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_colorramps(self):
"""make sure our colorramps are happy"""
c = plot.james()
self.assertEquals(c.N, 12)
c = plot.james2()
self.assertEquals(c.N, 12)
c = plot.whitebluegreenyellowred()
self.assertEquals(c.N, 236)
c = plot.nwssnow()
self.assertEquals(c.N, 11)
def test_colorramps():
"""make sure our colorramps are happy"""
c = plot.james()
assert c.N == 12
c = plot.james2()
assert c.N == 12
c = plot.whitebluegreenyellowred()
assert c.N == 236
c = plot.nwssnow()
assert c.N == 11
def test_colorramps(self):
"""make sure our colorramps are happy"""
c = plot.james()
self.assertEquals(c.N, 12)
c = plot.james2()
self.assertEquals(c.N, 12)
c = plot.whitebluegreenyellowred()
self.assertEquals(c.N, 236)
c = plot.nwssnow()
self.assertEquals(c.N, 11)
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 run(basets, endts, view):
"""Generate this plot for the given basets"""
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""", POSTGIS, params=(basets, endts), index_col=None)
df['used'] = False
df['textplot'] = True
df.sort_values(by='val', ascending=False, inplace=True)
# Now, we need to add in zeros, lets say we are looking at a .25 degree box
mybuffer = 0.75
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat+mybuffer)) &
(df['lon'] >= lon) & (df['lon'] < (lon+mybuffer))]
if len(df2.index) == 0:
newrows.append(dict(lon=(lon+mybuffer/2.),
lat=(lat+mybuffer/2.),
val=0, used=True, textplot=False))
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
dfnew = pd.DataFrame(newrows)
df = df.append(dfnew)
cdf = df[df['used']]
tdf = df[df['textplot']]
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
m.plot_values(tdf['lon'].values, tdf['lat'].values, tdf['val'].values,
fmt='%.1f')
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
# slightly different title to help uniqueness
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
def 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.))
& (df['lat'] < (lat + cellsize * 2.)) &
(df['lon'] >=
(lon - cellsize * 2.)) & (df['lon'] <
(lon + cellsize * 2.))]
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 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)