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pydle.features.rfc1459.client
on_user_invite
Callback called when another user was invited into a channel by someone.
def is_same_channel(self, left, right): """ Check if given nicknames are equal in the server's case mapping. """ return self.normalize(left) == self.normalize(right)
(self, target, channel, by)
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42,527
pydle.features.rfc1459.client
on_user_mode_change
Callback called when a user mode change occurred for the client.
def is_same_channel(self, left, right): """ Check if given nicknames are equal in the server's case mapping. """ return self.normalize(left) == self.normalize(right)
(self, modes)
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42,528
pydle.features.ircv3.monitor
on_user_offline
Callback called when a monitored users goes offline.
def is_monitoring(self, target): """ Return whether or not we are monitoring the target's online status. """ return target in self._monitoring
(self, nickname)
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42,529
pydle.features.ircv3.monitor
on_user_online
Callback called when a monitored user appears online.
def is_monitoring(self, target): """ Return whether or not we are monitoring the target's online status. """ return target in self._monitoring
(self, nickname)
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42,530
pydle.features.rfc1459.client
part
Leave channel, optionally with message.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, channel, message=None)
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0.013484440743923187, -0.00887988694012165, 0.01587465964257717 ]
42,531
pydle.features.rfc1459.client
quit
Quit network.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, message=None)
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42,536
pydle.features.rfc1459.client
set_mode
Set mode on target. Users should only rely on the mode actually being changed when receiving an on_{channel,user}_mode_change callback.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, target, *modes)
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42,537
pydle.features.rfc1459.client
set_nickname
Set nickname to given nickname. Users should only rely on the nickname actually being changed when receiving an on_nick_change callback.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, nickname)
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42,538
pydle.features.rfc1459.client
set_topic
Set topic on channel. Users should only rely on the topic actually being changed when receiving an on_topic_change callback.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, channel, topic)
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0.013484440743923187, -0.00887988694012165, 0.01587465964257717 ]
42,539
pydle.features.rfc1459.client
unban
Unban user from channel. Target can be either a user or a host. See ban documentation for the range parameter.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, channel, target, range=0)
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42,540
pydle.features.ircv3.monitor
unmonitor
Stop monitoring the online status of a user. Returns whether or not the server supports monitoring.
def _destroy_user(self, nickname, channel=None, monitor_override=False): # Override _destroy_user to not remove user if they are being monitored by us. if channel: channels = [self.channels[channel]] else: channels = self.channels.values() for ch in channels: # Remove from nicklist. ch['users'].discard(nickname) # Remove from statuses. for status in self._nickname_prefixes.values(): if status in ch['modes'] and nickname in ch['modes'][status]: ch['modes'][status].remove(nickname) # If we're not in any common channels with the user anymore, we have no reliable way to keep their info up-to-date. # Remove the user. if (monitor_override or not self.is_monitoring(nickname)) and (not channel or not any(nickname in ch['users'] for ch in self.channels.values())): del self.users[nickname]
(self, target)
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42,542
pydle.features.account
whois
null
def _create_user(self, nickname): super()._create_user(nickname) if nickname in self.users: self.users[nickname].update({ 'account': None, 'identified': False })
(self, nickname)
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42,543
pydle.features.rfc1459.client
whowas
Return information about offline user. This is an blocking asynchronous method: it has to be called from a coroutine, as follows: info = await self.whowas('Nick')
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, nickname)
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42,544
pydle.client
ClientPool
A pool of clients that are ran and handled in parallel.
class ClientPool: """ A pool of clients that are ran and handled in parallel. """ def __init__(self, clients=None, eventloop=None): self.eventloop = eventloop if eventloop else new_event_loop() self.clients = set(clients or []) self.connect_args = {} def connect(self, client: BasicClient, *args, **kwargs): """ Add client to pool. """ self.clients.add(client) self.connect_args[client] = (args, kwargs) # hack the clients event loop to use the pools own event loop client.eventloop = self.eventloop # necessary to run multiple clients in the same thread via the pool def disconnect(self, client): """ Remove client from pool. """ self.clients.remove(client) del self.connect_args[client] asyncio.run_coroutine_threadsafe(client.disconnect(expected=True), self.eventloop) def __contains__(self, item): return item in self.clients ## High-level. def handle_forever(self): """ Main loop of the pool: handle clients forever, until the event loop is stopped. """ # container for all the client connection coros connection_list = [] for client in self.clients: args, kwargs = self.connect_args[client] connection_list.append(client.connect(*args, **kwargs)) # single future for executing the connections connections = gather(*connection_list, loop=self.eventloop) # run the connections self.eventloop.run_until_complete(connections) # run the clients self.eventloop.run_forever()
(clients=None, eventloop=None)
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42,545
pydle.client
__contains__
null
def __contains__(self, item): return item in self.clients
(self, item)
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42,546
pydle.client
__init__
null
def __init__(self, clients=None, eventloop=None): self.eventloop = eventloop if eventloop else new_event_loop() self.clients = set(clients or []) self.connect_args = {}
(self, clients=None, eventloop=None)
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42,547
pydle.client
connect
Add client to pool.
def connect(self, client: BasicClient, *args, **kwargs): """ Add client to pool. """ self.clients.add(client) self.connect_args[client] = (args, kwargs) # hack the clients event loop to use the pools own event loop client.eventloop = self.eventloop # necessary to run multiple clients in the same thread via the pool
(self, client: pydle.client.BasicClient, *args, **kwargs)
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42,548
pydle.client
disconnect
Remove client from pool.
def disconnect(self, client): """ Remove client from pool. """ self.clients.remove(client) del self.connect_args[client] asyncio.run_coroutine_threadsafe(client.disconnect(expected=True), self.eventloop)
(self, client)
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42,549
pydle.client
handle_forever
Main loop of the pool: handle clients forever, until the event loop is stopped.
def handle_forever(self): """ Main loop of the pool: handle clients forever, until the event loop is stopped. """ # container for all the client connection coros connection_list = [] for client in self.clients: args, kwargs = self.connect_args[client] connection_list.append(client.connect(*args, **kwargs)) # single future for executing the connections connections = gather(*connection_list, loop=self.eventloop) # run the connections self.eventloop.run_until_complete(connections) # run the clients self.eventloop.run_forever()
(self)
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42,550
pydle.client
Error
Base class for all pydle errors.
class Error(Exception): """ Base class for all pydle errors. """ ...
null
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42,551
_asyncio
Future
This class is *almost* compatible with concurrent.futures.Future. Differences: - result() and exception() do not take a timeout argument and raise an exception when the future isn't done yet. - Callbacks registered with add_done_callback() are always called via the event loop's call_soon_threadsafe(). - This class is not compatible with the wait() and as_completed() methods in the concurrent.futures package.
from _asyncio import Future
(*, loop=None)
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42,552
pydle
MinimalClient
A cut-down, less-featured IRC client.
class MinimalClient(featurize(*features.LITE)): """ A cut-down, less-featured IRC client. """ ...
(*args, tls_client_cert=None, tls_client_cert_key=None, tls_client_cert_password=None, **kwargs)
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42,554
pydle.features.tls
__init__
null
def __init__(self, *args, tls_client_cert=None, tls_client_cert_key=None, tls_client_cert_password=None, **kwargs): super().__init__(*args, **kwargs) self.tls_client_cert = tls_client_cert self.tls_client_cert_key = tls_client_cert_key self.tls_client_cert_password = tls_client_cert_password
(self, *args, tls_client_cert=None, tls_client_cert_key=None, tls_client_cert_password=None, **kwargs)
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42,557
pydle.features.rfc1459.client
_create_message
null
def _create_message(self, command, *params, **kwargs): return parsing.RFC1459Message(command, params, **kwargs)
(self, command, *params, **kwargs)
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42,560
pydle.features.rfc1459.client
_destroy_user
null
def _destroy_user(self, user, channel=None): if channel: channels = [self.channels[channel]] else: channels = self.channels.values() # Remove user from status list too. for ch in channels: for status in self._nickname_prefixes.values(): if status in ch['modes'] and user in ch['modes'][status]: ch['modes'][status].remove(user)
(self, user, channel=None)
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42,568
pydle.features.rfc1459.client
_parse_message
null
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self)
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0.013484440743923187, -0.00887988694012165, 0.01587465964257717 ]
42,572
pydle.features.rfc1459.client
_register
Perform IRC connection registration.
def _format_host_range(self, host, range, allow_everything=False): # IPv4? try: addr = ipaddress.IPv4Network(host, strict=False) max = 4 if allow_everything else 3 # Round up subnet to nearest octet. subnet = addr.prefixlen + (8 - addr.prefixlen % 8) # Remove range mask. subnet -= min(range, max) * 8 rangeaddr = addr.supernet(new_prefix=subnet).exploded.split('/', 1)[0] return rangeaddr.replace('0', '*') except ValueError: pass # IPv6? try: addr = ipaddress.IPv6Network(host, strict=False) max = 4 if allow_everything else 3 # Round up subnet to nearest 32-et. subnet = addr.prefixlen + (32 - addr.prefixlen % 32) # Remove range mask. subnet -= min(range, max) * 32 rangeaddr = addr.supernet(new_prefix=subnet).exploded.split('/', 1)[0] return rangeaddr.replace(':0000', ':*') except ValueError: pass # Host? if '.' in host: # Split pieces. pieces = host.split('.') max = len(pieces) if not allow_everything: max -= 1 # Figure out how many to mask. to_mask = min(range, max) # Mask pieces. pieces[:to_mask] = '*' * to_mask return '.'.join(pieces) # Wat. if allow_everything and range >= 4: return '*' return host
(self)
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42,575
pydle.features.isupport
_reset_attributes
null
def _reset_attributes(self): super()._reset_attributes() self._isupport = {} self._extban_types = [] self._extban_prefix = None
(self)
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42,596
pydle.features.rfc1459.client
message
Message channel or user.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, target, message)
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0.013484440743923187, -0.00887988694012165, 0.01587465964257717 ]
42,598
pydle.features.rfc1459.client
notice
Notice channel or user.
def _parse_message(self): sep = protocol.MINIMAL_LINE_SEPARATOR.encode(self.encoding) message, _, data = self._receive_buffer.partition(sep) self._receive_buffer = data return parsing.RFC1459Message.parse(message + sep, encoding=self.encoding)
(self, target, message)
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42,683
pydle.features.rfc1459.client
on_raw_421
Server responded with 'unknown command'.
def is_same_channel(self, left, right): """ Check if given nicknames are equal in the server's case mapping. """ return self.normalize(left) == self.normalize(right)
(self, message)
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42,720
pydle.client
NotInChannel
null
class NotInChannel(Error): def __init__(self, channel): super().__init__('Not in channel: {}'.format(channel)) self.channel = channel
(channel)
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42,721
pydle.client
__init__
null
def __init__(self, channel): super().__init__('Not in channel: {}'.format(channel)) self.channel = channel
(self, channel)
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42,724
asyncio.coroutines
coroutine
Decorator to mark coroutines. If the coroutine is not yielded from before it is destroyed, an error message is logged.
def coroutine(func): """Decorator to mark coroutines. If the coroutine is not yielded from before it is destroyed, an error message is logged. """ warnings.warn('"@coroutine" decorator is deprecated since Python 3.8, use "async def" instead', DeprecationWarning, stacklevel=2) if inspect.iscoroutinefunction(func): # In Python 3.5 that's all we need to do for coroutines # defined with "async def". return func if inspect.isgeneratorfunction(func): coro = func else: @functools.wraps(func) def coro(*args, **kw): res = func(*args, **kw) if (base_futures.isfuture(res) or inspect.isgenerator(res) or isinstance(res, CoroWrapper)): res = yield from res else: # If 'res' is an awaitable, run it. try: await_meth = res.__await__ except AttributeError: pass else: if isinstance(res, collections.abc.Awaitable): res = yield from await_meth() return res coro = types.coroutine(coro) if not _DEBUG: wrapper = coro else: @functools.wraps(func) def wrapper(*args, **kwds): w = CoroWrapper(coro(*args, **kwds), func=func) if w._source_traceback: del w._source_traceback[-1] # Python < 3.5 does not implement __qualname__ # on generator objects, so we set it manually. # We use getattr as some callables (such as # functools.partial may lack __qualname__). w.__name__ = getattr(func, '__name__', None) w.__qualname__ = getattr(func, '__qualname__', None) return w wrapper._is_coroutine = _is_coroutine # For iscoroutinefunction(). return wrapper
(func)
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42,726
pydle
featurize
Put features into proper MRO order.
