def get_upper_air_data(date, station):
"""Get upper air observations from the test data cache.
Parameters
----------
time : datetime
The date and time of the desired observation.
station : str
The three letter ICAO identifier of the station for which data should be
downloaded.
Returns
-------
dict : upper air data
"""
sounding_key = '{0:%Y-%m-%dT%HZ}_{1:}'.format(date, station)
sounding_files = {'2016-05-22T00Z_DDC': 'may22_sounding.txt',
'2013-01-20T12Z_OUN': 'jan20_sounding.txt',
'1999-05-04T00Z_OUN': 'may4_sounding.txt',
'2002-11-11T00Z_BNA': 'nov11_sounding.txt',
'2010-12-09T12Z_BOI': 'dec9_sounding.txt'}
fname = sounding_files[sounding_key]
fobj = get_test_data(fname)
def to_float(s):
# Remove all whitespace and replace empty values with NaN
if not s.strip():
s = 'nan'
return float(s)
# Skip dashes, column names, units, and more dashes
for _ in range(4):
fobj.readline()
# Initiate lists for variables
arr_data = []
# Read all lines of data and append to lists only if there is some data
for row in fobj:
level = to_float(row[0:7])
values = (to_float(row[7:14]), to_float(row[14:21]), to_float(row[21:28]),
to_float(row[42:49]), to_float(row[49:56]))
if any(np.invert(np.isnan(values[1:]))):
arr_data.append((level,) + values)
p, z, t, td, direc, spd = np.array(arr_data).T
p = p * units.hPa
z = z * units.meters
t = t * units.degC
td = td * units.degC
direc = direc * units.degrees
spd = spd * units.knots
u, v = wind_components(spd, direc)
return {'pressure': p, 'height': z, 'temperature': t,
'dewpoint': td, 'direction': direc, 'speed': spd, 'u_wind': u, 'v_wind': v}
def test_wind_comps_with_north_and_calm():
"""Test that the wind component calculation handles northerly and calm winds."""
speed = np.array([0, 5, 5]) * units.mph
dirs = np.array([0, 360, 0]) * units.deg
u, v = wind_components(speed, dirs)
true_u = np.array([0, 0, 0]) * units.mph
true_v = np.array([0, -5, -5]) * units.mph
assert_array_almost_equal(true_u, u, 4)
assert_array_almost_equal(true_v, v, 4)
def test_wind_comps_with_north_and_calm(array_type):
"""Test that the wind component calculation handles northerly and calm winds."""
mask = [False, True, False]
speed = array_type([0, 5, 5], 'mph', mask=mask)
dirs = array_type([0, 360, 0], 'deg', mask=mask)
u, v = wind_components(speed, dirs)
true_u = array_type([0, 0, 0], 'mph', mask=mask)
true_v = array_type([0, -5, -5], 'mph', mask=mask)
assert_array_almost_equal(true_u, u, 4)
assert_array_almost_equal(true_v, v, 4)
def test_wind_comps_basic():
"""Test the basic wind component calculation."""
speed = np.array([4, 4, 4, 4, 25, 25, 25, 25, 10.]) * units.mph
dirs = np.array([0, 45, 90, 135, 180, 225, 270, 315, 360]) * units.deg
s2 = np.sqrt(2.)
u, v = wind_components(speed, dirs)
true_u = np.array([0, -4 / s2, -4, -4 / s2, 0, 25 / s2, 25, 25 / s2, 0]) * units.mph
true_v = np.array([-4, -4 / s2, 0, 4 / s2, 25, 25 / s2, 0, -25 / s2, -10]) * units.mph
assert_array_almost_equal(true_u, u, 4)
assert_array_almost_equal(true_v, v, 4)
def test_speed_direction_roundtrip():
"""Test round-tripping between speed/direction and components."""
# Test each quadrant of the whole circle
wspd = np.array([15., 5., 2., 10.]) * units.meters / units.seconds
wdir = np.array([160., 30., 225., 350.]) * units.degrees
u, v = wind_components(wspd, wdir)
wdir_out = wind_direction(u, v)
wspd_out = wind_speed(u, v)
assert_array_almost_equal(wspd, wspd_out, 4)
assert_array_almost_equal(wdir, wdir_out, 4)
def test_wind_comps_basic():
"""Test the basic wind component calculation."""
speed = np.array([4, 4, 4, 4, 25, 25, 25, 25, 10.]) * units.mph
dirs = np.array([0, 45, 90, 135, 180, 225, 270, 315, 360]) * units.deg
s2 = np.sqrt(2.)
u, v = wind_components(speed, dirs)
true_u = np.array([0, -4 / s2, -4, -4 / s2, 0, 25 / s2, 25, 25 / s2, 0]) * units.mph
true_v = np.array([-4, -4 / s2, 0, 4 / s2, 25, 25 / s2, 0, -25 / s2, -10]) * units.mph
assert_array_almost_equal(true_u, u, 4)
assert_array_almost_equal(true_v, v, 4)
def test_speed_direction_roundtrip():
"""Test round-tripping between speed/direction and components."""
# Test each quadrant of the whole circle
wspd = np.array([15., 5., 2., 10.]) * units.meters / units.seconds
wdir = np.array([160., 30., 225., 350.]) * units.degrees
u, v = wind_components(wspd, wdir)
wdir_out = wind_direction(u, v)
wspd_out = wind_speed(u, v)
assert_array_almost_equal(wspd, wspd_out, 4)
assert_array_almost_equal(wdir, wdir_out, 4)
def main(argv):
"""Go Main Go."""
year = utc().year if len(argv) == 1 else int(argv[1])
dbconn = get_dbconn("postgis")
cursor = dbconn.cursor()
nt = NetworkTable("RAOB")
df = read_sql(
"""
select f.fid, f.station, pressure, dwpc, tmpc, drct, smps, height,
levelcode from
raob_profile_""" + str(year) + """ p JOIN raob_flights f
on (p.fid = f.fid) WHERE not computed and height is not null
and pressure is not null
ORDER by pressure DESC
""",
dbconn,
)
if df.empty or pd.isnull(df["smps"].max()):
return
u, v = wind_components(
df["smps"].values * units("m/s"),
df["drct"].values * units("degrees_north"),
)
df["u"] = u.to(units("m/s")).m
df["v"] = v.to(units("m/s")).m
count = 0
progress = tqdm(df.groupby("fid"), disable=not sys.stdout.isatty())
for fid, gdf in progress:
progress.set_description("%s %s" % (year, fid))
try:
do_profile(cursor, fid, gdf, nt)
except (RuntimeError, ValueError, IndexError) as exp:
LOG.debug(
"Profile %s fid: %s failed calculation %s",
gdf.iloc[0]["station"],
fid,
exp,
)
cursor.execute(
"""
UPDATE raob_flights SET computed = 't' WHERE fid = %s
""",
(fid, ),
)
if count % 100 == 0:
cursor.close()
dbconn.commit()
cursor = dbconn.cursor()
count += 1
cursor.close()
dbconn.commit()
def plot_skewt(df):
# We will pull the data out of the example dataset into individual variables
# and assign units.
hght = df['height'].values * units.hPa
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
# Create a new figure. The dimensions here give a good aspect ratio.
fig = plt.figure(figsize=(9, 12))
skew = SkewT(fig, rotation=45)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
# Calculate LCL height and plot as black dot
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Create a hodograph
ax_hod = inset_axes(skew.ax, '40%', '40%', loc=2)
h = Hodograph(ax_hod, component_range=80.)
h.add_grid(increment=20)
h.plot_colormapped(u, v, hght)
return skew
def getDataFrame_UpperAir(model, station, init, hour):
bufrData = BUFKIT.getBufkitData(model, station, init)
if bufrData != False: # Verify data found
modelSoundings = bufrData.SoundingParameters
surfaceData = bufrData.SurfaceParameters[hour]
soundingData = modelSoundings[hour + 1]
z = [0 * units.meters]
p = [surfaceData.pres]
T = [surfaceData.t2ms]
Td = [surfaceData.td2m]
#Tw = [mpcalc.wet_bulb_temperature(surfaceData.pres, surfaceData.t2ms, surfaceData.td2m)]
theta = [
mpcalc.potential_temperature(surfaceData.pres, surfaceData.t2ms)
]
theta_e = [
mpcalc.equivalent_potential_temperature(surfaceData.pres,
surfaceData.t2ms,
surfaceData.td2m)
]
u = [surfaceData.uwnd]
v = [surfaceData.vwnd]
for level in soundingData:
z.append(level.hght)
p.append(level.pres)
T.append(level.tmpc)
Td.append(level.dwpc)
#Tw.append(mpcalc.wet_bulb_temperature(level.pres, level.tmpc, level.dwpc))
theta.append(mpcalc.potential_temperature(level.pres, level.tmpc))
theta_e.append(
mpcalc.equivalent_potential_temperature(
level.pres, level.tmpc, level.dwpc))
uv = mpcalc.wind_components(level.sknt, level.drct)
u.append(uv[0])
v.append(uv[1])
return pd.DataFrame(list(zip(z, p, T, Td, theta, theta_e, u, v)),
columns=[
'height', 'pressure', 'temperature',
'dewpoint', 'theta', 'theta_e', 'u_wind',
'v_wind'
])
else:
return False
def plot_metpy(data, title: str = "", saveplot: Path = None):
""" TODO DOC
:param data: TODO DOC
:param title: The title of the graph
:param saveplot: The path of file to save the plot
"""
