def basic_map(proj):
"""Make our basic default map for plotting"""
fig = plt.figure(figsize=(15, 10))
add_metpy_logo(fig, 0, 80, size='large')
view = fig.add_axes([0, 0, 1, 1], projection=proj)
view.set_extent([-120, -70, 20, 50])
view.add_feature(cfeature.STATES.with_scale('50m'))
view.add_feature(cfeature.OCEAN)
view.add_feature(cfeature.COASTLINE)
view.add_feature(cfeature.BORDERS, linestyle=':')
return fig, view
def make_skewt():
# Get the data
date = request.args.get('date')
time = request.args.get('time')
region = request.args.get('region')
station = request.args.get('station')
date = datetime.strptime(date, '%Y%m%d')
date = datetime(date.year, date.month, date.day, int(time))
df = get_sounding_data(date, region, station)
p = df['pressure'].values * units(df.units['pressure'])
T = df['temperature'].values * units(df.units['temperature'])
Td = df['dewpoint'].values * units(df.units['dewpoint'])
u = df['u_wind'].values * units(df.units['u_wind'])
v = df['v_wind'].values * units(df.units['v_wind'])
# Make the Skew-T
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, 115, 100)
skew = SkewT(fig, rotation=45)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'tab:red')
skew.plot(p, Td, 'tab:green')
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
# Calculate LCL height and plot as black dot
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)
# Shade areas of CAPE and CIN
skew.shade_cin(p, T, prof)
skew.shade_cape(p, T, prof)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
canvas = FigureCanvas(fig)
img = BytesIO()
fig.savefig(img)
img.seek(0)
return send_file(img, mimetype='image/png')
def basic_map(proj):
"""Make our basic default map for plotting"""
fig = plt.figure(figsize=(15, 10))
add_metpy_logo(fig, 0, 80, size='large')
view = fig.add_axes([0, 0, 1, 1], projection=proj)
view.set_extent([-120, -70, 20, 50])
view.add_feature(cfeature.STATES.with_scale('50m'))
view.add_feature(cfeature.OCEAN)
view.add_feature(cfeature.COASTLINE)
view.add_feature(cfeature.BORDERS, linestyle=':')
return fig, view
def basic_map(proj):
"""Make our basic default map for plotting"""
fig = plt.figure(figsize=(15, 10))
add_metpy_logo(fig, 0, 80, size='large')
view = fig.add_axes([0, 0, 1, 1], projection=proj)
view._hold = True # Work-around for CartoPy 0.16/Matplotlib 3.0.0 incompatibility
view.set_extent([-120, -70, 20, 50])
view.add_feature(cfeature.STATES.with_scale('50m'))
view.add_feature(cfeature.OCEAN)
view.add_feature(cfeature.COASTLINE)
view.add_feature(cfeature.BORDERS, linestyle=':')
return fig, view
def basic_map(proj):
"""Make our basic default map for plotting"""
fig = plt.figure(figsize=(15, 10))
add_metpy_logo(fig, 0, 80, size='large')
view = fig.add_axes([0, 0, 1, 1], projection=proj)
view.set_extent([-120, -70, 20, 50])
view.add_feature(
cartopy.feature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none'))
view.add_feature(cartopy.feature.OCEAN)
view.add_feature(cartopy.feature.COASTLINE)
view.add_feature(cartopy.feature.BORDERS, linestyle=':')
return view
def plot_skewt(df):
# We will pull the data out of the example dataset into individual variables
# and assign units.
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
# Create a new figure. The dimensions here give a good aspect ratio.
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, 115, 100)
skew = SkewT(fig, rotation=45)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
# Calculate LCL height and plot as black dot
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
return skew
def test_add_logo_invalid_size():
"""Test adding a logo to a figure with an invalid size specification."""
fig = plt.figure(figsize=(9, 9))
with pytest.raises(ValueError):
add_metpy_logo(fig, size='jumbo')
"""
NEXRAD Level 3 File
===================
Use MetPy to read information from a NEXRAD Level 3 (NIDS product) file and plot
"""
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.io import Level3File
from metpy.plots import add_metpy_logo, add_timestamp, colortables
###########################################
fig, axes = plt.subplots(1, 2, figsize=(15, 8))
add_metpy_logo(fig, 190, 85, size='large')
for v, ctable, ax in zip(('N0Q', 'N0U'), ('NWSReflectivity', 'NWSVelocity'), axes):
# Open the file
name = get_test_data('nids/KOUN_SDUS54_{}TLX_201305202016'.format(v), as_file_obj=False)
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array, then mask
data = np.ma.array(datadict['data'])
data[data == 0] = np.ma.masked
# Grab azimuths and calculate a range based on number of gates
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
###########################################
# Create CartoPy projection information for the file
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=proj_var.earth_radius,
semiminor_axis=proj_var.earth_radius)
proj = ccrs.LambertConformal(
central_longitude=proj_var.longitude_of_central_meridian,
central_latitude=proj_var.latitude_of_projection_origin,
standard_parallels=[proj_var.standard_parallel],
globe=globe)
###########################################
# Plot the image
fig = plt.figure(figsize=(10, 12))
