rabernat/dask-report.html

## dask-report.html


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      <meta charset="utf-8">
      <title>Dask Performance Report</title>

<link rel="shortcut icon" href="https://docs.dask.org/en/latest/_static/images/favicon.ico" />


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self.main(*args, **kwargs)\n","rv = self.invoke(ctx)\n","return ctx.invoke(self.callback, **ctx.params)\n","return callback(*args, **kwargs)\n","loop.run_sync(run)\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 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","await conn.write_message(header, 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.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","","self.nbytes_figure.x_range.end = max_limit\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","if self.property.matches(value, old) and (hint is None):\n","obj.trigger(self.name, old, value, hint, setter)\n","super().trigger(attr, 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","if s.startswith('\\ufeff'):\n","\"escaped_worker\": [escape.url_escape(ws.address) for ws in workers],\n","return quote(utf8(value))\n","return quote(string, safe, encoding, errors)\n","return quote_from_bytes(string, safe)\n","return ''.join([quoter(char) for char in bs])\n","return ''.join([quoter(char) for char in bs])\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(attr, 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","header = cls.create_header()\n","'msgid'   : bkserial.make_id(),\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 self[key]\n","raise KeyError(key) from None\n","return\n","def _gc_callback(self, phase, info):\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 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","array = convert_datetime_array(array)\n","if array.dtype.kind == 'M':\n","return transform_array_to_list(array)\n","if (array.dtype.kind in ('u', 'i', 'f') and (~np.isfinite(array)).any()):\n","return umr_any(a, axis, dtype, out, keepdims)\n","'dtype'       : array.dtype.name,\n","if issubclass(dtype.type, np.void):\n","handler_func(fileobj, events)\n","self._handle_read()\n","super()._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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","connection, address = sock.accept()\n","fd, addr = self._accept()\n","await self.comm_handler(comm)\n","await comm.write(result, serializers=serializers)\n","stream.write(b\"\".join([length_bytes, *frames]))\n","self._handle_write()\n","super()._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data)  # type: ignore\n","return self._sslobj.write(data)\n","return _to_frames()\n","protocol.dumps(\n","small_header, small_payload = dumps_msgpack(msg, **compress_opts)\n","fmt, payload = maybe_compress(payload, compression=compression)\n","compressed = compress(ensure_bytes(payload))\n","return lz4_compress(data)\n","n_frames = await stream.read_bytes(8)\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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","await self.handle_stream(comm=comm, extra={\"client\": client})\n","msgs = await comm.read()\n","n_frames = await stream.read_bytes(8)\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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","ret = callback()\n","return self.callback()\n","ioc = psutil.net_io_counters()\n","rawdict = _psplatform.net_io_counters()\n","lines = f.readlines()\n","return open(fname, \"rt\", **kwargs)\n","return self._proc.num_fds()\n","return fun(self, *args, **kwargs)\n","return len(os.listdir(\"%s/%s/fd\" % (self._procfs_path, self.pid)))\n","pt2 = self._proc.cpu_times()\n","return fun(self, *args, **kwargs)\n","values = self._parse_stat_file()\n","return fun(self, *args, **kwargs)\n","ret = self._cache[fun] = fun(self)\n","with open_binary(\"%s/%s/stat\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","ret = self._cache[fun] = fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","if self.proc.cpu_percent() &lt; 50:\n","pt2 = self._proc.cpu_times()\n","return fun(self, *args, **kwargs)\n","values = self._parse_stat_file()\n","return fun(self, *args, **kwargs)\n","return fun(self)\n","with open_binary(\"%s/%s/stat\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","self.finish()\n","future = self.flush(include_footers=True)\n","return self.request.connection.write_headers(\n","self._pending_write = self.stream.write(data)\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","future_add_done_callback(self._pending_write, self._finish_request)\n","callback(future)\n","self.close()\n","self.stream.close()\n","self.close_fd()\n","self.socket.close()\n","self._real_close()\n","_ss.close(self)\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","doc.validate()\n","self._safe_to_fork = False\n","doc.theme = BOKEH_THEME\n","self.task_stream_index = [0]\n","return source, root\n","return create_renderer(glyphclass, self, **kwargs)\n","return glyph_renderer\n","return glyphclass(**kws)\n","default_theme.apply_to_model(self)\n","self._initialized = True\n","raise AttributeError(\"unexpected attribute '%s' to %s, %s attributes are %s\" %\n","super().__set__(obj, value, setter)\n","work = await self._handle(message)\n","work = await self.handler.handle(message, self.connection)\n","work = await handler(message, connection)\n","result = func(self, *args, **kwargs)\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, include_defaults)\n","value = descriptor.serializable_value(self)\n","await self.send_message(work)\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","ret = await conn.read_response(request_delegate)\n","header_future = self.stream.read_until_regex(\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","await self.handle_worker(comm=comm, worker=address)\n","await self.handle_stream(comm=comm, extra={\"worker\": worker})\n","msgs = await comm.read()\n","n_frames = await stream.read_bytes(8)\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 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next(self.gen)\n","dsets = [stack.enter_context(self.open_input(i)) for i in inputs]\n","dsets = [stack.enter_context(self.open_input(i)) for i in inputs]\n","result = _cm_type.