utils/readfile.py:
1. modify get_slice_list() from get_2d_dataset_list() to get the list of slices for any hdf5 file by walking through the whole data structure except for the pre-defined file classes in pysar

2. implement similar walking through strategy to get_dataset_list() and read_attribute() for generic hdf5 file

3. split the content of read() into two functions:
read_hdf5_file() for generic data reading based on slice information
read_binary_file() for more consistent style of output and naming

4. remove obsolete check_variable_name()

utils/writefile.py: add remove_hdf5_dataset() and use it in remove_hdf5_dataset.py

add objects/giantobj.py:
1. add new class for giant stack files: giantIfgramStack
2. move giantTimeseries class from pysarobj.py to giantobj.py
now all giant files can be read and plot easily in pysar

unwrap_error_*.py
1. add MODIFICATION_TIME metadata for 3D dataset in ifgramStack: for --update option
2. modify run_check() for better handling of updating or not

info.py:
1. add --slice option to print out the slice information of any file
2. add support of --date option for giantTimeseries, giantIfgramStack and HDFEOS file
3. add --num support for --slice option