def main():
"""Map some CLI data"""
pgconn = get_dbconn('iem')
df = read_sql("""
WITH data as (
SELECT station, snow_jul1 - snow_jul1_normal as s
from cli_data where valid = '2019-02-18' and snow_jul1 > 0
and snow_jul1_normal > 0)
select station, st_x(geom) as lon, st_y(geom) as lat, c.s as val from
data c JOIN stations s on (s.id = c.station)
WHERE s.network = 'NWSCLI'
""", pgconn, index_col=None)
df['color'] = '#ff0000'
df.loc[df['val'] > 0, 'color'] = '#0000ff'
mp = MapPlot(sector='midwest', axisbg='white',
title=("2018-2019 Snowfall Total Departure "
"from Average [inches]"),
subtitle='18 Feb 2019 Based on NWS CLI Reporting Sites')
mp.plot_values(
df['lon'].values, df['lat'].values,
df['val'].values, fmt='%.1f', textsize=12, color=df['color'].values,
labelbuffer=1)
mp.postprocess(filename='test.png')
def plot(data, v):
''' Actually plot this data '''
nt = NetworkTable("ISUSM")
lats = []
lons = []
vals = []
valid = None
for sid in data.keys():
if data[sid][v] is None:
continue
lats.append(nt.sts[sid]['lat'])
lons.append(nt.sts[sid]['lon'])
vals.append(data[sid][v])
valid = data[sid]['valid']
if valid is None:
m = MapPlot(sector='iowa', axisbg='white',
title=('ISU Soil Moisture Network :: %s'
'') % (CTX[v]['title'], ),
figsize=(8.0, 6.4))
m.plot_values([-95, ], [41.99, ], ['No Data Found'], '%s', textsize=30)
m.postprocess(web=True)
return
m = MapPlot(sector='iowa', axisbg='white',
title='ISU Soil Moisture Network :: %s' % (CTX[v]['title'],),
subtitle='valid %s' % (valid.strftime("%-d %B %Y %I:%M %p"),),
figsize=(8.0, 6.4))
m.plot_values(lons, lats, vals, '%.1f')
m.drawcounties()
m.postprocess(web=True)
def plot(df):
from pyiem.plot import MapPlot
m = MapPlot(sector='midwest', continentalcolor='white')
m.plot_values(df['lon'].values, df['lat'].values, df['highdata'].values,
labelbuffer=0)
m.postprocess(filename='test.png')
m.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
state = ctx['state'][:2]
varname = ctx['var']
sector = ctx['sector']
opt = ctx['opt']
over = ctx['over']
month = ctx['month']
df = read_sql("""
WITH data as (
SELECT station, extract(month from valid) as month,
sum(precip) as total_precip, avg(high) as avg_high,
avg(low) as avg_low, avg((high+low)/2.) as avg_temp
from ncdc_climate81 GROUP by station, month)
SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat, month,
total_precip, avg_high, avg_low, avg_temp from data d JOIN stations t
ON (d.station = t.id) WHERE t.network = 'NCDC81' and
t.state in ('IA', 'ND', 'SD', 'NE', 'KS', 'MO', 'IL', 'WI', 'MN', 'MI',
'IN', 'OH', 'KY')
""", pgconn, index_col=['station', 'month'])
if df.empty:
return "No data was found for query, sorry."
if over == 'monthly':
title = "%s %s" % (calendar.month_name[month], PDICT3[varname])
df.reset_index(inplace=True)
df2 = df[df['month'] == month]
else:
title = "Annual %s" % (PDICT3[varname], )
if varname == 'total_precip':
df2 = df.sum(axis=0, level='station')
else:
df2 = df.mean(axis=0, level='station')
df2['lat'] = df['lat'].mean(axis=0, level='station')
df2['lon'] = df['lon'].mean(axis=0, level='station')
mp = MapPlot(sector=sector, state=state, axisbg='white',
title=('NCEI 1981-2010 Climatology of %s'
) % (title,),
subtitle=('based on National Centers for '
'Environmental Information (NCEI) 1981-2010'
' Climatology'))
levels = np.linspace(df2[varname].min(), df2[varname].max(), 10)
levels = [round(x, PRECISION[varname]) for x in levels]
if opt in ['both', 'contour']:
mp.contourf(df2['lon'].values, df2['lat'].values,
df2[varname].values, levels, units=UNITS[varname])
if sector == 'state':
mp.drawcounties()
if opt in ['both', 'values']:
mp.plot_values(df2['lon'].values, df2['lat'].values,
df2[varname].values,
fmt='%%.%if' % (PRECISION[varname],))
return mp.fig, df
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()
if not nt.sts.has_key(sid):
continue
labels.append( sid[2:] )
lats.append( nt.sts[sid]['lat'] )
lons.append( nt.sts[sid]['lon'] )
vals.append( row['days'] )
maxday = row['max']
#---------- Plot the points
m = MapPlot(title="Days with Measurable Precipitation (%s)" % (year,),
subtitle='Map valid January 1 - %s' % (maxday.strftime("%b %d")),
axisbg='white')
m.plot_values(lons, lats, vals, fmt='%.0f', labels=labels,
labeltextsize=8, labelcolor='tan')
m.drawcounties()
pqstr = "plot m %s bogus %s/summary/precip_days.png png" % (
now.strftime("%Y%m%d%H%M"), year,)
m.postprocess(pqstr=pqstr)
def main():
"""Go Main Go"""
pgconn = get_dbconn('iem', user='******')
icursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
lats = []
lons = []
vals = []
valmask = []
labels = []
icursor.execute("""
select id, ST_x(s.geom) as lon, ST_y(s.geom) as lat, pday
from summary c, stations s
WHERE day = 'TODAY' and pday >= 0 and pday < 20
and s.network = 'IA_COOP' and s.iemid = c.iemid
""")
for row in icursor:
lats.append(row['lat'])
lons.append(row['lon'])
vals.append(n(row['pday']))
labels.append(row['id'])
valmask.append(True)
mp = MapPlot(title="Iowa COOP 24 Hour Precipitation",
axisbg='white',
subtitle="ending approximately %s 7 AM" %
(datetime.datetime.now().strftime("%-d %b %Y"), ))
mp.plot_values(lons, lats, vals)
pqstr = "plot ac %s iowa_coop_precip.png iowa_coop_precip.png png" % (
datetime.datetime.now().strftime("%Y%m%d%H%M"), )
mp.postprocess(pqstr=pqstr)
mp.close()
def main():
"""Go!"""
title = 'NOAA MRMS Q3: RADAR + Guage Corrected Rainfall Estimates + NWS Storm Reports'
mp = MapPlot(sector='custom',
north=42.3, east=-93.0, south=41.65, west=-94.1,
axisbg='white',
titlefontsize=14,
title=title,
subtitle='Valid: 14 June 2018')
shp = shapefile.Reader('cities.shp')
for record in shp.shapeRecords():
geo = shape(record.shape)
mp.ax.add_geometries([geo], ccrs.PlateCarree(), zorder=Z_OVERLAY2,
facecolor='None', edgecolor='k', lw=2)
grbs = pygrib.open('MRMS_GaugeCorr_QPE_24H_00.00_20180614-200000.grib2')
grb = grbs.message(1)
pcpn = distance(grb['values'], 'MM').value('IN')
lats, lons = grb.latlons()
lons -= 360.
