def test_compare_empty(self):
"Compare two empty databases."
result = dbcompare.compare_databases(self.conn[0], self.conn[1],
verbosity=2)
self.assertTrue(result, "Identical empty databases are " +
"not comparing as equal.")
def test_compare_different(self):
"Compare two databases with differences."
file_name = 'test_compare_different_v3.txt'
output = StringIO.StringIO()
result = dbcompare.compare_databases(self.conn[0], self.conn[2],
verbosity=3, output=output)
output_file_path = os.path.join(self.OUTPUT_DIR, file_name)
with open(output_file_path, 'w') as output_file:
output_file.write(output.getvalue())
self.assertFalse(result, "Databases with differences are " +
"comparing as equal.")
expected_file_path = os.path.join(self.EXPECTED_DIR, file_name)
if os.path.isfile(expected_file_path):
with open(expected_file_path) as expected_file:
expected_str = expected_file.read()
self.assertEqual(output.getvalue(), expected_str)
else:
self.skipTest("expected output file not found.")
def test_landsat_tiler(self):
"""Test the cataloging and tiling of Landsat scences and compare
resulting database and tile contents with an ingestion benchmark"""
# This test is intended as an example, and so is extensively
# commented.
# Open a log file
if self.mode not in [0, 1, 2, 3]:
self.skipTest('Skipping test_landsat_tiler since flag is not in [0, 1, 2, 3]')
logfile_path = os.path.join(self.OUTPUT_DIR, "test_landsat_tiler.log")
self.logfile = open(logfile_path, "w")
#
# Create the initial database
#
# Randomise the name to avoid collisions with other users.
self.test_dbname = dbutil.random_name("test_tiler")
# Create the database.
print 'About to create dbase from %s' \
%(os.path.join(self.INPUT_DIR, "hypercube_empty.sql"))
if self.mode != 1:
dbutil.TESTSERVER.create(self.test_dbname,
self.INPUT_DIR, "hypercube_empty.sql")
#
# Run dbupdater on the test database and save the result
#
# Create updated datacube_conf file with the new dbname and tile_root
tile_root = os.path.join(self.OUTPUT_DIR, "tiles")
configuration_dict = {'dbname': self.test_dbname,
'tile_root': tile_root}
config_file_path = dbutil.update_config_file2(configuration_dict,
self.INPUT_DIR,
self.OUTPUT_DIR,
"test_datacube.conf")
# Run dbupdater
ingest_dir = os.path.join(self.INPUT_DIR, 'tiler_testing')
dbupdater_cmd = ["python",
"dbupdater.py",
"--debug",
"--config=%s" % config_file_path,
"--source=%s" % ingest_dir,
"--removedblist",
"--followsymlinks"]
if self.mode != 1:
subprocess.check_call(dbupdater_cmd, stdout=self.logfile,
stderr=subprocess.STDOUT)
# Run landsat_tiler
landsat_tiler_cmd = ["python",
"landsat_tiler.py",
"--config=%s" % config_file_path]
if self.mode != 1:
subprocess.check_call(landsat_tiler_cmd, stdout=self.logfile,
stderr=subprocess.STDOUT)
# Save the updated database
if self.mode != 1:
dbutil.TESTSERVER.save(self.test_dbname, self.OUTPUT_DIR,
"tiler_testing.sql")
#
# If an expected result exists then load it and compare
#
# Check for expected result
if self.mode > 0 and os.path.isfile(os.path.join(self.EXPECTED_DIR,
"tiler_testing.sql")):
print 'starting to check differences'
#MPHtemp create the output database
if self.mode == 1:
self.test_dbname = dbutil.random_name("tiler_testing")
dbutil.TESTSERVER.create(self.test_dbname,
self.OUTPUT_DIR, "tiler_testing.sql")
#END MPHtemp
# Create a randomised name...
self.expected_dbname = dbutil.random_name("expected_tiler_testing")
# load the database...
dbutil.TESTSERVER.create(self.expected_dbname,
self.EXPECTED_DIR, "tiler_testing.sql")
# create database connections...
self.test_conn = dbutil.TESTSERVER.connect(self.test_dbname)
self.expected_conn = \
dbutil.TESTSERVER.connect(self.expected_dbname)
# and compare.
dbases_agree = dbcompare.compare_databases(self.test_conn,
self.expected_conn,
output=self.logfile,
verbosity=3)
if self.mode == 2:
#Compare databases and fail test if they differ
assert dbases_agree, "Databases do not match."
