def test_cross_section_error_on_missing_coordinate(test_ds_lonlat):
"""Test that the proper error is raised with missing coordinate."""
# Use a variable with no crs coordinate
data_bad = test_ds_lonlat['temperature'].copy()
del data_bad['crs']
start, end = (30.5, 255.5), (44.5, 274.5)
with pytest.raises(ValueError):
cross_section(data_bad, start, end)
def test_cross_section_error_on_missing_coordinate(test_ds_lonlat):
"""Test that the proper error is raised with missing coordinate."""
# Use a variable with no crs coordinate
data_bad = test_ds_lonlat['temperature'].copy()
del data_bad['crs']
start, end = (30.5, 255.5), (44.5, 274.5)
with pytest.raises(ValueError):
cross_section(data_bad, start, end)
def test_cross_section_dataarray_and_linear_interp(test_ds_xy):
"""Test the cross_section function with a data array and linear interpolation."""
data = test_ds_xy['temperature']
start, end = ((36.46, -112.45), (42.95, -68.74))
data_cross = cross_section(data, start, end, steps=7)
truth_values = np.array([[[250.00095489, 251.53646673, 253.11586664, 254.73477364,
256.38991013, 258.0794356, 259.80334269],
[260.04880178, 261.58431362, 263.16371353, 264.78262053,
266.43775702, 268.12728249, 269.85118958],
[270.09664867, 271.63216051, 273.21156042, 274.83046742,
276.48560391, 278.17512938, 279.89903647],
[280.14449556, 281.6800074, 283.25940731, 284.87831431,
286.5334508, 288.22297627, 289.94688336],
[290.19234245, 291.72785429, 293.3072542, 294.9261612,
296.58129769, 298.27082316, 299.99473025]]])
truth_values_x = np.array([-499495.71907062, 98404.43537514, 688589.09865512,
1272690.44926197, 1852009.73516881, 2427525.45740665,
2999932.89862589])
truth_values_y = np.array([-1499865.98780602, -1268717.36799267, -1029139.66048478,
-782037.60343652, -528093.95678826, -267710.32566917,
-939.10769171])
index = xr.DataArray(range(7), name='index', dims=['index'])
data_truth_x = xr.DataArray(
truth_values_x,
name='x',
coords={
'crs': data['crs'],
'y': (['index'], truth_values_y),
'x': (['index'], truth_values_x),
'index': index,
},
dims=['index']
)
data_truth_y = xr.DataArray(
truth_values_y,
name='y',
coords={
'crs': data['crs'],
'y': (['index'], truth_values_y),
'x': (['index'], truth_values_x),
'index': index,
},
dims=['index']
)
data_truth = xr.DataArray(
truth_values * units.kelvin,
name='temperature',
coords={
'time': data['time'],
'isobaric': data['isobaric'],
'index': index,
'crs': data['crs'],
'y': data_truth_y,
'x': data_truth_x
},
dims=['time', 'isobaric', 'index']
)
xr.testing.assert_allclose(data_truth, data_cross)
def test_cross_section_dataset_and_nearest_interp(test_ds_lonlat):
"""Test the cross_section function with a dataset and nearest interpolation."""
start, end = (30.5, 255.5), (44.5, 274.5)
data_cross = cross_section(test_ds_lonlat, start, end, steps=7, interp_type='nearest')
nearest_values = test_ds_lonlat.isel(lat=xr.DataArray([0, 1, 2, 3, 3, 4, 5], dims='index'),
lon=xr.DataArray(range(7), dims='index'))
truth_temp = nearest_values['temperature'].metpy.unit_array
truth_rh = nearest_values['relative_humidity'].metpy.unit_array
truth_values_lon = np.array([255.5, 258.20305939, 261.06299342, 264.10041516,
267.3372208, 270.7961498, 274.5])
truth_values_lat = np.array([30.5, 33.02800969, 35.49306226, 37.88512911, 40.19271688,
42.40267088, 44.5])
index = xr.DataArray(range(7), name='index', dims=['index'])
data_truth = xr.Dataset(
{
'temperature': (['isobaric', 'index'], truth_temp),
'relative_humidity': (['isobaric', 'index'], truth_rh)
},
coords={
'isobaric': test_ds_lonlat['isobaric'],
'index': index,
'crs': test_ds_lonlat['crs'],
'lat': (['index'], truth_values_lat),
'lon': (['index'], truth_values_lon)
},
)
xr.testing.assert_allclose(data_truth, data_cross)
def plot_rh(ds, model):
data = ds.metpy.parse_cf()
data['lon'] = np.mod(ds.lon - 180.0, 360.0) - 180.0
start = (35., -120)
end = (35., -90)
cross = cross_section(data, start, end)
fig = plt.figure(1, figsize=(10., 10.))
