body_hash
stringlengths 64
64
| body
stringlengths 23
109k
| docstring
stringlengths 1
57k
| path
stringlengths 4
198
| name
stringlengths 1
115
| repository_name
stringlengths 7
111
| repository_stars
float64 0
191k
| lang
stringclasses 1
value | body_without_docstring
stringlengths 14
108k
| unified
stringlengths 45
133k
|
---|---|---|---|---|---|---|---|---|---|
6dad94e54ecdebe942218e0c4c84366feb2e5a6872b714ec1b6a5b78c3330914
|
async def retrieve_metadata_document(self, metadata_url):
'\n Retrieve the remote metadata document and make any necessary\n transformations on it.\n\n Parameters\n ----------\n metadata_url : str\n URL of remote metadata document\n identifier : str\n ID from JSON-LD description\n\n Returns\n -------\n the ElementTree object corresponding to the XML document\n '
msg = f'Requesting metadata URL {metadata_url}'
self.logger.debug(msg)
(content, _) = (await self.retrieve_url(metadata_url))
try:
doc = lxml.etree.parse(io.BytesIO(content))
except Exception as e:
msg = f'Unable to parse the metadata document at {metadata_url}: {e}.'
raise XMLMetadataParsingError(msg)
self.logger.debug('Got the metadata document')
return doc
|
Retrieve the remote metadata document and make any necessary
transformations on it.
Parameters
----------
metadata_url : str
URL of remote metadata document
identifier : str
ID from JSON-LD description
Returns
-------
the ElementTree object corresponding to the XML document
|
schema_org/schema_org/core.py
|
retrieve_metadata_document
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def retrieve_metadata_document(self, metadata_url):
'\n Retrieve the remote metadata document and make any necessary\n transformations on it.\n\n Parameters\n ----------\n metadata_url : str\n URL of remote metadata document\n identifier : str\n ID from JSON-LD description\n\n Returns\n -------\n the ElementTree object corresponding to the XML document\n '
msg = f'Requesting metadata URL {metadata_url}'
self.logger.debug(msg)
(content, _) = (await self.retrieve_url(metadata_url))
try:
doc = lxml.etree.parse(io.BytesIO(content))
except Exception as e:
msg = f'Unable to parse the metadata document at {metadata_url}: {e}.'
raise XMLMetadataParsingError(msg)
self.logger.debug('Got the metadata document')
return doc
|
async def retrieve_metadata_document(self, metadata_url):
'\n Retrieve the remote metadata document and make any necessary\n transformations on it.\n\n Parameters\n ----------\n metadata_url : str\n URL of remote metadata document\n identifier : str\n ID from JSON-LD description\n\n Returns\n -------\n the ElementTree object corresponding to the XML document\n '
msg = f'Requesting metadata URL {metadata_url}'
self.logger.debug(msg)
(content, _) = (await self.retrieve_url(metadata_url))
try:
doc = lxml.etree.parse(io.BytesIO(content))
except Exception as e:
msg = f'Unable to parse the metadata document at {metadata_url}: {e}.'
raise XMLMetadataParsingError(msg)
self.logger.debug('Got the metadata document')
return doc<|docstring|>Retrieve the remote metadata document and make any necessary
transformations on it.
Parameters
----------
metadata_url : str
URL of remote metadata document
identifier : str
ID from JSON-LD description
Returns
-------
the ElementTree object corresponding to the XML document<|endoftext|>
|
c8677e176c121676c34a87cc210a08bb2a6802012d7321a5a880d34fc71d9dcb
|
async def consume_sitemap(self, idx, sitemap_queue):
'\n In a producer/consumer paradigm, here we are consuming work items\n from the sitemap.\n\n Parameters\n ----------\n idx: int\n The only purpose for this is to identify the consumer in the logs.\n sitemap_queue : asyncio.Queue\n Holds jobs associated with the sitemap. Each job includes, among\n other things, a URL and a modification time.\n '
while True:
try:
job = (await sitemap_queue.get())
self.logger.debug(f'sitemap_consumer[{idx}] ==> {job.url}')
msg = f'last mod = {job.lastmod}: num failures so far = {self.failed_count}, queue size = {sitemap_queue.qsize()}'
self.logger.info(msg)
(await self.process_job(job))
except asyncio.CancelledError:
self.logger.debug('CancelledError')
break
except SkipError as e:
job.result = e
self.job_records.append(copy.copy(job))
msg = f'Unable to process {job.url}: {e}'
self.logger.warning(msg)
except Exception as e:
job.result = e
self.job_records.append(copy.copy(job))
msg = f'Unable to process {job.url}: {e}, {job.identifier} failures so far = {job.num_failures}'
self.logger.error(msg)
self.failed_count += 1
if (self.failed_count == self.max_num_errors):
self.logger.warning('Error threshold reached.')
(await self.shutdown())
if (job.num_failures < self.retry):
if isinstance(e, ERROR_RETRY_CANDIDATES):
self.logger.info(f'Throwing {job.url} back on queue')
job.num_failures += 1
sitemap_queue.put_nowait(job)
else:
job.result = None
self.job_records.append(copy.copy(job))
msg = f'sitemap_consumer[{idx}]: {SUCCESSFUL_INGEST_MESSAGE}: {job.identifier}'
self.logger.debug(msg)
msg = f'{SUCCESSFUL_INGEST_MESSAGE}: {job.identifier}'
self.logger.info(msg)
sitemap_queue.task_done()
|
In a producer/consumer paradigm, here we are consuming work items
from the sitemap.
Parameters
----------
idx: int
The only purpose for this is to identify the consumer in the logs.
sitemap_queue : asyncio.Queue
Holds jobs associated with the sitemap. Each job includes, among
other things, a URL and a modification time.
|
schema_org/schema_org/core.py
|
consume_sitemap
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def consume_sitemap(self, idx, sitemap_queue):
'\n In a producer/consumer paradigm, here we are consuming work items\n from the sitemap.\n\n Parameters\n ----------\n idx: int\n The only purpose for this is to identify the consumer in the logs.\n sitemap_queue : asyncio.Queue\n Holds jobs associated with the sitemap. Each job includes, among\n other things, a URL and a modification time.\n '
while True:
try:
job = (await sitemap_queue.get())
self.logger.debug(f'sitemap_consumer[{idx}] ==> {job.url}')
msg = f'last mod = {job.lastmod}: num failures so far = {self.failed_count}, queue size = {sitemap_queue.qsize()}'
self.logger.info(msg)
(await self.process_job(job))
except asyncio.CancelledError:
self.logger.debug('CancelledError')
break
except SkipError as e:
job.result = e
self.job_records.append(copy.copy(job))
msg = f'Unable to process {job.url}: {e}'
self.logger.warning(msg)
except Exception as e:
job.result = e
self.job_records.append(copy.copy(job))
msg = f'Unable to process {job.url}: {e}, {job.identifier} failures so far = {job.num_failures}'
self.logger.error(msg)
self.failed_count += 1
if (self.failed_count == self.max_num_errors):
self.logger.warning('Error threshold reached.')
(await self.shutdown())
if (job.num_failures < self.retry):
if isinstance(e, ERROR_RETRY_CANDIDATES):
self.logger.info(f'Throwing {job.url} back on queue')
job.num_failures += 1
sitemap_queue.put_nowait(job)
else:
job.result = None
self.job_records.append(copy.copy(job))
msg = f'sitemap_consumer[{idx}]: {SUCCESSFUL_INGEST_MESSAGE}: {job.identifier}'
self.logger.debug(msg)
msg = f'{SUCCESSFUL_INGEST_MESSAGE}: {job.identifier}'
self.logger.info(msg)
sitemap_queue.task_done()
|
async def consume_sitemap(self, idx, sitemap_queue):
'\n In a producer/consumer paradigm, here we are consuming work items\n from the sitemap.\n\n Parameters\n ----------\n idx: int\n The only purpose for this is to identify the consumer in the logs.\n sitemap_queue : asyncio.Queue\n Holds jobs associated with the sitemap. Each job includes, among\n other things, a URL and a modification time.\n '
while True:
try:
job = (await sitemap_queue.get())
self.logger.debug(f'sitemap_consumer[{idx}] ==> {job.url}')
msg = f'last mod = {job.lastmod}: num failures so far = {self.failed_count}, queue size = {sitemap_queue.qsize()}'
self.logger.info(msg)
(await self.process_job(job))
except asyncio.CancelledError:
self.logger.debug('CancelledError')
break
except SkipError as e:
job.result = e
self.job_records.append(copy.copy(job))
msg = f'Unable to process {job.url}: {e}'
self.logger.warning(msg)
except Exception as e:
job.result = e
self.job_records.append(copy.copy(job))
msg = f'Unable to process {job.url}: {e}, {job.identifier} failures so far = {job.num_failures}'
self.logger.error(msg)
self.failed_count += 1
if (self.failed_count == self.max_num_errors):
self.logger.warning('Error threshold reached.')
(await self.shutdown())
if (job.num_failures < self.retry):
if isinstance(e, ERROR_RETRY_CANDIDATES):
self.logger.info(f'Throwing {job.url} back on queue')
job.num_failures += 1
sitemap_queue.put_nowait(job)
else:
job.result = None
self.job_records.append(copy.copy(job))
msg = f'sitemap_consumer[{idx}]: {SUCCESSFUL_INGEST_MESSAGE}: {job.identifier}'
self.logger.debug(msg)
msg = f'{SUCCESSFUL_INGEST_MESSAGE}: {job.identifier}'
self.logger.info(msg)
sitemap_queue.task_done()<|docstring|>In a producer/consumer paradigm, here we are consuming work items
from the sitemap.
Parameters
----------
idx: int
The only purpose for this is to identify the consumer in the logs.
sitemap_queue : asyncio.Queue
Holds jobs associated with the sitemap. Each job includes, among
other things, a URL and a modification time.<|endoftext|>
|
f5381eb49dde041f6595b44a5a9ba192a448615e803c37424ed3154413adc6ec
|
async def process_job(self, job):
'\n Now that we have the record, validate and harvest it.\n\n Parameters\n ----------\n job : SlenderNodeJob\n Record containing at least the following attributes: landing page\n URL, last document modification time according to the site map.\n '
self.logger.debug(f'process_job: starting')
(series_id, pid, lastmod, doc) = (await self.retrieve_record(job.url))
if (lastmod is not None):
job.lastmod = lastmod
job.identifier = series_id
self.validate_document(doc)
(await self.harvest_document(series_id, pid, doc, job.lastmod))
self.logger.debug(f'process_job: finished')
|
Now that we have the record, validate and harvest it.
Parameters
----------
job : SlenderNodeJob
Record containing at least the following attributes: landing page
URL, last document modification time according to the site map.
|
schema_org/schema_org/core.py
|
process_job
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def process_job(self, job):
'\n Now that we have the record, validate and harvest it.\n\n Parameters\n ----------\n job : SlenderNodeJob\n Record containing at least the following attributes: landing page\n URL, last document modification time according to the site map.\n '
self.logger.debug(f'process_job: starting')
(series_id, pid, lastmod, doc) = (await self.retrieve_record(job.url))
if (lastmod is not None):
job.lastmod = lastmod
job.identifier = series_id
self.validate_document(doc)
(await self.harvest_document(series_id, pid, doc, job.lastmod))
self.logger.debug(f'process_job: finished')
|
async def process_job(self, job):
'\n Now that we have the record, validate and harvest it.\n\n Parameters\n ----------\n job : SlenderNodeJob\n Record containing at least the following attributes: landing page\n URL, last document modification time according to the site map.\n '
self.logger.debug(f'process_job: starting')
(series_id, pid, lastmod, doc) = (await self.retrieve_record(job.url))
if (lastmod is not None):
job.lastmod = lastmod
job.identifier = series_id
self.validate_document(doc)
(await self.harvest_document(series_id, pid, doc, job.lastmod))
self.logger.debug(f'process_job: finished')<|docstring|>Now that we have the record, validate and harvest it.
Parameters
----------
job : SlenderNodeJob
Record containing at least the following attributes: landing page
URL, last document modification time according to the site map.<|endoftext|>
|
befa4d3ef1fed6bb11bbfc55e9cd07f757ceaff0db4718e48ee2ebf0b58de4ff
|
def validate_document(self, doc):
'\n Verify that the format ID we have for the document is correct.\n\n Parameters\n ----------\n doc : bytes\n serialized version of XML metadata document\n '
format_id = self.sys_meta_dict['formatId_custom']
try:
d1_scimeta.validate.assert_valid(format_id, doc)
except Exception:
msg = f'Default validation failed with format ID {format_id}.'
self.logger.info(msg)
validator = XMLValidator(logger=self.logger)
format_id = validator.validate(doc)
if (format_id is None):
raise XMLValidationError('XML metadata validation failed.')
else:
self.sys_meta_dict['formatId_custom'] = format_id
|
Verify that the format ID we have for the document is correct.
Parameters
----------
doc : bytes
serialized version of XML metadata document
|
schema_org/schema_org/core.py
|
validate_document
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
def validate_document(self, doc):
'\n Verify that the format ID we have for the document is correct.\n\n Parameters\n ----------\n doc : bytes\n serialized version of XML metadata document\n '
format_id = self.sys_meta_dict['formatId_custom']
try:
d1_scimeta.validate.assert_valid(format_id, doc)
except Exception:
msg = f'Default validation failed with format ID {format_id}.'
self.logger.info(msg)
validator = XMLValidator(logger=self.logger)
format_id = validator.validate(doc)
if (format_id is None):
raise XMLValidationError('XML metadata validation failed.')
else:
self.sys_meta_dict['formatId_custom'] = format_id
|
def validate_document(self, doc):
'\n Verify that the format ID we have for the document is correct.\n\n Parameters\n ----------\n doc : bytes\n serialized version of XML metadata document\n '
format_id = self.sys_meta_dict['formatId_custom']
try:
d1_scimeta.validate.assert_valid(format_id, doc)
except Exception:
msg = f'Default validation failed with format ID {format_id}.'
self.logger.info(msg)
validator = XMLValidator(logger=self.logger)
format_id = validator.validate(doc)
if (format_id is None):
raise XMLValidationError('XML metadata validation failed.')
else:
self.sys_meta_dict['formatId_custom'] = format_id<|docstring|>Verify that the format ID we have for the document is correct.
Parameters
----------
doc : bytes
serialized version of XML metadata document<|endoftext|>
|
ae93fd515b2aa782c6dcd65b7b9729378b03296ced85c1564cecd8cac8497099
|
async def retrieve_record(self, document_url):
'\n Parameters\n ----------\n document_url : str\n URL for a remote document, could be a landing page, could be an\n XML document\n\n Returns\n -------\n identifier : str\n Ideally this is a DOI, but here it is a UUID.\n doc : ElementTree\n Metadata document\n '
raise NotImplementedError('must implement retrieve_record in sub class')
|
Parameters
----------
document_url : str
URL for a remote document, could be a landing page, could be an
XML document
Returns
-------
identifier : str
Ideally this is a DOI, but here it is a UUID.
doc : ElementTree
Metadata document
|
schema_org/schema_org/core.py
|
retrieve_record
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def retrieve_record(self, document_url):
'\n Parameters\n ----------\n document_url : str\n URL for a remote document, could be a landing page, could be an\n XML document\n\n Returns\n -------\n identifier : str\n Ideally this is a DOI, but here it is a UUID.\n doc : ElementTree\n Metadata document\n '
raise NotImplementedError('must implement retrieve_record in sub class')
|
async def retrieve_record(self, document_url):
'\n Parameters\n ----------\n document_url : str\n URL for a remote document, could be a landing page, could be an\n XML document\n\n Returns\n -------\n identifier : str\n Ideally this is a DOI, but here it is a UUID.\n doc : ElementTree\n Metadata document\n '
raise NotImplementedError('must implement retrieve_record in sub class')<|docstring|>Parameters
----------
document_url : str
URL for a remote document, could be a landing page, could be an
XML document
Returns
-------
identifier : str
Ideally this is a DOI, but here it is a UUID.
doc : ElementTree
Metadata document<|endoftext|>
|
07552ae0cadb67719c38d6eb84e0a967e17c8423b7525752f2ee5b7c81105694
|
async def process_sitemap(self, sitemap_url, last_harvest):
'\n Determine if the sitemap (or RSS feed or whatever) is an index file\n or whether it is a single document. If an index file, we need to\n descend recursively into it.\n\n Parameters\n ----------\n sitemap_url : str\n URL for a sitemap or sitemap index file\n last_harvest : datetime\n According to the MN, this is the last time we, uh, harvested any\n document.\n '
msg = f'process_sitemap: {sitemap_url}, {last_harvest}'
self.logger.debug(msg)
doc = (await self.get_sitemap_document(sitemap_url))
if self.is_sitemap_index_file(doc):
msg = 'process_sitemap: This is a sitemap index file.'
self.logger.debug(msg)
path = 'sm:sitemap/sm:loc/text()'
sitemap_urls = doc.xpath(path, namespaces=SITEMAP_NS)
for sitemap_url in sitemap_urls:
(await self.process_sitemap(sitemap_url, last_harvest))
else:
self.logger.debug('process_sitemap: This is a sitemap leaf.')
self._sitemaps.append(sitemap_url)
(await self.process_sitemap_leaf(doc, last_harvest))
|
Determine if the sitemap (or RSS feed or whatever) is an index file
or whether it is a single document. If an index file, we need to
descend recursively into it.
Parameters
----------
sitemap_url : str
URL for a sitemap or sitemap index file
last_harvest : datetime
According to the MN, this is the last time we, uh, harvested any
document.
|
schema_org/schema_org/core.py
|
process_sitemap
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def process_sitemap(self, sitemap_url, last_harvest):
'\n Determine if the sitemap (or RSS feed or whatever) is an index file\n or whether it is a single document. If an index file, we need to\n descend recursively into it.\n\n Parameters\n ----------\n sitemap_url : str\n URL for a sitemap or sitemap index file\n last_harvest : datetime\n According to the MN, this is the last time we, uh, harvested any\n document.\n '
msg = f'process_sitemap: {sitemap_url}, {last_harvest}'
self.logger.debug(msg)
doc = (await self.get_sitemap_document(sitemap_url))
if self.is_sitemap_index_file(doc):
msg = 'process_sitemap: This is a sitemap index file.'
self.logger.debug(msg)
path = 'sm:sitemap/sm:loc/text()'
sitemap_urls = doc.xpath(path, namespaces=SITEMAP_NS)
for sitemap_url in sitemap_urls:
(await self.process_sitemap(sitemap_url, last_harvest))
else:
self.logger.debug('process_sitemap: This is a sitemap leaf.')
self._sitemaps.append(sitemap_url)
(await self.process_sitemap_leaf(doc, last_harvest))
|
async def process_sitemap(self, sitemap_url, last_harvest):
'\n Determine if the sitemap (or RSS feed or whatever) is an index file\n or whether it is a single document. If an index file, we need to\n descend recursively into it.\n\n Parameters\n ----------\n sitemap_url : str\n URL for a sitemap or sitemap index file\n last_harvest : datetime\n According to the MN, this is the last time we, uh, harvested any\n document.\n '
msg = f'process_sitemap: {sitemap_url}, {last_harvest}'
self.logger.debug(msg)
doc = (await self.get_sitemap_document(sitemap_url))
if self.is_sitemap_index_file(doc):
msg = 'process_sitemap: This is a sitemap index file.'
self.logger.debug(msg)
path = 'sm:sitemap/sm:loc/text()'
sitemap_urls = doc.xpath(path, namespaces=SITEMAP_NS)
for sitemap_url in sitemap_urls:
(await self.process_sitemap(sitemap_url, last_harvest))
else:
self.logger.debug('process_sitemap: This is a sitemap leaf.')
self._sitemaps.append(sitemap_url)
(await self.process_sitemap_leaf(doc, last_harvest))<|docstring|>Determine if the sitemap (or RSS feed or whatever) is an index file
or whether it is a single document. If an index file, we need to
descend recursively into it.
Parameters
----------
sitemap_url : str
URL for a sitemap or sitemap index file
last_harvest : datetime
According to the MN, this is the last time we, uh, harvested any
document.<|endoftext|>
|
00e57a199f1b05802821b73bc12f1baa8999752039d9e2cba6bcfb99a7309f42
|
async def get_sitemap_document(self, sitemap_url):
'\n Retrieve a remote sitemap document.\n\n Parameters\n ---------\n sitemap_url : str\n URL for a sitemap or sitemap index file.\n '
self.logger.info(f'Requesting sitemap document from {sitemap_url}')
try:
(content, headers) = (await self.retrieve_url(sitemap_url))
self.check_xml_headers(headers)
except Exception as e:
msg = f'{SITEMAP_RETRIEVAL_FAILURE_MESSAGE} due to {repr(e)}'
self.logger.error(msg)
raise
try:
doc = lxml.etree.parse(io.BytesIO(content))
except lxml.etree.XMLSyntaxError as e:
msg1 = str(e)
try:
doc = lxml.etree.parse(io.BytesIO(gzip.decompress(content)))
except OSError:
msg = f'XMLSyntaxError: sitemap document at {sitemap_url}: {msg1}'
self.logger.error(msg)
msg = f'Unable to process the sitemap retrieved from {sitemap_url}.'
raise InvalidSitemapError(msg)
return doc
|
Retrieve a remote sitemap document.
Parameters
---------
sitemap_url : str
URL for a sitemap or sitemap index file.
|
schema_org/schema_org/core.py
|
get_sitemap_document
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def get_sitemap_document(self, sitemap_url):
'\n Retrieve a remote sitemap document.\n\n Parameters\n ---------\n sitemap_url : str\n URL for a sitemap or sitemap index file.\n '
self.logger.info(f'Requesting sitemap document from {sitemap_url}')
try:
(content, headers) = (await self.retrieve_url(sitemap_url))
self.check_xml_headers(headers)
except Exception as e:
msg = f'{SITEMAP_RETRIEVAL_FAILURE_MESSAGE} due to {repr(e)}'
self.logger.error(msg)
raise
try:
doc = lxml.etree.parse(io.BytesIO(content))
except lxml.etree.XMLSyntaxError as e:
msg1 = str(e)
try:
doc = lxml.etree.parse(io.BytesIO(gzip.decompress(content)))
except OSError:
msg = f'XMLSyntaxError: sitemap document at {sitemap_url}: {msg1}'
self.logger.error(msg)
msg = f'Unable to process the sitemap retrieved from {sitemap_url}.'
raise InvalidSitemapError(msg)
return doc
|
async def get_sitemap_document(self, sitemap_url):
'\n Retrieve a remote sitemap document.\n\n Parameters\n ---------\n sitemap_url : str\n URL for a sitemap or sitemap index file.\n '
self.logger.info(f'Requesting sitemap document from {sitemap_url}')
try:
(content, headers) = (await self.retrieve_url(sitemap_url))
self.check_xml_headers(headers)
except Exception as e:
msg = f'{SITEMAP_RETRIEVAL_FAILURE_MESSAGE} due to {repr(e)}'
self.logger.error(msg)
raise
try:
doc = lxml.etree.parse(io.BytesIO(content))
except lxml.etree.XMLSyntaxError as e:
msg1 = str(e)
try:
doc = lxml.etree.parse(io.BytesIO(gzip.decompress(content)))
except OSError:
msg = f'XMLSyntaxError: sitemap document at {sitemap_url}: {msg1}'
self.logger.error(msg)
msg = f'Unable to process the sitemap retrieved from {sitemap_url}.'
raise InvalidSitemapError(msg)
return doc<|docstring|>Retrieve a remote sitemap document.
Parameters
---------
sitemap_url : str
URL for a sitemap or sitemap index file.<|endoftext|>
|
5084072162c1cd8de089f8fa4e284308dbf5c6f7e373114da4fbe7d7261d2b0d
|
async def process_sitemap_leaf(self, doc, last_harvest):
'\n We are at a sitemap leaf, i.e. the sitemap does not reference other\n sitemaps. This is where we can retrieve landing pages instead of\n other sitemaps.\n\n Parameters\n ----------\n doc : ElementTree object\n Describes the sitemap leaf.\n last_harvest : datetime\n According to the MN, this is the last time we, uh, harvested any\n document.\n '
self.logger.debug(f'process_sitemap_leaf:')
records = self.extract_records_from_sitemap(doc)
records = self.post_process_sitemap_records(records, last_harvest)
self._sitemap_records.extend(records)
if self.no_harvest:
return
sitemap_queue = asyncio.Queue()
for (url, lastmod_time) in records:
job = SlenderNodeJob(url, '', lastmod_time, 0, None)
sitemap_queue.put_nowait(job)
tasks = []
for j in range(self.num_workers):
msg = f'process_sitemap_leaf: create task for sitemap_consumer[{j}]'
self.logger.debug(msg)
task = asyncio.create_task(self.consume_sitemap(j, sitemap_queue))
tasks.append(task)
(await sitemap_queue.join())
for task in tasks:
task.cancel()
(await asyncio.gather(*tasks, return_exceptions=True))
|
We are at a sitemap leaf, i.e. the sitemap does not reference other
sitemaps. This is where we can retrieve landing pages instead of
other sitemaps.
Parameters
----------
doc : ElementTree object
Describes the sitemap leaf.
last_harvest : datetime
According to the MN, this is the last time we, uh, harvested any
document.
|
schema_org/schema_org/core.py
|
process_sitemap_leaf
|
DataONEorg/d1_ncei_adapter
| 1 |
python
|
async def process_sitemap_leaf(self, doc, last_harvest):
'\n We are at a sitemap leaf, i.e. the sitemap does not reference other\n sitemaps. This is where we can retrieve landing pages instead of\n other sitemaps.\n\n Parameters\n ----------\n doc : ElementTree object\n Describes the sitemap leaf.\n last_harvest : datetime\n According to the MN, this is the last time we, uh, harvested any\n document.\n '
self.logger.debug(f'process_sitemap_leaf:')
records = self.extract_records_from_sitemap(doc)
records = self.post_process_sitemap_records(records, last_harvest)
self._sitemap_records.extend(records)
if self.no_harvest:
return
sitemap_queue = asyncio.Queue()
for (url, lastmod_time) in records:
job = SlenderNodeJob(url, , lastmod_time, 0, None)
sitemap_queue.put_nowait(job)
tasks = []
for j in range(self.num_workers):
msg = f'process_sitemap_leaf: create task for sitemap_consumer[{j}]'
self.logger.debug(msg)
task = asyncio.create_task(self.consume_sitemap(j, sitemap_queue))
tasks.append(task)
(await sitemap_queue.join())
for task in tasks:
task.cancel()
(await asyncio.gather(*tasks, return_exceptions=True))
|
async def process_sitemap_leaf(self, doc, last_harvest):
'\n We are at a sitemap leaf, i.e. the sitemap does not reference other\n sitemaps. This is where we can retrieve landing pages instead of\n other sitemaps.\n\n Parameters\n ----------\n doc : ElementTree object\n Describes the sitemap leaf.\n last_harvest : datetime\n According to the MN, this is the last time we, uh, harvested any\n document.\n '
self.logger.debug(f'process_sitemap_leaf:')
records = self.extract_records_from_sitemap(doc)
records = self.post_process_sitemap_records(records, last_harvest)
self._sitemap_records.extend(records)
if self.no_harvest:
return
sitemap_queue = asyncio.Queue()
for (url, lastmod_time) in records:
job = SlenderNodeJob(url, , lastmod_time, 0, None)
sitemap_queue.put_nowait(job)
tasks = []
for j in range(self.num_workers):
msg = f'process_sitemap_leaf: create task for sitemap_consumer[{j}]'
self.logger.debug(msg)
task = asyncio.create_task(self.consume_sitemap(j, sitemap_queue))
tasks.append(task)
(await sitemap_queue.join())
for task in tasks:
task.cancel()
(await asyncio.gather(*tasks, return_exceptions=True))<|docstring|>We are at a sitemap leaf, i.e. the sitemap does not reference other
sitemaps. This is where we can retrieve landing pages instead of
other sitemaps.
Parameters
----------
doc : ElementTree object
Describes the sitemap leaf.
last_harvest : datetime
According to the MN, this is the last time we, uh, harvested any
document.<|endoftext|>
|
2696cd3adf18d48a0469d65fe8f51e49052d1c77b4f7fbc03022beecee810607
|
def get_joke():
'Returns a joke from the WebKnox one liner API.\n\n Returns None if unable to retrieve a joke.\n '
headers = {'Accept': 'application/json'}
page = requests.get('https://icanhazdadjoke.com', headers=headers)
if (page.status_code == 200):
joke = json.loads(page.content.decode('UTF-8'))
return joke['joke']
return None
|
Returns a joke from the WebKnox one liner API.
Returns None if unable to retrieve a joke.
|
laughs/services/dadjokes.py
|
get_joke
|
seancallaway/laughs
| 0 |
python
|
def get_joke():
'Returns a joke from the WebKnox one liner API.\n\n Returns None if unable to retrieve a joke.\n '
headers = {'Accept': 'application/json'}
page = requests.get('https://icanhazdadjoke.com', headers=headers)
if (page.status_code == 200):
joke = json.loads(page.content.decode('UTF-8'))
return joke['joke']
return None
|
def get_joke():
'Returns a joke from the WebKnox one liner API.\n\n Returns None if unable to retrieve a joke.\n '
headers = {'Accept': 'application/json'}
page = requests.get('https://icanhazdadjoke.com', headers=headers)
if (page.status_code == 200):
joke = json.loads(page.content.decode('UTF-8'))
return joke['joke']
return None<|docstring|>Returns a joke from the WebKnox one liner API.
