def main():
"""Go Main"""
total = None
years = 0.
for yr in range(1981, 2018):
print(yr)
ncfn = "/mesonet/data/prism/%s_daily.nc" % (yr, )
nc = netCDF4.Dataset(ncfn)
if total is None:
lons = nc.variables['lon'][:]
lats = nc.variables['lat'][:]
total = np.zeros(nc.variables['tmax'].shape[1:], np.float)
days = np.zeros(nc.variables['tmax'].shape[1:], np.float)
sidx = daily_offset(datetime.date(yr, 1, 1))
eidx = daily_offset(datetime.date(yr, 7, 4))
for idx in range(sidx, eidx):
days += np.where(nc.variables['tmax'][idx, :, :] > THRESHOLD,
1, 0)
nc.close()
years += 1.
total += days
val = days - (total / years)
print(np.max(val))
print(np.min(val))
mp = MapPlot(sector='conus',
title=("OSU PRISM 2017 Days with High >= 90$^\circ$F "
"Departure"),
subtitle=("2017 thru 4 July against 1981-2016 "
"Year to Date Average"))
mp.contourf(lons, lats, val, np.arange(-25, 26, 5),
units='days', cmap=plt.get_cmap('seismic'))
mp.postprocess(filename='test.png')
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 """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
date1 = ctx["date1"]
date2 = ctx["date2"]
date1 = date1.replace(year=2000)
date2 = date2.replace(year=2000)
varname = ctx["varname"]
df = read_sql(
"""
WITH t2 as (
SELECT station, high, low from ncdc_climate81 WHERE
valid = %s
), t1 as (
SELECT station, high, low from ncdc_climate81 where
valid = %s
), data as (
SELECT t2.station, t1.high as t1_high, t2.high as t2_high,
t1.low as t1_low, t2.low as t2_low from t1 JOIN t2 on
(t1.station = t2.station)
)
SELECT d.station, ST_x(geom) as lon, ST_y(geom) as lat,
t2_high - t1_high as high, t2_low - t1_low as low from data d JOIN
stations s on (s.id = d.station) where s.network = 'NCDC81'
and s.state not in ('HI', 'AK')
""",
pgconn,
params=(date2, date1),
index_col="station",
)
if df.empty:
raise NoDataFound("No Data Found.")
days = int((date2 - date1).days)
extent = int(df[varname].abs().max())
mp = MapPlot(
sector="conus",
title=("%s Day Change in %s NCDC 81 Climatology") %
(days, PDICT[varname]),
subtitle="from %s to %s" %
(date1.strftime("%-d %B"), date2.strftime("%-d %B")),
)
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
np.arange(0 - extent, extent + 1, 2),
cmap=get_cmap(ctx["cmap"]),
units="F",
)
return mp.fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
if len(sector) != 2:
raise ValueError("Sorry, this app does not support multi-state plots.")
varname = ctx['var']
year = ctx['year']
popt = ctx['popt']
threshold = ctx['threshold']
table = "alldata_%s" % (sector, )
df = read_sql("""
WITH data as (
SELECT station, """ + SQLOPT[varname] + """ as doy
from """ + table + """
WHERE year = %s GROUP by station
)
select station, doy, st_x(geom) as lon, st_y(geom) as lat
from data d JOIN stations t on (d.station = t.id) WHERE
t.network = %s and substr(station, 3, 4) != '0000'
and substr(station, 3, 1) != 'C' and doy not in (0, 400) ORDER by doy
""",
pgconn,
params=(threshold, year, '%sCLIMATE' % (sector, )),
index_col='station')
if df.empty:
raise ValueError("No data found!")
