!\iaSrSSKJr SSKrSSKrSSKrSSKrSSKrSSK r SSK J r SSK J r SSK JrJrJrJr SSKJr SSKJrJr SS KJr SS KJr SS KJr SS KJr SS KJrJ r SSK!J"r" \RF"\$5r%\SSj5r&\SS\4Sj5r'\SSj5r(\SSSSS\S\RRSSSSS4 Sj5r*SSjr+SSjr,Sr-SSjr.g) z8Functions for working with features in a raster dataset.) ExitStackN)warp) DatasetBase)_shapes_sieve _rasterize_bounds)MergeAlg) ensure_env GDALVersion)ShapeSkipWarning) DatasetWriter)coords)Affine)IDENTITYguard_transform)WindowFc VU(aSOSupV[UUUUUUS9RS5$)aCreate a mask from shapes. By default, mask is intended for use as a numpy mask, where pixels that overlap shapes are False. Parameters ---------- geometries : iterable over geometries (GeoJSON-like objects) out_shape : tuple or list Shape of output :class:`numpy.ndarray`. transform : Affine transformation object Transformation from pixel coordinates of `source` to the coordinate system of the input `shapes`. See the `transform` property of dataset objects. all_touched : boolean, optional If True, all pixels touched by geometries will be burned in. If False, only pixels whose center is within the polygon or that are selected by Bresenham's line algorithm will be burned in. False by default invert: boolean, optional If True, mask will be True for pixels that overlap shapes. False by default. Returns ------- numpy.ndarray : Type is :class:`numpy.bool_` Notes ----- See rasterize() for performance notes. )r)rr) out_shape transform all_touchedfill default_valuebool) rasterizeastype) geometriesrrrinvertr mask_values T/var/www/html/kml_chatgpt/mouzaenv/lib/python3.13/site-packages/rasterio/features.py geometry_maskr"s;P"(vVD    " #)&. 1c## [US5(aUcUR)nURn[U5n[ XX#5ShvN gN7f)a)Get shapes and values of connected regions in a dataset or array. Parameters ---------- source : numpy.ndarray, dataset object, Band, or tuple(dataset, bidx) Data type must be one of rasterio.int8, rasterio.int16, rasterio.int32, rasterio.uint8, rasterio.uint16, rasterio.float32, or rasterio.float64. mask : numpy.ndarray or rasterio Band object, optional Must evaluate to bool (rasterio.bool_ or rasterio.uint8). Values of False or 0 will be excluded from feature generation. Note well that this is the inverse sense from Numpy's, where a mask value of True indicates invalid data in an array. If `source` is a :class:`numpy.ma.MaskedArray` and `mask` is None, the source's mask will be inverted and used in place of `mask`. connectivity : int, optional Use 4 or 8 pixel connectivity for grouping pixels into features transform : Affine transformation, optional If not provided, feature coordinates will be generated based on pixel coordinates Yields ------- polygon, value A pair of (polygon, value) for each feature found in the image. Polygons are GeoJSON-like dicts and the values are the associated value from the image, in the data type of the image. Note: due to floating point precision issues, values returned from a floating point image may not exactly match the original values. Notes ----- The amount of memory used by this algorithm is proportional to the number and complexity of polygons produced. This algorithm is most appropriate for simple thematic data. Data with high pixel-to-pixel variability, such as imagery, may produce one polygon per pixel and consume large amounts of memory. Because the low-level implementation uses either an int32 or float32 buffer, uint32 and float64 data cannot be operated on without truncation issues. maskN)hasattrr&datarr)sourcer& connectivityrs r!shapesr+NsGZvv4< | *Iv\===sAA A Ac[U[5(a [R"XR5nUc+[ R "URUR5n[XX#U5$)a)Remove small polygon regions from a raster. Polygons are found for each set of neighboring pixels of the same value. Parameters ---------- source : ndarray, dataset, or Band The source is a 2 or 3-D ndarray, a dataset opened in "r" mode, or a single or a multiple Rasterio Band object. Must