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|
@ -122,3 +122,7 @@ dmypy.json
|
|||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# vim
|
||||
.vim
|
||||
|
||||
|
|
26
README.md
26
README.md
|
@ -6,3 +6,29 @@ Cette fois ci, on utilise:
|
|||
- Des fichiers yaml pour les infos sur les élèves
|
||||
- Des notebooks pour l'analyse
|
||||
- Papermill pour produire les notesbooks à partir de template
|
||||
|
||||
## Les fichiers CSV
|
||||
|
||||
les paramètres sont décris dans ./recopytex/config.py
|
||||
|
||||
### Descriptions des questions
|
||||
|
||||
- Trimestre
|
||||
- Nom
|
||||
- Date
|
||||
- Exercice
|
||||
- Question
|
||||
- Competence
|
||||
- Domaine
|
||||
- Commentaire
|
||||
- Bareme
|
||||
- Est_nivele
|
||||
|
||||
|
||||
### Valeurs pour notes les élèves
|
||||
|
||||
- Score: 0, 1, 2, 3
|
||||
- Pas de réponses: .
|
||||
- Absent: a
|
||||
- Dispensé: (vide)
|
||||
|
||||
|
|
|
@ -0,0 +1,32 @@
|
|||
---
|
||||
source: ./
|
||||
output: ./
|
||||
templates: templates/
|
||||
|
||||
competences:
|
||||
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
|
||||
|
||||
|
||||
tribes:
|
||||
- name: Tribe1
|
||||
type: Type1
|
||||
students: tribe1.csv
|
||||
- name: Tribe2
|
||||
students: tribe2.csv
|
|
@ -0,0 +1,21 @@
|
|||
Nom,email
|
||||
Star Tice,stice0@jalbum.net
|
||||
Umberto Dingate,udingate1@tumblr.com
|
||||
Starlin Crangle,scrangle2@wufoo.com
|
||||
Humbert Bourcq,hbourcq3@g.co
|
||||
Gabriella Handyside,ghandyside4@patch.com
|
||||
Stewart Eaves,seaves5@ycombinator.com
|
||||
Erick Going,egoing6@va.gov
|
||||
Ase Praton,apraton7@va.gov
|
||||
Rollins Planks,rplanks8@delicious.com
|
||||
Dunstan Sarjant,dsarjant9@naver.com
|
||||
Stacy Guiton,sguitona@themeforest.net
|
||||
Ange Stanes,astanesb@marriott.com
|
||||
Amabelle Elleton,aelletonc@squidoo.com
|
||||
Darn Broomhall,dbroomhalld@cisco.com
|
||||
Dyan Chatto,dchattoe@npr.org
|
||||
Keane Rennebach,krennebachf@dot.gov
|
||||
Nari Paulton,npaultong@gov.uk
|
||||
Brandy Wase,bwaseh@ftc.gov
|
||||
Jaclyn Firidolfi,jfiridolfii@reuters.com
|
||||
Violette Lockney,vlockneyj@chron.com
|
|
|
@ -0,0 +1,21 @@
|
|||
Nom,email
|
||||
Elle McKintosh,emckintosh0@1und1.de
|
||||
Ty Megany,tmegany1@reuters.com
|
||||
Pippa Borrows,pborrows2@a8.net
|
||||
Sonny Eskrick,seskrick3@123-reg.co.uk
|
||||
Mollee Britch,mbritch4@usda.gov
|
||||
Ingram Plaistowe,iplaistowe5@purevolume.com
|
||||
Fay Vanyard,fvanyard6@sbwire.com
|
||||
Nancy Rase,nrase7@omniture.com
|
||||
Rachael Ruxton,rruxton8@bravesites.com
|
||||
Tallie Rushmer,trushmer9@home.pl
|
||||
Seward MacIlhagga,smacilhaggaa@hatena.ne.jp
|
||||
Lizette Searl,lsearlb@list-manage.com
|
||||
Talya Mannagh,tmannaghc@webnode.com
|
||||
Jordan Witherbed,jwitherbedd@unesco.org
|
||||
Reagan Botcherby,rbotcherbye@scientificamerican.com
|
||||
Libbie Shoulder,lshoulderf@desdev.cn
|
||||
Abner Khomich,akhomichg@youtube.com
|
||||
Zollie Kitman,zkitmanh@forbes.com
|
||||
Fiorenze Durden,fdurdeni@feedburner.com
|
||||
Kevyn Race,kracej@seattletimes.com
|
|
|
@ -17,7 +17,7 @@ def try_replace(x, old, new):
|
|||
|
||||
|
||||
def extract_students(df, no_student_columns=NO_ST_COLUMNS.values()):
|
||||
""" Extract the list of students from df
|
||||
"""Extract the list of students from df
|
||||
|
||||
:param df: the dataframe
|
||||
:param no_student_columns: columns that are not students
|
||||
|
@ -30,7 +30,7 @@ def extract_students(df, no_student_columns=NO_ST_COLUMNS.values()):
|
|||
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
|
||||
"""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
