def test_windchill_basic(array_type):
"""Test the basic wind chill calculation."""
temp = array_type([40, -10, -45, 20], 'degF')
speed = array_type([5, 55, 25, 15], 'mph')
wc = windchill(temp, speed)
values = array_type([36, -46, -84, 6], 'degF')
assert_array_almost_equal(wc, values, 0)
def test_windchill_basic():
"""Test the basic wind chill calculation."""
temp = np.array([40, -10, -45, 20]) * units.degF
speed = np.array([5, 55, 25, 15]) * units.mph
wc = windchill(temp, speed)
values = np.array([36, -46, -84, 6]) * units.degF
assert_array_almost_equal(wc, values, 0)
def test_windchill_undefined_flag():
"""Test whether masking values for windchill can be disabled."""
temp = units.Quantity(np.ma.array([49, 50, 49, 60, 80, 81]), units.degF)
speed = units.Quantity(([4, 4, 3, 1, 10, 39]), units.mph)
wc = windchill(temp, speed, mask_undefined=False)
mask = np.array([False] * 6)
assert_array_equal(wc.mask, mask)
def test_windchill_undefined_flag():
"""Test whether masking values for windchill can be disabled."""
temp = units.Quantity(np.ma.array([49, 50, 49, 60, 80, 81]), units.degF)
speed = units.Quantity(([4, 4, 3, 1, 10, 39]), units.mph)
wc = windchill(temp, speed, mask_undefined=False)
mask = np.array([False] * 6)
assert_array_equal(wc.mask, mask)
def test_windchill_basic():
"""Test the basic wind chill calculation."""
temp = np.array([40, -10, -45, 20]) * units.degF
speed = np.array([5, 55, 25, 15]) * units.mph
wc = windchill(temp, speed)
values = np.array([36, -46, -84, 6]) * units.degF
assert_array_almost_equal(wc, values, 0)
def test_windchill_face_level():
"""Test windchill using the face_level flag."""
temp = np.array([20, 0, -20, -40]) * units.degF
speed = np.array([15, 30, 45, 60]) * units.mph
wc = windchill(temp, speed, face_level_winds=True)
values = np.array([3, -30, -64, -98]) * units.degF
assert_array_almost_equal(wc, values, 0)
def test_windchill_invalid():
"""Test windchill for values that should be masked."""
temp = np.array([10, 51, 49, 60, 80, 81]) * units.degF
speed = np.array([4, 4, 3, 1, 10, 39]) * units.mph
wc = windchill(temp, speed)
mask = np.array([False, True, True, True, True, True])
assert_array_equal(wc.mask, mask)
def test_windchill_face_level():
"""Test windchill using the face_level flag."""
temp = np.array([20, 0, -20, -40]) * units.degF
speed = np.array([15, 30, 45, 60]) * units.mph
wc = windchill(temp, speed, face_level_winds=True)
values = np.array([3, -30, -64, -98]) * units.degF
assert_array_almost_equal(wc, values, 0)
def test_windchill_invalid():
"""Test windchill for values that should be masked."""
temp = np.array([10, 51, 49, 60, 80, 81]) * units.degF
speed = np.array([4, 4, 3, 1, 10, 39]) * units.mph
wc = windchill(temp, speed)
# We don't care about the masked values
truth = units.Quantity(np.ma.array([2.6230789, np.nan, np.nan, np.nan, np.nan, np.nan],
mask=[False, True, True, True, True, True]), units.degF)
assert_array_almost_equal(truth, wc)
def test_windchill_invalid():
"""Test windchill for values that should be masked."""
