API Reference

Here we provide the reference documentation for aospy’s public API. If you are new to the package and/or just trying to get a feel for the overall workflow, you are better off starting in the Overview, Using aospy, or Examples sections of this documentation.

Warning

aospy is under active development. While we strive to maintain backwards compatibility, it is likely that some breaking changes to the codebase will occur in the future as aospy is improved.

Core Hierarchy for Input Data

aospy provides three classes for specifying the location and characteristics of data saved on disk as netCDF files that the user wishes to use as input data for aospy calculations: Proj, Model, and Run.

Proj

class aospy.proj.Proj(name, description=None, models=None, default_models=None, regions=None, direc_out='', tar_direc_out='')[source]

An object that describes a single project that will use aospy.

This is the top-level class in the aospy hierarchy of data representations. It is meant to contain all of the Model, Run, and Region objects that are of relevance to a particular research project. (Any of these may be used by multiple Proj objects.)

The Proj class itself provides little functionality, but it is an important means of organizing a user’s work across different projects. In particular, the output of all calculations using aospy.Calc are saved in a directory structure whose root is that of the Proj object specified for that calculation.

Attributes:
name : str

The run’s name

description : str

A description of the run

direc_out, tar_direc_out : str

The paths to the root directories of, respectively, the standard and .tar versions of the output of aospy calculations saved to disk.

models : dict

A dictionary with entries of the form {model_obj.name: model_obj}, for each of this Proj’s child model objects

default_models : dict

The default model objects on which to perform calculations via aospy.Calc if not otherwise specified

regions : dict

A dictionary with entries of the form {regin_obj.name: region_obj}, for each of this Proj’s child region objects

__init__(name, description=None, models=None, default_models=None, regions=None, direc_out='', tar_direc_out='')[source]
Parameters:
name : str

The project’s name. This should be unique from that of any other Proj objects being used.

description : str, optional

A description of the model. This is not used internally by aospy; it is solely for the user’s information.

regions : {None, sequence of aospy.Region objects}, optional

The desired regions over which to perform region-average calculations.

models : {None, sequence of aospy.Model objects}, optional

The child Model objects of this project.

default_models : {None, sequence of aospy.Run objects}, optional

The subset of this Model’s runs over which to perform calculations by default.

direc_out, tar_direc_out : str

Path to the root directories of where, respectively, regular output and a .tar-version of the output will be saved to disk.

See also

aospy.Model, aospy.Region, aospy.Run

Notes

Instantiating a Proj object has the side-effect of setting the proj attribute of each of it’s child Model objects to itself.

Model

class aospy.model.Model(name=None, description=None, proj=None, grid_file_paths=None, default_start_date=None, default_end_date=None, runs=None, default_runs=None, load_grid_data=False, grid_attrs=None)[source]

An object that describes a single climate or weather model.

Each Model object is associated with a parent Proj object and also with one or more child Run objects.

If aospy is being used to work with non climate- or weather-model data, the Model object can be used e.g. to represent a gridded observational product, with its child Run objects representing different released versions of that dataset.

Attributes:
name : str

The model’s name

description : str

A description of the model

proj : {None, aospy.Proj}

The model’s parent aospy.Proj object

runs : list

A list of this model’s child Run objects

default_runs : list

The default subset of child run objects on which to perform calculations via aospy.Calc with this model if not otherwise specified

grid_file_paths : list

The paths to netCDF files stored on disk from which the model’s coordinate data can be taken.

default_start_date, default_end_date : datetime.datetime

The default start and end dates of any calculations using this Model

__init__(name=None, description=None, proj=None, grid_file_paths=None, default_start_date=None, default_end_date=None, runs=None, default_runs=None, load_grid_data=False, grid_attrs=None)[source]
Parameters:
name : str

The model’s name. This must be unique from that of any other Model objects being used by the parent Proj.

description : str, optional

A description of the model. This is not used internally by aospy; it is solely for the user’s information.

proj : {None, aospy.Proj}, optional

The parent Proj object. When the parent Proj object is instantiated with this Model included in its models attribute, this will be over-written with that Proj object.

grid_file_paths : {None, sequence of strings}, optional

The paths to netCDF files stored on disk from which the model’s coordinate data can be taken.

default_start_date : {None, datetime.datetime}, optional

Default start date of calculations to be performed using this Model.

default_end_date : {None, datetime.datetime}, optional

Default end date of calculations to be performed using this Model.

runs : {None, sequence of aospy.Run objects}, optional

The child run objects of this Model

default_runs : {None, sequence of aospy.Run objects}, optional

The subset of this Model’s runs over which to perform calculations by default.

load_grid_data : bool, optional (default False)

Whether or not to load the grid data specified by ‘grid_file_paths’ upon initilization

grid_attrs : dict, optional (default None)

Dictionary mapping aospy internal names of grid attributes to their corresponding names used in a particular model. E.g. {TIME_STR: 'T'}. While aospy checks for a number of alternative names for grid attributes used by various models, it is not possible to anticipate all possible names. This option allows the user to explicitly tell aospy which variables correspond to which internal names (internal names not provided in this dictionary will be attempted to be found in the usual way). For a list of built-in alternative names see the table here.

See also

aospy.DataLoader, aospy.Proj, aospy.Run

Notes

A side-effect of instantiating a Model object is that the parent attribute of all of the model’s Run objects is set to that model.

set_grid_data()[source]

Populate the attrs that hold grid data.

Run

class aospy.run.Run(name=None, description=None, proj=None, default_start_date=None, default_end_date=None, data_loader=None)[source]

An object that describes a single model ‘run’ (i.e. simulation).

Each Run object is associated with a parent Model object. This parent attribute is not set by Run itself, however; it is set during the instantation of the parent Model object.

If aospy is being used to work with non climate-model data, the Run object can be used e.g. to represent different versions of a gridded observational data product, with the parent Model representing that data product more generally.

Attributes:
name : str

The run’s name

description : str

A description of the run

proj : {None, aospy.Proj}

The run’s parent aospy.Proj object

default_start_date, default_end_date : datetime.datetime

The default start and end dates of any calculations using this Run

data_loader : aospy.DataLoader

The aospy.DataLoader object used to find data on disk corresponding to this Run object

__init__(name=None, description=None, proj=None, default_start_date=None, default_end_date=None, data_loader=None)[source]

Instantiate a Run object.

