def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
csector = ctx['csector']
date = ctx['date']
z = ctx['z']
period = ctx['f']
scale = ctx['scale']
valid = utc(date.year, date.month, date.day, int(z))
gribfn = valid.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/wpc/"
"p" + period + "m_%Y%m%d%Hf" + period + ".grb"))
if not os.path.isfile(gribfn):
raise ValueError("gribfn %s missing" % (gribfn, ))
grbs = pygrib.open(gribfn)
grb = grbs[1]
precip = distance(grb.values, 'MM').value('IN')
lats, lons = grb.latlons()
title = ("Weather Prediction Center %s Quantitative "
"Precipitation Forecast") % (PDICT[period])
subtitle = ("%sWPC Forcast %s UTC to %s UTC") % (
("US Drought Monitor Overlaid, " if ctx['opt'] == 'both' else ''),
valid.strftime("%d %b %Y %H"),
(valid +
datetime.timedelta(hours=int(period))).strftime("%d %b %Y %H"))
mp = MapPlot(sector=('state' if len(csector) == 2 else csector),
state=ctx['csector'],
title=title,
subtitle=subtitle,
continentalcolor='white',
titlefontsize=16)
cmap = plt.get_cmap('gist_ncar')
cmap.set_under('#EEEEEE')
cmap.set_over('black')
if scale == 'auto':
levs = np.linspace(0, np.max(precip) * 1.1, 10)
levs = [round(lev, 2) for lev in levs]
levs[0] = 0.01
elif scale == '10':
levs = np.arange(0, 10.1, 1.)
levs[0] = 0.01
elif scale == '7':
levs = np.arange(0, 7.1, 0.5)
levs[0] = 0.01
elif scale == '3.5':
levs = np.arange(0, 3.6, 0.25)
levs[0] = 0.01
mp.pcolormesh(lons,
lats,
precip,
levs,
cmap=cmap,
units='inch',
clip_on=False)
if ctx['opt'] == 'both':
mp.draw_usdm(valid=valid, filled=False, hatched=True)
return mp.fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
date1 = ctx['date1']
date2 = ctx['date2']
varname = ctx['var']
usdm = ctx['usdm']
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
df = read_sql("""
WITH obs as (
SELECT station, gddxx(50, 86, high, low) as gdd50,
sday, high, low, precip from """ + table + """ WHERE
day >= %s and day < %s and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip, high, low,
gdd50 from climate51),
combo as (
SELECT o.station, o.precip - c.precip as precip_diff,
o.precip as precip, c.precip as cprecip,
o.high, o.low, o.gdd50, c.gdd50 as cgdd50,
o.gdd50 - c.gdd50 as gdd50_diff,
(o.high + o.low)/2. - (c.high + c.low)/2. as temp_diff
from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
agg as (
SELECT station, sum(precip_diff) as precip_depart,
sum(precip) as precip, sum(cprecip) as cprecip,
min(low) as min_low_temp, sum(gdd50_diff) as gdd_depart,
avg(temp_diff) as avg_temp_depart, sum(gdd50) as gdd_sum,
sum(cgdd50) as cgdd_sum
from combo GROUP by station)
SELECT d.station, t.name,
d.precip as precip_sum, d.cprecip as cprecip_sum,
d.precip_depart, d.min_low_temp, d.avg_temp_depart,
d.gdd_depart, d.gdd_sum, d.cgdd_sum,
ST_x(t.geom) as lon, ST_y(t.geom) as lat
from agg d JOIN stations t on (d.station = t.id)
WHERE t.network ~* 'CLIMATE'
""",
pgconn,
params=(date1, date2),
index_col='station')
df = df.reindex(df[varname].abs().sort_values(ascending=False).index)
sector2 = "state" if sector != 'midwest' else 'midwest'
datefmt = "%d %b %Y" if varname != 'cgdd_sum' else '%d %b'
subtitle = ("Climatology is based on data from 1951-%s") % (
datetime.date.today().year - 1, )
if varname != 'cgdd_sum':
subtitle = ('%s is compared with 1951-%s Climatology'
' to compute departures') % (
date1.year, datetime.date.today().year - 1)
mp = MapPlot(sector=sector2,
state=sector,
axisbg='white',
title=('%s - %s %s [%s]') %
(date1.strftime(datefmt), date2.strftime(datefmt),
