def test_hodograph_wind_vectors():
"""Test plotting wind vectors onto a hodograph."""
u_wind = np.array([-10, -7, 0, 7, 10, 7, 0, -7])
v_wind = np.array([0, 7, 10, 7, 0, -7, -10, -7])
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(1, 1, 1)
h = Hodograph(ax, component_range=20)
h.plot(u_wind, v_wind, linewidth=3)
h.wind_vectors(u_wind, v_wind)
return fig
def test_hodograph_units():
"""Test passing unit-ed quantities to Hodograph."""
fig = plt.figure(figsize=(9, 9))
ax = fig.add_subplot(1, 1, 1)
hodo = Hodograph(ax)
u = np.arange(10) * units.kt
v = np.arange(10) * units.kt
hodo.plot(u, v)
hodo.plot_colormapped(u, v, np.sqrt(u * u + v * v), cmap='Greys')
return fig
def test_hodograph_api():
"""Basic test of Hodograph API."""
fig = plt.figure(figsize=(9, 9))
ax = fig.add_subplot(1, 1, 1)
hodo = Hodograph(ax, component_range=60)
hodo.add_grid(increment=5, color='k')
hodo.plot([1, 10], [1, 10], color='red')
hodo.plot_colormapped(np.array([1, 3, 5, 10]), np.array([2, 4, 6, 11]),
np.array([0.1, 0.3, 0.5, 0.9]), cmap='Greys')
return fig
def test_hodograph_units():
"""Test passing unit-ed quantities to Hodograph."""
fig = plt.figure(figsize=(9, 9))
ax = fig.add_subplot(1, 1, 1)
hodo = Hodograph(ax)
u = np.arange(10) * units.kt
v = np.arange(10) * units.kt
hodo.plot(u, v)
hodo.plot_colormapped(u, v, np.sqrt(u * u + v * v), cmap='Greys')
return fig
def test_hodograph_wind_vectors():
"""Test plotting wind vectors onto a hodograph."""
u_wind = np.array([-10, -7, 0, 7, 10, 7, 0, -7])
v_wind = np.array([0, 7, 10, 7, 0, -7, -10, -7])
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(1, 1, 1)
h = Hodograph(ax, component_range=20)
h.plot(u_wind, v_wind, linewidth=3)
h.wind_vectors(u_wind, v_wind)
return fig
def test_hodograph_api():
"""Basic test of Hodograph API."""
fig = plt.figure(figsize=(9, 9))
ax = fig.add_subplot(1, 1, 1)
hodo = Hodograph(ax, component_range=60)
hodo.add_grid(increment=5, color='k')
hodo.plot([1, 10], [1, 10], color='red')
hodo.plot_colormapped(np.array([1, 3, 5, 10]), np.array([2, 4, 6, 11]),
np.array([0.1, 0.3, 0.5, 0.9]), cmap='Greys')
return fig
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() skew.plot_mixing_lines() # Good bounds for aspect ratio skew.ax.set_xlim(-30, 40) # Create a hodograph ax = fig.add_subplot(gs[0, -1]) h = Hodograph(ax, component_range=60.) h.add_grid(increment=20) h.plot(u, v) # Show the plot plt.show()
# 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(pressure, temperature, 'r')
skew.plot(pressure, dewpoint, 'g')
skew.plot_barbs(pressure, u_wind, v_wind)
skew.ax.set_ylim(1000, 100)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Good bounds for aspect ratio
skew.ax.set_xlim(-40, 60)
# Create a hodograph
ax = fig.add_subplot(gs[0, -1])
h = Hodograph(ax, component_range=60.)
h.add_grid(increment=20)
h.plot(u_wind, v_wind)
# Add a title
skew.ax.set_title(station + ' ' + date.strftime("%d %b %Y %HZ"))
# Show the plot
plt.show()
pressure = df['pressure'].values * units(df.units['pressure'])
temperature = df['temperature'].values * units(df.units['temperature'])
wind_speed = df['speed'].values * units.knots
print(temperature)
dewpoint = df['dewpoint'].values * units(df.units['dewpoint'])
print(dewpoint)
u_wind = df['u_wind'].values * units(df.units['u_wind'])
print(u_wind)
v_wind = df['v_wind'].values * units(df.units['v_wind'])
heights = df['height'].values * units(df.units['height'])
print(mpcalc.wind_direction(u_wind,v_wind))
fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(1,1,1)
h= Hodograph(ax,component_range=60.)
