test_gms_preprocessing.py 31 KB
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#!/usr/bin/env python
# -*- coding: utf-8 -*-

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###################################################################################
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"""
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test_gms_preprocessing
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----------------------------------
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The testcases contained in this testscript, are parametrized testcases. They test
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the level-processing steps defined in the 'gms_preprocessing' module in the
"gms_preprocessing"-project with the help of the test datasets:
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- Landsat-5, Pre-Collection Data,
- Landsat-5, Collection Data,
- Landsat-7, SLC on, Pre-Collection Data,
- Landsat-7, SLC off, Pre-Collection Data,
- Landsat-7, SLC off, Collection Data,
- Landsat-8, Pre-Collection Data,
- Landsat-8, Collection Data,
- Sentinel-2A, Pre-Collection Data and
- Sentinel-2A, Collection Data.
The test datasets can be found in the directory "tests/data/archive_data/...". The
respective SRTM-datasets needed in the data-processing can be found in the directory
"tests/data/archive_data/Endeavor".

The tests, defined in a base-testcase (not executed), are triggered by creating
jobs (based on given job-IDs) in individual testcases that inherit the tests
from the base-testcase. The exception: The job-ID used in the last testclass
contains 3 different test datasets of the above listed datasets.

Note that the testresults are outputted in the console as well as a log-textfile
that can be found in the directory "tests/logs".

Program edited in July 2017.
"""

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# Import python standard libraries.
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import itertools
import logging
import os
import pandas
import sys
import time
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import unittest

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# Imports regarding the 'gms_preprocessing' module.
from gms_preprocessing import process_controller, __file__
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from gms_preprocessing.model.gms_object import GMS_object
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from gms_preprocessing.algorithms.L1A_P import L1A_object
from gms_preprocessing.algorithms.L1B_P import L1B_object
from gms_preprocessing.algorithms.L1C_P import L1C_object
from gms_preprocessing.algorithms.L2A_P import L2A_object
from gms_preprocessing.algorithms.L2B_P import L2B_object
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# from gms_preprocessing.algorithms.L2C_P import L2C_object
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from gms_preprocessing.misc.database_tools import get_info_from_postgreSQLdb
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from gms_preprocessing.model.gms_object import GMS_object_2_dataset_dict
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from . import db_host, index_host
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__author__ = 'Daniel Scheffler'  # edited by Jessica Palka.
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# Rootpath of the gms_preprocessing-repository.
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gmsRepo_rootpath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))

# Defining the configurations needed to start a job containing the different dataset scenes.
# TODO Change the job-configurations for selected datasets.
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job_config_kwargs = dict(parallelization_level='scenes', db_host=db_host, spatial_index_server_host=index_host,
                         delete_old_output=True, is_test=True)
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##########################
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# Test case: BaseTestCases
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##########################

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class BaseTestCases:
    """
    General testclass. The tests defined in this testclass test the processing steps Level-1A, Level-1B, Level-1C,
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    Level-2A, Level-2B and Level-2C defined in the "gms_preprocessing"-repository.
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    Note that the tests in this testclass are not executed directly. They are re-used in the other classes defined
    in this test-script.
    """
    class TestAll(unittest.TestCase):
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        PC = None  # default
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        @classmethod
        def tearDownClass(cls):
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            cls.PC.config.DB_job_record.delete_procdata_of_entire_job(force=True)
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        @classmethod
        def validate_db_entry(cls, filename):
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            sceneID_res = get_info_from_postgreSQLdb(cls.PC.config.conn_database, 'scenes', ['id'],
                                                     {'filename': filename})
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            assert sceneID_res and isinstance(sceneID_res[0][0], int), 'Invalid database entry.'

