def get_parcels(self):
'''
Function to generate various parcels and parcel
traces.
Returns nothing, but sets the following
variables:
self.mupcl : Most Unstable Parcel
self.sfcpcl : Surface Based Parcel
self.mlpcl : Mixed Layer Parcel
self.fcstpcl : Forecast Surface Parcel
self.ebottom : The bottom pressure level of
the effective inflow layer
self.etop : the top pressure level of
the effective inflow layer
self.ebotm : The bottom, meters (agl), of the
effective inflow layer
self.etopm : The top, meters (agl), of the
effective inflow layer
Parameters
----------
None
Returns
-------
None
'''
self.mupcl = params.parcelx( self, flag=3 )
if self.mupcl.lplvals.pres == self.pres[self.sfc]:
self.sfcpcl = self.mupcl
else:
self.sfcpcl = params.parcelx( self, flag=1 )
self.fcstpcl = params.parcelx( self, flag=2 )
self.mlpcl = params.parcelx( self, flag=4 )
self.usrpcl = params.Parcel()
## get the effective inflow layer data
self.ebottom, self.etop = params.effective_inflow_layer( self, mupcl=self.mupcl )
## if there was no effective inflow layer, set the values to masked
if self.etop is ma.masked or self.ebottom is ma.masked:
self.ebotm = ma.masked; self.etopm = ma.masked
self.effpcl = self.sfcpcl # Default to surface parcel, as in params.DefineProfile().
## otherwise, interpolate the heights given to above ground level
else:
self.ebotm = interp.to_agl(self, interp.hght(self, self.ebottom))
self.etopm = interp.to_agl(self, interp.hght(self, self.etop))
# The below code was adapted from params.DefineProfile()
# Lifting one additional parcel probably won't slow the program too much.
# It's just one more lift compared to all the lifts in the params.effective_inflow_layer() call.
mtha = params.mean_theta(self, self.ebottom, self.etop)
mmr = params.mean_mixratio(self, self.ebottom, self.etop)
effpres = (self.ebottom+self.etop)/2.
efftmpc = thermo.theta(1000., mtha, effpres)
effdwpc = thermo.temp_at_mixrat(mmr, effpres)
self.effpcl = params.parcelx(self, flag=5, pres=effpres, tmpc=efftmpc, dwpc=effdwpc) #This is the effective parcel.
def get_parcels(self):
'''
Function to generate various parcels and parcel
traces.
Returns nothing, but sets the following
variables:
self.mupcl : Most Unstable Parcel
self.sfcpcl : Surface Based Parcel
self.mlpcl : Mixed Layer Parcel
self.fcstpcl : Forecast Surface Parcel
self.ebottom : The bottom pressure level of
the effective inflow layer
self.etop : the top pressure level of
the effective inflow layer
self.ebotm : The bottom, meters (agl), of the
effective inflow layer
self.etopm : The top, meters (agl), of the
effective inflow layer
Parameters
----------
None
Returns
-------
None
'''
self.mupcl = params.parcelx( self, flag=3 )
if self.mupcl.lplvals.pres == self.pres[self.sfc]:
self.sfcpcl = self.mupcl
else:
self.sfcpcl = params.parcelx( self, flag=1 )
self.fcstpcl = params.parcelx( self, flag=2 )
self.mlpcl = params.parcelx( self, flag=4 )
self.usrpcl = params.Parcel()
## get the effective inflow layer data
self.ebottom, self.etop = params.effective_inflow_layer( self, mupcl=self.mupcl )
## if there was no effective inflow layer, set the values to masked
if self.etop is ma.masked or self.ebottom is ma.masked:
self.ebotm = ma.masked; self.etopm = ma.masked
self.effpcl = self.sfcpcl # Default to surface parcel, as in params.DefineProfile().
## otherwise, interpolate the heights given to above ground level
else:
self.ebotm = interp.to_agl(self, interp.hght(self, self.ebottom))
self.etopm = interp.to_agl(self, interp.hght(self, self.etop))
# The below code was adapted from params.DefineProfile()
# Lifting one additional parcel probably won't slow the program too much.
# It's just one more lift compared to all the lifts in the params.effective_inflow_layer() call.
mtha = params.mean_theta(self, self.ebottom, self.etop)
mmr = params.mean_mixratio(self, self.ebottom, self.etop)
effpres = (self.ebottom+self.etop)/2.
efftmpc = thermo.theta(1000., mtha, effpres)
effdwpc = thermo.temp_at_mixrat(mmr, effpres)
self.effpcl = params.parcelx(self, flag=5, pres=effpres, tmpc=efftmpc, dwpc=effdwpc) #This is the effective parcel.
def test_to_agl():
input_z = 1000.
correct_agl = 643.0
returned_agl = interp.to_agl(prof, input_z)
npt.assert_almost_equal(returned_agl, correct_agl)
input_z = [1000., 3000., 6000.]
correct_agl = [643., 2643., 5643.]
returned_agl = interp.to_agl(prof, input_z)
npt.assert_almost_equal(returned_agl, correct_agl)
def test_to_agl():
input_z = 1000.
correct_agl = 643.0
returned_agl = interp.to_agl(prof, input_z)
npt.assert_almost_equal(returned_agl, correct_agl)
input_z = [1000., 3000., 6000.]
correct_agl = [643., 2643., 5643.]
returned_agl = interp.to_agl(prof, input_z)
npt.assert_almost_equal(returned_agl, correct_agl)
def lift_parcels(prof):
"""Lift all the parcels within a given height interval and return the CAPEs, CINHs, and LFCs"""
## the height bottom, top, and interval
zvals = np.arange(0, 5000, 100)
pvals = interp.pres(prof, interp.to_msl(prof, zvals))
tvals = interp.temp(prof, pvals)
dvals = interp.dwpt(prof, pvals)
hvals = interp.hght(prof, pvals)
hvals = interp.to_agl(prof, hvals)
## empty lists for storing the result
cape_arr = []
cinh_arr = []
lfc_arr = []
## lift each parcel in the vertical profile
for p, t, td, h in zip(pvals, tvals, dvals, hvals):
## use SHARPpy to compute the parcel indices
pcl = params.parcelx(prof, pres=p, tmpc=t, dwpc=td)
## store the parcel indices
cape_arr.append(pcl.bplus)
cinh_arr.append(pcl.bminus)
lfc_arr.append(pcl.lfchght - h)
## return the data
return np.ma.masked_invalid(cape_arr), np.ma.masked_invalid(
cinh_arr), np.ma.masked_invalid(lfc_arr)
def get_fire(self):
'''
Function to generate different indices and information
regarding any fire weather in the sounding. This helps fill
the data shown in the FIRE inset.
