repytex/notes_tools/generate_bilan/generate_bilan.py
Benjamin Bertrand ae5a529602 basic log
2016-11-14 07:42:50 +03:00

134 lines
4.1 KiB
Python
Executable File

#!/usr/bin/env python
# encoding: utf-8
from notes_tools.tools import extract_flat_marks, get_class_ws, digest_flat_df, students_pov
from .texenv import texenv
import pandas as pd
import numpy as np
import xlrd
from path import Path
import logging
logger = logging.getLogger("generate_bilan")
notStudent = ["Trimestre", "Nom", "Date", "Exercice", "Question", "Competence", "Domaine", "Commentaire", "Bareme", "Niveau"]
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, path = Path("./")):
""" Build the path where the .tex will be sored
>>> build_target_name("312", "DS1")
Path('./312/bilan_DS1.tex')
>>> build_target_name("312", "DS1", Path("plop/"))
Path('plop/312/bilan_DS1.tex')
"""
return Path(path + classe + "/bilan_" + ds_name + ".tex")
def feed_bilan(target, datas, template = "tpl_bilan.tex"):
""" Get the template and feed it to create bilans
:param target: path where the bilan will be saved
:param datas: dictonnary to feed the template
:param template: the template
"""
bilan = texenv.get_template(template)
path_to_target = target.dirname()
if not path_to_target.exists():
path_to_target.mkdir()
with open(target, "w") as f:
f.write(bilan.render(**datas))
logger.info("{} est construit! Ya plus qu'à compiler!".format(target))
def generate_bilan(classe, ds_name, path = Path('./'), template = "tpl_bilan.tex"):
""" Generate the bilan of a evaluation for a class
:param classe: the classe name
:param ds_name: name of the evaluation
:param path: path where xlsx are stored
:param template: template for the bilan
"""
ws = get_class_ws(classe, path)
logger.info("Worksheets of {} imported".format(classe))
flat_df = extract_flat_marks(ws)
quest_df, exo_df, eval_df = digest_flat_df(flat_df)
logger.info("Worksheets parsed")
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, path)
feed_bilan(target, datas, template)
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