def workflow(ts):
"""Do Work"""
# Load up our netcdf file!
nc = ncopen(iemre.get_dailyc_ncname(), "a", timeout=300)
grid_day(nc, ts)
nc.close()
nc = ncopen(iemre.get_dailyc_mrms_ncname(), "a", timeout=300)
grid_day(nc, ts)
nc.close()
def plotter(fdict):
""" Go """
ctx = util.get_autoplot_context(fdict, get_description())
ptype = ctx["ptype"]
sdate = ctx["sdate"]
edate = ctx["edate"]
src = ctx["src"]
opt = ctx["opt"]
usdm = ctx["usdm"]
if sdate.year != edate.year:
raise NoDataFound("Sorry, do not support multi-year plots yet!")
days = (edate - sdate).days
sector = ctx["sector"]
x0 = 0
x1 = -1
y0 = 0
y1 = -1
state = None
if len(sector) == 2:
state = sector
sector = "state"
title = compute_title(src, sdate, edate)
if src == "mrms":
ncfn = iemre.get_daily_mrms_ncname(sdate.year)
clncfn = iemre.get_dailyc_mrms_ncname()
ncvar = "p01d"
source = "MRMS Q3"
subtitle = "NOAA MRMS Project, GaugeCorr and RadarOnly"
elif src == "iemre":
ncfn = iemre.get_daily_ncname(sdate.year)
clncfn = iemre.get_dailyc_ncname()
ncvar = "p01d_12z"
source = "IEM Reanalysis"
subtitle = "IEM Reanalysis is derived from various NOAA datasets"
else:
ncfn = "/mesonet/data/prism/%s_daily.nc" % (sdate.year, )
clncfn = "/mesonet/data/prism/prism_dailyc.nc"
ncvar = "ppt"
source = "OSU PRISM"
subtitle = ("PRISM Climate Group, Oregon State Univ., "
"http://prism.oregonstate.edu, created 4 Feb 2004.")
mp = MapPlot(
sector=sector,
state=state,
axisbg="white",
nocaption=True,
title="%s:: %s Precip %s" % (source, title, PDICT3[opt]),
subtitle="Data from %s" % (subtitle, ),
titlefontsize=14,
)
idx0 = iemre.daily_offset(sdate)
idx1 = iemre.daily_offset(edate) + 1
if not os.path.isfile(ncfn):
raise NoDataFound("No data for that year, sorry.")
with util.ncopen(ncfn) as nc:
if state is not None:
x0, y0, x1, y1 = util.grid_bounds(
nc.variables["lon"][:],
nc.variables["lat"][:],
state_bounds[state],
)
elif sector in SECTORS:
bnds = SECTORS[sector]
x0, y0, x1, y1 = util.grid_bounds(
nc.variables["lon"][:],
nc.variables["lat"][:],
[bnds[0], bnds[2], bnds[1], bnds[3]],
)
lats = nc.variables["lat"][y0:y1]
lons = nc.variables["lon"][x0:x1]
if sdate == edate:
p01d = mm2inch(nc.variables[ncvar][idx0, y0:y1, x0:x1])
elif (idx1 - idx0) < 32:
p01d = mm2inch(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0))
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 = mm2inch(
np.sum(nc.variables[ncvar][i:i2, y0:y1, x0:x1], 0))
else:
p01d += mm2inch(
np.sum(nc.variables[ncvar][i:i2, y0:y1, x0:x1], 0))
if np.ma.is_masked(np.max(p01d)):
raise NoDataFound("Data Unavailable")
plot_units = "inches"
cmap = get_cmap(ctx["cmap"])
cmap.set_bad("white")
if opt == "dep":
# Do departure work now
with util.ncopen(clncfn) as nc:
climo = mm2inch(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0))
p01d = p01d - climo
[maxv] = np.percentile(np.abs(p01d), [99])
clevs = np.around(np.linspace(0 - maxv, maxv, 11), decimals=2)
elif opt == "per":
with util.ncopen(clncfn) as nc:
climo = mm2inch(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0))
p01d = p01d / climo * 100.0
cmap.set_under("white")
cmap.set_over("black")
clevs = [1, 10, 25, 50, 75, 100, 125, 150, 200, 300, 500]
plot_units = "percent"
else:
p01d = np.where(p01d < 0.001, np.nan, p01d)
cmap.set_under("white")
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]
x2d, y2d = np.meshgrid(lons, lats)
if ptype == "c":
mp.contourf(x2d,
y2d,
p01d,
clevs,
cmap=cmap,
units=plot_units,
iline=False)
else:
res = mp.pcolormesh(x2d, y2d, p01d, clevs, cmap=cmap, units=plot_units)
res.set_rasterized(True)
if sector != "midwest":
mp.drawcounties()
mp.drawcities()
if usdm == "yes":
mp.draw_usdm(edate, filled=False, hatched=True)
return mp.fig
def plotter(fdict):
""" Go """
ctx = util.get_autoplot_context(fdict, get_description())
date = ctx['date']
sector = ctx['sector']
days = ctx['trailing']
threshold = ctx['threshold']
window_sts = date - datetime.timedelta(days=days)
if window_sts.year != date.year:
raise ValueError('Sorry, do not support multi-year plots yet!')
