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climakitae.util package

climakitae.util package#

Submodules#

climakitae.util.cluster module#

Wrapper for creating a Dask Gateway Cluster

class climakitae.util.cluster.Cluster(address=None, proxy_address=None, public_address=None, auth=None, cluster_options=None, shutdown_on_close=True, asynchronous=False, loop=None, **kwargs)#

Bases: GatewayCluster

A dask-gateway cluster allowing one cluster per user. Instead of always creating new clusters, connect to a previously running user cluster, and attempt to limit users to a single cluster.

Examples

>>> from climakitae.util.cluster import Cluster
>>> cluster = Cluster() # Create cluster
>>> cluster.adapt(minimum=0, maximum=8) # Specify the number of workers to use
>>> client = cluster.get_client()
>>> cluster # Output cluster information
extra_packages = ['git+https://github.com/cal-adapt/climakitae.git']#
get_client(set_as_default=True)#

Get client

Returns:

distributed.client.Client

climakitae.util.generate_gwl_tables module#

Util for generating warming level reference data in ../data/ ###

To run, type: <<python generate_gwl_tables.py>> in the command line and wait for printed model outputs showing progress. Generation takes ~1.5 hours for generating all 4 csv’s.

climakitae.util.generate_gwl_tables.get_sims_on_aws(df)#

Make a table of all of the relevant CMIP6 simulations on AWS.

climakitae.util.generate_gwl_tables.main()#

Call everything needed to write the global warming level reference files for all of the available GCMs.

climakitae.util.unit_conversions module#

Calculates alternative units for variables with multiple commonly used units, following NWS conversions for pressure and wind speed.

climakitae.util.unit_conversions.convert_units(da, selected_units)#

Converts units for any variable

Parameters:

  • da (DataArray) – data

  • selected_units (str) – selected units of data, from selections.units

Returns:

  • da (DataArray) – data with converted units and updated units attribute

  • References

  • -------

  • Wind speed (https://www.weather.gov/media/epz/wxcalc/windConversion.pdf)

  • Pressure (https://www.weather.gov/media/epz/wxcalc/pressureConversion.pdf)

climakitae.util.unit_conversions.get_unit_conversion_options()#

Get dictionary of unit conversion options offered for each unit

climakitae.util.utils module#

Miscellaneous utility functions.

climakitae.util.utils.area_average(dset)#

Weighted area-average

Parameters:

dset (Dataset) – one dataset from the catalog

Returns:

Dataset – sub-setted output data

climakitae.util.utils.combine_hdd_cdd(data)#

Drops specific unneeded coords from HDD/CDD data, independent of station or gridded data source

climakitae.util.utils.compute_annual_aggreggate(data, name, num_grid_cells)#

Calculates the annual sum of HDD and CDD

climakitae.util.utils.compute_multimodel_stats(data)#

Calculates model mean, min, max, median across simulations

climakitae.util.utils.convert_to_local_time(data, selections)#

Converts the inputted data to the local time of the selection.

climakitae.util.utils.downscaling_method_as_list(downscaling_method)#

Function to convert string based radio button values to python list.

Parameters:

downscaling_method (str) – one of “Dynamical”, “Statistical”, or “Dynamical+Statistical”

Returns:

method_list (list) – one of [“Dynamical”], [“Statistical”], or [“Dynamical”,”Statistical”]

climakitae.util.utils.get_closest_gridcell(data, lat, lon, print_coords=True)#

From input gridded data, get the closest gridcell to a lat, lon coordinate pair.

This function first transforms the lat,lon coords to the gridded data’s projection. Then, it uses xarray’s built in method .sel to get the nearest gridcell.

Parameters:

  • data (DataArray or Dataset) – Gridded data

  • lat (float) – Latitude of coordinate pair

  • lon (float) – Longitude of coordinate pair

  • print_coords (bool, optional) – Print closest coorindates? Default to True. Set to False for backend use.

Returns:

DataArray – Grid cell closest to input lat,lon coordinate pair

See also

xarray.DataArray.sel

climakitae.util.utils.hdd_cdd_lineplot(annual_data, trendline, title='title')#

Plots annual CDD/HDD with trendline provided

climakitae.util.utils.hdh_cdh_lineplot(data)#

Plots HDH/CDH

climakitae.util.utils.julianDay_to_str_date(julday, leap_year=True, str_format='%b-%d')#

Convert julian day of year to string format i.e. if str_format = “%b-%d”, the output will be Mon-Day (“Jan-01”)

Parameters:

  • julday (int) – Julian day

  • leap_year (boolean) – leap year? (default to True)

  • str_format (str) – string format of output date

Returns:

date (str) – Julian day in the input format month-day (i.e. “Jan-01”)

climakitae.util.utils.read_ae_colormap(cmap='ae_orange', cmap_hex=False)#

Read in AE colormap by name

Parameters:

  • cmap (str) – one of [“ae_orange”,”ae_blue”,”ae_diverging”]

  • cmap_hex (boolean) – return RGB or hex colors?

Returns:

climakitae.util.utils.read_csv_file(rel_path, index_col=None, parse_dates=False)#
climakitae.util.utils.readable_bytes(B)#

Return the given bytes as a human friendly KB, MB, GB, or TB string. Code from stackoverflow: https://stackoverflow.com/questions/12523586/python-format-size-application-converting-b-to-kb-mb-gb-tb

climakitae.util.utils.reproject_data(xr_da, proj='EPSG:4326', fill_value=nan)#

Reproject xr.DataArray using rioxarray.

Parameters:

  • xr_da (DataArray) – 2-or-3-dimensional DataArray, with 2 spatial dimensions

  • proj (str) – proj to use for reprojection (default to “EPSG:4326”– lat/lon coords)

  • fill_value (float) – fill value (default to np.nan)

Returns:

data_reprojected (DataArray) – 2-or-3-dimensional reprojected DataArray

Raises:

  • ValueError – if input data does not have spatial coords x,y

  • ValueError – if input data has more than 5 dimensions

climakitae.util.utils.scenario_to_experiment_id(scenario, reverse=False)#

Convert scenario format to experiment_id format matching catalog names. Set reverse=True to get scenario format from input experiement_id.

climakitae.util.utils.summary_table(data)#

Helper function to organize dataset object into a pandas dataframe for ease.

Parameters:

data (Dataset)

Returns:

df (DataFrame) – df is organized so that the simulations are stacked in individual columns by year/time

climakitae.util.utils.trendline(data, kind='mean')#

Calculates treadline of the multi-model mean or median.

Parameters:

  • data (Dataset)

  • kind (str (optional)) – Options are ‘mean’ and ‘median’

Returns:

trendline (Dataset)

Note

1. Development note: If an additional option to trendline ‘kind’ is required, compute_multimodel_stats must be modified to update optionality.

climakitae.util.utils.write_csv_file(df, rel_path)#

Module contents#

Contents