temp = np.array([10, 51, 49, 60, 80, 81]) * units.degF
speed = np.array([4, 4, 3, 1, 10, 39]) * units.mph
wc = windchill(temp, speed)
# We don't care about the masked values
truth = np.array([2.6230789, np.nan, np.nan, np.nan, np.nan, np.nan]) * units.degF
mask = np.array([False, True, True, True, True, True])
assert_array_almost_equal(truth, wc)
assert_array_equal(wc.mask, mask)
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
nt = NetworkTable(['AWOS', 'IA_ASOS'])
pgconn = get_dbconn('iem', user='******')
df = read_sql("""
SELECT id as station, tmpf, sknt from current_log c JOIN stations t
on (c.iemid = t.iemid) WHERE t.network in ('IA_ASOS', 'AWOS')
and valid >= %s and valid < %s + '24 hours'::interval
and tmpf is not null and sknt > 0
""",
pgconn,
params=(ts, ts),
index_col=None)
df['wcht'] = windchill(df['tmpf'].values * units.degF, df['sknt'].values *
units.knots).to(units.degF).magnitude
gdf = df.groupby('station').min()
routes = 'ac'
if not realtime:
routes = 'a'
lons = []
lats = []
vals = []
labels = []
for station, row in gdf.iterrows():
lons.append(nt.sts[station]['lon'])
lats.append(nt.sts[station]['lat'])
vals.append(row['wcht'])
labels.append(station)
pqstr = ("plot %s %s00 summary/iowa_min_windchill.png "
"summary/iowa_min_windchill.png png") % (routes,
ts.strftime("%Y%m%d%H"))
mp = MapPlot(title=(r"%s Minimum Wind Chill Temperature $^\circ$F") %
(ts.strftime("%-d %b %Y"), ),
subtitle="Calm conditions are excluded from analysis",
continentalcolor='white')
mp.plot_values(lons,
lats,
vals,
'%.1f',
labels=labels,
textsize=12,
labelbuffer=5,
labeltextsize=10)
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
def main(argv):
"""Go Main Go."""
school = argv[1]
req = requests.get(
('https://api.weather.gov/points/%s,%s/forecast/hourly'
) % (conf[school][2], conf[school][3]))
jdata = req.json()
rows = []
for data in jdata['properties']['periods']:
ts = datetime.datetime.strptime(data['endTime'][:13], '%Y-%m-%dT%H')
rows.append(
dict(valid=ts, tmpf=data['temperature'],
smph=int(data['windSpeed'].split()[0])))
df = pd.DataFrame(rows[:-24])
df['wcht'] = windchill(
df['tmpf'].values * units('degF'),
df['smph'].values * units('mph')
)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.plot(df['valid'], df['wcht'], lw=2, color='g', label='Wind Chill')
ax.plot(df['valid'], df['tmpf'], lw=2, color='r', label='Temperature')
ax.set_ylabel(r"Temperature $^\circ$F")
ax.set_title(
("NWS NDFD Grid Point Forecast for %s Airport"
) % (conf[school][4])
)
ax.grid(True)
ax.legend(loc=1, ncol=2)
ax.set_ylim(-60, 60)
ax.set_yticks(range(-60, 61, 10))
ax2 = ax.twinx()
ax2.scatter(df['valid'].values, df['smph'].values)
ax2.set_ylim(0, 24)
ax2.set_yticks(range(0, 25, 2))
ax2.set_ylabel("Wind Speed [mph], blue dots")
ax2.xaxis.set_major_locator(mdates.HourLocator(byhour=[0, 12]))
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%-I %p\n%-d %b'))
ax.axvspan(
datetime.datetime(2019, 1, 29, 17), datetime.datetime(2019, 1, 31, 12),
zorder=-1, color='tan')
ax.text(
datetime.datetime(2019, 1, 30, 14), -55, "%s CLOSED!" % (school, ),
bbox=dict(color=conf[school][0]), ha='center', va='center',
color=conf[school][1], fontsize=18
)
fig.savefig('%s.png' % (school, ))
def process():
"""Go Main Go"""
now = datetime.datetime(2019, 1, 29, 6)
ets = datetime.datetime(2019, 2, 4, 6)
interval = datetime.timedelta(hours=1)
minval = None
maxval = None
while now < ets:
fn = now.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/"
"model/rtma/%H/rtma.t%Hz.awp2p5f000.grib2"
))
if not os.path.isfile(fn):
print("missing %s" % (fn, ))
else:
grbs = None
grbs = pygrib.open(fn)
try:
t2 = grbs.select(shortName='2t')[0].values
u10 = grbs.select(shortName='10u')[0].values
v10 = grbs.select(shortName='10v')[0].values
except:
print("FAIL!")
