pysiral.core.flags
Attributes
Classes
Enum for hemispheres. |
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Enum for processing levels with a corresponding pysiral processor |
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Enum for processing levels with a corresponding pysiral processor |
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Enum for processing levels. |
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Enum for duration names for dateperiod.DatePeriod objects. |
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Container for surface type information. |
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Container for ice type information |
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Module Contents
- pysiral.core.flags.__author__ = 'Stefan Hendricks'
- pysiral.core.flags.ESA_SURFACE_TYPE_DICT
- pysiral.core.flags.SURFACE_TYPE_DICT
- pysiral.core.flags.WAVEFORM_CLASSIFICATION_BIT_DICT
- class pysiral.core.flags.Hemispheres
Bases:
enum.StrEnumEnum for hemispheres.
- NORTH = 'nh'
- SOUTH = 'sh'
- GLOBAL = 'global'
- classmethod get_choices() List[str]
- class pysiral.core.flags.DataRecordType
Bases:
enum.StrEnumEnum for processing levels with a corresponding pysiral processor
- CLIMATE_DATA_RECORD = 'cdr'
- INTERIM_CLIMATE_DATA_RECORD = 'icdr'
- NON_TIME_CRITICAL = 'ntc'
- NEAR_REAL_TIME = 'nrt'
- REPROCESSED = 'rep'
- classmethod get_choices() List[str]
- class pysiral.core.flags.PysiralProcessingLevels
Bases:
enum.StrEnumEnum for processing levels with a corresponding pysiral processor
- LEVEL1 = 'l1'
- LEVEL2 = 'l2'
- LEVEL3 = 'l3'
- class pysiral.core.flags.ProductProcessingLevels
Bases:
enum.StrEnumEnum for processing levels.
- LEVEL1_PREPROCESSED = 'l1p'
- LEVEL2 = 'l2'
- LEVEL2_INTERMEDIATE = 'l2i'
- LEVEL2_PREPROCESSED = 'l2p'
- LEVEL3_COLLATED = 'l3c'
- LEVEL3_SUPERCOLLATED = 'l3s'
- classmethod get_choices() List[str]
Returns a list of all processing level choices.
- class pysiral.core.flags.DurationType
Bases:
enum.StrEnumEnum for duration names for dateperiod.DatePeriod objects.
- P1D = 'day'
- P7D = 'isoweek'
- P1M = 'month'
- classmethod get_choices() List[str]
- class pysiral.core.flags.RadarModes
Bases:
object- flag_dict
- classmethod get_flag(mode_name: str) int | None
- classmethod get_name(flag: int) str | None
- name(index: int) str
- property num: int
- class pysiral.core.flags.SurfaceType
Bases:
objectContainer for surface type information.
- Possible classifications (Adapted from CryoSat-2 conventions)
unknown
ocean
closed sea/lakes
lead
large lead/polynya
sea ice (general sea ice class, not to be confused with ice type)
continental ice
land
- surface_type_dict
- _surface_type_flags = []
- _surface_type = None
- name(flag_value: int) str
Return the flag name for a give flag value
- Parameters:
flag_value
- Returns:
- set_flag(flag: numpy.ndarray) None
- add_flag(flag: numpy.ndarray, type_str: str) None
Add a surface type flag (boolean array)
- Parameters:
flag
type_str
- Returns:
- has_flag(type_str: str) bool
- get_by_name(name: str) FlagContainer
- append(annex: SurfaceType) None
- set_subset(subset_list)
- fill_gaps(corrected_n_records, indices_map)
API gap filler method. Note: Gaps will be filled with the nodata=unkown (8) surface_type
- invalid_n_records(n: int) bool
Check if flag array has the correct length
- _get_type_id(name: str) int
- property flag: numpy.ndarray
- property n_records: int
- property dimdict: collections.OrderedDict
Returns dictionary with dimensions
- property parameter_list: List[str]
- property lead: FlagContainer
- property ocean: FlagContainer
- property sea_ice: FlagContainer
- property land: FlagContainer
- class pysiral.core.flags.IceType
Bases:
objectContainer for ice type information
- Possible classifications
young thin ice
first year ice
multi-year ice
wet ice
- _ICE_TYPE_DICT
- _ice_type_flag = None
- class pysiral.core.flags.FlagContainer(flag: numpy.ndarray = None)
Bases:
object- _flag = None
- set_flag(flag: numpy.ndarray) None
- abstractmethod add(flag: numpy.ndarray) None
- property indices: numpy.ndarray
- property flag: numpy.ndarray
- property num: int
- class pysiral.core.flags.ANDCondition(**kwargs)
Bases:
FlagContainer- add(flag: numpy.ndarray | bool) None
- class pysiral.core.flags.ORCondition(**kwargs)
Bases:
FlagContainer- add(flag: numpy.ndarray | bool) None