def plot_sounding_data_csv(filename, output):
"""Plot SkewT from a CSV sounding data file
:param filename: The name of the file to open.
:type filename: str
:param output: The name of the file to output plot
:type output: str
The datafile format is CSV with the following columns:
- pressure (mb)
- height (m)
- temperature (C)
- dew point (C)
- wind direction (degrees)
- wind speed (m/s)
Missing values should be filled with the value -9999.00
"""
p, z, T, Td, wdir, wspd = np.loadtxt(filename, delimiter=',', unpack=True)
# Pressure to Pa
p = p * 100.
# z to km
#z = z / 1000.
# interpolate missing wind
nk = len(z)
u = np.empty(nk, np.float32)
v = np.empty(nk, np.float32)
for k in range(nk):
if wdir[k] == -9999. and wspd[k] == -9999.:
u[k] = v[k] = -9999.
else:
u[k], v[k] = dyn.wind_deg_to_uv(wdir[k], wspd[k])
#print('{0:5.2f} {1:5.2f} = {2:5.2f} {3:5.2f}'.format(wdir[k], wspd[k], u[k], v[k]))
_z = np.empty(2, np.float32)
_u = np.empty(2, np.float32)
_v = np.empty(2, np.float32)
print('INTERPOLATING')
for k in range(nk):
if wdir[k] == -9999. and wspd[k] == -9999.:
kb = ke = k
while kb >= 0 and wdir[kb] == -9999. and wspd[kb] == -9999.:
kb -= 1
while ke <= nk - 1 and wdir[ke] == -9999. and wspd[ke] == -9999.:
ke += 1
# everything in bounds
if kb >= 0 and ke <= nk - 1:
_z[0] = z[kb]
_z[1] = z[ke]
_u[0] = u[kb]
_u[1] = u[ke]
_v[0] = v[kb]
_v[1] = v[ke]
u[k] = pymeteo.interp.linear(_z, _u, z[k])
v[k] = pymeteo.interp.linear(_z, _v, z[k])
elif kb < 0:
u[k] = u[ke]
v[k] = v[ke]
elif ke > nk - 1:
u[k] = u[kb]
v[k] = v[kb]
for k in range(nk):
# kt to m/s
u[k] = u[k] * 0.5144444
v[k] = v[k] * 0.5144444
# print('{0:5.2f} {1:5.2f} = {2:5.2f} {3:5.2f}'.format(wdir[k], wspd[k], u[k], v[k]))
# calc theta
th = np.empty(nk, np.float32)
# calc qv
qv = np.empty(nk, np.float32)
for k in range(nk):
th[k] = met.theta(T[k] + met.T00, p[k])
w = met.es(Td[k] + met.T00) / met.es(T[k] + met.T00)
pp = met.es(T[k] + met.T00) / p[k]
qv[k] = 0.622 * pp * w
#qv[k] = met.es(Td[k]+met.T00) / (met.Rv * (T[k]+met.T00))
#print(z, th, p, qv, u, v)
plot(None, z, th, p, qv, u, v, output, title='Sounding Data')
def plot_sounding_data_csv(filename, output):
"""Plot SkewT from a CSV sounding data file
:param filename: The name of the file to open.
:type filename: str
:param output: The name of the file to output plot
:type output: str
The datafile format is CSV with the following columns:
- pressure (mb)
- height (m)
- temperature (C)
- dew point (C)
- wind direction (degrees)
- wind speed (m/s)
Missing values should be filled with the value -9999.00
"""
p,z,T,Td,wdir,wspd = np.loadtxt(filename, delimiter=',', unpack=True)
# Pressure to Pa
p = p * 100.
# z to km
#z = z / 1000.
# interpolate missing wind
nk = len(z)
u = np.empty(nk, np.float32)
v = np.empty(nk, np.float32)
for k in range(nk):
if wdir[k] == -9999. and wspd[k] == -9999.:
u[k] = v[k] = -9999.