# Convert data into a suitable format for metpy.
_altitude = data[:, 0] * units('m')
p = mpcalc.height_to_pressure_std(_altitude)
T = data[:, 3] * units.degC
Td = data[:, 4] * units.degC
wind_speed = data[:, 1] * units('m/s')
wind_direction = data[:, 2] * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_direction)
fig = plt.figure(figsize=(6, 8))
skew = SkewT(fig=fig)
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
my_interval = np.arange(300, 1000, 50) * units('mbar')
ix = mpcalc.resample_nn_1d(p, my_interval)
skew.plot_barbs(p[ix], u[ix], v[ix])
skew.ax.set_ylim(1000, 300)
skew.ax.set_xlim(-40, 30)
skew.plot_dry_adiabats()
heights = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) * units.km
std_pressures = mpcalc.height_to_pressure_std(heights)
for height_tick, p_tick in zip(heights, std_pressures):
trans, _, _ = skew.ax.get_yaxis_text1_transform(0)
skew.ax.text(0.02,
p_tick,
'---{:~d}'.format(height_tick),
transform=trans)
plt.title("Sounding: {}".format(title))
if saveplot is not None:
fig.savefig(str(saveplot), bbox_inches='tight')
def download_alldata(startts=datetime.datetime(2012, 8, 1),
endts=datetime.datetime(2012, 9, 1)):
"""An alternative method that fetches all available data.
Service supports up to 24 hours worth of data at a time."""
# timestamps in UTC to request data for
#startts = datetime.datetime(2012, 8, 1)
#endts = datetime.datetime(2012, 9, 1)
interval = datetime.timedelta(hours=24)
service = SERVICE + "data=all&tz=Etc/UTC&format=comma&latlon=yes&elev=yes&trace=0.0001&"
now = startts
while now < endts:
thisurl = service
thisurl += now.strftime("year1=%Y&month1=%m&day1=%d&")
thisurl += (now + interval).strftime("year2=%Y&month2=%m&day2=%d&")
print("Downloading: %s" % (now, ))
data = download_data(thisurl)
df = pd.read_csv(data,
skiprows=5,
header=0,
parse_dates=['valid'],
na_values=['M'],
low_memory=False)
# Convert wx codes to numeric values for plotting purposes
df['present_weather'] = wx_code_to_numeric(df['wxcodes'].fillna(''))
# Convert cloud cover strings to numeric values for plotting purposes
df['cloud_cover'] = df.skyc1.fillna('').apply(get_cloud_cover)
# Compute the u (eastward_wind) and v(northward_wind) components
df['eastward_wind'], df['northward_wind'] = mpcalc.wind_components(
df.sknt.values * units.knots, df.drct.values * units.degree)
# Compute the MSLP from the Altimeter reading using MetPy calculation
df['air_pressure_at_sea_level'] = mpcalc.altimeter_to_sea_level_pressure(
df.alti.values * units.inHg, df.elevation.values * units.meters,
df.tmpf.values * units.degF).to('hPa')
# Save CSV version of archive and new variables
df.to_csv(f'surface_data_{now:%Y%m%d}.csv', index=False)
#outfn = "surface_data_%s.txt" % (now.strftime("%Y%m%d"),)
#with open(outfn, "w") as fh:
# fh.write(data)
now += interval
def test_wind_comps_basic(array_type):
"""Test the basic wind component calculation."""
mask = [False, True, False, True, False, True, False, True, False]
speed = array_type([4, 4, 4, 4, 25, 25, 25, 25, 10.], 'mph', mask=mask)
dirs = array_type([0, 45, 90, 135, 180, 225, 270, 315, 360],
'deg',
mask=mask)
s2 = np.sqrt(2.)
u, v = wind_components(speed, dirs)
true_u = array_type([0, -4 / s2, -4, -4 / s2, 0, 25 / s2, 25, 25 / s2, 0],
'mph',
mask=mask)
true_v = array_type(
[-4, -4 / s2, 0, 4 / s2, 25, 25 / s2, 0, -25 / s2, -10],
'mph',
mask=mask)
assert_array_almost_equal(true_u, u, 4)
assert_array_almost_equal(true_v, v, 4)
def reader(self, sounding=None, wdShift=0):
if(type(sounding)==None):
logger.error("sounding data empty")
return
## basic variables
Time_released = sounding[self.soundingMeta["Time"]]
P_released = sounding[self.soundingMeta["P"]][:].to_numpy(dtype=float)
T_released = sounding[self.soundingMeta["T"]["name"]][:].to_numpy(dtype=float)
Z_released = sounding[self.soundingMeta["Z"]][:].to_numpy(dtype=float)
RH_released = sounding[self.soundingMeta["RH"]][:].to_numpy(dtype=float)
lat_released = sounding[self.soundingMeta["Lat"]][:].to_numpy(dtype=float)
lon_released = sounding[self.soundingMeta["Lon"]][:].to_numpy(dtype=float)
WS_released = sounding[self.soundingMeta["WS"]["name"]][:].to_numpy(dtype=float)
WD_released = ((sounding[self.soundingMeta["WD"]][:] - wdShift)%360).to_numpy(dtype=float)
## metpy cal
TD_released = dewpoint_from_relative_humidity(T_released * units(f'{self.soundingMeta["T"]["unit"]}'), RH_released/100).m
U_released, V_released = wind_components(WS_released * units(f'{self.soundingMeta["WS"]["unit"]}'), WD_released * units.degrees)
# T, P, T, TD, U, V
data = np.asarray([P_released,T_released,Z_released,TD_released,RH_released,U_released.to('m/s').m,V_released.to('m/s').m,lat_released,lon_released])
dataFrame = DataFrame(np.transpose(data),index=Time_released,columns=['P [hPa]','T [degC]','Z [m]','TD [degC]','RH [%]','U [m/s]','V [m/s]','Lat [o]','Lon [o]'])
return dataFrame, {'P [hPa]':"hPa", 'T [degC]':"degC", 'Z [m]':'m', 'TD [degC]':"degC", 'RH [%]':"percent", 'U [m/s]':'m/s', 'V [m/s]':'m/s', 'Lat [o]':'deg', 'Lon [o]':'deg'}
def add_wind_components_to_dataset(dataset):
"""
Input :
dataset : xarray dataset
Output :
dataset : xarray dataset
Original dataset, with added variables 'u_wind' and 'v_wind' calculated
from wind_speed and wind_direction of the given dataset
Function to compute u and v components of wind, from wind speed and direction in the given dataset,
and add them as variables to the dataset.
"""
u, v = mpcalc.wind_components(
dataset.wspd.values * units["m/s"],
dataset.wdir.values * units.deg,
)
dataset["u"] = (dataset.p.dims, u.magnitude)
dataset["v"] = (dataset.p.dims, v.magnitude)
return dataset
def get_sounding(file=None):
if file == None:
file = './KGRB_2019072000_raob.txt'
df = pd.read_fwf(file,
skiprows=1,
usecols=[1, 6, 7],
names=['height', 'direction', 'speed'])
wind_dir = df['direction'].values * units.degrees
wind_spd = df['speed'].values * .514
u, v = mpcalc.wind_components(wind_spd, wind_dir)
for n in np.arange(u.shape[0]):
print("%8.1f %6.3f %6.3f %5.2f %5.2f %5.2f" %
(df['height'].values[n], df['direction'].values[n],
wind_spd[n], u[n], v[n]))
return Gridded_Field('sounding',
height=df['height'].values,
u_wind=u,
v_wind=v)
else:
print('No sounding found')
return None
#지상 FLAG(null),권계면 FLAG(null),최대풍 FLAG(null)
df = pd.read_csv(dir_name + filename,
skiprows=1,
sep=',',
header=None,
index_col=0,
names=[
'site', 'time', 'pressure', 'height',
'temperature', 'dewpoint', 'direction', 'speed',
'FLAG1', 'FLAG2', 'FLAG3'
],
skipfooter=0,
engine='python')
df['u_wind'], df['v_wind'] = mpcalc.wind_components(
df['speed'].values * units('m/s'),
np.deg2rad(df['direction'].values * units.deg))
# Drop any rows with all NaN values for T, Td, winds
#df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed', 'u_wind', 'v_wind'),
#how='all').reset_index(drop=True)
df = df.dropna(subset=('temperature', 'dewpoint'),
how='all').reset_index(drop=True)
#df = df.dropna(subset=('temperature', 'dewpoint', 'height'), how='all').reset_index(drop=True)
# Rows that do not meet the condition alpha + num are eliminated
s_start_date = datetime(obs_year, 1,
1) #convert startdate to date type
s_end_date = datetime(obs_year + 1, 1, 1)
selected_times = []
dx,
dy,
dim_order='yx')
# Get the wind direction for each point
wdir = mpcalc.wind_direction(ugeo, vgeo)
# Compute the Gradient Wind via an approximation
dydx = mpcalc.first_derivative(Z, delta=dx, axis=1)
d2ydx2 = mpcalc.first_derivative(dydx, delta=dx, axis=1)
R = ((1 + dydx.m**2)**(3. / 2.)) / d2ydx2.m
geo_mag = mpcalc.wind_speed(ugeo, vgeo)
grad_mag = geo_mag.m - (geo_mag.m**2) / (f.magnitude * R)
ugrad, vgrad = mpcalc.wind_components(grad_mag * units('m/s'), wdir)
# Calculate Ageostrophic wind
uageo = ugrad - ugeo
vageo = vgrad - vgeo
# Compute QVectors
uqvect, vqvect = mpcalc.q_vector(ugeo, vgeo, T * units.degC, 500 * units.hPa,
dx, dy)
# Calculate divergence of the ageostrophic wind
div = mpcalc.divergence(uageo, vageo, dx, dy, dim_order='yx')
# Calculate Relative Vorticity Advection
relvor = mpcalc.vorticity(ugeo, vgeo, dx, dy, dim_order='yx')
adv = mpcalc.advection(relvor, (ugeo, vgeo), (dx, dy), dim_order='yx')
# Get wind information and mask where either speed or direction is unavailable
wind_speed = (data['wind_speed'].values * units('m/s')).to('knots')
wind_dir = data['wind_dir'].values * units.degree
good_indices = np.where((~np.isnan(wind_dir)) & (~np.isnan(wind_speed)))
x_masked = xp[good_indices]
y_masked = yp[good_indices]
wind_speed = wind_speed[good_indices]
wind_dir = wind_dir[good_indices]
