def avgWinds(self):
if not self.aSknt:
self.sped = None
self.drct = None
return
self.avg_sknt = int(float(sum(self.aSknt)) / float(len(self.aSknt)))
utot = 0
vtot = 0
for s, d in zip(self.aSknt, self.aDrct):
u, v = uv(s, d)
if s > self.xsped:
self.xsped = s * 1.150
self.xdrct = d
self.xdrctTxt = util.drct2text(d)
utot += u
vtot += v
uavg = utot / len(self.aSknt)
vavg = vtot / len(self.aSknt)
self.avg_drct = mydir(uavg, vavg)
self.avg_drctTxt = util.drct2text(self.avg_drct)
self.aSknt = []
self.aDrct = []
def test_drct2text():
""" Test conversion of drct2text """
assert util.drct2text(360) == "N"
assert util.drct2text(90) == "E"
# A hack to get move coverage
for i in range(360):
util.drct2text(i)
def avgWinds(self):
if not self.aSknt:
self.sped = None
self.drct = None
return
self.avg_sknt = int(float(sum(self.aSknt)) / float(len(self.aSknt)))
utot = 0
vtot = 0
for s, d in zip(self.aSknt, self.aDrct):
u, v = uv(s, d)
if s > self.xsped:
self.xsped = s * 1.150
self.xdrct = d
self.xdrctTxt = util.drct2text(d)
utot += u
vtot += v
uavg = utot / len(self.aSknt)
vavg = vtot / len(self.aSknt)
self.avg_drct = mydir(uavg, vavg)
self.avg_drctTxt = util.drct2text(self.avg_drct)
self.aSknt = []
self.aDrct = []
def computeOthers(d):
r = {}
# Need something to compute other values needed for output
for sid in d.keys():
ob = d[sid]
ob["ticks"] = calendar.timegm(ob['utc_valid'].timetuple())
if ob['sknt'] is not None:
ob["sped"] = ob["sknt"] * 1.17
if ob.get('tmpf') is not None and ob.get('dwpf') is not None:
tmpf = temperature(ob['tmpf'], 'F')
dwpf = temperature(ob['dwpf'], 'F')
ob["relh"] = meteorology.relh(tmpf, dwpf).value('%')
else:
ob['relh'] = None
if ob['relh'] == 'M':
ob['relh'] = None
if (ob.get('tmpf') is not None and ob.get('dwpf') is not None and
ob.get('sped') is not None):
ob['feel'] = meteorology.mcalc_feelslike(
masked_array([ob['tmpf'], ], units('degF'), mask=[False, ]),
masked_array([ob['dwpf'], ], units('degF'), mask=[False, ]),
masked_array([ob['sped'], ], units('mile per hour'),
mask=[False, ])
).to(units('degF')).magnitude[0]
else:
ob['feel'] = None
if ob['feel'] == 'M':
ob['feel'] = None
ob["altiTend"] = 'S'
ob["drctTxt"] = util.drct2text(ob["drct"])
if ob["max_drct"] is None:
ob["max_drct"] = 0
ob["max_drctTxt"] = util.drct2text(ob["max_drct"])
ob["20gu"] = 0
if ob['gust'] is not None:
ob["gmph"] = ob["gust"] * 1.17
if ob['max_gust'] is not None:
ob["max_sped"] = ob["max_gust"] * 1.17
else:
ob['max_sped'] = 0
ob['pday'] = 0 if ob['pday'] is None else ob['pday']
ob['pmonth'] = 0 if ob['pmonth'] is None else ob['pmonth']
ob["gtim"] = "0000"
ob["gtim2"] = "12:00 AM"
if ob["max_gust_ts"] is not None and ob["max_gust_ts"] != "null":
ob["gtim"] = ob["max_gust_ts"].strftime("%H%M")
ob["gtim2"] = ob["max_gust_ts"].strftime("%-I:%M %p")
r[sid] = ob
return r
def computeOthers(d):
r = {}
# Need something to compute other values needed for output
for sid in d.keys():
ob = d[sid]
ob["ticks"] = calendar.timegm(ob['utc_valid'].timetuple())
if ob['sknt'] is not None:
ob["sped"] = ob["sknt"] * 1.17
if ob.get('tmpf') is not None and ob.get('dwpf') is not None:
tmpf = temperature(ob['tmpf'], 'F')
dwpf = temperature(ob['dwpf'], 'F')
ob["relh"] = meteorology.relh(tmpf, dwpf).value('%')
else:
ob['relh'] = None
if ob['relh'] == 'M':
ob['relh'] = None
if (ob.get('tmpf') is not None and ob.get('dwpf') is not None and
ob.get('sped') is not None):
tmpf = temperature(ob['tmpf'], 'F')
dwpf = temperature(ob['dwpf'], 'F')
sknt = speed(ob['sped'], 'MPH')
ob["feel"] = meteorology.feelslike(tmpf, dwpf, sknt).value("F")
else:
ob['feel'] = None
if ob['feel'] == 'M':
ob['feel'] = None
ob["altiTend"] = 'S'
ob["drctTxt"] = util.drct2text(ob["drct"])
if ob["max_drct"] is None:
ob["max_drct"] = 0
ob["max_drctTxt"] = util.drct2text(ob["max_drct"])
ob["20gu"] = 0
if ob['gust'] is not None:
ob["gmph"] = ob["gust"] * 1.17
if ob['max_gust'] is not None:
ob["max_sped"] = ob["max_gust"] * 1.17
else:
ob['max_sped'] = 0
ob['pday'] = 0 if ob['pday'] is None else ob['pday']
ob['pmonth'] = 0 if ob['pmonth'] is None else ob['pmonth']
ob["gtim"] = "0000"
ob["gtim2"] = "12:00 AM"
if ob["max_gust_ts"] is not None and ob["max_gust_ts"] != "null":
ob["gtim"] = ob["max_gust_ts"].strftime("%H%M")
ob["gtim2"] = ob["max_gust_ts"].strftime("%-I:%M %p")
r[sid] = ob
return r
def wind_message(self):
"""Convert this into a Jabber style message"""
drct = 0
sknt = 0
time = self.time.replace(tzinfo=timezone.utc)
if self.wind_gust:
sknt = self.wind_gust.value("KT")
if self.wind_dir:
drct = self.wind_dir.value()
