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def prepare_auth(self, auth, url=''):
'Prepares the given HTTP auth data.'
if (auth is None):
url_auth = get_auth_from_url(self.url)
auth = (url_auth if any(url_auth) else None)
if auth:
if (isinstance(auth, tuple) and (len(auth) == 2)):
auth = HTTPBasicAuth(*auth)
r = auth(self)
self.__dict__.update(r.__dict__)
self.prepare_content_length(self.body) | 5,152,842,564,660,482,000 | Prepares the given HTTP auth data. | src/oci/_vendor/requests/models.py | prepare_auth | LaudateCorpus1/oci-python-sdk | python | def prepare_auth(self, auth, url=):
if (auth is None):
url_auth = get_auth_from_url(self.url)
auth = (url_auth if any(url_auth) else None)
if auth:
if (isinstance(auth, tuple) and (len(auth) == 2)):
auth = HTTPBasicAuth(*auth)
r = auth(self)
self.__dict__.update(r.__dict__)
self.prepare_content_length(self.body) |
def prepare_cookies(self, cookies):
'Prepares the given HTTP cookie data.\n\n This function eventually generates a ``Cookie`` header from the\n given cookies using cookielib. Due to cookielib\'s design, the header\n will not be regenerated if it already exists, meaning this function\n can only be called once for the life of the\n :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls\n to ``prepare_cookies`` will have no actual effect, unless the "Cookie"\n header is removed beforehand.\n '
if isinstance(cookies, cookielib.CookieJar):
self._cookies = cookies
else:
self._cookies = cookiejar_from_dict(cookies)
cookie_header = get_cookie_header(self._cookies, self)
if (cookie_header is not None):
self.headers['Cookie'] = cookie_header | -4,880,843,362,105,130,000 | Prepares the given HTTP cookie data.
This function eventually generates a ``Cookie`` header from the
given cookies using cookielib. Due to cookielib's design, the header
will not be regenerated if it already exists, meaning this function
can only be called once for the life of the
:class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls
to ``prepare_cookies`` will have no actual effect, unless the "Cookie"
header is removed beforehand. | src/oci/_vendor/requests/models.py | prepare_cookies | LaudateCorpus1/oci-python-sdk | python | def prepare_cookies(self, cookies):
'Prepares the given HTTP cookie data.\n\n This function eventually generates a ``Cookie`` header from the\n given cookies using cookielib. Due to cookielib\'s design, the header\n will not be regenerated if it already exists, meaning this function\n can only be called once for the life of the\n :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls\n to ``prepare_cookies`` will have no actual effect, unless the "Cookie"\n header is removed beforehand.\n '
if isinstance(cookies, cookielib.CookieJar):
self._cookies = cookies
else:
self._cookies = cookiejar_from_dict(cookies)
cookie_header = get_cookie_header(self._cookies, self)
if (cookie_header is not None):
self.headers['Cookie'] = cookie_header |
def prepare_hooks(self, hooks):
'Prepares the given hooks.'
hooks = (hooks or [])
for event in hooks:
self.register_hook(event, hooks[event]) | -4,515,863,383,951,718,400 | Prepares the given hooks. | src/oci/_vendor/requests/models.py | prepare_hooks | LaudateCorpus1/oci-python-sdk | python | def prepare_hooks(self, hooks):
hooks = (hooks or [])
for event in hooks:
self.register_hook(event, hooks[event]) |
def __bool__(self):
'Returns True if :attr:`status_code` is less than 400.\n\n This attribute checks if the status code of the response is between\n 400 and 600 to see if there was a client error or a server error. If\n the status code, is between 200 and 400, this will return True. This\n is **not** a check to see if the response code is ``200 OK``.\n '
return self.ok | -7,938,503,880,147,866,000 | Returns True if :attr:`status_code` is less than 400.
This attribute checks if the status code of the response is between
400 and 600 to see if there was a client error or a server error. If
the status code, is between 200 and 400, this will return True. This
is **not** a check to see if the response code is ``200 OK``. | src/oci/_vendor/requests/models.py | __bool__ | LaudateCorpus1/oci-python-sdk | python | def __bool__(self):
'Returns True if :attr:`status_code` is less than 400.\n\n This attribute checks if the status code of the response is between\n 400 and 600 to see if there was a client error or a server error. If\n the status code, is between 200 and 400, this will return True. This\n is **not** a check to see if the response code is ``200 OK``.\n '
return self.ok |
def __nonzero__(self):
'Returns True if :attr:`status_code` is less than 400.\n\n This attribute checks if the status code of the response is between\n 400 and 600 to see if there was a client error or a server error. If\n the status code, is between 200 and 400, this will return True. This\n is **not** a check to see if the response code is ``200 OK``.\n '
return self.ok | 4,933,757,067,486,797,000 | Returns True if :attr:`status_code` is less than 400.
This attribute checks if the status code of the response is between
400 and 600 to see if there was a client error or a server error. If
the status code, is between 200 and 400, this will return True. This
is **not** a check to see if the response code is ``200 OK``. | src/oci/_vendor/requests/models.py | __nonzero__ | LaudateCorpus1/oci-python-sdk | python | def __nonzero__(self):
'Returns True if :attr:`status_code` is less than 400.\n\n This attribute checks if the status code of the response is between\n 400 and 600 to see if there was a client error or a server error. If\n the status code, is between 200 and 400, this will return True. This\n is **not** a check to see if the response code is ``200 OK``.\n '
return self.ok |
def __iter__(self):
'Allows you to use a response as an iterator.'
return self.iter_content(128) | -7,277,316,857,547,251,000 | Allows you to use a response as an iterator. | src/oci/_vendor/requests/models.py | __iter__ | LaudateCorpus1/oci-python-sdk | python | def __iter__(self):
return self.iter_content(128) |
@property
def ok(self):
'Returns True if :attr:`status_code` is less than 400, False if not.\n\n This attribute checks if the status code of the response is between\n 400 and 600 to see if there was a client error or a server error. If\n the status code is between 200 and 400, this will return True. This\n is **not** a check to see if the response code is ``200 OK``.\n '
try:
self.raise_for_status()
except HTTPError:
return False
return True | -6,177,503,495,560,946,000 | Returns True if :attr:`status_code` is less than 400, False if not.
This attribute checks if the status code of the response is between
400 and 600 to see if there was a client error or a server error. If
the status code is between 200 and 400, this will return True. This
is **not** a check to see if the response code is ``200 OK``. | src/oci/_vendor/requests/models.py | ok | LaudateCorpus1/oci-python-sdk | python | @property
def ok(self):
'Returns True if :attr:`status_code` is less than 400, False if not.\n\n This attribute checks if the status code of the response is between\n 400 and 600 to see if there was a client error or a server error. If\n the status code is between 200 and 400, this will return True. This\n is **not** a check to see if the response code is ``200 OK``.\n '
try:
self.raise_for_status()
except HTTPError:
return False
return True |
@property
def is_redirect(self):
'True if this Response is a well-formed HTTP redirect that could have\n been processed automatically (by :meth:`Session.resolve_redirects`).\n '
return (('location' in self.headers) and (self.status_code in REDIRECT_STATI)) | 8,650,224,052,504,523,000 | True if this Response is a well-formed HTTP redirect that could have
been processed automatically (by :meth:`Session.resolve_redirects`). | src/oci/_vendor/requests/models.py | is_redirect | LaudateCorpus1/oci-python-sdk | python | @property
def is_redirect(self):
'True if this Response is a well-formed HTTP redirect that could have\n been processed automatically (by :meth:`Session.resolve_redirects`).\n '
return (('location' in self.headers) and (self.status_code in REDIRECT_STATI)) |
@property
def is_permanent_redirect(self):
'True if this Response one of the permanent versions of redirect.'
return (('location' in self.headers) and (self.status_code in (codes.moved_permanently, codes.permanent_redirect))) | 6,790,407,169,252,320,000 | True if this Response one of the permanent versions of redirect. | src/oci/_vendor/requests/models.py | is_permanent_redirect | LaudateCorpus1/oci-python-sdk | python | @property
def is_permanent_redirect(self):
return (('location' in self.headers) and (self.status_code in (codes.moved_permanently, codes.permanent_redirect))) |
@property
def next(self):
'Returns a PreparedRequest for the next request in a redirect chain, if there is one.'
return self._next | 6,609,836,077,647,073,000 | Returns a PreparedRequest for the next request in a redirect chain, if there is one. | src/oci/_vendor/requests/models.py | next | LaudateCorpus1/oci-python-sdk | python | @property
def next(self):
return self._next |
@property
def apparent_encoding(self):
'The apparent encoding, provided by the chardet library.'
return chardet.detect(self.content)['encoding'] | -1,142,759,535,317,406,500 | The apparent encoding, provided by the chardet library. | src/oci/_vendor/requests/models.py | apparent_encoding | LaudateCorpus1/oci-python-sdk | python | @property
def apparent_encoding(self):
return chardet.detect(self.content)['encoding'] |
def iter_content(self, chunk_size=1, decode_unicode=False):
'Iterates over the response data. When stream=True is set on the\n request, this avoids reading the content at once into memory for\n large responses. The chunk size is the number of bytes it should\n read into memory. This is not necessarily the length of each item\n returned as decoding can take place.\n\n chunk_size must be of type int or None. A value of None will\n function differently depending on the value of `stream`.\n stream=True will read data as it arrives in whatever size the\n chunks are received. If stream=False, data is returned as\n a single chunk.\n\n If decode_unicode is True, content will be decoded using the best\n available encoding based on the response.\n '
def generate():
if hasattr(self.raw, 'stream'):
try:
for chunk in self.raw.stream(chunk_size, decode_content=True):
(yield chunk)
except ProtocolError as e:
raise ChunkedEncodingError(e)
except DecodeError as e:
raise ContentDecodingError(e)
except ReadTimeoutError as e:
raise ConnectionError(e)
else:
while True:
chunk = self.raw.read(chunk_size)
if (not chunk):
break
(yield chunk)
self._content_consumed = True
if (self._content_consumed and isinstance(self._content, bool)):
raise StreamConsumedError()
elif ((chunk_size is not None) and (not isinstance(chunk_size, int))):
raise TypeError(('chunk_size must be an int, it is instead a %s.' % type(chunk_size)))
reused_chunks = iter_slices(self._content, chunk_size)
stream_chunks = generate()
chunks = (reused_chunks if self._content_consumed else stream_chunks)
if decode_unicode:
chunks = stream_decode_response_unicode(chunks, self)
return chunks | -1,745,230,368,267,707,000 | Iterates over the response data. When stream=True is set on the
request, this avoids reading the content at once into memory for
large responses. The chunk size is the number of bytes it should
read into memory. This is not necessarily the length of each item
returned as decoding can take place.
chunk_size must be of type int or None. A value of None will
function differently depending on the value of `stream`.
stream=True will read data as it arrives in whatever size the
chunks are received. If stream=False, data is returned as
a single chunk.
If decode_unicode is True, content will be decoded using the best
available encoding based on the response. | src/oci/_vendor/requests/models.py | iter_content | LaudateCorpus1/oci-python-sdk | python | def iter_content(self, chunk_size=1, decode_unicode=False):
'Iterates over the response data. When stream=True is set on the\n request, this avoids reading the content at once into memory for\n large responses. The chunk size is the number of bytes it should\n read into memory. This is not necessarily the length of each item\n returned as decoding can take place.\n\n chunk_size must be of type int or None. A value of None will\n function differently depending on the value of `stream`.\n stream=True will read data as it arrives in whatever size the\n chunks are received. If stream=False, data is returned as\n a single chunk.\n\n If decode_unicode is True, content will be decoded using the best\n available encoding based on the response.\n '
def generate():
if hasattr(self.raw, 'stream'):
try:
for chunk in self.raw.stream(chunk_size, decode_content=True):
(yield chunk)
except ProtocolError as e:
raise ChunkedEncodingError(e)
except DecodeError as e:
raise ContentDecodingError(e)
except ReadTimeoutError as e:
raise ConnectionError(e)
else:
while True:
chunk = self.raw.read(chunk_size)
if (not chunk):
break
(yield chunk)
self._content_consumed = True
if (self._content_consumed and isinstance(self._content, bool)):
raise StreamConsumedError()
elif ((chunk_size is not None) and (not isinstance(chunk_size, int))):
raise TypeError(('chunk_size must be an int, it is instead a %s.' % type(chunk_size)))
reused_chunks = iter_slices(self._content, chunk_size)
stream_chunks = generate()
chunks = (reused_chunks if self._content_consumed else stream_chunks)
if decode_unicode:
chunks = stream_decode_response_unicode(chunks, self)
return chunks |
def iter_lines(self, chunk_size=ITER_CHUNK_SIZE, decode_unicode=False, delimiter=None):
'Iterates over the response data, one line at a time. When\n stream=True is set on the request, this avoids reading the\n content at once into memory for large responses.\n\n .. note:: This method is not reentrant safe.\n '
pending = None
for chunk in self.iter_content(chunk_size=chunk_size, decode_unicode=decode_unicode):
if (pending is not None):
chunk = (pending + chunk)
if delimiter:
lines = chunk.split(delimiter)
else:
lines = chunk.splitlines()
if (lines and lines[(- 1)] and chunk and (lines[(- 1)][(- 1)] == chunk[(- 1)])):
pending = lines.pop()
else:
pending = None
for line in lines:
(yield line)
if (pending is not None):
(yield pending) | 5,187,716,744,981,455,000 | Iterates over the response data, one line at a time. When
stream=True is set on the request, this avoids reading the
content at once into memory for large responses.
.. note:: This method is not reentrant safe. | src/oci/_vendor/requests/models.py | iter_lines | LaudateCorpus1/oci-python-sdk | python | def iter_lines(self, chunk_size=ITER_CHUNK_SIZE, decode_unicode=False, delimiter=None):
'Iterates over the response data, one line at a time. When\n stream=True is set on the request, this avoids reading the\n content at once into memory for large responses.\n\n .. note:: This method is not reentrant safe.\n '
pending = None
for chunk in self.iter_content(chunk_size=chunk_size, decode_unicode=decode_unicode):
if (pending is not None):
chunk = (pending + chunk)
if delimiter:
lines = chunk.split(delimiter)
else:
lines = chunk.splitlines()
if (lines and lines[(- 1)] and chunk and (lines[(- 1)][(- 1)] == chunk[(- 1)])):
pending = lines.pop()
else:
pending = None
for line in lines:
(yield line)
if (pending is not None):
(yield pending) |
@property
def content(self):
'Content of the response, in bytes.'
if (self._content is False):
if self._content_consumed:
raise RuntimeError('The content for this response was already consumed')
if ((self.status_code == 0) or (self.raw is None)):
self._content = None
else:
self._content = (b''.join(self.iter_content(CONTENT_CHUNK_SIZE)) or b'')
self._content_consumed = True
return self._content | 4,100,658,395,437,519,400 | Content of the response, in bytes. | src/oci/_vendor/requests/models.py | content | LaudateCorpus1/oci-python-sdk | python | @property
def content(self):
if (self._content is False):
if self._content_consumed:
raise RuntimeError('The content for this response was already consumed')
if ((self.status_code == 0) or (self.raw is None)):
self._content = None
else:
self._content = (b.join(self.iter_content(CONTENT_CHUNK_SIZE)) or b)
self._content_consumed = True
return self._content |
@property
def text(self):
'Content of the response, in unicode.\n\n If Response.encoding is None, encoding will be guessed using\n ``chardet``.\n\n The encoding of the response content is determined based solely on HTTP\n headers, following RFC 2616 to the letter. If you can take advantage of\n non-HTTP knowledge to make a better guess at the encoding, you should\n set ``r.encoding`` appropriately before accessing this property.\n '
content = None
encoding = self.encoding
if (not self.content):
return str('')
if (self.encoding is None):
encoding = self.apparent_encoding
try:
content = str(self.content, encoding, errors='replace')
except (LookupError, TypeError):
content = str(self.content, errors='replace')
return content | -7,794,038,974,435,198,000 | Content of the response, in unicode.
If Response.encoding is None, encoding will be guessed using
``chardet``.
The encoding of the response content is determined based solely on HTTP
headers, following RFC 2616 to the letter. If you can take advantage of
non-HTTP knowledge to make a better guess at the encoding, you should
set ``r.encoding`` appropriately before accessing this property. | src/oci/_vendor/requests/models.py | text | LaudateCorpus1/oci-python-sdk | python | @property
def text(self):
'Content of the response, in unicode.\n\n If Response.encoding is None, encoding will be guessed using\n ``chardet``.\n\n The encoding of the response content is determined based solely on HTTP\n headers, following RFC 2616 to the letter. If you can take advantage of\n non-HTTP knowledge to make a better guess at the encoding, you should\n set ``r.encoding`` appropriately before accessing this property.\n '
content = None
encoding = self.encoding
if (not self.content):
return str()
if (self.encoding is None):
encoding = self.apparent_encoding
try:
content = str(self.content, encoding, errors='replace')
except (LookupError, TypeError):
content = str(self.content, errors='replace')
return content |
def json(self, **kwargs):
'Returns the json-encoded content of a response, if any.\n\n :param \\*\\*kwargs: Optional arguments that ``json.loads`` takes.\n :raises ValueError: If the response body does not contain valid json.\n '
if ((not self.encoding) and self.content and (len(self.content) > 3)):
encoding = guess_json_utf(self.content)
if (encoding is not None):
try:
return complexjson.loads(self.content.decode(encoding), **kwargs)
except UnicodeDecodeError:
pass
return complexjson.loads(self.text, **kwargs) | 6,506,800,846,072,340,000 | Returns the json-encoded content of a response, if any.
:param \*\*kwargs: Optional arguments that ``json.loads`` takes.
