fjetter/gh4471_dataframe_shuffle_main.html

## gh4471_dataframe_shuffle_main.html


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      <title>Dask Performance Report</title>

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import_term(self.function_path)(*args, **kwargs)\n","module = importlib.import_module(module_name)\n","return _bootstrap._gcd_import(name[level:], package, level)\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","","","","","","","","from .core import concatenate as _concatenate\n","","","","","","del PytestTester\n","","","","","","def ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False):\n","","","","","","del PytestTester\n","","","","","","","","","from fsspec import get_mapper\n","","","","","","from .core import get_fs_token_paths, open, open_files, open_local\n","","","","","","from .compression import compr\n","","","","","","pass\n","","","","","","class AbstractBufferedFile(io.IOBase):\n","return importlib.metadata.distribution(name).version\n","return self.metadata['Version']\n","return email.message_from_string(text)\n","return self._path.joinpath(filename).read_text(encoding='utf-8')\n","with self.open(mode='r', encoding=encoding, errors=errors) as f:\n","return io.open(self, mode, buffering, encoding, errors, newline,\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","with self.open(mode='r', encoding=encoding, errors=errors) as f:\n","return io.open(self, mode, buffering, encoding, errors, newline,\n","","","","","","","","","","","","","","","","def nlargest(self, n=5, split_every=None):\n","method.__doc__ = _derived_from(\n","return doc\n","return \"\\n\".join(lines)\n","","","","","","","","","return group_split_dispatch(df, c, k, ignore_index=ignore_index)\n","return meth(arg, *args, **kwargs)\n","indexer, locations = pd._libs.algos.groupsort_indexer(\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = [\n","blk.take_nd(\n","new_values = algos.take_nd(\n","mask = indexer == -1\n","elif is_extension_array_dtype(dtype) and isna(fill_value):\n","if mask.any():\n","result = NDArrayBackedExtensionIndex.take(\n","return self._shallow_copy(taken)\n","return result\n","return self._from_backing_data(new_data)\n","return type(self)._simple_new(arr, dtype=self.dtype)\n","return is_list_like(obj) and hasattr(obj, \"dtype\")\n","df2.iloc[a:b].reset_index(drop=True) if ignore_index else df2.iloc[a:b]\n","return self._getitem_axis(maybe_callable, axis=axis)\n","return self._get_slice_axis(key, axis=axis)\n","return self.obj._slice(slice_obj, axis=axis)\n","return result\n","return bm\n","new_blocks = [blk.getitem_block(slicer) for blk in self.blocks]\n","return type(self)._simple_new(new_values, new_mgr_locs, self.ndim)\n","obj = object.__new__(cls)\n","result = self._data[key]\n","return result\n","return result\n","return type(self)._simple_new(arr, dtype=self.dtype)\n","key = check_array_indexer(self, key)  # maybe ndarray[bool] -&gt; slice\n","if is_list_like(indexer):\n","dtarr = DatetimeArray._from_sequence_not_strict(\n","subarr, tz, inferred_freq = sequence_to_dt64ns(\n","data, copy = maybe_convert_dtype(data, copy)\n","if is_float_dtype(data.dtype):\n","return _is_dtype_type(arr_or_dtype, classes(np.floating))\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return _is_dtype(arr_or_dtype, condition)\n","dtype = get_dtype(arr_or_dtype)\n","NDFrame.__init__(self, data)\n","object.__setattr__(self, \"_flags\", Flags(self, allows_duplicate_labels=True))\n","self._allows_duplicate_labels = allows_duplicate_labels\n","self._is_copy = weakref.ref(ref)\n","self._validate_indexer(\"positional\", key.start, \"iloc\")\n","return pd.util.hash_pandas_object(\n","h = Series(h, index=obj.index, dtype=\"uint64\", copy=False)\n","self._set_axis(0, index, fastpath=True)\n","return subarr\n","return subarr\n","return casted\n","","return bool(asarray(a1 == a2).all())\n","dtype = pandas_dtype(dtype)\n","result = registry.find(dtype)\n","return dtype_type.construct_from_string(dtype)\n","first = next(arrays)\n","hashes = (hash_array(series._values) for _, series in obj.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.name = name\n","object.__getattribute__(self, name)\n","return self._name\n","return 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","","multiarray.copyto(res, z, casting='unsafe')\n","","data = self._take_with_is_copy(indexer, axis=1)\n","result = self.take(indices=indices, axis=axis)\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = self._slice_take_blocks_ax0(\n","nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n","new_values = algos.take_nd(\n","return type(self)(values, placement=placement, ndim=ndim)\n","self.mgr_locs = placement\n","dtype, fill_value = maybe_promote(arr.dtype, fill_value)\n","return dtype, fill_value\n","return func\n","return name\n","return True\n","mask = (indices &gt;= n) | (indices &lt; 0)\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","taken = algos.take(\n","if not self.is_consolidated():\n","self._protect_consolidate(f)\n","return result\n","self._mgr = self._mgr.consolidate()\n","return self\n","return self._is_consolidated\n","self._known_consolidated = True\n","dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]\n","NDFrame.__init__(self, data)\n","object.