def __modelProf(self):
if self.model == "GFS":
d = BufkitFile('ftp://ftp.meteo.psu.edu/pub/bufkit/' + self.model + '/' + self.runtime[:-1] + '/'
+ self.model.lower() + '3_' + self.loc.lower() + '.buf')
else:
d = BufkitFile('ftp://ftp.meteo.psu.edu/pub/bufkit/' + self.model + '/' + self.runtime[:-1] + '/'
+ self.model.lower() + '_' + self.loc.lower() + '.buf')
self.d = d
if self.model == "SREF":
for i in self.prof_idx:
profs = []
for j in range(len(d.wdir)):
##print "MAKING PROFILE OBJECT: " + datetime.strftime(d.dates[i], '%Y%m%d/%H%M')
if j == 0:
profs.append(profile.create_profile(profile='convective', omeg = d.omeg[j][i], hght = d.hght[j][i],
tmpc = d.tmpc[j][i], dwpc = d.dwpc[j][i], pres = d.pres[j][i], wspd=d.wspd[j][i], wdir=d.wdir[j][i]))
self.progress.emit()
else:
profs.append(profile.create_profile(profile='default', omeg = d.omeg[j][i], hght = d.hght[j][i],
tmpc = d.tmpc[j][i], dwpc = d.dwpc[j][i], pres = d.pres[j][i], wspd=d.wspd[j][i], wdir=d.wdir[j][i]))
self.profs.append(profs)
else:
for i in self.prof_idx:
##print "MAKING PROFILE OBJECT: " + datetime.strftime(d.dates[i], '%Y%m%d/%H%M')
self.profs.append(profile.create_profile(profile='convective', omeg = d.omeg[0][i], hght = d.hght[0][i],
tmpc = d.tmpc[0][i], dwpc = d.dwpc[0][i], pres = d.pres[0][i], wspd=d.wspd[0][i], wdir=d.wdir[0][i]))
self.progress.emit()
def __archiveProf(self):
"""
Get the archive sounding based on the user's selections.
"""
## construct the URL
arch_file = open(self.link, 'r')
## read in the file
data = np.array(arch_file.read().split('\n'))
## take care of possible whitespace issues
for i in range(len(data)):
data[i] = data[i].strip()
arch_file.close()
## 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
plot_title = data[title_idx + 1].upper() + ' (User Selected)'
## 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 )
## construct the Profile object
prof = profile.create_profile( profile='convective', pres=p, hght=h, tmpc=T, dwpc=Td,
wdir=wdir, wspd=wspd, location=self.loc)
return prof, plot_title
def _parse(self):
file_data = self._downloadFile()
## read in the file
data = np.array([l.strip() for l in file_data.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]
time = datetime.strptime(data_header[1][:11], '%y%m%d/%H%M')
if time > datetime.utcnow(
): #If the strptime accidently makes the sounding the future:
# If the strptime accidently makes the sounding in the future (like with SARS archive)
# i.e. a 1957 sounding becomes 2057 sounding...ensure that it's a part of the 20th century
time = datetime.strptime('19' + data_header[1][:11], '%Y%m%d/%H%M')
## 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)
# idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idx]
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw',
pres=pres,
hght=hght,
tmpc=tmpc,
dwpc=dwpc,
wdir=wdir,
wspd=wspd,
location=location,
date=time,
latitude=35.)
prof_coll = prof_collection.ProfCollection(
{'': [prof]},
[time],
)
prof_coll.setMeta('loc', location)
prof_coll.setMeta('observed', True)
prof_coll.setMeta('base_time', time)
return prof_coll
def __observedProf(self):
"""
Get the observed sounding based on the user's selections
"""
## if the profile is the latest, pull the latest profile
if self.prof_time == "Latest":
timestr = self.prof_time.upper()
## otherwise, convert the menu string to the URL format
else:
timestr = self.prof_time[2:4] + self.prof_time[5:7] + self.prof_time[8:10] + self.prof_time[11:-1]
timestr += "_OBS"
## construct the URL
url = urllib.urlopen('http://www.spc.noaa.gov/exper/soundings/' + timestr + '/' + self.loc.upper() + '.txt')
## read in the file
data = np.array(url.read().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
plot_title = data[title_idx + 1] + ' (Observed)'
## 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 )
## construct the Profile object
prof = profile.create_profile( profile='convective', pres=p, hght=h, tmpc=T, dwpc=Td,
wdir=wdir, wspd=wspd, location=self.loc)
return prof, plot_title
def _parse(self, file_name):
file_data = self._downloadFile(file_name)
## read in the file
data = np.array([l.strip() for l in file_data.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]
time = data_header[1][:11]
## 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 )
# idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idx]
prof = profile.create_profile(profile='raw', pres=pres, hght=hght, tmpc=tmpc, dwpc=dwpc,
wdir=wdir, wspd=wspd, location=location)
return {'':[ prof ]}, [ datetime.strptime(time, '%y%m%d/%H%M') ]
def _parseSection(self, section):
parts = section.split('\n')
if ' F' in parts[1]:
valid, fhr = parts[1].split(' F')
fhr = int(fhr)
else:
valid = parts[1]
fhr = 0
dt_obj = datetime.strptime(valid, 'TIME = %y%m%d/%H%M')
member = parts[0].split('=')[-1].strip()
location = parts[2].split('SLAT')[0].split('=')[-1].strip()
headers = [ h.lower() for h in parts[4].split(", ") ]
data = '\n'.join(parts[5:])
if not is_py3():
sound_data = StringIO(data)
else:
sound_data = BytesIO(data.encode())
prof_vars = np.genfromtxt( sound_data, delimiter=',', unpack=True)
prof_var_dict = dict(zip(headers, prof_vars))
def maybe_replace(old_var, new_var):
if old_var in prof_var_dict:
prof_var_dict[new_var] = prof_var_dict[old_var]
del prof_var_dict[old_var]
maybe_replace('omga', 'omeg')
maybe_replace('temp', 'tmpc')
maybe_replace('dewp', 'dwpc')
maybe_replace('uwin', 'u')
maybe_replace('vwin', 'v')
prof = profile.create_profile(profile='raw', location=location, date=dt_obj, missing=-999.0, **prof_var_dict)
return prof, dt_obj, dt_obj - timedelta(hours=fhr), member
def make_plot(index):
#index = (r,c)
r,c = index
print(r,c)
xlat = xlats[r,c]
xlon = xlons[r,c]
mean_hgt = np.nanmean(z_agl[:,:,r,c], axis=0)
mean_t = np.nanmean(t[:,:,r,c], axis=0)
mean_q = np.nanmean(q[:,:,r,c], axis=0)
mean_p = np.nanmean(p[:,:,r,c], axis=0)
mean_td = thermo.temp_at_mixrat(mean_q*1000., mean_p)
mean_u = np.nanmean(u[:,:,r,c], axis=0)
mean_v = np.nanmean(v[:,:,r,c], axis=0)
mean_omega = np.nanmean(omega[:,:,r,c], axis=0)
print("start prof")
prof = profile.create_profile(profile='convective', pres=mean_p, hght=mean_hgt, tmpc=mean_t, \
dwpc=mean_td, u=mean_u, v=mean_v, omeg=mean_omega, missing=-9999, strictQC=False, latitude=xlat,date=vdateobj)
print("end prof")
member_profs = np.empty(len(z_agl[:,1]), dtype=object)
print("start member profs")
