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 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 test_drawcities():
"""Draw Cities"""
mp = MapPlot(title='Fill and Draw Cities',
subtitle="This is my subtitle",
continentalcolor='blue',
sector='iowa', nocaption=True)
mp.drawcities()
return mp.fig
def test_colorbar():
"""Run tests against the colorbar algorithm"""
mp = MapPlot(sector='iowa', nocaption=True)
cmap = plot.maue()
cmap.set_under('white')
clevs = list(range(0, 101, 10))
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
mp.drawcities()
mp.draw_colorbar(clevs, cmap, norm)
return mp.fig
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 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 ValueError("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)
def run(basets, endts, view):
"""Generate this plot for the given basets"""
pgconn = get_dbconn('postgis', user='******')
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""",
pgconn,
params=(basets, endts),
index_col=None)
df['used'] = False
df['textplot'] = True
df.sort_values(by='val', ascending=False, inplace=True)
# Now, we need to add in zeros, lets say we are looking at a .25 degree box
mybuffer = 0.75
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat + mybuffer)) &
(df['lon'] >= lon) & (df['lon'] < (lon + mybuffer))]
if df2.empty:
newrows.append(
dict(lon=(lon + mybuffer / 2.),
lat=(lat + mybuffer / 2.),
val=0,
used=True,
textplot=False))
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
dfnew = pd.DataFrame(newrows)
df = df.append(dfnew)
cdf = df[df['used']]
tdf = df[df['textplot']]
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
mp = MapPlot(sector='iowa',
axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
if cdf['val'].max() > 0:
mp.contourf(cdf['lon'].values,
cdf['lat'].values,
cdf['val'].values,
rng,
cmap=cmap)
mp.drawcounties()
if len(tdf.index) > 0:
mp.plot_values(tdf['lon'].values,
tdf['lat'].values,
tdf['val'].values,
fmt='%.1f',
labelbuffer=5)
mp.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
# slightly different title to help uniqueness
mp = MapPlot(sector='iowa',
axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
if cdf['val'].max() > 0:
mp.contourf(cdf['lon'].values,
cdf['lat'].values,
cdf['val'].values,
rng,
cmap=cmap)
mp.drawcounties()
mp.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
mp = MapPlot(sector='midwest',
axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
if cdf['val'].max() > 0:
mp.contourf(cdf['lon'].values,
cdf['lat'].values,
cdf['val'].values,
rng,
cmap=cmap)
mp.drawcities()
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
def run(basets, endts, view):
"""Generate this plot for the given basets"""
pgconn = get_dbconn("postgis", user="******")
df = read_sql(
"""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""",
pgconn,
params=(basets, endts),
index_col=None,
)
df.sort_values(by="val", ascending=False, inplace=True)
df["useme"] = False
# First, we need to filter down the in-bound values to get rid of small
cellsize = 0.33
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, cellsize):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, cellsize):
# Look around this box at 1x
df2 = df[(df["lat"] >= (lat - cellsize))
& (df["lat"] < (lat + cellsize))
& (df["lon"] >= (lon - cellsize))
& (df["lon"] < (lon + cellsize))]
if df2.empty:
# If nothing was found, check 2x
df3 = df[(df["lat"] >= (lat - cellsize * 2.0))
& (df["lat"] < (lat + cellsize * 2.0))
