def test_drct(self):
"""Conversion of u and v to direction"""
self.assertEquals(
meteorology.drct(datatypes.speed(np.array([10, 20]), 'KT'),
datatypes.speed(np.array([10, 20]),
'KT')).value("DEG")[0], 225)
self.assertEquals(
meteorology.drct(datatypes.speed(-10, 'KT'),
datatypes.speed(10, 'KT')).value("DEG"), 135)
self.assertEquals(
meteorology.drct(datatypes.speed(-10, 'KT'),
datatypes.speed(-10, 'KT')).value("DEG"), 45)
self.assertEquals(
meteorology.drct(datatypes.speed(10, 'KT'),
datatypes.speed(-10, 'KT')).value("DEG"), 315)
def test_drct():
"""Conversion of u and v to direction"""
r = meteorology.drct(datatypes.speed(np.array([10, 20]), 'KT'),
datatypes.speed(np.array([10, 20]),
'KT')).value("DEG")
assert r[0] == 225
r = meteorology.drct(datatypes.speed(-10, 'KT'),
datatypes.speed(10, 'KT')).value("DEG")
assert r == 135
r = meteorology.drct(datatypes.speed(-10, 'KT'),
datatypes.speed(-10, 'KT')).value("DEG")
assert r == 45
r = meteorology.drct(datatypes.speed(10, 'KT'),
datatypes.speed(-10, 'KT')).value("DEG")
assert r == 315
def test_drct(self):
"""Conversion of u and v to direction"""
self.assertEquals(
meteorology.drct(datatypes.speed(np.array([10, 20]), 'KT'),
datatypes.speed(np.array([10, 20]), 'KT')
).value("DEG")[0], 225)
self.assertEquals(meteorology.drct(datatypes.speed(-10, 'KT'),
datatypes.speed(10, 'KT')
).value("DEG"), 135)
self.assertEquals(meteorology.drct(datatypes.speed(-10, 'KT'),
datatypes.speed(-10, 'KT')
).value("DEG"), 45)
self.assertEquals(meteorology.drct(datatypes.speed(10, 'KT'),
datatypes.speed(-10, 'KT')
).value("DEG"), 315)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
units = ctx['units']
nt = NetworkTable(network)
df = read_sql("""
select date_trunc('hour', valid) as ts, avg(sknt) as sknt,
max(drct) as drct from alldata
WHERE station = %s and sknt is not null and drct is not null
GROUP by ts
""", pgconn, params=(station, ), parse_dates=('ts',),
index_col=None)
sknt = speed(df['sknt'].values, 'KT')
drct = direction(df['drct'].values, 'DEG')
df['u'], df['v'] = [x.value('MPS') for x in meteorology.uv(sknt, drct)]
df['month'] = df['ts'].dt.month
grp = df[['month', 'u', 'v', 'sknt']].groupby('month').mean()
grp['u_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['v_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['sped_%s' % (units,)] = speed(grp['sknt'].values,
'KT').value(units.upper())
drct = meteorology.drct(speed(grp['u'].values, 'KT'),
speed(grp['v'].values, 'KT'))
grp['drct'] = drct.value('DEG')
maxval = grp['sped_%s' % (units,)].max()
(fig, ax) = plt.subplots(1, 1)
ax.barh(grp.index.values, grp['sped_%s' % (units,)].values,
align='center')
ax.set_xlabel("Average Wind Speed [%s]" % (UNITS[units],))
ax.set_yticks(grp.index.values)
ax.set_yticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlim(0, maxval * 1.2)
for mon, row in grp.iterrows():
ax.text(maxval * 1.1, mon, drct2text(row['drct']), ha='center',
va='center', bbox=dict(color='white'))
ax.text(row['sped_%s' % (units,)] * 0.98, mon,
"%.1f" % (row['sped_%s' % (units,)],), ha='right',
va='center', bbox=dict(color='white',
boxstyle='square,pad=0.03',))
ax.set_ylim(12.5, 0.5)
ax.set_title(("[%s] %s [%s-%s]\nMonthly Average Wind Speed and"
" Vector Average Direction"
) % (station, nt.sts[station]['name'],
df['ts'].min().year,
df['ts'].max().year))
return fig, grp
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
units = ctx['units']
nt = NetworkTable(network)
df = read_sql("""
select date_trunc('hour', valid at time zone 'UTC') as ts,
avg(sknt) as sknt, max(drct) as drct from alldata
WHERE station = %s and sknt is not null and drct is not null
GROUP by ts
""", pgconn, params=(station, ), index_col=None)
sknt = speed(df['sknt'].values, 'KT')
drct = direction(df['drct'].values, 'DEG')
df['u'], df['v'] = [x.value('MPS') for x in meteorology.uv(sknt, drct)]
df['month'] = df['ts'].dt.month
grp = df[['month', 'u', 'v', 'sknt']].groupby('month').mean()
