def get_thermo(self):
'''
Function to generate thermodynamic indices.
Function returns nothing, but sets the following
variables:
self.k_idx - K Index, a severe weather index
self.pwat - Precipitable Water Vapor (inches)
self.lapserate_3km - 0 to 3km AGL lapse rate (C/km)
self.lapserate_3_6km - 3 to 6km AGL lapse rate (C/km)
self.lapserate_850_500 - 850 to 500mb lapse rate (C/km)
self.lapserate_700_500 - 700 to 500mb lapse rate (C/km)
self.convT - The Convective Temperature (F)
self.maxT - The Maximum Forecast Surface Temp (F)
self.mean_mixr - Mean Mixing Ratio
self.low_rh - low level mean relative humidity
self.mid_rh - mid level mean relative humidity
self.totals_totals - Totals Totals index, a severe weather index
Parameters
----------
None
Returns
-------
None
'''
## either get or calculate the indices, round to the nearest int, and
## convert them to strings.
## K Index
self.k_idx = params.k_index( self )
## precipitable water
self.pwat = params.precip_water( self )
## 0-3km agl lapse rate
self.lapserate_3km = params.lapse_rate( self, 0., 3000., pres=False )
## 3-6km agl lapse rate
self.lapserate_3_6km = params.lapse_rate( self, 3000., 6000., pres=False )
## 850-500mb lapse rate
self.lapserate_850_500 = params.lapse_rate( self, 850., 500., pres=True )
## 700-500mb lapse rate
self.lapserate_700_500 = params.lapse_rate( self, 700., 500., pres=True )
## 2-6 km max lapse rate
self.max_lapse_rate_2_6 = params.max_lapse_rate( self )
## convective temperature
self.convT = thermo.ctof( params.convective_temp( self ) )
## sounding forecast surface temperature
self.maxT = thermo.ctof( params.max_temp( self ) )
#fzl = str(int(self.sfcparcel.hght0c))
## 100mb mean mixing ratio
self.mean_mixr = params.mean_mixratio( self )
## 150mb mean rh
self.low_rh = params.mean_relh( self )
self.mid_rh = params.mean_relh( self, pbot=(self.pres[self.sfc] - 150),
ptop=(self.pres[self.sfc] - 350) )
## calculate the totals totals index
self.totals_totals = params.t_totals( self )
## calculate the inferred temperature advection
self.inf_temp_adv = params.inferred_temp_adv(self, lat=self.latitude)
def get_thermo(self):
'''
Function to generate thermodynamic indices.
Function returns nothing, but sets the following
variables:
self.k_idx - K Index, a severe weather index
self.pwat - Precipitable Water Vapor (inches)
self.lapserate_3km - 0 to 3km AGL lapse rate (C/km)
self.lapserate_3_6km - 3 to 6km AGL lapse rate (C/km)
self.lapserate_850_500 - 850 to 500mb lapse rate (C/km)
self.lapserate_700_500 - 700 to 500mb lapse rate (C/km)
self.convT - The Convective Temperature (F)
self.maxT - The Maximum Forecast Surface Temp (F)
self.mean_mixr - Mean Mixing Ratio
self.low_rh - low level mean relative humidity
self.mid_rh - mid level mean relative humidity
self.totals_totals - Totals Totals index, a severe weather index
Parameters
----------
None
Returns
-------
None
'''