clevs = [0.01, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10]
cmap = nwsprecip()
cmap.set_over('k')
mp.pcolormesh(lons, lats, pcpn, clevs, cmap=cmap, latlon=True,
units='inch')
lons, lats, vals, labels = get_data()
mp.drawcounties()
mp.plot_values(lons, lats, vals, "%s", labels=labels,
labelbuffer=1, labelcolor='white')
mp.drawcities(labelbuffer=5, minarea=0.2)
mp.postprocess(filename='test.png')
def do_month(ts, routes='m'):
"""
Generate the plot for a given month, please
"""
sql = """SELECT station, sum(precip) as total, max(day) as lastday
from alldata_ia WHERE year = %s and month = %s
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (ts.year, ts.month)
lats = []
lons = []
vals = []
lastday = None
ccursor.execute(sql)
for row in ccursor:
if row['station'] not in nt.sts:
continue
if lastday is None:
lastday = row['lastday']
lats.append(nt.sts[row['station']]['lat'])
lons.append(nt.sts[row['station']]['lon'])
vals.append(row['total'])
m = MapPlot(title='%s - %s' %
(ts.strftime("%d %B %Y"), lastday.strftime("%d %B %Y")),
subtitle="%s Total Precipitation [inch]" %
(ts.strftime("%B %Y"), ))
m.contourf(lons, lats, vals,
[0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6, 7])
m.plot_values(lons, lats, vals, fmt='%.2f')
pqstr = ("plot %s %s summary/iemre_iowa_total_precip.png "
"%s/summary/iemre_iowa_total_precip.png png") % (
routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
m.postprocess(pqstr=pqstr)
def runYear(year):
# Grab the data
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 not nt.sts.has_key(sid):
continue
labels.append( sid[2:] )
lats.append( nt.sts[sid]['lat'] )
lons.append( nt.sts[sid]['lon'] )
vals.append( row['total'] )
maxday = row['max']
m = MapPlot(title="Total Precipitation [inch] (%s)" % (year,),
subtitle='1 January - %s' % (maxday.strftime("%d %B"),),
axisbg='white')
m.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,)
m.postprocess(pqstr=pqstr)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
cursor = pgconn.cursor()
sector = fdict.get('sector', 'IA')
date1 = datetime.datetime.strptime(fdict.get('date1', '2015-01-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2015-02-01'),
'%Y-%m-%d')
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
cursor.execute("""
WITH obs as (
SELECT station, sday, day, precip from """ + table + """ WHERE
day >= %s and day < %s and precip >= 0 and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip from
climate51),
combo as (
SELECT o.station, o.precip - c.precip as d from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
deltas as (
SELECT station, sum(d) from combo GROUP by station)
SELECT d.station, d.sum, ST_x(t.geom), ST_y(t.geom) from deltas d
JOIN stations t on (d.station = t.id) WHERE t.network ~* 'CLIMATE'
""", (date1, date2))
rows = []
for row in cursor:
rows.append(dict(station=row[0], delta=row[1], lon=row[2],
lat=row[3]))
df = pd.DataFrame(rows)
lons = np.array(df['lon'])
vals = np.array(df['delta'])
lats = np.array(df['lat'])
sector2 = "state" if sector != 'midwest' else 'midwest'
m = MapPlot(sector=sector2, state=sector, axisbg='white',
title=('%s - %s Precipitation Departure [inch]'
) % (date1.strftime("%d %b %Y"),
date2.strftime("%d %b %Y")),
subtitle='%s vs 1950-2014 Climatology' % (date1.year,))
rng = int(max([0 - np.min(vals), np.max(vals)]))
cmap = cm.get_cmap('RdYlBu')
cmap.set_bad('white')
m.contourf(lons, lats, vals, np.linspace(0 - rng - 0.5, rng + 0.6, 10,
dtype='i'),
cmap=cmap, units='inch')
m.plot_values(lons, lats, vals, fmt='%.2f')
if sector == 'iowa':
m.drawcounties()
return m.fig, df
def do_month(ts, routes='m'):
"""
Generate the plot for a given month, please
"""
sql = """SELECT station, sum(precip) as total, max(day) as lastday
from alldata_ia WHERE year = %s and month = %s
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (ts.year, ts.month)
lats = []
lons = []
vals = []
lastday = None
ccursor.execute(sql)
for row in ccursor:
if row['station'] not in nt.sts:
continue
if lastday is None:
lastday = row['lastday']
lats.append(nt.sts[row['station']]['lat'])
lons.append(nt.sts[row['station']]['lon'])
vals.append(row['total'])
m = MapPlot(title='%s - %s' % (ts.strftime("%d %B %Y"),
lastday.strftime("%d %B %Y")),
subtitle="%s Total Precipitation [inch]" % (
ts.strftime("%B %Y"),))
m.contourf(lons, lats, vals, [0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6,
7])
m.plot_values(lons, lats, vals, fmt='%.2f')
pqstr = ("plot %s %s summary/iemre_iowa_total_precip.png "
"%s/summary/iemre_iowa_total_precip.png png"
) % (routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
m.postprocess(pqstr=pqstr)
def doday():
"""
Create a plot of precipitation stage4 estimates for some day
"""
sts = mx.DateTime.DateTime(2013,5,25,12)
ets = mx.DateTime.DateTime(2013,5,31,12)
interval = mx.DateTime.RelativeDateTime(days=1)
now = sts
total = None
while now < ets:
fp = "/mesonet/ARCHIVE/data/%s/stage4/ST4.%s.24h.grib" % (
now.strftime("%Y/%m/%d"),
now.strftime("%Y%m%d%H") )
if os.path.isfile(fp):
lts = now
grbs = pygrib.open(fp)
if total is None:
g = grbs[1]
total = g["values"]
lats, lons = g.latlons()
else:
total += grbs[1]["values"]
grbs.close()
now += interval
m = MapPlot(sector='iowa', title='NOAA Stage IV & Iowa ASOS Precipitation',
subtitle='25-30 May 2013')
m.pcolormesh(lons, lats, total / 25.4, numpy.arange(0,14.1,1), latlon=True,
units='inch')
m.drawcounties()
m.plot_values(dlons, dlats, dvals, '%.02f')
m.postprocess(filename='test.svg')
import iemplot
iemplot.makefeature('test')
def main():
"""GO!"""
now = datetime.datetime.now()
df = get_df()
# df = pd.read_csv('example.csv')
rng = range(-30, 120, 2)
for sector in ['iowa', 'midwest', 'conus']:
mp = MapPlot(axisbg='white',
sector=sector,
title=("%s 2 meter Air Temperature") %
(sector.capitalize(), ),
subtitle=now.strftime("%d %b %Y %-I:%M %p"))
mp.contourf(df['lon'].values,
df['lat'].values,
df['tmpf'].values,
rng,
clevstride=5,
units='F')
mp.plot_values(df['lon'].values,
df['lat'].values,
df['tmpf'].values,
fmt='%.0f')
if sector == 'iowa':
mp.drawcounties()
pqstr = ("plot ac %s00 %s_tmpf.png %s_tmpf_%s.png png"
"") % (datetime.datetime.utcnow().strftime("%Y%m%d%H"),
sector, sector,
datetime.datetime.utcnow().strftime("%H"))
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""GO"""
pgconn = get_dbconn("iem", user="******")
cursor = pgconn.cursor()
# Compute normal from the climate database
sql = """
SELECT
id, network, vsby, ST_x(geom) as lon, ST_y(geom) as lat
FROM
current c JOIN stations s ON (s.iemid = c.iemid)
WHERE
s.network IN ('AWOS', 'IA_ASOS','IL_ASOS','MN_ASOS','WI_ASOS','SD_ASOS',
'NE_ASOS','MO_ASOS') and
valid + '60 minutes'::interval > now() and
vsby >= 0 and vsby <= 10
"""
lats = []
lons = []
vals = []
valmask = []
cursor.execute(sql)
for row in cursor:
lats.append(row[4])
lons.append(row[3])
vals.append(row[2])
valmask.append(row[1] in ["AWOS", "IA_ASOS"])
if len(lats) < 5:
return
now = datetime.datetime.now()
mp = MapPlot(
sector="iowa",
title="Iowa Visibility",
subtitle="Valid: %s" % (now.strftime("%d %b %Y %-I:%M %p"), ),
)
mp.contourf(
lons,
lats,
vals,
np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9]),
units="miles",
cmap=cm.get_cmap("gray"),
)
mp.plot_values(lons, lats, vals, "%.1f", valmask)
mp.drawcounties()
pqstr = ("plot ac %s00 iowa_vsby.png vsby_contour_%s00.png png"
"") % (
datetime.datetime.utcnow().strftime("%Y%m%d%H"),
datetime.datetime.utcnow().strftime("%H"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
pgconn = get_dbconn('iem', user='******')
df = read_sql("""
select station, st_x(geom), st_y(geom), snow_jul1, snow_jul1_normal
from cli_data c JOIN stations t on (t.id = c.station)
WHERE c.valid = 'YESTERDAY' and t.network = 'NWSCLI'
and snow_jul1 is not null and snow_jul1_normal is not null
and t.id not in ('RAP', 'DVN', 'FGF', 'OAX', 'MPX')
""",
pgconn,
index_col='station')
df['departure'] = df['snow_jul1'] - df['snow_jul1_normal']
df['colors'] = df['departure'].apply(lambda x: '#ff0000'
if x < 0 else '#0000ff')
yesterday = datetime.datetime.today() - datetime.timedelta(days=1)
