def plotter(fdict):
""" Go """
pgconn = get_dbconn('iem')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
varname = ctx['var']
sdate = ctx['sdate']
edate = ctx['edate']
# Get Climatology
cdf = read_sql("""
SELECT to_char(valid, 'mmdd') as sday, high, low,
(high + low) / 2. as avg,
precip from ncdc_climate81 WHERE station = %s
""", get_dbconn('coop'),
params=(
ctx['_nt'].sts[station]['ncdc81'],), index_col='sday')
if cdf.empty:
raise NoDataFound("No Data Found.")
cursor.execute("""
SELECT day, max_tmpf, min_tmpf, max_dwpf, min_dwpf,
(max_tmpf + min_tmpf) / 2. as avg_tmpf,
pday, coalesce(avg_sknt, 0) as avg_sknt from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""", (sdate, edate, station, ctx['network']))
rows = []
data = {}
for row in cursor:
hd = row['max_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'high']
ld = row['min_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'low']
ad = row['avg_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'avg']
rows.append(dict(day=row['day'], max_tmpf=row['max_tmpf'],
avg_smph=speed(row['avg_sknt'], 'KT').value('MPH'),
min_dwpf=row['min_dwpf'], max_dwpf=row['max_dwpf'],
high_departure=hd, low_departure=ld,
avg_departure=ad,
min_tmpf=row['min_tmpf'], pday=row['pday']))
data[row[0]] = {'val': safe(rows[-1], varname)}
if data[row[0]]['val'] == '0':
data[row[0]]['color'] = 'k'
elif varname == 'high_departure':
data[row[0]]['color'] = 'b' if hd < 0 else 'r'
elif varname == 'low_departure':
data[row[0]]['color'] = 'b' if ld < 0 else 'r'
elif varname == 'avg_departure':
data[row[0]]['color'] = 'b' if ad < 0 else 'r'
df = pd.DataFrame(rows)
title = '[%s] %s Daily %s' % (
station, ctx['_nt'].sts[station]['name'], PDICT.get(varname))
subtitle = '%s thru %s' % (
sdate.strftime("%-d %b %Y"), edate.strftime("%-d %b %Y"))
fig = calendar_plot(
sdate, edate, data, title=title, subtitle=subtitle)
return fig, df
def test_calendar(self):
"""See if we can make a calendar plot!"""
import matplotlib.pyplot as plt
sts = datetime.date(2015, 5, 1)
ets = datetime.date(2015, 7, 1)
data = dict()
fig = plot.calendar_plot(sts, ets, data)
self.assertTrue(isinstance(fig, plt.Figure))
def test_calendar(self):
"""See if we can make a calendar plot!"""
import matplotlib.pyplot as plt
sts = datetime.date(2015, 5, 1)
ets = datetime.date(2015, 7, 1)
data = dict()
fig = plot.calendar_plot(sts, ets, data)
self.assertTrue(isinstance(fig, plt.Figure))
def test_issue101():
"""We like June, it is a good month, don't drop it."""
sts = datetime.date(2017, 5, 29)
ets = datetime.date(2017, 9, 30)
data = dict()
data[datetime.date(2017, 6, 6)] = {'val': "0606"}
data[datetime.date(2017, 7, 6)] = {'val': "0506"}
return calendar_plot(
sts, ets, data, title='Whiz Bang, Wizzardry',
subtitle='This is officially unofficial and hacky.')
