repytex/notes_tools/tools/eval_tools.py

#!/usr/bin/env python
# encoding: utf-8

import pandas as pd
import numpy as np


def select(quest_df, exo_df, eval_df, evalname):
    """ Return quest, exo and eval rows which correspond to evalname

    :param quest_df: TODO
    :param exo_df: TODO
    :param eval_df: TODO

    """
    qu = quest_df[quest_df["Nom"] == evalname]
    exo = exo_df[exo_df["Nom"] == evalname]
    ev = eval_df[eval_df["Nom"] == evalname]
    return qu, exo, ev

def select_contains(quest_df, exo_df, eval_df, name_part):
    """ Return quest, exo and eval rows which contains name_part

    :param quest_df: TODO
    :param exo_df: TODO
    :param eval_df: TODO

    """
    qu = quest_df[quest_df["Nom"].str.contains(name_part)]
    exo = exo_df[exo_df["Nom"].str.contains(name_part)]
    ev = eval_df[eval_df["Nom"].str.contains(name_part)]
    return qu, exo, ev

def get_present_absent(eval_df):
    """ Return list of student who where present (Mark > 0) and 
    the list of those who weren't

    """
    presents = eval_df[eval_df["Mark"] > 0]["Eleve"]
    absents = eval_df[eval_df["Mark"] == 0]["Eleve"]
    return {"presents": presents, "absents": absents}

def keep_only_presents(quest_df, exo_df, eval_df, presents):
    """ Return quest, exo and eval rows of presents students """
    qu = quest_df[quest_df["Eleve"].isin(presents)]
    exo = exo_df[exo_df["Eleve"].isin(presents)]
    ev = eval_df[eval_df["Eleve"].isin(presents)]
    return qu, exo, ev

def students_pov(quest_df, exo_df, eval_df):
    """

    >>> from .df_marks_manip import digest_flat_df
    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
    ...    "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
    ...    "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
    ...    "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
    ...    "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
    ...    "Trimestre": ["1"]*12,
    ...    "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
    ...    "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
    ...    "Note":[1, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
    ...    }
    >>> df = pd.DataFrame(d)
    >>> quest_df, exo_df, eval_df = digest_flat_df(df)
    >>> std_pov = students_pov(quest_df, exo_df, eval_df)
    >>> std = std_pov[0]
    >>> std["Nom"]
    'E1'
    >>> "{} / {}".format(std["Total"]["Mark"], std["Total"]["Bareme"])
    '5.0 / 6.0'
    >>> for exo in std["Exercices"]:
    ...    print("{}: {} / {}".format(exo["Nom"], exo["Total"]["Mark"], exo["Total"]["Bareme"]))
    Ex1: 1.5 / 2.0
    Ex2: 3.5 / 4.0
    >>> exo = std["Exercices"][0]
    >>> for _,q in exo["Questions"].iterrows():
    ...    print("{} : {}".format(q["Question"], q["Latex_rep"]))
    Q1 : 1.0
    Q2 : 0.33
    Q1 : \RepU

    """
    es = []
    for e in eval_df["Eleve"].unique():
        eleve = {"Nom":e}
        e_quest = quest_df[quest_df["Eleve"] == e]
        eleve["quest"] = e_quest
        e_exo = exo_df[exo_df["Eleve"] == e]
        #e_df = ds_df[ds_df["Eleve"] == e][["Exercice", "Question", "Bareme", "Commentaire", "Niveau", "Mark", "Latex_rep"]]
        eleve["Total"] = eval_df[eval_df["Eleve"]==e].iloc[0]

        exos = []
        for exo in e_exo["Exercice"].unique():
            ex = {"Nom":exo}
            ex["Total"] = e_exo[e_exo["Exercice"]==exo].iloc[0]
            ex["Questions"] = e_quest[e_quest["Exercice"] == exo]
            exos.append(ex)
        eleve["Exercices"] = exos

        es.append(eleve)
    return es


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