Feat: add row to scores_table!!

.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:
+  Calculer:
+    name: Calculer
+    abrv: Cal
+  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/config.py

@@ -2,16 +2,16 @@
 # encoding: utf-8
 
 NO_ST_COLUMNS = {
-    "assessment": "Nom",
     "term": "Trimestre",
+    "assessment": "Nom",
     "date": "Date",
     "exercise": "Exercice",
     "question": "Question",
     "competence": "Competence",
     "theme": "Domaine",
     "comment": "Commentaire",
-    "is_leveled": "Est_nivele",
     "score_rate": "Bareme",
+    "is_leveled": "Est_nivele",
 }
 
 COLUMNS = {

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/exam.py

@@ -0,0 +1,341 @@
+#!/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
+import dash_bootstrap_components as dbc
+
+
+from .. import flat_df_students, pp_q_scores
+from ..config import NO_ST_COLUMNS
+from ..scripts.getconfig import config, CONFIGPATH
+
+COLORS = {
+    ".": "black",
+    0: "#E7472B",
+    1: "#FF712B",
+    2: "#F2EC4C",
+    3: "#68D42F",
+}
+
+app = dash.Dash(external_stylesheets=[dbc.themes.SIMPLEX])
+# external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
+# app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
+# app = dash.Dash(__name__)
+
+app.layout = html.Div(
+    children=[
+        dbc.NavbarSimple(
+            children=[
+                dbc.Alert("Dernière sauvegarde", id="lastsave", color="success"),
+            ],
+            brand="Analyse des notes",
+            brand_href="#",
+            color="success",
+            dark=True,
+        ),
+        html.Br(),
+        dbc.Row(
+            [
+                dbc.Col(
+                    [
+                        "Classe: ",
+                        dbc.Select(
+                            id="tribe",
+                            options=[
+                                {"label": t["name"], "value": t["name"]}
+                                for t in config["tribes"]
+                            ],
+                            value=config["tribes"][0]["name"],
+                        ),
+                    ]
+                ),
+                dbc.Col(
+                    [
+                        "Evaluation: ",
+                        dbc.Select(id="csv"),
+                    ]
+                ),
+            ],
+        ),
+        html.Br(),
+        dbc.Row(
+            [
+                dbc.Col(
+                    dash_table.DataTable(
+                        id="final_score_table",
+                        columns=[
+                            {"id": "Élève", "name": "Élève"},
+                            {"id": "Note", "name": "Note"},
+                            {"id": "Barème", "name": "Bareme"},
+                        ],
+                        data=[],
+                        style_data_conditional=[
+                            {
+                                "if": {"row_index": "odd"},
+                                "backgroundColor": "rgb(248, 248, 248)",
+                            }
+                        ],
+                        style_header={
+                            "backgroundColor": "rgb(230, 230, 230)",
+                            "fontWeight": "bold",
+                        },
+                        style_data={
+                            "width": "100px",
+                            "maxWidth": "100px",
+                            "minWidth": "100px",
+                        },
+                    )
+                ),
+                dbc.Col(
+                    [
+                        dash_table.DataTable(
+                            id="final_score_describe",
+                        ),
+                        dcc.Graph(
+                            id="fig_assessment_hist",
+                        ),
+                        # dcc.Graph(id="fig_competences"),
+                    ]
+                ),
+            ],
+        ),
+        html.Br(),
+        html.Div(
+            [
+                dash_table.DataTable(
+                    id="scores_table",
+                    columns=[{"id": c, "name": c} for c in NO_ST_COLUMNS.values()],
+                    style_cell={
+                        "whiteSpace": "normal",