Parameters:
name : str

The run’s name. This must be unique from that of any other Run objects being used by the parent Model.

description : str, optional

A description of the model. This is not used internally by aospy; it is solely for the user’s information.

proj : {None, aospy.Proj}, optional

The parent Proj object.

data_loader : aospy.DataLoader

The DataLoader object used to find the data on disk to be used as inputs for aospy calculations for this run.

default_start_date, default_end_date : datetime.datetime, optional

Default start and end dates of calculations to be performed using this Model.

See also

aospy.DataLoader, aospy.Model

DataLoaders

Run objects rely on a helper “data loader” to specify how to find their underlying data that is saved on disk. This mapping of variables, time ranges, and potentially other parameters to the location of the corresponding data on disk can differ among modeling centers or even between different models at the same center.

Currently supported data loader types are DictDataLoader, NestedDictDataLoader, and GFDLDataLoader Each of these inherit from the abstract base DataLoader class.

Note

An important consideration can be the datatype used to store values in your datasets. In particular, if the float32 datatype is used in storage, it can lead to undesired inaccuracies in the computation of reduction operations (like means) due to upstream issues (see pydata/xarray#1346 for more information). To address this it is recommended to always upcast float32 data to float64. This behavior is turned on by default. If you would like to disable this behavior you can set the upcast_float32 argument in your DataLoader constructors to False.

class aospy.data_loader.DataLoader[source]

A fundamental DataLoader object.

__init__($self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

load_variable(var=None, start_date=None, end_date=None, time_offset=None, grid_attrs=None, **DataAttrs)[source]

Load a DataArray for requested variable and time range.

Automatically renames all grid attributes to match aospy conventions.

Parameters:
var : Var

aospy Var object

start_date : datetime.datetime

start date for interval

end_date : datetime.datetime

end date for interval

time_offset : dict

Option to add a time offset to the time coordinate to correct for incorrect metadata.

grid_attrs : dict (optional)

Overriding dictionary of grid attributes mapping aospy internal names to names of grid attributes used in a particular model.

**DataAttrs

Attributes needed to identify a unique set of files to load from

Returns:
da : DataArray

DataArray for the specified variable, date range, and interval in

recursively_compute_variable(var, start_date=None, end_date=None, time_offset=None, model=None, **DataAttrs)[source]

Compute a variable recursively, loading data where needed.

An obvious requirement here is that the variable must eventually be able to be expressed in terms of model-native quantities; otherwise the recursion will never stop.

Parameters:
var : Var

aospy Var object

start_date : datetime.datetime

start date for interval

end_date : datetime.datetime

end date for interval

time_offset : dict

Option to add a time offset to the time coordinate to correct for incorrect metadata.

model : Model

aospy Model object (optional)

**DataAttrs

Attributes needed to identify a unique set of files to load from

Returns:
da : DataArray

DataArray for the specified variable, date range, and interval in

class aospy.data_loader.DictDataLoader(file_map=None, upcast_float32=True, data_vars='minimal', coords='minimal', preprocess_func=<function DictDataLoader.<lambda>>)[source]

A DataLoader that uses a dict mapping lists of files to string tags.

This is the simplest DataLoader; it is useful for instance if one is dealing with raw model history files, which tend to group all variables of a single output interval into single filesets. The intvl_in parameter is a string description of the time frequency of the data one is referencing (e.g. ‘monthly’, ‘daily’, ‘3-hourly’). In principle, one can give it any string value.

Parameters:
file_map : dict

A dict mapping an input interval to a list of files

upcast_float32 : bool (default True)

Whether to cast loaded DataArrays with the float32 datatype to float64 before doing calculations

data_vars : str (default ‘minimal’)

Mode for concatenating data variables in call to xr.open_mfdataset

coords : str (default ‘minimal’)

Mode for concatenating coordinate variables in call to xr.open_mfdataset.

preprocess_func : function (optional)

A function to apply to every Dataset before processing in aospy. Must take a Dataset and **kwargs as its two arguments.

Examples

Case of two sets of files, one with monthly average output, and one with 3-hourly output.

>>> file_map = {'monthly': '000[4-6]0101.atmos_month.nc',
...             '3hr': '000[4-6]0101.atmos_8xday.nc'}
>>> data_loader = DictDataLoader(file_map)

If one wanted to correct a CF-incompliant units attribute on each Dataset read in, which depended on the intvl_in of the fileset one could define a preprocess_func which took into account the intvl_in keyword argument.

>>> def preprocess(ds, **kwargs):
...     if kwargs['intvl_in'] == 'monthly':
...         ds['time'].attrs['units'] = 'days since 0001-01-0000'
...     if kwargs['intvl_in'] == '3hr':
...         ds['time'].attrs['units'] = 'hours since 0001-01-0000'
...     return ds
>>> data_loader = DictDataLoader(file_map, preprocess)
__init__(file_map=None, upcast_float32=True, data_vars='minimal', coords='minimal', preprocess_func=<function DictDataLoader.<lambda>>)[source]

Create a new DictDataLoader.

class aospy.data_loader.NestedDictDataLoader(file_map=None, upcast_float32=True, data_vars='minimal', coords='minimal', preprocess_func=<function NestedDictDataLoader.<lambda>>)[source]

DataLoader that uses a nested dictionary mapping to load files.

This is the most flexible existing type of DataLoader; it allows for the specification of different sets of files for different variables. The intvl_in parameter is a string description of the time frequency of the data one is referencing (e.g. ‘monthly’, ‘daily’, ‘3-hourly’). In principle, one can give it any string value. The variable name can be any variable name in your aospy object library (including alternative names).

Parameters:
file_map : dict

A dict mapping intvl_in to dictionaries mapping Var objects to lists of files

upcast_float32 : bool (default True)

Whether to cast loaded DataArrays with the float32 datatype to float64 before doing calculations

data_vars : str (default ‘minimal’)

Mode for concatenating data variables in call to xr.open_mfdataset

coords : str (default ‘minimal’)

Mode for concatenating coordinate variables in call to xr.open_mfdataset.

preprocess_func : function (optional)

A function to apply to every Dataset before processing in aospy. Must take a Dataset and **kwargs as its two arguments.

Examples

Case of a set of monthly average files for large scale precipitation, and another monthly average set of files for convective precipitation.

>>> file_map = {'monthly': {'precl': '000[4-6]0101.precl.nc',
...                         'precc': '000[4-6]0101.precc.nc'}}
>>> data_loader = NestedDictDataLoader(file_map)

See aospy.data_loader.DictDataLoader for an example of a possible function to pass as a preprocess_func.

