def underlay_usdm(axis, sts, ets, lon, lat):
"""Underlay the USDM as pretty bars, somehow"""
if ets < datetime.date(2000, 1, 1):
axis.text(0.0,
1.03,
"No Drought Information Prior to 2000",
transform=axis.transAxes)
return
rects = []
for color in COLORS:
rects.append(Rectangle((0, 0), 1, 1, fc=color))
axis.text(0.0,
1.03,
"Drought Category Underlain",
transform=axis.transAxes)
legend = plt.legend(rects, ['D%s' % (cat, ) for cat in range(5)],
ncol=5,
fontsize=11,
loc=(0.3, 1.01))
axis.add_artist(legend)
uri = ("http://iem.local/api/1/usdm_bypoint.json?sdate=%s&edate=%s&"
"lon=%s&lat=%s") % (sts.strftime("%Y-%m-%d"),
ets.strftime("%Y-%m-%d"), lon, lat)
data = requests.get(uri, timeout=30).json()
if not data['data']:
return
for row in data['data']:
ts = datetime.datetime.strptime(row['valid'], '%Y-%m-%d')
date = datetime.date(ts.year, ts.month, ts.day)
axis.add_patch(
Rectangle((date.toordinal(), -100),
7,
200,
color=COLORS[row['category']],
zorder=-3))
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
p1 = ctx['p1']
p2 = ctx['p2']
p3 = ctx['p3']
pvar = ctx['pvar']
sts = ctx['sdate']
ets = ctx['edate']
how = ctx['how']
maxdays = max([p1, p2, p3])
pgconn = get_dbconn('coop')
table = "alldata_%s" % (station[:2], )
df = read_sql("""
-- Get all period averages
with avgs as (
SELECT day, sday,
count(high) OVER (ORDER by day ASC ROWS %s PRECEDING) as counts,
avg(high) OVER (ORDER by day ASC ROWS %s PRECEDING) as p1_high,
avg(high) OVER (ORDER by day ASC ROWS %s PRECEDING) as p2_high,
avg(high) OVER (ORDER by day ASC ROWS %s PRECEDING) as p3_high,
avg(low) OVER (ORDER by day ASC ROWS %s PRECEDING) as p1_low,
avg(low) OVER (ORDER by day ASC ROWS %s PRECEDING) as p2_low,
avg(low) OVER (ORDER by day ASC ROWS %s PRECEDING) as p3_low,
avg((high+low)/2.)
OVER (ORDER by day ASC ROWS %s PRECEDING) as p1_avgt,
avg((high+low)/2.)
OVER (ORDER by day ASC ROWS %s PRECEDING) as p2_avgt,
avg((high+low)/2.)
OVER (ORDER by day ASC ROWS %s PRECEDING) as p3_avgt,
sum(precip) OVER (ORDER by day ASC ROWS %s PRECEDING) as p1_precip,
sum(precip) OVER (ORDER by day ASC ROWS %s PRECEDING) as p2_precip,
sum(precip) OVER (ORDER by day ASC ROWS %s PRECEDING) as p3_precip
from """ + table + """ WHERE station = %s
),
-- Get sday composites
sdays as (
SELECT sday,
avg(p1_high) as p1_high_avg, stddev(p1_high) as p1_high_stddev,
avg(p2_high) as p2_high_avg, stddev(p2_high) as p2_high_stddev,
avg(p3_high) as p3_high_avg, stddev(p3_high) as p3_high_stddev,
avg(p1_low) as p1_low_avg, stddev(p1_low) as p1_low_stddev,
avg(p2_low) as p2_low_avg, stddev(p2_low) as p2_low_stddev,
avg(p3_low) as p3_low_avg, stddev(p3_low) as p3_low_stddev,
avg(p1_avgt) as p1_avgt_avg, stddev(p1_avgt) as p1_avgt_stddev,
avg(p2_avgt) as p2_avgt_avg, stddev(p2_avgt) as p2_avgt_stddev,
avg(p3_avgt) as p3_avgt_avg, stddev(p3_avgt) as p3_avgt_stddev,
avg(p1_precip) as p1_precip_avg, stddev(p1_precip) as p1_precip_stddev,
avg(p2_precip) as p2_precip_avg, stddev(p2_precip) as p2_precip_stddev,
