def virt_temp(t, q):
"""
Compute virtual temperature in [K] from temperature and
specific humidity.
Arguments:
t -- temperature in [K]
q -- specific humidity in [kg/kg]
t and q can be scalars of NumPy arrays. They just have to either all
scalars, or all arrays.
Returns: Virtual temperature in [K]. Same dimension as input fields.
"""
t = units.Quantity(t, "K")
mix_rat = mpcalc.mixing_ratio_from_specific_humidity(q)
v_temp = mpcalc.virtual_temperature(t, mix_rat)
return v_temp
def test_virtual_temperature():
"""Test virtual temperature calculation."""
t = 288. * units.kelvin
qv = .0016 # kg/kg
tv = virtual_temperature(t, qv)
assert_almost_equal(tv, 288.2796 * units.kelvin, 3)
df_selected_time['mixing_ratio'] = mpcalc.mixing_ratio(
VP, p)
SMR = df_selected_time[
'saturation_mixing_ratio'].values * units('g/kg')
MR = df_selected_time['mixing_ratio'].values * units(
'g/kg')
# Calculate relative humidity
df_selected_time['relative_humidity_from_dewpoint'] \
= mpcalc.relative_humidity_from_dewpoint(T, Td)
df_selected_time['relative_humidity_from_mixing_ratio'] \
= mpcalc.relative_humidity_from_mixing_ratio(MR, T, p)
# Calculate virtual temperature
df_selected_time['virtual_temperature'] \
= mpcalc.virtual_temperature(T, MR)
# Calculate virtual potential temperature
df_selected_time['virtual_potential_temperature'] \
= mpcalc.virtual_potential_temperature(p, T, MR)
print('df_selected_time after drop nan.\n{0}'.format(
df_selected_time))
df_selected_time.to_csv(r'{0}{1}{2}_{3}_solution.csv'\
.format(dir_name, save_dir_name, filename_el[-5], selected_time[:13]))
p = df_selected_time['pressure'].values * units.hPa
T = df_selected_time['temperature'].values * units.degC
Td = df_selected_time['dewpoint'].values * units.degC
wind_speed = df_selected_time['speed'].values * units.knots
def EnergyMassPlot(pressure, temperature, dewpoint,
height, uwind, vwind, sphum=None, rh=None,
label='', size=(12,10), return_fig=False):
p=pressure
Z=height
T=temperature
Td=dewpoint
if isinstance(sphum,np.ndarray) and isinstance(rh,np.ndarray):
q=sphum
qs=q/rh
else:
q = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Td),p)
qs= mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T),p)
s = g*Z + Cp_d*T
sv= g*Z + Cp_d*mpcalc.virtual_temperature(T,q)
h = s + Lv*q
hs= s + Lv*qs
parcel_Tprofile = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
CAPE,CIN = mpcalc.cape_cin(p,T,Td,parcel_Tprofile)
ELp,ELT = mpcalc.el(p,T,Td)
ELindex = np.argmin(np.abs(p - ELp))
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
p_PWtop = max(200*units.mbar, min(p) +1*units.mbar)
PW = mpcalc.precipitable_water(Td,p, top=p_PWtop)
PWs = mpcalc.precipitable_water(T,p, top=p_PWtop)
CRH = (PW/PWs).magnitude *100.
fig,ax=setup_fig(size=size,label=label)
ax.plot(s /1000, p, color='r', linewidth=1.5) ### /1000 for kJ/kg
ax.plot(sv /1000, p, color='r', linestyle='-.')