__enter__(cm)\n","return next(self.gen)\n","with self.input_opener(fname) as f:\n","return next(self.gen)\n","with _maybe_open_or_copy_to_local(opener, self.copy_input_to_local_file, fname) as fp:\n","return next(self.gen)\n","_copy_btw_filesystems(opener, target_opener)\n","with input_opener as source:\n","return next(self.gen)\n","with self.fs.open(self._full_path(path), **kwargs) as f:\n","f = self._open(\n","return GCSFile(\n","super().__init__(\n","self.details = fs.info(path)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","out = self.cache._fetch(self.loc, self.loc + length)\n","self.cache = self.fetcher(start, end)  # new block replaces old\n","_, data = self.gcsfs.call(\"GET\", self.details[\"mediaLink\"], headers=head)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","gotit = waiter.acquire(True, timeout)\n","loop.call_soon_threadsafe(callback)\n","return handle\n","handle = events.Handle(callback, args, self, context)\n","next(self.gen)\n","yield f\n","self.close()\n","file = _io.open(fd, mode, buffering=buffering,\n","return prefix, suffix, dir, output_type\n","return tempdir\n","return dir\n","backend_ds = backend.open_dataset(\n","store = NetCDF4DataStore.open(\n","return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)\n","self.format = self.ds.data_model\n","return self._acquire()\n","with self._manager.acquire_context(needs_lock) as root:\n","return next(self.gen)\n","file, cached = self._acquire_with_cache_info(needs_lock)\n","file = self._opener(*self._args, **kwargs)\n","vars, attrs = store.load()\n","(_decode_variable_name(k), v) for k, v in self.get_variables().items()\n","dsvars = FrozenDict(\n","return Frozen(dict(*args, **kwargs))\n","(k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()\n","return Variable(dimensions, data, attributes, encoding)\n","self.shape = array.shape\n","ds = self.datastore._acquire(needs_lock)\n","with self._manager.acquire_context(needs_lock) as root:\n","return next(self.gen)\n","file, cached = self._acquire_with_cache_info(needs_lock)\n","return file, True\n","next(self.gen)\n","key = BasicIndexer((slice(None),) * array.ndim)\n","k = as_integer_slice(k)\n","return slice(start, stop, step)\n","self.close(needs_lock=False)\n","attrs = FrozenDict((k, self.ds.getncattr(k)) for k in self.ds.ncattrs())\n","return Frozen(dict(*args, **kwargs))\n","new_vars[k] = decode_cf_variable(\n","var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)\n","dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n","[first_n_items(values, 1) or [0], last_item(values) or [0]]\n","return np.asarray(array).flat[:n_desired]\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","return np.where(condition, decoded_fill_value, data)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","return result\n","return array\n","ds = self.datastore._acquire(needs_lock)\n","with self._manager.acquire_context(needs_lock) as root:\n","return _GeneratorContextManager(func, args, kwds)\n","return next(self.gen)\n","file, cached = self._acquire_with_cache_info(needs_lock)\n","with self._optional_lock(needs_lock):\n","return _GeneratorContextManager(func, args, kwds)\n","return _decompose_outer_indexer(indexer, shape, indexing_support)\n","return (OuterIndexer(tuple(backend_indexer)), OuterIndexer(tuple(np_indexer)))\n","k = as_integer_slice(k)\n","stop = as_integer_or_none(value.stop)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","return int(np.prod(self.shape))\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","return np.ravel(np.asarray(array[indexer])).tolist()\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","data = np.asarray(data, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)\n","flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(\n","return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors)\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 _abc_subclasscheck(cls, subclass)\n","self.encoding = encoding\n","return Variable(dims, data, attrs, encoding)\n","self.attrs = attrs\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","return self.array.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","if not pd.isnull(fv)\n","return _isna(obj)\n","","return asanyarray(a).ravel(order=order)\n","if np.issubdtype(dtype, np.floating):\n","return issubclass(arg1, arg2)\n","if is_duck_dask_array(array):\n","return is_duck_array(x) and is_dask_collection(x)\n","return (\n","return len(self.shape)\n","assert not is_duck_dask_array(array)\n","return is_duck_array(x) and is_dask_collection(x)\n","hasattr(value, \"ndim\")\n","return len(self.shape)\n","pop_to(attrs, encoding, attr, name=name)\n","self.key = key\n","return len(self.shape)\n","return self.array.shape\n","super().__init__(new_key)\n","return is_duck_array(x) and is_dask_collection(x)\n","and hasattr(value, \"dtype\")\n","return len(self.shape)\n","return self.array.shape\n","return self.array.shape\n","return Variable(dims, data, attrs, encoding)\n","self.encoding = encoding\n","return _maybe_wrap_data(data)\n","return dims\n","return len(self.shape)\n","return self._data.shape\n","return tuple(shape)\n","return Variable(dims, data, attrs, encoding)\n","self._dims = self._parse_dimensions(dims)\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","obj = obj.copy(deep=False)\n","return self._replace(data=data)\n","return type(self)(dims, data, attrs, encoding, fastpath=True)\n","self._data = as_compatible_data(data, fastpath=fastpath)\n","if fastpath and getattr(data, \"ndim\", 0) &gt; 0:\n","return len(self.shape)\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return dims\n","return len(self.shape)\n","return self._data.shape\n","return Variable(dims, data, attrs, encoding)\n","self._dims = self._parse_dimensions(dims)\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","return self.array.shape\n","return var.dims, var._data, var.attrs.copy(), var.encoding.copy()\n","dims, data, attrs, encoding = unpack_for_decoding(variable)\n","self._dims = self._parse_dimensions(dims)\n","if