def test_uv():
""" Test calculation of uv wind components """
speed = datatypes.speed([10], "KT")
mydir = datatypes.direction([0], "DEG")
u, v = meteorology.uv(speed, mydir)
assert u.value("KT") == 0.0
assert v.value("KT") == -10.0
speed = datatypes.speed([10, 20, 15], "KT")
mydir = datatypes.direction([90, 180, 135], "DEG")
u, v = meteorology.uv(speed, mydir)
assert u.value("KT")[0] == -10
assert v.value("KT")[1] == 20.0
assert abs(v.value("KT")[2] - 10.6) < 0.1
def test_uv():
""" Test calculation of uv wind components """
speed = datatypes.speed([10, ], 'KT')
mydir = datatypes.direction([0, ], 'DEG')
u, v = meteorology.uv(speed, mydir)
assert u.value("KT") == 0.
assert v.value("KT") == -10.
speed = datatypes.speed([10, 20, 15], 'KT')
mydir = datatypes.direction([90, 180, 135], 'DEG')
u, v = meteorology.uv(speed, mydir)
assert u.value("KT")[0] == -10
assert v.value("KT")[1] == 20.
assert abs(v.value("KT")[2] - 10.6) < 0.1
def test_uv(self):
""" Test calculation of uv wind components """
speed = datatypes.speed([10,], 'KT')
mydir = datatypes.direction([0,], 'DEG')
u,v = meteorology.uv(speed, mydir)
self.assertEqual(u.value("KT"), 0.)
self.assertEqual(v.value("KT"), -10.)
speed = datatypes.speed([10,20,15], 'KT')
mydir = datatypes.direction([90,180,135], 'DEG')
u,v = meteorology.uv(speed, mydir)
self.assertEqual(u.value("KT")[0], -10)
self.assertEqual(v.value("KT")[1], 20.)
self.assertAlmostEquals(v.value("KT")[2], 10.6, 1)
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 test_uv(self):
""" Test calculation of uv wind components """
speed = datatypes.speed([
10,
], 'KT')
mydir = datatypes.direction([
0,
], 'DEG')
u, v = meteorology.uv(speed, mydir)
self.assertEqual(u.value("KT"), 0.)
self.assertEqual(v.value("KT"), -10.)
speed = datatypes.speed([10, 20, 15], 'KT')
mydir = datatypes.direction([90, 180, 135], 'DEG')
u, v = meteorology.uv(speed, mydir)
self.assertEqual(u.value("KT")[0], -10)
self.assertEqual(v.value("KT")[1], 20.)
self.assertAlmostEquals(v.value("KT")[2], 10.6, 1)
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 do(valid, frame):
""" Generate plot for a given timestamp """
cursor.execute(
"""select turbineid, power, ST_x(geom), ST_y(geom), yaw,
windspeed
from sampled_data s JOIN turbines t on (t.id = s.turbineid)
WHERE valid = %s and power is not null and yaw is not null
and windspeed is not null""", (valid, ))
lons = []
lats = []
vals = []
u = []
v = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
a, b = uv(speed(row[5], 'MPS'), direction(row[4], 'deg'))
u.append(a.value('MPS'))
v.append(b.value('MPS'))
vals = np.array(vals)
avgv = np.average(vals)
vals2 = vals - avgv
print valid, min(vals2), max(vals2)
(fig, ax) = plt.subplots(1, 1)
cmap = plt.cm.get_cmap('RdYlBu_r')
cmap.set_under('white')
cmap.set_over('black')
clevs = np.arange(-300, 301, 50)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(lons,
lats,
c=vals2,
vmin=-500,
vmax=500,
cmap=cmap,
s=100,
zorder=2)
ax.set_title(
"Pomeroy Farm Turbine Power [kW] Diff from Farm Avg (1min sampled dataset)\nValid: %s"
% (valid.strftime("%d %b %Y %I:%M %p")))
make_colorbar(clevs, norm, cmap)
fig.savefig('power_movie/frame%05i.png' % (frame, ))
plt.close()
def grid_wind(df, domain):
"""
Grid winds based on u and v components
@return uwnd, vwnd
"""
# compute components
u = []
v = []
for _station, row in df.iterrows():
(_u, _v) = meteorology.uv(dt.speed(row["sknt"], "KT"),
dt.direction(row["drct"], "DEG"))
u.append(_u.value("MPS"))
v.append(_v.value("MPS"))
df["u"] = u
df["v"] = v
ugrid = generic_gridder(df, "u", domain)
vgrid = generic_gridder(df, "v", domain)
return ugrid, vgrid
def grid_wind(df, domain):
"""
Grid winds based on u and v components
@return uwnd, vwnd
"""
# compute components
u = []
v = []
for _station, row in df.iterrows():
(_u, _v) = meteorology.uv(dt.speed(row['sknt'], 'KT'),
dt.direction(row['drct'], 'DEG'))
u.append(_u.value("MPS"))
v.append(_v.value("MPS"))
df['u'] = u
df['v'] = v
ugrid = generic_gridder(df, 'u', domain)
vgrid = generic_gridder(df, 'v', domain)
return ugrid, vgrid
def do(valid, frame):
""" Generate plot for a given timestamp """
