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)

To prevent the diagnostic script from using the Dask Distributed scheduler, set no_distributed: true in the diagnostic script definition in the recipe or in the resulting settings.yml file.

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.

Shared diagnostic script code#

Iris helper functions#

Plotting#

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