#Compare data within corresponding files of the EXPECTED_DIR and
#OUTPUT_DIR. Get list of tile pathnames from EXPECTED and OUTPUT
#databases' repsective tile tables. There is an assumption here
#that, within each of the expected and output databases,
#the tile basename uniquely defines both the tile_type_id and the
#full tile pathname. However, if the tile type table has put ORTHO
#bands to be of a new tile_type, then
#corresponding tiles in expected and output may be of different
#tile_type. So we need to have self.bands_expected and
#self.bands_output
expected_tile_dict, output_tile_dict = \
self.get_tile_pathnames(self.expected_conn, self.test_conn)
tiles_expected = set(expected_tile_dict.keys())
tiles_output = set(output_tile_dict.keys())
tiles_expected_or_output = tiles_expected | tiles_output
#Construct band source table as per datacube module
self.bands_expected = \
self.construct_bands_source_dict(self.expected_conn)
self.bands_output =\
self.construct_bands_source_dict(self.test_conn)
#file_pattern to parse file name for information
file_pattern = [r'(?P<sat>\w+)_(?P<sensor>\w+)_',
r'(?P<processing_level>\w+)_',
r'(?P<xindex>-*\d+)_(?P<yindex>-*\d+)_'
r'(?P<year>\d+)-(?P<month>\d+)-'
r'(?P<day>\d+)T(?P<hour>\d+)-(?P<minute>\d+)-',
r'(?P<second_whole>\d+)\.(?P<second_fraction>\d+)'
r'\.(?P<file_extension>.+)']
pattern = re.compile(''.join(file_pattern))
#Set up dictionary of pixel counts to be accumulated per
#(procesing_level, tile_layer) over all tiles
#0: total_pixel_count_expected
#1: total_pixel_count_output
#2: total_pixel_count_both
#3: total_pixel_count_expected_not_output
#4: total_pixel_count_output_not_expected
pixel_count_dict = {}
#Set up nested dicts of differece counts
difference_count_dict = {}
#For each tile in EXPECTED_DIR and OUTPUT_DIR, get pixel counts and
#difference histograms
#There are five dictionaries involved:
### tile_name_dict {'sat': LS5, 'sensor': TM, ...}
### bands_dict_expected: those bands from self.bands_expected
### corresponding to current tile's
### tile_type_id and (satellite, sensor)
### bands_dict_output: output database's correspondent to
### bands_dict_expected
### level_dict_expected: those bands from bands_dict_expected
### for which the processing level
### matches that for the current tile
### level_dict_output: output database's correspondent to
### level_dict_expected
### all_levels_info_dict [level_dict_expected,
#### level_dict_output]
### for each processing level
all_levels_info_dict = {}
for tile_name in tiles_expected_or_output:
print 'processing tile %s' %tile_name
tile_type_id_expected = None
tile_type_id_output = None
fname_expected = None
fname_output = None
#If tile is in either database, extract tile_type and pathname
if tile_name in tiles_expected:
tile_type_id_expected, fname_expected = \
expected_tile_dict[tile_name]
if tile_name in tiles_output:
tile_type_id_output, fname_output = \
output_tile_dict[tile_name]
#Extract information from the tile name and select
#nested dictionary for this tile from bands table,
#given the (sat, sensor) [or("DERIVED', 'PQA') for PQA],
#which will be common to expected and output tiles, and the
#tile_type_id, which may be different for expected and output
matchobj = re.match(pattern, tile_name)
tile_name_dict = matchobj.groupdict()
full_key_expected = \
self.get_tiletype_sat_sens_level(tile_type_id_expected,
tile_name_dict)
full_key_output = \
self.get_tiletype_sat_sens_level(tile_type_id_output,
