def run(year):
cursor.execute(
"""
WITH obs as (
SELECT station, sum(precip) from alldata_ia where year = %s GROUP by station
), climate as (
SELECT station, sum(precip) from climate51 GROUP by station
)
SELECT o.station, o.sum - c.sum as diff from obs o JOIN climate c
on (c.station = o.station) ORDER by diff ASC
""", (year, ))
lats = []
lons = []
vals = []
for row in cursor:
if not nt.sts.has_key(row[0]) or row[0] in rejs or row[0][2] == 'C':
continue
print row
lats.append(nt.sts[row[0]]['lat'])
lons.append(nt.sts[row[0]]['lon'])
vals.append(row[1])
m = MapPlot(title='%s Precipitation Departure' % (year, ))
cmap = cm.get_cmap('BrBG')
#cmap.set_over('blue')
#cmap.set_under('red')
m.contourf(lons,
lats,
vals,
np.arange(-24, 24.1, 2),
cmap=cmap,
units='inch')
#m.plot_values(lons, lats, vals, '%.02f')
m.drawcounties()
m.postprocess(filename='%s.png' % (year, ))
def run(base, ceil, now, fn):
""" Generate the plot """
# Compute normal from the climate database
sql = """SELECT station,
sum(gddxx(%s, %s, high, low)) as gdd
from alldata_ia WHERE year = %s and month in (5,6,7,8,9,10)
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (base, ceil, now.year)
lats = []
lons = []
gdd50 = []
ccursor.execute(sql)
for row in ccursor:
if row[0] not in nt.sts:
continue
lats.append(nt.sts[row[0]]['lat'])
lons.append(nt.sts[row[0]]['lon'])
gdd50.append(float(row[1]))
m = MapPlot(title=("Iowa 1 May - %s GDD Accumulation"
) % (now.strftime("%-d %B %Y"), ),
subtitle="base %s" % (base,))
bins = np.linspace(min(gdd50)-1, max(gdd50)+1, num=10, dtype=np.int)
m.contourf(lons, lats, gdd50, bins)
m.drawcounties()
pqstr = "plot c 000000000000 summary/%s.png bogus png" % (fn,)
m.postprocess(pqstr=pqstr)
def test_contourf():
''' Test the contourf plot with labels specified '''
mp = MapPlot(sector='iowa', nocaption=True)
mp.contourf(np.arange(-94, -89), np.arange(40, 45),
np.arange(5), np.arange(5),
clevlabels=['a', 'b', 'c', 'd', 'e'])
return mp.fig
def do_month(ts, routes='m'):
"""
Generate the plot for a given month, please
"""
sql = """SELECT station, sum(precip) as total, max(day) as lastday
from alldata_ia WHERE year = %s and month = %s
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (ts.year, ts.month)
lats = []
lons = []
vals = []
lastday = None
ccursor.execute(sql)
for row in ccursor:
if row['station'] not in nt.sts:
continue
if lastday is None:
lastday = row['lastday']
lats.append(nt.sts[row['station']]['lat'])
lons.append(nt.sts[row['station']]['lon'])
vals.append(row['total'])
m = MapPlot(title='%s - %s' %
(ts.strftime("%d %B %Y"), lastday.strftime("%d %B %Y")),
subtitle="%s Total Precipitation [inch]" %
(ts.strftime("%B %Y"), ))
m.contourf(lons, lats, vals,
[0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6, 7])
m.plot_values(lons, lats, vals, fmt='%.2f')
pqstr = ("plot %s %s summary/iemre_iowa_total_precip.png "
"%s/summary/iemre_iowa_total_precip.png png") % (
routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
m.postprocess(pqstr=pqstr)
def do_month(ts, routes='m'):
"""
Generate the plot for a given month, please
"""
sql = """SELECT station, sum(precip) as total, max(day) as lastday
from alldata_ia WHERE year = %s and month = %s
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (ts.year, ts.month)
lats = []
lons = []
vals = []
lastday = None
ccursor.execute(sql)
for row in ccursor:
if row['station'] not in nt.sts:
continue
if lastday is None:
lastday = row['lastday']
lats.append(nt.sts[row['station']]['lat'])
lons.append(nt.sts[row['station']]['lon'])
vals.append(row['total'])
m = MapPlot(title='%s - %s' % (ts.strftime("%d %B %Y"),
lastday.strftime("%d %B %Y")),
subtitle="%s Total Precipitation [inch]" % (
ts.strftime("%B %Y"),))
m.contourf(lons, lats, vals, [0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6,
7])
m.plot_values(lons, lats, vals, fmt='%.2f')
pqstr = ("plot %s %s summary/iemre_iowa_total_precip.png "
"%s/summary/iemre_iowa_total_precip.png png"
) % (routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
m.postprocess(pqstr=pqstr)
def test_drawiowawfo():
"""Iowa Contour Plot"""
mp = MapPlot(sector='iowawfo', title='Iowa Contour plot', nocaption=True)
mp.contourf(np.arange(-94, -85), np.arange(36, 45), np.arange(9),
np.arange(9),
clevlabels=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'])
return mp.fig
def main(argv):
"""Go Main Go"""
st = NetworkTable("IACLIMATE")
now = datetime.datetime.now() - datetime.timedelta(days=1)
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor()
gfunc = "gdd50"
gbase = 50
if len(argv) == 2 and argv[1] == "gdd52":
gfunc = "gdd52"
gbase = 52
if len(argv) == 2 and argv[1] == "gdd48":
gfunc = "gdd48"
gbase = 48
# Compute normal from the climate database
ccursor.execute(
"""
SELECT station,
sum(%s(high, low)) as gdd
from alldata_ia WHERE station != 'IA0000'
and substr(station, 3, 1) != 'C' and year = %s
GROUP by station
"""
% (gfunc, now.year)
)
lats = []
lons = []
gdd50 = []
valmask = []
for row in ccursor:
station = row[0]
if station not in st.sts:
continue
lats.append(st.sts[station]["lat"])
lons.append(st.sts[station]["lon"])
gdd50.append(float(row[1]))
valmask.append(True)
mp = MapPlot(
axisbg="white",
title=("Iowa %s GDD (base=%s) Accumulation")
% (now.strftime("%Y"), gbase),
subtitle="1 Jan - %s" % (now.strftime("%d %b %Y"),),
)
minval = min(gdd50)
rng = max([int(max(gdd50) - minval), 10])
ramp = np.linspace(minval, minval + rng, 10, dtype=np.int)
mp.contourf(lons, lats, gdd50, ramp)
pqstr = "plot c 000000000000 summary/gdd_jan1.png bogus png"
if gbase == 52:
pqstr = "plot c 000000000000 summary/gdd52_jan1.png bogus png"
elif gbase == 48:
pqstr = "plot c 000000000000 summary/gdd48_jan1.png bogus png"
mp.drawcounties()
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
cursor = pgconn.cursor()
sector = fdict.get('sector', 'IA')
date1 = datetime.datetime.strptime(fdict.get('date1', '2015-01-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2015-02-01'),
'%Y-%m-%d')
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
cursor.execute("""
WITH obs as (
SELECT station, sday, day, precip from """ + table + """ WHERE
day >= %s and day < %s and precip >= 0 and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip from
climate51),
combo as (
SELECT o.station, o.precip - c.precip as d from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
deltas as (
SELECT station, sum(d) from combo GROUP by station)
SELECT d.station, d.sum, ST_x(t.geom), ST_y(t.geom) from deltas d
JOIN stations t on (d.station = t.id) WHERE t.network ~* 'CLIMATE'
""", (date1, date2))
rows = []
for row in cursor:
rows.append(dict(station=row[0], delta=row[1], lon=row[2],
lat=row[3]))
df = pd.DataFrame(rows)
lons = np.array(df['lon'])
vals = np.array(df['delta'])
lats = np.array(df['lat'])
sector2 = "state" if sector != 'midwest' else 'midwest'
m = MapPlot(sector=sector2, state=sector, axisbg='white',
title=('%s - %s Precipitation Departure [inch]'
) % (date1.strftime("%d %b %Y"),
date2.strftime("%d %b %Y")),
subtitle='%s vs 1950-2014 Climatology' % (date1.year,))
rng = int(max([0 - np.min(vals), np.max(vals)]))
cmap = cm.get_cmap('RdYlBu')
cmap.set_bad('white')
m.contourf(lons, lats, vals, np.linspace(0 - rng - 0.5, rng + 0.6, 10,
dtype='i'),
cmap=cmap, units='inch')
m.plot_values(lons, lats, vals, fmt='%.2f')
if sector == 'iowa':
m.drawcounties()
return m.fig, df
def test_michigan():
"""See what we do with Michigan"""
mp = MapPlot(sector='state', state='MI', nocaption=True)
mp.contourf(np.arange(-84, -75), np.arange(36, 45), np.arange(9),
np.arange(9),
clevlabels=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'])
return mp.fig
def main():
"""GO!"""
now = datetime.datetime.now()
df = get_df()
# df = pd.read_csv('example.csv')
rng = range(-30, 120, 2)
for sector in ['iowa', 'midwest', 'conus']:
mp = MapPlot(axisbg='white',
sector=sector,
title=("%s 2 meter Air Temperature") %
(sector.capitalize(), ),
subtitle=now.strftime("%d %b %Y %-I:%M %p"))
mp.contourf(df['lon'].values,
df['lat'].values,
df['tmpf'].values,
rng,
clevstride=5,
units='F')
mp.plot_values(df['lon'].values,
df['lat'].values,
df['tmpf'].values,
fmt='%.0f')
if sector == 'iowa':
mp.drawcounties()
pqstr = ("plot ac %s00 %s_tmpf.png %s_tmpf_%s.png png"
"") % (datetime.datetime.utcnow().strftime("%Y%m%d%H"),
sector, sector,
datetime.datetime.utcnow().strftime("%H"))
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
state = ctx['state'][:2]
varname = ctx['var']
sector = ctx['sector']
opt = ctx['opt']
over = ctx['over']
month = ctx['month']
df = read_sql("""
WITH data as (
SELECT station, extract(month from valid) as month,
sum(precip) as total_precip, avg(high) as avg_high,
avg(low) as avg_low, avg((high+low)/2.) as avg_temp
from ncdc_climate81 GROUP by station, month)
SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat, month,
total_precip, avg_high, avg_low, avg_temp from data d JOIN stations t
ON (d.station = t.id) WHERE t.network = 'NCDC81' and
t.state in ('IA', 'ND', 'SD', 'NE', 'KS', 'MO', 'IL', 'WI', 'MN', 'MI',
'IN', 'OH', 'KY')
""", pgconn, index_col=['station', 'month'])
if df.empty:
return "No data was found for query, sorry."