def test_calendar():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 5, 15)
data = dict()
data[datetime.date(2015, 5, 16)] = {'val': 300, 'color': '#ff0000'}
data[datetime.date(2015, 5, 6)] = {'val': 1, 'cellcolor': '#0000ff'}
return calendar_plot(
sts, ets, data, title='Whiz Bang, Wizzardry',
subtitle='This is officially unofficial and hacky.', heatmap=True)
def test_calendar4():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 8, 15)
data = dict()
data[datetime.date(2015, 6, 6)] = {'val': "0606"}
data[datetime.date(2015, 5, 6)] = {'val': "0506"}
return calendar_plot(
sts, ets, data, title='Whiz Bang, Wizzardry',
subtitle='This is officially unofficial and hacky.')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import calendar_plot
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
varname = ctx['var']
network = ctx['network']
sdate = ctx['sdate']
edate = ctx['edate']
nt = NetworkTable(network)
# Get Climatology
cdf = read_sql("""SELECT to_char(valid, 'mmdd') as sday, high, low,
precip from ncdc_climate81 WHERE station = %s
""", psycopg2.connect(database='coop', host='iemdb', user='******'),
params=(nt.sts[station]['ncdc81'],), index_col='sday')
cursor.execute("""
SELECT day, max_tmpf, min_tmpf, max_dwpf, min_dwpf,
pday, coalesce(avg_sknt, 0) as avg_sknt from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""", (sdate, edate, station, network))
rows = []
data = {}
for row in cursor:
hd = row['max_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'high']
ld = row['min_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'low']
rows.append(dict(day=row['day'], max_tmpf=row['max_tmpf'],
avg_smph=speed(row['avg_sknt'], 'KT').value('MPH'),
min_dwpf=row['min_dwpf'], max_dwpf=row['max_dwpf'],
high_departure=hd, low_departure=ld,
min_tmpf=row['min_tmpf'], pday=row['pday']))
data[row[0]] = {'val': safe(rows[-1], varname)}
if varname == 'high_departure':
data[row[0]]['color'] = 'b' if hd < 0 else 'r'
elif varname == 'low_departure':
data[row[0]]['color'] = 'b' if ld < 0 else 'r'
df = pd.DataFrame(rows)
title = ('[%s] %s Daily %s\n%s thru %s'
) % (station, nt.sts[station]['name'],
PDICT.get(varname), sdate.strftime("%-d %b %Y"),
edate.strftime("%-d %b %Y"))
fig = calendar_plot(sdate, edate, data,
title=title)
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import calendar_plot
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
varname = ctx['var']
network = ctx['network']
sdate = ctx['sdate']
edate = ctx['edate']
nt = NetworkTable(network)
# Get Climatology
cdf = read_sql("""SELECT to_char(valid, 'mmdd') as sday, high, low,
precip from ncdc_climate81 WHERE station = %s
""", psycopg2.connect(database='coop', host='iemdb', user='******'),
params=(nt.sts[station]['ncdc81'],), index_col='sday')
cursor.execute("""
SELECT day, max_tmpf, min_tmpf, max_dwpf, min_dwpf,
pday, coalesce(avg_sknt, 0) as avg_sknt from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""", (sdate, edate, station, network))
rows = []
data = {}
for row in cursor:
hd = row['max_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'high']
ld = row['min_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'low']
rows.append(dict(day=row['day'], max_tmpf=row['max_tmpf'],
avg_smph=speed(row['avg_sknt'], 'KT').value('MPH'),
min_dwpf=row['min_dwpf'], max_dwpf=row['max_dwpf'],
high_departure=hd, low_departure=ld,
min_tmpf=row['min_tmpf'], pday=row['pday']))
data[row[0]] = {'val': safe(rows[-1], varname)}
if varname == 'high_departure':
data[row[0]]['color'] = 'b' if hd < 0 else 'r'
elif varname == 'low_departure':
data[row[0]]['color'] = 'b' if ld < 0 else 'r'
df = pd.DataFrame(rows)
