def run(ts, routes):
""" Run for a given UTC timestamp """
fn = ts.strftime("/mesonet/ARCHIVE/data/%Y/%m/%d/model/rtma/%H/rtma.t%Hz.awp2p5f000.grib2")
if not os.path.isfile(fn):
print "wind_power.py missing", fn
return
grb = pygrib.open(fn)
u = grb.select(name="10 metre U wind component")[0]
v = grb.select(name="10 metre V wind component")[0]
mag = (u["values"] ** 2 + v["values"] ** 2) ** 0.5
mag = (mag * 1.35) ** 3 * 0.002641
# 0.002641
lats, lons = u.latlons()
lts = ts.astimezone(pytz.timezone("America/Chicago"))
pqstr = "plot %s %s00 midwest/rtma_wind_power.png midwest/rtma_wind_power_%s00.png png" % (
routes,
ts.strftime("%Y%m%d%H"),
ts.strftime("%H"),
)
m = MapPlot(
sector="midwest",
title=r"Wind Power Potential :: (speed_mps_10m * 1.35)$^3$ * 0.002641",
subtitle="valid: %s based on NOAA Realtime Mesoscale Analysis" % (lts.strftime("%d %b %Y %I %p")),
)
m.pcolormesh(lons, lats, mag, numpy.array(levels), units="MW")
m.postprocess(pqstr=pqstr)
def main():
"""Go Main"""
grbs = pygrib.open('ds.snow.bin')
# skip 1-off first field
total = None
lats = lons = None
for grb in grbs[1:]:
if lats is None:
lats, lons = grb.latlons()
total = grb['values']
continue
total += grb['values']
# TODO tz-hack here
analtime = grb.analDate - datetime.timedelta(hours=5)
mp = MapPlot(
sector='custom', west=-100, east=-92, north=45, south=41,
axisbg='tan',
title=("NWS Forecasted Accumulated Snowfall "
"thru 7 PM 12 April 2019"),
subtitle='NDFD Forecast Issued %s' % (
analtime.strftime("%-I %p %-d %B %Y"), )
)
cmap = nwssnow()
cmap.set_bad('tan')
mp.pcolormesh(
lons, lats, total * 39.3701,
[0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36],
cmap=cmap,
units='inch')
mp.drawcounties()
mp.drawcities()
mp.postprocess(filename='test.png')
mp.close()
def plot():
"""Do plotting work"""
cmap = plt.get_cmap('inferno_r')
# cmap.set_under('black')
# cmap.set_over('red')
minval = (np.load('minval.npy') * units.degK).to(units.degF)
maxval = (np.load('maxval.npy') * units.degK).to(units.degF)
diff = maxval - minval
lons = np.load('lons.npy')
lats = np.load('lats.npy')
mp = MapPlot(sector='conus',
title=(r"Difference between warmest 3 Oct and coldest 4 "
"Oct 2m Temperature"),
subtitle=("based on hourly NCEP Real-Time Mesoscale Analysis "
"(RTMA) ending midnight CDT"))
mp.ax.text(0.5, 0.97,
(r"Pixel Difference Range: %.1f$^\circ$F to %.1f$^\circ$F, "
r"Domain Analysis Range: %.1f$^\circ$F to %.1f$^\circ$F"
) % (np.min(diff).magnitude,
np.max(diff).magnitude,
np.min(minval).magnitude,
np.max(maxval).magnitude),
transform=mp.ax.transAxes, fontsize=12, ha='center',
bbox=dict(pad=0, color='white'), zorder=50)
mp.pcolormesh(lons, lats, diff, range(0, 61, 5),
cmap=cmap, clip_on=False,
units=r"$^\circ$F")
mp.postprocess(filename='test.png')
def make_plots(nc):
''' Generate some plots '''
sts = compute_sts(nc)
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
rts = (sts.astimezone(pytz.timezone("America/Chicago"))).strftime(
"%d %b %Y %H %p")
for i, tm in enumerate(nc.variables['time'][:]):
dt = sts + datetime.timedelta(minutes=float(tm))
if dt.minute != 0:
continue
fhour = int( tm / 60.0 )
fts = (dt.astimezone(pytz.timezone("America/Chicago"))).strftime(
"%d %b %Y %H %p")
for pvar in PVARS:
m = MapPlot(title='ISUMM5/Bridget Modelled %s' % (
PVARS[pvar]['title'],),
subtitle='Model Run: %s Forecast Valid: %s' % (rts, fts))
vals = nc.variables[pvar][i,:,:]
if pvar == 'bdeckt':
vals = temperature(vals, 'K').value('F')
m.pcolormesh(lons, lats, vals, PVARS[pvar]['levels'], units='mm')
pqstr = "plot c %s model/frost/bridget/%02i/%s_%02i_f%03i.png bogus png" % (
sts.strftime("%Y%m%d%H%M"), sts.hour,
pvar, sts.hour, fhour)
m.postprocess(pqstr=pqstr)
m.close()
def do_month(year, month, routes):
""" Generate a MRMS plot for the month!"""
sts = datetime.datetime(year,month,1)
ets = sts + datetime.timedelta(days=35)
ets = ets.replace(day=1)
today = datetime.datetime.now()
if ets > today:
ets = today
idx0 = iemre.daily_offset(sts)
idx1 = iemre.daily_offset(ets)
nc = netCDF4.Dataset("/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (year,),
'r')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
p01d = np.sum(nc.variables['p01d'][idx0:idx1,:,:],0) / 24.5
nc.close()
m = MapPlot(sector='iowa', title='MRMS %s - %s Total Precipitation' % (
sts.strftime("%-d %b"),
(ets - datetime.timedelta(days=1)).strftime("%-d %b %Y")),
subtitle='Data from NOAA MRMS Project')
x,y = np.meshgrid(lons, lats)
bins = [0.01, 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 12, 16, 20]
m.pcolormesh(x, y, p01d, bins, units='inches')
m.drawcounties()
currentfn = "summary/iowa_mrms_q3_month.png"
archivefn = sts.strftime("%Y/%m/summary/iowa_mrms_q3_month.png")
pqstr = "plot %s %s00 %s %s png" % (
routes, sts.strftime("%Y%m%d%H"), currentfn, archivefn)
m.postprocess(pqstr=pqstr)
def plot():
"""Do plotting work"""
cmap1 = plt.get_cmap('inferno_r')
colors = list(cmap1(np.arange(10) / 10.))
cmap2 = plt.get_cmap('Pastel1')
colors.extend(list(cmap2(np.arange(2) / 2.)))
cmap = ListedColormap(colors)
cmap.set_under('tan')
cmap.set_over('white')
minval = np.load('minval.npy')
maxval = np.load('maxval.npy')
diff = maxval - minval
lons = np.load('lons.npy')
lats = np.load('lats.npy')
mp = MapPlot(sector='midwest', statebordercolor='white',
title=(r"Diff between coldest wind chill and warmest "
"air temp 29 Jan - 3 Feb 2019"),
subtitle=("based on hourly NCEP Real-Time Mesoscale Analysis "
"(RTMA) ending midnight CST"))
levels = list(range(0, 101, 10))
levels.extend([105, 110])
mp.pcolormesh(lons, lats, diff, levels,
cmap=cmap, clip_on=False,
units=r"$^\circ$F", spacing='proportional')
mp.postprocess(filename='test.png')
def doday():
"""
Create a plot of precipitation stage4 estimates for some day
"""
sts = mx.DateTime.DateTime(2013,5,25,12)
ets = mx.DateTime.DateTime(2013,5,31,12)
interval = mx.DateTime.RelativeDateTime(days=1)
now = sts
total = None
while now < ets:
fp = "/mesonet/ARCHIVE/data/%s/stage4/ST4.%s.24h.grib" % (
now.strftime("%Y/%m/%d"),
now.strftime("%Y%m%d%H") )
if os.path.isfile(fp):
lts = now
grbs = pygrib.open(fp)
if total is None:
g = grbs[1]
total = g["values"]
lats, lons = g.latlons()
else:
total += grbs[1]["values"]
grbs.close()
now += interval
m = MapPlot(sector='iowa', title='NOAA Stage IV & Iowa ASOS Precipitation',
subtitle='25-30 May 2013')
m.pcolormesh(lons, lats, total / 25.4, numpy.arange(0,14.1,1), latlon=True,
units='inch')
m.drawcounties()
m.plot_values(dlons, dlats, dvals, '%.02f')
m.postprocess(filename='test.svg')
import iemplot
iemplot.makefeature('test')
def main():
"""Go!"""
title = 'NOAA MRMS Q3: RADAR + Guage Corrected Rainfall Estimates + NWS Storm Reports'
mp = MapPlot(sector='custom',
north=42.3, east=-93.0, south=41.65, west=-94.1,
axisbg='white',
titlefontsize=14,
title=title,
subtitle='Valid: 14 June 2018')
shp = shapefile.Reader('cities.shp')
for record in shp.shapeRecords():
geo = shape(record.shape)
mp.ax.add_geometries([geo], ccrs.PlateCarree(), zorder=Z_OVERLAY2,
facecolor='None', edgecolor='k', lw=2)
grbs = pygrib.open('MRMS_GaugeCorr_QPE_24H_00.00_20180614-200000.grib2')
grb = grbs.message(1)
pcpn = distance(grb['values'], 'MM').value('IN')
lats, lons = grb.latlons()
lons -= 360.
clevs = [0.01, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10]
cmap = nwsprecip()
cmap.set_over('k')
mp.pcolormesh(lons, lats, pcpn, clevs, cmap=cmap, latlon=True,
units='inch')
lons, lats, vals, labels = get_data()
mp.drawcounties()
mp.plot_values(lons, lats, vals, "%s", labels=labels,
labelbuffer=1, labelcolor='white')
mp.drawcities(labelbuffer=5, minarea=0.2)
mp.postprocess(filename='test.png')
def run(ts, routes):
""" Run for a given UTC timestamp """
fn = ts.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/rtma/%H/"
"rtma.t%Hz.awp2p5f000.grib2"))
if not os.path.isfile(fn):
print 'wind_power.py missing', fn
return
grb = pygrib.open(fn)
try:
u = grb.select(name='10 metre U wind component')[0]
v = grb.select(name='10 metre V wind component')[0]
except:
print('Missing u/v wind for wind_power.py\nFN: %s' % (fn,))
return
mag = (u['values']**2 + v['values']**2)**.5
mag = (mag * 1.35)**3 * 0.002641
# 0.002641
lats, lons = u.latlons()
lts = ts.astimezone(pytz.timezone("America/Chicago"))
pqstr = ("plot %s %s00 midwest/rtma_wind_power.png "
"midwest/rtma_wind_power_%s00.png png"
) % (routes, ts.strftime("%Y%m%d%H"), ts.strftime("%H"))
m = MapPlot(sector='midwest',
title=(r'Wind Power Potential :: '
'(speed_mps_10m * 1.35)$^3$ * 0.002641'),
subtitle=('valid: %s based on NOAA Realtime '
'Mesoscale Analysis'
) % (lts.strftime("%d %b %Y %I %p")))
m.pcolormesh(lons, lats, mag, numpy.array(levels), units='MW')
m.postprocess(pqstr=pqstr)
def compute(valid):
''' Get me files '''
prob = None
for hr in range(-15,0):
ts = valid + datetime.timedelta(hours=hr)
fn = ts.strftime("hrrr.ref.%Y%m%d%H00.grib2")
if not os.path.isfile(fn):
continue
grbs = pygrib.open(fn)
gs = grbs.select(level=1000,forecastTime=(-1 * hr * 60))
ref = generic_filter(gs[0]['values'], np.max, size=10)
if prob is None:
lats, lons = gs[0].latlons()
prob = np.zeros( np.shape(ref) )
prob = np.where(ref > 29, prob+1, prob)
prob = np.ma.array(prob / 15. * 100.)