__init__(file_map=None, upcast_float32=True, data_vars='minimal', coords='minimal', preprocess_func=<function NestedDictDataLoader.<lambda>>)[source]

Create a new NestedDictDataLoader

class aospy.data_loader.GFDLDataLoader(template=None, data_direc=None, data_dur=None, data_start_date=None, data_end_date=None, upcast_float32=None, data_vars=None, coords=None, preprocess_func=None)[source]

DataLoader for NOAA GFDL model output.

This is an example of a domain-specific custom DataLoader, designed specifically for finding files output by the Geophysical Fluid Dynamics Laboratory’s model history file post-processing tools.

Parameters:
template : GFDLDataLoader

Optional argument to specify a base GFDLDataLoader to inherit parameters from

data_direc : str

Root directory of data files

data_dur : int

Number of years included per post-processed file

data_start_date : datetime.datetime

Start date of data files

data_end_date : datetime.datetime

End date of data files

upcast_float32 : bool (default True)

Whether to cast loaded DataArrays with the float32 datatype to float64 before doing calculations

data_vars : str (default ‘minimal’)

Mode for concatenating data variables in call to xr.open_mfdataset

coords : str (default ‘minimal’)

Mode for concatenating coordinate variables in call to xr.open_mfdataset.

preprocess_func : function (optional)

A function to apply to every Dataset before processing in aospy. Must take a Dataset and **kwargs as its two arguments.

Examples

Case without a template to start from.

>>> base = GFDLDataLoader(data_direc='/archive/control/pp', data_dur=5,
...                       data_start_date=datetime(2000, 1, 1),
...                       data_end_date=datetime(2010, 12, 31))

Case with a starting template.

>>> data_loader = GFDLDataLoader(base, data_direc='/archive/2xCO2/pp')

See aospy.data_loader.DictDataLoader for an example of a possible function to pass as a preprocess_func.

__init__(template=None, data_direc=None, data_dur=None, data_start_date=None, data_end_date=None, upcast_float32=None, data_vars=None, coords=None, preprocess_func=None)[source]

Create a new GFDLDataLoader

Variables and Regions

The Var and Region classes are used to represent, respectively, physical quantities the user wishes to be able to compute and geographical regions over which the user wishes to aggregate their calculations.

Whereas the Proj - Model - Run hierarchy is used to describe the data resulting from particular model simulations, Var and Region represent the properties of generic physical entities that do not depend on the underlying data.

Var

class aospy.var.Var(name, alt_names=None, func=None, variables=None, units='', plot_units='', plot_units_conv=1, domain='atmos', description='', def_time=False, def_vert=False, def_lat=False, def_lon=False, math_str=False, colormap='RdBu_r', valid_range=None)[source]

An object representing a physical quantity to be computed.

Attributes:
name : str

The variable’s name

alt_names : tuple of strings

All other names that the variable may be referred to in the input data

names : tuple of strings

The combination of name and alt_names

description : str

A description of the variable

func : function

The function with which to compute the variable

variables : sequence of aospy.Var objects

The variables passed to func to compute it

units : str

The variable’s physical units

domain : str

The physical domain of the variable, e.g. ‘atmos’, ‘ocean’, or ‘land’

def_time, def_vert, def_lat, def_lon : bool

Whether the variable is defined, respectively, in time, vertically, in latitude, and in longitude

math_str : str

The mathematical representation of the variable

colormap : str

The name of the default colormap to be used in plots of this variable

valid_range : length-2 tuple

The range of values outside which to flag as unphysical/erroneous

__init__(name, alt_names=None, func=None, variables=None, units='', plot_units='', plot_units_conv=1, domain='atmos', description='', def_time=False, def_vert=False, def_lat=False, def_lon=False, math_str=False, colormap='RdBu_r', valid_range=None)[source]

Instantiate a Var object.

Parameters:
name : str

The variable’s name

alt_names : tuple of strings

All other names that the variable might be referred to in any input data. Each of these should be unique to this variable in order to avoid loading the wrong quantity.

description : str

A description of the variable

func : function

The function with which to compute the variable

variables : sequence of aospy.Var objects

The variables passed to func to compute it. Order matters: whenever calculations are performed to generate data corresponding to this Var, the data corresponding to the elements of variables will be passed to self.function in the same order.

units : str

The variable’s physical units

domain : str

The physical domain of the variable, e.g. ‘atmos’, ‘ocean’, or ‘land’. This is only used by aospy by some types of DataLoader, including GFDLDataLoader.

def_time, def_vert, def_lat, def_lon : bool

Whether the variable is defined, respectively, in time, vertically, in latitude, and in longitude

math_str : str

The mathematical representation of the variable. This is typically a raw string of LaTeX math-mode, e.g. r’$T_mathrm{sfc}$’ for surface temperature.

colormap : str

(Currently not used by aospy) The name of the default colormap to be used in plots of this variable.

valid_range : length-2 tuple

The range of values outside which to flag as unphysical/erroneous

mask_unphysical(data)[source]

Mask data array where values are outside physically valid range.

to_plot_units(data, dtype_vert=False)[source]

Convert the given data to plotting units.

Note

While for the sake of tracking metadata we encourage users to add a units attribute to each of their Var objects, these units attributes provide nothing more than descriptive value. One day we hope DataArrays produced by loading or computing variables will be truly units-aware (e.g. adding or subtracting two DataArrays with different units will lead to an error, or multiplying two DataArrays will result in a DataArray with new units), but we will leave that to upstream libraries to implement (see pydata/xarray#525 for more discussion).

Region

class aospy.region.Region(name='', description='', west_bound=None, east_bound=None, south_bound=None, north_bound=None, mask_bounds=None, do_land_mask=False)[source]

Geographical region over which to perform averages and other reductions.

Each Proj object includes a list of Region objects, which is used by Calc to determine which regions over which to perform time reductions over region-average quantities.

Region boundaries are specified as either a single “rectangle” in latitude and longitude or the union of multiple such rectangles. In addition, a land or ocean mask can be applied.

See also

aospy.Calc.region_calcs

Attributes:
name : str

The region’s name

description : str

A description of the region

mask_bounds : tuple

The coordinates definining the lat-lon rectangle(s) that define(s) the region’s boundaries

do_land_mask

Whether values occurring over land, ocean, or neither are excluded from the region, and whether the included points must be strictly land or ocean or if fractional land/ocean values are included.

