Shared diagnostic script code#

Code that is shared between multiple diagnostic scripts.

Classes:

`Datasets`(cfg)	Class to easily access a recipe's datasets in a diagnostic script.
`ProvenanceLogger`(cfg)	Open the provenance logger.
`Variable`(short_name, standard_name, ...)	Variable class containing all relevant information.
`Variables`([cfg])	Class to easily access a recipe's variables in a diagnostic.

Functions:

`apply_supermeans`(ctrl, exper, obs_list)	Apply supermeans on data components ie MEAN on time.
`extract_variables`(cfg[, as_iris])	Extract basic variable information from configuration dictionary.
`get_cfg`([filename])	Read diagnostic script configuration from settings.yml.
`get_control_exper_obs`(short_name, ...[, ...])	Get control, exper and obs datasets.
`get_diagnostic_filename`(basename, cfg[, ...])	Get a valid path for saving a diagnostic data file.
`get_plot_filename`(basename, cfg)	Get a valid path for saving a diagnostic plot.
`group_metadata`(metadata, attribute[, sort])	Group metadata describing preprocessed data by attribute.
`run_diagnostic`()	Run a Python diagnostic.
`save_data`(basename, provenance, cfg, cube, ...)	Save the data used to create a plot to file.
`save_figure`(basename, provenance, cfg[, ...])	Save a figure to file.
`select_metadata`(metadata, **attributes)	Select specific metadata describing preprocessed data.
`sorted_group_metadata`(metadata_groups, sort)	Sort grouped metadata.
`sorted_metadata`(metadata, sort)	Sort a list of metadata describing preprocessed data.
`variables_available`(cfg, short_names)	Check if data from certain variables is available.

class esmvaltool.diag_scripts.shared.Datasets(cfg)[source]#

Bases: object

Class to easily access a recipe’s datasets in a diagnostic script.

Note

This class has been deprecated in version 2.2 and will be removed two minor releases later in version 2.4.

Examples

Get all variables of a recipe configuration cfg:

datasets = Datasets(cfg)

Access data of a dataset with path dataset_path:

datasets.get_data(path=dataset_path)

Access dataset information of the dataset:

datasets.get_dataset_info(path=dataset_path)

Access the data of all datasets with exp=piControl:

datasets.get_data_list(exp=piControl)

Methods:

`add_dataset`(path[, data])	Add dataset to class.
`add_to_data`(data[, path])	Add element to a dataset's data.
`get_data`([path])	Access a dataset's data.
`get_data_list`(**dataset_info)	Access the datasets' data in a list.
`get_dataset_info`([path])	Access a dataset's information.
`get_dataset_info_list`(**dataset_info)	Access dataset's information in a list.
`get_info`(key[, path])	Access a 'dataset_info`'s key.
`get_info_list`(key, **dataset_info)	Access dataset_info's key values.
`get_path`(**dataset_info)	Access a dataset's path.
`get_path_list`(**dataset_info)	Access dataset's paths in a list.
`set_data`(data[, path])	Set element as a dataset's data.

add_dataset(path, data=None, **dataset_info)[source]#

Add dataset to class.

Parameters:

path (str) – (Unique) path to the dataset.
data (optional) – Arbitrary object to be saved as data for the dataset.
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

add_to_data(data, path=None, **dataset_info)[source]#

Add element to a dataset’s data.

Notes

Either path or a unique dataset_info description have to be given. Fails when given information is ambiguous.

Parameters:

data – Element to be added to the dataset’s data.
path (str, optional) – Path to the dataset
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

Raises:

RuntimeError – If data given by dataset_info is ambiguous.

get_data(path=None, **dataset_info)[source]#

Access a dataset’s data.

Notes

Either path or a unique dataset_info description have to be given. Fails when given information is ambiguous.

Parameters:

path (str, optional) – Path to the dataset
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

Returns:

Data of the selected dataset.

Return type:

data_object

Raises:

RuntimeError – If data given by dataset_info is ambiguous.

get_data_list(**dataset_info)[source]#

Access the datasets’ data in a list.

Notes

The returned data is sorted alphabetically respective to the paths.

