def mcalc_feelslike(tmpf, dwpf, smps):
"""Compute a feels like temperature
Args:
temperature (temperature): The dry bulb temperature
dewpoint (temperature): The dew point temperature
speed (speed): the wind speed
Returns:
temperature (temperature): The feels like temperature
"""
is_not_scalar = isinstance(tmpf.m, (list, tuple, np.ndarray))
rh = mcalc.relative_humidity_from_dewpoint(tmpf, dwpf)
# NB: update this once metpy 0.11 is released
app = mcalc.apparent_temperature(tmpf, rh, smps)
if hasattr(app, "mask"):
if is_not_scalar:
app[app.mask] = tmpf[app.mask]
else:
app = tmpf
elif hasattr(tmpf, "mask"):
app = masked_array(app.m, app.units)
app.mask = tmpf.mask
return app
def test_apparent_temperature_windchill():
"""Test that apparent temperature works when a windchill is calculated."""
temperature = -5. * units.degC
rel_humidity = 50. * units.percent
wind = 35. * units('m/s')
truth = -18.9357 * units.degC
res = apparent_temperature(temperature, rel_humidity, wind)
assert_almost_equal(res, truth, 0)
def test_apparent_temperature():
"""Test the apparent temperature calculation."""
temperature = np.array([[90, 90, 70], [20, 20, 60]]) * units.degF
rel_humidity = np.array([[60, 20, 60], [10, 10, 10]]) * units.percent
wind = np.array([[5, 3, 3], [10, 1, 10]]) * units.mph
truth = np.array([[99.6777178, 90, 70], [8.8140662, 20, 60]]) * units.degF
res = apparent_temperature(temperature, rel_humidity, wind)
assert_array_almost_equal(res, truth, 6)
def test_apparent_temperature_scalar_no_modification():
"""Test the apparent temperature calculation with a scalar that is NOOP."""
temperature = 70 * units.degF
rel_humidity = 60 * units.percent
wind = 5 * units.mph
truth = 70 * units.degF
res = apparent_temperature(temperature, rel_humidity, wind)
assert_almost_equal(res, truth, 6)
def test_apparent_temperature_scalar():
"""Test the apparent temperature calculation with a scalar."""
temperature = 90 * units.degF
rel_humidity = 60 * units.percent
wind = 5 * units.mph
truth = 99.6777178 * units.degF
res = apparent_temperature(temperature, rel_humidity, wind)
assert_almost_equal(res, truth, 6)
def test_apparent_temperature_mask_undefined_false():
"""Test that apparent temperature works when mask_undefined is False."""
temp = np.array([80, 55, 10]) * units.degF
rh = np.array([40, 50, 25]) * units.percent
wind = np.array([5, 4, 10]) * units('m/s')
app_temperature = apparent_temperature(temp, rh, wind, mask_undefined=False)
assert not hasattr(app_temperature, 'mask')
def test_apparent_temperature_windchill():
"""Test that apparent temperature works when a windchill is calculated."""
temperature = -5. * units.degC
rel_humidity = 50. * units.percent
wind = 35. * units('m/s')
truth = -18.9357 * units.degC
res = apparent_temperature(temperature, rel_humidity, wind)
assert_almost_equal(res, truth, 0)
def test_apparent_temperature_scalar_no_modification():
"""Test the apparent temperature calculation with a scalar that is NOOP."""
temperature = 70 * units.degF
rel_humidity = 60 * units.percent
wind = 5 * units.mph
truth = 70 * units.degF
res = apparent_temperature(temperature, rel_humidity, wind)
assert_almost_equal(res, truth, 6)
def test_apparent_temperature_scalar():
"""Test the apparent temperature calculation with a scalar."""
temperature = 90 * units.degF
rel_humidity = 60 * units.percent
wind = 5 * units.mph
truth = 99.6777178 * units.degF
res = apparent_temperature(temperature, rel_humidity, wind)
assert_almost_equal(res, truth, 6)
def test_apparent_temperature_mask_undefined_true():
"""Test that apparent temperature works when mask_undefined is True."""
temp = np.array([80, 55, 10]) * units.degF
rh = np.array([40, 50, 25]) * units.percent
wind = np.array([5, 4, 10]) * units('m/s')
app_temperature = apparent_temperature(temp, rh, wind, mask_undefined=True)
mask = [False, True, False]
assert_array_equal(app_temperature.mask, mask)
def test_apparent_temperature():
"""Test the apparent temperature calculation."""
