Grids🍋

`GridObject` 🍋

Bases: GeoRefObject

Base object representing a grid made of individual cells, supporting any dimension (2D, 3D, 4D, etc.).:

all indexed along the provided dimensions (e.g. UV in 2D, UVW in 3D, etc.)
with X, Y, Z coordinates at their center

There is no assumption on the grid actual representation and could be unstructured, irregular, regular, etc. You may use one of the provided children class or inherit this class to implement your own representation (e.g. hexahedrons).

Parameters:

Name	Type	Description	Default
`name`	`str`	object name.	required
`geometry`	`Optional[Data]`	geometry data, respectively X, Y and Z.	`None`
`coords`	`Optional[Data]`	grid coordinates, typically U, V, W.	`None`
`extra_coord_labels`	`List[str]`	labels corresponding to extra coordinates beyond the grid axes. For example, 4D cube would have an extra-dimension T beyond (U, V, W), therefore: `extra_coord_labels=["T"]`	`[]`
`read_only`	`Union[bool, List[bool]]`	whether XYZ coordinates are read-only. Use a single boolean for all, or a list a 3 booleans, respectively for each axis.	`True`

Raises:

Type	Description
`ValueError`	Object name is not provided. Shape is missing or incorrect. Number of coordinate labels doesn't match given coordinates shape.

Attributes:

Name	Type	Description
`n_cells`	`int`	total number of grid cells.
`origin`	`Vector`	grid origin.
`shape`	`ndarray`	shape of the grid.
`dimension`	`int`	number of dimensions.

`builtins: List[str]` `property` 🍋

Return the built-in object property names, defaults to X, Y, Z.

Returns:

Type	Description
`List[str]`	Built-in property names.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.builtins

Output

['X', 'Y', 'Z']

`default_support: AttributeSupportType` `property` 🍋

Return the default support type use for setting/getting attributes in the database.

Returns:

Type	Description
`AttributeSupportType`	The default support type.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.default_support

Output

<AttributeSupportType.NODE: 'NODE'>

`dimension: int` `property` 🍋

Return the number of dimensions of the grid (2D, 3D, 4D, etc.).

Returns:

Type	Description
`int`	The number of dimensions.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.dimension

Output

`n_cells: int` `property` 🍋

Return the total number of cells.

Returns:

Type	Description
`int`	The product of the number of cells in each dimension.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.n_cells

Output

`name: str` `property` `writable` 🍋

Return the object name.

Returns:

Type	Description
`str`	Object name.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.name

Output

'MyPointCloud'

`origin: Vector` `property` 🍋

Return the origin of the grid.

Returns:

Type	Description
`Vector`	Grid origin 3D point (X, Y, Z).

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.origin

Output

array([0., 0., 0.])

`shape: np.ndarray` `property` 🍋

Return the shape of the grid in each direction.

Returns:

Type	Description
`ndarray`	Grid shape as list.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.shape

Output

array([4, 3, 2])

`aggregate(properties=None, agg_methods=[AggregationMethod.SUM, AggregationMethod.MIN, AggregationMethod.MAX, AggregationMethod.MEAN])` 🍋

Return the object data grouped by location (X, Y) and grid index (U, V).

Parameters:

Name	Type	Description	Default
`properties`	`List[str]`	list of properties name to transfer to the GIS Object. Setting to None or empty list will select all existing properties.	`None`
`agg_methods`	`List[Union[AggregationMethod, Callable]]`	aggregation data methods. See Pandas - Group By to find out supported methods.	`[SUM, MIN, MAX, MEAN]`

Returns:

Type	Description
`DataFrame`	DataFrame of XY coordinates with aggregated data.

Raises:

Type	Description
`ValueError`	No aggregation method is provided. 'X', 'Y', 'U' or 'V' are part of the properties.
`AttributeError`	One of the aggregation methods is not supported.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.aggregate()

Output

    X    Y  U  V  Z_sum  Z_min  Z_max  Z_mean  W_sum  W_min  W_max  W_mean
0   0.5  0.5  0  0    2.0    0.5    1.5     1.0      1      0      1     0.5
1   0.5  1.5  0  1    2.0    0.5    1.5     1.0      1      0      1     0.5
2   0.5  2.5  0  2    2.0    0.5    1.5     1.0      1      0      1     0.5
3   1.5  0.5  1  0    2.0    0.5    1.5     1.0      1      0      1     0.5
4   1.5  1.5  1  1    2.0    0.5    1.5     1.0      1      0      1     0.5
5   1.5  2.5  1  2    2.0    0.5    1.5     1.0      1      0      1     0.5
6   2.5  0.5  2  0    2.0    0.5    1.5     1.0      1      0      1     0.5
7   2.5  1.5  2  1    2.0    0.5    1.5     1.0      1      0      1     0.5
8   2.5  2.5  2  2    2.0    0.5    1.5     1.0      1      0      1     0.5
9   3.5  0.5  3  0    2.0    0.5    1.5     1.0      1      0      1     0.5
10  3.5  1.5  3  1    2.0    0.5    1.5     1.0      1      0      1     0.5
11  3.5  2.5  3  2    2.0    0.5    1.5     1.0      1      0      1     0.5

`axis(axis=None)` 🍋

Return the cell unit vector for the given axis. If axis is not provided, the sum of all vectors is returned.

Parameters:

Name	Type	Description	Default
`axis`	`Optional[Axis]`	given axis to get the vector from. If None, the sum of all axes is computed.	`None`

Returns:

Type	Description
`Vector`	Grid directional vector (X, Y, Z).

Raises:

Type	Description
`ValueError`	Axis is out of dimensions when provided.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.axis(1)
vx.axis()

Output

array([0., 1., 0.])
array([1., 1., 1.])