ax = plt.axes()
cf = ax.contourf(cross['lon'], cross['isobaric'], cross['Relative_humidity_isobaric'],
levels=np.arange(-18, 20, 2), cmap='Spectral_r')
# Contour Fill the RH Values
cb = plt.colorbar(cf, orientation='vertical')
cb.set_label('Change in RH (%)')
# Contour the U-Component of Wind
clevs_500_hght = np.arange(4, 10, .5)
cs = ax.contour(cross['lon'], cross['isobaric'], cross['air_temperature'], clevs_500_hght, colors='black')
plt.clabel(cs, fmt='%d')
ax.set_yscale('symlog')
ax.set_yticklabels([1000, 850, 700, 500, 400, 300, 250, 200])
ax.set_ylim(1000, 200)
ax.set_yticks([1000, 850, 700, 500, 400, 300, 250, 200])
ax.set_xlim(cross['lon'].min(), cross['lon'].max())
plt.title('Delta RH (Filled) and Delta Temperature (Contoured) ' + model)
plt.savefig('RH_Delta_' + model + '.png', dpi=200)
return plt.show()
def test_cross_lonlat():
"""Return cross section on a lon/lat grid with no time coordinate for use in tests."""
data_u = np.linspace(-40, 40, 5 * 6 * 7).reshape((5, 6, 7))
data_v = np.linspace(40, -40, 5 * 6 * 7).reshape((5, 6, 7))
ds = xr.Dataset(
{
'u_wind': (['isobaric', 'lat', 'lon'], data_u),
'v_wind': (['isobaric', 'lat', 'lon'], data_v)
},
coords={
'isobaric': xr.DataArray(
np.linspace(1000, 500, 5),
name='isobaric',
dims=['isobaric'],
attrs={'units': 'hPa'}
),
'lat': xr.DataArray(
np.linspace(30, 45, 6),
name='lat',
dims=['lat'],
attrs={'units': 'degrees_north'}
),
'lon': xr.DataArray(
np.linspace(255, 275, 7),
name='lon',
dims=['lon'],
attrs={'units': 'degrees_east'}
)
}
)
ds['u_wind'].attrs['units'] = 'knots'
ds['v_wind'].attrs['units'] = 'knots'
start, end = (30.5, 255.5), (44.5, 274.5)
return cross_section(ds.metpy.parse_cf(), start, end, steps=7, interp_type='nearest')
def test_cross_lonlat():
"""Return cross section on a lon/lat grid with no time coordinate for use in tests."""
data_u = np.linspace(-40, 40, 5 * 6 * 7).reshape((5, 6, 7)) * units.knots
data_v = np.linspace(40, -40, 5 * 6 * 7).reshape((5, 6, 7)) * units.knots
ds = xr.Dataset(
{
'u_wind': (['isobaric', 'lat', 'lon'], data_u),
'v_wind': (['isobaric', 'lat', 'lon'], data_v)
},
coords={
'isobaric': xr.DataArray(
np.linspace(1000, 500, 5),
name='isobaric',
dims=['isobaric'],
attrs={'units': 'hPa'}
),
'lat': xr.DataArray(
np.linspace(30, 45, 6),
name='lat',
dims=['lat'],
attrs={'units': 'degrees_north'}
),
'lon': xr.DataArray(
np.linspace(255, 275, 7),
name='lon',
dims=['lon'],
attrs={'units': 'degrees_east'}
)
}
)
start, end = (30.5, 255.5), (44.5, 274.5)
return cross_section(ds.metpy.parse_cf(), start, end, steps=7, interp_type='nearest')
def test_cross_section_dataset_and_nearest_interp(test_ds_lonlat):
"""Test the cross_section function with a dataset and nearest interpolation."""