|
||||
|
@ -63,7 +63,7 @@ def flat_df_students(
|
|||
def flat_df_for(
|
||||
df, student, no_student_columns=NO_ST_COLUMNS.values(), postprocessing=True
|
||||
):
|
||||
""" Extract the data only for one student
|
||||
"""Extract the data only for one student
|
||||
|
||||
:param df: the dataframe (one row per questions)
|
||||
:param no_student_columns: columns that are not students
|
||||
|
@ -88,7 +88,7 @@ def flat_df_for(
|
|||
|
||||
|
||||
def postprocess(df):
|
||||
""" Postprocessing score dataframe
|
||||
"""Postprocessing score dataframe
|
||||
|
||||
- Replace na with an empty string
|
||||
- Replace "NOANSWER" with -1
|
||||
|
|
|
@ -0,0 +1,5 @@
|
|||
import dash
|
||||
|
||||
app = dash.Dash(__name__, suppress_callback_exceptions=True)
|
||||
# app = dash.Dash(__name__)
|
||||
server = app.server
|
|
@ -0,0 +1,66 @@
|
|||
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;
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,355 @@
|
|||
#!/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}"]
|
|
@ -0,0 +1,406 @@
|
|||
#!/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" or scores.iloc[0].str.contains("PPRE").any():
|
||||
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" or scores.iloc[0].str.contains("PPRE").any():
|
||||
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 = scores_table_column_order(scores)
|
||||
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
|
||||
|
||||
def scores_table_column_order(df):
|
||||
df_student_columns = [c for c in df.columns if c not in NO_ST_COLUMNS.values()]
|
||||
order = list(NO_ST_COLUMNS.values())+df_student_columns
|
||||
return df.loc[:, order]
|
||||
|
||||
|
||||
@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)
|
||||
stack = scores_table_column_order(stack)
|
||||
return (
|
||||
[
|
||||
{"id": c, "name": c}
|
||||
for c in stack.columns
|
||||
if c not in ["Trimestre", "Nom", "Date"]
|
||||
],
|
||||
stack.to_dict("records"),
|
||||
highlight_value(stack),
|
||||
)
|
|
@ -0,0 +1,29 @@
|
|||
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)
|
|
@ -0,0 +1,300 @@
|
|||
#!/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:
|
||||
if scores.iloc[0]["Commentaire"] == "commentaire" or scores.iloc[0].str.contains("PPRE").any():
|
||||
scores.drop([0], inplace=True)
|
||||
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)]
|
||||
|
|
@ -4,9 +4,11 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from math import ceil, floor
|
||||
from .config import COLUMNS, VALIDSCORE
|
||||
from .config import COLUMNS
|
||||
|
||||
# Values manipulations
|
||||
"""
|
||||
Functions for manipulate score dataframes
|
||||
"""
|
||||
|
||||
|
||||
def round_half_point(val):
|
||||
|
@ -19,12 +21,13 @@ def round_half_point(val):
|
|||
|
||||
|
||||
def score_to_mark(x):
|
||||
""" Compute the mark
|
||||
"""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,
|
||||
|
@ -43,8 +46,9 @@ def score_to_mark(x):
|
|||
|
||||
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_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 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)
|
||||
|
||||
if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]:
|
||||
|
@ -55,9 +59,10 @@ def score_to_mark(x):
|
|||
|
||||
|
||||
def score_to_level(x):
|
||||
""" Compute the level (".",0,1,2,3).
|
||||
"""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,
|
||||
|
@ -92,7 +97,9 @@ def score_to_level(x):
|
|||
|
||||
|
||||
def compute_mark(df):
|
||||
""" Add Mark column to 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.