ax.plot(h /1000, p, color='b', linewidth=1.5)
ax.plot(hs /1000, p, color='r', linewidth=1.5)
### RH rulings between s and h lines: annotate near 800 hPa level
annot_level = 800 #hPa
idx = np.argmin(np.abs(p - annot_level *units.hPa))
right_annot_loc = 380
for iRH in np.arange(10,100,10):
ax.plot( (s+ Lv*qs*iRH/100.)/1000, p, linewidth=0.5, linestyle=':', color='k')
ax.annotate(str(iRH), xy=( (s[idx]+Lv*qs[idx]*iRH/100.)/1000, annot_level),
horizontalalignment='center',fontsize=6)
ax.annotate('RH (%)', xy=(right_annot_loc, annot_level), fontsize=10)
if not np.isnan(CAPE.magnitude) and CAPE.magnitude >10:
parcelh = h [0] # for a layer mean: np.mean(h[idx1:idx2])
parcelsv = sv[0]
parcelp0 = p[0]
# Undilute parcel
ax.plot( (1,1)*parcelh/1000., (1,0)*parcelp0, linewidth=0.5, color='g')
maxbindex = np.argmax(parcel_Tprofile - T)
ax.annotate('CAPE='+str(int(CAPE.magnitude)),
xy=(parcelh/1000., p[maxbindex]), color='g')
# Plot LCL at saturation point, above the lifted sv of the surface parcel
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
ax.annotate('LCL', xy=(sv[0]/1000., lcl_pressure), fontsize=10, color='g', horizontalalignment='right')
# Purple fill for negative buoyancy below LCL:
ax.fill_betweenx(p, sv/1000., parcelsv/1000., where=p>lcl_pressure, facecolor='purple', alpha=0.4)
# Positive moist convective buoyancy in green
# Above LCL:
ax.fill_betweenx(p, hs/1000., parcelh/1000., where= parcelh>hs, facecolor='g', alpha=0.4)
# Depict Entraining parcels
# Parcel mass solves dM/dz = eps*M, solution is M = exp(eps*Z)
# M=1 at ground without loss of generality
entrainment_distance = 10000., 5000., 2000.
ax.annotate('entrain: 10,5,2 km', xy=(parcelh/1000, 140), color='g',
fontsize=8, horizontalalignment='right')
ax.annotate('parcel h', xy=(parcelh/1000, 120), color='g',
fontsize=10, horizontalalignment='right')
for ED in entrainment_distance:
eps = 1.0 / (ED*units.meter)
M = np.exp(eps * (Z-Z[0]).to('m')).to('dimensionless')
# dM is the mass contribution at each level, with 1 at the origin level.
M[0] = 0
dM = np.gradient(M)
# parcel mass is a sum of all the dM's at each level
# conserved linearly-mixed variables like h are weighted averages
hent = np.cumsum(dM*h) / np.cumsum(dM)
ax.plot( hent[0:ELindex+3]/1000., p[0:ELindex+3], linewidth=0.5, color='g')
### Internal waves (100m adiabatic displacements, assumed adiabatic: conserves s, sv, h).
dZ = 100 *units.meter
# depict displacements at sounding levels nearest these target levels
targetlevels = [900,800,700,600,500,400,300,200]*units.hPa
for ilev in targetlevels:
idx = np.argmin(np.abs(p - ilev))
# dT: Dry lapse rate dT/dz_dry is -g/Cp
dT = (-g/Cp_d *dZ).to('kelvin')
Tdisp = T[idx].to('kelvin') + dT
# dp: hydrostatic
rho = (p[idx]/Rd/T[idx])
dp = -rho*g*dZ
# dhsat
#qs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(T) ,p)
dqs = mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(Tdisp) ,p[idx]+dp) -qs[idx]
dhs = g*dZ + Cp_d*dT + Lv*dqs
# Whiskers on the data plots
ax.plot( (hs[idx]+dhs*(-1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.plot( (s [idx] *( 1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.plot( (h [idx] *( 1,1))/1000, p[idx]+dp*(-1,1), linewidth=3, color='b')
# annotation to explain it
if ilev == 600*ilev.units:
ax.plot(right_annot_loc*hs.units +dhs*(-1,1)/1000, p[idx]+dp*(-1,1), linewidth=3, color='r')
ax.annotate('+/- 100m', xy=(right_annot_loc,600), fontsize=8)
ax.annotate(' internal', xy=(right_annot_loc,630), fontsize=8)
ax.annotate(' waves', xy=(right_annot_loc,660), fontsize=8)
### Blue fill proportional to precipitable water, and blue annotation
ax.fill_betweenx(p, s/1000., h/1000., where=h > s, facecolor='b', alpha=0.4)