__setattr__(self, \"_flags\", Flags(self, allows_duplicate_labels=True))\n","return keyarr, indexer\n","return target, indexer\n","if not isinstance(other, Index):\n","indexer = self._engine.get_indexer(target._get_engine_target())\n","return Index(index_like)\n","return cls(\n","return self.get_indexer(target, **kwargs)\n","method = missing.clean_reindex_fill_method(method)\n","return Index(index_like)\n","if isinstance(data, ABCPandasArray):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","if not is_object_dtype(self.dtype) and is_object_dtype(other.dtype):\n","raise KeyError(\n","if isinstance(key, (ABCSeries, np.ndarray, ABCIndexClass)) or (\n","else methods.concat(args2, uniform=True, ignore_index=ignore_index)\n","return func(\n","out = pd.concat(dfs3, join=join, sort=False)\n","return op.get_result()\n","return cons(new_data).__finalize__(self, method=\"concat\")\n","return BlockManager(blocks, axes)\n","return np.concatenate(to_concat, axis=axis)\n","elif isinstance(arr, ABCRangeIndex):\n","non_empties = [x for x in to_concat if is_nonempty(x)]\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","values_flat = values.ravel(order=\"K\")\n","if not checker(arr[i : i + chunk_len]).all():\n","checker = lambda x: _isna_ndarraylike(x, inf_as_na=INF_AS_NA)\n","result = _isna_string_dtype(values, dtype, inf_as_na=inf_as_na)\n","_get_mgr_concatenation_plan(mgr, indexers) for mgr, indexers in mgrs_indexers\n","shape_list = list(mgr_shape)\n","self.block = block\n","yield placements[0], units\n","klass = get_block_type(values, dtype)\n","elif issubclass(vtype, np.floating):\n","from pandas.core.arrays.sparse import SparseDtype\n","allows_duplicate_labels = all(\n","x.flags.allows_duplicate_labels for x in other.objs\n","return array_equivalent(self._values, other._values)\n","left, right = np.asarray(left), np.asarray(right)\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","return self._concat(to_concat, name)\n","result = concat_compat(to_concat_vals)\n","return _concat_datetime(to_concat, axis=axis)\n","result = type(to_concat[0])._concat_same_type(to_concat, axis=axis)\n","new_obj = super()._concat_same_type(to_concat, axis)\n","return to_concat[0]._from_backing_data(new_values)\n","dtypes = {str(x.dtype) for x in to_concat}\n","return dtype.name\n","return name\n","elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):\n","if not issubclass_(arg1, generic):\n","return type(self)._simple_new(arr, dtype=self.dtype)\n","return len(self.shape)\n","return self._ndarray.shape\n","dtype = arr.dtype\n","non_empties = [x for x in to_concat if is_nonempty(x)]\n","if x.ndim &lt;= axis:\n","any_ea = any(is_extension_array_dtype(x.dtype) for x in to_concat)\n","return _maybe_asobject(dtype, DatetimeIndex, data, copy, name, **kwargs)\n","return klass(data, dtype=dtype, copy=copy, name=name, **kwargs)\n","dtarr = DatetimeArray._from_sequence_not_strict(\n","freq, freq_infer = dtl.maybe_infer_freq(freq)\n","tz = timezones.maybe_get_tz(tz)\n","names = {obj.name for obj in to_concat}\n","indexes = [x.axes[self.axis] for x in self.objs]\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, other._values)\n","return _array_equivalent_object(left, right, strict_nan)\n","return _is_dtype(arr_or_dtype, condition)\n","return condition(dtype)\n","return dtype.kind in (\"O\", \"S\", \"U\") and not is_excluded_dtype(dtype)\n","return any(is_excluded(dtype) for is_excluded in is_excluded_checks)\n","return any(is_excluded(dtype) for is_excluded in is_excluded_checks)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return names\n","name_tups = [tuple(i.names) for i in indexes]\n","new_index = self._shallow_copy(name=name)\n","result = self._simple_new(self._values, name=name)\n","obs_idxes = [obj._get_axis(axis) for obj in objs]\n","obj for obj in objs if sum(obj.shape) &gt; 0 or isinstance(obj, ABCSeries)\n","return len(self.index), len(self.columns)\n","return len(self._data)\n","return self.shape[0]\n","self._protect_consolidate(f)\n","result = f()\n","self._mgr = self._mgr.consolidate()\n","if self.is_consolidated():\n","self._consolidate_check()\n","dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]\n","dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]\n","return method(self, other)\n","return self._cmp_method(other, operator.eq)\n","return self._construct_result(res_values, name=res_name)\n","return res_values\n","return _isna(obj)\n","is_a_string_array = is_a_array and is_string_like_dtype(a)\n","return _is_dtype(arr_or_dtype, lambda dtype: dtype.kind in (\"S\", \"U\"))\n","return out\n","self._set_axis(0, index, fastpath=True)\n","return cls(block, index)\n","return klass(values, ndim=ndim, placement=placement)\n","raise ValueError(\n","return cls\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return False\n","return obj\n","return result\n","return self._constructor_sliced(data, index=self._info_axis, dtype=np.object_)\n","data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)\n","return subarr\n","subarr = maybe_cast_to_datetime(arr, dtype)\n","return value\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","if labels._is_all_dates:\n","block = make_block(array, placement=slice(0, len(index)), ndim=1)\n","return klass(values, ndim=ndim, placement=placement)\n","return cls\n","self.values = self._maybe_coerce_values(values)\n","return dtype\n","return is_none or is_simple_empty\n","return algos.take_1d(dtypes, self.blknos, allow_fill=False)\n","func = _get_take_nd_function(\n","func = _take_1d_dict.get(tup, None)\n","dtypes = np.array([blk.dtype for blk in self.blocks])\n","@doc(\n","return