pygeogrids package¶
Submodules¶
pygeogrids.geodetic_datum module¶
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class
pygeogrids.geodetic_datum.
GeodeticDatum
(ellps, **kwargs)[source]¶ Class representing a geodetic datum providing transformation and calculation functionality
Parameters: ellString (string) – String of geodetic datum (ellipsoid) as provided in pyproj -
EllM
(lat)[source]¶ Method to calculate the radius of the curvature
Parameters: lat (numpy.array, list or float) – Geodatic latitudes of the points in the grid Returns: r – radius of the curvature Return type: np.array, float
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EllN
(lat)[source]¶ Method to calculate the radius of the prime vertical
Parameters: lat (numpy.array, list or float) – Geodatic latitudes of the points in the grid Returns: r – radius of the prime vertical Return type: np.array, float
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GaussianRadi
(lat)[source]¶ Method to calculate the gaussian radius of the curvature
Parameters: lat (numpy.array, list or float) – Geodatic latitudes of the points in the grid Returns: r – gaussian radius of the curvature Return type: np.array, float
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GeocentricDistance
(lon, lat)[source]¶ Method to calculate the geocentric distance to given points
Parameters: - lon (numpy.array, list or float) – Geodatic longitude of the points in the grid
- lat (numpy.array, list or float) – Geodatic latitudes of the points in the grid
Returns: r – Geocentric radius
Return type: np.array, float
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GeocentricLat
(lat)[source]¶ Method to calculate the geocentric from the geodatic latitude.
Parameters: lat (numpy.array, list or float) – Geodatic latitudes of the points in the grid Returns: lat_geocentric – Geocentric latitude Return type: np.array, float
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GeodeticLat
(lat)[source]¶ Method to calculate the geodatic from the geocentric latitude.
Parameters: lat (numpy.array, list or float) – Geocentric latitudes of the points in the grid Returns: lat_geodatic – Geodatic latitude Return type: np.array, float
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MeridianArcDist
(lat1, lat2)[source]¶ Method to calculate the distance between two parallels (meridian arc distance)
Parameters: - lat1 (numpy.array, float) – Geodatic latitudes 1
- lat2 (numpy.array, float) – Geodatic latitudes 2
Returns: dist – Meridian arc distance
Return type: np.array, float
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ParallelArcDist
(lat, lon1, lon2)[source]¶ Method to calculate the distance between two points on a given parallel
Parameters: Returns: dist – Parallel arc distance
Return type: np.array, float
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ParallelRadi
(lat)[source]¶ Method to get the radius the parallel at a given latitude.
Parameters: lat (numpy.array, list or float) – latitudes of the points in the grid Returns: radius – Radius of parallel Return type: np.array, float
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ReducedLat
(lat)[source]¶ Method to calculate the reduced from the geodatic latitude.
Parameters: lat (numpy.array, list or float) – Geodatic latitudes of the points in the grid Returns: lat_reduced – Reduced latitude Return type: np.array, float
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getParameter
()[source]¶ Method to transform lon/lat to ECEF (Earth-Centered, Earth-Fixed) coordinates representing a 3d Cartesian coordinate system.
Parameters: - lon (numpy.array, list or float) – longitudes of the points in the grid
- lat (numpy.array, list or float) – latitudes of the points in the grid
Returns: x, y, z – 3D cartesian coordinates
Return type: np.array
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toECEF
(lon, lat)[source]¶ Method to transform lon/lat to ECEF (Earth-Centered, Earth-Fixed) coordinates representing a 3d Cartesian coordinate system.
Parameters: - lon (numpy.array, list or float) – longitudes of the points in the grid
- lat (numpy.array, list or float) – geodatic latitudes of the points in the grid
Returns: x, y, z – 3D cartesian coordinates
Return type: np.array
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pygeogrids.grids module¶
The grids module defines the grid classes.
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class
pygeogrids.grids.
