def get_varfeatures(lcfile,
                    outdir,
                    timecols=None,
                    magcols=None,
                    errcols=None,
                    mindet=1000,
                    lcformat='hat-sql',
                    lcformatdir=None):
    '''This runs :py:func:`astrobase.varclass.varfeatures.all_nonperiodic_features`
    on a single LC file.

    Parameters
    ----------

    lcfile : str
        The input light curve to process.

    outfile : str
        The filename of the output variable features pickle that will be
        generated.

    timecols : list of str or None
        The timecol keys to use from the lcdict in calculating the features.

    magcols : list of str or None
        The magcol keys to use from the lcdict in calculating the features.

    errcols : list of str or None
        The errcol keys to use from the lcdict in calculating the features.

    mindet : int
        The minimum number of LC points required to generate variability
        features.

    lcformat : str
        This is the `formatkey` associated with your light curve format, which
        you previously passed in to the `lcproc.register_lcformat`
        function. This will be used to look up how to find and read the light
        curves specified in `basedir` or `use_list_of_filenames`.

    lcformatdir : str or None
        If this is provided, gives the path to a directory when you've stored
        your lcformat description JSONs, other than the usual directories lcproc
        knows to search for them in. Use this along with `lcformat` to specify
        an LC format JSON file that's not currently registered with lcproc.

    Returns
    -------

    str
        The generated variability features pickle for the input LC, with results
        for each magcol in the input `magcol` or light curve format's default
        `magcol` list.

    '''

    try:
        formatinfo = get_lcformat(lcformat,
                                  use_lcformat_dir=lcformatdir)
        if formatinfo:
            (dfileglob, readerfunc,
             dtimecols, dmagcols, derrcols,
             magsarefluxes, normfunc) = formatinfo
        else:
            LOGERROR("can't figure out the light curve format")
            return None
    except Exception as e:
        LOGEXCEPTION("can't figure out the light curve format")
        return None

    # override the default timecols, magcols, and errcols
    # using the ones provided to the function
    if timecols is None:
        timecols = dtimecols
    if magcols is None:
        magcols = dmagcols
    if errcols is None:
        errcols = derrcols

    try:

        # get the LC into a dict
        lcdict = readerfunc(lcfile)

        # this should handle lists/tuples being returned by readerfunc
        # we assume that the first element is the actual lcdict
        # FIXME: figure out how to not need this assumption
        if ( (isinstance(lcdict, (list, tuple))) and
             (isinstance(lcdict[0], dict)) ):
            lcdict = lcdict[0]

        resultdict = {'objectid':lcdict['objectid'],
                      'info':lcdict['objectinfo'],
                      'lcfbasename':os.path.basename(lcfile)}


        # normalize using the special function if specified
        if normfunc is not None:
            lcdict = normfunc(lcdict)

        for tcol, mcol, ecol in zip(timecols, magcols, errcols):

            # dereference the columns and get them from the lcdict
            if '.' in tcol:
                tcolget = tcol.split('.')
            else:
                tcolget = [tcol]
            times = _dict_get(lcdict, tcolget)

            if '.' in mcol:
                mcolget = mcol.split('.')
            else:
                mcolget = [mcol]
            mags = _dict_get(lcdict, mcolget)

            if '.' in ecol:
                ecolget = ecol.split('.')
            else:
                ecolget = [ecol]
            errs = _dict_get(lcdict, ecolget)

            # normalize here if not using special normalization
            if normfunc is None:
                ntimes, nmags = normalize_magseries(
                    times, mags,
                    magsarefluxes=magsarefluxes
                )

                times, mags, errs = ntimes, nmags, errs


            # make sure we have finite values
            finind = np.isfinite(times) & np.isfinite(mags) & np.isfinite(errs)

            # make sure we have enough finite values
            if mags[finind].size < mindet:

                LOGINFO('not enough LC points: %s in normalized %s LC: %s' %
                        (mags[finind].size, mcol, os.path.basename(lcfile)))
                resultdict[mcol] = None

            else:

