pyorps.io package
Submodules
pyorps.io.geo_dataset module
PYORPS: An Open-Source Tool for Automated Power Line Routing
Reference: [1] Hofmann, M., Stetz, T., Kammer, F., Repo, S.: ‘PYORPS: An Open-Source Tool for
Automated Power Line Routing’, CIRED 2025 - 28th Conference and Exhibition on Electricity Distribution, 16 - 19 June 2025, Geneva, Switzerland
- class pyorps.io.geo_dataset.ABC[source]
Bases:
objectHelper class that provides a standard way to create an ABC using inheritance.
- class pyorps.io.geo_dataset.Affine(a, b, c, d, e, f, g=0.0, h=0.0, i=1.0)[source]
Bases:
AffineTwo dimensional affine transform for 2D linear mapping.
- Parameters:
a (
float) –Coefficients of an augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | 0 0 1 | | 1 |a, b, and c are the elements of the first row of the matrix. d, e, and f are the elements of the second row.
b (
float) –Coefficients of an augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | 0 0 1 | | 1 |a, b, and c are the elements of the first row of the matrix. d, e, and f are the elements of the second row.
c (
float) –Coefficients of an augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | 0 0 1 | | 1 |a, b, and c are the elements of the first row of the matrix. d, e, and f are the elements of the second row.
d (
float) –Coefficients of an augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | 0 0 1 | | 1 |a, b, and c are the elements of the first row of the matrix. d, e, and f are the elements of the second row.
e (
float) –Coefficients of an augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | 0 0 1 | | 1 |a, b, and c are the elements of the first row of the matrix. d, e, and f are the elements of the second row.
f (
float) –Coefficients of an augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | 0 0 1 | | 1 |a, b, and c are the elements of the first row of the matrix. d, e, and f are the elements of the second row.
g (float)
h (float)
i (float)
- a, b, c, d, e, f, g, h, i
The coefficients of the 3x3 augmented affine transformation matrix
x’ | | a b c | | x |y’ | = | d e f | | y |1 | | g h i | | 1 |g, h, and i are always 0, 0, and 1.
- Type:
- The Affine package is derived from Casey Duncan's Planar package.
- See the copyright statement below. Parallel lines are preserved by
- these transforms. Affine transforms can perform any combination of
- translations, scales/flips, shears, and rotations. Class methods
- are provided to conveniently compose transforms from these
- operations.
- Internally the transform is stored as a 3x3 transformation matrix.
- The transform may be constructed directly by specifying the first
- two rows of matrix values as 6 floats. Since the matrix is an affine
- transform, the last row is always ``(0, 0, 1)``.
- N.B.
- Type:
multiplicationofa transformandan (x,y) vector *always*
- returns the column vector that is the matrix multiplication product
- of the transform and (x, y) as a column vector, no matter which is
- on the left or right side. This is obviously not the case for
- matrices and vectors in general, but provides a convenience for
- users of this class.
- __getnewargs__()[source]
Pickle protocol support
Notes
Normal unpickling creates a situation where __new__ receives all 9 elements rather than the 6 that are required for the constructor. This method ensures that only the 6 are provided.
- __invert__()[source]
Return the inverse transform.
- Raises:
:except:`TransformNotInvertible` if the transform is degenerate.
- __mul__(other)[source]
Multiplication
Apply the transform using matrix multiplication, creating a resulting object of the same type. A transform may be applied to another transform, a vector, vector array, or shape.
- Parameters:
other (Affine,
Vec2,Vec2Array,Shape) – The object to transform.- Return type:
Same as
other
- static __new__(cls, a, b, c, d, e, f, g=0.0, h=0.0, i=1.0)[source]
Create a new object
- Parameters:
a (
float) – Elements of an augmented affine transformation matrix.b (
float) – Elements of an augmented affine transformation matrix.c (
float) – Elements of an augmented affine transformation matrix.d (
float) – Elements of an augmented affine transformation matrix.e (
float) – Elements of an augmented affine transformation matrix.f (
float) – Elements of an augmented affine transformation matrix.g (float)
h (float)
i (float)
- __rmul__(other)[source]
Right hand multiplication
Deprecated since version 2.3.0: Right multiplication will be prohibited in version 3.0. This method will raise AffineError.
Notes
We should not be called if other is an affine instance This is just a guarantee, since we would potentially return the wrong answer in that case.
- a
Alias for field number 0
- b
Alias for field number 1
- c
Alias for field number 2
- property column_vectors
The values of the transform as three 2D column vectors
- count(value, /)
Return number of occurrences of value.
- d
Alias for field number 3
- property determinant
The determinant of the transform matrix.
This value is equal to the area scaling factor when the transform is applied to a shape.
- e
Alias for field number 4
- property eccentricity: float
The eccentricity of the affine transformation.
This value represents the eccentricity of an ellipse under this affine transformation.
Raises NotImplementedError for improper transformations.
- f
Alias for field number 5
- classmethod from_gdal(c, a, b, f, d, e)[source]
Use same coefficient order as GDAL’s GetGeoTransform().
- g
Alias for field number 6
- h
Alias for field number 7
- i
Alias for field number 8
- index(value, start=0, stop=9223372036854775807, /)
Return first index of value.
Raises ValueError if the value is not present.
- property is_conformal: bool
True if the transform is conformal.
i.e., if angles between points are preserved after applying the transform, within rounding limits. This implies that the transform has no effective shear.
- property is_degenerate
True if this transform is degenerate.
Which means that it will collapse a shape to an effective area of zero. Degenerate transforms cannot be inverted.
- property is_identity: bool
True if this transform equals the identity matrix, within rounding limits.
- property is_orthonormal: bool
True if the transform is orthonormal.
Which means that the transform represents a rigid motion, which has no effective scaling or shear. Mathematically, this means that the axis vectors of the transform matrix are perpendicular and unit-length. Applying an orthonormal transform to a shape always results in a congruent shape.
- property is_proper
True if this transform is proper.
Which means that it does not include reflection.
- property is_rectilinear: bool
True if the transform is rectilinear.
i.e., whether a shape would remain axis-aligned, within rounding limits, after applying the transform.
- itransform(seq)[source]
Transform a sequence of points or vectors in place.
- Parameters:
seq – Mutable sequence of
Vec2to be transformed.- Return type:
- Returns:
None, the input sequence is mutated in place.
- classmethod permutation(*scaling)[source]
Create the permutation transform
For 2x2 matrices, there is only one permutation matrix that is not the identity.
- Return type:
- precision = 1e-05
- classmethod rotation(angle, pivot=None)[source]
Create a rotation transform at the specified angle.
A pivot point other than the coordinate system origin may be optionally specified.
- property rotation_angle: float
The rotation angle in degrees of the affine transformation.
This is the rotation angle in degrees of the affine transformation, assuming it is in the form M = R S, where R is a rotation and S is a scaling.
Raises UndefinedRotationError for improper and degenerate transformations.
- to_shapely()[source]
Return an affine transformation matrix compatible with shapely
Shapely’s affinity module expects an affine transformation matrix in (a,b,d,e,xoff,yoff) order.
- Return type:
- class pyorps.io.geo_dataset.Any(*args, **kwargs)[source]
Bases:
objectSpecial type indicating an unconstrained type.
Any is compatible with every type.
Any assumed to have all methods.
All values assumed to be instances of Any.
Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks.
- class pyorps.io.geo_dataset.GeoDataset(file_source, crs=None)[source]
Bases:
ABC-
data:
Union[GeoDataFrame,ndarray,None] = None
-
data:
- class pyorps.io.geo_dataset.InMemoryRasterDataset(file_source, crs, transform)[source]
Bases:
RasterDataset-
data:
Union[GeoDataFrame,ndarray,None] = None
-
data:
- class pyorps.io.geo_dataset.InMemoryVectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
VectorDataset- Parameters:
- __init__(file_source, crs=None, bbox=None, mask=None)
-
data:
Union[GeoDataFrame,ndarray,None] = None
- class pyorps.io.geo_dataset.LocalRasterDataset(file_source, crs=None)[source]
Bases:
RasterDataset-
data:
Union[GeoDataFrame,ndarray,None] = None
-
data:
- class pyorps.io.geo_dataset.LocalVectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
InMemoryVectorDataset- Parameters:
- __init__(file_source, crs=None, bbox=None, mask=None)
- correct_crs()
-
data:
Union[GeoDataFrame,ndarray,None] = None
- post_loading()
- class pyorps.io.geo_dataset.RasterDataset(file_source, crs=None)[source]
Bases:
GeoDataset,ABC-
data:
Union[GeoDataFrame,ndarray,None] = None
- abstractmethod load_data(**kwargs)
-
data:
- class pyorps.io.geo_dataset.VectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
GeoDataset,ABC- Parameters:
-
data:
Union[GeoDataFrame,ndarray,None] = None
- abstractmethod load_data(**kwargs)
- class pyorps.io.geo_dataset.WFSVectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
LocalVectorDataset- Parameters:
- __init__(file_source, crs=None, bbox=None, mask=None)
- apply_bbox()
- correct_crs()
-
data:
Union[GeoDataFrame,ndarray,None] = None
- post_loading()
- pyorps.io.geo_dataset.abstractmethod(funcobj)[source]
A decorator indicating abstract methods.
Requires that the metaclass is ABCMeta or derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods are overridden. The abstract methods can be called using any of the normal ‘super’ call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.
Usage:
- class C(metaclass=ABCMeta):
@abstractmethod def my_abstract_method(self, arg1, arg2, argN):
…
- class pyorps.io.geo_dataset.dtype(dtype, align=False, copy=False[, metadata])
Bases:
objectCreate a data type object.
A numpy array is homogeneous, and contains elements described by a dtype object. A dtype object can be constructed from different combinations of fundamental numeric types.
- Parameters:
dtype – Object to be converted to a data type object.
align (
bool, optional) – Add padding to the fields to match what a C compiler would output for a similar C-struct. Can beTrueonly if obj is a dictionary or a comma-separated string. If a struct dtype is being created, this also sets a sticky alignment flagisalignedstruct.copy (
bool, optional) – Make a new copy of the data-type object. IfFalse, the result may just be a reference to a built-in data-type object.metadata (
dict, optional) – An optional dictionary with dtype metadata.
