Fix: save csv with nice column order

.gitignore

@@ -122,3 +122,7 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# vim
+.vim
+

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)
+

example/recoconfig.yml

@@ -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

example/tribe1.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

example/tribe2.csv

@@ -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

recopytex/csv_extraction.py

@@ -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

recopytex/dashboard/app.py

@@ -0,0 +1,5 @@
+import dash
+
+app = dash.Dash(__name__, suppress_callback_exceptions=True)
+# app = dash.Dash(__name__)
+server = app.server

recopytex/dashboard/assets/style.css

@@ -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;
+}
+
+

recopytex/dashboard/create_exam/app.py

@@ -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}"]

recopytex/dashboard/exam_analysis/app.py

@@ -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),
+    )

recopytex/dashboard/index.py

@@ -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)

recopytex/dashboard/student_analysis/app.py

@@ -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)]
+

recopytex/df_marks_manip.py

@@ -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}")
+            raise ValueError(
-        #return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3)
+                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

recopytex/scripts/exam.py

@@ -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

recopytex/scripts/getconfig.py

@@ -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)
+

recopytex/scripts/prompts.py

@@ -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

recopytex/scripts/recopytex.py

@@ -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 .getconfig import config, CONFIGPATH
-from .config import config
+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):
+@cli.command()
-    # csv_file = Path(csv_file)
+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

requirements.txt

@@ -1,3 +1,4 @@
+prompt_toolkit
 ansiwrap==0.8.4
 appdirs==1.4.3
 attrs==19.1.0

requirements_dev.txt

@@ -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

setup.py

@@ -17,7 +17,6 @@ setup(
         'numpy',
         'papermill',
         'pyyaml',
-        'PyInquirer',
     ],
     entry_points='''
             [console_scripts]