& (df["lon"] >= (lon - cellsize * 2.0))
& (df["lon"] < (lon + cellsize * 2.0))]
if df3.empty:
# If nothing found, place a zero here
newrows.append({
"lon": lon,
"lat": lat,
"val": 0,
"useme": True,
"state": "NA",
})
continue
maxval = df.at[df2.index[0], "val"]
df.loc[df2[df2["val"] > (maxval * 0.8)].index, "useme"] = True
dfall = pd.concat([df, pd.DataFrame(newrows)], ignore_index=True)
df2 = dfall[dfall["useme"]]
xi = np.arange(reference.MW_WEST, reference.MW_EAST, cellsize)
yi = np.arange(reference.MW_SOUTH, reference.MW_NORTH, cellsize)
xi, yi = np.meshgrid(xi, yi)
gridder = Rbf(
df2["lon"].values,
df2["lat"].values,
pd.to_numeric(df2["val"].values, errors="ignore"),
function="thin_plate",
)
vals = gridder(xi, yi)
vals[np.isnan(vals)] = 0
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
mp = MapPlot(
sector="iowa",
axisbg="white",
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )),
)
if df["val"].max() > 0:
mp.contourf(xi, yi, vals, rng, cmap=cmap)
mp.drawcounties()
if not df.empty:
mp.plot_values(
df["lon"].values,
df["lat"].values,
df["val"].values,
fmt="%.1f",
labelbuffer=2,
)
mp.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
# slightly different title to help uniqueness
mp = MapPlot(
sector="iowa",
axisbg="white",
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )),
)
if df["val"].max() > 0:
mp.contourf(xi, yi, vals, rng, cmap=cmap)
mp.drawcounties()
mp.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
mp = MapPlot(
sector="midwest",
axisbg="white",
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )),
)
if df["val"].max() > 0:
mp.contourf(xi, yi, vals, rng, cmap=cmap)
mp.drawcities()
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
def main(argv):
"""Go Main Go"""
v = argv[1]
agg = argv[2]
ts = datetime.date(2008, 1, 1)
ts2 = datetime.date(2017, 12, 31)
scenario = 0
# suggested for runoff and precip
if V2UNITS[v] in ["mm", "inches"]:
colors = ["#ffffa6", "#9cf26d", "#76cc94", "#6399ba", "#5558a1"]
# suggested for detachment
elif v in ["avg_loss", "avg_loss_metric"]:
colors = [
"#cbe3bb",
"#c4ff4d",
"#ffff4d",
"#ffc44d",
"#ff4d4d",
"#c34dee",
]
# suggested for delivery
elif v in ["avg_delivery", "avg_delivery_metric"]:
colors = [
"#ffffd2",
"#ffff4d",
"#ffe0a5",
"#eeb74d",
"#ba7c57",
"#96504d",
]
cmap = mpcolors.ListedColormap(colors, "james")
cmap.set_under("white")
cmap.set_over("black")
pgconn = get_dbconn("idep")
title = "for %s" % (ts.strftime("%-d %B %Y"), )
if ts != ts2:
title = "between %s and %s" % (
ts.strftime("%-d %b %Y"),
ts2.strftime("%-d %b %Y"),
)
mp = MapPlot(
axisbg="#EEEEEE",
nologo=True,
sector="iowa",
nocaption=True,
title=("DEP %s %s %s") % (
V2NAME[v.replace("_metric", "")],
"Yearly Average" if agg == "avg" else "Total",
title,
),
caption="Daily Erosion Project",
)
df = read_postgis(
"""
WITH data as (
SELECT huc_12, extract(year from valid) as yr,
sum(""" + v.replace("_metric", "") + """) as d from results_by_huc12
WHERE scenario = %s and valid >= %s and valid <= %s
GROUP by huc_12, yr),
agg as (
SELECT huc_12, """ + agg + """(d) as d from data GROUP by huc_12)
SELECT ST_Transform(simple_geom, 4326) as geo, coalesce(d.d, 0) as data
from huc12 i LEFT JOIN agg d
ON (i.huc_12 = d.huc_12) WHERE i.scenario = %s and i.states ~* 'IA'
""",
pgconn,
params=(scenario, ts, ts2, scenario),
geom_col="geo",
index_col=None,
)
df["data"] = df["data"] * V2MULTI[v]
if df["data"].max() < 0.01:
bins = [0.01, 0.02, 0.03, 0.04, 0.05]
else:
bins = np.array(V2RAMP[v]) * (10.0 if agg == "sum" else 1.0)
