h[dHddlZddlmZddlZddlZddlmZddl m Z ddl m Z m Z  ddZd Zd d Zd d Zd Zdej&dej&fd ZdZdZ d dZ d dedededededededzfdZdZ d!dededededzdedededzdedefdZy)"N)partial) GeoDataFrame) PANDAS_GE_30) _check_crs_crs_mismatch_warnleftrightc |r1tt|j} td| dt |}t ||||||t |||||} t||| d||||| \} } | S)aSpatial join of two GeoDataFrames. See the User Guide page :doc:`../../user_guide/mergingdata` for details. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column predicate : string, default 'intersects' Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as ``left_df.sindex.valid_query_predicates`` or ``right_df.sindex.valid_query_predicates`` Replaces deprecated ``op`` parameter. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance : number or array_like, optional Distance(s) around each input geometry within which to query the tree for the 'dwithin' predicate. If array_like, must be one-dimesional with length equal to length of left GeoDataFrame. Required if ``predicate='dwithin'``. on_attribute : string, list or tuple Column name(s) to join on as an additional join restriction on top of the spatial predicate. These must be found in both DataFrames. If set, observations are joined only if the predicate applies and values in specified columns match. Examples -------- >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> chicago.head() # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... 2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801... 3 38 ... POLYGON ((-87.60671 41.81681, -87.60670 41.816... 4 39 ... POLYGON ((-87.59215 41.81693, -87.59215 41.816... [5 rows x 87 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT (-87.65661 41.97321) 1 18 41.696367 ... NaN MULTIPOINT (-87.68136 41.69713) 2 22 41.868634 ... NaN MULTIPOINT (-87.63918 41.86847) 3 23 41.877590 ... new MULTIPOINT (-87.65495 41.87783) 4 27 41.737696 ... NaN MULTIPOINT (-87.62715 41.73623) [5 rows x 8 columns] >>> groceries_w_communities = geopandas.sjoin(groceries, chicago) >>> groceries_w_communities.head() # doctest: +SKIP OBJECTID community geometry 0 16 UPTOWN MULTIPOINT ((-87.65661 41.97321)) 1 18 MORGAN PARK MULTIPOINT ((-87.68136 41.69713)) 2 22 NEAR WEST SIDE MULTIPOINT ((-87.63918 41.86847)) 3 23 NEAR WEST SIDE MULTIPOINT ((-87.65495 41.87783)) 4 27 CHATHAM MULTIPOINT ((-87.62715 41.73623)) [5 rows x 95 columns] See Also -------- overlay : overlay operation resulting in a new geometry GeoDataFrame.sjoin : equivalent method Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. z,sjoin() got an unexpected keyword argument '') on_attributeN)nextiterkeys TypeError_maybe_make_list _basic_checks_geom_predicate_query _frame_join) left_dfright_dfhow predicatelsuffixrsuffixdistancer kwargsfirstindicesjoined_s H/var/www/html/immo/lib/python3.12/site-packages/geopandas/tools/sjoin.pysjoinr" s~T&++-()FugQOPP#L1L'8S'7V#9h\G  ! IFA Mcft|tr t|S|t|ts|gS|SN) isinstancetuplelist)objs r!rrs0#uCy z#t4u Jr#ct|tstdt|t|tstdt|gd}||vrtd|d|t ||st ||d|r~|D]x}||vr||vrtd|d ||vrtd|d ||vrtd|d ||j j|j jfvsotd y y )a2Check the validity of join input parameters. `how` must be one of the valid options. `'index_'` concatenated with `lsuffix` or `rsuffix` must not already exist as columns in the left or right data frames. Parameters ---------- left_df : GeoDataFrame right_df : GeoData Frame how : str, one of 'left', 'right', 'inner' join type lsuffix : str left index suffix rsuffix : str right index suffix on_attribute : list, default None list of column names to merge on along with geometry z&'left_df' should be GeoDataFrame, got z''right_df' should be GeoDataFrame, got )rr innerz `how` was "z" but is expected to be in  stacklevelzExpected column z( is missing from both of the dataframes.z$ is missing from the left dataframe.z% is