def featurize(*features): """ Put features into proper MRO order. """ def compare_subclass(left, right): if issubclass(left, right): return -1 if issubclass(right, left): return 1 return 0 sorted_features = sorted(features, key=cmp_to_key(compare_subclass)) name = 'FeaturizedClient[{features}]'.format( features=', '.join(feature.__name__ for feature in sorted_features)) return type(name, tuple(sorted_features), {})
(*features)
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42,734
pyhmmer.hmmer
hmmalign
Align several sequences to a reference HMM, and return the MSA. Arguments: hmm (`~pyhmmer.plan7.HMM`): The reference HMM to use for the alignment. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): The sequences to align to the HMM. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. trim (`bool`): Trim off any residues that get assigned to flanking :math:`N` and :math:`C` states (in profile traces) or :math:`I_0` and :math:`I_m` (in core traces). digitize (`bool`): If set to `True`, returns a `DigitalMSA` instead of a `TextMSA`. all_consensus_cols (`bool`): Force a column to be created for every consensus column in the model, even if it means having all gap character in a column. Returns: `~pyhmmer.easel.MSA`: A multiple sequence alignment containing the aligned sequences, either a `TextMSA` or a `DigitalMSA` depending on the value of the ``digitize`` argument. See Also: The `~pyhmmer.plan7.TraceAligner` class, which lets you inspect the intermediate tracebacks obtained for each alignment before building a MSA. .. versionadded:: 0.4.7
def hmmalign( hmm: HMM, sequences: typing.Iterable[DigitalSequence], *, digitize: bool = False, trim: bool = False, all_consensus_cols: bool = True, ) -> MSA: """Align several sequences to a reference HMM, and return the MSA. Arguments: hmm (`~pyhmmer.plan7.HMM`): The reference HMM to use for the alignment. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): The sequences to align to the HMM. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. trim (`bool`): Trim off any residues that get assigned to flanking :math:`N` and :math:`C` states (in profile traces) or :math:`I_0` and :math:`I_m` (in core traces). digitize (`bool`): If set to `True`, returns a `DigitalMSA` instead of a `TextMSA`. all_consensus_cols (`bool`): Force a column to be created for every consensus column in the model, even if it means having all gap character in a column. Returns: `~pyhmmer.easel.MSA`: A multiple sequence alignment containing the aligned sequences, either a `TextMSA` or a `DigitalMSA` depending on the value of the ``digitize`` argument. See Also: The `~pyhmmer.plan7.TraceAligner` class, which lets you inspect the intermediate tracebacks obtained for each alignment before building a MSA. .. versionadded:: 0.4.7 """ aligner = TraceAligner() if not isinstance(sequences, DigitalSequenceBlock): sequences = DigitalSequenceBlock(hmm.alphabet, sequences) traces = aligner.compute_traces(hmm, sequences) return aligner.align_traces( hmm, sequences, traces, trim=trim, digitize=digitize, all_consensus_cols=all_consensus_cols, )
(hmm: pyhmmer.plan7.HMM, sequences: Iterable[pyhmmer.easel.DigitalSequence], *, digitize: bool = False, trim: bool = False, all_consensus_cols: bool = True) -> pyhmmer.easel.MSA
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42,736
pyhmmer.hmmer
hmmpress
Press several HMMs into a database. Calling this function will create 4 files at the given location: ``{output}.h3p`` (containing the optimized profiles), ``{output}.h3m`` (containing the binary HMMs), ``{output}.h3f`` (containing the MSV parameters), and ``{output}.h3i`` (the SSI index mapping the previous files). Arguments: hmms (iterable of `~pyhmmer.plan7.HMM`): The HMMs to be pressed together in the file. output (`str` or `os.PathLike`): The path to an output location where to write the different files.
def hmmpress( hmms: typing.Iterable[HMM], output: typing.Union[str, "os.PathLike[str]"], ) -> int: """Press several HMMs into a database. Calling this function will create 4 files at the given location: ``{output}.h3p`` (containing the optimized profiles), ``{output}.h3m`` (containing the binary HMMs), ``{output}.h3f`` (containing the MSV parameters), and ``{output}.h3i`` (the SSI index mapping the previous files). Arguments: hmms (iterable of `~pyhmmer.plan7.HMM`): The HMMs to be pressed together in the file. output (`str` or `os.PathLike`): The path to an output location where to write the different files. """ DEFAULT_L = 400 path = os.fspath(output) nmodel = 0 with contextlib.ExitStack() as ctx: h3p = ctx.enter_context(open("{}.h3p".format(path), "wb")) h3m = ctx.enter_context(open("{}.h3m".format(path), "wb")) h3f = ctx.enter_context(open("{}.h3f".format(path), "wb")) h3i = ctx.enter_context(SSIWriter("{}.h3i".format(path))) fh = h3i.add_file(path, format=0) for hmm in hmms: # create the background model on the first iteration if nmodel == 0: bg = Background(hmm.alphabet) bg.L = DEFAULT_L # build the optimized models gm = Profile(hmm.M, hmm.alphabet) gm.configure(hmm, bg, DEFAULT_L) om = gm.to_optimized() # update the disk offsets of the optimized model to be written om.offsets.model = h3m.tell() om.offsets.profile = h3p.tell() om.offsets.filter = h3f.tell() # check that hmm has a name if hmm.name is None: raise ValueError("HMMs must have a name to be pressed.") # add the HMM name, and optionally the HMM accession to the index h3i.add_key(hmm.name, fh, om.offsets.model, 0, 0) if hmm.accession is not None: h3i.add_alias(hmm.accession, hmm.name) # write the HMM in binary format, and the optimized profile hmm.write(h3m, binary=True) om.write(h3f, h3p) nmodel += 1 # return the number of written HMMs return nmodel
(hmms: Iterable[pyhmmer.plan7.HMM], output: Union[str, os.PathLike[str]]) -> int
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42,737
pyhmmer.hmmer
hmmscan
Scan query sequences against a profile database. In HMMER many-to-many comparisons, a *scan* is the operation of querying with sequences a database of profile HMMs. It is necessary slower than a *search* because reconfiguring profiles between each queries has additional overhead, so it's recommended to use a *search* if the order of the comparisons is not important. The `hmmscan` function offers two ways of managing the database that will be selected based on the type of the ``profiles`` argument. If ``profiles`` is an `HMMPressedFile` object, `hmmscan` will reopen the file in each thread, and load profiles *iteratively* to scan with the query. Otherwise, it will *pre-fetch* the optimized profiles into an `OptimizedProfileBlock` collection, and share them across queries. The *pre-fetching* gives much higher performance at the cost of extra startup time and much higher memory consumption. You may want to check how much memory is available (for instance with `psutil.virtual_memory`) before trying to load a whole pHMM database. Arguments: queries (iterable of `DigitalSequence`): The query sequences to scan with the database. Passing a single query is supported. profiles (iterable of `HMM`, `Profile` or `OptimizedProfile`): A database of profiles to query. If you plan on using the same targets several times, consider converting them into `OptimizedProfile` and storing them into an `OptimizedProfileBlock` ahead of time. If a `HMMPressedFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. background (`pyhmmer.plan7.Background`, *optional*): A background object to use for configuring the profiles. If `None` given, create a default one. Yields: `~pyhmmer.plan7.TopHits`: An object reporting *top hits* for each query, in the same order the queries were passed in the input. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query sequence and the profile do not share the same alphabet. Note: Any additional keyword arguments passed to the `phmmer` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created in each worker thread. Hint: If reading the profiles from a pressed HMM database, make sure to use the `HMMFile.optimized_profiles` method so that profiles are read iteratively from the file during the scan loop:: >>> with HMMFile("tests/data/hmms/db/PF02826.hmm") as hmm_file: ... targets = hmm_file.optimized_profiles() ... all_hits = list(hmmscan(proteins, targets, E=1e-10)) >>> sum(len(hits) for hits in all_hits) 6 Otherwise, passing ``hmm_file`` as the ``profiles`` argument of `hmmscan` would cause the entire HMM file to be loaded in memory into an `OptimizedProfileBlock` otherwise. .. versionadded:: 0.7.0
def hmmscan( queries: typing.Union[DigitalSequence, typing.Iterable[DigitalSequence]], profiles: typing.Iterable[typing.Union[HMM, Profile, OptimizedProfile]], *, cpus: int = 0, callback: typing.Optional[typing.Callable[[DigitalSequence, int], None]] = None, background: typing.Optional[Background] = None, **options, # type: typing.Dict[str, object] ) -> typing.Iterator[TopHits]: """Scan query sequences against a profile database. In HMMER many-to-many comparisons, a *scan* is the operation of querying with sequences a database of profile HMMs. It is necessary slower than a *search* because reconfiguring profiles between each queries has additional overhead, so it's recommended to use a *search* if the order of the comparisons is not important. The `hmmscan` function offers two ways of managing the database that will be selected based on the type of the ``profiles`` argument. If ``profiles`` is an `HMMPressedFile` object, `hmmscan` will reopen the file in each thread, and load profiles *iteratively* to scan with the query. Otherwise, it will *pre-fetch* the optimized profiles into an `OptimizedProfileBlock` collection, and share them across queries. The *pre-fetching* gives much higher performance at the cost of extra startup time and much higher memory consumption. You may want to check how much memory is available (for instance with `psutil.virtual_memory`) before trying to load a whole pHMM database. Arguments: queries (iterable of `DigitalSequence`): The query sequences to scan with the database. Passing a single query is supported. profiles (iterable of `HMM`, `Profile` or `OptimizedProfile`): A database of profiles to query. If you plan on using the same targets several times, consider converting them into `OptimizedProfile` and storing them into an `OptimizedProfileBlock` ahead of time. If a `HMMPressedFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. background (`pyhmmer.plan7.Background`, *optional*): A background object to use for configuring the profiles. If `None` given, create a default one. Yields: `~pyhmmer.plan7.TopHits`: An object reporting *top hits* for each query, in the same order the queries were passed in the input. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query sequence and the profile do not share the same alphabet. Note: Any additional keyword arguments passed to the `phmmer` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created in each worker thread. Hint: If reading the profiles from a pressed HMM database, make sure to use the `HMMFile.optimized_profiles` method so that profiles are read iteratively from the file during the scan loop:: >>> with HMMFile("tests/data/hmms/db/PF02826.hmm") as hmm_file: ... targets = hmm_file.optimized_profiles() ... all_hits = list(hmmscan(proteins, targets, E=1e-10)) >>> sum(len(hits) for hits in all_hits) 6 Otherwise, passing ``hmm_file`` as the ``profiles`` argument of `hmmscan` would cause the entire HMM file to be loaded in memory into an `OptimizedProfileBlock` otherwise. .. versionadded:: 0.7.0 """ _alphabet = Alphabet.amino() _cpus = cpus if cpus > 0 else psutil.cpu_count(logical=False) or os.cpu_count() or 1 _background = Background(_alphabet) if background is None else background options.setdefault("background", _background) # type: ignore if not isinstance(queries, collections.abc.Iterable): queries = (queries,) if isinstance(profiles, HMMPressedFile): alphabet = _alphabet # FIXME: try to detect from content instead? targets = profiles elif isinstance(profiles, OptimizedProfileBlock): alphabet = profiles.alphabet targets = profiles # type: ignore else: alphabet = _alphabet block = OptimizedProfileBlock(_alphabet) for item in profiles: if isinstance(item, HMM): profile = Profile(item.M, item.alphabet) profile.configure(item, _background) item = profile if isinstance(item, Profile): item = item.to_optimized() if isinstance(item, OptimizedProfile): block.append(item) else: ty = type(item).__name__ raise TypeError( "Expected HMM, Profile or OptimizedProfile, found {}".format(ty) ) targets = block # type: ignore dispatcher = _SCANDispatcher( queries=queries, targets=targets, cpus=_cpus, callback=callback, pipeline_class=Pipeline, alphabet=alphabet, builder=None, **options, ) return dispatcher.run()
(queries: Union[pyhmmer.easel.DigitalSequence, Iterable[pyhmmer.easel.DigitalSequence]], profiles: Iterable[Union[pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile]], *, cpus: int = 0, callback: Optional[Callable[[pyhmmer.easel.DigitalSequence, int], NoneType]] = None, background: Optional[pyhmmer.plan7.Background] = None, **options) -> Iterator[pyhmmer.plan7.TopHits]
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pyhmmer.hmmer
hmmsearch
Search HMM profiles against a sequence database. In HMMER many-to-many comparisons, a *search* is the operation of querying with profile HMMs a database of sequences. The `hmmsearch` function offers two ways of managing the database that will be selected based on the type of the ``sequences`` argument. If ``sequences`` is an `SequenceFile` object, `hmmsearch` will reopen the file in each thread, and load targets *iteratively* to scan with the query. Otherwise, it will *pre-fetch* the target sequences into a `DigitalSequenceBlock` collection, and share them across threads without copy. The *pre-fetching* gives much higher performance at the cost of extra startup time and much higher memory consumption. You may want to check how much memory is available (for instance with `psutil.virtual_memory`) before trying to load a whole sequence database, but it is really recommended to do so whenever possible. Arguments: queries (iterable of `HMM`, `Profile` or `OptimizedProfile`): The query HMMs or profiles to search for in the database. Note that passing a single object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. If a `SequenceFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. Yields: `~pyhmmer.plan7.TopHits`: An object reporting *top hits* for each query, in the same order the queries were passed in the input. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query HMMs and the sequences do not share the same alphabet. Note: Any additional arguments passed to the `hmmsearch` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created. For instance, to run a ``hmmsearch`` using a bitscore cutoffs of 5 instead of the default E-value cutoff, use:: >>> hits = next(hmmsearch(thioesterase, proteins, T=5)) >>> hits[0].score 8.601... .. versionadded:: 0.1.0 .. versionchanged:: 0.4.9 Allow using `Profile` and `OptimizedProfile` queries. .. versionchanged:: 0.7.0 Queries may now be an iterable of different types, or a single object.
