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 main documentation sections.

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='all', regions=None, direc_out='', tar_direc_out='')

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='all', regions=None, direc_out='', tar_direc_out='')

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

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)

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	(dict) A dictionary with entries of the form {run_obj.name: run_obj}, for each of this model’s child Run objects
default_runs	(dict) The default subset of chidl 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)

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, default_end_date : {None, datetime.datetime}, optional

Default start and end dates 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

See also

aospy.DataLoader, aospy.Proj, aospy.Run

set_grid_data()

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)

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)

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.

class aospy.data_loader.DataLoader

A fundamental DataLoader object

__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

load_variable(var=None, start_date=None, end_date=None, time_offset=None, **DataAttrs)

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.

**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)

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

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)

__init__(file_map=None)

Create a new DictDataLoader

class aospy.data_loader.NestedDictDataLoader(file_map=None)

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

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)

__init__(file_map=None)

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)

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

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')

__init__(template=None, data_direc=None, data_dur=None, data_start_date=None, data_end_date=None)

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, func_input_dtype='DataArray', 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)

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
func_input_dtype	({‘DataArray’, ‘Dataset’, ‘numpy’}) The datatype expected by func of its arguments
units	(aospy.units.Units object) 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, func_input_dtype='DataArray', 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)

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.

func_input_dtype : {None, ‘DataArray’, ‘Dataset’, ‘numpy’}

The datatype expected by func of its arguments

units : aospy.units.Units object

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)

Mask data array where values are outside physically valid range.

to_plot_units(data, dtype_vert=False)

Convert the given data to plotting units.

Region

class aospy.region.Region(name='', description='', lon_bounds=[], lat_bounds=[], mask_bounds=[], do_land_mask=False)

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='', lon_bounds=[], lat_bounds=[], mask_bounds=[], do_land_mask=False)

Instantiate a Region object.

If a region spans across the endpoint of the data’s longitude array (i.e. it crosses the Prime Meridian for data with longitudes spanning 0 to 360), it must be defined as the union of two sections extending to the east and to the west of the Prime Meridian.

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.

lon_bounds, lat_bounds : length-2 sequence, optional

The longitude and latitude bounds 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-2 tuple of the format (lat_bounds, lon_bounds), where each of lat_bounds and lon_bounds are of the form described above.

do_land_mask : { False, True, ‘ocean’, ‘strict_land’, ‘strict_ocean’},

optional

Determines what, if any, land mask is applied in addition to the mask defining the region’s boundaries. Default False.

• 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', lat_bounds=(-90, 90),
...                lon_bounds=(0, 360), do_land_mask=False)

Define a region corresponding to the Sahel region of Africa, which we’ll define as land points within 10N-20N latitude and 18W-40E longitude. Because this region crosses the 0 degrees longitude point, it has to be defined using mask_bounds as the union of two lat-lon rectangles.

>>> sahel = Region(name='sahel', do_land_mask=True,
...                mask_bounds=[((10, 20), (0, 40)),
...                             ((10, 20), (342, 360))])

av(data)

Time average of region-average time-series.

mask_var(data)

Mask the data of the given variable outside the region.

std(data)

Standard deviation of region-average time-series.

ts(data)

Create time-series of region-average data.

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 or it’s helper class, CalcInterface is required by the user, in which case this section should be skipped entirely.

Also included is the find_obj 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.

CalcInterface and Calc

class aospy.calc.CalcInterface(proj=None, model=None, run=None, ens_mem=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, verbose=True)

Interface to the Calc class.

__init__(proj=None, model=None, run=None, ens_mem=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, verbose=True)

Instantiate a CalcInterface 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.

ens_mem

: Currently not supported.

This will eventually be used to specify particular ensemble members of multi-member ensemble simulations.

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

class aospy.calc.Calc(calc_interface)

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

Calc objects are instantiated with a single argument: a CalcInterface object that includes all of the parameters necessary to determine what calculations to perform.

__init__(calc_interface)

ARR_XARRAY_NAME = 'aospy_result'

compute(save_files=True, save_tar_files=True)

Perform all desired calculations on the data and save externally.

key = 'level'

load(dtype_out_time, dtype_out_vert=False, region=False, time=False, vert=False, lat=False, lon=False, plot_units=False, mask_unphysical=False)

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

region_calcs(arr, func, n=0)

Perform a calculation for all regions.

save(data, dtype_out_time, dtype_out_vert=False, save_files=True, save_tar_files=False)

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

find_obj

Get aospy objects from an object containing them given their name string.

aospy.find_obj.to_iterable(obj)

Return the object if already iterable, otherwise return it as a list.

aospy.find_obj.to_model(model, proj, projs_module)

Convert string of a Model name to a Model instance.

aospy.find_obj.to_proj(proj, projs_module)

Convert string of an aospy.Proj name to an aospy.Proj instance.

aospy.find_obj.to_region(region, regions_module, proj=False)

Convert string of an aospy.Region name to an aospy.Region instance.

aospy.find_obj.to_run(run, model, proj, projs_module)

Convert string matching an aospy.run name to an aospy.run instance.

aospy.find_obj.to_var(var, vars_module)

Convert string of an aospy.var name to an aospy.var instance.

operator

Warning

The operator module is in the process of being re-vamped and is therefore currently not supported.

Abstraction of mathematical operators to be between aospy objects.

class aospy.operator.Operator(operator, objects)

Units and Constants

aospy provides the classes Constant and Units for representing, respectively, physical constants (e.g. Earth’s gravitational acceleration at the surface = 9.81 m/s^2) and physical units (e.g. meters per second squared in that example).

aospy comes with several commonly used constants saved within the constants module in which the Constant class is also defined. In contrast, there are no pre-defined Units objects; the user must define any Units objects they wish to use (e.g. to populate the units attribute of their Var objects).

