def main(argv):
inputfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv, "hi:o:", ["ifile=", "ofile="])
except getopt.GetoptError:
print 'test.py -i <inputfile> -o <outputfile>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'test.py -i <inputfile> -o <outputfile>'
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
print 'Input file is "', inputfile
# print 'Output file is "', outputfile
# path = 'L:\Radar2016\sample_data\pky1.vol'
path = inputfile
files = sorted(glob.glob(path))
tstart = dt.datetime.now()
for filename in files:
print 'Creating folder ' + filename + '.dir ...'
#filename = 'n:\\rason1\F2017111606S6008157.csv'
#StaNo = filename[-20:-15]
StaNo = '96253'
rason_datetime = filename[-14:-4]
#rason_datetime = filename[-22:-12]
YY = str(rason_datetime[0:4])
MM = str(rason_datetime[4:6])
DD = str(rason_datetime[6:8])
HH = str(rason_datetime[8:10])
dt_rason = dt.datetime.strptime(YY + '-' + MM + '-' + DD + '_' + HH,
'%Y-%m-%d_%H')
#dt_rason = dt_rason - dt.timedelta(hours=6) # 6 hours time difference
strdt_rason = dt_rason.strftime('%Y-%m-%d_%H')
df = pd.read_csv(filename, nrows=0)
rason_type = df.columns[0]
rason_release_date_local = df.columns[4]
rason_release_time_local = df.columns[5]
rason_sounding_length = df.columns[7]
rason_lat = str(round(float(df.columns[9]), 2))
rason_lon = str(round(float(df.columns[10]), 2))
rason_observer = df.columns[13]
newdir = filename + '.dir'
try:
os.mkdir(newdir, 0755)
except:
print newdir + ' already exists'
df = pd.read_csv(filename, skiprows=6)
obstime = df[df.columns[0]].map(
lambda x: dt.datetime.strptime(str(x), '%H:%M:%S')) #ObsTime
height = pad(df[df.columns[11]].replace('-----',
np.nan).astype(float)) #Height
lat = pad(df[df.columns[17]].replace('-----',
np.nan).astype(float)) #GeodetLat
lon = pad(df[df.columns[18]].replace('-----',
np.nan).astype(float)) #GeodetLon
wd = pad(df[df.columns[9]].replace(
'-----', np.nan).astype(float)) #Wind Direction
ws = pad(df[df.columns[10]].replace(
'-----', np.nan).astype(float)) # Wind Speed
press = df[df.columns[20]].replace('-----', np.nan) #Pressure
press = pad(press.replace('------', np.nan).astype(float))
temp = pad(df[df.columns[21]].replace(
'-----', np.nan).astype(float)) #Temperature
rh = pad(df[df.columns[22]].replace(
'-----', np.nan).astype(float)) #Relative Humidity
dewtemp = temp - ((100 - rh) / 5.) # Dewpoint temperature
#_AllowedKeys=['pres','hght','temp','dwpt','relh','mixr','drct','sknt','thta','thte','thtv']
mydata=dict(zip(('StationNumber','SoundingDate','hght','pres','temp','dwpt','relh','drct','sknt'),\
('Bengkulu ('+StaNo+')', strdt_rason +'UTC', height, press,temp,dewtemp,rh,wd,ws)))
S = SkewT.Sounding(soundingdata=mydata)
#S.do_thermodynamics()
#S.plot_skewt(tmin=-40.,tmax=40.,pmin=100.,pmax=1050.,parcel_type='sb')
try:
#S.plot_skewt(tmin=-40.,tmax=40.,pmin=100.,pmax=1050.,parcel_type='mu')
##S.plot_skewt(parcel_type='sb',imagename='f:/rason/test.png')
#S.fig.savefig(newdir+'\\'+StaNo+'_skewt-mu_'+strdt_rason+'.png', format='png', dpi=500)
#S.plot_skewt(tmin=-40.,tmax=40.,pmin=100.,pmax=1050.,parcel_type='ml')
##S.plot_skewt(parcel_type='sb',imagename='f:/rason/test.png')
#S.fig.savefig(newdir+'\\'+StaNo+'_skewt-ml_'+strdt_rason+'.png', format='png', dpi=500)
S.plot_skewt(tmin=-40.,
tmax=40.,
pmin=100.,
pmax=1050.,
parcel_type='sb')
#S.plot_skewt(parcel_type='sb',imagename='f:/rason/test.png')
S.fig.savefig(newdir + '\\' + StaNo + '_skewt-sb_' + strdt_rason +
'.png',
format='png',
dpi=500)
except:
#S.make_skewt_axes()
S.make_skewt_axes(tmin=-40., tmax=40., pmin=100., pmax=1050.)
