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Bertrand Benjamin 2021-04-05 19:02:10 +02:00
parent a0e94f52b1
commit c1fd060707
25 changed files with 268 additions and 2114 deletions
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5
example/Tribe1/210122_DS6.csv Normal file
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Trimestre,Nom,Date,Exercice,Question,Competence,Domaine,Commentaire,Bareme,Est_nivele,Star Tice,Umberto Dingate,Starlin Crangle,Humbert Bourcq,Gabriella Handyside,Stewart Eaves,Erick Going,Ase Praton,Rollins Planks,Dunstan Sarjant,Stacy Guiton,Ange Stanes,Amabelle Elleton,Darn Broomhall,Dyan Chatto,Keane Rennebach,Nari Paulton,Brandy Wase,Jaclyn Firidolfi,Violette Lockney
1,DS6,22/01/2021,Exercice 1,Sait pas,,,,,,,,,,,,,,,,,,,,,,,,,
1,DS6,22/01/2021,Exercice 1,Ha,,,,,,,,,,,,,,,,,,,,,,,,,
1,DS6,22/01/2021,Exercice 1,,,,,,,,,,,,,,,,,,,,,,,,,,
1,DS6,22/01/2021,Exercice 2,grr,,,,,,,,,,,,,,,,,,,,,,,,,
1 Trimestre Nom Date Exercice Question Competence Domaine Commentaire Bareme Est_nivele Star Tice Umberto Dingate Starlin Crangle Humbert Bourcq Gabriella Handyside Stewart Eaves Erick Going Ase Praton Rollins Planks Dunstan Sarjant Stacy Guiton Ange Stanes Amabelle Elleton Darn Broomhall Dyan Chatto Keane Rennebach Nari Paulton Brandy Wase Jaclyn Firidolfi Violette Lockney
2 1 DS6 22/01/2021 Exercice 1 Sait pas
3 1 DS6 22/01/2021 Exercice 1 Ha
4 1 DS6 22/01/2021 Exercice 1
5 1 DS6 22/01/2021 Exercice 2 grr

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recopytex/__init__.py
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#!/usr/bin/env python
# encoding: utf-8
from .csv_extraction import flat_df_students, flat_df_for
from .df_marks_manip import pp_q_scores

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recopytex/config.py
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#!/usr/bin/env python
# encoding: utf-8
NO_ST_COLUMNS = {
"term": "Trimestre",
"assessment": "Nom",
"date": "Date",
"exercise": "Exercice",
"question": "Question",
"competence": "Competence",
"theme": "Domaine",
"comment": "Commentaire",
"score_rate": "Bareme",
"is_leveled": "Est_nivele",
}
COLUMNS = {
**NO_ST_COLUMNS,
"student": "Eleve",
"score": "Score",
"mark": "Note",
"level": "Niveau",
"normalized": "Normalise",
}
VALIDSCORE = {
"NOTFILLED": "", # The item is not scored yet
"NOANSWER": ".", # Student gives no answer (this score will impact the fianl mark)
"ABS": "a", # Student has absent (this score won't be impact the final mark)
}

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recopytex/csv_extraction.py
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#!/usr/bin/env python
# encoding: utf-8
""" Extracting data from xlsx files """
import pandas as pd
from .config import NO_ST_COLUMNS, COLUMNS, VALIDSCORE
pd.set_option("Precision", 2)
def try_replace(x, old, new):
try:
return str(x).replace(old, new)
except ValueError:
return x
def extract_students(df, no_student_columns=NO_ST_COLUMNS.values()):
"""Extract the list of students from df
:param df: the dataframe
:param no_student_columns: columns that are not students
:return: list of students
"""
students = df.columns.difference(no_student_columns)
return students
def flat_df_students(
df, no_student_columns=NO_ST_COLUMNS.values(), postprocessing=True
):
"""Flat the dataframe by returning a dataframe with on student on each line
:param df: the dataframe (one row per questions)
:param no_student_columns: columns that are not students
:return: dataframe with one row per questions and students
Columns of csv files:
- NO_ST_COLUMNS meta data on questions
- one for each students
This function flat student's columns to "student" and "score"
"""
students = extract_students(df, no_student_columns)
scores = []
for st in students:
scores.append(
pd.melt(
df,
id_vars=no_student_columns,
value_vars=st,
var_name=COLUMNS["student"],
value_name=COLUMNS["score"],
).dropna(subset=[COLUMNS["score"]])
)
if postprocessing:
return postprocess(pd.concat(scores))
return pd.concat(scores)
def flat_df_for(
df, student, no_student_columns=NO_ST_COLUMNS.values(), postprocessing=True
):
"""Extract the data only for one student
:param df: the dataframe (one row per questions)
:param no_student_columns: columns that are not students
:return: dataframe with one row per questions and students
Columns of csv files:
- NO_ST_COLUMNS meta data on questions
- one for each students
"""
students = extract_students(df, no_student_columns)
if student not in students:
raise KeyError("This student is not in the table")
st_df = df[list(no_student_columns) + [student]]
st_df = st_df.rename(columns={student: COLUMNS["score"]}).dropna(
subset=[COLUMNS["score"]]
)
if postprocessing:
return postprocess(st_df)
return st_df
def postprocess(df):
"""Postprocessing score dataframe
- Replace na with an empty string
- Replace "NOANSWER" with -1
- Turn commas number to dot numbers
"""
df[COLUMNS["question"]].fillna("", inplace=True)
df[COLUMNS["exercise"]].fillna("", inplace=True)
df[COLUMNS["comment"]].fillna("", inplace=True)
df[COLUMNS["competence"]].fillna("", inplace=True)
df[COLUMNS["score"]] = pd.to_numeric(
df[COLUMNS["score"]]
.replace(VALIDSCORE["NOANSWER"], -1)
.apply(lambda x: try_replace(x, ",", "."))
)
df[COLUMNS["score_rate"]] = pd.to_numeric(
df[COLUMNS["score_rate"]].apply(lambda x: try_replace(x, ",", ".")),
errors="coerce",
)
return df
# -----------------------------
# Reglages pour 'vim'
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
# cursor: 16 del

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recopytex/dashboard/__init__.py
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recopytex/dashboard/app.py
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import dash
app = dash.Dash(__name__, suppress_callback_exceptions=True)
# app = dash.Dash(__name__)
server = app.server

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recopytex/dashboard/assets/style.css
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body {
margin: 0px;
font-family: 'Source Sans Pro','Roboto','Open Sans','Liberation Sans','DejaVu Sans','Verdana','Helvetica','Arial',sans-serif;
}
header {
margin: 0px 0px 20px 0px;
background-color: #333333;
color: #ffffff;
padding: 20px;
}
header > h1 {
margin: 0px;
}
main {
width: 95vw;
margin: auto;
}
section {
margin-top: 20px;
margin-bottom: 20px;
}
/* Exam analysis */
#select {
margin-bottom: 20px;
}
#select > div {
width: 40vw;
margin: auto;
}
#analysis {
display: flex;
flex-flow: row wrap;
}
#analysis > * {
display: flex;
flex-flow: column;
width: 45vw;
margin: auto;
}
/* Create new exam */
#new-exam {
display: flex;
flex-flow: row;
justify-content: space-between;
}
#new-exam label {
width: 20%;
display: flex;
flex-flow: column;
justify-content: space-between;
}

