Created
June 24, 2021 17:27
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GH 4962
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| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>Dask Performance Report</title> | |
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= _main(fd, parent_sentinel)\n","return self._bootstrap(parent_sentinel)\n","self.run()\n","self._target(*self._args, **self._kwargs)\n","target(*args, **kwargs)\n","loop.run_sync(run)\n","Worker._instances.add(self)\n","self.__stopped = False\n","return log\n","self._started.wait()\n","return signaled\n","return gotit\n","self.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","await self.handle_stream(\n","msgs = await comm.read()\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","return merge_and_deserialize(\n","return deserialize(header, merged_frames, deserializers=deserializers)\n","return loads(header, frames)\n","return pickle.loads(x, buffers=new)\n","return pickle.loads(x, buffers=buffers)\n","","","","","","","","","","","","from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib\n","","","","","","from pandas._libs.interval import Interval\n","","","","","","","","","","","","","","","","","","","","","","","","from .conversion import OutOfBoundsTimedelta, localize_pydatetime\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from ._parser import parse, parser, parserinfo, ParserError\n","","","","","","","from pandas.core.algorithms import factorize, unique, value_counts\n","","","","","","from pandas.core.construction import array, extract_array\n","","","","","","","","","","from pandas.core.arrays.interval import IntervalArray\n","","","","","","from pandas.core.indexes.base import ensure_index\n","","","","","","from pandas.core.dtypes.concat import concat_compat\n","","","","","","","","","","from pandas.core import ops\n","","","","","","","","from pandas.core.ops.methods import add_flex_arithmetic_methods # noqa:F401\n","","","","","","","from pandas.io.formats import console\n","","","","","","","from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy\n","","","","","","from pandas.core.frame import DataFrame\n","","","","","","from pandas.core import algorithms, common as com, generic, nanops, ops\n","","","","","","","","from pandas.core.window import Expanding, ExponentialMovingWindow, Rolling, Window\n","","","","","","__all__ = [\n","","","","","","def _extract_bool_array(mask: ArrayLike) -> np.ndarray:\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","import pandas.plotting\n","","","","","","__all__ = [\n","","","","","","","","","","","","","","","","","from pandas.core.dtypes.inference import is_bool, is_list_like\n","","","","","","","from pandas.io.json import read_json\n","","","","","","__all__ = [\n","","","","","","class FrameParser(Parser):\n","","","","","","","from pandas.io.excel._base import ExcelFile, ExcelWriter, read_excel\n","","","","","","class ExcelFile:\n","","","","","","","","","","","","","from pandas.core.computation.pytables import PyTablesExpr, maybe_expression\n","","","","","","","common_timezones_set = LazySet(common_timezones)\n","","","","","","","","","","","def _new_Index(cls, d):\n","deserialize(\n","return loads(header, frames)\n","return pickle.loads(x, buffers=new)\n","return pickle.loads(x)\n","","","","","","","","","","","","","","","from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib\n","","","","","","","","","","","","","","","","","","","from pandas._libs.interval import Interval\n","","","","","","","","","","","","","","","","","","","","","","","","from .conversion import OutOfBoundsTimedelta, localize_pydatetime\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from pandas.core.dtypes.dtypes import (\n","","","","","","from pandas.core.dtypes.base import ExtensionDtype, register_extension_dtype\n","","","","","","","from pandas.core.arrays.base import (\n","","","","","","from pandas.core import ops\n","","","","","","","","from pandas.core.ops.docstrings import (\n","","","","","","def _maybe_upcast_for_op(obj, shape: Shape):\n","","","","","","","","def dispatch_fill_zeros(op, left, right, result):\n","","","","","","","","","","from pandas.io.formats import console\n","","","","","","","","","","from pandas.core.indexes.base import ensure_index\n","","","","","","from pandas.core.dtypes.concat import concat_compat\n","","","","","","","","","","from .masked import BaseMaskedArray, BaseMaskedDtype\n","","","","","","class BaseMaskedArray(OpsMixin, ExtensionArray):\n","","","","","","","","","from pandas.core.arrays import datetimelike as dtl\n","","","","","","","","def maybe_infer_freq(freq):\n","","","","","","","","","","","","","","","","","from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy\n","","","","","","from pandas.core.aggregation import (\n","","","","","","","","from pandas.core import algorithms, common as com, generic, nanops, ops\n","","","","","","","","from pandas.core.internals import BlockManager\n","","","","","","","","","","","","","","","from pandas.core.window.expanding import Expanding, ExpandingGroupby # noqa:F401\n","","","","","","","","","","","","","","","","","","","","","","","def maybe_numeric_slice(df, slice_, include_bool: bool = False):\n","","","","","","","","","","","","","","","","","","","","","","from pandas.core.indexes.accessors import CombinedDatetimelikeProperties\n","","","","","","","","from pandas.core.indexes.api import Index\n","","","","","","","from pandas.core.groupby import base, numba_, ops\n","","","","","","","","","","from pandas.util._decorators import doc\n","","","","","","","","","","","","","","","","","","from pandas.api import extensions, indexers, types # noqa\n","","","","","","","","","from pandas.io.json import read_json\n","","","","","","from pandas.io.json._json import dumps, loads, read_json, to_json\n","","","","","","class FrameParser(Parser):\n","","","","","","","","","","","","","","from pandas.io.excel._base import ExcelFile, ExcelWriter, read_excel\n","","","","","","class ExcelFile:\n","","","","","","def _validate_skipfooter(kwds: Dict[str, Any]) -> None:\n","","","","","","","","","","from pandas.core.computation.eval import eval\n","","","","","","from pandas.core.computation.engines import ENGINES\n","","","","","","ENGINES: Dict[str, Type[AbstractEngine]] = {\n","","","","","","class FuncNode:\n","","","","","","","","","","","","","","","","","return [\n","compressions[c][\"decompress\"](frame)\n","return lz4_decompress(data)\n","def _gc_callback(self, phase, info):\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self._read_buffer_size >= self._read_bytes\n","self._read_future = Future()\n","return self._debug\n","self._try_inline_read()\n","self._read_from_buffer(pos)\n","self._finish_read(pos, False)\n","self._maybe_add_error_listener()\n","self._add_io_state(ioloop.IOLoop.READ)\n","self.io_loop.update_handler(self.fileno(), self._state)\n","self.selector_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)\n","self._add_reader(fd, callback, *args)\n","handle = events.Handle(callback, args, self, None)\n","self._loop = loop\n","def _ensure_fd_no_transport(self, fd):\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self.transition(ts, \"waiting\")\n","self._notify_plugins(\"transition\", ts.key, start, state or finish, **kwargs)\n","def _gc_callback(self, phase, info):\n","heapq.heappush(self.ready, (ts.priority, ts.key))\n","for name, plugin in self.plugins.items():\n","def _gc_callback(self, phase, info):\n","for dependent in ts.dependents:\n","if self.validate:\n","self.tasks[dep].who_has.update(workers)\n","@property\n","","def _gc_callback(self, phase, info):\n","self.key = key\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","return key in self.fast or key in self.slow\n","ts = self.tasks.get(key)\n","del self.data[key]\n","super().__delitem__(key)\n","del self.fast[key]\n","del self.d[key]\n","self.popitem()\n","self._min_heapify(0)\n","if l < n and h[l][0] < h[i][0]:\n","h[j][2] = j\n","self.traceback = None\n","return key in self.fast or key in self.slow\n","def __contains__(self, key):\n","if self.isEnabledFor(DEBUG):\n","return self._cache[level]\n","for name, plugin in self.plugins.items():\n","self.release_key(ts.key, reason=\"stolen\")\n","self.batched_stream.send(msg)\n","del self.data[key]\n","super().