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724,135 | pdfrw.tokens | __iter__ | null | def __iter__(self):
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724,136 | pdfrw.tokens | _gettoks | Given a source data string and a location inside it,
gettoks generates tokens. Each token is a tuple of the form:
<starting file loc>, <ending file loc>, <token string>
The ending file loc is past any trailing whitespace.
The main complication here is the literal strings, which
can contain nested parentheses. In order to cope with these
we can discard the current iterator and loop back to the
top to get a fresh one.
We could use re.search instead of re.finditer, but that's slower.
| def _gettoks(self, startloc, intern=intern,
delimiters=delimiters, findtok=findtok,
findparen=findparen, PdfString=PdfString,
PdfObject=PdfObject, BasePdfName=BasePdfName):
''' Given a source data string and a location inside it,
gettoks generates tokens. Each token is a tuple of the form:
<starting file loc>, <ending file loc>, <token string>
The ending file loc is past any trailing whitespace.
The main complication here is the literal strings, which
can contain nested parentheses. In order to cope with these
we can discard the current iterator and loop back to the
top to get a fresh one.
We could use re.search instead of re.finditer, but that's slower.
'''
fdata = self.fdata
current = self.current = [(startloc, startloc)]
cache = {}
get_cache = cache.get
while 1:
for match in findtok(fdata, current[0][1]):
current[0] = tokspan = match.span()
token = match.group(1)
firstch = token[0]
toktype = intern
if firstch not in delimiters:
toktype = PdfObject
elif firstch in '/<(%':
if firstch == '/':
# PDF Name
toktype = BasePdfName
elif firstch == '<':
# << dict delim, or < hex string >
if token[1:2] != '<':
toktype = PdfString
elif firstch == '(':
# Literal string
# It's probably simple, but maybe not
# Nested parentheses are a bear, and if
# they are present, we exit the for loop
# and get back in with a new starting location.
ends = None # For broken strings
if fdata[match.end(1) - 1] != ')':
nest = 2
m_start, loc = tokspan
for match in findparen(fdata, loc):
loc = match.end(1)
ending = fdata[loc - 1] == ')'
nest += 1 - ending * 2
if not nest:
break
if ending and ends is None:
ends = loc, match.end(), nest
token = fdata[m_start:loc]
current[0] = m_start, match.end()
if nest:
# There is one possible recoverable error
# seen in the wild -- some stupid generators
# don't escape (. If this happens, just
# terminate on first unescaped ). The string
# won't be quite right, but that's a science
# fair project for another time.
(self.error, self.exception)[not ends](
'Unterminated literal string')
loc, ends, nest = ends
token = fdata[m_start:loc] + ')' * nest
current[0] = m_start, ends
toktype = PdfString
elif firstch == '%':
# Comment
if self.strip_comments:
continue
else:
self.exception(('Tokenizer logic incorrect -- '
'should never get here'))
newtok = get_cache(token)
if newtok is None:
newtok = cache[token] = toktype(token)
yield newtok
if current[0] is not tokspan:
break
else:
if self.strip_comments:
break
raise StopIteration
| (self, startloc, intern=<built-in function intern>, delimiters='()<>{}[\\]/%', findtok=<built-in method finditer of re.Pattern object at 0x55b77025b280>, findparen=<built-in method finditer of re.Pattern object at 0x7ff9a4a59fc0>, PdfString=<class 'pdfrw.objects.pdfstring.PdfString'>, PdfObject=<class 'pdfrw.objects.pdfobject.PdfObject'>, BasePdfName=<class 'pdfrw.objects.pdfname.BasePdfName'>) | [
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724,137 | pdfrw.tokens | error | null | def error(self, *arg):
s = self.msg(*arg)
if s:
log.error(s)
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] |
724,138 | pdfrw.tokens | exception | null | def exception(self, *arg):
raise PdfParseError(self.msg(*arg))
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724,139 | pdfrw.tokens | msg | null | def msg(self, msg, *arg):
dumped = self.msgs_dumped
if dumped is not None:
if msg in dumped:
return
dumped.add(msg)
if arg:
msg %= arg
fdata = self.fdata
begin, end = self.current[0]
if begin >= len(fdata):
return '%s (filepos %s past EOF %s)' % (msg, begin, len(fdata))
line, col = linepos(fdata, begin)
if end > begin:
tok = fdata[begin:end].rstrip()
if len(tok) > 30:
tok = tok[:26] + ' ...'
return ('%s (line=%d, col=%d, token=%s)' %
(msg, line, col, repr(tok)))
return '%s (line=%d, col=%d)' % (msg, line, col)
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] |
724,140 | pdfrw.tokens | multiple | Retrieve multiple tokens
| def multiple(self, count, islice=itertools.islice, list=list):
''' Retrieve multiple tokens
'''
return list(islice(self, count))
| (self, count, islice=<class 'itertools.islice'>, list=<class 'list'>) | [
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724,141 | pdfrw.tokens | next_default | null | def next_default(self, default='nope'):
for result in self:
return result
return default
| (self, default='nope') | [
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724,142 | pdfrw.tokens | setstart | Change the starting location.
| def setstart(self, startloc):
''' Change the starting location.
'''
current = self.current
if startloc != current[0][1]:
current[0] = startloc, startloc
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724,143 | pdfrw.tokens | warning | null | def warning(self, *arg):
s = self.msg(*arg)
if s:
log.warning(s)
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724,164 | asyncmock | AsyncCallableMixin | null | class AsyncCallableMixin(CallableMixin):
def __init__(_mock_self, not_async=False, *args, **kwargs):
super().__init__(*args, **kwargs)
_mock_self.not_async = not_async
_mock_self.aenter_return_value = _mock_self
def __call__(_mock_self, *args, **kwargs):
# can't use self in-case a function / method we are mocking uses self
# in the signature
if _mock_self.not_async:
_mock_self._mock_check_sig(*args, **kwargs)
return _mock_self._mock_call(*args, **kwargs)
else:
async def wrapper():
_mock_self._mock_check_sig(*args, **kwargs)
return _mock_self._mock_call(*args, **kwargs)
return wrapper()
async def __aenter__(_mock_self):
return _mock_self.aenter_return_value
async def __aexit__(_mock_self, exc_type, exc_val, exc_tb):
pass
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724,165 | asyncmock | __aenter__ | null | def __call__(_mock_self, *args, **kwargs):
# can't use self in-case a function / method we are mocking uses self
# in the signature
if _mock_self.not_async:
_mock_self._mock_check_sig(*args, **kwargs)
return _mock_self._mock_call(*args, **kwargs)
else:
async def wrapper():
_mock_self._mock_check_sig(*args, **kwargs)
return _mock_self._mock_call(*args, **kwargs)
return wrapper()
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724,168 | asyncmock | __init__ | null | def __init__(_mock_self, not_async=False, *args, **kwargs):
super().__init__(*args, **kwargs)
_mock_self.not_async = not_async
_mock_self.aenter_return_value = _mock_self
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724,173 | asyncmock | AsyncMock |
Create a new `AsyncMock` object. `AsyncMock` several options that extends
the behaviour of the basic `Mock` object:
* `not_async`: This is a boolean flag used to indicate that when the mock
is called it should not return a normal Mock instance to make the mock
non-awaitable. If this flag is set the mock reverts to the default
behaviour of a `Mock` instance.
All other arguments are passed directly through to the underlying `Mock`
object.
| class AsyncMock(AsyncCallableMixin, NonCallableMock):
"""
Create a new `AsyncMock` object. `AsyncMock` several options that extends
the behaviour of the basic `Mock` object:
* `not_async`: This is a boolean flag used to indicate that when the mock
is called it should not return a normal Mock instance to make the mock
non-awaitable. If this flag is set the mock reverts to the default
behaviour of a `Mock` instance.
All other arguments are passed directly through to the underlying `Mock`
object.