        @classmethod
        def create_job(cls, jobID, config):
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            cls.PC = process_controller(jobID, **config)
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            # update attributes of DB_job_record and related DB entry
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            cls.PC.config.DB_job_record.reset_job_progress()
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            GMS_object.proc_status_all_GMSobjs.clear()  # reset
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            cls.PC.config.data_list = cls.PC.add_local_availability(cls.PC.config.data_list)
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            [cls.validate_db_entry(ds['filename']) for ds in cls.PC.config.data_list]
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            cls.PC.config.ac_estimate_accuracy = True  # FIXME
            cls.PC.config.spechomo_estimate_accuracy = True  # FIXME
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        def check_availability(self, GMS_objs, tgt_procL):
            dss = self.PC.add_local_availability([GMS_object_2_dataset_dict(obj) for obj in GMS_objs])
            for ds in dss:
                self.assertEqual(ds['proc_level'], tgt_procL,
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                                 msg='Written %s dataset cannot be found by PC.add_local_availability().'
                                     % (' '.join([ds['satellite'], ds['sensor'], ds['subsystem'], tgt_procL])))
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        def test_L1A_processing(self):
            self.L1A_newObjects = self.PC.L1A_processing()
            self.assertIsInstance(self.L1A_newObjects, list)
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            self.assertNotEqual(len(self.L1A_newObjects), 0, msg='L1A_processing did not output an L1A object.')
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            self.assertIsInstance(self.L1A_newObjects[0], L1A_object)

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            # check if PC.add_local_availability finds the written dataset
            self.check_availability(self.L1A_newObjects, 'L1A')

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        def test_L1B_processing(self):
            self.L1B_newObjects = self.PC.L1B_processing()
            self.assertIsInstance(self.L1B_newObjects, list)
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            self.assertNotEqual(len(self.L1B_newObjects), 0, msg='L1B_processing did not output an L1B object.')
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            self.assertIsInstance(self.L1B_newObjects[0], L1B_object)

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            # check if PC.add_local_availability finds the written dataset
            self.check_availability(self.L1B_newObjects, 'L1B')

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        def test_L1C_processing(self):
            self.L1C_newObjects = self.PC.L1C_processing()
            self.assertIsInstance(self.L1C_newObjects, list)
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            self.assertNotEqual(len(self.L1C_newObjects), 0, msg='L1C_processing did not output an L1C object.')
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            self.assertIsInstance(self.L1C_newObjects[0], L1C_object)

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            # check if PC.add_local_availability finds the written dataset
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            # self.check_availability(self.L1C_newObjects, 'L1C')
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        def test_L2A_processing(self):
            self.L2A_newObjects = self.PC.L2A_processing()
            self.assertIsInstance(self.L2A_newObjects, list)
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            self.assertNotEqual(len(self.L2A_newObjects), 0, msg='L2A_processing did not output an L2A object.')
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            self.assertIsInstance(self.L2A_newObjects[0], L2A_object)

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            # check if PC.add_local_availability finds the written dataset
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            # FIXME this will fail because AC outputs TOA-Ref if ECMWF data are missing
            # self.check_availability(self.L2A_newObjects, 'L2A')
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        def test_L2B_processing(self):
            self.L2B_newObjects = self.PC.L2B_processing()
            self.assertIsInstance(self.L2B_newObjects, list)
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            self.assertNotEqual(len(self.L2B_newObjects), 0, msg='L2B_processing did not output an L2B object.')
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            self.assertIsInstance(self.L2B_newObjects[0], L2B_object)

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            # check if PC.add_local_availability finds the written dataset
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            # FIXME this will fail because AC outputs TOA-Ref if ECMWF data are missing
            # self.check_availability(self.L2B_newObjects, 'L2B')
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        def test_L2C_processing(self):
            self.L2C_newObjects = self.PC.L2C_processing()
            self.assertIsInstance(self.L2C_newObjects, list)
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            self.assertNotEqual(len(self.L2C_newObjects), 0, msg='L2C_processing did not output an L2C object.')
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            # self.assertIsInstance(self.L2C_newObjects[0], L2C_object)
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            # check if PC.add_local_availability finds the written dataset
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            # FIXME this will fail because AC outputs TOA-Ref if ECMWF data are missing
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            # self.check_availability(self.L2C_newObjects, 'L2C')  # FIXME fails (not yet working)

