Feat: add row to scores_table!!

recopytex/dashboard/exam.py

@@ -11,6 +11,7 @@ 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
@@ -25,66 +26,87 @@ COLORS = {
     3: "#68D42F",
 }
 
-external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
+app = dash.Dash(external_stylesheets=[dbc.themes.SIMPLEX])
-app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
+# 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"),
+        dbc.NavbarSimple(
-        html.Div(
+            children=[
-            [
+                dbc.Alert("Dernière sauvegarde", id="lastsave", color="success"),
-                "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},
+            brand="Analyse des notes",
+            brand_href="#",
+            color="success",
+            dark=True,
         ),
-        html.Div(
+        html.Br(),
+        dbc.Row(
             [
-                dash_table.DataTable(
+                dbc.Col(
-                    id="final_score_table",
+                    [
-                    columns=[
+                        "Classe: ",
-                        {"id": "Élève", "name": "Élève"},
+                        dbc.Select(
-                        {"id": "Note", "name": "Note"},
+                            id="tribe",
-                        {"id": "Barème", "name": "Bareme"},
+                            options=[
-                    ],
+                                {"label": t["name"], "value": t["name"]}
-                    data=[],
+                                for t in config["tribes"]
-                    style_data_conditional=[
+                            ],
-                        {
+                            value=config["tribes"][0]["name"],
-                            "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(
+                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(
-                        dcc.Graph(id="fig_competences"),
+                            id="fig_assessment_hist",
+                        ),
+                        # dcc.Graph(id="fig_competences"),
                     ]
                 ),
             ],
-            style={"columnCount": 2},
         ),
         html.Br(),
         html.Div(
@@ -98,10 +120,10 @@ app.layout = html.Div(
                     },
                     style_data_conditional=[],
                     editable=True,
-                )
+                ),
+                dbc.Button("Ajouter un élément", id="btn_add_element"),
             ]
         ),
-        html.P(id="lastsave"),
         dcc.Store(id="final_score"),
     ]
 )
@@ -201,74 +223,78 @@ def update_final_scores_hist(data):
         hovertemplate="",
         marker_color="#4E89DE",
     )
-    # fig = go.Figure(
+    fig.update_layout(
-    #     data=go.Histogram(
+        height=300,
-    #         x=assessment_scores["Note"],
+        margin=dict(l=5, r=5, b=5, t=5),
-    #         xbins={"start": 0, "end": assessment_scores["Bareme"].max(), "size": 0.25},
+    )
-    #     ),
-    # )
     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("fig_competences", "figure"),
+        dash.dependencies.Output("lastsave", "children"),
+        dash.dependencies.Output("lastsave", "color"),
     ],
-    [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)
+    try:
-    print(f"save at {csv} ({datetime.today()})")
+        scores = pd.DataFrame.from_records(data)
-    scores.to_csv(csv, index=False)
+        scores.to_csv(csv, index=False)
-    return [datetime.today()]
+    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):
@@ -293,18 +319,23 @@ def highlight_value(df):
         dash.dependencies.Output("scores_table", "data"),
         dash.dependencies.Output("scores_table", "style_data_conditional"),
     ],
-    [dash.dependencies.Input("csv", "value")],
+    [
+        dash.dependencies.Input("csv", "value"),
+        dash.dependencies.Input("btn_add_element", "n_clicks"),
+        dash.dependencies.State("scores_table", "data"),
+    ],
 )
-def update_scores_table(value):
+def update_scores_table(csv, add_element, data):
-    if not value:
+    ctx = dash.callback_context
-        raise PreventUpdate
+    if ctx.triggered[0]['prop_id'] ==  "csv.value":
-    stack = pd.read_csv(value, encoding="UTF8")
+        stack = pd.read_csv(csv, encoding="UTF8")
-    # try:
+    elif ctx.triggered[0]['prop_id'] ==  "btn_add_element.n_clicks":
-    #     stack = stack.drop(columns=["Nom", "Trimestre", "Date", "Competence", "Domaine", "Est_nivele", "Bareme"])
+        stack = pd.DataFrame.from_records(data)
-    # except KeyError:
+        infos = pd.DataFrame.from_records([{k: stack.iloc[-1][k] for k in NO_ST_COLUMNS.values()}])
-    #     stack = stack
+        stack = stack.append(infos)
     return (
         [{"id": c, "name": c} for c in stack.columns],
         stack.to_dict("records"),
         highlight_value(stack),
     )
+