"""
| (spec=None, wraps=None, name=None, spec_set=None, parent=None, _spec_state=None, _new_name='', _new_parent=None, _spec_as_instance=False, _eat_self=None, unsafe=False, **kwargs) | [
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] |
724,211 | mock.mock | CallableMixin | null | class CallableMixin(Base):
def __init__(self, spec=None, side_effect=None, return_value=DEFAULT,
wraps=None, name=None, spec_set=None, parent=None,
_spec_state=None, _new_name='', _new_parent=None, **kwargs):
self.__dict__['_mock_return_value'] = return_value
_safe_super(CallableMixin, self).__init__(
spec, wraps, name, spec_set, parent,
_spec_state, _new_name, _new_parent, **kwargs
)
self.side_effect = side_effect
def _mock_check_sig(self, *args, **kwargs):
# stub method that can be replaced with one with a specific signature
pass
def __call__(_mock_self, *args, **kwargs):
# can't use self in-case a function / method we are mocking uses self
# in the signature
_mock_self._mock_check_sig(*args, **kwargs)
_mock_self._increment_mock_call(*args, **kwargs)
return _mock_self._mock_call(*args, **kwargs)
def _mock_call(_mock_self, *args, **kwargs):
return _mock_self._execute_mock_call(*args, **kwargs)
def _increment_mock_call(_mock_self, *args, **kwargs):
self = _mock_self
self.called = True
self.call_count += 1
# handle call_args
# needs to be set here so assertions on call arguments pass before
# execution in the case of awaited calls
_call = _Call((args, kwargs), two=True)
self.call_args = _call
self.call_args_list.append(_call)
# initial stuff for method_calls:
do_method_calls = self._mock_parent is not None
method_call_name = self._mock_name
# initial stuff for mock_calls:
mock_call_name = self._mock_new_name
is_a_call = mock_call_name == '()'
self.mock_calls.append(_Call(('', args, kwargs)))
# follow up the chain of mocks:
_new_parent = self._mock_new_parent
while _new_parent is not None:
# handle method_calls:
if do_method_calls:
_new_parent.method_calls.append(_Call((method_call_name, args, kwargs)))
do_method_calls = _new_parent._mock_parent is not None
if do_method_calls:
method_call_name = _new_parent._mock_name + '.' + method_call_name
# handle mock_calls:
this_mock_call = _Call((mock_call_name, args, kwargs))
_new_parent.mock_calls.append(this_mock_call)
if _new_parent._mock_new_name:
if is_a_call:
dot = ''
else:
dot = '.'
is_a_call = _new_parent._mock_new_name == '()'
mock_call_name = _new_parent._mock_new_name + dot + mock_call_name
# follow the parental chain:
_new_parent = _new_parent._mock_new_parent
def _execute_mock_call(_mock_self, *args, **kwargs):
self = _mock_self
# separate from _increment_mock_call so that awaited functions are
# executed separately from their call, also AsyncMock overrides this method
effect = self.side_effect
if effect is not None:
if _is_exception(effect):
raise effect
elif not _callable(effect):
result = next(effect)
if _is_exception(result):
raise result
else:
result = effect(*args, **kwargs)
if result is not DEFAULT:
return result
if self._mock_return_value is not DEFAULT:
return self.return_value
if self._mock_wraps is not None:
return self._mock_wraps(*args, **kwargs)
return self.return_value
| (spec=None, side_effect=None, return_value=sentinel.DEFAULT, wraps=None, name=None, spec_set=None, parent=None, _spec_state=None, _new_name='', _new_parent=None, **kwargs) | [
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] |
724,437 | flask_gzip | Gzip | null | class Gzip(object):
def __init__(self, app, compress_level=6, minimum_size=500):
self.app = app
self.compress_level = compress_level
self.minimum_size = minimum_size
self.app.after_request(self.after_request)
def after_request(self, response):
accept_encoding = request.headers.get('Accept-Encoding', '')
if response.status_code < 200 or \
response.status_code >= 300 or \
response.direct_passthrough or \
len(response.get_data()) < self.minimum_size or \
'gzip' not in accept_encoding.lower() or \
'Content-Encoding' in response.headers:
return response
gzip_buffer = BytesIO()
gzip_file = gzip.GzipFile(mode='wb', compresslevel=self.compress_level, fileobj=gzip_buffer)
gzip_file.write(response.get_data())
gzip_file.close()
response.set_data(gzip_buffer.getvalue())
response.headers['Content-Encoding'] = 'gzip'
response.headers['Content-Length'] = len(response.get_data())
return response
| (app, compress_level=6, minimum_size=500) | [
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724,438 | flask_gzip | __init__ | null | def __init__(self, app, compress_level=6, minimum_size=500):
self.app = app
self.compress_level = compress_level
self.minimum_size = minimum_size
self.app.after_request(self.after_request)
| (self, app, compress_level=6, minimum_size=500) | [
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] |
724,439 | flask_gzip | after_request | null | def after_request(self, response):
accept_encoding = request.headers.get('Accept-Encoding', '')
if response.status_code < 200 or \
response.status_code >= 300 or \
response.direct_passthrough or \
len(response.get_data()) < self.minimum_size or \
'gzip' not in accept_encoding.lower() or \
'Content-Encoding' in response.headers:
return response
gzip_buffer = BytesIO()
gzip_file = gzip.GzipFile(mode='wb', compresslevel=self.compress_level, fileobj=gzip_buffer)
gzip_file.write(response.get_data())
gzip_file.close()
response.set_data(gzip_buffer.getvalue())
response.headers['Content-Encoding'] = 'gzip'
response.headers['Content-Length'] = len(response.get_data())
return response
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] |
724,441 | hypertion.main | HyperFunction |
Handles the creation of a schema for LLM function calling, as well as the validation and invocation of functions based on the provided signature or metadata.
| class HyperFunction:
"""
Handles the creation of a schema for LLM function calling, as well as the validation and invocation of functions based on the provided signature or metadata.
"""
def __init__(self) -> None:
self._registered_functions: dict[str, info.FunctionInfo] = {}
"""Registered functions."""
def takeover(self, description: str | None = None):
"""Register the function by decorating it to generate function schema."""
def __wrapper__(func: Callable[..., Any]):
_description = description or func.__doc__
if _description is None:
raise RuntimeError(f"No description found for {func.__name__!r}")
_description = '\n'.join(
line.strip() for line in _description.split('\n')
)
self._registered_functions[func.__name__] = info.FunctionInfo(
memloc=func, description=description
)
return func
return __wrapper__
@staticmethod
def criteria(
default: Any | None = None, *, description: str
):
"""Adding criteria to parameters."""
return info.CriteriaInfo(description=description, default=default)
def _construct_mappings(self):
"""Construct schema mappings of registered functions."""
for f_name, f_info in self._registered_functions.items():
signature = inspect.signature(f_info.memloc)
properties, required = {}, []
for name, instance in signature.parameters.items():
criteria = instance.default
if not isinstance(criteria, info.CriteriaInfo):
raise TypeError(
f"Parameters of registered functions must be initialized with `criteria` method."
)
default, annotation = criteria.default, instance.annotation
if default and not isinstance(default, annotation):
raise TypeError(
f"{name!r} parameter is type-hinted as {annotation.__name__!r} but default value is of type {type(default).__name__!r}"
)
elif not default:
required.append(name)
properties[name] = helpers.construct_property_map(
annotation=annotation, description=criteria.description
)
parameter_map = {}
if properties:
parameter_map = {"type": "object", "properties": properties}
if required:
parameter_map['required'] = required
yield {'name': f_name, 'description': f_info.description} | {'parameters': parameter_map}
def attach_hyperfunction(self, __obj: "HyperFunction"):
"""Attach new `HyperFunction` instance in the current instance"""
self._registered_functions.update(__obj._registered_functions)
@property
def as_openai_functions(self) -> list[dict[str, Any]]:
"""Return GPT based function schema."""
return list(self._construct_mappings())
@property
def as_open_functions(self) -> list[dict[str, Any]]:
"""Return Gorilla based function schema."""
return [{'api_call': _['name']} | _ for _ in self._construct_mappings()]
def invoke(self, __signature_or_metadata: types.Signature | types.Metadata):
"""Validate and invoke the function from signature or metadata."""
function = __signature_or_metadata
if isinstance(function, types.Signature):
function = function.as_metadata()
function_info = self._registered_functions.get(function.name)
if function_info is None:
raise LookupError(f"{function.name!r} not found in registered functions.")
signature, forged_kwargs = inspect.signature(function_info.memloc), {}
for param_name, param in signature.parameters.items():
criteria = param.default
if param_name not in function.arguments and criteria.default is None:
raise KeyError(
f"{function.name!r} function required parameter {param_name!r} missing."
)
forged_kwargs[param_name] = helpers.forge_parameter(
annotation=param.annotation, value=function.arguments.get(param_name, criteria.default)
)
return function_info.memloc(**forged_kwargs)
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724,442 | hypertion.main | __init__ | null | def __init__(self) -> None:
self._registered_functions: dict[str, info.FunctionInfo] = {}
"""Registered functions."""
| (self) -> NoneType | [
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] |
724,443 | hypertion.main | _construct_mappings | Construct schema mappings of registered functions. | def _construct_mappings(self):
"""Construct schema mappings of registered functions."""
for f_name, f_info in self._registered_functions.items():
signature = inspect.signature(f_info.memloc)
properties, required = {}, []
for name, instance in signature.parameters.items():
criteria = instance.default
if not isinstance(criteria, info.CriteriaInfo):
raise TypeError(
f"Parameters of registered functions must be initialized with `criteria` method."