#S.make_skewt_axes(tmin=-50,tmax=40,pmin=100)
S.add_profile(lw=1)
#pc=S.most_unstable_parcel()
#sounding.lift_parcel(*pc,totalcape=True)
#S.fig.savefig('f:/rason/test.png', format='png', dpi=500)
#S.fig.savefig(newdir+'\\'+strdt_rason+'UTC-bengkulu-skewt.png', format='png', dpi=500)
#parcel=S.get_parcel()
#S.lift_parcel(*parcel)
#S.get_cape(*parcel)
# Create a Figure Manager
#mySkewT_Figure = figure()
# Add an Skew-T axes to the Figure
#mySkewT_Axes = mySkewT_Figure.add_subplot(111, projection='skewx')
# Add a profile to the Skew-T diagram
#mySkewT_Axes.addProfile(press.astype(float),temp.astype(float), dewtemp.astype(float) ,
# hPa=True, celsius=True, method=1, diagnostics=True, useVirtual=0)
# Show the figure
#mySkewT_Figure.show()
print 'Plotting figures in ' + filename + '.dir ...'
# draw figure
fig = pl.figure(figsize=(10, 8))
pl.plot(temp.astype(float), height.astype(float) / 1000, 'b')
pl.plot(dewtemp.astype(float), height.astype(float) / 1000, 'r')
#pl.xlabel('Temperature (C)')
pl.ylabel('Height (km)', fontsize=12)
pl.ylim(0, 30)
pl.xlim(-120, 40)
pl.text(-100, -3, 'Temperature (C)', color='b', fontsize=12)
pl.text(-50, -3, 'Dew Point Temperature (C)', color='r', fontsize=12)
pl.title('Temperature ' + HH + 'UTC ' + DD + '-' + MM + '-' + YY +
' LON=' + rason_lon + ',LAT=' + rason_lat,
fontsize=12)
#cbar = pl.colorbar(pm, shrink=0.75)
fig.savefig(newdir + '\\' + StaNo + '_t_' + strdt_rason + '.png',
format='png',
dpi=500)
fig = pl.figure(figsize=(10, 8))
pl.plot(rh.astype(float), height.astype(float) / 1000, 'b')
#pl.plot(dewtemp.astype(float),height.astype(float)/1000,'r')
#pl.xlabel('Relative Humidity (%)')
pl.ylabel('Height (km)', fontsize=12)
pl.ylim(0, 30)
pl.xlim(0, 100)
pl.text(10, -3, 'Relative Humidity (%)', color='b', fontsize=12)
#pl.text(-100,4000,'Dew point temperature',color='r')
pl.title('Humidity ' + HH + 'UTC ' + DD + '-' + MM + '-' + YY +
' LON=' + rason_lon + ',LAT=' + rason_lat,
fontsize=12)
#cbar = pl.colorbar(pm, shrink=0.75)
fig.savefig(newdir + '\\' + StaNo + '_rh_' + strdt_rason + '.png',
format='png',
dpi=500)
fig = pl.figure(figsize=(10, 8))
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax1.plot(wd.astype(float), height.astype(float) / 1000, 'y')
ax1.set_xlim(0, 360)
ax1.set_xticks(np.arange(0, 361, 45))
ax1.set_ylim(0, 30)
