import work
This commit is contained in:
Benjamin Bertrand
parent
c74dbe666e
commit
e0748227c9
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#!/usr/bin/env python
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# encoding: utf-8
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import pandas as pd
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import numpy as np
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from math import ceil
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# Values manipulations
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def round_half_point(val):
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try:
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return 0.5 * ceil(2.0 * val)
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except ValueError:
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return val
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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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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> note_to_rep(df.loc[0])
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1.0
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>>> note_to_rep(df.loc[4])
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'\\RepU'
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"""
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if x["Niveau"]:
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if pd.isnull(x["Note"]):
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return latex_caract[0]
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elif x["Note"] in range(4):
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return latex_caract[int(x["Note"])+1]
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return x["Note"]
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def note_to_mark(x):
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""" Compute the mark when it is a "Nivea" note
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> note_to_mark(df.loc[0])
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1.0
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>>> note_to_mark(df.loc[10])
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1.3333333333333333
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"""
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if x["Niveau"]:
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return x["Note"] * x["Bareme"] / 3
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return x["Note"]
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# DataFrame columns manipulations
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def compute_marks(df):
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""" Add Mark column to df
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_marks(df)
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0 1.000000
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1 0.330000
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2 2.000000
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3 1.500000
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4 0.666667
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5 2.000000
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6 0.666000
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7 1.000000
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8 1.500000
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9 1.000000
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10 1.333333
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11 2.000000
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dtype: float64
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"""
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return df[["Note", "Niveau", "Bareme"]].apply(note_to_mark, axis=1).fillna(0)
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def compute_latex_rep(df):
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""" Add Latex_rep column to df
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_latex_rep(df)
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0 1
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1 0.33
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2 2
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3 1.5
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4 \RepU
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5 \RepT
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6 0.666
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7 1
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8 1.5
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9 1
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10 \RepD
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11 \RepT
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dtype: object
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"""
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return df[["Note", "Niveau"]].apply(note_to_rep, axis=1).fillna("??")
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# Computing custom values
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def compute_exo_marks(df):
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""" Compute Exercice level marks
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:param df: the original marks
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:returns: DataFrame with computed marks
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df["Mark"] = compute_marks(df)
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>>> compute_exo_marks(df)
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Eleve Nom Exercice Date Trimestre Bareme Mark Question Niveau
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0 E1 N1 Ex1 16/09/2016 1 2 1.5 Total 0
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1 E1 N1 Ex2 16/09/2016 1 4 3.5 Total 0
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2 E1 N2 Ex1 01/10/2016 1 2 1.0 Total 0
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3 E1 N2 Ex2 01/10/2016 1 2 2.0 Total 0
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4 E2 N1 Ex1 16/09/2016 1 2 2.0 Total 0
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5 E2 N1 Ex2 16/09/2016 1 4 2.5 Total 0
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6 E2 N2 Ex1 01/10/2016 1 2 1.5 Total 0
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7 E2 N2 Ex2 01/10/2016 1 2 2.0 Total 0
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"""
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exo_pt = pd.pivot_table(df,
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index = [ "Eleve", "Nom", "Exercice", "Date", "Trimestre"],
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values = ["Bareme", "Mark"],
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aggfunc=np.sum,
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).applymap(round_half_point)
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exo = exo_pt.reset_index()
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exo["Question"] = "Total"
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exo["Niveau"] = 0
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return exo
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def compute_eval_marks(df):
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""" Compute Nom level marks from the dataframe using only row with Total in Question
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:param df: DataFrame with value Total in Question column
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:returns: DataFrame with evaluation marks
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df["Mark"] = compute_marks(df)
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>>> df_exo = compute_exo_marks(df)
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>>> compute_eval_marks(df_exo)
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Eleve Nom Date Trimestre Bareme Mark Exercice Niveau
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0 E1 N1 16/09/2016 1 6 5.0 Total 0
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1 E1 N2 01/10/2016 1 4 3.0 Total 0
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2 E2 N1 16/09/2016 1 6 4.5 Total 0
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3 E2 N2 01/10/2016 1 4 3.5 Total 0
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"""
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exo = df[df["Question"] == "Total"]
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eval_pt = pd.pivot_table(exo,
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index = [ "Eleve", "Nom", "Date", "Trimestre"],
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values = ["Bareme", "Mark"],
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aggfunc=np.sum,
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).applymap(round_half_point)
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eval_m = eval_pt.reset_index()
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eval_m["Exercice"] = "Total"
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eval_m["Niveau"] = 0
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return eval_m
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def digest_flat_df(flat_df):
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""" Compute necessary element to make a flat df usable for analysis.
