bi ddlmZddlZddlZddlZddlZddlm Z ddl m Z ddl m Z ddlmZmZddlmZddlmZd gZGd d eeZgd Zd ZdZdZddZeeeeeeeededeiZ GddZ! ddZ"y)) annotationsN)Task)new_collection)ArrowStringConversion) BlockwiseIOPartitionsFiltered)pyarrow_strings_enabled)Key timeseriesc eZdZdZgdZdddeeedddd id d d Zejd Z d Z e dZ ejdZejdZddZy ) TimeseriesT) startenddtypesfreqpartition_freqseedkwargscolumns _partitions_series 2000-01-01 2000-01-31stringnameidxy1s1dNF) rrrrrrrrrc|jdddjdd}|jr||jdS|S)N2000r)_make_timeseries_partilocrr)selfresults \/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/dask/dataframe/dask_expr/datasets.py_metazTimeseries._meta.sC++FFA>CCBQG <<&..+, , cntj|j|j|jS)N)rrr)pd date_rangerrrr&s r( _divisionszTimeseries._divisions5s#}}4::488$BUBUVVr*ct|jd}|jdDcic]}||| c}Scc}w)Nrr)operand)r&rcols r(_dtypeszTimeseries._dtypes8s6h',0LL,CDSVC[ DDDs 5ct|jdz }tt|jdd}tj j |j}||z}|j|dz}tt j|tjj|f}t||k(sJ|S)Nr)dtype) lenr/maxr1nprandom RandomStaterbyteslist frombufferuint32reshape)r& npartitionsndtypes random_staten random_datals r(rDzTimeseries.random_state=s$//+,q0 c$,,x0115yy,,TYY7 ' !"((Q/ {"))<DDaTJ K1v{{r*ct|jdt|jj |j |j S)Nr)MakeTimeseriesPartr1r>r3keysrrr.s r(r$z Timeseries._make_timeseries_partHs>! LL " ""$ % II KK   r*c F|j}tt|jdd}t ||j ||||dz|j ||z}|jr.t |tj||jddS|S)Nrr5rr) r/r9r8r1rr$rDroperatorgetitem)r&rindexfull_divisionsrCtasks r(_filtered_taskzTimeseries._filtered_taskQs*c$,,x0115   & & 5 ! 519 %   ego .   <<h..dll96Ma6PQ Q r*)rr rNintreturnr)__name__ __module__ __qualname___absorb_projections _parametersrRfloat _defaults functoolscached_propertyr)r/propertyr3rDr$rQr*r(r r s K#3UG I WEE   r*r )AliceBobCharlieDanEdithFrankGeorgeHannahIngridJerryKevinLauraMichaelNorbertOliverPatriciaQuinnRaySarahTimUrsulaVictorWendyXavierYvonneZeldac0|jt|SNsize)choicenamesrErstates r( make_stringr~s ==Q= ''r*ctjj|jdt t |t S)Nrr{)r, Categorical from_codesrandintr8r~rs r(make_categoricalrs, >> $ $V^^As5z^%JE RRr*c0|j|dzdz S)Nr5)randrs r( make_floatrs ;;q>A  !!r*c(|j||Srz)poisson)rErlams r(make_intrs >>#A> &&r*rcategoryceZdZdZdZy)rIc<||_||_||_||_y)N)rrrr)r&rrrrs r(__init__zMakeTimeseriesPart.__init__s    r*c j|j}|j}|j}|j}tj j |}tj|||d} i} |jD]v\} } |jD cic]3\} }| jddd| k(r| jddd|5}} }t| t| |fi|}| |vsr|| | <xtj| | |}|jd|k(r|jdd}|Scc}} w)N timestamp)rrrr_r5r)rNr)rrrrr:r;r<r,r-itemsrsplitmaker8 DataFramerNr%)r&rr state_datarrrrstaterNdatakdtkkvkwsr'dfs r(__call__zMakeTimeseriesPart.__call__s*,,yy %%j1 EsKP\\^ !EAr$\\^B99S!$Q'1, #q!!$a'C"Xc%j%737FG| Q !\\$eW = 88B<3 "B s8D/N)rTrUrVrrr^r*r(rIrIs  r*rIc |dtttd}|tjj d}t |||||||t |j}trtt|St|S)aXCreate timeseries dataframe with random data Parameters ---------- start: datetime (or datetime-like string) Start of time series end: datetime (or datetime-like string) End of time series dtypes: dict (optional) Mapping of column names to types. Valid types include {float, int, str, 'category'} freq: string String like '2s' or '1H' or '12W' for the time series frequency partition_freq: string String like '1M' or '2Y' to divide the dataframe into partitions seed: int (optional) Randomstate seed kwargs: Keywords to pass down to individual column creation functions. Keywords should be prefixed by the column name and then an underscore. Examples -------- >>> from dask.dataframe.dask_expr.datasets import timeseries >>> df = timeseries( ... start='2000', end='2010', ... dtypes={'value': float, 'name': str, 'id': int}, ... freq='2h', partition_freq='1D', seed=1 ... ) >>> df.head() # doctest: +SKIP id name value 2000-01-01 00:00:00 969 Jerry -0.309014 2000-01-01 02:00:00 1010 Ray -0.760675 2000-01-01 04:00:00 1016 Patricia -0.063261 2000-01-01 06:00:00 960 Charlie 0.788245 2000-01-01 08:00:00 1031 Kevin 0.466002 rrgeA)r) rRrYr:r;rr r>rJr rr)rrrrrrrexprs r(r r s\~"#EF |yy  %     V[[]# D 3D9:: $ r*)i)rrr 1DNN)# __future__rr[rLnumpyr:pandasr,dask._task_specr$dask.dataframe.dask_expr._collectionrdask.dataframe.dask_expr._exprrdask.dataframe.dask_expr.iorrdask.dataframe.utilsr dask.typingr __all__r r~rrrrrYrRstrobjectrrIr r^r*r(rs" ?@G8 .J#[JZ <(S"'  : K k F    @ r*