#Yh`G,UddlmZddlmZmZmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZmZddl m!Z!er.ddl"Z"ddl#m$Z$e"j%dkrdd lm&Z&ndd l'm&Z&dd l(m)Z)dd l*m+Z+e,e-d fZ.d e/d<e0e,e1e.fe,e1e.ffZ2d e/d<eeee eeefeeeefeeee fe e eefeeeefe e ee fiZ3e d7d8dZ4e d7d9dZ4d7dZ4 d:d;d!Z5dd,Z8d?d@d/Z9dAd3Z: d?dBd6Z;dS)C) annotations) TYPE_CHECKINGAnyoverload)Chart ConcatChartConcatSpecGenericSpec FacetChartFacetedUnitSpec FacetSpec HConcatChartHConcatSpecGenericSpec LayerChart LayerSpecNonNormalizedSpecTopLevelConcatSpecTopLevelFacetSpecTopLevelHConcatSpecTopLevelLayerSpecTopLevelUnitSpecTopLevelVConcatSpecUnitSpecUnitSpecWithFrame VConcatChartVConcatSpecGenericSpecdata_transformers)get_inline_tablesimport_vegafusion) UndefinedN)Iterable) ) TypeAlias) ChartType) DataFrameLike.r#Scope FacetMappingchartChart | FacetChart row_limit int | NoneexcludeIterable[str] | NonereturnDataFrameLike | NonecdSNr(r*r,s fD:\DownLoads\facial_attendance_system-main\venv311\Lib\site-packages\altair/utils/_transformed_data.pytransformed_datar5Cs 36LayerChart | HConcatChart | VConcatChart | ConcatChartlist[DataFrameLike]cdSr1r2r3s r4r5r5Ks #r6ct}t|tr$|jtkr|}|d}t|d|}tj d5| dddi }t|}d d d n #1swxYwYt|}t|||}g} |D]2} | |vr| || "d } t| |j|| || \} } t|tt$fr | sd S| dS| S) a Evaluate a Chart's transforms. Evaluate the data transforms associated with a Chart and return the transformed data as one or more DataFrames Parameters ---------- chart : Chart, FacetChart, LayerChart, HConcatChart, VConcatChart, or ConcatChart Altair chart to evaluate transforms on row_limit : int (optional) Maximum number of rows to return for each DataFrame. None (default) for unlimited exclude : iterable of str Set of the names of charts to exclude Returns ------- DataFrame or list of DataFrames or None If input chart is a Chart or Facet Chart, returns a DataFrame of the transformed data. Otherwise, returns a list of DataFrames of the transformed data T)deepr)r, vegafusionvega pre_transformF)formatcontextNzFailed to locate all datasets)r*inline_datasets)r isinstancermarkr mark_pointcopy name_viewsrenableto_dictrget_facet_mappingget_datasets_for_view_namesappend ValueErrorruntimepre_transform_datasetsr )r(r*r,vf chart_names vega_specrA facet_mappingdataset_mapping dataset_names chart_namemsgdatasets_s r4r5r5Ss.   B%#EJ)$;$;  "" JJDJ ! !EUAw777K  !, / /77MM/59QMRR +I66777777777777777 &i00M1)[-XXOM!""  ( (  !< = = = =1CS// !*33' 4KHa%%,-- 4A; s)B99B=B=r$iint list[str]c"|t|n t}t|ttttfr;|j|vr0|jdt hvrtj|_|jgSgSt|ttr|j }nt|ttr|j }nqt|ttr|j }nIt|ttr|j}n!dt!|}t#|g}|D]<}t%||t'|z|D]}||=|S)aM Name unnamed chart views. Name unnamed charts views so that we can look them up later in the compiled Vega spec. Note: This function mutates the input chart by applying names to unnamed views. Parameters ---------- chart : Chart, FacetChart, LayerChart, HConcatChart, VConcatChart, or ConcatChart Altair chart to apply names to i : int (default 0) Starting chart index exclude : iterable of str Names of charts to exclude Returns ------- list of str List of the names of the charts and subcharts Nztransformed_data accepts an instance of Chart, FacetChart, LayerChart, HConcatChart, VConcatChart, or ConcatChart Received value of type: )rYr,)setrB_chart_class_mappingrr namer _get_namerlayerr hconcatrvconcatrconcattyperLrFlenrK)r(rYr, subchartsrVrPsubchartr_s r4rFrFs4&1c'lllsuuG $U+-A*-MN  :W $ $zdI..."