def do_month(year, month, routes):
""" Generate a MRMS plot for the month!"""
sts = datetime.datetime(year, month, 1)
ets = sts + datetime.timedelta(days=35)
ets = ets.replace(day=1)
today = datetime.datetime.now()
if ets > today:
ets = today
idx0 = iemre.daily_offset(sts)
idx1 = iemre.daily_offset(ets)
nc = ncopen(iemre.get_daily_mrms_ncname(year), 'r')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
p01d = distance(np.sum(nc.variables['p01d'][idx0:idx1, :, :], 0),
'MM').value('IN')
nc.close()
mp = MapPlot(sector='iowa', title='MRMS %s - %s Total Precipitation' % (
sts.strftime("%-d %b"),
(ets - datetime.timedelta(days=1)).strftime("%-d %b %Y")),
subtitle='Data from NOAA MRMS Project')
x, y = np.meshgrid(lons, lats)
bins = [0.01, 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 12, 16, 20]
mp.pcolormesh(x, y, p01d, bins, units='inches')
mp.drawcounties()
currentfn = "summary/iowa_mrms_q3_month.png"
archivefn = sts.strftime("%Y/%m/summary/iowa_mrms_q3_month.png")
pqstr = "plot %s %s00 %s %s png" % (
routes, sts.strftime("%Y%m%d%H"), currentfn, archivefn)
mp.postprocess(pqstr=pqstr)
def run(ts):
"""Actually do the work, please"""
nc = ncopen(iemre.get_daily_mrms_ncname(ts.year), "a", timeout=300)
offset = iemre.daily_offset(ts)
ncprecip = nc.variables["p01d"]
ts += datetime.timedelta(hours=24)
gmtts = ts.astimezone(pytz.utc)
fn = findfile(gmtts)
if fn is None:
print("merge_mrms_q2 failed to find file for time: %s" % (gmtts, ))
return
img = Image.open(fn)
data = np.asarray(img)
# data is 3500,7000 , starting at upper L
data = np.flipud(data)
# Anything over 254 is bad
res = np.where(data > 254, 0, data)
res = np.where(np.logical_and(data >= 0, data < 100), data * 0.25, res)
res = np.where(
np.logical_and(data >= 100, data < 180),
25.0 + ((data - 100) * 1.25),
res,
)
res = np.where(
np.logical_and(data >= 180, data < 255),
125.0 + ((data - 180) * 5.0),
res,
)
y1 = int((iemre.NORTH - mrms.SOUTH) * 100.0)
y0 = int((iemre.SOUTH - mrms.SOUTH) * 100.0)
x0 = int((iemre.WEST - mrms.WEST) * 100.0)
x1 = int((iemre.EAST - mrms.WEST) * 100.0)
ncprecip[offset, :, :] = res[y0:y1, x0:x1]
nc.close()
def init_year(ts):
"""
Create a new NetCDF file for a year of our specification!
"""
fp = iemre.get_daily_mrms_ncname(ts.year)
nc = ncopen(fp, 'a', timeout=300)
nc.createDimension('nv', 2)
lat = nc.variables['lat']
lat.bounds = "lat_bnds"
lat[:] = np.arange(iemre.SOUTH + 0.005, iemre.NORTH, 0.01)
lat_bnds = nc.createVariable('lat_bnds', np.float, ('lat', 'nv'))
lat_bnds[:, 0] = np.arange(iemre.SOUTH, iemre.NORTH, 0.01)
lat_bnds[:, 1] = np.arange(iemre.SOUTH + 0.01, iemre.NORTH + 0.01, 0.01)
lon = nc.variables['lon']
lon.bounds = "lon_bnds"
lon[:] = np.arange(iemre.WEST, iemre.EAST, 0.01)
lon_bnds = nc.createVariable('lon_bnds', np.float, ('lon', 'nv'))
lon_bnds[:, 0] = np.arange(iemre.WEST, iemre.EAST, 0.01)
lon_bnds[:, 1] = np.arange(iemre.WEST + 0.01, iemre.EAST + 0.01, 0.01)
nc.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
lts = utc(ts.year, ts.month, ts.day, 12)
lts = lts.astimezone(pytz.timezone("America/Chicago"))
# make assumptions about the last valid MRMS data
if realtime:
# Up until :59 after of the last hour
lts = (datetime.datetime.now() -
datetime.timedelta(hours=1)).replace(minute=59)
else:
lts = lts.replace(year=ts.year,
month=ts.month,
day=ts.day,
hour=23,
minute=59)
idx = iemre.daily_offset(ts)
ncfn = iemre.get_daily_mrms_ncname(ts.year)
if not os.path.isfile(ncfn):
LOG.info("File %s missing, abort.", ncfn)
return
with ncopen(ncfn, timeout=300) as nc:
precip = nc.variables['p01d'][idx, :, :]
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
subtitle = "Total between 12:00 AM and %s" % (
lts.strftime("%I:%M %p %Z"), )
routes = 'ac'
if not realtime:
routes = 'a'
# clevs = np.arange(0, 0.25, 0.05)
# clevs = np.append(clevs, np.arange(0.25, 3., 0.25))
# clevs = np.append(clevs, np.arange(3., 10.0, 1))
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
]
(xx, yy) = np.meshgrid(lons, lats)
for sector in ['iowa', 'midwest']:
pqstr = ("plot %s %s00 %s_q2_1d.png %s_q2_1d.png png") % (
routes, ts.strftime("%Y%m%d%H"), sector, sector)
mp = MapPlot(title=("%s NCEP MRMS Q3 Today's Precipitation") %
(ts.strftime("%-d %b %Y"), ),
subtitle=subtitle,
sector=sector)
mp.pcolormesh(xx,
yy,
distance(precip, 'MM').value('IN'),
clevs,
cmap=nwsprecip(),
units='inch')
if sector == 'iowa':
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
def main():
"""Go Main Go"""
form = cgi.FieldStorage()
ts1 = datetime.datetime.strptime(form.getfirst("date1"), "%Y-%m-%d")
ts2 = datetime.datetime.strptime(form.getfirst("date2"), "%Y-%m-%d")
if ts1 > ts2:
send_error("date1 larger than date2")
if ts1.year != ts2.year:
send_error("multi-year query not supported yet...")