def hmmsearch( queries: typing.Union[_SEARCHQueryType, typing.Iterable[_SEARCHQueryType]], sequences: typing.Iterable[DigitalSequence], *, cpus: int = 0, callback: typing.Optional[typing.Callable[[_SEARCHQueryType, int], None]] = None, **options, # type: typing.Dict[str, object] ) -> typing.Iterator[TopHits]: """Search HMM profiles against a sequence database. In HMMER many-to-many comparisons, a *search* is the operation of querying with profile HMMs a database of sequences. The `hmmsearch` function offers two ways of managing the database that will be selected based on the type of the ``sequences`` argument. If ``sequences`` is an `SequenceFile` object, `hmmsearch` will reopen the file in each thread, and load targets *iteratively* to scan with the query. Otherwise, it will *pre-fetch* the target sequences into a `DigitalSequenceBlock` collection, and share them across threads without copy. The *pre-fetching* gives much higher performance at the cost of extra startup time and much higher memory consumption. You may want to check how much memory is available (for instance with `psutil.virtual_memory`) before trying to load a whole sequence database, but it is really recommended to do so whenever possible. Arguments: queries (iterable of `HMM`, `Profile` or `OptimizedProfile`): The query HMMs or profiles to search for in the database. Note that passing a single object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. If a `SequenceFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. Yields: `~pyhmmer.plan7.TopHits`: An object reporting *top hits* for each query, in the same order the queries were passed in the input. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query HMMs and the sequences do not share the same alphabet. Note: Any additional arguments passed to the `hmmsearch` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created. For instance, to run a ``hmmsearch`` using a bitscore cutoffs of 5 instead of the default E-value cutoff, use:: >>> hits = next(hmmsearch(thioesterase, proteins, T=5)) >>> hits[0].score 8.601... .. versionadded:: 0.1.0 .. versionchanged:: 0.4.9 Allow using `Profile` and `OptimizedProfile` queries. .. versionchanged:: 0.7.0 Queries may now be an iterable of different types, or a single object. """ _cpus = cpus if cpus > 0 else psutil.cpu_count(logical=False) or os.cpu_count() or 1 if not isinstance(queries, collections.abc.Iterable): queries = (queries,) if isinstance(sequences, SequenceFile): sequence_file: SequenceFile = sequences if sequence_file.name is None: raise ValueError("expected named `SequenceFile` for targets") if not sequence_file.digital: raise ValueError("expected digital mode `SequenceFile` for targets") assert sequence_file.alphabet is not None alphabet = sequence_file.alphabet targets: typing.Union[SequenceFile, DigitalSequenceBlock] = sequence_file elif isinstance(sequences, DigitalSequenceBlock): alphabet = sequences.alphabet targets = sequences else: queries = peekable(queries) try: alphabet = queries.peek().alphabet targets = DigitalSequenceBlock(alphabet, sequences) except StopIteration: alphabet = Alphabet.amino() targets = DigitalSequenceBlock(alphabet) dispatcher = _SEARCHDispatcher( queries=queries, targets=targets, cpus=_cpus, callback=callback, alphabet=alphabet, builder=None, pipeline_class=Pipeline, **options, ) return dispatcher.run()
(queries: Union[pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile, Iterable[Union[pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile]]], sequences: Iterable[pyhmmer.easel.DigitalSequence], *, cpus: int = 0, callback: Optional[Callable[[Union[pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile], int], NoneType]] = None, **options) -> Iterator[pyhmmer.plan7.TopHits]
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42,739
pyhmmer.hmmer
jackhmmer
Search protein sequences against a sequence database. Arguments: queries (iterable of `DigitalSequence`): The query sequences to search for in the sequence database. Passing a single sequence object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. `jackhmmer` does not support passing a `~pyhmmer.easel.SequenceFile` at the moment. max_iterations (`int`): The maximum number of iterations for the search. Hits will be returned early if the searched converged. select_hits (callable, optional): A function or callable object for manually selecting hits during each iteration. It should take a single `~pyhmmer.plan7.TopHits` argument and change the inclusion of individual hits with the `~pyhmmer.plan7.Hit.include` and `~pyhmmer.plan7.Hit.drop` methods of `~pyhmmer.plan7.Hit` objects. checkpoints (`bool`): A logical flag to return the results at each iteration 'checkpoint'. If `True`, then an iterable of up to ``max_iterations`` `~pyhmmer.plan7.IterationResult` will be returned, rather than just the final iteration. This is similar to ``--chkhmm`` amd ``--chkali`` flags from HMMER3's ``jackhmmer`` interface. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. builder (`~pyhmmer.plan7.Builder`, optional): A builder to configure how the queries are converted to HMMs. Passing `None` will create a default instance. Yields: `~pyhmmer.plan7.IterationResult`: An *iteration result* instance for each query, in the same order the queries were passed in the input. If ``checkpoint`` option is `True`, all iterations will be returned instead of the last one. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query sequence the profile or the optional builder do not share the same alphabet. Note: Any additional keyword arguments passed to the `jackhmmer` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created in each worker thread. Caution: Default values used for ``jackhmmer`` do not correspond to the default parameters used for creating a pipeline in the other cases. If no parameter value is given as a keyword argument, `jackhmmer` will create the pipeline with ``incE=0.001`` and ``incdomE=0.001``, where a default `~pyhmmer.plan7.Pipeline` would use ``incE=0.01`` and ``incdomE=0.01``. .. versionadded:: 0.8.0
def jackhmmer( queries: typing.Union[_JACKHMMERQueryType, typing.Iterable[_JACKHMMERQueryType]], sequences: typing.Iterable[DigitalSequence], *, max_iterations: typing.Optional[int] = 5, select_hits: typing.Optional[typing.Callable[[TopHits], None]] = None, checkpoints: bool = False, cpus: int = 0, callback: typing.Optional[typing.Callable[[_JACKHMMERQueryType, int], None]] = None, builder: typing.Optional[Builder] = None, **options, # type: object ) -> typing.Union[ typing.Iterator[IterationResult], typing.Iterator[typing.Iterable[IterationResult]] ]: """Search protein sequences against a sequence database. Arguments: queries (iterable of `DigitalSequence`): The query sequences to search for in the sequence database. Passing a single sequence object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. `jackhmmer` does not support passing a `~pyhmmer.easel.SequenceFile` at the moment. max_iterations (`int`): The maximum number of iterations for the search. Hits will be returned early if the searched converged. select_hits (callable, optional): A function or callable object for manually selecting hits during each iteration. It should take a single `~pyhmmer.plan7.TopHits` argument and change the inclusion of individual hits with the `~pyhmmer.plan7.Hit.include` and `~pyhmmer.plan7.Hit.drop` methods of `~pyhmmer.plan7.Hit` objects. checkpoints (`bool`): A logical flag to return the results at each iteration 'checkpoint'. If `True`, then an iterable of up to ``max_iterations`` `~pyhmmer.plan7.IterationResult` will be returned, rather than just the final iteration. This is similar to ``--chkhmm`` amd ``--chkali`` flags from HMMER3's ``jackhmmer`` interface. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. builder (`~pyhmmer.plan7.Builder`, optional): A builder to configure how the queries are converted to HMMs. Passing `None` will create a default instance. Yields: `~pyhmmer.plan7.IterationResult`: An *iteration result* instance for each query, in the same order the queries were passed in the input. If ``checkpoint`` option is `True`, all iterations will be returned instead of the last one. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query sequence the profile or the optional builder do not share the same alphabet. Note: Any additional keyword arguments passed to the `jackhmmer` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created in each worker thread. Caution: Default values used for ``jackhmmer`` do not correspond to the default parameters used for creating a pipeline in the other cases. If no parameter value is given as a keyword argument, `jackhmmer` will create the pipeline with ``incE=0.001`` and ``incdomE=0.001``, where a default `~pyhmmer.plan7.Pipeline` would use ``incE=0.01`` and ``incdomE=0.01``. .. versionadded:: 0.8.0 """ _alphabet = Alphabet.amino() _cpus = cpus if cpus > 0 else psutil.cpu_count(logical=False) or os.cpu_count() or 1 _builder = Builder(_alphabet, architecture="hand") if builder is None else builder options.setdefault("incE", 0.001) options.setdefault("incdomE", 0.001) if not isinstance(queries, collections.abc.Iterable): queries = (queries,) if isinstance(sequences, SequenceFile): sequence_file: SequenceFile = sequences if sequence_file.name is None: raise ValueError("expected named `SequenceFile` for targets") if not sequence_file.digital: raise ValueError("expected digital mode `SequenceFile` for targets") assert sequence_file.alphabet is not None alphabet = sequence_file.alphabet targets = typing.cast(DigitalSequenceBlock, sequence_file.read_block()) elif isinstance(sequences, DigitalSequenceBlock): alphabet = sequences.alphabet targets = sequences else: alphabet = _alphabet targets = DigitalSequenceBlock(_alphabet, sequences) dispatcher = _JACKHMMERDispatcher( # type: ignore queries=queries, targets=targets, cpus=_cpus, callback=callback, pipeline_class=Pipeline, alphabet=alphabet, builder=_builder, max_iterations=max_iterations, select_hits=select_hits, checkpoints=checkpoints, **options, ) return dispatcher.run()
(queries: Union[pyhmmer.easel.DigitalSequence, pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile, Iterable[Union[pyhmmer.easel.DigitalSequence, pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile]]], sequences: Iterable[pyhmmer.easel.DigitalSequence], *, max_iterations: Optional[int] = 5, select_hits: Optional[Callable[[pyhmmer.plan7.TopHits], NoneType]] = None, checkpoints: bool = False, cpus: int = 0, callback: Optional[Callable[[Union[pyhmmer.easel.DigitalSequence, pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile], int], NoneType]] = None, builder: Optional[pyhmmer.plan7.Builder] = None, **options) -> Union[Iterator[pyhmmer.plan7.IterationResult], Iterator[Iterable[pyhmmer.plan7.IterationResult]]]
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42,740
pyhmmer.hmmer
nhmmer
Search nucleotide sequences against a sequence database. Arguments: queries (iterable of `DigitalSequence`, `DigitalMSA`, `HMM`): The query sequences or profiles to search for in the sequence database. Passing a single object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. If a `SequenceFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. builder (`~pyhmmer.plan7.Builder`, optional): A builder to configure how the queries are converted to HMMs. Passing `None` will create a default instance. Yields: `~pyhmmer.plan7.TopHits`: A *top hits* instance for each query, in the same order the queries were passed in the input. Note: Any additional keyword arguments passed to the `nhmmer` function will be passed to the `~pyhmmer.plan7.LongTargetsPipeline` created in each worker thread. The ``strand`` argument can be used to restrict the search on the direct or reverse strand. Caution: This function is not just `phmmer` for nucleotide sequences; it actually uses a `~pyhmmer.plan7.LongTargetsPipeline` internally instead of processing each target sequence in its entirety when searching for hits. This avoids hitting the maximum target size that can be used (100,000 residues), which may be a problem for some larger genomes. .. versionadded:: 0.3.0 .. versionchanged:: 0.4.9 Allow using `Profile` and `OptimizedProfile` queries. .. versionchanged:: 0.7.0 Queries may now be an iterable of different types, or a single object.
def nhmmer( queries: typing.Union[_NHMMERQueryType, typing.Iterable[_NHMMERQueryType]], sequences: typing.Iterable[DigitalSequence], *, cpus: int = 0, callback: typing.Optional[typing.Callable[[_NHMMERQueryType, int], None]] = None, builder: typing.Optional[Builder] = None, **options, # type: typing.Dict[str, object] ) -> typing.Iterator[TopHits]: """Search nucleotide sequences against a sequence database. Arguments: queries (iterable of `DigitalSequence`, `DigitalMSA`, `HMM`): The query sequences or profiles to search for in the sequence database. Passing a single object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. If a `SequenceFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. builder (`~pyhmmer.plan7.Builder`, optional): A builder to configure how the queries are converted to HMMs. Passing `None` will create a default instance. Yields: `~pyhmmer.plan7.TopHits`: A *top hits* instance for each query, in the same order the queries were passed in the input. Note: Any additional keyword arguments passed to the `nhmmer` function will be passed to the `~pyhmmer.plan7.LongTargetsPipeline` created in each worker thread. The ``strand`` argument can be used to restrict the search on the direct or reverse strand. Caution: This function is not just `phmmer` for nucleotide sequences; it actually uses a `~pyhmmer.plan7.LongTargetsPipeline` internally instead of processing each target sequence in its entirety when searching for hits. This avoids hitting the maximum target size that can be used (100,000 residues), which may be a problem for some larger genomes. .. versionadded:: 0.3.0 .. versionchanged:: 0.4.9 Allow using `Profile` and `OptimizedProfile` queries. .. versionchanged:: 0.7.0 Queries may now be an iterable of different types, or a single object. """ _alphabet = Alphabet.dna() _cpus = cpus if cpus > 0 else psutil.cpu_count(logical=False) or os.cpu_count() or 1 if builder is None: _builder = Builder( _alphabet, seed=options.get("seed", 42), window_length=options.get("window_length"), window_beta=options.get("window_beta"), ) else: _builder = builder if not isinstance(queries, collections.abc.Iterable): queries = (queries,) if isinstance(sequences, SequenceFile): sequence_file: SequenceFile = sequences if sequence_file.name is None: raise ValueError("expected named `SequenceFile` for targets") if not sequence_file.digital: raise ValueError("expected digital mode `SequenceFile` for targets") assert sequence_file.alphabet is not None alphabet = sequence_file.alphabet targets: typing.Union[SequenceFile, DigitalSequenceBlock] = sequence_file elif isinstance(sequences, DigitalSequenceBlock): alphabet = sequences.alphabet targets = sequences else: alphabet = _alphabet targets = DigitalSequenceBlock(_alphabet, sequences) dispatcher = _NHMMERDispatcher( queries=queries, targets=targets, cpus=_cpus, callback=callback, pipeline_class=LongTargetsPipeline, alphabet=alphabet, builder=_builder, **options, ) return dispatcher.run()
(queries: Union[pyhmmer.easel.DigitalSequence, pyhmmer.easel.DigitalMSA, pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile, Iterable[Union[pyhmmer.easel.DigitalSequence, pyhmmer.easel.DigitalMSA, pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile]]], sequences: Iterable[pyhmmer.easel.DigitalSequence], *, cpus: int = 0, callback: Optional[Callable[[Union[pyhmmer.easel.DigitalSequence, pyhmmer.easel.DigitalMSA, pyhmmer.plan7.HMM, pyhmmer.plan7.Profile, pyhmmer.plan7.OptimizedProfile], int], NoneType]] = None, builder: Optional[pyhmmer.plan7.Builder] = None, **options) -> Iterator[pyhmmer.plan7.TopHits]
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42,741
pyhmmer.hmmer
phmmer
Search protein sequences against a sequence database. Arguments: queries (iterable of `DigitalSequence` or `DigitalMSA`): The query sequences to search for in the sequence database. Passing a single object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. If a `SequenceFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. builder (`~pyhmmer.plan7.Builder`, optional): A builder to configure how the queries are converted to HMMs. Passing `None` will create a default instance. Yields: `~pyhmmer.plan7.TopHits`: A *top hits* instance for each query, in the same order the queries were passed in the input. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query sequence the profile or the optional builder do not share the same alphabet. Note: Any additional keyword arguments passed to the `phmmer` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created in each worker thread. .. versionadded:: 0.2.0 .. versionchanged:: 0.3.0 Allow using `DigitalMSA` queries. .. versionchanged:: 0.7.0 Queries may now be an iterable of different types, or a single object.
