def test_speed_dir_roundtrip():
"""Test round-tripping between speed/direction and components."""
# Test each quadrant of the whole circle
wspd = np.array([15., 5., 2., 10.]) * units.meters / units.seconds
wdir = np.array([160., 30., 225., 350.]) * units.degrees
u, v = get_wind_components(wspd, wdir)
wdir_out = get_wind_dir(u, v)
wspd_out = get_wind_speed(u, v)
assert_array_almost_equal(wspd, wspd_out, 4)
assert_array_almost_equal(wdir, wdir_out, 4)
def test_speed():
"""Test calculating wind speed."""
u = np.array([4., 2., 0., 0.]) * units('m/s')
v = np.array([0., 2., 4., 0.]) * units('m/s')
speed = get_wind_speed(u, v)
s2 = np.sqrt(2.)
true_speed = np.array([4., 2 * s2, 4., 0.]) * units('m/s')
assert_array_almost_equal(true_speed, speed, 4)
def test_speed():
"""Test calculating wind speed."""
u = np.array([4., 2., 0., 0.]) * units('m/s')
v = np.array([0., 2., 4., 0.]) * units('m/s')
speed = get_wind_speed(u, v)
s2 = np.sqrt(2.)
true_speed = np.array([4., 2 * s2, 4., 0.]) * units('m/s')
assert_array_almost_equal(true_speed, speed, 4)
def test_speed_dir_roundtrip():
"""Test round-tripping between speed/direction and components."""
# Test each quadrant of the whole circle
wspd = np.array([15., 5., 2., 10.]) * units.meters / units.seconds
wdir = np.array([160., 30., 225., 350.]) * units.degrees
u, v = get_wind_components(wspd, wdir)
wdir_out = get_wind_dir(u, v)
wspd_out = get_wind_speed(u, v)
assert_array_almost_equal(wspd, wspd_out, 4)
assert_array_almost_equal(wdir, wdir_out, 4)
def test_get_wind_speed():
"""Test that get_wind_speed wrapper works (deprecated in 0.9)."""
with pytest.warns(MetpyDeprecationWarning):
s = get_wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
def test_scalar_speed():
"""Test wind speed with scalars."""
s = get_wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
def test_get_wind_speed():
"""Test that get_wind_speed wrapper works (deprecated in 0.9)."""
s = get_wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
# Determine the level of 500 hPa
levs = data.variables[dlev][:]
lev_500 = np.where(levs == 500)[0][0]
# Create more useable times for output
times = data.variables[dtime]
vtimes = num2date(times[:], times.units)
# Pull out the 500 hPa Heights
hght = data.variables['Geopotential_height'][:].squeeze() * units.meter
uwnd = data.variables['u_wind'][:].squeeze() * units('m/s')
vwnd = data.variables['v_wind'][:].squeeze() * units('m/s')
# Calculate the magnitude of the wind speed in kts
sped = get_wind_speed(uwnd, vwnd).to('knots')
##################################
# Set up the projection for LCC
plotcrs = ccrs.LambertConformal(central_longitude=-100.0,
central_latitude=45.0)
datacrs = ccrs.PlateCarree(central_longitude=0.)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
##################################
# Subset and smooth
dlon = data.variables['Geopotential_height_isobaric'].dimensions[3]
lat = data.variables[dlat][:]
lon = data.variables[dlon][:]
# Converting times using the num2date function available through netCDF4
times = data.variables[dtime]
vtimes = num2date(times[:], times.units)
# Smooth the 250-hPa heights using a gaussian filter from scipy.ndimage
hgt_250, lon = cutil.add_cyclic_point(data.variables['Geopotential_height_isobaric'][:],
coord=lon)
Z_250 = ndimage.gaussian_filter(hgt_250[0, 0, :, :], sigma=3, order=0)
u250 = cutil.add_cyclic_point(data.variables['u-component_of_wind_isobaric'][0, 0, :, :])
v250 = cutil.add_cyclic_point(data.variables['v-component_of_wind_isobaric'][0, 0, :, :])
