_cmd =
temp_cmd =
output = self._send_command(cpu_cmd)
environment.setdefault(, {})
environment[][0] = {}
environment[][0][] = 0.0
for line in output.splitlines():
if in line:
cpu_regex = r
match = re.search(cpu_regex, line)
environment[][0][] = float(match.group(1))
break
output = self._send_command(mem_cmd)
for line in output.splitlines():
if in line:
_, _, _, proc_used_mem, proc_free_mem = line.split()[:5]
elif in line or in line:
_, _, _, io_used_mem, io_free_mem = line.split()[:5]
used_mem = int(proc_used_mem) + int(io_used_mem)
free_mem = int(proc_free_mem) + int(io_free_mem)
environment.setdefault(, {})
environment[][] = used_mem
environment[][] = free_mem
environment.setdefault(, {})
output = self._send_command(temp_cmd)
if not in output:
for line in output.splitlines():
if in line:
system_temp = float(line.split()[1].split()[0])
elif in line:
system_temp_alert = float(line.split()[1].split()[0])
elif in line:
system_temp_crit = float(line.split()[1].split()[0])
env_value = {: system_temp >= system_temp_alert,
: system_temp >= system_temp_crit, : system_temp}
environment[][] = env_value
else:
env_value = {: False, : False, : -1.0}
environment[][] = env_value
environment.setdefault(, {})
environment[][] = {: True, : -1.0, : -1.0}
environment.setdefault(, {})
environment[][] = {: True}
return environment
|
Get environment facts.
power and fan are currently not implemented
cpu is using 1-minute average
cpu hard-coded to cpu0 (i.e. only a single CPU)
|
### Input:
Get environment facts.
power and fan are currently not implemented
cpu is using 1-minute average
cpu hard-coded to cpu0 (i.e. only a single CPU)
### Response:
#vtb
def get_environment(self):
environment = {}
cpu_cmd =
mem_cmd =
temp_cmd =
output = self._send_command(cpu_cmd)
environment.setdefault(, {})
environment[][0] = {}
environment[][0][] = 0.0
for line in output.splitlines():
if in line:
cpu_regex = r
match = re.search(cpu_regex, line)
environment[][0][] = float(match.group(1))
break
output = self._send_command(mem_cmd)
for line in output.splitlines():
if in line:
_, _, _, proc_used_mem, proc_free_mem = line.split()[:5]
elif in line or in line:
_, _, _, io_used_mem, io_free_mem = line.split()[:5]
used_mem = int(proc_used_mem) + int(io_used_mem)
free_mem = int(proc_free_mem) + int(io_free_mem)
environment.setdefault(, {})
environment[][] = used_mem
environment[][] = free_mem
environment.setdefault(, {})
output = self._send_command(temp_cmd)
if not in output:
for line in output.splitlines():
if in line:
system_temp = float(line.split()[1].split()[0])
elif in line:
system_temp_alert = float(line.split()[1].split()[0])
elif in line:
system_temp_crit = float(line.split()[1].split()[0])
env_value = {: system_temp >= system_temp_alert,
: system_temp >= system_temp_crit, : system_temp}
environment[][] = env_value
else:
env_value = {: False, : False, : -1.0}
environment[][] = env_value
environment.setdefault(, {})
environment[][] = {: True, : -1.0, : -1.0}
environment.setdefault(, {})
environment[][] = {: True}
return environment
|
#vtb
def loadScopeGroupbyID(self, id, callback=None, errback=None):
import ns1.ipam
scope_group = ns1.ipam.Scopegroup(self.config, id=id)
return scope_group.load(callback=callback, errback=errback)
|
Load an existing Scope Group by ID into a high level Scope Group object
:param int id: id of an existing ScopeGroup
|
### Input:
Load an existing Scope Group by ID into a high level Scope Group object
:param int id: id of an existing ScopeGroup
### Response:
#vtb
def loadScopeGroupbyID(self, id, callback=None, errback=None):
import ns1.ipam
scope_group = ns1.ipam.Scopegroup(self.config, id=id)
return scope_group.load(callback=callback, errback=errback)
|
#vtb
def mean(self, values, axis=0, weights=None, dtype=None):
values = np.asarray(values)
if weights is None:
result = self.reduce(values, axis=axis, dtype=dtype)
shape = [1] * values.ndim
shape[axis] = self.groups
weights = self.count.reshape(shape)
else:
weights = np.asarray(weights)
result = self.reduce(values * weights, axis=axis, dtype=dtype)
weights = self.reduce(weights, axis=axis, dtype=dtype)
return self.unique, result / weights
|
compute the mean over each group
Parameters
----------
values : array_like, [keys, ...]
values to take average of per group
axis : int, optional
alternative reduction axis for values
weights : ndarray, [keys, ...], optional
weight to use for each value
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups
|
### Input:
compute the mean over each group
Parameters
----------
values : array_like, [keys, ...]
values to take average of per group
axis : int, optional
alternative reduction axis for values
weights : ndarray, [keys, ...], optional
weight to use for each value
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups
### Response:
#vtb
def mean(self, values, axis=0, weights=None, dtype=None):
values = np.asarray(values)
if weights is None:
result = self.reduce(values, axis=axis, dtype=dtype)
shape = [1] * values.ndim
shape[axis] = self.groups
weights = self.count.reshape(shape)
else:
weights = np.asarray(weights)
result = self.reduce(values * weights, axis=axis, dtype=dtype)
weights = self.reduce(weights, axis=axis, dtype=dtype)
return self.unique, result / weights
|
#vtb
def create(self, name, data):
return self._update( %
urlparse.quote(name.encode()), data,
, dump_json=False)
|
Create a Job Binary Internal.
:param str data: raw data of script text
|
### Input:
Create a Job Binary Internal.
:param str data: raw data of script text
### Response:
#vtb
def create(self, name, data):
return self._update( %
urlparse.quote(name.encode()), data,
, dump_json=False)
|
#vtb
def update(request, ident, stateless=False, **kwargs):
dash_app, app = DashApp.locate_item(ident, stateless)
request_body = json.loads(request.body.decode())
if app.use_dash_dispatch():
view_func = app.locate_endpoint_function()
import flask
with app.test_request_context():
flask.request._cached_json = (request_body, flask.request._cached_json[True])
resp = view_func()
else:
app_state = request.session.get("django_plotly_dash", dict())
arg_map = {: ident,
: dash_app,
: request.user,
: app_state}
resp = app.dispatch_with_args(request_body, arg_map)
request.session[] = app_state
dash_app.handle_current_state()
if str(resp) == :
return HttpResponse("")
try:
rdata = resp.data
rtype = resp.mimetype
except:
rdata = resp
rtype = "application/json"
return HttpResponse(rdata,
content_type=rtype)
|
Generate update json response
|
### Input:
Generate update json response
### Response:
#vtb
def update(request, ident, stateless=False, **kwargs):
dash_app, app = DashApp.locate_item(ident, stateless)
request_body = json.loads(request.body.decode())
if app.use_dash_dispatch():
view_func = app.locate_endpoint_function()
import flask
with app.test_request_context():
flask.request._cached_json = (request_body, flask.request._cached_json[True])
resp = view_func()
else:
app_state = request.session.get("django_plotly_dash", dict())
arg_map = {: ident,
: dash_app,
: request.user,
: app_state}
resp = app.dispatch_with_args(request_body, arg_map)
request.session[] = app_state
dash_app.handle_current_state()
if str(resp) == :
return HttpResponse("")
try:
rdata = resp.data
rtype = resp.mimetype
except:
rdata = resp
rtype = "application/json"
return HttpResponse(rdata,
content_type=rtype)
|
#vtb
def processes(self, processes):
if self._processes > 1:
self._pool.close()
self._pool.join()
self._pool = multiprocessing.Pool(processes)
else:
self._pool = None
self._logger.log(, .format(
processes
))
|
Set the number of concurrent processes the ABC will utilize for
fitness function evaluation; if <= 1, single process is used
Args:
processes (int): number of concurrent processes
|
### Input:
Set the number of concurrent processes the ABC will utilize for
fitness function evaluation; if <= 1, single process is used
Args:
processes (int): number of concurrent processes
### Response:
#vtb
def processes(self, processes):
if self._processes > 1:
self._pool.close()
self._pool.join()
self._pool = multiprocessing.Pool(processes)
else:
self._pool = None
self._logger.log(, .format(
processes
))
|
#vtb
def load_path(path, overrides=None, **kwargs):
f = open(path, )
content = .join(f.readlines())
f.close()
if not isinstance(content, str):
raise AssertionError("Expected content to be of type str but it is "+str(type(content)))
return load(content, **kwargs)
|
Convenience function for loading a YAML configuration from a file.
Parameters
----------
path : str
The path to the file to load on disk.
overrides : dict, optional
A dictionary containing overrides to apply. The location of
the override is specified in the key as a dot-delimited path
to the desired parameter, e.g. "model.corruptor.corruption_level".
Returns
-------
graph : dict or object
The dictionary or object (if the top-level element specified an
Python object to instantiate).
Notes
-----
Other keyword arguments are passed on to `yaml.load`.
|
### Input:
Convenience function for loading a YAML configuration from a file.
Parameters
----------
path : str
The path to the file to load on disk.
overrides : dict, optional
A dictionary containing overrides to apply. The location of
the override is specified in the key as a dot-delimited path
to the desired parameter, e.g. "model.corruptor.corruption_level".
Returns
-------
graph : dict or object
The dictionary or object (if the top-level element specified an
Python object to instantiate).
Notes
-----
Other keyword arguments are passed on to `yaml.load`.
### Response:
#vtb
def load_path(path, overrides=None, **kwargs):
f = open(path, )
content = .join(f.readlines())
f.close()
if not isinstance(content, str):
raise AssertionError("Expected content to be of type str but it is "+str(type(content)))
return load(content, **kwargs)
|
#vtb
def stop_app(self):
try:
if self._conn:
finally:
self.clear_host_port()
|
Overrides superclass.
|
### Input:
Overrides superclass.
### Response:
#vtb
def stop_app(self):
try:
if self._conn:
finally:
self.clear_host_port()
|
#vtb
def get_evidence_by_hash(self, evidence_hash: str) -> Optional[Evidence]:
return self.session.query(Evidence).filter(Evidence.sha512 == evidence_hash).one_or_none()
|
Look up an evidence by its hash.
|
### Input:
Look up an evidence by its hash.
### Response:
#vtb
def get_evidence_by_hash(self, evidence_hash: str) -> Optional[Evidence]:
return self.session.query(Evidence).filter(Evidence.sha512 == evidence_hash).one_or_none()
|
#vtb
def reduce_data_frame (df, chunk_slicers,
avg_cols=(),
uavg_cols=(),
minmax_cols=(),
nchunk_colname=,
uncert_prefix=,
min_points_per_chunk=3):
subds = [df.iloc[idx] for idx in chunk_slicers]
subds = [sd for sd in subds if sd.shape[0] >= min_points_per_chunk]
chunked = df.__class__ ({nchunk_colname: np.zeros (len (subds), dtype=np.int)})
uncert_col_name = lambda c: uncert_prefix + c
for i, subd in enumerate (subds):
label = chunked.index[i]
chunked.loc[label,nchunk_colname] = subd.shape[0]
for col in avg_cols:
chunked.loc[label,col] = subd[col].mean ()
for col in uavg_cols:
ucol = uncert_col_name (col)
v, u = weighted_mean (subd[col], subd[ucol])
chunked.loc[label,col] = v
chunked.loc[label,ucol] = u
for col in minmax_cols:
chunked.loc[label, +col] = subd[col].min ()
chunked.loc[label, +col] = subd[col].max ()
return chunked
|
Reduce" a DataFrame by collapsing rows in grouped chunks. Returns another
DataFrame with similar columns but fewer rows.
