def plot_maxmin(ts, field):
"""Generate our plot."""
nc = ncopen(ts.strftime("/mesonet/data/ndfd/%Y%m%d%H_ndfd.nc"))
if field == 'high_tmpk':
data = np.max(nc.variables[field][:], 0)
elif field == 'low_tmpk':
data = np.min(nc.variables[field][:], 0)
data = masked_array(data, units.degK).to(units.degF).m
subtitle = ("Based on National Digital Forecast Database (NDFD) "
"00 UTC Forecast made %s") % (ts.strftime("%-d %b %Y"), )
mp = MapPlot(title='NWS NDFD 7 Day (%s through %s) %s Temperature' % (
ts.strftime("%-d %b"),
(ts + datetime.timedelta(days=6)).strftime("%-d %b"),
'Maximum' if field == 'high_tmpk' else 'Minimum',
),
subtitle=subtitle,
sector='iailin')
mp.pcolormesh(nc.variables['lon'][:],
nc.variables['lat'][:],
data,
np.arange(10, 121, 10),
cmap=plt.get_cmap('jet'),
units='Degrees F')
mp.drawcounties()
pqstr = (
"data c %s summary/cb_ndfd_7day_%s.png summary/cb_ndfd_7day_%s.png "
"png") % (ts.strftime("%Y%m%d%H%M"), "max" if field == 'high_tmpk' else
'min', "max" if field == 'high_tmpk' else 'min')
mp.postprocess(pqstr=pqstr)
mp.close()
nc.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from pyiem.plot.geoplot import MapPlot
ctx = util.get_autoplot_context(fdict, get_description())
date = ctx['date']
sector = ctx['sector']
threshold = ctx['threshold']
threshold_mm = distance(threshold, 'IN').value('MM')
window_sts = date - datetime.timedelta(days=90)
if window_sts.year != date.year:
raise Exception('Sorry, do not support multi-year plots yet!')
idx0 = iemre.daily_offset(window_sts)
idx1 = iemre.daily_offset(date)
ncfn = "/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (date.year, )
ncvar = 'p01d'
if not os.path.isfile(ncfn):
raise Exception("No data for that year, sorry.")
nc = netCDF4.Dataset(ncfn, 'r')
grid = np.zeros((len(nc.dimensions['lat']),
len(nc.dimensions['lon'])))
total = np.zeros((len(nc.dimensions['lat']),
len(nc.dimensions['lon'])))
for i, idx in enumerate(range(idx1, idx1-90, -1)):
total += nc.variables[ncvar][idx, :, :]
grid = np.where(np.logical_and(grid == 0,
total > threshold_mm), i, grid)
lon = np.append(nc.variables['lon'][:], [-80.5])
lat = np.append(nc.variables['lat'][:], [49.])
nc.close()
mp = MapPlot(sector='state', state=sector, titlefontsize=14,
subtitlefontsize=12,
title=("NOAA MRMS Q3: Number of Recent Days "
"till Accumulating %s\" of Precip"
) % (threshold, ),
subtitle=("valid %s: based on per calendar day "
"estimated preciptation, GaugeCorr and "
"RadarOnly products"
) % (date.strftime("%-d %b %Y"), ))
x, y = np.meshgrid(lon, lat)
cmap = plt.get_cmap('terrain')
cmap.set_over('k')
cmap.set_under('white')
mp.pcolormesh(x, y, grid,
np.arange(0, 81, 10), cmap=cmap, units='days')
mp.drawcounties()
mp.drawcities()
return mp.fig
def plotter(fdict):
""" Go """
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('gist_ncar')
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 plot_gdd(ts):
"""Generate our plot."""