add_metpy_logo(fig, 125, 145)
ax = fig.add_subplot(1, 1, 1, projection=proj)
wv_norm, wv_cmap = ctables.registry.get_with_range('WVCIMSS', 100, 260)
wv_cmap.set_under('k')
im = ax.imshow(dat[:],
cmap=wv_cmap,
norm=wv_norm,
extent=ds.img_extent,
origin='upper')
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
add_timestamp(ax, f.prod_desc.datetime, y=0.02, high_contrast=True)
plt.show()
def SkewT_plot(p,
T,
Td,
heights,
u=0,
v=0,
wind_barb=0,
p_lims=[1000, 100],
T_lims=[-50, 35],
metpy_logo=0,
plt_lfc=0,
plt_lcl=0,
plt_el=0,
title=None):
#plotting
fig = plt.figure(figsize=(10, 10))
skew = plots.SkewT(fig)
skew.plot(p, T, 'red') #Virtual Temperature
skew.plot(p, Td, 'green') #Dewpoint
skew.ax.set_ylim(p_lims)
skew.ax.set_xlim(T_lims)
if wind_barb == 1:
# resampling wind barbs
interval = np.logspace(2, 3) * units.hPa
idx = mpcalc.resample_nn_1d(p, interval)
skew.plot_barbs(p[idx], u[idx], v[idx])
#Showing Adiabasts and Mixing Ratio Lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
parcel_path = mpcalc.parcel_profile(p, T[0], Td[0])
skew.plot(p, parcel_path, color='k')
# CAPE and CIN
skew.shade_cape(p, T, parcel_path)
skew.shade_cin(p, T, parcel_path)
# Isotherms and Isobars
# skew.ax.axhline(500 * units.hPa, color='k')
# skew.ax.axvline(0 * units.degC, color='c')
# LCL, LFC, EL
lcl_p, lcl_T = mpcalc.lcl(p[0], T[0], Td[0])
lfc_p, lfc_T = mpcalc.lfc(p, T, Td)
el_p, el_T = mpcalc.el(p, T, Td)
if plt_lfc == 1:
skew.ax.axhline(lfc_p, color='k')
if plt_lcl == 1:
skew.ax.axhline(lcl_p, color='k')
# skew.ax.text(lcl_p, )
if plt_el == 1:
skew.ax.axhline(el_p, color='k')
if metpy_logo == 1: plots.add_metpy_logo(fig, x=55, y=50)
decimate = 3
for p, T, heights in zip(df['pressure'][::decimate],
df['temperature'][::decimate],
df['height'][::decimate]):
if p >= 700: skew.ax.text(T + 1, p, round(heights, 0))
plt.title(title)
plt.show()
return
########################################### # We will pull the data out of the example dataset into individual variables and # assign units. p = df['pressure'].values * units.hPa T = df['temperature'].values * units.degC Td = df['dewpoint'].values * units.degC wind_speed = df['speed'].values * units.knots wind_dir = df['direction'].values * units.degrees u, v = mpcalc.wind_components(wind_speed, wind_dir) ########################################### # Create a new figure. The dimensions here give a good aspect ratio fig = plt.figure(figsize=(9, 9)) add_metpy_logo(fig, 630, 80, size='large') # Grid for plots gs = gridspec.GridSpec(3, 3) skew = SkewT(fig, rotation=45, subplot=gs[:, :2]) # Plot the data using normal plotting functions, in this case using # log scaling in Y, as dictated by the typical meteorological plot skew.plot(p, T, 'r') skew.plot(p, Td, 'g') skew.plot_barbs(p, u, v) skew.ax.set_ylim(1000, 100) # Add the relevant special lines skew.plot_dry_adiabats() skew.plot_moist_adiabats()
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.')
isentrh = 100 * mpcalc.relative_humidity_from_specific_humidity(isentspech, isenttmp, isentprs)
#######################################
# **Plotting the Isentropic Analysis**
# Set up our projection
crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)
# Coordinates to limit map area
bounds = [(-122., -75., 25., 50.)]
# Choose a level to plot, in this case 296 K
level = 0
fig = plt.figure(figsize=(17., 12.))
add_metpy_logo(fig, 120, 245, size='large')
ax = fig.add_subplot(1, 1, 1, projection=crs)
ax.set_extent(*bounds, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.75)
ax.add_feature(cfeature.STATES, linewidth=0.5)
# Plot the surface
clevisent = np.arange(0, 1000, 25)
cs = ax.contour(lon, lat, isentprs[level, :, :], clevisent,
colors='k', linewidths=1.0, linestyles='solid', transform=ccrs.PlateCarree())
ax.clabel(cs, fontsize=10, inline=1, inline_spacing=7,
fmt='%i', rightside_up=True, use_clabeltext=True)
# Plot RH
cf = ax.contourf(lon, lat, isentrh[level, :, :], range(10, 106, 5),
cmap=plt.cm.gist_earth_r, transform=ccrs.PlateCarree())
# then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['longitude'].values,
data['latitude'].values)
data = data[reduce_point_density(point_locs, 300000.)]
###########################################
# The payoff
# ----------
# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering.
plt.rcParams['savefig.dpi'] = 255
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1100, 300, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS)
# Set plot bounds
ax.set_extent((-118, -73, 23, 50))
#
# Here's the actual station plot
def test_add_logo_invalid_size():
"""Test adding a logo to a figure with an invalid size specification."""
fig = plt.figure(figsize=(9, 9))
with pytest.raises(ValueError):
add_metpy_logo(fig, size='jumbo')
def test_add_metpy_logo_large():
"""Test adding a large MetPy logo to a figure."""
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, size='large')
return fig
def test_add_metpy_logo_small():
"""Test adding a MetPy logo to a figure."""
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig)
return fig
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.')