Arguments:
df
The input :class:`pandas.DataFrame`.
chunk_slicers
An iterable that returns values that are used to slice *df* with its
:meth:`pandas.DataFrame.iloc` indexer. An example value might be the
generator returned from :func:`slice_evenly_with_gaps`.
avg_cols
An iterable of names of columns that are to be reduced by taking the mean.
uavg_cols
An iterable of names of columns that are to be reduced by taking a
weighted mean.
minmax_cols
An iterable of names of columns that are to be reduced by reporting minimum
and maximum values.
nchunk_colname
The name of a column to create reporting the number of rows contributing
to each chunk.
uncert_prefix
The column name prefix for locating uncertainty estimates. By default, the
uncertainty on the column ``"temp"`` is given in the column ``"utemp"``.
min_points_per_chunk
Require at least this many rows in each chunk. Smaller chunks are discarded.
Returns a new :class:`pandas.DataFrame`.
|
### Input:
Reduce" a DataFrame by collapsing rows in grouped chunks. Returns another
DataFrame with similar columns but fewer rows.
Arguments:
df
The input :class:`pandas.DataFrame`.
chunk_slicers
An iterable that returns values that are used to slice *df* with its
:meth:`pandas.DataFrame.iloc` indexer. An example value might be the
generator returned from :func:`slice_evenly_with_gaps`.
avg_cols
An iterable of names of columns that are to be reduced by taking the mean.
uavg_cols
An iterable of names of columns that are to be reduced by taking a
weighted mean.
minmax_cols
An iterable of names of columns that are to be reduced by reporting minimum
and maximum values.
nchunk_colname
The name of a column to create reporting the number of rows contributing
to each chunk.
uncert_prefix
The column name prefix for locating uncertainty estimates. By default, the
uncertainty on the column ``"temp"`` is given in the column ``"utemp"``.
min_points_per_chunk
Require at least this many rows in each chunk. Smaller chunks are discarded.
Returns a new :class:`pandas.DataFrame`.
### Response:
#vtb
def reduce_data_frame (df, chunk_slicers,
avg_cols=(),
uavg_cols=(),
minmax_cols=(),
nchunk_colname=,
uncert_prefix=,
min_points_per_chunk=3):
subds = [df.iloc[idx] for idx in chunk_slicers]
subds = [sd for sd in subds if sd.shape[0] >= min_points_per_chunk]
chunked = df.__class__ ({nchunk_colname: np.zeros (len (subds), dtype=np.int)})
uncert_col_name = lambda c: uncert_prefix + c
for i, subd in enumerate (subds):
label = chunked.index[i]
chunked.loc[label,nchunk_colname] = subd.shape[0]
for col in avg_cols:
chunked.loc[label,col] = subd[col].mean ()
for col in uavg_cols:
ucol = uncert_col_name (col)
v, u = weighted_mean (subd[col], subd[ucol])
chunked.loc[label,col] = v
chunked.loc[label,ucol] = u
for col in minmax_cols:
chunked.loc[label, +col] = subd[col].min ()
chunked.loc[label, +col] = subd[col].max ()
return chunked
|
#vtb
def invoke_ssh_shell(cls, *args, **kwargs):
pty = kwargs.pop(, True)
echo = kwargs.pop(, False)
client = cls.connect_ssh(*args, **kwargs)
f = client.invoke_shell(pty=pty, echo=echo)
f.client = client
return f
|
invoke_ssh(arguments..., pty=False, echo=False)
Star a new shell on a remote server. It first calls
:meth:`Flow.connect_ssh` using all positional and keyword
arguments, then calls :meth:`SSHClient.invoke_shell` with the
pty / echo options.
Args:
arguments...: The options for the SSH connection.
pty(bool): Request a pseudo-terminal from the server.
echo(bool): Whether to echo read/written data to stdout by default.
Returns:
:class:`Flow`: A Flow instance initialised with the SSH channel.
|
### Input:
invoke_ssh(arguments..., pty=False, echo=False)
Star a new shell on a remote server. It first calls
:meth:`Flow.connect_ssh` using all positional and keyword
arguments, then calls :meth:`SSHClient.invoke_shell` with the
pty / echo options.
Args:
arguments...: The options for the SSH connection.
pty(bool): Request a pseudo-terminal from the server.
echo(bool): Whether to echo read/written data to stdout by default.
Returns:
:class:`Flow`: A Flow instance initialised with the SSH channel.
### Response:
#vtb
def invoke_ssh_shell(cls, *args, **kwargs):
pty = kwargs.pop(, True)
echo = kwargs.pop(, False)
client = cls.connect_ssh(*args, **kwargs)
f = client.invoke_shell(pty=pty, echo=echo)
f.client = client
return f
|
#vtb
def post_fork_child(self):
entry_point = .format(__name__)
exec_env = combined_dict(os.environ, dict(PANTS_ENTRYPOINT=entry_point))
cmd = [sys.executable] + sys.argv
self._logger.debug(.format(entry_point, .join(cmd)))
os.spawnve(os.P_NOWAIT, sys.executable, cmd, env=exec_env)
|
Post-fork() child callback for ProcessManager.daemon_spawn().
|
### Input:
Post-fork() child callback for ProcessManager.daemon_spawn().
### Response:
#vtb
def post_fork_child(self):
entry_point = .format(__name__)
exec_env = combined_dict(os.environ, dict(PANTS_ENTRYPOINT=entry_point))
cmd = [sys.executable] + sys.argv
self._logger.debug(.format(entry_point, .join(cmd)))
os.spawnve(os.P_NOWAIT, sys.executable, cmd, env=exec_env)
|
#vtb
def indication(self, pdu):
if _debug: StreamToPacket._debug("indication %r", pdu)
for packet in self.packetize(pdu, self.downstreamBuffer):
self.request(packet)
|
Message going downstream.
|
### Input:
Message going downstream.
### Response:
#vtb
def indication(self, pdu):
if _debug: StreamToPacket._debug("indication %r", pdu)
for packet in self.packetize(pdu, self.downstreamBuffer):
self.request(packet)
|
#vtb
def assignMgtKey(self, CorpNum, MgtKeyType, ItemKey, MgtKey, UserID=None):
if MgtKeyType == None or MgtKeyType == :
raise PopbillException(-99999999, "세금계산서 발행유형이 입력되지 않았습니다.")
if ItemKey == None or ItemKey == :
raise PopbillException(-99999999, "아이템키가 입력되지 않았습니다.")
if MgtKey == None or MgtKey == :
raise PopbillException(-99999999, "관리번호가 입력되지 않았습니다.")
postDate = "MgtKey=" + MgtKey
return self._httppost( + ItemKey + + MgtKeyType, postDate, CorpNum, UserID, "",
"application/x-www-form-urlencoded; charset=utf-8")
|
관리번호할당
args
CorpNum : 팝빌회원 사업자번호
MgtKeyType : 세금계산서 유형, SELL-매출, BUY-매입, TRUSTEE-위수탁
ItemKey : 아이템키 (Search API로 조회 가능)
MgtKey : 세금계산서에 할당할 파트너 관리 번호
UserID : 팝빌회원 아이디
return
처리결과. consist of code and message
raise
PopbillException
|
### Input:
관리번호할당
args
CorpNum : 팝빌회원 사업자번호
MgtKeyType : 세금계산서 유형, SELL-매출, BUY-매입, TRUSTEE-위수탁
ItemKey : 아이템키 (Search API로 조회 가능)
MgtKey : 세금계산서에 할당할 파트너 관리 번호
UserID : 팝빌회원 아이디
return
처리결과. consist of code and message
raise
PopbillException
### Response:
#vtb
def assignMgtKey(self, CorpNum, MgtKeyType, ItemKey, MgtKey, UserID=None):
if MgtKeyType == None or MgtKeyType == :
raise PopbillException(-99999999, "세금계산서 발행유형이 입력되지 않았습니다.")
if ItemKey == None or ItemKey == :
raise PopbillException(-99999999, "아이템키가 입력되지 않았습니다.")
if MgtKey == None or MgtKey == :
raise PopbillException(-99999999, "관리번호가 입력되지 않았습니다.")
postDate = "MgtKey=" + MgtKey
return self._httppost( + ItemKey + + MgtKeyType, postDate, CorpNum, UserID, "",
"application/x-www-form-urlencoded; charset=utf-8")
|
#vtb
def get_conditional_probs(self, source=None):
c_probs = np.zeros((self.m * (self.k + 1), self.k))
mu = self.mu.detach().clone().numpy()
for i in range(self.m):
mu_i = mu[i * self.k : (i + 1) * self.k, :]
c_probs[i * (self.k + 1) + 1 : (i + 1) * (self.k + 1), :] = mu_i
c_probs[i * (self.k + 1), :] = 1 - mu_i.sum(axis=0)
c_probs = np.clip(c_probs, 0.01, 0.99)
if source is not None:
return c_probs[source * (self.k + 1) : (source + 1) * (self.k + 1)]
else:
return c_probs
|
Returns the full conditional probabilities table as a numpy array,
where row i*(k+1) + ly is the conditional probabilities of source i
emmiting label ly (including abstains 0), conditioned on different
values of Y, i.e.:
c_probs[i*(k+1) + ly, y] = P(\lambda_i = ly | Y = y)
Note that this simply involves inferring the kth row by law of total
probability and adding in to mu.
If `source` is not None, returns only the corresponding block.
|
### Input:
Returns the full conditional probabilities table as a numpy array,
where row i*(k+1) + ly is the conditional probabilities of source i
emmiting label ly (including abstains 0), conditioned on different
values of Y, i.e.:
c_probs[i*(k+1) + ly, y] = P(\lambda_i = ly | Y = y)
Note that this simply involves inferring the kth row by law of total
probability and adding in to mu.
If `source` is not None, returns only the corresponding block.
### Response:
#vtb
def get_conditional_probs(self, source=None):
c_probs = np.zeros((self.m * (self.k + 1), self.k))
mu = self.mu.detach().clone().numpy()
for i in range(self.m):
mu_i = mu[i * self.k : (i + 1) * self.k, :]
c_probs[i * (self.k + 1) + 1 : (i + 1) * (self.k + 1), :] = mu_i
c_probs[i * (self.k + 1), :] = 1 - mu_i.sum(axis=0)
c_probs = np.clip(c_probs, 0.01, 0.99)
if source is not None:
return c_probs[source * (self.k + 1) : (source + 1) * (self.k + 1)]
else:
return c_probs
|
#vtb
def _build_register_function(universe: bool, in_place: bool):
def register(func):
return _register_function(func.__name__, func, universe, in_place)
return register
|
Build a decorator function to tag transformation functions.
:param universe: Does the first positional argument of this function correspond to a universe graph?
:param in_place: Does this function return a new graph, or just modify it in-place?
|
### Input:
Build a decorator function to tag transformation functions.
:param universe: Does the first positional argument of this function correspond to a universe graph?
:param in_place: Does this function return a new graph, or just modify it in-place?