)
default, annotation = criteria.default, instance.annotation
if default and not isinstance(default, annotation):
raise TypeError(
f"{name!r} parameter is type-hinted as {annotation.__name__!r} but default value is of type {type(default).__name__!r}"
)
elif not default:
required.append(name)
properties[name] = helpers.construct_property_map(
annotation=annotation, description=criteria.description
)
parameter_map = {}
if properties:
parameter_map = {"type": "object", "properties": properties}
if required:
parameter_map['required'] = required
yield {'name': f_name, 'description': f_info.description} | {'parameters': parameter_map}
| (self) | [
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724,444 | hypertion.main | attach_hyperfunction | Attach new `HyperFunction` instance in the current instance | def attach_hyperfunction(self, __obj: "HyperFunction"):
"""Attach new `HyperFunction` instance in the current instance"""
self._registered_functions.update(__obj._registered_functions)
| (self, _HyperFunction__obj: hypertion.main.HyperFunction) | [
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724,445 | hypertion.main | criteria | Adding criteria to parameters. | @staticmethod
def criteria(
default: Any | None = None, *, description: str
):
"""Adding criteria to parameters."""
return info.CriteriaInfo(description=description, default=default)
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724,446 | hypertion.main | invoke | Validate and invoke the function from signature or metadata. | def invoke(self, __signature_or_metadata: types.Signature | types.Metadata):
"""Validate and invoke the function from signature or metadata."""
function = __signature_or_metadata
if isinstance(function, types.Signature):
function = function.as_metadata()
function_info = self._registered_functions.get(function.name)
if function_info is None:
raise LookupError(f"{function.name!r} not found in registered functions.")
signature, forged_kwargs = inspect.signature(function_info.memloc), {}
for param_name, param in signature.parameters.items():
criteria = param.default
if param_name not in function.arguments and criteria.default is None:
raise KeyError(
f"{function.name!r} function required parameter {param_name!r} missing."
)
forged_kwargs[param_name] = helpers.forge_parameter(
annotation=param.annotation, value=function.arguments.get(param_name, criteria.default)
)
return function_info.memloc(**forged_kwargs)
| (self, _HyperFunction__signature_or_metadata: hypertion.types.Signature | hypertion.types.Metadata) | [
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724,447 | hypertion.main | takeover | Register the function by decorating it to generate function schema. | def takeover(self, description: str | None = None):
"""Register the function by decorating it to generate function schema."""
def __wrapper__(func: Callable[..., Any]):
_description = description or func.__doc__
if _description is None:
raise RuntimeError(f"No description found for {func.__name__!r}")
_description = '\n'.join(
line.strip() for line in _description.split('\n')
)
self._registered_functions[func.__name__] = info.FunctionInfo(
memloc=func, description=description
)
return func
return __wrapper__
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724,455 | nylas.client | Client |
API client for the Nylas API.
Attributes:
api_key: The Nylas API key to use for authentication
api_uri: The URL to use for communicating with the Nylas API
http_client: The HTTP client to use for requests to the Nylas API
| class Client:
"""
API client for the Nylas API.
Attributes:
api_key: The Nylas API key to use for authentication
api_uri: The URL to use for communicating with the Nylas API
http_client: The HTTP client to use for requests to the Nylas API
"""
def __init__(
self, api_key: str, api_uri: str = DEFAULT_SERVER_URL, timeout: int = 90
):
"""
Initialize the Nylas API client.
Args:
api_key: The Nylas API key to use for authentication
api_uri: The URL to use for communicating with the Nylas API
timeout: The timeout for requests to the Nylas API, in seconds
"""
self.api_key = api_key
self.api_uri = api_uri
self.http_client = HttpClient(self.api_uri, self.api_key, timeout)
@property
def auth(self) -> Auth:
"""
Access the Auth API.
Returns:
The Auth API.
"""
return Auth(self.http_client)
@property
def applications(self) -> Applications:
"""
Access the Applications API.
Returns:
The Applications API.
"""
return Applications(self.http_client)
@property
def attachments(self) -> Attachments:
"""
Access the Attachments API.
Returns:
The Attachments API.
"""
return Attachments(self.http_client)
@property
def connectors(self) -> Connectors:
"""
Access the Connectors API.
Returns:
The Connectors API.
"""
return Connectors(self.http_client)
@property
def calendars(self) -> Calendars:
"""
Access the Calendars API.
Returns:
The Calendars API.
"""
return Calendars(self.http_client)
@property
def contacts(self) -> Contacts:
"""
Access the Contacts API.
Returns:
The Contacts API.
"""
return Contacts(self.http_client)
@property
def drafts(self) -> Drafts:
"""
Access the Drafts API.
Returns:
The Drafts API.
"""
return Drafts(self.http_client)
@property
def events(self) -> Events:
"""
Access the Events API.
Returns:
The Events API.
"""
return Events(self.http_client)
@property
def folders(self) -> Folders:
"""
Access the Folders API.
Returns:
The Folders API.
"""
return Folders(self.http_client)
@property
def grants(self) -> Grants:
"""
Access the Grants API.
Returns:
The Grants API.
"""
return Grants(self.http_client)
@property
def messages(self) -> Messages:
"""
Access the Messages API.
Returns:
The Messages API.
"""
return Messages(self.http_client)
@property
def threads(self) -> Threads:
"""
Access the Threads API.
Returns:
The Threads API.
"""
return Threads(self.http_client)
@property
def webhooks(self) -> Webhooks:
"""
Access the Webhooks API.
Returns:
The Webhooks API.
"""
return Webhooks(self.http_client)
| (api_key: str, api_uri: str = 'https://api.us.nylas.com', timeout: int = 90) | [
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724,456 | nylas.client | __init__ |
Initialize the Nylas API client.
Args:
api_key: The Nylas API key to use for authentication
api_uri: The URL to use for communicating with the Nylas API
timeout: The timeout for requests to the Nylas API, in seconds
| def __init__(
self, api_key: str, api_uri: str = DEFAULT_SERVER_URL, timeout: int = 90
):
"""
Initialize the Nylas API client.
Args:
api_key: The Nylas API key to use for authentication
api_uri: The URL to use for communicating with the Nylas API
timeout: The timeout for requests to the Nylas API, in seconds
"""
self.api_key = api_key
self.api_uri = api_uri
self.http_client = HttpClient(self.api_uri, self.api_key, timeout)
| (self, api_key: str, api_uri: str = 'https://api.us.nylas.com', timeout: int = 90) | [
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] |
724,464 | json_logic | jsonLogic | null | def jsonLogic(tests, data=None):
# You've recursed to a primitive, stop!
if tests is None or type(tests) != dict:
return tests
data = data or {}
op = tests.keys()[0]
values = tests[op]
operations = {
"==" : (lambda a, b: a == b),
"===" : (lambda a, b: a is b),
"!=" : (lambda a, b: a != b),
"!==" : (lambda a, b: a is not b),
">" : (lambda a, b: a > b),
">=" : (lambda a, b: a >= b),
"<" : (lambda a, b, c=None:
a < b if (c is None) else (a < b) and (b < c)
),
"<=" : (lambda a, b, c=None:
a <= b if (c is None) else (a <= b) and (b <= c)
),
"!" : (lambda a: not a),
"%" : (lambda a, b: a % b),
"and" : (lambda *args:
reduce(lambda total, arg: total and arg, args, True)
),
"or" : (lambda *args:
reduce(lambda total, arg: total or arg, args, False)
),
"?:" : (lambda a, b, c: b if a else c),
"log" : (lambda a: a if sys.stdout.write(str(a)) else a),
"in" : (lambda a, b:
a in b if "__contains__" in dir(b) else False
),
"var" : (lambda a, not_found=None:
reduce(lambda data, key: (data.get(key, not_found)
if type(data) == dict
else data[int(key)]
if (type(data) in [list, tuple] and
str(key).lstrip("-").isdigit())
else not_found),
str(a).split("."),
data)
),
"cat" : (lambda *args:
"".join(args)
),
"+" : (lambda *args:
reduce(lambda total, arg: total + float(arg), args, 0.0)
),
"*" : (lambda *args:
reduce(lambda total, arg: total * float(arg), args, 1.0)
),
"-" : (lambda a, b=None: -a if b is None else a - b),
"/" : (lambda a, b=None: a if b is None else float(a) / float(b)),
"min" : (lambda *args: min(args)),
"max" : (lambda *args: max(args)),
"count": (lambda *args: sum(1 if a else 0 for a in args)),
}
if op not in operations:
raise RuntimeError("Unrecognized operation %s" % op)
# Easy syntax for unary operators, like {"var": "x"} instead of strict
# {"var": ["x"]}
if type(values) not in [list, tuple]:
values = [values]