norm = mpcolors.BoundaryNorm(bins, cmap.N)
# m.ax.add_geometries(df['geo'], ccrs.PlateCarree())
for _, row in df.iterrows():
c = cmap(norm([row["data"]]))[0]
arr = np.asarray(row["geo"].exterior)
points = mp.ax.projection.transform_points(ccrs.Geodetic(), arr[:, 0],
arr[:, 1])
p = Polygon(points[:, :2], fc=c, ec="k", zorder=2, lw=0.1)
mp.ax.add_patch(p)
mp.drawcounties()
mp.drawcities()
lbl = [round(_, 2) for _ in bins]
u = "%s, Avg: %.2f" % (V2UNITS[v], df["data"].mean())
mp.draw_colorbar(
bins,
cmap,
norm,
clevlabels=lbl,
units=u,
title="%s :: %s" % (V2NAME[v], V2UNITS[v]),
)
plt.savefig("%s_%s_%s%s.png" %
(ts.year, ts2.year, v, "_sum" if agg == "sum" else ""))
m = MapPlot(sector='custom', projection='aea', west=-93.2,
east=-90.3, south=42.5,
north=44.,
title='NOAA MRMS 24 Hour RADAR-Only Precipitation Estimate',
subtitle=("MRMS valid 7 AM 23 Aug 2016 to 7 AM 24 Aug 2016, "
"NWS Local Storm + COOP Reports Overlaid"))
clevs = [0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8,
10]
m.contourf(x[:, :-1], y[:, :-1], data, clevs, cmap=nwsprecip())
nt = NetworkTable("IA_ASOS")
lo = []
la = []
va = []
for sid in nt.sts.keys():
lo.append(nt.sts[sid]['lon'])
la.append(nt.sts[sid]['lat'])
va.append(nt.sts[sid]['name'])
# m.plot_values(lo, la, va, fmt='%s', textsize=10, color='black')
m.map.drawcounties(zorder=4, linewidth=1.)
m.drawcities(labelbuffer=25, textsize=10, color='white',
outlinecolor='#000000')
m.textmask[:, :] = 0
m.plot_values(llons, llats, vals, fmt='%s', labelbuffer=5)
m.postprocess(filename='test.png')
def main():
"""Go Main Go."""
pgconn = get_dbconn('iem')
cursor = pgconn.cursor()
cursor.execute("""SELECT ST_x(geom) as lon, ST_y(geom) as lat,
pday from summary_2016 s JOIN stations t on (s.iemid = t.iemid)
where day = '2016-08-24' and network in ('WI_COOP', 'MN_COOP', 'IA_COOP')
and pday > 0 ORDER by pday DESC""")
llons = []
llats = []
vals = []
for row in cursor:
llons.append(row[0])
llats.append(row[1])
vals.append("%.2f" % (row[2], ))
pgconn = get_dbconn('postgis')
cursor = pgconn.cursor()
cursor.execute("""SELECT ST_x(geom) as lon, ST_y(geom) as lat,
max(magnitude) from lsrs_2016
where wfo in ('DMX', 'DVN', 'ARX') and typetext = 'HEAVY RAIN' and
valid > '2016-08-23' GROUP by lon, lat ORDER by max DESC""")
for row in cursor:
llons.append(row[0])
llats.append(row[1])
vals.append("%.2f" % (row[2], ))
img = Image.open("p24h_201608241200.png")
data = np.flipud(np.asarray(img))
# 7000,3500 == -130,-60,55,25 === -100 to -90 then 38 to 45
sample = data[1800:2501, 3000:4501]
sample = np.where(sample == 255, 0, sample)
data = sample * 0.01
data = np.where(sample > 100, 1. + (sample - 100) * 0.05, data)
data = np.where(sample > 180, 5. + (sample - 180) * 0.2, data)
lons = np.arange(-100, -84.99, 0.01)
lats = np.arange(38, 45.01, 0.01)
x, y = np.meshgrid(lons, lats)
buff = 0.5
m = MapPlot(sector='custom', projection='aea', west=-93.2,
east=-90.3, south=42.5,
north=44.,
title='NOAA MRMS 24 Hour RADAR-Only Precipitation Estimate',
subtitle=("MRMS valid 7 AM 23 Aug 2016 to 7 AM 24 Aug 2016, "
"NWS Local Storm + COOP Reports Overlaid"))
clevs = [0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8,
10]
m.contourf(x[:, :-1], y[:, :-1], data, clevs, cmap=nwsprecip())
nt = NetworkTable("IA_ASOS")
lo = []
la = []
va = []
for sid in nt.sts.keys():
lo.append(nt.sts[sid]['lon'])
la.append(nt.sts[sid]['lat'])
va.append(nt.sts[sid]['name'])
# m.plot_values(lo, la, va, fmt='%s', textsize=10, color='black')
m.drawcounties(zorder=4, linewidth=1.)