_.. J< I e1*= > > " II 3LA B B " II 3LA B B " II 3K@ A A " II9+/;;99  S// !!# ! ) )H"8q3{3C3C/CWUUU ) )""4(((( )r6rQdict[str, Any]scopedict[str, Any] | Nonec|}|D]N}d}d}|dgD]*}|ddkr||kr|}n|dz }+|dS|}O|S)aF Get the group mark at a particular scope. Parameters ---------- vega_spec : dict Top-level Vega specification dictionary scope : tuple of int Scope tuple. If empty, the original Vega specification is returned. Otherwise, the nested group mark at the scope specified is returned. Returns ------- dict or None Top-level Vega spec (if scope is empty) or group mark (if scope is non-empty) or None (if group mark at scope does not exist) Examples -------- >>> spec = { ... "marks": [ ... {"type": "group", "marks": [{"type": "symbol"}]}, ... {"type": "group", "marks": [{"type": "rect"}]}, ... ] ... } >>> get_group_mark_for_scope(spec, (1,)) {'type': 'group', 'marks': [{'type': 'rect'}]} rNmarksregroup)get)rQrjrn scope_value group_index child_grouprCs r4get_group_mark_for_scoperts@ E     IIgr** ! !Dxx7**+--"&KEq  44 Lr6c@t||pi}g}|dgD]}||d|dididd}|r|||S)aL Get the names of the datasets that are defined at a given scope. Parameters ---------- vega_spec : dict Top-leve Vega specification scope : tuple of int Scope tuple. If empty, the names of top-level datasets are returned Otherwise, the names of the datasets defined in the nested group mark at the specified scope are returned. Returns ------- list of str List of the names of the datasets defined at the specified scope Examples -------- >>> spec = { ... "data": [{"name": "data1"}], ... "marks": [ ... { ... "type": "group", ... "data": [{"name": "data2"}], ... "marks": [{"type": "symbol"}], ... }, ... { ... "type": "group", ... "data": [ ... {"name": "data3"}, ... {"name": "data4"}, ... ], ... "marks": [{"type": "rect"}], ... }, ... ], ... } >>> get_datasets_for_scope(spec, ()) ['data1'] >>> get_datasets_for_scope(spec, (0,)) ['data2'] >>> get_datasets_for_scope(spec, (1,)) ['data3', 'data4'] Returns empty when no group mark exists at scope >>> get_datasets_for_scope(spec, (1, 3)) [] datar_fromfacetN)rtrprK)rQrjrnrWdataset facet_datasets r4get_datasets_for_scoper{sh %Y 6 6 <"EH99VR(())((((IIfb))--gr::>>vtLLM' &&& Or6 data_namestr usage_scope Scope | Nonecttt|dzD]$}|d|}t||}||vr|cS%dS)aR Return the scope that a dataset is defined at, for a given usage scope. Parameters ---------- vega_spec: dict Top-level Vega specification data_name: str The name of a dataset reference usage_scope: tuple of int The scope that the dataset is referenced in Returns ------- tuple of int The scope where the referenced dataset is defined, or None if no such dataset is found Examples -------- >>> spec = { ... "data": [{"name": "data1"}], ... "marks": [ ... { ... "type": "group", ... "data": [{"name": "data2"}], ... "marks": [ ... { ... "type": "symbol", ... "encode": { ... "update": { ... "x": {"field": "x", "data": "data1"}, ... "y": {"field": "y", "data": "data2"}, ... } ... }, ... } ... ], ... } ... ], ... } data1 is referenced at scope [0] and defined at scope [] >>> get_definition_scope_for_data_reference(spec, "data1", (0,)) () data2 is referenced at scope [0] and defined at scope [0] >>> get_definition_scope_for_data_reference(spec, "data2", (0,)) (0,) If data2 is not visible at scope [] (the top level), because it's defined in scope [0] >>> repr(get_definition_scope_for_data_reference(spec, "data2", ())) 'None' roN)reversedrangerfr{)rQr|r~rYrjrWs r4'get_definition_scope_for_data_referencerYskreC ,,q011 2 2BQB))U;;  LLL ! 