__delitem__(key)\n","del self.fast[key]\n","del self.d[key]\n","parent = self.heap[parentpos]\n","return key in self.fast or key in self.slow\n","return key in self.d\n","return key in self._keys\n","if self.next_deadline is None:\n","if self.isEnabledFor(DEBUG):\n","return self._cache[level]\n","self.release_key(key, reason=f\"delete data: {reason}\", report=False)\n","del self.data[key]\n","super().__delitem__(key)\n","del self.fast[key]\n","del self.d[key]\n","self.transition(self.tasks[d], \"flight\", worker=worker)\n","self.log.append((ts.key, start, finish))\n","ts = self.tasks.get(d)\n","return backend.get_address_host(loc)\n","return parse_host_port(loc)[0]\n","return host, int(port)\n","local = [w for w in workers if get_address_host(w) == host]\n","return backend.get_address_host(loc)\n","return parse_host_port(loc)[0]\n","return host, int(port)\n","return\n","ts.runspec = await self._maybe_deserialize_task(ts)\n","function, args, kwargs = _deserialize(*ts.runspec)\n","function = loads_function(function)\n","result = pickle.loads(bytes_object)\n","return pickle.loads(x)\n","","","","","","","","","","","","from . import backends, dispatch, rolling\n","","","","","","","","","","from .core import DataFrame, Index, Scalar, Series, _Frame\n","","","","","","from .. import array as da\n","","","","","","","","from . import backends, fft, lib, linalg, ma, overlap, random\n","","","","","","","","import scipy.fftpack\n","","","","","","","","","","","","","from .basic import *\n","","","","","","from scipy.fft import _pocketfft\n","","","","","","del PytestTester\n","","","","","","def _init_nd_shape_and_axes(x, shape, axes):\n","","","","","","del PytestTester\n","","","","","","","","","","from fsspec import get_mapper\n","","","","","","entry_points = entry_points()\n","ordered = sorted(eps, key=by_group)\n","dist.entry_points for dist in distributions())\n","return EntryPoint._from_text(self.read_text('entry_points.txt'))\n","return self._path.joinpath(filename).read_text(encoding='utf-8')\n","return f.read()\n","return io.open(self, mode, buffering, encoding, errors, newline,\n","return self._accessor.open(self, flags, mode)\n","yield self.joinpath(child)\n","return os.listdir(self.root or '')\n","import scipy.sparse\n","","","","","","from .csr import *\n","","","","","","def isspmatrix_csr(x):\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from ._reordering import reverse_cuthill_mckee, structural_rank\n","","","","","","","","","","","","","","","","","","","from .ufunc import greater_equal, rint\n","","","","","","rint = ufunc(np.rint)\n","derived_from(np)(self)\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","i: lines[i].strip()\n","s = inspect.signature(func)\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","return _signature_from_callable(obj, sigcls=cls,\n","raise ValueError('callable {!r} is not supported by signature'.format(obj))\n","return (isbuiltin(obj) or\n","return derived_from(source)(wrapped)\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","(i, line)\n","return _compile(pattern, flags).match(string)\n","return p\n","return _sre.compile(\n","return code\n","raise error(\"internal: unsupported operand type %r\" % (op,))\n","return op in _UNIT_CODES\n","","","del fft\n","","","","","","","","","","","","","","","","","","","","","","","","","from .routines import _average\n","","","","","","def triu_indices_from(arr, k=0):\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","(i, line)\n","return _compile(pattern, flags).match(string)\n","return _cache[type(pattern), pattern, flags]\n","return [\n","from .random import RandomState\n","","","","","","standard_t = _state.standard_t\n","def zipf(self, a, size=None, chunks=\"auto\", **kwargs):\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","","","","","","","","","","","def astype(self, dtype):\n","method.__doc__ = _derived_from(\n","doc = extra_titles(doc)\n","titles = {\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","subset = [\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","def match(pattern, string, flags=0):\n","def to_string(self, max_rows=5):\n","method.__doc__ = _derived_from(\n","doc = unsupported_arguments(doc, not_supported)\n","subset = [\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","return _compile(pattern, flags).match(string)\n","return _cache[type(pattern), pattern, flags]\n","def groupby(\n","method.__doc__ = _derived_from(\n","doc = extra_titles(doc)\n","titles = {\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","","","","","","","","","","","","","class SeriesGroupBy(_GroupBy):\n","def value_counts(self, split_every=None, split_out=1):\n","method.__doc__ = _derived_from(\n","doc = unsupported_arguments(doc, not_supported)\n","subset = [\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","return _compile(pattern, flags).match(string)\n","p = sre_compile.compile(pattern, flags)\n","code = _code(p, flags)\n","_compile(code, p.data, flags)\n","if not flags & SRE_FLAG_IGNORECASE:\n","from .parquet import read_parquet, to_parquet\n","","","","","","from .core import create_metadata_file, read_parquet, read_parquet_part, to_parquet\n","","","","","","","","","","from ...bytes import read_bytes\n","","","","","","","","","","","","","","value = super().__getitem__(key)\n","return self.data[key]\n","ret = callback()\n","return self.callback()\n","state = profile.process(\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","\"description\": info_frame(frame),\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","return lines\n","return text\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","stat = os.stat(fullname)\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","stat = os.stat(fullname)\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","return lines[lineno - 1]\n","return updatecache(filename, module_globals)\n","lines = fp.readlines()\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","return default, [first]\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","stat = os.stat(fullname)\n","buffer = _builtin_open(filename, 'rb')\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","diff = now - self._last_tick\n","ioc = psutil.net_io_counters()\n","return _common.snetio(*[sum(x) for x in zip(*rawdict.values())])\n","return _wn.run(input_dict, name)\n","return new_dict\n","self._timeout = self.io_loop.add_timeout(self._next_timeout, self._run)\n","return self.call_at(deadline, callback, *args, **kwargs)\n","return self.asyncio_loop.call_later(\n","timer = self.call_at(self.time() + delay, callback, *args,\n","timer = events.TimerHandle(when, callback, args, self, context)\n","super().__init__(callback, args, loop, context)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","lambda: self.batched_stream.send({\"op\": \"keep-alive\"}), 60000\n","self.waker.set()\n","fut.set_result(None)\n","def call_soon(self, callback, *args, context=None):\n","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded) # type: ignore\n","task = loop.create_task(coro_or_future)\n","return task\n","return handle\n","return multi_future(children, quiet_exceptions=quiet_exceptions)\n","def multi_future(\n","return (isinstance(object, types.CoroutineType) or\n","result.set_exception(TimeoutError(\"Timeout\"))\n","handle = self._call_soon(callback, args, context)\n","handle = events.Handle(callback, args, self, context)\n","def _gc_callback(self, phase, info):\n","self.ctx_run(self.run)\n","if not future.done():\n","with self._condition:\n","def __enter__(self):\n","handler_func(fileobj, events)\n","self._handle_read()\n","self._read_from_buffer(pos)\n","return self._find_read_pos()\n","return bytes_read\n","return self.socket.recv_into(buf, len(buf))\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","connection, address = sock.accept()\n","return sock, addr\n","stream.write(b\"\")\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","msg_size = sizeof(msg)\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + sum(map(sizeof, seq))\n","return meth(arg, *args, **kwargs)\n","+ sizeof(list(d.values()))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + sum(map(sizeof, seq))\n","return meth(arg, *args, **kwargs)\n","- 2 * sizeof(list())\n","protocol.dumps(\n","frames[0] = msgpack.dumps(msg, default=_encode_default, use_bin_type=True)\n","return Packer(**kwargs).pack(o)\n","return struct.pack(f\"Q{nframes}Q\", nframes, *nbytes_frames)\n","data = self._ssock.recv(4096)\n","df_sizeof += v.memory_usage(deep=True, index=True).sum()\n","result = self._constructor_sliced(\n","return concat(\n","return op.get_result()\n","return result.