def test_3d(self):
'Test of geostrophic wind calculation with 3D array'
z = np.array([[48, 49, 48], [49, 50, 49], [48, 49, 48]]) * 100.
# Using g as the value for f allows it to cancel out
z3d = np.dstack((z, z)) * units.meter
ug, vg = geostrophic_wind(z3d, g.magnitude / units.sec,
100. * units.meter, 100. * units.meter)
true_u = np.array([[-1, 0, 1]] * 3) * units('m/s')
true_v = -true_u.T
true_u = concatenate((true_u[..., None], true_u[..., None]), axis=2)
true_v = concatenate((true_v[..., None], true_v[..., None]), axis=2)
assert_array_equal(ug, true_u)
assert_array_equal(vg, true_v)
def test_3d(self):
'Test of geostrophic wind calculation with 3D array'
z = np.array([[48, 49, 48], [49, 50, 49], [48, 49, 48]]) * 100.
# Using g as the value for f allows it to cancel out
z3d = np.dstack((z, z)) * units.meter
ug, vg = geostrophic_wind(z3d, g.magnitude / units.sec,
100. * units.meter, 100. * units.meter)
true_u = np.array([[-1, 0, 1]] * 3) * units('m/s')
true_v = -true_u.T
true_u = concatenate((true_u[..., None], true_u[..., None]), axis=2)
true_v = concatenate((true_v[..., None], true_v[..., None]), axis=2)
assert_array_equal(ug, true_u)
assert_array_equal(vg, true_v)
def test_bunkers_motion():
"""Test Bunkers storm motion with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
motion = concatenate(bunkers_storm_motion(data['pressure'],
data['u_wind'], data['v_wind'],
data['height']))
truth = [1.4537892577864744, 2.0169333025630616, 10.587950761120482, 13.915130377372801,
6.0208700094534775, 7.9660318399679308] * units('m/s')
assert_almost_equal(motion.flatten(), truth, 8)
def test_bunkers_motion():
"""Test Bunkers storm motion with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
motion = concatenate(bunkers_storm_motion(data['pressure'],
data['u_wind'], data['v_wind'],
data['height']))
truth = [2.18346161, 0.86094706, 11.6006767, 12.53639395, 6.89206916,
6.69867051] * units('m/s')
assert_almost_equal(motion.flatten(), truth, 8)
def test_bunkers_motion():
"""Test Bunkers storm motion with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
motion = concatenate(bunkers_storm_motion(data['pressure'],
data['u_wind'], data['v_wind'],
data['height']))
truth = [1.4537892577864744, 2.0169333025630616, 10.587950761120482, 13.915130377372801,
6.0208700094534775, 7.9660318399679308] * units('m/s')
assert_almost_equal(motion.flatten(), truth, 8)
def parcel_profile(pressure, temperature, dewpt):
r"""Calculate the profile a parcel takes through the atmosphere.
The parcel starts at `temperature`, and `dewpt`, lifted up
dry adiabatically to the LCL, and then moist adiabatically from there.
`pressure` specifies the pressure levels for the profile.
Parameters
----------
pressure : `pint.Quantity`
The atmospheric pressure level(s) of interest. The first entry should be the starting
point pressure.
temperature : `pint.Quantity`
The starting temperature
dewpt : `pint.Quantity`
The starting dew point
Returns
-------
`pint.Quantity`
The parcel temperatures at the specified pressure levels.
See Also
--------
lcl, moist_lapse, dry_lapse
"""
# Find the LCL
lcl_pressure = lcl(pressure[0], temperature, dewpt)[0].to(pressure.units)
# Find the dry adiabatic profile, *including* the LCL. We need >= the LCL in case the
# LCL is included in the levels. It's slightly redundant in that case, but simplifies
# the logic for removing it later.
press_lower = concatenate(
(pressure[pressure >= lcl_pressure], lcl_pressure))
t1 = dry_lapse(press_lower, temperature)
# Find moist pseudo-adiabatic profile starting at the LCL
press_upper = concatenate(
(lcl_pressure, pressure[pressure < lcl_pressure]))
t2 = moist_lapse(press_upper, t1[-1]).to(t1.units)
# Return LCL *without* the LCL point
return concatenate((t1[:-1], t2[1:]))
def test_concatenate_masked():
"""Test concatenate preserves masks."""
d1 = units.Quantity(np.ma.array([1, 2, 3], mask=[False, True, False]), 'degC')
result = concatenate((d1, 32 * units.degF))
truth = np.ma.array([1, np.inf, 3, 0])
truth[1] = np.ma.masked
assert_array_almost_equal(result, units.Quantity(truth, 'degC'), 6)
assert_array_equal(result.mask, np.array([False, True, False, False]))
def _find_append_zero_crossings(x, y):
r"""
Find and interpolate zero crossings.
