Bug#1000273: satpy: autopkgtest regression on armhf and i386: ArrayMemoryError: Unable to allocate 73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
Paul Gevers
elbrus at debian.org
Sat Nov 20 18:59:50 GMT 2021
Source: satpy
Version: 0.31.0-2
X-Debbugs-CC: debian-ci at lists.debian.org
Severity: serious
User: debian-ci at lists.debian.org
Usertags: regression
Dear maintainer(s),
With a recent upload of satpy the autopkgtest of satpy fails in testing
when that autopkgtest is run with the binary packages of satpy from
unstable. It passes when run with only packages from testing. In tabular
form:
pass fail
satpy from testing 0.31.0-2
all others from testing from testing
I copied some of the output at the bottom of this report. Did the set of
tests get extended? It seems some tests are running out of memory space
on 32 bit architectures.
Currently this regression is blocking the migration to testing [1]. Can
you please investigate the situation and fix it?
More information about this bug and the reason for filing it can be found on
https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation
Paul
[1] https://qa.debian.org/excuses.php?package=satpy
https://ci.debian.net/data/autopkgtest/testing/armhf/s/satpy/16844833/log.gz
==================================== ERRORS
====================================
_ ERROR at setup of
TestModisL1b.test_scene_available_datasets[modis_l1b_nasa_mod021km_file-expected_names0-expected_data_res0-expected_geo_res0]
_
request = <FixtureRequest for <Function
test_scene_available_datasets[modis_l1b_nasa_mod021km_file-expected_names0-expected_data_res0-expected_geo_res0]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:316:
in modis_l1b_nasa_mod021km_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_scene_available_datasets[modis_l1b_imapp_1000m_file-expected_names1-expected_data_res1-expected_geo_res1]
_
request = <FixtureRequest for <Function
test_scene_available_datasets[modis_l1b_imapp_1000m_file-expected_names1-expected_data_res1-expected_geo_res1]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:330:
in modis_l1b_imapp_1000m_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_scene_available_datasets[modis_l1b_nasa_mod02hkm_file-expected_names2-expected_data_res2-expected_geo_res2]
_
request = <FixtureRequest for <Function
test_scene_available_datasets[modis_l1b_nasa_mod02hkm_file-expected_names2-expected_data_res2-expected_geo_res2]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:344:
in modis_l1b_nasa_mod02hkm_file
variable_infos.update(_get_visible_variable_info("EV_500_RefSB",
250, AVAILABLE_QKM_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 250, num_bands = 2, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
168. MiB for an array with shape (2, 8120, 5416) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_scene_available_datasets[modis_l1b_nasa_mod02qkm_file-expected_names3-expected_data_res3-expected_geo_res3]
_
request = <FixtureRequest for <Function
test_scene_available_datasets[modis_l1b_nasa_mod02qkm_file-expected_names3-expected_data_res3-expected_geo_res3]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:355:
in modis_l1b_nasa_mod02qkm_file
variable_infos.update(_get_visible_variable_info("EV_250_RefSB",
250, AVAILABLE_QKM_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 250, num_bands = 2, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
168. MiB for an array with shape (2, 8120, 5416) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_load_longitude_latitude[modis_l1b_nasa_mod021km_file-True-False-False-1000]
_
request = <FixtureRequest for <Function
test_load_longitude_latitude[modis_l1b_nasa_mod021km_file-True-False-False-1000]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:316:
in modis_l1b_nasa_mod021km_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_load_longitude_latitude[modis_l1b_imapp_1000m_file-True-False-False-1000]
_
request = <FixtureRequest for <Function
test_load_longitude_latitude[modis_l1b_imapp_1000m_file-True-False-False-1000]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:330:
in modis_l1b_imapp_1000m_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_load_longitude_latitude[modis_l1b_nasa_mod02hkm_file-False-True-True-250]
_
request = <FixtureRequest for <Function
test_load_longitude_latitude[modis_l1b_nasa_mod02hkm_file-False-True-True-250]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:344:
in modis_l1b_nasa_mod02hkm_file
variable_infos.update(_get_visible_variable_info("EV_500_RefSB",
250, AVAILABLE_QKM_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 250, num_bands = 2, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
168. MiB for an array with shape (2, 8120, 5416) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_load_longitude_latitude[modis_l1b_nasa_mod02qkm_file-False-True-True-250]
_
request = <FixtureRequest for <Function
test_load_longitude_latitude[modis_l1b_nasa_mod02qkm_file-False-True-True-250]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:355:
in modis_l1b_nasa_mod02qkm_file
variable_infos.update(_get_visible_variable_info("EV_250_RefSB",
250, AVAILABLE_QKM_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 250, num_bands = 2, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
168. MiB for an array with shape (2, 8120, 5416) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL1b.test_load_longitude_latitude[modis_l1b_nasa_1km_mod03_files-True-True-True-250]
_
request = <FixtureRequest for <Function
test_load_longitude_latitude[modis_l1b_nasa_1km_mod03_files-True-True-True-250]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:316:
in modis_l1b_nasa_mod021km_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
__________ ERROR at setup of TestModisL1b.test_load_sat_zenith_angle
___________
request = <FixtureRequest for <Function test_load_sat_zenith_angle>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
item.callspec.params[param] =
request.getfixturevalue(val.name)
elif param not in item.funcargs:
item.funcargs[param] = request.getfixturevalue(param)
> _fillfixtures()
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:39: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:316:
in modis_l1b_nasa_mod021km_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_________________ ERROR at setup of TestModisL1b.test_load_vis
_________________
request = <FixtureRequest for <Function test_load_vis>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
item.callspec.params[param] =
request.getfixturevalue(val.name)
elif param not in item.funcargs:
item.funcargs[param] = request.getfixturevalue(param)
> _fillfixtures()
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:39: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:316:
in modis_l1b_nasa_mod021km_file
variable_infos.update(_get_visible_variable_info("EV_1KM_RefSB",
1000, AVAILABLE_1KM_VIS_PRODUCT_NAMES))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:116:
in _get_visible_variable_info
data = _generate_visible_data(resolution, len(bands))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
resolution = 1000, num_bands = 14, dtype = <class 'numpy.uint16'>
def _generate_visible_data(resolution: int, num_bands: int,
dtype=np.uint16) -> np.ndarray:
shape = _shape_for_resolution(resolution)
> data = np.zeros((num_bands, shape[0], shape[1]), dtype=dtype)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
73.4 MiB for an array with shape (14, 2030, 1354) and data type uint16
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:76:
MemoryError
_ ERROR at setup of
TestModisL2.test_load_category_dataset[modis_l2_nasa_mod35_mod03_files-loadables0-1000-1000-True]
_
request = <FixtureRequest for <Function
test_load_category_dataset[modis_l2_nasa_mod35_mod03_files-loadables0-1000-1000-True]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:365:
in modis_l1b_nasa_mod03_file
variable_infos = _get_l1b_geo_variable_info(filename, 1000,
include_angles=True)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:188:
in _get_l1b_geo_variable_info
variables_info.update(_get_lonlat_variable_info(geo_resolution))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:81:
in _get_lonlat_variable_info
lon_5km, lat_5km = _generate_lonlat_data(resolution)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:61:
in _generate_lonlat_data
lat = np.repeat(np.linspace(35., 45., shape[0])[:, None], shape[1], 1)
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:479: in repeat
return _wrapfunc(a, 'repeat', repeats, axis=axis)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
obj = array([[35. ],
[35.00492854],
[35.00985707],
...,
[44.99014293],
[44.99507146],
[45. ]])
method = 'repeat', args = (1354,), kwds = {'axis': 1}
bound = <built-in method repeat of numpy.ndarray object at 0x10796548>
def _wrapfunc(obj, method, *args, **kwds):
bound = getattr(obj, method, None)
if bound is None:
return _wrapit(obj, method, *args, **kwds)
try:
> return bound(*args, **kwds)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
21.0 MiB for an array with shape (2030, 1354) and data type float64
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:58: MemoryError
_ ERROR at setup of
TestModisL2.test_load_category_dataset[modis_l2_imapp_mask_byte1_geo_files-loadables1-None-1000-True]
_
request = <FixtureRequest for <Function
test_load_category_dataset[modis_l2_imapp_mask_byte1_geo_files-loadables1-None-1000-True]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:365:
in modis_l1b_nasa_mod03_file
variable_infos = _get_l1b_geo_variable_info(filename, 1000,
include_angles=True)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:188:
in _get_l1b_geo_variable_info
variables_info.update(_get_lonlat_variable_info(geo_resolution))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:81:
in _get_lonlat_variable_info
lon_5km, lat_5km = _generate_lonlat_data(resolution)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:61:
in _generate_lonlat_data
lat = np.repeat(np.linspace(35., 45., shape[0])[:, None], shape[1], 1)
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:479: in repeat
return _wrapfunc(a, 'repeat', repeats, axis=axis)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
obj = array([[35. ],
[35.00492854],
[35.00985707],
...,
[44.99014293],
[44.99507146],
[45. ]])
method = 'repeat', args = (1354,), kwds = {'axis': 1}
bound = <built-in method repeat of numpy.ndarray object at 0x10796548>
def _wrapfunc(obj, method, *args, **kwds):
bound = getattr(obj, method, None)
if bound is None:
return _wrapit(obj, method, *args, **kwds)
try:
> return bound(*args, **kwds)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
21.0 MiB for an array with shape (2030, 1354) and data type float64
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:58: MemoryError
_ ERROR at setup of
TestModisL2.test_load_250m_cloud_mask_dataset[modis_l2_nasa_mod35_mod03_files-True]
_
request = <FixtureRequest for <Function
test_load_250m_cloud_mask_dataset[modis_l2_nasa_mod35_mod03_files-True]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:365:
in modis_l1b_nasa_mod03_file
variable_infos = _get_l1b_geo_variable_info(filename, 1000,
include_angles=True)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:188:
in _get_l1b_geo_variable_info
variables_info.update(_get_lonlat_variable_info(geo_resolution))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:81:
in _get_lonlat_variable_info
lon_5km, lat_5km = _generate_lonlat_data(resolution)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:61:
in _generate_lonlat_data
lat = np.repeat(np.linspace(35., 45., shape[0])[:, None], shape[1], 1)
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:479: in repeat
return _wrapfunc(a, 'repeat', repeats, axis=axis)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
obj = array([[35. ],
[35.00492854],
[35.00985707],
...,
[44.99014293],
[44.99507146],
[45. ]])
method = 'repeat', args = (1354,), kwds = {'axis': 1}
bound = <built-in method repeat of numpy.ndarray object at 0x10796548>
def _wrapfunc(obj, method, *args, **kwds):
bound = getattr(obj, method, None)
if bound is None:
return _wrapit(obj, method, *args, **kwds)
try:
> return bound(*args, **kwds)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
21.0 MiB for an array with shape (2030, 1354) and data type float64
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:58: MemoryError
_ ERROR at setup of
TestModisL2.test_load_l2_dataset[modis_l2_imapp_snowmask_geo_files-loadables2-1000-True]
_
request = <FixtureRequest for <Function
test_load_l2_dataset[modis_l2_imapp_snowmask_geo_files-loadables2-1000-True]>>
def fill(request):
item = request._pyfuncitem
fixturenames = getattr(item, "fixturenames", None)
if fixturenames is None:
fixturenames = request.fixturenames
if hasattr(item, 'callspec'):
for param, val in
sorted_by_dependency(item.callspec.params, fixturenames):
if val is not None and is_lazy_fixture(val):
> item.callspec.params[param] = request.getfixturevalue(val.name)
/usr/lib/python3/dist-packages/pytest_lazyfixture.py:35: _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:365:
in modis_l1b_nasa_mod03_file
variable_infos = _get_l1b_geo_variable_info(filename, 1000,
include_angles=True)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:188:
in _get_l1b_geo_variable_info
variables_info.update(_get_lonlat_variable_info(geo_resolution))
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:81:
in _get_lonlat_variable_info
lon_5km, lat_5km = _generate_lonlat_data(resolution)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/_modis_fixtures.py:61:
in _generate_lonlat_data
lat = np.repeat(np.linspace(35., 45., shape[0])[:, None], shape[1], 1)
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:479: in repeat
return _wrapfunc(a, 'repeat', repeats, axis=axis)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
obj = array([[35. ],
[35.00492854],
[35.00985707],
...,
[44.99014293],
[44.99507146],
[45. ]])
method = 'repeat', args = (1354,), kwds = {'axis': 1}
bound = <built-in method repeat of numpy.ndarray object at 0x10796548>
def _wrapfunc(obj, method, *args, **kwds):
bound = getattr(obj, method, None)
if bound is None:
return _wrapit(obj, method, *args, **kwds)
try:
> return bound(*args, **kwds)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
21.0 MiB for an array with shape (2030, 1354) and data type float64
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:58: MemoryError
=================================== FAILURES
===================================
_____________________________ TestScene.test_crop
______________________________
self = <satpy.tests.test_scene.TestScene object at 0xb4720760>
def test_crop(self):
"""Test the crop method."""
from satpy import Scene
from xarray import DataArray
from pyresample.geometry import AreaDefinition
scene1 = Scene()
area_extent = (-5570248.477339745, -5561247.267842293,
5567248.074173927,
5570248.477339745)
proj_dict = {'a': 6378169.0, 'b': 6356583.8, 'h': 35785831.0,
'lon_0': 0.0, 'proj': 'geos', 'units': 'm'}
x_size = 3712
y_size = 3712
area_def = AreaDefinition(
'test',
'test',
'test',
proj_dict,
x_size,
y_size,
area_extent,
)
area_def2 = AreaDefinition(
'test2',
'test2',
'test2',
proj_dict,
x_size // 2,
y_size // 2,
area_extent,
)
scene1["1"] = DataArray(np.zeros((y_size, x_size)))
scene1["2"] = DataArray(np.zeros((y_size, x_size)), dims=('y',
'x'))
> scene1["3"] = DataArray(np.zeros((y_size, x_size)), dims=('y', 'x'),
attrs={'area': area_def})
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
105. MiB for an array with shape (3712, 3712) and data type float64
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:422: MemoryError
_________________________ TestScene.test_crop_epsg_crs
_________________________
self = <satpy.tests.test_scene.TestScene object at 0xf3acbac0>
def test_crop_epsg_crs(self):
"""Test the crop method when source area uses an EPSG code."""
from satpy import Scene
from xarray import DataArray
from pyresample.geometry import AreaDefinition
scene1 = Scene()
area_extent = (699960.0, 5390220.0, 809760.0, 5500020.0)
x_size = 3712
y_size = 3712
area_def = AreaDefinition(
'test', 'test', 'test',
"EPSG:32630",
x_size,
y_size,
area_extent,
)
> scene1["1"] = DataArray(np.zeros((y_size, x_size)), dims=('y', 'x'),
attrs={'area': area_def})
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
105. MiB for an array with shape (3712, 3712) and data type float64
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:484: MemoryError
___________________________ TestScene.test_crop_rgb
____________________________
self = <satpy.tests.test_scene.TestScene object at 0xb4c6bc58>
def test_crop_rgb(self):
"""Test the crop method on multi-dimensional data."""
from satpy import Scene
from xarray import DataArray
from pyresample.geometry import AreaDefinition
scene1 = Scene()
area_extent = (-5570248.477339745, -5561247.267842293,
5567248.074173927,
5570248.477339745)
proj_dict = {'a': 6378169.0, 'b': 6356583.8, 'h': 35785831.0,
'lon_0': 0.0, 'proj': 'geos', 'units': 'm'}
x_size = 3712
y_size = 3712
area_def = AreaDefinition(
'test',
'test',
'test',
proj_dict,
x_size,
y_size,
area_extent,
)
area_def2 = AreaDefinition(
'test2',
'test2',
'test2',
proj_dict,
x_size // 2,
y_size // 2,
area_extent,
)
> scene1["1"] = DataArray(np.zeros((3, y_size, x_size)), dims=('bands', 'y', 'x'), attrs={'area': area_def})
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
315. MiB for an array with shape (3, 3712, 3712) and data type float64
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:521: MemoryError
_____________________ TestSceneAggregation.test_aggregate
______________________
self = <satpy.tests.test_scene.TestSceneAggregation
testMethod=test_aggregate>
def test_aggregate(self):
"""Test the aggregate method."""
x_size = 3712
y_size = 3712
> scene1 = self._create_test_data(x_size, y_size)
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:1810: _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:1836: in
_create_test_data
scene1["2"] = DataArray(np.ones((y_size, x_size)), dims=('y', 'x'),
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
shape = (3712, 3712), dtype = None, order = 'C'
@set_module('numpy')
def ones(shape, dtype=None, order='C'):
"""
Return a new array of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`.
Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: C
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
Returns
-------
out : ndarray
Array of ones with the given shape, dtype, and order.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
empty : Return a new uninitialized array.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Examples
--------
>>> np.ones(5)
array([1., 1., 1., 1., 1.])
>>> np.ones((5,), dtype=int)
array([1, 1, 1, 1, 1])
>>> np.ones((2, 1))
array([[1.],
[1.]])
>>> s = (2,2)
>>> np.ones(s)
array([[1., 1.],
[1., 1.]])
"""
> a = empty(shape, dtype, order)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
105. MiB for an array with shape (3712, 3712) and data type float64
/usr/lib/python3/dist-packages/numpy/core/numeric.py:192: MemoryError
______________ TestSceneAggregation.test_aggregate_with_boundary
_______________
self = <satpy.tests.test_scene.TestSceneAggregation
testMethod=test_aggregate_with_boundary>
def test_aggregate_with_boundary(self):
"""Test aggregation with boundary argument."""
x_size = 3711
y_size = 3711
> scene1 = self._create_test_data(x_size, y_size)
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:1860: _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/test_scene.py:1835: in
_create_test_data
scene1["1"] = DataArray(np.ones((y_size, x_size)),
attrs={'_satpy_id_keys': default_id_keys_config})
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
shape = (3711, 3711), dtype = None, order = 'C'
@set_module('numpy')
def ones(shape, dtype=None, order='C'):
"""
Return a new array of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`.
Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: C
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
Returns
-------
out : ndarray
Array of ones with the given shape, dtype, and order.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
empty : Return a new uninitialized array.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Examples
--------
>>> np.ones(5)
array([1., 1., 1., 1., 1.])
>>> np.ones((5,), dtype=int)
array([1, 1, 1, 1, 1])
>>> np.ones((2, 1))
array([[1.],
[1.]])
>>> s = (2,2)
>>> np.ones(s)
array([[1., 1.],
[1., 1.]])
"""
> a = empty(shape, dtype, order)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
105. MiB for an array with shape (3711, 3711) and data type float64
/usr/lib/python3/dist-packages/numpy/core/numeric.py:192: MemoryError
_____________________ TestMimicTPW2Reader.test_load_mimic
______________________
self = <satpy.tests.reader_tests.test_mimic_TPW2_nc.TestMimicTPW2Reader
testMethod=test_load_mimic>
def test_load_mimic(self):
"""Load Mimic data."""
from satpy.readers import load_reader
r = load_reader(self.reader_configs)
with mock.patch('satpy.readers.mimic_TPW2_nc.netCDF4.Variable',
xr.DataArray):
loadables = r.select_files_from_pathnames([
'comp20190619.130000.nc',
])
r.create_filehandlers(loadables)
> ds = r.load(['tpwGrid'])
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_mimic_TPW2_nc.py:126:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:943:
in load
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:839: in
_load_dataset_with_area
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:711: in
_load_dataset_data
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:701: in
_load_dataset
res = xr.concat(slice_list, dim=dim)
/usr/lib/python3/dist-packages/xarray/core/concat.py:242: in concat
return f(
/usr/lib/python3/dist-packages/xarray/core/concat.py:580: in
_dataarray_concat
ds = _dataset_concat(
/usr/lib/python3/dist-packages/xarray/core/concat.py:519: in _dataset_concat
combined = concat_vars(vars, dim, positions,
combine_attrs=combine_attrs)
/usr/lib/python3/dist-packages/xarray/core/variable.py:2897: in concat
return Variable.concat(variables, dim, positions, shortcut,
combine_attrs)
/usr/lib/python3/dist-packages/xarray/core/variable.py:1854: in concat
data = duck_array_ops.concatenate(arrays, axis=axis)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:302: in
concatenate
return _concatenate(as_shared_dtype(arrays), axis=axis)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:56: in f
return wrapped(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
args = ([array([[1.62000e+08, 1.62000e+08, 1.62000e+08, ..., 1.62018e+08,
1.62018e+08, 1.62018e+08],
[1.61982e...
[0.00000e+00, 1.00000e+00, 2.00000e+00, ..., 1.79970e+04,
1.79980e+04, 1.79990e+04]], dtype=float32)],)
kwargs = {'axis': 0}
relevant_args = [array([[1.62000e+08, 1.62000e+08, 1.62000e+08, ...,
1.62018e+08,
1.62018e+08, 1.62018e+08],
[1.61982e+...],
[0.00000e+00, 1.00000e+00, 2.00000e+00, ..., 1.79970e+04,
1.79980e+04, 1.79990e+04]], dtype=float32)]
> ???
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 618.
MiB for an array with shape (9001, 18000) and data type float32
<__array_function__ internals>:5: MemoryError
_
TestModisL2.test_load_longitude_latitude[modis_l2_nasa_mod35_file-True-False-False-1000]
_
self = <satpy.tests.reader_tests.test_modis_l2.TestModisL2 object at
0xe78c0328>
input_files =
['/tmp/pytest-of-debci/pytest-0/modis_l20/MOD35_L2.A2021324.1132.061.2021324113236.hdf']
has_5km = True, has_500 = False, has_250 = False, default_res = 1000
@pytest.mark.parametrize(
('input_files', 'has_5km', 'has_500', 'has_250', 'default_res'),
[
[lazy_fixture('modis_l2_nasa_mod35_file'),
True, False, False, 1000],
]
)
def test_load_longitude_latitude(self, input_files, has_5km,
has_500, has_250, default_res):
"""Test that longitude and latitude datasets are loaded
correctly."""
from .test_modis_l1b import _load_and_check_geolocation
scene = Scene(reader='modis_l2', filenames=input_files)
shape_5km = _shape_for_resolution(5000)
shape_500m = _shape_for_resolution(500)
shape_250m = _shape_for_resolution(250)
default_shape = _shape_for_resolution(default_res)
with dask.config.set(scheduler=CustomScheduler(max_computes=1 +
has_5km + has_500 + has_250)):
> _load_and_check_geolocation(scene, "*", default_res, default_shape, True,
check_callback=_check_shared_metadata)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_modis_l2.py:76:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_modis_l1b.py:56:
in _load_and_check_geolocation
lon_vals, lat_vals = dask.compute(lon_arr, lat_arr)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/satpy/tests/utils.py:265: in __call__
return dask.get(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/local.py:563: in get_sync
return get_async(
/usr/lib/python3/dist-packages/dask/local.py:506: in get_async
for key, res_info, failed in queue_get(queue).result():
/usr/lib/python3.9/concurrent/futures/_base.py:438: in result
return self.__get_result()
/usr/lib/python3.9/concurrent/futures/_base.py:390: in __get_result
raise self._exception
/usr/lib/python3/dist-packages/dask/local.py:548: in submit
fut.set_result(fn(*args, **kwargs))
/usr/lib/python3/dist-packages/dask/local.py:237: in batch_execute_tasks
return [execute_task(*a) for a in it]
/usr/lib/python3/dist-packages/dask/local.py:237: in <listcomp>
return [execute_task(*a) for a in it]
/usr/lib/python3/dist-packages/dask/local.py:228: in execute_task
result = pack_exception(e, dumps)
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/optimization.py:969: in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
/usr/lib/python3/dist-packages/dask/core.py:151: in get
result = _execute_task(task, cache)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in <genexpr>
return func(*(_execute_task(a, cache) for a in args))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
arg = (<built-in function mul>, (<built-in function add>,
'__dask_blockwise__2', '__dask_blockwise__3'), '__dask_blockwise__1')
cache = {'__dask_blockwise__0': 5, '__dask_blockwise__1': 1.0,
'__dask_blockwise__2': 0, '__dask_blockwise__3': array([[-2, -2.....,
[ 5, 5, 5, ..., 5, 5, 5],
[ 6, 6, 6, ..., 6, 6, 6],
[ 7, 7, 7, ..., 7, 7, 7]])}
dsk = None
def _execute_task(arg, cache, dsk=None):
"""Do the actual work of collecting data and executing a function
Examples
--------
>>> cache = {'x': 1, 'y': 2}
Compute tasks against a cache
>>> _execute_task((add, 'x', 1), cache) # Compute task in
naive manner
2
>>> _execute_task((add, (inc, 'x'), 1), cache) # Support
nested computation
3
Also grab data from cache
>>> _execute_task('x', cache)
1
Support nested lists
>>> list(_execute_task(['x', 'y'], cache))
[1, 2]
>>> list(map(list, _execute_task([['x', 'y'], ['y', 'x']],
cache)))
[[1, 2], [2, 1]]
>>> _execute_task('foo', cache) # Passes through on non-keys
'foo'
"""
if isinstance(arg, list):
return [_execute_task(a, cache) for a in arg]
elif istask(arg):
func, args = arg[0], arg[1:]
# Note: Don't assign the subtask results to a variable.
numpy detects
# temporaries by their reference count and can execute certain
# operations in-place.
> return func(*(_execute_task(a, cache) for a in args))
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
21.0 MiB for an array with shape (2030, 1354) and data type float64
/usr/lib/python3/dist-packages/dask/core.py:121: MemoryError
_
TestModisL2.test_load_250m_cloud_mask_dataset[modis_l2_nasa_mod35_file-False]
_
self = <satpy.tests.reader_tests.test_modis_l2.TestModisL2 object at
0xb4cc8a00>
input_files =
['/tmp/pytest-of-debci/pytest-0/modis_l20/MOD35_L2.A2021324.1132.061.2021324113236.hdf']
exp_area = False
@pytest.mark.parametrize(
('input_files', 'exp_area'),
[
[lazy_fixture('modis_l2_nasa_mod35_file'), False],
[lazy_fixture('modis_l2_nasa_mod35_mod03_files'), True],
]
)
def test_load_250m_cloud_mask_dataset(self, input_files, exp_area):
"""Test loading 250m cloud mask."""
scene = Scene(reader='modis_l2', filenames=input_files)
dataset_name = 'cloud_mask'
> scene.load([dataset_name], resolution=250)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_modis_l2.py:134:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/scene.py:1213: in load
self._read_datasets_from_storage(**kwargs)
/usr/lib/python3/dist-packages/satpy/scene.py:1233: in
_read_datasets_from_storage
return self._read_dataset_nodes_from_storage(nodes, **kwargs)
/usr/lib/python3/dist-packages/satpy/scene.py:1239: in
_read_dataset_nodes_from_storage
loaded_datasets = self._load_datasets_by_readers(reader_datasets,
**kwargs)
/usr/lib/python3/dist-packages/satpy/scene.py:1264: in
_load_datasets_by_readers
new_datasets = reader_instance.load(ds_ids, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:943: in load
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:839: in
_load_dataset_with_area
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:711: in
_load_dataset_data
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:687: in
_load_dataset
projectable = fh.get_dataset(dsid, ds_info)
/usr/lib/python3/dist-packages/satpy/readers/modis_l2.py:139: in get_dataset
dataset = self._extract_and_mask_category_dataset(dataset_id,
dataset_info, dataset_name_in_file)
/usr/lib/python3/dist-packages/satpy/readers/modis_l2.py:159: in
_extract_and_mask_category_dataset
dataset = _extract_byte_mask(dataset,
/usr/lib/python3/dist-packages/satpy/readers/modis_l2.py:204: in
_extract_byte_mask
dataset_a = np.uint16(dataset_a)
/usr/lib/python3/dist-packages/xarray/core/common.py:141: in __array__
return np.asarray(self.values, dtype=dtype)
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in <genexpr>
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/optimization.py:969: in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
/usr/lib/python3/dist-packages/dask/core.py:151: in get
result = _execute_task(task, cache)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/utils.py:35: in apply
return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
x = array([[[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
... ...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]]], dtype=int8)
astype_dtype = dtype('uint8'), kwargs = {}
def astype(x, astype_dtype=None, **kwargs):
> return x.astype(astype_dtype, **kwargs)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
15.7 MiB for an array with shape (6, 2030, 1354) and data type uint8
/usr/lib/python3/dist-packages/dask/array/chunk.py:281: MemoryError
------------------------------ Captured log call
-------------------------------
WARNING satpy.readers.yaml_reader:yaml_reader.py:771 Required file type
'hdf_eos_geo' not found or loaded for 'latitude'
WARNING satpy.readers.yaml_reader:yaml_reader.py:771 Required file type
'hdf_eos_geo' not found or loaded for 'longitude'
________________________ TestH5NWCSAF.test_get_dataset
_________________________
self = <satpy.tests.reader_tests.test_nwcsaf_msg.TestH5NWCSAF
testMethod=test_get_dataset>
def test_get_dataset(self):
"""Retrieve datasets from a NWCSAF msgv2013 hdf5 file."""
from satpy.readers.nwcsaf_msg2013_hdf5 import Hdf5NWCSAF
from satpy.tests.utils import make_dataid
filename_info = {}
filetype_info = {}
dsid = make_dataid(name="ct")
test = Hdf5NWCSAF(self.filename_ct, filename_info, filetype_info)
ds = test.get_dataset(dsid, {"file_key": "CT"})
self.assertEqual(ds.shape, (1856, 3712))
self.assertEqual(ds.dtype, np.uint8)
> np.testing.assert_allclose(ds.data[1000:1010, 1000:1010].compute(), CTYPE_TEST_FRAME)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_nwcsaf_msg.py:521:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in <genexpr>
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/optimization.py:969: in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
/usr/lib/python3/dist-packages/dask/core.py:151: in get
result = _execute_task(task, cache)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in <genexpr>
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:115: in _execute_task
return [_execute_task(a, cache) for a in arg]
/usr/lib/python3/dist-packages/dask/core.py:115: in <listcomp>
return [_execute_task(a, cache) for a in arg]
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/dask/core.py:121: in <genexpr>
return func(*(_execute_task(a, cache) for a in args))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
arg = (<built-in function mul>, '__dask_blockwise__1',
'__dask_blockwise__2')
cache = {'__dask_blockwise__0': 0.0, '__dask_blockwise__1': array([[ 91,
125, 81, ..., 244, 74, 89],
[ 28, 226, 131,..., 132, 60, ..., 106, 126, 5],
[100, 157, 165, ..., 169, 196, 199]], dtype=uint8),
'__dask_blockwise__2': 1.0}
dsk = None
def _execute_task(arg, cache, dsk=None):
"""Do the actual work of collecting data and executing a function
Examples
--------
>>> cache = {'x': 1, 'y': 2}
Compute tasks against a cache
>>> _execute_task((add, 'x', 1), cache) # Compute task in
naive manner
2
>>> _execute_task((add, (inc, 'x'), 1), cache) # Support
nested computation
3
Also grab data from cache
>>> _execute_task('x', cache)
1
Support nested lists
>>> list(_execute_task(['x', 'y'], cache))
[1, 2]
>>> list(map(list, _execute_task([['x', 'y'], ['y', 'x']],
cache)))
[[1, 2], [2, 1]]
>>> _execute_task('foo', cache) # Passes through on non-keys
'foo'
"""
if isinstance(arg, list):
return [_execute_task(a, cache) for a in arg]
elif istask(arg):
func, args = arg[0], arg[1:]
# Note: Don't assign the subtask results to a variable.
numpy detects
# temporaries by their reference count and can execute certain
# operations in-place.
> return func(*(_execute_task(a, cache) for a in args))
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
52.6 MiB for an array with shape (1856, 3712) and data type float64
/usr/lib/python3/dist-packages/dask/core.py:121: MemoryError
__________________ TestSMOSL2WINDReader.test_load_wind_speed
___________________
self = <satpy.tests.reader_tests.test_smos_l2_wind.TestSMOSL2WINDReader
testMethod=test_load_wind_speed>
def test_load_wind_speed(self):
"""Load wind_speed dataset."""
from satpy.readers import load_reader
r = load_reader(self.reader_configs)
with mock.patch('satpy.readers.smos_l2_wind.netCDF4.Variable',
xr.DataArray):
loadables = r.select_files_from_pathnames([
'SM_OPER_MIR_SCNFSW_20200420T021649_20200420T035013_110_001_7.nc',
])
r.create_filehandlers(loadables)
> ds = r.load(['wind_speed'])
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_smos_l2_wind.py:116:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:943:
in load
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:839: in
_load_dataset_with_area
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:711: in
_load_dataset_data
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:687: in
_load_dataset
projectable = fh.get_dataset(dsid, ds_info)
/usr/lib/python3/dist-packages/satpy/readers/smos_l2_wind.py:140: in
get_dataset
data = self._rename_coords(data)
/usr/lib/python3/dist-packages/satpy/readers/smos_l2_wind.py:112: in
_rename_coords
data = self._adjust_lon_coord(data)
/usr/lib/python3/dist-packages/satpy/readers/smos_l2_wind.py:106: in
_adjust_lon_coord
return data.where(data < 180., data - 360.)
/usr/lib/python3/dist-packages/xarray/core/common.py:1286: in where
return ops.where_method(self, cond, other)
/usr/lib/python3/dist-packages/xarray/core/ops.py:176: in where_method
return apply_ufunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:1174: in
apply_ufunc
return apply_dataarray_vfunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:293: in
apply_dataarray_vfunc
result_var = func(*data_vars)
/usr/lib/python3/dist-packages/xarray/core/computation.py:742: in
apply_variable_ufunc
result_data = func(*input_data)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:290: in
where_method
return where(cond, data, other)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:284: in where
return _where(condition, *as_shared_dtype([x, y]))
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:56: in f
return wrapped(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
args = (array([[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...60.],
[-360., -360., -360., ..., -360., -360., -360.],
[-360., -360., -360., ..., -360., -360., -360.]]))
kwargs = {}
relevant_args = (array([[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...60.],
[-360., -360., -360., ..., -360., -360., -360.],
[-360., -360., -360., ..., -360., -360., -360.]]))
> ???