###########################################
# Calculate u and v components of wind and then interpolate both.
#
# Both will have the same underlying grid so throw away grid returned from v interpolation.
u, v = wind_components(wind_speed, wind_dir)
windgridx, windgridy, uwind = interpolate_to_grid(x_masked,
y_masked,
np.array(u),
interp_type='cressman',
search_radius=400000,
hres=100000)
_, _, vwind = interpolate_to_grid(x_masked,
y_masked,
np.array(v),
interp_type='cressman',
search_radius=400000,
hres=100000)
# Use the cartopy map projection to transform station locations to the map and
# then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['lon'].values,
data['lat'].values)
data = data[reduce_point_density(point_locs, 300000.)]
###########################################
# Now that we have the data we want, we need to perform some conversions:
#
# - Get wind components from speed and direction
# - Convert cloud fraction values to integer codes [0 - 8]
# - Map METAR weather codes to WMO codes for weather symbols
# Get the wind components, converting from m/s to knots as will be appropriate
# for the station plot.
u, v = wind_components((data['wind_speed'].values * units('m/s')).to('knots'),
data['wind_dir'].values * units.degree)
# Convert the fraction value into a code of 0-8 and compensate for NaN values,
# which can be used to pull out the appropriate symbol
cloud_frac = (8 * data['cloud_fraction'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx = [
wx_code_map[s.split()[0] if ' ' in s else s]
for s in data['weather'].fillna('')
]
###########################################
def test_wind_comps_scalar():
"""Test wind components calculation with scalars."""
u, v = wind_components(8 * units('m/s'), 150 * units.deg)
assert_almost_equal(u, -4 * units('m/s'), 3)
assert_almost_equal(v, 6.9282 * units('m/s'), 3)
def get_upper_air_data(date, station):
"""Get upper air observations from the test data cache.
Parameters
----------
time : datetime
The date and time of the desired observation.
station : str
The three letter ICAO identifier of the station for which data should be
downloaded.
Returns
-------
dict : upper air data
"""
sounding_key = '{:%Y-%m-%dT%HZ}_{}'.format(date, station)
sounding_files = {
'2016-05-22T00Z_DDC': 'may22_sounding.txt',
'2013-01-20T12Z_OUN': 'jan20_sounding.txt',
'1999-05-04T00Z_OUN': 'may4_sounding.txt',
'2002-11-11T00Z_BNA': 'nov11_sounding.txt',
'2010-12-09T12Z_BOI': 'dec9_sounding.txt'
}
fname = sounding_files[sounding_key]
fobj = get_test_data(fname)
def to_float(s):
# Remove all whitespace and replace empty values with NaN
if not s.strip():
s = 'nan'
return float(s)
# Skip dashes, column names, units, and more dashes
for _ in range(4):
fobj.readline()
# Initiate lists for variables
arr_data = []
# Read all lines of data and append to lists only if there is some data
for row in fobj:
level = to_float(row[0:7])
values = (to_float(row[7:14]), to_float(row[14:21]),
to_float(row[21:28]), to_float(row[42:49]),
to_float(row[49:56]))
if any(np.invert(np.isnan(values[1:]))):
arr_data.append((level, ) + values)
p, z, t, td, direc, spd = np.array(arr_data).T
p = p * units.hPa
z = z * units.meters
t = t * units.degC
td = td * units.degC
direc = direc * units.degrees
spd = spd * units.knots
u, v = wind_components(spd, direc)
return {
'pressure': p,
'height': z,
'temperature': t,
'dewpoint': td,
'direction': direc,
'speed': spd,
'u_wind': u,
'v_wind': v
}
def do(ts):
"""Process this date timestamp"""
asos = get_dbconn("asos", user="******")
iemaccess = get_dbconn("iem")
icursor = iemaccess.cursor()
table = "summary_%s" % (ts.year,)
# Get what we currently know, just grab everything
current = read_sql(
"""
SELECT * from """
+ table
+ """ WHERE day = %s
""",
iemaccess,
params=(ts.strftime("%Y-%m-%d"),),
index_col="iemid",
)
df = read_sql(
"""
select station, network, iemid, drct, sknt, gust,
valid at time zone tzname as localvalid, valid,
tmpf, dwpf, relh, feel,
peak_wind_gust, peak_wind_drct, peak_wind_time,
peak_wind_time at time zone tzname as local_peak_wind_time from
alldata d JOIN stations t on (t.id = d.station)
where (network ~* 'ASOS' or network = 'AWOS')
and valid between %s and %s and t.tzname is not null
and date(valid at time zone tzname) = %s
ORDER by valid ASC
""",
asos,
params=(
ts - datetime.timedelta(days=2),
ts + datetime.timedelta(days=2),
ts.strftime("%Y-%m-%d"),
),
index_col=None,
)
if df.empty:
print("compute_daily no ASOS database entries for %s" % (ts,))
return
# derive some parameters
df["u"], df["v"] = mcalc.wind_components(
df["sknt"].values * munits.knots, df["drct"].values * munits.deg
)
df["localvalid_lag"] = df.groupby("iemid")["localvalid"].shift(1)
df["timedelta"] = df["localvalid"] - df["localvalid_lag"]
ndf = df[pd.isna(df["timedelta"])]
df.loc[ndf.index.values, "timedelta"] = pd.to_timedelta(
ndf["localvalid"].dt.hour * 3600.0
+ ndf["localvalid"].dt.minute * 60.0,
unit="s",
)
df["timedelta"] = df["timedelta"] / np.timedelta64(1, "s")
for iemid, gdf in df.groupby("iemid"):
if len(gdf.index) < 6:
# print(" Quorum not meet for %s" % (gdf.iloc[0]['station'], ))
continue
if iemid not in current.index:
print(
("compute_daily Adding %s for %s %s %s")
% (table, gdf.iloc[0]["station"], gdf.iloc[0]["network"], ts)
)
icursor.execute(
"""
INSERT into """
+ table
+ """
(iemid, day) values (%s, %s)
""",
(iemid, ts),
)
current.loc[iemid] = None
newdata = {}
currentrow = current.loc[iemid]
compute_wind_gusts(gdf, currentrow, newdata)
# take the nearest value
ldf = gdf.copy().fillna(method="bfill").fillna(method="ffill")
totsecs = ldf["timedelta"].sum()
is_new(
"avg_rh",
clean((ldf["relh"] * ldf["timedelta"]).sum() / totsecs, 1, 100),
currentrow,
newdata,
)
is_new("min_rh", clean(ldf["relh"].min(), 1, 100), currentrow, newdata)
is_new("max_rh", clean(ldf["relh"].max(), 1, 100), currentrow, newdata)
uavg = (ldf["u"] * ldf["timedelta"]).sum() / totsecs
vavg = (ldf["v"] * ldf["timedelta"]).sum() / totsecs
is_new(
"vector_avg_drct",
clean(
mcalc.wind_direction(uavg * munits.knots, vavg * munits.knots),
0,
360,
),
currentrow,
newdata,
)
is_new(
"avg_sknt",
clean((ldf["sknt"] * ldf["timedelta"]).sum() / totsecs, 0, 150),
currentrow,
newdata,
)
is_new(
"max_feel",
clean(ldf["feel"].max(), -150, 200),
currentrow,
newdata,
)
is_new(
"avg_feel",
clean((ldf["feel"] * ldf["timedelta"]).sum() / totsecs, -150, 200),
currentrow,
newdata,
)
is_new(
"min_feel",
clean(ldf["feel"].min(), -150, 200),
currentrow,
newdata,
)
if not newdata:
continue
cols = []
args = []
# print(gdf.iloc[0]['station'])
for key, val in newdata.items():
# print(" %s %s -> %s" % (key, currentrow[key], val))
cols.append("%s = %%s" % (key,))
args.append(val)
args.extend([iemid, ts])
sql = ", ".join(cols)
icursor.execute(
"""
UPDATE """
+ table
+ """
SET """
+ sql
+ """
WHERE
iemid = %s and day = %s
""",
args,
)
if icursor.rowcount == 0:
print(
"compute_daily update of %s[%s] was 0"
% (gdf.iloc[0]["station"], gdf.iloc[0]["network"])
)
icursor.close()
iemaccess.commit()
iemaccess.close()
def main():
### START OF USER SETTINGS BLOCK ###
# FILE/DATA SETTINGS
# file path to input
datafile = '/home/jgodwin/python/sfc_observations/surface_observations.txt'
timefile = '/home/jgodwin/python/sfc_observations/validtime.txt'
# MAP SETTINGS
# map names (for tracking purposes)
maps = ['CONUS','Texas','Floater 1']
restart_domain = [True,False]
# map boundaries
west = [-120,-108,-108]
east = [-70,-93,-85]
south = [20,25,37]
north = [50,38,52]
# OUTPUT SETTINGS
# save directory for output
savedir = '/var/www/html/images/'
# filenames ("_[variable].png" will be appended, so only a descriptor like "conus" is needed)
savenames = ['conus','texas','floater1']
# TEST MODE SETTINGS
test = False
testnum = 3
### END OF USER SETTINGS BLOCK ###
for i in range(len(maps)):
if test and i != testnum:
continue
print(maps[i])
# create the map projection
cenlon = (west[i] + east[i]) / 2.0
cenlat = (south[i] + north[i]) / 2.0
sparallel = cenlat
if cenlat > 0:
cutoff = -30
flip = False
elif cenlat < 0:
cutoff = 30
flip = True
if restart_domain:
to_proj = ccrs.LambertConformal(central_longitude=cenlon,central_latitude=cenlat,standard_parallels=[sparallel],cutoff=cutoff)
# open the data
vt = open(timefile).read()
with open(datafile) as f:
data = pd.read_csv(f,header=0,names=['siteID','lat','lon','elev','slp','temp','sky','dpt','wx','wdr',\
'wsp'],na_values=-99999)
# filter data by lat/lon
data = data[(data['lat'] >= south[i]-2.0) & (data['lat'] <= north[i]+2.0) & (data['lon'] >= west[i]-2.0)\
& (data['lon'] <= east[i]+2.0)]
# remove questionable data
data = data[(cToF(data['temp']) <= 120) & (cToF(data['dpt']) <= 80)]
# project lat/lon onto final projection
print("Creating map projection.")
