Feat: clean every thing
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# Encore une autre façon d'enregistrer et d'analyser mes notes
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# Recopytex
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Cette fois ci, on utilise:
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## Backend API
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- Des fichiers csv pour stocker les notes
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- Des fichiers yaml pour les infos sur les élèves
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- Des notebooks pour l'analyse
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- Papermill pour produire les notesbooks à partir de template
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## Frontend
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---
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source: sheets/
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output: reports/
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templates: templates/
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#!/usr/bin/env python
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# encoding: utf-8
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from .csv_extraction import flat_df_students, flat_df_for
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from .df_marks_manip import pp_q_scores
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#!/usr/bin/env python
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# encoding: utf-8
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NO_ST_COLUMNS = {
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"assessment": "Nom",
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"term": "Trimestre",
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"date": "Date",
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"exercise": "Exercice",
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"question": "Question",
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"competence": "Competence",
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"theme": "Domaine",
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"comment": "Commentaire",
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"is_leveled": "Est_nivele",
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"score_rate": "Bareme",
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}
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COLUMNS = {
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**NO_ST_COLUMNS,
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"student": "Eleve",
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"score": "Score",
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"mark": "Note",
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"level": "Niveau",
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"normalized": "Normalise",
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}
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VALIDSCORE = {
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"NOTFILLED": "", # The item is not scored yet
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"NOANSWER": ".", # Student gives no answer (this score will impact the fianl mark)
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"ABS": "a", # Student has absent (this score won't be impact the final mark)
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}
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#!/usr/bin/env python
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# encoding: utf-8
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""" Extracting data from xlsx files """
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import pandas as pd
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from .config import NO_ST_COLUMNS, COLUMNS, VALIDSCORE
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pd.set_option("Precision", 2)
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def try_replace(x, old, new):
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try:
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return str(x).replace(old, new)
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except ValueError:
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return x
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def extract_students(df, no_student_columns=NO_ST_COLUMNS.values()):
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""" Extract the list of students from df
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:param df: the dataframe
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:param no_student_columns: columns that are not students
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:return: list of students
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"""
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students = df.columns.difference(no_student_columns)
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return students
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def flat_df_students(
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df, no_student_columns=NO_ST_COLUMNS.values(), postprocessing=True
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):
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""" Flat the dataframe by returning a dataframe with on student on each line
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:param df: the dataframe (one row per questions)
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:param no_student_columns: columns that are not students
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:return: dataframe with one row per questions and students
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Columns of csv files:
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- NO_ST_COLUMNS meta data on questions
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- one for each students
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This function flat student's columns to "student" and "score"
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"""
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students = extract_students(df, no_student_columns)
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scores = []
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for st in students:
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scores.append(
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pd.melt(
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df,
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id_vars=no_student_columns,
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value_vars=st,
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var_name=COLUMNS["student"],
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value_name=COLUMNS["score"],
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).dropna(subset=[COLUMNS["score"]])
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)
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if postprocessing:
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return postprocess(pd.concat(scores))
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return pd.concat(scores)
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def flat_df_for(
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df, student, no_student_columns=NO_ST_COLUMNS.values(), postprocessing=True
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):
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""" Extract the data only for one student
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:param df: the dataframe (one row per questions)
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:param no_student_columns: columns that are not students
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:return: dataframe with one row per questions and students
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Columns of csv files:
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- NO_ST_COLUMNS meta data on questions
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- one for each students
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"""
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students = extract_students(df, no_student_columns)
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if student not in students:
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raise KeyError("This student is not in the table")
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st_df = df[list(no_student_columns) + [student]]
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st_df = st_df.rename(columns={student: COLUMNS["score"]}).dropna(
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subset=[COLUMNS["score"]]
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)
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if postprocessing:
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return postprocess(st_df)
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return st_df
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def postprocess(df):
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""" Postprocessing score dataframe
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- Replace na with an empty string
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- Replace "NOANSWER" with -1
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- Turn commas number to dot numbers
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"""
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df[COLUMNS["question"]].fillna("", inplace=True)
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df[COLUMNS["exercise"]].fillna("", inplace=True)
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df[COLUMNS["comment"]].fillna("", inplace=True)
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df[COLUMNS["competence"]].fillna("", inplace=True)
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df[COLUMNS["score"]] = pd.to_numeric(
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df[COLUMNS["score"]]
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.replace(VALIDSCORE["NOANSWER"], -1)
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.apply(lambda x: try_replace(x, ",", "."))
