s3analysis¶
Specific functions for felyx S3 SLSTR match-up database handling
| copyright: | Copyright 2016 Ifremer / Cersat. |
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| license: | Released under GPL v3 license, see license. |
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class
s3analysis.slstr.mdb.slstrmdb.SLSTRMDB(identifier=None, cfgfile=None, config=None)[source]¶ An abstraction class to read the SLSTR MDB records. It can be initialized in three different exclusive ways.
Parameters: - identifier (str, optional) – configuration identifier. If provided, a configuration file named <identifier>.cfg must exist in your $HOME/.s3analysis/mdb directory.
- cfgfile (str, optional) – explicit path to configuration file (exclusive with identifier)
- mdbroot (str, optional) – the path to the root directory where the match-up files are stored (with YYYY/DDD subfolders)
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__module__= 's3analysis.slstr.mdb.slstrmdb'¶
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extract_value(fieldname, data, row, cell)[source]¶ extract the pixel pointed to by row and cell indices.
Override here for specific cases
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get_algo_type_masks(matchups, use_wst_filter=True)[source]¶ return a selection mask for each algorithm type
Default settings as provided by G.Corlett
Parameters: dt_threshold (float) – maximum deviation to analysis
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get_wct_selection(matchups, across_track_range=[0, 1500], min_wind_speed=6.0, max_sat_zen=55.0, dt_thresh=5.0)[source]¶ return a selection of the match-ups in the proper range of validity.
Default settings as provided by G.Corlett
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get_wst_selection(matchups, insitu, dt_thresh=5.0, min_quality_level=2)[source]¶ return a selection of the match-ups in the proper range of validity.
Default settings as provided by G.Corlett
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read_aux_data(source, day, fields, end_day=None, filters=None)[source]¶ Read the auxiliary files generated by Gary Corlett
Parameters: - source (str) – the site collections (cmems_drifter, cmems_argo, etc…)
- day (datetime) – the date (or start date if end_date is also set) for which to retrieve the matchup in situ values
- matchup_root (str) – the path to the root repository where the matchup files are stored.
- fields (list) – a list of fields to read (remove the level suffix: for instance, use cmems_water_temperature instead of cmems_water_temperature_0 and the function will detect and reconstruct the two dimensional fields for you).
Returns: a dict where keys are field names, and values the retrieved data
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read_cloud_mask(source, day, matchup_root, end_day=None, box=False, max_distance=None, maskvar='S3A_SL_2_WCT__cloud_in', maskbits=None, multiple_mask=False)[source]¶ Return a cloud mask from ‘S3A_SL_2_WCT__cloud_in’ flag
Parameters: - maskvar (str) – the bitmask field name (ex: ‘S3A_SL_2_WCT__cloud_in’)
- maskbits (list, optional) – list of bits to test for cloud mask extraction, if you don’t want to use default values (preset for ‘S3A_SL_2_WCT__cloud_in’ variable), as a list of flag meanings.
- multiple_mask (boolean, optional) – if False (default), return a single mask array. If True, return a mask array for each mask bit within a dictionary.
Returns: an array of boolean (True is set when cloudy) if
multiple_maskkeyword is not set, or a dictionary of boolean arrays (one for each mask bit testes).Return type: numpy.ndarray or dict of numpy.ndarray
Created on Jun 26, 2017
@author: jfpiolle
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s3analysis.slstr.cloud.cloud_mask(bitmask, flagmeanings=['visible', '1.37_threshold', '1.6_small_histogram', '1.6_large_histogram', '2.25_small_histogram', '2.25_large_histogram', '11_spatial_coherence', 'gross_cloud', 'thin_cirrus', 'medium_high', 'fog_low_stratus', '11_12_view_difference', '3.7_11_view_difference', 'thermal_histogram', 'spare', 'spare'], flags=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768], bits=['visible', '1.37_threshold', '1.6_large_histogram', '2.25_large_histogram', 'gross_cloud', 'thin_cirrus', 'medium_high', '11_12_view_difference', '3.7_11_view_difference', 'fog_low_stratus', '11_spatial_coherence'])[source]¶ Build the cloud mask from the bitmask variable and the positional bits.
Parameters: - bitmask (numpy.ndarray) – the bitmask array (ex: content of ‘cloud_in’ variable)
- flagmeanings (list) – the list of flag meanings (explanation associated with each flag value, as provided in flag_meanings CF attribute
- flags (list) – the list of flag values, as provided in flag_masks CF attribute
- bits (list, optional) –
list of bits to test for cloud mask extraction, if you don’t want to use default values (preset for ‘S3A_SL_2_WCT__cloud_in’ variable). The default flag selection is:
- 1.37_threshold
- gross_cloud
- thin_cirrus
- medium_high
- 11_12_view_difference
- 3.7_11_view_difference
- fog_low_stratus
- 11_spatial_coherence
Returns: array of boolean (True when cloudy)
Return type: numpy.ndarray
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s3analysis.slstr.cloud.cloud_summary(bitmask, flagmeanings=['visible', '1.37_threshold', '1.6_small_histogram', '1.6_large_histogram', '2.25_small_histogram', '2.25_large_histogram', '11_spatial_coherence', 'gross_cloud', 'thin_cirrus', 'medium_high', 'fog_low_stratus', '11_12_view_difference', '3.7_11_view_difference', 'thermal_histogram', 'spare', 'spare'], flags=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768], bits=['visible', '1.37_threshold', '1.6_large_histogram', '2.25_large_histogram', 'gross_cloud', 'thin_cirrus', 'medium_high', '11_12_view_difference', '3.7_11_view_difference', 'fog_low_stratus', '11_spatial_coherence'])[source]¶ Return the cloud summary, flag by flag
Parameters: - bitmask (numpy.ndarray) – the bitmask array (ex: content of ‘cloud_in’ variable)
- flagmeanings (list) – the list of flag meanings (explanation associated with each flag value, as provided in flag_meanings CF attribute
- flags (list) – the list of flag values, as provided in flag_masks CF attribute
Returns: a dictionary of boolean arrays (True is set when cloudy), one for each mask bit tested.
Return type: dict of numpy.ndarray
Created on Jun 26, 2017
@author: jfpiolle