PDICT2.get(varname), UNITS.get(varname)),
subtitle=subtitle)
if varname in ['precip_depart', 'avg_temp_depart']:
rng = df[varname].abs().describe(percentiles=[0.95])['95%']
clevels = np.linspace(0 - rng, rng, 7)
fmt = '%.2f'
else:
minv = df[varname].min() - 5
maxv = df[varname].max() + 5
clevels = np.linspace(minv, maxv, 6, dtype='i')
fmt = '%.0f'
clevlabels = [fmt % x for x in clevels]
cmap = cm.get_cmap('RdYlBu' if varname == 'precip_depart' else 'RdYlBu_r')
cmap.set_bad('white')
mp.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
clevels,
clevlabels=clevlabels,
cmap=cmap,
units=UNITS.get(varname))
mp.plot_values(df['lon'].values,
df['lat'].values,
df[varname].values,
fmt=fmt,
labelbuffer=10)
if sector == 'IA':
mp.drawcounties()
if usdm == 'yes':
mp.draw_usdm(date2, filled=False, hatched=True)
return mp.fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
csector = ctx["csector"]
date = ctx["date"]
z = ctx["z"]
period = ctx["f"]
scale = ctx["scale"]
valid = utc(date.year, date.month, date.day, int(z))
gribfn = valid.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/wpc/"
"p" + period + "m_%Y%m%d%Hf" + period + ".grb"))
if not os.path.isfile(gribfn):
raise NoDataFound("gribfn %s missing" % (gribfn, ))
grbs = pygrib.open(gribfn)
grb = grbs[1]
precip = distance(grb.values, "MM").value("IN")
lats, lons = grb.latlons()
title = ("Weather Prediction Center %s Quantitative "
"Precipitation Forecast") % (PDICT[period])
subtitle = ("%sWPC Forecast %s UTC to %s UTC") % (
("US Drought Monitor Overlaid, " if ctx["opt"] == "both" else ""),
valid.strftime("%d %b %Y %H"),
(valid +
datetime.timedelta(hours=int(period))).strftime("%d %b %Y %H"),
)
mp = MapPlot(
sector=("state" if len(csector) == 2 else csector),
state=ctx["csector"],
title=title,
subtitle=subtitle,
continentalcolor="white",
titlefontsize=16,
)
cmap = plt.get_cmap(ctx["cmap"])
cmap.set_under("#EEEEEE")
cmap.set_over("black")
if scale == "auto":
levs = np.linspace(0, np.max(precip) * 1.1, 10)
levs = [round(lev, 2) for lev in levs]
levs[0] = 0.01
elif scale == "10":
levs = np.arange(0, 10.1, 1.0)
levs[0] = 0.01
elif scale == "7":
levs = np.arange(0, 7.1, 0.5)
levs[0] = 0.01
elif scale == "3.5":
levs = np.arange(0, 3.6, 0.25)
levs[0] = 0.01
mp.pcolormesh(
lons,
lats,
precip,
levs,
cmap=cmap,
units="inch",
clip_on=(ctx["csector"] == "iailin"),
)
if ctx["opt"] == "both":
mp.draw_usdm(valid=valid, filled=False, hatched=True)
if ctx["csector"] == "iailin":
mp.drawcounties()
return mp.fig
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
sector = ctx["sector"]
date1 = ctx["date1"]
date2 = ctx["date2"]
varname = ctx["var"]
table = "alldata_%s" % (sector, ) if len(sector) == 2 else "alldata"
state_limiter = ""
if sector == "iailin":
state_limiter = (
" and network in ('IACLIMATE', 'ILCLIMATE', 'INCLIMATE') ")
df = read_sql(
"""
WITH obs as (
SELECT station, gddxx(%s, 86, high, low) as gdd,
cdd(high, low, 65) as cdd65, hdd(high, low, 65) as hdd65,
sday, high, low, precip,
(high + low)/2. as avg_temp
from """ + table + """ WHERE
day >= %s and day < %s and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip, high, low,
gdd""" + str(ctx["gddbase"]) + """ as gdd, cdd65, hdd65
from climate51),
combo as (
SELECT o.station, o.precip - c.precip as precip_diff,
o.precip as precip, c.precip as cprecip,
o.avg_temp, o.cdd65, o.hdd65,
o.high, o.low, o.gdd, c.gdd as cgdd,
o.gdd - c.gdd as gdd_diff,
o.cdd65 - c.cdd65 as cdd_diff,
o.hdd65 - c.hdd65 as hdd_diff,
