def plotWinds(u_snd, v_snd, p_snd, **kwargs):
t_base = 45
stride = 1
idx_min = np.argmin(np.abs(_p_max - p_snd))
idx_max = np.argmin(np.abs(_p_min - p_snd))
if p_snd[idx_min] > _p_max:
idx_min += 1
if p_snd[idx_max] > _p_min:
idx_max += 1
thin_data = (slice(idx_min, idx_max, stride))
pylab.axvline(x=t_base, color='k', lw=0.5)
points = np.vstack((t_base * np.ones(p_snd.shape), p_snd)).T
x, base_y = _stlp_data_transform.transform(np.array([[t_base, 1000]]))[0]
trans_points = _stlp_data_transform.transform(points)
trans_points[:, 0] = x
axes_points = pylab.gca().transData.inverted().transform(trans_points)
plot_xs, plot_ys = zip(*axes_points)
pylab.barbs(plot_xs[thin_data], plot_ys[thin_data], u_snd[thin_data], v_snd[thin_data], clip_on=False)
return
def plot_ua_rhum(press):
print(" %smb RHUM" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width, height), frameon=False)
heights = hght_clevs[contlevid[str(press)]]
cdict = {
'red': ((0.00, 0.76, 0.76), (0.25, 0.64, 0.64), (0.50, 0.52, 0.52),
(0.75, 0.42, 0.42), (1.00, 0.32, 0.32)),
'green': ((0.00, 0.90, 0.90), (0.25, 0.80, 0.80), (0.50, 0.70, 0.70),
(0.75, 0.60, 0.60), (1.00, 0.50, 0.50)),
'blue': ((0.00, 0.49, 0.49), (0.25, 0.32, 0.32), (0.50, 0.17, 0.17),
(0.75, 0.06, 0.06), (1.00, 0.05, 0.05))
}
rhum_coltbl = LinearSegmentedColormap('RHUM_COLTBL', cdict)
# Temperature calculation here
#TC = temps_ua[time,level] - 273
TH_K = np.add(temps_ua[time, level], temps_base_ua)
TK = np.multiply(TH_K, math.pow((float(press) / 1000.), (287.04 / 1004.)))
TC = TK - 273
# RHUM Calculation here
es = 6.112 * np.exp(17.67 * TC / (TC + 243.5))
w = np.divide(qvap[time, level], (1 - qvap[time, level]))
e = (w * float(press)) / (0.622 + w)
relh = e / es * 100.
RHUM = pylab.contourf(x,
y,
relh,
rhum_clevs,
extend='max',
cmap=rhum_coltbl)
# Contour the heights
phtotal = np.add(phb[time, level], ph[time, level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT = pylab.contour(x, y, gheight, heights, colors='k', linewidths=1.5)
pylab.clabel(HGHT, inline=1, fontsize=8, fmt='%1.0f', inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time, level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time, level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb RELH, Height (m), Wind (kts)' % press
prodid = '%smb_rel_hum' % press
units = "percent"
drawmap(RHUM, title, prodid, units)
def plot_ua_vvel(press):
print(" %smb VVEL" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width, height), frameon=False)
cdict_vvel = {
'red': ((0.00, 0.50, 0.50), (0.10, 1.00, 1.00), (0.20, 1.00, 1.00),
(0.35, 1.00, 1.00), (0.40, 1.00, 1.00), (0.45, 0.80, 0.80),
(0.50, 0.72, 0.72), (0.55, 0.68, 0.68), (0.60, 0.64, 0.64),
(0.65, 0.60, 0.70), (0.70, 0.40, 0.45), (0.80, 0.20, 0.20),
(0.90, 0.00, 0.00), (1.00, 0.00, 0.00)),
'green': ((0.00, 0.35, 0.35), (0.10, 0.45, 0.45), (0.20, 0.55, 0.55),
(0.35, 1.00, 1.00), (0.40, 1.00, 1.00), (0.45, 1.00, 1.00),
(0.50, 1.00, 1.00), (0.55, 1.00, 1.00), (0.60, 1.00, 1.00),
(0.65, 1.00, 1.00), (0.70, 1.00, 1.00), (0.80, 1.00, 1.00),
(0.90, 1.00, 1.00), (1.00, 0.80, 0.80)),
'blue': ((0.00, 0.00, 0.00), (0.10, 0.00, 0.00), (0.20, 0.20, 0.20),
(0.35, 1.00, 1.00), (0.40, 1.00, 1.00), (0.45, 1.00, 1.00),
(0.50, 1.00, 1.00), (0.55, 1.00, 1.00), (0.60, 1.00, 1.00),
(0.65, 1.00, 1.00), (0.70, 1.00, 1.00), (0.80, 1.00, 1.00),
(0.90, 1.00, 1.00), (1.00, 0.80, 0.80))
}
vvel_coltbl = LinearSegmentedColormap('VVEL_COLTBL', cdict_vvel)
vert_vel = np.multiply(w_wind_ua[time, level], 100)
vert_vel = np.nan_to_num(vert_vel)
VVEL = pylab.contourf(x,
y,
vert_vel,
vvel_clevs,
cmap=vvel_coltbl,
extend='both')
# Contour the heights
heights = hght_clevs[contlevid[str(press)]]
phtotal = np.add(phb[time, level], ph[time, level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT = pylab.contour(x, y, gheight, heights, colors='k', linewidths=1.5)
pylab.clabel(HGHT, inline=1, fontsize=8, fmt='%1.0f', inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time, level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time, level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb VVel, Height (m), Wind (kts)' % press
prodid = '%smb_vert_vel' % press
units = "cm/s"
drawmap(VVEL, title, prodid, units)
def plotObservations(obs, map, title, file_name, refl=None):
pylab.clf()
if refl is not None:
nx, ny = refl.shape
xs, ys = np.meshgrid(np.arange(nx) * 1000, np.arange(ny) * 1000)
map.contour(xs, ys, refl, levels=np.arange(20, 80, 20), colors='k')
obs_x, obs_y = map(obs['longitude'], obs['latitude'])
for ob, x, y in zip(obs, obs_x, obs_y):
pylab.plot(x, y, 'ko', ms=3.)
pylab.text(x - 500, y + 500, "%4.1f" % ob['temp'], size='small', va='bottom', ha='right', color='r')
pylab.text(x - 500, y - 500, "%4.1f" % ob['dewp'], size='small', va='top', ha='right', color='g')
pylab.text(x + 500, y - 500, ob['id'], size='small', va='top', ha='left', color='#808080')
good_idxs = np.where((obs['wind_spd'] >= 0.) & (obs['wind_dir'] >= 0.))
u = -obs['wind_spd'] * np.sin(obs['wind_dir'] * np.pi / 180) * 1.94
v = -obs['wind_spd'] * np.cos(obs['wind_dir'] * np.pi / 180) * 1.94
u_rot, v_rot = map.rotate_vector(u, v, obs['longitude'], obs['latitude'])
pylab.barbs(obs_x[good_idxs], obs_y[good_idxs], u_rot[good_idxs], v_rot[good_idxs])
drawPolitical(map, scale_len=5)
pylab.title(title)
pylab.savefig(file_name)
return
def plot_sfwind():
print(" 10M WIND")
# Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms[time] * 1.94384449
v_wind_kts = v_wind_ms[time] * 1.94384449
windmag = np.power(np.power(u_wind_kts,2)+np.power(v_wind_kts,2), 0.5)
WIND_LEVS = range(10,46,2)
W=pylab.contourf(x,y,windmag,WIND_LEVS,extend='max')
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5,\
sizes={'spacing':0.2},pivot='middle')
# Convert Surface Pressure to Mean Sea Level Pressure
stemps = temps[time]+6.5*nc.variables['HGT'][time]/1000.
mslp = nc.variables['PSFC'][time]*np.exp(9.81/(287.0*stemps)*nc.variables['HGT'][time])*0.01 + (6.7 * nc.variables['HGT'][time] / 1000)
# Contour the pressure
#PLEVS = range(900,1050,5)
#P=pylab.contour(x,y,mslp,PLEVS,V=2,colors='k',linewidths=1.5)
#pylab.clabel(P,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
title = 'Sfc MSLP (mb), 10m Wind (kts)'
prodid = 'wind'
units = "kts"
drawmap(W, title, prodid, units)
def plot_srhel():
print(" SR Helicity")
# Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
P = nc.variables['P']
PB = nc.variables['PB']
UU = nc.variables['U']
VV = nc.variables['V']
PH = nc.variables['PH']
PHB = nc.variables['PHB']
# Need pressures, temps and mixing ratios
PR = P[time] + PB[time]
PHT = np.add(PH[time],PHB[time])
ZH = np.divide(PHT, 9.81)
U = UU[time]
V = VV[time]
for j in range(len(U[1,:,1])):
curcol_c = []
curcol_Umo = []
curcol_Vmo = []
for i in range(len(V[1,1,:])):
sparms = severe.SRHEL_CALC(U[:,j,i], V[:,j,i], ZH[:,j,i], PR[:,j,i])
curcol_c.append(sparms[0])
curcol_Umo.append(sparms[1])
curcol_Vmo.append(sparms[2])
np_curcol_c = np.array(curcol_c)
np_curcol_Umo = np.array(curcol_Umo)
np_curcol_Vmo = np.array(curcol_Vmo)
if j == 0:
srhel = np_curcol_c
U_srm = np_curcol_Umo
V_srm = np_curcol_Vmo
else:
srhel = np.row_stack((srhel, np_curcol_c))
U_srm = np.row_stack((U_srm, np_curcol_Umo))
V_srm = np.row_stack((V_srm, np_curcol_Vmo))
#print " SRHEL: ", np.shape(srhel)
# Now plot
SRHEL_LEVS = range(50,800,50)
srhel = np.nan_to_num(srhel)
SRHEL=pylab.contourf(x,y,srhel,SRHEL_LEVS)
u_mo_kts = U_srm * 1.94384449
v_mo_kts = V_srm * 1.94384449
pylab.barbs(x_th,y_th,u_mo_kts[::thin,::thin],\
v_mo_kts[::thin,::thin], length=5,\
sizes={'spacing':0.2},pivot='middle')
title = '0-3 km SRHelicity, Storm Motion (kt)'
prodid = 'hlcy'
units = "m" + u'\u00B2' + '/s' + u'\u00B2'
drawmap(SRHEL, title, prodid, units)
def _windbarbs(speed, theta, p): # in kt
x = np.cos(np.radians(270 - theta)) # direction only
y = np.sin(np.radians(270 - theta)) # direction only
u = x * speed
v = y * speed
for item in zip(p, theta, speed, x, y, u, v):
print(item)
i = 0
for pi in p * 100:
pl.barbs(45, pi, u[i], v[i])
i = i + 1
return
def plotObservations(obs, map, title, file_name, refl=None):
pylab.clf()
if refl is not None:
nx, ny = refl.shape
xs, ys = np.meshgrid(np.arange(nx) * 1000, np.arange(ny) * 1000)
map.contour(xs, ys, refl, levels=np.arange(20, 80, 20), colors='k')
obs_x, obs_y = map(obs['longitude'], obs['latitude'])
for ob, x, y in zip(obs, obs_x, obs_y):
pylab.plot(x, y, 'ko', ms=3.)