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> quest_df, exo_df, eval_df = digest_flat_df(df)
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"""
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df = flat_df.copy()
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df["Mark"] = compute_marks(flat_df)
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df["Latex_rep"] = compute_latex_rep(flat_df)
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exo_df = compute_exo_marks(df)
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eval_df = compute_eval_marks(exo_df)
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return df, exo_df, eval_df
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def students_pov(quest_df, exo_df, eval_df):
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"""
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
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... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
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... "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
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... "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
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... "Trimestre": ["1"]*12,
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... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> quest_df, exo_df, eval_df = digest_flat_df(df)
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>>> std_pov = students_pov(quest_df, exo_df, eval_df)
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>>> std = std_pov[0]
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>>> std["Nom"]
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'E1'
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>>> "{} / {}".format(std["Total"]["Mark"], std["Total"]["Bareme"])
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'5.0 / 6.0'
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>>> for exo in std["Exercices"]:
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... print("{}: {} / {}".format(exo["Nom"], exo["Total"]["Mark"], exo["Total"]["Bareme"]))
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Ex1: 1.5 / 2.0
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Ex2: 3.5 / 4.0
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>>> exo = std["Exercices"][0]
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>>> for _,q in exo["Questions"].iterrows():
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... print("{} : {}".format(q["Question"], q["Latex_rep"]))
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Q1 : 1.0
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Q2 : 0.33
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Q1 : \RepU
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"""
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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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e_exo = exo_df[exo_df["Eleve"] == e]
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#e_df = ds_df[ds_df["Eleve"] == e][["Exercice", "Question", "Bareme", "Commentaire", "Niveau", "Mark", "Latex_rep"]]
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eleve["Total"] = eval_df[eval_df["Eleve"]==e].iloc[0]
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exos = []
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for exo in e_exo["Exercice"].unique():
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ex = {"Nom":exo}
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ex["Total"] = e_exo[e_exo["Exercice"]==exo].iloc[0]
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ex["Questions"] = e_quest[e_quest["Exercice"] == exo]
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exos.append(ex)
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eleve["Exercices"] = exos
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es.append(eleve)
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return es
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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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@ -0,0 +1,134 @@
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#!/usr/bin/env python
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# encoding: utf-8
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import pandas as pd
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import numpy as np
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import xlrd
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from path import Path
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notes_path = Path("./")
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notStudent = ["Trimestre", "Nom", "Date", "Exercice", "Question", "Competence", "Domaine", "Commentaire", "Bareme", "Niveau"]
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pure_marks = ["Malus", "Bonus", "Presentation"]
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def list_classes(path = notes_path):
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"""
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List classes available in notes_path
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>>> list_classes()
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['509', '503', '308', '312']
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>>> p = Path("./")
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>>> list_classes(p)
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['509', '503', '308', '312']
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>>> list_classes("./")
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['509', '503', '308', '312']
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"""
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try:
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return [n.namebase for n in path.files("*.xlsx")]
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except AttributeError:
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p = Path(path)
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return [n.namebase for n in p.files("*.xlsx")]
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def get_class_ws(classe, path = notes_path):
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"""
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From the name of a classe, returns pd.ExcelFile
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"""
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if classe in list_classes(path):
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return pd.ExcelFile(notes_path/classe+".xlsx")
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else:
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raise ValueError("This class is not disponible in {p}. You have to choose in {c}".format(p = path, c = list_classes(path)))
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def extract_students(df, notStudent = notStudent):
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""" Extract the list of students from df """
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students = df.columns.difference(notStudent)
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return students
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def check_students(dfs, notStudent = notStudent):
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""" Build students list """
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dfs_students = [extract_students(df) for df in dfs]
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if not are_equal(dfs_students):
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raise ValueError("Not same list of students between df1 = {} ans df2 = {}".format(df1, df2))
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return dfs_students[0]
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def are_equal(elems):
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""" Test if item of elems are equal
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>>> L = [[1, 2, 3], [1, 3, 2], [1, 3, 2]]
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>>> are_equal(L)
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True
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>>> L = [[0, 2, 3], [1, 3, 2], [1, 3, 2]]
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>>> are_equal(L)
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False
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"""
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first = sorted(elems[0])
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||||
others = [sorted(e) for e in elems[1:]]
|
||||
diff = [e == first for e in others]
|
||||
|
||||
if False in diff:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def flat_df_students(df, students):
|
||||
""" Flat the ws for students """
|
||||
flat_df = pd.DataFrame()
|
||||
flat_data = []
|
||||
dfT = df.T
|
||||
for n in dfT:
|
||||
pre_di = dfT[n][notStudent].to_dict()
|
||||
for e in students:
|
||||
data = pre_di.copy()
|
||||
data["Eleve"] = e
|
||||
data["Note"] = dfT[n].loc[e]
|
||||
flat_data.append(data)
|
||||
return pd.DataFrame.from_dict(flat_data)
|
||||
|
||||
def get_all_marks(ws, marks_sheetnames = ["Notes", "Connaissances", "Calcul mental"]):
|
||||
""" Extract marks from marks_sheetnames
|
||||
|
||||
:param ws: TODO
|
||||
:returns: TODO
|
||||
|
||||
"""
|
||||
for sheetname in marks_sheetnames:
|
||||
try:
|
||||
marks = ws.parse(sheetname)
|
||||
except xlrd.biffh.XLRDError:
|
||||
pass
|
||||
|
||||
def extract_flat_marks(ws):
|
||||
""" Extract, flat and contact marks from the worksheet
|
||||
|
||||
:param ws: TODO
|
||||
:returns: TODO
|
||||
|
||||
"""
|
||||
marks_sheetnames = ["Notes", "Connaissances", "Calcul mental"]
|
||||
|
||||
sheets = []
|
||||
for sheetname in marks_sheetnames:
|
||||
try:
|
||||
sheets.append(ws.parse(sheetname))
|
||||
except xlrd.biffh.XLRDError:
|
||||
pass
|
||||
|
||||
students = check_students(sheets)
|
||||
|
||||
flat_df = pd.DataFrame()
|
||||
for sheet in sheets:
|
||||
flat = flat_df_students(sheet, students)
|
||||
flat_df = pd.concat([flat_df, flat])
|
||||
|
||||
return flat_df
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# -----------------------------
|
||||
# Reglages pour 'vim'
|
||||
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
|
||||
# cursor: 16 del
|
||||
|
|
@ -0,0 +1,118 @@
|
|||
#!/usr/bin/env python
|
||||
# encoding: utf-8
|
||||
|
||||
from extract import extract_flat_marks, get_class_ws
|
||||
from df_marks_manip import digest_flat_df, students_pov
|
||||
from opytex import texenv
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import optparse
|
||||
import xlrd
|
||||
|
||||
notStudent = ["Trimestre", "Nom", "Date", "Exercice", "Question", "Competence", "Domaine", "Commentaire", "Bareme", "Niveau"]
|
||||
pure_marks = ["Malus", "Bonus", "Presentation"]
|
||||
|
||||
def extract_students(df, notStudent = notStudent):
|
||||
""" Extract the list of students from df """
|
||||
students = df.columns.difference(notStudent)
|
||||
return students
|
||||
|
||||
def build_students(df1, df2, notStudent = notStudent):
|
||||
""" Build students list """
|
||||
students_from_notes = extract_students(df1, notStudent)
|
||||
students_from_conn = extract_students(df2, notStudent)
|
||||
if students_from_conn.equals(students_from_notes):
|
||||
return students_from_conn
|
||||
else:
|
||||
raise ValueError("Not same list of students between df1 = {} ans df2 = {}".format(df1, df2))
|
||||
|
||||
def flat_df_students(df, students):
|
||||
""" Flat the ws for students """
|
||||
flat_df = pd.DataFrame()
|
||||
flat_data = []
|
||||
dfT = df.T
|
||||
for n in dfT:
|
||||
pre_di = dfT[n][notStudent].to_dict()
|
||||
for e in students:
|
||||
data = pre_di.copy()
|
||||
data["Eleve"] = e
|
||||
data["Note"] = dfT[n].loc[e]
|
||||
flat_data.append(data)
|
||||
return pd.DataFrame.from_dict(flat_data)
|
||||
|
||||
def select_ds(ds_name, flat_df):
|
||||
"""TODO: Docstring for select_ds.