Returns None if unable to retrieve a joke.<|endoftext|>
|
c77365c637ac1afb1c1d36a0d78a367719ef501ca33b4e95c0607f1a2ad1901a
|
@property
def cursor(self) -> Position:
'\n The cursor position of the 1st selection.\n '
return self.selection.active
|
The cursor position of the 1st selection.
|
vscode/window.py
|
cursor
|
TTitcombe/vscode-ext
| 140 |
python
|
@property
def cursor(self) -> Position:
'\n \n '
return self.selection.active
|
@property
def cursor(self) -> Position:
'\n \n '
return self.selection.active<|docstring|>The cursor position of the 1st selection.<|endoftext|>
|
31461040ae3a5d0a1f8de8636dcc77bedae46ad8f9b839b004f80c29e657894d
|
def read_data(fp='../data/pmadata.csv'):
'\n Read clinic data from a csv into a pandas dataframe.\n\n :param str fp: the file path of the csv file\n '
return pd.read_csv(fp)
|
Read clinic data from a csv into a pandas dataframe.
:param str fp: the file path of the csv file
|
pmareport/predictors.py
|
read_data
|
gautsi/pmareport
| 0 |
python
|
def read_data(fp='../data/pmadata.csv'):
'\n Read clinic data from a csv into a pandas dataframe.\n\n :param str fp: the file path of the csv file\n '
return pd.read_csv(fp)
|
def read_data(fp='../data/pmadata.csv'):
'\n Read clinic data from a csv into a pandas dataframe.\n\n :param str fp: the file path of the csv file\n '
return pd.read_csv(fp)<|docstring|>Read clinic data from a csv into a pandas dataframe.
:param str fp: the file path of the csv file<|endoftext|>
|
c97eee076903c4d6163e8e4216bfedf26a7b53a7674efc813df4003d83a135e0
|
def percent_within(y_true, y_pred, thresh=5):
'\n Calculate the percentage of predictions are within\n `thresh` of the true value.\n\n :param array-like y_true: the true values\n :param array-like y_pred: the predicted values\n :param float thresh: the threshold for a close prediction\n\n :returns:\n the percent of predictions within the treshold from the true value\n :rtype: float\n '
return ((np.sum((np.abs((y_true - y_pred)) < thresh)) / float(len(y_true))) * 100)
|
Calculate the percentage of predictions are within
`thresh` of the true value.
:param array-like y_true: the true values
:param array-like y_pred: the predicted values
:param float thresh: the threshold for a close prediction
:returns:
the percent of predictions within the treshold from the true value
:rtype: float
|
pmareport/predictors.py
|
percent_within
|
gautsi/pmareport
| 0 |
python
|
def percent_within(y_true, y_pred, thresh=5):
'\n Calculate the percentage of predictions are within\n `thresh` of the true value.\n\n :param array-like y_true: the true values\n :param array-like y_pred: the predicted values\n :param float thresh: the threshold for a close prediction\n\n :returns:\n the percent of predictions within the treshold from the true value\n :rtype: float\n '
return ((np.sum((np.abs((y_true - y_pred)) < thresh)) / float(len(y_true))) * 100)
|
def percent_within(y_true, y_pred, thresh=5):
'\n Calculate the percentage of predictions are within\n `thresh` of the true value.\n\n :param array-like y_true: the true values\n :param array-like y_pred: the predicted values\n :param float thresh: the threshold for a close prediction\n\n :returns:\n the percent of predictions within the treshold from the true value\n :rtype: float\n '
return ((np.sum((np.abs((y_true - y_pred)) < thresh)) / float(len(y_true))) * 100)<|docstring|>Calculate the percentage of predictions are within
`thresh` of the true value.
:param array-like y_true: the true values
:param array-like y_pred: the predicted values
:param float thresh: the threshold for a close prediction
:returns:
the percent of predictions within the treshold from the true value
:rtype: float<|endoftext|>
|
ae6e021b660226d2641167800b542dba7fd2a8603ec434115beb2f6483ae0048
|
def make_int(self, col):
'\n Encode categorical variables of type other than int\n as ints for input into the decision tree.\n\n :param str col: the name of the column with categorical values\n '
categories = list(set(self.df[col]))
int_func = (lambda x: categories.index(x))
self.df[(col + 'i')] = self.df[col].apply(int_func)
self.feat_cols.remove(col)
self.feat_cols.append((col + 'i'))
self.int_funcs[col] = int_func
|
Encode categorical variables of type other than int
as ints for input into the decision tree.
:param str col: the name of the column with categorical values
|
pmareport/predictors.py
|
make_int
|
gautsi/pmareport
| 0 |
python
|
def make_int(self, col):
'\n Encode categorical variables of type other than int\n as ints for input into the decision tree.\n\n :param str col: the name of the column with categorical values\n '
categories = list(set(self.df[col]))
int_func = (lambda x: categories.index(x))
self.df[(col + 'i')] = self.df[col].apply(int_func)
self.feat_cols.remove(col)
self.feat_cols.append((col + 'i'))
self.int_funcs[col] = int_func
|
def make_int(self, col):
'\n Encode categorical variables of type other than int\n as ints for input into the decision tree.\n\n :param str col: the name of the column with categorical values\n '
categories = list(set(self.df[col]))
int_func = (lambda x: categories.index(x))
self.df[(col + 'i')] = self.df[col].apply(int_func)
self.feat_cols.remove(col)
self.feat_cols.append((col + 'i'))
self.int_funcs[col] = int_func<|docstring|>Encode categorical variables of type other than int
as ints for input into the decision tree.
:param str col: the name of the column with categorical values<|endoftext|>
|
ce8ce04ef68fb32d601b01790c27f7e156aef21c4d92db45cd259cd57f570dd4
|
def train_test(self, test_size=0.1):
'\n Split the data into train and test sets.\n\n :param float test_size: the percentage of rows to leave out as test\n '
(self.train, self.test) = cross_validation.train_test_split(self.df, test_size=test_size)
self.Xtrain = self.train[self.feat_cols]
self.ytrain = self.train[self.response_col]
self.Xtest = self.test[self.feat_cols]
self.ytest = self.test[self.response_col]
|
Split the data into train and test sets.
:param float test_size: the percentage of rows to leave out as test
|
pmareport/predictors.py
|
train_test
|
gautsi/pmareport
| 0 |
python
|
def train_test(self, test_size=0.1):
'\n Split the data into train and test sets.\n\n :param float test_size: the percentage of rows to leave out as test\n '
(self.train, self.test) = cross_validation.train_test_split(self.df, test_size=test_size)
self.Xtrain = self.train[self.feat_cols]
self.ytrain = self.train[self.response_col]
self.Xtest = self.test[self.feat_cols]
self.ytest = self.test[self.response_col]
|
def train_test(self, test_size=0.1):
'\n Split the data into train and test sets.\n\n :param float test_size: the percentage of rows to leave out as test\n '
(self.train, self.test) = cross_validation.train_test_split(self.df, test_size=test_size)
self.Xtrain = self.train[self.feat_cols]
self.ytrain = self.train[self.response_col]
self.Xtest = self.test[self.feat_cols]
self.ytest = self.test[self.response_col]<|docstring|>Split the data into train and test sets.
:param float test_size: the percentage of rows to leave out as test<|endoftext|>
|
0fbbfd19a8970a98e3fe68cb51d7d1aa9689fe06be8e697be6a1964fc5a3b37b
|
def make_model(self, max_depth=3):
'\n Make the model, a decision tree with maximum depth `max_depth`.\n\n :param max_depth: the maximum depth of the decision tree\n '
self.model = tree.DecisionTreeRegressor(max_depth=max_depth)
|
Make the model, a decision tree with maximum depth `max_depth`.
:param max_depth: the maximum depth of the decision tree
|
pmareport/predictors.py
|
make_model
|
gautsi/pmareport
| 0 |
python
|
def make_model(self, max_depth=3):
'\n Make the model, a decision tree with maximum depth `max_depth`.\n\n :param max_depth: the maximum depth of the decision tree\n '
self.model = tree.DecisionTreeRegressor(max_depth=max_depth)
|
def make_model(self, max_depth=3):
'\n Make the model, a decision tree with maximum depth `max_depth`.\n\n :param max_depth: the maximum depth of the decision tree\n '
self.model = tree.DecisionTreeRegressor(max_depth=max_depth)<|docstring|>Make the model, a decision tree with maximum depth `max_depth`.
:param max_depth: the maximum depth of the decision tree<|endoftext|>
|
66f8e09f51c531f271885d89acb3f1ec1177e4f41f135da80f85027d52cadef6
|
def cv_evalution(self, n_folds=10, thresh=5):
'\n Evaluate the model on a cross valdation split\n of the training data with `n_folds` nmber of folds.\n The metric is the percent of predictions within `thresh`\n of the true value.\n\n :param int n_folds: the number of folds for the cross validation\n :param float thresh:\n the threshold for considering a prediction close to the true value\n\n :returns: the average of metric values over the folds\n :rtype: float\n '
cv = cross_validation.KFold(len(self.train), n_folds=n_folds)
score_list = []
for (train, test) in cv:
cvXtrain = self.Xtrain.iloc[train]
cvXtest = self.Xtrain.iloc[test]
cvytrain = self.ytrain.iloc[train]
cvytest = self.ytrain.iloc[test]
self.model.fit(cvXtrain, cvytrain)
pred = self.model.predict(cvXtest)
score = percent_within(y_true=cvytest, y_pred=pred, thresh=5)
score_list.append(score)
return np.mean(score_list)
|
Evaluate the model on a cross valdation split
of the training data with `n_folds` nmber of folds.
The metric is the percent of predictions within `thresh`
of the true value.
:param int n_folds: the number of folds for the cross validation
:param float thresh:
the threshold for considering a prediction close to the true value
:returns: the average of metric values over the folds
:rtype: float
|
pmareport/predictors.py
|
cv_evalution
|
gautsi/pmareport
| 0 |
python
|
def cv_evalution(self, n_folds=10, thresh=5):
'\n Evaluate the model on a cross valdation split\n of the training data with `n_folds` nmber of folds.\n The metric is the percent of predictions within `thresh`\n of the true value.\n\n :param int n_folds: the number of folds for the cross validation\n :param float thresh:\n the threshold for considering a prediction close to the true value\n\n :returns: the average of metric values over the folds\n :rtype: float\n '
cv = cross_validation.KFold(len(self.train), n_folds=n_folds)
score_list = []
for (train, test) in cv:
cvXtrain = self.Xtrain.iloc[train]
cvXtest = self.Xtrain.iloc[test]
cvytrain = self.ytrain.iloc[train]
cvytest = self.ytrain.iloc[test]
self.model.fit(cvXtrain, cvytrain)
pred = self.model.predict(cvXtest)
score = percent_within(y_true=cvytest, y_pred=pred, thresh=5)
score_list.append(score)
return np.mean(score_list)
|
def cv_evalution(self, n_folds=10, thresh=5):
'\n Evaluate the model on a cross valdation split\n of the training data with `n_folds` nmber of folds.\n The metric is the percent of predictions within `thresh`\n of the true value.\n\n :param int n_folds: the number of folds for the cross validation\n :param float thresh:\n the threshold for considering a prediction close to the true value\n\n :returns: the average of metric values over the folds\n :rtype: float\n '
cv = cross_validation.KFold(len(self.train), n_folds=n_folds)
score_list = []
for (train, test) in cv:
cvXtrain = self.Xtrain.iloc[train]
cvXtest = self.Xtrain.iloc[test]
cvytrain = self.ytrain.iloc[train]
cvytest = self.ytrain.iloc[test]
self.model.fit(cvXtrain, cvytrain)
pred = self.model.predict(cvXtest)
score = percent_within(y_true=cvytest, y_pred=pred, thresh=5)
score_list.append(score)
return np.mean(score_list)<|docstring|>Evaluate the model on a cross valdation split
of the training data with `n_folds` nmber of folds.
The metric is the percent of predictions within `thresh`
of the true value.
:param int n_folds: the number of folds for the cross validation
:param float thresh:
the threshold for considering a prediction close to the true value
:returns: the average of metric values over the folds
:rtype: float<|endoftext|>
|
5540ebd126194e0bbfb4942a1665eb5f64865cc9889e88ec9c0b25972762f90d
|
def fit(self, thresh=5):
'\n Fit the model on the training set and evaluate it\n on the test set. The metric is the percent of\n predictions within `thresh` of the true value.\n\n :param float thresh:\n the threshold for considering a prediction close to the true value\n\n :returns: the score of the model on the test set\n :rtype: float\n '
self.model.fit(self.Xtrain, self.ytrain)
predictions = self.model.predict(self.Xtest)
score = percent_within(y_true=self.ytest, y_pred=predictions, thresh=thresh)
return score
|
Fit the model on the training set and evaluate it
on the test set. The metric is the percent of
predictions within `thresh` of the true value.
:param float thresh:
the threshold for considering a prediction close to the true value
:returns: the score of the model on the test set
:rtype: float
|
pmareport/predictors.py
|
fit
|
gautsi/pmareport
| 0 |
python
|
def fit(self, thresh=5):
'\n Fit the model on the training set and evaluate it\n on the test set. The metric is the percent of\n predictions within `thresh` of the true value.\n\n :param float thresh:\n the threshold for considering a prediction close to the true value\n\n :returns: the score of the model on the test set\n :rtype: float\n '
self.model.fit(self.Xtrain, self.ytrain)
predictions = self.model.predict(self.Xtest)
score = percent_within(y_true=self.ytest, y_pred=predictions, thresh=thresh)
return score
|
def fit(self, thresh=5):
'\n Fit the model on the training set and evaluate it\n on the test set. The metric is the percent of\n predictions within `thresh` of the true value.\n\n :param float thresh:\n the threshold for considering a prediction close to the true value\n\n :returns: the score of the model on the test set\n :rtype: float\n '
self.model.fit(self.Xtrain, self.ytrain)
predictions = self.model.predict(self.Xtest)
score = percent_within(y_true=self.ytest, y_pred=predictions, thresh=thresh)
return score<|docstring|>Fit the model on the training set and evaluate it
on the test set. The metric is the percent of
predictions within `thresh` of the true value.
:param float thresh:
the threshold for considering a prediction close to the true value
:returns: the score of the model on the test set
:rtype: float<|endoftext|>
|
74c6162a0aba99ad0b13e6285809f0974a787a60af4ea31c6076cabc91231c6b
|
def load_collections(fname):
'\n Open a geocodr mapping file and return all geocodr.search.Collection\n subclasses.\n '
collections = []
with open(fname, 'r') as f:
code = compile(f.read(), fname, 'exec')
ns = {}
exec(code, ns)
src_proj = None
for v in ns.values():
if (inspect.isclass(v) and issubclass(v, Collection) and (v != Collection) and v.name):
coll = v()
if src_proj:
assert (coll.src_proj == src_proj), 'all Collections need the same src_proj'
else:
src_proj = coll.src_proj
collections.append(coll)
return collections
|
Open a geocodr mapping file and return all geocodr.search.Collection
subclasses.
|
api/geocodr/mapping.py
|
load_collections
|
axza/geocodr
| 3 |
python
|
def load_collections(fname):
'\n Open a geocodr mapping file and return all geocodr.search.Collection\n subclasses.\n '
collections = []
with open(fname, 'r') as f:
code = compile(f.read(), fname, 'exec')
ns = {}
exec(code, ns)
src_proj = None
for v in ns.values():
if (inspect.isclass(v) and issubclass(v, Collection) and (v != Collection) and v.name):
coll = v()
if src_proj:
assert (coll.src_proj == src_proj), 'all Collections need the same src_proj'
else:
src_proj = coll.src_proj
collections.append(coll)
return collections
|
def load_collections(fname):
'\n Open a geocodr mapping file and return all geocodr.search.Collection\n subclasses.\n '
collections = []
with open(fname, 'r') as f:
code = compile(f.read(), fname, 'exec')
ns = {}
exec(code, ns)
src_proj = None
for v in ns.values():
if (inspect.isclass(v) and issubclass(v, Collection) and (v != Collection) and v.name):
coll = v()
if src_proj:
assert (coll.src_proj == src_proj), 'all Collections need the same src_proj'
else:
src_proj = coll.src_proj
collections.append(coll)
return collections<|docstring|>Open a geocodr mapping file and return all geocodr.search.Collection
subclasses.<|endoftext|>
|
fd8bc83e4dc3507f3117ef33adf48571cedd2bd391348c4209c1070e72ec6cc8
|
@classmethod
def from_dict(cls, adict):
'Convert dictionary to Arxiv document object\n\n Args:\n adict (dict): a python dictionary\n\n Returns:\n dict: a filtered list object\n '
invalid_req = req.InvalidRequestObject()
if (('filters' in adict) and (not isinstance(adict['filters'], collections.Mapping))):
invalid_req.add_error('filters', 'Is not iterable')
if invalid_req.has_errors():
return invalid_req
return ArxivDocumentListRequestObject(filters=adict.get('filters', None))
|
Convert dictionary to Arxiv document object
Args:
adict (dict): a python dictionary
Returns:
dict: a filtered list object
|
webminer/interface_adapters/rest_adapters/request_objects.py
|
from_dict
|
liadmagen/Keep-Current-Crawler
| 38 |
python
|
@classmethod
def from_dict(cls, adict):
'Convert dictionary to Arxiv document object\n\n Args:\n adict (dict): a python dictionary\n\n Returns:\n dict: a filtered list object\n '
invalid_req = req.InvalidRequestObject()
if (('filters' in adict) and (not isinstance(adict['filters'], collections.Mapping))):
invalid_req.add_error('filters', 'Is not iterable')
if invalid_req.has_errors():
return invalid_req
return ArxivDocumentListRequestObject(filters=adict.get('filters', None))
|
@classmethod
def from_dict(cls, adict):
'Convert dictionary to Arxiv document object\n\n Args:\n adict (dict): a python dictionary\n\n Returns:\n dict: a filtered list object\n '
invalid_req = req.InvalidRequestObject()
if (('filters' in adict) and (not isinstance(adict['filters'], collections.Mapping))):
invalid_req.add_error('filters', 'Is not iterable')
if invalid_req.has_errors():
return invalid_req
return ArxivDocumentListRequestObject(filters=adict.get('filters', None))<|docstring|>Convert dictionary to Arxiv document object
Args:
adict (dict): a python dictionary
Returns:
dict: a filtered list object<|endoftext|>
|
c160410c16a50d7a9b086330c9537e4120b8eaa162a7b1509373cd562f39cdc6
|
def test_nonSpecialCaseEvents(self):
'Test that the list of events without special cases matches expectations\n\t\t'
self.assertEqual(39, len(nonSpecialCaseEvents))
|
Test that the list of events without special cases matches expectations
|
tests/unit/test_orderedWinEventLimiter.py
|
test_nonSpecialCaseEvents
|
lukaszgo1/nvda
| 1,592 |
python
|
def test_nonSpecialCaseEvents(self):
'\n\t\t'
self.assertEqual(39, len(nonSpecialCaseEvents))
|
def test_nonSpecialCaseEvents(self):
'\n\t\t'
self.assertEqual(39, len(nonSpecialCaseEvents))<|docstring|>Test that the list of events without special cases matches expectations<|endoftext|>
|
ac4f2ae1edfb66ed3793d4a57915e9a3449589676dd332bd62a76edb65a74e90
|
def test_threadLimit_singleObject(self):
'Test that only the latest events are kept when the thread limit is exceeded\n\t\t'
source = (2, 2, 2)
limiter = OrderedWinEventLimiter(maxFocusItems=4)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
self.assertEqual(expectedEventCount, len(events))
|
Test that only the latest events are kept when the thread limit is exceeded
|
tests/unit/test_orderedWinEventLimiter.py
|
test_threadLimit_singleObject
|
lukaszgo1/nvda
| 1,592 |
python
|
def test_threadLimit_singleObject(self):
'\n\t\t'
source = (2, 2, 2)
limiter = OrderedWinEventLimiter(maxFocusItems=4)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
self.assertEqual(expectedEventCount, len(events))
|
def test_threadLimit_singleObject(self):
'\n\t\t'
source = (2, 2, 2)
limiter = OrderedWinEventLimiter(maxFocusItems=4)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
self.assertEqual(expectedEventCount, len(events))<|docstring|>Test that only the latest events are kept when the thread limit is exceeded<|endoftext|>
|
94e71ad6762dd7bd5a7bb1b8b6a8a2dd518a5f8141d22cca0decd0ae9a71f2cc
|
def test_threadLimit_noCanary(self):
'Test that only the latest events are kept when the thread limit is exceeded\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
self.assertListEqual([], errors)
|
Test that only the latest events are kept when the thread limit is exceeded
|
tests/unit/test_orderedWinEventLimiter.py
|
test_threadLimit_noCanary
|
lukaszgo1/nvda
| 1,592 |
python
|
def test_threadLimit_noCanary(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
self.assertListEqual([], errors)
|
def test_threadLimit_noCanary(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
self.assertListEqual([], errors)<|docstring|>Test that only the latest events are kept when the thread limit is exceeded<|endoftext|>
|
07d268f361cc6a0778c3c4692d1715b80f33aeb1d646c8b99964221ff4189d0b
|
def test_threadLimit_withCanaryAtStart(self):
'Test that only the latest events are kept when the thread limit is exceeded\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
softAssert(errors, self.assertNotIn, eventStartCanary, events)
self.assertListEqual([], errors)
|
Test that only the latest events are kept when the thread limit is exceeded
|
tests/unit/test_orderedWinEventLimiter.py
|
test_threadLimit_withCanaryAtStart
|
lukaszgo1/nvda
| 1,592 |
python
|
def test_threadLimit_withCanaryAtStart(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
softAssert(errors, self.assertNotIn, eventStartCanary, events)
self.assertListEqual([], errors)
|
def test_threadLimit_withCanaryAtStart(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
softAssert(errors, self.assertNotIn, eventStartCanary, events)
self.assertListEqual([], errors)<|docstring|>Test that only the latest events are kept when the thread limit is exceeded<|endoftext|>
|
81da0583d3566d88bf31df756f8a53ae45cab150b0e2367f3458ddee65555696
|
def test_threadLimit_canaryStartAndEnd(self):
'Test that only the latest events are kept when the thread limit is exceeded\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
eventEndCanary = (winUser.EVENT_OBJECT_NAMECHANGE, *canaryObject)
limiter.addEvent(*eventEndCanary, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
softAssert(errors, self.assertIn, eventEndCanary, events)
softAssert(errors, self.assertNotIn, eventStartCanary, events)
self.assertListEqual([], errors)
|
Test that only the latest events are kept when the thread limit is exceeded
|
tests/unit/test_orderedWinEventLimiter.py
|
test_threadLimit_canaryStartAndEnd
|
lukaszgo1/nvda
| 1,592 |
python
|
def test_threadLimit_canaryStartAndEnd(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
eventEndCanary = (winUser.EVENT_OBJECT_NAMECHANGE, *canaryObject)
limiter.addEvent(*eventEndCanary, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
softAssert(errors, self.assertIn, eventEndCanary, events)
softAssert(errors, self.assertNotIn, eventStartCanary, events)
self.assertListEqual([], errors)
|
def test_threadLimit_canaryStartAndEnd(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(500):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
eventEndCanary = (winUser.EVENT_OBJECT_NAMECHANGE, *canaryObject)
limiter.addEvent(*eventEndCanary, threadID=0)
events = limiter.flushEvents()
errors = []
expectedEventCount = orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD
softAssert(errors, self.assertEqual, expectedEventCount, len(events))
softAssert(errors, self.assertIn, eventEndCanary, events)
softAssert(errors, self.assertNotIn, eventStartCanary, events)
self.assertListEqual([], errors)<|docstring|>Test that only the latest events are kept when the thread limit is exceeded<|endoftext|>
|
be02cd8566e02c71dabc3ad50149ac68be95ba0df63486a55205f583723dade0
|
def test_alwaysAllowedObjects(self):
'Matches test_threadLimit_canaryStartAndEnd, but allows events from the first object\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
eventEndCanary = (winUser.EVENT_OBJECT_NAMECHANGE, *canaryObject)
limiter.addEvent(*eventEndCanary, threadID=0)
events = limiter.flushEvents(alwaysAllowedObjects=[canaryObject])
self.assertEqual(11, len(events))
self.assertIn(eventStartCanary, events)
self.assertEqual(eventStartCanary, events[0])
self.assertIn(eventEndCanary, events)
|
Matches test_threadLimit_canaryStartAndEnd, but allows events from the first object
|
tests/unit/test_orderedWinEventLimiter.py
|
test_alwaysAllowedObjects
|
lukaszgo1/nvda
| 1,592 |
python
|
def test_alwaysAllowedObjects(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
eventEndCanary = (winUser.EVENT_OBJECT_NAMECHANGE, *canaryObject)
limiter.addEvent(*eventEndCanary, threadID=0)
events = limiter.flushEvents(alwaysAllowedObjects=[canaryObject])
self.assertEqual(11, len(events))
self.assertIn(eventStartCanary, events)
self.assertEqual(eventStartCanary, events[0])
self.assertIn(eventEndCanary, events)
|
def test_alwaysAllowedObjects(self):
'\n\t\t'
limiter = OrderedWinEventLimiter(maxFocusItems=4)
canaryObject = (1, 1, 1)
eventStartCanary = (winUser.EVENT_OBJECT_VALUECHANGE, *canaryObject)
limiter.addEvent(*eventStartCanary, threadID=0)
for n in range(orderedWinEventLimiter.MAX_WINEVENTS_PER_THREAD):
eventId = nonSpecialCaseEvents[(n % len(nonSpecialCaseEvents))]
source = (2, 2, n)
limiter.addEvent(eventId, *source, threadID=0)
eventEndCanary = (winUser.EVENT_OBJECT_NAMECHANGE, *canaryObject)
limiter.addEvent(*eventEndCanary, threadID=0)
events = limiter.flushEvents(alwaysAllowedObjects=[canaryObject])
self.assertEqual(11, len(events))
self.assertIn(eventStartCanary, events)
self.assertEqual(eventStartCanary, events[0])
self.assertIn(eventEndCanary, events)<|docstring|>Matches test_threadLimit_canaryStartAndEnd, but allows events from the first object<|endoftext|>
|
ed1b8ec65f68ee3eaff51901d79275bd7b04fe38dbb8d3daaf8a67eba9c8703a
|
@staticmethod
@CachedMethods.register
def query_oxo(uid):
'\n This takes a curie id and send that id to EMBL-EBI OXO to convert to cui\n '
url_str = ('https://www.ebi.ac.uk/spot/oxo/api/mappings?fromId=' + str(uid))
requests = CacheControlHelper()
try:
res = requests.get(url_str, headers={'accept': 'application/json'}, timeout=120)
except requests.exceptions.Timeout:
print(('HTTP timeout in SemMedInterface.py; URL: ' + url_str), file=sys.stderr)
time.sleep(1)
return None
except requests.exceptions.ConnectionError:
print(('HTTP connection error in SemMedInterface.py; URL: ' + url_str), file=sys.stderr)
time.sleep(1)
return None
except sqlite3.OperationalError:
print(('Error reading sqlite cache; URL: ' + url_str), file=sys.stderr)
return None
status_code = res.status_code
if (status_code != 200):
print(((('HTTP response status code: ' + str(status_code)) + ' for URL:\n') + url_str), file=sys.stderr)
res = None
return res
|
This takes a curie id and send that id to EMBL-EBI OXO to convert to cui
|
code/reasoningtool/kg-construction/NormGoogleDistance.py
|
query_oxo
|
rtx-travis-tester/RTX
| 31 |
python
|
@staticmethod
@CachedMethods.register
def query_oxo(uid):
'\n \n '
url_str = ('https://www.ebi.ac.uk/spot/oxo/api/mappings?fromId=' + str(uid))
requests = CacheControlHelper()
try:
res = requests.get(url_str, headers={'accept': 'application/json'}, timeout=120)
except requests.exceptions.Timeout:
print(('HTTP timeout in SemMedInterface.py; URL: ' + url_str), file=sys.stderr)
time.sleep(1)
return None
except requests.exceptions.ConnectionError:
print(('HTTP connection error in SemMedInterface.py; URL: ' + url_str), file=sys.stderr)
time.sleep(1)
return None
except sqlite3.OperationalError:
print(('Error reading sqlite cache; URL: ' + url_str), file=sys.stderr)
return None
status_code = res.status_code
if (status_code != 200):
print(((('HTTP response status code: ' + str(status_code)) + ' for URL:\n') + url_str), file=sys.stderr)
res = None
return res
|
@staticmethod
@CachedMethods.register
def query_oxo(uid):
'\n \n '
url_str = ('https://www.ebi.ac.uk/spot/oxo/api/mappings?fromId=' + str(uid))
requests = CacheControlHelper()
try:
res = requests.get(url_str, headers={'accept': 'application/json'}, timeout=120)
except requests.exceptions.Timeout:
print(('HTTP timeout in SemMedInterface.py; URL: ' + url_str), file=sys.stderr)
time.sleep(1)
return None
except requests.exceptions.ConnectionError:
print(('HTTP connection error in SemMedInterface.py; URL: ' + url_str), file=sys.stderr)
time.sleep(1)
return None
except sqlite3.OperationalError:
print(('Error reading sqlite cache; URL: ' + url_str), file=sys.stderr)
return None
status_code = res.status_code
if (status_code != 200):
print(((('HTTP response status code: ' + str(status_code)) + ' for URL:\n') + url_str), file=sys.stderr)
res = None
return res<|docstring|>This takes a curie id and send that id to EMBL-EBI OXO to convert to cui<|endoftext|>
|
ee239bfd5168ffe7f80bf17e7beae6c6c63c301dd0cfba2b78930ab93ae2403f
|
@staticmethod
@CachedMethods.register
def get_mesh_term_for_all(curie_id, description):
'\n Takes a curie ID, detects the ontology from the curie id, and then finds the mesh term\n Params:\n curie_id - A string containing the curie id of the node. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description - A string containing the English name for the node\n current functionality (+ means has it, - means does not have it)\n "Reactome" +\n "GO" - found gene conversion but no biological process conversion\n "UniProt" +\n "HP" - +\n "UBERON" +\n "CL" - not supposed to be here?\n "NCBIGene" +\n "DOID" +\n "OMIM" +\n "ChEMBL" +\n '
if (type(description) != str):
description = str(description)
curie_list = curie_id.split(':')
names = None
if QueryNCBIeUtils.is_mesh_term(description):
return [(description + '[MeSH Terms]')]
names = NormGoogleDistance.get_mesh_from_oxo(curie_id)
if (names is None):
if curie_list[0].lower().startswith('react'):
res = QueryNCBIeUtils.get_reactome_names(curie_list[1])
if (res is not None):
names = res.split('|')
elif (curie_list[0] == 'GO'):
pass
elif curie_list[0].startswith('UniProt'):
res = QueryNCBIeUtils.get_uniprot_names(curie_list[1])
if (res is not None):
names = res.split('|')
elif (curie_list[0] == 'HP'):
names = QueryNCBIeUtils.get_mesh_terms_for_hp_id(curie_id)
elif (curie_list[0] == 'UBERON'):
if curie_id.endswith('PHENOTYPE'):
curie_id = curie_id[:(- 9)]
mesh_id = QueryEBIOLS.get_mesh_id_for_uberon_id(curie_id)
names = []
for entry in mesh_id:
if (len(entry.split('.')) > 1):
uids = QueryNCBIeUtils.get_mesh_uids_for_mesh_tree(entry.split(':')[1])
for uid in uids:
try:
uid_num = (int(uid.split(':')[1][1:]) + 68000000)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
except IndexError:
uid_num = int(uid)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
else:
try:
uid = entry.split(':')[1]
uid_num = (int(uid[1:]) + 68000000)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
except IndexError:
uid_num = int(entry)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
if (len(names) == 0):
names = None
else:
names[0] = (names[0] + '[MeSH Terms]')
elif (curie_list[0] == 'NCBIGene'):
gene_id = curie_id.split(':')[1]
names = QueryNCBIeUtils.get_pubmed_from_ncbi_gene(gene_id)
elif (curie_list[0] == 'DOID'):
mesh_id = QueryDisont.query_disont_to_mesh_id(curie_id)
names = []
for uid in mesh_id:
uid_num = (int(uid[1:]) + 68000000)
name = QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
if (name is not None):
names += name
if (len(names) == 0):
names = None
else:
names[0] = (names[0] + '[MeSH Terms]')
elif (curie_list[0] == 'OMIM'):
names = QueryNCBIeUtils.get_mesh_terms_for_omim_id(curie_list[1])
elif (curie_list[0] == 'ChEMBL'):
chembl_id = curie_id.replace(':', '').upper()
mesh_id = QueryMyChem.get_mesh_id(chembl_id)
if (mesh_id is not None):
mesh_id = (int(mesh_id[1:]) + 68000000)
names = QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(mesh_id)
if (names is not None):
if (type(names) == list):
for name in names:
if name.endswith('[MeSH Terms]'):
return [name]
return names
return [description.replace(';', '|')]
|
Takes a curie ID, detects the ontology from the curie id, and then finds the mesh term
Params:
curie_id - A string containing the curie id of the node. Formatted <source abbreviation>:<number> e.g. DOID:8398
description - A string containing the English name for the node
current functionality (+ means has it, - means does not have it)
"Reactome" +
"GO" - found gene conversion but no biological process conversion
"UniProt" +
"HP" - +
"UBERON" +
"CL" - not supposed to be here?
