Tethys package¶
tethys.model¶
@Date: 09/20/2022 @author: Isaac Thompson (isaac.thompson@pnnl.gov) @Project: Tethys V2.0
License: BSD 2-Clause, see LICENSE and DISCLAIMER files Copyright (c) 2022, Battelle Memorial Institute
- class tethys.model.Tethys(config_file=None, years=None, resolution=0.125, demand_type='withdrawals', perform_temporal=False, gcam_db=None, csv=None, output_file=None, downscaling_rules=None, proxy_files=None, map_files=None, temporal_files=None, temporal_methods=None)[source]¶
Bases:
objectModel wrapper for Tethys
- downscale(distribution, inputs, region_masks)[source]¶
Actual spatial downscaling happens here
- Parameters
distribution – DataArray (sector, year, lat, lon) spatial distribution of proxies
inputs – DataArray (region, sector, year) demand values by region, sector, year
region_masks – DataArray (lat, lon, region) of bools, True if (lat, lon) belongs to region
- Returns
distribution scaled to match inputs in all regions
tethys.datareader.gridded¶
- tethys.datareader.gridded.interp_helper(da, target_years=None)[source]¶
Linearly interpolate da to target_years more control over chunks, works with sparse
- Parameters
da – xarray DataArray with source years
target_years – list of target years to interpolate to. If None, then interpolate to annual
- Returns
da linearly interpolated to target_years
- tethys.datareader.gridded.load_file(filename, target_resolution, years, variables=None, flags=None, regrid_method='extensive')[source]¶
Prepare a dataset from single file to be merged into a dataset of all proxies
handles many oddities found in proxies
- Parameters
filename – name of file
target_resolution – resolution in degrees to regrid to
years – years to extract from the file
variables – variables to extract from the file
flags – list potentially containing ‘cell_area_share’ or ‘short_name_as_name’
regrid_method – passed along to regrid
- Returns
preprocessed data set
- tethys.datareader.gridded.pad_global(ds)[source]¶
pad inputs to global resolution
- Parameters
ds – xarray Dataset
- Returns
xarray Dataset with global extent
- tethys.datareader.gridded.percent_to_area(ds)[source]¶
Convert landcover dataset from cell proportion to area
- Parameters
ds – xarray Dataset where values represent proportion of grid cell
- Returns
xarray Dataset where values represent area in square kilometers
- tethys.datareader.gridded.regrid(ds, target_resolution, method='extensive')[source]¶
Simple regridding algorithm
- Parameters
ds – xarray Dataset or DataArray, needs lat and lon and global extent
target_resolution – target resolution in degrees
method – choice of ‘extensive’ (preserves sums, default), ‘intensive’ (take average), or ‘label’ (for maps)
- Returns
ds regridded to target_resolution
tethys.datareader.maps¶
- tethys.datareader.maps.load_region_map(mapfile, masks=False, namefile=None, target_resolution=None, nodata=None, flip_lat=False)[source]¶
Load region map.
- Parameters
mapfile – path to map file
masks – bool whether to convert categorical map to layer of region masks
namefile – optional path to csv with region names
target_resolution – resolution to coerce map to. If None (default), use base resolution
nodata – nodata value (like 9999), will be replaced with 0
flip_lat – bool, whether the map is “upside down”
tethys.datareader.regional¶
- tethys.datareader.regional.elec_sector_weights(gcam_db)[source]¶
Get the electricity sector weights from GCAM database
- tethys.datareader.regional.extract_basin_name(x)[source]¶
Maps ‘water_td_irr_basin_C’ to ‘_basin’, and water_td_elec_C to ‘’
- tethys.datareader.regional.friendly_sector_name(x)[source]¶
convert from GCAM water input name to friendly name
- tethys.datareader.regional.load_region_data(gcam_db, sectors, demand_type='withdrawals')[source]¶
Load region-scale water demand from GCAM needed to carry out a configuration
- Parameters
gcam_db – path to GCAM database (the folder containing the .basex files)
sectors – GCAM sectors to filter to (friendly names will be converted to unfriendly names)
demand_type – ‘withdrawals’ or ‘consumption’
- Returns
pandas dataframe with columns ‘region’, ‘sector’, ‘year’, ‘value’
tethys.datareader.easy_query¶
- tethys.datareader.easy_query.easy_query(variable, year_axis=None, **kwargs)[source]¶
Build a query for a GCAM database
kwargs act as filters on nodes, eg. “sector=’Beef’” is converted to the xpath “[@type=’sector’ and @name=’Beef’]” Lists are also accepted, so “sector=[‘Beef’, ‘Dairy’]” matches ‘Beef’ or ‘Dairy’
Order of kwargs must follow the sector -> subsector -> technology -> input/output/etc hierarchy
Some shortcuts are supported. ‘*’ can be used as a wildcard, so input=’water_td_irr_*_C’ matches any input starting with ‘water_td_irr’ and ending with ‘_C’
Starting a filter string with ‘!’ negates it, so sector=’!elec*’ will exclude any sector names that begin with ‘elec’. Combinations are allowed, so input=[‘water_td_irr_*_C’, ‘!water_td_irr_A*’] would select all input basins, except those beginning with the letter ‘A’.
- Parameters
variable – name of variable in database, such as ‘demand-physical’ or ‘emissions’
year_axis – optional name of year axis (‘year’ or ‘vintage’). If None, will be inferred from variable
kwargs – refer to description for details
- Returns
gcamreader Query object
- tethys.datareader.easy_query.handle_filter(key, value)[source]¶
Handle a kwarg for build_query
- Parameters
key – xml node @type attribute to match
value – string, or list of strings, indicating what the node @name attribute must match or not match
- Returns
xpath to filter nodes
- tethys.datareader.easy_query.parse_name(name)[source]¶
convert a string to an xpath condition the basic case converts “foo” to “@name=’foo’”
‘*’ acts as a wildcard, so “foo*” is converted to “(starts-with(@name, ‘foo’))”
- Parameters
name – the string to parse
- Returns
xpath condition indicated by string