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_assessment_overplot():
var_name = 'temp_mean'
files = sample_files.EXAMPLE_MET1
ds = arm.read_netcdf(files)
ds.load()
ds.clean.cleanup()
ds.qcfilter.set_test(var_name,
index=np.arange(100, 300, dtype=int),
test_number=2)
ds.qcfilter.set_test(var_name,
index=np.arange(420, 422, dtype=int),
test_number=3)
ds.qcfilter.set_test(var_name,
index=np.arange(500, 800, dtype=int),
test_number=4)
ds.qcfilter.set_test(var_name,
index=np.arange(900, 901, dtype=int),
test_number=4)
# Plot data
display = TimeSeriesDisplay(ds, subplot_shape=(1, ), figsize=(10, 6))
display.plot(var_name, day_night_background=True, assessment_overplot=True)
ds.close()
return display.fig
def test_plot_barbs_from_u_v2():
bins = list(np.linspace(0, 1, 10))
xbins = list(pd.date_range(pd.to_datetime('2020-01-01'), pd.to_datetime('2020-01-02'), 12))
y_data = np.full([len(xbins), len(bins)], 1.0)
x_data = np.full([len(xbins), len(bins)], 2.0)
y_array = xr.DataArray(y_data, dims={'xbins': xbins, 'ybins': bins}, attrs={'units': 'm/s'})
x_array = xr.DataArray(x_data, dims={'xbins': xbins, 'ybins': bins}, attrs={'units': 'm/s'})
xbins = xr.DataArray(xbins, dims={'xbins': xbins})
ybins = xr.DataArray(bins, dims={'ybins': bins})
fake_obj = xr.Dataset({'xbins': xbins, 'ybins': ybins, 'ydata': y_array, 'xdata': x_array})
BarbDisplay = TimeSeriesDisplay(fake_obj)
BarbDisplay.plot_barbs_from_u_v(
'xdata',
'ydata',
None,
num_barbs_x=20,
num_barbs_y=20,
set_title='test plot',
cmap='jet',
)
fake_obj.close()
try:
return BarbDisplay.fig
finally:
matplotlib.pyplot.close(BarbDisplay.fig)
def test_2d_as_1d():
obj = arm.read_netcdf(sample_files.EXAMPLE_CEIL1)
display = TimeSeriesDisplay(obj)
display.plot('backscatter', force_line_plot=True)
return display.fig
def test_plot_barbs_from_u_v():
sonde_ds = arm.read_netcdf(
sample_files.EXAMPLE_TWP_SONDE_WILDCARD)
BarbDisplay = TimeSeriesDisplay({'sonde_darwin': sonde_ds})
BarbDisplay.plot_barbs_from_u_v('u_wind', 'v_wind', 'pres',
num_barbs_x=20)
sonde_ds.close()
return BarbDisplay.fig
def test_time_plot2():
files = sample_files.EXAMPLE_MET1
obj = arm.read_netcdf(files,
decode_times=False,
cftime_to_datetime64=False)
display = TimeSeriesDisplay(obj)
display.plot('time')
return display.fig
def test_2D_timeseries_plot():
obj = arm.read_netcdf(sample_files.EXAMPLE_CEIL1)
display = TimeSeriesDisplay(obj)
display.plot('backscatter', y_rng=[0, 5000], use_var_for_y='range')
try:
return display.fig
finally:
matplotlib.pyplot.close(display.fig)
def test_time_height_scatter():
sonde_ds = arm.read_netcdf(sample_files.EXAMPLE_SONDE1)
display = TimeSeriesDisplay({'sgpsondewnpnC1.b1': sonde_ds},
figsize=(7, 3))
display.time_height_scatter('tdry', day_night_background=False)
sonde_ds.close()
return display.fig
def test_fill_between():
obj = arm.read_netcdf(sample_files.EXAMPLE_MET_WILDCARD)
accumulate_precip(obj, 'tbrg_precip_total')
display = TimeSeriesDisplay(obj)
display.fill_between('tbrg_precip_total_accumulated',
color='gray',
alpha=0.2)
return display.fig
def test_y_axis_flag_meanings():
variable = 'detection_status'
obj = arm.read_netcdf(
sample_files.EXAMPLE_CEIL1, keep_variables=[variable, 'lat', 'lon', 'alt']
)
obj.clean.clean_arm_state_variables(variable, override_cf_flag=True)