Parameters
----------
None
Returns
-------
None
'''
self.fosberg = fire.fosberg(self)
self.ppbl_top = params.pbl_top(self)
self.sfc_rh = thermo.relh(self.pres[self.sfc], self.tmpc[self.sfc],
self.dwpc[self.sfc])
pres_sfc = self.pres[self.sfc]
pres_1km = interp.pres(self, interp.to_msl(self, 1000.))
pbl_h = interp.to_agl(self, interp.hght(self, self.ppbl_top))
self.rh01km = params.mean_relh(self, pbot=pres_sfc, ptop=pres_1km)
self.pblrh = params.mean_relh(self, pbot=pres_sfc, ptop=self.ppbl_top)
self.meanwind01km = winds.mean_wind(self, pbot=pres_sfc, ptop=pres_1km)
self.meanwindpbl = winds.mean_wind(self,
pbot=pres_sfc,
ptop=self.ppbl_top)
self.pblmaxwind = winds.max_wind(self, lower=0, upper=pbl_h)
#self.pblmaxwind = [np.ma.masked, np.ma.masked]
mulplvals = params.DefineParcel(self, flag=3, pres=500)
mupcl = params.cape(self, lplvals=mulplvals)
self.bplus_fire = mupcl.bplus
def get_fire(self):
'''
Function to generate different indices and information
regarding any fire weather in the sounding. This helps fill
the data shown in the FIRE inset.
Parameters
----------
None
Returns
-------
None
'''
self.fosberg = fire.fosberg(self)
self.ppbl_top = params.pbl_top(self)
self.sfc_rh = thermo.relh(self.pres[self.sfc], self.tmpc[self.sfc], self.dwpc[self.sfc])
pres_sfc = self.pres[self.sfc]
pres_1km = interp.pres(self, interp.to_msl(self, 1000.))
pbl_h = interp.to_agl(self, interp.hght(self, self.ppbl_top))
self.rh01km = params.mean_relh(self, pbot=pres_sfc, ptop=pres_1km)
self.pblrh = params.mean_relh(self, pbot=pres_sfc, ptop=self.ppbl_top)
self.meanwind01km = winds.mean_wind(self, pbot=pres_sfc, ptop=pres_1km)
self.meanwindpbl = winds.mean_wind(self, pbot=pres_sfc, ptop=self.ppbl_top)
self.pblmaxwind = winds.max_wind(self, lower=0, upper=pbl_h)
#self.pblmaxwind = [np.ma.masked, np.ma.masked]
mulplvals = params.DefineParcel(self, flag=3, pres=500)
mupcl = params.cape(self, lplvals=mulplvals)
self.bplus_fire = mupcl.bplus
def add_hodo(ax,
prof,
lw=1,
color='black',
ls='solid',
size=5,
AGLs=[1, 2, 6, 10]):
# Return 2D hodograph line and list of text AGL labels.
# Draw hodo line
u_prof = prof.u[prof.pres >= 100]
v_prof = prof.v[prof.pres >= 100]
hodo, = ax.plot(u_prof, v_prof, 'k-', lw=lw, color=color, ls=ls)
# position AGL text labels
bbox_props = dict(boxstyle="square",
fc="w",
ec="0.5",
linewidth=0.5,
alpha=0.7)
AGL_labels = []
AGL_labels.append(ax.text(-35, -55, 'km AGL', size=size, bbox=bbox_props))
for a in AGLs:
in_meters = a * 1000
if in_meters <= np.max(interp.to_agl(prof, prof.hght)):
junk = ax.text(0,
0,
str(a),
ha='center',
va='center',
size=size,
bbox=bbox_props,
color=color)
ind = np.min(np.where(interp.to_agl(prof, prof.hght) > in_meters))
junk.set_position((prof.u[ind], prof.v[ind]))
junk.set_clip_on(True)
AGL_labels.append(junk)
return hodo, AGL_labels
def draw_ensemble_point(self, qp, prof):
# Plot the profile index on the scatter plot
if 'pbl_h' not in dir(
prof
): # Make sure a PBL top has been found in the profile object
ppbl_top = params.pbl_top(prof)
setattr(prof, 'pbl_h',
interp.to_agl(prof, interp.hght(prof, ppbl_top)))
if 'sfcpcl' not in dir(
prof
): # Make sure a surface parcel has been lifted in the profile object
setattr(prof, 'sfcpcl', params.parcelx(prof, flag=1))
#x = self.x_to_xpix()
#y = self.y_to_ypix()
color = QtCore.Qt.red
qp.setPen(QtGui.QPen(color))
qp.setBrush(QtGui.QBrush(color))
x = self.x_to_xpix(prof.pbl_h) - 50 / 2.
y = self.y_to_ypix(prof.sfcpcl.bplus) - (self.fsize - 1) / 2
qp.drawEllipse(x, y, 3, 3)
return
def init_phase(prof):
'''
Inital Precipitation Phase
Adapted from SHARP code donated by Rich Thompson (SPC)
This function determines the initial phase of any precipitation source in the profile.
It does this either by finding a source of precipitation by searching for the highest 50 mb
layer that has a relative humidity greater than 80 percent at the top and the bottom
of the layer. This layer may be found either in the lowest 5 km of the profile, and if
an OMEG profile is specified in the profile object, it will search for the layers with
upward motion.
The precipitation type is determined by using a.) the interpolated temperature in the middle
of the precipitation source layer and b.) set temperature thresholds to determine the
precipitation type. The type may be "Rain", "Freezing Rain", "ZR/S Mix", or "Snow".
