def plot_maxmin(ts, field):
"""Generate our plot."""
nc = ncopen(ts.strftime("/mesonet/data/ndfd/%Y%m%d%H_ndfd.nc"))
if field == 'high_tmpk':
data = np.max(nc.variables[field][:], 0)
elif field == 'low_tmpk':
data = np.min(nc.variables[field][:], 0)
data = masked_array(data, units.degK).to(units.degF).m
subtitle = ("Based on National Digital Forecast Database (NDFD) "
"00 UTC Forecast made %s") % (ts.strftime("%-d %b %Y"), )
mp = MapPlot(title='NWS NDFD 7 Day (%s through %s) %s Temperature' % (
ts.strftime("%-d %b"),
(ts + datetime.timedelta(days=6)).strftime("%-d %b"),
'Maximum' if field == 'high_tmpk' else 'Minimum',
),
subtitle=subtitle,
sector='iailin')
mp.pcolormesh(nc.variables['lon'][:],
nc.variables['lat'][:],
data,
np.arange(10, 121, 10),
cmap=plt.get_cmap('jet'),
units='Degrees F')
mp.drawcounties()
pqstr = (
"data c %s summary/cb_ndfd_7day_%s.png summary/cb_ndfd_7day_%s.png "
"png") % (ts.strftime("%Y%m%d%H%M"), "max" if field == 'high_tmpk' else
'min', "max" if field == 'high_tmpk' else 'min')
mp.postprocess(pqstr=pqstr)
mp.close()
nc.close()
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 """
ctx = get_autoplot_context(fdict, get_description())
ctx['qc'] = loadqc(date=ctx['date'])
ctx['pgconn'] = get_dbconn('isuag')
ctx['nt'] = NetworkTable("ISUSM")
# Adjust stations to make some room
ctx['nt'].sts['BOOI4']['lon'] -= 0.15
ctx['nt'].sts['BOOI4']['lat'] -= 0.15
ctx['nt'].sts['AHTI4']['lon'] += 0.25
ctx['nt'].sts['AHTI4']['lat'] += 0.25
title = 'TBD'
subtitle = 'TBD'
if ctx['opt'] == '1':
title = 'ISU Soil Moisture Max/Min 4 Inch Soil Temperature'
subtitle = 'based on available hourly observations'
data, df = plot1(ctx)
elif ctx['opt'] == '2':
title = 'ISU Soil Moisture Max/Min Air Temperature'
subtitle = 'based on available daily summary data'
data, df = plot2(ctx)
elif ctx['opt'] == '3':
title = 'ISU Soil Moisture Average 4 Inch Soil Temperature'
subtitle = 'based on available daily summary data'
data, df = plot3(ctx)
elif ctx['opt'] == '4':
title = 'ISU Soil Moisture Solar Radiation [MJ]'
subtitle = 'based on available daily summary data'
data, df = plot4(ctx)
elif ctx['opt'] == '5':
title = 'ISU Soil Moisture Potential Evapotranspiration [inch]'
subtitle = 'based on available daily summary data'
data, df = plot5(ctx, "dailyet")
elif ctx['opt'] == '6':
title = 'ISU Soil Moisture Precipitation [inch]'
subtitle = (
'based on available daily summary data, liquid equiv of snow '
'estimated'
)
data, df = plot5(ctx, "rain_mm_tot")
elif ctx['opt'] == '7':
title = 'ISU Soil Moisture Peak Wind Gust [MPH]'
subtitle = 'based on available daily summary data'
data, df = plot7(ctx)
elif ctx['opt'] == '8':
title = 'ISU Soil Moisture Average Wind Speed [MPH]'
subtitle = 'based on available daily summary data'
data, df = plot8(ctx)
tle = ctx['date'].strftime("%b %-d, %Y")
mp = MapPlot(
sector='iowa', continentalcolor='white', nocaption=True,
title='%s %s' % (tle, title),
subtitle=subtitle)
mp.drawcounties('#EEEEEE')
mp.plot_station(data, fontsize=12)
return mp.fig, df
def plotter(fdict):
""" Go """
from pyiem.plot.geoplot import MapPlot
import matplotlib.pyplot as plt
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
nt = NetworkTable(ctx['network'])
year = ctx['year']
dbconn = get_dbconn('coop')
table = "alldata_%s" % (station[:2], )
df = read_sql("""
with yearly as (
select year, sum(precip), station from alldata_ia
where precip is not null and sday < '1012' GROUP by station, year),
ames as (
select year, sum from yearly where station = %s),
combo as (
select y.year, y.station, y.sum as other, a.sum as ames
from yearly y JOIN ames a on (y.year = a.year)
WHERE y.station != %s and y.station != 'IA0807'),
y2017 as (
select * from combo where year = 2017),
agg as (
SELECT station, st_x(geom) as lon, st_y(geom) as lat,
sum(case when other > ames then 1 else 0 end) as hits, count(*)
from combo c JOIN stations t on (c.station = t.id)
WHERE t.network = %s and c.year < 2017
GROUP by station, lon, lat)
SELECT y.ames as ames, y.other as other, a.* from
y2017 y JOIN agg a on (y.station = a.station)
""",
dbconn,
params=(station, station, ctx['network']),
index_col='station')
df = df[df['count'] > 50]
df['h2017'] = 'red'
df.loc[df['other'] > df['ames'], 'h2017'] = 'blue'
df['freq'] = df['hits'] / df['count'] * 100.