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            # Setting the config.status manually.
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            # if self.L2C_newObjects:
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            #     self.PC.config.status = "finished"
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            # FIXME after updating the job.status-attribute for the level-processes, delete the code that is commented
            # FIXME out.
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    class TestCompletePipeline(unittest.TestCase):
        PC = None  # default

        @classmethod
        def tearDownClass(cls):
            cls.PC.config.DB_job_record.delete_procdata_of_entire_job(force=True)

        @classmethod
        def validate_db_entry(cls, filename):
            sceneID_res = get_info_from_postgreSQLdb(cls.PC.config.conn_database, 'scenes', ['id'],
                                                     {'filename': filename})
            assert sceneID_res and isinstance(sceneID_res[0][0], int), 'Invalid database entry.'

        @classmethod
        def create_job(cls, jobID, config):
            cls.PC = process_controller(jobID, **config)

            [cls.validate_db_entry(ds['filename']) for ds in cls.PC.config.data_list]

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            cls.PC.config.CPUs_all_jobs = 3
            cls.PC.config.max_parallel_reads_writes = 3
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            # cls.PC.config.spathomo_estimate_accuracy = True
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            # cls.PC.config.ac_estimate_accuracy = True  # FIXME
            # cls.PC.config.spechomo_estimate_accuracy = True  # FIXME
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        def test_run_all_processors(self):
            self.PC.run_all_processors()
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            self.assertIsInstance(self.PC.L2C_newObjects, list)
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            self.assertIsInstance(self.PC.summary_detailed, pandas.DataFrame)
            self.assertFalse(self.PC.summary_detailed.empty)
            self.assertIsInstance(self.PC.summary_quick, pandas.DataFrame)
            self.assertFalse(self.PC.summary_quick.empty)
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###################################################################################
# Test cases 1-9: Test_<Satelite-Dataset>_<PreCollection or Collection>Data
# Test case 10: Test_MultipleDatasetsInOneJob


# TESTDATA-CLASSES.
class Test_Landsat5_PreCollectionData(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a Landsat-5 TM scene (pre-collection data).
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    More information on the dataset will be output after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186263, job_config_kwargs)

# class Test_Landsat5_CollectionData(BaseTestCases.TestAll):
#     """
#     Parametrized testclass. Tests the level-processes on a Landsat-5 TM scene (collection data).
#     More information on the dataset will be outputted after the tests-classes are executed.
#     """
#     @classmethod
#     def setUpClass(cls):
#         cls.create_job(26186263, job_config_kwargs) # FIXME job_ID!

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class Test_Landsat7_SLC_on_PreCollectionData(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a Landsat-7 ETM+_SLC_ON scene (pre-collection data).
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    More information on the dataset will be output after after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186262, job_config_kwargs)

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class Test_Landsat7_SLC_off_PreCollectionData(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a Landsat-7 ETM+_SLC_OFF scene (pre-collection data).
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    More information on the dataset will be output after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186267, job_config_kwargs)

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# class Test_Landsat7_SLC_off_CollectionData(BaseTestCases.TestAll):
#     """
#     Parametrized testclass. Tests the level-processes on a Landsat-7 ETM+_SLC_OFF scene (collection data).
#     More information on the dataset will be outputted after the tests-classes are executed.
#     """
#     @classmethod
#     def setUpClass(cls):
#         cls.create_job(26186267, job_config_kwargs) # FIXME job_ID!