# Loop over all of the members and create profile objects for each of them.
for m in range(len(z_agl[:,1,r,c])):
member_profs[m] = profile.create_profile(profile='convective', pres=p[m,:,r,c], hght=z_agl[m,:,r,c], tmpc=t[m,:,r,c], \
dwpc=td[m,:,r,c], u=u[m,:,r,c], v=v[m,:,r,c], missing=-9999, strictQC=False, latitude=xlat,date=vdateobj)
members = {'hght': z_agl[:,:,r,c], 'pres': p[:,:,r,c], 'tmpc': t[:,:,r,c], 'dwpc': td[:,:,r,c], 'u': u[:,:,r,c], 'v': v[:,:,r,c], 'member_profs': member_profs}
print("end member profs")
#find summary file lat/lons
wherelatlon = np.where((sumlats == xlat)&(sumlons == xlon))
latwhere = wherelatlon[0][0]
lonwhere = wherelatlon[1][0]
cape_ml = cape_ml_0[:,latwhere-3:latwhere+4,lonwhere-3:lonwhere+4]
srh_0to1 = srh_0to1_0[:,latwhere-3:latwhere+4,lonwhere-3:lonwhere+4]
figname = os.path.join(outdir,'wofs_snd_{:02d}_{:02d}_f{:03d}.png'.format(r+1,c+1,leadminute))
# pass the data to the plotting script.
print('rounded lat/lon',round(xlat,2), round(xlon,2))
plot_wof(prof, members, figname, str(round(xlat,2)), str(round(xlon,2)), idateobj, vdateobj, x_pts=cape_ml, y_pts=srh_0to1)
def _parse(self):
file_data = self._downloadFile()
snfile = [l for l in file_data.split('\n')]
bgn = -1
end = -1
ttl = -1
stl = -1
for i in range(len(snfile)):
if snfile[i] == "<PRE>":
bgn = i+5
if snfile[i][:10] == "</PRE><H3>":
end = i-1
if snfile[i][:4] == "<H2>" and snfile[i][-5:] == "</H2>":
ttl = i
if 'Station latitude' in snfile[i]:
stl = i
if bgn == -1 or end == -1 or ttl == -1:
raise IOError("Looks like the server had difficulty handling the request. Try again in a few minutes.")
snd_data = []
for i in range(bgn, end+1):
vals = []
for j in [ 0, 1, 2, 3, 6, 7 ]:
val = snfile[i][(7 * j):(7 * (j + 1))].strip()
if val == "":
vals.append(UWYODecoder.MISSING)
else:
vals.append(float(val))
snd_data.append(vals)
col_names = ['pres', 'hght', 'tmpc', 'dwpc', 'wdir', 'wspd']
snd_dict = dict((v, p) for v, p in zip(col_names, list(zip(*snd_data))))
snd_date = datetime.strptime(snfile[ttl][-20:-5], "%HZ %d %b %Y")
loc = snfile[ttl][10:14]
if stl == -1:
lat = 35.
else:
lat = float(snfile[stl].split(':')[-1].strip())
prof = profile.create_profile(profile='raw', location=loc, date=snd_date, latitude=lat, missing=UWYODecoder.MISSING, **snd_dict)
prof_coll = prof_collection.ProfCollection(
{'':[ prof ]},
[ snd_date ],
)
prof_coll.setMeta('loc', loc)
prof_coll.setMeta('observed', True)
return prof_coll
def getProf(fname):
dec = spc_decoder.SPCDecoder(fname)
profs = dec.getProfiles()
stn_id = dec.getStnId()
all_profs = profs._profs
prof = all_profs[''][0]
dates = profs._dates
prof = profile.create_profile(pres=prof.pres, hght=prof.hght, tmpc=prof.tmpc, dwpc=prof.dwpc, wspd=prof.wspd, \
wdir=prof.wdir, strictQC=False, profile='convective', date=dates[0])
return prof
def _parseSection(self, section):
parts = section.split('\n')
dt_obj = datetime.strptime(parts[1], 'TIME = %y%m%d/%H%M')
member = parts[0].split('=')[-1].strip()
location = parts[2].split('SLAT')[0].split('=')[-1].strip()
data = '\n'.join(parts[5:])
sound_data = StringIO( data )
p, h, t, td, wdir, wspd, omeg = np.genfromtxt( sound_data, delimiter=',', unpack=True)
prof = profile.create_profile(profile='raw', pres=p[1:], hght=h[1:], tmpc=t[1:], dwpc=td[1:], wspd=wspd[1:],\
wdir=wdir[1:], omeg=omeg[1:], location=location, missing=-999.0)
return prof, dt_obj, member
def _parseSection(self, section):
parts = section.split('\n')
dt_obj = datetime.strptime(parts[1], 'TIME = %y%m%d/%H%M')
member = parts[0].split('=')[-1].strip()
location = parts[2].split('SLAT')[0].split('=')[-1].strip()
data = '\n'.join(parts[5:])
sound_data = StringIO(data)
p, h, t, td, wdir, wspd, omeg = np.genfromtxt(sound_data,
delimiter=',',
unpack=True)
prof = profile.create_profile(profile='raw', pres=p[1:], hght=h[1:], tmpc=t[1:], dwpc=td[1:], wspd=wspd[1:],\
wdir=wdir[1:], omeg=omeg[1:], location=location, date=dt_obj, missing=-999.0)
return prof, dt_obj, member
def _parse(self):
file_data = self._downloadFile()
## read in the file
data = np.array([l.strip() for l in file_data.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]
time = datetime.strptime(data_header[1][:11], '%y%m%d/%H%M')
if time > datetime.utcnow(): #If the strptime accidently makes the sounding the future:
# If the strptime accidently makes the sounding in the future (like with SARS archive)
# i.e. a 1957 sounding becomes 2057 sounding...ensure that it's a part of the 20th century
time = datetime.strptime('19' + data_header[1][:11], '%Y%m%d/%H%M')
## 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 )
# idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idx]
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw', pres=pres, hght=hght, tmpc=tmpc, dwpc=dwpc,
wdir=wdir, wspd=wspd, location=location, date=time, latitude=35.)
