Metadata-Version: 1.1
Name: untrendy
Version: 0.0.2
Summary: De-trending Kepler light curves in style
Home-page: https://github.com/dfm/untrendy
Author: Dan Foreman-Mackey
Author-email: danfm@nyu.edu
License: UNKNOWN
Description: Untrendy
        ========
        
        In an age where studying exoplanets is just the hippest thing ever, sometimes
        it's good to step out of line and be a little untrendy! This library is a set
        of hacks that can robustly remove the out-of-transit trends in light curve
        data.
        
        Installation
        ------------
        
        **Untrendy** depends on ``numpy`` and ``scipy`` so make sure that you install
        those first. Then, you can install using ``pip``:
        
        ::
        
            pip install untrendy
        
        Usage
        -----
        
        **Untrendy** is really complicated. It has approximately *one* function and
        about *200 lines of code (including documentation)*. It mostly runs on love
        and magic (more complete details are given below if you want).
        
        Let's say that you have a light curve with time samples ``t``, flux
        measurements ``f`` and uncertainties ``sigma``. You can simply run:
        
        .. code-block:: python
        
            import untrendy
            f_detrend, sigma_detrend = untrendy.untrend(t, f, sigma)
        
        to find a robust estimate of the global trends of the time series and remove
        it. The default settings are tuned to work well for finding the
        "out-of-transit" trends in Kepler data but a detailed description of the
        options is listed below. You can also just fit for the trends and get a
        callable representation of the trend:
        
        .. code-block:: python
        
            trend = untrendy.fit_trend(t, f, ferr)
        
        In this case, you can find the background level at some time ``t0`` by calling
        the function:
        
        .. code-block:: python
        
            bkg = trend(t0)
        
        Notes
        -----
        
        1. The spline sometimes goes to hell in regions where you don't have any
           samples so be careful with that.
        2. This whole procedure introduces correlated errors. You've been warned.
        
        Command Line Untrending
        -----------------------
        
        There is also the option of using **Untrendy** from the command line if you
        don't want to bother with all the Python stuff. If you have a whitespace
        separated ASCII file containing your light curve, you can de-trend it by
        running:
        
        ::
        
            untrend /path/to/data.txt
        
        The code will assume that your file has 2 or 3 columns with time, flux and
        (optionally) uncertainties for each observation. Then, the de-trended light
        curve will be written to standard out in the same format. Alternatively, the
        same program can read the data right from standard in:
        
        ::
        
            cat /path/to/data.txt | untrend
        
        This gives you the option of doing something crazy and then piping it all
        UNIX-like. Personally, I would just use Python.
        
        API
        ---
        
        Fit the trend
        +++++++++++++
        
        *untrendy.*\ **fit_trend** (``x``, ``y``, ``yerr=None``, ``Q=12``, ``dt=3.0``,
        ``tol=0.00125``, ``maxiter=15``, ``fill_times=None``, ``maxditer=4``,
        ``nfill=4``)
        
        Use iteratively re-weighted least squares to fit a spline to the
        out-of-transit trends in a time series. The input data should be "clean".
        In other words, bad data should be masked and it often helps to normalize
        the fluxes (by the median or something).
        
        **Parameters**
        
        :``x``:          The sampled times.
        :``y``:          The fluxes corresponding to the times in ``x``.
        :``yerr``:       (optional) The 1-sigma error bars on ``y``.
        :``Q``:          (optional) The parameter controlling the severity of the
                         re-weighting.
        :``dt``:         (optional) The initial spacing between time control
                         points.
        :``tol``:        (optional) The convergence criterion.
        :``maxiter``:    (optional) The maximum number of re-weighting iterations
                         to run.
        :``fill_times``: (optional) If provided, this number sets the minimum time
                         spacing between adjacent samples that is acceptable. If
                         the spacing is larger, knots will be added to fill in
                         the gap.
        :``maxditer``:   (optional) The maximum number of discontinuity search
                         iterations to run.
        :``nfill``:      (optional) The number of knots to use to fill in the
                         gaps.
        
        **Returns**
        
        :``trend``:      A callable representation of the trend.
        
        Remove the trend
        ++++++++++++++++
        
        *untrendy.*\ **untrend** (``x``, ``y``, ``yerr=None``, ``**kwargs``)
        
        Use iteratively re-weighted least squares to remove the out-of-transit
        trends in a light curve. Unlike ``fit_trend``, this function masks bad
        data (``NaN``) and normalizes the data before fitting.
        
        **Parameters**
        
        :``x``:          The sampled times.
        :``y``:          The fluxes corresponding to the times in ``x``.
        :``yerr``:       (optional) The 1-sigma error bars on ``y``.
        :``**kwargs``:   (optional) Other arguments passed to the ``fit_trend``
                         function.
        
        **Returns**
        
        :``flux``:       The de-trended relative fluxes.
        :``ferr``:       The de-trended uncertainties on ``flux``.
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