Parameters:: **dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.
Returns:: Data of the selected datasets.
Return type:: list

get_dataset_info(path=None, **dataset_info)[source]#

Access a dataset’s information.

Notes

Either path or a unique dataset_info description have to be given. Fails when given information is ambiguous.

Parameters:

path (str, optional) – Path to the dataset.
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

Returns:

All dataset information.

Return type:

dict

Raises:

RuntimeError – If data given by dataset_info is ambiguous.

get_dataset_info_list(**dataset_info)[source]#

Access dataset’s information in a list.

Notes

The returned data is sorted alphabetically respective to the paths.

Parameters:: **dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.
Returns:: Information dictionaries of the selected datasets.
Return type:: list

get_info(key, path=None, **dataset_info)[source]#

Access a ‘dataset_info`’s key.

Notes

Either path or a unique dataset_info description have to be given. Fails when given information is ambiguous. If the dataset_info does not contain the key, returns None.

Parameters:

key (str) – Desired dictionary key.
path (str) – Path to the dataset.
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

Returns:

key information of the given dataset.

Return type:

str

Raises:

RuntimeError – If data given by dataset_info is ambiguous.

get_info_list(key, **dataset_info)[source]#

Access dataset_info’s key values.

Notes

The returned data is sorted alphabetically respective to the paths.

Parameters:

key (str) – Desired dictionary key.
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

Returns:

key information of the selected datasets.

Return type:

list

get_path(**dataset_info)[source]#

Access a dataset’s path.

Notes

A unique dataset_info description has to be given. Fails when given information is ambiguous.

Parameters:: **dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.
Returns:: Path of the selected dataset.
Return type:: str
Raises:: RuntimeError – If data given by dataset_info is ambiguous.

get_path_list(**dataset_info)[source]#

Access dataset’s paths in a list.

Notes

The returned data is sorted alphabetically respective to the paths.

Parameters:: **dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.
Returns:: Paths of the selected datasets.
Return type:: list

set_data(data, path=None, **dataset_info)[source]#

Set element as a dataset’s data.

Notes

Either path or a unique dataset_info description have to be given. Fails when if given information is ambiguous.

Parameters:

data – Element to be set as the dataset’s data.
path (str, optional) – Path to the dataset.
**dataset_info (optional) – Keyword arguments describing the dataset, e.g. dataset=CanESM2, exp=piControl or short_name=tas.

Raises:

RuntimeError – If data given by dataset_info is ambiguous.

class esmvaltool.diag_scripts.shared.ProvenanceLogger(cfg)[source]#

Bases: object

Open the provenance logger.

Parameters:: cfg (dict) – Dictionary with diagnostic configuration.

Example

Use as a context manager:

record = {
    'caption': "This is a nice plot.",
    'statistics': ['mean'],
    'domain': ['global'],
    'plot_type': ['zonal'],
    'authors': [
        'first_author',
        'second_author',
    ],
    'references': [
        'author20journal',
    ],
    'ancestors': [
        '/path/to/input_file_1.nc',
        '/path/to/input_file_2.nc',
    ],
}
output_file = '/path/to/result.nc'

with ProvenanceLogger(cfg) as provenance_logger:
    provenance_logger.log(output_file, record)

Methods:

log(filename, record)

Record provenance.

log(filename, record)[source]#

Record provenance.

Parameters:

filename (str) – Name of the file containing the diagnostic data.
record (dict) –
Dictionary with the provenance information to be logged.
Typical keys are:
- ancestors
- authors
- caption
- domain
- plot_type
- references
- statistics

Note

See the provenance documentation for more information.

class esmvaltool.diag_scripts.shared.Variable(short_name, standard_name, long_name, units)[source]#

Bases: Variable

Variable class containing all relevant information.

Note

This class has been deprecated in version 2.2 and will be removed two minor releases later in version 2.4.

Methods:

`count`(value, /)	Return number of occurrences of value.
`index`(value[, start, stop])	Return first index of value.

Attributes:

`long_name`	Alias for field number 2
`short_name`	Alias for field number 0
`standard_name`	Alias for field number 1
`units`	Alias for field number 3

count(value, /)#: Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)#

Return first index of value.