avg(p3_precip) as p3_precip_avg, stddev(p3_precip) as p3_precip_stddev
from avgs WHERE counts = %s GROUP by sday
)
-- Now merge to get obs
SELECT day, s.sday,
p1_high - p1_high_avg as p1_high_diff,
p2_high - p2_high_avg as p2_high_diff,
p3_high - p3_high_avg as p3_high_diff,
p1_low - p1_low_avg as p1_low_diff,
p2_low - p2_low_avg as p2_low_diff,
p3_low - p3_low_avg as p3_low_diff,
p1_avgt - p1_avgt_avg as p1_avgt_diff,
p2_avgt - p2_avgt_avg as p2_avgt_diff,
p3_avgt - p3_avgt_avg as p3_avgt_diff,
p1_precip - p1_precip_avg as p1_precip_diff,
p2_precip - p2_precip_avg as p2_precip_diff,
p3_precip - p3_precip_avg as p3_precip_diff,
(p1_high - p1_high_avg) / p1_high_stddev as p1_high_sigma,
(p2_high - p2_high_avg) / p2_high_stddev as p2_high_sigma,
(p3_high - p3_high_avg) / p3_high_stddev as p3_high_sigma,
(p1_low - p1_low_avg) / p1_low_stddev as p1_low_sigma,
(p2_low - p2_low_avg) / p2_low_stddev as p2_low_sigma,
(p3_low - p3_low_avg) / p3_low_stddev as p3_low_sigma,
(p1_avgt - p1_avgt_avg) / p1_avgt_stddev as p1_avgt_sigma,
(p2_avgt - p2_avgt_avg) / p2_avgt_stddev as p2_avgt_sigma,
(p3_avgt - p3_avgt_avg) / p3_avgt_stddev as p3_avgt_sigma,
(p1_precip - p1_precip_avg) / p1_precip_stddev as p1_precip_sigma,
(p2_precip - p2_precip_avg) / p2_precip_stddev as p2_precip_sigma,
(p3_precip - p3_precip_avg) / p3_precip_stddev as p3_precip_sigma
from avgs a JOIN sdays s on (a.sday = s.sday) WHERE
day >= %s and day <= %s ORDER by day ASC
""",
pgconn,
params=(maxdays - 1, p1 - 1, p2 - 1, p3 - 1, p1 - 1, p2 - 1,
p3 - 1, p1 - 1, p2 - 1, p3 - 1, p1 - 1, p2 - 1,
p3 - 1, station, maxdays, sts, ets),
index_col='day')
if df.empty:
raise NoDataFound("No Data Found.")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.set_position([0.1, 0.14, 0.85, 0.71])
l1, = ax.plot(df.index.values,
df['p1_' + pvar + '_' + how],
lw=2,
label='%s Day' % (p1, ),
zorder=5)
l2, = ax.plot(df.index.values,
df['p2_' + pvar + '_' + how],
lw=2,
label='%s Day' % (p2, ),
zorder=5)
l3, = ax.plot(df.index.values,
df['p3_' + pvar + '_' + how],
lw=2,
label='%s Day' % (p3, ),
zorder=5)
fig.text(0.5,
0.93,
("[%s] %s\n"
"Trailing %s, %s, %s Day Departures & "
"US Drought Monitor") %
(station, ctx['_nt'].sts[station]['name'], p1, p2, p3),
ha='center',
fontsize=14)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
ax.set_ylabel(("%s [%s] %s") %
(PDICT.get(pvar),
UNITS[pvar] if how == 'diff' else r"$\sigma$", PDICT2[how]))
ax.grid(True)
legend = plt.legend(handles=[l1, l2, l3], ncol=3, fontsize=12, loc='best')
ax.add_artist(legend)
ax.text(1,
-0.14,
"%s to %s" %
(sts.strftime("%-d %b %Y"), ets.strftime("%-d %b %Y")),
va='bottom',
ha='right',
fontsize=12,
transform=ax.transAxes)
if station[2:] != "0000":
try:
underlay_usdm(ax, sts, ets, ctx['_nt'].sts[station]['lon'],
ctx['_nt'].sts[station]['lat'])
except Exception as exp:
sys.stderr.write(str(exp))
ax.set_xlim(df.index.min().toordinal() - 2, df.index.max().toordinal() + 2)
return fig, df