def draw(self):
''' For debugging, draw the polygons!'''
from descartes.patch import PolygonPatch
from pyiem.plot import MapPlot
for sig in self.sigmets:
m = MapPlot(sector='conus')
x, y = m.map(sig.geom.exterior.xy[0], sig.geom.exterior.xy[1])
patch = PolygonPatch(Polygon(zip(x, y)), fc='r', label='Outlook')
m.ax.add_patch(patch)
fn = '/tmp/%s.png' % (sig.label,)
print ':: creating plot %s' % (fn,)
m.postprocess(filename=fn)
m.close()
def draw(self):
''' For debugging, draw the polygons!'''
from descartes.patch import PolygonPatch
from pyiem.plot import MapPlot
for sig in self.sigmets:
m = MapPlot(sector='conus')
x, y = m.map(sig.geom.exterior.xy[0], sig.geom.exterior.xy[1])
patch = PolygonPatch(Polygon(zip(x, y)), fc='r', label='Outlook')
m.ax.add_patch(patch)
fn = '/tmp/%s.png' % (sig.label, )
print(':: creating plot %s' % (fn, ))
m.postprocess(filename=fn)
m.close()
def main():
"""Go Main Go."""
pgconn = get_dbconn('iem')
df = read_sql("""SELECT ST_x(geom) as x, ST_y(geom) as y,
min(min_tmpf) as val, state, count(*) from
summary s JOIN stations t on (t.iemid = s.iemid) WHERE
s.day > '2018-09-01' and min_tmpf is not null and t.country = 'US' and
t.network ~* 'COOP' and t.state in ('IA', 'MN', 'ND', 'SD', 'NE',
'KS', 'MO', 'WI', 'IL', 'IN', 'OH', 'MI', 'KY') GROUP by y, x, state
ORDER by val ASC""", pgconn, index_col=None)
df = df[(df['count'] > 110)]
x3 = df[(df.val < 32) & (df.val >= 10)]
x2 = df[(df.val < 10) & (df.val > -10)]
x1 = df[(df.val < -10) & (df.val > -30)]
x0 = df[df.val < -30]
m = MapPlot(title='Fall 2016 Minimum Temperature Reports', axisbg='white',
subtitle=('Based on NWS Cooperative Observer Data, '
'thru 27 Dec 2016'),
sector='midwest')
x, y = m.map(tuple(x3.x), tuple(x3.y))
m.map.scatter(x, y, marker='o', color='g', s=50, zorder=1,
label="10 to 32$^\circ$F")
x, y = m.map(tuple(x2.x), tuple(x2.y))
m.map.scatter(x, y, marker='s', color='b', zorder=1, s=50,
label="-10 to 10$^\circ$F")
x, y = m.map(tuple(x1.x), tuple(x1.y))
m.map.scatter(x, y, marker='+', color='r', s=50, zorder=2,
label="-30 to -10$^\circ$F")
x, y = m.map(tuple(x0.x), tuple(x0.y))
m.map.scatter(x, y, marker='v', facecolor='w', edgecolor='k', s=50, zorder=2,
label="Sub -30$^\circ$F")
m.ax.legend(loc=4, scatterpoints=1, ncol=4)
m.postprocess(filename='test.png')
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],),
ha='right', color=colors[i], fontsize=14)
m.postprocess(filename='test.ps')
import iemplot
iemplot.makefeature('test')
t.network ~* 'COOP' and t.state in ('IA', 'MN', 'ND', 'SD', 'NE',
'KS', 'MO', 'WI', 'IL', 'IN', 'OH', 'MI', 'KY') GROUP by st_y, st_x
ORDER by min ASC""")
res = []
for row in cursor:
res.append(dict(val=row[2], x=row[0], y=row[1]))
df = pd.DataFrame(res)
x35 = df[(df.val < 36) & (df.val >= 32)]
x32 = df[(df.val < 32) & (df.val > 28)]
x29 = df[df.val < 29]
m = MapPlot(title='Fall 2015 Minimum Temperature Reports', axisbg='white',
subtitle='Based on NWS Cooperative Observer Data, thru 7 Oct 2015',
sector='midwest')
x, y = m.map(tuple(x35.x), tuple(x35.y))
