def main():
"""Go Main"""
nt = NetworkTable(['AWOS', 'IA_ASOS'])
pgconn = psycopg2.connect(database='mos', host='localhost', user='******',
port=5555)
df = read_sql("""
select station, avg(pwater) from model_gridpoint where
model = 'NAM' and extract(hour from runtime at time zone 'UTC') = 0
and pwater > 0 and pwater < 100 and
extract(month from runtime) between 4 and 9 and ftime = runtime
GROUP by station ORDER by avg
""", pgconn, index_col='station')
df['lat'] = None
df['lon'] = None
df['pwater'] = distance(df['avg'].values, 'MM').value('IN')
for station in df.index.values:
df.at[station, 'lat'] = nt.sts[station[1:]]['lat']
df.at[station, 'lon'] = nt.sts[station[1:]]['lon']
mp = MapPlot(title=('00z Analysis NAM Warm-Season Average '
'Precipitable Water [in]'),
subtitle=("based on grid point samples "
"from 2004-2017 (April-September)"))
cmap = plt.get_cmap("plasma_r")
cmap.set_under('white')
cmap.set_over('black')
mp.contourf(df['lon'], df['lat'], df['pwater'],
np.arange(0.94, 1.13, 0.03), cmap=cmap,
units='inch')
mp.drawcounties()
mp.drawcities()
mp.postprocess(filename='170901.png')
mp.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 = 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())
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
def plotter(fdict):
""" Go """
pgconn = get_dbconn("postgis")
ctx = get_autoplot_context(fdict, get_description())
utcvalid = ctx.get("valid")
wfo = ctx["wfo"]
tzname = ctx["_nt"].sts[wfo]["tzname"]
p1 = ctx["phenomenav"][:2]
s1 = ctx["significancev"][:1]
etn = int(ctx["etn"])
year = int(ctx["year"])
table = "warnings_%s" % (year, )
df = read_postgis(
"""
SELECT w.ugc, simple_geom, u.name,
issue at time zone 'UTC' as issue,
expire at time zone 'UTC' as expire,
init_expire at time zone 'UTC' as init_expire,
1 as val,
status
from """ + table + """ w JOIN ugcs u on (w.gid = u.gid)
WHERE w.wfo = %s and eventid = %s and significance = %s and
phenomena = %s ORDER by issue ASC
""",
pgconn,
params=(wfo[-3:], etn, s1, p1),
index_col="ugc",
geom_col="simple_geom",
)
if df.empty:
raise NoDataFound("VTEC Event was not found, sorry.")
table = "sbw_%s" % (year, )
sbwdf = read_postgis(
"""
SELECT status, geom,
polygon_begin at time zone 'UTC' as polygon_begin,
polygon_end at time zone 'UTC' as polygon_end
from """ + table + """
WHERE wfo = %s and eventid = %s and significance = %s and
phenomena = %s ORDER by polygon_begin ASC
""",
pgconn,
params=(wfo[-3:], etn, s1, p1),
geom_col="geom",
)
if utcvalid is None:
utcvalid = df["issue"].max()
else:
# hack for an assumption below
utcvalid = pd.Timestamp(utcvalid.replace(tzinfo=None))
def m(valid):
"""Convert to our local timestamp."""
return (valid.tz_localize(pytz.UTC).astimezone(
pytz.timezone(tzname)).strftime(TFORMAT))
df["color"] = vtec.NWS_COLORS.get("%s.%s" % (p1, s1), "#FF0000")
if not sbwdf.empty:
df["color"] = "tan"
bounds = df["simple_geom"].total_bounds
buffer = 0.4
mp = MapPlot(
subtitle="Map Valid: %s, Event: %s to %s" %
(m(utcvalid), m(df["issue"].min()), m(df["expire"].max())),
title="%s %s %s %s (%s.%s) #%s" % (
year,
wfo,
vtec.VTEC_PHENOMENA.get(p1, p1),
vtec.VTEC_SIGNIFICANCE.get(s1, s1),
p1,
s1,
etn,
),
sector="custom",
west=bounds[0] - buffer,
south=bounds[1] - buffer,
east=bounds[2] + buffer,
north=bounds[3] + buffer,
nocaption=True,
)
mp.sector = "cwa"
mp.cwa = wfo[-3:]
# CAN statements come here with time == expire :/
df2 = df[(df["issue"] <= utcvalid) & (df["expire"] > utcvalid)]
if df2.empty:
mp.ax.text(
0.5,
0.5,
"Event No Longer Active",
zorder=1000,
transform=mp.ax.transAxes,
fontsize=24,
ha="center",
)
else:
mp.fill_ugcs(
df2["val"].to_dict(),
color=df2["color"].to_dict(),
nocbar=True,
labels=df2["name"].to_dict(),
missingval="",
ilabel=(len(df2.index) <= 10),
labelbuffer=5,
)
if not sbwdf.empty:
color = vtec.NWS_COLORS.get("%s.%s" % (p1, s1), "#FF0000")
poly = sbwdf.iloc[0]["geom"]
df2 = sbwdf[(sbwdf["polygon_begin"] <= utcvalid)
& (sbwdf["polygon_end"] > utcvalid)]
if not df2.empty:
# draw new
mp.ax.add_geometries(
[poly],
ccrs.PlateCarree(),
facecolor="None",
edgecolor="k",
zorder=Z_OVERLAY2 - 1,
)
poly = df2.iloc[0]["geom"]
mp.ax.add_geometries(
[poly],
ccrs.PlateCarree(),
facecolor=color,
alpha=0.5,
edgecolor="k",
zorder=Z_OVERLAY2,
)
if len(df.index) > 10:
mp.drawcities()
return mp.fig, df.drop("simple_geom", axis=1)
def make_overviewmap(form):
"""Draw a pretty map of just the HUC."""
