definition_dicts.py 7.14 KB
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# -*- coding: utf-8 -*-
__author__='Daniel Scheffler'

import collections
import re

import numpy as np

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from ..config import GMS_config as CFG

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########################### General dicts #####################################
dtype_lib_Python_IDL = {'bool_':0, 'uint8':1, 'int8':1, 'int_':1, 'int16':2, 'uint16':12, 'int32':3, 'uint32':13,
                        'int64':14, 'uint64':15, 'float32':4, 'float64':5, 'complex_':6, 'complex64':9}
dtype_lib_IDL_Python = {0:np.bool_, 1:np.uint8, 2:np.int16, 3:np.int32, 4:np.float32, 5:np.float64, 6:np.complex64,
                        9:np.complex128, 12:np.uint16, 13:np.uint32, 14:np.int64, 15:np.uint64}
dtype_lib_GDAL_Python= {"uint8": 1, "int8": 1, "uint16": 2, "int16": 3, "uint32": 4, "int32": 5, "float32": 6,
                        "float64": 7, "complex64": 10, "complex128": 11}
proc_chain = ['L1A','L1B','L1C','L2A','L2B','L2C']
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db_jobs_statistics_def = {'downloaded':1, 'started':2, None:2, 'L1A':3, 'L1B':4, 'L1C':5, 'L2A':6, 'L2B':7, 'L2C':8, 'FAILED':9}
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def get_GMS_sensorcode(GMS_identifier):
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    # type: (dict) -> str

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    Satellite,Sensor,Subsystem = (GMS_identifier['Satellite'], GMS_identifier['Sensor'], GMS_identifier['Subsystem'])
    Sensor          = Sensor[:-1] if re.match('SPOT', Satellite, re.I) and Sensor[-1] not in ['1', '2'] else Sensor
    meta_sensorcode = Satellite+'_'+Sensor+('_'+Subsystem if Subsystem not in ["", None] else "")
    sensorcode_dic = {
        'ALOS_AVNIR-2'       : 'AVNIR-2',
        'Landsat-4_TM'       : 'TM4', # call from layerstacker
        'Landsat-4_TM_SAM'   : 'TM4', # call from metadata object
        'Landsat-5_TM'       : 'TM5',
        'Landsat-5_TM_SAM'   : 'TM5',
        'Landsat-7_ETM+'     : 'TM7',
        'Landsat-7_ETM+_SAM' : 'TM7',
        'Landsat-8_OLI'      : 'LDCM',
        'Landsat-8_OLI_TIRS' : 'LDCM',
        'Landsat-8_LDCM'     : 'LDCM',
        'SPOT-1_HRV1'        : 'SPOT1a', #MS
        'SPOT-1_HRV2'        : 'SPOT1b',
        'SPOT-2_HRV1'        : 'SPOT2a',
        'SPOT-2_HRV2'        : 'SPOT2b',
        'SPOT-3_HRV1'        : 'SPOT3a',
        'SPOT-3_HRV2'        : 'SPOT3b',
        'SPOT-4_HRVIR1'      : 'SPOT4a',
        'SPOT-4_HRVIR2'      : 'SPOT4b',
        'SPOT-5_HRG1'        : 'SPOT5a', #PAN HRG2A
        'SPOT-5_HRG2'        : 'SPOT5b', #MS HRG2J
        'RapidEye-1_MSI'     : 'RE1',
        'RapidEye-2_MSI'     : 'RE2',
        'RapidEye-3_MSI'     : 'RE3',
        'RapidEye-4_MSI'     : 'RE4',
        'RapidEye-5_MSI'     : 'RE5',
        'SRTM_SRTM2'         : 'SRTM2' ,
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        'Terra_ASTER'        : 'AST_full',
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        'Terra_ASTER_VNIR1'  : 'AST_V1',
        'Terra_ASTER_VNIR2'  : 'AST_V2',
        'Terra_ASTER_SWIR'   : 'AST_S',
        'Terra_ASTER_TIR'    : 'AST_T',
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        'Sentinel-2A_MSI'       : 'S2A_full',
        'Sentinel-2B_MSI'       : 'S2B_full',
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        'Sentinel-2A_MSI_S2A10' : 'S2A10',
        'Sentinel-2A_MSI_S2A20' : 'S2A20',
        'Sentinel-2A_MSI_S2A60' : 'S2A60',
        'Sentinel-2B_MSI_S2B10' : 'S2B10',
        'Sentinel-2B_MSI_S2B20' : 'S2B20',
        'Sentinel-2B_MSI_S2B60' : 'S2B60'
    }
    try:
        return sensorcode_dic[meta_sensorcode]
    except KeyError:
        raise KeyError('Sensor %s is not included in sensorcode dictionary and can not be converted into GMS '
                       'sensorcode.' %meta_sensorcode)
    except:
        raise