if over == 'monthly':
title = "%s %s" % (calendar.month_name[month], PDICT3[varname])
df.reset_index(inplace=True)
df2 = df[df['month'] == month]
else:
title = "Annual %s" % (PDICT3[varname], )
if varname == 'total_precip':
df2 = df.sum(axis=0, level='station')
else:
df2 = df.mean(axis=0, level='station')
df2['lat'] = df['lat'].mean(axis=0, level='station')
df2['lon'] = df['lon'].mean(axis=0, level='station')
mp = MapPlot(sector=sector, state=state, axisbg='white',
title=('NCEI 1981-2010 Climatology of %s'
) % (title,),
subtitle=('based on National Centers for '
'Environmental Information (NCEI) 1981-2010'
' Climatology'))
levels = np.linspace(df2[varname].min(), df2[varname].max(), 10)
levels = [round(x, PRECISION[varname]) for x in levels]
if opt in ['both', 'contour']:
mp.contourf(df2['lon'].values, df2['lat'].values,
df2[varname].values, levels, units=UNITS[varname])
if sector == 'state':
mp.drawcounties()
if opt in ['both', 'values']:
mp.plot_values(df2['lon'].values, df2['lat'].values,
df2[varname].values,
fmt='%%.%if' % (PRECISION[varname],))
return mp.fig, df
def run(base, ceil, now, fn):
""" Generate the plot """
# Compute normal from the climate database
sql = """SELECT station,
sum(gddxx(%s, %s, high, low)) as gdd
from alldata_ia WHERE year = %s and month in (5,6,7,8,9,10)
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (
base,
ceil,
now.year,
)
lats = []
lons = []
gdd50 = []
ccursor.execute(sql)
for row in ccursor:
if row[0] not in nt.sts:
continue
lats.append(nt.sts[row[0]]["lat"])
lons.append(nt.sts[row[0]]["lon"])
gdd50.append(float(row[1]))
mp = MapPlot(
title=("Iowa 1 May - %s GDD Accumulation") %
(now.strftime("%-d %B %Y"), ),
subtitle="base %s" % (base, ),
)
bins = np.linspace(min(gdd50) - 1, max(gdd50) + 1, num=10, dtype=np.int)
mp.contourf(lons, lats, gdd50, bins)
mp.drawcounties()
pqstr = "plot c 000000000000 summary/%s.png bogus png" % (fn, )
mp.postprocess(pqstr=pqstr)
def run(year):
cursor.execute("""
WITH obs as (
SELECT station, sum(precip) from alldata_ia where year = %s GROUP by station
), climate as (
SELECT station, sum(precip) from climate51 GROUP by station
)
SELECT o.station, o.sum - c.sum as diff from obs o JOIN climate c
on (c.station = o.station) ORDER by diff ASC
""", (year,))
lats = []
lons = []
vals = []
for row in cursor:
if not nt.sts.has_key(row[0]) or row[0] in rejs or row[0][2] == 'C':
continue
print row
lats.append( nt.sts[row[0]]['lat'])
lons.append( nt.sts[row[0]]['lon'])
vals.append( row[1] )
m = MapPlot(title='%s Precipitation Departure' % (year,))
cmap = cm.get_cmap('BrBG')
#cmap.set_over('blue')
#cmap.set_under('red')
m.contourf(lons, lats, vals, np.arange(-24,24.1,2), cmap=cmap, units='inch')
#m.plot_values(lons, lats, vals, '%.02f')
m.drawcounties()
m.postprocess(filename='%s.png' % (year,))
def main():
"""GO"""
pgconn = get_dbconn("iem", user="******")
cursor = pgconn.cursor()
# Compute normal from the climate database
sql = """
SELECT
id, network, vsby, ST_x(geom) as lon, ST_y(geom) as lat
FROM
current c JOIN stations s ON (s.iemid = c.iemid)
WHERE
s.network IN ('AWOS', 'IA_ASOS','IL_ASOS','MN_ASOS','WI_ASOS','SD_ASOS',
'NE_ASOS','MO_ASOS') and
valid + '60 minutes'::interval > now() and
vsby >= 0 and vsby <= 10
"""
lats = []
lons = []
vals = []
valmask = []
cursor.execute(sql)
for row in cursor:
lats.append(row[4])
lons.append(row[3])
vals.append(row[2])
valmask.append(row[1] in ["AWOS", "IA_ASOS"])
if len(lats) < 5:
return
now = datetime.datetime.now()
mp = MapPlot(
sector="iowa",
title="Iowa Visibility",
subtitle="Valid: %s" % (now.strftime("%d %b %Y %-I:%M %p"), ),
)
mp.contourf(
lons,
lats,
vals,
np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9]),
units="miles",
cmap=cm.get_cmap("gray"),
)
mp.plot_values(lons, lats, vals, "%.1f", valmask)
mp.drawcounties()
pqstr = ("plot ac %s00 iowa_vsby.png vsby_contour_%s00.png png"
"") % (
datetime.datetime.utcnow().strftime("%Y%m%d%H"),
datetime.datetime.utcnow().strftime("%H"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
state = fdict.get('state', 'IA')[:2]
varname = fdict.get('var', 'total_precip')
sector = fdict.get('sector', 'state')
opt = fdict.get('opt', 'both')
over = fdict.get('over', 'monthly')
month = int(fdict.get('month', datetime.date.today().month))
df = read_sql("""
WITH data as (
SELECT station, extract(month from valid) as month,
sum(precip) as total_precip, avg(high) as avg_high,
avg(low) as avg_low, avg((high+low)/2.) as avg_temp
from ncdc_climate81 GROUP by station, month)
SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat, month,
total_precip, avg_high, avg_low, avg_temp from data d JOIN stations t
ON (d.station = t.id) WHERE t.network = 'NCDC81' and
t.state in ('IA', 'ND', 'SD', 'NE', 'KS', 'MO', 'IL', 'WI', 'MN', 'MI',
'IN', 'OH', 'KY')
""", pgconn, index_col=['station', 'month'])
if over == 'monthly':
title = "%s %s" % (calendar.month_name[month], PDICT3[varname])
df.reset_index(inplace=True)
df2 = df[df['month'] == month]
else:
title = "Annual %s" % (PDICT3[varname], )
if varname == 'total_precip':
df2 = df.sum(axis=0, level='station')
else:
df2 = df.mean(axis=0, level='station')
df2['lat'] = df['lat'].mean(axis=0, level='station')
df2['lon'] = df['lon'].mean(axis=0, level='station')
m = MapPlot(sector=sector, state=state, axisbg='white',
title=('NCEI 1981-2010 Climatology of %s'
) % (title,),
subtitle=('based on National Centers for '
'Environmental Information (NCEI) 1981-2010'
' Climatology'))
levels = np.linspace(df2[varname].min(), df2[varname].max(), 10)
levels = [round(x, PRECISION[varname]) for x in levels]
if opt in ['both', 'contour']:
m.contourf(df2['lon'].values, df2['lat'].values,
df2[varname].values, levels, units=UNITS[varname])
if sector == 'state':
m.drawcounties()
if opt in ['both', 'values']:
m.plot_values(df2['lon'].values, df2['lat'].values,
df2[varname].values,
fmt='%%.%if' % (PRECISION[varname],))
return m.fig, df
def test_cwa():
"""Exercise the cwa plotting routines"""
mp = MapPlot(sector='cwa', cwa='MKX', nocaption=True)
mp.contourf(np.arange(-94, -89), np.arange(40, 45),
np.arange(5), np.arange(5),
clevlabels=['a', 'b', 'c', 'd', 'e'])
mp.draw_cwas()
assert mp.cwa == 'MKX'
return mp.fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
varname = ctx['var']
year = ctx['year']
popt = ctx['popt']
threshold = ctx['threshold']
table = "alldata_%s" % (sector, )
df = read_sql("""
WITH data as (
SELECT station, """ + SQLOPT[varname] + """ as doy
from """ + table + """
WHERE year = %s GROUP by station
)
select station, doy, st_x(geom) as lon, st_y(geom) as lat
from data d JOIN stations t on (d.station = t.id) WHERE
t.network = %s and substr(station, 3, 4) != '0000'
and substr(station, 3, 1) != 'C' and doy not in (0, 400) ORDER by doy
""",
pgconn,
params=(threshold, year, '%sCLIMATE' % (sector, )),
index_col='station')
if df.empty:
return "No data found!"
def f(val):
ts = datetime.date(year, 1, 1) + datetime.timedelta(days=(val - 1))
return ts.strftime("%-m/%-d")
df['pdate'] = df['doy'].apply(f)
mp = MapPlot(sector='state',
state=sector,
continental_color='white',
nocaption=True,
title="%s %s %s$^\circ$F" %
(year, PDICT2[varname], threshold),
subtitle='based on NWS COOP and IEM Daily Estimates')
levs = np.linspace(df['doy'].min() - 3, df['doy'].max() + 3, 7, dtype='i')
levlables = map(f, levs)
if popt == 'contour':
mp.contourf(df['lon'],
df['lat'],
df['doy'],
levs,
clevlabels=levlables)
mp.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
mp.drawcounties()
return mp.fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
ptype = fdict.get('ptype', 'c')
date = datetime.datetime.strptime(fdict.get('date', '2015-01-01'),
'%Y-%m-%d')
varname = fdict.get('var', 'rsds')
idx0 = iemre.daily_offset(date)
nc = netCDF4.Dataset(("/mesonet/data/iemre/%s_mw_daily.nc"
) % (date.year, ), 'r')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
if varname == 'rsds':
# Value is in W m**-2, we want MJ
data = nc.variables[varname][idx0, :, :] * 86400. / 1000000.
units = 'MJ d-1'
clevs = np.arange(0, 37, 3.)
