#!/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


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# Reglages pour 'vim'
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