Similarly, whereas these baked-in Constant objects are used by aospy in various places, aospy currently does not actually use the Var.units attribute or the Units class more generally; they are for the user’s own informational purposes.

constants

Classes and objects pertaining to physical constants.

class aospy.constants.Constant(value, units, description='')

Physical constants used in atmospheric and oceanic sciences.

units

Functionality for representing physical units, e.g. meters.

class aospy.units.Units(units='', plot_units=False, plot_units_conv=1.0, vert_int_units=False, vert_int_plot_units=False, vert_int_plot_units_conv=False)

String representation of physical units and conversion methods.

Note

There has been discussion of implementing units-handling upstream within xarray (see here). If and when that happens, the Units class will likely be deprecated and replaced with the upstream version.

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), time arrays, andvertical 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)

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)

Determine the filename of GFDL model data output.

aospy.utils.io.data_out_label(time_intvl, dtype_time, dtype_vert=False)

aospy.utils.io.dict_name_keys(objs)

Create dict whose keys are the ‘name’ attr of the objects.

aospy.utils.io.dmget(files_list)

Call GFDL command ‘dmget’ to access archived files.

aospy.utils.io.ens_label(ens_mem)

Create label of the ensemble member for aospy data I/O.

aospy.utils.io.get_parent_attr(obj, attr, strict=False)

Search recursively through an object and its parent for an attribute.

Check if the object has the given attribute and it is non-empty. If not, check each parent object for the attribute and use the first one found.

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

Create time interval label for aospy data I/O.

aospy.utils.io.to_dup_list(x, n, single_to_list=True)

Convert singleton or iterable into length-n list. If the input is a list, with length-1, its lone value gets duplicated n times. If the input is a list with length-n, leave it the same. If the input is any other data type, replicate it as a length-n list.

aospy.utils.io.yr_label(yr_range)

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

utils.times

Utility functions for handling times, dates, etc.

aospy.utils.times.apply_time_offset(time, years=0, months=0, days=0, hours=0)

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) 
DatetimeIndex(['1900-01-01', '1899-02-01'], dtype='datetime64[ns]',
              freq=None)

aospy.utils.times.create_monthly_time_array(start_date, end_date, months)

Create an array of months compliant with numpy datetime limited range.

Parameters:

start_date, end_date : datetime.datetime

months : int, str, or xarray.DataArray of times

Specifies which months in each year to include via _month_conditonal

Returns:

xarray.DataArray

Array spans the specified date range and includes only the specified months of the year. Year is also reset to start at the beginning of the range of dates that np.datetime64 can support if originally out of range.

See also

_month_conditional

Generate boolean array specifying desired months

aospy.utils.times.datetime_or_default(date, default)

Return a datetime.datetime object or a default.

Parameters:

date : None or datetime-like object

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)

Return the object if it is of type datetime.datetime; else raise.

Parameters:obj : Object to be tested. Returns:The original object if it is a datetime.datetime object. Raises:TypeError if `obj` is not of type `datetime.datetime`.

aospy.utils.times.ensure_time_avg_has_cf_metadata(ds)

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 [R1]. 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.

[R1]	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_date_range_and_months(time, start_date, end_date, months)

Extract times within a specified date range and 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).

start_date, end_date : datetime.datetime

Desired start and end date

end_date : Desired end date

months : Desired months of the year to include

Returns:

xarray.DataArray of the desired times

aospy.utils.times.month_indices(months)

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)

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)

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.numpy_datetime_range_workaround(date)

Reset a date to earliest allowable year if outside of valid range.

Hack to address np.datetime64, and therefore pandas and xarray, not supporting dates outside the range 1677-09-21 and 2262-04-11 due to nanosecond precision. See e.g. https://github.com/spencerahill/aospy/issues/96.

Parameters:

date : datetime.datetime object

Returns:

datetime.datetime object

Original datetime.datetime object if the original date is within the permissible dates, otherwise a datetime.datetime object with the year offset to the earliest allowable year.

aospy.utils.times.numpy_datetime_workaround_encode_cf(ds)

Generate CF-compliant units for out-of-range dates.

Hack to address np.datetime64, and therefore pandas and xarray, not supporting dates outside the range 1677-09-21 and 2262-04-11 due to nanosecond precision. See e.g. https://github.com/spencerahill/aospy/issues/96.

Specifically, we coerce the data such that, when decoded, the earliest value starts in 1678 but with its month, day, and shorter timescales (hours, minutes, seconds, etc.) intact and with the time-spacing between values intact.

Parameters:ds : xarray.Dataset Returns:xarray.Dataset

aospy.utils.times.sel_time(da, start_date, end_date)

Subset a DataArray or Dataset for a given date range.

Ensures that data are present for full extent of requested range.

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

utils.vertcoord

Utility functions for dealing with vertical coordinates.

aospy.utils.vertcoord.d_deta_from_pfull(arr)

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)

Compute pressure level thickness from half level pressures.

aospy.utils.vertcoord.does_coord_increase_w_index(arr)

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)

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)

Compute pressure level thickness from surface pressure

aospy.utils.vertcoord.get_dim_name(arr, names)

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

aospy.utils.vertcoord.int_dp_g(arr, dp)

Mass weighted integral.

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

Integrate along the given dimension.

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

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)

Compute pressure at full levels from surface pressure.

aospy.utils.vertcoord.phalf_from_ps(bk, pk, ps)

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

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

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

aospy.utils.vertcoord.to_hpa(arr)

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

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

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

aospy.utils.vertcoord.to_pfull_from_phalf(arr, pfull_coord)

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)

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)

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

aospy.utils.vertcoord.vert_coord_name(arr)