def test_heatindex(self):
''' Test our heat index calculations '''
t = datatypes.temperature(80.0, 'F')
td = datatypes.temperature(70.0, 'F')
hdx = meteorology.heatindex(t, td)
self.assertAlmostEqual( hdx.value("F"), 83.93, 2)
t = datatypes.temperature(30.0, 'F')
hdx = meteorology.heatindex(t, td)
self.assertAlmostEqual( hdx.value("F"), 30.00, 2)
def test_heatindex(self):
''' Test our heat index calculations '''
t = datatypes.temperature(80.0, 'F')
td = datatypes.temperature(70.0, 'F')
hdx = meteorology.heatindex(t, td)
self.assertAlmostEqual(hdx.value("F"), 83.93, 2)
t = datatypes.temperature(30.0, 'F')
hdx = meteorology.heatindex(t, td)
self.assertAlmostEqual(hdx.value("F"), 30.00, 2)
def test_heatindex():
""" Test our heat index calculations """
t = datatypes.temperature(80.0, "F")
td = datatypes.temperature(70.0, "F")
hdx = meteorology.heatindex(t, td)
assert abs(hdx.value("F") - 83.93) < 0.01
t = datatypes.temperature(30.0, "F")
hdx = meteorology.heatindex(t, td)
assert abs(hdx.value("F") - 30.00) < 0.01
def test_heatindex():
''' Test our heat index calculations '''
t = datatypes.temperature(80.0, 'F')
td = datatypes.temperature(70.0, 'F')
hdx = meteorology.heatindex(t, td)
assert abs(hdx.value("F") - 83.93) < 0.01
t = datatypes.temperature(30.0, 'F')
hdx = meteorology.heatindex(t, td)
assert abs(hdx.value("F") - 30.00) < 0.01
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 one():
pgconn = psycopg2.connect(database='asos',
host='localhost',
port=5555,
user='******')
cursor = pgconn.cursor()
nt = NetworkTable(["IA_ASOS", "AWOS"])
ids = nt.sts.keys()
ids.sort()
print """
<table class="table table-condensed table-striped">
<thead>
<tr><th>ID</th><th>Station Name</th><th>3 Sep Peak Heat Index</th>
<th>Last Highest</th><th>Date</th></tr>
</thead>
"""
bah = nt.sts.keys()
for sid in ids:
cursor.execute(
"""
select valid, tmpf, dwpf from alldata where
station = %s and extract(month from valid) = 9
and dwpf is not null and tmpf > 84 and valid > '1990-01-01'
ORDER by valid DESC
""", (sid, ))
thisdate = [0, None, None, None]
for _, row in enumerate(cursor):
hdx = heatindex(temperature(row[1], 'F'),
temperature(row[2], 'F')).value('F')
if row[0].strftime("%Y%m%d") == '20150903':
if hdx > thisdate[0]:
thisdate = [hdx, row[0], row[1], row[2]]
continue
if thisdate[1] is None:
break
if hdx >= thisdate[0]:
bah.remove(sid)
print(('%s,%s,%.0f,(%.0f/%.0f),%.0f,(%.0f/%.0f),%s') %
(sid, nt.sts[sid]['name'], thisdate[0], thisdate[2],
thisdate[3], hdx, row[1], row[2],
row[0].strftime("%d %b %Y %I:%M %p")))
break
print 'missed', bah
print "</table>"
def one():
pgconn = psycopg2.connect(database='asos', host='localhost', port=5555,
user='******')
cursor = pgconn.cursor()
nt = NetworkTable(["IA_ASOS", "AWOS"])
ids = nt.sts.keys()
ids.sort()
print """
<table class="table table-condensed table-striped">
<thead>
<tr><th>ID</th><th>Station Name</th><th>3 Sep Peak Heat Index</th>
<th>Last Highest</th><th>Date</th></tr>
</thead>
"""
bah = nt.sts.keys()
for sid in ids:
cursor.execute("""
select valid, tmpf, dwpf from alldata where
station = %s and extract(month from valid) = 9
and dwpf is not null and tmpf > 84 and valid > '1990-01-01'
ORDER by valid DESC
""", (sid,))
thisdate = [0, None, None, None]
for _, row in enumerate(cursor):
hdx = heatindex(temperature(row[1], 'F'),
temperature(row[2], 'F')).value('F')
if row[0].strftime("%Y%m%d") == '20150903':
if hdx > thisdate[0]:
thisdate = [hdx, row[0], row[1], row[2]]
