def test_wetlift():
input_p = 700
input_t = 15
input_p2 = 100
correct_t = -81.27400812504021
returned_t = thermo.wetlift(input_p, input_t, input_p2)
npt.assert_almost_equal(returned_t, correct_t)
def test_wetlift():
input_p = 700
input_t = 15
input_p2 = 100
correct_t = -81.27400812504021
returned_t = thermo.wetlift(input_p, input_t, input_p2)
npt.assert_almost_equal(returned_t, correct_t)
def draw_background(ax,
dry=np.arange(-50, 110, 20),
moist=np.arange(-5, 40, 5),
presvals=np.arange(1050, 0, -10),
mix=[1, 2, 4, 8, 12, 16, 20, 24]):
# Plot dry adiabats
for t in dry:
ax.semilogy(thetas(t, presvals),
presvals,
'r-',
alpha=0.2,
linewidth=1)
# Plot moist adiabats to topp
topp = 200
moist_presvals = np.arange(np.max(presvals), topp, -10)
for t in moist:
tw = []
for p in moist_presvals:
tw.append(thermo.wetlift(1000., t, p))
ax.semilogy(tw,
moist_presvals,
'k-',
alpha=0.2,
linewidth=0.5,
linestyle='dashed')
# add moist adiabat text labels
ax.text(thermo.wetlift(1000., t, topp + 15),
topp + 15,
t,
va='center',
ha='center',
size=5.6,
alpha=0.3,
clip_on=True)
# Mixing ratio lines and labels to topp
topp = 650
for w in mix:
ww = []
for p in presvals:
ww.append(thermo.temp_at_mixrat(w, p))
ax.semilogy(ww,
presvals,
'g',
alpha=0.35,
linewidth=1,
linestyle="dotted")
ax.text(thermo.temp_at_mixrat(w, topp),
topp,
w,
va='bottom',
ha='center',
size=6.7,
color='g',
alpha=0.35)
# Disables the log-formatting (10 to the power of x) that comes with semilogy
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(100, 1000, 10))
ax.set_ylim(1050, 100)
plt.ylabel('Pressure (hPa)')
# The first time this axis object is returned, no xtick gridlines show up for -110 to -60C
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xlim(-50, 45)
plt.xlabel('Temperature (C)')
ax.grid(True, linestyle='solid', alpha=0.5)
def plot_wof(prof, members, figname, xlat, xlon, idateobj, vdateobj, **kwargs):
# '''
# Plots SHARPpy SPC window as .png
#
# Parameters
# ----------
# prof : a Profile Object from sharppy.sharptab.profile
#
# kwargs
# ------
# parcel_type: Parcel choice for plotting. 'sfc','ml','mu','fcst' Default is 'ml'
# filename: Filename as a string. Default is 'sounding.png'
# logo: Logo for upper-left portion of the skew-t. Default is 'None' and does not plot a logo.
# logo_dxdy: Size of logo. Actual dimensions are dT and dp as it is plotted on the skewT. Default is (20,13) This worked for a 489x132 pixel image.
# '''
#kwargs
parcel_type = kwargs.get('parcel_type', 'ml')
xpts = kwargs.get('x_pts')
ypts = kwargs.get('y_pts')
#Figure User Input
p_grid_labels = [
'1000', '', '', '850', '', '', '700', '', '', '', '500', '', '', '',
'300', '', '200', '', '100'
] #labels for the pressure ticks. Standard.
p_grid = [1000, 850, 700, 500, 300, 200,
100] #where horizontal lines go across the skew-T
my_dpi = 55 #dots per inch for the plot. This is a pretty hi-res image.
pmax = 1050 #lowest pressure on the skew-T
pmin = 100 #highest pressure on the skew-T
dp = -10 #pressure spacing for creating skew-T background lines
presvals = np.arange(
int(pmax),
int(pmin) + dp,
dp) #pressure values used for creating skew-T background lines
# Colors for wind speed bars and hodograph
hgt_list_bar = [0, 1000, 3000, 6000, 9000, 20000]
hgt_list_hodo = [0, 1000, 3000, 6000, 9000, 10000]
hodo_color = [
cb_colors.orange6, cb_colors.green6, cb_colors.blue6,
cb_colors.purple6, cb_colors.red6
]
hodo_label = ['0-1km', '1-3km', '3-6km', '6-9km', '9-10km']
#convoluted mess to get the title to be aligned how I wanted. This should be changed for others...
spaces = 10 #22
# title_text_1 = '' #site + ' ' + dt.strftime('%Y/%m/%d %H:%M UTC ' + data_type)
# title_text_3 = 'Sounding Powered by SHARPpy'
sharptext = 'Sounding Powered by SHARPpy'
# title_text_2 = title_text_3 = ''
title_text_3 = 'WoFS Sounding {}N, {}W'.format(
xlat, xlon) + (' ' * spaces) + 'Init: {} Valid: {}'.format(
idateobj.strftime('%Y-%m-%d %H%M UTC'),
vdateobj.strftime('%Y-%m-%d %H%M UTC'))
#xlat, xlon, initdate, validdate
title_text = title_text_3 #title_text_1 + (' '*spaces) +title_text_2 + (' '*spaces) + title_text_3
#Figures out where at which height the sounding reached in the list of h_new
#Then interpolates pressure to height levels
h_new = [0, 1000, 3000, 6000, 9000, 12000, 15000]
for i in range(len(h_new)):
if np.max(prof.hght) > h_new[i]:
index = i
h_new_labels = ['0 km', '1 km', '3 km', '6 km', '9 km', '12 km', '15 km']
h_new_labels = h_new_labels[0:index + 1]
#p_interped_func = interpolate.interp1d(prof.hght, prof.pres)
p_interped = sharptab.interp.pres(
prof, sharptab.interp.to_msl(prof, h_new[0:index + 1]))
#Thin out the winds for better plotting (significant level data points seem to bunch together to closely
minimum_separation = 250. #minimum spacing between wind barbs (meters)
h_barb = np.array(prof.hght).tolist()
p_barb = np.array(prof.pres).tolist()
spd_barb = np.array(prof.wspd).tolist()
direc_barb = np.array(prof.wdir).tolist()
#adds units to our newly created pressure, speed, and direction arrays for wind barb plotting
#p_barb = p_barb * units.mbar
#spd_barb = spd_barb * units.knot
#direc_barb = direc_barb * units.deg
# Convert wind speed and direction to components
#u, v = get_wind_components(prof.wspd * units.knot, prof.wdir * units.deg)
u, v = utils.vec2comp(prof.wdir, prof.wspd)
u_barb, v_barb = utils.vec2comp(prof.wdir, prof.wspd)
#SELECT PARCEL AND GET PARCEL DATA FROM SPC_UTILS
sfcpcl = prof.sfcpcl #params.parcelx( prof, flag=1 )
fcstpcl = prof.fcstpcl #params.parcelx( prof, flag=2)
mupcl = prof.mupcl #params.parcelx( prof, flag=3 )
mlpcl = prof.mlpcl #params.parcelx( prof, flag=4 )
if parcel_type == 'sfc':
pcl = sfcpcl
pcl_box_level = -0.065
pcl_type = 1
elif parcel_type == 'fcst':
pcl = fcstpcl
pcl_box_level = -0.0875
pcl_type = 2
elif parcel_type == 'mu':
pcl = mupcl
pcl_box_level = -0.1325
pcl_type = 4
elif parcel_type == 'ml':
pcl = mlpcl
pcl_box_level = -0.11
pcl_type = 3
else:
print(
"ERROR! Select 'sfc', 'fcst', 'mu', or 'ml' for parcel type. (plot_spc(prof,parcel_type)"
)
print("Defaulting to surface parcel...")
pcl = sfcpcl
pcl_box_level = -0.065
#PLOTTING *************************************************************************************************************
#Create full figure
fig = plt.figure(figsize=(1180 / my_dpi, 800 / my_dpi),
dpi=my_dpi,
frameon=False)
#SKEW T ***************************************************
ax = fig.add_subplot(111, projection='skewx',
facecolor="w") #skewed x-axis
# plot dashed temperature lines
ax.xaxis.grid(color='k',
linestyle='--',
dashes=(3, 3),
alpha=0.5,
zorder=0)
# plot the moist-adiabats
for temp in np.arange(-10, 45, 5):
tw = []
for pres in presvals:
tw.append(thermo.wetlift(1050., temp, pres))
ax.semilogy(tw,
presvals,
color=cb_colors.purple6,
linestyle='--',
dashes=(5, 2),
alpha=.3) #cb_colors.purple6
# plot the dry adiabats
for t in np.arange(-50, 80, 20):
thetas = ((t + thermo.ZEROCNK) / (np.power(
(1000. / presvals), thermo.ROCP))) - thermo.ZEROCNK
ax.semilogy(thetas, presvals, 'k', alpha=.3)
#plot mixing ratio lines
mixing_ratio_list = range(6, 36, 4)
for mr in mixing_ratio_list:
plt.plot((thermo.temp_at_mixrat(mr, 1050) - 273,
thermo.temp_at_mixrat(mr, 600) - 273), (1050, 600),
'g-',
lw=1.0,
zorder=3,
alpha=0.6)
ax.annotate(str(mr),
xy=((thermo.temp_at_mixrat(mr, 600) - 273), (600 - 3)),
xytext=((thermo.temp_at_mixrat(mr, 600) - 273), (600 - 3)),
ha='center',
color='g',
family='sans-serif',
weight='bold',
zorder=3,
fontsize=10,
alpha=0.6)
#plot horizontal lines at standard pressure levels
for p_loc in p_grid:
ax.axhline(y=p_loc, ls='-', c='k', alpha=0.5, linewidth=1.5, zorder=3)
# PLOT THE DATA ON THE SKEW-T
# 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.wetbulb,
prof.pres,
c="c",
linestyle='-',
lw=1,
alpha=1.0,
zorder=3) # Plot the wetbulb profile
ax.annotate(str(int(round(thermo.ctof(prof.wetbulb[prof.sfc])))),
xy=(prof.wetbulb[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.wetbulb[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color="c",
family='sans-serif',
weight='normal',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc wetbulb in F
ax.semilogy(prof.dwpc,
prof.pres,
c=cb_colors.blue6,
linestyle='-',
lw=3,
zorder=3) # plot the dewpoint profile
ax.annotate(str(int(round(thermo.ctof(prof.dwpc[prof.sfc])))),
xy=(prof.dwpc[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.dwpc[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color=cb_colors.blue6,
family='sans-serif',
weight='bold',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc dewpoint in F
ax.semilogy(prof.tmpc,
prof.pres,
c=cb_colors.orange6,
linestyle='-',
lw=3,
zorder=3) # Plot the temperature profile
ax.semilogy(prof.vtmp,
prof.pres,
c=cb_colors.orange6,
linestyle='--',
lw=3,
zorder=3) # Plot the temperature profile
ax.annotate(str(int(round(thermo.ctof(prof.tmpc[prof.sfc])))),
xy=(prof.tmpc[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.tmpc[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color=cb_colors.orange6,
family='sans-serif',
weight='bold',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc temp in F
ax.semilogy(pcl.ttrace,
pcl.ptrace,
c=cb_colors.gray6,
linestyle='--',
dashes=(3, 3),
lw=1.5,
alpha=1.0,
zorder=3) # plot the parcel trace
#member_cape = []
if members is not None:
# print( "Plotting members...")
for m_idx in range(len(members['tmpc'])):
ax.semilogy(members['dwpc'][m_idx],
members['pres'][m_idx],
c=cb_colors.blue6,
linestyle='-',
lw=1.,
zorder=1,
alpha=.6) # plot the dewpoint profile
ax.semilogy(members['tmpc'][m_idx],
members['pres'][m_idx],
c=cb_colors.orange6,
linestyle='-',
lw=1.,
zorder=1,
alpha=.6) # Plot the temperature profile
# set label and tick marks for pressure and temperature
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xticks(np.arange(-100, 60, 10))
ax.set_xticklabels([str(i) for i in np.arange(-100, 60, 10)],
color=cb_colors.gray7,
fontsize=12)
ax.set_xlim(-50, 50)
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(1000, 100, 19))
ax.set_yticklabels(p_grid_labels, color=cb_colors.gray7, fontsize=12)
ax.set_ylim(1050, 100)
#plot the title text
plt.text(0.05,
0.97,
title_text,
fontsize=15,
color=cb_colors.gray8,
weight='bold',
ha='left',
transform=fig.transFigure)
#x_hodo.annotate(sharptext, xy=(0.95, 0.95), xytext=(0.95, 0.95),xycoords='axes fraction',textcoords='axes fraction',ha='center', va='bottom', color=cb_colors.gray7, family='sans-serif', weight='bold', zorder=3,fontsize=14)
plt.text(0.8,
0.97,
sharptext,
fontsize=15,
color=cb_colors.gray8,
weight='bold',
ha='left',
transform=fig.transFigure)
#adjust the skew-T plot to make room for the rest of the figures. This was important to make everything line up.
plt.subplots_adjust(left=0.05, right=0.55, top=0.96, bottom=0.15)
#Plot the height labels on the left axis
ax2 = ax.twinx(
) #makes a twin of the skew-T subplot that's not skewed at 45 degrees
plt.yscale('log', nonposy='clip')
plt.yticks(p_interped, h_new_labels, color=cb_colors.green4, ha='left')
ax2.yaxis.tick_left()
ax2.tick_params(direction='in',
pad=-15,
axis='both',
which='major',
colors=cb_colors.green4,
length=10,
width=1.5)
ax2.set_yticklabels(h_new_labels,
fontsize=12,
weight='bold',
color=cb_colors.green4)
x = np.random.uniform(0.0, 10.0, 15)
y = np.random.uniform(0.0, 10.0, 15)
# Plot LCL and LFC levels
plt.plot((38, 42), (pcl.lfcpres, pcl.lfcpres),
c="darkgreen",
lw=2.0,
zorder=3)
ax2.annotate('LFC',
xy=(40, pcl.lfcpres),
xytext=(40, pcl.lfcpres),
ha='center',
va='bottom',
color=cb_colors.green5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
plt.plot((38, 42), (pcl.lclpres, pcl.lclpres),
c="r",
linestyle='-',
lw=2.0,
zorder=3)
ax2.annotate('LCL',
xy=(40, pcl.lclpres + 5.),
xytext=(40, pcl.lclpres + 5.),
ha='center',
va='top',
color=cb_colors.red5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
plt.plot((38, 42), (pcl.elpres, pcl.elpres),
c="m",
linestyle='-',
lw=2.0,
zorder=3)
ax2.annotate('EL',
xy=(40, pcl.elpres),
xytext=(40, pcl.elpres),
ha='center',
va='bottom',
color=cb_colors.purple5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
# Plot Eff Inflow Layer
eff_inflow = (prof.ebottom, prof.etop)
eff_inflow_top = sharptab.interp.to_agl(
prof, sharptab.interp.hght(prof, eff_inflow[1]))
eff_inflow_bottom = sharptab.interp.to_agl(
prof, sharptab.interp.hght(prof, eff_inflow[0]))
bunkers = prof.srwind
effective_srh = prof.right_esrh
plt.plot((-25, -20), (eff_inflow[0], eff_inflow[0]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
plt.plot((-25, -20), (eff_inflow[1], eff_inflow[1]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
plt.plot((-22.5, -22.5), (eff_inflow[0], eff_inflow[1]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
try:
plt.annotate(str(int(eff_inflow_bottom)) + 'm ',
xy=(-25, eff_inflow[0]),
xytext=(-25, eff_inflow[0]),
ha='right',
va='center',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
plt.annotate(str(int(eff_inflow_top)) + 'm ',
xy=(-25, eff_inflow[1]),
xytext=(-25, eff_inflow[1]),
ha='right',
va='center',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
plt.annotate(str(int(effective_srh[0])) + ' m$^2$/s$^2$',
xy=(-22.5, eff_inflow[1] - 10),
xytext=(-22.5, eff_inflow[1] - 10),
ha='center',
va='bottom',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
except:
print("NO EFF INFLOW")
# PLOT WINDBARBS
p_barb = np.asarray(p_barb)
pidx = idx = np.where(np.asarray(p_barb) >= 100.)[0]
wind_barbs = ax2.barbs(55 * np.ones(len(p_barb[idx])),
p_barb[idx],
u_barb[idx],
v_barb[idx],
barbcolor=cb_colors.gray7,
flagcolor='None',
zorder=10,
lw=1.0,
length=7)
wind_barbs.set_clip_on(False)
ax2.invert_yaxis()
ax2.set_xlim(-50, 50)
ax2.set_ylim(1050, 100)
spd_barb = np.asarray(spd_barb)
# Create hodograph ********************************************************************************************
ax_hod = fig.add_axes([0.60, 0.45, 0.38, 0.475],
frameon=False) #, facecolor='k')
# Set the characteristics of the tick marks
for tick in ax_hod.xaxis.get_major_ticks():
tick.label.set_fontsize(12)
tick.label.set_color(cb_colors.gray7)
tick.label.set_weight('bold')
for tick in ax_hod.yaxis.get_major_ticks():
tick.label.set_fontsize(12)
tick.label.set_color(cb_colors.gray7)
tick.label.set_weight('bold')
for i in range(10, 90, 10):
# Draw the range rings around the hodograph.
circle = plt.Circle((0, 0),
i,
linestyle='--',
color='k',
alpha=.3,
fill=False)
ax_hod.add_artist(circle)
# Set the tick parameters to displace the tick labels from the hodograph axes
ax_hod.tick_params(axis='x',
which='major',
labelsize=10,
color=cb_colors.gray7,
pad=-235,
length=0)
ax_hod.tick_params(axis='y',
which='major',
labelsize=10,
color=cb_colors.gray7,
pad=-315,
length=0)
# Plot the hodograph axes
ax_hod.axvline(0, color=cb_colors.gray7, linestyle='-', linewidth=2.)
ax_hod.axhline(0, color=cb_colors.gray7, linestyle='-', linewidth=2.)