be of type rasterio.int16, rasterio.int32, rasterio.uint8, rasterio.uint16, or rasterio.float32 size : int minimum polygon size (number of pixels) to retain. out : numpy ndarray, optional Array of same shape and data type as `source` in which to store results. mask : numpy ndarray or rasterio Band object, optional Values of False or 0 will be excluded from feature generation Must evaluate to bool (rasterio.bool_ or rasterio.uint8) connectivity : int, optional Use 4 or 8 pixel connectivity for grouping pixels into features Returns ------- out : numpy.ndarray Result Notes ----- GDAL only supports values that can be cast to 32-bit integers for this operation. The amount of memory used by this algorithm is proportional to the number and complexity of polygons found in the image. This algorithm is most appropriate for simple thematic data. Data with high pixel-to-pixel variability, such as imagery, may produce one polygon per pixel and consume large amounts of memory. ) isinstancerrasteriobandindexesnpzerosshapedtyper)r)sizeoutr&r*s r!siever7sPT&+&&v~~6 {hhv||V\\2 &< 88r#rTcSn[R"5RS5(aUS- nUbURRn O%U b![ R"U 5Rn OU bX;a [ S5e/n/n[U5GHunn[U[[45(a UunnUcUnOUnU n[USS5=(d Un[U5(a|URU5 USnUS:Xa!US HnURUU45 M MUS :Xa$US HnURS US .U45 M MURUU45 MU (a.[R"SR!UU5["5 GM[ S5e U (dU(a[ R$"U5nURRn X;a_U R'S5(aIUR)5UR+5nnSU::a US::aSn OSU::a US::aSn O [ S5e[-5nU b][U [.5(aU nO[1S#0U D6nUbUUS'U bU US'[2R4"U S40UD6nUR7U5 ORUbONUb@[9U5S:wa [ S5e[ R:"XS9nUR=U5 O [ S5e[)UR>5S:Xa [ S5e[AU5nU(a [CXXgU5 [U[ RD5(aAU(a/[GUS 5(d[ RHRKXSS!S"9nUsSSS5 $SSS5 g!,(df  g=f)$a Return an image array with input geometries burned in. Warnings will be raised for any invalid or empty geometries, and an exception will be raised if there are no valid shapes to rasterize. Parameters ---------- shapes : iterable of (`geometry`, `value`) pairs or geometries The `geometry` can either be an object that implements the geo interface or GeoJSON-like object. If no `value` is provided the `default_value` will be used. If `value` is `None` the `fill` value will be used. out_shape : tuple or list with 2 integers Shape of output :class:`numpy.ndarray`. fill : int or float, optional Used as fill value for all areas not covered by input geometries. nodata: float, optional nodata value to use in output file or masked array. masked: bool, optional. Default: False. If True, return a masked array. Note: nodata is always set in the case of file output. out : numpy.ndarray, optional Array in which to store results. If not provided, out_shape and dtype are required. transform : Affine transformation object, optional Transformation from pixel coordinates of `source` to the coordinate system of the input `shapes`. See the `transform` property of dataset objects. all_touched : boolean, optional If True, all pixels touched by geometries will be burned in. If false, only pixels whose center is within the polygon or that are selected by Bresenham's line algorithm will be burned in. merge_alg : MergeAlg, optional Merge algorithm to use. One of: MergeAlg.replace (default): the new value will overwrite the existing value. MergeAlg.add: the new value will be added to the existing raster. default_value : int or float, optional Used as value for all geometries, if not provided in `shapes`. dtype : rasterio or numpy.dtype, optional Used as data type for results, if `out` is not provided. skip_invalid : bool, optional If True (default), invalid shapes will be skipped. If False, ValueError will be raised. dst_path : str or PathLike, optional Path of output dataset dst_kwds : dict, optional Dictionary of creation options and other parameters that will be overlaid on the profile of the output dataset. Returns ------- numpy.ndarray : If `out` was not None then `out` is returned, it will have been modified in-place. If `out` was None, this