def load_table(state, date):
"""Update the station table"""
nt = NetworkTable("%sCLIMATE" % (state, ))
rows = []
istoday = date == datetime.date.today()
for sid in nt.sts:
# handled by compute_0000
if sid[2:] == "0000" or sid[2] == "C":
continue
if istoday and not nt.sts[sid]["temp24_hour"] in range(3, 12):
continue
i, j = iemre.find_ij(nt.sts[sid]["lon"], nt.sts[sid]["lat"])
nt.sts[sid]["gridi"] = i
nt.sts[sid]["gridj"] = j
rows.append({
"station":
sid,
"gridi":
i,
"gridj":
j,
"temp24_hour":
nt.sts[sid]["temp24_hour"],
"precip24_hour":
nt.sts[sid]["precip24_hour"],
"tracks":
nt.sts[sid]["attributes"].get("TRACKS_STATION", "|").split("|")[0],
})
if not rows:
return
df = pd.DataFrame(rows)
df.set_index("station", inplace=True)
for key in ["high", "low", "precip", "snow", "snowd"]:
df[key] = None
return df
def load_table(state, date):
"""Update the station table"""
nt = NetworkTable("%sCLIMATE" % (state, ))
rows = []
istoday = (date == datetime.date.today())
for sid in nt.sts:
# handled by compute_0000
if sid[2:] == '0000' or sid[2] == 'C':
continue
if istoday and not nt.sts[sid]['temp24_hour'] in range(3, 12):
# print('skipping %s as is_today' % (sid, ))
continue
i, j = iemre.find_ij(nt.sts[sid]['lon'], nt.sts[sid]['lat'])
nt.sts[sid]['gridi'] = i
nt.sts[sid]['gridj'] = j
rows.append(
{'station': sid, 'gridi': i, 'gridj': j,
'temp24_hour': nt.sts[sid]['temp24_hour'],
'precip24_hour': nt.sts[sid]['precip24_hour'],
'tracks': nt.sts[sid]['attributes'].get(
'TRACKS_STATION', '|').split("|")[0]}
)
if not rows:
return
df = pd.DataFrame(rows)
df.set_index('station', inplace=True)
for key in ['high', 'low', 'precip', 'snow', 'snowd']:
df[key] = None
return df
def do(netname, pname):
"""Do something please"""
mcursor = MESOSITE.cursor()
mcursor.execute("""
SELECT id, network, name from webcams where
network = %s
and online ORDER by id ASC
""", (netname, ))
NT = NetworkTable(None)
obs = {}
missing = 0
for row in mcursor:
NT.sts[row[0]] = dict(id=row[0], network=row[1], name=row[2],
tzname='America/Chicago')
fn = "%s/%s.jpg" % (mydir, row[0])
if not os.path.isfile(fn):
missing += 1
if missing > 1:
print 'Missing webcam file: %s' % (fn,)
continue
ticks = os.stat(fn)[stat.ST_MTIME]
valid = (datetime.datetime(1970, 1, 1) +
datetime.timedelta(seconds=ticks))
valid = valid.replace(tzinfo=pytz.timezone("UTC"))
obs[row[0]] = dict(valid=valid)
# Abort out if no obs are found
if len(obs) == 0:
return
tracker = TrackerEngine(IEM.cursor(), PORTFOLIO.cursor(), 10)
tracker.process_network(obs, pname, NT, threshold)
tracker.send_emails()
IEM.commit()
PORTFOLIO.commit()
def do(netname, pname):
"""Do something please"""
mcursor = MESOSITE.cursor()
mcursor.execute("""
SELECT id, network, name from webcams where
network = %s
and online ORDER by id ASC
""", (netname, ))
NT = NetworkTable(None)
obs = {}
for row in mcursor:
NT.sts[row[0]] = dict(id=row[0], network=row[1], name=row[2],
tzname='America/Chicago')
fn = "%s/%s.jpg" % (mydir, row[0])
if not os.path.isfile(fn):
print 'Missing webcam file: %s' % (fn,)
continue
ticks = os.stat(fn)[stat.ST_MTIME]
valid = (datetime.datetime(1970, 1, 1) +
datetime.timedelta(seconds=ticks))
valid = valid.replace(tzinfo=pytz.timezone("UTC"))
obs[row[0]] = dict(valid=valid)
# Abort out if no obs are found
if len(obs) == 0:
return
tracker = TrackerEngine(IEM.cursor(), PORTFOLIO.cursor(), 10)
tracker.process_network(obs, pname, NT, threshold)
tracker.send_emails()
IEM.commit()
PORTFOLIO.commit()
def plotter(fdict):
""" Go """
import seaborn as sns
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
phenomena = ctx['phenomena']
significance = ctx['significance']
opt = ctx['opt']
state = ctx['state']
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
wfo_limiter = (" and wfo = '%s' "
) % (station if len(station) == 3 else station[1:],)
if station == '_ALL':
wfo_limiter = ''
if opt == 'state':
wfo_limiter = " and substr(ugc, 1, 2) = '%s'" % (state, )
# NB we added a hack here that may lead to some false positives when events
# cross over months, sigh, recall the 2017 eventid pain
df = read_sql("""
with data as (
SELECT distinct
extract(year from issue)::int as yr,
extract(month from issue)::int as mo, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + """
GROUP by yr, mo, wfo, eventid)
SELECT yr, mo, count(*) from data GROUP by yr, mo ORDER by yr, mo ASC
""", pgconn, params=(phenomena, significance), index_col=None)
if df.empty:
raise ValueError("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 8))
df2 = df.pivot('yr', 'mo', 'count')
df2 = df2.reindex(
index=range(df2.index.min(), df2.index.max() + 1),
columns=range(1, 13))
title = "NWS %s" % (nt.sts[station]['name'], )
if opt == 'state':
title = ("NWS Issued for Counties/Zones for State of %s"
) % (reference.state_names[state],)
title += ("\n%s (%s.%s) Issued by Year,Month"
) % (vtec.get_ps_string(phenomena, significance),
phenomena, significance)
ax.set_title(title)
sns.heatmap(df2, annot=True, fmt=".0f", linewidths=.5, ax=ax, vmin=1)
ax.set_xticks(np.arange(12) + 0.5)
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_ylabel("Year")
ax.set_xlabel("Month")
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DMX')
limit = fdict.get('limit', 'no')
phenomena = fdict.get('phenomena', 'FF')
significance = fdict.get('significance', 'W')
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
lastdoy = 367
if limit.lower() == 'yes':
lastdoy = int(datetime.datetime.today().strftime("%j")) + 1
wfo_limiter = " and wfo = '%s' " % (station if len(station) == 3 else station[1:],)
if station == '_ALL':
wfo_limiter = ''
doy_limiter = ''
if limit == 'yes':
doy_limiter = (" and extract(doy from issue) < "
"extract(doy from 'TODAY'::date) ")
cursor.execute("""
with data as (
SELECT distinct extract(year from issue) as yr, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + doy_limiter + """)
SELECT yr, count(*) from data GROUP by yr ORDER by yr ASC
""", (phenomena, significance))
years = []
counts = []
for row in cursor:
years.append(row[0])
counts.append(row[1])
if len(years) == 0:
return("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1)
ax.bar(np.array(years)-0.4, counts)
ax.set_xlim(min(years)-0.5, max(years)+0.5)
ax.grid(True)
ax.set_ylabel("Yearly Count")
ax.set_title(("NWS %s\n%s %s (%s.%s) Count"
) % (nt.sts[station]['name'], vtec._phenDict[phenomena],
vtec._sigDict[significance], phenomena, significance))
if limit == 'yes':
ax.set_xlabel(("thru approximately %s"
) % (datetime.date.today().strftime("%-d %b"), ))
return fig
def plotter(fdict):
""" Go """
pgconn = psycopg2.connect(database="postgis", host="iemdb", user="******")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get("station", "DMX")
limit = fdict.get("limit", "no")
phenomena = fdict.get("phenomena", "FF")
significance = fdict.get("significance", "W")
nt = NetworkTable("WFO")
nt.sts["_ALL"] = {"name": "All Offices"}
lastdoy = 367
if limit.lower() == "yes":
lastdoy = int(datetime.datetime.today().strftime("%j")) + 1
wfo_limiter = " and wfo = '%s' " % (station,)
if station == "_ALL":
wfo_limiter = ""
doy_limiter = ""
if limit == "yes":
doy_limiter = " and extract(doy from issue) < " "extract(doy from 'TODAY'::date) "
cursor.execute(
"""
with data as (
SELECT distinct extract(year from issue) as yr, wfo, eventid
from warnings where phenomena = %s and significance = %s
"""
+ wfo_limiter
+ doy_limiter
+ """)
SELECT yr, count(*) from data GROUP by yr ORDER by yr ASC
""",
(phenomena, significance),
)
years = []
counts = []
for row in cursor:
years.append(row[0])
counts.append(row[1])
(fig, ax) = plt.subplots(1, 1)
ax.bar(np.array(years) - 0.4, counts)
ax.set_xlim(min(years) - 0.5, max(years) + 0.5)
ax.grid(True)
ax.set_ylabel("Yearly Count")
ax.set_title(
("NWS %s\n%s %s (%s.%s) Count")
% (nt.sts[station]["name"], vtec._phenDict[phenomena], vtec._sigDict[significance], phenomena, significance)
)
if limit == "yes":
ax.set_xlabel(("thru approximately %s") % (datetime.date.today().strftime("%-d %b"),))
return fig
def runYear(year):
"""Do Work."""