df.sort_values('freq', inplace=True)
mp = MapPlot(continentalcolor='white',
title=('Percentage of Years with Higher Precipitation Total '
'than Ames'),
subtitle=('For 1 January thru 12 October Period, sites '
'in blue are higher for 2017'))
mp.plot_values(df['lon'].values,
df['lat'].values,
df['freq'].values,
'%.0f',
labelbuffer=1,
color=df['h2017'].values)
#mp.contourf(df['lon'].values, df['lat'].values, df['freq'].values,
# range(0, 101, 10), cmap=plt.get_cmap('plasma_r'))
mp.drawcounties()
return mp.fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
# Covert datetime to UTC
ctx['sdate'] = ctx['sdate'].replace(tzinfo=pytz.utc)
ctx['edate'] = ctx['edate'].replace(tzinfo=pytz.utc)
state = ctx['state']
phenomena = ctx['phenomena']
significance = ctx['significance']
station = ctx['station'][:4]
t = ctx['t']
ilabel = (ctx['ilabel'] == 'yes')
geo = ctx['geo']
nt = NetworkTable("WFO")
if geo == 'ugc':
do_ugc(ctx)
elif geo == 'polygon':
do_polygon(ctx)
subtitle = "based on IEM Archives %s" % (ctx.get('subtitle', ''), )
if t == 'cwa':
subtitle = "Plotted for %s (%s), %s" % (nt.sts[station]['name'],
station, subtitle)
else:
subtitle = "Plotted for %s, %s" % (state_names[state], subtitle)
m = MapPlot(sector=('state' if t == 'state' else 'cwa'),
state=state,
cwa=(station if len(station) == 3 else station[1:]),
axisbg='white',
title=('%s %s (%s.%s)') %
(ctx['title'], vtec.get_ps_string(
phenomena, significance), phenomena, significance),
subtitle=subtitle,
nocaption=True,
titlefontsize=16)
if geo == 'ugc':
cmap = plt.get_cmap('Paired')
cmap.set_under('white')
cmap.set_over('white')
m.fill_ugcs(ctx['data'], ctx['bins'], cmap=cmap, ilabel=ilabel)
else:
cmap = plt.get_cmap('gist_ncar')
cmap.set_under('white')
cmap.set_over('black')
res = m.pcolormesh(ctx['lons'],
ctx['lats'],
ctx['data'],
ctx['bins'],
cmap=cmap,
units='count')
# Cut down on SVG et al size
res.set_rasterized(True)
if ctx['drawc'] == 'yes':
m.drawcounties()
return m.fig, ctx['df']
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 """
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 main():
"""Go Main Go."""