clevs[0] = 0.01
clevstride = 1
elif varname in ['p01d', 'p01d_12z']:
# Value is in W m**-2, we want MJ
data = nc.variables[varname][idx0, :, :] / 25.4
units = 'inch'
clevs = np.arange(0, 0.25, 0.05)
clevs = np.append(clevs, np.arange(0.25, 3., 0.25))
clevs = np.append(clevs, np.arange(3., 10.0, 1))
clevs[0] = 0.01
clevstride = 1
elif varname in ['high_tmpk', 'low_tmpk', 'high_tmpk_12z', 'low_tmpk_12z']:
# Value is in W m**-2, we want MJ
data = temperature(nc.variables[varname][idx0, :, :], 'K').value('F')
units = 'F'
clevs = np.arange(-30, 120, 2)
clevstride = 5
nc.close()
title = date.strftime("%-d %B %Y")
m = MapPlot(sector='midwest', axisbg='white', nocaption=True,
title='IEM Reanalysis of %s for %s' % (PDICT.get(varname),
title),
subtitle='Data derived from various NOAA datasets'
)
if np.ma.is_masked(np.max(data)):
return 'Data Unavailable'
x, y = np.meshgrid(lons, lats)
if ptype == 'c':
m.contourf(x, y, data, clevs, clevstride=clevstride, units=units)
else:
m.pcolormesh(x, y, data, clevs, clevstride=clevstride, units=units)
return m.fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
date1 = datetime.datetime.strptime(fdict.get('date1', '2014-09-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2014-09-22'),
'%Y-%m-%d')
date1 = date1.replace(year=2000)
date2 = date2.replace(year=2000)
varname = fdict.get('varname', 'low')
df = read_sql("""
WITH t2 as (
SELECT station, high, low from ncdc_climate81 WHERE
valid = %s
), t1 as (
SELECT station, high, low from ncdc_climate81 where
valid = %s
), data as (
SELECT t2.station, t1.high as t1_high, t2.high as t2_high,
t1.low as t1_low, t2.low as t2_low from t1 JOIN t2 on
(t1.station = t2.station)
)
SELECT d.station, ST_x(geom) as lon, ST_y(geom) as lat,
t2_high - t1_high as high, t2_low - t1_low as low from data d JOIN
stations s on (s.id = d.station) where s.network = 'NCDC81'
and s.state not in ('HI', 'AK')
""",
pgconn,
params=(date2, date1),
index_col='station')
days = int((date2 - date1).days)
extent = int(df[varname].abs().max())
m = MapPlot(sector='conus',
title=('%s Day Change in %s NCDC 81 Climatology') %
(days, PDICT[varname]),
subtitle='from %s to %s' %
(date1.strftime("%-d %B"), date2.strftime("%-d %B")))
cmap = cm.get_cmap("RdBu_r")
m.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
np.arange(0 - extent, extent + 1, 2),
cmap=cmap,
units='F')
return m.fig, df
def doit(now):
"""
Generate some plots for the COOP data!
"""
# We'll assume all COOP data is 12z, sigh for now
sql = """SELECT id, pday, network
from summary_%s s JOIN stations t ON (t.iemid = s.iemid)
WHERE day = '%s' and
t.network ~* 'COOP' and pday >= 0""" % (now.year,
now.strftime("%Y-%m-%d") )
lats = []
lons = []
vals = []
icursor.execute( sql )
iamax = 0.
for row in icursor:
sid = row[0]
if not st.sts.has_key(sid):
continue
#labels.append( id[2:] )
lats.append( st.sts[sid]['lat'] )
lons.append( st.sts[sid]['lon'] )
vals.append( row[1] )
if row[2] == 'IA_COOP' and row[1] > iamax:
iamax = row[1]
#if iamax == 0:
# Dummy in some bad data to prevent the contouring from going mad
# lats.append( 42. )
# lons.append( -96.0 )
# vals.append( 1. )
# Plot Iowa
m = MapPlot(sector='iowa',
title='24 Hour NWS COOP Precipitation [inch]',
subtitle='Ending %s at roughly 12Z' % (now.strftime("%d %B %Y"),))
m.contourf(lons, lats, vals, clevs, units='inch')
pqstr = "plot ac %s0000 iowa_coop_12z_precip.png iowa_coop_12z_precip.png png" % (now.strftime("%Y%m%d"),)
m.postprocess(pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest',
title='24 Hour NWS COOP Precipitation [inch]',
subtitle='Ending %s at roughly 12Z' % (now.strftime("%d %B %Y"),))
m.contourf(lons, lats, vals, clevs, units='inch')
pqstr = "plot ac %s0000 midwest_coop_12z_precip.png midwest_coop_12z_precip.png png" % (now.strftime("%Y%m%d"),)
m.postprocess(pqstr=pqstr)
m.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("iem", user="******")
icursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
day1 = datetime.date.today().replace(day=1)
day2 = (day1 + datetime.timedelta(days=35)).replace(day=1)
lats = []
lons = []
vals = []
valmask = []
table = "summary_%s" % (day1.year, )
icursor.execute(
"""
SELECT id, s.network, ST_x(s.geom) as lon, ST_y(s.geom) as lat,
avg( (max_tmpf + min_tmpf)/2.0 ) as avgt , count(*) as cnt
from """ + table + """ c JOIN stations s ON (s.iemid = c.iemid)
WHERE s.network in ('IA_ASOS', 'AWOS') and
day >= %s and day < %s
and max_tmpf > -30 and min_tmpf < 90 GROUP by id, s.network, lon, lat
""",
(day1, day2),
)
for row in icursor:
if row["cnt"] != now.day:
continue
lats.append(row["lat"])
lons.append(row["lon"])
vals.append(row["avgt"])
valmask.append(row["network"] in ["AWOS", "IA_ASOS"])
if len(vals) < 3:
return
mp = MapPlot(
axisbg="white",
title="Iowa %s Average Temperature" % (now.strftime("%Y %B"), ),
subtitle=("Average of the High + Low ending: %s"
"") % (now.strftime("%d %B"), ),
)
minval = int(min(vals))
maxval = max([int(max(vals)) + 3, minval + 11])
clevs = np.linspace(minval, maxval, 10, dtype="i")
mp.contourf(lons, lats, vals, clevs)
mp.drawcounties()
mp.plot_values(lons, lats, vals, "%.1f")
pqstr = "plot c 000000000000 summary/mon_mean_T.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
date1 = datetime.datetime.strptime(fdict.get('date1', '2014-09-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2014-09-22'),
'%Y-%m-%d')
date1 = date1.replace(year=2000)
date2 = date2.replace(year=2000)
varname = fdict.get('varname', 'low')
cursor.execute("""
WITH t2 as (
SELECT station, high, low from ncdc_climate81 WHERE
valid = %s
), t1 as (
SELECT station, high, low from ncdc_climate81 where
valid = %s
), data as (
SELECT t2.station, t1.high as t1_high, t2.high as t2_high,
t1.low as t1_low, t2.low as t2_low from t1 JOIN t2 on
(t1.station = t2.station)
)
SELECT d.station, ST_x(geom) as lon, ST_y(geom) as lat,
t2_high - t1_high as high, t2_low - t1_low as low from data d JOIN
stations s on (s.id = d.station) where s.network = 'NCDC81'
""", (date2, date1))
vals = []
lons = []
lats = []
for row in cursor:
lats.append(row['lat'])
lons.append(row['lon'])
vals.append(row[varname])
days = int((date2 - date1).days)
extent = int(max(abs(min(vals)), abs(max(vals))))
m = MapPlot(sector='conus',
title=('%s Day Change in %s NCDC 81 Climatology'
) % (days, PDICT[varname]),
subtitle='from %s to %s' % (date1.strftime("%-d %B"),
date2.strftime("%-d %B"))
)
cmap = cm.get_cmap("RdBu_r")
m.contourf(lons, lats, vals, np.arange(0-extent, extent+1, 2), cmap=cmap,
units='F')
return m.fig
def doit(now):
"""
Generate some plots for the COOP data!
"""
# We'll assume all COOP data is 12z, sigh for now
sql = """SELECT id, pday, network
from summary_%s s JOIN stations t ON (t.iemid = s.iemid)
WHERE day = '%s' and
t.network ~* 'COOP' and pday >= 0""" % (now.year,
now.strftime("%Y-%m-%d"))
lats = []
lons = []
vals = []
icursor.execute(sql)
iamax = 0.