# Grab the data
now = datetime.datetime.now()
nt = NetworkTable("IACLIMATE")
# Help plot readability
nt.sts["IA0200"]["lon"] = -93.4
nt.sts["IA5992"]["lat"] = 41.65
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
sql = """SELECT station, sum(precip) as total, max(day)
from alldata_ia WHERE year = %s and
station != 'IA0000' and
substr(station,3,1) != 'C' and
precip is not null GROUP by station""" % (
year,
)
lats = []
lons = []
vals = []
labels = []
ccursor.execute(sql)
for row in ccursor:
sid = row["station"]
if sid not in nt.sts:
continue
labels.append(sid[2:])
lats.append(nt.sts[sid]["lat"])
lons.append(nt.sts[sid]["lon"])
vals.append(row["total"])
maxday = row["max"]
# pre-1900 dates cause troubles
lastday = "31 December"
if now.year == maxday.year:
lastday = maxday.strftime("%d %B")
mp = MapPlot(
title="Total Precipitation [inch] (%s)" % (year,),
subtitle="1 January - %s" % (lastday,),
axisbg="white",
)
mp.plot_values(
lons,
lats,
vals,
labels=labels,
fmt="%.2f",
labeltextsize=8,
labelcolor="tan",
)
pqstr = "plot m %s bogus %s/summary/total_precip.png png" % (
now.strftime("%Y%m%d%H%M"),
year,
)
mp.postprocess(pqstr=pqstr)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
limit = ctx['limit']
phenomena = ctx['phenomena']
significance = ctx['significance']
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
wfo_limiter = (" and wfo = '%s' ") % (station if len(station) == 3 else
station[1:], )
if station == '_ALL':
wfo_limiter = ''
doy_limiter = ''
title = "Entire Year"
if limit.lower() == 'yes':
title = "thru ~%s" % (datetime.date.today().strftime("%-d %b"), )
doy_limiter = (" and extract(doy from issue) <= "
"extract(doy from 'TODAY'::date) ")
df = read_sql("""
with data as (
SELECT distinct extract(year from issue) as yr, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + doy_limiter + """)
SELECT yr, count(*) from data GROUP by yr ORDER by yr ASC
""",
pgconn,
params=(phenomena, significance))
if df.empty:
raise ValueError("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1)
ax.bar(df['yr'], df['count'], align='center')
ax.set_xlim(df['yr'].min() - 0.5, df['yr'].max() + 0.5)
ax.grid(True)
ax.set_ylabel("Yearly Count")
ax.set_title(
("NWS %s [%s]\n%s (%s.%s) Count") %
(nt.sts[station]['name'], title,
vtec.get_ps_string(phenomena, significance), phenomena, significance))
if limit == 'yes':
ax.set_xlabel(("thru approximately %s") %
(datetime.date.today().strftime("%-d %b"), ))
return fig, df
def runYear(year):
"""Do as I say"""
# Grab the data
now = datetime.datetime.now()
nt = NetworkTable("IACLIMATE")
nt.sts["IA0200"]["lon"] = -93.4
nt.sts["IA5992"]["lat"] = 41.65
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
lats = []
lons = []
vals = []
labels = []
ccursor.execute(
"""
SELECT station,
sum(case when precip > 0.009 then 1 else 0 end) as days, max(day)
from alldata_ia WHERE year = %s and substr(station,3,1) != 'C'
and station != 'IA0000' GROUP by station
""",
(year, ),
)
for row in ccursor:
sid = row["station"].upper()
if sid not in nt.sts:
continue
labels.append(sid[2:])
lats.append(nt.sts[sid]["lat"])
lons.append(nt.sts[sid]["lon"])
vals.append(row["days"])
maxday = row["max"]
mp = MapPlot(
title="Days with Measurable Precipitation (%s)" % (year, ),
subtitle="Map valid January 1 - %s" % (maxday.strftime("%b %d")),
axisbg="white",
)
mp.plot_values(
lons,
lats,
vals,
fmt="%.0f",
labels=labels,
labeltextsize=8,
labelcolor="tan",
)
mp.drawcounties()
pqstr = "plot m %s bogus %s/summary/precip_days.png png" % (
now.strftime("%Y%m%d%H%M"),
year,
)
mp.postprocess(pqstr=pqstr)
def test_workflow(pcursor, icursor):
""" Test that we can do stuff! """
sid1 = 'XXX'
sid2 = 'YYY'
pnetwork = 'xxxxxx'
nt = NetworkTable(None)
nt.sts[sid1] = dict(name='XXX Site Name', network='IA_XXXX',
tzname='America/Chicago')
nt.sts[sid2] = dict(name='YYY Site Name', network='IA_XXXX',
tzname='America/Chicago')
valid = datetime.datetime.utcnow()
valid = valid.replace(tzinfo=pytz.timezone("UTC"))
threshold = valid - datetime.timedelta(hours=3)
obs = {sid1: {'valid': valid},
sid2: {'valid': valid - datetime.timedelta(hours=6)}}
# Create dummy iem_site_contacts
pcursor.execute("""
INSERT into iem_site_contacts
(portfolio, s_mid, email) VALUES (%s, %s, %s)
""", (pnetwork, sid1, 'akrherz@localhost'))
pcursor.execute("""
INSERT into iem_site_contacts
(portfolio, s_mid, email) VALUES (%s, %s, %s)
""", (pnetwork, sid2, 'root@localhost'))
# Create some dummy tickets
pcursor.execute("""
INSERT into tt_base (portfolio, s_mid, subject,
status, author) VALUES (%s, %s, %s, %s, %s) RETURNING id
""", (pnetwork, sid1, 'FIXME PLEASE OPEN', 'OPEN', 'mesonet'))
pcursor.execute("""
INSERT into tt_base (portfolio, s_mid, subject,
status, author) VALUES (%s, %s, %s, %s, %s) RETURNING id
""", (pnetwork, sid1, 'FIXME PLEASE CLOSED', 'CLOSED', 'mesonet'))
tracker = TrackerEngine(icursor, pcursor)
tracker.process_network(obs, pnetwork, nt, threshold)
tracker.send_emails(really_send=False)
assert len(tracker.emails) == 1
tracker.emails = {}
obs[sid1]['valid'] = valid - datetime.timedelta(hours=6)
obs[sid2]['valid'] = valid
tracker.process_network(obs, pnetwork, nt, threshold)
tracker.send_emails(really_send=False)
assert len(tracker.emails) == 2
tracker.emails = {}
obs[sid1]['valid'] = valid - datetime.timedelta(hours=6)
obs[sid2]['valid'] = valid
tracker.process_network(obs, pnetwork, nt, threshold)
tracker.send_emails(really_send=False)
assert not tracker.emails
def plotter(fdict):
""" Go """
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DMX')[:4]
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
(fig, ax) = plt.subplots(1, 1, sharex=True)
if station == '_ALL':
cursor.execute("""
with obs as (
SELECT distinct extract(year from issue) as yr,
phenomena, significance from warnings WHERE
phenomena is not null and significance is not null and
issue > '2005-01-01' and issue is not null
)
SELECT yr, count(*) from obs GROUP by yr ORDER by yr ASC
""")
else:
cursor.execute("""
with obs as (
SELECT distinct extract(year from issue) as yr,
phenomena, significance from warnings WHERE
wfo = %s and phenomena is not null and significance is not null
and issue > '2005-01-01' and issue is not null
)
SELECT yr, count(*) from obs GROUP by yr ORDER by yr ASC
""", (station, ))
years = []
count = []
for row in cursor:
years.append(int(row[0]))
count.append(int(row[1]))
ax.bar(np.array(years)-0.4, count, width=0.8, fc='b', ec='b')
for yr, val in zip(years, count):
ax.text(yr, val+1, "%s" % (val,), ha='center')
ax.set_title(("[%s] NWS %s\nCount of Distinct VTEC Phenomena/"
"Significance - %i to %i"
) % (station, nt.sts[station]['name'],
years[0], years[-1]))
ax.grid()
ax.set_ylabel("Count")
return fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
station = fdict.get('station', 'DMX')[:4]
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
(fig, ax) = plt.subplots(1, 1, sharex=True)
if station == '_ALL':
df = read_sql("""
with obs as (
SELECT distinct extract(year from issue) as yr,
phenomena, significance from warnings WHERE
phenomena is not null and significance is not null and
issue > '2005-01-01' and issue is not null
)
SELECT yr as year, count(*) from obs GROUP by yr ORDER by yr ASC
""", pgconn, index_col=None)
else:
df = read_sql("""
with obs as (
SELECT distinct extract(year from issue) as yr,
phenomena, significance from warnings WHERE
wfo = %s and phenomena is not null and significance is not null
and issue > '2005-01-01' and issue is not null
)
SELECT yr as year, count(*) from obs GROUP by yr ORDER by yr ASC
""", pgconn, params=(station, ), index_col=None)
df['wfo'] = station
df['year'] = df['year'].astype('i')
ax.bar(df['year']-0.4, df['count'], width=0.8, fc='b', ec='b')
for yr, val in zip(df['year'], df['count']):
ax.text(yr, val+1, "%s" % (val,), ha='center')
ax.set_title(("[%s] NWS %s\nCount of Distinct VTEC Phenomena/"
"Significance - %i to %i"
) % (station, nt.sts[station]['name'],
df['year'].min(), df['year'].max()))
ax.grid()
ax.set_ylabel("Count")
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
station = fdict.get('station', 'DMX')
limit = fdict.get('limit', 'no')
phenomena = fdict.get('phenomena', 'FF')
significance = fdict.get('significance', 'W')
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
wfo_limiter = (" and wfo = '%s' "
) % (station if len(station) == 3 else station[1:],)
if station == '_ALL':
wfo_limiter = ''
doy_limiter = ''
if limit.lower() == 'yes':