ax2.plot(ws.astype(float), height.astype(float) / 1000, 'g')
ax2.set_xlim(0, 50)
ax2.set_ylim(0, 30)
#pl.xlabel('Temperature (C)')
ax1.set_ylabel('Height (km)', fontsize=12)
ax1.text(45, -3, 'Wind Direction (deg)', color='y', fontsize=12)
ax2.text(5, 32, 'Wind Speed (m/s)', color='g', fontsize=12)
ax2.text(23,
32,
'Wind ' + HH + 'UTC ' + DD + '-' + MM + '-' + YY + ' LON=' +
rason_lon + ',LAT=' + rason_lat,
fontsize=12)
#pl.title('Wind',fontsize=12)
#cbar = pl.colorbar(pm, shrink=0.75)
fig.savefig(newdir + '\\' + StaNo + '_wind_' + strdt_rason + '.png',
format='png',
dpi=500)
#*****************************************************************************\
#Cambiar fechas
timestamp = [datenum_to_datetime(t) for t in timesd]
time_my = np.array(timestamp)
#*****************************************************************************\
#Seleccionando Caso
caso=297 #1999-05-31 11:00:00
#caso=295 #1999-05-30 11:00:00
#caso=114 #2006-02-26 23:00:00
#print time_my[caso]
n_nnan=np.count_nonzero(~np.isnan(pres[:,caso]))
#*****************************************************************************\
height_m=hght[0:n_nnan-1,caso]
pressure_pa=pres[0:n_nnan-1,caso]
temperature_c=temp[0:n_nnan-1,caso]
dewpoint_c=dwpo[0:n_nnan-1,caso]
wsp=wspd[0:n_nnan-1,caso]
wdir=wdir[0:n_nnan-1,caso]
print n_nnan
mydata=dict(zip(('hght','pres','temp','dwpt','drct','sknt','StationNumber','SoundingDate'),(height_m,pressure_pa,temperature_c,dewpoint_c,wdir,wsp,'BoM',time_my[caso])))
S=SkewT.Sounding(soundingdata=mydata)
S.plot_skewt(color='r')
show()
def calc_cape(self, fnamelist, u, v, precip99, xy, idx, latlons):
r'''Description:
Variable required to calculate Convective Available Potential
Energy: $\int_{zf}^{zn} g
\left( \frac{T_{v, parcel} - T_{v,env}}{T_{v,env}}\right)
\mathrm{d}z$
If > 0 storms possible if enough moisture
Notes:
Blindly follows Rory's meathod
Args:
fnamelist (:obj:`list` of :obj:`str`): list of filenames:
[qf, q15f, Tf, t15f]
u (iris.cube): x_wind
v (iris.cube): y_wind
precip99 (float): 99th percentile precp
xy (iris.Constraint): slice from genslice
idx (int): index
latlons (:obj:`list` of :obj:`int`): list of lat-lons:
[llon, llat, ulat, ulon]
idx: row index
Returns:
CAPE(dataframe): dataframe containing Vars for cape calculations
TEPHI(dataframe): dataframe containg vars for tephigrams
'''