for row in icursor:
sid = row[0]
if sid not in st.sts:
continue
# labels.append( id[2:] )
lats.append(st.sts[sid]['lat'])
lons.append(st.sts[sid]['lon'])
vals.append(row[1])
if row[2] == 'IA_COOP' and row[1] > iamax:
iamax = row[1]
# Plot Iowa
m = MapPlot(sector='iowa',
title='24 Hour NWS COOP Precipitation [inch]',
subtitle=('Ending %s at roughly 12Z') %
(now.strftime("%d %B %Y"), ))
m.contourf(lons, lats, vals, clevs, units='inch')
pqstr = ("plot ac %s0000 iowa_coop_12z_precip.png "
"iowa_coop_12z_precip.png png") % (now.strftime("%Y%m%d"), )
m.postprocess(pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest',
title='24 Hour NWS COOP Precipitation [inch]',
subtitle=('Ending %s at roughly 12Z') %
(now.strftime("%d %B %Y"), ))
m.contourf(lons, lats, vals, clevs, units='inch')
pqstr = ("plot ac %s0000 midwest_coop_12z_precip.png "
"midwest_coop_12z_precip.png png") % (now.strftime("%Y%m%d"), )
m.postprocess(pqstr=pqstr)
m.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn('iem', user='******')
icursor = pgconn.cursor()
# Compute normal from the climate database
sql = """
select s.id, s.network,
ST_x(s.geom) as lon, ST_y(s.geom) as lat,
greatest(c.max_sknt, c.max_gust) as wind
from summary_%s c, current c2, stations s
WHERE s.iemid = c.iemid and c2.valid > 'TODAY' and c.day = 'TODAY'
and c2.iemid = s.iemid
and (s.network ~* 'ASOS' or s.network = 'AWOS') and s.country = 'US'
ORDER by lon, lat
""" % (now.year, )
lats = []
lons = []
vals = []
valmask = []
icursor.execute(sql)
for row in icursor:
if row[4] == 0 or row[4] is None:
continue
lats.append(row[3])
lons.append(row[2])
vals.append(speed(row[4], 'KT').value('MPH'))
valmask.append((row[1] in ['AWOS', 'IA_ASOS']))
if len(vals) < 5 or True not in valmask:
return
clevs = np.arange(0, 40, 2)
clevs = np.append(clevs, np.arange(40, 80, 5))
clevs = np.append(clevs, np.arange(80, 120, 10))
# Iowa
pqstr = "plot ac %s summary/today_gust.png iowa_wind_gust.png png" % (
now.strftime("%Y%m%d%H%M"), )
mp = MapPlot(title="Iowa ASOS/AWOS Peak Wind Speed Reports",
subtitle="%s" % (now.strftime("%d %b %Y"), ),
sector='iowa')
mp.contourf(lons, lats, vals, clevs, units='MPH')
mp.plot_values(lons, lats, vals, '%.0f', valmask=valmask, labelbuffer=10)
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("coop")
ccursor = pgconn.cursor()
nt = NetworkTable("IACLIMATE")
# Compute normal from the climate database
sql = """SELECT station,
sum(gdd50(high, low)) as gdd
from alldata_ia WHERE year = %s and month = %s
GROUP by station""" % (
now.year,
now.month,
)
vals = []
lats = []
lons = []
ccursor.execute(sql)
for row in ccursor:
if row[0] not in nt.sts:
continue
lats.append(nt.sts[row[0]]["lat"])
lons.append(nt.sts[row[0]]["lon"])
vals.append(float(row[1]))
if len(vals) < 5:
sys.exit()
mp = MapPlot(
title="Iowa %s GDD Accumulation" % (now.strftime("%B %Y"), ),
axisbg="white",
)
mp.contourf(
lons,
lats,
vals,
np.linspace(int(min(vals)),
int(max(vals)) + 3, 10),
units="base 50",
)
mp.plot_values(lons, lats, vals, fmt="%.0f")
pqstr = "plot c 000000000000 summary/gdd_mon.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
date1 = datetime.datetime.strptime(fdict.get('date1', '2014-09-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2014-09-22'),
'%Y-%m-%d')
date1 = date1.replace(year=2000)
date2 = date2.replace(year=2000)
varname = fdict.get('varname', 'low')
df = read_sql("""
WITH t2 as (
SELECT station, high, low from ncdc_climate81 WHERE
valid = %s
), t1 as (
SELECT station, high, low from ncdc_climate81 where
valid = %s
), data as (
SELECT t2.station, t1.high as t1_high, t2.high as t2_high,
t1.low as t1_low, t2.low as t2_low from t1 JOIN t2 on
(t1.station = t2.station)
)
SELECT d.station, ST_x(geom) as lon, ST_y(geom) as lat,
t2_high - t1_high as high, t2_low - t1_low as low from data d JOIN
stations s on (s.id = d.station) where s.network = 'NCDC81'
and s.state not in ('HI', 'AK')
""", pgconn, params=(date2, date1), index_col='station')
days = int((date2 - date1).days)
extent = int(df[varname].abs().max())
m = MapPlot(sector='conus',
title=('%s Day Change in %s NCDC 81 Climatology'
) % (days, PDICT[varname]),
subtitle='from %s to %s' % (date1.strftime("%-d %B"),
date2.strftime("%-d %B"))
)
cmap = cm.get_cmap("RdBu_r")
m.contourf(df['lon'].values, df['lat'].values, df[varname].values,
np.arange(0-extent, extent+1, 2), cmap=cmap, units='F')
return m.fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
pgconn = psycopg2.connect(dbname='coop', host='iemdb', user='******')
ctx = util.get_autoplot_context(fdict, get_description())
sector = ctx['sector']
varname = ctx['var']
year = ctx['year']
popt = ctx['popt']
threshold = ctx['threshold']
table = "alldata_%s" % (sector,)
df = read_sql("""
WITH data as (
SELECT station, """ + SQLOPT[varname] + """ as doy
from """ + table + """
WHERE year = %s GROUP by station
)
select station, doy, st_x(geom) as lon, st_y(geom) as lat
from data d JOIN stations t on (d.station = t.id) WHERE
t.network = %s and substr(station, 3, 4) != '0000'
and substr(station, 3, 1) != 'C' and doy not in (0, 400) ORDER by doy
""", pgconn, params=(threshold, year, '%sCLIMATE' % (sector,)),
index_col='station')
if len(df.index) == 0:
return "No data found!"
def f(val):
ts = datetime.date(year, 1, 1) + datetime.timedelta(days=(val - 1))
return ts.strftime("%-m/%-d")
df['pdate'] = df['doy'].apply(f)
m = MapPlot(sector='state', state=sector, axisbg='white', nocaption=True,
title="%s %s %s$^\circ$F" % (year, PDICT2[varname], threshold),
subtitle='based on NWS COOP and IEM Daily Estimates')
levs = np.linspace(df['doy'].min() - 3, df['doy'].max() + 3, 7, dtype='i')
levlables = map(f, levs)
if popt == 'contour':
m.contourf(df['lon'], df['lat'], df['doy'], levs, clevlabels=levlables)
m.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
m.drawcounties()
return m.fig, df
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor()
nt = NetworkTable("IACLIMATE")
# Compute normal from the climate database
sql = """SELECT station,
sum(sdd86(high, low)) as sdd
from alldata_ia WHERE year = %s and month = %s
GROUP by station""" % (
now.year,
now.month,
)
lats = []
lons = []
sdd86 = []
valmask = []
ccursor.execute(sql)
for row in ccursor:
lats.append(nt.sts[row[0]]["lat"])
lons.append(nt.sts[row[0]]["lon"])
sdd86.append(float(row[1]))
valmask.append(True)
if len(sdd86) < 5:
LOG.debug("aborting due to %s obs", len(sdd86))
sys.exit()
mp = MapPlot(
axisbg="white",
title="Iowa %s SDD Accumulation" % (now.strftime("%B %Y"), ),
)
if max(sdd86) > 5:
mp.contourf(lons, lats, sdd86,
range(int(min(sdd86) - 1), int(max(sdd86) + 1)))
else:
mp.plot_values(lons, lats, sdd86, fmt="%.0f")
pqstr = "plot c 000000000000 summary/sdd_mon.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def do_month(ts, routes="m"):
"""
Generate the plot for a given month, please
"""
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
nt = NetworkTable("IACLIMATE")
sql = """SELECT station, sum(precip) as total, max(day) as lastday
from alldata_ia WHERE year = %s and month = %s
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (
ts.year,
ts.month,
)
lats = []
lons = []
vals = []
lastday = None
ccursor.execute(sql)
for row in ccursor:
if row["station"] not in nt.sts:
continue
if lastday is None:
lastday = row["lastday"]
lats.append(nt.sts[row["station"]]["lat"])
lons.append(nt.sts[row["station"]]["lon"])
vals.append(row["total"])
mp = MapPlot(
title="%s - %s"
% (ts.strftime("%d %B %Y"), lastday.strftime("%d %B %Y")),
subtitle="%s Total Precipitation [inch]" % (ts.strftime("%B %Y"),),
)
mp.contourf(
lons, lats, vals, [0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6, 7]
)
mp.plot_values(lons, lats, vals, fmt="%.2f")
pqstr = (
"plot %s %s summary/iemre_iowa_total_precip.png "
"%s/summary/iemre_iowa_total_precip.png png"
) % (routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
mp.postprocess(pqstr=pqstr)
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
if ctx['t'] == 'state':
bnds = reference.state_bounds[ctx['state']]
title = reference.state_names[ctx['state']]
else:
bnds = reference.wfo_bounds[ctx['wfo']]
nt = NetworkTable("WFO")
title = "NWS CWA %s [%s]" % (nt.sts[ctx['wfo']]['name'], ctx['wfo'])
df, valid = get_df(ctx, bnds)
if df.empty:
raise ValueError("No data was found for your query")
mp = MapPlot(sector=('state' if ctx['t'] == 'state' else 'cwa'),
state=ctx['state'],
cwa=(ctx['wfo'] if len(ctx['wfo']) == 3 else ctx['wfo'][1:]),
axisbg='white',
title='%s for %s' % (PDICT2[ctx['v']], title),
subtitle=('Map valid: %s UTC') %
(valid.strftime("%d %b %Y %H:%M"), ),
nocaption=True,
titlefontsize=16)
mp.contourf(df['lon'].values,
df['lat'].values,
df['vsby'].values,
np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9]),
units='miles',
cmap=plt.get_cmap('gray'))
if ctx['t'] == 'state':
df2 = df[df['state'] == ctx['state']]
else:
df2 = df[df['wfo'] == ctx['wfo']]
mp.plot_values(df2['lon'].values, df2['lat'].values, df2['vsby'].values,
'%.1f')
mp.drawcounties()
if ctx['t'] == 'cwa':
mp.draw_cwas()
return mp.fig, df
def main():
"""Go Main Go."""
DBCONN = get_dbconn("postgis")
cursor = DBCONN.cursor()
cursor.execute("""
SELECT
ST_Transform(
ST_Simplify(
ST_Union(ST_Transform(the_geom,2163)),
500.),
4326) from states
WHERE state_abbr in ('IA', 'IL', 'IN')
--- where state_abbr in ('MI','WI', 'IL', 'IN', 'OH', 'KY', 'MO', 'KS',
--- 'NE', 'SD', 'ND', 'MN', 'IA')
""")
m = MapPlot("iowa")
lons = np.linspace(-110, -70, 50)
lats = np.linspace(28, 52, 50)
vals = np.linspace(0, 50, 50)
m.contourf(lons, lats, vals, vals)
# """
for row in cursor:
multipoly = MultiPolygon([loads(row[0], hex=True)])
for geo in multipoly.geoms:
if geo.area < 1:
continue
(lons, lats) = geo.exterior.xy
print("Masking with geo... %s %s" % (geo.area, len(lons)))
ar = list(zip(lons, lats))
ar.reverse()
np.save("iailin_ccw.npy", ar)
# mask_outside_polygon(ar, ax=m.ax)
poly = PolygonPatch(Polygon(ar), fc="r", zorder=10000)
m.ax.add_patch(poly)
# """
m.postprocess(filename="test.png")
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
ptype = fdict.get('ptype', 'c')
date = datetime.datetime.strptime(fdict.get('date', '2015-01-01'),
'%Y-%m-%d')
varname = fdict.get('var', 'rsds')
idx0 = iemre.daily_offset(date)
nc = netCDF4.Dataset(("/mesonet/data/iemre/%s_mw_daily.nc"
) % (date.year, ), 'r')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
if varname == 'rsds':
# Value is in W m**-2, we want MJ
data = nc.variables['rsds'][idx0, :, :] * 86400. / 1000000.
units = 'MJ d-1'
nc.close()
title = date.strftime("%-d %B %Y")
m = MapPlot(sector='midwest', axisbg='white', nocaption=True,
title='IEM Reanalysis of %s for %s' % (PDICT.get(varname),
title),
subtitle='Data derived from various NOAA datasets'
)
if np.ma.is_masked(np.max(data)):
return 'Data Unavailable'
clevs = np.arange(0, 37, 3.)