continue
if thisdate[1] is None:
break
if hdx >= thisdate[0]:
bah.remove(sid)
print(('%s,%s,%.0f,(%.0f/%.0f),%.0f,(%.0f/%.0f),%s'
) % (sid, nt.sts[sid]['name'],
thisdate[0], thisdate[2], thisdate[3],
hdx, row[1], row[2],
row[0].strftime("%d %b %Y %I:%M %p")))
break
print 'missed', bah
print "</table>"
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]
cursor.execute("""SELECT tmpf, dwpf from alldata where station = 'DSM' and
tmpf >= 80 and dwpf > 0 and tmpf < 110""")
for row in cursor:
otmpf.append(row[0])
odwpf.append(row[1])
otmpf = dt.temperature(numpy.array(otmpf), 'F')
odwpf = dt.temperature(numpy.array(odwpf), 'F')
orelh = meteorology.relh(otmpf, odwpf)
tmpf = dt.temperature(numpy.arange(80, 110), 'F')
dwpf = dt.temperature(numpy.arange(40, 80), 'F')
(t, d) = numpy.meshgrid(tmpf.value("F"), dwpf.value("F"))
hindex = meteorology.heatindex(dt.temperature(t, 'F'), dt.temperature(d, 'F'))
counts = numpy.zeros(numpy.shape(hindex.value("F")), 'f')
for otmp, odwp in zip(otmpf.value("F"), odwpf.value("F")):
if odwp < 40 or odwp >= 79.5:
continue
counts[(int(round(odwp)) - 40), int(round(otmp)) - 80] += 1.0
ttot = numpy.sum(counts, 0)
print ttot
ratio = numpy.ma.array(counts / ttot * 100.0)
#ratio.mask = numpy.where(ratio == 0, True, False)
(fig, ax) = plt.subplots(1, 1)
cmap = cm.get_cmap('jet')
cmap.set_under("tan")
idx = indices[sid]['idx']
name = nc.variables["stationName"][idx].tostring().replace('\x00','')
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,
cursor.execute("""SELECT tmpf, dwpf from alldata where station = 'DSM' and
tmpf >= 80 and dwpf > 0 and tmpf < 110""")
for row in cursor:
otmpf.append( row[0] )
odwpf.append( row[1] )
otmpf = dt.temperature(numpy.array(otmpf), 'F')
odwpf = dt.temperature(numpy.array(odwpf), 'F')
orelh = meteorology.relh(otmpf, odwpf)
tmpf = dt.temperature(numpy.arange(80,110), 'F')
dwpf = dt.temperature(numpy.arange(40,80), 'F')
(t, d) = numpy.meshgrid(tmpf.value("F"), dwpf.value("F"))
hindex = meteorology.heatindex(dt.temperature(t,'F'), dt.temperature(d, 'F'))
counts = numpy.zeros( numpy.shape(hindex.value("F")), 'f')
for otmp, odwp in zip(otmpf.value("F"), odwpf.value("F")):
if odwp < 40 or odwp >= 79.5:
continue
counts[(int(round(odwp)) - 40), int(round(otmp)) - 80 ] += 1.0
ttot = numpy.sum(counts,0)
print ttot
ratio = numpy.ma.array(counts / ttot * 100.0)
#ratio.mask = numpy.where(ratio == 0, True, False)
(fig, ax) = plt.subplots(1,1)
cmap = cm.get_cmap('jet')
cmap.set_under("tan")
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 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)
idx = indices[sid]['idx']
name = nc.variables["stationName"][idx].tostring().replace('\x00', '')
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(
acursor.execute("""SELECT valid, tmpf, dwpf, sknt, skyc1, skyc2, skyc3 from
alldata where station = 'AMW' and valid BETWEEN '%s 00:00' and '%s 23:59'
and tmpf > -30 and dwpf > -30
""" % (day.strftime("%Y-%m-%d"),day.strftime("%Y-%m-%d")))
relh = []
tmpf = []
heat = 'N'
windchill = 'N'
clcount = 0
for row2 in acursor:
t = temperature(row2[1], 'F')
td = temperature(row2[2], 'F')
if met.heatindex(t, td).value('F') > 90:
heat = 'Y'
if t.value('F') < 50:
print t.value('F'), td.value('F'), met.heatindex(t, td).value('F')
if row2[1] < 32 and row2[3] > 10:
windchill = 'Y' #approx
sky = 'Clear'
if 'OVC' in [row2[4], row2[5], row2[6]]:
sky = 'Overcast'
elif 'BKN' in [row2[4], row2[5], row2[6]]:
sky = 'Broken'
elif 'FEW' in [row2[4], row2[5], row2[6]]:
sky = 'Fair Skies'
if row2[0].hour > 7 and row2[0].hour < 18:
relh.append( met.relh(t,td).value('%') )
ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = ASOS.cursor()
cursor.execute("""
SELECT valid, tmpf, dwpf from alldata where station = 'DSM' and
tmpf >= 80 and dwpf > -50 and valid > '1935-01-01'
and (extract(minute from valid) = 0 or extract(minute from valid) > 52)
""")
hits3 = np.zeros( (2014-1935), 'f')
hits5 = np.zeros( (2014-1935), 'f')
total = np.zeros( (2014-1935), 'f')
for row in cursor:
t = dt.temperature(row[1], 'F')
d = dt.temperature(row[2], 'F')
hdx = met.heatindex(t, d)
if (hdx.value("F") - 3) >= row[1]:
hits3[ row[0].year - 1935 ] += 1.0
if (hdx.value("F") - 5) >= row[1]:
hits5[ row[0].year - 1935 ] += 1.0
total[ row[0].year - 1935 ] += 1.0
(fig, ax) = plt.subplots(2,1, sharex=True)
val = hits3 / total * 100.0
avg = np.average(val)
bars = ax[0].bar(np.arange(1935,2014)-0.4, val , ec='b', fc='b')
ax[0].plot([1933,2013], [avg,avg], c='k')
for bar in bars:
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = ASOS.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'DSM')
network = fdict.get('network', 'IA_ASOS')
highlightyear = int(fdict.get('year', 2015))
nt = NetworkTable(network)
cursor.execute("""
SELECT to_char(valid, 'YYYYmmddHH24') as d, avg(tmpf), avg(dwpf)
from alldata WHERE station = %s and tmpf >= 80 and dwpf >= 30
and dwpf <= tmpf and valid > '1973-01-01' GROUP by d
""", (station, ))
rows = []
for row in cursor:
t = temperature(row[1], 'F')
d = temperature(row[2], 'F')
h = pymet.heatindex(t, d)
if h.value('F') >= t.value('F'):
rows.append(dict(year=int(row[0][:4]), heatindex=h.value('F')))
minyear = max([1973, nt.sts[station]['archive_begin'].year])
maxyear = datetime.date.today().year
years = float((maxyear - minyear) + 1)
df = pd.DataFrame(rows)
x = []
y = []
y2 = []
(fig, ax) = plt.subplots(1, 1)
yloc = 0.9
ax.text(0.7, 0.94, 'Avg:',
transform=ax.transAxes, color='b')
ax.text(0.85, 0.94, '%s:' % (highlightyear,),
transform=ax.transAxes, color='r')
for level in range(90, 121):
x.append(level)
y.append(len(df[df['heatindex'] >= level]) / years)
y2.append(len(df[np.logical_and(df['heatindex'] >= level,
df['year'] == highlightyear)]))
if level % 2 == 0:
ax.text(0.6, yloc, '%s' % (level,),
transform=ax.transAxes)
ax.text(0.7, yloc, '%.1f' % (y[-1],),
transform=ax.transAxes, color='b')
ax.text(0.85, yloc, '%.0f' % (y2[-1],),
transform=ax.transAxes, color='r')
yloc -= 0.04
x = np.array(x, dtype=np.float64)
ax.scatter(x, y, color='b', label='Avg')
ax.scatter(x, y2, color='r', label="%s" % (highlightyear,))
ax.grid(True)
ax.set_ylim(-0.5, int(max(y)) + 5)
ax.set_xlim(89.5, 120.5)
ax.set_yticks(range(0, int(max(y)), 24))
ax.set_ylabel("Hours Per Year")
ax.set_xlabel("Heat Index Temp $^\circ$F")
ax.set_title(("[%s] %s %s-%s\n"
"Heat Index (when accretive to air temp) Histogram"
) % (station, nt.sts[station]['name'],
minyear,
datetime.date.today().year))
ax.legend(loc=(0.2, 0.8), scatterpoints=1)
return fig, df
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']
nt = NetworkTable(network)
doylimiter = ""
if ytd == 'yes':
doylimiter = (" and extract(doy from valid) < "
" extract(doy from 'TODAY'::date) ")
df = read_sql("""
SELECT to_char(valid, 'YYYYmmddHH24') as d, avg(tmpf)::int as tmpf,
avg(dwpf)::int as dwpf
from alldata WHERE station = %s and tmpf >= 50
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]