# Have to specify the top of the PW integral.
# I want whole atmosphere of course, but 200 hPa captures it all really.
#import metpy.calc as mpcalc
p_PWtop = max(200*units.mbar, min(p) +1*units.mbar)
PW = mpcalc.precipitable_water(Td,p, top=p_PWtop)
PWs = mpcalc.precipitable_water(T,p, top=p_PWtop)
CRH = (PW/PWs).magnitude *100.
# PW annotation arrow tip at 700 mb
idx = np.argmin(np.abs(p - 700*p.units))
centerblue = (s[idx]+h[idx])/2.0 /1000.
ax.annotate('CWV='+str(round(PW.to('mm').magnitude, 1))+'mm',
xy=(centerblue, 700), xytext=(285, 200),
color='blue', fontsize=15,
arrowprops=dict(width = 1, edgecolor='blue', shrink=0.02),
)
ax.annotate('(' + str(round(CRH,1)) +'% of sat)',
xy=(285, 230), color='blue', fontsize=12)
### Surface water values at 1C intervals, for eyeballing surface fluxes
sT = np.trunc(T[0].to('degC'))
sTint = int(sT.magnitude)
for idT in [-2,0,2,4]:
ssTint = sTint + idT # UNITLESS degC integers, for labels
# Kelvin values for computations
ssTC = ssTint * units.degC
ssTK = ssTC.to('kelvin')
ss = g*Z[0] + Cp_d*ssTK
hs = ss + Lv*mpcalc.mixing_ratio(mpcalc.saturation_vapor_pressure(ssTK) ,p[0])
ax.annotate(str(ssTint), xy=(ss/1000., p[0]+0*p.units),
verticalalignment='top', horizontalalignment='center',
color='red', fontsize=7)
ax.annotate(str(ssTint), xy=(hs/1000., p[0]+0*p.units),
verticalalignment='top', horizontalalignment='center',
color='red', fontsize=9)
ax.annotate('\u00b0C water', xy=(right_annot_loc, p[0]), verticalalignment='top',
fontsize=10, color='r')
if return_fig:
return ax,fig
def calculate_stability_indicies(
ds,
temp_name='temperature',
td_name='dewpoint_temperature',
p_name='pressure',
rh_name='relative_humidity',
moving_ave_window=0,
):
"""
Function for calculating stability indices from sounding data.
Parameters
----------
ds : ACT dataset
The dataset to compute the stability indicies of. Must have
temperature, dewpoint, and pressure in vertical coordinates.
temp_name : str
The name of the temperature field.
td_name : str
The name of the dewpoint field.
p_name : str
The name of the pressure field.
rh_name : str
The name of the relative humidity field.
moving_ave_window : int
Number of points to do a moving average on sounding data to reduce
noise. This is useful if noise in the sounding is preventing parcel
ascent.
Returns
-------
ds : ACT dataset
An ACT dataset with additional stability indicies added.
"""
if not METPY_AVAILABLE:
raise ImportError(
'MetPy need to be installed on your system to ' + 'calculate stability indices'
)
t = ds[temp_name]
td = ds[td_name]
p = ds[p_name]
rh = ds[rh_name]
if not hasattr(t, 'units'):
raise AttributeError('Temperature field must have units' + ' for ACT to discern!')
if not hasattr(td, 'units'):
raise AttributeError('Dewpoint field must have units' + ' for ACT to discern!')
if not hasattr(p, 'units'):
raise AttributeError('Pressure field must have units' + ' for ACT to discern!')