"NCBIGene" +
"DOID" +
"OMIM" +
"ChEMBL" +
|
code/reasoningtool/kg-construction/NormGoogleDistance.py
|
get_mesh_term_for_all
|
rtx-travis-tester/RTX
| 31 |
python
|
@staticmethod
@CachedMethods.register
def get_mesh_term_for_all(curie_id, description):
'\n Takes a curie ID, detects the ontology from the curie id, and then finds the mesh term\n Params:\n curie_id - A string containing the curie id of the node. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description - A string containing the English name for the node\n current functionality (+ means has it, - means does not have it)\n "Reactome" +\n "GO" - found gene conversion but no biological process conversion\n "UniProt" +\n "HP" - +\n "UBERON" +\n "CL" - not supposed to be here?\n "NCBIGene" +\n "DOID" +\n "OMIM" +\n "ChEMBL" +\n '
if (type(description) != str):
description = str(description)
curie_list = curie_id.split(':')
names = None
if QueryNCBIeUtils.is_mesh_term(description):
return [(description + '[MeSH Terms]')]
names = NormGoogleDistance.get_mesh_from_oxo(curie_id)
if (names is None):
if curie_list[0].lower().startswith('react'):
res = QueryNCBIeUtils.get_reactome_names(curie_list[1])
if (res is not None):
names = res.split('|')
elif (curie_list[0] == 'GO'):
pass
elif curie_list[0].startswith('UniProt'):
res = QueryNCBIeUtils.get_uniprot_names(curie_list[1])
if (res is not None):
names = res.split('|')
elif (curie_list[0] == 'HP'):
names = QueryNCBIeUtils.get_mesh_terms_for_hp_id(curie_id)
elif (curie_list[0] == 'UBERON'):
if curie_id.endswith('PHENOTYPE'):
curie_id = curie_id[:(- 9)]
mesh_id = QueryEBIOLS.get_mesh_id_for_uberon_id(curie_id)
names = []
for entry in mesh_id:
if (len(entry.split('.')) > 1):
uids = QueryNCBIeUtils.get_mesh_uids_for_mesh_tree(entry.split(':')[1])
for uid in uids:
try:
uid_num = (int(uid.split(':')[1][1:]) + 68000000)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
except IndexError:
uid_num = int(uid)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
else:
try:
uid = entry.split(':')[1]
uid_num = (int(uid[1:]) + 68000000)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
except IndexError:
uid_num = int(entry)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
if (len(names) == 0):
names = None
else:
names[0] = (names[0] + '[MeSH Terms]')
elif (curie_list[0] == 'NCBIGene'):
gene_id = curie_id.split(':')[1]
names = QueryNCBIeUtils.get_pubmed_from_ncbi_gene(gene_id)
elif (curie_list[0] == 'DOID'):
mesh_id = QueryDisont.query_disont_to_mesh_id(curie_id)
names = []
for uid in mesh_id:
uid_num = (int(uid[1:]) + 68000000)
name = QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
if (name is not None):
names += name
if (len(names) == 0):
names = None
else:
names[0] = (names[0] + '[MeSH Terms]')
elif (curie_list[0] == 'OMIM'):
names = QueryNCBIeUtils.get_mesh_terms_for_omim_id(curie_list[1])
elif (curie_list[0] == 'ChEMBL'):
chembl_id = curie_id.replace(':', ).upper()
mesh_id = QueryMyChem.get_mesh_id(chembl_id)
if (mesh_id is not None):
mesh_id = (int(mesh_id[1:]) + 68000000)
names = QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(mesh_id)
if (names is not None):
if (type(names) == list):
for name in names:
if name.endswith('[MeSH Terms]'):
return [name]
return names
return [description.replace(';', '|')]
|
@staticmethod
@CachedMethods.register
def get_mesh_term_for_all(curie_id, description):
'\n Takes a curie ID, detects the ontology from the curie id, and then finds the mesh term\n Params:\n curie_id - A string containing the curie id of the node. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description - A string containing the English name for the node\n current functionality (+ means has it, - means does not have it)\n "Reactome" +\n "GO" - found gene conversion but no biological process conversion\n "UniProt" +\n "HP" - +\n "UBERON" +\n "CL" - not supposed to be here?\n "NCBIGene" +\n "DOID" +\n "OMIM" +\n "ChEMBL" +\n '
if (type(description) != str):
description = str(description)
curie_list = curie_id.split(':')
names = None
if QueryNCBIeUtils.is_mesh_term(description):
return [(description + '[MeSH Terms]')]
names = NormGoogleDistance.get_mesh_from_oxo(curie_id)
if (names is None):
if curie_list[0].lower().startswith('react'):
res = QueryNCBIeUtils.get_reactome_names(curie_list[1])
if (res is not None):
names = res.split('|')
elif (curie_list[0] == 'GO'):
pass
elif curie_list[0].startswith('UniProt'):
res = QueryNCBIeUtils.get_uniprot_names(curie_list[1])
if (res is not None):
names = res.split('|')
elif (curie_list[0] == 'HP'):
names = QueryNCBIeUtils.get_mesh_terms_for_hp_id(curie_id)
elif (curie_list[0] == 'UBERON'):
if curie_id.endswith('PHENOTYPE'):
curie_id = curie_id[:(- 9)]
mesh_id = QueryEBIOLS.get_mesh_id_for_uberon_id(curie_id)
names = []
for entry in mesh_id:
if (len(entry.split('.')) > 1):
uids = QueryNCBIeUtils.get_mesh_uids_for_mesh_tree(entry.split(':')[1])
for uid in uids:
try:
uid_num = (int(uid.split(':')[1][1:]) + 68000000)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
except IndexError:
uid_num = int(uid)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
else:
try:
uid = entry.split(':')[1]
uid_num = (int(uid[1:]) + 68000000)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
except IndexError:
uid_num = int(entry)
names += QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
if (len(names) == 0):
names = None
else:
names[0] = (names[0] + '[MeSH Terms]')
elif (curie_list[0] == 'NCBIGene'):
gene_id = curie_id.split(':')[1]
names = QueryNCBIeUtils.get_pubmed_from_ncbi_gene(gene_id)
elif (curie_list[0] == 'DOID'):
mesh_id = QueryDisont.query_disont_to_mesh_id(curie_id)
names = []
for uid in mesh_id:
uid_num = (int(uid[1:]) + 68000000)
name = QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(uid_num)
if (name is not None):
names += name
if (len(names) == 0):
names = None
else:
names[0] = (names[0] + '[MeSH Terms]')
elif (curie_list[0] == 'OMIM'):
names = QueryNCBIeUtils.get_mesh_terms_for_omim_id(curie_list[1])
elif (curie_list[0] == 'ChEMBL'):
chembl_id = curie_id.replace(':', ).upper()
mesh_id = QueryMyChem.get_mesh_id(chembl_id)
if (mesh_id is not None):
mesh_id = (int(mesh_id[1:]) + 68000000)
names = QueryNCBIeUtils.get_mesh_terms_for_mesh_uid(mesh_id)
if (names is not None):
if (type(names) == list):
for name in names:
if name.endswith('[MeSH Terms]'):
return [name]
return names
return [description.replace(';', '|')]<|docstring|>Takes a curie ID, detects the ontology from the curie id, and then finds the mesh term
Params:
curie_id - A string containing the curie id of the node. Formatted <source abbreviation>:<number> e.g. DOID:8398
description - A string containing the English name for the node
current functionality (+ means has it, - means does not have it)
"Reactome" +
"GO" - found gene conversion but no biological process conversion
"UniProt" +
"HP" - +
"UBERON" +
"CL" - not supposed to be here?
"NCBIGene" +
"DOID" +
"OMIM" +
"ChEMBL" +<|endoftext|>
|
f2fee9fefa93787b84d525ce03335d195d612552bbaac29783ddcf1445262ff0
|
@staticmethod
def get_ngd_for_all(curie_id_list, description_list):
'\n Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.\n Params:\n curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description_list - a list of strings containing the English names for the nodes\n '
assert (len(curie_id_list) == len(description_list))
terms = ([None] * len(curie_id_list))
for a in range(len(description_list)):
terms[a] = NormGoogleDistance.get_mesh_term_for_all(curie_id_list[a], description_list[a])
if (type(terms[a]) != list):
terms[a] = [terms[a]]
if (len(terms[a]) == 0):
terms[a] = [description_list[a]]
if (len(terms[a]) > 30):
terms[a] = terms[a][:30]
terms_combined = ([''] * len(terms))
mesh_flags = ([True] * len(terms))
for a in range(len(terms)):
if (len(terms[a]) > 1):
if (not terms[a][0].endswith('[uid]')):
for b in range(len(terms[a])):
if (QueryNCBIeUtils.is_mesh_term(terms[a][b]) and (not terms[a][b].endswith('[MeSH Terms]'))):
terms[a][b] += '[MeSH Terms]'
terms_combined[a] = '|'.join(terms[a])
mesh_flags[a] = False
else:
terms_combined[a] = terms[a][0]
if terms[a][0].endswith('[MeSH Terms]'):
terms_combined[a] = terms[a][0][:(- 12)]
elif (not QueryNCBIeUtils.is_mesh_term(terms[a][0])):
mesh_flags[a] = False
ngd = QueryNCBIeUtils.multi_normalized_google_distance(terms_combined, mesh_flags)
return ngd
|
Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.
Params:
curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398
description_list - a list of strings containing the English names for the nodes
|
code/reasoningtool/kg-construction/NormGoogleDistance.py
|
get_ngd_for_all
|
rtx-travis-tester/RTX
| 31 |
python
|
@staticmethod
def get_ngd_for_all(curie_id_list, description_list):
'\n Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.\n Params:\n curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description_list - a list of strings containing the English names for the nodes\n '
assert (len(curie_id_list) == len(description_list))
terms = ([None] * len(curie_id_list))
for a in range(len(description_list)):
terms[a] = NormGoogleDistance.get_mesh_term_for_all(curie_id_list[a], description_list[a])
if (type(terms[a]) != list):
terms[a] = [terms[a]]
if (len(terms[a]) == 0):
terms[a] = [description_list[a]]
if (len(terms[a]) > 30):
terms[a] = terms[a][:30]
terms_combined = ([] * len(terms))
mesh_flags = ([True] * len(terms))
for a in range(len(terms)):
if (len(terms[a]) > 1):
if (not terms[a][0].endswith('[uid]')):
for b in range(len(terms[a])):
if (QueryNCBIeUtils.is_mesh_term(terms[a][b]) and (not terms[a][b].endswith('[MeSH Terms]'))):
terms[a][b] += '[MeSH Terms]'
terms_combined[a] = '|'.join(terms[a])
mesh_flags[a] = False
else:
terms_combined[a] = terms[a][0]
if terms[a][0].endswith('[MeSH Terms]'):
terms_combined[a] = terms[a][0][:(- 12)]
elif (not QueryNCBIeUtils.is_mesh_term(terms[a][0])):
mesh_flags[a] = False
ngd = QueryNCBIeUtils.multi_normalized_google_distance(terms_combined, mesh_flags)
return ngd
|
@staticmethod
def get_ngd_for_all(curie_id_list, description_list):
'\n Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.\n Params:\n curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description_list - a list of strings containing the English names for the nodes\n '
assert (len(curie_id_list) == len(description_list))
terms = ([None] * len(curie_id_list))
for a in range(len(description_list)):
terms[a] = NormGoogleDistance.get_mesh_term_for_all(curie_id_list[a], description_list[a])
if (type(terms[a]) != list):
terms[a] = [terms[a]]
if (len(terms[a]) == 0):
terms[a] = [description_list[a]]
if (len(terms[a]) > 30):
terms[a] = terms[a][:30]
terms_combined = ([] * len(terms))
mesh_flags = ([True] * len(terms))
for a in range(len(terms)):
if (len(terms[a]) > 1):
if (not terms[a][0].endswith('[uid]')):
for b in range(len(terms[a])):
if (QueryNCBIeUtils.is_mesh_term(terms[a][b]) and (not terms[a][b].endswith('[MeSH Terms]'))):
terms[a][b] += '[MeSH Terms]'
terms_combined[a] = '|'.join(terms[a])
mesh_flags[a] = False
else:
terms_combined[a] = terms[a][0]
if terms[a][0].endswith('[MeSH Terms]'):
terms_combined[a] = terms[a][0][:(- 12)]
elif (not QueryNCBIeUtils.is_mesh_term(terms[a][0])):
mesh_flags[a] = False
ngd = QueryNCBIeUtils.multi_normalized_google_distance(terms_combined, mesh_flags)
return ngd<|docstring|>Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.
Params:
curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398
description_list - a list of strings containing the English names for the nodes<|endoftext|>
|
eb60ae9f703b06a21c82bc2bccf3b299e2921a31deb092e39e36fb136697dd4c
|
@staticmethod
def get_pmids_for_all(curie_id_list, description_list):
'\n Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.\n Params:\n curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description_list - a list of strings containing the English names for the nodes\n '
assert (len(curie_id_list) == len(description_list))
terms = ([None] * len(curie_id_list))
for a in range(len(description_list)):
terms[a] = NormGoogleDistance.get_mesh_term_for_all(curie_id_list[a], description_list[a])
if (type(terms[a]) != list):
terms[a] = [terms[a]]
if (len(terms[a]) == 0):
terms[a] = [description_list[a]]
if (len(terms[a]) > 30):
terms[a] = terms[a][:30]
terms_combined = ([''] * len(terms))
mesh_flags = ([True] * len(terms))
for a in range(len(terms)):
if (len(terms[a]) > 1):
if (not terms[a][0].endswith('[uid]')):
for b in range(len(terms[a])):
if (QueryNCBIeUtils.is_mesh_term(terms[a][b]) and (not terms[a][b].endswith('[MeSH Terms]'))):
terms[a][b] += '[MeSH Terms]'
terms_combined[a] = '|'.join(terms[a])
mesh_flags[a] = False
else:
terms_combined[a] = terms[a][0]
if terms[a][0].endswith('[MeSH Terms]'):
terms_combined[a] = terms[a][0][:(- 12)]
elif (not QueryNCBIeUtils.is_mesh_term(terms[a][0])):
mesh_flags[a] = False
pmids = QueryNCBIeUtils.multi_normalized_pmids(terms_combined, mesh_flags)
pmids_with_prefix = []
for lst in pmids:
pmids_with_prefix.append([f'PMID:{x}' for x in lst])
return pmids_with_prefix
|
Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.
Params:
curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398
description_list - a list of strings containing the English names for the nodes
|
code/reasoningtool/kg-construction/NormGoogleDistance.py
|
get_pmids_for_all
|
rtx-travis-tester/RTX
| 31 |
python
|
@staticmethod
def get_pmids_for_all(curie_id_list, description_list):
'\n Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.\n Params:\n curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description_list - a list of strings containing the English names for the nodes\n '
assert (len(curie_id_list) == len(description_list))
terms = ([None] * len(curie_id_list))
for a in range(len(description_list)):
terms[a] = NormGoogleDistance.get_mesh_term_for_all(curie_id_list[a], description_list[a])
if (type(terms[a]) != list):
terms[a] = [terms[a]]
if (len(terms[a]) == 0):
terms[a] = [description_list[a]]
if (len(terms[a]) > 30):
terms[a] = terms[a][:30]
terms_combined = ([] * len(terms))
mesh_flags = ([True] * len(terms))
for a in range(len(terms)):
if (len(terms[a]) > 1):
if (not terms[a][0].endswith('[uid]')):
for b in range(len(terms[a])):
if (QueryNCBIeUtils.is_mesh_term(terms[a][b]) and (not terms[a][b].endswith('[MeSH Terms]'))):
terms[a][b] += '[MeSH Terms]'
terms_combined[a] = '|'.join(terms[a])
mesh_flags[a] = False
else:
terms_combined[a] = terms[a][0]
if terms[a][0].endswith('[MeSH Terms]'):
terms_combined[a] = terms[a][0][:(- 12)]
elif (not QueryNCBIeUtils.is_mesh_term(terms[a][0])):
mesh_flags[a] = False
pmids = QueryNCBIeUtils.multi_normalized_pmids(terms_combined, mesh_flags)
pmids_with_prefix = []
for lst in pmids:
pmids_with_prefix.append([f'PMID:{x}' for x in lst])
return pmids_with_prefix
|
@staticmethod
def get_pmids_for_all(curie_id_list, description_list):
'\n Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.\n Params:\n curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398\n description_list - a list of strings containing the English names for the nodes\n '
assert (len(curie_id_list) == len(description_list))
terms = ([None] * len(curie_id_list))
for a in range(len(description_list)):
terms[a] = NormGoogleDistance.get_mesh_term_for_all(curie_id_list[a], description_list[a])
if (type(terms[a]) != list):
terms[a] = [terms[a]]
if (len(terms[a]) == 0):
terms[a] = [description_list[a]]
if (len(terms[a]) > 30):
terms[a] = terms[a][:30]
terms_combined = ([] * len(terms))
mesh_flags = ([True] * len(terms))
for a in range(len(terms)):
if (len(terms[a]) > 1):
if (not terms[a][0].endswith('[uid]')):
for b in range(len(terms[a])):
if (QueryNCBIeUtils.is_mesh_term(terms[a][b]) and (not terms[a][b].endswith('[MeSH Terms]'))):
terms[a][b] += '[MeSH Terms]'
terms_combined[a] = '|'.join(terms[a])
mesh_flags[a] = False
else:
terms_combined[a] = terms[a][0]
if terms[a][0].endswith('[MeSH Terms]'):
terms_combined[a] = terms[a][0][:(- 12)]
elif (not QueryNCBIeUtils.is_mesh_term(terms[a][0])):
mesh_flags[a] = False
pmids = QueryNCBIeUtils.multi_normalized_pmids(terms_combined, mesh_flags)
pmids_with_prefix = []
for lst in pmids:
pmids_with_prefix.append([f'PMID:{x}' for x in lst])
return pmids_with_prefix<|docstring|>Takes a list of currie ids and descriptions then calculates the normalized google distance for the set of nodes.
Params:
curie_id_list - a list of strings containing the curie ids of the nodes. Formatted <source abbreviation>:<number> e.g. DOID:8398
description_list - a list of strings containing the English names for the nodes<|endoftext|>
|
593e8dfba974a9490a9e2a43a8a28fd271b135f1676af31aecc6e4c9ed4f7acf
|
@property
def AuxiliaryId(self):
'\n Returns\n -------\n - number: This describes the identifier for auxiliary connections.\n '
return self._get_attribute(self._SDM_ATT_MAP['AuxiliaryId'])
|
Returns
-------
- number: This describes the identifier for auxiliary connections.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
AuxiliaryId
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def AuxiliaryId(self):
'\n Returns\n -------\n - number: This describes the identifier for auxiliary connections.\n '
return self._get_attribute(self._SDM_ATT_MAP['AuxiliaryId'])
|
@property
def AuxiliaryId(self):
'\n Returns\n -------\n - number: This describes the identifier for auxiliary connections.\n '
return self._get_attribute(self._SDM_ATT_MAP['AuxiliaryId'])<|docstring|>Returns
-------
- number: This describes the identifier for auxiliary connections.<|endoftext|>
|
653b6ca5d0bbbe4b36891dcba1b9d3e856d17fa4a65338dc3b37be29b6ea3914
|
@property
def ConnectionType(self):
'\n Returns\n -------\n - str(tcp | tls | udp): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)\n '
return self._get_attribute(self._SDM_ATT_MAP['ConnectionType'])
|
Returns
-------
- str(tcp | tls | udp): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
ConnectionType
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def ConnectionType(self):
'\n Returns\n -------\n - str(tcp | tls | udp): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)\n '
return self._get_attribute(self._SDM_ATT_MAP['ConnectionType'])
|
@property
def ConnectionType(self):
'\n Returns\n -------\n - str(tcp | tls | udp): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)\n '
return self._get_attribute(self._SDM_ATT_MAP['ConnectionType'])<|docstring|>Returns
-------
- str(tcp | tls | udp): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)<|endoftext|>
|
a0bb7f85516cc1a8c943c8f999a16397dbb9a9a907b3b37d0e0b0e4a70fd9e1b
|
@property
def DataPathId(self):
'\n Returns\n -------\n - str: Indicates the datapath ID of the OpenFlow controller.\n '
return self._get_attribute(self._SDM_ATT_MAP['DataPathId'])
|
Returns
-------
- str: Indicates the datapath ID of the OpenFlow controller.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
DataPathId
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def DataPathId(self):
'\n Returns\n -------\n - str: Indicates the datapath ID of the OpenFlow controller.\n '
return self._get_attribute(self._SDM_ATT_MAP['DataPathId'])
|
@property
def DataPathId(self):
'\n Returns\n -------\n - str: Indicates the datapath ID of the OpenFlow controller.\n '
return self._get_attribute(self._SDM_ATT_MAP['DataPathId'])<|docstring|>Returns
-------
- str: Indicates the datapath ID of the OpenFlow controller.<|endoftext|>
|
4ca878a78ece21e2219ac3fd7d69c4ab0289f2f42ec6242cc02463290c9f6309
|
@property
def DataPathIdAsHex(self):
'\n Returns\n -------\n - str: Indicates the datapath ID of the OpenFlow controller in hexadecimal format.\n '
return self._get_attribute(self._SDM_ATT_MAP['DataPathIdAsHex'])
|
Returns
-------
- str: Indicates the datapath ID of the OpenFlow controller in hexadecimal format.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
DataPathIdAsHex
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def DataPathIdAsHex(self):
'\n Returns\n -------\n - str: Indicates the datapath ID of the OpenFlow controller in hexadecimal format.\n '
return self._get_attribute(self._SDM_ATT_MAP['DataPathIdAsHex'])
|
@property
def DataPathIdAsHex(self):
'\n Returns\n -------\n - str: Indicates the datapath ID of the OpenFlow controller in hexadecimal format.\n '
return self._get_attribute(self._SDM_ATT_MAP['DataPathIdAsHex'])<|docstring|>Returns
-------
- str: Indicates the datapath ID of the OpenFlow controller in hexadecimal format.<|endoftext|>
|
6cd11f9ee88bf47aa238f548b5c3cd3812495976af486ee07b85d4232bfea4c0
|
@property
def LocalIp(self):
'\n Returns\n -------\n - str: Signifies the local IP address of the selected interface.\n '
return self._get_attribute(self._SDM_ATT_MAP['LocalIp'])
|
Returns
-------
- str: Signifies the local IP address of the selected interface.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
LocalIp
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def LocalIp(self):
'\n Returns\n -------\n - str: Signifies the local IP address of the selected interface.\n '
return self._get_attribute(self._SDM_ATT_MAP['LocalIp'])
|
@property
def LocalIp(self):
'\n Returns\n -------\n - str: Signifies the local IP address of the selected interface.\n '
return self._get_attribute(self._SDM_ATT_MAP['LocalIp'])<|docstring|>Returns
-------
- str: Signifies the local IP address of the selected interface.<|endoftext|>
|
bff02b0d687b0cde35e787c71c2a352e1f18d2012dbd83864ea1150355fe321e
|
@property
def LocalPort(self):
'\n Returns\n -------\n - number: This describes the local port number identifier.\n '
return self._get_attribute(self._SDM_ATT_MAP['LocalPort'])
|
Returns
-------
- number: This describes the local port number identifier.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
LocalPort
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def LocalPort(self):
'\n Returns\n -------\n - number: This describes the local port number identifier.\n '
return self._get_attribute(self._SDM_ATT_MAP['LocalPort'])
|
@property
def LocalPort(self):
'\n Returns\n -------\n - number: This describes the local port number identifier.\n '
return self._get_attribute(self._SDM_ATT_MAP['LocalPort'])<|docstring|>Returns
-------
- number: This describes the local port number identifier.<|endoftext|>
|
8c9637b33d205fb380d1c0a7d2725b2cc05b209d5eebce9efaccaa51cd39e8f2
|
@property
def RemoteIp(self):
'\n Returns\n -------\n - str: This describes the IP address of the remote end of the OF Channel.\n '
return self._get_attribute(self._SDM_ATT_MAP['RemoteIp'])
|
Returns
-------
- str: This describes the IP address of the remote end of the OF Channel.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
RemoteIp
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def RemoteIp(self):
'\n Returns\n -------\n - str: This describes the IP address of the remote end of the OF Channel.\n '
return self._get_attribute(self._SDM_ATT_MAP['RemoteIp'])
|
@property
def RemoteIp(self):
'\n Returns\n -------\n - str: This describes the IP address of the remote end of the OF Channel.\n '
return self._get_attribute(self._SDM_ATT_MAP['RemoteIp'])<|docstring|>Returns
-------
- str: This describes the IP address of the remote end of the OF Channel.<|endoftext|>
|
3b769b54811d4822ecff3c5baec745fb2935e36aff46e15a010b28f549622b33
|
@property
def RemotePort(self):
'\n Returns\n -------\n - number: This describes the remote port number identifier.\n '
return self._get_attribute(self._SDM_ATT_MAP['RemotePort'])
|
Returns
-------
- number: This describes the remote port number identifier.
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
RemotePort
|
slieberth/ixnetwork_restpy
| 0 |
python
|
@property
def RemotePort(self):
'\n Returns\n -------\n - number: This describes the remote port number identifier.\n '
return self._get_attribute(self._SDM_ATT_MAP['RemotePort'])
|
@property
def RemotePort(self):
'\n Returns\n -------\n - number: This describes the remote port number identifier.\n '
return self._get_attribute(self._SDM_ATT_MAP['RemotePort'])<|docstring|>Returns
-------
- number: This describes the remote port number identifier.<|endoftext|>
|
1ca37cf7f36582ea26ace7be7f19abd80ef8d290cda849e91f9bee601b1be5e9
|
def find(self, AuxiliaryId=None, ConnectionType=None, DataPathId=None, DataPathIdAsHex=None, LocalIp=None, LocalPort=None, RemoteIp=None, RemotePort=None):
'Finds and retrieves controllerAuxiliaryConnectionLearnedInfo resources from the server.\n\n All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve controllerAuxiliaryConnectionLearnedInfo resources from the server.\n To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n By default the find method takes no parameters and will retrieve all controllerAuxiliaryConnectionLearnedInfo resources from the server.\n\n Args\n ----\n - AuxiliaryId (number): This describes the identifier for auxiliary connections.\n - ConnectionType (str(tcp | tls | udp)): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)\n - DataPathId (str): Indicates the datapath ID of the OpenFlow controller.\n - DataPathIdAsHex (str): Indicates the datapath ID of the OpenFlow controller in hexadecimal format.\n - LocalIp (str): Signifies the local IP address of the selected interface.\n - LocalPort (number): This describes the local port number identifier.\n - RemoteIp (str): This describes the IP address of the remote end of the OF Channel.\n - RemotePort (number): This describes the remote port number identifier.\n\n Returns\n -------\n - self: This instance with matching controllerAuxiliaryConnectionLearnedInfo resources retrieved from the server available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._select(self._map_locals(self._SDM_ATT_MAP, locals()))
|
Finds and retrieves controllerAuxiliaryConnectionLearnedInfo resources from the server.
