h5

The SlideRule Python API h5.py is used to access the H5Coro services provided by SlideRule. From Python, the module can be imported via:

from sliderule import h5

h5

sliderule.h5.h5(dataset, resource, asset, datatype=3, col=0, startrow=0, numrows=-1)[source]

Reads a dataset from an HDF5 file and returns the values of the dataset in a list

This function provides an easy way for locally run scripts to get direct access to HDF5 data stored in a cloud environment. But it should be noted that this method is not the most efficient way to access remote H5 data, as the data is accessed one dataset at a time. The h5p api is the preferred solution for reading multiple datasets.

One of the difficulties in reading HDF5 data directly from a Python script is converting the format of the data as it is stored in HDF5 to a data format that is easy to use in Python. The compromise that this function takes is that it allows the user to supply the desired data type of the returned data via the datatype parameter, and the function will then return a numpy array of values with that data type.

The data type is supplied as a sliderule.datatypes enumeration:

  • sliderule.datatypes["TEXT"]: return the data as a string of unconverted bytes

  • sliderule.datatypes["INTEGER"]: return the data as an array of integers

  • sliderule.datatypes["REAL"]: return the data as an array of double precision floating point numbers

  • sliderule.datatypes["DYNAMIC"]: return the data in the numpy data type that is the closest match to the data as it is stored in the HDF5 file

Parameters:

  • dataset (str) – full path to dataset variable (e.g. /gt1r/geolocation/segment_ph_cnt)

  • resource (str) – HDF5 filename

  • asset (str) – data source asset (see Assets)

  • datatype (int) – the type of data the returned dataset list should be in (datasets that are naturally of a different type undergo a best effort conversion to the specified data type before being returned)

  • col (int) – the column to read from the dataset for a multi-dimensional dataset; if there are more than two dimensions, all remaining dimensions are flattened out when returned.

  • startrow (int) – the first row to start reading from in a multi-dimensional dataset (or starting element if there is only one dimension)

  • numrows (int) – the number of rows to read when reading from a multi-dimensional dataset (or number of elements if there is only one dimension); if ALL_ROWS selected, it will read from the startrow to the end of the dataset.

Returns:

dataset values

Return type:

numpy array

Examples

>>> segments    = h5.h5("/gt1r/land_ice_segments/segment_id",  resource, asset)
>>> heights     = h5.h5("/gt1r/land_ice_segments/h_li",        resource, asset)
>>> latitudes   = h5.h5("/gt1r/land_ice_segments/latitude",    resource, asset)
>>> longitudes  = h5.h5("/gt1r/land_ice_segments/longitude",   resource, asset)
>>> df = pd.DataFrame(data=list(zip(heights, latitudes, longitudes)), index=segments, columns=["h_mean", "latitude", "longitude"])

h5p

sliderule.h5.h5p(datasets, resource, asset)[source]

Reads a list of datasets from an HDF5 file and returns the values of the dataset in a dictionary of lists.

This function is considerably faster than the icesat2.h5 function in that it not only reads the datasets in parallel on the server side, but also shares a file context between the reads so that portions of the file that need to be read multiple times do not result in multiple requests to S3.

For a full discussion of the data type conversion options, see h5.

Parameters:

  • datasets (dict) – list of full paths to dataset variable (e.g. /gt1r/geolocation/segment_ph_cnt); see below for additional parameters that can be added to each dataset

  • resource (str) – HDF5 filename

  • asset (str) –

    data source asset (see Assets)

Returns:

numpy arrays of dataset values, where the keys are the dataset names

The datasets dictionary can optionally contain the following elements per entry:

  • ”valtype” (int): the type of data the returned dataset list should be in (datasets that are naturally of a different type undergo a best effort conversion to the specified data type before being returned)

  • ”col” (int): the column to read from the dataset for a multi-dimensional dataset; if there are more than two dimensions, all remaining dimensions are flattened out when returned.

  • ”startrow” (int): the first row to start reading from in a multi-dimensional dataset (or starting element if there is only one dimension)

  • ”numrows” (int): the number of rows to read when reading from a multi-dimensional dataset (or number of elements if there is only one dimension); if ALL_ROWS selected, it will read from the startrow to the end of the dataset.

Return type:

dict

Examples

>>> from sliderule import icesat2
>>> icesat2.init(["127.0.0.1"], False)
>>> datasets = [
...         {"dataset": "/gt1l/land_ice_segments/h_li", "numrows": 5},
...         {"dataset": "/gt1r/land_ice_segments/h_li", "numrows": 5},
...         {"dataset": "/gt2l/land_ice_segments/h_li", "numrows": 5},
...         {"dataset": "/gt2r/land_ice_segments/h_li", "numrows": 5},
...         {"dataset": "/gt3l/land_ice_segments/h_li", "numrows": 5},
...         {"dataset": "/gt3r/land_ice_segments/h_li", "numrows": 5}
...     ]
>>> rsps = h5.h5p(datasets, "ATL06_20181019065445_03150111_003_01.h5", "atlas-local")
>>> print(rsps)
{'/gt2r/land_ice_segments/h_li': array([45.3146427 , 45.27640582, 45.23608027, 45.21131015, 45.15692304]),
 '/gt2l/land_ice_segments/h_li': array([45.35118977, 45.33535027, 45.27195617, 45.21816889, 45.18534204]),
 '/gt1l/land_ice_segments/h_li': array([45.68811156, 45.71368944, 45.74234326, 45.74614113, 45.79866465]),
 '/gt3l/land_ice_segments/h_li': array([45.29602321, 45.34764226, 45.31430979, 45.31471701, 45.30034622]),
 '/gt1r/land_ice_segments/h_li': array([45.72632446, 45.76512574, 45.76337375, 45.77102473, 45.81307948]),
 '/gt3r/land_ice_segments/h_li': array([45.14954134, 45.18970635, 45.16637644, 45.15235916, 45.17135806])}