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143 lines
5.3 KiB
Python
143 lines
5.3 KiB
Python
"""
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==============
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Array Creation
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==============
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Introduction
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============
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There are 5 general mechanisms for creating arrays:
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1) Conversion from other Python structures (e.g., lists, tuples)
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2) Intrinsic numpy array creation objects (e.g., arange, ones, zeros,
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etc.)
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3) Reading arrays from disk, either from standard or custom formats
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4) Creating arrays from raw bytes through the use of strings or buffers
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5) Use of special library functions (e.g., random)
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This section will not cover means of replicating, joining, or otherwise
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expanding or mutating existing arrays. Nor will it cover creating object
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arrays or structured arrays. Both of those are covered in their own sections.
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Converting Python array_like Objects to NumPy Arrays
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====================================================
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In general, numerical data arranged in an array-like structure in Python can
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be converted to arrays through the use of the array() function. The most
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obvious examples are lists and tuples. See the documentation for array() for
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details for its use. Some objects may support the array-protocol and allow
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conversion to arrays this way. A simple way to find out if the object can be
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converted to a numpy array using array() is simply to try it interactively and
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see if it works! (The Python Way).
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Examples: ::
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>>> x = np.array([2,3,1,0])
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>>> x = np.array([2, 3, 1, 0])
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>>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) # note mix of tuple and lists,
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and types
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>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
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Intrinsic NumPy Array Creation
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==============================
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NumPy has built-in functions for creating arrays from scratch:
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zeros(shape) will create an array filled with 0 values with the specified
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shape. The default dtype is float64. ::
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>>> np.zeros((2, 3))
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array([[ 0., 0., 0.], [ 0., 0., 0.]])
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ones(shape) will create an array filled with 1 values. It is identical to
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zeros in all other respects.
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arange() will create arrays with regularly incrementing values. Check the
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docstring for complete information on the various ways it can be used. A few
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examples will be given here: ::
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>>> np.arange(10)
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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>>> np.arange(2, 10, dtype=float)
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array([ 2., 3., 4., 5., 6., 7., 8., 9.])
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>>> np.arange(2, 3, 0.1)
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array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
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Note that there are some subtleties regarding the last usage that the user
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should be aware of that are described in the arange docstring.
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linspace() will create arrays with a specified number of elements, and
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spaced equally between the specified beginning and end values. For
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example: ::
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>>> np.linspace(1., 4., 6)
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array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])
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The advantage of this creation function is that one can guarantee the
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number of elements and the starting and end point, which arange()
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generally will not do for arbitrary start, stop, and step values.
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indices() will create a set of arrays (stacked as a one-higher dimensioned
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array), one per dimension with each representing variation in that dimension.
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An example illustrates much better than a verbal description: ::
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>>> np.indices((3,3))
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array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])
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This is particularly useful for evaluating functions of multiple dimensions on
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a regular grid.
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Reading Arrays From Disk
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========================
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This is presumably the most common case of large array creation. The details,
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of course, depend greatly on the format of data on disk and so this section
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can only give general pointers on how to handle various formats.
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Standard Binary Formats
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-----------------------
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Various fields have standard formats for array data. The following lists the
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ones with known python libraries to read them and return numpy arrays (there
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may be others for which it is possible to read and convert to numpy arrays so
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check the last section as well)
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::
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HDF5: h5py
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FITS: Astropy
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Examples of formats that cannot be read directly but for which it is not hard to
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convert are those formats supported by libraries like PIL (able to read and
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write many image formats such as jpg, png, etc).
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Common ASCII Formats
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------------------------
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Comma Separated Value files (CSV) are widely used (and an export and import
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option for programs like Excel). There are a number of ways of reading these
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files in Python. There are CSV functions in Python and functions in pylab
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(part of matplotlib).
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More generic ascii files can be read using the io package in scipy.
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Custom Binary Formats
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---------------------
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There are a variety of approaches one can use. If the file has a relatively
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simple format then one can write a simple I/O library and use the numpy
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fromfile() function and .tofile() method to read and write numpy arrays
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directly (mind your byteorder though!) If a good C or C++ library exists that
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read the data, one can wrap that library with a variety of techniques though
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that certainly is much more work and requires significantly more advanced
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knowledge to interface with C or C++.
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Use of Special Libraries
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------------------------
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There are libraries that can be used to generate arrays for special purposes
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and it isn't possible to enumerate all of them. The most common uses are use
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of the many array generation functions in random that can generate arrays of
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random values, and some utility functions to generate special matrices (e.g.
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diagonal).
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"""
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