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 7.92
MiB for an array with shape (721, 1440) and data type float64
<__array_function__ internals>:5: MemoryError
_____________________ TestTROPOMIL2Reader.test_load_bounds
_____________________
self = <satpy.tests.reader_tests.test_tropomi_l2.TestTROPOMIL2Reader
testMethod=test_load_bounds>
def test_load_bounds(self):
"""Load bounds dataset."""
from satpy.readers import load_reader
r = load_reader(self.reader_configs)
with mock.patch('satpy.readers.tropomi_l2.netCDF4.Variable',
xr.DataArray):
loadables = r.select_files_from_pathnames([
'S5P_OFFL_L2__NO2____20180709T170334_20180709T184504_03821_01_010002_20180715T184729.nc',
])
r.create_filehandlers(loadables)
keys = ['latitude_bounds', 'longitude_bounds']
> ds = r.load(keys)
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_tropomi_l2.py:173:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:943:
in load
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:839: in
_load_dataset_with_area
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:711: in
_load_dataset_data
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:687: in
_load_dataset
projectable = fh.get_dataset(dsid, ds_info)
/usr/lib/python3/dist-packages/satpy/readers/tropomi_l2.py:229: in
get_dataset
data = data.where(good_mask, new_fill)
/usr/lib/python3/dist-packages/xarray/core/common.py:1286: in where
return ops.where_method(self, cond, other)
/usr/lib/python3/dist-packages/xarray/core/ops.py:176: in where_method
return apply_ufunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:1174: in
apply_ufunc
return apply_dataarray_vfunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:293: in
apply_dataarray_vfunc
result_var = func(*data_vars)
/usr/lib/python3/dist-packages/xarray/core/computation.py:742: in
apply_variable_ufunc
result_data = func(*input_data)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:290: in
where_method
return where(cond, data, other)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:284: in where
return _where(condition, *as_shared_dtype([x, y]))
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:208: in
as_shared_dtype
return [x.astype(out_type, copy=False) for x in arrays]
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
.0 = <list_iterator object at 0xe94598>
> return [x.astype(out_type, copy=False) for x in arrays]
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 44.6
MiB for an array with shape (3246, 450, 4) and data type float64
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:208:
MemoryError
______________________ TestTROPOMIL2Reader.test_load_no2
_______________________
self = <satpy.tests.reader_tests.test_tropomi_l2.TestTROPOMIL2Reader
testMethod=test_load_no2>
def test_load_no2(self):
"""Load NO2 dataset."""
from satpy.readers import load_reader
r = load_reader(self.reader_configs)
with mock.patch('satpy.readers.tropomi_l2.netCDF4.Variable',
xr.DataArray):
loadables = r.select_files_from_pathnames([
'S5P_OFFL_L2__NO2____20180709T170334_20180709T184504_03821_01_010002_20180715T184729.nc',
])
r.create_filehandlers(loadables)
> ds = r.load(['nitrogen_dioxide_total_column'])
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_tropomi_l2.py:135:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:943:
in load
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:839: in
_load_dataset_with_area
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:711: in
_load_dataset_data
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:687: in
_load_dataset
projectable = fh.get_dataset(dsid, ds_info)
/usr/lib/python3/dist-packages/satpy/readers/tropomi_l2.py:229: in
get_dataset
data = data.where(good_mask, new_fill)
/usr/lib/python3/dist-packages/xarray/core/common.py:1286: in where
return ops.where_method(self, cond, other)
/usr/lib/python3/dist-packages/xarray/core/ops.py:176: in where_method
return apply_ufunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:1174: in
apply_ufunc
return apply_dataarray_vfunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:293: in
apply_dataarray_vfunc
result_var = func(*data_vars)
/usr/lib/python3/dist-packages/xarray/core/computation.py:742: in
apply_variable_ufunc
result_data = func(*input_data)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:290: in
where_method
return where(cond, data, other)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:284: in where
return _where(condition, *as_shared_dtype([x, y]))
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:208: in
as_shared_dtype
return [x.astype(out_type, copy=False) for x in arrays]
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
.0 = <list_iterator object at 0xb46b2238>
> return [x.astype(out_type, copy=False) for x in arrays]
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 11.1
MiB for an array with shape (3246, 450) and data type float64
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:208:
MemoryError
______________________ TestTROPOMIL2Reader.test_load_so2
_______________________
self = <satpy.tests.reader_tests.test_tropomi_l2.TestTROPOMIL2Reader
testMethod=test_load_so2>
def test_load_so2(self):
"""Load SO2 dataset."""
from satpy.readers import load_reader
r = load_reader(self.reader_configs)
with mock.patch('satpy.readers.tropomi_l2.netCDF4.Variable',
xr.DataArray):
loadables = r.select_files_from_pathnames([
'S5P_OFFL_L2__SO2____20181224T055107_20181224T073237_06198_01_010105_20181230T150634.nc',
])
r.create_filehandlers(loadables)
> ds = r.load(['sulfurdioxide_total_vertical_column'])
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_tropomi_l2.py:154:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:943:
in load
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:839: in
_load_dataset_with_area
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:711: in
_load_dataset_data
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:687: in
_load_dataset
projectable = fh.get_dataset(dsid, ds_info)
/usr/lib/python3/dist-packages/satpy/readers/tropomi_l2.py:229: in
get_dataset
data = data.where(good_mask, new_fill)
/usr/lib/python3/dist-packages/xarray/core/common.py:1286: in where
return ops.where_method(self, cond, other)
/usr/lib/python3/dist-packages/xarray/core/ops.py:176: in where_method
return apply_ufunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:1174: in
apply_ufunc
return apply_dataarray_vfunc(
/usr/lib/python3/dist-packages/xarray/core/computation.py:293: in
apply_dataarray_vfunc
result_var = func(*data_vars)
/usr/lib/python3/dist-packages/xarray/core/computation.py:742: in
apply_variable_ufunc
result_data = func(*input_data)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:290: in
where_method
return where(cond, data, other)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:284: in where
return _where(condition, *as_shared_dtype([x, y]))
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:56: in f
return wrapped(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
args = (array([[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...457e+04],
[1.8458e+04, 1.8459e+04, 1.8460e+04, ..., 1.8905e+04, 1.8906e+04,
1.8907e+04]]), array(-999.))
kwargs = {}
relevant_args = (array([[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...457e+04],
[1.8458e+04, 1.8459e+04, 1.8460e+04, ..., 1.8905e+04, 1.8906e+04,
1.8907e+04]]), array(-999.))
> ???
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 11.1
MiB for an array with shape (3246, 450) and data type float64
<__array_function__ internals>:5: MemoryError
_________________________ TestCompact.test_distributed
_________________________
self = <satpy.tests.reader_tests.test_viirs_compact.TestCompact
testMethod=test_distributed>
def setUp(self):
"""Create a fake file from scratch."""
fake_dnb = {
"All_Data": {
"ModeGran": {"value": 0},
"ModeScan": {
"value": np.array(
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
249,
],
dtype=np.uint8,
)
},
"NumberOfScans": {"value": np.array([47])},
"VIIRS-DNB-GEO_All": {
"AlignmentCoefficient": {
"value": np.array(
[
2.11257413e-02,
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],
dtype=np.float32,
)
},
"ExpansionCoefficient": {
"value": np.array(
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# noqa
],
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# noqa
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],
[
b"VIIRS-M3-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M4-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M5-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M6-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M7-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M8-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M9-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-RSBAUTOCAL-HISTORY-AUX_j01_20191024021527Z_20191024000000Z_ee00000000000000Z_-_nobc_ops_all-_ops"
# noqa
],
[
b"VIIRS-RSBAUTOCAL-VOLT-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-EDD154640-109C-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-BB-TEMP-COEFFS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-CAL-AUTOMATE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Pred-SideA-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-COEFF-A-LUT_j01_20180109114311Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-COEFF-B-LUT_j01_20180109101739Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-004-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DELTA-C-LUT_j01_20180109000000Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SideA-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-DN0-LUT_j01_20190930000000Z_20190928000000Z_ee00000000000000Z_PS-1-O-CCR-4262-026-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-FRAME-TO-ZONE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Op21-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-GAIN-RATIOS-LUT_j01_20190930000000Z_20190928000000Z_ee00000000000000Z_PS-1-O-CCR-4262-025-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-LGS-GAINS-LUT_j01_20180413122703Z_20180412000000Z_ee00000000000000Z_PS-1-O-CCR-3918-005-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-RVF-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Op21-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-STRAY-LIGHT-CORRECTION-LUT_j01_20190930160523Z_20191001000000Z_ee00000000000000Z_PS-1-O-CCR-4322-024-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-EBBT-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-EMISSIVE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-F-PREDICTED-LUT_j01_20180413123333Z_20180412000000Z_ee00000000000000Z_PS-1-O-CCR-3918-006-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-GAIN-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-HAM-ER-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-OBC-ER-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-OBC-RR-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-OBS-TO-PIXELS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SameAsSNPP-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-QA-LUT_j01_20180109121411Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RADIOMETRIC-PARAM-V3-LUT_j01_20161117000000Z_20180111000000Z_ee00000000000000Z_PS-1-O-CCR-17-3436-v003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-REFLECTIVE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SameAsSNPP-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RELATIVE-SPECTRAL-RESPONSE-LUT_j01_20161031000000Z_20180111000000Z_ee00000000000000Z_PS-1-O-CCR-17-3436-v003-FusedM9-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RTA-ER-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RVF-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-M16-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-SOLAR-IRAD-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Thuillier2002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-TELE-COEFFS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SideA-LE-PE_all-_all_all-_ops"
# noqa
],
],
dtype="|S151",
),
"N_Beginning_Orbit_Number": np.array(
[[10015]], dtype=np.uint64
),
"N_Beginning_Time_IET": np.array(
[[1950675122120971]], dtype=np.uint64
),
"N_Creation_Date":
np.array([[b"20191025"]], dtype="|S9"),
"N_Creation_Time": np.array(
[[b"062411.116253Z"]], dtype="|S15"
),
"N_Day_Night_Flag": np.array([[b"Night"]],
dtype="|S6"),
"N_Ending_Time_IET": np.array(
[[1950675204849492]], dtype=np.uint64
),
"N_Graceful_Degradation":
np.array([[b"No"]], dtype="|S3"),
"N_Granule_ID": np.array(
[[b"J01002526558865"]], dtype="|S16"
),
"N_Granule_Status": np.array([[b"N/A"]],
dtype="|S4"),
"N_Granule_Version": np.array([[b"A1"]],
dtype="|S3"),
"N_IDPS_Mode": np.array([[b"N/A"]],
dtype="|S4"),
"N_Input_Prod": np.array(
[
[b"GEO-VIIRS-OBC-IP:J01002526558865:A1"],
[b"SPACECRAFT-DIARY-RDR:J01002526558800:A1"],
[b"SPACECRAFT-DIARY-RDR:J01002526559000:A1"],
[b"VIIRS-DNB-GEO:J01002526558865:A1"],
[b"VIIRS-IMG-RGEO-TC:J01002526558865:A1"],
[b"VIIRS-MOD-RGEO-TC:J01002526558865:A1"],
[b"VIIRS-SCIENCE-RDR:J01002526558012:A1"],
[b"VIIRS-SCIENCE-RDR:J01002526558865:A1"],
],
dtype="|S40",
),
"N_JPSS_Document_Ref": np.array(
[
[
b"474-00448-02-06_JPSS-DD-Vol-II-Part-6_0200H.pdf"
],
[
b"474-00448-02-06_JPSS-VIIRS-SDR-DD-Part-6_0200H_VIIRS-DNB-SDR-PP.xml"
],
[
b"474-00448-03-06_JPSS-OAD-Vol-III-Part-6-VIIRS-RDR-SDR_-1.pdf"
],
],
dtype="|S68",
),
"N_LEOA_Flag": np.array([[b"On"]],
dtype="|S3"),
"N_Nadir_Latitude_Max": np.array(
[[45.3722]], dtype=np.float32
),
"N_Nadir_Latitude_Min": np.array(
[[40.6172]], dtype=np.float32
),
"N_Nadir_Longitude_Max": np.array(
[[-62.80047]], dtype=np.float32
),
"N_Nadir_Longitude_Min": np.array(
[[-64.51342]], dtype=np.float32
),
"N_Number_Of_Scans": np.array([[47]],
dtype=np.int32),
"N_Percent_Erroneous_Data": np.array(
[[0.0]], dtype=np.float32
),
"N_Percent_Missing_Data": np.array(
[[51.05127]], dtype=np.float32
),
"N_Percent_Not-Applicable_Data": np.array(
[[0.0]], dtype=np.float32
),
"N_Primary_Label": np.array(
[[b"Non-Primary"]], dtype="|S12"
),
"N_Quality_Summary_Names": np.array(
[
[b"Scan Quality Exclusion"],
[b"Summary VIIRS SDR Quality"],
],
dtype="|S26",
),
"N_Quality_Summary_Values": np.array(
[[24], [49]], dtype=np.int32
),
"N_RSB_Index": np.array([[17]],
dtype=np.int32),
"N_Reference_ID": np.array(
[[b"VIIRS-DNB-SDR:J01002526558865:A1"]], dtype="|S33"
),
"N_Satellite/Local_Azimuth_Angle_Max":
np.array(
[[179.9995]], dtype=np.float32
),
"N_Satellite/Local_Azimuth_Angle_Min":
np.array(
[[-179.9976]], dtype=np.float32
),
"N_Satellite/Local_Zenith_Angle_Max": np.array(
[[69.83973]], dtype=np.float32
),
"N_Satellite/Local_Zenith_Angle_Min": np.array(
[[0.00898314]], dtype=np.float32
),
"N_Software_Version": np.array(
[[b"CSPP_SDR_3_1_3"]], dtype="|S15"
),
"N_Solar_Azimuth_Angle_Max": np.array(
[[73.93496]], dtype=np.float32
),
"N_Solar_Azimuth_Angle_Min": np.array(
[[23.83542]], dtype=np.float32
),
"N_Solar_Zenith_Angle_Max": np.array(
[[147.5895]], dtype=np.float32
),
"N_Solar_Zenith_Angle_Min": np.array(
[[126.3929]], dtype=np.float32
),
"N_Spacecraft_Maneuver": np.array(
[[b"Normal Operations"]], dtype="|S18"
),
"North_Bounding_Coordinate": np.array(
[[46.8018]], dtype=np.float32
),
"South_Bounding_Coordinate": np.array(
[[36.53402]], dtype=np.float32
),
"West_Bounding_Coordinate": np.array(
[[-82.65787]], dtype=np.float32
),
}
},
"attrs": {
"Instrument_Short_Name": np.array([[b"VIIRS"]],
dtype="|S6"),
"N_Collection_Short_Name": np.array(
[[b"VIIRS-DNB-SDR"]], dtype="|S14"
),
"N_Dataset_Type_Tag": np.array([[b"SDR"]],
dtype="|S4"),
"N_Instrument_Flight_SW_Version": np.array(
[[20], [65534]], dtype=np.int32
),
"N_Processing_Domain": np.array([[b"ops"]],
dtype="|S4"),
"Operational_Mode": np.array(
[[b"J01 Normal Operations, VIIRS
Operational"]],
dtype="|S41",
),
},
},
},
"attrs": {
"CVIIRS_Version": np.array([[b"2.0.1"]], dtype="|S5"),
"Compact_VIIRS_SDR_Version": np.array([[b"3.1"]],
dtype="|S3"),
"Distributor": np.array([[b"cspp"]], dtype="|S5"),
"Mission_Name": np.array([[b"JPSS-1"]], dtype="|S7"),
"N_Dataset_Source": np.array([[b"all-"]], dtype="|S5"),
"N_GEO_Ref": np.array(
[
[
b"GDNBO_j01_d20191025_t0611251_e0612478_b10015_c20191025062405837630_cspp_dev.h5"
]
],
dtype="|S78",
),
"N_HDF_Creation_Date": np.array([[b"20191025"]],
dtype="|S8"),
"N_HDF_Creation_Time": np.array([[b"062502.927000Z"]],
dtype="|S14"),
"Platform_Short_Name": np.array([[b"J01"]], dtype="|S4"),
"Satellite_Id_Filename": np.array([[b"j01"]], dtype="|S3"),
},
}
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_viirs_compact.py:1485:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ mtrand.pyx:1169: in numpy.random.mtrand.RandomState.rand
???
mtrand.pyx:423: in numpy.random.mtrand.RandomState.random_sample
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
> ???
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 23.8
MiB for an array with shape (768, 4064) and data type float64
_common.pyx:270: MemoryError
_________________________ TestCompact.test_get_dataset
_________________________
self = <satpy.tests.reader_tests.test_viirs_compact.TestCompact
testMethod=test_get_dataset>
def setUp(self):
"""Create a fake file from scratch."""