|
||||
|
||||
:param ds_name: TODO
|
||||
:param flat_df: TODO
|
||||
:returns: TODO
|
||||
|
||||
"""
|
||||
ds = flat_df[flat_df["Nom"] == ds_name]
|
||||
if len(ds) == 0:
|
||||
raise ValueError("{} is not a registered evaluation".format(ds_name))
|
||||
return ds
|
||||
|
||||
def build_ds_info(classe, ds_df):
|
||||
"""TODO: Docstring for build_ds_info.
|
||||
|
||||
:param ds_df: TODO
|
||||
:returns: TODO
|
||||
|
||||
|
||||
# TODO: vérifier que toutes ces informations soient identiques sur les lignes |dim. nov. 6 16:06:58 EAT 2016
|
||||
"""
|
||||
ds_info = {}
|
||||
ds_info["Classe"] = classe
|
||||
ds_info["Nom"] = ds_df["Nom"].unique()[0]
|
||||
ds_info["Date"] = pd.to_datetime(ds_df["Date"].unique()[0]).strftime("%d-%m-%Y")
|
||||
ds_info["Trimestre"] = ds_df["Trimestre"].unique()[0]
|
||||
return ds_info
|
||||
|
||||
def build_target_name(classe, ds_name):
|
||||
return "./" + classe + "/bilan_" + ds_name + ".tex"
|
||||
|
||||
def feed_bilan(target, datas, template = "./tpl_bilan.tex"):
|
||||
""" get the template and feed it to create bilans
|
||||
|
||||
:param ???:
|
||||
:param datas: dictonnary to feed the template
|
||||
:param template: the template
|
||||
"""
|
||||
bilan = texenv.get_template(template)
|
||||
with open(target, "w") as f:
|
||||
f.write(bilan.render(**datas))
|
||||
print("{} est construit! Ya plus qu'à compiler!".format(target))
|
||||
|
||||
def build_bilan(classe, ds_name):
|
||||
ws = get_class_ws(classe)
|
||||
|
||||
flat_df = extract_flat_marks(ws)
|
||||
quest_df, exo_df, eval_df = digest_flat_df(flat_df)
|
||||
|
||||
quest_df = select_ds(ds_name, quest_df)
|
||||
exo_df = select_ds(ds_name, exo_df)
|
||||
eval_df = select_ds(ds_name, eval_df)
|
||||
|
||||
ds_info = build_ds_info(classe, eval_df)
|
||||
students_df = students_pov(quest_df, exo_df, eval_df)
|
||||
|
||||
datas = {"ds_info": ds_info, "students":students_df}
|
||||
|
||||
target = build_target_name(classe, ds_name)
|
||||
feed_bilan(target, datas)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = optparse.OptionParser()
|
||||
parser.add_option("-c","--classe",action="store",type="string",dest="classe", help="The classe")
|
||||
parser.add_option("-e","--evaluation",action="store",type="string",dest="ds_name", help="The evaluation name.")