Estimate the zero crossings of an x,y series and add estimated crossings to series,
returning a sorted array with no duplicate values.
Parameters
----------
x : `pint.Quantity`
x values of data
y : `pint.Quantity`
y values of data
Returns
-------
x : `pint.Quantity`
x values of data
y : `pint.Quantity`
y values of data
"""
# Find and append crossings to the data
crossings = find_intersections(x[1:], y[1:],
np.zeros_like(y[1:]) * y.units)
print(crossings)
x = concatenate((x, crossings[0]))
y = concatenate((y, crossings[1]))
# Resort so that data are in order
sort_idx = np.argsort(x)
x = x[sort_idx]
y = y[sort_idx]
# Remove duplicate data points if there are any
keep_idx = np.ediff1d(x, to_end=[1]) > 0
x = x[keep_idx]
y = y[keep_idx]
return x, y
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100) # Y limits from 1000 in the botton to 100 millibars to the top
skew.ax.set_xlim(-40, 60) # X limits
# Add the relevant special lines\n",
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
plt.show()
# Calculate LCL height and plot as black dot\n",
from metpy.calc import lcl, dry_lapse
from metpy.units import units, concatenate
l = lcl(p[0], T[0], Td[0]) # we can calculate the lcl level from the starting p, T, Td
lcl_temp = dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC') # Temperature of the lcl using the dry lapse
skew.plot(l, lcl_temp, 'ko', markerfacecolor='black') # plot the lcl level on the temperature with a black circle (ko) filled
# Calculate full parcel profile and add to plot as black line\n",
from metpy.calc import parcel_profile
prof = parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)
# Example of coloring area between profiles
skew.ax.fill_betweenx(p, T, prof, where=T>= prof, facecolor='blue', alpha=0.4)
skew.ax.fill_betweenx(p, T, prof, where=T< prof, facecolor='red', alpha=0.4)
# # An example of a slanted line at constant T -- in this case the 0 isotherm
level = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2) # highligh the 0 degree isoterm
from metpy.plots import Hodograpgh
def atmCalc(height, temp, humid):
print("ATMCALC", height, temp, humid, file=sys.stderr)
mtny = windh(MTNX, height, ratio=1,
yoffset=0)
windx = XVALUES
windy = windh(windx, height)
temp_ = temp * units.degC
initp = mc.height_to_pressure_std(windy[0] * units.meters)
dewpt = mc.dewpoint_from_relative_humidity(temp_, humid / 100.)
lcl_ = mc.lcl(initp, temp_, dewpt, max_iters=50, eps=1e-5)
LCL = mc.pressure_to_height_std(lcl_[0])
if (lcl_[0] > mc.height_to_pressure_std(max(windy) * units.meters)
and LCL > windy[0] * units.meters * 1.000009):
# add LCL to x
xlcl = windh(LCL.to('meters').magnitude, height, inv=True)
windx = np.sort(np.append(windx, xlcl))
windy = windh(windx, height)
pressures = mc.height_to_pressure_std(windy * units.meters)
wvmr0 = mc.mixing_ratio_from_relative_humidity(initp, temp_, humid / 100.)
# now calculate the air parcel temperatures and RH at each position
if (lcl_[0] <= min(pressures)):
T = mc.dry_lapse(pressures, temp_)
RH = [
mc.relative_humidity_from_mixing_ratio(
wvmr0, t, p) for t, p in zip(
T, pressures)]
else:
mini = np.argmin(pressures)
p1 = pressures[:mini + 1]
p2 = pressures[mini:] # with an overlap
p11 = p1[p1 >= lcl_[0] * .9999999] # lower (with tol) with lcl
p12 = p1[p1 < lcl_[0] * 1.000009] # upper (with tol) with lcl
T11 = mc.dry_lapse(p11, temp_)
T12 = mc.moist_lapse(p12, lcl_[1])
T1 = concatenate((T11[:-1], T12))
T2 = mc.dry_lapse(p2, T1[-1])
T = concatenate((T1, T2[1:]))
wvmrtop = mc.saturation_mixing_ratio(pressures[mini], T[mini])
RH=[]
for i in range(len(pressures)):
if pressures[i] > lcl_[0] and i <= mini:
v=mc.relative_humidity_from_mixing_ratio(pressures[i], T[i], wvmr0)
else:
if i < mini:
v=1
else:
v=mc.relative_humidity_from_mixing_ratio(pressures[i], T[i], wvmrtop)
RH.append(v)
#RH = [mc.relative_humidity_from_mixing_ratio(*tp, wvmr0) if tp[1] > lcl_[
#0] and i <= mini else 1.0 if i < mini else
#mc.relative_humidity_from_mixing_ratio(*tp, wvmrtop)
#for i, tp in enumerate(zip(pressures, T))]
RH = concatenate(RH)
return windx, mtny, windy, lcl_, LCL, T.to("degC"), RH
def test_concatenate():
"""Test basic functionality of unit-aware concatenate."""