__finalize__(self, method=\"concat\")\n","return np.concatenate(to_concat, axis=axis)\n","","_contains_datetime = any(typ.startswith(\"datetime\") for typ in typs)\n","self.name = name\n","object.__setattr__(self, name, value)\n","@name.setter\n","subarr = _try_cast(data, dtype, copy, raise_cast_failure)\n","if is_integer_dtype(dtype):\n","return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.integer))\n","def __len__(self) -> int:\n","block = make_block(array, placement=slice(0, len(index)), ndim=1)\n","klass = get_block_type(values, dtype)\n","elif is_extension_array_dtype(values.dtype):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","dtype_type = type(dtype)\n","self.new_axes = self._get_new_axes()\n","return [\n","self._get_concat_axis() if i == self.bm_axis else self._get_comb_axis(i)\n","concat_axis = _concat_indexes(indexes)\n","return indexes[0].append(indexes[1:])\n","names = {obj.name for obj in to_concat}\n","names = {obj.name for obj in to_concat}\n","@property\n","return Index(result, name=name)\n","return cls(\n","return cls._simple_new(subarr, name)\n","result._reset_identity()\n","@final\n","def is_datetime64_any_dtype(arr_or_dtype) -> bool:\n","return _is_dtype_type(\n","if isinstance(arr_or_dtype, np.dtype):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return getattr(inst, attr, \"_typ\") in comp\n","return _is_dtype_type(arr_or_dtype, classes(np.floating))\n","if isinstance(arr_or_dtype, ABCIndexClass):\n","inferred = lib.infer_dtype(subarr, skipna=False)\n","def _name_get(dtype):\n","","if isinstance(arr_or_dtype, (np.dtype, ExtensionDtype)):\n","return arr_or_dtype.kind == \"m\"\n","dtype = get_dtype(arr_or_dtype)\n","return pandas_dtype(arr_or_dtype)\n","elif isinstance(dtype, (np.dtype, ExtensionDtype)):\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","def find(\n","typs = _get_dtype_kinds(to_concat)\n","typ = str(dtype) # i.e. \"object\", \"bool\"\n","return dtype.name\n","if issubclass(dtype.type, np.void):\n","def _gc_callback(self, phase, info):\n","self._protect_consolidate(f)\n","result = f()\n","self._mgr = self._mgr.consolidate()\n","object.__getattribute__(self, name)\n","from pandas.core.frame import DataFrame\n","return cls._AXIS_TO_AXIS_NUMBER[axis]\n","@property\n","[c.memory_usage(index=False, deep=deep) for col, c in self.items()],\n","v = super().memory_usage(deep=deep)\n","v += lib.memory_usage_of_objects(self._values)\n","@Appender(base.IndexOpsMixin.array.__doc__) # type: ignore[misc]\n","return PandasArray(self.values)\n","if isinstance(values, type(self)):\n","return _abc_instancecheck(cls, instance)\n","return self.blocks[0]\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","def _is_dtype_type(arr_or_dtype, condition) -> bool:\n","if isinstance(dtype, np.ndarray):\n","return lambda tipo: issubclass(tipo, klasses)\n","yield k, self._get_item_cache(k)\n","data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)\n","subarr = maybe_convert_platform(data)\n","values = lib.maybe_convert_objects(values)\n","@set_array_function_like_doc\n","return array(a, dtype, copy=False, order=order)\n","","result = np.empty(len(values), dtype=\"object\")\n","subarr = maybe_cast_to_datetime(arr, dtype)\n","is_array = isinstance(value, np.ndarray)\n","v = np.array(v, copy=False)\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","subarr = np.array(values, dtype=dtype, copy=copy)\n","np.array(val, dtype=dtype)\n","value = maybe_infer_to_datetimelike(value)\n","if isinstance(\n","@classmethod # type: ignore[misc]\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","@classmethod # type: ignore[misc]\n","subarr = np.empty(length, dtype=dtype)\n","def __len__(self) -> int:\n","def is_excluded_dtype(dtype) -> bool:\n","if isinstance(obj, (ABCIndexClass, ABCSeries)):\n","@classmethod # type: ignore[misc]\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","if not isinstance(dtype, type):\n","converted, all_arrays = lib.clean_index_list(index_like)\n","@set_array_function_like_doc\n","if dtype.isbuiltin == 2:\n","if dtype.type == np.object_:\n","from pandas.core.indexes.numeric import (\n","elif is_extension_array_dtype(data_dtype) or is_extension_array_dtype(dtype):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","elif isinstance(dtype, (np.dtype, ExtensionDtype)):\n","if not (isinstance(values, (list, tuple)) or hasattr(values, \"__array__\")):\n","if name is None and isinstance(obj, (Index, ABCSeries)):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","if not isinstance(dtype, str):\n","from pandas.core.arrays import (\n","","def _name_get(dtype):\n","return arr_or_dtype.kind == \"M\"\n","return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.signedinteger))\n","def construct_1d_object_array_from_listlike(values: Sized) -> np.ndarray:\n","object.__setattr__(self, \"_index\", labels)\n","@cache_readonly\n","return self._data\n","block = make_block(array, placement=slice(0, len(index)), ndim=1)\n","klass = get_block_type(values, dtype)\n","elif is_categorical_dtype(values.dtype):\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return getattr(inst, attr, \"_typ\") in comp\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","@classmethod # type: ignore[misc]\n","from pandas.core.arrays.sparse import SparseDtype\n","","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","self.values = self._maybe_coerce_values(values)\n","def _maybe_coerce_values(self, values):\n","def _check_ndim(self, values, ndim):\n","if isinstance(obj, abc.Iterable) and not isinstance(obj, abc.Sized):\n","return _abc_instancecheck(cls, instance)\n","try:\n","return self._name\n","name in self._internal_names_set\n","if name is None and isinstance(obj, (Index, ABCSeries)):\n","@classmethod # type: ignore[misc]\n","all(hasattr(obj, attr) for attr in dict_like_attrs)\n","all(hasattr(obj, attr) for attr in dict_like_attrs)\n","object.__setattr__(self, \"_is_copy\", None)\n","return _abc_instancecheck(cls, instance)\n","def is_empty_data(data: Any) -> bool:\n","result = super().memory_usage(deep=deep)\n","if deep and is_object_dtype(self) and not PYPY:\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","if isinstance(arr_or_dtype, np.dtype):\n","result = getattr(self._data, name)\n","return self._dtype\n","state = func(ts, **kwargs)\n","self.put_key_in_memory(ts, value, transition=False)\n","self.data[ts.key] = value\n","super().