o.avg_temp - (c.high + c.low)/2. as temp_diff
from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
agg as (
SELECT station,
avg(avg_temp) as avg_temp,
sum(precip_diff) as precip_depart,
sum(precip) / sum(cprecip) * 100. as precip_percent,
sum(precip) as precip, sum(cprecip) as cprecip,
avg(high) as avg_high_temp,
avg(low) as avg_low_temp,
max(high) as max_high_temp,
min(low) as min_low_temp, sum(gdd_diff) as gdd_depart,
sum(gdd) / greatest(1, sum(cgdd)) * 100. as gdd_percent,
avg(temp_diff) as avg_temp_depart, sum(gdd) as gdd_sum,
sum(cgdd) as cgdd_sum,
sum(cdd65) as cdd_sum,
sum(hdd65) as hdd_sum,
sum(cdd_diff) as cdd_depart,
sum(hdd_diff) as hdd_depart
from combo GROUP by station)
SELECT d.station, t.name,
precip as precip_sum,
avg_temp,
cprecip as cprecip_sum,
precip_depart,
precip_percent,
min_low_temp,
avg_temp_depart,
gdd_depart,
gdd_sum,
gdd_percent,
cgdd_sum,
max_high_temp,
avg_high_temp,
avg_low_temp,
cdd_sum, hdd_sum, cdd_depart, hdd_depart,
ST_x(t.geom) as lon, ST_y(t.geom) as lat
from agg d JOIN stations t on (d.station = t.id)
WHERE t.network ~* 'CLIMATE' """ + state_limiter + """
""",
pgconn,
params=(ctx["gddbase"], date1, date2),
index_col="station",
)
if df.empty:
raise NoDataFound("No Data Found.")
df = df.reindex(df[varname].abs().sort_values(ascending=False).index)
datefmt = "%d %b %Y" if varname != "cgdd_sum" else "%d %b"
subtitle = ""
if varname.find("depart") > -1:
subtitle = ("%s is compared with 1951-%s Climatology"
" to compute departures") % (
date1.year, datetime.date.today().year - 1)
elif varname.startswith("c"):
subtitle = ("Climatology is based on data from 1951-%s") % (
datetime.date.today().year - 1, )
mp = MapPlot(
sector="state" if len(sector) == 2 else sector,
state=sector,
axisbg="white",
title="%s - %s %s [%s]" % (
date1.strftime(datefmt),
date2.strftime(datefmt),
PDICT2.get(varname).replace("$base", str(ctx["gddbase"])),
UNITS.get(varname),
),
subtitle=subtitle,
)
fmt = "%.2f"
cmap = cm.get_cmap(ctx["cmap"])
if varname in ["precip_depart", "avg_temp_depart", "gdd_depart"]:
rng = df[varname].abs().describe(percentiles=[0.95])["95%"]
clevels = np.linspace(0 - rng,
rng,
7,
dtype="i" if varname == "gdd_depart" else "f")
if varname == "gdd_depart":
fmt = "%.0f"
elif varname in ["precip_sum"]:
rng = df[varname].abs().describe(percentiles=[0.95])["95%"]
clevels = np.linspace(0, rng, 7)
cmap.set_under("white")
cmap.set_over("black")
elif varname.endswith("_percent"):
clevels = np.array([10, 25, 50, 75, 100, 125, 150, 175, 200])
fmt = "%.0f"
else:
minv = df[varname].min() - 5
maxv = df[varname].max() + 5
clevels = np.linspace(minv, maxv, 6, dtype="i")
fmt = "%.0f"
clevlabels = [fmt % x for x in clevels]
cmap.set_bad("white")
if ctx["p"] == "contour":
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
clevels,
clevlabels=clevlabels,
cmap=cmap,
units=UNITS.get(varname),
)
if ctx["c"] == "yes":
mp.plot_values(
df["lon"].values,
df["lat"].values,
df[varname].values,
fmt=fmt,
labelbuffer=5,
)
if len(sector) == 2 or sector == "iailin":
mp.drawcounties()
if ctx["usdm"] == "yes":
mp.draw_usdm(date2, filled=False, hatched=True)
return mp.fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
date1 = ctx['date1']
date2 = ctx['date2']
varname = ctx['var']
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
df = read_sql("""
WITH obs as (
SELECT station, gddxx(%s, 86, high, low) as gdd,
cdd(high, low, 65) as cdd65, hdd(high, low, 65) as hdd65,
sday, high, low, precip,
(high + low)/2. as avg_temp