nc = ncopen(ts.strftime("/mesonet/data/ndfd/%Y%m%d%H_ndfd.nc"))
# compute our daily GDDs
gddtot = np.zeros(np.shape(nc.variables["lon"][:]))
for i in range(7):
gddtot += gdd(
temperature(nc.variables["high_tmpk"][i, :, :], "K"),
temperature(nc.variables["low_tmpk"][i, :, :], "K"),
)
cnc = ncopen("/mesonet/data/ndfd/ndfd_dailyc.nc")
offset = daily_offset(ts)
avggdd = np.sum(cnc.variables["gdd50"][offset:offset + 7], 0)
data = gddtot - np.where(avggdd < 1, 1, avggdd)
subtitle = ("Based on National Digital Forecast Database (NDFD) "
"00 UTC Forecast made %s") % (ts.strftime("%-d %b %Y"), )
mp = MapPlot(
title="NWS NDFD 7 Day (%s through %s) GDD50 Departure from Avg" % (
ts.strftime("%-d %b"),
(ts + datetime.timedelta(days=6)).strftime("%-d %b"),
),
subtitle=subtitle,
sector="iailin",
)
mp.pcolormesh(
nc.variables["lon"][:],
nc.variables["lat"][:],
data,
np.arange(-80, 81, 20),
cmap=plt.get_cmap("RdBu_r"),
units=r"$^\circ$F",
spacing="proportional",
)
mp.drawcounties()
pqstr = (
"data c %s summary/cb_ndfd_7day_gdd.png summary/cb_ndfd_7day_gdd.png "
"png") % (ts.strftime("%Y%m%d%H%M"), )
mp.postprocess(pqstr=pqstr)
mp.close()
nc.close()
def plotter(ctx):
""" Go """
# Covert datetime to UTC
do_polygon(ctx)
m = MapPlot(
title='2009-2018 Flash Flood Emergency Polygon Heatmap',
sector='custom', axisbg='white',
# west=-107, south=25.5, east=-88, north=41,
# west=-82, south=36., east=-68, north=48,
west=-85, south=31.8, north=45.2, east=-69,
subtitle='based on unofficial IEM Archives', nocaption=True)
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)
m.postprocess(filename='test.png')
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 = get_autoplot_context(fdict, get_description())
level = ctx['level']
station = ctx['station'][:4]
t = ctx['t']
p = ctx['p']
month = ctx['month']
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
ones = np.ones((int(YSZ), int(XSZ)))
counts = np.zeros((int(YSZ), int(XSZ)))
# counts = np.load('counts.npy')
lons = np.arange(GRIDWEST, GRIDEAST, griddelta)
lats = np.arange(GRIDSOUTH, GRIDNORTH, griddelta)
pgconn = get_dbconn('postgis')
hour = int(p.split(".")[2])
df = read_postgis(
"""
WITH data as (
select product_issue, issue, expire, geom,
rank() OVER (PARTITION by issue ORDER by product_issue DESC)
from spc_outlooks where
outlook_type = %s and day = %s and threshold = %s and
category = %s and
ST_Within(geom, ST_GeomFromEWKT('SRID=4326;POLYGON((%s %s, %s %s,
%s %s, %s %s, %s %s))'))
and extract(hour from product_issue at time zone 'UTC') in %s
and extract(month from product_issue) in %s
)
SELECT * from data where rank = 1
""",
pgconn,
params=(p.split(".")[1], p.split(".")[0], level.split(".", 1)[1],
level.split(".")[0], GRIDWEST, GRIDSOUTH, GRIDWEST, GRIDNORTH,
GRIDEAST, GRIDNORTH, GRIDEAST, GRIDSOUTH, GRIDWEST, GRIDSOUTH,
tuple([hour - 1, hour, hour + 1]), tuple(months)),
geom_col='geom')
if df.empty:
raise NoDataFound("No results found for query")
for _, row in df.iterrows():
zs = zonal_stats(row['geom'],
ones,
affine=PRECIP_AFF,
nodata=-1,
all_touched=True,
raster_out=True)
for z in zs:
aff = z['mini_raster_affine']
west = aff.c
north = aff.f
raster = np.flipud(z['mini_raster_array'])
x0 = int((west - GRIDWEST) / griddelta)
y1 = int((north - GRIDSOUTH) / griddelta)
dy, dx = np.shape(raster)
x1 = x0 + dx
y0 = y1 - dy
counts[y0:y1, x0:x1] += np.where(raster.mask, 0, 1)
mindate = datetime.datetime(2014, 10, 1)
if level not in ['CATEGORICAL.MRGL', 'CATEGORICAL.ENH']:
mindate = datetime.datetime(2002, 1, 1)
if p.split(".")[1] == 'F':
mindate = datetime.datetime(2017, 1, 1)
years = (datetime.datetime.now() -
mindate).total_seconds() / 365.25 / 86400.