pylab.text(x - 500,
y + 500,
"%4.1f" % ob['temp'],
size='small',
va='bottom',
ha='right',
color='r')
pylab.text(x - 500,
y - 500,
"%4.1f" % ob['dewp'],
size='small',
va='top',
ha='right',
color='g')
pylab.text(x + 500,
y - 500,
ob['id'],
size='small',
va='top',
ha='left',
color='#808080')
good_idxs = np.where((obs['wind_spd'] >= 0.) & (obs['wind_dir'] >= 0.))
u = -obs['wind_spd'] * np.sin(obs['wind_dir'] * np.pi / 180) * 1.94
v = -obs['wind_spd'] * np.cos(obs['wind_dir'] * np.pi / 180) * 1.94
u_rot, v_rot = map.rotate_vector(u, v, obs['longitude'], obs['latitude'])
pylab.barbs(obs_x[good_idxs], obs_y[good_idxs], u_rot[good_idxs],
v_rot[good_idxs])
drawPolitical(map, scale_len=5)
pylab.title(title)
pylab.savefig(file_name)
return
def plotWinds(u_snd, v_snd, p_snd, **kwargs):
t_base = 45
stride = 50
thin_data = (slice(None, None, stride))
pylab.axvline(x=t_base, color='k', lw=0.5)
points = np.vstack((t_base * np.ones(p_snd.shape), p_snd)).T
x, base_y = _stlp_data_transform.transform(np.array([[t_base, 1000]]))[0]
trans_points = _stlp_data_transform.transform(points)
trans_points[:, 0] = x
axes_points = pylab.gca().transData.inverted().transform(trans_points)
plot_xs, plot_ys = zip(*axes_points)
pylab.barbs(plot_xs[thin_data], plot_ys[thin_data], u_snd[thin_data], v_snd[thin_data])
return
def plot_ua_winds(press):
from scipy.ndimage.filters import gaussian_filter
print(" %smb WINDS" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width, height), frameon=False)
heights = hght_clevs[contlevid[str(press)]]
phtotal = np.add(phb[time, level], ph[time, level])
gheight = gaussian_filter(np.divide(phtotal, 9.81), 1)
#gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time, level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time, level] * 1.94384449
u_wind_kts_sq = np.power(u_wind_kts, 2)
v_wind_kts_sq = np.power(v_wind_kts, 2)
wind_sum = np.add(u_wind_kts_sq, v_wind_kts_sq)
# Find actual wind speed and contour
windspd = np.sqrt(wind_sum)
WSPD = pylab.contourf(x,
y,
windspd,
wspd_clevs[contlevid[str(press)]],
extend='max')
# Contour the heights
HGHT = pylab.contour(x, y, gheight, heights, linewidths=1.5, colors='k')
pylab.clabel(HGHT, inline=1, fontsize=8, fmt='%1.0f', inline_spacing=1)
#pylab.clabel(T,inline=1,fontsize=10)
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb Height (m), Wind' % press
prodid = '%smb_wind' % press
units = 'kts'
drawmap(WSPD, title, prodid, units)
def plotWinds(u_snd, v_snd, p_snd, **kwargs):
t_base = 45
stride = 50
thin_data = (slice(None, None, stride))
pylab.axvline(x=t_base, color='k', lw=0.5)
points = np.vstack((t_base * np.ones(p_snd.shape), p_snd)).T
x, base_y = _stlp_data_transform.transform(np.array([[t_base, 1000]]))[0]
trans_points = _stlp_data_transform.transform(points)
trans_points[:, 0] = x
axes_points = pylab.gca().transData.inverted().transform(trans_points)
plot_xs, plot_ys = zip(*axes_points)
pylab.barbs(plot_xs[thin_data], plot_ys[thin_data], u_snd[thin_data],
v_snd[thin_data])
return
def plot_dwp():
print " DEWPOINT"
# Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
qhum = nc.variables['Q2']
# Convert Surface Pressure to Mean Sea Level Pressure
stemps = temps[time]+6.5*nc.variables['HGT'][time]/1000.
mslp = psfc[time]*np.exp(9.81/(287.0*stemps)*nc.variables['HGT'][time])*0.01
# Find saturation vapor pressure
es = 6.112 * np.exp(17.67 * temps[time]/(temps[time] + 243.5))
w = qhum[time]/(1-qhum[time])
e = (w * psfc[time] / (.622 + w)) / 100
Td_C = (243.5 * np.log(e/6.112))/(17.67-np.log(e/6.112))
Td_F = (Td_C * 9 / 5) + 32
DP_LEVS = range(-10,85,1)
# Contour and fill the dewpoint temperature
Td=pylab.contourf(x,y,Td_F,DP_LEVS,cmap=coltbls.dewpoint1(),extend='min')
Td_lev = pylab.contour(x,y,Td_F,DP_CLEVS,colors='k',linewidths=.5)
pylab.clabel(Td_lev,inline=1,fontsize=7,fmt='%1.0f',inline_spacing=1)
# Contour the pressure
# P=pylab.contour(x,y,mslp,V=2,colors='k',linewidths=1.5)
# pylab.clabel(P,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
#pylab.clabel(T,inline=1,fontsize=10)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms[time] * 1.94384449
v_wind_kts = v_wind_ms[time] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5,\
sizes={'spacing':0.2},pivot='middle')
title = 'Surface Dwp, 10m Wind (kts)'
prodid = 'dewp'
units = u"\u00B0" + "F"
drawmap(Td, title, prodid, units)
def plot_ua_winds(press):
from scipy.ndimage.filters import gaussian_filter
print(" %smb WINDS" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
heights=hght_clevs[contlevid[str(press)]]
phtotal = np.add(phb[time,level],ph[time,level])
gheight = gaussian_filter(np.divide(phtotal, 9.81),1)
#gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time,level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time,level] * 1.94384449
u_wind_kts_sq = np.power(u_wind_kts, 2)
v_wind_kts_sq = np.power(v_wind_kts, 2)
wind_sum = np.add(u_wind_kts_sq,v_wind_kts_sq)
# Find actual wind speed and contour
windspd = np.sqrt(wind_sum)
WSPD=pylab.contourf(x,y,windspd,wspd_clevs[contlevid[str(press)]],extend='max')
# Contour the heights
HGHT=pylab.contour(x,y,gheight,heights,linewidths=1.5, colors = 'k')
pylab.clabel(HGHT,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
#pylab.clabel(T,inline=1,fontsize=10)
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb Height (m), Wind' % press
prodid = '%smb_wind' % press
units = 'kts'
drawmap(WSPD, title, prodid, units)
def plot_thte():
"""Plot surface theta-e map"""
print " THETA-E"
pylab.figure(figsize=(width,height),frameon=False)
qhum = nc.variables['Q2']
thte = (temps[time] + qhum[time] * 2500000.0/1004.0) * (100000/psfc[time]) ** (287.0/1004.0)
THTE_LEVS = range(270,360,5)
THTE = pylab.contourf(x,y,thte,THTE_LEVS,cmap=coltbls.thetae(),extend='max')
u_wind_kts = u_wind_ms[time] * 1.94384449
v_wind_kts = v_wind_ms[time] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5,\
sizes={'spacing':0.2},pivot='middle')
title = 'Theta-e, 10 m Wind (kt)'
prodid = 'thte'
units = 'K'
drawmap(THTE, title, prodid, units)
def plot_ua_temps(press):
print(" %smb TEMPERATURE" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width, height), frameon=False)
heights = hght_clevs[contlevid[str(press)]]
# Temperature calculation here
#TC = temps_ua[time,level] - 273
TH_K = np.add(temps_ua[time, level], temps_base_ua)
TK = np.multiply(TH_K, math.pow((float(press) / 1000.), (287.04 / 1004.)))
TC = TK - 273
TEMP = pylab.contourf(x,
y,
TC,
temp_clevs[contlevid[str(press)]],
extend='both')
# Contour the heights
phtotal = np.add(phb[time, level], ph[time, level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT = pylab.contour(x, y, gheight, heights, colors='k', linewidths=1.5)
pylab.clabel(HGHT, inline=1, fontsize=8, fmt='%1.0f', inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time, level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time, level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb Temp, Height (m), Wind (kts)' % press
prodid = '%smb_temp' % press
units = u"\u00B0" + "C"
drawmap(TEMP, title, prodid, units)
def plot_ua_temps(press):
print(" %smb TEMPERATURE" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
heights=hght_clevs[contlevid[str(press)]]
# Temperature calculation here
#TC = temps_ua[time,level] - 273
TH_K = np.add(temps_ua[time,level],temps_base_ua)
TK = np.multiply(TH_K,math.pow((float(press)/1000.),(287.04/1004.)))