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#
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class Test_Landsat8_PreCollectionData(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a Landsat-8 OLI_TIRS scene (pre-collection data).
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    More information on the dataset will be output after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186196, job_config_kwargs)

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class Test_Landsat8_CollectionData(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a Landsat-8 OLI_TIRS scene (collection data).
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    More information on the dataset will be output after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
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        cls.create_job(26187391, job_config_kwargs)
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class Test_Landsat8_CollectionData_CompletePipeline(BaseTestCases.TestCompletePipeline):
    """
    Parametrized testclass. Tests the level-processes on a Landsat-8 OLI_TIRS scene (collection data).
    More information on the dataset will be output after the tests-classes are executed.
    """
    @classmethod
    def setUpClass(cls):
        cfg = job_config_kwargs
        # cfg.update(dict(inmem_serialization=True))
        cls.create_job(26187391, cfg)


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class Test_Sentinel2A_SingleGranuleFormat(BaseTestCases.TestAll):
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    """
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    Parametrized testclass. Tests the level-processes on a Sentinel-2A MSI scene (1 granule in archive: > 2017).
    More information on the dataset will be output after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186268, job_config_kwargs)

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class Test_Sentinel2A_SingleGranuleFormat_CompletePipeline(BaseTestCases.TestCompletePipeline):
    """
    Parametrized testclass. Tests the level-processes on a Sentinel-2A MSI scene (1 granule in archive: > 2017).
    More information on the dataset will be output after the tests-classes are executed.
    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186268, job_config_kwargs)

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    # @classmethod
    # def tearDownClass(cls):
    #     super().tearDownClass()
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        # PC = cls.PC

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class Test_Sentinel2A_MultiGranuleFormat(BaseTestCases.TestAll):
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    """
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    Parametrized testclass. Tests the level-processes on a Sentinel-2A MSI scene (multiple granules in archive: < 2017).
    More information on the dataset will be output after the tests-classes are executed.
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    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186272, job_config_kwargs)

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class Test_Sentinel2B_SingleGranuleFormat(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a Sentinel-2B MSI scene (1 granule in archive: > 2017).
    More information on the dataset will be output after the tests-classes are executed.
    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186937, job_config_kwargs)


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class Test_MultipleDatasetsInOneJob(BaseTestCases.TestAll):
    """
    Parametrized testclass. Tests the level-processes on a job containing a Landsat-5 (pre-collection data),
    Landsat-7 SLC_off (pre-collection data) and a Sentinel-2A (collection data) scene.
    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186273, job_config_kwargs)


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class Test_MultipleDatasetsInOneJob_CompletePipeline(BaseTestCases.TestCompletePipeline):
    """
    Parametrized testclass. Tests the level-processes on a job containing a Landsat-5 (pre-collection data),
    Landsat-7 SLC_off (pre-collection data) and a Sentinel-2A (collection data) scene.
    """
    @classmethod
    def setUpClass(cls):
        cls.create_job(26186273, job_config_kwargs)

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    # @classmethod
    # def tearDownClass(cls):
    #     super().tearDownClass()
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        # PC = cls.PC

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class Test_ProcessContinuing_CompletePipeline(unittest.TestCase):
    """
    Parametrized testclass. Tests the level-processes on a job containing a Landsat-5 (pre-collection data),
    Landsat-7 SLC_off (pre-collection data) and a Sentinel-2A (collection data) scene.
    """
    PC = None  # default

    @classmethod
    def tearDownClass(cls):
        cls.PC.config.DB_job_record.delete_procdata_of_entire_job(force=True)

    @classmethod
    def validate_db_entry(cls, filename):
        sceneID_res = get_info_from_postgreSQLdb(cls.PC.config.conn_database, 'scenes', ['id'],
                                                 {'filename': filename})
        assert sceneID_res and isinstance(sceneID_res[0][0], int), 'Invalid database entry.'