### Response:
#vtb
def _build_register_function(universe: bool, in_place: bool):
def register(func):
return _register_function(func.__name__, func, universe, in_place)
return register
|
#vtb
def p_expr_usr(p):
if p[2].type_ == TYPE.string:
p[0] = make_builtin(p.lineno(1), , p[2], type_=TYPE.uinteger)
else:
p[0] = make_builtin(p.lineno(1), ,
make_typecast(TYPE.uinteger, p[2], p.lineno(1)),
type_=TYPE.uinteger)
|
bexpr : USR bexpr %prec UMINUS
|
### Input:
bexpr : USR bexpr %prec UMINUS
### Response:
#vtb
def p_expr_usr(p):
if p[2].type_ == TYPE.string:
p[0] = make_builtin(p.lineno(1), , p[2], type_=TYPE.uinteger)
else:
p[0] = make_builtin(p.lineno(1), ,
make_typecast(TYPE.uinteger, p[2], p.lineno(1)),
type_=TYPE.uinteger)
|
#vtb
def WaitHotKeyReleased(hotkey: tuple) -> None:
mod = {ModifierKey.Alt: Keys.VK_MENU,
ModifierKey.Control: Keys.VK_CONTROL,
ModifierKey.Shift: Keys.VK_SHIFT,
ModifierKey.Win: Keys.VK_LWIN
}
while True:
time.sleep(0.05)
if IsKeyPressed(hotkey[1]):
continue
for k, v in mod.items():
if k & hotkey[0]:
if IsKeyPressed(v):
break
else:
break
|
hotkey: tuple, two ints tuple(modifierKey, key)
|
### Input:
hotkey: tuple, two ints tuple(modifierKey, key)
### Response:
#vtb
def WaitHotKeyReleased(hotkey: tuple) -> None:
mod = {ModifierKey.Alt: Keys.VK_MENU,
ModifierKey.Control: Keys.VK_CONTROL,
ModifierKey.Shift: Keys.VK_SHIFT,
ModifierKey.Win: Keys.VK_LWIN
}
while True:
time.sleep(0.05)
if IsKeyPressed(hotkey[1]):
continue
for k, v in mod.items():
if k & hotkey[0]:
if IsKeyPressed(v):
break
else:
break
|
#vtb
def _separate(self):
if self.total_free_space is None:
return 0
else:
sepa = self.default_column_space
if self.default_column_space_remainder > 0:
sepa += 1
self.default_column_space_remainder -= 1
logger.debug("remainder: %d, separator: %d",
self.default_column_space_remainder, sepa)
return sepa
|
get a width of separator for current column
:return: int
|
### Input:
get a width of separator for current column
:return: int
### Response:
#vtb
def _separate(self):
if self.total_free_space is None:
return 0
else:
sepa = self.default_column_space
if self.default_column_space_remainder > 0:
sepa += 1
self.default_column_space_remainder -= 1
logger.debug("remainder: %d, separator: %d",
self.default_column_space_remainder, sepa)
return sepa
|
#vtb
def p_expression_sla(self, p):
p[0] = Sll(p[1], p[3], lineno=p.lineno(1))
p.set_lineno(0, p.lineno(1))
|
expression : expression LSHIFTA expression
|
### Input:
expression : expression LSHIFTA expression
### Response:
#vtb
def p_expression_sla(self, p):
p[0] = Sll(p[1], p[3], lineno=p.lineno(1))
p.set_lineno(0, p.lineno(1))
|
#vtb
def _cb_inform_interface_change(self, msg):
self._logger.debug(, msg)
self._interface_changed.set()
|
Update the sensors and requests available.
|
### Input:
Update the sensors and requests available.
### Response:
#vtb
def _cb_inform_interface_change(self, msg):
self._logger.debug(, msg)
self._interface_changed.set()
|
#vtb
def to_dict(mapreduce_yaml):
all_configs = []
for config in mapreduce_yaml.mapreduce:
out = {
"name": config.name,
"mapper_input_reader": config.mapper.input_reader,
"mapper_handler": config.mapper.handler,
}
if config.mapper.params_validator:
out["mapper_params_validator"] = config.mapper.params_validator
if config.mapper.params:
param_defaults = {}
for param in config.mapper.params:
param_defaults[param.name] = param.default or param.value
out["mapper_params"] = param_defaults
if config.params:
param_defaults = {}
for param in config.params:
param_defaults[param.name] = param.default or param.value
out["params"] = param_defaults
if config.mapper.output_writer:
out["mapper_output_writer"] = config.mapper.output_writer
all_configs.append(out)
return all_configs
|
Converts a MapReduceYaml file into a JSON-encodable dictionary.
For use in user-visible UI and internal methods for interfacing with
user code (like param validation). as a list
Args:
mapreduce_yaml: The Pyton representation of the mapreduce.yaml document.
Returns:
A list of configuration dictionaries.
|
### Input:
Converts a MapReduceYaml file into a JSON-encodable dictionary.
For use in user-visible UI and internal methods for interfacing with
user code (like param validation). as a list
Args:
mapreduce_yaml: The Pyton representation of the mapreduce.yaml document.
Returns:
A list of configuration dictionaries.
### Response:
#vtb
def to_dict(mapreduce_yaml):
all_configs = []
for config in mapreduce_yaml.mapreduce:
out = {
"name": config.name,
"mapper_input_reader": config.mapper.input_reader,
"mapper_handler": config.mapper.handler,
}
if config.mapper.params_validator:
out["mapper_params_validator"] = config.mapper.params_validator
if config.mapper.params:
param_defaults = {}
for param in config.mapper.params:
param_defaults[param.name] = param.default or param.value
out["mapper_params"] = param_defaults
if config.params:
param_defaults = {}
for param in config.params:
param_defaults[param.name] = param.default or param.value
out["params"] = param_defaults
if config.mapper.output_writer:
out["mapper_output_writer"] = config.mapper.output_writer
all_configs.append(out)
return all_configs
|
#vtb
def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Explained variance between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
var_pct = torch.var(targ - pred) / torch.var(targ)
return 1 - var_pct
|
Explained variance between `pred` and `targ`.
|
### Input:
Explained variance between `pred` and `targ`.
### Response:
#vtb
def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Explained variance between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
var_pct = torch.var(targ - pred) / torch.var(targ)
return 1 - var_pct
|
#vtb
def tracefunc_xml(func):
funcname = meta_util_six.get_funcname(func)
def wrp_tracefunc2(*args, **kwargs):
verbose = kwargs.get(, True)
if verbose:
print( % (funcname,))
with util_print.Indenter():
ret = func(*args, **kwargs)
if verbose:
print( % (funcname,))
return ret
wrp_tracefunc2_ = ignores_exc_tb(wrp_tracefunc2)
wrp_tracefunc2_ = preserve_sig(wrp_tracefunc2_, func)
return wrp_tracefunc2_
|
Causes output of function to be printed in an XML style block
|
### Input:
Causes output of function to be printed in an XML style block
### Response:
#vtb
def tracefunc_xml(func):
funcname = meta_util_six.get_funcname(func)
def wrp_tracefunc2(*args, **kwargs):
verbose = kwargs.get(, True)
if verbose:
print( % (funcname,))
with util_print.Indenter():
ret = func(*args, **kwargs)
if verbose:
print( % (funcname,))
return ret
wrp_tracefunc2_ = ignores_exc_tb(wrp_tracefunc2)
wrp_tracefunc2_ = preserve_sig(wrp_tracefunc2_, func)
return wrp_tracefunc2_
|
#vtb
def tokenize_sents(string):
string = six.text_type(string)
spans = []
for match in re.finditer(, string):
spans.append(match)
spans_count = len(spans)
rez = []
off = 0
for i in range(spans_count):
tok = string[spans[i].start():spans[i].end()]
if i == spans_count - 1:
rez.append(string[off:spans[i].end()])
elif tok[-1] in [, , , , ]:
tok1 = tok[re.search(, tok).start()-1]
next_tok = string[spans[i + 1].start():spans[i + 1].end()]
if (next_tok[0].isupper()
and not tok1.isupper()
and not (tok[-1] !=
or tok1[0] ==
or tok in ABBRS)):
rez.append(string[off:spans[i].end()])
off = spans[i + 1].start()
return rez
|
Tokenize input text to sentences.
:param string: Text to tokenize
:type string: str or unicode
:return: sentences
:rtype: list of strings
|
### Input:
Tokenize input text to sentences.
:param string: Text to tokenize
:type string: str or unicode
:return: sentences
:rtype: list of strings
### Response:
#vtb
def tokenize_sents(string):
string = six.text_type(string)
spans = []
for match in re.finditer(, string):
spans.append(match)
spans_count = len(spans)
rez = []
off = 0
for i in range(spans_count):
tok = string[spans[i].start():spans[i].end()]
if i == spans_count - 1:
rez.append(string[off:spans[i].end()])
elif tok[-1] in [, , , , ]:
tok1 = tok[re.search(, tok).start()-1]
next_tok = string[spans[i + 1].start():spans[i + 1].end()]
if (next_tok[0].isupper()
and not tok1.isupper()
and not (tok[-1] !=
or tok1[0] ==
or tok in ABBRS)):
rez.append(string[off:spans[i].end()])
off = spans[i + 1].start()
return rez
|
#vtb
def turb_ice(turbice: [str], unit: str = ) -> str:
if not turbice:
return
if turbice[0][0] == :
conditions = TURBULANCE_CONDITIONS
elif turbice[0][0] == :
conditions = ICING_CONDITIONS
else:
return
split = []
for item in turbice:
if len(item) == 6:
split.append([item[1:2], item[2:5], item[5]])
for i in reversed(range(len(split) - 1)):
if split[i][2] == and split[i][0] == split[i + 1][0] \
and int(split[i + 1][1]) == (int(split[i][1]) + int(split[i][2]) * 10):
split[i][2] = str(int(split[i][2]) + int(split[i + 1][2]))
split.pop(i + 1)
return .join([.format(
conditions=conditions[item[0]], low_alt=int(item[1]) * 100,
high_alt=int(item[1]) * 100 + int(item[2]) * 1000, unit=unit) for item in split])
|
Translate the list of turbulance or icing into a readable sentence
Ex: Occasional moderate turbulence in clouds from 3000ft to 14000ft
|
### Input:
Translate the list of turbulance or icing into a readable sentence
Ex: Occasional moderate turbulence in clouds from 3000ft to 14000ft
### Response:
#vtb
def turb_ice(turbice: [str], unit: str = ) -> str:
if not turbice:
return
if turbice[0][0] == :
conditions = TURBULANCE_CONDITIONS
elif turbice[0][0] == :
conditions = ICING_CONDITIONS
else:
return
split = []
for item in turbice:
if len(item) == 6:
split.append([item[1:2], item[2:5], item[5]])
for i in reversed(range(len(split) - 1)):
if split[i][2] == and split[i][0] == split[i + 1][0] \
and int(split[i + 1][1]) == (int(split[i][1]) + int(split[i][2]) * 10):
split[i][2] = str(int(split[i][2]) + int(split[i + 1][2]))
split.pop(i + 1)
return .join([.format(
conditions=conditions[item[0]], low_alt=int(item[1]) * 100,
high_alt=int(item[1]) * 100 + int(item[2]) * 1000, unit=unit) for item in split])
|
#vtb
def by_position(self, position):
try:
return self.filter_by(position=position).one()
except sa.orm.exc.NoResultFound:
return None
|
Like `.get()`, but by position number.
|
### Input:
Like `.get()`, but by position number.
### Response:
#vtb
def by_position(self, position):
try:
return self.filter_by(position=position).one()
except sa.orm.exc.NoResultFound:
return None
|
#vtb
def get_tables(self):
url = self.build_url(self._endpoints.get())
response = self.session.get(url)
if not response:
return []
data = response.json()
return [self.table_constructor(parent=self, **{self._cloud_data_key: table})
for table in data.get(, [])]
|
Returns a collection of this worksheet tables
|
### Input:
Returns a collection of this worksheet tables
### Response:
#vtb
def get_tables(self):
url = self.build_url(self._endpoints.get())
response = self.session.get(url)
if not response:
return []
data = response.json()
return [self.table_constructor(parent=self, **{self._cloud_data_key: table})
for table in data.get(, [])]
|
#vtb
def to_vcf(self, path, rename=None, number=None, description=None,
fill=None, write_header=True):
r
write_vcf(path, callset=self, rename=rename, number=number,
description=description, fill=fill,
write_header=write_header)
|
r"""Write to a variant call format (VCF) file.
Parameters
----------
path : string
File path.
rename : dict, optional
Rename these columns in the VCF.
number : dict, optional
Override the number specified in INFO headers.
description : dict, optional
Descriptions for the INFO and FILTER headers.
fill : dict, optional
Fill values used for missing data in the table.
write_header : bool, optional
If True write VCF header.
