#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
# Create a new figure. The dimensions here give a good aspect ratio fig = plt.figure(figsize=(9, 9)) # 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() # Good bounds for aspect ratio skew.ax.set_xlim(-50, 60) # Create a hodograph ax_hod = inset_axes(skew.ax, '40%', '40%', loc=1) h = Hodograph(ax_hod, component_range=80.) h.add_grid(increment=20) h.plot_colormapped(u, v, np.hypot(u, v)) # Show the plot plt.show()
def test_united_hodograph_range():
"""Tests making a hodograph with a united ranged."""
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(1, 1, 1)
Hodograph(ax, component_range=60. * units.knots)
def main():
args = get_args()
setup_logging(args['verbose'])
# Define input file
file = args['inputfile']
output = args['outputfile']
ds = xr.open_dataset(file)
ds_sel = ds.isel({'sounding': 0})
ds_sel = ds_sel.sortby(ds_sel.pressure, ascending=False)
p = ds_sel.pressure.values
T = ds_sel.temperature.values
Td = ds_sel.dewPoint.values
wind_speed = ds_sel.windSpeed.values
wind_dir = ds_sel.windDirection.values
ascend_rate = ds_sel.ascentRate.values
launch_time = ds_sel.launch_unixtime
launch_time = dt.datetime.utcfromtimestamp(launch_time)
platform = ds_sel.platform_name.split('(')[1][:-1].strip()
resolution = ds_sel.resolution.replace(' ', '')
# Filter nans
idx = np.where((np.isnan(T) + np.isnan(Td) + np.isnan(p) +
np.isnan(wind_speed) + np.isnan(wind_dir)) == False, True,
False)
p = p[idx]
T = T[idx]
Td = Td[idx]
wind_speed = wind_speed[idx]
wind_dir = wind_dir[idx]
# Add units
p = p * units.hPa
T = T * units.degC
Td = Td * units.degC
wind_speed = wind_speed * (units.meter / units.second)
wind_dir = wind_dir * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
direction = find_direction_of_sounding(ascend_rate)
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(9, 10))
skew = SkewT(fig, rotation=30)
# 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')
# Plot only specific barbs to increase visibility
pressure_levels_barbs = np.logspace(0.1, 1, 50) * 100
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
# Search for levels by providing pressures
# (levels is the coordinate not pressure)
pres_vals = ds_sel.pressure.values[idx]
closest_pressure_levels = np.unique(
[find_nearest(pres_vals, p_) for p_ in pressure_levels_barbs])
_, closest_pressure_levels_idx, _ = np.intersect1d(pres_vals,
closest_pressure_levels,
return_indices=True)
p_barbs = ds_sel.pressure.isel({
'level': closest_pressure_levels_idx
}).values * units.hPa
wind_speed_barbs = ds_sel.windSpeed.isel({
'level':
closest_pressure_levels_idx
}).values * (units.meter / units.second)
wind_dir_barbs = ds_sel.windDirection.isel({
'level':
closest_pressure_levels_idx
}).values * units.degrees
u_barbs, v_barbs = mpcalc.wind_components(wind_speed_barbs, wind_dir_barbs)
# Find nans in pressure
# p_non_nan_idx = np.where(~np.isnan(pres_vals))
skew.plot_barbs(p_barbs, u_barbs, v_barbs, xloc=1.06)
skew.ax.set_ylim(1020, 100)
skew.ax.set_xlim(-50, 40)
# Plot LCL as black dot
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Plot the parcel profile as a black line
skew.plot(pres_vals, parcel_prof, 'k', linewidth=1.6)
# Shade areas of CAPE and CIN
skew.shade_cin(pres_vals, T, parcel_prof)
skew.shade_cape(pres_vals, T, parcel_prof)
# Plot a zero degree 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()
# Create a hodograph
# Create an inset axes object that is 40% width and height of the
# figure and put it in the upper right hand corner.
ax_hod = inset_axes(skew.ax, '35%', '35%', loc=1)
h = Hodograph(ax_hod, component_range=75.)
h.add_grid(increment=20)
h.plot_colormapped(u, v, wind_speed) # Plot a line colored by wind speed
# Set title
sounding_name = ds_sel.sounding.values
sounding_name_str = str(sounding_name.astype('str'))
skew.ax.set_title('{sounding}_{direction}'.format(
sounding=sounding_name_str, direction=direction))
if output is None:
filename_fmt = '{platform}_SoundingProfile_skewT_%Y%m%d_%H%M_{res}.png'.format(
platform=platform, res=resolution)
filename_fmt = launch_time.strftime(filename_fmt)
output = filename_fmt
else:
output = output.format(platform=platform,
direction=direction,
resolution=resolution)
output = launch_time.strftime(output)
logging.info('Write output to {}'.format(output))
plt.savefig(output)
def test_hodograph_alone():
"""Test to create Hodograph without specifying axes."""
Hodograph()