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recopytex/dashboard/create_exam/__init__.py
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recopytex/dashboard/create_exam/app.py
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#!/usr/bin/env python
# encoding: utf-8
import dash
import dash_html_components as html
import dash_core_components as dcc
import dash_table
import plotly.graph_objects as go
from datetime import date, datetime
import uuid
import pandas as pd
import yaml
from ...scripts.getconfig import config
from ...config import NO_ST_COLUMNS
from ..app import app
from ...scripts.exam import Exam
QUESTION_COLUMNS = [
{"id": "id", "name": "Question"},
{
"id": "competence",
"name": "Competence",
"presentation": "dropdown",
},
{"id": "theme", "name": "Domaine"},
{"id": "comment", "name": "Commentaire"},
{"id": "score_rate", "name": "Bareme"},
{"id": "is_leveled", "name": "Est_nivele"},
]
def get_current_year_limit():
today = date.today()
if today.month > 8:
return {
"min_date_allowed": date(today.year, 9, 1),
"max_date_allowed": date(today.year + 1, 7, 15),
"initial_visible_month": today,
}
return {
"min_date_allowed": date(today.year - 1, 9, 1),
"max_date_allowed": date(today.year, 7, 15),
"initial_visible_month": today,
}
layout = html.Div(
[
html.Header(
children=[
html.H1("Création d'une évaluation"),
html.P("Pas encore de sauvegarde", id="is-saved"),
html.Button("Enregistrer dans csv", id="save-csv"),
],
),
html.Main(
children=[
html.Section(
children=[
html.Form(
id="new-exam",
children=[
html.Label(
children=[
"Classe",
dcc.Dropdown(
id="tribe",
options=[
{"label": t["name"], "value": t["name"]}
for t in config["tribes"]
],
value=config["tribes"][0]["name"],
),
]
),
html.Label(
children=[
"Nom de l'évaluation",
dcc.Input(
id="exam_name",
type="text",
placeholder="Nom de l'évaluation",
),
]
),
html.Label(
children=[
"Date",
dcc.DatePickerSingle(
id="date",
date=date.today(),
**get_current_year_limit(),
),
]
),
html.Label(
children=[
"Trimestre",
dcc.Dropdown(
id="term",
options=[
{"label": i + 1, "value": i + 1}
for i in range(3)
],
value=1,
),
]
),
],
),
],
id="form",
),
html.Section(
children=[
html.Div(
id="exercises",
children=[],
),
html.Button(
"Ajouter un exercice",
id="add-exercise",
className="add-exercise",
),
html.Div(
id="summary",
),
],
id="exercises",
),
html.Section(
children=[
html.Div(
id="score_rate",
),
html.Div(
id="exercises-viz",
),
html.Div(
id="competences-viz",
),
html.Div(
id="themes-viz",
),
],
id="visualisation",
),
]
),
dcc.Store(id="exam_store"),
]
)
@app.callback(
dash.dependencies.Output("exercises", "children"),
dash.dependencies.Input("add-exercise", "n_clicks"),
dash.dependencies.State("exercises", "children"),
)
def add_exercise(n_clicks, children):
if n_clicks is None:
return children
element_table = pd.DataFrame(columns=[c["id"] for c in QUESTION_COLUMNS])
element_table = element_table.append(
pd.Series(
data={
"id": 1,
"competence": "Rechercher",
"theme": "",
"comment": "",
"score_rate": 1,
"is_leveled": 1,
},
name=0,
)
)
new_exercise = html.Div(
children=[
html.Div(
children=[
dcc.Input(
id={"type": "exercice", "index": str(n_clicks)},
type="text",
value=f"Exercice {len(children)+1}",
placeholder="Nom de l'exercice",
className="exercise-name",
),
html.Button(
"X",
id={"type": "rm_exercice", "index": str(n_clicks)},
className="delete-exercise",
),
],
className="exercise-head",
),
dash_table.DataTable(
id={"type": "elements", "index": str(n_clicks)},
columns=QUESTION_COLUMNS,
data=element_table.to_dict("records"),
editable=True,
row_deletable=True,
dropdown={
"competence": {
"options": [
{"label": i, "value": i} for i in config["competences"]
]
},
},
style_cell={
"whiteSpace": "normal",
"height": "auto",
},
),
html.Button(
"Ajouter un élément de notation",
id={"type": "add-element", "index": str(n_clicks)},
className="add-element",
),
],
className="exercise",
id=f"exercise-{n_clicks}",
)
children.append(new_exercise)
return children
@app.callback(
dash.dependencies.Output(
{"type": "elements", "index": dash.dependencies.MATCH}, "data"
),
dash.dependencies.Input(
{"type": "add-element", "index": dash.dependencies.MATCH}, "n_clicks"
),
[
dash.dependencies.State(
{"type": "elements", "index": dash.dependencies.MATCH}, "data"
),
],
prevent_initial_call=True,
)
def add_element(n_clicks, elements):
if n_clicks is None or n_clicks < len(elements):
return elements
df = pd.DataFrame.from_records(elements)
df = df.append(
pd.Series(
data={
"id": len(df) + 1,
"competence": "",
"theme": "",
"comment": "",
"score_rate": 1,
"is_leveled": 1,
},
name=n_clicks,
)
)
return df.to_dict("records")
def exam_generalities(tribe, exam_name, date, term, exercices=[], elements=[]):
return [
html.H1(f"{exam_name} pour les {tribe}"),
html.P(f"Fait le {date} (Trimestre {term})"),
]
def exercise_summary(identifier, name, elements=[]):
df = pd.DataFrame.from_records(elements)
return html.Div(
[
html.H2(name),
dash_table.DataTable(
columns=[{"id": c, "name": c} for c in df], data=elements
),
]
)
@app.callback(
dash.dependencies.Output("exam_store", "data"),
[
dash.dependencies.Input("tribe", "value"),
dash.dependencies.Input("exam_name", "value"),
dash.dependencies.Input("date", "date"),
dash.dependencies.Input("term", "value"),
dash.dependencies.Input(
{"type": "exercice", "index": dash.dependencies.ALL}, "value"
),
dash.dependencies.Input(
{"type": "elements", "index": dash.dependencies.ALL}, "data"
),
],
dash.dependencies.State({"type": "elements", "index": dash.dependencies.ALL}, "id"),
)
def store_exam(tribe, exam_name, date, term, exercices, elements, elements_id):
exam = Exam(exam_name, tribe, date, term)
for (i, name) in enumerate(exercices):
ex_elements_id = [el for el in elements_id if el["index"] == str(i + 1)][0]
index = elements_id.index(ex_elements_id)
ex_elements = elements[index]
exam.add_exercise(name, ex_elements)
return exam.to_dict()
@app.callback(
dash.dependencies.Output("score_rate", "children"),
dash.dependencies.Input("exam_store", "data"),
prevent_initial_call=True,
)
def score_rate(data):
exam = Exam(**data)
return [html.P(f"Barème /{exam.score_rate}")]
@app.callback(
dash.dependencies.Output("competences-viz", "figure"),
dash.dependencies.Input("exam_store", "data"),
prevent_initial_call=True,
)
def competences_viz(data):
exam = Exam(**data)
return [html.P(str(exam.competences_rate))]
@app.callback(
dash.dependencies.Output("themes-viz", "children"),
dash.dependencies.Input("exam_store", "data"),
prevent_initial_call=True,
)
def themes_viz(data):
exam = Exam(**data)
themes_rate = exam.themes_rate
fig = go.Figure()
if themes_rate:
fig.add_trace(go.Pie(labels=list(themes_rate.keys()), values=list(themes_rate.values())))
return [dcc.Graph(figure=fig)]
return []
@app.callback(
dash.dependencies.Output("is-saved", "children"),
dash.dependencies.Input("save-csv", "n_clicks"),
dash.dependencies.State("exam_store", "data"),
prevent_initial_call=True,
)
def save_to_csv(n_clicks, data):
exam = Exam(**data)
csv = exam.path(".csv")
exam.write_csv()
return [f"Dernière sauvegarde {datetime.today()} dans {csv}"]