prob.mask = np.ma.where(prob < 1, True, False)
m = MapPlot(sector='iowa',
title='HRRR Composite Forecast 4 PM 20 May 2014 30+ dbZ Reflectivity',
subtitle='frequency of previous 15 model runs all valid at %s, ~15km smoothed' % (valid.astimezone(pytz.timezone("America/Chicago")).strftime("%-d %b %Y %I:%M %p %Z"),))
m.pcolormesh(lons, lats, prob, np.arange(0,101,10), units='%',
clip_on=False)
m.map.drawcounties()
m.postprocess(filename='test.ps')
m.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
We should total files from 1 AM to midnight local time
"""
sts = ts.replace(hour=1)
ets = sts + datetime.timedelta(hours=24)
interval = datetime.timedelta(hours=1)
now = sts
total = None
lts = None
while now < ets:
gmt = now.astimezone(pytz.timezone("UTC"))
fn = gmt.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/"
+"stage4/ST4.%Y%m%d%H.01h.grib"))
if os.path.isfile(fn):
lts = now
grbs = pygrib.open(fn)
if total is None:
g = grbs[1]
total = g["values"]
lats, lons = g.latlons()
else:
total += grbs[1]["values"]
grbs.close()
now += interval
if lts is None and ts.hour > 1:
print 'stage4_today_total.py found no data!'
if lts is None:
return
lts = lts - datetime.timedelta(minutes=1)
subtitle = "Total between 12:00 AM and %s" % (lts.strftime("%I:%M %p %Z"),)
routes = 'ac'
if not realtime:
routes = 'a'
for sector in ['iowa', 'midwest', 'conus']:
pqstr = "plot %s %s00 %s_stage4_1d.png %s_stage4_1d.png png" % (routes,
ts.strftime("%Y%m%d%H"), sector, sector )
m = MapPlot(sector=sector,
title="%s NCEP Stage IV Today's Precipitation" % (
ts.strftime("%-d %b %Y"),),
subtitle=subtitle)
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
m.pcolormesh(lons, lats, total / 24.5, clevs, units='inch')
#map.drawstates(zorder=2)
if sector == 'iowa':
m.drawcounties()
m.postprocess(pqstr=pqstr)
m.close()
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 do(ts, hours):
"""
Create a plot of precipitation stage4 estimates for some day
"""
ts = ts.replace(minute=0)
sts = ts - datetime.timedelta(hours=hours)
ets = ts
interval = datetime.timedelta(hours=1)
now = sts
total = None
lts = None
while now < ets:
fn = "/mesonet/ARCHIVE/data/%s/stage4/ST4.%s.01h.grib" % (
now.strftime("%Y/%m/%d"),
now.strftime("%Y%m%d%H") )
if os.path.isfile(fn):
lts = now
grbs = pygrib.open(fn)
if total is None:
g = grbs[1]
total = g["values"]
lats, lons = g.latlons()
else:
total += grbs[1]["values"]
grbs.close()
now += interval
if lts is None and ts.hour > 1:
print 'Missing StageIV data!'
if lts is None:
return
cmap = cm.get_cmap("jet")
cmap.set_under('white')
cmap.set_over('black')
clevs = [0.01,0.1,0.25,0.5,1,2,3,5,8,9.9]
localtime = (ts - datetime.timedelta(minutes=1)).astimezone(
pytz.timezone("America/Chicago"))
for sector in ['iowa', 'midwest', 'conus']:
m = MapPlot(sector=sector,
title='NCEP Stage IV %s Hour Precipitation' % (hours,),
subtitle='Total up to %s' % (
localtime.strftime("%d %B %Y %I %p %Z"),))
m.pcolormesh(lons, lats, total / 24.5, clevs, units='inch')
pqstr = "plot %s %s00 %s_stage4_%sh.png %s_stage4_%sh_%s.png png" % (
'ac', ts.strftime("%Y%m%d%H"), sector, hours,
sector, hours, ts.strftime("%H"))
if sector == 'iowa':
m.drawcounties()
m.postprocess(pqstr=pqstr)
m.close()
def run( bcsdfn, bccafn, title ):
''' Run for a given filename! '''
nc = netCDF4.Dataset(bcsdfn, 'r')
# time
idx0, idx1 = get_time( nc )
# somewhat a hack for now
dmul = 1.0
if (idx1 - idx0) / 19.0 < 13:
dmul = 365.0 / 12.0
# Either pr or Prcp
pvar = 'pr' if nc.variables.has_key('pr') else 'Prcp'
pmul = 1.0 if nc.variables[pvar].units == 'mm/d' else 86400.0
bcsdprecip = np.sum(nc.variables[pvar][idx0:idx1+1,:,:],0) * dmul * pmul / 19.0 / 24.5
nc2 = netCDF4.Dataset(bccafn, 'r')
# time
idx0, idx1 = get_time( nc2 )
# somewhat a hack for now
dmul = 1.0
if (idx1 - idx0) / 19.0 < 13:
dmul = 365.0 / 12.0
# Either pr or Prcp
pvar = 'pr' if nc2.variables.has_key('pr') else 'Prcp'
pmul = 1.0 if nc2.variables[pvar].units == 'mm/d' else 86400.0
bccaprecip = np.sum(nc2.variables[pvar][idx0:idx1+1,:,:],0) * dmul * pmul / 19.0 / 24.5
# lat or latitude
latvar = 'lat' if nc.variables.has_key('lat') else 'latitude'
lats = nc.variables[latvar][:]
lats = np.concatenate([lats, [lats[-1]+(lats[-1]-lats[-2])]])
# lon or longitude
lonvar = 'lon' if nc.variables.has_key('lon') else 'longitude'
lons = nc.variables[lonvar][:]
lons = np.concatenate([lons, [lons[-1]+(lons[-1]-lons[-2])]])
print np.shape(lons), np.shape(lats), np.max(bccaprecip), np.max(bcsdprecip)
title = '81-99 Precip BCCA over BCSD %s' % (title,)
#subtitle = 'filename: %s' % (bccafn,)
m = MapPlot(title=title, subtitle='',
sector='conus', nologo=True, caption='')
x,y = np.meshgrid(lons, lats)
cmap = cm.get_cmap('Spectral')
cmap.set_over('black')
m.pcolormesh(x, y, bccaprecip / bcsdprecip * 100.0,
np.arange(0,201,20),
cmap=cmap, units='percentage')
png = '../cplots/%s.png' % (title.replace(" ", "_").lower(),)
print png
m.postprocess(filename=png)
nc.close()
nc2.close()
def test_pcolormesh():
"""See if we can do pcolormesh OKish"""
mp = MapPlot(sector='custom', north=43, east=-80, west=-96,
south=38, projection=reference.EPSG[2163],
continentalcolor='white', nocaption=True)
lons = np.arange(-100, -80, 0.25)
lats = np.arange(40, 50, 0.25)
vals = np.linspace(0, 1, lats.shape[0] * lons.shape[0]
).reshape([lats.shape[0], lons.shape[0]])
lons, lats = np.meshgrid(lons, lats)
mp.pcolormesh(lons, lats, vals, np.arange(0, 1, 0.1))
return mp.fig
def main():
"""Go Main Go"""
nc = ncopen("/mesonet/data/iemre/1979_narr.nc")
data = np.sum(nc.variables['apcp'][:, :, :], axis=0)
m = MapPlot(sector='conus', axisbg='tan',
title=(""),
subtitle='',
titlefontsize=16)
t = distance(data, 'MM').value('IN')
m.pcolormesh(nc.variables['lon'][:], nc.variables['lat'][:], t,
np.arange(0, 60, 2), units='F')
m.postprocess(filename='test.png')
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
lts = datetime.datetime.utcnow().replace(
tzinfo=pytz.timezone("UTC"))
lts = lts.astimezone(pytz.timezone("America/Chicago"))
# make assumptions about the last valid MRMS data
if realtime:
# Up until :59 after of the last hour
lts = (lts - datetime.timedelta(hours=1)).replace(minute=59)
else:
lts = lts.replace(year=ts.year, month=ts.month, day=ts.day,
hour=23, minute=59)
idx = iemre.daily_offset(ts)
ncfn = "/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (ts.year,)
nc = netCDF4.Dataset(ncfn)
precip = nc.variables['p01d'][idx, :, :]
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
subtitle = "Total between 12:00 AM and %s" % (
lts.strftime("%I:%M %p %Z"),)
routes = 'ac'
if not realtime:
routes = 'a'
# 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 = [0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8,
10]
sector = 'iowa'
pqstr = ("plot %s %s00 %s_q2_1d.png %s_q2_1d.png png"
) % (routes, ts.strftime("%Y%m%d%H"), sector, sector)
m = MapPlot(title=("%s NCEP MRMS Q3 Today's Precipitation"
) % (ts.strftime("%-d %b %Y"),),
subtitle=subtitle, sector=sector)
(x, y) = np.meshgrid(lons, lats)
m.pcolormesh(x, y, distance(precip, 'MM').value('IN'), clevs,
cmap=nwsprecip(), units='inch')
m.drawcounties()
m.postprocess(pqstr=pqstr, view=False)
m.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
nc = netCDF4.Dataset("/mesonet/data/iemre/%s_ifc_daily.nc" % (ts.year, ))
idx = daily_offset(ts)
xaxis = nc.variables['lon'][:]
yaxis = nc.variables['lat'][:]
total = nc.variables['p01d'][idx, :, :]
nc.close()
lastts = datetime.datetime(ts.year, ts.month, ts.day, 23, 59)
if realtime:
now = datetime.datetime.now() - datetime.timedelta(minutes=60)
lastts = now.replace(minute=59)
subtitle = "Total between 12:00 AM and %s" % (
lastts.strftime("%I:%M %p"), )
routes = 'ac'
if not realtime:
routes = 'a'
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
]
pqstr = ("plot %s %s00 iowa_ifc_1d.png iowa_ifc_1d.png png") % (
routes, ts.strftime("%Y%m%d%H"))
mp = MapPlot(title=("%s Iowa Flood Center Today's Precipitation") %
(ts.strftime("%-d %b %Y"), ),
subtitle=subtitle,
sector='custom',
west=xaxis[0],
east=xaxis[-1],
south=yaxis[0],
north=yaxis[-1])
(lons, lats) = np.meshgrid(xaxis, yaxis)
mp.pcolormesh(lons,
lats,
distance(total, 'MM').value("IN"),
clevs,
cmap=nwsprecip(),
units='inch')
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from pyiem.plot import MapPlot
ctx = get_autoplot_context(fdict, get_description())
# Covert datetime to UTC
ctx['sdate'] = ctx['sdate'].replace(tzinfo=pytz.utc)
ctx['edate'] = ctx['edate'].replace(tzinfo=pytz.utc)
state = ctx['state']
phenomena = ctx['phenomena']
significance = ctx['significance']
station = ctx['station'][:4]
t = ctx['t']
ilabel = (ctx['ilabel'] == 'yes')
geo = ctx['geo']
nt = NetworkTable("WFO")
if geo == 'ugc':
do_ugc(ctx)
elif geo == 'polygon':
do_polygon(ctx)
subtitle = "based on IEM Archives %s" % (ctx.get('subtitle', ''),)
if t == 'cwa':
subtitle = "Plotted for %s (%s), %s" % (nt.sts[station]['name'],
station, subtitle)
else:
subtitle = "Plotted for %s, %s" % (state_names[state],
subtitle)
m = MapPlot(sector=('state' if t == 'state' else 'cwa'),
state=state,
cwa=(station if len(station) == 3 else station[1:]),
axisbg='white',
title=('%s %s (%s.%s)'
) % (ctx['title'],
vtec.get_ps_string(phenomena, significance),
phenomena, significance),
subtitle=subtitle, nocaption=True,
titlefontsize=16
)
if geo == 'ugc':
cmap = plt.get_cmap('Paired')
cmap.set_under('white')
cmap.set_over('white')
m.fill_ugcs(ctx['data'], ctx['bins'], cmap=cmap, ilabel=ilabel)
else:
cmap = plt.get_cmap('jet')
cmap.set_under('white')
cmap.set_over('black')
res = m.pcolormesh(ctx['lons'], ctx['lats'], ctx['data'],
ctx['bins'], cmap=cmap, units='count')
# Cut down on SVG et al size
res.set_rasterized(True)
if ctx['drawc'] == 'yes':
m.drawcounties()
return m.fig, ctx['df']
def do_month(year, month, routes):
""" Generate a MRMS plot for the month!"""