                # get the features for this magcol
                lcfeatures = varfeatures.all_nonperiodic_features(
                    times, mags, errs
                )
                resultdict[mcol] = lcfeatures

        # now that we've collected all the magcols, we can choose which is the
        # "best" magcol. this is defined as the magcol that gives us the
        # smallest LC MAD.

        try:
            magmads = np.zeros(len(magcols))
            for mind, mcol in enumerate(magcols):
                if '.' in mcol:
                    mcolget = mcol.split('.')
                else:
                    mcolget = [mcol]

                magmads[mind] = resultdict[mcol]['mad']

            # smallest MAD index
            bestmagcolind = np.where(magmads == np.min(magmads))[0]
            resultdict['bestmagcol'] = magcols[bestmagcolind]

        except Exception as e:
            resultdict['bestmagcol'] = None

        outfile = os.path.join(outdir,
                               'varfeatures-%s.pkl' %
                               squeeze(resultdict['objectid']).replace(' ','-'))

        with open(outfile, 'wb') as outfd:
            pickle.dump(resultdict, outfd, protocol=4)

        return outfile

    except Exception as e:

        LOGEXCEPTION('failed to get LC features for %s because: %s' %
                     (os.path.basename(lcfile), e))
        return None
def get_varfeatures(lcfile,
                    outdir,
                    timecols=None,
                    magcols=None,
                    errcols=None,
                    mindet=1000,
                    lcformat='hat-sql',
                    lcformatdir=None):
    '''This runs :py:func:`astrobase.varclass.varfeatures.all_nonperiodic_features`
    on a single LC file.

    Parameters
    ----------

    lcfile : str
        The input light curve to process.

    outfile : str
        The filename of the output variable features pickle that will be
        generated.

    timecols : list of str or None
        The timecol keys to use from the lcdict in calculating the features.

    magcols : list of str or None
        The magcol keys to use from the lcdict in calculating the features.

    errcols : list of str or None
        The errcol keys to use from the lcdict in calculating the features.

    mindet : int
        The minimum number of LC points required to generate variability
        features.

    lcformat : str
        This is the `formatkey` associated with your light curve format, which
        you previously passed in to the `lcproc.register_lcformat`
        function. This will be used to look up how to find and read the light
        curves specified in `basedir` or `use_list_of_filenames`.

    lcformatdir : str or None
        If this is provided, gives the path to a directory when you've stored
        your lcformat description JSONs, other than the usual directories lcproc
        knows to search for them in. Use this along with `lcformat` to specify
        an LC format JSON file that's not currently registered with lcproc.

    Returns
    -------

    str
        The generated variability features pickle for the input LC, with results
        for each magcol in the input `magcol` or light curve format's default
        `magcol` list.

    '''

    try:
        formatinfo = get_lcformat(lcformat,
                                  use_lcformat_dir=lcformatdir)
        if formatinfo:
            (dfileglob, readerfunc,
             dtimecols, dmagcols, derrcols,
             magsarefluxes, normfunc) = formatinfo
        else:
            LOGERROR("can't figure out the light curve format")
            return None
    except Exception as e:
        LOGEXCEPTION("can't figure out the light curve format")
        return None

    # override the default timecols, magcols, and errcols
    # using the ones provided to the function
    if timecols is None:
        timecols = dtimecols
    if magcols is None:
        magcols = dmagcols
    if errcols is None:
        errcols = derrcols

    try:

        # get the LC into a dict
        lcdict = readerfunc(lcfile)

        # this should handle lists/tuples being returned by readerfunc
        # we assume that the first element is the actual lcdict
        # FIXME: figure out how to not need this assumption
        if ( (isinstance(lcdict, (list, tuple))) and
             (isinstance(lcdict[0], dict)) ):
            lcdict = lcdict[0]

        resultdict = {'objectid':lcdict['objectid'],
                      'info':lcdict['objectinfo'],
                      'lcfbasename':os.path.basename(lcfile)}


