aus400¶
aus400.cat¶
Tools for filtering and loading from the Aus400 catalogue
- aus400.cat.catalogue: pandas.DataFrame¶
The full Aus400 catalogue, as a
pandas.DataFrame. This catalogue may be filtered usingfilter_catalogue(), or the matching files opened asxarray.Datasetwithload()orload_all().The catalogue has the following columns:
- runid
Experiment run name (e.g. u-bq574)
- resolution
Data resolution (e.g. d0036) - A ‘d’ then the grid spacing in ten-thousandths of a degree
- ensemble
Ensemble member
- stream
Output stream (fx, cldrad, mdl, slv or spec)
- variable
BARRA variable name
- time
First timestamp in the file
- path
Path to the file
- standard_name
CF standard name
- description
Description of the variable
- methods
Variable processing
- aus400.cat.filter_catalogue(cat: Optional[pandas.core.frame.DataFrame] = None, **kwargs)¶
Returns a filtered view of the catalogue
By default the Aus400 catalogue is used as a starting point, if more complex filtering is required a different source may be provided.
- Parameters
- Returns
A filtered view of the catalogue
- aus400.cat.load(cat: Optional[pandas.core.frame.DataFrame] = None, **kwargs) xarray.core.dataset.Dataset¶
Load a single variable
Arguments should be used to narrow down what gets loaded from the full catalogue
- Parameters
**kwargs – See
filter_catalogue()- Returns
- aus400.cat.load_all(cat: Optional[pandas.core.frame.DataFrame] = None, **kwargs)¶
Load multiple variables, e.g. from different streams or resolutions
Arguments should be used to narrow down what gets loaded from the full catalogue
- Parameters
**kwargs – See
filter_catalogue()- Returns
Dict[str,
xarray.Dataset], with keys named like “{resolution}.{stream}.{variable}”
- aus400.cat.load_var(variable, cat: Optional[pandas.core.frame.DataFrame] = None, **kwargs) xarray.core.dataarray.DataArray¶
Load a single variable as a DataArray
Arguments should be used to narrow down what gets loaded from the full catalogue
- Parameters
**kwargs – See
filter_catalogue()- Returns
aus400.regrid¶
Regridding operations for Aus400 data
Aus400 data is on two main resolutions, ‘d0036’ - a 0.0036 degree grid spacing (equivalent to 400m at the equator) and ‘d0198’ - a 0.0198 degree grid spacing (equivalent to 2.2 km at the equator).
The Unified Model used to run the Aus400 experiment uses an Arakawa C grid. Scalar quantities are defined at grid centres, vector quantities on grid edges. Scalar quantites are on the ‘t’ grid, e.g. ‘d0036t’, and are offset half a grid spacing E-W from ‘d0036u’ and N-S from ‘d0036v’.
The default regridding uses bilinear interpolation. For custom regridding grid definitions may be found in the ‘grids/’ directory of the Aus400 published dataset, for use by e.g. ESMF_RegridWeightGen.
- aus400.regrid.identify_grid(data: xarray.core.dataset.Dataset)¶
Identify the grid of an Aus400 variable
- Parameters
data – Variable to identify
- Returns
strwith grid id of ‘data’
- aus400.regrid.identify_resolution(data: xarray.core.dataset.Dataset)¶
Identify the resolution of an Aus400 variable
- Parameters
data – Variable to identify
- Returns
strwith resolution id of ‘data’
- aus400.regrid.identify_subgrid(data)¶
Identify the subgrid of an Aus400 variable
- Parameters
data – Variable to identify
- Returns
strwith subgrid id of data (‘t’,’u’ or ‘v’)
- aus400.regrid.regrid_vector(data)¶
Redrigs vector quantities like u/v defined on grid edges to the scalar grid on gridpoint centres (same resolution as original)
Currently cannot regrid cross-sections of vector quantities – these must be regridded first before taking the cross-section afterwards. (this is because the cross-sections can mess with lat/lon spacing)
will need to load a ‘dummy’ variable to get the coordinates of the gridpoint centres at the same resolution as input data - might be able to just use grids in /g/data instead? – > these are actually different from the variable grids (?)
- Inputs:
data: input data
- Outputs:
data_regrid: regridded data
- aus400.regrid.to_barra(data: xarray.core.dataset.Dataset)¶
Regrid an Aus400 variable to the BARRA t (scalar) grid
- Parameters
data – Variable to regrid
- Returns
xarray.Datasetwith ‘data’ on the ‘barrat’ grid
- aus400.regrid.to_d0198(data: xarray.core.dataset.Dataset)¶
Regrid an Aus400 variable to the 2.2km t (scalar) grid
- Parameters
data – Variable to regrid
- Returns
xarray.Datasetwith ‘data’ on the ‘d0198t’ grid
aus400.render¶
Functions for rendering Aus400 data using Pillow
- aus400.render.field_to_image(field: xarray.core.dataarray.DataArray, vmin: Optional[float] = None, vmax: Optional[float] = None)¶
Render a Aus400 field as an image
If vmin, vmax are not provided they are set to the minimum, maximum of the field. Values outside of vmin, vmax are clipped.
- Parameters
field – Input 2d field
vmin – Minimum value for normalisation
vmax – Maximum value for normalisation
- Returns
PIL.Image.Imagein mode ‘L’
- aus400.render.set_image_mpl_cmap(image: PIL.Image.Image, cmap: str)¶
Set a PIL.Image to use a matplotlib colour map
See https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html
- Parameters
image – Image to modify
cmap – Matplotlib colour map name
- aus400.render.to_bytes(array)¶
Convert an array of floats between 0 and 1 to bytes between 0 and 255
- aus400.render.zoom_region(image: PIL.Image.Image, lat: float, lon: float, scale: float, size: Tuple[int, int])¶
Zooms to a specific region of an Aus400 rendered image
The output image is centred at (lat, lon), is ‘scale’ degrees wide, with the height determined by the aspect ratio of ‘size’
- Parameters
image – Source image, must cover the full d0036t grid
lat – Central latitude
lon – Central longitude
scale – Output longitude width in degrees
size – Output image size in pixels
- Returns
PIL.Image.Imagewith size ‘size’