gdal - Read HDF4 data using python

I am now working with VIIRS/NPP Active Fires by using python gdal. But I cannot read the data inside the files.
Filename = "NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.hdf"
Here is some information that I can get by using gdal.

gdal.RasterCount = 0

And gdalinfo:

gdalinfo NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.hdf
Driver: HDF4/Hierarchical Data Format Release 4
Files: NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.hdf

Size is 512, 512
Coordinate System is `'
Metadata:
  AlgorithmType=OPS
  Beginning_Time_IET=[1.7056441e+15]
  BeginningTime=060027.600000Z
  DayNightFlag=Day
  EastBoundingCoord=123.866
  Ending_Time_IET=[1.7056444e+15]
  EndingTime=060609.000000Z

  EndTime=2012-01-19 06:06:09.000
  HDFEOSVersion=HDFEOS_V2.17
InputPointer=NPP_GRCMAE_L1.A2012019.0555.P1_03110.2014057115525.hdf,NPP_GRCMAE_L1.A2012019.0600.P1_03110.2014057115623.hdf,NPP_GRCMAE_L1.A2012019.0605.P1_03110.2014057115525.hdf
  InstrumentShortname=VIIRS
  LocalGranuleID=NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.hdf
  LongName=VIIRS/NPP Active Fires 5-Min L2 Swath ARP 750m
  LPEATE_AlgorithmVersion=NPP_PRVAF 1.5.07.01
  LUTs_used=VIIRS-AF-EDR-AC-Int_v1.5.06.02_LP
  NorthBoundingCoord=27.8258
  Number_Fire_Pixels=256

  NumSCEA_RDR_TimeSegments=[18]
  NumSci_RDR_TimeSegments=[4]
  PGE_EndTime=2012-01-19 06:05:00.000
  PGE_Name=PGE330
  PGE_StartTime=2012-01-19 06:00:00.000
  PGEVersion=P2.3.0
  Platform_Short_Name=NPP
  ProcessingEnvironment=Linux minion5609 2.6.18-371.1.2.el5 #1 SMP Tue Oct 22 12:51:53 EDT 2013 x86_64 x86_64 x86_64 GNU/Linux
  ProcessVersion=P1_03110
  ProductionTime=2014-02-26 12:59:56.000

  ProxyDataType=Operational Data
  Resolution=Imagery
  SatelliteInstrument=NPP_OPS
  ShortName=NPP_AVAF_L2
  SouthBoundingCoord=3.93342
  StartTime=2012-01-19 06:00:27.600
  Unagg_DayNightFlag=TS 0: Day; TS 1: Day; TS 2: Day; TS 3: Day
  WestBoundingCoord=90.5542
Corner Coordinates:
Upper Left  (    0.0,    0.0)

Lower Left  (    0.0,  512.0)
Upper Right (  512.0,    0.0)
Lower Right (  512.0,  512.0)
Center      (  256.0,  256.0)

This is what I can see from HDFViewer on same file:

I know that the file carries information of active fire points, but I cannot read the data inside into an array.
From gdalinfo above, I can see Number_Fire_Pixels=256, but how can I get lat,lon of these points?

How can I read the data by using python?

UPDATE: Here's the link to the file that I'm working with.

Answer

Your data are stored as tables rather than gridded (raster) data which could be interpreted by GDAL.

It might be easier in the end to work in HDF5 rather than HDF4. Given you're on a Windows box it's easy to download and install the h4toh5 tools from the HDF group which can be used from the command line with (using your example file):

h4toh5convert NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.hdf NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.h5

Once you've got the file in HDF5 (you should not lose any data here as HDF4 attributes translate directly to HDF5) you've got a number of options in Python for working with the data. Your best bet is to install PyTables and Pandas and use the inbuilt HDFStore object to read in the data, which might look something like:

import pandas as pd

path = "NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.h5"
store = pd.io.pytables.HDFStore(path)


print store

Printing the store gives you a list of the series and frames stored in the dataset:

File path: /TEMP/NPP_AVAF_L2.A2012019.0600.P1_03110.2014057125956.h5
/ActiveFires_ARR/Data Fields/ColIndex                  frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[ColIndex])      
/ActiveFires_ARR/Data Fields/Latitude                  frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[Latitude])      
/ActiveFires_ARR/Data Fields/Longitude                 frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[Longitude])     
/ActiveFires_ARR/Data Fields/QF1_VIIRSAFARP            frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[QF1_VIIRSAFARP])

/ActiveFires_ARR/Data Fields/QF2_VIIRSAFARP            frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[QF2_VIIRSAFARP])
/ActiveFires_ARR/Data Fields/QF3_VIIRSAFARP            frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[QF3_VIIRSAFARP])
/ActiveFires_ARR/Data Fields/QF4_VIIRSAFARP            frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[QF4_VIIRSAFARP])
/ActiveFires_ARR/Data Fields/RowIndex                  frame_table [0.0.0] (typ->generic,nrows->256,ncols->1,indexers->[index],dc->[RowIndex])  

Which you can then select out the data using the name as a key:

latitude = store["/ActiveFires_ARR/Data Fields/Latitude"]
longitude = store["/ActiveFires_ARR/Data Fields/Longitude"]
qf1 = store["/ActiveFires_ARR/Data Fields/QF1_VIIRSAFARP"]
qf2 = store["/ActiveFires_ARR/Data Fields/QF2_VIIRSAFARP"]

qf3 = store["/ActiveFires_ARR/Data Fields/QF3_VIIRSAFARP"]
qf4 = store["/ActiveFires_ARR/Data Fields/QF4_VIIRSAFARP"]

And then join into a single pandas dataframe object:

df = latitude.join([longitude, qf1, qf2, qf3, qf4])

And from there you've got a complete dataframe object where you can do what you like, eg:

import seaborn
df.plot(kind="scatter",
    x="Longitude", y="Latitude",

    s=df["QF4_VIIRSAFARP"] / df["QF4_VIIRSAFARP"].max() * 200,
    alpha=0.5
)

And one last note, the store object can be used as a context manager so it's closed automatically:

with HDFStore(path) as store:
    # do something...