# Make sure we aren't in the future
tsend = datetime.date.today()
if ts2.date() > tsend:
ts2 = datetime.datetime.now() - datetime.timedelta(days=1)
lat = float(form.getfirst("lat"))
lon = float(form.getfirst("lon"))
if lon < iemre.WEST or lon > iemre.EAST:
send_error("lon value outside of bounds: %s to %s" %
(iemre.WEST, iemre.EAST))
if lat < iemre.SOUTH or lat > iemre.NORTH:
send_error("lat value outside of bounds: %s to %s" %
(iemre.SOUTH, iemre.NORTH))
# fmt = form["format"][0]
i, j = iemre.find_ij(lon, lat)
offset1 = iemre.daily_offset(ts1)
offset2 = iemre.daily_offset(ts2) + 1
# Get our netCDF vars
with ncopen(iemre.get_daily_ncname(ts1.year)) as nc:
hightemp = datatypes.temperature(
nc.variables['high_tmpk'][offset1:offset2, j, i], 'K').value("F")
high12temp = datatypes.temperature(
nc.variables['high_tmpk_12z'][offset1:offset2, j, i],
'K').value("F")
lowtemp = datatypes.temperature(
nc.variables['low_tmpk'][offset1:offset2, j, i], 'K').value("F")
low12temp = datatypes.temperature(
nc.variables['low_tmpk_12z'][offset1:offset2, j, i],
'K').value("F")
precip = nc.variables['p01d'][offset1:offset2, j, i] / 25.4
precip12 = nc.variables['p01d_12z'][offset1:offset2, j, i] / 25.4
# Get our climatology vars
c2000 = ts1.replace(year=2000)
coffset1 = iemre.daily_offset(c2000)
c2000 = ts2.replace(year=2000)
coffset2 = iemre.daily_offset(c2000) + 1
cnc = ncopen(iemre.get_dailyc_ncname())
chigh = datatypes.temperature(
cnc.variables['high_tmpk'][coffset1:coffset2, j, i], 'K').value("F")
clow = datatypes.temperature(
cnc.variables['low_tmpk'][coffset1:coffset2, j, i], 'K').value("F")
cprecip = cnc.variables['p01d'][coffset1:coffset2, j, i] / 25.4
cnc.close()
if ts1.year > 1980:
nc = ncopen("/mesonet/data/prism/%s_daily.nc" % (ts1.year, ))
i2, j2 = prismutil.find_ij(lon, lat)
prism_precip = nc.variables['ppt'][offset1:offset2, j2, i2] / 25.4
nc.close()
else:
prism_precip = [None] * (offset2 - offset1)
if ts1.year > 2010:
nc = ncopen(iemre.get_daily_mrms_ncname(ts1.year))
j2 = int((lat - iemre.SOUTH) * 100.0)
i2 = int((lon - iemre.WEST) * 100.0)
mrms_precip = nc.variables['p01d'][offset1:offset2, j2, i2] / 25.4
nc.close()
else:
mrms_precip = [None] * (offset2 - offset1)
res = {
'data': [],
}
for i in range(0, offset2 - offset1):
now = ts1 + datetime.timedelta(days=i)
res['data'].append({
'date': now.strftime("%Y-%m-%d"),
'mrms_precip_in': clean(mrms_precip[i]),
'prism_precip_in': clean(prism_precip[i]),
'daily_high_f': clean(hightemp[i]),
'12z_high_f': clean(high12temp[i]),
'climate_daily_high_f': clean(chigh[i]),
'daily_low_f': clean(lowtemp[i]),
'12z_low_f': clean(low12temp[i]),
'climate_daily_low_f': clean(clow[i]),
'daily_precip_in': clean(precip[i]),
'12z_precip_in': clean(precip12[i]),
'climate_daily_precip_in': clean(cprecip[i])
})
ssw('Content-type: application/json\n\n')
ssw(json.dumps(res))
def main():
"""Do Something Fun!"""