TC = TK - 273
TEMP=pylab.contourf(x,y,TC,temp_clevs[contlevid[str(press)]],extend='both')
# Contour the heights
phtotal = np.add(phb[time,level],ph[time,level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT=pylab.contour(x,y,gheight,heights,colors='k',linewidths=1.5)
pylab.clabel(HGHT,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time,level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time,level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb Temp, Height (m), Wind (kts)' % press
prodid = '%smb_temp' % press
units = u"\u00B0" + "C"
drawmap(TEMP, title, prodid, units)
def plot_surface():
print(" SURFACE")
# Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
# Convert Surface Pressure to Mean Sea Level Pressure
stemps = temps[time]+6.5*nc.variables['HGT'][time]/1000.
mslp = nc.variables['PSFC'][time]*np.exp(9.81/(287.0*stemps)*nc.variables['HGT'][time])*0.01 + (6.7 * nc.variables['HGT'][time] / 1000)
# Convert Celsius Temps to Fahrenheit
ftemps = (9./5.)*(temps[time]-273) + 32
T_LEVS = range(-10,125,5)
# Contour and fill the temperature
T=pylab.contourf(x,y,ftemps,T_LEVS,cmap=coltbls.sftemp())
# Contour the pressure
P=pylab.contour(x,y,mslp,V=2,colors='k',linewidths=1.5)
pylab.clabel(P,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
#pylab.clabel(T,inline=1,fontsize=10)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms[time] * 1.94384449
v_wind_kts = v_wind_ms[time] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5,\
sizes={'spacing':0.2},pivot='middle')
title = 'Sfc Temp, MSLP (mb), 10m Wind (kts)'
prodid = 'pmsl'
units = u"\u00B0" + "F"
drawmap(T, title, prodid, units)
def skewtlogp(olga, si):
"""
Get directory of script for saving arrays
"""
tmpPath = os.path.dirname(os.path.realpath(__file__))+'/tmp/'
if(not os.path.isdir(tmpPath)):
os.mkdir(tmpPath)
"""
Settings for high and low sounding top
"""
if(si.stype==0):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 20000. # lowest pressue in diagram (top)
ptop_thd = 40000 # top of dry adiabats
ptop_mxr = 60000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -35. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 7000
isotherms = np.arange(-100,30.001,10)
isobars = np.array([1050.,1000.,850.,700.,500.,400.,300.,250.,200.,150.,100.,50])*100.
dryadiabats = np.arange(-30,50.001,10)
moistadiabats = np.array([28.,24.,20.,16.,12.,8.,4.,0.])
mixrat = np.array([20.,12.,8.,5.,3.,2.,1.])
elif(si.stype==1):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 50000. # lowest pressue in diagram (top)
ptop_thd = 70000 # top of dry adiabats
ptop_mxr = 70000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -11. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 2500 # spacing (in pressure coords) of some label placement
isotherms = np.arange(-100,60.001,10)
isobars = np.array([1050.,1000.,900.,850.,700.,600.,500.])*100.
dryadiabats = np.arange(-10,30.001,5)
moistadiabats = np.array([28.,24.,20.,16.,12.,8.,4.,0.])
mixrat = np.array([20.,12.,8.,5.,3.,2.,1.])
else:
sys.exit('stype=%i not supported!'%stype)
"""
Setup figure
"""
if(olga != -1):
fig = pl.figure(figsize=(olga.fig_width_px/float(olga.fig_dpi), olga.fig_width_px/float(olga.fig_dpi)))
else:
fig = pl.figure(figsize=(8,8))
# L B R T ws hs
fig.subplots_adjust(0.08,0.10,1.0,0.93,0.2,0.08)
pl.subplot(111)
# Calculate bounds in figure coordinates
y00 = skewty(pbottom)
y11 = skewty(ptop)
x00 = skewtx(Tleft,y00)
x11 = skewtx(Tright,y00)
# Spacing for some labels
hs = np.abs(x11-x00)/200.
vs = np.abs(y11-y00)/200.
"""
1. Create isotherms
"""
for T in isotherms:
y = np.array([skewty(pbottom),skewty(ptop)])
x = np.array([skewtx(T,y[0]),skewtx(T,y[1])])
# Check if partially out of bounds
lloc = 0
if(x[0] < x00):
x[0] = x00
y[0] = iskewtxT(x00,T)
if(x[1] > x11):
x[1] = x11
y[1] = iskewtxT(x11,T)
if(x[1] >= x00 and y[1] >= y00):
pl.plot(x,y,color=c2,alpha=a1)
if(x[0] > x00 and x[0] < x11):
pl.text(x[0],y[0]-2*hs,int(T),ha='center',va='top',color=c2)
else:
pl.text(x[1]-5*hs,y[1]-5*vs,int(T),ha='center',va='center',color=c2,alpha=a2)
"""
2. Create isobars
"""
for p in isobars:
# Check if out of bounds
if((p >= ptop) and (p <= pbottom)):
y = skewty(p)
pl.plot([x00,x11],[y,y],color=c2,alpha=a1)
pl.text(x00-hs,y,int(p/100.),ha='right',va='center',color=c2)
"""
3. Create dry adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath+'theta_p_%i.arr'%si.stype)
y = np.load(tmpPath+'theta_y_%i.arr'%si.stype)
x = np.load(tmpPath+'theta_x_%i.arr'%si.stype)
except:
p = np.arange(pbottom,(np.max(([ptop,ptop_thd]))-dp),-dp)
x = np.zeros((dryadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(dryadiabats.size):
x[i,:] = 0.
for k in range(p.size):
xtmp = skewtx(((dryadiabats[i]+T0) * exner(p[k]))-T0, y[k])
if(xtmp >= x00 and xtmp <= x11):
x[i,k] = xtmp
else:
x[i,k] = -9999
p.dump(tmpPath+'theta_p_%i.arr'%si.stype)
y.dump(tmpPath+'theta_y_%i.arr'%si.stype)
x.dump(tmpPath+'theta_x_%i.arr'%si.stype)
# Plot the dry adiabats
for i in range(dryadiabats.size):
doPlot = np.where(x[i,:] != -9999)[0]
if(doPlot[0].size > 0):
lloc = max(0,np.size(x[i,doPlot])-int(5000./dp))
pl.plot(x[i,doPlot],y[doPlot],color=c1)
pl.text(x[i,doPlot][lloc],y[doPlot][lloc],int(dryadiabats[i]),color=c1,ha='center',va='center',backgroundcolor='w')
"""
4. Create moist adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath+'thetam_p_%i.arr'%si.stype)
y = np.load(tmpPath+'thetam_y_%i.arr'%si.stype)
x = np.load(tmpPath+'thetam_x_%i.arr'%si.stype)
except:
p = np.arange(np.min(([pbottom,pbottom_thw])),ptop-dp,-dp)
x = np.zeros((moistadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(moistadiabats.size):
for k in range(p.size):
thw = dsatlftskewt(moistadiabats[i], p[k])
xtmp = skewtx(thw, y[k])
if(xtmp >= x00 and xtmp <= x11):
x[i,k] = xtmp
else:
x[i,k] = -9999
p.dump(tmpPath+'thetam_p_%i.arr'%si.stype)
y.dump(tmpPath+'thetam_y_%i.arr'%si.stype)
x.dump(tmpPath+'thetam_x_%i.arr'%si.stype)
# Plot the moist adiabats
for i in range(moistadiabats.size):
doPlot = np.where(x[i,:] != -9999)[0]
if(doPlot[0].size > 0):
lloc = max(0,np.size(x[i,doPlot])-int(8000./dp))
pl.plot(x[i,doPlot],y[doPlot],'--',color=c3)
pl.text(x[i,doPlot][lloc],y[doPlot][lloc],int(moistadiabats[i]),color=c3,ha='center',va='center',backgroundcolor='w')
"""
5. Create isohumes / mixing ratio lines
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath+'mixr_p_%i.arr'%si.stype)
y = np.load(tmpPath+'mixr_y_%i.arr'%si.stype)
x = np.load(tmpPath+'mixr_x_%i.arr'%si.stype)
except:
p = np.arange(pbottom,(np.max(([ptop,ptop_mxr]))-dp),-dp)
x = np.zeros((mixrat.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(mixrat.size):
for k in range(p.size):
mix = Td(mixrat[i]/1000.,p[k])-T0
xtmp = skewtx(mix,y[k])
if(xtmp >= x00 and xtmp <= x11):
x[i,k] = xtmp
else:
x[i,k] = -9999
p.dump(tmpPath+'mixr_p_%i.arr'%si.stype)
y.dump(tmpPath+'mixr_y_%i.arr'%si.stype)
x.dump(tmpPath+'mixr_x_%i.arr'%si.stype)
# Plot the mixing ratio lines
for i in range(mixrat.size):
doPlot = np.where(x[i,:] != -9999)[0]
if(doPlot[0].size > 0):
pl.plot(x[i,doPlot],y[doPlot],color=c5,dashes=[3,2])
pl.text(x[i,doPlot][-1],y[doPlot][-1]+vs,int(mixrat[i]),color=c5,ha='center',va='bottom',backgroundcolor='w')
"""
6. Add sounding data
"""
# 6.1 Temperature and dewpoint temperature
if(si.T.size > 0):
y = skewty(si.p)
x1 = skewtx(si.T-T0,y)
x2 = skewtx(si.Td-T0,y)
pl.plot(x1,y,'-',color=cT,linewidth=2)
pl.plot(x2,y,'-',color=cTd,linewidth=2)
# 6.2 Add height labels to axis
if(si.z.size > 0 and si.z.size==si.p.size):
y = skewty(si.p)
p_last = 1e9
for k in range(si.z.size):
if(y[k] <= y11 and np.abs(si.p[k]-p_last) > dp_label):
pl.text(x11+hs,y[k],str(int(si.z[k]))+'m',color=c2,ha='right',va='center',size=7,backgroundcolor='w')
p_last = si.p[k]
# 6.2 Wind barbs
if(si.u.size > 0):
y = skewty(si.p)
u = si.u * 1.95
v = si.v * 1.95
xb = x11 + 9*hs
p_last = 1e9
for k in range(si.z.size):
if(y[k] <= y11 and np.abs(si.p[k]-p_last) > dp_label):
pl.barbs(xb,y[k],u[k],v[k],length=5.2,linewidth=0.5,pivot='middle')
p_last = si.p[k]
"""
6.1 :) Try to add measured data