qf, q15f, Tf, t15f = fnamelist
# ? 975 is a max value ?
T = iris.load_cube(Tf)
q = iris.load_cube(qf)
# Find mslp but keep it greater that 975
mslp = self.allvars['eve_mslp_mean'].loc[idx] / 100
if mslp > 975:
mslp = 975
# Special slice
xy850 = genslice(latlons, n1=mslp, n2=100)
T = iris.load_cube(Tf)
Q = iris.load_cube(qf)
T850 = T[3, :, :].extract(xy850)
q850 = Q[3, :, :].extract(xy850)
Tp = T850.data
Qp = q850.data
Tp[Tp < 100] = np.nan
Qp[Qp < 100] = np.nan
Tcol = np.nanmean(Tp, axis=(1, 2))
Qcol = np.nanmean(Qp, axis=(1, 2))
pressures = T850.coord('pressure').points
P = np.append(pressures, mslp)
pval = T850.data.shape[0] + 1
Temp = Tcol
T15 = cubemean(iris.load_cube(t15f)[11, :, :].extract(xy)).data
T = np.append(Temp, T15)
Tkel = T - 273.16
humcol = Qcol
Q15 = cubemean(iris.load_cube(q15f)[11, :, :].extract(xy)).data
hum = np.append(humcol, Q15)
dwpt = np.zeros_like(P)
if pval == 18:
humidity = ((0.263 * hum * P * 100) / 2.714**((17.67 * (Tkel)) /
(T - 29.65)))
RH_650 = (0.263 * hum * P) / 2.714**((17.67 * (Tkel)) /
(T - 29.65))
height = T * ((mslp / P)**(1. / 5.257) - 1) / 0.0065
height[-1] = 1.5
dwpt[:] = self.mcdp(Tkel, humidity) # vectorized dew_point calc
else:
height, dwpt, humidity, RH_650 = np.zeros((4, pval))
RH_650 = RH_650[np.where((P > 690) & (P <= 710))[0]]
xwind = cubemean(u[3, :, :])
ywind = cubemean(v[3, :, :])
self.allvars['Tephi_pressure'].loc[idx] = np.average(pressures, axis=0)
self.allvars['Tephi_T'].loc[idx] = np.average(T, axis=0)
self.allvars['Tephi_dewpT'].loc[idx] = np.average(dwpt, axis=0)
self.allvars['Tephi_height'].loc[idx] = np.average(height, axis=0)
self.allvars['Tephi_Q'].loc[idx] = np.average(humidity, axis=0)
self.allvars['Tephi_p99'].loc[idx] = precip99 * 3600.
self.allvars['Tephi_xwind'].loc[idx] = xwind.data
self.allvars['Tephi_ywind'].loc[idx] = ywind.data
self.allvars['Tephi_RH650'].loc[idx] = np.average(RH_650, axis=0)
mydata = dict(
zip(('hght', 'pres', 'temp', 'dwpt'),
(height[::-1], P[::-1], Tkel[::-1], dwpt[::-1])))
try:
S = sk.Sounding(soundingdata=mydata)
parcel = S.get_parcel('mu')
P_lcl, P_lfc, P_el, CAPE, CIN = S.get_cape(*parcel)
except AssertionError:
print('dew_point = ', dwpt[::-1])
print('height = ', height[::-1])
print('pressures = ', P[::-1])
print('Temp = ', Tkel[::-1])
print('AssertionError: Use a monotonically increasing abscissa')
print('Setting to np.nan')
P_lcl, P_lfc, P_el, CAPE, CIN = [
np.nan, np.nan, np.nan, np.nan, np.nan
]
self.allvars['CAPE_P_lcl'].loc[idx] = P_lcl
self.allvars['CAPE_P_lfc'].loc[idx] = P_lfc
self.allvars['CAPE_P_el'].loc[idx] = P_el
self.allvars['CAPE_CAPE'].loc[idx] = CAPE
self.allvars['CAPE_CIN'].loc[idx] = CIN
vert_x_array_km = vertgrid.x['data']/1000
vert_y_array_km = vertgrid.y['data']/1000