h.plot(u_wind,v_wind,linewidth=5)
h.add_grid(increment=10)
#h.add_grid(increment=20,color='tab:orange',linestyle='-')
h.plot_colormapped(u_wind, v_wind, wind_speed) # Plot a line colored by wind speed
plt.savefig('hodograph.png')
plt.show()
sys.exit()
#lcl_pressure, lcl_temperature = mpcalc.lcl(pressure[0], temperature[0], dewpoint[0])
#print(lcl_pressure, lcl_temperature)
# Calculate the parcel profile.
parcel_prof = mpcalc.parcel_profile(pressure, temperature[0], dewpoint[0]).to('degC')
# Create a new figure. The dimensions here give a good aspect ratio fig = plt.figure(figsize=(9, 9)) # 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() skew.plot_mixing_lines() # Good bounds for aspect ratio skew.ax.set_xlim(-30, 40) # Create a hodograph ax = fig.add_subplot(gs[0, -1]) h = Hodograph(ax, component_range=60.) h.add_grid(increment=20) h.plot(u, v) # Show the plot plt.show()
def plot_skewt(snd: Sounding, save_to: Optional[str] = None, p_top: int = 100):
"""
Plots a skew-T from the given Sounding.
:param snd: The Sounding.
:param save_to: Where to save the figure. Default None, which does not save the figure, and instead shows it.
:param p_top: Pressure at the top of the skew-T. If you change this, Metpy might change the rotation of the
isotherms. No known fix yet.
:return: None.
"""
####################################################################################################################
# Data extraction and masking
# Extract data from sounding.
p = snd.p
T = snd.T
Td = snd.Td
Tw = snd.Tw
Te = snd.thetaE
z = snd.z
cf = snd["CFRL"]
omega = snd.omega
u, v = snd.wind_components()
e = mpcalc.saturation_vapor_pressure(T)
rv = mpcalc.mixing_ratio(e, p)
w = mpcalc.vertical_velocity(omega, p, T, rv).to("cm/s")
# Create masks to filter what data is plotted.
mask_dewpoint = Td > -9000. * units.degC # Plot only non-missing dewpoints.
mask_wetbulb = Tw > -9000. * units.degC # Plot only non-missing dewpoints.
mask_thetae = Te > 0. * units.K # Plot only non-missing theta-es.
mask_barbs = p > p_top * units.hPa # Plot only winds below the top of the sounding.
####################################################################################################################
# Define intervals of height for coloring barbs and hodograph.
z_interval_levels = [1500, 3000, 6000, 9000, 12000, 99999]
z_interval_colors = ["red", "orange", "green", "blue", "purple", "grey"]
z_colors = []
for item in z:
for color, interval in zip(z_interval_colors, z_interval_levels):
if item <= interval * units.meter:
z_colors.append(color)
break
####################################################################################################################
# Plotting skew-T
fig = plt.figure(figsize=(11, 11))
ax_hodo = fig.add_axes([0.70, 0.675, 0.25, 0.25])
ax_thte = fig.add_axes([0.70, 0.375, 0.25, 0.25])
skew = SkewT(fig, rotation=45, rect=[0.05, 0.05, 0.60, 0.9])
# Plot temperature, dewpoint, and wet-bulb.
skew.plot(p, T, 'r')
skew.plot(p[mask_dewpoint], Td[mask_dewpoint], 'g')
skew.plot(p[mask_wetbulb], Tw[mask_wetbulb], color='#009999', linewidth=1)
# Calculate and plot surface parcel trace.
sfc_trace = snd.parcel_trace(0).to('degC')
sfc_trace_plot = skew.plot(p,
sfc_trace,
c='orange',
linewidth=2,
zorder=-10)
# Calculate and plot MU parcel trace.
mu_level_index = np.argmax(Te[p > 750. * units.hPa])
mu_trace = snd.parcel_trace(mu_level_index).to('degC')
mu_trace_plot = skew.plot(p[mu_level_index:],
mu_trace,
c='gray',
linewidth=2,
zorder=-9)