year = yesterday.year if yesterday.month > 6 else yesterday.year - 1
mp = MapPlot(sector='midwest',
axisbg='white',
title='NWS Total Snowfall (inches) thru %s' %
(yesterday.strftime("%-d %B %Y"), ),
subtitle=('1 July %s - %s') % (
year,
datetime.datetime.today().strftime("%-d %B %Y"),
))
mp.plot_values(df['st_x'].values,
df['st_y'].values,
df['snow_jul1'].values,
fmt='%.1f',
labelbuffer=5)
pqstr = ("data ac %s0000 summary/mw_season_snowfall.png "
"mw_season_snowfall.png png") % (
datetime.datetime.today().strftime("%Y%m%d"), )
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
# Depature
mp = MapPlot(sector='midwest',
axisbg='white',
title='NWS Total Snowfall Departure (inches) thru %s' %
(yesterday.strftime("%-d %B %Y"), ),
subtitle=('1 July %s - %s') % (
year,
datetime.datetime.today().strftime("%-d %B %Y"),
))
mp.plot_values(df['st_x'].values,
df['st_y'].values,
df['departure'].values,
color=df['colors'].values,
fmt='%.1f',
labelbuffer=5)
pqstr = ("data ac %s0000 summary/mw_season_snowfall_departure.png "
"mw_season_snowfall_departure.png png") % (
datetime.datetime.today().strftime("%Y%m%d"), )
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
state = fdict.get('state', 'IA')[:2]
varname = fdict.get('var', 'total_precip')
sector = fdict.get('sector', 'state')
opt = fdict.get('opt', 'both')
over = fdict.get('over', 'monthly')
month = int(fdict.get('month', datetime.date.today().month))
df = read_sql("""
WITH data as (
SELECT station, extract(month from valid) as month,
sum(precip) as total_precip, avg(high) as avg_high,
avg(low) as avg_low, avg((high+low)/2.) as avg_temp
from ncdc_climate81 GROUP by station, month)
SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat, month,
total_precip, avg_high, avg_low, avg_temp from data d JOIN stations t
ON (d.station = t.id) WHERE t.network = 'NCDC81' and
t.state in ('IA', 'ND', 'SD', 'NE', 'KS', 'MO', 'IL', 'WI', 'MN', 'MI',
'IN', 'OH', 'KY')
""", pgconn, index_col=['station', 'month'])
if over == 'monthly':
title = "%s %s" % (calendar.month_name[month], PDICT3[varname])
df.reset_index(inplace=True)
df2 = df[df['month'] == month]
else:
title = "Annual %s" % (PDICT3[varname], )
if varname == 'total_precip':
df2 = df.sum(axis=0, level='station')
else:
df2 = df.mean(axis=0, level='station')
df2['lat'] = df['lat'].mean(axis=0, level='station')
df2['lon'] = df['lon'].mean(axis=0, level='station')
m = MapPlot(sector=sector, state=state, axisbg='white',
title=('NCEI 1981-2010 Climatology of %s'
) % (title,),
subtitle=('based on National Centers for '
'Environmental Information (NCEI) 1981-2010'
' Climatology'))
levels = np.linspace(df2[varname].min(), df2[varname].max(), 10)
levels = [round(x, PRECISION[varname]) for x in levels]
if opt in ['both', 'contour']:
m.contourf(df2['lon'].values, df2['lat'].values,
df2[varname].values, levels, units=UNITS[varname])
if sector == 'state':
m.drawcounties()
if opt in ['both', 'values']:
m.plot_values(df2['lon'].values, df2['lat'].values,
df2[varname].values,
fmt='%%.%if' % (PRECISION[varname],))
return m.fig, df
def test_drawrandomtext():
"""See if we can handle the fun that is drawing random text"""
mp = MapPlot(sector='iowa', title='Fun Text, here and there',
continentalcolor='white', debug=True, nocaption=True)
mp.plot_values([-94, -92, -91, -92],
[42, 41, 43, 42.4],
['One', 'Two\nTwo', 'Three\nThree\nThree',
'Four\nFour\nFour\nFour'], showmarker=True)
return mp.fig
def test_overlap():
""" Do some checking of our overlaps logic """
mp = MapPlot(sector='midwest', continentalcolor='white', nocaption=True)
lons = np.linspace(-99, -90, 100)
lats = np.linspace(38, 44, 100)
vals = lats
labels = ['%.2f' % (s,) for s in lats]
mp.plot_values(lons, lats, vals, fmt='%.2f', labels=labels)
return mp.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')
from pyiem.plot import MapPlot
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
varname = ctx['var']
year = ctx['year']
popt = ctx['popt']
threshold = ctx['threshold']
table = "alldata_%s" % (sector, )
df = read_sql("""
WITH data as (
SELECT station, """ + SQLOPT[varname] + """ as doy
from """ + table + """
WHERE year = %s GROUP by station
)
select station, doy, st_x(geom) as lon, st_y(geom) as lat
from data d JOIN stations t on (d.station = t.id) WHERE
t.network = %s and substr(station, 3, 4) != '0000'
and substr(station, 3, 1) != 'C' and doy not in (0, 400) ORDER by doy
""",
pgconn,
params=(threshold, year, '%sCLIMATE' % (sector, )),
index_col='station')
if df.empty:
return "No data found!"
def f(val):
ts = datetime.date(year, 1, 1) + datetime.timedelta(days=(val - 1))
return ts.strftime("%-m/%-d")
df['pdate'] = df['doy'].apply(f)
mp = MapPlot(sector='state',
state=sector,
continental_color='white',
nocaption=True,
title="%s %s %s$^\circ$F" %
(year, PDICT2[varname], threshold),
subtitle='based on NWS COOP and IEM Daily Estimates')
levs = np.linspace(df['doy'].min() - 3, df['doy'].max() + 3, 7, dtype='i')
levlables = map(f, levs)
if popt == 'contour':
mp.contourf(df['lon'],
df['lat'],
df['doy'],
levs,
clevlabels=levlables)
mp.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
mp.drawcounties()
return mp.fig, df
def plot(df):
"""Diagnostic"""
m = MapPlot(sector="midwest", continentalcolor="white")
m.plot_values(
df["lon"].values,
df["lat"].values,
df["highdata"].values,
labelbuffer=0,
)
m.postprocess(filename="test.png")
m.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
nt = NetworkTable(["AWOS", "IA_ASOS"])
pgconn = get_dbconn("iem", user="******")
df = read_sql(
"""
SELECT id as station, min(feel) as wcht from current_log c JOIN stations t
on (c.iemid = t.iemid) WHERE t.network in ('IA_ASOS', 'AWOS')
and valid >= %s and valid < %s + '24 hours'::interval
and feel is not null and sknt > 0 GROUP by id
""",
pgconn,
params=(ts, ts),
index_col="station",
)
routes = "ac"
if not realtime:
routes = "a"
lons = []
lats = []
vals = []
labels = []
for station, row in df.iterrows():
lons.append(nt.sts[station]["lon"])
lats.append(nt.sts[station]["lat"])
vals.append(row["wcht"])
labels.append(station)
pqstr = ("plot %s %s00 summary/iowa_min_windchill.png "
"summary/iowa_min_windchill.png png") % (routes,
ts.strftime("%Y%m%d%H"))
mp = MapPlot(
title=(r"%s Minimum Wind Chill Temperature $^\circ$F") %
(ts.strftime("%-d %b %Y"), ),
subtitle="Calm conditions are excluded from analysis",
continentalcolor="white",
)
mp.plot_values(
lons,
lats,
vals,
"%.1f",
labels=labels,
textsize=12,
labelbuffer=5,
labeltextsize=10,
)
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
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 plot_precip_month(valid):
""" Go Main Go
Args:
valid (datetime): The timestamp we are interested in!
"""
pgconn = get_dbconn("iem", user="******")
cursor = pgconn.cursor()
d1 = valid.replace(day=1)
d2 = d1 + datetime.timedelta(days=35)
d2 = d2.replace(day=1)
cursor.execute(
"""SELECT sum(pday), id, st_x(geom), st_y(geom)
from summary s JOIN stations t on
(t.iemid = s.iemid) WHERE s.day >= %s and s.day < %s
and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
'MN_COOP')
and pday is not null and pday >= 0 and
extract(hour from coop_valid) between 5 and 10
GROUP by id, st_x, st_y""",
(d1.date(), d2.date()),
)
labels = []
vals = []
lats = []
lons = []
for row in cursor:
labels.append(row[1])
vals.append(pretty(row[0]))
lats.append(row[3])
lons.append(row[2])
mp = MapPlot(
title="%s NWS COOP Month Precipitation Totals [inch]"
% (valid.strftime("%-d %b %Y"),),
subtitle="Reports valid between 6 and 9 AM",
axisbg="white",
figsize=(10.24, 7.68),
)
mp.plot_values(lons, lats, vals, fmt="%s", labels=labels, labelcolor="tan")
mp.drawcounties()
pqstr = "plot ac %s0000 coopMonthPlot.gif coopMonthPlot.gif gif" % (
valid.strftime("%Y%m%d"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
pgconn.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("iem", user="******")
icursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
day1 = datetime.date.today().replace(day=1)
day2 = (day1 + datetime.timedelta(days=35)).replace(day=1)