:raises ValueError: If the response body does not contain valid json. | src/oci/_vendor/requests/models.py | json | LaudateCorpus1/oci-python-sdk | python | def json(self, **kwargs):
'Returns the json-encoded content of a response, if any.\n\n :param \\*\\*kwargs: Optional arguments that ``json.loads`` takes.\n :raises ValueError: If the response body does not contain valid json.\n '
if ((not self.encoding) and self.content and (len(self.content) > 3)):
encoding = guess_json_utf(self.content)
if (encoding is not None):
try:
return complexjson.loads(self.content.decode(encoding), **kwargs)
except UnicodeDecodeError:
pass
return complexjson.loads(self.text, **kwargs) |
@property
def links(self):
'Returns the parsed header links of the response, if any.'
header = self.headers.get('link')
l = {}
if header:
links = parse_header_links(header)
for link in links:
key = (link.get('rel') or link.get('url'))
l[key] = link
return l | 7,586,059,766,961,026,000 | Returns the parsed header links of the response, if any. | src/oci/_vendor/requests/models.py | links | LaudateCorpus1/oci-python-sdk | python | @property
def links(self):
header = self.headers.get('link')
l = {}
if header:
links = parse_header_links(header)
for link in links:
key = (link.get('rel') or link.get('url'))
l[key] = link
return l |
def raise_for_status(self):
'Raises :class:`HTTPError`, if one occurred.'
http_error_msg = ''
if isinstance(self.reason, bytes):
try:
reason = self.reason.decode('utf-8')
except UnicodeDecodeError:
reason = self.reason.decode('iso-8859-1')
else:
reason = self.reason
if (400 <= self.status_code < 500):
http_error_msg = (u'%s Client Error: %s for url: %s' % (self.status_code, reason, self.url))
elif (500 <= self.status_code < 600):
http_error_msg = (u'%s Server Error: %s for url: %s' % (self.status_code, reason, self.url))
if http_error_msg:
raise HTTPError(http_error_msg, response=self) | 6,135,942,110,397,613,000 | Raises :class:`HTTPError`, if one occurred. | src/oci/_vendor/requests/models.py | raise_for_status | LaudateCorpus1/oci-python-sdk | python | def raise_for_status(self):
http_error_msg =
if isinstance(self.reason, bytes):
try:
reason = self.reason.decode('utf-8')
except UnicodeDecodeError:
reason = self.reason.decode('iso-8859-1')
else:
reason = self.reason
if (400 <= self.status_code < 500):
http_error_msg = (u'%s Client Error: %s for url: %s' % (self.status_code, reason, self.url))
elif (500 <= self.status_code < 600):
http_error_msg = (u'%s Server Error: %s for url: %s' % (self.status_code, reason, self.url))
if http_error_msg:
raise HTTPError(http_error_msg, response=self) |
def close(self):
'Releases the connection back to the pool. Once this method has been\n called the underlying ``raw`` object must not be accessed again.\n\n *Note: Should not normally need to be called explicitly.*\n '
if (not self._content_consumed):
self.raw.close()
release_conn = getattr(self.raw, 'release_conn', None)
if (release_conn is not None):
release_conn() | 5,014,410,288,615,067,000 | Releases the connection back to the pool. Once this method has been
called the underlying ``raw`` object must not be accessed again.
*Note: Should not normally need to be called explicitly.* | src/oci/_vendor/requests/models.py | close | LaudateCorpus1/oci-python-sdk | python | def close(self):
'Releases the connection back to the pool. Once this method has been\n called the underlying ``raw`` object must not be accessed again.\n\n *Note: Should not normally need to be called explicitly.*\n '
if (not self._content_consumed):
self.raw.close()
release_conn = getattr(self.raw, 'release_conn', None)
if (release_conn is not None):
release_conn() |
def update_init_info(module, init_info):
'Update the `_params_init_info` in the module if the value of parameters\n are changed.\n\n Args:\n module (obj:`nn.Module`): The module of PyTorch with a user-defined\n attribute `_params_init_info` which records the initialization\n information.\n init_info (str): The string that describes the initialization.\n '
assert hasattr(module, '_params_init_info'), f'Can not find `_params_init_info` in {module}'
for (name, param) in module.named_parameters():
assert (param in module._params_init_info), f'Find a new :obj:`Parameter` named `{name}` during executing the `init_weights` of `{module.__class__.__name__}`. Please do not add or replace parameters during executing the `init_weights`. '
mean_value = param.data.mean()
if (module._params_init_info[param]['tmp_mean_value'] != mean_value):
module._params_init_info[param]['init_info'] = init_info
module._params_init_info[param]['tmp_mean_value'] = mean_value | -2,051,522,352,694,961,400 | Update the `_params_init_info` in the module if the value of parameters
are changed.
Args:
module (obj:`nn.Module`): The module of PyTorch with a user-defined
attribute `_params_init_info` which records the initialization
information.
init_info (str): The string that describes the initialization. | deep3dmap/core/utils/weight_init.py | update_init_info | achao2013/DeepRecon | python | def update_init_info(module, init_info):
'Update the `_params_init_info` in the module if the value of parameters\n are changed.\n\n Args:\n module (obj:`nn.Module`): The module of PyTorch with a user-defined\n attribute `_params_init_info` which records the initialization\n information.\n init_info (str): The string that describes the initialization.\n '
assert hasattr(module, '_params_init_info'), f'Can not find `_params_init_info` in {module}'
for (name, param) in module.named_parameters():
assert (param in module._params_init_info), f'Find a new :obj:`Parameter` named `{name}` during executing the `init_weights` of `{module.__class__.__name__}`. Please do not add or replace parameters during executing the `init_weights`. '
mean_value = param.data.mean()
if (module._params_init_info[param]['tmp_mean_value'] != mean_value):
module._params_init_info[param]['init_info'] = init_info
module._params_init_info[param]['tmp_mean_value'] = mean_value |
def bias_init_with_prob(prior_prob):
'initialize conv/fc bias value according to a given probability value.'
bias_init = float((- np.log(((1 - prior_prob) / prior_prob))))
return bias_init | 4,357,258,245,550,052,400 | initialize conv/fc bias value according to a given probability value. | deep3dmap/core/utils/weight_init.py | bias_init_with_prob | achao2013/DeepRecon | python | def bias_init_with_prob(prior_prob):
bias_init = float((- np.log(((1 - prior_prob) / prior_prob))))
return bias_init |
def initialize(module, init_cfg):
'Initialize a module.\n\n Args:\n module (``torch.nn.Module``): the module will be initialized.\n init_cfg (dict | list[dict]): initialization configuration dict to\n define initializer. OpenMMLab has implemented 6 initializers\n including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,\n ``Kaiming``, and ``Pretrained``.\n Example:\n >>> module = nn.Linear(2, 3, bias=True)\n >>> init_cfg = dict(type=\'Constant\', layer=\'Linear\', val =1 , bias =2)\n >>> initialize(module, init_cfg)\n\n >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))\n >>> # define key ``\'layer\'`` for initializing layer with different\n >>> # configuration\n >>> init_cfg = [dict(type=\'Constant\', layer=\'Conv1d\', val=1),\n dict(type=\'Constant\', layer=\'Linear\', val=2)]\n >>> initialize(module, init_cfg)\n\n >>> # define key``\'override\'`` to initialize some specific part in\n >>> # module\n >>> class FooNet(nn.Module):\n >>> def __init__(self):\n >>> super().__init__()\n >>> self.feat = nn.Conv2d(3, 16, 3)\n >>> self.reg = nn.Conv2d(16, 10, 3)\n >>> self.cls = nn.Conv2d(16, 5, 3)\n >>> model = FooNet()\n >>> init_cfg = dict(type=\'Constant\', val=1, bias=2, layer=\'Conv2d\',\n >>> override=dict(type=\'Constant\', name=\'reg\', val=3, bias=4))\n >>> initialize(model, init_cfg)\n\n >>> model = ResNet(depth=50)\n >>> # Initialize weights with the pretrained model.\n >>> init_cfg = dict(type=\'Pretrained\',\n checkpoint=\'torchvision://resnet50\')\n >>> initialize(model, init_cfg)\n\n >>> # Initialize weights of a sub-module with the specific part of\n >>> # a pretrained model by using "prefix".\n >>> url = \'http://download.openmmlab.com/mmdetection/v2.0/retinanet/\' >>> \'retinanet_r50_fpn_1x_coco/\' >>> \'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth\'\n >>> init_cfg = dict(type=\'Pretrained\',\n checkpoint=url, prefix=\'backbone.\')\n '
if (not isinstance(init_cfg, (dict, list))):
raise TypeError(f'init_cfg must be a dict or a list of dict, but got {type(init_cfg)}')
if isinstance(init_cfg, dict):
init_cfg = [init_cfg]
for cfg in init_cfg:
cp_cfg = copy.deepcopy(cfg)
override = cp_cfg.pop('override', None)
_initialize(module, cp_cfg)
if (override is not None):
cp_cfg.pop('layer', None)
_initialize_override(module, override, cp_cfg)
else:
pass | -8,268,513,196,977,106,000 | Initialize a module.
Args:
module (``torch.nn.Module``): the module will be initialized.
init_cfg (dict | list[dict]): initialization configuration dict to
define initializer. OpenMMLab has implemented 6 initializers
including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
``Kaiming``, and ``Pretrained``.
Example:
>>> module = nn.Linear(2, 3, bias=True)
>>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
>>> initialize(module, init_cfg)
>>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
>>> # define key ``'layer'`` for initializing layer with different
>>> # configuration
>>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
dict(type='Constant', layer='Linear', val=2)]
>>> initialize(module, init_cfg)
>>> # define key``'override'`` to initialize some specific part in
>>> # module
>>> class FooNet(nn.Module):
>>> def __init__(self):
>>> super().__init__()
>>> self.feat = nn.Conv2d(3, 16, 3)
>>> self.reg = nn.Conv2d(16, 10, 3)
>>> self.cls = nn.Conv2d(16, 5, 3)
>>> model = FooNet()
>>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
>>> override=dict(type='Constant', name='reg', val=3, bias=4))
>>> initialize(model, init_cfg)
>>> model = ResNet(depth=50)
>>> # Initialize weights with the pretrained model.
>>> init_cfg = dict(type='Pretrained',
checkpoint='torchvision://resnet50')
>>> initialize(model, init_cfg)
>>> # Initialize weights of a sub-module with the specific part of
>>> # a pretrained model by using "prefix".
>>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/' >>> 'retinanet_r50_fpn_1x_coco/' >>> 'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
>>> init_cfg = dict(type='Pretrained',
checkpoint=url, prefix='backbone.') | deep3dmap/core/utils/weight_init.py | initialize | achao2013/DeepRecon | python | def initialize(module, init_cfg):
'Initialize a module.\n\n Args:\n module (``torch.nn.Module``): the module will be initialized.\n init_cfg (dict | list[dict]): initialization configuration dict to\n define initializer. OpenMMLab has implemented 6 initializers\n including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,\n ``Kaiming``, and ``Pretrained``.\n Example:\n >>> module = nn.Linear(2, 3, bias=True)\n >>> init_cfg = dict(type=\'Constant\', layer=\'Linear\', val =1 , bias =2)\n >>> initialize(module, init_cfg)\n\n >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))\n >>> # define key ``\'layer\'`` for initializing layer with different\n >>> # configuration\n >>> init_cfg = [dict(type=\'Constant\', layer=\'Conv1d\', val=1),\n dict(type=\'Constant\', layer=\'Linear\', val=2)]\n >>> initialize(module, init_cfg)\n\n >>> # define key``\'override\'`` to initialize some specific part in\n >>> # module\n >>> class FooNet(nn.Module):\n >>> def __init__(self):\n >>> super().__init__()\n >>> self.feat = nn.Conv2d(3, 16, 3)\n >>> self.reg = nn.Conv2d(16, 10, 3)\n >>> self.cls = nn.Conv2d(16, 5, 3)\n >>> model = FooNet()\n >>> init_cfg = dict(type=\'Constant\', val=1, bias=2, layer=\'Conv2d\',\n >>> override=dict(type=\'Constant\', name=\'reg\', val=3, bias=4))\n >>> initialize(model, init_cfg)\n\n >>> model = ResNet(depth=50)\n >>> # Initialize weights with the pretrained model.\n >>> init_cfg = dict(type=\'Pretrained\',\n checkpoint=\'torchvision://resnet50\')\n >>> initialize(model, init_cfg)\n\n >>> # Initialize weights of a sub-module with the specific part of\n >>> # a pretrained model by using "prefix".\n >>> url = \'http://download.openmmlab.com/mmdetection/v2.0/retinanet/\' >>> \'retinanet_r50_fpn_1x_coco/\' >>> \'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth\'\n >>> init_cfg = dict(type=\'Pretrained\',\n checkpoint=url, prefix=\'backbone.\')\n '
if (not isinstance(init_cfg, (dict, list))):
raise TypeError(f'init_cfg must be a dict or a list of dict, but got {type(init_cfg)}')
if isinstance(init_cfg, dict):
init_cfg = [init_cfg]
for cfg in init_cfg:
cp_cfg = copy.deepcopy(cfg)
override = cp_cfg.pop('override', None)
_initialize(module, cp_cfg)
if (override is not None):
cp_cfg.pop('layer', None)
_initialize_override(module, override, cp_cfg)
else:
pass |
def trunc_normal_(tensor: Tensor, mean: float=0.0, std: float=1.0, a: float=(- 2.0), b: float=2.0) -> Tensor:
'Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the\n normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n with values outside :math:`[a, b]` redrawn until they are within\n the bounds. The method used for generating the random values works\n best when :math:`a \\leq \\text{mean} \\leq b`.\n\n Modified from\n https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py\n\n Args:\n tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.\n mean (float): the mean of the normal distribution.\n std (float): the standard deviation of the normal distribution.\n a (float): the minimum cutoff value.\n b (float): the maximum cutoff value.\n '
return _no_grad_trunc_normal_(tensor, mean, std, a, b) | -7,831,877,177,259,050,000 | Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Modified from
https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
Args:
tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
mean (float): the mean of the normal distribution.
std (float): the standard deviation of the normal distribution.
a (float): the minimum cutoff value.
b (float): the maximum cutoff value. | deep3dmap/core/utils/weight_init.py | trunc_normal_ | achao2013/DeepRecon | python | def trunc_normal_(tensor: Tensor, mean: float=0.0, std: float=1.0, a: float=(- 2.0), b: float=2.0) -> Tensor:
'Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the\n normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n with values outside :math:`[a, b]` redrawn until they are within\n the bounds. The method used for generating the random values works\n best when :math:`a \\leq \\text{mean} \\leq b`.\n\n Modified from\n https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py\n\n Args:\n tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.\n mean (float): the mean of the normal distribution.\n std (float): the standard deviation of the normal distribution.\n a (float): the minimum cutoff value.\n b (float): the maximum cutoff value.\n '
return _no_grad_trunc_normal_(tensor, mean, std, a, b) |
def _parse_inputs(self):
'A number of the command line options expect precisely one or two files.\n '
nr_input_files = len(self.inputs.input_files)
for n in self.input_spec.bool_or_const_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if isinstance(t, bool):
if (nr_input_files != 2):
raise ValueError(('Due to the %s option we expected 2 files but input_files is of length %d' % (n, nr_input_files)))
elif isinstance(t, float):
if (nr_input_files != 1):
raise ValueError(('Due to the %s option we expected 1 file but input_files is of length %d' % (n, nr_input_files)))
else:
raise ValueError(('Argument should be a bool or const, but got: %s' % t))
for n in self.input_spec.single_volume_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if (nr_input_files != 1):
raise ValueError(('Due to the %s option we expected 1 file but input_files is of length %d' % (n, nr_input_files)))
for n in self.input_spec.two_volume_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if (nr_input_files != 2):
raise ValueError(('Due to the %s option we expected 2 files but input_files is of length %d' % (n, nr_input_files)))
for n in self.input_spec.n_volume_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if (not (nr_input_files >= 1)):
raise ValueError(('Due to the %s option we expected at least one file but input_files is of length %d' % (n, nr_input_files)))
return super(Math, self)._parse_inputs() | -7,755,718,070,246,585,000 | A number of the command line options expect precisely one or two files. | nipype/interfaces/minc/minc.py | _parse_inputs | Inria-Visages/nipype | python | def _parse_inputs(self):
'\n '
nr_input_files = len(self.inputs.input_files)
for n in self.input_spec.bool_or_const_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if isinstance(t, bool):
if (nr_input_files != 2):
raise ValueError(('Due to the %s option we expected 2 files but input_files is of length %d' % (n, nr_input_files)))
elif isinstance(t, float):
if (nr_input_files != 1):
raise ValueError(('Due to the %s option we expected 1 file but input_files is of length %d' % (n, nr_input_files)))
else:
raise ValueError(('Argument should be a bool or const, but got: %s' % t))
for n in self.input_spec.single_volume_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if (nr_input_files != 1):
raise ValueError(('Due to the %s option we expected 1 file but input_files is of length %d' % (n, nr_input_files)))
for n in self.input_spec.two_volume_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if (nr_input_files != 2):
raise ValueError(('Due to the %s option we expected 2 files but input_files is of length %d' % (n, nr_input_files)))
for n in self.input_spec.n_volume_traits:
t = self.inputs.__getattribute__(n)
if isdefined(t):
if (not (nr_input_files >= 1)):
raise ValueError(('Due to the %s option we expected at least one file but input_files is of length %d' % (n, nr_input_files)))
return super(Math, self)._parse_inputs() |
def patch_settings():
'Merge settings with global cms settings, so all required attributes\n will exist. Never override, just append non existing settings.\n \n Also check for setting inconsistencies if settings.DEBUG\n '
if patch_settings.ALREADY_PATCHED:
return
patch_settings.ALREADY_PATCHED = True
if getattr(settings, 'CMS_MODERATOR', False):
warnings.warn('CMS_MODERATOR will be removed and replaced in django CMS 2.4!', CMSDeprecationWarning)
from cms.conf import global_settings
pre_patch()
for attr in dir(global_settings):
if ((attr == attr.upper()) and (not hasattr(settings, attr))):
setattr(settings._wrapped, attr, getattr(global_settings, attr))
post_patch()
if settings.DEBUG:
post_patch_check() | -1,568,719,852,966,767,000 | Merge settings with global cms settings, so all required attributes
will exist. Never override, just append non existing settings.
Also check for setting inconsistencies if settings.DEBUG | cms/conf/__init__.py | patch_settings | tonatos/django-cms | python | def patch_settings():
'Merge settings with global cms settings, so all required attributes\n will exist. Never override, just append non existing settings.\n \n Also check for setting inconsistencies if settings.DEBUG\n '
if patch_settings.ALREADY_PATCHED:
return
patch_settings.ALREADY_PATCHED = True
if getattr(settings, 'CMS_MODERATOR', False):
warnings.warn('CMS_MODERATOR will be removed and replaced in django CMS 2.4!', CMSDeprecationWarning)
from cms.conf import global_settings
pre_patch()
for attr in dir(global_settings):
if ((attr == attr.upper()) and (not hasattr(settings, attr))):
setattr(settings._wrapped, attr, getattr(global_settings, attr))
post_patch()
if settings.DEBUG:
post_patch_check() |
def list_database_account_keys(account_name: Optional[str]=None, resource_group_name: Optional[str]=None, opts: Optional[pulumi.InvokeOptions]=None) -> AwaitableListDatabaseAccountKeysResult:
'\n The access keys for the given database account.\n\n\n :param str account_name: Cosmos DB database account name.\n :param str resource_group_name: Name of an Azure resource group.\n '
__args__ = dict()
__args__['accountName'] = account_name
__args__['resourceGroupName'] = resource_group_name
if (opts is None):
opts = pulumi.InvokeOptions()
if (opts.version is None):
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:documentdb/v20191212:listDatabaseAccountKeys', __args__, opts=opts, typ=ListDatabaseAccountKeysResult).value
return AwaitableListDatabaseAccountKeysResult(primary_master_key=__ret__.primary_master_key, primary_readonly_master_key=__ret__.primary_readonly_master_key, secondary_master_key=__ret__.secondary_master_key, secondary_readonly_master_key=__ret__.secondary_readonly_master_key) | -3,061,702,169,182,575,600 | The access keys for the given database account.