Examples
--------
Setup a variant table to write out::
>>> import allel
>>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3']
>>> pos = [2, 6, 3, 8, 1]
>>> ids = ['a', 'b', 'c', 'd', 'e']
>>> ref = [b'A', b'C', b'T', b'G', b'N']
>>> alt = [(b'T', b'.'),
... (b'G', b'.'),
... (b'A', b'C'),
... (b'C', b'A'),
... (b'X', b'.')]
>>> qual = [1.2, 2.3, 3.4, 4.5, 5.6]
>>> filter_qd = [True, True, True, False, False]
>>> filter_dp = [True, False, True, False, False]
>>> dp = [12, 23, 34, 45, 56]
>>> qd = [12.3, 23.4, 34.5, 45.6, 56.7]
>>> flg = [True, False, True, False, True]
>>> ac = [(1, -1), (3, -1), (5, 6), (7, 8), (9, -1)]
>>> xx = [(1.2, 2.3), (3.4, 4.5), (5.6, 6.7), (7.8, 8.9),
... (9.0, 9.9)]
>>> columns = [chrom, pos, ids, ref, alt, qual, filter_dp,
... filter_qd, dp, qd, flg, ac, xx]
>>> records = list(zip(*columns))
>>> dtype = [('CHROM', 'S4'),
... ('POS', 'u4'),
... ('ID', 'S1'),
... ('REF', 'S1'),
... ('ALT', ('S1', 2)),
... ('qual', 'f4'),
... ('filter_dp', bool),
... ('filter_qd', bool),
... ('dp', int),
... ('qd', float),
... ('flg', bool),
... ('ac', (int, 2)),
... ('xx', (float, 2))]
>>> vt = allel.VariantTable(records, dtype=dtype)
Now write out to VCF and inspect the result::
>>> rename = {'dp': 'DP', 'qd': 'QD', 'filter_qd': 'QD'}
>>> fill = {'ALT': b'.', 'ac': -1}
>>> number = {'ac': 'A'}
>>> description = {'ac': 'Allele counts', 'filter_dp': 'Low depth'}
>>> vt.to_vcf('example.vcf', rename=rename, fill=fill,
... number=number, description=description)
>>> print(open('example.vcf').read())
##fileformat=VCFv4.1
##fileDate=...
##source=...
##INFO=<ID=DP,Number=1,Type=Integer,Description="">
##INFO=<ID=QD,Number=1,Type=Float,Description="">
##INFO=<ID=ac,Number=A,Type=Integer,Description="Allele counts">
##INFO=<ID=flg,Number=0,Type=Flag,Description="">
##INFO=<ID=xx,Number=2,Type=Float,Description="">
##FILTER=<ID=QD,Description="">
##FILTER=<ID=dp,Description="Low depth">
#CHROM POS ID REF ALT QUAL FILTER INFO
chr1 2 a A T 1.2 QD;dp DP=12;QD=12.3;ac=1;flg;xx=...
chr1 6 b C G 2.3 QD DP=23;QD=23.4;ac=3;xx=3.4,4.5
chr2 3 c T A,C 3.4 QD;dp DP=34;QD=34.5;ac=5,6;flg;x...
chr2 8 d G C,A 4.5 PASS DP=45;QD=45.6;ac=7,8;xx=7...
chr3 1 e N X 5.6 PASS DP=56;QD=56.7;ac=9;flg;xx=...
|
### Input:
r"""Write to a variant call format (VCF) file.
Parameters
----------
path : string
File path.
rename : dict, optional
Rename these columns in the VCF.
number : dict, optional
Override the number specified in INFO headers.
description : dict, optional
Descriptions for the INFO and FILTER headers.
fill : dict, optional
Fill values used for missing data in the table.
write_header : bool, optional
If True write VCF header.
Examples
--------
Setup a variant table to write out::
>>> import allel
>>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3']
>>> pos = [2, 6, 3, 8, 1]
>>> ids = ['a', 'b', 'c', 'd', 'e']
>>> ref = [b'A', b'C', b'T', b'G', b'N']
>>> alt = [(b'T', b'.'),
... (b'G', b'.'),
... (b'A', b'C'),
... (b'C', b'A'),
... (b'X', b'.')]
>>> qual = [1.2, 2.3, 3.4, 4.5, 5.6]
>>> filter_qd = [True, True, True, False, False]
>>> filter_dp = [True, False, True, False, False]
>>> dp = [12, 23, 34, 45, 56]
>>> qd = [12.3, 23.4, 34.5, 45.6, 56.7]
>>> flg = [True, False, True, False, True]
>>> ac = [(1, -1), (3, -1), (5, 6), (7, 8), (9, -1)]
>>> xx = [(1.2, 2.3), (3.4, 4.5), (5.6, 6.7), (7.8, 8.9),
... (9.0, 9.9)]
>>> columns = [chrom, pos, ids, ref, alt, qual, filter_dp,
... filter_qd, dp, qd, flg, ac, xx]
>>> records = list(zip(*columns))
>>> dtype = [('CHROM', 'S4'),
... ('POS', 'u4'),
... ('ID', 'S1'),
... ('REF', 'S1'),
... ('ALT', ('S1', 2)),
... ('qual', 'f4'),
... ('filter_dp', bool),
... ('filter_qd', bool),
... ('dp', int),
... ('qd', float),
... ('flg', bool),
... ('ac', (int, 2)),
... ('xx', (float, 2))]
>>> vt = allel.VariantTable(records, dtype=dtype)
Now write out to VCF and inspect the result::
>>> rename = {'dp': 'DP', 'qd': 'QD', 'filter_qd': 'QD'}
>>> fill = {'ALT': b'.', 'ac': -1}
>>> number = {'ac': 'A'}
>>> description = {'ac': 'Allele counts', 'filter_dp': 'Low depth'}
>>> vt.to_vcf('example.vcf', rename=rename, fill=fill,
... number=number, description=description)
>>> print(open('example.vcf').read())
##fileformat=VCFv4.1
##fileDate=...
##source=...
##INFO=<ID=DP,Number=1,Type=Integer,Description="">
##INFO=<ID=QD,Number=1,Type=Float,Description="">
##INFO=<ID=ac,Number=A,Type=Integer,Description="Allele counts">
##INFO=<ID=flg,Number=0,Type=Flag,Description="">
##INFO=<ID=xx,Number=2,Type=Float,Description="">
##FILTER=<ID=QD,Description="">
##FILTER=<ID=dp,Description="Low depth">
#CHROM POS ID REF ALT QUAL FILTER INFO
chr1 2 a A T 1.2 QD;dp DP=12;QD=12.3;ac=1;flg;xx=...
chr1 6 b C G 2.3 QD DP=23;QD=23.4;ac=3;xx=3.4,4.5
chr2 3 c T A,C 3.4 QD;dp DP=34;QD=34.5;ac=5,6;flg;x...
chr2 8 d G C,A 4.5 PASS DP=45;QD=45.6;ac=7,8;xx=7...
chr3 1 e N X 5.6 PASS DP=56;QD=56.7;ac=9;flg;xx=...
### Response:
#vtb
def to_vcf(self, path, rename=None, number=None, description=None,
fill=None, write_header=True):
r
write_vcf(path, callset=self, rename=rename, number=number,
description=description, fill=fill,
write_header=write_header)
|
#vtb
def scale_dtype(arr, dtype):
max_int = np.iinfo(dtype).max
return (arr * max_int).astype(dtype)
|
Convert an array from 0..1 to dtype, scaling up linearly
|
### Input:
Convert an array from 0..1 to dtype, scaling up linearly
### Response:
#vtb
def scale_dtype(arr, dtype):
max_int = np.iinfo(dtype).max
return (arr * max_int).astype(dtype)
|
#vtb
def index():
institute_objs = user_institutes(store, current_user)
institutes_count = ((institute_obj, store.cases(collaborator=institute_obj[]).count())
for institute_obj in institute_objs if institute_obj)
return dict(institutes=institutes_count)
|
Display a list of all user institutes.
|
### Input:
Display a list of all user institutes.
### Response:
#vtb
def index():
institute_objs = user_institutes(store, current_user)
institutes_count = ((institute_obj, store.cases(collaborator=institute_obj[]).count())
for institute_obj in institute_objs if institute_obj)
return dict(institutes=institutes_count)
|
#vtb
def conv_elems_1d(x, factor, out_depth=None):
out_depth = out_depth or x.get_shape().as_list()[-1]
x = tf.expand_dims(x, 1)
x = layers().Conv2D(
filters=out_depth,
kernel_size=(1, factor),
strides=(1, factor),
padding="valid",
data_format="channels_last",
)(x)
x = tf.squeeze(x, 1)
return x
|
Decrease the length and change the dimensionality.
Merge/restore/compress factors positions of dim depth of the input into
a single position of dim out_depth.
This is basically just a strided convolution without overlap
between each strides. The original length has to be divided by factor.
Args:
x (tf.Tensor): shape [batch_size, length, depth]
factor (int): Length compression factor.
out_depth (int): Output depth
Returns:
tf.Tensor: shape [batch_size, length//factor, out_depth]
|
### Input:
Decrease the length and change the dimensionality.
Merge/restore/compress factors positions of dim depth of the input into
a single position of dim out_depth.
This is basically just a strided convolution without overlap
between each strides. The original length has to be divided by factor.
Args:
x (tf.Tensor): shape [batch_size, length, depth]
factor (int): Length compression factor.
out_depth (int): Output depth
Returns:
tf.Tensor: shape [batch_size, length//factor, out_depth]
### Response:
#vtb
def conv_elems_1d(x, factor, out_depth=None):
out_depth = out_depth or x.get_shape().as_list()[-1]
x = tf.expand_dims(x, 1)
x = layers().Conv2D(
filters=out_depth,
kernel_size=(1, factor),
strides=(1, factor),
padding="valid",
data_format="channels_last",
)(x)
x = tf.squeeze(x, 1)
return x
|
#vtb
def _make_request(session, url, argument=None, params=None, raw=False):
if not params:
params = {}
params[] = session.auth.key
try:
if argument:
request_url = .format(session.auth.base_url, VOOBLY_API_URL, url, argument)
else:
request_url = .format(VOOBLY_API_URL, url)
resp = session.get(request_url, params=params)
except RequestException:
raise VooblyError()
if resp.text == :
raise VooblyError()
elif resp.text == :
raise VooblyError()
elif not resp.text:
raise VooblyError()
if raw:
return resp.text
try:
return tablib.Dataset().load(resp.text).dict
except UnsupportedFormat:
raise VooblyError(.format(resp.text))
|
Make a request to API endpoint.
|
### Input:
Make a request to API endpoint.
### Response:
#vtb
def _make_request(session, url, argument=None, params=None, raw=False):
if not params:
params = {}
params[] = session.auth.key
try:
if argument:
request_url = .format(session.auth.base_url, VOOBLY_API_URL, url, argument)
else:
request_url = .format(VOOBLY_API_URL, url)
resp = session.get(request_url, params=params)
except RequestException:
raise VooblyError()
if resp.text == :
raise VooblyError()
elif resp.text == :
raise VooblyError()
elif not resp.text:
raise VooblyError()
if raw:
return resp.text
try:
return tablib.Dataset().load(resp.text).dict
except UnsupportedFormat:
raise VooblyError(.format(resp.text))
|
#vtb
def tokenize(self, s, pattern=None, active=None):
if pattern is None:
if self.tokenize_pattern is None:
pattern = r
else:
pattern = self.tokenize_pattern
if active is None:
active = self.active
return self.group.tokenize(s, pattern=pattern, active=active)
|
Rewrite and tokenize the input string *s*.
Args:
s (str): the input string to process
pattern (str, optional): the regular expression pattern on
which to split tokens; defaults to `[ \t]+`
active (optional): a collection of external module names
that may be applied if called
Returns:
a :class:`~delphin.tokens.YyTokenLattice` containing the
tokens and their characterization information
|
### Input:
Rewrite and tokenize the input string *s*.