lats = []
lons = []
vals = []
valmask = []
table = "summary_%s" % (day1.year, )
icursor.execute(
"""
SELECT id, s.network, ST_x(s.geom) as lon, ST_y(s.geom) as lat,
avg( (max_tmpf + min_tmpf)/2.0 ) as avgt , count(*) as cnt
from """ + table + """ c JOIN stations s ON (s.iemid = c.iemid)
WHERE s.network in ('IA_ASOS', 'AWOS') and
day >= %s and day < %s
and max_tmpf > -30 and min_tmpf < 90 GROUP by id, s.network, lon, lat
""",
(day1, day2),
)
for row in icursor:
if row["cnt"] != now.day:
continue
lats.append(row["lat"])
lons.append(row["lon"])
vals.append(row["avgt"])
valmask.append(row["network"] in ["AWOS", "IA_ASOS"])
if len(vals) < 3:
return
mp = MapPlot(
axisbg="white",
title="Iowa %s Average Temperature" % (now.strftime("%Y %B"), ),
subtitle=("Average of the High + Low ending: %s"
"") % (now.strftime("%d %B"), ),
)
minval = int(min(vals))
maxval = max([int(max(vals)) + 3, minval + 11])
clevs = np.linspace(minval, maxval, 10, dtype="i")
mp.contourf(lons, lats, vals, clevs)
mp.drawcounties()
mp.plot_values(lons, lats, vals, "%.1f")
pqstr = "plot c 000000000000 summary/mon_mean_T.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("iem", user="******")
icursor = pgconn.cursor()
# Compute normal from the climate database
sql = """
select s.id, s.network,
ST_x(s.geom) as lon, ST_y(s.geom) as lat,
(case when c.pday < 0 or c.day is null then 0
else c.pday end) as rainfall
from summary_%s c, current c2, stations s
WHERE s.iemid = c2.iemid and c2.iemid = c.iemid and
c2.valid > (now() - '2 hours'::interval)
and c.day = 'TODAY'
and s.country = 'US' and (s.network ~* 'ASOS' or s.network = 'AWOS')
and s.state in ('IA','MN','WI','IL','MO','NE','KS','SD','ND')
""" % (
now.year,
)
lats = []
lons = []
vals = []
iavals = []
valmask = []
icursor.execute(sql)
for row in icursor:
lats.append(row[3])
lons.append(row[2])
vals.append(t(row[4]))
iowa = row[1] in ["AWOS", "IA_ASOS"]
valmask.append(iowa)
if iowa:
iavals.append(row[4])
if len(lats) < 3:
return
mp = MapPlot(
title="Iowa ASOS/AWOS Rainfall Reports",
axisbg="white",
subtitle="%s" % (now.strftime("%d %b %Y"),),
)
mp.drawcounties()
mp.plot_values(lons, lats, vals)
pqstr = "plot c 000000000000 summary/today_prec.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn('iem', user='******')
icursor = pgconn.cursor()
# Compute normal from the climate database
sql = """
select s.id, s.network,
ST_x(s.geom) as lon, ST_y(s.geom) as lat,
greatest(c.max_sknt, c.max_gust) as wind
from summary_%s c, current c2, stations s
WHERE s.iemid = c.iemid and c2.valid > 'TODAY' and c.day = 'TODAY'
and c2.iemid = s.iemid
and (s.network ~* 'ASOS' or s.network = 'AWOS') and s.country = 'US'
ORDER by lon, lat
""" % (now.year, )
lats = []
lons = []
vals = []
valmask = []
icursor.execute(sql)
for row in icursor:
if row[4] == 0 or row[4] is None:
continue
lats.append(row[3])
lons.append(row[2])
vals.append(speed(row[4], 'KT').value('MPH'))
valmask.append((row[1] in ['AWOS', 'IA_ASOS']))
if len(vals) < 5 or True not in valmask:
return
clevs = np.arange(0, 40, 2)
clevs = np.append(clevs, np.arange(40, 80, 5))
clevs = np.append(clevs, np.arange(80, 120, 10))
# Iowa
pqstr = "plot ac %s summary/today_gust.png iowa_wind_gust.png png" % (
now.strftime("%Y%m%d%H%M"), )
mp = MapPlot(title="Iowa ASOS/AWOS Peak Wind Speed Reports",
subtitle="%s" % (now.strftime("%d %b %Y"), ),
sector='iowa')
mp.contourf(lons, lats, vals, clevs, units='MPH')
mp.plot_values(lons, lats, vals, '%.0f', valmask=valmask, labelbuffer=10)
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
def doday(ts, realtime):
"""
Create a plot of precipitation stage4 estimates for some day
"""
nt = NetworkTable(['AWOS', 'IA_ASOS'])
pgconn = get_dbconn('iem', user='******')
df = read_sql("""
SELECT id as station, tmpf, sknt from current_log c JOIN stations t
on (c.iemid = t.iemid) WHERE t.network in ('IA_ASOS', 'AWOS')
and valid >= %s and valid < %s + '24 hours'::interval
and tmpf is not null and sknt > 0
""",
pgconn,
params=(ts, ts),
index_col=None)
df['wcht'] = windchill(df['tmpf'].values * units.degF, df['sknt'].values *
units.knots).to(units.degF).magnitude
gdf = df.groupby('station').min()
routes = 'ac'
if not realtime:
routes = 'a'
lons = []
lats = []
vals = []
labels = []
for station, row in gdf.iterrows():
lons.append(nt.sts[station]['lon'])
lats.append(nt.sts[station]['lat'])
vals.append(row['wcht'])
labels.append(station)
pqstr = ("plot %s %s00 summary/iowa_min_windchill.png "
"summary/iowa_min_windchill.png png") % (routes,
ts.strftime("%Y%m%d%H"))
mp = MapPlot(title=(r"%s Minimum Wind Chill Temperature $^\circ$F") %
(ts.strftime("%-d %b %Y"), ),
subtitle="Calm conditions are excluded from analysis",
continentalcolor='white')
mp.plot_values(lons,
lats,
vals,
'%.1f',
labels=labels,
textsize=12,
labelbuffer=5,
labeltextsize=10)
mp.drawcounties()
mp.postprocess(pqstr=pqstr, view=False)
mp.close()
def main():
"""Go Main Go"""
now = datetime.date.today()
pgconn = get_dbconn("iem", user="******")
icursor = pgconn.cursor()
day1 = now.replace(day=1)
day2 = (now + datetime.timedelta(days=35)).replace(day=1)
# Compute normal from the climate database
sql = """SELECT id,
sum(pday) as precip,
sum(CASE when pday is null THEN 1 ELSE 0 END) as missing,
ST_x(s.geom) as lon, ST_y(s.geom) as lat
from summary_%s c JOIN stations s
ON (s.iemid = c.iemid)
WHERE s.network in ('IA_COOP') and s.iemid = c.iemid and
day >= '%s' and day < '%s'
GROUP by id, lat, lon""" % (
now.year,
day1.strftime("%Y-%m-%d"),
day2.strftime("%Y-%m-%d"),
)
lats = []
lons = []
precip = []
labels = []
icursor.execute(sql)
for row in icursor:
if row[2] > (now.day / 3) or row[1] is None:
continue
sid = row[0]
labels.append(sid)
lats.append(row[4])
lons.append(row[3])
precip.append(row[1])
mp = MapPlot(
title="This Month's Precipitation [inch] (NWS COOP Network)",
subtitle=now.strftime("%b %Y"),
axisbg="white",
)
mp.plot_values(lons, lats, precip, fmt="%.2f", labels=labels)
mp.drawcounties()
pqstr = "plot c 000000000000 coopMonthPlot.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("coop")
ccursor = pgconn.cursor()
nt = NetworkTable("IACLIMATE")
# Compute normal from the climate database
sql = """SELECT station,
sum(gdd50(high, low)) as gdd
from alldata_ia WHERE year = %s and month = %s
GROUP by station""" % (
now.year,
now.month,
)
vals = []
lats = []
lons = []
ccursor.execute(sql)
for row in ccursor:
if row[0] not in nt.sts:
continue
lats.append(nt.sts[row[0]]["lat"])
lons.append(nt.sts[row[0]]["lon"])
vals.append(float(row[1]))
if len(vals) < 5:
sys.exit()
mp = MapPlot(
title="Iowa %s GDD Accumulation" % (now.strftime("%B %Y"), ),
axisbg="white",
)
mp.contourf(
lons,
lats,
vals,
np.linspace(int(min(vals)),
int(max(vals)) + 3, 10),
units="base 50",
)
mp.plot_values(lons, lats, vals, fmt="%.0f")
pqstr = "plot c 000000000000 summary/gdd_mon.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def plot_snowdepth(valid):
""" Go Main Go
Args:
valid (datetime): The timestamp we are interested in!
"""
pgconn = get_dbconn("iem", user="******")
cursor = pgconn.cursor()
cursor.execute(
"""SELECT snowd, id, st_x(geom), st_y(geom)
from summary s JOIN stations t on
(t.iemid = s.iemid) WHERE s.day = %s
and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
'MN_COOP')
and snowd is not null and snowd >= 0 and
extract(hour from coop_valid) between 5 and 10""",
(valid.date(),),
)
labels = []
vals = []
lats = []
lons = []
for row in cursor:
labels.append(row[1])
vals.append(pretty(row[0], precision=0))
lats.append(row[3])
lons.append(row[2])
mp = MapPlot(
title="%s NWS COOP Snowfall Depth Reports [inch]"
% (valid.strftime("%-d %b %Y"),),
subtitle="Reports valid between 6 and 9 AM",
axisbg="white",
figsize=(10.24, 7.68),
)
mp.plot_values(lons, lats, vals, fmt="%s", labels=labels, labelcolor="tan")
mp.drawcounties()
pqstr = "plot ac %s0000 coopSnowDepth.gif coopSnowDepth.gif gif" % (
valid.strftime("%Y%m%d"),
)
mp.postprocess(pqstr=pqstr)
mp.close()
pgconn.close()
def plot_snow_month(valid):
""" Go Main Go
Args:
valid (datetime): The timestamp we are interested in!