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def get_mask_classdefinition(maskname, satellite):
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    if   maskname== 'mask_nodata':
        return {'No data': 0,
                'Data'   : 1 }
    elif maskname == 'mask_clouds':
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        legends = {
            'FMASK':{
                'No Data': 0,
                'Clear'  : 1,
                'Cloud'  : 2,
                'Shadow' : 3,
                'Snow'   : 4,
                'Water'  : 5}, #{'Clear Land': 0, 'Clear Water': 1, 'Cloud Shadow': 2, 'Snow': 3, 'Cloud': 4, 'No data': 255} # seems to be outdated
            'Classical Bayesian':{
                'Clear'        : 10,
                'Thick Clouds' : 20,
                'Thin Clouds'  : 30,
                'Snow'         : 40 }, # Classical Bayesian py_tools_ah
            'SICOR':{
                'Clear' : 10,
                'Water' : 20,
                'Shadow': 30,
                'Cirrus': 40,
                'Cloud' : 50,
                'Snow'  : 60}  # SICOR
            }

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        return legends[CFG.job.cloud_masking_algorithm[satellite]]
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    else:
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        raise ValueError("'%s' is not a supported mask name." %maskname)
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Daniel Scheffler's avatar
Bugfix    
Daniel Scheffler committed
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def get_mask_colormap(maskname):
    if maskname == 'mask_clouds':
        #return collections.OrderedDict(zip(['No data','Clear','Thick Clouds','Thin Clouds','Snow','Unknown Class'],
        #                                    [[0,0,0] ,[0,255,0],[80,80,80], [175,175,175],[255,255,255],[255,0,0]]))
        return collections.OrderedDict( (
            ('No data',         [0,0,0]),
            ('Clear',           [0,255,0]),
            ('Water',           [0,0,255]),
            ('Shadow',          [50,50,50]),
            ('Cirrus',          [175,175,175]),
            ('Cloud',           [80,80,80]),
            ('Snow',            [255,255,255]),
            ('Unknown Class',   [255,0,0]), ) )
    else: return None


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def get_outFillZeroSaturated(dtype):
    """Returns the values for 'fill-', 'zero-' and 'saturated' pixels of an image
    to be written with regard to the target data type.
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    :param dtype: data type of the image to be written"""
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    dtype = str(np.dtype(dtype))
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    assert dtype in ['bool', 'int8', 'uint8', 'int16', 'uint16','float32'], \
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        "get_outFillZeroSaturated: Unknown dType: '%s'." %dtype
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    dict_outFill      = {'bool':None, 'int8':-128, 'uint8':0  , 'int16':-9999, 'uint16':9999 , 'float32':-9999.}
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    dict_outZero      = {'bool':None, 'int8':0   , 'uint8':1  , 'int16':0    , 'uint16':0    , 'float32':0.}
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    dict_outSaturated = {'bool':None, 'int8':127 , 'uint8':256, 'int16':32767, 'uint16':65535, 'float32':65535.}
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    return dict_outFill[dtype], dict_outZero[dtype], dict_outSaturated[dtype]


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def is_dataset_provided_as_fullScene(GMS_identifier):
    # type: (dict) -> bool
    """Returns True if the dataset belonging to the given GMS_identifier is provided as full scene and returns False if
     it is provided as multiple tiles.

    :param GMS_identifier:
    :return:
    """

    sensorcode = get_GMS_sensorcode(GMS_identifier)
    dict_fullScene_or_tiles = {
        'AVNIR-2'   : True,
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        'AST_full'  : False,
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        'AST_V1'    : True,
        'AST_V2'    : True,
        'AST_S'     : True,
        'AST_T'     : True,
        'TM4'       : True,
        'TM5'       : True,
        'TM7'       : True,
        'LDCM'      : True,
        'SPOT1a'    : True,
        'SPOT2a'    : True,
        'SPOT3a'    : True,
        'SPOT4a'    : True,
        'SPOT5a'    : True,
        'SPOT1b'    : True,
        'SPOT2b'    : True,
        'SPOT3b'    : True,
        'SPOT4b'    : True,
        'SPOT5b'    : True,
        'RE5'       : False,
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        'S2A_full'  : False, # FIXME this changed for S2 in 08/2016
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        'S2A10'     : False,
        'S2A20'     : False,
        'S2A60'     : False,
        'S2B10'     : False,
        'S2B20'     : False,
        'S2B60'     : False, }
    return dict_fullScene_or_tiles[sensorcode]