:param str account_name: Cosmos DB database account name.
:param str resource_group_name: Name of an Azure resource group. | sdk/python/pulumi_azure_native/documentdb/v20191212/list_database_account_keys.py | list_database_account_keys | polivbr/pulumi-azure-native | python | def list_database_account_keys(account_name: Optional[str]=None, resource_group_name: Optional[str]=None, opts: Optional[pulumi.InvokeOptions]=None) -> AwaitableListDatabaseAccountKeysResult:
'\n The access keys for the given database account.\n\n\n :param str account_name: Cosmos DB database account name.\n :param str resource_group_name: Name of an Azure resource group.\n '
__args__ = dict()
__args__['accountName'] = account_name
__args__['resourceGroupName'] = resource_group_name
if (opts is None):
opts = pulumi.InvokeOptions()
if (opts.version is None):
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:documentdb/v20191212:listDatabaseAccountKeys', __args__, opts=opts, typ=ListDatabaseAccountKeysResult).value
return AwaitableListDatabaseAccountKeysResult(primary_master_key=__ret__.primary_master_key, primary_readonly_master_key=__ret__.primary_readonly_master_key, secondary_master_key=__ret__.secondary_master_key, secondary_readonly_master_key=__ret__.secondary_readonly_master_key) |
@property
@pulumi.getter(name='primaryMasterKey')
def primary_master_key(self) -> str:
'\n Base 64 encoded value of the primary read-write key.\n '
return pulumi.get(self, 'primary_master_key') | -6,444,721,121,725,809,000 | Base 64 encoded value of the primary read-write key. | sdk/python/pulumi_azure_native/documentdb/v20191212/list_database_account_keys.py | primary_master_key | polivbr/pulumi-azure-native | python | @property
@pulumi.getter(name='primaryMasterKey')
def primary_master_key(self) -> str:
'\n \n '
return pulumi.get(self, 'primary_master_key') |
@property
@pulumi.getter(name='primaryReadonlyMasterKey')
def primary_readonly_master_key(self) -> str:
'\n Base 64 encoded value of the primary read-only key.\n '
return pulumi.get(self, 'primary_readonly_master_key') | 2,857,989,781,514,118,000 | Base 64 encoded value of the primary read-only key. | sdk/python/pulumi_azure_native/documentdb/v20191212/list_database_account_keys.py | primary_readonly_master_key | polivbr/pulumi-azure-native | python | @property
@pulumi.getter(name='primaryReadonlyMasterKey')
def primary_readonly_master_key(self) -> str:
'\n \n '
return pulumi.get(self, 'primary_readonly_master_key') |
@property
@pulumi.getter(name='secondaryMasterKey')
def secondary_master_key(self) -> str:
'\n Base 64 encoded value of the secondary read-write key.\n '
return pulumi.get(self, 'secondary_master_key') | 4,442,451,844,350,200,000 | Base 64 encoded value of the secondary read-write key. | sdk/python/pulumi_azure_native/documentdb/v20191212/list_database_account_keys.py | secondary_master_key | polivbr/pulumi-azure-native | python | @property
@pulumi.getter(name='secondaryMasterKey')
def secondary_master_key(self) -> str:
'\n \n '
return pulumi.get(self, 'secondary_master_key') |
@property
@pulumi.getter(name='secondaryReadonlyMasterKey')
def secondary_readonly_master_key(self) -> str:
'\n Base 64 encoded value of the secondary read-only key.\n '
return pulumi.get(self, 'secondary_readonly_master_key') | 1,901,077,849,702,143,000 | Base 64 encoded value of the secondary read-only key. | sdk/python/pulumi_azure_native/documentdb/v20191212/list_database_account_keys.py | secondary_readonly_master_key | polivbr/pulumi-azure-native | python | @property
@pulumi.getter(name='secondaryReadonlyMasterKey')
def secondary_readonly_master_key(self) -> str:
'\n \n '
return pulumi.get(self, 'secondary_readonly_master_key') |
def inference_data():
'\n Testing with a single type of nodes. Must do as well as EdgeFeatureGraphCRF\n '
(X, Y) = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
(x, y) = (X[0], Y[0])
n_states = x.shape[(- 1)]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
pw_horz = ((- 1) * np.eye(n_states))
(xx, yy) = np.indices(pw_horz.shape)
pw_horz[(xx > yy)] = 1
pw_vert = ((- 1) * np.eye(n_states))
pw_vert[(xx != yy)] = 1
pw_vert *= 10
edge_weights_horizontal = np.repeat(pw_horz[np.newaxis, :, :], edge_list[0].shape[0], axis=0)
edge_weights_vertical = np.repeat(pw_vert[np.newaxis, :, :], edge_list[1].shape[0], axis=0)
edge_weights = np.vstack([edge_weights_horizontal, edge_weights_vertical])
res = lp_general_graph((- x.reshape((- 1), n_states)), edges, edge_weights)
edge_features = edge_list_to_features(edge_list)
x = ([x.reshape((- 1), n_states)], [edges], [edge_features])
y = y.ravel()
return (x, y, pw_horz, pw_vert, res, n_states) | 3,998,788,590,160,578,600 | Testing with a single type of nodes. Must do as well as EdgeFeatureGraphCRF | pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py | inference_data | LemonLison/pystruct | python | def inference_data():
'\n \n '
(X, Y) = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
(x, y) = (X[0], Y[0])
n_states = x.shape[(- 1)]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
pw_horz = ((- 1) * np.eye(n_states))
(xx, yy) = np.indices(pw_horz.shape)
pw_horz[(xx > yy)] = 1
pw_vert = ((- 1) * np.eye(n_states))
pw_vert[(xx != yy)] = 1
pw_vert *= 10
edge_weights_horizontal = np.repeat(pw_horz[np.newaxis, :, :], edge_list[0].shape[0], axis=0)
edge_weights_vertical = np.repeat(pw_vert[np.newaxis, :, :], edge_list[1].shape[0], axis=0)
edge_weights = np.vstack([edge_weights_horizontal, edge_weights_vertical])
res = lp_general_graph((- x.reshape((- 1), n_states)), edges, edge_weights)
edge_features = edge_list_to_features(edge_list)
x = ([x.reshape((- 1), n_states)], [edges], [edge_features])
y = y.ravel()
return (x, y, pw_horz, pw_vert, res, n_states) |
def test_joint_feature_discrete():
'\n Testing with a single type of nodes. Must de aw well as EdgeFeatureGraphCRF\n '
(X, Y) = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
(x, y) = (X[0], Y[0])
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = ([x.reshape((- 1), 3)], [edges], [edge_features])
y_flat = y.ravel()
if True:
crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]])
joint_feature_y = crf.joint_feature(x, y_flat)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
n_states = crf.l_n_states[0]
n_features = crf.l_n_features[0]
(pw_joint_feature_horz, pw_joint_feature_vert) = joint_feature_y[(n_states * n_features):].reshape(2, n_states, n_states)
assert_array_equal(pw_joint_feature_vert, np.diag([(9 * 4), (9 * 4), (9 * 4)]))
vert_joint_feature = np.diag([(10 * 3), (10 * 3), (10 * 3)])
vert_joint_feature[(0, 1)] = 10
vert_joint_feature[(1, 2)] = 10
assert_array_equal(pw_joint_feature_horz, vert_joint_feature) | -6,468,331,002,279,117,000 | Testing with a single type of nodes. Must de aw well as EdgeFeatureGraphCRF | pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py | test_joint_feature_discrete | LemonLison/pystruct | python | def test_joint_feature_discrete():
'\n \n '
(X, Y) = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
(x, y) = (X[0], Y[0])
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = ([x.reshape((- 1), 3)], [edges], [edge_features])
y_flat = y.ravel()
if True:
crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]])
joint_feature_y = crf.joint_feature(x, y_flat)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
n_states = crf.l_n_states[0]
n_features = crf.l_n_features[0]
(pw_joint_feature_horz, pw_joint_feature_vert) = joint_feature_y[(n_states * n_features):].reshape(2, n_states, n_states)
assert_array_equal(pw_joint_feature_vert, np.diag([(9 * 4), (9 * 4), (9 * 4)]))
vert_joint_feature = np.diag([(10 * 3), (10 * 3), (10 * 3)])
vert_joint_feature[(0, 1)] = 10
vert_joint_feature[(1, 2)] = 10
assert_array_equal(pw_joint_feature_horz, vert_joint_feature) |
def test_joint_feature_continuous():
'\n Testing with a single type of nodes. Must de aw well as EdgeFeatureGraphCRF\n '
(X, Y) = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
(x, y) = (X[0], Y[0])
n_states = x.shape[(- 1)]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = ([x.reshape((- 1), 3)], [edges], [edge_features])
y = y.ravel()
pw_horz = ((- 1) * np.eye(n_states))
(xx, yy) = np.indices(pw_horz.shape)
pw_horz[(xx > yy)] = 1
pw_vert = ((- 1) * np.eye(n_states))
pw_vert[(xx != yy)] = 1
pw_vert *= 10
if True:
crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]])
w = np.hstack([np.eye(3).ravel(), (- pw_horz.ravel()), (- pw_vert.ravel())])
crf.initialize(x, y)
y_pred = crf.inference(x, w, relaxed=True)
joint_feature_y = crf.joint_feature(x, y_pred)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) | 52,569,035,070,579,250 | Testing with a single type of nodes. Must de aw well as EdgeFeatureGraphCRF | pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py | test_joint_feature_continuous | LemonLison/pystruct | python | def test_joint_feature_continuous():
'\n \n '
(X, Y) = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
(x, y) = (X[0], Y[0])
n_states = x.shape[(- 1)]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = ([x.reshape((- 1), 3)], [edges], [edge_features])
y = y.ravel()
pw_horz = ((- 1) * np.eye(n_states))
(xx, yy) = np.indices(pw_horz.shape)
pw_horz[(xx > yy)] = 1
pw_vert = ((- 1) * np.eye(n_states))
pw_vert[(xx != yy)] = 1
pw_vert *= 10
if True:
crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]])
w = np.hstack([np.eye(3).ravel(), (- pw_horz.ravel()), (- pw_vert.ravel())])
crf.initialize(x, y)
y_pred = crf.inference(x, w, relaxed=True)
joint_feature_y = crf.joint_feature(x, y_pred)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) |
def run_recipe(recipe, args, config):
'Given a recipe, calls the appropriate query and returns the result.\n\n The provided recipe name is used to make a call to the modules.\n\n :param str recipe: name of the recipe to be run.\n :param list args: remainder arguments that were unparsed.\n :param Configuration config: config object.\n :returns: string\n '
modname = '.recipes.{}'.format(recipe)
mod = importlib.import_module(modname, package='adr')
try:
output = mod.run(args, config)
except MissingDataError:
return "ActiveData didn't return any data."
if isinstance(config.fmt, string_types):
fmt = all_formatters[config.fmt]
log.debug('Result:')
return fmt(output) | -761,608,809,952,332,300 | Given a recipe, calls the appropriate query and returns the result.
The provided recipe name is used to make a call to the modules.
:param str recipe: name of the recipe to be run.
:param list args: remainder arguments that were unparsed.
:param Configuration config: config object.
:returns: string | adr/recipe.py | run_recipe | gmierz/active-data-recipes | python | def run_recipe(recipe, args, config):
'Given a recipe, calls the appropriate query and returns the result.\n\n The provided recipe name is used to make a call to the modules.\n\n :param str recipe: name of the recipe to be run.\n :param list args: remainder arguments that were unparsed.\n :param Configuration config: config object.\n :returns: string\n '
modname = '.recipes.{}'.format(recipe)
mod = importlib.import_module(modname, package='adr')
try:
output = mod.run(args, config)
except MissingDataError:
return "ActiveData didn't return any data."
if isinstance(config.fmt, string_types):
fmt = all_formatters[config.fmt]
log.debug('Result:')
return fmt(output) |
def get_chromsizes(bwpath):
'\n TODO: replace this with negspy\n\n Also, return NaNs from any missing chromosomes in bbi.fetch\n\n '
chromsizes = bbi.chromsizes(bwpath)
chromosomes = natsorted(chromsizes.keys())
chrom_series = pd.Series(chromsizes)[chromosomes]
return chrom_series | 5,693,233,413,802,413,000 | TODO: replace this with negspy
Also, return NaNs from any missing chromosomes in bbi.fetch | clodius/tiles/bigwig.py | get_chromsizes | 4dn-dcic/clodius | python | def get_chromsizes(bwpath):
'\n TODO: replace this with negspy\n\n Also, return NaNs from any missing chromosomes in bbi.fetch\n\n '
chromsizes = bbi.chromsizes(bwpath)
chromosomes = natsorted(chromsizes.keys())
chrom_series = pd.Series(chromsizes)[chromosomes]
return chrom_series |
def tileset_info(bwpath, chromsizes=None):
"\n Get the tileset info for a bigWig file\n\n Parameters\n ----------\n bwpath: string\n The path to the bigwig file from which to retrieve data\n chromsizes: [[chrom, size],...]\n A list of chromosome sizes associated with this tileset.\n Typically passed in to specify in what order data from\n the bigwig should be returned.\n\n Returns\n -------\n tileset_info: {'min_pos': [],\n 'max_pos': [],\n 'tile_size': 1024,\n 'max_zoom': 7\n }\n "
TILE_SIZE = 1024
if (chromsizes is None):
chromsizes = get_chromsizes(bwpath)
chromsizes_list = []
for (chrom, size) in chromsizes.iteritems():
chromsizes_list += [[chrom, int(size)]]
else:
chromsizes_list = chromsizes
min_tile_cover = np.ceil((sum([int(c[1]) for c in chromsizes_list]) / TILE_SIZE))
max_zoom = int(np.ceil(np.log2(min_tile_cover)))
tileset_info = {'min_pos': [0], 'max_pos': [(TILE_SIZE * (2 ** max_zoom))], 'max_width': (TILE_SIZE * (2 ** max_zoom)), 'tile_size': TILE_SIZE, 'max_zoom': max_zoom, 'chromsizes': chromsizes_list, 'aggregation_modes': aggregation_modes, 'range_modes': range_modes}
return tileset_info | 6,060,560,380,858,904,000 | Get the tileset info for a bigWig file
Parameters
----------
bwpath: string
The path to the bigwig file from which to retrieve data
chromsizes: [[chrom, size],...]
A list of chromosome sizes associated with this tileset.
Typically passed in to specify in what order data from
the bigwig should be returned.
Returns
-------
tileset_info: {'min_pos': [],
'max_pos': [],
'tile_size': 1024,
'max_zoom': 7
} | clodius/tiles/bigwig.py | tileset_info | 4dn-dcic/clodius | python | def tileset_info(bwpath, chromsizes=None):
"\n Get the tileset info for a bigWig file\n\n Parameters\n ----------\n bwpath: string\n The path to the bigwig file from which to retrieve data\n chromsizes: [[chrom, size],...]\n A list of chromosome sizes associated with this tileset.\n Typically passed in to specify in what order data from\n the bigwig should be returned.\n\n Returns\n -------\n tileset_info: {'min_pos': [],\n 'max_pos': [],\n 'tile_size': 1024,\n 'max_zoom': 7\n }\n "
TILE_SIZE = 1024
if (chromsizes is None):
chromsizes = get_chromsizes(bwpath)
chromsizes_list = []
for (chrom, size) in chromsizes.iteritems():
chromsizes_list += [[chrom, int(size)]]
else:
chromsizes_list = chromsizes
min_tile_cover = np.ceil((sum([int(c[1]) for c in chromsizes_list]) / TILE_SIZE))
max_zoom = int(np.ceil(np.log2(min_tile_cover)))
tileset_info = {'min_pos': [0], 'max_pos': [(TILE_SIZE * (2 ** max_zoom))], 'max_width': (TILE_SIZE * (2 ** max_zoom)), 'tile_size': TILE_SIZE, 'max_zoom': max_zoom, 'chromsizes': chromsizes_list, 'aggregation_modes': aggregation_modes, 'range_modes': range_modes}
return tileset_info |
def tiles(bwpath, tile_ids, chromsizes_map={}, chromsizes=None):
'\n Generate tiles from a bigwig file.\n\n Parameters\n ----------\n tileset: tilesets.models.Tileset object\n The tileset that the tile ids should be retrieved from\n tile_ids: [str,...]\n A list of tile_ids (e.g. xyx.0.0) identifying the tiles\n to be retrieved\n chromsizes_map: {uid: []}\n A set of chromsizes listings corresponding to the parameters of the\n tile_ids. To be used if a chromsizes id is passed in with the tile id\n with the `|cos:id` tag in the tile id\n chromsizes: [[chrom, size],...]\n A 2d array containing chromosome names and sizes. Overrides the\n chromsizes in chromsizes_map\n\n Returns\n -------\n tile_list: [(tile_id, tile_data),...]\n A list of tile_id, tile_data tuples\n '
TILE_SIZE = 1024
generated_tiles = []
for tile_id in tile_ids:
tile_option_parts = tile_id.split('|')[1:]
tile_no_options = tile_id.split('|')[0]
tile_id_parts = tile_no_options.split('.')
tile_position = list(map(int, tile_id_parts[1:3]))
return_value = (tile_id_parts[3] if (len(tile_id_parts) > 3) else 'mean')
aggregation_mode = (return_value if (return_value in aggregation_modes) else 'mean')
range_mode = (return_value if (return_value in range_modes) else None)
tile_options = dict([o.split(':') for o in tile_option_parts])
if chromsizes:
chromnames = [c[0] for c in chromsizes]
chromlengths = [int(c[1]) for c in chromsizes]
chromsizes_to_use = pd.Series(chromlengths, index=chromnames)
else:
chromsizes_id = None
if ('cos' in tile_options):
chromsizes_id = tile_options['cos']
if (chromsizes_id in chromsizes_map):
chromsizes_to_use = chromsizes_map[chromsizes_id]
else:
chromsizes_to_use = None
zoom_level = tile_position[0]
tile_pos = tile_position[1]
if (chromsizes_to_use is None):
chromsizes_to_use = get_chromsizes(bwpath)
max_depth = get_quadtree_depth(chromsizes_to_use)
tile_size = (TILE_SIZE * (2 ** (max_depth - zoom_level)))
start_pos = (tile_pos * tile_size)
end_pos = (start_pos + tile_size)
dense = get_bigwig_tile(bwpath, zoom_level, start_pos, end_pos, chromsizes_to_use, aggregation_mode=aggregation_mode, range_mode=range_mode)
tile_value = hgfo.format_dense_tile(dense)
generated_tiles += [(tile_id, tile_value)]
return generated_tiles | -4,122,499,748,940,527,000 | Generate tiles from a bigwig file.
