def test_cancel(self):
m = ConsoleMonitor()
with fetch_std_streams():
m.start('task A', total_work=10)
self.assertFalse(m.is_cancelled())
m.cancel()
self.assertTrue(m.is_cancelled())
def test_8days(self):
"""
Test nominal execution with a 8 Days frequency input dataset
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', '2000-12-31', freq='8D')
})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', freq='MS', periods=12)
})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
def test_nominal(self):
"""
Test nominal execution
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon'], np.ones([45, 90])),
'second': (['lat', 'lon'], np.ones([45, 90])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
m = ConsoleMonitor()
actual = reduce(ds, monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
actual = reduce(ds, dim=['lat', 'time', 'there_is_no_spoon'], monitor=m)
ex = xr.Dataset({
'first': (['lon'], np.ones([90])),
'second': (['lon'], np.ones([90])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
self.assertTrue(actual.broadcast_equals(ex))
def test_nominal(self):
"""
Test nominal execution
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=24)})
ds = adjust_temporal_attrs(ds)
# Test monitor
m = ConsoleMonitor()
actual = long_term_average(ds, monitor=m)
self.assertEqual(m._percentage, 100)
# Test CF attributes
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {'time': 12,
'nv': 2,
'lat': 45,
'lon': 90})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
# Test variable selection
actual = long_term_average(ds, var='first')
with self.assertRaises(KeyError):
actual['second']
def test_seasonal(self):
"""
Test aggregation to a seasonal dataset
"""
# daily -> seasonal
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 5])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 5])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('1999-12-01', freq='QS-DEC', periods=5)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, output_resolution='season', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# monthly -> seasonal
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
actual = temporal_aggregation(ds, output_resolution='season', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
def test_monitor(self):
"""
Test monitor integration
"""
ref = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90)
})
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time': [datetime(2000, x, 1) for x in range(1, 13)] +
[datetime(2001, x, 1) for x in range(1, 13)]
})
with create_tmp_file() as tmp_file:
ref.to_netcdf(tmp_file, 'w')
m = ConsoleMonitor()
anomaly.anomaly_external(ds, tmp_file, monitor=m)
self.assertEqual(m._percentage, 100)
def test_custom(self):
"""
Test aggergating to custom temporal resolutions
"""
# daily -> 8 days
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='8D')})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, custom_resolution='8D', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# daily -> weekly
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 53])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 53])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='W')})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
actual = temporal_aggregation(ds, custom_resolution='W', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# monthly -> 4 month seasons
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 4])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 4])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='4M', periods=4)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
actual = temporal_aggregation(ds, custom_resolution='4M', monitor=m)
print(actual)
self.assertTrue(actual.broadcast_equals(ex))
def test_console_monitor_with_progress_bar(self):
m = ConsoleMonitor(progress_bar_size=10)
with fetch_std_streams() as (stdout, stderr):
m.start('task A', total_work=10)
m.progress(work=1)
m.progress(work=5, msg='phase 1')
m.progress(msg='phase 2')
m.progress(work=4)
m.progress()
m.done()
actual_stdout = stdout.getvalue()
expected_stdout = 'task A: started\n' + \
'task A: [#---------] 10% \n' + \
'task A: [######----] 60% phase 1\n' + \
'task A: phase 2\n' + \
'task A: [##########] 100% \n' + \
'task A: in progress...\n' + \
'task A: done\n'
self.assertEqual(actual_stdout, expected_stdout)
self.assertEqual(stderr.getvalue(), '')
def test_console_monitor(self):
m = ConsoleMonitor()
with fetch_std_streams() as (stdout, stderr):
m.start('task A', total_work=10)
m.progress(work=1)
m.progress(work=5, msg='phase 1')
m.progress(msg='phase 2')
m.progress(work=4)
m.progress()
m.done()
actual_stdout = stdout.getvalue()
expected_stdout = 'task A: started\n' + \
'task A: 10%\n' + \
'task A: 60% phase 1\n' + \
'task A: phase 2\n' + \
'task A: 100%\n' + \
'task A: progress\n' + \
'task A: done\n'
self.assertEqual(actual_stdout, expected_stdout)
self.assertEqual(stderr.getvalue(), '')
self.assertTrue(True)
def test_child_monitor(self):
monitor = ConsoleMonitor()
child_monitor = monitor.child(work=7.5)
self.assertIsInstance(child_monitor, ChildMonitor)
def test_label_required(self):
monitor = ConsoleMonitor()
with fetch_std_streams():
with self.assertRaises(ValueError):
# "ValueError: label must be given"
monitor.start('', total_work=10)