data = counts / years
subtitle = "Found %s events for CONUS between %s and %s" % (
len(df.index), df['issue'].min().strftime("%d %b %Y"),
df['issue'].max().strftime("%d %b %Y"))
if t == 'cwa':
sector = 'cwa'
subtitle = "Plotted for %s (%s). %s" % (
ctx['_nt'].sts[station]['name'], station, subtitle)
else:
sector = 'state' if len(ctx['csector']) == 2 else ctx['csector']
mp = MapPlot(sector=sector,
state=ctx['csector'],
cwa=(station if len(station) == 3 else station[1:]),
axisbg='white',
title='SPC %s Outlook [%s] of at least %s' % (
ISSUANCE[p],
month.capitalize(),
OUTLOOKS[level].split("(")[0].strip(),
),
subtitle=subtitle,
nocaption=True,
titlefontsize=16)
# Get the main axes bounds
if t == 'state' and ctx['csector'] == 'conus':
domain = data
lons, lats = np.meshgrid(lons, lats)
df2 = pd.DataFrame()
else:
(west, east, south, north) = mp.ax.get_extent(ccrs.PlateCarree())
i0 = int((west - GRIDWEST) / griddelta)
j0 = int((south - GRIDSOUTH) / griddelta)
i1 = int((east - GRIDWEST) / griddelta)
j1 = int((north - GRIDSOUTH) / griddelta)
jslice = slice(j0, j1)
islice = slice(i0, i1)
domain = data[jslice, islice]
lons, lats = np.meshgrid(lons[islice], lats[jslice])
df2 = pd.DataFrame({
'lat': lats.ravel(),
'lon': lons.ravel(),
'freq': domain.ravel()
})
rng = [
round(x, 2) for x in np.linspace(max([0.01, np.min(domain) - 0.5]),
np.max(domain) + 0.5, 10)
]
cmap = plt.get_cmap(ctx['cmap'])
cmap.set_under('white')
cmap.set_over('black')
res = mp.pcolormesh(lons,
lats,
domain,
rng,
cmap=cmap,
clip_on=False,
units='days per year')
# Cut down on SVG et al size
res.set_rasterized(True)
if ctx['drawc'] == 'yes':
mp.drawcounties()
return mp.fig, df2
def plotter(fdict):
""" Go """
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"]
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" % (
ctx["_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,
)
cmap = plt.get_cmap(ctx["cmap"])
cmap.set_under("white")
cmap.set_over("white")
if geo == "ugc":
m.fill_ugcs(ctx["data"], ctx["bins"], cmap=cmap, ilabel=ilabel)
else:
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 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 plotter(fdict):
""" Go """
ctx = util.get_autoplot_context(fdict, get_description())
date = ctx['date']
sector = ctx['sector']
threshold = ctx['threshold']
threshold_mm = distance(threshold, 'IN').value('MM')
window_sts = date - datetime.timedelta(days=90)
if window_sts.year != date.year:
raise NoDataFound('Sorry, do not support multi-year plots yet!')
# idx0 = iemre.daily_offset(window_sts)
idx1 = iemre.daily_offset(date)
ncfn = iemre.get_daily_mrms_ncname(date.year)
if not os.path.isfile(ncfn):
raise NoDataFound("No data found.")
ncvar = 'p01d'
# 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)
with util.ncopen(ncfn) as nc:
czs.gen_stats(
np.zeros((nc.variables['lat'].size, nc.variables['lon'].size)),
df['the_geom'])
jslice = None
islice = None
for nav in czs.gridnav:
# 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)
grid = np.zeros(
(jslice.stop - jslice.start, islice.stop - islice.start))
total = np.zeros(
(jslice.stop - jslice.start, islice.stop - islice.start))
for i, idx in enumerate(range(idx1, idx1 - 90, -1)):
total += nc.variables[ncvar][idx, jslice, islice]
grid = np.where(np.logical_and(grid == 0, total > threshold_mm), i,
grid)
lon = nc.variables['lon'][islice]
lat = nc.variables['lat'][jslice]
mp = MapPlot(sector='state',
state=sector,
titlefontsize=14,
subtitlefontsize=12,
title=("NOAA MRMS Q3: Number of Recent Days "
"till Accumulating %s\" of Precip") % (threshold, ),
subtitle=("valid %s: based on per calendar day "
"estimated preciptation, GaugeCorr and "
"RadarOnly products") %
(date.strftime("%-d %b %Y"), ))
x, y = np.meshgrid(lon, lat)
cmap = plt.get_cmap(ctx['cmap'])
cmap.set_over('k')
cmap.set_under('white')
mp.pcolormesh(x, y, grid, np.arange(0, 81, 10), cmap=cmap, units='days')
mp.drawcounties()
mp.drawcities()
return mp.fig