__init__(name='', description='', west_bound=None, east_bound=None, south_bound=None, north_bound=None, mask_bounds=None, do_land_mask=False)[source]

Instantiate a Region object.

Note that longitudes spanning (-180, 180), (0, 360), or any other range are all supported: -180 to 0, 180 to 360, etc. are interpreted as the western hemisphere, and 0-180, 360-540, etc. are interpreted as the eastern hemisphere. This is true both of the region definition and of any data upon which the region mask is applied.

E.g. suppose some of your data is defined on a -180 to 180 longitude grid, some of it is defined on a 0 to 360 grid, and some of it is defined on a -70 to 290 grid. A single Region object will work with all three of these.

Conversely, latitudes are always treated as -90 as the South Pole, 0 as the Equator, and 90 as the North Pole. Latitudes larger than 90 are not physically meaningful.

Parameters:
name : str

The region’s name. This must be unique from that of any other Region objects being used by the overlying Proj.

description : str, optional

A description of the region. This is not used internally by aospy; it is solely for the user’s information.

west_bound, east_bound : { scalar, aospy.Longitude }, optional

The western and eastern boundaries of the region. All input longitudes are casted to aospy.Longitude objects, which essentially maps them to a 180W to 180E grid. The region’s longitudes always start at west_bound and move toward the east until reaching east_bound. This means that there are two distinct cases:

  • If, after this translation, west_bound is less than east_bound, the region includes the points east of west_bound and west of east_bound.
  • If west_bound is greater than east_bound, then the region is treated as wrapping around the dateline, i.e. it’s western-most point is east_bound, and it includes all points moving east from there until west_bound.

If the region boundaries are more complicated than a single lat-lon rectangle, use mask_bounds instead.

south_bound, north_bound : scalar, optional

The southern, and northern boundaries, respectively, of the region. If the region boundaries are more complicated than a single lat-lon rectangle, use mask_bounds instead.

mask_bounds : sequence, optional

Each element is a length-4 sequence of the format (west_bound, east_bound, south_bound, north_bound), where each of these _bound arguments is of the form described above.

do_land_mask, bool or str, optional

Determines what, if any, land mask is applied in addition to the mask defining the region’s boundaries. Default False. Must be one of False, True, ‘ocean’, ‘strict_land’, or ‘strict_ocean’:

  • True: apply the data’s full land mask
  • False: apply no mask
  • ‘ocean’: mask out land rather than ocean
  • ‘strict_land’: mask out all points that are not 100% land
  • ‘strict_ocean’: mask out all points that are not 100% ocean

Examples

Define a region spanning the entire globe:

>>> globe = Region(name='globe', west_bound=0, east_bound=360,
...                south_bound=-90, north_bound=90, do_land_mask=False)

Longitudes are handled as cyclic, so this definition could have equivalently used west_bound=-180, east_bound=180 or west_bound=200, east_bound=560, or anything else that spanned 360 degrees total.

Define a region corresponding to land in the mid-latitudes, which we’ll define as land points within 30-60 degrees latitude in both hemispheres. Because this region is the union of multiple lat-lon rectangles, it has to be defined using mask_bounds:

>>> land_mid_lats = Region(name='land_midlat', do_land_mask=True,
...                        mask_bounds=[(-180, 180, 30, 60),
...                                     (-180, 180, -30, -60)])

Define a region spanning the southern Tropical Atlantic ocean, which we’ll take to be all ocean points between 60W and 30E and between the Equator and 30S:

>>> atl_south_trop = Region(name='atl_sh_trop', west_bound=-60,
...                         east_bound=30, south_bound=-30,
...                         north_bound=0, do_land_mask='ocean')

Define the “opposite” region, i.e. all ocean points in the southern Tropics outside of the Atlantic. We simply swap west_bound and east_bound of the previous example:

>>> non_atl_south_trop = Region(name='non_atl_sh_trop', west_bound=30,
...                             east_bound=-60, south_bound=-30,
...                             north_bound=0, do_land_mask='ocean')
av(data, lon_str='lon', lat_str='lat', land_mask_str='land_mask', sfc_area_str='sfc_area')[source]

Time-average of region-averaged data.

Parameters:
data : xarray.DataArray

The array to compute the regional time-average of

lat_str, lon_str, land_mask_str, sfc_area_str : str, optional

The name of the latitude, longitude, land mask, and surface area coordinates, respectively, in data. Defaults are the corresponding values in aospy.internal_names.

Returns:
xarray.DataArray

The region-averaged and time-averaged data.

mask_var(data, lon_cyclic=True, lon_str='lon', lat_str='lat')[source]

Mask the given data outside this region.

Parameters:
data : xarray.DataArray

The array to be regionally masked.

lon_cyclic : bool, optional (default True)

Whether or not the longitudes of data span the whole globe, meaning that they should be wrapped around as necessary to cover the Region’s full width.

lon_str, lat_str : str, optional

The names of the longitude and latitude dimensions, respectively, in the data to be masked. Defaults are aospy.internal_names.LON_STR and aospy.internal_names.LON_STR, respectively.

Returns:
xarray.DataArray

The original array with points outside of the region masked.

std(data, lon_str='lon', lat_str='lat', land_mask_str='land_mask', sfc_area_str='sfc_area')[source]

Temporal standard deviation of region-averaged data.

Parameters:
data : xarray.DataArray

The array to compute the regional time-average of

lat_str, lon_str, land_mask_str, sfc_area_str : str, optional

The name of the latitude, longitude, land mask, and surface area coordinates, respectively, in data. Defaults are the corresponding values in aospy.internal_names.

Returns:
xarray.DataArray

The temporal standard deviation of the region-averaged data

ts(data, lon_cyclic=True, lon_str='lon', lat_str='lat', land_mask_str='land_mask', sfc_area_str='sfc_area')[source]

Create yearly time-series of region-averaged data.

Parameters:
data : xarray.DataArray

The array to create the regional timeseries of

lon_cyclic : { None, True, False }, optional (default True)

Whether or not the longitudes of data span the whole globe, meaning that they should be wrapped around as necessary to cover the Region’s full width.

lat_str, lon_str, land_mask_str, sfc_area_str : str, optional

The name of the latitude, longitude, land mask, and surface area coordinates, respectively, in data. Defaults are the corresponding values in aospy.internal_names.

Returns:
xarray.DataArray

The timeseries of values averaged within the region and within each year, one value per year.