# Recursion!
values = map(lambda val: jsonLogic(val, data), values)
return operations[op](*values)
| (tests, data=None) | [
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0.03598993644118309,
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0.01873095892369747,
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] |
724,466 | tlo.tl_config | TlConfig | null | class TlConfig(TlBase):
def __init__(
self,
):
self.types: List['TlType'] = []
self.id_to_type: Dict[int, 'TlType'] = {} # orig int32_t
self.name_to_type: Dict[str, 'TlType'] = {}
self.functions: List['TlCombinator'] = []
self.id_to_function: Dict[int, 'TlCombinator'] = {} # orig int32_t
self.name_to_function: Dict[str, 'TlCombinator'] = {}
def add_type(self, type_: 'TlType') -> None:
self.types.append(type_)
self.id_to_type[type_.id] = type_
self.name_to_type[type_.name] = type_
def get_type(self, type_id_or_name: Union[int, str]) -> 'TlType': # orig int32_t
if isinstance(type_id_or_name, int):
return self.id_to_type[type_id_or_name]
else:
return self.name_to_type[type_id_or_name]
def add_function(self, function: 'TlCombinator') -> None:
self.functions.append(function)
self.id_to_function[function.id] = function
self.name_to_function[function.name] = function
def get_function(self, function_id_or_name: Union[int, str]) -> 'TlCombinator': # orig int32_t
if isinstance(function_id_or_name, int):
return self.id_to_function[function_id_or_name]
else:
return self.name_to_function[function_id_or_name]
def get_type_count(self) -> int: # orig size_t
return len(self.types)
def get_type_by_num(self, num: int) -> 'TlType': # orig size_t
return self.types[num]
def get_function_count(self) -> int: # orig size_t
return len(self.functions)
def get_function_by_num(self, num: int) -> 'TlCombinator': # orig size_t
return self.functions[num]
| () | [
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] |
724,467 | tlo.tl_config | __init__ | null | def __init__(
self,
):
self.types: List['TlType'] = []
self.id_to_type: Dict[int, 'TlType'] = {} # orig int32_t
self.name_to_type: Dict[str, 'TlType'] = {}
self.functions: List['TlCombinator'] = []
self.id_to_function: Dict[int, 'TlCombinator'] = {} # orig int32_t
self.name_to_function: Dict[str, 'TlCombinator'] = {}
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724,468 | tlo.tl_core | __repr__ | null | def __repr__(self):
return f'<{__name__}.{type(self).__name__}> {vars(self)}'
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724,470 | tlo.tl_config | add_function | null | def add_function(self, function: 'TlCombinator') -> None:
self.functions.append(function)
self.id_to_function[function.id] = function
self.name_to_function[function.name] = function
| (self, function: tlo.tl_core.TlCombinator) -> NoneType | [
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724,471 | tlo.tl_config | add_type | null | def add_type(self, type_: 'TlType') -> None:
self.types.append(type_)
self.id_to_type[type_.id] = type_
self.name_to_type[type_.name] = type_
| (self, type_: tlo.tl_core.TlType) -> NoneType | [
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724,472 | tlo.tl_config | get_function | null | def get_function(self, function_id_or_name: Union[int, str]) -> 'TlCombinator': # orig int32_t
if isinstance(function_id_or_name, int):
return self.id_to_function[function_id_or_name]
else:
return self.name_to_function[function_id_or_name]
| (self, function_id_or_name: Union[int, str]) -> tlo.tl_core.TlCombinator | [
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] |
724,473 | tlo.tl_config | get_function_by_num | null | def get_function_by_num(self, num: int) -> 'TlCombinator': # orig size_t
return self.functions[num]
| (self, num: int) -> tlo.tl_core.TlCombinator | [
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] |
724,474 | tlo.tl_config | get_function_count | null | def get_function_count(self) -> int: # orig size_t
return len(self.functions)
| (self) -> int | [
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724,475 | tlo.tl_config | get_type | null | def get_type(self, type_id_or_name: Union[int, str]) -> 'TlType': # orig int32_t
if isinstance(type_id_or_name, int):
return self.id_to_type[type_id_or_name]
else:
return self.name_to_type[type_id_or_name]
| (self, type_id_or_name: Union[int, str]) -> tlo.tl_core.TlType | [
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] |
724,476 | tlo.tl_config | get_type_by_num | null | def get_type_by_num(self, num: int) -> 'TlType': # orig size_t
return self.types[num]
| (self, num: int) -> tlo.tl_core.TlType | [
0.052217207849025726,
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] |
724,477 | tlo.tl_config | get_type_count | null | def get_type_count(self) -> int: # orig size_t
return len(self.types)
| (self) -> int | [
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724,478 | tlo.tl_config_parser | TlConfigParser | null | class TlConfigParser(TlBase):
def __init__(self, data: bytes):
self.p = TlSimpleParser(data)
self.schema_version = -1
self.config = TlConfig() # should be TlConfig
def parse_config(self) -> 'TlConfig':
self.schema_version = self.get_schema_version(self.try_parse_int())
if self.schema_version < 2:
raise RuntimeError(f'Unsupported tl-schema version {self.schema_version}')
self.try_parse_int() # date
self.try_parse_int() # version
types_n = self.try_parse_int()
constructors_total = 0
for i in range(types_n):
tl_type = self.read_type()
self.config.add_type(tl_type)
constructors_total += tl_type.constructors_num
constructors_n = self.try_parse_int()
assert constructors_n == constructors_total
for i in range(constructors_n):
tl_combinator = self.read_combinator()
self.config.get_type(tl_combinator.type_id).add_constructor(tl_combinator)
functions_n = self.try_parse_int()
for i in range(functions_n):
self.config.add_function(self.read_combinator())
self.p.fetch_end()
self.try_parse(0)
return self.config
@staticmethod
def get_schema_version(version_id: int) -> int:
if version_id == TLS_SCHEMA_V4:
return 4
elif version_id == TLS_SCHEMA_V3:
return 3
elif version_id == TLS_SCHEMA_V2:
return 2
return -1
def read_type(self):
t = self.try_parse_int()
if t != TLS_TYPE:
raise RuntimeError(f'Wrong tls_type magic {t}')
tl_type = TlType()
tl_type.id = self.try_parse_int()
tl_type.name = self.try_parse_string()
tl_type.constructors_num = self.try_parse_int() # orig size_t
tl_type.constructors = []
tl_type.flags = self.try_parse_int()
tl_type.flags &= ~(1 | 8 | 16 | 1024)
if tl_type.flags != 0:
logger.warning(f'Type {tl_type.name} has non-zero flags: {tl_type.flags}')
tl_type.arity = self.try_parse_int()
self.try_parse_long() # unused
return tl_type
def read_combinator(self):
t = self.try_parse_int()
if t != TLS_COMBINATOR:
raise RuntimeError(f'Wrong tls_combinator magic {t}')
tl_combinator = TlCombinator()
tl_combinator.id = self.try_parse_int()
tl_combinator.name = self.try_parse_string()
tl_combinator.type_id = self.try_parse_int()
tl_combinator.var_count = 0
left_type = self.try_parse_int()
if left_type == TLS_COMBINATOR_LEFT:
tl_combinator.args = self.read_args_list(tl_combinator)
else:
if left_type != TLS_COMBINATOR_LEFT_BUILTIN:
raise RuntimeError(f'Wrong tls_combinator_left magic {left_type}')
right_ver = self.try_parse_int()
if right_ver != TLS_COMBINATOR_RIGHT_V2:
raise RuntimeError(f'Wrong tls_combinator_right magic {right_ver}')
tl_combinator.result = self.read_type_expr(tl_combinator)
return tl_combinator
def read_num_const(self) -> TlTree:
num = self.try_parse_int()
return TlTreeNatConst(FLAG_NOVAR, num)
def read_num_var(self, tl_combinator: TlCombinator) -> TlTree:
diff = self.try_parse_int()
var_num = self.try_parse_int()
if var_num >= tl_combinator.var_count:
tl_combinator.var_count = var_num + 1
return TlTreeVarNum(0, var_num, diff)
def read_nat_expr(self, tl_combinator: TlCombinator) -> TlTree:
tree_type = self.try_parse_int()
if tree_type in (TLS_NAT_CONST_OLD, TLS_NAT_CONST):
return self.read_num_const()
elif tree_type == TLS_NAT_VAR:
return self.read_num_var(tl_combinator)
else:
raise RuntimeError(f'tree_type = {tree_type}')
def read_expr(self, tl_combinator: TlCombinator) -> TlTree:
tree_type = self.try_parse_int()
if tree_type == TLS_EXPR_NAT:
return self.read_nat_expr(tl_combinator)
elif tree_type == TLS_EXPR_TYPE:
return self.read_type_expr(tl_combinator)
else:
raise RuntimeError(f'tree_type = {tree_type}')
def read_args_list(self, tl_combinator: TlCombinator) -> List[Arg]:
schema_flag_opt_field = 2 << int(self.schema_version >= 3)
schema_flag_has_vars = schema_flag_opt_field ^ 6
args_num = self.try_parse_int()
args_list = []
for i in range(args_num):
arg = Arg()
arg_v = self.try_parse_int()
if arg_v != TLS_ARG_V2:
raise RuntimeError(f'Wrong tls_arg magic {arg_v}')
arg.name = self.try_parse_string()
arg.flags = self.try_parse_int()
is_optional = False
if arg.flags & schema_flag_opt_field:
arg.flags &= ~schema_flag_opt_field
is_optional = True
if arg.flags & schema_flag_has_vars:
arg.flags &= ~schema_flag_has_vars
arg.var_num = self.try_parse_int()
else:
arg.var_num = -1