huc = form.get("huc")
plt.close()
projection = EPSG[5070]
if huc is None:
huclimiter = ""
elif len(huc) >= 8:
huclimiter = " and substr(huc_12, 1, 8) = '%s' " % (huc[:8],)
with get_sqlalchemy_conn("idep") as conn:
df = read_postgis(
f"""
SELECT simple_geom as geom, huc_12,
ST_x(ST_Transform(ST_Centroid(geom), 4326)) as centroid_x,
ST_y(ST_Transform(ST_Centroid(geom), 4326)) as centroid_y,
hu_12_name
from huc12 i WHERE i.scenario = 0 {huclimiter}
""",
conn,
geom_col="geom",
index_col="huc_12",
)
minx, miny, maxx, maxy = df["geom"].total_bounds
buf = float(form.get("zoom", 10.0)) * 1000.0 # 10km
hucname = "" if huc not in df.index else df.at[huc, "hu_12_name"]
subtitle = "The HUC8 is in tan"
if len(huc) == 12:
subtitle = "HUC12 highlighted in red, the HUC8 it resides in is in tan"
m = MapPlot(
axisbg="#EEEEEE",
logo="dep",
sector="custom",
south=miny - buf,
north=maxy + buf,
west=minx - buf,
east=maxx + buf,
projection=projection,
continentalcolor="white",
title="DEP HUC %s:: %s" % (huc, hucname),
subtitle=subtitle,
titlefontsize=20,
subtitlefontsize=18,
caption="Daily Erosion Project",
)
for _huc12, row in df.iterrows():
p = Polygon(
row["geom"].exterior.coords,
fc="red" if _huc12 == huc else "tan",
ec="k",
zorder=Z_OVERLAY2,
lw=0.1,
)
m.ax.add_patch(p)
# If this is our HUC, add some text to prevent cities overlay overlap
if _huc12 == huc:
m.plot_values(
[row["centroid_x"]],
[row["centroid_y"]],
[" . "],
color="None",
outlinecolor="None",
)
if huc is not None:
m.drawcounties()
m.drawcities()
ram = BytesIO()
plt.savefig(ram, format="png", dpi=100)
plt.close()
ram.seek(0)
return ram.read(), True
def make_map(huc, ts, ts2, scenario, v, form):
"""Make the map"""
projection = EPSG[5070]
plt.close()
# suggested for runoff and precip
if v in ["qc_precip", "avg_runoff"]:
# c = ['#ffffa6', '#9cf26d', '#76cc94', '#6399ba', '#5558a1']
cmap = james()
# suggested for detachment
elif v in ["avg_loss"]:
# c =['#cbe3bb', '#c4ff4d', '#ffff4d', '#ffc44d', '#ff4d4d', '#c34dee']
cmap = dep_erosion()
# suggested for delivery
elif v in ["avg_delivery"]:
# c =['#ffffd2', '#ffff4d', '#ffe0a5', '#eeb74d', '#ba7c57', '#96504d']
cmap = dep_erosion()
pgconn = get_dbconn("idep")
cursor = pgconn.cursor()
title = "for %s" % (ts.strftime("%-d %B %Y"),)
if ts != ts2:
title = "for period between %s and %s" % (
ts.strftime("%-d %b %Y"),
ts2.strftime("%-d %b %Y"),
)
if "averaged" in form:
title = "averaged between %s and %s (2008-2017)" % (
ts.strftime("%-d %b"),
ts2.strftime("%-d %b"),
)