See also
result_typeExamples
Using array-scalar type:
>>> import numpy as np >>> np.dtype(np.int16) dtype('int16')
Structured type, one field name ‘f1’, containing int16:
>>> np.dtype([('f1', np.int16)]) dtype([('f1', '<i2')])
Structured type, one field named ‘f1’, in itself containing a structured type with one field:
>>> np.dtype([('f1', [('f1', np.int16)])]) dtype([('f1', [('f1', '<i2')])])
Structured type, two fields: the first field contains an unsigned int, the second an int32:
>>> np.dtype([('f1', np.uint64), ('f2', np.int32)]) dtype([('f1', '<u8'), ('f2', '<i4')])
Using array-protocol type strings:
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')])
Using comma-separated field formats. The shape is (2,3):
>>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])
Using tuples.
intis a fixed type, 3 the field’s shape.voidis a flexible type, here of size 10:>>> np.dtype([('hello',(np.int64,3)),('world',np.void,10)]) dtype([('hello', '<i8', (3,)), ('world', 'V10')])
Subdivide
int16into 2int8’s, called x and y. 0 and 1 are the offsets in bytes:>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)})) dtype((numpy.int16, [('x', 'i1'), ('y', 'i1')]))
Using dictionaries. Two fields named ‘gender’ and ‘age’:
>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]}) dtype([('gender', 'S1'), ('age', 'u1')])
Offsets in bytes, here 0 and 25:
>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)}) dtype([('surname', 'S25'), ('age', 'u1')])
- alignment
The required alignment (bytes) of this data-type according to the compiler.
More information is available in the C-API section of the manual.
Examples
>>> import numpy as np >>> x = np.dtype('i4') >>> x.alignment 4
>>> x = np.dtype(float) >>> x.alignment 8
- base
Returns dtype for the base element of the subarrays, regardless of their dimension or shape.
See also
Examples
>>> import numpy as np >>> x = numpy.dtype('8f') >>> x.base dtype('float32')
>>> x = numpy.dtype('i2') >>> x.base dtype('int16')
- byteorder
A character indicating the byte-order of this data-type object.
One of:
‘=’
native
‘<’
little-endian
‘>’
big-endian
‘|’
not applicable
All built-in data-type objects have byteorder either ‘=’ or ‘|’.
Examples
>>> import numpy as np >>> dt = np.dtype('i2') >>> dt.byteorder '=' >>> # endian is not relevant for 8 bit numbers >>> np.dtype('i1').byteorder '|' >>> # or ASCII strings >>> np.dtype('S2').byteorder '|' >>> # Even if specific code is given, and it is native >>> # '=' is the byteorder >>> import sys >>> sys_is_le = sys.byteorder == 'little' >>> native_code = '<' if sys_is_le else '>' >>> swapped_code = '>' if sys_is_le else '<' >>> dt = np.dtype(native_code + 'i2') >>> dt.byteorder '=' >>> # Swapped code shows up as itself >>> dt = np.dtype(swapped_code + 'i2') >>> dt.byteorder == swapped_code True
- char
A unique character code for each of the 21 different built-in types.
Examples
>>> import numpy as np >>> x = np.dtype(float) >>> x.char 'd'
- descr
__array_interface__ description of the data-type.
The format is that required by the ‘descr’ key in the __array_interface__ attribute.
Warning: This attribute exists specifically for __array_interface__, and passing it directly to numpy.dtype will not accurately reconstruct some dtypes (e.g., scalar and subarray dtypes).
Examples
>>> import numpy as np >>> x = np.dtype(float) >>> x.descr [('', '<f8')]
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) >>> dt.descr [('name', '<U16'), ('grades', '<f8', (2,))]
- fields
Dictionary of named fields defined for this data type, or
None.The dictionary is indexed by keys that are the names of the fields. Each entry in the dictionary is a tuple fully describing the field:
(dtype, offset[, title])
Offset is limited to C int, which is signed and usually 32 bits. If present, the optional title can be any object (if it is a string or unicode then it will also be a key in the fields dictionary, otherwise it’s meta-data). Notice also that the first two elements of the tuple can be passed directly as arguments to the
ndarray.getfieldandndarray.setfieldmethods.See also
Examples
>>> import numpy as np >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) >>> print(dt.fields) {'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
- flags
Bit-flags describing how this data type is to be interpreted.
Bit-masks are in
numpy._core.multiarrayas the constants ITEM_HASOBJECT, LIST_PICKLE, ITEM_IS_POINTER, NEEDS_INIT, NEEDS_PYAPI, USE_GETITEM, USE_SETITEM. A full explanation of these flags is in C-API documentation; they are largely useful for user-defined data-types.The following example demonstrates that operations on this particular dtype requires Python C-API.
Examples
>>> import numpy as np >>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)]) >>> x.flags 16 >>> np._core.multiarray.NEEDS_PYAPI 16
- hasobject
Boolean indicating whether this dtype contains any reference-counted objects in any fields or sub-dtypes.
Recall that what is actually in the ndarray memory representing the Python object is the memory address of that object (a pointer). Special handling may be required, and this attribute is useful for distinguishing data types that may contain arbitrary Python objects and data-types that won’t.
- isalignedstruct
Boolean indicating whether the dtype is a struct which maintains field alignment. This flag is sticky, so when combining multiple structs together, it is preserved and produces new dtypes which are also aligned.
- isbuiltin
Integer indicating how this dtype relates to the built-in dtypes.
Read-only.
0
if this is a structured array type, with fields
1
if this is a dtype compiled into numpy (such as ints, floats etc)
2
if the dtype is for a user-defined numpy type A user-defined type uses the numpy C-API machinery to extend numpy to handle a new array type. See User-defined data-types in the NumPy manual.
Examples
>>> import numpy as np >>> dt = np.dtype('i2') >>> dt.isbuiltin 1 >>> dt = np.dtype('f8') >>> dt.isbuiltin 1 >>> dt = np.dtype([('field1', 'f8')]) >>> dt.isbuiltin 0
- isnative
Boolean indicating whether the byte order of this dtype is native to the platform.
- itemsize
The element size of this data-type object.
For 18 of the 21 types this number is fixed by the data-type. For the flexible data-types, this number can be anything.
Examples
>>> import numpy as np >>> arr = np.array([[1, 2], [3, 4]]) >>> arr.dtype dtype('int64') >>> arr.itemsize 8
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) >>> dt.itemsize 80
- kind
A character code (one of ‘biufcmMOSUV’) identifying the general kind of data.
b
boolean
i
signed integer
u
unsigned integer
f
floating-point
c
complex floating-point
m
timedelta
M
datetime
O
object
S
(byte-)string
U
Unicode
V
void
Examples
>>> import numpy as np >>> dt = np.dtype('i4') >>> dt.kind 'i' >>> dt = np.dtype('f8') >>> dt.kind 'f' >>> dt = np.dtype([('field1', 'f8')]) >>> dt.kind 'V'
- metadata
Either
Noneor a readonly dictionary of metadata (mappingproxy).The metadata field can be set using any dictionary at data-type creation. NumPy currently has no uniform approach to propagating metadata; although some array operations preserve it, there is no guarantee that others will.
Warning
Although used in certain projects, this feature was long undocumented and is not well supported. Some aspects of metadata propagation are expected to change in the future.
Examples
>>> import numpy as np >>> dt = np.dtype(float, metadata={"key": "value"}) >>> dt.metadata["key"] 'value' >>> arr = np.array([1, 2, 3], dtype=dt) >>> arr.dtype.metadata mappingproxy({'key': 'value'})
Adding arrays with identical datatypes currently preserves the metadata:
>>> (arr + arr).dtype.metadata mappingproxy({'key': 'value'})
But if the arrays have different dtype metadata, the metadata may be dropped:
>>> dt2 = np.dtype(float, metadata={"key2": "value2"}) >>> arr2 = np.array([3, 2, 1], dtype=dt2) >>> (arr + arr2).dtype.metadata is None True # The metadata field is cleared so None is returned
- name
A bit-width name for this data-type.
Un-sized flexible data-type objects do not have this attribute.
Examples
>>> import numpy as np >>> x = np.dtype(float) >>> x.name 'float64' >>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)]) >>> x.name 'void640'
- names
Ordered list of field names, or
Noneif there are no fields.The names are ordered according to increasing byte offset. This can be used, for example, to walk through all of the named fields in offset order.
Examples
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) >>> dt.names ('name', 'grades')
- ndim
Number of dimensions of the sub-array if this data type describes a sub-array, and
0otherwise.Examples
>>> import numpy as np >>> x = np.dtype(float) >>> x.ndim 0
>>> x = np.dtype((float, 8)) >>> x.ndim 1
>>> x = np.dtype(('i4', (3, 4))) >>> x.ndim 2
- newbyteorder(new_order='S', /)
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
- Parameters:
new_order (
string, optional) –Byte order to force; a value from the byte order specifications below. The default value (‘S’) results in swapping the current byte order. new_order codes can be any of:
’S’ - swap dtype from current to opposite endian
{‘<’, ‘little’} - little endian
{‘>’, ‘big’} - big endian
{‘=’, ‘native’} - native order
{‘|’, ‘I’} - ignore (no change to byte order)
- Returns:
new_dtype – New dtype object with the given change to the byte order.
- Return type:
Notes
Changes are also made in all fields and sub-arrays of the data type.
Examples
>>> import sys >>> sys_is_le = sys.byteorder == 'little' >>> native_code = '<' if sys_is_le else '>' >>> swapped_code = '>' if sys_is_le else '<' >>> import numpy as np >>> native_dt = np.dtype(native_code+'i2') >>> swapped_dt = np.dtype(swapped_code+'i2') >>> native_dt.newbyteorder('S') == swapped_dt True >>> native_dt.newbyteorder() == swapped_dt True >>> native_dt == swapped_dt.newbyteorder('S') True >>> native_dt == swapped_dt.newbyteorder('=') True >>> native_dt == swapped_dt.newbyteorder('N') True >>> native_dt == native_dt.newbyteorder('|') True >>> np.dtype('<i2') == native_dt.newbyteorder('<') True >>> np.dtype('<i2') == native_dt.newbyteorder('L') True >>> np.dtype('>i2') == native_dt.newbyteorder('>') True >>> np.dtype('>i2') == native_dt.newbyteorder('B') True
- num
A unique number for each of the 21 different built-in types.
These are roughly ordered from least-to-most precision.
Examples
>>> import numpy as np >>> dt = np.dtype(str) >>> dt.num 19
>>> dt = np.dtype(float) >>> dt.num 12
- shape
Shape tuple of the sub-array if this data type describes a sub-array, and
()otherwise.Examples
>>> import numpy as np >>> dt = np.dtype(('i4', 4)) >>> dt.shape (4,)
>>> dt = np.dtype(('i4', (2, 3))) >>> dt.shape (2, 3)
- str
The array-protocol typestring of this data-type object.
- subdtype
Tuple
(item_dtype, shape)if this dtype describes a sub-array, and None otherwise.The shape is the fixed shape of the sub-array described by this data type, and item_dtype the data type of the array.
If a field whose dtype object has this attribute is retrieved, then the extra dimensions implied by shape are tacked on to the end of the retrieved array.
See also
Examples
>>> import numpy as np >>> x = numpy.dtype('8f') >>> x.subdtype (dtype('float32'), (8,))
>>> x = numpy.dtype('i2') >>> x.subdtype >>>
- type = None
- pyorps.io.geo_dataset.initialize_geo_dataset(file_source, crs=None, bbox=None, mask=None, transform=None)[source]
Factory function to create the appropriate GeoDataset instance based on the provided input.
- Parameters:
file_source (
Union[str,dict,GeoDataFrame,GeoSeries,ndarray]) – Source data (file path, GeoDataFrame, URL dict, numpy array, etc.)bbox (
Union[Polygon,GeoDataFrame,GeoSeries,tuple[float,float,float,float],None]) – Bounding box for vector datasetsmask (
Union[Polygon,GeoDataFrame,tuple,None]) – Mask for vector datasetstransform (
Optional[Affine]) – Affine transform for in-memory raster datasets
- Return type:
- Returns:
An appropriate GeoDataset subclass instance
Examples
# From local vector file vector_dataset = create_geo_dataset(“path/to/shapefile.shp”, crs=”EPSG:4326”)
# From GeoDataFrame vector_dataset = create_geo_dataset(gdf, bbox=(x1, y1, x2, y2))
# From WFS source wfs_dataset = create_geo_dataset({“url”: “https://example.com/wfs”,
“layer”: “layer1”})
# From local raster file raster_dataset = create_geo_dataset(“path/to/dem.tif”)
# From numpy array raster_dataset = create_geo_dataset(array_data, transform=transform,
crs=”EPSG:4326”)
- pyorps.io.geo_dataset.load_from_wfs(url, layer, bbox=None, mask=None, filter_params=None, auto_match=True, max_workers=4)[source]
Load data from a Web Feature Service (WFS) using chunked loading.
- Parameters:
url (
str) – The base URL of the WFS servicelayer (
str) – Name of the layer to retrievebbox (
Union[Polygon,GeoDataFrame,GeoSeries,tuple[float,float,float,float],None]) – Optional bounding box to limit the query extent (minx, miny, maxx, maxy)mask (
Union[Polygon,GeoDataFrame,tuple,None]) – Optional geometry mask to limit the query (Shapely Polygon, GeoDataFrame, or GeoSeries)filter_params (
Optional[dict]) – Additional WFS parameters to filter resultsauto_match (
bool) – Whether to attempt finding similar layer names if exact match not foundmax_workers (
int) – Maximum number of parallel threads to use
- Return type:
- Returns:
Loaded GeoDataFrame or None if no data could be loaded
- Raises:
WFSLayerNotFoundError – If the layer cannot be found and auto_match is False
- class pyorps.io.geo_dataset.ndarray
Bases:
object- ndarray(shape, dtype=float, buffer=None, offset=0,
strides=None, order=None)
An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)
Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array.
For more information, refer to the numpy module and examine the methods and attributes of an array.
- Parameters:
below) ((for the __new__ method; see Notes)
shape (
tupleofints) – Shape of created array.dtype (
data-type, optional) – Any object that can be interpreted as a numpy data type.buffer (
object exposing buffer interface, optional) – Used to fill the array with data.offset (
int, optional) – Offset of array data in buffer.strides (
tupleofints, optional) – Strides of data in memory.order (
{'C', 'F'}, optional) – Row-major (C-style) or column-major (Fortran-style) order.
- data
The array’s elements, in memory.
- Type:
buffer
- dtype
Describes the format of the elements in the array.
- Type:
dtype object
- flags
Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.
- Type:
- flat
Flattened version of the array as an iterator. The iterator allows assignments, e.g.,
x.flat = 3(See ndarray.flat for assignment examples; TODO).- Type:
numpy.flatiter object
- nbytes
The total number of bytes required to store the array data, i.e.,
itemsize * size.- Type:
- strides
The step-size required to move from one element to the next in memory. For example, a contiguous
(3, 4)array of typeint16in C-order has strides(8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).- Type:
tupleofints
- ctypes
Class containing properties of the array needed for interaction with ctypes.
- Type:
ctypes object
- base
If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.
- Type:
See also
arrayConstruct an array.
zerosCreate an array, each element of which is zero.
emptyCreate an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).
dtypeCreate a data-type.
numpy.typing.NDArrayAn ndarray alias generic w.r.t. its dtype.type <numpy.dtype.type>.
Notes
There are two modes of creating an array using
__new__:If buffer is None, then only shape, dtype, and order are used.
If buffer is an object exposing the buffer interface, then all keywords are interpreted.
No
__init__method is needed because the array is fully initialized after the__new__method.Examples
These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray.
First mode, buffer is None:
>>> import numpy as np >>> np.ndarray(shape=(2,2), dtype=float, order='F') array([[0.0e+000, 0.0e+000], # random [ nan, 2.5e-323]])
Second mode:
>>> np.ndarray((2,), buffer=np.array([1,2,3]), ... offset=np.int_().itemsize, ... dtype=int) # offset = 1*itemsize, i.e. skip first element array([2, 3])
- T
View of the transposed array.
Same as
self.transpose().Examples
>>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> a array([[1, 2], [3, 4]]) >>> a.T array([[1, 3], [2, 4]])
>>> a = np.array([1, 2, 3, 4]) >>> a array([1, 2, 3, 4]) >>> a.T array([1, 2, 3, 4])
See also
- all(axis=None, out=None, keepdims=False, *, where=True)
Returns True if all elements evaluate to True.
Refer to numpy.all for full documentation.
See also
numpy.allequivalent function
- any(axis=None, out=None, keepdims=False, *, where=True)
Returns True if any of the elements of a evaluate to True.
Refer to numpy.any for full documentation.
See also
numpy.anyequivalent function
- argmax(axis=None, out=None, *, keepdims=False)
Return indices of the maximum values along the given axis.
Refer to numpy.argmax for full documentation.
See also
numpy.argmaxequivalent function
- argmin(axis=None, out=None, *, keepdims=False)
Return indices of the minimum values along the given axis.
Refer to numpy.argmin for detailed documentation.
See also
numpy.argminequivalent function
- argpartition(kth, axis=-1, kind='introselect', order=None)
Returns the indices that would partition this array.
Refer to numpy.argpartition for full documentation.
See also
numpy.argpartitionequivalent function
- argsort(axis=-1, kind=None, order=None)
Returns the indices that would sort this array.
Refer to numpy.argsort for full documentation.
See also
numpy.argsortequivalent function
- astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type.
- Parameters:
dtype (
strordtype) – Typecode or data-type to which the array is cast.order (
{'C', 'F', 'A', 'K'}, optional) – Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.casting (
{'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.
’no’ means the data types should not be cast at all.
’equiv’ means only byte-order changes are allowed.
’safe’ means only casts which can preserve values are allowed.
’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.
’unsafe’ means any data conversions may be done.
subok (
bool, optional) – If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.copy (
bool, optional) – By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.
- Returns:
arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.
- Return type:
- Raises:
ComplexWarning – When casting from complex to float or int. To avoid this, one should use
a.real.astype(t).
Examples
>>> import numpy as np >>> x = np.array([1, 2, 2.5]) >>> x array([1. , 2. , 2.5])
>>> x.astype(int) array([1, 2, 2])
- base
Base object if memory is from some other object.
Examples
The base of an array that owns its memory is None:
>>> import numpy as np >>> x = np.array([1,2,3,4]) >>> x.base is None True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:] >>> y.base is x True
- byteswap(inplace=False)
Swap the bytes of the array elements
Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.
- Parameters:
inplace (
bool, optional) – IfTrue, swap bytes in-place, default isFalse.- Returns:
out – The byteswapped array. If inplace is
True, this is a view to self.- Return type:
Examples
>>> import numpy as np >>> A = np.array([1, 256, 8755], dtype=np.int16) >>> list(map(hex, A)) ['0x1', '0x100', '0x2233'] >>> A.byteswap(inplace=True) array([ 256, 1, 13090], dtype=int16) >>> list(map(hex, A)) ['0x100', '0x1', '0x3322']
Arrays of byte-strings are not swapped
>>> A = np.array([b'ceg', b'fac']) >>> A.byteswap() array([b'ceg', b'fac'], dtype='|S3')
A.view(A.dtype.newbyteorder()).byteswap()produces an array with the same values but different representation in memory>>> A = np.array([1, 2, 3],dtype=np.int64) >>> A.view(np.uint8) array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], dtype=uint8) >>> A.view(A.dtype.newbyteorder()).byteswap(inplace=True) array([1, 2, 3], dtype='>i8') >>> A.view(np.uint8) array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3], dtype=uint8)
- choose(choices, out=None, mode='raise')
Use an index array to construct a new array from a set of choices.
Refer to numpy.choose for full documentation.
See also
numpy.chooseequivalent function
- clip(min=None, max=None, out=None, **kwargs)
Return an array whose values are limited to
[min, max]. One of max or min must be given.Refer to numpy.clip for full documentation.
See also
numpy.clipequivalent function
- compress(condition, axis=None, out=None)
Return selected slices of this array along given axis.
Refer to numpy.compress for full documentation.
See also
numpy.compressequivalent function
- conj()
Complex-conjugate all elements.
Refer to numpy.conjugate for full documentation.
See also
numpy.conjugateequivalent function
- conjugate()
Return the complex conjugate, element-wise.
Refer to numpy.conjugate for full documentation.
See also
numpy.conjugateequivalent function
- copy(order='C')
Return a copy of the array.
- Parameters:
order (
{'C', 'F', 'A', 'K'}, optional) – Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function andnumpy.copy()are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)
Notes
This function is the preferred method for creating an array copy. The function
numpy.copy()is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.Examples
>>> import numpy as np >>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x array([[0, 0, 0], [0, 0, 0]])
>>> y array([[1, 2, 3], [4, 5, 6]])
>>> y.flags['C_CONTIGUOUS'] True
For arrays containing Python objects (e.g. dtype=object), the copy is a shallow one. The new array will contain the same object which may lead to surprises if that object can be modified (is mutable):
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object) >>> b = a.copy() >>> b[2][0] = 10 >>> a array([1, 'm', list([10, 3, 4])], dtype=object)
To ensure all elements within an
objectarray are copied, use copy.deepcopy:>>> import copy >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object) >>> c = copy.deepcopy(a) >>> c[2][0] = 10 >>> c array([1, 'm', list([10, 3, 4])], dtype=object) >>> a array([1, 'm', list([2, 3, 4])], dtype=object)
- ctypes
An object to simplify the interaction of the array with the ctypes module.
This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.
- Parameters:
None
- Returns:
c – Possessing attributes data, shape, strides, etc.
- Return type:
Python object
See also
Notes
Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):
- _ctypes.data
A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as:
self._array_interface_['data'][0].Note that unlike
data_as, a reference won’t be kept to the array: code likectypes.c_void_p((a + b).ctypes.data)will result in a pointer to a deallocated array, and should be spelt(a + b).ctypes.data_as(ctypes.c_void_p)
- _ctypes.shape
A ctypes array of length self.ndim where the basetype is the C-integer corresponding to
dtype('p')on this platform (see ~numpy.ctypeslib.c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.- Type:
(c_intp*self.ndim)
- _ctypes.strides
A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.
- Type:
(c_intp*self.ndim)
- _ctypes.data_as(obj)
Return the data pointer cast to a particular c-types object. For example, calling
self._as_parameter_is equivalent toself.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data:self.data_as(ctypes.POINTER(ctypes.c_double)).The returned pointer will keep a reference to the array.
- _ctypes.shape_as(obj)
Return the shape tuple as an array of some other c-types type. For example:
self.shape_as(ctypes.c_short).
- _ctypes.strides_as(obj)
Return the strides tuple as an array of some other c-types type. For example:
self.strides_as(ctypes.c_longlong).
If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the
as_parameterattribute which will return an integer equal to the data attribute.Examples
>>> import numpy as np >>> import ctypes >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32) >>> x array([[0, 1], [2, 3]], dtype=int32) >>> x.ctypes.data 31962608 # may vary >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)) <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents c_uint(0) >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents c_ulong(4294967296) >>> x.ctypes.shape <numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary >>> x.ctypes.strides <numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary
- cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis.
Refer to numpy.cumprod for full documentation.
See also
numpy.cumprodequivalent function
- cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis.
Refer to numpy.cumsum for full documentation.
See also
numpy.cumsumequivalent function
- data
Python buffer object pointing to the start of the array’s data.
- device
- diagonal(offset=0, axis1=0, axis2=1)
Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.
Refer to
numpy.diagonal()for full documentation.See also
numpy.diagonalequivalent function
- dot()
- dtype
Data-type of the array’s elements.
Warning
Setting
arr.dtypeis discouraged and may be deprecated in the future. Setting will replace thedtypewithout modifying the memory (see also ndarray.view and ndarray.astype).- Parameters:
None
- Returns:
d
- Return type:
numpy dtype object
See also
ndarray.astypeCast the values contained in the array to a new data-type.
ndarray.viewCreate a view of the same data but a different data-type.
Examples
>>> x array([[0, 1], [2, 3]]) >>> x.dtype dtype('int32') >>> type(x.dtype) <type 'numpy.dtype'>
- dump(file)
Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.
- Parameters:
file (
strorPath) – A string naming the dump file.
- dumps()
Returns the pickle of the array as a string. pickle.loads will convert the string back to an array.
- Parameters:
None
- fill(value)
Fill the array with a scalar value.
- Parameters:
value (
scalar) – All elements of a will be assigned this value.
Examples
>>> import numpy as np >>> a = np.array([1, 2]) >>> a.fill(0) >>> a array([0, 0]) >>> a = np.empty(2) >>> a.fill(1) >>> a array([1., 1.])
Fill expects a scalar value and always behaves the same as assigning to a single array element. The following is a rare example where this distinction is important:
>>> a = np.array([None, None], dtype=object) >>> a[0] = np.array(3) >>> a array([array(3), None], dtype=object) >>> a.fill(np.array(3)) >>> a array([array(3), array(3)], dtype=object)
Where other forms of assignments will unpack the array being assigned:
>>> a[...] = np.array(3) >>> a array([3, 3], dtype=object)
- flags
Information about the memory layout of the array.
- C_CONTIGUOUS(C)
The data is in a single, C-style contiguous segment.
- F_CONTIGUOUS(F)
The data is in a single, Fortran-style contiguous segment.
- OWNDATA(O)
The array owns the memory it uses or borrows it from another object.
- WRITEABLE(W)
The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception.
- ALIGNED(A)
The data and all elements are aligned appropriately for the hardware.
- WRITEBACKIFCOPY(X)
This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.
- FNC
F_CONTIGUOUS and not C_CONTIGUOUS.
- FORC
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
- BEHAVED(B)
ALIGNED and WRITEABLE.
- CARRAY(CA)
BEHAVED and C_CONTIGUOUS.
- FARRAY(FA)
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
Notes
The flags object can be accessed dictionary-like (as in
a.flags['WRITEABLE']), or by using lowercased attribute names (as ina.flags.writeable). Short flag names are only supported in dictionary access.Only the WRITEBACKIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.
The array flags cannot be set arbitrarily:
WRITEBACKIFCOPY can only be set
False.ALIGNED can only be set
Trueif the data is truly aligned.WRITEABLE can only be set
Trueif the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.
Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays.
Even for contiguous arrays a stride for a given dimension
arr.strides[dim]may be arbitrary ifarr.shape[dim] == 1or the array has no elements. It does not generally hold thatself.strides[-1] == self.itemsizefor C-style contiguous arrays orself.strides[0] == self.itemsizefor Fortran-style contiguous arrays is true.
- flat
A 1-D iterator over the array.
This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.
Examples
>>> import numpy as np >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(x.flat) <class 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x array([[3, 3, 3], [3, 3, 3]]) >>> x.flat[[1,4]] = 1; x array([[3, 1, 3], [3, 1, 3]])
- flatten(order='C')
Return a copy of the array collapsed into one dimension.
- Parameters:
order (
{'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.- Returns:
y – A copy of the input array, flattened to one dimension.
- Return type:
Examples
>>> import numpy as np >>> a = np.array([[1,2], [3,4]]) >>> a.flatten() array([1, 2, 3, 4]) >>> a.flatten('F') array([1, 3, 2, 4])
- getfield(dtype, offset=0)
Returns a field of the given array as a certain type.
A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.
- Parameters:
Examples
>>> import numpy as np >>> x = np.diag([1.+1.j]*2) >>> x[1, 1] = 2 + 4.j >>> x array([[1.+1.j, 0.+0.j], [0.+0.j, 2.+4.j]]) >>> x.getfield(np.float64) array([[1., 0.], [0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the array for our view:
>>> x.getfield(np.float64, offset=8) array([[1., 0.], [0., 4.]])
- imag
The imaginary part of the array.
Examples
>>> import numpy as np >>> x = np.sqrt([1+0j, 0+1j]) >>> x.imag array([ 0. , 0.70710678]) >>> x.imag.dtype dtype('float64')
- item(*args)
Copy an element of an array to a standard Python scalar and return it.
- Parameters:
*args (
Arguments (variable numberandtype)) –none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.
int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.
tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.
- Returns:
z – A copy of the specified element of the array as a suitable Python scalar
- Return type:
Standard Python scalar object
Notes
When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.
item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.
Examples
>>> import numpy as np >>> np.random.seed(123) >>> x = np.random.randint(9, size=(3, 3)) >>> x array([[2, 2, 6], [1, 3, 6], [1, 0, 1]]) >>> x.item(3) 1 >>> x.item(7) 0 >>> x.item((0, 1)) 2 >>> x.item((2, 2)) 1
For an array with object dtype, elements are returned as-is.
>>> a = np.array([np.int64(1)], dtype=object) >>> a.item() #return np.int64 np.int64(1)
- itemset
- itemsize
Length of one array element in bytes.
Examples
>>> import numpy as np >>> x = np.array([1,2,3], dtype=np.float64) >>> x.itemsize 8 >>> x = np.array([1,2,3], dtype=np.complex128) >>> x.itemsize 16
- mT
View of the matrix transposed array.
The matrix transpose is the transpose of the last two dimensions, even if the array is of higher dimension.
Added in version 2.0.
- Raises:
ValueError – If the array is of dimension less than 2.
Examples
>>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> a array([[1, 2], [3, 4]]) >>> a.mT array([[1, 3], [2, 4]])
>>> a = np.arange(8).reshape((2, 2, 2)) >>> a array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) >>> a.mT array([[[0, 2], [1, 3]], [[4, 6], [5, 7]]])
- max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
Return the maximum along a given axis.
Refer to numpy.amax for full documentation.
See also
numpy.amaxequivalent function
- mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
Returns the average of the array elements along given axis.
Refer to numpy.mean for full documentation.
See also
numpy.meanequivalent function
- min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
Return the minimum along a given axis.
Refer to numpy.amin for full documentation.
See also
numpy.aminequivalent function
- nbytes
Total bytes consumed by the elements of the array.
Notes
Does not include memory consumed by non-element attributes of the array object.
See also
sys.getsizeofMemory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.
Examples
>>> import numpy as np >>> x = np.zeros((3,5,2), dtype=np.complex128) >>> x.nbytes 480 >>> np.prod(x.shape) * x.itemsize 480
- ndim
Number of array dimensions.
Examples
>>> import numpy as np >>> x = np.array([1, 2, 3]) >>> x.ndim 1 >>> y = np.zeros((2, 3, 4)) >>> y.ndim 3
- newbyteorder
- nonzero()
Return the indices of the elements that are non-zero.