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recopytex/dashboard/exam_analysis/__init__.py
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recopytex/dashboard/exam_analysis/app.py
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#!/usr/bin/env python
# encoding: utf-8
import dash
import dash_html_components as html
import dash_core_components as dcc
import dash_table
from dash.exceptions import PreventUpdate
import plotly.graph_objects as go
from pathlib import Path
from datetime import datetime
import pandas as pd
import numpy as np
from ... import flat_df_students, pp_q_scores
from ...config import NO_ST_COLUMNS
from ...scripts.getconfig import config
from ..app import app
COLORS = {
".": "black",
0: "#E7472B",
1: "#FF712B",
2: "#F2EC4C",
3: "#68D42F",
}
layout = html.Div(
children=[
html.Header(
children=[
html.H1("Analyse des notes"),
html.P("Dernière sauvegarde", id="lastsave"),
],
),
html.Main(
[
html.Section(
[
html.Div(
[
"Classe: ",
dcc.Dropdown(
id="tribe",
options=[
{"label": t["name"], "value": t["name"]}
for t in config["tribes"]
],
value=config["tribes"][0]["name"],
),
],
style={
"display": "flex",
"flex-flow": "column",
},
),
html.Div(
[
"Evaluation: ",
dcc.Dropdown(id="csv"),
],
style={
"display": "flex",
"flex-flow": "column",
},
),
],
id="select",
style={
"display": "flex",
"flex-flow": "row wrap",
},
),
html.Div(
[
html.Div(
dash_table.DataTable(
id="final_score_table",
columns=[
{"id": "Eleve", "name": "Élève"},
{"id": "Note", "name": "Note"},
{"id": "Bareme", "name": "Barème"},
],
data=[],
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
}
],
style_data={
"width": "100px",
"maxWidth": "100px",
"minWidth": "100px",
},
),
id="final_score_table_container",
),
html.Div(
[
dash_table.DataTable(
id="final_score_describe",
columns=[
{"id": "count", "name": "count"},
{"id": "mean", "name": "mean"},
{"id": "std", "name": "std"},
{"id": "min", "name": "min"},
{"id": "25%", "name": "25%"},
{"id": "50%", "name": "50%"},
{"id": "75%", "name": "75%"},
{"id": "max", "name": "max"},
],
),
dcc.Graph(
id="fig_assessment_hist",
),
dcc.Graph(id="fig_competences"),
],
id="desc_plots",
),
],
id="analysis",
),
html.Div(
[
dash_table.DataTable(
id="scores_table",
columns=[
{"id": "id", "name": "Question"},
{
"id": "competence",
"name": "Competence",
},
{"id": "theme", "name": "Domaine"},
{"id": "comment", "name": "Commentaire"},
{"id": "score_rate", "name": "Bareme"},
{"id": "is_leveled", "name": "Est_nivele"},
],
style_cell={
"whiteSpace": "normal",
"height": "auto",
},
fixed_columns={"headers": True, "data": 7},
style_table={"minWidth": "100%"},
style_data_conditional=[],
editable=True,
),
html.Button("Ajouter un élément", id="btn_add_element"),
],
id="big_table",
),
dcc.Store(id="final_score"),
],
className="content",
style={
"width": "95vw",
"margin": "auto",
},
),
],
)
@app.callback(
[
dash.dependencies.Output("csv", "options"),
dash.dependencies.Output("csv", "value"),
],
[dash.dependencies.Input("tribe", "value")],
)
def update_csvs(value):
if not value:
raise PreventUpdate
p = Path(value)
csvs = list(p.glob("*.csv"))
try:
return [{"label": str(c), "value": str(c)} for c in csvs], str(csvs[0])
except IndexError:
return []
@app.callback(
[
dash.dependencies.Output("final_score", "data"),
],
[dash.dependencies.Input("scores_table", "data")],
)
def update_final_scores(data):
if not data:
raise PreventUpdate
scores = pd.DataFrame.from_records(data)
try:
if scores.iloc[0]["Commentaire"] == "commentaire":
scores.drop([0], inplace=True)
except KeyError:
pass
scores = flat_df_students(scores).dropna(subset=["Score"])
if scores.empty:
return [{}]
scores = pp_q_scores(scores)
assessment_scores = scores.groupby(["Eleve"]).agg({"Note": "sum", "Bareme": "sum"})
return [assessment_scores.reset_index().to_dict("records")]
@app.callback(
[
dash.dependencies.Output("final_score_table", "data"),
],
[dash.dependencies.Input("final_score", "data")],
)
def update_final_scores_table(data):
assessment_scores = pd.DataFrame.from_records(data)
return [assessment_scores.to_dict("records")]
@app.callback(
[
dash.dependencies.Output("final_score_describe", "data"),
],