if len(sector) != 2:
raise ValueError("Sorry, this does not support multi-state plots yet.")
idx0 = iemre.daily_offset(window_sts)
idx1 = iemre.daily_offset(date)
ncfn = iemre.get_daily_mrms_ncname(date.year)
ncvar = 'p01d'
if not os.path.isfile(ncfn):
raise ValueError("No data for that year, sorry.")
nc = util.ncopen(ncfn)
# Get the state weight
df = gpd.GeoDataFrame.from_postgis("""
SELECT the_geom from states where state_abbr = %s
""",
util.get_dbconn('postgis'),
params=(sector, ),
index_col=None,
geom_col='the_geom')
czs = CachingZonalStats(iemre.MRMS_AFFINE)
czs.gen_stats(
np.zeros((nc.variables['lat'].size, nc.variables['lon'].size)),
df['the_geom'])
hasdata = None
jslice = None
islice = None
for nav in czs.gridnav:
hasdata = np.ones((nav.ysz, nav.xsz))
hasdata[nav.mask] = 0.
# careful here as y is flipped in this context
jslice = slice(nc.variables['lat'].size - (nav.y0 + nav.ysz),
nc.variables['lat'].size - nav.y0)
islice = slice(nav.x0, nav.x0 + nav.xsz)
hasdata = np.flipud(hasdata)
today = distance(nc.variables[ncvar][idx1, jslice, islice],
'MM').value('IN')
if (idx1 - idx0) < 32:
p01d = distance(
np.sum(nc.variables[ncvar][idx0:idx1, jslice, islice], 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, jslice, islice], 0),
'MM').value('IN')
else:
p01d += distance(
np.sum(nc.variables[ncvar][i:i2, jslice, islice], 0),
'MM').value('IN')
nc.close()
# Get climatology
nc = util.ncopen(iemre.get_dailyc_mrms_ncname())
if (idx1 - idx0) < 32:
c_p01d = distance(
np.sum(nc.variables[ncvar][idx0:idx1, jslice, islice], 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:
c_p01d = distance(
np.sum(nc.variables[ncvar][i:i2, jslice, islice], 0),
'MM').value('IN')
else:
c_p01d += distance(
np.sum(nc.variables[ncvar][i:i2, jslice, islice], 0),
'MM').value('IN')
nc.close()
# we actually don't care about weights at this fine of scale
cells = np.sum(np.where(hasdata > 0, 1, 0))
departure = p01d - c_p01d
# Update departure and today to values unconsidered below when out of state
departure = np.where(hasdata > 0, departure, -9999)
today = np.where(hasdata > 0, today, 0)
ranges = [[-99, -3], [-3, -2], [-2, -1], [-1, 0], [0, 1], [1, 2], [2, 3],
[3, 99]]
x = []
x2 = []
labels = []
for (minv, maxv) in ranges:
labels.append("%.0f to %.0f" % (minv, maxv))
# How many departure cells in this range
hits = np.logical_and(departure < maxv, departure > minv)
hits2 = np.logical_and(hits, today > threshold)
x.append(np.sum(np.where(hits, 1, 0)) / float(cells) * 100.)
x2.append(np.sum(np.where(hits2, 1, 0)) / float(cells) * 100.)
