def find_client_density(m,client_locations,latscale=1.0,lonscale=1.0,lat_smooth=1,lon_smooth=1):
lat_array=arange(m.ymin,m.ymax+latscale,latscale)
lon_array=arange(m.xmin,m.xmax+lonscale,lonscale)
maxlat=len(lat_array)-1
maxlon=len(lon_array)-1
Z=zeros((len(lat_array),len(lon_array)),dtype='float')
for client in client_locations:
lat=client[2]
i_lat=int(float((lat-lat_array[0]))/float(latscale))
lon=client[3]
i_lon=int(float((lon-lon_array[0]))/float(lonscale))
for i in xrange(-int(lat_smooth),int(lat_smooth+1),1):
for j in xrange(-int(lon_smooth),int(lon_smooth+1),1):
if ( i_lat+i >= 0 ) and (i_lat+i < maxlat) :
if ( i_lon+j >= 0) and ( i_lon+j < maxlon) :
Z[i_lat+i,i_lon+j]+=1.0
Lon,Lat=meshgrid(lon_array,lat_array)
X,Y=m(Lon,Lat)
Z= Z + 1.0
Z=log(Z)
Z = where(Z <= 0.,1.e10,Z)
Z = ma.masked_values(Z, 1.e10)
return X,Y,Z
fcstprob[icat] = 0.5 * (prob1 + prob2)
reliability[icat] = 1.e20
if totfreq[icat] > nsamps / 1000.:
reliability[icat] = 100. * obfreq[icat] / totfreq[icat]
frequse[icat] = 100. * totfreq[icat] / nsamps
print fcstprob[icat],reliability[icat],frequse[icat]\
# plot reliability diagram if matplotlib installed.
try:
from pylab import *
doplot = True
except:
doplot = False
if doplot:
from matplotlib.numerix import ma
reliability = ma.masked_values(reliability, 1.e20)
fig = figure(figsize=(6.5, 6.5))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
plot(fcstprob, reliability, 'bo-')
plot(arange(0, 110, 10), arange(0, 110, 10), 'r--')
xlabel('forecast probability')
ylabel('observed frequency')
title('Reliability Diagram')
text(55, 15, 'Brier Skill Score = %4.2f' % bss, fontsize=14)
ax2 = fig.add_axes([.2, .6, .25, .2], axisbg='y')
bar(10 * arange(10), frequse, width=10)
xlabel('forecast probability', fontsize=10)
ylabel('percent issued', fontsize=10)
title('Frequency of Use', fontsize=12)
ax2.set_xticklabels(arange(20, 120, 20), fontsize=9)
ax2.set_yticklabels(arange(20, 120, 20), fontsize=9)
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
m = Basemap(llcrnrlon=-145.5,llcrnrlat=1.,urcrnrlon=-2.566,urcrnrlat=46.352,\
rsphere=(6378137.00,6356752.3142),\
resolution='l',area_thresh=1000.,projection='lcc',\
lat_1=50.,lon_0=-107.)
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/40000.)+1; ny = int((m.ymax-m.ymin)/40000.)+1
topodat,x,y = m.transform_scalar(topoin,lonsin,latsin,nx,ny,returnxy=True)
# create the figure.
fig=figure(figsize=(8,8))
# add an axes, leaving room for colorbar on the right.
ax = fig.add_axes([0.1,0.1,0.7,0.7])
# make topodat a masked array, masking values lower than sea level.
topodat = where(topodat < 0.,1.e10,topodat)
topodatm = ma.masked_values(topodat, 1.e10)
palette = cm.YlOrRd
palette.set_bad('aqua', 1.0)
# plot image over map with imshow.
im = m.imshow(topodatm,palette,norm=colors.normalize(vmin=0.0,vmax=3000.0,clip=False))
# setup colorbar axes instance.
l,b,w,h = ax.get_position()
cax = axes([l+w+0.075, b, 0.05, h])