fake_dnb = {
"All_Data": {
"ModeGran": {"value": 0},
"ModeScan": {
"value": np.array(
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
254,
249,
],
dtype=np.uint8,
)
},
"NumberOfScans": {"value": np.array([47])},
"VIIRS-DNB-GEO_All": {
"AlignmentCoefficient": {
"value": np.array(
[
2.11257413e-02,
2.11152732e-02,
2.11079046e-02,
2.10680142e-02,
1.80840008e-02,
1.80402063e-02,
1.79968309e-02,
1.79477539e-02,
2.20463774e-03,
2.17431062e-03,
2.14360282e-03,
2.11503846e-03,
2.08630669e-03,
2.05924874e-03,
2.03177333e-03,
2.00573727e-03,
1.98072987e-03,
1.95503305e-03,
1.93077011e-03,
1.90702057e-03,
1.88353716e-03,
1.86104013e-03,
1.83863181e-03,
1.81696517e-03,
1.79550308e-03,
1.77481642e-03,
1.75439729e-03,
1.73398503e-03,
1.71459839e-03,
1.69516564e-03,
1.67622324e-03,
1.65758410e-03,
1.63990213e-03,
1.62128301e-03,
1.60375470e-03,
1.58667017e-03,
1.61543000e-03,
1.59775047e-03,
1.50719041e-03,
1.48937735e-03,
1.47257745e-03,
1.50070526e-03,
1.48288533e-03,
9.29064234e-04,
9.12246935e-04,
8.95748264e-04,
8.71886965e-04,
8.55044520e-04,
8.38686305e-04,
8.18263041e-04,
8.01501446e-04,
7.85346841e-04,
1.15984806e-03,
1.14326552e-03,
1.12648588e-03,
1.11018715e-03,
1.09399087e-03,
1.19698711e-03,
1.18051842e-03,
1.16404379e-03,
1.14832399e-03,
9.92591376e-04,
9.75896895e-04,
9.59663419e-04,
9.43415158e-04,
9.27662419e-04,
8.92253709e-04,
8.75947590e-04,
8.60177504e-04,
8.44484195e-04,
8.35279003e-04,
8.19236680e-04,
8.03303672e-04,
7.87482015e-04,
7.60449213e-04,
7.44239136e-04,
7.28625571e-04,
7.12990935e-04,
6.89090986e-04,
6.73000410e-04,
6.57248020e-04,
6.41623745e-04,
6.20219158e-04,
6.04308851e-04,
5.88596100e-04,
5.73108089e-04,
3.65344196e-04,
3.49639275e-04,
3.34273063e-04,
4.81286290e-04,
4.65485587e-04,
4.49862011e-04,
4.34543617e-04,
4.19324206e-04,
2.60536268e-04,
2.45052564e-04,
2.29740850e-04,
2.34466774e-04,
2.18822126e-04,
2.03370175e-04,
1.88058810e-04,
1.60192372e-04,
1.44485937e-04,
1.28920830e-04,
3.45615146e-04,
3.30171984e-04,
3.14682693e-04,
2.99300562e-04,
2.83925037e-04,
2.68518896e-04,
2.53254839e-04,
2.37950648e-04,
2.22716670e-04,
2.07562072e-04,
1.92296386e-04,
1.77147449e-04,
1.61994336e-04,
1.46895778e-04,
1.31844325e-04,
1.16730320e-04,
1.01757469e-04,
8.67861963e-05,
7.18669180e-05,
5.70719567e-05,
4.24701866e-05,
2.84846719e-05,
1.70599415e-05,
-1.47213286e-05,
-2.33691408e-05,
-3.68025649e-05,
-5.12388433e-05,
-6.59972284e-05,
-8.08926561e-05,
-9.58433884e-05,
-1.10882705e-04,
-1.25976600e-04,
-1.41044657e-04,
-1.56166439e-04,
-1.71307023e-04,
-1.86516074e-04,
-2.01731804e-04,
-2.16980450e-04,
-2.32271064e-04,
-2.47527263e-04,
-2.62940506e-04,
-2.78283434e-04,
-2.93711084e-04,
-3.09180934e-04,
-3.24661058e-04,
-3.40237195e-04,
-1.27807143e-04,
-1.43646437e-04,
-1.59638614e-04,
-1.87593061e-04,
-2.03169184e-04,
-2.18941437e-04,
-2.34920750e-04,
-2.30605408e-04,
-2.46262236e-04,
-2.62226094e-04,
-4.19838558e-04,
-4.35510388e-04,
-4.51152271e-04,
-4.67120990e-04,
-4.83241311e-04,
-3.37647041e-04,
-3.53568990e-04,
-3.69836489e-04,
-5.76354389e-04,
-5.92070050e-04,
-6.08178903e-04,
-6.24440494e-04,
-6.45648804e-04,
-6.61431870e-04,
-6.77491073e-04,
-6.93967624e-04,
-7.17683870e-04,
-7.33471534e-04,
-7.49999890e-04,
-7.66390527e-04,
-7.93468382e-04,
-8.09502264e-04,
-8.25728697e-04,
-8.42282083e-04,
-8.51265620e-04,
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],
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],
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],
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],
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b"Terrain-Eco-ANC-Tile_20030125000000Z_ee00000000000000Z_NA_NA_N0721_1.O.0.0"
],
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],
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],
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],
[
b"off_Planet-Eph-ANC_Static_JPL_000f_20151008_200001010000Z_20000101000000Z_ee00000000000000Z_np"
# noqa
],
[
b"off_USNO-PolarWander-UT1-ANC_Ser7_USNO_000f_20191025_201910250000Z_20191025000109Z_ee20191101120000Z_np"
# noqa
],
],
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# noqa
],
[
b"CmnGeo-SAA-AC_j01_20151008180000Z_20170807130000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"TLE-AUX_j01_20191024053224Z_20191024000000Z_ee00000000000000Z_-_nobc_ops_all-_ops"
# noqa
],
[
b"VIIRS-SDR-GEO-DNB-PARAM-LUT_j01_20180507121508Z_20180315000000Z_ee00000000000000Z_PS-1-O-CCR-3963-006-LE-PE_all-_all_all-_ops"
# noqa
],
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b"VIIRS-SDR-GEO-IMG-PARAM-LUT_j01_20180430182354Z_20180315000000Z_ee00000000000000Z_PS-1-O-CCR-3963-006-LE-PE_all-_all_all-_ops"
# noqa
],
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b"VIIRS-SDR-GEO-MOD-PARAM-LUT_j01_20180430182652Z_20180315000000Z_ee00000000000000Z_PS-1-O-CCR-3963-006-LE-PE_all-_all_all-_ops"
# noqa
],
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b"VIIRS-SDR-QA-LUT_j01_20180109121411Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
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[[b"J01002526558865"]], dtype="|S16"
),
"N_Granule_Status": np.array([[b"N/A"]],
dtype="|S4"),
"N_Granule_Version": np.array([[b"A1"]],
dtype="|S3"),
"N_IDPS_Mode": np.array([[b"N/A"]],
dtype="|S4"),
"N_Input_Prod": np.array(
[
[b"SPACECRAFT-DIARY-RDR:J01002526558800:A1"],
[b"SPACECRAFT-DIARY-RDR:J01002526559000:A1"],
[b"VIIRS-SCIENCE-RDR:J01002526558865:A1"],
],
dtype="|S40",
),
"N_JPSS_Document_Ref": np.array(
[
[
b"474-00448-02-06_JPSS-DD-Vol-II-Part-6_0200H.pdf"
],
[
b"474-00448-02-06_JPSS-VIIRS-SDR-DD-Part-6_0200H_VIIRS-DNB-GEO-PP.xml"
],
[
b"474-00448-03-06_JPSS-OAD-Vol-III-Part-6-VIIRS-RDR-SDR_-1.pdf"
],
],
dtype="|S68",
),
"N_LEOA_Flag": np.array([[b"On"]],
dtype="|S3"),
"N_Nadir_Latitude_Max": np.array(
[[45.3722]], dtype=np.float32
),
"N_Nadir_Latitude_Min": np.array(
[[40.6172]], dtype=np.float32
),
"N_Nadir_Longitude_Max": np.array(
[[-62.80047]], dtype=np.float32
),
"N_Nadir_Longitude_Min": np.array(
[[-64.51342]], dtype=np.float32
),
"N_Number_Of_Scans": np.array([[47]],
dtype=np.int32),
"N_Primary_Label": np.array(
[[b"Non-Primary"]], dtype="|S12"
),
"N_Quality_Summary_Names": np.array(
[
[b"Automatic Quality Flag"],
[b"Percent Missing Data"],
[b"Percent Out of Bounds"],
],
dtype="|S23",
),
"N_Quality_Summary_Values": np.array(
[[1], [61], [0]], dtype=np.int32
),
"N_Reference_ID": np.array(
[[b"VIIRS-DNB-GEO:J01002526558865:A1"]], dtype="|S33"
),
"N_Software_Version": np.array(
[[b"CSPP_SDR_3_1_3"]], dtype="|S15"
),
"N_Spacecraft_Maneuver": np.array(
[[b"Normal Operations"]], dtype="|S18"
),
"North_Bounding_Coordinate": np.array(
[[46.8018]], dtype=np.float32
),
"South_Bounding_Coordinate": np.array(
[[36.53401]], dtype=np.float32
),
"West_Bounding_Coordinate": np.array(
[[-82.66234]], dtype=np.float32
),
}
},
"attrs": {
"Instrument_Short_Name": np.array([[b"VIIRS"]],
dtype="|S6"),
"N_Anc_Type_Tasked": np.array([[b"Official"]],
dtype="|S9"),
"N_Collection_Short_Name": np.array(
[[b"VIIRS-DNB-GEO"]], dtype="|S14"
),
"N_Dataset_Type_Tag": np.array([[b"GEO"]],
dtype="|S4"),
"N_Processing_Domain": np.array([[b"ops"]],
dtype="|S4"),
"Operational_Mode": np.array(
[[b"J01 Normal Operations, VIIRS
Operational"]],
dtype="|S41",
),
},
},
"VIIRS-DNB-SDR": {
"VIIRS-DNB-SDR_Aggr": {
"attrs": {
"AggregateBeginningDate": np.array(
[[b"20191025"]], dtype="|S9"
),
"AggregateBeginningGranuleID": np.array(
[[b"J01002526558865"]], dtype="|S16"
),
"AggregateBeginningOrbitNumber": np.array(
[[10015]], dtype=np.uint64
),
"AggregateBeginningTime": np.array(
[[b"061125.120971Z"]], dtype="|S15"
),
"AggregateEndingDate": np.array(
[[b"20191025"]], dtype="|S9"
),
"AggregateEndingGranuleID": np.array(
[[b"J01002526558865"]], dtype="|S16"
),
"AggregateEndingOrbitNumber": np.array(
[[10015]], dtype=np.uint64
),
"AggregateEndingTime": np.array(
[[b"061247.849492Z"]], dtype="|S15"
),
"AggregateNumberGranules": np.array([[1]],
dtype=np.uint64),
}
},
"VIIRS-DNB-SDR_Gran_0": {
"attrs": {
"Ascending/Descending_Indicator": np.array(
[[1]], dtype=np.uint8
),
"Band_ID": np.array([[b"N/A"]], dtype="|S4"),
"Beginning_Date": np.array([[b"20191025"]],
dtype="|S9"),
"Beginning_Time": np.array(
[[b"061125.120971Z"]], dtype="|S15"
),
"East_Bounding_Coordinate": np.array(
[[-45.09281]], dtype=np.float32
),
"Ending_Date": np.array([[b"20191025"]],
dtype="|S9"),
"Ending_Time": np.array(
[[b"061247.849492Z"]], dtype="|S15"
),
"G-Ring_Latitude": np.array(
[
[41.84157],
[44.31069],
[46.78591],
[45.41409],
[41.07675],
[38.81512],
[36.53402],
[40.55788],
],
dtype=np.float32,
),
"G-Ring_Longitude": np.array(
[
[-82.65787],
[-82.55148],
[-82.47269],
[-62.80042],
[-45.09281],
[-46.58528],
[-47.95936],
[-64.54196],
],
dtype=np.float32,
),
"N_Algorithm_Version": np.array(
[[b"1.O.000.015"]], dtype="|S12"
),
"N_Anc_Filename": np.array(
[
[
b"off_Planet-Eph-ANC_Static_JPL_000f_20151008_200001010000Z_20000101000000Z_ee00000000000000Z_np"
# noqa
],
[
b"off_USNO-PolarWander-UT1-ANC_Ser7_USNO_000f_20191025_201910250000Z_20191025000109Z_ee20191101120000Z_np"
# noqa
],
],
dtype="|S104",
),
"N_Aux_Filename": np.array(
[
[
b"CMNGEO-PARAM-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-DNB-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-I1-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-I2-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-I3-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-I4-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-I5-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M1-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M10-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M11-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M12-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M13-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M14-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M15-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M16-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M2-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M3-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M4-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M5-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M6-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M7-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M8-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-M9-SDR-DQTT_j01_20151008180000Z_20020101010000Z_ee00000000000000Z_PS-1-O-NPP-1-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-RSBAUTOCAL-HISTORY-AUX_j01_20191024021527Z_20191024000000Z_ee00000000000000Z_-_nobc_ops_all-_ops"
# noqa
],
[
b"VIIRS-RSBAUTOCAL-VOLT-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-EDD154640-109C-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-BB-TEMP-COEFFS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-CAL-AUTOMATE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Pred-SideA-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-COEFF-A-LUT_j01_20180109114311Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-COEFF-B-LUT_j01_20180109101739Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-004-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DELTA-C-LUT_j01_20180109000000Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SideA-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-DN0-LUT_j01_20190930000000Z_20190928000000Z_ee00000000000000Z_PS-1-O-CCR-4262-026-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-FRAME-TO-ZONE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Op21-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-GAIN-RATIOS-LUT_j01_20190930000000Z_20190928000000Z_ee00000000000000Z_PS-1-O-CCR-4262-025-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-LGS-GAINS-LUT_j01_20180413122703Z_20180412000000Z_ee00000000000000Z_PS-1-O-CCR-3918-005-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-RVF-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Op21-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-DNB-STRAY-LIGHT-CORRECTION-LUT_j01_20190930160523Z_20191001000000Z_ee00000000000000Z_PS-1-O-CCR-4322-024-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-EBBT-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-EMISSIVE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-F-PREDICTED-LUT_j01_20180413123333Z_20180412000000Z_ee00000000000000Z_PS-1-O-CCR-3918-006-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-GAIN-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-HAM-ER-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-OBC-ER-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-OBC-RR-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-OBS-TO-PIXELS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SameAsSNPP-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-QA-LUT_j01_20180109121411Z_20180409000000Z_ee00000000000000Z_PS-1-O-CCR-3742-003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RADIOMETRIC-PARAM-V3-LUT_j01_20161117000000Z_20180111000000Z_ee00000000000000Z_PS-1-O-CCR-17-3436-v003-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-REFLECTIVE-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SameAsSNPP-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RELATIVE-SPECTRAL-RESPONSE-LUT_j01_20161031000000Z_20180111000000Z_ee00000000000000Z_PS-1-O-CCR-17-3436-v003-FusedM9-LE-PE_all-_all_all-_ops"
# noqa
],
[
b"VIIRS-SDR-RTA-ER-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-LE-PE_all-_all_all-_ops"
# noqa
],
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b"VIIRS-SDR-RVF-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-M16-LE-PE_all-_all_all-_ops"
# noqa
],
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b"VIIRS-SDR-SOLAR-IRAD-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-Thuillier2002-LE-PE_all-_all_all-_ops"
# noqa
],
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b"VIIRS-SDR-TELE-COEFFS-LUT_j01_20160331000000Z_20170807130000Z_ee00000000000000Z_PS-1-O-CCR-16-2859-v002-SideA-LE-PE_all-_all_all-_ops"
# noqa
],
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[b"VIIRS-DNB-GEO:J01002526558865:A1"],
[b"VIIRS-IMG-RGEO-TC:J01002526558865:A1"],
[b"VIIRS-MOD-RGEO-TC:J01002526558865:A1"],
[b"VIIRS-SCIENCE-RDR:J01002526558012:A1"],
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],
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],
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dtype="|S68",
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dtype="|S3"),
"N_Nadir_Latitude_Max": np.array(
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),
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),
"N_Nadir_Longitude_Max": np.array(
[[-62.80047]], dtype=np.float32
),
"N_Nadir_Longitude_Min": np.array(
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),
"N_Number_Of_Scans": np.array([[47]],
dtype=np.int32),
"N_Percent_Erroneous_Data": np.array(
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"N_Percent_Missing_Data": np.array(
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"N_Percent_Not-Applicable_Data": np.array(
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"N_Primary_Label": np.array(
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),
"N_Quality_Summary_Names": np.array(
[
[b"Scan Quality Exclusion"],
[b"Summary VIIRS SDR Quality"],
],
dtype="|S26",
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"N_Quality_Summary_Values": np.array(
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),
"N_RSB_Index": np.array([[17]],
dtype=np.int32),
"N_Reference_ID": np.array(
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),
"N_Satellite/Local_Azimuth_Angle_Max":
np.array(
[[179.9995]], dtype=np.float32
),
"N_Satellite/Local_Azimuth_Angle_Min":
np.array(
[[-179.9976]], dtype=np.float32
),
"N_Satellite/Local_Zenith_Angle_Max": np.array(
[[69.83973]], dtype=np.float32
),
"N_Satellite/Local_Zenith_Angle_Min": np.array(
[[0.00898314]], dtype=np.float32
),
"N_Software_Version": np.array(
[[b"CSPP_SDR_3_1_3"]], dtype="|S15"
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"N_Solar_Azimuth_Angle_Max": np.array(
[[73.93496]], dtype=np.float32
),
"N_Solar_Azimuth_Angle_Min": np.array(
[[23.83542]], dtype=np.float32
),
"N_Solar_Zenith_Angle_Max": np.array(
[[147.5895]], dtype=np.float32
),
"N_Solar_Zenith_Angle_Min": np.array(
[[126.3929]], dtype=np.float32
),
"N_Spacecraft_Maneuver": np.array(
[[b"Normal Operations"]], dtype="|S18"
),
"North_Bounding_Coordinate": np.array(
[[46.8018]], dtype=np.float32
),
"South_Bounding_Coordinate": np.array(
[[36.53402]], dtype=np.float32
),
"West_Bounding_Coordinate": np.array(
[[-82.65787]], dtype=np.float32
),
}
},
"attrs": {
"Instrument_Short_Name": np.array([[b"VIIRS"]],
dtype="|S6"),
"N_Collection_Short_Name": np.array(
[[b"VIIRS-DNB-SDR"]], dtype="|S14"
),
"N_Dataset_Type_Tag": np.array([[b"SDR"]],
dtype="|S4"),
"N_Instrument_Flight_SW_Version": np.array(
[[20], [65534]], dtype=np.int32
),
"N_Processing_Domain": np.array([[b"ops"]],
dtype="|S4"),
"Operational_Mode": np.array(
[[b"J01 Normal Operations, VIIRS
Operational"]],
dtype="|S41",
),
},
},
},
"attrs": {
"CVIIRS_Version": np.array([[b"2.0.1"]], dtype="|S5"),
"Compact_VIIRS_SDR_Version": np.array([[b"3.1"]],
dtype="|S3"),
"Distributor": np.array([[b"cspp"]], dtype="|S5"),
"Mission_Name": np.array([[b"JPSS-1"]], dtype="|S7"),
"N_Dataset_Source": np.array([[b"all-"]], dtype="|S5"),
"N_GEO_Ref": np.array(
[
[
b"GDNBO_j01_d20191025_t0611251_e0612478_b10015_c20191025062405837630_cspp_dev.h5"
]
],
dtype="|S78",
),
"N_HDF_Creation_Date": np.array([[b"20191025"]],
dtype="|S8"),
"N_HDF_Creation_Time": np.array([[b"062502.927000Z"]],
dtype="|S14"),
"Platform_Short_Name": np.array([[b"J01"]], dtype="|S4"),
"Satellite_Id_Filename": np.array([[b"j01"]], dtype="|S3"),
},
}
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_viirs_compact.py:1485:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ mtrand.pyx:1169: in numpy.random.mtrand.RandomState.rand
???
mtrand.pyx:423: in numpy.random.mtrand.RandomState.random_sample
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
> ???