grp['u_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['v_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['sped_%s' % (units,)] = speed(grp['sknt'].values,
'KT').value(units.upper())
drct = meteorology.drct(speed(grp['u'].values, 'KT'),
speed(grp['v'].values, 'KT'))
grp['drct'] = drct.value('DEG')
maxval = grp['sped_%s' % (units,)].max()
(fig, ax) = plt.subplots(1, 1)
ax.barh(grp.index.values, grp['sped_%s' % (units,)].values,
align='center')
ax.set_xlabel("Average Wind Speed [%s]" % (UNITS[units],))
ax.set_yticks(grp.index.values)
ax.set_yticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlim(0, maxval * 1.2)
for mon, row in grp.iterrows():
ax.text(maxval * 1.1, mon, drct2text(row['drct']), ha='center',
va='center', bbox=dict(color='white'))
ax.text(row['sped_%s' % (units,)] * 0.98, mon,
"%.1f" % (row['sped_%s' % (units,)],), ha='right',
va='center', bbox=dict(color='white',
boxstyle='square,pad=0.03',))
ax.set_ylim(12.5, 0.5)
ax.set_title(("[%s] %s [%s-%s]\nMonthly Average Wind Speed and"
" Vector Average Direction"
) % (station, nt.sts[station]['name'],
df['ts'].min().year,
df['ts'].max().year))
return fig, grp
def test_drct():
"""Conversion of u and v to direction"""
r = meteorology.drct(
datatypes.speed(np.array([10, 20]), 'KT'),
datatypes.speed(np.array([10, 20]), 'KT')
).value("DEG")
assert r[0] == 225
r = meteorology.drct(
datatypes.speed(-10, 'KT'),
datatypes.speed(10, 'KT')
).value("DEG")
assert r == 135
r = meteorology.drct(
datatypes.speed(-10, 'KT'),
datatypes.speed(-10, 'KT')
).value("DEG")
assert r == 45
r = meteorology.drct(
datatypes.speed(10, 'KT'),
datatypes.speed(-10, 'KT')
).value("DEG")
assert r == 315
def do(ts):
"""Process this date timestamp"""
asos = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = asos.cursor()
iemaccess = psycopg2.connect(database='iem', host='iemdb')
icursor = iemaccess.cursor()
cursor.execute("""
select station, network, iemid, drct, sknt, valid at time zone tzname,
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
ORDER by valid ASC
""", (ts - datetime.timedelta(days=2), ts + datetime.timedelta(days=2)))
wdata = dict()
rhdata = dict()
for row in cursor:
if row[5].strftime("%m%d") != ts.strftime("%m%d"):
continue
station = "%s|%s|%s" % (row[0], row[1], row[2])
if row[6] is not None and row[7] is not None:
tmpf = temperature(row[6], 'F')
dwpf = temperature(row[7], 'F')
rh = meteorology.relh(tmpf, dwpf)
if station not in rhdata:
rhdata[station] = dict(valid=[], rh=[])
rhdata[station]['valid'].append(row[5])
rhdata[station]['rh'].append(rh.value('%'))
if row[4] is not None and row[3] is not None:
sknt = speed(row[4], 'KT')
drct = direction(row[3], 'DEG')
(u, v) = meteorology.uv(sknt, drct)
if station not in wdata:
wdata[station] = {'valid': [], 'sknt': [], 'u': [], 'v': []}
wdata[station]['valid'].append(row[5])
wdata[station]['sknt'].append(row[4])
wdata[station]['u'].append(u.value('KT'))
wdata[station]['v'].append(v.value('KT'))
table = "summary_%s" % (ts.year,)
for stid in rhdata:
# Not enough data
if len(rhdata[stid]['valid']) < 6:
continue
station, network, iemid = stid.split("|")
now = datetime.datetime(ts.year, ts.month, ts.day)
runningrh = 0
runningtime = 0
for i, valid in enumerate(rhdata[stid]['valid']):
delta = (valid - now).seconds
runningtime += delta
runningrh += (delta * rhdata[stid]['rh'][i])
now = valid
if runningtime == 0:
print(("compute_daily %s has time domain %s %s"
) % (stid, rhdata[stid]['valid'][0],
rhdata[stid]['valid'][-1]))
continue
avg_rh = clean_rh(runningrh / runningtime)
min_rh = clean_rh(min(rhdata[stid]['rh']))
max_rh = clean_rh(max(rhdata[stid]['rh']))
def do_update():
icursor.execute("""UPDATE """ + table + """
SET avg_rh = %s, min_rh = %s, max_rh = %s WHERE
iemid = %s and day = %s""", (avg_rh, min_rh, max_rh, iemid, ts))
do_update()
if icursor.rowcount == 0:
print(('compute_daily Adding %s for %s %s %s'