`bounds(region=None)` 🍋

Return the object bounds, respectively separate minimum and maximum X, Y and Z.

Parameters:

Name	Type	Description	Default
`region`	`Optional[str]`	name of the region or condition to optionally filter the data to be returned.	`None`

Returns:

Type	Description
`ndarray`	XYZ bounding coordinates.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.bounds()

Output

array([[ 0,  1,  2],
    [27, 28, 29]])

`centroid(region=None)` 🍋

Return the object centroid, respectively separate mean X, Y and Z.

Parameters:

Name	Type	Description	Default
`region`	`Optional[str]`	name of the region or condition to optionally filter the data to be returned.	`None`

Returns:

Type	Description
`ndarray`	XYZ centroid coordinates.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.centroid()

Output

array([13.5, 14.5, 15.5])

`check_coords_bounds(coords)` 🍋

Check whether grid coordinates are out of bounds or not. If true, the exception will be returned, otherwise None.

Parameters:

Name	Type	Description	Default
`coords`	`Vector`	grid coordinates as (dimension,) or (N, dimension)	required

Returns:

Name	Type	Description
`ValueError`	`ValueError`	containing the error or None if coordinates are valid.

Example

Example 1Example 2

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.check_coords_bounds([0,0,0])

Output

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.check_coords_bounds([3,5,0])

Output

ValueError('idx 5.0 is out of bounds [0, 2] for axis 1')

`convert_to_gis_object(name, properties=None, agg_methods=[AggregationMethod.SUM, AggregationMethod.MIN, AggregationMethod.MAX, AggregationMethod.MEAN], region=None, region_agg=None)` 🍋

Create a new GISObject from the GeoRefObject. The original GeoRefObject is not modified.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the GISObject to create.	required
`properties`	`Optional[List[str]]`	list of properties name to transfer to the GIS Object. Setting to None or empty list will select all existing properties.	`None`
`agg_methods`	`List[Union[AggregationMethod, Callable]]`	aggregation data methods. See Pandas - Group By to find out supported methods.	`[SUM, MIN, MAX, MEAN]`
`region`	`Optional[str]`	Object region or condition to select data from.	`None`
`region_agg`	`Optional[RegionAggregationMethod]`	Aggregation region method.	`None`

Returns:

Type	Description
`GISObject`	GeoLime GISObject.

Raises:

Type	Description
`ValueError`	'X' or 'Y' are part of the properties.
`AttributeError`	One of the aggregation methods is not supported.

`coords(region=None)` 🍋

Return the object coordinates, respectively X, Y, and Z as data arrays.

Parameters:

Name	Type	Description	Default
`region`	`Optional[str]`	name of the region or condition to optionally filter the data to be returned.	`None`

Returns:

Type	Description
`ndarray`	XYZ coordinates.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.coords()

Output

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],
    [24, 25, 26],
    [27, 28, 29]])

`coords2idx(coords)` 🍋

Get the global grid index of the cell corresponding to the given (U, V, W) coordinates. See idx2coords() for inverse function.

Parameters:

Name	Type	Description	Default
`coords`	`Union[List[int], List[List[int]], ndarray]`	Grid coordinates as (n_dim,) or (N, n_dim)	required

Returns:

Type	Description
`Union[int, ndarray]`	The global index or list of indices.

Raises:

Type	Description
`ValueError`	Grid coords are out of bounds.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.coords2idx([1, 0, 0])

Output

`coords2xyz(coords)` 🍋

Get the (X, Y, Z) coordinates of the center of the cell corresponding to the given coordinate indices. Note that it returns the theoretical (X, Y, Z) position which may not exist (e.g. see BlockModel) or has been moved (e.g. actual positions have changed).

Parameters:

Name	Type	Description	Default
`coords`	`Union[List[int], List[List[int]], ndarray]`	Grid coordinates as (n_dim,) or (N, n_dim)	required

Returns:

Type	Description
`ndarray`	The (X, Y, Z) coordinates as an array of shape (n_dim,) if coords is a single
`ndarray`	coordinates set (n_dim,) or as an array of shape (N, n_dim) if coords is an array
`ndarray`	of N coordinates sets (N, n_dim).

Raises:

Type	Description
`ValueError`	Grid coords doesn't have the expected dimension. Grid coords are out of bounds.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.coords2xyz([[1, 0, 0], [3, 0, 1]])

Output

array([[1.5, 0.5, 0.5],
       [3.5, 0.5, 1.5]])

`copy(name, attributes=None)` 🍋

Copy the object.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the new object.	required
`attributes`	`Optional[Union[List[str], str]]`	list of attributes to copy.	`None`

Returns:

Type	Description
	GeoRefObject

Raises:

Type	Description
`ValueError`	Given attributes (properties and regions) do not exist.

`data(names, region=None, support=None)` 🍋

Return the attribute data corresponding to the given name(s) for the given support type and optionally in the given region. If no region is specified, it will return the whole data. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`names`	`Union[List[str], str]`	single or list of names to get data from.	required
`region`	`str`	name of the region or condition to optionally filter the data to be returned.	`None`
`support`	`AttributeSupportType`	type supporting the region (e.g. node = vertex, element = triangles).	`None`

Returns:

Type	Description
`ndarray`	Underlying data array associated to the given attribute names.

Raises:

Type	Description
`ValueError`	Attribute does not exist. Support type is not valid.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)
xyz_region = xyz_sum < 10
obj.set_region("Lower10", xyz_region)
point_cloud.data("XYZ", "Lower10")

Output

array([3])

`describe(properties=None, **kwargs)` 🍋

Generate descriptive statistics. For kwargs, see Pandas - Describe.