self._logical_func(\n","return self._reduce(\n","return op(delegate, skipna=skipna, **kwds)\n","self.oldcall = seterrcall(self.call)\n","return old\n","return values.any(axis)\n","return values, mask, dtype, dtype_max, fill_value\n","return None\n","return issubclass(dtype.type, np.bool_)\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return obj\n","return issubclass(dtype.type, np.bool_)\n","_add_filter(action, None, category, None, lineno, append=append)\n","args2 = [i for i in args if len(i)]\n","return len(self.index)\n","return len(self._data)\n","return self.shape[0]\n","return get_parallel_type(x) is not Scalar\n","meth = self.dispatch(type(arg))\n","return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\n","result = _execute_task(task, cache)\n","return func(*(_execute_task(a, cache) for a in args))\n","return make_timeseries_part(\n","columns[k] = make[dt](len(index), state, **kws)\n","mgr = init_dict(data, index, columns, dtype=dtype)\n","return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)\n","return create_block_manager_from_arrays(arrays, arr_names, axes)\n","return mgr\n","return blocks\n","return new_blocks\n","klass = get_block_type(values, dtype)\n","return cls\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return False\n","self.mgr_locs = placement\n","new_mgr_locs = libinternals.BlockPlacement(new_mgr_locs)\n","values, placement = _stack_arrays(tuples, dtype)\n","if isinstance(other, ABCMultiIndex):\n","left, right = np.asarray(left), np.asarray(right)\n","val = sanitize_array(\n","subarr = np.array(data, dtype=object, copy=copy)\n","return _isna(obj)\n","return _isna_ndarraylike(obj, inf_as_na=inf_as_na)\n","result = _isna_string_dtype(values, dtype, inf_as_na=inf_as_na)\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","return condition(type(None))\n","if maybe_castable(arr) and not copy and dtype is None:\n","return arr.dtype.name not in POSSIBLY_CAST_DTYPES\n","return map(self._values.item, range(self._values.size))\n","return self._mgr.internal_values()\n","return self._block.internal_values()\n","return Index(index_like)\n","return cls(\n","from pandas.core.indexes.range import RangeIndex\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","data, index = self._init_dict(data, index, dtype)\n","s = s.reindex(index, copy=False)\n","return super().reindex(index=index, **kwargs)\n","return self._reindex_axes(\n","new_index, indexer = ax.reindex(\n","if self.equals(target):\n","return self.is_unique\n","return self._engine.is_unique\n","return self._constructor(new_data).__finalize__(self)\n","self._get_axis(axis).identical(ax)\n","self.equals(other)\n","return array_equivalent(self._values, other._values)\n","return _array_equivalent_object(left, right, strict_nan)\n","if not is_hashable(name):\n","return Index(index_like)\n","return cls(\n","elif is_period_dtype(data_dtype) or is_period_dtype(dtype):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","dtarr = DatetimeArray._generate_range(\n","return cls._simple_new(index.asi8, freq=freq, dtype=dtype)\n","result = self._data[key]\n","result = super().__getitem__(key)\n","return self._box_func(result)\n","return self._getitem_axis(maybe_callable, axis=axis)\n","return self._get_slice_axis(key, axis=axis)\n","return self.obj._slice(slice_obj, axis=axis)\n","return result\n","return bm\n","return type(self)(result, name=self.name)\n","return subarr\n","return result\n","data = self._take_with_is_copy(indexer, axis=1)\n","result = self.take(indices=indices, axis=axis)\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = self._slice_take_blocks_ax0(\n","nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n","new_values = algos.take_nd(\n","return out\n","return dtype, fill_value\n","return dtype.type(value)\n","return _isna(obj)\n","return func\n","return name\n","return True\n","return tuple(len(ax) for ax in self.axes)\n","return tuple(len(ax) for ax in self.axes)\n","if mask.any():\n","allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)\n","return result\n","return is_list_like(obj) and hasattr(obj, \"dtype\")\n","self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)\n","missing_mask = indexer &lt; 0\n","return self.get_indexer(target, **kwargs)\n","target = ensure_index(target)\n","return Index(index_like)\n","elif is_datetime64_any_dtype(data_dtype) or is_datetime64_any_dtype(dtype):\n","return clean_fill_method(method, allow_nearest=True)\n","if self.equals(target):\n","return array_equivalent(self._values, other._values)\n","axis_number = self._get_axis_number(axis)\n","if isinstance(key, (ABCSeries, np.ndarray, ABCIndexClass)) or (\n","return is_list_like(obj) and hasattr(obj, \"dtype\")\n","return [_execute_task(a, cache) for a in arg]\n","return arg\n","return _toposort(dsk, dependencies=dependencies)\n","dependencies = dict((k, get_dependencies(dsk, k)) for k in dsk)\n","dependencies = dict((k, get_dependencies(dsk, k)) for k in dsk)\n","arg = dsk[key]\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","+ sizeof(list(d.values()))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + int(num_items / num_samples * sum(map(sizeof, samples)))\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","values = self._mgr.iget(loc)\n","block = self.blocks[self.blknos[i]]\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 type(self)(values, placement=placement, ndim=ndim)\n","raise ValueError(\n","self._mgr_locs = new_mgr_locs\n","return klass(values, index=self.index, name=name, fastpath=True)\n","self.name = name\n","object.