def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
ts = ctx['ts'].replace(tzinfo=pytz.utc)
hour = int(ctx['hour'])
ilabel = (ctx['ilabel'] == 'yes')
column = "hour%02i" % (hour,)
pgconn = get_dbconn('postgis')
df = read_sql("""
WITH data as (
SELECT ugc, rank() OVER (PARTITION by ugc ORDER by valid DESC),
hour01, hour03, hour06, hour12, hour24
from ffg WHERE valid >= %s and valid <= %s)
SELECT *, substr(ugc, 3, 1) as ztype from data where rank = 1
""", pgconn, params=(ts - datetime.timedelta(hours=24), ts),
index_col='ugc')
plot = MapPlot(sector=ctx['t'], continentalcolor='white',
state=ctx['state'], cwa=ctx['wfo'],
title=("NWS RFC %s Hour Flash Flood Guidance on "
"%s UTC"
) % (hour, ts.strftime("%-d %b %Y %H")),
subtitle=("Estimated amount of %s Rainfall "
"needed for non-urban Flash Flooding to commence"
) % (HOURS[ctx['hour']], ))
bins = [0.01, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.25, 2.5, 2.75, 3.,
3.5, 4.0, 5.0]
cmap = plt.get_cmap('gist_rainbow_r')
df2 = df[df['ztype'] == 'C']
plot.fill_ugcs(df2[column].to_dict(), bins, cmap=cmap, plotmissing=False,
ilabel=ilabel)
df2 = df[df['ztype'] == 'Z']
plot.fill_ugcs(df2[column].to_dict(), bins, cmap=cmap, plotmissing=False,
units='inches', ilabel=ilabel)
return plot.fig, df
def test_drawugcs2():
"""3 filled zones"""
mp = MapPlot(sector='iowa', title='Zones, 3 filled in Iowa, label',
subtitle='test_drawugcs2',
nocaption=True)
mp.fill_ugcs({"IAZ001": 10, "IAZ003": 20, "IAZ005": 30}, ilabel=True)
return mp.fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from pyiem.plot import MapPlot
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('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)
if ctx['drawc'] == 'yes':
m.drawcounties()
return m.fig, ctx['df']
def main():
"""Do Something"""
pgconn = get_dbconn('postgis')
df = read_sql("""
SELECT ugc, count(*) from warnings_2011 where phenomena = 'TO'
and significance = 'A' and issue < '2011-05-18'
GROUP by ugc ORDER by count DESC
""", pgconn, index_col='ugc')
mp = MapPlot(sector='nws',
title=('1 Jan - 17 May 2011 Number of Storm Prediction Center'
' Tornado Watches by County'),
subtitle=('count by county, '
'based on unofficial archives maintained by the '
'IEM'))
bins = list(range(0, 19, 2))
bins[0] = 1
print(df['count'].max())
cmap = plt.get_cmap('plasma')
cmap.set_under('white')
cmap.set_over('black')
mp.fill_ugcs(df['count'].to_dict(), bins, cmap=cmap)
mp.postprocess(filename='test.png')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from pyiem.plot import MapPlot
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
state = fdict.get('state', 'IA')
phenomena = fdict.get('phenomena', 'TO')
significance = fdict.get('significance', 'A')
station = fdict.get('station', 'DMX')[:4]
sdate = datetime.datetime.strptime(fdict.get('sdate', '2015-01-01'),
'%Y-%m-%d')
edate = datetime.datetime.strptime(fdict.get('edate', '2015-01-01'),
'%Y-%m-%d')
t = fdict.get('t', 'state')
varname = fdict.get('v', 'lastyear')
year = int(fdict.get('year', 2015))
year2 = int(fdict.get('year2', 2015))
ilabel = (fdict.get('ilabel', 'no') == 'yes')
nt = NetworkTable("WFO")
if varname == 'lastyear':
if t == 'cwa':
cursor.execute(
"""
select ugc, max(issue at time zone 'UTC') from warnings
WHERE wfo = %s and phenomena = %s and significance = %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:], phenomena,
significance))
else:
cursor.execute(
"""
select ugc, max(issue at time zone 'UTC') from warnings
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s GROUP by ugc
""", (state, phenomena, significance))
rows = []
data = {}
for row in cursor:
rows.append(dict(valid=row[1], year=row[1].year, ugc=row[0]))
data[row[0]] = row[1].year
title = "Year of Last"
datavar = "year"
elif varname == 'yearcount':
table = "warnings_%s" % (year, )
if t == 'cwa':
cursor.execute(
"""
select ugc, count(*) from """ + table + """
WHERE wfo = %s and phenomena = %s and significance = %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:], phenomena,
significance))
else:
cursor.execute(
"""