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)
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df[COLUMNS["score_rate"]] = pd.to_numeric(
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df[COLUMNS["score_rate"]].apply(lambda x: try_replace(x, ",", ".")),
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errors="coerce",
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)
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return df
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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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@ -1,206 +0,0 @@
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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, floor
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from .config import COLUMNS, VALIDSCORE
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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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except TypeError:
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return val
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def score_to_mark(x):
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""" Compute the mark
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if the item is leveled then the score is multiply by the score_rate
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otherwise it copies the score
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:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
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... COLUMNS["score"]:[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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>>> score_to_mark(df.loc[0])
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1.0
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>>> score_to_mark(df.loc[10])
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1.3333333333333333
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"""
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# -1 is no answer
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if x[COLUMNS["score"]] == -1:
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return 0
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if x[COLUMNS["is_leveled"]]:
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if x[COLUMNS["score"]] not in [0, 1, 2, 3]:
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raise ValueError(f"The evaluation is out of range: {x[COLUMNS['score']]} at {x}")
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#return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3)
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return round(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3, 2)
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if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]:
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raise ValueError(
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f"The score ({x['score']}) is greated than the rating scale ({x[COLUMNS['score_rate']]}) at {x}"
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)
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return x[COLUMNS["score"]]
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def score_to_level(x):
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""" Compute the level (".",0,1,2,3).
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:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
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... COLUMNS["score"]:[1, 0.33, np.nan, 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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>>> score_to_level(df.loc[0])
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3
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>>> score_to_level(df.loc[1])
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1
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>>> score_to_level(df.loc[2])
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'na'
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>>> score_to_level(df.loc[3])
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3
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>>> score_to_level(df.loc[5])
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3
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>>> score_to_level(df.loc[10])
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2
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"""
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# negatives are no answer or negatives points
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if x[COLUMNS["score"]] <= -1:
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return np.nan
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if x[COLUMNS["is_leveled"]]:
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return int(x[COLUMNS["score"]])
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return int(ceil(x[COLUMNS["score"]] / x[COLUMNS["score_rate"]] * 3))
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# DataFrame columns manipulations
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def compute_mark(df):
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""" Add Mark column to df
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:param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
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... COLUMNS["score"]:[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_mark(df)
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0 1.00
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1 0.33
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2 2.00
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3 1.50
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4 0.67
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5 2.00
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6 0.67
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7 1.00
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8 1.50
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9 1.00