if t.units == 'C':
t_units = units.degC
else:
t_units = getattr(units, t.units)
if td.units == 'C':
td_units = units.degC
else:
td_units = getattr(units, td.units)
p_units = getattr(units, p.units)
rh_units = getattr(units, rh.units)
# Sort all values by decreasing pressure
t_sorted = np.array(t.values)
td_sorted = np.array(td.values)
p_sorted = np.array(p.values)
rh_sorted = np.array(rh.values)
ind_sort = np.argsort(p_sorted)
t_sorted = t_sorted[ind_sort[-1:0:-1]]
td_sorted = td_sorted[ind_sort[-1:0:-1]]
p_sorted = p_sorted[ind_sort[-1:0:-1]]
rh_sorted = rh_sorted[ind_sort[-1:0:-1]]
if moving_ave_window > 0:
t_sorted = np.convolve(t_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
td_sorted = np.convolve(td_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
p_sorted = np.convolve(p_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
rh_sorted = np.convolve(rh_sorted, np.ones((moving_ave_window,)) / moving_ave_window)
t_sorted = t_sorted * t_units
td_sorted = td_sorted * td_units
p_sorted = p_sorted * p_units
rh_sorted = rh_sorted * rh_units
# Calculate mixing ratio
mr = mpcalc.mixing_ratio_from_relative_humidity(p_sorted, t_sorted, rh_sorted)
# Discussion of issue #361 use virtual temperature.
vt = mpcalc.virtual_temperature(t_sorted, mr)
t_profile = mpcalc.parcel_profile(p_sorted, t_sorted[0], td_sorted[0])
# Calculate parcel trajectory
ds['parcel_temperature'] = t_profile.magnitude
ds['parcel_temperature'].attrs['units'] = t_profile.units
# Calculate CAPE, CIN, LCL
sbcape, sbcin = mpcalc.surface_based_cape_cin(p_sorted, vt, td_sorted)
lcl = mpcalc.lcl(p_sorted[0], t_sorted[0], td_sorted[0])
try:
lfc = mpcalc.lfc(p_sorted[0], t_sorted[0], td_sorted[0])
except IndexError:
lfc = np.nan * p_sorted.units
mucape, mucin = mpcalc.most_unstable_cape_cin(p_sorted, vt, td_sorted)
where_500 = np.argmin(np.abs(p_sorted - 500 * units.hPa))
li = t_sorted[where_500] - t_profile[where_500]
ds['surface_based_cape'] = sbcape.magnitude
ds['surface_based_cape'].attrs['units'] = 'J/kg'
ds['surface_based_cape'].attrs['long_name'] = 'Surface-based CAPE'
ds['surface_based_cin'] = sbcin.magnitude
ds['surface_based_cin'].attrs['units'] = 'J/kg'
ds['surface_based_cin'].attrs['long_name'] = 'Surface-based CIN'
ds['most_unstable_cape'] = mucape.magnitude
ds['most_unstable_cape'].attrs['units'] = 'J/kg'
ds['most_unstable_cape'].attrs['long_name'] = 'Most unstable CAPE'
ds['most_unstable_cin'] = mucin.magnitude
ds['most_unstable_cin'].attrs['units'] = 'J/kg'
ds['most_unstable_cin'].attrs['long_name'] = 'Most unstable CIN'
ds['lifted_index'] = li.magnitude
ds['lifted_index'].attrs['units'] = t_profile.units
ds['lifted_index'].attrs['long_name'] = 'Lifted index'
ds['level_of_free_convection'] = lfc.magnitude
ds['level_of_free_convection'].attrs['units'] = lfc.units
ds['level_of_free_convection'].attrs['long_name'] = 'Level of free convection'
ds['lifted_condensation_level_temperature'] = lcl[1].magnitude
ds['lifted_condensation_level_temperature'].attrs['units'] = lcl[1].units
ds['lifted_condensation_level_temperature'].attrs[
'long_name'
] = 'Lifted condensation level temperature'
ds['lifted_condensation_level_pressure'] = lcl[0].magnitude
ds['lifted_condensation_level_pressure'].attrs['units'] = lcl[0].units
ds['lifted_condensation_level_pressure'].attrs[
'long_name'
] = 'Lifted condensation level pressure'
return ds
def f(fullname, selected_time):
fullname_el = fullname.split('/')
#fullname_el = fullname.split('\\')
filename = fullname_el[-1]
filename_el = filename.split('_')