missing from the right dataframe.zMActive geometry column cannot be used as an input for on_attribute parameter.N)r&r ValueErrortyperrgeometryname)rrrrrr allowed_howsattrs r!rrsD( g| ,A$w-QRR h -B4>BRSTT-L ,;se+F|nUVV gx (7H;  DG#$h*> &tf,TU7" &tf,PQ8# &tf,QR((--x/@/@/E/EFF 2 r#c|}|dk(rd}|j}|j}n|j}|j}|r|j||d|\}} nJtjgtj tjgtj } }|dk(r%|| }} tj | |f} || }| | } |r|D]} t|||| | \\}} } || fS)aCompute geometric comparisons and get matching indices. Parameters ---------- left_df : GeoDataFrame right_df : GeoDataFrame predicate : string Binary predicate to query. on_attribute: list, default None list of column names to merge on along with geometry Returns ------- DataFrame DataFrame with matching indices in columns named `_key_left` and `_key_right`. withincontainsF)rsortrdtype)sindexr1querynparrayintplexsort_filter_shared_attribute) rrrrr original_predicater; input_geomsl_idxr_idxindexerr4r s r!rrs&#H '' &&  || 958$ u xx"''2BHHRrww4OuX%eu**eU^,gg  D 85%! NUEA  %<r#c|jj}tr|j}n|}|jd|jj d}t |D]b\}}| ||}d|vr|jdd} d|| }nd|}||jvs||jvrtd|d |||<d|tj|fS) zf Equivalent of df.reset_index(), but with adding 'suffix' to auto-generated column names. Tinplacecopylevelr index_r z4' cannot be a column name in the frames being joined) indexnamesr reset_indexcolumnsto_numpy enumeratesplitr/pdIndex) dfsuffixotherindex_originaldf_reset column_namesilabel new_labellevs r!_reset_index_with_suffixrbs XX^^N>>#T*##,,$,7Ln-(5 =$QI)#ooc*1-$VHSE2 $VH- BJJ&)u}}*D  {"VW(LO( RXXl+ ++r#c |j|tdk(r||fS|\}}|s|stdfd}t||t |dd}t||t |dd} t j |D cgc] } ||  c} } t j |D cgc] } | |  c} } g} | js3| | j|jzj} | jsB| j| | j|jzj| r)tjdt| dtd | | fScc} wcc} w) z Add suffixes to overlapping labels (ignoring the geometry column). This is based on pandas' merge logic at https://github.com/pandas-dev/pandas/blob/ a0779adb183345a8eb4be58b3ad00c223da58768/pandas/core/reshape/merge.py#L2300-L2370 rz)columns overlap but no suffix specified: c,|vr||k7r ||d|S|S)Nr )xrYr1 to_renames r!renamerz2_process_column_names_with_suffix..renamer1s* >a8m0BS&? "r#_geometry_column_nameN)rYr1z1Passing 'suffixes' which cause duplicate columns zM in the result is deprecated and will raise a MergeError in a future version.r,r-) intersectionlenr/rgetattrrVrW is_unique duplicatedtolistextendwarningswarnset FutureWarning)rr suffixesrrrrrhlrenamerrrenamerlab left_renamed right_renameddupsrgs @r!!_process_column_names_with_suffixr|s!!%(I 9~U{GW 7DYKPQQ "94@H #:DAH88d;sXc];M D  ! !\446DOO9 $$r#c n|r|j|d}|dvr(|j|jjd}n'|j|jjd}|jd}|jj } |jj } t|||\}} |jd}|jj } |jj } t|||\}}t| |||f||\} }| |_ ||_ |jd| }|jd| }|dk(r t|n t|}t|||||\\}}}tjt|}|jd||fi}|jd||fi}tri}n t!d }tj"||gfd di|}|dvrt%||| }||fS|j'|jj}t%||| }||fS) abJoin the GeoDataFrames at the DataFrame level. Parameters ---------- left_df : GeoDataFrame right_df : GeoDataFrame indices : tuple of ndarray Indices returned by the geometric join. Tuple with with integer indices representing the matches from `left_df` and `right_df` respectively. distances : ndarray, optional Passed trough and adapted based on the indices, if needed. how : string The type of join to use on the DataFrame level. lsuffix : string Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string Suffix to apply to overlapping column names (right GeoDataFrame). on_attribute: list, default None list of column names to merge on along with geometry Returns ------- GeoDataFrame Joined GeoDataFrame. rM)axis)r+rF)deepNr rrJr)dropr1r2rKrOnlevelsrPrbr|rRrkrrV RangeIndex_reindex_with_indexersrdictconcatr set_geometry)rrrrrrrrr left_nlevelsleft_index_originalleft_column_names right_nlevelsright_index_originalright_column_names left_index right_indexrrDrE new_indexrr rrs r!rrsHL==A=6 ==!2!2!7!7a=@,,w//441,=lll&G==((L!