def phmmer( queries: typing.Union[_PHMMERQueryType, typing.Iterable[_PHMMERQueryType]], sequences: typing.Iterable[DigitalSequence], *, cpus: int = 0, callback: typing.Optional[typing.Callable[[_PHMMERQueryType, int], None]] = None, builder: typing.Optional[Builder] = None, **options, # type: typing.Dict[str, object] ) -> typing.Iterator[TopHits]: """Search protein sequences against a sequence database. Arguments: queries (iterable of `DigitalSequence` or `DigitalMSA`): The query sequences to search for in the sequence database. Passing a single object is supported. sequences (iterable of `~pyhmmer.easel.DigitalSequence`): A database of sequences to query. If you plan on using the same sequences several times, consider storing them into a `~pyhmmer.easel.DigitalSequenceBlock` directly. If a `SequenceFile` is given, profiles will be loaded iteratively from disk rather than prefetched. cpus (`int`): The number of threads to run in parallel. Pass ``1`` to run everything in the main thread, ``0`` to automatically select a suitable number (using `psutil.cpu_count`), or any positive number otherwise. callback (callable): A callback that is called everytime a query is processed with two arguments: the query, and the total number of queries. This can be used to display progress in UI. builder (`~pyhmmer.plan7.Builder`, optional): A builder to configure how the queries are converted to HMMs. Passing `None` will create a default instance. Yields: `~pyhmmer.plan7.TopHits`: A *top hits* instance for each query, in the same order the queries were passed in the input. Raises: `~pyhmmer.errors.AlphabetMismatch`: When any of the query sequence the profile or the optional builder do not share the same alphabet. Note: Any additional keyword arguments passed to the `phmmer` function will be passed transparently to the `~pyhmmer.plan7.Pipeline` to be created in each worker thread. .. versionadded:: 0.2.0 .. versionchanged:: 0.3.0 Allow using `DigitalMSA` queries. .. versionchanged:: 0.7.0 Queries may now be an iterable of different types, or a single object. """ _alphabet = Alphabet.amino() _cpus = cpus if cpus > 0 else psutil.cpu_count(logical=False) or os.cpu_count() or 1 _builder = Builder(_alphabet) if builder is None else builder if not isinstance(queries, collections.abc.Iterable): queries = (queries,) if isinstance(sequences, SequenceFile): sequence_file: SequenceFile = sequences if sequence_file.name is None: raise ValueError("expected named `SequenceFile` for targets") if not sequence_file.digital: raise ValueError("expected digital mode `SequenceFile` for targets") assert sequence_file.alphabet is not None alphabet = sequence_file.alphabet targets: typing.Union[SequenceFile, DigitalSequenceBlock] = sequence_file elif isinstance(sequences, DigitalSequenceBlock): alphabet = sequences.alphabet targets = sequences else: alphabet = _alphabet targets = DigitalSequenceBlock(_alphabet, sequences) dispatcher = _PHMMERDispatcher( queries=queries, targets=targets, cpus=_cpus, callback=callback, pipeline_class=Pipeline, alphabet=alphabet, builder=_builder, **options, ) return dispatcher.run()
(queries: Union[pyhmmer.easel.DigitalSequence, pyhmmer.easel.DigitalMSA, Iterable[Union[pyhmmer.easel.DigitalSequence, pyhmmer.easel.DigitalMSA]]], sequences: Iterable[pyhmmer.easel.DigitalSequence], *, cpus: int = 0, callback: Optional[Callable[[Union[pyhmmer.easel.DigitalSequence, pyhmmer.easel.DigitalMSA], int], NoneType]] = None, builder: Optional[pyhmmer.plan7.Builder] = None, **options) -> Iterator[pyhmmer.plan7.TopHits]
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42,744
preprocess_cancellation
HullTracker
null
class HullTracker: def add_point(self, point: Point): ... def center(self) -> Point: ... def exterior(self) -> list[Point]: ... @classmethod def create(cls): if shapely: return ShapelyHullTracker() return SimpleHullTracker()
()
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42,745
preprocess_cancellation
add_point
null
def add_point(self, point: Point): ...
(self, point: preprocess_cancellation.Point)
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42,746
preprocess_cancellation
center
null
def center(self) -> Point: ...
(self) -> preprocess_cancellation.Point
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42,747
preprocess_cancellation
exterior
null
def exterior(self) -> list[Point]: ...
(self) -> list[preprocess_cancellation.Point]
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42,748
preprocess_cancellation
KnownObject
KnownObject(name, hull)
class KnownObject(NamedTuple): name: str hull: HullTracker
(name: str, hull: preprocess_cancellation.HullTracker)
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42,750
namedtuple_KnownObject
__new__
Create new instance of KnownObject(name, hull)
from builtins import function
(_cls, name: ForwardRef('str'), hull: ForwardRef('HullTracker'))
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42,753
collections
_replace
Return a new KnownObject object replacing specified fields with new values
def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None): """Returns a new subclass of tuple with named fields. >>> Point = namedtuple('Point', ['x', 'y']) >>> Point.__doc__ # docstring for the new class 'Point(x, y)' >>> p = Point(11, y=22) # instantiate with positional args or keywords >>> p[0] + p[1] # indexable like a plain tuple 33 >>> x, y = p # unpack like a regular tuple >>> x, y (11, 22) >>> p.x + p.y # fields also accessible by name 33 >>> d = p._asdict() # convert to a dictionary >>> d['x'] 11 >>> Point(**d) # convert from a dictionary Point(x=11, y=22) >>> p._replace(x=100) # _replace() is like str.replace() but targets named fields Point(x=100, y=22) """ # Validate the field names. At the user's option, either generate an error # message or automatically replace the field name with a valid name. if isinstance(field_names, str): field_names = field_names.replace(',', ' ').split() field_names = list(map(str, field_names)) typename = _sys.intern(str(typename)) if rename: seen = set() for index, name in enumerate(field_names): if (not name.isidentifier() or _iskeyword(name) or name.startswith('_') or name in seen): field_names[index] = f'_{index}' seen.add(name) for name in [typename] + field_names: if type(name) is not str: raise TypeError('Type names and field names must be strings') if not name.isidentifier(): raise ValueError('Type names and field names must be valid ' f'identifiers: {name!r}') if _iskeyword(name): raise ValueError('Type names and field names cannot be a ' f'keyword: {name!r}') seen = set() for name in field_names: if name.startswith('_') and not rename: raise ValueError('Field names cannot start with an underscore: ' f'{name!r}') if name in seen: raise ValueError(f'Encountered duplicate field name: {name!r}') seen.add(name) field_defaults = {} if defaults is not None: defaults = tuple(defaults) if len(defaults) > len(field_names): raise TypeError('Got more default values than field names') field_defaults = dict(reversed(list(zip(reversed(field_names), reversed(defaults))))) # Variables used in the methods and docstrings field_names = tuple(map(_sys.intern, field_names)) num_fields = len(field_names) arg_list = ', '.join(field_names) if num_fields == 1: arg_list += ',' repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')' tuple_new = tuple.__new__ _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip # Create all the named tuple methods to be added to the class namespace namespace = { '_tuple_new': tuple_new, '__builtins__': {}, '__name__': f'namedtuple_{typename}', } code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))' __new__ = eval(code, namespace) __new__.__name__ = '__new__' __new__.__doc__ = f'Create new instance of {typename}({arg_list})' if defaults is not None: __new__.__defaults__ = defaults @classmethod def _make(cls, iterable): result = tuple_new(cls, iterable) if _len(result) != num_fields: raise TypeError(f'Expected {num_fields} arguments, got {len(result)}') return result _make.__func__.__doc__ = (f'Make a new {typename} object from a sequence ' 'or iterable') def _replace(self, /, **kwds): result = self._make(_map(kwds.pop, field_names, self)) if kwds: raise ValueError(f'Got unexpected field names: {list(kwds)!r}') return result _replace.__doc__ = (f'Return a new {typename} object replacing specified ' 'fields with new values') def __repr__(self): 'Return a nicely formatted representation string' return self.__class__.__name__ + repr_fmt % self def _asdict(self): 'Return a new dict which maps field names to their values.' return _dict(_zip(self._fields, self)) def __getnewargs__(self): 'Return self as a plain tuple. Used by copy and pickle.' return _tuple(self) # Modify function metadata to help with introspection and debugging for method in ( __new__, _make.__func__, _replace, __repr__, _asdict, __getnewargs__, ): method.__qualname__ = f'{typename}.{method.__name__}' # Build-up the class namespace dictionary # and use type() to build the result class class_namespace = { '__doc__': f'{typename}({arg_list})', '__slots__': (), '_fields': field_names, '_field_defaults': field_defaults, '__new__': __new__, '_make': _make, '_replace': _replace, '__repr__': __repr__, '_asdict': _asdict, '__getnewargs__': __getnewargs__, '__match_args__': field_names, } for index, name in enumerate(field_names): doc = _sys.intern(f'Alias for field number {index}') class_namespace[name] = _tuplegetter(index, doc) result = type(typename, (tuple,), class_namespace) # For pickling to work, the __module__ variable needs to be set to the frame # where the named tuple is created. Bypass this step in environments where # sys._getframe is not defined (Jython for example) or sys._getframe is not # defined for arguments greater than 0 (IronPython), or where the user has # specified a particular module. if module is None: try: module = _sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): pass if module is not None: result.__module__ = module return result
(self, /, **kwds)
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42,755
preprocess_cancellation
Point
Point(x, y)
class Point(NamedTuple): x: float y: float
(x: float, y: float)
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42,757
namedtuple_Point
__new__
Create new instance of Point(x, y)
from builtins import function
(_cls, x: ForwardRef('float'), y: ForwardRef('float'))
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42,760
collections
_replace
Return a new Point object replacing specified fields with new values
def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None): """Returns a new subclass of tuple with named fields. >>> Point = namedtuple('Point', ['x', 'y']) >>> Point.__doc__ # docstring for the new class 'Point(x, y)' >>> p = Point(11, y=22) # instantiate with positional args or keywords >>> p[0] + p[1] # indexable like a plain tuple 33 >>> x, y = p # unpack like a regular tuple >>> x, y (11, 22) >>> p.x + p.y # fields also accessible by name 33 >>> d = p._asdict() # convert to a dictionary >>> d['x'] 11 >>> Point(**d) # convert from a dictionary Point(x=11, y=22) >>> p._replace(x=100) # _replace() is like str.replace() but targets named fields Point(x=100, y=22) """ # Validate the field names. At the user's option, either generate an error # message or automatically replace the field name with a valid name. if isinstance(field_names, str): field_names = field_names.replace(',', ' ').split() field_names = list(map(str, field_names)) typename = _sys.intern(str(typename)) if rename: seen = set() for index, name in enumerate(field_names): if (not name.isidentifier() or _iskeyword(name) or name.startswith('_') or name in seen): field_names[index] = f'_{index}' seen.add(name) for name in [typename] + field_names: if type(name) is not str: raise TypeError('Type names and field names must be strings') if not name.isidentifier(): raise ValueError('Type names and field names must be valid ' f'identifiers: {name!r}') if _iskeyword(name): raise ValueError('Type names and field names cannot be a ' f'keyword: {name!r}') seen = set() for name in field_names: if name.startswith('_') and not rename: raise ValueError('Field names cannot start with an underscore: ' f'{name!r}') if name in seen: raise ValueError(f'Encountered duplicate field name: {name!r}') seen.add(name) field_defaults = {} if defaults is not None: defaults = tuple(defaults) if len(defaults) > len(field_names): raise TypeError('Got more default values than field names') field_defaults = dict(reversed(list(zip(reversed(field_names), reversed(defaults))))) # Variables used in the methods and docstrings field_names = tuple(map(_sys.intern, field_names)) num_fields = len(field_names) arg_list = ', '.join(field_names) if num_fields == 1: arg_list += ',' repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')' tuple_new = tuple.__new__ _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip # Create all the named tuple methods to be added to the class namespace namespace = { '_tuple_new': tuple_new, '__builtins__': {}, '__name__': f'namedtuple_{typename}', } code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))' __new__ = eval(code, namespace) __new__.__name__ = '__new__' __new__.__doc__ = f'Create new instance of {typename}({arg_list})' if defaults is not None: __new__.__defaults__ = defaults @classmethod def _make(cls, iterable): result = tuple_new(cls, iterable) if _len(result) != num_fields: raise TypeError(f'Expected {num_fields} arguments, got {len(result)}') return result _make.__func__.__doc__ = (f'Make a new {typename} object from a sequence ' 'or iterable') def _replace(self, /, **kwds): result = self._make(_map(kwds.pop, field_names, self)) if kwds: raise ValueError(f'Got unexpected field names: {list(kwds)!r}') return result _replace.__doc__ = (f'Return a new {typename} object replacing specified ' 'fields with new values') def __repr__(self): 'Return a nicely formatted representation string' return self.__class__.__name__ + repr_fmt % self def _asdict(self): 'Return a new dict which maps field names to their values.' return _dict(_zip(self._fields, self)) def __getnewargs__(self): 'Return self as a plain tuple. Used by copy and pickle.' return _tuple(self) # Modify function metadata to help with introspection and debugging for method in ( __new__, _make.__func__, _replace, __repr__, _asdict, __getnewargs__, ): method.__qualname__ = f'{typename}.{method.__name__}' # Build-up the class namespace dictionary # and use type() to build the result class class_namespace = { '__doc__': f'{typename}({arg_list})', '__slots__': (), '_fields': field_names, '_field_defaults': field_defaults, '__new__': __new__, '_make': _make, '_replace': _replace, '__repr__': __repr__, '_asdict': _asdict, '__getnewargs__': __getnewargs__, '__match_args__': field_names, } for index, name in enumerate(field_names): doc = _sys.intern(f'Alias for field number {index}') class_namespace[name] = _tuplegetter(index, doc) result = type(typename, (tuple,), class_namespace) # For pickling to work, the __module__ variable needs to be set to the frame # where the named tuple is created. Bypass this step in environments where # sys._getframe is not defined (Jython for example) or sys._getframe is not # defined for arguments greater than 0 (IronPython), or where the user has # specified a particular module. if module is None: try: module = _sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): pass if module is not None: result.__module__ = module return result