title = ('[%s] %s Daily %s\n%s thru %s'
) % (station, nt.sts[station]['name'],
PDICT.get(varname), sdate.strftime("%-d %b %Y"),
edate.strftime("%-d %b %Y"))
fig = calendar_plot(sdate, edate, data,
title=title)
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import calendar_plot
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DSM')
varname = fdict.get('var', 'pday')
network = fdict.get('network', 'IA_ASOS')
sdate = datetime.datetime.strptime(fdict.get('sdate', '2015-01-01'),
'%Y-%m-%d')
edate = datetime.datetime.strptime(fdict.get('edate', '2015-02-01'),
'%Y-%m-%d')
sdate = sdate.date()
edate = edate.date()
if PDICT.get(varname) is None:
return
nt = NetworkTable(network)
# Get Climatology
cdf = read_sql("""SELECT to_char(valid, 'mmdd') as sday, high, low,
precip from ncdc_climate81 WHERE station = %s
""", psycopg2.connect(database='coop', host='iemdb', user='******'),
params=(nt.sts[station]['ncdc81'],), index_col='sday')
cursor.execute("""
SELECT day, max_tmpf, min_tmpf, pday from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""", (sdate, edate, station, network))
rows = []
data = {}
for row in cursor:
hd = row['max_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'high']
ld = row['min_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'low']
rows.append(dict(day=row['day'], max_tmpf=row['max_tmpf'],
high_departure=hd, low_departure=ld,
min_tmpf=row['min_tmpf'], pday=row['pday']))
data[row[0]] = {'val': safe(rows[-1], varname)}
if varname == 'high_departure':
data[row[0]]['color'] = 'b' if hd < 0 else 'r'
elif varname == 'low_departure':
data[row[0]]['color'] = 'b' if ld < 0 else 'r'
df = pd.DataFrame(rows)
title = '[%s] %s Daily %s' % (station, nt.sts[station]['name'],
PDICT.get(varname))
fig = calendar_plot(sdate, edate, data,
title=title)
return fig, df
def test_issue101():
"""We like June, it is a good month, don't drop it."""
sts = datetime.date(2017, 5, 29)
ets = datetime.date(2017, 9, 30)
data = dict()
data[datetime.date(2017, 6, 6)] = {'val': "0606"}
data[datetime.date(2017, 7, 6)] = {'val': "0506"}
return calendar_plot(sts,
ets,
data,
title='Whiz Bang, Wizzardry',
subtitle='This is officially unofficial and hacky.')
def test_calendar4():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 8, 15)
data = dict()
data[datetime.date(2015, 6, 6)] = {'val': "0606"}
data[datetime.date(2015, 5, 6)] = {'val': "0506"}
return calendar_plot(sts,
ets,
data,
title='Whiz Bang, Wizzardry',
subtitle='This is officially unofficial and hacky.')
def test_calendar():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 5, 15)
data = dict()
data[datetime.date(2015, 5, 16)] = {'val': 300, 'color': '#ff0000'}
data[datetime.date(2015, 5, 6)] = {'val': 1, 'cellcolor': '#0000ff'}
return calendar_plot(sts,
ets,
data,
title='Whiz Bang, Wizzardry',
subtitle='This is officially unofficial and hacky.',
heatmap=True)
def test_calendar4():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 8, 15)
data = dict()
data[datetime.date(2015, 6, 6)] = {"val": "0606"}
data[datetime.date(2015, 5, 6)] = {"val": "0506"}
return calendar_plot(
sts,
ets,
data,
title="Whiz Bang, Wizzardry",
subtitle="This is officially unofficial and hacky.\nAnd a second line",
)
def test_calendar():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 5, 15)
data = dict()
data[datetime.date(2015, 5, 16)] = {"val": 300, "color": "#ff0000"}
data[datetime.date(2015, 5, 6)] = {"val": 1, "cellcolor": "#0000ff"}
return calendar_plot(
sts,
ets,
data,
title="Whiz Bang, Wizzardry",
subtitle="This is officially unofficial and hacky.",
heatmap=True,
)
def test_calendar2():
"""See if we can make a calendar plot!"""