display = TimeSeriesDisplay(obj, figsize=(12, 8), subplot_shape=(1,))
display.plot(variable, subplot_index=(0,), day_night_background=True, y_axis_flag_meanings=18)
display.fig.subplots_adjust(left=0.15, right=0.95, bottom=0.1, top=0.94)
return display.fig
def test_2d_as_1d():
obj = arm.read_netcdf(sample_files.EXAMPLE_CEIL1)
display = TimeSeriesDisplay(obj)
display.plot('backscatter', force_line_plot=True)
obj.close()
del obj
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)
# 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_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_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_fill_between():
obj = arm.read_netcdf(sample_files.EXAMPLE_MET_WILDCARD)
accumulate_precip(obj, 'tbrg_precip_total')
display = TimeSeriesDisplay(obj)
display.fill_between('tbrg_precip_total_accumulated', color='gray', alpha=0.2)
obj.close()
del obj
try:
return display.fig
finally:
matplotlib.pyplot.close(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_colorbar_labels():
variable = 'cloud_phase_hsrl'
obj = arm.read_netcdf(sample_files.EXAMPLE_CLOUDPHASE)
obj.clean.clean_arm_state_variables(variable)
display = TimeSeriesDisplay(obj, figsize=(12, 8), subplot_shape=(1,))
y_axis_labels = {}
flag_colors = ['white', 'green', 'blue', 'red', 'cyan', 'orange', 'yellow', 'black', 'gray']
for value, meaning, color in zip(
obj[variable].attrs['flag_values'], obj[variable].attrs['flag_meanings'], flag_colors
):
y_axis_labels[value] = {'text': meaning, 'color': color}
display.plot(variable, subplot_index=(0,), colorbar_labels=y_axis_labels, cbar_h_adjust=0)
display.fig.subplots_adjust(left=0.08, right=0.88, bottom=0.1, top=0.94)
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_assessment_overplot_multi():
var_name1, var_name2 = 'wspd_arith_mean', 'wspd_vec_mean'
files = sample_files.EXAMPLE_MET1
ds = arm.read_netcdf(files)
ds.load()
ds.clean.cleanup()
ds.qcfilter.set_test(var_name1,
index=np.arange(100, 200, dtype=int),
test_number=2)
ds.qcfilter.set_test(var_name1,
index=np.arange(500, 600, dtype=int),
test_number=4)
ds.qcfilter.set_test(var_name2,
index=np.arange(300, 400, dtype=int),
test_number=4)
# Plot data
display = TimeSeriesDisplay(ds, subplot_shape=(1, ), figsize=(10, 6))
display.plot(var_name1,
label=var_name1,
assessment_overplot=True,
overplot_behind=True)
display.plot(var_name2,
day_night_background=True,
color='green',
label=var_name2,
assessment_overplot=True)
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)
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_barb_sounding_plot():
sonde_ds = arm.read_netcdf(sample_files.EXAMPLE_TWP_SONDE_WILDCARD)
BarbDisplay = TimeSeriesDisplay({'sonde_darwin': sonde_ds})
BarbDisplay.plot_time_height_xsection_from_1d_data('rh',
'pres',
cmap='coolwarm_r',
vmin=0,
vmax=100,
num_time_periods=25)
BarbDisplay.plot_barbs_from_spd_dir('deg', 'wspd', 'pres', num_barbs_x=20)
sonde_ds.close()
return BarbDisplay.fig
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)
# 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
files.sort()
# Read in data. This will read in one or more files and do a bit of work
# updating the variable attributes with units, long_name and quality
# control variables.
ds_obj = read_gml(files)