__getattribute__(self, name)\n","validate_all_hashable(value, error_name=f\"{type(self).__name__}.name\")\n","return self._name\n","return object.__getattribute__(self, name)\n","object.__setattr__(self, \"_flags\", Flags(self, allows_duplicate_labels=True))\n","self._obj = weakref.ref(obj)\n","getitem = self._data.__getitem__\n","casted_key = self._maybe_cast_indexer(key)\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","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 meth(arg, *args, **kwargs)\n","p = int(i.memory_usage())\n","for name, col in df.iteritems():\n","yield from self.items()\n","yield k, self._get_item_cache(k)\n","values = self._mgr.iget(loc)\n","block = self.blocks[self.blknos[i]]\n","self._rebuild_blknos_and_blklocs()\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","if not isinstance(self._values, np.ndarray):\n","return self._engine.is_unique\n","sample = np.random.choice(x, size=20, replace=True)\n","return meth(arg, *args, **kwargs)\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n","return meth(arg, *args, **kwargs)\n","p = int(i.memory_usage())\n","result += self._engine.sizeof(deep=deep)\n","result = getattr(self._data, name)\n","return self.nbytes\n","v = super().memory_usage(deep=deep)\n","v = self.array.nbytes\n","return self._mgr._block.array_values()\n","return PandasArray(self.values)\n","self._ndarray = values\n","return self._mgr.dtype\n","return self._block.dtype\n","return self._mgr.internal_values()\n","return self._block.internal_values()\n","p += object_size(s.index)\n","self._bootstrap_inner()\n","self.run()\n","self._target(*self._args, **self._kwargs)\n","task.run()\n","self.future.set_result(result)\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","out = df._constructor(res).iloc[0]\n","return self._getitem_axis(maybe_callable, axis=axis)\n","return self.obj._ixs(key, axis=axis)\n","new_values = self._mgr.fast_xs(i)\n","dtype = _interleaved_dtype(self.blocks)\n","return find_common_type([b.dtype for b in blocks])\n","return np.find_common_type(types, [])\n","return maxa\n","self._set_axis(0, index, fastpath=True)\n","self._mgr.set_axis(axis, labels)\n","return subarr\n","return subarr\n","object.__setattr__(self, \"_flags\", Flags(self, allows_duplicate_labels=True))\n","is_array_like(key) and is_extension_array_dtype(key.dtype)\n","return is_list_like(obj) and hasattr(obj, \"dtype\")\n","return _abc_instancecheck(cls, instance)\n","return _abc_subclasscheck(cls, subclass)\n","return _abc_subclasscheck(cls, subclass)\n","return _abc_subclasscheck(cls, subclass)\n","return new_mgr, indexer\n","return type(self).from_blocks(new_blocks, axes)\n","self._mgr_locs = new_mgr_locs\n","return arr\n","","return self._shallow_copy(taken)\n","return result\n","result = func(self.values)\n","return op(values, axis=1, skipna=skipna, **kwds)\n","return f(*args, **kwargs)\n","return result\n","return the_sum\n","return values, mask, dtype, dtype_max, fill_value\n","return mask\n","return _isna(obj)\n","return _isna_ndarraylike(obj, inf_as_na=inf_as_na)\n","return result\n","return issubclass(dtype.type, np.bool_)\n","null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) &lt; 0\n","","self.oldcall = seterrcall(self.call)\n","return old\n","if any(self.check(obj) for obj in obj_iter):\n","","def make_block(self, values, placement=None) -&gt; \"Block\":\n","return klass(values, ndim=ndim, placement=placement)\n","return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)\n","new_data, new_dtype = _maybe_cast_data_without_dtype(subarr)\n","return construct_1d_object_array_from_listlike(values)\n","","if is_dict_like(dtype):\n","all(hasattr(obj, attr) for attr in dict_like_attrs)\n","return self.apply(\"astype\", dtype=dtype, copy=copy, errors=errors)\n","applied = getattr(b, f)(**kwargs)\n","if inspect.isclass(dtype) and issubclass(dtype, ExtensionDtype):\n","self.name = name\n","object.__getattribute__(self, name)\n","own_dtypes = [arr.dtype for arr in self._iter_column_arrays()]\n","yield self._get_column_array(i)\n","return self._mgr.iget_values(i)\n","df = self._constructor_expanddim(self)\n","mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)\n","index, columns = _get_axes(\n","index = ensure_index(index)\n","return RangeIndex(0, n, name=None)\n","return cls._simple_new(rng, name=name)\n","values=blocks[0], placement=slice(0, len(axes[0])), ndim=2\n","return self.transpose()\n","return result.__finalize__(self, method=\"transpose\")\n","return self._mgr.as_array(transpose=self._AXIS_REVERSED)\n","def is_series_like(s):\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","return isna_all(values_flat)\n","return False\n","checker = lambda x: _isna_ndarraylike(x, inf_as_na=INF_AS_NA)\n","return result\n","yield from self.items()\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","concat_axis = _concat_indexes(indexes)\n","return indexes[0].append(indexes[1:])\n","return self._concat(to_concat, name)\n","result = Int64Index(np.concatenate([x._values for x in indexes]))\n","new_data = concatenate_block_managers(\n","elif _is_uniform_join_units(join_units):\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","return 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**self._kwargs)\n","loop.