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate 23.8
MiB for an array with shape (768, 4064) and data type float64
_common.pyx:270: MemoryError
________________ TestAWIPSTiledWriter.test_basic_lettered_tiles
________________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xf3bf69e8>
def test_basic_lettered_tiles(self):
"""Test creating a lettered grid."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data(shape=(2000, 1000), chunks=500)
area_def = self._get_test_area(shape=(2000, 1000),
extents=(-1000000., -1500000.,
1000000., 1500000.))
ds = self._get_test_lcc_data(data, area_def)
# tile_count should be ignored since we specified lettered_grid
> w.save_datasets([ds], sector_id='LCC', source_name="TESTS", tile_count=(3, 3), lettered_grid=True)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:261:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_34, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
------------------------------ Captured log call
-------------------------------
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
________ TestAWIPSTiledWriter.test_basic_lettered_tiles_diff_projection
________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0x608eb0>
def test_basic_lettered_tiles_diff_projection(self):
"""Test creating a lettered grid from data with differing
projection.."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
crs = CRS("+proj=lcc +datum=WGS84 +ellps=WGS84 +lon_0=-95.
+lat_0=45 +lat_1=45 +units=m +no_defs")
> data = self._get_test_data(shape=(2000, 1000), chunks=500)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:276:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:144:
in _get_test_data
data = np.linspace(0., 1., shape[0] * shape[1],
dtype=np.float32).reshape(shape)
<__array_function__ internals>:5: in linspace
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
start = 0.0, stop = 1.0, num = 2000000, endpoint = True, retstep = False
dtype = <class 'numpy.float32'>, axis = 0
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False,
dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to
False.
In that case, the sequence consists of all but the last of
``num + 1``
evenly spaced samples, so that `stop` is excluded. Note
that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be
non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not
included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the
spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given,
infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only
if start
or stop are array-like. By default (0), the samples will
be along a
new axis inserted at the beginning. Use -1 to get an axis
at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead
of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced
evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points
specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be
non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an
__array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
> y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
15.3 MiB for an array with shape (2000000,) and data type float64
/usr/lib/python3/dist-packages/numpy/core/function_base.py:128: MemoryError
___________ TestAWIPSTiledWriter.test_lettered_tiles_update_existing
___________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0x4eeb98>
def test_lettered_tiles_update_existing(self):
"""Test updating lettered tiles with additional data."""
import shutil
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import dask
first_base_dir = os.path.join(self.base_dir, 'first')
w = AWIPSTiledWriter(base_dir=first_base_dir, compress=True)
shape = (2000, 1000)
> data = np.linspace(0., 1., shape[0] * shape[1], dtype=np.float32).reshape(shape)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:300:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ <__array_function__ internals>:5: in linspace
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
start = 0.0, stop = 1.0, num = 2000000, endpoint = True, retstep = False
dtype = <class 'numpy.float32'>, axis = 0
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False,
dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to
False.
In that case, the sequence consists of all but the last of
``num + 1``
evenly spaced samples, so that `stop` is excluded. Note
that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be
non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not
included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the
spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given,
infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only
if start
or stop are array-like. By default (0), the samples will
be along a
new axis inserted at the beginning. Use -1 to get an axis
at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead
of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced
evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points
specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be
non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an
__array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
> y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
15.3 MiB for an array with shape (2000000,) and data type float64
/usr/lib/python3/dist-packages/numpy/core/function_base.py:128: MemoryError
_____________ TestAWIPSTiledWriter.test_lettered_tiles_sector_ref
______________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xbbd58e68>
def test_lettered_tiles_sector_ref(self):
"""Test creating a lettered grid using the sector as reference."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
> data = self._get_test_data(shape=(2000, 1000), chunks=500)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:366:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:144:
in _get_test_data
data = np.linspace(0., 1., shape[0] * shape[1],
dtype=np.float32).reshape(shape)
<__array_function__ internals>:5: in linspace
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
start = 0.0, stop = 1.0, num = 2000000, endpoint = True, retstep = False
dtype = <class 'numpy.float32'>, axis = 0
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False,
dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to
False.
In that case, the sequence consists of all but the last of
``num + 1``
evenly spaced samples, so that `stop` is excluded. Note
that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be
non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not
included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the
spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given,
infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only
if start
or stop are array-like. By default (0), the samples will
be along a
new axis inserted at the beginning. Use -1 to get an axis
at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead
of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced
evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points
specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be
non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an
__array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
> y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
15.3 MiB for an array with shape (2000000,) and data type float64
/usr/lib/python3/dist-packages/numpy/core/function_base.py:128: MemoryError
_______________ TestAWIPSTiledWriter.test_lettered_tiles_no_fit
________________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xe9d7760>
def test_lettered_tiles_no_fit(self):
"""Test creating a lettered grid with no data overlapping the
grid."""
from satpy.writers.awips_tiled import AWIPSTiledWriter
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
> data = self._get_test_data(shape=(2000, 1000), chunks=500)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:386:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:144:
in _get_test_data
data = np.linspace(0., 1., shape[0] * shape[1],
dtype=np.float32).reshape(shape)
<__array_function__ internals>:5: in linspace
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
start = 0.0, stop = 1.0, num = 2000000, endpoint = True, retstep = False
dtype = <class 'numpy.float32'>, axis = 0
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False,
dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to
False.
In that case, the sequence consists of all but the last of
``num + 1``
evenly spaced samples, so that `stop` is excluded. Note
that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be
non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not
included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the
spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given,
infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only
if start
or stop are array-like. By default (0), the samples will
be along a
new axis inserted at the beginning. Use -1 to get an axis
at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead
of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced
evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points
specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be
non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an
__array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
> y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
15.3 MiB for an array with shape (2000000,) and data type float64
/usr/lib/python3/dist-packages/numpy/core/function_base.py:128: MemoryError
____________ TestAWIPSTiledWriter.test_lettered_tiles_no_valid_data
____________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xe9f7e38>
def test_lettered_tiles_no_valid_data(self):
"""Test creating a lettered grid with no valid data."""
from satpy.writers.awips_tiled import AWIPSTiledWriter
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = da.full((2000, 1000), np.nan, chunks=500, dtype=np.float32)
area_def = self._get_test_area(shape=(2000, 1000),
extents=(-1000000., -1500000.,
1000000., 1500000.))
ds = self._get_test_lcc_data(data, area_def)
> w.save_datasets([ds], sector_id='LCC', source_name="TESTS", tile_count=(3, 3), lettered_grid=True)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:403:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_34, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
------------------------------ Captured log call
-------------------------------
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
WARNING satpy.writers.awips_tiled:awips_tiled.py:935 environment
ORGANIZATION not set for .production_location attribute, using hostname
____________ TestAWIPSTiledWriter.test_lettered_tiles_bad_filename
_____________
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xc9fdd30>
def test_lettered_tiles_bad_filename(self):
"""Test creating a lettered grid with a bad filename."""
from satpy.writers.awips_tiled import AWIPSTiledWriter
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True,
filename="{Bad Key}.nc")
> data = self._get_test_data(shape=(2000, 1000), chunks=500)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:412:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:144:
in _get_test_data
data = np.linspace(0., 1., shape[0] * shape[1],
dtype=np.float32).reshape(shape)
<__array_function__ internals>:5: in linspace
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
start = 0.0, stop = 1.0, num = 2000000, endpoint = True, retstep = False
dtype = <class 'numpy.float32'>, axis = 0
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False,
dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to
False.
In that case, the sequence consists of all but the last of
``num + 1``
evenly spaced samples, so that `stop` is excluded. Note
that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be
non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not
included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the
spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given,
infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only
if start
or stop are array-like. By default (0), the samples will
be along a
new axis inserted at the beginning. Use -1 to get an axis
at the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead
of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced
evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points
specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be
non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an
__array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) *
ndim(delta))
# In-place multiplication y *= delta/div is faster, but
prevents the multiplicant
# from overriding what class is produced, and thus prevents,
e.g. use of Quantities,
# see gh-7142. Hence, we multiply in place only for standard
scalar types.
_mult_inplace = _nx.isscalar(delta)
if div > 0:
step = delta / div
if _nx.any(step == 0):
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
else:
if _mult_inplace:
y *= step
else:
y = y * step
else:
# sequences with 0 items or 1 item with endpoint=True (i.e.
div <= 0)
# have an undefined step
step = NaN
# Multiply with delta to allow possible override of output
class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if retstep:
return y.astype(dtype, copy=False), step
else:
> return y.astype(dtype, copy=False)
E numpy.core._exceptions._ArrayMemoryError: Unable to allocate
7.63 MiB for an array with shape (2000000,) and data type float32
/usr/lib/python3/dist-packages/numpy/core/function_base.py:165: MemoryError
____
TestAWIPSTiledWriter.test_multivar_numbered_tiles_glm[extra_kwargs0-C] ____
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0x5eebb50>
sector = 'C', extra_kwargs = {}
@pytest.mark.parametrize(
"sector",
['C',
'F']
)
@pytest.mark.parametrize(
"extra_kwargs",
[
{},
{'environment_prefix': 'AA'},
{'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'},
]
)
def test_multivar_numbered_tiles_glm(self, sector, extra_kwargs):
"""Test creating a tiles with multiple variables."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import os
os.environ['ORGANIZATION'] = '1' * 50
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data()
area_def = self._get_test_area()
ds1 = self._get_test_lcc_data(data, area_def)
ds1.attrs.update(
dict(
name='total_energy',
platform_name='GOES-17',
sensor='SENSOR',
units='1',
scan_mode='M3',
scene_abbr=sector,
platform_shortname="G17"
)
)
ds2 = ds1.copy()
ds2.attrs.update({
'name': 'flash_extent_density',
})
ds3 = ds1.copy()
ds3.attrs.update({
'name': 'average_flash_area',
})
dqf = ds1.copy()
dqf = (dqf * 255).astype(np.uint8)
dqf.attrs = ds1.attrs.copy()
dqf.attrs.update({
'name': 'DQF',
'_FillValue': 1,
})
> w.save_datasets([ds1, ds2, ds3, dqf], sector_id='TEST',
source_name="TESTS",
tile_count=(3, 3),
template='glm_l2_rad{}'.format(sector.lower()),
**extra_kwargs)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_34, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
____
TestAWIPSTiledWriter.test_multivar_numbered_tiles_glm[extra_kwargs0-F] ____
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xeb1640>
sector = 'F', extra_kwargs = {}
@pytest.mark.parametrize(
"sector",
['C',
'F']
)
@pytest.mark.parametrize(
"extra_kwargs",
[
{},
{'environment_prefix': 'AA'},
{'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'},
]
)
def test_multivar_numbered_tiles_glm(self, sector, extra_kwargs):
"""Test creating a tiles with multiple variables."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import os
os.environ['ORGANIZATION'] = '1' * 50
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data()
area_def = self._get_test_area()
ds1 = self._get_test_lcc_data(data, area_def)
ds1.attrs.update(
dict(
name='total_energy',
platform_name='GOES-17',
sensor='SENSOR',
units='1',
scan_mode='M3',
scene_abbr=sector,
platform_shortname="G17"
)
)
ds2 = ds1.copy()
ds2.attrs.update({
'name': 'flash_extent_density',
})
ds3 = ds1.copy()
ds3.attrs.update({
'name': 'average_flash_area',
})
dqf = ds1.copy()
dqf = (dqf * 255).astype(np.uint8)
dqf.attrs = ds1.attrs.copy()
dqf.attrs.update({
'name': 'DQF',
'_FillValue': 1,
})
> w.save_datasets([ds1, ds2, ds3, dqf], sector_id='TEST',
source_name="TESTS",
tile_count=(3, 3),
template='glm_l2_rad{}'.format(sector.lower()),
**extra_kwargs)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
____
TestAWIPSTiledWriter.test_multivar_numbered_tiles_glm[extra_kwargs1-C] ____
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xfd406e8>
sector = 'C', extra_kwargs = {'environment_prefix': 'AA'}
@pytest.mark.parametrize(
"sector",
['C',
'F']
)
@pytest.mark.parametrize(
"extra_kwargs",
[
{},
{'environment_prefix': 'AA'},
{'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'},
]
)
def test_multivar_numbered_tiles_glm(self, sector, extra_kwargs):
"""Test creating a tiles with multiple variables."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import os
os.environ['ORGANIZATION'] = '1' * 50
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data()
area_def = self._get_test_area()
ds1 = self._get_test_lcc_data(data, area_def)
ds1.attrs.update(
dict(
name='total_energy',
platform_name='GOES-17',
sensor='SENSOR',
units='1',
scan_mode='M3',
scene_abbr=sector,
platform_shortname="G17"
)
)
ds2 = ds1.copy()
ds2.attrs.update({
'name': 'flash_extent_density',
})
ds3 = ds1.copy()
ds3.attrs.update({
'name': 'average_flash_area',
})
dqf = ds1.copy()
dqf = (dqf * 255).astype(np.uint8)
dqf.attrs = ds1.attrs.copy()
dqf.attrs.update({
'name': 'DQF',
'_FillValue': 1,
})
> w.save_datasets([ds1, ds2, ds3, dqf], sector_id='TEST',
source_name="TESTS",
tile_count=(3, 3),
template='glm_l2_rad{}'.format(sector.lower()),
**extra_kwargs)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
____
TestAWIPSTiledWriter.test_multivar_numbered_tiles_glm[extra_kwargs1-F] ____
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xd59ac70>
sector = 'F', extra_kwargs = {'environment_prefix': 'AA'}
@pytest.mark.parametrize(
"sector",
['C',
'F']
)
@pytest.mark.parametrize(
"extra_kwargs",
[
{},
{'environment_prefix': 'AA'},
{'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'},
]
)
def test_multivar_numbered_tiles_glm(self, sector, extra_kwargs):
"""Test creating a tiles with multiple variables."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import os
os.environ['ORGANIZATION'] = '1' * 50
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data()
area_def = self._get_test_area()
ds1 = self._get_test_lcc_data(data, area_def)
ds1.attrs.update(
dict(
name='total_energy',
platform_name='GOES-17',
sensor='SENSOR',
units='1',
scan_mode='M3',
scene_abbr=sector,
platform_shortname="G17"
)
)
ds2 = ds1.copy()
ds2.attrs.update({
'name': 'flash_extent_density',
})
ds3 = ds1.copy()
ds3.attrs.update({
'name': 'average_flash_area',
})
dqf = ds1.copy()
dqf = (dqf * 255).astype(np.uint8)
dqf.attrs = ds1.attrs.copy()
dqf.attrs.update({
'name': 'DQF',
'_FillValue': 1,
})
> w.save_datasets([ds1, ds2, ds3, dqf], sector_id='TEST',
source_name="TESTS",
tile_count=(3, 3),
template='glm_l2_rad{}'.format(sector.lower()),
**extra_kwargs)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
____
TestAWIPSTiledWriter.test_multivar_numbered_tiles_glm[extra_kwargs2-C] ____
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xabbac88>
sector = 'C'
extra_kwargs = {'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'}
@pytest.mark.parametrize(
"sector",
['C',
'F']
)
@pytest.mark.parametrize(
"extra_kwargs",
[
{},
{'environment_prefix': 'AA'},
{'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'},
]
)
def test_multivar_numbered_tiles_glm(self, sector, extra_kwargs):
"""Test creating a tiles with multiple variables."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import os
os.environ['ORGANIZATION'] = '1' * 50
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data()
area_def = self._get_test_area()
ds1 = self._get_test_lcc_data(data, area_def)
ds1.attrs.update(
dict(
name='total_energy',
platform_name='GOES-17',
sensor='SENSOR',
units='1',
scan_mode='M3',
scene_abbr=sector,
platform_shortname="G17"
)
)
ds2 = ds1.copy()
ds2.attrs.update({
'name': 'flash_extent_density',
})
ds3 = ds1.copy()
ds3.attrs.update({
'name': 'average_flash_area',
})
dqf = ds1.copy()
dqf = (dqf * 255).astype(np.uint8)
dqf.attrs = ds1.attrs.copy()
dqf.attrs.update({
'name': 'DQF',
'_FillValue': 1,
})
> w.save_datasets([ds1, ds2, ds3, dqf], sector_id='TEST',
source_name="TESTS",
tile_count=(3, 3),
template='glm_l2_rad{}'.format(sector.lower()),
**extra_kwargs)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
____
TestAWIPSTiledWriter.test_multivar_numbered_tiles_glm[extra_kwargs2-F] ____
self = <satpy.tests.writer_tests.test_awips_tiled.TestAWIPSTiledWriter
object at 0xc9d0328>
sector = 'F'
extra_kwargs = {'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'}
@pytest.mark.parametrize(
"sector",
['C',
'F']
)
@pytest.mark.parametrize(
"extra_kwargs",
[
{},
{'environment_prefix': 'AA'},
{'environment_prefix': 'BB', 'filename':
'{environment_prefix}_{name}_GLM_T{tile_number:04d}.nc'},
]
)
def test_multivar_numbered_tiles_glm(self, sector, extra_kwargs):
"""Test creating a tiles with multiple variables."""