# Make an LCC map projection
proj = ccrs.LambertConformal()
# Plot the map
fig = plt.figure(figsize=(12, 7))
ax = plt.axes(projection=proj)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.add_feature(cfeature.BORDERS.with_scale('50m'), linestyle=':')
ax.add_feature(cfeature.STATES.with_scale('50m'), linestyle=':')
ax.add_feature(cfeature.LAKES.with_scale('50m'), alpha=0.5)
ax.add_feature(cfeature.RIVERS.with_scale('50m'), alpha=0.5)
add_timestamp(ax)
add_metpy_logo(fig, x=300, y=350)
scatter = ax.scatter(df.longitude, df.latitude,
c=df[args.var], transform=ccrs.PlateCarree(),
cmap=plt.get_cmap(args.cmap), vmin=args.min, vmax=args.max,
s=args.msize) # cm.Oranges or Use plt.get_cmap(str)
plt.colorbar(scatter, orientation='horizontal',
label=args.var.replace('_', ' ').title(),
shrink=0.6, pad=0.05)
#u, v = mpcalc.wind_components(df.wind_direction.values * units('m/s'), df.wind_direction.values * units.degrees)
#x = df.longitude.values
#y = df.latitude.values
#ax.quiver(x, y, u.m, v.m, transform=ccrs.PlateCarree(), units='dots')
def produce(data, conn, client):
Year = data['inputData']['Year']
Month = data['inputData']['Month']
Day = data['inputData']['Day']
Radar = data['inputData']['Radar']
uid = data['uid']
inputData = data['inputData']
userID = data["userID"]
numberOfPlots = 1
scans = conn.get_avail_scans(Year, Month, Day,
Radar) # year, month and day
results = conn.download(scans[numberOfPlots - 1], 'templocation')
# fig = plt.figure(figsize=(16,12))
for i, scan in enumerate(results.iter_success(), start=1):
# ax = fig.add_subplot(1,1,i)
# radar = scan.open_pyart()
# display = pyart.graph.RadarDisplay(radar)
# display.plot('reflectivity',0,ax=ax,title="{} {}".format(scan.radar_id,scan.scan_time))
# display.set_limits((-150, 150), (-150, 150), ax=ax)
sweep = 0
name = scan.open()
f = Level2File(name)
# First item in ray is header, which has azimuth angle
az = np.array([ray[0].az_angle for ray in f.sweeps[sweep]])
# 5th item is a dict mapping a var name (byte string) to a tuple
# of (header, data array)
ref_hdr = f.sweeps[sweep][0][4][b'REF'][0]
ref_range = np.arange(
ref_hdr.num_gates) * ref_hdr.gate_width + ref_hdr.first_gate
ref = np.array([ray[4][b'REF'][1] for ray in f.sweeps[sweep]])
rho_hdr = f.sweeps[sweep][0][4][b'RHO'][0]
rho_range = (np.arange(rho_hdr.num_gates + 1) -
0.5) * rho_hdr.gate_width + rho_hdr.first_gate
rho = np.array([ray[4][b'RHO'][1] for ray in f.sweeps[sweep]])
fig, axes = plt.subplots(1, 2, figsize=(15, 8))
add_metpy_logo(fig, 190, 85, size='large')
for var_data, var_range, ax in zip((ref, rho), (ref_range, rho_range),
axes):
# Turn into an array, then mask
data = np.ma.array(var_data)
data[np.isnan(data)] = np.ma.masked
# Convert az,range to x,y
xlocs = var_range * np.sin(np.deg2rad(az[:, np.newaxis]))
ylocs = var_range * np.cos(np.deg2rad(az[:, np.newaxis]))
# Plot the data
ax.pcolormesh(xlocs, ylocs, data, cmap='viridis')
ax.set_aspect('equal', 'datalim')
ax.set_xlim(-40, 20)
ax.set_ylim(-30, 30)
add_timestamp(ax, f.dt, y=0.02, high_contrast=True)
pltName = 'images/' + str(uid + str(i) + '.png')
plt.savefig(pltName)
plt.close(fig)
uploadImage = im.upload_image(pltName, title="Uploaded with PyImgur")
link = str(uploadImage.link)
body = {
"inputData": inputData,
"outputData": link,
"uid": uid,
"userID": userID
}
with (client.topics['dataAnalysisConsumerF']
).get_sync_producer() as producer:
producer.produce(bytes(json.dumps(body), 'utf-8'))