Parameters
----------
tileset: tilesets.models.Tileset object
The tileset that the tile ids should be retrieved from
tile_ids: [str,...]
A list of tile_ids (e.g. xyx.0.0) identifying the tiles
to be retrieved
chromsizes_map: {uid: []}
A set of chromsizes listings corresponding to the parameters of the
tile_ids. To be used if a chromsizes id is passed in with the tile id
with the `|cos:id` tag in the tile id
chromsizes: [[chrom, size],...]
A 2d array containing chromosome names and sizes. Overrides the
chromsizes in chromsizes_map
Returns
-------
tile_list: [(tile_id, tile_data),...]
A list of tile_id, tile_data tuples | clodius/tiles/bigwig.py | tiles | 4dn-dcic/clodius | python | def tiles(bwpath, tile_ids, chromsizes_map={}, chromsizes=None):
'\n Generate tiles from a bigwig file.\n\n Parameters\n ----------\n tileset: tilesets.models.Tileset object\n The tileset that the tile ids should be retrieved from\n tile_ids: [str,...]\n A list of tile_ids (e.g. xyx.0.0) identifying the tiles\n to be retrieved\n chromsizes_map: {uid: []}\n A set of chromsizes listings corresponding to the parameters of the\n tile_ids. To be used if a chromsizes id is passed in with the tile id\n with the `|cos:id` tag in the tile id\n chromsizes: [[chrom, size],...]\n A 2d array containing chromosome names and sizes. Overrides the\n chromsizes in chromsizes_map\n\n Returns\n -------\n tile_list: [(tile_id, tile_data),...]\n A list of tile_id, tile_data tuples\n '
TILE_SIZE = 1024
generated_tiles = []
for tile_id in tile_ids:
tile_option_parts = tile_id.split('|')[1:]
tile_no_options = tile_id.split('|')[0]
tile_id_parts = tile_no_options.split('.')
tile_position = list(map(int, tile_id_parts[1:3]))
return_value = (tile_id_parts[3] if (len(tile_id_parts) > 3) else 'mean')
aggregation_mode = (return_value if (return_value in aggregation_modes) else 'mean')
range_mode = (return_value if (return_value in range_modes) else None)
tile_options = dict([o.split(':') for o in tile_option_parts])
if chromsizes:
chromnames = [c[0] for c in chromsizes]
chromlengths = [int(c[1]) for c in chromsizes]
chromsizes_to_use = pd.Series(chromlengths, index=chromnames)
else:
chromsizes_id = None
if ('cos' in tile_options):
chromsizes_id = tile_options['cos']
if (chromsizes_id in chromsizes_map):
chromsizes_to_use = chromsizes_map[chromsizes_id]
else:
chromsizes_to_use = None
zoom_level = tile_position[0]
tile_pos = tile_position[1]
if (chromsizes_to_use is None):
chromsizes_to_use = get_chromsizes(bwpath)
max_depth = get_quadtree_depth(chromsizes_to_use)
tile_size = (TILE_SIZE * (2 ** (max_depth - zoom_level)))
start_pos = (tile_pos * tile_size)
end_pos = (start_pos + tile_size)
dense = get_bigwig_tile(bwpath, zoom_level, start_pos, end_pos, chromsizes_to_use, aggregation_mode=aggregation_mode, range_mode=range_mode)
tile_value = hgfo.format_dense_tile(dense)
generated_tiles += [(tile_id, tile_value)]
return generated_tiles |
def chromsizes(filename):
'\n Get a list of chromosome sizes from this [presumably] bigwig\n file.\n\n Parameters:\n -----------\n filename: string\n The filename of the bigwig file\n\n Returns\n -------\n chromsizes: [(name:string, size:int), ...]\n An ordered list of chromosome names and sizes\n '
try:
chrom_series = get_chromsizes(filename)
data = []
for (chrom, size) in chrom_series.iteritems():
data.append([chrom, size])
return data
except Exception as ex:
logger.error(ex)
raise Exception('Error loading chromsizes from bigwig file: {}'.format(ex)) | 2,550,086,058,640,435,700 | Get a list of chromosome sizes from this [presumably] bigwig
file.
Parameters:
-----------
filename: string
The filename of the bigwig file
Returns
-------
chromsizes: [(name:string, size:int), ...]
An ordered list of chromosome names and sizes | clodius/tiles/bigwig.py | chromsizes | 4dn-dcic/clodius | python | def chromsizes(filename):
'\n Get a list of chromosome sizes from this [presumably] bigwig\n file.\n\n Parameters:\n -----------\n filename: string\n The filename of the bigwig file\n\n Returns\n -------\n chromsizes: [(name:string, size:int), ...]\n An ordered list of chromosome names and sizes\n '
try:
chrom_series = get_chromsizes(filename)
data = []
for (chrom, size) in chrom_series.iteritems():
data.append([chrom, size])
return data
except Exception as ex:
logger.error(ex)
raise Exception('Error loading chromsizes from bigwig file: {}'.format(ex)) |
def read_args():
'Reads command line arguments.\n\n Returns: Parsed arguments.'
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--file', type=str, help='path to .csv file', default='orbit.csv')
parser.add_argument('-u', '--units', type=str, help='units of distance (m or km)', default='km')
return parser.parse_args() | 6,337,914,842,948,013,000 | Reads command line arguments.
Returns: Parsed arguments. | orbitdeterminator/kep_determination/ellipse_fit.py | read_args | Alexandros23Kazantzidis/orbitdeterminator | python | def read_args():
'Reads command line arguments.\n\n Returns: Parsed arguments.'
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--file', type=str, help='path to .csv file', default='orbit.csv')
parser.add_argument('-u', '--units', type=str, help='units of distance (m or km)', default='km')
return parser.parse_args() |
def plane_err(data, coeffs):
'Calculates the total squared error of the data wrt a plane.\n\n The data should be a list of points. coeffs is an array of\n 3 elements - the coefficients a,b,c in the plane equation\n ax+by+c = 0.\n\n Arguments:\n data: A numpy array of points.\n coeffs: The coefficients of the plane ax+by+c=0.\n\n Returns: The total squared error wrt the plane defined by ax+by+cz = 0.'
(a, b, c) = coeffs
return (np.sum(((((a * data[:, 0]) + (b * data[:, 1])) + (c * data[:, 2])) ** 2)) / (((a ** 2) + (b ** 2)) + (c ** 2))) | -5,432,732,931,796,351,000 | Calculates the total squared error of the data wrt a plane.
The data should be a list of points. coeffs is an array of
3 elements - the coefficients a,b,c in the plane equation
ax+by+c = 0.
Arguments:
data: A numpy array of points.
coeffs: The coefficients of the plane ax+by+c=0.
Returns: The total squared error wrt the plane defined by ax+by+cz = 0. | orbitdeterminator/kep_determination/ellipse_fit.py | plane_err | Alexandros23Kazantzidis/orbitdeterminator | python | def plane_err(data, coeffs):
'Calculates the total squared error of the data wrt a plane.\n\n The data should be a list of points. coeffs is an array of\n 3 elements - the coefficients a,b,c in the plane equation\n ax+by+c = 0.\n\n Arguments:\n data: A numpy array of points.\n coeffs: The coefficients of the plane ax+by+c=0.\n\n Returns: The total squared error wrt the plane defined by ax+by+cz = 0.'
(a, b, c) = coeffs
return (np.sum(((((a * data[:, 0]) + (b * data[:, 1])) + (c * data[:, 2])) ** 2)) / (((a ** 2) + (b ** 2)) + (c ** 2))) |
def project_to_plane(points, coeffs):
'Projects points onto a plane.\n\n Projects a list of points onto the plane ax+by+c=0,\n where a,b,c are elements of coeffs.\n\n Arguments:\n coeffs: The coefficients of the plane ax+by+c=0.\n points: A numpy array of points.\n\n Returns:\n A list of projected points.'
(a, b, c) = coeffs
proj_mat = [[((b ** 2) + (c ** 2)), ((- a) * b), ((- a) * c)], [((- a) * b), ((a ** 2) + (c ** 2)), ((- b) * c)], [((- a) * c), ((- b) * c), ((a ** 2) + (b ** 2))]]
return (np.matmul(points, proj_mat) / (((a ** 2) + (b ** 2)) + (c ** 2))) | 7,388,186,402,707,117,000 | Projects points onto a plane.
Projects a list of points onto the plane ax+by+c=0,
where a,b,c are elements of coeffs.
Arguments:
coeffs: The coefficients of the plane ax+by+c=0.
points: A numpy array of points.
Returns:
A list of projected points. | orbitdeterminator/kep_determination/ellipse_fit.py | project_to_plane | Alexandros23Kazantzidis/orbitdeterminator | python | def project_to_plane(points, coeffs):
'Projects points onto a plane.\n\n Projects a list of points onto the plane ax+by+c=0,\n where a,b,c are elements of coeffs.\n\n Arguments:\n coeffs: The coefficients of the plane ax+by+c=0.\n points: A numpy array of points.\n\n Returns:\n A list of projected points.'
(a, b, c) = coeffs
proj_mat = [[((b ** 2) + (c ** 2)), ((- a) * b), ((- a) * c)], [((- a) * b), ((a ** 2) + (c ** 2)), ((- b) * c)], [((- a) * c), ((- b) * c), ((a ** 2) + (b ** 2))]]
return (np.matmul(points, proj_mat) / (((a ** 2) + (b ** 2)) + (c ** 2))) |
def conv_to_2D(points, x, y):
'Finds coordinates of points in a plane wrt a basis.\n\n Given a list of points in a plane, and a basis of the plane,\n this function returns the coordinates of those points\n wrt this basis.\n\n Arguments:\n points: A numpy array of points.\n x: One vector of the basis.\n y: Another vector of the basis.\n\n Returns:\n Coordinates of the points wrt the basis [x,y].'
mat = [x[0:2], y[0:2]]
mat_inv = np.linalg.inv(mat)
coords = np.matmul(points[:, 0:2], mat_inv)
return coords | -1,297,365,435,381,717,800 | Finds coordinates of points in a plane wrt a basis.
Given a list of points in a plane, and a basis of the plane,
this function returns the coordinates of those points
wrt this basis.
Arguments:
points: A numpy array of points.
x: One vector of the basis.
y: Another vector of the basis.
Returns:
Coordinates of the points wrt the basis [x,y]. | orbitdeterminator/kep_determination/ellipse_fit.py | conv_to_2D | Alexandros23Kazantzidis/orbitdeterminator | python | def conv_to_2D(points, x, y):
'Finds coordinates of points in a plane wrt a basis.\n\n Given a list of points in a plane, and a basis of the plane,\n this function returns the coordinates of those points\n wrt this basis.\n\n Arguments:\n points: A numpy array of points.\n x: One vector of the basis.\n y: Another vector of the basis.\n\n Returns:\n Coordinates of the points wrt the basis [x,y].'
mat = [x[0:2], y[0:2]]
mat_inv = np.linalg.inv(mat)
coords = np.matmul(points[:, 0:2], mat_inv)
return coords |
def cart_to_pol(points):
'Converts a list of cartesian coordinates into polar ones.\n\n Arguments:\n points: The list of points in the format [x,y].\n\n Returns:\n A list of polar coordinates in the format [radius,angle].'
pol = np.empty(points.shape)
pol[:, 0] = np.sqrt(((points[:, 0] ** 2) + (points[:, 1] ** 2)))
pol[:, 1] = np.arctan2(points[:, 1], points[:, 0])
return pol | -8,661,686,831,716,060,000 | Converts a list of cartesian coordinates into polar ones.
Arguments:
points: The list of points in the format [x,y].
Returns:
A list of polar coordinates in the format [radius,angle]. | orbitdeterminator/kep_determination/ellipse_fit.py | cart_to_pol | Alexandros23Kazantzidis/orbitdeterminator | python | def cart_to_pol(points):
'Converts a list of cartesian coordinates into polar ones.\n\n Arguments:\n points: The list of points in the format [x,y].\n\n Returns:\n A list of polar coordinates in the format [radius,angle].'
pol = np.empty(points.shape)
pol[:, 0] = np.sqrt(((points[:, 0] ** 2) + (points[:, 1] ** 2)))
pol[:, 1] = np.arctan2(points[:, 1], points[:, 0])
return pol |
def ellipse_err(polar_coords, params):
'Calculates the total squared error of the data wrt an ellipse.\n\n params is a 3 element array used to define an ellipse.\n It contains 3 elements a,e, and t0.\n\n a is the semi-major axis\n e is the eccentricity\n t0 is the angle of the major axis wrt the x-axis.\n\n These 3 elements define an ellipse with one focus at origin.\n Equation of the ellipse is r = a(1-e^2)/(1+ecos(t-t0))\n\n The function calculates r for every theta in the data.\n It then takes the square of the difference and sums it.\n\n Arguments:\n polar_coords: A list of polar coordinates in the format [radius,angle].\n params: The array [a,e,t0].\n\n Returns:\n The total squared error of the data wrt the ellipse.'
(a, e, t0) = params
dem = (1 + (e * np.cos((polar_coords[:, 1] - t0))))
num = (a * (1 - (e ** 2)))
r = np.divide(num, dem)
err = np.sum(((r - polar_coords[:, 0]) ** 2))
return err | -2,029,023,196,265,276,000 | Calculates the total squared error of the data wrt an ellipse.
params is a 3 element array used to define an ellipse.
It contains 3 elements a,e, and t0.
a is the semi-major axis
e is the eccentricity
t0 is the angle of the major axis wrt the x-axis.
These 3 elements define an ellipse with one focus at origin.
Equation of the ellipse is r = a(1-e^2)/(1+ecos(t-t0))
The function calculates r for every theta in the data.
It then takes the square of the difference and sums it.
Arguments:
polar_coords: A list of polar coordinates in the format [radius,angle].
params: The array [a,e,t0].
Returns:
The total squared error of the data wrt the ellipse. | orbitdeterminator/kep_determination/ellipse_fit.py | ellipse_err | Alexandros23Kazantzidis/orbitdeterminator | python | def ellipse_err(polar_coords, params):
'Calculates the total squared error of the data wrt an ellipse.\n\n params is a 3 element array used to define an ellipse.\n It contains 3 elements a,e, and t0.\n\n a is the semi-major axis\n e is the eccentricity\n t0 is the angle of the major axis wrt the x-axis.\n\n These 3 elements define an ellipse with one focus at origin.\n Equation of the ellipse is r = a(1-e^2)/(1+ecos(t-t0))\n\n The function calculates r for every theta in the data.\n It then takes the square of the difference and sums it.\n\n Arguments:\n polar_coords: A list of polar coordinates in the format [radius,angle].\n params: The array [a,e,t0].\n\n Returns:\n The total squared error of the data wrt the ellipse.'