+                        "height": "auto",
+                    },
+                    style_data_conditional=[],
+                    editable=True,
+                ),
+                dbc.Button("Ajouter un élément", id="btn_add_element"),
+            ]
+        ),
+        dcc.Store(id="final_score"),
+    ]
+)
+
+
+@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
+    try:
+        scores = pd.DataFrame.from_records(data)
+        scores = flat_df_students(scores).dropna(subset=["Score"])
+        scores = pp_q_scores(scores)
+        assessment_scores = scores.groupby(["Eleve"]).agg(
+            {"Note": "sum", "Bareme": "sum"}
+        )
+        return [assessment_scores.reset_index().to_dict("records")]
+    except KeyError:
+        raise PreventUpdate
+
+
+@app.callback(
+    [
+        dash.dependencies.Output("final_score_table", "columns"),
+        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 [
+        {"id": c, "name": c} for c in assessment_scores.columns
+    ], assessment_scores.to_dict("records")
+
+
+@app.callback(
+    [
+        dash.dependencies.Output("final_score_describe", "columns"),
+        dash.dependencies.Output("final_score_describe", "data"),
+    ],
+    [dash.dependencies.Input("final_score", "data")],
+)
+def update_final_scores_descr(data):
+    desc = pd.DataFrame.from_records(data)["Note"].describe()
+    return [{"id": c, "name": c} for c in desc.keys()], [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)
+
+    ranges = np.linspace(
+        0, assessment_scores.Bareme.max(), int(assessment_scores.Bareme.max() * 2 + 1)
+    )
+    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)
+#     scores = flat_df_students(scores).dropna(subset=["Score"])
+#     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)
+#         + ":"
+#         + pt.index.get_level_values(1)
+#         + " "
+#         + pt.index.get_level_values(2)
+#     )
+#
+#     fig = go.Figure()
+#     bars = [
+#         {"score": -1, "name": "Pas de réponse", "color": COLORS["."]},
+#         {"score": 0, "name": "Faut", "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")
+#     return [fig]
+
+
+@app.callback(
+    [
+        dash.dependencies.Output("lastsave", "children"),
+        dash.dependencies.Output("lastsave", "color"),
+    ],
+    [
+        dash.dependencies.Input("scores_table", "data"),
+        dash.dependencies.State("csv", "value"),
+    ],
+)
+def save_scores(data, csv):
+    try:
+        scores = pd.DataFrame.from_records(data)
+        scores.to_csv(csv, index=False)
+    except:
+        return [f"Soucis pour sauvegarder à  {datetime.today()} dans {csv}"], "warning"
+    else:
+        return [f"Dernière sauvegarde {datetime.today()} dans {csv}"], "success"
+
+
+def highlight_value(df):
+    """ Cells style  """
+    hight = []
+    for v, color in COLORS.items():
+        hight += [
+            {
+                "if": {"filter_query": "{{{}}} = {}".format(col, v), "column_id": col},
+                "backgroundColor": color,
+                "color": "white",
+            }
+            for col in df.columns
+            if col not in NO_ST_COLUMNS.values()
+        ]
+    return hight
+
+
+@app.callback(
+    [
+        dash.dependencies.Output("scores_table", "columns"),
+        dash.dependencies.Output("scores_table", "data"),
+        dash.dependencies.Output("scores_table", "style_data_conditional"),
+    ],
+    [
+        dash.dependencies.Input("csv", "value"),
+        dash.dependencies.Input("btn_add_element", "n_clicks"),
+        dash.dependencies.State("scores_table", "data"),
+    ],
+)
+def update_scores_table(csv, add_element, data):
+    ctx = dash.callback_context
+    if ctx.triggered[0]['prop_id'] ==  "csv.value":
+        stack = pd.read_csv(csv, encoding="UTF8")
+    elif ctx.triggered[0]['prop_id'] ==  "btn_add_element.n_clicks":
+        stack = pd.DataFrame.from_records(data)
+        infos = pd.DataFrame.from_records([{k: stack.iloc[-1][k] for k in NO_ST_COLUMNS.values()}])
+        stack = stack.append(infos)
+    return (
+        [{"id": c, "name": c} for c in stack.columns],
+        stack.to_dict("records"),
+        highlight_value(stack),
+    )
+