save_base_dir_name = '../1data/'
save_dir_name = '{0}{1}/'.format(save_base_dir_name + dir_name, obs_year)
print('site : {0}'.format(dir_name))
if os.path.isfile('{0}{1}_{2}_student.csv'\
.format(save_dir_name, filename_el[-5], selected_time[:13]))\
and os.path.isfile('{0}{1}_{2}_solution.csv'\
.format(save_dir_name, filename_el[-5], selected_time[:13])):
write_log(log_file, '{3} ::: {0}{1}_{2} files are already exist'\
.format(save_dir_name, filename_el[-5], selected_time[:13], datetime.now()))
else:
try:
f = lambda s: selected_time in s
ids = df['time'].apply(f)
df_selected_time = df[ids]
df_selected_time = df_selected_time.sort_values('pressure',
ascending=False)
print('filename : {0}'.format(fullname))
print('df_selected_time.\n{0}'.format(df_selected_time))
df_selected_time.to_csv(r'{0}{1}_{2}_student.csv'\
.format(save_dir_name, filename_el[-5], selected_time[:13]))
#################################################################################
### We will pull the data out of the example dataset into individual variables and
### assign units.
#################################################################################
p = df_selected_time['pressure'].values * units.hPa
T = df_selected_time['temperature'].values * units.degC
Td = df_selected_time['dewpoint'].values * units.degC
wind_speed = df_selected_time['speed'].values * units.knots
wind_dir = df_selected_time['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
# Calculate web bulb temperature
df_selected_time['wet_bulb_T'] = mpcalc.wet_bulb_temperature(
p, T, Td)
# Calculate potential temperature
df_selected_time['potential_T'] = mpcalc.potential_temperature(
p, T)
df_selected_time['potential_T_C'] = df_selected_time[
'potential_T'].values - 273.15
# Calculate saturation vaper pressure
df_selected_time[
'saturation_vaper_pressure'] = mpcalc.saturation_vapor_pressure(
T)
df_selected_time[
'vaper_pressure'] = mpcalc.saturation_vapor_pressure(Td)
SVP = df_selected_time[
'saturation_vaper_pressure'].values * units.hPa
VP = df_selected_time['vaper_pressure'].values * units.hPa
# Calculate mixing ratio
df_selected_time['saturation_mixing_ratio'] = mpcalc.mixing_ratio(
SVP, p)
df_selected_time['mixing_ratio'] = mpcalc.mixing_ratio(VP, p)
SMR = df_selected_time['saturation_mixing_ratio'].values * units(
'g/kg')
MR = df_selected_time['mixing_ratio'].values * units('g/kg')
# Calculate relative humidity
df_selected_time['relative_humidity_from_dewpoint'] \
= mpcalc.relative_humidity_from_dewpoint(T, Td)
df_selected_time['relative_humidity_from_mixing_ratio'] \
= mpcalc.relative_humidity_from_mixing_ratio(MR, T, p)
# Calculate virtual temperature
df_selected_time['virtual_temperature'] \
= mpcalc.virtual_temperature(T, MR)
# Calculate virtual potential temperature
df_selected_time['virtual_potential_temperature'] \
= mpcalc.virtual_potential_temperature(p, T, MR)
print('df_selected_time after drop nan.\n{0}'.format(
df_selected_time))
df_selected_time.to_csv(r'{0}{1}_{2}_solution.csv'\
.format(save_dir_name, filename_el[-5], selected_time[:13]))
except Exception as err:
write_log(err_log_file, '{4} ::: {0} with {1}{2} on {3}'\
.format(err, dir_name, filename, selected_time[:13], datetime.now()))
print('Thread {0} is finished'.format(selected_time))
return 0 # Return a dummy value
def cape(filelist,storm,track,show):
#Sort filelist.
filelist=np.sort(filelist)
# Get sampling periods (this will be a dictionary). See the toolbox
print('Retrieving sampling periods')
sampleperiods=getsamplingperiods(filelist,3.)