vert_z_array_km = vertgrid.z['data']/1000
#assign grid lat/lon data to a variable
vertlat = vertgrid.point_latitude['data']
vertlon = vertgrid.point_longitude['data']
vertalt = vertgrid.point_altitude['data']/1000 #in km
#==============================================================================
#Sounding data
#==============================================================================
#read in sounding data
# sndfile = '/Users/kurtispinkney/Desktop/MastersThesis/sounding_data/2016/'+snd_date+'_snd.txt'
sndfile = '/Users/kurtispinkney/Desktop/MastersThesis/sounding_data/2015/'+snd_date+'.txt'
sounding = SkewT.Sounding(sndfile)
#assign temp and height data to variables
stemp = sounding.soundingdata['temp']
stemp = ma.getdata(stemp)
shght = sounding.soundingdata['hght']
shght = ma.getdata(shght)
#find values below 15 km (size of grid in vertical)
below15hght = shght[np.where(shght < 15000)]
below15temp = stemp[np.where(shght < 15000)]
#find important isotherms
#0°C
freezelayeridx = np.abs(0-below15temp).argmin()
freezelayerhght = below15hght[freezelayeridx]/1000 #km
def process_thermal_wx(thermal):
print 'Calculating WX for {}'.format(thermal)
lon = thermal.longitude
lat = thermal.latitude
terrain = retrieve_band(lat, lon)
if terrain == -1:
return
df = pd.read_csv(thermal_file.format(thermal.thermal_id))
if len(df.index) < 185:
df.to_csv(home + "/RAP/CSV/{}.error".format(thermal.thermal_id))
return
df['paramId'] = pd.to_numeric(df.paramId, errors='coerce')
df['value'] = pd.to_numeric(df.value, errors='coerce')
df['level'] = pd.to_numeric(df.level, errors='coerce')
# Geopotential Height
df_hgt = df.loc[df['paramId'] == 156][0:37]
df_hgt = df_hgt.rename(columns={'value': 'HGT'}).drop('paramId', 1)
# Temperature
df_tmp = df.loc[df['paramId'] == 130][0:37]
df_tmp = df_tmp.rename(columns={'value': 'TMP_K'}).drop('paramId', 1)
# Relative Humidity
df_rh = df.loc[df['paramId'] == 157][0:37]
df_rh = df_rh.rename(columns={'value': 'RH'}).drop('paramId', 1)
# U component of wind
df_uw = df.loc[df['paramId'] == 131][0:37]
df_uw = df_uw.rename(columns={'value': 'W_U'}).drop('paramId', 1)
# V component of windcd R
df_vw = df.loc[df['paramId'] == 132][0:37]
df_vw = df_vw.rename(columns={'value': 'W_V'}).drop('paramId', 1)
# Ground Temperature
# df_gtmp = df.loc[df['paramId'] == 167]
dfs = [df_hgt, df_tmp, df_rh, df_uw, df_vw]
df_snd = reduce(lambda left, right: pd.merge(left, right, on='level'), dfs)
# Wind Speed
df_snd['W_SPD_MS'] = (df_snd.W_U**2 + df_snd.W_V**2)**(0.5)
df_snd['W_SPD_KTS'] = df_snd.W_SPD_MS * 1.94384
# Wind Direction
df_snd['W_DIR'] = np.arctan2(df_snd.W_U, df_snd.W_V) * (180. / np.pi)
# Temperature in Celcius
df_snd['TMP_C'] = df_snd.TMP_K - 273.15
# Dewpoint Temperature
dew_point(df_snd)