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","handler_func(fileobj, events)\n","self._handle_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","connection, address = sock.accept()\n","fd, addr = self._accept()\n","future = self.handle_stream(stream, address)\n","context = _HTTPRequestContext(\n","self.address_family = stream.socket.family\n","await self.handle_worker(comm=comm, worker=address)\n","await self.handle_stream(comm=comm, extra={\"worker\": worker})\n","handler(**merge(extra, msg))\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","def _gc_callback(self, phase, info):\n","return self._nbytes if self._nbytes &gt;= 0 else DEFAULT_DATA_SIZE\n","parent._transitions(recommendations, client_msgs, worker_msgs)\n","new = self._transition(key, finish)\n","a: tuple = func(key, *args, **kwargs)\n","if not ts._run_spec:  # pure data\n","def __hash__(self):\n","return self._nbytes if self._nbytes &gt;= 0 else DEFAULT_DATA_SIZE\n","worker_msgs[worker] = [_task_to_msg(self, ts)]\n","msg.update(task)\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","msg[\"nbytes\"] = {dts._key: dts._nbytes for dts in deps}\n","ws = decide_worker(\n","ws = min(candidates, key=objective)\n","nbytes = dts.get_nbytes()\n","return self._nbytes if self._nbytes &gt;= 0 else DEFAULT_DATA_SIZE\n","def __hash__(self):\n","assert all([dts._who_has for dts in deps])\n","candidates = {wws for dts in deps for wws in dts._who_has}\n","if ts._host_restrictions:\n","comm: double = self.get_comm_cost(ts, ws)\n","return nbytes / self._bandwidth\n","return self._hash\n","saturated: set = self._saturated\n","@state.setter\n","self.put_key_in_stealable(ts)\n","cost_multiplier, level = self.steal_time_ratio(ts)\n","split = ts.prefix.name\n","return self._name\n","return self._processing\n","nbytes += ts.get_nbytes()\n","return self._nbytes if self._nbytes &gt;= 0 else DEFAULT_DATA_SIZE\n","@property\n","def __hash__(self):\n","def cast(T, v, *a, **k):\n","r: tuple = parent._transition(key, \"memory\", worker=worker, **kwargs)\n","dependents = set(ts._dependents)\n","_add_to_memory(\n","recommendations[dts._key] = \"processing\"\n","return self._priority\n","def _gc_callback(self, phase, info):\n","for ws in self._who_has:\n","state.check_idle_saturated(ws)\n","idle[ws._address] = ws\n","dict.__setitem__(self, key, value)\n","def __hash__(self):\n","return self._address == other_ws._address\n","self.remove_key_from_stealable(ts)\n","worker, level = result\n","w.send(*msgs)\n","self.waker.set()\n","fut.set_result(None)\n","handle = self._call_soon(callback, args, context)\n","def _call_soon(self, callback, args, context):\n","self._source_traceback = None\n","return self._debug\n","ts: TaskState = parent._tasks.get(key)\n","return self._hash\n","def cast(T, v, *a, **k):\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","def _gc_callback(self, phase, info):\n","def _decode_default(obj):\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","buf[:] = memoryview(self._read_buffer)[self._read_buffer_pos : end]\n","r: tuple = self.stimulus_missing_data(key=key, ensure=False, **msg)\n","parent._transitions(recommendations, client_msgs, worker_msgs)\n","new = self._transition(key, finish)\n","self.transition_log.append((key, start, finish2, recommendations, time()))\n","worker_msgs[worker] = [_task_to_msg(self, ts)]\n","\"priority\": ts._priority,\n","dts._waiters.add(ts)\n","def transition_processing_released(self, key):\n","if ts is None or not ts._who_has:\n","IOLoop.current().call_later(\n","return self.call_at(self.time() + delay, 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","self._when = when\n","def _gc_callback(self, phase, info):\n","return IOLoop._ioloop_for_asyncio[loop]\n","def _gc_callback(self, phase, info):\n","call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))\n","self._check_closed()\n","raise RuntimeError('Event loop is closed')\n","handle = events.Handle(callback, args, self, context)\n","if self._loop.get_debug():\n","return self._debug\n","await p\n","await message.send(self)\n","await conn.write_message(self.header_json, locked=False)\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","size = len(data)\n","return value.encode(\"utf-8\")\n","return self.stream.closed() or self.client_terminated or self.server_terminated\n","return self._block.acquire(timeout)\n","self._content_json = json_encode(self.content)\n","return json.dumps(value).replace(\"&lt;/\", \"&lt;\\\\/\")\n","return _default_encoder.encode(obj)\n","chunks = self.iterencode(o, _one_shot=True)\n","return _iterencode(o, 0)\n","if not self._header_json:\n","return json.dumps(value).replace(\"&lt;/\", \"&lt;\\\\/\")\n","return _default_encoder.encode(obj)\n","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return\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","rectangles = self.plugin.rectangles(\n","return rectangles(msgs, workers=workers, start_boundary=start_boundary)\n","name = key_split(key)\n","def format_time(n):\n","self._stream(new_data, rollover)\n","self.data._stream(self.document, self, new_data, rollover, setter)\n","del L[:-rollover]\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","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","msg = self.protocol.create('PATCH-DOC', [event])\n","return self._messages[msgtype].create(*args, **kwargs)\n","content = loads(patch_json)\n","return _default_decoder.decode(s)\n","obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n","return obj, end\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","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return transform_array(obj, force_list=True)\n","","return serialize_array(array, force_list=force_list, buffers=buffers)\n","return transform_array_to_list(array)\n","return array.tolist()\n","self.hint = hint\n","return\n","","return concatenate((arr, values), axis=axis)\n","","","return asanyarray(a).ravel(order=order)\n","return array(a, dtype, copy=False, order=order, subok=True)\n","@set_array_function_like_doc\n","self.root.title.text = (\n","super().