    @classmethod
    def create_job(cls, jobID, config):
        cls.PC = process_controller(jobID, **config)

        cls.PC.logger.info('Execution of entire GeoMultiSens pre-processing chain started for job ID %s...'
                           % cls.PC.config.ID)

        [cls.validate_db_entry(ds['filename']) for ds in cls.PC.config.data_list]

    def setUp(self):
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        self.cfg_kw = job_config_kwargs.copy()  # copy, because job_config_kwargs is modified otherwise
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        self.cfg_kw.update(dict(
            reset_status=True,
            exec_L1BP=[False, False, False],
            exec_L1CP=[False, False, False],
            exec_L2AP=[False, False, False],
            exec_L2BP=[False, False, False],
            exec_L2CP=[False, False, False]
        ))

        # produce L1A data and stop processing there
        self.create_job(26186263, self.cfg_kw)  # 1x L5 pre-collection
        self.PC.run_all_processors()

    def test_continue_from_L1A(self):
        # create a new job and try to continue from L1A
        cfg_kw = self.cfg_kw
        cfg_kw.update(dict(
            exec_L1BP=[True, True, False],
            delete_old_output=False
        ))
        self.create_job(26186263, cfg_kw)  # 1x L5 pre-collection
        self.PC.run_all_processors()


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###################################################################################
# Summarizing the information regarding the test datasets.

# The information: 'country' (3-letter country code, UN), 'characteristic features of the shown scene', 'cloud cover
# present' and 'overlap area present' of each dataset are summarized in the dictionary "testdata_scenes". The
# information are sorted according to the testdata.
# 3-letter code:
# UKR-Ukraine, KGZ-Kyrgyztan, POL-Poland, AUT-Austria, JPN-Japan, BOL-Bolivia, TUR-Turkey, DEU-Germany, CHE-Switzerland.
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testdata_scenes = \
    {'Landsat5_PreCollectionData': list(['UKR', 'City region, forest', 'Sparsely', 'Zone 34/35']),
     # 'Landsat5_CollectionData': list(['KGZ', 'Snowy Mountains', 'Yes', 'None']),
     'Landsat7_SLC_on_PreCollectionData': list(['POL', 'City region, lakes', 'Yes', 'None']),
     'Landsat7_SLC_off_PreCollectionData': list(['AUT', 'Stripes (partly), Mountains', 'None', 'None']),
     # 'Landsat7_SLC_off_CollectionData': list(['JPN', 'Stripes (completly), Mountains', 'Yes', 'Zone 53/54']),
     'Landsat8_PreCollectionData': list(['BOL', 'Forest', 'Yes', 'None']),
     'Landsat8_CollectionData': list(['TUR', 'Snowy Mountains', 'Yes', 'None']),
     'Sentinel2A_PreCollectionData': list(['DEU', 'Potsdam', 'Sparsely', 'None']),
     'Sentinel2A_CollectionData': list(['CHE', 'City region, on the Rhine', 'Yes', 'None'])
     }
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# The key of the dictionary is the key-value to parametrize the testclasses so that each testclass is executed
# automatically.
testdata = list(testdata_scenes.keys())
testdata.append('MultipleDatasetsInOneJob')


###################################################################################
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# Parameterizing the test cases and creating a summary of the test results.
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summary_testResults, summary_errors, summary_failures, summary_skipped, jobstatus = [[] for _ in range(5)]

