pysiral.surface

Created on Mon Jul 27 11:25:04 2015

Classes

`ClassifierContainer`
`SurfaceTypeClassifier`	This is a parent class that allows to quickly generate surface type classication schemes
`SICCI2SurfaceType`	new and unified surface type classifier for cryosat2 and envisat
`ClassifierThresholdSurfaceType`	Simplified surface type classification based on the positive
`ClassifierAuxiliarySurfaceType`	Surface type classification using a parameter already present in l1p that

Module Contents

class pysiral.surface.ClassifierContainer

Bases: object

_parameters

add_parameter(parameter: numpy.ndarray, parameter_name: str) → None

get(parameter_name: str, raise_on_error: bool = False) → numpy.ndarray | None

__getattr__(item): Modify the attribute getter to provide a shortcut to the data content :param item: Name of the parameter :return:

property n_parameters: int

property parameter_list: List[str]

property shape: int | Tuple

class pysiral.surface.SurfaceTypeClassifier(*args, **kwargs)

Bases: object

This is a parent class that allows to quickly generate surface type classication schemes for the Level-2 processpr

surface_type

classifier

radar_modes

_classes = []

set_initial_classification(surface_type: pysiral.core.flags.SurfaceType) → None: This method sets an initial surface type classification :param surface_type: :return:

transfer_l1b_classifier(l1: pysiral.l1data.L1bdataNCFile) → None: A standard functionality to transfer all l1b classifier :param l1: :return:

add_classifier_parameter(l2: pysiral.l2data.Level2Data, parameter_list: List[str]) → None: Retrieve a list of parameters from the l2 and add to the classifier parameter container :param l2: :param parameter_list: :return:

set_unknown_default(n_records: int) → None: This method can be used to initialize the surface type with unknown values :return:

set_l1b_land_mask(l1: pysiral.l1data.L1bdataNCFile) → None

Use this method to transfer the l1 land mask to the l2 surface type classification.

NOTE: Highly recommended to do this at the end of the surface type classication: in order to overwrite potential mis-classifications.

Parameters:: l1
Returns:

has_class(name: str) → bool

class pysiral.surface.SICCI2SurfaceType(*args, **kwargs)

Bases: pysiral.l2proc.procsteps.Level2ProcessorStep, SurfaceTypeClassifier

new and unified surface type classifier for cryosat2 and envisat based on similar (pulse peakiness, backscatter, leading edge width)

#TODO: This could be generalized to automatically compare parameters from config file

_classes = ['unknown', 'ocean', 'lead', 'sea_ice', 'land']

execute_procstep(l1b, l2): The mandatory class for a Level2ProcessorStep. :param l1b: :param l2: :return:

classify_ocean(opt, is_radar_mode): Classify ocean waveforms. :param opt: :param is_radar_mode: :return:

classify_leads(opt, month_num, is_radar_mode): Classify leads in sea ice :param opt: :param month_num: :param is_radar_mode: :return:

classify_sea_ice(opt, month_num, is_radar_mode): Classify waveforms as sea ice :param opt: The option attribute dictionary :param month_num: :param is_radar_mode: :return:

static get_threshold_value(options, name, month_num): A unified method to retrieve threshold values from lists (one per month) or a scalar :param options: configuration object :param name: (str) parameter name (must be attribute of) options :param month_num: (int) number of the month (1 - 12) :return: threshold value to use for specific month

property l2_input_vars

property l2_output_vars

property error_bit

class pysiral.surface.ClassifierThresholdSurfaceType(*args, **kwargs)

Bases: pysiral.l2proc.procsteps.Level2ProcessorStep, SurfaceTypeClassifier

Simplified surface type classification based on the positive classification of leads and lazy sea ice classifcation (everything that is not a lead, has a decent leading edge and is within the ice mask)

reference_date = None

execute_procstep(l1: pysiral.l1data.L1bdataNCFile, l2: pysiral.l2data.Level2Data) → numpy.ndarray: The mandatory class for a Level2ProcessorStep. :param l1: :param l2: :return:

_classifiy_surface_types() → None

Classifiy surface types using the options in the l2 configuration file.

Returns:

_classify_surface_type(opt_dict: dict) → pysiral.core.flags.ANDCondition

Compute the surface type flag from the conditions in the config file for a specific radar mode. The expected structure of opt_dict is:

[exclude: surface_type_name] (Optional) radar_mode: <radar mode id> conditions:

logical expression with parameter name in brackets {}

The expression will be evaluated with eval() at run time and merged via AND:

flag = condition1 AND condition2 AND condition3 ….

If the exclude option is set, a condition will be added that the the current surface type cannot be any waveform that was previously (see order in cfg.options.surface_types) attributed to another surface type.

Parameters:: opt_dict
Returns:

_get_parameter_list() → List[str]: Construct a list of required parameters from the configuration file. This method checks all conditions for parameter name in curly brackets. :return: list of parameters that can be retrieved with l2.get_parameter_by_name()

static _get_expr_param(expression: str) → str | None

Get the parameter from an expression in the config file. E.g. for the expression:

{sea_ice_concentration} >= 15.0

the return value will be sea_ice_concentration :param expression: The expression from the config file :return:

property l2_input_vars: List[str]

property l2_output_vars: List[str]

property error_bit: numpy.ndarray

class pysiral.surface.ClassifierAuxiliarySurfaceType(*args, **kwargs)

Bases: pysiral.l2proc.procsteps.Level2ProcessorStep, SurfaceTypeClassifier

Surface type classification using a parameter already present in l1p that was read in from auxiliary files during preprocessing

reference_date = None

_classes = ['unknown', 'ocean', 'lead', 'sea_ice']

execute_procstep(l1, l2) → numpy.ndarray: The mandatory class for a Level2ProcessorStep. :param l1: :param l2: :return:

classify_ocean(): Classify ocean waveforms. :return:

classify_leads(): Classify leads in sea ice :return:

classify_sea_ice(): Classify waveforms as sea ice :return:

property l2_input_vars: List[str]

property l2_output_vars: List[str]

property error_bit: numpy.ndarray