clevs[0] = 0.01
x, y = np.meshgrid(lons, lats)
if ptype == 'c':
m.contourf(x, y, data, clevs, units=units)
else:
m.pcolormesh(x, y, data, clevs, units=units)
return m.fig
def main():
"""GO!"""
now = datetime.datetime.now()
df = get_df()
# df = pd.read_csv('example.csv')
rng = range(-30, 120, 2)
for sector in ["iowa", "midwest", "conus"]:
mp = MapPlot(
axisbg="white",
sector=sector,
title=("%s 2 meter Air Temperature") % (sector.capitalize(), ),
subtitle=now.strftime("%d %b %Y %-I:%M %p"),
)
mp.contourf(
df["lon"].values,
df["lat"].values,
df["tmpf"].values,
rng,
clevstride=5,
units="F",
)
mp.plot_values(df["lon"].values,
df["lat"].values,
df["tmpf"].values,
fmt="%.0f")
if sector == "iowa":
mp.drawcounties()
pqstr = ("plot ac %s00 %s_tmpf.png %s_tmpf_%s.png png"
"") % (
datetime.datetime.utcnow().strftime("%Y%m%d%H"),
sector,
sector,
datetime.datetime.utcnow().strftime("%H"),
)
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn('coop', user='******')
ccursor = pgconn.cursor()
nt = NetworkTable("IACLIMATE")
# Compute normal from the climate database
sql = """SELECT station,
sum(sdd86(high, low)) as sdd
from alldata_ia WHERE year = %s and month = %s
GROUP by station""" % (now.year, now.month)
lats = []
lons = []
sdd86 = []
valmask = []
ccursor.execute(sql)
for row in ccursor:
lats.append(nt.sts[row[0]]['lat'])
lons.append(nt.sts[row[0]]['lon'])
sdd86.append(float(row[1]))
valmask.append(True)
if len(sdd86) < 5 or max(sdd86) == 0:
sys.exit()
mp = MapPlot(axisbg='white',
title="Iowa %s SDD Accumulation" % (now.strftime("%B %Y"), ))
mp.contourf(lons, lats, sdd86,
range(int(min(sdd86) - 1), int(max(sdd86) + 1)))
pqstr = "plot c 000000000000 summary/sdd_mon.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
rise2 = loc.next_rising(sun).datetime()
loc.date = "2013/06/23"
set2 = loc.next_setting(sun).datetime()
day1 = (set1 - rise1).seconds + (set1 - rise1).microseconds / 1000000.0
day2 = (set2 - rise2).seconds + (set2 - rise2).microseconds / 1000000.0
return day1 - day2
xs, ys = np.meshgrid(np.concatenate([iemre.XAXIS, [iemre.XAXIS[-1] + 0.25, ]]),
np.concatenate([iemre.YAXIS, [iemre.YAXIS[-1] + 0.25, ]]))
secs = np.zeros(np.shape(w), 'f')
for i, lon in enumerate(iemre.XAXIS):
print i, len(iemre.XAXIS)
for j, lat in enumerate(iemre.YAXIS):
secs[j, i] = do(lat, lon)
print np.shape(w)
print np.shape(secs)
# secs = np.where(w == 1, secs, 0)
m = MapPlot(sector='midwest',
title='21 Jun to 22 Jun 2013 Decrease in Daylight Time',
subtitle='No local topography considered')
m.contourf(iemre.XAXIS, iemre.YAXIS, secs, np.arange(2, 6.1, 0.25),
units='seconds')
m.postprocess(filename='test.png')
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx["v"]
if ctx["t"] == "state":
bnds = reference.state_bounds[ctx["state"]]
title = reference.state_names[ctx["state"]]
else:
bnds = reference.wfo_bounds[ctx["wfo"]]
title = "NWS CWA %s [%s]" % (
ctx["_nt"].sts[ctx["wfo"]]["name"],
ctx["wfo"],
)
df, valid = get_df(ctx, bnds)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(
sector=("state" if ctx["t"] == "state" else "cwa"),
state=ctx["state"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="white",
title="%s for %s" % (PDICT2[ctx["v"]], title),
subtitle=("Map valid: %s UTC") % (valid.strftime("%d %b %Y %H:%M"),),
nocaption=True,
titlefontsize=16,
)
if varname == "vsby":
ramp = np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9])
valunit = "miles"
elif varname == "feel":
valunit = "F"
df["feel"] = (
apparent_temperature(
df["tmpf"].values * units("degF"),
df["relh"].values * units("percent"),
df["sknt"].values * units("knots"),
)
.to(units("degF"))
.m
)
# Data QC, cough
if ctx.get("above"):
df = df[df[varname] < ctx["above"]]
if ctx.get("below"):
df = df[df[varname] > ctx["below"]]
# with QC done, we compute ramps
if varname != "vsby":
ramp = np.linspace(
df[varname].min() - 5, df[varname].max() + 5, 10, dtype="i"
)
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
ramp,
units=valunit,
cmap=get_cmap(ctx["cmap"]),
)
if ctx["t"] == "state":
df2 = df[df["state"] == ctx["state"]]
else:
df2 = df[df["wfo"] == ctx["wfo"]]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2[varname].values,
"%.1f",
labelbuffer=10,
)
mp.drawcounties()
if ctx["t"] == "cwa":
mp.draw_cwas()
return mp.fig, df
lons = []
vals = []
valmask = []
icursor.execute(sql)
for row in icursor:
if row[4] == 0 or row[4] is None:
continue
lats.append(row[3])
lons.append(row[2])
vals.append(speed(row[4], 'KT').value('MPH'))
valmask.append((row[1] in ['AWOS', 'IA_ASOS']))
if len(vals) < 5 or True not in valmask:
sys.exit(0)
clevs = numpy.arange(0, 40, 2)
clevs = numpy.append(clevs, numpy.arange(40, 80, 5))
clevs = numpy.append(clevs, numpy.arange(80, 120, 10))
# Iowa
pqstr = "plot ac %s summary/today_gust.png iowa_wind_gust.png png" % (
now.strftime("%Y%m%d%H%M"), )
m = MapPlot(title="Iowa ASOS/AWOS Peak Wind Speed Reports",
subtitle="%s" % (now.strftime("%d %b %Y"), ),
sector='iowa')
m.contourf(lons, lats, vals, clevs, units='MPH')
m.plot_values(lons, lats, vals, '%.0f', valmask=valmask)
m.drawcounties()
m.postprocess(pqstr=pqstr, view=False)
m.close()
for row in ccursor:
station = row[0]
if station not in st.sts:
continue
nrain.append(row[1])
lats.append(st.sts[station]['lat'])
lons.append(st.sts[station]['lon'])
m = MapPlot(axisbg='white',
title=("Iowa %s Normal Precipitation Accumulation"
) % (ts.strftime("%Y"), ),
subtitle="1 Jan - %s" % (ts.strftime("%d %b %Y"), ))
rng = np.arange(int(min(nrain))-1, int(max(nrain))+1)
if max(nrain) < 10:
rng = np.arange(0, 10)
m.contourf(lons, lats, nrain, rng, units='inch')
pqstr = "plot c 000000000000 summary/year/normals.png bogus png"
m.postprocess(view=False, pqstr=pqstr)
m.close()
# ----------------------------------
# - Compute departures
nrain = []
lats = []
lons = []
ccursor.execute("""select climate.station, norm, obs from
(select c.station, sum(c.precip) as norm from climate51 c
where c.valid < '2000-%s' and substr(station,0,3) = 'IA'
GROUP by c.station) as climate,
(select a.station, sum(a.precip) as obs from alldata a
WHERE a.year = %s and substr(a.station,0,3) = 'IA'
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
ptype = ctx["ptype"]
date = ctx["date"]
varname = ctx["var"]
csector = ctx["csector"]
title = date.strftime("%-d %B %Y")
mp = MapPlot(
sector=("state" if len(csector) == 2 else csector),
state=ctx["csector"],
axisbg="white",
nocaption=True,
title="IEM Reanalysis of %s for %s" % (PDICT.get(varname), title),
subtitle="Data derived from various NOAA datasets",
)
(west, east, south, north) = mp.ax.get_extent(ccrs.PlateCarree())
i0, j0 = iemre.find_ij(west, south)
i1, j1 = iemre.find_ij(east, north)
jslice = slice(j0, j1)
islice = slice(i0, i1)
idx0 = iemre.daily_offset(date)
ncfn = iemre.get_daily_ncname(date.year)
if not os.path.isfile(ncfn):
raise NoDataFound("No Data Found.")