len(dims) != self.ndim:\n","@property\n","return self._data.shape\n","return self.array.shape\n","self._dims = self._parse_dimensions(dims)\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","variables, coord_names, dims, indexes, _ = merge_data_and_coords(\n","return merge_core(\n","collected = collect_variables_and_indexes(aligned)\n","variable = as_variable(variable, name=name)\n","return obj\n","return IndexVariable(\n","self._data = PandasIndexAdapter(self._data)\n","self._dtype = dtype_\n","return _maybe_cast_to_cftimeindex(index)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","return dates.reshape(num_dates.shape)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","return result\n","","res = np.argmin(a, axis=axis)\n","","return _wrapfunc(a, 'argmin', axis=axis, out=out)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","return self._replace(data=data)\n","return type(self)(dims, data, attrs, encoding, fastpath=True)\n","self._data = as_compatible_data(data, fastpath=fastpath)\n","if fastpath and getattr(data, \"ndim\", 0) &gt; 0:\n","return len(self.shape)\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","assert self.ndim == 1\n","encoding[\"unlimited_dims\"] = {\n","obj = self.copy()\n","return self._replace(variables, attrs=attrs)\n","obj = self._construct_direct(\n","obj._indexes = indexes\n","variables = {k: v.copy(deep=deep) for k, v in self._variables.items()}\n","return self._replace(data=data)\n","attrs = copy.copy(self._attrs)\n","self._dims = self._parse_dimensions(dims)\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","if fastpath and getattr(data, \"ndim\", 0) &gt; 0:\n","return len(self.shape)\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","return self.array.shape\n","data = self._data.copy(deep=deep)\n","return PandasIndexAdapter(array, self._dtype)\n","self.array = utils.safe_cast_to_index(array)\n","return _maybe_cast_to_cftimeindex(index)\n","return CFTimeIndex(index)\n","super().__init__(dims, data, attrs, encoding, fastpath)\n","self.encoding = encoding\n","_protect_dataset_variables_inplace(backend_ds, cache)\n","if name not in variable.dims:\n","if data.shape != self.shape:\n","return self.array.shape\n","return self.array.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self._data.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","return engine\n","return build_engines(pkg_entrypoints)\n","return engines\n","return unique_pkg_entrypoints\n","entry\n","backend.open_dataset_parameters = detect_parameters(open_dataset)\n","return tuple(parameters_list)\n","ds[v].attrs.update(_fixed_attrs(ds[v].attrs))\n","return self._construct_dataarray(key)\n","return DataArray(variable, coords, name=name, indexes=indexes, fastpath=True)\n","return Frozen(self._variables)\n","assert isinstance(coords, dict)\n","variables = {k: v.copy(deep=deep) for k, v in self._variables.items()}\n","variables = {k: v.copy(deep=deep) for k, v in self._variables.items()}\n","return self._replace(data=data)\n","return type(self)(dims, data, attrs, encoding, fastpath=True)\n","self._data = as_compatible_data(data, fastpath=fastpath)\n","if fastpath and getattr(data, \"ndim\", 0) &gt; 0:\n","return len(self.shape)\n","return self.array.shape\n","return self.array.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","if len(dims) != self.ndim:\n","return len(self.shape)\n","return self._data.shape\n","return self.array.shape\n","return self.array.shape\n","for size, k in zip(self.array.shape, self.key.tuple):\n","return self.array.shape\n","return type(self)(self.dims, data, self._attrs, self._encoding, fastpath=True)\n","return PandasIndexAdapter(array, self._dtype)\n","self._dtype = dtype_\n","return _maybe_cast_to_cftimeindex(index)\n","return index\n","result._data = np.array(data, dtype=\"O\")\n","return self.variable.encoding\n","return self.variable.attrs\n","return formatting.dataset_repr(self)\n","summary.append(coords_repr(ds.coords, col_width=col_width))\n","return _mapping_repr(\n","summary += [summarizer(k, v, col_width) for k, v in mapping.items()]\n","summary += [summarizer(k, v, col_width) for k, v in mapping.items()]\n","return summarize_variable(name, var.variable, col_width, marker)\n","values_str = inline_variable_array_repr(var, values_width)\n","return format_array_flat(var, max_width)\n","first_n_items(array, (max_possibly_relevant + 1) // 2)\n","indexer = _get_indexer_at_least_n_items(array.shape, n_desired, from_end=False)\n","stop = int(np.ceil(float(n_desired) / np.r_[1, cum_items][n_steps]))\n","res = self.concatenate(tuple(objs), axis=axis)\n","","return _wrapfunc(a, 'argmax', axis=axis, out=out)\n","return int(np.prod(self.shape))\n","indexer = _get_indexer_at_least_n_items(array.shape, n_desired, from_end=True)\n","stop = int(np.ceil(float(n_desired) / np.r_[1, cum_items][n_steps]))\n","res = self.concatenate(tuple(objs), axis=axis)\n","","return _wrapfunc(a, 'argmax', axis=axis, out=out)\n","","return _wrapfunc(a, 'argmax', axis=axis, out=out)\n","return int(np.prod(self.shape))\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","passkwargs = {k: v for k, v in kwargs.items()\n","return dtype.name\n","return name\n","return True\n","return issubclass(arg1, arg2)\n","index = self.to_index()\n","index = index.set_names(self.name)\n","idx._set_names(names, level=level)\n","for key in self._mapping:\n","for k in self.variables:\n","return Frozen(\n","{k: v for k, v in self._data.variables.items() if k in self._names}\n","return cast(\"DataArray\", self._data[key])\n","return self._construct_dataarray(key)\n","indexes = {k: v for k, v in self._indexes.items() if k in coords}\n","for k, v in mapping.items():\n","return ItemsView(self)\n","index = self.to_index()\n","index = index.set_names(self.name)\n","if not is_list_like(names) and level is None and self.nlevels &gt; 1:\n","items = list(mapping.items())\n","yield (key, self._mapping[key])\n","return cast(\"DataArray\", self._dataset[key])\n","return self._construct_dataarray(key)\n","return DataArray(variable, coords, name=name, indexes=indexes, fastpath=True)\n","return Frozen(self._variables)\n","return self._construct_dataarray(key)\n","indexes = {k: v for k, v in self._indexes.items() if k in coords}\n","return Frozen(self._variables)\n","def __init__(\n","self.gen.throw(type, value, traceback)\n","yield fp\n","self.close()\n","object.