Raises ValueError if the value is not present.

long_name#: Alias for field number 2

short_name#: Alias for field number 0

standard_name#: Alias for field number 1

units#: Alias for field number 3

class esmvaltool.diag_scripts.shared.Variables(cfg=None, **names)[source]#

Bases: object

Class to easily access a recipe’s variables in a diagnostic.

Note

This class has been deprecated in version 2.2 and will be removed two minor releases later in version 2.4.

Examples

Get all variables of a recipe configuration cfg:

variables = Variables(cfg)

Access information of a variable tas:

variables.short_name('tas')
variables.standard_name('tas')
variables.long_name('tas')
variables.units('tas')

Access iris-suitable dictionary of a variable tas:

variables.iris_dict('tas')

Check if variables tas and pr are available:

variables.vars_available('tas', 'pr')

Methods:

`add_vars`(**names)	Add custom variables to the class.
`iris_dict`(var)	Access `iris` dictionary of the variable.
`long_name`(var)	Access long name.
`modify_var`(var, **names)	Modify an already existing variable of the class.
`short_name`(var)	Access short name.
`short_names`()	Get list of all short_names.
`standard_name`(var)	Access standard name.
`standard_names`()	Get list of all standard_names.
`units`(var)	Access units.
`var_name`(var)	Access var name.
`vars_available`(*args)	Check if given variables are available.

add_vars(**names)[source]#

Add custom variables to the class.

Parameters:: **names (dict or Variable, optional) – Keyword arguments of the form short_name=Variable_object where Variable_object can be given as dict or Variable.

iris_dict(var)[source]#

Access iris dictionary of the variable.

Parameters:: var (str) – (Short) name of the variable.
Returns:: Dictionary containing all attributes of the variable which can be used directly in iris (short_name replaced by var_name).
Return type:: dict

long_name(var)[source]#

Access long name.

Parameters:: var (str) – (Short) name of the variable.
Returns:: Long name of the variable.
Return type:: str

modify_var(var, **names)[source]#

Modify an already existing variable of the class.

Parameters:

var (str) – (Short) name of the existing variable.
**names – Keyword arguments of the form short_name=tas.

Raises:

ValueError – If var is not an existing variable.
TypeError – If a non-valid keyword argument is given.

short_name(var)[source]#

Access short name.

Parameters:: var (str) – (Short) name of the variable.
Returns:: Short name of the variable.
Return type:: str

short_names()[source]#

Get list of all short_names.

Returns:: List of all short_names.
Return type:: list

standard_name(var)[source]#

Access standard name.

Parameters:: var (str) – (Short) name of the variable.
Returns:: Standard name of the variable.
Return type:: str

standard_names()[source]#

Get list of all standard_names.

Returns:: List of all standard_names.
Return type:: list

units(var)[source]#

Access units.

Parameters:: var (str) – (Short) name of the variable.
Returns:: Units of the variable.
Return type:: str

var_name(var)[source]#

Access var name.

Parameters:: var (str) – (Short) name of the variable.
Returns:: Var name (=short name) of the variable.
Return type:: str

vars_available(*args)[source]#

Check if given variables are available.

Parameters:: *args – Short names of the variables to be tested.
Returns:: True if variables are available, False if not.
Return type:: bool

esmvaltool.diag_scripts.shared.apply_supermeans(ctrl, exper, obs_list)[source]#

Apply supermeans on data components ie MEAN on time.

This function is an extension of climate_statistics() meant to ease the time-meaning procedure when dealing with CONTROL, EXPERIMENT and OBS (if any) datasets. ctrl: dictionary of CONTROL dataset exper: dictionary of EXPERIMENT dataset obs_lis: list of dicts for OBS datasets (0, 1 or many)

Returns: control and experiment cubes and list of obs cubes

esmvaltool.diag_scripts.shared.extract_variables(cfg, as_iris=False)[source]#

Extract basic variable information from configuration dictionary.

Returns short_name, standard_name, long_name and units keys for each variable.

Parameters:

cfg (dict) – Diagnostic script configuration.
as_iris (bool, optional) – Replace short_name by var_name, this can be used directly in iris classes.

Returns:

Variable information in dict`s (values) for each `short_name (key).

