def test_give_equal_weights_on_example():
# given
columns = ["hw01", "hw02", "hw03", "lab01"]
p1 = pd.Series(data=[1, 30, 90, 20], index=columns, name="A1")
p2 = pd.Series(data=[2, 7, 15, 20], index=columns, name="A2")
points = pd.DataFrame([p1, p2])
maximums = pd.Series([2, 50, 100, 20], index=columns)
gradebook = gradelib.Gradebook(points, maximums)
homeworks = gradebook.assignments.starting_with("hw")
# when
actual = gradebook.give_equal_weights(within=homeworks)
# then
assert actual.maximums.loc["hw01"] == 1
assert actual.maximums.loc["hw02"] == 1
assert actual.maximums.loc["hw03"] == 1
assert actual.maximums.loc["lab01"] == 20
assert actual.points.loc["A1", "hw01"] == 1 / 2
assert actual.points.loc["A1", "hw02"] == 30 / 50
def test_drop_lowest_counts_lates_as_zeros():
# given
columns = ["hw01", "hw02"]
p1 = pd.Series(data=[10, 5], index=columns, name="A1")
p2 = pd.Series(data=[10, 10], index=columns, name="A2")
points = pd.DataFrame([p1, p2])
maximums = pd.Series([10, 10], index=columns)
gradebook = gradelib.Gradebook(points, maximums)
gradebook.late.iloc[0, 0] = True
# since A1's perfect homework is late, it should count as zero and be
# dropped
# when
actual = gradebook.drop_lowest(1)
# then
assert actual.dropped.iloc[0, 0]
assert list(actual.dropped.sum(axis=1)) == [1, 1]
assert_gradebook_is_sound(actual)
def test_drop_lowest_on_simple_example_2():
# given
columns = ["hw01", "hw02", "hw03", "lab01"]
p1 = pd.Series(data=[1, 30, 90, 20], index=columns, name="A1")
p2 = pd.Series(data=[2, 7, 15, 20], index=columns, name="A2")
points = pd.DataFrame([p1, p2])
maximums = pd.Series([2, 50, 100, 20], index=columns)
gradebook = gradelib.Gradebook(points, maximums)
homeworks = gradebook.assignments.starting_with("hw")
# if we are dropping 1 HW, the right strategy is to drop the 50 point HW
# for A1 and to drop the 100 point homework for A2
# when
actual = gradebook.drop_lowest(2, within=homeworks)
# then
assert not actual.dropped.iloc[0, 2]
assert not actual.dropped.iloc[1, 0]
assert list(actual.dropped.sum(axis=1)) == [2, 2]
assert_gradebook_is_sound(actual)
def test_add_assignment_default_none_dropped_or_late():
# given
columns = ["hw01", "hw01 - programming", "hw02", "lab01"]
p1 = pd.Series(data=[1, 30, 90, 20], index=columns, name="A1")
p2 = pd.Series(data=[2, 7, 15, 20], index=columns, name="A2")
points = pd.DataFrame([p1, p2])
maximums = pd.Series([2, 50, 100, 20], index=columns)
gradebook = gradelib.Gradebook(points, maximums)
assignment_points = pd.Series([10, 20], index=["A1", "A2"])
assignment_max = 20
# when
result = gradebook.add_assignment(
"new",
assignment_points,
20,
)
# then
assert result.late.loc["A1", "new"] == False
assert result.dropped.loc["A1", "new"] == False
def test_drop_lowest_ignores_assignments_alread_dropped():
# given
columns = ["hw01", "hw02", "hw03", "hw04"]
p1 = pd.Series(data=[9, 0, 7, 0], index=columns, name="A1")
p2 = pd.Series(data=[10, 10, 10, 10], index=columns, name="A2")
points = pd.DataFrame([p1, p2])
maximums = pd.Series([10, 10, 10, 10], index=columns)
gradebook = gradelib.Gradebook(points, maximums)
gradebook.dropped.loc["A1", "hw02"] = True
gradebook.dropped.loc["A1", "hw04"] = True
# since A1's perfect homeworks are already dropped, we should drop a third
# homework, too: this will be HW03
# when
actual = gradebook.drop_lowest(1)
# then
assert actual.dropped.loc["A1", "hw04"]
assert actual.dropped.loc["A1", "hw02"]
assert actual.dropped.loc["A1", "hw03"]
assert list(actual.dropped.sum(axis=1)) == [3, 1]
assert_gradebook_is_sound(actual)
def test_unify_assignments():
"""test that points / maximums are added across unified assignments"""
# given
columns = ["hw01", "hw01 - programming", "hw02", "lab01"]
p1 = pd.Series(data=[1, 30, 90, 20], index=columns, name="A1")
p2 = pd.Series(data=[2, 7, 15, 20], index=columns, name="A2")
points = pd.DataFrame([p1, p2])
maximums = pd.Series([2, 50, 100, 20], index=columns)
gradebook = gradelib.Gradebook(points, maximums)
HOMEWORK_01_PARTS = gradebook.assignments.starting_with("hw01")
# when
result = gradebook.unify_assignments({"hw01": HOMEWORK_01_PARTS})
# then
assert len(result.assignments) == 3
assert result.maximums["hw01"] == 52
assert result.points.loc["A1", "hw01"] == 31
assert result.maximums.shape[0] == 3
assert result.late.shape[1] == 3
assert result.dropped.shape[1] == 3
assert result.points.shape[1] == 3
import pytest
import pandas as pd
import numpy as np
import gradelib
EXAMPLES_DIRECTORY = pathlib.Path(__file__).parent / "examples"
GRADESCOPE_EXAMPLE = gradelib.Gradebook.from_gradescope(EXAMPLES_DIRECTORY /
"gradescope.csv")
CANVAS_EXAMPLE = gradelib.Gradebook.from_canvas(EXAMPLES_DIRECTORY /
"canvas.csv")
# the canvas example has Lab 01, which is also in Gradescope. Let's remove it
CANVAS_WITHOUT_LAB_EXAMPLE = gradelib.Gradebook(
points=CANVAS_EXAMPLE.points.drop(columns="lab 01"),
maximums=CANVAS_EXAMPLE.maximums.drop(index="lab 01"),
late=CANVAS_EXAMPLE.late.drop(columns="lab 01"),
dropped=CANVAS_EXAMPLE.dropped.drop(columns="lab 01"),
)
# given
ROSTER = gradelib.read_egrades_roster(EXAMPLES_DIRECTORY / "egrades.csv")
def assert_gradebook_is_sound(gradebook):
assert gradebook.points.shape == gradebook.dropped.shape == gradebook.late.shape
assert (gradebook.points.columns == gradebook.dropped.columns).all()
assert (gradebook.points.columns == gradebook.late.columns).all()
assert (gradebook.points.index == gradebook.dropped.index).all()
assert (gradebook.points.index == gradebook.late.index).all()
assert (gradebook.points.columns == gradebook.maximums.index).all()