# variable for plotting.
height, temp = log_interpolate_1d(plevs, pres, hgt, temperature, axis=1)
####################################
# **Plotting the Data for 700 hPa.**
# Set up our projection
crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)
# Set the forecast hour
FH = 1
# Create the figure and grid for subplots
fig = plt.figure(figsize=(17, 12))
add_metpy_logo(fig, 470, 320, size='large')
# Plot 700 hPa
ax = plt.subplot(111, projection=crs)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.75)
ax.add_feature(cfeature.STATES, linewidth=0.5)
# Plot the heights
cs = ax.contour(lon, lat, height[FH, 0, :, :], transform=ccrs.PlateCarree(),
colors='k', linewidths=1.0, linestyles='solid')
ax.clabel(cs, fontsize=10, inline=1, inline_spacing=7,
fmt='%i', rightside_up=True, use_clabeltext=True)
# Contour the temperature
cf = ax.contourf(lon, lat, temp[FH, 0, :, :], range(-20, 20, 1), cmap=plt.cm.RdBu_r,
transform=ccrs.PlateCarree())
from metpy.io import Level3File
from metpy.plots import add_metpy_logo, add_timestamp, ctables
# Copyright (c) 2015 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
NEXRAD Level 3 File
===================
Use MetPy to read information from a NEXRAD Level 3 (NIDS product) file and plot
"""
###########################################
fig, axes = plt.subplots(1, 2, figsize=(15, 8))
add_metpy_logo(fig, 190, 85, size="large")
for v, ctable, ax in zip(("N0Q", "N0U"), ("NWSReflectivity", "NWSVelocity"),
axes):
# Open the file
name = get_test_data("nids/KOUN_SDUS54_{}TLX_201305202016".format(v),
as_file_obj=False)
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array, then mask
data = np.ma.array(datadict["data"])
data[data == 0] = np.ma.masked
# Grab azimuths and calculate a range based on number of gates
def Map_500_Vort(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("Absolute_vorticity_isobaric",'Geopotential_height_isobaric')
# Finally attempt to access the data
data = subset.get_data(query_data)
#for i in data.variables.keys(): print "Variables:",i,"\n"
# Pull out variables you want to use
Vort = data.variables['Absolute_vorticity_isobaric']
lev_500mb = np.where(data.variables['isobaric4'][:] == 50000)[0][0]
Vort_500 = data.variables['Absolute_vorticity_isobaric'][0, lev_500mb, :, :]
lat = data.variables['lat'][:].squeeze()
lon = data.variables['lon'][:].squeeze()
time_var = data.variables[find_time_var(data.variables['Absolute_vorticity_isobaric'])]
# Convert number of hours since the reference time into an actual date
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"
# Combine 1D latitude and longitudes into a 2D grid of locations
#lon_2d, lat_2d = np.meshgrid(lon, lat)
# Create new figure
fig = plt.figure(figsize=(17., 11.))
add_metpy_logo(fig, 30, 940, size='small')
# Add the map and set the extent
ax = plt.subplot(111, projection=plotcrs)
#Set the lat and lon boundaries
ax.set_extent(extent, datacrs)
# Add state boundaries to plot
ax.add_feature(states_provinces, edgecolor='blue', linewidth=1)
# Add country borders to plot
ax.add_feature(country_borders, edgecolor='black', linewidth=1)
# Plot Title
plt.title('GFS: 500mb Vorticity and Heights', fontdict=font,loc='left')
plt.title(' {0:%d %B %Y %H:%MZ}'.format(time_final), fontdict=font,loc='right')
Min = Vort_500.min()
Max = Vort_500.max()
#print(Min,Max)
#print hght_500.min(),hght_500.max()
#hght_500 = ndimage.gaussian_filter(hght_500, sigma=3, order=0) * units.meter
lev_500 = np.where(data.variables['isobaric4'][:] == 50000)[0][0]
hght_500 = data.variables['Geopotential_height_isobaric'][0, lev_500, :, :]
MIN = hght_500.min()
MAX = hght_500.max()
clev500 = np.arange(MIN, MAX, 70)
cs = ax.contour(lon, lat, hght_500,clev500 ,colors='black', linewidths=2.0,
linestyles='solid', transform=ccrs.PlateCarree())
vort_levels = np.arange(-.00055,.0007,0.00001)
cs2 = ax.contourf(lon, lat, Vort_500,vort_levels,
transform=datacrs,cmap=my_cmap)
cbaxes = fig.add_axes(colorbar_axis) # [left, bottom, width, height]
#def myfmt(x, pos):
# return '{0:.1e}'.format(x)
# apply formatter for colorbar labels
#cbar = plt.colorbar(cs2, orientation='horizontal',cax=cbaxes,format=mpl.ticker.FuncFormatter(myfmt))
cbar = plt.colorbar(cs2, orientation='horizontal',cax=cbaxes)
#cbar = plt.colorbar(cs2, orientation='horizontal',cax=cbaxes,ticks = np.arange(-0.0006, 0.0006, 17),format=sfmt)
cbar.set_label(r'$s{^-1}$')
#plt.show()
plt.close(fig)
VORT = im_save_path+"GFS/Vorticity/"
if not os.path.isdir(VORT):
os.makedirs(VORT)
outfile=VORT+"Vort_Heights_500mb_"+file_time+".png"
fig.savefig(outfile,bbox_inches='tight',dpi=120)
print("done.")