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@unittest.SkipTest
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class Test_in_normal_mode(unittest.TestCase):
    def setUp(self):
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        # self.job_id = 26184107
        # self.job_id = 26185175   # 1x TM5
        # self.job_id = 26185176   # 1x Landsat
        # self.job_id = 26185177  # 1. Sentinel-2-Testszene
        # self.job_id = 26185189   # direkt benachbarte Granules von 1. Sentinel-2-Testszene
        # self.job_id = 26185237  # 4 x Landsat-8 -> Job per database tools erstellt
        # self.job_id = 26185239  # 50 x Landsat-8 -> Job per database tools erstellt - 1. L8 Beta-Testjob
        # self.job_id = 26185242  # 1 x Landsat-8 - Bug files_in_archive=None
        # self.job_id = 26185250  # Beta-Job - 219 x L8, 172 x L7, 111 x S2, spatref S2
        # self.job_id = 26185251  # 1x L8, Zielsensor L8
        # self.job_id = 26185252  # 1x L8, Zielsensor L8, spat.ref L8
        # self.job_id = 26185253  # 25x L8, Zielsensor L8, spat.ref L8
        # self.job_id = 26185254  # 10x L8, Zielsensor L8, spat.ref L8
        # Grund=Schreibfehler L1A im tiled Python-mode bei mehr als 1 Szene im Job:
        # self.job_id = 26185255  # 1x L8 Bug 5 corners found
        # self.job_id = 26185256  # 1x L7 SLC off, Zielsensor L8, spat.ref L8
        # self.job_id = 26185257  # Beta-Job - 219 x L8, 172 x L7, 111 x S2, spatref L8
        # self.job_id = 26185258  # Beta-Job - 219 x L8, spatref L8
        # self.job_id = 26185259  # Beta-Job - 172 x L7, spatref L8
        # self.job_id = 26185260  # Beta-Job - 111 x S2, spatref L8
        # self.job_id = 26185268  # 25x L7 SLC off, Zielsensor L8, spat.ref L8
        # self.job_id = 26185269  # 1x L7 SLC off, Bug SpatialIndexMediator
        # self.job_id = 26185270  # 5x L7 SLC off, Bug SpatialIndexMediator
        # self.job_id = 26185275  # 1x L8, spat. Ref. L8 Bug L1B_mask not found
        # self.job_id = 26185264  # 1x L8, Bug L1B_masks not found
        # self.job_id = 26185265  # 1x L8, Bug L2B_masks not found
        # self.job_id = 26185268  # "2x L8, Bug L2B_masks not found, incl. 1x bad archive"
        # self.job_id = 26185269 # "10x L8, Bug L2B_masks not found"
        # self.job_id = 26185272 # "1x S2A Sips"
        # self.job_id = 26185273  # "1x L7, target L8, spat.ref L8"
        # self.job_id = 26185275 # "1x L7, target L8, spat.ref L8 L1B Matching failed"
        # self.job_id = 26185276 # "1x L7, target L8, spat.ref L8 L1B Matching window became too small."
        # self.job_id = 26185279 # "GEOMS: 25x L7, target L8, spat.ref L8"
        # "GEOMS: 1x L7, target L8, spat.ref L8, debugging NoneType object is not subscriptable within
        # mapinfo2geotransform":
        # self.job_id = 26185280
        # self.job_id = 26185281 # "GEOMS: 4x L7, target L8, spat.ref L8, freeze of pool.map"
        # self.job_id = 26185283 # "GEOMS: 10x L7, target L8, spat.ref L8, freeze of pool.map"
        # self.job_id = 26185284 # "GEOMS: 11x L7, target L8, spat.ref L8, freeze of pool.map"
        # self.job_id = 26185321 # "GEOMS: 1x L7, target L8, spat.ref L8, debugging L1B_P"
        # "GEOMS: 1x L7, target L8, spat.ref L8, Bug calc_shifted_cross_power_spectrum: NoneType object not iterable":
        # self.job_id = 26185322
        # self.job_id = 26185277 # "GMS41: 10x L7, target L8, spat.ref L8, Permission errors during logging"
        # self.job_id = 26185278 # "Beta-Job - 172 x L7, spatref L8"
        # self.job_id = 26185284 # "GMS41: "all beta-L8 with cloud cover <30% (74 scenes)"
        # self.job_id = 26185285 # "GMS41: "all beta-L7 with cloud cover <30% (49 scenes)"
        # self.job_id = 26185396 # "GEOMS: 1x S2A multi GSD testing"
        # self.job_id = 26185398  # "GEOMS: 1x S2A granule multi GSD testing"