select ugc, count(*) from """ + table + """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s GROUP by ugc
""", (state, phenomena, significance))
rows = []
data = {}
for row in cursor:
rows.append(dict(count=row[1], year=year, ugc=row[0]))
data[row[0]] = row[1]
title = "Count for %s" % (year, )
datavar = "count"
elif varname == 'total':
table = "warnings"
if t == 'cwa':
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE wfo = %s and phenomena = %s and significance = %s
and issue >= %s and issue <= %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:], phenomena,
significance, sdate, edate))
else:
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s and issue >= %s and issue < %s
GROUP by ugc
""", (state, phenomena, significance, sdate, edate))
rows = []
data = {}
for row in cursor:
rows.append(
dict(count=row[1],
year=year,
ugc=row[0],
minissue=row[2],
maxissue=row[3]))
data[row[0]] = row[1]
title = "Total between %s and %s" % (sdate.strftime("%d %b %Y"),
edate.strftime("%d %b %Y"))
datavar = "count"
elif varname == 'yearavg':
table = "warnings"
if t == 'cwa':
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE wfo = %s and phenomena = %s and significance = %s
and issue >= %s and issue <= %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:], phenomena,
significance, datetime.date(
year, 1, 1), datetime.date(year2 + 1, 1, 1)))
else:
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s and issue >= %s and issue < %s
GROUP by ugc
""", (state, phenomena, significance, datetime.date(
year, 1, 1), datetime.date(year2 + 1, 1, 1)))
rows = []
data = {}
minv = datetime.datetime(2050, 1, 1)
maxv = datetime.datetime(1986, 1, 1)
for row in cursor:
if row[2] < minv:
minv = row[2]
if row[3] > maxv:
maxv = row[3]
rows.append(
dict(count=row[1],
year=year,
ugc=row[0],
minissue=row[2],
maxissue=row[3]))
data[row[0]] = row[1]
title = ("Yearly Avg: %s and %s") % (minv.strftime("%d %b %Y"),
maxv.strftime("%d %b %Y"))
datavar = "average"
if len(rows) == 0:
return ("Sorry, no data found for query!")
df = pd.DataFrame(rows)
if varname == 'yearavg':
years = maxv.year - minv.year + 1
df['average'] = df['count'] / years
for key in data:
data[key] = round(data[key] / float(years), 2)
bins = range(0, int(np.max(df[datavar][:])) + 2, 1)
else:
bins = range(np.min(df[datavar][:]), np.max(df[datavar][:]) + 2, 1)
if len(bins) < 3:
bins.append(bins[-1] + 1)
if len(bins) > 8:
bins = np.linspace(np.min(df[datavar][:]),
np.max(df[datavar][:]) + 2,
8,
dtype='i')
subtitle = "based on IEM Archives of NWS WWA"
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.%s)') %
(title, vtec._phenDict[phenomena], vtec._sigDict[significance],
phenomena, significance),
subtitle=subtitle,
nocaption=True,
titlefontsize=16)
cmap = plt.get_cmap('Paired')
cmap.set_over('white')
cmap.set_under('white')
m.fill_ugcs(data, bins, cmap=cmap, ilabel=ilabel)
return m.fig, df
GROUP by ugc),
u as (SELECT ugc, ST_Area(ST_Transform(geom, 2163)) / 1000000. as area
from ugcs where substr(ugc,3,1) = 'C' and end_ts is null)
SELECT data.ugc, data.data, data.data / u.area
from data JOIN u on (u.ugc = data.ugc)
'''
pcursor.execute("""
WITH data as (
SELECT ugc, count(*) / %s as data from
(select distinct ugc, generate_series(issue, expire, '1 minute'::interval)
from warnings where phenomena in %s and significance = 'W'
and ugc is not null and (expire - issue) < '1440 minutes'::interval
and issue > %s and issue < %s) as foo2
GROUP by ugc),
u as (SELECT ugc, ST_Area(ST_Transform(geom, 2163)) / 1000000. as area
from ugcs where substr(ugc,3,1) = 'C' and end_ts is null)
SELECT data.ugc, data.data, data.data / u.area
from data JOIN u on (u.ugc = data.ugc)
""", (opts['years'], opts['dbcols'], opts['sdate'], opts['edate']))
data = {}
for row in pcursor:
data[row[0]] = float(row[2 if opts['normalized'] else 1])
m.fill_ugcs(data, bins, cmap=cmap, units=opts['units'])
m.postprocess(filename=fn)
subprocess.call("xv %s" % (fn,), shell=True)
GROUP by ugc_county ORDER by count DESC
""")
data = {}
for row in icursor:
data[row[0]] = row[1]