if self.wind_speed_peak:
v1 = self.wind_speed_peak.value("KT")
d1 = self.wind_dir_peak.value()
t1 = self.peak_wind_time.replace(tzinfo=timezone.utc)
if v1 > sknt:
sknt = v1
drct = d1
time = t1
key = "%s;%s;%s" % (self.station_id, sknt, time)
if key not in WIND_ALERTS:
WIND_ALERTS[key] = 1
speed = datatypes.speed(sknt, "KT")
return ("gust of %.0f knots (%.1f mph) from %s @ %s") % (
speed.value("KT"),
speed.value("MPH"),
drct2text(drct),
time.strftime("%H%MZ"),
)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
units = fdict.get('units', 'MPH').upper()
if units not in PDICT:
units = 'MPH'
year = int(fdict.get('year', datetime.datetime.now().year))
month = int(fdict.get('month', datetime.datetime.now().month))
sts = datetime.date(year, month, 1)
ets = (sts + datetime.timedelta(days=35)).replace(day=1)
nt = NetworkTable(network)
cursor.execute("""
SELECT day, avg_sknt, vector_avg_drct from summary s JOIN stations t
ON (t.iemid = s.iemid) WHERE t.id = %s and t.network = %s and
s.day >= %s and s.day < %s ORDER by day ASC
""", (station, network, sts, ets))
days = []
drct = []
sknt = []
for row in cursor:
if row[1] is None:
continue
days.append(row[0].day)
drct.append(row[2])
sknt.append(row[1])
if len(sknt) == 0:
return "ERROR: No Data Found"
df = pd.DataFrame(dict(day=pd.Series(days),
drct=pd.Series(drct),
sknt=pd.Series(sknt)))
sknt = speed(np.array(sknt), 'KT').value(units)
(fig, ax) = plt.subplots(1, 1)
ax.bar(np.array(days)-0.4, sknt, ec='green', fc='green')
pos = max([min(sknt) / 2.0, 0.5])
for d, _, r in zip(days, sknt, drct):
draw_line(plt, d, max(sknt)+0.5, (270. - r) / 180. * np.pi)
txt = ax.text(d, pos, drct2text(r), ha='center', rotation=90,
color='white', va='center')
txt.set_path_effects([PathEffects.withStroke(linewidth=2,
foreground="k")])
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\n%s Daily Average Wind Speed and Direction"
) % (nt.sts[station]['name'], station,
sts.strftime("%b %Y")))
ax.set_xlim(0.5, max(days)+0.5)
ax.set_ylim(top=max(sknt)+2)
ax.set_ylabel("Average Wind Speed [%s]" % (PDICT.get(units),))
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('iem')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
units = ctx['units']
year = ctx['year']
month = ctx['month']
sts = datetime.date(year, month, 1)
ets = (sts + datetime.timedelta(days=35)).replace(day=1)
nt = NetworkTable(network)
cursor.execute(
"""
SELECT day, avg_sknt, vector_avg_drct from summary s JOIN stations t
ON (t.iemid = s.iemid) WHERE t.id = %s and t.network = %s and
s.day >= %s and s.day < %s ORDER by day ASC
""", (station, network, sts, ets))
days = []
drct = []
sknt = []
for row in cursor:
if row[1] is None:
continue
days.append(row[0].day)
drct.append(row[2])
sknt.append(row[1])
if not sknt:
raise ValueError("ERROR: No Data Found")
df = pd.DataFrame(
dict(day=pd.Series(days), drct=pd.Series(drct), sknt=pd.Series(sknt)))
sknt = speed(np.array(sknt), 'KT').value(units)
(fig, ax) = plt.subplots(1, 1)
ax.bar(np.array(days), sknt, ec='green', fc='green', align='center')
pos = max([min(sknt) / 2.0, 0.5])
for d, _, r in zip(days, sknt, drct):
draw_line(d, max(sknt) + 0.5, (270. - r) / 180. * np.pi)
txt = ax.text(d,
pos,
drct2text(r),
ha='center',
rotation=90,
color='white',
va='center')
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="k")])
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\n%s Daily Average Wind Speed and Direction") %
(nt.sts[station]['name'], station, sts.strftime("%b %Y")))
ax.set_xlim(0.5, 31.5)
ax.set_xticks(range(1, 31, 5))
ax.set_ylim(top=max(sknt) + 2)
ax.set_ylabel("Average Wind Speed [%s]" % (PDICT.get(units), ))
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("iem")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
plot_units = ctx["units"]
year = ctx["year"]
month = ctx["month"]
sts = datetime.date(year, month, 1)
ets = (sts + datetime.timedelta(days=35)).replace(day=1)
df = read_sql(
"""
SELECT day, avg_sknt as sknt, vector_avg_drct as drct
from summary s JOIN stations t
ON (t.iemid = s.iemid) WHERE t.id = %s and t.network = %s and
s.day >= %s and s.day < %s and avg_sknt is not null
ORDER by day ASC
""",
pgconn,
params=(station, ctx["network"], sts, ets),
)
if df.empty:
raise NoDataFound("ERROR: No Data Found")
df["day"] = pd.to_datetime(df["day"])
sknt = (df["sknt"].values * units("knot")).to(XREF_UNITS[plot_units]).m
(fig, ax) = plt.subplots(1, 1)
ax.bar(df["day"].dt.day.values,
sknt,
ec="green",
fc="green",
align="center")
pos = max([min(sknt) / 2.0, 0.5])
for d, _, r in zip(df["day"].dt.day.values, sknt, df["drct"].values):