Refer to numpy.nonzero for full documentation.
See also
numpy.nonzeroequivalent function
- partition(kth, axis=-1, kind='introselect', order=None)
Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two partitions on the either side of the k-th element in the output array is undefined.
- Parameters:
kth (
intorsequenceofints) –Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.
Deprecated since version 1.22.0: Passing booleans as index is deprecated.
axis (
int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.kind (
{'introselect'}, optional) – Selection algorithm. Default is ‘introselect’.order (
strorlistofstr, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
See also
numpy.partitionReturn a partitioned copy of an array.
argpartitionIndirect partition.
sortFull sort.
Notes
See
np.partitionfor notes on the different algorithms.Examples
>>> import numpy as np >>> a = np.array([3, 4, 2, 1]) >>> a.partition(3) >>> a array([2, 1, 3, 4]) # may vary
>>> a.partition((1, 3)) >>> a array([1, 2, 3, 4])
- prod()
- a.prod(axis=None, dtype=None, out=None, keepdims=False,
initial=1, where=True)
Return the product of the array elements over the given axis
Refer to numpy.prod for full documentation.
See also
numpy.prodequivalent function
- ptp
- put(indices, values, mode='raise')
Set
a.flat[n] = values[n]for all n in indices.Refer to numpy.put for full documentation.
See also
numpy.putequivalent function
- ravel([order])
Return a flattened array.
Refer to numpy.ravel for full documentation.
See also
numpy.ravelequivalent function
ndarray.flata flat iterator on the array.
- real
The real part of the array.
Examples
>>> import numpy as np >>> x = np.sqrt([1+0j, 0+1j]) >>> x.real array([ 1. , 0.70710678]) >>> x.real.dtype dtype('float64')
See also
numpy.realequivalent function
- repeat(repeats, axis=None)
Repeat elements of an array.
Refer to numpy.repeat for full documentation.
See also
numpy.repeatequivalent function
- reshape(shape, /, *, order='C', copy=None)
Returns an array containing the same data with a new shape.
Refer to numpy.reshape for full documentation.
See also
numpy.reshapeequivalent function
Notes
Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example,
a.reshape(10, 11)is equivalent toa.reshape((10, 11)).
- resize(new_shape, refcheck=True)
Change shape and size of array in-place.
- Parameters:
- Return type:
- Raises:
ValueError – If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.
SystemError – If the order keyword argument is specified. This behaviour is a bug in NumPy.
See also
resizeReturn a new array with the specified shape.
Notes
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be resized.
The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.
Examples
Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:
>>> import numpy as np
>>> a = np.array([[0, 1], [2, 3]], order='C') >>> a.resize((2, 1)) >>> a array([[0], [1]])
>>> a = np.array([[0, 1], [2, 3]], order='F') >>> a.resize((2, 1)) >>> a array([[0], [2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]]) >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple >>> b array([[0, 1, 2], [3, 0, 0]])
Referencing an array prevents resizing…
>>> c = a >>> a.resize((1, 1)) Traceback (most recent call last): ... ValueError: cannot resize an array that references or is referenced ...
Unless refcheck is False:
>>> a.resize((1, 1), refcheck=False) >>> a array([[0]]) >>> c array([[0]])
- round(decimals=0, out=None)
Return a with each element rounded to the given number of decimals.
Refer to numpy.around for full documentation.
See also
numpy.aroundequivalent function
- searchsorted(v, side='left', sorter=None)
Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see numpy.searchsorted
See also
numpy.searchsortedequivalent function
- setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type.
Place val into a’s field defined by dtype and beginning offset bytes into the field.
- Parameters:
- Return type:
See also
Examples
>>> import numpy as np >>> x = np.eye(3) >>> x.getfield(np.float64) array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) >>> x.setfield(3, np.int32) >>> x.getfield(np.int32) array([[3, 3, 3], [3, 3, 3], [3, 3, 3]], dtype=int32) >>> x array([[1.0e+000, 1.5e-323, 1.5e-323], [1.5e-323, 1.0e+000, 1.5e-323], [1.5e-323, 1.5e-323, 1.0e+000]]) >>> x.setfield(np.eye(3), np.int32) >>> x array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
- setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)
- Parameters:
Notes
Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only three of which can be changed by the user: WRITEBACKIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well as the full name.
Examples
>>> import numpy as np >>> y = np.array([[3, 1, 7], ... [2, 0, 0], ... [8, 5, 9]]) >>> y array([[3, 1, 7], [2, 0, 0], [8, 5, 9]]) >>> y.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False >>> y.setflags(write=0, align=0) >>> y.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : False ALIGNED : False WRITEBACKIFCOPY : False >>> y.setflags(uic=1) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: cannot set WRITEBACKIFCOPY flag to True
- shape
Tuple of array dimensions.
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.
Warning
Setting
arr.shapeis discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.Examples
>>> import numpy as np >>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]]) >>> y.shape = (3, 6) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: total size of new array must be unchanged >>> np.zeros((4,2))[::2].shape = (-1,) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: Incompatible shape for in-place modification. Use `.reshape()` to make a copy with the desired shape.
See also
numpy.shapeEquivalent getter function.
numpy.reshapeFunction similar to setting
shape.ndarray.reshapeMethod similar to setting
shape.
- size
Number of elements in the array.
Equal to
np.prod(a.shape), i.e., the product of the array’s dimensions.Notes
a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested
np.prod(a.shape), which returns an instance ofnp.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.Examples
>>> import numpy as np >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30
- sort(axis=-1, kind=None, order=None)
Sort an array in-place. Refer to numpy.sort for full documentation.
- Parameters:
axis (
int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.kind (
{'quicksort', 'mergesort', 'heapsort', 'stable'}, optional) – Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.order (
strorlistofstr, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
See also
numpy.sortReturn a sorted copy of an array.
numpy.argsortIndirect sort.
numpy.lexsortIndirect stable sort on multiple keys.
numpy.searchsortedFind elements in sorted array.
numpy.partitionPartial sort.
Notes
See numpy.sort for notes on the different sorting algorithms.
Examples
>>> import numpy as np >>> a = np.array([[1,4], [3,1]]) >>> a.sort(axis=1) >>> a array([[1, 4], [1, 3]]) >>> a.sort(axis=0) >>> a array([[1, 3], [1, 4]])
Use the order keyword to specify a field to use when sorting a structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) >>> a.sort(order='y') >>> a array([(b'c', 1), (b'a', 2)], dtype=[('x', 'S1'), ('y', '<i8')])
- squeeze(axis=None)
Remove axes of length one from a.
Refer to numpy.squeeze for full documentation.
See also
numpy.squeezeequivalent function
- std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
Returns the standard deviation of the array elements along given axis.
Refer to numpy.std for full documentation.
See also
numpy.stdequivalent function
- strides
Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element
(i[0], i[1], ..., i[n])in an array a is:offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in The N-dimensional array (ndarray).
Warning
Setting
arr.stridesis discouraged and may be deprecated in the future. numpy.lib.stride_tricks.as_strided should be preferred to create a new view of the same data in a safer way.Notes
Imagine an array of 32-bit integers (each 4 bytes):
x = np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be
(20, 4).See also
Examples
>>> import numpy as np >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) >>> y array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) >>> y.strides (48, 16, 4) >>> y[1,1,1] 17 >>> offset=sum(y.strides * np.array((1,1,1))) >>> offset/y.itemsize 17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) >>> x.strides (32, 4, 224, 1344) >>> i = np.array([3,5,2,2]) >>> offset = sum(i * x.strides) >>> x[3,5,2,2] 813 >>> offset / x.itemsize 813
- sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
Return the sum of the array elements over the given axis.
Refer to numpy.sum for full documentation.
See also
numpy.sumequivalent function
- swapaxes(axis1, axis2)
Return a view of the array with axis1 and axis2 interchanged.
Refer to numpy.swapaxes for full documentation.
See also
numpy.swapaxesequivalent function
- take(indices, axis=None, out=None, mode='raise')
Return an array formed from the elements of a at the given indices.
Refer to numpy.take for full documentation.
See also
numpy.takeequivalent function
- to_device()
- tobytes(order='C')
Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object is produced in C-order by default. This behavior is controlled by the
orderparameter.- Parameters:
order (
{'C', 'F', 'A'}, optional) – Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.- Returns:
s – Python bytes exhibiting a copy of a’s raw data.
- Return type:
See also
frombufferInverse of this operation, construct a 1-dimensional array from Python bytes.
Examples
>>> import numpy as np >>> x = np.array([[0, 1], [2, 3]], dtype='<u2') >>> x.tobytes() b'\x00\x00\x01\x00\x02\x00\x03\x00' >>> x.tobytes('C') == x.tobytes() True >>> x.tobytes('F') b'\x00\x00\x02\x00\x01\x00\x03\x00'
- tofile(fid, sep='', format='%s')
Write array to a file as text or binary (default).
Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().
- Parameters:
fid (
fileorstrorPath) – An open file object, or a string containing a filename.sep (
str) – Separator between array items for text output. If “” (empty), a binary file is written, equivalent tofile.write(a.tobytes()).format (
str) – Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.
Notes
This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.
When fid is a file object, array contents are directly written to the file, bypassing the file object’s
writemethod. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not supportfileno()(e.g., BytesIO).
- tolist()
Return the array as an
a.ndim-levels deep nested list of Python scalars.Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.
If
a.ndimis 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.- Parameters:
none
- Returns:
y – The possibly nested list of array elements.
- Return type:
object, orlistofobject, orlistoflistofobject, or...
Notes
The array may be recreated via
a = np.array(a.tolist()), although this may sometimes lose precision.Examples
For a 1D array,
a.tolist()is almost the same aslist(a), except thattolistchanges numpy scalars to Python scalars:>>> import numpy as np >>> a = np.uint32([1, 2]) >>> a_list = list(a) >>> a_list [np.uint32(1), np.uint32(2)] >>> type(a_list[0]) <class 'numpy.uint32'> >>> a_tolist = a.tolist() >>> a_tolist [1, 2] >>> type(a_tolist[0]) <class 'int'>
Additionally, for a 2D array,
tolistapplies recursively:>>> a = np.array([[1, 2], [3, 4]]) >>> list(a) [array([1, 2]), array([3, 4])] >>> a.tolist() [[1, 2], [3, 4]]
The base case for this recursion is a 0D array:
>>> a = np.array(1) >>> list(a) Traceback (most recent call last): ... TypeError: iteration over a 0-d array >>> a.tolist() 1
- tostring(order='C')
A compatibility alias for ~ndarray.tobytes, with exactly the same behavior.