Calculations

Calc is the engine that combines the user’s specifications of (1) the data on disk via Proj, Model, and Run, (2) the physical quantity to compute and regions to aggregate over via Var and Region, and (3) the desired date range, time reduction method, and other characteristics to actually perform the calculation

Whereas Proj, Model, Run, Var, and Region are all intended to be saved in .py files for reuse, Calc objects are intended to be generated dynamically by a main script and then not retained after they have written their outputs to disk following the user’s specifications.

Moreover, if the main.py script is used to execute calculations, no direct interfacing with Calc is required by the user, in which case this section should be skipped entirely.

Also included is the automate module, which enables aospy e.g. in the main script to find objects in the user’s object library that the user specifies via their string names rather than having to import the objects themselves.

Calc

class aospy.calc.Calc(proj=None, model=None, run=None, var=None, date_range=None, region=None, intvl_in=None, intvl_out=None, dtype_in_time=None, dtype_in_vert=None, dtype_out_time=None, dtype_out_vert=None, level=None, time_offset=None)[source]

Class for executing, saving, and loading a single computation.

__init__(proj=None, model=None, run=None, var=None, date_range=None, region=None, intvl_in=None, intvl_out=None, dtype_in_time=None, dtype_in_vert=None, dtype_out_time=None, dtype_out_vert=None, level=None, time_offset=None)[source]

Instantiate a Calc object.

Parameters:
proj : aospy.Proj object

The project for this calculation.

model : aospy.Model object

The model for this calculation.

run : aospy.Run object

The run for this calculation.

var : aospy.Var object

The variable for this calculation.

region : sequence of aospy.Region objects

The region(s) over which any regional reductions will be performed.

date_range : tuple of datetime.datetime objects

The range of dates over which to perform calculations.

intvl_in : {None, ‘annual’, ‘monthly’, ‘daily’, ‘6hr’, ‘3hr’}, optional

The time resolution of the input data.

dtype_in_time : {None, ‘inst’, ‘ts’, ‘av’, ‘av_ts’}, optional

What the time axis of the input data represents:

  • ‘inst’ : Timeseries of instantaneous values
  • ‘ts’ : Timeseries of averages over the period of each time-index
  • ‘av’ : A single value averaged over a date range
dtype_in_vert : {None, ‘pressure’, ‘sigma’}, optional

The vertical coordinate system used by the input data:

  • None : not defined vertically
  • ‘pressure’ : pressure coordinates
  • ‘sigma’ : hybrid sigma-pressure coordinates
intvl_out : {‘ann’, season-string, month-integer}

The sub-annual time interval over which to compute:

  • ‘ann’ : Annual mean
  • season-string : E.g. ‘JJA’ for June-July-August
  • month-integer : 1 for January, 2 for February, etc.
dtype_out_time : tuple with elements being one or more of:
  • Gridpoint-by-gridpoint output:
    • ‘av’ : Gridpoint-by-gridpoint time-average
    • ‘std’ : Gridpoint-by-gridpoint temporal standard deviation
    • ‘ts’ : Gridpoint-by-gridpoint time-series
  • Averages over each region specified via region:
    • ‘reg.av’, ‘reg.std’, ‘reg.ts’ : analogous to ‘av’, ‘std’, ‘ts’
dtype_out_vert : {None, ‘vert_av’, ‘vert_int’}, optional

How to reduce the data vertically:

  • None : no vertical reduction (i.e. output is defined vertically)
  • ‘vert_av’ : mass-weighted vertical average
  • ‘vert_int’ : mass-weighted vertical integral
time_offset : {None, dict}, optional

How to offset input data in time to correct for metadata errors

ARR_XARRAY_NAME = 'aospy_result'
compute(write_to_tar=True)[source]

Perform all desired calculations on the data and save externally.

load(dtype_out_time, dtype_out_vert=False, region=False, plot_units=False, mask_unphysical=False)[source]

Load the data from the object if possible or from disk.

region_calcs(arr, func)[source]

Perform a calculation for all regions.

save(data, dtype_out_time, dtype_out_vert=False, save_files=True, write_to_tar=False)[source]

Save aospy data to data_out attr and to an external file.

automate

Functionality for specifying and cycling through multiple calculations.

exception aospy.automate.AospyException[source]

Base exception class for the aospy package.

class aospy.automate.CalcSuite(calc_suite_specs)[source]

Suite of Calc objects generated from provided specifications.

create_calcs()[source]

Generate a Calc object for each requested parameter combination.

aospy.automate.submit_mult_calcs(calc_suite_specs, exec_options=None)[source]

Generate and execute all specified computations.

Once the calculations are prepped and submitted for execution, any calculation that triggers any exception or error is skipped, and the rest of the calculations proceed unaffected. This prevents an error in a single calculation from crashing a large suite of calculations.

Parameters:
calc_suite_specs : dict

The specifications describing the full set of calculations to be generated and potentially executed. Accepted keys and their values:

library : module or package comprising an aospy object library

The aospy object library for these calculations.

projects : list of aospy.Proj objects

The projects to permute over.

models : ‘all’, ‘default’, or list of aospy.Model objects

The models to permute over. If ‘all’, use all models in the models attribute of each Proj. If ‘default’, use all models in the default_models attribute of each Proj.

runs : ‘all’, ‘default’, or list of aospy.Run objects

The runs to permute over. If ‘all’, use all runs in the runs attribute of each Model. If ‘default’, use all runs in the default_runs attribute of each Model.

variables : list of aospy.Var objects

The variables to be calculated.

regions : ‘all’ or list of aospy.Region objects

The region(s) over which any regional reductions will be performed. If ‘all’, use all regions in the regions attribute of each Proj.

date_ranges : ‘default’ or a list of tuples

The range of dates (inclusive) over which to perform calculations. If ‘default’, use the default_start_date and default_end_date attribute of each Run. Else provide a list of tuples, each containing a pair of start and end dates, such as date_ranges=[(start, end)] where start and end are each datetime.datetime objects, partial datetime strings (e.g. ‘0001’), np.datetime64 objects, or cftime.datetime objects.

output_time_intervals : {‘ann’, season-string, month-integer}

The sub-annual time interval over which to aggregate.

