2017-2018/Notes/reports.py

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2017-09-10 15:41:11 +00:00
#!/usr/bin/env python
# encoding: utf-8
import sqlite3
import pandas as pd
import numpy as np
from math import ceil
NOANSWER = "."
NOTRATED = ""
COMPETENCES = {
"Cher": {"fullname": "Chercher", "latex": "\\Cher"},
"Mod": {"fullname": "Modéliser", "latex": "\\Mod"},
"Rep": {"fullname": "Représenter", "latex": "\\Rep"},
"Rai": {"fullname": "Raisonner", "latex": "\\Rai"},
"Cal": {"fullname": "Calculer", "latex": "\\Cal"},
"Com": {"fullname": "Communiquer", "latex": "\\Com"},
"Con": {"fullname": "Connaître", "latex": "\\Con"},
}
# Pick from db
def df_from_db(eval_id):
db = "recopytex.db"
conn = sqlite3.connect(db)
df = pd.read_sql_query("SELECT \
student.name as name,\
student.surname as surname,\
score.value as value, \
question.competence as competence,\
question.name as question,\
question.comment as comment,\
exercise.name as exercise, \
eval.name as eval\
FROM score\
JOIN question ON score.question_id==question.id \
JOIN exercise ON question.exercise_id==exercise.id \
JOIN eval ON exercise.eval_id==eval.id \
JOIN student ON score.student_id==student.id\
WHERE eval.id == (?)",
conn,
params = (eval_id,))
return df
def prepare_df(df):
df = df[df["value"]!=NOTRATED]
df["score"] = df.apply(col2score, axis=1)
df["fullname"] = df["name"] + " " + df["surname"]
df['competence'] = df["competence"].astype("category",
categories = list(COMPETENCES.keys()), ordered=True,)
#df["abv_competence"] = df["competence"]
#df["competence"] = df.apply(competence_fullname, axis=1)
df["latex"] = df.apply(col2latex, axis=1)
#df["latex_competence"] = df.apply(competence_latex, axis=1)
return df
# Value transformations
def val2score(x):
if x == '.':
return 0
if x not in [0, 1, 2, 3]:
raise ValueError(f"The evaluation is out of range. Got {x}")
return x
def val2latex(x):
latex_caract = ["\\NoRep", "\\RepZ", "\\RepU", "\\RepD", "\\RepT"]
if x == NOANSWER:
return latex_caract[0]
elif x in range(4):
return latex_caract[int(x)+1]
return x
val2latex.__name__ = "Aquisition"
def rounded_mean(x, rounded=0):
""" Rounded x mean """
mean = np.mean(x)
return round(mean, rounded)
rounded_mean.__name__ = "Moyenne discrète"
# Columns transformations
def col2score(x):
return val2score(x["value"])
def col2latex(x):
return val2latex(x["value"])
def competence_fullname(x):
try:
return COMPETENCES[x['competence']]["fullname"]
except KeyError:
return ""
def competence_latex(x):
try:
return COMPETENCES[x['competence']]["latex"]
except KeyError:
return ""
# Df transforms
def competence_report(df):
report_comp = pd.pivot_table(df,
index=["competence"],
columns = ['fullname'],
values = ["score"],
aggfunc = [rounded_mean])
return report_comp.dropna()
def exercise_gpby(df):
""" Group the dataframe in exercise POV """
pass
def eval_gpby(df):
""" Group the dataframe in eval POV """
pass
def term_gpby(df):
""" Group the dataframe in term POV """
pass
# -----------------------------
# Reglages pour 'vim'
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
# cursor: 16 del