Feat: import html for 404 layout

see https://dash.plotly.com/urls

example/Tribe1/210112_DS.csv

@@ -1,5 +1,5 @@
 Trimestre,Nom,Date,Exercice,Question,Competence,Domaine,Commentaire,Bareme,Est_nivele,Star Tice,Umberto Dingate,Starlin Crangle,Humbert Bourcq,Gabriella Handyside,Stewart Eaves,Erick Going,Ase Praton,Rollins Planks,Dunstan Sarjant,Stacy Guiton,Ange Stanes,Amabelle Elleton,Darn Broomhall,Dyan Chatto,Keane Rennebach,Nari Paulton,Brandy Wase,Jaclyn Firidolfi,Violette Lockney
-1,DS,12/01/2021,Exercice 1,1,Calculer,Plop,Coucou,1,1,,,1.0,0,1.0,2.0,3.0,0.0,3.0,3.0,2.0,,1.0,,,,,,,
-1,DS,12/01/2021,Exercice 1,2,Calculer,C'est trop chouette!,Coucou,1,1,,,1.0,2,,,3.0,3.0,,,,,2.0,,,,,,,
-1,DS,12/01/2021,Exercice 1,3,Calculer,Null,Coucou,1,1,,,,3,2.0,,,,,,,,3.0,,,,,,,
-1,DS,12/01/2021,Exercice 1,3,Calculer,Nié,DChic,1,1,,,,2,,,,,,,,,,,,,,,,
+1,DS,12/01/2021,Exercice 1,1,Calculer,Plop,Coucou,1,1,,,1,0,1,2,3,0,3,3,2,,1,,,,,,,
+1,DS,12/01/2021,Exercice 1,2,Calculer,C'est trop chouette!,Coucou,1,1,,,1,2,,,3,3,,,,,2,,,,,,,
+1,DS,12/01/2021,Exercice 1,3,Calculer,Null,Coucou,1,1,,,,3,2,,,,,,,,3,,,,,,,
+1,DS,12/01/2021,Exercice 1,3,Calculer,Nié,DChic,1,1,,,,2,.,,,,,,,,,,,,,,,

recopytex/dashboard/pages/exams_scores/app.py

@@ -7,62 +7,106 @@ from .models import get_tribes, get_exams
 from .callbacks import *
 