(a, e, t0) = params
dem = (1 + (e * np.cos((polar_coords[:, 1] - t0))))
num = (a * (1 - (e ** 2)))
r = np.divide(num, dem)
err = np.sum(((r - polar_coords[:, 0]) ** 2))
return err |
def configure_app(app):
'Multiple app configurations'
Compress(app) | -8,213,292,863,896,477,000 | Multiple app configurations | config.py | configure_app | Rwothoromo/Flask-Okta | python | def configure_app(app):
Compress(app) |
def _param_check():
' 受信パラメータチェック '
msg = ''
if (not HachiUtil.LocalAddress().is_localaddress(RecvParams.ip.get())):
msg += '・指定した待受IPアドレスがインターフェースにありません。\n'
if (not (0 <= RecvParams.port.get() <= 65535)):
msg += '・ポート番号は 0～65535 の範囲で指定してください。\n'
return msg | -3,071,456,578,103,253,500 | 受信パラメータチェック | controller/RxController.py | _param_check | kinformation/hachi | python | def _param_check():
' '
msg =
if (not HachiUtil.LocalAddress().is_localaddress(RecvParams.ip.get())):
msg += '・指定した待受IPアドレスがインターフェースにありません。\n'
if (not (0 <= RecvParams.port.get() <= 65535)):
msg += '・ポート番号は 0～65535 の範囲で指定してください。\n'
return msg |
def recv_tcp_start(self):
' TCPパケット送信スレッド '
self.th_recv = RecvTcpThread.RecvTcpThread(RecvParams(), self.shareObj)
self.th_recv.setDaemon(True)
self.th_recv.start() | -1,761,130,429,100,993,800 | TCPパケット送信スレッド | controller/RxController.py | recv_tcp_start | kinformation/hachi | python | def recv_tcp_start(self):
' '
self.th_recv = RecvTcpThread.RecvTcpThread(RecvParams(), self.shareObj)
self.th_recv.setDaemon(True)
self.th_recv.start() |
def recv_udp_start(self):
' UDPパケット受信スレッド '
self.th_recv = RecvUdpThread.RecvUdpThread(RecvParams(), self.shareObj)
self.th_recv.setDaemon(True)
self.th_recv.start() | 8,602,522,311,085,771,000 | UDPパケット受信スレッド | controller/RxController.py | recv_udp_start | kinformation/hachi | python | def recv_udp_start(self):
' '
self.th_recv = RecvUdpThread.RecvUdpThread(RecvParams(), self.shareObj)
self.th_recv.setDaemon(True)
self.th_recv.start() |
def monitor_start(self):
' パケット受信監視スレッド '
self.th_monitor = RecvMonitorThread.RecvMonitorThread(MonitorParams(), self.shareObj)
self.th_monitor.setDaemon(True)
self.th_monitor.start() | -5,020,833,981,410,120,000 | パケット受信監視スレッド | controller/RxController.py | monitor_start | kinformation/hachi | python | def monitor_start(self):
' '
self.th_monitor = RecvMonitorThread.RecvMonitorThread(MonitorParams(), self.shareObj)
self.th_monitor.setDaemon(True)
self.th_monitor.start() |
@pytest.fixture(scope='module', autouse=True)
def create_kmc_db():
'\n Set up tests and clean up after.\n '
kmer_len = 17
memory = 2
cutoff_min = 1
sig_len = 9
reads_src = 'input.fastq'
reads = ('GGCATTGCATGCAGTNNCAGTCATGCAGTCAGGCAGTCATGGCATGCAACGACGATCAGTCATGGTCGAG', 'GGCATTGCATGCAGTNNCAGTCATGCAGTCAGGCAGTCATGGCATGCAACGACGATCAGTCATGGTCGAG', 'GTCGATGCATCGATGCTGATGCTGCTGTGCTAGTAGCGTCTGAGGGCTA')
_save_reads_as_fastq(reads, reads_src)
kmers = _cout_kmers(reads, kmer_len)
absent_kmers = _generate_not_existing_kmers(kmers, kmer_len)
_run_kmc(cutoff_min, kmer_len, memory, sig_len, reads_src)
result = {'kmers': kmers, 'kmer_len': kmer_len, 'sig_len': sig_len, 'absent_kmers': absent_kmers}
(yield result)
os.remove(reads_src)
os.remove('kmc_db.kmc_pre')
os.remove('kmc_db.kmc_suf') | 1,694,867,008,165,371,600 | Set up tests and clean up after. | tests/py_kmc_api/test_py_kmc_file.py | create_kmc_db | refresh-bio/KMC | python | @pytest.fixture(scope='module', autouse=True)
def create_kmc_db():
'\n \n '
kmer_len = 17
memory = 2
cutoff_min = 1
sig_len = 9
reads_src = 'input.fastq'
reads = ('GGCATTGCATGCAGTNNCAGTCATGCAGTCAGGCAGTCATGGCATGCAACGACGATCAGTCATGGTCGAG', 'GGCATTGCATGCAGTNNCAGTCATGCAGTCAGGCAGTCATGGCATGCAACGACGATCAGTCATGGTCGAG', 'GTCGATGCATCGATGCTGATGCTGCTGTGCTAGTAGCGTCTGAGGGCTA')
_save_reads_as_fastq(reads, reads_src)
kmers = _cout_kmers(reads, kmer_len)
absent_kmers = _generate_not_existing_kmers(kmers, kmer_len)
_run_kmc(cutoff_min, kmer_len, memory, sig_len, reads_src)
result = {'kmers': kmers, 'kmer_len': kmer_len, 'sig_len': sig_len, 'absent_kmers': absent_kmers}
(yield result)
os.remove(reads_src)
os.remove('kmc_db.kmc_pre')
os.remove('kmc_db.kmc_suf') |
def _cout_kmers(reads, kmer_len):
' Simple k-mer counting routine. '
kmers = {}
for read in reads:
for start in range(0, ((len(read) - kmer_len) + 1)):
kmer = read[start:(start + kmer_len)]
if ('N' in kmer):
continue
rev = kmer_utils.rev_comp(kmer)
if (rev < kmer):
kmer = rev
if (kmer in kmers.keys()):
kmers[kmer] += 1
else:
kmers[kmer] = 1
return kmers | 292,329,010,136,147,000 | Simple k-mer counting routine. | tests/py_kmc_api/test_py_kmc_file.py | _cout_kmers | refresh-bio/KMC | python | def _cout_kmers(reads, kmer_len):
' '
kmers = {}
for read in reads:
for start in range(0, ((len(read) - kmer_len) + 1)):
kmer = read[start:(start + kmer_len)]
if ('N' in kmer):
continue
rev = kmer_utils.rev_comp(kmer)
if (rev < kmer):
kmer = rev
if (kmer in kmers.keys()):
kmers[kmer] += 1
else:
kmers[kmer] = 1
return kmers |
def _save_reads_as_fastq(reads, file_name):
' Save reads from input to file named file_name. '
file = open(file_name, 'w')
for read in reads:
file.write('@TEST\n')
file.write((read + '\n'))
file.write('+TEST\n')
file.write((('I' * len(read)) + '\n'))
file.close() | 7,954,480,761,115,530,000 | Save reads from input to file named file_name. | tests/py_kmc_api/test_py_kmc_file.py | _save_reads_as_fastq | refresh-bio/KMC | python | def _save_reads_as_fastq(reads, file_name):
' '
file = open(file_name, 'w')
for read in reads:
file.write('@TEST\n')
file.write((read + '\n'))
file.write('+TEST\n')
file.write((('I' * len(read)) + '\n'))
file.close() |
def _generate_not_existing_kmers(kmers, kmer_len):
' Generate k-mers that are not present in the database.\n\n :kmers: existing k-mers\n :kmer_len: length of k-mers\n\n '
def increment_kmer(kmer, start):
' Increments k-mer to next lexographical.\n\n Start from pos :start: (from end, i.e. start = 0 means last k-mer symbol). '
def replace_char(string, pos, new_char):
' Create new string with character at :pos: changed to :new_char:. '
if (pos < 0):
pos = (len(string) + pos)
return ((string[:pos] + new_char) + string[(pos + 1):])
for i in range(start, len(kmer)):
if (kmer[((- 1) - i)] == 'A'):
return replace_char(kmer, ((- 1) - i), 'C')
if (kmer[((- 1) - i)] == 'C'):
return replace_char(kmer, ((- 1) - i), 'G')
if (kmer[((- 1) - i)] == 'T'):
return replace_char(kmer, ((- 1) - i), 'T')
kmer = replace_char(kmer, ((- 1) - i), 'T')
return kmer
absent_kmers = []
for i in range(0, kmer_len):
for kmer_str in kmers.keys():
inc_kmer = increment_kmer(kmer_str, i)
if (not (inc_kmer in kmers.keys())):
absent_kmers.append(inc_kmer)
return absent_kmers | -3,914,537,680,329,714,700 | Generate k-mers that are not present in the database.
:kmers: existing k-mers
:kmer_len: length of k-mers | tests/py_kmc_api/test_py_kmc_file.py | _generate_not_existing_kmers | refresh-bio/KMC | python | def _generate_not_existing_kmers(kmers, kmer_len):
' Generate k-mers that are not present in the database.\n\n :kmers: existing k-mers\n :kmer_len: length of k-mers\n\n '
def increment_kmer(kmer, start):
' Increments k-mer to next lexographical.\n\n Start from pos :start: (from end, i.e. start = 0 means last k-mer symbol). '
def replace_char(string, pos, new_char):
' Create new string with character at :pos: changed to :new_char:. '
if (pos < 0):
pos = (len(string) + pos)
return ((string[:pos] + new_char) + string[(pos + 1):])
for i in range(start, len(kmer)):
if (kmer[((- 1) - i)] == 'A'):
return replace_char(kmer, ((- 1) - i), 'C')
if (kmer[((- 1) - i)] == 'C'):
return replace_char(kmer, ((- 1) - i), 'G')
if (kmer[((- 1) - i)] == 'T'):
return replace_char(kmer, ((- 1) - i), 'T')
kmer = replace_char(kmer, ((- 1) - i), 'T')
return kmer
absent_kmers = []
for i in range(0, kmer_len):
for kmer_str in kmers.keys():
inc_kmer = increment_kmer(kmer_str, i)
if (not (inc_kmer in kmers.keys())):
absent_kmers.append(inc_kmer)
return absent_kmers |
def _run_kmc(cutoff_min, kmer_len, memory, sig_len, reads_src):
' Runs kmc. '
if (init_sys_path.is_linux() or init_sys_path.is_mac()):
kmc_path = os.path.join(os.path.dirname(__file__), '../../bin/kmc')
elif init_sys_path.is_windows():
kmc_path = os.path.join(os.path.dirname(__file__), '../../x64/Release/kmer_counter.exe')
if init_sys_path.is_mac():
resource.setrlimit(resource.RLIMIT_NOFILE, (2048, 2048))
subprocess.call([kmc_path, '-ci{}'.format(cutoff_min), '-k{}'.format(kmer_len), '-m{}'.format(memory), '-p{}'.format(sig_len), reads_src, 'kmc_db', '.']) | 6,122,891,625,812,580,000 | Runs kmc. | tests/py_kmc_api/test_py_kmc_file.py | _run_kmc | refresh-bio/KMC | python | def _run_kmc(cutoff_min, kmer_len, memory, sig_len, reads_src):
' '
if (init_sys_path.is_linux() or init_sys_path.is_mac()):
kmc_path = os.path.join(os.path.dirname(__file__), '../../bin/kmc')
elif init_sys_path.is_windows():
kmc_path = os.path.join(os.path.dirname(__file__), '../../x64/Release/kmer_counter.exe')
if init_sys_path.is_mac():
resource.setrlimit(resource.RLIMIT_NOFILE, (2048, 2048))
subprocess.call([kmc_path, '-ci{}'.format(cutoff_min), '-k{}'.format(kmer_len), '-m{}'.format(memory), '-p{}'.format(sig_len), reads_src, 'kmc_db', '.']) |
def _open_for_listing():
' Open kmc database for listing and check if opened sucessfully. '
kmc_file = pka.KMCFile()
assert kmc_file.OpenForListing('kmc_db')
return kmc_file | 3,280,609,107,479,147,500 | Open kmc database for listing and check if opened sucessfully. | tests/py_kmc_api/test_py_kmc_file.py | _open_for_listing | refresh-bio/KMC | python | def _open_for_listing():
' '
kmc_file = pka.KMCFile()
assert kmc_file.OpenForListing('kmc_db')
return kmc_file |
def _open_for_ra():
' Open kmc database for random access and check if opened sucessfully. '
kmc_file = pka.KMCFile()
assert kmc_file.OpenForRA('kmc_db')
return kmc_file | 7,697,411,584,319,123,000 | Open kmc database for random access and check if opened sucessfully. | tests/py_kmc_api/test_py_kmc_file.py | _open_for_ra | refresh-bio/KMC | python | def _open_for_ra():
' '
kmc_file = pka.KMCFile()
assert kmc_file.OpenForRA('kmc_db')
return kmc_file |
def test_info(create_kmc_db):
'\n Test if some fields in object returned from Info are set properly.\n\n '
pattern = create_kmc_db
kmc_file = _open_for_listing()
info = kmc_file.Info()
assert (info.kmer_length == pattern['kmer_len'])
assert (info.mode == 0)
assert (info.counter_size == 1)
assert (info.signature_len == pattern['sig_len'])
assert (info.min_count == 1)
assert info.both_strands
assert (info.total_kmers == len(pattern['kmers'])) | -3,972,196,797,885,729,000 | Test if some fields in object returned from Info are set properly. | tests/py_kmc_api/test_py_kmc_file.py | test_info | refresh-bio/KMC | python | def test_info(create_kmc_db):
'\n \n\n '
pattern = create_kmc_db
kmc_file = _open_for_listing()
info = kmc_file.Info()
assert (info.kmer_length == pattern['kmer_len'])
assert (info.mode == 0)
assert (info.counter_size == 1)
assert (info.signature_len == pattern['sig_len'])
assert (info.min_count == 1)
assert info.both_strands
assert (info.total_kmers == len(pattern['kmers'])) |
def test_kmc_file_next_kmer(create_kmc_db):
' Test if all counted k-mers are returned by KMC API using NextKmer method. '
pattern = create_kmc_db['kmers']
kmc_file = _open_for_listing()
counter = pka.Count()
kmer = pka.KmerAPI(create_kmc_db['kmer_len'])
res = {}
while kmc_file.ReadNextKmer(kmer, counter):
res[str(kmer)] = counter.value
assert (res == pattern) | -5,991,943,377,933,929,000 | Test if all counted k-mers are returned by KMC API using NextKmer method. | tests/py_kmc_api/test_py_kmc_file.py | test_kmc_file_next_kmer | refresh-bio/KMC | python | def test_kmc_file_next_kmer(create_kmc_db):
' '
pattern = create_kmc_db['kmers']
kmc_file = _open_for_listing()
counter = pka.Count()
kmer = pka.KmerAPI(create_kmc_db['kmer_len'])
res = {}
while kmc_file.ReadNextKmer(kmer, counter):
res[str(kmer)] = counter.value
assert (res == pattern) |
def test_get_counters_for_read(create_kmc_db):
' Test case for GetCountersForRead method of KMCFile. '
kmers = create_kmc_db['kmers']
read = 'GGCATTGCATGCAGTNNCAGTCATGCAGTCAGGCAGTCATGGCATGCGTAAACGACGATCAGTCATGGTCGAG'
pattern = []
kmer_len = create_kmc_db['kmer_len']
for i in range(0, ((len(read) - kmer_len) + 1)):
kmer = read[i:(i + kmer_len)]
if ('N' in kmer):
pattern.append(0)
continue
rev = kmer_utils.rev_comp(kmer)
if (rev < kmer):
kmer = rev
if (not (kmer in kmers.keys())):
pattern.append(0)
else:
pattern.append(kmers[kmer])
kmc_file = _open_for_ra()
res = pka.CountVec()
kmc_file.GetCountersForRead(read, res)
assert (res.value == pattern) | -1,974,169,297,877,745,200 | Test case for GetCountersForRead method of KMCFile. | tests/py_kmc_api/test_py_kmc_file.py | test_get_counters_for_read | refresh-bio/KMC | python | def test_get_counters_for_read(create_kmc_db):
' '
kmers = create_kmc_db['kmers']
read = 'GGCATTGCATGCAGTNNCAGTCATGCAGTCAGGCAGTCATGGCATGCGTAAACGACGATCAGTCATGGTCGAG'
pattern = []
kmer_len = create_kmc_db['kmer_len']
for i in range(0, ((len(read) - kmer_len) + 1)):
kmer = read[i:(i + kmer_len)]
if ('N' in kmer):
pattern.append(0)
continue
rev = kmer_utils.rev_comp(kmer)
if (rev < kmer):
kmer = rev
if (not (kmer in kmers.keys())):
pattern.append(0)
else:
pattern.append(kmers[kmer])
kmc_file = _open_for_ra()
res = pka.CountVec()
kmc_file.GetCountersForRead(read, res)
assert (res.value == pattern) |
def test_check_kmer(create_kmc_db):
'\n Test case for CheckKmer method.\n\n Check if are k-mers from input are present in the database and\n if some not present in the input are absent in the output.\n '
kmers = create_kmc_db['kmers']
kmer_len = create_kmc_db['kmer_len']
kmer = pka.KmerAPI(kmer_len)
counter = pka.Count()
kmc_file = _open_for_ra()
for (kmer_str, count) in kmers.items():
kmer.from_string(kmer_str)
assert kmc_file.CheckKmer(kmer, counter)
assert (counter.value == count)
absent_kmers = create_kmc_db['absent_kmers']
for kmer_str in absent_kmers:
kmer.from_string(kmer_str)
assert (not kmc_file.CheckKmer(kmer, counter)) | -785,399,526,982,835,100 | Test case for CheckKmer method.
Check if are k-mers from input are present in the database and
if some not present in the input are absent in the output. | tests/py_kmc_api/test_py_kmc_file.py | test_check_kmer | refresh-bio/KMC | python | def test_check_kmer(create_kmc_db):
'\n Test case for CheckKmer method.\n\n Check if are k-mers from input are present in the database and\n if some not present in the input are absent in the output.\n '
kmers = create_kmc_db['kmers']
kmer_len = create_kmc_db['kmer_len']
kmer = pka.KmerAPI(kmer_len)
counter = pka.Count()
kmc_file = _open_for_ra()
for (kmer_str, count) in kmers.items():
kmer.from_string(kmer_str)
assert kmc_file.CheckKmer(kmer, counter)
assert (counter.value == count)
absent_kmers = create_kmc_db['absent_kmers']
for kmer_str in absent_kmers:
kmer.from_string(kmer_str)
assert (not kmc_file.CheckKmer(kmer, counter)) |
def increment_kmer(kmer, start):
' Increments k-mer to next lexographical.\n\n Start from pos :start: (from end, i.e. start = 0 means last k-mer symbol). '
def replace_char(string, pos, new_char):
' Create new string with character at :pos: changed to :new_char:. '
if (pos < 0):
pos = (len(string) + pos)
return ((string[:pos] + new_char) + string[(pos + 1):])
for i in range(start, len(kmer)):
if (kmer[((- 1) - i)] == 'A'):
return replace_char(kmer, ((- 1) - i), 'C')
if (kmer[((- 1) - i)] == 'C'):
return replace_char(kmer, ((- 1) - i), 'G')
if (kmer[((- 1) - i)] == 'T'):
return replace_char(kmer, ((- 1) - i), 'T')
kmer = replace_char(kmer, ((- 1) - i), 'T')
return kmer | -6,267,665,744,541,755,000 | Increments k-mer to next lexographical.