import xarray as xr
from satpy.writers.awips_tiled import AWIPSTiledWriter
import os
os.environ['ORGANIZATION'] = '1' * 50
w = AWIPSTiledWriter(base_dir=self.base_dir, compress=True)
data = self._get_test_data()
area_def = self._get_test_area()
ds1 = self._get_test_lcc_data(data, area_def)
ds1.attrs.update(
dict(
name='total_energy',
platform_name='GOES-17',
sensor='SENSOR',
units='1',
scan_mode='M3',
scene_abbr=sector,
platform_shortname="G17"
)
)
ds2 = ds1.copy()
ds2.attrs.update({
'name': 'flash_extent_density',
})
ds3 = ds1.copy()
ds3.attrs.update({
'name': 'average_flash_area',
})
dqf = ds1.copy()
dqf = (dqf * 255).astype(np.uint8)
dqf.attrs = ds1.attrs.copy()
dqf.attrs.update({
'name': 'DQF',
'_FillValue': 1,
})
> w.save_datasets([ds1, ds2, ds3, dqf], sector_id='TEST',
source_name="TESTS",
tile_count=(3, 3),
template='glm_l2_rad{}'.format(sector.lower()),
**extra_kwargs)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_awips_tiled.py:499:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1615: in
save_datasets
delayeds = self._delay_netcdf_creation(delayed_gen)
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1632: in
_delay_netcdf_creation
for dataset_to_save, output_filename, mode in
dataset_iter(delayed_gen):
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:1654: in
dataset_iter
results = dask.compute(_delayed_gen)[0]
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
__________ TestMITIFFWriter.test_get_test_dataset_three_bands_prereq
___________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_get_test_dataset_three_bands_prereq>
def test_get_test_dataset_three_bands_prereq(self):
"""Test basic writer operation with 3 bands with DataQuery
prerequisites with missing name."""
import os
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
IMAGEDESCRIPTION = 270
dataset = self._get_test_dataset_three_bands_prereq()
w = MITIFFWriter(base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:988:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:104: in
_delayed_create
self._save_datasets_as_mitiff(dataset, image_description,
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:728: in
_save_datasets_as_mitiff
self._save_as_enhanced(tif, datasets, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:664: in
_save_as_enhanced
data = chn.values.clip(0, 1) * 254. + 1
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-2_0, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
______________ TestMITIFFWriter.test_save_dataset_with_bad_value
_______________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_save_dataset_with_bad_value>
def test_save_dataset_with_bad_value(self):
"""Test writer operation with bad values."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected = np.array([[0, 4, 1, 37, 73],
[110, 146, 183, 219, 255]])
dataset = self._get_test_dataset_with_bad_values()
w = MITIFFWriter(base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:831:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:104: in
_delayed_create
self._save_datasets_as_mitiff(dataset, image_description,
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:728: in
_save_datasets_as_mitiff
self._save_as_enhanced(tif, datasets, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:664: in
_save_as_enhanced
data = chn.values.clip(0, 1) * 254. + 1
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-3_0, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
_____________ TestMITIFFWriter.test_save_dataset_with_calibration
______________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_save_dataset_with_calibration>
def test_save_dataset_with_calibration(self):
"""Test writer operation with calibration."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected_ir = np.full((100, 200), 255)
expected_vis = np.full((100, 200), 0)
expected = np.stack([expected_vis, expected_vis, expected_ir,
expected_ir, expected_ir, expected_vis])
expected_key_channel = ['Table_calibration: 1-VIS0.63,
Reflectance(Albedo), [%], 8, [ 0.00 0.39 0.78 1.18 1.57 '
'1.96 2.35 2.75 3.14 3.53 3.92 4.31
4.71 5.10 5.49 5.88 6.27 6.67 7.06 7.45 7.84 8.24 '
'8.63 9.02 9.41 9.80 10.20 10.59 10.98
11.37 11.76 12.16 12.55 12.94 13.33 13.73 14.12 '
'14.51 14.90 15.29 15.69 16.08 16.47
16.86 17.25 17.65 18.04 18.43 18.82 19.22 19.61 '
'20.00 20.39 20.78 21.18 21.57 21.96
22.35 22.75 23.14 23.53 23.92 24.31 24.71 25.10 '
'25.49 25.88 26.27 26.67 27.06 27.45
27.84 28.24 28.63 29.02 29.41 29.80 30.20 30.59 '
'30.98 31.37 31.76 32.16 32.55 32.94
33.33 33.73 34.12 34.51 34.90 35.29 35.69 36.08 '
'36.47 36.86 37.25 37.65 38.04 38.43
38.82 39.22 39.61 40.00 40.39 40.78 41.18 41.57 '
'41.96 42.35 42.75 43.14 43.53 43.92
44.31 44.71 45.10 45.49 45.88 46.27 46.67 47.06 '
'47.45 47.84 48.24 48.63 49.02 49.41
49.80 50.20 50.59 50.98 51.37 51.76 52.16 52.55 '
'52.94 53.33 53.73 54.12 54.51 54.90
55.29 55.69 56.08 56.47 56.86 57.25 57.65 58.04 '
'58.43 58.82 59.22 59.61 60.00 60.39
60.78 61.18 61.57 61.96 62.35 62.75 63.14 63.53 '
'63.92 64.31 64.71 65.10 65.49 65.88
66.27 66.67 67.06 67.45 67.84 68.24 68.63 69.02 '
'69.41 69.80 70.20 70.59 70.98 71.37
71.76 72.16 72.55 72.94 73.33 73.73 74.12 74.51 '
'74.90 75.29 75.69 76.08 76.47 76.86
77.25 77.65 78.04 78.43 78.82 79.22 79.61 80.00 '
'80.39 80.78 81.18 81.57 81.96 82.35
82.75 83.14 83.53 83.92 84.31 84.71 85.10 85.49 '
'85.88 86.27 86.67 87.06 87.45 87.84
88.24 88.63 89.02 89.41 89.80 90.20 90.59 90.98 '
'91.37 91.76 92.16 92.55 92.94 93.33
93.73 94.12 94.51 94.90 95.29 95.69 96.08 96.47 '
'96.86 97.25 97.65 98.04 98.43 98.82
99.22 99.61 100.00 ]',
'Table_calibration: 2-VIS0.86,
Reflectance(Albedo), [%], 8, [ 0.00 0.39 0.78 1.18 1.57 '
'1.96 2.35 2.75 3.14 3.53 3.92 4.31
4.71 5.10 5.49 5.88 6.27 6.67 7.06 7.45 7.84 8.24 '
'8.63 9.02 9.41 9.80 10.20 10.59 10.98
11.37 11.76 12.16 12.55 12.94 13.33 13.73 14.12 '
'14.51 14.90 15.29 15.69 16.08 16.47
16.86 17.25 17.65 18.04 18.43 18.82 19.22 19.61 '
'20.00 20.39 20.78 21.18 21.57 21.96
22.35 22.75 23.14 23.53 23.92 24.31 24.71 25.10 '
'25.49 25.88 26.27 26.67 27.06 27.45
27.84 28.24 28.63 29.02 29.41 29.80 30.20 30.59 '
'30.98 31.37 31.76 32.16 32.55 32.94
33.33 33.73 34.12 34.51 34.90 35.29 35.69 36.08 '
'36.47 36.86 37.25 37.65 38.04 38.43
38.82 39.22 39.61 40.00 40.39 40.78 41.18 41.57 '
'41.96 42.35 42.75 43.14 43.53 43.92
44.31 44.71 45.10 45.49 45.88 46.27 46.67 47.06 '
'47.45 47.84 48.24 48.63 49.02 49.41
49.80 50.20 50.59 50.98 51.37 51.76 52.16 52.55 '
'52.94 53.33 53.73 54.12 54.51 54.90
55.29 55.69 56.08 56.47 56.86 57.25 57.65 58.04 '
'58.43 58.82 59.22 59.61 60.00 60.39
60.78 61.18 61.57 61.96 62.35 62.75 63.14 63.53 '
'63.92 64.31 64.71 65.10 65.49 65.88
66.27 66.67 67.06 67.45 67.84 68.24 68.63 69.02 '
'69.41 69.80 70.20 70.59 70.98 71.37
71.76 72.16 72.55 72.94 73.33 73.73 74.12 74.51 '
'74.90 75.29 75.69 76.08 76.47 76.86
77.25 77.65 78.04 78.43 78.82 79.22 79.61 80.00 '
'80.39 80.78 81.18 81.57 81.96 82.35
82.75 83.14 83.53 83.92 84.31 84.71 85.10 85.49 '
'85.88 86.27 86.67 87.06 87.45 87.84
88.24 88.63 89.02 89.41 89.80 90.20 90.59 90.98 '
'91.37 91.76 92.16 92.55 92.94 93.33
93.73 94.12 94.51 94.90 95.29 95.69 96.08 96.47 '
'96.86 97.25 97.65 98.04 98.43 98.82
99.22 99.61 100.00 ]',
u'Table_calibration: 3(3B)-IR3.7, BT,
°[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 '
'45.29 44.51 43.73 42.94 42.16 41.37
40.59 39.80 39.02 38.24 37.45 36.67 35.88 35.10 '
'34.31 33.53 32.75 31.96 31.18 30.39
29.61 28.82 28.04 27.25 26.47 25.69 24.90 24.12 '
'23.33 22.55 21.76 20.98 20.20 19.41
18.63 17.84 17.06 16.27 15.49 14.71 13.92 13.14 '
'12.35 11.57 10.78 10.00 9.22 8.43 7.65
6.86 6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 '
'-0.20 -0.98 -1.76 -2.55 -3.33 -4.12
-4.90 -5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 '
'-11.18 -11.96 -12.75 -13.53 -14.31
-15.10 -15.88 -16.67 -17.45 -18.24 -19.02 -19.80 '
'-20.59 -21.37 -22.16 -22.94 -23.73
-24.51 -25.29 -26.08 -26.86 -27.65 -28.43 -29.22 '
'-30.00 -30.78 -31.57 -32.35 -33.14
-33.92 -34.71 -35.49 -36.27 -37.06 -37.84 -38.63 '
'-39.41 -40.20 -40.98 -41.76 -42.55
-43.33 -44.12 -44.90 -45.69 -46.47 -47.25 -48.04 '
'-48.82 -49.61 -50.39 -51.18 -51.96
-52.75 -53.53 -54.31 -55.10 -55.88 -56.67 -57.45 '
'-58.24 -59.02 -59.80 -60.59 -61.37
-62.16 -62.94 -63.73 -64.51 -65.29 -66.08 -66.86 '
'-67.65 -68.43 -69.22 -70.00 -70.78
-71.57 -72.35 -73.14 -73.92 -74.71 -75.49 -76.27 '
'-77.06 -77.84 -78.63 -79.41 -80.20
-80.98 -81.76 -82.55 -83.33 -84.12 -84.90 -85.69 '
'-86.47 -87.25 -88.04 -88.82 -89.61
-90.39 -91.18 -91.96 -92.75 -93.53 -94.31 -95.10 '
'-95.88 -96.67 -97.45 -98.24 -99.02
-99.80 -100.59 -101.37 -102.16 -102.94 -103.73 '
'-104.51 -105.29 -106.08 -106.86
-107.65 -108.43 -109.22 -110.00 -110.78 -111.57 '
'-112.35 -113.14 -113.92 -114.71
-115.49 -116.27 -117.06 -117.84 -118.63 -119.41 '
'-120.20 -120.98 -121.76 -122.55
-123.33 -124.12 -124.90 -125.69 -126.47 -127.25 '
'-128.04 -128.82 -129.61 -130.39
-131.18 -131.96 -132.75 -133.53 -134.31 -135.10 '
'-135.88 -136.67 -137.45 -138.24
-139.02 -139.80 -140.59 -141.37 -142.16 -142.94 '
'-143.73 -144.51 -145.29 -146.08
-146.86 -147.65 -148.43 -149.22 -150.00 ]',
u'Table_calibration: 4-IR10.8, BT,
°[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 '
'45.29 '
'44.51 43.73 42.94 42.16 41.37 40.59
39.80 39.02 38.24 37.45 36.67 35.88 35.10 34.31 '
'33.53 32.75 31.96 31.18 30.39 29.61
28.82 28.04 27.25 26.47 25.69 24.90 24.12 23.33 '
'22.55 21.76 20.98 20.20 19.41 18.63
17.84 17.06 16.27 15.49 14.71 13.92 13.14 12.35 '
'11.57 10.78 10.00 9.22 8.43 7.65 6.86
6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 -0.20 '
'-0.98 -1.76 -2.55 -3.33 -4.12 -4.90
-5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 -11.18 '
'-11.96 -12.75 -13.53 -14.31 -15.10
-15.88 -16.67 -17.45 -18.24 -19.02 -19.80 -20.59 '
'-21.37 -22.16 -22.94 -23.73 -24.51
-25.29 -26.08 -26.86 -27.65 -28.43 -29.22 -30.00 '
'-30.78 -31.57 -32.35 -33.14 -33.92
-34.71 -35.49 -36.27 -37.06 -37.84 -38.63 -39.41 '
'-40.20 -40.98 -41.76 -42.55 -43.33
-44.12 -44.90 -45.69 -46.47 -47.25 -48.04 -48.82 '
'-49.61 -50.39 -51.18 -51.96 -52.75
-53.53 -54.31 -55.10 -55.88 -56.67 -57.45 -58.24 '
'-59.02 -59.80 -60.59 -61.37 -62.16
-62.94 -63.73 -64.51 -65.29 -66.08 -66.86 -67.65 '
'-68.43 -69.22 -70.00 -70.78 -71.57
-72.35 -73.14 -73.92 -74.71 -75.49 -76.27 -77.06 '
'-77.84 -78.63 -79.41 -80.20 -80.98
-81.76 -82.55 -83.33 -84.12 -84.90 -85.69 -86.47 '
'-87.25 -88.04 -88.82 -89.61 -90.39
-91.18 -91.96 -92.75 -93.53 -94.31 -95.10 -95.88 '
'-96.67 -97.45 -98.24 -99.02 -99.80
-100.59 -101.37 -102.16 -102.94 -103.73 -104.51 '
'-105.29 -106.08 -106.86 -107.65
-108.43 -109.22 -110.00 -110.78 -111.57 -112.35 '
'-113.14 -113.92 -114.71 -115.49
-116.27 -117.06 -117.84 -118.63 -119.41 -120.20 '
'-120.98 -121.76 -122.55 -123.33
-124.12 -124.90 -125.69 -126.47 -127.25 -128.04 '
'-128.82 -129.61 -130.39 -131.18
-131.96 -132.75 -133.53 -134.31 -135.10 -135.88 '
'-136.67 -137.45 -138.24 -139.02
-139.80 -140.59 -141.37 -142.16 -142.94 -143.73 '
'-144.51 -145.29 -146.08 -146.86
-147.65 -148.43 -149.22 -150.00 ]',
u'Table_calibration: 5-IR11.5, BT,
°[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 '
'45.29 '
'44.51 43.73 42.94 42.16 41.37 40.59
39.80 39.02 38.24 37.45 36.67 35.88 35.10 34.31 '
'33.53 32.75 31.96 31.18 30.39 29.61
28.82 28.04 27.25 26.47 25.69 24.90 24.12 23.33 '
'22.55 21.76 20.98 20.20 19.41 18.63
17.84 17.06 16.27 15.49 14.71 13.92 13.14 12.35 '
'11.57 10.78 10.00 9.22 8.43 7.65 6.86
6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 -0.20 '
'-0.98 -1.76 -2.55 -3.33 -4.12 -4.90
-5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 -11.18 '
'-11.96 -12.75 -13.53 -14.31 -15.10
-15.88 -16.67 -17.45 -18.24 -19.02 -19.80 -20.59 '
'-21.37 -22.16 -22.94 -23.73 -24.51
-25.29 -26.08 -26.86 -27.65 -28.43 -29.22 -30.00 '
'-30.78 -31.57 -32.35 -33.14 -33.92
-34.71 -35.49 -36.27 -37.06 -37.84 -38.63 -39.41 '
'-40.20 -40.98 -41.76 -42.55 -43.33
-44.12 -44.90 -45.69 -46.47 -47.25 -48.04 -48.82 '
'-49.61 -50.39 -51.18 -51.96 -52.75
-53.53 -54.31 -55.10 -55.88 -56.67 -57.45 -58.24 '
'-59.02 -59.80 -60.59 -61.37 -62.16
-62.94 -63.73 -64.51 -65.29 -66.08 -66.86 -67.65 '
'-68.43 -69.22 -70.00 -70.78 -71.57
-72.35 -73.14 -73.92 -74.71 -75.49 -76.27 -77.06 '
'-77.84 -78.63 -79.41 -80.20 -80.98
-81.76 -82.55 -83.33 -84.12 -84.90 -85.69 -86.47 '
'-87.25 -88.04 -88.82 -89.61 -90.39
-91.18 -91.96 -92.75 -93.53 -94.31 -95.10 -95.88 '
'-96.67 -97.45 -98.24 -99.02 -99.80
-100.59 -101.37 -102.16 -102.94 -103.73 -104.51 '
'-105.29 -106.08 -106.86 -107.65
-108.43 -109.22 -110.00 -110.78 -111.57 -112.35 '
'-113.14 -113.92 -114.71 -115.49
-116.27 -117.06 -117.84 -118.63 -119.41 -120.20 '
'-120.98 -121.76 -122.55 -123.33
-124.12 -124.90 -125.69 -126.47 -127.25 -128.04 '
'-128.82 -129.61 -130.39 -131.18
-131.96 -132.75 -133.53 -134.31 -135.10 -135.88 '
'-136.67 -137.45 -138.24 -139.02
-139.80 -140.59 -141.37 -142.16 -142.94 -143.73 '
'-144.51 -145.29 -146.08 -146.86
-147.65 -148.43 -149.22 -150.00 ]',
'Table_calibration: 6(3A)-VIS1.6,
Reflectance(Albedo), [%], 8, [ 0.00 0.39 0.78 1.18 '
'1.57 1.96 2.35 2.75 3.14 3.53 3.92
4.31 4.71 5.10 5.49 5.88 6.27 6.67 7.06 7.45 7.84 '
'8.24 8.63 9.02 9.41 9.80 10.20 10.59
10.98 11.37 11.76 12.16 12.55 12.94 13.33 13.73 '
'14.12 14.51 14.90 15.29 15.69 16.08
16.47 16.86 17.25 17.65 18.04 18.43 18.82 19.22 '
'19.61 20.00 20.39 20.78 21.18 21.57
21.96 22.35 22.75 23.14 23.53 23.92 24.31 24.71 '
'25.10 25.49 25.88 26.27 26.67 27.06
27.45 27.84 28.24 28.63 29.02 29.41 29.80 30.20 '
'30.59 30.98 31.37 31.76 32.16 32.55
32.94 33.33 33.73 34.12 34.51 34.90 35.29 35.69 '
'36.08 36.47 36.86 37.25 37.65 38.04
38.43 38.82 39.22 39.61 40.00 40.39 40.78 41.18 '
'41.57 41.96 42.35 42.75 43.14 43.53
43.92 44.31 44.71 45.10 45.49 45.88 46.27 46.67 '
'47.06 47.45 47.84 48.24 48.63 49.02
49.41 49.80 50.20 50.59 50.98 51.37 51.76 52.16 '
'52.55 52.94 53.33 53.73 54.12 54.51
54.90 55.29 55.69 56.08 56.47 56.86 57.25 57.65 '
'58.04 58.43 58.82 59.22 59.61 60.00
60.39 60.78 61.18 61.57 61.96 62.35 62.75 63.14 '
'63.53 63.92 64.31 64.71 65.10 65.49
65.88 66.27 66.67 67.06 67.45 67.84 68.24 68.63 '
'69.02 69.41 69.80 70.20 70.59 70.98
71.37 71.76 72.16 72.55 72.94 73.33 73.73 74.12 '
'74.51 74.90 75.29 75.69 76.08 76.47
76.86 77.25 77.65 78.04 78.43 78.82 79.22 79.61 '
'80.00 80.39 80.78 81.18 81.57 81.96
82.35 82.75 83.14 83.53 83.92 84.31 84.71 85.10 '
'85.49 85.88 86.27 86.67 87.06 87.45
87.84 88.24 88.63 89.02 89.41 89.80 90.20 90.59 '
'90.98 91.37 91.76 92.16 92.55 92.94
93.33 93.73 94.12 94.51 94.90 95.29 95.69 96.08 '
'96.47 96.86 97.25 97.65 98.04 98.43
98.82 99.22 99.61 100.00 ]']
dataset = self._get_test_dataset_calibration()
w =
MITIFFWriter(filename=dataset.attrs['metadata_requirements']['file_pattern'],
base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:731:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
____________________ TestMITIFFWriter.test_save_one_dataset
____________________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_save_one_dataset>
def test_save_one_dataset(self):
"""Test basic writer operation with one dataset ie. no bands."""