doy_limiter = (" and extract(doy from issue) < "
"extract(doy from 'TODAY'::date) ")
df = read_sql("""
with data as (
SELECT distinct extract(year from issue) as yr, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + doy_limiter + """)
SELECT yr, count(*) from data GROUP by yr ORDER by yr ASC
""", pgconn, params=(phenomena, significance))
if len(df.index) == 0:
return("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1)
ax.bar(df['yr']-0.4, df['count'])
ax.set_xlim(df['yr'].min()-0.5, df['yr'].max()+0.5)
ax.grid(True)
ax.set_ylabel("Yearly Count")
ax.set_title(("NWS %s\n%s %s (%s.%s) Count"
) % (nt.sts[station]['name'], vtec._phenDict[phenomena],
vtec._sigDict[significance], phenomena, significance))
if limit == 'yes':
ax.set_xlabel(("thru approximately %s"
) % (datetime.date.today().strftime("%-d %b"), ))
return fig, df
def workflow(netname, pname):
"""Do something please"""
pgconn_iem = get_dbconn("iem")
pgconn_mesosite = get_dbconn("mesosite")
pgconn_portfolio = get_dbconn("portfolio")
# Now lets check files
mydir = "/home/ldm/data/camera/stills"
threshold = datetime.datetime.utcnow() - datetime.timedelta(hours=2)
threshold = threshold.replace(tzinfo=pytz.UTC)
mcursor = pgconn_mesosite.cursor()
mcursor.execute(
"""
SELECT id, network, name from webcams where
network = %s
and online ORDER by id ASC
""",
(netname, ),
)
nt = NetworkTable(None)
obs = {}
missing = 0
for row in mcursor:
nt.sts[row[0]] = dict(id=row[0],
network=row[1],
name=row[2],
tzname="America/Chicago")
fn = "%s/%s.jpg" % (mydir, row[0])
if not os.path.isfile(fn):
missing += 1
if missing > 1:
print("Missing webcam file: %s" % (fn, ))
continue
ticks = os.stat(fn)[stat.ST_MTIME]
valid = datetime.datetime(1970, 1,
1) + datetime.timedelta(seconds=ticks)
valid = valid.replace(tzinfo=pytz.UTC)
obs[row[0]] = dict(valid=valid)
# Abort out if no obs are found
if not obs:
return
tracker = TrackerEngine(pgconn_iem.cursor(), pgconn_portfolio.cursor(), 10)
tracker.process_network(obs, pname, nt, threshold)
tracker.send_emails()
pgconn_iem.commit()
pgconn_portfolio.commit()
def main():
"""Go Main Go"""
today = datetime.date.today()
now = today.replace(year=2000)
nt = NetworkTable("IACLIMATE")
nt.sts["IA0200"]["lon"] = -93.6
nt.sts["IA5992"]["lat"] = 41.65
coop = get_dbconn("coop", user="******")
obs = []
cursor = coop.cursor()
cursor.execute(
"SELECT station, max_high, min_low from climate WHERE valid = %s "
"and substr(station,0,3) = 'IA'",
(now,),
)
for row in cursor:
sid = row[0]
if sid[2] == "C" or sid[2:] == "0000" or sid not in nt.sts:
continue
obs.append(
dict(
id=sid[2:],
lat=nt.sts[sid]["lat"],
lon=nt.sts[sid]["lon"],
tmpf=row[1],
dwpf=row[2],
)
)
mp = MapPlot(
title=("Record High + Low Temperature [F] (1893-%s)") % (today.year,),
subtitle="For Date: %s" % (now.strftime("%d %b"),),
continentalcolor="white",
)
mp.drawcounties()
mp.plot_station(obs)
pqstr = (
"plot ac %s0000 climate/iowa_today_rec_hilo_pt.png "
"coop_rec_temp.png png"
) % (today.strftime("%Y%m%d"),)
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
nt = NetworkTable("IACLIMATE")
nt.sts["IA0200"]["lon"] = -93.6
nt.sts["IA5992"]["lat"] = 41.65
coop = get_dbconn("coop", user="******")
# Compute normal from the climate database
sql = """
SELECT station, high, low from climate WHERE valid = '2000-%s'
and substr(station,0,3) = 'IA'
""" % (now.strftime("%m-%d"), )
obs = []
cursor = coop.cursor(cursor_factory=psycopg2.extras.DictCursor)
cursor.execute(sql)
for row in cursor:
sid = row["station"]
if sid[2] == "C" or sid[2:] == "0000" or sid not in nt.sts:
continue
obs.append(
dict(
id=sid[2:],
lat=nt.sts[sid]["lat"],
lon=nt.sts[sid]["lon"],
tmpf=row["high"],
dwpf=row["low"],
))
mp = MapPlot(
title=("Average High + Low Temperature [F] (1893-%s)") % (now.year, ),
subtitle="For Date: %s" % (now.strftime("%d %b"), ),
axisbg="white",
)
mp.drawcounties()
mp.plot_station(obs)
pqstr = ("plot ac %s0000 climate/iowa_today_avg_hilo_pt.png "
"coop_avg_temp.png png") % (now.strftime("%Y%m%d"), )
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
nt = NetworkTable('IACLIMATE')
nt.sts["IA0200"]["lon"] = -93.6
nt.sts["IA5992"]["lat"] = 41.65
coop = get_dbconn('coop', user='******')
# Compute normal from the climate database
sql = """
SELECT station, max_high, min_low from climate WHERE valid = '2000-%s'
and substr(station,0,3) = 'IA'
""" % (now.strftime("%m-%d"), )
obs = []
cursor = coop.cursor()
cursor.execute(sql)
for row in cursor:
sid = row[0]
if sid[2] == 'C' or sid[2:] == '0000' or sid not in nt.sts:
continue
obs.append(
dict(id=sid[2:],
lat=nt.sts[sid]['lat'],
lon=nt.sts[sid]['lon'],
tmpf=row[1],
dwpf=row[2]))
mp = MapPlot(title=("Record High + Low Temperature [F] (1893-%s)") %
(now.year, ),
subtitle="For Date: %s" % (now.strftime("%d %b"), ),
continentalcolor='white')
mp.drawcounties()
mp.plot_station(obs)
pqstr = ("plot ac %s0000 climate/iowa_today_rec_hilo_pt.png "
"coop_rec_temp.png png") % (now.strftime("%Y%m%d"), )
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DMX')
limit = fdict.get('limit', 'no')
combo = fdict.get('c', 'svrtor')
phenomena = fdict.get('phenomena', 'TO')
significance = fdict.get('significance', 'W')
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
lastdoy = 367
if limit.lower() == 'yes':
lastdoy = int(datetime.datetime.today().strftime("%j")) + 1
if combo == 'svrtor':
if station == '_ALL':
cursor.execute("""
with counts as (
select extract(year from issue) as yr,
extract(doy from issue) as doy, count(*) from sbw
where status = 'NEW' and phenomena in ('SV', 'TO')
and significance = 'W' and issue > '2003-01-01'
and extract(doy from issue) < %s
GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr
ORDER by doy ASC)
from counts ORDER by yr ASC, doy ASC
""", (lastdoy, ))
else:
cursor.execute("""
with counts as (
select extract(year from issue) as yr,
extract(doy from issue) as doy, count(*) from sbw
where status = 'NEW' and phenomena in ('SV', 'TO')
and significance = 'W' and wfo = %s
and issue > '2003-01-01'
and extract(doy from issue) < %s
GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr
ORDER by doy ASC)
from counts ORDER by yr ASC, doy ASC
""", (station, lastdoy))
else:
if station == '_ALL':
cursor.execute("""
WITH data as (
SELECT extract(year from issue) as yr,
issue, eventid, wfo from warnings WHERE
phenomena = %s and significance = %s
and extract(doy from issue) < %s
and issue > '2003-01-01'),
agg1 as (
SELECT yr, min(issue) as min_issue, eventid, wfo from data
GROUP by yr, eventid, wfo),
agg2 as (
SELECT yr, extract(doy from min_issue) as doy, count(*)
from agg1 GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from agg2 ORDER by yr ASC, doy ASC
""", (phenomena, significance, lastdoy))
else:
cursor.execute("""
WITH data as (
SELECT extract(year from issue) as yr,
issue, eventid, wfo from warnings WHERE
phenomena = %s and significance = %s and wfo = %s
and extract(doy from issue) < %s
and issue > '2003-01-01'),
agg1 as (
SELECT yr, min(issue) as min_issue, eventid, wfo from data
GROUP by yr, eventid, wfo),
agg2 as (
SELECT yr, extract(doy from min_issue) as doy, count(*)
from agg1 GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from agg2 ORDER by yr ASC, doy ASC
""", (phenomena, significance, station, lastdoy))
data = {}
for yr in range(2003, datetime.datetime.now().year + 1):
data[yr] = {'doy': [], 'counts': []}
rows = []
for row in cursor:
data[row[0]]['doy'].append(row[1])
data[row[0]]['counts'].append(row[2])
rows.append(dict(year=row[0], day_of_year=row[1], count=row[2]))
# append on a lastdoy value so all the plots go to the end
for yr in range(2003, datetime.datetime.now().year):
if len(data[yr]['doy']) == 0 or data[yr]['doy'][-1] >= lastdoy:
continue
data[yr]['doy'].append(lastdoy)
data[yr]['counts'].append(data[yr]['counts'][-1])
data[datetime.datetime.now().year]['doy'].append(
int(datetime.datetime.today().strftime("%j")) + 1)
data[datetime.datetime.now().year]['counts'].append(
data[datetime.datetime.now().year]['counts'][-1])
df = pd.DataFrame(rows)
(fig, ax) = plt.subplots(1, 1)
ann = []
for yr in range(2003, datetime.datetime.now().year + 1):
if len(data[yr]['doy']) < 2:
continue
l = ax.plot(data[yr]['doy'], data[yr]['counts'], lw=2,
label="%s (%s)" % (str(yr), data[yr]['counts'][-1]),
drawstyle='steps-post')
ann.append(
ax.text(data[yr]['doy'][-1]+1, data[yr]['counts'][-1],
"%s" % (yr,), color='w', va='center',
fontsize=10, bbox=dict(facecolor=l[0].get_color(),
edgecolor=l[0].get_color()))
)