"""
# 6.1 Temperature and dewpoint temperature
if(si.Tm.size > 0):
y = skewty(si.pm)
x1 = skewtx(si.Tm,y)
x2 = skewtx(si.Tdm,y)
pl.plot(x1,y,'--',color=cT,linewidth=1)
pl.plot(x2,y,'--',color=cTd,linewidth=1)
"""
7. Lauch parcel
"""
if(si.parcel == True):
# Plot starting position:
p0s = skewty(si.ps)
T0s = skewtx(si.Ts-T0, p0s)
Td0s = skewtx(Td(si.rs,si.ps)-T0, p0s)
pl.scatter(T0s, p0s, facecolor='none')
pl.scatter(Td0s,p0s, facecolor='none')
# Lists to hold parcel pressure, temp and dewpoint temp during ascent
pp = [si.ps]
Tp = [si.Ts]
Tdp = [Td(si.rs, si.ps)]
# Launch parcel
n = 0
while(Tp[-1] > Tdp[-1] and n<1000):
n += 1
dp2 = max(1,(Tp[-1] - Tdp[-1])*300) # bit weird, but fast
pp. append(pp[-1] - dp2)
Tp. append(si.Ts*exner(pp[-1], si.ps))
Tdp.append(Td(si.rs, pp[-1]))
# Plot lines from surface --> LCL
ps = skewty(np.array(pp))
Tps = skewtx(np.array(Tp)-T0, ps)
Tdps = skewtx(np.array(Tdp)-T0, ps)
pl.plot(Tps, ps, 'k', linewidth=1.5, dashes=[4,2])
pl.plot(Tdps, ps, 'k', linewidth=1.5, dashes=[4,2])
pl.scatter(Tps[-1], ps[-1], facecolor='none')
# Iteratively find the moist adiabat starting at p0, which goes through the temperature at LCL
Ths = si.Ts / exner(si.ps, p0) # potential temperature surface (@p0)
ThwsLCL = dsatlftskewt(Ths-T0, pp[-1])+T0 # Temp moist adiabat at plcl, through Ths
thw0 = Ths - (ThwsLCL - Tp[-1]) # First estimate of moist adiabat passing through Tlcl
ThwLCL = dsatlftskewt(thw0-T0, pp[-1])+T0
while(np.abs(ThwLCL-Tp[-1]) > 0.1):
thw0 -= ThwLCL-Tp[-1]
ThwLCL = dsatlftskewt(thw0-T0, pp[-1])+T0
# Plot moist adiabat from LCL upwards
p = np.arange(pp[-1], ptop, -dp)
x = np.zeros(p.size)
y = np.zeros(p.size)
for k in range(p.size):
thw = dsatlftskewt(thw0-T0,p[k])
y[k] = skewty(p[k])
x[k] = skewtx(thw,y[k])
pl.plot(x,y,'k', linewidth=1.5, dashes=[4,2])
"""
Add info from TEMF boundary layer scheme
"""
if(si.Tu.size > 0):
# Cloud fraction
dw = (x11-x00) # width of diagram
y = skewty(si.p)
x = x00 + si.cfru * 0.2 * (x11-x00)
pl.plot(x,y,'-',linewidth=1.5,color=c6) # cloud cover
cfr_pos = np.where(si.cfru > 0.001)[0]
if(np.size(cfr_pos)>1):
pl.text(x00,y[cfr_pos[0]-1],'- %im'%(si.z[cfr_pos[0]-1]),ha='left',va='center',size=8,color=c6)
pl.text(x00,y[cfr_pos[-1]],'- %im'%(si.z[cfr_pos[-1]]),ha='left',va='center',size=8,color=c6)
kmax=np.where(si.cfru == si.cfru.max())[0][0]
pl.text(x.max(),y[kmax],'- %i%%'%(si.cfru[kmax]*100.),ha='left',va='center',size=8,color=c6)
"""
6. Finish diagram
"""
pl.xticks([])
pl.yticks([])
pl.box('off')
pl.xlim(x00-0.1,x11+16*hs)
pl.ylim(y00-0.1,y11+0.1)
# draw axis
pl.plot([x00,x11],[y00,y00],'k-')
pl.plot([x00,x00],[y00,y11],'k-')
pl.figtext(0.5,0.05,'Temperature (C)',ha='center')
pl.figtext(0.015,0.52,'Pressure (hPa)',rotation=90)
label = 'Skew-T log-P, %s, %s UTC'%(si.name,si.time)
pl.figtext(0.5,0.97,label,ha='center')
if(olga != -1):
img = image.imread(olga.olgaRoot+'include/olga_left.png')
pl.figimage(img,7,5)
#pl.figimage(img,10,olga.fig_width_px-45)
pl.figtext(0.99,0.011,'%s'%olga.map_desc[0],size=7,ha='right')
return fig
def main():
base_time = datetime(2009, 6, 5, 18, 0, 0)
epoch = datetime(1970, 1, 1, 0, 0, 0)
base_epoch = (base_time - epoch).total_seconds()
times_seconds = range(14700, 18300, 300)
times = [ base_time + timedelta(seconds=t) for t in times_seconds ]
bounds = (slice(100, 180), slice(90, 170))
rev_bounds = [ 0 ]
rev_bounds.extend(bounds[::-1])
rev_bounds = tuple(rev_bounds)
proj = setupMapProjection(goshen_1km_proj, goshen_1km_gs, bounds=bounds)
map = Basemap(**proj)
obs_file_names = ['psu_straka_mesonet.pkl', 'ttu_sticknet.pkl', 'asos.pkl', 'soundings_clip.pkl']
all_obs = loadObs(obs_file_names, times, map, (goshen_1km_proj['width'], goshen_1km_proj['height']), sounding_obs=['soundings_clip.pkl'])
refl_base = "hdf/KCYS/1km/goshen.hdfrefl2d"
refl_times = np.array([ int(f[-6:]) for f in glob.glob("%s??????" % refl_base) ])
refl_keep_times = []
refl_data = {}
for tt in times_seconds:
idx = np.argmin(np.abs(refl_times - tt))
if refl_times[idx] > tt and idx > 0:
idx -= 1
file_name = "%s%06d" % (refl_base, refl_times[idx])
hdf = nio.open_file(file_name, mode='r', format='hdf')
refl_keep_times.append(refl_times[idx])
refl_data[tt] = hdf.variables['refl2d'][rev_bounds]
for time, reg in inflow_stations.iteritems():
pylab.figure()
gs_x, gs_y = goshen_1km_gs
nx, ny = refl_data[time].shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl_data[time], levels=np.arange(10, 80, 10))
for region, stations in reg.iteritems():
if region != 'sounding':
for station in stations:
idxs = np.where((all_obs['id'] == station) & (all_obs['time'] == base_epoch + time))
ob_xs, ob_ys = map(all_obs['longitude'][idxs], all_obs['latitude'][idxs])
if region == 'inflow': color='r'
elif region == 'outflow': color='b'
wdir = all_obs['wind_dir'][idxs]
wspd = all_obs['wind_spd'][idxs]
u = -wspd * np.sin(wdir * np.pi / 180.) * 1.94
v = -wspd * np.cos(wdir * np.pi / 180.) * 1.94
pylab.plot(ob_xs, ob_ys, "%so" % color)
pylab.barbs(ob_xs, ob_ys, u, v)
drawPolitical(map, scale_len=10)
pylab.savefig("inflow_stations_%06d.png" % time)
pylab.close()
return
def main():
base_time = datetime(2009, 6, 5, 18, 0, 0)
epoch = datetime(1970, 1, 1, 0, 0, 0)
base_epoch = (base_time - epoch).total_seconds()
times_seconds = range(14700, 18300, 300)
times = [base_time + timedelta(seconds=t) for t in times_seconds]
bounds = (slice(100, 180), slice(90, 170))
rev_bounds = [0]
rev_bounds.extend(bounds[::-1])
rev_bounds = tuple(rev_bounds)
proj = setupMapProjection(goshen_1km_proj, goshen_1km_gs, bounds=bounds)
map = Basemap(**proj)
obs_file_names = [
'psu_straka_mesonet.pkl', 'ttu_sticknet.pkl', 'asos.pkl',
'soundings_clip.pkl'
]
all_obs = loadObs(obs_file_names,
times,
map,
(goshen_1km_proj['width'], goshen_1km_proj['height']),
sounding_obs=['soundings_clip.pkl'])
refl_base = "hdf/KCYS/1km/goshen.hdfrefl2d"
refl_times = np.array(
[int(f[-6:]) for f in glob.glob("%s??????" % refl_base)])
refl_keep_times = []
refl_data = {}
for tt in times_seconds:
idx = np.argmin(np.abs(refl_times - tt))
if refl_times[idx] > tt and idx > 0:
idx -= 1
file_name = "%s%06d" % (refl_base, refl_times[idx])
hdf = nio.open_file(file_name, mode='r', format='hdf')
refl_keep_times.append(refl_times[idx])
refl_data[tt] = hdf.variables['refl2d'][rev_bounds]
for time, reg in inflow_stations.iteritems():
pylab.figure()
gs_x, gs_y = goshen_1km_gs
nx, ny = refl_data[time].shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl_data[time], levels=np.arange(10, 80, 10))
for region, stations in reg.iteritems():
if region != 'sounding':
for station in stations:
idxs = np.where((all_obs['id'] == station)
& (all_obs['time'] == base_epoch + time))
ob_xs, ob_ys = map(all_obs['longitude'][idxs],
all_obs['latitude'][idxs])
if region == 'inflow': color = 'r'
elif region == 'outflow': color = 'b'
wdir = all_obs['wind_dir'][idxs]
wspd = all_obs['wind_spd'][idxs]
u = -wspd * np.sin(wdir * np.pi / 180.) * 1.94
v = -wspd * np.cos(wdir * np.pi / 180.) * 1.94
pylab.plot(ob_xs, ob_ys, "%so" % color)
pylab.barbs(ob_xs, ob_ys, u, v)
drawPolitical(map, scale_len=10)
pylab.savefig("inflow_stations_%06d.png" % time)
pylab.close()
return
pylab.semilogy(temp_trans,actual_press_levels, \
color = 'red',basey=10, linewidth = 2)
#print Tp, press_p
#raw_input()
#pylab.semilogy(Tp_trans,press_p,color = 'brown',\
# basey = 10, linewidth = 2, linestyle = 'dashed')
baraxis = []
# Need this -3 to the list to stop them from going off the top
for n in range(len(u_wind_kts[:-5])):
baraxis.append(45.)
pylab.plot([45,45],[100,1000],linewidth = .75, color = 'k')
bb = pylab.barbs(baraxis,actual_wind_levs[:-5],u_wind_kts[:-5],v_wind_kts[:-5],\
linewidth = .75)
bb.set_clip_box(None)
ax = pylab.gca()
#ax.set_ylim(ax.get_ylim()[::-1])
ax.set_ylim([1000,100])
ax.set_xlim([-40,50])
majorLocator = MultipleLocator(100.)
majorFormatter = FormatStrFormatter('%4.0f')
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_major_formatter(majorFormatter)
stemps = sfcT[time,point_y,point_x]+6.5*ncsfc.variables['HGT'][time,point_y,point_x]/1000.