tile_name_dict)
#Following will raise assertion error if a tile's
#tile_type_id has changed since benchmark ingestion
full_key = self.check_equal_or_null(full_key_expected,
full_key_output)
level_dict_expected = {}
level_dict_output = {}
#full_key is (tile_type, sat, sensor, processing_level)
if full_key in all_levels_info_dict:
(level_dict_expected, level_dict_output) = \
all_levels_info_dict[full_key]
if level_dict_expected == {} and full_key_expected != None:
level_dict_expected = \
self.collect_source_bands(self.bands_expected,
full_key)
if level_dict_output == {} and full_key_output != None:
level_dict_output = \
self.collect_source_bands(self.bands_output,
full_key)
if full_key not in all_levels_info_dict:
all_levels_info_dict[full_key] = [level_dict_expected,
level_dict_output]
if all_levels_info_dict[full_key][0] == {} and \
level_dict_expected != {}:
all_levels_info_dict[full_key][0] = level_dict_expected
if all_levels_info_dict[full_key][1] == {} and \
level_dict_output != {}:
all_levels_info_dict[full_key][1] = level_dict_output
#Check that the number of bands is as expected, adding
#singleton dimension if only one band
([data_expected, data_output], number_layers) = \
self.load_and_check(fname_expected, fname_output,
level_dict_expected,
level_dict_output)
assert bool(fname_expected) == (data_expected != None) and \
bool(fname_output) == (data_output != None), \
"data array should exist if and only if fname exists"
for ilayer in range(number_layers):
#Define expected and output band data
band_expected, dtype_expected = \
self.get_band_data(data_expected, ilayer)
band_output, dtype_output = \
self.get_band_data(data_output, ilayer)
assert (band_expected == None) == (dtype_expected == None)\
and (band_output == None) == (dtype_output == None), \
"band data should exist if and only if dtype exists"
dtype_this = self.check_equal_or_null(dtype_expected,
dtype_output)
#calculate the number of bins required to store the
#histogram of differences from this datatype
if tile_name_dict['processing_level'] == 'PQA':
#possible difference values are 0 through 16,
#(number of tests which differ)
bin_count = 16 + 1
else:
#possible difference vals are min through max of dtype
bin_count = numpy.iinfo(dtype_this).max - \
numpy.iinfo(dtype_this).min + 1
assert bin_count < 66000, "datatype is more than 16" \
"bits, need to add code to coarsen the" \
"histogram bins or use apriori max and" \
"min values of the data"
#The histograms are per (level, layer).
#Could have one histogram per (sat, sensor, level, layer)
#and then, depending on verbosity, aggregate during report.
#But for now, just key by (level, layer).
result_key = (full_key[3], ilayer + 1)
if result_key not in pixel_count_dict:
pixel_count_dict[result_key] = numpy.zeros(shape=(5),
dtype=numpy.uint64)
difference_count_dict[result_key] = \
numpy.zeros(shape=(bin_count), dtype=numpy.uint64)
pixel_count = pixel_count_dict[result_key]
difference_count = difference_count_dict[result_key]
if tile_name_dict['processing_level'] == 'PQA':
if band_expected is None:
band_expected = 0
if band_output is None:
band_output = 0
#define index as those pixels with contiguity bit set
index_expected = \
numpy.bitwise_and(band_expected,
1 << self.PQA_CONTIGUITY_BIT) > 0
index_output = \
numpy.bitwise_and(band_output,
1 << self.PQA_CONTIGUITY_BIT) > 0
else:
#For NBAR and ORTHO use nodata_value
nodata_value = \
level_dict_output[ilayer + 1]['nodata_value']
if band_expected is None:
band_expected = nodata_value
if band_output is None:
band_output = nodata_value
index_expected = band_expected != nodata_value
index_output = band_output != nodata_value
pixel_count[0] += numpy.count_nonzero(index_expected)
pixel_count[1] += numpy.count_nonzero(index_output)
pixel_count[2] += \
numpy.count_nonzero(numpy.logical_and(index_expected,
index_output))
pixel_count[3] += \
numpy.count_nonzero(numpy.logical_and
(index_expected, ~index_output))
pixel_count[4] += \
numpy.count_nonzero(numpy.logical_and
(~index_expected, index_output))
#Only want to calculate differences at common pixels
index_both = numpy.logical_and(index_expected,
index_output)
if numpy.count_nonzero(index_both) == 0:
continue
valid_data_expected = band_expected[index_both].ravel()
valid_data_output = band_output[index_both].ravel()
#Calculate difference histogram and add to running total
if tile_name_dict['processing_level'] == 'PQA':
difference = \
self.count_bitwise_diffs(valid_data_expected,
valid_data_output)
else:
difference = abs(valid_data_output.astype(numpy.int64)
- valid_data_expected.astype(numpy.int64))
hist, dummy_bin_edges = \
numpy.histogram(difference,
numpy.array(range(bin_count + 1),
dtype=numpy.uint64))
difference_count += hist
#dereference band data
band_expected = None
band_output = None
difference = None
#end of layer loop
#dereference tile data
data_expected = None
data_output = None
#Output
#for sat_sen, band_dict in all_bands_dict:
fp = open(os.path.join(self.OUTPUT_DIR,
'Histogram_output.txt'), 'w')
fp.writelines('##### COMPARISON OF TILED DATA IN FOLLOWING '\
'DIRECTORES\n%s\n%s\n' %(self.EXPECTED_DIR,
self.OUTPUT_DIR))
result_keys_processed = []
for full_key in all_levels_info_dict.keys():
dummy, dummy, dummy, processing_level = full_key
top_layer_result_key = (processing_level, 1)
if top_layer_result_key in result_keys_processed:
continue
fp.writelines('#### Processing Level: %s\n' %processing_level)
level_dict_expected, level_dict_output = \
all_levels_info_dict[full_key]
assert set(level_dict_expected.keys()) == \
set(level_dict_output.keys()), "different key sets"
number_layers = len(level_dict_output.keys())
for this_layer in range(1, number_layers + 1):
result_key = (processing_level, this_layer)
result_keys_processed.append(result_key)
fp.writelines('### tile_layer = %d\n' %this_layer)
for key, val in level_dict_expected[this_layer].items():
if key == 'tile_layer' or key == 'level_name':
continue
outline = '# %s = %s' %(key, val)
if str(level_dict_output[this_layer][key]) != str(val):
outline = '%s (%s in output database)' \
%(outline, level_dict_output[this_layer][key])
fp.writelines('%s\n' %outline)
#get key for pixel_count_dict and difference_count_dict
#Print counts of pixels with valid data
fp.writelines('#Valid data counts\n')
pixel_count = pixel_count_dict[result_key]
count_desc = ['Expected\t', 'Output\t\t', 'Common\t\t',
'Missing\t\t', 'Extra\t\t']
for desc, num in zip(count_desc, pixel_count):
fp.writelines('\t\t%s%d\n' %(desc, num))
#Print histograms of differences in valid data
fp.writelines('#Histogram of differences in valid data\n')
difference_count = difference_count_dict[result_key]
index_nonzero_bins = difference_count > 0
for bin_no in range(len(difference_count)):
if index_nonzero_bins[bin_no]:
fp.writelines('\t\tDifference of %d: %d\n'
%(bin_no, difference_count[bin_no]))
fp.close()
else:
if self.mode > 0:
self.skipTest("Expected database save file not found.")