Parameters:

Name	Type	Description	Default
`properties`	`Optional[List[str]]`	List of names to get data summary from.	`None`

Returns:

Type	Description
`DataFrame`	pd.DataFrame

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.describe()

Output

               X          Y          Z
count  10.000000  10.000000  10.000000
mean   13.500000  14.500000  15.500000
std     9.082951   9.082951   9.082951
min     0.000000   1.000000   2.000000
25%     6.750000   7.750000   8.750000
50%    13.500000  14.500000  15.500000
75%    20.250000  21.250000  22.250000
max    27.000000  28.000000  29.000000

`downscale(name, discr)` 🍋

Downscale the GridObject by a given discretisation.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the newly created GridObject.	required
`discr`	`Vector`	Discretisation of the new GridObject.	required

Returns:

Type	Description
`None`	GridObject object downscaled.

`element_count(region=None)` 🍋

Return the actual number of cells defined in the grid. This number may be different from n_cells depending on the grid type. Same as sample_count().

Parameters:

Name	Type	Description	Default
`region`	`Optional[str]`	name of the region to optionally filter the data to be counted.	`None`

Returns:

Type	Description
`int`	Number of cells defined.

Example

Count all the elements in the GridObjectCount all the elements within a given region

import geolime as geo
vx = geo.Voxel("GridObj", [4, 3], axis=[[1., 0., 0.], [0., 1., 0.]])
vx.element_count()

Output

import geolime as geo
import numpy as np
vx = geo.Voxel("GridObj", [4, 3], axis=[[1., 0., 0.], [0., 1., 0.]])

vx.set_property(
    name="PROP",
    data=np.array([1.2, 5.1, 6., 1.4, 1.6, 2.9, 4.1, 8.2, 6.1, 8.7, 3.9, 9.1]),
)
vx.set_region_condition(
    name="REG",
    condition="PROP < 5.5",
)
vx.element_count(region="REG")

Output

`generate_attribute_name(prefix='_', support=None)` 🍋

Return a random unused name with the given prefix.

Parameters:

Name	Type	Description	Default
`prefix`	`str`	Prefix to start the attribute name with.	`'_'`
`support`	`Optional[AttributeSupportType]`	Type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Returns:

Type	Description
`str`	The attribute name string.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.generate_attribute_name(prefix="new_prop")

Output

'new_prop1b750b'

`idx2coords(idx)` 🍋

Get the grid coordinates of the cell corresponding to the given global grid index. See coords2idx() for inverse function.

Parameters:

Name	Type	Description	Default
`idx`	`Union[int, List[int], ndarray]`	global index or list of indices - index must be in range [0, n_cells[	required

Returns:

Type	Description
`ndarray`	The grid coordinates as an array of shape (n_dim,) if idx is an integer or as an array of shape (N, n_dim) if idx is an array of size N.

Raises:

Type	Description
`ValueError`	idx is out of bounds.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.idx2coords(3)

Output

array([3, 0, 0])

`indexed_coords(region=None)` 🍋

Return the object geometry along with its indexed coordinates.

Parameters:

Name	Type	Description	Default
`region`	`str`	Region or condition to filter the data.	`None`

Returns:

Type	Description
`ndarray`	Object geometry and indices, respectively X, Y, Z, coords data arrays.

Example

import geolime as geo
vx = geo.Voxel("MyGridObj", [4, 3, 2])
vx.indexed_coords()

Output

array([[0.5, 0.5, 0.5, 0. , 0. , 0. ],
    [1.5, 0.5, 0.5, 1. , 0. , 0. ],
    [2.5, 0.5, 0.5, 2. , 0. , 0. ],
    [3.5, 0.5, 0.5, 3. , 0. , 0. ],
    [0.5, 1.5, 0.5, 0. , 1. , 0. ],
    [1.5, 1.5, 0.5, 1. , 1. , 0. ],
    [2.5, 1.5, 0.5, 2. , 1. , 0. ],
    [3.5, 1.5, 0.5, 3. , 1. , 0. ],
    [0.5, 2.5, 0.5, 0. , 2. , 0. ],
    [1.5, 2.5, 0.5, 1. , 2. , 0. ],
    [2.5, 2.5, 0.5, 2. , 2. , 0. ],
    [3.5, 2.5, 0.5, 3. , 2. , 0. ],
    [0.5, 0.5, 1.5, 0. , 0. , 1. ],
    [1.5, 0.5, 1.5, 1. , 0. , 1. ],
    [2.5, 0.5, 1.5, 2. , 0. , 1. ],
    [3.5, 0.5, 1.5, 3. , 0. , 1. ],
    [0.5, 1.5, 1.5, 0. , 1. , 1. ],
    [1.5, 1.5, 1.5, 1. , 1. , 1. ],
    [2.5, 1.5, 1.5, 2. , 1. , 1. ],
    [3.5, 1.5, 1.5, 3. , 1. , 1. ],
    [0.5, 2.5, 1.5, 0. , 2. , 1. ],
    [1.5, 2.5, 1.5, 1. , 2. , 1. ],
    [2.5, 2.5, 1.5, 2. , 2. , 1. ],
    [3.5, 2.5, 1.5, 3. , 2. , 1. ]])

`internals(support=None)` 🍋

Return list of all existing internal properties for the specified support. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`support`	`Optional[AttributeSupportType]`	type supporting the property (e.g. node = vertex, element = triangles).	`None`

Returns:

Type	Description
`List`	List containing all object internal property names.

`plot_2d(property, agg_method, region=None, region_agg=None, interactive_map=False, interactive_max_sample=5000, **kwargs)` 🍋

Display the data on a regular plot or an interactive map.

Note

interactive map requires folium and mapclassify packages to be installed.
a CRS must be defined for the data to be properly plotted. If the data doesn't have a CRS, the Project CRS will be used by default. See Project.
interactive map works within a Jupyter Notebook context, please see folium for more information.

