o i(@sNddlZddlZddlmZddlmZddlmZddlmZm Z m Z ddl m Z ddl m Z ddlZddlZddlZdd lmZdd lmZdd lmZmZdd lmZmZmZGd ddZeGdddeZ eGdddeZ!eGdddeZ"GdddZ#GdddeZ$d%ddZ%d&ddZ&dd Z'd!d"Z(d'd#d$Z)dS)(Ncopy)partial) UserString)IterableSequenceMapping)Number)datetime)share_init_params_with_map)Version) QUAL_PALETTES color_palette)_check_argumentget_color_cycle remove_nac@sDeZdZdZdZdZdZddZddZddZ d d Z d d Z dS) SemanticMappingz6Base class for mapping data values to plot attributes.NcCs ||_dSNplotter)selfrrK/var/www/edux/Edux_v2/venv/lib/python3.10/site-packages/seaborn/_oldcore.py__init__+s zSemanticMapping.__init__cOs<d|jddd}t||||g|Ri||S)N_i_map)__name__lowersetattr)clsrargskwargs method_namerrrmap4szSemanticMapping.mapcCsd}t|t|kr/dd|dt|ddt|ddg}d d t|t|D}n&t|t|krUdd |d t|ddt|d g}|dt|}|r_tj|tdd|S)z/Input check when values are provided as a list. z The z list has fewer values ()z than needed (z) and will cycle, which mayz produce an uninterpretable plot.cSsg|]\}}|qSrr).0rxrrr Dz6SemanticMapping._check_list_length..The z list has more values (z), which may not be intended.N stacklevel)lenjoinzip itertoolscyclewarningswarn UserWarning)rlevelsvaluesvariablemessagerrr_check_list_length:s"z"SemanticMapping._check_list_lengthcCs |j|S)z)Apply the mapping to a single data value. lookup_table)rkeyrrr_lookup_singleRs zSemanticMapping._lookup_singlecsBt|ttjtjfrfdd|DSj|gRiS)z-Get the attribute(s) values for the data key.cs$g|]}j|gRiqSr)r@r(kr!r"rrrr*Ys$z,SemanticMapping.__call__..) isinstancelistnpndarraypdSeriesr@)rr?r!r"rrCr__call__VszSemanticMapping.__call__) r __module__ __qualname____doc__map_typer8r>rr$r<r@rJrrrrrs  rcsPeZdZdZdZdZdZ d fdd ZddZddZ d d Z d d Z Z S) HueMappingz9Mapping that sets artist colors according to data values.Nc st||jdtjtd}|r(|dur&d}t j |dddSdS| |||j |j d}|dkrIt|}||||\}} }} n!|dkr[d} }||||\}} nd} }|t|||\}} ||_| |_||_||_||_| |_dS) zMap the levels of the `hue` variable to distinct colors. Parameters ---------- # TODO add generic parameters huedtypeNz?Ignoring `palette` because no `hue` variable has been assigned.r.numeric categorical)superr plot_datagetrHrIfloatisnaallr5r6infer_map_type input_format var_types to_numericnumeric_mappingcategorical_mappingrErNr>paletter8normcmap) rrrborderrcdatamsgrNr8r>rd __class__rrrjs<      zHueMapping.__init__c Csz|j|}W|StyN|jdurYdSz||}Wnty<}zt|r.d}n|WYd}~Y|Sd}~wwtj|rFtj}| |}Y|Sw)z@Get the color for a single value, using colormap to interpolate.N)rrrr) r>KeyErrorrc TypeErrorrFisnanma is_maskednanrd)rr?valuenormederrrrrr@s*      zHueMapping._lookup_singlecCsN|tvrd}|S|durd}|St|ttfrd}|S|dkr#d}|S|}|S)z'Determine how to implement the mapping.rUNrTwide)rrDdictrE)rrbrcr]var_typerNrrrr\s zHueMapping.infer_map_typec Cst||}t|}t|tr)t|t|}t|r#d}t|||}||fS|dur@|ttkr:t d|} nt d|} nt|t rM| ||d} nt ||} tt || }||fS)z5Determine colors when the hue mapping is