Parameters
----------
prof : Profile object (omega profile optional)
Returns
-------
plevel : the pressure level of the precipitation source (mb)
phase : the phase type of the precipitation (int)
phase == 0 for "Rain"
phase == 1 for "Freezing Rain" or "ZR/S Mix"
phase == 3 for "Snow"
tmp : the temperature at the level that is the precipitation source
st : a string naming the precipitation type
'''
# Needs to be tested
plevel = 0
phase = -1
# First, determine whether Upward VVELS are available. If they are,
# use them to determine level where precipitation will develop.
avail = np.ma.where(prof.omeg < .1)[0]
hght_agl = interp.to_agl(prof, prof.hght)
if len(avail) < 5:
# No VVELS...must look for saturated level
# Find the highest near-saturated 50mb layer below 5km agl
below_5km_idx = np.ma.where((hght_agl < 5000.) &\
(hght_agl >= 0))[0]
else:
# Use the VV to find the source of precip.
below_5km_idx = np.ma.where((hght_agl < 5000.) &\
(hght_agl >= 0) &\
(prof.omeg <= 0))[0]
# Compute the RH at the top and bottom of 50 mb layers
rh = thermo.relh(prof.pres, prof.tmpc, prof.dwpc)[below_5km_idx]
sats = np.ma.where(rh > 80)[0]
new_pres = prof.pres[below_5km_idx][sats] + 50.
new_temp = interp.temp(prof, new_pres)
new_dwpt = interp.dwpt(prof, new_pres)
rh_plus50 = thermo.relh(new_pres, new_temp, new_dwpt)
# Find layers where the RH is >80% at the top and bottom
layers_idx = np.ma.where(rh_plus50 > 80)[0]
if len(layers_idx) == 0:
# Found no precipitation source layers
st = "N/A"
return prof.missing, phase, prof.missing, st
# Find the highest layer up via the largest index
top_most_layer = np.ma.max(layers_idx)
plevel = new_pres[top_most_layer] - 25.
# Determine the initial precip type based on the temp in the layer
tmp = interp.temp(prof, plevel)
if tmp > 0:
phase = 0
st = "Rain"
elif tmp <= 0 and tmp > -5:
phase = 1
st = "Freezing Rain"
elif tmp <=-5 and tmp > -9:
phase = 1
st = "ZR/S Mix"
elif tmp <= -9:
phase = 3
st = "Snow"
else:
st = "N/A"
return plevel, phase, tmp, st
def best_guess_precip(prof, init_phase, init_lvl, init_temp, tpos, tneg):
'''
Best Guess Precipitation type
Adapted from SHARP code donated by Rich Thompson (SPC)
Description:
This algorithm utilizes the output from the init_phase() and posneg_temperature()
functions to make a best guess at the preciptation type one would observe
at the surface given a thermodynamic profile.
Precipitation Types Supported:
- None
- Rain
- Snow
- Sleet and Snow
- Sleet
- Freezing Rain/Drizzle
- Unknown
Parameters
----------
prof : Profile object
init_phase : the initial phase of the precipitation (int) (see 2nd value returned from init_phase())
init_lvl : the inital level of the precipitation source (mb) (see 1st value returned from init_phase())
init_temp : the inital level of the precipitation source (C) (see 3rd value returned from init_phase())
tpos : the positive area (> 0 C) in the temperature profile (J/kg)
Returns
-------
precip_type : a string containing the best guess precipitation type
'''
# Needs to be tested
precip_type = None
# Case: No precip
if init_phase < 0:
precip_type = "None."
# Case: Always too warm - Rain
elif init_phase == 0 and tneg >= 0 and prof.tmpc[prof.get_sfc()] > 0:
precip_type = "Rain."
# Case: always too cold
elif init_phase == 3 and tpos <= 0 and prof.tmpc[prof.get_sfc()] <= 0:
precip_type = "Snow."
# Case: ZR too warm at sfc - Rain
elif init_phase == 1 and tpos <= 0 and prof.tmpc[prof.get_sfc()] > 0:
precip_type = "Rain."
# Case: non-snow init...always too cold - Initphase & sleet
elif init_phase == 1 and tpos <= 0 and prof.tmpc[prof.get_sfc()] <= 0:
#print interp.to_agl(prof, interp.hght(prof, init_lvl))
if interp.to_agl(prof, interp.hght(prof, init_lvl)) >= 3000:
if init_temp <= -4:
precip_type = "Sleet and Snow."
else:
precip_type = "Sleet."
else:
precip_type = "Freezing Rain/Drizzle."
# Case: Snow...but warm at sfc
elif init_phase == 3 and tpos <= 0 and prof.tmpc[prof.get_sfc()] > 0:
if prof.tmpc[prof.get_sfc()] > 4:
precip_type = "Rain."
else:
precip_type = "Snow."
# Case: Warm layer.
elif tpos > 0:
x1 = tpos
y1 = -tneg
y2 = (0.62 * x1) + 60.0
if y1 > y2:
precip_type = "Sleet."
else:
if prof.tmpc[prof.get_sfc()] <= 0:
precip_type = "Freezing Rain."
else:
precip_type = "Rain."
else:
precip_type = "Unknown."
return precip_type
def init_phase(prof):
'''
Inital Precipitation Phase
Adapted from SHARP code donated by Rich Thompson (SPC)
This function determines the initial phase of any precipitation source in the profile.
It does this either by finding a source of precipitation by searching for the highest 50 mb
layer that has a relative humidity greater than 80 percent at the top and the bottom
of the layer. This layer may be found either in the lowest 5 km of the profile, and if
an OMEG profile is specified in the profile object, it will search for the layers with
upward motion.
The precipitation type is determined by using a.) the interpolated temperature in the middle
of the precipitation source layer and b.) set temperature thresholds to determine the
precipitation type. The type may be "Rain", "Freezing Rain", "ZR/S Mix", or "Snow".
Parameters
----------
prof : Profile object (omega profile optional)
Returns
-------
plevel : the pressure level of the precipitation source (mb)
phase : the phase type of the precipitation (int)
phase == 0 for "Rain"
phase == 1 for "Freezing Rain" or "ZR/S Mix"
phase == 3 for "Snow"
tmp : the temperature at the level that is the precipitation source
st : a string naming the precipitation type
'''
# Needs to be tested
plevel = 0
phase = -1
# First, determine whether Upward VVELS are available. If they are,
# use them to determine level where precipitation will develop.
avail = np.ma.where(prof.omeg < .1)[0]
hght_agl = interp.to_agl(prof, prof.hght)
if len(avail) < 5:
# No VVELS...must look for saturated level
# Find the highest near-saturated 50mb layer below 5km agl
below_5km_idx = np.ma.where((hght_agl < 5000.) &\
(hght_agl >= 0))[0]
else:
# Use the VV to find the source of precip.
below_5km_idx = np.ma.where((hght_agl < 5000.) &\
(hght_agl >= 0) &\
(prof.omeg <= 0))[0]
# Compute the RH at the top and bottom of 50 mb layers
rh = thermo.relh(prof.pres, prof.tmpc, prof.dwpc)[below_5km_idx]
sats = np.ma.where(rh > 80)[0]
new_pres = prof.pres[below_5km_idx][sats] + 50.
new_temp = interp.temp(prof, new_pres)
new_dwpt = interp.dwpt(prof, new_pres)
rh_plus50 = thermo.relh(new_pres, new_temp, new_dwpt)
# Find layers where the RH is >80% at the top and bottom
layers_idx = np.ma.where(rh_plus50 > 80)[0]
if len(layers_idx) == 0:
# Found no precipitation source layers
st = "N/A"
return prof.missing, phase, prof.missing, st
# Find the highest layer up via the largest index
top_most_layer = np.ma.max(layers_idx)
plevel = new_pres[top_most_layer] - 25.
