def test_multidataset_plot_dict():
conn = boto3.resource('s3')
conn.meta.client.meta.events.register('choose-signer.s3.*',
disable_signing)
bucket = conn.Bucket('act-tests')
if not os.path.isdir((os.getcwd() + '/data/')):
os.makedirs((os.getcwd() + '/data/'))
for item in bucket.objects.all():
bucket.download_file(item.key, (os.getcwd() + '/data/' + item.key))
ceil_ds = arm.read_netcdf('data/sgpceilC1.b1*')
sonde_ds = arm.read_netcdf(sample_files.EXAMPLE_MET_WILDCARD)
ceil_ds = ceil.correct_ceil(ceil_ds, fill_value=-9999.)
display = TimeSeriesDisplay(
{
'ceiliometer': ceil_ds,
'rawinsonde': sonde_ds
},
subplot_shape=(2, ),
figsize=(15, 10))
display.plot('backscatter', 'ceiliometer', subplot_index=(0, ))
display.plot('temp_mean', 'rawinsonde', subplot_index=(1, ))
display.day_night_background('rawinsonde', subplot_index=(1, ))
ceil_ds.close()
sonde_ds.close()
return display.fig
def test_qc_bar_plot():
ds_object = arm.read_netcdf(sample_files.EXAMPLE_MET1)
ds_object.clean.cleanup()
var_name = 'temp_mean'
ds_object.qcfilter.set_test(var_name, index=range(100, 600), test_number=2)
# Testing out when the assessment is not listed
ds_object.qcfilter.set_test(var_name, index=range(500, 800), test_number=4)
ds_object['qc_' + var_name].attrs['flag_assessments'][3] = 'Wonky'
display = TimeSeriesDisplay({'sgpmetE13.b1': ds_object},
subplot_shape=(2, ),
figsize=(7, 4))
display.plot(var_name, subplot_index=(0, ), assessment_overplot=True)
display.day_night_background('sgpmetE13.b1', subplot_index=(0, ))
color_lookup = {
'Bad': 'red',
'Incorrect': 'red',
'Indeterminate': 'orange',
'Suspect': 'orange',
'Missing': 'darkgray',
'Not Failing': 'green',
'Acceptable': 'green'
}
display.qc_flag_block_plot(var_name,
subplot_index=(1, ),
assessment_color=color_lookup)
ds_object.close()
try:
return display.fig
finally:
matplotlib.pyplot.close(display.fig)
def test_multidataset_plot_tuple():
conn = boto3.resource('s3')
conn.meta.client.meta.events.register('choose-signer.s3.*',
disable_signing)
bucket = conn.Bucket('act-tests')
if not os.path.isdir((os.getcwd() + '/data/')):
os.makedirs((os.getcwd() + '/data/'))
for item in bucket.objects.all():
bucket.download_file(item.key, (os.getcwd() + '/data/' + item.key))
ceil_ds = arm.read_netcdf('data/sgpceilC1.b1*')
sonde_ds = arm.read_netcdf(sample_files.EXAMPLE_MET_WILDCARD)
# Removing fill value of -9999 as it was causing some warnings
ceil_ds = ceil.correct_ceil(ceil_ds)
# You can use tuples if the datasets in the tuple contain a
# datastream attribute. This is required in all ARM datasets.
display = TimeSeriesDisplay((ceil_ds, sonde_ds),
subplot_shape=(2, ),
figsize=(15, 10))
display.plot('backscatter', 'sgpceilC1.b1', subplot_index=(0, ))
display.plot('temp_mean', 'sgpmetE13.b1', subplot_index=(1, ))
display.day_night_background('sgpmetE13.b1', subplot_index=(1, ))
ceil_ds.close()
sonde_ds.close()
return display.fig
def test_multidataset_plot_tuple():
obj = arm.read_netcdf(sample_files.EXAMPLE_MET1)
obj2 = arm.read_netcdf(sample_files.EXAMPLE_SIRS)
obj = obj.rename({'lat': 'fun_time'})
obj['fun_time'].attrs['standard_name'] = 'latitude'
obj = obj.rename({'lon': 'not_so_fun_time'})
obj['not_so_fun_time'].attrs['standard_name'] = 'longitude'