 layout = html.Div(
-        children=[
-            html.Header(
-                children=[
-                    html.H1("Analyse des notes"),
-                    html.P("Dernière sauvegarde", id="lastsave"),
+    children=[
+        html.Header(
+            children=[
+                html.H1("Analyse des notes"),
+                html.P("Dernière sauvegarde", id="lastsave"),
+            ],
+        ),
+        html.Main(
+            children=[
+                html.Section(
+                    children=[
+                        html.Div(
+                            children=[
+                                "Classe: ",
+                                dcc.Dropdown(
+                                    id="tribe",
+                                    options=[
+                                        {"label": t["name"], "value": t["name"]}
+                                        for t in get_tribes().values()
+                                    ],
+                                    value=next(iter(get_tribes().values()))["name"],
+                                ),
+                            ],
+                        ),
+                        html.Div(
+                            children=[
+                                "Evaluation: ",
+                                dcc.Dropdown(id="exam_select"),
+                            ],
+                        ),
                     ],
+                    id="selects",
                 ),
-            html.Main(
-                children=[
-                    html.Section(
-                        children=[
-                            html.Div(
-                                children=[
-                                    "Classe: ",
-                                    dcc.Dropdown(
-                                        id="tribe",
-                                        options=[
-                                            {"label": t["name"], "value": t["name"]}
-                                            for t in get_tribes().values()
-                                            ],
-                                        value=next(iter(get_tribes().values()))["name"],
-                                        ),
+                html.Section(
+                    children=[
+                        html.Div(
+                            children=[
+                                dash_table.DataTable(
+                                    id="final_score_table",
+                                    columns=[
+                                        {"name": "Étudiant", "id": "student_name"},
+                                        {"name": "Note", "id": "mark"},
+                                        {"name": "Barème", "id": "score_rate"},
                                     ],
-                                ),
-                            html.Div(
-                                children=[
-                                    "Evaluation: ",
-                                    dcc.Dropdown(id="exam_select"),
-                                    ],
-                                ),
-                            ],
-                        id="selects",
-                        ),
-                    html.Section(
-                        children=[
-                            html.Div(
-                                children=[],
-                                id="final_score_table_container",
-                                ),
-                            ],
-                        id="analysis",
-                        ),
-                    html.Section(
-                        children=[
-                            dash_table.DataTable(
-                                id="scores_table",
-                                columns=[],
-                                style_data_conditional=[],
-                                fixed_columns={},
-                                editable=True,
                                 )
                             ],
-                        id="edit",
+                            id="final_score_table_container",
+                        ),
+                        html.Div(
+                            children=[
+                                dash_table.DataTable(
+                                    id="score_statistics_table",
+                                    columns=[],
+                                )
+                            ],
+                            id="score_statistics_table_container",
+                        ),
+                        html.Div(
+                            children=[
+                                dcc.Graph(
+                                    id="fig_exam_histo",
+                                    config={"displayModeBar": False},
+                                )
+                            ],
+                            id="fig_exam_histo_container",
+                        ),
+                        html.Div(
+                            children=[
+                                dcc.Graph(
+                                    id="fig_questions_bar",
+                                    config={"displayModeBar": False},
+                                )
+                            ],
+                            id="fig_questions_bar_container",
                         ),
                     ],
+                    id="analysis",
+                ),
+                html.Section(
+                    children=[
+                        dash_table.DataTable(
+                            id="scores_table",
+                            columns=[],
+                            style_data_conditional=[],
+                            fixed_columns={},
+                            editable=True,
+                            style_table={"minWidth": "100%"},
+                            style_cell={
+                                "minWidth": "100px",
+                                "width": "100px",
+                                "maxWidth": "100px",
+                                "overflow": "hidden",
+                                "textOverflow": "ellipsis",
+                            },
+                        )
+                    ],
+                    id="edit",
                 ),
-            dcc.Store(id="scores"),
             ],
-        )
+        ),
+        dcc.Store(id="scores"),
+    ],
+)

recopytex/dashboard/pages/exams_scores/callbacks.py

@@ -2,11 +2,12 @@
 # encoding: utf-8
 
 from dash.dependencies import Input, Output, State
-import dash
 from dash.exceptions import PreventUpdate
+import plotly.graph_objects as go
 import dash_table
 import json
 import pandas as pd
+import numpy as np
 
 from recopytex.dashboard.app import app
 from recopytex.dashboard.common.formating import highlight_scores
@@ -17,6 +18,10 @@ from .models import (
     get_unstack_scores,
     get_students_from_exam,
     get_score_colors,
+    get_level_color_bar,
+    score_to_final_mark,
+    stack_scores,
+    pivot_score_on,
 )
 