# Get the lift parcel for the terrain altitude
parcel = get_parcel_at_hgt(terrain, df_snd)
df_snd = strip_to_terrain(df_snd, parcel)
# Retrieve surface temperature
print parcel
base_tmp = parcel[1]
base_hgt = parcel[4]
thermal['ground_temp_c'] = base_tmp
thermal['ground_elev'] = base_hgt
thermal['ground_w_dir'] = parcel[5]
thermal['ground_w_spd_kts'] = parcel[6]
# Add the DALR
df_snd['DALR'] = base_tmp - ((df_snd.HGT - base_hgt) / 1000) * 9.8
# Virtual Temperature
df_snd['VIRTT'] = (df_snd.TMP_K) / (1 - 0.379 * (6.11 * np.power(
((7.5 * df_snd.DEWP_C) /
(237.7 + df_snd.DEWP_C)), 10)) / df_snd.level) - 273.15
# Thermal Index
df_snd['TI'] = df_snd.TMP_C - df_snd.DALR
df_snd['TI_ROUND'] = df_snd['TI'].round()
# Top of lift
lift_top = np.NAN
df_lift = df_snd.loc[df_snd['TI_ROUND'] <= 0]
if len(df_lift.index > 0):
lift_top = df_lift.iloc[-1]['HGT']
thermal['lift_top'] = lift_top
hght = df_snd[['HGT']].as_matrix().flatten()
pres = df_snd[['level']].as_matrix().flatten()
temp = df_snd[['TMP_C']].as_matrix().flatten()
dwpt = df_snd[['DEWP_C']].as_matrix().flatten()
sknt = df_snd[['W_DIR']].as_matrix().flatten()
drct = df_snd[['W_SPD_KTS']].as_matrix().flatten()
mydata = dict(
zip(('hght', 'pres', 'temp', 'dwpt', 'sknt', 'drct'),
(hght, pres, temp, dwpt, sknt, drct)))
S = SkewT.Sounding(soundingdata=mydata)
S.make_skewt_axes()
S.add_profile()
S.lift_parcel(*parcel[0:4])
Plcl, Plfc, P_el, CAPE, CIN = S.get_cape(*parcel[0:4])
# S.plot_skewt(title=thermal.time)
plt.title('Test')
plt.savefig(home + "/RAP/PNG/{}.png".format(thermal.thermal_id))
Hlcl = calc_hgt(df_snd, Plcl)
thermal['H_lcl'] = Hlcl
Hlfc = Plfc
if not (math.isnan(Plfc)):
Hlfc = calc_hgt(df_snd, Plfc)
thermal['H_lfc'] = Hlfc
H_el = P_el
if not (math.isnan(P_el)):
H_el = calc_hgt(df_snd, P_el)
thermal['H_el'] = H_el
thermal['CAPE'] = CAPE
thermal['CIN'] = CIN
return thermal
url = 'http://weather.uwyo.edu/cgi-bin/sounding?region=naconf&TYPE=TEXT%3ALIST&YEAR=' + year + '&MONTH=' + month + '&FROM=' + day + hour + '&TO=' + day + hour + '&STNM=' + stn
content = urllib2.urlopen(url).read()
# 2)
# Remove the html tags
soup = BeautifulSoup(content)
data_text = soup.get_text()
# 3)
# Split the content by new line.
splitted = data_text.split("\n", data_text.count("\n"))
#4)
# Must save the processed data as a .txt file to be read in by the skewt module.
# Write this splitted text to a .txt document. Save in current directory.
Sounding_dir = './'
Sounding_filename = str(stn) + '.' + str(year) + str(month) + str(day) + str(
hour) + '.txt'
f = open(Sounding_dir + Sounding_filename, 'w')
for line in splitted[4:]:
f.write(line + '\n')
f.close()
#5)
# Add skewt data to plot
S = SkewT.Sounding(filename=Sounding_dir + Sounding_filename)
S.make_skewt_axes(tmin=-40., tmax=50., pmin=100., pmax=1050.)