) % (table, station, network, ts))
icursor.execute("""INSERT into """ + table + """
(iemid, day) values (%s, %s)""", (iemid, ts))
do_update()
for stid in wdata:
# Not enough data
if len(wdata[stid]['valid']) < 6:
continue
station, network, iemid = stid.split("|")
now = datetime.datetime(ts.year, ts.month, ts.day)
runningsknt = 0
runningtime = 0
runningu = 0
runningv = 0
for i, valid in enumerate(wdata[stid]['valid']):
delta = (valid - now).seconds
runningtime += delta
runningsknt += (delta * wdata[stid]['sknt'][i])
runningu += (delta * wdata[stid]['u'][i])
runningv += (delta * wdata[stid]['v'][i])
now = valid
if runningtime == 0:
print(("compute_daily %s has time domain %s %s"
) % (stid, wdata[stid]['valid'][0],
wdata[stid]['valid'][-1]))
continue
sknt = runningsknt / runningtime
u = speed(runningu / runningtime, 'KT')
v = speed(runningv / runningtime, 'KT')
drct = meteorology.drct(u, v).value("DEG")
def do_update():
icursor.execute("""UPDATE """ + table + """
SET avg_sknt = %s, vector_avg_drct = %s WHERE
iemid = %s and day = %s""", (sknt, drct, iemid, ts))
do_update()
if icursor.rowcount == 0:
print(('compute_daily Adding %s for %s %s %s'
) % (table, station, network, ts))
icursor.execute("""INSERT into """ + table + """
(iemid, day) values (%s, %s)""", (iemid, ts))
do_update()
icursor.close()
iemaccess.commit()
iemaccess.close()
def write_grids(fp, valid, fhour):
"""Do the write to disk"""
gribfn = "%s/%sF%03i.grib2" % (TMP, valid.strftime("%Y%m%d%H%M"), fhour)
if not os.path.isfile(gribfn):
print("Skipping write_grids because of missing fn: %s" % (gribfn, ))
return
gribs = pygrib.open(gribfn)
grids = dict()
for grib in gribs:
grids[grib.name] = grib
d = dict()
if '2 metre temperature' in grids:
g = grids['2 metre temperature']
if G['LATS'] is None:
G['LATS'], G['LONS'] = g.latlons()
vals = temperature(g.values, 'K').value('C')
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['tmpc'] = nn(XI, YI)
if 'Relative humidity' in grids:
g = grids['Relative humidity']
vals = g.values
nn = NearestNDInterpolator(
(G['LONS'].flatten(), G['LATS'].flatten()), vals.flatten())
rh = nn(XI, YI)
d['dwpc'] = dewpoint(temperature(d['tmpc'], 'C'),
humidity(rh, '%')).value('C')
if ('10 metre U wind component' in grids
and '10 metre V wind component' in grids):
u = grids['10 metre U wind component'].values
v = grids['10 metre V wind component'].values
vals = ((u**2) + (v**2))**0.5
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['smps'] = nn(XI, YI)
vals = drct(speed(u, 'MPS'), speed(v, 'MPS')).value('deg')
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['drct'] = nn(XI, YI)
if 'Total Precipitation' in grids:
vals = grids['Total Precipitation'].values
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['pcpn'] = nn(XI, YI)
if 'Visibility' in grids:
vals = grids['Visibility'].values / 1000. # km
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['vsby'] = nn(XI, YI)
fp.write("""{"forecast_hour": "%03i",
"gids": [
""" % (fhour, ))
fmt = ('{"gid": %s, "tmpc": %s, "dwpc": %s, '
'"smps": %s, "drct": %s, "vsby": %s, "pcpn": %s}')
i = 1
ar = []
def f(label, row, col, fmt):
if label not in d:
return 'null'
return fmt % d[label][row, col]
for row in range(len(YAXIS)):
for col in range(len(XAXIS)):
ar.append(
fmt %
(i, f('tmpc', row, col, '%.2f'), f('dwpc', row, col, '%.2f'),
f('smps', row, col, '%.1f'), f('drct', row, col, '%i'),
f('vsby', row, col, '%.3f'), f('pcpn', row, col, '%.2f')))
i += 1
fp.write(",\n".join(ar))
fp.write("]}%s\n" % ("," if fhour != 84 else '', ))
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
units = ctx["units"]
df = read_sql(
"""
select date_trunc('hour', valid at time zone 'UTC') as ts,
avg(sknt) as sknt, max(drct) as drct from alldata
WHERE station = %s and sknt is not null and drct is not null
GROUP by ts
""",
pgconn,
params=(station, ),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data Found.")