 
@@ -51,7 +56,6 @@ def update_exams_choices(tribe):
     ],
 )
 def update_scores_store(exam):
-    ctx = dash.callback_context
     if not exam:
         return [[], [], [], {}]
     exam = pd.DataFrame.from_dict([json.loads(exam)])
@@ -77,3 +81,136 @@ def update_scores_store(exam):
         highlight_scores(students, score_color),
         {"headers": True, "data": len(fixed_columns)},
     ]
+
+
+@app.callback(
+    [
+        Output("final_score_table", "data"),
+    ],
+    [
+        Input("scores_table", "data"),
+    ],
+)
+def update_finale_score_table(scores):
+    scores_df = pd.DataFrame.from_records(scores)
+    stacked_scores = stack_scores(scores_df)
+    return score_to_final_mark(stacked_scores)
+
+
+@app.callback(
+    [
+        Output("score_statistics_table", "columns"),
+        Output("score_statistics_table", "data"),
+    ],
+    [
+        Input("final_score_table", "data"),
+    ],
+)
+def update_statictics_table(finale_score):
+    df = pd.DataFrame.from_records(finale_score)
+    statistics = df["mark"].describe().to_frame().T
+    return [
+        [{"id": c, "name": c} for c in statistics.columns],
+        statistics.to_dict("records"),
+    ]
+
+
+@app.callback(
+    [
+        Output("fig_exam_histo", "figure"),
+    ],
+    [
+        Input("final_score_table", "data"),
+    ],
+)
+def update_exam_histo(finale_scores):
+    scores = pd.DataFrame.from_records(finale_scores)
+
+    if scores.empty:
+        return [go.Figure(data=[go.Scatter(x=[], y=[])])]
+
+    ranges = np.linspace(
+        -0.5,
+        scores["score_rate"].max(),
+        int(scores["score_rate"].max() * 2 + 2),
+    )
+
+    bins = pd.cut(scores["mark"], ranges)
+    scores["Bin"] = bins
+    grouped = (
+        scores.reset_index()
+        .groupby("Bin")
+        .agg({"score_rate": "count", "student_name": lambda x: "\n".join(x)})
+    )
+    grouped.index = grouped.index.map(lambda i: i.right)
+    fig = go.Figure()
+    fig.add_bar(
+        x=grouped.index,
+        y=grouped["score_rate"],
+        text=grouped["student_name"],
+        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(
+    [
+        Output("fig_questions_bar", "figure"),
+    ],
+    [
+        Input("scores_table", "data"),
+    ],
+)
+def update_questions_bar(finale_scores):
+    scores = pd.DataFrame.from_records(finale_scores)
+    scores = stack_scores(scores)
+
+    if scores.empty:
+        return [go.Figure(data=[go.Scatter(x=[], y=[])])]
+
+    pt = pivot_score_on(scores, ["exercise", "question", "comment"], "score")
+
+    # separation  between exercises
+    for i in {i for i in pt.index.get_level_values(0)}:
+        pt.loc[(str(i), "", ""), :] = ""
+    pt.sort_index(inplace=True)
+
+    # Bar label
+    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 = get_level_color_bar()
+
+    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),
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+            )
+        )
+    return [fig]

recopytex/dashboard/pages/exams_scores/models.py

@@ -1,10 +1,44 @@
-#!/usr/bin/env python
+#!/use/bin/env python
 # encoding: utf-8
 
 from recopytex.database.filesystem.loader import CSVLoader
 from recopytex.datalib.dataframe import column_values_to_column
+import recopytex.datalib.on_score_column as on_column
+import pandas as pd
 
-LOADER = CSVLoader("./test_config.yml")
+LOADER = CSVLoader("./test_confia.ml")
+SCORES_CONFIG = LOADER.get_config()["scores"]
+
+
+def unstack_scores(scores):
+    """Put student_name values to columns
+
+    :param scores: Score dataframe with one line per score
+    :returns: Scrore dataframe with student_name in columns
+
+    """
+    kept_columns = [col for col in LOADER.score_columns if col != "score"]
+    return column_values_to_column("student_name", "score", kept_columns, scores)
+
+
+def stack_scores(scores):
+    """Student columns are melt to rows with student_name column
+
+    :param scores: Score dataframe with student_name in columns
+    :returns: Scrore dataframe with one line per score
+
+    """
+    kept_columns = [
+        c for c in LOADER.score_columns if c not in ["score", "student_name"]
+    ]
+    student_names = [c for c in scores.columns if c not in kept_columns]
+    return pd.melt(
+        scores,
+        id_vars=kept_columns,
+        value_vars=student_names,
+        var_name="student_name",
+        value_name="score",
+    )
 
 
 def get_tribes():
@@ -21,8 +55,7 @@ def get_record_scores(exam):
 
 def get_unstack_scores(exam):
     flat_scores = LOADER.get_exam_scores(exam)
-    kept_columns = [col for col in LOADER.score_columns if col != "score"]
-    return column_values_to_column("student_name", "score", kept_columns, flat_scores)
+    return unstack_scores(flat_scores)
 
 
 def get_students_from_exam(exam):
@@ -31,8 +64,65 @@ def get_students_from_exam(exam):
 