(fig, ax) = plt.subplots(1, 1)
ax.set_title(("%s NOAA MRMS %s %.2f inch Precip Coverage") %
(state_names[sector], date.strftime("%-d %b %Y"), threshold))
ax.bar(np.arange(8) - 0.2,
x,
align='center',
width=0.4,
label='Trailing %s Day Departure' % (days, ))
ax.bar(np.arange(8) + 0.2,
x2,
align='center',
width=0.4,
label='%s Coverage (%.1f%% Tot)' %
(date.strftime("%-d %b %Y"), sum(x2)))
for i, (_x1, _x2) in enumerate(zip(x, x2)):
ax.text(i - 0.2, _x1 + 1, "%.1f" % (_x1, ), ha='center')
ax.text(i + 0.2, _x2 + 1, "%.1f" % (_x2, ), ha='center')
ax.set_xticks(np.arange(8))
ax.set_xticklabels(labels)
ax.set_xlabel("Trailing %s Day Precip Departure [in]" % (days, ))
ax.set_position([0.1, 0.2, 0.8, 0.7])
ax.legend(loc=(0., -0.2), ncol=2)
ax.set_ylabel("Areal Coverage of %s [%%]" % (state_names[sector], ))
ax.grid(True)
ax.set_xlim(-0.5, 7.5)
ax.set_ylim(0, max([max(x2), max(x)]) + 5)
return fig
def plotter(fdict):
""" Go """
ctx = util.get_autoplot_context(fdict, get_description())
ptype = ctx['ptype']
sdate = ctx['sdate']
edate = ctx['edate']
src = ctx['src']
opt = ctx['opt']
usdm = ctx['usdm']
if sdate.year != edate.year:
raise NoDataFound('Sorry, do not support multi-year plots yet!')
days = (edate - sdate).days
sector = ctx['sector']
if sdate == edate:
title = sdate.strftime("%-d %B %Y")
else:
title = "%s to %s (inclusive)" % (sdate.strftime("%-d %b"),
edate.strftime("%-d %b %Y"))
x0 = 0
x1 = -1
y0 = 0
y1 = -1
state = None
if len(sector) == 2:
state = sector
sector = 'state'
if src == 'mrms':
ncfn = iemre.get_daily_mrms_ncname(sdate.year)
clncfn = iemre.get_dailyc_mrms_ncname()
ncvar = 'p01d'
source = 'MRMS Q3'
subtitle = 'NOAA MRMS Project, GaugeCorr and RadarOnly'
elif src == 'iemre':
ncfn = iemre.get_daily_ncname(sdate.year)
clncfn = iemre.get_dailyc_ncname()
ncvar = 'p01d_12z'
source = 'IEM Reanalysis'
subtitle = 'IEM Reanalysis is derived from various NOAA datasets'
else:
ncfn = "/mesonet/data/prism/%s_daily.nc" % (sdate.year, )
clncfn = "/mesonet/data/prism/prism_dailyc.nc"
ncvar = 'ppt'
source = 'OSU PRISM'
subtitle = ('PRISM Climate Group, Oregon State Univ., '
'http://prism.oregonstate.edu, created 4 Feb 2004.')
mp = MapPlot(sector=sector,
state=state,
axisbg='white',
nocaption=True,
title='%s:: %s Precip %s' % (source, title, PDICT3[opt]),
subtitle='Data from %s' % (subtitle, ),
titlefontsize=14)
idx0 = iemre.daily_offset(sdate)
idx1 = iemre.daily_offset(edate) + 1
if not os.path.isfile(ncfn):
raise NoDataFound("No data for that year, sorry.")
with util.ncopen(ncfn) as nc:
if state is not None:
x0, y0, x1, y1 = util.grid_bounds(nc.variables['lon'][:],
nc.variables['lat'][:],
state_bounds[state])
elif sector in SECTORS:
bnds = SECTORS[sector]
x0, y0, x1, y1 = util.grid_bounds(
nc.variables['lon'][:], nc.variables['lat'][:],
[bnds[0], bnds[2], bnds[1], bnds[3]])
lats = nc.variables['lat'][y0:y1]
lons = nc.variables['lon'][x0:x1]
if sdate == edate:
p01d = distance(nc.variables[ncvar][idx0, y0:y1, x0:x1],
'MM').value('IN')
elif (idx1 - idx0) < 32:
p01d = distance(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 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, y0:y1, x0:x1], 0),
'MM').value('IN')
else:
p01d += distance(
np.sum(nc.variables[ncvar][i:i2, y0:y1, x0:x1], 0),
'MM').value('IN')
if np.ma.is_masked(np.max(p01d)):
raise NoDataFound("Data Unavailable")
units = 'inches'
cmap = plt.get_cmap(ctx['cmap'])
cmap.set_bad('white')
if opt == 'dep':
# Do departure work now
with util.ncopen(clncfn) as nc:
climo = distance(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0),
'MM').value('IN')
p01d = p01d - climo
[maxv] = np.percentile(np.abs(p01d), [
99,
])
clevs = np.around(np.linspace(0 - maxv, maxv, 11), decimals=2)
elif opt == 'per':
with util.ncopen(clncfn) as nc:
climo = distance(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0),
'MM').value('IN')
p01d = p01d / climo * 100.