surface_temp.metpy.convert_units('degF')
precip_water.metpy.convert_units('inches')
winds_300.metpy.convert_units('knots')
###########################################
# Smooth the height data
heights_300 = ndimage.gaussian_filter(ds['heights_300'][0], sigma=1.5, order=0)
heights_500 = ndimage.gaussian_filter(ds['heights_500'][0], sigma=1.5, order=0)
###########################################
# Create the figure and plot background on different axes
fig, axarr = plt.subplots(nrows=2, ncols=2, figsize=(20, 13), constrained_layout=True,
subplot_kw={'projection': crs})
add_metpy_logo(fig, 140, 120, size='large')
axlist = axarr.flatten()
for ax in axlist:
plot_background(ax)
# Upper left plot - 300-hPa winds and geopotential heights
cf1 = axlist[0].contourf(lon_2d, lat_2d, winds_300, cmap='cool', transform=ccrs.PlateCarree())
c1 = axlist[0].contour(lon_2d, lat_2d, heights_300, colors='black', linewidths=2,
transform=ccrs.PlateCarree())
axlist[0].clabel(c1, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)
axlist[0].set_title('300-hPa Wind Speeds and Heights', fontsize=16)
cb1 = fig.colorbar(cf1, ax=axlist[0], orientation='horizontal', shrink=0.74, pad=0)
cb1.set_label('knots', size='x-large')
# Upper right plot - 500mb absolute vorticity and geopotential heights
cf2 = axlist[1].contourf(lon_2d, lat_2d, vort_500, cmap='BrBG', transform=ccrs.PlateCarree(),
# Set up the map projection and set up a cartopy feature for state borders
proj = ccrs.LambertConformal(central_longitude=-95,
central_latitude=35,
standard_parallels=[35])
###########################################
# The payoff
# ----------
# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering
plt.rcParams['savefig.dpi'] = 255
# Create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Add some various map elements to the plot to make it recognizable
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS, linewidth=2)
# Set plot bounds
ax.set_extent((-118, -73, 23, 50))
#
# Here's the actual station plot
wind_direction = data['WDIR']
latitude = data['LAT']
longitude = data['LON']
station_id = data['STID']
# Take cardinal direction and convert to degrees, then convert to components
wind_direction = mpcalc.parse_angle(list(wind_direction))
u, v = mpcalc.wind_components(wind_speed.to('knots'), wind_direction)
###########################################
# Create the figure
# -----------------
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 8))
add_metpy_logo(fig, 70, 30, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.STATES.with_scale('50m'))
# Set plot bounds
ax.set_extent((-104, -93, 33.4, 37.2))
stationplot = StationPlot(ax,
longitude.values,
latitude.values,
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=12)
t,
interp_type='cressman',
minimum_neighbors=3,
search_radius=400000,
hres=35000)
temp = np.ma.masked_where(np.isnan(temp), temp)
###########################################
# Set up the map and plot the interpolated grids appropriately.
levels = list(range(-20, 20, 1))
cmap = plt.get_cmap('viridis')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 360, 120, size='large')
view = fig.add_subplot(1, 1, 1, projection=to_proj)
view.set_extent([-120, -70, 20, 50])
view.add_feature(cfeature.STATES.with_scale('50m'))
view.add_feature(cfeature.OCEAN)
view.add_feature(cfeature.COASTLINE.with_scale('50m'))
view.add_feature(cfeature.BORDERS, linestyle=':')
cs = view.contour(slpgridx,
slpgridy,
slp,
colors='k',
levels=list(range(990, 1034, 4)))
plt.clabel(cs, inline=1, fontsize=12, fmt='%i')
# Temporary variables for ease
temp = testdata['T']
pres = testdata['P']
rh = testdata['RH']
ws = testdata['WS']
wsmax = testdata['WSMAX']
wd = testdata['WD']
date = testdata['DATE']
# ID For Plotting on Meteogram
probe_id = '0102A'
data = {'wind_speed': (np.array(ws) * units('m/s')).to(units('knots')),
'wind_speed_max': (np.array(wsmax) * units('m/s')).to(units('knots')),
'wind_direction': np.array(wd) * units('degrees'),
'dewpoint': dewpoint_rh((np.array(temp) * units('degC')).to(units('K')),
np.array(rh) / 100.).to(units('degF')),
'air_temperature': (np.array(temp) * units('degC')).to(units('degF')),
'mean_slp': calc_mslp(np.array(temp), np.array(pres), hgt_example) * units('hPa'),
'relative_humidity': np.array(rh), 'times': np.array(date)}
fig = plt.figure(figsize=(20, 16))
add_metpy_logo(fig, 250, 180)
meteogram = Meteogram(fig, data['times'], probe_id)
meteogram.plot_winds(data['wind_speed'], data['wind_direction'], data['wind_speed_max'])
meteogram.plot_thermo(data['air_temperature'], data['dewpoint'])
meteogram.plot_rh(data['relative_humidity'])
meteogram.plot_pressure(data['mean_slp'])
fig.subplots_adjust(hspace=0.5)
plt.show()
########################################### # We will pull the data out of the example dataset into individual variables and # assign units. p = df['pressure'].values * units.hPa T = df['temperature'].values * units.degC Td = df['dewpoint'].values * units.degC wind_speed = df['speed'].values * units.knots wind_dir = df['direction'].values * units.degrees u, v = mpcalc.get_wind_components(wind_speed, wind_dir) ########################################### # Create a new figure. The dimensions here give a good aspect ratio fig = plt.figure(figsize=(9, 9)) add_metpy_logo(fig, 115, 100) # Grid for plots skew = SkewT(fig, rotation=45) # Plot the data using normal plotting functions, in this case using # log scaling in Y, as dictated by the typical meteorological plot skew.plot(p, T, 'r') skew.plot(p, Td, 'g') skew.plot_barbs(p, u, v) skew.ax.set_ylim(1000, 100) # Add the relevant special lines skew.plot_dry_adiabats() skew.plot_moist_adiabats() skew.plot_mixing_lines()
tlons = tlatlons[:, :, 0]
tlats = tlatlons[:, :, 1]
# Coordinates to limit map area
bounds = [(-122., -75., 25., 50.)]