lon = data['lon'].values
lat = data['lat'].values
xp, yp, _ = to_proj.transform_points(ccrs.Geodetic(), lon, lat).T
# remove missing data from pressure and interpolate
# we'll give this a try and see if it can help with my CPU credit problem
if restart_domain:
print("Performing Cressman interpolation.")
x_masked, y_masked, pres = remove_nan_observations(xp, yp, data['slp'].values)
slpgridx, slpgridy, slp = interpolate_to_grid(x_masked, y_masked, pres, interp_type='cressman',
minimum_neighbors=1, search_radius=400000,
hres=100000)
# get wind information and remove missing data
wind_speed = (data['wsp'].values * units('knots'))
wind_dir = data['wdr'].values * units.degree
good_indices = np.where((~np.isnan(wind_dir)) & (~np.isnan(wind_speed)))
x_masked = xp[good_indices]
y_masked = yp[good_indices]
wind_speed = wind_speed[good_indices]
wind_dir = wind_dir[good_indices]
u, v = wind_components(wind_speed, wind_dir)
windgridx, windgridy, uwind = interpolate_to_grid(x_masked, y_masked, np.array(u),
interp_type='cressman', search_radius=400000,
hres=100000)
_, _, vwind = interpolate_to_grid(x_masked, y_masked, np.array(v), interp_type='cressman',
search_radius=400000, hres=100000)
# get temperature information
data['temp'] = cToF(data['temp'])
x_masked, y_masked, t = remove_nan_observations(xp, yp, data['temp'].values)
tempx, tempy, temp = interpolate_to_grid(x_masked, y_masked, t, interp_type='cressman',
minimum_neighbors=3, search_radius=200000, hres=18000)
temp = np.ma.masked_where(np.isnan(temp), temp)
# get dewpoint information
data['dpt'] = cToF(data['dpt'])
x_masked,y_masked,td = remove_nan_observations(xp,yp,data['dpt'].values)
dptx,dpty,dewp = interpolate_to_grid(x_masked,y_masked,td,interp_type='cressman',\
minimum_neighbors=3,search_radius=200000,hres=18000)
dewp = np.ma.masked_where(np.isnan(dewp),dewp)
# interpolate wind speed
x_masked,y_masked,wspd = remove_nan_observations(xp,yp,data['wsp'].values)
wspx,wspy,speed = interpolate_to_grid(x_masked,y_masked,wspd,interp_type='cressman',\
minimum_neighbors=3,search_radius=200000,hres=18000)
speed = np.ma.masked_where(np.isnan(speed),speed)
# derived values
# station pressure
data['pres'] = stationPressure(data['slp'],data['elev'])
# theta-E
data['thetae'] = equivalent_potential_temperature(data['pres'].values*units.hPa,data['temp'].values*units.degF,data['dpt'].values*units.degF)
x_masked,y_masked,thetae = remove_nan_observations(xp,yp,data['thetae'].values)
thex,they,thte = interpolate_to_grid(x_masked,y_masked,thetae,interp_type='cressman',\
minimum_neighbors=3,search_radius=200000,hres=18000)
thte = np.ma.masked_where(np.isnan(thte),thte)
# mixing ratio
relh = relative_humidity_from_dewpoint(data['temp'].values*units.degF,data['dpt'].values*units.degF)
mixr = mixing_ratio_from_relative_humidity(relh,data['temp'].values*units.degF,data['pres'].values*units.hPa) * 1000.0
x_masked,y_masked,mixrat = remove_nan_observations(xp,yp,mixr)
mrx,mry,mrat = interpolate_to_grid(x_masked,y_masked,mixrat,interp_type='cressman',\
minimum_neighbors=3,search_radius=200000,hres=18000)
mrat = np.ma.masked_where(np.isnan(mrat),mrat)
# set up the state borders
state_boundaries = cfeature.NaturalEarthFeature(category='cultural',\
name='admin_1_states_provinces_lines',scale='50m',facecolor='none')
# SCALAR VARIABLES TO PLOT
# variable names (will appear in plot title)
variables = ['Temperature','Dewpoint','Wind Speed','Theta-E','Mixing Ratio']
# units (for colorbar label)
unitlabels = ['F','F','kt','K','g/kg']
# list of actual variables to plot
vardata = [temp,dewp,speed,thte,mrat]
# tag in output filename
varplots = ['temp','dewp','wspd','thte','mrat']
# levels: (lower,upper,step)
levs = [[-20,105,5],[30,85,5],[0,70,5],[250,380,5],[0,22,2]]
# colormaps
colormaps = ['hsv_r','Greens','plasma','hsv_r','Greens']
for j in range(len(variables)):
print("\t%s" % variables[j])
fig = plt.figure(figsize=(20, 10))
view = fig.add_subplot(1, 1, 1, projection=to_proj)
# set up the map and plot the interpolated grids
levels = list(range(levs[j][0],levs[j][1],levs[j][2]))
cmap = plt.get_cmap(colormaps[j])
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
labels = variables[j] + " (" + unitlabels[j] + ")"
# add map features
view.set_extent([west[i],east[i],south[i],north[i]])
view.add_feature(state_boundaries,edgecolor='black')
view.add_feature(cfeature.OCEAN,zorder=-1)
view.add_feature(cfeature.COASTLINE,zorder=2)
view.add_feature(cfeature.BORDERS, linewidth=2,edgecolor='black')
# plot the sea-level pressure
cs = view.contour(slpgridx, slpgridy, slp, colors='k', levels=list(range(990, 1034, 4)))
view.clabel(cs, inline=1, fontsize=12, fmt='%i')
# plot the scalar background
mmb = view.pcolormesh(tempx, tempy, vardata[j], cmap=cmap, norm=norm)
fig.colorbar(mmb, shrink=.4, orientation='horizontal', pad=0.02, boundaries=levels, \
extend='both',label=labels)
# plot the wind barbs
view.barbs(windgridx, windgridy, uwind, vwind, alpha=.4, length=5,flip_barb=flip)
# plot title and save
view.set_title('%s (shaded), SLP, and Wind (valid %s)' % (variables[j],vt))
plt.savefig('/var/www/html/images/%s_%s.png' % (savenames[i],varplots[j]),bbox_inches='tight')
# close everything
fig.clear()
view.clear()
plt.close(fig)
f.close()
print("Script finished.")