"""
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor()
d1 = valid.replace(day=1)
d2 = d1 + datetime.timedelta(days=35)
d2 = d2.replace(day=1)
cursor.execute(
"""SELECT sum(snow), id, st_x(geom), st_y(geom)
from summary s JOIN stations t on
(t.iemid = s.iemid) WHERE s.day >= %s and s.day < %s
and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
'MN_COOP')
and snow is not null and snow >= 0 and
extract(hour from coop_valid) between 5 and 10
GROUP by id, st_x, st_y""", (d1.date(), d2.date()))
labels = []
vals = []
lats = []
lons = []
for row in cursor:
labels.append(row[1])
vals.append(pretty(row[0], 0))
lats.append(row[3])
lons.append(row[2])
mp = MapPlot(title='%s NWS COOP Month Snowfall Totals [inch]' %
(valid.strftime("%-d %b %Y"), ),
subtitle='Reports valid between 6 and 9 AM',
axisbg='white',
figsize=(10.24, 7.68))
mp.plot_values(lons, lats, vals, fmt='%s', labels=labels, labelcolor='tan')
mp.drawcounties()
pqstr = "plot ac %s0000 coopMonthSPlot.gif coopMonthSPlot.gif gif" % (
valid.strftime("%Y%m%d"), )
mp.postprocess(pqstr=pqstr)
mp.close()
pgconn.close()
def main():
"""Go Main Go"""
now = datetime.datetime.now()
qdict = pyiem.tracker.loadqc()
pgconn = get_dbconn("iem")
icursor = pgconn.cursor()
# Compute normal from the climate database
sql = """SELECT id,
sum(pday) as precip,
ST_x(geom) as lon, ST_y(geom) as lat from summary_%s s, stations t
WHERE t.network in ('IA_ASOS', 'AWOS') and
extract(month from day) = %s
and extract(year from day) = extract(year from now())
and t.iemid = s.iemid GROUP by id, lat, lon
""" % (
now.year,
now.strftime("%m"),
)
lats = []
lons = []
precip = []
labels = []
icursor.execute(sql)
for row in icursor:
sid = row[0]
labels.append(sid)
lats.append(row[3])
lons.append(row[2])
if not qdict.get(sid, {}).get("precip", False) and row[1] is not None:
precip.append("%.2f" % (row[1], ))
else:
precip.append("M")
mp = MapPlot(
title="This Month's Precipitation [inch]",
subtitle=now.strftime("%b %Y"),
axisbg="white",
)
mp.plot_values(lons, lats, precip, labels=labels)
pqstr = "plot c 000000000000 summary/month_prec.png bogus png"
mp.postprocess(pqstr=pqstr)
mp.close()
def plot_precip_month(valid):
""" Go Main Go
Args:
valid (datetime): The timestamp we are interested in!
"""
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor()
d1 = valid.replace(day=1)
d2 = d1 + datetime.timedelta(days=35)
d2 = d2.replace(day=1)
cursor.execute("""SELECT sum(pday), id, st_x(geom), st_y(geom)
from summary s JOIN stations t on
(t.iemid = s.iemid) WHERE s.day >= %s and s.day < %s
and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
'MN_COOP')
and pday is not null and pday >= 0 and
extract(hour from coop_valid) between 5 and 10
GROUP by id, st_x, st_y""", (d1.date(), d2.date()))
labels = []
vals = []
lats = []
lons = []
for row in cursor:
labels.append(row[1])
vals.append(pretty(row[0]))
lats.append(row[3])
lons.append(row[2])
m = MapPlot(title='%s NWS COOP Month Precipitation Totals [inch]' % (
valid.strftime("%-d %b %Y"),),
subtitle='Reports valid between 6 and 9 AM',
axisbg='white', figsize=(10.24, 7.68))
m.plot_values(lons, lats, vals, fmt='%s', labels=labels,
labelcolor='tan')
m.drawcounties()
pqstr = "plot ac %s0000 coopMonthPlot.gif coopMonthPlot.gif gif" % (
valid.strftime("%Y%m%d"),)
m.postprocess(pqstr=pqstr)
m.close()
pgconn.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
pgconn = psycopg2.connect(dbname='coop', host='iemdb', user='******')
ctx = util.get_autoplot_context(fdict, get_description())
sector = ctx['sector']
varname = ctx['var']
year = ctx['year']
popt = ctx['popt']
threshold = ctx['threshold']
table = "alldata_%s" % (sector,)
df = read_sql("""
WITH data as (
SELECT station, """ + SQLOPT[varname] + """ as doy
from """ + table + """
WHERE year = %s GROUP by station
)
select station, doy, st_x(geom) as lon, st_y(geom) as lat
from data d JOIN stations t on (d.station = t.id) WHERE
t.network = %s and substr(station, 3, 4) != '0000'
and substr(station, 3, 1) != 'C' and doy not in (0, 400) ORDER by doy
""", pgconn, params=(threshold, year, '%sCLIMATE' % (sector,)),
index_col='station')
if len(df.index) == 0:
return "No data found!"
def f(val):
ts = datetime.date(year, 1, 1) + datetime.timedelta(days=(val - 1))
return ts.strftime("%-m/%-d")
df['pdate'] = df['doy'].apply(f)
m = MapPlot(sector='state', state=sector, axisbg='white', nocaption=True,
title="%s %s %s$^\circ$F" % (year, PDICT2[varname], threshold),
subtitle='based on NWS COOP and IEM Daily Estimates')
levs = np.linspace(df['doy'].min() - 3, df['doy'].max() + 3, 7, dtype='i')
levlables = map(f, levs)
if popt == 'contour':
m.contourf(df['lon'], df['lat'], df['doy'], levs, clevlabels=levlables)
m.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
m.drawcounties()
return m.fig, df
def main():
"""Go Main Go"""
now = datetime.datetime.now()
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor()
nt = NetworkTable("IACLIMATE")
# Compute normal from the climate database
sql = """SELECT station,
sum(sdd86(high, low)) as sdd
from alldata_ia WHERE year = %s and month = %s
GROUP by station""" % (
now.year,
now.month,
)
lats = []
lons = []
sdd86 = []
valmask = []
ccursor.execute(sql)
for row in ccursor:
lats.append(nt.sts[row[0]]["lat"])
lons.append(nt.sts[row[0]]["lon"])
sdd86.append(float(row[1]))
valmask.append(True)
if len(sdd86) < 5:
LOG.debug("aborting due to %s obs", len(sdd86))
sys.exit()
mp = MapPlot(
axisbg="white",
title="Iowa %s SDD Accumulation" % (now.strftime("%B %Y"), ),
)
if max(sdd86) > 5:
mp.contourf(lons, lats, sdd86,
range(int(min(sdd86) - 1), int(max(sdd86) + 1)))
else:
mp.plot_values(lons, lats, sdd86, fmt="%.0f")
pqstr = "plot c 000000000000 summary/sdd_mon.png bogus png"
mp.postprocess(view=False, pqstr=pqstr)
mp.close()
def test_issue98_labelbar():
"""Sometimes our label bar sucks."""
mp = MapPlot(
title='Proportional Colorbar with some rotation',
sector='iowa', nocaption=True)
cmap = plot.maue()
cmap.set_under('white')
cmap.set_over('black')
clevs = np.arange(0, 1., 0.1)
clevs[-1] = 3.987654
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
mp.plot_values(
[-94, -92, -91, -92], [42, 41, 43, 42.4],
['0.5', '0.25', '1.0', '5.0'], color=cmap(norm([0.5, 0.25, 1.0, 5.0])),
showmarker=True
)
mp.draw_colorbar(clevs, cmap, norm, spacing='proportional')
return mp.fig
def main():
"""Go Main Go"""
pgconn = get_dbconn("iem", user="******")
icursor = pgconn.cursor()
now = datetime.datetime.now()
sql = """
WITH lows as (
SELECT c.iemid, min(tmpf) as low12z from current_log c JOIN stations s
ON (s.iemid = c.iemid)
WHERE tmpf > -90 and valid > '%s 00:00:00+00' and valid < '%s 12:00:00+00'
and s.network in ('IA_ASOS', 'AWOS') GROUP by c.iemid
)
select t.id, ST_x(t.geom) as lon, ST_y(t.geom) as lat, l.low12z from
lows l JOIN stations t on (t.iemid = l.iemid)
""" % (
now.strftime("%Y-%m-%d"),
now.strftime("%Y-%m-%d"),
)
lats = []
lons = []
vals = []
valmask = []
labels = []
icursor.execute(sql)
for row in icursor:
lats.append(row[2])
lons.append(row[1])
vals.append(float(row[3]))
labels.append(row[0])
valmask.append(True)
mp = MapPlot(
title="Iowa ASOS/AWOS 12Z Morning Low Temperature",
subtitle="%s" % (now.strftime("%d %b %Y"), ),
axisbg="white",
)
mp.drawcounties()
mp.plot_values(lons, lats, vals, fmt="%.0f", labels=labels, labelbuffer=5)
pqstr = ("plot ac %s summary/iowa_asos_12z_low.png "
"iowa_asos_12z_low.png png") % (now.strftime("%Y%m%d%H%M"), )
mp.postprocess(pqstr=pqstr)
def main():
"""Go Main!"""
nt = NetworkTable("IACLIMATE")
pgconn = get_dbconn('coop')
df = read_sql("""
with monthly as (
select station, year, month, avg((high+low)/2.) from alldata_ia
WHERE day < '2018-06-01' and high is not null
GROUP by station, year, month),
agg as (
select station, year, month, avg,
lag(avg) OVER (PARTITION by station ORDER by year ASC, month ASC)
from monthly),
agg2 as (
select station, year, month, avg, lag, avg - lag as val,
rank() OVER (PARTITION by station ORDER by avg - lag DESC)
from agg WHERE lag is not null)
select * from agg2 where rank = 1 ORDER by station
""", pgconn, index_col='station')
df['lat'] = 0.
