o h| @sUdZddlmZddlZddlZddlZddlZddlZddlZddl m Z m Z m Z ddl mZddlmZddlmZddlmZmZmZmZmZmZmZddlZddlmmZdd lm Z m!Z!dd l"m#Z#dd l$m%Z%m&Z&m'Z'ej(d krdd lm)Z)m*Z*m+Z+n dd l,m)Z)m*Z*m+Z+ej(dkrddlm-Z-m.Z.nddl,m-Z-m.Z.erddlZ/ddl0Z1ddl"m2Z2ddl3m4Z5ddl6m7Z8ede#dZ9edZ:e.dZ;edZe*dde:eszDataFrameLike.__dataframe__N)FT)r,r-r.r-r/r0)__name__ __module__ __qualname__r6r3r3r3r5r+<sr+ONQG)ordinalnominal quantitativetemporalgeojsoncCsi|]\}}||qSr3r3).0kvr3r3r5 KrF)argmaxargminaveragecountdistinctmaxmeanmedianminmissingproductq1q3ci0ci1stderrstdevstdevpsumvalidvaluesvariance variancep exponential exponentialb) row_numberrank dense_rank percent_rank cume_distntilelaglead first_value last_value nth_value)Qyearquartermonthweekday dayofyeardatehoursminutesseconds milliseconds yearquarteryearquartermonth yearmonth yearmonthdateyearmonthdatehoursyearmonthdatehoursminutes yearmonthdatehoursminutessecondsyearweek yearweekdayyearweekdayhoursyearweekdayhoursminutesyearweekdayhoursminutesseconds yeardayofyear quartermonth monthdatemonthdatehoursmonthdatehoursminutesmonthdatehoursminutessecondsweekday weeksdayhours weekdayhoursweekdayhoursminutesweekdayhoursminutessecondsdayhoursdayhoursminutesdayhoursminutesseconds hoursminuteshoursminutessecondsminutessecondssecondsmillisecondsutcyear utcquarterutcmonthutcweekutcday utcdayofyearutcdateutchours utcminutes utcsecondsutcmillisecondsutcyearquarterutcyearquartermonth utcyearmonthutcyearmonthdateutcyearmonthdatehoursutcyearmonthdatehoursminutes#utcyearmonthdatehoursminutesseconds utcyearweekutcyearweekdayutcyearweekdayhoursutcyearweekdayhoursminutes!utcyearweekdayhoursminutessecondsutcyeardayofyearutcquartermonth utcmonthdateutcmonthdatehoursutcmonthdatehoursminutesutcmonthdatehoursminutesseconds utcweekdayutcweekdayhoursutcweekdayhoursminutesutcweekdayhoursminutesseconds utcdayhoursutcdayhoursminutesutcdayhoursminutessecondsutchoursminutesutchoursminutessecondsutcminutessecondsutcsecondsmillisecondsz (?P.*)z (?P{})|z(?Pcount)z (?Pcount)z(?P{})z (?P{})z(?P{}))fieldtype agg_countop_count aggregate window_optimeUnit)rrrrz mixed-integermixed-integer-floatcomplexintegerfloatingr@ categoricalcatr>>bytesmixedstringbooleanunicoderr?>rrtimeperioddatetime timedelta datetime64 timedelta64rAz.I don't know how to infer vegalite type from 'z'. Defaulting to nominal. stacklevel) pandas.api.typesrhasattrrordered categoriestolistwarningswarn)rrtypr3r3r5infer_vegalite_type_for_pandass   rfeatdict[str, Any]c sLfdddD}zd|d}W|Sttfy%|}Y|Sw)zf Merge properties with geometry. * Overwrites 'type' and 'geometry' entries if existing. csi|]}||qSr3r3)rCrDrr3r5rFrGz$merge_props_geom..rgeometry properties)updateAttributeErrorKeyError)rgeom props_geomr3rr5merge_props_geoms rgeot.MutableMapping[Any, Any]cCst|}|D]}tt||jdr||||<qtt|}|ddkrF|d}t |dkrDt |D] \}}t |||<q9|S|ddkrRt |}|Sd|d}|S)z Santize a geo_interface to prepare it for serialization. * Make a copy * Convert type array or _Array to list * Convert tuples to lists (using json.loads/dumps) * Merge properties with geometry )_ArrayarrayrFeatureCollectionfeaturesrFeaturer) rstrrr7 startswithrjsonloadsdumpslen enumerater)rkeygeo_dctidxrr3r3r5sanitize_geo_interface"s"     rdtypesubtyper-c Cs0ddl}z|||WSttfyYdSw)NrF)numpy issubdtypeNotImplementedError