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10 1.33
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11 2.00
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dtype: float64
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"""
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return df[[COLUMNS["score"], COLUMNS["is_leveled"], COLUMNS["score_rate"]]].apply(
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score_to_mark, axis=1
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)
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def compute_level(df):
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""" Add Mark column to df
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:param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
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... COLUMNS["score"]:[np.nan, 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_level(df)
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0 na
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1 1
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2 3
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3 3
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4 1
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5 3
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6 2
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7 3
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8 3
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9 2
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10 2
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11 3
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dtype: object
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"""
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return df[[COLUMNS["score"], COLUMNS["is_leveled"], COLUMNS["score_rate"]]].apply(
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score_to_level, axis=1
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)
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def compute_normalized(df):
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""" Compute the normalized mark (Mark / score_rate)
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:param df: DataFrame with "Mark" and COLUMNS["score_rate"] columns
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
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... COLUMNS["score"]:[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_normalized(df)
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0 1.00
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1 0.33
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2 1.00
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3 0.75
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4 0.33
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5 1.00
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6 0.67
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7 1.00
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8 0.75
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9 0.50
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10 0.67
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11 1.00
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dtype: float64
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"""
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return df[COLUMNS["mark"]] / df[COLUMNS["score_rate"]]
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# Postprocessing question scores
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def pp_q_scores(df):
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""" Postprocessing questions scores dataframe
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:param df: questions-scores dataframe
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:return: same data frame with mark, level and normalize columns
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"""
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assign = {
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COLUMNS["mark"]: compute_mark,
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COLUMNS["level"]: compute_level,
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COLUMNS["normalized"]: compute_normalized,
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}
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return df.assign(**assign)
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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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#!/usr/bin/env python
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# encoding: utf-8
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import yaml
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CONFIGPATH = "recoconfig.yml"
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with open(CONFIGPATH, "r") as configfile:
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config = yaml.load(configfile, Loader=yaml.FullLoader)
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#!/usr/bin/env python
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# encoding: utf-8
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import click
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from pathlib import Path
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from datetime import datetime
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from PyInquirer import prompt, print_json
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import pandas as pd
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import numpy as np
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from .config import config
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from ..config import NO_ST_COLUMNS
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class PromptAbortException(EOFError):
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def __init__(self, message, errors=None):
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# Call the base class constructor with the parameters it needs
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super(PromptAbortException, self).__init__("Abort questionnary", errors)