(self, /, **kwds)
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0.03675394132733345, 0.005624341778457165, 0.03271595388650894 ]
42,761
preprocess_cancellation
ShapelyHullTracker
null
class ShapelyHullTracker(HullTracker): def __init__(self): self.pos = None self.points: Set[Point] = set() def add_point(self, point: Point): self.points.add(point) def center(self): if not self.points: return return Point( statistics.mean(p[0] for p in self.points), statistics.mean(p[1] for p in self.points), ) def exterior(self): if not self.points: return return list( shapely.geometry.MultiPoint(list(self.points)) .convex_hull.simplify(0.02, preserve_topology=False) .exterior.coords )
()
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42,762
preprocess_cancellation
__init__
null
def __init__(self): self.pos = None self.points: Set[Point] = set()
(self)
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42,763
preprocess_cancellation
add_point
null
def add_point(self, point: Point): self.points.add(point)
(self, point: preprocess_cancellation.Point)
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42,764
preprocess_cancellation
center
null
def center(self): if not self.points: return return Point( statistics.mean(p[0] for p in self.points), statistics.mean(p[1] for p in self.points), )
(self)
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42,765
preprocess_cancellation
exterior
null
def exterior(self): if not self.points: return return list( shapely.geometry.MultiPoint(list(self.points)) .convex_hull.simplify(0.02, preserve_topology=False) .exterior.coords )
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42,766
preprocess_cancellation
SimpleHullTracker
null
class SimpleHullTracker(HullTracker): def __init__(self) -> None: self.xmin = 999999 self.ymin = 999999 self.xmax = -999999 self.ymax = -999999 self.count_points = 0 self.xsum = 0 self.ysum = 0 def add_point(self, point: Point): self.xsum += point.x self.ysum += point.y self.count_points += 1 if point.x < self.xmin: self.xmin = point.x if point.y < self.ymin: self.ymin = point.y if point.x > self.xmax: self.xmax = point.x if point.y > self.ymax: self.ymax = point.y def center(self): if not self.count_points: return return Point( self.xsum / self.count_points, self.ysum / self.count_points, ) def exterior(self): if not self.count_points: return return boundingbox((self.xmin, self.ymin), (self.xmax, self.ymax))
() -> 'None'
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42,767
preprocess_cancellation
__init__
null
def __init__(self) -> None: self.xmin = 999999 self.ymin = 999999 self.xmax = -999999 self.ymax = -999999 self.count_points = 0 self.xsum = 0 self.ysum = 0
(self) -> NoneType
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42,768
preprocess_cancellation
add_point
null
def add_point(self, point: Point): self.xsum += point.x self.ysum += point.y self.count_points += 1 if point.x < self.xmin: self.xmin = point.x if point.y < self.ymin: self.ymin = point.y if point.x > self.xmax: self.xmax = point.x if point.y > self.ymax: self.ymax = point.y
(self, point: preprocess_cancellation.Point)
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42,769
preprocess_cancellation
center
null
def center(self): if not self.count_points: return return Point( self.xsum / self.count_points, self.ysum / self.count_points, )
(self)
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42,770
preprocess_cancellation
exterior
null
def exterior(self): if not self.count_points: return return boundingbox((self.xmin, self.ymin), (self.xmax, self.ymax))
(self)
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42,779
preprocess_cancellation
_clean_id
null
def _clean_id(id): return re.sub(r"\W+", "_", id).strip("_")
(id)
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42,780
preprocess_cancellation
_dump_coords
null
def _dump_coords(coords: List[float]) -> str: return ",".join(map("{:0.3f}".format, coords))
(coords: List[float]) -> str
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42,781
preprocess_cancellation
_main
null
def _main(): argparser = argparse.ArgumentParser() argparser.add_argument( "--output-suffix", "-o", help="Add a suffix to gcoode output. Without this, gcode will be rewritten in place", ) argparser.add_argument( "--disable-shapely", help="Disable using shapely to generate a hull polygon for objects", action="store_true" ) argparser.add_argument("gcode", nargs="*") exitcode = 0 args = argparser.parse_args() if args.disable_shapely: global shapely shapely = None for filename in args.gcode: if not process_file_for_cancellation(filename, args.output_suffix): exitcode = 1 sys.exit(exitcode)
()
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42,783
preprocess_cancellation
boundingbox
null
def boundingbox(pmin: Point, pmax: Point): return [ (pmin[0], pmin[1]), (pmin[0], pmax[1]), (pmax[0], pmax[1]), (pmax[0], pmin[1]), ]
(pmin: preprocess_cancellation.Point, pmax: preprocess_cancellation.Point)
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42,784
preprocess_cancellation
define_object
null
def define_object( name, center: Optional[Point] = None, polygon: Optional[Point] = None, region: Optional[List[Point]] = None, ): yield f"EXCLUDE_OBJECT_DEFINE NAME={name}" if center: yield f" CENTER={_dump_coords(center)}" if polygon: yield f" POLYGON={json.dumps(polygon, separators=(',', ':'))}" if region: yield f" REGION={_dump_coords(region, separators=(',', ':'))}" yield "\n"
(name, center: Optional[preprocess_cancellation.Point] = None, polygon: Optional[preprocess_cancellation.Point] = None, region: Optional[List[preprocess_cancellation.Point]] = None)
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42,785
preprocess_cancellation
header
null
def header(object_count): yield "\n\n" yield HEADER_MARKER yield f"; {object_count} known objects\n"
(object_count)
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42,786
preprocess_cancellation
identify_slicer_marker
null
def identify_slicer_marker(line): for name, (marker, processor) in SLICERS.items(): if line.strip().startswith(marker): logger.debug("Identified slicer %s", name) return processor
(line)
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42,789
preprocess_cancellation
object_end_marker
null
def object_end_marker(object_name): yield f"EXCLUDE_OBJECT_END NAME={object_name}\n"
(object_name)
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42,790
preprocess_cancellation
object_start_marker
null
def object_start_marker(object_name): yield f"EXCLUDE_OBJECT_START NAME={object_name}\n"
(object_name)
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42,791
preprocess_cancellation
parse_gcode
null
def parse_gcode(line): # drop comments line = line.split(";", maxsplit=1)[0] command, *params = line.strip().split() parsed = {} for param in params: if "=" in param: parsed.update(dict(zip(param.split("=", maxsplit=1)))) else: parsed.update({param[0].upper(): param[1:]}) return command, parsed
(line)
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42,793
preprocess_cancellation
preprocess_cura
null
def preprocess_cura(infile): known_objects: Dict[str, KnownObject] = {} current_hull: Optional[HullTracker] = None last_time_elapsed: str = None # iterate the file twice, to be able to inject the header markers for line in infile: if line.startswith(";MESH:"): object_name = line.split(":", maxsplit=1)[1].strip() if object_name == "NONMESH": continue if object_name not in known_objects: known_objects[object_name] = KnownObject(_clean_id(object_name), HullTracker.create()) current_hull = known_objects[object_name].hull if current_hull and line.strip().lower().startswith("g"): _, params = parse_gcode(line) if float(params.get("E", -1)) > 0 and "X" in params and "Y" in params: x = float(params["X"]) y = float(params["Y"]) current_hull.add_point(Point(x, y)) if line.startswith(";TIME_ELAPSED:"): last_time_elapsed = line infile.seek(0) for line in infile: if line.strip() and not line.startswith(";"): # Inject custom marker yield from header(len(known_objects)) for mesh_id, hull in known_objects.values(): yield from define_object( mesh_id, center=hull.center(), polygon=hull.exterior(), ) yield line break yield line current_object = None for line in infile: yield line if line.startswith(";MESH:"): if current_object: yield from object_end_marker(current_object) current_object = None mesh = line.split(":", maxsplit=1)[1].strip() if mesh == "NONMESH": continue current_object, _ = known_objects[mesh] yield from object_start_marker(current_object) if line == last_time_elapsed and current_object: yield from object_end_marker(current_object) current_object = None if current_object: yield from object_end_marker(current_object)
(infile)
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42,794
preprocess_cancellation
preprocess_ideamaker
null
def preprocess_ideamaker(infile): # This one is funnier # theres blocks like this, we can grab all these to get the names and ideamaker's IDs for them. # ;PRINTING: test_bed_part0.3mf # ;PRINTING_ID: 0 known_objects: Dict[str, KnownObject] = {} current_hull: HullTracker = None for line in infile: if line.startswith(";TOTAL_NUM:"): total_num = int(line.split(":")[1].strip()) if line.startswith(";PRINTING:"): name = line.split(":")[1].strip() id_line = next(infile) assert id_line.startswith(";PRINTING_ID:") id = id_line.split(":")[1].strip() # Ignore the internal non-object meshes if id == "-1": continue if id not in known_objects: known_objects[id] = KnownObject(_clean_id(name), HullTracker.create()) current_hull = known_objects[id].hull if current_hull and line.strip().lower().startswith("g"): command, params = parse_gcode(line) if float(params.get("E", -1)) > 0 and "X" in params and "Y" in params: x = float(params["X"]) y = float(params["Y"]) current_hull.add_point(Point(x, y)) infile.seek(0) current_object: Optional[KnownObject] = None for line in infile: yield line if line.strip() and not line.startswith(";"): break assert total_num == len(known_objects) yield from header(total_num) for id, (name, hull) in known_objects.items(): yield from define_object( name, center=hull.center(), polygon=hull.exterior(), ) yield "\n\n" for line in infile: yield line if line.startswith(";PRINTING_ID:"): printing_id = line.split(":")[1].strip() if current_object: yield from object_end_marker(current_object.name) current_object = None if printing_id == "-1": continue current_object = known_objects[printing_id] yield from object_start_marker(current_object.name) if line == ";REMAINING_TIME: 0\n" and current_object: yield from object_end_marker(current_object.name) current_object = None if current_object: yield from object_end_marker(current_object.name)
(infile)
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42,795
preprocess_cancellation
preprocess_m486
null
def preprocess_m486(infile): known_objects: Dict[str, KnownObject] = {} current_hull: Optional[HullTracker] = None for line in infile: if line.startswith("M486"): _, params = parse_gcode(line) if "T" in params: for i in range(-1, int(params["T"])): known_objects[f"{i}"] = KnownObject(f"{i}", HullTracker.create()) elif "S" in params: current_hull = known_objects[params["S"]].hull if current_hull and line.strip().lower().startswith("g"): _, params = parse_gcode(line) if float(params.get("E", -1)) > 0 and "X" in params and "Y" in params: x = float(params["X"]) y = float(params["Y"]) current_hull.add_point(Point(x, y)) infile.seek(0) current_object = None for line in infile: if line.upper().startswith("M486"): _, params = parse_gcode(line) if "T" in params: # Inject custom marker yield from header(len(known_objects)) for mesh_id, hull in known_objects.values(): if mesh_id == "-1": continue yield from define_object( mesh_id, center=hull.center(), polygon=hull.exterior(), ) if "S" in params: if current_object: yield from object_end_marker(current_object.name) current_object = None if params["S"] != "-1": current_object = known_objects[params["S"]] yield from object_start_marker(current_object.name) yield "; " # Comment out the original M486 lines yield line
(infile)
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42,796
preprocess_cancellation
preprocess_pipe
null
def preprocess_pipe(infile): yield from infile
(infile)
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-0.025837186723947525, 0.027281705290079117, 0.013829625211656094 ]
42,797
preprocess_cancellation
preprocess_slicer
null
def preprocess_slicer(infile): known_objects: Dict[str, KnownObject] = {} current_hull: Optional[HullTracker] = None for line in infile: if line.startswith("; printing object "): object_id = line.split("printing object")[1].strip() if object_id not in known_objects: known_objects[object_id] = KnownObject(_clean_id(object_id), HullTracker.create()) current_hull = known_objects[object_id].hull if line.startswith("; stop printing object "): current_hull = None if current_hull and line.strip().lower().startswith("g"): command, params = parse_gcode(line) if float(params.get("E", -1)) > 0 and "X" in params and "Y" in params: x = float(params["X"]) y = float(params["Y"]) current_hull.add_point(Point(x, y)) infile.seek(0) for line in infile: if line.strip() and not line.startswith(";"): # Inject custom marker yield from header(len(known_objects)) for object_id, hull in known_objects.values(): yield from define_object( object_id, center=hull.center(), polygon=hull.exterior(), ) yield line break yield line for line in infile: yield line if line.startswith("; printing object "): yield from object_start_marker(known_objects[line.split("printing object")[1].strip()].name) if line.startswith("; stop printing object "): yield from object_end_marker(known_objects[line.split("printing object")[1].strip()].name)
(infile)
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42,798
preprocess_cancellation
preprocessor
null
def preprocessor(infile, outfile): logger.debug("Identifying slicer") found_m486 = False processor = None for line in infile: if line.startswith("EXCLUDE_OBJECT_DEFINE") or line.startswith("DEFINE_OBJECT"): logger.info("GCode already supports cancellation") infile.seek(0) outfile.write(infile.read()) return True if line.startswith("M486"): if not found_m486: logger.info("File has existing M486 markers, converting") found_m486 = True processor = preprocess_m486 if not processor: processor = identify_slicer_marker(line) infile.seek(0) for line in processor(infile): outfile.write(line) return True