TypeError)rrnpr3r3r5numpy_is_subtypeCs r df pd.DataFramec sddlddl}|}t|j|jr|jt|_|jD]}t|ts.d|d}t|qt|j |j r.to_list_if_arrayrcategoryrr-r)r timestampcSs|Sr2) isoformat)xr3r3r5sz+sanitize_pandas_dataframe..NaTrField " " has type "^" which is not supported by Altair. Please convert to either a timestamp or a numerical value.r> Int8Int16Int32Int64UInt8UInt16UInt32UInt64Float32Float64)rpandascopyrcolumns RangeIndexastyper ValueErrorindex MultiIndexdtypesitemsrobjectwherenotnullrapplyreplacer rrisnullisinf) rpdcol_namemsgr dtype_itemr dtype_namecol bad_valuesr3rr5sanitize_pandas_dataframeMst        rAnw.DataFrame[TIntoDataFrame]cCs|j}g}d}t|}|D]Z\}}|tjkr-|r-|t|tj j |q|tjkr?|t|j |q|tj krT|t|j |dq|tj krfd|d|d}t |||q||S)z3Sanitize narwhals.DataFrame for JSON serialization.z%Y-%m-%dT%H:%M:%Sz%.frrr)schemar to_nativer2nwDateappendr?rDatetimedt to_stringDurationr.select)rrCr+local_iso_fmt_string is_polarsnamerr<r3r3r5sanitize_narwhals_dataframes&       rPrnw.DataFrame[Any]cCsBtj|dd}t|dkrddlm}||}tj|dd}|S)z Wrap `data` in `narwhals.DataFrame`. If `data` is not supported by Narwhals, but it is convertible to a PyArrow table, then first convert to a PyArrow Table, and then wrap in `narwhals.DataFrame`. Teager_or_interchange_only interchanger)arrow_table_from_dfi_dataframe) eager_only)rE from_native get_levelaltair.utils.datarU)rdata_nwrUpa_tabler3r3r5to_eager_narwhals_dataframes  r\TF shorthanddict[str, Any] | strIntoDataFrame | Noneparse_aggregatesparse_window_opsparse_timeunits parse_typescs0ddlm}s iSg}|r|dg|dg|r(|dg|dg|r0|dg|dg|rDttjd d |D}d d |D}ttrS} n tfd d |D} d | vrnt | d | d | d <| ddikrxd| d <d| vrd | vrd| d <d | vr||r| d dd} t j |dd} | j} | | vr| | } | | t jt jhvrt| rt| | d <nt| | d <t| d tr| d d| d<| d d| d <d| vrd| dvr| d| ddddkrtd| dddddtdd d!| S)"a General tool to parse shorthand values. These are of the form: - "col_name" - "col_name:O" - "average(col_name)" - "average(col_name):O" Optionally, a dataframe may be supplied, from which the type will be inferred if not specified in the shorthand. Parameters ---------- shorthand : dict or string The shorthand representation to be parsed data : DataFrame, optional If specified and of type DataFrame, then use these values to infer the column type if not provided by the shorthand. parse_aggregates : boolean If True (default), then parse aggregate functions within the shorthand. parse_window_ops : boolean If True then parse window operations within the shorthand (default:False) parse_timeunits : boolean If True (default), then parse timeUnits from within the shorthand parse_types : boolean If True (default), then parse typecodes within the shorthand Returns ------- attrs : dict a dictionary of attributes extracted from the shorthand Examples -------- >>> import pandas as pd >>> data = pd.DataFrame({"foo": ["A", "B", "A", "B"], "bar": [1, 2, 3, 4]}) >>> parse_shorthand("name") == {"field": "name"} True >>> parse_shorthand("name:Q") == {"field": "name", "type": "quantitative"} True >>> parse_shorthand("average(col)") == {"aggregate": "average", "field": "col"} True >>> parse_shorthand("foo:O") == {"field": "foo", "type": "ordinal"} True >>> parse_shorthand("min(foo):Q") == { ... "aggregate": "min", ... "field": "foo", ... "type": "quantitative", ... } True >>> parse_shorthand("month(col)") == { ... "field": "col", ... "timeUnit": "month", ... "type": "temporal", ... } True >>> parse_shorthand("year(col):O") == { ... "field": "col", ... "timeUnit": "year", ... "type": "ordinal", ... } True >>> parse_shorthand("foo", data) == {"field": "foo", "type": "nominal"} True >>> parse_shorthand("bar", data) == {"field": "bar", "type": "quantitative"} True >>> parse_shorthand("bar:O", data) == {"field": "bar", "type": "ordinal"} True >>> parse_shorthand("sum(bar)", data) == { ... "aggregate": "sum", ... "field": "bar", ... "type": "quantitative", ... } True >>> parse_shorthand("count()", data) == { ... "aggregate": "count", ... "type": "quantitative", ... } True r) is_data_typez{agg_count}\(\)z{aggregate}\({field}\)z{op_count}\(\)z{window_op}\({field}\)z{timeUnit}\({field}\)z{field}css|] }|d|fVqdS)z:{type}Nr3rCpr3r3r5 ~sz"parse_shorthand..css2|]}td|jditdtjVqdS)z\Az\ZNr3)recompileformatSHORTHAND_UNITSDOTALLrer3r3r5rgs  c3s,|]}|dur|VqdSr2)match groupdict)rCexpr]r3r5rgs rrrKr@rrAr\rTrRrsort:z"{}" z0is not one of the valid encoding data types: {}.z, zi For more details, see https://altair-viz.github.io/user_guide/encodings/index.html#encoding-data-types. z>If you are trying to use a column name that contains a colon, zPprefix it with a backslash; for example "column\:name" instead of "column:name".)rYrdextendlist itertoolschainrdictnextINV_TYPECODE_MAPgetr7rErWrCObjectUnknownrrDr infer_vegalite_type_for_narwhalstuplerfindr.rjsplitjoin TYPECODE_MAPr\)r]rr`rarbrcrdpatternsregexpsattrsunescaped_fieldrZrCcolumnr3rpr5parse_shorthands~ f               rr nw.Series8InferredVegaLiteType | tuple[InferredVegaLiteType, list]cCs|j}t|r|j}sd|fS|tjks&|tjks&|tj kr(dS| r.dS|tj ks8|tj kr:dSd|}t |)Nr>r?r@rAzUnexpected DtypeKind: )rrEis_ordered_categoricalrget_categoriesis_emptyto_listString CategoricalBoolean is_numericrHrFr.)rrrr<r3r3r5rs  rtpCallable[P, Any]cs.td dd}tdd d}dfd d }|S)a_ Use the signature and doc of ``tp`` for the decorated callable ``cb``. - **Overload 1**: Decorating method - **Overload 2**: Decorating function Returns ------- **Adding the annotation breaks typing**: Overload[Callable[[WrapsMethod[T, R]], WrappedMethod[T, P, R]], Callable[[WrapsFunc[R]], WrappedFunc[P, R]]] cbWrapsMethod[T, R]r/WrappedMethod[T, P, R]cSr1r2r3rr3r3r5decoratezuse_signature..decorate WrapsFunc[R]WrappedFunc[P, R]cSr1r2r3rr3r3r5rr*WrappedMethod[T, P, R] | WrappedFunc[P, R]csltd|_j}r-|jpdjdd}d|g|jdddd R|_|Sd }t|) z Raises when no doc was found. 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Parameters ---------- original : MutableMapping the original (nested) dictionary, which will be updated in-place update : Mapping the nested dictionary of updates copy : bool, default False if True, then copy the original dictionary rather than modifying it Returns ------- original : MutableMapping a reference to the (modified) original dict Examples -------- >>> original = {"x": {"b": 2, "c": 4}} >>> update = {"x": {"b": 5, "d": 6}, "y": 40} >>> update_nested(original, update) # doctest: +SKIP {'x': {'b': 5, 'c': 4, 'd': 6}, 'y': 40} >>> original # doctest: +SKIP {'x': {'b': 5, 'c': 4, 'd': 6}, 'y': 40} )rr2rrr|rr)rrr*rrorig_valr3r3r5r