BasicGrid
(lon, lat, gpis=None, geodatum='WGS84', subset=None, setup_kdTree=True, shape=None)[source]¶ Bases:
object
Grid that just has lat,lon coordinates and can find the nearest neighbour. It can also yield the gpi, lat, lon information in order.
Parameters: - lon (numpy.array) – longitudes of the points in the grid
- lat (numpy.array) – latitudes of the points in the grid
- geodatum (basestring) – Name of the geodatic datum associated with the grid
- gpis (numpy.array, optional) – if the gpi numbers are in a different order than the lon and lat arrays an array containing the gpi numbers can be given if no array is given here the lon lat arrays are given gpi numbers starting at 0
- subset (numpy.array, optional) – if the active part of the array is only a subset of all the points then the subset array which is a index into lon and lat can be given here
- setup_kdTree (boolean, optional) – if set (default) then the kdTree for nearest neighbour search will be built on initialization
- shape (tuple, optional) – The shape of the grid array in 2-d space. e.g. for a 1x1 degree global regular grid the shape would be (180,360). if given the grid can be reshaped into the given shape this indicates that it is a regular grid and fills the attributes self.lon2d and self.lat2d which define the grid only be the meridian coordinates(self.lon2d) and the coordinates of the circles of latitude(self.lat2d). The shape has to be given as (lat2d, lon2d) It it is not given the shape is set to the length of the input lon and lat arrays.
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arrlon
¶ numpy.array – 1D array of all longitudes of the grid
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arrlat
¶ numpy.array – 1D array of all latitudes of the grid
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n_gpi
¶ int – number of gpis in the grid
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gpidirect
¶ boolean – if true the gpi number is equal to the index of arrlon and arrlat
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gpis
¶ numpy.array – gpi number for elements in arrlon and arrlat gpi[i] is located at arrlon[i],arrlat[i]
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subset
¶ numpy.array – if given then this contains the indices of a subset of the grid. This can be used if only a part of a grid is interesting for a application. e.g. land points, or only a specific country
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allpoints
¶ boolean – if False only a subset of the grid is active
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activearrlon
¶ numpy.array – array of longitudes that are active, is defined by arrlon[subset] if a subset is given otherwise equal to arrlon
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activearrlat
¶ numpy.array – array of latitudes that are active, is defined by arrlat[subset] if a subset is given otherwise equal to arrlat
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activegpis
¶ numpy.array – array of gpis that are active, is defined by gpis[subset] if a subset is given otherwise equal to gpis
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geodatum
¶ object – pygeogrids.geodatic_datum object (reference ellipsoid) associated with the grid
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issplit
¶ boolean – if True then the array was split in n parts with the self.split function
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kdTree
¶ object – grid.nearest_neighbor.findGeoNN object for nearest neighbor search
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shape
¶ tuple, optional – if given during initialization then this is the shape the grid can be reshaped to
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lat2d
¶ numpy.array, optional – if shape is given this attribute contains all latitudes according to the provided 2d-shape that make up the grid
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lon2d
¶ numpy.array, optional – if shape is given this attribute contains all longitudes according to the provided 2d-shape that make up the grid
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calc_lut
(other, max_dist=<Mock name='mock.Inf' id='140029349277008'>, into_subset=False)[source]¶ Takes other BasicGrid or CellGrid objects and computes a lookup table between them. The lut will have the size of self.n_gpis and will for every grid point have the nearest index into other.arrlon etc.
Parameters: - other (grid object) – to which to calculate the lut to
- max_dist (float, optional) – maximum allowed distance in meters
- into_subset (boolean, optional) – if set the returned lut will have the index into the subset if the other grid is a subset of a grid. Example: if e.g. ind_l is used for the warp_grid some datasets will be given as arrays with len(ind_l) elements. These datasets can not be indexed with gpi numbers but have to be indexed with indices into the subset
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find_nearest_gpi
(lon, lat, max_dist=<Mock name='mock.Inf' id='140029349277008'>)[source]¶ Finds nearest gpi, builds kdTree if it does not yet exist.