with ncopen(ncfn) as nc:
lats = nc.variables["lat"][jslice]
lons = nc.variables["lon"][islice]
cmap = ctx["cmap"]
if varname in ["rsds", "power_swdn"]:
# Value is in W m**-2, we want MJ
multi = (86400.0 / 1000000.0) if varname == "rsds" else 1
data = nc.variables[varname][idx0, jslice, islice] * multi
plot_units = "MJ d-1"
clevs = np.arange(0, 37, 3.0)
clevs[0] = 0.01
clevstride = 1
elif varname in ["wind_speed"]:
data = (masked_array(
nc.variables[varname][idx0, jslice, islice],
units("meter / second"),
).to(units("mile / hour")).m)
plot_units = "mph"
clevs = np.arange(0, 41, 2)
clevs[0] = 0.01
clevstride = 2
elif varname in ["p01d", "p01d_12z", "snow_12z", "snowd_12z"]:
# Value is in W m**-2, we want MJ
data = (masked_array(nc.variables[varname][idx0, jslice, islice],
units("mm")).to(units("inch")).m)
plot_units = "inch"
clevs = np.arange(0, 0.25, 0.05)
clevs = np.append(clevs, np.arange(0.25, 3.0, 0.25))
clevs = np.append(clevs, np.arange(3.0, 10.0, 1))
clevs[0] = 0.01
clevstride = 1
cmap = stretch_cmap(ctx["cmap"], clevs)
elif varname in [
"high_tmpk",
"low_tmpk",
"high_tmpk_12z",
"low_tmpk_12z",
"avg_dwpk",
]:
# Value is in W m**-2, we want MJ
data = (masked_array(nc.variables[varname][idx0, jslice, islice],
units("degK")).to(units("degF")).m)
plot_units = "F"
clevs = np.arange(-30, 120, 5)
clevstride = 2
elif varname in ["range_tmpk", "range_tmpk_12z"]:
vname1 = "high_tmpk%s" % ("_12z"
if varname == "range_tmpk_12z" else "", )
vname2 = "low_tmpk%s" % ("_12z"
if varname == "range_tmpk_12z" else "", )
d1 = nc.variables[vname1][idx0, jslice, islice]
d2 = nc.variables[vname2][idx0, jslice, islice]
data = (masked_array(d1, units("degK")).to(units("degF")).m -
masked_array(d2, units("degK")).to(units("degF")).m)
plot_units = "F"
clevs = np.arange(0, 61, 5)
clevstride = 2
if np.ma.is_masked(np.max(data)):
raise NoDataFound("Data Unavailable")
x, y = np.meshgrid(lons, lats)
if ptype == "c":
# in the case of contour, use the centroids on the grids
mp.contourf(
x + 0.125,
y + 0.125,
data,
clevs,
clevstride=clevstride,
units=plot_units,
ilabel=True,
labelfmt="%.0f",
cmap=cmap,
)
else:
x, y = np.meshgrid(lons, lats)
mp.pcolormesh(
x,
y,
data,
clevs,
clevstride=clevstride,
cmap=cmap,
units=plot_units,
)
return mp.fig
delta = rise.hour * 60 + rise.minute
now += interval
while True:
ames.date = now.strftime("%Y/%m/%d")
rise2 = mx.DateTime.strptime(str(ames.next_rising(sun)), "%Y/%m/%d %H:%M:%S")
rise2 = rise2.localtime()
delta2 = rise2.hour * 60 + rise2.minute
if delta2 < delta:
return (rise2 - rise).days
now += interval
return doy, arr, returnD
lats = []
lons = []
vals = []
for lon in tqdm.tqdm(range(-130, -60, 2)):
for lat in range(20, 55, 1):
lats.append(lat)
lons.append(lon)
vals.append(compute_sunrise(str(lat), str(lon)))
m = MapPlot(sector='conus',
title='Days to Recover Morning Hour after Spring Saving Time Change',
subtitle=('days until local time of sunrise is earlier '
'than on 10 March, local DST rules ignored for plot'))
m.contourf(lons, lats, vals, range(27, 78, 3), units='days')
m.postprocess(filename='180313.png')
def run(basets, endts, view):
"""Generate this plot for the given basets"""
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""",
POSTGIS,
params=(basets, endts),
index_col=None)
df['used'] = False
df['textplot'] = True
df.sort_values(by='val', ascending=False, inplace=True)
# Now, we need to add in zeros, lets say we are looking at a .25 degree box
mybuffer = 0.75
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat + mybuffer)) &
(df['lon'] >= lon) & (df['lon'] < (lon + mybuffer))]
if len(df2.index) == 0:
newrows.append(
dict(lon=(lon + mybuffer / 2.),
lat=(lat + mybuffer / 2.),
val=0,
used=True,
textplot=False))
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
dfnew = pd.DataFrame(newrows)
df = df.append(dfnew)
cdf = df[df['used']]
tdf = df[df['textplot']]
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
m = MapPlot(sector='iowa',
axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values,
cdf['lat'].values,
cdf['val'].values,
rng,
cmap=cmap)
m.drawcounties()
m.plot_values(tdf['lon'].values,
tdf['lat'].values,
tdf['val'].values,
fmt='%.1f')
m.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
# slightly different title to help uniqueness
m = MapPlot(sector='iowa',
axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values,
cdf['lat'].values,
cdf['val'].values,
rng,
cmap=cmap)
m.drawcounties()
m.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest',
axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values,
cdf['lat'].values,
cdf['val'].values,
rng,
cmap=cmap)
m.drawcities()
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
tmpf >= -50 and tmpf < 140
"""
lats = []
lons = []
vals = []
valmask = []
icursor.execute(sql)
for row in icursor:
lats.append(row['lat'])
lons.append(row['lon'])
vals.append(row['tmpf'])
valmask.append((row['network'] in ['AWOS', 'IA_ASOS']))
rng = np.arange(-30, 120, 2)
for sector in ['iowa', 'midwest', 'conus']:
m = MapPlot(axisbg='white',
sector=sector,
title="%s 2 meter Air Temperature" % (sector.capitalize(), ),
subtitle=now.strftime("%d %b %Y %-I:%M %p"))
m.contourf(lons, lats, vals, rng, clevstride=5, units='F')
m.plot_values(lons, lats, vals, fmt='%.0f')
if sector == 'iowa':
m.drawcounties()
pqstr = ("plot ac %s00 %s_tmpf.png %s_tmpf_%s.png png"
"") % (datetime.datetime.utcnow().strftime("%Y%m%d%H"), sector,
sector, datetime.datetime.utcnow().strftime("%H"))
m.postprocess(view=False, pqstr=pqstr)
m.close()
dates = []
sites = []
for row in cursor:
station = row[0]
if station == 'IA0000' or station[2] == 'C':
continue
if station not in nt.sts:
continue
if station in ['IA3909', 'IA0364', 'IA3473', 'IA1394']:
continue
sites.append(station)
lats.append(nt.sts[station]['lat'])
lons.append(nt.sts[station]['lon'])
vals.append(float(row[1]))
d = datetime.date(2015, 1, 1) + datetime.timedelta(days=int(row[1]))
dates.append(d.strftime("%-d %b"))
rng = range(int(min(vals)-2), int(max(vals)+4), 4)
labels = []
for i in rng:
dt = datetime.date(2015, 1, 1) + datetime.timedelta(days=i)
labels.append(dt.strftime("%-d %b"))
m = MapPlot(title="Average First Date of Low Temperature At or Below 28$^\circ$F",
subtitle="based on stations with data between 1951-2014")
m.contourf(lons, lats, vals, rng, clevlabels=labels,
cmap=plt.get_cmap("jet"))
m.drawcounties()
#m.plot_values(lons, lats, dates, fmt='%s', labels=sites)
m.postprocess(filename='test.png')
def doit(ts, hours):
"""
Create a plot of precipitation stage4 estimates for some day
"""
# Start at 1 AM
ts = ts.replace(minute=0, second=0, microsecond=0)
now = ts - datetime.timedelta(hours=hours-1)
interval = datetime.timedelta(hours=1)
ets = datetime.datetime.utcnow()
ets = ets.replace(tzinfo=pytz.timezone("UTC"))
total = None
while now < ets:
gmt = now.astimezone(pytz.timezone("UTC"))
gribfn = None
for prefix in ['GaugeCorr', 'RadarOnly']:
fn = gmt.strftime((prefix + "_QPE_01H_00.00_%Y%m%d-%H%M00"
".grib2.gz"))
res = requests.get(gmt.strftime(
("http://mtarchive.geol.iastate.edu/%Y/%m/%d/mrms/ncep/" +
prefix + "_QPE_01H/" + fn)), timeout=30)
if res.status_code != 200:
continue
o = open(TMP + "/" + fn, 'wb')
o.write(res.content)
o.close()
gribfn = "%s/%s" % (TMP, fn)
break
if gribfn is None:
print("q3_Xhour.py[%s] MISSING %s" % (hours, now))
now += interval
continue
fp = gzip.GzipFile(gribfn, 'rb')
(tmpfp, tmpfn) = tempfile.mkstemp()
tmpfp = open(tmpfn, 'wb')
tmpfp.write(fp.read())
tmpfp.close()
grbs = pygrib.open(tmpfn)
grb = grbs[1]
os.unlink(tmpfn)
# careful here, how we deal with the two missing values!
if total is None:
total = grb['values']
else:
maxgrid = np.maximum(grb['values'], total)
total = np.where(np.logical_and(grb['values'] >= 0,
total >= 0),
grb['values'] + total, maxgrid)
now += interval
os.unlink(gribfn)
if total is None:
print("q3_Xhour.py no data ts: %s hours: %s" % (ts, hours))
return
# Scale factor is 10
routes = "c"
if ts.minute == 0:
routes = "ac"
pqstr = "plot %s %s iowa_q2_%sh.png q2/iowa_q2_%sh_%s00.png png" % (
routes, ts.strftime("%Y%m%d%H%M"), hours, hours,
ts.strftime("%H"))
lts = ts.astimezone(pytz.timezone("America/Chicago"))
subtitle = 'Total up to %s' % (lts.strftime("%d %B %Y %I:%M %p %Z"),)
m = MapPlot(title=("NCEP MRMS Q3 (RADAR Only) %s Hour "
"Precipitation [inch]") % (hours,),
subtitle=subtitle)
clevs = [0.01, 0.1, 0.3, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10]
m.contourf(mrms.XAXIS, mrms.YAXIS,
distance(np.flipud(total), 'MM').value('IN'), clevs,
cmap=nwsprecip())
m.drawcounties()
m.postprocess(pqstr=pqstr, view=False)
m.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
sector = fdict.get('sector', 'IA')
date1 = datetime.datetime.strptime(fdict.get('date1', '2015-01-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2015-02-01'),
'%Y-%m-%d')
varname = fdict.get('var', 'precip_depart')
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
df = read_sql("""
WITH obs as (
SELECT station, gddxx(50, 86, high, low) as gdd50,
sday, high, low, precip from """ + table + """ WHERE
day >= %s and day < %s and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip, high, low,
gdd50 from climate51),
combo as (
SELECT o.station, o.precip - c.precip as precip_diff,
o.high, o.low, o.gdd50, c.gdd50 as cgdd50,
o.gdd50 - c.gdd50 as gdd50_diff,
(o.high + o.low)/2. - (c.high + c.low)/2. as temp_diff
from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
agg as (
SELECT station, sum(precip_diff) as precip_depart,
min(low) as min_low_temp, sum(gdd50_diff) as gdd_depart,
avg(temp_diff) as avg_temp_depart, sum(gdd50) as gdd_sum,
sum(cgdd50) as cgdd_sum
from combo GROUP by station)
SELECT d.station, d.precip_depart, d.min_low_temp, d.avg_temp_depart,
d.gdd_depart, d.gdd_sum, d.cgdd_sum,
ST_x(t.geom) as lon, ST_y(t.geom) as lat
from agg d JOIN stations t on (d.station = t.id)
WHERE t.network ~* 'CLIMATE'
""", pgconn, params=(date1, date2), index_col='station')
sector2 = "state" if sector != 'midwest' else 'midwest'
m = MapPlot(sector=sector2, state=sector, axisbg='white',
title=('%s - %s %s [%s]'
) % (date1.strftime("%d %b %Y"),
date2.strftime("%d %b %Y"), PDICT2.get(varname),
UNITS.get(varname)),
subtitle=('%s is compared with 1951-%s Climatology'
' to compute departures'
) % (date1.year, datetime.date.today().year - 1))
if varname in ['precip_depart', 'avg_temp_depart']:
rng = df[varname].abs().describe(percentiles=[0.95])['95%']
clevels = np.linspace(0 - rng, rng, 7)
fmt = '%.2f'
else:
minv = df[varname].min() - 5
maxv = df[varname].max() + 5
clevels = np.linspace(minv, maxv, 6, dtype='i')
fmt = '%.0f'
clevlabels = [fmt % x for x in clevels]
cmap = cm.get_cmap('RdYlBu' if varname == 'precip_depart' else 'RdYlBu_r')
cmap.set_bad('white')
m.contourf(df['lon'].values, df['lat'].values,
df[varname].values, clevels, clevlabels=clevlabels,
cmap=cmap, units=UNITS.get(varname))
m.plot_values(df['lon'].values, df['lat'].values,
df[varname].values, fmt=fmt, labelbuffer=10)
if sector == 'IA':
m.drawcounties()
return m.fig, df
def doit(ts, hours):
"""
Create a plot of precipitation stage4 estimates for some day
"""
# Start at 1 AM
ts = ts.replace(minute=0, second=0, microsecond=0)
now = ts - datetime.timedelta(hours=hours - 1)
interval = datetime.timedelta(hours=1)
ets = datetime.datetime.utcnow()
ets = ets.replace(tzinfo=pytz.timezone("UTC"))
total = None
while now < ets:
gmt = now.astimezone(pytz.timezone("UTC"))
for prefix in ["GaugeCorr", "RadarOnly"]:
gribfn = gmt.strftime(
(
"/mnt/a4/data/%Y/%m/%d/mrms/ncep/" + prefix + "_QPE_01H/" + prefix + "_QPE_01H_00.00_%Y%m%d-%H%M00"
".grib2.gz"
)
)
if os.path.isfile(gribfn):
break
if not os.path.isfile(gribfn):
print("q3_Xhour.py MISSING %s" % (gribfn,))
now += interval
continue
fp = gzip.GzipFile(gribfn, "rb")
(tmpfp, tmpfn) = tempfile.mkstemp()
tmpfp = open(tmpfn, "wb")
tmpfp.write(fp.read())
tmpfp.close()
grbs = pygrib.open(tmpfn)
grb = grbs[1]
os.unlink(tmpfn)