start, end = (30.5, 255.5), (44.5, 274.5)
data_cross = cross_section(test_ds_lonlat, start, end, steps=7, interp_type='nearest')
nearest_values = test_ds_lonlat.isel(lat=xr.DataArray([0, 1, 2, 3, 3, 4, 5], dims='index'),
lon=xr.DataArray(range(7), dims='index'))
truth_temp = nearest_values['temperature'].values
truth_rh = nearest_values['relative_humidity'].values
truth_values_lon = np.array([255.5, 258.20305939, 261.06299342, 264.10041516,
267.3372208, 270.7961498, 274.5])
truth_values_lat = np.array([30.5, 33.02800969, 35.49306226, 37.88512911, 40.19271688,
42.40267088, 44.5])
index = xr.DataArray(range(7), name='index', dims=['index'])
data_truth = xr.Dataset(
{
'temperature': (['isobaric', 'index'], truth_temp),
'relative_humidity': (['isobaric', 'index'], truth_rh)
},
coords={
'isobaric': test_ds_lonlat['isobaric'],
'index': index,
'crs': test_ds_lonlat['crs'],
'lat': (['index'], truth_values_lat),
'lon': (['index'], truth_values_lon)
},
)
xr.testing.assert_allclose(data_truth, data_cross)
def interpolate_csec(self, plevs, start, end, steps=100):
start = (start['lat'], start['lon'])
end = (end['lat'], end['lon'])
xp_interp = cross_section(self.__press_inter, start, end, steps=steps)
for name, (d_arr, conf) in list(self.var_conf.items()):
d_arr = cross_section(d_arr, start, end, steps=steps)
d_arr_interp = log_interpolate_1d(plevs, xp_interp, d_arr)
# Back to DataArray:
d_arr_interp = xr.DataArray(data=d_arr_interp,
dims=['plevs', 'index'],
coords={'lat': ('index', d_arr['lat']),
'lon': ('index', d_arr['lon']),
'plevs': ('plevs', plevs),
'index': ('index', d_arr['index'])},
attrs=d_arr.attrs) # Anpassen, nicht identisch
# Change dict entry for interpolated values
self.var_conf[name] = (d_arr_interp, conf)
def test_cross_xy():
"""Return cross section on a x/y grid with a time coordinate for use in tests."""
data_u = np.linspace(-25, 25, 5 * 6 * 7).reshape((1, 5, 6, 7))
data_v = np.linspace(25, -25, 5 * 6 * 7).reshape((1, 5, 6, 7))
ds = xr.Dataset(
{
'u_wind': (['time', 'isobaric', 'y', 'x'], data_u),
'v_wind': (['time', 'isobaric', 'y', 'x'], data_v),
'lambert_conformal': ([], '')
},
coords={
'time': xr.DataArray(
np.array([np.datetime64('2018-07-01T00:00')]),
name='time',
dims=['time']
),
'isobaric': xr.DataArray(
np.linspace(1000, 500, 5),
name='isobaric',
dims=['isobaric'],
attrs={'units': 'hPa'}
),
'y': xr.DataArray(
np.linspace(-1500, 0, 6),
name='y',
dims=['y'],
attrs={'units': 'km'}
),
'x': xr.DataArray(
np.linspace(-500, 3000, 7),
name='x',
dims=['x'],
attrs={'units': 'km'}
)
}
)
ds['u_wind'].attrs = ds['v_wind'].attrs = {
'units': 'm/s',
'grid_mapping': 'lambert_conformal'
}
ds['lambert_conformal'].attrs = {
'grid_mapping_name': 'lambert_conformal_conic',
'standard_parallel': 50.0,
'longitude_of_central_meridian': -107.0,
'latitude_of_projection_origin': 50.0,
'earth_shape': 'spherical',
'earth_radius': 6367470.21484375
}
start, end = ((36.46, -112.45), (42.95, -68.74))
return cross_section(ds.metpy.parse_cf(), start, end, steps=7)
def test_absolute_momentum_xarray_units_attr():
"""Test absolute momentum when `u` and `v` are DataArrays with a `units` attribute."""