cursor.execute("""select turbineid, power, ST_x(geom), ST_y(geom), yaw,
windspeed
from sampled_data s JOIN turbines t on (t.id = s.turbineid)
WHERE valid = %s and power is not null and yaw is not null
and windspeed is not null""", (valid,))
lons = []
lats = []
vals = []
u = []
v = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
a,b = uv(speed(row[5], 'MPS'), direction(row[4], 'deg'))
u.append( a.value('MPS') )
v.append( b.value('MPS') )
vals = np.array(vals)
avgv = np.average(vals)
vals2 = vals - avgv
print valid, min(vals2), max(vals2)
(fig, ax) = plt.subplots(1,1)
cmap = plt.cm.get_cmap('RdYlBu_r')
cmap.set_under('white')
cmap.set_over('black')
clevs = np.arange(-300,301,50)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(lons, lats, c=vals2, vmin=-500, vmax=500,
cmap=cmap, s=100, zorder=2)
ax.set_title("Pomeroy Farm Turbine Power [kW] Diff from Farm Avg (1min sampled dataset)\nValid: %s" % (
valid.strftime("%d %b %Y %I:%M %p")))
make_colorbar(clevs, norm, cmap)
fig.savefig('power_movie/frame%05i.png' % (frame,))
plt.close()
def grid_wind(rs):
"""
Grid winds based on u and v components
@param rs array of dicts
@return uwnd, vwnd
"""
lats = []
lons = []
udata = []
vdata = []
for row in rs:
if row['sknt'] is None or row['drct'] is None:
continue
# mps
(u, v) = meteorology.uv(dt.speed(row['sknt'], 'KT'),
dt.direction(row['drct'], 'DEG'))
if v is not None:
lats.append(nt.sts[row['station']]['lat'])
lons.append(nt.sts[row['station']]['lon'])
vdata.append(v.value("MPS"))
udata.append(u.value("MPS"))
if len(vdata) < 4:
print "No wind data at all"
return None
xi, yi = np.meshgrid(iemre.XAXIS, iemre.YAXIS)
nn = NearestNDInterpolator((lons, lats), np.array(udata))
ugrid = nn(xi, yi)
nn = NearestNDInterpolator((lons, lats), np.array(vdata))
vgrid = nn(xi, yi)
if ugrid is not None:
ugt = ugrid
vgt = vgrid
return ugt, vgt
else:
return None, None
def grid_wind(rs):
"""
Grid winds based on u and v components
@param rs array of dicts
@return uwnd, vwnd
"""
lats = []
lons = []
udata = []
vdata = []
for row in rs:
if row["sknt"] is None or row["drct"] is None:
continue
# mps
(u, v) = meteorology.uv(dt.speed(row["sknt"], "KT"), dt.direction(row["drct"], "DEG"))
if v is not None:
lats.append(nt.sts[row["station"]]["lat"])
lons.append(nt.sts[row["station"]]["lon"])
vdata.append(v.value("MPS"))
udata.append(u.value("MPS"))
if len(vdata) < 4:
print "No wind data at all"
return None
xi, yi = np.meshgrid(iemre.XAXIS, iemre.YAXIS)
nn = NearestNDInterpolator((lons, lats), np.array(udata))
ugrid = nn(xi, yi)
nn = NearestNDInterpolator((lons, lats), np.array(vdata))
vgrid = nn(xi, yi)
if ugrid is not None:
ugt = ugrid
vgt = vgrid
return ugt, vgt
else:
return None, None
def do(valid, yawsource):
""" Generate plot for a given timestamp """
if yawsource not in ['yaw', 'yaw2', 'yaw3']:
return
yawdict = {'yaw': 'Orginal', 'yaw2': 'daryl corrected', 'yaw3': 'daryl v2'}
if os.environ.get("SERVER_NAME", "") == 'iem.local':
PGCONN = psycopg2.connect(database='mec',
host='localhost',
port='5555',
user='******')
else:
PGCONN = psycopg2.connect(database='mec',
host='iemdb',
port='5432',
user='******')
cursor = PGCONN.cursor()
cursor.execute(
"""select turbineid, power, ST_x(geom), ST_y(geom),
""" + yawsource + """, windspeed, pitch
from sampled_data s JOIN turbines t on (t.id = s.turbineid)
WHERE valid = %s and power is not null and """ + yawsource +
""" is not null
and windspeed is not null and pitch is not null""", (valid, ))
lons = []
lats = []
vals = []
u = []
v = []
ws = []
yaw = []
pitch = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
ws.append(row[5])
yaw.append(row[4])
a, b = uv(speed(row[5], 'MPS'), direction(row[4], 'deg'))
u.append(a.value('MPS'))
v.append(b.value('MPS'))
pitch.append(row[6])
pitch = np.array(pitch)
vals = np.array(vals)
avgv = np.average(vals)
#vals2 = vals - avgv
fig = plt.figure(figsize=(12.8, 7.2))
ax = fig.add_axes([0.14, 0.1, 0.52, 0.8])
cmap = plt.cm.get_cmap('jet')
cmap.set_under('tan')
cmap.set_over('black')
clevs = np.arange(0, 1651, 150)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(lons,
lats,
c=vals,
norm=norm,
edgecolor='none',
cmap=cmap,