if arg.var_num >= tl_combinator.var_count:
tl_combinator.var_count = arg.var_num + 1
if is_optional:
arg.exist_var_num = self.try_parse_int()
arg.exist_var_bit = self.try_parse_int()
else:
arg.exist_var_num = -1
arg.exist_var_bit = 0
arg.type = self.read_type_expr(tl_combinator)
if arg.type.flags & FLAG_NOVAR:
arg.flags |= FLAG_NOVAR
args_list.append(arg)
return args_list
def read_type_expr(self, tl_combinator: TlCombinator) -> TlTree:
tree_type = self.try_parse_int()
if tree_type == TLS_TYPE_VAR:
return self.read_type_var(tl_combinator)
elif tree_type == TLS_TYPE_EXPR:
return self.read_type_tree(tl_combinator)
elif tree_type == TLS_ARRAY:
return self.read_array(tl_combinator)
else:
raise RuntimeError(f'tree_type = {tree_type}')
def read_type_var(self, tl_combinator: TlCombinator) -> TlTree:
var_num = self.try_parse_int()
flags = self.try_parse_int()
if var_num >= tl_combinator.var_count:
tl_combinator.var_count = var_num + 1
assert not (flags & (FLAG_NOVAR | FLAG_BARE))
return TlTreeVarType(flags, var_num)
def read_type_tree(self, tl_combinator: TlCombinator) -> TlTree:
tl_type = self.config.get_type(self.try_parse_int())
# there is assert not needed because we have KeyError exception
flags = self.try_parse_int() | FLAG_NOVAR
arity = self.try_parse_int()
assert tl_type.arity == arity
tl_tree_type = TlTreeType(flags, tl_type, arity)
for i in range(arity):
child = self.read_expr(tl_combinator)
tl_tree_type.children.append(child)
if not (child.flags & FLAG_NOVAR):
tl_tree_type.flags &= ~FLAG_NOVAR
return tl_tree_type
def read_array(self, tl_combinator: TlCombinator) -> TlTree:
flags = FLAG_NOVAR
multiplicity = self.read_nat_expr(tl_combinator)
tl_tree_array = TlTreeArray(flags, multiplicity, self.read_args_list(tl_combinator))
for i in range(len(tl_tree_array.args)):
if not (tl_tree_array.args[i].flags & FLAG_NOVAR):
tl_tree_array.flags &= ~FLAG_NOVAR
return tl_tree_array
def try_parse(self, res):
if self.p.get_error():
raise RuntimeError(f'Wrong TL-scheme specified: {self.p.get_error()} at {self.p.get_error_pos()}')
return res
def try_parse_int(self) -> int: # orig int32_t
return self.try_parse(self.p.fetch_int())
def try_parse_long(self) -> int: # orig int64_t
return self.try_parse(self.p.fetch_long())
def try_parse_string(self) -> str:
return self.try_parse(self.p.fetc | (data: bytes) | [
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724,479 | tlo.tl_config_parser | __init__ | null | def __init__(self, data: bytes):
self.p = TlSimpleParser(data)
self.schema_version = -1
self.config = TlConfig() # should be TlConfig
| (self, data: bytes) | [
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] |
724,482 | tlo.tl_config_parser | get_schema_version | null | @staticmethod
def get_schema_version(version_id: int) -> int:
if version_id == TLS_SCHEMA_V4:
return 4
elif version_id == TLS_SCHEMA_V3:
return 3
elif version_id == TLS_SCHEMA_V2:
return 2
return -1
| (version_id: int) -> int | [
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724,483 | tlo.tl_config_parser | parse_config | null | def parse_config(self) -> 'TlConfig':
self.schema_version = self.get_schema_version(self.try_parse_int())
if self.schema_version < 2:
raise RuntimeError(f'Unsupported tl-schema version {self.schema_version}')
self.try_parse_int() # date
self.try_parse_int() # version
types_n = self.try_parse_int()
constructors_total = 0
for i in range(types_n):
tl_type = self.read_type()
self.config.add_type(tl_type)
constructors_total += tl_type.constructors_num
constructors_n = self.try_parse_int()
assert constructors_n == constructors_total
for i in range(constructors_n):
tl_combinator = self.read_combinator()
self.config.get_type(tl_combinator.type_id).add_constructor(tl_combinator)
functions_n = self.try_parse_int()
for i in range(functions_n):
self.config.add_function(self.read_combinator())
self.p.fetch_end()
self.try_parse(0)
return self.config
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724,484 | tlo.tl_config_parser | read_args_list | null | def read_args_list(self, tl_combinator: TlCombinator) -> List[Arg]:
schema_flag_opt_field = 2 << int(self.schema_version >= 3)
schema_flag_has_vars = schema_flag_opt_field ^ 6
args_num = self.try_parse_int()
args_list = []
for i in range(args_num):
arg = Arg()
arg_v = self.try_parse_int()
if arg_v != TLS_ARG_V2:
raise RuntimeError(f'Wrong tls_arg magic {arg_v}')
arg.name = self.try_parse_string()
arg.flags = self.try_parse_int()
is_optional = False
if arg.flags & schema_flag_opt_field:
arg.flags &= ~schema_flag_opt_field
is_optional = True
if arg.flags & schema_flag_has_vars:
arg.flags &= ~schema_flag_has_vars
arg.var_num = self.try_parse_int()
else:
arg.var_num = -1
if arg.var_num >= tl_combinator.var_count:
tl_combinator.var_count = arg.var_num + 1
if is_optional:
arg.exist_var_num = self.try_parse_int()
arg.exist_var_bit = self.try_parse_int()
else:
arg.exist_var_num = -1
arg.exist_var_bit = 0
arg.type = self.read_type_expr(tl_combinator)
if arg.type.flags & FLAG_NOVAR:
arg.flags |= FLAG_NOVAR
args_list.append(arg)
return args_list
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> List[tlo.tl_core.Arg] | [
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724,485 | tlo.tl_config_parser | read_array | null | def read_array(self, tl_combinator: TlCombinator) -> TlTree:
flags = FLAG_NOVAR
multiplicity = self.read_nat_expr(tl_combinator)
tl_tree_array = TlTreeArray(flags, multiplicity, self.read_args_list(tl_combinator))
for i in range(len(tl_tree_array.args)):
if not (tl_tree_array.args[i].flags & FLAG_NOVAR):
tl_tree_array.flags &= ~FLAG_NOVAR
return tl_tree_array
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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724,486 | tlo.tl_config_parser | read_combinator | null | def read_combinator(self):
t = self.try_parse_int()
if t != TLS_COMBINATOR:
raise RuntimeError(f'Wrong tls_combinator magic {t}')
tl_combinator = TlCombinator()
tl_combinator.id = self.try_parse_int()
tl_combinator.name = self.try_parse_string()
tl_combinator.type_id = self.try_parse_int()
tl_combinator.var_count = 0
left_type = self.try_parse_int()
if left_type == TLS_COMBINATOR_LEFT:
tl_combinator.args = self.read_args_list(tl_combinator)
else:
if left_type != TLS_COMBINATOR_LEFT_BUILTIN:
raise RuntimeError(f'Wrong tls_combinator_left magic {left_type}')
right_ver = self.try_parse_int()
if right_ver != TLS_COMBINATOR_RIGHT_V2:
raise RuntimeError(f'Wrong tls_combinator_right magic {right_ver}')
tl_combinator.result = self.read_type_expr(tl_combinator)
return tl_combinator
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724,487 | tlo.tl_config_parser | read_expr | null | def read_expr(self, tl_combinator: TlCombinator) -> TlTree:
tree_type = self.try_parse_int()
if tree_type == TLS_EXPR_NAT:
return self.read_nat_expr(tl_combinator)
elif tree_type == TLS_EXPR_TYPE:
return self.read_type_expr(tl_combinator)
else:
raise RuntimeError(f'tree_type = {tree_type}')
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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724,488 | tlo.tl_config_parser | read_nat_expr | null | def read_nat_expr(self, tl_combinator: TlCombinator) -> TlTree:
tree_type = self.try_parse_int()
if tree_type in (TLS_NAT_CONST_OLD, TLS_NAT_CONST):
return self.read_num_const()
elif tree_type == TLS_NAT_VAR:
return self.read_num_var(tl_combinator)
else:
raise RuntimeError(f'tree_type = {tree_type}')
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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] |
724,489 | tlo.tl_config_parser | read_num_const | null | def read_num_const(self) -> TlTree:
num = self.try_parse_int()
return TlTreeNatConst(FLAG_NOVAR, num)
| (self) -> tlo.tl_core.TlTree | [
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724,490 | tlo.tl_config_parser | read_num_var | null | def read_num_var(self, tl_combinator: TlCombinator) -> TlTree:
diff = self.try_parse_int()
var_num = self.try_parse_int()
if var_num >= tl_combinator.var_count:
tl_combinator.var_count = var_num + 1
return TlTreeVarNum(0, var_num, diff)
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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724,491 | tlo.tl_config_parser | read_type | null | def read_type(self):
t = self.try_parse_int()
if t != TLS_TYPE:
raise RuntimeError(f'Wrong tls_type magic {t}')
tl_type = TlType()
tl_type.id = self.try_parse_int()
tl_type.name = self.try_parse_string()
tl_type.constructors_num = self.try_parse_int() # orig size_t
tl_type.constructors = []
tl_type.flags = self.try_parse_int()
tl_type.flags &= ~(1 | 8 | 16 | 1024)
if tl_type.flags != 0:
logger.warning(f'Type {tl_type.name} has non-zero flags: {tl_type.flags}')
tl_type.arity = self.try_parse_int()
self.try_parse_long() # unused
return tl_type
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724,492 | tlo.tl_config_parser | read_type_expr | null | def read_type_expr(self, tl_combinator: TlCombinator) -> TlTree:
tree_type = self.try_parse_int()
if tree_type == TLS_TYPE_VAR:
return self.read_type_var(tl_combinator)