mslp = sfcP[time,point_y,point_x]*np.exp(9.81/(287.0*stemps)*ncsfc.variables['HGT'][time,point_y,point_x])*0.01 + (6.7 * ncsfc.variables['HGT'][time,point_y,point_x] / 1000)
# Convert Celsius Temps to Fahrenheit
ftemp = (9./5.)*(sfcT[time,point_y,point_x]-273) + 32
def create_timeseries(olga, wrfout, dom, times):
# colormap for cloud cover
cld = make_colormap({0.: '#05b5ff', 0.3: '#bdecff', 1.0: '#ffffff'})
olga_logo = pl.matplotlib.image.imread(olga.olgaRoot +
'include/olga_left.png')
# Loop over requested locations
for name, longName, lon, lat, type in zip(olga.soundLoc[dom].shortName,
olga.soundLoc[dom].longName,
olga.soundLoc[dom].lon,
olga.soundLoc[dom].lat,
olga.soundLoc[dom].type):
if (type == 0 or type == 2):
# Read WRF data
d = readwrf_loc(olga, wrfout, lon, lat, times[0], times[-1])
# DEBUGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
#figure()
#for t in range(np.size(d.t0_ana)):
# t0 = d.t0_ana[t]
# t1 = d.t1_ana[t]
# figure()
# for tt in range(t0,t1):
# sp = tt-t0
# ax=subplot(5,4,sp+1)
# title(str(tt))
# d.w[tt,d.w[tt,:]<0] = 0
# plot(d.w[tt,:], d.zf[tt,:],'r-',label='w_up')
# plot(d.qltemf[tt,:]*10000, d.zf[tt,:],'b-',label='ql_up*1e4')
# ylim(0,3000)
# xlim(0,3)
# if(tt == t0): legend(frameon=False)
# if(sp % 4 != 0): ax.tick_params(labelleft='off')
# if(sp < 16): ax.tick_params(labelbottom='off')
# grid()
# savefig('test_%s.png'%name)
# Loop over diffent day (if available):
for t in range(np.size(d.t0_ana)):
t0 = d.t0_ana[t]
t1 = d.t1_ana[t]
x0 = d.hour[t0]
x1 = d.hour[t1]
fig = pl.figure(
figsize=(olga.fig_width_px / float(olga.fig_dpi),
olga.fig_width_px / float(olga.fig_dpi)))
# L B R T ws hs
fig.subplots_adjust(0.10, 0.11, 0.98, 0.88, 0.35, 0.55)
pl.figtext(0.5,
0.95,
'%s [%.2fN, %.2fE]' % (longName, lat, lon),
size=9,
ha='center')
pl.figtext(0.5, 0.93, '%s' % (d.date[t0]), size=8, ha='center')
gs = pl.matplotlib.gridspec.GridSpec(
4, 2, height_ratios=[1., 1., 0.4, 1], width_ratios=[2, 1])
# -------------------------------------------------
# Updraft velocity / height: wstar
# -------------------------------------------------
#ax = pl.subplot(gs[:2,0]); modplot(ax)
#ax.set_title('Updraft velocity and height',loc='left')
#zs = d.z[0,0] # terrain height (lowest half level)
#wm = 3.5 # scaling for colormap
#bw1 = 0.6 # width of sub-cloud updrafts
#bw2 = 0.8 # width of cloud updrafts
#for i in range(t0,t1+1):
# if(d.wglider[i] > 0.0):
# color = wup((np.floor(d.wglider[i])+0.5)/wm)
# pl.bar(d.hour[i]-0.5*bw1, d.zi[i], width=bw1, bottom=zs, color=color, edgecolor='none')
# pl.bar(d.hour[i]-0.5*bw2, d.ct[i]-d.zi[i], width=bw2, bottom=d.zi[i]+zs, color='k', alpha=0.3, edgecolor='none')
## Add surface
#pl.plot([d.hour[t0], d.hour[t1]],[zs, zs], 'k:')
#pl.text(d.hour[t0]+0.2,zs,'surface',size=7,ha='left',va='bottom')
## Add sort-of colorbar
#wups = ([0.5,1.5,2.5,3.5])
#names = (['0-1 m/s','1-2 m/s','2-3 m/s','>3 m/s'])
#for wu,nam in zip(wups,names):
# pl.scatter([-10],[300],color=wup(wu/wm),label=nam)
#pl.legend(frameon=False,loc=2)
#pl.xlim(d.hour[t0],d.hour[t1])
#pl.ylim(0,3000)
#pl.ylabel('z [m AMSL]')
#pl.xticks(np.arange(d.hour[t0],d.hour[t1]+0.001,2))
#pl.yticks(np.arange(0,3000.001,500))
# -------------------------------------------------
# Updraft velocity / height: vertical velocity TEMF
# -------------------------------------------------
ax = pl.subplot(gs[:2, 0])
modplot(ax)
ax.set_title('Updraft velocity and height', loc='left', size=8)
zs = d.z[0, 0] # terrain height (lowest half level)
wm = 4 # scaling for colormap
bw1 = 0.70 # width of sub-cloud updrafts
bw2 = 0.85 # width of cloud updrafts
# Loop over all time steps
for tt in range(d.nt):
# TO-DO: TEMF often doesn't decay updraft velocity after convection stop.
# Limit plot to conditions with unstable near-surface layer:
if (d.thv[tt, 1] < d.thv[tt, 0]):
# Plot updraft velocity
wc_p, cc_p = w_discretized(d.w[tt, 0])
k_p = 0
wc_max = 0
for k in range(key_nearest(d.zf[tt, :], 3000) + 1):
# Current discretized updraft velocity
wc_a, cc_a = w_discretized(d.w[tt, k])
wc_max = np.max((wc_max, wc_a))
# If current velocity differs from previous, draw bar from k_p to k
if (wc_a != wc_p):
pl.bar(d.hour[tt] - 0.5 * bw1,
d.z[tt, k] - d.z[tt, k_p],
width=bw1,
bottom=d.z[tt, k_p],
color=cc_p,
edgecolor='none')
wc_p = wc_a
cc_p = cc_a
k_p = k
# Plot cumulus clouds, only if there is something like an updraft below
if (wc_max > 0):
cloud = False # flag to see if we have cumulus
for k in range(key_nearest(d.zf[tt, :], 3000) + 1):
ql = np.max((0, d.qltemf[tt, k]))
if (ql >= 5e-5 and cloud == False):
cloud = True
kc_p = k
if (ql < 5e-5 and cloud == True):
pl.bar(d.hour[tt] - 0.5 * bw2,
d.z[tt, k + 1] - d.z[tt, kc_p],
width=bw2,
bottom=d.z[tt, kc_p],
color='0.9',
edgecolor='k',
alpha=0.5)
cloud = False
# Add line at surface
pl.plot([d.hour[t0], d.hour[t1]], [zs, zs], 'k:')
pl.text(d.hour[t0] + 0.2,
zs,
'surface',
size=7,
ha='left',
va='bottom')
# Add sort-of colorbar
wups = ([0.7, 1, 2, 3])
names = (['0.7-1 m/s', '1-2 m/s', '2-3 m/s', '>3 m/s'])
for wu, nam in zip(wups, names):
tmp, cc = w_discretized(wu)
pl.scatter([-10], [300], marker='s', color=cc, label=nam)
pl.plot([-10, -10], [300, 300], 'k-', label='cumulus')
pl.legend(frameon=False, loc=2, scatterpoints=1)
pl.xlim(d.hour[t0], d.hour[t1])
pl.ylim(0, 3000)
pl.ylabel('z [m AMSL]')
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
pl.yticks(np.arange(0, 3000.001, 500))
# -------------------------------------------------
# Pressure
# -------------------------------------------------
ax = pl.subplot(gs[0, 1])
modplot(ax)
ax.set_title('Surface pressure', loc='left', size=8)
slp = d.slps[t0:t1] / 100.
