def test_vectorized(self):
"""See that heatindex and windchill can do lists"""
temp = datatypes.temperature([0, 10], 'F')
sknt = datatypes.speed([30, 40], 'MPH')
val = meteorology.windchill(temp, sknt).value('F')
self.assertAlmostEquals(val[0], -24.50, 2)
t = datatypes.temperature([80.0, 90.0], 'F')
td = datatypes.temperature([70.0, 60.0], 'F')
hdx = meteorology.heatindex(t, td)
self.assertAlmostEqual(hdx.value("F")[0], 83.93, 2)
def test_vectorized():
"""See that heatindex and windchill can do lists"""
temp = datatypes.temperature([0, 10], "F")
sknt = datatypes.speed([30, 40], "MPH")
val = meteorology.windchill(temp, sknt).value("F")
assert abs(val[0] - -24.50) < 0.01
t = datatypes.temperature([80.0, 90.0], "F")
td = datatypes.temperature([70.0, 60.0], "F")
hdx = meteorology.heatindex(t, td)
assert abs(hdx.value("F")[0] - 83.93) < 0.01
tmpf = np.array([80.0, 90.0]) * units("degF")
dwpf = np.array([70.0, 60.0]) * units("degF")
smps = np.array([10.0, 20.0]) * units("meter per second")
feels = meteorology.mcalc_feelslike(tmpf, dwpf, smps)
assert abs(feels.to(units("degF")).magnitude[0] - 83.15) < 0.01
tmpf = masked_array([80.0, np.nan], units("degF"), mask=[False, True])
feels = meteorology.mcalc_feelslike(tmpf, dwpf, smps)
assert abs(feels.to(units("degF")).magnitude[0] - 83.15) < 0.01
assert feels.mask[1]
def test_vectorized():
"""See that heatindex and windchill can do lists"""
temp = datatypes.temperature([0, 10], 'F')
sknt = datatypes.speed([30, 40], 'MPH')
val = meteorology.windchill(temp, sknt).value('F')
assert abs(val[0] - -24.50) < 0.01
t = datatypes.temperature([80.0, 90.0], 'F')
td = datatypes.temperature([70.0, 60.0], 'F')
hdx = meteorology.heatindex(t, td)
assert abs(hdx.value("F")[0] - 83.93) < 0.01
tmpf = np.array([80., 90.]) * units('degF')
dwpf = np.array([70., 60.]) * units('degF')
smps = np.array([10., 20.]) * units('meter per second')
feels = meteorology.mcalc_feelslike(tmpf, dwpf, smps)
assert abs(feels.to(units("degF")).magnitude[0] - 83.15) < 0.01
tmpf = masked_array([80., np.nan], units('degF'), mask=[False, True])
feels = meteorology.mcalc_feelslike(tmpf, dwpf, smps)
assert abs(feels.to(units("degF")).magnitude[0] - 83.15) < 0.01
assert feels.mask[1]
if tmpf != "M" and dwpf != "M":
t = temperature(nc.variables['temperature'][idx], 'K')
d = temperature(nc.variables['dewpoint'][idx], 'K')
relh = meteorology.relh(t, d).value("%")
heat = "%5.1f" % (meteorology.heatindex(t, d).value("F"),)
drct = s2( nc.variables['windDir'][idx])
smps = s2( nc.variables['windSpeed'][idx])
sped = "M"
if smps != "M":
sped = "%5.1f" % (nc.variables['windSpeed'][idx] * 2.23694,)
wcht = "M"
if tmpf != "M" and sped != "M":
t = temperature(nc.variables['temperature'][idx], 'K')
sped = speed( nc.variables['windSpeed'][idx], 'MPS')
wcht = "%5.1f" % (meteorology.windchill(t, sped).value("F"),)
ts = indices[sid]['ts']
out.write("%5.5s %25.25s %8.4f %10.4f %02i %02i %5s %5s %5s %5s %5s %5s\n" % (sid, name, latitude,
longitude, ts.hour,
ts.minute, tmpf, dwpf,
drct, sped, heat, wcht))
nc.close()
out.close()
pqstr = "data c 000000000000 wxc/wxc_%s.txt bogus txt" % (network.lower(),)
subprocess.call("/home/ldm/bin/pqinsert -p '%s' %s" % (
pqstr, wxcfn), shell=True)
os.remove(wxcfn)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
highlightyear = ctx['year']
ytd = ctx['ytd']
varname = ctx['var']
inc = ctx['inc']
nt = NetworkTable(network)
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
