问题
I have a hdf file and want to extract data from it. For some reason i cant able to extract latitude and longitude values:
the code that i tried is :
from pyhdf import SD
hdf = SD.SD('MOD10C2.A2001033.006.2016092173057.hdf')
data = hdf.select('Eight_Day_CMG_Snow_Cover')
lat = (hdf.select('Latitude'))[:]
it gives me an error:
HDF4Error: select: non-existent dataset
I tried with:
lat = (hdf.select('Lat'))[:]
still does not help!
data can be found in this link
any help will be highly appreciated!
data format looks like:
and the error I got is:
---------------------------------------------------------------------------
HDF4Error Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/pyhdf/SD.py in select(self, name_or_index)
1635 try:
-> 1636 idx = self.nametoindex(name_or_index)
1637 except HDF4Error:
~/anaconda3/lib/python3.6/site-packages/pyhdf/SD.py in nametoindex(self, sds_name)
1528 sds_idx = _C.SDnametoindex(self._id, sds_name)
-> 1529 _checkErr('nametoindex', sds_idx, 'non existent SDS')
1530 return sds_idx
~/anaconda3/lib/python3.6/site-packages/pyhdf/error.py in _checkErr(procName, val, msg)
22 err = "%s : %s" % (procName, msg)
---> 23 raise HDF4Error(err)
HDF4Error: nametoindex : non existent SDS
During handling of the above exception, another exception occurred:
HDF4Error Traceback (most recent call last)
<ipython-input-11-21e6a4fdf8eb> in <module>()
----> 1 hdf.select('Lat')
~/anaconda3/lib/python3.6/site-packages/pyhdf/SD.py in select(self, name_or_index)
1636 idx = self.nametoindex(name_or_index)
1637 except HDF4Error:
-> 1638 raise HDF4Error("select: non-existent dataset")
1639 id = _C.SDselect(self._id, idx)
1640 _checkErr('select', id, "cannot execute")
HDF4Error: select: non-existent dataset
回答1:
The datafile that you are using is a MODIS Level 3 product. All Level 3 products are interpolated onto some regular grid. In the case of MOD10C2 the grid is the so called Climate Modeling Grid (CMG). This grid is regularly spaced over 0.05 degree intervals. Panoply knows that.
The CMG is a regular rectangular grid in the cylindrical projection. We can use this information to geolocate the data. Consider the following example.
from pyhdf import SD
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LinearSegmentedColormap
hdf = SD.SD('MOD10C2.A2001033.006.2016092173057.hdf')
data = hdf.select('Eight_Day_CMG_Snow_Cover')
snowcover=np.array(data[:,:],np.float)
snowcover[np.where(snowcover==255)]=np.nan
m = Basemap(projection='cyl', resolution = 'l',
llcrnrlat=-90, urcrnrlat=90,llcrnrlon=-180,urcrnrlon=180)
cdict = {'red' : [(0,0.,0.), (100./255.,1.,1.),(1.,0.,0.)],
'green' : [(0,0.,0.),(1.,0.,0.)] ,
'blue' : [(0,0.,0.),(100./255.,0.,0.),(1.,1.,1.)] }
blue_red = LinearSegmentedColormap('BlueRed',cdict)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90,120,30), labels=[1,0,0,0])
m.drawmeridians(np.arange(-180,181,45),labels=[0,0,0,1])
m.imshow(np.flipud(snowcover),cmap=blue_red)
plt.title('MOD10C2: Eight Day Global Snow Cover')
plt.show()
This code should display a picture of snowcover.
If you need to work with the data in a different projection you could use python GDAL interface to turn snowcover array into a geolocated dataset.
Working with the data as an irregular grid is also possible but very inefficient.
lon,lat = np.meshgrid(np.arange(-180,180,0.05),np.arange(-90,90,0.05))
would be the corresponding longitude and latitude grids.