__setattr__(self, name, value)\n","self.variable.encoding = value\n","return formatting.dataset_repr(self)\n","summary.append(coords_repr(ds.coords, col_width=col_width))\n","return _mapping_repr(\n","summary += [summarizer(k, v, col_width) for k, v in mapping.items()]\n","summary += [summarizer(k, v, col_width) for k, v in mapping.items()]\n","return summarize_variable(name, var.variable, col_width, marker)\n","values_str = inline_variable_array_repr(var, values_width)\n","return format_array_flat(var, max_width)\n","first_n_items(array, (max_possibly_relevant + 1) // 2)\n","indexer = _get_indexer_at_least_n_items(array.shape, n_desired, from_end=False)\n","stop = int(np.ceil(float(n_desired) / np.r_[1, cum_items][n_steps]))\n","res = self.concatenate(tuple(objs), axis=axis)\n","","return _wrapfunc(a, 'argmax', axis=axis, out=out)\n","indexer = _get_indexer_at_least_n_items(array.shape, n_desired, from_end=True)\n","stop = int(np.ceil(float(n_desired) / np.r_[1, cum_items][n_steps]))\n","objs[k] = objs[k].astype(final_dtype)\n","","return _wrapfunc(a, 'argmax', axis=axis, out=out)\n","data = as_indexable(self._data)[indexer]\n","result = self.array[key]\n","return promote(getitem(key))\n","return super()._shallow_copy(values=values, name=name)\n","return self._simple_new(values, name=name)\n","","return _wrapfunc(a, 'argmax', axis=axis, out=out)\n","return int(np.prod(self.shape))\n","","return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n","formatted = [format_item(xi, timedelta_format) for xi in x]\n","formatted = [format_item(xi, timedelta_format) for xi in x]\n","elif np.issubdtype(type(x), np.floating):\n","yield (key, self._mapping[key])\n","return cast(\"DataArray\", self._data[key])\n","return self._construct_dataarray(key)\n","if k in self._coord_names and set(self.variables[k].dims) &lt;= needed_dims:\n","return Frozen(self._variables)\n","summary += [summarizer(k, v, col_width) for k, v in items[-last_rows:]]\n","summary += [summarizer(k, v, col_width) for k, v in items[-last_rows:]]\n","return summarize_variable(name, var.variable, col_width)\n","values_str = inline_variable_array_repr(var, values_width)\n","return inline_dask_repr(var.data)\n","if is_duck_array(self._data):\n","and hasattr(value, \"dtype\")\n","yield (key, self._mapping[key])\n","return cast(\"DataArray\", self._dataset[key])\n","return self._construct_dataarray(key)\n","return DataArray(variable, coords, name=name, indexes=indexes, fastpath=True)\n","return Frozen(self._variables)\n","self._coords = coords\n","summary += [summarizer(k, v, col_width) for k, v in items[:first_rows]]\n","return summarize_variable(name, var.variable, col_width)\n","front_str = f\"{first_col}{dims_str}{var.dtype} \"\n","dsets = [ds.chunk() for ds in dsets]\n","variables = {\n","k: _maybe_chunk(k, v, chunks, token, lock, name_prefix)\n","token2 = tokenize(name, token if token else var._data, chunks)\n","return md5(str(tuple(map(normalize_token, args))).encode()).hexdigest()\n","return meth(arg, *args, **kwargs)\n","return normalize_function(o) if callable(o) else uuid.uuid4().hex\n","return UUID(bytes=os.urandom(16), version=4)\n","data = da.from_array(data, chunks, name=name, lock=lock, **kwargs)\n","chunks = normalize_chunks(\n","chunks = sum(\n","blockdims_from_blockshape((s,), (c,))\n","shape = tuple(map(int, shape))\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return isinstance(i, Integral) or (isinstance(i, float) and i.is_integer())\n","return _abc_instancecheck(cls, instance)\n","def __new__(cls, dask, name, chunks, dtype=None, meta=None, shape=None):\n","return cls(layers, deps)\n","self.layers = {\n","k: v if isinstance(v, Layer) else BasicLayer(v)\n","self.mapping = mapping\n","self.annotations = copy.copy(config.get(\"annotations\", None))\n","return default\n","return len(self.shape)\n","val = self.func(instance)\n","return tuple(cached_cumsum(c, initial_zero=True)[-1] for c in self.chunks)\n","return tuple(cached_cumsum(c, initial_zero=True)[-1] for c in self.chunks)\n","result = _cumsum(_HashIdWrapper(seq), initial_zero)\n","return tuple(accumulate(add, seq, 0))\n","return\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return pmem(rss, vms, shared, text, lib, data, dirty)\n","chunks = normalize_chunks(chunks, shape, dtype=dtype)\n","chunks = tuple(c if c not in {None, -1} else s for c, s in zip(chunks, shape))\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return tuple(tuple(int(x) if not math.isnan(x) else x for x in c) for c in chunks)\n","cumdims = [cached_cumsum(bds, initial_zero=True) for bds in chunks]\n","is_distributed = client._get_global_client() is not None\n","L = sorted(list(_global_clients), reverse=True)\n","with _IterationGuard(self):\n","if contains_array(store, path=path):\n","return key in store\n","return self.fs.exists(path) and self.fs.isfile(path)\n","self.info(path, **kwargs)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","return key in store\n","return self.fs.exists(path) and self.fs.isfile(path)\n","self.info(path, **kwargs)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","return self.info(path)[\"type\"] == \"file\"\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","if contains_array(store, path=self._path):\n","return key in store\n","return self.fs.exists(path) and self.fs.isfile(path)\n","self.info(path, **kwargs)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","result = self.fs.cat(k)\n","out = maybe_sync(self._cat, self, paths, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","meta = parse_metadata(s)\n","meta = json_loads(s)\n","return json.loads(ensure_text(s, 'ascii'))\n","return s\n","return entry\n","","","","","","","","","","if contains_array(self._store, path):\n","return key in store\n","return self.fs.exists(path) and self.fs.isfile(path)\n","self.info(path, **kwargs)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","self._condition = threading.Condition()\n","return _has_code_flag(obj, CO_COROUTINE)\n","return bool(f.