# Determine the initial precip type based on the temp in the layer
tmp = interp.temp(prof, plevel)
if tmp > 0:
phase = 0
st = "Rain"
elif tmp <= 0 and tmp > -5:
phase = 1
st = "Freezing Rain"
elif tmp <= -5 and tmp > -9:
phase = 1
st = "ZR/S Mix"
elif tmp <= -9:
phase = 3
st = "Snow"
else:
st = "N/A"
return plevel, phase, tmp, st
def indices(prof, debug=False):
# return a formatted-string list of stability and kinematic indices
sfcpcl = params.parcelx(prof, flag=1)
mupcl = params.parcelx(prof, flag=3) # most unstable
mlpcl = params.parcelx(prof, flag=4) # 100 mb mean layer parcel
pcl = mupcl
sfc = prof.pres[prof.sfc]
p3km = interp.pres(prof, interp.to_msl(prof, 3000.))
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
#print "0-3 km Pressure-Weighted Mean Wind (kt):", utils.comp2vec(mean_3km[0], mean_3km[1])[1]
#print "0-6 km Shear (kt):", utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1]
srwind = params.bunkers_storm_motion(prof)
srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
#print "0-3 km Storm Relative Helicity [m2/s2]:",srh3km[0]
#### Calculating variables based off of the effective inflow layer:
# The effective inflow layer concept is used to obtain the layer of buoyant parcels that feed a storm's inflow.
# Here are a few examples of how to compute variables that require the effective inflow layer in order to calculate them:
stp_fixed = params.stp_fixed(
sfcpcl.bplus, sfcpcl.lclhght, srh1km[0],
utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = params.ship(prof)
# If you get an error about not converting masked constant to python int
# use the round() function instead of int() - Ahijevych May 11 2016
# 2nd element of list is the # of decimal places
indices = {
'SBCAPE': [sfcpcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'SBCIN': [sfcpcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'SBLCL': [sfcpcl.lclhght, 0, 'm AGL'],
'SBLFC': [sfcpcl.lfchght, 0, 'm AGL'],
'SBEL': [sfcpcl.elhght, 0, 'm AGL'],
'SBLI': [sfcpcl.li5, 0, 'C'],
'MLCAPE': [mlpcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'MLCIN': [mlpcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'MLLCL': [mlpcl.lclhght, 0, 'm AGL'],
'MLLFC': [mlpcl.lfchght, 0, 'm AGL'],
'MLEL': [mlpcl.elhght, 0, 'm AGL'],
'MLLI': [mlpcl.li5, 0, 'C'],
'MUCAPE': [mupcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'MUCIN': [mupcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'MULCL': [mupcl.lclhght, 0, 'm AGL'],
'MULFC': [mupcl.lfchght, 0, 'm AGL'],
'MUEL': [mupcl.elhght, 0, 'm AGL'],
'MULI': [mupcl.li5, 0, 'C'],
'0-1 km SRH': [srh1km[0], 0, '$\mathregular{m^{2}s^{-2}}$'],
'0-1 km Shear':
[utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1], 0, 'kt'],
'0-3 km SRH': [srh3km[0], 0, '$\mathregular{m^{2}s^{-2}}$'],
'0-6 km Shear':
[utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1], 0, 'kt'],
'PWV': [params.precip_water(prof), 2, 'inch'],
'K-index': [params.k_index(prof), 0, ''],
'STP(fix)': [stp_fixed, 1, ''],
'SHIP': [ship, 1, '']
}
eff_inflow = params.effective_inflow_layer(prof)
if any(eff_inflow):
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
#print "Effective Inflow Layer Bottom Height (m AGL):", ebot_hght
#print "Effective Inflow Layer Top Height (m AGL):", etop_hght
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
indices['Eff. SRH'] = [
effective_srh[0], 0, '$\mathregular{m^{2}s^{-2}}$'
]
#print "Effective Inflow Layer SRH (m2/s2):", effective_srh[0]
ebwd = winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = utils.mag(*ebwd)
indices['EBWD'] = [ebwspd, 0, 'kt']
#print "Effective Bulk Wind Difference:", ebwspd
scp = params.scp(mupcl.bplus, effective_srh[0], ebwspd)
indices['SCP'] = [scp, 1, '']
stp_cin = params.stp_cin(mlpcl.bplus, effective_srh[0], ebwspd,
mlpcl.lclhght, mlpcl.bminus)
indices['STP(cin)'] = [stp_cin, 1, '']
#print "Supercell Composite Parameter:", scp
#print "Significant Tornado Parameter (w/CIN):", stp_cin
#print "Significant Tornado Parameter (fixed):", stp_fixed
# Update the indices within the indices dictionary on the side of the plot.
string = ''
for index, value in sorted(indices.items()):
if np.ma.is_masked(value[0]):
if debug:
print("skipping masked value for index=", index)
continue
if debug:
print("index=", index)
print("value=", value)
format = '%.' + str(value[1]) + 'f'
string += index + ": " + format % value[0] + " " + value[2] + '\n'
return string
''' Create the Sounding (Profile) Object '''
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
srwind = params.bunkers_storm_motion(prof)
srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
stp_fixed = params.stp_fixed(
sfcpcl.bplus, sfcpcl.lclhght, srh1km[0],
utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = params.ship(prof)
eff_inflow = params.effective_inflow_layer(prof)
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
ebwd = winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = utils.mag(ebwd[0], ebwd[1])
scp = params.scp(mupcl.bplus, effective_srh[0], ebwspd)
stp_cin = params.stp_cin(mlpcl.bplus, effective_srh[0], ebwspd, mlpcl.lclhght,
mlpcl.bminus)
indices = {'SBCAPE': [int(sfcpcl.bplus), 'J/kg'],\
'SBCIN': [int(sfcpcl.bminus), 'J/kg'],\
'SBLCL': [int(sfcpcl.lclhght), 'm AGL'],\
# annotate dewpoint in F at bottom of dewpoint profile
dewpointF = skew.ax.text(prof.dwpc[0], prof.pres[0]+10, utils.INT2STR(thermo.ctof(prof.dwpc[0])),
verticalalignment='top', horizontalalignment='center', size=7, color=dwpt_trace.get_color())
skew.plot(pcl.ptrace, pcl.ttrace, 'brown', linestyle="dashed" ) # parcel temperature trace
skew.ax.set_ylim(1050,100)
skew.ax.set_xlim(-50,45)
# Plot the effective inflow layer using purple horizontal lines
eff_inflow = params.effective_inflow_layer(prof)
inflow_bot = skew.ax.axhline(eff_inflow[0], color='purple',xmin=0.38, xmax=0.45)
inflow_top = skew.ax.axhline(eff_inflow[1], color='purple',xmin=0.38, xmax=0.45)
srwind = params.bunkers_storm_motion(prof)
# annotate effective inflow layer SRH
if eff_inflow[0]:
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
effective_srh = winds.helicity(prof, ebot_hght, etop_hght, stu = srwind[0], stv = srwind[1])