# Choose a level to plot, in this case 296 K
level = 0
# Get data to plot state and province boundaries
states_provinces = cfeature.NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
fig = plt.figure(1, figsize=(17., 12.))
add_metpy_logo(fig, 120, 245, size='large')
ax = fig.add_subplot(1, 1, 1, projection=crs)
ax.set_extent(*bounds, crs=ccrs.PlateCarree())
ax.coastlines('50m', edgecolor='black', linewidth=0.75)
ax.add_feature(states_provinces, edgecolor='black', linewidth=0.5)
# Plot the surface
clevisent = np.arange(0, 1000, 25)
cs = ax.contour(tlons, tlats, isentprs[level, :, :], clevisent,
colors='k', linewidths=1.0, linestyles='solid')
plt.clabel(cs, fontsize=10, inline=1, inline_spacing=7,
fmt='%i', rightside_up=True, use_clabeltext=True)
# Plot RH
cf = ax.contourf(tlons, tlats, isentrh[level, :, :], range(10, 106, 5),
cmap=plt.cm.gist_earth_r)
def test_add_metpy_logo_small():
"""Test adding a MetPy logo to a figure."""
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig)
return fig
# variable for plotting.
height, temp = log_interpolate_1d(plevs, pres, hgt, temperature, axis=1)
####################################
# **Plotting the Data for 700 hPa.**
# Set up our projection
crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)
# Set the forecast hour
FH = 1
# Create the figure and grid for subplots
fig = plt.figure(figsize=(17, 12))
add_metpy_logo(fig, 470, 320, size='large')
# Plot 700 hPa
ax = plt.subplot(111, projection=crs)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.75)
ax.add_feature(cfeature.STATES, linewidth=0.5)
# Plot the heights
cs = ax.contour(lon,
lat,
height[FH, 0, :, :],
transform=ccrs.PlateCarree(),
colors='k',
linewidths=1.0,
linestyles='solid')
cs.clabel(fontsize=10,
###########################################
# Build dictionary of desired sweeps
sweeps_dict = sweep_info(radar_file,elevation_angle_limit=2)
# Create data array mappe to longitude/latitude
lons, lats, data = build_radar_data_array()
# Define extent of desired plot and get associated ticks
extent, x_ticks, y_ticks = plot_extent(lons,lats,rda_lon,rda_lat,0.45)
# Create plot
fig, axes = plt.subplots(1, 2, figsize=(11, 6),sharey=True,
subplot_kw={'projection': ccrs.PlateCarree()})
add_metpy_logo(fig, 575, 55, size='small')
plt.labelsize : 8
plt.tick_params(labelsize=8)
for y,a in zip([0,1],axes.ravel()):
this_sweep = sweeps_dict[y]
a.set_extent(extent, crs=ccrs.PlateCarree())
a.tick_params(axis='both', labelsize=8)
a.pcolormesh(this_sweep['lons'], this_sweep['lats'],
this_sweep['data'], cmap=this_sweep['cmap'],
vmin=this_sweep['vmin'],
vmax=this_sweep['vmax'],
transform=ccrs.PlateCarree())
a.add_feature(cfeature.STATES, linewidth=0.5)
# ID For Plotting on Meteogram
probe_id = '0102A'
data = {
'wind_speed': (np.array(ws) * units('m/s')).to(units('knots')),
'wind_speed_max': (np.array(wsmax) * units('m/s')).to(units('knots')),
'wind_direction':
np.array(wd) * units('degrees'),
'dewpoint':
dewpoint_from_relative_humidity((np.array(temp) * units.degC).to(units.K),
np.array(rh) / 100.).to(units('degF')),
'air_temperature': (np.array(temp) * units('degC')).to(units('degF')),
'mean_slp':
calc_mslp(np.array(temp), np.array(pres), hgt_example) * units('hPa'),
'relative_humidity':
np.array(rh),
'times':
np.array(date)
}
fig = plt.figure(figsize=(20, 16))
add_metpy_logo(fig, 250, 180)
meteogram = Meteogram(fig, data['times'], probe_id)
meteogram.plot_winds(data['wind_speed'], data['wind_direction'],
data['wind_speed_max'])
meteogram.plot_thermo(data['air_temperature'], data['dewpoint'])
meteogram.plot_rh(data['relative_humidity'])
meteogram.plot_pressure(data['mean_slp'])
fig.subplots_adjust(hspace=0.5)
plt.show()
def Map_6hrPrecip(i, im_save_path):
from siphon.catalog import TDSCatalog
top_cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog.xml')
ref_anl = top_cat.catalog_refs['Forecast Products and Analyses']
new_cat_anl = ref_anl.follow()
model_anl = new_cat_anl.catalog_refs[1]
gfs_anl_cat = model_anl.follow()
ds_anl = gfs_anl_cat.datasets[1]
subset = ds_anl.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(
"Total_precipitation_surface_6_Hour_Accumulation").add_lonlat()
# Finally attempt to access the data
data = subset.get_data(query_data)
dew = data.variables[
'Total_precipitation_surface_6_Hour_Accumulation'][:].squeeze()
dew = np.ma.masked_where(dew < 2., dew)
lat = data.variables['lat'][:].squeeze()
lon = data.variables['lon'][:].squeeze()
#lats = data.variables['lat'][:]
#lons = data.variables['lon'][:]
time_var = data.variables[find_time_var(
data.variables['Total_precipitation_surface_6_Hour_Accumulation'])]
# Convert number of hours since the reference time into an actual date
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"
# Create new figure
fig = plt.figure(figsize=(17., 11.))