ax_hod.set_yticks(range(-60, 65, 10))
ax_hod.set_xticks(range(-70, 75, 10))
hod_yticklabels = [str(abs(i)) for i in range(-60, 65, 10)]
#hod_yticklabels[len(hod_yticklabels)/2] = ''
hod_xticklabels = [str(abs(i)) for i in range(-70, 75, 10)]
#hod_xticklabels[len(hod_xticklabels)/2] = ''
ax_hod.set_yticklabels(hod_yticklabels,
fontsize=12,
weight='bold',
color=cb_colors.gray7)
ax_hod.set_xticklabels(hod_xticklabels,
fontsize=12,
weight='bold',
color=cb_colors.gray7)
# Plot the hodograph using the color scheme for different layers (0-3, 3-6, etc.)
bounds = [0, 1000, 3000, 6000, 9000, 12000]
for i in range(1, len(bounds), 1):
subset_idxs = np.where(
(prof.hght <= sharptab.interp.to_msl(prof, bounds[i]))
& (prof.hght >= sharptab.interp.to_msl(prof, bounds[i - 1])))
subset_hghts = np.ma.concatenate(
([sharptab.interp.to_msl(prof, bounds[i - 1])],
prof.hght[subset_idxs], [sharptab.interp.to_msl(prof,
bounds[i])]))
u, v = sharptab.interp.components(
prof, sharptab.interp.pres(prof, subset_hghts))
ax_hod.plot(u,
v,
c=hodo_color[i - 1],
linewidth=2.5,
label=hodo_label[i - 1],
zorder=3)
if members is not None:
for mprof in members['member_profs']:
for i in range(1, len(bounds), 1):
subset_idxs = np.where(
(mprof.hght <= sharptab.interp.to_msl(mprof, bounds[i]))
& (mprof.hght >= sharptab.interp.to_msl(
mprof, bounds[i - 1])))
subset_hghts = np.ma.concatenate(
([sharptab.interp.to_msl(mprof, bounds[i - 1])
], mprof.hght[subset_idxs],
[sharptab.interp.to_msl(mprof, bounds[i])]))
u, v = sharptab.interp.components(
mprof, sharptab.interp.pres(mprof, subset_hghts))
ax_hod.plot(u,
v,
c=hodo_color[i - 1],
linewidth=1.25,
alpha=0.6,
label=hodo_label[i - 1],
zorder=1)
# Get the Bunkers storm motions and convert them to strings to plot
bunkers = srwind = prof.srwind
bunkers_rt = utils.comp2vec(bunkers[0], bunkers[1])
bunkers_lf = utils.comp2vec(bunkers[2], bunkers[3])
bunkers_rt_str = str(int(np.ma.around(bunkers_rt[0], 0))) + "/" + str(
int(np.ma.around(bunkers_rt[1], 0)))
bunkers_lf_str = str(int(np.ma.around(bunkers_lf[0], 0))) + "/" + str(
int(np.ma.around(bunkers_lf[1], 0)))
# Plot the effective inflow layer on the hodograph
effubot, effvbot = sharptab.interp.components(prof, eff_inflow[0])
effutop, effvtop = sharptab.interp.components(prof, eff_inflow[1])
ax_hod.plot([effubot, srwind[0]], [effvbot, srwind[1]],
c='c',
linewidth=1.5)
ax_hod.plot([effutop, srwind[0]], [effvtop, srwind[1]],
c='c',
linewidth=1.5)
# Annotate where the Bunkers storm motion vectors are on the hodograph
ax_hod.plot(srwind[0],
srwind[1],
marker='o',
fillstyle='none',
markeredgecolor=cb_colors.blue8,
markeredgewidth=1.5,
markersize=11)
ax_hod.annotate(bunkers_rt_str + ' RM', (srwind[0] + 1.5, srwind[1] - 1.5),
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold',
zorder=10)
ax_hod.plot(srwind[2],
srwind[3],
marker='o',
fillstyle='none',
markeredgecolor=cb_colors.blue8,
markeredgewidth=1.5,
markersize=11)
ax_hod.annotate(bunkers_lf_str + ' LM', (srwind[2] + 1.5, srwind[3] - 1.5),
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold',
zorder=10)
# Annotate where the Corfidi MBE vectors are on the hodograph
corfidi = prof.upshear_downshear
corfidi_up = utils.comp2vec(corfidi[0], corfidi[1])
corfidi_dn = utils.comp2vec(corfidi[2], corfidi[3])
c = 'k' #'#0A74C6'
ax_hod.plot(corfidi[0],
corfidi[1],
marker='o',
fillstyle='none',
markeredgecolor=c,
markeredgewidth=1.5,
markersize=9)
ax_hod.annotate(str(int(corfidi_up[0])) + '/' + str(int(corfidi_up[1])) +
' UP', (corfidi[0] + 1.5, corfidi[1] - 1.5),
fontsize=12,
va="top",
ha="left",
color=cb_colors.purple8,
weight='bold',
zorder=10)
ax_hod.plot(corfidi[2],
corfidi[3],
marker='o',
fillstyle='none',
markeredgecolor=c,
markeredgewidth=1.5,
markersize=9)
ax_hod.annotate(str(int(corfidi_dn[0])) + '/' + str(int(corfidi_dn[1])) +
' DN', (corfidi[2] + 1.5, corfidi[3] - 1.5),
fontsize=12,
va="top",
ha="left",
color=cb_colors.purple8,
weight='bold',
zorder=10)
# Get the cloud-layer mean wind
mean_cloudlayer = winds.mean_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres)
mean_cloudlayer_comp = utils.comp2vec(mean_cloudlayer[0],
mean_cloudlayer[1])
try:
mean_cloudlayer_str = str(int(np.ma.around(
mean_cloudlayer_comp[0], 0))) + "/" + str(
int(np.ma.around(mean_cloudlayer_comp[1], 0)))
except:
mean_cloudlayer_str = 'M/M'
# Write the critical angle to the hodograph.
if eff_inflow[0] == prof.pres[prof.sfc]:
ax_hod.annotate('Critical Angle = ' + str(int(prof.critical_angle)),
(-65, -50),
fontsize=12,
va="bottom",
ha="left",
color=cb_colors.green6,
weight='bold',
zorder=10)
ax_hod.set_xlim(-80, 80)
ax_hod.set_ylim(-70, 70)
#BELOW IS STUFF FOR BOXES/BORDERS ******************************************************************************
ax3 = ax2.twinx()
ax3.axes.get_xaxis().set_visible(False)
ax3.axes.get_yaxis().set_visible(False)
ax3.set_yticks([])
ax3.set_yticklabels([])
#Big Thick Box around Skew-T
#box = ax3.add_patch(patches.Rectangle((-50, 0), 110.0, 1.0,fill=False,linewidth=2,edgecolor="w",zorder=3))
#box.set_clip_on(False)
#box around hodograph
#box = ax_hod.add_patch(patches.Rectangle((-80., -70.), 160., 140.,fill=False,linewidth=2,edgecolor="w",zorder=4))
#box.set_clip_on(False)
inset_color = cb_colors.gray7
#THICK TEXT BOX around Thermodynamics Text
box = ax3.add_patch(
patches.Rectangle((-58.0, -0.035),
54,
-0.12,
fill=False,
linewidth=2,
edgecolor=inset_color,
zorder=4))
box.set_clip_on(False)
#THICK TEXT BOX around Kinematics Text
box = ax3.add_patch(
patches.Rectangle((-3.0, -0.035),
60,
-0.12,
fill=False,
linewidth=2,
edgecolor=inset_color,
zorder=4))
box.set_clip_on(False)
#THICK TEXT BOX Around Dynamics Text
# box = ax3.add_patch(patches.Rectangle((9.0, -0.04), 60, -0.38,fill=False,linewidth=2,edgecolor=inset_color,zorder=4))
# box.set_clip_on(False)
#THICK TEXT BOX Around SARS Text
# box = ax3.add_patch(patches.Rectangle((69.0, -0.04), 55, -0.38,fill=False,linewidth=2,edgecolor=inset_color,zorder=4))
# box.set_clip_on(False)
#Thermodynamics
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.04), 64.0, -0.12,fill=False,linewidth=1,edgecolor=inset_color,zorder=4))
#box.set_clip_on(False)
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.04), 64.0, -0.025,fill=False,linewidth=1,edgecolor=inset_color,zorder=4))
#box.set_clip_on(False)
# Write the parcel properties to the inset.
#x_list = [0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47]
x_list = np.array([0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47]) - 0.075
y_list = [-0.045, -0.07, -0.0925, -0.115, -0.1375]
A = [
"SFC", prof.sfcpcl.bplus,
int(prof.sfcpcl.bminus), prof.sfcpcl.lclhght, prof.sfcpcl.li5,
prof.sfcpcl.lfchght, prof.sfcpcl.elhght
]
# B = ["FCST", prof.fcstpcl.bplus, int(prof.fcstpcl.bminus), prof.fcstpcl.lclhght, prof.fcstpcl.li5, prof.fcstpcl.lfchght, prof.fcstpcl.elhght]
C = [
"ML", prof.mlpcl.bplus,
int(prof.mlpcl.bminus), prof.mlpcl.lclhght, prof.mlpcl.li5,
prof.mlpcl.lfchght, prof.mlpcl.elhght
]
D = [
"MU", prof.mupcl.bplus,
int(prof.mupcl.bminus), prof.mupcl.lclhght, prof.mupcl.li5,
prof.mupcl.lfchght, prof.mupcl.elhght
]
#mlcape = C[1]
#print('mlcape',mlcape)
data = np.array([["PCL", "CAPE", "CINH", "LCL", "LI", "LFC", "EL"],
[
str(int(np.ma.around(A[i], 0))) if
(type(A[i]) == np.float64) else str(A[i])
for i in range(len(A))
],
[
str(int(np.ma.around(C[i], 0))) if
(type(C[i]) == np.float64) else str(C[i])
for i in range(len(C))
],
[
str(int(np.ma.around(D[i], 0))) if
(type(D[i]) == np.float64) else str(D[i])
for i in range(len(D))
]])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color=cb_colors.gray7,
weight='bold')
# Draw a box around the selected parcel being shown in the Skew-T
box = ax3.add_patch(
patches.Rectangle((-57.7, pcl_box_level),
53.,
0.0225,
fill=False,
linewidth=1,
edgecolor=cb_colors.purple4,
zorder=4))
box.set_clip_on(False)
# Write the lapse rates to the inset.
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.305), 43.0, -0.115,fill=False,linewidth=1,edgecolor="w",zorder=4))
#box.set_clip_on(False)
x_list = [0.15, 0.16]
y_list = np.arange(-0.315, -.40, -0.0225)
data = np.array([[
"0-3km AGL LR =",
str(np.ma.around(prof.lapserate_3km, 1)) + " C/km"
], [
"3-6km AGL LR =",
str(np.ma.around(prof.lapserate_3_6km, 1)) + " C/km"
],
[
"850-500mb LR =",
str(np.ma.around(prof.lapserate_850_500, 1)) + " C/km"
],
[
"700-500mb LR =",
str(np.ma.around(prof.lapserate_700_500, 1)) + " C/km"
]])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if (j % 2 == 0):
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="right",
color='k',
weight='bold')
else:
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold')
#Severe Indices
#box = ax3.add_patch(patches.Rectangle((-12.0, -0.305), 21.0, -0.115,fill=False,linewidth=1,edgecolor="w",zorder=4))
#box.set_clip_on(False)
x_list = [0.52, 0.53]
y_list = np.arange(-0.315, -.40, -0.0225)
# This looks lifted from the Profile class. Don't need this.
sfc = prof.pres[prof.sfc]
p6km = sharptab.interp.pres(prof, sharptab.interp.to_msl(prof, 6000.))
p8km = sharptab.interp.pres(prof, sharptab.interp.to_msl(prof, 8000.))
# ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
# etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
# Mean winds
#mean_1km = winds.mean_wind(prof, pbot=sfc, ptop=p1km)
mean_1km_comp = prof.mean_1km #utils.comp2vec(mean_1km[0],mean_1km[1])
#mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
mean_3km_comp = prof.mean_3km #utils.comp2vec(mean_3km[0],mean_3km[1])
#mean_eff = winds.mean_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
mean_eff_comp = utils.comp2vec(
*prof.mean_eff) #utils.comp2vec(mean_eff[0],mean_eff[1])
if type(eff_inflow[0]) != np.float64:
mean_eff_comp = ['---', '--']
mean_6km = winds.mean_wind(prof, pbot=sfc, ptop=p6km)
mean_6km_comp = prof.mean_6km #utils.comp2vec(mean_6km[0],mean_6km[1])
mean_8km = winds.mean_wind(prof, pbot=sfc, ptop=p8km)
mean_8km_comp = prof.mean_8km #utils.comp2vec(mean_8km[0],mean_8km[1])
mean_cloudlayer = winds.mean_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres)
mean_cloudlayer_comp = prof.mean_lcl_el #utils.comp2vec(mean_cloudlayer[0],mean_cloudlayer[1])
mean_ebwd = winds.mean_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
mean_ebwd_comp = utils.comp2vec(
*prof.mean_ebw) #utils.comp2vec(mean_ebwd[0],mean_ebwd[1])
if type(eff_inflow[0]) != np.float64:
mean_ebwd_comp = ['---', '--']
bunkers_rt = utils.comp2vec(bunkers[0], bunkers[1])
bunkers_lf = utils.comp2vec(bunkers[2], bunkers[3])
corfidi_up = utils.comp2vec(corfidi[0], corfidi[1])
corfidi_dn = utils.comp2vec(corfidi[2], corfidi[3])
srw_1km = prof.srw_1km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p1km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_3km = prof.srw_3km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p3km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_eff = utils.comp2vec(
*prof.srw_eff
) #utils.comp2vec(*winds.sr_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1], stu=prof.srwind[0], stv=prof.srwind[1]))
if type(eff_inflow[0]) != np.float64:
srw_eff = ['---', '--']
srw_6km = prof.srw_6km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p6km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_8km = prof.srw_8km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p8km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_cloudlayer = prof.srw_lcl_el #utils.comp2vec(*winds.sr_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_ebwd = utils.comp2vec(
*prof.srw_ebw
) #utils.comp2vec(*winds.sr_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1], stu=prof.srwind[0], stv=prof.srwind[1]))
if type(eff_inflow[0]) != np.float64:
srw_ebwd = ['---', '--']
srw_46km = utils.comp2vec(
*prof.srw_4_6km
) #utils.comp2vec(*winds.sr_wind(prof, pbot=p4km, ptop=p6km, stu=prof.srwind[0], stv=prof.srwind[1]))
sfc_8km_shear = prof.sfc_8km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p8km)
sfc_6km_shear = prof.sfc_6km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = prof.sfc_3km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = prof.sfc_1km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p1km)
effective_shear = prof.eff_shear #winds.wind_shear(prof, pbot=eff_inflow[0], ptop=etop_hght)
cloudlayer_shear = prof.lcl_el_shear #winds.wind_shear(prof,pbot= pcl.lclpres, ptop=pcl.elpres)
srh3km = prof.srh3km #winds.helicity(prof, 0, 3000., stu = bunkers[0], stv = bunkers[1])
srh1km = prof.srh1km #winds.helicity(prof, 0, 1000., stu = bunkers[0], stv = bunkers[1])
stp_fixed = prof.stp_fixed #params.stp_fixed(pcl.bplus, pcl.lclhght, srh1km[0], utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = prof.ship
effective_srh = prof.right_esrh #winds.helicity(prof, ebot_hght, etop_hght, stu = bunkers[0], stv = bunkers[1])
ebwd = prof.ebwd #winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = prof.ebwspd
scp = prof.right_scp
stp_cin = prof.stp_cin #params.stp_cin(pcl.bplus, effective_srh[0], ebwspd, pcl.lclhght, pcl.bminus)
brn_shear = pcl.brnshear
# Draw the SCP, STP, SHIP indices to the plot
# TODO: Include the color variations on this to denote intensity of the index.
# if prof.stp_fixed is ma.masked:
# temp_stp_fixed = '0.0'
# else:
# temp_stp_fixed = str(round(prof.stp_fixed,1))
# if prof.stp_cin is ma.masked:
# temp_stp_cin = '0.0'
# else:
# temp_stp_cin = str(round(prof.stp_cin,1))
# if prof.right_scp is ma.masked:
# temp_right_scp = '0.0'
# else:
# temp_right_scp = str(round(prof.right_scp,1))
# data = np.array([["Supercell =",temp_right_scp],
# ["STP (cin) =",temp_stp_cin],
# ["STP (fix) =",temp_stp_fixed]])
data = np.array([["Supercell =",
str(np.ma.around(prof.right_scp, 1))],
["STP (cin) =",
str(np.ma.around(prof.stp_cin, 1))],
["STP (fix) =",
str(np.ma.around(prof.stp_fixed, 1))]])
data = np.array([["Supercell =",
str(np.ma.around(prof.right_scp, 1))],
["STP (cin) =",
str(np.ma.around(prof.stp_cin, 1))],
["STP (fix) =",
str(np.ma.around(prof.stp_fixed, 1))],
["SHIP =", str(np.ma.around(prof.ship, 1))]])
'''
for i in range(data.shape[0]):
for j in range(data.shape[1]):
d = float(data[i,1])
if i == 0:
if d >= 19.95:
c = MAGENTA
elif d >= 11.95:
c = RED
elif d >= 1.95:
c = YELLOW
elif d >= .45:
c = WHITE
elif d >= -.45:
c = LBROWN
elif d < -0.45:
c = CYAN
elif i == 1:
if d >= 8:
c = MAGENTA
elif d >= 4:
c = RED
elif d >= 2:
c = YELLOW
elif d >= 1:
c = WHITE
elif d >= .5:
c = LBROWN
elif d < .5:
c = DBROWN
stpCinColor = c
elif i == 2:
if d >= 7:
c = MAGENTA
elif d >= 5:
c = RED
elif d >= 2:
c = YELLOW
elif d >= 1:
c = WHITE
elif d >= 0.5:
c = LBROWN
else:
c = DBROWN
elif i == 3:
if d >= 5:
c = MAGENTA
elif d >= 2:
c = RED
elif d >= 1:
c = YELLOW
elif d >= .5:
c = WHITE
else:
c = DBROWN
if (j % 2 == 0):
ax2.annotate(data[i,j], (x_list[j], y_list[i]), xycoords="axes fraction",
fontsize=12, va="top", ha="right", color=c, weight='bold')
else:
ax2.annotate(data[i,j], (x_list[j], y_list[i]), xycoords="axes fraction",
fontsize=12, va="top", ha="left", color=cb_colors.gray7, weight='bold')
'''
# Draw the kinematic inset on the plot
# box = ax3.add_patch(patches.Rectangle((9.0, -0.04), 60.0, -0.025,fill=False,linewidth=1,edgecolor="w",zorder=4))
# box.set_clip_on(False)
x_list = np.array([0.60, 0.84, 0.97, 1.08, 1.18]) - 0.12
y_list = [-0.045]
y_list.extend(np.arange(-.07, -0.12, -.0225).tolist())
y_list.extend(np.arange(-.145, -0.22, -.0225).tolist())
y_list.extend(np.arange(-.2425, -0.27, -.0225).tolist())
y_list.extend(np.arange(-0.295, -.4, -0.0225).tolist())
A = [
"SFC-1km", srh1km[0],
utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1]
]
A2 = [mean_1km_comp, srw_1km]
B = [
"SFC-3km", srh3km[0],
utils.comp2vec(sfc_3km_shear[0], sfc_3km_shear[1])[1]
]
B2 = [mean_3km_comp, srw_3km]
C = [
"Eff Inflow Layer", effective_srh[0],
utils.comp2vec(effective_shear[0], effective_shear[1])[1]
]
C2 = [mean_eff_comp, srw_eff]
