Feat: add PyInquirer to setup

.gitignore

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

README.md

@@ -6,29 +6,3 @@ 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

@@ -1,32 +0,0 @@
----
-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

@@ -1,21 +0,0 @@
-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

@@ -1,21 +0,0 @@
-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 = {
-    "term": "Trimestre",
     "assessment": "Nom",
+    "term": "Trimestre",
     "date": "Date",
     "exercise": "Exercice",
     "question": "Question",
     "competence": "Competence",
     "theme": "Domaine",
     "comment": "Commentaire",
-    "score_rate": "Bareme",
     "is_leveled": "Est_nivele",
+    "score_rate": "Bareme",
 }
 
 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/df_marks_manip.py

@@ -6,9 +6,7 @@ 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):
@@ -21,13 +19,12 @@ 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,
@@ -46,10 +43,9 @@ def score_to_mark(x):
 
     if x[COLUMNS["is_leveled"]]:
         if x[COLUMNS["score"]] not in [0, 1, 2, 3]:
-            raise ValueError(
+            raise ValueError(f"The evaluation is out of range: {x[COLUMNS['score']]} at {x}")
-                f"The evaluation is out of range: {x[COLUMNS['score']]} at {x}"
+        #return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3)
-            )
+        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(
@@ -59,10 +55,9 @@ 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,
@@ -97,9 +92,7 @@ def score_to_level(x):
 
 
 def compute_mark(df):
-    """Compute the mark for the dataframe
+    """ Add Mark column to df
-
-    apply score_to_mark to each row
 
     :param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
 
@@ -130,12 +123,9 @@ def compute_mark(df):
 
 
 def compute_level(df):
-    """Compute level for the dataframe
+    """ Add Mark column to df
-
-    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,
@@ -164,10 +154,9 @@ 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,
@@ -198,9 +187,7 @@ 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

@@ -0,0 +1,10 @@
+#!/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

@@ -1,132 +0,0 @@
-#!/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/exam_dash.py

@@ -1,299 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
-import dash
-import dash_html_components as html
-import dash_core_components as dcc
-import dash_table
-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 .getconfig import config, CONFIGPATH
-
-COLORS = {
-    ".": "black",
-    0: "#E7472B",
-    1: "#FF712B",
-    2: "#F2EC4C",
-    3: "#68D42F",
-}
-
-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=[
-        html.H1("Analyse des notes"),
-        html.Div(
-            [
-                "Classe: ",
-                dcc.Dropdown(
-                    id="tribe",
-                    options=[
-                        {"label": t["name"], "value": t["name"]}
-                        for t in config["tribes"]
-                    ],
-                    value=config["tribes"][0]["name"],
-                ),
-                "Evaluation: ",
-                dcc.Dropdown(id="csv"),
-            ],
-            style={"columnCount": 2},
-        ),
-        html.Div(
-            [
-                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",
-                    },
-                ),
-                html.Div(
-                    [
-                        dash_table.DataTable(
-                            id="final_score_describe",
-                        ),
-                        dcc.Graph(id="fig_assessment_hist"),
-                        dcc.Graph(id="fig_competences"),
-                    ]
-                ),
-            ],
-            style={"columnCount": 2},
-        ),
-        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,
-                )
-            ]
-        ),
-        html.P(id="lastsave"),
-        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",
-    )
-    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.Input("scores_table", "data"),
-        dash.dependencies.State("csv", "value"),
-    ],
-)
-def save_scores(data, csv):
-    scores = pd.DataFrame.from_records(data)
-    print(f"save at {csv} ({datetime.today()})")
-    scores.to_csv(csv, index=False)
-    return [datetime.today()]
-
-
-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")],
-)
-def update_scores_table(value):
-    if not value:
-        raise PreventUpdate
-    stack = pd.read_csv(value, encoding="UTF8")
-    # try:
-    #     stack = stack.drop(columns=["Nom", "Trimestre", "Date", "Competence", "Domaine", "Est_nivele", "Bareme"])
-    # except KeyError:
-    #     stack = stack
-    return (
-        [{"id": c, "name": c} for c in stack.columns],
-        stack.to_dict("records"),
-        highlight_value(stack),
-    )

recopytex/scripts/getconfig.py

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

recopytex/scripts/prepare_csv.py

@@ -0,0 +1,160 @@
+#!/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

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

recopytex/scripts/recopytex.py

@@ -3,17 +3,13 @@
 
 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 .getconfig import config, CONFIGPATH
+from .prepare_csv import prepare_csv
-from .prompts import prompt_exam, prompt_exercise, prompt_validate
+from .config import config
-from ..config import NO_ST_COLUMNS
-from .exam import Exam
-from .exam_dash import app as exam_app
 
 
 @click.group()
@@ -28,79 +24,8 @@ def print_config():
     click.echo(config)
 
 
-@cli.command()
+def reporting(csv_file):
-def setup():
+    # csv_file = Path(csv_file)
-    """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]
 
@@ -109,7 +34,7 @@ def report(csv_file):
     try:
         date = datetime.strptime(date, "%y%m%d")
     except ValueError:
-        date = None
+        date = datetime.today().strptime(date, "%y%m%d")
 
     tribe = str(tribe_dir).split("/")[-1]
 
@@ -129,3 +54,49 @@ def report(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

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

requirements_dev.txt

@@ -1,69 +0,0 @@
-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='0.1',
+    version='1.1.1',
     description='Assessment analysis',
     author='Benjamin Bertrand',
     author_email='',
@@ -13,6 +13,11 @@ setup(
     include_package_data=True,
     install_requires=[
         'Click',
+        'pandas',
+        'numpy',
+        'papermill',
+        'pyyaml',
+        'PyInquirer',
     ],
     entry_points='''
             [console_scripts]