# Change variable units from degree C to degree F. Can specify specific variables.
# Or list no variables and will attempt to change all variables to the desired
# unit if the units are conicially matching.
ds_obj = ds_obj.utils.change_units(variables='air_temperature_10m',
desired_unit='degF')
# Set up plotting ACT object to contain data to plot and other configurations
my_disp = TimeSeriesDisplay({'noaa_gml_brw': ds_obj},
subplot_shape=(2, ),
figsize=(15, 10))
# Plot two variables on one plot and set the plot axes title. Also color
# background to indicate when sun is shining and local solar noon time.
my_disp.plot('downwelling_global_solar',
subplot_index=(0, ),
label='Downwelling')
my_disp.plot('upwelling_global_solar',
subplot_index=(0, ),
label='Upwelling',
set_title=f"NOAA GML {ds_obj.attrs['location']} Global Solar",
day_night_background=True)
# Add second plot of air temperature. Use variable long name for plot axes title
# and add background color to indicate when sun is shining and solar noon.
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
# Reduce met data from full month to one day
met_obj = met_obj.sel(
time=slice(rad_obj['time'].values[0], rad_obj['time'].values[-1]))
# MET and Ozone do not have location variables in the file. Add location variables
# from rad_obj to allow day/night background plotting.
for var_name in ['lat', 'lon', 'alt']:
ozone_obj[var_name] = rad_obj[var_name]
met_obj[var_name] = rad_obj[var_name]
# Create dictionary of all data objects to allow plotting from different
# Xarray Datasets.
location = "Barrow"
plot_obj = {'met': met_obj, 'rad': rad_obj, 'ozone': ozone_obj}
my_disp = TimeSeriesDisplay(plot_obj, subplot_shape=(3, ), figsize=(10, 8))
# Create first plot of two variables from Radiation file.
my_disp.plot('downwelling_global_solar',
dsname='rad',
subplot_index=(0, ),
label='Downwelling')
my_disp.plot('upwelling_global_solar',
dsname='rad',
subplot_index=(0, ),
label='Upwelling',
set_title=f"NOAA GML {location} Global Solar",
day_night_background=True)
# Add second plot of Ozone. Use variable long name for plot axes title
# and add background color to indicate when sun is shining and solar noon.
def test_time_plot():
files = sample_files.EXAMPLE_MET1
obj = arm.read_netcdf(files)
display = TimeSeriesDisplay(obj)
display.plot('time')
return display.fig
import re
filename = Path('data', 'pocketLab_aqi_2021-05-18_082549.csv')
ds = read_csv(filename, parse_dates={'time': [0,1]})
ds['time'].values = ds['time'].values + np.timedelta64(5, 'h')
ds = ds.assign_coords(time=ds['time'])
ds = ds.swap_dims({'index': 'time'})
ds = ds.drop('index')
ds = ds.rename({'Latitude': 'latitude', 'Longitude': 'longitude'})
var_names = list(set(ds.keys()) - set(['time', 'Elapsed Seconds', 'latitude', 'longitude']))
var_names = [ii for ii in var_names if 'Avg.' not in ii]
var_names.sort()
my_disp = TimeSeriesDisplay({'pocketLab': ds}, subplot_shape=(int(len(var_names)/2), 2),
figsize=(8*2, 2*len(var_names)/2))
row=0
col=0
for plot_num, var_name in enumerate(var_names):
match = re.match(r".+\(([\S]+)\).*", var_name)
units = '(1)'
if match is not None:
units = f"({match.groups()[0]})"
axes = my_disp.plot(var_name, subplot_index=(row, col), day_night_background=True)
axes.set_ylabel(units)
row += 1
if plot_num == 4:
col += 1
row = 0
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()