will be a new array. Notes ----- Valid data types for `fill`, `default_value`, `out`, `dtype` and shape values are "int16", "int32", "uint8", "uint16", "uint32", "float32", and "float64". This function requires significant memory resources. The shapes iterator will be materialized to a Python list and another C copy of that list will be made. The `out` array will be copied and additional temporary raster memory equal to 2x the smaller of `out` data or GDAL's max cache size (controlled by GDAL_CACHEMAX, default is 5% of the computer's physical memory) is required. If GDAL max cache size is smaller than the output data, the array of shapes will be iterated multiple times. Performance is thus a linear function of buffer size. For maximum speed, ensure that GDAL_CACHEMAX is larger than the size of `out` or `out_shape`. ) int16int32int64uint8uint16uint32uint64float32float64z3.7)int8NzEData type specified by out array or dtype parameter is not supported.__geo_interface__typeGeometryCollectionr MultiPolygon coordinatesPolygon)rDrGz=Invalid or empty shape {} at index {} will not be rasterized.z,Invalid or empty shape cannot be rasterized.intiir:rlr>z2GDAL versions < 3.6 cannot rasterize int64 values.nodatar4wzInvalid out_shape, must be 2Dr4z-Either an out_shape or image must be providedzwidth and height must be > 0r&F)copy)&r runtimeat_leastr4namer1 ValueError enumerater-tuplelistgetattr is_valid_geomappendwarningswarnformatr array startswithminmaxrrdictr.open enter_contextlenemptyrr3rrndarrayr'ma masked_equal)r+rrrJmaskedr6rr merge_algrr4 skip_invaliddst_pathdst_kwds valid_dtypes valid_shapes shape_valuesindexitemgeomvalue geom_typepartpoly values_arrlohi exit_stack out_profiles r!rrsz|L%%e,, !     $$  U6 S  LL ( t dUDM * *KD%}D!Et0$7?4       &V I00 !.D ''u 6/n, /D '')D)QSX(YZ0##T5M2 SZZe% !!OPPS)Z \XXl+   %%  $)9)9%)@)@^^%z~~'7Bb R:%5bR:-  !UVV   (M22".X. %,2K)$+0K(mmHcA[A((- _   "9~" !@AA((92C HHTNLM M syy>Q ;< <#I.  |)) L c2:: & &gc622ee((5(AI B 'C s E N// N=c[USS5=(d UnSU;a[US5$URS5=(d UnSU;dSU;dSU;d [S5e[ X1US 9$) a>Return a (left, bottom, right, top) bounding box. From Fiona 1.4.8. Modified to return bbox from geometry if available. Parameters ---------- geometry: GeoJSON-like feature (implements __geo_interface__), feature collection, or geometry. Returns ------- tuple Bounding box: (left, bottom, right, top) rCNbboxgeometryrGrfeatureszRgeometry must be a GeoJSON-like geometry, GeometryCollection, or FeatureCollection)north_upr)rWrUgetrSr )rrrrss r!boundsrs| x!4d;GxH Xf%&& << # /xD T !\T%9Z4=O #  4i @@r#c HUVs/sHn[XR)S9PM n nU V V V V Vs/sHuppX- X-X-X- 4HnUPM M nn n n n nU V V V V Vs/sHuppX- X- X-X-4HnUPM M nn n n n n[[R"[ U555[[R "[U555nn[[R"[ U555[[R "[U555nn[UU[UU- S5[UU- S5S9n[SSURUR5nU(dURU5nU$s snfs snn n n n fs snn n n n f)a Calculate the window within the raster that fits the bounds of the geometry plus optional padding. The window is the outermost pixel indices that contain the geometry (floor of offsets, ceiling of width and height). If shapes do not overlap raster, a WindowError is raised. Parameters ---------- dataset : dataset object opened in 'r' mode Raster for which the mask will be created. shapes : iterable over geometries. A geometry is a GeoJSON-like object or implements the geo interface. Must be in same coordinate system as dataset. pad_x : float Amount of padding (as fraction of raster's x pixel size) to add to left and right side of bounds. pad_y : float Amount of padding (as fraction of raster's y pixel size) to add to top and bottom of bounds. north_up : optional This parameter is ignored since version 1.2.1. A deprecation warning will be emitted in 1.3.0. rotated : optional This parameter is ignored since version 1.2.1. A deprecation warning will be emitted in 1.3.0. pixel_precision : int or float, optional Number of