colorbar(cax=cax) # draw colorbar
axes(ax) # make the original axes current again
# plot blue dot on boulder, colorado and label it as such.
xpt,ypt = m(-104.237,40.125)
m.plot([xpt],[ypt],'bo')
text(xpt+100000,ypt+100000,'Boulder')
# draw coastlines and political boundaries.
m.drawcoastlines()
from matplotlib.toolkits.basemap import Basemap
# read in data from netCDF file.
infile = 'ccsm_popgrid.nc'
fpin = NetCDFFile(infile)
tlat = fpin.variables['TLAT'][:]
tlon = fpin.variables['TLONG'][:]
temp = fpin.variables['TEMP'][:]
fillvalue = fpin.variables['TEMP'].attributes['_FillValue']
fpin.close()
# make longitudes monotonically increasing.
tlon = N.where(N.greater_equal(tlon,min(tlon[:,0])),tlon-360,tlon)
# create a masked array with temperature data (continents masked).
temp = MA.masked_values(temp,fillvalue)
# stack grids side-by-side (in longitiudinal direction), so
# any range of longitudes may be plotted on a world map.
tlon = N.concatenate((tlon,tlon+360),1)
tlat = N.concatenate((tlat,tlat),1)
temp = MA.concatenate((temp,temp),1)
tlon = tlon-360.
pl.figure(figsize=(8.5,11))
pl.subplot(2,1,1)
# subplot 1 just shows POP grid cells.
map = Basemap(projection='merc', lat_ts=20, llcrnrlon=-180, \
urcrnrlon=180, llcrnrlat=-84, urcrnrlat=84, resolution='c')
map.drawcoastlines()
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
m = Basemap(llcrnrlon=-145.5,llcrnrlat=1.,urcrnrlon=-2.566,urcrnrlat=46.352,\
rsphere=(6378137.00,6356752.3142),\
resolution='l',area_thresh=1000.,projection='lcc',\
lat_1=50.,lon_0=-107.)
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/40000.)+1; ny = int((m.ymax-m.ymin)/40000.)+1
topodat,x,y = m.transform_scalar(topoin,lonsin,latsin,nx,ny,returnxy=True)
# set up figure with same aspect ratio as map.
fig=m.createfigure()
ax = fig.add_axes([0.1,0.1,0.7,0.7],axisbg='aqua')
# make topodat a masked array, masking values lower than sea level.
topodat = where(topodat < 0.,1.e10,topodat)
topodat = ma.masked_values(topodat, 1.e10)
# plot image over map with imshow.
im = m.imshow(topodat,cm.jet,norm=colors.normalize(vmin=-4000.0,vmax=3000.0,clip=False))
cax = axes([0.875, 0.1, 0.05, 0.7]) # setup colorbar axes
colorbar(tickfmt='%d', cax=cax) # draw colorbar
axes(ax) # make the original axes current again
# plot blue dot on boulder, colorado and label it as such.
xpt,ypt = m(-104.237,40.125)
m.plot([xpt],[ypt],'bo')
text(xpt+100000,ypt+100000,'Boulder')
# draw coastlines and political boundaries.
m.drawcoastlines()
m.drawcountries()
m.drawstates()
# draw parallels and meridians.
# label on left, right and bottom of map.
fdate = hrs_since_day1CE_todate(int(time*24.0))
verifdates.append(fdate.strftime('%Y%m%d%H'))
print fcsthrs
print verifdates
levs = levels[:]
lats = latitudes[:]
lons = longitudes[:]
lons, lats = meshgrid(lons,lats)
# unpack 2-meter temp forecast data.
t2mvar = data['tmp2m']
missval = t2mvar.missing_value
t2m = t2mvar[:,:,:]
if missval < 0:
t2m = ma.masked_values(where(t2m>-1.e20,t2m,1.e20), 1.e20)
else:
t2m = ma.masked_values(where(t2m<1.e20,t2m,1.e20), 1.e20)
t2min = amin(t2m.compressed()); t2max= amax(t2m.compressed())
print t2min,t2max
clevs = frange(around(t2min/10.)*10.-5.,around(t2max/10.)*10.+5.,4)
print clevs[0],clevs[-1]
llcrnrlat = 22.0
urcrnrlat = 48.0
latminout = 22.0
llcrnrlon = -125.0
urcrnrlon = -60.0
standardpar = 50.0
centerlon=-105.