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def get_tribes(answers):
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""" List tribes based on subdirectory of config["source"] which have an "eleves.csv" file inside """
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return [
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p.name for p in Path(config["source"]).iterdir() if (p / "eleves.csv").exists()
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]
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def prepare_csv():
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items = new_eval()
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item = items[0]
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# item = {"tribe": "308", "date": datetime.today(), "assessment": "plop"}
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csv_output = (
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Path(config["source"])
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/ item["tribe"]
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/ f"{item['date']:%y%m%d}_{item['assessment']}.csv"
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)
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students = pd.read_csv(Path(config["source"]) / item["tribe"] / "eleves.csv")["Nom"]
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columns = list(NO_ST_COLUMNS.keys())
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items = [[it[c] for c in columns] for it in items]
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columns = list(NO_ST_COLUMNS.values())
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items_df = pd.DataFrame.from_records(items, columns=columns)
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for s in students:
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items_df[s] = np.nan
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items_df.to_csv(csv_output, index=False, date_format="%d/%m/%Y")
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click.echo(f"Saving csv file to {csv_output}")
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def new_eval(answers={}):
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click.echo(f"Préparation d'un nouveau devoir")
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eval_questions = [
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{"type": "input", "name": "assessment", "message": "Nom de l'évaluation",},
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{
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"type": "list",
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"name": "tribe",
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"message": "Classe concernée",
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"choices": get_tribes,
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},
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{
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"type": "input",
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"name": "date",
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"message": "Date du devoir (%y%m%d)",
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"default": datetime.today().strftime("%y%m%d"),
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"filter": lambda val: datetime.strptime(val, "%y%m%d"),
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},
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{
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"type": "list",
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"name": "term",
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"message": "Trimestre",
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"choices": ["1", "2", "3"],
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},
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]
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eval_ans = prompt(eval_questions)
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items = []
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add_exo = True
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while add_exo:
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ex_items = new_exercice(eval_ans)
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items += ex_items
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add_exo = prompt(
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[
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{
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"type": "confirm",
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"name": "add_exo",
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"message": "Ajouter un autre exercice",
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"default": True,
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}
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]
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||||
)["add_exo"]
|
||||
return items
|
||||
|
||||
|
||||
def new_exercice(answers={}):
|
||||
exercise_questions = [
|
||||
{"type": "input", "name": "exercise", "message": "Nom de l'exercice"},
|
||||
]
|
||||
|
||||
click.echo(f"Nouvel exercice")
|
||||
exercise_ans = prompt(exercise_questions, answers=answers)
|
||||
|
||||
items = []
|
||||
|
||||
add_item = True
|
||||
while add_item:
|
||||
try:
|
||||
item_ans = new_item(exercise_ans)
|
||||
except PromptAbortException:
|
||||
click.echo("Création de l'item annulée")
|
||||
else:
|
||||
items.append(item_ans)
|
||||
add_item = prompt(
|
||||
[
|
||||
{
|
||||
"type": "confirm",
|
||||
"name": "add_item",
|
||||
"message": f"Ajouter un autre item pour l'exercice {exercise_ans['exercise']}",
|
||||
"default": True,
|
||||
}
|
||||
]
|
||||
)["add_item"]
|
||||
|
||||
return items
|
||||
|
||||
|
||||
def new_item(answers={}):
|
||||
item_questions = [
|
||||
{"type": "input", "name": "question", "message": "Nom de l'item",},
|
||||
{"type": "input", "name": "comment", "message": "Commentaire",},
|
||||
{
|
||||
"type": "list",
|
||||
"name": "competence",
|
||||
"message": "Competence",
|
||||
"choices": ["Cher", "Rep", "Mod", "Rai", "Cal", "Com"],