s=100,
zorder=2)
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.set_title(
"'I050' Farm Turbine Power [kW] (1min sampled dataset)\nValid: %s, yaw source: %s"
% (valid.strftime("%d %b %Y %I:%M %p"),
yawdict.get(yawsource, yawsource)))
make_colorbar(clevs, norm, cmap)
ax.text(0.05,
0.05,
"Turbine Power: $\mu$= %.1f $\sigma$= %.1f kW" %
(avgv, np.std(vals)),
transform=ax.transAxes)
ax.text(0.05,
0.01,
"Wind $\mu$= %.1f $\sigma$= %.1f $ms^{-1}$" %
(np.average(ws), np.std(ws)),
transform=ax.transAxes)
ax.set_xlabel("Longitude $^\circ$E")
ax.set_ylabel("Latitude $^\circ$N")
ax.set_xlim(-94.832, -94.673)
ax.set_ylim(42.545, 42.671)
# Next plot
ax2 = fig.add_axes([0.7, 0.80, 0.28, 0.18])
ax2.scatter(ws, vals, edgecolor='k', c='k')
ax2.text(0.5,
-0.25,
"Wind Speed $ms^{-1}$",
transform=ax2.transAxes,
ha='center')
ax2.set_xlim(0, 20)
#ax2.set_ylabel("Power kW")
ax2.grid(True)
# Next plot
ax3 = fig.add_axes([0.7, 0.57, 0.28, 0.18], sharey=ax2)
ax3.scatter(yaw, vals, edgecolor='k', c='k')
ax3.text(0.5, -0.25, "Yaw", transform=ax3.transAxes, ha='center')
#ax3.set_ylabel("Power kW")
ax3.set_xlim(0, 360)
ax3.set_xticks(np.arange(0, 361, 45))
ax3.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax3.grid(True)
# Next plot
ax4 = fig.add_axes([0.7, 0.32, 0.28, 0.18], sharey=ax2)
ax4.scatter(pitch, vals, edgecolor='k', c='k')
ax4.text(0.5,
-0.25,
"Pitch $^\circ$",
transform=ax4.transAxes,
ha='center')
ax4.set_ylim(-10, 1600)
ax4.grid(True)
# Next plot
ax5 = fig.add_axes([0.7, 0.07, 0.28, 0.18], sharex=ax4)
ax5.scatter(pitch, ws, edgecolor='k', c='k')
ax5.text(0.5,
-0.25,
"Pitch $^\circ$",
transform=ax5.transAxes,
ha='center')
ax5.grid(True)
ax5.set_ylim(bottom=-10)
#maxpitch = max(np.where(pitch > 20, 0, pitch))
#ax5.set_xlim(np.ma.minimum(pitch)-0.5, maxpitch+0.5)
ax5.set_xlim(-3, 20.1)
ax5.set_ylim(0, 20)
ax5.text(-0.1,
0.5,
"Wind Speed $ms^{-1}$",
transform=ax5.transAxes,
ha='center',
va='center',
rotation=90)
plt.savefig(sys.stdout)
def simple(grids, valid, iarchive):
"""Simple gridder (stub for now)"""
if iarchive:
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from alldata c JOIN stations t on
(c.station = t.id)
WHERE c.valid >= %s and c.valid < %s and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""", pgconn, params=((valid - datetime.timedelta(minutes=30)),
(valid + datetime.timedelta(minutes=30))),
index_col=None)
else:
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from current c JOIN stations t on (c.iemid = t.iemid)
WHERE c.valid > now() - '1 hour'::interval and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""", pgconn, index_col=None)
if len(df.index) < 5:
print(("i5gridder abort len(data): %s for %s iarchive: %s"
% (len(df.index), valid, iarchive)))
sys.exit()
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['tmpf'].values, 'F').value('C'))
grids['tmpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['dwpf'].values, 'F').value('C'))
grids['dwpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
speed(df['sknt'].values, 'KT').value('MPS'))
grids['smps'] = nn(XI, YI)
u, v = meteorology.uv(speed(df['sknt'].values, 'KT'),
direction(df['drct'].values, 'DEG'))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
u.value('MPS'))
ugrid = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
v.value('MPS'))
vgrid = nn(XI, YI)
drct = meteorology.drct(speed(ugrid.ravel(), 'MPS'),
speed(vgrid.ravel(), 'MPS')
).value('DEG').astype('i')
grids['drct'] = np.reshape(drct, (len(YAXIS), len(XAXIS)))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
distance(df['vsby'].values, 'MI').value('KM'))
grids['vsby'] = nn(XI, YI)
def do(valid, yawsource):
""" Generate plot for a given timestamp """
pgconn = get_dbconn('scada')
cursor = pgconn.cursor()
cursor.execute("""select turbine_id, power, lon, lat,
yawangle, windspeed, alpha1
from data s JOIN turbines t on (t.id = s.turbine_id)
WHERE valid = %s and power is not null and yawangle is not null
and windspeed is not null and alpha1 is not null""", (valid,))
lons = []
lats = []
vals = []
u = []
v = []
ws = []
yaw = []
pitch = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
ws.append(row[5])