# create Basemap instance for Lambert Conformal Conic projection.
m = Basemap(llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat,
from matplotlib.toolkits.basemap import Basemap
# read in data from netCDF file.
infile = 'ccsm_popgrid.nc'
fpin = NetCDFFile(infile)
tlat = fpin.variables['TLAT'][:]
tlon = fpin.variables['TLONG'][:]
temp = fpin.variables['TEMP'][:]
fillvalue = fpin.variables['TEMP'].attributes['_FillValue']
fpin.close()
# make longitudes monotonically increasing.
tlon = N.where(N.greater_equal(tlon, min(tlon[:, 0])), tlon - 360, tlon)
# create a masked array with temperature data (continents masked).
temp = MA.masked_values(temp, fillvalue)
# stack grids side-by-side (in longitiudinal direction), so
# any range of longitudes may be plotted on a world map.
tlon = N.concatenate((tlon, tlon + 360), 1)
tlat = N.concatenate((tlat, tlat), 1)
temp = MA.concatenate((temp, temp), 1)
tlon = tlon - 360.
pl.figure(figsize=(8.5, 11))
pl.subplot(2, 1, 1)
# subplot 1 just shows POP grid cells.
map = Basemap(projection='merc', lat_ts=20, llcrnrlon=-180, \
urcrnrlon=180, llcrnrlat=-84, urcrnrlat=84, resolution='c')
map.drawcoastlines()
verifdates.append(fdate.strftime("%Y%m%d%H"))
print fcsthrs
print verifdates
levs = levels[:]
lats = latitudes[:]
lons = longitudes[:]
lons, lats = meshgrid(lons, lats)
# unpack 2-meter temp forecast data.
print data.variables.keys()
t2mvar = data.variables["t2m"]
missval = t2mvar.missing_value
t2m = t2mvar[:, :, :]
if missval < 0:
t2m = ma.masked_values(where(t2m > -1.0e20, t2m, 1.0e20), 1.0e20)
else:
t2m = ma.masked_values(where(t2m < 1.0e20 .e - 12, t2m, 1.0e20), 1.0e20)
t2min = amin(t2m.compressed())
t2max = amax(t2m.compressed())
print t2min, t2max
clevs = frange(around(t2min / 10.0) * 10.0, around(t2max / 10.0) * 10.0, 4)
llcrnrlat = 22.0
urcrnrlat = 48.0
latminout = 22.0
llcrnrlon = -125.0
urcrnrlon = -60.0
standardpar = 50.0
centerlon = -105.0
# create Basemap instance for Lambert Conformal Conic projection.
m = Basemap(
prob2 = (icat+1)*10.
fcstprob[icat] = 0.5*(prob1+prob2)
reliability[icat]=1.e20
if totfreq[icat] > nsamps/1000.:
reliability[icat] = 100.*obfreq[icat]/totfreq[icat]
frequse[icat] = 100.*totfreq[icat]/nsamps
print fcstprob[icat],reliability[icat],frequse[icat]\
# plot reliability diagram if matplotlib installed.
try:
from pylab import *
doplot = True
except:
doplot = False
if doplot:
from matplotlib.numerix import ma
reliability = ma.masked_values(reliability, 1.e20)
fig=figure(figsize=(6.5,6.5))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
plot(fcstprob,reliability,'bo-')
plot(arange(0,110,10),arange(0,110,10),'r--')
xlabel('forecast probability')
ylabel('observed frequency')
title('Reliability Diagram')
text(55,15,'Brier Skill Score = %4.2f' % bss,fontsize=14)
ax2 = fig.add_axes([.2, .6, .25, .2], axisbg='y')
bar(10*arange(10), frequse, width=10)
xlabel('forecast probability',fontsize=10)
ylabel('percent issued',fontsize=10)
title('Frequency of Use',fontsize=12)
ax2.set_xticklabels(arange(20,120,20),fontsize=9)
ax2.set_yticklabels(arange(20,120,20),fontsize=9)