__setattr__(name, value)\n","self._internal_set(obj, value, setter=setter)\n","self._real_set(obj, old, value, hint=hint, setter=setter)\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","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","receiver._document_model_changed(self)\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","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return\n","return input_function(*args, **kwargs)\n","source.data.update(d)\n","descriptor._notify_mutated(owner, old, hint=hint)\n","value = self.property.prepare_value(obj, self.name, value)\n","result = fn(obj, value)\n",").asserts(lambda _, data: len({len(x) for x in data.values()}) &lt;= 1,\n","def _gc_callback(self, phase, info):\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","receiver._document_model_changed(self)\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 self.hint.generate(references, buffers)\n","return { 'kind'          : 'ColumnDataChanged',\n","return data_copy\n","return obj_copy\n","return\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","if set(new.keys()) != set(old.keys()):\n","def _gc_callback(self, phase, info):\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 self._convert(self._default)\n","return convert_bool(os.environ.get(\"BOKEH_DEV\", False))\n","return default\n","raise KeyError(key) from None\n","return\n","return\n","return cls(\n","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return\n","return self.hint.generate(references, buffers)\n","return { 'kind'          : 'ColumnDataChanged',\n","return data_copy\n","return obj_copy\n","return\n","return header\n","return str(_simple_id)\n","return self._convert(self._default)\n","return False\n","raise KeyError(key) from None\n","return\n","return _default_decoder.decode(s)\n","obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n","return all(self.matches(new[k], old[k]) for k in new)\n","super().__setattr__(name, value)\n","self._internal_set(obj, value, setter=setter)\n","self._real_set(obj, old, value, hint=hint, setter=setter)\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","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","receiver._document_model_changed(self)\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","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return\n","return accumulate_from_superclasses(cls, \"__properties__\")\n","if cachename not in cls.__dict__:\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","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","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","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","receiver._document_model_changed(self)\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","return ''.join(chunks)\n","return _iterencode(o, 0)\n","return header\n","return str(_simple_id)\n","return self._convert(self._default)\n","return False\n","raise KeyError(key) from None\n","return\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","msg = self.protocol.create('PATCH-DOC', [event])\n","return self._messages[msgtype].create(*args, **kwargs)\n","patch_json, buffers = process_document_events(events, use_buffers)\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","chunks = self.iterencode(o, _one_shot=True)\n","return _iterencode(o, 0)\n","def _gc_callback(self, phase, info):\n","return self.hint.generate(references, buffers)\n","data_dict = transform_column_source_data(self.column_source.data, buffers=buffers, cols=self.cols)\n","data_copy[key] = transform_array(data[key], buffers=buffers)\n","return serialize_array(array, force_list=force_list, buffers=buffers)\n","return encode_binary_dict(array, buffers)\n","return {\n","return name\n","return True\n","if settings.simple_ids():\n","if is_dev() and self._dev_default is not _Unset:\n","return convert_bool(os.environ.get(\"BOKEH_DEV\", False))\n","return default\n","return\n","result = fn(obj, value)\n",").asserts(lambda _, data: len({len(x) for x in data.values()}) &lt;= 1,\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 get_debug(self):\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","sub_header, sub_frames = serialize_and_split(\n","header, frames = serialize(x, serializers, on_error, context)\n","return serialize(\n","headers_frames = [\n","serialize(\n","header, frames = dumps(x, context=context) if wants_context else dumps(x)\n","return\n","def _gc_callback(self, phase, info):\n","return struct.pack(f\"Q{nframes}Q\", nframes, *nbytes_frames)\n","self.scheduler.send_task_to_worker(thief.address, ts)\n","msg: dict = _task_to_msg(parent, ts, duration)\n","task = ts._run_spec\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","stream_comms[worker].send(msg)\n","self.waker.set()\n","fut.set_result(None)\n","return handle\n","return handle\n","self._source_traceback = None\n","idle.pop(ws._address, None)\n","self._list_remove(key)\n","pos = bisect_left(_maxes, value)\n","cost_multiplier, level = self.steal_time_ratio(ts)\n","nbytes = ts.get_nbytes_deps()\n","nbytes += ts.get_nbytes()\n","return self._nbytes if self._nbytes &gt;= 0 else DEFAULT_DATA_SIZE\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","def advance(self, size: int) -&gt; None:\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","def _decode_default(obj):\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","buf = bytearray(self.read_chunk_size)\n","return self.socket.recv_into(buf, len(buf))\n","timestamp, size = ws._memory_other_history[0]\n","parent._tasks[key]: duration for key, duration in executing.items()\n","addr = resolve_address(addr)\n","return unparse_address(scheme, backend.resolve_address(loc))\n","return unparse_host_port(ensure_ip(host), port)\n","return \"%s:%s\" % (host, port)\n","ret = callback()\n","return self.callback()\n","ioc = psutil.net_io_counters()\n","rawdict = _psplatform.net_io_counters()\n","return _wn.run(input_dict, name)\n","bits.append(input_value + self.reminders[name][remkey])\n","def _gc_callback(self, phase, info):\n","return next(self.gen)\n","self.cpu_times.cache_activate(self)\n","proc._cache = {}\n","maybe_move_task(\n","self.move_task_request(ts, sat, idl)\n","key = ts.key\n","return self._key\n","worker, level = result\n","deps: set = ts._dependencies.difference(ws._has_what)\n","if self.isEnabledFor(DEBUG):\n","return ws.occupancy + self.in_flight_occupancy[ws]\n","return self._hash\n","if self._total_nthreads == 0 or ws.status == Status.closed:\n","saturated = [\n","return ws.occupancy + self.in_flight_occupancy[ws]\n","return not ts.loose_restrictions and (\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","memory = proc.memory_info().rss\n","ret = self._cache[fun]\n","def _measure_tick(self):\n","for i in range(len(self.intervals) - 1):\n","_reevaluate_occupancy_worker(parent, ws)\n","steal.put_key_in_stealable(ts)\n","self.key_stealable[ts] = (worker, level)\n","return cost_multiplier, level\n","return nbytes\n","return self._nbytes if self._nbytes &gt;= 0 else DEFAULT_DATA_SIZE\n","def _gc_callback(self, phase, info):\n","@property\n","return self._processing\n","return self._hash\n","@property\n","worker, level = result\n","def __hash__(self):\n","@ccall\n","deps: set = ts._dependencies.difference(ws._has_what)\n","result.set_exception(TimeoutError(\"Timeout\"))\n","return handle\n","return handle\n","return\n","future.add_done_callback(callback)\n","await self._schedule(work)\n","await self.send_message(work)\n","await message.send(self)\n","await conn.write_message(self.content_json, locked=False)\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","self._content_json = json_encode(self.content)\n","return json.dumps(value).replace(\"&lt;/\", \"&lt;\\\\/\")\n","return _default_encoder.encode(obj)\n","chunks = self.iterencode(o, _one_shot=True)\n","return _iterencode(o, 0)\n","work = await self.handler.handle(message, self.connection)\n","work = await handler(message, connection)\n","result = func(self, *args, **kwargs)\n","message.apply_to_document(self.document, self)\n","doc._with_self_as_curdoc(lambda: doc.apply_json_patch(self.content, setter))\n","return f()\n","doc._with_self_as_curdoc(lambda: doc.apply_json_patch(self.content, setter))\n","patched_obj.set_from_json(attr, value, models=references, setter=setter)\n","descriptor.set_from_json(self, json, models, setter)\n","return super().set_from_json(obj, self.property.from_json(json, models), models, setter)\n","self._internal_set(obj, json, setter=setter)\n","old = self._get(obj)\n","return self._get_default(obj)\n","self.validate(value)\n","return\n","if isinstance(value, dict) and all(key_is_valid(key) and value_is_valid(val) for key, val in value.items()):\n","self.validate(value, False)\n","if self._is_seq(value) and all(self.item_type.is_valid(item) for item in value):\n","if self._is_seq(value) and all(self.item_type.is_valid(item) for item in value):\n","self.validate(value, False)\n","def validation_on():\n","def validate(self, value, detail=True):\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","new_data[key] = decode_base64_dict(value)\n","b64 = base64.b64decode(data['__ndarray__'])\n","return binascii.a2b_base64(s)\n","return connection.protocol.create('PULL-DOC-REPLY', message.header['msgid'], self.document)\n","return self._messages[msgtype].create(*args, **kwargs)\n","content = { 'doc' : document.to_json() }\n","doc_json = self.to_json_string()\n","'references' : references_json(root_references)\n","struct['attributes'] = r._to_json_like(include_defaults=False)\n","all_attrs = self.properties_with_values(include_defaults=include_defaults)\n","return self.query_properties_with_values(lambda prop: prop.serialized,\n","value = descriptor.serializable_value(self)\n","value = self.__get__(obj, obj.__class__)\n","value = self._get(obj)\n","return self._get_default(obj)\n","default = self.instance_default(obj)\n","return self.property.themed_default(obj.