Changeset 708c667 in flex_extract.git


Ignore:
Timestamp:
Oct 18, 2018, 2:44:55 PM (7 months ago)
Author:
Anne Philipp <anne.philipp@…>
Branches:
dev
Children:
274f9ef
Parents:
e585e1b
Message:

generated first sphinx instance and adapted code doctsrings for automated api generation for disaggregation module as a first test

Files:
141 added
2 edited

Legend:

Unmodified
Added
Removed
  • source/python/classes/ControlFile.py

    r70fee58 r708c667  
    6767class ControlFile(object):
    6868    '''
    69     Class containing the information of the flex_extract CONTROL file.
    70 
    71     Contains all the parameters of CONTROL file, which are e.g.:
    72     DAY1(start_date), DAY2(end_date), DTIME, MAXSTEP, TYPE, TIME,
    73     STEP, CLASS(marsclass), STREAM, NUMBER, EXPVER, GRID, LEFT,
    74     LOWER, UPPER, RIGHT, LEVEL, LEVELIST, RESOL, GAUSS, ACCURACY,
    75     OMEGA, OMEGADIFF, ETA, ETADIFF, DPDETA, SMOOTH, FORMAT,
    76     ADDPAR, WRF, CWC, PREFIX, ECSTORAGE, ECTRANS, ECFSDIR,
    77     MAILOPS, MAILFAIL, GRIB2FLEXPART, FLEXPARTDIR,
    78     BASETIME, DATE_CHUNK, DEBUG, INPUTDIR, OUTPUTDIR, FLEXPART_ROOT_SCRIPTS
    79 
    80     For more information about format and content of the parameter
    81     see documentation.
    82 
     69    Contains the information which are stored in the CONTROL files.
    8370    '''
    8471
  • source/python/mods/disaggregation.py

    rae88f7d r708c667  
    5252# ------------------------------------------------------------------------------
    5353def dapoly(alist):
    54     '''
    55     @Author: P. JAMES
    56 
    57     @Date: 2000-03-29
    58 
    59     @ChangeHistory:
    60         June 2003     - A. BECK (2003-06-01)
    61             adaptaions
    62         November 2015 - Leopold Haimberger (University of Vienna)
    63             migration from Fortran to Python
    64 
    65     @Description:
    66         Interpolation of deaccumulated fluxes of an ECMWF model FG field
    67         using a cubic polynomial solution which conserves the integrals
    68         of the fluxes within each timespan.
    69         disaggregationregation is done for 4 accumluated timespans which generates
    70         a new, disaggregated value which is output at the central point
    71         of the 4 accumulation timespans. This new point is used for linear
    72         interpolation of the complete timeseries afterwards.
    73 
    74     @Input:
    75         alist: list of size 4, array(2D), type=float
    76             List of 4 timespans as 2-dimensional, horizontal fields.
    77             E.g. [[array_t1], [array_t2], [array_t3], [array_t4]]
    78 
    79     @Return:
    80         nfield: array(2D), type=float
    81             New field which replaces the field at the second position
    82             of the accumulation timespans.
    83 
    84     '''
     54    """Cubic polynomial interpolation of deaccumulated fluxes.
     55
     56    Interpolation of deaccumulated fluxes of an ECMWF model FG field
     57    using a cubic polynomial solution which conserves the integrals
     58    of the fluxes within each timespan.
     59    Disaggregation is done for 4 accumluated timespans which
     60    generates a new, disaggregated value which is output at the
     61    central point of the 4 accumulation timespans.
     62    This new point is used for linear interpolation of the complete
     63    timeseries afterwards.
     64
     65    Parameters
     66    ----------
     67    alist : :obj:`list` of :obj:`array` of :obj:`float`
     68        List of 4 timespans as 2-dimensional, horizontal fields.
     69        E.g. [[array_t1], [array_t2], [array_t3], [array_t4]]
     70
     71    Return
     72    ------
     73    nfield : :obj:`array` of :obj:`float`
     74        Interpolated flux at central point of accumulation timespan.
     75
     76    Note
     77    ----
     78    March 2000    : P. JAMES
     79        Original author
     80
     81    June 2003     : A. BECK
     82        Adaptations
     83
     84    November 2015 : Leopold Haimberger (University of Vienna)
     85        Migration from Fortran to Python
     86
     87    """
     88
    8589    pya = (alist[3] - alist[0] + 3. * (alist[1] - alist[2])) / 6.
    8690    pyb = (alist[2] + alist[0]) / 2. - alist[1] - 9. * pya / 2.
     