import os
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_one_dataset()
w = MITIFFWriter(base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:571:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:104: in
_delayed_create
self._save_datasets_as_mitiff(dataset, image_description,
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:728: in
_save_datasets_as_mitiff
self._save_as_enhanced(tif, datasets, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:664: in
_save_as_enhanced
data = chn.values.clip(0, 1) * 254. + 1
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-4_0, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
______________ TestMITIFFWriter.test_save_one_dataset_sesnor_set
_______________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_save_one_dataset_sesnor_set>
def test_save_one_dataset_sesnor_set(self):
"""Test basic writer operation with one dataset ie. no bands."""
import os
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_one_dataset_sensor_set()
w = MITIFFWriter(base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:586:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:104: in
_delayed_create
self._save_datasets_as_mitiff(dataset, image_description,
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:728: in
_save_datasets_as_mitiff
self._save_as_enhanced(tif, datasets, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:664: in
_save_as_enhanced
data = chn.values.clip(0, 1) * 254. + 1
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-5_0, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
------------------------------ Captured log call
-------------------------------
WARNING satpy.writers.mitiff:mitiff.py:81 Sensor is set, will use the
first value: {'avhrr'}
______________________ TestMITIFFWriter.test_simple_write
______________________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_simple_write>
def test_simple_write(self):
"""Test basic writer operation."""
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_dataset()
w = MITIFFWriter(base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:530:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:104: in
_delayed_create
self._save_datasets_as_mitiff(dataset, image_description,
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:728: in
_save_datasets_as_mitiff
self._save_as_enhanced(tif, datasets, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:664: in
_save_as_enhanced
data = chn.values.clip(0, 1) * 254. + 1
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-6_0, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
_________________ TestMITIFFWriter.test_simple_write_two_bands
_________________
self = <satpy.tests.writer_tests.test_mitiff.TestMITIFFWriter
testMethod=test_simple_write_two_bands>
def test_simple_write_two_bands(self):
"""Test basic writer operation with 3 bands from 2
prerequisites."""
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_dataset_three_bands_two_prereq()
w = MITIFFWriter(base_dir=self.base_dir)
> w.save_dataset(dataset)
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_mitiff.py:977:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/mitiff.py:113: in
save_dataset
return delayed.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:517: in get_async
raise_exception(exc, tb)
/usr/lib/python3/dist-packages/dask/local.py:325: in reraise
raise exc
/usr/lib/python3/dist-packages/dask/local.py:223: in execute_task
result = _execute_task(task, data)
/usr/lib/python3/dist-packages/dask/core.py:121: in _execute_task
return func(*(_execute_task(a, cache) for a in args))
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:104: in
_delayed_create
self._save_datasets_as_mitiff(dataset, image_description,
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:728: in
_save_datasets_as_mitiff
self._save_as_enhanced(tif, datasets, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/mitiff.py:664: in
_save_as_enhanced
data = chn.values.clip(0, 1) * 254. + 1
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-7_0, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
_________________________ test_write_and_read_file_RGB
_________________________
test_image_large_asia_RGB = <trollimage.xrimage.XRImage object at
0xb4697760>
tmp_path =
PosixPath('/tmp/pytest-of-debci/pytest-0/test_write_and_read_file_RGB0')
def test_write_and_read_file_RGB(test_image_large_asia_RGB, tmp_path):
"""Test writing and reading RGB."""
import rasterio
from satpy.writers.ninjogeotiff import NinJoGeoTIFFWriter
fn = os.fspath(tmp_path / "test.tif")
ngtw = NinJoGeoTIFFWriter()
> ngtw.save_dataset(
test_image_large_asia_RGB.data,
filename=fn,
fill_value=0,
PhysicUnit="N/A",
PhysicValue="N/A",
SatelliteNameID=6400014,
ChannelID=900015,
DataType="GORN",
DataSource="dowsing rod")
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_ninjogeotiff.py:467:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/satpy/writers/__init__.py:809: in
save_dataset
return self.save_image(img, filename=filename, compute=compute,
fill_value=fill_value, **kwargs)
/usr/lib/python3/dist-packages/satpy/writers/ninjogeotiff.py:178: in
save_image
return super().save_image(
/usr/lib/python3/dist-packages/satpy/writers/geotiff.py:228: in save_image
return img.save(filename, fformat='tif', fill_value=fill_value,
/usr/lib/python3/dist-packages/trollimage/xrimage.py:419: in save
return self.rio_save(filename, fformat=fformat,
/usr/lib/python3/dist-packages/trollimage/xrimage.py:590: in rio_save
res = da.store(*to_store)
/usr/lib/python3/dist-packages/dask/array/core.py:1043: in store
compute_as_if_collection(Array, store_dsk, store_keys, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:315: in compute_as_if_collection
return schedule(dsk2, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
_________________________ test_get_min_gray_value_RGB
__________________________
ntg2 = <satpy.writers.ninjogeotiff.NinJoTagGenerator object at 0xb466e070>
def test_get_min_gray_value_RGB(ntg2):
"""Test getting min gray value for RGB.
Note that min/max gray value is mandatory in NinJo even for
RGBs?
"""
> assert ntg2.get_min_gray_value().compute().item() == 1 # fill value 0
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_ninjogeotiff.py:696:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/xarray/core/dataarray.py:955: in
compute
return new.load(**kwargs)
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:929: in load
ds = self._to_temp_dataset().load(**kwargs)
/usr/lib/python3/dist-packages/xarray/core/dataset.py:865: in load
evaluated_data = da.compute(*lazy_data.values(), **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
_________________________ test_get_max_gray_value_RGB
__________________________
ntg2 = <satpy.writers.ninjogeotiff.NinJoTagGenerator object at 0xb466e070>
def test_get_max_gray_value_RGB(ntg2):
"""Test max gray value for RGB."""
> assert ntg2.get_max_gray_value() == 255
/usr/lib/python3/dist-packages/satpy/tests/writer_tests/test_ninjogeotiff.py:713:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ /usr/lib/python3/dist-packages/xarray/core/common.py:129: in
__bool__
return bool(self.values)
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:651: in values
return self.variable.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:517: in values
return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:259: in
_as_array_or_item
data = np.asarray(data)
/usr/lib/python3/dist-packages/numpy/core/_asarray.py:83: in asarray
return array(a, dtype, copy=False, order=order)
/usr/lib/python3/dist-packages/dask/array/core.py:1491: in __array__
x = self.compute()
/usr/lib/python3/dist-packages/dask/base.py:288: in compute
(result,) = compute(self, traverse=False, **kwargs)
/usr/lib/python3/dist-packages/dask/base.py:570: in compute
results = schedule(dsk, keys, **kwargs)
/usr/lib/python3/dist-packages/dask/threaded.py:79: in get
results = get_async(
/usr/lib/python3/dist-packages/dask/local.py:505: in get_async
fire_tasks(chunksize)
/usr/lib/python3/dist-packages/dask/local.py:500: in fire_tasks
fut = submit(batch_execute_tasks, each_args)
/usr/lib/python3.9/concurrent/futures/thread.py:176: in submit
self._adjust_thread_count()
/usr/lib/python3.9/concurrent/futures/thread.py:199: in _adjust_thread_count
t.start()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
self = <Thread(ThreadPoolExecutor-0_35, initial)>
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It
arranges for the
object's run() method to be invoked in a separate thread of
control.
This method will raise a RuntimeError if called more than
once on the
same thread object.
"""
if not self._initialized:
raise RuntimeError("thread.__init__() not called")
if self._started.is_set():
raise RuntimeError("threads can only be started once")
with _active_limbo_lock:
_limbo[self] = self
try:
> _start_new_thread(self._bootstrap, ())
E RuntimeError: can't start new thread
/usr/lib/python3.9/threading.py:892: RuntimeError
=============================== warnings summary
===============================
../../../../usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_mviri_l1b_fiduceo_nc.py:535
/usr/lib/python3/dist-packages/satpy/tests/reader_tests/test_mviri_l1b_fiduceo_nc.py:535:
PytestUnknownMarkWarning: Unknown pytest.mark.file_handler_data - is
this a typo? You can register custom marks to avoid this warning - for
details, see https://docs.pytest.org/en/stable/mark.html
@pytest.mark.file_handler_data(mask_bad_quality=False)
tests/test_composites.py: 13 warnings
tests/test_config.py: 112 warnings
tests/test_modifiers.py: 2 warnings
tests/test_multiscene.py: 10 warnings
tests/test_regressions.py: 6 warnings
tests/test_resample.py: 14 warnings
tests/test_scene.py: 15 warnings
tests/test_writers.py: 4 warnings
tests/test_yaml_reader.py: 3 warnings
tests/compositor_tests/test_abi.py: 1 warning
tests/compositor_tests/test_ahi.py: 1 warning
tests/compositor_tests/test_glm.py: 1 warning
tests/modifier_tests/test_crefl.py: 12 warnings
tests/reader_tests/test_ahi_hsd.py: 2 warnings
tests/reader_tests/test_ahi_l1b_gridded_bin.py: 1 warning
tests/reader_tests/test_cmsaf_claas.py: 2 warnings
tests/reader_tests/test_fci_l1c_nc.py: 16 warnings
tests/reader_tests/test_generic_image.py: 3 warnings
tests/reader_tests/test_geocat.py: 6 warnings
tests/reader_tests/test_geos_area.py: 1 warning
tests/reader_tests/test_gpm_imerg.py: 1 warning
tests/reader_tests/test_hrit_base.py: 1 warning
tests/reader_tests/test_mviri_l1b_fiduceo_nc.py: 12 warnings
tests/reader_tests/test_nwcsaf_msg.py: 1 warning
tests/reader_tests/test_nwcsaf_nc.py: 3 warnings
tests/reader_tests/test_seviri_l1b_hrit.py: 3 warnings
tests/reader_tests/test_seviri_l1b_native.py: 2 warnings
tests/writer_tests/test_mitiff.py: 23 warnings
/usr/lib/python3/dist-packages/pyproj/crs/crs.py:1256: UserWarning:
You will likely lose important projection information when converting to
a PROJ string from another format. See:
https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems
return self._crs.to_proj4(version=version)
tests/test_composites.py::TestMatchDataArrays::test_nondimensional_coords
tests/test_composites.py::TestMatchDataArrays::test_nondimensional_coords
tests/reader_tests/test_goes_imager_nc.py::GOESNCEUMFileHandlerRadianceTest::test_get_dataset_radiance
tests/reader_tests/test_goes_imager_nc.py::GOESNCEUMFileHandlerRadianceTest::test_get_dataset_radiance
tests/reader_tests/test_goes_imager_nc.py::GOESNCEUMFileHandlerRadianceTest::test_get_dataset_radiance
tests/reader_tests/test_goes_imager_nc.py::GOESNCEUMFileHandlerRadianceTest::test_get_dataset_radiance
tests/reader_tests/test_goes_imager_nc.py::GOESNCEUMFileHandlerReflectanceTest::test_get_dataset_reflectance
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:2343:
PendingDeprecationWarning: dropping variables using `drop` will be
deprecated; using drop_vars is encouraged.
ds = self._to_temp_dataset().drop(labels, dim, errors=errors)
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers0-writers0-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers0-None-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers0-writers2-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[None-writers0-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[None-None-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[None-writers2-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers2-writers0-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers2-None-None]
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers2-writers2-None]
/usr/lib/python3/dist-packages/satpy/modifiers/_crefl.py:56:
DeprecationWarning: 'dem_filename' for 'ReflectanceCorrector' is
deprecated. Use 'url' instead.
warnings.warn("'dem_filename' for 'ReflectanceCorrector' is "
tests/test_data_download.py::TestDataDownload::test_find_registerable[readers0-None-comp_sensors0]
/usr/lib/python3/dist-packages/pyninjotiff/tifffile.py:154:
UserWarning: failed to import the optional _tifffile C extension module.
Loading of some compressed images will be slow.
Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/
warnings.warn(
tests/test_dataset.py::test_combine_dicts_different[test_mda5]
/usr/lib/python3/dist-packages/satpy/dataset/metadata.py:198:
FutureWarning: elementwise comparison failed; returning scalar instead,
but in the future will perform elementwise comparison
res = comp_func(a, b)
tests/test_dataset.py::TestIDQueryInteractions::test_seviri_hrv_has_priority_over_vis008
/usr/lib/python3/dist-packages/satpy/tests/test_dataset.py:662:
UserWarning: Attribute access to DataIDs is deprecated, use key access
instead.
assert res[0].name == "HRV"
tests/test_dependency_tree.py::TestMultipleSensors::test_compositor_loaded_sensor_order
/usr/lib/python3/dist-packages/satpy/tests/test_dependency_tree.py:223:
UserWarning: Attribute access to DataIDs is deprecated, use key access
instead.
self.assertEqual(comp_nodes[0].name.resolution, 500)
tests/test_modifiers.py::TestPSPAtmosphericalCorrection::test_call
tests/modifier_tests/test_crefl.py::TestReflectanceCorrectorModifier::test_reflectance_corrector_abi
tests/modifier_tests/test_crefl.py::TestReflectanceCorrectorModifier::test_reflectance_corrector_abi
/usr/lib/python3/dist-packages/dask/core.py:121: RuntimeWarning:
invalid value encountered in remainder
return func(*(_execute_task(a, cache) for a in args))
tests/test_readers.py::TestReaderLoader::test_missing_requirements
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:495:
UserWarning: No handler for reading requirement 'HRIT_EPI' for
H-000-MSG4__-MSG4________-IR_108___-000006___-201809050900-__
warnings.warn(msg)
tests/test_readers.py::TestReaderLoader::test_missing_requirements
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:495:
UserWarning: No handler for reading requirement 'HRIT_PRO' for
H-000-MSG4__-MSG4________-IR_108___-000006___-201809050900-__
warnings.warn(msg)
tests/test_readers.py::TestReaderLoader::test_missing_requirements
/usr/lib/python3/dist-packages/satpy/readers/yaml_reader.py:498:
UserWarning: No matching requirement file of type HRIT_PRO for
H-000-MSG4__-MSG4________-IR_108___-000006___-201809051000-__
warnings.warn(str(err) + ' for {}'.format(filename))
tests/test_resample.py::TestHLResample::test_type_preserve
tests/test_resample.py::TestHLResample::test_type_preserve
/usr/lib/python3/dist-packages/pyresample/geometry.py:567:
DeprecationWarning: This function is deprecated. See:
https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1
xyz = np.stack(transform(src, dst, lons, lats, alt), axis=1)
tests/test_resample.py::TestKDTreeResampler::test_check_numpy_cache
/usr/lib/python3/dist-packages/satpy/resample.py:551: UserWarning:
Using Numpy files as resampling cache is deprecated.
warnings.warn("Using Numpy files as resampling cache is "
tests/test_resample.py::TestBucketAvg::test_compute_and_not_use_skipna_handling
tests/test_resample.py::TestBucketAvg::test_compute_and_not_use_skipna_handling
tests/test_resample.py::TestBucketSum::test_compute_and_not_use_skipna_handling
tests/test_resample.py::TestBucketSum::test_compute_and_not_use_skipna_handling
/usr/lib/python3/dist-packages/satpy/resample.py:1072:
DeprecationWarning: Argument mask_all_nan is deprecated.Please update
Pyresample and use skipna for missing values handling.
warnings.warn('Argument mask_all_nan is deprecated.'
tests/test_resample.py::TestBucketAvg::test_compute_and_use_skipna_handling
tests/test_resample.py::TestBucketSum::test_compute_and_use_skipna_handling
/usr/lib/python3/dist-packages/satpy/resample.py:1067:
DeprecationWarning: Argument mask_all_nan is deprecated. Please use
skipna for missing values handling. Continuing with default skipna=True,
if not provided differently.
warnings.warn('Argument mask_all_nan is deprecated. Please use
skipna for missing values handling. '
tests/test_scene.py: 2 warnings
tests/test_writers.py: 14 warnings
tests/writer_tests/test_geotiff.py: 4 warnings
/usr/lib/python3/dist-packages/rasterio/__init__.py:230:
NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The
identity matrix be returned.
s = writer(path, mode, driver=driver,
tests/test_scene.py: 3 warnings
tests/test_writers.py: 10 warnings
tests/reader_tests/test_aapp_l1b.py: 3 warnings
tests/writer_tests/test_geotiff.py: 2 warnings
tests/writer_tests/test_simple_image.py: 2 warnings
/usr/lib/python3/dist-packages/dask/core.py:121: RuntimeWarning:
divide by zero encountered in true_divide
return func(*(_execute_task(a, cache) for a in args))
tests/test_scene.py: 3 warnings
tests/test_writers.py: 10 warnings
tests/writer_tests/test_geotiff.py: 2 warnings
tests/writer_tests/test_simple_image.py: 2 warnings
/usr/lib/python3/dist-packages/dask/core.py:121: RuntimeWarning:
invalid value encountered in multiply
return func(*(_execute_task(a, cache) for a in args))
tests/enhancement_tests/test_enhancements.py::TestEnhancementStretch::test_crefl_scaling
/usr/lib/python3/dist-packages/satpy/enhancements/__init__.py:114:
DeprecationWarning: 'crefl_scaling' is deprecated, use
'piecewise_linear_stretch' instead.
warnings.warn("'crefl_scaling' is deprecated, use
'piecewise_linear_stretch' instead.", DeprecationWarning)
tests/enhancement_tests/test_enhancements.py::TestColormapLoading::test_cmap_from_file_rgb_1
tests/enhancement_tests/test_enhancements.py::TestColormapLoading::test_cmap_list
/usr/lib/python3/dist-packages/trollimage/colormap.py:207:
UserWarning: Colormap 'colors' should be flotaing point numbers between
0 and 1.
warnings.warn("Colormap 'colors' should be flotaing point numbers
between 0 and 1.")