prof_coll = prof_collection.ProfCollection(
{'':[ prof ]},
[ time ],
)
prof_coll.setMeta('loc', location)
prof_coll.setMeta('observed', True)
prof_coll.setMeta('base_time', time)
return prof_coll
def _parse(self):
# time = datetime.now()
file_data = self._downloadFile()
modInit, modName, fHour, coords = file_data.split(' ')
# if len(modInit) == 2:
# import time
# modInit = time.strftime('%Y%m%d')+modInit
locationStr = coords.strip('\n')
textFile = '/home/apache/climate/data/forecast/sndgs/'+modInit+'_'+modName+'_'+fHour+'_'+coords.strip('\n')+'_raw.txt'
#writeTimes(textFile, 'Begin')
data_header = 'Location: '
time = datetime.strptime(modInit, '%Y%m%d%H') + timedelta(hours=int(fHour))
# Determine if it's a site ID:
if ',' not in coords:
import numpy as np
sites, siteCoords = np.genfromtxt('/home/apache/climate/hanis/model/fsound/text/sid.txt', dtype=str, unpack=True, delimiter=' ')
i = np.where(sites == 'KORD')
coords = siteCoords[i[0][0]]
variables = fsonde_decoder.decode(modInit, modName, fHour, coords)
#writeTimes(textFile, 'After Decode')
pres = variables['pres']
hght = variables['hght']
tmpc = variables['temp']
dwpc = variables['dewp']
u = variables['ugrd']
v = variables['vgrd']
omeg = variables['omeg']
wdir, wspd = utils.comp2vec(u, v)
# wspd = [s*1.94384 for s in wspd]
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw', pres=pres, hght=hght, tmpc=tmpc, dwpc=dwpc, wdir=wdir, wspd=wspd, omeg=omeg, location=locationStr, date=time, latitude=35.)
prof_coll = prof_collection.ProfCollection({'':[ prof ]},[ time ],)
prof_coll.setMeta('loc', locationStr)
#writeTimes(textFile, 'End')
return prof_coll
def test_url():
path = 'examples/data/14061619.OAX'
# Sys.argv[1] should be the URL to the file that is being tested.
profs, stn_id = decode(path)
print((profs._profs))
for k in profs._profs.keys():
all_prof = profs._profs[k]
dates = profs._dates
for i in range(len(all_prof)):
prof = all_prof[i]
new_prof = profile.create_profile(pres=prof.pres, hght=prof.hght, tmpc=prof.tmpc, dwpc=prof.dwpc, wspd=prof.wspd, \
wdir=prof.wdir, strictQC=False, profile='convective', date=dates[i])
#for key in dir(new_prof):
# print((key, getattr(new_prof,key)))
print(new_prof.mupcl.bplus)
def decode(filename):
dec = SPCDecoder(filename)
if dec is None:
raise IOError("Could not figure out the format of '%s'!" % filename)
# Returns the set of profiles from the file that are from the "Profile" class.
profs = dec.getProfiles()
stn_id = dec.getStnId()
for k in list(profs._profs.keys()):
all_prof = profs._profs[k]
dates = profs._dates
for i in range(len(all_prof)):
prof = all_prof[i]
new_prof = profile.create_profile(pres=prof.pres, hght=prof.hght, tmpc=prof.tmpc, dwpc=prof.dwpc, wspd=prof.wspd, \
wdir=prof.wdir, strictQC=False, profile='convective', date=dates[i])
return new_prof, dates[i], stn_id
def make_profile(self, i):
d = self.d
prof = profile.create_profile(profile='convective', hght = d.hght[0][i],
tmpc = d.tmpc[0][i], dwpc = d.dwpc[0][i], pres = d.pres[0][i],
wspd=d.wspd[0][i], wdir=d.wdir[0][i])
return prof
p_std = data_std[:, 0]
T = data_all[:, 1]
Td = data_all[:, 2]
RH = data_all[:, 3]
#Td = 243.04*(np.log(RH/100)+((17.625*T)/(243.04+T)))/(17.625-np.log(RH/100)-((17.625*T)/(243.04+T)))
h = data_all[:, 4]
h = [i - np.min(h) for i in h] #reduce to ground level
spd = data_all[:, 5]
spd_std = data_std[:, 5]
direc = data_all[:, 6]
direc_std = data_std[:, 6]
prof = profile.create_profile(profile='default',
pres=p,
hght=h,
tmpc=T,
dwpc=Td,
wspd=spd,
wdir=direc,
strictQC=False)
#interpolate pressure to important height levels
h_new = [1000] + [3000, 6000, 9000, 12000, 15000]
for i in range(len(h_new)):
if np.max(h) > h_new[i]:
index = i
h_new_labels = ['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(h, p)
p_interped = p_interped_func(h_new[0:index + 1])
# Add units to the data arrays
if fhr % args.interval != 0:
print "fhr not multiple of", args.interval, "skipping", sfile
continue
if len(station) > 3 and station not in no_ignore_station:
print "skipping", sfile
continue
skew.ax.set_title(title, horizontalalignment="left", x=0, fontsize=12)
print "reading", sfile
data = open(sfile).read()
pres, hght, tmpc, dwpc, wdir, wspd, latitude, longitude = parseGEMPAK(data)
if wdir.size == 0:
print "no good data lines. empty profile"
continue
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc,
dwpc=dwpc, wspd=wspd, wdir=wdir, latitude=latitude, longitude=longitude, missing=-999., strictQC=True)
#### Adding a Parcel Trace
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
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = mupcl
# Temperature, dewpoint, virtual temperature, wetbulb, parcel profiles
temperature_trace, = skew.plot(prof.pres, prof.tmpc, 'r', linewidth=2) # temperature profile
# annotate temperature in F at bottom of T profile
temperatureF = skew.ax.text(prof.tmpc[0], prof.pres[0]+10, utils.INT2STR(thermo.ctof(prof.tmpc[0])),
verticalalignment='top', horizontalalignment='center', size=7, color=temperature_trace.get_color())
skew.plot(prof.pres, prof.vtmp, 'r', linewidth=0.5) # Virtual temperature profile
hghts = ds.z.values / G
uwnd = ds.u.values
vwnd = ds.v.values
wdirs = wind_direction(uwnd, vwnd)
wspds = wind_speed(uwnd, vwnd)
rh = ds.r.values / 100.
