def bsigchi(fit):
'''
Some code from ZEN which calculates the binned-sigma
relation chi-squared used in the PLD method.
'''
zeropoint = fit.sdnr
sdnr = []
binlevel = []
err = []
resppb = 1.
resbin = float(len(fit.abscissa))
num = float(len(fit.normresiduals))
sigma = np.std(fit.normresiduals)
while resbin > 16:
binnedres = bd.bindata(int(resbin), mean=[fit.normresiduals])
sdnr.append(np.std(binnedres))
binlevel.append(resppb)
ebar = sigma / np.sqrt(2. * resbin)
err.append(ebar)
resppb *= 2
resbin = np.floor(num / resppb)
sdnr[0] = sigma
err[0] = sigma / np.sqrt(2 * num)
sdnr = np.asarray(sdnr)
binlevel = np.asarray(binlevel)
sdnrchisq, slope = reschisq(sdnr, binlevel, err, zeropoint)
return sdnrchisq
plt.figure()
# contour the gridded data, plotting dots at the randomly spaced data points.
#CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
#CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.Spectral_r)
plotbins(xi, yi, zi)
plt.colorbar()
plt.scatter(x, y, marker='o', c='b', s=3)
plt.xlim(xi.min(), xi.max())
plt.ylim(yi.min(), yi.max())
plt.title('Gridded data (%d points)' % npts)
plt.savefig('gridded-data.png')
#####
# bin the data.
zi = bindata(x, y, z, xi, yi)
#zi[zi<-0.3] = np.nan
plt.figure()
# contour the gridded data, plotting dots at the randomly spaced data points.
#CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
#CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.Spectral_r)
plotbins(xi, yi, zi)
plt.colorbar() # draw colorbar
#####
# plot data points.
plt.scatter(x, y, marker='o', c='b', s=3)
plt.xlim(xi.min(), xi.max())
plt.ylim(yi.min(), yi.max())
plt.title('Binned data (%d points)' % npts)
z = z[ind]
# grid
dx = 0.5 # for contourf !!!
dy = 0.15
#dx = 1.2
#dy = 0.3
left, right = x.min(), x.max()
bottom, top = y.min(), y.max()
xi = np.arange(left, right + dx, dx)
yi = np.arange(bottom, top + dy, dy)
xx, yy = np.meshgrid(xi, yi)
grid, bins = bindata.bindata(x, y, z, xi, yi, ppbin=True)
#grid = griddata(x, y, z, xi, yi)
##### PLOT
fig = plt.figure(figsize=(9, 9))
ax = plt.axes([0, 0, 1, 1])
# Antactic Peninsula
left, right, bottom, top = -80, -50, -74, -61
lon0, lat0, lon1, lat1 = -67.5, -67, -56, -68.7
# use major and minor sphere radii from WGS84 ellipsoid
m = Basemap(projection='lcc', lat_1=-72, lat_2=-62, lon_0=0,
llcrnrlon=lon0, llcrnrlat=lat0, urcrnrlon=lon1, urcrnrlat=lat1,\
rsphere=(6378137.00, 6356752.3142))
plt.figure()
# contour the gridded data, plotting dots at the randomly spaced data points.
#CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
#CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.Spectral_r)
plotbins(xi, yi, zi)
plt.colorbar()
plt.scatter(x, y, marker='o', c='b', s=3)
plt.xlim(xi.min(), xi.max())
plt.ylim(yi.min(), yi.max())
plt.title('Gridded data (%d points)' % npts)
plt.savefig('gridded-data.png')
#####
# bin the data.
zi = bindata(x, y, z, xi, yi)
#zi[zi<-0.3] = np.nan
plt.figure()
# contour the gridded data, plotting dots at the randomly spaced data points.
#CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
#CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.Spectral_r)
plotbins(xi, yi, zi)
plt.colorbar() # draw colorbar
#####
# plot data points.
plt.scatter(x, y, marker='o', c='b', s=3)
plt.xlim(xi.min(), xi.max())
plt.ylim(yi.min(), yi.max())
plt.title('Binned data (%d points)' % npts)
z = z[ind]
# grid
dx = 0.5 # for contourf !!!
dy = 0.15
#dx = 1.2
#dy = 0.3
left, right = x.min(), x.max()
bottom, top = y.min(), y.max()
xi = np.arange(left, right+dx, dx)
yi = np.arange(bottom, top+dy, dy)
xx, yy = np.meshgrid(xi, yi)
grid, bins = bindata.bindata(x, y, z, xi, yi, ppbin=True)
#grid = griddata(x, y, z, xi, yi)
##### PLOT
fig = plt.figure(figsize=(9,9))
ax = plt.axes([0,0,1,1])
# Antactic Peninsula
left, right, bottom, top = -80, -50, -74, -61
lon0, lat0, lon1, lat1 = -67.5, -67, -56, -68.7
# use major and minor sphere radii from WGS84 ellipsoid
m = Basemap(projection='lcc', lat_1=-72, lat_2=-62, lon_0=0,
llcrnrlon=lon0, llcrnrlat=lat0, urcrnrlon=lon1, urcrnrlat=lat1,\
rsphere=(6378137.00, 6356752.3142))