form = cgi.FieldStorage()
ts = datetime.datetime.strptime(form.getfirst("date"), "%Y-%m-%d")
lat = float(form.getfirst("lat"))
lon = float(form.getfirst("lon"))
fmt = form.getfirst("format")
if fmt != 'json':
ssw("Content-type: text/plain\n\n")
ssw("ERROR: Service only emits json at this time")
return
i, j = iemre.find_ij(lon, lat)
offset = iemre.daily_offset(ts)
res = {
'data': [],
}
fn = iemre.get_daily_ncname(ts.year)
ssw('Content-type: application/json\n\n')
if not os.path.isfile(fn):
ssw(json.dumps(res))
sys.exit()
if i is None or j is None:
ssw(json.dumps({'error': 'Coordinates outside of domain'}))
return
if ts.year > 1980:
ncfn = "/mesonet/data/prism/%s_daily.nc" % (ts.year, )
if not os.path.isfile(ncfn):
prism_precip = None
else:
i2, j2 = prismutil.find_ij(lon, lat)
with ncopen(ncfn) as nc:
prism_precip = nc.variables['ppt'][offset, j2, i2] / 25.4
else:
prism_precip = None
if ts.year > 2010:
ncfn = iemre.get_daily_mrms_ncname(ts.year)
if not os.path.isfile(ncfn):
mrms_precip = None
else:
j2 = int((lat - iemre.SOUTH) * 100.0)
i2 = int((lon - iemre.WEST) * 100.0)
with ncopen(ncfn) as nc:
mrms_precip = nc.variables['p01d'][offset, j2, i2] / 25.4
else:
mrms_precip = None
nc = ncopen(fn)
c2000 = ts.replace(year=2000)
coffset = iemre.daily_offset(c2000)
cnc = ncopen(iemre.get_dailyc_ncname())
res['data'].append({
'prism_precip_in':
myrounder(prism_precip, 2),
'mrms_precip_in':
myrounder(mrms_precip, 2),
'daily_high_f':
myrounder(
datatypes.temperature(nc.variables['high_tmpk'][offset, j, i],
'K').value('F'), 1),
'12z_high_f':
myrounder(
datatypes.temperature(nc.variables['high_tmpk_12z'][offset, j, i],
'K').value('F'), 1),
'climate_daily_high_f':
myrounder(
datatypes.temperature(cnc.variables['high_tmpk'][coffset, j, i],
'K').value("F"), 1),
'daily_low_f':
myrounder(
datatypes.temperature(nc.variables['low_tmpk'][offset, j, i],
'K').value("F"), 1),
'12z_low_f':
myrounder(
datatypes.temperature(nc.variables['low_tmpk_12z'][offset, j, i],
'K').value('F'), 1),
'avg_dewpoint_f':
myrounder(
datatypes.temperature(nc.variables['avg_dwpk'][offset, j, i],
'K').value('F'), 1),
'climate_daily_low_f':
myrounder(
datatypes.temperature(cnc.variables['low_tmpk'][coffset, j, i],
'K').value("F"), 1),
'daily_precip_in':
myrounder(nc.variables['p01d'][offset, j, i] / 25.4, 2),
'12z_precip_in':
myrounder(nc.variables['p01d_12z'][offset, j, i] / 25.4, 2),
'climate_daily_precip_in':
myrounder(cnc.variables['p01d'][coffset, j, i] / 25.4, 2),
'srad_mj':
myrounder(nc.variables['rsds'][offset, j, i] * 86400. / 1000000., 2),
'avg_windspeed_mps':
myrounder(nc.variables['wind_speed'][offset, j, i], 2),
})
nc.close()
cnc.close()
ssw(json.dumps(res))
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']
days = ctx['trailing']
threshold = ctx['threshold']
window_sts = date - datetime.timedelta(days=days)
if window_sts.year != date.year:
raise ValueError('Sorry, do not support multi-year plots yet!')
if len(sector) != 2:
raise ValueError("Sorry, this does not support multi-state plots yet.")
idx0 = iemre.daily_offset(window_sts)
idx1 = iemre.daily_offset(date)
ncfn = iemre.get_daily_mrms_ncname(date.year)
ncvar = 'p01d'
if not os.path.isfile(ncfn):
raise ValueError("No data for that year, sorry.")
nc = util.ncopen(ncfn)
# 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)
czs.gen_stats(
np.zeros((nc.variables['lat'].size, nc.variables['lon'].size)),
df['the_geom'])
hasdata = None
jslice = None
islice = None
for nav in czs.gridnav:
hasdata = np.ones((nav.ysz, nav.xsz))
hasdata[nav.mask] = 0.