tmpc = ds.t.values
dwpc = dewpoint_from_rh(tmpc, rh)
tmpc = tmpc - ZEROCNK
t = 0
for j in range(tmpc.shape[2]):
for i in range(tmpc.shape[3]):
prof = SHARPPY_PROFILE.create_profile(pres=pres,
tmpc=tmpc[t, :, j, i],
hght=hghts[t, :, j, i],
dwpc=dwpc[t, :, j, i],
wspd=wspds[t, :, j, i],
wdir=wdirs[t, :, j, i])
eff_inflow = params.effective_inflow_layer(prof)
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
srwind = params.bunkers_storm_motion(prof)
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
print('... reading file: ' + filename)
url = open(filename)
data = np.array(url.read().split('\n'))
title_idx = np.where(data == '%TITLE%')[0][0]
start_idx = np.where(data == '%RAW%')[0]
finish_idx = np.where(data == '%END%')[0]
plot_title = data[title_idx + 1]
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
P, h, T, Td, wdir, wspd = np.genfromtxt(sound_data,
delimiter=',',
comments="%",
unpack=True)
prof = profile.create_profile(pres=P,
hght=h,
tmpc=T,
dwpc=Td,
wdir=wdir,
wspd=wspd)
pcl = parcelx(prof)
sfcpcl = tab.params.parcelx(prof, flag=1) #Surface Parcel
fcstpcl = tab.params.parcelx(prof, flag=2) #Forecast Parcel
mupcl = tab.params.parcelx(prof, flag=3) #Most-Unstable Parcel
mlpcl = tab.params.parcelx(prof, flag=4) #100 mb Mean Layer Parcel
csvfile = open(('Lift_Parcel_' + filename[:-4] + '.txt'), 'wb')
writer = csv.writer(csvfile, delimiter=' ')
writer.writerow([plot_title])
writer.writerow('\r')
loop_idx = range(1)
for idx in loop_idx:
a = [('Surface CAPE = ' + str(sfcpcl.bplus))] #J/Kg
sys.argv[2])
data = np.array(url.read().split('\n'))
title_idx = np.where(data == '%TITLE%')[0][0]
start_idx = np.where(data == '%RAW%')[0] + 1
finish_idx = np.where(data == '%END%')[0]
plot_title = data[title_idx + 1] + ' (Observed)'
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
p, h, T, Td, wdir, wspd = np.genfromtxt(sound_data,
delimiter=',',
comments="%",
unpack=True)
prof = profile.create_profile(profile='convective',
pres=p,
hght=h,
tmpc=T,
dwpc=Td,
wdir=wdir,
wspd=wspd,
location=sys.argv[1])
elif len(sys.argv) > 1 and sys.argv[1] != "test":
gmtime = datetime.datetime.utcnow()
t_str = str(gmtime)
year = t_str[2:4]
month = t_str[5:7]
day = t_str[8:10]
hour = t_str[11:13]
if int(hour) > 12:
current_ob = '12'
else:
current_ob = '00'
def _parseMember(self, text):
data = np.array(text.split('\r\n'))
data_idxs = []
new_record = False
begin_idx = 0
member_name = data[0]
dates = []
# Figure out the indices for the data chunks
for i in range(len(data)):
if "STID" in data[i]:
# Here is information about the record
spl = data[i].split()
station = spl[2]
wmo_id = spl[5]
dates.append(datetime.strptime(spl[8], '%y%m%d/%H%M'))
slat = float(data[i+1].split()[2])
slon = float(data[i+1].split()[5])
selv = float(data[i+1].split()[8])
stim = float(data[i+2].split()[2])
if data[i].find('HGHT') >= 0 and new_record == False:
# we've found a new data chunk
new_record = True
begin_idx = i+1
elif 'STID' in data[i] and new_record == True:
# We've found the end of the data chunk
new_record = False
data_idxs.append((begin_idx, i-1))
elif 'STN' in data[i] and new_record == True:
# We've found the end of the last data chunk of the file
new_record = False
data_idxs.append((begin_idx, i))
elif new_record == True:
continue
##print data[i]
data_idxs = data_idxs[1:]
# Make arrays to store the data
profiles = []
# Parse out the profiles
for i in range(len(data_idxs)):
data_stuff = data[data_idxs[i][0]: data_idxs[i][1]]
profile_length = len(data[data_idxs[i][0]: data_idxs[i][1]])/2
hght = np.zeros((profile_length,), dtype=float)
pres = np.zeros((profile_length,), dtype=float)
tmpc = np.zeros((profile_length,), dtype=float)
dwpc = np.zeros((profile_length,), dtype=float)
wdir = np.zeros((profile_length,), dtype=float)
wspd = np.zeros((profile_length,), dtype=float)
omeg = np.zeros((profile_length,), dtype=float)
for j in np.arange(0, profile_length * 2, 2):
if len(data_stuff[j+1].split()) == 1:
hght[j / 2] = float(data_stuff[j+1].split()[0])
else:
hght[j / 2] = float(data_stuff[j+1].split()[1])
tmpc[j / 2] = float(data_stuff[j].split()[1])
dwpc[j / 2] = float(data_stuff[j].split()[3])
pres[j / 2] = float(data_stuff[j].split()[0])
wspd[j / 2] = float(data_stuff[j].split()[6])
wdir[j / 2] = float(data_stuff[j].split()[5])
omeg[j / 2] = float(data_stuff[j].split()[7])
prof = profile.create_profile(profile='raw', pres=pres, hght=hght, tmpc=tmpc, dwpc=dwpc,
wdir=wdir, wspd=wspd, omeg=omeg, location=station, date=dates[i], latitude=slat)
profiles.append(prof)
return member_name, profiles, dates
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 ##
"""
continue
skew.ax.set_title(title, horizontalalignment="left", x=0, fontsize=12)
print("reading " + sfile)
data = open(sfile).read()
pres, hght, tmpc, dwpc, wdir, wspd, latitude, longitude = parseGEMPAK(data)
print("finished reading " + sfile)
if wdir.size == 0:
print("no good data lines. empty profile")
continue
prof = profile.create_profile(profile='default',
pres=pres,
hght=hght,
tmpc=tmpc,
dwpc=dwpc,
wspd=wspd,
wdir=wdir,
latitude=latitude,
longitude=longitude,
strictQC=True)
#### Adding a Parcel Trace
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
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = mupcl
# Temperature, dewpoint, virtual temperature, wetbulb, parcel profiles
temperature_trace, = skew.plot(prof.pres, prof.tmpc, 'r',
def _parse(self):
global dyn_inset
file_data = self._downloadFile()
## read in the file
data = np.array([l.strip() for l in file_data.split('\n')])
## necessary index points
#title_idx = np.where( 'Station:' in data )
date_idx = data[0]
title_idx = data[1]
#print date_idx
#print title_idx
start_idx = (np.where(np.char.find(data, 'OMEGASTART') > -1)[0][0]) + 1
#print data[start_idx]
finish_idx = np.where(np.char.find(data, 'OMEGAEND') > -1)[0][0]
## create the plot title
location = title_idx
time = datetime.strptime(date_idx, '%a %d %b %Y | %H%M UTC')
#print time.strftime('%Y %M %d %H')
# data_header = 'Location: ' + location + ' ' + data[date_idx]
# if 'analysis' in data[date_idx]:
# #print "analysis"
# timeStr = str(data[date_idx].split('for ')[1]).upper()
# time = datetime.strptime(timeStr, '%H%MZ %d %b %y')
# else:
# #print "forecast"
# timeStr = str(data[date_idx].split('valid ')[1]).upper()
# time = datetime.strptime(timeStr, '%HZ %a %d %b %y')
# if time > datetime.utcnow(): #If the strptime accidently makes the sounding the future:
# # If the strptime accidently makes the sounding in the future (like with SARS archive)
# # i.e. a 1957 sounding becomes 2057 sounding...ensure that it's a part of the 20th century
# time = datetime.strptime('19' + data_header[1][:11], '%Y%m%d/%H%M')
## put it all together for StringIO
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
#print datetime.strftime('%Y %m %d %H',time)
#full_data = np.array(full_data)
#print sound_data
## read the data into arrays
p, T, Td, h, wspd, wdir, omeg = np.genfromtxt(sound_data,
unpack=True,
usecols=(0, 1, 2, 3, 4,
5, 6))
#idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idxi]
omeg = omeg #[idx]
print omeg
if dwpc[0] < 40:
dyn_inset = 'winter'
else:
dyn_inset = 'severe'
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw',
pres=pres,
hght=hght,
tmpc=tmpc,
dwpc=dwpc,
wdir=wdir,
wspd=wspd,
omeg=omeg,
location=location,
date=time,
latitude=35.)
