pytuflow.Grid#

class Grid(fpath)#

Bases: MapOutput, LineStringMixin, PointMixin

Generic Grid result class. Can be used to load raster files that are supported by GDAL or rasterio. The raster data extracted from an input file is assumed to be static (i.e. no time dimension). Alternatively, it is possible to initialise the Grid class with a dictionary containing already extracted grid data in the form of an array. The dictionary should include metadata about the grid such as cell size, origin, and number of rows/columns. The data can be either static or temporal.

Parameters:

fpath (PathLike | dict) –

The file path to the grid file to load, or a dictionary containing grid data (as a np.ndarray) and metadata.

If using a dictionary, the following keys should be present:

dx : float
dy : float
ncol : int
nrow : int
ox : float : optional, default is 0.0
oy : float : optional, default is 0.0
nodatavalue : float, optional, default is np.nan
data : np.ndarray. Array can be 2D (static scalar, NRow,NCol), 3D (temporal scalar, NTime,NRow,NCol), 3D (static vector, NRow,NCol,2), or 4D (temporal vector, NTime,NRow,NCol,2). If data represents vector data, then the dtype key must also be set to "vector". If data is temporal, then the timesteps key must also be provided.
data_type : str, optional data type (result type) identifier
timesteps : list[float] | int, optional. If data is temporal, this should be a list of time values.
dtype : str, optional. Either "scalar" (default) or "vector".

Examples

Load a static grid from a GeoTIFF file:

>>> grid = Grid('path/to/static_grid.tif')
>>> grid.dx
10.0
>>> df = grid.surface()
                x           y  value  active
0       292723.75  6177421.25    NaN   False
1       292726.25  6177421.25    NaN   False
2       292728.75  6177421.25    NaN   False
3       292731.25  6177421.25    NaN   False
4       292733.75  6177421.25    NaN   False
...           ...         ...    ...     ...
197536  293768.75  6178586.25    NaN   False
197537  293771.25  6178586.25    NaN   False
197538  293773.75  6178586.25    NaN   False
197539  293776.25  6178586.25    NaN   False
197540  293778.75  6178586.25    NaN   False

__init__(fpath)#

Parameters:: fpath (Path | str | dict)

Methods

`data_point`	Extracts the data value for the given point locations and data types at the specified time.
`data_types`	Returns all the available data types (result types) for the output given the context.
`maximum`	Returns the maximum values for the given data types.
`minimum`	Returns the minimum values for the given data types.
`section`	Extracts section data for the given locations and data types.
`surface`	Returns the value for every cell/vertex at the specified time.
`time_series`	Extracts time-series data for the given locations and data types.
`times`	Returns all the available times for the given context.
`to_mesh`	Converts the grid to a `GridMesh` object, essentially converting the grid data structure into a mesh data structure.

Attributes

`ATTRIBUTE_TYPES`
`DOMAIN_TYPES`
`GEOMETRY_TYPES`
`ID_COLUMNS`
`dx`	The grid cell size in the x-direction.
`dy`	The grid cell size in the y-direction.
`ncol`	The number of columns in the grid.
`no_data_value`	The no data value for the grid.
`nrow`	The number of rows in the grid.
`ox`	The x-origin of the grid.
`oy`	The y-origin of the grid.
`name`	The result name
`has_reference_time`	Does the result have an inherent reference time.
`reference_time`	The reference time for the output

pytuflow.Grid

Contents

pytuflow.Grid#