# 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)
hasdata = np.flipud(hasdata)
today = distance(nc.variables[ncvar][idx1, jslice, islice],
'MM').value('IN')
if (idx1 - idx0) < 32:
p01d = distance(
np.sum(nc.variables[ncvar][idx0:idx1, jslice, islice], 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, jslice, islice], 0),
'MM').value('IN')
else:
p01d += distance(
np.sum(nc.variables[ncvar][i:i2, jslice, islice], 0),
'MM').value('IN')
nc.close()
# Get climatology
nc = util.ncopen(iemre.get_dailyc_mrms_ncname())
if (idx1 - idx0) < 32:
c_p01d = distance(
np.sum(nc.variables[ncvar][idx0:idx1, jslice, islice], 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:
c_p01d = distance(
np.sum(nc.variables[ncvar][i:i2, jslice, islice], 0),
'MM').value('IN')
else:
c_p01d += distance(
np.sum(nc.variables[ncvar][i:i2, jslice, islice], 0),
'MM').value('IN')
nc.close()
# we actually don't care about weights at this fine of scale
cells = np.sum(np.where(hasdata > 0, 1, 0))
departure = p01d - c_p01d
# Update departure and today to values unconsidered below when out of state
departure = np.where(hasdata > 0, departure, -9999)
today = np.where(hasdata > 0, today, 0)
ranges = [[-99, -3], [-3, -2], [-2, -1], [-1, 0], [0, 1], [1, 2], [2, 3],
[3, 99]]
x = []
x2 = []
labels = []
for (minv, maxv) in ranges:
labels.append("%.0f to %.0f" % (minv, maxv))
# How many departure cells in this range
hits = np.logical_and(departure < maxv, departure > minv)
hits2 = np.logical_and(hits, today > threshold)
x.append(np.sum(np.where(hits, 1, 0)) / float(cells) * 100.)
x2.append(np.sum(np.where(hits2, 1, 0)) / float(cells) * 100.)
(fig, ax) = plt.subplots(1, 1)
ax.set_title(("%s NOAA MRMS %s %.2f inch Precip Coverage") %
(state_names[sector], date.strftime("%-d %b %Y"), threshold))
ax.bar(np.arange(8) - 0.2,
x,
align='center',
width=0.4,
label='Trailing %s Day Departure' % (days, ))
ax.bar(np.arange(8) + 0.2,
x2,
align='center',
width=0.4,
label='%s Coverage (%.1f%% Tot)' %
(date.strftime("%-d %b %Y"), sum(x2)))
for i, (_x1, _x2) in enumerate(zip(x, x2)):
ax.text(i - 0.2, _x1 + 1, "%.1f" % (_x1, ), ha='center')
ax.text(i + 0.2, _x2 + 1, "%.1f" % (_x2, ), ha='center')
ax.set_xticks(np.arange(8))
ax.set_xticklabels(labels)
ax.set_xlabel("Trailing %s Day Precip Departure [in]" % (days, ))
ax.set_position([0.1, 0.2, 0.8, 0.7])
ax.legend(loc=(0., -0.2), ncol=2)
ax.set_ylabel("Areal Coverage of %s [%%]" % (state_names[sector], ))
ax.grid(True)
ax.set_xlim(-0.5, 7.5)
ax.set_ylim(0, max([max(x2), max(x)]) + 5)
return 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 test_ncname():
"""Test the responses for get_names."""
assert iemre.get_daily_ncname(2020) is not None
assert iemre.get_hourly_ncname(2020) is not None
assert iemre.get_daily_mrms_ncname(2020) is not None
def application(environ, start_response):
"""Do Something Fun!"""