yaw.append(row[4])
a, b = uv(speed(row[5], 'MPS'), direction(row[4], 'deg'))
u.append(a.value('MPS'))
v.append(b.value('MPS'))
pitch.append(row[6])
pitch = np.array(pitch)
vals = np.array(vals)
avgv = np.average(vals)
# vals2 = vals - avgv
fig = plt.figure(figsize=(12.8, 7.2))
ax = fig.add_axes([0.14, 0.1, 0.52, 0.8])
cmap = plt.cm.get_cmap('jet')
cmap.set_under('tan')
cmap.set_over('black')
# cmap = plt.cm.get_cmap('seismic')
# clevs = np.arange(-250, 251, 50)
clevs = np.arange(0, 1501, 150)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(lons, lats, c=vals, norm=norm, edgecolor='none',
cmap=cmap, s=100, zorder=2)
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.set_title(("Turbine Power [kW]\n"
"Valid: %s"
) % (valid.strftime("%d %b %Y %I:%M %p")))
make_colorbar(clevs, norm, cmap)
ax.text(0.05, 0.05, "Turbine Power: $\mu$= %.1f $\sigma$= %.1f kW" % (
avgv, np.std(vals)),
transform=ax.transAxes)
ax.text(0.05, 0.01, "Wind $\mu$= %.1f $\sigma$= %.1f $ms^{-1}$" % (
np.average(ws),
np.std(ws)),
transform=ax.transAxes)
ax.set_xlabel("Longitude $^\circ$E")
ax.set_ylabel("Latitude $^\circ$N")
ax.set_xlim(-93.475, -93.328)
ax.set_ylim(42.20, 42.31)
# Next plot
ax2 = fig.add_axes([0.7, 0.80, 0.28, 0.18])
ax2.scatter(ws, vals, edgecolor='k', c='k')
ax2.text(0.5, -0.25, "Wind Speed $ms^{-1}$", transform=ax2.transAxes,
ha='center')
ax2.set_xlim(0, 20)
# ax2.set_ylabel("Power kW")
ax2.grid(True)
# Next plot
ax3 = fig.add_axes([0.7, 0.57, 0.28, 0.18], sharey=ax2)
ax3.scatter(yaw, vals, edgecolor='k', c='k')
ax3.text(0.5, -0.25, "Yaw", transform=ax3.transAxes, ha='center')
# ax3.set_ylabel("Power kW")
ax3.set_xlim(0, 360)
ax3.set_xticks(np.arange(0, 361, 45))
ax3.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax3.grid(True)
# Next plot
ax4 = fig.add_axes([0.7, 0.32, 0.28, 0.18], sharey=ax2)
ax4.scatter(pitch, vals, edgecolor='k', c='k')
ax4.text(0.5, -0.25, "Pitch $^\circ$", transform=ax4.transAxes,
ha='center')
ax4.set_ylim(-10, 1600)
ax4.grid(True)
# Next plot
ax5 = fig.add_axes([0.7, 0.07, 0.28, 0.18], sharex=ax4)
ax5.scatter(pitch, ws, edgecolor='k', c='k')
ax5.text(0.5, -0.25, "Pitch $^\circ$", transform=ax5.transAxes,
ha='center')
ax5.grid(True)
ax5.set_ylim(bottom=-10)
# maxpitch = max(np.where(pitch > 20, 0, pitch))
# ax5.set_xlim(np.ma.minimum(pitch)-0.5, maxpitch+0.5)
ax5.set_xlim(-3, 20.1)
ax5.set_ylim(0, 20)
ax5.text(-0.1, 0.5, "Wind Speed $ms^{-1}$", transform=ax5.transAxes,
ha='center', va='center', rotation=90)
plt.savefig(getattr(sys.stdout, 'buffer', sys.stdout))
def do(valid, yawsource):
""" Generate plot for a given timestamp """
PGCONN = psycopg2.connect(database='scada', host='iemdb',
user='******')
cursor = PGCONN.cursor()
cursor.execute("""select turbine_id, power, lon, lat,
yawangle, windspeed, alpha1
from data s JOIN turbines t on (t.id = s.turbine_id)
WHERE valid = %s and power is not null and yawangle is not null
and windspeed is not null and alpha1 is not null""", (valid,))
lons = []
lats = []
vals = []
u = []
v = []
ws = []
yaw = []
pitch = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
ws.append(row[5])
yaw.append(row[4])
a, b = uv(speed(row[5], 'MPS'), direction(row[4], 'deg'))
u.append(a.value('MPS'))
v.append(b.value('MPS'))
pitch.append(row[6])
pitch = np.array(pitch)
vals = np.array(vals)
avgv = np.average(vals)
vals2 = vals - avgv
fig = plt.figure(figsize=(12.8, 7.2))
ax = fig.add_axes([0.14, 0.1, 0.52, 0.8])
cmap = plt.cm.get_cmap('jet')
cmap.set_under('tan')
cmap.set_over('black')
# cmap = plt.cm.get_cmap('seismic')
# clevs = np.arange(-250, 251, 50)
clevs = np.arange(0, 1501, 150)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(lons, lats, c=vals, norm=norm, edgecolor='none',
cmap=cmap, s=100, zorder=2)
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.set_title(("Turbine Power [kW]\n"
"Valid: %s"
) % (valid.strftime("%d %b %Y %I:%M %p")))
make_colorbar(clevs, norm, cmap)
ax.text(0.05, 0.05, "Turbine Power: $\mu$= %.1f $\sigma$= %.1f kW" % (
avgv, np.std(vals)),
transform=ax.transAxes)
ax.text(0.05, 0.01, "Wind $\mu$= %.1f $\sigma$= %.1f $ms^{-1}$" % (
np.average(ws),
np.std(ws)),
transform=ax.transAxes)