# Set position of label
# x position is mean of horizontal line bounds
# For some reason this makes a big white space on the left side and for all subsequent plots.
inflow_SRH = skew.ax.text(
np.mean(inflow_top.get_xdata()), eff_inflow[1],
'%.0f' % effective_srh[0] + ' ' + '$\mathregular{m^{2}s^{-2}}$',
verticalalignment='bottom', horizontalalignment='center', size=6, transform=inflow_bot.get_transform(), color=inflow_top.get_color()
)
# draw indices text string to the right of plot.
indices_text = plt.text(1.08, 1.0, myskewt.indices(prof), verticalalignment='top', size=5.6, transform=plt.gca().transAxes)
# globe with dot
def append_wbz():
#Load each ERA-Interim netcdf file, and append wbz
start_lat = -44.525
end_lat = -9.975
start_lon = 111.975
end_lon = 156.275
domain = [start_lat, end_lat, start_lon, end_lon]
model = "erai"
region = "aus"
dates = []
for y in np.arange(1979, 2019):
for m in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
if (m != 12):
dates.append([dt.datetime(y,m,1,0,0,0),\
dt.datetime(y,m+1,1,0,0,0)-dt.timedelta(hours = 6)])
else:
dates.append([dt.datetime(y,m,1,0,0,0),\
dt.datetime(y+1,1,1,0,0,0)-dt.timedelta(hours = 6)])
for t in np.arange(0, len(dates)):
print(str(dates[t][0]) + " - " + str(dates[t][1]))
fname = "/g/data/eg3/ab4502/ExtremeWind/"+region+"/"+model+"/"+model+"_"+\
dt.datetime.strftime(dates[t][0],"%Y%m%d")+"_"+\
dt.datetime.strftime(dates[t][-1],"%Y%m%d")+".nc"
ta,dp,hur,hgt,terrain,p,ps,wap,ua,va,uas,vas,tas,ta2d,cp,wg10,cape,lon,lat,date_list = \
read_erai(domain,dates[t])
dp = get_dp(ta, hur, dp_mask=False)
agl_idx = (p <= ps)
#Replace masked dp values
dp = replace_dp(dp)
try:
prof = profile.create_profile(pres = np.insert(p[agl_idx],0,ps), \
hght = np.insert(hgt[agl_idx],0,terrain), \
tmpc = np.insert(ta[agl_idx],0,tas), \
dwpc = np.insert(dp[agl_idx],0,ta2d), \
u = np.insert(ua[agl_idx],0,uas), \
v = np.insert(va[agl_idx],0,vas), \
strictqc=False, omeg=np.insert(wap[agl_idx],0,wap[agl_idx][0]) )
except:
p = p[agl_idx]
ua = ua[agl_idx]
va = va[agl_idx]
hgt = hgt[agl_idx]
ta = ta[agl_idx] \
dp = dp[agl_idx]
p[0] = ps
ua[0] = uas
va[0] = vas
hgt[0] = terrain
ta[0] = tas
dp[0] = ta2d
prof = profile.create_profile(pres = p, \
hght = hgt, \
tmpc = ta, \
dwpc = dp, \
u = ua, \
v = va, \
strictqc=False, omeg=wap[agl_idx])
pwb0 = params.temp_lvl(prof, 0, wetbulb=True)
hwb0 = interp.to_agl(prof, interp.hght(prof, pwb0))
param_file = nc.Dataset(fname, "a")
wbz_var = param_file.createVariable("wbz",float,\
("time","lat","lon"))
wbz_var.units = "m"
wbz_var.long_name = "wet_bulb_zero_height"
wbz_var[:] = hwb0
T1 = abs(
thermo.wetlift(prof.pres[0], prof.tmpc[0], 600) -
interp.temp(prof, 600))
T2 = abs(
thermo.wetlift(pwb0, interp.temp(prof, pwb0), sfc) - prof.tmpc[0])
Vprime = utils.KTS2MS(13 * np.sqrt((T1 + T2) / 2) + (1 / 3 *
(Umean01)))
Vprime_var = param_file.createVariable("Vprime",float,\
("time","lat","lon"))
Vprime_var.units = "m/s"
Vprime_var.long_name = "miller_1972_wind_speed"
Vprime_var[:] = Vprime
param_file.close()
def best_guess_precip(prof, init_phase, init_lvl, init_temp, tpos, tneg):
'''
Best Guess Precipitation type
Adapted from SHARP code donated by Rich Thompson (SPC)
Description:
This algorithm utilizes the output from the init_phase() and posneg_temperature()
functions to make a best guess at the preciptation type one would observe
at the surface given a thermodynamic profile.
Precipitation Types Supported:
- None
- Rain
- Snow
- Sleet and Snow
- Sleet
- Freezing Rain/Drizzle
- Unknown
Parameters
----------
prof : Profile object
init_phase : the initial phase of the precipitation (int) (see 2nd value returned from init_phase())
init_lvl : the inital level of the precipitation source (mb) (see 1st value returned from init_phase())
init_temp : the inital level of the precipitation source (C) (see 3rd value returned from init_phase())
tpos : the positive area (> 0 C) in the temperature profile (J/kg)
Returns
-------
precip_type : a string containing the best guess precipitation type
'''
# Needs to be tested
precip_type = None
# Case: No precip
if init_phase < 0:
precip_type = "None."
# Case: Always too warm - Rain
elif init_phase == 0 and tneg >=0 and prof.tmpc[prof.get_sfc()] > 0:
precip_type = "Rain."
# Case: always too cold
elif init_phase == 3 and tpos <= 0 and prof.tmpc[prof.get_sfc()] <= 0:
precip_type = "Snow."
