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# encoding: utf-8 # encoding: utf-8
import dash_html_components as html import dash_html_components as html
from ....database.filesystem.loader import CSVLoader from recopytex.database.filesystem.loader import CSVLoader
from .models import get_tribes, get_exams, get_students from .models import get_tribes, get_exams, get_students
loader = CSVLoader("./test_config.yml") loader = CSVLoader("./test_config.yml")

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# encoding: utf-8 # encoding: utf-8
from dash.dependencies import Input, Output from dash.dependencies import Input, Output
from ...app import app from recopytex.dashboard.app import app

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#!/usr/bin/env python
# encoding: utf-8
from math import ceil
def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
"""Compute the mark from the score
if the item is leveled then the score is multiply by the score_rate
otherwise it copies the score
:param x: dictionnary with "is_leveled", "score" and "score_rate" keys
:param score_max:
:param rounding: rounding mark function
:return: the mark
>>> import pandas as pd
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1.2, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df.loc[0]
Eleve E1
score_rate 1
is_leveled 0
score 1.0
Name: 0, dtype: object
>>> score_to_mark(df.loc[0], 3)
1.0
>>> df.loc[10]
Eleve E2
score_rate 2
is_leveled 1
score 2.0
Name: 10, dtype: object
>>> score_to_mark(df.loc[10], 3)
1.33
>>> from .on_value import round_half_point
>>> score_to_mark(df.loc[10], 3, round_half_point)
1.5
>>> df.loc[1]
Eleve E1
score_rate 1
is_leveled 0
score 0.33
Name: 1, dtype: object
>>> score_to_mark(df.loc[1], 3)
0.33
"""
if x["is_leveled"]:
if x["score"] not in list(range(score_max + 1)):
raise ValueError(f"The evaluation is out of range: {x['score']} at {x}")
return rounding(x["score"] * x["score_rate"] / score_max)
return rounding(x["score"])
def score_to_level(x, level_max=3):
"""Compute the level (".",0,1,2,3).
:param x: dictionnary with "is_leveled", "score" and "score_rate" keys
:return: the level
>>> import pandas as pd
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[1, 0.33, 0, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df
Eleve score_rate is_leveled score
0 E1 1 0 1.000
1 E1 1 0 0.330
2 E1 2 0 0.000
3 E1 2 0 1.500
4 E1 2 1 1.000
5 E1 2 1 3.000
6 E2 1 0 0.666
7 E2 1 0 1.000
8 E2 2 0 1.500
9 E2 2 0 1.000
10 E2 2 1 2.000
11 E2 2 1 3.000
>>> df.apply(score_to_level, axis=1)
0 3
1 1
2 0
3 3
4 1
5 3
6 2
7 3
8 3
9 2
10 2
11 3
dtype: int64
>>> df.apply(lambda x: score_to_level(x, 5), axis=1)
0 5
1 2
2 0
3 4
4 1
5 3
6 4
7 5
8 4
9 3
10 2
11 3
dtype: int64
"""
if x["is_leveled"]:
return int(x["score"])
if x["score"] > x["score_rate"]:
raise ValueError(
f"score is higher than score_rate ({x['score']} > {x['score_rate']}) for {x}"
)
return int(ceil(x["score"] / x["score_rate"] * level_max))
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#!/usr/bin/env python
# encoding: utf-8
from .on_score_column import score_to_mark, score_to_level
def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
"""Compute the mark for the dataframe
apply score_to_mark to each row
:param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
>>> import pandas as pd
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df
Eleve score_rate is_leveled score
0 E1 1 0 1.000
1 E1 1 0 0.330
2 E1 2 0 2.000
3 E1 2 0 1.500
4 E1 2 1 1.000
5 E1 2 1 3.000
6 E2 1 0 0.666
7 E2 1 0 1.000
8 E2 2 0 1.500
9 E2 2 0 1.000
10 E2 2 1 2.000
11 E2 2 1 3.000
>>> compute_marks(df, 3)
0 1.00
1 0.33
2 2.00
3 1.50
4 0.67
5 2.00
6 0.67
7 1.00
8 1.50
9 1.00
10 1.33
11 2.00
dtype: float64
>>> from .on_value import round_half_point
>>> compute_marks(df, 3, round_half_point)
0 1.0
1 0.5
2 2.0
3 1.5
4 0.5
5 2.0
6 0.5
7 1.0
8 1.5
9 1.0
10 1.5
11 2.0
dtype: float64
"""
return df[["score", "is_leveled", "score_rate"]].apply(
lambda x: score_to_mark(x, score_max, rounding), axis=1
)
def compute_level(df, level_max=3):
"""Compute level for the dataframe
Applies score_to_level to each row
:param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
:return: Columns with level
>>> import pandas as pd
>>> import numpy as np
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> compute_level(df)
0 0
1 1
2 3
3 3
4 1
5 3
6 2
7 3
8 3
9 2
10 2
11 3
dtype: int64
"""
return df[["score", "is_leveled", "score_rate"]].apply(
lambda x: score_to_level(x, level_max), axis=1
)
def compute_normalized(df, rounding=lambda x: round(x, 2)):
"""Compute the normalized mark (Mark / score_rate)
:param df: DataFrame with "Mark" and "score_rate" columns
:return: column with normalized mark
>>> import pandas as pd
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df["mark"] = compute_marks(df, 3)
>>> compute_normalized(df)
0 1.00
1 0.33
2 1.00
3 0.75
4 0.34
5 1.00
6 0.67
7 1.00
8 0.75
9 0.50
10 0.66
11 1.00
dtype: float64
"""
return rounding(df["mark"] / df["score_rate"])
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#!/usr/bin/env python
# encoding: utf-8
from math import ceil, floor
def round_with_base(x, base=0.5):
"""Round to a multiple of base
:example:
>>> round_with_base(1.33, 0.1)
1.3
>>> round_with_base(1.33, 0.2)
1.4
>>> round_with_base(1.33, 1)
1
>>> round_with_base(1.33, 2)
2
"""
try:
prec = len(str(base).split(".")[1])
except IndexError:
prec = 0
return round(base * round(float(x) / base), prec)
def round_half_point(x):
"""Round to nearest half point
:example:
>>> round_half_point(1.33)
1.5
>>> round_half_point(1.1)
1.0
>>> round_half_point(1.66)
1.5
>>> round_half_point(1.76)
2.0
"""
return round_with_base(x, base=0.5)

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# encoding: utf-8 # encoding: utf-8
import click import click
from ..dashboard.app import app as dash from recopytex.dashboard.app import app as dash
@click.group() @click.group()