prof_coll = prof_collection.ProfCollection(
{'': [prof]},
[time],
)
prof_coll.setMeta('loc', location)
print "Using Omega Decoder."
return prof_coll
import sharppy.io.spc_decoder as spc_decoder
import sharppy.sharptab.profile as profile
import sharppy.sharptab.watch_type as watch
import numpy.testing as npt
import numpy as np
files = ['examples/data/14061619.OAX']
dec = spc_decoder.SPCDecoder(files[0])
profs = dec.getProfiles()
stn_id = dec.getStnId()
all_profs = profs._profs
prof = all_profs[''][0]
dates = profs._dates
prof = profile.create_profile(pres=prof.pres, hght=prof.hght, tmpc=prof.tmpc, dwpc=prof.dwpc, wspd=prof.wspd, \
wdir=prof.wdir, strictQC=False, profile='convective', date=dates[0])
def test_heat_index():
temps = np.array([104, 100, 92, 92, 86, 80, 80, 60, 30])
rh = np.array([55, 65, 60, 90, 90, 75, 40, 90, 50])
correct_hi = np.array([
137.361, 135.868, 104.684, 131.256, 105.294, 83.5751, 79.79, 59.965,
30.00
])
returned_hi = []
for i in range(len(temps)):
returned_hi.append(watch.heat_index(temps[i], rh[i]))
returned_hi = np.asarray(returned_hi)
npt.assert_almost_equal(returned_hi, correct_hi, 0)
def append_wbz():
#Load each ERA-Interim netcdf file, and append wbz
start_lat = -44.525
end_lat = -9.975
start_lon = 111.975
end_lon = 156.275
domain = [start_lat, end_lat, start_lon, end_lon]
model = "erai"
region = "aus"
dates = []
for y in np.arange(1979, 2019):
for m in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
if (m != 12):
dates.append([dt.datetime(y,m,1,0,0,0),\
dt.datetime(y,m+1,1,0,0,0)-dt.timedelta(hours = 6)])
else:
dates.append([dt.datetime(y,m,1,0,0,0),\
dt.datetime(y+1,1,1,0,0,0)-dt.timedelta(hours = 6)])
for t in np.arange(0, len(dates)):
print(str(dates[t][0]) + " - " + str(dates[t][1]))
fname = "/g/data/eg3/ab4502/ExtremeWind/"+region+"/"+model+"/"+model+"_"+\
dt.datetime.strftime(dates[t][0],"%Y%m%d")+"_"+\
dt.datetime.strftime(dates[t][-1],"%Y%m%d")+".nc"
ta,dp,hur,hgt,terrain,p,ps,wap,ua,va,uas,vas,tas,ta2d,cp,wg10,cape,lon,lat,date_list = \
read_erai(domain,dates[t])
dp = get_dp(ta, hur, dp_mask=False)
agl_idx = (p <= ps)
#Replace masked dp values
dp = replace_dp(dp)
try:
prof = profile.create_profile(pres = np.insert(p[agl_idx],0,ps), \
hght = np.insert(hgt[agl_idx],0,terrain), \
tmpc = np.insert(ta[agl_idx],0,tas), \
dwpc = np.insert(dp[agl_idx],0,ta2d), \
u = np.insert(ua[agl_idx],0,uas), \
v = np.insert(va[agl_idx],0,vas), \
strictqc=False, omeg=np.insert(wap[agl_idx],0,wap[agl_idx][0]) )
except:
p = p[agl_idx]
ua = ua[agl_idx]
va = va[agl_idx]
hgt = hgt[agl_idx]
ta = ta[agl_idx] \
dp = dp[agl_idx]
p[0] = ps
ua[0] = uas
va[0] = vas
hgt[0] = terrain
ta[0] = tas
dp[0] = ta2d
prof = profile.create_profile(pres = p, \
hght = hgt, \
tmpc = ta, \
dwpc = dp, \
u = ua, \
v = va, \
strictqc=False, omeg=wap[agl_idx])
pwb0 = params.temp_lvl(prof, 0, wetbulb=True)
hwb0 = interp.to_agl(prof, interp.hght(prof, pwb0))
param_file = nc.Dataset(fname, "a")
wbz_var = param_file.createVariable("wbz",float,\
("time","lat","lon"))
wbz_var.units = "m"
wbz_var.long_name = "wet_bulb_zero_height"
wbz_var[:] = hwb0
T1 = abs(
thermo.wetlift(prof.pres[0], prof.tmpc[0], 600) -
interp.temp(prof, 600))
T2 = abs(
thermo.wetlift(pwb0, interp.temp(prof, pwb0), sfc) - prof.tmpc[0])
Vprime = utils.KTS2MS(13 * np.sqrt((T1 + T2) / 2) + (1 / 3 *
(Umean01)))
Vprime_var = param_file.createVariable("Vprime",float,\
("time","lat","lon"))
Vprime_var.units = "m/s"
Vprime_var.long_name = "miller_1972_wind_speed"
Vprime_var[:] = Vprime
param_file.close()
def 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 _parse(self):
file_data = self._downloadFile()
## read in the file
data = np.array([l.strip() for l in file_data.split('\n')])
## necessary index points
start_idx = np.where(data == '<PRE>')[0]
finish_idx = np.where(np.char.find(data, '</H3>') > -1)[0]
time_idx = np.where(np.char.find(data, 'time') > -1)[0][0]
latitude_idx = np.where(np.char.find(data, 'latitude') > -1)[0][0]
## create the plot title and time
location = data[4].split()[1]
time = datetime.strptime(data[time_idx].strip().split()[2],
'%y%m%d/%H%M')
latitude = data[latitude_idx].strip().split()[2]
if time > datetime.utcnow(
): #If the strptime accidently makes the sounding the future:
# If the strptime accidently makes the sounding in the future (like with SARS archive)
# i.e. a 1957 sounding becomes 2057 sounding...ensure that it's a part of the 20th century
time = datetime.strptime('19' + data[time_idx].strip().split()[2],
'%y%m%d/%H%M')
## put it all together for StringIO
data = data[10:finish_idx][:]
data_final = []
max = 0
for m in data:
while ' ' in m:
m = m.replace(' ', ' ')
if len(m.split(' ')) != 11:
continue
if int(float(m.split(' ')[1])) <= max:
continue
data_final.append(m)
max = int(float(m.split(' ')[1]))
full_data = '\n'.join(data_final)
while ' ' in full_data:
full_data = full_data.replace(' ', ' ')
sound_data = StringIO(full_data.strip())
## read the data into arrays
p, h, T, Td, rh, mr, wdir, wspd, ta, te, tv = np.genfromtxt(
sound_data, delimiter=' ', comments="%", unpack=True)
#idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idx]
wdir_final = []
for m in wdir:
s = '0'
if int(m) < 360:
s = m
wdir_final.append(s)
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw',
pres=pres,
hght=hght,
tmpc=tmpc,
dwpc=dwpc,
wdir=wdir_final,
wspd=wspd,
location=location,
date=time,
latitude=float(latitude))
prof_coll = prof_collection.ProfCollection(
{'': [prof]},
[time],
)
prof_coll.setMeta('loc', location)
return prof_coll
def test_plotting():
dec = spc_decoder.SPCDecoder(files[0])
profs = dec.getProfiles()
stn_id = dec.getStnId()
print(profs)
all_profs = profs._profs
print(all_profs)
prof = all_profs[''][0]
dates = profs._dates
print(dates)
prof = profile.create_profile(pres=prof.pres, hght=prof.hght, tmpc=prof.tmpc, dwpc=prof.dwpc, wspd=prof.wspd, \
wdir=prof.wdir, strictQC=False, profile='convective', date=dates[0])
time = dates[0]
location = "OAX"
pb_plot = 1050
pt_plot = 100
dp_plot = 10
plevs_plot = np.arange(pb_plot, pt_plot - 1, -dp_plot)
# Open up the text file with the data in columns (e.g. the sample OAX file distributed with SHARPpy)
title = time.strftime('%Y%m%d/%H%M') + ' ' + location + ' (Observed)'