categorical.z*The palette dictionary is missing keys: {}Nhuslrb) categorical_orderr0rDrtsetany ValueErrorformatrrrEr<r2) rrfrbrer8n_colorsmissingrrr>colorsrrrras$      zHueMapping.categorical_mappingc s ttr!tt}fddtD}tj|}}n^tt t | }dur2dnttjj r>}nt dd}|durNtj}nt|trZtjj|}n t|tjjsgd}t||st|t|tt||||}||||fS)z7Determine colors when the hue variable is quantitative.csg|]}|qSrrrArbrrr*r+z.HueMapping.numeric_mapping..Nzch:T)as_cmapz6``hue_norm`` must be None, tuple, or Normalize object.)rDrtrEsortedmplr~ListedColormaprrFsortruniqueColormapr Normalizetuplerzscaledasarraydropnar2) rrfrbrcr8r~rdr>rrrrrr`s*        zHueMapping.numeric_mappingNNN) rrKrLrMrbrcrdrr@r\rar` __classcell__rrrhrrO^s@"rOcsHeZdZdZdZ d fdd ZddZddZd d Zd d Z Z S) SizeMappingz8Mapping that sets artist sizes according to data values.Nc st||jdtjtd}|rb| |||j d}|dkr1| |||\}}}} n|dkrA| |||\}}d} n | t |||\}}d} ||_||_||_||_| |_||_dSdS)zMap the levels of the `size` variable to distinct values. Parameters ---------- # TODO add generic parameters sizerQrTrUN)rVrrWrXrHrIrYnotnaryr\r^r`rarErNr8rcsizes size_ranger>) rrrrercrfrNr8r>rrhrrr)s4     zSizeMapping.__init__cCs.|durd}|St|ttfrd}|S|}|S)NrTrU)rDrtrE)rrcrrurNrrrr\aszSizeMapping.infer_map_typecCs\z|j|}W|Sty-||}tj|rtj}|jd|t|j}Y|Sw)Nr) r>rjrcrFrmrnrorptp)rr?rprqrrrr@ls   zSizeMapping._lookup_singlecCst||}t|tr't|t|}t|rd|}t||}||fSt|tr>|||d}tt ||}||fSt|t rPt |dkrOd}t|n|dur]d|}t||j j }tjg|t |Rddd}tt ||}||fS)Nz(Missing sizes for the following levels: r'A `sizes` tuple must have only 2 values"Value for `sizes` not understood: )rwrDrtrxryrzrrEr<r2rr0r_default_size_rangerFlinspace)rrfrrer8r}rrr>rrrraws0   - '  "zSizeMapping.categorical_mappingc CsJt|trttt|}|}t|t|f}n0ttt| }t|t r:t |dkr7d}t ||}n|durGd|}t ||j j}|durUtj}n t|t ratjj|}nt|tjjsqd|}t |t|}d|_|s||||}t|tr|} n|\} } | || | }tt||} || ||fS)Nrrrz0Value for size `norm` parameter not understood: T)rDrtrErFrr9minmaxrrrr0rzrrrr~rrcliprr2) rrfrrcr8 size_valuesrrr sizes_scaledr>lohirrrr`s@         zSizeMapping.numeric_mappingr) rrKrLrMrcrr\r@rar`rrrrhrr#s8  :rcs:eZdZdZdZ d fdd Zd ddZdd ZZS) StyleMappingz8Mapping that sets artist style according to data values.rUNcsRt||jdtjtd}|rt |dkr!t |}t ||}| ||t t|d}| ||tt|d}i}g}|D]#\} } t| tjjsWtj| } | | || <|| qFt|rxt|sxd} t| i} |D]"} i| | <|r|| | | d<|| | | d<|r|| | | d<q|||_| |_d Sd S) zMap the levels of the `style` variable to distinct values. Parameters ---------- # TODO add generic parameters stylerQr markersdashesz+Filled and line art markers cannot be mixedmarkerpathN)rVrrWrXrHrIrYrry variable_typerErw_map_attributesunique_markersr0 unique_dashesitemsrDrr MarkerStyleget_path transformed get_transformappend is_filledr[rzr8r>)rrrrrerfr8pathsfilled_markersrBmrrr>r?rhrrrsD      zStyleMapping.