Parameters: Returns: - gpi (long) – Grid point index.
- distance (float) – Distance of gpi to given lon, lat. At the moment not on a great circle but in spherical cartesian coordinates.
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get_bbox_grid_points
(latmin=-90, latmax=90, lonmin=-180, lonmax=180, coords=False, both=False)[source]¶ Returns all grid points located in a submitted geographic box, optinal as coordinates
Parameters: - latmin (float, optional) – minimum latitude
- latmax (float, optional) – maximum latitude
- lonmin (float, optional) – minimum latitude
- lonmax (float, optional) – maximum latitude
- coords (boolean, optional) – set to True if coordinates should be returned
- both (boolean, optional) – set to True if gpis and coordinates should be returned
Returns: - gpi (numpy.ndarray) – grid point indices, if coords=False
- lat (numpy.ndarray) – longitudes of gpis, if coords=True
- lon (numpy.ndarray) – longitudes of gpis, if coords=True
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get_grid_points
(*args)[source]¶ Returns all active grid points.
Parameters: n (int, optional) – if the grid is split in n parts using the split function then this function will only return the nth part of the grid Returns: - gpis (numpy.ndarray) – Grid point indices.
- arrlon (numpy.ndarray) – Longitudes.
- arrlat (numpy.ndarray) – Latitudes.
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get_shp_grid_points
(ply)[source]¶ Returns all grid points located in a submitted shapefile, optinal as coordinates. Currently only works in WGS84.
Parameters: ply (object, OGRGeometryShadow) – the Geometry of the Feature as returned from ogr.GetGeometryRef Returns: grid – Subgrid. Return type: BasicGrid
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gpi2lonlat
(gpi)[source]¶ Longitude and latitude for given gpi.
Parameters: gpi (int32 or iterable) – Grid point index. Returns: - lon (float) – Longitude of gpi.
- lat (float) – Latitude of gpi
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gpi2rowcol
(gpi)[source]¶ If the grid can be reshaped into a sensible 2D shape then this function gives the row(latitude dimension) and column(longitude dimension) indices of the gpi in the 2D grid.
Parameters: gpi (int32) – Grid point index. Returns: - row (int) – Row in 2D array.
- col (int) – Column in 2D array.
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grid_points
(*args)[source]¶ Yields all grid points in order
Parameters: n (int, optional) – if the grid is split in n parts using the split function then this iterator will only iterate of the nth part of the grid Returns: - gpi (long) – grid point index
- lon (float) – longitude of gpi
- lat (float) – longitude of gpi
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split
(n)[source]¶ Function splits the grid into n parts this changes not function but grid_points() which takes the argument n and will only iterate through this part of the grid.
Parameters: n (int) – Number of parts the grid should be split into
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subgrid_from_gpis
(gpis)[source]¶ Generate a subgrid for given gpis.
Parameters: gpis (int, numpy.ndarray) – Grid point indices. Returns: grid – Subgrid. Return type: BasicGrid
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class
pygeogrids.grids.
CellGrid
(lon, lat, cells, gpis=None, geodatum='WGS84', subset=None, setup_kdTree=False, **kwargs)[source]¶ Bases:
pygeogrids.grids.BasicGrid
Grid that has lat,lon coordinates as well as cell informatin. It can find nearest neighbour. It can also yield the gpi, lat, lon, cell information in cell order. This is important if the data on the grid is saved in cell files on disk as we can go through all grid points with optimized IO performance.
Parameters: - lon (numpy.ndarray) – Longitudes of the points in the grid.
- lat (numpy.ndarray) – Latitudes of the points in the grid.
- cells (numpy.ndarray) – Of same shape as lon and lat, containing the cell number of each gpi.
- gpis (numpy.ndarray, optional) – If the gpi numbers are in a different order than the lon and lat arrays an array containing the gpi numbers can be given.