data = xr.open_dataset(get_test_data('narr_example.nc', False))
data = data.metpy.parse_cf().squeeze()
start = (37.0, -105.0)
end = (35.5, -65.0)
cross = cross_section(data, start, end)
u = cross['u_wind'][0].sel(index=slice(0, 2))
v = cross['v_wind'][0].sel(index=slice(0, 2))
momentum = absolute_momentum(u, v)
true_momentum_values = np.array([137.46164031, 134.11450232, 133.85196023])
true_momentum = xr.DataArray(units.Quantity(true_momentum_values, 'm/s'),
coords=u.coords)
assert_xarray_allclose(momentum, true_momentum)
def Crosssection_Wind_Temp_RH(
initial_time=None,
fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
day_back=0,
model='ECMWF',
output_dir=None,
st_point=[43.5, 111.5],
ed_point=[33, 125.0],
map_extent=[70, 140, 15, 55],
h_pos=[0.125, 0.665, 0.25, 0.2]):
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl='500'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
# retrieve data from micaps server
rh = get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
rh = rh.metpy.parse_cf().squeeze()
u = get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
u = u.metpy.parse_cf().squeeze()
v = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
v = v.metpy.parse_cf().squeeze()
v2 = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v2 is None:
return
v2 = v2.metpy.parse_cf().squeeze()
t = get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
t = t.metpy.parse_cf().squeeze()
gh = get_model_grid(data_dir[4], filename=filename)
psfc = get_model_grid(data_dir[5], filename=filename)
psfc = psfc.metpy.parse_cf().squeeze()
mask1 = ((psfc['lon'] >= t['lon'].values.min()) &
(psfc['lon'] <= t['lon'].values.max()) &
(psfc['lat'] >= t['lat'].values.min()) &
(psfc['lat'] <= t['lat'].values.max()))
t2, psfc_bdcst = xr.broadcast(t['data'], psfc['data'].where(mask1,
drop=True))
mask2 = (psfc_bdcst > -10000)
psfc_bdcst = psfc_bdcst.where(mask2, drop=True)
#psfc_bdcst=psfc_bdcst.metpy.parse_cf().squeeze()
if t is None:
return
resolution = u['lon'][1] - u['lon'][0]
x, y = np.meshgrid(u['lon'], u['lat'])
dx, dy = mpcalc.lat_lon_grid_deltas(u['lon'], u['lat'])
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh = cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u = cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v = cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
#cross_psfc=cross.set_coords(('lat', 'lon'))
cross_u['data'].attrs['units'] = units.meter / units.second
cross_v['data'].attrs['units'] = units.meter / units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(
cross_u['data'], cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_Temp = cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_Temp['data'].values * units.celsius,
cross_rh['data'].values * units.percent)
rh, pressure = xr.broadcast(cross_rh['data'], cross_Temp['level'])
cross_terrain = pressure - cross_psfc
crossection_graphics.draw_Crosssection_Wind_Temp_RH(
cross_rh=cross_rh,
cross_Temp=cross_Temp,
cross_u=cross_u,
cross_v=cross_v,
cross_terrain=cross_terrain,
gh=gh,
h_pos=h_pos,
st_point=st_point,
ed_point=ed_point,
levels=levels,
map_extent=map_extent,
model=model,
output_dir=output_dir)
def Crosssection_Wind_Temp_RH(
initTime=None, fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200],
day_back=0,model='ECMWF',data_source='MICAPS',
output_dir=None,
st_point = [43.5, 111.5],
ed_point = [33, 125.0],
map_extent=[70,140,15,55],
lw_ratio=[16,9],
h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ):
if(data_source == 'MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
if(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False)
u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False)
v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False)
gh=get_model_grid(data_dir[4], filename=filename)
psfc=get_model_grid(data_dir[5], filename=filename)
if(data_source == 'CIMISS'):
if(initTime != None):
filename = utl.model_filename(initTime, fhour,UTC=True)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True)
try:
rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%')
u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s')
v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K')
t['data'].values=t['data'].values-273.15
gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'),
fcst_level=500, fcst_ele="GPH", units='gpm')
gh['data'].values=gh['data'].values/10.