__init__cCs(|dur |j|}|S|j||}|S)z(Get attribute(s) for a given data point.Nr=)rr?attrrprrrr@Ds  zStyleMapping._lookup_singlecCs|dur tt||}|St|tr,t|t|}|r(d|d|}t||}|St|trA||||}tt||}|S|rOd|d|}t|i}|S)z5Handle the specification for a given style attribute.TzThese `z` levels are missing values: zThis `z` argument was not understood: )rtr2rDrxrzrr<)rargr8defaultsrr>r}rrrrrrLs&  zStyleMapping._map_attributesrr) rrKrLrMrNrr@rrrrrhrrs <rc@seZdZdZeeedZdZdddddZ dddZ d Z d ifd d Z e d1d dZeddZeddZd ifddZd1ddZd1ddZ d1ddddddddZeddZd d!Z d2d"d#Zd$d%Zd3d'd(Zd)d*Zd+d,Zd-d.Zd2d/d0Zd S)4 VectorPlotterz2Base class for objects underlying *plot functions.)rPrr)r)yrPrrunits@index@values@columns)r)rrPrr)r)r rNcCshi|_ddd|_||||jD]\}}t|j|d}t|d||t|d|qdS)NFrrmap_) _var_levels _var_orderedassign_variables_semantic_mappingsrrr$rgetattr)rrf variablesvarr map_funcrrrrxs  zVectorPlotter.__init__cCs@|dur|j}i}|D]\}}||vr|dur|||<q |S)zsz2VectorPlotter.assign_variables..)rXr]_assign_variables_wideform_assign_variables_longformrWrr^)rrfrr)rrrrrs*     zVectorPlotter.assign_variablescKsdd|D}t|r-t|dkrdnd}d|d}|dd d |D7}t||d up5t| }t|tr@|}n t tj |t d }td d |D }|r`t } i} | | fS|rt |} | j| jjd} i} i} dD] } | |jvr|j| }t| |dd | | <| |j| | | <qwt | } | | fSt|tri}t|D]\}} t| d|}t | ||<q|}t|trdd|D}t j |dd}dd|D}||}ddd}d|jv}|rd|d<z|jj}|jj}|jd|_Wn tyd}Ynwd}|j|d<|jdi|} |r5|r5t | d||| d<|jD] \} }| || | <q:i} |jD]\} }t||dd }t|dd | | <qM| t | } | | fS)a5Define plot variables given wide-form data. Parameters ---------- data : flat vector or collection of vectors Data can be a vector or mapping that is coerceable to a Series or a sequence- or mapping-based collection of such vectors, or a rectangular numpy array, or a Pandas DataFrame. kwargs : variable -> data mappings Behavior with keyword arguments is currently undefined. Returns ------- plot_data : :class:`pandas.DataFrame` Long-form data object mapping seaborn variables (x, y, hue, ...) to data vectors. variables : dict Keys are defined seaborn variables; values are names inferred from the inputs (or None when no name can be determined). cSsg|] \}}|dur|qSrrr(rBrrrrr*z.r sr%zThe following variablez) cannot be assigned with wide-form data: z, css|] }d|dVqdS)`Nrrrrr sz;VectorPlotter._assign_variables_wideform..NrQcss*|]}t|tot|ttf VqdSr)rDrstrbytesrrrrrs  )rrrnamecSsi|] \}}|t|qSr)rHrI)r(r?rrrrr(rz.TrcSs g|] \}}t|dkr|qSrTrrrrrr*1srr)var_name value_namerid_varsFr)!rryr0r1rzrDrtr9rF atleast_1drobjectrH DataFramerIrrindexflat_structurerr enumeraterwide_structurecolumns categoriesorderedadd_categoriesr to_seriesmelt CategoricalrE)rrfr"assignedrrremptyr9flatrWr flat_datanamesrr data_dictir? wide_data numeric_colsmelt_kws use_indexorig_categories orig_orderedcategory_columnsobjrrrrs    ]   G       z(VectorPlotter._assign_variables_wideformc sii}|dur i}z|j}Wn tyi}Ynw|D]\}}z||vp4t|ttfo4||v}Wn ttfyCd}Ynw|r`||vrQ|||<n ||vr[|||<|||<q!t|ttfrtd|d|d}t |t|t j rt|t j st |rt|t|kr|jj} d| dt|d|d t|d }t |||<t|d d||<q!t fd d |D}|fS)aDefine plot variables given long-form data and/or vector inputs. Parameters ---------- data : dict-like collection of vectors Input data where variable names map to vector values. kwargs : variable -> data mappings Keys are seaborn variables (x, y, hue, ...) and values are vectors in any format that can construct a :class:`pandas.DataFrame` or names of columns or index levels in ``data``. Returns ------- plot_data : :class:`pandas.DataFrame` Long-form data object mapping seaborn variables (x, y, hue, ...) to data vectors. variables : dict Keys are defined seaborn variables; values are names inferred from the inputs (or None when no name can be determined). Raises ------ ValueError When variables are strings that don't appear in ``data``. NFzCould not interpret value `z` for parameter `rz Length of zO vectors must match length of `data` when both are used, but `data` has length z and the vector passed to `z ` has length .rcs&i|]\}}|r||qSr)notnullry)r(rrrrrrs z.)rto_framerrrDrrrjrkrzrHrrIrFndimr0rirr) rrfr"rrr?rval_as_data_keyrrval_clsrrrr[sZ      z(VectorPlotter._assign_variables_longformFT)reversefrom_comp_databy_facet allow_emptyrc #s.|durg}nt|tr|g}n t|trt|}|r.ddh}||tjt|@fdd|D}|r=j}nj}|rF| }j } |rddht|@D]S} j | dkrj | ruj| jd } | | | | | <qUt|| | | <qUj | d krtj| | | | <qUj | d kr| rt| | | | <qU|r|j|d d d } g} |D] }| | |gqtj| }|rtt|}|D]8}t|dkr|d n|}z| |}Wnt!y|j"g}Ynw|j#r|sqt$t%||}|| fVqdSi| fVdS)aGenerator for getting subsets of data defined by semantic variables. Also injects "col" and "row" into grouping semantics. Parameters ---------- grouping_vars : string or list of strings Semantic variables that define the subsets of data. reverse : bool If True, reverse the order of iteration. from_comp_data : bool If True, use self.comp_data rather than self.plot_data by_facet : bool If True, add faceting variables to the set of grouping variables. allow_empty : bool If True, yield an empty dataframe when no observations exist for combinations of grouping variables. dropna : bool If True, remove rows with missing data. Yields ------ sub_vars : dict Keys are semantic names, values are the level of that semantic. sub_data : :class:`pandas.DataFrame` Subset of ``plot_data`` for this combination of semantic values. Ncolrowcsg|] }|jvr|qSr)r)r(rrrrr*sz+VectorPlotter.iter_data..r)rrUrr rTF)ras_indexr )&rDrrrEextendrxr comp_datarWrrrr^r convertersiloc convert_unitsrFrrrdatesdate2num _log_scaledlog10groupbyrrXr3productreversedr0 get_grouprjlocrrtr2)r grouping_varsr r r r r facet_varsrfr8axis converter grouped_data grouping_keysr iter_keysr?pd_key data_subsetsub_varsrrr iter_datasp#         zVectorPlotter.iter_datac CsDt|ds|jSt|ds|jjddjddgddd }d D]}}||jvr&qg}|j|j|j|dd }|D]M\}}td d | }||j vrU|| |j |}Wdn1s_wYt | |}|dkrwt|}|tj||j|jdq7|rt|}ntjt|d}|d||q||_|jS)zFDataframe