f.write(item)
###############################################################################
############# Read WRF data, only the variable of interest ###########
ncfiles = [Dataset(x) for x in wrf_file] # leer todos los wrfout
# print ncfiles
print("vientos")
wind = getvar(ncfiles, 'uvmet10_wspd_wdir', timeidx=ALL_TIMES, method='cat')
ws = wind.sel(wspd_wdir='wspd')
wd = wind.sel(wspd_wdir='wdir')
###############################################################################
##################### cambiar de m/s para knots ###############################
ws10 = (ws.values * units('m/s')).to('knots')
wd10 = wd.values * units.degree
##################### calcular los componentes U y V ##########################
u10, v10 = wind_components(ws10, wd10)
############################### AOD #######################################
tau1 = getvar(ncfiles, "TAUAER1", timeidx=ALL_TIMES,
method='cat') # AOD in 300nm
tau2 = getvar(ncfiles, "TAUAER2", timeidx=ALL_TIMES,
method='cat') # AOD in 400nm
tau3 = getvar(ncfiles, "TAUAER3", timeidx=ALL_TIMES,
method='cat') # AOD in 600nm
tau4 = getvar(ncfiles, "TAUAER4", timeidx=ALL_TIMES,
method='cat') # AOD in 1000nm
angstrom = np.zeros(
(tau1.shape[0], tau1.shape[1], tau1.shape[2], tau1.shape[3]))
aod_550 = np.zeros(
(tau1.shape[0], tau1.shape[1], tau1.shape[2], tau1.shape[3]))
def plot_map_temperature(proj,
point_locs,
df_t,
area='EU',
west=-5.5,
east=32,
south=42,
north=62,
fonts=14,
cm='gist_ncar',
path=None,
SLP=False):
if path is None:
# set up the paths and test for existence
path = expanduser('~') + '/Documents/Metar_plots'
try:
os.listdir(path)
except FileNotFoundError:
os.mkdir(path)
else:
path = path
df = df_t
plt.rcParams['savefig.dpi'] = 300
# =========================================================================
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 16))
ax = fig.add_subplot(1, 1, 1, projection=proj)
if area == 'Antarctica':
df = df.loc[df['latitude'] < north]
ax.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
elif area == 'Arctic':
df = df.loc[df['latitude'] > south]
ax.set_extent([-180, 180, 60, 90], ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
else:
ax.set_extent((west, east, south, north))
# Set up a cartopy feature for state borders.
state_boundaries = feat.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='10m',
facecolor='#d8dcd6',
alpha=0.5)
ax.coastlines(resolution='10m', zorder=1, color='black')
ax.add_feature(state_boundaries, zorder=1, edgecolor='black')
# ax.add_feature(cartopy.feature.OCEAN, zorder=0)
# Set plot bounds
# reset index for easier loop
df = df.dropna(how='any', subset=['TT'])
df = df.reset_index()
cmap = matplotlib.cm.get_cmap(cm)
norm = matplotlib.colors.Normalize(vmin=-30.0, vmax=30.0)
# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
index = 0
a = np.arange(-30, 30, 1)
for x in a:
if index == 0:
df_min = df.loc[df['TT'] < min(a)]
df_max = df.loc[df['TT'] > max(a)]
j = 0
list_ex = [min(a) - 5, max(a) + 5]
for arr in [df_min, df_max]:
stationplot = StationPlot(ax,
arr['longitude'],
arr['latitude'],
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
Temp = stationplot.plot_parameter('NW',
arr['TT'],
color=cmap(norm(list_ex[j])))
try:
Temp.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
except AttributeError:
pass
j += 1
# slice out values between x and x+1
df_cur = df.loc[(df['TT'] < x + 1) & (df['TT'] >= x)]
stationplot = StationPlot(ax,
df_cur['longitude'],
df_cur['latitude'],
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
# plot the sliced values with a different color for each loop
Temp = stationplot.plot_parameter('NW',
df_cur['TT'],
color=cmap(norm(x + 0.5)))
try:
Temp.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
except AttributeError:
pass
print('x={} done correctly '.format(x))
index += 1
# fontweight = 'bold'
# More complex ex. uses custom formatter to control how sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of
# the pressure value in tenths of millibars.
stationplot = StationPlot(ax,
df['longitude'].values,
df['latitude'].values,
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
try:
u, v = wind_components(((df['ff'].values) * units('knots')),
(df['dd'].values * units.degree))
cloud_frac = df['cloud_cover']
if area != 'Arctic':
stationplot.plot_barb(u, v, zorder=1000, linewidth=2)
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# stationplot.plot_text((2, 0), df['Station'])
for val in range(0, 2):
wx = df[['ww', 'StationType']]
if val == 0:
# mask all the unmanned stations
wx['ww'].loc[wx['StationType'] > 3] = np.nan
wx2 = wx['ww'].fillna(00).astype(int).values.tolist()
stationplot.plot_symbol('W', wx2, current_weather, zorder=2000)
else:
# mask all the manned stations
wx['ww'].loc[(wx['StationType'] <= 3)] = np.nan
# mask all reports smaller than 9
# =7 is an empty symbol!
wx['ww'].loc[wx['ww'] <= 9] = 7
wx2 = wx['ww'].fillna(7).astype(int).values.tolist()
stationplot.plot_symbol('W',
wx2,
current_weather_auto,
zorder=2000)
# print(u, v)
except (ValueError, TypeError) as error:
pass
if SLP is True:
lon = df['longitude'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)].values
lat = df['latitude'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)].values
xp, yp, _ = proj.transform_points(ccrs.PlateCarree(), lon, lat).T
sea_levelp = df['SLP'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)]
x_masked, y_masked, pres = remove_nan_observations(
xp, yp, sea_levelp.values)
slpgridx, slpgridy, slp = interpolate_to_grid(x_masked,
y_masked,
pres,
interp_type='cressman',
search_radius=400000,
rbf_func='quintic',
minimum_neighbors=1,
hres=100000,
rbf_smooth=100000)
Splot_main = ax.contour(slpgridx,
slpgridy,
slp,
colors='k',
linewidths=2,
extent=(west, east, south, north),
levels=list(range(950, 1050, 10)))
plt.clabel(Splot_main, inline=1, fontsize=12, fmt='%i')
Splot = ax.contour(slpgridx,
slpgridy,
slp,
colors='k',
linewidths=1,
linestyles='--',
extent=(west, east, south, north),
levels=[
x for x in range(950, 1050, 1)
if x not in list(range(950, 1050, 10))
])
plt.clabel(Splot, inline=1, fontsize=10, fmt='%i')
# stationplot.plot_text((2, 0), df['Station'])
# Also plot the actual text of the station id. Instead of cardinal
# directions, plot further out by specifying a location of 2 increments
# in x and 0 in y.stationplot.plot_text((2, 0), df['station'])
if (area == 'Antarctica' or area == 'Arctic'):
plt.savefig(path + '/CURR_SYNOP_color_' + area + '.png',
bbox_inches='tight',
pad_inches=0)
else:
plt.savefig(path + '/CURR_SYNOP_color_' + area + '.png',
bbox_inches='tight',
transparent="True",
pad_inches=0)
from metpy.plots import Hodograph, SkewT
from metpy.units import units
#########################################################################
# Getting Data
# ------------
#
# Upper air data can be obtained using the siphon package, but for this tutorial we will use
# some of MetPy's sample data. This event is the Veterans Day tornado outbreak in 2002.
col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
df = pd.read_fwf(get_test_data('nov11_sounding.txt', as_file_obj=False),
skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)
df['u_wind'], df['v_wind'] = mpcalc.wind_components(df['speed'],
np.deg2rad(df['direction']))
# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
'u_wind', 'v_wind'), how='all').reset_index(drop=True)
##########################################################################
# We will pull the data out of the example dataset into individual variables and
# assign units.
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
def test_warning_dir():
"""Test that warning is raised wind direction > 2Pi."""
with pytest.warns(UserWarning):
wind_components(3. * units('m/s'), 270)
def main():
### START OF USER SETTINGS BLOCK ###
# FILE/DATA SETTINGS
# file path to input file
datafile = '/home/jgodwin/python/sfc_observations/surface_observations.txt'
timefile = '/home/jgodwin/python/sfc_observations/validtime.txt'
# file path to county shapefile
ctyshppath = '/home/jgodwin/python/sfc_observations/shapefiles/counties/countyl010g.shp'
# file path to ICAO list
icaopath = '/home/jgodwin/python/sfc_observations/icao_list.csv'
icaodf = pd.read_csv(icaopath, index_col='STATION')
# MAP SETTINGS
# map names (doesn't go anywhere (yet), just for tracking purposes)
maps = ['CONUS', 'Texas', 'Tropical Atlantic']
# minimum radius allowed between points (in km)
radius = [100.0, 50.0, 75.0]
# map boundaries (longitude/latitude degrees)
west = [-122, -108, -100]
east = [-73, -93, -60]
south = [23, 25, 10]
north = [50, 38, 35]
restart_projection = [True, False, True]