m.drawcities(labelbuffer=25, textsize=10, color='white',
outlinecolor='#000000')
m.textmask[:, :] = 0
m.plot_values(llons, llats, vals, fmt='%s', labelbuffer=5)
m.postprocess(filename='test.png')
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()
west=-93.2,
east=-90.3,
south=42.5,
north=44.,
title='NOAA MRMS 24 Hour RADAR-Only Precipitation Estimate',
subtitle=("MRMS valid 7 AM 23 Aug 2016 to 7 AM 24 Aug 2016, "
"NWS Local Storm + COOP Reports Overlaid"))
clevs = [0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10]
m.contourf(x[:, :-1], y[:, :-1], data, clevs, cmap=nwsprecip())
nt = NetworkTable("IA_ASOS")
lo = []
la = []
va = []
for sid in nt.sts.keys():
lo.append(nt.sts[sid]['lon'])
la.append(nt.sts[sid]['lat'])
va.append(nt.sts[sid]['name'])
# m.plot_values(lo, la, va, fmt='%s', textsize=10, color='black')
m.map.drawcounties(zorder=4, linewidth=1.)
m.drawcities(labelbuffer=25,
textsize=10,
color='white',
outlinecolor='#000000')
m.textmask[:, :] = 0
m.plot_values(llons, llats, vals, fmt='%s', labelbuffer=5)
m.postprocess(filename='test.png')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
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']
opt = ctx['opt']
usdm = ctx['usdm']
if sdate.year != edate.year:
raise ValueError('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
if sector == 'midwest':
state = None
else:
state = sector
sector = 'state'
if src == 'mrms':
ncfn = "/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (sdate.year, )
clncfn = "/mesonet/data/iemre/mw_mrms_dailyc.nc"
ncvar = 'p01d'
source = 'MRMS Q3'
subtitle = 'NOAA MRMS Project, GaugeCorr and RadarOnly'
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 ValueError("No data for that year, sorry.")
nc = netCDF4.Dataset(ncfn, 'r')
if state is not None:
x0, y0, x1, y1 = util.grid_bounds(nc.variables['lon'][:],
nc.variables['lat'][:],
state_bounds[state])
lats = nc.variables['lat'][y0:y1]
lons = nc.variables['lon'][x0:x1]
if (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')
nc.close()
if np.ma.is_masked(np.max(p01d)):
raise ValueError("Data Unavailable")
units = 'inches'
if opt == 'dep':
# Do departure work now
nc = netCDF4.Dataset(clncfn)
climo = distance(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0),
'MM').value('IN')
p01d = p01d - climo
cmap = plt.get_cmap('RdBu')
[maxv] = np.percentile(np.abs(p01d), [
99,
])
clevs = np.around(np.linspace(0 - maxv, maxv, 11), decimals=2)
elif opt == 'per':
nc = netCDF4.Dataset(clncfn)
climo = distance(
np.sum(nc.variables[ncvar][idx0:idx1, y0:y1, x0:x1], 0),
'MM').value('IN')
p01d = p01d / climo * 100.