sknt = speed(df["sknt"].values, "KT")
drct = direction(df["drct"].values, "DEG")
df["u"], df["v"] = [x.value("MPS") for x in meteorology.uv(sknt, drct)]
df["month"] = df["ts"].dt.month
grp = df[["month", "u", "v", "sknt"]].groupby("month").mean()
grp["u_%s" % (units, )] = speed(grp["u"].values, "KT").value(units.upper())
grp["v_%s" % (units, )] = speed(grp["u"].values, "KT").value(units.upper())
grp["sped_%s" % (units, )] = speed(grp["sknt"].values,
"KT").value(units.upper())
drct = meteorology.drct(speed(grp["u"].values, "KT"),
speed(grp["v"].values, "KT"))
grp["drct"] = drct.value("DEG")
maxval = grp["sped_%s" % (units, )].max()
(fig, ax) = plt.subplots(1, 1)
ax.barh(grp.index.values,
grp["sped_%s" % (units, )].values,
align="center")
ax.set_xlabel("Average Wind Speed [%s]" % (UNITS[units], ))
ax.set_yticks(grp.index.values)
ax.set_yticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlim(0, maxval * 1.2)
for mon, row in grp.iterrows():
ax.text(
maxval * 1.1,
mon,
drct2text(row["drct"]),
ha="center",
va="center",
bbox=dict(color="white"),
)
ax.text(
row["sped_%s" % (units, )] * 0.98,
mon,
"%.1f" % (row["sped_%s" % (units, )], ),
ha="right",
va="center",
bbox=dict(color="white", boxstyle="square,pad=0.03"),
)
ax.set_ylim(12.5, 0.5)
ax.set_title(("[%s] %s [%s-%s]\nMonthly Average Wind Speed and"
" Vector Average Direction") % (
station,
ctx["_nt"].sts[station]["name"],
df["ts"].min().year,
df["ts"].max().year,
))
return fig, grp
def write_grids(fp, valid, fhour):
"""Do the write to disk"""
gribfn = "%s/%sF%03i.grib2" % (TMP, valid.strftime("%Y%m%d%H%M"),
fhour)
if not os.path.isfile(gribfn):
print("Skipping write_grids because of missing fn: %s" % (gribfn,))
return
gribs = pygrib.open(gribfn)
grids = dict()
for grib in gribs:
grids[grib.name] = grib
d = dict()
if '2 metre temperature' in grids:
g = grids['2 metre temperature']
if G['LATS'] is None:
G['LATS'], G['LONS'] = g.latlons()
vals = temperature(g.values, 'K').value('C')
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['tmpc'] = nn(XI, YI)
if 'Relative humidity' in grids:
g = grids['Relative humidity']
vals = g.values
nn = NearestNDInterpolator((G['LONS'].flatten(),
G['LATS'].flatten()),
vals.flatten())
rh = nn(XI, YI)
d['dwpc'] = dewpoint(temperature(d['tmpc'], 'C'),
humidity(rh, '%')).value('C')
if ('10 metre U wind component' in grids and
'10 metre V wind component' in grids):
u = grids['10 metre U wind component'].values
v = grids['10 metre V wind component'].values
vals = ((u ** 2) + (v ** 2)) ** 0.5
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['smps'] = nn(XI, YI)
vals = drct(speed(u, 'MPS'), speed(v, 'MPS')).value('deg')
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['drct'] = nn(XI, YI)
if 'Total Precipitation' in grids:
vals = grids['Total Precipitation'].values
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['pcpn'] = nn(XI, YI)
if 'Visibility' in grids:
vals = grids['Visibility'].values / 1000. # km
nn = NearestNDInterpolator((G['LONS'].flatten(), G['LATS'].flatten()),
vals.flatten())
d['vsby'] = nn(XI, YI)
fp.write("""{"forecast_hour": "%03i",
"gids": [
""" % (fhour,))
fmt = ('{"gid": %s, "tmpc": %s, "dwpc": %s, '
'"smps": %s, "drct": %s, "vsby": %s, "pcpn": %s}')
i = 1
ar = []
def f(label, row, col, fmt):
if label not in d:
return 'null'
return fmt % d[label][row, col]
for row in range(len(YAXIS)):
for col in range(len(XAXIS)):
ar.append(fmt % (i, f('tmpc', row, col, '%.2f'),
f('dwpc', row, col, '%.2f'),