df['lon'] = 0.
for station, _ in df.iterrows():
if station in nt.sts and station != 'IA0000' and station[2] != 'C':
df.at[station, 'lat'] = nt.sts[station]['lat']
df.at[station, 'lon'] = nt.sts[station]['lon']
mp = MapPlot(title="Largest Positive Change in Month to Month Average Temperature",
subtitle=('values in red set record for April to May 2018'), sector='state',
state='IA',
drawstates=True, continentalcolor='white')
df2 = df[df['year'] == 2018]
mp.plot_values(df2['lon'].values,
df2['lat'].values,
df2['val'].values, fmt='%.1f', textsize=12, labelbuffer=5,
color='r')
df2 = df[df['year'] != 2018]
mp.plot_values(df2['lon'].values,
df2['lat'].values,
df2['val'].values, fmt='%.1f', textsize=12, labelbuffer=5,
color='b')
mp.drawcounties()
mp.postprocess(filename='test.png')
mp.close()
def do_month(ts, routes="m"):
"""
Generate the plot for a given month, please
"""
pgconn = get_dbconn("coop", user="******")
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
nt = NetworkTable("IACLIMATE")
sql = """SELECT station, sum(precip) as total, max(day) as lastday
from alldata_ia WHERE year = %s and month = %s
and station != 'IA0000' and substr(station,2,1) != 'C'
GROUP by station""" % (
ts.year,
ts.month,
)
lats = []
lons = []
vals = []
lastday = None
ccursor.execute(sql)
for row in ccursor:
if row["station"] not in nt.sts:
continue
if lastday is None:
lastday = row["lastday"]
lats.append(nt.sts[row["station"]]["lat"])
lons.append(nt.sts[row["station"]]["lon"])
vals.append(row["total"])
mp = MapPlot(
title="%s - %s"
% (ts.strftime("%d %B %Y"), lastday.strftime("%d %B %Y")),
subtitle="%s Total Precipitation [inch]" % (ts.strftime("%B %Y"),),
)
mp.contourf(
lons, lats, vals, [0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6, 7]
)
mp.plot_values(lons, lats, vals, fmt="%.2f")
pqstr = (
"plot %s %s summary/iemre_iowa_total_precip.png "
"%s/summary/iemre_iowa_total_precip.png png"
) % (routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
mp.postprocess(pqstr=pqstr)
def main(days, argv):
"""Go!"""
today = datetime.date.today()
routes = "ac"
if len(argv) == 4:
today = datetime.date(int(argv[1]), int(argv[2]), int(argv[3]))
routes = "a"
sixago = today - datetime.timedelta(days=(days - 1))
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
# Compute normal from the climate database
df = read_sql("""
select s.id, ST_x(s.geom) as lon, ST_y(s.geom) as lat,
sum(pday) as rainfall
from summary c JOIN stations s on (c.iemid = s.iemid)
WHERE day >= %s and day <= %s
and s.network in ('AWOS', 'IA_ASOS')
and pday >= 0 and pday < 30
GROUP by s.id, lon, lat
""",
pgconn,
params=(sixago, today),
index_col='id')
df['label'] = df['rainfall'].apply(fmter)
mp = MapPlot(title=('Iowa %s Day Precipitation Total [inch] (ASOS/AWOS)') %
(days, ),
subtitle=("%s - %s inclusive") %
(sixago.strftime("%d %b %Y"), today.strftime("%d %b %Y")),
continentalcolor='white')
mp.plot_values(df['lon'].values,
df['lat'].values,
df['label'].values,
'%s',
labels=df.index.values,
labelbuffer=5)
mp.drawcounties()
pqstr = ("plot %s %s0000 summary/%sday/ia_precip.png "
"summary/%sday/ia_precip.png png") % (
routes, today.strftime("%Y%m%d"), days, days)
mp.postprocess(pqstr=pqstr)
mp.close()
def plot_precip(valid):
""" Go Main Go
Args:
valid (datetime): The timestamp we are interested in!
"""
pgconn = get_dbconn('iem', user='******')
cursor = pgconn.cursor()
cursor.execute(
"""SELECT pday, id, st_x(geom), st_y(geom)
from summary s JOIN stations t on
(t.iemid = s.iemid) WHERE s.day = %s
and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
'MN_COOP')
and pday is not null and pday >= 0 and
extract(hour from coop_valid) between 5 and 10""", (valid.date(), ))
labels = []
vals = []
lats = []
lons = []
for row in cursor:
labels.append(row[1])
vals.append(pretty(row[0]))
lats.append(row[3])
lons.append(row[2])
mp = MapPlot(title='%s NWS COOP 24 Hour Precipitation Reports [inch]' %
(valid.strftime("%-d %b %Y"), ),
subtitle='Reports valid between 6 and 9 AM',
axisbg='white',
figsize=(10.24, 7.68))
mp.plot_values(lons, lats, vals, fmt='%s', labels=labels, labelcolor='tan')
mp.drawcounties()
pqstr = "plot ac %s0000 coopPrecPlot.gif coopPrecPlot.gif gif" % (
valid.strftime("%Y%m%d"), )
mp.postprocess(pqstr=pqstr)
mp.close()
pgconn.close()
def plot_snowdepth(valid):
""" Go Main Go
Args:
valid (datetime): The timestamp we are interested in!
"""
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor()
cursor.execute("""SELECT snowd, id, st_x(geom), st_y(geom)
from summary s JOIN stations t on
(t.iemid = s.iemid) WHERE s.day = %s
and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
'MN_COOP')
and snowd is not null and snowd >= 0 and
extract(hour from coop_valid) between 5 and 10""", (valid.date(),))
labels = []
vals = []
lats = []
lons = []
for row in cursor:
labels.append(row[1])
vals.append(pretty(row[0], precision=0))
lats.append(row[3])
lons.append(row[2])
m = MapPlot(title='%s NWS COOP Snowfall Depth Reports [inch]' % (
valid.strftime("%-d %b %Y"),),
subtitle='Reports valid between 6 and 9 AM',
axisbg='white', figsize=(10.24, 7.68))
m.plot_values(lons, lats, vals, fmt='%s', labels=labels,
labelcolor='tan')
m.drawcounties()
pqstr = "plot ac %s0000 coopSnowDepth.gif coopSnowDepth.gif gif" % (
valid.strftime("%Y%m%d"),)
m.postprocess(pqstr=pqstr)
m.close()
pgconn.close()
def main():
"""Go Main Go."""