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
tests/reader_tests/test_aapp_l1b.py::TestAAPPL1BAllChannelsPresent::test_read
/usr/lib/python3/dist-packages/dask/core.py:121: RuntimeWarning:
invalid value encountered in log
return func(*(_execute_task(a, cache) for a in args))
tests/reader_tests/test_abi_l2_nc.py::TestMCMIPReading::test_mcmip_get_dataset
/usr/lib/python3/dist-packages/satpy/readers/abi_l2_nc.py:40:
UserWarning: Attribute access to DataIDs is deprecated, use key access
instead.
var += "_" + key.name
tests/reader_tests/test_ahi_hsd.py::TestAHIHSDFileHandler::test_read_band
tests/reader_tests/test_ahi_hsd.py::TestAHIHSDFileHandler::test_read_band
tests/reader_tests/test_ahi_hsd.py::TestAHIHSDFileHandler::test_scene_loading
tests/reader_tests/test_utils.py::TestHelpers::test_get_earth_radius
tests/reader_tests/test_utils.py::TestHelpers::test_get_earth_radius
tests/reader_tests/test_utils.py::TestHelpers::test_get_earth_radius
tests/reader_tests/test_utils.py::TestHelpers::test_get_earth_radius
tests/reader_tests/test_utils.py::TestHelpers::test_get_earth_radius
tests/reader_tests/test_utils.py::TestHelpers::test_get_earth_radius
/usr/lib/python3/dist-packages/satpy/readers/utils.py:320:
DeprecationWarning: This function is deprecated. See:
https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1
x, y, z = pyproj.transform(latlong, geocent, lon, lat, 0.)
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDF::test_get_dataset
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDF::test_get_dataset_counts
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDF::test_get_dataset_vis
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDFIRCal::test_default_calibrate
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDFIRCal::test_gsics_radiance_corr
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDFIRCal::test_infile_calibrate
tests/reader_tests/test_ami_l1b.py::TestAMIL1bNetCDFIRCal::test_user_radiance_corr
/usr/lib/python3/dist-packages/satpy/readers/ami_l1b.py:165:
DeprecationWarning: This function is deprecated. See:
https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1
sc_position = pyproj.transform(
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTGetCalibratedReflectances::test_calibrated_reflectances_values
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTGetCalibratedBT::test_calibrated_bt_values
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTChannel3::test_channel_3a_masking
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTChannel3::test_channel_3b_masking
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTNavigation::test_latitudes_are_returned
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTNavigation::test_longitudes_are_returned
/usr/lib/python3/dist-packages/satpy/readers/hrpt.py:80:
DeprecationWarning: parsing timezone aware datetimes is deprecated; this
will raise an error in the future
return (np.datetime64(
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTGetCalibratedReflectances::test_calibrated_reflectances_values
tests/reader_tests/test_avhrr_l0_hrpt.py::TestHRPTChannel3::test_channel_3a_masking
/usr/lib/python3/dist-packages/satpy/readers/hrpt.py:222:
DeprecationWarning: parsing timezone aware datetimes is deprecated; this
will raise an error in the future
- np.datetime64(str(self.year) + '-01-01T00:00:00Z'))
tests/reader_tests/test_fci_l2_nc.py::TestFciL2NCReadingByteData::test_byte_extraction
/usr/lib/python3/dist-packages/pyresample/geometry.py:1282:
RuntimeWarning: invalid value encountered in double_scalars
self.pixel_offset_x = -self.area_extent[0] / self.pixel_size_x
tests/reader_tests/test_fci_l2_nc.py::TestFciL2NCReadingByteData::test_byte_extraction
/usr/lib/python3/dist-packages/pyresample/geometry.py:1283:
RuntimeWarning: invalid value encountered in double_scalars
self.pixel_offset_y = self.area_extent[3] / self.pixel_size_y
tests/reader_tests/test_generic_image.py: 7 warnings
tests/reader_tests/test_smos_l2_wind.py: 2 warnings
tests/writer_tests/test_mitiff.py: 5 warnings
/usr/lib/python3/dist-packages/pyproj/crs/crs.py:131: FutureWarning:
'+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is
the preferred initialization method. When making the change, be mindful
of axis order changes:
https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
in_crs_string = _prepare_from_proj_string(in_crs_string)
tests/reader_tests/test_generic_image.py::TestGenericImage::test_png_scene
tests/reader_tests/test_generic_image.py::TestGenericImage::test_png_scene
/usr/lib/python3/dist-packages/rasterio/__init__.py:220:
NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The
identity matrix be returned.
s = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)
tests/reader_tests/test_goes_imager_nc.py: 28 warnings
/usr/lib/python3/dist-packages/satpy/readers/goes_imager_nc.py:738:
DeprecationWarning: an integer is required (got type DataArray).
Implicit conversion to integers using __int__ is deprecated, and may be
removed in a future version of Python.
return datetime(year=dt.year, month=dt.month, day=dt.day,
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_angles
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_angles
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_angles
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_angles
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_meteo
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_meteo
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_meteo
tests/reader_tests/test_olci_nc.py::TestOLCIReader::test_olci_meteo
/usr/lib/python3/dist-packages/geotiepoints/interpolator.py:239:
DeprecationWarning: elementwise comparison failed; this will raise an
error in the future.
if np.all(self.hrow_indices == self.row_indices):
tests/reader_tests/test_satpy_cf_nc.py: 8 warnings
tests/writer_tests/test_cf.py: 19 warnings
/usr/lib/python3/dist-packages/satpy/writers/cf_writer.py:754:
FutureWarning: The default behaviour of the CF writer will soon change
to not compress data by default.
warnings.warn("The default behaviour of the CF writer will soon
change to not compress data by default.",
tests/reader_tests/test_satpy_cf_nc.py: 18 warnings
/usr/lib/python3/dist-packages/satpy/readers/satpy_cf_nc.py:240:
DeprecationWarning: The truth value of an empty array is ambiguous.
Returning False, but in future this will result in an error. Use
`array.size > 0` to check that an array is not empty.
if 'modifiers' in ds_info and not ds_info['modifiers']:
tests/reader_tests/test_satpy_cf_nc.py::TestCFReader::test_read_prefixed_channels_by_user_no_prefix
tests/writer_tests/test_cf.py::TestCFWriter::test_save_dataset_a_digit_no_prefix_include_attr
/usr/lib/python3/dist-packages/satpy/writers/cf_writer.py:566:
UserWarning: Invalid NetCDF dataset name: 1 starts with a digit.
warnings.warn('Invalid NetCDF dataset name: {} starts with a
digit.'.format(name))
tests/reader_tests/test_seviri_base.py::TestOrbitPolynomialFinder::test_get_orbit_polynomial[orbit_polynomials1-time1-orbit_polynomial_exp1]
tests/reader_tests/test_seviri_base.py::TestOrbitPolynomialFinder::test_get_orbit_polynomial_exceptions[orbit_polynomials1-time1]
/usr/lib/python3/dist-packages/satpy/readers/seviri_base.py:770:
UserWarning: No orbit polynomial valid for 2006-01-01T12:15:00.000000.
Using closest match.
warnings.warn(
tests/reader_tests/test_seviri_base.py::TestOrbitPolynomialFinder::test_get_orbit_polynomial_exceptions[orbit_polynomials0-time0]
/usr/lib/python3/dist-packages/satpy/readers/seviri_base.py:770:
UserWarning: No orbit polynomial valid for 2006-01-02T12:15:00.000000.
Using closest match.
warnings.warn(
tests/reader_tests/test_seviri_l1b_hrit.py::TestHRITMSGFileHandler::test_satpos_no_valid_orbit_polynomial
tests/reader_tests/test_seviri_l1b_native.py::TestNativeMSGDataset::test_satpos_no_valid_orbit_polynomial
/usr/lib/python3/dist-packages/satpy/readers/seviri_base.py:770:
UserWarning: No orbit polynomial valid for 2006-01-01T12:15:09.304888.
Using closest match.
warnings.warn(
tests/reader_tests/test_seviri_l1b_nc.py::TestNCSEVIRIFileHandler::test_satpos_no_valid_orbit_polynomial
/usr/lib/python3/dist-packages/satpy/readers/seviri_base.py:770:
UserWarning: No orbit polynomial valid for 2020-01-01T00:00:00.000000.
Using closest match.
warnings.warn(
tests/reader_tests/test_slstr_l1b.py::TestSLSTRReader::test_instantiate
/usr/lib/python3/dist-packages/satpy/readers/slstr_l1b.py:174:
UserWarning: Warning: No radiance adjustment supplied for channel foo_nadir
warnings.warn("Warning: No radiance adjustment supplied " +
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_no_valid_data
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_no_valid_data
/usr/lib/python3/dist-packages/dask/utils.py:35: RuntimeWarning:
All-NaN slice encountered
return func(*args, **kwargs)
tests/writer_tests/test_awips_tiled.py: 54 warnings
/usr/lib/python3/dist-packages/satpy/writers/awips_tiled.py:940:
UserWarning: Production location attribute is longer than 31 characters
(AWIPS limit). Set it to a smaller value with the 'ORGANIZATION'
environment variable. Defaults to hostname and is currently set to
'11111111111111111111111111111111111111111111111111'.
warnings.warn("Production location attribute is longer than 31 "
tests/writer_tests/test_cf.py::TestCFWriter::test_groups
/usr/lib/python3/dist-packages/satpy/writers/cf_writer.py:361:
UserWarning: Cannot pretty-format "acq_time" coordinates because they
are not unique among the given datasets
warnings.warn('Cannot pretty-format "{}" coordinates because they
are not unique among the '
tests/writer_tests/test_cf.py::TestCFWriter::test_link_coords
/usr/lib/python3/dist-packages/satpy/writers/cf_writer.py:305:
UserWarning: Coordinate "not_exist" referenced by dataarray var4 does
not exist, dropping reference.
warnings.warn('Coordinate "{}" referenced by dataarray {} does not
exist, dropping reference.'
tests/writer_tests/test_cf.py::TestCFWriter::test_save_with_compression
/usr/lib/python3/dist-packages/satpy/writers/cf_writer.py:759:
FutureWarning: The `compression` keyword will soon be deprecated. Please
use the `encoding` of the DataArrays to tune compression from now on.
warnings.warn("The `compression` keyword will soon be deprecated.
Please use the `encoding` of the "
-- Docs: https://docs.pytest.org/en/stable/warnings.html
=========================== short test summary info
============================
FAILED tests/test_scene.py::TestScene::test_crop -
numpy.core._exceptions._Ar...
FAILED tests/test_scene.py::TestScene::test_crop_epsg_crs -
numpy.core._excep...
FAILED tests/test_scene.py::TestScene::test_crop_rgb -
numpy.core._exceptions...
FAILED tests/test_scene.py::TestSceneAggregation::test_aggregate -
numpy.core...
FAILED
tests/test_scene.py::TestSceneAggregation::test_aggregate_with_boundary
FAILED
tests/reader_tests/test_mimic_TPW2_nc.py::TestMimicTPW2Reader::test_load_mimic
FAILED
tests/reader_tests/test_modis_l2.py::TestModisL2::test_load_longitude_latitude[modis_l2_nasa_mod35_file-True-False-False-1000]
FAILED
tests/reader_tests/test_modis_l2.py::TestModisL2::test_load_250m_cloud_mask_dataset[modis_l2_nasa_mod35_file-False]
FAILED tests/reader_tests/test_nwcsaf_msg.py::TestH5NWCSAF::test_get_dataset
FAILED
tests/reader_tests/test_smos_l2_wind.py::TestSMOSL2WINDReader::test_load_wind_speed
FAILED
tests/reader_tests/test_tropomi_l2.py::TestTROPOMIL2Reader::test_load_bounds
FAILED
tests/reader_tests/test_tropomi_l2.py::TestTROPOMIL2Reader::test_load_no2
FAILED
tests/reader_tests/test_tropomi_l2.py::TestTROPOMIL2Reader::test_load_so2
FAILED
tests/reader_tests/test_viirs_compact.py::TestCompact::test_distributed
FAILED
tests/reader_tests/test_viirs_compact.py::TestCompact::test_get_dataset
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_basic_lettered_tiles
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_basic_lettered_tiles_diff_projection
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_update_existing
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_sector_ref
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_no_fit
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_no_valid_data
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_lettered_tiles_bad_filename
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_multivar_numbered_tiles_glm[extra_kwargs0-C]
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_multivar_numbered_tiles_glm[extra_kwargs0-F]
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_multivar_numbered_tiles_glm[extra_kwargs1-C]
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_multivar_numbered_tiles_glm[extra_kwargs1-F]
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_multivar_numbered_tiles_glm[extra_kwargs2-C]
FAILED
tests/writer_tests/test_awips_tiled.py::TestAWIPSTiledWriter::test_multivar_numbered_tiles_glm[extra_kwargs2-F]
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_get_test_dataset_three_bands_prereq
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_save_dataset_with_bad_value
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_save_dataset_with_calibration
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_save_one_dataset
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_save_one_dataset_sesnor_set
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_simple_write
FAILED
tests/writer_tests/test_mitiff.py::TestMITIFFWriter::test_simple_write_two_bands
FAILED tests/writer_tests/test_ninjogeotiff.py::test_write_and_read_file_RGB
FAILED tests/writer_tests/test_ninjogeotiff.py::test_get_min_gray_value_RGB
FAILED tests/writer_tests/test_ninjogeotiff.py::test_get_max_gray_value_RGB
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_scene_available_datasets[modis_l1b_nasa_mod021km_file-expected_names0-expected_data_res0-expected_geo_res0]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_scene_available_datasets[modis_l1b_imapp_1000m_file-expected_names1-expected_data_res1-expected_geo_res1]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_scene_available_datasets[modis_l1b_nasa_mod02hkm_file-expected_names2-expected_data_res2-expected_geo_res2]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_scene_available_datasets[modis_l1b_nasa_mod02qkm_file-expected_names3-expected_data_res3-expected_geo_res3]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_longitude_latitude[modis_l1b_nasa_mod021km_file-True-False-False-1000]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_longitude_latitude[modis_l1b_imapp_1000m_file-True-False-False-1000]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_longitude_latitude[modis_l1b_nasa_mod02hkm_file-False-True-True-250]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_longitude_latitude[modis_l1b_nasa_mod02qkm_file-False-True-True-250]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_longitude_latitude[modis_l1b_nasa_1km_mod03_files-True-True-True-250]
ERROR
tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_sat_zenith_angle
ERROR tests/reader_tests/test_modis_l1b.py::TestModisL1b::test_load_vis
- num...
ERROR
tests/reader_tests/test_modis_l2.py::TestModisL2::test_load_category_dataset[modis_l2_nasa_mod35_mod03_files-loadables0-1000-1000-True]
ERROR
tests/reader_tests/test_modis_l2.py::TestModisL2::test_load_category_dataset[modis_l2_imapp_mask_byte1_geo_files-loadables1-None-1000-True]
ERROR
tests/reader_tests/test_modis_l2.py::TestModisL2::test_load_250m_cloud_mask_dataset[modis_l2_nasa_mod35_mod03_files-True]
ERROR
tests/reader_tests/test_modis_l2.py::TestModisL2::test_load_l2_dataset[modis_l2_imapp_snowmask_geo_files-loadables2-1000-True]
= 38 failed, 1289 passed, 10 skipped, 5 deselected, 4 xfailed, 566
warnings, 15 errors in 226.63s (0:03:46) =
autopkgtest [11:33:26]: test python3
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