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        # self.job_id = 26186740  # Testjob Landsat-8
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        # self.job_id = 26186906  # Bug Input Validator
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        # self.job_id = 26186925  # 1 Sentinel-2A, Bug NoneType' object has no attribute 'find'
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        # self.job_id = 26187051  # GMS41: 1 Landsat, FileNotFoundError
        # self.job_id = 26187052  # GMS41: 1 Landsat, DB query returns no DEM
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        # self.job_id = 26187053  # GMS41: AC: The input 'list_GMS_objs' contains duplicates: ['', '']
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        # self.job_id = 26187750  # GEOMS: [AC]: RuntimeWarning: All-NaN slice encountered
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        # self.job_id = 26187760  # GEOMS: [L2C]: ValueError: 'axis' entry is out of bounds
        self.job_id = 26187804  # GEOMS: Spatial homogenization leaves resampling artifacs at the image edges.
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        self.PC = process_controller(self.job_id, **dict(is_test=False, parallelization_level='scenes', db_host=db_host,
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                                                         delete_old_output=True, disable_exception_handler=True))
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        self.PC.config.spathomo_estimate_accuracy = True
        self.PC.config.ac_estimate_accuracy = True  # FIXME
        self.PC.config.spechomo_estimate_accuracy = True  # FIXME
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        self.PC.config.exec_L1CP = [1, 1, 0]
        self.PC.config.exec_2ACP = [1, 1, 0]
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    def test(self):
        self.PC.run_all_processors()


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if __name__ == '__main__':
    # Part 1: Creating and running a testsuite for each dataset-testcase, and querying the job.status of the job.
    for items in testdata:
        suite = unittest.TestLoader().loadTestsFromTestCase(eval("Test_"+items))
        alltests = unittest.TestSuite(suite)

        # Part 2: Saving the results of each testsuite and the query for the job.status in individual variables.
        testResult = unittest.TextTestRunner(verbosity=2).run(alltests)

        summary_testResults.append([testResult.testsRun, testResult.wasSuccessful(),
                                    len(testResult.errors), len(testResult.failures),
                                    len(testResult.skipped)])
        summary_errors.append(testResult.errors)
        summary_failures.append(testResult.failures)
        summary_skipped.append(testResult.skipped)

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        # FIXME: If the job.status-issue is fixed, the commented out section can be nullified.
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        # jobstatus.append(eval("Test_"+items).PC.status)
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    # Part 3: Summarizing the testresults of each testsuite and outputting the results in an orderly fashion on the
    # console and in a textfile.
    # Note that the testresults are outputted as usual after each test is executed. Since the output of each
    # level-process is rather long, the output of the testresults become lost. Therefore, the purpose to output the
    # testresults again is simply to summarize the testresults in one place and to give an overview over the results.

    # Output: a) Information on the test datasets (table), b) testresults summarized in a table, c)if existing,
    # a list of errors, failures and skips in the testcases and d) the job.status that is not set to "finished".

    time.sleep(0.5)

    # Path of the textfile the results will be logged to.
    test_log_path = os.path.join(gmsRepo_rootpath, 'tests', 'data', 'logs', time.strftime('%Y%m%d_%H%M%S_log.txt'))

    # Creating a logging system for the testresults.
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    # Source: The "GMS_logger"-function in the "gms_preprocessing" --> "misc" --> "logging.py"-script was used and
    # slightly altered to meet the needs of the current problem.
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    logger = logging.getLogger("log_Test")
    logger.setLevel(logging.INFO)

    # Defining the format of the console and the file-output.
    formatter_fileH = logging.Formatter('')
    formatter_ConsoleH = logging.Formatter('')

    # Creating a handler for the file for the logging level "INFO".
    fileHandler = logging.FileHandler(test_log_path)
    fileHandler.setFormatter(formatter_fileH)
    fileHandler.setLevel(logging.INFO)