draw_line(d, max(sknt) + 0.5, (270.0 - r) / 180.0 * np.pi)
txt = ax.text(
d,
pos,
drct2text(r),
ha="center",
rotation=90,
color="white",
va="center",
)
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="k")])
ax.grid(True, zorder=11)
ax.set_title(
("%s [%s]\n%s Daily Average Wind Speed and Direction") %
(ctx["_nt"].sts[station]["name"], station, sts.strftime("%b %Y")))
ax.set_xlim(0.5, 31.5)
ax.set_xticks(range(1, 31, 5))
ax.set_ylim(top=max(sknt) + 2)
ax.set_ylabel("Average Wind Speed [%s]" % (PDICT[plot_units], ))
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
units = ctx['units']
nt = NetworkTable(network)
df = read_sql("""
select date_trunc('hour', valid) as ts, avg(sknt) as sknt,
max(drct) as drct from alldata
WHERE station = %s and sknt is not null and drct is not null
GROUP by ts
""", pgconn, params=(station, ), parse_dates=('ts',),
index_col=None)
sknt = speed(df['sknt'].values, 'KT')
drct = direction(df['drct'].values, 'DEG')
df['u'], df['v'] = [x.value('MPS') for x in meteorology.uv(sknt, drct)]
df['month'] = df['ts'].dt.month
grp = df[['month', 'u', 'v', 'sknt']].groupby('month').mean()
grp['u_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['v_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['sped_%s' % (units,)] = speed(grp['sknt'].values,
'KT').value(units.upper())
drct = meteorology.drct(speed(grp['u'].values, 'KT'),
speed(grp['v'].values, 'KT'))
grp['drct'] = drct.value('DEG')
maxval = grp['sped_%s' % (units,)].max()
(fig, ax) = plt.subplots(1, 1)
ax.barh(grp.index.values, grp['sped_%s' % (units,)].values,
align='center')
ax.set_xlabel("Average Wind Speed [%s]" % (UNITS[units],))
ax.set_yticks(grp.index.values)
ax.set_yticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlim(0, maxval * 1.2)
for mon, row in grp.iterrows():
ax.text(maxval * 1.1, mon, drct2text(row['drct']), ha='center',
va='center', bbox=dict(color='white'))
ax.text(row['sped_%s' % (units,)] * 0.98, mon,
"%.1f" % (row['sped_%s' % (units,)],), ha='right',
va='center', bbox=dict(color='white',
boxstyle='square,pad=0.03',))
ax.set_ylim(12.5, 0.5)
ax.set_title(("[%s] %s [%s-%s]\nMonthly Average Wind Speed and"
" Vector Average Direction"
) % (station, nt.sts[station]['name'],
df['ts'].min().year,
df['ts'].max().year))
return fig, grp
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
units = ctx['units']
nt = NetworkTable(network)
df = read_sql("""
select date_trunc('hour', valid at time zone 'UTC') as ts,
avg(sknt) as sknt, max(drct) as drct from alldata
WHERE station = %s and sknt is not null and drct is not null
GROUP by ts
""", pgconn, params=(station, ), index_col=None)
sknt = speed(df['sknt'].values, 'KT')
drct = direction(df['drct'].values, 'DEG')
df['u'], df['v'] = [x.value('MPS') for x in meteorology.uv(sknt, drct)]
df['month'] = df['ts'].dt.month
grp = df[['month', 'u', 'v', 'sknt']].groupby('month').mean()
grp['u_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['v_%s' % (units,)] = speed(grp['u'].values, 'KT').value(units.upper())
grp['sped_%s' % (units,)] = speed(grp['sknt'].values,
'KT').value(units.upper())
drct = meteorology.drct(speed(grp['u'].values, 'KT'),
speed(grp['v'].values, 'KT'))
grp['drct'] = drct.value('DEG')
maxval = grp['sped_%s' % (units,)].max()
(fig, ax) = plt.subplots(1, 1)
ax.barh(grp.index.values, grp['sped_%s' % (units,)].values,
align='center')
ax.set_xlabel("Average Wind Speed [%s]" % (UNITS[units],))
ax.set_yticks(grp.index.values)
ax.set_yticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlim(0, maxval * 1.2)
for mon, row in grp.iterrows():
ax.text(maxval * 1.1, mon, drct2text(row['drct']), ha='center',
va='center', bbox=dict(color='white'))
ax.text(row['sped_%s' % (units,)] * 0.98, mon,
"%.1f" % (row['sped_%s' % (units,)],), ha='right',
va='center', bbox=dict(color='white',
boxstyle='square,pad=0.03',))
ax.set_ylim(12.5, 0.5)
ax.set_title(("[%s] %s [%s-%s]\nMonthly Average Wind Speed and"
" Vector Average Direction"
) % (station, nt.sts[station]['name'],
df['ts'].min().year,
df['ts'].max().year))
return fig, grp
def avgWinds(self):
"""My wind averager"""
if not self.aSPED:
self.sped = None
self.drct = None
return
self.sped = sum(self.aSPED) / len(self.aSPED)
utot = 0
vtot = 0
for i in range(len(self.aSPED)):
u, v = uv(self.aSPED[i], self.aDrct[i])
utot += u
vtot += v
uavg = utot / len(self.aSPED)
vavg = vtot / len(self.aSPED)
self.drct = dir(uavg, vavg)
self.drctTxt = util.drct2text(self.drct)
def do_imagework(cameras, cid, tokens, now):
"""Process the images"""