 
 def get_score_colors():
-    scores_config = LOADER.get_config()["valid_scores"]
     score_color = {}
-    for key, score in scores_config.items():
+    for key, score in SCORES_CONFIG.items():
         score_color[score["value"]] = score["color"]
     return score_color
+
+
+def get_level_color_bar():
+    return [
+        {"score": str(s["value"]), "name": s["comment"], "color": s["color"]}
+        for s in SCORES_CONFIG.values()
+    ]
+
+
+is_none_score = lambda x: on_column.is_none_score(x, SCORES_CONFIG)
+format_score = lambda x: on_column.format_score(x, SCORES_CONFIG)
+score_to_numeric_score = lambda x: on_column.score_to_numeric_score(x, SCORES_CONFIG)
+score_to_mark = lambda x: on_column.score_to_mark(
+    x, max([v["value"] for v in SCORES_CONFIG.values() if isinstance(v["value"], int)])
+)
+
+
+def filter_clean_score(scores):
+    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
+    filtered_scores = filtered_scores.assign(
+        score=filtered_scores.apply(format_score, axis=1)
+    )
+    return filtered_scores
+
+
+def score_to_final_mark(scores):
+    """ Compute marks then reduce to final mark per student """
+
+    filtered_scores = filter_clean_score(scores)
+    filtered_scores = filtered_scores.assign(
+        score=filtered_scores.apply(score_to_numeric_score, axis=1)
+    )
+    filtered_scores = filtered_scores.assign(
+        mark=filtered_scores.apply(score_to_mark, axis=1)
+    )
+    final_score = filtered_scores.groupby(["student_name"])[
+        ["mark", "score_rate"]
+    ].sum()
+    return [final_score.reset_index().to_dict("records")]
+
+
+def pivot_score_on(scores, index, columns, aggfunc="size"):
+    """Pivot scores on index, columns with aggfunc
+
+    It assumes thant scores are levels
+
+    """
+    filtered_scores = filter_clean_score(scores)
+    filtered_scores["score"] = filtered_scores["score"].astype(str)
+    pt = pd.pivot_table(
+        filtered_scores,
+        index=index,
+        columns=columns,
+        aggfunc=aggfunc,
+        fill_value=0,
+    )
+    return pt
+

recopytex/dashboard/routes.py

@@ -6,6 +6,7 @@ from dash.dependencies import Input, Output
 from .app import app
 from .pages.home import app as home
 from .pages.exams_scores import app as exams_scores
+import dash_html_components as html
 
 
 @app.callback(Output("page-content", "children"), [Input("url", "pathname")])

recopytex/database/filesystem/default_config.yml

@@ -26,30 +26,37 @@ scores: #
     value: 0 
     numeric_value: 0 
     color: "#E7472B"
+    comment: Faux
   FEW: # Few good things
     value: 1 
     numeric_value: 1 
     color: "#FF712B"
+    comment: Peu juste
   NEARLY: # Nearly good but things are missing
     value: 2
     numeric_value: 2
     color: "#F2EC4C"
+    comment: Presque juste
   GOOD: # Everything is good
     value: 3
     numeric_value: 3
     color: "#68D42F"
+    comment: Juste
   NOTFILLED: # The item is not scored yet
     value: ""
     numeric_value: None
     color: white
+    comment: En attente
   NOANSWER: # Student gives no answer (count as 0)
     value: "."
     numeric_value: 0
     color: black
+    comment: Pas de réponse
   ABS: # Student has absent (this score won't be impact the final mark)
     value: a
     numeric_value: None
     color: lightgray
+    comment: Non noté
 
 csv_fields: # dataframe_field: csv_field 
   term: Trimestre

recopytex/database/filesystem/loader.py

@@ -31,10 +31,11 @@ class CSVLoader(Loader):
     :example:
     >>> loader = CSVLoader()
     >>> loader.get_config()
-    {'source': './', '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'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
+    {'source': './', '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'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B', 'comment': 'Faux'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B', 'comment': 'Peu juste'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C', 'comment': 'Presque juste'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F', 'comment': 'Juste'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white', 'comment': 'En attente'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black', 'comment': 'Pas de réponse'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray', 'comment': 'Non noté'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
+
     >>> loader = CSVLoader("./test_config.yml")
     >>> loader.get_config()
-    {'source': './example', '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'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
+    {'source': './example', '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'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B', 'comment': 'Faux'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B', 'comment': 'Peu juste'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C', 'comment': 'Presque juste'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F', 'comment': 'Juste'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white', 'comment': 'En attente'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black', 'comment': 'Pas de réponse'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray', 'comment': 'Non noté'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
     """
 