def test_landsat_tiler(self):
"""Test the cataloging and tiling of Landsat scences and compare
resulting database and tile contents with an ingestion benchmark"""
# This test is intended as an example, and so is extensively
# commented.
# Open a log file
if self.mode not in [0, 1, 2, 3]:
self.skipTest(
'Skipping test_landsat_tiler since flag is not in [0, 1, 2, 3]'
)
logfile_path = os.path.join(self.OUTPUT_DIR, "test_landsat_tiler.log")
self.logfile = open(logfile_path, "w")
#
# Create the initial database
#
# Randomise the name to avoid collisions with other users.
self.test_dbname = dbutil.random_name("test_tiler")
# Create the database.
print 'About to create dbase from %s' \
%(os.path.join(self.INPUT_DIR, "hypercube_empty.sql"))
if self.mode != 1:
dbutil.TESTSERVER.create(self.test_dbname, self.INPUT_DIR,
"hypercube_empty.sql")
#
# Run dbupdater on the test database and save the result
#
# Create updated datacube_conf file with the new dbname and tile_root
tile_root = os.path.join(self.OUTPUT_DIR, "tiles")
configuration_dict = {
'dbname': self.test_dbname,
'tile_root': tile_root
}
config_file_path = dbutil.update_config_file2(configuration_dict,
self.INPUT_DIR,
self.OUTPUT_DIR,
"test_datacube.conf")
# Run dbupdater
ingest_dir = os.path.join(self.INPUT_DIR, 'tiler_testing')
dbupdater_cmd = [
"python", "dbupdater.py", "--debug",
"--config=%s" % config_file_path,
"--source=%s" % ingest_dir, "--removedblist", "--followsymlinks"
]
if self.mode != 1:
subprocess.check_call(dbupdater_cmd,
stdout=self.logfile,
stderr=subprocess.STDOUT)
# Run landsat_tiler
landsat_tiler_cmd = [
"python", "landsat_tiler.py",
"--config=%s" % config_file_path
]
if self.mode != 1:
subprocess.check_call(landsat_tiler_cmd,
stdout=self.logfile,
stderr=subprocess.STDOUT)
# Save the updated database
if self.mode != 1:
dbutil.TESTSERVER.save(self.test_dbname, self.OUTPUT_DIR,
"tiler_testing.sql")
#
# If an expected result exists then load it and compare
#
# Check for expected result
if self.mode > 0 and os.path.isfile(
os.path.join(self.EXPECTED_DIR, "tiler_testing.sql")):
print 'starting to check differences'
#MPHtemp create the output database
if self.mode == 1:
self.test_dbname = dbutil.random_name("tiler_testing")
dbutil.TESTSERVER.create(self.test_dbname, self.OUTPUT_DIR,
"tiler_testing.sql")
#END MPHtemp
# Create a randomised name...
self.expected_dbname = dbutil.random_name("expected_tiler_testing")
# load the database...
dbutil.TESTSERVER.create(self.expected_dbname, self.EXPECTED_DIR,
"tiler_testing.sql")
# create database connections...
self.test_conn = dbutil.TESTSERVER.connect(self.test_dbname)
self.expected_conn = \
dbutil.TESTSERVER.connect(self.expected_dbname)
# and compare.
dbases_agree = dbcompare.compare_databases(self.test_conn,
self.expected_conn,
output=self.logfile,
verbosity=3)
if self.mode == 2:
#Compare databases and fail test if they differ
assert dbases_agree, "Databases do not match."