# Hard coded...
drct = cameras[cid]["pan0"]
drct_text = util.drct2text(drct)
# Create 320x240 variant
fn = "165.206.203.34/rwis_images/Vid-000512%s.jpg" % ("-".join(tokens), )
try:
i0 = Image.open(fn)
i320 = i0.resize((320, 240), Image.ANTIALIAS)
except Exception:
if os.path.isfile(fn):
os.unlink(fn)
return
draw = ImageDraw.Draw(i0)
text = "(%s) %s %s" % (
drct_text,
cameras[cid]["name"],
now.strftime("%-2I:%M:%S %p - %d %b %Y"),
)
(width, height) = FONT.getsize(text)
draw.rectangle([5, 475 - height, 5 + width, 475], fill="#000000")
draw.text((5, 475 - height), text, font=FONT)
# Save 640x480
i0.save("%s-640x480.jpg" % (cid, ))
draw = ImageDraw.Draw(i320)
text = "(%s) %s %s" % (
drct_text,
cameras[cid]["name"],
now.strftime("%-2I:%M:%S %p - %d %b %Y"),
)
(width, height) = FONT.getsize(text)
draw.rectangle([5, 235 - height, 5 + width, 235], fill="#000000")
draw.text((5, 235 - height), text, font=FONT)
# Save 640x480
i320.save("%s-320x240.jpg" % (cid, ))
del i0
del i320
return drct
def highcharts(fdict):
""" Generate the highcharts variant"""
import matplotlib
matplotlib.use('agg')
from windrose.windrose import histogram
ctx = get_context(fdict)
dir_edges, var_bins, table = histogram(ctx['df']['drct'].values,
ctx['df']['smph'].values,
np.array([0, 2, 5, 7, 10, 15,
20]), 18, True)
arr = [drct2text(mydir) for mydir in dir_edges]
return """
var arr = """ + str(arr) + """;
$("#ap_container").highcharts({
series: [{name: '2 - 5',
data: """ + str(zip(arr, table[1])) + """,
_colorIndex: 0},
{name: '5 - 7',
data: """ + str(zip(arr, table[2])) + """,
_colorIndex: 1},
{name: '7 - 10',
data: """ + str(zip(arr, table[3])) + """,
_colorIndex: 2},
{name: '10 - 15',
data: """ + str(zip(arr, table[4])) + """,
_colorIndex: 3},
{name: '15 - 20',
data: """ + str(zip(arr, table[5])) + """,
_colorIndex: 4},
{name: '20 +',
data: """ + str(zip(arr, table[6])) + """,
_colorIndex: 5}],
chart: {
polar: true,
type: 'column'
},
title: {
'text': '""" + ctx['title'] + """'
},
subtitle: {
'text': '""" + ctx['subtitle'] + """'
def highcharts(fdict):
""" Generate the highcharts variant"""
import matplotlib
matplotlib.use('agg')
from windrose.windrose import histogram
ctx = get_context(fdict)
dir_edges, var_bins, table = histogram(ctx['df']['drct'].values,
ctx['df']['smph'].values,
np.array([0, 2, 5, 7, 10, 15, 20]),
18, True)
arr = [drct2text(mydir) for mydir in dir_edges]
return """
var arr = """+str(arr)+""";
$("#ap_container").highcharts({
series: [{name: '2 - 5',
data: """+str(zip(arr, table[1]))+""",
_colorIndex: 0},
{name: '5 - 7',
data: """+str(zip(arr, table[2]))+""",
_colorIndex: 1},
{name: '7 - 10',
data: """+str(zip(arr, table[3]))+""",
_colorIndex: 2},
{name: '10 - 15',
data: """+str(zip(arr, table[4]))+""",
_colorIndex: 3},
{name: '15 - 20',
data: """+str(zip(arr, table[5]))+""",
_colorIndex: 4},
{name: '20 +',
data: """+str(zip(arr, table[6]))+""",
_colorIndex: 5}],
chart: {
polar: true,
type: 'column'
},
title: {
'text': '"""+ctx['title']+"""'
},
subtitle: {
'text': '"""+ctx['subtitle']+"""'
def wind_message(self):
"""Convert this into a Jabber style message"""
drct = 0
sknt = 0
time = self.time.replace(tzinfo=pytz.UTC)
if self.wind_gust:
sknt = self.wind_gust.value("KT")
if self.wind_dir:
drct = self.wind_dir.value()
if self.wind_speed_peak:
v1 = self.wind_speed_peak.value("KT")
d1 = self.wind_dir_peak.value()
t1 = self.peak_wind_time.replace(tzinfo=pytz.UTC)
if v1 > sknt:
sknt = v1
drct = d1
time = t1
key = "%s;%s;%s" % (self.station_id, sknt, time)
if key not in WIND_ALERTS:
WIND_ALERTS[key] = 1
speed = datatypes.speed(sknt, 'KT')
return ("gust of %.0f knots (%.1f mph) from %s @ %s"
) % (speed.value('KT'), speed.value('MPH'),
drct2text(drct), time.strftime("%H%MZ"))
def get_data(ts):
""" Get the data for this timestamp """
iemcursor = IEM.cursor()
cursor = ISUAG.cursor(cursor_factory=psycopg2.extras.DictCursor)
qcdict = loadqc()
nt = NetworkTable("ISUSM")
data = {
"type": "FeatureCollection",
"crs": {
"type": "EPSG",
"properties": {
"code": 4326,
"coordinate_order": [1, 0]
}
},
"features": []
}