(infile, outfile)
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42,799
preprocess_cancellation
process_file_for_cancellation
null
def process_file_for_cancellation(filename: PathLike, output_suffix=None) -> int: filepath = pathlib.Path(filename) outfilepath = filepath if output_suffix: outfilepath = outfilepath.with_name(outfilepath.stem + output_suffix + outfilepath.suffix) tempfilepath = pathlib.Path(tempfile.mktemp()) with filepath.open("r") as fin: with tempfilepath.open("w") as fout: res = preprocessor(fin, fout) if res: if outfilepath.exists(): outfilepath.unlink() shutil.move(tempfilepath, outfilepath) else: tempfilepath.unlink() return res
(filename: ~PathLike, output_suffix=None) -> int
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42,805
linearmodels.iv.absorbing
AbsorbingLS
Linear regression with high-dimensional effects Parameters ---------- dependent : array_like Endogenous variables (nobs by 1) exog : array_like Exogenous regressors (nobs by nexog) absorb: {DataFrame, Interaction} The effects or continuous variables to absorb. When using a DataFrame, effects must be categorical variables. Other variable types are treated as continuous variables that should be absorbed. When using an Interaction, variables in the `cat` argument are treated as effects and variables in the `cont` argument are treated as continuous. interactions : {DataFrame, Interaction, list[DataFrame, Interaction]} Interactions containing both categorical and continuous variables. Each interaction is constructed using the Cartesian product of the categorical variables to produce the dummy, which are then separately interacted with each continuous variable. weights : array_like Observation weights used in estimation drop_absorbed : bool Flag indicating whether to drop absorbed variables Notes ----- Capable of estimating models with millions of effects. Estimates models of the form .. math:: y_i = x_i \beta + z_i \gamma + \epsilon_i where :math:`\beta` are parameters of interest and :math:`\gamma` are not. z may be high-dimensional, although must have fewer variables than the number of observations in y. The syntax simplifies specifying high-dimensional z when z consists of categorical (factor) variables, also known as effects, or when z contains interactions between continuous variables and categorical variables, also known as fixed slopes. The high-dimensional effects are fit using LSMR which avoids inverting or even constructing the inner product of the regressors. This is combined with Frish-Waugh-Lovell to orthogonalize x and y from z. z can contain factors that are perfectly linearly dependent. LSMR estimates a particular restricted set of parameters that captures the effect of non-redundant components in z. See also -------- Interaction linearmodels.iv.model.IVLIML linearmodels.iv.model.IV2SLS scipy.sparse.linalg.lsmr Examples -------- Estimate a model by absorbing 2 categoricals and 2 continuous variables >>> import numpy as np >>> import pandas as pd >>> from linearmodels.iv import AbsorbingLS, Interaction >>> dep = np.random.standard_normal((20000,1)) >>> exog = np.random.standard_normal((20000,2)) >>> cats = pd.DataFrame({i: pd.Categorical(np.random.randint(1000, size=20000)) ... for i in range(2)}) >>> cont = pd.DataFrame({i+2: np.random.standard_normal(20000) for i in range(2)}) >>> absorb = pd.concat([cats, cont], axis=1) >>> mod = AbsorbingLS(dep, exog, absorb=absorb) >>> res = mod.fit() Add interactions between the cartesian product of the categorical and each continuous variables >>> iaction = Interaction(cat=cats, cont=cont) >>> absorb = Interaction(cat=cats) # Other encoding of categoricals >>> mod = AbsorbingLS(dep, exog, absorb=absorb, interactions=iaction)
class AbsorbingLS: r""" Linear regression with high-dimensional effects Parameters ---------- dependent : array_like Endogenous variables (nobs by 1) exog : array_like Exogenous regressors (nobs by nexog) absorb: {DataFrame, Interaction} The effects or continuous variables to absorb. When using a DataFrame, effects must be categorical variables. Other variable types are treated as continuous variables that should be absorbed. When using an Interaction, variables in the `cat` argument are treated as effects and variables in the `cont` argument are treated as continuous. interactions : {DataFrame, Interaction, list[DataFrame, Interaction]} Interactions containing both categorical and continuous variables. Each interaction is constructed using the Cartesian product of the categorical variables to produce the dummy, which are then separately interacted with each continuous variable. weights : array_like Observation weights used in estimation drop_absorbed : bool Flag indicating whether to drop absorbed variables Notes ----- Capable of estimating models with millions of effects. Estimates models of the form .. math:: y_i = x_i \beta + z_i \gamma + \epsilon_i where :math:`\beta` are parameters of interest and :math:`\gamma` are not. z may be high-dimensional, although must have fewer variables than the number of observations in y. The syntax simplifies specifying high-dimensional z when z consists of categorical (factor) variables, also known as effects, or when z contains interactions between continuous variables and categorical variables, also known as fixed slopes. The high-dimensional effects are fit using LSMR which avoids inverting or even constructing the inner product of the regressors. This is combined with Frish-Waugh-Lovell to orthogonalize x and y from z. z can contain factors that are perfectly linearly dependent. LSMR estimates a particular restricted set of parameters that captures the effect of non-redundant components in z. See also -------- Interaction linearmodels.iv.model.IVLIML linearmodels.iv.model.IV2SLS scipy.sparse.linalg.lsmr Examples -------- Estimate a model by absorbing 2 categoricals and 2 continuous variables >>> import numpy as np >>> import pandas as pd >>> from linearmodels.iv import AbsorbingLS, Interaction >>> dep = np.random.standard_normal((20000,1)) >>> exog = np.random.standard_normal((20000,2)) >>> cats = pd.DataFrame({i: pd.Categorical(np.random.randint(1000, size=20000)) ... for i in range(2)}) >>> cont = pd.DataFrame({i+2: np.random.standard_normal(20000) for i in range(2)}) >>> absorb = pd.concat([cats, cont], axis=1) >>> mod = AbsorbingLS(dep, exog, absorb=absorb) >>> res = mod.fit() Add interactions between the cartesian product of the categorical and each continuous variables >>> iaction = Interaction(cat=cats, cont=cont) >>> absorb = Interaction(cat=cats) # Other encoding of categoricals >>> mod = AbsorbingLS(dep, exog, absorb=absorb, interactions=iaction) """ def __init__( self, dependent: ArrayLike, exog: ArrayLike | None = None, *, absorb: InteractionVar | None = None, interactions: InteractionVar | Iterable[InteractionVar] | None = None, weights: ArrayLike | None = None, drop_absorbed: bool = False, ) -> None: self._dependent = IVData(dependent, "dependent") self._nobs = nobs = self._dependent.shape[0] self._exog = IVData(exog, "exog", nobs=self._nobs) self._absorb = absorb if isinstance(absorb, DataFrame): self._absorb_inter = Interaction.from_frame(absorb) elif absorb is None: self._absorb_inter = Interaction(None, None, nobs) elif isinstance(absorb, Interaction): self._absorb_inter = absorb else: raise TypeError("absorb must ba a DataFrame or an Interaction") self._weights = weights self._is_weighted = False self._drop_absorbed = drop_absorbed self._check_weights() self._interactions = interactions self._interaction_list: list[Interaction] = [] self._prepare_interactions() self._absorbed_dependent: DataFrame | None = None self._absorbed_exog: DataFrame | None = None self._check_shape() self._original_index = self._dependent.pandas.index self._drop_locs = self._drop_missing() self._columns = self._exog.cols self._index = self._dependent.rows self._method = "Absorbing LS" self._const_col = 0 self._has_constant = False self._has_constant_exog = self._check_constant() self._constant_absorbed = False self._num_params = 0 self._regressors: sp.csc_matrix | None = None self._regressors_hash: tuple[tuple[str, ...], ...] | None = None def _drop_missing(self) -> BoolArray: missing = require(self.dependent.isnull.to_numpy(), requirements="W") missing |= self.exog.isnull.to_numpy() missing |= self._absorb_inter.cat.isnull().any(axis=1).to_numpy() missing |= self._absorb_inter.cont.isnull().any(axis=1).to_numpy() for interact in self._interaction_list: missing |= interact.isnull.to_numpy() if npany(missing): self.dependent.drop(missing) self.exog.drop(missing) self._absorb_inter.drop(missing) for interact in self._interaction_list: interact.drop(missing) missing_warning(missing, stacklevel=4) return missing def _check_constant(self) -> bool: col_delta = ptp(self.exog.ndarray, 0) has_constant = npany(col_delta == 0) self._const_col = where(col_delta == 0)[0][0] if has_constant else None return bool(has_constant) def _check_weights(self) -> None: if self._weights is None: nobs = self._dependent.shape[0] self._is_weighted = False self._weight_data = IVData(ones(nobs), "weights") else: self._is_weighted = True weights = IVData(self._weights).ndarray weights = weights / nanmean(weights) self._weight_data = IVData(weights, var_name="weights", nobs=self._nobs) def _check_shape(self) -> None: nobs = self._nobs if self._absorb is not None: if self._absorb_inter.nobs != nobs: raise ValueError( "absorb and dependent have different number of observations" ) for interact in self._interaction_list: if interact.nobs != nobs: raise ValueError( "interactions ({}) and dependent have different number of " "observations".format(str(interact)) ) @property def absorbed_dependent(self) -> DataFrame: """ Dependent variable with effects absorbed Returns ------- DataFrame Dependent after effects have been absorbed Raises ------ RuntimeError If called before `fit` has been used once """ if self._absorbed_dependent is not None: return self._absorbed_dependent raise RuntimeError( "fit must be called once before absorbed_dependent is available" ) @property def absorbed_exog(self) -> DataFrame: """ Exogenous variables with effects absorbed Returns ------- DataFrame Exogenous after effects have been absorbed Raises ------ RuntimeError If called before `fit` has been used once """ if self._absorbed_exog is not None: return self._absorbed_exog raise RuntimeError("fit must be called
(dependent: 'ArrayLike', exog: 'ArrayLike | None' = None, *, absorb: 'InteractionVar | None' = None, interactions: 'InteractionVar | Iterable[InteractionVar] | None' = None, weights: 'ArrayLike | None' = None, drop_absorbed: 'bool' = False) -> 'None'
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42,806
linearmodels.iv.absorbing
__init__
null
def __init__( self, dependent: ArrayLike, exog: ArrayLike | None = None, *, absorb: InteractionVar | None = None, interactions: InteractionVar | Iterable[InteractionVar] | None = None, weights: ArrayLike | None = None, drop_absorbed: bool = False, ) -> None: self._dependent = IVData(dependent, "dependent") self._nobs = nobs = self._dependent.shape[0] self._exog = IVData(exog, "exog", nobs=self._nobs) self._absorb = absorb if isinstance(absorb, DataFrame): self._absorb_inter = Interaction.from_frame(absorb) elif absorb is None: self._absorb_inter = Interaction(None, None, nobs) elif isinstance(absorb, Interaction): self._absorb_inter = absorb else: raise TypeError("absorb must ba a DataFrame or an Interaction") self._weights = weights self._is_weighted = False self._drop_absorbed = drop_absorbed self._check_weights() self._interactions = interactions self._interaction_list: list[Interaction] = [] self._prepare_interactions() self._absorbed_dependent: DataFrame | None = None self._absorbed_exog: DataFrame | None = None self._check_shape() self._original_index = self._dependent.pandas.index self._drop_locs = self._drop_missing() self._columns = self._exog.cols self._index = self._dependent.rows self._method = "Absorbing LS" self._const_col = 0 self._has_constant = False self._has_constant_exog = self._check_constant() self._constant_absorbed = False self._num_params = 0 self._regressors: sp.csc_matrix | None = None self._regressors_hash: tuple[tuple[str, ...], ...] | None = None
(self, dependent: Union[numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series], exog: Union[numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series, NoneType] = None, *, absorb: Union[pandas.core.frame.DataFrame, linearmodels.iv.absorbing.Interaction, NoneType] = None, interactions: Union[pandas.core.frame.DataFrame, linearmodels.iv.absorbing.Interaction, collections.abc.Iterable[Union[pandas.core.frame.DataFrame, linearmodels.iv.absorbing.Interaction]], NoneType] = None, weights: Union[numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series, NoneType] = None, drop_absorbed: bool = False) -> NoneType
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42,807
linearmodels.iv.absorbing
_check_constant
null
def _check_constant(self) -> bool: col_delta = ptp(self.exog.ndarray, 0) has_constant = npany(col_delta == 0) self._const_col = where(col_delta == 0)[0][0] if has_constant else None return bool(has_constant)
(self) -> bool
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42,808
linearmodels.iv.absorbing
_check_shape
null
def _check_shape(self) -> None: nobs = self._nobs if self._absorb is not None: if self._absorb_inter.nobs != nobs: raise ValueError( "absorb and dependent have different number of observations" ) for interact in self._interaction_list: if interact.nobs != nobs: raise ValueError( "interactions ({}) and dependent have different number of " "observations".format(str(interact)) )
(self) -> NoneType
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42,809
linearmodels.iv.absorbing
_check_weights
null
def _check_weights(self) -> None: if self._weights is None: nobs = self._dependent.shape[0] self._is_weighted = False self._weight_data = IVData(ones(nobs), "weights") else: self._is_weighted = True weights = IVData(self._weights).ndarray weights = weights / nanmean(weights) self._weight_data = IVData(weights, var_name="weights", nobs=self._nobs)
(self) -> NoneType
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0.0329933762550354, 0.05053039267659187, 0.048187460750341415 ]
42,810
linearmodels.iv.absorbing
_drop_missing
null
def _drop_missing(self) -> BoolArray: missing = require(self.dependent.isnull.to_numpy(), requirements="W") missing |= self.exog.isnull.to_numpy() missing |= self._absorb_inter.cat.isnull().any(axis=1).to_numpy() missing |= self._absorb_inter.cont.isnull().any(axis=1).to_numpy() for interact in self._interaction_list: missing |= interact.isnull.to_numpy() if npany(missing): self.dependent.drop(missing) self.exog.drop(missing) self._absorb_inter.drop(missing) for interact in self._interaction_list: interact.drop(missing) missing_warning(missing, stacklevel=4) return missing
