emc2.core.model.DHARMA¶

class emc2.core.model.DHARMA(file_path, time_range=None, time_dim='dom_col', single_pi_class=True, load_processed=False, bounding_box=None)[source]¶

This loads a DHARMA simulation with all of the necessary parameters for EMC^2 to run.

Parameters:

file_path: str: Path to a ModelE simulation.
time_range: tuple or None: Start and end time to include. If this is None, the entire simulation will be included.
time_dim: str: name of time dimension.
single_pi_class: bool: If True, using a single precipitating ice class (pi). If False, using two precipitation ice classes (pi - snow and pir - rimed ice - both using the same LUTs).
load_processed: bool: If True, treating the ‘file_path’ variable as an EMC2-processed dataset; thus skipping appended string removal and dimension stacking, which are typically part of pre-processing.
bounding_box: None or 4-tuple: If not None, then a tuple representing the bounding box (x_min, y_min, x_max, y_max).

__init__(file_path, time_range=None, time_dim='dom_col', single_pi_class=True, load_processed=False, bounding_box=None)[source]¶

This loads a DHARMA simulation with all of the necessary parameters for EMC^2 to run.

Parameters:

file_path: str: Path to a ModelE simulation.
time_range: tuple or None: Start and end time to include. If this is None, the entire simulation will be included.
time_dim: str: name of time dimension.
single_pi_class: bool: If True, using a single precipitating ice class (pi). If False, using two precipitation ice classes (pi - snow and pir - rimed ice - both using the same LUTs).
load_processed: bool: If True, treating the ‘file_path’ variable as an EMC2-processed dataset; thus skipping appended string removal and dimension stacking, which are typically part of pre-processing.
bounding_box: None or 4-tuple: If not None, then a tuple representing the bounding box (x_min, y_min, x_max, y_max).

Methods

`__init__`(file_path[, time_range, time_dim, ...])	This loads a DHARMA simulation with all of the necessary parameters for EMC^2 to run.
`check_and_stack_time_lat_lon`([...])	Stack the time dim together with the lat and lon dims (if the lat and/or lon dims are longer than 1) to enable EMC^2 processing of regional model output.
`finalize_subcol_fields`([more_fieldnames])	Remove all zero values from subcolumn output fields enabling better visualization.
`load_subcolumns_from_netcdf`(file_name)	Load all of the subcolumn data from a previously saved netCDF file.
`permute_dims_for_processing`([base_order, ...])	Reorder dims for consistent processing such that the order is: subcolumn x time x height.
`remove_appended_str`([all_appended_in_lat])	Remove appended strings from xr.Dataset coords and fieldnames based on lat/lon coord names (typically required when using post-processed output data files).
`remove_subcol_fields`([cloud_class])	Remove all subcolumn output fields for the given cloud class to save memory (mainly releveant for CESM and E3SM).
`set_hyd_types`(hyd_types)
`subcolumns_to_netcdf`(file_name)	Saves all of the simulated subcolumn parameters to a netCDF file.
`unstack_time_lat_lon`([order_dim, ...])	Unstack the time, lat, and lon dims if they were previously stacked together (self.stacked_time_dim is not None).

Attributes

`hydrometeor_classes`	The list of hydrometeor classes.
`num_hydrometeor_classes`	The number of hydrometeor classes used
`num_subcolumns`	Gets the number of subcolumns in the model.