wspd250 = mpcalc.get_wind_speed(u250, v250) * 1.94384
#################################################
# The next cell sets up the geographic details for the plot that we are going to do later.
# This is done using the Cartopy package. We will also bring in some geographic data to
# geo-reference the image for us.
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.NorthPolarStereo(central_longitude=-100.0)
states_provinces = cfeature.NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
# Make a grid of lat/lon values to use for plotting with Basemap.
lons, lats = np.meshgrid(lon, lat)
def plot_background(ax):
ax.set_extent([WLON, ELON, SLAT, NLAT])
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.5)
ax.add_feature(cfeature.LAKES.with_scale('50m'), linewidth=0.5)
ax.add_feature(cfeature.STATES, linewidth=0.5)
ax.add_feature(cfeature.BORDERS, linewidth=0.5)
return ax
# =============================================================================
# FIG #1: 250: JET STREAM, GEOPOTENTIAL HEIGHT, DIVERGENCE
# =============================================================================
H250 = HGT_DATA.variables['hgt'][TIME_INDEX, 8, :, :]
U250 = UWND_DATA.variables['uwnd'][TIME_INDEX, 8, :, :] * units('m/s')
V250 = VWND_DATA.variables['vwnd'][TIME_INDEX, 8, :, :] * units('m/s')
SPEED250 = mpcalc.get_wind_speed(U250, V250)
DIV250 = mpcalc.divergence(U250, V250, DX, DY, dim_order='YX')
DIV250 = (DIV250 * (units('1/s')))
# =============================================================================
# FIG #2: 500: VORTICITY, GEOPOTENTIAL HEIGHT, VORTICITY ADVECTION
# =============================================================================
H500 = HGT_DATA.variables['hgt'][TIME_INDEX, 5, :, :]
U500 = UWND_DATA.variables['uwnd'][TIME_INDEX, 5, :, :] * units('m/s')
V500 = VWND_DATA.variables['vwnd'][TIME_INDEX, 5, :, :] * units('m/s')
DX, DY = mpcalc.lat_lon_grid_spacing(LON, LAT)
VORT500 = mpcalc.vorticity(U500, V500, DX, DY, dim_order='YX')
VORT500 = (VORT500 * (units('1/s')))
VORT_ADV500 = mpcalc.advection(VORT500, [U500, V500], (DX, DY), dim_order='yx')
# =============================================================================
# FIG #3: 700: Q-VECTORS+CONVERGENCE, GEOPOTENTIAL HEIGHT
# =============================================================================
def Map_Jets(i, im_save_path):
from siphon.catalog import TDSCatalog
top_cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog.xml')
ref = top_cat.catalog_refs['Forecast Model Data']
new_cat = ref.follow()
model = new_cat.catalog_refs[4]
gfs_cat = model.follow()
ds = gfs_cat.datasets[1]
subset = ds.subset()
query_data = subset.query()
query_data.lonlat_box(west=-130, east=-50, south=10, north=60)
# Allow for NetCDF files
query_data.accept('netcdf4')
query_data.time(i)
data = query_data.variables('Geopotential_height_isobaric',
'Pressure_reduced_to_MSL_msl',
'u-component_of_wind_isobaric',
'v-component_of_wind_isobaric')
# Finally attempt to access the data
data = subset.get_data(query_data)
lat = data.variables['lat'][:].squeeze()
lon = data.variables['lon'][:].squeeze()
lev_250 = np.where(data.variables['isobaric4'][:] == 25000)[0][0]
hght_250 = data.variables['Geopotential_height_isobaric'][0, lev_250, :, :]
u_250 = data.variables['u-component_of_wind_isobaric'][0, lev_250, :, :]
v_250 = data.variables['v-component_of_wind_isobaric'][0, lev_250, :, :]
# Create a figure object, title it, and do the plots.
fig = plt.figure(figsize=(17., 11.))