__setitem__(key, value)\n","self.slow[key] = value\n","return safe_sizeof(value)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return getsizeof(d)\n","return total_size\n","return meth(arg, *args, **kwargs)\n","return int(p) + 1000\n","return sum(sample) / 20 * len(x)\n","return meth(arg, *args, **kwargs)\n","@sizeof.register(object)\n","p += object_size(col._values)\n","sample = np.random.choice(x, size=20, replace=True)\n","weight = self.weight(key, value)\n","return safe_sizeof(value)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return getsizeof(d)\n","return total_size\n","return meth(arg, *args, **kwargs)\n","return int(p) + 1000\n","return sum(sample) / 20 * len(x)\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","try:\n","yield from self.items()\n","for k in self.columns:\n","return map(self._values.item, range(self._values.size))\n","v = super().memory_usage(deep=deep)\n","if hasattr(self.array, \"memory_usage\"):\n","return self._mgr._block.array_values()\n","return PandasArray(self.values)\n","self._dtype = PandasDtype(values.dtype)\n","def __init__(self, dtype: object):\n","def __instancecheck__(cls, instance):\n","lk = self._lookup\n","p += object_size(col._values)\n","sample = np.random.choice(x, size=20, replace=True)\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n","yield from self.items()\n","yield k, self._ixs(i, axis=1)\n","return res\n","typ_serialized = dumps_function(typ)\n","try:\n","ts.runspec = await self._maybe_deserialize_task(ts)\n","return key in self.fast or key in self.slow\n","return key in self.d\n","return key in self._keys\n","return self.fast[key]\n","self.heap[key] = self.i\n","self.pop(key)\n","del self[key]\n","parent = self.heap[parentpos]\n","return self.d[key][0]\n","yielded = ctx_run(next, result)\n","future = executor.submit(function, *args, **kwargs)\n","if self._shutdown:\n","self.not_empty.notify()\n","pass\n","return\n","self._done_callbacks = []\n","self._waiters = _deque()\n","logger.warning(\n","self._schedule_next()\n","self._timeout = self.io_loop.add_timeout(self._next_timeout, self._run)\n","return self.call_at(deadline, callback, *args, **kwargs)\n","functools.partial(callback, *args, **kwargs),\n","def _gc_callback(self, phase, info):\n","return _abc_instancecheck(cls, instance)\n","def _gc_callback(self, phase, info):\n","if self.handle_yield(first_yielded):\n","self.io_loop.add_future(self.future, inner)\n","future_add_done_callback(\n","if future.done():\n","return self._state in [CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED]\n","return\n","return dispatch(args[0].__class__)(*args, **kw)\n","impl = dispatch_cache[cls]\n","def __getitem__(self, key):\n","return NDFrame.sum(\n","return self._min_count_stat_function(\n","return self._reduce(\n","return op(delegate, skipna=skipna, **kwds)\n","@functools.wraps(f)\n","result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)\n","values, mask, dtype, dtype_max, _ = _get_values(\n","values = extract_array(values, extract_numpy=True)\n","if isinstance(obj, (ABCIndexClass, ABCSeries)):\n","@classmethod # type: ignore[misc]\n","lambda f: self._run_callback(functools.partial(callback, future))\n","ret = callback()\n","self.ctx_run(self.run)\n","return\n","yield self.waker.wait(self.next_deadline)\n","return timeout_fut\n","return result\n","return self.call_at(deadline, callback, *args, **kwargs)\n","return _abc_instancecheck(cls, instance)\n","logger.warning(\n","return self.asyncio_loop.call_later(\n","logger.warning(\n","timer = self.call_at(self.time() + delay, callback, *args,\n","def _gc_callback(self, phase, info):\n","future = Future() # type: Future\n","def _gc_callback(self, phase, info):\n","IOLoop.current().add_future(a, copy)\n","return\n","self.future = convert_yielded(yielded)\n","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded) # type: ignore\n","return task\n","return task\n","return\n","def _gc_callback(self, phase, info):\n","response = await retry_operation(\n","return await retry(\n","return await coro()\n","return result\n","return response\n","return frames_nbytes_total\n","return future\n","future_set_result_unless_cancelled(future, None)\n","return self.socket.send(data) # type: ignore\n","frames_nbytes = await stream.read_bytes(fmt_size)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","def _gc_callback(self, phase, info):\n","out.update(self.monitor.recent())\n","try:\n","response = await get_data_from_worker(\n","return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n","return await retry(\n","return await coro()\n","response = await send_recv(\n","response = await comm.read(deserializers=deserializers)\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","return merge_and_deserialize(\n","return deserialize(header, merged_frames, deserializers=deserializers)\n","return loads(header, frames)\n","return pickle.loads(x, buffers=new)\n","return pickle.loads(x, buffers=buffers)\n","self._post_setstate()\n","self._rebuild_blknos_and_blklocs()\n","new_blklocs[rl.indexer] = np.arange(len(rl))\n","return tuple(len(ax) for ax in self.axes)\n","return tuple(len(ax) for ax in self.axes)\n","return len(self._data)\n","return self.shape[0]\n","return self._ndarray.shape\n","unpickle_block(b[\"values\"], b[\"mgr_locs\"], ndim=ndim)\n","return make_block(values, placement=mgr_locs, ndim=ndim)\n","if isinstance(values, ABCPandasArray):\n","def _gc_callback(self, phase, info):\n","elif is_interval_dtype(dtype) or is_period_dtype(dtype):\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","def _gc_callback(self, phase, info):\n","@classmethod\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","def _gc_callback(self, phase, info):\n","@classmethod # type: ignore[misc]\n","from pandas.core.arrays.sparse import SparseDtype\n","","@classmethod # type: ignore[misc]\n","self.mgr_locs = placement\n","new_mgr_locs = libinternals.BlockPlacement(new_mgr_locs)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","def _new_Index(cls, d):\n","from pandas.core.indexes.numeric import (\n","if isinstance(arr_or_dtype, ExtensionDtype):\n","return super().is_dtype(dtype)\n","def _gc_callback(self, phase, info):\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","inferred = lib.infer_dtype(subarr, skipna=False)\n","def _name_get(dtype):\n","try:\n","","def _gc_callback(self, phase, info):\n","elif isinstance(data, (np.ndarray, Index, ABCSeries)):\n","def is_signed_integer_dtype(arr_or_dtype) -> bool:\n","if isinstance(arr_or_dtype, np.dtype):\n","def _gc_callback(self, phase, info):\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","arr_or_dtype, classes_and_not_datetimelike(np.unsignedinteger)\n","return condition(arr_or_dtype.type)\n","issubclass(tipo, klasses)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","@classmethod\n","return getattr(inst, attr, \"_typ\") in comp\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","@classmethod\n","@classmethod # type: ignore[misc]\n","def _gc_callback(self, phase, info):\n","data = np.array(data, dtype=dtype, copy=copy)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return\n","result._reset_identity()\n","self._id = _Identity(object())\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","logger.warning(\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","if isinstance(arr_or_dtype, np.dtype):\n","return condition(type(None))\n","if isinstance(arr_or_dtype, (np.dtype, ExtensionDtype)):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","elif is_extension_array_dtype(arr_or_dtype):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return pandas_dtype(arr_or_dtype)\n","elif isinstance(dtype, (np.dtype, ExtensionDtype)):\n","@classmethod # type: ignore[misc]\n","if com.all_none(start, stop, step):\n","return all(arg is None for arg in args)\n","logger.warning(\n","def _frombuffer(buf, dtype, shape, order):\n","meta = set(self._internal_names + self._metadata)\n","def _gc_callback(self, phase, info):\n","self._obj = weakref.ref(obj)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","def _new_DatetimeIndex(cls, d):\n","def _gc_callback(self, phase, info):\n","return [\n","compressions[c][\"decompress\"](frame)\n","return lz4_decompress(data)\n","def _gc_callback(self, phase, info):\n","return await loop.run_in_executor(_offload_executor, lambda: fn(*args, **kwargs))\n","executor.submit(func, *args), loop=self)\n","self._adjust_thread_count()\n","_threads_queues[t] = self._work_queue\n","self._started.wait()\n","return signaled\n","return gotit\n","def _gc_callback(self, phase, info):\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self._finish_read(pos, False)\n","self._maybe_add_error_listener()\n","self._add_io_state(ioloop.IOLoop.READ)\n","self.io_loop.update_handler(self.fileno(), self._state)\n","if events & IOLoop.WRITE:\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","frames.append(b[start:end])\n","def _gc_callback(self, phase, info):\n","stream.write(b\"\")\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","def _to_frames():\n","def _gc_callback(self, phase, info):\n","return meth(arg, *args, **kwargs)\n","+ sizeof(list(d.keys()))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + sum(map(sizeof, seq))\n","def _gc_callback(self, phase, info):\n","stream.write(b\"\")\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","def _gc_callback(self, phase, info):\n","state = func(ts, **kwargs)\n","self.put_key_in_memory(ts, value)\n","self.data[ts.key] = value\n","super().