[dash.dependencies.Input("final_score", "data")],
)
def update_final_scores_descr(data):
scores = pd.DataFrame.from_records(data)
if scores.empty:
return [[{}]]
desc = scores["Note"].describe().T.round(2)
return [[desc.to_dict()]]
@app.callback(
[
dash.dependencies.Output("fig_assessment_hist", "figure"),
],
[dash.dependencies.Input("final_score", "data")],
)
def update_final_scores_hist(data):
assessment_scores = pd.DataFrame.from_records(data)
if assessment_scores.empty:
return [go.Figure(data=[go.Scatter(x=[], y=[])])]
ranges = np.linspace(
-0.5,
assessment_scores.Bareme.max(),
int(assessment_scores.Bareme.max() * 2 + 2),
)
bins = pd.cut(assessment_scores["Note"], ranges)
assessment_scores["Bin"] = bins
assessment_grouped = (
assessment_scores.reset_index()
.groupby("Bin")
.agg({"Bareme": "count", "Eleve": lambda x: "\n".join(x)})
)
assessment_grouped.index = assessment_grouped.index.map(lambda i: i.right)
fig = go.Figure()
fig.add_bar(
x=assessment_grouped.index,
y=assessment_grouped.Bareme,
text=assessment_grouped.Eleve,
textposition="auto",
hovertemplate="",
marker_color="#4E89DE",
)
fig.update_layout(
height=300,
margin=dict(l=5, r=5, b=5, t=5),
)
return [fig]
@app.callback(
[
dash.dependencies.Output("fig_competences", "figure"),
],
[dash.dependencies.Input("scores_table", "data")],
)
def update_competence_fig(data):
scores = pd.DataFrame.from_records(data)
try:
if scores.iloc[0]["Commentaire"] == "commentaire":
scores.drop([0], inplace=True)
except KeyError:
pass
scores = flat_df_students(scores).dropna(subset=["Score"])
if scores.empty:
return [go.Figure(data=[go.Scatter(x=[], y=[])])]
scores = pp_q_scores(scores)
pt = pd.pivot_table(
scores,
index=["Exercice", "Question", "Commentaire"],
columns="Score",
aggfunc="size",
fill_value=0,
)
for i in {i for i in pt.index.get_level_values(0)}:
pt.loc[(str(i), "", ""), :] = ""
pt.sort_index(inplace=True)
index = (
pt.index.get_level_values(0).map(str)
+ ":"
+ pt.index.get_level_values(1).map(str)
+ " "
+ pt.index.get_level_values(2).map(str)
)
fig = go.Figure()
bars = [
{"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
{"score": 0, "name": "Faux", "color": COLORS[0]},
{"score": 1, "name": "Peu juste", "color": COLORS[1]},
{"score": 2, "name": "Presque juste", "color": COLORS[2]},
{"score": 3, "name": "Juste", "color": COLORS[3]},
]
for b in bars:
try:
fig.add_bar(
x=index, y=pt[b["score"]], name=b["name"], marker_color=b["color"]
)
except KeyError:
pass
fig.update_layout(barmode="relative")
fig.update_layout(
height=500,
margin=dict(l=5, r=5, b=5, t=5),
)
return [fig]
@app.callback(
[
dash.dependencies.Output("lastsave", "children"),
],
[
dash.dependencies.Input("scores_table", "data"),
dash.dependencies.State("csv", "value"),
],
)
def save_scores(data, csv):
try:
scores = pd.DataFrame.from_records(data)
scores.to_csv(csv, index=False)
except:
return [f"Soucis pour sauvegarder à {datetime.today()} dans {csv}"]
else:
return [f"Dernière sauvegarde {datetime.today()} dans {csv}"]
def highlight_value(df):
""" Cells style """
hight = []
for v, color in COLORS.items():
hight += [
{
"if": {"filter_query": "{{{}}} = {}".format(col, v), "column_id": col},
"backgroundColor": color,
"color": "white",
}
for col in df.columns
if col not in NO_ST_COLUMNS.values()
]
return hight
@app.callback(
[
dash.dependencies.Output("scores_table", "columns"),
dash.dependencies.Output("scores_table", "data"),
dash.dependencies.Output("scores_table", "style_data_conditional"),
],
[
dash.dependencies.Input("csv", "value"),
dash.dependencies.Input("btn_add_element", "n_clicks"),
dash.dependencies.State("scores_table", "data"),
],
)
def update_scores_table(csv, add_element, data):
ctx = dash.callback_context
if ctx.triggered[0]["prop_id"] == "csv.value":
stack = pd.read_csv(csv, encoding="UTF8")
elif ctx.triggered[0]["prop_id"] == "btn_add_element.n_clicks":
stack = pd.DataFrame.from_records(data)
infos = pd.DataFrame.from_records(
[{k: stack.iloc[-1][k] for k in NO_ST_COLUMNS.values()}]
)
stack = stack.append(infos)
return (
[
{"id": c, "name": c}
for c in stack.columns
if c not in ["Trimestre", "Nom", "Date"]
],
stack.to_dict("records"),
highlight_value(stack),
)