__code__.co_flags &amp; flag)\n","return self.info(path)[\"type\"] == \"file\"\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","self._load_metadata()\n","self._load_metadata_nosync()\n","meta_bytes = self._store[mkey]\n","result = self.fs.cat(k)\n","out = maybe_sync(self._cat, self, paths, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","if not absolute and self._path:\n","return self.asdict().keys()\n","d = self._get_nosync()\n","data = self.store[self.key]\n","result = self.fs.cat(k)\n","out = maybe_sync(self._cat, self, paths, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","self.set_basic_selection(selection, value, fields=fields)\n","return self._set_basic_selection_nd(selection, value, fields=fields)\n","self._set_selection(indexer, value, fields=fields)\n","self._chunk_setitems(lchunk_coords, lchunk_selection, chunk_values,\n","self.chunk_store.setitems({k: v for k, v in zip(ckeys, cdatas)})\n","self.fs.pipe(values)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","return _has_code_flag(obj, CO_COROUTINE)\n","if not isfunction(f):\n","cdatas = [self._process_for_setitem(key, sel, val, fields=fields)\n","return self._encode_chunk(chunk)\n","cdata = self._compressor.encode(chunk)\n","if np.isscalar(value):\n","return all(\n","for dim_projections in itertools.product(*self.dim_indexers):\n","yield ChunkDimProjection(dim_chunk_ix, dim_chunk_sel, dim_out_sel)\n","if np.isscalar(value):\n","if is_integer(dim_sel):\n","return isinstance(x, numbers.Integral)\n","check_selection_length(selection, shape)\n","def pop_fields(selection):\n","return array(a, dtype, copy=False, order=order)\n","x = self.compute()\n","(result,) = compute(self, traverse=False, **kwargs)\n","results = schedule(dsk, keys, **kwargs)\n","return get_async(apply_sync, 1, dsk, keys, **kwargs)\n","fire_task()\n","apply_async(\n","res = func(*args, **kwds)\n","result = _execute_task(task, data)\n","return func(*(_execute_task(a, cache) for a in args))\n","c = np.asarray(c)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","self._ensure_cached()\n","self.array = NumpyIndexingAdapter(np.asarray(self.array))\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","data = np.asarray(data, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","array = getitem(original_array, key)\n","ds = self.datastore._acquire(needs_lock)\n","with self._manager.acquire_context(needs_lock) as root:\n","return next(self.gen)\n","file, cached = self._acquire_with_cache_info(needs_lock)\n","with self._optional_lock(needs_lock):\n","return _GeneratorContextManager(func, args, kwds)\n","self.gen = func(*args, **kwds)\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return array(a, dtype, copy=False, order=order)\n","def __array__(self, dtype=None):\n","return len(self.shape)\n","with self.not_full:\n","out = uuid.uuid4().hex\n","dsk = repack_dsk.copy()\n","var = conventions.encode_cf_variable(var, name=name)\n","var = coder.encode(var, name=name)\n","data, units = encode_cf_timedelta(data, encoding.pop(\"units\", None))\n","num = cast_to_int_if_safe(num)\n","int_num = np.array(num, dtype=np.int64)\n","x = self.compute()\n","(result,) = compute(self, traverse=False, **kwargs)\n","results = schedule(dsk, keys, **kwargs)\n","return get_async(apply_sync, 1, dsk, keys, **kwargs)\n","fire_task()\n","apply_async(\n","res = func(*args, **kwds)\n","result = _execute_task(task, data)\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","c = np.asarray(c)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","self._ensure_cached()\n","self.array = NumpyIndexingAdapter(np.asarray(self.array))\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","num_timedeltas = np.asarray(num_timedeltas)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","data = np.asarray(data, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","result = pd.to_timedelta(value, **kwargs).to_numpy()\n","return _convert_listlike(arg, unit=unit, errors=errors)\n","return value\n","return type(self)(self.array[key], self.func, self.dtype)\n","assert not is_duck_dask_array(array)\n","return is_duck_array(x) and is_dask_collection(x)\n","hasattr(value, \"ndim\")\n","return len(self.shape)\n","return self.array.shape\n","out = uuid.uuid4().hex\n","dsk = opt(dsk, keys, **kwargs)\n","dsk = optimize_blockwise(dsk, keys=keys)\n","out = _optimize_blockwise(graph, keys=keys)\n","return HighLevelGraph(out, dependencies)\n","return result\n","return\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return pmem(rss, vms, shared, text, lib, data, dirty)\n","return bool(self.compute())\n","(result,) = compute(self, traverse=False, **kwargs)\n","results = schedule(dsk, keys, **kwargs)\n","return get_async(apply_sync, 1, dsk, keys, **kwargs)\n","fire_task()\n","apply_async(\n","res = func(*args, **kwds)\n","result = _execute_task(task, data)\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","c = np.asarray(c)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","self._ensure_cached()\n","self.array = NumpyIndexingAdapter(np.asarray(self.array))\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","result = to_timedelta_unboxed(num_timedeltas.ravel(), unit=units)\n","result = pd.to_timedelta(value, **kwargs).to_numpy()\n","return _convert_listlike(arg, unit=unit, errors=errors)\n","return value\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","data = np.asarray(data, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","return type(self)(self.array[key], self.func, self.dtype)\n","return type(self)(self.array[key], self.func, self.dtype)\n","return type(self)(self.array, self._updated_key(indexer))\n","out = uuid.uuid4().hex\n","return simple_get(dsk, out)\n","for key in toposort(dsk):\n","return _toposort(dsk, dependencies=dependencies)\n","return all(self, axis=axis, keepdims=keepdims, split_every=split_every, out=out)\n","return reduction(\n","reduced_meta = compute_meta(\n","args_meta = [meta_from_array(x) if is_arraylike(x) else x for x in args]\n","args_meta = [meta_from_array(x) if is_arraylike(x) else x for