All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve controllerAuxiliaryConnectionLearnedInfo resources from the server.
To retrieve an exact match ensure the parameter value starts with ^ and ends with $
By default the find method takes no parameters and will retrieve all controllerAuxiliaryConnectionLearnedInfo resources from the server.
Args
----
- AuxiliaryId (number): This describes the identifier for auxiliary connections.
- ConnectionType (str(tcp | tls | udp)): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)
- DataPathId (str): Indicates the datapath ID of the OpenFlow controller.
- DataPathIdAsHex (str): Indicates the datapath ID of the OpenFlow controller in hexadecimal format.
- LocalIp (str): Signifies the local IP address of the selected interface.
- LocalPort (number): This describes the local port number identifier.
- RemoteIp (str): This describes the IP address of the remote end of the OF Channel.
- RemotePort (number): This describes the remote port number identifier.
Returns
-------
- self: This instance with matching controllerAuxiliaryConnectionLearnedInfo resources retrieved from the server available through an iterator or index
Raises
------
- ServerError: The server has encountered an uncategorized error condition
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
find
|
slieberth/ixnetwork_restpy
| 0 |
python
|
def find(self, AuxiliaryId=None, ConnectionType=None, DataPathId=None, DataPathIdAsHex=None, LocalIp=None, LocalPort=None, RemoteIp=None, RemotePort=None):
'Finds and retrieves controllerAuxiliaryConnectionLearnedInfo resources from the server.\n\n All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve controllerAuxiliaryConnectionLearnedInfo resources from the server.\n To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n By default the find method takes no parameters and will retrieve all controllerAuxiliaryConnectionLearnedInfo resources from the server.\n\n Args\n ----\n - AuxiliaryId (number): This describes the identifier for auxiliary connections.\n - ConnectionType (str(tcp | tls | udp)): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)\n - DataPathId (str): Indicates the datapath ID of the OpenFlow controller.\n - DataPathIdAsHex (str): Indicates the datapath ID of the OpenFlow controller in hexadecimal format.\n - LocalIp (str): Signifies the local IP address of the selected interface.\n - LocalPort (number): This describes the local port number identifier.\n - RemoteIp (str): This describes the IP address of the remote end of the OF Channel.\n - RemotePort (number): This describes the remote port number identifier.\n\n Returns\n -------\n - self: This instance with matching controllerAuxiliaryConnectionLearnedInfo resources retrieved from the server available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._select(self._map_locals(self._SDM_ATT_MAP, locals()))
|
def find(self, AuxiliaryId=None, ConnectionType=None, DataPathId=None, DataPathIdAsHex=None, LocalIp=None, LocalPort=None, RemoteIp=None, RemotePort=None):
'Finds and retrieves controllerAuxiliaryConnectionLearnedInfo resources from the server.\n\n All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve controllerAuxiliaryConnectionLearnedInfo resources from the server.\n To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n By default the find method takes no parameters and will retrieve all controllerAuxiliaryConnectionLearnedInfo resources from the server.\n\n Args\n ----\n - AuxiliaryId (number): This describes the identifier for auxiliary connections.\n - ConnectionType (str(tcp | tls | udp)): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)\n - DataPathId (str): Indicates the datapath ID of the OpenFlow controller.\n - DataPathIdAsHex (str): Indicates the datapath ID of the OpenFlow controller in hexadecimal format.\n - LocalIp (str): Signifies the local IP address of the selected interface.\n - LocalPort (number): This describes the local port number identifier.\n - RemoteIp (str): This describes the IP address of the remote end of the OF Channel.\n - RemotePort (number): This describes the remote port number identifier.\n\n Returns\n -------\n - self: This instance with matching controllerAuxiliaryConnectionLearnedInfo resources retrieved from the server available through an iterator or index\n\n Raises\n ------\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._select(self._map_locals(self._SDM_ATT_MAP, locals()))<|docstring|>Finds and retrieves controllerAuxiliaryConnectionLearnedInfo resources from the server.
All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve controllerAuxiliaryConnectionLearnedInfo resources from the server.
To retrieve an exact match ensure the parameter value starts with ^ and ends with $
By default the find method takes no parameters and will retrieve all controllerAuxiliaryConnectionLearnedInfo resources from the server.
Args
----
- AuxiliaryId (number): This describes the identifier for auxiliary connections.
- ConnectionType (str(tcp | tls | udp)): Specifies how this controllerPort is connected to another controller (internal/external) or host or there is no connection (noConnection)
- DataPathId (str): Indicates the datapath ID of the OpenFlow controller.
- DataPathIdAsHex (str): Indicates the datapath ID of the OpenFlow controller in hexadecimal format.
- LocalIp (str): Signifies the local IP address of the selected interface.
- LocalPort (number): This describes the local port number identifier.
- RemoteIp (str): This describes the IP address of the remote end of the OF Channel.
- RemotePort (number): This describes the remote port number identifier.
Returns
-------
- self: This instance with matching controllerAuxiliaryConnectionLearnedInfo resources retrieved from the server available through an iterator or index
Raises
------
- ServerError: The server has encountered an uncategorized error condition<|endoftext|>
|
e819d9cbbc624f4ec8438a71cc9c0866c97e72e2e82990e0bd0e5418132a39b4
|
def read(self, href):
'Retrieves a single instance of controllerAuxiliaryConnectionLearnedInfo data from the server.\n\n Args\n ----\n - href (str): An href to the instance to be retrieved\n\n Returns\n -------\n - self: This instance with the controllerAuxiliaryConnectionLearnedInfo resources from the server available through an iterator or index\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._read(href)
|
Retrieves a single instance of controllerAuxiliaryConnectionLearnedInfo data from the server.
Args
----
- href (str): An href to the instance to be retrieved
Returns
-------
- self: This instance with the controllerAuxiliaryConnectionLearnedInfo resources from the server available through an iterator or index
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/controllerauxiliaryconnectionlearnedinfo_1c2f8f11bff25ef8bd7fd65ee1072e9b.py
|
read
|
slieberth/ixnetwork_restpy
| 0 |
python
|
def read(self, href):
'Retrieves a single instance of controllerAuxiliaryConnectionLearnedInfo data from the server.\n\n Args\n ----\n - href (str): An href to the instance to be retrieved\n\n Returns\n -------\n - self: This instance with the controllerAuxiliaryConnectionLearnedInfo resources from the server available through an iterator or index\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._read(href)
|
def read(self, href):
'Retrieves a single instance of controllerAuxiliaryConnectionLearnedInfo data from the server.\n\n Args\n ----\n - href (str): An href to the instance to be retrieved\n\n Returns\n -------\n - self: This instance with the controllerAuxiliaryConnectionLearnedInfo resources from the server available through an iterator or index\n\n Raises\n ------\n - NotFoundError: The requested resource does not exist on the server\n - ServerError: The server has encountered an uncategorized error condition\n '
return self._read(href)<|docstring|>Retrieves a single instance of controllerAuxiliaryConnectionLearnedInfo data from the server.
Args
----
- href (str): An href to the instance to be retrieved
Returns
-------
- self: This instance with the controllerAuxiliaryConnectionLearnedInfo resources from the server available through an iterator or index
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition<|endoftext|>
|
a81b413ddd41cec247151aefceaaef37ef175c23bb2ada961d531974330ed91f
|
def to_string(self):
' Return object as string '
url = self.scheme
if self.user:
if self.password:
url += ('://%s@%s' % (self.user, self.password))
else:
url += '://'
url += self.netloc
if self.port:
url += (':%d' % self.port)
else:
url += ':80'
url += ('%s' % self.path)
return url
|
Return object as string
|
apps/orgapp/app/models.py
|
to_string
|
dorneanu/crudappify
| 1 |
python
|
def to_string(self):
' '
url = self.scheme
if self.user:
if self.password:
url += ('://%s@%s' % (self.user, self.password))
else:
url += '://'
url += self.netloc
if self.port:
url += (':%d' % self.port)
else:
url += ':80'
url += ('%s' % self.path)
return url
|
def to_string(self):
' '
url = self.scheme
if self.user:
if self.password:
url += ('://%s@%s' % (self.user, self.password))
else:
url += '://'
url += self.netloc
if self.port:
url += (':%d' % self.port)
else:
url += ':80'
url += ('%s' % self.path)
return url<|docstring|>Return object as string<|endoftext|>
|
e00feb417a93d5f095f02d25e9aeecaed93ecebdf6314abbba89dfb04e451806
|
def load_data(database_filepath):
"\n Load data from the specified database\n\n Args:\n database_filepath: string. A relative path to the database file\n\n Returns:\n X: Array of features data which is data in the 'message' column\n y: Array of labels data which is the 36 categories in the dataset\n category_names: List of category names corresponding to columns of y\n "
engine = create_engine('sqlite:///{}'.format(database_filepath))
df = pd.read_sql_table('InsertTableName', engine)
X = np.array(df['message'])
cat_values = df.drop(['id', 'message', 'original', 'genre'], axis=1)
if pd.__version__.startswith('0.24'):
Y = cat_values.to_numpy()
else:
Y = cat_values.values
category_names = cat_values.columns.tolist()
return (X, Y, category_names)
|
Load data from the specified database
Args:
database_filepath: string. A relative path to the database file
Returns:
X: Array of features data which is data in the 'message' column
y: Array of labels data which is the 36 categories in the dataset
category_names: List of category names corresponding to columns of y
|
models/train_classifier.py
|
load_data
|
nongnoochr/diaster-response-app
| 0 |
python
|
def load_data(database_filepath):
"\n Load data from the specified database\n\n Args:\n database_filepath: string. A relative path to the database file\n\n Returns:\n X: Array of features data which is data in the 'message' column\n y: Array of labels data which is the 36 categories in the dataset\n category_names: List of category names corresponding to columns of y\n "
engine = create_engine('sqlite:///{}'.format(database_filepath))
df = pd.read_sql_table('InsertTableName', engine)
X = np.array(df['message'])
cat_values = df.drop(['id', 'message', 'original', 'genre'], axis=1)
if pd.__version__.startswith('0.24'):
Y = cat_values.to_numpy()
else:
Y = cat_values.values
category_names = cat_values.columns.tolist()
return (X, Y, category_names)
|
def load_data(database_filepath):
"\n Load data from the specified database\n\n Args:\n database_filepath: string. A relative path to the database file\n\n Returns:\n X: Array of features data which is data in the 'message' column\n y: Array of labels data which is the 36 categories in the dataset\n category_names: List of category names corresponding to columns of y\n "
engine = create_engine('sqlite:///{}'.format(database_filepath))
df = pd.read_sql_table('InsertTableName', engine)
X = np.array(df['message'])
cat_values = df.drop(['id', 'message', 'original', 'genre'], axis=1)
if pd.__version__.startswith('0.24'):
Y = cat_values.to_numpy()
else:
Y = cat_values.values
category_names = cat_values.columns.tolist()
return (X, Y, category_names)<|docstring|>Load data from the specified database
Args:
database_filepath: string. A relative path to the database file
Returns:
X: Array of features data which is data in the 'message' column
y: Array of labels data which is the 36 categories in the dataset
category_names: List of category names corresponding to columns of y<|endoftext|>
|
e65cf0669b7cf9111387975ed8163250ee8bd49dc217fa9286aba57046477890
|
def get_wordnet_pos(tag):
' \n Get a TreeBank tag from the specified WordNet part of speech name\n\n Args:\n tag: string. WordNet part of speech name.\n\n Returns:\n A corresponding TreeBank tag\n '
treebank_tag = ''
if tag.startswith('J'):
treebank_tag = wordnet.ADJ
elif tag.startswith('V'):
treebank_tag = wordnet.VERB
elif tag.startswith('N'):
treebank_tag = wordnet.NOUN
elif tag.startswith('R'):
treebank_tag = wordnet.ADV
else:
treebank_tag = wordnet.NOUN
return treebank_tag
|
Get a TreeBank tag from the specified WordNet part of speech name
Args:
tag: string. WordNet part of speech name.
Returns:
A corresponding TreeBank tag
|
models/train_classifier.py
|
get_wordnet_pos
|
nongnoochr/diaster-response-app
| 0 |
python
|
def get_wordnet_pos(tag):
' \n Get a TreeBank tag from the specified WordNet part of speech name\n\n Args:\n tag: string. WordNet part of speech name.\n\n Returns:\n A corresponding TreeBank tag\n '
treebank_tag =
if tag.startswith('J'):
treebank_tag = wordnet.ADJ
elif tag.startswith('V'):
treebank_tag = wordnet.VERB
elif tag.startswith('N'):
treebank_tag = wordnet.NOUN
elif tag.startswith('R'):
treebank_tag = wordnet.ADV
else:
treebank_tag = wordnet.NOUN
return treebank_tag
|
def get_wordnet_pos(tag):
' \n Get a TreeBank tag from the specified WordNet part of speech name\n\n Args:\n tag: string. WordNet part of speech name.\n\n Returns:\n A corresponding TreeBank tag\n '
treebank_tag =
if tag.startswith('J'):
treebank_tag = wordnet.ADJ
elif tag.startswith('V'):
treebank_tag = wordnet.VERB
elif tag.startswith('N'):
treebank_tag = wordnet.NOUN
elif tag.startswith('R'):
treebank_tag = wordnet.ADV
else:
treebank_tag = wordnet.NOUN
return treebank_tag<|docstring|>Get a TreeBank tag from the specified WordNet part of speech name
Args:
tag: string. WordNet part of speech name.
Returns:
A corresponding TreeBank tag<|endoftext|>
|
ec66bcc257954739476efb560111d068b628ca001aff95556c4f8f8e328a4c36
|
def tokenize(text):
'\n Perform a tokenization process on the input text\n\n Args:\n text: string. A message to be tokenized\n\n Returns:\n clean_tokens\n '
text = text.lower()
text = re.sub('[^a-zA-Z0-9]', ' ', text)
words = word_tokenize(text)
words = [w for w in words if (w not in stopwords.words('english'))]
pv_tags = pos_tag(words)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
for cur_tag in pv_tags:
cur_text = cur_tag[0]
w_tag = get_wordnet_pos(cur_tag[1])
clean_tok = lemmatizer.lemmatize(cur_text, w_tag)
clean_tokens.append(clean_tok)
return clean_tokens
|
Perform a tokenization process on the input text
Args:
text: string. A message to be tokenized
Returns:
clean_tokens
|
models/train_classifier.py
|
tokenize
|
nongnoochr/diaster-response-app
| 0 |
python
|
def tokenize(text):
'\n Perform a tokenization process on the input text\n\n Args:\n text: string. A message to be tokenized\n\n Returns:\n clean_tokens\n '
text = text.lower()
text = re.sub('[^a-zA-Z0-9]', ' ', text)
words = word_tokenize(text)
words = [w for w in words if (w not in stopwords.words('english'))]
pv_tags = pos_tag(words)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
for cur_tag in pv_tags:
cur_text = cur_tag[0]
w_tag = get_wordnet_pos(cur_tag[1])
clean_tok = lemmatizer.lemmatize(cur_text, w_tag)
clean_tokens.append(clean_tok)
return clean_tokens
|
def tokenize(text):
'\n Perform a tokenization process on the input text\n\n Args:\n text: string. A message to be tokenized\n\n Returns:\n clean_tokens\n '
text = text.lower()
text = re.sub('[^a-zA-Z0-9]', ' ', text)
words = word_tokenize(text)
words = [w for w in words if (w not in stopwords.words('english'))]
pv_tags = pos_tag(words)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
for cur_tag in pv_tags:
cur_text = cur_tag[0]
w_tag = get_wordnet_pos(cur_tag[1])
clean_tok = lemmatizer.lemmatize(cur_text, w_tag)
clean_tokens.append(clean_tok)
return clean_tokens<|docstring|>Perform a tokenization process on the input text
Args:
text: string. A message to be tokenized
Returns:
clean_tokens<|endoftext|>
|
d9bb12308b321eb0c8cab317ec110ecc12b80045d779047f5a14d11dd98eeb28
|
def build_model():
'\n Create a GridsearchCV object of a pipeline where the MultiOutputClassifier \n is used along with the RandomForestClassifier as an estimator\n\n Returns:\n model: GridsearchCV object.\n '
pipeline = Pipeline([('vect', CountVectorizer(tokenizer=tokenize)), ('tfidf', TfidfTransformer()), ('clf', MultiOutputClassifier(RandomForestClassifier(random_state=1, n_jobs=(- 1))))])
parameters = {'clf__estimator__min_samples_leaf': [2, 5], 'clf__estimator__n_estimators': [10, 30]}
model = GridSearchCV(pipeline, param_grid=parameters, cv=5)
return model
|
Create a GridsearchCV object of a pipeline where the MultiOutputClassifier
is used along with the RandomForestClassifier as an estimator
Returns:
model: GridsearchCV object.
|
models/train_classifier.py
|
build_model
|
nongnoochr/diaster-response-app
| 0 |
python
|
def build_model():
'\n Create a GridsearchCV object of a pipeline where the MultiOutputClassifier \n is used along with the RandomForestClassifier as an estimator\n\n Returns:\n model: GridsearchCV object.\n '
pipeline = Pipeline([('vect', CountVectorizer(tokenizer=tokenize)), ('tfidf', TfidfTransformer()), ('clf', MultiOutputClassifier(RandomForestClassifier(random_state=1, n_jobs=(- 1))))])
parameters = {'clf__estimator__min_samples_leaf': [2, 5], 'clf__estimator__n_estimators': [10, 30]}
model = GridSearchCV(pipeline, param_grid=parameters, cv=5)
return model
|
def build_model():
'\n Create a GridsearchCV object of a pipeline where the MultiOutputClassifier \n is used along with the RandomForestClassifier as an estimator\n\n Returns:\n model: GridsearchCV object.\n '
pipeline = Pipeline([('vect', CountVectorizer(tokenizer=tokenize)), ('tfidf', TfidfTransformer()), ('clf', MultiOutputClassifier(RandomForestClassifier(random_state=1, n_jobs=(- 1))))])
parameters = {'clf__estimator__min_samples_leaf': [2, 5], 'clf__estimator__n_estimators': [10, 30]}
model = GridSearchCV(pipeline, param_grid=parameters, cv=5)
return model<|docstring|>Create a GridsearchCV object of a pipeline where the MultiOutputClassifier
is used along with the RandomForestClassifier as an estimator
Returns:
model: GridsearchCV object.<|endoftext|>
|
05fde6d985ed9c85dad3277805aa4d25260bf7132654c40dad0aaf6bcdbbf653
|
def evaluate_model(model, X_test, Y_test, category_names):
"\n Run the predict method of the specified model with a given input data and\n print out the best parameter found by the model (GridSearchCV object) and also\n report the f1 score, precision and recall for each output category of the dataset\n\n Args:\n model: A trained GridSearchCV object\n X_Test: Array of feature's test data\n Y_Test: Array of label's test data\n category_names: List of category names corresponding to each column in Y_Test\n "
print('{} : Start model.predict'.format(datetime.datetime.now()))
Y_pred = model.predict(X_test)
print('{} : Finish model.predict'.format(datetime.datetime.now()))
print('Best parameters:\n{}'.format(model.best_params_))
for (index, col_name) in enumerate(category_names):
print('Column#{} - {}'.format(index, col_name))
print(classification_report(Y_test[(:, index)], Y_pred[(:, index)]))
|
Run the predict method of the specified model with a given input data and
print out the best parameter found by the model (GridSearchCV object) and also
report the f1 score, precision and recall for each output category of the dataset
Args:
model: A trained GridSearchCV object
X_Test: Array of feature's test data
Y_Test: Array of label's test data
category_names: List of category names corresponding to each column in Y_Test
|
models/train_classifier.py
|
evaluate_model
|
nongnoochr/diaster-response-app
| 0 |
python
|
def evaluate_model(model, X_test, Y_test, category_names):
"\n Run the predict method of the specified model with a given input data and\n print out the best parameter found by the model (GridSearchCV object) and also\n report the f1 score, precision and recall for each output category of the dataset\n\n Args:\n model: A trained GridSearchCV object\n X_Test: Array of feature's test data\n Y_Test: Array of label's test data\n category_names: List of category names corresponding to each column in Y_Test\n "
print('{} : Start model.predict'.format(datetime.datetime.now()))
Y_pred = model.predict(X_test)
print('{} : Finish model.predict'.format(datetime.datetime.now()))
print('Best parameters:\n{}'.format(model.best_params_))
for (index, col_name) in enumerate(category_names):
print('Column#{} - {}'.format(index, col_name))
print(classification_report(Y_test[(:, index)], Y_pred[(:, index)]))
|
def evaluate_model(model, X_test, Y_test, category_names):
"\n Run the predict method of the specified model with a given input data and\n print out the best parameter found by the model (GridSearchCV object) and also\n report the f1 score, precision and recall for each output category of the dataset\n\n Args:\n model: A trained GridSearchCV object\n X_Test: Array of feature's test data\n Y_Test: Array of label's test data\n category_names: List of category names corresponding to each column in Y_Test\n "
print('{} : Start model.predict'.format(datetime.datetime.now()))
Y_pred = model.predict(X_test)
print('{} : Finish model.predict'.format(datetime.datetime.now()))
print('Best parameters:\n{}'.format(model.best_params_))
for (index, col_name) in enumerate(category_names):
print('Column#{} - {}'.format(index, col_name))
print(classification_report(Y_test[(:, index)], Y_pred[(:, index)]))<|docstring|>Run the predict method of the specified model with a given input data and
print out the best parameter found by the model (GridSearchCV object) and also
report the f1 score, precision and recall for each output category of the dataset
Args:
model: A trained GridSearchCV object
X_Test: Array of feature's test data
Y_Test: Array of label's test data
category_names: List of category names corresponding to each column in Y_Test<|endoftext|>
|
44145b7d17f229bcbcf966e1175040bafade619ecdd4d3d03ddeb8d20c79074b
|
def save_model(model, model_filepath):
'\n Save a model to a pickle file at the speicified file path\n\n Args:\n model: A model object\n model_filepath: A relative path of the output file path\n '
with open(model_filepath, 'wb') as file:
pickle.dump(model, file)
|
Save a model to a pickle file at the speicified file path
Args:
model: A model object
model_filepath: A relative path of the output file path
|
models/train_classifier.py
|
save_model
|
nongnoochr/diaster-response-app
| 0 |
python
|
def save_model(model, model_filepath):
'\n Save a model to a pickle file at the speicified file path\n\n Args:\n model: A model object\n model_filepath: A relative path of the output file path\n '
with open(model_filepath, 'wb') as file:
pickle.dump(model, file)
|
def save_model(model, model_filepath):
'\n Save a model to a pickle file at the speicified file path\n\n Args:\n model: A model object\n model_filepath: A relative path of the output file path\n '
with open(model_filepath, 'wb') as file:
pickle.dump(model, file)<|docstring|>Save a model to a pickle file at the speicified file path
Args:
model: A model object
model_filepath: A relative path of the output file path<|endoftext|>
|
701edf4c27ca473c3dbe243ce87bb069255462a81a9f6238e78d5b00b5e59b85
|
def run_update(ydl):
'\n Update the program file with the latest version from the repository\n Returns whether the program should terminate\n '
JSON_URL = 'https://api.github.com/repos/yt-dlp/yt-dlp/releases/latest'
def report_error(msg, expected=False):
ydl.report_error(msg, tb=('' if expected else None))
def report_unable(action, expected=False):
report_error(f'Unable to {action}', expected)
def report_permission_error(file):
report_unable(f'write to {file}; Try running as administrator', True)
def report_network_error(action, delim=';'):
report_unable(f'{action}{delim} Visit https://github.com/yt-dlp/yt-dlp/releases/latest', True)
def calc_sha256sum(path):
h = hashlib.sha256()
b = bytearray((128 * 1024))
mv = memoryview(b)
with open(os.path.realpath(path), 'rb', buffering=0) as f:
for n in iter((lambda : f.readinto(mv)), 0):
h.update(mv[:n])
return h.hexdigest()
try:
version_info = ydl._opener.open(JSON_URL).read().decode('utf-8')
version_info = json.loads(version_info)
except Exception:
return report_network_error('obtain version info', delim='; Please try again later or')
def version_tuple(version_str):
return tuple(map(int, version_str.split('.')))
version_id = version_info['tag_name']
ydl.to_screen(f'Latest version: {version_id}, Current version: {__version__}')
if (version_tuple(__version__) >= version_tuple(version_id)):
ydl.to_screen(f'yt-dlp is up to date ({__version__})')
return
err = is_non_updateable()
if err:
return report_error(err, True)
filename = compat_realpath((sys.executable if hasattr(sys, 'frozen') else sys.argv[0]))
ydl.to_screen(f'Current Build Hash {calc_sha256sum(filename)}')
ydl.to_screen(f'Updating to version {version_id} ...')
version_labels = {'zip_3': '', 'win_exe_64': '.exe', 'py2exe_64': '_min.exe', 'win_exe_32': '_x86.exe', 'mac_exe_64': '_macos'}
def get_bin_info(bin_or_exe, version):
label = version_labels[('%s_%s' % (bin_or_exe, version))]
return next((i for i in version_info['assets'] if (i['name'] == ('yt-dlp%s' % label))), {})
def get_sha256sum(bin_or_exe, version):
filename = ('yt-dlp%s' % version_labels[('%s_%s' % (bin_or_exe, version))])
urlh = next((i for i in version_info['assets'] if (i['name'] in 'SHA2-256SUMS')), {}).get('browser_download_url')
if (not urlh):
return None
hash_data = ydl._opener.open(urlh).read().decode('utf-8')
return dict((ln.split()[::(- 1)] for ln in hash_data.splitlines())).get(filename)
if (not os.access(filename, os.W_OK)):
return report_permission_error(filename)
variant = detect_variant()
if (variant in ('win_exe', 'py2exe')):
directory = os.path.dirname(filename)
if (not os.access(directory, os.W_OK)):
return report_permission_error(directory)
try:
if os.path.exists((filename + '.old')):
os.remove((filename + '.old'))
except (IOError, OSError):
return report_unable('remove the old version')
try:
arch = platform.architecture()[0][:2]
url = get_bin_info(variant, arch).get('browser_download_url')
if (not url):
return report_network_error('fetch updates')
urlh = ydl._opener.open(url)
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
return report_network_error('download latest version')
try:
with open((filename + '.new'), 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
return report_permission_error(f'{filename}.new')
expected_sum = get_sha256sum(variant, arch)
if (not expected_sum):
ydl.report_warning('no hash information found for the release')
elif (calc_sha256sum((filename + '.new')) != expected_sum):
report_network_error('verify the new executable')
try:
os.remove((filename + '.new'))
except OSError:
return report_unable('remove corrupt download')
try:
os.rename(filename, (filename + '.old'))
except (IOError, OSError):
return report_unable('move current version')
try:
os.rename((filename + '.new'), filename)
except (IOError, OSError):
report_unable('overwrite current version')
os.rename((filename + '.old'), filename)
return
try:
Popen(('ping 127.0.0.1 -n 5 -w 1000 & del /F "%s.old"' % filename), shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
ydl.to_screen(('Updated yt-dlp to version %s' % version_id))
return True
except OSError:
report_unable('delete the old version')
elif (variant in ('zip', 'mac_exe')):
pack_type = ('3' if (variant == 'zip') else '64')
try:
url = get_bin_info(variant, pack_type).get('browser_download_url')
if (not url):
return report_network_error('fetch updates')
urlh = ydl._opener.open(url)
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
return report_network_error('download the latest version')
expected_sum = get_sha256sum(variant, pack_type)
if (not expected_sum):
ydl.report_warning('no hash information found for the release')
elif (hashlib.sha256(newcontent).hexdigest() != expected_sum):
return report_network_error('verify the new package')
try:
with open(filename, 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
return report_unable('overwrite current version')
ydl.to_screen(('Updated yt-dlp to version %s; Restart yt-dlp to use the new version' % version_id))
return
assert False, f'Unhandled variant: {variant}'
|
Update the program file with the latest version from the repository
Returns whether the program should terminate
|
yt_dlp/update.py
|
run_update
|
wlritchi/yt-dlp
| 64 |
python
|
def run_update(ydl):
'\n Update the program file with the latest version from the repository\n Returns whether the program should terminate\n '
JSON_URL = 'https://api.github.com/repos/yt-dlp/yt-dlp/releases/latest'
def report_error(msg, expected=False):
ydl.report_error(msg, tb=( if expected else None))
def report_unable(action, expected=False):
report_error(f'Unable to {action}', expected)
def report_permission_error(file):
report_unable(f'write to {file}; Try running as administrator', True)
def report_network_error(action, delim=';'):
report_unable(f'{action}{delim} Visit https://github.com/yt-dlp/yt-dlp/releases/latest', True)
def calc_sha256sum(path):
h = hashlib.sha256()
b = bytearray((128 * 1024))
mv = memoryview(b)
with open(os.path.realpath(path), 'rb', buffering=0) as f:
for n in iter((lambda : f.readinto(mv)), 0):
h.update(mv[:n])
return h.hexdigest()
try:
version_info = ydl._opener.open(JSON_URL).read().decode('utf-8')
version_info = json.loads(version_info)
except Exception:
return report_network_error('obtain version info', delim='; Please try again later or')
def version_tuple(version_str):
return tuple(map(int, version_str.split('.')))
version_id = version_info['tag_name']
ydl.to_screen(f'Latest version: {version_id}, Current version: {__version__}')
if (version_tuple(__version__) >= version_tuple(version_id)):
ydl.to_screen(f'yt-dlp is up to date ({__version__})')
return
err = is_non_updateable()
if err:
return report_error(err, True)
filename = compat_realpath((sys.executable if hasattr(sys, 'frozen') else sys.argv[0]))
ydl.to_screen(f'Current Build Hash {calc_sha256sum(filename)}')
ydl.to_screen(f'Updating to version {version_id} ...')