     CONFIG = DEFAULT_CONFIG

recopytex/datalib/on_score_column.py

@@ -2,10 +2,137 @@
 # encoding: utf-8
 
 from math import ceil
+import pandas as pd
+
+
+def is_none_score(x, score_config):
+    """Is a score correspond to a None numeric_value which
+
+    >>> import pandas as pd
+    >>> d = {"Eleve":["E1"]*7,
+    ...    "score_rate": [1]*7,
+    ...    "is_leveled":[0]+[1]*6,
+    ...    "score":[0.33, "", ".", "a", 1, 2, 3],
+    ...    }
+    >>> score_config = {
+    ...   'BAD': {'value': 0, 'numeric_value': 0},
+    ...   'FEW': {'value': 1, 'numeric_value': 1},
+    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
+    ...   'GOOD': {'value': 3, 'numeric_value': 3},
+    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
+    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
+    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
+    ...    }
+    >>> df = pd.DataFrame(d)
+    >>> df.apply(lambda x:is_none_score(x, score_config), axis=1)
+    0    False
+    1     True
+    2    False
+    3     True
+    4    False
+    5    False
+    6    False
+    dtype: bool
+
+    """
+    none_values = [
+        v["value"]
+        for v in score_config.values()
+        if str(v["numeric_value"]).lower() == "none"
+    ]
+    return x["score"] in none_values or pd.isnull(x["score"])
+
+
+def format_score(x, score_config):
+    """Make sure that score have the appropriate format
+
+    >>> import pandas as pd
+    >>> d = {"Eleve":["E1"]*6,
+    ...    "score_rate": [1]*6,
+    ...    "is_leveled":[0]+[1]*5,
+    ...    "score":[0.33,  ".", "a", 1, 2, 3],
+    ...    }
+    >>> score_config = {
+    ...   'BAD': {'value': 0, 'numeric_value': 0},
+    ...   'FEW': {'value': 1, 'numeric_value': 1},
+    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
+    ...   'GOOD': {'value': 3, 'numeric_value': 3},
+    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
+    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
+    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
+    ...    }
+    >>> df = pd.DataFrame(d)
+    >>> df.apply(lambda x:format_score(x, score_config), axis=1)
+    0    0.33
+    1       .
+    2       a
+    3       1
+    4       2
+    5       3
+    dtype: object
+    >>> format_score({"score": "1.0", "is_leveled": 1}, score_config)
+    1
+    >>> format_score({"score": "3.0", "is_leveled": 1}, score_config)
+    3
+    >>> format_score({"score": 4, "is_leveled": 1}, score_config)
+    Traceback (most recent call last):
+        ...
+    ValueError: 4 (<class 'int'>) can't be a score
+
+    """
+    if not x["is_leveled"]:
+        return float(x["score"])
+
+    try:
+        score = int(float(x["score"]))
+    except ValueError:
+        score = str(x["score"])
+
+    if score in [v["value"] for v in score_config.values()]:
+        return score
+
+    raise ValueError(f"{x['score']} ({type(x['score'])}) can't be a score")
+
+
+def score_to_numeric_score(x, score_config):
+    """Convert a score to the corresponding numeric value
+
+    >>> import pandas as pd
+    >>> d = {"Eleve":["E1"]*7,
+    ...    "score_rate": [1]*7,
+    ...    "is_leveled":[0]+[1]*6,
+    ...    "score":[0.33, "", ".", "a", 1, 2, 3],
+    ...    }
+    >>> score_config = {
+    ...   'BAD': {'value': 0, 'numeric_value': 0},
+    ...   'FEW': {'value': 1, 'numeric_value': 1},
+    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
+    ...   'GOOD': {'value': 3, 'numeric_value': 3},
+    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
+    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
+    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
+    ...    }
+    >>> df = pd.DataFrame(d)
+    >>> df.apply(lambda x:score_to_numeric_score(x, score_config), axis=1)
+    0    0.33
+    1    None
+    2       0
+    3    None
+    4       1
+    5       2
+    6       3
+    dtype: object
+
+    """
+    if x["is_leveled"]:
+        replacements = {v["value"]: v["numeric_value"] for v in score_config.values()}
+        return replacements[x["score"]]
+
+    return x["score"]
 
 
 def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
-    """Compute the mark from the score
+    """Compute the mark from "score" which have to be filtered and in numeric form
 
     if the item is leveled then the score is multiply by the score_rate
     otherwise it copies the score
@@ -16,39 +143,38 @@ def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
     :return: the mark
 