Despite its name, it returns
bytesnotstrs.Deprecated since version 1.19.0.
- trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array.
Refer to numpy.trace for full documentation.
See also
numpy.traceequivalent function
- transpose(*axes)
Returns a view of the array with axes transposed.
Refer to numpy.transpose for full documentation.
- Parameters:
axes (
None,tupleofints, or n ints) –None or no argument: reverses the order of the axes.
tuple of ints: i in the j-th place in the tuple means that the array’s i-th axis becomes the transposed array’s j-th axis.
n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form).
- Returns:
p – View of the array with its axes suitably permuted.
- Return type:
See also
transposeEquivalent function.
ndarray.TArray property returning the array transposed.
ndarray.reshapeGive a new shape to an array without changing its data.
Examples
>>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> a array([[1, 2], [3, 4]]) >>> a.transpose() array([[1, 3], [2, 4]]) >>> a.transpose((1, 0)) array([[1, 3], [2, 4]]) >>> a.transpose(1, 0) array([[1, 3], [2, 4]])
>>> a = np.array([1, 2, 3, 4]) >>> a array([1, 2, 3, 4]) >>> a.transpose() array([1, 2, 3, 4])
- var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
Returns the variance of the array elements, along given axis.
Refer to numpy.var for full documentation.
See also
numpy.varequivalent function
- view([dtype][, type])
New view of array with the same data.
Note
Passing None for
dtypeis different from omitting the parameter, since the former invokesdtype(None)which is an alias fordtype('float64').- Parameters:
dtype (
data-typeorndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting thetypeparameter).type (
Python type, optional) – Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter results in type preservation.
Notes
a.view()is used two different ways:a.view(some_dtype)ora.view(dtype=some_dtype)constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.a.view(ndarray_subclass)ora.view(type=ndarray_subclass)just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.For
a.view(some_dtype), ifsome_dtypehas a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the last axis ofamust be contiguous. This axis will be resized in the result.Changed in version 1.23.0: Only the last axis needs to be contiguous. Previously, the entire array had to be C-contiguous.
Examples
>>> import numpy as np >>> x = np.array([(-1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> nonneg = np.dtype([("a", np.uint8), ("b", np.uint8)]) >>> y = x.view(dtype=nonneg, type=np.recarray) >>> x["a"] array([-1], dtype=int8) >>> y.a array([255], dtype=uint8)
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) >>> xv = x.view(dtype=np.int8).reshape(-1,2) >>> xv array([[1, 2], [3, 4]], dtype=int8) >>> xv.mean(0) array([2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20 >>> x array([(1, 20), (3, 4)], dtype=[('a', 'i1'), ('b', 'i1')])
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray) >>> z.a array([1, 3], dtype=int8)
Views share data:
>>> x[0] = (9, 10) >>> z[0] np.record((9, 10), dtype=[('a', 'i1'), ('b', 'i1')])
Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
>>> x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16) >>> y = x[:, ::2] >>> y array([[1, 3], [4, 6]], dtype=int16) >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) Traceback (most recent call last): ... ValueError: To change to a dtype of a different size, the last axis must be contiguous >>> z = y.copy() >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) array([[(1, 3)], [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])
However, views that change dtype are totally fine for arrays with a contiguous last axis, even if the rest of the axes are not C-contiguous:
>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4) >>> x.transpose(1, 0, 2).view(np.int16) array([[[ 256, 770], [3340, 3854]], [[1284, 1798], [4368, 4882]], [[2312, 2826], [5396, 5910]]], dtype=int16)
- pyorps.io.geo_dataset.rio_open(fp, mode='r', driver=None, width=None, height=None, count=None, crs=None, transform=None, dtype=None, nodata=None, sharing=False, opener=None, **kwargs)
Open a dataset for reading or writing.
The dataset may be located in a local file, in a resource located by a URL, or contained within a stream of bytes. This function accepts different types of fp parameters. However, it is almost always best to pass a string that has a dataset name as its value. These are passed directly to GDAL protocol and format handlers. A path to a zipfile is more efficiently used by GDAL than a Python ZipFile object, for example.
In read (‘r’) or read/write (‘r+’) mode, no keyword arguments are required: these attributes are supplied by the opened dataset.
In write (‘w’ or ‘w+’) mode, the driver, width, height, count, and dtype keywords are strictly required.
- Parameters:
fp (
str,os.PathLike,file-like, orrasterio.io.MemoryFile) – A filename or URL, a file object opened in binary (‘rb’) mode, a Path object, or one of the rasterio classes that provides the dataset-opening interface (has an open method that returns a dataset). Use a string when possible: GDAL can more efficiently access a dataset if it opens it natively.mode (
str, optional) – ‘r’ (read, the default), ‘r+’ (read/write), ‘w’ (write), or ‘w+’ (write/read).driver (
str, optional) – A short format driver name (e.g. “GTiff” or “JPEG”) or a list of such names (see GDAL docs at https://gdal.org/drivers/raster/index.html). In ‘w’ or ‘w+’ modes a single name is required. In ‘r’ or ‘r+’ modes the driver can usually be omitted. Registered drivers will be tried sequentially until a match is found. When multiple drivers are available for a format such as JPEG2000, one of them can be selected by using this keyword argument.width (
int, optional) – The number of columns of the raster dataset. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.height (
int, optional) – The number of rows of the raster dataset. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.count (
int, optional) – The count of dataset bands. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.crs (
str,dict, orCRS, optional) – The coordinate reference system. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.transform (
affine.Affine, optional) – Affine transformation mapping the pixel space to geographic space. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.dtype (
strornumpy.dtype, optional) – The data type for bands. For example: ‘uint8’ or rasterio.uint16. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.nodata (
int,float, ornan, optional) – Defines the pixel value to be interpreted as not valid data. Required in ‘w’ or ‘w+’ modes, it is ignored in ‘r’ or ‘r+’ modes.sharing (
bool, optional) – To reduce overhead and prevent programs from running out of file descriptors, rasterio maintains a pool of shared low level dataset handles. If True this function will use a shared handle if one is available. Multithreaded programs must avoid sharing and should set sharing to False.opener (
callable, optional) – A custom dataset opener which can serve GDAL’s virtual filesystem machinery via Python file-like objects. The underlying file-like object is obtained by calling opener with (fp, mode) or (fp, mode + “b”) depending on the format driver’s native mode. opener must return a Python file-like object that provides read, seek, tell, and close methods. Note: only one opener at a time per fp, mode pair is allowed.kwargs (optional) – These are passed to format drivers as directives for creating or interpreting datasets. For example: in ‘w’ or ‘w+’ modes a tiled=True keyword argument will direct the GeoTIFF format driver to create a tiled, rather than striped, TIFF.
- Returns:
rasterio.io.DatasetReader– If mode is “r”.rasterio.io.DatasetWriter– If mode is “r+”, “w”, or “w+”.
- Raises:
TypeError – If arguments are of the wrong Python type.
rasterio.errors.RasterioIOError – If the dataset can not be opened. Such as when there is no dataset with the given name.
rasterio.errors.DriverCapabilityError – If the detected format driver does not support the requested opening mode.
Examples
To open a local GeoTIFF dataset for reading using standard driver discovery and no directives:
>>> import rasterio >>> with rasterio.open('example.tif') as dataset: ... print(dataset.profile)
To open a local JPEG2000 dataset using only the JP2OpenJPEG driver:
>>> with rasterio.open( ... 'example.jp2', driver='JP2OpenJPEG') as dataset: ... print(dataset.profile)
To create a new 8-band, 16-bit unsigned, tiled, and LZW-compressed GeoTIFF with a global extent and 0.5 degree resolution:
>>> from rasterio.transform import from_origin >>> with rasterio.open( ... 'example.tif', 'w', driver='GTiff', dtype='uint16', ... width=720, height=360, count=8, crs='EPSG:4326', ... transform=from_origin(-180.0, 90.0, 0.5, 0.5), ... nodata=0, tiled=True, compress='lzw') as dataset: ... dataset.write(...)
pyorps.io.vector_loader module
PYORPS: An Open-Source Tool for Automated Power Line Routing
Reference: [1] Hofmann, M., Stetz, T., Kammer, F., Repo, S.: ‘PYORPS: An Open-Source Tool for
Automated Power Line Routing’, CIRED 2025 - 28th Conference and Exhibition on Electricity Distribution, 16 - 19 June 2025, Geneva, Switzerland
- class pyorps.io.vector_loader.Path(*args, **kwargs)[source]
Bases:
PurePathPurePath subclass that can make system calls.
Path represents a filesystem path but unlike PurePath, also offers methods to do system calls on path objects. Depending on your system, instantiating a Path will return either a PosixPath or a WindowsPath object. You can also instantiate a PosixPath or WindowsPath directly, but cannot instantiate a WindowsPath on a POSIX system or vice versa.
- __bytes__()
Return the bytes representation of the path. This is only recommended to use under Unix.
- __str__()
Return the string representation of the path, suitable for passing to system calls.
- absolute()[source]
Return an absolute version of this path by prepending the current working directory. No normalization or symlink resolution is performed.
Use resolve() to get the canonical path to a file.
- property anchor
The concatenation of the drive and root, or ‘’.
- as_posix()
Return the string representation of the path with forward (/) slashes.
- as_uri()
Return the path as a ‘file’ URI.
- classmethod cwd()[source]
Return a new path pointing to the current working directory (as returned by os.getcwd()).
- property drive
The drive prefix (letter or UNC path), if any.
- expanduser()[source]
Return a new path with expanded ~ and ~user constructs (as returned by os.path.expanduser)
- glob(pattern)[source]
Iterate over this subtree and yield all existing files (of any kind, including directories) matching the given relative pattern.
- hardlink_to(target)[source]
Make this path a hard link pointing to the same file as target.
Note the order of arguments (self, target) is the reverse of os.link’s.
- classmethod home()[source]
Return a new path pointing to the user’s home directory (as returned by os.path.expanduser(‘~’)).
