class POV for term
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@ -14,6 +14,8 @@ 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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@ -112,11 +114,56 @@ class Student(object):
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class Classe(object):
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"""Docstring for Classe. """
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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):
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"""TODO: to be defined1. """
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pass
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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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@ -17,6 +17,15 @@ def round_half_point(val):
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except TypeError:
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return val
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def num_format(num):
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""" Tranform a number into an appropriate string """
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try:
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if int(num) == num:
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return str(int(num))
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except ValueError:
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pass
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return f"{num:.1f}".replace(".", ",")
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latex_caract = ["\\NoRep", "\\RepZ", "\\RepU", "\\RepD", "\\RepT"]
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def note_to_rep(x):
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r""" Transform a Note to the latex caracter
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@ -112,6 +121,10 @@ def note_to_level(x):
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else:
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return int(ceil(x["Note"] / x["Bareme"] * 3))
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def mark_bareme_formater(row):
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""" Create m/b string """
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return f"{num_format(row['Mark'])} / {num_format(row['Bareme'])}"
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def question_uniq_formater(row):
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""" Create a kind of unique description of the question
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@ -294,6 +307,10 @@ def compute_normalized(df):
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"""
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return df["Mark"] / df["Bareme"]
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def compute_mark_barem(df):
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""" Build the string mark m/b """
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return df.apply(mark_bareme_formater, axis=1)
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def compute_question_description(df):
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""" Compute the unique description of a question """
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return df.apply(question_uniq_formater, axis = 1)
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@ -458,8 +475,10 @@ def digest_flat_df(flat_df):
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exo_df = compute_exo_marks(df)
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exo_df["Normalized"] = compute_normalized(exo_df)
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exo_df["Mark_barem"] = compute_mark_barem(exo_df)
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eval_df = compute_eval_marks(exo_df)
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eval_df["Normalized"] = compute_normalized(eval_df)
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eval_df["Mark_barem"] = compute_mark_barem(eval_df)
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return df, exo_df, eval_df
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@ -81,7 +81,7 @@ 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 + 1
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nbr_rows = nbr_pies // nbr_cols
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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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