cmap = plt.get_cmap('RdBu')
cmap.set_under('white')
cmap.set_over('black')
clevs = [1, 10, 25, 50, 75, 100, 125, 150, 200, 300, 500]
units = 'percent'
else:
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]
cmap = nwsprecip()
cmap.set_over('k')
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
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 run(basets, endts, view):
"""Generate this plot for the given basets"""
pgconn = get_dbconn('postgis', user='******')
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""", pgconn, params=(basets, endts), index_col=None)
df.sort_values(by='val', ascending=False, inplace=True)
df['useme'] = False
# First, we need to filter down the in-bound values to get rid of small
cellsize = 0.25
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH,
cellsize * 2):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST,
cellsize * 2):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat + cellsize * 2)) &
(df['lon'] >= lon) & (df['lon'] < (lon + cellsize * 2))]
if df2.empty:
df3 = df[(df['lat'] >= lat) &
(df['lat'] < (lat + cellsize * 4)) &
(df['lon'] >= lon) &
(df['lon'] < (lon + cellsize * 4))]
if df3.empty:
newrows.append({'lon': lon + cellsize * 1.5,
'lat': lat + cellsize * 1.5,
'val': 0,
'useme': True,
'state': 'NA'})
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.8)].index, 'useme'] = True
dfall = pd.concat([df, pd.DataFrame(newrows)], ignore_index=True)
df2 = dfall[dfall['useme']]
xi = np.arange(reference.MW_WEST, reference.MW_EAST, cellsize)
yi = np.arange(reference.MW_SOUTH, reference.MW_NORTH, cellsize)
xi, yi = np.meshgrid(xi, yi)
gridder = Rbf(df2['lon'].values, df2['lat'].values,
df2['val'].values, function='thin_plate')
vals = gridder(xi, yi)
vals[np.isnan(vals)] = 0
window = np.ones((2, 2))
vals = convolve2d(vals, window / window.sum(), mode='same',
boundary='symm')
vals[vals < 0.1] = 0
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
mp = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
if df['val'].max() > 0:
mp.contourf(xi, yi, vals, rng, cmap=cmap)
mp.drawcounties()
if not df.empty:
mp.plot_values(df['lon'].values, df['lat'].values, df['val'].values,
fmt='%.1f', labelbuffer=2)
mp.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
# slightly different title to help uniqueness
mp = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
if df['val'].max() > 0:
mp.contourf(xi, yi, vals, rng, cmap=cmap)
mp.drawcounties()
mp.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
mp = MapPlot(sector='midwest', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
if df['val'].max() > 0:
mp.contourf(xi, yi, vals, rng, cmap=cmap)
mp.drawcities()
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
mp.postprocess(view=view, pqstr=pqstr)
mp.close()
def main(argv):
"""Go Main Go"""
v = argv[1]
agg = argv[2]
ts = datetime.date(2008, 1, 1)
ts2 = datetime.date(2016, 12, 31)
scenario = 0
# suggested for runoff and precip
if v in ['qc_precip', 'avg_runoff']:
colors = ['#ffffa6', '#9cf26d', '#76cc94', '#6399ba', '#5558a1']
# suggested for detachment
elif v in ['avg_loss']:
colors = ['#cbe3bb', '#c4ff4d', '#ffff4d', '#ffc44d', '#ff4d4d',
'#c34dee']
# suggested for delivery
elif v in ['avg_delivery']:
colors = ['#ffffd2', '#ffff4d', '#ffe0a5', '#eeb74d', '#ba7c57',
'#96504d']
cmap = mpcolors.ListedColormap(colors, 'james')
cmap.set_under('white')
cmap.set_over('black')
pgconn = psycopg2.connect(database='idep', host='localhost', port=5555,
user='******')
title = "for %s" % (ts.strftime("%-d %B %Y"),)
if ts != ts2:
title = "between %s and %s" % (ts.strftime("%-d %b %Y"),
ts2.strftime("%-d %b %Y"))
mp = MapPlot(axisbg='#EEEEEE', nologo=True, sector='iowa',
nocaption=True,
title=("DEP %s %s %s"
) % (V2NAME[v],
"Yearly Average" if agg == 'avg' else 'Total',
title),
caption='Daily Erosion Project')
df = read_postgis("""
WITH data as (
SELECT huc_12, extract(year from valid) as yr,
sum("""+v+""") as d from results_by_huc12
WHERE scenario = %s and valid >= %s and valid <= %s
GROUP by huc_12, yr),
agg as (
SELECT huc_12, """ + agg + """(d) as d from data GROUP by huc_12)
SELECT ST_Transform(simple_geom, 4326) as geo, coalesce(d.d, 0) as data
from huc12 i LEFT JOIN agg d
ON (i.huc_12 = d.huc_12) WHERE i.scenario = %s and i.states ~* 'IA'
""", pgconn, params=(scenario, ts, ts2, scenario), geom_col='geo',
index_col=None)
df['data'] = df['data'] * V2MULTI[v]
if df['data'].max() < 0.01:
bins = [0.01, 0.02, 0.03, 0.04, 0.05]
else:
bins = np.array(V2RAMP[v]) * (10. if agg == 'sum' else 1.)