Args:
s (str): the input string to process
pattern (str, optional): the regular expression pattern on
which to split tokens; defaults to `[ \t]+`
active (optional): a collection of external module names
that may be applied if called
Returns:
a :class:`~delphin.tokens.YyTokenLattice` containing the
tokens and their characterization information
### Response:
#vtb
def tokenize(self, s, pattern=None, active=None):
if pattern is None:
if self.tokenize_pattern is None:
pattern = r
else:
pattern = self.tokenize_pattern
if active is None:
active = self.active
return self.group.tokenize(s, pattern=pattern, active=active)
|
#vtb
def conv_block(name, x, mid_channels, dilations=None, activation="relu",
dropout=0.0):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x_shape = common_layers.shape_list(x)
is_2d = len(x_shape) == 4
num_steps = x_shape[1]
if is_2d:
first_filter = [3, 3]
second_filter = [1, 1]
else:
if num_steps == 1:
first_filter = [1, 3, 3]
else:
first_filter = [2, 3, 3]
second_filter = [1, 1, 1]
x = conv("1_1", x, output_channels=mid_channels, filter_size=first_filter,
dilations=dilations)
x = tf.nn.relu(x)
x = get_dropout(x, rate=dropout)
if activation == "relu":
x = conv("1_2", x, output_channels=mid_channels,
filter_size=second_filter, dilations=dilations)
x = tf.nn.relu(x)
elif activation == "gatu":
x_tanh = conv("1_tanh", x, output_channels=mid_channels,
filter_size=second_filter, dilations=dilations)
x_sigm = conv("1_sigm", x, output_channels=mid_channels,
filter_size=second_filter, dilations=dilations)
x = tf.nn.tanh(x_tanh) * tf.nn.sigmoid(x_sigm)
x = get_dropout(x, rate=dropout)
return x
|
2 layer conv block used in the affine coupling layer.
Args:
name: variable scope.
x: 4-D or 5-D Tensor.
mid_channels: Output channels of the second layer.
dilations: Optional, list of integers.
activation: relu or gatu.
If relu, the second layer is relu(W*x)
If gatu, the second layer is tanh(W1*x) * sigmoid(W2*x)
dropout: Dropout probability.
Returns:
x: 4-D Tensor: Output activations.
|
### Input:
2 layer conv block used in the affine coupling layer.
Args:
name: variable scope.
x: 4-D or 5-D Tensor.
mid_channels: Output channels of the second layer.
dilations: Optional, list of integers.
activation: relu or gatu.
If relu, the second layer is relu(W*x)
If gatu, the second layer is tanh(W1*x) * sigmoid(W2*x)
dropout: Dropout probability.
Returns:
x: 4-D Tensor: Output activations.
### Response:
#vtb
def conv_block(name, x, mid_channels, dilations=None, activation="relu",
dropout=0.0):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x_shape = common_layers.shape_list(x)
is_2d = len(x_shape) == 4
num_steps = x_shape[1]
if is_2d:
first_filter = [3, 3]
second_filter = [1, 1]
else:
if num_steps == 1:
first_filter = [1, 3, 3]
else:
first_filter = [2, 3, 3]
second_filter = [1, 1, 1]
x = conv("1_1", x, output_channels=mid_channels, filter_size=first_filter,
dilations=dilations)
x = tf.nn.relu(x)
x = get_dropout(x, rate=dropout)
if activation == "relu":
x = conv("1_2", x, output_channels=mid_channels,
filter_size=second_filter, dilations=dilations)
x = tf.nn.relu(x)
elif activation == "gatu":
x_tanh = conv("1_tanh", x, output_channels=mid_channels,
filter_size=second_filter, dilations=dilations)
x_sigm = conv("1_sigm", x, output_channels=mid_channels,
filter_size=second_filter, dilations=dilations)
x = tf.nn.tanh(x_tanh) * tf.nn.sigmoid(x_sigm)
x = get_dropout(x, rate=dropout)
return x
|
#vtb
def inverse_transform(self, maps):
out = {}
xi1 = conversions.primary_xi(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
xi2 = conversions.secondary_xi(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
out["phi_a"] = conversions.phi_a(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
out["phi_s"] = conversions.phi_s(
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
if isinstance(xi1, numpy.ndarray):
mass1, mass2 = map(numpy.array, [maps[parameters.mass1],
maps[parameters.mass2]])
mask_mass1_gte_mass2 = mass1 >= mass2
mask_mass1_lt_mass2 = mass1 < mass2
out["xi1"] = numpy.concatenate((
xi1[mask_mass1_gte_mass2],
xi2[mask_mass1_lt_mass2]))
out["xi2"] = numpy.concatenate((
xi1[mask_mass1_gte_mass2],
xi2[mask_mass1_lt_mass2]))
elif maps["mass1"] > maps["mass2"]:
out["xi1"] = xi1
out["xi2"] = xi2
else:
out["xi1"] = xi2
out["xi2"] = xi1
return self.format_output(maps, out)
|
This function transforms from component masses and cartesian spins to
mass-weighted spin parameters perpendicular with the angular momentum.
Parameters
----------
maps : a mapping object
Returns
-------
out : dict
A dict with key as parameter name and value as numpy.array or float
of transformed values.
|
### Input:
This function transforms from component masses and cartesian spins to
mass-weighted spin parameters perpendicular with the angular momentum.
Parameters
----------
maps : a mapping object
Returns
-------
out : dict
A dict with key as parameter name and value as numpy.array or float
of transformed values.
### Response:
#vtb
def inverse_transform(self, maps):
out = {}
xi1 = conversions.primary_xi(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
xi2 = conversions.secondary_xi(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
out["phi_a"] = conversions.phi_a(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
out["phi_s"] = conversions.phi_s(
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
if isinstance(xi1, numpy.ndarray):
mass1, mass2 = map(numpy.array, [maps[parameters.mass1],
maps[parameters.mass2]])
mask_mass1_gte_mass2 = mass1 >= mass2
mask_mass1_lt_mass2 = mass1 < mass2
out["xi1"] = numpy.concatenate((
xi1[mask_mass1_gte_mass2],
xi2[mask_mass1_lt_mass2]))
out["xi2"] = numpy.concatenate((
xi1[mask_mass1_gte_mass2],
xi2[mask_mass1_lt_mass2]))
elif maps["mass1"] > maps["mass2"]:
out["xi1"] = xi1
out["xi2"] = xi2
else:
out["xi1"] = xi2
out["xi2"] = xi1
return self.format_output(maps, out)
|
#vtb
def run(*args):
import os
import sys
import argparse
import pkg_resources
parser = argparse.ArgumentParser(prog=, description=)
parser.add_argument(, help=)
parser.add_argument(, dest=, action=,
help=)
parser.add_argument(, dest=, action=,
help=)
ver = pkg_resources.require()[0].version
parser.add_argument(, action=,
version= % ver, help=)
if args:
clargs, remaining = parser.parse_known_args(args=args)
else:
clargs, remaining = parser.parse_known_args()
if clargs.verbose:
logger.setLevel(logging.DEBUG)
args, kwargs = parse(remaining)
shovel = Shovel()
shovel.extend(Task.clear())
for path in [
os.path.expanduser(),
os.path.expanduser()]:
if os.path.exists(path):
shovel.read(path, os.path.expanduser())
shovel_home = os.environ.get()
if shovel_home and os.path.exists(shovel_home):
shovel.read(shovel_home, shovel_home)
for path in [, ]:
if os.path.exists(path):
shovel.read(path)
if clargs.method == :
print(help.shovel_help(shovel, *args, **kwargs))
elif clargs.method == :
tasks = list(v for _, v in shovel.items())
if not tasks:
print()
else:
names = list(t.fullname for t in tasks)
docs = list(t.doc for t in tasks)
width = 80
import shutil
try:
width, _ = shutil.get_terminal_size(fallback=(0, width))
except AttributeError:
pass
format = % (
max(len(name) for name in names), width)
for name, doc in zip(names, docs):
print(format % (name, doc))
elif clargs.method:
try:
tasks = shovel.tasks(clargs.method)
except KeyError:
print( % clargs.method, file=sys.stderr)
exit(1)
if len(tasks) > 1:
print( % clargs.method, file=sys.stderr)
for task in tasks:
print( % task.fullname, file=sys.stderr)
exit(2)
task = tasks[0]
if clargs.dryRun:
print(task.dry(*args, **kwargs))
else:
task(*args, **kwargs)
|
Run the normal shovel functionality
|
### Input:
Run the normal shovel functionality
### Response:
#vtb
def run(*args):
import os
import sys
import argparse
import pkg_resources
parser = argparse.ArgumentParser(prog=, description=)
parser.add_argument(, help=)
parser.add_argument(, dest=, action=,
help=)
parser.add_argument(, dest=, action=,
help=)
ver = pkg_resources.require()[0].version
parser.add_argument(, action=,
version= % ver, help=)
if args:
clargs, remaining = parser.parse_known_args(args=args)
else:
clargs, remaining = parser.parse_known_args()
if clargs.verbose:
logger.setLevel(logging.DEBUG)
args, kwargs = parse(remaining)
shovel = Shovel()
shovel.extend(Task.clear())
for path in [
os.path.expanduser(),
os.path.expanduser()]:
if os.path.exists(path):
shovel.read(path, os.path.expanduser())
shovel_home = os.environ.get()
if shovel_home and os.path.exists(shovel_home):
shovel.read(shovel_home, shovel_home)
for path in [, ]:
if os.path.exists(path):
shovel.read(path)
if clargs.method == :
print(help.shovel_help(shovel, *args, **kwargs))
elif clargs.method == :
tasks = list(v for _, v in shovel.items())
if not tasks:
print()
else:
names = list(t.fullname for t in tasks)
docs = list(t.doc for t in tasks)
width = 80
import shutil
try:
width, _ = shutil.get_terminal_size(fallback=(0, width))
except AttributeError:
pass
format = % (
max(len(name) for name in names), width)
for name, doc in zip(names, docs):
print(format % (name, doc))
elif clargs.method:
try:
tasks = shovel.tasks(clargs.method)
except KeyError:
print( % clargs.method, file=sys.stderr)
exit(1)
if len(tasks) > 1:
print( % clargs.method, file=sys.stderr)
for task in tasks:
print( % task.fullname, file=sys.stderr)
exit(2)
task = tasks[0]
if clargs.dryRun:
print(task.dry(*args, **kwargs))
else:
task(*args, **kwargs)
|
#vtb
def remove(self, first, count):
if first < 0 or count < 1:
return
new_range = []
last = first + count - 1
for r in self.__range:
if first <= r.last and r.first <= last:
if r.first < first:
new_range.append(IdRange(r.first, first-r.first))
if last < r.last:
new_range.append(IdRange(last+1, r.last-last))
else:
new_range.append(r)
self.__range = new_range
|
Remove a range of count consecutive ids starting at id first
from all the ranges in the set.
|
### Input:
Remove a range of count consecutive ids starting at id first
from all the ranges in the set.
### Response:
#vtb
def remove(self, first, count):
if first < 0 or count < 1:
return
new_range = []
last = first + count - 1
for r in self.__range:
if first <= r.last and r.first <= last:
if r.first < first:
new_range.append(IdRange(r.first, first-r.first))
if last < r.last:
new_range.append(IdRange(last+1, r.last-last))
else:
new_range.append(r)
self.__range = new_range
|
#vtb
def _get_current_object(self):
if not hasattr(self.__local, ):
return self.__local()
try:
return getattr(self.__local, self.__name__)
except AttributeError:
raise RuntimeError( % self.__name__)
|
Return the current object. This is useful if you want the real
object behind the proxy at a time for performance reasons or because
you want to pass the object into a different context.
|
### Input:
Return the current object. This is useful if you want the real
object behind the proxy at a time for performance reasons or because
you want to pass the object into a different context.
