How to use the metpy.calc.get_wind_components function in MetPy

# Use the cartopy map projection to transform station locations to the map and
# then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['lon'].values, data['lat'].values)
data = data[reduce_point_density(point_locs, 300000.)]

###########################################
# Now that we have the data we want, we need to perform some conversions:
#
# - Get wind components from speed and direction
# - Convert cloud fraction values to integer codes [0 - 8]
# - Map METAR weather codes to WMO codes for weather symbols

# Get the wind components, converting from m/s to knots as will be appropriate
# for the station plot.
u, v = get_wind_components((data['wind_speed'].values * units('m/s')).to('knots'),
                           data['wind_dir'].values * units.degree)

# Convert the fraction value into a code of 0-8 and compensate for NaN values,
# which can be used to pull out the appropriate symbol
cloud_frac = (8 * data['cloud_fraction'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)

# 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
# ----------

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

##########################################################################

# 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)

##########################################################################
# Thermodynamic Calculations
# --------------------------
#
# Often times we will want to calculate some thermodynamic parameters of a
# sounding. The MetPy calc module has many such calculations already implemented!
#
# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's
#   relative humidity becomes 100% when lifted along a dry adiabatic path.
# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning
#   at the surface temperature/pressure and rising dry adiabatically until
#   reaching the LCL, then rising moist adiabatially.

# Calculate the LCL
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])

from metpy.plots import Hodograph, SkewT
from metpy.units import units

#########################################################################
# Getting Data
# ------------
#
# Upper air data can be obtained using the siphon package, but for this tutorial we will use
# some of MetPy's sample data. This event is the Veterans Day tornado outbreak in 2002.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('nov11_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.get_wind_components(df['speed'],
                                                        np.deg2rad(df['direction']))

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

##########################################################################

# 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

# coding: utf-8
import matplotlib.pyplot as plt
import numpy as np

from metpy.calc import get_wind_components
from metpy.plots import SkewT

# Change default to be better for skew-T
plt.rcParams['figure.figsize'] = (9, 9)

# Parse the data
p, T, Td, direc, spd = np.loadtxt('../testdata/sounding_data.txt',
        usecols=(0, 2, 3, 6, 7), unpack=True)
u, v = get_wind_components(spd, direc)

# Create a skewT using matplotlib's default figure size
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')
skew.plot_barbs(p, u, v)

# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)

import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units

###########################################
# Upper air data can be obtained using the siphon package, but for this example we will use
# some of MetPy's sample data.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.get_wind_components(df['speed'],
                                                        np.deg2rad(df['direction']))

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

###########################################
# 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)

#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
from scipy.constants import C2K, K2C

from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile
from metpy.plots import SkewT

# Parse the data
p, T, Td, direc, spd = np.loadtxt('testdata/sounding_data.txt',
        usecols=(0, 2, 3, 6, 7), unpack=True)
u,v = get_wind_components(spd, direc)

# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(6.5875, 6.2125))
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)

l = lcl(p[0], C2K(T[0]), C2K(Td[0]))
skew.plot(l, K2C(dry_lapse(l, C2K(T[0]), p[0])), 'ko',
        markerfacecolor='black')

prof = K2C(parcel_profile(p, C2K(T[0]), C2K(Td[0])))

import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units

###########################################
# Upper air data can be obtained using the siphon package, but for this example we will use
# some of MetPy's sample data.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.get_wind_components(df['speed'],
                                                        np.deg2rad(df['direction']))

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

###########################################
# 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)

np.deg2rad(df['direction']))

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

###########################################
# 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)
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)