else:
u[k], v[k] = dyn.wind_deg_to_uv(wdir[k], wspd[k])
#print('{0:5.2f} {1:5.2f} = {2:5.2f} {3:5.2f}'.format(wdir[k], wspd[k], u[k], v[k]))
_z = np.empty(2,np.float32)
_u = np.empty(2,np.float32)
_v = np.empty(2,np.float32)
print('INTERPOLATING')
for k in range(nk):
if wdir[k] == -9999. and wspd[k] == -9999.:
kb = ke = k
while kb >= 0 and wdir[kb] == -9999. and wspd[kb] == -9999.:
kb -= 1
while ke <= nk-1 and wdir[ke] == -9999. and wspd[ke] == -9999.:
ke += 1
# everything in bounds
if kb >= 0 and ke <= nk-1:
_z[0] = z[kb]
_z[1] = z[ke]
_u[0] = u[kb]
_u[1] = u[ke]
_v[0] = v[kb]
_v[1] = v[ke]
u[k] = pymeteo.interp.linear(_z, _u, z[k])
v[k] = pymeteo.interp.linear(_z, _v, z[k])
elif kb < 0:
u[k] = u[ke]
v[k] = v[ke]
elif ke > nk-1:
u[k] = u[kb]
v[k] = v[kb]
for k in range(nk):
# kt to m/s
u[k] = u[k] * 0.5144444
v[k] = v[k] * 0.5144444
# print('{0:5.2f} {1:5.2f} = {2:5.2f} {3:5.2f}'.format(wdir[k], wspd[k], u[k], v[k]))
# calc theta
th = np.empty(nk, np.float32)
# calc qv
qv = np.empty(nk, np.float32)
for k in range(nk):
th[k] = met.theta(T[k]+met.T00, p[k])
w = met.es(Td[k]+met.T00) / met.es(T[k]+met.T00)
pp = met.es(T[k]+met.T00) / p[k]
qv[k] = 0.622 * pp * w
#qv[k] = met.es(Td[k]+met.T00) / (met.Rv * (T[k]+met.T00))
#print(z, th, p, qv, u, v)
plot(None, z, th, p, qv, u, v, output, title='Sounding Data')
def processDataSet(data):
print("[+] Writing data into temporary file")
tdata = tempfile.NamedTemporaryFile()
tdata.write(data.read())
print("[-] Data written to {0}".format(tdata.name))
print("[+] Opening data as NetCDF")
d = data.read()
with netCDF4.Dataset(tdata.name, mode='r') as nc:
print("[-] Dataset open with")
_z = nc["altitude"][:]
_T = nc["temperature"][:]
_qv = nc["waterVaporMR"][:]
windSpeed = nc["windSpeed"][:]
windDir = nc["windDir"][:]
_lon = nc["longitude"][:]
_lat = nc["latitude"][:]
flag = nc["sounding_flag"][:]
_airport = nc["sounding_airport_id"][:]
time = nc["soundingSecs"][:]
print("[-] {0} Records".format(len(_z)))
#conversions
_p = thermo.p_from_pressure_altitude(_z, _T)
_u, _v = dynamics.wind_deg_to_uv(windDir, windSpeed)
_th = thermo.theta(_T, _p)
# split the arrays when the flag changes sign
splits = np.where(np.diff(time))[0] + 1
_z = np.split(_z, splits)
_p = np.split(_p, splits)
_th = np.split(_th, splits)
_qv = np.split(_qv, splits)
_u = np.split(_u, splits)
_v = np.split(_v, splits)
_lat = np.split(_lat, splits)
_lon = np.split(_lon, splits)
_airport = np.split(_airport, splits)
time = np.split(time, splits)
flag = np.split(flag, splits)
print("[-] Found {0} profiles".format(len(_z)))
#re-shape data
outputData = []
for i in range(len(_z)):
ts = time[i].compressed()
if len(ts) == 0:
# profiles without timestamps invalid?
continue
profileDir = flag[i][0]
if (profileDir == 0):
continue
z = _z[i].filled()
p = _p[i].filled()
th = _th[i].filled()
qv = _qv[i].filled()
u = _u[i].filled()
v = _v[i].filled()
lat = _lat[i].filled()
lon = _lon[i].filled()
airport = getAirportByCode(_airport[i][0])
profileData = {
"i": i,
"n": len(z),
"z": z if profileDir > 0 else z[::-1],
"p": p if profileDir > 0 else p[::-1],
"th": th if profileDir > 0 else th[::-1],
"qv": qv if profileDir > 0 else qv[::-1],
"u": u if profileDir > 0 else u[::-1],
"v": v if profileDir > 0 else v[::-1],
"lat": lat if profileDir > 0 else lat[::-1],
"lon": lon if profileDir > 0 else lon[::-1],
"airport": airport,
"time": datetime.utcfromtimestamp(ts.mean()).strftime("%H%MZ"),
"flag": profileDir
}
outputData.append(profileData)
return outputData
file = sys.argv[0]
# Read data
ds = xr.open_dataset(file)
p = ds['pressure'].isel({'sounding': 0}).values * units.hPa
z = ds['altitude'].isel({'sounding': 0}).values * units.meters
T = ds['temperature'].isel({'sounding': 0}).values * units.degC
Td = ds['dewPoint'].isel({'sounding': 0}).values * units.degC
wind_speed = ds['windSpeed'].isel({
'sounding': 0
}).values * (units.meter / units.second)
wind_dir = ds['windDirection'].isel({'sounding': 0}).values * units.degrees
# Calculate input for skewt
u, v = mpcalc.wind_components(wind_speed, wind_dir)
th = met.theta(np.array(T) + met.T00, np.array(p) * 100)
w = met.es(np.array(Td) + met.T00) / met.es(np.array(T) + met.T00)
pp = met.es(np.array(T) + met.T00) / (np.array(p) * 100)
qv = 0.622 * pp * w
# Create skewt
skewt.plot(None,
np.array(z),
th,
np.array(p) * 100,
qv,
np.array(u),
np.array(v),
'sounding.pdf',
title=os.path.basename(file))