__class__, self.name, obj.themed_values())\n","return self.prepare_value(cls, name, default)\n","if value is Undefined:\n","return transform_column_source_data(value)\n","data_copy[key] = traverse_data(data[key], buffers=buffers)\n","elif isinstance(item, (list, tuple)):  # check less common type second\n","return serialize_array(array, force_list=force_list, buffers=buffers)\n","return encode_base64_dict(array)\n","'__ndarray__' : base64.b64encode(array.data).decode('utf-8'),\n","return json.dumps(obj, cls=BokehJSONEncoder, allow_nan=False, indent=indent, separators=separators, sort_keys=True, **kwargs)\n","return cls(\n","chunks = self.iterencode(o, _one_shot=True)\n","return _iterencode(o, 0)\n","def default(self, obj):\n","return _default_decoder.decode(s)\n","obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n","obj, end = self.scan_once(s, idx)\n","message = await self.receiver.consume(fragment)\n","self._current_consumer(fragment)\n","self._check_complete()\n","return self._messages[header['msgtype']].assemble(\n","return msg\n","return json.loads(to_basestring(value))\n","return _default_decoder.decode(s)\n","return obj\n","return obj, end\n","result = await result\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","await handled_future\n","return data\n","return future\n","return\n","return self._find_read_pos()\n","return bytes_read\n","return self.socket.recv_into(buf, len(buf))\n","session = await self.get_session()\n","session = await self.application_context.create_session_if_needed(session_id, self.request, token)\n","self._application.initialize_document(doc)\n","h.modify_document(doc)\n","self._func(doc)\n","ts = TaskStream(\n","self.source, self.root = task_stream_figure(clear_interval, **kwargs)\n","root = figure(\n","return Figure(**kwargs)\n","self.x_range = get_range(opts.x_range)\n","if pd and isinstance(range_input, pd.core.groupby.GroupBy):\n","if len(args) &gt; len(glyphclass._args):\n","return input_function(*args, **kwargs)\n","rectangles = self.plugin.rectangles(\n","msg = self.buffer[i]\n","key = msg[\"key\"]\n","return \"%.2f ms\" % (n * 1e3)\n","return color_of(split)\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","","self._pop_all_models_freeze()\n","self._recompute_all_models()\n","a._attach_document(self)\n","doc.theme.apply_to_model(self)\n","model.apply_theme(self._for_class(model.__class__))\n","descriptor.trigger_if_changed(self, v)\n","return\n","self.set_etag_header()\n","etag = self.compute_etag()\n","hasher = hashlib.sha1()\n","lambda f: self._run_callback(functools.partial(callback, future))\n","ret = callback()\n","self.ctx_run(self.run)\n","yielded = self.gen.send(value)\n","nbytes = yield self.comm.write(\n","return fut\n","chain_future(future_converted, result)\n","def copy(future: \"Future[_T]\") -&gt; None:\n","return\n","return\n","return self.call_at(deadline, callback, *args, **kwargs)\n","return self.asyncio_loop.call_later(\n","return timer\n","return timer\n","return self._when &lt; other._when\n","return\n","self._scheduled = False\n","return\n","return _abc_instancecheck(cls, instance)\n","def _gc_callback(self, phase, info):\n","return\n","self.io_loop.add_future(self.future, inner)\n","def add_future(\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded)  # type: ignore\n","return task\n","return task\n","return self._debug\n","def _gc_callback(self, phase, info):\n","self._loop.add_future(invoke(), on_done)\n","return gen.multi([sleep_future, callback_future])\n","return multi_future(children, quiet_exceptions=quiet_exceptions)\n","def _gc_callback(self, phase, info):\n","self._loop.call_later(self._period / 1000.0, lambda: f.set_result(None))\n","return self.call_at(self.time() + delay, callback, *args, **kwargs)\n","return self.asyncio_loop.call_later(\n","return timer\n","return timer\n","self._scheduled = False\n","return\n","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded)  # type: ignore\n","return task\n","return task\n","return handle\n","future_add_done_callback(\n","handle = self._call_soon(callback, args, context)\n","handle = events.Handle(callback, args, self, context)\n","self._cancelled = False\n","self._scheduler_identity = SchedulerInfo(await self.scheduler.identity())\n","result = await send_recv(comm=comm, op=key, **kwargs)\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","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","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","def _decode_default(obj):\n","lambda tf: fut.cancel() if not fut.done() else None\n","return handle\n","return handle\n","return\n","future_converted, lambda future: io_loop.remove_timeout(timeout_handle)\n","timeout.cancel()  # type: ignore\n","super().cancel()\n","request_delegate = delegate.start_request(self, conn)\n","delegate = self.request_callback.start_request(server_conn, request_conn)\n","def start_request(\n","header_future = self.stream.read_until_regex(\n","self._read_regex = re.compile(regex)\n","return _compile(pattern, flags)\n","return _cache[type(pattern), pattern, flags]\n","headers = httputil.HTTPHeaders.parse(data_str[eol:])\n","h.parse_line(line)\n","self.add(name, value.strip())\n","self[norm_name] = value\n","def copy(future: \"Future[_T]\") -&gt; None:\n","result = handler(**msg)\n","type = loads(type)\n","return pickle.loads(x)\n","result = await self.client._gather([self])\n","unpacked, future_set = unpack_remotedata(futures, byte_keys=True)\n","return first(w for w in Worker._instances if w.status == Status.running)\n","response = await retry_operation(self.scheduler.gather, keys=keys)\n","return await retry(\n","return await coro()\n","result = await send_recv(comm=comm, op=key, **kwargs)\n","response = await comm.read(deserializers=deserializers)\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","def __lt__(self, 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