Metadata-Version: 2.1
Name: MetaArray
Version: 2.0.0
Summary: N-dimensional array with metadata such as axis titles, units, and column names.
Home-page: https://github.com/outofculture/metaarray
Author: Luke Campagnola
Author-email: luke.campagnola@gmail.com
License: MIT
Description: MetaArray
        =========
        
        MetaArray is a class that extends ndarray, adding support for per-axis metadata storage. This class is useful for
        storing data arrays along with units, axis names, column names, axis values, etc. MetaArray objects can be indexed and
        sliced arbitrarily using named axes and columns.
        
        Justification
        -------------
        
        Consider data in the following shape:
        
        ![](example.png)
        
        Notice that each axis has a name and can store different types of meta information:
        
        * The Signal axis has named columns with different units for each column
        * The Time axis associates a numerical value with each row
        * The Trial axis uses normal integer indexes
        
        Data from this array would best be accessed variously using those names:
        
        ```python
        initial_v1s = data[:, "Voltage 1", 0]
        trial1_v0 = data["Trial": 1, "Signal": "Voltage 0"]
        time3_to_7 = data["Time": slice(3, 7)]
        ```
        
        Features
        --------
        
        * Per axis meta-information:
            * Named axes
            * Numerical values with units (e.g., "Time" axis above)
            * Column names/units (e.g., "Signal" axis above)
        * Indexing by name:
            * Index each axis by name, so there is no need to remember order of axes
            * Within an axis, index each column by name, so there is no need to remember the order of columns
        * Read/write files easily (in HDF5 format)
        * Append, extend, and sort convenience functions
        
        Documentation
        -------------
        
        ### Installation
        
        `pip install MetaArray`
        
        ### Instantiation
        
        Accepted Syntaxes:
        
        ```python
        # Constructs MetaArray from a preexisting ndarray with the provided info
        MetaArray(ndarray, info)
        
        # Constructs MetaArray from file written using MetaArray.write()
        MetaArray(file='fileName')
        ```
        
        `info` parameter: This parameter specifies the entire set of metadata for this MetaArray and must follow a specific
        format. First, info is a list of axis descriptions:`
        
        ```python
        info = [axis1, axis2, axis3, ...]
        ```
        
        Each axis description is a dict which may contain:
        
        * "name": the name of the axis
        * "values": a list or 1D ndarray of values, one per index in the axis
        * "cols": a list of column descriptions `[col1, col2, col3, ...]`
        * "units": the units associated with the numbers listed in "values"
        
        All of these parameters are optional. A column description, likewise, is a dict which may contain:
        
        * "name": the name of the column
        * "units": the units for all values under this column
        
        In the case where meta information is to apply to the entire array, (for example, if the entire array uses the same
        units) simply add an extra axis description to the end of the info list. All dicts may contain any extra information you
        want.
        
        For example, the data set shown above would look like:
        
        ```python
        MetaArray((3, 6, 3), dtype=float, info=[
            {"name": "Signal", "cols": [
                {"name": "Voltage 0", "units": "V"},
                {"name": "Voltage 1", "units": "V"},
                {"name": "Current 0", "units": "A"}
            ]
             },
            {"name": "Time", "units": "msec", "values": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]},
            {"name": "Trial"},
            {"note": "Just some extra info"}
        ]
        ```
        
        ### Accessing Data
        
        Data can be accessed through a variety of methods:
        
        * Standard indexing -- You may always just index the array exactly as you would any ndarray
        * Named axes -- If you don't remember the order of axes, you may specify the axis to be indexed or sliced like this:
        
        ```python
        data["AxisName": index]
        data["AxisName": slice(...)]
        ```
        
        Note that since this syntax hijacks the original slice mechanism, you must specify a slice using slice() if you want to
        use named axes.
        
        * Column selection--If you don't remember the index of a column you wish to select, you may substitute the column's name
          for the index number. Lists of column names are also acceptable. For example:
        
        ```python
        data["AxisName": "ColumnName"]
        data["ColumnName"]  # Works only if the named column exists for this axis
        data[["ColumnName1", "ColumnName2"]]
        ```
        
        * Boolean selection--works as you might normally expect, for example:
        
        ```python
        sel = data["ColumnName", 0, 0] > 0.2
        data[sel]
        ```
        
        * Access axis values using MetaArray.axisValues(), or .xvals() for short.
        * Access axis units using .axisUnits(), column units using .columnUnits()
        * Access any other parameter directly through the info list with .infoCopy()
        
        ### File I/O
        
        ```python
        data.write('fileName')
        newData = MetaArray(file='fileName')
        ```
        
        ### Performance Tips
        
        MetaArray is a subclass of ndarray which overrides the `__getitem__` and `__setitem__` methods. Since these methods must
        alter the structure of the meta information for each access, they are quite slow compared to the native methods. As a
        result, many builtin functions will run very slowly when operating on a MetaArray. It is recommended, therefore, that
        you recast your arrays before performing these operations like this:
        
        ```python
        data = MetaArray(...)
        data.mean()  # very slow
        data.view(ndarray).mean()  # native speed
        ```
        
        ### More Examples
        
        A 2D array of altitude values for a topographical map might look like
        
        ```python
        info = [
            {'name': 'lat', 'title': 'Latitude'},
            {'name': 'lon', 'title': 'Longitude'},
            {'title': 'Altitude', 'units': 'm'}
        ]
        ```
        
        In this case, every value in the array represents the altitude in feet at the lat, lon position represented by the array
        index. All of the following return the value at lat=10, lon=5:
        
        ```python
        array[10, 5]
        array['lon': 5, 'lat': 10]
        array['lat': 10][5]
        ```
        
        Now suppose we want to combine this data with another array of equal dimensions that represents the average rainfall for
        each location. We could easily store these as two separate arrays or combine them into a 3D array with this description:
        
        ```python
        info = [
            {
                'name': 'vals',
                'cols': [
                    {'name': 'altitude', 'units': 'm'},
                    {'name': 'rainfall', 'units': 'cm/year'},
                ],
            },
            {'name': 'lat', 'title': 'Latitude'},
            {'name': 'lon', 'title': 'Longitude'},
        ]
        ```
        
        We can now access the altitude values with `array[0]` or `array['altitude']`, and the rainfall values with `array[1]`
        or `array['rainfall']`. All of the following return the rainfall value at lat=10, lon=5:
        
        ```python
        array[1, 10, 5]
        array['lon': 5, 'lat': 10, 'val': 'rainfall']
        array['rainfall', 'lon': 5, 'lat': 10]
        ```
        
        Notice that in the second example, there is no need for an extra (4th) axis description since the actual values are
        described (name and units) in the column info for the first axis.
        
        Contact
        ---------
        Luke Campagnola - `[firstname][lastname]@gmail.com`
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
