o +&]iJ@s UddlmZddlmZmZmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZmZddl m!Z!eryddl"Z"ddl#m$Z$e"j%dkrgdd 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  dEdFddZ4e  dEdGddZ4dEddZ4dHd d!Z5dId#d$Z6 dJdKd(d)Z7dLd.d/Z8dMd0d1Z9dNd6d7Z:dOdPd:d;Z;dQd?d@Z< 8dOdRdCdDZ=dS)S) 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 | NonecCdSNr'r)r+r1r1\/var/www/html/IGF-ODF-V3/venv/lib/python3.10/site-packages/altair/utils/_transformed_data.pytransformed_dataCr46LayerChart | HConcatChart | VConcatChart | ConcatChartlist[DataFrameLike]cCr/r0r1r2r1r1r3r4Kr5cCst}t|tr|jtkr|}|jdd}t|d|d}t d|j dddid }t |}Wd n1s;wYt |}t |||}g} |D]} | |vr\| || qNd } t| |jj|| ||d \} } t|ttfr}| syd 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_specr> facet_mappingdataset_mapping dataset_names chart_namemsgdatasets_r1r1r3r4Ss8     r#NonecCsx|jdthvr:t|dr||_dS|jj}|dddddd}|d}d|dt |d |_dSdS) z0Assign a name to a chart if it doesn't have one.N_get_view_hash_namespecgenericconcatrUview_x) namerhasattrrW __class____name__lowerreplace removesuffixid)r' chart_typer1r1r3_assign_chart_names    rg list[Any]cCsft|ttr |jSt|ttr|jSt|ttr|jSt|ttr(|j Sdt |}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 hconcatrvconcatrr[typerI)r'rSr1r1r3_get_subchartssrniint list[str]cCs|durt|nt}t|ttttfr%|j|vr#t||jgSgSt|}g}|D]}t||t ||dD]}| |q:q-|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 N)ror+) setr?rirr r^rgrnrClenrH)r'ror+ subchartsrMsubchartr^r1r1r3rCs  rCrNdict[str, Any]scopedict[str, Any] | NonecCsd|}|D]+}d}d}|dgD]}|ddkr%||kr!|}n|d7}q|dur-dS|}q|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'}]} rNmarksrmgroup)get)rNrwrz scope_value group_index child_groupr@r1r1r3get_group_mark_for_scopes rcCs`t||pi}g}|dgD] }||dq|dididd}|r.|||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)) [] datar^fromfacetN)rr|rH)rNrwrzrTdataset facet_datasetr1r1r3get_datasets_for_scope/s4 r data_namestr usage_scope Scope | NonecCsDttt|dD]}|d|}t||}||vr|Sq 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' r{N)reversedrangersr)rNrrrorwrTr1r1r3'get_definition_scope_for_data_referenceqs9  rr1rzc Csi}d}t||p i}|dgD]T}|dddkreg||R}|didd}|durV|dd}|d d} |durV| durVt|| |} | durV| | f|||f<t||d } || |d 7}q|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', ())} rryrmNrzrrr^rrwr{)rr|rrFupdate) rzrwrOr~ mark_groupr@ group_scoper facet_name facet_datadefinition_scope child_mappingr1r1r3rFs.$    rFscoped_datasettuple[str, Scope]rOcCs||vr ||}||vs|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', ()) r1)rrOr1r1r3get_from_facet_mappingsrvl_chart_namesdict[str, tuple[str, Scope]]cCs<i}d}t||p i}|dgD]}|D](}|dd|dkr=|diddd d} | |f} t| |||<nq|dd} |d dd krmt|||g||Rd } | D] \} }|| |q]|d 7}q|D]+}| |r| dr|did d} t|| |}|durt| |f|||<nqoq|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 rryr^rY_cellrrNrrmrzrr{_marks) rr|rrGitems setdefault startswithendswithr)rzrrOrwrTr~rr@ vl_chart_namerscoped_data_namer^group_data_nameskv scoped_datar1r1r3rGsD  rG)NN)r'r(r)r*r+r,r-r.)r'r6r)r*r+r,r-r7)r'r#r-rV)r'r#r-rh)rN)r'r#rorpr+r,r-rq)rNrvrwr%r-rx)rNrvrwr%r-rq)rNrvrrrr%r-r)r1)rzrvrwr%r-r&)rrrOr&r-r) rzrvrrqrOr&rwr%r-r)> __future__rtypingrrraltairrrrr r r r r rrrrrrrrrrrrrraltair.utils._vegafusion_datarraltair.utils.schemapirsyscollections.abcr version_infor"typing_extensions altair.typingr#altair.utils.corer$tuplerpr%__annotations__dictrr&rir4rgrnrCrrrrFrrGr1r1r1r3sb`      $       S  . 3 B A A%