# Fetch the daily values
iemcursor.execute(
"""
SELECT id, pday, max_tmpf, min_tmpf from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE t.network = 'ISUSM' and day = %s
""", (ts.date(), ))
daily = {}
for row in iemcursor:
daily[row[0]] = {
'pday': row[1],
'max_tmpf': row[2],
'min_tmpf': row[3]
}
cursor.execute("""
SELECT * from sm_hourly where valid = %s
""", (ts, ))
for row in cursor:
sid = row['station']
lon = nt.sts[sid]['lon']
lat = nt.sts[sid]['lat']
q = qcdict.get(sid, {})
data['features'].append({
"type": "Feature",
"id": sid,
"properties": {
"encrh_avg":
("%s%%" %
safe(row['encrh_avg'], 1) if row['encrh_avg'] > 0 else "M"),
"rh":
"%s%%" % (safe(row["rh"], 0), ),
"hrprecip": (safe_p(row['rain_mm_tot'])
if not q.get('precip', False) else 'M'),
"et":
safe_p(row['etalfalfa']),
"bat":
safe(row['battv_min'], 2),
"radmj":
safe(row['slrmj_tot'], 2),
"tmpf":
safe_t(row['tair_c_avg']),
"high":
safe_t(daily.get(sid, {}).get('max_tmpf', None), 'F'),
"low":
safe_t(daily.get(sid, {}).get('min_tmpf', None), 'F'),
"pday": (safe(daily.get(sid, {}).get('pday', None), 2)
if not q.get('precip', False) else 'M'),
"soil04t": (safe_t(row['tsoil_c_avg'])
if not q.get('soil4', False) else 'M'),
"soil12t": (safe_t(row['t12_c_avg'])
if not q.get('soil12', False) else 'M'),
"soil24t": (safe_t(row['t24_c_avg'])
if not q.get('soil24', False) else 'M'),
"soil50t": (safe_t(row['t50_c_avg'])
if not q.get('soil50', False) else 'M'),
"soil12m": (safe_m(row['vwc_12_avg'])
if not q.get('soil12', False) else 'M'),
"soil24m": (safe_m(row['vwc_24_avg'])
if not q.get('soil24', False) else 'M'),
"soil50m": (safe_m(row['vwc_50_avg'])
if not q.get('soil50', False) else 'M'),
"gust":
safe(row['ws_mph_max'], 1),
"wind": ("%s@%.0f") %
(drct2text(row['winddir_d1_wvt']), row['ws_mps_s_wvt'] * 2.23),
'name':
nt.sts[sid]['name']
},
"geometry": {
"type": "Point",
"coordinates": [lon, lat]
}
})
sys.stdout.write(json.dumps(data))
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
units = ctx["units"]
df = read_sql(
"""
select date_trunc('hour', valid at time zone 'UTC') as ts,
avg(sknt) as sknt, max(drct) as drct from alldata
WHERE station = %s and sknt is not null and drct is not null
GROUP by ts
""",
pgconn,
params=(station, ),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data Found.")
sknt = speed(df["sknt"].values, "KT")
drct = direction(df["drct"].values, "DEG")
df["u"], df["v"] = [x.value("MPS") for x in meteorology.uv(sknt, drct)]
df["month"] = df["ts"].dt.month
grp = df[["month", "u", "v", "sknt"]].groupby("month").mean()
grp["u_%s" % (units, )] = speed(grp["u"].values, "KT").value(units.upper())
grp["v_%s" % (units, )] = speed(grp["u"].values, "KT").value(units.upper())
grp["sped_%s" % (units, )] = speed(grp["sknt"].values,
"KT").value(units.upper())
drct = meteorology.drct(speed(grp["u"].values, "KT"),
speed(grp["v"].values, "KT"))
grp["drct"] = drct.value("DEG")
maxval = grp["sped_%s" % (units, )].max()
(fig, ax) = plt.subplots(1, 1)
ax.barh(grp.index.values,
grp["sped_%s" % (units, )].values,
align="center")
ax.set_xlabel("Average Wind Speed [%s]" % (UNITS[units], ))
ax.set_yticks(grp.index.values)
ax.set_yticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlim(0, maxval * 1.2)
for mon, row in grp.iterrows():
ax.text(
maxval * 1.1,
mon,
drct2text(row["drct"]),
ha="center",
va="center",
bbox=dict(color="white"),
)
ax.text(
row["sped_%s" % (units, )] * 0.98,
mon,
"%.1f" % (row["sped_%s" % (units, )], ),
ha="right",
va="center",
bbox=dict(color="white", boxstyle="square,pad=0.03"),
)
ax.set_ylim(12.5, 0.5)
ax.set_title(("[%s] %s [%s-%s]\nMonthly Average Wind Speed and"
" Vector Average Direction") % (
station,
ctx["_nt"].sts[station]["name"],
df["ts"].min().year,
df["ts"].max().year,
))
return fig, grp
continue
t = tokens[0]
cid = "IDOT-%s-%s" % (t[0], t[2])
gmt = datetime.datetime(int(t[3]), int(t[4]), int(t[5]), int(t[6]),
int(t[7]))
gmt = gmt.replace(tzinfo=pytz.timezone("UTC"))
now = gmt.astimezone(pytz.timezone("America/Chicago"))
if cid not in cameras:
print "ingest_dot_webcams.py unknown CameraID: %s" % (cid, )