|
||||
},
|
||||
{"type": "input", "name": "theme", "message": "Domaine",},
|
||||
{
|
||||
"type": "confirm",
|
||||
"name": "is_leveled",
|
||||
"message": "Évaluation par niveau",
|
||||
"default": True,
|
||||
},
|
||||
{"type": "input", "name": "score_rate", "message": "Bareme"},
|
||||
{
|
||||
"type": "confirm",
|
||||
"name": "correct",
|
||||
"message": "Tout est correct?",
|
||||
"default": True,
|
||||
},
|
||||
]
|
||||
click.echo(f"Nouvelle question pour l'exercice {answers['exercise']}")
|
||||
item_ans = prompt(item_questions, answers=answers)
|
||||
if item_ans["correct"]:
|
||||
return item_ans
|
||||
raise PromptAbortException("Abort item creation")
|
||||
@ -1,102 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# encoding: utf-8
|
||||
|
||||
import click
|
||||
from pathlib import Path
|
||||
import yaml
|
||||
import sys
|
||||
import papermill as pm
|
||||
from datetime import datetime
|
||||
|
||||
from .prepare_csv import prepare_csv
|
||||
from .config import config
|
||||
|
||||
|
||||
@click.group()
|
||||
def cli():
|
||||
pass
|
||||
|
||||
|
||||
@cli.command()
|
||||
def print_config():
|
||||
click.echo(f"Config file is {CONFIGPATH}")
|
||||
click.echo("It contains")
|
||||
click.echo(config)
|
||||
|
||||
|
||||
def reporting(csv_file):
|
||||
# csv_file = Path(csv_file)
|
||||
tribe_dir = csv_file.parent
|
||||
csv_filename = csv_file.name.split(".")[0]
|
||||
|
||||
assessment = str(csv_filename).split("_")[-1].capitalize()
|
||||
date = str(csv_filename).split("_")[0]
|
||||
try:
|
||||
date = datetime.strptime(date, "%y%m%d")
|
||||
except ValueError:
|
||||
date = datetime.today().strptime(date, "%y%m%d")
|
||||
|
||||
tribe = str(tribe_dir).split("/")[-1]
|
||||
|
||||
template = Path(config["templates"]) / "tpl_evaluation.ipynb"
|
||||
|
||||
dest = Path(config["output"]) / tribe / csv_filename
|
||||
dest.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
click.echo(f"Building {assessment} ({date:%d/%m/%y}) report")
|
||||
pm.execute_notebook(
|
||||
str(template),
|
||||
str(dest / f"{assessment}.ipynb"),
|
||||
parameters=dict(
|
||||
tribe=tribe,
|
||||
assessment=assessment,
|
||||
date=f"{date:%d/%m/%y}",
|
||||
csv_file=str(csv_file.absolute()),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@cli.command()
|
||||
@click.argument("target", required=False)
|
||||
def report(target=""):
|
||||
""" Make a report for the eval
|
||||
|
||||
:param target: csv file or a directory where csvs are
|
||||
"""
|
||||
try:
|
||||
if target.endswith(".csv"):
|
||||
csv = Path(target)
|
||||
if not csv.exists():
|
||||
click.echo(f"{target} does not exists")
|
||||
sys.exit(1)
|
||||
if csv.suffix != ".csv":
|
||||
click.echo(f"{target} has to be a csv file")
|
||||
sys.exit(1)
|
||||
csvs = [csv]
|
||||
else:
|
||||
csvs = list(Path(target).glob("**/*.csv"))
|
||||
except AttributeError:
|
||||
csvs = list(Path(config["source"]).glob("**/*.csv"))
|
||||
|
||||
for csv in csvs:
|
||||
click.echo(f"Processing {csv}")
|
||||
try:
|
||||
reporting(csv)
|
||||
except pm.exceptions.PapermillExecutionError as e:
|
||||
click.echo(f"Error with {csv}: {e}")
|
||||
|
||||
|
||||
@cli.command()
|
||||
def prepare():
|
||||
""" Prepare csv file """
|
||||
|
||||
items = prepare_csv()
|
||||
|
||||
click.echo(items)
|
||||
|
||||
|
||||
@cli.command()
|
||||
@click.argument("tribe")
|
||||
def random_pick(tribe):
|
||||
""" Randomly pick a student """
|
||||
pass
|
||||
@ -1,76 +0,0 @@
|
||||
ansiwrap==0.8.4
|
||||
appdirs==1.4.3
|
||||
attrs==19.1.0
|
||||
backcall==0.1.0
|
||||
black==19.10b0
|
||||
bleach==3.1.0
|
||||
certifi==2019.6.16
|
||||
chardet==3.0.4
|
||||
Click==7.0
|
||||
colorama==0.4.1
|
||||
cycler==0.10.0
|
||||
decorator==4.4.0
|
||||
defusedxml==0.6.0
|
||||
entrypoints==0.3
|
||||
future==0.17.1
|
||||
idna==2.8
|
||||
importlib-resources==1.0.2
|
||||
ipykernel==5.1.3
|
||||
ipython==7.11.1
|
||||
ipython-genutils==0.2.0
|
||||
ipywidgets==7.5.1
|
||||
jedi==0.15.2
|
||||
Jinja2==2.10.3
|
||||
jsonschema==3.2.0
|
||||
jupyter==1.0.0
|
||||
jupyter-client==5.3.4
|
||||
jupyter-console==6.1.0
|
||||
jupyter-core==4.6.1
|
||||
jupytex==0.0.3
|
||||
kiwisolver==1.1.0
|
||||
Markdown==3.1.1
|
||||
MarkupSafe==1.1.1
|
||||
matplotlib==3.1.2
|
||||
mistune==0.8.4
|
||||
nbconvert==5.6.1
|
||||
nbformat==5.0.3
|
||||
notebook==6.0.3
|
||||
numpy==1.18.1
|
||||
pandas==0.25.3
|
||||
pandocfilters==1.4.2
|
||||
papermill==1.2.1
|
||||
parso==0.5.2
|
||||
pathspec==0.7.0
|
||||
pexpect==4.8.0
|
||||
pickleshare==0.7.5
|
||||
prometheus-client==0.7.1
|
||||
prompt-toolkit==1.0.14
|
||||
ptyprocess==0.6.0
|
||||
Pygments==2.5.2
|
||||
PyInquirer==1.0.3
|
||||
pyparsing==2.4.6
|
||||
pyrsistent==0.15.7
|
||||
python-dateutil==2.8.0
|
||||
pytz==2019.3
|
||||
PyYAML==5.3
|
||||
pyzmq==18.1.1
|
||||
qtconsole==4.6.0
|
||||
-e git+git_opytex:/lafrite/recopytex.git@7e026bedb24c1ca8bef3b71b3d63f8b0d6916e81#egg=Recopytex
|
||||
regex==2020.1.8
|
||||
requests==2.22.0
|
||||
scipy==1.4.1
|
||||
Send2Trash==1.5.0
|
||||
six==1.12.0
|
||||
tenacity==6.0.0
|
||||
terminado==0.8.3
|
||||
testpath==0.4.4
|
||||
textwrap3==0.9.2
|
||||
toml==0.10.0
|
||||
tornado==6.0.3
|
||||
tqdm==4.41.1
|
||||
traitlets==4.3.2
|
||||
typed-ast==1.4.1
|
||||
urllib3==1.25.8
|
||||
wcwidth==0.1.8
|
||||
webencodings==0.5.1
|
||||
widgetsnbextension==3.5.1
|
||||
31
setup.py
31
setup.py
@ -1,31 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# encoding: utf-8
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name='Recopytex',
|
||||
version='1.1.1',
|
||||
description='Assessment analysis',
|
||||
author='Benjamin Bertrand',
|
||||
author_email='',
|
||||
packages=find_packages(),
|
||||
include_package_data=True,
|
||||
install_requires=[
|
||||
'Click',
|
||||
'pandas',
|
||||
'numpy',
|
||||
'papermill',
|
||||
'pyyaml',
|
||||
'PyInquirer',
|
||||
],
|
||||
entry_points='''
|
||||
[console_scripts]
|
||||
recopytex=recopytex.scripts.recopytex:cli
|
||||
''',
|
||||
)
|
||||
|
||||
# -----------------------------
|
||||
# Reglages pour 'vim'
|
||||
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
|
||||
# cursor: 16 del
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user