- is_absolute()
True if the path is absolute (has both a root and, if applicable, a drive).
- is_file()[source]
Whether this path is a regular file (also True for symlinks pointing to regular files).
- is_relative_to(*other)
Return True if the path is relative to another path or False.
- is_reserved()
Return True if the path contains one of the special names reserved by the system, if any.
- iterdir()[source]
Iterate over the files in this directory. Does not yield any result for the special paths ‘.’ and ‘..’.
- joinpath(*args)
Combine this path with one or several arguments, and return a new path representing either a subpath (if all arguments are relative paths) or a totally different path (if one of the arguments is anchored).
- lchmod(mode)[source]
Like chmod(), except if the path points to a symlink, the symlink’s permissions are changed, rather than its target’s.
- link_to(target)[source]
Make the target path a hard link pointing to this path.
Note this function does not make this path a hard link to target, despite the implication of the function and argument names. The order of arguments (target, link) is the reverse of Path.symlink_to, but matches that of os.link.
Deprecated since Python 3.10 and scheduled for removal in Python 3.12. Use hardlink_to() instead.
- lstat()[source]
Like stat(), except if the path points to a symlink, the symlink’s status information is returned, rather than its target’s.
- match(path_pattern)
Return True if this path matches the given pattern.
- property name
The final path component, if any.
- open(mode='r', buffering=-1, encoding=None, errors=None, newline=None)[source]
Open the file pointed by this path and return a file object, as the built-in open() function does.
- property parent
The logical parent of the path.
- property parents
A sequence of this path’s logical parents.
- property parts
An object providing sequence-like access to the components in the filesystem path.
- read_text(encoding=None, errors=None)[source]
Open the file in text mode, read it, and close the file.
- relative_to(*other)
Return the relative path to another path identified by the passed arguments. If the operation is not possible (because this is not a subpath of the other path), raise ValueError.
- rename(target)[source]
Rename this path to the target path.
The target path may be absolute or relative. Relative paths are interpreted relative to the current working directory, not the directory of the Path object.
Returns the new Path instance pointing to the target path.
- replace(target)[source]
Rename this path to the target path, overwriting if that path exists.
The target path may be absolute or relative. Relative paths are interpreted relative to the current working directory, not the directory of the Path object.
Returns the new Path instance pointing to the target path.
- resolve(strict=False)[source]
Make the path absolute, resolving all symlinks on the way and also normalizing it.
- rglob(pattern)[source]
Recursively yield all existing files (of any kind, including directories) matching the given relative pattern, anywhere in this subtree.
- property root
The root of the path, if any.
- samefile(other_path)[source]
Return whether other_path is the same or not as this file (as returned by os.path.samefile()).
- stat(*, follow_symlinks=True)[source]
Return the result of the stat() system call on this path, like os.stat() does.
- property stem
The final path component, minus its last suffix.
- property suffix
The final component’s last suffix, if any.
This includes the leading period. For example: ‘.txt’
- property suffixes
A list of the final component’s suffixes, if any.
These include the leading periods. For example: [‘.tar’, ‘.gz’]
- symlink_to(target, target_is_directory=False)[source]
Make this path a symlink pointing to the target path. Note the order of arguments (link, target) is the reverse of os.symlink.
- touch(mode=438, exist_ok=True)[source]
Create this file with the given access mode, if it doesn’t exist.
- unlink(missing_ok=False)[source]
Remove this file or link. If the path is a directory, use rmdir() instead.
- with_name(name)
Return a new path with the file name changed.
- with_stem(stem)
Return a new path with the stem changed.
- with_suffix(suffix)
Return a new path with the file suffix changed. If the path has no suffix, add given suffix. If the given suffix is an empty string, remove the suffix from the path.
- class pyorps.io.vector_loader.SequenceMatcher(isjunk=None, a='', b='', autojunk=True)[source]
Bases:
objectSequenceMatcher is a flexible class for comparing pairs of sequences of any type, so long as the sequence elements are hashable. The basic algorithm predates, and is a little fancier than, an algorithm published in the late 1980’s by Ratcliff and Obershelp under the hyperbolic name “gestalt pattern matching”. The basic idea is to find the longest contiguous matching subsequence that contains no “junk” elements (R-O doesn’t address junk). The same idea is then applied recursively to the pieces of the sequences to the left and to the right of the matching subsequence. This does not yield minimal edit sequences, but does tend to yield matches that “look right” to people.
SequenceMatcher tries to compute a “human-friendly diff” between two sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the longest contiguous & junk-free matching subsequence. That’s what catches peoples’ eyes. The Windows(tm) windiff has another interesting notion, pairing up elements that appear uniquely in each sequence. That, and the method here, appear to yield more intuitive difference reports than does diff. This method appears to be the least vulnerable to syncing up on blocks of “junk lines”, though (like blank lines in ordinary text files, or maybe “<P>” lines in HTML files). That may be because this is the only method of the 3 that has a concept of “junk” <wink>.
Example, comparing two strings, and considering blanks to be “junk”:
>>> s = SequenceMatcher(lambda x: x == " ", ... "private Thread currentThread;", ... "private volatile Thread currentThread;") >>>
.ratio() returns a float in [0, 1], measuring the “similarity” of the sequences. As a rule of thumb, a .ratio() value over 0.6 means the sequences are close matches:
>>> print(round(s.ratio(), 3)) 0.866 >>>
If you’re only interested in where the sequences match, .get_matching_blocks() is handy:
>>> for block in s.get_matching_blocks(): ... print("a[%d] and b[%d] match for %d elements" % block) a[0] and b[0] match for 8 elements a[8] and b[17] match for 21 elements a[29] and b[38] match for 0 elements
Note that the last tuple returned by .get_matching_blocks() is always a dummy, (len(a), len(b), 0), and this is the only case in which the last tuple element (number of elements matched) is 0.
If you want to know how to change the first sequence into the second, use .get_opcodes():
>>> for opcode in s.get_opcodes(): ... print("%6s a[%d:%d] b[%d:%d]" % opcode) equal a[0:8] b[0:8] insert a[8:8] b[8:17] equal a[8:29] b[17:38]
See the Differ class for a fancy human-friendly file differencer, which uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines.
See also function get_close_matches() in this module, which shows how simple code building on SequenceMatcher can be used to do useful work.
Timing: Basic R-O is cubic time worst case and quadratic time expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear.
- __init__(isjunk=None, a='', b='', autojunk=True)[source]
Construct a SequenceMatcher.
Optional arg isjunk is None (the default), or a one-argument function that takes a sequence element and returns true iff the element is junk. None is equivalent to passing “lambda x: 0”, i.e. no elements are considered to be junk. For example, pass
lambda x: x in “ t”
if you’re comparing lines as sequences of characters, and don’t want to synch up on blanks or hard tabs.
Optional arg a is the first of two sequences to be compared. By default, an empty string. The elements of a must be hashable. See also .set_seqs() and .set_seq1().
Optional arg b is the second of two sequences to be compared. By default, an empty string. The elements of b must be hashable. See also .set_seqs() and .set_seq2().
Optional arg autojunk should be set to False to disable the “automatic junk heuristic” that treats popular elements as junk (see module documentation for more information).
- find_longest_match(alo=0, ahi=None, blo=0, bhi=None)[source]
Find longest matching block in a[alo:ahi] and b[blo:bhi].
By default it will find the longest match in the entirety of a and b.
If isjunk is not defined:
- Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where
alo <= i <= i+k <= ahi blo <= j <= j+k <= bhi
- and for all (i’,j’,k’) meeting those conditions,
k >= k’ i <= i’ and if i == i’, j <= j’
In other words, of all maximal matching blocks, return one that starts earliest in a, and of all those maximal matching blocks that start earliest in a, return the one that starts earliest in b.
>>> s = SequenceMatcher(None, " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=0, b=4, size=5)
If isjunk is defined, first the longest matching block is determined as above, but with the additional restriction that no junk element appears in the block. Then that block is extended as far as possible by matching (only) junk elements on both sides. So the resulting block never matches on junk except as identical junk happens to be adjacent to an “interesting” match.
Here’s the same example as before, but considering blanks to be junk. That prevents “ abcd” from matching the “ abcd” at the tail end of the second sequence directly. Instead only the “abcd” can match, and matches the leftmost “abcd” in the second sequence:
>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=1, b=0, size=4)
If no blocks match, return (alo, blo, 0).
>>> s = SequenceMatcher(None, "ab", "c") >>> s.find_longest_match(0, 2, 0, 1) Match(a=0, b=0, size=0)
- get_grouped_opcodes(n=3)[source]
Isolate change clusters by eliminating ranges with no changes.
Return a generator of groups with up to n lines of context. Each group is in the same format as returned by get_opcodes().
>>> from pprint import pprint >>> a = list(map(str, range(1,40))) >>> b = a[:] >>> b[8:8] = ['i'] # Make an insertion >>> b[20] += 'x' # Make a replacement >>> b[23:28] = [] # Make a deletion >>> b[30] += 'y' # Make another replacement >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes())) [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)], [('equal', 16, 19, 17, 20), ('replace', 19, 20, 20, 21), ('equal', 20, 22, 21, 23), ('delete', 22, 27, 23, 23), ('equal', 27, 30, 23, 26)], [('equal', 31, 34, 27, 30), ('replace', 34, 35, 30, 31), ('equal', 35, 38, 31, 34)]]
- get_matching_blocks()[source]
Return list of triples describing matching subsequences.
Each triple is of the form (i, j, n), and means that a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i and in j. New in Python 2.5, it’s also guaranteed that if (i, j, n) and (i’, j’, n’) are adjacent triples in the list, and the second is not the last triple in the list, then i+n != i’ or j+n != j’. IOW, adjacent triples never describe adjacent equal blocks.
The last triple is a dummy, (len(a), len(b), 0), and is the only triple with n==0.
>>> s = SequenceMatcher(None, "abxcd", "abcd") >>> list(s.get_matching_blocks()) [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]
- get_opcodes()[source]
Return list of 5-tuples describing how to turn a into b.
Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the tuple preceding it, and likewise for j1 == the previous j2.
The tags are strings, with these meanings:
‘replace’: a[i1:i2] should be replaced by b[j1:j2] ‘delete’: a[i1:i2] should be deleted.