  • ‘ann’ : Annual mean
  • season-string : E.g. ‘JJA’ for June-July-August
  • month-integer : 1 for January, 2 for February, etc. Each one is
    a separate reduction, e.g. [1, 2] would produce averages (or other specified time reduction) over all Januaries, and separately over all Februaries.
output_time_regional_reductions : list of reduction string identifiers

Unlike most other keys, these are not permuted over when creating the aospy.Calc objects that execute the calculations; each aospy.Calc performs all of the specified reductions. Accepted string identifiers are:

  • Gridpoint-by-gridpoint output:
    • ‘av’ : Gridpoint-by-gridpoint time-average
    • ‘std’ : Gridpoint-by-gridpoint temporal standard deviation
    • ‘ts’ : Gridpoint-by-gridpoint time-series
  • Averages over each region specified via region:
    • ‘reg.av’, ‘reg.std’, ‘reg.ts’ : analogous to ‘av’, ‘std’, ‘ts’
output_vertical_reductions : {None, ‘vert_av’, ‘vert_int’}, optional

How to reduce the data vertically:

  • None : no vertical reduction
  • ‘vert_av’ : mass-weighted vertical average
  • ‘vert_int’ : mass-weighted vertical integral
input_time_intervals : {‘annual’, ‘monthly’, ‘daily’, ‘#hr’}

A string specifying the time resolution of the input data. In ‘#hr’ above, the ‘#’ stands for a number, e.g. 3hr or 6hr, for sub-daily output. These are the suggested specifiers, but others may be used if they are also used by the DataLoaders for the given Runs.

input_time_datatypes : {‘inst’, ‘ts’, ‘av’}

What the time axis of the input data represents:

  • ‘inst’ : Timeseries of instantaneous values
  • ‘ts’ : Timeseries of averages over the period of each time-index
  • ‘av’ : A single value averaged over a date range
input_vertical_datatypes : {False, ‘pressure’, ‘sigma’}, optional

The vertical coordinate system used by the input data:

  • False : not defined vertically
  • ‘pressure’ : pressure coordinates
  • ‘sigma’ : hybrid sigma-pressure coordinates
input_time_offsets : {None, dict}, optional

How to offset input data in time to correct for metadata errors

exec_options : dict or None (default None)

Options regarding how the calculations are reported, submitted, and saved. If None, default settings are used for all options. Currently supported options (each should be either True or False):

  • prompt_verify : (default False) If True, print summary of
    calculations to be performed and prompt user to confirm before submitting for execution.
  • parallelize : (default False) If True, submit calculations in
    parallel.
  • client : distributed.Client or None (default None) The
    dask.distributed Client used to schedule computations. If None and parallelize is True, a LocalCluster will be started.
  • write_to_tar : (default True) If True, write results of calculations
    to .tar files, one for each aospy.Run object. These tar files have an identical directory structures the standard output relative to their root directory, which is specified via the tar_direc_out argument of each Proj object’s instantiation.
Returns:
A list of the return values from each :py:meth:`aospy.Calc.compute` call

If a calculation ran without error, this value is the aospy.Calc object itself, with the results of its calculations saved in its data_out attribute. data_out is a dictionary, with the keys being the temporal-regional reduction identifiers (e.g. ‘reg.av’), and the values being the corresponding result.

If any error occurred during a calculation, the return value is None.
Raises:
AospyException

If the prompt_verify option is set to True and the user does not respond affirmatively to the prompt.

Utilities

aospy includes a number of utility functions that are used internally and may also be useful to users for their own purposes. These include functions pertaining to input/output (IO), longitudes, time arrays, and vertical coordinates.

utils.io

Utility functions for data input and output.

aospy.utils.io.data_in_label(intvl_in, dtype_in_time, dtype_in_vert=False)[source]

Create string label specifying the input data of a calculation.

aospy.utils.io.data_name_gfdl(name, domain, data_type, intvl_type, data_yr, intvl, data_in_start_yr, data_in_dur)[source]

Determine the filename of GFDL model data output.

aospy.utils.io.data_out_label(time_intvl, dtype_time, dtype_vert=False)[source]
aospy.utils.io.dmget(files_list)[source]

Call GFDL command ‘dmget’ to access archived files.

aospy.utils.io.time_label(intvl, return_val=True)[source]

Create time interval label for aospy data I/O.

aospy.utils.io.yr_label(yr_range)[source]

Create label of start and end years for aospy data I/O.

utils.longitude

Functionality relating to parsing and comparing longitudes.

class aospy.utils.longitude.Longitude(value)[source]

Geographic longitude.

Enables unambiguous comparison of longitudes using the standard comparison operators, regardless of they were initially represented with a 0 to 360 convention, -180 to 180 convention, or anything else, and even if the original convention differs between them.

Specifically, the < operator assesses if the first object is to the west of the second object, with the standard convention that longitudes in the Western Hemisphere are always to the west of longitudes in the Eastern Hemisphere. The > operator is defined analogously. ==, >=, and <= are also all defined.

In addition to other Longitude objects, the operators can be used to compare a Longitude object to anything that can be casted to a Longitude object, or to any sequence (e.g. a list or xarray.DataArray) whose elements can be casted to Longitude objects.

hemisphere

{‘W’, ‘E’} The longitude’s hemisphere, either western or eastern.

longitude

(scalar) The unsigned numerical value of the longitude.

Always in the range 0 to 180. Must be combined with the hemisphere attribute to specify the exact latitude.

to_0360()[source]

Convert longitude to its numerical value within [0, 360).

to_pm180()[source]

Convert longitude to its numerical value within [-180, 180).

aospy.utils.longitude.lon_to_0360(lon)[source]

Convert longitude(s) to be within [0, 360).

The Eastern hemisphere corresponds to 0 <= lon + (n*360) < 180, and the Western Hemisphere corresponds to 180 <= lon + (n*360) < 360, where ‘n’ is any integer (positive, negative, or zero).

Parameters:
lon : scalar or sequence of scalars

One or more longitude values to be converted to lie in the [0, 360) range

Returns:
If ``lon`` is a scalar, then a scalar of the same type in the range [0,

360). If lon is array-like, then an array-like of the same type with each element a scalar in the range [0, 360).

aospy.utils.longitude.lon_to_pm180(lon)[source]

Convert longitude(s) to be within [-180, 180).

The Eastern hemisphere corresponds to 0 <= lon + (n*360) < 180, and the Western Hemisphere corresponds to 180 <= lon + (n*360) < 360, where ‘n’ is any integer (positive, negative, or zero).