version_labels = {'zip_3': , 'win_exe_64': '.exe', 'py2exe_64': '_min.exe', 'win_exe_32': '_x86.exe', 'mac_exe_64': '_macos'}
def get_bin_info(bin_or_exe, version):
label = version_labels[('%s_%s' % (bin_or_exe, version))]
return next((i for i in version_info['assets'] if (i['name'] == ('yt-dlp%s' % label))), {})
def get_sha256sum(bin_or_exe, version):
filename = ('yt-dlp%s' % version_labels[('%s_%s' % (bin_or_exe, version))])
urlh = next((i for i in version_info['assets'] if (i['name'] in 'SHA2-256SUMS')), {}).get('browser_download_url')
if (not urlh):
return None
hash_data = ydl._opener.open(urlh).read().decode('utf-8')
return dict((ln.split()[::(- 1)] for ln in hash_data.splitlines())).get(filename)
if (not os.access(filename, os.W_OK)):
return report_permission_error(filename)
variant = detect_variant()
if (variant in ('win_exe', 'py2exe')):
directory = os.path.dirname(filename)
if (not os.access(directory, os.W_OK)):
return report_permission_error(directory)
try:
if os.path.exists((filename + '.old')):
os.remove((filename + '.old'))
except (IOError, OSError):
return report_unable('remove the old version')
try:
arch = platform.architecture()[0][:2]
url = get_bin_info(variant, arch).get('browser_download_url')
if (not url):
return report_network_error('fetch updates')
urlh = ydl._opener.open(url)
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
return report_network_error('download latest version')
try:
with open((filename + '.new'), 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
return report_permission_error(f'{filename}.new')
expected_sum = get_sha256sum(variant, arch)
if (not expected_sum):
ydl.report_warning('no hash information found for the release')
elif (calc_sha256sum((filename + '.new')) != expected_sum):
report_network_error('verify the new executable')
try:
os.remove((filename + '.new'))
except OSError:
return report_unable('remove corrupt download')
try:
os.rename(filename, (filename + '.old'))
except (IOError, OSError):
return report_unable('move current version')
try:
os.rename((filename + '.new'), filename)
except (IOError, OSError):
report_unable('overwrite current version')
os.rename((filename + '.old'), filename)
return
try:
Popen(('ping 127.0.0.1 -n 5 -w 1000 & del /F "%s.old"' % filename), shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
ydl.to_screen(('Updated yt-dlp to version %s' % version_id))
return True
except OSError:
report_unable('delete the old version')
elif (variant in ('zip', 'mac_exe')):
pack_type = ('3' if (variant == 'zip') else '64')
try:
url = get_bin_info(variant, pack_type).get('browser_download_url')
if (not url):
return report_network_error('fetch updates')
urlh = ydl._opener.open(url)
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
return report_network_error('download the latest version')
expected_sum = get_sha256sum(variant, pack_type)
if (not expected_sum):
ydl.report_warning('no hash information found for the release')
elif (hashlib.sha256(newcontent).hexdigest() != expected_sum):
return report_network_error('verify the new package')
try:
with open(filename, 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
return report_unable('overwrite current version')
ydl.to_screen(('Updated yt-dlp to version %s; Restart yt-dlp to use the new version' % version_id))
return
assert False, f'Unhandled variant: {variant}'
|
def run_update(ydl):
'\n Update the program file with the latest version from the repository\n Returns whether the program should terminate\n '
JSON_URL = 'https://api.github.com/repos/yt-dlp/yt-dlp/releases/latest'
def report_error(msg, expected=False):
ydl.report_error(msg, tb=( if expected else None))
def report_unable(action, expected=False):
report_error(f'Unable to {action}', expected)
def report_permission_error(file):
report_unable(f'write to {file}; Try running as administrator', True)
def report_network_error(action, delim=';'):
report_unable(f'{action}{delim} Visit https://github.com/yt-dlp/yt-dlp/releases/latest', True)
def calc_sha256sum(path):
h = hashlib.sha256()
b = bytearray((128 * 1024))
mv = memoryview(b)
with open(os.path.realpath(path), 'rb', buffering=0) as f:
for n in iter((lambda : f.readinto(mv)), 0):
h.update(mv[:n])
return h.hexdigest()
try:
version_info = ydl._opener.open(JSON_URL).read().decode('utf-8')
version_info = json.loads(version_info)
except Exception:
return report_network_error('obtain version info', delim='; Please try again later or')
def version_tuple(version_str):
return tuple(map(int, version_str.split('.')))
version_id = version_info['tag_name']
ydl.to_screen(f'Latest version: {version_id}, Current version: {__version__}')
if (version_tuple(__version__) >= version_tuple(version_id)):
ydl.to_screen(f'yt-dlp is up to date ({__version__})')
return
err = is_non_updateable()
if err:
return report_error(err, True)
filename = compat_realpath((sys.executable if hasattr(sys, 'frozen') else sys.argv[0]))
ydl.to_screen(f'Current Build Hash {calc_sha256sum(filename)}')
ydl.to_screen(f'Updating to version {version_id} ...')
version_labels = {'zip_3': , 'win_exe_64': '.exe', 'py2exe_64': '_min.exe', 'win_exe_32': '_x86.exe', 'mac_exe_64': '_macos'}
def get_bin_info(bin_or_exe, version):
label = version_labels[('%s_%s' % (bin_or_exe, version))]
return next((i for i in version_info['assets'] if (i['name'] == ('yt-dlp%s' % label))), {})
def get_sha256sum(bin_or_exe, version):
filename = ('yt-dlp%s' % version_labels[('%s_%s' % (bin_or_exe, version))])
urlh = next((i for i in version_info['assets'] if (i['name'] in 'SHA2-256SUMS')), {}).get('browser_download_url')
if (not urlh):
return None
hash_data = ydl._opener.open(urlh).read().decode('utf-8')
return dict((ln.split()[::(- 1)] for ln in hash_data.splitlines())).get(filename)
if (not os.access(filename, os.W_OK)):
return report_permission_error(filename)
variant = detect_variant()
if (variant in ('win_exe', 'py2exe')):
directory = os.path.dirname(filename)
if (not os.access(directory, os.W_OK)):
return report_permission_error(directory)
try:
if os.path.exists((filename + '.old')):
os.remove((filename + '.old'))
except (IOError, OSError):
return report_unable('remove the old version')
try:
arch = platform.architecture()[0][:2]
url = get_bin_info(variant, arch).get('browser_download_url')
if (not url):
return report_network_error('fetch updates')
urlh = ydl._opener.open(url)
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
return report_network_error('download latest version')
try:
with open((filename + '.new'), 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
return report_permission_error(f'{filename}.new')
expected_sum = get_sha256sum(variant, arch)
if (not expected_sum):
ydl.report_warning('no hash information found for the release')
elif (calc_sha256sum((filename + '.new')) != expected_sum):
report_network_error('verify the new executable')
try:
os.remove((filename + '.new'))
except OSError:
return report_unable('remove corrupt download')
try:
os.rename(filename, (filename + '.old'))
except (IOError, OSError):
return report_unable('move current version')
try:
os.rename((filename + '.new'), filename)
except (IOError, OSError):
report_unable('overwrite current version')
os.rename((filename + '.old'), filename)
return
try:
Popen(('ping 127.0.0.1 -n 5 -w 1000 & del /F "%s.old"' % filename), shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
ydl.to_screen(('Updated yt-dlp to version %s' % version_id))
return True
except OSError:
report_unable('delete the old version')
elif (variant in ('zip', 'mac_exe')):
pack_type = ('3' if (variant == 'zip') else '64')
try:
url = get_bin_info(variant, pack_type).get('browser_download_url')
if (not url):
return report_network_error('fetch updates')
urlh = ydl._opener.open(url)
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
return report_network_error('download the latest version')
expected_sum = get_sha256sum(variant, pack_type)
if (not expected_sum):
ydl.report_warning('no hash information found for the release')
elif (hashlib.sha256(newcontent).hexdigest() != expected_sum):
return report_network_error('verify the new package')
try:
with open(filename, 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
return report_unable('overwrite current version')
ydl.to_screen(('Updated yt-dlp to version %s; Restart yt-dlp to use the new version' % version_id))
return
assert False, f'Unhandled variant: {variant}'<|docstring|>Update the program file with the latest version from the repository
Returns whether the program should terminate<|endoftext|>
|
c846159964fca60e8619abcad7e150d6ffa91e4e6c86eb1b89290165c28ae2e3
|
def escape_tags(value, valid_tags):
'\n Strips text from the given html string, leaving only tags.\n This functionality requires BeautifulSoup, nothing will be\n done otherwise.\n\n This isn\'t perfect. Someone could put javascript in here:\n <a onClick="alert(\'hi\');">test</a>\n\n So if you use valid_tags, you still need to trust your data entry.\n Or we could try:\n - only escape the non matching bits\n - use BeautifulSoup to understand the elements, escape everything\n else and remove potentially harmful attributes (onClick).\n - Remove this feature entirely. Half-escaping things securely is\n very difficult, developers should not be lured into a false\n sense of security.\n '
value = conditional_escape(value)
if valid_tags:
tag_re = re.compile(('<(\\s*/?\\s*(%s))(.*?\\s*)>' % '|'.join((re.escape(tag) for tag in valid_tags))))
value = tag_re.sub(_replace_quot, value)
value = value.replace('<!--', '<!--').replace('-->', '-->')
return mark_safe(value)
|
Strips text from the given html string, leaving only tags.
This functionality requires BeautifulSoup, nothing will be
done otherwise.
This isn't perfect. Someone could put javascript in here:
<a onClick="alert('hi');">test</a>
So if you use valid_tags, you still need to trust your data entry.
Or we could try:
- only escape the non matching bits
- use BeautifulSoup to understand the elements, escape everything
else and remove potentially harmful attributes (onClick).
- Remove this feature entirely. Half-escaping things securely is
very difficult, developers should not be lured into a false
sense of security.
|
djangoseo/utils.py
|
escape_tags
|
adilshehzad786/django-seo2
| 66 |
python
|
def escape_tags(value, valid_tags):
'\n Strips text from the given html string, leaving only tags.\n This functionality requires BeautifulSoup, nothing will be\n done otherwise.\n\n This isn\'t perfect. Someone could put javascript in here:\n <a onClick="alert(\'hi\');">test</a>\n\n So if you use valid_tags, you still need to trust your data entry.\n Or we could try:\n - only escape the non matching bits\n - use BeautifulSoup to understand the elements, escape everything\n else and remove potentially harmful attributes (onClick).\n - Remove this feature entirely. Half-escaping things securely is\n very difficult, developers should not be lured into a false\n sense of security.\n '
value = conditional_escape(value)
if valid_tags:
tag_re = re.compile(('<(\\s*/?\\s*(%s))(.*?\\s*)>' % '|'.join((re.escape(tag) for tag in valid_tags))))
value = tag_re.sub(_replace_quot, value)
value = value.replace('<!--', '<!--').replace('-->', '-->')
return mark_safe(value)
|
def escape_tags(value, valid_tags):
'\n Strips text from the given html string, leaving only tags.\n This functionality requires BeautifulSoup, nothing will be\n done otherwise.\n\n This isn\'t perfect. Someone could put javascript in here:\n <a onClick="alert(\'hi\');">test</a>\n\n So if you use valid_tags, you still need to trust your data entry.\n Or we could try:\n - only escape the non matching bits\n - use BeautifulSoup to understand the elements, escape everything\n else and remove potentially harmful attributes (onClick).\n - Remove this feature entirely. Half-escaping things securely is\n very difficult, developers should not be lured into a false\n sense of security.\n '
value = conditional_escape(value)
if valid_tags:
tag_re = re.compile(('<(\\s*/?\\s*(%s))(.*?\\s*)>' % '|'.join((re.escape(tag) for tag in valid_tags))))
value = tag_re.sub(_replace_quot, value)
value = value.replace('<!--', '<!--').replace('-->', '-->')
return mark_safe(value)<|docstring|>Strips text from the given html string, leaving only tags.
This functionality requires BeautifulSoup, nothing will be
done otherwise.
This isn't perfect. Someone could put javascript in here:
<a onClick="alert('hi');">test</a>
So if you use valid_tags, you still need to trust your data entry.
Or we could try:
- only escape the non matching bits
- use BeautifulSoup to understand the elements, escape everything
else and remove potentially harmful attributes (onClick).
- Remove this feature entirely. Half-escaping things securely is
very difficult, developers should not be lured into a false
sense of security.<|endoftext|>
|
7fc5ede4b1a5d1260f251f9c98bb114fe0de04ce620481ded00334fc7c9f4e3b
|
def _get_seo_content_types(seo_models):
'Returns a list of content types from the models defined in settings.'
try:
return [ContentType.objects.get_for_model(m).id for m in seo_models]
except Exception:
return []
|
Returns a list of content types from the models defined in settings.
|
djangoseo/utils.py
|
_get_seo_content_types
|
adilshehzad786/django-seo2
| 66 |
python
|
def _get_seo_content_types(seo_models):
try:
return [ContentType.objects.get_for_model(m).id for m in seo_models]
except Exception:
return []
|
def _get_seo_content_types(seo_models):
try:
return [ContentType.objects.get_for_model(m).id for m in seo_models]
except Exception:
return []<|docstring|>Returns a list of content types from the models defined in settings.<|endoftext|>
|
dad0551a029b244d0d5ae3f898c0dcdf06aaeb679b96dbf761a4d01fcd0f5272
|
def __init__(self, Lower=(- 10.0), Upper=10.0):
'Initialize of Quintic benchmark.\n\n Args:\n Lower (Optional[float]): Lower bound of problem.\n Upper (Optional[float]): Upper bound of problem.\n\n See Also:\n :func:`NiaPy.benchmarks.Benchmark.__init__`\n '
Benchmark.__init__(self, Lower, Upper)
|
Initialize of Quintic benchmark.
Args:
Lower (Optional[float]): Lower bound of problem.
Upper (Optional[float]): Upper bound of problem.
See Also:
:func:`NiaPy.benchmarks.Benchmark.__init__`
|
NiaPy/benchmarks/quintic.py
|
__init__
|
lucijabrezocnik/NiaPy
| 0 |
python
|
def __init__(self, Lower=(- 10.0), Upper=10.0):
'Initialize of Quintic benchmark.\n\n Args:\n Lower (Optional[float]): Lower bound of problem.\n Upper (Optional[float]): Upper bound of problem.\n\n See Also:\n :func:`NiaPy.benchmarks.Benchmark.__init__`\n '
Benchmark.__init__(self, Lower, Upper)
|
def __init__(self, Lower=(- 10.0), Upper=10.0):
'Initialize of Quintic benchmark.\n\n Args:\n Lower (Optional[float]): Lower bound of problem.\n Upper (Optional[float]): Upper bound of problem.\n\n See Also:\n :func:`NiaPy.benchmarks.Benchmark.__init__`\n '
Benchmark.__init__(self, Lower, Upper)<|docstring|>Initialize of Quintic benchmark.
Args:
Lower (Optional[float]): Lower bound of problem.
Upper (Optional[float]): Upper bound of problem.
See Also:
:func:`NiaPy.benchmarks.Benchmark.__init__`<|endoftext|>
|
7c125cdd7e7a9d612f575ba81ebb9e273042e1c9ac72b9717ce7dd148c02bf1c
|
@staticmethod
def latex_code():
'Return the latex code of the problem.\n\n Returns:\n str: Latex code\n '
return '$f(\\mathbf{x}) = \\sum_{i=1}^D \\left| x_i^5 - 3x_i^4 +\n 4x_i^3 + 2x_i^2 - 10x_i - 4\\right|$'
|
Return the latex code of the problem.
Returns:
str: Latex code
|
NiaPy/benchmarks/quintic.py
|
latex_code
|
lucijabrezocnik/NiaPy
| 0 |
python
|
@staticmethod
def latex_code():
'Return the latex code of the problem.\n\n Returns:\n str: Latex code\n '
return '$f(\\mathbf{x}) = \\sum_{i=1}^D \\left| x_i^5 - 3x_i^4 +\n 4x_i^3 + 2x_i^2 - 10x_i - 4\\right|$'
|
@staticmethod
def latex_code():
'Return the latex code of the problem.\n\n Returns:\n str: Latex code\n '
return '$f(\\mathbf{x}) = \\sum_{i=1}^D \\left| x_i^5 - 3x_i^4 +\n 4x_i^3 + 2x_i^2 - 10x_i - 4\\right|$'<|docstring|>Return the latex code of the problem.
Returns:
str: Latex code<|endoftext|>
|
396e8e5145b1a11c22095d219a15dc0ed9d00098b17739d493fcb801fa96602c
|
def function(self):
'Return benchmark evaluation function.\n\n Returns:\n Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function\n '
def evaluate(D, sol):
'Fitness function.\n\n Args:\n D (int): Dimensionality of the problem\n sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.\n\n Returns:\n float: Fitness value for the solution.\n '
val = 0.0
for i in range(D):
val += abs((((((math.pow(sol[i], 5) - (3.0 * math.pow(sol[i], 4))) + (4.0 * math.pow(sol[i], 3))) + (2.0 * math.pow(sol[i], 2))) - (10.0 * sol[i])) - 4))
return val
return evaluate
|
Return benchmark evaluation function.
Returns:
Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
|
NiaPy/benchmarks/quintic.py
|
function
|
lucijabrezocnik/NiaPy
| 0 |
python
|
def function(self):
'Return benchmark evaluation function.\n\n Returns:\n Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function\n '
def evaluate(D, sol):
'Fitness function.\n\n Args:\n D (int): Dimensionality of the problem\n sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.\n\n Returns:\n float: Fitness value for the solution.\n '
val = 0.0
for i in range(D):
val += abs((((((math.pow(sol[i], 5) - (3.0 * math.pow(sol[i], 4))) + (4.0 * math.pow(sol[i], 3))) + (2.0 * math.pow(sol[i], 2))) - (10.0 * sol[i])) - 4))
return val
return evaluate
|
def function(self):
'Return benchmark evaluation function.\n\n Returns:\n Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function\n '
def evaluate(D, sol):
'Fitness function.\n\n Args:\n D (int): Dimensionality of the problem\n sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.\n\n Returns:\n float: Fitness value for the solution.\n '
val = 0.0
for i in range(D):
val += abs((((((math.pow(sol[i], 5) - (3.0 * math.pow(sol[i], 4))) + (4.0 * math.pow(sol[i], 3))) + (2.0 * math.pow(sol[i], 2))) - (10.0 * sol[i])) - 4))
return val
return evaluate<|docstring|>Return benchmark evaluation function.
Returns:
Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function<|endoftext|>
|
4fa9f34f37ba61fb5f19b88f45cd5e3693dc2c3ba4441bb72ed36f7a7174a804
|
def evaluate(D, sol):
'Fitness function.\n\n Args:\n D (int): Dimensionality of the problem\n sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.\n\n Returns:\n float: Fitness value for the solution.\n '
val = 0.0
for i in range(D):
val += abs((((((math.pow(sol[i], 5) - (3.0 * math.pow(sol[i], 4))) + (4.0 * math.pow(sol[i], 3))) + (2.0 * math.pow(sol[i], 2))) - (10.0 * sol[i])) - 4))
return val
|
Fitness function.
Args:
D (int): Dimensionality of the problem
sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
Returns:
float: Fitness value for the solution.
|
NiaPy/benchmarks/quintic.py
|
evaluate
|
lucijabrezocnik/NiaPy
| 0 |
python
|
def evaluate(D, sol):
'Fitness function.\n\n Args:\n D (int): Dimensionality of the problem\n sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.\n\n Returns:\n float: Fitness value for the solution.\n '
val = 0.0
for i in range(D):
val += abs((((((math.pow(sol[i], 5) - (3.0 * math.pow(sol[i], 4))) + (4.0 * math.pow(sol[i], 3))) + (2.0 * math.pow(sol[i], 2))) - (10.0 * sol[i])) - 4))
return val
|
def evaluate(D, sol):
'Fitness function.\n\n Args:\n D (int): Dimensionality of the problem\n sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.\n\n Returns:\n float: Fitness value for the solution.\n '
val = 0.0
for i in range(D):
val += abs((((((math.pow(sol[i], 5) - (3.0 * math.pow(sol[i], 4))) + (4.0 * math.pow(sol[i], 3))) + (2.0 * math.pow(sol[i], 2))) - (10.0 * sol[i])) - 4))
return val<|docstring|>Fitness function.
Args:
D (int): Dimensionality of the problem
sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
Returns:
float: Fitness value for the solution.<|endoftext|>
|
c9332bd7166bc5a8b2a3e636499e6cbc8e9084177b3b0df8fa6e2c6eb64fe2af
|
def freeParameters(self):
'\n Roadrunner models do not have a concept of "free" or "fixed"\n parameters (maybe it should?). Either way, we add a cheeky method\n to the tellurium interface to roadrunner to return the names\n of the parameters we want to fit\n '
return ['k1', 'k2', 'k3']
|
Roadrunner models do not have a concept of "free" or "fixed"
parameters (maybe it should?). Either way, we add a cheeky method
to the tellurium interface to roadrunner to return the names
of the parameters we want to fit
|
sresFromMoonfit/test/RoadrunnerProblemWithRL.py
|
freeParameters
|
CiaranWelsh/SRES
| 1 |
python
|
def freeParameters(self):
'\n Roadrunner models do not have a concept of "free" or "fixed"\n parameters (maybe it should?). Either way, we add a cheeky method\n to the tellurium interface to roadrunner to return the names\n of the parameters we want to fit\n '
return ['k1', 'k2', 'k3']
|
def freeParameters(self):
'\n Roadrunner models do not have a concept of "free" or "fixed"\n parameters (maybe it should?). Either way, we add a cheeky method\n to the tellurium interface to roadrunner to return the names\n of the parameters we want to fit\n '
return ['k1', 'k2', 'k3']<|docstring|>Roadrunner models do not have a concept of "free" or "fixed"
parameters (maybe it should?). Either way, we add a cheeky method
to the tellurium interface to roadrunner to return the names
of the parameters we want to fit<|endoftext|>
|
51c6a0667dd4eaa1878d61acd1f5affd589bdd1f8ba90740669cfc01da4cec69
|
@SRES.COST_FUNCTION_CALLBACK
def cost_fun(parameters, fitness, constraints):
"\n Brief\n -----\n Compute difference between experimental dataset and model simulation with candidate parameters.\n This cost function is user defined and used as input to the main SRES algorithm. The input\n to this function is always [parameters, fitness and constraints]. You do not need to worry\n about generating candidate parameters as they are generated by the underlying algorithm. You do\n however have to worry about updating the fitness value, which you do like this:\n\n fitness.contents.value = calculated_cost\n\n Where calculated_cost is a float computed by your function. Note, that even though\n we haven't used the constraints argument in this cost function, we still need to pass it in\n as an input parameter.\n\n Details\n -------\n The underlying SRES C code requires as input a function pointer to a cost function\n that has the following signature:\n\n typedef void(*ESfcnFG)(double *, double *, double *);\n\n We can create a cost function in Python to pass to C by using the\n :py:class:`SRES.COST_FUNCTION_CALLBACK` decorator. Since the C end is\n expecting a function with three double pointer types, we must have\n as arguments to our cost function, three arguments.\n\n When coding the cost function, you need to remember that the types of\n parameter, fitness and constraints are ctypes pointers to double\n arrays in the case of the parameter and constraints argument and\n a pointer to a double in the case of fitness. To do computation\n with these types you need the value that the pointer points to, not\n the pointer. To get these, you use:\n >>> parameters.contents[0]\n In the case of pointer to a double array or\n >>> fitness.contents.value\n in the case of a pointer to a double.\n\n Args\n ----\n parameters: A list of candidate parameters with the same size as the\n dimensionality of your defined optimization problem.\n fitness: This is the value that you must compute and assign.\n\n "
(x, y, sel) = get_data(**dict(zip(r.freeParameters(), parameters.contents)))
cost = np.sum(np.sum(((y - y_data) ** 2)))
fitness.contents.value = cost
|
Brief
-----
Compute difference between experimental dataset and model simulation with candidate parameters.
This cost function is user defined and used as input to the main SRES algorithm. The input
to this function is always [parameters, fitness and constraints]. You do not need to worry
about generating candidate parameters as they are generated by the underlying algorithm. You do
however have to worry about updating the fitness value, which you do like this:
fitness.contents.value = calculated_cost
Where calculated_cost is a float computed by your function. Note, that even though
we haven't used the constraints argument in this cost function, we still need to pass it in
as an input parameter.
Details
-------
The underlying SRES C code requires as input a function pointer to a cost function
that has the following signature:
typedef void(*ESfcnFG)(double *, double *, double *);
We can create a cost function in Python to pass to C by using the
:py:class:`SRES.COST_FUNCTION_CALLBACK` decorator. Since the C end is
expecting a function with three double pointer types, we must have
as arguments to our cost function, three arguments.
When coding the cost function, you need to remember that the types of
parameter, fitness and constraints are ctypes pointers to double
arrays in the case of the parameter and constraints argument and
a pointer to a double in the case of fitness. To do computation
with these types you need the value that the pointer points to, not
the pointer. To get these, you use:
>>> parameters.contents[0]
In the case of pointer to a double array or
>>> fitness.contents.value
in the case of a pointer to a double.
Args
----
parameters: A list of candidate parameters with the same size as the
dimensionality of your defined optimization problem.
fitness: This is the value that you must compute and assign.
|
sresFromMoonfit/test/RoadrunnerProblemWithRL.py
|
cost_fun
|
CiaranWelsh/SRES
| 1 |
python
|
@SRES.COST_FUNCTION_CALLBACK
def cost_fun(parameters, fitness, constraints):
"\n Brief\n -----\n Compute difference between experimental dataset and model simulation with candidate parameters.\n This cost function is user defined and used as input to the main SRES algorithm. The input\n to this function is always [parameters, fitness and constraints]. You do not need to worry\n about generating candidate parameters as they are generated by the underlying algorithm. You do\n however have to worry about updating the fitness value, which you do like this:\n\n fitness.contents.value = calculated_cost\n\n Where calculated_cost is a float computed by your function. Note, that even though\n we haven't used the constraints argument in this cost function, we still need to pass it in\n as an input parameter.\n\n Details\n -------\n The underlying SRES C code requires as input a function pointer to a cost function\n that has the following signature:\n\n typedef void(*ESfcnFG)(double *, double *, double *);\n\n We can create a cost function in Python to pass to C by using the\n :py:class:`SRES.COST_FUNCTION_CALLBACK` decorator. Since the C end is\n expecting a function with three double pointer types, we must have\n as arguments to our cost function, three arguments.\n\n When coding the cost function, you need to remember that the types of\n parameter, fitness and constraints are ctypes pointers to double\n arrays in the case of the parameter and constraints argument and\n a pointer to a double in the case of fitness. To do computation\n with these types you need the value that the pointer points to, not\n the pointer. To get these, you use:\n >>> parameters.contents[0]\n In the case of pointer to a double array or\n >>> fitness.contents.value\n in the case of a pointer to a double.\n\n Args\n ----\n parameters: A list of candidate parameters with the same size as the\n dimensionality of your defined optimization problem.\n fitness: This is the value that you must compute and assign.\n\n "
(x, y, sel) = get_data(**dict(zip(r.freeParameters(), parameters.contents)))
cost = np.sum(np.sum(((y - y_data) ** 2)))
fitness.contents.value = cost
|
@SRES.COST_FUNCTION_CALLBACK
def cost_fun(parameters, fitness, constraints):
"\n Brief\n -----\n Compute difference between experimental dataset and model simulation with candidate parameters.\n This cost function is user defined and used as input to the main SRES algorithm. The input\n to this function is always [parameters, fitness and constraints]. You do not need to worry\n about generating candidate parameters as they are generated by the underlying algorithm. You do\n however have to worry about updating the fitness value, which you do like this:\n\n fitness.contents.value = calculated_cost\n\n Where calculated_cost is a float computed by your function. Note, that even though\n we haven't used the constraints argument in this cost function, we still need to pass it in\n as an input parameter.\n\n Details\n -------\n The underlying SRES C code requires as input a function pointer to a cost function\n that has the following signature:\n\n typedef void(*ESfcnFG)(double *, double *, double *);\n\n We can create a cost function in Python to pass to C by using the\n :py:class:`SRES.COST_FUNCTION_CALLBACK` decorator. Since the C end is\n expecting a function with three double pointer types, we must have\n as arguments to our cost function, three arguments.\n\n When coding the cost function, you need to remember that the types of\n parameter, fitness and constraints are ctypes pointers to double\n arrays in the case of the parameter and constraints argument and\n a pointer to a double in the case of fitness. To do computation\n with these types you need the value that the pointer points to, not\n the pointer. To get these, you use:\n >>> parameters.contents[0]\n In the case of pointer to a double array or\n >>> fitness.contents.value\n in the case of a pointer to a double.\n\n Args\n ----\n parameters: A list of candidate parameters with the same size as the\n dimensionality of your defined optimization problem.\n fitness: This is the value that you must compute and assign.\n\n "
(x, y, sel) = get_data(**dict(zip(r.freeParameters(), parameters.contents)))
cost = np.sum(np.sum(((y - y_data) ** 2)))
fitness.contents.value = cost<|docstring|>Brief
-----
Compute difference between experimental dataset and model simulation with candidate parameters.
This cost function is user defined and used as input to the main SRES algorithm. The input
to this function is always [parameters, fitness and constraints]. You do not need to worry
about generating candidate parameters as they are generated by the underlying algorithm. You do
however have to worry about updating the fitness value, which you do like this:
fitness.contents.value = calculated_cost
Where calculated_cost is a float computed by your function. Note, that even though
we haven't used the constraints argument in this cost function, we still need to pass it in
as an input parameter.