m.map.scatter(x, y, marker='o', color='g', s=50, zorder=1,
label="Sub 36$^\circ$F")
x, y = m.map(tuple(x32.x), tuple(x32.y))
m.map.scatter(x, y, marker='s', zorder=1, s=50,
label="Sub 32$^\circ$F")
x, y = m.map(tuple(x29.x), tuple(x29.y))
m.map.scatter(x, y, marker='+', color='r', s=50, zorder=2,
label="Sub 29$^\circ$F")
m.ax.legend(loc=4, scatterpoints=1)
m.postprocess(filename='test.png')
cursor.execute("""SELECT ST_x(geom), ST_y(geom), min(min_tmpf) from
summary_2014 s JOIN stations t on (t.iemid = s.iemid) WHERE
s.day > '2014-05-12' and min_tmpf > 0 and t.country = 'US' and
t.network ~* 'COOP' GROUP by st_y, st_x
ORDER by min ASC""")
res = []
for row in cursor:
res.append( dict(val=row[2], x=row[0], y=row[1]) )
df = pd.DataFrame(res)
x32 = df[(df.val<32)&(df.val>28)]
x29 = df[df.val<29]
m = MapPlot(title='13-19 May 2014 Freezing Reports',
subtitle='Based on NWS Cooperative Observer Data',
sector='midwest')
x,y = m.map(x32.x, x32.y)
m.map.scatter(x,y, marker='s',zorder=1, label="Sub 32$^\circ$F")
x,y = m.map(x29.x, x29.y)
m.map.scatter(x,y, marker='+', color='r', s=40, zorder=2, label="Sub 29$^\circ$F")
m.ax.legend(loc=4, scatterpoints=1)
m.postprocess(filename='test.ps')
import iemplot
iemplot.makefeature('test')
res = []
for row in cursor:
res.append(dict(val=row[2], x=row[0], y=row[1]))
df = pd.DataFrame(res)
x35 = df[(df.val < 36) & (df.val >= 32)]
x32 = df[(df.val < 32) & (df.val > 28)]
x29 = df[df.val < 29]
m = MapPlot(title='Fall 2015 Minimum Temperature Reports',
axisbg='white',
subtitle='Based on NWS Cooperative Observer Data, thru 7 Oct 2015',
sector='midwest')
x, y = m.map(tuple(x35.x), tuple(x35.y))
m.map.scatter(x,
y,
marker='o',
color='g',
s=50,
zorder=1,
label="Sub 36$^\circ$F")
x, y = m.map(tuple(x32.x), tuple(x32.y))
m.map.scatter(x, y, marker='s', zorder=1, s=50, label="Sub 32$^\circ$F")
x, y = m.map(tuple(x29.x), tuple(x29.y))
m.map.scatter(x,
y,
marker='+',
color='r',
s=50,
clons = []
clats = []
for row in pcursor:
clats.append( row['lat'] )
clons.append( row['lon'] )
cobs = nn(clons, clats)
#cobs = griddata((lons, lats), soil_obs, (clons, clats))
m = MapPlot(sector='iowa',
title="Iowa 4in Soil Temperatures %s" % (ts.strftime("%b %d %Y"), ),
subtitle='Based on gridded analysis of ISUAG, ISUSM network observations'
)
m.contourf(clons, clats, cobs, np.arange(10,101,5), cmap=cm.get_cmap('jet'),
units=r'$^\circ$F')
for lo, la, ob in zip(clons, clats, cobs):
#for lo, la, ob in zip(lons, lats, soil_obs):
xi, yi = m.map(lo, la)
txt = m.ax.text(xi, yi, "%.0f" % (ob,))
m.drawcounties()
m.postprocess(filename='highs.ps')
os.system("convert -trim highs.ps obs.png")
routes = "a"
if day_ago < 4:
routes = "ac"
os.system("/home/ldm/bin/pqinsert -p 'plot %s %s0000 soilt_day%s.png isuag_county_4inch_soil.png png' obs.png" % (
routes, ts.strftime("%Y%m%d"), day_ago) )
os.unlink('highs.ps')
os.unlink('obs.png')
if (m.map.roads_info[nshape]['US1'] in (69,30) or
m.map.roads_info[nshape]['INT1'] == 35):
data = np.array( seg )
m.ax.plot(data[:,0], data[:,1], lw=2, linestyle='--',color='k',