# D = ["SFC-6km", "", utils.comp2vec(sfc_6km_shear[0],sfc_6km_shear[1])[1]]
# D2 = [mean_6km_comp, srw_6km]
# E = ["SFC-8km", "", utils.comp2vec(sfc_8km_shear[0],sfc_8km_shear[1])[1]]
# E2 = [mean_8km_comp, srw_8km]
# F = ["LCL-EL (CLoud Layer)", "", utils.comp2vec(cloudlayer_shear[0],cloudlayer_shear[1])[1]]
# F2 = [mean_cloudlayer_comp, srw_cloudlayer]
# G = ["Eff Shear (EBWD)", "", utils.comp2vec(ebwd[0],ebwd[1])[1]]
# G2 = [mean_ebwd_comp, srw_ebwd]
# H = ["BRN Shear (m2/s2)", "", brn_shear, "", ""]
# I = ["4-6km SR Wind", ""]
# I2 = [str(int(round(srw_46km[0],0)))+"/"+str(int(round(srw_46km[1],0)))]
# I3 = ["", ""]
# J = ["...Storm Motion Vectors...", "", "", "", ""]
# K = ["Bunkers Right", ""]
# K2 = [str(int(round(bunkers_rt[0],0)))+"/"+str(int(round(bunkers_rt[1],0)))]
# K3 = ["", ""]
# L = ["Bunkers Left", ""]
# L2 = [str(int(round(bunkers_lf[0],0)))+"/"+str(int(round(bunkers_lf[1],0)))]
# L3 = ["", ""]
# M = ["Corfidi Downshear", ""]
# M2 = [str(int(round(corfidi_dn[0],0)))+"/"+str(int(round(corfidi_dn[1],0)))]
# M3 = ["", ""]
# N = ["Corfidi Upshear", ""]
# N2 = [str(int(round(corfidi_up[0],0)))+"/"+str(int(round(corfidi_up[1],0)))]
# N3 = ["", ""]
data = np.array([np.array(["", "SRH (m2/s2)", "Shear (kt)", "MnWind", "SRW"]),
np.array([ str(int(round(A[i],0))) if (type(A[i])== np.float64) else str(A[i]) for i in range(len(A)) ]+\
[ str(int(np.ma.around(A2[i][0],0)))+"/"+str(int(np.ma.around(A2[i][1],0))) if (type(A2[i][0])== np.ma.core.MaskedArray) else str(A2[i][0])+"/"+str(A2[i][1]) for i in range(len(A2)) ]),
np.array([ str(int(round(B[i],0))) if (type(B[i])== np.float64) else str(B[i]) for i in range(len(B)) ]+\
[ str(int(np.ma.around(B2[i][0],0)))+"/"+str(int(np.ma.around(B2[i][1],0))) if (type(B2[i][0])== np.ma.core.MaskedArray) else str(B2[i][0])+"/"+str(B2[i][1]) for i in range(len(B2)) ]),
np.array([ str(int(np.ma.around(C[i],0))) if (type(C[i])== np.float64) else str(C[i]) for i in range(len(C)) ]+\
[ str(int(np.ma.around(C2[i][0],0)))+"/"+str(int(np.ma.around(C2[i][1],0))) if (type(C2[i][0])== np.ma.core.MaskedArray) else str(C2[i][0])+"/"+str(C2[i][1]) for i in range(len(C2)) ])]) #,
# np.array([ str(int(round(D[i],0))) if (type(D[i])== np.float64) else str(D[i]) for i in range(len(D)) ]+\
# [ str(int(round(D2[i][0],0)))+"/"+str(int(round(D2[i][1],0))) if (type(D2[i][0])== np.ma.core.MaskedArray) else str(D2[i][0])+"/"+str(D2[i][1]) for i in range(len(D2)) ]),
# np.array([ str(int(round(E[i],0))) if (type(E[i])== np.float64) else str(E[i]) for i in range(len(E)) ]+\
# [ str(int(round(E2[i][0],0)))+"/"+str(int(round(E2[i][1],0))) if (type(E2[i][0])== np.ma.core.MaskedArray) else str(E2[i][0])+"/"+str(E2[i][1]) for i in range(len(E2)) ]),
# np.array([ str(int(round(F[i],0))) if (type(F[i])== np.float64) else str(F[i]) for i in range(len(F)) ]+\
# [ str(int(round(F2[i][0],0)))+"/"+str(int(round(F2[i][1],0))) if (type(F2[i][0])== np.ma.core.MaskedArray) else str(F2[i][0])+"/"+str(F2[i][1]) for i in range(len(F2)) ]),
# np.array([ str(int(round(G[i],0))) if (type(G[i])== np.float64) else str(G[i]) for i in range(len(G)) ]+\
# [ str(int(round(G2[i][0],0)))+"/"+str(int(round(G2[i][1],0))) if (type(G2[i][0])== np.ma.core.MaskedArray) else str(G2[i][0])+"/"+str(G2[i][1]) for i in range(len(G2)) ]),
# np.array([ str(int(round(H[i],0))) if (type(H[i])== np.float64) else str(H[i]) for i in range(len(H)) ]),
# np.array(I+I2+I3),
# np.array(J),
# np.array(K+K2+K3),
# np.array(L+L2+L3),
# np.array(M+M2+M3),
# np.array(N+N2+N3)])
#x_list = np.array([0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47])-0.05
y_list = [-0.045, -0.07, -0.0925, -0.115, -0.1375]
for i in range(data.shape[0]):
for j in range(data[0].shape[0]):
if j > 0:
ax2.annotate(data[i][j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="right",
color=cb_colors.gray7,
weight='bold')
else:
ax2.annotate(data[i][j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color=cb_colors.gray7,
weight='bold')
# wind_1km = utils.vec2comp(prof.wind1km[0], prof.wind1km[1])
# wind_6km = utils.vec2comp(prof.wind6km[0], prof.wind6km[1])
# wind_barbs = ax2.barbs(58, 2200, wind_1km[0], wind_1km[1], color='#AA0000', zorder=3, lw=1.25,length=9)
# wind_barbs.set_clip_on(False)
# wind_barbs = ax2.barbs(58, 2200, wind_6km[0], wind_6km[1], color='#0A74C6', zorder=3, lw=1.25,length=9)
# wind_barbs.set_clip_on(False)
# ax2.annotate("1km & 6km AGL\nWind Barbs", (1.08,-0.37), xycoords="axes fraction", fontsize=12, va="top", ha="center", color='#0A74C6', weight='bold')
# Draw the CAPE vs. SRH Scatter
#ax_EFSTP = fig.add_axes([0.74625, 0.0229, 0.20375, 0.2507], frameon=False)
#x_EFSTP = fig.add_axes([0.72625, 0.0229, 0.20375, 0.2507], frameon=False)
ax_EFSTP = fig.add_axes([0.625, 0.05, 0.35, 0.3], frameon=False)
for member_no, c in zip(
np.arange(1, 19, 1),
np.tile([
cb_colors.orange6, cb_colors.orange6, cb_colors.green6,
cb_colors.green6, cb_colors.purple6, cb_colors.purple6
], (3, 1))):
memidx = [0, 10, 11, 12, 13, 14, 15, 16, 17, 1, 2, 3, 4, 5, 6, 7, 8, 9]
ax_EFSTP.scatter(xpts[memidx[member_no - 1], :, :].ravel(),
ypts[memidx[member_no - 1], :, :].ravel(),
color=c,
marker='o',
s=5,
alpha=0.7)
ax_EFSTP.set_xticks(np.arange(0, 5001, 1000))
ax_EFSTP.set_yticks(np.arange(0, 601, 100))
#ax_EFSTP.set_xticklabels(np.arange(0,5001,1000),color='k',fontsize=12)
#ax_EFSTP.set_yticklabels(np.arange(0,601,100),color='k',fontsize=12)
ax_EFSTP.set_xlabel('MLCAPE', weight='bold', fontsize=14)
ax_EFSTP.set_ylabel('0-1km SRH', weight='bold', fontsize=14)
ax_EFSTP.tick_params(axis='both', labelsize=12, labelcolor='k')
ax_EFSTP.set_xlim(-200, 5000)
ax_EFSTP.set_ylim(-100, 600)
#ax_EFSTP.tick_params(direction='in', axis='x', which='major', colors=cb_colors.gray4,length=0,width=1.5,size=12)#pad=-10
#ax_EFSTP.tick_params(direction='in', axis='y', which='major', colors=cb_colors.gray4,length=0,width=1.5,size=12)#pad=-23
ax_EFSTP.grid(color=cb_colors.gray4,
linestyle='--',
dashes=(3, 3),
alpha=0.75,
zorder=0,
linewidth=1.25)
ax_EFSTP.text(0.8,
0.95,
'YSU',
color=cb_colors.orange6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
ax_EFSTP.text(0.8,
0.89,
'MYJ',
color=cb_colors.green6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
ax_EFSTP.text(0.8,
0.83,
'MYNN',
color=cb_colors.purple6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
box = ax_EFSTP.add_patch(
patches.Rectangle((0, -1),
13,
13,
fill=False,
linewidth=2,
edgecolor=cb_colors.gray4,
zorder=10))
box.set_clip_on(False)
ax_EFSTP.annotate("0-1 km SRH vs. 100-mb MLCAPE", (0.5, 1.075),
xycoords="axes fraction",
fontsize=14,
va="center",
ha="center",
color=cb_colors.gray7,
weight='bold')
ax_EFSTP.annotate("(9 km neighborhood)", (0.5, 1.025),
xycoords="axes fraction",
fontsize=12,
va="center",
ha="center",
color=cb_colors.gray7,
weight='bold')
plt.savefig(figname, facecolor=fig.get_facecolor()) #, edgecolor=None)
def unstable_level(prof, lower, upper):
'''
Finds the most unstable level between the lower and upper levels.
Inputs
------
prof (profile object) Profile Object
lower (float) Bottom level (hPa) [-1=SFC]
upper (float) Top level (hPa) [-1=SFC-100hPa]
Returns
-------
Pressure Level of most unstable level (float [hPa])
'''
if lower == -1: lower = prof.gSndg[prof.sfc][prof.pind]
if upper == -1: upper = prof.gSndg[prof.sfc][prof.pind] - 400.