form = parse_formvars(environ)
ts = datetime.datetime.strptime(form.get("date", "2019-03-01"), "%Y-%m-%d")
lat = float(form.get("lat", 41.99))
lon = float(form.get("lon", -95.1))
fmt = form.get("format", "json")
if fmt != "json":
headers = [("Content-type", "text/plain")]
start_response("200 OK", headers)
return [b"ERROR: Service only emits json at this time"]
i, j = iemre.find_ij(lon, lat)
offset = iemre.daily_offset(ts)
res = {"data": []}
fn = iemre.get_daily_ncname(ts.year)
headers = [("Content-type", "application/json")]
start_response("200 OK", headers)
if not os.path.isfile(fn):
return [json.dumps(res).encode("ascii")]
if i is None or j is None:
data = {"error": "Coordinates outside of domain"}
return [json.dumps(data).encode("ascii")]
if ts.year > 1980:
ncfn = "/mesonet/data/prism/%s_daily.nc" % (ts.year, )
if not os.path.isfile(ncfn):
prism_precip = None
else:
i2, j2 = prismutil.find_ij(lon, lat)
with ncopen(ncfn) as nc:
prism_precip = nc.variables["ppt"][offset, j2, i2] / 25.4
else:
prism_precip = None
if ts.year > 2010:
ncfn = iemre.get_daily_mrms_ncname(ts.year)
if not os.path.isfile(ncfn):
mrms_precip = None
else:
j2 = int((lat - iemre.SOUTH) * 100.0)
i2 = int((lon - iemre.WEST) * 100.0)
with ncopen(ncfn) as nc:
mrms_precip = nc.variables["p01d"][offset, j2, i2] / 25.4
else:
mrms_precip = None
c2000 = ts.replace(year=2000)
coffset = iemre.daily_offset(c2000)
with ncopen(fn) as nc:
with ncopen(iemre.get_dailyc_ncname()) as cnc:
res["data"].append({
"prism_precip_in":
myrounder(prism_precip, 2),
"mrms_precip_in":
myrounder(mrms_precip, 2),
"daily_high_f":
myrounder(
datatypes.temperature(
nc.variables["high_tmpk"][offset, j, i],
"K").value("F"),
1,
),
"12z_high_f":
myrounder(
datatypes.temperature(
nc.variables["high_tmpk_12z"][offset, j, i],
"K").value("F"),
1,
),
"climate_daily_high_f":
myrounder(
datatypes.temperature(
cnc.variables["high_tmpk"][coffset, j, i],
"K").value("F"),
1,
),
"daily_low_f":
myrounder(
datatypes.temperature(
nc.variables["low_tmpk"][offset, j, i],
"K").value("F"),
1,
),
"12z_low_f":
myrounder(
datatypes.temperature(
nc.variables["low_tmpk_12z"][offset, j, i],
"K").value("F"),
1,
),
"avg_dewpoint_f":
myrounder(
datatypes.temperature(
nc.variables["avg_dwpk"][offset, j, i],
"K").value("F"),
1,
),
"climate_daily_low_f":
myrounder(
datatypes.temperature(
cnc.variables["low_tmpk"][coffset, j, i],
"K").value("F"),
1,
),
"daily_precip_in":
myrounder(nc.variables["p01d"][offset, j, i] / 25.4, 2),
"12z_precip_in":
myrounder(nc.variables["p01d_12z"][offset, j, i] / 25.4, 2),
"climate_daily_precip_in":
myrounder(cnc.variables["p01d"][coffset, j, i] / 25.4, 2),
"srad_mj":
myrounder(
nc.variables["rsds"][offset, j, i] * 86400.0 / 1000000.0,
2,
),
"avg_windspeed_mps":
myrounder(nc.variables["wind_speed"][offset, j, i], 2),
})
return [json.dumps(res).encode("ascii")]
def application(environ, start_response):
"""Go Main Go"""
form = parse_formvars(environ)
ts1 = datetime.datetime.strptime(form.get("date1"), "%Y-%m-%d")
ts2 = datetime.datetime.strptime(form.get("date2"), "%Y-%m-%d")
if ts1 > ts2:
return [send_error(start_response, "date1 larger than date2")]
if ts1.year != ts2.year:
return [
send_error(start_response, "multi-year query not supported yet...")
]
# Make sure we aren't in the future
tsend = datetime.date.today()
if ts2.date() > tsend:
ts2 = datetime.datetime.now() - datetime.timedelta(days=1)
lat = float(form.get("lat"))
lon = float(form.get("lon"))
if lon < iemre.WEST or lon > iemre.EAST:
return [
send_error(
start_response,
"lon value outside of bounds: %s to %s"
% (iemre.WEST, iemre.EAST),
)
]
if lat < iemre.SOUTH or lat > iemre.NORTH:
return [
send_error(
start_response,
"lat value outside of bounds: %s to %s"
% (iemre.SOUTH, iemre.NORTH),
)
]
# fmt = form["format"][0]
i, j = iemre.find_ij(lon, lat)
offset1 = iemre.daily_offset(ts1)
offset2 = iemre.daily_offset(ts2) + 1
# Get our netCDF vars
with ncopen(iemre.get_daily_ncname(ts1.year)) as nc:
hightemp = datatypes.temperature(
nc.variables["high_tmpk"][offset1:offset2, j, i], "K"
).value("F")
high12temp = datatypes.temperature(
nc.variables["high_tmpk_12z"][offset1:offset2, j, i], "K"
).value("F")
lowtemp = datatypes.temperature(
nc.variables["low_tmpk"][offset1:offset2, j, i], "K"
).value("F")
low12temp = datatypes.temperature(
nc.variables["low_tmpk_12z"][offset1:offset2, j, i], "K"
).value("F")
precip = nc.variables["p01d"][offset1:offset2, j, i] / 25.4
precip12 = nc.variables["p01d_12z"][offset1:offset2, j, i] / 25.4
# Get our climatology vars
c2000 = ts1.replace(year=2000)
