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3 Commits
235019102b
...
8cdeecfc53
Author | SHA1 | Date | |
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8cdeecfc53 | |||
1a7c97d869 | |||
ab5de2711e |
@ -55,6 +55,23 @@ layout = html.Div(
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],
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id="final_score_table_container",
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),
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html.Div(
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children=[
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dash_table.DataTable(
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id="score_statistics_table",
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columns=[],
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)
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],
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id="score_statistics_table_container",
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),
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html.Div(
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children=[
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dcc.Graph(
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id="fig_exam_histo",
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)
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],
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id="fig_exam_histo_container",
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),
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],
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id="analysis",
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),
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@ -66,6 +83,14 @@ layout = html.Div(
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style_data_conditional=[],
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fixed_columns={},
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editable=True,
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style_table={"minWidth": "100%"},
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style_cell={
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"minWidth": "100px",
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"width": "100px",
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"maxWidth": "100px",
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"overflow": "hidden",
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"textOverflow": "ellipsis",
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},
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)
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],
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id="edit",
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@ -2,11 +2,12 @@
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# encoding: utf-8
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from dash.dependencies import Input, Output, State
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import dash
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from dash.exceptions import PreventUpdate
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import plotly.graph_objects as go
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import dash_table
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import json
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import pandas as pd
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import numpy as np
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from recopytex.dashboard.app import app
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from recopytex.dashboard.common.formating import highlight_scores
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@ -52,7 +53,6 @@ def update_exams_choices(tribe):
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],
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)
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def update_scores_store(exam):
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ctx = dash.callback_context
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if not exam:
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return [[], [], [], {}]
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exam = pd.DataFrame.from_dict([json.loads(exam)])
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@ -88,7 +88,70 @@ def update_scores_store(exam):
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Input("scores_table", "data"),
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],
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)
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def update_scores_store(scores):
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def update_finale_score_table(scores):
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scores_df = pd.DataFrame.from_records(scores)
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# print(scores_df)
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return score_to_final_mark(scores_df)
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@app.callback(
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[
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Output("score_statistics_table", "columns"),
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Output("score_statistics_table", "data"),
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],
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[
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Input("final_score_table", "data"),
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],
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)
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def update_statictics_table(finale_score):
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df = pd.DataFrame.from_records(finale_score)
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statistics = df["mark"].describe().to_frame().T
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print(statistics)
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return [
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[{"id": c, "name": c} for c in statistics.columns],
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statistics.to_dict("records"),
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]
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@app.callback(
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[
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Output("fig_exam_histo", "figure"),
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],
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[
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Input("final_score_table", "data"),
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],
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)
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def update_exam_histo(finale_scores):
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scores = pd.DataFrame.from_records(finale_scores)
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if scores.empty:
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return [go.Figure(data=[go.Scatter(x=[], y=[])])]
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ranges = np.linspace(
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-0.5,
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scores["score_rate"].max(),
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int(scores["score_rate"].max() * 2 + 2),
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)
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bins = pd.cut(scores["mark"], ranges)
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scores["Bin"] = bins
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grouped = (
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scores.reset_index()
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.groupby("Bin")
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.agg({"score_rate": "count", "student_name": lambda x: "\n".join(x)})
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)
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grouped.index = grouped.index.map(lambda i: i.right)
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fig = go.Figure()
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fig.add_bar(
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x=grouped.index,
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y=grouped["score_rate"],
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text=grouped["student_name"],
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textposition="auto",
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hovertemplate="",
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marker_color="#4E89DE",
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)
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fig.update_layout(
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height=300,
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margin=dict(l=5, r=5, b=5, t=5),
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)
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return [fig]
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