from alldata WHERE station = %s """ + tmpflimit + """
and dwpf <= tmpf and valid > '1973-01-01'
and report_type = 2 """ + doylimiter + """ GROUP by d
""",
ASOS,
params=(station, ),
index_col=None)
df['year'] = df['d'].apply(lambda x: int(x[:4]))
df2 = df
title2 = VDICT[varname]
compop = np.greater
inctitle = ''
if varname == 'heatindex':
df['heatindex'] = pymet.heatindex(temperature(df['tmpf'].values, 'F'),
temperature(df['dwpf'].values,
'F')).value('F')
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(maxval - 31, maxval)
elif varname == 'windchill':
compop = np.less
df['year'] = df['d'].apply(lambda x: (int(x[:4]) - 1)
if int(x[4:6]) < 7 else int(x[:4]))
df['windchill'] = pymet.windchill(temperature(df['tmpf'].values, 'F'),
speed(df['sknt'].values,
'KT')).value('F')
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)
minyear = max([1973, nt.sts[station]['archive_begin'].year])
maxyear = datetime.date.today().year
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("%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, nt.sts[station]['name'], minyear,
datetime.date.today().year, title2, title, inctitle))
ax.legend(loc='best', scatterpoints=1)
return fig, rdf
def test_windchill():
"""Wind Chill Conversion"""
temp = datatypes.temperature(0, "F")
sknt = datatypes.speed(30, "MPH")
val = meteorology.windchill(temp, sknt).value("F")
assert abs(val - -24.50) < 0.01
heat = "M"
if tmpf != "M" and dwpf != "M":
t = temperature(nc.variables['temperature'][idx], 'K')
d = temperature(nc.variables['dewpoint'][idx], 'K')
relh = meteorology.relh(t, d).value("%")
heat = "%5.1f" % (meteorology.heatindex(t, d).value("F"), )
drct = s2(nc.variables['windDir'][idx])
smps = s2(nc.variables['windSpeed'][idx])
sped = "M"
if smps != "M":
sped = "%5.1f" % (nc.variables['windSpeed'][idx] * 2.23694, )
wcht = "M"
if tmpf != "M" and sped != "M":
t = temperature(nc.variables['temperature'][idx], 'K')
sped = speed(nc.variables['windSpeed'][idx], 'MPS')
wcht = "%5.1f" % (meteorology.windchill(t, sped).value("F"), )
ts = indices[sid]['ts']
out.write(
"%5.5s %25.25s %8.4f %10.4f %02i %02i %5s %5s %5s %5s %5s %5s\n" %
(sid, name, latitude, longitude, ts.hour, ts.minute, tmpf, dwpf, drct,
sped, heat, wcht))
nc.close()
out.close()
pqstr = "data c 000000000000 wxc/wxc_%s.txt bogus txt" % (network.lower(), )
subprocess.call("/home/ldm/bin/pqinsert -p '%s' %s" % (pqstr, wxcfn),
shell=True)
os.remove(wxcfn)
def main(argv):
"""Go Main Go"""
network = argv[1]
wxcfn = argv[2]
utc = datetime.datetime.utcnow()
utc = utc.replace(tzinfo=pytz.UTC)
out = open(wxcfn, 'w')
out.write("""Weather Central 001d0300 Surface Data TimeStamp=%s
12
5 Station
25 Station Name
8 Lat
10 Lon
2 Hour
2 Minute
5 Air Temperature F
5 Dew Point F
5 Wind Direction deg
5 Wind Speed mph
5 Heat Index F
5 Wind Chill F
""" % (utc.strftime("%Y.%m.%d.%H%M"), ))
fn = None
for i in range(4):
now = utc - datetime.timedelta(hours=i)
testfn = now.strftime("/mesonet/data/madis/mesonet1/%Y%m%d_%H00.nc")
if os.path.isfile(testfn):
fn = testfn
break
if fn is None:
sys.exit()
indices = {}
BOGUS = datetime.datetime(2000, 1, 1)
BOGUS = BOGUS.replace(tzinfo=pytz.UTC)
nc = ncopen(fn)
providers = chartostring(nc.variables["dataProvider"][:])
stations = chartostring(nc.variables["stationId"][:])
names = chartostring(nc.variables["stationName"][:])
for i, provider in enumerate(providers):
if provider != network:
continue
sid = stations[i]