# Query out centroids of counties...
pcursor.execute("""
SELECT ugc, ST_x(ST_centroid(geom)) as lon,
ST_y(ST_centroid(geom)) as lat
from ugcs WHERE state = 'IA' and end_ts is null and
substr(ugc,3,1) = 'C'
""")
clons = []
clats = []
cvals = []
for row in pcursor:
cvals.append(data.get(row[0], 0))
clats.append(row[2])
clons.append(row[1])
m = MapPlot(axisbg='white', title='Iowa CoCoRaHS Observers Per County',
subtitle=("Sites with at least one report in past year "
"(Sep 2015-2016)"))
m.fill_ugcs(data, [1, 2, 3, 4, 5, 7, 10, 15, 20])
m.plot_values(clons, clats, cvals, labelbuffer=0)
m.drawcounties()
m.postprocess(filename='test.png')
bins = np.arange(0, 25, 1)
norm = mpcolors.BoundaryNorm(bins, cmap.N)
pcursor.execute("""
WITH data as (
SELECT ugc, issue at time zone tzname as v
from warnings w JOIN stations t
ON (w.wfo = (case when length(t.id) = 4 then substr(t.id, 1, 3) else t.id end))
WHERE t.network = 'WFO' and
phenomena = 'SV' and significance = 'W' and issue is not null),
agg as (
SELECT ugc, extract(hour from v) as hr, count(*) from data
GROUP by ugc, hr),
ranks as (
SELECT ugc, hr, rank() OVER (PARTITION by ugc ORDER by count DESC)
from agg)
SELECT ugc, hr from ranks where rank = 1
""")
data = {}
for row in pcursor:
data[row[0]] = float(row[1])
cl = ['Mid', '', '2 AM', '', '4 AM', '', '6 AM', '', '8 AM', '',
'10 AM', '', 'Noon',
'', '2 PM', '', '4 PM', '', '6 PM', '', '8 PM', '', '10 PM', '']
m.fill_ugcs(data, bins, cmap=cmap, units='Hour of Day',
clevstride=2, clevlabels=cl)
m.postprocess(filename='test.png')
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
ts = ctx["ts"].replace(tzinfo=pytz.utc)
hour = int(ctx["hour"])
ilabel = ctx["ilabel"] == "yes"
plot = MapPlot(
sector=ctx["t"],
continentalcolor="white",
state=ctx["state"],
cwa=ctx["wfo"],
title=("NWS RFC %s Hour Flash Flood Guidance on %s UTC") %
(hour, ts.strftime("%-d %b %Y %H")),
subtitle=("Estimated amount of %s Rainfall "
"needed for non-urban Flash Flooding to commence") %
(HOURS[ctx["hour"]], ),
)
cmap = plt.get_cmap(ctx["cmap"])
bins = [
0.01,
0.6,
0.8,
1.0,
1.2,
1.4,
1.6,
1.8,
2.0,
2.25,
2.5,
2.75,
3.0,
3.5,
4.0,
5.0,
]
if ts.year < 2019:
column = "hour%02i" % (hour, )
pgconn = get_dbconn("postgis")
df = read_sql(
"""
WITH data as (
SELECT ugc, rank() OVER (PARTITION by ugc ORDER by valid DESC),
hour01, hour03, hour06, hour12, hour24
from ffg WHERE valid >= %s and valid <= %s)
SELECT *, substr(ugc, 3, 1) as ztype from data where rank = 1
""",
pgconn,
params=(ts - datetime.timedelta(hours=24), ts),
index_col="ugc",
)
df2 = df[df["ztype"] == "C"]
plot.fill_ugcs(
df2[column].to_dict(),
bins,
cmap=cmap,
plotmissing=False,
ilabel=ilabel,
)
df2 = df[df["ztype"] == "Z"]
plot.fill_ugcs(
df2[column].to_dict(),
bins,
cmap=cmap,
plotmissing=False,
units="inches",
ilabel=ilabel,
)
else:
# use grib data
ts -= datetime.timedelta(hours=(ts.hour % 6))
ts = ts.replace(minute=0)
fn = None
for offset in range(0, 24, 4):
ts2 = ts - datetime.timedelta(hours=offset)
testfn = ts2.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/ffg/"
"5kmffg_%Y%m%d00.grib2"))
if os.path.isfile(testfn):
fn = testfn
break
if fn is None:
raise NoDataFound("No valid grib data found!")
grbs = pygrib.index(fn, "stepRange")
grb = grbs.select(stepRange="0-%s" % (hour, ))[0]
lats, lons = grb.latlons()