# Case: ZR too warm at sfc - Rain
elif init_phase == 1 and tpos <= 0 and prof.tmpc[prof.get_sfc()] > 0:
precip_type = "Rain."
# Case: non-snow init...always too cold - Initphase & sleet
elif init_phase == 1 and tpos <= 0 and prof.tmpc[prof.get_sfc()] <= 0:
#print interp.to_agl(prof, interp.hght(prof, init_lvl))
if interp.to_agl(prof, interp.hght(prof, init_lvl)) >= 3000:
if init_temp <= -4:
precip_type = "Sleet and Snow."
else:
precip_type = "Sleet."
else:
precip_type = "Freezing Rain/Drizzle."
# Case: Snow...but warm at sfc
elif init_phase == 3 and tpos <= 0 and prof.tmpc[prof.get_sfc()] > 0:
if prof.tmpc[prof.get_sfc()] > 4:
precip_type = "Rain."
else:
precip_type = "Snow."
# Case: Warm layer.
elif tpos > 0:
x1 = tpos
y1 = -tneg
y2 = (0.62 * x1) + 60.0
if y1 > y2:
precip_type = "Sleet."
else:
if prof.tmpc[prof.get_sfc()] <= 0:
precip_type = "Freezing Rain."
else:
precip_type = "Rain."
else:
precip_type = "Unknown."
return precip_type
linestyle="dashed") # parcel temperature trace
# Plot the effective inflow layer using purple horizontal lines
eff_inflow = params.effective_inflow_layer(prof)
inflow_bot = skew.ax.axhline(eff_inflow[0],
color='purple',
xmin=0.38,
xmax=0.45)
inflow_top = skew.ax.axhline(eff_inflow[1],
color='purple',
xmin=0.38,
xmax=0.45)
srwind = params.bunkers_storm_motion(prof)
# annotate effective inflow layer SRH
if eff_inflow[0]:
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
# Set position of label
# x position is mean of horizontal line bounds
# For some reason this makes a big white space on the left side and for all subsequent plots.
inflow_SRH = skew.ax.text(np.mean(inflow_top.get_xdata()),
eff_inflow[1],
'%.0f' % effective_srh[0] + ' ' +
'$\mathregular{m^{2}s^{-2}}$',
verticalalignment='bottom',
horizontalalignment='center',
def do_sharppy(spc_file):
"""
Based on the tutorial which can be found here: http://nbviewer.ipython.org/github/sharppy/SHARPpy/blob/master/tutorials/SHARPpy_basics.ipynb
SHARPpy can be found here: https://github.com/sharppy/SHARPpy
Credit goes to:
Patrick Marsh (SPC)
Kelton Halbert (OU School of Meteorology)
Greg Blumberg (OU/CIMMS)
Tim Supinie (OU School of Meteorology)
"""
import sharppy
import sharppy.sharptab.profile as profile
import sharppy.sharptab.interp as interp
import sharppy.sharptab.winds as winds
import sharppy.sharptab.utils as utils
import sharppy.sharptab.params as params
import sharppy.sharptab.thermo as thermo
import matplotlib.pyplot as plt
from StringIO import StringIO
from matplotlib.axes import Axes
import matplotlib.transforms as transforms
import matplotlib.axis as maxis
import matplotlib.spines as mspines
import matplotlib.path as mpath
from matplotlib.projections import register_projection
spc_file = open('skewt_data', 'r').read()
def parseSPC(spc_file):
## read in the file
data = np.array([l.strip() for l in spc_file.split('\n')])
## necessary index points
title_idx = np.where( data == '%TITLE%')[0][0]
start_idx = np.where( data == '%RAW%' )[0] + 1
finish_idx = np.where( data == '%END%')[0]
## create the plot title
data_header = data[title_idx + 1].split()
location = data_header[0]+' '+data_header[1]
time = data_header[2]
title = location+' '+time
## put it all together for StringIO
full_data = '\n'.join(data[start_idx : finish_idx][:])
sound_data = StringIO( full_data )
## read the data into arrays
p, h, T, Td, wdir, wspd = np.genfromtxt( sound_data, delimiter=',', comments="%", unpack=True )
return p, h, T, Td, wdir, wspd, title
pres, hght, tmpc, dwpc, wdir, wspd, title = parseSPC(spc_file)
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc, \
dwpc=dwpc, wspd=wspd, wdir=wdir, missing=-9999, strictQC=True)
sfcpcl = params.parcelx( prof, flag=1 ) # Surface Parcel
fcstpcl = params.parcelx( prof, flag=2 ) # Forecast Parcel
mupcl = params.parcelx( prof, flag=3 ) # Most-Unstable Parcel
mlpcl = params.parcelx( prof, flag=4 ) # 100 mb Mean Layer Parcel
msl_hght = prof.hght[prof.sfc] # Grab the surface height value
print "SURFACE HEIGHT (m MSL):",msl_hght
agl_hght = interp.to_agl(prof, msl_hght) # Converts to AGL
print "SURFACE HEIGHT (m AGL):", agl_hght
msl_hght = interp.to_msl(prof, agl_hght) # Converts to MSL
print "SURFACE HEIGHT (m MSL):",msl_hght
print "Most-Unstable CAPE:", mupcl.bplus # J/kg
print "Most-Unstable CIN:", mupcl.bminus # J/kg
print "Most-Unstable LCL:", mupcl.lclhght # meters AGL
print "Most-Unstable LFC:", mupcl.lfchght # meters AGL
print "Most-Unstable EL:", mupcl.elhght # meters AGL
print "Most-Unstable LI:", mupcl.li5 # C
class SkewXTick(maxis.XTick):
def draw(self, renderer):
if not self.get_visible(): return
renderer.open_group(self.__name__)
lower_interval = self.axes.xaxis.lower_interval
upper_interval = self.axes.xaxis.upper_interval
if self.gridOn and transforms.interval_contains(
self.axes.xaxis.get_view_interval(), self.get_loc()):
self.gridline.draw(renderer)
if transforms.interval_contains(lower_interval, self.get_loc()):
if self.tick1On:
self.tick1line.draw(renderer)
if self.label1On:
self.label1.draw(renderer)
if transforms.interval_contains(upper_interval, self.get_loc()):
if self.tick2On:
self.tick2line.draw(renderer)
if self.label2On:
self.label2.draw(renderer)
renderer.close_group(self.__name__)
# This class exists to provide two separate sets of intervals to the tick,
# as well as create instances of the custom tick
class SkewXAxis(maxis.XAxis):
def __init__(self, *args, **kwargs):
maxis.XAxis.__init__(self, *args, **kwargs)
self.upper_interval = 0.0, 1.0
def _get_tick(self, major):
return SkewXTick(self.axes, 0, '', major=major)
@property
def lower_interval(self):
return self.axes.viewLim.intervalx
def get_view_interval(self):
return self.upper_interval[0], self.axes.viewLim.intervalx[1]
# This class exists to calculate the separate data range of the
# upper X-axis and draw the spine there. It also provides this range
# to the X-axis artist for ticking and gridlines
class SkewSpine(mspines.Spine):
def _adjust_location(self):
trans = self.axes.transDataToAxes.inverted()
if self.spine_type == 'top':
yloc = 1.0
else:
yloc = 0.0
left = trans.transform_point((0.0, yloc))[0]
right = trans.transform_point((1.0, yloc))[0]
pts = self._path.vertices
pts[0, 0] = left
pts[1, 0] = right
self.axis.upper_interval = (left, right)