# use county map? (True/False): warning, counties load slow!
usecounties = [False, False, False]
# OUTPUT SETTINGS
# save directory for output
savedir = '/var/www/html/images/'
# filenames for output
savenames = ['conus.png', 'texas.png', 'atlantic.png']
# TEST MODE SETTINGS
test = False # True/False
testnum = 5 # which map are you testing? corresponds to index in "maps" above
### END OF USER SETTING SECTION ###
# if there are any missing weather codes, add them here
wx_code_map.update({
'-RADZ': 59,
'-TS': 17,
'VCTSSH': 80,
'-SGSN': 77,
'SHUP': 76,
'FZBR': 48,
'FZUP': 76
})
### READ IN DATA / SETUP MAP ###
# read in the valid time file
vt = open(timefile).read()
# read in the data
for i in range(len(maps)):
if test and i != testnum:
continue
with open(datafile) as f:
data = pd.read_csv(f,header=0,names=['siteID','lat','lon','elev','slp','temp','sky','dpt','wx','wdr',\
'wsp'],na_values=-99999)
# drop rows with missing winds
data = data.dropna(how='any', subset=['wdr', 'wsp'])
# remove data not within our domain
data = data[(data['lat'] >= south[i]-2.0) & (data['lat'] <= north[i]+2.0) \
& (data['lon'] >= west[i]-2.0) & (data['lon'] <= east[i]+2.0)]
# filter data (there seems to be one site always reporting a really anomalous temperature
data = data[data['temp'] <= 50]
print("Working on %s" % maps[i])
# set up the map projection central longitude/latitude and the standard parallels
cenlon = (west[i] + east[i]) / 2.0
cenlat = (south[i] + north[i]) / 2.0
sparallel = cenlat
if cenlat > 0:
cutoff = -30
flip = False
elif cenlat < 0:
cutoff = 30
flip = True
# create the projection
if restart_projection:
proj = ccrs.LambertConformal(central_longitude=cenlon,
central_latitude=cenlat,
standard_parallels=[sparallel],
cutoff=cutoff)
point_locs = proj.transform_points(ccrs.PlateCarree(),
data['lon'].values,
data['lat'].values)
data = data[reduce_point_density(point_locs, radius[i] * 1000)]
# state borders
state_boundaries = cfeature.NaturalEarthFeature(category='cultural',\
name='admin_1_states_provinces_lines',scale='50m',facecolor='none')
# county boundaries
if usecounties[i]:
county_reader = shpreader.Reader(ctyshppath)
counties = list(county_reader.geometries())
COUNTIES = cfeature.ShapelyFeature(counties, ccrs.PlateCarree())
### DO SOME CONVERSIONS ###
# get the wind components
u, v = wind_components(data['wsp'].values * units('knots'),
data['wdr'].values * units.degree)
# convert temperature from Celsius to Fahrenheit
data['temp'] = cToF(data['temp'])
data['dpt'] = cToF(data['dpt'])
# convert the cloud fraction value into a code of 0-8 (oktas) and compenate for NaN values
cloud_frac = (8 * data['sky'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# map weather strings to WMO codes (only use first symbol if multiple are present
data['wx'] = data.wx.str.split('/').str[0] + ''
wx = [
wx_code_map[s.split()[0] if ' ' in s else s]
for s in data['wx'].fillna('')
]
# get the minimum and maximum temperatures in domain
searchdata = data[(data['lat'] >= south[i]) & (data['lat'] <= north[i]) \
& (data['lon'] >= west[i]) & (data['lon'] <= east[i])]
min_temp = searchdata.loc[searchdata['temp'].idxmin()]
max_temp = searchdata.loc[searchdata['temp'].idxmax()]
max_dewp = searchdata.loc[searchdata['dpt'].idxmax()]
# look up the site names for the min/max temp locations
min_temp_loc = icaoLookup(min_temp['siteID'], icaodf)
max_temp_loc = icaoLookup(max_temp['siteID'], icaodf)
max_dewp_loc = icaoLookup(max_dewp['siteID'], icaodf)
text_str = "Min temp: %.0f F at %s (%s)\nMax temp: %.0f F at %s (%s)\nMax dewpoint: %.0f F at %s (%s)"\
% (min_temp['temp'],min_temp['siteID'],min_temp_loc,\
max_temp['temp'],max_temp['siteID'],max_temp_loc,\
max_dewp['dpt'],max_dewp['siteID'],max_dewp_loc)
### PLOTTING SECTION ###
# change the DPI to increase the resolution
plt.rcParams['savefig.dpi'] = 255
# create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# add various map elements
ax.add_feature(cfeature.LAND, zorder=-1)
ax.add_feature(cfeature.OCEAN, zorder=-1)
ax.add_feature(cfeature.LAKES, zorder=-1)
ax.add_feature(cfeature.COASTLINE, zorder=2, edgecolor='black')
ax.add_feature(state_boundaries, edgecolor='black')
if usecounties[i]:
ax.add_feature(COUNTIES,
facecolor='none',
edgecolor='gray',
zorder=-1)
ax.add_feature(cfeature.BORDERS, linewidth=2, edgecolor='black')
# set plot bounds
ax.set_extent((west[i], east[i], south[i], north[i]))
### CREATE STATION PLOTS ###
# lat/lon of the station plots
stationplot = StationPlot(ax,data['lon'].values,data['lat'].values,clip_on=True,\
transform=ccrs.PlateCarree(),fontsize=6)
# plot the temperature and dewpoint
stationplot.plot_parameter('NW', data['temp'], color='red')
stationplot.plot_parameter('SW', data['dpt'], color='darkgreen')
# plot the SLP using the standard trailing three digits
stationplot.plot_parameter(
'NE', data['slp'], formatter=lambda v: format(10 * v, '.0f')[-3:])
# plot the sky condition
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# plot the present weather
stationplot.plot_symbol('W', wx, current_weather)
# plot the wind barbs
stationplot.plot_barb(u, v, flip_barb=flip)
# plot the text of the station ID
stationplot.plot_text((2, 0), data['siteID'])
# plot the valid time
plt.title('Surface Observations valid %s' % vt)
# plot the min/max temperature info and draw circle around warmest and coldest obs
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
plt.text(west[i],
south[i],
text_str,
fontsize=12,
verticalalignment='top',
bbox=props,
transform=ccrs.Geodetic())
projx1, projy1 = proj.transform_point(min_temp['lon'], min_temp['lat'],
ccrs.Geodetic())
ax.add_patch(
matplotlib.patches.Circle(xy=[projx1, projy1],
radius=50000,
facecolor="None",
edgecolor='blue',
linewidth=3,
transform=proj))
projx2, projy2 = proj.transform_point(max_temp['lon'], max_temp['lat'],
ccrs.Geodetic())
ax.add_patch(
matplotlib.patches.Circle(xy=[projx2, projy2],
radius=50000,
facecolor="None",
edgecolor='red',
linewidth=3,
transform=proj))
projx3, projy3 = proj.transform_point(max_dewp['lon'], max_dewp['lat'],
ccrs.Geodetic())
ax.add_patch(
matplotlib.patches.Circle(xy=[projx3, projy3],
radius=30000,
facecolor="None",
edgecolor='green',
linewidth=3,
transform=proj))
# save the figure
outfile_name = savedir + savenames[i]
plt.savefig(outfile_name, bbox_inches='tight')
# clear and close everything
fig.clear()
ax.clear()
plt.close(fig)
f.close()
print("Script finished.")
def preprocessDataResample(file, path, spatialResolution, lambda1, lambda2,
order):
#delete gloabal data variable as soon as troubleshooting is complete
""" prepare data for hodograph analysis. non numeric values & values > 999999 removed, brunt-viasala freq
calculated, background wind removed
Different background removal techniques used: rolling average, savitsky-golay filter, nth order polynomial fits
"""
data = openFile(file, path)
data = interpolateVertically(data)
#change data container name, sounds silly but useful for troubleshooting data-cleaning bugs
global df
df = data
#print(df)
#make following vars availabale outside of function - convenient for time being, but consider changing in future
"""
global Time
global Pres
global Temp
global Hu
global Wd
global Long
global Lat
global Alt
global potentialTemp
global bv2
global u, v
global uBackground
global vBackground
global tempBackground
#for comparing rolling ave to savitsky golay
#global uBackgroundRolling
#global vBackgroundRolling
#global tempBackgroundRolling
#global uRolling
#global vRolling
#global tRolling
"""
#individual series for each variable, local
Time = df['Time'].to_numpy()
Pres = df['P'].to_numpy() * units.hPa
Temp = df['T'].to_numpy() * units.degC
Ws = df['Ws'].to_numpy() * units.m / units.second
Wd = df['Wd'].to_numpy() * units.degree
Long = df['Long.'].to_numpy()
Lat = df['Lat.'].to_numpy()
Alt = df['Alt'].to_numpy().astype(int) * units.meter
#calculate brunt-viasala frequency **2
tempK = Temp.to('kelvin')
potentialTemperature = tempK * (p_0 / Pres)**(
2 / 7) #https://glossary.ametsoc.org/wiki/Potential_temperature
bv2 = mpcalc.brunt_vaisala_frequency_squared(
Alt, potentialTemperature).magnitude #N^2
#bv2 = bruntViasalaFreqSquared(potentialTemperature, heightSamplingFreq) #Maybe consider using metpy version of N^2 ? Height sampling is not used in hodo method, why allow it to affect bv ?