add_metpy_logo(fig, 30, 1000, size='small')
# Add the map and set the extent
ax = plt.subplot(111, projection=plotcrs)
#Set the lat and lon boundaries
ax.set_extent(extent, datacrs)
# Add state boundaries to plot
ax.add_feature(states_provinces, edgecolor='blue', linewidth=1)
# Add country borders to plot
ax.add_feature(country_borders, edgecolor='black', linewidth=1)
# Plot Title
plt.title('6-hr Precip Accumulation ($kg/m^{2}$)',
loc='left',
fontdict=font)
plt.title(' {0:%d %B %Y %H:%MZ}'.format(time_final),
loc='right',
fontdict=font)
cs = ax.contourf(lon, lat, dew, 40, cmap="jet",
transform=datacrs) #norm=Normalize(-1, 80)
cbaxes = fig.add_axes(colorbar_axis) # [left, bottom, width, height]
cbar = plt.colorbar(cs, orientation='horizontal', cax=cbaxes)
#cbar.set_label(r'$s^-1$')
#plt.show()
plt.close(fig)
#---------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------
PV_Jet = im_save_path + "GFS/Precip_6/"
if not os.path.isdir(PV_Jet):
os.makedirs(PV_Jet)
fig.savefig(PV_Jet + "Precip_Accum" + file_time + ".png",
bbox_inches='tight',
dpi=120)
print('done.')
"""
NEXRAD Level 3 File
===================
Use MetPy to read information from a NEXRAD Level 3 (NIDS product) file and plot
"""
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.io import Level3File
from metpy.plots import add_metpy_logo, ctables
###########################################
fig, axes = plt.subplots(1, 2, figsize=(15, 8))
add_metpy_logo(fig, 1200, 85, size='large')
for v, ctable, ax in zip(('N0Q', 'N0U'), ('NWSReflectivity', 'NWSVelocity'), axes):
# Open the file
name = get_test_data('nids/KOUN_SDUS54_{}TLX_201305202016'.format(v), as_file_obj=False)
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array, then mask
data = np.ma.array(datadict['data'])
data[data == 0] = np.ma.masked
# Grab azimuths and calculate a range based on number of gates
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
########################################### # We will pull the data out of the example dataset into individual variables and # assign units. p = df['pressure'].values * units.hPa T = df['temperature'].values * units.degC Td = df['dewpoint'].values * units.degC wind_speed = df['speed'].values * units.knots wind_dir = df['direction'].values * units.degrees u, v = mpcalc.wind_components(wind_speed, wind_dir) ########################################### # Create a new figure. The dimensions here give a good aspect ratio. fig = plt.figure(figsize=(9, 9)) add_metpy_logo(fig, 115, 100) skew = SkewT(fig, rotation=45) # Plot the data using normal plotting functions, in this case using # log scaling in Y, as dictated by the typical meteorological plot. skew.plot(p, T, 'r') skew.plot(p, Td, 'g') skew.plot_barbs(p, u, v) skew.ax.set_ylim(1000, 100) skew.ax.set_xlim(-40, 60) # Calculate LCL height and plot as black dot. Because `p`'s first value is # ~1000 mb and its last value is ~250 mb, the `0` index is selected for # `p`, `T`, and `Td` to lift the parcel from the surface. If `p` was inverted, # i.e. start from low value, 250 mb, to a high value, 1000 mb, the `-1` index # should be selected.
tlats,
msf[level, :, :],
clevmsf,
colors='k',
linewidths=1.0,
linestyles='solid')
plt.clabel(cs,
fontsize=10,
inline=1,
inline_spacing=7,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# Add metpy logo
add_metpy_logo()
# Plot RH
cf = ax.contourf(tlons,
tlats,
isentrh[level, :, :],
range(10, 106, 5),
cmap=plt.cm.gist_earth_r)
cb = plt.colorbar(cf,
orientation='horizontal',
extend=max,
aspect=65,
shrink=0.5,
pad=0,
extendrect='True')
cb.set_label('Relative Humidity', size='x-large')
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx = [wx_code_map[s.split()[0] if ' ' in s else s] for s in data['weather'].fillna('')]
###########################################
# The payoff
# ----------
# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering.
plt.rcParams['savefig.dpi'] = 255
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS)
# Set plot bounds
ax.set_extent((-118, -73, 23, 50))
#
# Here's the actual station plot
def test_add_metpy_logo_large():
"""Test adding a large MetPy logo to a figure."""
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, size='large')
return fig
###########################################
# Open the GINI file from the test data
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
print(f)
###########################################
# Get a Dataset view of the data (essentially a NetCDF-like interface to the
# underlying data). Pull out the data and (x, y) coordinates. We use `metpy.parse_cf` to
# handle parsing some netCDF Climate and Forecasting (CF) metadata to simplify working with
# projections.
ds = xr.open_dataset(f)
x = ds.variables['x'][:]
y = ds.variables['y'][:]
dat = ds.metpy.parse_cf('WV')