# This class handles registration of the skew-xaxes as a projection as well
# as setting up the appropriate transformations. It also overrides standard
# spines and axes instances as appropriate.
class SkewXAxes(Axes):
# The projection must specify a name. This will be used be the
# user to select the projection, i.e. ``subplot(111,
# projection='skewx')``.
name = 'skewx'
def _init_axis(self):
#Taken from Axes and modified to use our modified X-axis
self.xaxis = SkewXAxis(self)
self.spines['top'].register_axis(self.xaxis)
self.spines['bottom'].register_axis(self.xaxis)
self.yaxis = maxis.YAxis(self)
self.spines['left'].register_axis(self.yaxis)
self.spines['right'].register_axis(self.yaxis)
def _gen_axes_spines(self):
spines = {'top':SkewSpine.linear_spine(self, 'top'),
'bottom':mspines.Spine.linear_spine(self, 'bottom'),
'left':mspines.Spine.linear_spine(self, 'left'),
'right':mspines.Spine.linear_spine(self, 'right')}
return spines
def _set_lim_and_transforms(self):
"""
This is called once when the plot is created to set up all the
transforms for the data, text and grids.
"""
rot = 30
#Get the standard transform setup from the Axes base class
Axes._set_lim_and_transforms(self)
# Need to put the skew in the middle, after the scale and limits,
# but before the transAxes. This way, the skew is done in Axes
# coordinates thus performing the transform around the proper origin
# We keep the pre-transAxes transform around for other users, like the
# spines for finding bounds
self.transDataToAxes = self.transScale + (self.transLimits +
transforms.Affine2D().skew_deg(rot, 0))
# Create the full transform from Data to Pixels
self.transData = self.transDataToAxes + self.transAxes
# Blended transforms like this need to have the skewing applied using
# both axes, in axes coords like before.
self._xaxis_transform = (transforms.blended_transform_factory(
self.transScale + self.transLimits,
transforms.IdentityTransform()) +
transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
# Now register the projection with matplotlib so the user can select
# it.
register_projection(SkewXAxes)
pcl = mupcl
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(6.5875, 6.2125))
ax = fig.add_subplot(111, projection='skewx')
ax.grid(True)
pmax = 1000
pmin = 10
dp = -10
presvals = np.arange(int(pmax), int(pmin)+dp, dp)
# plot the moist-adiabats
for t in np.arange(-10,45,5):
tw = []
for p in presvals:
tw.append(thermo.wetlift(1000., t, p))
ax.semilogy(tw, presvals, 'k-', alpha=.2)
def thetas(theta, presvals):
return ((theta + thermo.ZEROCNK) / (np.power((1000. / presvals),thermo.ROCP))) - thermo.ZEROCNK
# plot the dry adiabats
for t in np.arange(-50,110,10):
ax.semilogy(thetas(t, presvals), presvals, 'r-', alpha=.2)
plt.title(title, fontsize=14, loc='left')
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dicatated by the typical meteorological plot
ax.semilogy(prof.tmpc, prof.pres, 'r', lw=2)
ax.semilogy(prof.dwpc, prof.pres, 'g', lw=2)
ax.semilogy(pcl.ttrace, pcl.ptrace, 'k-.', lw=2)
# An example of a slanted line at constant X
l = ax.axvline(0, color='b', linestyle='--')
l = ax.axvline(-20, color='b', linestyle='--')
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(100,1000,10))
ax.set_ylim(1050,100)
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xlim(-50,50)
plt.show()
##PLOTS SKEWT OK ABOVE HERE ##
"""
def do_sharppy(spc_file):
"""
Based on the tutorial which can be found here: http://nbviewer.ipython.org/github/sharppy/SHARPpy/blob/master/tutorials/SHARPpy_basics.ipynb
SHARPpy can be found here: https://github.com/sharppy/SHARPpy
Credit goes to:
Patrick Marsh (SPC)
Kelton Halbert (OU School of Meteorology)
Greg Blumberg (OU/CIMMS)
Tim Supinie (OU School of Meteorology)
"""
import sharppy
import sharppy.sharptab.profile as profile
import sharppy.sharptab.interp as interp
import sharppy.sharptab.winds as winds
import sharppy.sharptab.utils as utils
import sharppy.sharptab.params as params
import sharppy.sharptab.thermo as thermo
import matplotlib.pyplot as plt
from StringIO import StringIO
from matplotlib.axes import Axes
import matplotlib.transforms as transforms
import matplotlib.axis as maxis
import matplotlib.spines as mspines
import matplotlib.path as mpath
from matplotlib.projections import register_projection
spc_file = open('skewt_data', 'r').read()
def parseSPC(spc_file):
## read in the file
data = np.array([l.strip() for l in spc_file.split('\n')])
## necessary index points
title_idx = np.where(data == '%TITLE%')[0][0]
start_idx = np.where(data == '%RAW%')[0] + 1
finish_idx = np.where(data == '%END%')[0]
## create the plot title
data_header = data[title_idx + 1].split()
location = data_header[0] + ' ' + data_header[1]
time = data_header[2]
title = location + ' ' + time
## put it all together for StringIO
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
## read the data into arrays
p, h, T, Td, wdir, wspd = np.genfromtxt(sound_data,
delimiter=',',
comments="%",
unpack=True)
return p, h, T, Td, wdir, wspd, title
pres, hght, tmpc, dwpc, wdir, wspd, title = parseSPC(spc_file)
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc, \
dwpc=dwpc, wspd=wspd, wdir=wdir, missing=-9999, strictQC=True)
sfcpcl = params.parcelx(prof, flag=1) # Surface Parcel
fcstpcl = params.parcelx(prof, flag=2) # Forecast Parcel
mupcl = params.parcelx(prof, flag=3) # Most-Unstable Parcel
mlpcl = params.parcelx(prof, flag=4) # 100 mb Mean Layer Parcel
msl_hght = prof.hght[prof.sfc] # Grab the surface height value
print "SURFACE HEIGHT (m MSL):", msl_hght
agl_hght = interp.to_agl(prof, msl_hght) # Converts to AGL