#convert wind from polar to cartesian c.s.
u, v = mpcalc.wind_components(
Ws, Wd) #raw u,v components - no different than using trig fuctions
print("Size of u: ", len(u))
#subtract nth order polynomials to find purturbation profile
return
# - Convert cardinal wind direction to degrees
# - Get wind components from speed and direction
# Read in the data and assign units as defined by the Mesonet
temperature = data['TAIR'].values * units.degF
dewpoint = data['TDEW'].values * units.degF
pressure = data['PRES'].values * units.hPa
wind_speed = data['WSPD'].values * units.mph
wind_direction = data['WDIR']
latitude = data['LAT']
longitude = data['LON']
station_id = data['STID']
# Take cardinal direction and convert to degrees, then convert to components
wind_direction = mpcalc.parse_angle(list(wind_direction))
u, v = mpcalc.wind_components(wind_speed.to('knots'), wind_direction)
###########################################
# Create the figure
# -----------------
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 8))
add_metpy_logo(fig, 70, 30, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.STATES.with_scale('50m'))
# Set plot bounds
# Drop any rows with all NaN values for T, Td, winds
# df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
# 'u_wind', 'v_wind'), how='all').reset_index(drop=True)
##########################################################################
# We will pull the data out of the example dataset into individual variables and
# assign units.
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
u_wind = df['u_wind'].values * units(df.units['u_wind'])
v_wind = df['v_wind'].values * units(df.units['v_wind'])
##########################################################################
# Thermodynamic Calculations
# --------------------------
#
# Often times we will want to calculate some thermodynamic parameters of a
# sounding. The MetPy calc module has many such calculations already implemented!
#
# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's
# relative humidity becomes 100% when lifted along a dry adiabatic path.
# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning
# at the surface temperature/pressure and rising dry adiabatically until
# reaching the LCL, then rising moist adiabatically.
def wind_rh_according_to_4D_data(
initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
sta_fcs={
'lon': [101.82, 101.32, 101.84, 102.23, 102.2681],
'lat': [28.35, 27.91, 28.32, 27.82, 27.8492],
'altitude': [3600, 3034.62, 3240, 1669, 1941.5],
'name': ['健美乡', '项脚乡', '\n锦屏镇', '\n马道镇', 'S9005 ']
},
draw_zd=True,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500],
map_ratio=19 / 9,
zoom_ratio=1,
south_China_sea=False,
area='全国',
city=False,
output_dir=None,
bkgd_type='satellite',
data_source='MICAPS'):
# micaps data directory
if (area != '全国'):
south_China_sea = False
# prepare data
if (area != '全国'):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
cntr_pnt = np.append(np.mean(sta_fcs['lon']), np.mean(sta_fcs['lat']))
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
bkgd_level = utl.cal_background_zoom_ratio(zoom_ratio)
# micaps data directory
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='u10m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='v10m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='Td2m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='T2m')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
initTime = filename[0:8]
# retrieve data from micaps server
gh = MICAPS_IO.get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels)
if (gh is None):
return
gh['data'].values = gh['data'].values * 10
rh = MICAPS_IO.get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
u = MICAPS_IO.get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
v = MICAPS_IO.get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
u10m = MICAPS_IO.get_model_grid(directory=data_dir[4],
filename=filename)
if u10m is None:
return
v10m = MICAPS_IO.get_model_grid(directory=data_dir[5],
filename=filename)
if v10m is None:
return
td2m = MICAPS_IO.get_model_grid(directory=data_dir[6],
filename=filename)
if td2m is None:
return
t2m = MICAPS_IO.get_model_grid(directory=data_dir[7],
filename=filename)
if t2m is None:
return
if (draw_zd == True):
validtime = (datetime.strptime('20' + initTime, '%Y%m%d%H') +
timedelta(hours=fhour)).strftime("%Y%m%d%H")
directory_obs = utl.Cassandra_dir(data_type='surface',
data_source='OBS',
var_name='PLOT_ALL')
try:
zd_sta = MICAPS_IO.get_station_data(filename=validtime +
'0000.000',
directory=directory_obs,
dropna=True,
cache=False)
obs_valid = True
except:
zd_sta = MICAPS_IO.get_station_data(directory=directory_obs,
dropna=True,
cache=False)
obs_valid = False
zd_lon = zd_sta['lon'].values
zd_lat = zd_sta['lat'].values
zd_alt = zd_sta['Alt'].values
zd_u, zd_v = mpcalc.wind_components(
zd_sta['Wind_speed_2m_avg'].values * units('m/s'),
zd_sta['Wind_angle_2m_avg'].values * units.deg)
idx_zd = np.where((zd_lon > map_extent[0])
& (zd_lon < map_extent[1])
& (zd_lat > map_extent[2])
& (zd_lat < map_extent[3]))
zd_sm_lon = zd_lon[idx_zd[0]]
zd_sm_lat = zd_lat[idx_zd[0]]
zd_sm_alt = zd_alt[idx_zd[0]]
zd_sm_u = zd_u[idx_zd[0]]
zd_sm_v = zd_v[idx_zd[0]]
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour, UTC=True)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
try:
# retrieve data from CMISS server
gh = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='GPH'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="GPH",
units='gpm')
if gh is None:
return
rh = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='RHU'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="RHU",
units='%')
if rh is None:
return
u = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="WIU",
units='m/s')
if u is None:
return
v = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="WIV",
units='m/s')
if v is None:
return
if (model == 'ECMWF'):
td2m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='DPT'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="DPT",
units='K')
if td2m is None:
return
t2m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEF2'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TEF2",
units='K')
if t2m is None:
return
v10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="WIV10",
units='m/s')
if v10m is None:
return
u10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="WIU10",
units='m/s')
if u10m is None:
return
if (model == 'GRAPES_GFS'):
rh2m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='RHF2'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=2,
fcst_ele="RHF2",
units='%')
if rh2m is None:
return
v10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=10,
fcst_ele="WIV10",
units='m/s')
if v10m is None:
return
u10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=10,
fcst_ele="WIU10",
units='m/s')
if u10m is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
if (draw_zd == True):
if (initTime == None):
initTime1 = CIMISS_IO.cimiss_get_obs_latest_time(
data_code="SURF_CHN_MUL_HOR")
initTime = (datetime.strptime('20' + initTime1, '%Y%m%d%H') -
timedelta(days=day_back)).strftime("%Y%m%d%H")[2:]
validtime = (datetime.strptime('20' + initTime, '%Y%m%d%H') +
timedelta(hours=fhour)).strftime("%Y%m%d%H")
data_code = utl.CMISS_data_code(data_source='OBS',
var_name='PLOT_sfc')
zd_sta = CIMISS_IO.cimiss_obs_by_time(
times=validtime + '0000',
data_code=data_code,
sta_levels="011,012,013,014",
elements=
"Station_Id_C,Station_Id_d,lat,lon,Alti,TEM,WIN_D_Avg_2mi,WIN_S_Avg_2mi,RHU"
)
obs_valid = True
if (zd_sta is None):
CIMISS_IO.cimiss_get_obs_latest_time(data_code=data_code,
latestTime=6)
zd_sta = CIMISS_IO.cimiss_obs_by_time(directory=directory_obs,
dropna=True,
cache=False)
obs_valid = False
zd_lon = zd_sta['lon'].values
zd_lat = zd_sta['lat'].values
zd_alt = zd_sta['Alti'].values
zd_u, zd_v = mpcalc.wind_components(
zd_sta['WIN_S_Avg_2mi'].values * units('m/s'),
zd_sta['WIN_D_Avg_2mi'].values * units.deg)
idx_zd = np.where((zd_lon > map_extent[0])
& (zd_lon < map_extent[1])
& (zd_lat > map_extent[2])
& (zd_lat < map_extent[3])
& (zd_sta['WIN_S_Avg_2mi'].values < 1000))
zd_sm_lon = zd_lon[idx_zd[0]]
zd_sm_lat = zd_lat[idx_zd[0]]
zd_sm_alt = zd_alt[idx_zd[0]]
zd_sm_u = zd_u[idx_zd[0]]
zd_sm_v = zd_v[idx_zd[0]]
#maskout area
delt_xy = rh['lon'].values[1] - rh['lon'].values[0]
#+ to solve the problem of labels on all the contours
mask1 = (rh['lon'] > map_extent[0] - delt_xy) & (
rh['lon'] < map_extent[1] + delt_xy) & (
rh['lat'] > map_extent[2] - delt_xy) & (rh['lat'] <
map_extent[3] + delt_xy)
mask2 = (u10m['lon'] > map_extent[0] - delt_xy) & (
u10m['lon'] < map_extent[1] + delt_xy) & (
u10m['lat'] > map_extent[2] - delt_xy) & (u10m['lat'] <
map_extent[3] + delt_xy)
#- to solve the problem of labels on all the contours
rh = rh.where(mask1, drop=True)
u = u.where(mask1, drop=True)
v = v.where(mask1, drop=True)
gh = gh.where(mask1, drop=True)
u10m = u10m.where(mask2, drop=True)
v10m = v10m.where(mask2, drop=True)
#prepare interpolator
Ex1 = np.squeeze(u['data'].values).flatten()
Ey1 = np.squeeze(v['data'].values).flatten()