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
rh = rh.metpy.parse_cf().squeeze()
u = u.metpy.parse_cf().squeeze()
v = v.metpy.parse_cf().squeeze()
t = t.metpy.parse_cf().squeeze()
psfc=psfc.metpy.parse_cf().squeeze()
#if(psfc['lon'].values[0] != t['lon'].values[0]):
mask1 = (
(psfc['lon']>=t['lon'].values.min())&
(psfc['lon']<=t['lon'].values.max())&
(psfc['lat']>=t['lat'].values.min())&
(psfc['lat']<=t['lat'].values.max())
)
t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True))
mask2=(psfc_bdcst > -10000)
psfc_bdcst=psfc_bdcst.where(mask2, drop=True)
#else:
# psfc_bdcst=psfc['data'].copy
resolution=u['lon'][1]-u['lon'][0]
x,y=np.meshgrid(u['lon'], u['lat'])
dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat'])
cross = cross_section(rh, st_point, ed_point)
cross_rh=cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u=cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v=cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
cross_u['data'].attrs['units']=units.meter/units.second
cross_v['data'].attrs['units']=units.meter/units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_Temp=cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_Temp['data'].values*units.celsius,
cross_rh['data'].values* units.percent)
rh,pressure = xr.broadcast(cross_rh['data'],cross_Temp['level'])
cross_terrain=pressure-cross_psfc
crossection_graphics.draw_Crosssection_Wind_Temp_RH(
cross_rh=cross_rh, cross_Temp=cross_Temp, cross_u=cross_u,
cross_v=cross_v,cross_terrain=cross_terrain,gh=gh,
h_pos=h_pos,st_point=st_point,ed_point=ed_point,lw_ratio=lw_ratio,
levels=levels,map_extent=map_extent,model=model,
output_dir=output_dir)
def Crosssection_Wind_Theta_e_Qv(
initial_time=None,
fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
day_back=0,
model='ECMWF',
output_dir=None,
st_point=[20, 120.0],
ed_point=[50, 130.0],
map_extent=[70, 140, 15, 55],
h_pos=[0.125, 0.665, 0.25, 0.2]):
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl='500')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
# retrieve data from micaps server
rh = get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
rh = rh.metpy.parse_cf().squeeze()
u = get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
u = u.metpy.parse_cf().squeeze()
v = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
v = v.metpy.parse_cf().squeeze()
v2 = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v2 is None:
return
v2 = v2.metpy.parse_cf().squeeze()
t = get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
t = t.metpy.parse_cf().squeeze()
gh = get_model_grid(data_dir[4], filename=filename)
if t is None:
return
resolution = u['lon'][1] - u['lon'][0]
x, y = np.meshgrid(u['lon'], u['lat'])
dx, dy = mpcalc.lat_lon_grid_deltas(u['lon'], u['lat'])
for ilvl in levels:
u2d = u.sel(level=ilvl)
#u2d['data'].attrs['units']=units.meter/units.second
v2d = v.sel(level=ilvl)
#v2d['data'].attrs['units']=units.meter/units.second
absv2d = mpcalc.absolute_vorticity(
u2d['data'].values * units.meter / units.second,
v2d['data'].values * units.meter / units.second, dx, dy,
y * units.degree)
if (ilvl == levels[0]):
absv3d = v2
absv3d['data'].loc[dict(level=ilvl)] = np.array(absv2d)
else:
absv3d['data'].loc[dict(level=ilvl)] = np.array(absv2d)
absv3d['data'].attrs['units'] = absv2d.units
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh = cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u = cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v = cross.set_coords(('lat', 'lon'))
cross_u['data'].attrs['units'] = units.meter / units.second
cross_v['data'].attrs['units'] = units.meter / units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(
cross_u['data'], cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_t = cross.set_coords(('lat', 'lon'))
cross = cross_section(absv3d, st_point, ed_point)
cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values * units.celsius,
cross_rh['data'].values * units.percent)
rh, pressure = xr.broadcast(cross_rh['data'], cross_t['level'])
Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td, pressure)
cross_Qv = xr.DataArray(np.array(Qv) * 1000.,
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': units('g/kg')})
Theta_e = mpcalc.equivalent_potential_temperature(
pressure, cross_t['data'].values * units.celsius, cross_Td)
cross_Theta_e = xr.DataArray(np.array(Theta_e),
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv(
cross_Qv=cross_Qv,
cross_Theta_e=cross_Theta_e,
cross_u=cross_u,
cross_v=cross_v,
gh=gh,
h_pos=h_pos,
st_point=st_point,
ed_point=ed_point,
levels=levels,
map_extent=map_extent,
output_dir=output_dir)
'https://thredds.met.no/thredds/dodsC/mepslatest/meps_det_2_5km_%sT%sZ.ncml'
% (date, ini_time),
decode_times=True,
use_cftime=True)
data = fnx.metpy.parse_cf().squeeze()
# Cross section along 69.3 latitude and between 1 and 25 longitude
# Andenes = 16deg longitude
start = (69.3, 1)
end = (69.3, 25)
cross_data = data[[
'cloud_area_fraction_pl', 'air_temperature_pl', 'relative_humidity_pl'
]]
cross = cross_section(cross_data, start, end).set_coords(
('latitude', 'longitude'))
# Inverse the pressure axes (doesn't work as intended)
# cross = cross.reindex(pressure=list(reversed(cross.pressure)))
temperature, clouds, relative_humidity = xr.broadcast(
cross['air_temperature_pl'], cross['cloud_area_fraction_pl'],
cross['relative_humidity_pl'])
# Plot the cross section
fig, axs = plt.subplots(nrows=3,
ncols=3,
sharey=True,
sharex=True,
figsize=(14, 10))
ax = axs.ravel().tolist()
