improver.wxcode.weather_symbols module

Module containing weather symbol implementation.

class improver.wxcode.weather_symbols.WeatherSymbols(wxtree='high_resolution')

Bases: object

Definition and implementation of a weather symbol decision tree. This plugin uses a variety of diagnostic inputs and the decision tree logic to determine the most representative weather symbol for each site defined in the input cubes.

__init__(wxtree='high_resolution')

Define a decision tree for determining weather symbols based upon the input diagnostics. Use this decision tree to allocate a weather symbol to each point.

Key Args:

wxtree (str):

Choose weather symbol decision tree. Default is ‘high_resolution’ ‘global’ will load the global weather symbol decision tree.

float_tolerance defines the tolerance when matching thresholds to allow for the difficulty of float comparisons.

check_input_cubes(cubes)

Check that the input cubes contain all the diagnostics and thresholds required by the decision tree.

Parameters:cubes (iris.cube.CubeList) – A CubeList containing the input diagnostic cubes. Raises:IOError – Raises an IOError if any of the required input data is missing. The error includes details of which fields are missing.

static construct_condition(extract_constraint, condition, probability_threshold, gamma)

Create a string representing a comparison condition.

Parameters:

• extract_constraint (string or list of strings) – A string, or list of strings, encoding iris constraints that will be used to extract the correct diagnostic cube (by name) from the input cube list and the correct threshold from that cube.
• condition (string) – The condition statement (e.g. greater than, >).
• probability_threshold (float) – The probability value to use in the comparison.
• gamma (float or None) – The gamma factor to multiply one field by when performing a subtraction. This value will be None in the case that extract_constraint is not a list; it will not be used.

Returns:

The formatted condition statement, e.g.:

cubes.extract(Constraint(
        name='probability_of_rainfall_rate_above_threshold',
        coord_values={'threshold': 0.03})
              )[0].data < 0.5)

Return type:

string

static construct_extract_constraint(diagnostics, thresholds)

Construct an iris constraint.

Parameters:

• diagnostics (string or list of strings) – The names of the diagnostics to be extracted from the CubeList.
• thresholds (iris.AuxCoord or list of iris.AuxCoord) – A thresholds within the given diagnostic cubes that are needed. Including units.

Returns:

String, or list of strings, encoding iris cube constraints.

Return type:

string or list of strings

static create_condition_chain(test_conditions)

A wrapper to call the construct_condition function for all the conditions specified in a single query.

Parameters:test_conditions (dict) – A query from the decision tree. Returns:A list of strings that describe the conditions comprising the query. e.g.:

[
  "(cubes.extract(Constraint(
        name='probability_of_rainfall_rate_above_threshold',
        coord_values={'threshold': 0.03})
   )[0].data < 0.5) |
   (cubes.extract(Constraint(
        name=
        'probability_of_lwe_snowfall_rate_above_threshold',
        coord_values={'threshold': 0.03})
   )[0].data < 0.5)"
]

Return type:condition_chain (list)

static create_symbol_cube(cube)

Create an empty weather_symbol cube initialised with -1 across the grid.

Parameters:cube (iris.cube.Cube) – An x-y slice of one of the input cubes, used to define the size of the weather symbol grid. Returns:A cube full of -1 values, with suitable metadata to describe the weather symbols that will fill it. Return type:symbols (iris.cube.Cube)

static find_all_routes(graph, start, end, route=None)

Function to trace all routes through the decision tree.

Parameters:

• graph (dict) – A dictionary that describes each node in the tree, e.g. {<node_name>: [<succeed_name>, <fail_name>]}
• start (string) – The node name of the tree root (currently always heavy_precipitation).
• end (int) – The weather symbol code to which we are tracing all routes.
• route (list) – A list of node names found so far.

Returns:

A list of node names that defines the route from the tree root to the weather symbol leaf (end of chain).

Return type:

routes (list)

References

Method based upon Python Patterns - Implementing Graphs essay https://www.python.org/doc/essays/graphs/

static format_condition_chain(conditions, condition_combination='AND')

Chain individual condition statements together in a format that numpy.where can use to make a series of comparisons.

Parameters:

• conditions (list) – A list of conditions to be combined into a single comparison statement.
• condition_combination (string) – The method by which multiple conditions should be combined, either AND or OR.

Returns:

A string formatted as a chain of conditions suitable for use in a numpy.where statement. e.g. (condition 1) & (condition 2)

Return type:

string

static invert_condition(test_conditions)

Invert a comparison condition to select the negative case.

Parameters:test_conditions (dict) – A single query from the decision tree. Returns:

tuple containing:

inverted_threshold (string):

A string representing the inverted comparison.

inverted_combination (string):

A string representing the inverted combination

Return type:(tuple)

process(cubes)

Apply the decision tree to the input cubes to produce weather symbol output.

Parameters:cubes (iris.cube.CubeList) – A cubelist containing the diagnostics required for the weather symbols decision tree, these at co-incident times. Returns:A cube of weather symbols. Return type:symbols (iris.cube.Cube)