Ez1 = np.squeeze(rh['data'].values).flatten()
z = (np.squeeze(gh['data'].values)).flatten()
coords = np.zeros((np.size(levels), u['lat'].size, u['lon'].size, 3))
coords[..., 1] = u['lat'].values.reshape((1, u['lat'].size, 1))
coords[..., 2] = u['lon'].values.reshape((1, 1, u['lon'].size))
coords = coords.reshape((Ex1.size, 3))
coords[:, 0] = z
interpolator_U = LinearNDInterpolator(coords, Ex1, rescale=True)
interpolator_V = LinearNDInterpolator(coords, Ey1, rescale=True)
interpolator_RH = LinearNDInterpolator(coords, Ez1, rescale=True)
#process sta_fcs 10m wind
coords2 = np.zeros((np.size(sta_fcs['lon']), 3))
coords2[:, 0] = sta_fcs['altitude']
coords2[:, 1] = sta_fcs['lat']
coords2[:, 2] = sta_fcs['lon']
u_sta = interpolator_U(coords2)
v_sta = interpolator_V(coords2)
RH_sta = interpolator_RH(coords2)
wsp_sta = (u_sta**2 + v_sta**2)**0.5
u10m_2D = u10m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
v10m_2D = v10m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
if (model == 'GRAPES_GFS' and data_source == 'CIMISS'):
rh2m_2D = rh2m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))['data'].values
else:
td2m_2D = td2m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
t2m_2D = t2m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
if (data_source == 'MICAPS'):
rh2m_2D = mpcalc.relative_humidity_from_dewpoint(
t2m_2D['data'].values * units('degC'),
td2m_2D['data'].values * units('degC')) * 100
else:
rh2m_2D = mpcalc.relative_humidity_from_dewpoint(
t2m_2D['data'].values * units('kelvin'),
td2m_2D['data'].values * units('kelvin')) * 100
wsp10m_2D = (u10m_2D['data'].values**2 + v10m_2D['data'].values**2)**0.5
winddir10m = mpcalc.wind_direction(u10m_2D['data'].values * units('m/s'),
v10m_2D['data'].values * units('m/s'))
if (np.isnan(wsp_sta).any()):
if (wsp_sta.size == 1):
wsp_sta[np.isnan(wsp_sta)] = np.squeeze(
wsp10m_2D[np.isnan(wsp_sta)])
RH_sta[np.isnan(RH_sta)] = np.squeeze(
np.array(rh2m_2D)[np.isnan(RH_sta)])
else:
wsp_sta[np.isnan(wsp_sta)] = np.squeeze(wsp10m_2D)[np.isnan(
wsp_sta)]
RH_sta[np.isnan(RH_sta)] = np.squeeze(
np.array(rh2m_2D))[np.isnan(RH_sta)]
u_sta, v_sta = mpcalc.wind_components(wsp_sta * units('m/s'), winddir10m)
#process zd_sta 10m wind
zd_fcst_obs = None
if (draw_zd is True):
coords3 = np.zeros((np.size(zd_sm_alt), 3))
coords3[:, 0] = zd_sm_alt
coords3[:, 1] = zd_sm_lat
coords3[:, 2] = zd_sm_lon
u_sm_sta = interpolator_U(coords3)
v_sm_sta = interpolator_V(coords3)
wsp_sm_sta = (u_sm_sta**2 + v_sm_sta**2)**0.5
u10m_sm = u10m.interp(lon=('points', zd_sm_lon),
lat=('points', zd_sm_lat))
v10m_sm = v10m.interp(lon=('points', zd_sm_lon),
lat=('points', zd_sm_lat))
wsp10m_sta = np.squeeze(
(u10m_sm['data'].values**2 + v10m_sm['data'].values**2)**0.5)
winddir10m_sm = mpcalc.wind_direction(
u10m_sm['data'].values * units('m/s'),
v10m_sm['data'].values * units('m/s'))
if (np.isnan(wsp_sm_sta).any()):
wsp_sm_sta[np.isnan(wsp_sm_sta)] = wsp10m_sta[np.isnan(wsp_sm_sta)]
u_sm_sta, v_sm_sta = mpcalc.wind_components(wsp_sm_sta * units('m/s'),
winddir10m_sm)
zd_fcst_obs = {
'lon': zd_sm_lon,
'lat': zd_sm_lat,
'altitude': zd_sm_alt,
'U': np.squeeze(np.array(u_sm_sta)),
'V': np.squeeze(np.array(v_sm_sta)),
'obs_valid': obs_valid,
'U_obs': np.squeeze(np.array(zd_sm_u)),
'V_obs': np.squeeze(np.array(zd_sm_v))
}
#prepare for graphics
sta_fcs_fcst = {
'lon': sta_fcs['lon'],
'lat': sta_fcs['lat'],
'altitude': sta_fcs['altitude'],
'name': sta_fcs['name'],
'RH': np.array(RH_sta),
'U': np.squeeze(np.array(u_sta)),
'V': np.squeeze(np.array(v_sta))
}
fcst_info = gh.coords
local_scale_graphics.draw_wind_rh_according_to_4D_data(
sta_fcs_fcst=sta_fcs_fcst,
zd_fcst_obs=zd_fcst_obs,
fcst_info=fcst_info,
map_extent=map_extent,
draw_zd=draw_zd,
bkgd_type=bkgd_type,
bkgd_level=bkgd_level,
output_dir=None)
def test_warning_dir():
"""Test that warning is raised wind direction > 2Pi."""
with pytest.warns(UserWarning):
wind_components(3. * units('m/s'), 270)
'dew_point_temperature'].values * units.degC
data['air_pressure_at_sea_level'] = data_arr['slp'].values * units('mbar')
###########################################
# Notice that the names (the keys) in the dictionary are the same as those that the
# layout is expecting.
#
# Now perform a few conversions:
#
# - Get wind components from speed and direction
# - Convert cloud fraction values to integer codes [0 - 8]
# - Map METAR weather codes to WMO codes for weather symbols
# Get the wind components, converting from m/s to knots as will be appropriate
# for the station plot
u, v = wind_components(data_arr['wind_speed'].values * units('m/s'),
data_arr['wind_dir'].values * units.degree)
data['eastward_wind'], data['northward_wind'] = u, v
# Convert the fraction value into a code of 0-8, which can be used to pull out
# the appropriate symbol
data['cloud_coverage'] = (
8 * data_arr['cloud_fraction']).fillna(10).values.astype(int)
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx_text = data_arr['weather'].fillna('')
data['current_wx1_symbol'] = [
wx_code_map[s.split()[0] if ' ' in s else s] for s in wx_text
]
###########################################
print (dir_name+save_dir_name, 'is exist')
filename = 'UPPER_SONDE_47185_ALL_2014_2014_2015.csv'
fullname = dir_name + filename
fullname_el = fullname.split('\\')
#filename = fullname_el[-1]
filename_el = filename.split('_')
obs_year = int(filename_el[-3])
#지점,일시(UTC),기압(hPa),고도(gpm),기온(°C),이슬점온도(°C),풍향(deg),풍속(knot),지상 FLAG(null),권계면 FLAG(null),최대풍 FLAG(null)
df = pd.read_csv(dir_name+filename, skiprows=1, sep=',', header=None, index_col=0,
names = ['site', 'time', 'pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed', 'FLAG1', 'FLAG2', 'FLAG3'],
skipfooter=1, engine='python')
df['u_wind'], df['v_wind'] = mpcalc.wind_components(df['speed'],
np.deg2rad(df['direction']))
#%%
# Drop any rows with all NaN values for T, Td, winds
#df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed', 'u_wind', 'v_wind'), how='all').reset_index(drop=True)
#df = df.dropna(subset=('temperature', 'dewpoint'), how='all').reset_index(drop=True)
df = df.dropna(subset=('temperature', 'dewpoint', 'height'), how='all').reset_index(drop=True)
# Rows that do not meet the condition alpha + num are eliminated
s_start_date = datetime(obs_year, 1, 1) #convert startdate to date type
s_end_date = datetime(obs_year+1, 1, 1)
date1 = s_start_date
selected_times = []
while date1 < s_end_date :
date1_strf = date1.strftime('%Y-%m-%d %H:%M')
selected_times.append(date1_strf)
# Get wind information and mask where either speed or direction is unavailable
wind_speed = (data['wind_speed'].values * units('m/s')).to('knots')
wind_dir = data['wind_dir'].values * units.degree
good_indices = np.where((~np.isnan(wind_dir)) & (~np.isnan(wind_speed)))
x_masked = xp[good_indices]
y_masked = yp[good_indices]
wind_speed = wind_speed[good_indices]
wind_dir = wind_dir[good_indices]
###########################################
# Calculate u and v components of wind and then interpolate both.
#
# Both will have the same underlying grid so throw away grid returned from v interpolation.
u, v = wind_components(wind_speed, wind_dir)
windgridx, windgridy, uwind = interpolate_to_grid(x_masked, y_masked, np.array(u),
interp_type='cressman', search_radius=400000,
hres=100000)
_, _, vwind = interpolate_to_grid(x_masked, y_masked, np.array(v), interp_type='cressman',
search_radius=400000, hres=100000)
###########################################
# Get temperature information
x_masked, y_masked, t = remove_nan_observations(xp, yp, data['temperature'].values)
tempx, tempy, temp = interpolate_to_grid(x_masked, y_masked, t, interp_type='cressman',
minimum_neighbors=3, search_radius=400000, hres=35000)
temp = np.ma.masked_where(np.isnan(temp), temp)
# Use the cartopy map projection to transform station locations to the map and
# then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['lon'].values, data['lat'].values)
data = data[reduce_point_density(point_locs, 300000.)]
###########################################
# Now that we have the data we want, we need to perform some conversions:
#
# - Get wind components from speed and direction
# - Convert cloud fraction values to integer codes [0 - 8]
# - Map METAR weather codes to WMO codes for weather symbols
# Get the wind components, converting from m/s to knots as will be appropriate
# for the station plot.
u, v = wind_components((data['wind_speed'].values * units('m/s')).to('knots'),
data['wind_dir'].values * units.degree)
# Convert the fraction value into a code of 0-8 and compensate for NaN values,
# which can be used to pull out the appropriate symbol
cloud_frac = (8 * data['cloud_fraction'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx = [wx_code_map[s.split()[0] if ' ' in s else s] for s in data['weather'].fillna('')]
###########################################
# The payoff
# ----------
def test_wind_comps_scalar():
"""Test wind components calculation with scalars."""
u, v = wind_components(8 * units('m/s'), 150 * units.deg)
assert_almost_equal(u, -4 * units('m/s'), 3)
assert_almost_equal(v, 6.9282 * units('m/s'), 3)