#Compare data within corresponding files of the EXPECTED_DIR and
#OUTPUT_DIR. Get list of tile pathnames from EXPECTED and OUTPUT
#databases' repsective tile tables. There is an assumption here
#that, within each of the expected and output databases,
#the tile basename uniquely defines both the tile_type_id and the
#full tile pathname. However, if the tile type table has put ORTHO
#bands to be of a new tile_type, then
#corresponding tiles in expected and output may be of different
#tile_type. So we need to have self.bands_expected and
#self.bands_output
expected_tile_dict, output_tile_dict = \
self.get_tile_pathnames(self.expected_conn, self.test_conn)
tiles_expected = set(expected_tile_dict.keys())
tiles_output = set(output_tile_dict.keys())
tiles_expected_or_output = tiles_expected | tiles_output
#Construct band source table as per datacube module
self.bands_expected = \
self.construct_bands_source_dict(self.expected_conn)
self.bands_output =\
self.construct_bands_source_dict(self.test_conn)
#file_pattern to parse file name for information
file_pattern = [
r'(?P<sat>\w+)_(?P<sensor>\w+)_',
r'(?P<processing_level>\w+)_',
r'(?P<xindex>-*\d+)_(?P<yindex>-*\d+)_'
r'(?P<year>\d+)-(?P<month>\d+)-'
r'(?P<day>\d+)T(?P<hour>\d+)-(?P<minute>\d+)-',
r'(?P<second_whole>\d+)\.(?P<second_fraction>\d+)'
r'\.(?P<file_extension>.+)'
]
pattern = re.compile(''.join(file_pattern))
#Set up dictionary of pixel counts to be accumulated per
#(procesing_level, tile_layer) over all tiles
#0: total_pixel_count_expected
#1: total_pixel_count_output
#2: total_pixel_count_both
#3: total_pixel_count_expected_not_output
#4: total_pixel_count_output_not_expected
pixel_count_dict = {}
#Set up nested dicts of differece counts
difference_count_dict = {}
#For each tile in EXPECTED_DIR and OUTPUT_DIR, get pixel counts and
#difference histograms
#There are five dictionaries involved:
### tile_name_dict {'sat': LS5, 'sensor': TM, ...}
### bands_dict_expected: those bands from self.bands_expected
### corresponding to current tile's
### tile_type_id and (satellite, sensor)
### bands_dict_output: output database's correspondent to
### bands_dict_expected
### level_dict_expected: those bands from bands_dict_expected
### for which the processing level
### matches that for the current tile
### level_dict_output: output database's correspondent to
### level_dict_expected
### all_levels_info_dict [level_dict_expected,
#### level_dict_output]
### for each processing level
all_levels_info_dict = {}
for tile_name in tiles_expected_or_output:
print 'processing tile %s' % tile_name
tile_type_id_expected = None
tile_type_id_output = None
fname_expected = None
fname_output = None
#If tile is in either database, extract tile_type and pathname
if tile_name in tiles_expected:
tile_type_id_expected, fname_expected = \
expected_tile_dict[tile_name]
if tile_name in tiles_output:
tile_type_id_output, fname_output = \
output_tile_dict[tile_name]
#Extract information from the tile name and select
#nested dictionary for this tile from bands table,
#given the (sat, sensor) [or("DERIVED', 'PQA') for PQA],
#which will be common to expected and output tiles, and the
#tile_type_id, which may be different for expected and output
matchobj = re.match(pattern, tile_name)
tile_name_dict = matchobj.groupdict()
full_key_expected = \
self.get_tiletype_sat_sens_level(tile_type_id_expected,
tile_name_dict)
full_key_output = \
self.get_tiletype_sat_sens_level(tile_type_id_output,
tile_name_dict)
#Following will raise assertion error if a tile's
#tile_type_id has changed since benchmark ingestion
full_key = self.check_equal_or_null(full_key_expected,
full_key_output)
level_dict_expected = {}
level_dict_output = {}
#full_key is (tile_type, sat, sensor, processing_level)
if full_key in all_levels_info_dict:
(level_dict_expected, level_dict_output) = \
all_levels_info_dict[full_key]
if level_dict_expected == {} and full_key_expected != None:
level_dict_expected = \
self.collect_source_bands(self.bands_expected,
full_key)
if level_dict_output == {} and full_key_output != None:
level_dict_output = \
self.collect_source_bands(self.bands_output,
full_key)
if full_key not in all_levels_info_dict:
all_levels_info_dict[full_key] = [
level_dict_expected, level_dict_output
]
if all_levels_info_dict[full_key][0] == {} and \
level_dict_expected != {}:
all_levels_info_dict[full_key][0] = level_dict_expected
if all_levels_info_dict[full_key][1] == {} and \
level_dict_output != {}:
all_levels_info_dict[full_key][1] = level_dict_output
#Check that the number of bands is as expected, adding
#singleton dimension if only one band
([data_expected, data_output], number_layers) = \
self.load_and_check(fname_expected, fname_output,
level_dict_expected,
level_dict_output)
assert bool(fname_expected) == (data_expected != None) and \
bool(fname_output) == (data_output != None), \
"data array should exist if and only if fname exists"
for ilayer in range(number_layers):
#Define expected and output band data
band_expected, dtype_expected = \
self.get_band_data(data_expected, ilayer)
band_output, dtype_output = \
self.get_band_data(data_output, ilayer)
assert (band_expected == None) == (dtype_expected == None)\
and (band_output == None) == (dtype_output == None), \
"band data should exist if and only if dtype exists"
dtype_this = self.check_equal_or_null(
dtype_expected, dtype_output)
#calculate the number of bins required to store the
#histogram of differences from this datatype
if tile_name_dict['processing_level'] == 'PQA':
#possible difference values are 0 through 16,
#(number of tests which differ)
bin_count = 16 + 1
else:
#possible difference vals are min through max of dtype
bin_count = numpy.iinfo(dtype_this).max - \
numpy.iinfo(dtype_this).min + 1
assert bin_count < 66000, "datatype is more than 16" \
"bits, need to add code to coarsen the" \
"histogram bins or use apriori max and" \
"min values of the data"
#The histograms are per (level, layer).