Start from pos :start: (from end, i.e. start = 0 means last k-mer symbol). | tests/py_kmc_api/test_py_kmc_file.py | increment_kmer | refresh-bio/KMC | python | def increment_kmer(kmer, start):
' Increments k-mer to next lexographical.\n\n Start from pos :start: (from end, i.e. start = 0 means last k-mer symbol). '
def replace_char(string, pos, new_char):
' Create new string with character at :pos: changed to :new_char:. '
if (pos < 0):
pos = (len(string) + pos)
return ((string[:pos] + new_char) + string[(pos + 1):])
for i in range(start, len(kmer)):
if (kmer[((- 1) - i)] == 'A'):
return replace_char(kmer, ((- 1) - i), 'C')
if (kmer[((- 1) - i)] == 'C'):
return replace_char(kmer, ((- 1) - i), 'G')
if (kmer[((- 1) - i)] == 'T'):
return replace_char(kmer, ((- 1) - i), 'T')
kmer = replace_char(kmer, ((- 1) - i), 'T')
return kmer |
def replace_char(string, pos, new_char):
' Create new string with character at :pos: changed to :new_char:. '
if (pos < 0):
pos = (len(string) + pos)
return ((string[:pos] + new_char) + string[(pos + 1):]) | -4,285,610,178,083,164,000 | Create new string with character at :pos: changed to :new_char:. | tests/py_kmc_api/test_py_kmc_file.py | replace_char | refresh-bio/KMC | python | def replace_char(string, pos, new_char):
' '
if (pos < 0):
pos = (len(string) + pos)
return ((string[:pos] + new_char) + string[(pos + 1):]) |
def leakyrelu(x, leak=0.01):
'\n leakyrelu激活函数\n Args:\n x (Tensor): input\n leak (int): x<0时的斜率\n\n Returns:\n Tensor\n '
f1 = (0.5 * (1 + leak))
f2 = (0.5 * (1 - leak))
return ((f1 * x) + (f2 * tf.abs(x))) | 6,906,050,861,386,286,000 | leakyrelu激活函数
Args:
x (Tensor): input
leak (int): x<0时的斜率
Returns:
Tensor | algorithm/BST/leakyrelu.py | leakyrelu | tangxyw/RecAlgorithm | python | def leakyrelu(x, leak=0.01):
'\n leakyrelu激活函数\n Args:\n x (Tensor): input\n leak (int): x<0时的斜率\n\n Returns:\n Tensor\n '
f1 = (0.5 * (1 + leak))
f2 = (0.5 * (1 - leak))
return ((f1 * x) + (f2 * tf.abs(x))) |
def read_novel_info(self):
'Get novel title, autor, cover etc'
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup((self.novel_url + '?waring=1'))
self.novel_title = soup.find('div', {'class': 'manga-detail'}).find('h1').text
logger.info('Novel title: %s', self.novel_title)
self.novel_cover = self.absolute_url(soup.find('div', {'class': 'manga-detail'}).find('img')['src'])
logger.info('Novel cover: %s', self.novel_cover)
author = soup.find('div', {'class': 'detail-info'}).find('a').text.split(',')
if (len(author) == 2):
self.novel_author = (((author[0] + ' (') + author[1]) + ')')
else:
self.novel_author = ' '.join(author)
logger.info('Novel author: %s', self.novel_author)
chapters = soup.find('div', {'class': 'manga-detailchapter'}).findAll('a', title=True)
chapters.reverse()
for a in chapters:
for span in a.findAll('span'):
span.extract()
for x in chapters:
chap_id = (len(self.chapters) + 1)
if ((len(self.chapters) % 100) == 0):
vol_id = ((chap_id // 100) + 1)
vol_title = ('Volume ' + str(vol_id))
self.volumes.append({'id': vol_id, 'title': vol_title})
self.chapters.append({'id': chap_id, 'volume': vol_id, 'url': self.absolute_url(x['href']), 'title': (x['title'] or ('Chapter %d' % chap_id))}) | -1,875,111,746,532,289,300 | Get novel title, autor, cover etc | sources/novelall.py | read_novel_info | BorgSquared/lightnovel-crawler | python | def read_novel_info(self):
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup((self.novel_url + '?waring=1'))
self.novel_title = soup.find('div', {'class': 'manga-detail'}).find('h1').text
logger.info('Novel title: %s', self.novel_title)
self.novel_cover = self.absolute_url(soup.find('div', {'class': 'manga-detail'}).find('img')['src'])
logger.info('Novel cover: %s', self.novel_cover)
author = soup.find('div', {'class': 'detail-info'}).find('a').text.split(',')
if (len(author) == 2):
self.novel_author = (((author[0] + ' (') + author[1]) + ')')
else:
self.novel_author = ' '.join(author)
logger.info('Novel author: %s', self.novel_author)
chapters = soup.find('div', {'class': 'manga-detailchapter'}).findAll('a', title=True)
chapters.reverse()
for a in chapters:
for span in a.findAll('span'):
span.extract()
for x in chapters:
chap_id = (len(self.chapters) + 1)
if ((len(self.chapters) % 100) == 0):
vol_id = ((chap_id // 100) + 1)
vol_title = ('Volume ' + str(vol_id))
self.volumes.append({'id': vol_id, 'title': vol_title})
self.chapters.append({'id': chap_id, 'volume': vol_id, 'url': self.absolute_url(x['href']), 'title': (x['title'] or ('Chapter %d' % chap_id))}) |
def download_chapter_body(self, chapter):
'Download body of a single chapter and return as clean html format.'
logger.info('Downloading %s', chapter['url'])
soup = self.get_soup(chapter['url'])
contents = soup.find('div', {'class': 'reading-box'})
self.clean_contents(contents)
return str(contents) | 2,756,602,651,324,911,000 | Download body of a single chapter and return as clean html format. | sources/novelall.py | download_chapter_body | BorgSquared/lightnovel-crawler | python | def download_chapter_body(self, chapter):
logger.info('Downloading %s', chapter['url'])
soup = self.get_soup(chapter['url'])
contents = soup.find('div', {'class': 'reading-box'})
self.clean_contents(contents)
return str(contents) |
def create_url_search(self, parameters):
'Creates the search url, combining the standard url and various\n search parameters.'
url = self.standard
url += parameters[0]
for i in parameters[1:]:
url += '&{}'.format(i)
url += '&apikey={}'.format(self.key_api)
return url | -7,296,441,941,738,629,000 | Creates the search url, combining the standard url and various
search parameters. | src/arcas/IEEE/main.py | create_url_search | ArcasProject/Arcas | python | def create_url_search(self, parameters):
'Creates the search url, combining the standard url and various\n search parameters.'
url = self.standard
url += parameters[0]
for i in parameters[1:]:
url += '&{}'.format(i)
url += '&apikey={}'.format(self.key_api)
return url |
@staticmethod
@ratelimit.rate_limited(3)
def make_request(url):
'Request from an API and returns response.'
response = requests.get(url, stream=True, verify=False)
if (response.status_code != 200):
raise APIError(response.status_code)
return response | 2,011,241,343,862,152,700 | Request from an API and returns response. | src/arcas/IEEE/main.py | make_request | ArcasProject/Arcas | python | @staticmethod
@ratelimit.rate_limited(3)
def make_request(url):
response = requests.get(url, stream=True, verify=False)
if (response.status_code != 200):
raise APIError(response.status_code)
return response |
def to_dataframe(self, raw_article):
'A function which takes a dictionary with structure of the IEEE\n results and transform it to a standardized format.\n '
raw_article['url'] = raw_article.get('html_url', None)
try:
raw_article['author'] = [author['full_name'] for author in raw_article['authors']['authors']]
except KeyError:
raw_article['author'] = ['No authors found for this document.']
raw_article['abstract'] = raw_article.get('abstract', None)
if (raw_article['content_type'] == 'Conferences'):
date = raw_article.get('conference_dates', None)
else:
date = raw_article.get('publication_date', None)
if (date is not None):
date = int(date.split(' ')[(- 1)])
raw_article['date'] = date
category = raw_article.get('index_terms', None)
if (category is not None):
try:
category = category['author_terms']['terms']
except KeyError:
try:
category = category['ieee_terms']['terms']
except KeyError:
category = None
raw_article['doi'] = raw_article.get('doi', None)
raw_article['category'] = category
raw_article['journal'] = raw_article.get('publication_title', None)
raw_article['provenance'] = 'IEEE'
(raw_article['key'], raw_article['unique_key']) = self.create_keys(raw_article)
raw_article['open_access'] = (raw_article['access_type'] == 'OPEN_ACCESS')
raw_article['score'] = 'Not available'
return self.dict_to_dataframe(raw_article) | -7,890,503,207,656,345,000 | A function which takes a dictionary with structure of the IEEE
results and transform it to a standardized format. | src/arcas/IEEE/main.py | to_dataframe | ArcasProject/Arcas | python | def to_dataframe(self, raw_article):
'A function which takes a dictionary with structure of the IEEE\n results and transform it to a standardized format.\n '
raw_article['url'] = raw_article.get('html_url', None)
try:
raw_article['author'] = [author['full_name'] for author in raw_article['authors']['authors']]
except KeyError:
raw_article['author'] = ['No authors found for this document.']
raw_article['abstract'] = raw_article.get('abstract', None)
if (raw_article['content_type'] == 'Conferences'):
date = raw_article.get('conference_dates', None)
else:
date = raw_article.get('publication_date', None)
if (date is not None):
date = int(date.split(' ')[(- 1)])
raw_article['date'] = date
category = raw_article.get('index_terms', None)
if (category is not None):
try:
category = category['author_terms']['terms']
except KeyError:
try:
category = category['ieee_terms']['terms']
except KeyError:
category = None
raw_article['doi'] = raw_article.get('doi', None)
raw_article['category'] = category
raw_article['journal'] = raw_article.get('publication_title', None)
raw_article['provenance'] = 'IEEE'
(raw_article['key'], raw_article['unique_key']) = self.create_keys(raw_article)
raw_article['open_access'] = (raw_article['access_type'] == 'OPEN_ACCESS')
raw_article['score'] = 'Not available'
return self.dict_to_dataframe(raw_article) |
def parse(self, root):
'Parsing the xml file'
if (root['total_records'] == 0):
return False
return root['articles'] | 7,165,902,714,162,163,000 | Parsing the xml file | src/arcas/IEEE/main.py | parse | ArcasProject/Arcas | python | def parse(self, root):
if (root['total_records'] == 0):
return False
return root['articles'] |
def parse_args():
'\n Parse input arguments\n '
parser = argparse.ArgumentParser(description='Generate txt result file')
parser.add_argument('--dir', dest='base_dir', help='result base dir', default='/home/hezheqi/data/frame/result', type=str)
parser.add_argument('--gt', dest='gt_dir', help='gt base dir', default='/data/hezheqi/frame/test/gt', type=str)
parser.add_argument('--name', dest='name', help='out name', default=None, type=str)
parser.add_argument('--list', dest='img_list_dir', help='image list', default='/data/hezheqi/frame/test/img_list.txt', type=str)
if (len(sys.argv) == 1):
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args | -3,541,902,435,000,393,000 | Parse input arguments | tools/eval_frame.py | parse_args | lz20061213/quadrilateral | python | def parse_args():
'\n \n '
parser = argparse.ArgumentParser(description='Generate txt result file')
parser.add_argument('--dir', dest='base_dir', help='result base dir', default='/home/hezheqi/data/frame/result', type=str)
parser.add_argument('--gt', dest='gt_dir', help='gt base dir', default='/data/hezheqi/frame/test/gt', type=str)
parser.add_argument('--name', dest='name', help='out name', default=None, type=str)
parser.add_argument('--list', dest='img_list_dir', help='image list', default='/data/hezheqi/frame/test/img_list.txt', type=str)
if (len(sys.argv) == 1):
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args |
def eval_one(results, gts, point_dis=False, rect_label=None):
'\n :param results:\n :param gts:\n :param point_dis:\n :param rect_label: use rectangle or not\n :return right_num, error_num, mid_num\n '
m = len(gts)
is_used = ([False] * m)
right_num = 0
err_num = 0
mid_num = 0
for res in results:
if (not point_dis):
max_iou = (- 1)
max_index = (- 1)
for (j, gt) in enumerate(gts):
if is_used[j]:
continue
iou = calculate_iou(res, gt)
if (max_iou < iou):
max_iou = iou
max_index = j
if (max_iou > th):
is_used[max_index] = True
if (rect_label == None):
right_num += 1
elif rect_label[max_index]:
right_num += 1
elif (not rect_label[max_index]):
mid_num += 1
else:
err_num += 1
else:
flag = False
for (j, gt) in enumerate(gts):
if is_used[j]:
continue
if verify_point_distance(res, gt):
is_used[j] = True
right_num += 1
flag = True
break
if (not flag):
err_num += 1
assert (right_num <= m)
assert (err_num <= len(results))
return (right_num, err_num, mid_num) | 1,268,701,586,528,160,300 | :param results:
:param gts:
:param point_dis:
:param rect_label: use rectangle or not
:return right_num, error_num, mid_num | tools/eval_frame.py | eval_one | lz20061213/quadrilateral | python | def eval_one(results, gts, point_dis=False, rect_label=None):
'\n :param results:\n :param gts:\n :param point_dis:\n :param rect_label: use rectangle or not\n :return right_num, error_num, mid_num\n '
m = len(gts)
is_used = ([False] * m)
right_num = 0
err_num = 0
mid_num = 0
for res in results:
if (not point_dis):
max_iou = (- 1)
max_index = (- 1)
for (j, gt) in enumerate(gts):
if is_used[j]:
continue
iou = calculate_iou(res, gt)
if (max_iou < iou):
max_iou = iou
max_index = j
if (max_iou > th):
is_used[max_index] = True
if (rect_label == None):
right_num += 1
elif rect_label[max_index]:
right_num += 1
elif (not rect_label[max_index]):
mid_num += 1
else:
err_num += 1
else:
flag = False
for (j, gt) in enumerate(gts):
if is_used[j]:
continue
if verify_point_distance(res, gt):
is_used[j] = True
right_num += 1
flag = True
break
if (not flag):
err_num += 1
assert (right_num <= m)
assert (err_num <= len(results))
return (right_num, err_num, mid_num) |
@property
def chat(self):
"\n Returns the :tl:`User`, :tl:`Chat` or :tl:`Channel` where this object\n belongs to. It may be ``None`` if Telegram didn't send the chat.\n\n If you're using `telethon.events`, use `get_chat` instead.\n "
return self._chat | -3,510,885,467,875,371,000 | Returns the :tl:`User`, :tl:`Chat` or :tl:`Channel` where this object
belongs to. It may be ``None`` if Telegram didn't send the chat.
If you're using `telethon.events`, use `get_chat` instead. | telethon/tl/custom/chatgetter.py | chat | bb010g/Telethon | python | @property
def chat(self):
"\n Returns the :tl:`User`, :tl:`Chat` or :tl:`Channel` where this object\n belongs to. It may be ``None`` if Telegram didn't send the chat.\n\n If you're using `telethon.events`, use `get_chat` instead.\n "
return self._chat |
async def get_chat(self):
"\n Returns `chat`, but will make an API call to find the\n chat unless it's already cached.\n "
if (((self._chat is None) or getattr(self._chat, 'min', None)) and (await self.get_input_chat())):
try:
self._chat = (await self._client.get_entity(self._input_chat))
except ValueError:
(await self._refetch_chat())
return self._chat | 7,656,971,415,182,909,000 | Returns `chat`, but will make an API call to find the
chat unless it's already cached. | telethon/tl/custom/chatgetter.py | get_chat | bb010g/Telethon | python | async def get_chat(self):
"\n Returns `chat`, but will make an API call to find the\n chat unless it's already cached.\n "
if (((self._chat is None) or getattr(self._chat, 'min', None)) and (await self.get_input_chat())):
try:
self._chat = (await self._client.get_entity(self._input_chat))
except ValueError:
(await self._refetch_chat())
return self._chat |
@property
def input_chat(self):
"\n This :tl:`InputPeer` is the input version of the chat where the\n message was sent. Similarly to `input_sender`, this doesn't have\n things like username or similar, but still useful in some cases.\n\n Note that this might not be available if the library doesn't\n have enough information available.\n "
if ((self._input_chat is None) and self._chat_peer and self._client):
try:
self._input_chat = self._client._entity_cache[self._chat_peer]
except KeyError:
pass
return self._input_chat | -8,939,400,300,496,340,000 | This :tl:`InputPeer` is the input version of the chat where the
message was sent. Similarly to `input_sender`, this doesn't have
things like username or similar, but still useful in some cases.
Note that this might not be available if the library doesn't
have enough information available. | telethon/tl/custom/chatgetter.py | input_chat | bb010g/Telethon | python | @property
def input_chat(self):
"\n This :tl:`InputPeer` is the input version of the chat where the\n message was sent. Similarly to `input_sender`, this doesn't have\n things like username or similar, but still useful in some cases.\n\n Note that this might not be available if the library doesn't\n have enough information available.\n "
if ((self._input_chat is None) and self._chat_peer and self._client):
try:
self._input_chat = self._client._entity_cache[self._chat_peer]
except KeyError:
pass
return self._input_chat |
async def get_input_chat(self):
"\n Returns `input_chat`, but will make an API call to find the\n input chat unless it's already cached.\n "
if ((self.input_chat is None) and self.chat_id and self._client):
try:
target = self.chat_id
async for d in self._client.iter_dialogs(100):
if (d.id == target):
self._chat = d.entity
self._input_chat = d.input_entity
break
except errors.RPCError:
pass
return self._input_chat | -3,886,584,894,192,146,400 | Returns `input_chat`, but will make an API call to find the
input chat unless it's already cached. | telethon/tl/custom/chatgetter.py | get_input_chat | bb010g/Telethon | python | async def get_input_chat(self):
"\n Returns `input_chat`, but will make an API call to find the\n input chat unless it's already cached.\n "
if ((self.input_chat is None) and self.chat_id and self._client):
try:
target = self.chat_id
async for d in self._client.iter_dialogs(100):
if (d.id == target):
self._chat = d.entity
self._input_chat = d.input_entity
break
except errors.RPCError:
pass
return self._input_chat |
@property
def chat_id(self):
'\n Returns the marked chat integer ID. Note that this value **will\n be different** from `to_id` for incoming private messages, since\n the chat *to* which the messages go is to your own person, but\n the *chat* itself is with the one who sent the message.\n\n TL;DR; this gets the ID that you expect.\n '
return (utils.get_peer_id(self._chat_peer) if self._chat_peer else None) | 1,561,789,777,970,038,000 | Returns the marked chat integer ID. Note that this value **will
be different** from `to_id` for incoming private messages, since
the chat *to* which the messages go is to your own person, but
the *chat* itself is with the one who sent the message.