__setitem__(key, value)\n","if self.weight(key, value) <= self.n:\n","return safe_sizeof(value)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","for name, col in df.iteritems():\n","yield from self.items()\n","yield k, self._get_item_cache(k)\n","res = self._box_col_values(values, loc).__finalize__(self)\n","return klass(values, index=self.index, name=name, fastpath=True)\n","generic.NDFrame.__init__(self, data)\n","object.__setattr__(self, \"_flags\", Flags(self, allows_duplicate_labels=True))\n","self._obj = weakref.ref(obj)\n","return\n","def _gc_callback(self, phase, info):\n","object.__getattribute__(self, name)\n","return self._name\n","return object.__getattribute__(self, name)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","validate_all_hashable(value, error_name=f\"{type(self).__name__}.name\")\n","if not all(is_hashable(arg) for arg in args):\n","if not all(is_hashable(arg) for arg in args):\n","return True\n","promote = self._shallow_copy\n","def _gc_callback(self, phase, info):\n","self._cacher = (item, weakref.ref(cacher))\n","object.__getattribute__(self, name)\n","casted_key = self._maybe_cast_indexer(key)\n","if not self.is_floating():\n","return self.inferred_type in [\"floating\", \"mixed-integer-float\", \"integer-na\"]\n","return lib.infer_dtype(self._values, skipna=False)\n","def _gc_callback(self, phase, info):\n","return self._data\n","def _gc_callback(self, phase, info):\n","return SingleBlockManager(\n","return type(self)(values, placement=placement, ndim=ndim)\n","self.mgr_locs = placement\n","new_mgr_locs = libinternals.BlockPlacement(new_mgr_locs)\n","def _gc_callback(self, phase, info):\n","return self.values[i]\n","self.axes = [axis]\n","@property\n","for name in other.attrs:\n","if self._attrs is None:\n","@property\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","v = super().memory_usage(deep=deep)\n","if hasattr(self.array, \"memory_usage\"):\n","return self._mgr._block.array_values()\n","return PandasArray(self.values)\n","self._ndarray = values\n","sample = np.random.choice(x, size=20, replace=True)\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n","return\n","return getsizeof(d)\n","total_size += sizeof(df)\n","return meth(arg, *args, **kwargs)\n","for name, col in df.iteritems():\n","yield from self.items()\n","yield k, self._get_item_cache(k)\n","res = self._box_col_values(values, loc).__finalize__(self)\n","return klass(values, index=self.index, name=name, fastpath=True)\n","self._set_axis(0, index, fastpath=True)\n","deep_labels, (DatetimeIndex, PeriodIndex, TimedeltaIndex)\n","object.__getattribute__(self, name)\n","def _gc_callback(self, phase, info):\n","return self._name\n","return object.__getattribute__(self, name)\n","def _gc_callback(self, phase, info):\n","validate_all_hashable(value, error_name=f\"{type(self).__name__}.name\")\n","if not all(is_hashable(arg) for arg in args):\n","if not all(is_hashable(arg) for arg in args):\n","hash(obj)\n","object.__setattr__(self, \"_flags\", Flags(self, allows_duplicate_labels=True))\n","def _gc_callback(self, phase, info):\n","self._obj = weakref.ref(obj)\n","def _gc_callback(self, phase, info):\n","def __getitem__(self, key):\n","def _gc_callback(self, phase, info):\n","if lib.is_float(val) and val.is_integer():\n","casted_key = self._maybe_cast_indexer(key)\n","return com.cast_scalar_indexer(key)\n","if lib.is_float(val) and val.is_integer():\n","for name in set(self._metadata) & set(other._metadata):\n","value = bool(value)\n","return self._flags\n","return SingleBlockManager(\n","if fastpath is not lib.no_default:\n","return type(self)(values, placement=placement, ndim=ndim)\n","self.mgr_locs = placement\n","new_mgr_locs = libinternals.BlockPlacement(new_mgr_locs)\n","def _gc_callback(self, phase, info):\n","@property\n","self._cacher = (item, weakref.ref(cacher))\n","return map(self._values.item, range(self._values.size))\n","v = super().memory_usage(deep=deep)\n","if hasattr(self.array, \"memory_usage\"):\n","return self._mgr._block.array_values()\n","return PandasArray(self.values)\n","self._dtype = PandasDtype(values.dtype)\n","return _abc_instancecheck(cls, instance)\n","self._dtype = np.dtype(dtype)\n","@property\n","sample = np.random.choice(x, size=20, replace=True)\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","passkwargs = {k: v for k, v in kwargs.items()\n","def __call__(self, arg, *args, **kwargs):\n","p = int(i.memory_usage())\n","result += self._engine.sizeof(deep=deep)\n","target_values = self._get_engine_target()\n","def _get_engine_target(self) -> np.ndarray:\n","def dispatch(self, cls):\n","weight = self.weight(key, value)\n","return safe_sizeof(value)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","p += col.memory_usage(index=False)\n","v = super().memory_usage(deep=deep)\n","if hasattr(self.array, \"memory_usage\"):\n","return self._mgr._block.array_values()\n","return PandasArray(self.values)\n","if isinstance(values, type(self)):\n","def _gc_callback(self, phase, info):\n","return _abc_instancecheck(cls, instance)\n","self._dtype = np.dtype(dtype)\n","sample = np.random.choice(x, size=20, replace=True)\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","reduction = getattr(obj, method)\n","def _gc_callback(self, phase, info):\n","return getsizeof(d)\n","total_size += sizeof(df)\n","return meth(arg, *args, **kwargs)\n","p += col.memory_usage(index=False)\n","v = super().memory_usage(deep=deep)\n","if hasattr(self.array, \"memory_usage\"):\n","return self._mgr._block.array_values()\n","return PandasArray(self.values)\n","if isinstance(values, type(self)):\n","def __instancecheck__(cls, instance):\n","def _gc_callback(self, phase, info):\n","self._dtype = np.dtype(dtype)\n","@cache_readonly\n","@property\n","return self._block.dtype\n","return self.values.dtype\n","sample = np.random.choice(x, size=20, replace=True)\n","yield from self.items()\n","for k in self.columns:\n","if not isinstance(self._values, np.ndarray):\n","@property\n","wrapper = [value, key, len(self)]\n","return len(self.d)\n","state = func(ts, **kwargs)\n","self.has_what[self.address].discard(ts.key)\n","self.transition(self.tasks[d], \"flight\", worker=worker)\n","self.log.append((ts.key, start, finish))\n","return deps, total_bytes\n","return seq[self._randbelow(len(seq))]\n","compressed = await comm.write(msg, serializers=serializers)\n","frames = await to_frames(\n","return _to_frames()\n","protocol.dumps(\n","frames[0] = msgpack.dumps(msg, default=_encode_default, use_bin_type=True)\n","return Packer(**kwargs).pack(o)\n","_inplace_compress_frames(sub_header, sub_frames)\n","compression[i], frames[i] = maybe_compress(\n","if len(compress(sample)) > 0.9 * len(sample): # sample not very compressible\n","return lz4_compress(data)\n","return b\"\".join(map(ensure_bytes, parts))\n","def ensure_bytes(s):\n","starts = [random.randint(0, len(b) - size) for j in range(n)]\n","return self.randrange(a, b+1)\n","return istart + self._randbelow(width)\n","return r\n","parts = [b[start:end] for start, end in zip(starts, ends)]\n","return bytes(s)\n","header, frames = serialize(x, serializers, on_error, context)\n","return serialize(\n","header, frames = dumps(x, context=context) if wants_context else dumps(x)\n","frames[0] = pickle.dumps(\n","result = pickle.dumps(x, **dump_kwargs)\n","def __reduce__(self):\n","return {k: getattr(self, k, None) for k in self._attributes}\n","return {k: getattr(self, k, None) for k in self._attributes}\n","def _gc_callback(self, phase, info):\n","@property\n","def _gc_callback(self, phase, info):\n","mod, _, _ = x.__class__.__module__.partition(\".