sts = datetime.date(2015, 5, 4)
ets = datetime.date(2015, 6, 15)
data = dict()
data[datetime.date(2015, 6, 6)] = {"val": "0606"}
data[datetime.date(2015, 5, 6)] = {"val": "0506"}
return calendar_plot(
sts,
ets,
data,
title=("Whiz Bang, Wizzardry. This is even more text and we "
"have even more."),
subtitle="This is officially unofficial and hacky.",
)
def main():
"""Go Main Go"""
pgconn = get_dbconn('postgis')
cursor = pgconn.cursor()
cursor.execute("""
WITH data as (
SELECT eventid, min(issue) from warnings_2017 where wfo = 'OUN' and
phenomena = 'FG' and significance = 'Y' GROUP by eventid
)
SELECT date(min), count(*) from data GROUP by date
""")
data = {}
for row in cursor:
data[row[0]] = {'val': row[1]}
fig = calendar_plot(datetime.date(2017, 1, 1),
datetime.date(2017, 12, 31), data, heatmap=True)
fig.text(0.5, 0.95, "2017 OUN Daily Severe Thunderstorm Warnings",
ha='center', fontsize=20)
fig.savefig('test.png')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import calendar_plot
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DSM')
varname = fdict.get('var', 'pday')
network = fdict.get('network', 'IA_ASOS')
sdate = datetime.datetime.strptime(fdict.get('sdate', '2015-01-01'),
'%Y-%m-%d')
edate = datetime.datetime.strptime(fdict.get('edate', '2015-02-01'),
'%Y-%m-%d')
sdate = sdate.date()
edate = edate.date()
if PDICT.get(varname) is None:
return
nt = NetworkTable(network)
cursor.execute("""
SELECT day, max_tmpf, min_tmpf, pday from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""", (sdate, edate, station, network))
rows = []
data = {}
for row in cursor:
data[row[0]] = {'val': safe(row, varname)}
rows.append(dict(day=row['day'], max_tmpf=row['max_tmpf'],
min_tmpf=row['min_tmpf'], pday=row['pday']))
df = pd.DataFrame(rows)
title = '[%s] %s Daily %s' % (station, nt.sts[station]['name'],
PDICT.get(varname))
fig = calendar_plot(sdate, edate, data,
title=title)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("postgis")
ctx = get_autoplot_context(fdict, get_description())
sts = ctx["sdate"]
ets = ctx["edate"]
wfo = ctx["wfo"]
p1 = ctx["phenomenav1"]
p2 = ctx["phenomenav2"]
p3 = ctx["phenomenav3"]
p4 = ctx["phenomenav4"]
phenomena = []
for p in [p1, p2, p3, p4]:
if p is not None:
phenomena.append(p[:2])
s1 = ctx["significancev1"]
s2 = ctx["significancev2"]
s3 = ctx["significancev3"]
s4 = ctx["significancev4"]
significance = []
for s in [s1, s2, s3, s4]:
if s is not None:
significance.append(s[0])
pstr = []
title = ""
for i, (p, s) in enumerate(zip(phenomena, significance)):
pstr.append("(phenomena = '%s' and significance = '%s')" % (p, s))
if i == 2:
title += "\n"
title += "%s %s.%s, " % (vtec.get_ps_string(p, s), p, s)
pstr = " or ".join(pstr)
pstr = "(%s)" % (pstr, )
if ctx["w"] == "wfo":
ctx["_nt"].sts["_ALL"] = {
"name": "All Offices",
"tzname": "America/Chicago",
}
if wfo not in ctx["_nt"].sts:
raise NoDataFound("No Data Found.")
