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Usage

The functionalities of gcm_toolkit is likely to expand in the future, since gcm_toolkit is in active development. However, gcm_toolkit can already be used to perform a variety of tasks. These docs explain how gcm_toolkit can be used to load data and do plots with the data.

GCMT class

The GCMT class is the main point of user interaction. It is used to load data into xarray datasets, which can then be used for analysis.

from gcm_toolkit import GCMT

# Load data
tools = GCMT()
tools.read_raw(..., tag='model_name')
# tools.read_raw(..., tag='model_name_2')  # if you have multiple models
ds = tools['model_name']

The GCMT class can also be used for quick plotting of data, since it wraps a few plotting functions, which are outlined in :ref:`Plotting`.

Note

You can also load netcdf data:

tools.read_reduced(..., tag='model_name')
.. autoclass:: gcm_toolkit.GCMT
    :members: __init__, get, get_models, models, read_raw, read_reduced, load, save

Postprocessing

The GCMT class has quite a few methods that can calculate diagnostics such as total energy, location of the RCB and so on. You find their documentation here.

Example for the total energy:

from gcm_toolkit import GCMT
import gcm_toolkit.gcm_plotting as gcmp

# Load data
tools = GCMT()
tools.read_raw(..., tag='model_name')
E = tools.add_total_energy(var_key_out='E', tag='model_name')

# You will also have the energy stored in the dataset if var_key_out is not None:
ds = tools['model_name']
E == ds.E
.. autoclass:: gcm_toolkit.GCMT
    :members: add_theta, add_horizontal_average, add_total_energy, add_meridional_overturning, add_rcb, add_total_momentum

Plotting

gcm_toolkit includes some very simple (but yet powerful and highly customizable) plotting functions that can be used in two different ways. You can either use the gcm_toolkit class to invoke these functions or call these functions directly.

Example:

from gcm_toolkit import GCMT
import gcm_toolkit.gcm_plotting as gcmp

# Load data
tools = GCMT()
tools.read_raw(..., tag='model_name')
ds = tools['model_name']

# Plot data with GCMT
tools.isobaric_slice(tag = 'model_name', p=1e-2, lookup_method='nearest', var_key='T', wind_kwargs={'windstream':False, 'sample_one_in':2})

# Plot data with GCMTtools.gcm_plotting
tools.isobaric_slice(ds, p=1e-2, lookup_method='nearest', var_key='T', wind_kwargs={'windstream':False, 'sample_one_in':2})

Both ways are equally powerful and flexible.

.. autofunction:: gcm_toolkit.gcm_plotting.isobaric_slice


.. autofunction:: gcm_toolkit.gcm_plotting.zonal_mean


.. autofunction:: gcm_toolkit.gcm_plotting.time_evol

Interface

gcm_toolkit comes with some interfaces to other codes. We currently have support for petitRADTRANS to calculate spectra and phasecurves with prt_phasecure.

.. autoclass:: gcm_toolkit.utils.interface.PrtInterface
    :members: __init__, set_data, chem_from_poorman, calc_phase_spectrum, phase_curve