# careful here, how we deal with the two missing values!
if total is None:
total = grb["values"]
else:
maxgrid = np.maximum(grb["values"], total)
total = np.where(np.logical_and(grb["values"] >= 0, total >= 0), grb["values"] + total, maxgrid)
now += interval
if total is None:
print("q3_Xhour.py no data ts: %s hours: %s" % (ts, hours))
return
# Scale factor is 10
routes = "c"
if ts.minute == 0:
routes = "ac"
pqstr = "plot %s %s iowa_q2_%sh.png q2/iowa_q2_%sh_%s00.png png" % (
routes,
ts.strftime("%Y%m%d%H%M"),
hours,
hours,
ts.strftime("%H"),
)
lts = ts.astimezone(pytz.timezone("America/Chicago"))
subtitle = "Total up to %s" % (lts.strftime("%d %B %Y %I:%M %p %Z"),)
m = MapPlot(title=("NCEP MRMS Q3 (RADAR Only) %s Hour " "Precipitation [inch]") % (hours,), subtitle=subtitle)
clevs = np.arange(0, 0.2, 0.05)
clevs = np.append(clevs, np.arange(0.2, 1.0, 0.1))
clevs = np.append(clevs, np.arange(1.0, 5.0, 0.25))
clevs = np.append(clevs, np.arange(5.0, 10.0, 1.0))
clevs[0] = 0.01
m.contourf(mrms.XAXIS, mrms.YAXIS, np.flipud(total) / 24.5, clevs)
m.drawcounties()
m.postprocess(pqstr=pqstr)
import pygrib
from pyiem.plot import MapPlot
from pyiem import reference
import numpy as np
from pyiem.datatypes import distance
g = pygrib.open('p168m_2016090600f168.grb')
grb = g[1]
lats, lons = grb.latlons()
m = MapPlot(sector='custom', project='aea',
south=reference.MW_SOUTH, north=reference.MW_NORTH,
east=reference.MW_EAST, west=reference.MW_WEST,
axisbg='tan',
title=("Weather Prediction Center (WPC) "
"7 Day Forecasted Precipitation"),
subtitle='7 PM 5 September thru 7 PM 12 September 2016')
m.contourf(lons, lats, distance(grb['values'], 'MM').value('IN'),
np.arange(0, 4.6, 0.25), units='inches')
m.postprocess(filename='test.png')
s.iemid = c.iemid and
(valid + '30 minutes'::interval) > now() and
tmpf >= -50 and tmpf < 140
"""
lats = []
lons = []
vals = []
valmask = []
icursor.execute(sql)
for row in icursor:
lats.append(row['lat'])
lons.append(row['lon'])
vals.append(row['tmpf'])
valmask.append((row['network'] in ['AWOS', 'IA_ASOS']))
rng = np.arange(-30, 120, 10)
for sector in ['iowa', 'midwest', 'conus']:
m = MapPlot(axisbg='white', sector=sector,
title="%s 2 meter Air Temperature" % (sector.capitalize(), ),
subtitle=now.strftime("%d %b %Y %-I:%M %p"))
m.contourf(lons, lats, vals, rng)
if sector == 'iowa':
m.plot_values(lons, lats, vals, fmt='%.0f')
pqstr = ("plot ac %s00 %s_tmpf.png %s_tmpf_%s.png png"
"") % (datetime.datetime.utcnow().strftime("%Y%m%d%H"),
sector, sector, datetime.datetime.utcnow().strftime("%H"))
m.postprocess(view=False, pqstr=pqstr)
m.close()
dates = []
sites = []
for row in cursor:
station = row[0]
if station == 'IA0000' or station[2] == 'C':
continue
if station not in nt.sts:
continue
if station in ['IA3909', 'IA0364', 'IA3473', 'IA1394']:
continue
sites.append(station)
lats.append(nt.sts[station]['lat'])
lons.append(nt.sts[station]['lon'])
vals.append(float(row[1]))
d = datetime.date(2015, 1, 1) + datetime.timedelta(days=int(row[1]))
dates.append(d.strftime("%-d %b"))
rng = range(int(min(vals) - 2), int(max(vals) + 4), 4)
labels = []
for i in rng:
dt = datetime.date(2015, 1, 1) + datetime.timedelta(days=i)
labels.append(dt.strftime("%-d %b"))
m = MapPlot(
title="Average First Date of Low Temperature At or Below 28$^\circ$F",
subtitle="based on stations with data between 1951-2014")
m.contourf(lons, lats, vals, rng, clevlabels=labels, cmap=plt.get_cmap("jet"))
m.drawcounties()
#m.plot_values(lons, lats, dates, fmt='%s', labels=sites)
m.postprocess(filename='test.png')
def run(basets, endts, view):
"""Generate this plot for the given basets"""
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""", POSTGIS, params=(basets, endts), index_col=None)
df['used'] = False
df['textplot'] = True
df.sort_values(by='val', ascending=False, inplace=True)
# Now, we need to add in zeros, lets say we are looking at a .25 degree box
mybuffer = 0.75
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat+mybuffer)) &
(df['lon'] >= lon) & (df['lon'] < (lon+mybuffer))]
if len(df2.index) == 0:
newrows.append(dict(lon=(lon+mybuffer/2.),
lat=(lat+mybuffer/2.),
val=0, used=True, textplot=False))
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
dfnew = pd.DataFrame(newrows)
df = df.append(dfnew)
cdf = df[df['used']]
tdf = df[df['textplot']]
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
m.plot_values(tdf['lon'].values, tdf['lat'].values, tdf['val'].values,
fmt='%.1f')
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
# slightly different title to help uniqueness
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
state = ctx['state'][:2]
sector = ctx['sector']
opt = ctx['opt']
p1syear = ctx['p1syear']
p1eyear = ctx['p1eyear']
p2syear = ctx['p2syear']
p2eyear = ctx['p2eyear']
varname = ctx['var']
table = "alldata"
if sector == 'state':
table = "alldata_%s" % (state, )
df = read_sql("""
WITH season1 as (
SELECT station, year,
min(case when month > 7 and low < 32 then
extract(doy from day) else 366 end) as first_freeze,
max(case when month < 7 and low < 32 then
extract(doy from day) else 0 end) as last_freeze
from """ + table + """ WHERE
year >= %s and year <= %s GROUP by station, year),
season2 as (
SELECT station, year,
min(case when month > 7 and low < 32 then
extract(doy from day) else 366 end) as first_freeze,
max(case when month < 7 and low < 32 then
extract(doy from day) else 0 end) as last_freeze
from """ + table + """ WHERE
year >= %s and year <= %s GROUP by station, year),
agg as (
SELECT p1.station, avg(p1.first_freeze) as p1_first_fall,
avg(p1.last_freeze) as p1_last_spring,
avg(p2.first_freeze) as p2_first_fall,
avg(p2.last_freeze) as p2_last_spring
from season1 as p1 JOIN season2 as p2 on (p1.station = p2.station)
GROUP by p1.station)
SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat,
d.* from agg d JOIN stations t ON (d.station = t.id)
WHERE t.network in ('IACLIMATE', 'NDCLIMATE', 'SDCLIMATE', 'NECLIMATE',
'KSCLIMATE', 'MOCLIMATE', 'ILCLIMATE', 'WICLIMATE', 'MNCLIMATE',
'MICLIMATE', 'INCLIMATE', 'OHCLIMATE', 'KYCLIMATE')
and substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'
""",
pgconn,
params=[p1syear, p1eyear, p2syear, p2eyear],
index_col='station')
df['p1_season'] = df['p1_first_fall'] - df['p1_last_spring']
df['p2_season'] = df['p2_first_fall'] - df['p2_last_spring']
df['season_delta'] = df['p2_season'] - df['p1_season']
df['spring_delta'] = df['p2_last_spring'] - df['p1_last_spring']
df['fall_delta'] = df['p2_first_fall'] - df['p1_first_fall']
# Reindex so that most extreme values are first
df = df.reindex(df[varname +
'_delta'].abs().sort_values(ascending=False).index)
title = PDICT3[varname]
m = MapPlot(sector=sector,
state=state,
axisbg='white',
title=('%.0f-%.0f minus %.0f-%.0f %s Difference') %
(p2syear, p2eyear, p1syear, p1eyear, title),
subtitle=('based on IEM Archives'),
titlefontsize=14)
# Create 9 levels centered on zero
abval = df[varname + '_delta'].abs().max()
levels = centered_bins(abval)
if opt in ['both', 'contour']:
m.contourf(df['lon'].values,
df['lat'].values,
df[varname + '_delta'].values,
levels,
cmap=plt.get_cmap('seismic'),
units='days')
if sector == 'state':
m.drawcounties()
if opt in ['both', 'values']:
m.plot_values(df['lon'].values,
df['lat'].values,
df[varname + '_delta'].values,
fmt='%.1f',
labelbuffer=5)
return m.fig, df
from
(SELECT station, year, min(low) as m from alldata_ia where
month > 6 and sday < '1005' and year > 1950 GROUP by station, year) as foo
) as foo2 WHERE station != 'IA0000' and substr(station,3,1) != 'C'
GROUP by station ORDER by ma DESC
""")
lats = []
lons = []
vals = []
for row in ccursor:
if not nt.sts.has_key(row[0]):
continue
#if nt.sts[row[0]]['archive_begin'].year > 1893:
# continue
print row
#vals.append( float(row[3]) / float(row[4]) * 100.0 )
vals.append( row[1] / float(row[2]) * 100.0 )
lats.append( nt.sts[row[0]]['lat'] )
lons.append( nt.sts[row[0]]['lon'] )
import numpy as np
m = MapPlot(sector='iowa', title='Fall Minimum Temperature Percentile [100=warmest]',
subtitle='thru 5 October, 2014 vs 1951-2013')
cmap = cm.get_cmap('jet')
m.contourf(lons, lats, vals, np.arange(0,101,10), cmap=cmap)
m.plot_values(lons, lats, vals, '%.0f')
m.drawcounties()
m.postprocess(filename='test.ps')
iemplot.makefeature('test')
def main():
"""Go Main Go."""