j = 0
# dimension.
data = xr.open_dataset(get_test_data('narr_example.nc', False))
data = data.metpy.parse_cf().squeeze()
print(data)
##############################
# Define start and end points:
start = (37.0, -105.0)
end = (35.5, -65.0)
##############################
# Get the cross section, and convert lat/lon to supplementary coordinates:
cross = cross_section(data, start, end)
cross.set_coords(('lat', 'lon'), True)
print(cross)
##############################
# For this example, we will be plotting potential temperature, relative humidity, and
# tangential/normal winds. And so, we need to calculate those, and add them to the dataset:
temperature, pressure, specific_humidity = xr.broadcast(
cross['Temperature'], cross['isobaric'], cross['Specific_humidity'])
theta = mpcalc.potential_temperature(pressure, temperature)
rh = mpcalc.relative_humidity_from_specific_humidity(specific_humidity,
temperature, pressure)
# These calculations return unit arrays, so put those back into DataArrays in our Dataset
# dimension.
data = xr.open_dataset(get_test_data('narr_example.nc', False))
data = data.metpy.parse_cf().squeeze()
print(data)
##############################
# Define start and end points:
start = (37.0, -105.0)
end = (35.5, -65.0)
##############################
# Get the cross section, and convert lat/lon to supplementary coordinates:
cross = cross_section(data, start, end)
cross.set_coords(('lat', 'lon'), True)
print(cross)
##############################
# For this example, we will be plotting potential temperature, relative humidity, and
# tangential/normal winds. And so, we need to calculate those, and add them to the dataset:
temperature, pressure, specific_humidity = xr.broadcast(cross['Temperature'],
cross['isobaric'],
cross['Specific_humidity'])
theta = mpcalc.potential_temperature(pressure, temperature)
rh = mpcalc.relative_humidity_from_specific_humidity(specific_humidity, temperature, pressure)
# These calculations return unit arrays, so put those back into DataArrays in our Dataset
data = xr.open_dataset('./CFile.nc')
data["rhum"] = (['time', 'level', 'lat', 'lon'], c)
data = data.metpy.parse_cf().squeeze()
print(data)
##############################
# Define start and end points:
start = (5.0, 75.0)
end = (15.0, 80.0)
##############################
# Get the cross section, and convert lat/lon to supplementary coordinates:
cross = cross_section(data, start, end).set_coords(('lat', 'lon'))
##############################
# For this example, we will be plotting potential temperature, relative humidity, and
# tangential/normal winds. And so, we need to calculate those, and add them to the dataset:
cross['rhum'].metpy.convert_units('percent')
temperature, pressure, relative_humidity = xr.broadcast(
cross['air'], cross['level'], cross['rhum'])
theta = mpcalc.potential_temperature(pressure, temperature)
print(max(theta.flatten()))
print(min(theta.flatten()))
# These calculations return unit arrays, so put those back into DataArrays in our Dataset
def test_cross_section_dataarray_and_linear_interp(test_ds_xy):
"""Test the cross_section function with a data array and linear interpolation."""