x in args]\n","return keyword in inspect.signature(func).parameters\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","return _signature_from_callable(obj, sigcls=cls,\n","return _signature_from_function(sigcls, obj,\n","return f(self, other)\n","return elemwise(operator.eq, self, other)\n","result = blockwise(\n","meta = compute_meta(func, dtype, *args[::2], **kwargs)\n","return _isna(obj)\n","return _isna_ndarraylike(np.asarray(obj), inf_as_na=inf_as_na)\n","return array(a, dtype, copy=False, order=order)\n","x = self.compute()\n","(result,) = compute(self, traverse=False, **kwargs)\n","results = schedule(dsk, keys, **kwargs)\n","return get_async(apply_sync, 1, dsk, keys, **kwargs)\n","fire_task()\n","apply_async(\n","res = func(*args, **kwds)\n","result = _execute_task(task, data)\n","return func(*(_execute_task(a, cache) for a in args))\n","c = np.asarray(c)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","self._ensure_cached()\n","self.array = NumpyIndexingAdapter(np.asarray(self.array))\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","num_timedeltas = np.asarray(num_timedeltas)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","data = np.asarray(data, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","result = pd.to_timedelta(value, **kwargs).to_numpy()\n","return _convert_listlike(arg, unit=unit, errors=errors)\n","value = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","collections_token = uuid.uuid4().hex\n","return f(self, other)\n","return elemwise(operator.truediv, self, other)\n","result = blockwise(\n","return new_da_object(graph, out, chunks, meta=meta, dtype=dtype)\n","return Array(dsk, name=name, chunks=chunks, meta=meta, dtype=dtype)\n","return meta\n","return keyword in inspect.signature(func).parameters\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","return _signature_from_callable(obj, sigcls=cls,\n","return _signature_from_builtin(sigcls, obj,\n","return _signature_fromstr(cls, func, s, skip_bound_arg)\n","parameters.pop(0)\n","for t in token_stream:\n","return len(self.shape)\n","if not is_scalar_for_elemwise(a)\n","shape_condition = not isinstance(maybe_shape, Iterable) or any(\n","is_dask_collection(x) for x in maybe_shape\n","return elemwise(operator.mul, other, self)\n","result = blockwise(\n","meta = compute_meta(func, dtype, *args[::2], **kwargs)\n","if has_keyword(func, \"computing_meta\"):\n","return keyword in inspect.signature(func).parameters\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","return _signature_from_callable(obj, sigcls=cls,\n","return _signature_from_builtin(sigcls, obj,\n","return _signature_fromstr(cls, func, s, skip_bound_arg)\n","","return da_func(*args, **kwargs)\n","return elemwise(np.where, condition, x, y)\n","result = blockwise(\n","meta = compute_meta(func, dtype, *args[::2], **kwargs)\n","args_meta = [meta_from_array(x) if is_arraylike(x) else x for x in args]\n","args_meta = [meta_from_array(x) if is_arraylike(x) else x for x in args]\n","arrays.append(a.rechunk(chunks))\n","return rechunk(self, chunks, threshold, block_size_limit, balance)\n","chunks = normalize_chunks(\n","chunks = sum(\n","blockdims_from_blockshape((s,), (c,))\n","shape = tuple(map(int, shape))\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","(data, units, calendar) = encode_cf_datetime(\n","dates = np.asarray(dates)\n","return array(a, dtype, copy=False, order=order)\n","x = self.compute()\n","(result,) = compute(self, traverse=False, **kwargs)\n","collections, repack = unpack_collections(*args, traverse=traverse)\n","collections_token = uuid.uuid4().hex\n","return get_async(apply_sync, 1, dsk, keys, **kwargs)\n","fire_task()\n","apply_async(\n","res = func(*args, **kwds)\n","result = _execute_task(task, data)\n","return func(*(_execute_task(a, cache) for a in args))\n","c = np.asarray(c)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","self._ensure_cached()\n","self.array = NumpyIndexingAdapter(np.asarray(self.array))\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","num_dates = np.asarray(num_dates)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","data = np.asarray(data, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","result = raw_indexing_method(raw_key.tuple)\n","array = getitem(original_array, key)\n","result = meth(_maybe_unwrap_index(other))\n","return method(self, other)\n","return self - (self // other) * other\n","return method(self, other)\n","next(self.gen)\n","yield ds\n","if cb(*exc_details):\n","next(self.gen)\n","yield ds\n","next(self.gen)\n","yield fp\n","next(self.gen)\n","ntf.close()  # cleans up the temporary file\n","self._closer.close()\n","","return self._construct_dataarray(key)\n","return DataArray(variable, coords, name=name, indexes=indexes, fastpath=True)\n","return Frozen(self._variables)\n","return self._replace_with_new_dims(\n","dims = calculate_dimensions(variables)\n","for dim, size in zip(var.dims, var.shape):\n","return self._data.shape\n","attrs = copy.copy(self._attrs)\n","all_chunk_conflicts = calc_chunk_conflicts(input_sequence_lens, self._sequence_dim_chunks)\n","chunk_stop = (stop - 1) // zchunks\n","","return _nx.concatenate(arrs, 0)\n","","return _wrapfunc(a, 'cumsum', axis=axis, dtype=dtype, out=out)\n","return _wrapit(obj, method, *args, **kwds)\n","return result\n","self.close(needs_lock=False)\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","return c\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","self.array = NumpyIndexingAdapter(np.asarray(self.array))\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(self.array, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","return result.reshape(num_timedeltas.shape)\n","return array(a, dtype, copy=False, order=order)\n","return self.func(self.array)\n","return np.where(condition, decoded_fill_value, data)\n","return array(a, dtype, copy=False, order=order)\n","return np.asarray(array[self.key], dtype=None)\n","return indexing.explicit_indexing_adapter(\n","return 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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","self.