- subset (numpy.array, optional) – If the active part of the array is only a subset of all the points then the subset array which is a index into lon, lat and cells can be given here.
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arrcell
¶ numpy.ndarray – Array of cell number with same shape as arrlon, arrlat.
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activearrcell
¶ numpy.ndarray – Array of longitudes that are active, is defined by arrlon[subset] if a subset is given otherwise equal to arrlon.
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get_cells
()[source]¶ Function to get all cell numbers of the grid.
Returns: cells – Unique cell numbers. Return type: numpy.ndarray
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get_grid_points
(*args)[source]¶ Returns all active grid points.
Parameters: n (int, optional) – If the grid is split in n parts using the split function then this function will only return the nth part of the grid. Returns: - gpis (numpy.ndarray) – Grid point indices.
- arrlon (numpy.ndarray) – Longitudes.
- arrlat (numpy.ndarray) – Latitudes.
- cells (numpy.ndarray) – Cell numbers.
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gpi2cell
(gpi)[source]¶ Cell for given gpi.
Parameters: gpi (int32 or iterable) – Grid point index. Returns: cell – Cell number of gpi. Return type: int or iterable
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grid_points_for_cell
(cells)[source]¶ Get all grid points for a given cell number.
Parameters: cell (int, numpy.ndarray) – Cell numbers. Returns: - gpis (numpy.ndarray) – Gpis belonging to cell.
- lons (numpy.array) – Longitudes belonging to the gpis.
- lats (numpy.array) – Latitudes belonging to the gpis.
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split
(n)[source]¶ Function splits the grid into n parts this changes not function but grid_points() which takes the argument n and will only iterate through this part of the grid.
Parameters: n (int) – Number of parts the grid should be split into.
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subgrid_from_cells
(cells)[source]¶ Generate a subgrid for given cells.
Parameters: cells (int, numpy.ndarray) – Cell numbers. Returns: grid – Subgrid. Return type: CellGrid
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subgrid_from_gpis
(gpis)[source]¶ Generate a subgrid for given gpis.
Parameters: gpis (int, numpy.ndarray) – Grid point indices. Returns: grid – Subgrid. Return type: BasicGrid
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exception
pygeogrids.grids.
GridDefinitionError
[source]¶ Bases:
exceptions.Exception
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exception
pygeogrids.grids.
GridIterationError
[source]¶ Bases:
exceptions.Exception
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pygeogrids.grids.
genreg_grid
(grd_spc_lat=1, grd_spc_lon=1, minlat=-90.0, maxlat=90.0, minlon=-180.0, maxlon=180.0, **kwargs)[source]¶ Define a global regular lon lat grid which starts in the North Western Corner of minlon, maxlat. The grid points are defined to be in the middle of a grid cell. e.g. the first point on a 1x1 degree grid with minlon -180.0 and maxlat 90.0 will be at -179.5 longitude, 89.5 latitude.
Parameters: - grd_spc_lat (float, optional) – Grid spacing in latitude direction.
- grd_spc_lon (float, optional) – Grid spacing in longitude direction.
- minlat (float, optional) – Minimum latitude of the grid.
- maxlat (float, optional) – Maximum latitude of the grid.
- minlon (float, optional) – Minimum longitude of the grid.
- maxlon (float, optional) – Maximum longitude of the grid.
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pygeogrids.grids.
gridfromdims
(londim, latdim, **kwargs)[source]¶ Defines new grid object from latitude and longitude dimensions. Latitude and longitude dimensions are 1D arrays that give the latitude and longitude values of a 2D latitude-longitude array.
Parameters: - londim (numpy.ndarray) – longitude dimension
- latdim (numpy.ndarray) – latitude dimension
Returns: grid – New grid object.
Return type:
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pygeogrids.grids.
lonlat2cell
(lon, lat, cellsize=5.0, cellsize_lon=None, cellsize_lat=None)[source]¶ Partition lon, lat points into cells.