data = test_ds_xy['temperature']
start, end = ((36.46, -112.45), (42.95, -68.74))
data_cross = cross_section(data, start, end, steps=7)
truth_values = np.array([[[250.00095489, 251.53646673, 253.11586664, 254.73477364,
256.38991013, 258.0794356, 259.80334269],
[260.04880178, 261.58431362, 263.16371353, 264.78262053,
266.43775702, 268.12728249, 269.85118958],
[270.09664867, 271.63216051, 273.21156042, 274.83046742,
276.48560391, 278.17512938, 279.89903647],
[280.14449556, 281.6800074, 283.25940731, 284.87831431,
286.5334508, 288.22297627, 289.94688336],
[290.19234245, 291.72785429, 293.3072542, 294.9261612,
296.58129769, 298.27082316, 299.99473025]]])
truth_values_x = np.array([-499495.71907062, 98404.43537514, 688589.09865512,
1272690.44926197, 1852009.73516881, 2427525.45740665,
2999932.89862589])
truth_values_y = np.array([-1499865.98780602, -1268717.36799267, -1029139.66048478,
-782037.60343652, -528093.95678826, -267710.32566917,
-939.10769171])
index = xr.DataArray(range(7), name='index', dims=['index'])
data_truth_x = xr.DataArray(
truth_values_x,
name='x',
coords={
'crs': data['crs'],
'y': (['index'], truth_values_y),
'x': (['index'], truth_values_x),
'index': index,
},
dims=['index']
)
data_truth_y = xr.DataArray(
truth_values_y,
name='y',
coords={
'crs': data['crs'],
'y': (['index'], truth_values_y),
'x': (['index'], truth_values_x),
'index': index,
},
dims=['index']
)
data_truth = xr.DataArray(
truth_values,
name='temperature',
coords={
'time': data['time'],
'isobaric': data['isobaric'],
'index': index,
'crs': data['crs'],
'y': data_truth_y,
'x': data_truth_x
},
dims=['time', 'isobaric', 'index'],
attrs={'units': 'kelvin'}
)
xr.testing.assert_allclose(data_truth, data_cross)
def test_cross_section_dataarray_projection_noop(test_ds_xy):
"""Test the cross_section function with a projection dataarray."""
data = test_ds_xy['lambert_conformal']
start, end = ((36.46, -112.45), (42.95, -68.74))
data_cross = cross_section(data, start, end, steps=7)
xr.testing.assert_identical(data, data_cross)
def test_cross_section_dataarray_projection_noop(test_ds_xy):
"""Test the cross_section function with a projection dataarray."""
data = test_ds_xy['lambert_conformal']
start, end = ((36.46, -112.45), (42.95, -68.74))
data_cross = cross_section(data, start, end, steps=7)
xr.testing.assert_identical(data, data_cross)
def metpy_read_wrf_cross(fname, plevels, tidx_in, lons_out, lats_out, start,
end):
ds = xr.open_dataset(fname).metpy.parse_cf().squeeze()
ds = ds.isel(Time=tidx)
print(ds)
ds1 = xr.Dataset()
p = units.Quantity(to_np(ds.p), 'hPa')
z = units.Quantity(to_np(ds.z), 'meter')
u = units.Quantity(to_np(ds.u), 'm/s')
v = units.Quantity(to_np(ds.v), 'm/s')
w = units.Quantity(to_np(ds.w), 'm/s')
tk = units.Quantity(to_np(ds.tk), 'kelvin')
th = units.Quantity(to_np(ds.th), 'kelvin')
eth = units.Quantity(to_np(ds.eth), 'kelvin')
wspd = units.Quantity(to_np(ds.wspd), 'm/s')
omega = units.Quantity(to_np(ds.omega), 'Pa/s')
plevels_unit = plevels * units.hPa
z, u, v, w, tk, th, eth, wspd, omega = log_interpolate_1d(plevels_unit,
p,
z,
u,
v,
w,
tk,
th,
eth,
wspd,
omega,
axis=0)
coords, dims = [plevs, ds.lat.values,
ds.lon.values], ["level", "lat", "lon"]
for name, var in zip(
['z', 'u', 'v', 'w', 'tk', 'th', 'eth', 'wspd', 'omega'],
[z, u, v, w, tk, th, eth, wspd, omega]):
#g = ndimage.gaussian_filter(var, sigma=3, order=0)
ds1[name] = xr.DataArray(to_np(mpcalc.smooth_n_point(var, 9)),
coords=coords,
dims=dims)
dx, dy = mpcalc.lat_lon_grid_deltas(ds.lon.values * units('degrees_E'),
ds.lat.values * units('degrees_N'))
# Calculate temperature advection using metpy function
for i, plev in enumerate(plevs):
adv = mpcalc.advection(eth[i, :, :], [u[i, :, :], v[i, :, :]],
(dx, dy),
dim_order='yx') * units('K/sec')
adv = ndimage.gaussian_filter(adv, sigma=3, order=0) * units('K/sec')
ds1['eth_adv_{:03d}'.format(plev)] = xr.DataArray(
np.array(adv),
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
div = mpcalc.divergence(u[i, :, :], v[i, :, :], dx, dy, dim_order='yx')
div = ndimage.gaussian_filter(div, sigma=3, order=0) * units('1/sec')
ds1['div_{:03d}'.format(plev)] = xr.DataArray(
np.array(div),
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
