repytex/repytex/tools/term.py

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

import pandas as pd
import numpy as np
from repytex.tools.marks_plottings import (pie_pivot_table,
        parallel_on,
        radar_on,
        )

import seaborn as sns

__all__ = ["students_pov", "class_pov"]

class Student(object):

    """ 
    Informations on a student which can be use inside template.
    
    Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
    """

    def __init__(self, quest_df, exo_df, eval_df):
        """ 
        Description of a student from quest, exo and eval

        """

        name = {*quest_df["Eleve"].unique(),
                *exo_df["Eleve"].unique(),
                *eval_df["Eleve"].unique(),
                }

        if len(name) != 1:
            raise ValueError("Can't initiate Student: dfs contains different student names")

        self.name = name.pop()

        self.quest_df = quest_df
        self.exo_df = exo_df
        self.eval_df = eval_df

    @property
    def marks_tabular(self):
        """ Latex tabular with all of his marks of the term """
        try:
            self._marks_tabular
        except AttributeError:
            self._marks_tabular = self.eval_df[["Nom", "Mark_barem"]]
            self._marks_tabular.columns = ["Devoir", "Note"]
        return self._marks_tabular.to_latex()

    @property
    def pies_on_competence(self):
        """ Pies chart on competences """
        return  pie_pivot_table(self.quest_df,
                    index = "Level",
                    columns = "Competence",
                    values = "Eleve",
                    aggfunc = len,
                    fill_value = 0,
                    )

    @property
    def pies_on_domaine(self):
        """ Pies chart on domaines """
        return  pie_pivot_table(self.quest_df,
                    index = "Level",
                    columns = "Domaine",
                    values = "Eleve",
                    aggfunc = len,
                    fill_value = 0,
                    )

    @property
    def radar_on_competence(self):
        """ Radar plot on competence """
        return  radar_on(self.quest_df,
                    "Competence")

    @property
    def radar_on_domaine(self):
        """ Radar plot on domaine """
        return radar_on(self.quest_df,
                    "Domaine")

    @property
    def heatmap_on_domain(self):
        """ Heatmap over evals on domains """
        comp = pd.pivot_table(self.quest_df,
                index = "Domaine",
                columns = ["Date","Nom"],
                values = ["Normalized"],
                aggfunc = np.mean,
                )
        comp.columns = [i[1].strftime("%Y-%m-%d") + "\n" + i[2] for i in comp.columns]
        return  sns.heatmap(comp)

    @property
    def heatmap_on_competence(self):
        """ Heatmap over evals on competences """
        comp = pd.pivot_table(self.quest_df,
                index = "Competence",
                columns = ["Date","Nom"],
                values = ["Normalized"],
                aggfunc = np.mean,
                )
        comp.columns = [i[1].strftime("%Y-%m-%d") + "\n" + i[2] for i in comp.columns]
        return  sns.heatmap(comp)

    def parallel_on_evals(self, classe_evals):
        """ Parallel coordinate plot of the class with student line highlight """
        return parallel_on(classe_evals, "Nom", self.name)

class Classe(object):

    """ 
    Informations on a class which can be use inside template.
    
    Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
    """

    def __init__(self, quest_df, exo_df, eval_df):
        """ Init of a class from quest, exo and eval """
        self.quest_df = quest_df
        self.exo_df = exo_df
        self.eval_df = eval_df

    @property
    def evals_tabular(self):
        """ Summary of all evaluations for all students """
        try:
            self._evals_tabular
        except AttributeError:
            self._evals_tabular = pd.pivot_table(self.eval_df,
                    index = "Eleve",
                    columns = "Nom", 
                    values = "Mark_barem",
                    aggfunc = lambda x: " ".join(x)).to_latex()
        return self._evals_tabular


    @property
    def parallel_on_evals(self):
        """ Parallel coordinate plot of the class """
        return parallel_on(self.eval_df, "Nom")

    @property
    def pies_eff_pts_on_competence(self):
        """ Pie charts on competence with repartition of evaluated times and attributed points """
        return pie_pivot_table(self.quest_df[["Competence", "Bareme", "Exercice", "Question", "Commentaire"]].drop_duplicates(),
                index = "Competence",
                #columns = "Level",
                values = "Bareme",
                aggfunc=[len,np.sum],
                fill_value=0)

    @property
    def pies_eff_pts_on_domaine(self):
        """ Pie charts on domaine with repartition of evaluated times and attributed points """
        return pie_pivot_table(self.quest_df[["Domaine", "Bareme", "Exercice", "Question", "Commentaire"]].drop_duplicates(),
                index = "Domaine",
                #columns = "Level",
                values = "Bareme",
                aggfunc=[len,np.sum],
                fill_value=0)

def select(quest_df, exo_df, eval_df, index, value):
    """ Return quest, exo and eval rows which correspond index == value

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

    """
    qu = quest_df[quest_df[index] == value]
    exo = exo_df[exo_df[index] == value]
    ev = eval_df[eval_df[index] == value]
    return qu, exo, ev

def students_pov(quest_df, exo_df, eval_df):
    es = []
    for e in eval_df["Eleve"].unique():
        d = select(quest_df, exo_df, eval_df, "Eleve", e)
        eleve = Student(*d)
        es.append(eleve)
    return es

def class_pov(quest_df, exo_df, eval_df):
    return Classe(quest_df, exo_df, eval_df)


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