# Make sure this is a valid layer
while not QC(interp.dwpt(upper, prof)):
upper += 50.
if not QC(interp.temp(lower, prof)): lower = prof.gSndg[prof.sfc][0]
# Find lowest observations in the layer
i = 0
while prof.gSndg[i][prof.pind] > lower:
i += 1
while not QC(prof.gSndg[i][prof.tind]):
i += 1
lptr = i
if prof.gSndg[i][prof.pind] == lower: lptr += 1
# Find highest observations in the layer
i = prof.gNumLevels - 1
while prof.gSndg[i][prof.pind] < upper:
i -= 1
uptr = i
if prof.gSndg[i][prof.pind] == upper: uptr -= 1
# Start with interpolated bottom layer
p1 = lower
t1 = interp.temp(p1, prof)
td1 = interp.dwpt(p1, prof)
p2, t2 = thermo.drylift(p1, t1, td1)
tmax = thermo.wetlift(p2, t2, 1000.)
pmax = p1
# Calculate every level that reports a dew point
for i in range(lptr, uptr + 1):
if QC(prof.gSndg[i][prof.tdind]):
p1 = prof.gSndg[i][prof.pind]
t1 = prof.gSndg[i][prof.tind]
td1 = prof.gSndg[i][prof.tdind]
p2, t2 = thermo.drylift(p1, t1, td1)
t1 = thermo.wetlift(p2, t2, 1000.)
if t1 > tmax:
tmax = t1
pmax = p1
# Finish with interpolated top layer
p1 = upper
t1 = interp.temp(p1, prof)
td1 = interp.dwpt(p1, prof)
p2, t2 = thermo.drylift(p1, t1, td1)
t1 = thermo.wetlift(p2, t2, 1000.)
if t1 > tmax:
pmax = prof.gSndg[i][prof.pind]
return pmax
def parcelx(lower, upper, pres, temp, dwpt, prof, **kwargs):
'''
Lifts the specified parcel, calculated various levels and parameters from
the profile object. B+/B- are calculated based on the specified layer.
!! All calculations use the virtual temperature correction unless noted. !!
Inputs
------
lower (float) Lower-bound lifting level (hPa)
upper (float) Upper-bound lifting level
pres (float) Pressure of parcel to lift (hPa)
temp (float) Temperature of parcel to lift (C)
dwpt (float) Dew Point of parcel to lift (C)
prof (profile object) Profile Object
Returns
-------
pcl (parcel object) Parcel Object
'''
pcl = Parcel(-1, -1, pres, temp, dwpt)
if 'lplvals' in kwargs: pcl.lplvals = kwargs.get('lplvals')
else:
lplvals = DefineParcel(prof, 5, pres=pres, temp=temp, dwpt=dwpt)
pcl.lplvals = lplvals
if prof.gNumLevels < 1: return pcl
lyre = -1
cap_strength = RMISSD
cap_strengthpres = RMISSD
li_max = RMISSD
li_maxpres = RMISSD
totp = 0.
totn = 0.
tote = 0.
cinh_old = 0.
# See if default layer is specified
if lower == -1:
lower = prof.gSndg[prof.sfc][prof.pind]
pcl.blayer = lower
if upper == -1:
upper = prof.gSndg[prof.gNumLevels - 1][prof.pind]
pcl.tlayer = upper
# Make sure that this is a valid layer
if lower > pres:
lower = pres
pcl.blayer = lower
if not QC(interp.vtmp(lower, prof)) or \
not QC(interp.vtmp(upper, prof)):
return RMISSD
# Begin with the Mixing Layer
te1 = interp.vtmp(pres, prof)
pe1 = lower
h1 = interp.hght(pe1, prof)
tp1 = thermo.virtemp(pres, temp, dwpt)
# te1 = tp1
# Lift parcel and return LCL pres (hPa) and LCL temp (c)
pe2, tp2 = thermo.drylift(pres, temp, dwpt)
blupper = pe2 # Define top of layer as LCL pres
h2 = interp.hght(pe2, prof)
te2 = interp.vtmp(pe2, prof)
pcl.lclpres = pe2
pcl.lclhght = interp.agl(h2, prof)
# Calculate lifted parcel theta for use in iterative CINH loop below
# RECALL: lifted parcel theta is CONSTANT from LPL to LCL
theta_parcel = thermo.theta(pe2, tp2, 1000.)
# Environmental theta and mixing ratio at LPL
bltheta = thermo.theta(pres, interp.temp(pres, prof), 1000.)
blmr = thermo.mixratio(pres, dwpt)
# ACCUMULATED CINH IN MIXING LAYER BELOW THE LCL
# This will be done in 10mb increments, and will use the virtual
# temperature correction where possible
pinc = -10
a = int(lower)
b = int(blupper)
for pp in range(a, b, int(pinc)):
pp1 = pp
pp2 = pp + pinc
if pp2 < blupper: pp2 = blupper
dz = interp.hght(pp2, prof) - interp.hght(pp1, prof)
# Calculate difference between Tv_parcel and Tv_environment at top
# and bottom of 10mb layers. Make use of constant lifted parcel
# theta and mixing ratio from LPL to LCL
tv_env_bot = thermo.virtemp(
pp1, thermo.theta(pp1, interp.temp(pp1, prof), 1000.),
interp.dwpt(pp1, prof))
tdef1 = (thermo.virtemp(pp1, theta_parcel,
thermo.temp_at_mixrat(blmr, pp1)) - tv_env_bot) / \
(thermo.ctok(tv_env_bot))
tv_env_top = thermo.virtemp(
pp2, thermo.theta(pp2, interp.temp(pp2, prof), 1000.),
interp.dwpt(pp2, prof))
tdef2 = (thermo.virtemp(pp2, theta_parcel,
thermo.temp_at_mixrat(blmr, pp2)) - tv_env_top) / \
(thermo.ctok(tv_env_bot))
lyre = G * (tdef1 + tdef2) / 2. * dz
if lyre < 0: totn += lyre
# Move the bottom layer to the top of the boundary layer
if lower > pe2:
lower = pe2
pcl.blayer = lower
# Calculate height of various temperature levels
p0c = temp_lvl(0., prof)
pm10c = temp_lvl(-10., prof)
pm20c = temp_lvl(-20., prof)
pm30c = temp_lvl(-30., prof)
hgt0c = interp.hght(p0c, prof)
hgtm10c = interp.hght(pm10c, prof)
hgtm20c = interp.hght(pm20c, prof)
hgtm30c = interp.hght(pm30c, prof)
pcl.p0c = p0c
pcl.pm10c = pm10c
pcl.pm20c = pm20c
pcl.pm30c = pm30c
pcl.hght0c = hgt0c
pcl.hghtm10c = hgtm10c
pcl.hghtm20c = hgtm20c
pcl.hghtm30c = hgtm30c
# Find lowest observation in layer
i = 0
while prof.gSndg[i][prof.pind] > lower:
if i == prof.gNumLevels - 1: break
i += 1
while not QC(prof.gSndg[i][prof.tdind]):
if i == prof.gNumLevels - 1: break
i += 1
lptr = i
if prof.gSndg[i][prof.pind] == lower:
if i != prof.gNumLevels - 1: lptr += 1
# Find highest observation in layer
i = prof.gNumLevels - 1
while prof.gSndg[i][prof.pind] < upper:
if i < lptr: break
i -= 1
uptr = i
if prof.gSndg[i][prof.pind] == upper:
if i > lptr: uptr -= 1
# START WITH INTERPOLATED BOTTOM LAYER
# Begin moist ascent from lifted parcel LCL (pe2, tp2)
pe1 = lower
h1 = interp.hght(pe1, prof)
te1 = interp.vtmp(pe1, prof)
tp1 = thermo.wetlift(pe2, tp2, pe1)
lyre = 0
lyrlast = 0
for i in range(lptr, prof.gNumLevels):
if not QC(prof.gSndg[i][prof.tind]): continue
pe2 = prof.gSndg[i][prof.pind]
h2 = prof.gSndg[i][prof.zind]
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe1, tp1, pe2)
tdef1 = (thermo.virtemp(pe1, tp1, tp1) - te1) / thermo.ctok(te1)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / thermo.ctok(te2)
lyrlast = lyre
lyre = G * (tdef1 + tdef2) / 2. * (h2 - h1)
# Add layer energy to total positive if lyre > 0
if lyre > 0:
totp += lyre
# Add layer energy to total negative if lyre < 0, only up to EL
else:
if pe2 > 500.: totn += lyre
# Check for Max LI
mli = thermo.virtemp(pe2, tp2, tp2) - te2
if mli > li_max:
li_max = mli
li_maxpres = pe2
# Check for Max Cap Strength
mcap = te2 - mli
if mcap > cap_strength:
cap_strength = mcap
cap_strengthpres = pe2
tote += lyre
pelast = pe1
pe1 = pe2
h1 = h2
te1 = te2
tp1 = tp2
# Is this the top of the specified layer
if i >= uptr and not QC(pcl.bplus):
pe3 = pe1
h3 = h1
te3 = te1
tp3 = tp1
lyrf = lyre
if lyrf > 0:
pcl.bplus = totp - lyrf
pcl.bminus = totn
else:
pcl.bplus = totp
if pe2 > 500.: pcl.bminus = totn + lyrf
else: pcl.bminus = totn
pe2 = upper
h2 = interp.hght(pe2, prof)
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
if lyrf > 0: pcl.bplus += lyrf
else:
if pe2 > 500.: pcl.bminus += lyrf
if pcl.bplus == 0: pcl.bminus = 0.
