def test_resample_nn():
"""Test 1d nearest neighbor functionality."""
a = np.arange(5.)
b = np.array([2, 3.8])
truth = np.array([2, 4])
assert_array_equal(truth, resample_nn_1d(a, b))
def test_resample_nn():
"""Test 1d nearest neighbor functionality."""
a = np.arange(5.)
b = np.array([2, 3.8])
truth = np.array([2, 4])
assert_array_equal(truth, resample_nn_1d(a, b))
def plot_sounding(P, T, Td, U, V, r_time, ID):
##need the metpy package
##from metpy.plots import SkewT
##from metpy.units import units
##import metpy.calc as mcalc
fig = plt.figure(figsize=(7, 6))
skew = SkewT(fig)
skew.plot(P, T, 'r')
skew.plot(P, Td, 'g')
idx = mcalc.resample_nn_1d(
P,
np.array([1000, 975, 950, 925, 900, 850, 800, 750, 700, 650, 600,
500]))
skew.plot_barbs(P[idx],
U[idx] * units('m/s'),
V[idx] * units('m/s'),
plot_units=units('m/s'))
##special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats(color='blue')
skew.plot_mixing_lines(p=np.linspace(1100, 300) * units.hPa)
##title etc.,
plt.title(str(ID) + '_' + str(r_time) + 'LST')
plt.xlim(-40, 40)
plt.ylim(1100, 300)
for i in range(-70, 40, 20):
plt.fill_between(range(i, i + 11), 1100, 300, color='#C4FF8C')
#plt.savefig('merged_data_plot/' + fname + '.png')
plt.show()
def plot_metpy(data, title="", saveplot=None, showplot=True):
# Convert data into a suitable format for metpy.
_altitude = data[:,0] * units('m')
p = mpcalc.height_to_pressure_std(_altitude)
T = data[:,3] * units.degC
Td = data[:,4] * units.degC
wind_speed = data[:,1] * units('m/s')
wind_direction = data[:,2] * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_direction)
fig = plt.figure(figsize=(6,8))
skew = SkewT(fig=fig)
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
my_interval = np.arange(300, 1000, 50) * units('mbar')
ix = mpcalc.resample_nn_1d(p, my_interval)
skew.plot_barbs(p[ix], u[ix], v[ix])
skew.ax.set_ylim(1000,300)
skew.ax.set_xlim(-40, 30)
skew.plot_dry_adiabats()
heights = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) * units.km
std_pressures = mpcalc.height_to_pressure_std(heights)
for height_tick, p_tick in zip(heights, std_pressures):
trans, _, _ = skew.ax.get_yaxis_text1_transform(0)
skew.ax.text(0.02, p_tick, '---{:~d}'.format(height_tick), transform=trans)
plt.title("Sounding: " + title)
if saveplot != None:
fig.savefig(saveplot, bbox_inches='tight')
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)
###########################################
# Example of defining your own vertical barb spacing
skew = SkewT()
# 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')
# Set spacing interval--Every 50 mb from 1000 to 100 mb
my_interval = np.arange(100, 1000, 50) * units('mbar')
# Get indexes of values closest to defined interval
ix = resample_nn_1d(p, my_interval)
# Plot only values nearest to defined interval values
skew.plot_barbs(p[ix], u[ix], v[ix])
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)
# Show the plot
plt.show()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)
###########################################
# Example of defining your own vertical barb spacing
skew = SkewT()
# 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')
# Set spacing interval--Every 50 mb from 1000 to 100 mb
my_interval = np.arange(100, 1000, 50) * units('mbar')
# Get indexes of values closest to defined interval
ix = mpcalc.resample_nn_1d(p, my_interval)
# Plot only values nearest to defined interval values
skew.plot_barbs(p[ix], u[ix], v[ix])
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)
# Show the plot
plt.show()
'u_wind', 'v_wind'), how='all').reset_index(drop=True)
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)
###wind speed and dir are cols = 5,6; only plot winds every 50 mb. You can modify this if desired.
interval = np.arange(100,1000,50) * units('mbar')
ix = resample_nn_1d(p,interval)
#%%
#Can change the size of the figure by modifying 'figsize' to desired dimensions
fig = plt.figure(figsize=(8, 8))
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')
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