Return type:

dict

esmvaltool.diag_scripts.shared.get_cfg(filename=None)[source]#: Read diagnostic script configuration from settings.yml.

esmvaltool.diag_scripts.shared.get_control_exper_obs(short_name, input_data, cfg, cmip_type=None)[source]#

Get control, exper and obs datasets.

This function is used when running recipes that need a clear distinction between a control dataset, an experiment dataset and have optional obs (OBS, obs4MIPs etc) datasets; such recipes include recipe_validation, and all the autoassess ones; short_name: variable short name input_data: dict containing the input data info cfg: config file as used in this module cmip_type: optional, CMIP project type (CMIP5 or CMIP6)

esmvaltool.diag_scripts.shared.get_diagnostic_filename(basename, cfg, extension='nc')[source]#

Get a valid path for saving a diagnostic data file.

Parameters:

basename (str) – The basename of the file.
cfg (dict) – Dictionary with diagnostic configuration.
extension (str) – File name extension.

Returns:

A valid path for saving a diagnostic data file.

Return type:

str

esmvaltool.diag_scripts.shared.get_plot_filename(basename, cfg)[source]#

Get a valid path for saving a diagnostic plot.

Parameters:

basename (str) – The basename of the file.
cfg (dict) – Dictionary with diagnostic configuration.

Returns:

A valid path for saving a diagnostic plot.

Return type:

str

esmvaltool.diag_scripts.shared.group_metadata(metadata, attribute, sort=None)[source]#

Group metadata describing preprocessed data by attribute.

Parameters:

metadata (list of dict) – A list of metadata describing preprocessed data.
attribute (str) – The attribute name that the metadata should be grouped by.
sort – See sorted_group_metadata.

Returns:

A dictionary containing the requested groups.

Return type:

dict of list of dict

esmvaltool.diag_scripts.shared.run_diagnostic()[source]#

Run a Python diagnostic.

This context manager is the main entry point for most Python diagnostics.

Example

See esmvaltool/diag_scripts/examples/diagnostic.py for an extensive example of how to start your diagnostic.

Basic usage is as follows, add these lines at the bottom of your script:

def main(cfg):
    # Your diagnostic code goes here.
    print(cfg)

if __name__ == '__main__':
    with run_diagnostic() as cfg:
        main(cfg)

The cfg dict passed to main contains the script configuration that can be used with the other functions in this module.

esmvaltool.diag_scripts.shared.save_data(basename, provenance, cfg, cube, **kwargs)[source]#

Save the data used to create a plot to file.

Parameters:

basename (str) – The basename of the file.
provenance (dict) – The provenance record for the data.
cfg (dict) – Dictionary with diagnostic configuration.
cube (iris.cube.Cube) – Data cube to save.
**kwargs – Extra keyword arguments to pass to iris.save.

Iris helper functions#

Convenience functions for iris objects.

Functions:

`check_coordinate`(cubes, coord_name)	Compare coordinate of cubes and raise error if not identical.
`convert_to_iris`(dict_)	Change all appearances of `short_name` to `var_name`.
`get_mean_cube`(datasets)	Get mean cube of a list of datasets.
`intersect_dataset_coordinates`(cubes)	Compare dataset coordinates of cubes and match them if necessary.
`iris_project_constraint`(projects, input_data)	Create `iris.Constraint` to select specific projects from data.
`prepare_cube_for_merging`(cube, cube_label)	Prepare single `iris.cube.Cube` in order to merge it later.
`unify_1d_cubes`(cubes, coord_name)	Unify 1D cubes by transforming them to identical coordinates.
`unify_time_coord`(cube[, target_units])	Unify time coordinate of cube in-place.

esmvaltool.diag_scripts.shared.iris_helpers.check_coordinate(cubes, coord_name)[source]#

Compare coordinate of cubes and raise error if not identical.

Parameters:

cubes (iris.cube.CubeList) – Cubes to be compared.
coord_name (str) – Name of the coordinate.

Returns:

Points of the coordinate.

Return type:

numpy.array

Raises:

iris.exceptions.CoordinateNotFoundError – Coordinate coord_name is not a coordinate of one of the cubes.
ValueError – Given coordinate differs for the input cubes.

esmvaltool.diag_scripts.shared.iris_helpers.convert_to_iris(dict_)[source]#

Change all appearances of short_name to var_name.