coffset1 = iemre.daily_offset(c2000)
c2000 = ts2.replace(year=2000)
coffset2 = iemre.daily_offset(c2000) + 1
with ncopen(iemre.get_dailyc_ncname()) as cnc:
chigh = datatypes.temperature(
cnc.variables["high_tmpk"][coffset1:coffset2, j, i], "K"
).value("F")
clow = datatypes.temperature(
cnc.variables["low_tmpk"][coffset1:coffset2, j, i], "K"
).value("F")
cprecip = cnc.variables["p01d"][coffset1:coffset2, j, i] / 25.4
if ts1.year > 1980:
i2, j2 = prismutil.find_ij(lon, lat)
with ncopen("/mesonet/data/prism/%s_daily.nc" % (ts1.year,)) as nc:
prism_precip = nc.variables["ppt"][offset1:offset2, j2, i2] / 25.4
else:
prism_precip = [None] * (offset2 - offset1)
if ts1.year > 2010:
j2 = int((lat - iemre.SOUTH) * 100.0)
i2 = int((lon - iemre.WEST) * 100.0)
with ncopen(iemre.get_daily_mrms_ncname(ts1.year)) as nc:
mrms_precip = nc.variables["p01d"][offset1:offset2, j2, i2] / 25.4
else:
mrms_precip = [None] * (offset2 - offset1)
res = {"data": []}
for i in range(0, offset2 - offset1):
now = ts1 + datetime.timedelta(days=i)
res["data"].append(
{
"date": now.strftime("%Y-%m-%d"),
"mrms_precip_in": clean(mrms_precip[i]),
"prism_precip_in": clean(prism_precip[i]),
"daily_high_f": clean(hightemp[i]),
"12z_high_f": clean(high12temp[i]),
"climate_daily_high_f": clean(chigh[i]),
"daily_low_f": clean(lowtemp[i]),
"12z_low_f": clean(low12temp[i]),
"climate_daily_low_f": clean(clow[i]),
"daily_precip_in": clean(precip[i]),
"12z_precip_in": clean(precip12[i]),
"climate_daily_precip_in": clean(cprecip[i]),
}
)
start_response("200 OK", [("Content-type", "application/json")])
return [json.dumps(res).encode("ascii")]
def service(
fmt: SupportedFormatsNoGeoJSON,
sdate: datetime.date = Query(..., description="Start Date."),
edate: datetime.date = Query(..., description="End Date."),
lon: float = Query(..., description="Longitude of point of interest"),
lat: float = Query(..., description="Latitude of point of interest"),
):
"""Go Main Go"""
# Make sure we aren't in the future
tsend = datetime.date.today()
if edate > tsend:
edate = datetime.date.today() - datetime.timedelta(days=1)
i, j = iemre.find_ij(lon, lat)
offset1 = iemre.daily_offset(sdate)
offset2 = iemre.daily_offset(edate) + 1
# Get our netCDF vars
with ncopen(iemre.get_daily_ncname(sdate.year)) as nc:
hightemp = convert_value(
nc.variables["high_tmpk"][offset1:offset2, j, i], "degK", "degF"
)
high12temp = convert_value(
nc.variables["high_tmpk_12z"][offset1:offset2, j, i],
"degK",
"degF",
)
lowtemp = convert_value(
nc.variables["low_tmpk"][offset1:offset2, j, i], "degK", "degF"
)
low12temp = convert_value(
nc.variables["low_tmpk_12z"][offset1:offset2, j, i], "degK", "degF"
)
precip = mm2inch(nc.variables["p01d"][offset1:offset2, j, i])
precip12 = mm2inch(nc.variables["p01d_12z"][offset1:offset2, j, i])
# Get our climatology vars
c2000 = sdate.replace(year=2000)
coffset1 = iemre.daily_offset(c2000)
c2000 = edate.replace(year=2000)
coffset2 = iemre.daily_offset(c2000) + 1
with ncopen(iemre.get_dailyc_ncname()) as cnc:
chigh = convert_value(
cnc.variables["high_tmpk"][coffset1:coffset2, j, i], "degK", "degF"
)
clow = convert_value(
cnc.variables["low_tmpk"][coffset1:coffset2, j, i], "degK", "degF"
)
cprecip = mm2inch(
cnc.variables["p01d"][coffset1:coffset2, j, i],
)
if sdate.year > 1980:
i2, j2 = prismutil.find_ij(lon, lat)
with ncopen(f"/mesonet/data/prism/{sdate.year}_daily.nc") as nc:
prism_precip = mm2inch(
nc.variables["ppt"][offset1:offset2, j2, i2],
)
else:
prism_precip = [None] * (offset2 - offset1)
if sdate.year > 2000:
j2 = int((lat - iemre.SOUTH) * 100.0)
i2 = int((lon - iemre.WEST) * 100.0)
with ncopen(iemre.get_daily_mrms_ncname(sdate.year)) as nc:
mrms_precip = mm2inch(
nc.variables["p01d"][offset1:offset2, j2, i2],
)
else:
mrms_precip = [None] * (offset2 - offset1)
res = []
for i in range(0, offset2 - offset1):
now = sdate + datetime.timedelta(days=i)
res.append(
{
"date": now.strftime("%Y-%m-%d"),
"mrms_precip_in": clean(mrms_precip[i]),
"prism_precip_in": clean(prism_precip[i]),
"daily_high_f": clean(hightemp[i]),
"12z_high_f": clean(high12temp[i]),
"climate_daily_high_f": clean(chigh[i]),
"daily_low_f": clean(lowtemp[i]),
"12z_low_f": clean(low12temp[i]),
"climate_daily_low_f": clean(clow[i]),
"daily_precip_in": clean(precip[i]),
"12z_precip_in": clean(precip12[i]),
"climate_daily_precip_in": clean(cprecip[i]),
}
)
return deliver_df(pd.DataFrame(res), fmt)
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 init_year(ts):
"""
Create a new NetCDF file for a year of our specification!
