def main():
"""Go Main"""
nt = NetworkTable(['AWOS', 'IA_ASOS'])
pgconn = psycopg2.connect(database='mos', host='localhost', user='******',
port=5555)
df = read_sql("""
select station, avg(pwater) from model_gridpoint where
model = 'NAM' and extract(hour from runtime at time zone 'UTC') = 0
and pwater > 0 and pwater < 100 and
extract(month from runtime) between 4 and 9 and ftime = runtime
GROUP by station ORDER by avg
""", pgconn, index_col='station')
df['lat'] = None
df['lon'] = None
df['pwater'] = distance(df['avg'].values, 'MM').value('IN')
for station in df.index.values:
df.at[station, 'lat'] = nt.sts[station[1:]]['lat']
df.at[station, 'lon'] = nt.sts[station[1:]]['lon']
mp = MapPlot(title=('00z Analysis NAM Warm-Season Average '
'Precipitable Water [in]'),
subtitle=("based on grid point samples "
"from 2004-2017 (April-September)"))
cmap = plt.get_cmap("plasma_r")
cmap.set_under('white')
cmap.set_over('black')
mp.contourf(df['lon'], df['lat'], df['pwater'],
np.arange(0.94, 1.13, 0.03), cmap=cmap,
units='inch')
mp.drawcounties()
mp.drawcities()
mp.postprocess(filename='170901.png')
mp.close()
def plot_maxmin(ts, field):
"""Generate our plot."""
nc = ncopen(ts.strftime("/mesonet/data/ndfd/%Y%m%d%H_ndfd.nc"))
if field == 'high_tmpk':
data = np.max(nc.variables[field][:], 0)
elif field == 'low_tmpk':
data = np.min(nc.variables[field][:], 0)
data = masked_array(data, units.degK).to(units.degF).m
subtitle = ("Based on National Digital Forecast Database (NDFD) "
"00 UTC Forecast made %s") % (ts.strftime("%-d %b %Y"), )
mp = MapPlot(title='NWS NDFD 7 Day (%s through %s) %s Temperature' % (
ts.strftime("%-d %b"),
(ts + datetime.timedelta(days=6)).strftime("%-d %b"),
'Maximum' if field == 'high_tmpk' else 'Minimum',
),
subtitle=subtitle,
sector='iailin')
mp.pcolormesh(nc.variables['lon'][:],
nc.variables['lat'][:],
data,
np.arange(10, 121, 10),
cmap=plt.get_cmap('jet'),
units='Degrees F')
mp.drawcounties()
pqstr = (
"data c %s summary/cb_ndfd_7day_%s.png summary/cb_ndfd_7day_%s.png "
"png") % (ts.strftime("%Y%m%d%H%M"), "max" if field == 'high_tmpk' else
'min', "max" if field == 'high_tmpk' else 'min')
mp.postprocess(pqstr=pqstr)
mp.close()
nc.close()
def plot_gdd(ts):
"""Generate our plot."""
nc = ncopen(ts.strftime("/mesonet/data/ndfd/%Y%m%d%H_ndfd.nc"))
# compute our daily GDDs
gddtot = np.zeros(np.shape(nc.variables["lon"][:]))
for i in range(7):
gddtot += gdd(
temperature(nc.variables["high_tmpk"][i, :, :], "K"),
temperature(nc.variables["low_tmpk"][i, :, :], "K"),
)
cnc = ncopen("/mesonet/data/ndfd/ndfd_dailyc.nc")
offset = daily_offset(ts)
avggdd = np.sum(cnc.variables["gdd50"][offset:offset + 7], 0)
data = gddtot - np.where(avggdd < 1, 1, avggdd)
subtitle = ("Based on National Digital Forecast Database (NDFD) "
"00 UTC Forecast made %s") % (ts.strftime("%-d %b %Y"), )
mp = MapPlot(
title="NWS NDFD 7 Day (%s through %s) GDD50 Departure from Avg" % (
ts.strftime("%-d %b"),
(ts + datetime.timedelta(days=6)).strftime("%-d %b"),
),
subtitle=subtitle,
sector="iailin",
)
mp.pcolormesh(
nc.variables["lon"][:],
nc.variables["lat"][:],
data,
np.arange(-80, 81, 20),
cmap=plt.get_cmap("RdBu_r"),
units=r"$^\circ$F",
spacing="proportional",
)
mp.drawcounties()
pqstr = (
"data c %s summary/cb_ndfd_7day_gdd.png summary/cb_ndfd_7day_gdd.png "
"png") % (ts.strftime("%Y%m%d%H%M"), )
mp.postprocess(pqstr=pqstr)
mp.close()
nc.close()
def main():
"""Go Main Go"""
data = get_data()
mp = MapPlot(sector='midwest',
title='8 July 2018 USDA NASS Corn Progress Percent Silking',
subtitle=('Top value is 2018 percentage, bottom value is '
'departure from 2008-2017 avg'))
data2 = {}
labels = {}
for state in data:
val = data[state]['d2017'] - data[state]['avg']
data2[state] = val
labels[state.encode('utf-8')] = "%i%%\n%s%.1f%%" % (data[state]['d2017'],
"+" if val > 0 else "", val)
print(labels)
levels = range(-40, 41, 10)
mp.fill_states(data2, ilabel=True, labels=labels, bins=levels,
cmap=plt.get_cmap('RdBu_r'), units='Absolute %',
labelfontsize=16)