pgconn = get_dbconn("idep")
df = read_postgis(
"""
with centroids as (
select huc_12, st_centroid(geom) as center, simple_geom from huc12
where scenario = 0),
agg as (
select c.huc_12,
sum(case when st_y(center) < st_ymax(geom) then 1 else 0 end) as west,
count(*) from flowpaths f JOIN centroids c on
(f.huc_12 = c.huc_12) WHERE f.scenario = 0
GROUP by c.huc_12)
select a.huc_12, st_transform(c.simple_geom, 4326) as geo,
a.west, a.count from agg a JOIN centroids c
ON (a.huc_12 = c.huc_12)
""",
pgconn,
index_col=None,
geom_col="geo",
)
df["percent"] = df["west"] / df["count"] * 100.0
bins = np.arange(0, 101, 10)
cmap = plt.get_cmap("RdBu")
norm = mpcolors.BoundaryNorm(bins, cmap.N)
mp = MapPlot(
continentalcolor="thistle",
nologo=True,
sector="custom",
south=36.8,
north=48.0,
west=-99.2,
east=-88.9,
subtitle="",
title=("DEP Flowpaths North of HUC12 Centroid (%.0f/%.0f %.2f%%)" % (
df["west"].sum(),
df["count"].sum(),
df["west"].sum() / df["count"].sum() * 100.0,
)),
)
for _i, row in df.iterrows():
c = cmap(norm([row["percent"]]))[0]
arr = np.asarray(row["geo"].exterior)
points = mp.ax.projection.transform_points(ccrs.Geodetic(), arr[:, 0],
arr[:, 1])
p = Polygon(points[:, :2], fc=c, ec="None", zorder=2, lw=0.1)
mp.ax.add_patch(p)
mp.drawcounties()
mp.draw_colorbar(bins, cmap, norm, title="Percent", extend="neither")
mp.postprocess(filename="/tmp/huc12_north.png")
def main():
"""Go Main Go."""
pgconn = get_dbconn("idep")
df = read_postgis(
"""
select f.huc_12, count(*) as fps,
st_transform(h.simple_geom, 4326) as geo
from flowpaths f JOIN huc12 h on
(f.huc_12 = h.huc_12) WHERE f.scenario = 0 and h.scenario = 0
GROUP by f.huc_12, geo ORDER by fps ASC
""",
pgconn,
index_col=None,
geom_col="geo",
)
bins = np.arange(1, 42, 2)
cmap = plt.get_cmap("copper")
cmap.set_over("white")
cmap.set_under("thistle")
norm = mpcolors.BoundaryNorm(bins, cmap.N)
mp = MapPlot(
continentalcolor="thistle",
nologo=True,
sector="custom",
south=36.8,
north=45.0,
west=-99.2,
east=-88.9,
subtitle="",
title=("DEP HUCs with <40 Flowpaths (%.0f/%.0f %.2f%%)" % (
len(df[df["fps"] < 40].index),
len(df.index),
len(df[df["fps"] < 40].index) / len(df.index) * 100.0,
)),
)
for _i, row in df.iterrows():
c = cmap(norm([row["fps"]]))[0]
arr = np.asarray(row["geo"].exterior)
points = mp.ax.projection.transform_points(ccrs.Geodetic(), arr[:, 0],
arr[:, 1])
p = Polygon(points[:, :2], fc=c, ec="None", zorder=2, lw=0.1)
mp.ax.add_patch(p)
mp.drawcounties()
mp.draw_colorbar(bins, cmap, norm, title="Count")
mp.postprocess(filename="/tmp/huc12_cnts.png")
def plot_gdd(ts):
"""Generate our plot."""
nc = ncopen(ts.strftime("/mesonet/data/ndfd/%Y%m%d%H_ndfd.nc"))
# compute our daily GDDs
gddtot = np.zeros(np.shape(nc.variables["lon"][:]))
for i in range(7):
gddtot += gdd(
temperature(nc.variables["high_tmpk"][i, :, :], "K"),
temperature(nc.variables["low_tmpk"][i, :, :], "K"),
)
cnc = ncopen("/mesonet/data/ndfd/ndfd_dailyc.nc")
offset = daily_offset(ts)
avggdd = np.sum(cnc.variables["gdd50"][offset:offset + 7], 0)
data = gddtot - np.where(avggdd < 1, 1, avggdd)
subtitle = ("Based on National Digital Forecast Database (NDFD) "
"00 UTC Forecast made %s") % (ts.strftime("%-d %b %Y"), )
mp = MapPlot(
title="NWS NDFD 7 Day (%s through %s) GDD50 Departure from Avg" % (
ts.strftime("%-d %b"),
(ts + datetime.timedelta(days=6)).strftime("%-d %b"),
),
subtitle=subtitle,
sector="iailin",
)
mp.pcolormesh(
nc.variables["lon"][:],
nc.variables["lat"][:],
data,
np.arange(-80, 81, 20),
cmap=plt.get_cmap("RdBu_r"),
units=r"$^\circ$F",
spacing="proportional",
)
mp.drawcounties()
pqstr = (
"data c %s summary/cb_ndfd_7day_gdd.png summary/cb_ndfd_7day_gdd.png "
"png") % (ts.strftime("%Y%m%d%H%M"), )
mp.postprocess(pqstr=pqstr)
mp.close()
nc.close()
def main():
"""Go Main Go."""