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","memory = proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","[int(x) * PAGESIZE for x in f.readline().split()[:7]]\n","ret = callback()\n","return self.callback()\n","cpu = self.proc.cpu_percent()\n","pt2 = self._proc.cpu_times()\n","return fun(self, *args, **kwargs)\n","values = self._parse_stat_file()\n","return fun(self, *args, **kwargs)\n","ret = self._cache[fun] = fun(self)\n","data = f.read()\n","return open(fname, \"rb\", **kwargs)\n","return self._proc.num_fds()\n","return fun(self, *args, **kwargs)\n","return len(os.listdir(\"%s/%s/fd\" % (self._procfs_path, self.pid)))\n","ret = self._cache[fun] = fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","rawdict = _psplatform.net_io_counters()\n","with open_text(\"%s/net/dev\" % get_procfs_path()) as f:\n","return open(fname, \"rt\", **kwargs)\n","state = profile.process(\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","stat = os.stat(fullname)\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\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","stat = os.stat(fullname)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","d = {\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","stat = os.stat(fullname)\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","stat = os.stat(fullname)\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","stat = os.stat(fullname)\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","self.close(needs_lock=False)\n","file.close()\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","if 1 &lt;= lineno &lt;= len(lines):\n","return updatecache(filename, module_globals)\n","stat = os.stat(fullname)\n","buffer = _builtin_open(filename, 'rb')\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\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","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","def __del__(self, _warnings: Any = warnings) -&gt; None:\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","def __del__(self, _warn=warnings.warn):\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","if self._loop.get_debug():\n","def get_debug(self):\n","return self._when &lt; other._when\n","def __instancecheck__(cls, instance):\n","return _abc_subclasscheck(cls, subclass)\n","math.floor((current_time - self._next_timeout) / callback_time_sec) + 1\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","handler_func(fileobj, events)\n","self._handle_read()\n","super()._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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","self.socket.do_handshake()\n","self._sslobj.do_handshake()\n","self._do_ssl_handshake()\n","self.socket.do_handshake()\n","self._sslobj.do_handshake()\n","callback(connection, address)\n","app_log.error(\"Error in connection callback\", exc_info=True)\n","return context.wrap_socket(socket, server_hostname=server_hostname, **kwargs)\n","return self.sslsocket_class._create(\n","return self\n","return sock, addr\n","response = await retry_operation(\n","return await retry(\n","return await coro()\n","result = await send_recv(comm=comm, op=key, **kwargs)\n","await comm.write(msg, serializers=serializers, on_error=\"raise\")\n","stream.write(b\"\".join([length_bytes, *frames]))\n","self._handle_write()\n","super()._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data)  # type: ignore\n","return self._sslobj.write(data)\n","msg_size = sizeof(msg)\n","return meth(arg, *args, **kwargs)\n","return (\n","_atomic_removal(self.data, wr.key)\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","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","return meth(arg, *args, **kwargs)\n","s = getsizeof(seq) + num_items / samples * sum(\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","def __call__(self, arg, *args, **kwargs):\n","protocol.dumps(\n","msg, data, bytestrings = extract_serialize(msg)\n","_extract_serialize(x_items, x2, ser, bytestrings, path)\n","x2[k] = v\n","x2[k] = v\n","return [header_bytes, payload]\n","return Packer(**kwargs).pack(o)\n","n_frames = await stream.read_bytes(8)\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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","return _abc_instancecheck(cls, instance)\n","def __subclasscheck__(cls, subclass):\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","await self.ensure_computing()\n","ts.runspec = await self._maybe_deserialize_task(ts)\n","function, args, kwargs = _deserialize(*ts.runspec)\n","function = loads_function(function)\n","return pickle.loads(bytes_object)\n","return pickle.loads(x)\n","self.close(needs_lock=False)\n","file.close()\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","stream.write(b\"\".join([length_bytes, *frames]))\n","self._handle_write()\n","super()._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data)  # type: ignore\n","return self._sslobj.write(data)\n","await self.handle_stream(\n","msgs = await comm.read()\n","n_frames = await stream.read_bytes(8)\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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\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","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","self.release_key(key)\n","del ts\n","prof = profile.merge(*pluck(1, history))\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","def _gc_callback(self, phase, info):\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","children = {k: merge(*v) for k, v in children.items()}\n","def _gc_callback(self, phase, info):\n","lambda f: self._run_callback(functools.partial(callback, future))\n","data = self._ssock.recv(4096)\n","await self.comm_handler(comm)\n","await comm.write(result, serializers=serializers)\n","frames = await to_frames(\n","msg_size = sizeof(msg)\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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.values()))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(seq) + sum(map(sizeof, seq))\n","return time.monotonic()\n","","self.set_basic_selection(selection, value, fields=fields)\n","return self._set_basic_selection_nd(selection, value, fields=fields)\n","self._set_selection(indexer, value, fields=fields)\n","self._chunk_setitems(lchunk_coords, lchunk_selection, chunk_values,\n","self.chunk_store.setitems({k: v for k, v in zip(ckeys, cdatas)})\n","self.fs.pipe(values)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","cdatas = [self._process_for_setitem(key, sel, val, fields=fields)\n","return self._encode_chunk(chunk)\n","return next(self.gen)\n","dsets = [stack.enter_context(self.open_input(i)) for i in inputs]\n","dsets = [stack.enter_context(self.open_input(i)) for i in inputs]\n","result = _cm_type.