4r6r2rnci}d}t||pi}|dgD]}|dddkrg||R}|didd}|L|dd}|d d} || t|| |} | | | f|||f<t|| } || |d z }|S) a Create mapping from facet definitions to source datasets. Parameters ---------- group : dict Top-level Vega spec or nested group mark scope : tuple of int Scope of the group dictionary within a top-level Vega spec Returns ------- dict Dictionary from (facet_name, facet_scope) to (dataset_name, dataset_scope) Examples -------- >>> spec = { ... "data": [{"name": "data1"}], ... "marks": [ ... { ... "type": "group", ... "from": { ... "facet": { ... "name": "facet1", ... "data": "data1", ... "groupby": ["colA"], ... } ... }, ... } ... ], ... } >>> get_facet_mapping(spec) {('facet1', (0,)): ('data1', ())} rrmreNrnrwrxr_rvrjro)rtrprrIupdate) rnrjrRrr mark_grouprC group_scoperx facet_name facet_datadefinition_scope child_mappings r4rIrIs5HMK)%77=2Jw++ 88FD ! !W , ,/E/;//KHHVR((,,Wd;;E "YYvt44 "YYvt44 )j.D'Nz5(($(3&,B j+&=> .e;GGGM   / / / 1 K r6scoped_datasettuple[str, Scope]rRc&||vr ||}||v |S)a Apply facet mapping to a scoped dataset. Parameters ---------- scoped_dataset : (str, tuple of int) A dataset name and scope tuple facet_mapping : dict from (str, tuple of int) to (str, tuple of int) The facet mapping produced by get_facet_mapping Returns ------- (str, tuple of int) Dataset name and scope tuple that has been mapped as many times as possible Examples -------- Facet mapping as produced by get_facet_mapping >>> facet_mapping = { ... ("facet1", (0,)): ("data1", ()), ... ("facet2", (0, 1)): ("facet1", (0,)), ... } >>> get_from_facet_mapping(("facet2", (0, 1)), facet_mapping) ('data1', ()) r2)rrRs r4get_from_facet_mappingrs,8 M ) )&~6 M ) ) r6vl_chart_namesdict[str, tuple[str, Scope]]c\i}d}t||pi}|dgD]}}|D]v}|dd|dkrW|diddd d} | |f} t| |||<nw|dd} |d dd krNt|||g||R } | D]\} }|| ||d z }|D]}| |ri| drT|did d} t|| |}|t| |f|||<n|S)aH Get the Vega datasets that correspond to the provided Altair view names. Parameters ---------- group : dict Top-level Vega spec or nested group mark vl_chart_names : list of str List of the Vega-Lite facet_mapping : dict from (str, tuple of int) to (str, tuple of int) The facet mapping produced by get_facet_mapping scope : tuple of int Scope of the group dictionary within a top-level Vega spec Returns ------- dict from str to (str, tuple of int) Dict from Altair view names to scoped datasets rrmr__cellrwrxNrvrernrro_marks) rtrprrJitems setdefault startswithendswithr)rnrrRrjrWrrrrC vl_chart_namer|scoped_data_namer_group_data_nameskv scoped_datas r4rJrJs2HK)%77=2Jw+++  Mxx##-'>'>'>>> HHVR0044WdCCGGPTUU $-u#5 *@$m++' ?xx## 88FB  7 * *:~}rs"""""""//////////0ONNNNNNN++++++ 0JJJ(((((( 7""$$$$$$$//////''''''//////c?""""uS%Z0%U 2CCD DDDD     . :+13HI>>>B>>>>>BJ ;;;;;;;r6