ospreyx / scikits.timeseries-sandbox Goto Github PK
View Code? Open in Web Editor NEWThis project forked from pydata/scikits.timeseries-sandbox
A library to handle time series in NumPy/SciPy
Home Page: pytseries.sourceforge.net
License: Other
This project forked from pydata/scikits.timeseries-sandbox
A library to handle time series in NumPy/SciPy
Home Page: pytseries.sourceforge.net
License: Other
************ Introduction ************ About ===== The :mod:`scikits.timeseries` module provides classes and functions for manipulating, reporting, and plotting time series of various frequencies. The focus is on convenient data access and manipulation while leveraging the existing mathematical functionality in numpy and scipy. If the following scenarios sound familiar to you, then you will likely find the :mod:`scikits.timeseries` module useful: * Compare many time series with different ranges of data (eg. stock prices); * Create time series plots with intelligently spaced axis labels; * Convert a daily time series to monthly by taking the average value during each month; * Work with data that has missing values; * Determine the last business day of the previous month/quarter/year for reporting purposes; * Compute a moving standard deviation *efficiently*. These are just some of the scenarios that are made very simple with the :mod:`scikits.timeseries` module. ********** Installing ********** Dependencies ============ Requirements ------------ In order to use the `scikits.timeseries` package, the following external packages must be installed beforehand: Python_ 2.4 or later. Please note that Python_ 3 is not supported yet. setuptools_ `scikits` is a namespace package, and as a result every `scikit` requires setuptools_ to be installed to function properly. Numpy_ 1.3.0 or later. Numpy_ is a library to manipulate large arrays of numerical data. Version 1.3.0 provides improved support to `numpy.ma.MaskedArray` objects with structured datatype. .. _Python: http://www.python.org/download/ .. _setuptools: http://pypi.python.org/pypi/setuptools .. _Numpy: http://www.scipy.org/Download Optional -------- The following packages are strongly recommended: SciPy_ 0.7.0 or later: SciPy_ is a set of Numpy_\-based tools for engineering and scientific applications. Some of the `scikits.timeseries.lib` sub-modules (`lib.interpolate`, `lib.moving_funcs`...) use SciPy_ interpolation and signal functions. matplotlib_ 0.98.0 or later: matplotlib_ is a Python 2D plotting library. `scikits.timeseries` includes some extensions to matplotlib_ to plot time series. PyTables_ 2.0 or later: PyTables_ is a package for managing hierarchical datasets, using the `HDF5 <http://www.hdfgroup.org/HDF5/>`_ format. `scikits.timeseries` provides support to store time series with missing data. .. _SciPy: http://www.scipy.org/Download .. _matplotlib: http://matplotlib.sourceforge.net .. _PyTables: http://www.pytables.org Download ======== You can download source code and windows installers from the `sourceforge project page <https://sourceforge.net/project/showfiles.php?group_id=239604>`_. For svn repository access:: svn co http://svn.scipy.org/svn/scikits/trunk/timeseries timeseries Installation ============ To install on windows, it is recommend that you use the pre-built installers from the `sourceforge project page <https://sourceforge.net/project/showfiles.php?group_id=239604>`_. To install the `scikits.timeseries` package directly from source, run the command:: python setup.py install
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.