sts = datetime.datetime(year, month, 1)
ets = sts + datetime.timedelta(days=35)
ets = ets.replace(day=1)
today = datetime.datetime.now()
if ets > today:
ets = today
idx0 = iemre.daily_offset(sts)
idx1 = iemre.daily_offset(ets)
nc = ncopen(iemre.get_daily_mrms_ncname(year), "r")
lats = nc.variables["lat"][:]
lons = nc.variables["lon"][:]
p01d = distance(np.sum(nc.variables["p01d"][idx0:idx1, :, :], 0),
"MM").value("IN")
nc.close()
mp = MapPlot(
sector="iowa",
title="MRMS %s - %s Total Precipitation" % (
sts.strftime("%-d %b"),
(ets - datetime.timedelta(days=1)).strftime("%-d %b %Y"),
),
subtitle="Data from NOAA MRMS Project",
)
x, y = np.meshgrid(lons, lats)
bins = [0.01, 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 12, 16, 20]
mp.pcolormesh(x, y, p01d, bins, units="inches")
mp.drawcounties()
currentfn = "summary/iowa_mrms_q3_month.png"
archivefn = sts.strftime("%Y/%m/summary/iowa_mrms_q3_month.png")
pqstr = "plot %s %s00 %s %s png" % (
routes,
sts.strftime("%Y%m%d%H"),
currentfn,
archivefn,
)
mp.postprocess(pqstr=pqstr)
def compute(valid):
''' Get me files '''
prob = None
for hr in range(-15, 0):
ts = valid + datetime.timedelta(hours=hr)
fn = ts.strftime("/tmp/ncep_hrrr_%Y%m%d%H.grib2")
print hr, fn
if not os.path.isfile(fn):
continue
grbs = pygrib.open(fn)
try:
gs = grbs.select(level=1000, forecastTime=(-1 * hr * 60))
except:
print fn, 'ERROR'
continue
ref = gs[0]['values']
#ref = generic_filter(gs[0]['values'], np.max, size=10)
if prob is None:
lats, lons = gs[0].latlons()
prob = np.zeros(np.shape(ref))
prob = np.where(ref > 29, prob + 1, prob)
prob = np.ma.array(prob / 15. * 100.)
prob.mask = np.ma.where(prob < 1, True, False)
m = MapPlot(
sector='iowa',
title='HRRR Composite Forecast 6 PM 22 Sep 2015 30+ dbZ Reflectivity',
subtitle='frequency of previous 15 NCEP model runs all valid at %s' %
(valid.astimezone(pytz.timezone("America/Chicago")).strftime(
"%-d %b %Y %I:%M %p %Z"), ))
m.pcolormesh(lons,
lats,
prob,
np.arange(0, 101, 10),
units='% of runs',
clip_on=False)
m.map.drawcounties()
m.postprocess(filename='test.png')
m.close()
def run( fn ):
''' Run for a given filename! '''
nc = netCDF4.Dataset(fn, 'r')
# time
idx0, idx1 = get_time( nc )
# somewhat a hack for now
dmul = 1.0
if (idx1 - idx0) == 19:
dmul = 1.0
elif (idx1 - idx0) / 19.0 < 13:
dmul = 365.0 / 12.0
# Either pr or Prcp
pvar = 'pr' if nc.variables.has_key('pr') else 'Prcp'
pmul = 1.0 if nc.variables[pvar].units in ['mm/d', 'mm'] else 86400.0
precip = np.sum(nc.variables[pvar][idx0:idx1+1,:,:],0) * dmul * pmul / 19.0 / 24.5
# lat or latitude
latvar = 'lat' if nc.variables.has_key('lat') else 'latitude'
lats = nc.variables[latvar][:]
lats = np.concatenate([lats, [lats[-1]+(lats[-1]-lats[-2])]])
# lon or longitude
lonvar = 'lon' if nc.variables.has_key('lon') else 'longitude'
lons = nc.variables[lonvar][:] - 360.
lons = np.concatenate([lons, [lons[-1]+(lons[-1]-lons[-2])]])
print fn, np.shape(lons), np.shape(lats), np.max(precip)
try:
title = '2046-2065 Precip %s' % (nc.title,)
except:
title = '2046-2065 Yearly Average Precipitation '
subtitle = 'filename: %s' % (fn,)
m = MapPlot(title=title, subtitle=subtitle,
sector='conus', nologo=True, caption='')
x,y = np.meshgrid(lons, lats)
cmap = cm.get_cmap('gist_rainbow')
cmap.set_over('black')
m.pcolormesh(x, y, precip, np.array([0,1,5,10,15,20,25,30,35,40,50,60,70,80,100]),
cmap=cmap, units='inch/yr')
m.postprocess(filename='/tmp/plots/%s.png' % (fn.replace(".nc", ""),))
nc.close()
m.close()
def doit(ts):
"""
Generate hourly plot of stage4 data
"""
gmtnow = datetime.datetime.utcnow()
gmtnow = gmtnow.replace(tzinfo=pytz.utc)
routes = "a"
if ((gmtnow - ts).days * 86400. + (gmtnow - ts).seconds) < 7200:
routes = "ac"
fn = "/mesonet/ARCHIVE/data/%s/stage4/ST4.%s.01h.grib" % (
ts.strftime("%Y/%m/%d"), ts.strftime("%Y%m%d%H"))
if not os.path.isfile(fn):
print('current/stage4_hourly.py Missing stage4 %s' % (fn,))
return
grbs = pygrib.open(fn)
grib = grbs[1]
lats, lons = grib.latlons()
vals = grib.values / 25.4
cmap = cm.get_cmap("jet")
cmap.set_under('white')
cmap.set_over('black')
clevs = [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1,
1.5, 2, 3]
localtime = ts.astimezone(pytz.timezone("America/Chicago"))
for sector in ['iowa', 'midwest', 'conus']:
mp = MapPlot(sector=sector,
title='Stage IV One Hour Precipitation',
subtitle='Hour Ending %s' % (
localtime.strftime("%d %B %Y %I %p %Z"),))
mp.pcolormesh(lons, lats, vals, clevs, units='inch')
pqstr = "plot %s %s00 %s_stage4_1h.png %s_stage4_1h_%s.png png" % (
routes, ts.strftime("%Y%m%d%H"), sector,
sector, ts.strftime("%H"))
if sector == 'iowa':
mp.drawcounties()
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def run( fn ):
''' Run for a given filename! '''
nc = netCDF4.Dataset(fn, 'r')
# time
idx0, idx1 = get_time( nc )
# somewhat a hack for now
dmul = 1.0
if (idx1 - idx0) == 19:
dmul = 1.0
elif (idx1 - idx0) / 19.0 < 13:
dmul = 365.0 / 12.0
pvar = 'tmin'
tmpf = c2f(np.average(nc.variables[pvar][idx0:idx1+1,:,:],0) * dmul )
# lat or latitude
latvar = 'lat' if nc.variables.has_key('lat') else 'latitude'
lats = nc.variables[latvar][:]
lats = np.concatenate([lats, [lats[-1]+(lats[-1]-lats[-2])]])
# lon or longitude
lonvar = 'lon' if nc.variables.has_key('lon') else 'longitude'
lons = nc.variables[lonvar][:] - 360.
