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()
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()
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("""
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:
