def test_plot():
# Process MET data to get simple LCL
files = sample_files.EXAMPLE_MET_WILDCARD
met = arm.read_netcdf(files)
met_temp = met.temp_mean
met_rh = met.rh_mean
met_lcl = (20. + met_temp / 5.) * (100. - met_rh) / 1000.
met['met_lcl'] = met_lcl * 1000.
met['met_lcl'].attrs['units'] = 'm'
met['met_lcl'].attrs['long_name'] = 'LCL Calculated from SGP MET E13'
# Plot data
display = TimeSeriesDisplay(met)
display.add_subplots((2, 2), figsize=(15, 10))
display.plot('wspd_vec_mean', subplot_index=(0, 0))
display.plot('temp_mean', subplot_index=(1, 0))
display.plot('rh_mean', subplot_index=(0, 1))
windrose = WindRoseDisplay(met)
display.put_display_in_subplot(windrose, subplot_index=(1, 1))
windrose.plot('wdir_vec_mean',
'wspd_vec_mean',
spd_bins=np.linspace(0, 10, 4))
windrose.axes[0].legend(loc='best')
met.close()
return display.fig
def test_plot():
# Process MET data to get simple LCL
files = sample_files.EXAMPLE_SONDE_WILDCARD
met = arm.read_netcdf(files)
met_temp = met.temp_mean
met_rh = met.rh_mean
met_lcl = (20. + met_temp / 5.) * (100. - met_rh) / 1000.
met['met_lcl'] = met_lcl * 1000.
met['met_lcl'].attrs['units'] = 'm'
met['met_lcl'].attrs['long_name'] = 'LCL Calculated from SGP MET E13'
# Plot data
# Plot data
display = TimeSeriesDisplay(met)
display.add_subplots((3, ), figsize=(15, 10))
display.plot('wspd_vec_mean', subplot_index=(0, ))
display.plot('temp_mean', subplot_index=(1, ))
display.plot('rh_mean', subplot_index=(2, ))
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)