### Response:
#vtb
def _get_current_object(self):
if not hasattr(self.__local, ):
return self.__local()
try:
return getattr(self.__local, self.__name__)
except AttributeError:
raise RuntimeError( % self.__name__)
|
#vtb
def days_in_month(year, month):
eom = _days_per_month[month - 1]
if is_leap_year(year) and month == 2:
eom += 1
return eom
|
returns number of days for the given year and month
:param int year: calendar year
:param int month: calendar month
:return int:
|
### Input:
returns number of days for the given year and month
:param int year: calendar year
:param int month: calendar month
:return int:
### Response:
#vtb
def days_in_month(year, month):
eom = _days_per_month[month - 1]
if is_leap_year(year) and month == 2:
eom += 1
return eom
|
#vtb
def get_map_values(self, lons, lats, ibin=None):
pix_idxs = self.get_pixel_indices(lons, lats, ibin)
idxs = copy.copy(pix_idxs)
m = np.empty_like(idxs[0], dtype=bool)
m.fill(True)
for i, p in enumerate(pix_idxs):
m &= (pix_idxs[i] >= 0) & (pix_idxs[i] < self._npix[i])
idxs[i][~m] = 0
vals = self.counts.T[idxs]
vals[~m] = np.nan
return vals
|
Return the map values corresponding to a set of coordinates.
Parameters
----------
lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
'Latitidues' (DEC or GLAT)
ibin : int or array-like
Extract data only for a given energy bin. None -> extract data for all bins
Returns
----------
vals : numpy.ndarray((n))
Values of pixels in the flattened map, np.nan used to flag
coords outside of map
|
### Input:
Return the map values corresponding to a set of coordinates.
Parameters
----------
lons : array-like
'Longitudes' (RA or GLON)
lats : array-like
'Latitidues' (DEC or GLAT)
ibin : int or array-like
Extract data only for a given energy bin. None -> extract data for all bins
Returns
----------
vals : numpy.ndarray((n))
Values of pixels in the flattened map, np.nan used to flag
coords outside of map
### Response:
#vtb
def get_map_values(self, lons, lats, ibin=None):
pix_idxs = self.get_pixel_indices(lons, lats, ibin)
idxs = copy.copy(pix_idxs)
m = np.empty_like(idxs[0], dtype=bool)
m.fill(True)
for i, p in enumerate(pix_idxs):
m &= (pix_idxs[i] >= 0) & (pix_idxs[i] < self._npix[i])
idxs[i][~m] = 0
vals = self.counts.T[idxs]
vals[~m] = np.nan
return vals
|
#vtb
def _add_url(self, chunk):
if in chunk:
return chunk
public_path = chunk.get()
if public_path:
chunk[] = public_path
else:
fullpath = posixpath.join(self.state.static_view_path,
chunk[])
chunk[] = self._request.static_url(fullpath)
return chunk
|
Add a 'url' property to a chunk and return it
|
### Input:
Add a 'url' property to a chunk and return it
### Response:
#vtb
def _add_url(self, chunk):
if in chunk:
return chunk
public_path = chunk.get()
if public_path:
chunk[] = public_path
else:
fullpath = posixpath.join(self.state.static_view_path,
chunk[])
chunk[] = self._request.static_url(fullpath)
return chunk
|
#vtb
def chunks(iterable, n):
for i in range(0, len(iterable), n):
yield iterable[i:i + n]
|
Yield successive n-sized chunks from iterable object. https://stackoverflow.com/a/312464
|
### Input:
Yield successive n-sized chunks from iterable object. https://stackoverflow.com/a/312464
### Response:
#vtb
def chunks(iterable, n):
for i in range(0, len(iterable), n):
yield iterable[i:i + n]
|
#vtb
def unmodified_isinstance(*bases):
class UnmodifiedIsInstance(type):
if sys.version_info[0] == 2 and sys.version_info[1] <= 6:
@classmethod
def __instancecheck__(cls, instance):
if cls.__name__ in (str(base.__name__) for base in bases):
return isinstance(instance, bases)
subclass = getattr(instance, , None)
subtype = type(instance)
instance_type = getattr(abc, , None)
if not instance_type:
class test_object:
pass
instance_type = type(test_object)
if subtype is instance_type:
subtype = subclass
if subtype is subclass or subclass is None:
return cls.__subclasscheck__(subtype)
return (cls.__subclasscheck__(subclass) or cls.__subclasscheck__(subtype))
else:
@classmethod
def __instancecheck__(cls, instance):
if cls.__name__ in (str(base.__name__) for base in bases):
return isinstance(instance, bases)
return type.__instancecheck__(cls, instance)
return with_metaclass(UnmodifiedIsInstance, *bases)
|
When called in the form
MyOverrideClass(unmodified_isinstance(BuiltInClass))
it allows calls against passed in built in instances to pass even if there not a subclass
|
### Input:
When called in the form
MyOverrideClass(unmodified_isinstance(BuiltInClass))
it allows calls against passed in built in instances to pass even if there not a subclass
### Response:
#vtb
def unmodified_isinstance(*bases):
class UnmodifiedIsInstance(type):
if sys.version_info[0] == 2 and sys.version_info[1] <= 6:
@classmethod
def __instancecheck__(cls, instance):
if cls.__name__ in (str(base.__name__) for base in bases):
return isinstance(instance, bases)
subclass = getattr(instance, , None)
subtype = type(instance)
instance_type = getattr(abc, , None)
if not instance_type:
class test_object:
pass
instance_type = type(test_object)
if subtype is instance_type:
subtype = subclass
if subtype is subclass or subclass is None:
return cls.__subclasscheck__(subtype)
return (cls.__subclasscheck__(subclass) or cls.__subclasscheck__(subtype))
else:
@classmethod
def __instancecheck__(cls, instance):
if cls.__name__ in (str(base.__name__) for base in bases):
return isinstance(instance, bases)
return type.__instancecheck__(cls, instance)
return with_metaclass(UnmodifiedIsInstance, *bases)
|
#vtb
def write(self, buffer):
if type(buffer) == type(0):
buffer = chr(buffer)
elif not isinstance(buffer, bytes):
buffer = buffer.encode(self.encoding)
if IAC in buffer:
buffer = buffer.replace(IAC, IAC+IAC)
self.msg("send %s", repr(buffer))
self.sock.send(buffer)
|
Write a string to the socket, doubling any IAC characters.
Can block if the connection is blocked. May raise
socket.error if the connection is closed.
|
### Input:
Write a string to the socket, doubling any IAC characters.
Can block if the connection is blocked. May raise
socket.error if the connection is closed.
### Response:
#vtb
def write(self, buffer):
if type(buffer) == type(0):
buffer = chr(buffer)
elif not isinstance(buffer, bytes):
buffer = buffer.encode(self.encoding)
if IAC in buffer:
buffer = buffer.replace(IAC, IAC+IAC)
self.msg("send %s", repr(buffer))
self.sock.send(buffer)
|
#vtb
def first_interval_starting(self, start: datetime.datetime) -> \
Optional[Interval]:
for i in self.intervals:
if i.start == start:
return i
return None
|
Returns our first interval that starts with the ``start`` parameter, or
``None``.
|
### Input:
Returns our first interval that starts with the ``start`` parameter, or
``None``.
### Response:
#vtb
def first_interval_starting(self, start: datetime.datetime) -> \
Optional[Interval]:
for i in self.intervals:
if i.start == start:
return i
return None
|
#vtb
def _convert(cls, record):
if not record:
return {}
converted_dict = {}
for field in cls.conversion:
key = field[0]
if len(field) >= 2 and field[1]:
converted_key = field[1]
else:
converted_key = key
if len(field) >= 3 and field[2]:
conversion_method = field[2]
else:
conversion_method = cls.default_conversion_method
if len(field) >= 4:
converter = field[3]
else:
converter = None
try:
value = conversion_method(record[key])
except KeyError:
continue
if converter:
value = converter._convert_internal(value)
if converted_key is APPEND:
if isinstance(value, list):
for v in value:
converted_dict.update(v)
else:
converted_dict.update(value)
else:
converted_dict[converted_key] = value
return converted_dict
|
Core method of the converter. Converts a single dictionary into another dictionary.
|
### Input:
Core method of the converter. Converts a single dictionary into another dictionary.
### Response:
#vtb
def _convert(cls, record):
if not record:
return {}
converted_dict = {}
for field in cls.conversion:
key = field[0]
if len(field) >= 2 and field[1]:
converted_key = field[1]
else:
converted_key = key
if len(field) >= 3 and field[2]:
conversion_method = field[2]
else:
conversion_method = cls.default_conversion_method
if len(field) >= 4:
converter = field[3]
else:
converter = None
try:
value = conversion_method(record[key])
except KeyError:
continue
if converter:
value = converter._convert_internal(value)
if converted_key is APPEND:
if isinstance(value, list):
for v in value:
converted_dict.update(v)
else:
converted_dict.update(value)
else:
converted_dict[converted_key] = value
return converted_dict
|
#vtb
def _set_link_error_disable(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=link_error_disable.link_error_disable, is_container=, presence=False, yang_name="link-error-disable", rest_name="link-error-disable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u: {u: u, u: u, u: None, u: None, u: None, u: None}}, namespace=, defining_module=, yang_type=, is_config=True)
except (TypeError, ValueError):
raise ValueError({
: ,
: "container",
: ,
})
self.__link_error_disable = t
if hasattr(self, ):
self._set()
|
Setter method for link_error_disable, mapped from YANG variable /interface/ethernet/link_error_disable (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_link_error_disable is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_link_error_disable() directly.
|
### Input:
Setter method for link_error_disable, mapped from YANG variable /interface/ethernet/link_error_disable (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_link_error_disable is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_link_error_disable() directly.
### Response:
#vtb
def _set_link_error_disable(self, v, load=False):
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=link_error_disable.link_error_disable, is_container=, presence=False, yang_name="link-error-disable", rest_name="link-error-disable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u: {u: u, u: u, u: None, u: None, u: None, u: None}}, namespace=, defining_module=, yang_type=, is_config=True)
except (TypeError, ValueError):
raise ValueError({
: ,
: "container",
: ,
})
self.__link_error_disable = t
if hasattr(self, ):
self._set()
|
#vtb
def _locate_free_sectors(self, ignore_chunk=None):
sectors = self._sectors(ignore_chunk=ignore_chunk)
return [not i for i in sectors]
|
Return a list of booleans, indicating the free sectors.
|
### Input:
Return a list of booleans, indicating the free sectors.
### Response:
#vtb
def _locate_free_sectors(self, ignore_chunk=None):
sectors = self._sectors(ignore_chunk=ignore_chunk)
return [not i for i in sectors]
|
#vtb
def handleGetValue(self, topContainer):
value = self.__referenceDict if self.__referenceDict is not None else topContainer
for key in self.__dictKeyChain:
value = value[key]
return value
|
This method overrides ValueGetterBase's "pure virtual" method. It
returns the referenced value. The derived class is NOT responsible for
fully resolving the reference'd value in the event the value resolves to
another ValueGetterBase-based instance -- this is handled automatically
within ValueGetterBase implementation.
topContainer: The top-level container (dict, tuple, or list [sub-]instance)
within whose context the value-getter is applied. If
self.__referenceDict is None, then topContainer will be used
as the reference dictionary for resolving our dictionary key
chain.
Returns: The value referenced by this instance (which may be another
value-getter instance)
|
### Input:
This method overrides ValueGetterBase's "pure virtual" method. It
returns the referenced value. The derived class is NOT responsible for
fully resolving the reference'd value in the event the value resolves to
another ValueGetterBase-based instance -- this is handled automatically
within ValueGetterBase implementation.
topContainer: The top-level container (dict, tuple, or list [sub-]instance)
within whose context the value-getter is applied. If
self.__referenceDict is None, then topContainer will be used
as the reference dictionary for resolving our dictionary key
chain.