if varname == 'heatindex':
title2 = "Heat Index (when accretive to air temp)"
df['heatindex'] = df[['tmpf', 'dwpf']].apply(
lambda x: pymet.heatindex(temperature(x[0], 'F'),
temperature(x[1],
'F')).value('F'), axis=1)
df2 = df[df['heatindex'] > df['tmpf']]
minyear = max([1973, nt.sts[station]['archive_begin'].year])
maxyear = datetime.date.today().year
years = float((maxyear - minyear) + 1)
x = []
y = []
y2 = []
(fig, ax) = plt.subplots(1, 1)
yloc = 0.9
ax.text(0.7, 0.94, 'Avg:',
transform=ax.transAxes, color='b')
ax.text(0.85, 0.94, '%s:' % (highlightyear,),
transform=ax.transAxes, color='r')
for level in LEVELS[varname]:
x.append(level)
y.append(len(df2[df2[varname] >= level]) / years)
y2.append(len(df[np.logical_and(df[varname] >= level,
df['year'] == highlightyear)]))
if level % 2 == 0:
ax.text(0.6, yloc, '%s' % (level,),
transform=ax.transAxes)
ax.text(0.7, yloc, '%.1f' % (y[-1],),
transform=ax.transAxes, color='b')
ax.text(0.85, yloc, '%.0f' % (y2[-1],),
transform=ax.transAxes, color='r')
yloc -= 0.04
for x0, y0, y02 in zip(x, y, y2):
c = 'r' if y02 > y0 else 'b'
ax.plot([x0, x0], [y0, y02], color=c)
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="%s" % (highlightyear,))
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)"
) % (station, nt.sts[station]['name'],
minyear,
datetime.date.today().year, title2, title))
ax.legend(loc=(0.2, 0.8), scatterpoints=1)
return fig, rdf
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = ASOS.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'DSM')
network = fdict.get('network', 'IA_ASOS')
highlightyear = int(fdict.get('year', 2015))
nt = NetworkTable(network)
cursor.execute(
"""
SELECT to_char(valid, 'YYYYmmddHH24') as d, avg(tmpf), avg(dwpf)
from alldata WHERE station = %s and tmpf >= 80 and dwpf >= 30
and dwpf <= tmpf and valid > '1973-01-01' GROUP by d
""", (station, ))
rows = []
for row in cursor:
t = temperature(row[1], 'F')
d = temperature(row[2], 'F')
h = pymet.heatindex(t, d)
if h.value('F') >= t.value('F'):
rows.append(dict(year=int(row[0][:4]), heatindex=h.value('F')))
minyear = max([1973, nt.sts[station]['archive_begin'].year])
maxyear = datetime.date.today().year
years = float((maxyear - minyear) + 1)
df = pd.DataFrame(rows)
x = []
y = []
y2 = []
(fig, ax) = plt.subplots(1, 1)
yloc = 0.9
ax.text(0.7, 0.94, 'Avg:', transform=ax.transAxes, color='b')
ax.text(0.85,
0.94,
'%s:' % (highlightyear, ),
transform=ax.transAxes,
color='r')
for level in range(90, 121):
x.append(level)
y.append(len(df[df['heatindex'] >= level]) / years)
y2.append(
len(df[np.logical_and(df['heatindex'] >= level,
df['year'] == highlightyear)]))
if level % 2 == 0:
ax.text(0.6, yloc, '%s' % (level, ), transform=ax.transAxes)
ax.text(0.7,
yloc,
'%.1f' % (y[-1], ),
transform=ax.transAxes,
color='b')
ax.text(0.85,
yloc,
'%.0f' % (y2[-1], ),
transform=ax.transAxes,
color='r')
yloc -= 0.04
x = np.array(x, dtype=np.float64)
ax.scatter(x, y, color='b', label='Avg')
ax.scatter(x, y2, color='r', label="%s" % (highlightyear, ))
ax.grid(True)
ax.set_ylim(-0.5, int(max(y)) + 5)
ax.set_xlim(89.5, 120.5)
ax.set_yticks(range(0, int(max(y)), 24))
ax.set_ylabel("Hours Per Year")
ax.set_xlabel("Heat Index Temp $^\circ$F")
ax.set_title(("[%s] %s %s-%s\n"
"Heat Index (when accretive to air temp) Histogram") %
(station, nt.sts[station]['name'], minyear,
datetime.date.today().year))
ax.legend(loc=(0.2, 0.8), scatterpoints=1)
return fig, df