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Feat: basic score_table

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This commit is contained in:
Bertrand Benjamin 2021-04-18 16:44:40 +02:00
parent 6ed55c07d4
commit 2031ade1ab
3 changed files with 104 additions and 78 deletions
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84
recopytex/dashboard/pages/exams_scores/app.py
View File

@ -15,43 +15,53 @@ layout = html.Div(
],
),
html.Main(
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.Section(
# children=[
# html.Div(
# children=[],
# id="final_score_table_container",
# ),
# ],
# id="analysis",
# ),
# html.Section(
# children=[],
# id="scores_table",
# ),
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.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",
),
],
),
dcc.Store(id="scores"),
],

78
recopytex/dashboard/pages/exams_scores/callbacks.py
View File

@ -1,14 +1,15 @@
#!/usr/bin/env python
# encoding: utf-8
from dash.dependencies import Input, Output
from dash.dependencies import Input, Output, State
import dash
from dash.exceptions import PreventUpdate
import dash_table
import json
import pandas as pd
from ...app import app
from .models import get_tribes, get_exams, get_scores
from .models import get_tribes, get_exams, get_unstack_scores, get_students_from_exam
@app.callback(
@ -18,10 +19,10 @@ from .models import get_tribes, get_exams, get_scores
],
[Input("tribe", "value")],
)
def update_exams_choices(value):
if not value:
def update_exams_choices(tribe):
if not tribe:
raise PreventUpdate
exams = get_exams(value)
exams = get_exams(tribe)
exams.reset_index(inplace=True)
if not exams.empty:
return [
@ -29,39 +30,38 @@ def update_exams_choices(value):
], exams.loc[0].to_json()
return [], None
@app.callback(
[Output("scores", "data")],
[Input("exam_select", "value")]
)
def update_scores_store(value):
if not value:
return [[]]
exam = pd.DataFrame.from_dict([json.loads(value)])
return [get_scores(exam)]
[
Output("scores_table", "columns"),
Output("scores_table", "data"),
Output("scores_table", "style_data_conditional"),
Output("scores_table", "fixed_columns"),
],
[
Input("exam_select", "value"),
],
)
def update_scores_store(exam):
ctx = dash.callback_context
if not exam:
return [[], [], [], {}]
exam = pd.DataFrame.from_dict([json.loads(exam)])
scores = get_unstack_scores(exam, "score", "student_name")
fixed_columns = [
"exercise",
"question",
"competence",
"theme",
"comment",
"score_rate",
"is_leveled",
]
columns = fixed_columns + list(get_students_from_exam(exam))
# @app.callback(
# [
# Output("final_score", "data"),
# ],
# [Input("scores_table", "data")],
# )
# def update_final_scores(data):
# if not data:
# raise PreventUpdate
#
# scores = pd.DataFrame.from_records(data)
# try:
# if scores.iloc[0]["Commentaire"] == "commentaire":
# scores.drop([0], inplace=True)
# except KeyError:
# pass
# scores = flat_df_students(scores).dropna(subset=["Score"])
# if scores.empty:
# return [{}]
#
# scores = pp_q_scores(scores)
# assessment_scores = scores.groupby(["Eleve"]).agg({"Note": "sum", "Bareme": "sum"})
# return [assessment_scores.reset_index().to_dict("records")]
return [
[{"id": c, "name": c} for c in columns],
scores.to_dict("records"),
[],
{"headers": True, "data": len(fixed_columns)},
]

20
recopytex/dashboard/pages/exams_scores/models.py
View File

@ -14,5 +14,21 @@ def get_exams(tribe):
return LOADER.get_exams([tribe])
def get_scores(exam):
return LOADER.get_exam_scores(exam).to_dict('records')
def get_record_scores(exam):
return LOADER.get_exam_scores(exam)
def get_unstack_scores(exam, value_column="score", unstack_column="student_name"):
flat_scores = LOADER.get_exam_scores(exam)
pivot_columns = [col for col in LOADER.score_columns if col != value_column]
unstacked_df = (
flat_scores.set_index(pivot_columns)[value_column]
.unstack(unstack_column)
.reset_index()
)
return unstacked_df
def get_students_from_exam(exam):
flat_scores = LOADER.get_exam_scores(exam)
return flat_scores["student_name"].unique()