Returns: The value referenced by this instance (which may be another
value-getter instance)
### Response:
#vtb
def handleGetValue(self, topContainer):
value = self.__referenceDict if self.__referenceDict is not None else topContainer
for key in self.__dictKeyChain:
value = value[key]
return value
|
#vtb
def installed(name,
cyg_arch=,
mirrors=None):
ret = {: name, : None, : , : {}}
if cyg_arch not in [, ]:
ret[] = False
ret[] = cyg_arch\x86\x86_64\
return ret
LOG.debug(, mirrors)
if not __salt__[](name,
cyg_arch=cyg_arch,
mirrors=mirrors):
ret[] = False
ret[] =
return ret
pkgs = __salt__[](name, cyg_arch)
if name in pkgs:
ret[] = True
ret[] =
return ret
if __opts__[]:
ret[] = .format(name)
return ret
if __salt__[](name,
cyg_arch=cyg_arch,
mirrors=mirrors):
ret[] = True
ret[][name] =
ret[] =
else:
ret[] = False
ret[] =
return ret
|
Make sure that a package is installed.
name
The name of the package to install
cyg_arch : x86_64
The cygwin architecture to install the package into.
Current options are x86 and x86_64
mirrors : None
List of mirrors to check.
None will use a default mirror (kernel.org)
CLI Example:
.. code-block:: yaml
rsync:
cyg.installed:
- mirrors:
- http://mirror/without/public/key: ""
- http://mirror/with/public/key: http://url/of/public/key
|
### Input:
Make sure that a package is installed.
name
The name of the package to install
cyg_arch : x86_64
The cygwin architecture to install the package into.
Current options are x86 and x86_64
mirrors : None
List of mirrors to check.
None will use a default mirror (kernel.org)
CLI Example:
.. code-block:: yaml
rsync:
cyg.installed:
- mirrors:
- http://mirror/without/public/key: ""
- http://mirror/with/public/key: http://url/of/public/key
### Response:
#vtb
def installed(name,
cyg_arch=,
mirrors=None):
ret = {: name, : None, : , : {}}
if cyg_arch not in [, ]:
ret[] = False
ret[] = cyg_arch\x86\x86_64\
return ret
LOG.debug(, mirrors)
if not __salt__[](name,
cyg_arch=cyg_arch,
mirrors=mirrors):
ret[] = False
ret[] =
return ret
pkgs = __salt__[](name, cyg_arch)
if name in pkgs:
ret[] = True
ret[] =
return ret
if __opts__[]:
ret[] = .format(name)
return ret
if __salt__[](name,
cyg_arch=cyg_arch,
mirrors=mirrors):
ret[] = True
ret[][name] =
ret[] =
else:
ret[] = False
ret[] =
return ret
|
#vtb
def loads(cls, s):
with closing(StringIO(s)) as fileobj:
return cls.load(fileobj)
|
Load an instance of this class from YAML.
|
### Input:
Load an instance of this class from YAML.
### Response:
#vtb
def loads(cls, s):
with closing(StringIO(s)) as fileobj:
return cls.load(fileobj)
|
#vtb
def cmd_func(self, command: str) -> Optional[Callable]:
func_name = self.cmd_func_name(command)
if func_name:
return getattr(self, func_name)
|
Get the function for a command
:param command: the name of the command
|
### Input:
Get the function for a command
:param command: the name of the command
### Response:
#vtb
def cmd_func(self, command: str) -> Optional[Callable]:
func_name = self.cmd_func_name(command)
if func_name:
return getattr(self, func_name)
|
#vtb
def get_vcs_root(path):
previous_path = path
while get_vcs_info(path) is None:
path = abspardir(path)
if path == previous_path:
return
else:
previous_path = path
return osp.abspath(path)
|
Return VCS root directory path
Return None if path is not within a supported VCS repository
|
### Input:
Return VCS root directory path
Return None if path is not within a supported VCS repository
### Response:
#vtb
def get_vcs_root(path):
previous_path = path
while get_vcs_info(path) is None:
path = abspardir(path)
if path == previous_path:
return
else:
previous_path = path
return osp.abspath(path)
|
#vtb
def _do_multipart_upload(self, stream, metadata, size, num_retries):
data = stream.read(size)
if len(data) < size:
msg = _READ_LESS_THAN_SIZE.format(size, len(data))
raise ValueError(msg)
headers = _get_upload_headers(self._connection.USER_AGENT)
upload_url = _MULTIPART_URL_TEMPLATE.format(project=self.project)
upload = MultipartUpload(upload_url, headers=headers)
if num_retries is not None:
upload._retry_strategy = resumable_media.RetryStrategy(
max_retries=num_retries
)
response = upload.transmit(self._http, data, metadata, _GENERIC_CONTENT_TYPE)
return response
|
Perform a multipart upload.
:type stream: IO[bytes]
:param stream: A bytes IO object open for reading.
:type metadata: dict
:param metadata: The metadata associated with the upload.
:type size: int
:param size: The number of bytes to be uploaded (which will be read
from ``stream``). If not provided, the upload will be
concluded once ``stream`` is exhausted (or :data:`None`).
:type num_retries: int
:param num_retries: Number of upload retries. (Deprecated: This
argument will be removed in a future release.)
:rtype: :class:`~requests.Response`
:returns: The "200 OK" response object returned after the multipart
upload request.
:raises: :exc:`ValueError` if the ``stream`` has fewer than ``size``
bytes remaining.
|
### Input:
Perform a multipart upload.
:type stream: IO[bytes]
:param stream: A bytes IO object open for reading.
:type metadata: dict
:param metadata: The metadata associated with the upload.
:type size: int
:param size: The number of bytes to be uploaded (which will be read
from ``stream``). If not provided, the upload will be
concluded once ``stream`` is exhausted (or :data:`None`).
:type num_retries: int
:param num_retries: Number of upload retries. (Deprecated: This
argument will be removed in a future release.)
:rtype: :class:`~requests.Response`
:returns: The "200 OK" response object returned after the multipart
upload request.
:raises: :exc:`ValueError` if the ``stream`` has fewer than ``size``
bytes remaining.
### Response:
#vtb
def _do_multipart_upload(self, stream, metadata, size, num_retries):
data = stream.read(size)
if len(data) < size:
msg = _READ_LESS_THAN_SIZE.format(size, len(data))
raise ValueError(msg)
headers = _get_upload_headers(self._connection.USER_AGENT)
upload_url = _MULTIPART_URL_TEMPLATE.format(project=self.project)
upload = MultipartUpload(upload_url, headers=headers)
if num_retries is not None:
upload._retry_strategy = resumable_media.RetryStrategy(
max_retries=num_retries
)
response = upload.transmit(self._http, data, metadata, _GENERIC_CONTENT_TYPE)
return response
|
#vtb
def to_dict(self):
rv = {: self.code}
if not self.is_native():
rv[] = self.issuer
rv[] = self.type
else:
rv[] =
return rv
|
Generate a dict for this object's attributes.
:return: A dict representing an :class:`Asset`
|
### Input:
Generate a dict for this object's attributes.
:return: A dict representing an :class:`Asset`
### Response:
#vtb
def to_dict(self):
rv = {: self.code}
if not self.is_native():
rv[] = self.issuer
rv[] = self.type
else:
rv[] =
return rv
|
#vtb
def cli(debug, cache, incremental):
settings.HTTP_CACHE = cache
settings.INCREMENTAL = incremental
settings.DEBUG = debug
if settings.DEBUG:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
init_memorious()
|
Crawler framework for documents and structured scrapers.
|
### Input:
Crawler framework for documents and structured scrapers.
### Response:
#vtb
def cli(debug, cache, incremental):
settings.HTTP_CACHE = cache
settings.INCREMENTAL = incremental
settings.DEBUG = debug
if settings.DEBUG:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
init_memorious()
|
#vtb
def ensure_dir(self, mode=0777):
if not self.exists() or not self.isdir():
os.makedirs(self, mode)
|
Make sure the directory exists, create if necessary.
|
### Input:
Make sure the directory exists, create if necessary.
### Response:
#vtb
def ensure_dir(self, mode=0777):
if not self.exists() or not self.isdir():
os.makedirs(self, mode)
|
#vtb
def feed_interval_get(feed_id, parameters):
val = cache.get(getkey( T_INTERVAL,
key=feed_interval_key(feed_id, parameters) ))
return val if isinstance(val, tuple) else (val, None)
|
Get adaptive interval between checks for a feed.
|
### Input:
Get adaptive interval between checks for a feed.
### Response:
#vtb
def feed_interval_get(feed_id, parameters):
val = cache.get(getkey( T_INTERVAL,
key=feed_interval_key(feed_id, parameters) ))
return val if isinstance(val, tuple) else (val, None)
|
#vtb
def impulse_noise(x, severity=1):
c = [.03, .06, .09, 0.17, 0.27][severity - 1]
x = tfds.core.lazy_imports.skimage.util.random_noise(
np.array(x) / 255., mode=, amount=c)
x_clip = np.clip(x, 0, 1) * 255
return around_and_astype(x_clip)
|
Impulse noise corruption to images.
Args:
x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255].
severity: integer, severity of corruption.
Returns:
numpy array, image with uint8 pixels in [0,255]. Added impulse noise.
|
### Input:
Impulse noise corruption to images.
Args:
x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255].
severity: integer, severity of corruption.
Returns:
numpy array, image with uint8 pixels in [0,255]. Added impulse noise.
### Response:
#vtb
def impulse_noise(x, severity=1):
c = [.03, .06, .09, 0.17, 0.27][severity - 1]
x = tfds.core.lazy_imports.skimage.util.random_noise(
np.array(x) / 255., mode=, amount=c)
x_clip = np.clip(x, 0, 1) * 255
return around_and_astype(x_clip)
|
#vtb
def _open(self, name=None, fileobj=None, mymap=None, block=None):
if block is not None:
if not name:
name =
self.unpack_from(block)
if fileobj:
fileobj.close()
return self
if mymap is not None:
block = mymap
elif fileobj:
try:
mymap = mmap.mmap(fileobj.fileno(), 0,
mmap.MAP_SHARED, mmap.PROT_READ)
except:
mymap = 0
block = fileobj.read()
elif name:
fileobj = io.open(os.path.normpath(os.path.expanduser(name)), )
else:
assert False
return self._open(name=name,
fileobj=fileobj,
mymap=mymap,
block=block)
|
The _open function takes some form of file identifier and creates
an :py:class:`CpioFile` instance from it.
:param :py:class:`str` name: a file name
:param :py:class:`file` fileobj: if given, this overrides *name*
:param :py:class:`mmap.mmap` mymap: if given, this overrides *fileobj*
:param :py:class:`bytes` block: file contents in a block of memory, (if given, this overrides *mymap*)
The file to be used can be specified in any of four different
forms, (in reverse precedence):
#. a file name
#. :py:class:`file` object
#. :py:mod:`mmap.mmap`, or
#. a block of memory
|
### Input:
The _open function takes some form of file identifier and creates
an :py:class:`CpioFile` instance from it.
:param :py:class:`str` name: a file name
:param :py:class:`file` fileobj: if given, this overrides *name*
:param :py:class:`mmap.mmap` mymap: if given, this overrides *fileobj*
:param :py:class:`bytes` block: file contents in a block of memory, (if given, this overrides *mymap*)
The file to be used can be specified in any of four different
forms, (in reverse precedence):
#. a file name
#. :py:class:`file` object
#. :py:mod:`mmap.mmap`, or
#. a block of memory
### Response:
#vtb
def _open(self, name=None, fileobj=None, mymap=None, block=None):
if block is not None:
if not name:
name =
self.unpack_from(block)
if fileobj:
fileobj.close()
return self
if mymap is not None:
block = mymap
elif fileobj:
try:
mymap = mmap.mmap(fileobj.fileno(), 0,
mmap.MAP_SHARED, mmap.PROT_READ)
except:
mymap = 0
block = fileobj.read()
elif name:
fileobj = io.open(os.path.normpath(os.path.expanduser(name)), )
else:
assert False
return self._open(name=name,
fileobj=fileobj,
mymap=mymap,
block=block)
|
#vtb
def vectorize(self, sentence_list):
test_observed_arr = self.__setup_dataset(sentence_list, self.__token_master_list, self.__seq_len)
inferenced_arr = self.__rbm.inference(
test_observed_arr,
training_count=1,
r_batch_size=-1
)
return inferenced_arr
|
Args:
sentence_list: The list of tokenized sentences.