|
||||
(options, args) = parser.parse_args()
|
||||
|
||||
build_bilan(options.classe, options.ds_name)
|
||||
|
||||
|
||||
# -----------------------------
|
||||
# Reglages pour 'vim'
|
||||
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
|
||||
# cursor: 16 del
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
\documentclass{/media/documents/Cours/Prof/Enseignements/2016-2017/tools/style/classBilan}
|
||||
\usepackage{/media/documents/Cours/Prof/Enseignements/2016-2017/theme}
|
||||
|
||||
\usepackage{multicol}
|
||||
|
||||
% Title Page
|
||||
\titre{\Var{ds_info["Nom"]}}
|
||||
% \seconde \premiereS \PSTMG \TSTMG
|
||||
\classe{\Var{ds_info["Classe"]}}
|
||||
\date{\Var{ds_info["Date"]}}
|
||||
|
||||
|
||||
\begin{document}
|
||||
|
||||
\Block{for e in students}
|
||||
\maketitle
|
||||
|
||||
\begin{minipage}{0.5\linewidth}
|
||||
\large
|
||||
\Var{e["Nom"]}
|
||||
\end{minipage}
|
||||
\begin{minipage}{0.3\linewidth}
|
||||
\begin{flushright}
|
||||
\Large \Var{e["Total"]["Mark"]} / \Var{e["Total"]["Bareme"]}
|
||||
\end{flushright}
|
||||
\end{minipage}
|
||||
|
||||
\vfill
|
||||
|
||||
\fbox{%
|
||||
\begin{minipage}{0.9\linewidth}
|
||||
\hfill
|
||||
\vspace{3cm}
|
||||
\end{minipage}
|
||||
}
|
||||
|
||||
\vfill
|
||||
|
||||
\scriptsize
|
||||
\begin{multicols}{3}
|
||||
|
||||
\Block{for exo in e["Exercices"]}
|
||||
\begin{tabular}{|p{2cm}|c|}
|
||||
|
||||
\Block{if exo["Nom"] in ["Bonus", "Malus", "Presentation"]}
|
||||
\Block{for _,q in exo["Questions"].iterrows()}
|
||||
\Block{if q["Mark"]}
|
||||
\hline
|
||||
\rowcolor{highlightbg}
|
||||
\Var{exo["Nom"]} (\Var{q["Question"]}) & \Var{q["Latex_rep"]} \\
|
||||
\Block{endif}
|
||||
\Block{endfor}
|
||||
\Block{else}
|
||||
|
||||
\hline
|
||||
\rowcolor{highlightbg}
|
||||
Exerice \Var{exo["Nom"]} & \Var{exo["Total"]["Mark"]} / \Var{exo["Total"]["Bareme"]} \\
|
||||
\Block{for _,q in exo["Questions"].iterrows()}
|
||||
\hline
|
||||
\Var{q["Question"]} \newline \Var{q["Commentaire"]} & \Var{q["Latex_rep"]} \\
|
||||
\Block{endfor}
|
||||
\Block{endif}
|
||||
|
||||
|
||||
\hline
|
||||
\end{tabular}
|
||||
\Block{endfor}
|
||||
\end{multicols}
|
||||
\vfill
|
||||
|
||||
\begin{center}
|
||||
Pas de réponse \NoRep \hfill
|
||||
Tout faux \RepZ \hfill
|
||||
Beaucoup d'erreurs \RepU \hfill
|
||||
Quelques erreurs \RepD \hfill
|
||||
Juste \RepT \hfill
|
||||
\end{center}
|
||||
\normalsize
|
||||
\pagebreak
|
||||
\Block{endfor}
|
||||
|
||||
\end{document}
|
||||
|
||||
%%% Local Variables:
|
||||
%%% mode: latex
|
||||
%%% TeX-master: "master"
|
||||
%%% End:
|
||||
|
||||
Loading…
Reference in New Issue