ax.set_xlabel("Longitude $^\circ$E")
ax.set_ylabel("Latitude $^\circ$N")
ax.set_xlim(-93.475, -93.328)
ax.set_ylim(42.20, 42.31)
# Next plot
ax2 = fig.add_axes([0.7, 0.80, 0.28, 0.18])
ax2.scatter(ws, vals, edgecolor='k', c='k')
ax2.text(0.5, -0.25, "Wind Speed $ms^{-1}$", transform=ax2.transAxes,
ha='center')
ax2.set_xlim(0, 20)
# ax2.set_ylabel("Power kW")
ax2.grid(True)
# Next plot
ax3 = fig.add_axes([0.7, 0.57, 0.28, 0.18], sharey=ax2)
ax3.scatter(yaw, vals, edgecolor='k', c='k')
ax3.text(0.5, -0.25, "Yaw", transform=ax3.transAxes, ha='center')
# ax3.set_ylabel("Power kW")
ax3.set_xlim(0, 360)
ax3.set_xticks(np.arange(0, 361, 45))
ax3.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax3.grid(True)
# Next plot
ax4 = fig.add_axes([0.7, 0.32, 0.28, 0.18], sharey=ax2)
ax4.scatter(pitch, vals, edgecolor='k', c='k')
ax4.text(0.5, -0.25, "Pitch $^\circ$", transform=ax4.transAxes,
ha='center')
ax4.set_ylim(-10, 1600)
ax4.grid(True)
# Next plot
ax5 = fig.add_axes([0.7, 0.07, 0.28, 0.18], sharex=ax4)
ax5.scatter(pitch, ws, edgecolor='k', c='k')
ax5.text(0.5, -0.25, "Pitch $^\circ$", transform=ax5.transAxes,
ha='center')
ax5.grid(True)
ax5.set_ylim(bottom=-10)
# maxpitch = max(np.where(pitch > 20, 0, pitch))
# ax5.set_xlim(np.ma.minimum(pitch)-0.5, maxpitch+0.5)
ax5.set_xlim(-3, 20.1)
ax5.set_ylim(0, 20)
ax5.text(-0.1, 0.5, "Wind Speed $ms^{-1}$", transform=ax5.transAxes,
ha='center', va='center', rotation=90)
plt.savefig(sys.stdout)
def do(ts):
"""Process this date timestamp"""
asos = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = asos.cursor()
iemaccess = psycopg2.connect(database='iem', host='iemdb')
icursor = iemaccess.cursor()
cursor.execute("""
select station, network, iemid, drct, sknt, valid at time zone tzname,
tmpf, dwpf from
alldata d JOIN stations t on (t.id = d.station)
where (network ~* 'ASOS' or network = 'AWOS')
and valid between %s and %s and t.tzname is not null
ORDER by valid ASC
""", (ts - datetime.timedelta(days=2), ts + datetime.timedelta(days=2)))
wdata = dict()
rhdata = dict()
for row in cursor:
if row[5].strftime("%m%d") != ts.strftime("%m%d"):
continue
station = "%s|%s|%s" % (row[0], row[1], row[2])
if row[6] is not None and row[7] is not None:
tmpf = temperature(row[6], 'F')
dwpf = temperature(row[7], 'F')
rh = meteorology.relh(tmpf, dwpf)
if station not in rhdata:
rhdata[station] = dict(valid=[], rh=[])
rhdata[station]['valid'].append(row[5])
rhdata[station]['rh'].append(rh.value('%'))
if row[4] is not None and row[3] is not None:
sknt = speed(row[4], 'KT')
drct = direction(row[3], 'DEG')
(u, v) = meteorology.uv(sknt, drct)
if station not in wdata:
wdata[station] = {'valid': [], 'sknt': [], 'u': [], 'v': []}
wdata[station]['valid'].append(row[5])
wdata[station]['sknt'].append(row[4])
wdata[station]['u'].append(u.value('KT'))
wdata[station]['v'].append(v.value('KT'))
table = "summary_%s" % (ts.year,)
for stid in rhdata:
# Not enough data
if len(rhdata[stid]['valid']) < 6:
continue
station, network, iemid = stid.split("|")
now = datetime.datetime(ts.year, ts.month, ts.day)
runningrh = 0
runningtime = 0
for i, valid in enumerate(rhdata[stid]['valid']):
delta = (valid - now).seconds
runningtime += delta
runningrh += (delta * rhdata[stid]['rh'][i])
now = valid
if runningtime == 0:
print(("compute_daily %s has time domain %s %s"
) % (stid, rhdata[stid]['valid'][0],
rhdata[stid]['valid'][-1]))
continue
avg_rh = clean_rh(runningrh / runningtime)
min_rh = clean_rh(min(rhdata[stid]['rh']))
max_rh = clean_rh(max(rhdata[stid]['rh']))
def do_update():
icursor.execute("""UPDATE """ + table + """
SET avg_rh = %s, min_rh = %s, max_rh = %s WHERE
iemid = %s and day = %s""", (avg_rh, min_rh, max_rh, iemid, ts))
do_update()
if icursor.rowcount == 0:
print(('compute_daily Adding %s for %s %s %s'
) % (table, station, network, ts))
icursor.execute("""INSERT into """ + table + """
(iemid, day) values (%s, %s)""", (iemid, ts))
do_update()
for stid in wdata:
# Not enough data
if len(wdata[stid]['valid']) < 6:
continue
station, network, iemid = stid.split("|")
now = datetime.datetime(ts.year, ts.month, ts.day)
runningsknt = 0
runningtime = 0
