mxiJUddlmZddlmZmZmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZmZddl m!Z!er2ddl"Z"ddl#m$Z$e"jJdk\rdd 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 d ddZ4e d ddZ4ddZ4ddZ5d dZ6 d! d"dZ7 d#dZ8d$dZ9 d%dZ:d&d'dZ; d(dZ< d& d)dZ=y)*) annotations) TYPE_CHECKINGAnyoverload)Chart ConcatChartConcatSpecGenericSpec FacetChartFacetedUnitSpec FacetSpec HConcatChartHConcatSpecGenericSpec LayerChart LayerSpecNonNormalizedSpecTopLevelConcatSpecTopLevelFacetSpecTopLevelHConcatSpecTopLevelLayerSpecTopLevelUnitSpecTopLevelVConcatSpecUnitSpecUnitSpecWithFrame VConcatChartVConcatSpecGenericSpecdata_transformers)get_inline_tablesimport_vegafusion) UndefinedN)Iterable) ) TypeAlias) ChartType) DataFrameLike.r#Scope FacetMappingcyNchart row_limitexcludes U/home/htdocs/ttos/venv/lib/python3.12/site-packages/altair/utils/_transformed_data.pytransformed_datar0Cs cyr)r*r+s r/r0r0Ks r1c`t}t|tr#|jtk(r|j }|j d}t|d|}tjd5|jdddi }t|}d d d t}t|||}g} |D]'} | |vr| j|| d } t| |j j#|| | \} } t|tt$fr| sy | dS| S#1swYxYw) 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_specr: facet_mappingdataset_mapping dataset_names chart_namemsgdatasets_s r/r0r0SsE.  B%EJJ)$;  " JJDJ !EUAw7K  ! !, /7MM/59QMR +I67 &i0M1)[-XOM!"  (  !< =1CS/ ! "**33' 4KHa%%,-A; E77s 3!D$$D-cn|jdthvrt|dr|j|_y|jj j }|jddjddjdd}|jd}d|dt|d |_yy) z0Assign a name to a chart if it doesn't have one.N_get_view_hash_namespecgenericconcatrQview_x) namerhasattrrS __class____name__lowerreplace removesuffixid)r, chart_types r/_assign_chart_namercs zzdI&& 5/ 0224EJ11779J""62.B'2&  $005J  Abi];EJ!'r1cLt|ttr |jSt|ttr |j St|tt r |jSt|ttr |jSdt|}t|)z(Get the subcharts for a composite chart.ztransformed_data accepts an instance of Chart, FacetChart, LayerChart, HConcatChart, VConcatChart, or ConcatChart Received value of type: ) r;_chart_class_mappingrlayerr hconcatrvconcatrrWtyperE)r,rOs r/_get_subchartsrjs%-j9:{{ E/ = >}} E/ = >}} E/ < =|| ''+E{m 5 or1cT| t|n t}t|ttttfr(|j |vrt ||j gSgSt|}g}|D]2}t||t|z|D]}|j|4|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 )ir.) setr;rerr rZrcrjr?lenrD)r,rlr. subchartsrIsubchartrZs r/r?r?s4&1c'lsuG $U+-A*-MN ::W $ u %JJ<  u%IK%x1s;/?+?Q %D   t $ %% r1c|}|D]F}d}d}|jdgD]%}|jddk(s||k(r|}n|dz }'|y|}H|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'}]} rNmarksrigroup)get)rJscopers scope_value group_index child_groupr<s r/get_group_mark_for_scoperzs@ E    IIgr* !Dxx7*+-"&Kq  !    Lr1c t||xsi}g}|jdgD]}|j|d|jdijdijdd}|r|j||S)aM Get the names of the datasets that are defined at a given scope. Parameters ---------- vega_spec : dict Top-level 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)) [] datarZfromfacetN)rzrurD)rJrvrsrPdataset facet_datasets r/get_datasets_for_scoper/sh %Y 6 <"EH99VR()()IIfb)--gr:>>vtLM & Or1cttt|dzD]}|d|}t||}||vs|cSy)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' rtN)reversedrangernr)rJ data_name usage_scoperlrvrPs r/'get_definition_scope_for_data_referencerqsQreC ,q01 2BQ))U;  L  r1ci}d}t||xsi}|jdgD]}|jdddk(sg||}|jdijdd}|@|jdd}|jd d} || t|| |} | | | f|||f<t|| } |j | |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', ())} rrrriNrsr}r~rZr|rvrt)rzrurrBupdate) rsrvrKrx mark_groupr< group_scoper~ facet_name facet_datadefinition_scope child_mappings r/rBrBs HMK)%7=2Jw+ 88FD !W ,/E/;/KHHVR(,,Wd;E "YYvt4 "YYvt4 )j.D'Nz5($(3&,B j+&=> .e;GM   / 1 K+. r1c"||vr ||}||vr |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', ()) r*)scoped_datasetrKs r/get_from_facet_mappingrs&8 M )&~6 M ) r1ci}d}t||xsi}|jdgD]D}|D]a}|jdd|dk(s|jdijddjd d} | |f} t| |||<n|jdd} |jd dd k(rDt|||g|| } | j D]\} }|j | ||d z }|D]k}| j |s| jds'|jdijd d} t|| |}|Yt| |f|||<DG|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 rrrrZrU_cellr}r~Nr|rirsrrt_marks) rzrurrCitems setdefault startswithendswithr)rsvl_chart_namesrKrvrPrxrr< vl_chart_namerscoped_data_namerZgroup_data_nameskv scoped_datas r/rCrCs2HK)%7=2Jw++ Mxx#-'>> HHVR044WdCGGPTU $-u#5 *@$m+' xx# 88FB 7 *:~} Or1)NN)r,zChart | FacetChartr- int | Noner.Iterable[str] | NonereturnzDataFrameLike | None)r,z6LayerChart | HConcatChart | VConcatChart | ConcatChartr-rr.rrzlist[DataFrameLike])r,r$rNone)r,r$rz list[Any])rN)r,r$rlintr.rr list[str])rJdict[str, Any]rvr&rzdict[str, Any] | None)rJrrvr&rr)rJrrstrrr&rz Scope | None)r*)rsrrvr&rr')rtuple[str, Scope]rKr'rr) rsrrrrKr'rvr&rzdict[str, tuple[str, Scope]])> __future__rtypingrrraltairrrr r r r r rrrrrrrrrrrrrrraltair.utils._vegafusion_datarraltair.utils.schemapirsyscollections.abcr version_infor#typing_extensions altair.typingr$altair.utils.corer%tuplerr&__annotations__dictrr'rer0rcrjr?rzrrrBrrCr*r1r/rs^"//0O+( 7"$/'/c?y"uS%Z0%U 2CCD iD     . :+13HI>*->>B>B%6BJ ; ;; ;  ; " ;r1