add_metpy_logo(fig, 30, 940, size='small')
# Add the map and set the extent
ax6 = plt.subplot(1, 1, 1, projection=plotcrs)
# Add state boundaries to plot
ax6.add_feature(states_provinces, edgecolor='b', linewidth=1)
# Add country borders to plot
ax6.add_feature(country_borders, edgecolor='k', linewidth=1)
# Convert number of hours since the reference time into an actual date
time_var = data.variables[find_time_var(
data.variables['v-component_of_wind_isobaric'])]
time_final = num2date(time_var[:].squeeze(), time_var.units)
print(
str(time_final)[:4] + "_" + str(time_final)[5:7] + "_" +
str(time_final)[8:10] + "_" + str(time_final)[11:13] + "Z")
file_time = str(time_final)[:4] + "_" + str(time_final)[5:7] + "_" + str(
time_final)[8:10] + "_" + str(time_final)[11:13] + "Z"
# Plot Title
plt.title('GFS: 250mb Heights and Jet Streaks (m/s)',
loc='left',
fontsize=16)
plt.title(' {0:%d %B %Y %H:%MZ}'.format(time_final),
loc='right',
fontsize=16)
# Heights
#---------------------------------------------------------------------------------------------------
MIN = hght_250.min()
MAX = hght_250.max()
#print hght_250.min(),hght_250.max()
hght_250 = ndimage.gaussian_filter(hght_250, sigma=3,
order=0) * units.meter
clev250 = np.arange(MIN, MAX, 80)
cs = ax6.contour(lon,
lat,
hght_250.m,
clev250,
colors='black',
linewidths=2.0,
linestyles='solid',
transform=ccrs.PlateCarree())
#plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',
# rightside_up=True, use_clabeltext=True)
# Winds
#---------------------------------------------------------------------------------------------------
#lon_slice = slice(None, None, 7)
#lat_slice = slice(None, None, 7)
#ax4.barbs(lon[lon_slice], lat[lat_slice],
# u_250[lon_slice, lat_slice].magnitude,
# v_250[lon_slice, lat_slice].magnitude,
# transform=ccrs.PlateCarree(), zorder=2)
wspd250 = mpcalc.get_wind_speed(u_250, v_250)
clevsped250 = np.arange(50, 100, 1)
cf = ax6.contourf(lon,
lat,
wspd250,
clevsped250,
cmap="gist_ncar",
transform=ccrs.PlateCarree())
#cbar = plt.colorbar(cf, cax=cax, orientation='horizontal', extend='max', extendrect=True,pad=0.2)
cbaxes = fig.add_axes(colorbar_axis)
cbar = plt.colorbar(cf, orientation='horizontal', cax=cbaxes)
ax6.set_extent(extent, datacrs)
plt.close(fig)
#---------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------
GFS_Jet = im_save_path + "GFS/Jets/"
if not os.path.isdir(GFS_Jet):
os.makedirs(GFS_Jet)
fig.savefig(GFS_Jet + "250mb_Heights_Winds_" + file_time + ".png",
bbox_inches='tight',
dpi=120)
print('done.')
def test_get_wind_speed():
"""Test that get_wind_speed wrapper works (deprecated in 0.9)."""
with pytest.warns(MetpyDeprecationWarning):
s = get_wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
def Map_PVJet(i, im_save_path):
from siphon.catalog import TDSCatalog
top_cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog.xml')
ref = top_cat.catalog_refs['Forecast Model Data']
new_cat = ref.follow()
model = new_cat.catalog_refs[4]
gfs_cat = model.follow()
ds = gfs_cat.datasets[1]
gfs_cat.datasets[1]
subset = ds.subset()
query_data = subset.query()
query_data.lonlat_box(west=-130, east=-50, south=10, north=60)
# Allow for NetCDF files
query_data.accept('netcdf4')
query_data.time(i)
query_data.variables('Geopotential_height_potential_vorticity_surface',
'Geopotential_height_isobaric',
'u-component_of_wind_isobaric',
'v-component_of_wind_isobaric',
'Relative_humidity_isobaric',
"Pressure_reduced_to_MSL_msl")
#230., 295., 15., 45.
#query.lonlat_box(west=230., east=295., south=15., north=45.)
query_data.lonlat_box(west=-130, east=-50, south=10, north=60)
query_data.accept('netcdf4')
data = subset.get_data(query_data)
PV_Heights = data.variables[
'Geopotential_height_potential_vorticity_surface'][:].squeeze()
PV_1 = np.where(data.variables['potential_vorticity_surface'][:] ==
1.9999999949504854E-6)[0][0]
PV_1st = PV_Heights[PV_1]
lev_250 = np.where(data.variables['isobaric4'][:] == 25000)[0][0]
hght_250 = data.variables['Geopotential_height_isobaric'][0, lev_250, :, :]
u_250 = data.variables['u-component_of_wind_isobaric'][0, lev_250, :, :]
v_250 = data.variables['v-component_of_wind_isobaric'][0, lev_250, :, :]
lat = data.variables['lat'][:]
lon = data.variables['lon'][:]
mslp = data.variables['Pressure_reduced_to_MSL_msl'][:].squeeze()
# Create a figure object, title it, and do the plots.
fig = plt.figure(figsize=(17., 11.))