nt = NetworkTable(['IA_ASOS', 'AWOS'])
pgconn = get_dbconn('iem')
cursor = pgconn.cursor()
lats = []
lons = []
vals = []
cursor.execute("""WITH today as (
SELECT id, max_tmpf from summary_2017 s JOIN stations t
ON (s.iemid = t.iemid) WHERE t.network in ('IA_ASOS', 'AWOS')
and day = '2017-01-18'),
agg as (
SELECT t.id, max(day) from summary s, stations t, today t2
WHERE s.iemid = t.iemid and t.id = t2.id and
t.network in ('IA_ASOS', 'AWOS') and day > '2016-12-01'
and day < '2017-01-18' and s.max_tmpf >= t2.max_tmpf
GROUP by t.id)
SELECT id, max from agg ORDER by max ASC
""")
colors = []
for row in cursor:
lats.append(nt.sts[row[0]]['lat'])
lons.append(nt.sts[row[0]]['lon'])
vals.append(row[1].strftime("%-m/%-d"))
colors.append('r' if row[1] == datetime.date(2016, 12, 25) else 'k')
m = MapPlot(continentalcolor='white',
title=("Iowa ASOS/AWOS Last Date of as Warm of "
"Daily High as 18 Jan 2017"))
m.plot_values(lons, lats, vals, fmt='%s', color=colors, labelbuffer=5)
m.drawcounties()
m.postprocess(filename='170119.png')
def main():
"""Go"""
pgconn = psycopg2.connect(database='iem', host='localhost', port=5555,
user='******')
df = read_sql("""
SELECT id, network, ST_x(geom) as lon,
ST_y(geom) as lat, min(min_tmpf)
from summary_2017 s JOIN stations t on (t.iemid = s.iemid)
WHERE network IN ('IA_ASOS','AWOS') and min_tmpf > -50 and
day > '2017-08-01' and id not in ('XXX')
GROUP by id, network, lon, lat ORDER by min ASC
""", pgconn, index_col=None)
mp = MapPlot(title=r"2017 Fall Season Minimum Temperature $^\circ$F",
axisbg='white',
subtitle=('Automated Weather Stations ASOS/AWOS, '
'Valid Fall 2017 thru %s'
) % (datetime.datetime.now().strftime("%d %b %Y"),))
# m.contourf(lons, lats, vals, np.arange(-30,1,4))
mp.plot_values(df['lon'].values, df['lat'].values, df['min'].values,
'%.0f', labels=df['id'].values, labelbuffer=3)
mp.drawcounties()
mp.postprocess(filename='test.png')
import psycopg2
from pyiem.plot import MapPlot
IEM = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = IEM.cursor()
cursor.execute("""select station, st_x(geom), st_y(geom),
precip_jun1 - precip_jun1_normal from
cli_data c JOIN stations s on (s.id = c.station)
WHERE s.network = 'NWSCLI' and c.valid = '2015-08-12' """)
lats = []
lons = []
colors = []
vals = []
for row in cursor:
if row[3] is None or row[0] in ['KGFK', 'KRAP']:
continue
lats.append(row[2])
lons.append(row[1])
vals.append(row[3])
colors.append('#ff0000' if row[3] < 0 else '#00ff00')
m = MapPlot(sector='midwest', axisbg='white',
title='NWS 1 June - 12 August Precipitation Departure [inches]',
subtitle='12 August 2015 based on NWS issued CLI Reports')
m.plot_values(lons, lats, vals, fmt='%.2f', textsize=16, color=colors)
m.postprocess(filename='150813.png')
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
from pyiem.plot import MapPlot
import matplotlib.cm as cm
pgconn = psycopg2.connect(database='coop', host='iemdb', user='******')
sector = fdict.get('sector', 'IA')
date1 = datetime.datetime.strptime(fdict.get('date1', '2015-01-01'),
'%Y-%m-%d')
date2 = datetime.datetime.strptime(fdict.get('date2', '2015-02-01'),
'%Y-%m-%d')
varname = fdict.get('var', 'precip_depart')
table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
df = read_sql("""
WITH obs as (
SELECT station, gddxx(50, 86, high, low) as gdd50,
sday, high, low, precip from """ + table + """ WHERE
day >= %s and day < %s and
substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
climo as (
SELECT station, to_char(valid, 'mmdd') as sday, precip, high, low,
gdd50 from climate51),
combo as (
SELECT o.station, o.precip - c.precip as precip_diff,
o.high, o.low, o.gdd50, c.gdd50 as cgdd50,
o.gdd50 - c.gdd50 as gdd50_diff,
(o.high + o.low)/2. - (c.high + c.low)/2. as temp_diff
from obs o JOIN climo c ON
(o.station = c.station and o.sday = c.sday)),
agg as (
SELECT station, sum(precip_diff) as precip_depart,
min(low) as min_low_temp, sum(gdd50_diff) as gdd_depart,
avg(temp_diff) as avg_temp_depart, sum(gdd50) as gdd_sum,
sum(cgdd50) as cgdd_sum
from combo GROUP by station)
SELECT d.station, d.precip_depart, d.min_low_temp, d.avg_temp_depart,
d.gdd_depart, d.gdd_sum, d.cgdd_sum,
ST_x(t.geom) as lon, ST_y(t.geom) as lat
from agg d JOIN stations t on (d.station = t.id)
WHERE t.network ~* 'CLIMATE'
""", pgconn, params=(date1, date2), index_col='station')
sector2 = "state" if sector != 'midwest' else 'midwest'
m = MapPlot(sector=sector2, state=sector, axisbg='white',
title=('%s - %s %s [%s]'
) % (date1.strftime("%d %b %Y"),
date2.strftime("%d %b %Y"), PDICT2.get(varname),
UNITS.get(varname)),
subtitle=('%s is compared with 1951-%s Climatology'
' to compute departures'
) % (date1.year, datetime.date.today().year - 1))
if varname in ['precip_depart', 'avg_temp_depart']:
rng = df[varname].abs().describe(percentiles=[0.95])['95%']
clevels = np.linspace(0 - rng, rng, 7)
fmt = '%.2f'
else:
minv = df[varname].min() - 5
maxv = df[varname].max() + 5
clevels = np.linspace(minv, maxv, 6, dtype='i')
fmt = '%.0f'
clevlabels = [fmt % x for x in clevels]
cmap = cm.get_cmap('RdYlBu' if varname == 'precip_depart' else 'RdYlBu_r')
cmap.set_bad('white')
m.contourf(df['lon'].values, df['lat'].values,
df[varname].values, clevels, clevlabels=clevlabels,
cmap=cmap, units=UNITS.get(varname))
m.plot_values(df['lon'].values, df['lat'].values,
df[varname].values, fmt=fmt, labelbuffer=10)
if sector == 'IA':
m.drawcounties()
return m.fig, df
hole = np.array(hole)
lons = []
lats = []
vals = []
for key in cnts.keys():
if key[2] == 'C' or key == 'IA0000':
continue
lons.append( nt.sts[key]['lon'] )
lats.append( nt.sts[key]['lat'] )
vals.append( cnts[key] )
m = MapPlot(sector='iowa', title='10 Sept 2013 US Drought Monitor',
subtitle=r"Count of 30 / 60 / 90 / 120 Day Standardized Precip Index below -1.2 (D2+) $\sigma$")
m.plot_values(lons, lats, vals, textsize=18)
colors = ['#f6eb13', '#ffcc66', '#ff9900', '#ff3333', '#FF00FF']
classes = ['D0: Abnormal', 'D1: Moderate', 'D2: Severe']
m.map.readshapefile('dm_current', 'dm', ax=m.ax)
for nshape, seg in enumerate(m.map.dm):
cat = m.map.dm_info[nshape]['DM']
poly=Polygon(seg, fc=colors[cat], ec='k', lw=.4, zorder=3)
m.ax.add_patch(poly)
x,y = m.map(hole[:,0], hole[:,1])
poly=Polygon(zip(x,y), fc='w', ec='k', lw=.4, zorder=3)
m.ax.add_patch(poly)
for i in range(len(classes)):
m.fig.text(0.9, 0.91 + ((i*2.5) /100.0), "%s" % (classes[i],),
GROUP by ugc_county ORDER by count DESC
""")
data = {}
for row in icursor:
data[row[0]] = row[1]
# Query out centroids of counties...
pcursor.execute("""
SELECT ugc, ST_x(ST_centroid(geom)) as lon,
ST_y(ST_centroid(geom)) as lat
from ugcs WHERE state = 'IA' and end_ts is null and
substr(ugc,3,1) = 'C'
""")
clons = []
clats = []
cvals = []
for row in pcursor:
cvals.append(data.get(row[0], 0))
clats.append(row[2])
clons.append(row[1])
m = MapPlot(axisbg='white', title='Iowa CoCoRaHS Observers Per County',
subtitle=("Sites with at least one report in past year "
"(Sep 2015-2016)"))
m.fill_ugcs(data, [1, 2, 3, 4, 5, 7, 10, 15, 20])
m.plot_values(clons, clats, cvals, labelbuffer=0)
m.drawcounties()
m.postprocess(filename='test.png')
sum(CASE when pday is null THEN 1 ELSE 0 END) as missing,
ST_x(s.geom) as lon, ST_y(s.geom) as lat from summary_%s c JOIN stations s
ON (s.iemid = c.iemid)
WHERE s.network in ('IA_COOP') and s.iemid = c.iemid and
day >= '%s' and day < '%s'
GROUP by id, lat, lon""" % (now.year, day1.strftime("%Y-%m-%d"),
day2.strftime("%Y-%m-%d"))
lats = []
lons = []
precip = []
labels = []
icursor.execute(sql)
for row in icursor:
if row[2] > (now.day / 3) or row[1] is None:
continue
sid = row[0]
labels.append(sid)
lats.append(row[4])
lons.append(row[3])
precip.append(row[1])
m = MapPlot(title="This Month's Precipitation [inch] (NWS COOP Network)",
subtitle=now.strftime("%b %Y"), axisbg='white')
m.plot_values(lons, lats, precip, fmt='%.2f', labels=labels)
m.drawcounties()
pqstr = "plot c 000000000000 coopMonthPlot.png bogus png"
m.postprocess(view=False, pqstr=pqstr)
from
(SELECT station, year, min(low) as m from alldata_ia where
month > 6 and sday < '1005' and year > 1950 GROUP by station, year) as foo
) as foo2 WHERE station != 'IA0000' and substr(station,3,1) != 'C'
GROUP by station ORDER by ma DESC
""")
lats = []
lons = []
vals = []
for row in ccursor:
if not nt.sts.has_key(row[0]):
continue