data = (masked_array(grb.values,
data_units=units("mm")).to(units("inch")).m)
plot.pcolormesh(lons, lats, data, bins, cmap=cmap)
if ilabel:
plot.drawcounties()
df = pd.DataFrame()
return plot.fig, df
def test_drawugcs():
"""test drawing of UGCS"""
mp = MapPlot(sector='conus', title='Counties, 3 filled in Iowa',
nocaption=True)
mp.fill_ugcs({"IAC001": 10, "IAC003": 20, "IAC005": 30})
return mp.fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
from pyiem.plot import MapPlot
pgconn = psycopg2.connect(database="postgis", host="iemdb", user="******")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
state = fdict.get("state", "IA")
phenomena = fdict.get("phenomena", "TO")
significance = fdict.get("significance", "A")
station = fdict.get("station", "DMX")[:4]
sdate = datetime.datetime.strptime(fdict.get("sdate", "2015-01-01"), "%Y-%m-%d")
edate = datetime.datetime.strptime(fdict.get("edate", "2015-01-01"), "%Y-%m-%d")
t = fdict.get("t", "state")
varname = fdict.get("v", "lastyear")
year = int(fdict.get("year", 2015))
year2 = int(fdict.get("year2", 2015))
ilabel = fdict.get("ilabel", "no") == "yes"
nt = NetworkTable("WFO")
if varname == "lastyear":
if t == "cwa":
cursor.execute(
"""
select ugc, max(issue at time zone 'UTC') from warnings
WHERE wfo = %s and phenomena = %s and significance = %s
GROUP by ugc
""",
(station if len(station) == 3 else station[1:], phenomena, significance),
)
else:
cursor.execute(
"""
select ugc, max(issue at time zone 'UTC') from warnings
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s GROUP by ugc
""",
(state, phenomena, significance),
)
rows = []
data = {}
for row in cursor:
rows.append(dict(valid=row[1], year=row[1].year, ugc=row[0]))
data[row[0]] = row[1].year
title = "Year of Last"
datavar = "year"
elif varname == "yearcount":
table = "warnings_%s" % (year,)
if t == "cwa":
cursor.execute(
"""
select ugc, count(*) from """
+ table
+ """
WHERE wfo = %s and phenomena = %s and significance = %s
GROUP by ugc
""",
(station if len(station) == 3 else station[1:], phenomena, significance),
)
else:
cursor.execute(
"""
select ugc, count(*) from """
+ table
+ """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s GROUP by ugc
""",
(state, phenomena, significance),
)
rows = []
data = {}
for row in cursor:
rows.append(dict(count=row[1], year=year, ugc=row[0]))
data[row[0]] = row[1]
title = "Count for %s" % (year,)
datavar = "count"
elif varname == "total":
table = "warnings"
if t == "cwa":
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """
+ table
+ """
WHERE wfo = %s and phenomena = %s and significance = %s
and issue >= %s and issue <= %s
GROUP by ugc
""",
(station if len(station) == 3 else station[1:], phenomena, significance, sdate, edate),
)
else:
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """
+ table
+ """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s and issue >= %s and issue < %s
GROUP by ugc
""",
(state, phenomena, significance, sdate, edate),
)
rows = []
data = {}
for row in cursor:
rows.append(dict(count=row[1], year=year, ugc=row[0], minissue=row[2], maxissue=row[3]))
data[row[0]] = row[1]
title = "Total between %s and %s" % (sdate.strftime("%d %b %Y"), edate.strftime("%d %b %Y"))
datavar = "count"
elif varname == "yearavg":
table = "warnings"