add_metpy_logo(fig, 30, 940, size='small')
# Add the map and set the extent
ax = plt.subplot(1, 1, 1, projection=plotcrs)
# Add state boundaries to plot
ax.add_feature(states_provinces, edgecolor='b', linewidth=1)
# Add country borders to plot
ax.add_feature(country_borders, edgecolor='k', linewidth=1)
# Convert number of hours since the reference time into an actual date
time_var = data.variables[find_time_var(
data.variables['v-component_of_wind_isobaric'])]
time_final = num2date(time_var[:].squeeze(), time_var.units)
print(
str(time_final)[:4] + "_" + str(time_final)[5:7] + "_" +
str(time_final)[8:10] + "_" + str(time_final)[11:13] + "Z")
file_time = str(time_final)[:4] + "_" + str(time_final)[5:7] + "_" + str(
time_final)[8:10] + "_" + str(time_final)[11:13] + "Z"
# Plot Title
plt.title('GFS: PV and Jet Streaks (m/s)', loc='left', fontsize=16)
plt.title(' {0:%d %B %Y %H:%MZ}'.format(time_final),
loc='right',
fontsize=16)
# Heights
#---------------------------------------------------------------------------------------------------
MIN = hght_250.min()
MAX = hght_250.max()
#print hght_250.min(),hght_250.max()
hght_250_smooth = ndimage.gaussian_filter(hght_250, sigma=3,
order=0) * units.meter
clev250 = np.arange(MIN, MAX, 80)
#cs = ax.contour(lon_2d, lat_2d, hght_250_smooth, clev250, colors='black', linewidths=2.0,
# linestyles='solid', transform=datacrs)
#plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',
# rightside_up=True, use_clabeltext=True)
# Winds
#---------------------------------------------------------------------------------------------------
#lon_slice = slice(None, None, 7)
#lat_slice = slice(None, None, 7)
#ax4.barbs(lon[lon_slice], lat[lat_slice],
# u_250[lon_slice, lat_slice].magnitude,
# v_250[lon_slice, lat_slice].magnitude,
# transform=ccrs.PlateCarree(), zorder=2)
#clev_mslp = np.arange(0, 1200, 3)
#cs = ax.contour(lon, lat, mslp/100, clev_mslp, colors='k',alpha=0.5,
#linestyles='solid', transform=datacrs,zorder=5) # cmap='rainbow, linewidths=3
#plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',
# rightside_up=True, use_clabeltext=True,colors='k')
wspd250 = mpcalc.get_wind_speed(u_250, v_250)
#print wspd250.min()
clevsped250 = np.arange(50, 100, 5)
cf = ax.contour(lon,
lat,
wspd250,
clevsped250,
colors='r',
transform=datacrs)
plt.clabel(cf,
fontsize=10,
inline=1,
inline_spacing=10,
fmt='%i',
rightside_up=True,
use_clabeltext=True,
colors='k')
#cf = ax.contourf(lon_2d, lat_2d, wspd250, clevsped250, cmap="gist_ncar", transform=datacrs)
#cbar = plt.colorbar(cf, cax=cax, orientation='horizontal', extend='max', extendrect=True,pad=0.2)
#cbaxes = fig.add_axes(colorbar_axis)
#cbar = plt.colorbar(cf, orientation='horizontal',cax=cbaxes)
cs2 = ax.contourf(lon,
lat,
PV_1st,
100,
alpha=0.7,
antialiased=True,
transform=datacrs,
cmap='cubehelix_r')
cbaxes = fig.add_axes(colorbar_axis) # [left, bottom, width, height]
cbar = plt.colorbar(cs2, orientation='horizontal', cax=cbaxes)
ax.set_extent(extent, datacrs)
plt.close(fig)
#---------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------
PV_Jet = im_save_path + "GFS/PV_Jet/"
if not os.path.isdir(PV_Jet):
os.makedirs(PV_Jet)
fig.savefig(PV_Jet + "Jet_PV_" + file_time + ".png",
bbox_inches='tight',
dpi=120)
print('done.')
def test_scalar_speed():
"""Test wind speed with scalars."""