runningu = 0
runningv = 0
for i, valid in enumerate(wdata[stid]['valid']):
delta = (valid - now).seconds
runningtime += delta
runningsknt += (delta * wdata[stid]['sknt'][i])
runningu += (delta * wdata[stid]['u'][i])
runningv += (delta * wdata[stid]['v'][i])
now = valid
if runningtime == 0:
print(("compute_daily %s has time domain %s %s"
) % (stid, wdata[stid]['valid'][0],
wdata[stid]['valid'][-1]))
continue
sknt = runningsknt / runningtime
u = speed(runningu / runningtime, 'KT')
v = speed(runningv / runningtime, 'KT')
drct = meteorology.drct(u, v).value("DEG")
def do_update():
icursor.execute("""UPDATE """ + table + """
SET avg_sknt = %s, vector_avg_drct = %s WHERE
iemid = %s and day = %s""", (sknt, drct, iemid, ts))
do_update()
if icursor.rowcount == 0:
print(('compute_daily Adding %s for %s %s %s'
) % (table, station, network, ts))
icursor.execute("""INSERT into """ + table + """
(iemid, day) values (%s, %s)""", (iemid, ts))
do_update()
icursor.close()
iemaccess.commit()
iemaccess.close()
def do(valid, yawsource):
""" Generate plot for a given timestamp """
if yawsource not in ['yaw', 'yaw2', 'yaw3']:
return
yawdict = {'yaw': 'Orginal', 'yaw2': 'daryl corrected',
'yaw3': 'daryl v2'}
if os.environ.get("SERVER_NAME", "") == 'iem.local':
PGCONN = psycopg2.connect(database='mec', host='localhost',
port='5555', user='******')
else:
PGCONN = psycopg2.connect(database='mec', host='iemdb', port='5432',
user='******')
cursor = PGCONN.cursor()
cursor.execute("""select turbineid, power, ST_x(geom), ST_y(geom),
"""+yawsource+""", windspeed, pitch
from sampled_data s JOIN turbines t on (t.id = s.turbineid)
WHERE valid = %s and power is not null and """+yawsource+""" is not null
and windspeed is not null and pitch is not null""", (valid,))
lons = []
lats = []
vals = []
u = []
v = []
ws = []
yaw = []
pitch = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
ws.append( row[5] )
yaw.append( row[4])
a,b = uv(speed(row[5], 'MPS'), direction(row[4], 'deg'))
u.append( a.value('MPS') )
v.append( b.value('MPS') )
pitch.append(row[6])
pitch = np.array(pitch)
vals = np.array(vals)
avgv = np.average(vals)
#vals2 = vals - avgv
fig = plt.figure(figsize=(12.8,7.2))
ax = fig.add_axes([0.14, 0.1, 0.52, 0.8])
cmap = plt.cm.get_cmap('jet')
cmap.set_under('tan')
cmap.set_over('black')
clevs = np.arange(0,1651,150)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(lons, lats, c=vals, norm=norm, edgecolor='none',
cmap=cmap, s=100, zorder=2)
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.set_title("'I050' Farm Turbine Power [kW] (1min sampled dataset)\nValid: %s, yaw source: %s" % (
valid.strftime("%d %b %Y %I:%M %p"),
yawdict.get(yawsource, yawsource)))
make_colorbar(clevs, norm, cmap)
ax.text(0.05, 0.05, "Turbine Power: $\mu$= %.1f $\sigma$= %.1f kW" % (
avgv, np.std(vals)),
transform=ax.transAxes)
ax.text(0.05, 0.01, "Wind $\mu$= %.1f $\sigma$= %.1f $ms^{-1}$" % (
np.average(ws),
np.std(ws)),
transform=ax.transAxes)
ax.set_xlabel("Longitude $^\circ$E")
ax.set_ylabel("Latitude $^\circ$N")
ax.set_xlim(-94.832, -94.673)
ax.set_ylim(42.545, 42.671)
# Next plot
ax2 = fig.add_axes([0.7, 0.80, 0.28, 0.18])
ax2.scatter(ws, vals, edgecolor='k', c='k')
ax2.text(0.5, -0.25, "Wind Speed $ms^{-1}$", transform=ax2.transAxes,
ha='center')
ax2.set_xlim(0,20)
#ax2.set_ylabel("Power kW")
ax2.grid(True)
# Next plot
ax3 = fig.add_axes([0.7, 0.57, 0.28, 0.18], sharey=ax2)
ax3.scatter(yaw, vals, edgecolor='k', c='k')
ax3.text(0.5, -0.25, "Yaw", transform=ax3.transAxes, ha='center')
#ax3.set_ylabel("Power kW")
ax3.set_xlim(0,360)
ax3.set_xticks(np.arange(0,361,45))
ax3.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax3.grid(True)
# Next plot
ax4 = fig.add_axes([0.7, 0.32, 0.28, 0.18], sharey=ax2)
ax4.scatter(pitch, vals, edgecolor='k', c='k')
ax4.text(0.5, -0.25, "Pitch $^\circ$", transform=ax4.transAxes, ha='center')
ax4.set_ylim(-10,1600)
ax4.grid(True)
# Next plot
ax5 = fig.add_axes([0.7, 0.07, 0.28, 0.18], sharex=ax4)
ax5.scatter(pitch, ws, edgecolor='k', c='k')
ax5.text(0.5, -0.25, "Pitch $^\circ$", transform=ax5.transAxes, ha='center')
ax5.grid(True)
ax5.set_ylim(bottom=-10)