mask = np.zeros(fig.canvas.get_width_height(), bool)
fig.canvas.draw()
attempts = 10
while len(ann) > 0 and attempts > 0:
attempts -= 1
removals = []
for a in ann:
bbox = a.get_window_extent()
x0 = int(bbox.x0)
x1 = int(math.ceil(bbox.x1))
y0 = int(bbox.y0)
y1 = int(math.ceil(bbox.y1))
s = np.s_[x0:x1+1, y0:y1+1]
if np.any(mask[s]):
a.set_position([a._x-int(lastdoy/14), a._y])
else:
mask[s] = True
removals.append(a)
for rm in removals:
ann.remove(rm)
ax.legend(loc=2, ncol=2, fontsize=10)
ax.set_xlim(1, 367)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_ylabel("Accumulated Count")
title = "%s %s" % (vtec._phenDict[phenomena], vtec._sigDict[significance])
if combo == 'svrtor':
title = "Severe Thunderstorm + Tornado Warning"
ax.set_title(("NWS %s\n %s Count"
) % (nt.sts[station]['name'], title))
ax.set_xlim(0, lastdoy)
if lastdoy < 367:
ax.set_xlabel(("thru approximately %s"
) % (datetime.date.today().strftime("%-d %b"), ))
return fig, df
'IA2070': 'DVN',
'IA2203': 'DSM',
'IA2367': 'DBQ',
'IA2723': 'EST',
'IA4106': 'IOW',
'IA4587': 'LWD',
'IA5199': 'MIW',
'IA5235': 'MCW',
'IA6389': 'OTM',
'IA7708': 'SUX',
'IA7844': 'SPW',
'IA8706': 'ALO',
}
# Pre-compute the grid location of each climate site
nt = NetworkTable("%sCLIMATE" % (state.upper(),))
for sid in nt.sts.keys():
i, j = iemre.find_ij(nt.sts[sid]['lon'], nt.sts[sid]['lat'])
nt.sts[sid]['gridi'] = i
nt.sts[sid]['gridj'] = j
for key in ['high', 'low', 'precip', 'snow', 'snowd']:
nt.sts[sid][key] = None
def estimate_precip(ts):
"""Estimate precipitation based on IEMRE"""
idx = iemre.daily_offset(ts)
nc = netCDF4.Dataset("/mesonet/data/iemre/%s_mw_daily.nc" % (ts.year, ),
'r')
grid12 = nc.variables['p01d_12z'][idx, :, :] / 25.4
grid00 = nc.variables['p01d'][idx, :, :] / 25.4
'SD6597': 'PIR',
'SD6947': 'RAP',
# SD6947 | KUNR | RAPID CITY 4NW | Rapid City
# Wisconsin
'WI5479': 'MKE',
'WI3269': 'GRB',
'WI7113': 'RHI',
'WI2428': 'EAU',
'WI4961': 'MSN',
'WI4370': 'LSE',
'WI2428': 'AUW',
}
# Pre-compute the grid location of each climate site
nt = NetworkTable("%sCLIMATE" % (state.upper(),))
for sid in nt.sts.keys():
i, j = iemre.find_ij(nt.sts[sid]['lon'], nt.sts[sid]['lat'])
nt.sts[sid]['gridi'] = i
nt.sts[sid]['gridj'] = j
for key in ['high', 'low', 'precip', 'snow', 'snowd']:
nt.sts[sid][key] = None
def estimate_precip(ts):
"""Estimate precipitation based on IEMRE"""
idx = iemre.daily_offset(ts)
nc = netCDF4.Dataset("/mesonet/data/iemre/%s_mw_daily.nc" % (ts.year, ),
'r')
grid12 = nc.variables['p01d_12z'][idx, :, :] / 25.4
grid00 = nc.variables['p01d'][idx, :, :] / 25.4
"""
Generate a map of Number of days with precip
"""
import sys
from pyiem.plot import MapPlot
import datetime
now = datetime.datetime.now()
from pyiem.network import Table as NetworkTable
nt = NetworkTable("IACLIMATE")
nt.sts["IA0200"]["lon"] = -93.4
nt.sts["IA5992"]["lat"] = 41.65
import psycopg2.extras
COOP = psycopg2.connect(database='coop', host='iemdb', user='******')
ccursor = COOP.cursor(cursor_factory=psycopg2.extras.DictCursor)
def runYear(year):
# Grab the data
sql = """SELECT station,
sum(case when precip >= 0.01 then 1 else 0 end) as days, max(day)
from alldata_ia WHERE year = %s and substr(station,3,1) != 'C'
and station != 'IA0000' GROUP by station""" % (year,)
lats = []
lons = []
vals = []
labels = []
ccursor.execute( sql )
for row in ccursor:
sid = row['station'].upper()
import psycopg2.extras
import subprocess
import tempfile
import calendar
import pytz
utc = datetime.datetime.utcnow()
utc = utc.replace(tzinfo=pytz.timezone("UTC"))
tstr = utc.strftime("%Y%m%d%H%M")
now = utc.astimezone(pytz.timezone("America/Chicago"))
IEM = psycopg2.connect(database='iem', host='iemdb', user='******')
icursor = IEM.cursor(cursor_factory=psycopg2.extras.DictCursor)
st = NetworkTable(['KCCI', 'KELO', 'KIMT'])
st.sts["SMAI4"]["plot_name"] = "M-town"
st.sts["SBZI4"]["plot_name"] = "Zoo"
st.sts["SMSI4"]["plot_name"] = "Barnum"
st.sts["STQI4"]["plot_name"] = "Tama"
st.sts["SBOI4"]["plot_name"] = "Boone"
def altiTxt(d):
if d == "":
return "S"
if d < 0:
return "F"
if d > 0:
return "R"
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.colors as mpcolors
import matplotlib.patheffects as PathEffects
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
phenomena = ctx['phenomena']
significance = ctx['significance']
opt = ctx['opt']
state = ctx['state']
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
wfo_limiter = (" and wfo = '%s' ") % (station if len(station) == 3 else
station[1:], )
if station == '_ALL':
wfo_limiter = ''
if opt == 'state':
wfo_limiter = " and substr(ugc, 1, 2) = '%s'" % (state, )
df = read_sql("""
with data as (
SELECT distinct extract(year from issue) as yr2,
min(issue) as i, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + """ and issue is not null
GROUP by yr2, wfo, eventid)
SELECT extract(year from i) as yr, extract(month from i) as mo,
count(*) from data GROUP by yr, mo ORDER by yr, mo ASC
""",
pgconn,
params=(phenomena, significance),
index_col=None)
if len(df.index) == 0:
raise Exception("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 8))
minyear = df['yr'].min()
maxyear = df['yr'].max()
data = np.zeros((int(maxyear - minyear + 1), 12))
for _, row in df.iterrows():
data[int(row['yr'] - minyear), int(row['mo'] - 1)] = row['count']
txt = ax.text(row['mo'],
row['yr'],
"%.0f" % (row['count'], ),
va='center',
ha='center',
color='white')
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="k")])
cmap = plt.get_cmap('jet')
cmap.set_under('white')
maxval = max([df['count'].max(), 11])
bounds = np.linspace(1, maxval, 10, dtype='i')
norm = mpcolors.BoundaryNorm(bounds, cmap.N)
res = ax.imshow(data,
extent=[0.5, 12.5, maxyear + 0.5, minyear - 0.5],
interpolation='nearest',
aspect='auto',
norm=norm,
cmap=cmap)
fig.colorbar(res, label='count')
ax.grid(True)
ax.set_xticks(range(1, 13))
ax.set_xticklabels(calendar.month_abbr[1:])
title = "NWS %s" % (nt.sts[station]['name'], )
if opt == 'state':
title = ("NWS Issued for Counties/Zones for State of %s") % (
reference.state_names[state], )
title += ("\n%s %s (%s.%s) Issued by Year,Month") % (
vtec._phenDict[phenomena], vtec._sigDict[significance], phenomena,
significance)
ax.set_title(title)
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
station = fdict.get('station', 'DMX')[:4]
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
fig = plt.figure(figsize=(8,
14 if station != '_ALL' else 21))
ax = [None, None]
ax[0] = plt.axes([0.1, 0.75, 0.85, 0.2])
ax[1] = plt.axes([0.1, 0.05, 0.85, 0.65])
if station == '_ALL':
df = read_sql("""
SELECT distinct extract(year from issue) as year,
phenomena, significance from warnings WHERE
phenomena is not null and significance is not null and
issue > '2005-01-01' and issue is not null
""", pgconn, index_col=None)
else:
df = read_sql("""
SELECT distinct extract(year from issue) as year,
phenomena, significance from warnings WHERE
wfo = %s and phenomena is not null and significance is not null
and issue > '2005-01-01' and issue is not null
""", pgconn, params=(station, ), index_col=None)
df['wfo'] = station
df['year'] = df['year'].astype('i')
gdf = df.groupby('year').count()
ax[0].bar(gdf.index.values, gdf['wfo'], width=0.8, fc='b', ec='b',
align='center')
for yr, row in gdf.iterrows():
ax[0].text(yr, row['wfo'] + 1, "%s" % (row['wfo'],), ha='center')
ax[0].set_title(("[%s] NWS %s\nCount of Distinct VTEC Phenomena/"
"Significance - %i to %i"
) % (station, nt.sts[station]['name'],
df['year'].min(), df['year'].max()))
ax[0].grid()
ax[0].set_ylabel("Count")
ax[0].set_xlim(gdf.index.values.min() - 0.5,
gdf.index.values.max() + 0.5)
pos = {}
i = 1
df.sort_values(['phenomena', 'significance'], inplace=True)
for _, row in df.iterrows():
key = "%s.%s" % (row['phenomena'], row['significance'])
if key not in pos:
pos[key] = i
i += 1
ax[1].text(row['year'], pos[key], key, ha='center',
va='center', fontsize=10,
bbox=dict(color='white'))
ax[1].set_title("VTEC <Phenomena.Significance> Issued by Year")
ax[1].set_ylim(0, i)
ax[1].grid(True)
ax[1].set_xlim(gdf.index.values.min() - 0.5,
gdf.index.values.max() + 0.5)
return fig, df
def runYear(year):
"""Hack"""
now = datetime.datetime.now()
nt = NetworkTable("IACLIMATE")
nt.sts["IA0200"]["lon"] = -93.4
nt.sts["IA5992"]["lat"] = 41.65
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
sql = """SELECT station, avg(high) as avg_high, avg(low) as avg_low,
avg( (high+low)/2 ) as avg_tmp, max(day)