temperature = np.array([[90, 90, 70], [20, 20, 60]]) * units.degF
rel_humidity = np.array([[60, 20, 60], [10, 10, 10]]) * units.percent
wind = np.array([[5, 3, 3], [10, 1, 10]]) * units.mph
truth = units.Quantity(
np.ma.array([[99.6777178, 86.3357671, 70], [8.8140662, 20, 60]],
mask=[[False, False, True], [False, True, True]]),
units.degF)
res = apparent_temperature(temperature, rel_humidity, wind)
assert_array_almost_equal(res, truth, 6)
def test_apparent_temperature():
"""Test the apparent temperature calculation."""
temperature = np.array([[90, 90, 70],
[20, 20, 60]]) * units.degF
rel_humidity = np.array([[60, 20, 60],
[10, 10, 10]]) * units.percent
wind = np.array([[5, 3, 3],
[10, 1, 10]]) * units.mph
truth = np.array([[99.6777178, 90, 70],
[8.8140662, 20, 60]]) * units.degF
res = apparent_temperature(temperature, rel_humidity, wind)
assert_array_almost_equal(res, truth, 6)
def calc(self):
"""Compute things not usually computed"""
if self.data["relh"] is None and None not in [
self.data["tmpf"],
self.data["dwpf"],
]:
self.data["relh"] = bounded(
mcalc.relative_humidity_from_dewpoint(
self.data["tmpf"] * munits.degF,
self.data["dwpf"] * munits.degF,
).to(munits.percent).magnitude,
0.5,
100.5,
)
if (self.data["dwpf"] is None
and None not in [self.data["tmpf"], self.data["relh"]]
and self.data["relh"] >= 1 and self.data["relh"] <= 100):
self.data["dwpf"] = bounded(
mcalc.dewpoint_from_relative_humidity(
self.data["tmpf"] * munits.degF,
self.data["relh"] * munits.percent,
).to(munits.degF).magnitude,
-100.0,
100.0,
)
if self.data["feel"] is None and None not in [
self.data["tmpf"],
self.data["relh"],
self.data["sknt"],
]:
self.data["feel"] = bounded(
mcalc.apparent_temperature(
self.data["tmpf"] * munits.degF,
self.data["relh"] * munits.percent,
self.data["sknt"] * munits.knots,
).to(munits.degF).magnitude,
-150.0,
200.0,
)
def calc(self):
"""Compute things not usually computed"""
if (self.data['relh'] is None
and None not in [self.data['tmpf'], self.data['dwpf']]):
self.data['relh'] = bounded(
mcalc.relative_humidity_from_dewpoint(
self.data['tmpf'] * munits.degF, self.data['dwpf'] *
munits.degF).to(munits.percent).magnitude, 0.5, 100.5)
if (self.data['dwpf'] is None
and None not in [self.data['tmpf'], self.data['relh']]
and self.data['relh'] >= 1 and self.data['relh'] <= 100):
self.data['dwpf'] = bounded(
mcalc.dewpoint_rh(self.data['tmpf'] * munits.degF,
self.data['relh'] * munits.percent).to(
munits.degF).magnitude, -100., 100.)
if (self.data['feel'] is None and None not in [
self.data['tmpf'], self.data['relh'], self.data['sknt']
]):
self.data['feel'] = bounded(
mcalc.apparent_temperature(
self.data['tmpf'] * munits.degF,
self.data['relh'] * munits.percent, self.data['sknt'] *
munits.knots).to(munits.degF).magnitude, -150., 200.)