elif tree_type == TLS_TYPE_EXPR:
return self.read_type_tree(tl_combinator)
elif tree_type == TLS_ARRAY:
return self.read_array(tl_combinator)
else:
raise RuntimeError(f'tree_type = {tree_type}')
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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724,493 | tlo.tl_config_parser | read_type_tree | null | def read_type_tree(self, tl_combinator: TlCombinator) -> TlTree:
tl_type = self.config.get_type(self.try_parse_int())
# there is assert not needed because we have KeyError exception
flags = self.try_parse_int() | FLAG_NOVAR
arity = self.try_parse_int()
assert tl_type.arity == arity
tl_tree_type = TlTreeType(flags, tl_type, arity)
for i in range(arity):
child = self.read_expr(tl_combinator)
tl_tree_type.children.append(child)
if not (child.flags & FLAG_NOVAR):
tl_tree_type.flags &= ~FLAG_NOVAR
return tl_tree_type
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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724,494 | tlo.tl_config_parser | read_type_var | null | def read_type_var(self, tl_combinator: TlCombinator) -> TlTree:
var_num = self.try_parse_int()
flags = self.try_parse_int()
if var_num >= tl_combinator.var_count:
tl_combinator.var_count = var_num + 1
assert not (flags & (FLAG_NOVAR | FLAG_BARE))
return TlTreeVarType(flags, var_num)
| (self, tl_combinator: tlo.tl_core.TlCombinator) -> tlo.tl_core.TlTree | [
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] |
724,495 | tlo.tl_config_parser | try_parse | null | def try_parse(self, res):
if self.p.get_error():
raise RuntimeError(f'Wrong TL-scheme specified: {self.p.get_error()} at {self.p.get_error_pos()}')
return res
| (self, res) | [
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] |
724,496 | tlo.tl_config_parser | try_parse_int | null | def try_parse_int(self) -> int: # orig int32_t
return self.try_parse(self.p.fetch_int())
| (self) -> int | [
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] |
724,497 | tlo.tl_config_parser | try_parse_long | null | def try_parse_long(self) -> int: # orig int64_t
return self.try_parse(self.p.fetch_long())
| (self) -> int | [
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724,498 | tlo.tl_config_parser | try_parse_string | null | def try_parse_string(self) -> str:
return self.try_parse(self.p.fetch_string())
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] |
724,499 | tlo | read_tl_config | null | def read_tl_config(data: bytes) -> TlConfig:
if not data:
raise RuntimeError(f'Config data is empty')
if len(data) % struct.calcsize('i') != 0:
raise RuntimeError(f'Config size = {len(data)} is not multiple of {struct.calcsize("i")}')
parser = TlConfigParser(data)
return parser.parse_config()
| (data: bytes) -> tlo.tl_config.TlConfig | [
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724,500 | tlo | read_tl_config_from_file | null | def read_tl_config_from_file(file_name: str) -> TlConfig:
with open(file_name, 'rb') as config:
return read_tl_config(config.read())
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] |
724,506 | domain2idna.converter | Converter |
Provides a base for every core logic we add.
:param subject: The subject to convert.
:type subject: str, list
:param str original_encoding:
The encoding to provide as output.
| class Converter:
"""
Provides a base for every core logic we add.
:param subject: The subject to convert.
:type subject: str, list
:param str original_encoding:
The encoding to provide as output.
"""
to_ignore = [
"0.0.0.0",
"localhost",
"127.0.0.1",
"localdomain",
"local",
"broadcasthost",
"allhosts",
"allnodes",
"allrouters",
"localnet",
"loopback",
"mcastprefix",
]
def __init__(self, subject, original_encoding="utf-8"):
self.subject = subject
self.encoding = original_encoding
def convert_to_idna(self, subject, original_encoding="utf-8"):
"""
Converts the given subject to IDNA.
:param str subject: The subject to convert.
:rtype: str
"""
if subject in self.to_ignore:
return subject
if "://" not in subject:
try:
return subject.encode("idna").decode(original_encoding)
except UnicodeError: # pragma: no cover
return subject
if subject.startswith("://"):
to_convert = urlparse(f"https://{subject[3:]}").netloc
converted = self.convert_to_idna(to_convert)
return subject.replace(to_convert, converted)
parsed_url = urlparse(subject)
result = f"{parsed_url.scheme}://{self.convert_to_idna(parsed_url.netloc)}{parsed_url.path}"
if parsed_url.params:
result += f";{parsed_url.params}"
if parsed_url.query:
result += f"?{parsed_url.query}"
if parsed_url.fragment:
result += f"#{parsed_url.fragment}"
return result
def __get_converted(self, subject):
"""
Process the actual conversion.
:param str subject: The subject to convert.
:rtype: str
"""
if (
not subject
or not subject.strip()
or subject in self.to_ignore
or subject.startswith("#")
):
return subject
if "#" in subject and "://" not in subject:
comment = " " + subject[subject.find("#") :]
subject = subject[: subject.find("#")].strip()
else:
comment = ""
return (
" ".join(
[
self.convert_to_idna(x, original_encoding=self.encoding)
for x in subject.split()
]
)
+ comment
).strip()
def get_converted(self):
"""
Provides the converted data.
"""
if isinstance(self.subject, list):
return [self.__get_converted(x) for x in self.subject]
return self.__get_converted(self.subject)
| (subject, original_encoding='utf-8') | [
-0.016016362234950066,
0.01554726343601942,
-0.010932866483926773,
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-0.050624340772628784,
0.019166022539138794,
0.014867549762129784,
0.04759913682937622
] |
724,507 | domain2idna.converter | __get_converted |
Process the actual conversion.
:param str subject: The subject to convert.
:rtype: str
| def __get_converted(self, subject):
"""
Process the actual conversion.
:param str subject: The subject to convert.
:rtype: str
"""
if (
not subject
or not subject.strip()
or subject in self.to_ignore
or subject.startswith("#")
):
return subject
if "#" in subject and "://" not in subject:
comment = " " + subject[subject.find("#") :]
subject = subject[: subject.find("#")].strip()
else:
comment = ""
return (
" ".join(
[
self.convert_to_idna(x, original_encoding=self.encoding)
for x in subject.split()
]
)
+ comment
).strip()
| (self, subject) | [
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724,508 | domain2idna.converter | __init__ | null | def __init__(self, subject, original_encoding="utf-8"):
self.subject = subject
self.encoding = original_encoding
| (self, subject, original_encoding='utf-8') | [
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724,509 | domain2idna.converter | convert_to_idna |
Converts the given subject to IDNA.
:param str subject: The subject to convert.
:rtype: str
| def convert_to_idna(self, subject, original_encoding="utf-8"):
"""
Converts the given subject to IDNA.
:param str subject: The subject to convert.
:rtype: str
"""
if subject in self.to_ignore:
return subject
if "://" not in subject:
try:
return subject.encode("idna").decode(original_encoding)
except UnicodeError: # pragma: no cover
return subject
if subject.startswith("://"):
to_convert = urlparse(f"https://{subject[3:]}").netloc
converted = self.convert_to_idna(to_convert)
return subject.replace(to_convert, converted)
parsed_url = urlparse(subject)
result = f"{parsed_url.scheme}://{self.convert_to_idna(parsed_url.netloc)}{parsed_url.path}"
if parsed_url.params:
result += f";{parsed_url.params}"
if parsed_url.query:
result += f"?{parsed_url.query}"
if parsed_url.fragment:
result += f"#{parsed_url.fragment}"
return result
| (self, subject, original_encoding='utf-8') | [
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724,510 | domain2idna.converter | get_converted |
Provides the converted data.
| def get_converted(self):
"""
Provides the converted data.
"""
if isinstance(self.subject, list):
return [self.__get_converted(x) for x in self.subject]
return self.__get_converted(self.subject)
| (self) | [
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] |
724,512 | domain2idna | domain2idna |
Process the conversion of the given subject.
:param subject: The subject to convert.
:type subject: str, list
:param str encoding: The encoding to provide.
:rtype: list, str
| def domain2idna(subject, encoding="utf-8"):
"""
Process the conversion of the given subject.
:param subject: The subject to convert.
:type subject: str, list
:param str encoding: The encoding to provide.
:rtype: list, str
"""
return Converter(subject, original_encoding=encoding).get_converted()
| (subject, encoding='utf-8') | [
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0.02068593166768551,
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0.12113139033317566,
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0.0125472042709589,
0.0018926779739558697,
0.018583426252007484,
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] |
724,513 | domain2idna | get |
This function is a passerelle between the front
and the backend of this module.
:param str domain_to_convert:
The domain to convert.