Details
-------
The underlying SRES C code requires as input a function pointer to a cost function
that has the following signature:
typedef void(*ESfcnFG)(double *, double *, double *);
We can create a cost function in Python to pass to C by using the
:py:class:`SRES.COST_FUNCTION_CALLBACK` decorator. Since the C end is
expecting a function with three double pointer types, we must have
as arguments to our cost function, three arguments.
When coding the cost function, you need to remember that the types of
parameter, fitness and constraints are ctypes pointers to double
arrays in the case of the parameter and constraints argument and
a pointer to a double in the case of fitness. To do computation
with these types you need the value that the pointer points to, not
the pointer. To get these, you use:
>>> parameters.contents[0]
In the case of pointer to a double array or
>>> fitness.contents.value
in the case of a pointer to a double.
Args
----
parameters: A list of candidate parameters with the same size as the
dimensionality of your defined optimization problem.
fitness: This is the value that you must compute and assign.<|endoftext|>
|
b7c3225891a10d859420a5e44a5cd9500d838e0d06ef9c8aed7e46ceb4d02e6d
|
def step(self, action: dict):
"Run one timestep of the environment's dynamics. When end of\n episode is reached, you are responsible for calling `reset()`\n to reset this environment's state.\n\n Accepts an action and returns a tuple (observation, reward, done, info).\n\n Args:\n action (object): an action provided by the agent\n\n Returns:\n observation (object): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)\n "
sres = SRES(cost_function=cost_fun, ngen=100, lb=([0.01] * 3), ub=([10] * 3), parent_popsize=action['parent_popsize'], child_popsize=action['child_popsize'], retry=100)
best_val = sres.fit(False)
best_params = sres.getBestParameters()
observation = best_params
reward = np.log10((1 / best_val))
if (self.current_episode == self.episide_length):
self.done = True
else:
self.current_episode += 1
return (observation, reward, self.done, '')
|
Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
Args:
action (object): an action provided by the agent
Returns:
observation (object): agent's observation of the current environment
reward (float) : amount of reward returned after previous action
done (bool): whether the episode has ended, in which case further step() calls will return undefined results
info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)
|
sresFromMoonfit/test/RoadrunnerProblemWithRL.py
|
step
|
CiaranWelsh/SRES
| 1 |
python
|
def step(self, action: dict):
"Run one timestep of the environment's dynamics. When end of\n episode is reached, you are responsible for calling `reset()`\n to reset this environment's state.\n\n Accepts an action and returns a tuple (observation, reward, done, info).\n\n Args:\n action (object): an action provided by the agent\n\n Returns:\n observation (object): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)\n "
sres = SRES(cost_function=cost_fun, ngen=100, lb=([0.01] * 3), ub=([10] * 3), parent_popsize=action['parent_popsize'], child_popsize=action['child_popsize'], retry=100)
best_val = sres.fit(False)
best_params = sres.getBestParameters()
observation = best_params
reward = np.log10((1 / best_val))
if (self.current_episode == self.episide_length):
self.done = True
else:
self.current_episode += 1
return (observation, reward, self.done, )
|
def step(self, action: dict):
"Run one timestep of the environment's dynamics. When end of\n episode is reached, you are responsible for calling `reset()`\n to reset this environment's state.\n\n Accepts an action and returns a tuple (observation, reward, done, info).\n\n Args:\n action (object): an action provided by the agent\n\n Returns:\n observation (object): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)\n "
sres = SRES(cost_function=cost_fun, ngen=100, lb=([0.01] * 3), ub=([10] * 3), parent_popsize=action['parent_popsize'], child_popsize=action['child_popsize'], retry=100)
best_val = sres.fit(False)
best_params = sres.getBestParameters()
observation = best_params
reward = np.log10((1 / best_val))
if (self.current_episode == self.episide_length):
self.done = True
else:
self.current_episode += 1
return (observation, reward, self.done, )<|docstring|>Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
Args:
action (object): an action provided by the agent
Returns:
observation (object): agent's observation of the current environment
reward (float) : amount of reward returned after previous action
done (bool): whether the episode has ended, in which case further step() calls will return undefined results
info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)<|endoftext|>
|
82d9b483e59ace549efd5cf7d17bcee277fc0ee33d47f105b79ece3d38bb0ab9
|
def reset(self):
"Resets the environment to an initial state and returns an initial\n observation.\n\n Note that this function should not reset the environment's random\n number generator(s); random variables in the environment's state should\n be sampled independently between multiple calls to `reset()`. In other\n words, each call of `reset()` should yield an environment suitable for\n a new episode, independent of previous episodes.\n\n Returns:\n observation (object): the initial observation.\n "
self.done = False
self.current_episode = 0
return self
|
Resets the environment to an initial state and returns an initial
observation.
Note that this function should not reset the environment's random
number generator(s); random variables in the environment's state should
be sampled independently between multiple calls to `reset()`. In other
words, each call of `reset()` should yield an environment suitable for
a new episode, independent of previous episodes.
Returns:
observation (object): the initial observation.
|
sresFromMoonfit/test/RoadrunnerProblemWithRL.py
|
reset
|
CiaranWelsh/SRES
| 1 |
python
|
def reset(self):
"Resets the environment to an initial state and returns an initial\n observation.\n\n Note that this function should not reset the environment's random\n number generator(s); random variables in the environment's state should\n be sampled independently between multiple calls to `reset()`. In other\n words, each call of `reset()` should yield an environment suitable for\n a new episode, independent of previous episodes.\n\n Returns:\n observation (object): the initial observation.\n "
self.done = False
self.current_episode = 0
return self
|
def reset(self):
"Resets the environment to an initial state and returns an initial\n observation.\n\n Note that this function should not reset the environment's random\n number generator(s); random variables in the environment's state should\n be sampled independently between multiple calls to `reset()`. In other\n words, each call of `reset()` should yield an environment suitable for\n a new episode, independent of previous episodes.\n\n Returns:\n observation (object): the initial observation.\n "
self.done = False
self.current_episode = 0
return self<|docstring|>Resets the environment to an initial state and returns an initial
observation.
Note that this function should not reset the environment's random
number generator(s); random variables in the environment's state should
be sampled independently between multiple calls to `reset()`. In other
words, each call of `reset()` should yield an environment suitable for
a new episode, independent of previous episodes.
Returns:
observation (object): the initial observation.<|endoftext|>
|
e70a2f98e8f7f555b7433ed1ffc9d2d472b2dcfd284e3b21df63429d0885b182
|
def _query_scihub(opener, query):
'\n Get the data from the scihub catalogue\n and write it to a GeoPandas GeoDataFrame\n '
columns = ['identifier', 'polarisationmode', 'orbitdirection', 'acquisitiondate', 'relativeorbitnumber', 'orbitnumber', 'producttype', 'slicenumber', 'size', 'beginposition', 'endposition', 'lastrelativeorbitnumber', 'lastorbitnumber', 'uuid', 'platformidentifier', 'missiondatatakeid', 'swathidentifier', 'ingestiondate', 'sensoroperationalmode', 'geometry']
crs = 'epsg:4326'
geo_df = gpd.GeoDataFrame(columns=columns, crs=crs)
(index, rows, next_page) = (0, 99, 1)
while next_page:
url = (query + f'&rows={rows}&start={index}')
try:
req = opener.open(url)
except URLError as error:
if hasattr(error, 'reason'):
logger.info(f'{CONNECTION_ERROR}{error.reason}')
sys.exit()
elif hasattr(error, 'code'):
logger.info(f'{CONNECTION_ERROR_2}{error.code}')
sys.exit()
else:
response = req.read().decode('utf-8')
dom = xml.dom.minidom.parseString(response)
acq_list = _read_xml(dom)
gdf = gpd.GeoDataFrame(acq_list, columns=columns, crs=crs)
geo_df = geo_df.append(gdf)
next_page = scihub.next_page(dom)
index += rows
return geo_df
|
Get the data from the scihub catalogue
and write it to a GeoPandas GeoDataFrame
|
ost/s1/search.py
|
_query_scihub
|
d-chambers/OpenSarToolkit
| 131 |
python
|
def _query_scihub(opener, query):
'\n Get the data from the scihub catalogue\n and write it to a GeoPandas GeoDataFrame\n '
columns = ['identifier', 'polarisationmode', 'orbitdirection', 'acquisitiondate', 'relativeorbitnumber', 'orbitnumber', 'producttype', 'slicenumber', 'size', 'beginposition', 'endposition', 'lastrelativeorbitnumber', 'lastorbitnumber', 'uuid', 'platformidentifier', 'missiondatatakeid', 'swathidentifier', 'ingestiondate', 'sensoroperationalmode', 'geometry']
crs = 'epsg:4326'
geo_df = gpd.GeoDataFrame(columns=columns, crs=crs)
(index, rows, next_page) = (0, 99, 1)
while next_page:
url = (query + f'&rows={rows}&start={index}')
try:
req = opener.open(url)
except URLError as error:
if hasattr(error, 'reason'):
logger.info(f'{CONNECTION_ERROR}{error.reason}')
sys.exit()
elif hasattr(error, 'code'):
logger.info(f'{CONNECTION_ERROR_2}{error.code}')
sys.exit()
else:
response = req.read().decode('utf-8')
dom = xml.dom.minidom.parseString(response)
acq_list = _read_xml(dom)
gdf = gpd.GeoDataFrame(acq_list, columns=columns, crs=crs)
geo_df = geo_df.append(gdf)
next_page = scihub.next_page(dom)
index += rows
return geo_df
|
def _query_scihub(opener, query):
'\n Get the data from the scihub catalogue\n and write it to a GeoPandas GeoDataFrame\n '
columns = ['identifier', 'polarisationmode', 'orbitdirection', 'acquisitiondate', 'relativeorbitnumber', 'orbitnumber', 'producttype', 'slicenumber', 'size', 'beginposition', 'endposition', 'lastrelativeorbitnumber', 'lastorbitnumber', 'uuid', 'platformidentifier', 'missiondatatakeid', 'swathidentifier', 'ingestiondate', 'sensoroperationalmode', 'geometry']
crs = 'epsg:4326'
geo_df = gpd.GeoDataFrame(columns=columns, crs=crs)
(index, rows, next_page) = (0, 99, 1)
while next_page:
url = (query + f'&rows={rows}&start={index}')
try:
req = opener.open(url)
except URLError as error:
if hasattr(error, 'reason'):
logger.info(f'{CONNECTION_ERROR}{error.reason}')
sys.exit()
elif hasattr(error, 'code'):
logger.info(f'{CONNECTION_ERROR_2}{error.code}')
sys.exit()
else:
response = req.read().decode('utf-8')
dom = xml.dom.minidom.parseString(response)
acq_list = _read_xml(dom)
gdf = gpd.GeoDataFrame(acq_list, columns=columns, crs=crs)
geo_df = geo_df.append(gdf)
next_page = scihub.next_page(dom)
index += rows
return geo_df<|docstring|>Get the data from the scihub catalogue
and write it to a GeoPandas GeoDataFrame<|endoftext|>
|
05076f54d2b9c0e34e2119b37eca3d025a6cda4a05fa1c945047bedb99850889
|
def check_availability(inventory_gdf, download_dir, data_mount):
'This function checks if the data is already downloaded or\n available through a mount point on DIAS cloud\n\n :param inventory_gdf:\n :param download_dir:\n :param data_mount:\n :return:\n '
from ost import Sentinel1Scene
inventory_gdf['download_path'] = inventory_gdf.identifier.apply((lambda row: str(Sentinel1Scene(row).get_path(download_dir, data_mount))))
return inventory_gdf
|
This function checks if the data is already downloaded or
available through a mount point on DIAS cloud
:param inventory_gdf:
:param download_dir:
:param data_mount:
:return:
|
ost/s1/search.py
|
check_availability
|
d-chambers/OpenSarToolkit
| 131 |
python
|
def check_availability(inventory_gdf, download_dir, data_mount):
'This function checks if the data is already downloaded or\n available through a mount point on DIAS cloud\n\n :param inventory_gdf:\n :param download_dir:\n :param data_mount:\n :return:\n '
from ost import Sentinel1Scene
inventory_gdf['download_path'] = inventory_gdf.identifier.apply((lambda row: str(Sentinel1Scene(row).get_path(download_dir, data_mount))))
return inventory_gdf
|
def check_availability(inventory_gdf, download_dir, data_mount):
'This function checks if the data is already downloaded or\n available through a mount point on DIAS cloud\n\n :param inventory_gdf:\n :param download_dir:\n :param data_mount:\n :return:\n '
from ost import Sentinel1Scene
inventory_gdf['download_path'] = inventory_gdf.identifier.apply((lambda row: str(Sentinel1Scene(row).get_path(download_dir, data_mount))))
return inventory_gdf<|docstring|>This function checks if the data is already downloaded or
available through a mount point on DIAS cloud
:param inventory_gdf:
:param download_dir:
:param data_mount:
:return:<|endoftext|>
|
35cfd1c6b70df3752d3aefd7e6a66c2710d42010bddf66a5d4ff49dfdcc62236
|
def scihub_catalogue(query_string, output, append=False, uname=None, pword=None, base_url='https://apihub.copernicus.eu/apihub'):
'This is the main search function on scihub\n\n :param query_string:\n :param output:\n :param append:\n :param uname:\n :param pword:\n :return:\n '
output = str(output)
hub = f'{base_url}/search?q='
opener = scihub.connect(uname, pword, base_url)
query = f'{hub}{query_string}'
gdf = _query_scihub(opener, query)
if (output[(- 4):] == '.shp'):
logger.info(f'Writing inventory data to shape file: {output}')
_to_shapefile(gdf, output, append)
elif (output[(- 5):] == '.gpkg'):
logger.info(f'Writing inventory data to geopackage file: {output}')
_to_geopackage(gdf, output, append)
else:
logger.info(f'Writing inventory data toPostGIS table: {output}')
db_connect = pgHandler()
_to_postgis(gdf, db_connect, output)
|
This is the main search function on scihub
:param query_string:
:param output:
:param append:
:param uname:
:param pword:
:return:
|
ost/s1/search.py
|
scihub_catalogue
|
d-chambers/OpenSarToolkit
| 131 |
python
|
def scihub_catalogue(query_string, output, append=False, uname=None, pword=None, base_url='https://apihub.copernicus.eu/apihub'):
'This is the main search function on scihub\n\n :param query_string:\n :param output:\n :param append:\n :param uname:\n :param pword:\n :return:\n '
output = str(output)
hub = f'{base_url}/search?q='
opener = scihub.connect(uname, pword, base_url)
query = f'{hub}{query_string}'
gdf = _query_scihub(opener, query)
if (output[(- 4):] == '.shp'):
logger.info(f'Writing inventory data to shape file: {output}')
_to_shapefile(gdf, output, append)
elif (output[(- 5):] == '.gpkg'):
logger.info(f'Writing inventory data to geopackage file: {output}')
_to_geopackage(gdf, output, append)
else:
logger.info(f'Writing inventory data toPostGIS table: {output}')
db_connect = pgHandler()
_to_postgis(gdf, db_connect, output)
|
def scihub_catalogue(query_string, output, append=False, uname=None, pword=None, base_url='https://apihub.copernicus.eu/apihub'):
'This is the main search function on scihub\n\n :param query_string:\n :param output:\n :param append:\n :param uname:\n :param pword:\n :return:\n '
output = str(output)
hub = f'{base_url}/search?q='
opener = scihub.connect(uname, pword, base_url)
query = f'{hub}{query_string}'
gdf = _query_scihub(opener, query)
if (output[(- 4):] == '.shp'):
logger.info(f'Writing inventory data to shape file: {output}')
_to_shapefile(gdf, output, append)
elif (output[(- 5):] == '.gpkg'):
logger.info(f'Writing inventory data to geopackage file: {output}')
_to_geopackage(gdf, output, append)
else:
logger.info(f'Writing inventory data toPostGIS table: {output}')
db_connect = pgHandler()
_to_postgis(gdf, db_connect, output)<|docstring|>This is the main search function on scihub
:param query_string:
:param output:
:param append:
:param uname:
:param pword:
:return:<|endoftext|>
|
938497d7fe580abe80493a41f86db303f965e2f7856cbcf9aa1f49f5202c3e0c
|
def __get_acceptable(self):
'A list of symbols that the configuration would accept in its current state.'
return self.configuration.acceptableSymbols()
|
A list of symbols that the configuration would accept in its current state.
|
pyxb/utils/fac.py
|
__get_acceptable
|
maciekwawro/pyxb
| 123 |
python
|
def __get_acceptable(self):
return self.configuration.acceptableSymbols()
|
def __get_acceptable(self):
return self.configuration.acceptableSymbols()<|docstring|>A list of symbols that the configuration would accept in its current state.<|endoftext|>
|
c09a4cd6e05eafaaec184ccce93dbdd93f66870143caa53749371583d5406554
|
def __init__(self, symbol, is_initial, final_update=None, is_unordered_catenation=False):
'Create a FAC state.\n\n @param symbol: The symbol associated with the state.\n Normally initialized from the L{Symbol.metadata} value. The\n state may be entered if, among other conditions, the L{match}\n routine accepts the proposed input as being consistent with\n this value.\n\n @param is_initial: C{True} iff this state may serve as the\n first state of the automaton.\n\n @param final_update: C{None} if this state is not an\n accepting state of the automaton; otherwise a set of\n L{UpdateInstruction} values that must be satisfied by the\n counter values in a configuration as a further restriction of\n acceptance.\n\n @param is_unordered_catenation: C{True} if this state has\n subautomata that must be matched to execute the unordered\n catenation of an L{All} node; C{False} if this is a regular\n symbol.'
self.__symbol = symbol
self.__isInitial = (not (not is_initial))
self.__finalUpdate = final_update
self.__isUnorderedCatenation = is_unordered_catenation
|
Create a FAC state.
@param symbol: The symbol associated with the state.
Normally initialized from the L{Symbol.metadata} value. The
state may be entered if, among other conditions, the L{match}
routine accepts the proposed input as being consistent with
this value.
@param is_initial: C{True} iff this state may serve as the
first state of the automaton.
@param final_update: C{None} if this state is not an
accepting state of the automaton; otherwise a set of
L{UpdateInstruction} values that must be satisfied by the
counter values in a configuration as a further restriction of
acceptance.
@param is_unordered_catenation: C{True} if this state has
subautomata that must be matched to execute the unordered
catenation of an L{All} node; C{False} if this is a regular
symbol.
|
pyxb/utils/fac.py
|
__init__
|
maciekwawro/pyxb
| 123 |
python
|
def __init__(self, symbol, is_initial, final_update=None, is_unordered_catenation=False):
'Create a FAC state.\n\n @param symbol: The symbol associated with the state.\n Normally initialized from the L{Symbol.metadata} value. The\n state may be entered if, among other conditions, the L{match}\n routine accepts the proposed input as being consistent with\n this value.\n\n @param is_initial: C{True} iff this state may serve as the\n first state of the automaton.\n\n @param final_update: C{None} if this state is not an\n accepting state of the automaton; otherwise a set of\n L{UpdateInstruction} values that must be satisfied by the\n counter values in a configuration as a further restriction of\n acceptance.\n\n @param is_unordered_catenation: C{True} if this state has\n subautomata that must be matched to execute the unordered\n catenation of an L{All} node; C{False} if this is a regular\n symbol.'
self.__symbol = symbol
self.__isInitial = (not (not is_initial))
self.__finalUpdate = final_update
self.__isUnorderedCatenation = is_unordered_catenation
|
def __init__(self, symbol, is_initial, final_update=None, is_unordered_catenation=False):
'Create a FAC state.\n\n @param symbol: The symbol associated with the state.\n Normally initialized from the L{Symbol.metadata} value. The\n state may be entered if, among other conditions, the L{match}\n routine accepts the proposed input as being consistent with\n this value.\n\n @param is_initial: C{True} iff this state may serve as the\n first state of the automaton.\n\n @param final_update: C{None} if this state is not an\n accepting state of the automaton; otherwise a set of\n L{UpdateInstruction} values that must be satisfied by the\n counter values in a configuration as a further restriction of\n acceptance.\n\n @param is_unordered_catenation: C{True} if this state has\n subautomata that must be matched to execute the unordered\n catenation of an L{All} node; C{False} if this is a regular\n symbol.'
self.__symbol = symbol
self.__isInitial = (not (not is_initial))
self.__finalUpdate = final_update
self.__isUnorderedCatenation = is_unordered_catenation<|docstring|>Create a FAC state.
@param symbol: The symbol associated with the state.
Normally initialized from the L{Symbol.metadata} value. The
state may be entered if, among other conditions, the L{match}
routine accepts the proposed input as being consistent with
this value.
@param is_initial: C{True} iff this state may serve as the
first state of the automaton.
@param final_update: C{None} if this state is not an
accepting state of the automaton; otherwise a set of
L{UpdateInstruction} values that must be satisfied by the
counter values in a configuration as a further restriction of
acceptance.
@param is_unordered_catenation: C{True} if this state has
subautomata that must be matched to execute the unordered
catenation of an L{All} node; C{False} if this is a regular
symbol.<|endoftext|>
|
2b16cbe72363e624c097738680fa0b8a5adb6289dac56f2da630cf6aae50faa3
|
def __get_automaton(self):
'Link to the L{Automaton} to which the state belongs.'
return self.__automaton
|
Link to the L{Automaton} to which the state belongs.
|
pyxb/utils/fac.py
|
__get_automaton
|
maciekwawro/pyxb
| 123 |
python
|
def __get_automaton(self):
return self.__automaton
|
def __get_automaton(self):
return self.__automaton<|docstring|>Link to the L{Automaton} to which the state belongs.<|endoftext|>
|
37662e4fa8dbeebd992a198650733dcf4ddbaa42aaff3ae1026de2d074e73868
|
def _set_automaton(self, automaton):
'Method invoked during automaton construction to set state owner.'
assert (self.__automaton is None)
self.__automaton = automaton
return self
|
Method invoked during automaton construction to set state owner.
|
pyxb/utils/fac.py
|
_set_automaton
|
maciekwawro/pyxb
| 123 |
python
|
def _set_automaton(self, automaton):
assert (self.__automaton is None)
self.__automaton = automaton
return self
|
def _set_automaton(self, automaton):
assert (self.__automaton is None)
self.__automaton = automaton
return self<|docstring|>Method invoked during automaton construction to set state owner.<|endoftext|>
|
fa309d42871994a6bdd612d59c196522b16e90389b77f3d9ea17a57c522047a3
|
def __get_symbol(self):
'Application-specific metadata identifying the symbol.\n\n See also L{match}.'
return self.__symbol
|
Application-specific metadata identifying the symbol.
See also L{match}.
|
pyxb/utils/fac.py
|
__get_symbol
|
maciekwawro/pyxb
| 123 |
python
|
def __get_symbol(self):
'Application-specific metadata identifying the symbol.\n\n See also L{match}.'
return self.__symbol
|
def __get_symbol(self):
'Application-specific metadata identifying the symbol.\n\n See also L{match}.'
return self.__symbol<|docstring|>Application-specific metadata identifying the symbol.
See also L{match}.<|endoftext|>
|
d02b5e75dee64ac894d3971c03bc4d70f837c44ab27b2650792be81187f558a8
|
def __get_isUnorderedCatenation(self):
'Indicate whether the state has subautomata for unordered\n catenation.\n\n To reduce state explosion due to non-determinism, such a state\n executes internal transitions in subautomata until all terms\n have matched or a failure is discovered.'
return self.__isUnorderedCatenation
|
Indicate whether the state has subautomata for unordered
catenation.
To reduce state explosion due to non-determinism, such a state
executes internal transitions in subautomata until all terms
have matched or a failure is discovered.
|
pyxb/utils/fac.py
|
__get_isUnorderedCatenation
|
maciekwawro/pyxb
| 123 |
python
|
def __get_isUnorderedCatenation(self):
'Indicate whether the state has subautomata for unordered\n catenation.\n\n To reduce state explosion due to non-determinism, such a state\n executes internal transitions in subautomata until all terms\n have matched or a failure is discovered.'
return self.__isUnorderedCatenation
|
def __get_isUnorderedCatenation(self):
'Indicate whether the state has subautomata for unordered\n catenation.\n\n To reduce state explosion due to non-determinism, such a state\n executes internal transitions in subautomata until all terms\n have matched or a failure is discovered.'
return self.__isUnorderedCatenation<|docstring|>Indicate whether the state has subautomata for unordered
catenation.
To reduce state explosion due to non-determinism, such a state
executes internal transitions in subautomata until all terms
have matched or a failure is discovered.<|endoftext|>
|
e76b5190169a256051a961683b72231ace6fda558dacb13a0632ce551cae1183
|
def __get_subAutomata(self):
'A sequence of sub-automata supporting internal state transitions.\n\n This will return C{None} unless L{isUnorderedCatenation} is C{True}.'
return self.__subAutomata
|
A sequence of sub-automata supporting internal state transitions.
This will return C{None} unless L{isUnorderedCatenation} is C{True}.
|
pyxb/utils/fac.py
|
__get_subAutomata
|
maciekwawro/pyxb
| 123 |
python
|
def __get_subAutomata(self):
'A sequence of sub-automata supporting internal state transitions.\n\n This will return C{None} unless L{isUnorderedCatenation} is C{True}.'
return self.__subAutomata
|
def __get_subAutomata(self):
'A sequence of sub-automata supporting internal state transitions.\n\n This will return C{None} unless L{isUnorderedCatenation} is C{True}.'
return self.__subAutomata<|docstring|>A sequence of sub-automata supporting internal state transitions.
This will return C{None} unless L{isUnorderedCatenation} is C{True}.<|endoftext|>
|
99c052bd935b4f8cf212477da4633da288b574a0bb39c6e38b1897c988e4b44a
|
def __get_isInitial(self):
'C{True} iff this state may be the first state the automaton enters.'
return self.__isInitial
|
C{True} iff this state may be the first state the automaton enters.
|
pyxb/utils/fac.py
|
__get_isInitial
|
maciekwawro/pyxb
| 123 |
python
|
def __get_isInitial(self):
return self.__isInitial
|
def __get_isInitial(self):
return self.__isInitial<|docstring|>C{True} iff this state may be the first state the automaton enters.<|endoftext|>
|
24b2a6100dc2713d34a637ff4339d8b2a79596baa223ca829282ba5132c9f3ff
|
def __get_automatonEntryTransitions(self):
'Return the set of initial transitions allowing entry to the automata through this state.\n\n These are structurally-permitted transitions only, and must be\n filtered based on the symbol that might trigger the\n transition. The results are not filtered based on counter\n value, since this value is used to determine how the\n containing automaton might be entered. Consequently the\n return value is the empty set unless this is an initial state.\n\n The returned set is closed under entry to sub-automata,\n i.e. it is guaranteed that each transition includes a\n consuming state even if it requires a multi-element chain of\n transitions into subautomata to reach one.'
if (self.__automatonEntryTransitions is None):
transitions = []
if self.__isInitial:
xit = Transition(self, set())
if (self.__subAutomata is None):
transitions.append(xit)
else:
for sa in self.__subAutomata:
for saxit in sa.initialTransitions:
transitions.append(xit.chainTo(saxit.makeEnterAutomatonTransition()))
self.__automatonEntryTransitions = transitions
return self.__automatonEntryTransitions
|
Return the set of initial transitions allowing entry to the automata through this state.
These are structurally-permitted transitions only, and must be
filtered based on the symbol that might trigger the
transition. The results are not filtered based on counter
value, since this value is used to determine how the
containing automaton might be entered. Consequently the
return value is the empty set unless this is an initial state.
The returned set is closed under entry to sub-automata,
i.e. it is guaranteed that each transition includes a
consuming state even if it requires a multi-element chain of
transitions into subautomata to reach one.
|
pyxb/utils/fac.py
|
__get_automatonEntryTransitions
|
maciekwawro/pyxb
| 123 |
python
|
def __get_automatonEntryTransitions(self):
'Return the set of initial transitions allowing entry to the automata through this state.\n\n These are structurally-permitted transitions only, and must be\n filtered based on the symbol that might trigger the\n transition. The results are not filtered based on counter\n value, since this value is used to determine how the\n containing automaton might be entered. Consequently the\n return value is the empty set unless this is an initial state.\n\n The returned set is closed under entry to sub-automata,\n i.e. it is guaranteed that each transition includes a\n consuming state even if it requires a multi-element chain of\n transitions into subautomata to reach one.'
if (self.__automatonEntryTransitions is None):
transitions = []
if self.__isInitial:
xit = Transition(self, set())
if (self.__subAutomata is None):
transitions.append(xit)
else:
for sa in self.__subAutomata:
for saxit in sa.initialTransitions:
transitions.append(xit.chainTo(saxit.makeEnterAutomatonTransition()))
self.__automatonEntryTransitions = transitions
return self.__automatonEntryTransitions
|
def __get_automatonEntryTransitions(self):
'Return the set of initial transitions allowing entry to the automata through this state.\n\n These are structurally-permitted transitions only, and must be\n filtered based on the symbol that might trigger the\n transition. The results are not filtered based on counter\n value, since this value is used to determine how the\n containing automaton might be entered. Consequently the\n return value is the empty set unless this is an initial state.\n\n The returned set is closed under entry to sub-automata,\n i.e. it is guaranteed that each transition includes a\n consuming state even if it requires a multi-element chain of\n transitions into subautomata to reach one.'
if (self.__automatonEntryTransitions is None):
transitions = []
if self.__isInitial:
xit = Transition(self, set())
if (self.__subAutomata is None):
transitions.append(xit)
else:
for sa in self.__subAutomata:
for saxit in sa.initialTransitions:
transitions.append(xit.chainTo(saxit.makeEnterAutomatonTransition()))
self.__automatonEntryTransitions = transitions
return self.__automatonEntryTransitions<|docstring|>Return the set of initial transitions allowing entry to the automata through this state.