Parameters:

Name	Type	Description	Default
`property`	`str`	object attribute to aggregate and display.	required
`agg_method`	`AggregationMethod`	aggregation data method. See Pandas - Group By to find out supported methods.	required
`region`	`Optional[str]`	Object region or condition to select data from.	`None`
`region_agg`	`Optional[RegionAggregationMethod]`	Aggregation region method.	`None`
`interactive_map`	`bool`	if True, `folium` interactive map is used.	`False`
`interactive_max_sample`	`int`	maximum number of samples to display in interactive mode. Above 10 000 samples, loading time and interactivity will be impacted.	`5000`
`**kwargs`		extra arguments to pass along to GeoPandas - Plot or GeoPandas - Explore.	`{}`

Returns:

Type	Description
`Union[Axes, Map]`	A folium Map in interactive mode, or an instance of Matplotlib Axes otherwise.

Raises:

Type	Description
`ValueError`	Packages are missing for interactive mode.

`properties(support=None)` 🍋

Return list of all existing properties for the specified support. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`support`	`AttributeSupportType`	type supporting the property (e.g. node = vertex, element = triangles).	`None`

Returns:

Type	Description
`List[str]`	List containing all object property names.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)
point_cloud.properties()

Output

['X', 'Y', 'Z', 'XYZ']

`property(name, support=None, return_none=False)` 🍋

Return the object property for the given name for the given support type. The support databased are disjoints, so it is possible to have properties carried by the nodes but not by the elements and vice-versa. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	name to get property from.	required
`support`	`AttributeSupportType`	type supporting the property (e.g. node = vertex, element = triangles).	`None`
`return_none`	`bool`	if True, returns None instead of raising an error.	`False`

Returns:

Type	Description
`ObjectProperty`	Object property associated to the given name.

Raises:

Type	Description
`ValueError`	Property does not exist. Attribute is not a property. Support type is not valid.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)
point_cloud.property("XYZ")

Output

ObjectProperty
└─data()
└─dynamic ⇨ False
└─expr ⇨ None
└─kind ⇨ None
└─name ⇨ XYZ
└─read_only ⇨ False
└─unit ⇨ None

`read_file(filepath, attributes=None)` `classmethod` 🍋

Read the object from its folder path. If it does not end with .geo, the suffix will automatically be appended. See global read_file() provided for convenience.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	path to the object folder path.	required
`attributes`	`Optional[List[str]]`	list of attributes to read from the object.	`None`

Returns:

Type	Description
`Union[GeoRefObject, GISObject]`	The newly created object read from the file.

Raise

ValueError: - if the path is invalid. - if the format version is not found in the manifest. - if the object type in the manifest does not match this object reader. - if there is any data file read error.

`read_manifest(filepath)` `staticmethod` 🍋

Read the manifest first from the given object folder path (ending with .geo).

Parameters:

Name	Type	Description	Default
`filepath`	`str`	path to the object folder path.	required

Raise

ValueError: Path is invalid.

Returns:

Type	Description
	The contents of the manifest file as JSON dictionary.

`refresh_attributes(names, support=None)` 🍋

Recompute the given attribute(s) for the given support type. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`names`	`Union[List[str], str]`	list of names or single name of the attribute(s) to refresh.	required
`support`	`AttributeSupportType`	type supporting the region (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid.

`region(name, support=None, return_none=False)` 🍋

Return the object region for the given name for the given support type. The support databased are disjoints, so it is possible to have regions carried by the nodes but not by the elements and vice-versa. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	name to get region from.	required
`support`	`AttributeSupportType`	type supporting the region (e.g. node = vertex, element = triangles).	`None`
`return_none`	`bool`	if True, returns None instead of raising an error.	`False`

Returns:

Type	Description
`ObjectRegion`	Object region associated to the given name.

Raises:

Type	Description
`ValueError`	Region does not exist. Attribute is not a region. Support type is not valid.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)
xyz_region = xyz_sum < 10
point_cloud.set_region("Lower10", xyz_region)
point_cloud.region("Lower10")

Output

ObjectRegion
└─data()
└─dynamic ⇨ False
└─expr ⇨ None
└─name ⇨ Lower10
└─read_only ⇨ False

`region_mask(region, support=None)` 🍋

Get the boolean mask corresponding to the given region.

Parameters:

Name	Type	Description	Default
`region`	`str`	region or condition to filter data.	required
`support`	`Optional[AttributeSupportType]`	type supporting the property (e.g. node = vertex, element = triangles).	`None`

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)
xyz_region = xyz_sum < 10
point_cloud.set_region("Lower10", xyz_region)
point_cloud.region_mask("Lower10")

Output

array([ True, False, False, False, False, False, False, False, False, False])

`regions(support=None)` 🍋

Return a list of regions for a given GeoRefObject.

Parameters:

Name	Type	Description	Default
`support`	`Optional[AttributeSupportType]`	support on which the regions are defined. Default value is None.	`None`

Returns:

Type	Description
`List[str]`	List containing all user region names.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)
xyz_region = xyz_sum < 10
point_cloud.set_region("Lower10", xyz_region)
point_cloud.regions()

Output

['Lower10']

`remove_attribute(name, support=None)` 🍋

Remove the object attribute (property or region) associated to the given name. Use a list of attribute names to remove all of them at once.

Parameters:

Name	Type	Description	Default
`name`	`Union[str, List[str]]`	name of the attribute to remove or a list of names of the attribute(s) to remove.	required
`support`	`Optional[AttributeSupportType]`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid.
`ValueError`	Property is built-in or internal or doesn't exist.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)

xyz_region_inf10 = xyz_sum < 10
point_cloud.set_region("Lower10", xyz_region_inf10)

xyz_region_sup10 = xyz_sum > 10
point_cloud.set_region("Upper10", xyz_region_sup10)

# Remove only one attribute
point_cloud.remove_attribute("Lower10")
# Or remove multiple attributes
point_cloud.remove_attribute(["Lower10", "Upper10"])

Output

True

`remove_property(name, support=None)` 🍋

Remove the object property associated to the given name. Use a list of property names to remove all of them at once.