:return:
str:
if a string is given.
list:
if a list is given.
:rtype: str, list
.. deprecated:: 1.10.0
Use :func:`~domain2idna.domain2idna` instead.
| def get(domain_to_convert): # pragma: no cover
"""
This function is a passerelle between the front
and the backend of this module.
:param str domain_to_convert:
The domain to convert.
:return:
str:
if a string is given.
list:
if a list is given.
:rtype: str, list
.. deprecated:: 1.10.0
Use :func:`~domain2idna.domain2idna` instead.
"""
warnings.warn(
"`domain2idna.get` will be removed in future version. "
"Please use `domain2idna.domain2idna` instead.",
DeprecationWarning,
)
return domain2idna(domain_to_convert)
| (domain_to_convert) | [
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724,566 | markov_clustering.utils | MessagePrinter | null | class MessagePrinter(object):
def __init__(self, enabled):
self._enabled = enabled
def enable(self):
self._enabled = True
def disable(self):
self._enabled = False
def print(self, string):
if self._enabled:
print(string)
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724,567 | markov_clustering.utils | __init__ | null | def __init__(self, enabled):
self._enabled = enabled
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724,568 | markov_clustering.utils | disable | null | def disable(self):
self._enabled = False
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724,569 | markov_clustering.utils | enable | null | def enable(self):
self._enabled = True
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] |
724,570 | markov_clustering.utils | print | null | def print(self, string):
if self._enabled:
print(string)
| (self, string) | [
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724,571 | markov_clustering.mcl | add_self_loops |
Add self-loops to the matrix by setting the diagonal
to loop_value
:param matrix: The matrix to add loops to
:param loop_value: Value to use for self-loops
:returns: The matrix with self-loops
| def add_self_loops(matrix, loop_value):
"""
Add self-loops to the matrix by setting the diagonal
to loop_value
:param matrix: The matrix to add loops to
:param loop_value: Value to use for self-loops
:returns: The matrix with self-loops
"""
shape = matrix.shape
assert shape[0] == shape[1], "Error, matrix is not square"
if isspmatrix(matrix):
new_matrix = matrix.todok()
else:
new_matrix = matrix.copy()
for i in range(shape[0]):
new_matrix[i, i] = loop_value
if isspmatrix(matrix):
return new_matrix.tocsc()
return new_matrix
| (matrix, loop_value) | [
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] |
724,572 | markov_clustering.mcl | converged |
Check for convergence by determining if
matrix1 and matrix2 are approximately equal.
:param matrix1: The matrix to compare with matrix2
:param matrix2: The matrix to compare with matrix1
:returns: True if matrix1 and matrix2 approximately equal
| def converged(matrix1, matrix2):
"""
Check for convergence by determining if
matrix1 and matrix2 are approximately equal.
:param matrix1: The matrix to compare with matrix2
:param matrix2: The matrix to compare with matrix1
:returns: True if matrix1 and matrix2 approximately equal
"""
if isspmatrix(matrix1) or isspmatrix(matrix2):
return sparse_allclose(matrix1, matrix2)
return np.allclose(matrix1, matrix2)
| (matrix1, matrix2) | [
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0.07769665867090225
] |
724,573 | markov_clustering.modularity | convert_to_adjacency_matrix |
Converts transition matrix into adjacency matrix
:param matrix: The matrix to be converted
:returns: adjacency matrix
| def convert_to_adjacency_matrix(matrix):
"""
Converts transition matrix into adjacency matrix
:param matrix: The matrix to be converted
:returns: adjacency matrix
"""
for i in range(matrix.shape[0]):
if isspmatrix(matrix):
col = find(matrix[:,i])[2]
else:
col = matrix[:,i].T.tolist()[0]
coeff = max( Fraction(c).limit_denominator().denominator for c in col )
matrix[:,i] *= coeff
return matrix
| (matrix) | [
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724,574 | scipy.sparse._csc | csc_matrix |
Compressed Sparse Column matrix.
This can be instantiated in several ways:
csc_matrix(D)
where D is a 2-D ndarray
csc_matrix(S)
with another sparse array or matrix S (equivalent to S.tocsc())
csc_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N)
dtype is optional, defaulting to dtype='d'.
csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where ``data``, ``row_ind`` and ``col_ind`` satisfy the
relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
csc_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSC representation where the row indices for
column i are stored in ``indices[indptr[i]:indptr[i+1]]``
and their corresponding values are stored in
``data[indptr[i]:indptr[i+1]]``. If the shape parameter is
not supplied, the matrix dimensions are inferred from
the index arrays.
Attributes
----------
dtype : dtype
Data type of the matrix
shape : 2-tuple
Shape of the matrix
ndim : int
Number of dimensions (this is always 2)
nnz
size
data
CSC format data array of the matrix
indices
CSC format index array of the matrix
indptr
CSC format index pointer array of the matrix
has_sorted_indices
has_canonical_format
T
Notes
-----
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSC format
- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
- efficient column slicing
- fast matrix vector products (CSR, BSR may be faster)
Disadvantages of the CSC format
- slow row slicing operations (consider CSR)
- changes to the sparsity structure are expensive (consider LIL or DOK)
Canonical format
- Within each column, indices are sorted by row.
- There are no duplicate entries.
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> csc_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = np.array([0, 2, 2, 0, 1, 2])
>>> col = np.array([0, 0, 1, 2, 2, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
>>> indptr = np.array([0, 2, 3, 6])
>>> indices = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
| class csc_matrix(spmatrix, _csc_base):
"""
Compressed Sparse Column matrix.
This can be instantiated in several ways:
csc_matrix(D)
where D is a 2-D ndarray
csc_matrix(S)
with another sparse array or matrix S (equivalent to S.tocsc())
csc_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N)
dtype is optional, defaulting to dtype='d'.
csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where ``data``, ``row_ind`` and ``col_ind`` satisfy the
relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
csc_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSC representation where the row indices for
column i are stored in ``indices[indptr[i]:indptr[i+1]]``
and their corresponding values are stored in
``data[indptr[i]:indptr[i+1]]``. If the shape parameter is
not supplied, the matrix dimensions are inferred from
the index arrays.
Attributes
----------
dtype : dtype
Data type of the matrix
shape : 2-tuple
Shape of the matrix
ndim : int
Number of dimensions (this is always 2)
nnz
size
data
CSC format data array of the matrix
indices
CSC format index array of the matrix
indptr
CSC format index pointer array of the matrix
has_sorted_indices
has_canonical_format
T
Notes
-----
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSC format
- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
- efficient column slicing
- fast matrix vector products (CSR, BSR may be faster)
Disadvantages of the CSC format
- slow row slicing operations (consider CSR)
- changes to the sparsity structure are expensive (consider LIL or DOK)
Canonical format
- Within each column, indices are sorted by row.
- There are no duplicate entries.
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> csc_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = np.array([0, 2, 2, 0, 1, 2])
>>> col = np.array([0, 0, 1, 2, 2, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
>>> indptr = np.array([0, 2, 3, 6])
>>> indices = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
"""
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724,575 | scipy.sparse._data | __abs__ | null | def __abs__(self):
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724,576 | scipy.sparse._base | __add__ | null | def __add__(self, other): # self + other
if isscalarlike(other):
if other == 0:
return self.copy()
# Now we would add this scalar to every element.
raise NotImplementedError('adding a nonzero scalar to a '
'sparse array is not supported')
elif issparse(other):
if other.shape != self.shape:
raise ValueError("inconsistent shapes")
return self._add_sparse(other)
elif isdense(other):
other = np.broadcast_to(other, self.shape)
return self._add_dense(other)
else:
return NotImplemented
| (self, other) | [
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724,577 | scipy.sparse._base | __bool__ | null | def __bool__(self): # Simple -- other ideas?
if self.shape == (1, 1):
return self.nnz != 0
else:
raise ValueError("The truth value of an array with more than one "
"element is ambiguous. Use a.any() or a.all().")