[[`token`, `token`, `token`, ...],
[`token`, `token`, `token`, ...],
[`token`, `token`, `token`, ...]]
Returns:
`np.ndarray` of tokens.
[vector of token, vector of token, vector of token]
|
### Input:
Args:
sentence_list: The list of tokenized sentences.
[[`token`, `token`, `token`, ...],
[`token`, `token`, `token`, ...],
[`token`, `token`, `token`, ...]]
Returns:
`np.ndarray` of tokens.
[vector of token, vector of token, vector of token]
### Response:
#vtb
def vectorize(self, sentence_list):
test_observed_arr = self.__setup_dataset(sentence_list, self.__token_master_list, self.__seq_len)
inferenced_arr = self.__rbm.inference(
test_observed_arr,
training_count=1,
r_batch_size=-1
)
return inferenced_arr
|
#vtb
def obj(self):
if not getattr(self, , None):
self._obj = self.get_object()
if self._obj is None and not self.allow_none:
self.return_error(404)
return self._obj
|
Returns the value of :meth:`ObjectMixin.get_object` and sets a private
property called _obj. This property ensures the logic around allow_none
is enforced across Endpoints using the Object interface.
:raises: :class:`werkzeug.exceptions.BadRequest`
:returns: The result of :meth:ObjectMixin.get_object`
|
### Input:
Returns the value of :meth:`ObjectMixin.get_object` and sets a private
property called _obj. This property ensures the logic around allow_none
is enforced across Endpoints using the Object interface.
:raises: :class:`werkzeug.exceptions.BadRequest`
:returns: The result of :meth:ObjectMixin.get_object`
### Response:
#vtb
def obj(self):
if not getattr(self, , None):
self._obj = self.get_object()
if self._obj is None and not self.allow_none:
self.return_error(404)
return self._obj
|
#vtb
def relabel_non_zero(label_image, start = 1):
r
if start <= 0: raise ArgumentError()
l = list(scipy.unique(label_image))
if 0 in l: l.remove(0)
mapping = dict()
mapping[0] = 0
for key, item in zip(l, list(range(start, len(l) + start))):
mapping[key] = item
return relabel_map(label_image, mapping)
|
r"""
Relabel the regions of a label image.
Re-processes the labels to make them consecutively and starting from start.
Keeps all zero (0) labels, as they are considered background.
Parameters
----------
label_image : array_like
A nD label map.
start : integer
The id of the first label to assign
Returns
-------
relabel_map : ndarray
The relabelled label map.
See also
--------
relabel
|
### Input:
r"""
Relabel the regions of a label image.
Re-processes the labels to make them consecutively and starting from start.
Keeps all zero (0) labels, as they are considered background.
Parameters
----------
label_image : array_like
A nD label map.
start : integer
The id of the first label to assign
Returns
-------
relabel_map : ndarray
The relabelled label map.
See also
--------
relabel
### Response:
#vtb
def relabel_non_zero(label_image, start = 1):
r
if start <= 0: raise ArgumentError()
l = list(scipy.unique(label_image))
if 0 in l: l.remove(0)
mapping = dict()
mapping[0] = 0
for key, item in zip(l, list(range(start, len(l) + start))):
mapping[key] = item
return relabel_map(label_image, mapping)
|
#vtb
def register(self, request, **cleaned_data):
if Site._meta.installed:
site = Site.objects.get_current()
else:
site = RequestSite(request)
create_user = RegistrationProfile.objects.create_inactive_user
new_user = create_user(
cleaned_data[],
cleaned_data[],
cleaned_data[],
site,
send_email=False
)
new_user.first_name = cleaned_data[]
new_user.last_name = cleaned_data[]
new_user.save()
user_info = UserInfo(
user=new_user,
company=cleaned_data[],
function=cleaned_data[],
address=cleaned_data[],
postal_code=cleaned_data[],
city=cleaned_data[],
country=cleaned_data[],
phone=cleaned_data[],
)
user_info.save()
send_activation_email(new_user, site, user_info)
send_activation_pending_email(new_user, site, user_info)
signals.user_registered.send(sender=self.__class__, user=new_user, request=request)
return new_user
|
Given a username, email address and password, register a new
user account, which will initially be inactive.
Along with the new ``User`` object, a new
``registration.models.RegistrationProfile`` will be created,
tied to that ``User``, containing the activation key which
will be used for this account.
Two emails will be sent. First one to the admin; this email should
contain an activation link and a resume of the new user infos.
Second one, to the user, for inform him that his request is pending.
After the ``User`` and ``RegistrationProfile`` are created and
the activation email is sent, the signal
``registration.signals.user_registered`` will be sent, with
the new ``User`` as the keyword argument ``user`` and the
class of this backend as the sender.
|
### Input:
Given a username, email address and password, register a new
user account, which will initially be inactive.
Along with the new ``User`` object, a new
``registration.models.RegistrationProfile`` will be created,
tied to that ``User``, containing the activation key which
will be used for this account.
Two emails will be sent. First one to the admin; this email should
contain an activation link and a resume of the new user infos.
Second one, to the user, for inform him that his request is pending.
After the ``User`` and ``RegistrationProfile`` are created and
the activation email is sent, the signal
``registration.signals.user_registered`` will be sent, with
the new ``User`` as the keyword argument ``user`` and the
class of this backend as the sender.
### Response:
#vtb
def register(self, request, **cleaned_data):
if Site._meta.installed:
site = Site.objects.get_current()
else:
site = RequestSite(request)
create_user = RegistrationProfile.objects.create_inactive_user
new_user = create_user(
cleaned_data[],
cleaned_data[],
cleaned_data[],
site,
send_email=False
)
new_user.first_name = cleaned_data[]
new_user.last_name = cleaned_data[]
new_user.save()
user_info = UserInfo(
user=new_user,
company=cleaned_data[],
function=cleaned_data[],
address=cleaned_data[],
postal_code=cleaned_data[],
city=cleaned_data[],
country=cleaned_data[],
phone=cleaned_data[],
)
user_info.save()
send_activation_email(new_user, site, user_info)
send_activation_pending_email(new_user, site, user_info)
signals.user_registered.send(sender=self.__class__, user=new_user, request=request)
return new_user
|
#vtb
def _join_factory(cls, gap, pad):
if issubclass(cls, dict):
def _join(data):
out = cls()
data = list(data)
while data:
tsd = data.pop(0)
out.append(tsd, gap=gap, pad=pad)
del tsd
return out
else:
from .. import TimeSeriesBaseList
def _join(arrays):
list_ = TimeSeriesBaseList(*arrays)
return list_.join(pad=pad, gap=gap)
return _join
|
Build a joiner for the given cls, and the given padding options
|
### Input:
Build a joiner for the given cls, and the given padding options
### Response:
#vtb
def _join_factory(cls, gap, pad):
if issubclass(cls, dict):
def _join(data):
out = cls()
data = list(data)
while data:
tsd = data.pop(0)
out.append(tsd, gap=gap, pad=pad)
del tsd
return out
else:
from .. import TimeSeriesBaseList
def _join(arrays):
list_ = TimeSeriesBaseList(*arrays)
return list_.join(pad=pad, gap=gap)
return _join
|
#vtb
def chi2(T1, T2):
rs2 = T2.sum(axis=1)
rs1 = T1.sum(axis=1)
rs2nz = rs2 > 0
rs1nz = rs1 > 0
dof1 = sum(rs1nz)
dof2 = sum(rs2nz)
rs2 = rs2 + (rs2 == 0)
dof = (dof1 - 1) * (dof2 - 1)
p = np.diag(1 / rs2) * np.matrix(T2)
E = np.diag(rs1) * np.matrix(p)
num = T1 - E
num = np.multiply(num, num)
E = E + (E == 0)
chi2 = num / E
chi2 = chi2.sum()
pvalue = 1 - stats.chi2.cdf(chi2, dof)
return chi2, pvalue, dof
|
chi-squared test of difference between two transition matrices.
Parameters
----------
T1 : array
(k, k), matrix of transitions (counts).
T2 : array
(k, k), matrix of transitions (counts) to use to form the
probabilities under the null.
Returns
-------
: tuple
(3 elements).
(chi2 value, pvalue, degrees of freedom).
Examples
--------
>>> import libpysal
>>> from giddy.markov import Spatial_Markov, chi2
>>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
>>> years = list(range(1929, 2010))
>>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
>>> rpci = pci/(pci.mean(axis=0))
>>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
>>> w.transform='r'
>>> sm = Spatial_Markov(rpci, w, fixed=True)
>>> T1 = sm.T[0]
>>> T1
array([[562., 22., 1., 0.],
[ 12., 201., 22., 0.],
[ 0., 17., 97., 4.],
[ 0., 0., 3., 19.]])
>>> T2 = sm.transitions
>>> T2
array([[884., 77., 4., 0.],
[ 68., 794., 87., 3.],
[ 1., 92., 815., 51.],
[ 1., 0., 60., 903.]])
>>> chi2(T1,T2)
(23.39728441473295, 0.005363116704861337, 9)
Notes
-----
Second matrix is used to form the probabilities under the null.
Marginal sums from first matrix are distributed across these probabilities
under the null. In other words the observed transitions are taken from T1
while the expected transitions are formed as follows
.. math::
E_{i,j} = \sum_j T1_{i,j} * T2_{i,j}/\sum_j T2_{i,j}
Degrees of freedom corrected for any rows in either T1 or T2 that have
zero total transitions.
|
### Input:
chi-squared test of difference between two transition matrices.
Parameters
----------
T1 : array
(k, k), matrix of transitions (counts).
T2 : array
(k, k), matrix of transitions (counts) to use to form the
probabilities under the null.
Returns
-------
: tuple
(3 elements).
(chi2 value, pvalue, degrees of freedom).
Examples
--------
>>> import libpysal
>>> from giddy.markov import Spatial_Markov, chi2
>>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
>>> years = list(range(1929, 2010))
>>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
>>> rpci = pci/(pci.mean(axis=0))
>>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
>>> w.transform='r'
>>> sm = Spatial_Markov(rpci, w, fixed=True)
>>> T1 = sm.T[0]
>>> T1
array([[562., 22., 1., 0.],
[ 12., 201., 22., 0.],
[ 0., 17., 97., 4.],
[ 0., 0., 3., 19.]])
>>> T2 = sm.transitions
>>> T2
array([[884., 77., 4., 0.],
[ 68., 794., 87., 3.],
[ 1., 92., 815., 51.],
[ 1., 0., 60., 903.]])
>>> chi2(T1,T2)
(23.39728441473295, 0.005363116704861337, 9)
Notes
-----
Second matrix is used to form the probabilities under the null.
Marginal sums from first matrix are distributed across these probabilities
under the null. In other words the observed transitions are taken from T1
while the expected transitions are formed as follows
.. math::
E_{i,j} = \sum_j T1_{i,j} * T2_{i,j}/\sum_j T2_{i,j}
Degrees of freedom corrected for any rows in either T1 or T2 that have
zero total transitions.
### Response:
#vtb
def chi2(T1, T2):
rs2 = T2.sum(axis=1)
rs1 = T1.sum(axis=1)
rs2nz = rs2 > 0
rs1nz = rs1 > 0
dof1 = sum(rs1nz)
dof2 = sum(rs2nz)
rs2 = rs2 + (rs2 == 0)
dof = (dof1 - 1) * (dof2 - 1)
p = np.diag(1 / rs2) * np.matrix(T2)
E = np.diag(rs1) * np.matrix(p)
num = T1 - E
num = np.multiply(num, num)
E = E + (E == 0)
chi2 = num / E
chi2 = chi2.sum()
pvalue = 1 - stats.chi2.cdf(chi2, dof)
return chi2, pvalue, dof
|
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