|
||||
|
||||
|
@ -123,9 +130,12 @@ def compute_mark(df):
|
|||
|
||||
|
||||
def compute_level(df):
|
||||
""" Add Mark column to 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,
|
||||
|
@ -154,9 +164,10 @@ def compute_level(df):
|
|||
|
||||
|
||||
def compute_normalized(df):
|
||||
""" Compute the normalized mark (Mark / score_rate)
|
||||
"""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,
|
||||
|
@ -187,7 +198,9 @@ def compute_normalized(df):
|
|||
|
||||
|
||||
def pp_q_scores(df):
|
||||
""" Postprocessing questions scores dataframe
|
||||
"""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
|
||||
|
|
|
@ -0,0 +1,211 @@
|
|||
#!/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(question) for question in 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()
|
||||
|
||||
print(rows)
|
||||
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
|
|
@ -0,0 +1,9 @@
|
|||
#!/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)
|
||||
|
|
@ -0,0 +1,233 @@
|
|||
#!/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
|
|
@ -3,13 +3,16 @@
|
|||
|
||||
import click
|
||||
from pathlib import Path
|
||||
import yaml
|
||||
import sys
|
||||
import papermill as pm
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
import yaml
|
||||
|
||||
from .prepare_csv import prepare_csv
|
||||
from .config import config
|
||||
from .getconfig import config, CONFIGPATH
|
||||
from ..config import NO_ST_COLUMNS
|
||||
from .exam import Exam
|
||||
from ..dashboard.index import app as dash
|
||||
|
||||
|
||||
@click.group()
|
||||
|
@ -24,8 +27,33 @@ def print_config():
|
|||
click.echo(config)
|
||||
|
||||
|
||||
def reporting(csv_file):
|
||||
# csv_file = Path(csv_file)
|
||||
@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()
|
||||
@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]
|
||||
|
||||
|
@ -54,49 +82,3 @@ def reporting(csv_file):
|
|||
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
|
||||
|
|
|
@ -1,3 +1,4 @@
|
|||
prompt_toolkit
|
||||
ansiwrap==0.8.4
|
||||
appdirs==1.4.3
|
||||
attrs==19.1.0
|
||||
|
|
|
@ -0,0 +1,69 @@
|
|||
ansiwrap
|
||||
attrs
|
||||
backcall
|
||||
bleach
|
||||
certifi
|
||||
chardet
|
||||
Click
|
||||
colorama
|
||||
cycler
|
||||
decorator
|
||||
defusedxml
|
||||
entrypoints
|
||||
future
|
||||
idna
|
||||
importlib-resources
|
||||
ipykernel
|
||||
ipython
|
||||
ipython-genutils
|
||||
ipywidgets
|
||||
jedi
|
||||
Jinja2
|
||||
jsonschema
|
||||
jupyter
|
||||
jupyter-client
|
||||
jupyter-console
|
||||
jupyter-core
|
||||
jupytex
|
||||
kiwisolver
|
||||
MarkupSafe
|
||||
matplotlib
|
||||
mistune
|
||||
nbconvert
|
||||
nbformat
|
||||
notebook
|
||||
numpy
|
||||
pandas
|
||||
pandocfilters
|
||||
papermill
|
||||
parso
|
||||
pexpect
|
||||
pickleshare
|
||||
prometheus-client
|
||||
prompt-toolkit
|
||||
ptyprocess
|
||||
Pygments
|
||||
pyparsing
|
||||
pyrsistent
|
||||
python-dateutil
|
||||
pytz
|
||||
PyYAML
|
||||
pyzmq
|
||||
qtconsole
|
||||
-e git+git_opytex:/lafrite/recopytex.git@e9a8310f151ead60434ae944d726a2fd22b23d06#egg=Recopytex
|
||||
requests
|
||||
scipy
|
||||
seaborn
|
||||
Send2Trash
|
||||
six
|
||||
tenacity
|
||||
terminado
|
||||
testpath
|
||||
textwrap3
|
||||
tornado
|
||||
tqdm
|
||||
traitlets
|
||||
urllib3
|
||||
wcwidth
|
||||
webencodings
|
||||
widgetsnbextension
|
Loading…
Reference in New Issue