#maxpitch = max(np.where(pitch > 20, 0, pitch))
#ax5.set_xlim(np.ma.minimum(pitch)-0.5, maxpitch+0.5)
ax5.set_xlim(-3, 20.1)
ax5.set_ylim(0,20)
ax5.text(-0.1, 0.5, "Wind Speed $ms^{-1}$", transform=ax5.transAxes,
ha='center', va='center', rotation=90)
plt.savefig( sys.stdout )
def do(valid, yawsource):
""" Generate plot for a given timestamp """
if yawsource not in ["yaw", "yaw2", "yaw3"]:
return
yawdict = {"yaw": "Orginal", "yaw2": "daryl corrected", "yaw3": "daryl v2"}
pgconn = get_dbconn("mec")
cursor = pgconn.cursor()
cursor.execute(
"""select turbineid, power, ST_x(geom), ST_y(geom),
""" + yawsource + """, windspeed, pitch
from sampled_data s JOIN turbines t on (t.id = s.turbineid)
WHERE valid = %s and power is not null and """ + yawsource +
""" is not null
and windspeed is not null and pitch is not null""",
(valid, ),
)
lons = []
lats = []
vals = []
u = []
v = []
ws = []
yaw = []
pitch = []
for row in cursor:
lons.append(row[2])
lats.append(row[3])
vals.append(row[1])
ws.append(row[5])
yaw.append(row[4])
a, b = uv(speed(row[5], "MPS"), direction(row[4], "deg"))
u.append(a.value("MPS"))
v.append(b.value("MPS"))
pitch.append(row[6])
pitch = np.array(pitch)
vals = np.array(vals)
avgv = np.average(vals)
# vals2 = vals - avgv
fig = plt.figure(figsize=(12.8, 7.2))
ax = fig.add_axes([0.14, 0.1, 0.52, 0.8])
cmap = plt.cm.get_cmap("jet")
cmap.set_under("tan")
cmap.set_over("black")
clevs = np.arange(0, 1651, 150)
norm = mpcolors.BoundaryNorm(clevs, cmap.N)
ax.quiver(lons, lats, u, v, zorder=1)
ax.scatter(
lons,
lats,
c=vals,
norm=norm,
edgecolor="none",
cmap=cmap,
s=100,
zorder=2,
)
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.set_title(("Farm Turbine Power [kW] (1min sampled dataset)\n"
"Valid: %s, yaw source: %s") % (
valid.strftime("%d %b %Y %I:%M %p"),
yawdict.get(yawsource, yawsource),
))
make_colorbar(clevs, norm, cmap)
ax.text(
0.05,
0.05,
"Turbine Power: $\mu$= %.1f $\sigma$= %.1f kW" % (avgv, np.std(vals)),
transform=ax.transAxes,
)
ax.text(
0.05,
0.01,
"Wind $\mu$= %.1f $\sigma$= %.1f $ms^{-1}$" %
(np.average(ws), np.std(ws)),
transform=ax.transAxes,
)
ax.set_xlabel("Longitude $^\circ$E")
ax.set_ylabel("Latitude $^\circ$N")
ax.set_xlim(-94.832, -94.673)
ax.set_ylim(42.545, 42.671)
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
# Next plot
ax2 = fig.add_axes([0.7, 0.80, 0.28, 0.18])
ax2.scatter(ws, vals, edgecolor="k", c="k")
ax2.text(
0.5,
-0.25,
"Wind Speed $ms^{-1}$",
transform=ax2.transAxes,
ha="center",
)
ax2.set_xlim(0, 20)
# ax2.set_ylabel("Power kW")
ax2.grid(True)
# Next plot
ax3 = fig.add_axes([0.7, 0.57, 0.28, 0.18], sharey=ax2)
ax3.scatter(yaw, vals, edgecolor="k", c="k")
ax3.text(0.5, -0.25, "Yaw", transform=ax3.transAxes, ha="center")
# ax3.set_ylabel("Power kW")
ax3.set_xlim(0, 360)
ax3.set_xticks(np.arange(0, 361, 45))
ax3.set_xticklabels(["N", "NE", "E", "SE", "S", "SW", "W", "NW", "N"])
ax3.grid(True)
# Next plot
ax4 = fig.add_axes([0.7, 0.32, 0.28, 0.18], sharey=ax2)
ax4.scatter(pitch, vals, edgecolor="k", c="k")
ax4.text(0.5,
-0.25,
"Pitch $^\circ$",
transform=ax4.transAxes,
ha="center")
ax4.set_ylim(-10, 1600)
ax4.grid(True)
# Next plot
ax5 = fig.add_axes([0.7, 0.07, 0.28, 0.18], sharex=ax4)
ax5.scatter(pitch, ws, edgecolor="k", c="k")
ax5.text(0.5,
-0.25,
"Pitch $^\circ$",
transform=ax5.transAxes,
ha="center")
ax5.grid(True)
ax5.set_ylim(bottom=-10)
# maxpitch = max(np.where(pitch > 20, 0, pitch))
# ax5.set_xlim(np.ma.minimum(pitch)-0.5, maxpitch+0.5)
ax5.set_xlim(-3, 20.1)
ax5.set_ylim(0, 20)
ax5.text(
-0.1,
0.5,
"Wind Speed $ms^{-1}$",
transform=ax5.transAxes,
ha="center",
va="center",
rotation=90,
)
def simple(grids, valid, iarchive):
"""Simple gridder (stub for now)"""
if iarchive:
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from alldata c JOIN stations t on
(c.station = t.id)
WHERE c.valid >= %s and c.valid < %s and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""",
pgconn,
params=((valid - datetime.timedelta(minutes=30)),
(valid + datetime.timedelta(minutes=30))),
index_col=None)
else:
pgconn = psycopg2.connect(database='iem', host='iemdb', user='******')
df = read_sql("""
SELECT ST_x(geom) as lon, ST_y(geom) as lat,