cameras[cid] = {'pan0': 0, 'name': 'unknown'}
# Hard coded...
d = cameras[cid]
drct = d['pan0']
drctTxt = util.drct2text(drct)
# Create 320x240 variant
fn = "165.206.203.34/rwis_images/Vid-000512%s.jpg" % ("-".join(t), )
try:
i0 = Image.open(fn)
i320 = i0.resize((320, 240), Image.ANTIALIAS)
except:
if os.path.isfile(fn):
os.unlink(fn)
continue
draw = ImageDraw.Draw(i0)
s = "(%s) %s %s" % (drctTxt, d['name'],
now.strftime("%-2I:%M:%S %p - %d %b %Y"))
(w, h) = font.getsize(s)
def main():
''' Go!'''
ncursor.execute("""
DELETE from lsrs WHERE valid > %s and valid < %s
""", (RT_T0 - datetime.timedelta(minutes=300),
RT_T1 + datetime.timedelta(minutes=300)))
print('Removed %s rows from nwa lsr table' % (ncursor.rowcount,))
# Get DMX coords in 26915
pcursor.execute("""SELECT
ST_x( ST_transform(
ST_GeomFromEWKT('SRID=4326;POINT(-93.723892 41.731220)'), 26915)) as x,
ST_y( ST_transform(
ST_GeomFromEWKT('SRID=4326;POINT(-93.723892 41.731220)'), 26915)) as y
""")
row = pcursor.fetchone()
radx = row['x']
rady = row['y']
# Get all LSRs within 230m of the nexrad
pcursor.execute("""SELECT *, ST_astext(geom) as tgeom,
ST_x( ST_transform( geom, 26915) ) -
ST_x( ST_transform(
ST_GeomFromEWKT('SRID=4326;POINT(%s %s)'), 26915)) as offset_x,
ST_y( ST_transform( geom, 26915) ) -
ST_y( ST_transform(
ST_GeomFromEWKT('SRID=4326;POINT(%s %s)'), 26915)) as offset_y
from lsrs WHERE valid BETWEEN '%s' and '%s'
and ST_distance( ST_transform(geom, 26915),
ST_transform( ST_GeomFromEWKT('SRID=4326;POINT(%s %s)'), 26915))
< (230.0 / 0.6214 * 1000.0)
""" % (NEXRAD_LON, NEXRAD_LAT, NEXRAD_LON, NEXRAD_LAT,
(ARCHIVE_T0 - datetime.timedelta(minutes=120)
).strftime("%Y-%m-%d %H:%M+00"),
(ARCHIVE_T1 + datetime.timedelta(minutes=120)
).strftime("%Y-%m-%d %H:%M+00"), NEXRAD_LON, NEXRAD_LAT))
for row in pcursor:
locx = radx + row['offset_x']
locy = rady + row['offset_y']
# Locate nearest city and distance, hmm
sql = """
SELECT name, ST_distance(ST_transform(the_geom, 26915),
ST_GeomFromEWKT('SRID=26915;POINT(%s %s)')) as d,
%s - ST_x(ST_transform(the_geom,26915)) as offsetx,
%s - ST_y(ST_transform(the_geom,26915)) as offsety
from cities_iowa
ORDER by d ASC LIMIT 1
""" % (locx, locy, locx, locy)
ncursor.execute(sql)
row2 = ncursor.fetchone()
deg = getdir(0 - row2['offsetx'], 0 - row2['offsety'])
drct = util.drct2text(deg)
miles = row2['d'] * 0.0006214 # meters -> miles
newcity = "%.1f %s %s" % (miles, drct, row2['name'])
city = row['city'] if REAL88D == FAKE88D else newcity
# Compute the new valid time
ts = row['valid']
offset = ((ts - ARCHIVE_T0).days * 86400. +
(ts - ARCHIVE_T0).seconds) / SPEEDUP # Speed up!