# Plot each barb individually for control over color. Unfortunately, the c arg of plot_barbs doesn't work for this
# purpose.
for p_, u_, v_, c_ in zip(p[mask_barbs][::BARB_DENSITY],
u[mask_barbs][::BARB_DENSITY],
v[mask_barbs][::BARB_DENSITY],
np.array(z_colors)[mask_barbs][::BARB_DENSITY]):
skew.plot_barbs(p_, u_, v_, y_clip_radius=0.03, barbcolor=c_)
####################################################################################################################
# Cloud fraction and omega
zero_line = 1 / 15
cf_plot = (cf * zero_line) / 100
w_plot = (w.magnitude / 20) + zero_line
skew.ax.plot(np.zeros(cf_plot.shape) + 1 / 15,
snd.p,
transform=skew.ax.get_yaxis_transform(),
color="grey")
skew.ax.plot(cf_plot,
snd.p,
transform=skew.ax.get_yaxis_transform(),
color="black")
skew.ax.plot(w_plot,
snd.p,
transform=skew.ax.get_yaxis_transform(),
color="purple")
skew.ax.text(np.max(w_plot),
snd.p[np.argmax(w_plot)],
" {:.1f}".format(np.max(w.magnitude)),
color="purple",
ha="left",
va="center",
transform=skew.ax.get_yaxis_transform())
skew.ax.text(max(np.min(w_plot), 0),
snd.p[np.argmin(w_plot)],
" {:.1f}".format(np.min(w.magnitude)),
color="purple",
ha="left",
va="center",
transform=skew.ax.get_yaxis_transform())
# skew.ax.fill_betweenx(snd.p, cloud_fractions, np.zeros(cloud_fractions.shape))
####################################################################################################################
# Tweaking
skew.ax.set_xlim(-30, 40)
skew.ax.set_ylim(1020, p_top)
skew.ax.set_xlabel("")
skew.ax.set_ylabel("")
# Add legend for the parcel traces.
skew.ax.legend(handles=[
mlines.Line2D([], [], color='orange', label='Surface parcel'),
mlines.Line2D([], [],
color='gray',
label=r"Max $\theta_e$ below 750mb"),
mlines.Line2D([], [], color='black', label=r"Cloud fraction"),
mlines.Line2D([], [],
color='purple',
label=r"Vertical velocity (cm/s)"),
],
loc="upper center")
# Add adiabats and isohumes.
skew.plot_dry_adiabats(t0=np.arange(233, 533, 10) * units.K,
alpha=0.25,
color='orangered')
skew.plot_moist_adiabats(t0=np.arange(233, 400, 5) * units.K,
alpha=0.25,
color='tab:green')
# Reshape required as a quirk of metpy.
skew.plot_mixing_lines(w=np.array(
[1, 2, 3, 4, 6, 8, 10, 12, 16, 20, 24, 28, 36]).reshape(-1, 1) / 1000.,
p=np.arange(1000, 99, -100) * units.hPa,
linestyle='dotted',
color='tab:blue')
plt.title('RAP sounding at {}'.format(snd.params["STID"]), loc='left')
plt.title('{:.0f}-hour forecast valid at {}'.format(
snd.params["STIM"], snd.params["TIME"]),
loc='right')
####################################################################################################################
# Theta-E plot
# Set up axis for theta-e plot.
ax_thte.plot(Te[mask_thetae], p[mask_thetae])
ax_thte.set_xlim(300, 360)
ax_thte.set_ylim(1020, p_top)
ax_thte.set_yscale("log")
ax_thte.set_yticks(np.arange(p_top, 1001, 100))
ax_thte.set_yticklabels(np.arange(p_top, 1001, 100))
ax_thte.set_xlabel("")
ax_thte.grid(axis="both")
plt.text(0.5,
0.9,
"Theta-E (Kelvins)",
ha="center",
va="center",
transform=ax_thte.transAxes)
####################################################################################################################
# Hodograph
# Set up axis for hodograph.
h = Hodograph(ax_hodo, component_range=100)
h.add_grid(20)
# Plot each segment individually for control over color, reversed so that the full hodograph is plotted first,
# followed by all but the last segment, etc. Unfortunately, the plot_colormapped() function doesn't work for this
# purpose.
for color, interval in zip(reversed(z_interval_colors),
reversed(z_interval_levels)):
mask = z < interval * units.meter
h.plot(u.magnitude[mask], v.magnitude[mask], c=color)
for vector in snd.bunkers_storm_motion():
h.plot(vector[0], vector[1], c="black", markersize=3, marker="o")
ax_hodo.set_xticks([])
ax_hodo.set_yticks([])
ax_hodo.set_xlim(-60, 100)
ax_hodo.set_ylim(-60, 100)
plt.text(0.1,
0.9,
"Velocity (knots)",
ha="left",
va="center",
transform=ax_hodo.transAxes)
for a in range(20, 61, 20):
ax_hodo.text(-a * 0.71, -a * 0.71, a, ha="center", va="center")
########################################################################################################################
parameter_names = ["SB STP", "0-1 SRH", "SB CAPE", "SB CIN"]
parameters = [
snd.significant_tornado()[0],
snd.storm_relative_helicity()[2],
snd.cape_cin(0)[0],
snd.cape_cin(0)[1]
]
for name, value, i in zip(parameter_names, parameters,
range(len(parameters))):
s = "{:15} {:10.3f}".format(name, value.magnitude)
fig.text(0.70,
0.32 - (0.02 * i),
s,
ha="left",
va="top",
family="monospace",
transform=fig.transFigure)
########################################################################################################################
if save_to is None:
plt.show()
else:
plt.savefig(save_to)
plt.close()
# Shade areas of CAPE and CIN
skew.shade_cin(p, T, parcel_prof, Td)
skew.shade_cape(p, T, parcel_prof)
# Plot a zero degree isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=3)
# Add the relevant special lines
skew.plot_dry_adiabats(linewidth=2.5)
skew.plot_moist_adiabats(linewidth=2.5)
skew.plot_mixing_lines(linewidth=2.5)
ax1 = fig.add_subplot(gs[0:7, 4])
h = Hodograph(ax1, component_range=50.)
h.add_grid(increment=12.5)
h.plot(u, v, color='#0080ff')
ax1.tick_params(labelsize=15.)
ax1.set_xticks(np.arange(-50., 75., 25.))
ax1.set_yticks(np.arange(-50., 75., 25.))
ax1.set_xticklabels([])
ax1.set_title('Hodograph', fontsize=20.)
ax1.set_xlabel('wind speed ($m$ $s^{-1}$)', fontsize=15.)
ax1.set_ylabel('wind speed ($m$ $s^{-1}$)', fontsize=15.)
ax2 = fig.add_subplot(gs[9:15, 4])
[ax2.add_patch(PolygonPatch(shape, fc='none')) for shape in shapes]
ax2.scatter(lon[10:], lat[10:], c='r', s=10)
ax2.tick_params(labelsize=15.)
ax2.set_xticks(np.arange(119.5, 122., 0.5))
ax2.set_yticks(np.arange(22., 24.5, 0.5))
ax2.set_xticklabels(['', '120°E', '', '121°E', ''])
ax2.yaxis.tick_left()
ax2.tick_params(direction='in', pad=-5)
#PLOT PARCEL STUFF
plt.plot((37, 43), (mupcl.lfcpres,mupcl.lfcpres), 'r-',lw=1.5)
ax2.annotate('LFC', xy=(40, mupcl.lfcpres-10), xytext=(40, mupcl.lfcpres-10),ha='center', color='r')
plt.plot((37, 43), (mupcl.lclpres,mupcl.lclpres), 'g-',lw=1.5)
ax2.annotate('LCL', xy=(40, mupcl.lclpres+50), xytext=(40, mupcl.lclpres+50),ha='center', color='g')
# Create windbarbs and hodograph
skew.plot_barbs(p, u, v, xloc=1.1)
hgt_list = [0,3000,6000,9000,np.max(h)]
hodo_color = ['r','g','y','c']
hodo_label = ['0-3km','3-6km','6-9km','>9km']
ax_hod = inset_axes(skew.ax, '40%', '40%', loc=1)
for tick in ax_hod.xaxis.get_major_ticks():
tick.label.set_fontsize(10)
tick.label.set_rotation(45)
for tick in ax_hod.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
hodo = Hodograph(ax_hod, component_range=80.)
hodo.add_grid(increment=20)
for k in range(len(hgt_list)-1):
index1 = min(range(len(h)), key=lambda i: abs(h[i]-hgt_list[k]))
index2 = min(range(len(h)), key=lambda i: abs(h[i]-hgt_list[k+1]))
hodo.plot(u[index1:index2+1],v[index1:index2+1],c=hodo_color[k],linewidth=2.0,label=hodo_label[k])
ax_hod.legend(loc=2,prop={'size':8})
plt.savefig('/home/kgoebber/http/soundings/launches/'+launch+'_'+ascent+'/'+launch+'_'+ascent+'_KVUM.png',dpi=150)
#plt.show()
#cambiando la direccion cardinal a angular
angle = np.zeros(len(direction) +
2) #añado el dos para igualar size de ambas columnas
#corregir
#cambiar la denominacion: NS por NW
for i in range(2, len(direction) + 2):
angle[i] = portolan.middle(direction.direccion[i])
#5. GENERACIÓN DE LA HODOGRAFA
angle = pd.DataFrame(angle[2:], columns=['angulo'])
wind_speed = wind.values * units.knots
wind_dir = angle.values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
#visualizando la hodografa
fig = plt.figure(figsize=(6, 6))
fig.suptitle('Hodografa - Estacion 000527')
ax = fig.add_subplot(1, 1, 1)
h = Hodograph(ax, component_range=10.)
h.plot(u, v, linewidth=5)
h.add_grid(increment=5)
plt.savefig(dir_file + '/hodografa.png')
#resultado: dado que la hodografa es casi constante en todo la figura
#concluimos que la velocidad del viento es casi constante en modulo y direccion