     >>> import pandas as pd
-    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
-    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
-    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
-    ...    "score":[1, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1.2, 2, 3],
+    >>> d = {"Eleve":["E1"]*7,
+    ...    "score_rate": [1]*7,
+    ...    "is_leveled":[0]+[1]*6,
+    ...    "score":[0.33, "", ".", "a", 1, 2, 3],
+    ...    }
+    >>> score_config = {
+    ...   'BAD': {'value': 0, 'numeric_value': 0},
+    ...   'FEW': {'value': 1, 'numeric_value': 1},
+    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
+    ...   'GOOD': {'value': 3, 'numeric_value': 3},
+    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
+    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
+    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
     ...    }
     >>> df = pd.DataFrame(d)
-    >>> df.loc[0]
-    Eleve          E1
-    score_rate      1
-    is_leveled      0
-    score         1.0
-    Name: 0, dtype: object
-    >>> score_to_mark(df.loc[0], 3)
-    1.0
-    >>> df.loc[10]
-    Eleve          E2
-    score_rate      2
-    is_leveled      1
-    score         2.0
-    Name: 10, dtype: object
-    >>> score_to_mark(df.loc[10], 3)
-    1.33
+    >>> df = df[~df.apply(lambda x:is_none_score(x, score_config), axis=1)]
+    >>> df["score"] = df.apply(lambda x:score_to_numeric_score(x, score_config), axis=1)
+    >>> df.apply(lambda x:score_to_mark(x, 3), axis=1)
+    0    0.33
+    2    0.00
+    4    0.33
+    5    0.67
+    6    1.00
+    dtype: float64
     >>> from .on_value import round_half_point
-    >>> score_to_mark(df.loc[10], 3, round_half_point)
-    1.5
-    >>> df.loc[1]
-    Eleve           E1
-    score_rate       1
-    is_leveled       0
-    score         0.33
-    Name: 1, dtype: object
-    >>> score_to_mark(df.loc[1], 3)
-    0.33
+    >>> df.apply(lambda x:score_to_mark(x, 3, round_half_point), axis=1)
+    0    0.5
+    2    0.0
+    4    0.5
+    5    0.5
+    6    1.0
+    dtype: float64
     """
     if x["is_leveled"]:
         if x["score"] not in list(range(score_max + 1)):
@@ -125,10 +251,6 @@ def score_to_level(x, level_max=3):
     return int(ceil(x["score"] / x["score_rate"] * level_max))
 
 
-def score_to_numeric_score(x, score_config):
-    pass
-
-
 # -----------------------------
 # Reglages pour 'vim'
 # vim:set autoindent expandtab tabstop=4 shiftwidth=4:

recopytex/datalib/on_score_dataframe.py

@@ -1,196 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
-from .on_score_column import score_to_mark, score_to_level
-import pandas as pd
-
-
-def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
-    """Compute the mark for the dataframe
-
-    apply score_to_mark to each row
-
-    :param df: DataFrame with "score" (need to be number), "is_leveled" and "score_rate" columns.
-
-    >>> import pandas as pd
-    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
-    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
-    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
-    ...    "score":[1, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> df
-       Eleve  score_rate  is_leveled  score
-    0     E1           1           0  1.000
-    1     E1           1           0  0.330
-    2     E1           2           0  2.000
-    3     E1           2           0  1.500
-    4     E1           2           1  1.000
-    5     E1           2           1  3.000
-    6     E2           1           0  0.666
-    7     E2           1           0  1.000
-    8     E2           2           0  1.500
-    9     E2           2           0  1.000
-    10    E2           2           1  2.000
-    11    E2           2           1  3.000
-    >>> compute_marks(df, 3)
-    0     1.00
-    1     0.33
-    2     2.00
-    3     1.50
-    4     0.67
-    5     2.00
-    6     0.67
-    7     1.00
-    8     1.50
-    9     1.00
-    10    1.33
-    11    2.00
-    dtype: float64
-    >>> from .on_value import round_half_point
-    >>> compute_marks(df, 3, round_half_point)
-    0     1.0
-    1     0.5
-    2     2.0
-    3     1.5
-    4     0.5
-    5     2.0
-    6     0.5
-    7     1.0
-    8     1.5
-    9     1.0
-    10    1.5
-    11    2.0
-    dtype: float64
-    """
-    return df[["score", "is_leveled", "score_rate"]].apply(
-        lambda x: score_to_mark(x, score_max, rounding), axis=1
-    )
-
-
-def compute_level(df, level_max=3):
-    """Compute level for the dataframe
-
-    Applies score_to_level to each row
-
-    :param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
-    :return: Columns with level
-
-    >>> import pandas as pd
-    >>> import numpy as np
-    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
-    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
-    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
-    ...    "score":[0, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> compute_level(df)
-    0     0
-    1     1
-    2     3
-    3     3
-    4     1
-    5     3
-    6     2
-    7     3
-    8     3
-    9     2
-    10    2
-    11    3
-    dtype: int64
-    """
-    return df[["score", "is_leveled", "score_rate"]].apply(
-        lambda x: score_to_level(x, level_max), axis=1
-    )
-
-
-def compute_normalized(df, rounding=lambda x: round(x, 2)):
-    """Compute the normalized mark (Mark / score_rate)
-
-    :param df: DataFrame with "Mark" and "score_rate" columns
-    :return: column with normalized mark
-
-    >>> import pandas as pd
-    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
-    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
-    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
-    ...    "score":[0, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> df["mark"] = compute_marks(df, 3)
-    >>> compute_normalized(df)
-    0     0.00
-    1     0.33
-    2     1.00
-    3     0.75
-    4     0.34
-    5     1.00
-    6     0.67
-    7     1.00
-    8     0.75
-    9     0.50
-    10    0.66
-    11    1.00
-    dtype: float64
-    """
-    return rounding(df["mark"] / df["score_rate"])
-
-
-def filter_none_score(df, score_config):
-    """Filter rows where scores have None numeric values
-
-    :example:
-
-    >>> import pandas as pd
-    >>> d = {"Eleve":["E1"]*7,
-    ...    "score_rate": [1]*7,
-    ...    "is_leveled":[0]+[1]*6,
-    ...    "score":[0.33, "", ".", "a", 1, 2, 3],
-    ...    }
-    >>> score_config = {
-    ...   'BAD': {'value': 0, 'numeric_value': 0},
-    ...   'FEW': {'value': 1, 'numeric_value': 1},
-    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
-    ...   'GOOD': {'value': 3, 'numeric_value': 3},
-    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
-    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
-    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> filter_none_score(df, score_config)
-      Eleve  score_rate  is_leveled score
-    0    E1           1           0  0.33
-    2    E1           1           1     .
-    4    E1           1           1     1
-    5    E1           1           1     2
-    6    E1           1           1     3
-    """
-    not_leveled_df = df[df["is_leveled"] != 1]
-    leveled_df = df[df["is_leveled"] == 1]
-
-    not_none_values = [
-        v["value"]
-        for v in score_config.values()
-        if str(v["numeric_value"]).lower() != "none"
-    ]
-    filtered_leveled_df = leveled_df[leveled_df["score"].isin(not_none_values)]
-
-    return pd.concat([not_leveled_df, filtered_leveled_df])
-
-
-def score_to_numeric_score(df, score_config):
-    """Transform a score to the corresponding numeric value
-
-    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
-    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
-    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
-    ...    "score":[0, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
-    ...    }
-    """
-    pass
-
-
-# -----------------------------
-# Reglages pour 'vim'
-# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
-# cursor: 16 del

recopytex/scripts/recopytex.py

@@ -2,8 +2,7 @@
 # encoding: utf-8
 
 import click
-from recopytex.dashboard.app import app as dash
-
+from recopytex.dashboard.index import app as dash
 
 @click.group()
 def cli():
@@ -14,3 +13,6 @@ def cli():
 @click.option("--debug", default=0, help="Debug mode for dash")
 def dashboard(debug):
     dash.run_server(debug=bool(debug))
+
+if __name__ == "__main__":
+    cli()