zorder=3)
m.map.readshapefile('cities', 'cities', ax=m.ax)
for nshape, seg in enumerate(m.map.cities):
if m.map.cities_info[nshape]['NAME10'] != 'Ames':
continue
poly=Polygon(seg, fc='None', ec='k', lw=1.5, zorder=3)
m.ax.add_patch(poly)
nc = netCDF4.Dataset('narr-a_221_20100626_0300_000.grb.nc')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
pcpn = nc.variables['Total_precipitation'][0,:,:] / 24.5
mask = lons > -100.0
n1p = [0,0.01,0.1,0.3,0.5,0.8,1.0,1.3,1.5,1.8,2.0,2.5,3.0,4.0,6.0,8.0]
m.pcolormesh(lons, lats, pcpn, n1p, latlon=True)
xx, yy = m.map(-93.62, 41.99)
txt = m.ax.text(xx, yy, "%.2f" % (0.19,) , zorder=5, ha='center',
va='center', fontsize=20)
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground="w")])
m.postprocess(filename='test.png')
sector='iowa',
title=
'Amount of additional June precipitation needed to break local record',
subtitle='1-23 June 2014 totals shown')
for row in data:
#cursor.execute("""SELECT year, sum(precip) from alldata_ia where month =6
#and year < 2014 and station = %s
#GROUP by year ORDER by sum DESC LIMIT 1""", (nt.sts[row[0]]['climate_site'],) )
#row2 = cursor.fetchone()
#ratio = row[1] / row2[1] * 360.
#print row[0], row[1], row2[1], ratio
ratio = min(row[1] / row[2] * 360., 360.)
xy = m.map(nt.sts[row[0]]['lon'], nt.sts[row[0]]['lat'])
patches.append(
Wedge(xy, 20000, 0, ratio, width=10000, fc='b', fill=True, zorder=2))
print "%.0f" % (ratio / 360. * 100., ), ratio / 360.0
remaining = max(0, row[2] - row[1])
txt = m.ax.text(xy[0],
xy[1],
"%.2f" % (remaining, ),
fontsize=24,
color='yellow',
ha='center',
va='center')
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="purple")])
txt = m.ax.text(xy[0],
X, Y = numpy.mgrid[reference.IA_WEST:reference.IA_EAST:100j,
reference.IA_SOUTH:reference.IA_NORTH:100j]
positions = numpy.vstack([X.ravel(), Y.ravel()])
values = numpy.vstack([lons, lats])
kernel = stats.gaussian_kde(values)
Z = numpy.reshape(kernel(positions).T, X.shape)
m = MapPlot(sector='iowa',
title='NEXRAD Tornado Vortex Signature Reports',
subtitle='%s - %s, (TVS storm attribute present)' % (minvalid.strftime("%d %b %Y"),
maxvalid.strftime("%d %b %Y")))
#m.pcolormesh(X, Y, Z, numpy.arange(0,.11,.01), cmap=plt.cm.gist_earth_r,
# latlon=True)
xs, ys = m.map([-93.72,-96.37,-91.18,-90.57,-96.72], [41.72,41.32,43.82,41.6,43.58])
m.ax.scatter(xs, ys, marker='o', zorder=20, s=100, color='r')
xs, ys = m.map(lons, lats)
m.ax.scatter(xs, ys, marker='+', zorder=20, s=40, color='k')
m.drawcounties()
m.postprocess(filename='test.svg')
import iemplot
iemplot.makefeature('test')
"""
fig = plt.figure()
m = MapPlot(sector='iowa',
title='Amount of additional June precipitation needed to break local record',
subtitle='1-23 June 2014 totals shown')
for row in data:
#cursor.execute("""SELECT year, sum(precip) from alldata_ia where month =6
#and year < 2014 and station = %s
#GROUP by year ORDER by sum DESC LIMIT 1""", (nt.sts[row[0]]['climate_site'],) )
#row2 = cursor.fetchone()
#ratio = row[1] / row2[1] * 360.