now += interval
continue
smps = (u10 ** 2 + v10 ** 2) ** 0.5
wcht = windchill(
t2 * units('degK'), smps * units('meter / second'))
wcht = wcht.to(units('degF')).magnitude
t2 = (t2 * units('degK')).to(units('degF')).magnitude
if minval is None:
minval = wcht
maxval = (t2 * units('degK')).to(units('degF')).magnitude
lats, lons = grbs.select(shortName='2t')[0].latlons()
np.save('lons', lons)
np.save('lats', lats)
minval = np.where(wcht < minval, wcht, minval)
maxval = np.where(t2 > maxval, t2, maxval)
print("%s min: %.1f max: %.1f maxdel: %.1f" % (
now, np.min(minval), np.max(maxval), np.max(maxval - minval)))
grbs.close()
now += interval
np.save('maxval', maxval)
np.save('minval', minval)
def wind_chill(temperature, wind_speed):
r"""This function calls the metpy method that calculates
the wind chill. The arguments are pint Quantities that
carry their units. The metpy method returns a wind chill
with the units of degrees Fahrenheit.
Args:
temperature(pint.Quantity): atmospheric temperature.
wind_speed(pint.Quantity): horizontal wind speed.
Returns:
wchill(pint.Quantity): wind chill in units of degree Fahrenheit.
"""
wchill = calc.windchill(temperature, wind_speed)
return wchill
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
"""
rh = mcalc.relative_humidity_from_dewpoint(tmpf, dwpf)
hidx = mcalc.heat_index(tmpf, rh, mask_undefined=True)
wcht = mcalc.windchill(tmpf, smps, mask_undefined=True)
# Where heat index is masked, replace with temperature
hidx[hidx.mask] = tmpf[hidx.mask]
# where ever wcht is not masked, replace with wind chill
hidx[~ wcht.mask] = wcht[~ wcht.mask]
return hidx
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
"""
rh = mcalc.relative_humidity_from_dewpoint(tmpf, dwpf)
hidx = mcalc.heat_index(tmpf, rh, mask_undefined=True)
wcht = mcalc.windchill(tmpf, smps, mask_undefined=True)
# Where heat index is masked, replace with temperature
hidx[hidx.mask] = tmpf[hidx.mask]
# where ever wcht is not masked, replace with wind chill
hidx[~wcht.mask] = wcht[~wcht.mask]
return hidx
#Calculate dewpoint in F from relative humidity and temperature
dewpt = C2F(dewpoint(data['TAIR'], data['RELH']/100.))
data = rec_append_fields(data, ('dewpoint',), (dewpt,))
#Convert temperature and dewpoint to Farenheit
mod_units = mesonet_units.copy()
mod_units['TAIR'] = 'F'
mod_units['dewpoint'] = 'F'
data['TAIR'] = C2F(data['TAIR'])
#Convert wind speeds to MPH
data['WSPD'] *= sconsts.hour / sconsts.mile
data['WMAX'] *= sconsts.hour / sconsts.mile
mod_units['WSPD'] = 'MPH'
mod_units['WMAX'] = 'MPH'
#Convert rainfall to inches
data['RAIN'] *= sconsts.milli / sconsts.inch
mod_units['RAIN'] = 'in.'