s      in_ipythoncCsHt}|rddlm}|}nd}|dur||dStj|dS)Nr) get_ipython)sysexc_infoIPython.core.getipythonr showtraceback tracebackprint_exception)rrripr3r3r5display_traceback:s r)rdatumvalue _ChannelCache_CHANNEL_CACHEc@sJeZdZUded<ded<edddZdd d ZdddZdddZdS)rdict[type[SchemaBase], str]channel_to_name/dict[str, dict[_ChannelType, type[SchemaBase]]]name_to_channelr/cCsBzt}WtSty ||}t|_t|j|_|aYtSwr2)r NameError__new___init_channel_to_namer_invert_group_channelsr)clscachedr3r3r5 from_cacheVs  z_ChannelCache.from_cacher type[Any]rcCs,|j|}r |Sdt|j}t|)Nzpositional of type )rr|rr7r )r4rencodingr<r3r3r5 get_encodingbsz_ChannelCache.get_encodingobjr rcst|tr|St|trd|i}nt|ttfr"fdd|DSt|tr+|}j}rR|d|vr:dnd}z|j |ddWSt j yQ|YSwt j dd d |S) Nr]csg|]}|qSr3)_wrap_in_channel)rCelrr4r3r5 nsz2_ChannelCache._wrap_in_channel..rrF)validatezUnrecognized encoding channel rr)rrrrvrrto_dictrr| from_dict jsonschemaValidationErrorrr)r4rrchannelrr3rr5rhs"    z_ChannelCache._wrap_in_channelkwargsrcsfdd|DS)Ncs&i|]\}}|tur|||qSr3)rr)rCrrr4r3r5rFs  z6_ChannelCache.infer_encoding_types..)r2)r4rr3rr5infer_encoding_types~s z"_ChannelCache.infer_encoding_typesN)r/r)rrr/r)rr rr)rr) r7r8r9__annotations__ classmethodrrrrr3r3r3r5rRs   cs4ddlm}|j|j|jffdd|jDS)a Construct a dictionary of channel type to encoding name. Note ---- The return type is not expressible using annotations, but is used internally by `mypy`/`pyright` and avoids the need for type ignores. Returns ------- mapping: dict[type[``] | type[``] | type[``], str] r)channelscs2i|]}t|trt|rt|tr||jqSr3)rr issubclassr_encoding_name)rCcmixinsr3r5rFsz)_init_channel_to_name..)altair.vegalite.v5.schemarFieldChannelMixinValueChannelMixinDatumChannelMixin__dict__r\)chr3rr5rs  rmrrcs.d ddt|td}fdd |DS) z;Grouped inverted index for `_ChannelCache.channel_to_name`.itIterator[tuple[type[Any], str]]r/r cSsDi}|D]\}}|j}|drq|drd}nd}|||<q|S)z Returns a 1-2 item dict, per channel. Never includes `datum`, as it is never utilized in `wrap_in_channel`. DatumValuerr)r7endswith)ritemr_rOsub_keyr3r3r5_reduces    z'_invert_group_channels.._reducercsi|] \}}||qSr3r3)rCrDchansrr3r5rFsz*_invert_group_channels..N)rrr/r )rr2r)rgrouperr3rr5rs rargstuple[Any, ...]rcCsnt}|D]+}t|ttfrtt|dn|}|t|}||vr(|||<qd|d}t || |S)aa Infer typed keyword arguments for args and kwargs. Parameters ---------- args : Sequence Sequence of function args kwargs : MutableMapping Dict of function kwargs Returns ------- kwargs : dict All args and kwargs in a single dict, with keys and types based on the channels mapping. Nz encoding z specified twice.) rrrrvrrziterrrr.r)rrcacheargrrr<r3r3r5rs    r)rr r/r)rrr/r)rrr/r)rr rr r/r-)rrr/r)rrBr/rB)rrr/rQ)NTFTT)r]r^rr_r`r-rar-rbr-rcr-r/r)rrr/r)rr).)rrrrr*rr/r)rrrrr*rr/r)F)rr rrr*r-r/r)T)rr-)rrr/r)rrrr)cr __future__rrwrrhrrrcollections.abcrrrr*rroperatorrtypingr r r r r rrrnarwhals.stable.v1stablev1rEnarwhals.stable.v1.dependenciesrrnarwhals.stable.v1.typingraltair.utils.schemapirrr version_inforrrtyping_extensionsrrtr)r:raltair.utils._dfi_typesrr0!altair.vegalite.v5.schema._typingr InferredVegaLiteTyper!r#r$r%r&r(r)r*r+rr2r{ AGGREGATESWINDOW_AGGREGATES TIMEUNITSrvrxrVALID_TYPECODESrjrrk frozensetrrrrrr rArPr\rrrrr _ChannelTyperrrrr3r3r3r5s    $        T  0  !   !  < *  -4