----!9'7H!UG }}%}(HNN**M#>>//#;Hgw#W H -N ' -))(GO)H,/J"">M2K(+g~c(m3wr?float64rA)rrrrrrr use_left_as_sindexr;r<res input_idxtree_idxrrDrE sort_orderr4shared_attribute_rowss r!_nearest_queryrs]!!   nn %+   /2 , !Y9$' !YI #Y5EE9J ,eJ.?5E$%j1 $h5Exx"''2BHHRrww4Ou 2::6II  9D4L5%5 1NUE1""78I  9 5>9 $$r#c||j|j||j|jk(}||}||}||f|fS)zReturn the indices for the left and right dataframe that share the same entry in the attribute column. Also returns a Boolean `shared_attribute_rows` for rows with the same entry. )ilocvalues)rrrDrE attributers r!rArA.se  &--)1D1I1I%1P1W1WW ' (E ' (E 5>0 00r#rr distance_colreturnc "t||||||jjjd|jjjd|du}t ||||||\} } t ||| | |||d\} } |r| | |<| S)aSpatial join of two GeoDataFrames based on the distance between their geometries. Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors. Distance is calculated in CRS units and can be returned using the `distance_col` parameter. See the User Guide page https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html for more details. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column max_distance : float, default None Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance_col : string, default None If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame. exclusive : bool, default False If True, the nearest geometries that are equal to the input geometry will not be returned, default False. Examples -------- >>> import geodatasets >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ) >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ).to_crs(groceries.crs) >>> chicago.head() # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... 2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801... 3 38 ... POLYGON ((-87.60671 41.81681, -87.60670 41.816... 4 39 ... POLYGON ((-87.59215 41.81693, -87.59215 41.816... [5 rows x 87 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT ((-87.65661 41.97321)) 1 18 41.696367 ... NaN MULTIPOINT ((-87.68136 41.69713)) 2 22 41.868634 ... NaN MULTIPOINT ((-87.63918 41.86847)) 3 23 41.877590 ... new MULTIPOINT ((-87.65495 41.87783)) 4 27 41.737696 ... NaN MULTIPOINT ((-87.62715 41.73623)) [5 rows x 8 columns] >>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago) >>> groceries_w_communities[["Chain", "community", "geometry"]].head(2) Chain community geometry 0 VIET HOA PLAZA UPTOWN MULTIPOINT ((1168268.672 1933554.35)) 1 COUNTY FAIR FOODS MORGAN PARK MULTIPOINT ((1162302.618 1832900.224)) To include the distances: >>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago, distance_col="distances") >>> groceries_w_communities[["Chain", "community", "distances"]].head(2) Chain community distances 0 VIET HOA PLAZA UPTOWN 0.0 1 COUNTY FAIR FOODS MORGAN PARK 0.0 In the following example, we get multiple groceries for Uptown because all results are equidistant (in this case zero because they intersect). In fact, we get 4 results in total: >>> chicago_w_groceries = geopandas.sjoin_nearest(groceries, chicago, distance_col="distances", how="right") >>> uptown_results = chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"] >>> uptown_results[["Chain", "community"]] Chain community 30 VIET HOA PLAZA UPTOWN 30 JEWEL OSCO UPTOWN 30 TARGET UPTOWN 30 Mariano's UPTOWN See Also -------- sjoin : binary predicate joins GeoDataFrame.sjoin_nearest : equivalent method Notes ----- Since this join relies on distances, results will be inaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. rMr-N)rr1rcheck_geographic_crsrr) rrrrrrrrrrrrs r! sjoin_nearestr=sv'8S'7; 00A0> 11Q1?"$.O'  GY$   FI(| Mr#)r+ intersectsrr NNr%)r+Nrr NF)rq functoolsrnumpyr=pandasrV geopandasrgeopandas._compatrgeopandas.arrayrrr"rrrrbrWr|rrrfloatstrboolr(rrArrer#r!rsX"*:    wt3l8v,@8' ((8'888'v&'%f\L!%5% 5%5%5% 5%  5%  5%+5%p 1$!%#X XX X$, X  X  X*XXXr#