#Calculate windchill
wchill = windchill(data['TAIR'], data['WSPD'], metric=False)
data = rec_append_fields(data, ('windchill',), (wchill,))
fig = plt.figure(figsize=(8,10))
layout = {0:['temperature', 'dewpoint', 'windchill']}
axs = meteogram(data, fig, num_panels=5, units=mod_units, time_range=times,
layout=layout)
axs[0].set_ylabel('Temperature (F)')
plt.show()
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
highlightyear = ctx["year"]
sdate = datetime.date(ctx["syear"], 1, 1)
edate = datetime.date(ctx["eyear"] + 1, 1, 1)
ytd = ctx["ytd"]
varname = ctx["var"]
inc = ctx["inc"]
doylimiter = get_doylimit(ytd, varname)
tmpflimit = "and tmpf >= 50" if varname != "windchill" else "and tmpf < 50"
if varname not in ["windchill", "heatindex"]:
tmpflimit = ""
df = read_sql(
"SELECT to_char(valid, 'YYYYmmddHH24') as d, avg(tmpf)::int as tmpf, "
"avg(dwpf)::int as dwpf, avg(coalesce(sknt, 0)) as sknt "
f"from alldata WHERE station = %s {tmpflimit} "
"and dwpf <= tmpf and valid > %s and valid < %s and report_type = 2 "
f"{doylimiter} GROUP by d",
pgconn,
params=(station, sdate, edate),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data Found.")
df["year"] = df["d"].apply(lambda x: int(x[:4]))
df2 = df
title2 = VDICT[varname]
compop = np.greater_equal
inctitle = ""
if varname == "heatindex":
df["heatindex"] = (heat_index(
df["tmpf"].values * units("degF"),
relative_humidity_from_dewpoint(
df["tmpf"].values * units("degF"),
df["dwpf"].values * units("degF"),
),
).to(units("degF")).m)
inctitle = " [All Obs Included]"
if inc == "no":
df2 = df[df["heatindex"] > df["tmpf"]]
inctitle = " [Only Additive]"
else:
df2 = df
maxval = int(df2["heatindex"].max() + 1)
LEVELS[varname] = np.arange(80, maxval)
elif varname == "windchill":
compop = np.less_equal
df["year"] = df["d"].apply(lambda x: (int(x[:4]) - 1)
if int(x[4:6]) < 7 else int(x[:4]))
df["windchill"] = (windchill(
df["tmpf"].values * units("degF"),
df["sknt"].values * units("knot"),
).to(units("degF")).m)
inctitle = " [All Obs Included]"
if inc == "no":
df2 = df[df["windchill"] < df["tmpf"]]
inctitle = " [Only Additive]"
else:
df2 = df
minval = int(df2["windchill"].min() - 1)
LEVELS[varname] = np.arange(minval, minval + 51)
else:
maxval = int(df2[varname].max() + 1)
LEVELS[varname] = np.arange(maxval - 31, maxval)
bs = ctx["_nt"].sts[station]["archive_begin"]
if bs is None:
raise NoDataFound("Unknown station metadata.")