Parameters:
lon : scalar or sequence of scalars

One or more longitude values to be converted to lie in the [-180, 180) range

Returns:
If ``lon`` is a scalar, then a scalar of the same type in the range

[-180, 180). If lon is array-like, then an array-like of the same type with each element a scalar in the range [-180, 180).

utils.times

Utility functions for handling times, dates, etc.

aospy.utils.times.add_uniform_time_weights(ds)[source]

Append uniform time weights to a Dataset.

All DataArrays with a time coordinate require a time weights coordinate. For Datasets read in without a time bounds coordinate or explicit time weights built in, aospy adds uniform time weights at each point in the time coordinate.

Parameters:
ds : Dataset

Input data

Returns:
Dataset
aospy.utils.times.apply_time_offset(time, years=0, months=0, days=0, hours=0)[source]

Apply a specified offset to the given time array.

This is useful for GFDL model output of instantaneous values. For example, 3 hourly data postprocessed to netCDF files spanning 1 year each will actually have time values that are offset by 3 hours, such that the first value is for 1 Jan 03:00 and the last value is 1 Jan 00:00 of the subsequent year. This causes problems in xarray, e.g. when trying to group by month. It is resolved by manually subtracting off those three hours, such that the dates span from 1 Jan 00:00 to 31 Dec 21:00 as desired.

Parameters:
time : xarray.DataArray representing a timeseries
years, months, days, hours : int, optional

The number of years, months, days, and hours, respectively, to offset the time array by. Positive values move the times later.

Returns:
pandas.DatetimeIndex

Examples

Case of a length-1 input time array:

>>> times = xr.DataArray(datetime.datetime(1899, 12, 31, 21))
>>> apply_time_offset(times)
Timestamp('1900-01-01 00:00:00')

Case of input time array with length greater than one:

>>> times = xr.DataArray([datetime.datetime(1899, 12, 31, 21),
...                       datetime.datetime(1899, 1, 31, 21)])
>>> apply_time_offset(times) # doctest: +NORMALIZE_WHITESPACE
DatetimeIndex(['1900-01-01', '1899-02-01'], dtype='datetime64[ns]',
              freq=None)
aospy.utils.times.assert_matching_time_coord(arr1, arr2)[source]

Check to see if two DataArrays have the same time coordinate.

Parameters:
arr1 : DataArray or Dataset

First DataArray or Dataset

arr2 : DataArray or Dataset

Second DataArray or Dataset

Raises:
ValueError

If the time coordinates are not identical between the two Datasets

aospy.utils.times.average_time_bounds(ds)[source]

Return the average of each set of time bounds in the Dataset.

Useful for creating a new time array to replace the Dataset’s native time array, in the case that the latter matches either the start or end bounds. This can cause errors in grouping (akin to an off-by-one error) if the timesteps span e.g. one full month each. Note that the Dataset’s times must not have already undergone “CF decoding”, wherein they are converted from floats using the ‘units’ attribute into datetime objects.

Parameters:
ds : xarray.Dataset

A Dataset containing a time bounds array with name matching internal_names.TIME_BOUNDS_STR. This time bounds array must have two dimensions, one of which’s coordinates is the Dataset’s time array, and the other is length-2.

Returns:
xarray.DataArray

The mean of the start and end times of each timestep in the original Dataset.

Raises:
ValueError

If the time bounds array doesn’t match the shape specified above.

aospy.utils.times.datetime_or_default(date, default)[source]

Return a datetime-like object or a default.

Parameters:
date : None or datetime-like object or str
default : The value to return if date is None
Returns:
`default` if `date` is `None`, otherwise returns the result of
`utils.times.ensure_datetime(date)`
aospy.utils.times.ensure_datetime(obj)[source]

Return the object if it is a datetime-like object

Parameters:
obj : Object to be tested.
Returns:
The original object if it is a datetime-like object
Raises:
TypeError if `obj` is not datetime-like
aospy.utils.times.ensure_time_as_index(ds)[source]

Ensures that time is an indexed coordinate on relevant quantites.

Sometimes when the data we load from disk has only one timestep, the indexing of time-defined quantities in the resulting xarray.Dataset gets messed up, in that the time bounds array and data variables don’t get indexed by time, even though they should. Therefore, we need this helper function to (possibly) correct this.

Note that this must be applied before CF-conventions are decoded; otherwise it casts np.datetime64[ns] as int values.

Parameters:
ds : Dataset

Dataset with a time coordinate

Returns:
Dataset
aospy.utils.times.ensure_time_avg_has_cf_metadata(ds)[source]

Add time interval length and bounds coordinates for time avg data.

If the Dataset or DataArray contains time average data, enforce that there are coordinates that track the lower and upper bounds of the time intervals, and that there is a coordinate that tracks the amount of time per time average interval.

CF conventions require that a quantity stored as time averages over time intervals must have time and time_bounds coordinates [1]. aospy further requires AVERAGE_DT for time average data, for accurate time-weighted averages, which can be inferred from the CF-required time_bounds coordinate if needed. This step should be done prior to decoding CF metadata with xarray to ensure proper computed timedeltas for different calendar types.

[1]http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_data_representative_of_cells
Parameters:
ds : Dataset or DataArray

Input data

Returns:
Dataset or DataArray

Time average metadata attributes added if needed.

aospy.utils.times.extract_months(time, months)[source]

Extract times within specified months of the year.

Parameters:
time : xarray.DataArray

Array of times that can be represented by numpy.datetime64 objects (i.e. the year is between 1678 and 2262).

months : Desired months of the year to include
Returns:
xarray.DataArray of the desired times
aospy.utils.times.infer_year(date)[source]

Given a datetime-like object or string infer the year.

Parameters:
date : datetime-like object or str

Input date

Returns:
int

Examples

>>> infer_year('2000')
2000
>>> infer_year('2000-01')
2000
>>> infer_year('2000-01-31')
2000
>>> infer_year(datetime.datetime(2000, 1, 1))
2000
>>> infer_year(np.datetime64('2000-01-01'))
2000
>>> infer_year(DatetimeNoLeap(2000, 1, 1))
2000
>>>
aospy.utils.times.maybe_convert_to_index_date_type(index, date)[source]

Convert a datetime-like object to the index’s date type.

Datetime indexing in xarray can be done using either a pandas DatetimeIndex or a CFTimeIndex. Both support partial-datetime string indexing regardless of the calendar type of the underlying data; therefore if a string is passed as a date, we return it unchanged. If a datetime-like object is provided, it will be converted to the underlying date type of the index. For a DatetimeIndex that is np.datetime64; for a CFTimeIndex that is an object of type cftime.datetime specific to the calendar used.