pl.plot(d.hour[t0:t1], slp, 'k-')
pl.xlim(d.hour[t0], d.hour[t1])
pl.ylabel('hPa')
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
if (slp.max() - slp.min() < 10):
pl.ylim(
roundNumber(slp.mean(), 1, DOWN) - 5,
roundNumber(slp.mean(), 1, UP) + 5)
# -------------------------------------------------
# Air temperature / dew poiunt temperature
# -------------------------------------------------
ax = pl.subplot(gs[1, 1])
modplot(ax)
ax.set_title('T and Td at 2m', loc='left', size=8)
pl.plot(d.hour[t0:t1], d.T2[t0:t1] - 273.15, 'k-', label='T')
pl.plot(d.hour[t0:t1],
d.Td2[t0:t1] - 273.15,
'k-',
label='Td',
dashes=[4, 2])
pl.ylabel('celcius')
pl.xlim(d.hour[t0], d.hour[t1])
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
pl.legend(frameon=False, loc=2)
# -------------------------------------------------
# Rain
# -------------------------------------------------
ax = pl.subplot(gs[2, 1])
modplot(ax)
ax.set_title('Rain', loc='left', size=8)
pl.bar(d.hour[t0:t1],
d.drr_mp[t0:t1],
color='g',
edgecolor='none')
pl.bar(d.hour[t0:t1],
d.drr_con[t0:t1],
bottom=d.drr_mp[t0:t1],
color='b',
edgecolor='none')
pl.ylabel('mm')
pl.xlim(d.hour[t0], d.hour[t1])
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
pl.ylim(
0,
max(
1,
roundNumber((d.drr_mp[t0:t1] + d.drr_con[t0:t1]).max(),
1, UP)))
# -------------------------------------------------
# Shortwave radiation
# -------------------------------------------------
ax = pl.subplot(gs[3, 1])
modplot(ax)
ax.set_title('Shortwave radiation', loc='left', size=8)
pl.plot(d.hour[t0:t1], d.swd[t0:t1], 'k-')
pl.plot(d.hour[t0:t1],
d.swdc[t0:t1],
'k-',
label='Pot.',
dashes=[4, 2])
pl.ylabel('W/m2')
pl.xlabel('time UTC [h]')
pl.xlim(d.hour[t0], d.hour[t1])
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
pl.ylim(0, 1000)
pl.yticks(np.arange(0, 1000.01, 200))
pl.legend(frameon=False, loc=2, borderpad=0)
# -------------------------------------------------
# Cloud cover
# -------------------------------------------------
ax = pl.subplot(gs[2, 0])
modplot(ax)
ax.set_title('Cloud cover', loc='left', size=8)
pl.pcolormesh(d.ccl, cmap=cld, vmin=0, vmax=1)
pl.text(d.hour[t0] - 0.4,
0.5,
'low',
size=7,
ha='right',
va='center')
pl.text(d.hour[t0] - 0.4,
1.5,
'middle',
size=7,
ha='right',
va='center')
pl.text(d.hour[t0] - 0.4,
2.5,
'high',
size=7,
ha='right',
va='center')
pl.xlim(d.hour[t0], d.hour[t1])
ax.set_yticks([])
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
# -------------------------------------------------
# Wind
# -------------------------------------------------
ax = pl.subplot(gs[3, 0])
modplot(ax)
ax.set_title('Wind', loc='left', size=8)
k500 = key_nearest(d.zf[0, :], 500)
k1000 = key_nearest(d.zf[0, :], 1000)
k2000 = key_nearest(d.zf[0, :], 2000)
pl.barbs(d.hour[t0:t1 + 1],
0.5,
d.u10[t0:t1 + 1] * m2k,
d.v10[t0:t1 + 1] * m2k,
length=5,
linewidth=0.5,
pivot='middle')
pl.barbs(d.hour[t0:t1 + 1],
1.5,
d.u[t0:t1 + 1, k500] * m2k,
d.v[t0:t1 + 1, k500] * m2k,
length=5,
linewidth=0.5,
pivot='middle')
pl.barbs(d.hour[t0:t1 + 1],
2.5,
d.u[t0:t1 + 1, k1000] * m2k,
d.v[t0:t1 + 1, k1000] * m2k,
length=5,
linewidth=0.5,
pivot='middle')
pl.barbs(d.hour[t0:t1 + 1],
3.5,
d.u[t0:t1 + 1, k2000] * m2k,
d.v[t0:t1 + 1, k2000] * m2k,
length=5,
linewidth=0.5,
pivot='middle')
pl.text(d.hour[t0] - 0.4,
0.5,
'10m',
size=7,
ha='right',
va='center')
pl.text(d.hour[t0] - 0.4,
1.5,
'500m',
size=7,
ha='right',
va='center')
pl.text(d.hour[t0] - 0.4,
2.5,
'1000m',
size=7,
ha='right',
va='center')
pl.text(d.hour[t0] - 0.4,
3.5,
'2000m',
size=7,
ha='right',
va='center')
pl.xlim(0, 24)
pl.xlim(d.hour[t0], d.hour[t1])
ax.set_yticks([])
pl.xticks(np.arange(d.hour[t0], d.hour[t1] + 0.001, 2))
pl.xlabel('time UTC [h]')
# Add logo (105px wide)
pl.figimage(olga_logo, 10, olga.fig_width_px - 40)
pl.figtext(0.01,
0.011,
'%s' % (olga.map_desc[dom]),
size=7,
ha='left')
# Save figure
tmp = '%06i' % (olga.islice * 24 * 100)
name = '%s%04i%02i%02i_t%02iz/tser_%s_%02i_%s.png' % (
olga.figRoot, olga.year, olga.month, olga.day, olga.cycle,
name, dom + 1, tmp)
pl.savefig(name, dpi=olga.fig_dpi)
pylab.semilogy(temp_trans,actual_press_levels, \
color = 'red',basey=10, linewidth = 2)
#print Tp, press_p
#raw_input()
#pylab.semilogy(Tp_trans,press_p,color = 'brown',\
# basey = 10, linewidth = 2, linestyle = 'dashed')
baraxis = []
# Need this -3 to the list to stop them from going off the top
for n in range(len(u_wind_kts[:-5])):
baraxis.append(45.)
pylab.plot([45,45],[100,1000],linewidth = .75, color = 'k')
bb = pylab.barbs(baraxis,actual_wind_levs[:-5],u_wind_kts[:-5],v_wind_kts[:-5],\
linewidth = .75)
bb.set_clip_box(None)
ax = pylab.gca()
#ax.set_ylim(ax.get_ylim()[::-1])
ax.set_ylim([1000,100])
ax.set_xlim([-40,50])
majorLocator = MultipleLocator(100.)
majorFormatter = FormatStrFormatter('%4.0f')
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_major_formatter(majorFormatter)
stemps = sfcT[time,point_y,point_x]+6.5*ncsfc.variables['HGT'][time,point_y,point_x]/1000.
mslp = sfcP[time,point_y,point_x]*np.exp(9.81/(287.0*stemps)*ncsfc.variables['HGT'][time,point_y,point_x])*0.01 + (6.7 * ncsfc.variables['HGT'][time,point_y,point_x] / 1000)
# Convert Celsius Temps to Fahrenheit
ftemp = (9./5.)*(sfcT[time,point_y,point_x]-273) + 32
def skewtlogp(olga, si):
"""
Get directory of script for saving arrays
"""
tmpPath = os.path.dirname(os.path.realpath(__file__)) + '/tmp/'
if (not os.path.isdir(tmpPath)):
os.mkdir(tmpPath)
"""
Settings for high and low sounding top
"""
if (si.stype == 0):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 20000. # lowest pressue in diagram (top)
ptop_thd = 40000 # top of dry adiabats
ptop_mxr = 60000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -35. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 7000
isotherms = np.arange(-100, 30.001, 10)
isobars = np.array([
1050., 1000., 850., 700., 500., 400., 300., 250., 200., 150., 100.,
50
]) * 100.
dryadiabats = np.arange(-30, 50.001, 10)
moistadiabats = np.array([28., 24., 20., 16., 12., 8., 4., 0.])
mixrat = np.array([20., 12., 8., 5., 3., 2., 1.])
elif (si.stype == 1):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 50000. # lowest pressue in diagram (top)
ptop_thd = 70000 # top of dry adiabats
ptop_mxr = 70000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -11. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 2500 # spacing (in pressure coords) of some label placement
isotherms = np.arange(-100, 60.001, 10)
isobars = np.array([1050., 1000., 900., 850., 700., 600., 500.]) * 100.
dryadiabats = np.arange(-10, 30.001, 5)
moistadiabats = np.array([28., 24., 20., 16., 12., 8., 4., 0.])
mixrat = np.array([20., 12., 8., 5., 3., 2., 1.])
else:
sys.exit('stype=%i not supported!' % stype)
"""
Setup figure
"""
if (olga != -1):
fig = pl.figure(figsize=(olga.fig_width_px / float(olga.fig_dpi),
olga.fig_width_px / float(olga.fig_dpi)))
else:
fig = pl.figure(figsize=(8, 8))
# L B R T ws hs
fig.subplots_adjust(0.08, 0.10, 1.0, 0.93, 0.2, 0.08)
pl.subplot(111)
# Calculate bounds in figure coordinates
y00 = skewty(pbottom)
y11 = skewty(ptop)
x00 = skewtx(Tleft, y00)
x11 = skewtx(Tright, y00)
# Spacing for some labels
hs = np.abs(x11 - x00) / 200.
vs = np.abs(y11 - y00) / 200.
"""
1. Create isotherms
"""
for T in isotherms:
y = np.array([skewty(pbottom), skewty(ptop)])
x = np.array([skewtx(T, y[0]), skewtx(T, y[1])])
# Check if partially out of bounds
lloc = 0
if (x[0] < x00):
x[0] = x00
y[0] = iskewtxT(x00, T)
if (x[1] > x11):
x[1] = x11
y[1] = iskewtxT(x11, T)
if (x[1] >= x00 and y[1] >= y00):
pl.plot(x, y, color=c2, alpha=a1)
if (x[0] > x00 and x[0] < x11):
pl.text(x[0],
y[0] - 2 * hs,
int(T),
ha='center',
va='top',
color=c2)
else:
pl.text(x[1] - 5 * hs,
y[1] - 5 * vs,
int(T),
ha='center',
va='center',
color=c2,
alpha=a2)
"""
2. Create isobars
"""
for p in isobars:
# Check if out of bounds
if ((p >= ptop) and (p <= pbottom)):
y = skewty(p)
pl.plot([x00, x11], [y, y], color=c2, alpha=a1)
pl.text(x00 - hs,
y,
int(p / 100.),
ha='right',
va='center',
color=c2)
"""
3. Create dry adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath + 'theta_p_%i.arr' % si.stype)
y = np.load(tmpPath + 'theta_y_%i.arr' % si.stype)
x = np.load(tmpPath + 'theta_x_%i.arr' % si.stype)
except:
p = np.arange(pbottom, (np.max(([ptop, ptop_thd])) - dp), -dp)
x = np.zeros((dryadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(dryadiabats.size):
x[i, :] = 0.