#print row[0], row[1], row2[1], ratio
ratio = min(row[1] / row[2] * 360., 360.)
xy = m.map(nt.sts[row[0]]['lon'], nt.sts[row[0]]['lat'])
patches.append(
Wedge(xy, 20000, 0, ratio, width=10000, fc='b', fill=True, zorder=2)
)
print "%.0f" % (ratio/360. * 100.,), ratio / 360.0
remaining = max(0, row[2] - row[1])
txt = m.ax.text(xy[0], xy[1], "%.2f" % (remaining,), fontsize=24, color='yellow',
ha='center', va='center')
txt.set_path_effects([PathEffects.withStroke(linewidth=2,
foreground="purple")])
txt = m.ax.text(xy[0], xy[1]-12000, "%s" % (row[3],), fontsize=14,
ha='center', va='center')
txt.set_path_effects([PathEffects.withStroke(linewidth=2,
foreground="yellow")])
X, Y = numpy.mgrid[reference.IA_WEST:reference.IA_EAST:100j,
reference.IA_SOUTH:reference.IA_NORTH:100j]
positions = numpy.vstack([X.ravel(), Y.ravel()])
values = numpy.vstack([lons, lats])
kernel = stats.gaussian_kde(values)
Z = numpy.reshape(kernel(positions).T, X.shape)
m = MapPlot(sector='iowa',
title='NEXRAD Tornado Vortex Signature Reports',
subtitle='%s - %s, (TVS storm attribute present)' %
(minvalid.strftime("%d %b %Y"), maxvalid.strftime("%d %b %Y")))
#m.pcolormesh(X, Y, Z, numpy.arange(0,.11,.01), cmap=plt.cm.gist_earth_r,
# latlon=True)
xs, ys = m.map([-93.72, -96.37, -91.18, -90.57, -96.72],
[41.72, 41.32, 43.82, 41.6, 43.58])
m.ax.scatter(xs, ys, marker='o', zorder=20, s=100, color='r')
xs, ys = m.map(lons, lats)
m.ax.scatter(xs, ys, marker='+', zorder=20, s=40, color='k')
m.drawcounties()
m.postprocess(filename='test.svg')
import iemplot
iemplot.makefeature('test')
"""
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(numpy.rot90(Z), cmap=plt.cm.gist_earth_r,
extent=[reference.IA_WEST,reference.IA_EAST, reference.IA_SOUTH,
lons, lats = np.meshgrid( lons1x, lats1x)
res = m.pcolormesh(lons, lats, data, n1p, latlon=True, units='inch')
ys = np.digitize([41.965,42.085], lats1x)
xs = np.digitize([-93.7,-93.55], lons1x)
for x in range(xs[0], xs[1], 3):
for y in range(ys[1], ys[0], 3):
xx, yy = m.map(lons1x[x] + 0.0025, lats1x[y] - 0.0025)
txt = m.ax.text(xx, yy, "%s" % (data[y,x],) , zorder=5, ha='center',
va='center')
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground="w")])
print np.average( data[ys[1]:ys[0],xs[0]:xs[1]] )
"""
grbs = pygrib.open('ST4.2010062605.01h.grib')
g = grbs.message(1)
data = g['values'] / 25.4
lats, lons = g.latlons()
n1p = [0,0.01,0.1,0.3,0.5,0.8,1.0,1.3,1.5,1.8,2.0,2.5,3.0,4.0,6.0,8.0]
m.pcolormesh(lons, lats, data, n1p, latlon=True)
for lo, la, val in zip(lons[np.logical_and(np.logical_and(np.logical_and(lats > 41.965, lats < 42.085),lons > -93.7), lons < -93.55)],
lats[np.logical_and(np.logical_and(np.logical_and(lats > 41.965, lats < 42.085),lons > -93.7), lons < -93.55)],
data[np.logical_and(np.logical_and(np.logical_and(lats > 41.965, lats < 42.085),lons > -93.7), lons < -93.55)]):
