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# Copyright (c) 2022 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
========
Q-Vector
========
Use `metpy.calc.q_vector`.
This example demonstrates the q_vector calculation by computing them from the example xarray
Dataset and plotting using Matplotlib.
"""
import matplotlib.pyplot as plt
import numpy as np
import metpy.calc as mpcalc
from metpy.cbook import example_data
from metpy.units import units
# load example data
ds = example_data()
# Calculate the temperature advection of the flow
tadv = mpcalc.advection(ds.temperature, ds.uwind, ds.vwind)
# Calculate the q-vectors. Passing in a fixed value of static stability, but could also
# use `mpcalc.static_stability()`.
u_qvect, v_qvect = mpcalc.q_vector(ds.uwind, ds.vwind, ds.temperature, 850 * units.hPa,
static_stability=2e-6 * units('J / kg / Pa^2'))
# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))
# plot isotherms
cs = ax.contour(ds.lon, ds.lat, ds.temperature, range(4, 26, 2), colors='tab:red',
linestyles='dashed', linewidths=3)
plt.clabel(cs, fmt='%d', fontsize=16)
# plot temperature advection in Kelvin per 3 hours
cf = ax.contourf(ds.lon, ds.lat, tadv.metpy.convert_units('kelvin/hour') * 3, range(-6, 7, 1),
cmap=plt.cm.bwr, alpha=0.75)
plt.colorbar(cf, pad=0, aspect=50)
# calculate a scale length quantity of our vectors based on their mean
scale = (np.hypot(u_qvect, v_qvect).mean() * np.sqrt(u_qvect.size)).data
# plot Q-vectors as arrows, every other arrow
qvec = ax.quiver(ds.lon.values[::2], ds.lat.values[::2],
u_qvect[::2, ::2], v_qvect[::2, ::2],
color='black', scale=scale.m, alpha=0.5, width=0.01)
# calculate representative arrow for key
key = scale / 10
# add key for arrow length
qk = ax.quiverkey(qvec, 0.65, 0.905, key.m, f'{key:0.2~P}', labelpos='E',
coordinates='figure')
ax.set(xlim=(260, 270), ylim=(30, 40))
ax.set_title('Q-Vector Calculation', loc='left')
plt.show()
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