places of rounding precision or absolute precision for evaluating bounds of shapes. boundless : bool, optional Whether to allow a boundless window or not. Returns ------- rasterio.windows.Window )rg)col_offrow_offwidthheightr) rrrImathfloorr_ceilr`rrr intersection)datasetr+pad_xpad_yrrotatedpixel_precision boundlessr3 all_boundsleftbottomrighttopxcolsyrows row_startrow_stop col_startcol_stopwindow raster_windows r!geometry_windowrs`LRR6%&+<+<*<=6JR+5 *4 &T5, u}dlKA K *4  +5 *4 &T5+s{FNFNKA K *4   djjT34c$))CI:N6OxIdjjT34c$))CI:N6OxI (Y&,8i'- F1a?M $$]3 M9S   sF %F %F cT1Skn[USS5=(d UnUSnX!RS15;agX!;GaSU;agUSnUS:Xa[ U5S :$US :Xa&[ U5S :=(a [ US 5S :$US :Xa&[ U5S :=(a [ US 5S :$US :Xa&[ U5S:=(a [ US 5S :$US:XaA[ U5S :=(a, [ US 5S :=(a [ US S 5S :$US:XaA[ U5S :=(a, [ US 5S:=(a [ US S 5S :$US:Xa_[ U5S :=(aJ [ US 5S :=(a2 [ US S 5S:=(a [ US S S 5S :$US:Xa8SU;ag[ US5S :dgUSHn[ U5(aM g g![[4a gf=f)a Checks to see if geometry is a valid GeoJSON geometry type or GeometryCollection. Geometry must be GeoJSON or implement the geo interface. Geometries must be non-empty, and have at least x, y coordinates. Note: only the first coordinate is checked for validity. Parameters ---------- geom: an object that implements the geo interface or GeoJSON-like object Returns ------- bool: True if object is a valid GeoJSON geometry type >PointrH LineString LinearRing MultiPointrFMultiLineStringrCNrDrEFrGrrLrrrrr$rrHrFrT)rWunionKeyError TypeErrorrdrX)rs geom_typesrurgs r!rXrXsb&@J 4,d 3 ;tDL ,,.B-CD D E   $m$  v;!# #  $v;?:s6!9~': :  $v;!#;F1I!(; ;  $v;!#;F1I!(; ; ) )K!O+F1I!(;+q ! %* ,  !K!O+F1I!(;+q ! %* ,  &K!OIF1I(:Iq ! %*I/26!9Q<?/Cq/H J(( t #4 %&*l#A ##$ q i sHH'&H'c #~# UbXR:a [S5eSnSn URn US:a[[R "UR U- 55[[R "URU- 554n URU S- n UR U S- n U [R"URU -UR U -5-n U [R"X5-n U(aU(aU(dU(dUS:XaURU5n OSW nUc&[R"UR4U-S5n O[R"US5n URX5n Uc.[RRU 5R!S5n U nU(abUS:XaUR#USS9nOK[R"W UR$UR&R)U5S9nUR#XSS9nU(a,[R*"US5S -nSXS:H'UnU(dUn S U 0nU(dU US '[,R.R1UR25n[5[6R8R:"U40UD65HununnU(a"[<R>"UR@S US US9n[C[EU56unnSUSU3UUS.[GU5[GU5[IU5[IU5/US.v M g7f)aAYield GeoJSON features for the dataset The geometries are polygons bounding contiguous regions of the same raster value. Parameters ---------- src: Rasterio Dataset bidx: int band index sampling: int (DEFAULT: 1) Inverse of the sampling fraction; a value of 10 decimates band: boolean (DEFAULT: True) extract features from a band (True) or a mask (False) as_mask: boolean (DEFAULT: False) Interpret band as a mask and output only one class of valid data shapes? with_nodata: boolean (DEFAULT: False) Include nodata regions? geographic: str (DEFAULT: True) Output shapes in EPSG:4326? Otherwise use the native CRS. precision: int (DEFAULT: -1) Decimal precision of coordinates. -1 for full float precision output Yields ------ GeoJSON-like Feature dictionaries for shapes found in the given band Nzbidx is out of range for rasterrrr<F)rirMrr&z EPSG:4326T)antimeridian_cutting precisionFeature:)valfilename)rDid propertiesr~r)%countrSrrIrrrrr translationscale read_masksr1r2 logical_orreducerreaddtypesr0rq ones_likeospathbasenamerRrTr.rr+rtransform_geomcrsziprr_r`)srcbidxsamplingr/as_mask with_nodata geographicrimgmskrr3 x_sampling y_sampling msk_shapetmpkwargs src_basenameirrxsyss r!dataset_featuresrXsT D99,:;; C C I!|TYYszzH456TYYsyy83457YYq) ZZ%(*  V'' II "CJJ$;= =  V\\*99 D q=..&CI|hhYYL9,g7hhy'2..+C <--&&s+227;C q=((4(.C((jj!2!24!89;C((4U(3C ll3(3.1H C9 %F v77##CHH-L!    $ $S 3F 35 8As ##a%)Y@AfQiB!N!A3'"%<@Wc"gs2wB8   5sL;L=)FF)NNr$)TN)rrNNNF)NrTFFT)/__doc__ contextlibrloggingrrrZnumpyr1r.rrasterio._baserrasterio._featuresrrrr rasterio.enumsr rasterio.envr r rasterio.errorsr rasterio.iorrasterio.rio.helpersrrasterio.transformrrrrasterio.windowsr getLogger__name__logr"r+r7replacerrrrXrrOr#r!rs+>  &CC#0,%'%8# !  /1 /1d 11> 1>h /9 /9d       V VrAH  L^Ut B r#