recopytex/df_marks_manip.py

@@ -6,7 +6,9 @@ import numpy as np
 from math import ceil, floor
 from .config import COLUMNS, VALIDSCORE
 
-# 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,
@@ -43,9 +46,10 @@ 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)
+            )
+        return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3)
 
     if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]:
         raise ValueError(
@@ -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/config.py

@@ -1,10 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
-import yaml
-
-CONFIGPATH = "recoconfig.yml"
-
-with open(CONFIGPATH, "r") as configfile:
-    config = yaml.load(configfile, Loader=yaml.FullLoader)
-

recopytex/scripts/exam.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+# encoding: utf-8
+
+from datetime import datetime
+from pathlib import Path
+from prompt_toolkit import HTML
+import yaml
+from .getconfig import config
+
+
+class Exam:
+    def __init__(self, name, tribename, date, term, **kwrds):
+        self._name = name
+        self._tribename = tribename
+        try:
+            self._date = datetime.strptime(date, "%y%m%d")
+        except:
+            self._date = date
+
+        self._term = term
+
+        self._exercises = {}
+
+    @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] = 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] += questions
+            else:
+                self._exercises[name] = 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(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()))

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/prepare_csv.py

@@ -1,160 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
-import click
-from pathlib import Path
-from datetime import datetime
-from PyInquirer import prompt, print_json
-import pandas as pd
-import numpy as np
-
-from .config import config
-from ..config import NO_ST_COLUMNS
-
-
-class PromptAbortException(EOFError):
-    def __init__(self, message, errors=None):
-
-        # Call the base class constructor with the parameters it needs
-        super(PromptAbortException, self).__init__("Abort questionnary", errors)
-
-
-def get_tribes(answers):
-    """ List tribes based on subdirectory of config["source"] which have an "eleves.csv" file inside """
-    return [
-        p.name for p in Path(config["source"]).iterdir() if (p / "eleves.csv").exists()
-    ]
-
-
-def prepare_csv():
-    items = new_eval()
-
-    item = items[0]
-    # item = {"tribe": "308", "date": datetime.today(), "assessment": "plop"}
-    csv_output = (
-        Path(config["source"])
-        / item["tribe"]
-        / f"{item['date']:%y%m%d}_{item['assessment']}.csv"
-    )
-
-    students = pd.read_csv(Path(config["source"]) / item["tribe"] / "eleves.csv")["Nom"]
-
-    columns = list(NO_ST_COLUMNS.keys())
-    items = [[it[c] for c in columns] for it in items]
-    columns = list(NO_ST_COLUMNS.values())
-    items_df = pd.DataFrame.from_records(items, columns=columns)
-    for s in students:
-        items_df[s] = np.nan
-
-    items_df.to_csv(csv_output, index=False, date_format="%d/%m/%Y")
-    click.echo(f"Saving csv file to {csv_output}")
-
-
-def new_eval(answers={}):
-    click.echo(f"Préparation d'un nouveau devoir")
-
-    eval_questions = [
-        {"type": "input", "name": "assessment", "message": "Nom de l'évaluation",},
-        {
-            "type": "list",
-            "name": "tribe",
-            "message": "Classe concernée",
-            "choices": get_tribes,
-        },
-        {
-            "type": "input",
-            "name": "date",
-            "message": "Date du devoir (%y%m%d)",
-            "default": datetime.today().strftime("%y%m%d"),
-            "filter": lambda val: datetime.strptime(val, "%y%m%d"),
-        },
-        {
-            "type": "list",
-            "name": "term",
-            "message": "Trimestre",
-            "choices": ["1", "2", "3"],
-        },
-    ]
-
-    eval_ans = prompt(eval_questions)
-
-    items = []
-    add_exo = True
-    while add_exo:
-        ex_items = new_exercice(eval_ans)
-        items += ex_items
-        add_exo = prompt(
-            [
-                {
-                    "type": "confirm",
-                    "name": "add_exo",
-                    "message": "Ajouter un autre exercice",
-                    "default": True,
-                }
-            ]
-        )["add_exo"]
-    return items
-
-
-def new_exercice(answers={}):
-    exercise_questions = [
-        {"type": "input", "name": "exercise", "message": "Nom de l'exercice"},
-    ]
-
-    click.echo(f"Nouvel exercice")
-    exercise_ans = prompt(exercise_questions, answers=answers)
-
-    items = []
-
-    add_item = True
-    while add_item:
-        try:
-            item_ans = new_item(exercise_ans)
-        except PromptAbortException:
-            click.echo("Création de l'item annulée")
-        else:
-            items.append(item_ans)
-        add_item = prompt(
-            [
-                {
-                    "type": "confirm",
-                    "name": "add_item",
-                    "message": f"Ajouter un autre item pour l'exercice {exercise_ans['exercise']}",
-                    "default": True,
-                }
-            ]
-        )["add_item"]
-
-    return items
-
-
-def new_item(answers={}):
-    item_questions = [
-        {"type": "input", "name": "question", "message": "Nom de l'item",},
-        {"type": "input", "name": "comment", "message": "Commentaire",},
-        {
-            "type": "list",
-            "name": "competence",
-            "message": "Competence",
-            "choices": ["Cher", "Rep", "Mod", "Rai", "Cal", "Com"],
-        },
-        {"type": "input", "name": "theme", "message": "Domaine",},
-        {
-            "type": "confirm",
-            "name": "is_leveled",
-            "message": "Évaluation par niveau",
-            "default": True,
-        },
-        {"type": "input", "name": "score_rate", "message": "Bareme"},
-        {
-            "type": "confirm",
-            "name": "correct",
-            "message": "Tout est correct?",
-            "default": True,
-        },
-    ]
-    click.echo(f"Nouvelle question pour l'exercice {answers['exercise']}")
-    item_ans = prompt(item_questions, answers=answers)
-    if item_ans["correct"]:
-        return item_ans
-    raise PromptAbortException("Abort item creation")

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 +3,17 @@
 
 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 .prompts import prompt_exam, prompt_exercise, prompt_validate
+from ..config import NO_ST_COLUMNS
+from .exam import Exam
+from ..dashboard.exam import app as exam_app
 