wfo_limiter = (" and wfo = '%s' ") % (wfo
if len(wfo) == 3 else wfo[1:], )
if wfo == "_ALL":
wfo_limiter = ""
tzname = ctx["_nt"].sts[wfo]["tzname"]
else:
wfo_limiter = " and substr(ugc, 1, 2) = '%s' " % (ctx["state"], )
tzname = "America/Chicago"
df = read_sql(
"""
with events as (
select wfo, min(issue at time zone %s) as localissue,
extract(year from issue) as year,
phenomena, significance, eventid from warnings
where """ + pstr + """ """ + wfo_limiter + """ and
issue >= %s and issue < %s GROUP by wfo, year, phenomena, significance,
eventid
)
SELECT date(localissue), count(*) from events GROUP by date(localissue)
""",
pgconn,
params=(
tzname,
sts - datetime.timedelta(days=2),
ets + datetime.timedelta(days=2),
),
index_col="date",
)
data = {}
now = sts
while now <= ets:
data[now] = {"val": 0}
now += datetime.timedelta(days=1)
for date, row in df.iterrows():
data[date] = {"val": row["count"]}
if ctx["w"] == "wfo":
title2 = "NWS %s [%s]" % (ctx["_nt"].sts[wfo]["name"], wfo)
if wfo == "_ALL":
title2 = "All NWS Offices"
else:
title2 = state_names[ctx["state"]]
fig = calendar_plot(
sts,
ets,
data,
heatmap=(ctx["heatmap"] == "yes"),
title=("Number of VTEC Events for %s by Local Calendar Date") %
(title2, ),
subtitle="Valid %s - %s for %s" %
(sts.strftime("%d %b %Y"), ets.strftime("%d %b %Y"), title),
)
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
sts = ctx['sdate']
ets = ctx['edate']
wfo = ctx['wfo']
p1 = ctx['phenomenav1']
p2 = ctx['phenomenav2']
p3 = ctx['phenomenav3']
p4 = ctx['phenomenav4']
phenomena = []
for p in [p1, p2, p3, p4]:
if p is not None:
phenomena.append(p[:2])
s1 = ctx['significancev1']
s2 = ctx['significancev2']
s3 = ctx['significancev3']
s4 = ctx['significancev4']
significance = []
for s in [s1, s2, s3, s4]:
if s is not None:
significance.append(s[0])
pstr = []
title = ""
for i, (p, s) in enumerate(zip(phenomena, significance)):
pstr.append("(phenomena = '%s' and significance = '%s')" % (p, s))
if i == 2:
title += "\n"
title += "%s %s.%s, " % (vtec.get_ps_string(p, s), p, s)
pstr = " or ".join(pstr)
pstr = "(%s)" % (pstr, )
if ctx['w'] == 'wfo':
nt = NetworkTable("WFO")
nt.sts['_ALL'] = {'name': 'All Offices', 'tzname': 'America/Chicago'}
wfo_limiter = (" and wfo = '%s' ") % (wfo
if len(wfo) == 3 else wfo[1:], )
if wfo == '_ALL':
wfo_limiter = ''
tzname = nt.sts[wfo]['tzname']
else:
wfo_limiter = " and substr(ugc, 1, 2) = '%s' " % (ctx['state'], )
tzname = 'America/Chicago'
df = read_sql("""
with events as (
select wfo, min(issue at time zone %s) as localissue,
extract(year from issue) as year,
phenomena, significance, eventid from warnings
where """ + pstr + """ """ + wfo_limiter + """ and
issue >= %s and issue < %s GROUP by wfo, year, phenomena, significance,
eventid
)
SELECT date(localissue), count(*) from events GROUP by date(localissue)
""",
pgconn,
params=(tzname, sts - datetime.timedelta(days=2),
ets + datetime.timedelta(days=2)),
index_col='date')
data = {}
now = sts
while now <= ets:
data[now] = {'val': 0}
now += datetime.timedelta(days=1)
for date, row in df.iterrows():
data[date] = {'val': row['count']}
fig = calendar_plot(sts, ets, data, heatmap=(ctx['heatmap'] == 'yes'))
if ctx['w'] == 'wfo':
title2 = "NWS %s [%s]" % (nt.sts[wfo]['name'], wfo)
if wfo == '_ALL':
title2 = "All NWS Offices"
else:
title2 = state_names[ctx['state']]
fig.text(
0.5,
0.95,
("Number of VTEC Events for %s by Local Calendar Date"
"\nValid %s - %s for %s") %
(title2, sts.strftime("%d %b %Y"), ets.strftime("%d %b %Y"), title),
ha='center',
va='center')
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
sts = ctx['sdate']
ets = ctx['edate']
wfo = ctx['wfo']
outlook_type = ctx['outlook_type']
day = int(ctx['day'])
ugc = ctx['ugc']
sqllimiter = ""
category = "CATEGORICAL"
if day >= 4 and outlook_type == 'C':
category = 'ANY SEVERE'
elif day >= 3 and outlook_type == 'F':
category = 'CRITICAL FIRE WEATHER AREA'
elif outlook_type == 'F':