tmpf, dwpf, sknt, drct, vsby
from current c JOIN stations t on (c.iemid = t.iemid)
WHERE c.valid > now() - '1 hour'::interval and
t.network in ('IA_ASOS', 'AWOS', 'MN_ASOS', 'WI_ASOS', 'IL_ASOS',
'MO_ASOS', 'NE_ASOS', 'KS_ASOS', 'SD_ASOS') and sknt is not null
and drct is not null and tmpf is not null and dwpf is not null
and vsby is not null
""",
pgconn,
index_col=None)
if len(df.index) < 5:
print(("i5gridder abort len(data): %s for %s iarchive: %s" %
(len(df.index), valid, iarchive)))
sys.exit()
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['tmpf'].values, 'F').value('C'))
grids['tmpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
temperature(df['dwpf'].values, 'F').value('C'))
grids['dwpc'] = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
speed(df['sknt'].values, 'KT').value('MPS'))
grids['smps'] = nn(XI, YI)
u, v = meteorology.uv(speed(df['sknt'].values, 'KT'),
direction(df['drct'].values, 'DEG'))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
u.value('MPS'))
ugrid = nn(XI, YI)
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
v.value('MPS'))
vgrid = nn(XI, YI)
drct = meteorology.drct(speed(ugrid.ravel(), 'MPS'),
speed(vgrid.ravel(),
'MPS')).value('DEG').astype('i')
grids['drct'] = np.reshape(drct, (len(YAXIS), len(XAXIS)))
nn = NearestNDInterpolator((df['lon'].values, df['lat'].values),
distance(df['vsby'].values, 'MI').value('KM'))
grids['vsby'] = nn(XI, YI)
def plot_station(self, data, **kwargs):
"""Plot values on a map in a station plot like manner.
Args:
data (list): list of dicts with station data to plot
fontsize (int): font size to use for plotted text
"""
(x0, x1) = self.ax.set_xlim()
# size to use for circles
circlesz = (x1 - x0) / 180.
# (y0, y1) = self.ax.set_ylim()
offsets = {1: [-4, 4, 'right', 'bottom'],
2: [0, 4, 'center', 'bottom'],
3: [4, 4, 'left', 'bottom'],
4: [-4, 0, 'right', 'center'],
5: [0, 0, 'center', 'center'],
6: [4, 0, 'left', 'center'],
7: [-4, -4, 'right', 'top'],
8: [0, -4, 'center', 'top'],
9: [4, -4, 'left', 'top']}
mask = np.zeros(self.fig.canvas.get_width_height(), bool)
for stdata in data:
(x, y) = self.ax.projection.transform_point(stdata['lon'],
stdata['lat'],
ccrs.Geodetic())
(imgx, imgy) = self.ax.transData.transform([x, y])
imgx = int(imgx)
imgy = int(imgy)
# Check to see if this overlaps
_cnt = np.sum(np.where(mask[imgx-15:imgx+15, imgy-15:imgy+15], 1,
0))
if _cnt > 5:
continue
mask[imgx-15:imgx+15, imgy-15:imgy+15] = True
# Plot bars
if stdata.get('sknt') is not None and stdata['sknt'] > 1:
(u, v) = meteorology.uv(speed(stdata.get('sknt', 0), 'KT'),
direction(stdata.get('drct', 0),
'DEG'))
if u is not None and v is not None:
self.ax.barbs(x, y, u.value('KT'), v.value('KT'), zorder=1)
# Sky Coverage
skycoverage = stdata.get('coverage')
if (skycoverage is not None and skycoverage >= 0 and
skycoverage <= 100):
w = Wedge((x, y), circlesz, 0, 360, ec='k', fc='white',
zorder=2)
self.ax.add_artist(w)
w = Wedge((x, y), circlesz, 0, 360. * skycoverage / 100.,
ec='k', fc='k', zorder=3)
self.ax.add_artist(w)
# Temperature
val = stdata.get('tmpf')
if val is not None:
(offx, offy, ha, va) = offsets[1]
self.ax.annotate(
stdata.get("tmpf_format", "%.0f") % (val, ),
xy=(x, y), ha=ha, va=va,
xytext=(offx, offy), color=stdata.get('tmpf_color', 'r'),
textcoords="offset points",
zorder=Z_OVERLAY+2,
clip_on=True, fontsize=kwargs.get('fontsize', 8))
# Dew Point
val = stdata.get('dwpf')
if val is not None:
(offx, offy, ha, va) = offsets[7]
self.ax.annotate(
stdata.get("dwpf_format", "%.0f") % (val, ),
xy=(x, y), ha=ha, va=va,
xytext=(offx, offy), color=stdata.get('dwpf_color', 'b'),
textcoords="offset points",
zorder=Z_OVERLAY+2,
clip_on=True, fontsize=kwargs.get('fontsize', 8))
# Plot identifier
val = stdata.get('id')
if val is not None:
(offx, offy, ha, va) = (
offsets[6] if skycoverage is not None else offsets[5]
)
self.ax.annotate(
"%s" % (val, ), xy=(x, y), ha=ha, va=va,
xytext=(offx, offy),
color=stdata.get('id_color', 'tan'),
textcoords="offset points",
zorder=Z_OVERLAY+2,
clip_on=True,
fontsize=kwargs.get('fontsize', 8))
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