# Set up the figure in matplotlib.
fig = plt.figure(figsize=(9, 8))
gs = gridspec.GridSpec(4, 4, width_ratios=[1, 5, 1, 1])
ax = plt.subplot(gs[0:3, 0:2], projection='skewx')
skew.draw_title(ax, title)
skew.draw_dry_adiabats(ax)
skew.draw_mixing_ratio_lines(ax)
skew.draw_moist_adiabats(ax)
skew.draw_heights(ax, prof)
skew.draw_effective_inflow_layer(ax, prof)
ax.grid(True)
plt.grid(True)
# Plot the background variables
presvals = np.arange(1000, 0, -10)
ax.semilogy(prof.tmpc[~prof.tmpc.mask],
prof.pres[~prof.tmpc.mask],
'r',
lw=2)
ax.semilogy(prof.dwpc[~prof.dwpc.mask],
prof.pres[~prof.dwpc.mask],
'g',
lw=2)
ax.semilogy(prof.vtmp[~prof.dwpc.mask], prof.pres[~prof.dwpc.mask], 'r--')
ax.semilogy(prof.wetbulb[~prof.dwpc.mask], prof.pres[~prof.dwpc.mask],
'c-')
# Plot the parcel trace, but this may fail. If it does so, inform the user.
try:
ax.semilogy(prof.mupcl.ttrace, prof.mupcl.ptrace, 'k--')
except:
print("Couldn't plot parcel traces...")
skew.plot_sig_levels(ax, prof)
# Highlight the 0 C and -20 C isotherms.
l = ax.axvline(0, color='b', ls='--')
l = ax.axvline(-20, color='b', ls='--')
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.set_yticks(np.linspace(100, 1000, 10))
ax.set_ylim(1050, 100)
# Plot the hodograph data.
inset_axes = skew.draw_hodo_inset(ax, prof)
skew.plotHodo(inset_axes, prof.hght, prof.u, prof.v, color='r')
#inset_axes.text(srwind[0], srwind[1], 'RM', color='r', fontsize=8)
#inset_axes.text(srwind[2], srwind[3], 'LM', color='b', fontsize=8)
# Draw the wind barbs axis and everything that comes with it.
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.set_xlim(-50, 50)
ax2 = plt.subplot(gs[0:3, 2])
ax3 = plt.subplot(gs[3, 0:3])
skew.plot_wind_axes(ax2)
skew.plot_wind_barbs(ax2, prof.pres, prof.u, prof.v)
gs.update(left=0.05, bottom=0.05, top=0.95, right=1, wspace=0.025)
def _parseMember(self, text):
data = np.array(text.split('\r\n'))
data_idxs = []
new_record = False
begin_idx = 0
member_name = data[0]
dates = []
# Figure out the indices for the data chunks
for i in range(len(data)):
if "STID" in data[i]:
# Here is information about the record
spl = data[i].split()
if spl[2].strip() == "STNM":
station = "" # The bufkit file has a blank space for the station name
wmo_id = spl[4]
dates.append(datetime.strptime(spl[7], '%y%m%d/%H%M'))
else:
station = spl[2]
wmo_id = spl[5]
dates.append(datetime.strptime(spl[8], '%y%m%d/%H%M'))
slat = float(data[i + 1].split()[2])
slon = float(data[i + 1].split()[5])
selv = float(data[i + 1].split()[8])
stim = float(data[i + 2].split()[2])
if data[i].find('HGHT') >= 0 and new_record == False:
# we've found a new data chunk
new_record = True
begin_idx = i + 1
elif 'STID' in data[i] and new_record == True:
# We've found the end of the data chunk
new_record = False
data_idxs.append((begin_idx, i - 1))
elif 'STN' in data[i] and new_record == True:
# We've found the end of the last data chunk of the file
new_record = False
data_idxs.append((begin_idx, i))
elif new_record == True:
continue
##print data[i]
data_idxs = data_idxs[1:]
# Make arrays to store the data
profiles = []
# Parse out the profiles
for i in range(len(data_idxs)):
data_stuff = data[data_idxs[i][0]:data_idxs[i][1]]
profile_length = len(data[data_idxs[i][0]:data_idxs[i][1]]) / 2
hght = np.zeros((profile_length, ), dtype=float)
pres = np.zeros((profile_length, ), dtype=float)
tmpc = np.zeros((profile_length, ), dtype=float)
dwpc = np.zeros((profile_length, ), dtype=float)
wdir = np.zeros((profile_length, ), dtype=float)
wspd = np.zeros((profile_length, ), dtype=float)
omeg = np.zeros((profile_length, ), dtype=float)
for j in np.arange(0, profile_length * 2, 2):
if len(data_stuff[j + 1].split()) == 1:
hght[j / 2] = float(data_stuff[j + 1].split()[0])
else:
hght[j / 2] = float(data_stuff[j + 1].split()[1])
tmpc[j / 2] = float(data_stuff[j].split()[1])
dwpc[j / 2] = float(data_stuff[j].split()[3])
pres[j / 2] = float(data_stuff[j].split()[0])
wspd[j / 2] = float(data_stuff[j].split()[6])
wdir[j / 2] = float(data_stuff[j].split()[5])
omeg[j / 2] = float(data_stuff[j].split()[7])
prof = profile.create_profile(profile='raw',
pres=pres,
hght=hght,
tmpc=tmpc,
dwpc=dwpc,
wdir=wdir,
wspd=wspd,
omeg=omeg,
location=station,
date=dates[i],
latitude=slat)
profiles.append(prof)
return member_name, profiles, dates
def _parse(self):
"""
Parse the netCDF file according to the variable naming and
dimmensional conventions of the WRF-ARW.