f('smps', row, col, '%.1f'),
f('drct', row, col, '%i'),
f('vsby', row, col, '%.3f'),
f('pcpn', row, col, '%.2f')))
i += 1
fp.write(",\n".join(ar))
fp.write("]}%s\n" % ("," if fhour != 84 else '',))
def simple(grids, valid, iarchive):
"""Simple gridder (stub for now)"""
if iarchive:
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from alldata c JOIN stations t on
(c.station = t.id)
WHERE c.valid >= %s and c.valid < %s and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""",
pgconn,
params=((valid - datetime.timedelta(minutes=30)),
(valid + datetime.timedelta(minutes=30))),
index_col=None)
else:
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from current c JOIN stations t on (c.iemid = t.iemid)
WHERE c.valid > now() - '1 hour'::interval and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""",
pgconn,
index_col=None)
if len(df.index) < 5:
print(("i5gridder abort len(data): %s for %s iarchive: %s" %
(len(df.index), valid, iarchive)))
sys.exit()
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['tmpf'].values, 'F').value('C'))
grids['tmpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['dwpf'].values, 'F').value('C'))
grids['dwpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
speed(df['sknt'].values, 'KT').value('MPS'))
grids['smps'] = nn(XI, YI)
u, v = meteorology.uv(speed(df['sknt'].values, 'KT'),
direction(df['drct'].values, 'DEG'))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
u.value('MPS'))
ugrid = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
v.value('MPS'))
vgrid = nn(XI, YI)
drct = meteorology.drct(speed(ugrid.ravel(), 'MPS'),
speed(vgrid.ravel(),
'MPS')).value('DEG').astype('i')
grids['drct'] = np.reshape(drct, (len(YAXIS), len(XAXIS)))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
distance(df['vsby'].values, 'MI').value('KM'))
grids['vsby'] = nn(XI, YI)
def simple(grids, valid, iarchive):
"""Simple gridder (stub for now)"""
if iarchive:
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from alldata c JOIN stations t on
(c.station = t.id)
WHERE c.valid >= %s and c.valid < %s and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""", pgconn, params=((valid - datetime.timedelta(minutes=30)),
(valid + datetime.timedelta(minutes=30))),
index_col=None)
else:
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from current c JOIN stations t on (c.iemid = t.iemid)
WHERE c.valid > now() - '1 hour'::interval and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""", pgconn, index_col=None)
if len(df.index) < 5:
print(("i5gridder abort len(data): %s for %s iarchive: %s"
% (len(df.index), valid, iarchive)))
sys.exit()
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['tmpf'].values, 'F').value('C'))
grids['tmpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['dwpf'].values, 'F').value('C'))
grids['dwpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
speed(df['sknt'].values, 'KT').value('MPS'))
grids['smps'] = nn(XI, YI)
u, v = meteorology.uv(speed(df['sknt'].values, 'KT'),
direction(df['drct'].values, 'DEG'))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
u.value('MPS'))
ugrid = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
v.value('MPS'))
vgrid = nn(XI, YI)
drct = meteorology.drct(speed(ugrid.ravel(), 'MPS'),
speed(vgrid.ravel(), 'MPS')
).value('DEG').astype('i')
grids['drct'] = np.reshape(drct, (len(YAXIS), len(XAXIS)))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
distance(df['vsby'].values, 'MI').value('KM'))
grids['vsby'] = nn(XI, YI)