for k in range(p.size):
xtmp = skewtx(((dryadiabats[i] + T0) * exner(p[k])) - T0, y[k])
if (xtmp >= x00 and xtmp <= x11):
x[i, k] = xtmp
else:
x[i, k] = -9999
p.dump(tmpPath + 'theta_p_%i.arr' % si.stype)
y.dump(tmpPath + 'theta_y_%i.arr' % si.stype)
x.dump(tmpPath + 'theta_x_%i.arr' % si.stype)
# Plot the dry adiabats
for i in range(dryadiabats.size):
doPlot = np.where(x[i, :] != -9999)[0]
if (doPlot[0].size > 0):
lloc = max(0, np.size(x[i, doPlot]) - int(5000. / dp))
pl.plot(x[i, doPlot], y[doPlot], color=c1)
pl.text(x[i, doPlot][lloc],
y[doPlot][lloc],
int(dryadiabats[i]),
color=c1,
ha='center',
va='center',
backgroundcolor='w')
"""
4. Create moist adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath + 'thetam_p_%i.arr' % si.stype)
y = np.load(tmpPath + 'thetam_y_%i.arr' % si.stype)
x = np.load(tmpPath + 'thetam_x_%i.arr' % si.stype)
except:
p = np.arange(np.min(([pbottom, pbottom_thw])), ptop - dp, -dp)
x = np.zeros((moistadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(moistadiabats.size):
for k in range(p.size):
thw = dsatlftskewt(moistadiabats[i], p[k])
xtmp = skewtx(thw, y[k])
if (xtmp >= x00 and xtmp <= x11):
x[i, k] = xtmp
else:
x[i, k] = -9999
p.dump(tmpPath + 'thetam_p_%i.arr' % si.stype)
y.dump(tmpPath + 'thetam_y_%i.arr' % si.stype)
x.dump(tmpPath + 'thetam_x_%i.arr' % si.stype)
# Plot the moist adiabats
for i in range(moistadiabats.size):
doPlot = np.where(x[i, :] != -9999)[0]
if (doPlot[0].size > 0):
lloc = max(0, np.size(x[i, doPlot]) - int(8000. / dp))
pl.plot(x[i, doPlot], y[doPlot], '--', color=c3)
pl.text(x[i, doPlot][lloc],
y[doPlot][lloc],
int(moistadiabats[i]),
color=c3,
ha='center',
va='center',
backgroundcolor='w')
"""
5. Create isohumes / mixing ratio lines
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath + 'mixr_p_%i.arr' % si.stype)
y = np.load(tmpPath + 'mixr_y_%i.arr' % si.stype)
x = np.load(tmpPath + 'mixr_x_%i.arr' % si.stype)
except:
p = np.arange(pbottom, (np.max(([ptop, ptop_mxr])) - dp), -dp)
x = np.zeros((mixrat.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(mixrat.size):
for k in range(p.size):
mix = Td(mixrat[i] / 1000., p[k]) - T0
xtmp = skewtx(mix, y[k])
if (xtmp >= x00 and xtmp <= x11):
x[i, k] = xtmp
else:
x[i, k] = -9999
p.dump(tmpPath + 'mixr_p_%i.arr' % si.stype)
y.dump(tmpPath + 'mixr_y_%i.arr' % si.stype)
x.dump(tmpPath + 'mixr_x_%i.arr' % si.stype)
# Plot the mixing ratio lines
for i in range(mixrat.size):
doPlot = np.where(x[i, :] != -9999)[0]
if (doPlot[0].size > 0):
pl.plot(x[i, doPlot], y[doPlot], color=c5, dashes=[3, 2])
pl.text(x[i, doPlot][-1],
y[doPlot][-1] + vs,
int(mixrat[i]),
color=c5,
ha='center',
va='bottom',
backgroundcolor='w')
"""
6. Add sounding data
"""
# 6.1 Temperature and dewpoint temperature
if (si.T.size > 0):
y = skewty(si.p)
x1 = skewtx(si.T - T0, y)
x2 = skewtx(si.Td - T0, y)
pl.plot(x1, y, '-', color=cT, linewidth=2)
pl.plot(x2, y, '-', color=cTd, linewidth=2)
# 6.2 Add height labels to axis
if (si.z.size > 0 and si.z.size == si.p.size):
y = skewty(si.p)
p_last = 1e9
for k in range(si.z.size):
if (y[k] <= y11 and np.abs(si.p[k] - p_last) > dp_label):
pl.text(x11 + hs,
y[k],
str(int(si.z[k])) + 'm',
color=c2,
ha='right',
va='center',
size=7,
backgroundcolor='w')
p_last = si.p[k]
# 6.2 Wind barbs
if (si.u.size > 0):
y = skewty(si.p)
u = si.u * 1.95
v = si.v * 1.95
xb = x11 + 9 * hs
p_last = 1e9
for k in range(si.z.size):
if (y[k] <= y11 and np.abs(si.p[k] - p_last) > dp_label):
pl.barbs(xb,
y[k],
u[k],
v[k],
length=5.2,
linewidth=0.5,
pivot='middle')
p_last = si.p[k]
"""
6.1 :) Try to add measured data
"""
# 6.1 Temperature and dewpoint temperature
if (si.Tm.size > 0):
y = skewty(si.pm)
x1 = skewtx(si.Tm, y)
x2 = skewtx(si.Tdm, y)
pl.plot(x1, y, '--', color=cT, linewidth=1)
pl.plot(x2, y, '--', color=cTd, linewidth=1)
"""
7. Lauch parcel
"""
if (si.parcel == True):
# Plot starting position:
p0s = skewty(si.ps)
T0s = skewtx(si.Ts - T0, p0s)
Td0s = skewtx(Td(si.rs, si.ps) - T0, p0s)
pl.scatter(T0s, p0s, facecolor='none')
pl.scatter(Td0s, p0s, facecolor='none')
# Lists to hold parcel pressure, temp and dewpoint temp during ascent
pp = [si.ps]
Tp = [si.Ts]
Tdp = [Td(si.rs, si.ps)]
# Launch parcel
n = 0
while (Tp[-1] > Tdp[-1] and n < 1000):
n += 1
dp2 = max(1, (Tp[-1] - Tdp[-1]) * 300) # bit weird, but fast
pp.append(pp[-1] - dp2)
Tp.append(si.Ts * exner(pp[-1], si.ps))
Tdp.append(Td(si.rs, pp[-1]))
# Plot lines from surface --> LCL
ps = skewty(np.array(pp))
Tps = skewtx(np.array(Tp) - T0, ps)
Tdps = skewtx(np.array(Tdp) - T0, ps)
pl.plot(Tps, ps, 'k', linewidth=1.5, dashes=[4, 2])
pl.plot(Tdps, ps, 'k', linewidth=1.5, dashes=[4, 2])
pl.scatter(Tps[-1], ps[-1], facecolor='none')
# Iteratively find the moist adiabat starting at p0, which goes through the temperature at LCL
Ths = si.Ts / exner(si.ps, p0) # potential temperature surface (@p0)
ThwsLCL = dsatlftskewt(
Ths - T0, pp[-1]) + T0 # Temp moist adiabat at plcl, through Ths
thw0 = Ths - (ThwsLCL - Tp[-1]
) # First estimate of moist adiabat passing through Tlcl
ThwLCL = dsatlftskewt(thw0 - T0, pp[-1]) + T0
while (np.abs(ThwLCL - Tp[-1]) > 0.1):
thw0 -= ThwLCL - Tp[-1]
ThwLCL = dsatlftskewt(thw0 - T0, pp[-1]) + T0
# Plot moist adiabat from LCL upwards
p = np.arange(pp[-1], ptop, -dp)
x = np.zeros(p.size)
y = np.zeros(p.size)
for k in range(p.size):
thw = dsatlftskewt(thw0 - T0, p[k])
y[k] = skewty(p[k])
x[k] = skewtx(thw, y[k])
pl.plot(x, y, 'k', linewidth=1.5, dashes=[4, 2])
"""
Add info from TEMF boundary layer scheme
"""
if (si.Tu.size > 0):
# Cloud fraction
dw = (x11 - x00) # width of diagram
y = skewty(si.p)
x = x00 + si.cfru * 0.2 * (x11 - x00)
pl.plot(x, y, '-', linewidth=1.5, color=c6) # cloud cover
cfr_pos = np.where(si.cfru > 0.001)[0]
if (np.size(cfr_pos) > 1):
pl.text(x00,
y[cfr_pos[0] - 1],
'- %im' % (si.z[cfr_pos[0] - 1]),
ha='left',
va='center',
size=8,
color=c6)
pl.text(x00,
y[cfr_pos[-1]],
'- %im' % (si.z[cfr_pos[-1]]),
ha='left',
va='center',
size=8,
color=c6)
kmax = np.where(si.cfru == si.cfru.max())[0][0]
pl.text(x.max(),
y[kmax],
'- %i%%' % (si.cfru[kmax] * 100.),
ha='left',
va='center',
size=8,
color=c6)
"""
6. Finish diagram
"""
pl.xticks([])
pl.yticks([])
pl.box('off')
pl.xlim(x00 - 0.1, x11 + 16 * hs)
pl.ylim(y00 - 0.1, y11 + 0.1)
# draw axis
pl.plot([x00, x11], [y00, y00], 'k-')
pl.plot([x00, x00], [y00, y11], 'k-')
pl.figtext(0.5, 0.05, 'Temperature (C)', ha='center')
pl.figtext(0.015, 0.52, 'Pressure (hPa)', rotation=90)
label = 'Skew-T log-P, %s, %s UTC' % (si.name, si.time)
pl.figtext(0.5, 0.97, label, ha='center')
if (olga != -1):
img = image.imread(olga.olgaRoot + 'include/olga_left.png')
pl.figimage(img, 7, 5)
#pl.figimage(img,10,olga.fig_width_px-45)
pl.figtext(0.99, 0.011, '%s' % olga.map_desc[0], size=7, ha='right')
return fig
def plot_ua_vvel(press):
print(" %smb VVEL" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
cdict_vvel ={'red': ((0.00, 0.50, 0.50),
(0.10, 1.00, 1.00),
(0.20, 1.00, 1.00),
(0.35, 1.00, 1.00),
(0.40, 1.00, 1.00),
(0.45, 0.80, 0.80),
(0.50, 0.72, 0.72),
(0.55, 0.68, 0.68),
(0.60, 0.64, 0.64),
(0.65, 0.60, 0.70),
(0.70, 0.40, 0.45),
(0.80, 0.20, 0.20),
(0.90, 0.00, 0.00),
(1.00, 0.00, 0.00)),
'green': ((0.00, 0.35, 0.35),
(0.10, 0.45, 0.45),
(0.20, 0.55, 0.55),
(0.35, 1.00, 1.00),
(0.40, 1.00, 1.00),
(0.45, 1.00, 1.00),
(0.50, 1.00, 1.00),
(0.55, 1.00, 1.00),
(0.60, 1.00, 1.00),
(0.65, 1.00, 1.00),
(0.70, 1.00, 1.00),
(0.80, 1.00, 1.00),
(0.90, 1.00, 1.00),
(1.00, 0.80, 0.80)),
'blue': ((0.00, 0.00, 0.00),
(0.10, 0.00, 0.00),
(0.20, 0.20, 0.20),
(0.35, 1.00, 1.00),
(0.40, 1.00, 1.00),
(0.45, 1.00, 1.00),
(0.50, 1.00, 1.00),
(0.55, 1.00, 1.00),
(0.60, 1.00, 1.00),
(0.65, 1.00, 1.00),
(0.70, 1.00, 1.00),
(0.80, 1.00, 1.00),
(0.90, 1.00, 1.00),
(1.00, 0.80, 0.80))}
vvel_coltbl = LinearSegmentedColormap('VVEL_COLTBL',cdict_vvel)
vert_vel = np.multiply(w_wind_ua[time,level],100)
vert_vel = np.nan_to_num(vert_vel)
VVEL=pylab.contourf(x,y,vert_vel,vvel_clevs,cmap=vvel_coltbl,extend='both')
# Contour the heights
heights=hght_clevs[contlevid[str(press)]]
phtotal = np.add(phb[time,level],ph[time,level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT=pylab.contour(x,y,gheight,heights,colors='k',linewidths=1.5)
pylab.clabel(HGHT,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time,level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time,level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb VVel, Height (m), Wind (kts)' % press
prodid = '%smb_vert_vel' % press
units = "cm/s"
drawmap(VVEL, title, prodid, units)
def plot_ua_rhum(press):
print(" %smb RHUM" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
heights=hght_clevs[contlevid[str(press)]]
cdict = {'red': ((0.00, 0.76, 0.76),
(0.25, 0.64, 0.64),
(0.50, 0.52, 0.52),
(0.75, 0.42, 0.42),
(1.00, 0.32, 0.32)),
'green': ((0.00, 0.90, 0.90),
(0.25, 0.80, 0.80),
(0.50, 0.70, 0.70),
(0.75, 0.60, 0.60),
(1.00, 0.50, 0.50)),
'blue': ((0.00, 0.49, 0.49),
(0.25, 0.32, 0.32),
(0.50, 0.17, 0.17),
(0.75, 0.06, 0.06),
(1.00, 0.05, 0.05))}
rhum_coltbl = LinearSegmentedColormap('RHUM_COLTBL',cdict)
# Temperature calculation here
#TC = temps_ua[time,level] - 273
TH_K = np.add(temps_ua[time,level],temps_base_ua)
TK = np.multiply(TH_K,math.pow((float(press)/1000.),(287.04/1004.)))
