livvext.common

module documentation

(source)

Generic class for LIVVkit Extensions.

This supplies several common methods for plotting and data analysis.

Class	`LEX`	Define a LIVVkit extension.
Function	`annotate_plot`	Add land / ocean, gridlines, colourbar.
Function	`area_avg`	Compute a masked and weighted area average of some field.
Function	`check_longitude`	Check that longitudes are -180 - +180.
Function	`closest_points`	Determine closest model points to set of observation x/y points.
Function	`compute_clevs`	Compute reasonable min / max levels for one to three arrays which are comparable.
Function	`gen_file_list`	Undocumented
Function	`gen_file_list_old`	Undocumented
Function	`gen_file_list_timeseries`	Undocumented
Function	`get_cycle`	Get the name of which type of averaging is used (annual, seasonal, monthly).
Function	`get_season_bounds`	Determine season bounds for climatology files.
Function	`img_file_prefix`	Convert the module name into a file prefix for the image output.
Function	`load_obs`	Undocumented
Function	`load_timeseries_data`	Load data for timeseries.
Function	`parse_var`	Undocumented
Function	`parse_var_name`	Undocumented
Function	`plot_grid`	Undocumented
Function	`plot_points`	Undocumented
Function	`proc_climo_file`	Process the climatology file to maintain backward compatibility with standalone LEX.
Function	`summarize_result`	Provides a snapshot of the extension's results to be provided on the summary webpage and printed to STDOUT via the print_summary method
Function	`var_filename_format`	Undocumented
Constant	`DAYS_PER_MONTH`	Undocumented
Constant	`DAYS_PER_SEASON`	Undocumented
Constant	`MON_NAMES`	Undocumented
Constant	`SEASON_NAME`	Undocumented
Constant	`TFORM`	Undocumented

def annotate_plot(axis, color_field=None, label=None): (source) ¶

Add land / ocean, gridlines, colourbar.

def area_avg(data, config, area_file, area_var, mask_file=None, mask_var=None, sum_out=False, land_only=False): (source) ¶

Compute a masked and weighted area average of some field.

data : array_like: Array of data to be averaged
config : dict: LIVVkit configuration dictionary, at least contains the variable maskv if mask_var is not set
area_file : Path: Path to a netCDF file containing the grid cell area which matches data
area_var : str: Name of the netCDF variable which contains the area data
mask_file : Path, optional: Path to a netCDF file containing the ice sheet mask whose shape matches data. If not set, the mask is assumed to be in the area_file file
mask_var : str, optional: Name of the netCDF variable which contains the ice sheet mask data, if not set, then use maskv from config

Returns
avg: `float` - Masked and area-weighted average of `data` isheet_mask: `array_like` - Mask of ice sheet used in generating `avg` area_maskice: `array_like` - Masked area used in generating `avg` _data: `array_like` - Input `data` masked by `isheet_mask`

def check_longitude(data, lon_coord='lon'): (source) ¶

Check that longitudes are -180 - +180.

def closest_points(model_x, model_y, obs_x, obs_y): (source) ¶

Determine closest model points to set of observation x/y points.

def compute_clevs(data, bnds=(5, 95), even=False, round=True, keys=None): (source) ¶

Compute reasonable min / max levels for one to three arrays which are comparable.

Parameters
data:`dictionary`	Masked array of data for which to compute contour levels
bnds:`tuple, optional`	Upper / lower percentiles to use for bounds (default: (5%, 95%))
even:`bool, optional`	Use an even interval about 0 (default: False)
round	Undocumented
keys:`list, optional`	List of keys within `data` for which bounds will be computed, default is all keys in `data`

Returns
`float`	bnd_l, bnd_h - Lower, upper bounds for contouring

def gen_file_list(config: dict, var_name: list | tuple | str, overs: str, sea: list | tuple | str, cycle: str): (source) ¶

Undocumented

def gen_file_list_old(config: dict, var_name: list | tuple | str, overs: str, sea: list | tuple | str, cycle: str, mode: str): (source) ¶

Undocumented

def gen_file_list_timeseries(config: dict, var_name: list | tuple | str, overs: str): (source) ¶

Undocumented

def get_cycle(sea): (source) ¶

Get the name of which type of averaging is used (annual, seasonal, monthly).

def get_season_bounds(season, year_s, year_e): (source) ¶

Determine season bounds for climatology files.

def img_file_prefix(config): (source) ¶

Convert the module name into a file prefix for the image output.

def load_obs(config, sea='ANN', mode='climoS', single_ds=None, expect_one_time=True): (source) ¶

Undocumented

@logger.catch

def load_timeseries_data(config): (source) ¶

Load data for timeseries.

def parse_var(data_var, dataset, scale): (source) ¶

Undocumented

def parse_var_name(data_var): (source) ¶

Undocumented

def plot_grid(lon, lat, data, axis, cmap=None, vmin=None, vmax=None, outline=False): (source) ¶

Undocumented

def plot_points(lon, lat, data, axis, cmap=None, vmin=None, vmax=None, outline=False): (source) ¶

Undocumented

def proc_climo_file(config, file_tag, sea): (source) ¶

Process the climatology file to maintain backward compatibility with standalone LEX.

Parameters
config:`dict`	LIVVkit /LEX configuration dict
file_tag:`str`	Configuration item which points to climatology filename to be formatted, usually `climo` or `climo_remap`
sea:`str`	Season identifier

Returns
`str`	climo_file - Formatted name of climatology file

def summarize_result(result): (source) ¶

Provides a snapshot of the extension's results to be provided on the summary webpage and printed to STDOUT via the print_summary method

def var_filename_format(file_pattern, _var, isheet, _sea, year_s, year_e, sep='_'): (source) ¶

Undocumented

DAYS_PER_MONTH = (source) ¶

Undocumented

Value

np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])

DAYS_PER_SEASON = (source) ¶

Undocumented

Value

{'DJF': 31 + 31 + 28,
 'MAM': 31 + 30 + 31,
 'JJA': 30 + 31 + 31,
 'SON': 30 + 31 + 30,
 'JFM': 31 + 28 + 31,
 'AMJ': 30 + 31 + 30,
 'JAS': 31 + 31 + 30,
...

MON_NAMES = (source) ¶

Undocumented

Value

[dt.datetime(2000, mon, 1).strftime('%b') for mon in range(1, 12 + 1)]

SEASON_NAME: dict[str, str] = (source) ¶

Undocumented

Value

{'ANN': 'annual',
 'DJF': 'winter',
 'JJA': 'summer',
 'MAM': 'spring',
 'SON': 'autumn'}

TFORM = (source) ¶

Undocumented

Value

ccrs.PlateCarree()