Parameters:

Name	Type	Description	Default
`name`	`Union[str, List[str]]`	name of the property or a list of names of propertie(s) to remove.	required
`support`	`Optional[AttributeSupportType]`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid.
`ValueError`	Property does not exist. Attribute is not a property.
`ValueError`	Property is built-in or internal.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)

xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)

xyz_sum_off1 = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ_off1", xyz_sum_off1)

# Remove only one property
point_cloud.remove_property("XYZ")
# Or remove multiple properties
point_cloud.remove_attribute(["XYZ", "XYZ_off1"])

Output

True

`remove_region(name, support=None)` 🍋

Remove the object region associated to the given name. Use a list of region names to remove all of them at once.

Parameters:

Name	Type	Description	Default
`name`	`Union[str, List[str]]`	name of the region or a list of names of region(s) to remove.	required
`support`	`Optional[AttributeSupportType]`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Region does not exist. Attribute is not a region.
`ValueError`	Support type is not valid.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum)

xyz_region_inf10 = xyz_sum < 10
point_cloud.set_region("Lower10", xyz_region_inf10)

xyz_region_sup10 = xyz_sum > 10
point_cloud.set_region("Upper10", xyz_region_sup10)

# Remove only one region
point_cloud.remove_attribute("Lower10")
# Or remove multiple regions
point_cloud.remove_attribute(["Lower10", "Upper10"])

Output

True

`rename_attribute(names, support=None)` 🍋

Rename the object attribute (property or region).

Parameters:

Name	Type	Description	Default
`names`	`Dict[str, str]`	a dictionary where each item is a pair of current name and new name.	required
`support`	`Optional[AttributeSupportType]`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid. Attribute is built-in or internal or doesn't exist. Attribute new name already exists. Attribute new name is invalid (e.g. whitespace in name, special character, starting with a number, read-only, etc.).
`KeyError`	Any of the current names is not found in the database.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
pc = geo.PointCloud("test", xyz)

data = np.arange(0., 10., 1.)
pc.set_property("PROP_1", data)
pc.set_property_expr("PROP_2", "PROP_1 + 1.")
pc.set_property_expr("PROP_3", "PROP_1 + 10.")

pc.rename_property({"PROP_1": "PROP_1_renamed"})
pc.rename_property({"PROP_2": "PROP_2_renamed", "PROP_3": "PROP_3_renamed"})

`rename_property(names, support=None)` 🍋

Rename the object property.

Parameters:

Name	Type	Description	Default
`names`	`Dict[str, str]`	a dictionary where each item is a pair of current name and new name.	required
`support`	`Optional[AttributeSupportType]`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid. Attribute is built-in or internal or doesn't exist. Attribute new name already exists. Attribute new name is invalid (e.g. whitespace in name, special character, starting with a number, read-only, etc.).
`KeyError`	Any of the current names is not found in the database.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
pc = geo.PointCloud("test", xyz)

data = np.arange(0., 10., 1.)
pc.set_property("PROP_1", data)
pc.set_property_expr("PROP_2", "PROP_1 + 1.")
pc.set_property_expr("PROP_3", "PROP_1 + 10.")

pc.rename_property(names={"PROP_1": "PROP_1_renamed})
pc.rename_property(names={"PROP_2": "PROP_2_renamed", "PROP_3": "PROP_3_renamed"})

`rename_region(names, support=None)` 🍋

Rename the object region.

Parameters:

Name	Type	Description	Default
`names`	`Dict[str, str]`	a dictionary where each item is a pair of current name and new name.	required
`support`	`Optional[AttributeSupportType]`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid. Attribute is built-in or internal or doesn't exist. Attribute new name already exists. Attribute new name is invalid (e.g. whitespace in name, special character, starting with a number, read-only, etc.).
`KeyError`	Any of the current names is not found in the database.

!! example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
pc = geo.PointCloud("MyPointCloud", xyz)

xyz_sum = np.sum(xyz, axis=1)
pc.set_property("XYZ", xyz_sum)

xyz_region = xyz_sum < 2
pc.set_region("Lower2", xyz_region)

xyz_region = xyz_sum > 3
pc.set_region("Upper3", xyz_region)

xyz_region = xyz_sum < 10
pc.set_region("Lower10", xyz_region)

pc.rename_region(names={"Lower2": "Lower2_renamed"})
pc.rename_region(names={"Upper3": "Upper3_renamed", "Lower10": "Lower10_renamed"})

`sample_count(region=None)` 🍋

Return the actual number of cells defined in the grid. This number may be different from n_cells depending on the grid type. Same as element_count().

Parameters:

Name	Type	Description	Default
`region`	`Optional[str]`	name of the region to optionally filter the data to be counted.	`None`

Returns:

Type	Description
`int`	Number of cells defined.

Example

Count all the elements in the GridObjectCount all the elements within a given region

import geolime as geo
vx = geo.Voxel("GridObj", [4, 3], axis=[[1., 0., 0.], [0., 1., 0.]])
vx.sample_count()

Output

import geolime as geo
import numpy as np
vx = geo.Voxel("GridObj", [4, 3], axis=[[1., 0., 0.], [0., 1., 0.]])

vx.set_property(
    name="PROP",
    data=np.array([1.2, 5.1, 6., 1.4, 1.6, 2.9, 4.1, 8.2, 6.1, 8.7, 3.9, 9.1]),
)
vx.set_region_condition(
    name="REG",
    condition="PROP < 5.5",
)
vx.sample_count(region="REG")

Output

`set_property(name, data, kind=None, unit=None, read_only=False, support=None)` 🍋

Add or update the property corresponding to the given name(s) for the given support type. See also set_property_value() if you only need to update the data value of an existing property. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the property to create or update.	required
`data`	`Data`	property data array.	required
`kind`	`PropertyKind`	property kind, see PropertyKind for available kinds.	`None`
`unit`	`Unit`	property unit, see Pint Unit for more information.	`None`
`read_only`	`bool`	whether attribute data is protected.	`False`
`support`	`AttributeSupportType`	type supporting the property (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Attribute exists but is not a property. Support type is not valid.