        # normalize using the special function if specified
        if normfunc is not None:
            lcdict = normfunc(lcdict)

        for tcol, mcol, ecol in zip(timecols, magcols, errcols):

            # dereference the columns and get them from the lcdict
            if '.' in tcol:
                tcolget = tcol.split('.')
            else:
                tcolget = [tcol]
            times = _dict_get(lcdict, tcolget)

            if '.' in mcol:
                mcolget = mcol.split('.')
            else:
                mcolget = [mcol]
            mags = _dict_get(lcdict, mcolget)

            if '.' in ecol:
                ecolget = ecol.split('.')
            else:
                ecolget = [ecol]
            errs = _dict_get(lcdict, ecolget)

            # normalize here if not using special normalization
            if normfunc is None:
                ntimes, nmags = normalize_magseries(
                    times, mags,
                    magsarefluxes=magsarefluxes
                )

                times, mags, errs = ntimes, nmags, errs


            # make sure we have finite values
            finind = np.isfinite(times) & np.isfinite(mags) & np.isfinite(errs)

            # make sure we have enough finite values
            if mags[finind].size < mindet:

                LOGINFO('not enough LC points: %s in normalized %s LC: %s' %
                        (mags[finind].size, mcol, os.path.basename(lcfile)))
                resultdict[mcol] = None

            else:

                # get the features for this magcol
                lcfeatures = varfeatures.all_nonperiodic_features(
                    times, mags, errs
                )
                resultdict[mcol] = lcfeatures

        # now that we've collected all the magcols, we can choose which is the
        # "best" magcol. this is defined as the magcol that gives us the
        # smallest LC MAD.

        try:
            magmads = np.zeros(len(magcols))
            for mind, mcol in enumerate(magcols):
                if '.' in mcol:
                    mcolget = mcol.split('.')
                else:
                    mcolget = [mcol]

                magmads[mind] = resultdict[mcol]['mad']

            # smallest MAD index
            bestmagcolind = np.where(magmads == np.min(magmads))[0]
            resultdict['bestmagcol'] = magcols[bestmagcolind]

        except Exception as e:
            resultdict['bestmagcol'] = None

        outfile = os.path.join(outdir,
                               'varfeatures-%s.pkl' %
                               squeeze(resultdict['objectid']).replace(' ','-'))

        with open(outfile, 'wb') as outfd:
            pickle.dump(resultdict, outfd, protocol=4)

        return outfile

    except Exception as e:

        LOGEXCEPTION('failed to get LC features for %s because: %s' %
                     (os.path.basename(lcfile), e))
        return None
def get_varfeatures ( lcfile , outdir , timecols = None , magcols = None , errcols = None , mindet = 1000 , lcformat = 'hat-sql' , lcformatdir = None ) : try : formatinfo = get_lcformat ( lcformat , use_lcformat_dir = lcformatdir ) if formatinfo : ( dfileglob , readerfunc , dtimecols , dmagcols , derrcols , magsarefluxes , normfunc ) = formatinfo else : LOGERROR ( "can't figure out the light curve format" ) return None except Exception as e : LOGEXCEPTION ( "can't figure out the light curve format" ) return None # override the default timecols, magcols, and errcols # using the ones provided to the function if timecols is None : timecols = dtimecols if magcols is None : magcols = dmagcols if errcols is None : errcols = derrcols try : # get the LC into a dict lcdict = readerfunc ( lcfile ) # this should handle lists/tuples being returned by readerfunc # we assume that the first element is the actual lcdict # FIXME: figure out how to not need this assumption if ( ( isinstance ( lcdict , ( list , tuple ) ) ) and ( isinstance ( lcdict [ 0 ] , dict ) ) ) : lcdict = lcdict [ 0 ] resultdict = { 'objectid' : lcdict [ 'objectid' ] , 'info' : lcdict [ 'objectinfo' ] , 'lcfbasename' : os . path . basename ( lcfile ) } # normalize using the special function if specified if normfunc is not None : lcdict = normfunc ( lcdict ) for tcol , mcol , ecol in zip ( timecols , magcols , errcols ) : # dereference the columns and get them from the lcdict if '.' in tcol : tcolget = tcol . split ( '.' ) else : tcolget = [ tcol ] times = _dict_get ( lcdict , tcolget ) if '.' in mcol : mcolget = mcol . split ( '.' ) else : mcolget = [ mcol ] mags = _dict_get ( lcdict , mcolget ) if '.' in ecol : ecolget = ecol . split ( '.' ) else : ecolget = [ ecol ] errs = _dict_get ( lcdict , ecolget ) # normalize here if not using special normalization if normfunc is None : ntimes , nmags = normalize_magseries ( times , mags , magsarefluxes = magsarefluxes ) times , mags , errs = ntimes , nmags , errs # make sure we have finite values finind = np . isfinite ( times ) & np . isfinite ( mags ) & np . isfinite ( errs ) # make sure we have enough finite values if mags [ finind ] . size < mindet : LOGINFO ( 'not enough LC points: %s in normalized %s LC: %s' % ( mags [ finind ] . size , mcol , os . path . basename ( lcfile ) ) ) resultdict [ mcol ] = None else : # get the features for this magcol lcfeatures = varfeatures . all_nonperiodic_features ( times , mags , errs ) resultdict [ mcol ] = lcfeatures # now that we've collected all the magcols, we can choose which is the # "best" magcol. this is defined as the magcol that gives us the # smallest LC MAD. try : magmads = np . zeros ( len ( magcols ) ) for mind , mcol in enumerate ( magcols ) : if '.' in mcol : mcolget = mcol . split ( '.' ) else : mcolget = [ mcol ] magmads [ mind ] = resultdict [ mcol ] [ 'mad' ] # smallest MAD index bestmagcolind = np . where ( magmads == np . min ( magmads ) ) [ 0 ] resultdict [ 'bestmagcol' ] = magcols [ bestmagcolind ] except Exception as e : resultdict [ 'bestmagcol' ] = None outfile = os . path . join ( outdir , 'varfeatures-%s.pkl' % squeeze ( resultdict [ 'objectid' ] ) . replace ( ' ' , '-' ) ) with open ( outfile , 'wb' ) as outfd : pickle . dump ( resultdict , outfd , protocol = 4 ) return outfile except Exception as e : LOGEXCEPTION ( 'failed to get LC features for %s because: %s' % ( os . path . basename ( lcfile ) , e ) ) return None
This runs :py:func:`astrobase.varclass.varfeatures.all_nonperiodic_features`
    on a single LC file.

    Parameters
    ----------

    lcfile : str
        The input light curve to process.

    outfile : str
        The filename of the output variable features pickle that will be
        generated.

    timecols : list of str or None
        The timecol keys to use from the lcdict in calculating the features.

    magcols : list of str or None
        The magcol keys to use from the lcdict in calculating the features.

    errcols : list of str or None
        The errcol keys to use from the lcdict in calculating the features.

    mindet : int
        The minimum number of LC points required to generate variability
        features.

    lcformat : str
        This is the `formatkey` associated with your light curve format, which
        you previously passed in to the `lcproc.register_lcformat`
        function. This will be used to look up how to find and read the light
        curves specified in `basedir` or `use_list_of_filenames`.

    lcformatdir : str or None
        If this is provided, gives the path to a directory when you've stored
        your lcformat description JSONs, other than the usual directories lcproc
        knows to search for them in. Use this along with `lcformat` to specify
        an LC format JSON file that's not currently registered with lcproc.

    Returns
    -------

    str
        The generated variability features pickle for the input LC, with results
        for each magcol in the input `magcol` or light curve format's default
        `magcol` list.
This runs : py : func : astrobase . varclass . varfeatures . all_nonperiodic_features on a single LC file .