TC = TK - 273
# RHUM Calculation here
es = 6.112*np.exp(17.67*TC/(TC+243.5))
w = np.divide(qvap[time,level],(1-qvap[time,level]))
e = (w*float(press))/(0.622+w)
relh = e / es * 100.
RHUM=pylab.contourf(x,y,relh,rhum_clevs,extend='max',cmap=rhum_coltbl)
# Contour the heights
phtotal = np.add(phb[time,level],ph[time,level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT=pylab.contour(x,y,gheight,heights,colors='k',linewidths=1.5)
pylab.clabel(HGHT,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time,level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time,level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb RELH, Height (m), Wind (kts)' % press
prodid = '%smb_rel_hum' % press
units = "percent"
drawmap(RHUM, title, prodid, units)
def plot_ua_avort(press):
print(" %smb VORICITY" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width, height), frameon=False)
cdict_vort = {
'red': ((0.00, 1.00, 1.00), (0.10, 1.00, 1.00), (0.25, 1.00, 1.00),
(0.35, 0.82, 0.82), (0.40, 0.63, 0.63), (0.45, 0.00, 0.00),
(0.50, 0.12, 0.12), (0.60, 0.00, 0.00), (0.70, 0.25, 0.25),
(0.85, 0.50, 0.50), (1.00, 0.65, 0.65)),
'green': ((0.00, 0.43, 0.43), (0.10, 0.88, 0.88), (0.25, 1.00, 1.00),
(0.35, 0.96, 0.96), (0.40, 0.82, 0.82), (0.45, 0.75, 0.75),
(0.50, 0.56, 0.56), (0.60, 0.41, 0.41), (0.70, 0.00, 0.00),
(0.85, 0.00, 0.00), (1.00, 0.13, 0.13)),
'blue': ((0.00, 0.00, 0.00), (0.10, 0.20, 0.20), (0.25, 1.00, 1.00),
(0.35, 1.00, 1.00), (0.40, 1.00, 1.00), (0.45, 1.00, 1.00),
(0.50, 1.00, 1.00), (0.60, 0.88, 0.88), (0.70, 0.80, 0.80),
(0.85, 0.59, 0.59), (1.00, 0.94, 0.94))
}
vort_coltbl = LinearSegmentedColormap('VORT_COLTBL', cdict_vort)
# Vorticity calculation goes here
dx_2 = 2 * dx
dy_2 = 2 * dy
for xs in range(x_dim - 2):
cur_column = []
for ys in range(y_dim - 2):
du = np.subtract(u_wind_ms_ua[time, level, (ys + 1), (xs + 2)],
u_wind_ms_ua[time, level, (ys + 1), xs])
dv = np.subtract(v_wind_ms_ua[time, level, (ys + 2), (xs + 1)],
v_wind_ms_ua[time, level, ys, (xs + 1)])
f_val = f[time, (ys + 1), (xs + 1)]
cur_avort = (dv / dx_2) - (du / dy_2) + f_val
cur_column.append(cur_avort)
np_column = np.array(cur_column)
if xs == 0:
vort_grid = np_column
else:
vort_grid = np.column_stack((vort_grid, np_column))
avort = np.multiply(vort_grid, 100000)
#np.reshape(avort,-1)
avort = np.nan_to_num(avort)
VORT = pylab.contourf(x_1,
y_1,
avort,
vort_clevs,
cmap=vort_coltbl,
extend='both')
# Contour the heights
heights = hght_clevs[contlevid[str(press)]]
phtotal = np.add(phb[time, level], ph[time, level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT = pylab.contour(x, y, gheight, heights, colors='k', linewidths=1.5)
pylab.clabel(HGHT, inline=1, fontsize=8, fmt='%1.0f', inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time, level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time, level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb avort (10^-5 s-1) Height, Wind (kts)' % press
prodid = '%smb_vort' % press
units = "10^-5 / s"
drawmap(VORT, title, prodid, units)
def plot_ua_avort(press):
print(" %smb VORICITY" % press)
level = levid[str(press)]
#Set Figure Size (1000 x 800)
pylab.figure(figsize=(width,height),frameon=False)
cdict_vort ={'red': ((0.00, 1.00, 1.00),
(0.10, 1.00, 1.00),
(0.25, 1.00, 1.00),
(0.35, 0.82, 0.82),
(0.40, 0.63, 0.63),
(0.45, 0.00, 0.00),
(0.50, 0.12, 0.12),
(0.60, 0.00, 0.00),
(0.70, 0.25, 0.25),
(0.85, 0.50, 0.50),
(1.00, 0.65, 0.65)),
'green': ((0.00, 0.43, 0.43),
(0.10, 0.88, 0.88),
(0.25, 1.00, 1.00),
(0.35, 0.96, 0.96),
(0.40, 0.82, 0.82),
(0.45, 0.75, 0.75),
(0.50, 0.56, 0.56),
(0.60, 0.41, 0.41),
(0.70, 0.00, 0.00),
(0.85, 0.00, 0.00),
(1.00, 0.13, 0.13)),
'blue': ((0.00, 0.00, 0.00),
(0.10, 0.20, 0.20),
(0.25, 1.00, 1.00),
(0.35, 1.00, 1.00),
(0.40, 1.00, 1.00),
(0.45, 1.00, 1.00),
(0.50, 1.00, 1.00),
(0.60, 0.88, 0.88),
(0.70, 0.80, 0.80),
(0.85, 0.59, 0.59),
(1.00, 0.94, 0.94))}
vort_coltbl = LinearSegmentedColormap('VORT_COLTBL',cdict_vort)
# Vorticity calculation goes here
dx_2 = 2 * dx
dy_2 = 2 * dy
for xs in range(x_dim - 2):
cur_column = []
for ys in range(y_dim - 2):
du = np.subtract(u_wind_ms_ua[time,level,(ys+1),(xs+2)],u_wind_ms_ua[time,level,(ys+1),xs])
dv = np.subtract(v_wind_ms_ua[time,level,(ys+2),(xs+1)],v_wind_ms_ua[time,level,ys,(xs+1)])
f_val = f[time,(ys+1),(xs+1)]
cur_avort = (dv/dx_2) - (du/dy_2) + f_val
cur_column.append(cur_avort)
np_column=np.array(cur_column)
if xs == 0:
vort_grid=np_column
else:
vort_grid = np.column_stack((vort_grid,np_column))
avort = np.multiply(vort_grid, 100000)
#np.reshape(avort,-1)
avort = np.nan_to_num(avort)
VORT=pylab.contourf(x_1,y_1,avort,vort_clevs,cmap=vort_coltbl, extend='both')
# Contour the heights
heights=hght_clevs[contlevid[str(press)]]
phtotal = np.add(phb[time,level],ph[time,level])
gheight = np.divide(phtotal, 9.81)
#gheight = ght[time,level]
HGHT=pylab.contour(x,y,gheight,heights,colors='k',linewidths=1.5)
pylab.clabel(HGHT,inline=1,fontsize=8,fmt='%1.0f',inline_spacing=1)
# Convert winds from m/s to kts and then draw barbs
u_wind_kts = u_wind_ms_ua[time,level] * 1.94384449
v_wind_kts = v_wind_ms_ua[time,level] * 1.94384449
pylab.barbs(x_th,y_th,u_wind_kts[::thin,::thin],\
v_wind_kts[::thin,::thin], length=5, sizes={'spacing':0.2},pivot='middle')
title = '%s mb avort (10^-5 s-1) Height, Wind (kts)' % press
prodid = '%smb_vort' % press
units = "10^-5 / s"
drawmap(VORT, title, prodid, units)
pylab.gca().add_line(Line2D([x + rad, x - rad], [y, y], color='k', linewidth=line_width, solid_capstyle='butt'))
return
if __name__ == "__main__":
l_scale = .01
oktas = 2
pylab.figure(figsize=(12, 8))
pylab.axes((0, 0, 1, 1))
map = Basemap(resolution='i', projection='stere', lat_ts=60.,
lat_0=60., lon_0=-97.5,
llcrnrlat=30.0, llcrnrlon=-106.0,
urcrnrlat=40.0, urcrnrlon=-93.0)
unit_scale = l_scale * max(map.urcrnry - map.llcrnry, map.urcrnrx - map.llcrnrx)
map.drawcoastlines()
map.drawcountries()
map.drawstates()
point = map(-97.4167, 35.2167)
drawSkyCover(point, .7 * unit_scale, oktas)
# drawRSDPrecip(_vec_add(point, (-1.5 * unit_scale, 0)), '+', .3 * unit_scale, snow=True, rain=True, intermittent=False)
# drawFreezingPrecip(_vec_add(point, (-2 * unit_scale, 0)), '-', 'drizzle', 1.5 * unit_scale)
# drawThunderPrecip(_vec_add(point, (-1.5 * unit_scale, 0)), '-', 'hail', .8 * unit_scale)
# drawPelletSymbol(_vec_add(point, (-1.5 * unit_scale, 0)), .8 * unit_scale, sleet=True)
drawSkyObscuration(_vec_add(point, (-1.5 * unit_scale, 0)), .5 * unit_scale, freezing=True)
pylab.barbs(point[0], point[1], 5., 10.)
pylab.savefig("mpl_test.png")