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recopytex
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Feat: add competence fig and better error management

This commit is contained in:
Bertrand Benjamin 2021-01-15 13:48:57 +01:00
parent 0a5a931d01
commit 09ac9f01f8
1 changed files with 93 additions and 62 deletions
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155
recopytex/dashboard/exam.py
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@ -75,7 +75,7 @@ app.layout = html.Div(
columns=[
{"id": "Élève", "name": "Élève"},
{"id": "Note", "name": "Note"},
{"id": "Barème", "name": "Bareme"},
{"id": "Barème", "name": "Barème"},
],
data=[],
style_data_conditional=[
@ -99,11 +99,21 @@ app.layout = html.Div(
[
dash_table.DataTable(
id="final_score_describe",
columns=[{"id": "count", "name": "count"},
{"id": "mean", "name": "mean"},
{"id": "std", "name": "std"},
{"id": "min", "name": "min"},
{"id": "25%", "name": "25%"},
{"id": "50%", "name": "50%"},
{"id": "75%", "name": "75%"},
{"id": "max", "name": "max"},
]
),
dcc.Graph(
id="fig_assessment_hist",
),
# dcc.Graph(id="fig_competences"),
dcc.Graph(id="fig_competences"),
]
),
],
@ -156,42 +166,47 @@ def update_csvs(value):
def update_final_scores(data):
if not data:
raise PreventUpdate
scores = pd.DataFrame.from_records(data)
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")]
if scores.iloc[0]["Commentaire"] == "commentaire":
scores.drop([0], inplace=True)
except KeyError:
raise PreventUpdate
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")]
@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")
return [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()]
scores = pd.DataFrame.from_records(data)
if scores.empty:
return [[{}]]
desc = scores["Note"].describe().T
return [[desc.to_dict()]]
@app.callback(
@ -203,6 +218,9 @@ def update_final_scores_descr(data):
def update_final_scores_hist(data):
assessment_scores = pd.DataFrame.from_records(data)
if assessment_scores.empty:
return [{}]
ranges = np.linspace(
0, assessment_scores.Bareme.max(), int(assessment_scores.Bareme.max() * 2 + 1)
)
@ -230,51 +248,64 @@ def update_final_scores_hist(data):
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.Input("scores_table", "data")],
)
def update_competence_fig(data):
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)
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")
fig.update_layout(
height=500,
margin=dict(l=5, r=5, b=5, t=5),
)
return [fig]
@app.callback(