cmap.set_under('white')
cmap.set_over('black')
clevs = [1, 10, 25, 50, 75, 100, 125, 150, 200, 300, 500]
units = 'percent'
else:
p01d = np.where(p01d < 0.001, np.nan, p01d)
cmap.set_under('white')
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]
x2d, y2d = np.meshgrid(lons, lats)
if ptype == 'c':
mp.contourf(x2d, y2d, p01d, clevs, cmap=cmap, units=units, iline=False)
else:
res = mp.pcolormesh(x2d, y2d, p01d, clevs, cmap=cmap, units=units)
res.set_rasterized(True)
if sector != 'midwest':
mp.drawcounties()
mp.drawcities()
if usdm == 'yes':
mp.draw_usdm(edate, filled=False, hatched=True)
return mp.fig
def init_year(ts):
"""
Create a new NetCDF file for a year of our specification!
"""
fn = iemre.get_dailyc_mrms_ncname()
nc = ncopen(fn, "w")
nc.title = "IEM Daily Reanalysis Climatology %s" % (ts.year, )
nc.platform = "Grided Climatology"
nc.description = "IEM daily analysis on a 0.01 degree grid"
nc.institution = "Iowa State University, Ames, IA, USA"
nc.source = "Iowa Environmental Mesonet"
nc.project_id = "IEM"
nc.realization = 1
nc.Conventions = "CF-1.0" # *cough*
nc.contact = "Daryl Herzmann, [email protected], 515-294-5978"
nc.history = "%s Generated" % (
datetime.datetime.now().strftime("%d %B %Y"), )
nc.comment = "No Comment at this time"
# Setup Dimensions
nc.createDimension("lat", (iemre.NORTH - iemre.SOUTH) * 100.0)
nc.createDimension("lon", (iemre.EAST - iemre.WEST) * 100.0)
ts2 = datetime.datetime(ts.year + 1, 1, 1)
days = (ts2 - ts).days
nc.createDimension("time", int(days))
nc.createDimension("nv", 2)
# Setup Coordinate Variables
lat = nc.createVariable("lat", np.float, ("lat", ))
lat.units = "degrees_north"
lat.long_name = "Latitude"
lat.standard_name = "latitude"
lat.bounds = "lat_bnds"
lat.axis = "Y"
# Grid centers
lat[:] = np.arange(iemre.SOUTH + 0.005, iemre.NORTH, 0.01)
lat_bnds = nc.createVariable("lat_bnds", np.float, ("lat", "nv"))
lat_bnds[:, 0] = np.arange(iemre.SOUTH, iemre.NORTH, 0.01)
lat_bnds[:, 1] = np.arange(iemre.SOUTH + 0.01, iemre.NORTH + 0.01, 0.01)
lon = nc.createVariable("lon", np.float, ("lon", ))
lon.units = "degrees_east"
lon.long_name = "Longitude"
lon.standard_name = "longitude"
lon.bounds = "lon_bnds"
lon.axis = "X"
lon[:] = np.arange(iemre.WEST, iemre.EAST, 0.01)
lon_bnds = nc.createVariable("lon_bnds", np.float, ("lon", "nv"))
lon_bnds[:, 0] = np.arange(iemre.WEST, iemre.EAST, 0.01)
lon_bnds[:, 1] = np.arange(iemre.WEST + 0.01, iemre.EAST + 0.01, 0.01)
tm = nc.createVariable("time", np.float, ("time", ))
tm.units = "Days since %s-01-01 00:00:0.0" % (ts.year, )
tm.long_name = "Time"
tm.standard_name = "time"
tm.axis = "T"
tm.calendar = "gregorian"
tm[:] = np.arange(0, int(days))
p01d = nc.createVariable("p01d",
np.uint16, ("time", "lat", "lon"),
fill_value=65535)
p01d.units = "mm"
p01d.scale_factor = 0.01
p01d.long_name = "Precipitation"
p01d.standard_name = "Precipitation"
p01d.coordinates = "lon lat"
p01d.description = "Precipitation accumulation for the day"
nc.close()