norm = mpcolors.BoundaryNorm(bins, cmap.N)
# m.ax.add_geometries(df['geo'], ccrs.PlateCarree())
for _, row in df.iterrows():
c = cmap(norm([row['data'], ]))[0]
arr = np.asarray(row['geo'].exterior)
points = mp.ax.projection.transform_points(ccrs.Geodetic(),
arr[:, 0], arr[:, 1])
p = Polygon(points[:, :2], fc=c, ec='k', zorder=2, lw=0.1)
mp.ax.add_patch(p)
mp.drawcounties()
mp.drawcities()
lbl = [round(_, 2) for _ in bins]
u = "%s, Avg: %.2f" % (V2UNITS[v], df['data'].mean())
mp.draw_colorbar(bins, cmap, norm,
clevlabels=lbl, units=u,
title="%s :: %s" % (V2NAME[v], V2UNITS[v]))
plt.savefig('%s_%s_%s%s.eps' % (ts.year, ts2.year, v,
"_sum" if agg == 'sum' else ''))
def main(argv):
"""Go Main Go"""
v = argv[1]
agg = argv[2]
ts = datetime.date(2008, 1, 1)
ts2 = datetime.date(2017, 12, 31)
scenario = 0
# suggested for runoff and precip
if V2UNITS[v] in ['mm', 'inches']:
colors = ['#ffffa6', '#9cf26d', '#76cc94', '#6399ba', '#5558a1']
# suggested for detachment
elif v in ['avg_loss', 'avg_loss_metric']:
colors = ['#cbe3bb', '#c4ff4d', '#ffff4d', '#ffc44d', '#ff4d4d',
'#c34dee']
# suggested for delivery
elif v in ['avg_delivery', 'avg_delivery_metric']:
colors = ['#ffffd2', '#ffff4d', '#ffe0a5', '#eeb74d', '#ba7c57',
'#96504d']
cmap = mpcolors.ListedColormap(colors, 'james')
cmap.set_under('white')
cmap.set_over('black')
pgconn = get_dbconn('idep')
title = "for %s" % (ts.strftime("%-d %B %Y"),)
if ts != ts2:
title = "between %s and %s" % (ts.strftime("%-d %b %Y"),
ts2.strftime("%-d %b %Y"))
mp = MapPlot(axisbg='#EEEEEE', nologo=True, sector='iowa',
nocaption=True,
title=("DEP %s %s %s"
) % (V2NAME[v.replace("_metric", "")],
"Yearly Average" if agg == 'avg' else 'Total',
title),
caption='Daily Erosion Project')
df = read_postgis("""
WITH data as (
SELECT huc_12, extract(year from valid) as yr,
sum(""" + v.replace("_metric", "") + """) as d from results_by_huc12
WHERE scenario = %s and valid >= %s and valid <= %s
GROUP by huc_12, yr),
agg as (
SELECT huc_12, """ + agg + """(d) as d from data GROUP by huc_12)
SELECT ST_Transform(simple_geom, 4326) as geo, coalesce(d.d, 0) as data
from huc12 i LEFT JOIN agg d
ON (i.huc_12 = d.huc_12) WHERE i.scenario = %s and i.states ~* 'IA'
""", pgconn, params=(scenario, ts, ts2, scenario), geom_col='geo',
index_col=None)
df['data'] = df['data'] * V2MULTI[v]
if df['data'].max() < 0.01:
bins = [0.01, 0.02, 0.03, 0.04, 0.05]
else:
bins = np.array(V2RAMP[v]) * (10. if agg == 'sum' else 1.)