lons = np.concatenate([lons, [lons[-1]+(lons[-1]-lons[-2])]])
print fn, np.shape(lons), np.shape(lats), np.max(tmpf)
try:
title = '2046-2065 Average Daily High Temp %s' % (nc.title,)
except:
title = '2046-2065 Average Daily Low Temperature '
subtitle = 'filename: %s' % (fn,)
m = MapPlot(title=title, subtitle=subtitle,
sector='conus', nologo=True, caption='')
x,y = np.meshgrid(lons, lats)
cmap = cm.get_cmap('gist_rainbow')
cmap.set_over('black')
m.pcolormesh(x, y, tmpf, np.arange(50,120,10),
cmap=cmap, units='F')
m.postprocess(filename='/tmp/plots/%s.png' % (fn.replace(".nc", ""),))
nc.close()
m.close()
def main():
"""Go Main"""
nc = netCDF4.Dataset('/tmp/sfav2_CONUS_2018093012_to_2019021312.nc')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
data = nc.variables['Data'][:] * 1000. / 25.4
nc.close()
mp = MapPlot(
sector='iowa', continentalcolor='tan',
title=("National Snowfall Analysis - NOHRSC "
"- Season Total Snowfall"),
subtitle='Snowfall up until 7 AM 13 Feb 2019')
cmap = plt.get_cmap('terrain_r')
levs = [0.1, 2, 5, 8, 12, 18, 24, 30, 36, 42, 48]
mp.pcolormesh(
lons, lats, data, levs, cmap=cmap, units='inch', clip_on=False,
spacing='proportional'
)
mp.drawcounties()
mp.drawcities()
mp.postprocess(filename='test.png')
def compute(valid):
""" Get me files """
prob = None
for hr in range(-15, 0):
ts = valid + datetime.timedelta(hours=hr)
fn = ts.strftime("/tmp/ncep_hrrr_%Y%m%d%H.grib2")
print hr, fn
if not os.path.isfile(fn):
continue
grbs = pygrib.open(fn)
try:
gs = grbs.select(level=1000, forecastTime=(-1 * hr * 60))
except:
print fn, "ERROR"
continue
ref = gs[0]["values"]
# ref = generic_filter(gs[0]['values'], np.max, size=10)
if prob is None:
lats, lons = gs[0].latlons()
prob = np.zeros(np.shape(ref))
prob = np.where(ref > 29, prob + 1, prob)
prob = np.ma.array(prob / 15.0 * 100.0)
prob.mask = np.ma.where(prob < 1, True, False)
m = MapPlot(
sector="iowa",
title="HRRR Composite Forecast 6 PM 22 Sep 2015 30+ dbZ Reflectivity",
subtitle="frequency of previous 15 NCEP model runs all valid at %s"
% (valid.astimezone(pytz.timezone("America/Chicago")).strftime("%-d %b %Y %I:%M %p %Z"),),
)
m.pcolormesh(lons, lats, prob, np.arange(0, 101, 10), units="% of runs", clip_on=False)
m.map.drawcounties()
m.postprocess(filename="test.png")
m.close()
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 doit(ts):
"""
Generate hourly plot of stage4 data
"""
routes = "a"
if ((gmtnow - ts).days * 86400. + (gmtnow - ts).seconds) < 7200:
routes = "ac"
fn = "/mesonet/ARCHIVE/data/%s/stage4/ST4.%s.01h.grib" % (
ts.strftime("%Y/%m/%d"), ts.strftime("%Y%m%d%H") )
if not os.path.isfile(fn):
print 'current/stage4_hourly.py Missing stage4 %s' % (fn,)
return
grbs = pygrib.open(fn)
grib = grbs[1]
lats, lons = grib.latlons()
vals = grib.values / 25.4
cmap = cm.get_cmap("jet")
cmap.set_under('white')
cmap.set_over('black')
clevs = [0.01,0.05,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.5,2,3]
localtime = ts.astimezone(pytz.timezone("America/Chicago"))
for sector in ['iowa', 'midwest', 'conus']:
m = MapPlot(sector=sector,
title='Stage IV One Hour Precipitation',
subtitle='Hour Ending %s' % (
localtime.strftime("%d %B %Y %I %p %Z"),))
m.pcolormesh(lons, lats, vals, clevs, units='inch')
pqstr = "plot %s %s00 %s_stage4_1h.png %s_stage4_1h_%s.png png" % (
routes, ts.strftime("%Y%m%d%H"), sector,
sector, ts.strftime("%H"))
if sector == 'iowa':
m.drawcounties()
m.postprocess(view=False, pqstr=pqstr)
m.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
nc = netCDF4.Dataset("/mesonet/data/iemre/%s_ifc_daily.nc" % (ts.year,))
idx = daily_offset(ts)
xaxis = nc.variables['lon'][:]
yaxis = nc.variables['lat'][:]
total = nc.variables['p01d'][idx, :, :]
nc.close()
lastts = datetime.datetime(ts.year, ts.month, ts.day, 23, 59)
if realtime:
now = datetime.datetime.now() - datetime.timedelta(minutes=60)
lastts = now.replace(minute=59)
subtitle = "Total between 12:00 AM and %s" % (
lastts.strftime("%I:%M %p"),)
routes = 'ac'
if not realtime:
routes = 'a'
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]
pqstr = ("plot %s %s00 iowa_ifc_1d.png iowa_ifc_1d.png png"
) % (routes, ts.strftime("%Y%m%d%H"))
m = MapPlot(title=("%s Iowa Flood Center Today's Precipitation"
) % (ts.strftime("%-d %b %Y"),),
subtitle=subtitle, sector='custom',
west=xaxis[0], east=xaxis[-1],
south=yaxis[0], north=yaxis[-1])
(x, y) = np.meshgrid(xaxis, yaxis)
m.pcolormesh(x, y, distance(total, 'MM').value("IN"), clevs,
cmap=nwsprecip(), units='inch')
m.drawcounties()
m.postprocess(pqstr=pqstr, view=False)
m.close()
def main():
"""Go Main Go"""
pgconn = get_dbconn('postgis')
cursor = pgconn.cursor()
sts = datetime.date(2018, 1, 24)
ets = datetime.date(2017, 8, 1)
now = sts
xaxis = np.arange(reference.IA_WEST, reference.IA_EAST, GX)
yaxis = np.arange(reference.IA_SOUTH, reference.IA_NORTH, GX)
"""
days = np.ones((len(yaxis), len(xaxis)), 'i') * -1
count = 0
while now >= ets:
cursor.execute(""
select distinct st_x(geom) as lon, st_y(geom) as lat
from nldn_all n, states s
WHERE n.valid >= %s and n.valid < %s
and s.state_abbr = 'IA' and n.geom && s.the_geom
"", (now, now + datetime.timedelta(days=1)))
print("date: %s rows: %s" % (now, cursor.rowcount))
for row in cursor:
yidx = int((row[1] - reference.IA_SOUTH) / GX)
xidx = int((row[0] - reference.IA_WEST) / GX)
if days[yidx, xidx] < 0:
days[yidx, xidx] = count
now -= datetime.timedelta(days=1)
count += 1
np.save('days', days)
"""
days = np.load('days.npy')
mp = MapPlot()
ramp = [1, 24, 55, 85, 116, 146, 177]
mp.pcolormesh(xaxis, yaxis, days, ramp,
cmap=plt.get_cmap('afmhot_r'))
mp.postprocess(filename='test.png')
mp.close()
def main():
"""Go Main Go"""
ets = datetime.datetime.now() - datetime.timedelta(days=1)
sts = datetime.datetime(ets.year, 1, 1)
# Get the normal accumm
with ncopen(iemre.get_dailyc_ncname()) as cnc:
lons = cnc.variables["lon"][:]
lats = cnc.variables["lat"][:]
index0 = iemre.daily_offset(sts)
index1 = iemre.daily_offset(ets)
clprecip = np.sum(cnc.variables["p01d"][index0:index1, :, :], 0)
with ncopen(iemre.get_daily_ncname(sts.year)) as nc:
obprecip = np.sum(nc.variables["p01d"][index0:index1, :, :], 0)
lons, lats = np.meshgrid(lons, lats)
# Plot departure from normal
mp = MapPlot(
sector="midwest",
title=("Precipitation Departure %s - %s") %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle="based on IEM Estimates",
)
mp.pcolormesh(lons, lats, (obprecip - clprecip) / 25.4,
np.arange(-10, 10, 1))
mp.postprocess(
pqstr="plot c 000000000000 summary/year/stage4_diff.png bogus png")
mp.close()
# Plot normals
mp = MapPlot(
sector="midwest",
title=("Normal Precipitation:: %s - %s") %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle="based on IEM Estimates",
)
mp.pcolormesh(lons, lats, (clprecip) / 25.4, np.arange(0, 30, 2))
mp.postprocess(
pqstr="plot c 000000000000 summary/year/stage4_normals.png bogus png")
mp.close()
# Plot Obs
mp = MapPlot(
sector="midwest",
title=("Estimated Precipitation:: %s - %s") %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle="based on IEM Estimates",
)
mp.pcolormesh(lons, lats, (obprecip) / 25.4, np.arange(0, 30, 2))
mp.postprocess(
pqstr="plot c 000000000000 summary/year/stage4obs.png bogus png")
mp.close()
def do_month(year, month, routes):
""" Generate a MRMS plot for the month!"""
sts = datetime.datetime(year, month, 1)
ets = sts + datetime.timedelta(days=35)
ets = ets.replace(day=1)
today = datetime.datetime.now()
if ets > today:
ets = today
idx0 = iemre.daily_offset(sts)
idx1 = iemre.daily_offset(ets)
nc = netCDF4.Dataset("/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (year, ),
'r')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
p01d = distance(np.sum(nc.variables['p01d'][idx0:idx1, :, :], 0),
'MM').value('IN')
nc.close()
mp = MapPlot(sector='iowa',
title='MRMS %s - %s Total Precipitation' %
(sts.strftime("%-d %b"),
(ets - datetime.timedelta(days=1)).strftime("%-d %b %Y")),
subtitle='Data from NOAA MRMS Project')
x, y = np.meshgrid(lons, lats)
bins = [0.01, 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 12, 16, 20]
mp.pcolormesh(x, y, p01d, bins, units='inches')
mp.drawcounties()
currentfn = "summary/iowa_mrms_q3_month.png"
archivefn = sts.strftime("%Y/%m/summary/iowa_mrms_q3_month.png")
pqstr = "plot %s %s00 %s %s png" % (routes, sts.strftime("%Y%m%d%H"),
currentfn, archivefn)
mp.postprocess(pqstr=pqstr)
def run(ts, routes):
""" Run for a given UTC timestamp """
fn = ts.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/rtma/%H/"
"rtma.t%Hz.awp2p5f000.grib2"))
if not os.path.isfile(fn):
print("rtma/wind_power.py missing %s" % (fn, ))
return
grb = pygrib.open(fn)
try:
u = grb.select(name="10 metre U wind component")[0]
v = grb.select(name="10 metre V wind component")[0]
except Exception as exp:
print("Missing u/v wind for wind_power.py\nFN: %s\n%s" % (fn, exp))
return
mag = np.hypot(u["values"], v["values"])
mag = (mag * 1.35)**3 * 0.002641
# 0.002641
lats, lons = u.latlons()
lts = ts.astimezone(pytz.timezone("America/Chicago"))
pqstr = ("plot %s %s00 midwest/rtma_wind_power.png "
"midwest/rtma_wind_power_%s00.png png") % (
routes, ts.strftime("%Y%m%d%H"), ts.strftime("%H"))
mp = MapPlot(
sector="midwest",
title=(r"Wind Power Potential :: "
"(speed_mps_10m * 1.35)$^3$ * 0.002641"),
subtitle=("valid: %s based on NOAA Realtime "
"Mesoscale Analysis") % (lts.strftime("%d %b %Y %I %p")),
)
mp.pcolormesh(lons, lats, mag, np.array(LEVELS), units="MW")
mp.postprocess(pqstr=pqstr)
mp.close()
def do(valid):
"""Do Something"""
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
precip = load_precip(valid)
stage4 = load_precip(valid, 'stage4')
inqcprecip = load_precip(valid, 'inqcprecip')
outqcprecip = load_precip(valid, 'outqcprecip')
multiplier = load_precip(valid, 'multiplier')
yidx = int((43.27 - SOUTH) / 0.01)
xidx = int((-94.39 - WEST) / 0.01)
print(("yidx:%s xidx:%s precip:%.2f stage4: %.2f inqc: %.2f outqc: %.2f "
"mul: %.2f"
) % (yidx, xidx, precip[yidx, xidx], stage4[yidx, xidx],
inqcprecip[yidx, xidx], outqcprecip[yidx, xidx],
multiplier[yidx, xidx]))
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s DEP Quality Controlled Precip Totals'
) % (valid.strftime("%-d %b %Y"), ))
(lons, lats) = np.meshgrid(XS, YS)
mp.pcolormesh(lons, lats, precip / 25.4, clevs,
units='inch')
mp.drawcounties()
mp.postprocess(filename='qc.png')
mp.close()
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s Stage IV Precip Totals'
) % (valid.strftime("%-d %b %Y"), ))
mp.pcolormesh(lons, lats, stage4 / 25.4, clevs,
units='inch')
mp.drawcounties()
mp.postprocess(filename='stageIV.png')
mp.close()
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s High-res total prior to QC'
) % (valid.strftime("%-d %b %Y"), ))
mp.pcolormesh(lons, lats, inqcprecip / 25.4, clevs,
units='inch')
mp.drawcounties()
mp.postprocess(filename='inqcprecip.png')
mp.close()
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s High-res total after QC'
) % (valid.strftime("%-d %b %Y"), ))
mp.pcolormesh(lons, lats, outqcprecip / 25.4, clevs,
units='inch')
mp.drawcounties()
mp.postprocess(filename='outqcprecip.png')
mp.close()
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s QC Change in Precip Out - In'
) % (valid.strftime("%-d %b %Y"), ))
diff = (outqcprecip - inqcprecip) / 25.4
mp.pcolormesh(lons, lats, diff,
np.arange(-1.4, 1.5, 0.1),
cmap=plt.get_cmap('BrBG'),
units='inch')
mp.drawcounties()
mp.postprocess(filename='qcprecipdiff.png')
mp.close()
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s multiplier'
) % (valid.strftime("%-d %b %Y"), ))
mp.pcolormesh(lons, lats, multiplier,
np.arange(0.0, 2.5, 0.2),
cmap=plt.get_cmap('jet'),
units='ratio')
mp.drawcounties()
mp.postprocess(filename='multiplier.png')
mp.close()
mp = MapPlot(sector=SECTOR, axisbg='white',
title=('%s manually computed QC Precip mul * stage4'
) % (valid.strftime("%-d %b %Y"), ))
mp.pcolormesh(lons, lats, multiplier * stage4 / 25.4,
clevs,
units='inch')
mp.drawcounties()
mp.postprocess(filename='qcmancalc.png')
mp.close()
# Go MRMS
ncfn = "/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (valid.year, )
if not os.path.isfile(ncfn):
return
nc = netCDF4.Dataset(ncfn, 'r')
xidx = int((WEST - nc.variables['lon'][0]) * 100.)