def do(ts):
"""Process this date timestamp"""
asos = get_dbconn('asos', user='******')
iemaccess = get_dbconn('iem')
icursor = iemaccess.cursor()
df = read_sql("""
select station, network, iemid, drct, sknt,
valid at time zone tzname as localvalid,
tmpf, dwpf 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)
# derive some parameters
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * munits.degF,
df['dwpf'].values * munits.degF).to(munits.percent)
df['feel'] = mcalc.apparent_temperature(
df['tmpf'].values * munits.degF,
df['relh'].values * munits.percent,
df['sknt'].values * munits.knots
)
df['u'], df['v'] = mcalc.get_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. +
ndf['localvalid'].dt.minute * 60., unit='s'
)
df['timedelta'] = df['timedelta'] / np.timedelta64(1, 's')
table = "summary_%s" % (ts.year,)
for iemid, gdf in df.groupby('iemid'):
if len(gdf.index) < 6:
# print(" Quorum not meet for %s" % (gdf.iloc[0]['station'], ))
continue
ldf = gdf.copy()
ldf.interpolate(inplace=True)
totsecs = ldf['timedelta'].sum()
avg_rh = clean((ldf['relh'] * ldf['timedelta']).sum() / totsecs, 1,
100)
min_rh = clean(ldf['relh'].min(), 1, 100)
max_rh = clean(ldf['relh'].max(), 1, 100)
uavg = (ldf['u'] * ldf['timedelta']).sum() / totsecs
vavg = (ldf['u'] * ldf['timedelta']).sum() / totsecs
drct = clean(
mcalc.get_wind_dir(uavg * munits.knots, vavg * munits.knots),
0, 360)
avg_sknt = clean(
(ldf['sknt'] * ldf['timedelta']).sum() / totsecs, 0, 150 # arb
)
max_feel = clean(ldf['feel'].max(), -150, 200)
avg_feel = clean(
(ldf['feel'] * ldf['timedelta']).sum() / totsecs, -150, 200
)
min_feel = clean(ldf['feel'].min(), -150, 200)
def do_update():
"""Inline updating"""
icursor.execute("""
UPDATE """ + table + """
SET avg_rh = %s, min_rh = %s, max_rh = %s,
avg_sknt = %s, vector_avg_drct = %s,
min_feel = %s, avg_feel = %s, max_feel = %s
WHERE
iemid = %s and day = %s
""", (avg_rh, min_rh, max_rh, avg_sknt, drct,
min_feel, avg_feel, max_feel,
iemid, ts))
do_update()
if icursor.rowcount == 0:
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))
do_update()
icursor.close()
iemaccess.commit()
iemaccess.close()
def process_metar(mstr, now):
""" Do the METAR Processing """
mtr = None
while mtr is None:
try:
mtr = Metar(mstr, now.month, now.year)
except MetarParserError as exp:
try:
msg = str(exp)
except Exception as exp:
return None
tokens = ERROR_RE.findall(str(exp))
orig_mstr = mstr
if tokens:
for token in tokens[0].split():
mstr = mstr.replace(" %s" % (token, ), "")
if orig_mstr == mstr:
print("Can't fix badly formatted metar: " + mstr)
return None
else:
print("MetarParserError: "+msg)
return None
except Exception as exp:
print("Double Fail: %s %s" % (mstr, exp))
return None
if mtr is None or mtr.time is None:
return None
ob = OB()
ob.metar = mstr[:254]
ob.valid = now
if mtr.temp:
ob.tmpf = mtr.temp.value("F")
if mtr.dewpt:
ob.dwpf = mtr.dewpt.value("F")
if mtr.wind_speed:
ob.sknt = mtr.wind_speed.value("KT")
if mtr.wind_gust:
ob.gust = mtr.wind_gust.value("KT")
# Calc some stuff
if ob.tmpf is not None and ob.dwpf is not None:
ob.relh = relative_humidity_from_dewpoint(
ob.tmpf * units('degF'),
ob.dwpf * units('degF')
).to(units('percent')).magnitude
if ob.sknt is not None:
ob.feel = apparent_temperature(
ob.tmpf * units('degF'),
ob.relh * units('percent'),
ob.sknt * units('knots')
).to(units('degF')).magnitude
if mtr.wind_dir and mtr.wind_dir.value() != "VRB":
ob.drct = mtr.wind_dir.value()
if mtr.vis:
ob.vsby = mtr.vis.value("SM")
# see pull request #38
if mtr.press and mtr.press != mtr.press_sea_level:
ob.alti = mtr.press.value("IN")
if mtr.press_sea_level:
ob.mslp = mtr.press_sea_level.value("MB")
if mtr.precip_1hr:
ob.p01i = mtr.precip_1hr.value("IN")
# Do something with sky coverage
for i in range(len(mtr.sky)):
(c, h, _) = mtr.sky[i]
setattr(ob, 'skyc%s' % (i+1), c)
if h is not None:
setattr(ob, 'skyl%s' % (i+1), h.value("FT"))
if mtr.max_temp_6hr:
ob.max_tmpf_6hr = mtr.max_temp_6hr.value("F")