These are structurally-permitted transitions only, and must be
filtered based on the symbol that might trigger the
transition. The results are not filtered based on counter
value, since this value is used to determine how the
containing automaton might be entered. Consequently the
return value is the empty set unless this is an initial state.
The returned set is closed under entry to sub-automata,
i.e. it is guaranteed that each transition includes a
consuming state even if it requires a multi-element chain of
transitions into subautomata to reach one.<|endoftext|>
|
42e28a5355768fce8a342df453258586ac68d0fc330a0728a4b9035a9ae7df33
|
def __get_finalUpdate(self):
'Return the update instructions that must be satisfied for this to be a final state.'
return self.__finalUpdate
|
Return the update instructions that must be satisfied for this to be a final state.
|
pyxb/utils/fac.py
|
__get_finalUpdate
|
maciekwawro/pyxb
| 123 |
python
|
def __get_finalUpdate(self):
return self.__finalUpdate
|
def __get_finalUpdate(self):
return self.__finalUpdate<|docstring|>Return the update instructions that must be satisfied for this to be a final state.<|endoftext|>
|
155c5a656446b9b51c19666b941d47bbc1ce485a310e4ddf60a0e304891c4f08
|
def subAutomataInitialTransitions(self, sub_automata=None):
'Return the set of candidate transitions to enter a sub-automaton of this state.\n\n @param sub_automata: A subset of the sub-automata of this\n state which should contribute to the result. If C{None}, all\n sub-automata are used.\n\n @return: A pair C{(nullable, transitions)} where C{nullable}\n is C{True} iff there is at least one sub-automaton that is in\n an accepting state on entry, and C{transitions} is a list of\n L{Transition} instances describing how to reach some state in\n a sub-automaton via a consumed symbol.\n '
assert (self.__subAutomata is not None)
is_nullable = True
transitions = []
if (sub_automata is None):
sub_automata = self.__subAutomata
for sa in sub_automata:
if (not sa.nullable):
is_nullable = False
transitions.extend(sa.initialTransitions)
return (is_nullable, transitions)
|
Return the set of candidate transitions to enter a sub-automaton of this state.
@param sub_automata: A subset of the sub-automata of this
state which should contribute to the result. If C{None}, all
sub-automata are used.
@return: A pair C{(nullable, transitions)} where C{nullable}
is C{True} iff there is at least one sub-automaton that is in
an accepting state on entry, and C{transitions} is a list of
L{Transition} instances describing how to reach some state in
a sub-automaton via a consumed symbol.
|
pyxb/utils/fac.py
|
subAutomataInitialTransitions
|
maciekwawro/pyxb
| 123 |
python
|
def subAutomataInitialTransitions(self, sub_automata=None):
'Return the set of candidate transitions to enter a sub-automaton of this state.\n\n @param sub_automata: A subset of the sub-automata of this\n state which should contribute to the result. If C{None}, all\n sub-automata are used.\n\n @return: A pair C{(nullable, transitions)} where C{nullable}\n is C{True} iff there is at least one sub-automaton that is in\n an accepting state on entry, and C{transitions} is a list of\n L{Transition} instances describing how to reach some state in\n a sub-automaton via a consumed symbol.\n '
assert (self.__subAutomata is not None)
is_nullable = True
transitions = []
if (sub_automata is None):
sub_automata = self.__subAutomata
for sa in sub_automata:
if (not sa.nullable):
is_nullable = False
transitions.extend(sa.initialTransitions)
return (is_nullable, transitions)
|
def subAutomataInitialTransitions(self, sub_automata=None):
'Return the set of candidate transitions to enter a sub-automaton of this state.\n\n @param sub_automata: A subset of the sub-automata of this\n state which should contribute to the result. If C{None}, all\n sub-automata are used.\n\n @return: A pair C{(nullable, transitions)} where C{nullable}\n is C{True} iff there is at least one sub-automaton that is in\n an accepting state on entry, and C{transitions} is a list of\n L{Transition} instances describing how to reach some state in\n a sub-automaton via a consumed symbol.\n '
assert (self.__subAutomata is not None)
is_nullable = True
transitions = []
if (sub_automata is None):
sub_automata = self.__subAutomata
for sa in sub_automata:
if (not sa.nullable):
is_nullable = False
transitions.extend(sa.initialTransitions)
return (is_nullable, transitions)<|docstring|>Return the set of candidate transitions to enter a sub-automaton of this state.
@param sub_automata: A subset of the sub-automata of this
state which should contribute to the result. If C{None}, all
sub-automata are used.
@return: A pair C{(nullable, transitions)} where C{nullable}
is C{True} iff there is at least one sub-automaton that is in
an accepting state on entry, and C{transitions} is a list of
L{Transition} instances describing how to reach some state in
a sub-automaton via a consumed symbol.<|endoftext|>
|
f1828f55a517ecd95e52cd8ee0661b976f361e1fc2d3dea3392fd8f502405e7a
|
def isAccepting(self, counter_values):
'C{True} iff this state is an accepting state for the automaton.\n\n @param counter_values: Counter values that further validate\n whether the requirements of the automaton have been met.\n\n @return: C{True} if this is an accepting state and the\n counter values relevant at it are satisfied.'
if (self.__finalUpdate is None):
return False
return UpdateInstruction.Satisfies(counter_values, self.__finalUpdate)
|
C{True} iff this state is an accepting state for the automaton.
@param counter_values: Counter values that further validate
whether the requirements of the automaton have been met.
@return: C{True} if this is an accepting state and the
counter values relevant at it are satisfied.
|
pyxb/utils/fac.py
|
isAccepting
|
maciekwawro/pyxb
| 123 |
python
|
def isAccepting(self, counter_values):
'C{True} iff this state is an accepting state for the automaton.\n\n @param counter_values: Counter values that further validate\n whether the requirements of the automaton have been met.\n\n @return: C{True} if this is an accepting state and the\n counter values relevant at it are satisfied.'
if (self.__finalUpdate is None):
return False
return UpdateInstruction.Satisfies(counter_values, self.__finalUpdate)
|
def isAccepting(self, counter_values):
'C{True} iff this state is an accepting state for the automaton.\n\n @param counter_values: Counter values that further validate\n whether the requirements of the automaton have been met.\n\n @return: C{True} if this is an accepting state and the\n counter values relevant at it are satisfied.'
if (self.__finalUpdate is None):
return False
return UpdateInstruction.Satisfies(counter_values, self.__finalUpdate)<|docstring|>C{True} iff this state is an accepting state for the automaton.
@param counter_values: Counter values that further validate
whether the requirements of the automaton have been met.
@return: C{True} if this is an accepting state and the
counter values relevant at it are satisfied.<|endoftext|>
|
4cd4cb51ed7a04d068beae4bd1e4b46c5e346b8eff2b3148e32d9476d9685f8c
|
def __get_transitionSet(self):
'Definitions of viable transitions from this state.\n\n The transition set of a state is a set of L{Transition} nodes\n identifying a state reachable in a single step from this\n state, and a set of counter updates that must apply if the\n transition is taken.\n\n These transitions may not in themselves consume a symbol. For\n example, if the destination state represents a match of an\n L{unordered catenation of terms<All>}, then secondary\n processing must be done to traverse into the automata for\n those terms and identify transitions that include a symbol\n consumption.\n\n @note: Although conceptually the viable transitions are a set,\n this implementation maintains them in a list so that order is\n preserved when automata processing becomes non-deterministic.\n PyXB is careful to build the transition list so that the\n states are attempted in the order in which they appear in the\n schema that define the automata.\n '
return self.__transitionSet
|
Definitions of viable transitions from this state.
The transition set of a state is a set of L{Transition} nodes
identifying a state reachable in a single step from this
state, and a set of counter updates that must apply if the
transition is taken.
These transitions may not in themselves consume a symbol. For
example, if the destination state represents a match of an
L{unordered catenation of terms<All>}, then secondary
processing must be done to traverse into the automata for
those terms and identify transitions that include a symbol
consumption.
@note: Although conceptually the viable transitions are a set,
this implementation maintains them in a list so that order is
preserved when automata processing becomes non-deterministic.
PyXB is careful to build the transition list so that the
states are attempted in the order in which they appear in the
schema that define the automata.
|
pyxb/utils/fac.py
|
__get_transitionSet
|
maciekwawro/pyxb
| 123 |
python
|
def __get_transitionSet(self):
'Definitions of viable transitions from this state.\n\n The transition set of a state is a set of L{Transition} nodes\n identifying a state reachable in a single step from this\n state, and a set of counter updates that must apply if the\n transition is taken.\n\n These transitions may not in themselves consume a symbol. For\n example, if the destination state represents a match of an\n L{unordered catenation of terms<All>}, then secondary\n processing must be done to traverse into the automata for\n those terms and identify transitions that include a symbol\n consumption.\n\n @note: Although conceptually the viable transitions are a set,\n this implementation maintains them in a list so that order is\n preserved when automata processing becomes non-deterministic.\n PyXB is careful to build the transition list so that the\n states are attempted in the order in which they appear in the\n schema that define the automata.\n '
return self.__transitionSet
|
def __get_transitionSet(self):
'Definitions of viable transitions from this state.\n\n The transition set of a state is a set of L{Transition} nodes\n identifying a state reachable in a single step from this\n state, and a set of counter updates that must apply if the\n transition is taken.\n\n These transitions may not in themselves consume a symbol. For\n example, if the destination state represents a match of an\n L{unordered catenation of terms<All>}, then secondary\n processing must be done to traverse into the automata for\n those terms and identify transitions that include a symbol\n consumption.\n\n @note: Although conceptually the viable transitions are a set,\n this implementation maintains them in a list so that order is\n preserved when automata processing becomes non-deterministic.\n PyXB is careful to build the transition list so that the\n states are attempted in the order in which they appear in the\n schema that define the automata.\n '
return self.__transitionSet<|docstring|>Definitions of viable transitions from this state.
The transition set of a state is a set of L{Transition} nodes
identifying a state reachable in a single step from this
state, and a set of counter updates that must apply if the
transition is taken.
These transitions may not in themselves consume a symbol. For
example, if the destination state represents a match of an
L{unordered catenation of terms<All>}, then secondary
processing must be done to traverse into the automata for
those terms and identify transitions that include a symbol
consumption.
@note: Although conceptually the viable transitions are a set,
this implementation maintains them in a list so that order is
preserved when automata processing becomes non-deterministic.
PyXB is careful to build the transition list so that the
states are attempted in the order in which they appear in the
schema that define the automata.<|endoftext|>
|
c93d0036efa66ece1cbb633273760cfd9996dd9e00192fd4b9fdf8ee18d4153e
|
def _set_transitionSet(self, transition_set):
'Method invoked during automaton construction to set the\n legal transitions from the state.\n\n The set of transitions cannot be defined until all states that\n appear in it are available, so the creation of the automaton\n requires that the association of the transition set be\n delayed. (Though described as a set, the transitions are a\n list where order reflects priority.)\n\n @param transition_set: a list of pairs where the first\n member is the destination L{State} and the second member is the\n set of L{UpdateInstruction}s that apply when the automaton\n transitions to the destination state.'
self.__transitionSet = []
seen = set()
for xit in transition_set:
if (not (xit in seen)):
seen.add(xit)
self.__transitionSet.append(xit)
|
Method invoked during automaton construction to set the
legal transitions from the state.
The set of transitions cannot be defined until all states that
appear in it are available, so the creation of the automaton
requires that the association of the transition set be
delayed. (Though described as a set, the transitions are a
list where order reflects priority.)
@param transition_set: a list of pairs where the first
member is the destination L{State} and the second member is the
set of L{UpdateInstruction}s that apply when the automaton
transitions to the destination state.
|
pyxb/utils/fac.py
|
_set_transitionSet
|
maciekwawro/pyxb
| 123 |
python
|
def _set_transitionSet(self, transition_set):
'Method invoked during automaton construction to set the\n legal transitions from the state.\n\n The set of transitions cannot be defined until all states that\n appear in it are available, so the creation of the automaton\n requires that the association of the transition set be\n delayed. (Though described as a set, the transitions are a\n list where order reflects priority.)\n\n @param transition_set: a list of pairs where the first\n member is the destination L{State} and the second member is the\n set of L{UpdateInstruction}s that apply when the automaton\n transitions to the destination state.'
self.__transitionSet = []
seen = set()
for xit in transition_set:
if (not (xit in seen)):
seen.add(xit)
self.__transitionSet.append(xit)
|
def _set_transitionSet(self, transition_set):
'Method invoked during automaton construction to set the\n legal transitions from the state.\n\n The set of transitions cannot be defined until all states that\n appear in it are available, so the creation of the automaton\n requires that the association of the transition set be\n delayed. (Though described as a set, the transitions are a\n list where order reflects priority.)\n\n @param transition_set: a list of pairs where the first\n member is the destination L{State} and the second member is the\n set of L{UpdateInstruction}s that apply when the automaton\n transitions to the destination state.'
self.__transitionSet = []
seen = set()
for xit in transition_set:
if (not (xit in seen)):
seen.add(xit)
self.__transitionSet.append(xit)<|docstring|>Method invoked during automaton construction to set the
legal transitions from the state.
The set of transitions cannot be defined until all states that
appear in it are available, so the creation of the automaton
requires that the association of the transition set be
delayed. (Though described as a set, the transitions are a
list where order reflects priority.)
@param transition_set: a list of pairs where the first
member is the destination L{State} and the second member is the
set of L{UpdateInstruction}s that apply when the automaton
transitions to the destination state.<|endoftext|>
|
b1be5774f35e5cf718ec504714c4f6144cd2fe545651aad79f9adee7f54d1e90
|
def match(self, symbol):
'Return C{True} iff the symbol matches for this state.\n\n This may be overridden by subclasses when matching by\n equivalence does not work. Alternatively, if the symbol\n stored in this node is a subclass of L{SymbolMatch_mixin}, then\n its match method will be used. Otherwise C{symbol} matches\n only if it is equal to the L{symbol} of this state.\n\n @param symbol: A candidate symbol corresponding to the\n expression symbol for this state.\n\n @return: C{True} iff C{symbol} is a match for this state.\n '
if isinstance(self.__symbol, SymbolMatch_mixin):
return self.__symbol.match(symbol)
return (self.__symbol == symbol)
|
Return C{True} iff the symbol matches for this state.
This may be overridden by subclasses when matching by
equivalence does not work. Alternatively, if the symbol
stored in this node is a subclass of L{SymbolMatch_mixin}, then
its match method will be used. Otherwise C{symbol} matches
only if it is equal to the L{symbol} of this state.
@param symbol: A candidate symbol corresponding to the
expression symbol for this state.
@return: C{True} iff C{symbol} is a match for this state.
|
pyxb/utils/fac.py
|
match
|
maciekwawro/pyxb
| 123 |
python
|
def match(self, symbol):
'Return C{True} iff the symbol matches for this state.\n\n This may be overridden by subclasses when matching by\n equivalence does not work. Alternatively, if the symbol\n stored in this node is a subclass of L{SymbolMatch_mixin}, then\n its match method will be used. Otherwise C{symbol} matches\n only if it is equal to the L{symbol} of this state.\n\n @param symbol: A candidate symbol corresponding to the\n expression symbol for this state.\n\n @return: C{True} iff C{symbol} is a match for this state.\n '
if isinstance(self.__symbol, SymbolMatch_mixin):
return self.__symbol.match(symbol)
return (self.__symbol == symbol)
|
def match(self, symbol):
'Return C{True} iff the symbol matches for this state.\n\n This may be overridden by subclasses when matching by\n equivalence does not work. Alternatively, if the symbol\n stored in this node is a subclass of L{SymbolMatch_mixin}, then\n its match method will be used. Otherwise C{symbol} matches\n only if it is equal to the L{symbol} of this state.\n\n @param symbol: A candidate symbol corresponding to the\n expression symbol for this state.\n\n @return: C{True} iff C{symbol} is a match for this state.\n '
if isinstance(self.__symbol, SymbolMatch_mixin):
return self.__symbol.match(symbol)
return (self.__symbol == symbol)<|docstring|>Return C{True} iff the symbol matches for this state.
This may be overridden by subclasses when matching by
equivalence does not work. Alternatively, if the symbol
stored in this node is a subclass of L{SymbolMatch_mixin}, then
its match method will be used. Otherwise C{symbol} matches
only if it is equal to the L{symbol} of this state.
@param symbol: A candidate symbol corresponding to the
expression symbol for this state.
@return: C{True} iff C{symbol} is a match for this state.<|endoftext|>
|
678cbf810b869b86bcc279470d1224ae7c130953c6b1448efd74cbee3809639d
|
def __get_min(self):
'The minimum legal value for the counter.\n\n This is a non-negative integer.'
return self.__min
|
The minimum legal value for the counter.
This is a non-negative integer.
|
pyxb/utils/fac.py
|
__get_min
|
maciekwawro/pyxb
| 123 |
python
|
def __get_min(self):
'The minimum legal value for the counter.\n\n This is a non-negative integer.'
return self.__min
|
def __get_min(self):
'The minimum legal value for the counter.\n\n This is a non-negative integer.'
return self.__min<|docstring|>The minimum legal value for the counter.
This is a non-negative integer.<|endoftext|>
|
5722ad9ddaac8898a6576c607f8276ce697cc0323b32cc4710e70af81dd83a69
|
def __get_max(self):
'The maximum legal value for the counter.\n\n This is a positive integer, or C{None} to indicate that the\n counter is unbounded.'
return self.__max
|
The maximum legal value for the counter.
This is a positive integer, or C{None} to indicate that the
counter is unbounded.
|
pyxb/utils/fac.py
|
__get_max
|
maciekwawro/pyxb
| 123 |
python
|
def __get_max(self):
'The maximum legal value for the counter.\n\n This is a positive integer, or C{None} to indicate that the\n counter is unbounded.'
return self.__max
|
def __get_max(self):
'The maximum legal value for the counter.\n\n This is a positive integer, or C{None} to indicate that the\n counter is unbounded.'
return self.__max<|docstring|>The maximum legal value for the counter.
This is a positive integer, or C{None} to indicate that the
counter is unbounded.<|endoftext|>
|
42db0a57764598881bf96b6ac1042504c0e009a045e89f82f40c81d46d2583fc
|
def __get_metadata(self):
'A pointer to application metadata provided when the condition was created.'
return self.__metadata
|
A pointer to application metadata provided when the condition was created.
|
pyxb/utils/fac.py
|
__get_metadata
|
maciekwawro/pyxb
| 123 |
python
|
def __get_metadata(self):
return self.__metadata
|
def __get_metadata(self):
return self.__metadata<|docstring|>A pointer to application metadata provided when the condition was created.<|endoftext|>
|
073c53aa7789c2f3224340df7b5572112728849b348404319aa037e31894aa3f
|
def __init__(self, min, max, metadata=None):
'Create a counter condition.\n\n @param min: The value for L{min}\n @param max: The value for L{max}\n @param metadata: The value for L{metadata}\n '
self.__min = min
self.__max = max
self.__metadata = metadata
|
Create a counter condition.
@param min: The value for L{min}
@param max: The value for L{max}
@param metadata: The value for L{metadata}
|
pyxb/utils/fac.py
|
__init__
|
maciekwawro/pyxb
| 123 |
python
|
def __init__(self, min, max, metadata=None):
'Create a counter condition.\n\n @param min: The value for L{min}\n @param max: The value for L{max}\n @param metadata: The value for L{metadata}\n '
self.__min = min
self.__max = max
self.__metadata = metadata
|
def __init__(self, min, max, metadata=None):
'Create a counter condition.\n\n @param min: The value for L{min}\n @param max: The value for L{max}\n @param metadata: The value for L{metadata}\n '
self.__min = min
self.__max = max
self.__metadata = metadata<|docstring|>Create a counter condition.
@param min: The value for L{min}
@param max: The value for L{max}
@param metadata: The value for L{metadata}<|endoftext|>
|
b60947aa9029a3d761275db6a4eb2e849b901ed1333d946edbd8b685171f265d
|
def __get_counterCondition(self):
'A reference to the L{CounterCondition} identifying the\n counter to be updated.\n\n The counter condition instance is used as a key to the\n dictionary maintaining current counter values.'
return self.__counterCondition
|
A reference to the L{CounterCondition} identifying the
counter to be updated.
The counter condition instance is used as a key to the
dictionary maintaining current counter values.
|
pyxb/utils/fac.py
|
__get_counterCondition
|
maciekwawro/pyxb
| 123 |
python
|
def __get_counterCondition(self):
'A reference to the L{CounterCondition} identifying the\n counter to be updated.\n\n The counter condition instance is used as a key to the\n dictionary maintaining current counter values.'
return self.__counterCondition
|
def __get_counterCondition(self):
'A reference to the L{CounterCondition} identifying the\n counter to be updated.\n\n The counter condition instance is used as a key to the\n dictionary maintaining current counter values.'
return self.__counterCondition<|docstring|>A reference to the L{CounterCondition} identifying the
counter to be updated.
The counter condition instance is used as a key to the
dictionary maintaining current counter values.<|endoftext|>
|
906c331571439917d1924bb98fa139edb7239ee82e69074c246e83bf129078f2
|
def __get_doIncrement(self):
'C{True} if the counter is to be incremented; C{False} if it is to be reset.'
return self.__doIncrement
|
C{True} if the counter is to be incremented; C{False} if it is to be reset.
|
pyxb/utils/fac.py
|
__get_doIncrement
|
maciekwawro/pyxb
| 123 |
python
|
def __get_doIncrement(self):
return self.__doIncrement
|
def __get_doIncrement(self):
return self.__doIncrement<|docstring|>C{True} if the counter is to be incremented; C{False} if it is to be reset.<|endoftext|>
|
7b48f68ff00b29b45d75e1e6aa7d86cad24b4fe95ca3b049bd0841eabe208740
|
def __init__(self, counter_condition, do_increment):
'Create an update instruction.\n\n @param counter_condition: A L{CounterCondition} identifying a\n minimum and maximum value for a counter, and serving as a map\n key for the value of the corresponding counter.\n\n @param do_increment: C{True} if the update is to increment\n the value of the counter; C{False} if the update is to reset\n the counter.\n '
self.__counterCondition = counter_condition
self.__doIncrement = (not (not do_increment))
self.__min = counter_condition.min
self.__max = counter_condition.max
|
Create an update instruction.
@param counter_condition: A L{CounterCondition} identifying a
minimum and maximum value for a counter, and serving as a map
key for the value of the corresponding counter.
@param do_increment: C{True} if the update is to increment
the value of the counter; C{False} if the update is to reset
the counter.
|
pyxb/utils/fac.py
|
__init__
|
maciekwawro/pyxb
| 123 |
python
|
def __init__(self, counter_condition, do_increment):
'Create an update instruction.\n\n @param counter_condition: A L{CounterCondition} identifying a\n minimum and maximum value for a counter, and serving as a map\n key for the value of the corresponding counter.\n\n @param do_increment: C{True} if the update is to increment\n the value of the counter; C{False} if the update is to reset\n the counter.\n '
self.__counterCondition = counter_condition
self.__doIncrement = (not (not do_increment))
self.__min = counter_condition.min
self.__max = counter_condition.max
|
def __init__(self, counter_condition, do_increment):
'Create an update instruction.\n\n @param counter_condition: A L{CounterCondition} identifying a\n minimum and maximum value for a counter, and serving as a map\n key for the value of the corresponding counter.\n\n @param do_increment: C{True} if the update is to increment\n the value of the counter; C{False} if the update is to reset\n the counter.\n '
self.__counterCondition = counter_condition
self.__doIncrement = (not (not do_increment))
self.__min = counter_condition.min
self.__max = counter_condition.max<|docstring|>Create an update instruction.
@param counter_condition: A L{CounterCondition} identifying a
minimum and maximum value for a counter, and serving as a map
key for the value of the corresponding counter.
@param do_increment: C{True} if the update is to increment
the value of the counter; C{False} if the update is to reset
the counter.<|endoftext|>
|
2c2cfa3c8086b06ebe5347b86a43c9e0dbac98f530dc233e37ad483b473b45ed
|
def satisfiedBy(self, counter_values):
'Implement a component of definition 5 from B{HOV09}.\n\n The update instruction is satisfied by the counter values if\n its action may be legitimately applied to the value of its\n associated counter.\n\n @param counter_values: A map from L{CounterCondition}s to\n non-negative integers\n\n @return: C{True} or C{False}\n '
value = counter_values[self.__counterCondition]
if (self.__doIncrement and (self.__max is not None) and (value >= self.__max)):
return False
if ((not self.__doIncrement) and (value < self.__min)):
return False
return True
|
Implement a component of definition 5 from B{HOV09}.
The update instruction is satisfied by the counter values if
its action may be legitimately applied to the value of its
associated counter.
@param counter_values: A map from L{CounterCondition}s to
non-negative integers
@return: C{True} or C{False}
|
pyxb/utils/fac.py
|
satisfiedBy
|
maciekwawro/pyxb
| 123 |
python
|
def satisfiedBy(self, counter_values):
'Implement a component of definition 5 from B{HOV09}.\n\n The update instruction is satisfied by the counter values if\n its action may be legitimately applied to the value of its\n associated counter.\n\n @param counter_values: A map from L{CounterCondition}s to\n non-negative integers\n\n @return: C{True} or C{False}\n '
value = counter_values[self.__counterCondition]
if (self.__doIncrement and (self.__max is not None) and (value >= self.__max)):
return False
if ((not self.__doIncrement) and (value < self.__min)):
return False
return True
|
def satisfiedBy(self, counter_values):
'Implement a component of definition 5 from B{HOV09}.\n\n The update instruction is satisfied by the counter values if\n its action may be legitimately applied to the value of its\n associated counter.\n\n @param counter_values: A map from L{CounterCondition}s to\n non-negative integers\n\n @return: C{True} or C{False}\n '
value = counter_values[self.__counterCondition]
if (self.__doIncrement and (self.__max is not None) and (value >= self.__max)):
return False
if ((not self.__doIncrement) and (value < self.__min)):
return False
return True<|docstring|>Implement a component of definition 5 from B{HOV09}.
The update instruction is satisfied by the counter values if
its action may be legitimately applied to the value of its
associated counter.
@param counter_values: A map from L{CounterCondition}s to
non-negative integers
@return: C{True} or C{False}<|endoftext|>
|
b53954c0ca54a255c3cb5cb1d6db01af81adfce5593073e09cd899668e7e603a
|
@classmethod
def Satisfies(cls, counter_values, update_instructions):
'Return C{True} iff the counter values satisfy the update\n instructions.\n\n @param counter_values: A map from L{CounterCondition} to\n integer counter values\n\n @param update_instructions: A set of L{UpdateInstruction}\n instances\n\n @return: C{True} iff all instructions are satisfied by the\n values and limits.'
for psi in update_instructions:
if (not psi.satisfiedBy(counter_values)):
return False
return True
|
Return C{True} iff the counter values satisfy the update
instructions.
@param counter_values: A map from L{CounterCondition} to
integer counter values
@param update_instructions: A set of L{UpdateInstruction}
instances
@return: C{True} iff all instructions are satisfied by the
values and limits.
|
pyxb/utils/fac.py
|
Satisfies
|
maciekwawro/pyxb
| 123 |
python
|
@classmethod
def Satisfies(cls, counter_values, update_instructions):
'Return C{True} iff the counter values satisfy the update\n instructions.\n\n @param counter_values: A map from L{CounterCondition} to\n integer counter values\n\n @param update_instructions: A set of L{UpdateInstruction}\n instances\n\n @return: C{True} iff all instructions are satisfied by the\n values and limits.'
for psi in update_instructions:
if (not psi.satisfiedBy(counter_values)):
return False
return True
|
@classmethod
def Satisfies(cls, counter_values, update_instructions):
'Return C{True} iff the counter values satisfy the update\n instructions.\n\n @param counter_values: A map from L{CounterCondition} to\n integer counter values\n\n @param update_instructions: A set of L{UpdateInstruction}\n instances\n\n @return: C{True} iff all instructions are satisfied by the\n values and limits.'
for psi in update_instructions:
if (not psi.satisfiedBy(counter_values)):
return False
return True<|docstring|>Return C{True} iff the counter values satisfy the update
instructions.
@param counter_values: A map from L{CounterCondition} to
integer counter values
@param update_instructions: A set of L{UpdateInstruction}
instances
@return: C{True} iff all instructions are satisfied by the
values and limits.<|endoftext|>
|
84a3ba255bed317474ebcca2b55ae6f6467f2ed50d2baf41edc08687834dec7c
|
def apply(self, counter_values):
'Apply the update instruction to the provided counter values.\n\n @param counter_values: A map from L{CounterCondition} to\n integer counter values. This map is updated in-place.'
if (not self.satisfiedBy(counter_values)):
raise UpdateApplicationError(self, counter_values)
value = counter_values[self.__counterCondition]
if self.__doIncrement:
value += 1
else:
value = 1
counter_values[self.__counterCondition] = value
|
Apply the update instruction to the provided counter values.
@param counter_values: A map from L{CounterCondition} to
integer counter values. This map is updated in-place.
|
pyxb/utils/fac.py
|
apply
|
maciekwawro/pyxb
| 123 |
python
|
def apply(self, counter_values):
'Apply the update instruction to the provided counter values.\n\n @param counter_values: A map from L{CounterCondition} to\n integer counter values. This map is updated in-place.'
if (not self.satisfiedBy(counter_values)):
raise UpdateApplicationError(self, counter_values)
value = counter_values[self.__counterCondition]
if self.__doIncrement:
value += 1
else:
value = 1
counter_values[self.__counterCondition] = value
|
def apply(self, counter_values):
'Apply the update instruction to the provided counter values.\n\n @param counter_values: A map from L{CounterCondition} to\n integer counter values. This map is updated in-place.'
if (not self.satisfiedBy(counter_values)):
raise UpdateApplicationError(self, counter_values)
value = counter_values[self.__counterCondition]
if self.__doIncrement:
value += 1
else:
value = 1
counter_values[self.__counterCondition] = value<|docstring|>Apply the update instruction to the provided counter values.