#Could have one histogram per (sat, sensor, level, layer)
#and then, depending on verbosity, aggregate during report.
#But for now, just key by (level, layer).
result_key = (full_key[3], ilayer + 1)
if result_key not in pixel_count_dict:
pixel_count_dict[result_key] = numpy.zeros(
shape=(5), dtype=numpy.uint64)
difference_count_dict[result_key] = \
numpy.zeros(shape=(bin_count), dtype=numpy.uint64)
pixel_count = pixel_count_dict[result_key]
difference_count = difference_count_dict[result_key]
if tile_name_dict['processing_level'] == 'PQA':
if band_expected is None:
band_expected = 0
if band_output is None:
band_output = 0
#define index as those pixels with contiguity bit set
index_expected = \
numpy.bitwise_and(band_expected,
1 << self.PQA_CONTIGUITY_BIT) > 0
index_output = \
numpy.bitwise_and(band_output,
1 << self.PQA_CONTIGUITY_BIT) > 0
else:
#For NBAR and ORTHO use nodata_value
nodata_value = \
level_dict_output[ilayer + 1]['nodata_value']
if band_expected is None:
band_expected = nodata_value
if band_output is None:
band_output = nodata_value
index_expected = band_expected != nodata_value
index_output = band_output != nodata_value
pixel_count[0] += numpy.count_nonzero(index_expected)
pixel_count[1] += numpy.count_nonzero(index_output)
pixel_count[2] += \
numpy.count_nonzero(numpy.logical_and(index_expected,
index_output))
pixel_count[3] += \
numpy.count_nonzero(numpy.logical_and
(index_expected, ~index_output))
pixel_count[4] += \
numpy.count_nonzero(numpy.logical_and
(~index_expected, index_output))
#Only want to calculate differences at common pixels
index_both = numpy.logical_and(index_expected,
index_output)
if numpy.count_nonzero(index_both) == 0:
continue
valid_data_expected = band_expected[index_both].ravel()
valid_data_output = band_output[index_both].ravel()
#Calculate difference histogram and add to running total
if tile_name_dict['processing_level'] == 'PQA':
difference = \
self.count_bitwise_diffs(valid_data_expected,
valid_data_output)
else:
difference = abs(
valid_data_output.astype(numpy.int64) -
valid_data_expected.astype(numpy.int64))
hist, dummy_bin_edges = \
numpy.histogram(difference,
numpy.array(range(bin_count + 1),
dtype=numpy.uint64))
difference_count += hist
#dereference band data
band_expected = None
band_output = None
difference = None
#end of layer loop
#dereference tile data
data_expected = None
data_output = None
#Output
#for sat_sen, band_dict in all_bands_dict:
fp = open(os.path.join(self.OUTPUT_DIR, 'Histogram_output.txt'),
'w')
fp.writelines('##### COMPARISON OF TILED DATA IN FOLLOWING '\
'DIRECTORES\n%s\n%s\n' %(self.EXPECTED_DIR,
self.OUTPUT_DIR))
result_keys_processed = []
for full_key in all_levels_info_dict.keys():
dummy, dummy, dummy, processing_level = full_key
top_layer_result_key = (processing_level, 1)
if top_layer_result_key in result_keys_processed:
continue
fp.writelines('#### Processing Level: %s\n' % processing_level)
level_dict_expected, level_dict_output = \
all_levels_info_dict[full_key]
assert set(level_dict_expected.keys()) == \
set(level_dict_output.keys()), "different key sets"
number_layers = len(level_dict_output.keys())
for this_layer in range(1, number_layers + 1):
