def test_qc_flag_block_plot():
obj = arm.read_netcdf(sample_files.EXAMPLE_SURFSPECALB1MLAWER)
display = TimeSeriesDisplay(obj, subplot_shape=(2, ), figsize=(8, 2 * 4))
display.plot('surface_albedo_mfr_narrowband_10m',
force_line_plot=True,
labels=True)
display.qc_flag_block_plot('surface_albedo_mfr_narrowband_10m',
subplot_index=(1, ))
obj.close()
del obj
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)
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()
del ds_object
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_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()
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)
from act.io.armfiles import read_netcdf from act.plotting import TimeSeriesDisplay from act.tests import EXAMPLE_SURFSPECALB1MLAWER # Read a data file that has a 2D DataArray of multiple 1D data. # The corresponding quality control DataArray is also read in and # will be used to make a summary plot of quality control infomation # of each assessment category. obj = read_netcdf(EXAMPLE_SURFSPECALB1MLAWER) # The name of the data variable we wish to plot var_name = 'surface_albedo_mfr_narrowband_10m' # Create the ACT display object used for plotting. This will have two # vertical plots of 800 by 400 pixels. display = TimeSeriesDisplay(obj, subplot_shape=(2, ), figsize=(8, 2 * 4)) # Create the top plot of data using the force_line_plot option. # This will force the plotting to not assume the data are 2D data that # would normally be plotted as a 2D plot. Rather for each index into the # filter dimention plot a 1D time series plot. Setting labels=True # will create a legend using the filter dimention DataArray. display.plot(var_name, force_line_plot=True, labels=True) # Create the bottom plot of summarized quality control by assessment # cateory. display.qc_flag_block_plot(var_name, subplot_index=(1, )) # Show the plot in a new window. plt.show()