yidx = int((SOUTH - nc.variables['lat'][0]) * 100.)
idx = daily_offset(valid)
mrms = nc.variables['p01d'][idx, yidx:(yidx+800), xidx:(xidx+921)]
nc.close()
lons = fut_nc.variables['longitude'][:]
x, y = numpy.meshgrid(lons, lats)
offset1, offset2 = numpy.digitize([days1,days2], obs_nc.variables['time'][:])
offset3, offset4 = numpy.digitize([days3,days4], fut_nc.variables['time'][:])
print offset1, offset2, offset2 - offset1
print offset3, offset4, offset4 - offset3
jan = numpy.average(obs_nc.variables['Prcp'][offset1+0:offset2:12,:,:],0)
feb = numpy.average(obs_nc.variables['Prcp'][offset1+1:offset2:12,:,:],0)
dec = numpy.average(obs_nc.variables['Prcp'][offset1+11:offset2:12,:,:],0)
obs = (jan + feb + dec)/3.0
jan = numpy.average(obs_nc.variables['Prcp'][offset3+0:offset4:12,:,:],0)
feb = numpy.average(obs_nc.variables['Prcp'][offset3+1:offset4:12,:,:],0)
dec = numpy.average(obs_nc.variables['Prcp'][offset3+11:offset4:12,:,:],0)
fut = (jan + feb + dec)/3.0
ratio = numpy.ma.array(fut / obs)
ratio.mask = numpy.where(ratio == 1, True, False)
print numpy.min(ratio), numpy.max(ratio)
m = MapPlot(sector='conus', caption='', nologo=True,
title=r'MIROC3.2 exp1 BCSD DJF Precipitation Ratio A1B / C20',
subtitle='Period: Jan 2046 - Dec 2064 divided by Jan 1981- Dec 1999')
m.pcolormesh(x, y, ratio, numpy.arange(0,2.5,0.1), latlon=True, units='ratio')
m.postprocess(filename='djf_ratio_bcsd_miroc_only.png')
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
We should total files from 1 AM to midnight local time
"""
sts = ts.replace(hour=1)
ets = sts + datetime.timedelta(hours=24)
interval = datetime.timedelta(hours=1)
now = sts
total = None
lts = None
while now < ets:
gmt = now.astimezone(pytz.utc)
fn = gmt.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/"
"stage4/ST4.%Y%m%d%H.01h.grib"))
if os.path.isfile(fn):
lts = now
grbs = pygrib.open(fn)
if total is None:
total = grbs[1]["values"]
lats, lons = grbs[1].latlons()
else:
total += grbs[1]["values"]
grbs.close()
now += interval
if lts is None:
if ts.hour > 1:
LOG.info("found no data for date: %s", ts)
return
lts = lts - datetime.timedelta(minutes=1)
subtitle = "Total between 12:00 AM and %s" % (
lts.strftime("%I:%M %p %Z"), )
routes = "ac"
if not realtime:
routes = "a"
total = masked_array(total, units("mm")).to(units("inch")).m
for sector in ["iowa", "midwest", "conus"]:
pqstr = ("plot %s %s00 %s_stage4_1d.png %s_stage4_1d.png png") % (
routes,
ts.strftime("%Y%m%d%H"),
sector,
sector,
)
mp = MapPlot(
sector=sector,
title=("%s NCEP Stage IV Today's Precipitation") %
(ts.strftime("%-d %b %Y"), ),
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]
mp.pcolormesh(lons, lats, total, clevs, cmap=nwsprecip(), units="inch")
# map.drawstates(zorder=2)
if sector == "iowa":
mp.drawcounties()
mp.postprocess(pqstr=pqstr)
mp.close()
continue
g = temperature(gs[0]['values'], 'K')
g2 = temperature(gs2[0]['values'], 'K')
if now == sts:
lats, lons = gs[0].latlons()
largest = np.zeros(np.shape(g))
delta = g.value('F') - g2.value('F')
largest = np.minimum(delta, largest)
now += interval
np.save('maxinterval.npy', np.array(largest))
np.save('lats.npy', lats)
np.save('lons.npy', lons)
#do()
#sys.exit()
maxinterval = np.load('maxinterval.npy')
print np.max(maxinterval), np.min(maxinterval)
lats = np.load('lats.npy')
lons = np.load('lons.npy')
m = MapPlot(
sector='conus',
axisbg='#EEEEEE',
title='16 - 18 March 2015 :: Coldest 24 Hour Temperature Change',
subtitle='based on hourly HRRR Analyses 7 AM 16 Mar thru 5 AM 18 Mar')
m.pcolormesh(lons, lats, maxinterval, range(-50, 1, 5), units='F')
m.postprocess(filename='test.png')
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
csector = ctx["csector"]
date = ctx["date"]
z = ctx["z"]
period = ctx["f"]
scale = ctx["scale"]
valid = utc(date.year, date.month, date.day, int(z))
gribfn = valid.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/wpc/"
"p" + period + "m_%Y%m%d%Hf" + period + ".grb"))
if not os.path.isfile(gribfn):
raise NoDataFound("gribfn %s missing" % (gribfn, ))
grbs = pygrib.open(gribfn)
grb = grbs[1]
precip = distance(grb.values, "MM").value("IN")
lats, lons = grb.latlons()
title = ("Weather Prediction Center %s Quantitative "
"Precipitation Forecast") % (PDICT[period])
subtitle = ("%sWPC Forecast %s UTC to %s UTC") % (
("US Drought Monitor Overlaid, " if ctx["opt"] == "both" else ""),
valid.strftime("%d %b %Y %H"),
(valid +
datetime.timedelta(hours=int(period))).strftime("%d %b %Y %H"),
)
mp = MapPlot(
sector=("state" if len(csector) == 2 else csector),
state=ctx["csector"],
title=title,
subtitle=subtitle,
continentalcolor="white",
titlefontsize=16,
)
cmap = plt.get_cmap(ctx["cmap"])
cmap.set_under("#EEEEEE")
cmap.set_over("black")
if scale == "auto":
levs = np.linspace(0, np.max(precip) * 1.1, 10)
levs = [round(lev, 2) for lev in levs]
levs[0] = 0.01
elif scale == "10":
levs = np.arange(0, 10.1, 1.0)
levs[0] = 0.01
elif scale == "7":
levs = np.arange(0, 7.1, 0.5)
levs[0] = 0.01
elif scale == "3.5":
levs = np.arange(0, 3.6, 0.25)
levs[0] = 0.01
mp.pcolormesh(
lons,
lats,
precip,
levs,
cmap=cmap,
units="inch",
clip_on=(ctx["csector"] == "iailin"),
)
if ctx["opt"] == "both":
mp.draw_usdm(valid=valid, filled=False, hatched=True)
if ctx["csector"] == "iailin":
mp.drawcounties()
return mp.fig
import numpy as np
import matplotlib.patheffects as PathEffects
img = Image.open("p48h_201410150000.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)
m = MapPlot(sector='iowa',
title='NOAA MRMS 48 Hour Precipitation Estimate',
subtitle='7 PM 12 Oct 2014 to 7 PM 14 Oct 2014')
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.pcolormesh(x, y, data, clevs)
m.map.drawcounties(zorder=10, linewidth=1.)
m.postprocess(filename='test.png')
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
csector = ctx['csector']
date = ctx['date']
z = ctx['z']
period = ctx['f']
scale = ctx['scale']
valid = utc(date.year, date.month, date.day, int(z))
gribfn = valid.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/wpc/"
"p" + period + "m_%Y%m%d%Hf" + period + ".grb"))
if not os.path.isfile(gribfn):
raise NoDataFound("gribfn %s missing" % (gribfn, ))
grbs = pygrib.open(gribfn)
grb = grbs[1]
precip = distance(grb.values, 'MM').value('IN')
lats, lons = grb.latlons()
title = ("Weather Prediction Center %s Quantitative "
"Precipitation Forecast") % (PDICT[period])
subtitle = ("%sWPC Forecast %s UTC to %s UTC") % (
("US Drought Monitor Overlaid, " if ctx['opt'] == 'both' else ''),
valid.strftime("%d %b %Y %H"),
(valid +
datetime.timedelta(hours=int(period))).strftime("%d %b %Y %H"))
mp = MapPlot(sector=('state' if len(csector) == 2 else csector),
state=ctx['csector'],
title=title,
subtitle=subtitle,
continentalcolor='white',
titlefontsize=16)
cmap = plt.get_cmap(ctx['cmap'])
cmap.set_under('#EEEEEE')
cmap.set_over('black')
if scale == 'auto':
levs = np.linspace(0, np.max(precip) * 1.1, 10)
levs = [round(lev, 2) for lev in levs]
levs[0] = 0.01
elif scale == '10':
levs = np.arange(0, 10.1, 1.)
levs[0] = 0.01
elif scale == '7':
levs = np.arange(0, 7.1, 0.5)
levs[0] = 0.01
elif scale == '3.5':
levs = np.arange(0, 3.6, 0.25)
levs[0] = 0.01
mp.pcolormesh(lons,
lats,
precip,
levs,
cmap=cmap,
units='inch',
clip_on=(ctx['csector'] == 'iailin'))
if ctx['opt'] == 'both':
mp.draw_usdm(valid=valid, filled=False, hatched=True)
if ctx['csector'] == 'iailin':
mp.drawcounties()
return mp.fig
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
csector = ctx['csector']
sdate = make_tuesday(ctx['sdate'])
edate = make_tuesday(ctx['edate'])
dlevel = ctx['d']
griddelta = 0.1
mp = MapPlot(sector=('state' if len(csector) == 2 else csector),
state=ctx['csector'],
title=('%s at or above "%s" %s - %s') %
(PDICT2[ctx['w']], PDICT[dlevel],
sdate.strftime("%b %-d, %Y"), edate.strftime("%b %-d, %Y")),
subtitle=('based on weekly US Drought Monitor Analysis, '
'%.2f$^\circ$ grid analysis') % (griddelta, ),
continentalcolor='white',
titlefontsize=14)
# compute the affine
(west, east, south, north) = mp.ax.get_extent(ccrs.PlateCarree())
raster = np.zeros((int(
(north - south) / griddelta), int((east - west) / griddelta)))
lons = np.arange(raster.shape[1]) * griddelta + west
lats = np.arange(0, 0 - raster.shape[0], -1) * griddelta + north
lats = lats[::-1]
affine = Affine(griddelta, 0., west, 0., 0 - griddelta, north)
# get the geopandas data
pgconn = get_dbconn('postgis')
df = read_postgis("""
with d as (
select valid, (ST_Dump(st_simplify(geom, 0.01))).geom from usdm where
valid >= %s and valid <= %s and dm >= %s and
ST_Intersects(geom, ST_GeomFromEWKT('SRID=4326;POLYGON((%s %s, %s %s,
%s %s, %s %s, %s %s))'))
)
select valid, st_collect(geom) as the_geom from d GROUP by valid
""",
pgconn,
params=(sdate, edate, dlevel, west, south, west, north,
east, north, east, south, west, south),
geom_col='the_geom')
if df.empty:
raise NoDataFound("No Data Found, sorry!")
