Change organisation in term report to use POV
This commit is contained in:
Benjamin Bertrand
parent
412b174027
commit
37e9cdd27f
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@ -1,176 +0,0 @@
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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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# vim:fenc=utf-8
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#
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# Copyright © 2017 lafrite <lafrite@Poivre>
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#
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# Distributed under terms of the MIT license.
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"""
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POV tools on term
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"""
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from notes_tools.tools.marks_plottings import (pie_pivot_table,
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parallel_on,
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radar_on,
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)
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import pandas as pd
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import numpy as np
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__all__ = ["Student", "Classe"]
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class Student(object):
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"""
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Informations on a student which can be use inside template.
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Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
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"""
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def __init__(self, quest_df, exo_df, eval_df):
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"""
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Description of a student from quest, exo and eval
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"""
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if len(quest_df["Eleve"].unique()) != 1:
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raise ValueError("Can't initiate Student: quest_df contains different student names")
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elif len(exo_df["Eleve"].unique()) != 1:
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raise ValueError("Can't initiate Student: exo_df contains different student names")
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elif len(eval_df["Eleve"].unique()) != 1:
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raise ValueError("Can't initiate Student: eval_df contains different student names")
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elif quest_df["Eleve"].iloc[0] != exo_df["Eleve"].iloc[0] or \
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quest_df["Eleve"].iloc[0] != eval_df["Eleve"].iloc[0]:
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raise ValueError("Can't initiate Student: dfs contains different student names")
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self.name = quest_df["Eleve"].iloc[0]
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self.quest_df = quest_df
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self.exo_df = exo_df
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self.eval_df = eval_df
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@property
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def marks_tabular(self):
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""" Latex tabular with all of his marks of the term """
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try:
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self._marks_tabular
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except AttributeError:
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self._marks_tabular = self.eval_df[["Nom", "Mark", "Bareme"]].to_latex()
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return self._marks_tabular
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@property
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def pies_on_competence(self):
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""" Pies chart on competences """
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try:
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self._pies_on_competence
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except AttributeError:
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self._pies_on_competence = pie_pivot_table(self.quest_df,
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index = "Level",
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columns = "Competence",
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values = "Eleve",
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aggfunc = len,
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fill_value = 0,
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)
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return self._pies_on_competence
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@property
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def pies_on_domaine(self):
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""" Pies chart on domaines """
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try:
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self._pies_on_domaine
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except AttributeError:
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self._pies_on_domaine = pie_pivot_table(self.quest_df,
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index = "Level",
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columns = "Domaine",
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values = "Eleve",
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aggfunc = len,
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fill_value = 0,
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)
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return self._pies_on_domaine
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@property
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def radar_on_competence(self):
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""" Radar plot on competence """
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try:
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self._radar_on_competence
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except AttributeError:
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self._radar_on_competence = radar_on(self.quest_df,
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"Competence")
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return self._radar_on_competence
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@property
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def radar_on_domaine(self):
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""" Radar plot on domaine """
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try:
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self._radar_on_domaine
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except AttributeError:
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self._radar_on_domaine = radar_on(self.quest_df,
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"Domaine")
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return self._radar_on_domaine
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def parallel_on_evals(self, classe_evals):
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""" Parallel coordinate plot of the class with student line highlight """
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return parallel_on(classe_evals, "Nom", self.name)
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class Classe(object):
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"""
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Informations on a class which can be use inside template.