category = 'FIRE WEATHER CATEGORICAL'
if ctx['w'] == 'all':
df = read_sql("""
with data as (
select expire, o.threshold from spc_outlooks o
WHERE category = %s
and o.day = %s and o.outlook_type = %s and expire > %s
and expire < %s),
agg as (
select date(expire - '1 day'::interval), d.threshold, priority,
rank() OVER (PARTITION by date(expire - '1 day'::interval)
ORDER by priority DESC)
from data d JOIN spc_outlook_thresholds t
on (d.threshold = t.threshold))
SELECT distinct date, threshold from agg where rank = 1
ORDER by date ASC
""",
pgconn,
params=(category, day, outlook_type, sts,
ets + datetime.timedelta(days=2)),
index_col='date')
title2 = "Continental US"
else:
if ctx['w'] == 'wfo':
table = "cwa"
abbrcol = "wfo"
geoval = wfo
geomcol = "the_geom"
if wfo not in ctx['_nt'].sts:
raise NoDataFound("Unknown station metadata.")
title2 = "NWS %s [%s]" % (ctx['_nt'].sts[wfo]['name'], wfo)
elif ctx['w'] == 'ugc':
table = "ugcs"
abbrcol = "ugc"
geomcol = "simple_geom"
geoval = ugc
sqllimiter = " and t.end_ts is null "
cursor = pgconn.cursor()
cursor.execute(
"""
SELECT name from ugcs where ugc = %s and end_ts is null
""", (ugc, ))
name = "Unknown"
if cursor.rowcount == 1:
name = cursor.fetchone()[0]
title2 = "%s [%s] %s" % ("County" if ugc[2] == 'C' else 'Zone',
ugc, name)
else:
table = "states"
geomcol = "the_geom"
abbrcol = "state_abbr"
geoval = ctx['mystate']
title2 = state_names[ctx['mystate']]
df = read_sql("""
with data as (
select expire, o.threshold from spc_outlooks o,
""" + table + """ t
WHERE t.""" + abbrcol + """ = %s and category = %s
and ST_Intersects(st_buffer(o.geom, 0), t.""" + geomcol + """)
and o.day = %s and o.outlook_type = %s and expire > %s
and expire < %s """ + sqllimiter + """),
agg as (
select date(expire - '1 day'::interval), d.threshold, priority,
rank() OVER (PARTITION by date(expire - '1 day'::interval)
ORDER by priority DESC)
from data d JOIN spc_outlook_thresholds t
on (d.threshold = t.threshold))
SELECT distinct date, threshold from agg where rank = 1
ORDER by date ASC
""",
pgconn,
params=(geoval, category, day, outlook_type, sts,
ets + datetime.timedelta(days=2)),
index_col='date')
data = {}
now = sts
while now <= ets:
data[now] = {'val': " "}
now += datetime.timedelta(days=1)
for date, row in df.iterrows():
if row['threshold'] == 'TSTM' and ctx.get('g', 'yes') == 'no':
continue
data[date] = {
'val': row['threshold'],
'cellcolor': COLORS.get(row['threshold'], '#EEEEEE')
}
fig = calendar_plot(sts,
ets,
data,
title="Highest SPC Day %s %s Outlook for %s" %
(day, PDICT[outlook_type], title2),
subtitle="Valid %s - %s" %
(sts.strftime("%d %b %Y"), ets.strftime("%d %b %Y")))
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("postgis")
ctx = get_autoplot_context(fdict, get_description())
sts = ctx["sdate"]
ets = ctx["edate"]
wfo = ctx["wfo"]
outlook_type = ctx["outlook_type"]
day = int(ctx["day"])
ugc = ctx["ugc"]
sqllimiter = ""
category = "CATEGORICAL"
if day >= 4 and outlook_type == "C":
category = "ANY SEVERE"
elif day >= 3 and outlook_type == "F":
category = "CRITICAL FIRE WEATHER AREA"
elif outlook_type == "F":
category = "FIRE WEATHER CATEGORICAL"
if ctx["w"] == "all":
df = read_sql(
"""
with data as (
select expire, o.threshold from spc_outlooks o
WHERE category = %s
and o.day = %s and o.outlook_type = %s and expire > %s
and expire < %s),
agg as (
select date(expire - '1 day'::interval), d.threshold, priority,
rank() OVER (PARTITION by date(expire - '1 day'::interval)
ORDER by priority DESC)
from data d JOIN spc_outlook_thresholds t
on (d.threshold = t.threshold))
SELECT distinct date, threshold from agg where rank = 1
ORDER by date ASC
""",
pgconn,
params=(
category,
day,
outlook_type,
sts,
ets + datetime.timedelta(days=2),
),
index_col="date",
)
title2 = "Continental US"
else:
if ctx["w"] == "wfo":
table = "cwa"
abbrcol = "wfo"
geoval = wfo
geomcol = "the_geom"
if wfo not in ctx["_nt"].sts:
raise NoDataFound("Unknown station metadata.")