# Is this the freezing level
if te2 < 0. and not QC(pcl.bfzl):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.bfzl = totp - lyrf
else: pcl.bfzl = totp
if not QC(p0c) or p0c > pe3:
pcl.bfzl = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgt0c - h3)
if lyrf > 0: pcl.bfzl += lyrf
# Is this the -10C level
if te2 < -10. and not QC(pcl.wm10c):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.wm10c = totp - lyrf
else: pcl.wm10c = totp
if not QC(pm10c) or pm10c > pcl.lclpres:
pcl.wm10c = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgtm10c - h3)
if lyrf > 0: pcl.wm10c += lyrf
# Is this the -20C level
if te2 < -20. and not QC(pcl.wm20c):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.wm20c = totp - lyrf
else: pcl.wm20c = totp
if not QC(pm20c) or pm20c > pcl.lclpres:
pcl.wm20c = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgtm20c - h3)
if lyrf > 0: pcl.wm20c += lyrf
# Is this the -30C level
if te2 < -30. and not QC(pcl.wm30c):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.wm30c = totp - lyrf
else: pcl.wm30c = totp
if not QC(pm30c) or pm30c > pcl.lclpres:
pcl.wm30c = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgtm30c - h3)
if lyrf > 0: pcl.wm30c += lyrf
# Is this the 3km level
if pcl.lclhght < 3000.:
h = interp.agl(interp.hght(pe2, prof), prof)
if h >= 3000. and not QC(pcl.b3km):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0: pcl.b3km = totp - lyrf
else: pcl.b3km = totp
h2 = interp.msl(3000., prof)
pe2 = interp.pres(h2, prof)
if QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
if lyrf > 0: pcl.b3km += lyrf
else: pcl.b3km = 0.
# Is this the 6km level
if pcl.lclhght < 6000.:
h = interp.agl(interp.hght(pe2, prof), prof)
if h >= 6000. and not QC(pcl.b6km):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0: pcl.b6km = totp - lyrf
else: pcl.b6km = totp
h2 = interp.msl(6000., prof)
pe2 = interp.pres(h2, prof)
if QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
if lyrf > 0: pcl.b6km += lyrf
else: pcl.b6km = 0.
# LFC Possibility
if lyre >= 0. and lyrlast <= 0.:
tp3 = tp1
te3 = te1
pe2 = pe1
pe3 = pelast
while interp.vtmp(pe3, prof) > thermo.virtemp(
pe3, thermo.wetlift(pe2, tp3, pe3),
thermo.wetlift(pe2, tp3, pe3)):
pe3 -= 5
pcl.lfcpres = pe3
pcl.lfchght = interp.agl(interp.hght(pe3, prof), prof)
cinh_old = totn
tote = 0.
pcl.elpres = RMISSD
li_max = RMISSD
if cap_strength < 0.: cap_strength = 0.
pcl.cap = cap_strength
pcl.cappres = cap_strengthpres
# Hack to force LFC to be at least at the LCL
if pcl.lfcpres > pcl.lclpres:
pcl.lfcpres = pcl.lclpres
pcl.lfchght = pcl.lclhght
# EL Possibility
if lyre <= 0. and lyrlast >= 0.:
tp3 = tp1
te3 = te1
pe2 = pe1
pe3 = pelast
while interp.vtmp(pe3, prof) < thermo.virtemp(
pe3, thermo.wetlift(pe2, tp3, pe3),
thermo.wetlift(pe2, tp3, pe3)):
pe3 -= 5
pcl.elpres = pe3
pcl.elhght = interp.agl(interp.hght(pe3, prof), prof)
pcl.mplpres = RMISSD
pcl.limax = -li_max
pcl.limaxpress = li_maxpres
# MPL Possibility
if tote < 0. and not QC(pcl.mplpres) and QC(pcl.elpres):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
totx = tote - lyre
pe2 = pelast
while totx > 0:
pe2 -= 1
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
h2 = interp.hght(pe2, prof)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
totx += lyrf
tp3 = tp2
te3 = te2
pe3 = pe2
pcl.mplpres = pe2
pcl.mplhght = interp.agl(interp.hght(pe2, prof), prof)
# 500 hPa Lifted Index
if prof.gSndg[i][prof.pind] <= 500. and pcl.li5 == RMISSD:
a = interp.vtmp(500., prof)
b = thermo.wetlift(pe1, tp1, 500.)
pcl.li5 = a - thermo.virtemp(500, b, b)
# 300 hPa Lifted Index
if prof.gSndg[i][prof.pind] <= 300. and pcl.li3 == RMISSD:
a = interp.vtmp(300., prof)
b = thermo.wetlift(pe1, tp1, 300.)
pcl.li3 = a - thermo.virtemp(300, b, b)
# Calculate BRN if available
pcl = bulk_rich(pcl, prof)
pcl.bminus = cinh_old
if pcl.bplus == 0: pcl.bminus = 0.
return pcl
def unstable_level(prof, lower, upper):
'''
Finds the most unstable level between the lower and upper levels.
Inputs
------
prof (profile object) Profile Object
lower (float) Bottom level (hPa) [-1=SFC]
upper (float) Top level (hPa) [-1=SFC-100hPa]
Returns
-------
Pressure Level of most unstable level (float [hPa])
'''
if lower == -1: lower = prof.gSndg[prof.sfc][prof.pind]
if upper == -1: upper = prof.gSndg[prof.sfc][prof.pind] - 400.
# Make sure this is a valid layer
while not QC(interp.dwpt(upper, prof)): upper += 50.
if not QC(interp.temp(lower, prof)): lower = prof.gSndg[prof.sfc][0]
# Find lowest observations in the layer
i = 0
while prof.gSndg[i][prof.pind] > lower: i+=1
while not QC(prof.gSndg[i][prof.tind]): i+=1
lptr = i
if prof.gSndg[i][prof.pind] == lower: lptr+=1
# Find highest observations in the layer
i = prof.gNumLevels - 1
while prof.gSndg[i][prof.pind] < upper: i-=1
uptr = i
if prof.gSndg[i][prof.pind] == upper: uptr-=1
# Start with interpolated bottom layer
p1 = lower
t1 = interp.temp(p1, prof)
td1 = interp.dwpt(p1, prof)
p2, t2 = thermo.drylift(p1, t1, td1)
tmax = thermo.wetlift(p2, t2, 1000.)
pmax = p1
# Calculate every level that reports a dew point
for i in range(lptr, uptr+1):
if QC(prof.gSndg[i][prof.tdind]):
p1 = prof.gSndg[i][prof.pind]
t1 = prof.gSndg[i][prof.tind]
td1 = prof.gSndg[i][prof.tdind]
p2, t2 = thermo.drylift(p1, t1, td1)
t1 = thermo.wetlift(p2, t2, 1000.)
if t1 > tmax:
tmax = t1
pmax = p1
# Finish with interpolated top layer
p1 = upper
t1 = interp.temp(p1, prof)
td1 = interp.dwpt(p1, prof)
p2, t2 = thermo.drylift(p1, t1, td1)
t1 = thermo.wetlift(p2, t2, 1000.)
if t1 > tmax:
pmax = prof.gSndg[i][prof.pind]
return pmax
def parcelx(lower, upper, pres, temp, dwpt, prof, **kwargs):
'''
Lifts the specified parcel, calculated various levels and parameters from
the profile object. B+/B- are calculated based on the specified layer.
!! All calculations use the virtual temperature correction unless noted. !!
Inputs
------
lower (float) Lower-bound lifting level (hPa)
upper (float) Upper-bound lifting level
pres (float) Pressure of parcel to lift (hPa)
temp (float) Temperature of parcel to lift (C)
dwpt (float) Dew Point of parcel to lift (C)
prof (profile object) Profile Object
Returns
-------
pcl (parcel object) Parcel Object
'''
pcl = Parcel(-1, -1, pres, temp, dwpt)
if 'lplvals' in kwargs: pcl.lplvals = kwargs.get('lplvals')
else:
lplvals = DefineParcel(prof, 5, pres=pres, temp=temp, dwpt=dwpt)
pcl.lplvals = lplvals
if prof.gNumLevels < 1: return pcl
lyre = -1
cap_strength = RMISSD
cap_strengthpres = RMISSD
li_max = RMISSD
li_maxpres = RMISSD
totp = 0.
totn = 0.
tote = 0.
cinh_old = 0.
# See if default layer is specified
if lower == -1:
lower = prof.gSndg[prof.sfc][prof.pind]
pcl.blayer = lower
if upper == -1:
upper = prof.gSndg[prof.gNumLevels-1][prof.pind]
pcl.tlayer = upper
# Make sure that this is a valid layer
if lower > pres:
lower = pres
pcl.blayer = lower
if not QC(interp.vtmp(lower, prof)) or \
not QC(interp.vtmp(upper, prof)):
return RMISSD
# Begin with the Mixing Layer
te1 = interp.vtmp(pres, prof)
pe1 = lower
h1 = interp.hght(pe1, prof)
tp1 = thermo.virtemp(pres, temp, dwpt)
# te1 = tp1
# Lift parcel and return LCL pres (hPa) and LCL temp (c)
pe2, tp2 = thermo.drylift(pres, temp, dwpt)
blupper = pe2 # Define top of layer as LCL pres
h2 = interp.hght(pe2, prof)
te2 = interp.vtmp(pe2, prof)
pcl.lclpres = pe2
pcl.lclhght = interp.agl(h2, prof)
# Calculate lifted parcel theta for use in iterative CINH loop below
# RECALL: lifted parcel theta is CONSTANT from LPL to LCL
theta_parcel = thermo.theta(pe2, tp2, 1000.)