valid = RT_T0 + datetime.timedelta(seconds=offset)
# Query for WFO
sql = """
SELECT * from nws_ugc WHERE
ST_transform(ST_GeomFromEWKT('SRID=26915;POINT(%s %s)'),4326) && geom
and ST_Contains(geom,
ST_transform(ST_GeomFromEWKT('SRID=26915;POINT(%s %s)'),4326))
""" % (locx, locy, locx, locy)
ncursor.execute(sql)
row2 = ncursor.fetchone()
wfo = row['wfo'] if FAKE88D == REAL88D else row2['wfo']
cnty = row['county'] if FAKE88D == REAL88D else row2['name']
st = row['state'] if FAKE88D == REAL88D else row2['state']
# newremark = "%s\n[WAS: %s %s]" % (row['source'], row['city'],
# row['county'])
# remark = row['remark'] if FAKE88D == REAL88D else newremark
sql = """
INSERT into lsrs(valid, type, magnitude, city, county, state,
source, remark, wfo, typetext, geom) values ('%s', '%s', %s, '%s',
'%s', '%s', '%s', '%s', '%s', '%s',
ST_transform( ST_GeomFromEWKT('SRID=26915;POINT(%s %s)'), 4326))
RETURNING ST_x(geom) as x, ST_y(geom) as y
""" % (valid.strftime("%Y-%m-%d %H:%M+00"), row['type'],
row['magnitude'] or 0, city.replace("'", ""),
cnty.replace("'", ""), st, row['source'],
row['remark'], wfo, row['typetext'], locx, locy)
ncursor.execute(sql)
row2 = ncursor.fetchone()
print(("%s,%s,%.3f,%.3f,%s,%s,%s,%s,%s,%s"
) % (ts.strftime("%Y-%m-%d %H:%M"),
valid.strftime("%Y-%m-%d %H:%M"),
row2['x'], row2['y'],
row['magnitude'], row['typetext'],
row['source'], city, row['city'],
row['remark']))
ncursor.close()
NWA.commit()
NWA.close()
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
units = fdict.get('units', 'MPH').upper()
if units not in PDICT:
units = 'MPH'
year = int(fdict.get('year', datetime.datetime.now().year))
month = int(fdict.get('month', datetime.datetime.now().month))
sts = datetime.date(year, month, 1)
ets = (sts + datetime.timedelta(days=35)).replace(day=1)
nt = NetworkTable(network)
cursor.execute(
"""
SELECT day, avg_sknt, vector_avg_drct from summary s JOIN stations t
ON (t.iemid = s.iemid) WHERE t.id = %s and t.network = %s and
s.day >= %s and s.day < %s ORDER by day ASC
""", (station, network, sts, ets))
days = []
drct = []
sknt = []
for row in cursor:
if row[1] is None:
continue
days.append(row[0].day)
drct.append(row[2])
sknt.append(row[1])
if len(sknt) == 0:
return "ERROR: No Data Found"
df = pd.DataFrame(
dict(day=pd.Series(days), drct=pd.Series(drct), sknt=pd.Series(sknt)))
sknt = speed(np.array(sknt), 'KT').value(units)
(fig, ax) = plt.subplots(1, 1)
ax.bar(np.array(days) - 0.4, sknt, ec='green', fc='green')
pos = max([min(sknt) / 2.0, 0.5])
for d, _, r in zip(days, sknt, drct):
draw_line(plt, d, max(sknt) + 0.5, (270. - r) / 180. * np.pi)
txt = ax.text(d,
pos,
drct2text(r),
ha='center',
rotation=90,
color='white',
va='center')
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="k")])
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\n%s Daily Average Wind Speed and Direction") %
(nt.sts[station]['name'], station, sts.strftime("%b %Y")))
ax.set_xlim(0.5, max(days) + 0.5)
ax.set_ylim(top=max(sknt) + 2)
ax.set_ylabel("Average Wind Speed [%s]" % (PDICT.get(units), ))
return fig, df
def highcharts(fdict):
""" Generate the highcharts variant"""
ctx = get_context(fdict)
dir_edges, _, table = histogram(
ctx["df"]["drct"].values,
ctx["df"]["smph"].values,
np.array([0, 2, 5, 7, 10, 15, 20]),
18,
True,
)
arr = [drct2text(mydir) for mydir in dir_edges]
return ("""
var arr = """ + str(arr) + """;
$("#ap_container").highcharts({
series: [{name: '2 - 5',
data: """ + str(list(zip(arr, table[1]))) + """,
_colorIndex: 0},
{name: '5 - 7',
data: """ + str(list(zip(arr, table[2]))) + """,
_colorIndex: 1},
{name: '7 - 10',
data: """ + str(list(zip(arr, table[3]))) + """,
_colorIndex: 2},
{name: '10 - 15',
data: """ + str(list(zip(arr, table[4]))) + """,
_colorIndex: 3},
{name: '15 - 20',
data: """ + str(list(zip(arr, table[5]))) + """,
_colorIndex: 4},
{name: '20 +',
data: """ + str(list(zip(arr, table[6]))) + """,
_colorIndex: 5}],
chart: {
polar: true,
type: 'column'
},
title: {
'text': '""" + ctx["title"] + """'
},
subtitle: {
'text': '""" + ctx["subtitle"] + """'
},
pane: {
'size': '85%'
},
legend: {
title: {text: 'Wind Speed [MPH]'},
verticalAlign: 'bottom',
layout: 'horizontal'
},
xAxis: {
'tickInterval': 18./8.,
'labels': {
formatter: function(){
var v = this.value.toFixed(1);
if (v == '0.0') {return 'N';}
if (v == '2.3') {return 'NE';}
if (v == '4.5') {return 'E';}
if (v == '6.8') {return 'SE';}
if (v == '9.0') {return 'S';}
if (v == '11.3') {return 'SW';}
if (v == '13.5') {return 'W';}
if (v == '15.8') {return 'NW';}
return v;
}
}
},
yAxis: {
'min': 0,
'endOnTick': false,
'showLastLabel': true,
'title': {
'text': 'Frequency (%)'
},
'reversedStacks': false
},
tooltip: {
positioner: function () {
return { x: 10, y: 10 };
},
'valueSuffix': '%',
shared: true,
valueDecimals: 1,
formatter: function () {
var s = '<b>' + arr[this.x] +
' ('+ this.points[0].total.toFixed(1)+'%)</b>';
$.each(this.points, function () {
s += '<br/>' + this.series.name + ': ' +
this.y.toFixed(1) + '%';
});
return s;
},
},
plotOptions: {
'series': {
'stacking': 'normal',
'shadow': false,
'groupPadding': 0,
'pointPlacement': 'on'
}
}
});
""")
continue
t = tokens[0]
cid = "IDOT-%s-%s" % (t[0], t[2])
gmt = datetime.datetime(int(t[3]), int(t[4]), int(t[5]),
int(t[6]), int(t[7]))
gmt = gmt.replace(tzinfo=pytz.timezone("UTC"))
now = gmt.astimezone(pytz.timezone("America/Chicago"))
if cid not in cameras:
print "ingest_dot_webcams.py unknown CameraID: %s" % (cid,)