result_key = (processing_level, this_layer)
result_keys_processed.append(result_key)
fp.writelines('### tile_layer = %d\n' % this_layer)
for key, val in level_dict_expected[this_layer].items():
if key == 'tile_layer' or key == 'level_name':
continue
outline = '# %s = %s' % (key, val)
if str(level_dict_output[this_layer][key]) != str(val):
outline = '%s (%s in output database)' \
%(outline, level_dict_output[this_layer][key])
fp.writelines('%s\n' % outline)
#get key for pixel_count_dict and difference_count_dict
#Print counts of pixels with valid data
fp.writelines('#Valid data counts\n')
pixel_count = pixel_count_dict[result_key]
count_desc = [
'Expected\t', 'Output\t\t', 'Common\t\t',
'Missing\t\t', 'Extra\t\t'
]
for desc, num in zip(count_desc, pixel_count):
fp.writelines('\t\t%s%d\n' % (desc, num))
#Print histograms of differences in valid data
fp.writelines('#Histogram of differences in valid data\n')
difference_count = difference_count_dict[result_key]
index_nonzero_bins = difference_count > 0
for bin_no in range(len(difference_count)):
if index_nonzero_bins[bin_no]:
fp.writelines('\t\tDifference of %d: %d\n' %
(bin_no, difference_count[bin_no]))
fp.close()
else:
if self.mode > 0:
self.skipTest("Expected database save file not found.")
def test_onescene(self):
"""Test database update for a single scene."""
# This test is intended as an example, and so is extensively
# commented.
# Open a log file
logfile_path = os.path.join(self.OUTPUT_DIR, "test_onescene.log")
self.logfile = open(logfile_path, "w")
#
# Create the initial database
#
# Randomise the name to avoid collisons with other users.
self.test_dbname = dbutil.random_name("test_onescene")
# Create the database.
dbutil.TESTSERVER.create(self.test_dbname,
self.INPUT_DIR, "hypercube_empty.sql")
#
# Run dbupdater on the test database and save the result
#
# Create an updated datacube_conf file with the new dbname
config_file_path = dbutil.update_config_file(self.test_dbname,
self.INPUT_DIR,
self.OUTPUT_DIR,
"test_datacube.conf")
# Run dbupdater
ingest_dir = os.path.join(self.INPUT_DIR, 'onescene')
dbupdater_cmd = ["python",
"dbupdater.py",
"--debug",
"--config=%s" % config_file_path,
"--source=%s" % ingest_dir,
"--removedblist",
"--followsymlinks"]
subprocess.check_call(dbupdater_cmd, stdout=self.logfile,
stderr=subprocess.STDOUT)
# Save the updated database
dbutil.TESTSERVER.save(self.test_dbname, self.OUTPUT_DIR,
"onescene.sql")
#
# If an expected result exists then load it and compare
#
# Check for expected result
if os.path.isfile(os.path.join(self.EXPECTED_DIR, "onescene.sql")):
# Create a randomised name...
self.expected_dbname = dbutil.random_name("expected_onescene")
# load the database...
dbutil.TESTSERVER.create(self.expected_dbname,
self.EXPECTED_DIR, "onescene.sql")
# create database connections...
self.test_conn = dbutil.TESTSERVER.connect(self.test_dbname)
self.expected_conn = dbutil.TESTSERVER.connect(
self.expected_dbname)
# and compare.
self.assertTrue(dbcompare.compare_databases(self.test_conn,
self.expected_conn,
output=self.logfile,
verbosity=3),
"Databases do not match.")
else:
self.skipTest("Expected database save file not found.")