Parameters:
index : pd.Index

Input time index

date : datetime-like object or str

Input datetime

Returns:
date of the type appropriate for the time index of the Dataset
aospy.utils.times.month_indices(months)[source]

Convert string labels for months to integer indices.

Parameters:
months : str, int

If int, number of the desired month, where January=1, February=2, etc. If str, must match either ‘ann’ or some subset of ‘jfmamjjasond’. If ‘ann’, use all months. Otherwise, use the specified months.

Returns:
np.ndarray of integers corresponding to desired month indices
Raises:
TypeError : If months is not an int or str

See also

_month_conditional

aospy.utils.times.monthly_mean_at_each_ind(monthly_means, sub_monthly_timeseries)[source]

Copy monthly mean over each time index in that month.

Parameters:
monthly_means : xarray.DataArray

array of monthly means

sub_monthly_timeseries : xarray.DataArray

array of a timeseries at sub-monthly time resolution

Returns:
xarray.DataArray with eath monthly mean value from `monthly_means` repeated
at each time within that month from `sub_monthly_timeseries`

See also

monthly_mean_ts
Create timeseries of monthly mean values
aospy.utils.times.monthly_mean_ts(arr)[source]

Convert a sub-monthly time-series into one of monthly means.

Also drops any months with no data in the original DataArray.

Parameters:
arr : xarray.DataArray

Timeseries of sub-monthly temporal resolution data

Returns:
xarray.DataArray

Array resampled to comprise monthly means

See also

monthly_mean_at_each_ind
Copy monthly means to each submonthly time
aospy.utils.times.sel_time(da, start_date, end_date)[source]

Subset a DataArray or Dataset for a given date range.

Ensures that data are present for full extent of requested range. Appends start and end date of the subset to the DataArray.

Parameters:
da : DataArray or Dataset

data to subset

start_date : np.datetime64

start of date interval

end_date : np.datetime64

end of date interval

Returns:
da : DataArray or Dataset

subsetted data

Raises:
AssertionError

if data for requested range do not exist for part or all of requested range

aospy.utils.times.yearly_average(arr, dt)[source]

Average a sub-yearly time-series over each year.

Resulting timeseries comprises one value for each year in which the original array had valid data. Accounts for (i.e. ignores) masked values in original data when computing the annual averages.

Parameters:
arr : xarray.DataArray

The array to be averaged

dt : xarray.DataArray

Array of the duration of each timestep

Returns:
xarray.DataArray

Has the same shape and mask as the original arr, except for the time dimension, which is truncated to one value for each year that arr spanned

utils.vertcoord

Utility functions for dealing with vertical coordinates.

aospy.utils.vertcoord.d_deta_from_pfull(arr)[source]

Compute $partial/partialeta$ of the array on full hybrid levels.

$eta$ is the model vertical coordinate, and its value is assumed to simply increment by 1 from 0 at the surface upwards. The data to be differenced is assumed to be defined at full pressure levels.

Parameters:
arr : xarray.DataArray containing the ‘pfull’ dim
Returns:
deriv : xarray.DataArray with the derivative along ‘pfull’ computed via

2nd order centered differencing.

aospy.utils.vertcoord.d_deta_from_phalf(arr, pfull_coord)[source]

Compute pressure level thickness from half level pressures.

aospy.utils.vertcoord.does_coord_increase_w_index(arr)[source]

Determine if the array values increase with the index.

Useful, e.g., for pressure, which sometimes is indexed surface to TOA and sometimes the opposite.

aospy.utils.vertcoord.dp_from_p(p, ps, p_top=0.0, p_bot=110000.0)[source]

Get level thickness of pressure data, incorporating surface pressure.

Level edges are defined as halfway between the levels, as well as the user- specified uppermost and lowermost values. The dp of levels whose bottom pressure is less than the surface pressure is not changed by ps, since they don’t intersect the surface. If ps is in between a level’s top and bottom pressures, then its dp becomes the pressure difference between its top and ps. If ps is less than a level’s top and bottom pressures, then that level is underground and its values are masked.

Note that postprocessing routines (e.g. at GFDL) typically mask out data wherever the surface pressure is less than the level’s given value, not the level’s upper edge. This masks out more levels than the

aospy.utils.vertcoord.dp_from_ps(bk, pk, ps, pfull_coord)[source]

Compute pressure level thickness from surface pressure

aospy.utils.vertcoord.get_dim_name(arr, names)[source]

Determine if an object has an attribute name matching a given list.

aospy.utils.vertcoord.int_dp_g(arr, dp)[source]

Mass weighted integral.

aospy.utils.vertcoord.integrate(arr, ddim, dim=False, is_pressure=False)[source]

Integrate along the given dimension.

aospy.utils.vertcoord.level_thickness(p, p_top=0.0, p_bot=101325.0)[source]

Calculates the thickness, in Pa, of each pressure level.

Assumes that the pressure values given are at the center of that model level, except for the lowest value (typically 1000 hPa), which is the bottom boundary. The uppermost level extends to 0 hPa.

Unlike dp_from_p, this does not incorporate the surface pressure.

aospy.utils.vertcoord.pfull_from_ps(bk, pk, ps, pfull_coord)[source]

Compute pressure at full levels from surface pressure.

aospy.utils.vertcoord.phalf_from_ps(bk, pk, ps)[source]

Compute pressure of half levels of hybrid sigma-pressure coordinates.

aospy.utils.vertcoord.replace_coord(arr, old_dim, new_dim, new_coord)[source]

Replace a coordinate with new one; new and old must have same shape.

aospy.utils.vertcoord.to_hpa(arr)[source]

Convert pressure array from Pa to hPa (if needed).

aospy.utils.vertcoord.to_pascal(arr, is_dp=False)[source]

Force data with units either hPa or Pa to be in Pa.

aospy.utils.vertcoord.to_pfull_from_phalf(arr, pfull_coord)[source]

Compute data at full pressure levels from values at half levels.

aospy.utils.vertcoord.to_phalf_from_pfull(arr, val_toa=0, val_sfc=0)[source]

Compute data at half pressure levels from values at full levels.

Could be the pressure array itself, but it could also be any other data defined at pressure levels. Requires specification of values at surface and top of atmosphere.

aospy.utils.vertcoord.to_radians(arr, is_delta=False)[source]

Force data with units either degrees or radians to be radians.

aospy.utils.vertcoord.vert_coord_name(arr)[source]