# Iterate over all sampling periods.
for sampindex,periodskey in enumerate(sampleperiods):
#Allocate starting (stdt) and ending date (endt). Remeber dt is the convetional short-name for date.
stdt=periodskey
endt=sampleperiods[periodskey]
# Define sampling period string
period=str(stdt.hour)+'_'+str(stdt.day)+'-'+str(endt.hour)+'_'+str(endt.day)
# Create new-empty lists.
lats=[]
lons=[]
xs=[]
ys=[]
capes=[]
cins=[]
distfig = plt.figure(figsize=(13, 9))
ax=distfig.add_subplot(111)
print('start filelist loop')
# Iterate over all files.
for filename in filelist:
# Select end-name of file by inspecting filename string. Notice how filename can change how file is read.
if 'radazm' in filename.split('/')[-1] or 'eol' in filename.split('/')[-1]:
end='radazm'
else:
end='avp'
# Obtain properties of file, i.e., launch time and location into a dictionary (dicc).
dicc=findproperties(filename,end)
# Condition to see if current file is in sampling period.
# Notice how if structure is constructed, condition finds times outside of sampling period and
# if found outside the sampling period, continue to next file.
if dicc['Launch Time']<stdt or dicc['Launch Time'] > endt:
continue
nump=np.genfromtxt(filename,skip_header=16,skip_footer=0)
temperature=clean1(nump[:,5])
pressure=clean1(nump[:,4])
Height=clean1(nump[:,13])
if np.nanmax(Height)<3500:
continue
#Clean for cape
RelH=clean1(nump[:,7])
lon=clean1(nump[:,14])
lat=clean1(nump[:,15])
lon=clean1(lon)
lat=clean1(lat)
mlon=np.nanmean(lon)
mlat=np.nanmean(lat)
RH=RelH/100
T,P,rh,dz=cleanforcape(temperature,pressure,RH,Height)
#Metpy set-up
T=np.flip(T,0)
rh=np.flip(rh,0)
p=np.flip(P,0)
dz=np.flip(dz,0)
p=p*units.hPa
T=T*units.celsius
mixing=rh*mpcalc.saturation_mixing_ratio(p,T)
epsilon=0.6219800858985514
Tv=mpcalc.virtual_temperature(T, mixing,
molecular_weight_ratio=epsilon)
dwpoint=mpcalc.dewpoint_rh(T, rh)
blh_indx=np.where(dz<500)
try:
parcelprofile=mpcalc.parcel_profile(p,np.nanmean(T[blh_indx])*units.celsius,mpcalc.dewpoint_rh(np.nanmean(T[blh_indx])*units.celsius, np.nanmean(rh[blh_indx]))).to('degC')
Tv_parcelprofile=mpcalc.virtual_temperature(parcelprofile, mixing,
molecular_weight_ratio=epsilon)
cape,cin=cape_cin(p,Tv,dwpoint,Tv_parcelprofile,dz,T)
except:
continue
plotskewT=True
if plotskewT==True:
os.system('mkdir figs/skewt')
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
skew.plot(p, dwpoint, 'g',label=r'$T_{dp}$')
skew.plot(p, Tv, 'r',label=r'$T_v$')
plt.text(-120,120,str(np.around(cape,2)),fontsize=14,fontweight='bold')
# 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,Tv_parcelprofile,'k',label=r'$T_{v env}$')
skew.shade_cin(p, T, parcelprofile,label='CIN')
skew.shade_cape(p, Tv, Tv_parcelprofile,label='CAPE')
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
plt.legend()
plt.title(storm + ' on' + period,fontsize=14)
plt.savefig('figs/skewt/'+storm+str(dicc['Launch Time'].time())+'.png')
#plt.show()