# loop over the cached stats
czs = CachingZonalStats(affine)
czs.compute_gridnav(df['the_geom'], raster)
for nav in czs.gridnav:
if nav is None:
continue
grid = np.ones((nav.ysz, nav.xsz))
grid[nav.mask] = 0.
jslice = slice(nav.y0, nav.y0 + nav.ysz)
islice = slice(nav.x0, nav.x0 + nav.xsz)
raster[jslice, islice] += grid
maxval = 10 if np.max(raster) < 11 else np.max(raster)
ramp = np.linspace(1, maxval + 1, 11, dtype='i')
if ctx['w'] == 'percent':
ramp = np.arange(0, 101, 10)
ramp[0] = 1.
ramp[-1] = 100.1
# we add one since we are rectified to tuesdays, so we have an extra
# week in there
raster = raster / ((edate - sdate).days / 7. + 1.) * 100.
# plot
cmap = stretch_cmap(ctx['cmap'], ramp)
cmap.set_under('white')
cmap.set_bad('white')
mp.pcolormesh(lons,
lats,
np.flipud(raster),
ramp,
cmap=cmap,
units='count' if ctx['w'] == 'weeks' else 'Percent')
if len(csector) == 2:
mp.drawcounties()
mp.drawcities()
rows = []
for j in range(raster.shape[0]):
for i in range(raster.shape[1]):
rows.append(dict(lon=lons[i], lat=lats[j], value=raster[j, i]))
return mp.fig, pd.DataFrame(rows)
maxinterval = np.where(current > maxinterval, current, maxinterval)
now += interval
np.save('maxinterval.npy', np.array(maxinterval))
np.save('lats.npy', lats)
np.save('lons.npy', lons)
#do()
maxinterval = np.load('maxinterval.npy')
lats = np.load('lats.npy')
lons = np.load('lons.npy')
m = MapPlot(
sector='conus',
axisbg='#EEEEEE',
title='1-22 November 2014 :: NCEP HRRR Maximum Period Below Freezing',
subtitle=
'based on hourly HRRR 2 meter AGL Temperature Analysis, thru 22 Nov 12 PM CST'
)
m.pcolormesh(
lons,
lats,
maxinterval, [
1, 3, 6, 12, 24, 48, 72, 5 * 24, 7 * 24, 9 * 24, 11 * 24, 13 * 24,
15 * 24
],
units='time',
clevlabels=['1h', '3h', '6h', '12h', '1d', '2', 3, 5, 7, 9, 11, 13, 15])
m.postprocess(filename='test.png')
x, y = np.meshgrid(lons, lats)
buff = 0.5
m = MapPlot(
sector='iowa', #west=min(llons)-buff,
#east=max(llons)+buff, south=min(llats)-buff,
#north=max(llats)+buff,
title='NOAA MRMS 48 Hour RADAR-Only Precipitation Estimate',
subtitle='MRMS valid 5 AM 6 Sep 2015 to 5 AM 8 Sep 2015')
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.pcolormesh(x, y, data, clevs)
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.plot_values(llons, llats, vals, fmt='%s')
m.map.drawcounties(zorder=10, linewidth=1.)
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
ts = ctx["ts"].replace(tzinfo=pytz.utc)
hour = int(ctx["hour"])
ilabel = ctx["ilabel"] == "yes"
plot = MapPlot(
sector=ctx["t"],
continentalcolor="white",
state=ctx["state"],
cwa=ctx["wfo"],
title=("NWS RFC %s Hour Flash Flood Guidance on %s UTC") %
(hour, ts.strftime("%-d %b %Y %H")),
subtitle=("Estimated amount of %s Rainfall "
"needed for non-urban Flash Flooding to commence") %
(HOURS[ctx["hour"]], ),
)
cmap = plt.get_cmap(ctx["cmap"])
bins = [
0.01,
0.6,
0.8,
1.0,
1.2,
1.4,
1.6,
1.8,
2.0,
2.25,
2.5,
2.75,
3.0,
3.5,
4.0,
5.0,
]
if ts.year < 2019:
column = "hour%02i" % (hour, )
pgconn = get_dbconn("postgis")
df = read_sql(
"""
WITH data as (
SELECT ugc, rank() OVER (PARTITION by ugc ORDER by valid DESC),
hour01, hour03, hour06, hour12, hour24
from ffg WHERE valid >= %s and valid <= %s)
SELECT *, substr(ugc, 3, 1) as ztype from data where rank = 1
""",
pgconn,
params=(ts - datetime.timedelta(hours=24), ts),
index_col="ugc",
)
df2 = df[df["ztype"] == "C"]
plot.fill_ugcs(
df2[column].to_dict(),
bins,
cmap=cmap,
plotmissing=False,
ilabel=ilabel,
)
df2 = df[df["ztype"] == "Z"]
plot.fill_ugcs(
df2[column].to_dict(),
bins,
cmap=cmap,
plotmissing=False,
units="inches",
ilabel=ilabel,
)
else:
# use grib data
ts -= datetime.timedelta(hours=(ts.hour % 6))
ts = ts.replace(minute=0)
fn = None
for offset in range(0, 24, 4):
ts2 = ts - datetime.timedelta(hours=offset)
testfn = ts2.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/ffg/"
"5kmffg_%Y%m%d00.grib2"))
if os.path.isfile(testfn):
fn = testfn
break
if fn is None:
raise NoDataFound("No valid grib data found!")
grbs = pygrib.index(fn, "stepRange")
grb = grbs.select(stepRange="0-%s" % (hour, ))[0]
lats, lons = grb.latlons()
data = (masked_array(grb.values,
data_units=units("mm")).to(units("inch")).m)
plot.pcolormesh(lons, lats, data, bins, cmap=cmap)
if ilabel:
plot.drawcounties()
df = pd.DataFrame()
return plot.fig, df
import matplotlib.pyplot as plt
import datetime
import numpy as np
nc = netCDF4.Dataset("/mesonet/data/iemre/2016_mw_mrms_daily.nc", 'r')
idx = iemre.daily_offset(datetime.date(2016, 7, 3))
grid = np.zeros((len(nc.dimensions['lat']), len(nc.dimensions['lon'])))
total = np.zeros((len(nc.dimensions['lat']), len(nc.dimensions['lon'])))
for i, x in enumerate(range(idx, idx - 60, -1)):
total += nc.variables['p01d'][x, :, :]
grid = np.where(np.logical_and(grid == 0, total > 25.4), i, grid)
m = MapPlot(
sector='iowa',
title='NOAA MRMS Q3: Number of Recent Days till Accumulating 1" of Precip',
subtitle=
'valid 4 July 2016: based on per calendar day estimated preciptation, GaugeCorr and RadarOnly products'
)
lon = np.append(nc.variables['lon'][:], [-80.5])
print lon
lat = np.append(nc.variables['lat'][:], [49.])
print lat
x, y = np.meshgrid(lon, lat)
cmap = nwsprecip()
m.pcolormesh(x, y, grid, np.arange(0, 53, 7), cmap=cmap, units='days')
m.drawcounties()
m.drawcities()
m.postprocess(filename='test.png')
m.close()
nc.close()
def main(argv):
"""Go Main Go"""
nt = Table("ISUSM")
qdict = loadqc()
idbconn = get_dbconn("isuag", user="******")
pdbconn = get_dbconn("postgis", user="******")
day_ago = int(argv[1])
ts = datetime.date.today() - datetime.timedelta(days=day_ago)
hlons, hlats, hvals = do_nam(ts)
nam = temperature(hvals, "K").value("F")
window = np.ones((3, 3))
nam = convolve2d(nam, window / window.sum(), mode="same", boundary="symm")
# mp = MapPlot(sector='midwest')
# mp.pcolormesh(hlons, hlats, nam,
# range(20, 90, 5))
# mp.postprocess(filename='test.png')
# sys.exit()
# Query out the data
df = read_sql(
"""
WITH ranges as (
select station, count(*), min(tsoil_c_avg_qc),
max(tsoil_c_avg_qc) from sm_hourly WHERE
valid >= %s and valid < %s and tsoil_c_avg_qc > -40
and tsoil_c_avg_qc < 50 GROUP by station
)
SELECT d.station, d.tsoil_c_avg_qc,
r.max as hourly_max_c, r.min as hourly_min_c, r.count
from sm_daily d JOIN ranges r on (d.station = r.station)
where valid = %s and tsoil_c_avg_qc > -40 and r.count > 19
""",
idbconn,
params=(ts, ts + datetime.timedelta(days=1), ts),
index_col="station",
)
for col, newcol in zip(
["tsoil_c_avg_qc", "hourly_min_c", "hourly_max_c"],
["ob", "min", "max"],
):
df[newcol] = temperature(df[col].values, "C").value("F")
df.drop(col, axis=1, inplace=True)
for stid, row in df.iterrows():
df.at[stid, "ticket"] = qdict.get(stid, {}).get("soil4", False)
x, y = get_idx(hlons, hlats, nt.sts[stid]["lon"], nt.sts[stid]["lat"])
df.at[stid, "nam"] = nam[x, y]
df.at[stid, "lat"] = nt.sts[stid]["lat"]
df.at[stid, "lon"] = nt.sts[stid]["lon"]
# ticket is an object type from above
df = df[~df["ticket"].astype("bool")]
df["diff"] = df["ob"] - df["nam"]
bias = df["diff"].mean()
nam = nam + bias
print("fancy_4inch NAM bias correction of: %.2fF applied" % (bias, ))
# apply nam bias to sampled data
df["nam"] += bias
df["diff"] = df["ob"] - df["nam"]
# we are going to require data be within 1 SD of sampled or 5 deg
std = 5.0 if df["nam"].std() < 5.0 else df["nam"].std()
for station in df[df["diff"].abs() > std].index.values:
print(("fancy_4inch %s QC'd %s out std: %.2f, ob:%.1f nam:%.1f") % (
ts.strftime("%Y%m%d"),
station,
std,
df.at[station, "ob"],
df.at[station, "nam"],
))
df.drop(station, inplace=True)
# Query out centroids of counties...
cdf = read_sql(
"""SELECT ST_x(ST_centroid(the_geom)) as lon,
ST_y(ST_centroid(the_geom)) as lat
from uscounties WHERE state_name = 'Iowa'
""",
pdbconn,
index_col=None,
)
for i, row in cdf.iterrows():
x, y = get_idx(hlons, hlats, row["lon"], row["lat"])
cdf.at[i, "nam"] = nam[x, y]
mp = MapPlot(
sector="iowa",
title=("Average 4 inch Depth Soil Temperatures for %s") %
(ts.strftime("%b %d, %Y"), ),
subtitle=("County est. based on bias adj. "
"NWS NAM Model (black numbers), "
"ISUSM network observations (red numbers)"),
)
mp.pcolormesh(
hlons,
hlats,
nam,
np.arange(10, 101, 5),
cmap=cm.get_cmap("jet"),
units=r"$^\circ$F",
)
mp.plot_values(df["lon"],
df["lat"],
df["ob"],
fmt="%.0f",
color="r",
labelbuffer=5)
mp.plot_values(
cdf["lon"],
cdf["lat"],
cdf["nam"],
fmt="%.0f",
textsize=11,
labelbuffer=5,
)
mp.drawcounties()
routes = "a" if day_ago >= 4 else "ac"
pqstr = ("plot %s %s0000 soilt_day%s.png isuag_county_4inch_soil.png png"
) % (routes, ts.strftime("%Y%m%d"), day_ago)
mp.postprocess(pqstr=pqstr)
mp.close()
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)
with ncopen(iemre.get_daily_ncname(date.year)) 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. / 1000000.) if varname == 'rsds' else 1
data = nc.variables[varname][idx0, jslice, islice] * multi
units = 'MJ d-1'
clevs = np.arange(0, 37, 3.)