if t == "cwa":
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """
+ table
+ """
WHERE wfo = %s and phenomena = %s and significance = %s
and issue >= %s and issue <= %s
GROUP by ugc
""",
(
station if len(station) == 3 else station[1:],
phenomena,
significance,
datetime.date(year, 1, 1),
datetime.date(year2 + 1, 1, 1),
),
)
else:
cursor.execute(
"""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """
+ table
+ """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s and issue >= %s and issue < %s
GROUP by ugc
""",
(state, phenomena, significance, datetime.date(year, 1, 1), datetime.date(year2 + 1, 1, 1)),
)
rows = []
data = {}
minv = datetime.datetime(2050, 1, 1)
maxv = datetime.datetime(1986, 1, 1)
for row in cursor:
if row[2] < minv:
minv = row[2]
if row[3] > maxv:
maxv = row[3]
rows.append(dict(count=row[1], year=year, ugc=row[0], minissue=row[2], maxissue=row[3]))
data[row[0]] = row[1]
title = ("Yearly Avg: %s and %s") % (minv.strftime("%d %b %Y"), maxv.strftime("%d %b %Y"))
datavar = "average"
if len(rows) == 0:
return "Sorry, no data found for query!"
df = pd.DataFrame(rows)
if varname == "yearavg":
years = maxv.year - minv.year + 1
df["average"] = df["count"] / years
for key in data:
data[key] = round(data[key] / float(years), 2)
bins = range(0, int(np.max(df[datavar][:])) + 2, 1)
else:
bins = range(np.min(df[datavar][:]), np.max(df[datavar][:]) + 2, 1)
if len(bins) < 3:
bins.append(bins[-1] + 1)
if len(bins) > 8:
bins = np.linspace(np.min(df[datavar][:]), np.max(df[datavar][:]) + 2, 8, dtype="i")
subtitle = "based on IEM Archives of NWS WWA"
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.%s)")
% (title, vtec._phenDict[phenomena], vtec._sigDict[significance], phenomena, significance),
subtitle=subtitle,
nocaption=True,
)
cmap = plt.get_cmap("Paired")
cmap.set_over("white")
cmap.set_under("white")
m.fill_ugcs(data, bins, cmap=cmap, ilabel=ilabel)
return m.fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from pyiem.plot import MapPlot
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
state = fdict.get('state', 'IA')
phenomena = fdict.get('phenomena', 'TO')
significance = fdict.get('significance', 'A')
station = fdict.get('station', 'DMX')[:4]
sdate = datetime.datetime.strptime(fdict.get('sdate', '2015-01-01'),
'%Y-%m-%d')
edate = datetime.datetime.strptime(fdict.get('edate', '2015-01-01'),
'%Y-%m-%d')
t = fdict.get('t', 'state')
varname = fdict.get('v', 'lastyear')
year = int(fdict.get('year', 2015))
year2 = int(fdict.get('year2', 2015))
ilabel = (fdict.get('ilabel', 'no') == 'yes')
nt = NetworkTable("WFO")
if varname == 'lastyear':
if t == 'cwa':
cursor.execute("""
select ugc, max(issue at time zone 'UTC') from warnings
WHERE wfo = %s and phenomena = %s and significance = %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:],
phenomena, significance))
else:
cursor.execute("""
select ugc, max(issue at time zone 'UTC') from warnings
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s GROUP by ugc
""", (state, phenomena, significance))
rows = []
data = {}
for row in cursor:
rows.append(dict(valid=row[1],
year=row[1].year,
ugc=row[0]))
data[row[0]] = row[1].year
title = "Year of Last"
datavar = "year"
elif varname == 'yearcount':
table = "warnings_%s" % (year, )
if t == 'cwa':
cursor.execute("""