@param counter_values: A map from L{CounterCondition} to
integer counter values. This map is updated in-place.<|endoftext|>
|
771f09018cc23ca61c527bba4abb2196e64382ed9be6160ab9ba583870488a4a
|
@classmethod
def Apply(cls, update_instructions, counter_values):
'Apply the update instructions to the counter values.\n\n @param update_instructions: A set of L{UpdateInstruction}\n instances.\n\n @param counter_values: A map from L{CounterCondition}\n instances to non-negative integers. This map is updated\n in-place by applying each instruction in\n C{update_instructions}.'
for psi in update_instructions:
psi.apply(counter_values)
|
Apply the update instructions to the counter values.
@param update_instructions: A set of L{UpdateInstruction}
instances.
@param counter_values: A map from L{CounterCondition}
instances to non-negative integers. This map is updated
in-place by applying each instruction in
C{update_instructions}.
|
pyxb/utils/fac.py
|
Apply
|
maciekwawro/pyxb
| 123 |
python
|
@classmethod
def Apply(cls, update_instructions, counter_values):
'Apply the update instructions to the counter values.\n\n @param update_instructions: A set of L{UpdateInstruction}\n instances.\n\n @param counter_values: A map from L{CounterCondition}\n instances to non-negative integers. This map is updated\n in-place by applying each instruction in\n C{update_instructions}.'
for psi in update_instructions:
psi.apply(counter_values)
|
@classmethod
def Apply(cls, update_instructions, counter_values):
'Apply the update instructions to the counter values.\n\n @param update_instructions: A set of L{UpdateInstruction}\n instances.\n\n @param counter_values: A map from L{CounterCondition}\n instances to non-negative integers. This map is updated\n in-place by applying each instruction in\n C{update_instructions}.'
for psi in update_instructions:
psi.apply(counter_values)<|docstring|>Apply the update instructions to the counter values.
@param update_instructions: A set of L{UpdateInstruction}
instances.
@param counter_values: A map from L{CounterCondition}
instances to non-negative integers. This map is updated
in-place by applying each instruction in
C{update_instructions}.<|endoftext|>
|
5cd41080797adcbb3a3b99b3dea2ea97ff261949455c810a88e45bc7f8b1438b
|
def __get_destination(self):
'The transition destination state.'
return self.__destination
|
The transition destination state.
|
pyxb/utils/fac.py
|
__get_destination
|
maciekwawro/pyxb
| 123 |
python
|
def __get_destination(self):
return self.__destination
|
def __get_destination(self):
return self.__destination<|docstring|>The transition destination state.<|endoftext|>
|
0af89c42ad447b5391b121c8029b37040d6e713203e2e0e4b68278a4c36d1955
|
def __get_updateInstructions(self):
'The set of counter updates that are applied when the transition is taken.'
return self.__updateInstructions
|
The set of counter updates that are applied when the transition is taken.
|
pyxb/utils/fac.py
|
__get_updateInstructions
|
maciekwawro/pyxb
| 123 |
python
|
def __get_updateInstructions(self):
return self.__updateInstructions
|
def __get_updateInstructions(self):
return self.__updateInstructions<|docstring|>The set of counter updates that are applied when the transition is taken.<|endoftext|>
|
49110cefcf48cca8c6944a08217dfaaf8b16ce7a1a38fee934bc0fa01e706c1a
|
def __get_nextTransition(self):
'The next transition to apply in this chain.\n\n C{None} if this is the last transition in the chain.'
return self.__nextTransition
|
The next transition to apply in this chain.
C{None} if this is the last transition in the chain.
|
pyxb/utils/fac.py
|
__get_nextTransition
|
maciekwawro/pyxb
| 123 |
python
|
def __get_nextTransition(self):
'The next transition to apply in this chain.\n\n C{None} if this is the last transition in the chain.'
return self.__nextTransition
|
def __get_nextTransition(self):
'The next transition to apply in this chain.\n\n C{None} if this is the last transition in the chain.'
return self.__nextTransition<|docstring|>The next transition to apply in this chain.
C{None} if this is the last transition in the chain.<|endoftext|>
|
92b597d5c493066c1f562222e50c629cca475a486c379346de3d47d0931bd4a7
|
def __get_layerLink(self):
'A directive relating to changing automaton layer on transition.\n\n C{None} indicates this transition is from one state to another\n within a single automaton.\n\n An instance of L{Configuration} is a transition on completion\n of a subautomaton back to the configuration in the parent\n automaton. The L{destination} is the state in the parent automaton.\n\n An instance of L{Automaton} requires creation of a\n sub-configuration and initial entry into the automaton. The\n L{destination} is the state in the sub-automaton.\n '
return self.__layerLink
|
A directive relating to changing automaton layer on transition.
C{None} indicates this transition is from one state to another
within a single automaton.
An instance of L{Configuration} is a transition on completion
of a subautomaton back to the configuration in the parent
automaton. The L{destination} is the state in the parent automaton.
An instance of L{Automaton} requires creation of a
sub-configuration and initial entry into the automaton. The
L{destination} is the state in the sub-automaton.
|
pyxb/utils/fac.py
|
__get_layerLink
|
maciekwawro/pyxb
| 123 |
python
|
def __get_layerLink(self):
'A directive relating to changing automaton layer on transition.\n\n C{None} indicates this transition is from one state to another\n within a single automaton.\n\n An instance of L{Configuration} is a transition on completion\n of a subautomaton back to the configuration in the parent\n automaton. The L{destination} is the state in the parent automaton.\n\n An instance of L{Automaton} requires creation of a\n sub-configuration and initial entry into the automaton. The\n L{destination} is the state in the sub-automaton.\n '
return self.__layerLink
|
def __get_layerLink(self):
'A directive relating to changing automaton layer on transition.\n\n C{None} indicates this transition is from one state to another\n within a single automaton.\n\n An instance of L{Configuration} is a transition on completion\n of a subautomaton back to the configuration in the parent\n automaton. The L{destination} is the state in the parent automaton.\n\n An instance of L{Automaton} requires creation of a\n sub-configuration and initial entry into the automaton. The\n L{destination} is the state in the sub-automaton.\n '
return self.__layerLink<|docstring|>A directive relating to changing automaton layer on transition.
C{None} indicates this transition is from one state to another
within a single automaton.
An instance of L{Configuration} is a transition on completion
of a subautomaton back to the configuration in the parent
automaton. The L{destination} is the state in the parent automaton.
An instance of L{Automaton} requires creation of a
sub-configuration and initial entry into the automaton. The
L{destination} is the state in the sub-automaton.<|endoftext|>
|
3f812b0b40599f56b386563b6ae87d84f6598cb47a49dba20b6c27a444d8843c
|
def __init__(self, destination, update_instructions, layer_link=None):
'Create a transition to a state.\n\n @param destination: the state into which the transition is\n made\n\n @param update_instructions: A iterable of L{UpdateInstruction}s\n denoting the changes that must be made to counters as a\n consequence of taking the transition.\n\n @keyword layer_link: The value for L{layerLink}.'
self.__destination = destination
if (not isinstance(update_instructions, list)):
update_instructions = list(update_instructions)
self.__updateInstructions = update_instructions
self.__layerLink = layer_link
|
Create a transition to a state.
@param destination: the state into which the transition is
made
@param update_instructions: A iterable of L{UpdateInstruction}s
denoting the changes that must be made to counters as a
consequence of taking the transition.
@keyword layer_link: The value for L{layerLink}.
|
pyxb/utils/fac.py
|
__init__
|
maciekwawro/pyxb
| 123 |
python
|
def __init__(self, destination, update_instructions, layer_link=None):
'Create a transition to a state.\n\n @param destination: the state into which the transition is\n made\n\n @param update_instructions: A iterable of L{UpdateInstruction}s\n denoting the changes that must be made to counters as a\n consequence of taking the transition.\n\n @keyword layer_link: The value for L{layerLink}.'
self.__destination = destination
if (not isinstance(update_instructions, list)):
update_instructions = list(update_instructions)
self.__updateInstructions = update_instructions
self.__layerLink = layer_link
|
def __init__(self, destination, update_instructions, layer_link=None):
'Create a transition to a state.\n\n @param destination: the state into which the transition is\n made\n\n @param update_instructions: A iterable of L{UpdateInstruction}s\n denoting the changes that must be made to counters as a\n consequence of taking the transition.\n\n @keyword layer_link: The value for L{layerLink}.'
self.__destination = destination
if (not isinstance(update_instructions, list)):
update_instructions = list(update_instructions)
self.__updateInstructions = update_instructions
self.__layerLink = layer_link<|docstring|>Create a transition to a state.
@param destination: the state into which the transition is
made
@param update_instructions: A iterable of L{UpdateInstruction}s
denoting the changes that must be made to counters as a
consequence of taking the transition.
@keyword layer_link: The value for L{layerLink}.<|endoftext|>
|
781f07226e6d18d177e5f49e23e882a2f490c513050407cd420186d1947751d0
|
def consumingState(self):
'Return the state in this transition chain that must match a symbol.'
if (self.__destination.subAutomata is not None):
if (not self.__nextTransition):
return None
return self.__nextTransition.consumingState()
assert (self.__nextTransition is None)
return self.__destination
|
Return the state in this transition chain that must match a symbol.
|
pyxb/utils/fac.py
|
consumingState
|
maciekwawro/pyxb
| 123 |
python
|
def consumingState(self):
if (self.__destination.subAutomata is not None):
if (not self.__nextTransition):
return None
return self.__nextTransition.consumingState()
assert (self.__nextTransition is None)
return self.__destination
|
def consumingState(self):
if (self.__destination.subAutomata is not None):
if (not self.__nextTransition):
return None
return self.__nextTransition.consumingState()
assert (self.__nextTransition is None)
return self.__destination<|docstring|>Return the state in this transition chain that must match a symbol.<|endoftext|>
|
0945b3fb5749bb218f3ddc9e1259bfbebd7e6b0c1afe954023b064185b567bb4
|
def consumedSymbol(self):
'Return the L{symbol<State.symbol>} of the L{consumingState}.'
return self.consumingState().symbol
|
Return the L{symbol<State.symbol>} of the L{consumingState}.
|
pyxb/utils/fac.py
|
consumedSymbol
|
maciekwawro/pyxb
| 123 |
python
|
def consumedSymbol(self):
return self.consumingState().symbol
|
def consumedSymbol(self):
return self.consumingState().symbol<|docstring|>Return the L{symbol<State.symbol>} of the L{consumingState}.<|endoftext|>
|
7a325a859ac885b2747ddb977384ada4b59008f232dc412c461ca24d74a2ab37
|
def satisfiedBy(self, configuration):
'Check the transition update instructions against\n configuration counter values.\n\n This implementation follows layer changes, updating the\n configuration used as counter value source as necessary.\n\n @param configuration: A L{Configuration} instance containing\n counter data against which update instruction satisfaction is\n checked.\n\n @return: C{True} iff all update instructions along the\n transition chain are satisfied by their relevant\n configuration.'
if isinstance(self.__layerLink, Automaton):
return True
if isinstance(self.__layerLink, Configuration):
configuration = self.__layerLink
assert (self.destination.automaton == configuration.automaton)
if (not configuration.satisfies(self)):
return False
if self.__nextTransition:
return self.__nextTransition.satisfiedBy(configuration)
return True
|
Check the transition update instructions against
configuration counter values.
This implementation follows layer changes, updating the
configuration used as counter value source as necessary.
@param configuration: A L{Configuration} instance containing
counter data against which update instruction satisfaction is
checked.
@return: C{True} iff all update instructions along the
transition chain are satisfied by their relevant
configuration.
|
pyxb/utils/fac.py
|
satisfiedBy
|
maciekwawro/pyxb
| 123 |
python
|
def satisfiedBy(self, configuration):
'Check the transition update instructions against\n configuration counter values.\n\n This implementation follows layer changes, updating the\n configuration used as counter value source as necessary.\n\n @param configuration: A L{Configuration} instance containing\n counter data against which update instruction satisfaction is\n checked.\n\n @return: C{True} iff all update instructions along the\n transition chain are satisfied by their relevant\n configuration.'
if isinstance(self.__layerLink, Automaton):
return True
if isinstance(self.__layerLink, Configuration):
configuration = self.__layerLink
assert (self.destination.automaton == configuration.automaton)
if (not configuration.satisfies(self)):
return False
if self.__nextTransition:
return self.__nextTransition.satisfiedBy(configuration)
return True
|
def satisfiedBy(self, configuration):
'Check the transition update instructions against\n configuration counter values.\n\n This implementation follows layer changes, updating the\n configuration used as counter value source as necessary.\n\n @param configuration: A L{Configuration} instance containing\n counter data against which update instruction satisfaction is\n checked.\n\n @return: C{True} iff all update instructions along the\n transition chain are satisfied by their relevant\n configuration.'
if isinstance(self.__layerLink, Automaton):
return True
if isinstance(self.__layerLink, Configuration):
configuration = self.__layerLink
assert (self.destination.automaton == configuration.automaton)
if (not configuration.satisfies(self)):
return False
if self.__nextTransition:
return self.__nextTransition.satisfiedBy(configuration)
return True<|docstring|>Check the transition update instructions against
configuration counter values.
This implementation follows layer changes, updating the
configuration used as counter value source as necessary.
@param configuration: A L{Configuration} instance containing
counter data against which update instruction satisfaction is
checked.
@return: C{True} iff all update instructions along the
transition chain are satisfied by their relevant
configuration.<|endoftext|>
|
45c2cd231d04cbf9896f9fd86fd55094092eec55945a0a6c5e28388a0a41115d
|
def apply(self, configuration, clone_map=None):
'Apply the transitition to a configuration.\n\n This updates the configuration counter values based on the\n update instructions, and sets the new configuration state.\n\n @note: If the transition involves leaving a sub-automaton or\n creating a new sub-automaton, the returned configuration\n structure will be different from the one passed in. You\n should invoke this as::\n\n cfg = transition.apply(cfg)\n\n @param configuration: A L{Configuration} of an executing automaton\n\n @param clone_map: A map from L{Configuration} to\n L{Configuration} reflecting the replacements made when the\n configuration for which the transition was calculated was\n subsequently cloned into the C{configuration} passed into this\n method. This is only necessary when the transition includes\n layer transitions.\n\n @return: The resulting configuration\n '
layer_link = self.__layerLink
if isinstance(layer_link, Configuration):
if (clone_map is not None):
layer_link = clone_map[layer_link]
configuration = layer_link.leaveAutomaton(configuration)
elif isinstance(layer_link, Automaton):
configuration = configuration.enterAutomaton(layer_link)
UpdateInstruction.Apply(self.updateInstructions, configuration._get_counterValues())
configuration._set_state(self.destination, (layer_link is None))
if (self.__nextTransition is None):
return configuration
return self.__nextTransition.apply(configuration, clone_map)
|
Apply the transitition to a configuration.
This updates the configuration counter values based on the
update instructions, and sets the new configuration state.
@note: If the transition involves leaving a sub-automaton or
creating a new sub-automaton, the returned configuration
structure will be different from the one passed in. You
should invoke this as::
cfg = transition.apply(cfg)
@param configuration: A L{Configuration} of an executing automaton
@param clone_map: A map from L{Configuration} to
L{Configuration} reflecting the replacements made when the
configuration for which the transition was calculated was
subsequently cloned into the C{configuration} passed into this
method. This is only necessary when the transition includes
layer transitions.
@return: The resulting configuration
|
pyxb/utils/fac.py
|
apply
|
maciekwawro/pyxb
| 123 |
python
|
def apply(self, configuration, clone_map=None):
'Apply the transitition to a configuration.\n\n This updates the configuration counter values based on the\n update instructions, and sets the new configuration state.\n\n @note: If the transition involves leaving a sub-automaton or\n creating a new sub-automaton, the returned configuration\n structure will be different from the one passed in. You\n should invoke this as::\n\n cfg = transition.apply(cfg)\n\n @param configuration: A L{Configuration} of an executing automaton\n\n @param clone_map: A map from L{Configuration} to\n L{Configuration} reflecting the replacements made when the\n configuration for which the transition was calculated was\n subsequently cloned into the C{configuration} passed into this\n method. This is only necessary when the transition includes\n layer transitions.\n\n @return: The resulting configuration\n '
layer_link = self.__layerLink
if isinstance(layer_link, Configuration):
if (clone_map is not None):
layer_link = clone_map[layer_link]
configuration = layer_link.leaveAutomaton(configuration)
elif isinstance(layer_link, Automaton):
configuration = configuration.enterAutomaton(layer_link)
UpdateInstruction.Apply(self.updateInstructions, configuration._get_counterValues())
configuration._set_state(self.destination, (layer_link is None))
if (self.__nextTransition is None):
return configuration
return self.__nextTransition.apply(configuration, clone_map)
|
def apply(self, configuration, clone_map=None):
'Apply the transitition to a configuration.\n\n This updates the configuration counter values based on the\n update instructions, and sets the new configuration state.\n\n @note: If the transition involves leaving a sub-automaton or\n creating a new sub-automaton, the returned configuration\n structure will be different from the one passed in. You\n should invoke this as::\n\n cfg = transition.apply(cfg)\n\n @param configuration: A L{Configuration} of an executing automaton\n\n @param clone_map: A map from L{Configuration} to\n L{Configuration} reflecting the replacements made when the\n configuration for which the transition was calculated was\n subsequently cloned into the C{configuration} passed into this\n method. This is only necessary when the transition includes\n layer transitions.\n\n @return: The resulting configuration\n '
layer_link = self.__layerLink
if isinstance(layer_link, Configuration):
if (clone_map is not None):
layer_link = clone_map[layer_link]
configuration = layer_link.leaveAutomaton(configuration)
elif isinstance(layer_link, Automaton):
configuration = configuration.enterAutomaton(layer_link)
UpdateInstruction.Apply(self.updateInstructions, configuration._get_counterValues())
configuration._set_state(self.destination, (layer_link is None))
if (self.__nextTransition is None):
return configuration
return self.__nextTransition.apply(configuration, clone_map)<|docstring|>Apply the transitition to a configuration.
This updates the configuration counter values based on the
update instructions, and sets the new configuration state.
@note: If the transition involves leaving a sub-automaton or
creating a new sub-automaton, the returned configuration
structure will be different from the one passed in. You
should invoke this as::
cfg = transition.apply(cfg)
@param configuration: A L{Configuration} of an executing automaton
@param clone_map: A map from L{Configuration} to
L{Configuration} reflecting the replacements made when the
configuration for which the transition was calculated was
subsequently cloned into the C{configuration} passed into this
method. This is only necessary when the transition includes
layer transitions.
@return: The resulting configuration<|endoftext|>
|
588848a31c52d339fed10c76395885524875c9964db6f27b73da8bf3412623ee
|
def chainTo(self, next_transition):
'Duplicate the state and chain the duplicate to a successor\n transition.\n\n This returns a new transition which applies the operation for\n this transition, then proceeds to apply the next transition in\n the chain.\n\n @note: The node that is invoking this must not have successor\n transitions.\n\n @param next_transition: A L{Transition} node describing a\n subsequent transition.\n\n @return: a clone of this node, augmented with a link to\n C{next_transition}.'
assert (not self.__nextTransition)
head = type(self)(self.__destination, self.__updateInstructions, layer_link=self.__layerLink)
head.__nextTransition = next_transition
return head
|
Duplicate the state and chain the duplicate to a successor
transition.
This returns a new transition which applies the operation for
this transition, then proceeds to apply the next transition in
the chain.
@note: The node that is invoking this must not have successor
transitions.
@param next_transition: A L{Transition} node describing a
subsequent transition.
@return: a clone of this node, augmented with a link to
C{next_transition}.
|
pyxb/utils/fac.py
|
chainTo
|
maciekwawro/pyxb
| 123 |
python
|
def chainTo(self, next_transition):
'Duplicate the state and chain the duplicate to a successor\n transition.\n\n This returns a new transition which applies the operation for\n this transition, then proceeds to apply the next transition in\n the chain.\n\n @note: The node that is invoking this must not have successor\n transitions.\n\n @param next_transition: A L{Transition} node describing a\n subsequent transition.\n\n @return: a clone of this node, augmented with a link to\n C{next_transition}.'
assert (not self.__nextTransition)
head = type(self)(self.__destination, self.__updateInstructions, layer_link=self.__layerLink)
head.__nextTransition = next_transition
return head
|
def chainTo(self, next_transition):
'Duplicate the state and chain the duplicate to a successor\n transition.\n\n This returns a new transition which applies the operation for\n this transition, then proceeds to apply the next transition in\n the chain.\n\n @note: The node that is invoking this must not have successor\n transitions.\n\n @param next_transition: A L{Transition} node describing a\n subsequent transition.\n\n @return: a clone of this node, augmented with a link to\n C{next_transition}.'
assert (not self.__nextTransition)
head = type(self)(self.__destination, self.__updateInstructions, layer_link=self.__layerLink)
head.__nextTransition = next_transition
return head<|docstring|>Duplicate the state and chain the duplicate to a successor
transition.
This returns a new transition which applies the operation for
this transition, then proceeds to apply the next transition in
the chain.
@note: The node that is invoking this must not have successor
transitions.
@param next_transition: A L{Transition} node describing a
subsequent transition.
@return: a clone of this node, augmented with a link to
C{next_transition}.<|endoftext|>
|
41f0992b50b9299a7ded6989a2a840f959906509c4b40f0562f30ec8011d0d17
|
def makeEnterAutomatonTransition(self):
'Replicate the transition as a layer link into its automaton.\n\n This is used on initial transitions into sub-automata where a\n sub-configuration must be created and recorded.'
assert (self.__layerLink is None)
assert (self.__nextTransition is None)
head = type(self)(self.__destination, self.__updateInstructions)
head.__layerLink = self.__destination.automaton
return head
|
Replicate the transition as a layer link into its automaton.
This is used on initial transitions into sub-automata where a
sub-configuration must be created and recorded.
|
pyxb/utils/fac.py
|
makeEnterAutomatonTransition
|
maciekwawro/pyxb
| 123 |
python
|
def makeEnterAutomatonTransition(self):
'Replicate the transition as a layer link into its automaton.\n\n This is used on initial transitions into sub-automata where a\n sub-configuration must be created and recorded.'
assert (self.__layerLink is None)
assert (self.__nextTransition is None)
head = type(self)(self.__destination, self.__updateInstructions)
head.__layerLink = self.__destination.automaton
return head
|
def makeEnterAutomatonTransition(self):
'Replicate the transition as a layer link into its automaton.\n\n This is used on initial transitions into sub-automata where a\n sub-configuration must be created and recorded.'
assert (self.__layerLink is None)
assert (self.__nextTransition is None)
head = type(self)(self.__destination, self.__updateInstructions)
head.__layerLink = self.__destination.automaton
return head<|docstring|>Replicate the transition as a layer link into its automaton.
This is used on initial transitions into sub-automata where a
sub-configuration must be created and recorded.<|endoftext|>
|
1df0714402dca463279e0b0370295278128cb382758eb0a7dc9b496f6449f984
|
def acceptableSymbols(self):
'Return the acceptable L{Symbol}s given the current\n configuration.\n\n This method extracts the symbol from all candidate transitions\n that are permitted based on the current counter values.\n Because transitions are presented in a preferred order, the\n symbols are as well.'
raise NotImplementedError(('%s.acceptableSymbols' % (type(self).__name__,)))
|
Return the acceptable L{Symbol}s given the current
configuration.
This method extracts the symbol from all candidate transitions
that are permitted based on the current counter values.
Because transitions are presented in a preferred order, the
symbols are as well.
|
pyxb/utils/fac.py
|
acceptableSymbols
|
maciekwawro/pyxb
| 123 |
python
|
def acceptableSymbols(self):
'Return the acceptable L{Symbol}s given the current\n configuration.\n\n This method extracts the symbol from all candidate transitions\n that are permitted based on the current counter values.\n Because transitions are presented in a preferred order, the\n symbols are as well.'
raise NotImplementedError(('%s.acceptableSymbols' % (type(self).__name__,)))
|
def acceptableSymbols(self):
'Return the acceptable L{Symbol}s given the current\n configuration.\n\n This method extracts the symbol from all candidate transitions\n that are permitted based on the current counter values.\n Because transitions are presented in a preferred order, the\n symbols are as well.'
raise NotImplementedError(('%s.acceptableSymbols' % (type(self).__name__,)))<|docstring|>Return the acceptable L{Symbol}s given the current
configuration.
This method extracts the symbol from all candidate transitions
that are permitted based on the current counter values.
Because transitions are presented in a preferred order, the
symbols are as well.<|endoftext|>
|
17a03268c5198a4f9e008c0d34af4029044e8079db12ade2bdc5d36456050a81
|
def step(self, symbol):
"Execute an automaton transition using the given symbol.\n\n @param symbol: A symbol from the alphabet of the automaton's\n language. This is a Python value that should be accepted by\n the L{SymbolMatch_mixin.match} method of a L{State.symbol}.\n It is not a L{Symbol} instance.\n\n @return: The new configuration resulting from the step.\n\n @raises AutomatonStepError: L{UnrecognizedSymbolError}\n when no transition compatible with C{symbol} is available, and\n L{NondeterministicSymbolError} if C{symbol} admits multiple\n transitions and the subclass does not support\n non-deterministic steps (see L{MultiConfiguration}).\n\n @warning: If the step entered or left a sub-automaton the\n return value will not be the configuration that was used to\n execute the step. The proper pattern for using this method\n is::\n\n cfg = cfg.step(sym)\n\n "
raise NotImplementedError(('%s.step' % (type(self).__name__,)))
|
Execute an automaton transition using the given symbol.
@param symbol: A symbol from the alphabet of the automaton's
language. This is a Python value that should be accepted by
the L{SymbolMatch_mixin.match} method of a L{State.symbol}.
It is not a L{Symbol} instance.
@return: The new configuration resulting from the step.
@raises AutomatonStepError: L{UnrecognizedSymbolError}
when no transition compatible with C{symbol} is available, and
L{NondeterministicSymbolError} if C{symbol} admits multiple
transitions and the subclass does not support
non-deterministic steps (see L{MultiConfiguration}).
@warning: If the step entered or left a sub-automaton the
return value will not be the configuration that was used to
execute the step. The proper pattern for using this method
is::
cfg = cfg.step(sym)
|
pyxb/utils/fac.py
|
step
|
maciekwawro/pyxb
| 123 |
python
|
def step(self, symbol):
"Execute an automaton transition using the given symbol.\n\n @param symbol: A symbol from the alphabet of the automaton's\n language. This is a Python value that should be accepted by\n the L{SymbolMatch_mixin.match} method of a L{State.symbol}.\n It is not a L{Symbol} instance.\n\n @return: The new configuration resulting from the step.\n\n @raises AutomatonStepError: L{UnrecognizedSymbolError}\n when no transition compatible with C{symbol} is available, and\n L{NondeterministicSymbolError} if C{symbol} admits multiple\n transitions and the subclass does not support\n non-deterministic steps (see L{MultiConfiguration}).\n\n @warning: If the step entered or left a sub-automaton the\n return value will not be the configuration that was used to\n execute the step. The proper pattern for using this method\n is::\n\n cfg = cfg.step(sym)\n\n "
raise NotImplementedError(('%s.step' % (type(self).__name__,)))
|
def step(self, symbol):
"Execute an automaton transition using the given symbol.\n\n @param symbol: A symbol from the alphabet of the automaton's\n language. This is a Python value that should be accepted by\n the L{SymbolMatch_mixin.match} method of a L{State.symbol}.\n It is not a L{Symbol} instance.\n\n @return: The new configuration resulting from the step.\n\n @raises AutomatonStepError: L{UnrecognizedSymbolError}\n when no transition compatible with C{symbol} is available, and\n L{NondeterministicSymbolError} if C{symbol} admits multiple\n transitions and the subclass does not support\n non-deterministic steps (see L{MultiConfiguration}).\n\n @warning: If the step entered or left a sub-automaton the\n return value will not be the configuration that was used to\n execute the step. The proper pattern for using this method\n is::\n\n cfg = cfg.step(sym)\n\n "
raise NotImplementedError(('%s.step' % (type(self).__name__,)))<|docstring|>Execute an automaton transition using the given symbol.
@param symbol: A symbol from the alphabet of the automaton's
language. This is a Python value that should be accepted by
the L{SymbolMatch_mixin.match} method of a L{State.symbol}.
It is not a L{Symbol} instance.
@return: The new configuration resulting from the step.
@raises AutomatonStepError: L{UnrecognizedSymbolError}
when no transition compatible with C{symbol} is available, and
L{NondeterministicSymbolError} if C{symbol} admits multiple
transitions and the subclass does not support
non-deterministic steps (see L{MultiConfiguration}).
@warning: If the step entered or left a sub-automaton the
return value will not be the configuration that was used to
execute the step. The proper pattern for using this method
is::
cfg = cfg.step(sym)<|endoftext|>
|
e2a649ec4d8015e2614a2ca3a85cdc4174b2fa1dddf4afca1d1fb7bdf24a7896
|
def __get_state(self):
"The state of the configuration.\n\n This is C{None} to indicate an initial state, or one of the underlying automaton's states."
return self.__state
|
The state of the configuration.
This is C{None} to indicate an initial state, or one of the underlying automaton's states.
|
pyxb/utils/fac.py
|
__get_state
|
maciekwawro/pyxb
| 123 |
python
|
def __get_state(self):
"The state of the configuration.\n\n This is C{None} to indicate an initial state, or one of the underlying automaton's states."
return self.__state
|
def __get_state(self):
"The state of the configuration.\n\n This is C{None} to indicate an initial state, or one of the underlying automaton's states."
return self.__state<|docstring|>The state of the configuration.
This is C{None} to indicate an initial state, or one of the underlying automaton's states.<|endoftext|>
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.