Example

import geolime as geo
from pint import Unit
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)

# Setting a property using the method allowing to also define kind and units.
point_cloud.set_property("XYZ", xyz_sum, geo.PropertyKind.LENGTH, Unit("m"))

# Or using the shortcut method for simplicity. Note here, unit and kind
# are default to None.
point_cloud["XYZ"] = xyz_sum

# Setting the value to a string.
point_cloud.set_property("domain", "'hg'")
# or
point_cloud["domain"] = "'hg'"

`set_property_expr(name, expr, dynamic=False, kind=None, unit=None, read_only=False, support=None, region=None, default=None)` 🍋

Add or update the property corresponding to the given name(s) and expression for the given support type. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	property name.	required
`expr`	`Union[str, int, float]`	expression use to compute the property values. Use backquotes when attribute names contains whitespace.	required
`dynamic`	`bool`	set to True to have property values recomputed each time the data is requested.	`False`
`kind`	`Optional[PropertyKind]`	property kind, see PropertyKind for available kinds.	`None`
`unit`	`Optional[Unit]`	property unit, see Pint Unit for more information.	`None`
`read_only`	`bool`	set to True if attribute data is protected.	`False`
`support`	`Optional[AttributeSupportType]`	type supporting the property (e.g. node = vertex, element = triangles).	`None`
`region`	`Optional[str]`	region or condition in which to set the property data.	`None`
`default`	`Optional[str]`	default value used outside of the region. This must be used in combination with a region parameter.	`None`

Raises:

Type	Description
`ValueError`	Expression is invalid (e.g. starts with "=" character). Support type is not valid. Property doesn't exist and region is specified

Example

import geolime as geo

# Or using the shortcut method for simplicity. Note that unit and kind
# default to None.
point_cloud["XYZ"] = "X * Y * Z"

# Create a property with an expression defined for a specific region
# and a default value for the remaining data.
point_cloud.set_property(
    name="PROP",
    expr="X * Y * Z",
    dynamic=False,
    region="(Z >= 14) & (Z <= 17)",
    default="Y"
)

# Setting the value to a string.
point_cloud.set_property_expr("domain", "'hg'")
# or
point_cloud["domain"] = "'hg'"

`set_property_value(name, data, support=None, region=None)` 🍋

Update the named property with the given data for the given support type. The property must already exist, otherwise use set_property(). If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the property to update.	required
`data`	`Data`	property data array.	required
`support`	`Optional[AttributeSupportType]`	type supporting the property (e.g. node = vertex, element = triangles).	`None`
`region`	`Optional[str]`	region or condition in which to set the property data.	`None`

Raises:

Type	Description
`ValueError`	Property does not exist. Support type is not valid.

Example

import geolime as geo
import numpy as np
from pint import Unit
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum, geo.PropertyKind.LENGTH, Unit("m"))
point_cloud.set_property_value("XYZ", 2 * point_cloud.data("XYZ"))

`set_region(name, data, read_only=False, support=None)` 🍋

Add or update the region data corresponding to the given name(s) for the given support type. See also set_region_values() if you only need to update the data value of an existing region. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the property to create or update.	required
`data`	`Data`	region data array.	required
`read_only`	`bool`	set to True if region data is protected.	`False`
`support`	`AttributeSupportType`	type supporting the region (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Attribute exists but is not a region. Support type is not valid. Region in which to set the data.

Example

import geolime as geo
import numpy as np
from pint import Unit
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_property("XYZ", xyz_sum, geo.PropertyKind.LENGTH, Unit("m"))
xyz_region = xyz_sum < 10
point_cloud.set_region('RegionTest', xyz_region)

`set_region_condition(name, condition, dynamic=False, read_only=False, support=None, region=None)` 🍋

Add or update the region data corresponding to the given name(s) and condition for the given support type. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	region name.	required
`condition`	`str`	condition use to compute the region values. Use backquotes when attribute names contains whitespace.	required
`dynamic`	`bool`	set to True to have region values recomputed each time the data is requested.	`False`
`read_only`	`bool`	set to True if region data is protected.	`False`
`support`	`Optional[AttributeSupportType]`	type supporting the region (e.g. node = vertex, element = triangles).	`None`
`region`	`Optional[str]`	region or condition in which to set the region data.	`None`

Raises:

Type	Description
`ValueError`	Expression is invalid (e.g. starts with "=" character). Support type is not valid. Region doesn't exist and region/condition is specified.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.set_region_condition('RegionTest', 'X + Y + Z < 10', False)

`set_region_value(name, data, support=None, region=None)` 🍋

Update the named region with the given data for the given support type. The region must already exist, otherwise use set_region(). If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of the region to update.	required
`data`	`Data`	region data array.	required
`support`	`Optional[AttributeSupportType]`	type supporting the region (e.g. node = vertex, element = triangles).	`None`
`region`	`Optional[str]`	region or condition in which to set the property data.	`None`

Raises:

Type	Description
`ValueError`	Region does not exist. Support type is not valid.

