scripts.plotting.cam_taylor_diagram
===================================

.. py:module:: scripts.plotting.cam_taylor_diagram

.. autoapi-nested-parse::

   Module: cam_taylor_diagram

   Provides a Taylor diagram following the AMWG package. Uses spatial information only.

   This module, for better or worse, provides both the computation and plotting functionality.
   It depends on an ADF instance to obtain the `climo` files.
   It is designed to have one "reference" case (could be observations) and arbitrary test cases.
   When multiple test cases are provided, they are plotted with different colors.



Functions
---------

.. autoapisummary::

   scripts.plotting.cam_taylor_diagram.my_formatwarning
   scripts.plotting.cam_taylor_diagram.cam_taylor_diagram
   scripts.plotting.cam_taylor_diagram.vertical_average
   scripts.plotting.cam_taylor_diagram.find_landmask
   scripts.plotting.cam_taylor_diagram.get_prect
   scripts.plotting.cam_taylor_diagram.get_tropical_land_precip
   scripts.plotting.cam_taylor_diagram.get_tropical_ocean_precip
   scripts.plotting.cam_taylor_diagram.get_surface_pressure
   scripts.plotting.cam_taylor_diagram.get_var_at_plev
   scripts.plotting.cam_taylor_diagram.get_u_at_plev
   scripts.plotting.cam_taylor_diagram.get_vertical_average
   scripts.plotting.cam_taylor_diagram.get_virh
   scripts.plotting.cam_taylor_diagram.get_vit
   scripts.plotting.cam_taylor_diagram.get_landt2m
   scripts.plotting.cam_taylor_diagram.get_eqpactaux
   scripts.plotting.cam_taylor_diagram.get_derive_func
   scripts.plotting.cam_taylor_diagram._retrieve
   scripts.plotting.cam_taylor_diagram.weighted_correlation
   scripts.plotting.cam_taylor_diagram.weighted_std
   scripts.plotting.cam_taylor_diagram.taylor_stats_single
   scripts.plotting.cam_taylor_diagram.taylor_plot_setup
   scripts.plotting.cam_taylor_diagram.plot_taylor_data
   scripts.plotting.cam_taylor_diagram.taylor_plot_finalize


Module Contents
---------------

.. py:function:: my_formatwarning(msg, *args, **kwargs)

.. py:function:: cam_taylor_diagram(adfobj)

   Make a Taylor Diagram, eh?


.. py:function:: vertical_average(fld, ps, acoef, bcoef)

   Calculate weighted vertical average using trapezoidal rule. Uses full column.


.. py:function:: find_landmask(adf, casename, location)

.. py:function:: get_prect(casename, location, **kwargs)

.. py:function:: get_tropical_land_precip(adf, casename, location, **kwargs)

.. py:function:: get_tropical_ocean_precip(adf, casename, location, **kwargs)

.. py:function:: get_surface_pressure(dset, casename, location)

.. py:function:: get_var_at_plev(adf, casename, location, variable, plev)

   Get `variable` from the data and then interpolate it to isobaric level `plev` (units of hPa).


.. py:function:: get_u_at_plev(adf, casename, location)

.. py:function:: get_vertical_average(adf, casename, location, varname)

   Collect data from case and use `vertical_average` to get result.


.. py:function:: get_virh(adf, casename, location, **kwargs)

   Calculate vertically averaged relative humidity.


.. py:function:: get_vit(adf, casename, location, **kwargs)

   Calculate vertically averaged temperature.


.. py:function:: get_landt2m(adf, casename, location)

   Gets TREFHT (T_2m) and removes non-land points.


.. py:function:: get_eqpactaux(adf, casename, location)

   Gets zonal surface wind stress 5S to 5N.


.. py:function:: get_derive_func(fld)

.. py:function:: _retrieve(adfobj, variable, casename, location, return_dataset=False)

   Custom function that retrieves a variable. Returns the variable as a DataArray.
   kwarg:
   return_dataset -> if true, return the dataset object, otherwise return the DataArray
                     with `variable`
                     This option allows get_u_at_plev to use _retrieve.


.. py:function:: weighted_correlation(x, y, weights)

.. py:function:: weighted_std(x, weights)

   Weighted standard deviation.
   x -> xr.DataArray
   weights -> array-like of weights, probably xr.DataArray
   If weights is not the same shape as x, will use `broadcast_like` to
   create weights array.
   Returns the weighted standard deviation of the full x array.


.. py:function:: taylor_stats_single(casedata, refdata, w=True)

   This replicates the basic functionality of 'taylor_stats' from NCL.
   input:
       casedata : input data, DataArray
       refdata  : reference case data, DataArray
       w        : if true use cos(latitude) as spatial weight, if false assume uniform weight
   :returns: pattern_correlation, ratio of standard deviation (case/ref), bias


.. py:function:: taylor_plot_setup(title, baseline)

   Constructs Figure and Axes objects for basic Taylor Diagram.


.. py:function:: plot_taylor_data(wks, df, **kwargs)

   Apply data on top of the Taylor Diagram Axes.
   wks -> Axes object, probably from taylor_plot_setup
   df  -> DataFrame holding the Taylor stats.
   kwargs -> optional arguments
     look for 'use_bias'
     look for 'case_color'


.. py:function:: taylor_plot_finalize(wks, test_nicknames, casecolors, syear_cases, eyear_cases, needs_bias_labels=True)

   Apply final formatting to a Taylor diagram.
   wks -> Axes object that has passed through taylor_plot_setup and plot_taylor_data
   casenames -> list of case names for the legend
   casecolors -> list of colors for the cases
   needs_bias_labels -> Bool, if T make the legend for the bias-sized markers.