 
 @click.group()
@@ -24,8 +28,81 @@ 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()
+def new_exam():
+    """ Create new exam csv file """
+    exam = Exam(**prompt_exam())
+
+    if exam.path(".yml").exists():
+        print(f"Fichier sauvegarde trouvé à {exam.path('.yml')} -- importation")
+        with open(exam.path(".yml"), "r") as f:
+            for name, questions in yaml.load(f, Loader=yaml.SafeLoader)[
+                "exercices"
+            ].items():
+                exam.add_exercise(name, questions)
+
+        print(exam.themes)
+        # print(yaml.dump(exam.to_dict()))
+
+    exam.write()
+
+    for name, questions in exam.exercices.items():
+        exam.modify_exercise(
+            **prompt_exercise(
+                name=name, completer={"theme": exam.themes}, questions=questions
+            )
+        )
+        exam.write()
+
+    new_exercise = prompt_validate("Ajouter un exercice? ")
+    while new_exercise:
+        exam.add_exercise(
+            **prompt_exercise(len(exam.exercices) + 1, completer={"theme": exam.themes})
+        )
+        exam.write()
+        new_exercise = prompt_validate("Ajouter un exercice? ")
+
+    rows = exam.to_row()
+
+    base_df = pd.DataFrame.from_dict(rows)[NO_ST_COLUMNS.keys()]
+    base_df.rename(columns=NO_ST_COLUMNS, inplace=True)
+
+    students = pd.read_csv(exam.tribe_student_path)["Nom"]
+    for student in students:
+        base_df[student] = ""
+
+    exam.tribe_path.mkdir(exist_ok=True)
+
+    base_df.to_csv(exam.path(".csv"), index=False)
+    print(f"Le fichier note a été enregistré à {exam.path('.csv')}")
+
+
+@cli.command()
+def exam_analysis():
+    exam_app.run_server(debug=True)
+
+
+@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]
 
@@ -34,7 +111,7 @@ def reporting(csv_file):
     try:
         date = datetime.strptime(date, "%y%m%d")
     except ValueError:
-        date = datetime.today().strptime(date, "%y%m%d")
+        date = None
 
     tribe = str(tribe_dir).split("/")[-1]
 
@@ -54,49 +131,3 @@ 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

@@ -1,76 +1,4 @@
-ansiwrap==0.8.4
+pandas
-appdirs==1.4.3
+click
-attrs==19.1.0
+papermill
-backcall==0.1.0
+prompt_toolkit
-black==19.10b0
-bleach==3.1.0
-certifi==2019.6.16
-chardet==3.0.4
-Click==7.0
-colorama==0.4.1
-cycler==0.10.0
-decorator==4.4.0
-defusedxml==0.6.0
-entrypoints==0.3
-future==0.17.1
-idna==2.8
-importlib-resources==1.0.2
-ipykernel==5.1.3
-ipython==7.11.1
-ipython-genutils==0.2.0
-ipywidgets==7.5.1
-jedi==0.15.2
-Jinja2==2.10.3
-jsonschema==3.2.0
-jupyter==1.0.0
-jupyter-client==5.3.4
-jupyter-console==6.1.0
-jupyter-core==4.6.1
-jupytex==0.0.3
-kiwisolver==1.1.0
-Markdown==3.1.1
-MarkupSafe==1.1.1
-matplotlib==3.1.2
-mistune==0.8.4
-nbconvert==5.6.1
-nbformat==5.0.3
-notebook==6.0.3
-numpy==1.18.1
-pandas==0.25.3
-pandocfilters==1.4.2
-papermill==1.2.1
-parso==0.5.2
-pathspec==0.7.0
-pexpect==4.8.0
-pickleshare==0.7.5
-prometheus-client==0.7.1
-prompt-toolkit==1.0.14
-ptyprocess==0.6.0
-Pygments==2.5.2
-PyInquirer==1.0.3
-pyparsing==2.4.6
-pyrsistent==0.15.7
-python-dateutil==2.8.0
-pytz==2019.3
-PyYAML==5.3
-pyzmq==18.1.1
-qtconsole==4.6.0
--e git+git_opytex:/lafrite/recopytex.git@7e026bedb24c1ca8bef3b71b3d63f8b0d6916e81#egg=Recopytex
-regex==2020.1.8
-requests==2.22.0
-scipy==1.4.1
-Send2Trash==1.5.0
-six==1.12.0
-tenacity==6.0.0
-terminado==0.8.3
-testpath==0.4.4
-textwrap3==0.9.2
-toml==0.10.0
-tornado==6.0.3
-tqdm==4.41.1
-traitlets==4.3.2
-typed-ast==1.4.1
-urllib3==1.25.8
-wcwidth==0.1.8
-webencodings==0.5.1
-widgetsnbextension==3.5.1

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

@@ -5,7 +5,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='Recopytex',
-    version='1.1.1',
+    version='0.1',
     description='Assessment analysis',
     author='Benjamin Bertrand',
     author_email='',
@@ -13,11 +13,6 @@ setup(
     include_package_data=True,
     install_requires=[
         'Click',
-        'pandas',
-        'numpy',
-        'papermill',
-        'pyyaml',
-        'PyInquirer',
     ],
     entry_points='''
             [console_scripts]