a 1$eN @sddlmZddlZddlZddlZddlmZddlm Z ddl m Z m Z dd d Zd d ZddZddZeeeeeedddZdeeeeeeeeeeed ddZdS))OptionalN) GeoDataFrame)_compat) _check_crs_crs_mismatch_warninner intersectsleftrightc Ksd|vrb|d}d}|dkrN||krNd|d|d|} tj| tddntj|tdd|}|rtt|} td | d t |||||t |||} t | |||||} | S) a Spatial 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). 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 Ycoord Xcoord ... GonorrF GonorrM Tuberc 0 16 41.973266 -87.657073 ... 170.8 468.7 13.6 87 365 41.961707 -87.654058 ... 170.8 468.7 13.6 90 373 41.963131 -87.656352 ... 170.8 468.7 13.6 140 582 41.969131 -87.674882 ... 170.8 468.7 13.6 1 18 41.696367 -87.681315 ... 800.5 741.1 2.6 [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. opzwThe `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.rz@A non-default value for `predicate` was passed (got `predicate="z"` in combination with `op="zr"`). The value of `predicate` will be overridden by the value of `op`, , which may result in unexpected behavior.  stacklevelz,sjoin() got an unexpected keyword argument '') popwarningswarn UserWarning FutureWarningnextiterkeys TypeError _basic_checks_geom_predicate_query _frame_join) left_dfright_dfhow predicatelsuffixrsuffixkwargsr Zdeprecation_messageZoverride_messagefirstindicesjoinedr&Q/var/www/html/django/DPS/env/lib/python3.9/site-packages/geopandas/tools/sjoin.pysjoin s.S  r(cCst|tstdt|t|ts8tdt|gd}||vrXtd||t||spt||ddd|}d|}t|j ||gst|j ||grtd||d S) aChecks 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 z('left_df' should be GeoDataFrame, got {}z)'right_df' should be GeoDataFrame, got {})r r rz*`how` was "{}" but is expected to be in {}r r index_{}z:'{0}' and '{1}' cannot be names in the frames being joinedN) isinstancer ValueErrorformattyperranycolumnsisin)rrrr r!Z allowed_hows index_left index_rightr&r&r'rs2        rc CstHtddt|}|dkr6d}|j}|j}n |j}|j}Wdn1sV0Y|r|j||dd\}}t||d}ntjd d gt d }|dkr|j d d dd }|S) aRCompute geometric comparisons and get matching indices. Parameters ---------- left_df : GeoDataFrame right_df : GeoDataFrame predicate : string Binary predicate to query. Returns ------- DataFrame DataFrame with matching indices in columns named `_key_left` and `_key_right`. ignorez Generated spatial index is emptyZwithincontainsNF)rsort) _key_left _key_rightr6r7r/Zdtyper/) rcatch_warningsfilterwarningsrsindexgeometryquerypd DataFramefloatrename) rrrZoriginal_predicater<Z input_geomsl_idxr_idxr$r&r&r'rs( $rc sd}|jdd}z|jj}|j||_WnBtyrfddt|jjD}|jj}|j||_Yn0|}d}|jdd}z|jj} |j||_WnBtyfddt|jjD}|jj} |j||_Yn0|}|dkr|| d}|j |ddd j |j |j jd d d dd d fd |j d gd d } t |trp|| j_n|| j_n|dkr| d}|j |ddddj |j |j jd d dd dd d fd |j d gd d } t |tr|| j_n|| j_n||j |j jd d j |j |d ddddddd d fd |j dd gd d |j j} t |trv| | j_n| | j_| S)a2Join the GeoDataFrames at the DataFrame level. Parameters ---------- join_df : DataFrame Indices and join data returned by the geometric join. Must have columns `_key_left` and `_key_right` with integer indices representing the matches from `left_df` and `right_df` respectively. Additional columns may be included and will be copied to the resultant GeoDataFrame. left_df : GeoDataFrame right_df : GeoDataFrame lsuffix : string Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string Suffix to apply to overlapping column names (right GeoDataFrame). how : string The type of join to use on the DataFrame level. Returns ------- GeoDataFrame Joined GeoDataFrame. r)T)deepcs"g|]\}}dt|qSr)r,str.0posZix)r r&r' sz_frame_join..cs"g|]\}}dt|qSrFrGrI)r!r&r'rLsrr6) left_index right_index)Zaxisr7z_{})left_onrNsuffixesr )rMrNr)rrPrNrQr )rPrNr)rMZright_onrrQ)r,copyindexnamerBr enumeratenamesZ reset_indexZ set_indexmergeZdropr=r*listZ set_geometry) join_dfrrrr r!r1Zleft_index_namer2Zright_index_namer%r&)r r!r'rs                          r)rr max_distancerreturn_distance exclusivecCstjs tjrtjs tdtj|dk}|r:|j}|j}n |j}|j}|r|j|d|||d} |rp| \\} } } n | \} } d} |r| | } }t j | dd}| |||} }| dur| |} n | | } }t | || d}nt j gdt d}|S) NzKCurrently, only PyGEOS >= 0.10.0 or Shapely >= 2.0 supports `nearest_all`. r T)Z return_allrZr[r\Zstable)kind)r6r7 distancesr8)compatZUSE_SHAPELY_20Z USE_PYGEOSZ PYGEOS_GE_010NotImplementedErrorZINSTALL_PYGEOS_ERRORr<r=ZnearestnpZargsortr?r@rA)rrrZrr[r\Zuse_left_as_sindexr<r>resZ input_idxZtree_idxr^rCrDZ sort_orderrYr&r&r'_nearest_queryasL    rcF) rrrrZr r! distance_colr\returnc st||||||jjjdd|jjjdddu}t||||||} |r`| jdid} n | dt| |||||} |rfdd| jDg} | | } | 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. Requires Shapely >= 2.0. 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 1933554.350) 87 JEWEL OSCO UPTOWN MULTIPOINT (1168837.980 1929246.962) To include the distances: >>> groceries_w_communities = geopandas.sjoin_nearest(groceries, chicago, distance_col="distances") >>> groceries_w_communities[["Chain", "community", "distances"]].head(2) # doctest: +SKIP Chain community distances 0 VIET HOA PLAZA UPTOWN 0.0 87 JEWEL OSCO UPTOWN 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"]] # doctest: +SKIP 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. rOr Nr^r9csg|]}|kr|qSr&r&)rJcrdr&r'rL&z!sjoin_nearest..) rr=valuesZcheck_geographic_crsrcrBrrr/) rrrrZr r!rdr\r[rYr%r/r&rgr' sjoin_nearests|  rj)rrr r )rNr r NF)typingrrnumpyraZpandasr?Z geopandasrrr_Zgeopandas.arrayrrr(rrrrArHboolrcrjr&r&r&r'sL    t27y 9