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 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 plot(argv):
"""Make a plot"""
df = pd.read_csv("%s_maxdailyprecip.txt" % (argv[1], ), dtype={'huc12': str})
df.set_index('huc12', inplace=True)
pgconn = get_dbconn('idep')
huc12df = gpd.GeoDataFrame.from_postgis("""
SELECT huc12, simple_geom as geo from wbd_huc12
WHERE swat_use ORDER by huc12
""", pgconn, index_col='huc12', geom_col='geo')
mp = MapPlot(
sector='custom', south=34, north=48, west=-98, east=-77,
title="%s Max Daily Precipitation" % (argv[1].split("_", 1)[1], )
)
bins = range(0, 201, 20)
cmap = stretch_cmap('terrain_r', bins)
norm = mpcolors.BoundaryNorm(bins, cmap.N)
for huc12, row in huc12df.iterrows():
for poly in row['geo']:
arr = np.asarray(poly.exterior)
points = mp.ax.projection.transform_points(ccrs.Geodetic(),
arr[:, 0], arr[:, 1])
color = cmap(norm([df.at[huc12, 'maxp'], ]))[0]
poly = Polygon(points[:, :2], fc=color, ec='None', zorder=2, lw=.1)
mp.ax.add_patch(poly)
mp.draw_colorbar(bins, cmap, norm, units='mm')
mp.postprocess(filename='test.png')
def main():
"""Go Main Go."""
years = 12.0 # 2008 - 2019
pgconn = get_dbconn("idep")
postgis = get_dbconn("postgis")
# Get the initial geometries
df = read_postgis(
"""
SELECT ugc, name, geom from ugcs WHERE end_ts is null and
substr(ugc, 1, 3) = 'IAC'
""",
postgis,
index_col="ugc",
crs="EPSG:4326",
)
scenario = 0
df2 = read_sql(
"""WITH data as (
SELECT r.huc_12,
sum(avg_loss) * 4.163 / %s as detach,
sum(avg_delivery) * 4.163 / %s as delivery,
sum(avg_runoff) / 25.4 / %s as runoff
from results_by_huc12 r
, huc12 h WHERE r.huc_12 = h.huc_12 and h.states ~* 'IA'
and r.scenario = %s and h.scenario = 0 and r.valid < '2020-01-01'
and r.valid > '2008-01-01'
GROUP by r.huc_12)
SELECT ugc, avg(detach) as detach, avg(delivery) as delivery,
avg(runoff) as runoff from data d JOIN huc12 h on (d.huc_12 = h.huc_12)
WHERE h.scenario = 0 GROUP by ugc ORDER by delivery desc
""",
pgconn,
params=(years, years, years, scenario),
index_col="ugc",
)
newcols = {
"detach": "det%s" % (0, ),
"delivery": "del%s" % (0, ),
"runoff": "run%s" % (0, ),
}
for key, val in newcols.items():
df[val] = df2[key]
df = df.sort_values("del0", ascending=False)
print(df.head(10))
mp = MapPlot(title="2008-2019 DEP Top 10 Erosive Counties",
logo="dep",
caption="")
df2 = df.head(10)
mp.fill_ugcs(df2["del0"].to_dict())
mp.postprocess(filename="test.png")
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 plotter(ctx):
""" Go """
# Covert datetime to UTC
do_polygon(ctx)
m = MapPlot(
title='2009-2018 Flash Flood Emergency Polygon Heatmap',
sector='custom', axisbg='white',
# west=-107, south=25.5, east=-88, north=41,
# west=-82, south=36., east=-68, north=48,
west=-85, south=31.8, north=45.2, east=-69,
subtitle='based on unofficial IEM Archives', nocaption=True)
cmap = plt.get_cmap('jet')
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)
m.postprocess(filename='test.png')
def main():
"""Go Main Go."""
dbconn = get_dbconn("postgis")
df = read_sql("""
WITH data as (
SELECT wfo, eventid, extract(year from issue) as year,
max(case when svs is not null then 1 else 0 end) as hit from
warnings where product_issue > '2014-04-01' and
product_issue < '2019-02-22' and phenomena = 'SV'
and date(issue) not in ('2017-08-25', '2017-08-26', '2017-08-27',
'2017-08-28', '2017-08-29', '2017-08-30')
and significance = 'W' GROUP by wfo, eventid, year
)
SELECT wfo, sum(hit) as got_update, count(*) as total_events from data
GROUP by wfo ORDER by total_events DESC
""", dbconn, index_col='wfo')
if 'JSJ' in df.index and 'SJU' not in df.index:
df.loc['SJU'] = df.loc['JSJ']
df['no_update_percent'] = (
100. - df['got_update'] / df['total_events'] * 100.
)
df.to_csv("140401_190221_svr_nofls.csv")
# NOTE: FFW followup is FFS
mp = MapPlot(
sector='nws',
title='Percentage of Severe TStorm Warnings without a SVS Update Issued',
subtitle=('1 April 2014 - 21 February 2019 (exclude Harvey 26-30 Aug '
'2017), based on unofficial data')
)
cmap = plt.get_cmap("copper_r")
cmap.set_under('white')
cmap.set_over('black')
ramp = range(0, 101, 5)
mp.fill_cwas(
df['no_update_percent'], bins=ramp, cmap=cmap, units='%', ilabel=True,
lblformat='%.1f'
)
mp.postprocess(filename='140401_190221_svr_nosvs.png')
def main():
"""Go Main Go"""
data = get_data()
mp = MapPlot(sector='midwest',
title='8 July 2018 USDA NASS Corn Progress Percent Silking',
subtitle=('Top value is 2018 percentage, bottom value is '
'departure from 2008-2017 avg'))
data2 = {}
labels = {}
for state in data:
val = data[state]['d2017'] - data[state]['avg']
data2[state] = val
labels[state.encode('utf-8')] = "%i%%\n%s%.1f%%" % (data[state]['d2017'],
"+" if val > 0 else "", val)
print(labels)
levels = range(-40, 41, 10)
mp.fill_states(data2, ilabel=True, labels=labels, bins=levels,
cmap=plt.get_cmap('RdBu_r'), units='Absolute %',
labelfontsize=16)
mp.postprocess(filename='test.png')
mp.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:
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 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()