## either get or calculate the indices, round to the nearest int, and
## convert them to strings.
## K Index
self.k_idx = params.k_index( self )
## precipitable water
self.pwat = params.precip_water( self )
## 0-3km agl lapse rate
self.lapserate_3km = params.lapse_rate( self, 0., 3000., pres=False )
## 3-6km agl lapse rate
self.lapserate_3_6km = params.lapse_rate( self, 3000., 6000., pres=False )
## 850-500mb lapse rate
self.lapserate_850_500 = params.lapse_rate( self, 850., 500., pres=True )
## 700-500mb lapse rate
self.lapserate_700_500 = params.lapse_rate( self, 700., 500., pres=True )
## convective temperature
self.convT = thermo.ctof( params.convective_temp( self ) )
## sounding forecast surface temperature
self.maxT = thermo.ctof( params.max_temp( self ) )
#fzl = str(int(self.sfcparcel.hght0c))
## 100mb mean mixing ratio
self.mean_mixr = params.mean_mixratio( self )
## 150mb mean rh
self.low_rh = params.mean_relh( self )
self.mid_rh = params.mean_relh( self, pbot=(self.pres[self.sfc] - 150),
ptop=(self.pres[self.sfc] - 350) )
## calculate the totals totals index
self.totals_totals = params.t_totals( self )
## calculate the inferred temperature advection
self.inf_temp_adv = params.inferred_temp_adv(self)
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 '''
def plot_sounding(file, imgName):
try:
prof, time, location = decode(file)
except Exception as e:
print(
"\n Oops! Couldn't decode the sounding data. No plot produced!\n")
print(e)
# return None
# Open up the text file with the data in columns (e.g. the sample OAX file distributed with SHARPpy)
locInfo = location.split('_')
title = locInfo[0] + ' ' + locInfo[1] + ' ' + locInfo[
2] + ' ' + time.strftime('%Y%m%d/%H%M') + ' (Observed)'
# Set up the figure in matplotlib.
fig = plt.figure(figsize=(14, 7.25))
gs = gridspec.GridSpec(4, 6, width_ratios=[1, 5, 1, 0.5, 3, 3])
ax = plt.subplot(gs[0:3, 0:2], projection='skewx')
plt.title(title, fontsize=14, loc='left', color='w')
ax.set_facecolor('k')
ax.spines['left'].set_color('w')
ax.spines['right'].set_color('w')
ax.spines['bottom'].set_color('w')
ax.spines['top'].set_color('w')
# xticks = ax.xaxis.get_major_ticks() #mute a tick label outside plot
# xticks[-4].label1.set_visible(False)
ax.tick_params(axis='both', colors='w', grid_color='silver')
ax.ticklabel_format(style='plain')
# ax.xaxis.label.set_color('w')
# ax.yaxis.label.set_color('w')
ax.grid(True)
plt.grid(True)
# Ask user for default limits or custom limits
pt_plot, t_lower, t_upper = ask_limits(prof.pres[~prof.dwpc.mask],
prof.dwpc[~prof.dwpc.mask])
# Bounds of the pressure axis
pb_plot = 1050
dp_plot = 10
plevs_plot = np.arange(pb_plot, pt_plot - 1, -dp_plot)
# Plot the background variables
# presvals = np.arange(1000, 0, -10)
#draw mixing ratio lines
draw_mixing_ratio_lines(ax)
ax.semilogy(prof.tmpc[~prof.tmpc.mask],
prof.pres[~prof.tmpc.mask],
'r',
lw=2)
ax.semilogy(prof.dwpc[~prof.dwpc.mask],
prof.pres[~prof.dwpc.mask],
'lime',
lw=2)
ax.semilogy(prof.vtmp[~prof.dwpc.mask],
prof.pres[~prof.dwpc.mask],
'r--',
lw=1)
ax.semilogy(prof.wetbulb[~prof.dwpc.mask],
prof.pres[~prof.dwpc.mask],
'cyan',
'-',
lw=1)
#write sfc temp and dewpoint in F
sfcT = prof.tmpc[~prof.tmpc.mask][0]
sfcTd = prof.dwpc[~prof.dwpc.mask][0]
sfcW = prof.wetbulb[~prof.dwpc.mask][0]
sfcP = prof.pres[~prof.tmpc.mask][0]
ax.annotate(str(int(sfcW * (9 / 5) + 32)), (sfcW, sfcP),
xytext=(-6, -9),
textcoords='offset points',
color='cyan',
weight='black',
size=8,
path_effects=[pe.withStroke(linewidth=2, foreground="black")])
ax.annotate(str(int(sfcT * (9 / 5) + 32)), (sfcT, sfcP),
xytext=(-2, -9),
textcoords='offset points',
color='r',
weight='black',
size=8,
path_effects=[pe.withStroke(linewidth=2, foreground="black")])
ax.annotate(str(int(sfcTd * (9 / 5) + 32)), (sfcTd, sfcP),
xytext=(-12, -9),
textcoords='offset points',
color='lime',
weight='black',
size=8,
path_effects=[pe.withStroke(linewidth=2, foreground="black")])
#plot significant levels
plot_sig_levels(ax, prof)