pgconn = get_dbconn('idep')
df = read_postgis("""
select f.huc_12, count(*) as fps,
st_transform(h.simple_geom, 4326) as geo
from flowpaths f JOIN huc12 h on
(f.huc_12 = h.huc_12) WHERE f.scenario = 0 and h.scenario = 0
GROUP by f.huc_12, geo ORDER by fps ASC
""", pgconn, index_col=None, geom_col='geo')
bins = np.arange(1, 42, 2)
cmap = plt.get_cmap('copper')
cmap.set_over('white')
cmap.set_under('thistle')
norm = mpcolors.BoundaryNorm(bins, cmap.N)
mp = MapPlot(
continentalcolor='thistle', nologo=True,
sector='custom',
south=36.8, north=45.0, west=-99.2, east=-88.9,
subtitle='',
title=('DEP HUCs with <40 Flowpaths (%.0f/%.0f %.2f%%)' % (
len(df[df['fps'] < 40].index), len(df.index),
len(df[df['fps'] < 40].index) / len(df.index) * 100.
)))
for _i, row in df.iterrows():
c = cmap(norm([row['fps'], ]))[0]
arr = np.asarray(row['geo'].exterior)
points = mp.ax.projection.transform_points(
ccrs.Geodetic(), arr[:, 0], arr[:, 1])
p = Polygon(points[:, :2], fc=c, ec='None', zorder=2, lw=0.1)
mp.ax.add_patch(p)
mp.drawcounties()
mp.draw_colorbar(
bins, cmap, norm,
title='Count')
mp.postprocess(filename='/tmp/huc12_cnts.png')
def makeplot(ts, routes="ac"):
"""
Generate two plots for a given time GMT
"""
pgconn = get_dbconn("smos", user="******")
df = read_sql(
"""
WITH obs as (
SELECT grid_idx, avg(soil_moisture) * 100. as sm,
avg(optical_depth) as od from data where valid BETWEEN %s and %s
GROUP by grid_idx)
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
CASE WHEN sm is Null THEN -1 ELSE sm END as sm,
CASE WHEN od is Null THEN -1 ELSE od END as od
from obs o JOIN grid g ON (o.grid_idx = g.idx)
""",
pgconn,
params=(
ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6),
),
index_col=None,
)
if df.empty:
LOG.info(
"Did not find SMOS data for: %s-%s",
ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6),
)
return
for sector in ["midwest", "iowa"]:
clevs = np.arange(0, 71, 5)
mp = MapPlot(
sector=sector,
axisbg="white",
title="SMOS Satellite: Soil Moisture (0-5cm)",
subtitle="Satelite passes around %s UTC" %
(ts.strftime("%d %B %Y %H"), ),
)
if sector == "iowa":
mp.drawcounties()
cmap = get_cmap("jet_r")
cmap.set_under("#EEEEEE")
cmap.set_over("k")
mp.hexbin(
df["lon"].values,
df["lat"].values,
df["sm"],
clevs,
units="%",
cmap=cmap,
)
pqstr = "plot %s %s00 smos_%s_sm%s.png smos_%s_sm%s.png png" % (
routes,
ts.strftime("%Y%m%d%H"),
sector,
ts.strftime("%H"),
sector,
ts.strftime("%H"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
for sector in ["midwest", "iowa"]:
clevs = np.arange(0, 1.001, 0.05)
mp = MapPlot(
sector=sector,