# Check that we have data for this date!
cursor.execute(
"SELECT value from properties where key = 'last_date_0'",
)
lastts = datetime.datetime.strptime(cursor.fetchone()[0], "%Y-%m-%d")
floor = datetime.date(2007, 1, 1)
if ts > lastts.date() or ts2 > lastts.date() or ts < floor:
plt.text(
0.5,
0.5,
"Data Not Available\nPlease Check Back Later!",
fontsize=20,
ha="center",
)
ram = BytesIO()
plt.savefig(ram, format="png", dpi=100)
plt.close()
ram.seek(0)
return ram.read(), False
if huc is None:
huclimiter = ""
elif len(huc) == 8:
huclimiter = " and substr(i.huc_12, 1, 8) = '%s' " % (huc,)
elif len(huc) == 12:
huclimiter = " and i.huc_12 = '%s' " % (huc,)
if "iowa" in form:
huclimiter += " and i.states ~* 'IA' "
if "mn" in form:
huclimiter += " and i.states ~* 'MN' "
if "averaged" in form:
# 11 years of data is standard
# 10 years is for the switchgrass one-off
with get_sqlalchemy_conn("idep") as conn:
df = read_postgis(
f"""
WITH data as (
SELECT huc_12, sum({v}) / 10. as d from results_by_huc12
WHERE scenario = %s and to_char(valid, 'mmdd') between %s and %s
and valid between '2008-01-01' and '2018-01-01'
GROUP by huc_12)
SELECT simple_geom as geom,
coalesce(d.d, 0) * %s as data
from huc12 i LEFT JOIN data d
ON (i.huc_12 = d.huc_12) WHERE i.scenario = %s {huclimiter}
""",
conn,
params=(
scenario,
ts.strftime("%m%d"),
ts2.strftime("%m%d"),
V2MULTI[v],
0,
),
geom_col="geom",
)
else:
with get_sqlalchemy_conn("idep") as conn:
df = read_postgis(
f"""
WITH data as (
SELECT huc_12, sum({v}) as d from results_by_huc12
WHERE scenario = %s and valid between %s and %s
GROUP by huc_12)
SELECT simple_geom as geom,
coalesce(d.d, 0) * %s as data
from huc12 i LEFT JOIN data d
ON (i.huc_12 = d.huc_12) WHERE i.scenario = %s {huclimiter}
""",
conn,
params=(
scenario,
ts.strftime("%Y-%m-%d"),
ts2.strftime("%Y-%m-%d"),
V2MULTI[v],
0,
),
geom_col="geom",
)
minx, miny, maxx, maxy = df["geom"].total_bounds
buf = 10000.0 # 10km
m = MapPlot(
axisbg="#EEEEEE",
logo="dep",
sector="custom",
south=miny - buf,
north=maxy + buf,
west=minx - buf,
east=maxx + buf,
projection=projection,
title="DEP %s by HUC12 %s" % (V2NAME[v], title),
titlefontsize=16,
caption="Daily Erosion Project",
)
if ts == ts2:
# Daily
bins = RAMPS["english"][0]
else:
bins = RAMPS["english"][1]
norm = mpcolors.BoundaryNorm(bins, cmap.N)
for _, row in df.iterrows():
p = Polygon(
row["geom"].exterior.coords,
fc=cmap(norm([row["data"]]))[0],
ec="k",
zorder=5,
lw=0.1,
)
m.ax.add_patch(p)
label_scenario(m.ax, scenario, pgconn)
lbl = [round(_, 2) for _ in bins]
if huc is not None:
m.drawcounties()
m.drawcities()
m.draw_colorbar(
bins, cmap, norm, units=V2UNITS[v], clevlabels=lbl, spacing="uniform"
)
if "progressbar" in form:
fig = plt.gcf()
avgval = df["data"].mean()
fig.text(
0.01,
0.905,
"%s: %4.1f T/a"
% (ts.year if "averaged" not in form else "Avg", avgval),
fontsize=14,
)
bar_width = 0.758
# yes, a small one off with years having 366 days
proportion = (ts2 - ts).days / 365.0 * bar_width
rect1 = Rectangle(
(0.15, 0.905),
bar_width,
0.02,
color="k",
zorder=40,
transform=fig.transFigure,
figure=fig,
)
fig.patches.append(rect1)
rect2 = Rectangle(
(0.151, 0.907),
proportion,
0.016,
color=cmap(norm([avgval]))[0],
zorder=50,
transform=fig.transFigure,
figure=fig,
)
fig.patches.append(rect2)
if "cruse" in form:
# Crude conversion of T/a to mm depth
depth = avgval / 5.0
m.ax.text(
0.9,
0.92,
"%.2fmm" % (depth,),
zorder=1000,
fontsize=24,
transform=m.ax.transAxes,
ha="center",
va="center",
bbox=dict(color="k", alpha=0.5, boxstyle="round,pad=0.1"),
color="white",
)
ram = BytesIO()
plt.savefig(ram, format="png", dpi=100)
plt.close()
ram.seek(0)
return ram.read(), True
