def write_interpolated(filename, f0, f1, fact, datasetNames):
'''
interpolate two hdf files f0 and f1 using factor fact, and
write the result to filename
'''
hdf = SD(filename, SDC.WRITE|SDC.CREATE)
for datasetName in datasetNames:
try:
info = SD(f0).select(datasetName).info()
except:
print >> stderr, 'Error loading %s in %s' % (datasetName, f0)
raise
typ = info[3]
shp = info[2]
sds_in1 = SD(f0).select(datasetName)
met0 = sds_in1.get()
met1 = SD(f1).select(datasetName).get()
interp = (1-fact)*met0 + fact*met1
interp = interp.astype({
SDC.INT16: 'int16',
SDC.FLOAT32: 'float32',
SDC.FLOAT64: 'float64',
}[typ])
# write
sds = hdf.create(datasetName, typ, shp)
sds[:] = interp[:]
# copy attributes
attr = sds_in1.attributes()
if len(attr) > 0:
for name in attr.keys():
setattr(sds, name, attr[name])
sds.endaccess()
hdf.end()
def plot_sd(SD):
# TODO:
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib import ticker
name = SD.info()[0]
rank = SD.info()[1]
dims = SD.info()[2]
fill = SD._FillValue
scale = SD.scale_factor
offset = SD.add_offset
# unit = SD.units
data = SD.get()
# print data.shape, rank
scl_data = data * scale + offset
vrng = np.array(SD.valid_range) * scale + offset
if rank == 3:
nc = 3 # number of columns in figure
nr = dims[0] / nc + 1 if dims[0] % nc > 0 else dims[0] / nc
fig = plt.figure(figsize=(12, nr * 3))
gs = gridspec.GridSpec(nr, nc)
for i in range(0, dims[0]):
if data[i, :, :].max() == fill: continue
ax = fig.add_subplot(gs[i])
frame = plt.gca()
frame.axes.get_xaxis().set_ticks([])
frame.axes.get_yaxis().set_ticks([])
ax.set_title('{0}:{1}'.format(name, i + 1), fontsize=10)
mdata = np.ma.masked_where(data[i, :, :] == fill,
scl_data[i, :, :])
# print 'Reading array', i
# print mdata.min(), mdata.max(), mdata.ptp()
vrng = [mdata.min(), mdata.max()]
plt.imshow(mdata.T,
vmin=vrng[0],
vmax=vrng[1],
cmap=plt.cm.Spectral_r) # @UndefinedVariable
cb = plt.colorbar(orientation='vertical',
fraction=.03,
format='%0.2f')
tick_locator = ticker.MaxNLocator(nbins=6)
cb.locator = tick_locator
cb.update_ticks()
elif rank == 2:
if data.max() == fill:
# No valid sds in the selected field, skip further processing..
raise SystemExit('** W: No valid data in {0} for '\
'plotting **\n'.format(name))
else:
# Create masked array of the selected field filtering out fill_values
mdata = np.ma.masked_where(data == fill, scl_data)
fig = plt.figure(figsize=(4, 3))
ax = fig.add_subplot(111)
ax.set_title(name)
plt.imshow(mdata.T,
vmin=mdata.min(),
vmax=mdata.max(),
cmap=plt.cm.Spectral_r) # @UndefinedVariable
ax.set_aspect('equal')
plt.colorbar(orientation='vertical', fraction=.03)
fig.tight_layout()
plt.show()
