#! /usr/bin/env python
# -*- coding: utf-8 -*-
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#
# Copyright © 2017 lafrite <lafrite@Poivre>
#
# Distributed under terms of the MIT license.

"""
Manipulating rating scale of an evaluation.

Those functions are made to be applied over eval_df
"""

from .df_marks_manip import round_half_point, compute_mark_barem

__all__ = []

def new_scale_min(x):
    """ Change the scale by selecting min between scale and mark """
    return min(x["Mark_old"], x["Bareme"])

def new_scale_proportionnal(x):
    """ Changing the scale proportionally """
    return round_half_point(x["Mark_old"] * x["Bareme"] / x["Bareme_old"])

def tranform_scale(eval_df, new_scale, method):
    """ Change the rating scale of the exam

    It backups Bareme, Mark, Mark_barem columns adding "_old". The backup is done once then it is ignored.

    It changes Bareme value to new_scale, applies method to marks and remake mark_bareme

    :param eval_df: dataframe on evaluations
    :param new_scale: replacement scale value
    :param method: "min", "prop" or a function on eval_df rows
    :returns: the transformed eval_df

    """
    df = eval_df.copy()
    for c in ["Bareme", "Mark", "Mark_barem"]:
        try:
            df[c+"_old"]
        except KeyError:
            df[c+"_old"] = df[c]

    df["Bareme"] = new_scale 

    TRANFS = {"min": new_scale_min,
            "prop": new_scale_proportionnal,
            }
    try:
        t = TRANFS[method]
    except KeyError:
        df["Mark"] = df.apply(method)
    else:
        df["Mark"] = df.apply(t, axis=1)

    df["Mark_barem"] = compute_mark_barem(df)
    return df





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