Parameters: - lat (float64, or numpy.ndarray) – Latitude.
- lon (float64, or numpy.ndarray) – Longitude.
- cellsize (float) – Cell size in degrees.
- cellsize_lon (float, optional) – Cell size in degrees on the longitude axis.
- cellsize_lat (float, optional) – Cell size in degrees on the latitude axis.
Returns: cell – Cell numbers.
Return type: int32, or numpy.ndarray
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pygeogrids.grids.
reorder_to_cellsize
(grid, cellsize_lat, cellsize_lon)[source]¶ Reorder grid points in one grid to follow the ordering of differently sized cells. This is useful if e.g. a 10x10 degree CellGrid should be traversed in an order compatible with a 5x5 degree CellGrid.
Parameters: - grid (
pygeogrids.grids.CellGrid
) – input grid - cellsize_lat (float) – cellsize in latitude direction
- cellsize_lon (float) – cellsize in longitude direction
Returns: new_grid – output grid with original cell sizes but different ordering.
Return type: - grid (
pygeogrids.nearest_neighbor module¶
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class
pygeogrids.nearest_neighbor.
findGeoNN
(lon, lat, geodatum, grid=False, kd_tree_name='pykdtree')[source]¶ Bases:
object
class that takes lat,lon coordinates, transformes them to cartesian (X,Y,Z) coordinates and provides a interface to scipy.spatial.kdTree as well as pykdtree if installed
Parameters: - lon (numpy.array or list) – longitudes of the points in the grid
- lat (numpy.array or list) – latitudes of the points in the grid
- geodatum (object) – pygeogrids.geodatic_datum.GeodeticDatum object associated with lons/lats coordinates
- grid (boolean, optional) – if True then lon and lat are assumed to be the coordinates of a grid and will be used in numpy.meshgrid to get coordinates for all grid points
- kd_tree_name (string, optional) – name of kdTree implementation to use, either ‘pykdtree’ to use pykdtree or ‘scipy’ to use scipy.spatial.kdTree Fallback is always scipy if any other string is given or if pykdtree is not installed. standard is pykdtree since it is faster
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geodatum
¶ object – pygeogrids.geodatic_datum.GeodeticDatum object used for x,y,z coordinates calculations
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coords
¶ numpy.array – 3D array of cartesian x,y,z coordinates
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kd_tree_name
¶ string – name of kdTree implementation to use, either ‘pykdtree’ to use pykdtree or ‘scipy’ to use scipy.spatial.kdTree Fallback is always scipy if any other string is given or if pykdtree is not installed
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kdtree
¶ object – kdTree object that is built only once and saved in this attribute
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find_nearest_index
(lon, lat)[source]¶ finds the nearest neighbor of the given lon,lat coordinates in the lon,lat arrays given during initialization and returns the index of the nearest neighbour in those arrays.
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find_nearest_index
(lon, lat, max_dist=<Mock name='mock.Inf' id='140029349277008'>)[source] finds nearest index, builds kdTree if it does not yet exist
Parameters: - lon (float, list or numpy.array) – longitude of point
- lat (float, list or numpy.array) – latitude of point
- max_dist (float, optional) – maximum distance to consider for search
Returns: - d (float, numpy.array) – distances of query coordinates to the nearest grid point, distance is given in cartesian coordinates and is not the great circle distance at the moment. This should be OK for most applications that look for the nearest neighbor which should not be hundreds of kilometers away.
- ind (int, numpy.array) – indices of nearest neighbor
- index_lon (numpy.array, optional) – if self.grid is True then return index into lon array of grid definition
- index_lat (numpy.array, optional) – if self.grid is True then return index into lat array of grid definition
pygeogrids.netcdf module¶
Module for saving grid to netCDF.