clevs[0] = 0.01
clevstride = 1
elif varname in [
'wind_speed',
]:
data = speed(nc.variables[varname][idx0, jslice, islice],
'MPS').value('MPH')
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 = distance(nc.variables[varname][idx0, jslice, islice],
'MM').value('IN')
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
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 = temperature(nc.variables[varname][idx0, jslice, islice],
'K').value('F')
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 = (temperature(d1, 'K').value('F') -
temperature(d2, 'K').value('F'))
units = 'F'
clevs = np.arange(0, 61, 5)
clevstride = 2
if np.ma.is_masked(np.max(data)):
raise ValueError("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=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=units)
return mp.fig
onc.close()
else:
ncfn = "/mesonet/data/iemre/%s_mw_daily.nc" % (sts.year, )
onc = netCDF4.Dataset(ncfn, 'r')
obprecip = np.sum(onc.variables['p01d'][index0:index1, :, :], 0)
onc.close()
lons, lats = np.meshgrid(lons, lats)
# Plot departure from normal
m = MapPlot(sector='midwest',
title='Precipitation Departure %s - %s' %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle='based on IEM Estimates')
m.pcolormesh(lons, lats, (obprecip - clprecip) / 25.4, np.arange(-10, 10, 1))
m.postprocess(pqstr="plot c 000000000000 summary/4mon_diff.png bogus png")
# Plot normals
m = MapPlot(sector='midwest',
title='Normal Precipitation:: %s - %s' %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle='based on IEM Estimates')
m.pcolormesh(lons, lats, (clprecip) / 25.4, np.arange(0, 30, 2))
m.postprocess(pqstr="plot c 000000000000 summary/4mon_normals.png bogus png")
# Plot Obs
m = MapPlot(sector='midwest',
title='Estimated Precipitation:: %s - %s' %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
#pr = numpy.average(miroc_nc.variables['pr'][offset3:offset4,:,:] ,0)
#pr2 = numpy.average(pr_nc2.variables['pr'][0,offset3:offset4,:,:] ,0)
pr = numpy.average(bc3_nc.variables['Prcp'][0,offset1:offset2,:,:] ,0)
m = MapPlot(sector='conus', caption='', nologo=True,
title=r'1981-1999 MIROC3.2 Bias Correction 3 (bc3)',
subtitle='Yearly Average Precipitation')
# urcrnrlon=-78,urcrnrlat=49, projection='merc',
# lat_0=45.,lon_0=-92.,lat_ts=42.,
# resolution='i', fix_aspect=False)
x, y = numpy.meshgrid(lons, lats)
#colormap = LevelColormap([1,6,12,18,24,30,36,42,48,54,60,66,72,78,84], plt.cm.gist_ncar)
levels = [1,6,12,18,24,30,36,42,48,54,60,66,72,78,84]
#levels = numpy.arange(-14,14,2)
m.pcolormesh(x, y, pr * 365.0 / 25.4, levels, latlon=True, units='inches')
#m.drawstates()
#m.drawcoastlines()
#ax.set_title(r"1981-1999 MIROC 3.2 downscaled BCCA [inch/yr]")
#fig.colorbar(cs)
#fig.savefig('test.png')
m.postprocess(filename='test.png')
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
# Start at 1 AM
now = ts.replace(hour=1, minute=0)
ets = now + datetime.timedelta(hours=24)
interval = datetime.timedelta(hours=1)
currenttime = datetime.datetime.utcnow()
currenttime = currenttime.replace(tzinfo=pytz.timezone("UTC"))
total = None
lastts = None
while now < ets:
gmt = now.astimezone(pytz.timezone("UTC"))
if gmt > currenttime:
break
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_today_total.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)
lastts = now
now += interval
if lastts is None:
print(('No MRMS Q3 Data found for date: %s') %
(now.strftime("%d %B %Y"), ))
return
lastts = lastts - datetime.timedelta(minutes=1)
subtitle = "Total between 12:00 AM and %s" % (
lastts.strftime("%I:%M %p %Z"), )
routes = 'ac'
if not realtime:
routes = 'a'
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
sector = 'iowa'
pqstr = ("plot %s %s00 %s_q2_1d.png %s_q2_1d.png png") % (
routes, ts.strftime("%Y%m%d%H"), sector, sector)
m = MapPlot(title=("%s NCEP MRMS Q3 Today's Precipitation") %
(ts.strftime("%-d %b %Y"), ),
subtitle=subtitle,
sector=sector)
(x, y) = np.meshgrid(mrms.XAXIS, mrms.YAXIS)
m.pcolormesh(x, y, np.flipud(total) / 24.5, clevs, units='inch')
m.drawcounties()
m.postprocess(pqstr=pqstr, view=False)
m.close()
except:
print fn2
now += interval
continue
g = temperature(gs[0]['values'], 'K')
g2 = temperature(gs2[0]['values'], 'K')
if now == sts:
lats,lons = gs[0].latlons()
largest = np.zeros(np.shape(g))
delta = g.value('F') - g2.value('F')
largest = np.minimum(delta, largest)
now += interval
np.save('maxinterval.npy', np.array(largest))
np.save('lats.npy', lats)
np.save('lons.npy', lons)
#do()
#sys.exit()
maxinterval = np.load('maxinterval.npy')
print np.max(maxinterval), np.min(maxinterval)
lats = np.load('lats.npy')
lons = np.load('lons.npy')
m = MapPlot(sector='conus', axisbg='#EEEEEE',
title='16 - 18 March 2015 :: Coldest 24 Hour Temperature Change',
subtitle='based on hourly HRRR Analyses 7 AM 16 Mar thru 5 AM 18 Mar')
m.pcolormesh(lons, lats, maxinterval, range(-50,1,5), units='F')
m.postprocess(filename='test.png')
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
def run(utc, routes):
''' Generate the plot for the given UTC time '''
fn = utc.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/hrrr/%H/"
"hrrr.t%Hz.refd.grib2"))
grbs = pygrib.open(fn)
subprocess.call("rm /tmp/hrrr_ref_???.gif",
shell=True,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
lats = None
lons = None
i = 0
for minute in range(0, 18 * 60 + 1, 15):
now = utc + datetime.timedelta(minutes=minute)
now = now.astimezone(pytz.timezone("America/Chicago"))
grbs.seek(0)
try:
gs = grbs.select(level=1000, forecastTime=minute)
except ValueError:
continue
g = gs[0]
if lats is None:
lats, lons = g.latlons()
x1, x2, y1, y2 = compute_bounds(lons, lats)
lats = lats[x1:x2, y1:y2]
lons = lons[x1:x2, y1:y2]
ref = g['values'][x1:x2, y1:y2]
mp = MapPlot(sector='midwest',
axisbg='tan',
title=('%s UTC NCEP HRRR 1 km AGL Reflectivity') %
(utc.strftime("%-d %b %Y %H"), ),
subtitle=('valid: %s') %
(now.strftime("%-d %b %Y %I:%M %p %Z"), ))
mp.pcolormesh(lons,
lats,
ref,
np.arange(0, 75, 5),
units='dBZ',
clip_on=False)
mp.postprocess(filename='/tmp/hrrr_ref_%03i.png' % (i, ))
mp.close()
subprocess.call(("convert /tmp/hrrr_ref_%03i.png "
"/tmp/hrrr_ref_%03i.gif") % (i, i),
shell=True)
i += 1
# Generate anim GIF
subprocess.call(("gifsicle --loopcount=0 --delay=50 "
"/tmp/hrrr_ref_???.gif > /tmp/hrrr_ref.gif"),
shell=True,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
pqstr = ("plot %s %s model/hrrr/hrrr_1km_ref.gif "
"model/hrrr/hrrr_1km_ref_%02i.gif gif") % (
routes, utc.strftime("%Y%m%d%H%M"), utc.hour)
subprocess.call("/home/ldm/bin/pqinsert -p '%s' /tmp/hrrr_ref.gif" %
(pqstr, ),
shell=True,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
os.remove("/tmp/hrrr_ref.gif")
def main():
"""Go Main Go"""
# Run for a period of 121 days
ets = datetime.datetime.now() - datetime.timedelta(days=1)
sts = ets - datetime.timedelta(days=121)
# Get the normal accumm
cnc = ncopen(iemre.get_dailyc_ncname(), "r")
lons = cnc.variables["lon"][:]
lats = cnc.variables["lat"][:]
index0 = iemre.daily_offset(sts)
index1 = iemre.daily_offset(ets)
if index1 < index0: # Uh oh, we are spanning a year
clprecip = np.sum(cnc.variables["p01d"][:index1, :, :], 0)
clprecip = clprecip + np.sum(cnc.variables["p01d"][index0:, :, :], 0)
else:
clprecip = np.sum(cnc.variables["p01d"][index0:index1, :, :], 0)
cnc.close()
# Get the observed precip
if sts.year != ets.year: # spanner, darn
onc = ncopen(iemre.get_daily_ncname(sts.year))
obprecip = np.sum(onc.variables["p01d"][index0:, :, :], 0)
onc.close()
onc = ncopen(iemre.get_daily_ncname(ets.year))
obprecip = obprecip + np.sum(onc.variables["p01d"][:index1, :, :], 0)
onc.close()
else:
ncfn = iemre.get_daily_ncname(sts.year)
onc = ncopen(ncfn, "r")
obprecip = np.sum(onc.variables["p01d"][index0:index1, :, :], 0)
onc.close()
lons, lats = np.meshgrid(lons, lats)
# Plot departure from normal
mp = MapPlot(
sector="midwest",
title=("Precipitation Departure %s - %s") %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle="based on IEM Estimates",
)
mp.pcolormesh(lons, lats, (obprecip - clprecip) / 25.4,
np.arange(-10, 10, 1))
mp.postprocess(pqstr="plot c 000000000000 summary/4mon_diff.png bogus png")
mp.close()
# Plot normals
mp = MapPlot(
sector="midwest",
title=("Normal Precipitation:: %s - %s") %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle="based on IEM Estimates",
)
mp.pcolormesh(lons, lats, (clprecip) / 25.4, np.arange(0, 30, 2))
mp.postprocess(
pqstr="plot c 000000000000 summary/4mon_normals.png bogus png")
mp.close()
# Plot Obs
mp = MapPlot(
sector="midwest",
title=("Estimated Precipitation:: %s - %s") %
(sts.strftime("%b %d %Y"), ets.strftime("%b %d %Y")),
subtitle="based on IEM Estimates",
)
mp.pcolormesh(lons, lats, (obprecip) / 25.4, np.arange(0, 30, 2))
mp.postprocess(
pqstr="plot c 000000000000 summary/4mon_stage4obs.png bogus png")
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot, nwsprecip
ctx = util.get_autoplot_context(fdict, get_description())
ptype = ctx['ptype']
sdate = ctx['sdate']
edate = ctx['edate']
src = ctx['src']
if sdate.year != edate.year:
raise Exception('Sorry, do not support multi-year plots yet!')