__enter__(cm)\n","return next(self.gen)\n","with self.input_opener(fname) as f:\n","return next(self.gen)\n","with _maybe_open_or_copy_to_local(opener, self.copy_input_to_local_file, fname) as fp:\n","return next(self.gen)\n","_copy_btw_filesystems(opener, target_opener)\n","data = source.read(BLOCK_SIZE)\n","out = self.cache._fetch(self.loc, self.loc + length)\n","self.cache = self.fetcher(start, end)  # new block replaces old\n","_, data = self.gcsfs.call(\"GET\", self.details[\"mediaLink\"], headers=head)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","if contains_array(self._store, path):\n","return key in store\n","return self.fs.exists(path) and self.fs.isfile(path)\n","return self.info(path)[\"type\"] == \"file\"\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = self._cond.wait(timeout)\n","self.info(path, **kwargs)\n","return maybe_sync(func, self, *args, **kwargs)\n","return sync(loop, func, *args, **kwargs)\n","e.wait(10)\n","signaled = 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Stream"},"id":"105730","type":"Panel"},{"attributes":{"text":"\n        &lt;h1&gt; Dask Performance Report &lt;/h1&gt;\n\n        &lt;i&gt; Select different tabs on the top for additional information &lt;/i&gt;\n\n        &lt;h2&gt; Duration: 2441.54 s &lt;/h2&gt;\n        &lt;h2&gt; Tasks Information &lt;/h2&gt;\n        &lt;ul&gt;\n         &lt;li&gt; number of tasks: 326 &lt;/li&gt;\n         \n&lt;li&gt; compute time: 62655.00 s &lt;/li&gt;\n&lt;li&gt; deserialize time: 257.85 s &lt;/li&gt;\n        &lt;/ul&gt;\n\n        &lt;h2&gt; Scheduler Information &lt;/h2&gt;\n        &lt;ul&gt;\n          &lt;li&gt; Address: tls://10.36.209.12:8786 &lt;/li&gt;\n          &lt;li&gt; Workers: 30 &lt;/li&gt;\n          &lt;li&gt; Threads: 30 &lt;/li&gt;\n          &lt;li&gt; Memory: 128.85 GB &lt;/li&gt;\n          &lt;li&gt; Dask Version: 2021.01.1 &lt;/li&gt;\n          &lt;li&gt; Dask.Distributed Version: 2021.01.1 &lt;/li&gt;\n        &lt;/ul&gt;\n\n        &lt;h2&gt; Calling Code &lt;/h2&gt;\n        &lt;pre&gt;\nfrom dask.distributed import performance_report\n\nwith performance_report(filename=\"dask-report.html\"):\n    delayed.compute(retries=5)\n\n        &lt;/pre&gt;\n        "},"id":"105725","type":"Div"},{"attributes":{"callback":null,"point_policy":"follow_mouse","tooltips":"\n            &lt;div&gt;\n                &lt;span style=\"font-size: 14px; font-weight: bold;\"&gt;Name:&lt;/span&gt;&amp;nbsp;\n                &lt;span style=\"font-size: 10px; font-family: Monaco, monospace;\"&gt;@name&lt;/span&gt;\n            &lt;/div&gt;\n            &lt;div&gt;\n                &lt;span style=\"font-size: 14px; font-weight: bold;\"&gt;Filename:&lt;/span&gt;&amp;nbsp;\n                &lt;span style=\"font-size: 10px; font-family: Monaco, monospace;\"&gt;@filename&lt;/span&gt;\n            &lt;/div&gt;\n            &lt;div&gt;\n                &lt;span style=\"font-size: 14px; font-weight: bold;\"&gt;Line number:&lt;/span&gt;&amp;nbsp;\n                &lt;span style=\"font-size: 10px; font-family: Monaco, monospace;\"&gt;@line_number&lt;/span&gt;\n            &lt;/div&gt;\n            &lt;div&gt;\n                &lt;span style=\"font-size: 14px; font-weight: bold;\"&gt;Line:&lt;/span&gt;&amp;nbsp;\n                &lt;span style=\"font-size: 10px; font-family: Monaco, monospace;\"&gt;@line&lt;/span&gt;\n            &lt;/div&gt;\n            &lt;div&gt;\n                &lt;span style=\"font-size: 14px; font-weight: bold;\"&gt;Time:&lt;/span&gt;&amp;nbsp;\n                &lt;span style=\"font-size: 10px; font-family: Monaco, monospace;\"&gt;@time&lt;/span&gt;\n            &lt;/div&gt;\n            &lt;div&gt;\n                &lt;span style=\"font-size: 14px; font-weight: bold;\"&gt;Percentage:&lt;/span&gt;&amp;nbsp;\n                &lt;span style=\"font-size: 10px; font-family: Monaco, monospace;\"&gt;@percentage&lt;/span&gt;\n            &lt;/div&gt;\n            "},"id":"105549","type":"HoverTool"},{"attributes":{"data_source":{"id":"105516"},"glyph":{"id":"105545"},"hover_glyph":null,"muted_glyph":null,"nonselection_glyph":null,"selection_glyph":null,"view":{"id":"105548"}},"id":"105547","type":"GlyphRenderer"},{"attributes":{"overlay":{"id":"105608"}},"id":"105605","type":"BoxZoomTool"},{"attributes":{"callback":null,"point_policy":"follow_mouse","tooltips":"\n            &lt;div&gt;\n                &lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; @source &lt;/p&gt;\n                &lt;p&gt;&lt;b&gt;Destination:&lt;/b&gt; @destination &lt;/p&gt;\n                &lt;p&gt;&lt;b&gt;Bandwidth:&lt;/b&gt; @bandwidth_text / s&lt;/p&gt;\n            &lt;/div&gt;\n            "},"id":"105691","type":"HoverTool"},{"attributes":{},"id":"105786","type":"UnionRenderers"},{"attributes":{"dimensions":"width"},"id":"105606","type":"WheelZoomTool"},{"attributes":{"fill_alpha":{"field":"alpha"},"fill_color":{"field":"color"},"height":{"units":"data","value":0.4},"line_alpha":{"value":0.6},"line_color":{"field":"color"},"line_width":{"value":3},"width":{"field":"duration","units":"data"},"x":{"field":"start"},"y":{"field":"y"}},"id":"105643","type":"Rect"},{"attributes":{},"id":"105787","type":"Selection"},{"attributes":{"formatter":{"id":"105746"},"major_label_text_alpha":0,"major_tick_line_alpha":0,"minor_tick_line_alpha":0,"ticker":{"id":"105638"}},"id":"105637","type":"LinearAxis"},{"attributes":{"text":"Task Stream"},"id":"105625","type":"Title"}],"root_ids":["105733"]},"title":"Bokeh Application","version":"2.2.3"}}
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