    # Creating a handler for the console for the logging level "INFO". "sys.stdout" is used for the logging output.
    consoleHandler_out = logging.StreamHandler(stream=sys.stdout)
    consoleHandler_out.setFormatter(formatter_ConsoleH)
    consoleHandler_out.set_name('console handler stdout')
    consoleHandler_out.setLevel(logging.INFO)

    # Adding the defined handlers to the instantiated logger.
    logger.addHandler(fileHandler)
    logger.addHandler(consoleHandler_out)

    # OUPUT, START.
    # Header of the file.
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    logger.info("\ntest_gms_preprocessing.py"
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                "\nREVIEW OF ALL TEST RESULTS, SUMMARY:"
                "\n***************************************************************************************"
                "\n--> SPECIFIC FEATURES OF DATA:")

    # Adding a table displaying the characteristic features of each dataset.
    logger.info(pandas.DataFrame.from_items(testdata_scenes.items(),
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                                            orient='index',
                                            columns=['Country', 'Characteristic', 'Clouds', 'Overlap_area']))
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    logger.info("\nThe jobID used in Test_" + testdata[-1] + " contains the datasets: "
                "\n-Landsat5_PreCollectionData,\n-Landsat7_SLC_off_PreCollectionData and "
                "\n-Sentinel2A_CollectionData.")

    # Adding a table displaying the testresults.
    logger.info("\n***************************************************************************************"
                "\n--> TESTRESULTS:")

    results = ["Run", "Success", "Errors", "Failures", "Skips"]
    testdata_index = ["Test_" + item for item in testdata]
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    logger.info(pandas.DataFrame(summary_testResults, columns=results, index=testdata_index))
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    # If errors, failures or skips (there is yet nothing to skip in the code) occurres, the respective message will
    # be printed.
    logger.info("\n***************************************************************************************")
    if list(itertools.chain(*summary_errors)) or list(itertools.chain(*summary_failures)) or \
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       list(itertools.chain(*summary_skipped)):
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        logger.info("--> ERRORS/FAILURES/SKIPS:")
        logger.info("\n---------------------------------------------------------------------------------------")

        for index, test in enumerate(testdata):
            logger.info("Test_" + test + ", ERRORS:")
            if summary_errors[index]:
                logger.info(summary_errors[index][0][1])
            else:
                logger.info("None. \n")

            logger.info("Test_" + test + ", FAILURES:")
            if summary_failures[index]:
                logger.info(summary_failures[index][0][1])
            else:
                logger.info("None. \n")
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            logger.info("Test_" + test + ", SKIPS:")
            if summary_skipped[index]:
                logger.info(summary_skipped[index][0][1])
            else:
                logger.info("None.")
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            if not index == (len(testdata) - 1):
                logger.info("\n---------------------------------------------------------------------------------------")
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        logger.info("\n***************************************************************************************")
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    else:
        pass
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    # Checking, if the job.status of each job is set to "finished". Is it not set to "finished", a dataframe is created
    # containing the test-name with and the different job.status itself.
    # FIXME: If the job.status-issue is fixed, the commented out section can be nullified.
    # jobstatus_table, index_table = [[] for _ in range(2)]
    # for index, test in enumerate(testdata):
    #     if jobstatus[index] != "finished":
    #         jobstatus_table.append(jobstatus[index])
    #         index_table.append("Test_" + test)
    #
    # if jobstatus_table:
    #     logger.info("--> WARNING!!! JOBSTATUS of the following testcase(s) is not set to 'finished': \n")
    #     logger.info(pandas.DataFrame(jobstatus_table, columns=["jobstatus"], index=index_table))
    #     logger.info("\n***************************************************************************************")
    # else:
    #     pass
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    logger.info("END.")  # OUTPUT, END.
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    # Delete the handlers added to the "log_Test"-logger to ensure that no message is outputted twice in a row, when
    # the logger is used again.
    logger.handlers = []