xx, yy = m.map(lo + 0.02, la + 0.02)
txt = m.ax.text(xx, yy, "%.2f" % (val,) , zorder=5, ha='center',
va='center', fontsize=20)
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground="w")])
m.postprocess(filename='test.png')
SELECT ST_Transform(simple_geom, 4326),
(scenario_loss - baseline_loss) / 8.0 as val, i.huc_12
from huc12 i JOIN agg d on (d.huc_12 = i.huc_12)
WHERE i.states ~* 'IA' ORDER by val DESC
""", (scenario, ))
# bins = np.arange(0, 101, 10)
bins = [-25, -10, -5, -2, 0, 2, 5, 10, 25]
cmap = plt.get_cmap("BrBG_r")
cmap.set_under('purple')
cmap.set_over('black')
norm = mpcolors.BoundaryNorm(bins, cmap.N)
patches = []
for row in cursor:
# print "%s,%s" % (row[2], row[1])
polygon = loads(row[0].decode('hex'))
a = np.asarray(polygon.exterior)
x, y = m.map(a[:, 0], a[:, 1])
a = zip(x, y)
c = cmap(norm([float(row[1]), ]))[0]
p = Polygon(a, fc=c, ec='None', zorder=2, lw=.1)
patches.append(p)
m.ax.add_collection(PatchCollection(patches, match_original=True))
m.draw_colorbar(bins, cmap, norm, units='T/a')
m.drawcounties()
m.postprocess(filename='test.png')
""")
bins = np.arange(0, 18, 2)
# cmap = cm.get_cmap('BrBG')
# cmap.set_under('orange')
# cmap.set_over('black')
cmap = james()
cmap.set_under("white")
cmap.set_over("black")
norm = mpcolors.BoundaryNorm(bins, cmap.N)
patches = []
for row in cursor:
geom = loads(row[0].decode("hex"))
for polygon in geom:
a = np.asarray(polygon.exterior)
x, y = m.map(a[:, 0], a[:, 1])
a = zip(x, y)
# c = cmap( norm([float(row[1]),]) )[0]
diff = data.get(row[1], 0) - data1.get(row[1], 0)
diff = data.get(row[1], 0)
c = cmap(norm([float(diff)]))[0]
p = Polygon(a, fc=c, ec="None", zorder=2, lw=0.1)
patches.append(p)
m.ax.add_collection(PatchCollection(patches, match_original=True))
m.draw_colorbar(bins, cmap, norm, units="%")
m.drawcounties()
m.postprocess(filename="test.png")
m.map.readshapefile('cities', 'cities', ax=m.ax)
for nshape, seg in enumerate(m.map.cities):
if m.map.cities_info[nshape]['NAME10'] != 'Ames':
continue
poly=Polygon(seg, fc='None', ec='k', lw=1.5, zorder=3)
m.ax.add_patch(poly)
nc = netCDF4.Dataset('3B42.100626.6.6A.nc')
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
lons, lats = np.meshgrid(lons, lats)
pcpn = nc.variables['precipitation'][:,:] / 24.5 * 3
n1p = [0,0.01,0.1,0.3,0.5,0.8,1.0,1.3,1.5,1.8,2.0,2.5,3.0,4.0,6.0,8.0]
m.pcolormesh(lons, lats, pcpn, n1p, latlon=True)
xx, yy = m.map(-93.6, 42.02)
txt = m.ax.text(xx, yy, "%.2f" % (0.65,) , zorder=5, ha='center',
va='center', fontsize=20)
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground="w")])
xx, yy = m.map(-93.64, 42.02)
txt = m.ax.text(xx, yy, "%.2f" % (0.39,) , zorder=5, ha='center',
va='center', fontsize=20)
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground="w")])
m.postprocess(filename='test.png')