29
recopytex/dashboard/index.py
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@ -1,29 +0,0 @@
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from .app import app
from .exam_analysis import app as exam_analysis
from .create_exam import app as create_exam
from .student_analysis import app as student_analysis
app.layout = html.Div(
[dcc.Location(id="url", refresh=False), html.Div(id="page-content")]
)
@app.callback(Output("page-content", "children"), Input("url", "pathname"))
def display_page(pathname):
if pathname == "/":
return exam_analysis.layout
elif pathname == "/create-exam":
return create_exam.layout
elif pathname == "/students":
return student_analysis.layout
else:
return "404"
if __name__ == "__main__":
app.run_server(debug=True)

0
recopytex/dashboard/student_analysis/__init__.py
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303
recopytex/dashboard/student_analysis/app.py
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@ -1,303 +0,0 @@
#!/usr/bin/env python
# encoding: utf-8
import dash
import dash_html_components as html
import dash_core_components as dcc
import dash_table
import plotly.graph_objects as go
from datetime import date, datetime
import uuid
import pandas as pd
import yaml
from pathlib import Path
from ...scripts.getconfig import config
from ... import flat_df_students, pp_q_scores
from ...config import NO_ST_COLUMNS
from ..app import app
from ...scripts.exam import Exam
def get_students(csv):
return list(pd.read_csv(csv).T.to_dict().values())
COLORS = {
".": "black",
0: "#E7472B",
1: "#FF712B",
2: "#F2EC4C",
3: "#68D42F",
}
QUESTION_COLUMNS = [
{"id": "id", "name": "Question"},
{
"id": "competence",
"name": "Competence",
"presentation": "dropdown",
},
{"id": "theme", "name": "Domaine"},
{"id": "comment", "name": "Commentaire"},
{"id": "score_rate", "name": "Bareme"},
{"id": "is_leveled", "name": "Est_nivele"},
]
layout = html.Div(
[
html.Header(
children=[
html.H1("Bilan des élèves"),
],
),
html.Main(
children=[
html.Section(
children=[
html.Form(
id="select-student",
children=[
html.Label(
children=[
"Classe",
dcc.Dropdown(
id="tribe",
options=[
{"label": t["name"], "value": t["name"]}
for t in config["tribes"]
],
value=config["tribes"][0]["name"],
),
]
),
html.Label(
children=[
"Élève",
dcc.Dropdown(
id="student",
options=[
{"label": t["Nom"], "value": t["Nom"]}
for t in get_students(config["tribes"][0]["students"])
],
value=get_students(config["tribes"][0]["students"])[0]["Nom"],
),
]
),
html.Label(
children=[
"Trimestre",
dcc.Dropdown(
id="term",
options=[
{"label": i + 1, "value": i + 1}
for i in range(3)
],
value=1,
),
]
),
],
),
],
id="form",
),
html.Section(
children=[
html.H2("Évaluations"),
html.Div(
dash_table.DataTable(
id="exam_scores",
columns=[
{"id": "Nom", "name": "Évaluations"},
{"id": "Note", "name": "Note"},
{"id": "Bareme", "name": "Barème"},
],
data=[],
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
}
],
style_data={
"width": "100px",
"maxWidth": "100px",
"minWidth": "100px",
},
),
id="eval-table",
),
],
id="Évaluations",
),
html.Section(
children=[
html.Div(
id="competences-viz",
),
html.Div(
id="themes-vizz",
),
],
id="visualisation",
),
]
),
dcc.Store(id="student-scores"),
]
)
@app.callback(
[
dash.dependencies.Output("student", "options"),
dash.dependencies.Output("student", "value"),
],
[
dash.dependencies.Input("tribe", "value")
],)
def update_students_list(tribe):
tribe_config = [t for t in config["tribes"] if t["name"] == tribe][0]
students = get_students(tribe_config["students"])
options = [
{"label": t["Nom"], "value": t["Nom"]}
for t in students
]
value = students[0]["Nom"]
return options, value
@app.callback(
[
dash.dependencies.Output("student-scores", "data"),
],
[
dash.dependencies.Input("tribe", "value"),
dash.dependencies.Input("student", "value"),
dash.dependencies.Input("term", "value"),
],
)
def update_student_scores(tribe, student, term):
tribe_config = [t for t in config["tribes"] if t["name"] == tribe][0]
p = Path(tribe_config["name"])
csvs = list(p.glob("*.csv"))
dfs = []
for csv in csvs:
try:
scores = pd.read_csv(csv)
except pd.errors.ParserError:
pass
else:
try:
if scores.iloc[0]["Commentaire"] == "commentaire":
scores.drop([0], inplace=True)
except KeyError:
pass
scores = flat_df_students(scores).dropna(subset=["Score"])
scores = scores[scores["Eleve"] == student]
scores = scores[scores["Trimestre"] == term]
dfs.append(scores)
df = pd.concat(dfs)
return [df.to_dict("records")]
@app.callback(
[
dash.dependencies.Output("exam_scores", "data"),
],
[
dash.dependencies.Input("student-scores", "data"),
],
)
def update_exam_scores(data):
scores = pd.DataFrame.from_records(data)
scores = pp_q_scores(scores)
assessment_scores = scores.groupby(["Nom"]).agg({"Note": "sum", "Bareme": "sum"})
return [assessment_scores.reset_index().to_dict("records")]
@app.callback(
[
dash.dependencies.Output("competences-viz", "children"),
],
[
dash.dependencies.Input("student-scores", "data"),
],
)
def update_competences_viz(data):
scores = pd.DataFrame.from_records(data)
scores = pp_q_scores(scores)
pt = pd.pivot_table(
scores,
index=["Competence"],
columns="Score",
aggfunc="size",
fill_value=0,
)
fig = go.Figure()
bars = [
{"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
{"score": 0, "name": "Faux", "color": COLORS[0]},
{"score": 1, "name": "Peu juste", "color": COLORS[1]},
{"score": 2, "name": "Presque juste", "color": COLORS[2]},
{"score": 3, "name": "Juste", "color": COLORS[3]},
]
for b in bars:
try:
fig.add_bar(
x=list(config["competences"].keys()), y=pt[b["score"]], name=b["name"], marker_color=b["color"]
)
except KeyError:
pass
fig.update_layout(barmode="relative")
fig.update_layout(
height=500,
margin=dict(l=5, r=5, b=5, t=5),
)
return [dcc.Graph(figure=fig)]
@app.callback(
[
dash.dependencies.Output("themes-vizz", "children"),
],
[
dash.dependencies.Input("student-scores", "data"),
],
)
def update_themes_viz(data):
scores = pd.DataFrame.from_records(data)
scores = pp_q_scores(scores)
pt = pd.pivot_table(
scores,
index=["Domaine"],
columns="Score",
aggfunc="size",
fill_value=0,
)
fig = go.Figure()
bars = [
{"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
{"score": 0, "name": "Faux", "color": COLORS[0]},
{"score": 1, "name": "Peu juste", "color": COLORS[1]},
{"score": 2, "name": "Presque juste", "color": COLORS[2]},
{"score": 3, "name": "Juste", "color": COLORS[3]},
]
for b in bars:
try:
fig.add_bar(
x=list(pt.index), y=pt[b["score"]], name=b["name"], marker_color=b["color"]
)
except KeyError:
pass
fig.update_layout(barmode="relative")
fig.update_layout(
height=500,
margin=dict(l=5, r=5, b=5, t=5),
)
return [dcc.Graph(figure=fig)]