\")\n","buffer_callback = lambda f: frames.append(memoryview(f))\n","@final\n","meta = {k: getattr(self, k, None) for k in self._metadata}\n","\"_flags\": {k: self.flags[k] for k in self.flags._keys},\n","return getattr(self, key)\n","@property\n","return self._flags\n","if self._attrs is None:\n","\"blocks\": [\n","block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]\n","return promote(getitem(key))\n","return self._simple_new(values, name=name)\n","result._index_data = values\n","def _gc_callback(self, phase, info):\n","self._id = _Identity(object())\n","def _gc_callback(self, phase, info):\n","@property\n","def _gc_callback(self, phase, info):\n","return self.axes[0]\n","block_values = [b.values for b in self.blocks]\n","\"blocks\": [\n","d.update(self._get_attributes_dict())\n","return {k: getattr(self, k, None) for k in self._attributes}\n","return {k: getattr(self, k, None) for k in self._attributes}\n","def _gc_callback(self, phase, info):\n","result = getattr(self._data, name)\n","return getattr(self.dtype, \"tz\", None)\n","return self._dtype\n","return self._name\n","def _gc_callback(self, phase, info):\n","def _DType_reduce(DType):\n","def _gc_callback(self, phase, info):\n","\"writeable\": tuple(not f.readonly for f in frames[1:]),\n","def frame_split_size(frame, n=BIG_BYTES_SHARD_SIZE) -> list:\n","def _gc_callback(self, phase, info):\n","return Packer(**kwargs).pack(o)\n","def _gc_callback(self, phase, info):\n","return meth(arg, *args, **kwargs)\n","+ sizeof(list(d.values()))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + sum(map(sizeof, seq))\n","return meth(arg, *args, **kwargs)\n","+ sizeof(list(d.keys()))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + sum(map(sizeof, seq))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(o)\n","def _gc_callback(self, phase, info):\n","pool = list(population)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","self._buffers.append((False, bytearray(data)))\n","return struct.pack(f\"Q{nframes}Q\", nframes, *nbytes_frames)\n","return frame.nbytes\n","nbytes = {k: self.tasks[k].nbytes for k in data if k in self.tasks}\n","frames_nbytes = await stream.read_bytes(fmt_size)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self._try_inline_read()\n","self._read_from_buffer(pos)\n","self._finish_read(pos, False)\n","self._maybe_add_error_listener()\n","self._add_io_state(ioloop.IOLoop.READ)\n","self.io_loop.update_handler(self.fileno(), self._state)\n","self.selector_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)\n","self._add_reader(fd, callback, *args)\n","key = self._selector.get_key(fd)\n","def _gc_callback(self, phase, info):\n","data = {k: self.data[k] for k in keys if k in self.data}\n","return self.fast[key]\n","self.heap[key] = self.i\n","self.pop(key)\n","del self[key]\n","self.popitem()\n","return wrapper[1], wrapper[0]\n","i = low\n","return self.d[key][0]\n","await self.comm_handler(comm)\n","msg = await comm.read()\n","frames_nbytes = await stream.read_bytes(fmt_size)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","frames = await to_frames(\n","return _to_frames()\n","protocol.dumps(\n","frames[0] = msgpack.dumps(msg, default=_encode_default, use_bin_type=True)\n","return Packer(**kwargs).pack(o)\n","_inplace_compress_frames(sub_header, sub_frames)\n","compression[i], frames[i] = maybe_compress(\n","if len(compress(sample)) > 0.9 * len(sample): # sample not very compressible\n","return lz4_compress(data)\n","header, frames = serialize(x, serializers, on_error, context)\n","return serialize(\n","header, frames = dumps(x, context=context) if wants_context else dumps(x)\n","frames[0] = pickle.dumps(\n","result = pickle.dumps(x, **dump_kwargs)\n","yielded = ctx_run(next, result)\n","stream.socket.shutdown(socket.SHUT_RDWR)\n","if self.isEnabledFor(DEBUG):\n","return self._cache[level]\n","def call_soon(self, callback, *args, context=None):\n","await worker\n","await self.start()\n","await self._register_with_scheduler()\n","response = await future\n","return d\n","return host\n","return itertools.chain(\n","return val\n","return _unknown_as_blank(_Processor.get())\n","return func() or ''\n","return subprocess.check_output(\n","return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n","return CompletedProcess(process.args, retcode, stdout, stderr)\n","return (stdout, stderr)\n","raise\n","raise child_exception_type(err_msg)\n","if has_arg(function, \"dask_worker\"):\n","if argname in inspect.getfullargspec(func).args:\n","sig = _signature_from_callable(func,\n","return _signature_from_function(sigcls, obj,\n","return cls(parameters,\n","params = OrderedDict((param.name, param) for param in parameters)\n","stream = await self.client.connect(\n","addrinfo = await self.resolver.resolve(host, port, af)\n","return async_future\n","return f\n","t.start()\n","self._started.wait()\n","return signaled\n","return gotit\n","self.try_connect(iter(self.primary_addrs))\n","stream, future = self.connect(af, addr)\n","return stream, stream.connect(addr)\n","self.socket.connect(address)\n","_socket.socket.__init__(self, family, type, proto, fileno)\n","o = aca_chunk(x, **kwargs)\n","return getattr(obj, self.method)(*args, **kwargs)\n","return NDFrame.sum(\n","return self._min_count_stat_function(\n","return self._reduce(\n","res, _ = df._mgr.reduce(blk_func, ignore_failures=ignore_failures)\n","nbs = blk.reduce(func, ignore_failures)\n","result = func(self.values)\n","return op(values, axis=1, skipna=skipna, **kwds)\n","return f(*args, **kwargs)\n","result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)\n","values, mask, dtype, dtype_max, _ = _get_values(\n","mask = _maybe_get_mask(values, skipna, mask)\n","mask = isna(values)\n","return _isna(obj)\n","return _isna_ndarraylike(obj, inf_as_na=inf_as_na)\n","else methods.concat(args2, uniform=True, ignore_index=ignore_index)\n","return func(\n","out = pd.concat(dfs3, join=join, sort=False)\n","op = _Concatenator(\n","self.new_axes = self._get_new_axes()\n","return [\n","self._get_concat_axis() if i == self.bm_axis else self._get_comb_axis(i)\n","return get_objs_combined_axis(\n","return _get_combined_index(obs_idxes, intersect=intersect, sort=sort, copy=copy)\n","return index\n","return index\n","if not all(index.equals(other) for other in indexes[1:]):\n","return array_equivalent(self._values, 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**self._kwargs)\n","loop.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle = None # Needed to break cycles when an exception occurs.\n","self = None # Needed to break cycles when an exception occurs.\n","self.cluster = await LocalCluster(\n","await self._correct_state()\n","raise RuntimeError(f\"Cluster failed to start. {str(e)}\") from e\n","self.status = Status.undefined\n","super().__init__(**kwargs)\n","self.__stopped = False\n","return log\n","self._started.wait()\n","return signaled\n","return gotit\n","frames = await to_frames(\n","return _to_frames()\n","return list(\n","return frames\n","return Packer(**kwargs).pack(o)\n","return {\"__Serialized__\": offset}\n","return header, out_frames\n","return serialize(\n","return headers, frames\n","serialize(\n","return header, frames\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","await self.handle_stream(comm=comm, extra={\"client\": client})\n","msgs = await comm.read()\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","sub_header = msgpack.loads(\n","def _gc_callback(self, phase, info):\n","unpacked_graph = HighLevelGraph.