minyear = df["year"].min()
maxyear = df["year"].max()
years = float((maxyear - minyear) + 1)
x = []
y = []
y2 = []
fig = plt.figure(figsize=(9, 6))
ax = fig.add_axes([0.1, 0.1, 0.6, 0.8])
yloc = 1.0
xloc = 1.13
yrlabel = ("%s" %
(highlightyear, ) if varname != "windchill" else "%s-%s" %
(highlightyear, highlightyear + 1))
ax.text(xloc + 0.08,
yloc + 0.04,
"Avg:",
transform=ax.transAxes,
color="b")
ax.text(xloc + 0.21,
yloc + 0.04,
yrlabel,
transform=ax.transAxes,
color="r")
df3 = df2[df2["year"] == highlightyear]
for level in LEVELS[varname]:
x.append(level)
y.append(len(df2[compop(df2[varname], level)]) / years)
y2.append(len(df3[compop(df3[varname], level)]))
if level % 2 == 0:
ax.text(xloc, yloc, "%s" % (level, ), transform=ax.transAxes)
ax.text(
xloc + 0.08,
yloc,
"%.1f" % (y[-1], ),
transform=ax.transAxes,
color="b",
)
ax.text(
xloc + 0.21,
yloc,
"%.0f" % (y2[-1], ),
transform=ax.transAxes,
color="r",
)
yloc -= 0.04
ax.text(xloc, yloc, "n=%s" % (len(df2.index), ), transform=ax.transAxes)
for x0, y0, y02 in zip(x, y, y2):
ax.plot([x0, x0], [y0, y02], color="k")
rdf = pd.DataFrame({"level": x, "avg": y, "d%s" % (highlightyear, ): y2})
x = np.array(x, dtype=np.float64)
ax.scatter(x, y, color="b", label="Avg")
ax.scatter(x, y2, color="r", label=yrlabel)
ax.grid(True)
ymax = int(max([max(y), max(y2)]))
ax.set_xlim(x[0] - 0.5, x[-1] + 0.5)
dy = 24 * (int(ymax / 240) + 1)
ax.set_yticks(range(0, ymax, dy))
ax.set_ylim(-0.5, ymax + 5)
ax2 = ax.twinx()
ax2.set_ylim(-0.5, ymax + 5)
ax2.set_yticks(range(0, ymax, dy))
ax2.set_yticklabels(["%.0f" % (s, ) for s in np.arange(0, ymax, dy) / 24])
ax2.set_ylabel("Expressed in 24 Hour Days")
ax.set_ylabel("Hours Per Year")
ax.set_xlabel(r"%s $^\circ$F" % (VDICT[varname], ))
title = "till %s" % (datetime.date.today().strftime("%-d %b"), )
title = "Entire Year" if ytd == "no" else title
ax.set_title(("[%s] %s %s-%s\n"
"%s Histogram (%s)%s") % (
station,
ctx["_nt"].sts[station]["name"],
minyear,
maxyear,
title2,
title,
inctitle,
))
ax.legend(loc="best", scatterpoints=1)
return fig, rdf
def test_windchill_kelvin():
"""Test wind chill when given Kelvin temperatures."""
wc = windchill(268.15 * units.kelvin, 35 * units('m/s'))
assert_almost_equal(wc, -18.9357 * units.degC, 0)
def test_windchill_scalar():
"""Test wind chill with scalars."""
wc = windchill(-5 * units.degC, 35 * units('m/s'))
assert_almost_equal(wc, -18.9357 * units.degC, 0)
def test_windchill_scalar():
"""Test wind chill with scalars."""
wc = windchill(-5 * units.degC, 35 * units('m/s'))
assert_almost_equal(wc, -18.9357 * units.degC, 0)
def test_windchill_kelvin():
"""Test wind chill when given Kelvin temperatures."""
wc = windchill(268.15 * units.kelvin, 35 * units('m/s'))
assert_almost_equal(wc, -18.9357 * units.degC, 0)
#Convert temperature and dewpoint to Farenheit
mod_units = mesonet_units.copy()
mod_units['TAIR'] = 'F'
mod_units['dewpoint'] = 'F'
data['TAIR'] = C2F(data['TAIR'])
#Convert wind speeds to MPH
data['WSPD'] *= sconsts.hour / sconsts.mile
data['WMAX'] *= sconsts.hour / sconsts.mile
mod_units['WSPD'] = 'MPH'
mod_units['WMAX'] = 'MPH'
#Convert rainfall to inches
data['RAIN'] *= sconsts.milli / sconsts.inch
mod_units['RAIN'] = 'in.'
#Calculate windchill
wchill = windchill(data['TAIR'], data['WSPD'], metric=False)
data = rec_append_fields(data, ('windchill', ), (wchill, ))
fig = plt.figure(figsize=(8, 10))
layout = {0: ['temperature', 'dewpoint', 'windchill']}
axs = meteogram(data,
fig,
num_panels=5,
units=mod_units,
time_range=times,
layout=layout)
axs[0].set_ylabel('Temperature (F)')
plt.show()