# Plot the parcel trace, but this may fail. If it does so, inform the user.
try:
ax.semilogy(prof.mupcl.ttrace, prof.mupcl.ptrace, 'w--')
except:
print("Couldn't plot parcel traces...")
# Highlight the 0 C and -20 C isotherms.
l = ax.axvline(0, color='b', ls='--')
l = ax.axvline(-20, color='b', ls='--')
#plot dry adiabats
skew.draw_dry_adiabats(ax, color='silver')
#draw heights
skew.draw_heights(ax, prof)
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(ScalarFormatter())
pmin = prof.pres[~prof.dwpc.mask][-1]
if pmin > 700.:
ax.set_yticks(np.arange(100, 1000, 50))
else:
ax.set_yticks(np.linspace(100, 1000, 10))
ax.set_ylim(pb_plot, pt_plot)
# Plot the hodograph data.
# inset_axes = draw_hodo_inset(ax, prof)
hodoAx = plt.subplot(gs[0:3, 3:])
hodoAx.set_facecolor('k')
hodoAx.axis('off')
hodoAx = draw_hodo_inset(hodoAx, prof)
# plotHodo(inset_axes, prof.hght, prof.u, prof.v, color='r')
plotHodo(hodoAx, prof.hght, prof.u, prof.v, color='r')
#plot bunkers motion unless the most unstable EL does not exist
srwind = params.bunkers_storm_motion(prof)
if isinstance(prof.mupcl.elpres, np.float64):
hodoAx.text(srwind[0], srwind[1], 'RM', color='w', fontsize=8)
hodoAx.text(srwind[2], srwind[3], 'LM', color='w', fontsize=8)
else:
print("couldn't plot Bunkers vectors")
# inset_axes.text(srwind[0], srwind[1], 'RM', color='r', fontsize=8)
# inset_axes.text(srwind[2], srwind[3], 'LM', color='b', fontsize=8)
#mask out barbs above the top of the plot
below_pmin = np.where(prof.pres >= pt_plot)[0]
# Draw the wind barbs axis and everything that comes with it.
if pmin > 700.:
ax.xaxis.set_major_locator(MultipleLocator(5))
else:
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.set_xlim(t_lower, t_upper)
ax2 = plt.subplot(gs[0:3, 2])
ax3 = plt.subplot(gs[3, 0:3])
plot_wind_axes(ax2, pb_plot, pt_plot, plevs_plot)
#setting the stride for how many wind barbs plot
# st = 15
# plot_wind_barbs(ax2, prof.pres[below_pmin][~prof.pres.mask[below_pmin]][::st],
# prof.u[below_pmin][~prof.u.mask[below_pmin]][::st],
# prof.v[below_pmin][~prof.v.mask[below_pmin]][::st],
# pt_plot)
plot_wind_barbs(ax2, prof.pres[below_pmin][~prof.pres.mask[below_pmin]],
prof.u[below_pmin][~prof.u.mask[below_pmin]],
prof.v[below_pmin][~prof.v.mask[below_pmin]], pt_plot)
gs.update(left=0.05, bottom=0.05, top=0.95, right=1, wspace=0.025)