__dask_distributed_unpack__(hlg)\n","unpacked_layer = unpack_state(layer[\"state\"], dsk, deps)\n","layer_dsk = {\n","stringify(k): stringify_collection_keys(v) for k, v in layer_dsk.items()\n","return (obj0,) + tuple(stringify_collection_keys(x) for x in obj[1:])\n","return (obj0,) + tuple(stringify_collection_keys(x) for x in obj[1:])\n","return stringify(obj)\n","return str(obj)\n","def _gc_callback(self, phase, info):\n","return [stringify_collection_keys(v) for v in obj]\n","return stringify(obj)\n","if typ is str or typ is bytes:\n","return str(obj)\n","deps = {k: keys_in_tasks(keys, [v]) for k, v in layer_dsk.items()}\n","ret.append(w)\n","return {\"function\": dumps_function(task[0]), \"args\": warn_dumps(task[1:])}\n","return b\n","return result\n","dsk[(shuffle_split_name, _idx, _inp)] = (\n","def _gc_callback(self, phase, info):\n","k: v.intersection(dsk_keys)\n","def _gc_callback(self, phase, info):\n","dependents = reverse_dict(dependencies)\n","return result\n","num_needed[parent] -= 1\n","num_needed = {k: len(v) for k, v in dependents.items() if v}\n","num_needed = {k: len(v) for k, v in dependents.items() if v}\n","def _gc_callback(self, phase, info):\n","root_nodes = {k for k, v in dependents.items() if not v}\n","-total_dependencies[x], # go where the work is\n","try:\n","for k, deps in list(dependencies.items()):\n","ts: TaskState = TaskState(key, spec)\n","self._metadata = {}\n","def _gc_callback(self, phase, info):\n","break\n","other._dependents.add(self)\n","return self._hash\n","def _gc_callback(self, phase, info):\n","@property\n","parent._transitions(recommendations, client_msgs, worker_msgs)\n","new = self._transition(key, finish)\n","for plugin in list(self.plugins):\n","def transition(\n","ts._waiting_on.add(dts)\n","def __hash__(self):\n","ts._waiters = {dts for dts in ts._dependents if dts._state == \"waiting\"}\n","def _gc_callback(self, phase, info):\n","result = await result\n","session = await self.get_session()\n","return session\n","return session\n","doc.validate()\n","self._safe_to_fork = False\n","doc.template_variables.update(extra)\n","self.task_stream_index = [0]\n","return source, root\n","return Figure(**kwargs)\n","process_active_tools(self.toolbar, tool_map, opts.active_drag, opts.active_inspect, opts.active_scroll, opts.active_tap)\n","default_theme.apply_to_model(self)\n","self._initialized = True\n","raise AttributeError(\"unexpected attribute '%s' to %s, %s attributes are %s\" %\n","self._internal_set(obj, value, setter=setter)\n","self._real_set(obj, old, value, hint=hint, setter=setter)\n","return self._get_default(obj)\n","return default\n","return self.property.themed_default(obj.__class__, self.name, obj.themed_values())\n","return self.prepare_value(cls, name, default)\n","return accumulate_dict_from_superclasses(cls, \"__overridden_defaults__\")\n","return cls.__dict__[cachename]\n","return input_function(*args, **kwargs)\n","self.source.stream(rectangles, self.n_rectangles)\n","return rectangles(msgs, workers=workers, start_boundary=start_boundary)\n","return {\n","cpu = figure(\n","return Figure(**kwargs)\n","process_axis_and_grid(self, opts.x_axis_type, opts.x_axis_location, opts.x_minor_ticks, opts.x_axis_label, self.x_range, 0)\n","grid = Grid(dimension=dim, axis=axis)\n","default_theme.apply_to_model(self)\n","self._initialized = True\n","raise AttributeError(\"unexpected attribute '%s' to %s, %s attributes are %s\" %\n","self._internal_set(obj, value, setter=setter)\n","self._real_set(obj, old, value, hint=hint, setter=setter)\n","return self.wrap(value)\n","return value\n","doc.template = env.get_template(\"simple.html\")\n","return self._load_template(name, globals)\n","return template\n","return self._uptodate()\n","return os.path.getmtime(filename) == mtime\n","return os.stat(filename).st_mtime\n","check_integrity(refs)\n","log.warning(\"W-%d (%s): %s: %s\" % msg)\n","self.set_headers()\n","self.set_etag_header()\n","etag = self.compute_etag()\n","version_hash = self._get_cached_version(self.absolute_path)\n","hashes[abs_path] = cls.get_content_version(abs_path)\n","for chunk in data:\n","chunk = file.read(chunk_size)\n","await self.ws_connection.accept_connection(self)\n","await self._accept_connection(handler)\n","await self._receive_frame_loop()\n","await self._receive_frame()\n","data = await self._read_bytes(2)\n","data = await self.stream.read_bytes(n)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","handler_func(fileobj, events)\n","self._handle_read()\n","self._read_from_buffer(pos)\n","return self._find_read_pos()\n","return bytes_read\n","return self.socket.recv_into(buf, len(buf))\n","self.selector_loop.remove_reader(fd)\n","def remove_reader(self, fd):\n","await self._schedule(work)\n","await self.send_message(work)\n","await message.send(self)\n","await conn.write_message(self.metadata_json, locked=False)\n","await super().write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","return asyncio.ensure_future(wrapper())\n","return self.stream.write(frame)\n","return future\n","future_set_result_unless_cancelled(future, None)\n","return self.socket.send(data) # type: ignore\n","return value.encode(\"utf-8\")\n","work = await self.handler.handle(message, self.connection)\n","work = await handler(message, connection)\n","return result\n","return connection.protocol.create('PULL-DOC-REPLY', message.header['msgid'], self.document)\n","return self._messages[msgtype].create(*args, **kwargs)\n","return msg\n","return loads(doc_json)\n","return serialize_json(json, indent=indent)\n","return json.dumps(obj, cls=BokehJSONEncoder, allow_nan=False, indent=indent, separators=separators, sort_keys=True, **kwargs)\n","return cls(\n","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return obj.ref\n","struct['attributes'] = r._to_json_like(include_defaults=False)\n","return attrs\n","return self.query_properties_with_values(lambda prop: prop.serialized,\n","return result\n","return self.property.to_serializable(obj, self.name, getattr(obj, self.name))\n","return dict(value=val)\n","raise ValueError(msg)\n","return connection.ok(message)\n","return self.protocol.create('OK', message.header['msgid'])\n","return self._messages[msgtype].create(*args, **kwargs)\n","return cls(header, metadata, {})\n","await p\n","return None\n","return sent\n","await super().write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","return asyncio.ensure_future(wrapper())\n","return self.stream.write(frame)\n","return future\n","future_set_result_unless_cancelled(future, None)\n","return self.socket.send(data) # type: ignore\n","self._content_json = json_encode(self.content)\n","return json.dumps(value).replace(\"</\", \"<\\\\/\")\n","await conn.write_message(payload, binary=True, locked=locked)\n","await super().write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","return func(*args, **kwargs)\n","return doc._with_self_as_curdoc(invoke)\n","return f()\n","return f(*args, **kwargs)\n","doc.add_periodic_callback(lambda: update(ref), interval)\n","comp.update()\n","return input_function(*args, **kwargs)\n","self.root.title.text = (\n","return input_function(*args, **kwargs)\n","source.data.update(d)\n","if not np or not any(isinstance(v, np.ndarray) for v in source.data.values()):\n","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","if self.property.matches(value, old) and (hint is None):\n","if set(new.keys()) != set(old.keys()):\n","def _gc_callback(self, phase, info):\n","obj.trigger(self.name, old, value, hint, setter)\n","super().trigger(descriptor.name, old, new, hint=hint, setter=setter)\n","self._document._notify_change(self, attr, old, new, hint, setter, invoke)\n","self._trigger_on_change(event)\n","self._with_self_as_curdoc(invoke_callbacks)\n","return f()\n","cb(event)\n","self.data.discard(item)\n","self.source.stream(rectangles, self.n_rectangles)\n","self._stream(new_data, rollover)\n","self.data._stream(self.document, self, new_data, rollover, setter)\n","data = np.append(self[k], new_data[k])\n","","return concatenate((arr, values), axis=axis)\n","","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","if self.property.matches(value, old) and (hint is None):\n","return all(self.matches(new[k], old[k]) for k in new)\n","return all(self.matches(new[k], old[k]) for k in new)\n","return np.array_equal(new, old)\n","","return bool(asarray(a1 == a2).all())\n","return umr_all(a, axis, dtype, out, keepdims)\n","log = self.scheduler.get_events(topic=\"stealing\")\n","return tuple(self.events[topic])\n","data = func(data)\n","return d\n","sigspec, rv = _check_sigspec(sigspec, func, _sigs._is_partial_args,\n","return None, builtin_func(*builtin_args)\n","return any(check_partial(sig, args, kwargs) for sig in sigs)\n","return any(check_partial(sig, args, kwargs) for sig in sigs)\n","return is_partial_args(func, args, kwargs, sigspec)\n","return True\n","return