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pygeogrids.netcdf.
load_grid
(filename, subset_flag='subset_flag', location_var_name='gpi')[source]¶ load a grid from netCDF file
Parameters: Returns: grid – grid instance initialized with the loaded data
Return type: BasicGrid or CellGrid instance
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pygeogrids.netcdf.
save_grid
(filename, grid, subset_name='subset_flag', subset_meaning='water land', global_attrs=None)[source]¶ save a BasicGrid or CellGrid to netCDF it is assumed that a subset should be used as land_points
Parameters: - filename (string) – name of file
- grid (BasicGrid or CellGrid object) – grid whose definition to save to netCDF
- subset_name (string, optional) – long_name of the netcdf variable if the subset symbolises something other than a land/sea mask
- subset_meaning (string, optional) – will be written into flag_meanings metadata of variable ‘subset_name’
- global_attrs (dict, optional) – if given will be written as global attributs into netCDF file
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pygeogrids.netcdf.
save_lonlat
(filename, arrlon, arrlat, geodatum, arrcell=None, gpis=None, subsets={}, global_attrs=None, format='NETCDF4', zlib=False, complevel=4, shuffle=True)[source]¶ saves grid information to netCDF file
Parameters: - filename (string) – name of file
- arrlon (numpy.array) – array of longitudes
- arrlat (numpy.array) – array of latitudes
- geodatum (object) – pygeogrids.geodetic_datum.GeodeticDatum object associated with lon/lat
- arrcell (numpy.array, optional) – array of cell numbers
- gpis (numpy.array, optional) – gpi numbers if not index of arrlon, arrlat
- subsets (dict of dicts, optional) –
keys : long_name of the netcdf variables values : dict with the following keys: points, meaning e.g. subsets = {‘subset_flag’: {‘points’: numpy.array,
’meaning’: ‘water, land’}} - global_attrs (dict, optional) – if given will be written as global attributs into netCDF file
- format (string, optional) –
- choose either from one of these NetCDF formats
- ’NETCDF4’ ‘NETCDF4_CLASSIC’ ‘NETCDF3_CLASSIC’ ‘NETCDF3_64BIT_OFFSET’
- zlib (boolean, optional) – see netCDF documentation
- shuffle (boolean, optional) – see netCDF documentation
- complevel (int, opational) – see netCDF documentation
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pygeogrids.netcdf.
sort_for_netcdf
(lons, lats, values)[source]¶ Sort an 2D array for storage in a netCDF file. This mans that the latitudes are stored from 90 to -90 and the longitudes from -180 to 180. Input arrays have to have shape latdim, londim which would mean for a global 10 degree grid (18, 36).
Parameters: - lons (numpy.ndarray) – 2D numpy array of longitudes
- lats (numpy.ndarray) – 2D numpy array of latitudes
- values (numpy.ndarray) – 2D numpy array of values to sort
Returns: - lons (numpy.ndarray) – 2D numpy array of longitudes, sorted
- lats (numpy.ndarray) – 2D numpy array of latitudes, sorted
- values (numpy.ndarray) – 2D numpy array of values to sort, sorted
pygeogrids.plotting module¶
pygeogrids.shapefile module¶
Module for extracting grid points from global administrative areas
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pygeogrids.shapefile.
get_gad_grid_points
(grid, gadm_shp_path, level, name=None, oid=None)[source]¶ Returns all grid points located in a administrative area. For this function the files from http://biogeo.ucdavis.edu/data/gadm2.8/gadm28_levels.shp.zip need to be available in the folder gadm_shp_path Optinal as coordinates. Currently only works in WGS84.
Parameters: - grid (object) –
- gadm_shp_path (path) – Location to GADM28 shapefiles
- level (int) – Global Administrative Database Level 0 : country 1 : province/county/state/region/municipality/… 2 : municipality/District/county/…
- name (str) – name of region at indicated level. For countries the english name
- oid (int) – OBJECTID of feature. This only works with the correct level shp.
Returns: grid – Subgrid.
Return type: Raises: - ValueError: If name or oid are not found in shapefile of given level
- ImportError: If gdal or osgeo are not installed