"""
## open the file and also store the lat/lon of the selected point
file_data = self._downloadFile()
gridx = self._file_name[1]
gridy = self._file_name[2]
## calculate the nearest grid point to the map point
idx = self._find_nearest_point(file_data, gridx, gridy)
## check to see if this is a 4D netCDF4 that includes all available times.
## If it does, open and compute the variables as 4D variables
if len(file_data.variables["T"][:].shape) == 4:
## read in the data from the WRF file and conduct necessary processing
theta = file_data.variables["T"][:, :, idx[0], idx[1]] + 300.0
qvapr = file_data.variables["QVAPOR"][:, :, idx[0],
idx[1]] * 10**3 #g/kg
mpres = (file_data.variables["P"][:, :, idx[0], idx[1]] +
file_data.variables["PB"][:, :, idx[0], idx[1]]) * .01
mhght = file_data.variables[
"PH"][:, :, idx[0],
idx[1]] + file_data.variables["PHB"][:, :, idx[0],
idx[1]] / G
## unstagger the height grid
mhght = (mhght[:, :-1, :, :] + mhght[:, 1:, :, :]) / 2.
muwin = file_data.variables["U"][:, :, idx[0], idx[1]]
mvwin = file_data.variables["V"][:, :, idx[0], idx[1]]
## convert the potential temperature to air temperature
mtmpc = thermo.theta(1000.0, theta - 273.15, p2=mpres)
## convert the mixing ratio to dewpoint
mdwpc = thermo.temp_at_mixrat(qvapr, mpres)
## convert the grid relative wind to earth relative
U = muwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] - mvwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
V = mvwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] + muwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
## convert from m/s to kts
muwin = utils.MS2KTS(U)
mvwin = utils.MS2KTS(V)
## if the data is not 4D, then it must be assumed that this is a file containing only a single time
else:
## read in the data from the WRF file and conduct necessary processing
theta = file_data.variables["T"][:, idx[0], idx[1]] + 300.0
qvapr = file_data.variables["QVAPOR"][:, idx[0],
idx[1]] * 10**3 #g/kg
mpres = (file_data.variables["P"][:, idx[0], idx[1]] +
file_data.variables["PB"][:, idx[0], idx[1]]) * .01
mhght = file_data.variables["PH"][:, idx[0],
idx[1]] + file_data.variables[
"PHB"][:, idx[0], idx[1]] / G
## unstagger the height grid
mhght = (mhght[:-1, :, :] + mhght[1:, :, :]) / 2.
muwin = file_data.variables["U"][:, idx[0], idx[1]]
mvwin = file_data.variables["V"][:, idx[0], idx[1]]
## convert the potential temperature to air temperature
mtmpc = thermo.theta(1000.0, theta - 273.15, p2=mpres)
## convert the mixing ratio to dewpoint
mdwpc = thermo.temp_at_mixrat(qvapr, mpres)
## convert the grid relative wind to earth relative
U = muwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] - mvwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
V = mvwin * file_data.variables['COSALPHA'][
0, idx[0], idx[1]] + muwin * file_data.variables['SINALPHA'][
0, idx[0], idx[1]]
## convert from m/s to kts
muwin = utils.MS2KTS(U)
mvwin = utils.MS2KTS(V)
## get the model start time of the file
inittime = dattim.datetime.strptime(str(file_data.START_DATE),
'%Y-%m-%d_%H:%M:%S')
profiles = []
dates = []
## loop over the available times
for i in range(file_data.variables["T"][:].shape[0]):
## make sure the arrays are 1D
prof_pres = mpres[i].flatten()
prof_hght = mhght[i].flatten()
prof_tmpc = mtmpc[i].flatten()
prof_dwpc = mdwpc[i].flatten()
prof_uwin = muwin[i].flatten()
prof_vwin = mvwin[i].flatten()
## compute the time of the profile
try:
delta = dattim.timedelta(
minutes=int(file_data.variables["XTIME"][i]))
curtime = inittime + delta
except KeyError:
var = ''.join(
np.asarray(file_data.variables['Times'][i], dtype=str))
curtime = dattim.datetime.strptime(var, '%Y-%m-%d_%H:%M:%S')
date_obj = curtime
## construct the profile object
prof = profile.create_profile(profile="raw",
pres=prof_pres,
hght=prof_hght,
tmpc=prof_tmpc,
dwpc=prof_dwpc,
u=prof_uwin,
v=prof_vwin,
location=str(gridx) + "," +
str(gridy),
date=date_obj,
missing=-999.0,
latitude=gridy,
strictQC=False)
## append the dates and profiles
profiles.append(prof)
dates.append(date_obj)
## create a profile collection - dictionary has no key since this
## is not an ensemble model
prof_coll = prof_collection.ProfCollection({'': profiles}, dates)
return prof_coll
def _parse(self):
file_data = self._downloadFile()
## read in the file
data = np.array(
[l.strip() for l in file_data.split('\n') if l.strip()])
## necessary index points
title_idx = np.where(data == '%TITLE%')[0][0]
start_idx = np.where(data == '%RAW%')[0][0] + 1
finish_idx = np.where(data == '%END%')[0]
# Made %END% unnecessary
if finish_idx:
finish_idx = finish_idx[0]
else:
finish_idx = max(n for n, line in enumerate(data)
if len(line.split(',')) == 6) + 1
## create the plot title
data_header = data[title_idx + 1].split()
location = data_header[0]
time = datetime.strptime(data_header[1][:11], '%y%m%d/%H%M')
if len(data_header) > 2:
lat, lon = data_header[2].split(',')
lat = float(lat)
lon = float(lon)
else:
lat = 35.
lon = -97.