with numeric x and y, after unit conversion and log scaling.ax _comp_dataF)deepr)rr ignore)r!errorsyxrzmode.use_inf_as_naTNlog)r)rRrr)hasattrrWrdroprrrrHoption_contextrrisinr_r get_scalerFrrrIrrconcatrYinsertr+) rrrpartsgroupedr"origcompcomp_colrrrrFs>         zVectorPlotter.comp_datacCsx|dd}|dd}|dur|dur|jj||fS|dur&|jj|S|dur0|jj|S|jdur9|jjS|jS)z>Return an Axes object based on existence of row/col variables.rNr)rXfacets axes_dictr*)rr(rrrrr _get_axests     zVectorPlotter._get_axesc Csddlm}t||r-d|_||_|j}|jdur!|j|jd<|j dur,|j |jd<n ||_d|_|g}t d |j }|durGgd}nt|t rO|g}|D]}|j|}||vrld|d |d |d } t| qQddd } i|_|D]}d dd |} tj|jj|td} t|jd|d} | dus| | | krt|d|d| jdd<nS| dur|D]\}}||}t||d| j|j<qn4t|j| d}t|D]&\}}| dkr|dfnd|f}t|jj||d}|| j|j| |k<q| |j|<|j|j|j|dd}|D]%\} }|j|dkr4|j|r-|j|}nd}t||}| |qqw|durFd}}n%z|\}}Wntyjd|j vr\|nd}d |j vrf|nd}Ynwtd||fD]@\}}|r|D]5}t|d|d}|dur|dq{t t!j"t dkr|d|dq{|did||iq{qr|j#d ddkr|D]!}z |j$%dWqt&y|'s|(YqwdSdS)aAssociate the plotter with an Axes manager and initialize its units. Parameters ---------- obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid` Structural object that we will eventually plot onto. allowed_types : str or list of str If provided, raise when either the x or y variable does not have one of the declared seaborn types. log_scale : bool, number, or pair of bools or numbers If not False, set the axes to use log scaling, with the given base or defaulting to 10. If a tuple, interpreted as separate arguments for the x and y axes. r ) FacetGridNrrrrTr rUr,z variable is z , but one of z is requiredrrr))rrrR_shareTrr!F_namesr0rUset_scaler1z3.3)baserG)r1))axisgridrArDr*r>axesflatten col_namesr row_namesrx intersectionrrr^rkrrHrIrWrrrrr)r@rrrrw update_unitsr2r r __version__rXyaxis set_invertedryaxis_inverted invert_yaxis)rr allowed_types log_scalerAax_listaxis_variablesrrurr facet_dim other_varr" share_state axes_vars axes_datar*rrlevelidxr!r: seed_datarescalexscaleyrF set_scalerrr_attachs                        zVectorPlotter._attachcCs~t|dsdS|jdur|jj}n|jg}g}|D]}t||d}||dkqt|r;t |s;t dt|S)zAReturn True if specified axis is log scaled on all attached axes.r*FNr!r1zAxis scaling is not consistent) r2r*r>rIrJrrr6ryr[ RuntimeError)rr! axes_list log_scaledr* data_axisrrrrs  zVectorPlotter._log_scaledr%cCst|stdd|D}|j|jd||d|s8tdd|D}|j|jd||ddSdS)zCAdd axis labels if not present, set visibility to match ticklabels.cs|]}|VqdSr get_visibler(trrrr0z1VectorPlotter._add_axis_labels..r))visiblecsrhrrirkrrrr3rmrN) get_xlabelryget_xticklabels set_xlabelrrX get_ylabelget_yticklabels set_ylabel)rr* default_x default_y x_visible y_visiblerrr_add_axis_labels)szVectorPlotter._add_axis_labelscOtrNotImplementedErrorrr!r!r"rrr scale_native;szVectorPlotter.scale_nativecOrzrr{r}rrr scale_numericAzVectorPlotter.scale_numericcOrzrr{r}rrrscale_datetimeHrzVectorPlotter.scale_datetimecCstdddg|||jvrd|j|<d|j|<d|j|<|j|dkr,|jj|dd |_|j|}|dup:|jjd k|j|<t t ||}|durU| |}| |}n | t }| t }||j|<d|j|<||j|<|S) a Enforce categorical (fixed-scale) rules for the data on given axis. Parameters ---------- axis : "x" or "y" Axis of the plot to operate on. order : list Order that unique values should appear in. formatter : callable Function mapping values to a string representation. Returns ------- self r!r)rNrUr%rT mergesort)kindcategory)rrr^rW sort_valuesrRrrrHIndexrwr$astyperr)rr!re formattercat_datarrrscale_categoricalOs&*             zVectorPlotter.scale_categoricalr)NN)r%r%)rrKrLrMrOrrrrrrrr classmethodrpropertyrrrrrr)rr@rcrryr~rrrrrrrresP      s y -  rcs,eZdZdZdZfddZddZZS) VariableTypez Prevent comparisons elsewhere in the library from using the wrong name. Errors are simple assertions because users should not be able to trigger them. If that changes, they should be more verbose. rBcs"||jvs J|t|dSr)allowedrVr)rrfrhrrrszVariableType.__init__cCs||jvs J||j|kSr)rrf)rotherrrr__eq__s zVariableType.__eq__)rrKrLrMrrrrrrrhrrs  rrTcCstjj|r tdSt|rtdSt(tj dt t fdt |ddt jgr;t|WdSWdn1sEwYtjj|rUtdStjj|r`tdSd d }||rltdSd d }||rxtdStdS) a+ Determine whether a vector contains numeric, categorical, or datetime data. This function differs from the pandas typing API in two ways: - Python sequences or object-typed PyData objects are considered numeric if all of their entries are numeric. - String or mixed-type data are considered categorical even if not explicitly represented as a :class:`pandas.api.types.CategoricalDtype`. Parameters ---------- vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence Input data to test. boolean_type : 'numeric' or 'categorical' Type to use for vectors containing only 0s and 1s (and NAs). Returns ------- var_type : 'numeric', 'categorical', or 'datetime' Name identifying the type of data in the vector. rUrTr-)actionrrr Nr cSs|D] }t|ts dSqdSNFT)rDr r)x_irrr all_numerics  z"variable_type..all_numericcSs$|D] }t|ttjfsdSqdSr)rDr rF datetime64rrrr all_datetimes z#variable_type..all_datetime)rHapitypesis_categorical_dtyperrZr[r5catch_warnings simplefilter FutureWarningDeprecationWarningrFr5rois_numeric_dtypeis_datetime64_dtype)vectorrrrrrrrs0 rTc Cs|durdnt|}|durdnt|}d}d}|dur.) r0r3combinations_with_replacementchainr2rErrr)nrpab segment_listsegmentsspecrrrrfs    rcCszgd}d}t||kr7d|dd}||dd|f|dd|f|ddf|ddfg|d7}t||ks |d|S)aWBuild an arbitrarily long list of unique marker styles for points. Parameters ---------- n : int Number of unique marker specs to generate. Returns ------- markers : list of string or tuples Values for defining :class:`matplotlib.markers.MarkerStyle` objects. All markers will be filled. ) oX)rSr-P)rSrr)rSr r^)rSr rrrihr rrN)r0r)rrrrrrrrs    rc Cs|durEt|dr |j}n2z|jj}Wn+ttfy>z|}Wnty0t|}Ynwt|dkrsJ       AE^cN  SR3.