title2 = "NWS %s [%s]" % (ctx["_nt"].sts[wfo]["name"], wfo)
elif ctx["w"] == "ugc":
table = "ugcs"
abbrcol = "ugc"
geomcol = "simple_geom"
geoval = ugc
sqllimiter = " and t.end_ts is null "
cursor = pgconn.cursor()
cursor.execute(
"""
SELECT name from ugcs where ugc = %s and end_ts is null
""",
(ugc, ),
)
name = "Unknown"
if cursor.rowcount == 1:
name = cursor.fetchone()[0]
title2 = "%s [%s] %s" % (
"County" if ugc[2] == "C" else "Zone",
ugc,
name,
)
else:
table = "states"
geomcol = "the_geom"
abbrcol = "state_abbr"
geoval = ctx["mystate"]
title2 = state_names[ctx["mystate"]]
df = read_sql(
"""
with data as (
select expire, o.threshold from spc_outlooks o,
""" + table + """ t
WHERE t.""" + abbrcol + """ = %s and category = %s
and ST_Intersects(st_buffer(o.geom, 0), t.""" + geomcol + """)
and o.day = %s and o.outlook_type = %s and expire > %s
and expire < %s """ + sqllimiter + """),
agg as (
select date(expire - '1 day'::interval), d.threshold, priority,
rank() OVER (PARTITION by date(expire - '1 day'::interval)
ORDER by priority DESC)
from data d JOIN spc_outlook_thresholds t
on (d.threshold = t.threshold))
SELECT distinct date, threshold from agg where rank = 1
ORDER by date ASC
""",
pgconn,
params=(
geoval,
category,
day,
outlook_type,
sts,
ets + datetime.timedelta(days=2),
),
index_col="date",
)
data = {}
now = sts
while now <= ets:
data[now] = {"val": " "}
now += datetime.timedelta(days=1)
for date, row in df.iterrows():
if row["threshold"] == "TSTM" and ctx.get("g", "yes") == "no":
continue
data[date] = {
"val": row["threshold"],
"cellcolor": COLORS.get(row["threshold"], "#EEEEEE"),
}
fig = calendar_plot(
sts,
ets,
data,
title="Highest SPC Day %s %s Outlook for %s" %
(day, PDICT[outlook_type], title2),
subtitle="Valid %s - %s" %
(sts.strftime("%d %b %Y"), ets.strftime("%d %b %Y")),
)
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import calendar_plot
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DSM')
varname = fdict.get('var', 'pday')
network = fdict.get('network', 'IA_ASOS')
sdate = datetime.datetime.strptime(fdict.get('sdate', '2015-01-01'),
'%Y-%m-%d')
edate = datetime.datetime.strptime(fdict.get('edate', '2015-02-01'),
'%Y-%m-%d')
sdate = sdate.date()
edate = edate.date()
if PDICT.get(varname) is None:
return
nt = NetworkTable(network)
# Get Climatology
cdf = read_sql("""SELECT to_char(valid, 'mmdd') as sday, high, low,
precip from ncdc_climate81 WHERE station = %s
""",
psycopg2.connect(database='coop',
host='iemdb',
user='******'),
params=(nt.sts[station]['ncdc81'], ),
index_col='sday')
cursor.execute(
"""
SELECT day, max_tmpf, min_tmpf, pday from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""", (sdate, edate, station, network))
