Feat: add row to scores_table!!

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/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/recopytex.py

@@ -13,7 +13,7 @@ from .getconfig import config, CONFIGPATH
 from .prompts import prompt_exam, prompt_exercise, prompt_validate
 from ..config import NO_ST_COLUMNS
 from .exam import Exam
-from .exam_dash import app as exam_app
+from ..dashboard.exam import app as exam_app
 
 
 @click.group()
@@ -85,10 +85,12 @@ def new_exam():
     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):