select ugc, count(*) from """ + table + """
WHERE wfo = %s and phenomena = %s and significance = %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:],
phenomena, significance))
else:
cursor.execute("""
select ugc, count(*) from """ + table + """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s GROUP by ugc
""", (state, phenomena, significance))
rows = []
data = {}
for row in cursor:
rows.append(dict(count=row[1], year=year,
ugc=row[0]))
data[row[0]] = row[1]
title = "Count for %s" % (year,)
datavar = "count"
elif varname == 'total':
table = "warnings"
if t == 'cwa':
cursor.execute("""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE wfo = %s and phenomena = %s and significance = %s
and issue >= %s and issue <= %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:],
phenomena, significance,
sdate, edate))
else:
cursor.execute("""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s and issue >= %s and issue < %s
GROUP by ugc
""", (state, phenomena, significance,
sdate, edate))
rows = []
data = {}
for row in cursor:
rows.append(dict(count=row[1], year=year,
ugc=row[0], minissue=row[2], maxissue=row[3]))
data[row[0]] = row[1]
title = "Total between %s and %s" % (sdate.strftime("%d %b %Y"),
edate.strftime("%d %b %Y"))
datavar = "count"
elif varname == 'yearavg':
table = "warnings"
if t == 'cwa':
cursor.execute("""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE wfo = %s and phenomena = %s and significance = %s
and issue >= %s and issue <= %s
GROUP by ugc
""", (station if len(station) == 3 else station[1:],
phenomena, significance,
datetime.date(year, 1, 1), datetime.date(year2 + 1, 1, 1)))
else:
cursor.execute("""
select ugc, count(*), min(issue at time zone 'UTC'),
max(issue at time zone 'UTC') from """ + table + """
WHERE substr(ugc, 1, 2) = %s and phenomena = %s
and significance = %s and issue >= %s and issue < %s
GROUP by ugc
""", (state, phenomena, significance,
datetime.date(year, 1, 1), datetime.date(year2 + 1, 1, 1)))
rows = []
data = {}
minv = datetime.datetime(2050, 1, 1)
maxv = datetime.datetime(1986, 1, 1)
for row in cursor:
if row[2] < minv:
minv = row[2]
if row[3] > maxv:
maxv = row[3]
rows.append(dict(count=row[1], year=year,
ugc=row[0], minissue=row[2], maxissue=row[3]))
data[row[0]] = row[1]
title = ("Yearly Avg: %s and %s"
) % (minv.strftime("%d %b %Y"), maxv.strftime("%d %b %Y"))
datavar = "average"
if len(rows) == 0:
return("Sorry, no data found for query!")
df = pd.DataFrame(rows)
if varname == 'yearavg':
years = maxv.year - minv.year + 1
df['average'] = df['count'] / years
for key in data:
data[key] = round(data[key] / float(years), 2)
bins = range(0, int(np.max(df[datavar][:]))+2, 1)
else:
bins = range(np.min(df[datavar][:]), np.max(df[datavar][:])+2, 1)
if len(bins) < 3:
bins.append(bins[-1]+1)
if len(bins) > 8:
bins = np.linspace(np.min(df[datavar][:]), np.max(df[datavar][:])+2,
8, dtype='i')
subtitle = "based on IEM Archives of NWS WWA"
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.%s)'
) % (title, vtec._phenDict[phenomena],
vtec._sigDict[significance],
phenomena, significance),
subtitle=subtitle, nocaption=True,
titlefontsize=16
)
cmap = plt.get_cmap('Paired')
cmap.set_over('white')
cmap.set_under('white')
m.fill_ugcs(data, bins, cmap=cmap, ilabel=ilabel)
return m.fig, df