Example

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.set_region_condition('RegionTest', 'X + Y + Z < 10', False)
xyz_sum = np.sum(xyz, axis=1)
point_cloud.set_region_value('RegionTest', xyz_sum < 23)

`to_csv(filename, names=None, region=None, skip_nan=False, sort_by=None, sort_ascending=True, mapping=None, support=None, **kwargs)` 🍋

Export the given attribute(s) to a CSV-file. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`filename`	`str`	path to export the file to.	required
`names`	`Union[List[str], str]`	list of names or single name of the attribute(s) to export. If not specified, all attributes are exported.	`None`
`region`	`str`	region name or condition to filter data to export.	`None`
`skip_nan`	`bool`	True will drop NaN values from the exported file. Otherwise, NaN values will be exported as NumericalConstants.NDV (-99999). To overwrite the NaN values, use `na_rep` argument of `pandas.to_csv()`.	`False`
`sort_by`	`Union[str, List[str]]`	sort the data by the given attributes.	`None`
`sort_ascending`	`Union[bool, List[bool]]`	sorting order when `sort_by` is provided.	`True`
`mapping`	`Dict`	rename given columns in the exported CSV.	`None`
`support`	`AttributeSupportType`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`
`**kwargs`		extra arguments to pass along to Pandas - To CSV.	`{}`

Raises:

Type	Description
`ValueError`	Support type is not valid. Attribute does not exist.

`to_dataframe(names=None, region=None, support=None)` 🍋

Export the given attribute(s) to a Pandas DataFrame. If no support is specified, the default database will be used.

Parameters:

Name	Type	Description	Default
`names`	`Union[List[str], str]`	list of names or single name of the attribute(s) to export. If not specified, all attributes are exported.	`None`
`region`	`str`	region name or condition to filter data to export.	`None`
`support`	`AttributeSupportType`	type supporting the attributes (e.g. node = vertex, element = triangles).	`None`

Raises:

Type	Description
`ValueError`	Support type is not valid. Attribute does not exist.

`to_file(filepath, attributes=None, version=None)` 🍋

Write the object to the given folder path. If it does not end with .geo, the suffix will automatically be appended.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	path to the object folder path.	required
`attributes`	`Optional[List[str]]`	list of attributes to write to the object.	`None`
`version`	`Optional[str]`	specific version format to use. Use None will default to the latest available version.	`None`

Raises:

Type	Description
`ValueError`	There is any data file write error.

`to_pyvista(properties=None, region=None)` 🍋

Export GridObject and selected properties to Pyvista ImageData.

Parameters:

Name	Type	Description	Default
`properties`	`Optional[Union[str, List[str]]]`	Property or list of properties to export to Pyvista.	`None`
`region`	`Optional[str]`	Name of the region to filter the data.	`None`

Returns:

Type	Description
`Union[ImageData, UnstructuredGrid]`	ImageData object if full Object or UnstructuredGrid if a region is selected.

`transform(properties=None, agg_methods=None)` 🍋

Return the object data grouped by location (X, Y).

Parameters:

Name	Type	Description	Default
`properties`	`Optional[List[str]]`	list of properties name to transfer to the GIS Object. Setting to None or empty list will select all existing properties.	`None`
`agg_methods`	`Optional[Union[AggregationMethod, Callable]]`	aggregation data methods. See Pandas - Group By to find out supported methods.	`None`

Returns:

Type	Description
`DataFrame`	DataFrame of XY coordinates with aggregated data.

Raises:

Type	Description
`ValueError`	No aggregation method is provided. 'X' or 'Y' are part of the properties. One of the aggregation methods is not supported.

Example

Example 1Example 2

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.aggregate(["Z"], ["max"])

Output

    X   Y  Z_max
0   0   1      2
1   3   4      5
2   6   7      8
3   9  10     11
4  12  13     14
5  15  16     17
6  18  19     20
7  21  22     23
8  24  25     26
9  27  28     29

import geolime as geo
import numpy as np
xyz = np.arange(30).reshape(10, 3)
point_cloud = geo.PointCloud("MyPointCloud", xyz)
point_cloud.aggregate()

Output

    X   Y  Z_sum  Z_min  Z_max  Z_mean
0   0   1      2      2      2     2.0
1   3   4      5      5      5     5.0
2   6   7      8      8      8     8.0
3   9  10     11     11     11    11.0
4  12  13     14     14     14    14.0
5  15  16     17     17     17    17.0
6  18  19     20     20     20    20.0
7  21  22     23     23     23    23.0
8  24  25     26     26     26    26.0
9  27  28     29     29     29    29.0

`translate_by(coord, expr, region=None)` 🍋

Shift the given existing X, Y or Z coordinate by the given expression or value.

Parameters:

Name	Type	Description	Default
`coord`	`Union[Attribute, Coord]`	coordinate property.	required
`expr`	`str`	expression use to compute the property values. Use backquotes when attribute names contains whitespace.	required
`region`	`Optional[str]`	region or condition in which to set the property data.	`None`

Raises:

Type	Description
`ValueError`	Coordinate doesn't exist.

`user_properties(support=None)` 🍋

Return a list of properties for a given GeoRefObject including all the objects properties excluding built-in ones.

Parameters:

Name	Type	Description	Default
`support`	`Optional[AttributeSupportType]`	support on which the properties are defined. Default value is None.	`None`

Returns:

Type	Description
`List[str]`	List containing all user property names.