result = concatenate((3 * units.meter, 400 * units.cm))
assert_array_equal(result, np.array([3, 4]) * units.meter)
assert not isinstance(result.m, np.ma.MaskedArray)
###########################################
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(9, 9))
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)
# Calculate LCL height and plot as black dot
l = mpcalc.lcl(p[0], T[0], Td[0])
lcl_temp = mpcalc.dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC')
skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)
# Example of coloring area between profiles
greater = T >= prof
skew.ax.fill_betweenx(p, T, prof, where=greater, facecolor='blue', alpha=0.4)
skew.ax.fill_betweenx(p, T, prof, where=~greater, facecolor='red', alpha=0.4)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
#
# 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 level
parcel_lcl = mpcalc.lcl(p[0], T[0], Td[0])
# Determine the LCL temperature by lifting a parcel from the surface
# pressure and temperature via a dry adiabatic process.
lcl_temperature = mpcalc.dry_lapse(concatenate((p[0], parcel_lcl)),
T[0])[-1].to('degC')
print(parcel_lcl, lcl_temperature)
# Calculate the parcel profile.
parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
##########################################################################
# Basic Skew-T Plotting
# ---------------------
#
# The Skew-T (log-P) diagram is the standard way to view rawinsonde data. The
# y-axis is height in pressure coordinates and the x-axis is temperature. The
# y coordinates are plotted on a logarithmic scale and the x coordinate system
# is skewed. An explanation of skew-T interpretation is beyond the scope of this
def plot(self, t, td, p, u, v, lat, long, time):
r"""Displays the Skew-T data on a matplotlib figure.
Args:
t (array-like): A list of temperature values.
td (array-like): A list of dewpoint values.
p (array-like): A list of pressure values.
u (array-like): A list of u-wind component values.
v (array-like): A list of v-wind component values.
lat (string): A string containing the requested latitude value.
long (string): A string containing the requested longitude value.
time (string): A string containing the UTC time requested with seconds truncated.
Returns:
None.
Raises:
None.
"""
# Create a new figure. The dimensions here give a good aspect ratio
self.skew = SkewT(self.figure, rotation=40)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
self.skew.plot(p, t, 'r')
self.skew.plot(p, td, 'g')
self.skew.plot_barbs(p, u, v, barbcolor='#FF0000', flagcolor='#FF0000')
self.skew.ax.set_ylim(1000, 100)
self.skew.ax.set_xlim(-40, 60)
# Axis colors
self.skew.ax.tick_params(axis='x', colors='#A3A3A4')
self.skew.ax.tick_params(axis='y', colors='#A3A3A4')
# Calculate LCL height and plot as black dot
l = lcl(p[0], t[0], td[0])
lcl_temp = dry_lapse(concatenate((p[0], l)), t[0])[-1].to('degC')
self.skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = parcel_profile(p, t[0], td[0]).to('degC')
self.skew.plot(p, prof, 'k', linewidth=2)
# Color shade areas between profiles
self.skew.ax.fill_betweenx(p, t, prof, where=t >= prof, facecolor='#5D8C53', alpha=0.7)
self.skew.ax.fill_betweenx(p, t, prof, where=t < prof, facecolor='#CD6659', alpha=0.7)
# Add the relevant special lines
self.skew.plot_dry_adiabats()
self.skew.plot_moist_adiabats()
self.skew.plot_mixing_lines()
# Set title
deg = u'\N{DEGREE SIGN}'
self.skew.ax.set_title('Sounding for ' + lat + deg + ', ' + long + deg + ' at ' + time + 'z', y=1.02,
color='#A3A3A4')
# Discards old graph, works poorly though
# skew.ax.hold(False)
# Figure and canvas widgets that display the figure in the GUI
# set canvas size to display Skew-T appropriately
self.canvas.setMaximumSize(QtCore.QSize(800, 2000))
# refresh canvas
self.canvas.draw()