API Reference

APIs

Below is a list of Application Programming Interfaces (APIs) available in pcmdi_metrics (> v3.6.1).

`utils.custom_season_average`(ds, data_var, season)	Calculates the average of a user defined season in each year.
`utils.custom_season_departure`(ds, data_var, ...)	Calculate the departure from a reference seasonal climatology for each season in a given year.
`utils.replace_date_pattern`(filename, replacement)	Replace the 'YYYYMM-YYYYMM' pattern in a filename with a specified replacement.

io.xcdat_open(infile[, data_var, decode_times])

Open input file (netCDF, or xml generated by cdscan)

`utils.date_to_str`(date_obj)	Convert a date object to a string in 'YYYY-MM-DD' format.
`utils.extract_date_components`(ds[, index])	Extract year, month, and day from a dataset's time dimension.
`utils.find_overlapping_dates`(ds, start_date, ...)	Find the overlapping period between given dates and a dataset's time range.

`utils.create_land_sea_mask`(obj[, lon_key, ...])	Generate a land-sea mask (1 for land, 0 for sea) for a given xarray Dataset or DataArray.
`utils.apply_landmask`(obj[, data_var, ...])	Apply a land-sea mask to a given DataArray or Dataset.
`utils.apply_oceanmask`(obj[, data_var, ...])	Apply an ocean mask to a given DataArray or Dataset.

Example usages of the following plotting functions are available in the demo notebooks.

`graphics.parallel_coordinate_plot`(data, ...)	Create a parallel coordinate plot for visualizing multi-dimensional data.
`graphics.portrait_plot`(data, xaxis_labels, ...)	Create a portrait plot for visualizing 2D data arrays.
`graphics.TaylorDiagram`(stddev, corrcoef, refstd)	Create a Taylor diagram.

`utils.create_target_grid`([lat1, lat2, lon1, ...])	Generate a uniform grid for given latitude/longitude ranges and resolution
`utils.regrid`(ds, data_var, target_grid[, ...])	Regrid the dataset to a given grid.

`io.load_regions_specs`()	Load predefined geographic region specifications for climate data analysis.
`io.region_subset`(ds, region[, data_var, ...])	Subset a dataset or data array based on a specified region.
`io.region_from_file`(data, rgn_path, attr, ...)	Return data masked from a feature in the input file.

`io.get_grid`(d)	Get grid information
`io.get_axis_list`(ds)	Retrieve coordinate key names from the dataset or data array.
`io.get_data_list`(ds)	Retrieve data variable names from the dataset or data array.
`io.get_latitude`(ds)	Extract latitude coordinate data.
`io.get_latitude_bounds`(ds)	Extract latitude bounds data.
`io.get_latitude_key`(ds)	Get the key for the latitude dimension.
`io.get_longitude`(ds)	Extract longitude coordinate data.
`io.get_longitude_bounds`(ds)	Extract longitude bounds data.
`io.get_longitude_key`(ds)	Get the key for the longitude dimension.
`io.get_time`(ds)	Extract time coordinate data.
`io.get_time_bounds`(ds)	Extract time bounds data.
`io.get_time_bounds_key`(ds)	Get the key for the time bounds.
`io.get_time_key`(ds)	Get the key for the time dimension.
`io.select_subset`(ds[, lat, lon, time])	Select a subset of the given xarray dataset based on specified latitude, longitude, and time ranges.

`utils.check_daily_time_axis`(ds[, time_key])	Check if the time axis in an xarray dataset follows a correct daily sequence, considering all CFTime calendars.
`utils.check_monthly_time_axis`(ds[, time_key])	Check if the time axis of a dataset follows a correct monthly sequence.
`utils.regenerate_time_axis`(ds[, start_str, ...])	Regenerate the time axis and bounds for an xarray Dataset.

`utils.sort_human`(input_list)	Sort a list of strings in natural order.
`utils.fill_template`(template, **kwargs)	Fill in a template string with keyword values.
`utils.tree`()	Create a nested defaultdict with infinite depth.