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724,578 | scipy.sparse._base | __div__ | null | def __div__(self, other):
# Always do true division
return self._divide(other, true_divide=True)
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] |
724,579 | scipy.sparse._compressed | __eq__ | null | def __eq__(self, other):
# Scalar other.
if isscalarlike(other):
if np.isnan(other):
return self.__class__(self.shape, dtype=np.bool_)
if other == 0:
warn("Comparing a sparse matrix with 0 using == is inefficient"
", try using != instead.", SparseEfficiencyWarning,
stacklevel=3)
all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
inv = self._scalar_binopt(other, operator.ne)
return all_true - inv
else:
return self._scalar_binopt(other, operator.eq)
# Dense other.
elif isdense(other):
return self.todense() == other
# Pydata sparse other.
elif is_pydata_spmatrix(other):
return NotImplemented
# Sparse other.
elif issparse(other):
warn("Comparing sparse matrices using == is inefficient, try using"
" != instead.", SparseEfficiencyWarning, stacklevel=3)
# TODO sparse broadcasting
if self.shape != other.shape:
return False
elif self.format != other.format:
other = other.asformat(self.format)
res = self._binopt(other, '_ne_')
all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
return all_true - res
else:
return NotImplemented
| (self, other) | [
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] |
724,580 | scipy.sparse._compressed | __ge__ | null | def __ge__(self, other):
return self._inequality(other, operator.ge, '_ge_',
"Comparing a sparse matrix with a scalar "
"less than zero using >= is inefficient, "
"try using < instead.")
| (self, other) | [
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] |
724,581 | scipy.sparse._index | __getitem__ | null | def __getitem__(self, key):
row, col = self._validate_indices(key)
# Dispatch to specialized methods.
if isinstance(row, INT_TYPES):
if isinstance(col, INT_TYPES):
return self._get_intXint(row, col)
elif isinstance(col, slice):
self._raise_on_1d_array_slice()
return self._get_intXslice(row, col)
elif col.ndim == 1:
self._raise_on_1d_array_slice()
return self._get_intXarray(row, col)
elif col.ndim == 2:
return self._get_intXarray(row, col)
raise IndexError('index results in >2 dimensions')
elif isinstance(row, slice):
if isinstance(col, INT_TYPES):
self._raise_on_1d_array_slice()
return self._get_sliceXint(row, col)
elif isinstance(col, slice):
if row == slice(None) and row == col:
return self.copy()
return self._get_sliceXslice(row, col)
elif col.ndim == 1:
return self._get_sliceXarray(row, col)
raise IndexError('index results in >2 dimensions')
elif row.ndim == 1:
if isinstance(col, INT_TYPES):
self._raise_on_1d_array_slice()
return self._get_arrayXint(row, col)
elif isinstance(col, slice):
return self._get_arrayXslice(row, col)
else: # row.ndim == 2
if isinstance(col, INT_TYPES):
return self._get_arrayXint(row, col)
elif isinstance(col, slice):
raise IndexError('index results in >2 dimensions')
elif row.shape[1] == 1 and (col.ndim == 1 or col.shape[0] == 1):
# special case for outer indexing
return self._get_columnXarray(row[:,0], col.ravel())
# The only remaining case is inner (fancy) indexing
row, col = _broadcast_arrays(row, col)
if row.shape != col.shape:
raise IndexError('number of row and column indices differ')
if row.size == 0:
return self.__class__(np.atleast_2d(row).shape, dtype=self.dtype)
return self._get_arrayXarray(row, col)
| (self, key) | [
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724,582 | scipy.sparse._compressed | __gt__ | null | def __gt__(self, other):
return self._inequality(other, operator.gt, '_gt_',
"Comparing a sparse matrix with a scalar "
"less than zero using > is inefficient, "
"try using <= instead.")
| (self, other) | [
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724,583 | scipy.sparse._base | __iadd__ | null | def __iadd__(self, other):
return NotImplemented
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724,584 | scipy.sparse._base | __idiv__ | null | def __idiv__(self, other):
return self.__itruediv__(other)
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724,585 | scipy.sparse._data | __imul__ | null | def __imul__(self, other): # self *= other
if isscalarlike(other):
self.data *= other
return self
else:
return NotImplemented
| (self, other) | [
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724,586 | scipy.sparse._compressed | __init__ | null | def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if issparse(arg1):
if arg1.format == self.format and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.asformat(self.format)
self.indptr, self.indices, self.data, self._shape = (
arg1.indptr, arg1.indices, arg1.data, arg1._shape
)
elif isinstance(arg1, tuple):
if isshape(arg1):
# It's a tuple of matrix dimensions (M, N)
# create empty matrix
self._shape = check_shape(arg1)
M, N = self.shape
# Select index dtype large enough to pass array and
# scalar parameters to sparsetools
idx_dtype = self._get_index_dtype(maxval=max(M, N))
self.data = np.zeros(0, getdtype(dtype, default=float))
self.indices = np.zeros(0, idx_dtype)
self.indptr = np.zeros(self._swap((M, N))[0] + 1,
dtype=idx_dtype)
else:
if len(arg1) == 2:
# (data, ij) format
coo = self._coo_container(arg1, shape=shape, dtype=dtype)
arrays = coo._coo_to_compressed(self._swap)
self.indptr, self.indices, self.data, self._shape = arrays
elif len(arg1) == 3:
# (data, indices, indptr) format
(data, indices, indptr) = arg1
# Select index dtype large enough to pass array and
# scalar parameters to sparsetools
maxval = None
if shape is not None:
maxval = max(shape)
idx_dtype = self._get_index_dtype((indices, indptr),
maxval=maxval,
check_contents=True)
if not copy:
copy = copy_if_needed
self.indices = np.array(indices, copy=copy, dtype=idx_dtype)
self.indptr = np.array(indptr, copy=copy, dtype=idx_dtype)
self.data = np.array(data, copy=copy, dtype=dtype)
else:
raise ValueError(f"unrecognized {self.format}_matrix "
"constructor usage")
else:
# must be dense
try:
arg1 = np.asarray(arg1)
except Exception as e:
msg = f"unrecognized {self.format}_matrix constructor usage"
raise ValueError(msg) from e
coo = self._coo_container(arg1, dtype=dtype)
arrays = coo._coo_to_compressed(self._swap)
self.indptr, self.indices, self.data, self._shape = arrays
# Read matrix dimensions given, if any
if shape is not None:
self._shape = check_shape(shape)
else:
if self.shape is None:
# shape not already set, try to infer dimensions
try:
major_dim = len(self.indptr) - 1
minor_dim = self.indices.max() + 1
except Exception as e:
raise ValueError('unable to infer matrix dimensions') from e
else:
self._shape = check_shape(self._swap((major_dim, minor_dim)))
if dtype is not None:
self.data = self.data.astype(dtype, copy=False)
self.check_format(full_check=False)
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724,587 | scipy.sparse._base | __isub__ | null | def __isub__(self, other):
return NotImplemented
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724,588 | scipy.sparse._csc | __iter__ | null | def __iter__(self):
yield from self.tocsr()
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724,589 | scipy.sparse._data | __itruediv__ | null | def __itruediv__(self, other): # self /= other
if isscalarlike(other):
recip = 1.0 / other
self.data *= recip
return self
else:
return NotImplemented
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724,590 | scipy.sparse._compressed | __le__ | null | def __le__(self, other):
return self._inequality(other, operator.le, '_le_',
"Comparing a sparse matrix with a scalar "
"greater than zero using <= is inefficient, "
"try using > instead.")
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724,591 | scipy.sparse._base | __len__ | null | def __len__(self):
raise TypeError("sparse array length is ambiguous; use getnnz()"
" or shape[0]")
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724,592 | scipy.sparse._compressed | __lt__ | null | def __lt__(self, other):
return self._inequality(other, operator.lt, '_lt_',
"Comparing a sparse matrix with a scalar "
"greater than zero using < is inefficient, "
"try using >= instead.")
| (self, other) | [
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] |
724,593 | scipy.sparse._base | __matmul__ | null | def __matmul__(self, other):
if isscalarlike(other):
raise ValueError("Scalar operands are not allowed, "
"use '*' instead")
return self._matmul_dispatch(other)
| (self, other) | [
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0.08121117204427719,
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724,594 | scipy.sparse._matrix | __mul__ | null | def __mul__(self, other):
return self._matmul_dispatch(other)
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724,595 | scipy.sparse._compressed | __ne__ | null | def __ne__(self, other):
# Scalar other.
if isscalarlike(other):
if np.isnan(other):
warn("Comparing a sparse matrix with nan using != is"
" inefficient", SparseEfficiencyWarning, stacklevel=3)
all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
return all_true
elif other != 0:
warn("Comparing a sparse matrix with a nonzero scalar using !="
" is inefficient, try using == instead.",
SparseEfficiencyWarning, stacklevel=3)
all_true = self.__class__(np.ones(self.shape), dtype=np.bool_)
inv = self._scalar_binopt(other, operator.eq)
return all_true - inv
else:
return self._scalar_binopt(other, operator.ne)
# Dense other.
elif isdense(other):
return self.todense() != other
# Pydata sparse other.
elif is_pydata_spmatrix(other):
return NotImplemented
# Sparse other.
elif issparse(other):
# TODO sparse broadcasting
if self.shape != other.shape:
return True
elif self.format != other.format:
other = other.asformat(self.format)
return self._binopt(other, '_ne_')
else:
return NotImplemented
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724,596 | scipy.sparse._data | __neg__ | null | def __neg__(self):
if self.dtype.kind == 'b':
raise NotImplementedError('negating a boolean sparse array is not '
'supported')
return self._with_data(-self.data)
| (self) | [
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724,598 | scipy.sparse._matrix | __pow__ | null | def __pow__(self, power):
from .linalg import matrix_power
return matrix_power(self, power)
| (self, power) | [
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724,599 | scipy.sparse._base | __radd__ | null | def __radd__(self,other): # other + self
return self.__add__(other)
| (self, other) | [
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724,600 | scipy.sparse._base | __rdiv__ | null | def __rdiv__(self, other):
# Implementing this as the inverse would be too magical -- bail out
return NotImplemented
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