rows = []
data = {}
for row in cursor:
hd = row['max_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'high']
ld = row['min_tmpf'] - cdf.at[row[0].strftime("%m%d"), 'low']
rows.append(
dict(day=row['day'],
max_tmpf=row['max_tmpf'],
high_departure=hd,
low_departure=ld,
min_tmpf=row['min_tmpf'],
pday=row['pday']))
data[row[0]] = {'val': safe(rows[-1], varname)}
if varname == 'high_departure':
data[row[0]]['color'] = 'b' if hd < 0 else 'r'
elif varname == 'low_departure':
data[row[0]]['color'] = 'b' if ld < 0 else 'r'
df = pd.DataFrame(rows)
title = '[%s] %s Daily %s' % (station, nt.sts[station]['name'],
PDICT.get(varname))
fig = calendar_plot(sdate, edate, data, title=title)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("iem")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
varname = ctx["var"]
sdate = ctx["sdate"]
edate = ctx["edate"]
# Get Climatology
cdf = read_sql(
"""
SELECT to_char(valid, 'mmdd') as sday, high, low,
(high + low) / 2. as avg,
precip from ncdc_climate81 WHERE station = %s
""",
get_dbconn("coop"),
params=(ctx["_nt"].sts[station]["ncdc81"], ),
index_col="sday",
)
if cdf.empty:
raise NoDataFound("No Data Found.")
cursor.execute(
"""
SELECT day, max_tmpf, min_tmpf, max_dwpf, min_dwpf,
(max_tmpf + min_tmpf) / 2. as avg_tmpf,
pday, avg_sknt from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE s.day >= %s and s.day <= %s and
t.id = %s and t.network = %s ORDER by day ASC
""",
(sdate, edate, station, ctx["network"]),
)
rows = []
data = {}
for row in cursor:
hd = row["max_tmpf"] - cdf.at[row[0].strftime("%m%d"), "high"]
ld = row["min_tmpf"] - cdf.at[row[0].strftime("%m%d"), "low"]
ad = row["avg_tmpf"] - cdf.at[row[0].strftime("%m%d"), "avg"]
avg_sknt = row["avg_sknt"]
if avg_sknt is None:
if varname == "avg_smph":
continue
avg_sknt = 0
rows.append(
dict(
day=row["day"],
max_tmpf=row["max_tmpf"],
avg_smph=(avg_sknt * units("knot")).to(units("mile / hour")).m,
min_dwpf=row["min_dwpf"],
max_dwpf=row["max_dwpf"],
high_departure=hd,
low_departure=ld,
avg_departure=ad,
min_tmpf=row["min_tmpf"],
pday=row["pday"],
))
data[row[0]] = {"val": safe(rows[-1], varname)}
if data[row[0]]["val"] == "0":
data[row[0]]["color"] = "k"
elif varname == "high_departure":
data[row[0]]["color"] = "b" if hd < 0 else "r"
elif varname == "low_departure":
data[row[0]]["color"] = "b" if ld < 0 else "r"
elif varname == "avg_departure":
data[row[0]]["color"] = "b" if ad < 0 else "r"
df = pd.DataFrame(rows)
title = "[%s] %s Daily %s" % (
station,
ctx["_nt"].sts[station]["name"],
PDICT.get(varname),
)
subtitle = "%s thru %s" % (
sdate.strftime("%-d %b %Y"),
edate.strftime("%-d %b %Y"),
)
fig = calendar_plot(sdate, edate, data, title=title, subtitle=subtitle)
return fig, df