self._bind(args, kwargs, partial=True)\n","return self._bound_arguments_cls(self, arguments)\n","return\n","self._stream(new_data, rollover)\n","self.data._stream(self.document, self, new_data, rollover, setter)\n","self._notify_owners(old,\n","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","if self.property.matches(value, old) and (hint is None):\n","return all(self.matches(new[k], old[k]) for k in new)\n","return all(self.matches(new[k], old[k]) for k in new)\n","return new == old\n","obj.trigger(self.name, old, value, hint, setter)\n","descriptor = self.lookup(attr)\n","resolved_name = cls._property_aliases().get(name, name)\n","update(self.source, result)\n","return input_function(*args, **kwargs)\n","source.data.update(d)\n","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","self._trigger(obj, old, value, hint=hint, setter=setter)\n","obj.trigger(self.name, old, value, hint, setter)\n","super().trigger(descriptor.name, old, new, hint=hint, setter=setter)\n","self._document._notify_change(self, attr, old, new, hint, setter, invoke)\n","self._trigger_on_change(event)\n","self._with_self_as_curdoc(invoke_callbacks)\n","return f()\n","cb(event)\n","self._callbacks[receiver] = lambda event: event.dispatch(receiver)\n","super().dispatch(receiver)\n","receiver._document_patched(self)\n","self._pending_writes.append(connection.send_patch_document(event))\n","return self._socket.send_message(msg)\n","return self._messages[msgtype].create(*args, **kwargs)\n","return msg\n","return serialize_json(json), buffers if use_buffers else []\n","return json.dumps(obj, cls=BokehJSONEncoder, allow_nan=False, indent=indent, separators=separators, sort_keys=True, **kwargs)\n","return cls(\n","self.source.stream(self.get_data(), 1000)\n","self._stream(new_data, rollover)\n","self.data._stream(self.document, self, new_data, rollover, setter)\n","self._notify_owners(old,\n","","return concatenate((arr, values), axis=axis)\n","","ret = callback()\n","raise\n","steal.put_key_in_stealable(ts)\n","saturated.discard(ws)\n","return self._hash\n","def pop(self, key, default=__not_given):\n","exec_time: double = ws._executing.get(ts, 0)\n","deps: set = ts._dependencies.difference(ws._has_what)\n","duration: double = ts._prefix._duration_average\n","def __hash__(self):\n","cost_multiplier, level = self.steal_time_ratio(ts)\n","compute_time = ws.processing[ts]\n","@property\n","@property\n","return self._prefix\n","@property\n","def __hash__(self):\n","return self._name\n","nbytes += ts.get_nbytes()\n","@ccall\n","def _gc_callback(self, phase, info):\n","return self._key\n","result = self.key_stealable.pop(ts, None)\n","def __hash__(self):\n","if isinstance(deadline, numbers.Real):\n","max(0, when - self.time()),\n","timer = self.call_at(self.time() + delay, callback, *args,\n","timer = events.TimerHandle(when, callback, args, self, context)\n","super().__init__(callback, args, loop, context)\n","self._context = context\n","return _abc_instancecheck(cls, instance)\n","self._schedule_next()\n","self._timeout = self.io_loop.add_timeout(self._next_timeout, self._run)\n","return self.call_at(deadline, callback, *args, **kwargs)\n","return self.asyncio_loop.call_later(\n","timer = self.call_at(self.time() + delay, callback, *args,\n","timer = events.TimerHandle(when, callback, args, self, context)\n","super().__init__(callback, args, loop, context)\n","result = {\"cpu\": cpu, \"memory\": memory, \"time\": now, \"count\": self.count}\n","rawdict = _psplatform.net_io_counters()\n","memory = proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return pmem(\n","return fun(self, *args, **kwargs)\n","return fun(self)\n","return ret\n","return next(self.gen)\n","raise\n","maybe_move_task(\n","if occ_idl + cost_multiplier * duration <= occ_sat - duration / 2:\n","if self._total_nthreads == 0 or ws.status == Status.closed:\n","if self.scheduler.validate:\n","deps: set = ts._dependencies.difference(ws._has_what)\n","return self._key\n","return ws.occupancy + self.in_flight_occupancy[ws]\n","return self._occupancy\n","def __hash__(self):\n","return ws.occupancy + self.in_flight_occupancy[ws]\n","@property\n","@property\n","def __hash__(self):\n","return not ts.loose_restrictions and (\n","@property\n","@property\n","if not result.done():\n","await self.handle_worker(comm=comm, worker=address)\n","await self.handle_stream(comm=comm, extra={\"worker\": worker})\n","msgs = await comm.read()\n","frames_nbytes = await stream.read_bytes(fmt_size)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self._try_inline_read()\n","self._read_from_buffer(pos)\n","self._finish_read(pos, False)\n","self._maybe_add_error_listener()\n","self._add_io_state(ioloop.IOLoop.READ)\n","self.io_loop.update_handler(self.fileno(), self._state)\n","self.selector_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)\n","self._add_reader(fd, callback, *args)\n","self._selector.register(fd, selectors.EVENT_READ,\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","def _decode_default(obj):\n","def _remove(item, selfref=ref(self)):\n","r: tuple = self.stimulus_task_finished(key=key, worker=worker, **msg)\n","r: tuple = parent._transition(key, \"memory\", worker=worker, **kwargs)\n","a: tuple = func(key, *args, **kwargs)\n","_remove_from_processing(self, ts)\n","state.check_idle_saturated(ws)\n","idle.pop(ws._address, None)\n","def pop(self, key, default=__not_given):\n","ts._group._types.add(typename)\n","return self._priority\n","new = self._transition(key, finish)\n","plugin.transition(key, start, finish2, *args, **kwargs)\n","self.put_key_in_stealable(ts)\n","cost_multiplier, level = self.steal_time_ratio(ts)\n","if not ts.dependencies: # no dependencies fast path\n","@property\n","worker_msgs[worker] = [_task_to_msg(self, ts)]\n","task = ts._run_spec\n","ws = decide_worker(\n","assert all([dts._who_has for dts in deps])\n","def _gc_callback(self, phase, info):\n","for dts in ts._dependencies:\n","return self._nbytes if self._nbytes >= 0 else DEFAULT_DATA_SIZE\n","w.send(*msgs)\n","if self.comm is not None and self.comm.closed():\n","self.put_key_in_stealable(ts)\n","cost_multiplier, level = self.steal_time_ratio(ts)\n","if not ts.dependencies: # no dependencies fast path\n","return self._dependencies\n","msg: dict = _task_to_msg(parent, ts, duration)\n","msg[\"who_has\"] = {\n","dts._key: [ws._address for ws in dts._who_has] for dts in deps\n","dts._key: [ws._address for ws in dts._who_has] for dts in deps\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","stream_comms[worker].send(msg)\n","self.waker.set()\n","if not self._value:\n","return self._closed\n","@ccall\n","dict.__setitem__(self, key, value)\n","def __hash__(self):\n","@property\n","@property\n","result = self.key_stealable.pop(ts, None)\n","return self._hash\n","return self._total_occupancy\n","removed_tasks: set = tasks.intersection(ws._has_what)\n","def __hash__(self):\n","tasks: set = {parent._tasks[k] for k in keys if k in parent._tasks}\n","return self._hash\n","if ts._processing_on != ws:\n","ts._who_has.add(ws)\n","return (yield from awaitable.__await__())\n","await self.start()\n","response = await self.instantiate()\n","self.listeners.append(listener)\n","return self\n","self.get_host_port() # trigger assignment to self.bound_address\n","return sockets\n","self._closed = False\n","await self.comm_handler(comm)\n","await comm.write(result, serializers=serializers)\n","stream.write(b\"\")\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","frames_nbytes = await stream.read_bytes(fmt_size)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","stream = await self.client.connect(\n","af, addr, stream = await connector.start(connect_timeout=timeout)\n","return self.future\n","future_add_done_callback(\n","return stream, stream.connect(addr)\n","self._closed = False\n","resp = await send_recv(comm, close=True, serializers=serializers, **msg)\n","await comm.write(msg, serializers=serializers, on_error=\"raise\")\n","stream.write(b\"\")\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","yielded = ctx_run(next, 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