axisbg="white",
title=("SMOS Satellite: Land Cover Optical Depth "
"(microwave L-band)"),
subtitle="Satelite passes around %s UTC" %
(ts.strftime("%d %B %Y %H"), ),
)
if sector == "iowa":
mp.drawcounties()
cmap = get_cmap("jet")
cmap.set_under("#EEEEEE")
cmap.set_over("k")
mp.hexbin(df["lon"].values,
df["lat"].values,
df["od"],
clevs,
cmap=cmap)
pqstr = "plot %s %s00 smos_%s_od%s.png smos_%s_od%s.png png" % (
routes,
ts.strftime("%Y%m%d%H"),
sector,
ts.strftime("%H"),
sector,
ts.strftime("%H"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
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 = get_autoplot_context(fdict, get_description())
level = ctx['level']
station = ctx['station'][:4]
t = ctx['t']
p = ctx['p']
month = ctx['month']
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
ones = np.ones((int(YSZ), int(XSZ)))
counts = np.zeros((int(YSZ), int(XSZ)))
# counts = np.load('counts.npy')
lons = np.arange(GRIDWEST, GRIDEAST, griddelta)
lats = np.arange(GRIDSOUTH, GRIDNORTH, griddelta)
pgconn = get_dbconn('postgis')
hour = int(p.split(".")[2])
df = read_postgis(
"""
WITH data as (
select product_issue, issue, expire, geom,
rank() OVER (PARTITION by issue ORDER by product_issue DESC)
from spc_outlooks where
outlook_type = %s and day = %s and threshold = %s and
category = %s and
ST_Within(geom, ST_GeomFromEWKT('SRID=4326;POLYGON((%s %s, %s %s,
%s %s, %s %s, %s %s))'))
and extract(hour from product_issue at time zone 'UTC') in %s
and extract(month from product_issue) in %s
)
SELECT * from data where rank = 1
""",
pgconn,
params=(p.split(".")[1], p.split(".")[0], level.split(".", 1)[1],
level.split(".")[0], GRIDWEST, GRIDSOUTH, GRIDWEST, GRIDNORTH,
GRIDEAST, GRIDNORTH, GRIDEAST, GRIDSOUTH, GRIDWEST, GRIDSOUTH,
tuple([hour - 1, hour, hour + 1]), tuple(months)),
geom_col='geom')
if df.empty:
raise NoDataFound("No results found for query")
for _, row in df.iterrows():
zs = zonal_stats(row['geom'],
ones,
affine=PRECIP_AFF,
nodata=-1,
all_touched=True,
raster_out=True)
for z in zs:
aff = z['mini_raster_affine']
west = aff.c
north = aff.f
raster = np.flipud(z['mini_raster_array'])
x0 = int((west - GRIDWEST) / griddelta)
y1 = int((north - GRIDSOUTH) / griddelta)
dy, dx = np.shape(raster)
x1 = x0 + dx
y0 = y1 - dy
counts[y0:y1, x0:x1] += np.where(raster.mask, 0, 1)
mindate = datetime.datetime(2014, 10, 1)
if level not in ['CATEGORICAL.MRGL', 'CATEGORICAL.ENH']:
mindate = datetime.datetime(2002, 1, 1)
if p.split(".")[1] == 'F':
mindate = datetime.datetime(2017, 1, 1)
years = (datetime.datetime.now() -
mindate).total_seconds() / 365.25 / 86400.