220
recopytex/df_marks_manip.py
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@ -1,220 +0,0 @@
#!/usr/bin/env python
# encoding: utf-8
import pandas as pd
import numpy as np
from math import ceil, floor
from .config import COLUMNS
"""
Functions for manipulate score dataframes
"""
def round_half_point(val):
try:
return 0.5 * ceil(2.0 * val)
except ValueError:
return val
except TypeError:
return val
def score_to_mark(x):
"""Compute the mark
if the item is leveled then the score is multiply by the score_rate
otherwise it copies the score
:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
:return: the mark
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
... COLUMNS["score"]:[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> score_to_mark(df.loc[0])
1.0
>>> score_to_mark(df.loc[10])
1.3333333333333333
"""
# -1 is no answer
if x[COLUMNS["score"]] == -1:
return 0
if x[COLUMNS["is_leveled"]]:
if x[COLUMNS["score"]] not in [0, 1, 2, 3]:
raise ValueError(
f"The evaluation is out of range: {x[COLUMNS['score']]} at {x}"
)
return round(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3, 2)
#return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3)
if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]:
raise ValueError(
f"The score ({x['score']}) is greated than the rating scale ({x[COLUMNS['score_rate']]}) at {x}"
)
return x[COLUMNS["score"]]
def score_to_level(x):
"""Compute the level (".",0,1,2,3).
:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
:return: the level
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
... COLUMNS["score"]:[1, 0.33, np.nan, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> score_to_level(df.loc[0])
3
>>> score_to_level(df.loc[1])
1
>>> score_to_level(df.loc[2])
'na'
>>> score_to_level(df.loc[3])
3
>>> score_to_level(df.loc[5])
3
>>> score_to_level(df.loc[10])
2
"""
# negatives are no answer or negatives points
if x[COLUMNS["score"]] <= -1:
return np.nan
if x[COLUMNS["is_leveled"]]:
return int(x[COLUMNS["score"]])
return int(ceil(x[COLUMNS["score"]] / x[COLUMNS["score_rate"]] * 3))
# DataFrame columns manipulations
def compute_mark(df):
"""Compute the mark for the dataframe
apply score_to_mark to each row
:param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
... COLUMNS["score"]:[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> compute_mark(df)
0 1.00
1 0.33
2 2.00
3 1.50
4 0.67
5 2.00
6 0.67
7 1.00
8 1.50
9 1.00
10 1.33
11 2.00
dtype: float64
"""
return df[[COLUMNS["score"], COLUMNS["is_leveled"], COLUMNS["score_rate"]]].apply(
score_to_mark, axis=1
)
def compute_level(df):
"""Compute level for the dataframe
Applies score_to_level to each row
:param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
:return: Columns with level
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
... COLUMNS["score"]:[np.nan, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> compute_level(df)
0 na
1 1
2 3
3 3
4 1
5 3
6 2
7 3
8 3
9 2
10 2
11 3
dtype: object
"""
return df[[COLUMNS["score"], COLUMNS["is_leveled"], COLUMNS["score_rate"]]].apply(
score_to_level, axis=1
)
def compute_normalized(df):
"""Compute the normalized mark (Mark / score_rate)
:param df: DataFrame with "Mark" and COLUMNS["score_rate"] columns
:return: column with normalized mark
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... COLUMNS["is_leveled"]:[0]*4+[1]*2 + [0]*4+[1]*2,
... COLUMNS["score"]:[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df["Mark"] = compute_marks(df)
>>> compute_normalized(df)
0 1.00
1 0.33
2 1.00
3 0.75
4 0.33
5 1.00
6 0.67
7 1.00
8 0.75
9 0.50
10 0.67
11 1.00
dtype: float64
"""
return df[COLUMNS["mark"]] / df[COLUMNS["score_rate"]]
# Postprocessing question scores
def pp_q_scores(df):
"""Postprocessing questions scores dataframe
Add 3 columns: mark, level and normalized
:param df: questions-scores dataframe
:return: same data frame with mark, level and normalize columns
"""
assign = {
COLUMNS["mark"]: compute_mark,
COLUMNS["level"]: compute_level,
COLUMNS["normalized"]: compute_normalized,
}
return df.assign(**assign)
# -----------------------------
# Reglages pour 'vim'
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
# cursor: 16 del

0
recopytex/scripts/__init__.py
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207
recopytex/scripts/exam.py
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@ -1,207 +0,0 @@
#!/usr/bin/env python
# encoding: utf-8
from datetime import datetime
from pathlib import Path
from prompt_toolkit import HTML
from ..config import NO_ST_COLUMNS
import pandas as pd
import yaml
from .getconfig import config
def try_parsing_date(text, formats=["%Y-%m-%d", "%Y.%m.%d", "%Y/%m/%d"]):
for fmt in formats:
try:
return datetime.strptime(text[:10], fmt)
except ValueError:
pass
raise ValueError("no valid date format found")
def format_question(question):
question["score_rate"] = float(question["score_rate"])
return question
class Exam:
def __init__(self, name, tribename, date, term, **kwrds):
self._name = name
self._tribename = tribename
self._date = try_parsing_date(date)
self._term = term
try:
kwrds["exercices"]
except KeyError:
self._exercises = {}
else:
self._exercises = kwrds["exercices"]
@property
def name(self):
return self._name
@property
def tribename(self):
return self._tribename
@property
def date(self):
return self._date
@property
def term(self):
return self._term
def add_exercise(self, name, questions):
""" Add key with questions in ._exercises """
try:
self._exercises[name]
except KeyError:
self._exercises[name] = format_question(questions)
else:
raise KeyError("The exercise already exsists. Use modify_exercise")
def modify_exercise(self, name, questions, append=False):
"""Modify questions of an exercise
If append==True, add questions to the exercise questions
"""
try:
self._exercises[name]
except KeyError:
raise KeyError("The exercise already exsists. Use modify_exercise")
else:
if append:
self._exercises[name] += format_question(questions)
else:
self._exercises[name] = format_question(questions)
@property
def exercices(self):
return self._exercises
@property
def tribe_path(self):
return Path(config["source"]) / self.tribename
@property
def tribe_student_path(self):
return (
Path(config["source"])
/ [t["students"] for t in config["tribes"] if t["name"] == self.tribename][
0
]
)
@property
def long_name(self):
""" Get exam name with date inside """
return f"{self.date.strftime('%y%m%d')}_{self.name}"
def path(self, extention=""):
return self.tribe_path / (self.long_name + extention)
def to_dict(self):
return {
"name": self.name,
"tribename": self.tribename,
"date": self.date,
"term": self.term,
"exercices": self.exercices,
}
def to_row(self):
rows = []
for ex, questions in self.exercices.items():
for q in questions:
rows.append(
{
"term": self.term,
"assessment": self.name,
"date": self.date.strftime("%d/%m/%Y"),
"exercise": ex,
"question": q["id"],
**q,
}
)
return rows
@property
def themes(self):
themes = set()
for questions in self._exercises.values():
themes.update([q["theme"] for q in questions])
return themes
def display_exercise(self, name):
pass
def display(self, name):
pass
def write_yaml(self):
print(f"Sauvegarde temporaire dans {self.path('.yml')}")
self.tribe_path.mkdir(exist_ok=True)
with open(self.path(".yml"), "w") as f:
f.write(yaml.dump(self.to_dict()))
def write_csv(self):
rows = self.to_row()
base_df = pd.DataFrame.from_dict(rows)[NO_ST_COLUMNS.keys()]
base_df.rename(columns=NO_ST_COLUMNS, inplace=True)
students = pd.read_csv(self.tribe_student_path)["Nom"]
for student in students:
base_df[student] = ""
self.tribe_path.mkdir(exist_ok=True)
base_df.to_csv(self.path(".csv"), index=False)
@property
def score_rate(self):
total = 0
for ex, questions in self._exercises.items():
total += sum([q["score_rate"] for q in questions])
return total
@property
def competences_rate(self):
""" Dictionnary with competences as key and total rate as value"""
rates = {}
for ex, questions in self._exercises.items():
for q in questions:
try:
q["competence"]
except KeyError:
pass
else:
try:
rates[q["competence"]] += q["score_rate"]
except KeyError:
rates[q["competence"]] = q["score_rate"]
return rates
@property
def themes_rate(self):
""" Dictionnary with themes as key and total rate as value"""
rates = {}
for ex, questions in self._exercises.items():
for q in questions:
try:
q["theme"]
except KeyError:
pass
else:
if q["theme"]:
try:
rates[q["theme"]] += q["score_rate"]
except KeyError:
rates[q["theme"]] = q["score_rate"]
return rates

9
recopytex/scripts/getconfig.py
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@ -1,9 +0,0 @@
#!/usr/bin/env python
# encoding: utf-8
import yaml
CONFIGPATH = "recoconfig.yml"
with open(CONFIGPATH, "r") as config:
config = yaml.load(config, Loader=yaml.FullLoader)