Parameters:: dict (dict) – Dictionary to convert.
Returns:: Converted dictionary.
Return type:: dict
Raises:: KeyError – dict contains keys``’short_name’`` and 'var_name'.

esmvaltool.diag_scripts.shared.iris_helpers.get_mean_cube(datasets)[source]#

Get mean cube of a list of datasets.

Parameters:: datasets (list of dict) – List of datasets (given as metadata dict).
Returns:: Mean cube.
Return type:: iris.cube.Cube

esmvaltool.diag_scripts.shared.iris_helpers.intersect_dataset_coordinates(cubes)[source]#

Compare dataset coordinates of cubes and match them if necessary.

Use intersection of coordinate ‘dataset’ of all given cubes and remove elements which are not given in all cubes.

Parameters:

cubes (iris.cube.CubeList) – Cubes to be compared.

Returns:

Transformed cubes.

Return type:

iris.cube.CubeList

Raises:

iris.exceptions.CoordinateNotFoundError – Coordinate dataset is not a coordinate of one of the cubes.
ValueError – At least one of the cubes contains a dataset coordinate with duplicate elements or the cubes do not share common elements.

esmvaltool.diag_scripts.shared.iris_helpers.iris_project_constraint(projects, input_data, negate=False)[source]#

Create iris.Constraint to select specific projects from data.

Parameters:

projects (list of str) – Projects to be selected.
input_data (list of dict) – List of dataset metadata used to extract all relevant datasets belonging to given projects.
negate (bool, optional (default: False)) – Negate constraint (False: select all elements that fit projects, True`: select all elements that do not fit projects).

Returns:

constraint for coordinate dataset.

Return type:

iris.Constraint

esmvaltool.diag_scripts.shared.iris_helpers.prepare_cube_for_merging(cube, cube_label)[source]#

Prepare single iris.cube.Cube in order to merge it later.

Parameters:

cube (iris.cube.Cube) – Cube to be pre-processed.
cube_label (str) – Label for the new scalar coordinate cube_label.

esmvaltool.diag_scripts.shared.iris_helpers.unify_1d_cubes(cubes, coord_name)[source]#

Unify 1D cubes by transforming them to identical coordinates.

Use union of all coordinates as reference and transform other cubes to it by adding missing values.

Parameters:

cubes (iris.cube.CubeList) – Cubes to be processed.
coord_name (str) – Name of the coordinate.

Returns:

Transformed cubes.

Return type:

iris.cube.CubeList

Raises:

ValueError – Cubes are not 1D, coordinate name differs or not all cube coordinates are subsets of longest coordinate.

esmvaltool.diag_scripts.shared.iris_helpers.unify_time_coord(cube, target_units='days since 1850-01-01 00:00:00')[source]#

Unify time coordinate of cube in-place.

Parameters:

cube (iris.cube.Cube) – Cube whose time coordinate is transformed in-place.
target_units (str or cf_units.Unit, optional) – Target time units.

Raises:

iris.exceptions.CoordinateNotFoundError – Cube does not contain coordinate time.

Plotting#

Module that provides common plot functions.

Functions:

`get_dataset_style`(dataset[, style_file])	Retrieve the style information for the given dataset.
`get_path_to_mpl_style`([style_file])	Get path to matplotlib style file.
`global_contourf`(cube[, cbar_center, ...])	Plot global filled contour plot.
`global_pcolormesh`(cube[, cbar_center, ...])	Plot global color mesh.
`multi_dataset_scatterplot`(x_data, y_data, ...)	Plot a multi dataset scatterplot.
`quickplot`(cube, plot_type[, filename])	Plot a cube using one of the iris.quickplot functions.
`scatterplot`(x_data, y_data, filepath, **kwargs)	Plot a scatterplot.

esmvaltool.diag_scripts.shared.plot.get_dataset_style(dataset, style_file=None)[source]#: Retrieve the style information for the given dataset.

esmvaltool.diag_scripts.shared.plot.get_path_to_mpl_style(style_file=None)[source]#: Get path to matplotlib style file.

esmvaltool.diag_scripts.shared.plot.global_contourf(cube, cbar_center=None, cbar_label=None, cbar_range=None, cbar_ticks=None, **kwargs)[source]#

Plot global filled contour plot.