data = counts / years
subtitle = "Found %s events for CONUS between %s and %s" % (
len(df.index), df['issue'].min().strftime("%d %b %Y"),
df['issue'].max().strftime("%d %b %Y"))
if t == 'cwa':
sector = 'cwa'
subtitle = "Plotted for %s (%s). %s" % (
ctx['_nt'].sts[station]['name'], station, subtitle)
else:
sector = 'state' if len(ctx['csector']) == 2 else ctx['csector']
mp = MapPlot(sector=sector,
state=ctx['csector'],
cwa=(station if len(station) == 3 else station[1:]),
axisbg='white',
title='SPC %s Outlook [%s] of at least %s' % (
ISSUANCE[p],
month.capitalize(),
OUTLOOKS[level].split("(")[0].strip(),
),
subtitle=subtitle,
nocaption=True,
titlefontsize=16)
# Get the main axes bounds
if t == 'state' and ctx['csector'] == 'conus':
domain = data
lons, lats = np.meshgrid(lons, lats)
df2 = pd.DataFrame()
else:
(west, east, south, north) = mp.ax.get_extent(ccrs.PlateCarree())
i0 = int((west - GRIDWEST) / griddelta)
j0 = int((south - GRIDSOUTH) / griddelta)
i1 = int((east - GRIDWEST) / griddelta)
j1 = int((north - GRIDSOUTH) / griddelta)
jslice = slice(j0, j1)
islice = slice(i0, i1)
domain = data[jslice, islice]
lons, lats = np.meshgrid(lons[islice], lats[jslice])
df2 = pd.DataFrame({
'lat': lats.ravel(),
'lon': lons.ravel(),
'freq': domain.ravel()
})
rng = [
round(x, 2) for x in np.linspace(max([0.01, np.min(domain) - 0.5]),
np.max(domain) + 0.5, 10)
]
cmap = plt.get_cmap(ctx['cmap'])
cmap.set_under('white')
cmap.set_over('black')
res = mp.pcolormesh(lons,
lats,
domain,
rng,
cmap=cmap,
clip_on=False,
units='days per year')
# Cut down on SVG et al size
res.set_rasterized(True)
if ctx['drawc'] == 'yes':
mp.drawcounties()
return mp.fig, df2
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
# Covert datetime to UTC
ctx["sdate"] = ctx["sdate"].replace(tzinfo=pytz.utc)
ctx["edate"] = ctx["edate"].replace(tzinfo=pytz.utc)
state = ctx["state"]
phenomena = ctx["phenomena"]
significance = ctx["significance"]
station = ctx["station"][:4]
t = ctx["t"]
ilabel = ctx["ilabel"] == "yes"
geo = ctx["geo"]
if geo == "ugc":
do_ugc(ctx)
elif geo == "polygon":
do_polygon(ctx)
subtitle = "based on IEM Archives %s" % (ctx.get("subtitle", ""), )
if t == "cwa":
subtitle = "Plotted for %s (%s), %s" % (
ctx["_nt"].sts[station]["name"],
station,
subtitle,
)
else:
subtitle = "Plotted for %s, %s" % (state_names[state], subtitle)
m = MapPlot(
sector=("state" if t == "state" else "cwa"),
state=state,
cwa=(station if len(station) == 3 else station[1:]),
axisbg="white",
title=("%s %s (%s.%s)") % (
ctx["title"],
vtec.get_ps_string(phenomena, significance),
phenomena,
significance,
),
subtitle=subtitle,
nocaption=True,
titlefontsize=16,
)
cmap = plt.get_cmap(ctx["cmap"])
cmap.set_under("white")
cmap.set_over("white")
if geo == "ugc":
m.fill_ugcs(ctx["data"], ctx["bins"], cmap=cmap, ilabel=ilabel)
else:
res = m.pcolormesh(
ctx["lons"],
ctx["lats"],
ctx["data"],
ctx["bins"],
cmap=cmap,
units="count",
)
# Cut down on SVG et al size
res.set_rasterized(True)
if ctx["drawc"] == "yes":
m.drawcounties()
return m.fig, ctx["df"]
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
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"]]
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 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 makeplot(ts, routes='ac'):
"""
Generate two plots for a given time GMT
"""
pgconn = get_dbconn('smos', user='******')
df = read_sql("""
WITH obs as (
SELECT grid_idx, avg(soil_moisture) * 100. as sm,
avg(optical_depth) as od from data where valid BETWEEN %s and %s
GROUP by grid_idx)
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