###########################################
# Plot the image. We use MetPy's xarray/cartopy integration to automatically handle parsing
# the projection information.
fig = plt.figure(figsize=(10, 12))
add_metpy_logo(fig, 125, 145)
ax = fig.add_subplot(1, 1, 1, projection=dat.metpy.cartopy_crs)
wv_norm, wv_cmap = colortables.get_with_range('WVCIMSS', 100, 260)
wv_cmap.set_under('k')
im = ax.imshow(dat[:], cmap=wv_cmap, norm=wv_norm,
extent=(x.min(), x.max(), y.min(), y.max()), origin='upper')
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
add_timestamp(ax, f.prod_desc.datetime, y=0.02, high_contrast=True)
plt.show()
def Map_1000_500(i, im_save_path):
"""
i is the forecast time
"""
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')
# Finally attempt to access the data
data = subset.get_data(query_data)
lat = data.variables['lat'][:].squeeze()
lon = data.variables['lon'][:].squeeze()
lev_500 = np.where(data.variables['isobaric4'][:] == 50000)[0][0]
hght_500 = data.variables['Geopotential_height_isobaric'][0, lev_500, :, :]
# 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
ax5 = plt.subplot(1, 1, 1, projection=plotcrs)
ax5.set_extent(extent, datacrs)
# Add state boundaries to plot
ax5.add_feature(states_provinces, edgecolor='k', linewidth=1)
# Add country borders to plot
ax5.add_feature(country_borders, edgecolor='black', linewidth=1)
# Convert number of hours since the reference time into an actual date
time_var = data.variables[find_time_var(
data.variables['Geopotential_height_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: 1000 and 500mb Heights (m)', loc='left', fontdict=font)
plt.title(' {0:%d %B %Y %H:%MZ}'.format(time_final),
loc='right',
fontdict=font)
# Heights - 1000mb
#---------------------------------------------------------------------------------------------------
lev_1000 = np.where(data.variables['isobaric4'][:] == 100000)[0][0]
hght_1000 = data.variables['Geopotential_height_isobaric'][0,
lev_1000, :, :]
MIN = hght_1000.min()
MAX = hght_1000.max()
#print hght_1000.min(),hght_1000.max()
hght_1000 = ndimage.gaussian_filter(hght_1000, sigma=3,
order=0) * units.meter
clev1000 = np.arange(MIN, MAX, 50)
cs = ax5.contour(lon,
lat,
hght_1000.m,
clev1000,
colors='blue',
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)
# Heights - 500mb
#---------------------------------------------------------------------------------------------------
MIN = hght_500.min()
MAX = hght_500.max()
#print hght_500.min(),hght_500.max()
hght_500 = ndimage.gaussian_filter(hght_500, sigma=3,
order=0) * units.meter
clev500 = np.arange(MIN, MAX, 70)
cs = ax5.contour(lon,
lat,
hght_500.m,
clev500,
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)
#plt.show()
plt.close(fig)
#---------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------
GFS_1000_500 = im_save_path + "GFS/1000_500mb/"
if not os.path.isdir(GFS_1000_500):
os.makedirs(GFS_1000_500)
fig.savefig(GFS_1000_500 + "1000_500mb_Heights_" + file_time + ".png",
bbox_inches='tight',
dpi=120)
print('done.')
def Map_Theta(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("Potential_temperature_sigma",
"Pressure_reduced_to_MSL_msl",
"u-component_of_wind_height_above_ground",
"v-component_of_wind_height_above_ground")
# Finally attempt to access the data
data = subset.get_data(query_data)
theta = data.variables['Potential_temperature_sigma'][:].squeeze()
mslp = data.variables['Pressure_reduced_to_MSL_msl'][:] * units.Pa
mslp.ito('hPa')
mslp = gaussian_filter(mslp[0], sigma=3.0)
u_wind = units('m/s') * data.variables[
'u-component_of_wind_height_above_ground'][:].squeeze()
v_wind = units('m/s') * data.variables[
'v-component_of_wind_height_above_ground'][:].squeeze()
u_wind.ito('kt')
v_wind.ito('kt')
lev_10m = np.where(data.variables['height_above_ground3'][:] == 10)[0][0]
u_wind_10m = u_wind[lev_10m]
v_wind_10m = v_wind[lev_10m]
lat = data.variables['lat'][:]
lon = data.variables['lon'][:]
# 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['Potential_temperature_sigma'])]
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: Potential Temperature (K)', loc='left', fontsize=16)
plt.title(' {0:%d %B %Y %H:%MZ}'.format(time_final),
loc='right',
fontsize=16)
# Dew Points
#---------------------------------------------------------------------------------------------------
levs = np.arange(50, 5000, 100)
cs = ax.contour(lon,
lat,
theta,
30,
cmap="nipy_spectral",
transform=datacrs)
#plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',
# rightside_up=True, use_clabeltext=True)
cbaxes = fig.add_axes(colorbar_axis) # [left, bottom, width, height]
cbar = plt.colorbar(cs, orientation='horizontal', cax=cbaxes)
ax.set_extent(extent, datacrs)
#ax.set_extent([-105, -98, 30, 50])
kw_clabels = {
'fontsize': 11,
'inline': True,
'inline_spacing': 5,
'fmt': '%i',
'rightside_up': True,
'use_clabeltext': True
}
clevmslp = np.arange(800., 1120., 4)
cs2 = ax.contour(lon,
lat,
mslp,
clevmslp,
colors='k',
linewidths=1.25,
linestyles='solid',
transform=datacrs)
plt.clabel(cs2, **kw_clabels)
# High and Low Symbols
#---------------------------------------------------------------------------------------------------
plot_maxmin_points(ax,
lon,
lat,
mslp,
'max',
50,
symbol='H',
color='b',
transform=datacrs)
plot_maxmin_points(ax,
lon,
lat,
mslp,
'min',
25,
symbol='L',
color='r',
transform=datacrs)
#---------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------
ax.barbs(lon,
lat,
u_wind_10m.magnitude,
v_wind_10m.magnitude,
length=6,
regrid_shape=20,
pivot='middle',
transform=datacrs,
barbcolor='k')
GFS_theta = im_save_path + "GFS/Theta/"
if not os.path.isdir(GFS_theta):
os.makedirs(GFS_theta)
fig.savefig(GFS_theta + "PotTemp_" + file_time + ".png",
bbox_inches='tight',
dpi=120)
plt.close(fig)
print('done.')