print "SURFACE HEIGHT (m AGL):", agl_hght
msl_hght = interp.to_msl(prof, agl_hght) # Converts to MSL
print "SURFACE HEIGHT (m MSL):", msl_hght
print "Most-Unstable CAPE:", mupcl.bplus # J/kg
print "Most-Unstable CIN:", mupcl.bminus # J/kg
print "Most-Unstable LCL:", mupcl.lclhght # meters AGL
print "Most-Unstable LFC:", mupcl.lfchght # meters AGL
print "Most-Unstable EL:", mupcl.elhght # meters AGL
print "Most-Unstable LI:", mupcl.li5 # C
class SkewXTick(maxis.XTick):
def draw(self, renderer):
if not self.get_visible(): return
renderer.open_group(self.__name__)
lower_interval = self.axes.xaxis.lower_interval
upper_interval = self.axes.xaxis.upper_interval
if self.gridOn and transforms.interval_contains(
self.axes.xaxis.get_view_interval(), self.get_loc()):
self.gridline.draw(renderer)
if transforms.interval_contains(lower_interval, self.get_loc()):
if self.tick1On:
self.tick1line.draw(renderer)
if self.label1On:
self.label1.draw(renderer)
if transforms.interval_contains(upper_interval, self.get_loc()):
if self.tick2On:
self.tick2line.draw(renderer)
if self.label2On:
self.label2.draw(renderer)
renderer.close_group(self.__name__)
# This class exists to provide two separate sets of intervals to the tick,
# as well as create instances of the custom tick
class SkewXAxis(maxis.XAxis):
def __init__(self, *args, **kwargs):
maxis.XAxis.__init__(self, *args, **kwargs)
self.upper_interval = 0.0, 1.0
def _get_tick(self, major):
return SkewXTick(self.axes, 0, '', major=major)
@property
def lower_interval(self):
return self.axes.viewLim.intervalx
def get_view_interval(self):
return self.upper_interval[0], self.axes.viewLim.intervalx[1]
# This class exists to calculate the separate data range of the
# upper X-axis and draw the spine there. It also provides this range
# to the X-axis artist for ticking and gridlines
class SkewSpine(mspines.Spine):
def _adjust_location(self):
trans = self.axes.transDataToAxes.inverted()
if self.spine_type == 'top':
yloc = 1.0
else:
yloc = 0.0
left = trans.transform_point((0.0, yloc))[0]
right = trans.transform_point((1.0, yloc))[0]
pts = self._path.vertices
pts[0, 0] = left
pts[1, 0] = right
self.axis.upper_interval = (left, right)
# This class handles registration of the skew-xaxes as a projection as well
# as setting up the appropriate transformations. It also overrides standard
# spines and axes instances as appropriate.
class SkewXAxes(Axes):
# The projection must specify a name. This will be used be the
# user to select the projection, i.e. ``subplot(111,
# projection='skewx')``.
name = 'skewx'
def _init_axis(self):
#Taken from Axes and modified to use our modified X-axis
self.xaxis = SkewXAxis(self)
self.spines['top'].register_axis(self.xaxis)
self.spines['bottom'].register_axis(self.xaxis)
self.yaxis = maxis.YAxis(self)
self.spines['left'].register_axis(self.yaxis)
self.spines['right'].register_axis(self.yaxis)
def _gen_axes_spines(self):
spines = {
'top': SkewSpine.linear_spine(self, 'top'),
'bottom': mspines.Spine.linear_spine(self, 'bottom'),
'left': mspines.Spine.linear_spine(self, 'left'),
'right': mspines.Spine.linear_spine(self, 'right')
}
return spines
def _set_lim_and_transforms(self):
"""
This is called once when the plot is created to set up all the
transforms for the data, text and grids.
"""
rot = 30
#Get the standard transform setup from the Axes base class
Axes._set_lim_and_transforms(self)
# Need to put the skew in the middle, after the scale and limits,
# but before the transAxes. This way, the skew is done in Axes
# coordinates thus performing the transform around the proper origin
# We keep the pre-transAxes transform around for other users, like the
# spines for finding bounds
self.transDataToAxes = self.transScale + (
self.transLimits + transforms.Affine2D().skew_deg(rot, 0))
# Create the full transform from Data to Pixels
self.transData = self.transDataToAxes + self.transAxes
# Blended transforms like this need to have the skewing applied using
# both axes, in axes coords like before.
self._xaxis_transform = (
transforms.blended_transform_factory(
self.transScale + self.transLimits,
transforms.IdentityTransform()) +
transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
# Now register the projection with matplotlib so the user can select
# it.
register_projection(SkewXAxes)
pcl = mupcl
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(6.5875, 6.2125))
ax = fig.add_subplot(111, projection='skewx')
ax.grid(True)
pmax = 1000
pmin = 10
dp = -10
presvals = np.arange(int(pmax), int(pmin) + dp, dp)
# plot the moist-adiabats
for t in np.arange(-10, 45, 5):
tw = []
for p in presvals:
tw.append(thermo.wetlift(1000., t, p))
ax.semilogy(tw, presvals, 'k-', alpha=.2)
def thetas(theta, presvals):
return ((theta + thermo.ZEROCNK) / (np.power(
(1000. / presvals), thermo.ROCP))) - thermo.ZEROCNK
# plot the dry adiabats
for t in np.arange(-50, 110, 10):
ax.semilogy(thetas(t, presvals), presvals, 'r-', alpha=.2)
plt.title(title, fontsize=14, loc='left')
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dicatated by the typical meteorological plot
ax.semilogy(prof.tmpc, prof.pres, 'r', lw=2)
ax.semilogy(prof.dwpc, prof.pres, 'g', lw=2)
ax.semilogy(pcl.ttrace, pcl.ptrace, 'k-.', lw=2)
# An example of a slanted line at constant X
l = ax.axvline(0, color='b', linestyle='--')
l = ax.axvline(-20, color='b', linestyle='--')
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(100, 1000, 10))
ax.set_ylim(1050, 100)
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xlim(-50, 50)
plt.show()
##PLOTS SKEWT OK ABOVE HERE ##
"""