if mtr.min_temp_6hr:
ob.min_tmpf_6hr = mtr.min_temp_6hr.value("F")
if mtr.max_temp_24hr:
ob.max_tmpf_24hr = mtr.max_temp_24hr.value("F")
if mtr.min_temp_24hr:
ob.min_tmpf_6hr = mtr.min_temp_24hr.value("F")
if mtr.precip_3hr:
ob.p03i = mtr.precip_3hr.value("IN")
if mtr.precip_6hr:
ob.p06i = mtr.precip_6hr.value("IN")
if mtr.precip_24hr:
ob.p24i = mtr.precip_24hr.value("IN")
# Presentwx
if mtr.weather:
pwx = []
for wx in mtr.weather:
val = "".join([a for a in wx if a is not None])
if val == "" or val == len(val) * "/":
continue
pwx.append(val)
ob.wxcodes = pwx
return ob
T2M = T2M_DATA.variables['air'][TIME_INDEX, :, :] * units('kelvin')
U2M = U2M_DATA.variables['uwnd'][TIME_INDEX, :, :] * units('m/s')
V2M = V2M_DATA.variables['vwnd'][TIME_INDEX, :, :] * units('m/s')
# =============================================================================
# FIG #8: SFC: HEAT INDEX OR WINDCHILL
# =============================================================================
T2M = T2M_DATA.variables['air'][TIME_INDEX, :, :] * units('kelvin')
SH2M = SH2M_DATA.variables['shum'][TIME_INDEX, :, :]
U2M = U2M_DATA.variables['uwnd'][TIME_INDEX, :, :] * units('m/s')
V2M = V2M_DATA.variables['vwnd'][TIME_INDEX, :, :] * units('m/s')
PRES = PRES_DATA.variables['pres'][TIME_INDEX, :, :] * units('Pa')
RHUM = mpcalc.relative_humidity_from_specific_humidity(SH2M, T2M, PRES)
T2M = T2M.to('degF')
SFC_SPEED = mpcalc.get_wind_speed(U2M, V2M)
SFC_SPEED = SFC_SPEED.to('mph')
APPARENT_TEMP = mpcalc.apparent_temperature(T2M, RHUM, SFC_SPEED)
# =============================================================================
# =============================================================================
# =============================================================================
# Make a grid of lat/lon values to use for plotting with Basemap.
lons, lats = np.meshgrid(np.squeeze(LON), np.squeeze(LAT))
slons, slats = np.meshgrid(np.squeeze(SFLON), np.squeeze(SFLAT))
fig, axarr = plt.subplots(nrows=2,
ncols=4,
figsize=(35, 25),
subplot_kw={'projection': crs})
axlist = axarr.flatten()
fig.tight_layout()
for ax in axlist:
plot_background(ax)
# =============================================================================
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx["v"]
if ctx["t"] == "state":
bnds = reference.state_bounds[ctx["state"]]
title = reference.state_names[ctx["state"]]
else:
bnds = reference.wfo_bounds[ctx["wfo"]]
title = "NWS CWA %s [%s]" % (
ctx["_nt"].sts[ctx["wfo"]]["name"],
ctx["wfo"],
)
df, valid = get_df(ctx, bnds)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(
sector=("state" if ctx["t"] == "state" else "cwa"),
state=ctx["state"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="white",
title="%s for %s" % (PDICT2[ctx["v"]], title),
subtitle=("Map valid: %s UTC") % (valid.strftime("%d %b %Y %H:%M"),),
nocaption=True,
titlefontsize=16,
)
if varname == "vsby":
ramp = np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9])
valunit = "miles"
elif varname == "feel":
valunit = "F"
df["feel"] = (
apparent_temperature(
df["tmpf"].values * units("degF"),
df["relh"].values * units("percent"),
df["sknt"].values * units("knots"),
)
.to(units("degF"))
.m
)
# Data QC, cough
if ctx.get("above"):
df = df[df[varname] < ctx["above"]]
if ctx.get("below"):
df = df[df[varname] > ctx["below"]]
# with QC done, we compute ramps
if varname != "vsby":
ramp = np.linspace(
df[varname].min() - 5, df[varname].max() + 5, 10, dtype="i"
)
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
ramp,
units=valunit,
cmap=get_cmap(ctx["cmap"]),
)
if ctx["t"] == "state":
df2 = df[df["state"] == ctx["state"]]
else:
df2 = df[df["wfo"] == ctx["wfo"]]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2[varname].values,
"%.1f",
labelbuffer=10,
)
mp.drawcounties()
if ctx["t"] == "cwa":
mp.draw_cwas()
return mp.fig, df
def process_metar(mstr, now):
""" Do the METAR Processing """
mtr = None
while mtr is None:
try:
mtr = Metar(mstr, now.month, now.year)
except MetarParserError as exp:
try:
msg = str(exp)
except Exception as exp:
return None
tokens = ERROR_RE.findall(str(exp))
orig_mstr = mstr
if tokens:
for token in tokens[0].split():
mstr = mstr.replace(" %s" % (token, ), "")
if orig_mstr == mstr:
print("Can't fix badly formatted metar: " + mstr)
return None
else:
print("MetarParserError: " + msg)
return None
except Exception as exp:
print("Double Fail: %s %s" % (mstr, exp))
return None
if mtr is None or mtr.time is None:
return None
ob = OB()
ob.metar = mstr[:254]
ob.valid = now
if mtr.temp:
ob.tmpf = mtr.temp.value("F")
if mtr.dewpt:
ob.dwpf = mtr.dewpt.value("F")
if mtr.wind_speed:
ob.sknt = mtr.wind_speed.value("KT")
if mtr.wind_gust:
ob.gust = mtr.wind_gust.value("KT")
# Calc some stuff
if ob.tmpf is not None and ob.dwpf is not None:
ob.relh = relative_humidity_from_dewpoint(
ob.tmpf * units('degF'),
ob.dwpf * units('degF')).to(units('percent')).magnitude
if ob.sknt is not None:
ob.feel = apparent_temperature(ob.tmpf * units('degF'),
ob.relh * units('percent'),
ob.sknt * units('knots')).to(
units('degF')).magnitude
if mtr.wind_dir and mtr.wind_dir.value() != "VRB":
ob.drct = mtr.wind_dir.value()
if mtr.vis:
ob.vsby = mtr.vis.value("SM")
# see pull request #38
if mtr.press and mtr.press != mtr.press_sea_level:
ob.alti = mtr.press.value("IN")
if mtr.press_sea_level:
ob.mslp = mtr.press_sea_level.value("MB")
if mtr.precip_1hr:
ob.p01i = mtr.precip_1hr.value("IN")
# Do something with sky coverage
for i in range(len(mtr.sky)):
(c, h, _) = mtr.sky[i]
setattr(ob, 'skyc%s' % (i + 1), c)
if h is not None:
setattr(ob, 'skyl%s' % (i + 1), h.value("FT"))
if mtr.max_temp_6hr:
ob.max_tmpf_6hr = mtr.max_temp_6hr.value("F")
if mtr.min_temp_6hr:
ob.min_tmpf_6hr = mtr.min_temp_6hr.value("F")
if mtr.max_temp_24hr:
ob.max_tmpf_24hr = mtr.max_temp_24hr.value("F")
if mtr.min_temp_24hr:
ob.min_tmpf_6hr = mtr.min_temp_24hr.value("F")
if mtr.precip_3hr:
ob.p03i = mtr.precip_3hr.value("IN")
if mtr.precip_6hr:
ob.p06i = mtr.precip_6hr.value("IN")
if mtr.precip_24hr:
ob.p24i = mtr.precip_24hr.value("IN")
# Presentwx
if mtr.weather:
pwx = []
for wx in mtr.weather:
val = "".join([a for a in wx if a is not None])
if val == "" or val == len(val) * "/":
continue
pwx.append(val)
ob.wxcodes = pwx
return ob
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx['v']
if ctx['t'] == 'state':
bnds = reference.state_bounds[ctx['state']]
title = reference.state_names[ctx['state']]
else:
bnds = reference.wfo_bounds[ctx['wfo']]
title = "NWS CWA %s [%s]" % (ctx['_nt'].sts[ctx['wfo']]['name'],
ctx['wfo'])
df, valid = get_df(ctx, bnds)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(sector=('state' if ctx['t'] == 'state' else 'cwa'),
state=ctx['state'],
cwa=(ctx['wfo'] if len(ctx['wfo']) == 3 else ctx['wfo'][1:]),
axisbg='white',
title='%s for %s' % (PDICT2[ctx['v']], title),
subtitle=('Map valid: %s UTC') %
(valid.strftime("%d %b %Y %H:%M"), ),
nocaption=True,
titlefontsize=16)
if varname == 'vsby':
ramp = np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9])
valunit = 'miles'
elif varname == 'feel':
valunit = 'F'
df['feel'] = apparent_temperature(df['tmpf'].values * units('degF'),
df['relh'].values * units('percent'),
df['sknt'].values *
units('knots')).to(units('degF')).m
# Data QC, cough
if ctx.get('above'):
df = df[df[varname] < ctx['above']]
if ctx.get('below'):
df = df[df[varname] > ctx['below']]
# with QC done, we compute ramps
if varname != 'vsby':
ramp = np.linspace(df[varname].min() - 5,
df[varname].max() + 5,
10,
dtype='i')
mp.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
ramp,
units=valunit,
cmap=plt.get_cmap(ctx['cmap']))
if ctx['t'] == 'state':
df2 = df[df['state'] == ctx['state']]
else:
df2 = df[df['wfo'] == ctx['wfo']]
mp.plot_values(df2['lon'].values,
df2['lat'].values,
df2[varname].values,
'%.1f',
labelbuffer=10)
mp.drawcounties()
if ctx['t'] == 'cwa':
mp.draw_cwas()
return mp.fig, df