from alldata_ia WHERE year = %s and station != 'IA0000' and
high is not Null and low is not Null and substr(station,3,1) != 'C'
GROUP by station""" % (
year,
)
ccursor.execute(sql)
# Plot Average Highs
lats = []
lons = []
vals = []
labels = []
for row in ccursor:
sid = row["station"].upper()
if sid not in nt.sts:
continue
labels.append(sid[2:])
lats.append(nt.sts[sid]["lat"])
lons.append(nt.sts[sid]["lon"])
vals.append(row["avg_high"])
maxday = row["max"]
# ---------- Plot the points
mp = MapPlot(
title="Average Daily High Temperature [F] (%s)" % (year,),
subtitle="1 January - %s" % (maxday.strftime("%d %B"),),
axisbg="white",
)
mp.plot_values(
lons,
lats,
vals,
labels=labels,
labeltextsize=8,
labelcolor="tan",
fmt="%.1f",
)
pqstr = "plot m %s bogus %s/summary/avg_high.png png" % (
now.strftime("%Y%m%d%H%M"),
year,
)
mp.postprocess(pqstr=pqstr)
mp.close()
# Plot Average Lows
lats = []
lons = []
vals = []
labels = []
ccursor.execute(sql)
for row in ccursor:
sid = row["station"].upper()
if sid not in nt.sts:
continue
labels.append(sid[2:])
lats.append(nt.sts[sid]["lat"])
lons.append(nt.sts[sid]["lon"])
vals.append(row["avg_low"])
# ---------- Plot the points
mp = MapPlot(
title="Average Daily Low Temperature [F] (%s)" % (year,),
subtitle="1 January - %s" % (maxday.strftime("%d %B"),),
axisbg="white",
)
mp.plot_values(
lons,
lats,
vals,
labels=labels,
labeltextsize=8,
labelcolor="tan",
fmt="%.1f",
)
pqstr = "plot m %s bogus %s/summary/avg_low.png png" % (
now.strftime("%Y%m%d%H%M"),
year,
)
mp.postprocess(pqstr=pqstr)
mp.close()
# Plot Average Highs
lats = []
lons = []
vals = []
labels = []
ccursor.execute(sql)
for row in ccursor:
sid = row["station"].upper()
if sid not in nt.sts:
continue
labels.append(sid[2:])
lats.append(nt.sts[sid]["lat"])
lons.append(nt.sts[sid]["lon"])
vals.append(row["avg_tmp"])
# ---------- Plot the points
mp = MapPlot(
title="Average Daily Temperature [F] (%s)" % (year,),
subtitle="1 January - %s" % (maxday.strftime("%d %B"),),
axisbg="white",
)
mp.plot_values(
lons,
lats,
vals,
labels=labels,
labeltextsize=8,
labelcolor="tan",
fmt="%.1f",
)
pqstr = "plot m %s bogus %s/summary/avg_temp.png png" % (
now.strftime("%Y%m%d%H%M"),
year,
)
mp.postprocess(pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
pgconn = get_dbconn('postgis')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
limit = ctx['limit']
combo = ctx['c']
phenomena = ctx['phenomena'][:2]
significance = ctx['significance'][:2]
opt = ctx['opt']
state = ctx['state'][:2]
syear = ctx['syear']
eyear = ctx['eyear']
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
lastdoy = 367
if limit.lower() == 'yes':
lastdoy = int(datetime.datetime.today().strftime("%j")) + 1
wfolimiter = " and wfo = '%s' " % (station, )
if opt == 'state':
wfolimiter = " and substr(ugc, 1, 2) = '%s' " % (state, )
if opt == 'wfo' and station == '_ALL':
wfolimiter = ''
eventlimiter = ""
if combo == 'svrtor':
eventlimiter = " or (phenomena = 'SV' and significance = 'W') "
phenomena = 'TO'
significance = 'W'
cursor.execute(
"""
WITH data as (
SELECT extract(year from issue) as yr,
issue, phenomena, significance, eventid, wfo from warnings WHERE
((phenomena = %s and significance = %s) """ + eventlimiter + """)
and extract(year from issue) >= %s and
extract(year from issue) <= %s
and extract(doy from issue) <= %s """ + wfolimiter + """),
agg1 as (
SELECT yr, min(issue) as min_issue, eventid, wfo, phenomena,
significance from data
GROUP by yr, eventid, wfo, phenomena, significance),
agg2 as (
SELECT yr, extract(doy from min_issue) as doy, count(*)
from agg1 GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from agg2 ORDER by yr ASC, doy ASC
""", (phenomena, significance, syear, eyear, lastdoy))
data = {}
for yr in range(syear, eyear + 1):
data[yr] = {'doy': [0], 'counts': [0]}
rows = []
for row in cursor:
data[row[0]]['doy'].append(row[1])
data[row[0]]['counts'].append(row[2])
rows.append(dict(year=row[0], day_of_year=row[1], count=row[2]))
# append on a lastdoy value so all the plots go to the end
for yr in range(syear, eyear):
if len(data[yr]['doy']) == 1 or data[yr]['doy'][-1] >= lastdoy:
continue
data[yr]['doy'].append(lastdoy)
data[yr]['counts'].append(data[yr]['counts'][-1])
if data[eyear]['doy']:
data[eyear]['doy'].append(
int(datetime.datetime.today().strftime("%j")) + 1)
data[eyear]['counts'].append(data[eyear]['counts'][-1])
df = pd.DataFrame(rows)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ann = []
for yr in range(syear, eyear + 1):
if len(data[yr]['doy']) < 2:
continue
lp = ax.plot(data[yr]['doy'],
data[yr]['counts'],
lw=2,
label="%s (%s)" % (str(yr), data[yr]['counts'][-1]),
drawstyle='steps-post')
ann.append(
ax.text(data[yr]['doy'][-1] + 1,
data[yr]['counts'][-1],
"%s" % (yr, ),
color='w',
va='center',
fontsize=10,
bbox=dict(facecolor=lp[0].get_color(),
edgecolor=lp[0].get_color())))
mask = np.zeros(fig.canvas.get_width_height(), bool)
fig.canvas.draw()
attempts = 10
while ann and attempts > 0:
attempts -= 1
removals = []
for a in ann:
bbox = a.get_window_extent()
x0 = int(bbox.x0)
x1 = int(math.ceil(bbox.x1))
y0 = int(bbox.y0)
y1 = int(math.ceil(bbox.y1))
s = np.s_[x0:x1 + 1, y0:y1 + 1]
if np.any(mask[s]):
a.set_position([a._x - int(lastdoy / 14), a._y])
else:
mask[s] = True
removals.append(a)
for rm in removals:
ann.remove(rm)
ax.legend(loc=2, ncol=2, fontsize=10)
ax.set_xlim(1, 367)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_ylabel("Accumulated Count")
ax.set_ylim(bottom=0)
title = vtec.get_ps_string(phenomena, significance)
if combo == 'svrtor':
title = "Severe Thunderstorm + Tornado Warning"
ptitle = "%s" % (nt.sts[station]['name'], )
if opt == 'state':
ptitle = ("NWS Issued for Counties/Parishes in %s") % (
reference.state_names[state], )
ax.set_title(("%s\n %s Count") % (ptitle, title))
ax.set_xlim(0, lastdoy)
if lastdoy < 367:
ax.set_xlabel(("thru approximately %s") %
(datetime.date.today().strftime("%-d %B"), ))
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('postgis')
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
ctx = get_autoplot_context(fdict, get_description())
opt = ctx['opt']
station = ctx['station']
state = ctx['state']
date1 = ctx.get('date1', datetime.date(2010, 4, 1))
date2 = ctx.get(
'date2', datetime.date.today() + datetime.timedelta(days=1))
pgconn = get_dbconn('postgis')
wfo_limiter = ("and wfo = '%s' "
) % (station if len(station) == 3 else station[1:],)
if station == '_ALL':
wfo_limiter = ''
sql = """
select windtag, hailtag,
min(issue) as min_issue, max(issue) as max_issue, count(*)
from sbw WHERE issue >= '%s' and issue <= '%s'
%s
and (windtag > 0 or hailtag > 0)
and status = 'NEW' and phenomena = 'SV'
GROUP by windtag, hailtag
""" % (date1, date2, wfo_limiter)
supextra = ""
if opt == 'wfo' and station != '_ALL':
supextra = "For warnings issued by %s %s.\n" % (
station, nt.sts[station]['name'])
if opt == 'state':
supextra = (
"For warnings that covered some portion of %s.\n"
) % (state_names[state], )
sql = """
SELECT windtag, hailtag,
min(issue) as min_issue, max(issue) as max_issue, count(*)
from sbw w, states s
WHERE issue >= '%s' and issue <= '%s' and
s.state_abbr = '%s' and ST_Intersects(s.the_geom, w.geom) and
(windtag > 0 or hailtag > 0)
and status = 'NEW' and phenomena = 'SV'
GROUP by windtag, hailtag
""" % (date1, date2, state)
df = read_sql(sql, pgconn, index_col=None)
minvalid = df['min_issue'].min()
maxvalid = df['max_issue'].max()
df.fillna(0, inplace=True)
total = df['count'].sum()
uniquehail = df['hailtag'].unique().tolist()
uniquehail.sort()
uniquehail = uniquehail[::-1]
uniquewind = df['windtag'].astype(int).unique().tolist()
uniquewind.sort()
gdf = df.set_index(['hailtag', 'windtag'])
(fig, ax) = plt.subplots(figsize=(8, 6))
for (hailtag, windtag), row in gdf.iterrows():
y = uniquehail.index(hailtag)
x = uniquewind.index(windtag)
val = row['count'] / total * 100.
ax.text(x, y, "%.2f" % (val, ), ha='center', fontsize=FONTSIZE,
color='r' if val >= 10 else 'k',
va='center', bbox=dict(color='white', boxstyle='square,pad=0'))
for hailtag, row in df.groupby('hailtag').sum().iterrows():
y = uniquehail.index(hailtag)
x = len(uniquewind)
val = row['count'] / total * 100.
ax.text(x, y, "%.2f" % (val, ), ha='center', fontsize=FONTSIZE,
color='r' if val >= 10 else 'k',
va='center', bbox=dict(color='white', boxstyle='square,pad=0'))
for windtag, row in df.groupby('windtag').sum().iterrows():
y = -1
x = uniquewind.index(windtag)
val = row['count'] / total * 100.