#if nt.sts[row[0]]['archive_begin'].year > 1893:
# continue
print row
#vals.append( float(row[3]) / float(row[4]) * 100.0 )
vals.append( row[1] / float(row[2]) * 100.0 )
lats.append( nt.sts[row[0]]['lat'] )
lons.append( nt.sts[row[0]]['lon'] )
import numpy as np
m = MapPlot(sector='iowa', title='Fall Minimum Temperature Percentile [100=warmest]',
subtitle='thru 5 October, 2014 vs 1951-2013')
cmap = cm.get_cmap('jet')
m.contourf(lons, lats, vals, np.arange(0,101,10), cmap=cmap)
m.plot_values(lons, lats, vals, '%.0f')
m.drawcounties()
m.postprocess(filename='test.ps')
iemplot.makefeature('test')
def run(basets, endts, view):
"""Generate this plot for the given basets"""
df = read_sql("""SELECT state,
max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
from lsrs WHERE type in ('S') and magnitude >= 0 and
valid > %s and valid < %s GROUP by state, lon, lat
""", POSTGIS, params=(basets, endts), index_col=None)
df['used'] = False
df['textplot'] = True
df.sort_values(by='val', ascending=False, inplace=True)
# Now, we need to add in zeros, lets say we are looking at a .25 degree box
mybuffer = 0.75
newrows = []
for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat+mybuffer)) &
(df['lon'] >= lon) & (df['lon'] < (lon+mybuffer))]
if len(df2.index) == 0:
newrows.append(dict(lon=(lon+mybuffer/2.),
lat=(lat+mybuffer/2.),
val=0, used=True, textplot=False))
continue
maxval = df.at[df2.index[0], 'val']
df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
dfnew = pd.DataFrame(newrows)
df = df.append(dfnew)
cdf = df[df['used']]
tdf = df[df['textplot']]
rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
cmap = nwssnow()
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
m.plot_values(tdf['lon'].values, tdf['lat'].values, tdf['val'].values,
fmt='%.1f')
pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
# slightly different title to help uniqueness
m = MapPlot(sector='iowa', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports valid over past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
m.drawcounties()
pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
m = MapPlot(sector='midwest', axisbg='white',
title="Local Storm Report Snowfall Total Analysis",
subtitle=("Reports past 12 hours: %s"
"" % (endts.strftime("%d %b %Y %I:%M %p"), )))
m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
cmap=cmap)
pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
m.postprocess(view=view, pqstr=pqstr)
m.close()
import psycopg2
from pyiem.network import Table as NetworkTable
from pyiem.plot import MapPlot
nt = NetworkTable(['IA_ASOS', 'AWOS'])
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor()
lats = []
lons = []
vals = []
cursor.execute("""SELECT id, max(day) from summary_2015 s JOIN stations t
ON (t.iemid = s.iemid) WHERE t.network in ('IA_ASOS', 'AWOS')
and min_tmpf < 31.5 and day < '2015-04-22' GROUP by id ORDER by max ASC""")
for row in cursor:
lats.append(nt.sts[row[0]]['lat'])
lons.append(nt.sts[row[0]]['lon'])
vals.append(row[1].strftime("%-d %b"))
m = MapPlot(axisbg='white',
title='Iowa ASOS/AWOS Last Date of Sub-Freezing Temperature',
subtitle='thru 12 AM 22 April 2015')
m.plot_values(lons, lats, vals, fmt='%s')
m.drawcounties()
m.postprocess(filename='150422.png')
vals = []
valmask = []
icursor.execute(sql)
for row in icursor:
if row[4] == 0 or row[4] is None:
continue
lats.append(row[3])
lons.append(row[2])
vals.append(speed(row[4], 'KT').value('MPH'))
valmask.append((row[1] in ['AWOS', 'IA_ASOS']))
if len(vals) < 5 or True not in valmask:
sys.exit(0)
clevs = numpy.arange(0, 40, 2)
clevs = numpy.append(clevs, numpy.arange(40, 80, 5))
clevs = numpy.append(clevs, numpy.arange(80, 120, 10))
# Iowa
pqstr = "plot ac %s summary/today_gust.png iowa_wind_gust.png png" % (
now.strftime("%Y%m%d%H%M"), )
m = MapPlot(title="Iowa ASOS/AWOS Peak Wind Speed Reports",
subtitle="%s" % (now.strftime("%d %b %Y"), ),
sector='iowa')
m.contourf(lons, lats, vals, clevs, units='MPH')
m.plot_values(lons, lats, vals, '%.0f', valmask=valmask,
labelbuffer=10)
m.drawcounties()
m.postprocess(pqstr=pqstr, view=False)
m.close()
def main():
"""Go Main Go."""
pgconn = get_dbconn('iem')
cursor = pgconn.cursor()
cursor.execute("""SELECT ST_x(geom) as lon, ST_y(geom) as lat,
pday from summary_2016 s JOIN stations t on (s.iemid = t.iemid)
where day = '2016-08-24' and network in ('WI_COOP', 'MN_COOP', 'IA_COOP')
and pday > 0 ORDER by pday DESC""")
llons = []
llats = []
vals = []
for row in cursor:
llons.append(row[0])
llats.append(row[1])
vals.append("%.2f" % (row[2], ))
pgconn = get_dbconn('postgis')
cursor = pgconn.cursor()
cursor.execute("""SELECT ST_x(geom) as lon, ST_y(geom) as lat,
max(magnitude) from lsrs_2016
where wfo in ('DMX', 'DVN', 'ARX') and typetext = 'HEAVY RAIN' and
valid > '2016-08-23' GROUP by lon, lat ORDER by max DESC""")
for row in cursor:
llons.append(row[0])
llats.append(row[1])
vals.append("%.2f" % (row[2], ))
img = Image.open("p24h_201608241200.png")
data = np.flipud(np.asarray(img))
# 7000,3500 == -130,-60,55,25 === -100 to -90 then 38 to 45
sample = data[1800:2501, 3000:4501]
sample = np.where(sample == 255, 0, sample)
data = sample * 0.01
data = np.where(sample > 100, 1. + (sample - 100) * 0.05, data)
data = np.where(sample > 180, 5. + (sample - 180) * 0.2, data)
lons = np.arange(-100, -84.99, 0.01)
lats = np.arange(38, 45.01, 0.01)
x, y = np.meshgrid(lons, lats)
buff = 0.5
m = MapPlot(sector='custom', projection='aea', west=-93.2,
east=-90.3, south=42.5,
north=44.,
title='NOAA MRMS 24 Hour RADAR-Only Precipitation Estimate',
subtitle=("MRMS valid 7 AM 23 Aug 2016 to 7 AM 24 Aug 2016, "
"NWS Local Storm + COOP Reports Overlaid"))
clevs = [0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8,
10]
m.contourf(x[:, :-1], y[:, :-1], data, clevs, cmap=nwsprecip())
nt = NetworkTable("IA_ASOS")
lo = []
la = []
va = []
for sid in nt.sts.keys():
lo.append(nt.sts[sid]['lon'])
la.append(nt.sts[sid]['lat'])
va.append(nt.sts[sid]['name'])
# m.plot_values(lo, la, va, fmt='%s', textsize=10, color='black')
m.drawcounties(zorder=4, linewidth=1.)
m.drawcities(labelbuffer=25, textsize=10, color='white',
outlinecolor='#000000')
m.textmask[:, :] = 0
m.plot_values(llons, llats, vals, fmt='%s', labelbuffer=5)
m.postprocess(filename='test.png')
from pyiem.plot import MapPlot
import datetime
import psycopg2
import numpy as np
IEM = psycopg2.connect(database='iem', host='iemdb', user='******')
icursor = IEM.cursor()
vals = []
lats = []
lons = []
valmask = []
icursor.execute("""SELECT id, network, ST_x(geom) as lon,
ST_y(geom) as lat, min(min_tmpf)
from summary_2015 s JOIN stations t on (t.iemid = s.iemid)
WHERE network IN ('IA_ASOS','AWOS') and min_tmpf > -50 and
day > '2012-08-01' and id not in ('XXX')
GROUP by id, network, lon, lat ORDER by min ASC""")
for row in icursor:
vals.append(row[4])
lats.append(row[3])
lons.append(row[2])
valmask.append(row[1] in ['IA_ASOS', 'AWOS'])
print row[4], row[0]
m = MapPlot(title="2014-2015 Winter Minimum Temperature $^\circ$F", axisbg='white',
subtitle='Automated Weather Stations, Valid Fall 2014 - %s' % (datetime.datetime.now().strftime("%d %b %Y"),)
)
#m.contourf(lons, lats, vals, np.arange(-30,1,4))
m.plot_values(lons, lats, vals, '%.0f', valmask)
m.drawcounties()
m.postprocess(filename='150223.png')
nc = netCDF4.Dataset("data/hght12_%s.nc" % (row[0].strftime("%Y%m%d"),), 'r')
idx = np.digitize([500.0], nc.variables['isobaric'][:])[0]
mslp = nc.variables['Geopotential_height'][0,idx,:,:]
mx = np.max(mslp[mslp >0])
if total is None:
total = mslp
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
else:
total += mslp
mlons = lons[mslp==mx]
mlats = lats[mslp==mx]
if np.max(mlons) - np.min(mlons) < 10 and np.min(lats) - np.max(lats) < 10:
xs.append( np.average(mlons) )
ys.append( np.average(mlats) )
xx.append('X')
else:
print mlons, mlats
print np.max(mlons) - np.min(mlons), np.min(lats) - np.max(lats)
nc.close()
m = MapPlot('conus', title='1979-2012 NCEP NARR Composite 500 hPa Geopotential Heights',
subtitle='12 UTC analysis for %s days in August where Des Moines hit 100+$^\circ$F High' % (cursor.rowcount,))
m.pcolormesh(lons, lats, total / float(cursor.rowcount), np.arange(5650,5951,20),
units='meters')
#x,y = m.map(xs,ys)
#print xx
m.plot_values(xs,ys, xx, '%s', textsize=16)
m.postprocess(filename='test.ps')
import iemplot
iemplot.makefeature('test')