233
recopytex/scripts/prompts.py
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@ -1,233 +0,0 @@
#!/usr/bin/env python
# encoding: utf-8
from prompt_toolkit import prompt, HTML, ANSI
from prompt_toolkit import print_formatted_text as print
from prompt_toolkit.styles import Style
from prompt_toolkit.validation import Validator
from prompt_toolkit.completion import WordCompleter
from unidecode import unidecode
from datetime import datetime
from functools import wraps
import sys
from .getconfig import config
VALIDATE = [
"o",
"ok",
"OK",
"oui",
"OUI",
"yes",
"YES",
]
REFUSE = ["n", "non", "NON", "no", "NO"]
CANCEL = ["a", "annuler"]
STYLE = Style.from_dict(
{
"": "#93A1A1",
"validation": "#884444",
"appending": "#448844",
}
)
class CancelError(Exception):
pass
def prompt_validate(question, cancelable=False, empty_means=1, style="validation"):
"""Prompt for validation
:param question: Text to print to ask the question.
:param cancelable: enable cancel answer
:param empty_means: result for no answer
:return:
0 -> Refuse
1 -> Validate
-1 -> cancel
"""
question_ = question
choices = VALIDATE + REFUSE
if cancelable:
question_ += "(a ou annuler pour sortir)"
choices += CANCEL
ans = prompt(
[
(f"class:{style}", question_),
],
completer=WordCompleter(choices),
style=STYLE,
).lower()
if ans == "":
return empty_means
if ans in VALIDATE:
return 1
if cancelable and ans in CANCEL:
return -1
return 0
def prompt_until_validate(question="C'est ok? ", cancelable=False):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwrd):
ans = func(*args, **kwrd)
confirm = prompt_validate(question, cancelable)
if confirm == -1:
raise CancelError
while not confirm:
sys.stdout.flush()
ans = func(*args, **ans, **kwrd)
confirm = prompt_validate(question, cancelable)
if confirm == -1:
raise CancelError
return ans
return wrapper
return decorator
@prompt_until_validate()
def prompt_exam(**kwrd):
""" Prompt questions to edit an exam """
print(HTML("<b>Nouvelle évaluation</b>"))
exam = {}
exam["name"] = prompt("Nom de l'évaluation: ", default=kwrd.get("name", "DS"))
tribes_name = [t["name"] for t in config["tribes"]]
exam["tribename"] = prompt(
"Nom de la classe: ",
default=kwrd.get("tribename", ""),
completer=WordCompleter(tribes_name),
validator=Validator.from_callable(lambda x: x in tribes_name),
)
exam["tribe"] = [t for t in config["tribes"] if t["name"] == exam["tribename"]][0]
exam["date"] = prompt(
"Date de l'évaluation (%y%m%d): ",
default=kwrd.get("date", datetime.today()).strftime("%y%m%d"),
validator=Validator.from_callable(lambda x: (len(x) == 6) and x.isdigit()),
)
exam["date"] = datetime.strptime(exam["date"], "%y%m%d")
exam["term"] = prompt(
"Trimestre: ",
validator=Validator.from_callable(lambda x: x.isdigit()),
default=kwrd.get("term", "1"),
)
return exam
@prompt_until_validate()
def prompt_exercise(number=1, completer={}, **kwrd):
exercise = {}
try:
kwrd["name"]
except KeyError:
print(HTML("<b>Nouvel exercice</b>"))
exercise["name"] = prompt(
"Nom de l'exercice: ", default=kwrd.get("name", f"Exercice {number}")
)
else:
print(HTML(f"<b>Modification de l'exercice: {kwrd['name']}</b>"))
exercise["name"] = kwrd["name"]
exercise["questions"] = []
try:
kwrd["questions"][0]
except KeyError:
last_question_id = "1a"
except IndexError:
last_question_id = "1a"
else:
for ques in kwrd["questions"]:
try:
exercise["questions"].append(
prompt_question(completer=completer, **ques)
)
except CancelError:
print("Cette question a été supprimée")
last_question_id = exercise["questions"][-1]["id"]
appending = prompt_validate(
question="Ajouter un élément de notation? ", style="appending"
)
while appending:
try:
exercise["questions"].append(
prompt_question(last_question_id, completer=completer)
)
except CancelError:
print("Cette question a été supprimée")
else:
last_question_id = exercise["questions"][-1]["id"]
appending = prompt_validate(
question="Ajouter un élément de notation? ", style="appending"
)
return exercise
@prompt_until_validate(cancelable=True)
def prompt_question(last_question_id="1a", completer={}, **kwrd):
try:
kwrd["id"]
except KeyError:
print(HTML("<b>Nouvel élément de notation</b>"))
else:
print(
HTML(f"<b>Modification de l'élément {kwrd['id']} ({kwrd['comment']})</b>")
)
question = {}
question["id"] = prompt(
"Identifiant de la question: ",
default=kwrd.get("id", "1a"),
)
question["competence"] = prompt(
"Competence: ",
default=kwrd.get("competence", list(config["competences"].keys())[0]),
completer=WordCompleter(config["competences"].keys()),
validator=Validator.from_callable(lambda x: x in config["competences"].keys()),
)
question["theme"] = prompt(
"Domaine: ",
default=kwrd.get("theme", ""),
completer=WordCompleter(completer.get("theme", [])),
)
question["comment"] = prompt(
"Commentaire: ",
default=kwrd.get("comment", ""),
)
question["is_leveled"] = prompt(
"Évaluation par niveau: ",
default=kwrd.get("is_leveled", "1"),
# validate
)
question["score_rate"] = prompt(
"Barème: ",
default=kwrd.get("score_rate", "1"),
# validate
)
return question