Grids🍋

GridObject 🍋

builtins: List[str] property 🍋

default_support: AttributeSupportType property 🍋

dimension: int property 🍋

n_cells: int property 🍋

name: str property writable 🍋

origin: Vector property 🍋

shape: np.ndarray property 🍋

aggregate(properties=None, agg_methods=[AggregationMethod.SUM, AggregationMethod.MIN, AggregationMethod.MAX, AggregationMethod.MEAN]) 🍋

axis(axis=None) 🍋

bounds(region=None) 🍋

centroid(region=None) 🍋

check_coords_bounds(coords) 🍋

convert_to_gis_object(name, properties=None, agg_methods=[AggregationMethod.SUM, AggregationMethod.MIN, AggregationMethod.MAX, AggregationMethod.MEAN], region=None, region_agg=None) 🍋

coords(region=None) 🍋

coords2idx(coords) 🍋

coords2xyz(coords) 🍋

copy(name, attributes=None) 🍋

data(names, region=None, support=None) 🍋

describe(properties=None, **kwargs) 🍋

downscale(name, discr) 🍋

element_count(region=None) 🍋

generate_attribute_name(prefix='_', support=None) 🍋

idx2coords(idx) 🍋

indexed_coords(region=None) 🍋

internals(support=None) 🍋

plot_2d(property, agg_method, region=None, region_agg=None, interactive_map=False, interactive_max_sample=5000, **kwargs) 🍋

properties(support=None) 🍋

property(name, support=None, return_none=False) 🍋

read_file(filepath, attributes=None) classmethod 🍋

read_manifest(filepath) staticmethod 🍋

refresh_attributes(names, support=None) 🍋

region(name, support=None, return_none=False) 🍋

region_mask(region, support=None) 🍋

regions(support=None) 🍋

remove_attribute(name, support=None) 🍋

remove_property(name, support=None) 🍋

remove_region(name, support=None) 🍋

rename_attribute(names, support=None) 🍋

rename_property(names, support=None) 🍋

rename_region(names, support=None) 🍋

sample_count(region=None) 🍋

set_property(name, data, kind=None, unit=None, read_only=False, support=None) 🍋

set_property_expr(name, expr, dynamic=False, kind=None, unit=None, read_only=False, support=None, region=None, default=None) 🍋

set_property_value(name, data, support=None, region=None) 🍋

set_region(name, data, read_only=False, support=None) 🍋

set_region_condition(name, condition, dynamic=False, read_only=False, support=None, region=None) 🍋

set_region_value(name, data, support=None, region=None) 🍋

to_csv(filename, names=None, region=None, skip_nan=False, sort_by=None, sort_ascending=True, mapping=None, support=None, **kwargs) 🍋

to_dataframe(names=None, region=None, support=None) 🍋

to_file(filepath, attributes=None, version=None) 🍋

to_pyvista(properties=None, region=None) 🍋

transform(properties=None, agg_methods=None) 🍋

translate_by(coord, expr, region=None) 🍋

user_properties(support=None) 🍋

`GridObject` 🍋

`builtins: List[str]` `property` 🍋

`default_support: AttributeSupportType` `property` 🍋

`dimension: int` `property` 🍋

`n_cells: int` `property` 🍋

`name: str` `property` `writable` 🍋

`origin: Vector` `property` 🍋

`shape: np.ndarray` `property` 🍋

`aggregate(properties=None, agg_methods=[AggregationMethod.SUM, AggregationMethod.MIN, AggregationMethod.MAX, AggregationMethod.MEAN])` 🍋

`axis(axis=None)` 🍋

`bounds(region=None)` 🍋

`centroid(region=None)` 🍋

`check_coords_bounds(coords)` 🍋

`convert_to_gis_object(name, properties=None, agg_methods=[AggregationMethod.SUM, AggregationMethod.MIN, AggregationMethod.MAX, AggregationMethod.MEAN], region=None, region_agg=None)` 🍋

`coords(region=None)` 🍋

`coords2idx(coords)` 🍋

`coords2xyz(coords)` 🍋

`copy(name, attributes=None)` 🍋

`data(names, region=None, support=None)` 🍋

`describe(properties=None, **kwargs)` 🍋

`downscale(name, discr)` 🍋

`element_count(region=None)` 🍋

`generate_attribute_name(prefix='_', support=None)` 🍋

`idx2coords(idx)` 🍋

`indexed_coords(region=None)` 🍋

`internals(support=None)` 🍋

`plot_2d(property, agg_method, region=None, region_agg=None, interactive_map=False, interactive_max_sample=5000, **kwargs)` 🍋

`properties(support=None)` 🍋

`property(name, support=None, return_none=False)` 🍋

`read_file(filepath, attributes=None)` `classmethod` 🍋

`read_manifest(filepath)` `staticmethod` 🍋

`refresh_attributes(names, support=None)` 🍋

`region(name, support=None, return_none=False)` 🍋

`region_mask(region, support=None)` 🍋

`regions(support=None)` 🍋

`remove_attribute(name, support=None)` 🍋

`remove_property(name, support=None)` 🍋

`remove_region(name, support=None)` 🍋

`rename_attribute(names, support=None)` 🍋

`rename_property(names, support=None)` 🍋

`rename_region(names, support=None)` 🍋

`sample_count(region=None)` 🍋

`set_property(name, data, kind=None, unit=None, read_only=False, support=None)` 🍋

`set_property_expr(name, expr, dynamic=False, kind=None, unit=None, read_only=False, support=None, region=None, default=None)` 🍋

`set_property_value(name, data, support=None, region=None)` 🍋

`set_region(name, data, read_only=False, support=None)` 🍋

`set_region_condition(name, condition, dynamic=False, read_only=False, support=None, region=None)` 🍋

`set_region_value(name, data, support=None, region=None)` 🍋

`to_csv(filename, names=None, region=None, skip_nan=False, sort_by=None, sort_ascending=True, mapping=None, support=None, **kwargs)` 🍋

`to_dataframe(names=None, region=None, support=None)` 🍋

`to_file(filepath, attributes=None, version=None)` 🍋

`to_pyvista(properties=None, region=None)` 🍋

`transform(properties=None, agg_methods=None)` 🍋

`translate_by(coord, expr, region=None)` 🍋

`user_properties(support=None)` 🍋