def makeSkewWet(ax, corners=[-30, 25], skew=30):
"""
draw a skew-T lnP diagram on an axis
Parameters
----------
ax : matplotlib.axes
matplotlib figure axis
corners : [float]
x axis temperature limits (degC)
skew : float
adjustable coefficient to make isotherms slope
compared to adiabats
Returns
-------
ax : matplotlib.axes
the modified figure axis
"""
yplot = range(1000, 190, -10) #
xcorners = find_corners(corners, skew=skew)
xplot = list(np.linspace(xcorners[0], xcorners[1], 35))
pvals = np.size(yplot)
tvals = np.size(xplot)
temp = np.zeros([pvals, tvals])
theTheta = np.zeros_like(temp)
the_rsat = np.zeros_like(temp)
theThetae = np.zeros([pvals, tvals])
# lay down a reference grid that labels xplot,yplot points
# in the new (skewT-lnP) coordinate system .
# Each value of the temp matrix holds the actual (data)
# temperature label (in deg C) of the xplot, yplot coordinate.
# pairs. The transformation is given by W&H 3.56, p. 78. Note
# that there is a sign difference, because rather than
# taking y= -log(P) like W&H, I take y= +log(P) and
# then reverse the y axis
for presshPa in yplot: #loop over pressures
for skewed in xplot: #loop over skewed xcoords
# Note that we don't have to transform the y
# coordinate, as it is still pressure.
iInd = yplot.index(presshPa)
jInd = xplot.index(skewed)
temp[iInd, jInd] = convertSkewToTemp(skewed, presshPa, skew)
Tk = c.Tc + temp[iInd, jInd]
pressPa = presshPa * 100.
theTheta[iInd, jInd] = find_theta(Tk, pressPa)
the_rsat[iInd, jInd] = find_rsat(Tk, pressPa)
theThetae[iInd, jInd] = find_thetaes(Tk, pressPa)
#
# Contour the temperature matrix.
#
# First, make sure that all plotted lines are solid.
mpl.rcParams['contour.negative_linestyle'] = 'solid'
tempLabels = range(-40, 50, 10)
ax.contour(xplot, yplot, temp, tempLabels, \
colors='k')
#
# contour theta
#
thetaLabels = list(range(200, 390, 10))
thetaLevs = ax.contour(xplot, yplot, theTheta, thetaLabels, \
colors='b')
#
# contour rsat
#
rsLabels = [0.1, 0.25, 0.5, 1, 2, 3] + list(range(4, 20, 2)) + [20, 24, 28]
rsLevs = ax.contour(xplot,
yplot,
the_rsat * 1.e3,
levels=rsLabels,
colors='g',
linewidths=.5)
thetaeLabels = np.arange(250, 410, 10)
thetaeLevs = ax.contour(xplot, yplot, theThetae, thetaeLabels, \
colors='r')
#
# Customize the plot
#
ax.set_yscale('log')
locs = np.array(range(100, 1100, 100))
labels = locs
ax.set_yticks(locs)
ax.set_yticklabels(labels) # Conventionally labels semilog graph.
ax.set_ybound((200, 1000))
plt.setp(ax.get_xticklabels(), weight='bold')
plt.setp(ax.get_yticklabels(), weight='bold')
ax.yaxis.grid(True)
ax.set_title('skew T - lnp chart')
ax.set_ylabel('pressure (hPa)')
ax.set_xlabel('temperature (deg C)')
#
# Crop image to a more usable size
#
TempTickLabels = range(-30, 40, 5)
TempTickCoords = TempTickLabels
skewTickCoords = convertTempToSkew(TempTickCoords, 1.e3, skew)
ax.set_xticks(skewTickCoords)
ax.set_xticklabels(TempTickLabels)
skewLimits = convertTempToSkew([-15, 35], 1.e3, skew)
ax.axis([skewLimits[0], skewLimits[1], 300, 1.e3])
#
# Create line labels
#
fntsz = 9 # Handle for 'fontsize' of the line label.
ovrlp = True # Handle for 'inline'. Any integer other than 0
# creates a white space around the label.
#tempLevs.clabel(inline=ovrlp, inline_spacing=0,fmt='%2d', fontsize=fntsz,use_clabeltext=True)
thetaLevs.clabel(inline=ovrlp,
inline_spacing=0,
fmt='%3d',
fontsize=fntsz,
use_clabeltext=True)
rsLevs.clabel(inline=ovrlp,
inline_spacing=0,
fmt='%3.2g',
fontsize=fntsz,
use_clabeltext=True)
thetaeLevs.clabel(thetaeLabels,
inline_spacing=0,
inline=ovrlp,
fmt='%5g',
fontsize=fntsz,
use_clabeltext=True)
ax.invert_yaxis()
#ax.figure.canvas.draw()
xcorners = find_corners(corners, skew=skew)
ax.set(ylim=(1000, 300), xlim=xcorners)
return ax, skew
xplot=convertTempToSkew(Tdew_800 - c.Tc,press*pa2hPa,skew)
bot=ax.plot(xplot, press*pa2hPa, 'bd', markersize=14, markerfacecolor='b')
#
# draw the two LCLs as black circles
#
press=860.e2
xplot=convertTempToSkew(Temp_860 - c.Tc,press*pa2hPa,skew)
bot=ax.plot(xplot, press*pa2hPa, 'ko', markersize=14, markerfacecolor='k')
press=702.e2 #add 2 hPa so we can see it
xplot=convertTempToSkew(Temp_700 - c.Tc,press*pa2hPa,skew)
bot=ax.plot(xplot, press*pa2hPa, 'ko', markersize=14, markerfacecolor='k')
fig.savefig('mid-tephi.pdf')
press=700.e2
thetaes_700 = tl.find_thetaes(Temp_700,press)
print('thetaes at {} = {} K'.format(press*1.e-2,n(thetaes_700)))
press=860.e2
thetaes_860 = tl.find_thetaes(Temp_860,press)
print(' thetaes at {} = {} K'.format(press*1.e-2,n(thetaes_860)))
print('entropy for 860 hPa = {}'.format(e(c.cpd*np.log(thetaes_860))))
#
#wet bulb temp potential temperature for 900 hPa -- bring air to 1000 hPa
#along a moist adiabat
#
press = 1.e5
Temp_1000=find_Tmoist(thetae_900,press)
print('wet bulb potential temperature for 900 hPa air = {} C'.format(n(k2c(Temp_1000))))
#