days = (edate - sdate).days
sector = ctx['sector']
idx0 = iemre.daily_offset(sdate)
idx1 = iemre.daily_offset(edate) + 1
if src == 'mrms':
ncfn = "/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (sdate.year, )
ncvar = 'p01d'
source = 'NOAA MRMS Q3'
subtitle = 'NOAA MRMS Project, GaugeCorr and RadarOnly'
else:
ncfn = "/mesonet/data/prism/%s_daily.nc" % (sdate.year, )
ncvar = 'ppt'
source = 'OSU PRISM'
subtitle = ('PRISM Climate Group, Oregon State Univ., '
'http://prism.oregonstate.edu, created 4 Feb 2004.')
if not os.path.isfile(ncfn):
raise Exception("No data for that year, sorry.")
nc = netCDF4.Dataset(ncfn, 'r')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
if (idx1 - idx0) < 32:
p01d = distance(np.sum(nc.variables[ncvar][idx0:idx1, :, :], 0),
'MM').value('IN')
else:
# Too much data can overwhelm this app, need to chunk it
for i in range(idx0, idx1, 10):
i2 = min([i + 10, idx1])
if idx0 == i:
p01d = distance(np.sum(nc.variables[ncvar][i:i2, :, :], 0),
'MM').value('IN')
else:
p01d += distance(np.sum(nc.variables[ncvar][i:i2, :, :], 0),
'MM').value('IN')
nc.close()
if sdate == edate:
title = sdate.strftime("%-d %B %Y")
else:
title = "%s to %s (inclusive)" % (sdate.strftime("%-d %b %Y"),
edate.strftime("%-d %b %Y"))
if sector == 'midwest':
state = None
else:
state = sector
sector = 'state'
m = MapPlot(sector=sector,
state=state,
axisbg='white',
nocaption=True,
title='%s:: %s Total Precip' % (source, title),
subtitle='Data from %s' % (subtitle, ))
if np.ma.is_masked(np.max(p01d)):
return 'Data Unavailable'
clevs = [0.01, 0.1, 0.3, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10]
if days > 6:
clevs = [0.01, 0.3, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 10, 15, 20]
if days > 29:
clevs = [0.01, 0.5, 1, 2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30, 35]
if days > 90:
clevs = [0.01, 1, 2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30, 35, 40]
x, y = np.meshgrid(lons, lats)
cmap = nwsprecip()
cmap.set_over('k')
if ptype == 'c':
m.contourf(x, y, p01d, clevs, cmap=cmap, label='inches')
else:
m.pcolormesh(x, y, p01d, clevs, cmap=cmap, label='inches')
if sector != 'midwest':
m.drawcounties()
m.drawcities()
return m.fig
X, Y = numpy.mgrid[reference.MW_WEST:reference.MW_EAST:50j,
reference.MW_SOUTH:reference.MW_NORTH:50j]
positions = numpy.vstack([X.ravel(), Y.ravel()])
values = numpy.vstack([lons, lats])
kernel = stats.gaussian_kde(values)
Z = numpy.reshape(kernel(positions).T, X.shape)
m = MapPlot(
sector='midwest',
title='Local Storm Reports of Hail (1+") :: Kernel Density Estimate',
subtitle=
'2003 - May 2013, gaussian kernel, 15min/15km duplicate rule applied')
m.pcolormesh(X,
Y,
Z,
numpy.arange(0, 0.006, .0003),
cmap=plt.cm.gist_earth_r,
latlon=True)
#xs, ys = m.map(-93.72, 41.72)
#m.ax.scatter(xs, ys, marker='o', zorder=20, s=50, color='k')
#xs, ys = m.map(lons, lats)
#m.ax.scatter(xs, ys, marker='+', zorder=20, s=100, color='k')
#m.drawcounties()
m.postprocess(filename='test.png')
"""
fig = plt.figure()
ax = fig.add_subplot(111)
utcnow = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
for i, lon in enumerate(tqdm(lons)):
for j, lat in enumerate(lats):
cursor.execute("""
select eventid from sbw where
ST_Covers(geom, ST_GeomFromEWKT('SRID=4326;POINT(%s %s)'))
and status = 'NEW' and phenomena = 'TO' and significance = 'W'
and issue < '2017-01-01' and issue > '2002-01-01'
""", (lon, lat))
val = cursor.rowcount
vals[j, i] = val / 15.0 # Number of years 2002-2016
print("Maximum is: %.1f" % (np.max(vals),))
m = MapPlot(sector='iowa',
title='Avg Number of Storm Based Tornado Warnings per Year',
subtitle=("(2003 through 2016) based on unofficial "
"archives maintained by the IEM, %sx%s analysis grid"
) % (dx, dx))
cmap = plt.get_cmap('jet')
cmap.set_under('white')
cmap.set_over('black')
lons, lats = np.meshgrid(lons, lats)
rng = np.arange(0, 2.1, 0.2)
rng[0] = 0.01
m.pcolormesh(lons, lats, vals,
rng, cmap=cmap, units='count')
m.drawcounties()
m.postprocess(filename='count.png')
m.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
We should total files from 1 AM to midnight local time
"""
sts = ts.replace(hour=1)
ets = sts + datetime.timedelta(hours=24)
interval = datetime.timedelta(hours=1)
now = sts
total = None
lts = None
while now < ets:
gmt = now.astimezone(pytz.timezone("UTC"))
fn = gmt.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/"
"stage4/ST4.%Y%m%d%H.01h.grib"))
if os.path.isfile(fn):
lts = now
grbs = pygrib.open(fn)
if total is None:
g = grbs[1]
total = g["values"]
lats, lons = g.latlons()
else:
total += grbs[1]["values"]
grbs.close()
now += interval
if lts is None and ts.hour > 1:
print 'stage4_today_total.py found no data!'
if lts is None:
return
lts = lts - datetime.timedelta(minutes=1)
subtitle = "Total between 12:00 AM and %s" % (
lts.strftime("%I:%M %p %Z"), )
routes = 'ac'
if not realtime:
routes = 'a'
for sector in ['iowa', 'midwest', 'conus']:
pqstr = ("plot %s %s00 %s_stage4_1d.png %s_stage4_1d.png png") % (
routes, ts.strftime("%Y%m%d%H"), sector, sector)
m = MapPlot(sector=sector,
title="%s NCEP Stage IV Today's Precipitation" %
(ts.strftime("%-d %b %Y"), ),
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.pcolormesh(lons,
lats,
distance(total, 'MM').value('IN'),
clevs,
cmap=nwsprecip(),
units='inch')
# map.drawstates(zorder=2)
if sector == 'iowa':
m.drawcounties()
m.postprocess(pqstr=pqstr)
m.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
ctx = get_autoplot_context(fdict, get_description())
ptype = ctx['ptype']
date = ctx['date']
varname = ctx['var']
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 [
'wind_speed',
]:
data = speed(nc.variables[varname][idx0, :, :], 'MPS').value('MPH')
units = 'mph'
clevs = np.arange(0, 41, 2)
clevs[0] = 0.01
clevstride = 2
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',
'avg_dwpk'
]:
# 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, 5)
clevstride = 2
nc.close()
title = date.strftime("%-d %B %Y")
mp = 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':
# in the case of contour, use the centroids on the grids
mp.contourf(x + 0.125,
y + 0.125,
data,
clevs,
clevstride=clevstride,
units=units,
ilabel=True,
labelfmt='%.0f')
else:
x, y = np.meshgrid(lons, lats)
mp.pcolormesh(x, y, data, clevs, clevstride=clevstride, units=units)
return mp.fig
def run(valid, routes):
''' Generate the plot for the given UTC time '''
fn = valid.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/hrrr/%H/"
"hrrr.t%Hz.refd.grib2"))
if not os.path.isfile(fn):
print("hrrr/plot_ref missing %s" % (fn, ))
return
grbs = pygrib.open(fn)
lats = None
lons = None
i = 0
for minute in range(0, HOURS[valid.hour] * 60 + 1, 15):
if minute > (18 * 60) and minute % 60 != 0:
continue
now = valid + datetime.timedelta(minutes=minute)
now = now.astimezone(pytz.timezone("America/Chicago"))
grbs.seek(0)
try:
gs = grbs.select(level=1000,
forecastTime=(minute if minute <=
(18 * 60) else int(minute / 60)))
except ValueError:
continue
if lats is None:
lats, lons = gs[0].latlons()
x1, x2, y1, y2 = compute_bounds(lons, lats)
lats = lats[x1:x2, y1:y2]
lons = lons[x1:x2, y1:y2]
# HACK..............
if len(gs) > 1 and minute > (18 * 60):
reflect = gs[-1]['values'][x1:x2, y1:y2]
else:
reflect = gs[0]['values'][x1:x2, y1:y2]
mp = MapPlot(sector='midwest',
axisbg='tan',
title=('%s UTC NCEP HRRR 1 km AGL Reflectivity') %
(valid.strftime("%-d %b %Y %H"), ),
subtitle=('valid: %s') %
(now.strftime("%-d %b %Y %I:%M %p %Z"), ))
mp.pcolormesh(lons,
lats,
reflect,
np.arange(0, 75, 5),
units='dBZ',
clip_on=False)
pngfn = '/tmp/hrrr_ref_%s_%03i.png' % (valid.strftime("%Y%m%d%H"), i)
mp.postprocess(filename=pngfn)
mp.close()
subprocess.call(("convert %s "
"%s.gif") % (pngfn, pngfn[:-4]),
shell=True)
i += 1
# Generate anim GIF
subprocess.call(("gifsicle --loopcount=0 --delay=50 "
"/tmp/hrrr_ref_%s_???.gif > /tmp/hrrr_ref_%s.gif") %
(valid.strftime("%Y%m%d%H"), valid.strftime("%Y%m%d%H")),
shell=True,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
pqstr = ("plot %s %s model/hrrr/hrrr_1km_ref.gif "
"model/hrrr/hrrr_1km_ref_%02i.gif gif") % (
routes, valid.strftime("%Y%m%d%H%M"), valid.hour)
subprocess.call(("/home/ldm/bin/pqinsert -p '%s' /tmp/hrrr_ref_%s.gif") %
(pqstr, valid.strftime("%Y%m%d%H")),
shell=True,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
subprocess.call("rm -f /tmp/hrrr_ref_%s*" % (valid.strftime("%Y%m%d%H"), ),
shell=True)