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recopytex/scripts/recopytex.py
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#!/usr/bin/env python
# encoding: utf-8
import click
from pathlib import Path
import sys
import papermill as pm
import pandas as pd
from datetime import datetime
import yaml
from .getconfig import config, CONFIGPATH
from .prompts import prompt_exam, prompt_exercise, prompt_validate
from ..config import NO_ST_COLUMNS
from .exam import Exam
from ..dashboard.index import app as dash
@click.group()
def cli():
pass
@cli.command()
def print_config():
click.echo(f"Config file is {CONFIGPATH}")
click.echo("It contains")
click.echo(config)
@cli.command()
def setup():
"""Setup the environnement using recoconfig.yml"""
for tribe in config["tribes"]:
Path(tribe["name"]).mkdir(exist_ok=True)
if not Path(tribe["students"]).exists():
print(f"The file {tribe['students']} does not exists")
@cli.command()
def new_exam():
""" Create new exam csv file """
exam = Exam(**prompt_exam())
if exam.path(".yml").exists():
print(f"Fichier sauvegarde trouvé à {exam.path('.yml')} -- importation")
with open(exam.path(".yml"), "r") as f:
for name, questions in yaml.load(f, Loader=yaml.SafeLoader)[
"exercices"
].items():
exam.add_exercise(name, questions)
print(exam.themes)
# print(yaml.dump(exam.to_dict()))
exam.write()
for name, questions in exam.exercices.items():
exam.modify_exercise(
**prompt_exercise(
name=name, completer={"theme": exam.themes}, questions=questions
)
)
exam.write()
new_exercise = prompt_validate("Ajouter un exercice? ")
while new_exercise:
exam.add_exercise(
**prompt_exercise(len(exam.exercices) + 1, completer={"theme": exam.themes})
)
exam.write()
new_exercise = prompt_validate("Ajouter un exercice? ")
rows = exam.to_row()
base_df = pd.DataFrame.from_dict(rows)[NO_ST_COLUMNS.keys()]
base_df.rename(columns=NO_ST_COLUMNS, inplace=True)
students = pd.read_csv(exam.tribe_student_path)["Nom"]
for student in students:
base_df[student] = ""
exam.tribe_path.mkdir(exist_ok=True)
base_df.to_csv(exam.path(".csv"), index=False)
print(f"Le fichier note a été enregistré à {exam.path('.csv')}")
@cli.command()
@click.option("--debug", default=0, help="Debug mode for dash")
def dashboard(debug):
dash.run_server(debug=bool(debug))
@cli.command()
@click.argument("csv_file")
def report(csv_file):
csv = Path(csv_file)
if not csv.exists():
click.echo(f"{csv_file} does not exists")
sys.exit(1)
if csv.suffix != ".csv":
click.echo(f"{csv_file} has to be a csv file")
sys.exit(1)
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 = None
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()),
),
)

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recopytex/store/__init__.py Normal file
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#!/usr/bin/env python
# encoding: utf-8
from abc import ABC, abstractmethod
import yaml
"""
Adapter to pull data from the filesystem
# Loader
# Writer
"""
class Loader(ABC):
"""Load data from source"""
def __init__(self, configfile="recoconfig.yml"):
"""Init loader
:param configfile: yaml file with informations on data source
"""
with open(CONFIGPATH, "r") as config:
sefl._config = yaml.load(config, Loader=yaml.FullLoader)
@abstractmethod
def get_tribes(self):
""" Get tribes list """
pass
@abstractmethod
def get_exams(self, tribes=[]):
"""Get exams list
:param tribes: get only exams for those tribes
"""
pass
@abstractmethod
def get_students(self, tribes=[]):
"""Get student list
:param filters: list of filters
"""
pass
@abstractmethod
def get_exam_questions(self, exams=[]):
"""Get questions for the exam
:param exams: questions for those exams only
"""
pass
@abstractmethod
def get_questions_scores(self, questions=[]):
"""Get scores of those questions
:param questions: score for those questions
"""
pass
@abstractmethod
def get_student_scores(self, student):
"""Get scores of the student
:param student:
"""
pass
class Writer(ABC):
""" Write datas to the source """
@abstractmethod
def __init__(self):
pass

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recopytex/store/filesystem/__init__.py Normal file
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#!/usr/bin/env python
# encoding: utf-8
"""
Store data using filesystem for organisation, csv for scores
## Organisation
- tribe1.csv # list of students for the tribe
- tribe1/
- exam1.csv # questions and scores for exam1
- exam1.yml # Extra information about exam1
- exam2.csv # questions and scores for exam2
"""

33
recopytex/store/filesystem/lib.py Normal file
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#!/usr/bin/env python
# encoding: utf-8
import pandas as pd
from pathlib import Path
__all__ = ["list_csvs", "extract_exam"]
def list_csvs(path):
""" list csv files in path """
return list(Path(path).glob("*.csv"))
def extract_fields(csv_filename, fields=[], remove_duplicates=True):
"""Extract fields in csv
:param csv_filename: csv filename (with header)
:param fields: list of fields to extract (all fields if empty list - default)
:param remove_duplicates: keep uniques rows (default True)
:example:
>>> extract_fields("./example/Tribe1/210122_DS6.csv", ["Trimestre", "Nom", "Date"])
Trimestre Nom Date
0 1 DS6 22/01/2021
"""
df = pd.read_csv(csv_filename)
if fields:
df = df[fields]
if remove_duplicates:
return df.drop_duplicates()
return df

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recopytex/store/filesystem/loader.py Normal file
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#!/usr/bin/env python
# encoding: utf-8
from .. import Loader
import yaml
def list_csvs(path):
""" list csv files in path """
pass
class CSVLoader(Loader):
"""Loader when scores and metadatas are stored in csv files
## configfile (`recoconfig.yml` by default
source: ./ # basepath where to start (default value)
templates: # directory where templates are stored
tribes: # All the tribes (required)
Tribe1: # Tribe name
directory: tribe1 # tribe directory
type: type1 # Type of tribe (2nd, 1, T...)
students: tribe1.csv # csv with infos on students
competences: # Competences (default values)
Chercher:
name: Chercher
abrv: Cher
Représenter:
name: Représenter
abrv: Rep
Modéliser:
name: Modéliser
abrv: Mod
Raisonner:
name: Raisonner
abrv: Rai
Calculer:
name: Calculer
abrv: Cal
Communiquer:
name: Communiquer
abrv: Com
valid_scores: # (default values)
BAD: 0 # Everything is bad
FEW: 1 # Few good things
NEARLY: 2 # Nearly good but things are missing
GOOD: 3 # Everything is good
NOTFILLED: # The item is not scored yet
NOANSWER: . # Student gives no answer (count as 0)
ABS: "a" # Student has absent (this score won't be impact the final mark)
csv_fields: # dataframe_field: csv_field (default values)
term: Trimestre,
exam: Nom,
date: Date,
exercise: Exercice,
question: Question,
competence: Competence,
theme: Domaine,
comment: Commentaire,
score_rate: Bareme,
is_leveled: Est_nivele,
"""
def get_config(self):
""" Get config"""
return self._config
def get_tribes(self, only_names=False):
""" Get tribes list """
if only_names:
return list(self._config["tribes"].keys())
return self._config["tribes"]
def get_exams(self, tribes=[]):
"""Get exams list
:param tribes: get only exams for those tribes
:return: list of dictionaries of exams (fields: `["name", "tribe", "date", "term"])
"""
exams = []
for tribe in tribes:
csvs = list_csvs()
for csv in csvs:
fields = [
self._config["csv_fields"][k] for k in ["exam", "date", "term"]
]
exam = extract_fields(csv, fields)
exam.rename(columns=self._config["csv_fields"], inplace=True).rename(
columns={"exam": "name"}, inplace=True
)
return df.concate(exams)
def get_students(self, tribes=[]):
"""Get student list
:param filters: list of filters
"""
pass
def get_exam_questions(self, exams=[]):
"""Get questions for the exam
:param exams: questions for those exams only
"""
pass
def get_questions_scores(self, questions=[]):
"""Get scores of those questions
:param questions: score for those questions
"""
pass
def get_student_scores(self, student):
"""Get scores of the student
:param student:
"""
pass

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recopytex/store/filesystem/writer.py Normal file
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#!/usr/bin/env python
# encoding: utf-8
"""
"""