    9397
    9498def darain(alist):
    95     '''
    96     @Author: P. JAMES
    97 
    98     @Date: 2000-03-29
    99 
    100     @ChangeHistory:
    101         June 2003     - A. BECK (2003-06-01)
    102             adaptaions
    103         November 2015 - Leopold Haimberger (University of Vienna)
    104             migration from Fortran to Python
    105 
    106     @Description:
    107         Interpolation of deaccumulated fluxes of an ECMWF model FG rainfall
    108         field using a modified linear solution which conserves the integrals
    109         of the fluxes within each timespan.
    110         disaggregationregation is done for 4 accumluated timespans which generates
    111         a new, disaggregated value which is output at the central point
    112         of the 4 accumulation timespans. This new point is used for linear
    113         interpolation of the complete timeseries afterwards.
    114 
    115     @Input:
    116         alist: list of size 4, array(2D), type=float
    117             List of 4 timespans as 2-dimensional, horizontal fields.
    118             E.g. [[array_t1], [array_t2], [array_t3], [array_t4]]
    119 
    120     @Return:
    121         nfield: array(2D), type=float
    122             New field which replaces the field at the second position
    123             of the accumulation timespans.
    124     '''
     99    """Linear interpolation of deaccumulated fluxes.
     100
     101    Interpolation of deaccumulated fluxes of an ECMWF model FG rainfall
     102    field using a modified linear solution which conserves the integrals
     103    of the fluxes within each timespan.
     104    Disaggregation is done for 4 accumluated timespans which generates
     105    a new, disaggregated value which is output at the central point
     106    of the 4 accumulation timespans. This new point is used for linear
     107    interpolation of the complete timeseries afterwards.
     108
     109    Parameters
     110    ----------
     111    alist : :obj:`list` of :obj:`array` of :obj:`float`
     112        List of 4 timespans as 2-dimensional, horizontal fields.
     113        E.g. [[array_t1], [array_t2], [array_t3], [array_t4]]
     114
     115    Return
     116    ------
     117    nfield : :obj:`array` of :obj:`float`
     118        Interpolated flux at central point of accumulation timespan.
     119
     120    Note
     121    ----
     122    March 2000    : P. JAMES
     123        Original author
     124
     125    June 2003     : A. BECK
     126        Adaptations
     127
     128    November 2015 : Leopold Haimberger (University of Vienna)
     129        Migration from Fortran to Python
     130    """
     131
    125132    xa = alist[0]
    126133    xb = alist[1]
     
    143150
    144151def IA3(g):
    145     """
    146 
    147     ***************************************************************************
    148     * Copyright 2017                                                          *
    149     * Sabine Hittmeir, Anne Philipp, Petra Seibert                            *
    150     *                                                                         *
    151     * This work is licensed under the Creative Commons Attribution 4.0        *
    152     * International License. To view a copy of this license, visit            *
    153     * http://creativecommons.org/licenses/by/4.0/ or send a letter to         *
    154     * Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.            *
    155     ***************************************************************************
    156 
    157     @Description
    158        The given data series will be interpolated with a non-negative geometric
    159        mean based algorithm. The original grid is reconstructed by adding two
    160        sampling points in each data series interval. This subgrid is used to
    161        keep all information during the interpolation within the associated
    162        interval. Additionally, an advanced monotonicity filter is applied to
    163        improve the monotonicity properties of the series.
    164 
    165        For more information see article:
    166        Hittmeir, S.; Philipp, A.; Seibert, P. (2017): A conservative
    167        interpolation scheme for extensive quantities with application to the
    168        Lagrangian particle dispersion model FLEXPART.,
    169        Geoscientific Model Development
    170 
    171     @Input
    172        g: list of float values
    173           A list of float values which represents the complete data series that
    174           will be interpolated having the dimension of the original raw series.
    175 
    176     @Return
    177        f: list of float values
    178           The interpolated data series with additional subgrid points.
    179           Its dimension is equal to the length of the input data series
    180           times three.
     152    """ Interpolation with a non-negative geometric mean based algorithm.
     153
     154    The original grid is reconstructed by adding two sampling points in each
     155    data series interval. This subgrid is used to keep all information during
     156    the interpolation within the associated interval. Additionally, an advanced
     157    monotonicity filter is applied to improve the monotonicity properties of
     158    the series.
     159
     160    Note
     161    ----
     162    Copyright 2017
     163    Sabine Hittmeir, Anne Philipp, Petra Seibert
     164
     165    This work is licensed under the Creative Commons Attribution 4.0
     166    International License. To view a copy of this license, visit
     167    http://creativecommons.org/licenses/by/4.0/ or send a letter to
     168    Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
     169
     170    Parameters
     171    ----------
     172    g : :obj:`list` of :obj:`float`
     173        Complete data series that will be interpolated having
     174        the dimension of the original raw series.
     175
     176    Return
     177    ------
     178    f : :obj:`list` of :obj:`float`
     179        The interpolated data series with additional subgrid points.
     180        Its dimension is equal to the length of the input data series
     181        times three.
     182
     183
     184    References
     185    ----------
     186    For more information see article:
     187    Hittmeir, S.; Philipp, A.; Seibert, P. (2017): A conservative
     188    interpolation scheme for extensive quantities with application to the
     189    Lagrangian particle dispersion model FLEXPART.,
     190    Geoscientific Model Development
    181191    """
    182192
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