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Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
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"""
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def __init__(self, quest_df, exo_df, eval_df):
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""" Init of a class from quest, exo and eval """
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self.quest_df = quest_df
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self.exo_df = exo_df
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self.eval_df = eval_df
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@property
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def evals_tabular(self):
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""" Summary of all evaluations for all students """
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try:
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self._evals_tabular
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except AttributeError:
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self._evals_tabular = pd.pivot_table(self.eval_df,
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index = "Eleve",
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columns = "Nom",
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values = "Mark_barem",
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aggfunc = lambda x: " ".join(x)).to_latex()
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return self._evals_tabular
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@property
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def parallel_on_evals(self):
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""" Parallel coordinate plot of the class """
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return parallel_on(self.eval_df, "Nom")
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@property
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def pies_eff_pts_on_competence(self):
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""" Pie charts on competence with repartition of evaluated times and attributed points """
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return pie_pivot_table(self.quest_df,
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index = "Competence",
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#columns = "Level",
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values = "Bareme",
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aggfunc=[len,np.sum],
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fill_value=0)
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@property
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def pies_eff_pts_on_domaine(self):
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""" Pie charts on domaine with repartition of evaluated times and attributed points """
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return pie_pivot_table(self.quest_df,
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index = "Domaine",
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#columns = "Level",
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values = "Bareme",
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aggfunc=[len,np.sum],
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fill_value=0)
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# -----------------------------
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# Reglages pour 'vim'
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# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
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# cursor: 16 del
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@ -54,14 +54,17 @@ def term_report(classe, term, path = Path('./'),
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term_df = flat_df[flat_df['Trimestre'] == term]
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quest_df, exo_df, eval_df = digest_flat_df(term_df)
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conn_df = exo_df[exo_df["Nom"].str.contains('Conn')]
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#conn_df = exo_df[exo_df["Nom"].str.contains('Conn')]
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report_info = term_info(classe, eval_df)
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students = term_tools.students_pov(quest_df, exo_df, eval_df)
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datas = {"report_info": report_info, "students":students,
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"quest_df":quest_df, "exo_df":exo_df, "eval_df":eval_df,
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"conn_df": conn_df}
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students_pov = term_tools.students_pov(quest_df, exo_df, eval_df)
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class_pov = term_tools.class_pov(quest_df, exo_df, eval_df)
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datas = {"report_info": report_info,
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"classe": class_pov,
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"students":students_pov,
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}
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target = build_target_name(classe, term, path)
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feed_template(target, datas, template)
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@ -80,8 +80,8 @@ def my_autopct(values):
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def pivot_table_to_pie(pv, pies_per_lines = 3):
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nbr_pies = len(pv.columns)
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nbr_cols = min(pies_per_lines, nbr_pies)
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nbr_rows = nbr_pies // nbr_cols
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nbr_cols = pies_per_lines
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nbr_rows = nbr_pies // nbr_cols + 1
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f, axs = plt.subplots(nbr_rows, nbr_cols, figsize = (4*nbr_cols,4*nbr_rows))
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for (c, ax) in zip(pv, axs.flatten()):
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datas = pv[c]
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@ -3,35 +3,183 @@
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import pandas as pd
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import numpy as np
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from notes_tools.tools.marks_plottings import (pie_pivot_table,
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parallel_on,
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radar_on,
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)
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__all__ = ["students_pov", "class_pov"]
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class Student(object):
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"""
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Informations on a student which can be use inside template.
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Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
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"""
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def __init__(self, quest_df, exo_df, eval_df):
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"""
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Description of a student from quest, exo and eval
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"""
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if len(quest_df["Eleve"].unique()) != 1:
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raise ValueError("Can't initiate Student: quest_df contains different student names")
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elif len(exo_df["Eleve"].unique()) != 1:
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raise ValueError("Can't initiate Student: exo_df contains different student names")
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elif len(eval_df["Eleve"].unique()) != 1:
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raise ValueError("Can't initiate Student: eval_df contains different student names")
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elif quest_df["Eleve"].iloc[0] != exo_df["Eleve"].iloc[0] or \
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quest_df["Eleve"].iloc[0] != eval_df["Eleve"].iloc[0]:
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raise ValueError("Can't initiate Student: dfs contains different student names")
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self.name = quest_df["Eleve"].iloc[0]
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self.quest_df = quest_df
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self.exo_df = exo_df
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self.eval_df = eval_df
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@property
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def marks_tabular(self):
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""" Latex tabular with all of his marks of the term """
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try:
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self._marks_tabular
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except AttributeError:
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self._marks_tabular = self.eval_df[["Nom", "Mark_barem"]].to_latex()
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return self._marks_tabular
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@property
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def pies_on_competence(self):
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""" Pies chart on competences """
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try:
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self._pies_on_competence
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except AttributeError:
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self._pies_on_competence = pie_pivot_table(self.quest_df,
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index = "Level",
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columns = "Competence",
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values = "Eleve",
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aggfunc = len,
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fill_value = 0,
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)
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return self._pies_on_competence
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@property
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def pies_on_domaine(self):
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""" Pies chart on domaines """
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try:
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self._pies_on_domaine
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except AttributeError:
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self._pies_on_domaine = pie_pivot_table(self.quest_df,
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index = "Level",
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columns = "Domaine",
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values = "Eleve",
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aggfunc = len,
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fill_value = 0,
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)
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return self._pies_on_domaine
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@property
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def radar_on_competence(self):
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""" Radar plot on competence """
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try:
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self._radar_on_competence
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except AttributeError:
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self._radar_on_competence = radar_on(self.quest_df,
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"Competence")
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return self._radar_on_competence
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@property
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def radar_on_domaine(self):
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""" Radar plot on domaine """
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try:
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self._radar_on_domaine
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except AttributeError:
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self._radar_on_domaine = radar_on(self.quest_df,
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"Domaine")
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return self._radar_on_domaine
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def select(quest_df, exo_df, eval_df, term):
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""" Return quest, exo and eval rows which correspond to evalname
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def parallel_on_evals(self, classe_evals):
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""" Parallel coordinate plot of the class with student line highlight """
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return parallel_on(classe_evals, "Nom", self.name)
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class Classe(object):
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"""
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Informations on a class which can be use inside template.
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Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
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"""
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def __init__(self, quest_df, exo_df, eval_df):
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""" Init of a class from quest, exo and eval """
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self.quest_df = quest_df
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self.exo_df = exo_df
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self.eval_df = eval_df
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@property
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def evals_tabular(self):
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""" Summary of all evaluations for all students """
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try:
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self._evals_tabular
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except AttributeError:
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self._evals_tabular = pd.pivot_table(self.eval_df,
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index = "Eleve",
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columns = "Nom",
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values = "Mark_barem",
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aggfunc = lambda x: " ".join(x)).to_latex()
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return self._evals_tabular
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@property
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def parallel_on_evals(self):
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""" Parallel coordinate plot of the class """
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return parallel_on(self.eval_df, "Nom")
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@property
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def pies_eff_pts_on_competence(self):
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""" Pie charts on competence with repartition of evaluated times and attributed points """
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return pie_pivot_table(self.quest_df,
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index = "Competence",
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#columns = "Level",
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values = "Bareme",
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aggfunc=[len,np.sum],
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fill_value=0)
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@property
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def pies_eff_pts_on_domaine(self):
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""" Pie charts on domaine with repartition of evaluated times and attributed points """
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return pie_pivot_table(self.quest_df,
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index = "Domaine",
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#columns = "Level",
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values = "Bareme",
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aggfunc=[len,np.sum],
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fill_value=0)
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def select(quest_df, exo_df, eval_df, index, value):
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""" Return quest, exo and eval rows which correspond index == value
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:param quest_df: TODO
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:param exo_df: TODO
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:param eval_df: TODO
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"""
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qu = quest_df[quest_df["Trimestre"] == term]
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exo = exo_df[exo_df["Trimestre"] == term]
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ev = eval_df[eval_df["Trimestre"] == term]
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qu = quest_df[quest_df[index] == value]
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exo = exo_df[exo_df[index] == value]
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ev = eval_df[eval_df[index] == value]
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return qu, exo, ev
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def students_pov(quest_df, exo_df, eval_df):
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es = []
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for e in eval_df["Eleve"].unique():
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eleve = {"Nom":e}
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e_quest = quest_df[quest_df["Eleve"] == e]
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eleve["quest"] = e_quest
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e_exo = exo_df[exo_df["Eleve"] == e]
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eleve["exo"] = e_exo
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e_eval = eval_df[eval_df["Eleve"] == e]
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eleve["eval"] = e_eval
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d = select(quest_df, exo_df, eval_df, "Eleve", e)
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eleve = Student(*d)
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es.append(eleve)
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return es
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def class_pov(quest_df, exo_df, eval_df):
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return Classe(quest_df, exo_df, eval_df)
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# -----------------------------
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# Reglages pour 'vim'
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