# Environmental theta and mixing ratio at LPL
bltheta = thermo.theta(pres, interp.temp(pres, prof), 1000.)
blmr = thermo.mixratio(pres, dwpt)
# ACCUMULATED CINH IN MIXING LAYER BELOW THE LCL
# This will be done in 10mb increments, and will use the virtual
# temperature correction where possible
pinc = -10
a = int(lower)
b = int(blupper)
for pp in range(a, b, int(pinc)):
pp1 = pp
pp2 = pp + pinc
if pp2 < blupper: pp2 = blupper
dz = interp.hght(pp2, prof) - interp.hght(pp1, prof)
# Calculate difference between Tv_parcel and Tv_environment at top
# and bottom of 10mb layers. Make use of constant lifted parcel
# theta and mixing ratio from LPL to LCL
tv_env_bot = thermo.virtemp(pp1, thermo.theta(pp1,
interp.temp(pp1, prof), 1000.), interp.dwpt(pp1, prof))
tdef1 = (thermo.virtemp(pp1, theta_parcel,
thermo.temp_at_mixrat(blmr, pp1)) - tv_env_bot) / \
(thermo.ctok(tv_env_bot))
tv_env_top = thermo.virtemp(pp2, thermo.theta(pp2,
interp.temp(pp2, prof), 1000.), interp.dwpt(pp2, prof))
tdef2 = (thermo.virtemp(pp2, theta_parcel,
thermo.temp_at_mixrat(blmr, pp2)) - tv_env_top) / \
(thermo.ctok(tv_env_bot))
lyre = G * (tdef1 + tdef2) / 2. * dz
if lyre < 0: totn += lyre
# Move the bottom layer to the top of the boundary layer
if lower > pe2:
lower = pe2
pcl.blayer = lower
# Calculate height of various temperature levels
p0c = temp_lvl(0., prof)
pm10c = temp_lvl(-10., prof)
pm20c = temp_lvl(-20., prof)
pm30c = temp_lvl(-30., prof)
hgt0c = interp.hght(p0c, prof)
hgtm10c = interp.hght(pm10c, prof)
hgtm20c = interp.hght(pm20c, prof)
hgtm30c = interp.hght(pm30c, prof)
pcl.p0c = p0c
pcl.pm10c = pm10c
pcl.pm20c = pm20c
pcl.pm30c = pm30c
pcl.hght0c = hgt0c
pcl.hghtm10c = hgtm10c
pcl.hghtm20c = hgtm20c
pcl.hghtm30c = hgtm30c
# Find lowest observation in layer
i = 0
while prof.gSndg[i][prof.pind] > lower:
if i == prof.gNumLevels-1: break
i += 1
while not QC(prof.gSndg[i][prof.tdind]):
if i == prof.gNumLevels-1: break
i += 1
lptr = i
if prof.gSndg[i][prof.pind] == lower:
if i != prof.gNumLevels-1: lptr += 1
# Find highest observation in layer
i = prof.gNumLevels-1
while prof.gSndg[i][prof.pind] < upper:
if i < lptr: break
i -= 1
uptr = i
if prof.gSndg[i][prof.pind] == upper:
if i > lptr: uptr -= 1
# START WITH INTERPOLATED BOTTOM LAYER
# Begin moist ascent from lifted parcel LCL (pe2, tp2)
pe1 = lower
h1 = interp.hght(pe1, prof)
te1 = interp.vtmp(pe1, prof)
tp1 = thermo.wetlift(pe2, tp2, pe1)
lyre = 0
lyrlast = 0
for i in range(lptr, prof.gNumLevels):
if not QC(prof.gSndg[i][prof.tind]): continue
pe2 = prof.gSndg[i][prof.pind]
h2 = prof.gSndg[i][prof.zind]
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe1, tp1, pe2)
tdef1 = (thermo.virtemp(pe1, tp1, tp1) - te1) / thermo.ctok(te1)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / thermo.ctok(te2)
lyrlast = lyre
lyre = G * (tdef1 + tdef2) / 2. * (h2 - h1)
# Add layer energy to total positive if lyre > 0
if lyre > 0: totp += lyre
# Add layer energy to total negative if lyre < 0, only up to EL
else:
if pe2 > 500.: totn += lyre
# Check for Max LI
mli = thermo.virtemp(pe2, tp2, tp2) - te2
if mli > li_max:
li_max = mli
li_maxpres = pe2
# Check for Max Cap Strength
mcap = te2 - mli
if mcap > cap_strength:
cap_strength = mcap
cap_strengthpres = pe2
tote += lyre
pelast = pe1
pe1 = pe2
h1 = h2
te1 = te2
tp1 = tp2
# Is this the top of the specified layer
if i >= uptr and not QC(pcl.bplus):
pe3 = pe1
h3 = h1
te3 = te1
tp3 = tp1
lyrf = lyre
if lyrf > 0:
pcl.bplus = totp - lyrf
pcl.bminus = totn
else:
pcl.bplus = totp
if pe2 > 500.: pcl.bminus = totn + lyrf
else: pcl.bminus = totn
pe2 = upper
h2 = interp.hght(pe2, prof)
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
if lyrf > 0: pcl.bplus += lyrf
else:
if pe2 > 500.: pcl.bminus += lyrf
if pcl.bplus == 0: pcl.bminus = 0.
# Is this the freezing level
if te2 < 0. and not QC(pcl.bfzl):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.bfzl = totp - lyrf
else: pcl.bfzl = totp
if not QC(p0c) or p0c > pe3:
pcl.bfzl = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgt0c - h3)
if lyrf > 0: pcl.bfzl += lyrf
# Is this the -10C level
if te2 < -10. and not QC(pcl.wm10c):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.wm10c = totp - lyrf
else: pcl.wm10c = totp
if not QC(pm10c) or pm10c > pcl.lclpres:
pcl.wm10c = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgtm10c - h3)
if lyrf > 0: pcl.wm10c += lyrf
# Is this the -20C level
if te2 < -20. and not QC(pcl.wm20c):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.wm20c = totp - lyrf
else: pcl.wm20c = totp
if not QC(pm20c) or pm20c > pcl.lclpres:
pcl.wm20c = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgtm20c - h3)
if lyrf > 0: pcl.wm20c += lyrf
# Is this the -30C level
if te2 < -30. and not QC(pcl.wm30c):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0.: pcl.wm30c = totp - lyrf
else: pcl.wm30c = totp
if not QC(pm30c) or pm30c > pcl.lclpres:
pcl.wm30c = 0
elif QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (hgtm30c - h3)
if lyrf > 0: pcl.wm30c += lyrf
# Is this the 3km level
if pcl.lclhght < 3000.:
h = interp.agl(interp.hght(pe2, prof), prof)
if h >= 3000. and not QC(pcl.b3km):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0: pcl.b3km = totp - lyrf
else: pcl.b3km = totp
h2 = interp.msl(3000., prof)
pe2 = interp.pres(h2, prof)
if QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
if lyrf > 0: pcl.b3km += lyrf
else: pcl.b3km = 0.
# Is this the 6km level
if pcl.lclhght < 6000.:
h = interp.agl(interp.hght(pe2, prof), prof)
if h >= 6000. and not QC(pcl.b6km):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
lyrf = lyre
if lyrf > 0: pcl.b6km = totp - lyrf
else: pcl.b6km = totp
h2 = interp.msl(6000., prof)
pe2 = interp.pres(h2, prof)
if QC(pe2):
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
if lyrf > 0: pcl.b6km += lyrf
else: pcl.b6km = 0.
# LFC Possibility
if lyre >= 0. and lyrlast <= 0.:
tp3 = tp1
te3 = te1
pe2 = pe1
pe3 = pelast
while interp.vtmp(pe3, prof) > thermo.virtemp(pe3,
thermo.wetlift(pe2, tp3, pe3), thermo.wetlift(pe2, tp3, pe3)):
pe3 -= 5
pcl.lfcpres = pe3
pcl.lfchght = interp.agl(interp.hght(pe3, prof), prof)
cinh_old = totn
tote = 0.
pcl.elpres = RMISSD
li_max = RMISSD
if cap_strength < 0.: cap_strength = 0.
pcl.cap = cap_strength
pcl.cappres = cap_strengthpres
# Hack to force LFC to be at least at the LCL
if pcl.lfcpres > pcl.lclpres:
pcl.lfcpres = pcl.lclpres
pcl.lfchght = pcl.lclhght
# EL Possibility
if lyre <= 0. and lyrlast >= 0.:
tp3 = tp1
te3 = te1
pe2 = pe1
pe3 = pelast
while interp.vtmp(pe3, prof) < thermo.virtemp(pe3,
thermo.wetlift(pe2, tp3, pe3), thermo.wetlift(pe2, tp3, pe3)):
pe3 -= 5
pcl.elpres = pe3
pcl.elhght = interp.agl(interp.hght(pe3, prof), prof)
pcl.mplpres = RMISSD
pcl.limax = -li_max
pcl.limaxpress = li_maxpres
# MPL Possibility
if tote < 0. and not QC(pcl.mplpres) and QC(pcl.elpres):
pe3 = pelast
h3 = interp.hght(pe3, prof)
te3 = interp.vtmp(pe3, prof)
tp3 = thermo.wetlift(pe1, tp1, pe3)
totx = tote - lyre
pe2 = pelast
while totx > 0:
pe2 -= 1
te2 = interp.vtmp(pe2, prof)
tp2 = thermo.wetlift(pe3, tp3, pe2)
h2 = interp.hght(pe2, prof)
tdef3 = (thermo.virtemp(pe3, tp3, tp3) - te3) / \
thermo.ctok(te3)
tdef2 = (thermo.virtemp(pe2, tp2, tp2) - te2) / \
thermo.ctok(te2)
lyrf = G * (tdef3 + tdef2) / 2. * (h2 - h3)
totx += lyrf
tp3 = tp2
te3 = te2
pe3 = pe2
pcl.mplpres = pe2
pcl.mplhght = interp.agl(interp.hght(pe2, prof), prof)
# 500 hPa Lifted Index
if prof.gSndg[i][prof.pind] <= 500. and pcl.li5 == RMISSD:
a = interp.vtmp(500., prof)
b = thermo.wetlift(pe1, tp1, 500.)
pcl.li5 = a - thermo.virtemp(500, b, b)
# 300 hPa Lifted Index
if prof.gSndg[i][prof.pind] <= 300. and pcl.li3 == RMISSD:
a = interp.vtmp(300., prof)
b = thermo.wetlift(pe1, tp1, 300.)
pcl.li3 = a - thermo.virtemp(300, b, b)
# Calculate BRN if available
pcl = bulk_rich(pcl, prof)
pcl.bminus = cinh_old
if pcl.bplus == 0: pcl.bminus = 0.
return pcl
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 ##
"""
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()