plt.close()
r,theta=cart_to_cylindr(mlon,mlat,track,dicc['Launch Time'])
if not(np.isnan(r)) and not(np.isnan(theta)) and not(np.isnan(cape.magnitude)):
xs.append(r*np.cos(theta))
ys.append(r*np.sin(theta))
capes.append(cape.magnitude)
cins.append(cin)
cs=ax.scatter(xs,ys,c=np.asarray(capes),cmap='jet')
for i,xi in enumerate(xs):
ax.text(xi,ys[i]+10,str(np.around(capes[i],1)))
plt.colorbar(cs)
ax.scatter(0,0,marker='v',s=100,color='black')
ax.grid()
ax.set_xlabel('X distance [km]')
ax.set_ylabel('Y distance [km]')
ax.set_title('CAPE distribution for '+storm+' on '+period,fontsize=14)
distfig.savefig('figs/cape'+storm+period+'.png')
if show:
plt.show()
ds = ds.sel(time=dt.strftime('%Y-%m-%dT%H:%M:%S')) # reduce the dataset to the RAP analysis (starting) time from 'dt'
#### Want to list the field names + some info from this xarray Dataset? Uncomment the below 6 lines!
#fieldNames = list(ds.variables.keys())
#for fieldName in fieldNames:
# try:
# print('%s -- "%s" [%s]' % (fieldName, ds[fieldName].long_name, ds[fieldName].units))
# except AttributeError:
# print(fieldName)
#### Compute 1000-500-hPa geopotential thickness (pressure coordinates are in units of Pa)
thick = ds.Geopotential_height_isobaric.sel(isobaric=50000) - ds.Geopotential_height_isobaric.sel(isobaric=100000)
#### Compute 1000-500-hPa pressure-weighted mean virtual temperature
MR = mpcalc.mixing_ratio_from_relative_humidity(ds.isobaric, ds.Temperature_isobaric, ds.Relative_humidity_isobaric)
Tvirt = mpcalc.virtual_temperature(ds.Temperature_isobaric, MR, molecular_weight_ratio=0.6219569100577033)
layer = slice(50000,100000) # 1000-500-hPa layer
dp = np.zeros(ds.isobaric.sel(isobaric=layer).size, dtype=float) # NumPy array to store dp, the depth in Pa of each layer
dp[:-1] = np.abs(np.ediff1d(ds.isobaric.sel(isobaric=layer).values))
dp[-1] = dp[-2] # np.ediff1d produces an array 1 smaller than the input, so make an assumption here for final value
dp = xr.DataArray(data=dp.copy(), dims=['isobaric'], coords={'isobaric':ds.isobaric.sel(isobaric=layer)},
attrs={'units': ds.isobaric.units})
TvirtLayer = np.sum(Tvirt.sel(isobaric=layer)*ds.isobaric.sel(isobaric=layer)*dp, axis=0) / \
np.sum(ds.isobaric.sel(isobaric=layer)*dp, axis=0)
#### Convert x/y coordinates to latitude & longitude (note: steps are NOT via xarray as I'm debugging that approach)
dsProjDict = ds.LambertConformal_Projection.attrs # obtain the source data map projection
R = dsProjDict['earth_radius']
dsProj = ccrs.LambertConformal(central_longitude=dsProjDict['longitude_of_central_meridian']-360.,
central_latitude=dsProjDict['latitude_of_projection_origin'],
standard_parallels=[dsProjDict['standard_parallel']],
def test_virtual_temperature():
"""Test virtual temperature calculation."""
t = 288. * units.kelvin
qv = .0016 * units.dimensionless # kg/kg
tv = virtual_temperature(t, qv)
assert_almost_equal(tv, 288.2796 * units.kelvin, 3)