# Calculate indices to be shown. More indices can be calculated here using the tutorial and reading the params module.
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
sfc = prof.pres[prof.sfc]
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
scp = params.scp(prof.mupcl.bplus, prof.right_esrh[0], prof.ebwspd)
stp_cin = params.stp_cin(prof.mlpcl.bplus, prof.right_esrh[0], prof.ebwspd,
prof.mlpcl.lclhght, prof.mlpcl.bminus)
stp_fixed = params.stp_fixed(
prof.sfcpcl.bplus, prof.sfcpcl.lclhght, srh1km[0],
utils.comp2vec(prof.sfc_6km_shear[0], prof.sfc_6km_shear[1])[1])
ship = params.ship(prof)
# A routine to perform the correct formatting when writing the indices out to the figure.
def fmt(value, fmt='int'):
if fmt == 'int':
try:
val = int(value)
except:
val = str("M")
else:
try:
val = round(value, 1)
except:
val = "M"
return val
# Setting a dictionary that is a collection of all of the indices we'll be showing on the figure.
# the dictionary includes the index name, the actual value, and the units.
indices = {'SBCAPE': [fmt(prof.sfcpcl.bplus), 'J/kg'],\
'SBCIN': [fmt(prof.sfcpcl.bminus), 'J/kg'],\
'SBLCL': [fmt(prof.sfcpcl.lclhght), 'm AGL'],\
'SBLFC': [fmt(prof.sfcpcl.lfchght), 'm AGL'],\
'SBEL': [fmt(prof.sfcpcl.elhght), 'm AGL'],\
'SBLI': [fmt(prof.sfcpcl.li5), 'C'],\
'MLCAPE': [fmt(prof.mlpcl.bplus), 'J/kg'],\
'MLCIN': [fmt(prof.mlpcl.bminus), 'J/kg'],\
'MLLCL': [fmt(prof.mlpcl.lclhght), 'm AGL'],\
'MLLFC': [fmt(prof.mlpcl.lfchght), 'm AGL'],\
'MLEL': [fmt(prof.mlpcl.elhght), 'm AGL'],\
'MLLI': [fmt(prof.mlpcl.li5), 'C'],\
'MUCAPE': [fmt(prof.mupcl.bplus), 'J/kg'],\
'MUCIN': [fmt(prof.mupcl.bminus), 'J/kg'],\
'MULCL': [fmt(prof.mupcl.lclhght), 'm AGL'],\
'MULFC': [fmt(prof.mupcl.lfchght), 'm AGL'],\
'MUEL': [fmt(prof.mupcl.elhght), 'm AGL'],\
'MULI': [fmt(prof.mupcl.li5), 'C'],\
'0-1 km SRH': [fmt(srh1km[0]), 'm2/s2'],\
'0-1 km Shear': [fmt(utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1]), 'kts'],\
'0-3 km SRH': [fmt(srh3km[0]), 'm2/s2'],\
'0-6 km Shear': [fmt(utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1]), 'kts'],\
'Eff. SRH': [fmt(prof.right_esrh[0]), 'm2/s2'],\
'EBWD': [fmt(prof.ebwspd), 'kts'],\
'PWV': [round(prof.pwat, 2), 'inch'],\
'K-index': [fmt(params.k_index(prof)), ''],\
'STP(fix)': [fmt(stp_fixed, 'flt'), ''],\
'SHIP': [fmt(ship, 'flt'), ''],\
'SCP': [fmt(scp, 'flt'), ''],\
'STP(cin)': [fmt(stp_cin, 'flt'), '']}
# List the indices within the indices dictionary on the side of the plot.
trans = transforms.blended_transform_factory(ax.transAxes, ax.transData)