TL;DR; this gets the ID that you expect. | telethon/tl/custom/chatgetter.py | chat_id | bb010g/Telethon | python | @property
def chat_id(self):
'\n Returns the marked chat integer ID. Note that this value **will\n be different** from `to_id` for incoming private messages, since\n the chat *to* which the messages go is to your own person, but\n the *chat* itself is with the one who sent the message.\n\n TL;DR; this gets the ID that you expect.\n '
return (utils.get_peer_id(self._chat_peer) if self._chat_peer else None) |
@property
def is_private(self):
'True if the message was sent as a private message.'
return isinstance(self._chat_peer, types.PeerUser) | 2,072,770,881,503,792,600 | True if the message was sent as a private message. | telethon/tl/custom/chatgetter.py | is_private | bb010g/Telethon | python | @property
def is_private(self):
return isinstance(self._chat_peer, types.PeerUser) |
@property
def is_group(self):
'True if the message was sent on a group or megagroup.'
if ((self._broadcast is None) and self.chat):
self._broadcast = getattr(self.chat, 'broadcast', None)
return (isinstance(self._chat_peer, (types.PeerChat, types.PeerChannel)) and (not self._broadcast)) | 8,039,516,671,244,592,000 | True if the message was sent on a group or megagroup. | telethon/tl/custom/chatgetter.py | is_group | bb010g/Telethon | python | @property
def is_group(self):
if ((self._broadcast is None) and self.chat):
self._broadcast = getattr(self.chat, 'broadcast', None)
return (isinstance(self._chat_peer, (types.PeerChat, types.PeerChannel)) and (not self._broadcast)) |
@property
def is_channel(self):
'True if the message was sent on a megagroup or channel.'
return isinstance(self._chat_peer, types.PeerChannel) | 2,578,293,180,495,241,700 | True if the message was sent on a megagroup or channel. | telethon/tl/custom/chatgetter.py | is_channel | bb010g/Telethon | python | @property
def is_channel(self):
return isinstance(self._chat_peer, types.PeerChannel) |
async def _refetch_chat(self):
'\n Re-fetches chat information through other means.\n ' | 4,903,209,581,249,528,000 | Re-fetches chat information through other means. | telethon/tl/custom/chatgetter.py | _refetch_chat | bb010g/Telethon | python | async def _refetch_chat(self):
'\n \n ' |
def est_fpos_rate(token, trace=None, stats=None):
"\n Estimate false positive rate of a single-token signature.\n \n Estimates using the 'tokensplit' and trace-modeling methods,\n and returns the higher (most pessimistic of the two). Note that both\n of these estimates are strictly equal to or higher than the actual \n fraction of streams that 'token' occurs in within the trace.\n "
global estd_fpos_rate
if (not (estd_fpos_rate.has_key(trace) and estd_fpos_rate[trace].has_key(token))):
if (not estd_fpos_rate.has_key(trace)):
estd_fpos_rate[trace] = {}
import polygraph.sigprob.tokensplit as tokensplit
import polygraph.sigprob.sigprob as sigprob
if trace:
split_prob = tokensplit.mpp(token, trace, minlen=3)[0]
stat_prob = tokensplit.maxcontextprob(token, trace)[0]
estd_fpos_rate[trace][token] = max(split_prob, stat_prob)
else:
estd_fpos_rate[trace][token] = sigprob.token_prob(token, 1000, stats=stats)[(- 1)]
rv = estd_fpos_rate[trace][token]
if (len(token) > 20):
del estd_fpos_rate[trace][token]
if (len(estd_fpos_rate[trace].keys()) > 200):
estd_fpos_rate[trace].clear()
return rv | -4,091,892,719,482,200,600 | Estimate false positive rate of a single-token signature.
Estimates using the 'tokensplit' and trace-modeling methods,
and returns the higher (most pessimistic of the two). Note that both
of these estimates are strictly equal to or higher than the actual
fraction of streams that 'token' occurs in within the trace. | polygraph/sig_gen/sig_gen.py | est_fpos_rate | hadisfr/Polygraph | python | def est_fpos_rate(token, trace=None, stats=None):
"\n Estimate false positive rate of a single-token signature.\n \n Estimates using the 'tokensplit' and trace-modeling methods,\n and returns the higher (most pessimistic of the two). Note that both\n of these estimates are strictly equal to or higher than the actual \n fraction of streams that 'token' occurs in within the trace.\n "
global estd_fpos_rate
if (not (estd_fpos_rate.has_key(trace) and estd_fpos_rate[trace].has_key(token))):
if (not estd_fpos_rate.has_key(trace)):
estd_fpos_rate[trace] = {}
import polygraph.sigprob.tokensplit as tokensplit
import polygraph.sigprob.sigprob as sigprob
if trace:
split_prob = tokensplit.mpp(token, trace, minlen=3)[0]
stat_prob = tokensplit.maxcontextprob(token, trace)[0]
estd_fpos_rate[trace][token] = max(split_prob, stat_prob)
else:
estd_fpos_rate[trace][token] = sigprob.token_prob(token, 1000, stats=stats)[(- 1)]
rv = estd_fpos_rate[trace][token]
if (len(token) > 20):
del estd_fpos_rate[trace][token]
if (len(estd_fpos_rate[trace].keys()) > 200):
estd_fpos_rate[trace].clear()
return rv |
def train(self, pos_samples):
'\n Generate one or more signatures from pos_samples (suspicious pool).\n Returns a sequence of Sig objects.\n '
raise NotImplementedError | 4,773,307,708,365,914,000 | Generate one or more signatures from pos_samples (suspicious pool).
Returns a sequence of Sig objects. | polygraph/sig_gen/sig_gen.py | train | hadisfr/Polygraph | python | def train(self, pos_samples):
'\n Generate one or more signatures from pos_samples (suspicious pool).\n Returns a sequence of Sig objects.\n '
raise NotImplementedError |
def match(self, sample):
'Return whether current signature matches the sample'
raise NotImplementedError | -4,908,549,882,155,932,000 | Return whether current signature matches the sample | polygraph/sig_gen/sig_gen.py | match | hadisfr/Polygraph | python | def match(self, sample):
raise NotImplementedError |
def runTest(self):
'This function will fetch added table under schema node.'
table_response = tables_utils.verify_table(self.server, self.db_name, self.table_id)
if (not table_response):
raise Exception('Could not find the table to update.')
if self.is_partition:
data = {'id': self.table_id}
tables_utils.set_partition_data(self.server, self.db_name, self.schema_name, self.table_name, self.partition_type, data, self.mode)
else:
data = {'description': 'This is test comment for table', 'id': self.table_id}
response = self.tester.put((((((((((self.url + str(utils.SERVER_GROUP)) + '/') + str(self.server_id)) + '/') + str(self.db_id)) + '/') + str(self.schema_id)) + '/') + str(self.table_id)), data=json.dumps(data), follow_redirects=True)
self.assertEquals(response.status_code, 200) | -8,427,420,854,452,124,000 | This function will fetch added table under schema node. | pgAdmin4/pgAdmin4/lib/python2.7/site-packages/pgadmin4/pgadmin/browser/server_groups/servers/databases/schemas/tables/tests/test_table_put.py | runTest | Anillab/One-Minute-Pitch | python | def runTest(self):
table_response = tables_utils.verify_table(self.server, self.db_name, self.table_id)
if (not table_response):
raise Exception('Could not find the table to update.')
if self.is_partition:
data = {'id': self.table_id}
tables_utils.set_partition_data(self.server, self.db_name, self.schema_name, self.table_name, self.partition_type, data, self.mode)
else:
data = {'description': 'This is test comment for table', 'id': self.table_id}
response = self.tester.put((((((((((self.url + str(utils.SERVER_GROUP)) + '/') + str(self.server_id)) + '/') + str(self.db_id)) + '/') + str(self.schema_id)) + '/') + str(self.table_id)), data=json.dumps(data), follow_redirects=True)
self.assertEquals(response.status_code, 200) |
def dataset_parser(self, value):
'Parse an ImageNet record from a serialized string Tensor.'
keys_to_features = {'image/encoded': tf.FixedLenFeature((), tf.string, ''), 'image/format': tf.FixedLenFeature((), tf.string, 'jpeg'), 'image/class/label': tf.FixedLenFeature([], tf.int64, (- 1)), 'image/class/text': tf.FixedLenFeature([], tf.string, ''), 'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32), 'image/object/class/label': tf.VarLenFeature(dtype=tf.int64)}
parsed = tf.parse_single_example(value, keys_to_features)
image_bytes = tf.reshape(parsed['image/encoded'], shape=[])
image = self.image_preprocessing_fn(image_bytes=image_bytes, is_training=self.is_training, use_bfloat16=self.use_bfloat16)
label = tf.cast((tf.cast(tf.reshape(parsed['image/class/label'], shape=[1]), dtype=tf.int32) - 1), dtype=tf.float32)
return (image, label) | -2,487,874,934,583,650,000 | Parse an ImageNet record from a serialized string Tensor. | models/experimental/distribution_strategy/imagenet_input_keras.py | dataset_parser | aidangomez/tpu | python | def dataset_parser(self, value):
keys_to_features = {'image/encoded': tf.FixedLenFeature((), tf.string, ), 'image/format': tf.FixedLenFeature((), tf.string, 'jpeg'), 'image/class/label': tf.FixedLenFeature([], tf.int64, (- 1)), 'image/class/text': tf.FixedLenFeature([], tf.string, ), 'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32), 'image/object/class/label': tf.VarLenFeature(dtype=tf.int64)}
parsed = tf.parse_single_example(value, keys_to_features)
image_bytes = tf.reshape(parsed['image/encoded'], shape=[])
image = self.image_preprocessing_fn(image_bytes=image_bytes, is_training=self.is_training, use_bfloat16=self.use_bfloat16)
label = tf.cast((tf.cast(tf.reshape(parsed['image/class/label'], shape=[1]), dtype=tf.int32) - 1), dtype=tf.float32)
return (image, label) |
def input_fn(self):
'Input function which provides a single batch for train or eval.\n\n Returns:\n A `tf.data.Dataset` object.\n '
if (self.data_dir is None):
tf.logging.info('Using fake input.')
return self.input_fn_null()
file_pattern = os.path.join(self.data_dir, ('train-*' if self.is_training else 'validation-*'))
dataset = tf.data.Dataset.list_files(file_pattern, shuffle=self.is_training)
if self.is_training:
dataset = dataset.repeat()
def fetch_dataset(filename):
buffer_size = ((8 * 1024) * 1024)
dataset = tf.data.TFRecordDataset(filename, buffer_size=buffer_size)
return dataset
dataset = dataset.apply(tf.contrib.data.parallel_interleave(fetch_dataset, cycle_length=16, sloppy=True))
dataset = dataset.shuffle(1024)
dataset = dataset.apply(tf.contrib.data.map_and_batch(self.dataset_parser, batch_size=self.batch_size, num_parallel_batches=2, drop_remainder=True))
dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)
return dataset | -7,626,540,773,962,126,000 | Input function which provides a single batch for train or eval.
Returns:
A `tf.data.Dataset` object. | models/experimental/distribution_strategy/imagenet_input_keras.py | input_fn | aidangomez/tpu | python | def input_fn(self):
'Input function which provides a single batch for train or eval.\n\n Returns:\n A `tf.data.Dataset` object.\n '
if (self.data_dir is None):
tf.logging.info('Using fake input.')
return self.input_fn_null()
file_pattern = os.path.join(self.data_dir, ('train-*' if self.is_training else 'validation-*'))
dataset = tf.data.Dataset.list_files(file_pattern, shuffle=self.is_training)
if self.is_training:
dataset = dataset.repeat()
def fetch_dataset(filename):
buffer_size = ((8 * 1024) * 1024)
dataset = tf.data.TFRecordDataset(filename, buffer_size=buffer_size)
return dataset
dataset = dataset.apply(tf.contrib.data.parallel_interleave(fetch_dataset, cycle_length=16, sloppy=True))
dataset = dataset.shuffle(1024)
dataset = dataset.apply(tf.contrib.data.map_and_batch(self.dataset_parser, batch_size=self.batch_size, num_parallel_batches=2, drop_remainder=True))
dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)
return dataset |
def input_fn_null(self):
'Input function which provides null (black) images.'
dataset = tf.data.Dataset.range(1).repeat().map(self._get_null_input)
dataset = dataset.prefetch(self.batch_size)
dataset = dataset.batch(self.batch_size, drop_remainder=True)
dataset = dataset.prefetch(32)
tf.logging.info('Input dataset: %s', str(dataset))
return dataset | -9,042,925,970,187,984,000 | Input function which provides null (black) images. | models/experimental/distribution_strategy/imagenet_input_keras.py | input_fn_null | aidangomez/tpu | python | def input_fn_null(self):
dataset = tf.data.Dataset.range(1).repeat().map(self._get_null_input)
dataset = dataset.prefetch(self.batch_size)
dataset = dataset.batch(self.batch_size, drop_remainder=True)
dataset = dataset.prefetch(32)
tf.logging.info('Input dataset: %s', str(dataset))
return dataset |
def retrieval_fall_out(preds: Tensor, target: Tensor, k: Optional[int]=None) -> Tensor:
'Computes the Fall-out (for information retrieval), as explained in `IR Fall-out`_ Fall-out is the fraction\n of non-relevant documents retrieved among all the non-relevant documents.\n\n ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``,\n ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be `float`,\n otherwise an error is raised. If you want to measure Fall-out@K, ``k`` must be a positive integer.\n\n Args:\n preds: estimated probabilities of each document to be relevant.\n target: ground truth about each document being relevant or not.\n k: consider only the top k elements (default: `None`, which considers them all)\n\n Returns:\n a single-value tensor with the fall-out (at ``k``) of the predictions ``preds`` w.r.t. the labels ``target``.\n\n Raises:\n ValueError:\n If ``k`` parameter is not `None` or an integer larger than 0\n\n Example:\n >>> from torchmetrics.functional import retrieval_fall_out\n >>> preds = tensor([0.2, 0.3, 0.5])\n >>> target = tensor([True, False, True])\n >>> retrieval_fall_out(preds, target, k=2)\n tensor(1.)\n '
(preds, target) = _check_retrieval_functional_inputs(preds, target)
k = (preds.shape[(- 1)] if (k is None) else k)
if (not (isinstance(k, int) and (k > 0))):
raise ValueError('`k` has to be a positive integer or None')
target = (1 - target)
if (not target.sum()):
return tensor(0.0, device=preds.device)
relevant = target[torch.argsort(preds, dim=(- 1), descending=True)][:k].sum().float()
return (relevant / target.sum()) | 6,820,039,520,958,153,000 | Computes the Fall-out (for information retrieval), as explained in `IR Fall-out`_ Fall-out is the fraction
of non-relevant documents retrieved among all the non-relevant documents.
``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``,
``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be `float`,
otherwise an error is raised. If you want to measure Fall-out@K, ``k`` must be a positive integer.
Args:
preds: estimated probabilities of each document to be relevant.
target: ground truth about each document being relevant or not.
k: consider only the top k elements (default: `None`, which considers them all)
Returns:
a single-value tensor with the fall-out (at ``k``) of the predictions ``preds`` w.r.t. the labels ``target``.
