Python PDS4 Tools
This document describes the current status and usage of Python tools developed at PDS-SBN to read and visualize PDS4 data in Python. Please note that a PDS4 reader and visualizer for IDL is also available.
Reading and Displaying PDS4 Data
This section describes a Python package that can read and display PDS4 data and meta data. In the future this tool is expected to support all PDS4 objects, currently support is limited to objects given in the Supported Objects section. The package expects labels that pass PDS4 Schema and Schematron validation.
Contact Lev Nagdimunov with questions or comments regarding this code or its description.
Python 2.6 or 2.7
You may use pds4_read to read-in data without any extra packages; pds4_viewer requires recent versions of the additional packages.
Recommended for Arrays and Tables containing GROUP fields to allow for multi-dimensional indexing. Can result in significant improvements in memory usage and read-in speed for some data objects.
Supported Data Structures
PDS4 Data Standards < v1.3 are not officially supported but may work.
PDS4 Data Standards >= v1.3 are supported.
PDS3 Data Standards are not supported.
The table below lists the main PDS4 data objects and the current status.
Read-in column indicates support by pds4_read()
Display columns indicate support by pds4_viewer().
|Structure||Read-in||Display as Table||Display as Image||Display Columns as Plot|
|Array||Yes||Yes||2D and 3D only||Under Development|
|Table_Binary||Yes, except BitFields||Yes||No||Under Development|
|Table_Delimited||Future development||Future development||Future Development||Future Development|
Download the ZIP file File:PDS4 tools-0.3.zip
Note: This is Alpha quality software that is actively being developed, use at your own risk.
Use "pip install PDS4_tools-0.3.zip" or "easy_install PDS4_tools-0.3.zip". You can also extract the ZIP file and use "python /path/to/extracted/setup.py install". Note that there is no uninstall script provided (although "pip uninstall pds4_tools" should work), and that this tool will be updated in the future.
Extract the downloaded file to a directory Python can find. To use it follow the instructions in Example Usage except with the following lines first,
import sys sys.path.extend(['/path/to/your/extraction/directory']) # On a windows machine use backslashes (/) instead of windows' normal forward slashes to specify paths
You may call pds4_read from command line or from your own script. The following is the docstring for pds4_read:
Reads PDS4 compliant data into a `StructureList` Given a PDS4 label, reads the PDS4 data described in the label and associated label meta data into an `StructureList`, with each PDS4 data structure (e.g. Array_2D, Table_Binary, etc) as its own `Structure`. By default all data structures described in the label are read-in. NOTES: Currently supports Array structures, Table_Character and Table_Binary. Packed bit fields in Table_Binary are not yet supported, all other features of previously mentioned structures are fully supported. Parameters: filename: str The filename, including full or relative path if necessary, of the PDS4 label describing the data. quiet: bool, optional Suppresses all info/warnings from being output. use_numpy: bool, optional Returned data will be an ndarray and use NumPy data types. On by default if NumPy is installed. structure_num: integer, optional Instead of reading all data structures, only read the n^th structure, where n = structure_num. structure_name: str, optional Instead of reading all data structures, only read the structure with a name equal to structure_name. structure_lid: str, optional Instead of reading all data structures, only read the structure with a local identifier equal to structure_lid. Returns: `StructureList` Contains PDS4 structures and label data. Can be treated/accessed/used like a `dict` or `list`. Example usage: Below we document how to read data described by an example label which has two data structures, an Array_2D_Image and a Table_Binary. An outline of the label, including the array and a table with 3 fields, is given. >>> struct_list = pds4_read('/path/to/Example_Label.xml') Example Label Outline: Array_2D_Image: unnamed Table_Binary: Observations Field: order Field: wavelength Group: unnamed Field: pos_vector All below documentation assumes that the above outlined label, containing an array that does not have a name indicated in the label, and a table has the name 'Observations' with 3 fields as shown has been read-in. Accessing Example Structures: To access the data structures in `StructureList`, which is returned by pds4_read(), you may use any combination of `dict` or `list`. >>> unnamed_array = struct_list >>> or struct_list['ARRAY_0'] >>> obs_table = struct_list >>> or struct_list['Observations'] Label or Structure Overview: To see a summary of the data structures, which for Arrays shows the type and dimensions of the array, and for Tables shows the type and number of fields, you may use the info() method. Calling info() on the `Structure` instead of `StructureList` will provide a more detailed summary, including all Fields for a table. >>> struct_list.info() >>> unnamed_array.info() >>> obs_table.info() Accessing Example Label data: To access the read-in data, as an array-like (either list, array.array or ndarray), you can use the data attribute for a PDS4 Array data structure, or the field() method to access a field for a table. >>> unnamed_array.data >>> obs_table.field('wavelength') >>> obs_table.field('pos_vector') Accessing Example Label meta data: You can access all meta data in the label for a given PDS4 data structure or field via the `OrderedDict` meta_data attribute. The below examples use the 'description' element. >>> unnamed_array.meta_data['description'] >>> obs_table.field('wavelength').meta_data['description'] >>> obs_table.field('pos_vector').meta_data['description'] Accessing Example Label: The XML for a label is also accessible via the label attribute, either the entire label or for each PDS4 data structure. Entire label: >>> struct_list.label Part of label describing Observations table: >>> struct_list['Observations'].label >>> struct_list.label The returned object is similar to an ElementTree instance. It is searchable via find() and findall() methods and XPATH. Consult ElementTree manual for more details. For example, >>> struct_list.label.findall('.//disp:Display_Settings') Will find all elements in the entire label named 'Display_Settings' which are in the 'disp' namespace. You can additionally use the to_dict() and to_string() methods.
To display the objects in a label you may call pds4_viewer from the command line, or import it in the Python interpreter:
usage: pds4_viewer.py [-h] [--quiet] [--structure_num STRUCTURE_NUM] [--structure_name STRUCTURE_NAME] [--structure_lid STRUCTURE_LID] [filename] positional arguments: filename Filename, including full path, of the label optional arguments: -h, --help show this help message and exit --quiet Suppresses all info/warnings --structure_num STRUCTURE_NUM Only reads the data structure specified by zero-based order (integer) --structure_name STRUCTURE_NAME Only reads the data structure specified by name --structure_lid STRUCTURE_LID Only reads the data structure specified by local identifier
It is not necessary to include the filename parameter for pds4_viewer, you may simplify call it without any options or arguments and a GUI will open from which you can open labels.
You may also call pds4_viewer from another module or script. All the above arguments are available as optional named parameters. A basic example usage is as follows:
""" Basic pds4_viewer example """ from pds4_tools import pds4_read, pds4_viewer pds4_viewer() # or pds4_viewer('/path/to/label.xml') # or struct_list = pds4_read('label.xml') pds4_viewer('label.xml', from_existing_structures=struct_list) # Won't re-read the data