CASE WHEN sm is Null THEN -1 ELSE sm END as sm,
CASE WHEN od is Null THEN -1 ELSE od END as od
from obs o JOIN grid g ON (o.grid_idx = g.idx)
""", pgconn, params=(ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6)), index_col=None)
if df.empty:
print(("Did not find SMOS data for: %s-%s"
) % (ts - datetime.timedelta(hours=6),
ts + datetime.timedelta(hours=6)))
return
for sector in ['midwest', 'iowa']:
clevs = np.arange(0, 71, 5)
mp = MapPlot(sector=sector, axisbg='white',
title='SMOS Satellite: Soil Moisture (0-5cm)',
subtitle="Satelite passes around %s UTC" % (
ts.strftime("%d %B %Y %H"),))
if sector == 'iowa':
mp.drawcounties()
cmap = cm.get_cmap('jet_r')
cmap.set_under('#EEEEEE')
cmap.set_over("k")
mp.hexbin(df['lon'].values, df['lat'].values, df['sm'], clevs,
units='%', cmap=cmap)
pqstr = "plot %s %s00 smos_%s_sm%s.png smos_%s_sm%s.png png" % (
routes, ts.strftime("%Y%m%d%H"), sector, ts.strftime("%H"),
sector, ts.strftime("%H"))
mp.postprocess(pqstr=pqstr)
mp.close()
for sector in ['midwest', 'iowa']:
clevs = np.arange(0, 1.001, 0.05)
mp = MapPlot(sector=sector, axisbg='white',
title=('SMOS Satellite: Land Cover Optical Depth '
'(microwave L-band)'),
subtitle="Satelite passes around %s UTC" % (
ts.strftime("%d %B %Y %H"),))
if sector == 'iowa':
mp.drawcounties()
cmap = cm.get_cmap('jet')
cmap.set_under('#EEEEEE')
cmap.set_over("k")
mp.hexbin(df['lon'].values, df['lat'].values, df['od'], clevs,
cmap=cmap)
pqstr = "plot %s %s00 smos_%s_od%s.png smos_%s_od%s.png png" % (
routes, ts.strftime("%Y%m%d%H"), sector, ts.strftime("%H"),
sector, ts.strftime("%H"))
mp.postprocess(pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
ctx["qc"] = loadqc(date=ctx["date"])
ctx["pgconn"] = get_dbconn("isuag")
ctx["nt"] = NetworkTable("ISUSM")
if not ctx["nt"].sts:
raise NoDataFound("No station metadata found.")
# Adjust stations to make some room
ctx["nt"].sts["BOOI4"]["lon"] -= 0.15
ctx["nt"].sts["BOOI4"]["lat"] -= 0.15
ctx["nt"].sts["AHTI4"]["lon"] += 0.25
ctx["nt"].sts["AHTI4"]["lat"] += 0.25
title = "TBD"
subtitle = "TBD"
if ctx["opt"] == "1":
title = "ISU Soil Moisture Max/Min 4 Inch Soil Temperature"
subtitle = "based on available hourly observations"
data, df = plot1(ctx)
elif ctx["opt"] == "2":
title = "ISU Soil Moisture Max/Min Air Temperature"
subtitle = "based on available daily summary data"
data, df = plot2(ctx)
elif ctx["opt"] == "3":
title = "ISU Soil Moisture Average 4 Inch Soil Temperature"
subtitle = "based on available daily summary data"
data, df = plot3(ctx)
elif ctx["opt"] == "4":
title = "ISU Soil Moisture Solar Radiation [MJ]"
subtitle = "based on available daily summary data"
data, df = plot4(ctx)
elif ctx["opt"] == "5":
title = "ISU Soil Moisture Potential Evapotranspiration [inch]"
subtitle = "based on available daily summary data"
data, df = plot5(ctx, "dailyet")
elif ctx["opt"] == "6":
title = "ISU Soil Moisture Precipitation [inch]"
subtitle = (
"based on available daily summary data, liquid equiv of snow "
"estimated")
data, df = plot5(ctx, "rain_mm_tot")
elif ctx["opt"] == "7":
title = "ISU Soil Moisture Peak Wind Gust [MPH]"
subtitle = "based on available daily summary data"
data, df = plot7(ctx)
elif ctx["opt"] == "8":
title = "ISU Soil Moisture Average Wind Speed [MPH]"
subtitle = "based on available daily summary data"
data, df = plot8(ctx)
tle = ctx["date"].strftime("%b %-d, %Y")
mp = MapPlot(
sector="iowa",
continentalcolor="white",
nocaption=True,
title="%s %s" % (tle, title),
subtitle=subtitle,
)
mp.drawcounties("#EEEEEE")
mp.plot_station(data, fontsize=12)
return mp.fig, df