Note

This is only possible if the cube is 2D with dimensional coordinates latitude and longitude.

Parameters:

cube (iris.cube.Cube) – Cube to plot.
cbar_center (float, optional) – Central value for the colormap, useful for diverging colormaps. Can only be used if cbar_range is given.
cbar_label (str, optional) – Label for the colorbar.
cbar_range (list of float, optional) – Range of the colorbar (first and second list element) and number of distinct colors (third element). See numpy.linspace.
cbar_ticks (list, optional) – Ticks for the colorbar.
**kwargs – Keyword argument for iris.plot.contourf().

Returns:

Plot object.

Return type:

matplotlib.contour.QuadContourSet

Raises:

iris.exceptions.CoordinateNotFoundError – Input iris.cube.Cube does not contain the necessary dimensional coordinates 'latitude' and 'longitude'.
ValueError – Input iris.cube.Cube is not 2D.

esmvaltool.diag_scripts.shared.plot.global_pcolormesh(cube, cbar_center=None, cbar_label=None, cbar_ticks=None, **kwargs)[source]#

Plot global color mesh.

Note

This is only possible if the cube is 2D with dimensional coordinates latitude and longitude.

Parameters:

cube (iris.cube.Cube) – Cube to plot.
cbar_center (float, optional) – Central value for the colormap, useful for diverging colormaps. Can only be used if vmin and vmax are given.
cbar_label (str, optional) – Label for the colorbar.
cbar_ticks (list, optional) – Ticks for the colorbar.
**kwargs – Keyword argument for iris.plot.pcolormesh().

Returns:

Plot object.

Return type:

matplotlib.contour.QuadContourSet

Raises:

iris.exceptions.CoordinateNotFoundError – Input iris.cube.Cube does not contain the necessary dimensional coordinates 'latitude' and 'longitude'.
ValueError – Input iris.cube.Cube is not 2D.

esmvaltool.diag_scripts.shared.plot.multi_dataset_scatterplot(x_data, y_data, datasets, filepath, **kwargs)[source]#

Plot a multi dataset scatterplot.

Notes

Allowed keyword arguments:

mpl_style_file (str): Path to the matplotlib style file.
dataset_style_file (str): Path to the dataset style file.
plot_kwargs (array-like): Keyword arguments for the plot (e.g. label, makersize, etc.).
save_kwargs (dict): Keyword arguments for saving the plot.
axes_functions (dict): Arbitrary functions for axes, i.e. axes.set_title(‘title’).

Parameters:

x_data (array-like) – x data of each dataset.
y_data (array-like) – y data of each dataset.
datasets (array-like) – Names of the datasets.
filepath (str) – Path to which plot is written.
**kwargs – Keyword arguments.

Raises:

TypeError – A non-valid keyword argument is given or x_data, y_data, datasets or (if given) plot_kwargs is not array-like.
ValueError – x_data, y_data, datasets or plot_kwargs do not have the same size.

esmvaltool.diag_scripts.shared.plot.quickplot(cube, plot_type, filename=None, **kwargs)[source]#: Plot a cube using one of the iris.quickplot functions.

esmvaltool.diag_scripts.shared.plot.scatterplot(x_data, y_data, filepath, **kwargs)[source]#

Plot a scatterplot.

Notes

Allowed keyword arguments:

mpl_style_file (str): Path to the matplotlib style file.
plot_kwargs (array-like): Keyword arguments for the plot (e.g. label, makersize, etc.).
save_kwargs (dict): Keyword arguments for saving the plot.
axes_functions (dict): Arbitrary functions for axes, i.e. axes.set_title(‘title’).

Parameters:

x_data (array-like) – x data of each dataset.
y_data (array-like) – y data of each dataset.
filepath (str) – Path to which plot is written.
**kwargs – Keyword arguments.

Raises:

TypeError – A non-valid keyword argument is given or x_data, y_data or (if given) plot_kwargs is not array-like.
ValueError – x_data, y_data or plot_kwargs do not have the same size.