def f(val):
ts = datetime.date(year, 1, 1) + datetime.timedelta(days=int(val - 1))
return ts.strftime("%-m/%-d")
df['pdate'] = df['doy'].apply(f)
mp = MapPlot(sector=('state' if len(sector) == 2 else sector),
state=ctx['sector'],
continental_color='white',
nocaption=True,
title=r"%s %s %s$^\circ$F" %
(year, PDICT2[varname], threshold),
subtitle='based on NWS COOP and IEM Daily Estimates')
levs = np.linspace(df['doy'].min() - 3, df['doy'].max() + 3, 7, dtype='i')
levlables = list(map(f, levs))
if popt == 'contour':
mp.contourf(df['lon'],
df['lat'],
df['doy'],
levs,
clevlabels=levlables)
mp.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
mp.drawcounties()
return mp.fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
date1 = ctx['date1']
date2 = ctx['date2']
date1 = date1.replace(year=2000)
date2 = date2.replace(year=2000)
varname = ctx['varname']
df = read_sql("""
WITH t2 as (
SELECT station, high, low from ncdc_climate81 WHERE
valid = %s
), t1 as (
SELECT station, high, low from ncdc_climate81 where
valid = %s
), data as (
SELECT t2.station, t1.high as t1_high, t2.high as t2_high,
t1.low as t1_low, t2.low as t2_low from t1 JOIN t2 on
(t1.station = t2.station)
)
SELECT d.station, ST_x(geom) as lon, ST_y(geom) as lat,
t2_high - t1_high as high, t2_low - t1_low as low from data d JOIN
stations s on (s.id = d.station) where s.network = 'NCDC81'
and s.state not in ('HI', 'AK')
""",
pgconn,
params=(date2, date1),
index_col='station')
days = int((date2 - date1).days)
extent = int(df[varname].abs().max())
mp = MapPlot(sector='conus',
title=('%s Day Change in %s NCDC 81 Climatology') %
(days, PDICT[varname]),
subtitle='from %s to %s' %
(date1.strftime("%-d %B"), date2.strftime("%-d %B")))
mp.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
np.arange(0 - extent, extent + 1, 2),
cmap=cm.get_cmap(ctx['cmap']),
units='F')
return mp.fig, 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"]
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 """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
sector = ctx["sector"]
if len(sector) != 2:
raise NoDataFound("Sorry, this app doesn't support multi-state plots.")
varname = ctx["var"]
year = ctx["year"]
popt = ctx["popt"]
threshold = ctx["threshold"]
table = "alldata_%s" % (sector,)
nt = NetworkTable("%sCLIMATE" % (sector,))
syear = ctx.get("syear", 1893)
eyear = ctx.get("eyear", datetime.date.today().year)
df = read_sql(
"""
-- get the domain of data
WITH events as (
SELECT
station, month,
case when month < 7 then year - 1 else year end as winter_year,
year,
extract(doy from day) as doy,
day
from """
+ table
+ """
WHERE """
+ SQLOPT[varname]
+ """ and
month in %s and
substr(station, 3, 4) != '0000'
and substr(station, 3, 1) not in ('C', 'T')
and year >= %s and year <= %s
), agg as (
SELECT station, winter_year, year, doy, day,
case when month < 7 then doy + 366 else doy end as winter_doy,
rank() OVER (
PARTITION by """
+ YRGP[varname]
+ """, station
ORDER by day """
+ ORDER[varname]
+ """)
from events)
select * from agg where rank = 1
""",
pgconn,
params=(threshold, tuple(MONTH_DOMAIN[varname]), syear, eyear),
index_col="station",
)
doy = USEDOY[varname]
def f(val):
"""Make a pretty date."""
base = datetime.date(2000, 1, 1)
date = base + datetime.timedelta(days=int(val))
return date.strftime("%-m/%-d")
if ctx.get("p") is None:
df2 = df[df[YRGP[varname]] == year].copy()
title = r"%s %s %s$^\circ$F" % (year, PDICT2[varname], threshold)
df2["pdate"] = df2["day"].apply(lambda x: x.strftime("%-m/%-d"))
extra = ""
else:
df2 = df[[doy]].groupby("station").quantile(ctx["p"] / 100.0).copy()
title = r"%.0f%s Percentile Date of %s %s$^\circ$F" % (
ctx["p"],
th(str(ctx["p"])),
PDICT2[varname],
threshold,
)
df2["pdate"] = df2[doy].apply(f)
extra = ", period of record: %.0f-%.0f" % (
df["year"].min(),
df["year"].max(),
)
if df2.empty:
raise NoDataFound("No Data was found")
for station in df2.index.values:
if station not in nt.sts:
continue
df2.at[station, "lat"] = nt.sts[station]["lat"]
df2.at[station, "lon"] = nt.sts[station]["lon"]
mp = MapPlot(
sector="state",
state=ctx["sector"],
continental_color="white",
nocaption=True,
title=title,
subtitle="based on NWS COOP and IEM Daily Estimates%s" % (extra,),
)
levs = np.linspace(df2[doy].min() - 1, df2[doy].max() + 1, 7, dtype="i")
if "cint" in ctx:
levs = np.arange(
df2[doy].min() - 1, df2[doy].max() + 1, ctx["cint"], dtype="i"
)
levlables = list(map(f, levs))
if popt == "contour":
mp.contourf(
df2["lon"],
df2["lat"],
df2[doy],
levs,
clevlabels=levlables,
cmap=ctx["cmap"],
)
mp.plot_values(df2["lon"], df2["lat"], df2["pdate"], labelbuffer=5)
mp.drawcounties()
return mp.fig, df[["year", "winter_doy", "doy"]]