if time > datetime.utcnow() + timedelta(hours=1):
# If the strptime accidently makes the sounding in the future (like with SARS archive)
# i.e. a 1957 sounding becomes 2057 sounding...ensure that it's a part of the 20th century
time = datetime.strptime('19' + data_header[1][:11], '%Y%m%d/%H%M')
## put it all together for StringIO
full_data = '\n'.join(data[start_idx:finish_idx][:])
if not is_py3():
sound_data = StringIO(full_data)
else:
sound_data = BytesIO(full_data.encode())
## read the data into arrays
p, h, T, Td, wdir, wspd = np.genfromtxt(sound_data,
delimiter=',',
comments="%",
unpack=True)
# idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idx]
# Br00tal hack
if hght[0] > 30000:
hght[0] = -9999.00
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw',
pres=pres,
hght=hght,
tmpc=tmpc,
dwpc=dwpc,
wdir=wdir,
wspd=wspd,
location=location,
date=time,
latitude=lat,
missing=-9999.00)
prof_coll = prof_collection.ProfCollection(
{'': [prof]},
[time],
)
prof_coll.setMeta('loc', location)
prof_coll.setMeta('observed', True)
prof_coll.setMeta('base_time', time)
return prof_coll
def process_site(file):
try:
## switch out the reading function for the anticipated data type
data = read_wyoming_file(file)
#data = read_sounding_file(file)
## depending on the data reader used, different block of code is requires
#"""
p = data[:, 0]
Z = data[:, 1]
T = data[:, 2]
Td = data[:, 3]
U = np.zeros(T.shape)
V = np.zeros(T.shape)
"""
p = data[:, 0]
T = data[:, 1]
Td = data[:, 2]
Z = data[:, 5]
U = np.zeros(T.shape)
V = np.zeros(T.shape)
"""
## comment out this triple quote if using the above block
dz = None
p_og = None
z_og = None
p[p == 9999.0] = np.nan
T[T == 999.0] = np.nan
Td[Td == 999.0] = np.nan
Z[Z == 9999.0] = np.nan
p = np.ma.masked_invalid(p)
T = np.ma.masked_invalid(T)
Td = np.ma.masked_invalid(Td)
Z = np.ma.masked_invalid(Z)
except:
## read in Kevins beautiful data
data = read_kevins_file(file)
Z = data[:, 0]
p = data[:, 1]
T = data[:, 2]
U = np.zeros(T.shape)
V = np.zeros(T.shape)
Td = data[:, 3]
dz = data[:, 4][::10]
p_og = data[:, 5][::10]
z_og = data[:, 6][::10]
p = np.ma.masked_invalid(p)
T = np.ma.masked_invalid(T)
Td = np.ma.masked_invalid(Td)
Z = np.ma.masked_invalid(Z)
print file
## create profile object for processing
prof = profile.create_profile(profile='default',
pres=p,
hght=Z,
tmpc=T,
dwpc=Td,
u=U,
v=V,
strictQC=False)
if dz is not None:
pvals = interp.pres(prof, interp.to_msl(prof, np.arange(0, 5000, 100)))
dz = interp.generic_interp_pres(np.log10(pvals),
np.log10(p_og)[::-1], dz[::-1])
print dz
## return the lifted parcel indices and the bore dz
return lift_parcels(prof), dz
## get the current utc time and format it into
## a string that can be used for the SPC url.
if sys.argv[1] == "SARS":
url = open("/Users/keltonhalbert/Downloads/snd/supercell/violent/" + sys.argv[2])
data = np.array(url.read().split('\n'))
title_idx = np.where( data == '%TITLE%')[0][0]
start_idx = np.where( data == '%RAW%' )[0] + 1
finish_idx = np.where( data == '%END%')[0]
plot_title = data[title_idx + 1] + ' (Observed)'
full_data = '\n'.join(data[start_idx : finish_idx][:])
sound_data = StringIO( full_data )
p, h, T, Td, wdir, wspd = np.genfromtxt( sound_data, delimiter=',', comments="%", unpack=True )
prof = profile.create_profile( profile='convective', pres=p, hght=h, tmpc=T, dwpc=Td,
wdir=wdir, wspd=wspd, location=sys.argv[1])
elif sys.argv[1] != "test":
gmtime = datetime.datetime.utcnow()
t_str = str( gmtime )
year = t_str[2:4]
month = t_str[5:7]
day = t_str[8:10]
hour = t_str[11:13]
if int( hour ) > 12:
current_ob = '12'
else:
current_ob = '00'
obstring = year + month + day + current_ob
obstime = datetime.datetime.strptime( obstring, '%y%m%d%H')
def _parse(self):
global dyn_inset
file_data = self._downloadFile()
## read in the file
data = np.array([l.strip() for l in file_data.split('\n')])
## necessary index points
#title_idx = np.where( 'Station:' in data )
title_idx = np.where( np.char.find(data,'Station:') > -1)[0][0]
date_idx = np.where( np.char.find(data,'Date: ') > -1 )[0][0]
start_idx = np.where( np.char.find(data,'SFC') > -1 )[0][0]
finish_idx = np.where( np.char.find(data,'TRP') > -1 )[0][0]
## create the plot title
location = data[title_idx].split('Station: ')[1]
data_header = 'Location: ' + location + ' ' + data[date_idx]
timeStr = str(data[date_idx].split('Date: ')[1]).upper()
time = datetime.strptime(timeStr, '%H%MZ %d %b %y')
# if time > datetime.utcnow(): #If the strptime accidently makes the sounding the future:
# # If the strptime accidently makes the sounding in the future (like with SARS archive)
# # i.e. a 1957 sounding becomes 2057 sounding...ensure that it's a part of the 20th century
# time = datetime.strptime('19' + data_header[1][:11], '%Y%m%d/%H%M')
# ---------------------------- Clean up the data ------------------------------#
# Make sure we have the right number of fields.
# Commonly at the end of the sounding, wind speed and direciton are missing.
# This check takes care of that, eliminating a row if it's undersized.
dirtyData = data[start_idx : (finish_idx)][:]
cleanData = []
for line in dirtyData:
numItems = 0
items = line.split(' ')
for item in items:
if item != '':
numItems += 1
if numItems == 15:
cleanData.append(line)
# -----------------------------------------------------------------------------#
## put it all together for StringIO
full_data = '\n'.join(cleanData)
sound_data = StringIO( full_data )
## read the data into arrays
p, h, T, Td, wdir, wspd = np.genfromtxt( sound_data, unpack=True, usecols=(1,2,3,4,8,9) )
#idx = np.argsort(p, kind='mergesort')[::-1] # sort by pressure in case the pressure array is off.
# ----------------------- More Cleaning ----------------------- #
# SHARPpy doesn't like directions of 360, convert those to 0:
wdir = [0 if x==360 else x for x in wdir]
# If there is a duplicate height entry (common),
# Just add an extra meter, and that'll be our little secret. ;)
for key,height in enumerate(h):
if key == 0:
continue
if height == h[key-1]:
h[key] += 1
# ------------------------------------------------------------- #
pres = p #[idx]
hght = h #[idx]
tmpc = T #[idx]
dwpc = Td #[idx]
wspd = wspd #[idx]
wdir = wdir #[idxi]
if dwpc[0] < 40:
dyn_inset = 'winter'
else:
dyn_inset = 'severe'
# Force latitude to be 35 N. Figure out a way to fix this later.
prof = profile.create_profile(profile='raw', pres=pres, hght=hght, tmpc=tmpc, dwpc=dwpc, wdir=wdir, wspd=wspd, location=location, date=time, latitude=35.)
prof_coll = prof_collection.ProfCollection({'':[ prof ]},[ time ],)
prof_coll.setMeta('loc', location)
return prof_coll