# Write out all of the indices to the figure.
#print("##############")
#print(" INDICES ")
#print("##############")
string = ''
keys = np.sort(list(indices.keys()))
x = 0
counter = 0
for key in keys:
string = string + key + ': ' + str(
indices[key][0]) + ' ' + indices[key][1] + '\n'
# print((key + ": " + str(indices[key][0]) + ' ' + indices[key][1]))
if counter < 7:
counter += 1
continue
else:
counter = 0
ax3.text(x,
1,
string,
verticalalignment='top',
transform=ax3.transAxes,
fontsize=11,
color='w')
string = ''
x += 0.3
ax3.text(x,
1,
string,
verticalalignment='top',
transform=ax3.transAxes,
fontsize=11,
color='w')
ax3.set_axis_off()
# Show SARS matches (edited for Keith Sherburn)
#try:
# supercell_matches = prof.supercell_matches
# hail_matches = prof.matches
#except:
# supercell_matches = prof.right_supercell_matches
# hail_matches = prof.right_matches
#print()
#print("#############")
#print(" SARS OUTPUT ")
#print("#############")
#for mtype, matches in zip(['Supercell', 'Hail'], [supercell_matches, hail_matches]):
# print(mtype)
# print('-----------')
# if len(matches[0]) == 0:
# print("NO QUALITY MATCHES")
# for i in range(len(matches[0])):
# print(matches[0][i] + ' ' + matches[1][i])
# print("Total Loose Matches:", matches[2])
# print("# of Loose Matches that met Criteria:", matches[3])
# print("SVR Probability:", matches[4])
# print()
#plot logos
im = plt.imread('logo.png')
#left, bottom, width, height = [0.25, 0.6, 0.2, 0.2]
#left, bottom, width, height = [0.1, 0.175, 0.4, 0.4] #bottom left
left, bottom, width, height = [0.035, 0.65, 0.4, 0.4]
# ax4 = fig.add_axes([left, bottom, width, height])
ax4 = plt.subplot(gs[3, 4])
implot = ax4.imshow(im, alpha=0.99)
ax4.axis('off')
ax4.set_facecolor('k')
im2 = plt.imread('essc_logo.png')
ax5 = plt.subplot(gs[3, 5])
implot = ax5.imshow(im2, alpha=0.99)
ax5.axis('off')
ax5.set_facecolor('k')
#plot SHARPpy acknowledgement
# plt.text(1, 1, 'Plotted with SHARPpy', horizontalalignment='right',
# verticalalignment='top', transform=ax.transAxes, color='w')
hodoAx.annotate(
'Plotted with SHARPpy - https://sharppy.github.io/SHARPpy/',
(0.7, 0.96),
xycoords='figure fraction',
va='center',
color='w')
#filename for the plot
# plotName = os.path.splitext(file)[0] + '.png'
# Finalize the image formatting and alignments, and save the image to the file.
#gs.tight_layout(fig)
plt.style.use('dark_background')
fn = time.strftime(
'%Y%m%d.%H%M') + '_' + locInfo[0] + '_' + locInfo[1] + '.png'
fn = fn.replace('/', '')
print('SHARPpy quick-look image output at: ' + imgName)
#plt.savefig(fn, bbox_inches='tight', dpi=180)
plt.savefig(imgName, dpi=180)
'MLLFC': [int(mlpcl.lfchght), 'm AGL'],\
'MLEL': [int(mlpcl.elhght), 'm AGL'],\
'MLLI': [int(mlpcl.li5), 'C'],\
'MUCAPE': [int(mupcl.bplus), 'J/kg'],\
'MUCIN': [int(mupcl.bminus), 'J/kg'],\
'MULCL': [int(mupcl.lclhght), 'm AGL'],\
'MULFC': [int(mupcl.lfchght), 'm AGL'],\
'MUEL': [int(mupcl.elhght), 'm AGL'],\
'MULI': [int(mupcl.li5), 'C'],\
'0-1 km SRH': [int(srh1km[0]), 'm2/s2'],\
'0-1 km Shear': [int(utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1]), 'kts'],\
'0-3 km SRH': [int(srh3km[0]), 'm2/s2'],\
'Eff. SRH': [int(effective_srh[0]), 'm2/s2'],\
'EBWD': [int(ebwspd), 'kts'],\
'PWV': [round(params.precip_water(prof), 2), 'inch'],\
'K-index': [int(params.k_index(prof)), ''],\
'STP(fix)': [round(stp_fixed, 1), ''],\
'SHIP': [round(ship, 1), ''],\
'SCP': [round(scp, 1), ''],\
'STP(cin)': [round(stp_cin, 1), '']}
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = mupcl
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='skewx')
ax.grid(True)
pmax = 1000
pmin = 10