ds1['accrain'] = xr.DataArray(ds.accrain.values,
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
eth2 = mpcalc.equivalent_potential_temperature(
ds.slp.values * units.hPa, ds.t2m.values * units('K'),
ds.td2.values * units('celsius'))
ds1['eth2'] = xr.DataArray(eth2,
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
#ds1['sst'] = xr.DataArray(ndimage.gaussian_filter(ds.sst.values, sigma=3, order=0)-273.15, coords=[ds.lat.values,ds.lon.values], dims=["lat","lon"])
ds1 = ds1.metpy.parse_cf().squeeze()
cross = cross_section(ds1, start, end).set_coords(('lat', 'lon'))
cross.u.attrs['units'] = 'm/s'
cross.v.attrs['units'] = 'm/s'
cross['t_wind'], cross['n_wind'] = mpcalc.cross_section_components(
cross['u'], cross['v'])
weights = np.cos(np.deg2rad(ds.lat))
ds_weighted = ds.weighted(weights)
weighted = ds_weighted.mean(("lat"))
return ds1, cross, weighted
def Crosssection_Wind_Theta_e_Qv(
initTime=None, fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200],
day_back=0,model='GRAPES_GFS',data_source='MICAPS',
lw_ratio=[16,9],
output_dir=None,
st_point = [20, 120.0],
ed_point = [50, 130.0],
map_extent=[70,140,15,55],
h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ):
if(data_source == 'MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
# retrieve data from micaps server
rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False)
u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False)
v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
v2=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False)
gh=get_model_grid(data_dir[4], filename=filename)
psfc=get_model_grid(data_dir[5], filename=filename)
if(data_source == 'CIMISS'):
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour,UTC=True)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True)
try:
rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%')
u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s')
v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
v2=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K')
t['data'].values=t['data'].values-273.15
gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'),
fcst_level=500, fcst_ele="GPH", units='gpm')
gh['data'].values=gh['data'].values/10.
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
rh = rh.metpy.parse_cf().squeeze()
u = u.metpy.parse_cf().squeeze()
v = v.metpy.parse_cf().squeeze()
v2 = v2.metpy.parse_cf().squeeze()
psfc=psfc.metpy.parse_cf().squeeze()
t = t.metpy.parse_cf().squeeze()
resolution=u['lon'][1]-u['lon'][0]
x,y=np.meshgrid(u['lon'], u['lat'])
# +form 3D psfc
mask1 = (
(psfc['lon']>=t['lon'].values.min())&
(psfc['lon']<=t['lon'].values.max())&
(psfc['lat']>=t['lat'].values.min())&
(psfc['lat']<=t['lat'].values.max())
)
t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True))
mask2=(psfc_bdcst > -10000)
psfc_bdcst=psfc_bdcst.where(mask2, drop=True)
# -form 3D psfc
dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat'])
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh=cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u=cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v=cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
cross_u['data'].attrs['units']=units.meter/units.second
cross_v['data'].attrs['units']=units.meter/units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_t=cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values*units.celsius,
cross_rh['data'].values* units.percent)
rh,pressure = xr.broadcast(cross_rh['data'],cross_t['level'])
pressure.attrs['units']='hPa'
Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td,
pressure)
cross_Qv = xr.DataArray(np.array(Qv)*1000.,
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': units('g/kg')})
Theta_e=mpcalc.equivalent_potential_temperature(pressure,
cross_t['data'].values*units.celsius,
cross_Td)
cross_terrain=pressure-cross_psfc
cross_Theta_e = xr.DataArray(np.array(Theta_e),
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv(
cross_Qv=cross_Qv, cross_Theta_e=cross_Theta_e, cross_u=cross_u,
cross_v=cross_v,cross_terrain=cross_terrain,gh=gh,
h_pos=h_pos,st_point=st_point,ed_point=ed_point,
levels=levels,map_extent=map_extent,lw_ratio=lw_ratio,
output_dir=output_dir)