S.add_profile(bloc=0)
parcel = S.get_parcel()
S.lift_parcel(*parcel)
wdir=wdir_CL4_G3
mydataG3=dict(zip(('hght','pres','temp','dwpt','drct','sknt','StationNumber','SoundingDate'),(height_m,pressure_pa,temperature_c,dewpoint_c,wdir,wsp,' ','Cluster 3')))
#C4
height_m=hght_CL4_G4
temperature_c=temp_CL4_G4
dewpoint_c=dewp_CL4_G4
wsp=wsp_CL4_G4*1.943844
wdir=wdir_CL4_G4
mydataG4=dict(zip(('hght','pres','temp','dwpt','drct','sknt','StationNumber','SoundingDate'),(height_m,pressure_pa,temperature_c,dewpoint_c,wdir,wsp,' ','Cluster 4')))
#Individuals
S1=SkewT.Sounding(soundingdata=mydataG1)
S1.plot_skewt(color='r')
plt.savefig(path_data_save + '4K_C1.png', format='png', dpi=1200)
S2=SkewT.Sounding(soundingdata=mydataG2)
S2.plot_skewt(color='r')
plt.savefig(path_data_save + '4K_C2.png', format='png', dpi=1200)
S3=SkewT.Sounding(soundingdata=mydataG3)
S3.plot_skewt(color='r')
plt.savefig(path_data_save + '4K_C3.png', format='png', dpi=1200)
S4=SkewT.Sounding(soundingdata=mydataG4)
S4.plot_skewt(color='r')
plt.savefig(path_data_save + '4K_C4.png', format='png', dpi=1200)
S=SkewT.Sounding(soundingdata=mydataG1)
S.make_skewt_axes()
S.add_profile(color='r',bloc=0)
zip(('hght', 'pres', 'temp', 'dwpt', 'drct', 'sknt', 'StationNumber',
'SoundingDate'), (height_m, pressure_pa, temperature_c, dewpoint_c,
wdir, wsp, 'Cold Fronts 4K', 'C1')))
#DCF
temperature_c = temp_CL4_G1_DCF
dewpoint_c = dewp_CL4_G1_DCF
wsp = wsp_CL4_G1_DCF * 1.943844
wdir = wdir_CL4_G1_DCF
mydataG1_DCF = dict(
zip(('hght', 'pres', 'temp', 'dwpt', 'drct', 'sknt', 'StationNumber',
'SoundingDate'), (height_m, pressure_pa, temperature_c, dewpoint_c,
wdir, wsp, 'Cold Fronts 4K', 'C1')))
# #Individuals
S = SkewT.Sounding(soundingdata=mydataG1_CF)
S2 = SkewT.Sounding(soundingdata=mydataG1_DCF)
S.make_skewt_axes()
S.add_profile(color='r', bloc=0)
S.soundingdata = S2.soundingdata
S.add_profile(color='b', bloc=1)
plt.savefig(path_data_save + '4K_C1_CF_Dif.png', format='png', dpi=1200)
#*****************************************************************************\
# Cluster 2
#*****************************************************************************\
df_CL4_G2 = df_myclucfro[df_myclucfro['CL_4'] == 2]
df_CL4_G2_DCF = df_CL4_G2[np.isfinite(df_CL4_G2['Dist CFront'])]
df_CL4_G2_CF = df_CL4_G2[np.isnan(df_CL4_G2['Dist CFront'])]
print len(df_CL4_G2), len(df_CL4_G2_CF), len(df_CL4_G2_DCF)
zip(('hght', 'pres', 'temp', 'dwpt', 'drct', 'sknt', 'StationNumber',
'SoundingDate'), (height_m, pressure_pa, temperature_c, dewpoint_c,
wdir, wsp, 'Post Front', '')))
#Pre
temperature_c = temp_pref
dewpoint_c = dewp_pref
wsp = wsp_pref * 1.943844
wdir = wdir_pref
mydatapref = dict(
zip(('hght', 'pres', 'temp', 'dwpt', 'drct', 'sknt', 'StationNumber',
'SoundingDate'), (height_m, pressure_pa, temperature_c, dewpoint_c,
wdir, wsp, 'Pre Front', '')))
S1 = SkewT.Sounding(soundingdata=mydataposf)
S1.plot_skewt(color='r')
plt.savefig(path_data_save + 'sounding_postcoldfront.png',
format='png',
dpi=1200)
S2 = SkewT.Sounding(soundingdata=mydatapref)
S2.plot_skewt(color='r')
plt.savefig(path_data_save + 'sounding_precoldfront.png',
format='png',
dpi=1200)
S = SkewT.Sounding(soundingdata=mydataposf)
S.make_skewt_axes()
S.add_profile(color='r', bloc=0)
S.soundingdata = S2.soundingdata
S.add_profile(color='b', bloc=1)