s = get_wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
# Surface dewpoint
#
# 700-hPa dewpoint depression
#
# 12-hr surface pressure falls and 500-hPa height changes
# 500 hPa CVA
dx, dy = mpcalc.lat_lon_grid_spacing(lon, lat)
vort_adv_500 = mpcalc.advection(avor_500, [u_500.to('m/s'), v_500.to('m/s')],
(dx, dy), dim_order='yx') * 1e9
vort_adv_500_smooth = gaussian_filter(vort_adv_500, 4)
####################################
# For the jet axes, we will calculate the windspeed at each level, and plot the highest values
wspd_300 = gaussian_filter(mpcalc.get_wind_speed(u_300, v_300), 5)
wspd_500 = gaussian_filter(mpcalc.get_wind_speed(u_500, v_500), 5)
wspd_850 = gaussian_filter(mpcalc.get_wind_speed(u_850, v_850), 5)
#################################
# 850-hPa dewpoint will be calculated from RH and Temperature_isobaric
Td_850 = mpcalc.dewpoint_rh(tmp_850, rh_850 / 100.)
################################
# 700-hPa dewpoint depression will be calculated from Temperature_isobaric and RH
Td_dep_700 = tmp_700 - mpcalc.dewpoint_rh(tmp_700, rh_700 / 100.)
######################################
# 12-hr surface pressure falls and 500-hPa height changes
pmsl_change = pmsl - pmsl_00z
hgt_500_change = hgt_500 - hgt_500_00z
skew.plot_barbs(p[ix], u[ix], v[ix])
skew.ax.set_ylim(1075, 100)
skew.ax.set_ylabel('Pressure (hPa)')
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0]) #LCL
pwat = mpcalc.precipitable_water(Td, p, 500 * units.hectopascal).to('in') #PWAT
cape, cin = mpcalc.most_unstable_cape_cin(p[:], T[:], Td[:]) #MUCAPE
cape_sfc, cin_sfc = mpcalc.surface_based_cape_cin(p, T, Td) #SBCAPE
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') #parcel profile
equiv_pot_temp = mpcalc.equivalent_potential_temperature(p, T, Td) #equivalent potential temperature
el_pressure, el_temperature = mpcalc.el(p, T, Td) #elevated level
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td) #LFC
#calculates shear
u_threekm_bulk_shear, v_threekm_bulk_shear = mpcalc.bulk_shear(p, u, v, hgt, bottom = min(hgt), depth = 3000 * units.meter)
threekm_bulk_shear = mpcalc.get_wind_speed(u_threekm_bulk_shear, v_threekm_bulk_shear)
u_onekm_bulk_shear, v_onekm_bulk_shear = mpcalc.bulk_shear(p, u, v, hgt, bottom = min(hgt), depth = 1000 * units.meter)
onekm_bulk_shear = mpcalc.get_wind_speed(u_onekm_bulk_shear, v_onekm_bulk_shear)
#shows the level of the LCL, LFC, and EL.
skew.ax.text(T[0].magnitude, p[0].magnitude + 5, str(int(np.round(T[0].to('degF').magnitude))), fontsize = 'medium', horizontalalignment = 'left', verticalalignment = 'top', color = 'red')
skew.ax.text(Td[0].magnitude, p[0].magnitude + 5, str(int(np.round(Td[0].to('degF').magnitude))), fontsize = 'medium', horizontalalignment = 'right', verticalalignment = 'top', color = 'green')
skew.ax.text(lcl_temperature.magnitude + 5, lcl_pressure.magnitude, "---- LCL", fontsize = 'medium', verticalalignment = 'center')
skew.ax.text(Td[0].magnitude - 10, p[0].magnitude, 'SFC: {}hPa ----'.format(p[0].magnitude), fontsize = 'medium', horizontalalignment = 'right', verticalalignment = 'center', color = 'black')
if str(lfc_temperature.magnitude) != 'nan': #checks to see if LFC/EL exists. If not, skip.
skew.ax.text(lfc_temperature.magnitude + 5, lfc_pressure.magnitude, "---- LFC", fontsize = 'medium', verticalalignment = 'center')
skew.ax.text(el_temperature.magnitude + 5, el_pressure.magnitude, "---- EL", fontsize = 'medium', verticalalignment = 'center')
skew.plot(p, prof, 'k-', linewidth=1) #plots parcel profile
skew.shade_cape(p, T, prof) #shades cape