pgconn = get_dbconn('iem')
cursor = pgconn.cursor()
cursor.execute("""SELECT ST_x(geom) as lon, ST_y(geom) as lat,
pday from summary_2016 s JOIN stations t on (s.iemid = t.iemid)
where day = '2016-08-24' and network in ('WI_COOP', 'MN_COOP', 'IA_COOP')
and pday > 0 ORDER by pday DESC""")
llons = []
llats = []
vals = []
for row in cursor:
llons.append(row[0])
llats.append(row[1])
vals.append("%.2f" % (row[2], ))
pgconn = get_dbconn('postgis')
cursor = pgconn.cursor()
cursor.execute("""SELECT ST_x(geom) as lon, ST_y(geom) as lat,
max(magnitude) from lsrs_2016
where wfo in ('DMX', 'DVN', 'ARX') and typetext = 'HEAVY RAIN' and
valid > '2016-08-23' GROUP by lon, lat ORDER by max DESC""")
for row in cursor:
llons.append(row[0])
llats.append(row[1])
vals.append("%.2f" % (row[2], ))
img = Image.open("p24h_201608241200.png")
data = np.flipud(np.asarray(img))
# 7000,3500 == -130,-60,55,25 === -100 to -90 then 38 to 45
sample = data[1800:2501, 3000:4501]
sample = np.where(sample == 255, 0, sample)
data = sample * 0.01
data = np.where(sample > 100, 1. + (sample - 100) * 0.05, data)
data = np.where(sample > 180, 5. + (sample - 180) * 0.2, data)
lons = np.arange(-100, -84.99, 0.01)
lats = np.arange(38, 45.01, 0.01)
x, y = np.meshgrid(lons, lats)
buff = 0.5
m = MapPlot(sector='custom', projection='aea', west=-93.2,
east=-90.3, south=42.5,
north=44.,
title='NOAA MRMS 24 Hour RADAR-Only Precipitation Estimate',
subtitle=("MRMS valid 7 AM 23 Aug 2016 to 7 AM 24 Aug 2016, "
"NWS Local Storm + COOP Reports Overlaid"))
clevs = [0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8,
10]
m.contourf(x[:, :-1], y[:, :-1], data, clevs, cmap=nwsprecip())
nt = NetworkTable("IA_ASOS")
lo = []
la = []
va = []
for sid in nt.sts.keys():
lo.append(nt.sts[sid]['lon'])
la.append(nt.sts[sid]['lat'])
va.append(nt.sts[sid]['name'])
# m.plot_values(lo, la, va, fmt='%s', textsize=10, color='black')
m.drawcounties(zorder=4, linewidth=1.)
m.drawcities(labelbuffer=25, textsize=10, color='white',
outlinecolor='#000000')
m.textmask[:, :] = 0
m.plot_values(llons, llats, vals, fmt='%s', labelbuffer=5)
m.postprocess(filename='test.png')
vals = []
valmask = []
cursor.execute(sql)
for row in cursor:
lats.append(row[4])
lons.append(row[3])
vals.append(row[2])
valmask.append(row[1] in ['AWOS', 'IA_ASOS'])
if len(lats) < 5:
sys.exit(0)
now = datetime.datetime.now()
m = MapPlot(sector='iowa',
title='Iowa Visibility',
subtitle='Valid: %s' % (now.strftime("%d %b %Y %-I:%M %p"),))
m.contourf(lons, lats, vals, np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3,
5, 8, 9.9]),
units='miles', cmap=cm.get_cmap('gray'))
m.plot_values(lons, lats, vals, '%.1f', valmask)
m.drawcounties()
pqstr = ("plot ac %s00 iowa_vsby.png vsby_contour_%s00.png png"
"") % (datetime.datetime.utcnow().strftime("%Y%m%d%H"),
datetime.datetime.utcnow().strftime("%H"))
m.postprocess(pqstr=pqstr)
m.close()
("IACLIMATE", "MNCLIMATE", "NDCLIMATE", "SDCLIMATE",
"NECLIMATE", "KSCLIMATE", "MOCLIMATE", "KYCLIMATE", "ILCLIMATE",
"WICLIMATE", "INCLIMATE", "OHCLIMATE", "MICLIMATE"))
cursor.execute("""
SELECT station, sum(case when precip >= 2 then precip else 0 end) /
sum(precip) from alldata WHERE year > 1980 and precip > 0
and year < 2011 GROUP by station
""")
lats = []
lons = []
vals = []
for row in cursor:
if row[0][2] == 'C' or row[0][2:] == '0000':
continue
if row[0] not in nt.sts:
continue
if row[0][:2] == 'MI' and row[1] > 0.2:
print row
lats.append(nt.sts[row[0]]['lat'])
lons.append(nt.sts[row[0]]['lon'])
vals.append(row[1] * 100.0)
m = MapPlot(sector='midwest',
title=('Contribution of Daily 2+ inch '
'Precip Events to Yearly Total'),
subtitle='1981-2010 based on IEM archive of NWS COOP Data')
m.contourf(lons, lats, vals, np.arange(0, 41, 2), units='%')
m.postprocess(filename='twoinch_1981_2010.png')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
date1 = ctx['date1']
date2 = ctx['date2']
varname = ctx['var']
usdm = ctx['usdm']
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
df = read_sql("""
WITH obs as (
SELECT station, gddxx(50, 86, high, low) as gdd50,
sday, high, low, precip from """ + table + """ WHERE
day >= %s and day < %s and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip, high, low,
gdd50 from climate51),
combo as (
SELECT o.station, o.precip - c.precip as precip_diff,
o.precip as precip, c.precip as cprecip,
o.high, o.low, o.gdd50, c.gdd50 as cgdd50,
o.gdd50 - c.gdd50 as gdd50_diff,
(o.high + o.low)/2. - (c.high + c.low)/2. as temp_diff
from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
agg as (
SELECT station, sum(precip_diff) as precip_depart,
sum(precip) as precip, sum(cprecip) as cprecip,
min(low) as min_low_temp, sum(gdd50_diff) as gdd_depart,
avg(temp_diff) as avg_temp_depart, sum(gdd50) as gdd_sum,
sum(cgdd50) as cgdd_sum
from combo GROUP by station)
SELECT d.station, t.name,
d.precip as precip_sum, d.cprecip as cprecip_sum,
d.precip_depart, d.min_low_temp, d.avg_temp_depart,
d.gdd_depart, d.gdd_sum, d.cgdd_sum,
ST_x(t.geom) as lon, ST_y(t.geom) as lat
from agg d JOIN stations t on (d.station = t.id)
WHERE t.network ~* 'CLIMATE'
""",
pgconn,
params=(date1, date2),
index_col='station')
df = df.reindex(df[varname].abs().sort_values(ascending=False).index)
sector2 = "state" if sector != 'midwest' else 'midwest'
datefmt = "%d %b %Y" if varname != 'cgdd_sum' else '%d %b'
subtitle = ("Climatology is based on data from 1951-%s") % (
datetime.date.today().year - 1, )
if varname != 'cgdd_sum':
subtitle = ('%s is compared with 1951-%s Climatology'
' to compute departures') % (
date1.year, datetime.date.today().year - 1)
mp = MapPlot(sector=sector2,
state=sector,
axisbg='white',
title=('%s - %s %s [%s]') %
(date1.strftime(datefmt), date2.strftime(datefmt),
PDICT2.get(varname), UNITS.get(varname)),
subtitle=subtitle)
if varname in ['precip_depart', 'avg_temp_depart']:
rng = df[varname].abs().describe(percentiles=[0.95])['95%']
clevels = np.linspace(0 - rng, rng, 7)
fmt = '%.2f'
else:
minv = df[varname].min() - 5
maxv = df[varname].max() + 5
clevels = np.linspace(minv, maxv, 6, dtype='i')
fmt = '%.0f'
clevlabels = [fmt % x for x in clevels]
cmap = cm.get_cmap('RdYlBu' if varname == 'precip_depart' else 'RdYlBu_r')
cmap.set_bad('white')
mp.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
clevels,
clevlabels=clevlabels,
cmap=cmap,
units=UNITS.get(varname))
mp.plot_values(df['lon'].values,
df['lat'].values,
df[varname].values,
fmt=fmt,
labelbuffer=10)
if sector == 'IA':
mp.drawcounties()
if usdm == 'yes':
mp.draw_usdm(date2, filled=False, hatched=True)
return mp.fig, df