(self) -> numpy.ndarray
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42,811
linearmodels.iv.absorbing
_f_statistic
null
def _f_statistic( self, params: Float64Array, cov: Float64Array, debiased: bool ) -> WaldTestStatistic | InvalidTestStatistic: const_loc = find_constant(cast(Float64Array, self._exog.ndarray)) resid_df = self._nobs - self._num_params return f_statistic(params, cov, debiased, resid_df, const_loc)
(self, params: numpy.ndarray, cov: numpy.ndarray, debiased: bool) -> linearmodels.shared.hypotheses.WaldTestStatistic | linearmodels.shared.hypotheses.InvalidTestStatistic
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42,812
linearmodels.iv.absorbing
_first_time_fit
null
def _first_time_fit( self, use_cache: bool, absorb_options: None | ( dict[str, bool | float | str | ArrayLike | None | dict[str, Any]] ), method: str, ) -> None: weights = ( cast(Float64Array, self.weights.ndarray) if self._is_weighted else None ) use_hdfe = weights is None and method in ("auto", "hdfe") use_hdfe = use_hdfe and not self._absorb_inter.cont.shape[1] use_hdfe = use_hdfe and not self._interaction_list if not use_hdfe and method == "hdfe": raise RuntimeError( "HDFE has been set as the method but the model cannot be estimated " "using HDFE. HDFE requires that the model is unweighted and that the " "absorbed regressors include only fixed effects (dummy variables)." ) areg = AbsorbingRegressor( cat=self._absorb_inter.cat, cont=self._absorb_inter.cont, interactions=self._interaction_list, weights=weights, ) areg_constant = areg.has_constant self._regressors = areg.regressors self._num_params += areg.approx_rank # Do not double count intercept-like terms self._has_constant = self._has_constant_exog or areg_constant self._num_params -= min(self._has_constant_exog, areg_constant) self._regressors_hash = areg.hash self._constant_absorbed = self._has_constant_exog and areg_constant dep = self._dependent.ndarray exog = cast(Float64Array, self._exog.ndarray) root_w = sqrt(self._weight_data.ndarray) dep = root_w * dep exog = root_w * exog denom = root_w.T @ root_w mu_dep = (root_w.T @ dep) / denom mu_exog = (root_w.T @ exog) / denom absorb_options = {} if absorb_options is None else absorb_options assert isinstance(self._regressors, sp.csc_matrix) if self._regressors.shape[1] > 0: if use_hdfe: from pyhdfe import create absorb_options["drop_singletons"] = False algo = create(self._absorb_inter.cat, **absorb_options) dep_exog = column_stack((dep, exog)) resids = algo.residualize(dep_exog) dep_resid = resids[:, :1] exog_resid = resids[:, 1:] else: self._regressors = preconditioner(self._regressors)[0] dep_exog = column_stack((dep, exog)) resid = lsmr_annihilate( self._regressors, dep_exog, use_cache, self._regressors_hash, **absorb_options, ) dep_resid = resid[:, :1] exog_resid = resid[:, 1:] else: dep_resid = dep exog_resid = exog if self._constant_absorbed: dep_resid += root_w * mu_dep exog_resid += root_w * mu_exog if not self._drop_absorbed: check_absorbed(exog_resid, self.exog.cols, exog) else: ncol = exog_resid.shape[1] retain = not_absorbed(exog_resid) if not retain: raise ValueError( "All columns in exog have been fully absorbed by the " "included effects. This model cannot be estimated." ) elif len(retain) < ncol: drop = set(range(ncol)).difference(retain) dropped = ", ".join([str(self.exog.cols[i]) for i in drop]) warnings.warn( absorbing_warn_msg.format(absorbed_variables=dropped), AbsorbingEffectWarning, stacklevel=3, ) exog_resid = exog_resid[:, retain] self._columns = [self._columns[i] for i in retain] self._absorbed_dependent = DataFrame( dep_resid, index=self._dependent.pandas.index, columns=self._dependent.pandas.columns, ) self._absorbed_exog = DataFrame( exog_resid, index=self._exog.pandas.index, columns=self._columns )
(self, use_cache: bool, absorb_options: None | dict[str, typing.Union[bool, float, str, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series, NoneType, dict[str, typing.Any]]], method: str) -> NoneType
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42,813
linearmodels.iv.absorbing
_post_estimation
null
def _post_estimation( self, params: Float64Array, cov_estimator: ( HomoskedasticCovariance | HeteroskedasticCovariance | KernelCovariance | ClusteredCovariance ), cov_type: str, ) -> dict[str, Any]: columns = self._columns index = self._index eps = self.resids(params) fitted_values = self._dependent.ndarray - eps fitted = DataFrameWrapper( fitted_values, index=self._dependent.rows, columns=["fitted_values"], ) assert isinstance(self._absorbed_dependent, DataFrame) absorbed_effects = DataFrameWrapper( self._absorbed_dependent.to_numpy() - fitted_values, columns=["absorbed_effects"], index=self._dependent.rows, ) weps = self.wresids(params) cov = cov_estimator.cov debiased = cov_estimator.debiased residual_ss = (weps.T @ weps)[0, 0] w = self.weights.ndarray root_w = sqrt(w) e = self._dependent.ndarray * root_w if self.has_constant: e = e - root_w * average(self._dependent.ndarray, weights=w) total_ss = float(squeeze(e.T @ e)) r2 = max(1 - residual_ss / total_ss, 0.0) e = self._absorbed_dependent.to_numpy() # already scaled by root_w # If absorbing contains a constant, but exog does not, no need to demean assert isinstance(self._absorbed_exog, DataFrame) if self._const_col is not None: col = self._const_col x = self._absorbed_exog.to_numpy()[:, col : col + 1] mu = (lstsq(x, e, rcond=None)[0]).squeeze() e = e - x * mu aborbed_total_ss = float(squeeze(e.T @ e)) r2_absorbed = max(1 - residual_ss / aborbed_total_ss, 0.0) fstat = self._f_statistic(params, cov, debiased) out = { "params": Series(params.squeeze(), columns, name="parameter"), "eps": SeriesWrapper(eps.squeeze(), index=index, name="residual"), "weps": SeriesWrapper( weps.squeeze(), index=index, name="weighted residual" ), "cov": DataFrame(cov, columns=columns, index=columns), "s2": float(squeeze(cov_estimator.s2)), "debiased": debiased, "residual_ss": float(residual_ss), "total_ss": float(total_ss), "r2": float(r2), "fstat": fstat, "vars": columns, "instruments": [], "cov_config": cov_estimator.config, "cov_type": cov_type, "method": self._method, "cov_estimator": cov_estimator, "fitted": fitted, "original_index": self._original_index, "absorbed_effects": absorbed_effects, "absorbed_r2": r2_absorbed, } return out
(self, params: numpy.ndarray, cov_estimator: linearmodels.iv.covariance.HomoskedasticCovariance | linearmodels.iv.covariance.HeteroskedasticCovariance | linearmodels.iv.covariance.KernelCovariance | linearmodels.iv.covariance.ClusteredCovariance, cov_type: str) -> dict[str, typing.Any]
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42,814
linearmodels.iv.absorbing
_prepare_interactions
null
def _prepare_interactions(self) -> None: if self._interactions is None: return elif isinstance(self._interactions, DataFrame): self._interaction_list = [Interaction.from_frame(self._interactions)] elif isinstance(self._interactions, Interaction): self._interaction_list = [self._interactions] else: for interact in self._interactions: if isinstance(interact, DataFrame): self._interaction_list.append(Interaction.from_frame(interact)) elif isinstance(interact, Interaction): self._interaction_list.append(interact) else: raise TypeError( "interactions must contain DataFrames or Interactions" )
(self) -> NoneType
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42,815
linearmodels.iv.absorbing
fit
Estimate model parameters Parameters ---------- cov_type : str Name of covariance estimator to use. Supported covariance estimators are: * "unadjusted", "homoskedastic" - Classic homoskedastic inference * "robust", "heteroskedastic" - Heteroskedasticity robust inference * "kernel" - Heteroskedasticity and autocorrelation robust inference * "cluster" - One-way cluster dependent inference. Heteroskedasticity robust debiased : bool Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment. method : str One of: * "auto" - (Default). Use HDFE when applicable and fallback to LSMR. * "lsmr" - Force LSMR. * "hdfe" - Force HDFE. Raises RuntimeError if the model contains continuous variables or continuous-binary interactions to absorb or if the model is weighted. absorb_options : dict Dictionary of options to pass to the absorber. Passed to either scipy.sparse.linalg.lsmr or pyhdfe.create depending on the method used to absorb the absorbed regressors. use_cache : bool Flag indicating whether the variables, once purged from the absorbed variables and interactions, should be stored in the cache, and retrieved if available. Cache can dramatically speed up re-fitting large models when the set of absorbed variables and interactions are identical. lsmr_options : dict Options to ass to scipy.sparse.linalg.lsmr. .. deprecated:: 4.17 Use absorb_options to pass options **cov_config Additional parameters to pass to covariance estimator. The list of optional parameters differ according to ``cov_type``. See the documentation of the alternative covariance estimators for the complete list of available commands. Returns ------- AbsorbingLSResults Results container Notes ----- Additional covariance parameters depend on specific covariance used. The see the docstring of specific covariance estimator for a list of supported options. Defaults are used if no covariance configuration is provided. If use_cache is True, then variables are hashed based on their contents using either a 64-bit value (if xxhash is installed) or a 256-bit value. This allows variables to be reused in different models if the set of absorbing variables and interactions is held constant. See also -------- linearmodels.iv.covariance.HomoskedasticCovariance linearmodels.iv.covariance.HeteroskedasticCovariance linearmodels.iv.covariance.KernelCovariance linearmodels.iv.covariance.ClusteredCovariance
def fit( self, *, cov_type: str = "robust", debiased: bool = False, method: str = "auto", absorb_options: None | ( dict[str, bool | float | str | ArrayLike | None | dict[str, Any]] ) = None, use_cache: bool = True, lsmr_options: dict[str, float | bool] | None = None, **cov_config: Any, ) -> AbsorbingLSResults: """ Estimate model parameters Parameters ---------- cov_type : str Name of covariance estimator to use. Supported covariance estimators are: * "unadjusted", "homoskedastic" - Classic homoskedastic inference * "robust", "heteroskedastic" - Heteroskedasticity robust inference * "kernel" - Heteroskedasticity and autocorrelation robust inference * "cluster" - One-way cluster dependent inference. Heteroskedasticity robust debiased : bool Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment. method : str One of: * "auto" - (Default). Use HDFE when applicable and fallback to LSMR. * "lsmr" - Force LSMR. * "hdfe" - Force HDFE. Raises RuntimeError if the model contains continuous variables or continuous-binary interactions to absorb or if the model is weighted. absorb_options : dict Dictionary of options to pass to the absorber. Passed to either scipy.sparse.linalg.lsmr or pyhdfe.create depending on the method used to absorb the absorbed regressors. use_cache : bool Flag indicating whether the variables, once purged from the absorbed variables and interactions, should be stored in the cache, and retrieved if available. Cache can dramatically speed up re-fitting large models when the set of absorbed variables and interactions are identical. lsmr_options : dict Options to ass to scipy.sparse.linalg.lsmr. .. deprecated:: 4.17 Use absorb_options to pass options **cov_config Additional parameters to pass to covariance estimator. The list of optional parameters differ according to ``cov_type``. See the documentation of the alternative covariance estimators for the complete list of available commands. Returns ------- AbsorbingLSResults Results container Notes ----- Additional covariance parameters depend on specific covariance used. The see the docstring of specific covariance estimator for a list of supported options. Defaults are used if no covariance configuration is provided. If use_cache is True, then variables are hashed based on their contents using either a 64-bit value (if xxhash is installed) or a 256-bit value. This allows variables to be reused in different models if the set of absorbing variables and interactions is held constant. See also -------- linearmodels.iv.covariance.HomoskedasticCovariance linearmodels.iv.covariance.HeteroskedasticCovariance linearmodels.iv.covariance.KernelCovariance linearmodels.iv.covariance.ClusteredCovariance """ if lsmr_options is not None: if absorb_options is not None: raise ValueError("absorb_options cannot be used with lsmr_options") warnings.warn( "lsmr_options is deprecated. Use absorb_options.", FutureWarning, stacklevel=2, ) absorb_options = {k: v for k, v in lsmr_options.items()} if self._absorbed_dependent is None: self._first_time_fit(use_cache, absorb_options, method) exog_resid = self.absorbed_exog.to_numpy() dep_resid = self.absorbed_dependent.to_numpy() if self._exog.shape[1] == 0: params = empty((0, 1)) else: params = lstsq(exog_resid, dep_resid, rcond=None)[0] self._num_params += exog_resid.shape[1] cov_estimator = COVARIANCE_ESTIMATORS[cov_type] cov_config["debiased"] = debiased cov_config["kappa"] = 0.0 cov_config_copy = {k: v for k, v in cov_config.items()} if "center" in cov_config_copy: del cov_config_copy["center"] cov_estimator_inst = cov_estimator( exog_resid, dep_resid, exog_resid, params, **cov_config_copy ) results = {"kappa": 0.0, "liml_kappa": 0.0} pe = self._post_estimation(params, cov_estimator_inst, cov_type) results.update(pe) results["df_model"] = self._num_params return AbsorbingLSResults(results, self)
(self, *, cov_type: str = 'robust', debiased: bool = False, method: str = 'auto', absorb_options: Optional[dict[str, Union[bool, float, str, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series, NoneType, dict[str, Any]]]] = None, use_cache: bool = True, lsmr_options: Optional[dict[str, float | bool]] = None, **cov_config: Any) -> linearmodels.iv.results.AbsorbingLSResults
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42,816
linearmodels.iv.absorbing
resids
Compute model residuals Parameters ---------- params : ndarray Model parameters (nvar by 1) Returns ------- ndarray Model residuals
def resids(self, params: Float64Array) -> Float64Array: """ Compute model residuals Parameters ---------- params : ndarray Model parameters (nvar by 1) Returns ------- ndarray Model residuals """ resids = self.wresids(params) return resids / sqrt(self.weights.ndarray)
(self, params: numpy.ndarray) -> numpy.ndarray
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42,817
linearmodels.iv.absorbing
wresids
Compute weighted model residuals Parameters ---------- params : ndarray Model parameters (nvar by 1) Returns ------- ndarray Weighted model residuals Notes ----- Uses weighted versions of data instead of raw data. Identical to resids if all weights are unity.