cameras[cid] = {'pan0': 0, 'name': 'unknown'}
# Hard coded...
d = cameras[cid]
drct = d['pan0']
drctTxt = util.drct2text(drct)
# Create 320x240 variant
fn = "165.206.203.34/rwis_images/Vid-000512%s.jpg" % ("-".join(t),)
try:
i0 = Image.open(fn)
i320 = i0.resize((320, 240), Image.ANTIALIAS)
except:
if os.path.isfile(fn):
os.unlink(fn)
continue
draw = ImageDraw.Draw(i0)
s = "(%s) %s %s" % (drctTxt, d['name'],
now.strftime("%-2I:%M:%S %p - %d %b %Y"))
(w, h) = font.getsize(s)
def get_data(ts):
""" Get the data for this timestamp """
iemcursor = IEM.cursor()
cursor = ISUAG.cursor(cursor_factory=psycopg2.extras.DictCursor)
qcdict = loadqc()
nt = NetworkTable("ISUSM", only_online=False)
data = {"type": "FeatureCollection", "features": []}
# Fetch the daily values
iemcursor.execute(
"""
SELECT id, pday, max_tmpf, min_tmpf from summary s JOIN stations t
on (t.iemid = s.iemid) WHERE t.network = 'ISUSM' and day = %s
""",
(ts.date(),),
)
daily = {}
for row in iemcursor:
daily[row[0]] = {
"pday": row[1],
"max_tmpf": row[2],
"min_tmpf": row[3],
}
cursor.execute(
"""
SELECT h.station,
h.encrh_avg,
coalesce(m.rh_avg_qc, h.rh_qc) as rh,
h.rain_mm_tot,
etalfalfa,
battv_min,
coalesce(m.slrkj_tot_qc * 3600 / 1000000, h.slrmj_tot_qc) as slrmj_tot,
coalesce(m.tair_c_avg, h.tair_c_avg) as tair_c_avg,
coalesce(m.tsoil_c_avg_qc, h.tsoil_c_avg_qc) as tsoil_c_avg_qc,
coalesce(m.t12_c_avg_qc, h.t12_c_avg_qc) as t12_c_avg_qc,
coalesce(m.t24_c_avg_qc, h.t24_c_avg_qc) as t24_c_avg_qc,
coalesce(m.t50_c_avg_qc, h.t50_c_avg_qc) as t50_c_avg_qc,
coalesce(m.calcvwc12_avg_qc, h.calc_vwc_12_avg_qc)
as calc_vwc_12_avg_qc,
coalesce(m.calcvwc24_avg_qc, h.calc_vwc_24_avg_qc)
as calc_vwc_24_avg_qc,
coalesce(m.calcvwc50_avg_qc, h.calc_vwc_50_avg_qc)
as calc_vwc_50_avg_qc,
coalesce(m.ws_mph_max, h.ws_mph_max) as ws_mph_max,
coalesce(m.winddir_d1_wvt, h.winddir_d1_wvt) as winddir_d1_wvt,
coalesce(m.ws_mph_s_wvt * 0.447, h.ws_mps_s_wvt)as ws_mps_s_wvt
from sm_hourly h LEFT JOIN sm_minute m on (h.station = m.station and
h.valid = m.valid)
where h.valid = %s
""",
(ts,),
)
for row in cursor:
sid = row["station"]
if sid not in nt.sts:
continue
lon = nt.sts[sid]["lon"]
lat = nt.sts[sid]["lat"]
q = qcdict.get(sid, {})
data["features"].append(
{
"type": "Feature",
"id": sid,
"properties": {
"encrh_avg": (
"%s%%" % safe(row["encrh_avg"], 1)
if row["encrh_avg"] is not None
and row["encrh_avg"] > 0
else "M"
),
"rh": "%s%%" % (safe(row["rh"], 0),),
"hrprecip": (
safe_p(row["rain_mm_tot"])
if not q.get("precip", False)
else "M"
),
"et": safe_p(row["etalfalfa"]),
"bat": safe(row["battv_min"], 2),
"radmj": safe(row["slrmj_tot"], 2),
"tmpf": safe_t(row["tair_c_avg"]),
"high": safe_t(
daily.get(sid, {}).get("max_tmpf", None), "F"
),
"low": safe_t(
daily.get(sid, {}).get("min_tmpf", None), "F"
),
"pday": (
safe(daily.get(sid, {}).get("pday", None), 2)
if not q.get("precip", False)
else "M"
),
"soil04t": (
safe_t(row["tsoil_c_avg_qc"])
if not q.get("soil4", False)
else "M"
),
"soil12t": (
safe_t(row["t12_c_avg_qc"])
if not q.get("soil12", False)
else "M"
),
"soil24t": (
safe_t(row["t24_c_avg_qc"])
if not q.get("soil24", False)
else "M"
),
"soil50t": (
safe_t(row["t50_c_avg_qc"])
if not q.get("soil50", False)
else "M"
),
"soil12m": (
safe_m(row["calc_vwc_12_avg_qc"])
if not q.get("soil12", False)
else "M"
),
"soil24m": (
safe_m(row["calc_vwc_24_avg_qc"])
if not q.get("soil24", False)
else "M"
),
"soil50m": (
safe_m(row["calc_vwc_50_avg_qc"])
if not q.get("soil50", False)
else "M"
),
"gust": safe(row["ws_mph_max"], 1),
"wind": ("%s@%.0f")
% (
drct2text(row["winddir_d1_wvt"]),
row["ws_mps_s_wvt"] * 2.23,
),
"name": nt.sts[sid]["name"],
},
"geometry": {"type": "Point", "coordinates": [lon, lat]},
}
)
return json.dumps(data)