ax.text(x, y, "%.2f" % (val, ), ha='center', fontsize=FONTSIZE,
color='r' if val >= 10 else 'k',
va='center', bbox=dict(color='white', boxstyle='square,pad=0'))
ax.set_xticks(range(len(uniquewind) + 1))
ax.set_yticks(range(-1, len(uniquehail) + 1))
ax.set_xlim(-0.5, len(uniquewind) + 0.5)
ax.set_ylim(-1.5, len(uniquehail) - 0.5)
ax.set_xticklabels(uniquewind + ['Total'], fontsize=14)
ax.set_yticklabels(['Total'] + uniquehail, fontsize=14)
ax.xaxis.tick_top()
ax.set_xlabel("Wind Speed [mph]", fontsize=14)
ax.set_ylabel("Hail Size [inch]", fontsize=14)
ax.xaxis.set_label_position('top')
plt.tick_params(top=False, bottom=False, left=False, right=False)
fig.suptitle(
("Frequency [%%] of NWS Wind/Hail Tags for "
"Severe Thunderstorm Warning Issuance\n"
"%s through %s, %.0f warnings\n%s"
"Values larger than 10%% in red"
) % (minvalid.strftime("%d %b %Y"),
maxvalid.strftime("%d %b %Y"), df['count'].sum(), supextra))
ax.set_position([0.15, 0.05, 0.8, 0.72])
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
phenomena = ctx['phenomena']
significance = ctx['significance']
opt = ctx['opt']
state = ctx['state']
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
wfo_limiter = (" and wfo = '%s' ") % (station if len(station) == 3 else
station[1:], )
if station == '_ALL':
wfo_limiter = ''
if opt == 'state':
wfo_limiter = " and substr(ugc, 1, 2) = '%s'" % (state, )
# NB we added a hack here that may lead to some false positives when events
# cross over months, sigh, recall the 2017 eventid pain
df = read_sql("""
with data as (
SELECT distinct
extract(year from issue) as yr,
extract(month from issue) as mo, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + """
GROUP by yr, mo, wfo, eventid)
SELECT yr, mo, count(*) from data GROUP by yr, mo ORDER by yr, mo ASC
""",
pgconn,
params=(phenomena, significance),
index_col=None)
if df.empty:
raise ValueError("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 8))
minyear = df['yr'].min()
maxyear = df['yr'].max()
data = np.zeros((int(maxyear - minyear + 1), 12), 'i')
for _, row in df.iterrows():
data[int(row['yr'] - minyear), int(row['mo'] - 1)] = row['count']
txt = ax.text(row['mo'],
row['yr'],
"%.0f" % (row['count'], ),
va='center',
ha='center',
color='white')
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="k")])
cmap = plt.get_cmap('jet')
cmap.set_under('white')
maxval = max([df['count'].max(), 11])
bounds = np.linspace(1, maxval, 10, dtype='i')
norm = mpcolors.BoundaryNorm(bounds, cmap.N)
res = ax.imshow(data,
extent=[0.5, 12.5, maxyear + 0.5, minyear - 0.5],
interpolation='nearest',
aspect='auto',
norm=norm,
cmap=cmap)
fig.colorbar(res, label='count')
ax.grid(True)
ax.set_xticks(range(1, 13))
ax.set_xticklabels(calendar.month_abbr[1:])
title = "NWS %s" % (nt.sts[station]['name'], )
if opt == 'state':
title = ("NWS Issued for Counties/Zones for State of %s") % (
reference.state_names[state], )
title += ("\n%s (%s.%s) Issued by Year,Month") % (vtec.get_ps_string(
phenomena, significance), phenomena, significance)
ax.set_title(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 main(argv):
"""Go Main Go"""
nt = Table("ISUSM")
qdict = loadqc()
idbconn = get_dbconn("isuag", user="******")
pdbconn = get_dbconn("postgis", user="******")
day_ago = int(argv[1])
ts = datetime.date.today() - datetime.timedelta(days=day_ago)
hlons, hlats, hvals = do_nam(ts)
nam = temperature(hvals, "K").value("F")
window = np.ones((3, 3))
nam = convolve2d(nam, window / window.sum(), mode="same", boundary="symm")
# mp = MapPlot(sector='midwest')
# mp.pcolormesh(hlons, hlats, nam,
# range(20, 90, 5))
# mp.postprocess(filename='test.png')
# sys.exit()
# Query out the data
df = read_sql(
"""
WITH ranges as (
select station, count(*), min(tsoil_c_avg_qc),
max(tsoil_c_avg_qc) from sm_hourly WHERE
valid >= %s and valid < %s and tsoil_c_avg_qc > -40
and tsoil_c_avg_qc < 50 GROUP by station
)
SELECT d.station, d.tsoil_c_avg_qc,
r.max as hourly_max_c, r.min as hourly_min_c, r.count
from sm_daily d JOIN ranges r on (d.station = r.station)
where valid = %s and tsoil_c_avg_qc > -40 and r.count > 19
""",
idbconn,
params=(ts, ts + datetime.timedelta(days=1), ts),
index_col="station",
)
for col, newcol in zip(
["tsoil_c_avg_qc", "hourly_min_c", "hourly_max_c"],
["ob", "min", "max"],
):
df[newcol] = temperature(df[col].values, "C").value("F")
df.drop(col, axis=1, inplace=True)
for stid, row in df.iterrows():
df.at[stid, "ticket"] = qdict.get(stid, {}).get("soil4", False)
x, y = get_idx(hlons, hlats, nt.sts[stid]["lon"], nt.sts[stid]["lat"])
df.at[stid, "nam"] = nam[x, y]
df.at[stid, "lat"] = nt.sts[stid]["lat"]
df.at[stid, "lon"] = nt.sts[stid]["lon"]
# ticket is an object type from above
df = df[~df["ticket"].astype("bool")]
df["diff"] = df["ob"] - df["nam"]
bias = df["diff"].mean()
nam = nam + bias
print("fancy_4inch NAM bias correction of: %.2fF applied" % (bias, ))
# apply nam bias to sampled data
df["nam"] += bias
df["diff"] = df["ob"] - df["nam"]
# we are going to require data be within 1 SD of sampled or 5 deg
std = 5.0 if df["nam"].std() < 5.0 else df["nam"].std()
for station in df[df["diff"].abs() > std].index.values:
print(("fancy_4inch %s QC'd %s out std: %.2f, ob:%.1f nam:%.1f") % (
ts.strftime("%Y%m%d"),
station,
std,
df.at[station, "ob"],
df.at[station, "nam"],
))
df.drop(station, inplace=True)
# Query out centroids of counties...
cdf = read_sql(
"""SELECT ST_x(ST_centroid(the_geom)) as lon,
ST_y(ST_centroid(the_geom)) as lat
from uscounties WHERE state_name = 'Iowa'
""",
pdbconn,
index_col=None,
)
for i, row in cdf.iterrows():
x, y = get_idx(hlons, hlats, row["lon"], row["lat"])
cdf.at[i, "nam"] = nam[x, y]
mp = MapPlot(
sector="iowa",
title=("Average 4 inch Depth Soil Temperatures for %s") %
(ts.strftime("%b %d, %Y"), ),
subtitle=("County est. based on bias adj. "
"NWS NAM Model (black numbers), "
"ISUSM network observations (red numbers)"),
)
mp.pcolormesh(
hlons,
hlats,
nam,
np.arange(10, 101, 5),
cmap=cm.get_cmap("jet"),
units=r"$^\circ$F",
)
mp.plot_values(df["lon"],
df["lat"],
df["ob"],
fmt="%.0f",
color="r",
labelbuffer=5)
mp.plot_values(
cdf["lon"],
cdf["lat"],
cdf["nam"],
fmt="%.0f",
textsize=11,
labelbuffer=5,
)
mp.drawcounties()
routes = "a" if day_ago >= 4 else "ac"
pqstr = ("plot %s %s0000 soilt_day%s.png isuag_county_4inch_soil.png png"
) % (routes, ts.strftime("%Y%m%d"), day_ago)
mp.postprocess(pqstr=pqstr)
mp.close()
PAJN | OSO
PHFO | OSO
PACR | RVF
PAMC | SCD
PAFA | SCD
PANC | SHP
PTSA | SSM
PAER | STQ
PALU | STQ
PAAQ | TST
PHEB | TST
PAFG | ZFP"""
from pyiem.network import Table
nt = Table("WFO")
data = {}
labels = {}
uniq = []
for line in text.split("\n"):
tokens = line.replace(" ", "").split("|")
wfo = tokens[0][1:]
if tokens[0][0] == 'P':
wfo = tokens[0]
key = "%s" % (tokens[1], )
if not nt.sts.has_key(wfo):
continue
# P
wfo = tokens[0][1:]
if not key in uniq:
def plotter(fdict):
""" Go """
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DMX')
limit = fdict.get('limit', 'no')
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
lastdoy = 367
if limit.lower() == 'yes':
lastdoy = int(datetime.datetime.today().strftime("%j")) + 1
if station == '_ALL':
cursor.execute("""
with counts as (
select extract(year from issue) as yr,
extract(doy from issue) as doy, count(*) from sbw
where status = 'NEW' and phenomena in ('SV', 'TO')
and significance = 'W' and issue > '2003-01-01'
and extract(doy from issue) < %s
GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from counts ORDER by yr ASC, doy ASC
""", (lastdoy, ))
else:
cursor.execute("""
with counts as (
select extract(year from issue) as yr,
extract(doy from issue) as doy, count(*) from sbw
where status = 'NEW' and phenomena in ('SV', 'TO')
and significance = 'W' and wfo = %s and issue > '2003-01-01'
and extract(doy from issue) < %s
GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from counts ORDER by yr ASC, doy ASC
""", (station, lastdoy))
data = {}
for yr in range(2003, datetime.datetime.now().year + 1):
data[yr] = {'doy': [], 'counts': []}
for row in cursor:
data[row[0]]['doy'].append(row[1])
data[row[0]]['counts'].append(row[2])
(fig, ax) = plt.subplots(1, 1)
ann = []
for yr in range(2003, datetime.datetime.now().year + 1):
