def display_asc(fname, path='', radians=True, *args, **kwargs):
"""Displays an ascii file as a pylab image."""
from pylab import imshow
lon,lat,data = asc_to_ndarray(fname,path)
if radians:
lon *= np.pi/180.
lat *= np.pi/180.
imshow(grid_convert(data,'y-x+','y+x+'),extent=[lon.min(),lon.max(),lat.min(),lat.max()],*args,**kwargs)
def asc_to_locs(fname, path='', thin=1, bufsize=1):
"""Converts an ascii grid to a list of locations where prediction is desired."""
lon,lat,data = asc_to_ndarray(fname,path)
data = grid_convert(data,'y-x+','x+y+')
unmasked = buffer(True-data[::thin,::thin].mask, n=bufsize)
# unmasked = None
lat,lon = [dir[unmasked] for dir in np.meshgrid(lat[::thin],lon[::thin])]
if np.any(np.abs(lon)>180.) or np.any(np.abs(lat)>90.):
raise ValueError, 'Ascii file %s has incorrect header. Lower left corner is (%f,%f); upper right corner is (%f,%f).'%(fname,lat.min(),lon.min(),lat.max(),lon.max())
# lon,lat = [dir.ravel() for dir in np.meshgrid(lon[::thin],lat[::thin])]
return np.vstack((lon,lat)).T*np.pi/180., unmasked
def vec_to_asc(vec, fname, out_fname, unmasked, path=''):
"""
Converts a vector of outputs on a thin, unmasked, ravelled subset of an
ascii grid to an ascii file matching the original grid.
"""
if np.any(np.isnan(vec)):
raise ValueError, 'NaN in vec'
header, headlines = get_header(fname,path)
lon,lat,data = asc_to_ndarray(fname,path)
data = grid_convert(data,'y-x+','x+y+')
data_thin = np.zeros(unmasked.shape)
data_thin[unmasked] = vec
mapgrid = np.array(mgrid[0:data.shape[0],0:data.shape[1]], dtype=float)
normalize_for_mapcoords(mapgrid[0], data_thin.shape[0]-1)
normalize_for_mapcoords(mapgrid[1], data_thin.shape[1]-1)
if data_thin.shape == data.shape:
out = np.ma.masked_array(data_thin, mask=data.mask)
else:
out = np.ma.masked_array(ndimage.map_coordinates(data_thin, mapgrid), mask=data.mask)
if np.any(np.isnan(out)):
raise ValueError, 'NaN in output'
# import pylab as pl
# pl.close('all')
# pl.figure()
# pl.imshow(out, interpolation='nearest', vmin=0.)
# pl.colorbar()
# pl.title('Resampled')
#
# pl.figure()
# pl.imshow(np.ma.masked_array(data_thin, mask=True-unmasked), interpolation='nearest', vmin=0)
# pl.colorbar()
# pl.title('Original')
out_conv = grid_convert(out,'x+y+','y-x+')
header['NODATA_value'] = -9999
exportAscii(out_conv.data,out_fname,header,True-out_conv.mask)
return out
def asc_to_vals(fname, path='', thin=1, unmasked=None):
"""
Converts an ascii grid to a list of values where prediction is desired.
If the unmasked argument is provided, the mask of the ascii grid is
checked against that mask.
"""
lon,lat,data = asc_to_ndarray(fname, path)
data = grid_convert(data,'y-x+','x+y+')
if unmasked is not None:
input_unmasked = True-data[::thin,::thin].mask
if not (unmasked == input_unmasked).all():
where_mismatch = np.where(input_unmasked != unmasked)
import pylab as pl
pl.clf()
pl.plot(lon[where_mismatch[0]],lat[where_mismatch[1]],'k.',markersize=2)
pl.savefig('mismatch.pdf')
msg = '%s: mask does not match input mask at the following pixels (in decimal degrees):\n'%fname
for i,j in zip(*where_mismatch):
msg += "\t%f, %f\n"%(lon[i],lat[j])
msg += 'Image of mismatched points saved as mismatch.pdf'
raise ValueError, msg
return data.data[unmasked]