Raises:
ValueError:
If ``k`` parameter is not `None` or an integer larger than 0
Example:
>>> from torchmetrics.functional import retrieval_fall_out
>>> preds = tensor([0.2, 0.3, 0.5])
>>> target = tensor([True, False, True])
>>> retrieval_fall_out(preds, target, k=2)
tensor(1.) | torchmetrics/functional/retrieval/fall_out.py | retrieval_fall_out | Abdelrhman-Hosny/metrics | python | def retrieval_fall_out(preds: Tensor, target: Tensor, k: Optional[int]=None) -> Tensor:
'Computes the Fall-out (for information retrieval), as explained in `IR Fall-out`_ Fall-out is the fraction\n of non-relevant documents retrieved among all the non-relevant documents.\n\n ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``,\n ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be `float`,\n otherwise an error is raised. If you want to measure Fall-out@K, ``k`` must be a positive integer.\n\n Args:\n preds: estimated probabilities of each document to be relevant.\n target: ground truth about each document being relevant or not.\n k: consider only the top k elements (default: `None`, which considers them all)\n\n Returns:\n a single-value tensor with the fall-out (at ``k``) of the predictions ``preds`` w.r.t. the labels ``target``.\n\n Raises:\n ValueError:\n If ``k`` parameter is not `None` or an integer larger than 0\n\n Example:\n >>> from torchmetrics.functional import retrieval_fall_out\n >>> preds = tensor([0.2, 0.3, 0.5])\n >>> target = tensor([True, False, True])\n >>> retrieval_fall_out(preds, target, k=2)\n tensor(1.)\n '
(preds, target) = _check_retrieval_functional_inputs(preds, target)
k = (preds.shape[(- 1)] if (k is None) else k)
if (not (isinstance(k, int) and (k > 0))):
raise ValueError('`k` has to be a positive integer or None')
target = (1 - target)
if (not target.sum()):
return tensor(0.0, device=preds.device)
relevant = target[torch.argsort(preds, dim=(- 1), descending=True)][:k].sum().float()
return (relevant / target.sum()) |
def index(request):
'\n Return landing page\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Rendered home (index) page\n '
return render(request, 'webservice/index.html', {'demo_input_repo_name': DEMO_REPO_INPUT_NAME, 'supported_languages': sorted([lang.capitalize() for lang in SUPPORTED_LANGUAGES.keys()])}) | 2,300,159,280,462,162,700 | Return landing page
arguments:
:request: GET HTTP request
returns:
Rendered home (index) page | webserver/pkgpkr/webservice/views.py | index | pkgpkr/Package-Picker | python | def index(request):
'\n Return landing page\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Rendered home (index) page\n '
return render(request, 'webservice/index.html', {'demo_input_repo_name': DEMO_REPO_INPUT_NAME, 'supported_languages': sorted([lang.capitalize() for lang in SUPPORTED_LANGUAGES.keys()])}) |
def about(request):
'\n Return about info\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Rendered about page\n '
return render(request, 'webservice/about.html') | 1,604,955,779,372,363,300 | Return about info
arguments:
:request: GET HTTP request
returns:
Rendered about page | webserver/pkgpkr/webservice/views.py | about | pkgpkr/Package-Picker | python | def about(request):
'\n Return about info\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Rendered about page\n '
return render(request, 'webservice/about.html') |
def login(request):
' Log user in using GitHub OAuth\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Redirects to index\n '
if (not request.session.get('github_token')):
request.session['github_token'] = None
request.session['github_info'] = None
if (os.environ.get('SELENIUM_TEST') == '1'):
assert os.environ.get('GH_TOKEN'), 'GH_TOKEN not set'
request.session['github_token'] = os.environ.get('GH_TOKEN')
request.session['github_info'] = github_util.get_user_info(request.session['github_token'])
return HttpResponseRedirect(reverse('index'))
return HttpResponseRedirect(GITHUB_OATH_AUTH_PATH) | 1,179,897,966,924,708,600 | Log user in using GitHub OAuth
arguments:
:request: GET HTTP request
returns:
Redirects to index | webserver/pkgpkr/webservice/views.py | login | pkgpkr/Package-Picker | python | def login(request):
' Log user in using GitHub OAuth\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Redirects to index\n '
if (not request.session.get('github_token')):
request.session['github_token'] = None
request.session['github_info'] = None
if (os.environ.get('SELENIUM_TEST') == '1'):
assert os.environ.get('GH_TOKEN'), 'GH_TOKEN not set'
request.session['github_token'] = os.environ.get('GH_TOKEN')
request.session['github_info'] = github_util.get_user_info(request.session['github_token'])
return HttpResponseRedirect(reverse('index'))
return HttpResponseRedirect(GITHUB_OATH_AUTH_PATH) |
def callback(request):
'\n GitHub redirect here, then retrieves token for API\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Redirects to index\n '
code = request.GET.get('code')
payload = {'client_id': GITHUB_CLIENT_ID, 'client_secret': GITHUB_CLIENT_SECRET, 'code': code}
headers = {'accept': 'application/json'}
res = requests.post(GITHUB_OATH_ACCESS_TOKEN_PATH, data=payload, headers=headers)
request.session['github_token'] = res.json()['access_token']
request.session['github_info'] = github_util.get_user_info(request.session['github_token'])
return HttpResponseRedirect(reverse('index')) | -6,453,799,905,267,306,000 | GitHub redirect here, then retrieves token for API
arguments:
:request: GET HTTP request
returns:
Redirects to index | webserver/pkgpkr/webservice/views.py | callback | pkgpkr/Package-Picker | python | def callback(request):
'\n GitHub redirect here, then retrieves token for API\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Redirects to index\n '
code = request.GET.get('code')
payload = {'client_id': GITHUB_CLIENT_ID, 'client_secret': GITHUB_CLIENT_SECRET, 'code': code}
headers = {'accept': 'application/json'}
res = requests.post(GITHUB_OATH_ACCESS_TOKEN_PATH, data=payload, headers=headers)
request.session['github_token'] = res.json()['access_token']
request.session['github_info'] = github_util.get_user_info(request.session['github_token'])
return HttpResponseRedirect(reverse('index')) |
def logout(request):
'\n Logs user out but keep authorization ot OAuth GitHub\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Redirects to index\n '
request.session['github_token'] = None
request.session['github_info'] = None
return HttpResponseRedirect(reverse('index')) | -1,557,519,377,283,205,400 | Logs user out but keep authorization ot OAuth GitHub
arguments:
:request: GET HTTP request
returns:
Redirects to index | webserver/pkgpkr/webservice/views.py | logout | pkgpkr/Package-Picker | python | def logout(request):
'\n Logs user out but keep authorization ot OAuth GitHub\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Redirects to index\n '
request.session['github_token'] = None
request.session['github_info'] = None
return HttpResponseRedirect(reverse('index')) |
def repositories(request):
'\n Get full list (up to 100) for the current user\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Rendered repositories page\n '
if (not request.session.get('github_token')):
return HttpResponseRedirect(reverse('index'))
repos_per_language = github_util.get_repositories(request.session['github_token'])
combined_repos = dict()
for (language, repos) in repos_per_language.items():
for repo in repos:
if (not repo['object']):
continue
date_time = repo['updatedAt']
date = date_time.split('T')[0]
repo['date'] = date
repo['nameWithOwnerEscaped'] = urllib.parse.quote_plus(repo['nameWithOwner'])
repo['language'] = language
if parse_dependencies(repo['object']['text'], language, True):
combined_repos[repo['nameWithOwner']] = repo
return render(request, 'webservice/repositories.html', {'repos': combined_repos.values()}) | 4,976,217,205,767,315,000 | Get full list (up to 100) for the current user
arguments:
:request: GET HTTP request
returns:
Rendered repositories page | webserver/pkgpkr/webservice/views.py | repositories | pkgpkr/Package-Picker | python | def repositories(request):
'\n Get full list (up to 100) for the current user\n\n arguments:\n :request: GET HTTP request\n\n returns:\n Rendered repositories page\n '
if (not request.session.get('github_token')):
return HttpResponseRedirect(reverse('index'))
repos_per_language = github_util.get_repositories(request.session['github_token'])
combined_repos = dict()
for (language, repos) in repos_per_language.items():
for repo in repos:
if (not repo['object']):
continue
date_time = repo['updatedAt']
date = date_time.split('T')[0]
repo['date'] = date
repo['nameWithOwnerEscaped'] = urllib.parse.quote_plus(repo['nameWithOwner'])
repo['language'] = language
if parse_dependencies(repo['object']['text'], language, True):
combined_repos[repo['nameWithOwner']] = repo
return render(request, 'webservice/repositories.html', {'repos': combined_repos.values()}) |
def recommendations(request, name):
'\n Get recommended packages for the repo\n\n arguments:\n :request: GET/POST HTTP request\n :name: repo name\n\n returns:\n Rendered recommendation page\n '
repo_name = urllib.parse.unquote_plus(name)
if (request.method == 'POST'):
language = request.POST.get('language')
language = language.lower()
dependencies = request.POST.get('dependencies')
dependencies = dependencies.strip(',')
if (language not in SUPPORTED_LANGUAGES.keys()):
return HttpResponse(f'Demo language {language} not supported', status=404)
request.session['dependencies'] = dependencies
request.session['language'] = language
branch_name = None
branch_names = None
else:
if (not request.session.get('github_token')):
return HttpResponseRedirect(reverse('index'))
branch_name = request.GET.get('branch', default='master')
(_, branch_names, language) = github_util.get_dependencies(request.session['github_token'], repo_name, branch_name)
return render(request, 'webservice/recommendations.html', {'repository_name': repo_name, 'recommendation_url': f'/recommendations/{urllib.parse.quote_plus(name)}?branch={branch_name}', 'branch_names': branch_names, 'current_branch': branch_name, 'language': language}) | -4,413,514,821,107,826,700 | Get recommended packages for the repo
arguments:
:request: GET/POST HTTP request
:name: repo name
returns:
Rendered recommendation page | webserver/pkgpkr/webservice/views.py | recommendations | pkgpkr/Package-Picker | python | def recommendations(request, name):
'\n Get recommended packages for the repo\n\n arguments:\n :request: GET/POST HTTP request\n :name: repo name\n\n returns:\n Rendered recommendation page\n '
repo_name = urllib.parse.unquote_plus(name)
if (request.method == 'POST'):
language = request.POST.get('language')
language = language.lower()
dependencies = request.POST.get('dependencies')
dependencies = dependencies.strip(',')
if (language not in SUPPORTED_LANGUAGES.keys()):
return HttpResponse(f'Demo language {language} not supported', status=404)
request.session['dependencies'] = dependencies
request.session['language'] = language
branch_name = None
branch_names = None
else:
if (not request.session.get('github_token')):
return HttpResponseRedirect(reverse('index'))
branch_name = request.GET.get('branch', default='master')
(_, branch_names, language) = github_util.get_dependencies(request.session['github_token'], repo_name, branch_name)
return render(request, 'webservice/recommendations.html', {'repository_name': repo_name, 'recommendation_url': f'/recommendations/{urllib.parse.quote_plus(name)}?branch={branch_name}', 'branch_names': branch_names, 'current_branch': branch_name, 'language': language}) |
def recommendations_json(request, name):
'\n Get recommended packages for the repo in JSON format\n\n arguments:\n :request: GET HTTP request\n :name: repo name\n\n returns:\n JSON object with recommendations\n '
repo_name = urllib.parse.unquote_plus(name)
if (name == DEMO_REPO_INPUT_NAME):
dependencies = github_util.parse_dependencies(request.session.get('dependencies'), request.session.get('language'))
branch_name = None
else:
if (not request.session.get('github_token')):
return HttpResponse('Unauthorized', status=401)
branch_name = request.GET.get('branch', default='master')
(dependencies, _, _) = github_util.get_dependencies(request.session['github_token'], repo_name, branch_name)
recommended_dependencies = RECOMMENDER_SERVICE.get_recommendations(dependencies)
data = {'repository_name': repo_name, 'current_branch': branch_name, 'data': recommended_dependencies}
return HttpResponse(json.dumps(data), content_type='application/json') | 2,116,296,651,441,857,800 | Get recommended packages for the repo in JSON format
arguments:
:request: GET HTTP request
:name: repo name
returns:
JSON object with recommendations | webserver/pkgpkr/webservice/views.py | recommendations_json | pkgpkr/Package-Picker | python | def recommendations_json(request, name):
'\n Get recommended packages for the repo in JSON format\n\n arguments:\n :request: GET HTTP request\n :name: repo name\n\n returns:\n JSON object with recommendations\n '
repo_name = urllib.parse.unquote_plus(name)
if (name == DEMO_REPO_INPUT_NAME):
dependencies = github_util.parse_dependencies(request.session.get('dependencies'), request.session.get('language'))
branch_name = None
else:
if (not request.session.get('github_token')):
return HttpResponse('Unauthorized', status=401)
branch_name = request.GET.get('branch', default='master')
(dependencies, _, _) = github_util.get_dependencies(request.session['github_token'], repo_name, branch_name)
recommended_dependencies = RECOMMENDER_SERVICE.get_recommendations(dependencies)
data = {'repository_name': repo_name, 'current_branch': branch_name, 'data': recommended_dependencies}
return HttpResponse(json.dumps(data), content_type='application/json') |
@csrf_exempt
def recommendations_service_api(request):
'\n Returns package recommendations for API POST call without authentication\n\n arguments:\n :request: POST request of application/json type\n\n returns:\n list of package recommendations\n '
if (request.method == 'POST'):
try:
json_data = json.loads(request.body)
except json.JSONDecodeError:
return HttpResponseBadRequest('Could not parse JSON.')
try:
dependencies = json_data['dependencies']
language = json_data['language'].lower()
except KeyError:
return HttpResponseBadRequest('Required JSON keys: `dependencies`, `language`')
except AttributeError as e:
return HttpResponseBadRequest(f'Error casting language to lower(): {e}')
if ((not isinstance(dependencies, list)) or (not dependencies)):
return HttpResponseBadRequest(f'{language.capitalize()} dependencies must be non-empty and of type LIST (i.e. [...]).')
if (language == PYTHON):
dependencies = '\n'.join(dependencies)
elif (language == JAVASCRIPT):
formatted_dependencies_list = [(('"' + dep.replace('@', '":"')) + '"') for dep in dependencies]
dependencies = ','.join(formatted_dependencies_list)
else:
return HttpResponseBadRequest(f'Language not supported: [{language}].')
dependencies = github_util.parse_dependencies(dependencies, language)
if ('max_recommendations' in json_data):
recommended_dependencies = RECOMMENDER_SERVICE.get_recommendations(dependencies, json_data['max_recommendations'])
else:
recommended_dependencies = RECOMMENDER_SERVICE.get_recommendations(dependencies)
output_recommended_dependencies = []
for recommended_dependency in recommended_dependencies:
d = dict()
d['forPackage'] = recommended_dependency[0]
d['recommendedPackage'] = recommended_dependency[1]
d['url'] = recommended_dependency[2]
d['pkgpkrScore'] = recommended_dependency[3]
d['absoluteTrendScore'] = recommended_dependency[4]
d['relativeTrendScore'] = recommended_dependency[5]
d['boundedPopularityScore'] = recommended_dependency[6]
d['boundedSimilarityScore'] = recommended_dependency[7]
d['categories'] = recommended_dependency[8]
d['displayDate'] = recommended_dependency[9]
d['monthlyDownloadsLastMonth'] = recommended_dependency[10]
output_recommended_dependencies.append(d)
data = {'language': language, 'recommended_dependencies': output_recommended_dependencies}
return HttpResponse(json.dumps(data), content_type='application/json')
return HttpResponseNotAllowed(['POST']) | 6,866,179,884,337,457,000 | Returns package recommendations for API POST call without authentication
arguments:
:request: POST request of application/json type
returns:
list of package recommendations | webserver/pkgpkr/webservice/views.py | recommendations_service_api | pkgpkr/Package-Picker | python | @csrf_exempt
def recommendations_service_api(request):
'\n Returns package recommendations for API POST call without authentication\n\n arguments:\n :request: POST request of application/json type\n\n returns:\n list of package recommendations\n '
if (request.method == 'POST'):
try:
json_data = json.loads(request.body)
except json.JSONDecodeError:
return HttpResponseBadRequest('Could not parse JSON.')
try:
dependencies = json_data['dependencies']
language = json_data['language'].lower()
except KeyError:
return HttpResponseBadRequest('Required JSON keys: `dependencies`, `language`')
except AttributeError as e:
return HttpResponseBadRequest(f'Error casting language to lower(): {e}')
if ((not isinstance(dependencies, list)) or (not dependencies)):
return HttpResponseBadRequest(f'{language.capitalize()} dependencies must be non-empty and of type LIST (i.e. [...]).')
if (language == PYTHON):
dependencies = '\n'.join(dependencies)
elif (language == JAVASCRIPT):
formatted_dependencies_list = [(('"' + dep.replace('@', '":"')) + '"') for dep in dependencies]
dependencies = ','.join(formatted_dependencies_list)
else:
return HttpResponseBadRequest(f'Language not supported: [{language}].')
dependencies = github_util.parse_dependencies(dependencies, language)
if ('max_recommendations' in json_data):
recommended_dependencies = RECOMMENDER_SERVICE.get_recommendations(dependencies, json_data['max_recommendations'])
else:
recommended_dependencies = RECOMMENDER_SERVICE.get_recommendations(dependencies)
output_recommended_dependencies = []
for recommended_dependency in recommended_dependencies:
d = dict()
d['forPackage'] = recommended_dependency[0]
d['recommendedPackage'] = recommended_dependency[1]
d['url'] = recommended_dependency[2]
d['pkgpkrScore'] = recommended_dependency[3]
d['absoluteTrendScore'] = recommended_dependency[4]
d['relativeTrendScore'] = recommended_dependency[5]
d['boundedPopularityScore'] = recommended_dependency[6]
d['boundedSimilarityScore'] = recommended_dependency[7]
d['categories'] = recommended_dependency[8]
d['displayDate'] = recommended_dependency[9]
d['monthlyDownloadsLastMonth'] = recommended_dependency[10]
output_recommended_dependencies.append(d)
data = {'language': language, 'recommended_dependencies': output_recommended_dependencies}
return HttpResponse(json.dumps(data), content_type='application/json')
return HttpResponseNotAllowed(['POST']) |
def _get_modifiable_indices(self, current_text):
' \n Returns the word indices in ``current_text`` which are able to be modified.\n '
non_stopword_indices = set()
for (i, word) in enumerate(current_text.words):
if (word not in self.stopwords):
non_stopword_indices.add(i)
return non_stopword_indices | -7,270,054,439,923,210,000 | Returns the word indices in ``current_text`` which are able to be modified. | textattack/constraints/pre_transformation/stopword_modification.py | _get_modifiable_indices | fighting41love/TextAttack | python | def _get_modifiable_indices(self, current_text):
' \n \n '
non_stopword_indices = set()
for (i, word) in enumerate(current_text.words):
if (word not in self.stopwords):
non_stopword_indices.add(i)
return non_stopword_indices |
def check_compatibility(self, transformation):
' \n The stopword constraint only is concerned with word swaps since paraphrasing phrases\n containing stopwords is OK.\n\n Args:\n transformation: The ``Transformation`` to check compatibility with.\n '
return transformation_consists_of_word_swaps(transformation) | -5,874,091,402,889,851,000 | The stopword constraint only is concerned with word swaps since paraphrasing phrases
containing stopwords is OK.
Args:
transformation: The ``Transformation`` to check compatibility with. | textattack/constraints/pre_transformation/stopword_modification.py | check_compatibility | fighting41love/TextAttack | python | def check_compatibility(self, transformation):
' \n The stopword constraint only is concerned with word swaps since paraphrasing phrases\n containing stopwords is OK.\n\n Args:\n transformation: The ``Transformation`` to check compatibility with.\n '
return transformation_consists_of_word_swaps(transformation) |
def link_iterable_by_fields(unlinked, other=None, fields=None, kind=None, internal=False, relink=False):
'Generic function to link objects in ``unlinked`` to objects in ``other`` using fields ``fields``.\n\n The database to be linked must have uniqueness for each object for the given ``fields``.\n\n If ``kind``, limit objects in ``unlinked`` of type ``kind``.\n\n If ``relink``, link to objects which already have an ``input``. Otherwise, skip already linked objects.\n\n If ``internal``, linked ``unlinked`` to other objects in ``unlinked``. Each object must have the attributes ``database`` and ``code``.'
if kind:
kind = ({kind} if isinstance(kind, str) else kind)
if relink:
filter_func = (lambda x: (x.get('type') in kind))
else:
filter_func = (lambda x: ((x.get('type') in kind) and (not x.get('input'))))
elif relink:
filter_func = (lambda x: True)
else:
filter_func = (lambda x: (not x.get('input')))
if internal:
other = unlinked
(duplicates, candidates) = ({}, {})
try:
for ds in other:
key = activity_hash(ds, fields)
if (key in candidates):
duplicates.setdefault(key, []).append(ds)
else:
candidates[key] = (ds['database'], ds['code'])
except KeyError:
raise StrategyError('Not all datasets in database to be linked have ``database`` or ``code`` attributes')
for container in unlinked:
for obj in filter(filter_func, container.get('exchanges', [])):
key = activity_hash(obj, fields)
if (key in duplicates):
raise StrategyError(format_nonunique_key_error(obj, fields, duplicates[key]))
elif (key in candidates):
obj['input'] = candidates[key]
return unlinked | 2,774,126,575,220,764,000 | Generic function to link objects in ``unlinked`` to objects in ``other`` using fields ``fields``.
The database to be linked must have uniqueness for each object for the given ``fields``.
If ``kind``, limit objects in ``unlinked`` of type ``kind``.
If ``relink``, link to objects which already have an ``input``. Otherwise, skip already linked objects.
If ``internal``, linked ``unlinked`` to other objects in ``unlinked``. Each object must have the attributes ``database`` and ``code``. | bw2io/strategies/generic.py | link_iterable_by_fields | pjamesjoyce/brightway2-io | python | def link_iterable_by_fields(unlinked, other=None, fields=None, kind=None, internal=False, relink=False):
'Generic function to link objects in ``unlinked`` to objects in ``other`` using fields ``fields``.\n\n The database to be linked must have uniqueness for each object for the given ``fields``.\n\n If ``kind``, limit objects in ``unlinked`` of type ``kind``.\n\n If ``relink``, link to objects which already have an ``input``. Otherwise, skip already linked objects.\n\n If ``internal``, linked ``unlinked`` to other objects in ``unlinked``. Each object must have the attributes ``database`` and ``code``.'
if kind:
kind = ({kind} if isinstance(kind, str) else kind)
if relink:
filter_func = (lambda x: (x.get('type') in kind))
else:
filter_func = (lambda x: ((x.get('type') in kind) and (not x.get('input'))))
elif relink:
filter_func = (lambda x: True)
else:
filter_func = (lambda x: (not x.get('input')))
if internal:
other = unlinked
(duplicates, candidates) = ({}, {})
try:
for ds in other:
key = activity_hash(ds, fields)
if (key in candidates):
duplicates.setdefault(key, []).append(ds)
else:
candidates[key] = (ds['database'], ds['code'])
except KeyError:
raise StrategyError('Not all datasets in database to be linked have ``database`` or ``code`` attributes')
for container in unlinked:
for obj in filter(filter_func, container.get('exchanges', [])):
key = activity_hash(obj, fields)
if (key in duplicates):
raise StrategyError(format_nonunique_key_error(obj, fields, duplicates[key]))
elif (key in candidates):
obj['input'] = candidates[key]
return unlinked |
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