mp.postprocess(filename='test.png')
mp.close()
def makeplot(ts, routes='ac'):
"""
Generate two plots for a given time GMT
"""
pgconn = get_dbconn('smos', user='******')
df = read_sql("""
WITH obs as (
SELECT grid_idx, avg(soil_moisture) * 100. as sm,
avg(optical_depth) as od from data where valid BETWEEN %s and %s
GROUP by grid_idx)
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
CASE WHEN sm is Null THEN -1 ELSE sm END as sm,
CASE WHEN od is Null THEN -1 ELSE od END as od
from obs o JOIN grid g ON (o.grid_idx = g.idx)
""", pgconn, params=(ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6)), index_col=None)
if df.empty:
print(("Did not find SMOS data for: %s-%s"
) % (ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6)))
return
for sector in ['midwest', 'iowa']:
clevs = np.arange(0, 71, 5)
mp = MapPlot(sector=sector, axisbg='white',
title='SMOS Satellite: Soil Moisture (0-5cm)',
subtitle="Satelite passes around %s UTC" % (
ts.strftime("%d %B %Y %H"),))
if sector == 'iowa':
mp.drawcounties()
cmap = cm.get_cmap('jet_r')
cmap.set_under('#EEEEEE')
cmap.set_over("k")
mp.hexbin(df['lon'].values, df['lat'].values, df['sm'], clevs,
units='%', cmap=cmap)
pqstr = "plot %s %s00 smos_%s_sm%s.png smos_%s_sm%s.png png" % (
routes, ts.strftime("%Y%m%d%H"), sector, ts.strftime("%H"),
sector, ts.strftime("%H"))
mp.postprocess(pqstr=pqstr)
mp.close()
for sector in ['midwest', 'iowa']:
clevs = np.arange(0, 1.001, 0.05)
mp = MapPlot(sector=sector, axisbg='white',
title=('SMOS Satellite: Land Cover Optical Depth '
'(microwave L-band)'),
subtitle="Satelite passes around %s UTC" % (
ts.strftime("%d %B %Y %H"),))
if sector == 'iowa':
mp.drawcounties()
cmap = cm.get_cmap('jet')
cmap.set_under('#EEEEEE')
cmap.set_over("k")
mp.hexbin(df['lon'].values, df['lat'].values, df['od'], clevs,
cmap=cmap)
pqstr = "plot %s %s00 smos_%s_od%s.png smos_%s_od%s.png png" % (
routes, ts.strftime("%Y%m%d%H"), sector, ts.strftime("%H"),
sector, ts.strftime("%H"))
mp.postprocess(pqstr=pqstr)
mp.close()
def makeplot(ts, routes="ac"):
"""
Generate two plots for a given time GMT
"""
pgconn = get_dbconn("smos", user="******")
df = read_sql(
"""
WITH obs as (
SELECT grid_idx, avg(soil_moisture) * 100. as sm,
avg(optical_depth) as od from data where valid BETWEEN %s and %s
GROUP by grid_idx)
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
CASE WHEN sm is Null THEN -1 ELSE sm END as sm,
CASE WHEN od is Null THEN -1 ELSE od END as od
from obs o JOIN grid g ON (o.grid_idx = g.idx)
""",
pgconn,
params=(
ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6),
),
index_col=None,
)
if df.empty:
LOG.info(
"Did not find SMOS data for: %s-%s",
ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6),
)
return
for sector in ["midwest", "iowa"]:
clevs = np.arange(0, 71, 5)
mp = MapPlot(
sector=sector,
axisbg="white",
title="SMOS Satellite: Soil Moisture (0-5cm)",
subtitle="Satelite passes around %s UTC" %
(ts.strftime("%d %B %Y %H"), ),
)
if sector == "iowa":
mp.drawcounties()
cmap = get_cmap("jet_r")
cmap.set_under("#EEEEEE")
cmap.set_over("k")
mp.hexbin(
df["lon"].values,
df["lat"].values,
df["sm"],
clevs,
units="%",
cmap=cmap,
)
pqstr = "plot %s %s00 smos_%s_sm%s.png smos_%s_sm%s.png png" % (
routes,
ts.strftime("%Y%m%d%H"),
sector,
ts.strftime("%H"),
sector,
ts.strftime("%H"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
for sector in ["midwest", "iowa"]:
clevs = np.arange(0, 1.001, 0.05)
mp = MapPlot(
sector=sector,
axisbg="white",
title=("SMOS Satellite: Land Cover Optical Depth "
"(microwave L-band)"),
subtitle="Satelite passes around %s UTC" %
(ts.strftime("%d %B %Y %H"), ),
)
if sector == "iowa":
mp.drawcounties()
cmap = get_cmap("jet")
cmap.set_under("#EEEEEE")
cmap.set_over("k")
mp.hexbin(df["lon"].values,
df["lat"].values,
df["od"],
clevs,
cmap=cmap)
pqstr = "plot %s %s00 smos_%s_od%s.png smos_%s_od%s.png png" % (
routes,
ts.strftime("%Y%m%d%H"),
sector,
ts.strftime("%H"),
sector,
ts.strftime("%H"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