回答2:
Normaly latitude and longitude information aren´t in the scientific mode of the hdf file, it is the mainly reason because lat = (hdf.select('Lat'))[:] doesn´t work like the other variables. With the following function you can extract any type of variable store in the hdf file
from pyhdf.HDF import *
from pyhdf.V import *
from pyhdf.VS import *
from pyhdf.SD import *
def HDFread(filename, variable, Class=None):
"""
Extract the data for non scientific data in V mode of hdf file
"""
hdf = HDF(filename, HC.READ)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename)
vs = hdf.vstart()
v = hdf.vgstart()
# Encontrar el puto id de las Geolocation Fields
if Class == None:
ref = v.findclass('SWATH Vgroup')
else:
ref = v.findclass(Class)
# Open all data of the class
vg = v.attach(ref)
# All fields in the class
members = vg.tagrefs()
nrecs = []
names = []
for tag, ref in members:
# Vdata tag
vd = vs.attach(ref)
# nrecs, intmode, fields, size, name = vd.inquire()
nrecs.append(vd.inquire()[0]) # number of records of the Vdata
names.append(vd.inquire()[-1])# name of the Vdata
vd.detach()
idx = names.index(variable)
var = vs.attach(members[idx][1])
V = var.read(nrecs[idx])
var.detach()
# Terminate V, VS and SD interfaces.
v.end()
vs.end()
sd.end()
# Close HDF file.
hdf.close()
return np.array(V).ravel()
If you don't know the exact variable name, you can try whit pyhdf.V using the following program that shows the contents of the vgroups contained inside any HDF file.
from pyhdf.HDF import *
from pyhdf.V import *
from pyhdf.VS import *
from pyhdf.SD import *
def describevg(refnum):
# Describe the vgroup with the given refnum.
# Open vgroup in read mode.
vg = v.attach(refnum)
print "----------------"
print "name:", vg._name, "class:",vg._class, "tag,ref:",
print vg._tag, vg._refnum
# Show the number of members of each main object type.
print "members: ", vg._nmembers,
print "datasets:", vg.nrefs(HC.DFTAG_NDG),
print "vdatas: ", vg.nrefs(HC.DFTAG_VH),
print "vgroups: ", vg.nrefs(HC.DFTAG_VG)
# Read the contents of the vgroup.
members = vg.tagrefs()
# Display info about each member.
index = -1
for tag, ref in members:
index += 1
print "member index", index
# Vdata tag
if tag == HC.DFTAG_VH:
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
print " vdata:",name, "tag,ref:",tag, ref
print " fields:",fields
print " nrecs:",nrecs
vd.detach()
# SDS tag
elif tag == HC.DFTAG_NDG:
sds = sd.select(sd.reftoindex(ref))
name, rank, dims, type, nattrs = sds.info()
print " dataset:",name, "tag,ref:", tag, ref
print " dims:",dims
print " type:",type
sds.endaccess()
# VS tag
elif tag == HC.DFTAG_VG:
vg0 = v.attach(ref)
print " vgroup:", vg0._name, "tag,ref:", tag, ref
vg0.detach()
# Unhandled tag
else:
print "unhandled tag,ref",tag,ref
# Close vgroup
vg.detach()
# Open HDF file in readonly mode.
filename = 'yourfile.hdf'
hdf = HDF(filename)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename)
vs = hdf.vstart()
v = hdf.vgstart()
# Scan all vgroups in the file.
ref = -1
while 1:
try:
ref = v.getid(ref)
print ref
except HDF4Error,msg: # no more vgroup
break
describevg(ref)
回答3:
I think the problem is that the file dosen't have traditional latitude and longitude data (like many .nc files). I meet similar problem when I want to process the MYD14 data (it's a MODIS file about fire-mask). I search long time to find the solution. Here are my finding： ①If the MODIS file use SIN-Grid(Sinusoidal Projection) to define data, the file will not give you the traditional lon,lat data. ②More the detail of Sinusoidal Projection, you can read this website: https://code.env.duke.edu/projects/mget/wiki/SinusoidalMODIS
来源：https://stackoverflow.com/questions/51557613/lat-lon-information-from-hdf-file-python