"""
fn = iemre.get_daily_mrms_ncname(ts.year)
nc = ncopen(fn, "w")
nc.title = "MRMS Daily Precipitation %s" % (ts.year, )
nc.platform = "Grided Estimates"
nc.description = "MRMS 0.01 degree grid"
nc.institution = "Iowa State University, Ames, IA, USA"
nc.source = "Iowa Environmental Mesonet"
nc.project_id = "IEM"
nc.realization = 1
nc.Conventions = "CF-1.0"
nc.contact = "Daryl Herzmann, [email protected], 515-294-5978"
nc.history = "%s Generated" % (
datetime.datetime.now().strftime("%d %B %Y"), )
nc.comment = "No Comment at this time"
# Setup Dimensions
nc.createDimension("lat", (iemre.NORTH - iemre.SOUTH) * 100.0)
nc.createDimension("lon", (iemre.EAST - iemre.WEST) * 100.0)
days = ((ts.replace(year=ts.year + 1)) - ts).days
nc.createDimension("time", int(days))
nc.createDimension("nv", 2)
# Setup Coordinate Variables
lat = nc.createVariable("lat", np.float, ("lat", ))
lat.units = "degrees_north"
lat.long_name = "Latitude"
lat.standard_name = "latitude"
lat.bounds = "lat_bnds"
lat.axis = "Y"
# Grid centers
lat[:] = np.arange(iemre.SOUTH + 0.005, iemre.NORTH, 0.01)
lat_bnds = nc.createVariable("lat_bnds", np.float, ("lat", "nv"))
lat_bnds[:, 0] = np.arange(iemre.SOUTH, iemre.NORTH, 0.01)
lat_bnds[:, 1] = np.arange(iemre.SOUTH + 0.01, iemre.NORTH + 0.01, 0.01)
lon = nc.createVariable("lon", np.float, ("lon", ))
lon.units = "degrees_east"
lon.long_name = "Longitude"
lon.standard_name = "longitude"
lon.bounds = "lon_bnds"
lon.axis = "X"
lon[:] = np.arange(iemre.WEST, iemre.EAST, 0.01)
lon_bnds = nc.createVariable("lon_bnds", np.float, ("lon", "nv"))
lon_bnds[:, 0] = np.arange(iemre.WEST, iemre.EAST, 0.01)
lon_bnds[:, 1] = np.arange(iemre.WEST + 0.01, iemre.EAST + 0.01, 0.01)
tm = nc.createVariable("time", np.float, ("time", ))
tm.units = "Days since %s-01-01 00:00:0.0" % (ts.year, )
tm.long_name = "Time"
tm.standard_name = "time"
tm.axis = "T"
tm.calendar = "gregorian"
tm[:] = np.arange(0, int(days))
# 0 to 65535 by 0.01
p01d = nc.createVariable("p01d",
np.uint16, ("time", "lat", "lon"),
fill_value=65535)
p01d.units = "mm"
p01d.scale_factor = 0.01
p01d.long_name = "Precipitation"
p01d.standard_name = "Precipitation"
p01d.coordinates = "lon lat"
p01d.description = "Precipitation accumulation for the day"
nc.close()
def run(ts):
"""Update netcdf file with the MRMS data
Args:
ts (datetime): timestamptz at midnight central time and we are running
forward in time
"""
nc = ncopen(iemre.get_daily_mrms_ncname(ts.year), "a", timeout=300)
offset = iemre.daily_offset(ts)
ncprecip = nc.variables["p01d"]
gmtts = ts.astimezone(pytz.UTC)
utcnow = utc()
total = None
lats = None
for _ in range(1, 25):
gmtts += datetime.timedelta(hours=1)
if gmtts > utcnow:
continue
gribfn = None
for prefix in ["GaugeCorr_QPE_01H", "RadarOnly_QPE_01H"]:
fn = mrms.fetch(prefix, gmtts)
if fn is None:
continue
fp = gzip.GzipFile(fn, "rb")
(_, gribfn) = tempfile.mkstemp()
tmpfp = open(gribfn, "wb")
tmpfp.write(fp.read())
tmpfp.close()
os.unlink(fn)
break
if gribfn is None:
if gmtts < utcnow:
print("merge_mrms_q3.py MISSING %s" % (gmtts,))
continue
grbs = pygrib.open(gribfn)
grb = grbs[1]
if lats is None:
lats, _ = grb.latlons()
os.unlink(gribfn)
val = grb["values"]
# Anything less than zero, we set to zero
val = np.where(val < 0, 0, val)
if total is None:
total = val
else:
total += val
if lats is None:
print("merge_mrms_q3 nodata for %s" % (ts.date(),))
return
# CAREFUL HERE! The MRMS grid is North to South
# set top (smallest y)
y0 = int((lats[0, 0] - iemre.NORTH) * 100.0)
y1 = int((lats[0, 0] - iemre.SOUTH) * 100.0)
x0 = int((iemre.WEST - mrms.WEST) * 100.0)
x1 = int((iemre.EAST - mrms.WEST) * 100.0)
# print(('y0:%s y1:%s x0:%s x1:%s lat0:%s offset:%s '
# ) % (y0, y1, x0, x1, lats[0, 0], offset))
ncprecip[offset, :, :] = np.flipud(total[y0:y1, x0:x1])
nc.close()
def service(
fmt: SupportedFormatsNoGeoJSON,
date: datetime.date = Query(..., description="The date of interest."),
lon: float = Query(..., description="Longitude of point of interest"),
lat: float = Query(..., description="Latitude of point of interest"),
):
"""Do Something Fun!"""
i, j = iemre.find_ij(lon, lat)
offset = iemre.daily_offset(date)
res = []
fn = iemre.get_daily_ncname(date.year)
if date.year > 1980:
ncfn = f"/mesonet/data/prism/{date.year}_daily.nc"
if not os.path.isfile(ncfn):
prism_precip = None
else:
i2, j2 = prismutil.find_ij(lon, lat)
with ncopen(ncfn) as nc:
prism_precip = mm2inch(nc.variables["ppt"][offset, j2, i2])
else:
prism_precip = None
if date.year > 2000:
ncfn = iemre.get_daily_mrms_ncname(date.year)
if not os.path.isfile(ncfn):
mrms_precip = None
else:
j2 = int((lat - iemre.SOUTH) * 100.0)
i2 = int((lon - iemre.WEST) * 100.0)
with ncopen(ncfn) as nc:
mrms_precip = mm2inch(nc.variables["p01d"][offset, j2, i2])
else:
mrms_precip = None
c2000 = date.replace(year=2000)
coffset = iemre.daily_offset(c2000)
with ncopen(fn) as nc:
with ncopen(iemre.get_dailyc_ncname()) as cnc:
res.append({
"prism_precip_in":
myrounder(prism_precip, 2),
"mrms_precip_in":
myrounder(mrms_precip, 2),
"daily_high_f":
myrounder(
convert_value(
nc.variables["high_tmpk"][offset, j, i],
"degK",
"degF",
),
1,
),
"12z_high_f":
myrounder(
convert_value(
nc.variables["high_tmpk_12z"][offset, j, i],
"degK",
"degF",
),
1,
),
"climate_daily_high_f":
myrounder(
convert_value(
cnc.variables["high_tmpk"][coffset, j, i],
"degK",
"degF",
),
1,
),
"daily_low_f":
myrounder(
convert_value(
nc.variables["low_tmpk"][offset, j, i],
"degK",
"degF",
),
1,
),
"12z_low_f":
myrounder(
convert_value(
nc.variables["low_tmpk_12z"][offset, j, i],
"degK",
"degF",
),
1,
),
"avg_dewpoint_f":
myrounder(
convert_value(
nc.variables["avg_dwpk"][offset, j, i],
"degK",
"degF",
),
1,
),
"climate_daily_low_f":
myrounder(
convert_value(
cnc.variables["low_tmpk"][coffset, j, i],
"degK",
"degF",
),
1,
),
"daily_precip_in":
myrounder(mm2inch(nc.variables["p01d"][offset, j, i]), 2),
"12z_precip_in":
myrounder(mm2inch(nc.variables["p01d_12z"][offset, j, i]), 2),
"climate_daily_precip_in":
myrounder(mm2inch(cnc.variables["p01d"][coffset, j, i]), 2),
"srad_mj":
myrounder(
nc.variables["rsds"][offset, j, i] * 86400.0 / 1000000.0,
2,
),
"avg_windspeed_mps":
myrounder(nc.variables["wind_speed"][offset, j, i], 2),
})
return deliver_df(pd.DataFrame(res), fmt)