# You can use tuples if the datasets in the tuple contain a
# datastream attribute. This is required in all ARM datasets.
display = TimeSeriesDisplay((obj, obj2),
subplot_shape=(2, ),
figsize=(15, 10))
display.plot('short_direct_normal', 'sgpsirsE13.b1', subplot_index=(0, ))
display.day_night_background('sgpsirsE13.b1', subplot_index=(0, ))
display.plot('temp_mean', 'sgpmetE13.b1', subplot_index=(1, ))
display.day_night_background('sgpmetE13.b1', subplot_index=(1, ))
ax = act.plotting.common.parse_ax(ax=None)
ax, fig = act.plotting.common.parse_ax_fig(ax=None, fig=None)
obj.close()
obj2.close()
try:
return display.fig
finally:
matplotlib.pyplot.close(display.fig)
def test_qc_bar_plot():
ds_object = arm.read_netcdf(sample_files.EXAMPLE_MET1)
ds_object.clean.cleanup()
var_name = 'temp_mean'
ds_object.qcfilter.set_test(var_name, index=range(100, 600), test_number=2)
display = TimeSeriesDisplay({'sgpmetE13.b1': ds_object},
subplot_shape=(2, ),
figsize=(7, 4))
display.plot(var_name, subplot_index=(0, ), assessment_overplot=True)
display.day_night_background('sgpmetE13.b1', subplot_index=(0, ))
display.qc_flag_block_plot(var_name, subplot_index=(1, ))
return display.fig
def test_multidataset_plot_dict():
obj = arm.read_netcdf(sample_files.EXAMPLE_MET1)
obj2 = arm.read_netcdf(sample_files.EXAMPLE_SIRS)
# You can use tuples if the datasets in the tuple contain a
# datastream attribute. This is required in all ARM datasets.
display = TimeSeriesDisplay(
{'sirs': obj2, 'met': obj},
subplot_shape=(2,), figsize=(15, 10))
display.plot('short_direct_normal', 'sirs', subplot_index=(0,))
display.day_night_background('sirs', subplot_index=(0,))
display.plot('temp_mean', 'met', subplot_index=(1,))
display.day_night_background('met', subplot_index=(1,))
obj.close()
obj2.close()
return display.fig
def test_qc_bar_plot():
ds_object = arm.read_netcdf(sample_files.EXAMPLE_MET1)
ds_object.clean.cleanup()
var_name = 'temp_mean'
ds_object.qcfilter.set_test(var_name, index=range(100, 600), test_number=2)
# Testing out when the assessment is not listed
ds_object.qcfilter.set_test(var_name, index=range(500, 800), test_number=4)
ds_object['qc_' + var_name].attrs['flag_assessments'][3] = 'Wonky'
display = TimeSeriesDisplay({'sgpmetE13.b1': ds_object},
subplot_shape=(2, ),
figsize=(7, 4))
display.plot(var_name, subplot_index=(0, ), assessment_overplot=True)
display.day_night_background('sgpmetE13.b1', subplot_index=(0, ))
display.qc_flag_block_plot(var_name, subplot_index=(1, ))
ds_object.close()
return display.fig
def test_errors():
files = sample_files.EXAMPLE_MET_WILDCARD
obj = arm.read_netcdf(files)
display = TimeSeriesDisplay(obj)
display.axes = None
with np.testing.assert_raises(RuntimeError):
display.day_night_background()
display = TimeSeriesDisplay({'met': obj, 'met2': obj})
with np.testing.assert_raises(ValueError):
display.plot('temp_mean')
with np.testing.assert_raises(ValueError):
display.qc_flag_block_plot('qc_temp_mean')
with np.testing.assert_raises(ValueError):
display.plot_barbs_from_spd_dir('wdir_vec_mean', 'wspd_vec_mean')
with np.testing.assert_raises(ValueError):
display.plot_barbs_from_u_v('wdir_vec_mean', 'wspd_vec_mean')
del obj.attrs['_file_dates']
data = np.empty(len(obj['time'])) * np.nan
lat = obj['lat'].values
lon = obj['lon'].values
obj['lat'].values = data
obj['lon'].values = data
display = TimeSeriesDisplay(obj)
display.plot('temp_mean')
display.set_yrng([0, 0])
with np.testing.assert_warns(RuntimeWarning):
display.day_night_background()
obj['lat'].values = lat
with np.testing.assert_warns(RuntimeWarning):
display.day_night_background()
obj['lon'].values = lon * 100.
with np.testing.assert_warns(RuntimeWarning):
display.day_night_background()
obj['lat'].values = lat * 100.
with np.testing.assert_warns(RuntimeWarning):
display.day_night_background()
obj.close()
# Test some of the other errors
obj = arm.read_netcdf(files)
del obj['temp_mean'].attrs['units']
display = TimeSeriesDisplay(obj)
display.axes = None
with np.testing.assert_raises(RuntimeError):
display.set_yrng([0, 10])
with np.testing.assert_raises(RuntimeError):
display.set_xrng([0, 10])
display.fig = None
display.plot('temp_mean', add_nan=True)
assert display.fig is not None
assert display.axes is not None
with np.testing.assert_raises(AttributeError):
display = TimeSeriesDisplay([])
fig, ax = matplotlib.pyplot.subplots()
display = TimeSeriesDisplay(obj)
display.add_subplots((2, 2), figsize=(15, 10))
display.assign_to_figure_axis(fig, ax)
assert display.fig is not None
assert display.axes is not None
obj = arm.read_netcdf(files)
display = TimeSeriesDisplay(obj)
obj.clean.cleanup()
display.axes = None
display.fig = None
display.qc_flag_block_plot('atmos_pressure')
assert display.fig is not None
assert display.axes is not None
matplotlib.pyplot.close(fig=display.fig)