if len(data[yr]['doy']) < 2:
continue
l = ax.plot(data[yr]['doy'], data[yr]['counts'], lw=2,
label="%s (%s)" % (str(yr), data[yr]['counts'][-1]))
ann.append(
ax.text(data[yr]['doy'][-1]+1, data[yr]['counts'][-1],
"%s" % (yr,), color='w', va='center',
fontsize=10, bbox=dict(facecolor=l[0].get_color(),
edgecolor=l[0].get_color()))
)
mask = np.zeros(fig.canvas.get_width_height(), bool)
fig.canvas.draw()
attempts = 10
while len(ann) > 0 and attempts > 0:
attempts -= 1
removals = []
for a in ann:
bbox = a.get_window_extent()
x0 = int(bbox.x0)
x1 = int(math.ceil(bbox.x1))
y0 = int(bbox.y0)
y1 = int(math.ceil(bbox.y1))
s = np.s_[x0:x1+1, y0:y1+1]
if np.any(mask[s]):
a.set_position([a._x-int(lastdoy/14), a._y])
else:
mask[s] = True
removals.append(a)
for rm in removals:
ann.remove(rm)
ax.legend(loc=2, ncol=2, fontsize=10)
ax.set_xlim(1, 367)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_ylabel("Accumulated Count")
ax.set_title(("NWS %s\nSevere Thunderstorm + Tornado Warning Count"
) % (nt.sts[station]['name'],))
ax.set_xlim(0, lastdoy)
if lastdoy < 367:
ax.set_xlabel(("thru approximately %s"
) % (datetime.date.today().strftime("%-d %b"), ))
return fig
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.colors as mpcolors
import matplotlib.patheffects as PathEffects
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
phenomena = ctx['phenomena']
significance = ctx['significance']
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
wfo_limiter = (" and wfo = '%s' "
) % (station if len(station) == 3 else station[1:],)
if station == '_ALL':
wfo_limiter = ''
df = read_sql("""
with data as (
SELECT distinct extract(year from issue) as yr2,
min(issue) as i, wfo, eventid
from warnings where phenomena = %s and significance = %s
""" + wfo_limiter + """ and issue is not null
GROUP by yr2, wfo, eventid)
SELECT extract(year from i) as yr, extract(month from i) as mo,
count(*) from data GROUP by yr, mo ORDER by yr, mo ASC
""", pgconn, params=(phenomena, significance), index_col=None)
if len(df.index) == 0:
return("Sorry, no data found!")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 8))
minyear = df['yr'].min()
maxyear = df['yr'].max()
data = np.zeros((maxyear - minyear + 1, 12))
for _, row in df.iterrows():
data[row['yr'] - minyear, row['mo'] - 1] = row['count']
txt = ax.text(row['mo'], row['yr'], "%.0f" % (row['count'],),
va='center', ha='center', color='white')
txt.set_path_effects([PathEffects.withStroke(linewidth=2,
foreground="k")])
cmap = plt.get_cmap('jet')
cmap.set_under('white')
maxval = max([df['count'].max(), 11])
bounds = np.linspace(1, maxval, 10, dtype='i')
norm = mpcolors.BoundaryNorm(bounds, cmap.N)
res = ax.imshow(data, extent=[0.5, 12.5, maxyear + 0.5, minyear - 0.5],
interpolation='nearest', aspect='auto', norm=norm)
fig.colorbar(res, label='count')
ax.grid(True)
ax.set_xticks(range(1, 13))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_title(("NWS %s\n%s %s (%s.%s) Issued by Year,Month"
) % (nt.sts[station]['name'], vtec._phenDict[phenomena],
vtec._sigDict[significance], phenomena, significance))
return fig, df
import psycopg2.extras
import subprocess
import tempfile
import calendar
import pytz
utc = datetime.datetime.utcnow()
utc = utc.replace(tzinfo=pytz.timezone("UTC"))
tstr = utc.strftime("%Y%m%d%H%M")
now = utc.astimezone(pytz.timezone("America/Chicago"))
IEM = psycopg2.connect(database='iem', host='iemdb', user='******')
icursor = IEM.cursor(cursor_factory=psycopg2.extras.DictCursor)
st = NetworkTable(['KCCI', 'KIMT'])
st.sts["SMAI4"]["plot_name"] = "M-town"
st.sts["SBZI4"]["plot_name"] = "Zoo"
st.sts["SMSI4"]["plot_name"] = "Barnum"
st.sts["STQI4"]["plot_name"] = "Tama"
st.sts["SBOI4"]["plot_name"] = "Boone"
def altiTxt(d):
if d == "":
return "S"
if d < 0:
return "F"
if d > 0:
return "R"
def test_workflow(pcursor, icursor):
""" Test that we can do stuff! """
sid1 = 'XXX'
sid2 = 'YYY'
pnetwork = 'xxxxxx'
nt = NetworkTable(None)
nt.sts[sid1] = dict(name='XXX Site Name',
network='IA_XXXX',
tzname='America/Chicago')
nt.sts[sid2] = dict(name='YYY Site Name',
network='IA_XXXX',
tzname='America/Chicago')
valid = datetime.datetime.utcnow()
valid = valid.replace(tzinfo=pytz.timezone("UTC"))
threshold = valid - datetime.timedelta(hours=3)
obs = {
sid1: {
'valid': valid
},
sid2: {
'valid': valid - datetime.timedelta(hours=6)
}
}
# Create dummy iem_site_contacts
pcursor.execute(
"""
INSERT into iem_site_contacts
(portfolio, s_mid, email) VALUES (%s, %s, %s)
""", (pnetwork, sid1, 'akrherz@localhost'))
pcursor.execute(
"""
INSERT into iem_site_contacts
(portfolio, s_mid, email) VALUES (%s, %s, %s)
""", (pnetwork, sid2, 'root@localhost'))
# Create some dummy tickets
pcursor.execute(
"""
INSERT into tt_base (portfolio, s_mid, subject,
status, author) VALUES (%s, %s, %s, %s, %s) RETURNING id
""", (pnetwork, sid1, 'FIXME PLEASE OPEN', 'OPEN', 'mesonet'))
pcursor.execute(
"""
INSERT into tt_base (portfolio, s_mid, subject,
status, author) VALUES (%s, %s, %s, %s, %s) RETURNING id
""", (pnetwork, sid1, 'FIXME PLEASE CLOSED', 'CLOSED', 'mesonet'))
tracker = TrackerEngine(icursor, pcursor)
tracker.process_network(obs, pnetwork, nt, threshold)
tracker.send_emails(really_send=False)
assert len(tracker.emails) == 1
tracker.emails = {}
obs[sid1]['valid'] = valid - datetime.timedelta(hours=6)
obs[sid2]['valid'] = valid
tracker.process_network(obs, pnetwork, nt, threshold)
tracker.send_emails(really_send=False)
assert len(tracker.emails) == 2
tracker.emails = {}
obs[sid1]['valid'] = valid - datetime.timedelta(hours=6)
obs[sid2]['valid'] = valid
tracker.process_network(obs, pnetwork, nt, threshold)
tracker.send_emails(really_send=False)
assert not tracker.emails
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('station', 'DMX')
limit = fdict.get('limit', 'no')
combo = fdict.get('c', 'svrtor')
phenomena = fdict.get('phenomena', 'TO')
significance = fdict.get('significance', 'W')
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
lastdoy = 367
if limit.lower() == 'yes':
lastdoy = int(datetime.datetime.today().strftime("%j")) + 1
if combo == 'svrtor':
if station == '_ALL':
cursor.execute(
"""
with counts as (
select extract(year from issue) as yr,
extract(doy from issue) as doy, count(*) from sbw
where status = 'NEW' and phenomena in ('SV', 'TO')
and significance = 'W' and issue > '2003-01-01'
and extract(doy from issue) < %s
GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr
ORDER by doy ASC)
from counts ORDER by yr ASC, doy ASC
""", (lastdoy, ))
else:
cursor.execute(
"""
with counts as (
select extract(year from issue) as yr,
extract(doy from issue) as doy, count(*) from sbw
where status = 'NEW' and phenomena in ('SV', 'TO')
and significance = 'W' and wfo = %s
and issue > '2003-01-01'
and extract(doy from issue) < %s
GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr
ORDER by doy ASC)
from counts ORDER by yr ASC, doy ASC
""", (station, lastdoy))
else:
if station == '_ALL':
cursor.execute(
"""
WITH data as (
SELECT extract(year from issue) as yr,
issue, eventid, wfo from warnings WHERE
phenomena = %s and significance = %s
and extract(doy from issue) < %s
and issue > '2003-01-01'),
agg1 as (
SELECT yr, min(issue) as min_issue, eventid, wfo from data
GROUP by yr, eventid, wfo),
agg2 as (
SELECT yr, extract(doy from min_issue) as doy, count(*)
from agg1 GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from agg2 ORDER by yr ASC, doy ASC
""", (phenomena, significance, lastdoy))
else:
cursor.execute(
"""
WITH data as (
SELECT extract(year from issue) as yr,
issue, eventid, wfo from warnings WHERE
phenomena = %s and significance = %s and wfo = %s
and extract(doy from issue) < %s
and issue > '2003-01-01'),
agg1 as (
SELECT yr, min(issue) as min_issue, eventid, wfo from data
GROUP by yr, eventid, wfo),
agg2 as (
SELECT yr, extract(doy from min_issue) as doy, count(*)
from agg1 GROUP by yr, doy)
SELECT yr, doy, sum(count) OVER (PARTITION by yr ORDER by doy ASC)
from agg2 ORDER by yr ASC, doy ASC
""", (phenomena, significance, station, lastdoy))
data = {}
for yr in range(2003, datetime.datetime.now().year + 1):
data[yr] = {'doy': [], 'counts': []}
rows = []
for row in cursor:
data[row[0]]['doy'].append(row[1])
data[row[0]]['counts'].append(row[2])
rows.append(dict(year=row[0], day_of_year=row[1], count=row[2]))
# append on a lastdoy value so all the plots go to the end
for yr in range(2003, datetime.datetime.now().year):
if len(data[yr]['doy']) == 0 or data[yr]['doy'][-1] >= lastdoy:
continue
data[yr]['doy'].append(lastdoy)
data[yr]['counts'].append(data[yr]['counts'][-1])
if len(data[datetime.datetime.now().year]['doy']) > 0:
data[datetime.datetime.now().year]['doy'].append(
int(datetime.datetime.today().strftime("%j")) + 1)
data[datetime.datetime.now().year]['counts'].append(
data[datetime.datetime.now().year]['counts'][-1])
df = pd.DataFrame(rows)
(fig, ax) = plt.subplots(1, 1)
ann = []
for yr in range(2003, datetime.datetime.now().year + 1):
if len(data[yr]['doy']) < 2:
continue
l = ax.plot(data[yr]['doy'],
data[yr]['counts'],
lw=2,
label="%s (%s)" % (str(yr), data[yr]['counts'][-1]),
drawstyle='steps-post')
ann.append(
ax.text(data[yr]['doy'][-1] + 1,
data[yr]['counts'][-1],
"%s" % (yr, ),
color='w',
va='center',
fontsize=10,
bbox=dict(facecolor=l[0].get_color(),
edgecolor=l[0].get_color())))
mask = np.zeros(fig.canvas.get_width_height(), bool)
fig.canvas.draw()
attempts = 10
while len(ann) > 0 and attempts > 0:
attempts -= 1
removals = []
for a in ann:
bbox = a.get_window_extent()
x0 = int(bbox.x0)
x1 = int(math.ceil(bbox.x1))
y0 = int(bbox.y0)
y1 = int(math.ceil(bbox.y1))
s = np.s_[x0:x1 + 1, y0:y1 + 1]
if np.any(mask[s]):
a.set_position([a._x - int(lastdoy / 14), a._y])
else:
mask[s] = True
removals.append(a)
for rm in removals:
ann.remove(rm)
ax.legend(loc=2, ncol=2, fontsize=10)
ax.set_xlim(1, 367)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_ylabel("Accumulated Count")
title = "%s %s" % (vtec._phenDict[phenomena], vtec._sigDict[significance])
if combo == 'svrtor':
title = "Severe Thunderstorm + Tornado Warning"
ax.set_title(("NWS %s\n %s Count") % (nt.sts[station]['name'], title))
ax.set_xlim(0, lastdoy)
if lastdoy < 367:
ax.set_xlabel(("thru approximately %s") %
(datetime.date.today().strftime("%-d %b"), ))
return fig, df
"""
Generate a map of Number of days with precip
"""
import sys
from pyiem.plot import MapPlot
import datetime
from pyiem.network import Table as NetworkTable
import psycopg2.extras
now = datetime.datetime.now()
nt = NetworkTable("IACLIMATE")
nt.sts["IA0200"]["lon"] = -93.4
nt.sts["IA5992"]["lat"] = 41.65
COOP = psycopg2.connect(database='coop', host='iemdb', user='******')
ccursor = COOP.cursor(cursor_factory=psycopg2.extras.DictCursor)
def runYear(year):
# Grab the data
sql = """SELECT station,
sum(case when precip >= 0.01 then 1 else 0 end) as days, max(day)
from alldata_ia WHERE year = %s and substr(station,3,1) != 'C'
and station != 'IA0000' GROUP by station""" % (year, )
lats = []
lons = []
vals = []
labels = []
ccursor.execute(sql)
for row in ccursor:
def main(argv):
"""Go Main Go"""
nt = Table("ISUSM")
qdict = loadqc()
idbconn = get_dbconn('isuag', user='******')
icursor = idbconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
pdbconn = get_dbconn('postgis', user='******')
pcursor = pdbconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
day_ago = int(argv[1])
ts = datetime.datetime.now() - datetime.timedelta(days=day_ago)
# Query out the data
soil_obs = []
lats = []
lons = []
icursor.execute("""
SELECT station, tsoil_c_avg_qc from sm_daily
where valid = '%s' and tsoil_c_avg_qc > -40
and station not in ('AHTI4', 'FRUI4')
""" % (ts.strftime("%Y-%m-%d"), ))
for row in icursor:
stid = row['station']
if qdict.get(stid, {}).get('soil4', False):
# print '%s was QCd out' % (stid,)
continue
soil_obs.append(temperature(row['tsoil_c_avg_qc'], 'C').value('F'))
lats.append(nt.sts[stid]['lat'])
lons.append(nt.sts[stid]['lon'])
if len(lats) < 5:
print(("isuag/fancy_4inch found %s obs for %s") %
(len(lats), ts.strftime("%Y-%m-%d")))
return
# Grid it
# numxout = 40
# numyout = 40
# xmin = min(lons) - 2.
# ymin = min(lats) - 2.
# xmax = max(lons) + 2.
# ymax = max(lats) + 2.
# xc = (xmax-xmin)/(numxout-1)
# yc = (ymax-ymin)/(numyout-1)
# xo = xmin + xc * np.arange(0, numxout)
# yo = ymin + yc * np.arange(0, numyout)
# analysis = griddata((lons, lats), soil_obs, (xo, yo) )
# rbfi = Rbf(lons, lats, soil_obs, function='cubic')
# analysis = rbfi(xo, yo)
nn = NearestNDInterpolator((lons, lats), np.array(soil_obs))
# analysis = nn(xo, yo)
# Query out centroids of counties...
pcursor.execute("""SELECT ST_x(ST_centroid(the_geom)) as lon,
ST_y(ST_centroid(the_geom)) as lat
from uscounties WHERE state_name = 'Iowa'
""")
clons = []
clats = []
for row in pcursor:
clats.append(row['lat'])
clons.append(row['lon'])
cobs = nn(clons, clats)
mp = MapPlot(sector='iowa',
title=("Iowa Average 4 inch Soil Temperatures %s") %
(ts.strftime("%b %d %Y"), ),
subtitle=("Based on gridded analysis (black numbers) of "
"ISUSM network observations (red numbers)"))
mp.contourf(clons,
clats,
cobs,
np.arange(10, 101, 5),
cmap=cm.get_cmap('jet'),
units=r'$^\circ$F')
mp.plot_values(lons, lats, soil_obs, fmt='%.0f', color='r', labelbuffer=5)
mp.plot_values(clons, clats, cobs, fmt='%.0f', textsize=11, labelbuffer=5)
# for lo, la, ob in zip(clons, clats, cobs):
# xi, yi = m.map(lo, la)
# txt = m.ax.text(xi, yi, "%.0f" % (ob,))
mp.drawcounties()
routes = "a" if day_ago >= 4 else "ac"
pqstr = ("plot %s %s0000 soilt_day%s.png isuag_county_4inch_soil.png png"
) % (routes, ts.strftime("%Y%m%d"), day_ago)
mp.postprocess(pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='postgis', host='iemdb', user='******')
station = fdict.get('station', 'DMX')[:4]
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
fig = plt.figure(figsize=(8, 14 if station != '_ALL' else 21))
ax = [None, None]
ax[0] = plt.axes([0.1, 0.75, 0.85, 0.2])
ax[1] = plt.axes([0.1, 0.05, 0.85, 0.65])
if station == '_ALL':
df = read_sql("""
SELECT distinct extract(year from issue) as year,
phenomena, significance from warnings WHERE
phenomena is not null and significance is not null and
issue > '2005-01-01' and issue is not null
""",
pgconn,
index_col=None)
else:
df = read_sql("""
SELECT distinct extract(year from issue) as year,
phenomena, significance from warnings WHERE
wfo = %s and phenomena is not null and significance is not null
and issue > '2005-01-01' and issue is not null
""",
pgconn,
params=(station, ),
index_col=None)
df['wfo'] = station
df['year'] = df['year'].astype('i')
gdf = df.groupby('year').count()
ax[0].bar(gdf.index.values,
gdf['wfo'],
width=0.8,
fc='b',
ec='b',
align='center')
for yr, row in gdf.iterrows():
ax[0].text(yr, row['wfo'] + 1, "%s" % (row['wfo'], ), ha='center')
ax[0].set_title(
("[%s] NWS %s\nCount of Distinct VTEC Phenomena/"
"Significance - %i to %i") %
(station, nt.sts[station]['name'], df['year'].min(), df['year'].max()))
ax[0].grid()
ax[0].set_ylabel("Count")
ax[0].set_xlim(gdf.index.values.min() - 0.5, gdf.index.values.max() + 0.5)
pos = {}
i = 1
df.sort_values(['phenomena', 'significance'], inplace=True)
for _, row in df.iterrows():
key = "%s.%s" % (row['phenomena'], row['significance'])
if key not in pos:
pos[key] = i
i += 1
ax[1].text(row['year'],
pos[key],
key,
ha='center',
va='center',
fontsize=10,
bbox=dict(color='white'))
ax[1].set_title("VTEC <Phenomena.Significance> Issued by Year")
ax[1].set_ylim(0, i)
ax[1].grid(True)
ax[1].set_xlim(gdf.index.values.min() - 0.5, gdf.index.values.max() + 0.5)
return fig, df
import datetime
import sys
# thirdparty
import numpy as np
from scipy.interpolate import NearestNDInterpolator
import matplotlib.cm as cm
import psycopg2.extras
# pyiem
from pyiem.plot import MapPlot
from pyiem.datatypes import temperature
from pyiem.tracker import loadqc
from pyiem.network import Table
nt = Table("ISUSM")
qdict = loadqc()
ISUAG = psycopg2.connect(database='isuag', host='iemdb', user='******')
icursor = ISUAG.cursor(cursor_factory=psycopg2.extras.DictCursor)
POSTGIS = psycopg2.connect(database='postgis', host='iemdb', user='******')
pcursor = POSTGIS.cursor(cursor_factory=psycopg2.extras.DictCursor)
day_ago = int(sys.argv[1])
ts = datetime.datetime.now() - datetime.timedelta(days=day_ago)
# Query out the data
soil_obs = []
lats = []
lons = []
icursor.execute("""