from """ + table + """ WHERE
day >= %s and day < %s and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip, high, low,
gdd""" + str(ctx['gddbase']) + """ as gdd, cdd65, hdd65
from climate51),
combo as (
SELECT o.station, o.precip - c.precip as precip_diff,
o.precip as precip, c.precip as cprecip,
o.avg_temp, o.cdd65, o.hdd65,
o.high, o.low, o.gdd, c.gdd as cgdd,
o.gdd - c.gdd as gdd_diff,
o.cdd65 - c.cdd65 as cdd_diff,
o.hdd65 - c.hdd65 as hdd_diff,
o.avg_temp - (c.high + c.low)/2. as temp_diff
from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
agg as (
SELECT station,
avg(avg_temp) as avg_temp,
sum(precip_diff) as precip_depart,
sum(precip) as precip, sum(cprecip) as cprecip,
avg(high) as avg_high_temp,
avg(low) as avg_low_temp,
max(high) as max_high_temp,
min(low) as min_low_temp, sum(gdd_diff) as gdd_depart,
avg(temp_diff) as avg_temp_depart, sum(gdd) as gdd_sum,
sum(cgdd) as cgdd_sum,
sum(cdd65) as cdd_sum,
sum(hdd65) as hdd_sum,
sum(cdd_diff) as cdd_depart,
sum(hdd_diff) as hdd_depart
from combo GROUP by station)
SELECT d.station, t.name,
precip as precip_sum,
avg_temp,
cprecip as cprecip_sum,
precip_depart,
min_low_temp,
avg_temp_depart,
gdd_depart,
gdd_sum,
cgdd_sum,
max_high_temp,
avg_high_temp,
avg_low_temp,
cdd_sum, hdd_sum, cdd_depart, hdd_depart,
ST_x(t.geom) as lon, ST_y(t.geom) as lat
from agg d JOIN stations t on (d.station = t.id)
WHERE t.network ~* 'CLIMATE'
""",
pgconn,
params=(ctx['gddbase'], date1, date2),
index_col='station')
df = df.reindex(df[varname].abs().sort_values(ascending=False).index)
sector2 = "state" if sector != 'midwest' else 'midwest'
datefmt = "%d %b %Y" if varname != 'cgdd_sum' else '%d %b'
subtitle = ''
if varname.find('depart') > -1:
subtitle = ('%s is compared with 1951-%s Climatology'
' to compute departures') % (
date1.year, datetime.date.today().year - 1)
elif varname.startswith('c'):
subtitle = ("Climatology is based on data from 1951-%s") % (
datetime.date.today().year - 1, )
mp = MapPlot(sector=sector2,
state=sector,
axisbg='white',
title=('%s - %s %s [%s]') %
(date1.strftime(datefmt), date2.strftime(datefmt),
PDICT2.get(varname).replace("$base", str(
ctx['gddbase'])), UNITS.get(varname)),
subtitle=subtitle)
fmt = '%.2f'
if varname in ['precip_depart', 'avg_temp_depart']:
rng = df[varname].abs().describe(percentiles=[0.95])['95%']
clevels = np.linspace(0 - rng, rng, 7)
cmap = cm.get_cmap('RdYlBu' if varname ==
'precip_depart' else 'RdYlBu_r')
elif varname in ['precip_sum']:
rng = df[varname].abs().describe(percentiles=[0.95])['95%']
clevels = np.linspace(0, rng, 7)
cmap = cm.get_cmap('plasma_r')
cmap.set_under('white')
cmap.set_over('black')
else:
minv = df[varname].min() - 5
maxv = df[varname].max() + 5
clevels = np.linspace(minv, maxv, 6, dtype='i')
fmt = '%.0f'
cmap = cm.get_cmap('RdYlBu_r')
clevlabels = [fmt % x for x in clevels]
cmap.set_bad('white')
if ctx['p'] == 'contour':
mp.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
clevels,
clevlabels=clevlabels,
cmap=cmap,
units=UNITS.get(varname))
mp.plot_values(df['lon'].values,
df['lat'].values,
df[varname].values,
fmt=fmt,
labelbuffer=5)
if len(sector) == 2:
mp.drawcounties()
if ctx['usdm'] == 'yes':
mp.draw_usdm(date2, filled=False, hatched=True)
return mp.fig, df
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
def test_usdm():
"""Can we plot the current USDM"""
mp = MapPlot(sector='conus', nocaption=True)
mp.draw_usdm(valid=datetime.date(2018, 5, 7), hatched=True, filled=False)
return mp.fig