# We have an ob!
ticks = int(nc.variables["observationTime"][i])
ts = datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=ticks)
ts = ts.replace(tzinfo=pytz.UTC)
if ts > indices.get(sid, {'ts': BOGUS})['ts']:
indices[sid] = {'ts': ts, 'idx': i}
for sid in indices:
idx = indices[sid]['idx']
name = names[idx]
latitude = nc.variables['latitude'][idx]
longitude = nc.variables['longitude'][idx]
tmpf = s(nc.variables['temperature'][idx])
dwpf = s(nc.variables['dewpoint'][idx])
qcd = nc.variables['temperatureQCD'][idx][0]
if qcd < -10 or qcd > 10:
tmpf = "M"
dwpf = "M"
heat = "M"
if tmpf != "M" and dwpf != "M":
t = temperature(nc.variables['temperature'][idx], 'K')
d = temperature(nc.variables['dewpoint'][idx], 'K')
# relh = meteorology.relh(t, d).value("%")
heat = "%5.1f" % (meteorology.heatindex(t, d).value("F"), )
drct = s2(nc.variables['windDir'][idx])
smps = s2(nc.variables['windSpeed'][idx])
sped = "M"
if smps != "M":
sped = "%5.1f" % (nc.variables['windSpeed'][idx] * 2.23694, )
wcht = "M"
if tmpf != "M" and sped != "M":
t = temperature(nc.variables['temperature'][idx], 'K')
sped = speed(nc.variables['windSpeed'][idx], 'MPS')
wcht = "%5.1f" % (meteorology.windchill(t, sped).value("F"), )
ts = indices[sid]['ts']
out.write(("%5.5s %25.25s %8.4f %10.4f "
"%02i %02i %5s %5s %5s %5s %5s %5s\n") %
(sid, name, latitude, longitude, ts.hour, ts.minute, tmpf,
dwpf, drct, sped, heat, wcht))
nc.close()
out.close()
pqstr = "data c 000000000000 wxc/wxc_%s.txt bogus txt" % (
network.lower(), )
subprocess.call("/home/ldm/bin/pqinsert -p '%s' %s" % (pqstr, wxcfn),
shell=True)
os.remove(wxcfn)
def test_windchill(self):
"""Wind Chill Conversion"""
temp = datatypes.temperature(0, 'F')
sknt = datatypes.speed(30, 'MPH')
val = meteorology.windchill(temp, sknt).value('F')
self.assertAlmostEquals(val, -24.50, 2)
def test_windchill():
"""Wind Chill Conversion"""
temp = datatypes.temperature(0, 'F')
sknt = datatypes.speed(30, 'MPH')
val = meteorology.windchill(temp, sknt).value('F')
assert abs(val - -24.50) < 0.01
def test_windchill(self):
"""Wind Chill Conversion"""
temp = datatypes.temperature(0, 'F')
sknt = datatypes.speed(30, 'MPH')
val = meteorology.windchill(temp, sknt).value('F')
self.assertAlmostEquals(val, -24.50, 2)