def wresids(self, params: Float64Array) -> Float64Array: """ Compute weighted model residuals Parameters ---------- params : ndarray Model parameters (nvar by 1) Returns ------- ndarray Weighted model residuals Notes ----- Uses weighted versions of data instead of raw data. Identical to resids if all weights are unity. """ assert isinstance(self._absorbed_dependent, DataFrame) assert isinstance(self._absorbed_exog, DataFrame) return ( self._absorbed_dependent.to_numpy() - self._absorbed_exog.to_numpy() @ params )
(self, params: numpy.ndarray) -> numpy.ndarray
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42,818
linearmodels.panel.model
BetweenOLS
Between estimator for panel data Parameters ---------- dependent : array_like Dependent (left-hand-side) variable (time by entity) exog : array_like Exogenous or right-hand-side variables (variable by time by entity). weights : array_like Weights to use in estimation. Assumes residual variance is proportional to inverse of weight to that the residual time the weight should be homoskedastic. Notes ----- The model is given by .. math:: \bar{y}_{i}= \beta^{\prime}\bar{x}_{i}+\bar{\epsilon}_{i} where :math:`\bar{z}` is the time-average.
class BetweenOLS(_PanelModelBase): r""" Between estimator for panel data Parameters ---------- dependent : array_like Dependent (left-hand-side) variable (time by entity) exog : array_like Exogenous or right-hand-side variables (variable by time by entity). weights : array_like Weights to use in estimation. Assumes residual variance is proportional to inverse of weight to that the residual time the weight should be homoskedastic. Notes ----- The model is given by .. math:: \bar{y}_{i}= \beta^{\prime}\bar{x}_{i}+\bar{\epsilon}_{i} where :math:`\bar{z}` is the time-average. """ def __init__( self, dependent: PanelDataLike, exog: PanelDataLike, *, weights: PanelDataLike | None = None, check_rank: bool = True, ) -> None: super().__init__(dependent, exog, weights=weights, check_rank=check_rank) self._cov_estimators = CovarianceManager( self.__class__.__name__, HomoskedasticCovariance, HeteroskedasticCovariance, ClusteredCovariance, ) def _setup_clusters( self, cov_config: Mapping[str, bool | float | str | IntArray | DataFrame | PanelData], ) -> dict[str, bool | float | str | IntArray | DataFrame | PanelData]: """Return covariance estimator reformat clusters""" cov_config_upd = dict(cov_config) if "clusters" not in cov_config: return cov_config_upd clusters = cov_config.get("clusters", None) if clusters is not None: cluster_data = cast(Union[IntArray, DataFrame, PanelData], clusters) clusters_panel = self.reformat_clusters(cluster_data) cluster_max = np.nanmax(clusters_panel.values3d, axis=1) delta = cluster_max - np.nanmin(clusters_panel.values3d, axis=1) if np.any(delta != 0): raise ValueError("clusters must not vary within an entity") index = clusters_panel.panel.minor_axis reindex = clusters_panel.entities clusters_frame = DataFrame( cluster_max.T, index=index, columns=clusters_panel.vars ) # TODO: Bug in pandas-stubs prevents using Hashable | None clusters_frame = clusters_frame.loc[reindex].astype( np.int64 ) # type: ignore cov_config_upd["clusters"] = clusters_frame return cov_config_upd def fit( self, *, reweight: bool = False, cov_type: str = "unadjusted", debiased: bool = True, **cov_config: bool | float | str | IntArray | DataFrame | PanelData, ) -> PanelResults: """ Estimate model parameters Parameters ---------- reweight : bool Flag indicating to reweight observations if the input data is unbalanced using a WLS estimator. If weights are provided, these are accounted for when reweighting. Has no effect on balanced data. cov_type : str Name of covariance estimator. See Notes. debiased : bool Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment. **cov_config Additional covariance-specific options. See Notes. Returns ------- PanelResults Estimation results Examples -------- >>> from linearmodels import BetweenOLS >>> mod = BetweenOLS(y, x) >>> res = mod.fit(cov_type='robust') Notes ----- Three covariance estimators are supported: * "unadjusted", "homoskedastic" - Assume residual are homoskedastic * "robust", "heteroskedastic" - Control for heteroskedasticity using White's estimator * "clustered` - One- or two-way clustering. Configuration options are: * ``clusters`` - Input containing 1 or 2 variables. Clusters should be integer values, although other types will be coerced to integer values by treating as categorical variables When using a clustered covariance estimator, all cluster ids must be identical within an entity. """ y, x, w = self._prepare_between() if np.all(self.weights.values2d == 1.0) and not reweight: w = root_w = np.ones_like(y) else: root_w = cast(Float64Array, np.sqrt(w)) wx = root_w * x wy = root_w * y params = _lstsq(wx, wy, rcond=None)[0] df_resid = y.shape[0] - x.shape[1] df_model = (x.shape[1],) nobs = y.shape[0] cov_config = self._setup_clusters(cov_config) extra_df = 0 if "extra_df" in cov_config: cov_config = cov_config.copy() _extra_df = cov_config.pop("extra_df") assert isinstance(_extra_df, (str, int)) extra_df = int(_extra_df) cov = setup_covariance_estimator( self._cov_estimators, cov_type, wy, wx, params, self.dependent.entity_ids, self.dependent.time_ids, debiased=debiased, extra_df=extra_df, **cov_config, ) weps = wy - wx @ params index = self.dependent.index fitted = DataFrame(self.exog.values2d @ params, index, ["fitted_values"]) eps = y - x @ params effects = DataFrame(eps, self.dependent.entities, ["estimated_effects"]) idx = cast(MultiIndex, fitted.index) entities = idx.levels[0][idx.codes[0]] effects = effects.loc[entities] effects.index = idx dep = self.dependent.dataframe fitted = fitted.reindex(dep.index) effects = effects.reindex(dep.index) idiosyncratic = DataFrame( np.asarray(dep) - np.asarray(fitted) - np.asarray(effects), dep.index, ["idiosyncratic"], ) residual_ss = float(np.squeeze(weps.T @ weps)) e = y if self._constant: e = y - (w * y).sum() / w.sum() total_ss = float(np.squeeze(w.T @ (e**2))) r2 = 1 - residual_ss / total_ss res = self._postestimation( params, cov, debiased, df_resid, weps, wy, wx, root_w ) res.update( dict( df_resid=df_resid, df_model=df_model, nobs=nobs, residual_ss=residual_ss, total_ss=total_ss, r2=r2, wresids=weps, resids=eps, index=self.dependent.entities, fitted=fitted, effects=effects, idiosyncratic=idiosyncratic, ) ) return PanelResults(res) @classmethod def from_formula( cls, formula: str, data: PanelDataLike, *, weights: PanelDataLike | None = None, check_rank: bool = True, ) -> BetweenOLS: """ Create a model from a formula Parameters ---------- formula : str Formula to transform into model. Conforms to formulaic formula rules. data : array_like Data structure that can be coerced into a PanelData. In most cases, this should be a multi-index DataFrame where the level 0 index contains the entities and the level 1 contains the time. weights: array_like Weights to use in estimation. Assumes residual variance is proportional to inverse of weight to that the residual times the weight should be homoskedastic. check_rank : bool Flag indicating whether to perform a rank check on the exogenous variables to ensure that the model is identified. Skipping this check can reduce the time required to validate a model specification. Results may be numerically unstable if this check
(dependent: 'PanelDataLike', exog: 'PanelDataLike', *, weights: 'PanelDataLike | None' = None, check_rank: 'bool' = True) -> 'None'
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42,819
linearmodels.panel.model
__init__
null
def __init__( self, dependent: PanelDataLike, exog: PanelDataLike, *, weights: PanelDataLike | None = None, check_rank: bool = True, ) -> None: super().__init__(dependent, exog, weights=weights, check_rank=check_rank) self._cov_estimators = CovarianceManager( self.__class__.__name__, HomoskedasticCovariance, HeteroskedasticCovariance, ClusteredCovariance, )
(self, dependent: Union[linearmodels.panel.data.PanelData, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series], exog: Union[linearmodels.panel.data.PanelData, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series], *, weights: Union[linearmodels.panel.data.PanelData, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series, NoneType] = None, check_rank: bool = True) -> NoneType
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42,820
linearmodels.panel.model
__repr__
null
def __repr__(self) -> str: return self.__str__() + "\nid: " + str(hex(id(self)))
(self) -> str
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42,821
linearmodels.panel.model
__str__
null
def __str__(self) -> str: out = "{name} \nNum exog: {num_exog}, Constant: {has_constant}" return out.format( name=self.__class__.__name__, num_exog=self.exog.dataframe.shape[1], has_constant=self.has_constant, )
(self) -> str
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42,822
linearmodels.panel.model
_adapt_weights
Check and transform weights depending on size
def _adapt_weights(self, weights: PanelDataLike | None) -> PanelData: """Check and transform weights depending on size""" if weights is None: self._is_weighted = False frame = self.dependent.dataframe.copy() frame.iloc[:, :] = 1 # TODO: Remove once pandas typing fixed frame.columns = Index(["weight"]) return PanelData(frame) frame = DataFrame(columns=self.dependent.entities, index=self.dependent.time) nobs, nentity = self.exog.nobs, self.exog.nentity if weights.ndim == 3 or weights.shape == (nobs, nentity): return PanelData(weights) if isinstance(weights, np.ndarray): weights = cast(Float64Array, np.squeeze(weights)) if weights.shape[0] == nobs and nobs == nentity: raise AmbiguityError( "Unable to distinguish nobs form nentity since they are " "equal. You must use an 2-d array to avoid ambiguity." ) if ( isinstance(weights, (Series, DataFrame)) and isinstance(weights.index, MultiIndex) and weights.shape[0] == self.dependent.dataframe.shape[0] ): frame = DataFrame(weights) elif weights.shape[0] == nobs: weights_arr = np.asarray(weights)[:, None] weights_arr = weights_arr @ np.ones((1, nentity)) frame.iloc[:, :] = weights_arr elif weights.shape[0] == nentity: weights_arr = np.asarray(weights)[None, :] weights_arr = np.ones((nobs, 1)) @ weights_arr frame.iloc[:, :] = weights_arr elif weights.shape[0] == nentity * nobs: frame = self.dependent.dataframe.copy() frame.iloc[:, :] = np.asarray(weights)[:, None] else: raise ValueError("Weights do not have a supported shape.") return PanelData(frame)
(self, weights: Union[linearmodels.panel.data.PanelData, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series, NoneType]) -> linearmodels.panel.data.PanelData
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0.00443523982539773, 0.04005113244056702, 0.03362038731575012 ]
42,823
linearmodels.panel.model
_check_exog_rank
null
def _check_exog_rank(self) -> int: if not self._check_rank: return self.exog.shape[1] x = cast(Float64Array, self.exog.values2d) _, _, rank_of_x, _ = _lstsq(x, np.ones(x.shape[0])) if rank_of_x < x.shape[1]: raise ValueError( "exog does not have full column rank. If you wish to proceed with " "model estimation irrespective of the numerical accuracy of " "coefficient estimates, you can set check_rank=False." ) return rank_of_x
(self) -> int
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-0.00014858828217256814, -0.028909336775541306, -0.012039659544825554 ]
42,824
linearmodels.panel.model
_f_statistic
Compute model F-statistic
def _f_statistic( self, weps: Float64Array, y: Float64Array, x: Float64Array, root_w: Float64Array, df_resid: int, ) -> WaldTestStatistic | InvalidTestStatistic: """Compute model F-statistic""" weps_const = y num_df = x.shape[1] name = "Model F-statistic (homoskedastic)" if self.has_constant: if num_df == 1: return InvalidTestStatistic("Model contains only a constant", name=name) num_df -= 1 weps_const = cast( Float64Array, y - float(np.squeeze((root_w.T @ y) / (root_w.T @ root_w))), ) resid_ss = float(np.squeeze(weps.T @ weps)) num = float(np.squeeze(weps_const.T @ weps_const - resid_ss)) denom = resid_ss denom_df = df_resid stat = float((num / num_df) / (denom / denom_df)) if denom > 0.0 else 0.0 return WaldTestStatistic( stat, null="All parameters ex. constant are zero", df=num_df, df_denom=denom_df, name=name, )
(self, weps: numpy.ndarray, y: numpy.ndarray, x: numpy.ndarray, root_w: numpy.ndarray, df_resid: int) -> linearmodels.shared.hypotheses.WaldTestStatistic | linearmodels.shared.hypotheses.InvalidTestStatistic
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42,825
linearmodels.panel.model
_f_statistic_robust
Compute Wald test that all parameters are 0, ex. constant
def _f_statistic_robust( self, params: Float64Array, ) -> FInfo: """Compute Wald test that all parameters are 0, ex. constant""" sel = np.ones(params.shape[0], dtype=bool) name = "Model F-statistic (robust)" if self.has_constant: if len(sel) == 1: return FInfo( sel, name, InvalidTestStatistic("Model contains only a constant", name=name), True, ) assert isinstance(self._constant_index, int) sel[self._constant_index] = False return FInfo(sel, name, None, False)
(self, params: numpy.ndarray) -> linearmodels.panel.model.FInfo
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