Note that j1==j2 in this case.
- ‘insert’: b[j1:j2] should be inserted at a[i1:i1].
Note that i1==i2 in this case.
‘equal’: a[i1:i2] == b[j1:j2]
>>> a = "qabxcd" >>> b = "abycdf" >>> s = SequenceMatcher(None, a, b) >>> for tag, i1, i2, j1, j2 in s.get_opcodes(): ... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" % ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))) delete a[0:1] (q) b[0:0] () equal a[1:3] (ab) b[0:2] (ab) replace a[3:4] (x) b[2:3] (y) equal a[4:6] (cd) b[3:5] (cd) insert a[6:6] () b[5:6] (f)
- quick_ratio()[source]
Return an upper bound on ratio() relatively quickly.
This isn’t defined beyond that it is an upper bound on .ratio(), and is faster to compute.
- ratio()[source]
Return a measure of the sequences’ similarity (float in [0,1]).
Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is 1 if the sequences are identical, and 0 if they have nothing in common.
.ratio() is expensive to compute if you haven’t already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound.
>>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.quick_ratio() 0.75 >>> s.real_quick_ratio() 1.0
- real_quick_ratio()[source]
Return an upper bound on ratio() very quickly.
This isn’t defined beyond that it is an upper bound on .ratio(), and is faster to compute than either .ratio() or .quick_ratio().
- set_seq1(a)[source]
Set the first sequence to be compared.
The second sequence to be compared is not changed.
>>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq1("bcde") >>> s.ratio() 1.0 >>>
SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences.
See also set_seqs() and set_seq2().
- set_seq2(b)[source]
Set the second sequence to be compared.
The first sequence to be compared is not changed.
>>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq2("abcd") >>> s.ratio() 1.0 >>>
SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences.
See also set_seqs() and set_seq1().
- class pyorps.io.vector_loader.ThreadPoolExecutor(max_workers=None, thread_name_prefix='', initializer=None, initargs=())[source]
Bases:
Executor- __init__(max_workers=None, thread_name_prefix='', initializer=None, initargs=())[source]
Initializes a new ThreadPoolExecutor instance.
- Parameters:
max_workers – The maximum number of threads that can be used to execute the given calls.
thread_name_prefix – An optional name prefix to give our threads.
initializer – A callable used to initialize worker threads.
initargs – A tuple of arguments to pass to the initializer.
- map(fn, *iterables, timeout=None, chunksize=1)
Returns an iterator equivalent to map(fn, iter).
- Parameters:
fn – A callable that will take as many arguments as there are passed iterables.
timeout – The maximum number of seconds to wait. If None, then there is no limit on the wait time.
chunksize – The size of the chunks the iterable will be broken into before being passed to a child process. This argument is only used by ProcessPoolExecutor; it is ignored by ThreadPoolExecutor.
- Returns:
map(func, *iterables) but the calls may be evaluated out-of-order.
- Return type:
An iterator equivalent to
- Raises:
TimeoutError – If the entire result iterator could not be generated before the given timeout.
- shutdown(wait=True, *, cancel_futures=False)[source]
Clean-up the resources associated with the Executor.
It is safe to call this method several times. Otherwise, no other methods can be called after this one.
- Parameters:
wait – If True then shutdown will not return until all running futures have finished executing and the resources used by the executor have been reclaimed.
cancel_futures – If True then shutdown will cancel all pending futures. Futures that are completed or running will not be cancelled.
- exception pyorps.io.vector_loader.WFSConnectionError[source]
Bases:
WFSErrorException raised for connection issues with WFS services.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- exception pyorps.io.vector_loader.WFSError[source]
Bases:
ExceptionBase exception for WFS-related errors.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- exception pyorps.io.vector_loader.WFSLayerNotFoundError[source]
Bases:
WFSErrorException raised when a requested layer cannot be found.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- exception pyorps.io.vector_loader.WFSResponseParsingError[source]
Bases:
WFSErrorException raised when parsing WFS responses fails.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- pyorps.io.vector_loader.box(minx, miny, maxx, maxy, ccw=True)[source]
Return a rectangular polygon with configurable normal vector.
- pyorps.io.vector_loader.load_from_wfs(url, layer, bbox=None, mask=None, filter_params=None, auto_match=True, max_workers=4)[source]
Load data from a Web Feature Service (WFS) using chunked loading.
- Parameters:
url (
str) – The base URL of the WFS servicelayer (
str) – Name of the layer to retrievebbox (
Union[Polygon,GeoDataFrame,GeoSeries,tuple[float,float,float,float],None]) – Optional bounding box to limit the query extent (minx, miny, maxx, maxy)mask (
Union[Polygon,GeoDataFrame,tuple,None]) – Optional geometry mask to limit the query (Shapely Polygon, GeoDataFrame, or GeoSeries)filter_params (
Optional[dict]) – Additional WFS parameters to filter resultsauto_match (
bool) – Whether to attempt finding similar layer names if exact match not foundmax_workers (
int) – Maximum number of parallel threads to use
- Return type:
- Returns:
Loaded GeoDataFrame or None if no data could be loaded
- Raises:
WFSLayerNotFoundError – If the layer cannot be found and auto_match is False
- pyorps.io.vector_loader.unary_union(geoms)
Return the union of a sequence of geometries.
Usually used to convert a collection into the smallest set of polygons that cover the same area.
Module contents
Input/output operations for geospatial data.
This module provides: 1. Base classes for working with vector and raster geospatial data 2. Dataset implementations for local and in-memory data sources 3. Web Feature Service (WFS) data loading capabilities 4. Factory functions to create appropriate dataset instances
- class pyorps.io.GeoDataset(file_source, crs=None)[source]
Bases:
ABC-
data:
Union[GeoDataFrame,ndarray,None] = None
-
data:
- class pyorps.io.InMemoryRasterDataset(file_source, crs, transform)[source]
Bases:
RasterDataset-
data:
Union[GeoDataFrame,ndarray,None] = None
-
data:
- class pyorps.io.InMemoryVectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
VectorDataset- Parameters:
- __init__(file_source, crs=None, bbox=None, mask=None)
-
data:
Union[GeoDataFrame,ndarray,None] = None
- class pyorps.io.LocalRasterDataset(file_source, crs=None)[source]
Bases:
RasterDataset-
data:
Union[GeoDataFrame,ndarray,None] = None
-
data:
- class pyorps.io.LocalVectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
InMemoryVectorDataset- Parameters:
- __init__(file_source, crs=None, bbox=None, mask=None)
- correct_crs()
-
data:
Union[GeoDataFrame,ndarray,None] = None
- post_loading()
- class pyorps.io.RasterDataset(file_source, crs=None)[source]
Bases:
GeoDataset,ABC-
data:
Union[GeoDataFrame,ndarray,None] = None
- abstractmethod load_data(**kwargs)
-
data:
- class pyorps.io.VectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
GeoDataset,ABC- Parameters:
-
data:
Union[GeoDataFrame,ndarray,None] = None
- abstractmethod load_data(**kwargs)
- exception pyorps.io.WFSConnectionError[source]
Bases:
WFSErrorException raised for connection issues with WFS services.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- exception pyorps.io.WFSError[source]
Bases:
ExceptionBase exception for WFS-related errors.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- exception pyorps.io.WFSLayerNotFoundError[source]
Bases:
WFSErrorException raised when a requested layer cannot be found.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- exception pyorps.io.WFSResponseParsingError[source]
Bases:
WFSErrorException raised when parsing WFS responses fails.
- __init__(*args, **kwargs)
- add_note()
Exception.add_note(note) – add a note to the exception
- args
- with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
- class pyorps.io.WFSVectorDataset(file_source, crs=None, bbox=None, mask=None)[source]
Bases:
LocalVectorDataset- Parameters:
- __init__(file_source, crs=None, bbox=None, mask=None)
- apply_bbox()
- correct_crs()
-
data:
Union[GeoDataFrame,ndarray,None] = None
- post_loading()
- pyorps.io.initialize_geo_dataset(file_source, crs=None, bbox=None, mask=None, transform=None)[source]
Factory function to create the appropriate GeoDataset instance based on the provided input.
- Parameters:
file_source (
Union[str,dict,GeoDataFrame,GeoSeries,ndarray]) – Source data (file path, GeoDataFrame, URL dict, numpy array, etc.)bbox (
Union[Polygon,GeoDataFrame,GeoSeries,tuple[float,float,float,float],None]) – Bounding box for vector datasetsmask (
Union[Polygon,GeoDataFrame,tuple,None]) – Mask for vector datasetstransform (
Optional[Affine]) – Affine transform for in-memory raster datasets
- Return type:
- Returns:
An appropriate GeoDataset subclass instance
Examples
# From local vector file vector_dataset = create_geo_dataset(“path/to/shapefile.shp”, crs=”EPSG:4326”)
# From GeoDataFrame vector_dataset = create_geo_dataset(gdf, bbox=(x1, y1, x2, y2))
# From WFS source wfs_dataset = create_geo_dataset({“url”: “https://example.com/wfs”,
“layer”: “layer1”})
# From local raster file raster_dataset = create_geo_dataset(“path/to/dem.tif”)
# From numpy array raster_dataset = create_geo_dataset(array_data, transform=transform,
crs=”EPSG:4326”)
- pyorps.io.load_from_wfs(url, layer, bbox=None, mask=None, filter_params=None, auto_match=True, max_workers=4)[source]
Load data from a Web Feature Service (WFS) using chunked loading.
- Parameters:
url (
str) – The base URL of the WFS servicelayer (
str) – Name of the layer to retrievebbox (
Union[Polygon,GeoDataFrame,GeoSeries,tuple[float,float,float,float],None]) – Optional bounding box to limit the query extent (minx, miny, maxx, maxy)mask (
Union[Polygon,GeoDataFrame,tuple,None]) – Optional geometry mask to limit the query (Shapely Polygon, GeoDataFrame, or GeoSeries)filter_params (
Optional[dict]) – Additional WFS parameters to filter resultsauto_match (
bool) – Whether to attempt finding similar layer names if exact match not foundmax_workers (
int) – Maximum number of parallel threads to use
- Return type:
- Returns:
Loaded GeoDataFrame or None if no data could be loaded
- Raises:
WFSLayerNotFoundError – If the layer cannot be found and auto_match is False