norm = mpcolors.BoundaryNorm(bins, cmap.N)
# m.ax.add_geometries(df['geo'], ccrs.PlateCarree())
for _, row in df.iterrows():
c = cmap(norm([row['data'], ]))[0]
arr = np.asarray(row['geo'].exterior)
points = mp.ax.projection.transform_points(ccrs.Geodetic(),
arr[:, 0], arr[:, 1])
p = Polygon(points[:, :2], fc=c, ec='k', zorder=2, lw=0.1)
mp.ax.add_patch(p)
mp.drawcounties()
mp.drawcities()
lbl = [round(_, 2) for _ in bins]
u = "%s, Avg: %.2f" % (V2UNITS[v], df['data'].mean())
mp.draw_colorbar(bins, cmap, norm,
clevlabels=lbl, units=u,
title="%s :: %s" % (V2NAME[v], V2UNITS[v]))
plt.savefig('%s_%s_%s%s.png' % (ts.year, ts2.year, v,
"_sum" if agg == 'sum' else ''))
def run(basets, endts, view):
"""Generate this plot for the given basets"""
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""", POSTGIS, params=(basets, endts), index_col=None)
df['used'] = False
df['textplot'] = True
df.sort_values(by='val', ascending=False, inplace=True)
# Now, we need to add in zeros, lets say we are looking at a .25 degree box
mybuffer = 0.75
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat+mybuffer)) &
(df['lon'] >= lon) & (df['lon'] < (lon+mybuffer))]
if len(df2.index) == 0:
newrows.append(dict(lon=(lon+mybuffer/2.),
lat=(lat+mybuffer/2.),
val=0, used=True, textplot=False))
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
dfnew = pd.DataFrame(newrows)
df = df.append(dfnew)
cdf = df[df['used']]
tdf = df[df['textplot']]
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
m.plot_values(tdf['lon'].values, tdf['lat'].values, tdf['val'].values,
fmt='%.1f')
m.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
# slightly different title to help uniqueness
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
m.drawcities()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcities()
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
if ctx["sz"] < 5:
ctx["sz"] = 5
if ctx["hours"] > 300:
ctx["hours"] = 300
endts = ctx["endts"]
basets = endts - datetime.timedelta(hours=ctx["hours"])
# Retrieve available obs
df = load_data(ctx, basets, endts)
# figure out our grid bounds
ctx["bnds2163"] = compute_grid_bounds(ctx)
# add zeros and QC
df = add_zeros(df, ctx)
# do gridding
df2 = df[df[USEME]]
lons, lats, vals = do_analysis(df2, ctx)
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
if ctx["t"] == "cwa":
sector = "cwa"
else:
sector = "state" if len(ctx["csector"]) == 2 else ctx["csector"]
mp = MapPlot(
sector=sector,
state=ctx["csector"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="white",
title=("NWS Local Storm Report%s Snowfall Total Analysis") %
(" & COOP" if ctx["coop"] == "yes" else "", ),
subtitle=("%.0f reports over past %.0f hours till %s, "
"grid size: %.0fkm, Rbf: %s"
"" % (
len(df2.index),
ctx["hours"],
endts.strftime("%d %b %Y %I:%M %p"),
ctx["sz"],
ctx["f"],
)),
)
if df2["val"].max() > 0 and ctx["p"] in ["both", "contour"]:
mp.contourf(lons,
lats,
vals,
rng,
cmap=cmap,
clip_on=(ctx["t"] != "cwa"))
# Allow analysis to bleed outside the CWA per request.
if ctx["t"] == "cwa":
mp.draw_mask(sector="conus")
mp.draw_cwas(linewidth=2)
mp.drawcounties()
if not df.empty and ctx["p"] in ["both", "plot"]:
df2 = df[df["plotme"]]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2["val"].values,
fmt="%.1f",
labelbuffer=2,
)
if ctx["z"] == "plot":
df2 = df[df["state"] == "Z"]
if not df2.empty:
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2["state"].values,
fmt="%s",
color="#FF0000",
labelbuffer=0,
)
mp.drawcities()
return mp.fig, df.drop(["geo", "plotme"], axis=1)
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']
if sdate.year != edate.year:
return '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
ncfn = "/mesonet/data/iemre/%s_mw_mrms_daily.nc" % (sdate.year, )
if not os.path.isfile(ncfn):
return "No MRMS 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['p01d'][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['p01d'][i:i2, :, :], 0),
'MM').value('IN')
else:
p01d += distance(np.sum(nc.variables['p01d'][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='NOAA MRMS Q3:: %s Total Precip' % (title,),
subtitle='Data from NOAA MRMS Project, GaugeCorr and RadarOnly'
)
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
