def test_create_plot_geolife(self, get_geolife_triplegs_with_modes):
"""Check if we can run the plot function with geolife data without error"""
modal_split = calculate_modal_split(get_geolife_triplegs_with_modes,
freq='d',
per_user=False)
plot_modal_split(modal_split)
assert True
def test_multi_user_error(self, get_test_triplegs_with_modes):
"""Create a modal split plot based on randomly generated test data"""
modal_split = calculate_modal_split(get_test_triplegs_with_modes,
freq='d',
per_user=True,
norm=True)
with pytest.raises(ValueError):
plot_modal_split(modal_split)
# make sure that there is no error if the data was correctly created
modal_split = calculate_modal_split(get_test_triplegs_with_modes,
freq='d',
per_user=False,
norm=True)
plot_modal_split(modal_split)
assert True
def test_run_modal_split_with_geolife(self, read_geolife_with_modes):
"""check if we can run all possible combinations with a small sample of the geolife data"""
tpls = read_geolife_with_modes
metric_list = ['duration', 'distance', 'count']
freq_list = [None, 'D', 'W-MON']
per_user_list = [False, True]
norm_list = [True, False]
for metric in metric_list:
for freq in freq_list:
for per_user in per_user_list:
for norm in norm_list:
calculate_modal_split(tpls, metric=metric, freq=freq, per_user=per_user, norm=norm)
# we only check if it runs through successfully
assert True
def test_modal_split_total_distance(self, test_triplegs_modal_split):
"""Check distances per user and mode without temporal binning"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric='distance', per_user=True)
# if the users would get merged, walk would be larger than bike
assert modal_split.loc[0, 'bike'] > modal_split.loc[0, 'walk']
assert modal_split.loc[0, 'walk'] > modal_split.loc[0, 'car']
def test_modal_split_total_duration(self, test_triplegs_modal_split):
"""Check duration per user and mode without temporal binning"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric='duration', per_user=True)
assert np.isclose(modal_split.loc[0, 'bike'], datetime.timedelta(minutes=1).total_seconds())
assert np.isclose(modal_split.loc[0, 'car'], datetime.timedelta(hours=1).total_seconds())
assert np.isclose(modal_split.loc[0, 'walk'], datetime.timedelta(hours=2).total_seconds())
assert np.isclose(modal_split.loc[1, 'walk'], datetime.timedelta(hours=2).total_seconds())
def test_modal_split_total_no_user_count(self, test_triplegs_modal_split):
"""Check counts without users and without temporal binning"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric='count', per_user=False)
# if the user get merged, walk would be larger than bike
assert modal_split.loc[0, 'bike'] == 1
assert modal_split.loc[0, 'car'] == 1
assert modal_split.loc[0, 'walk'] == 4
def test_modal_split_total_count(self, test_triplegs_modal_split):
"""Check counts per user and mode without temporal binning"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric="count", per_user=True)
# if the user get merged, walk would be larger than bike
assert modal_split.loc[0, "bike"] == 1
assert modal_split.loc[0, "car"] == 1
assert modal_split.loc[0, "walk"] == 2
assert modal_split.loc[1, "walk"] == 2
def test_check_dtype_error(self, get_geolife_triplegs_with_modes):
"""Check if error is thrown correctly when index is not datetime
freq=None calculates the modal split over the whole period
"""
modal_split = calculate_modal_split(get_geolife_triplegs_with_modes,
freq=None,
per_user=False)
with pytest.raises(ValueError):
plot_modal_split(modal_split)
assert True
def test_modal_split_weekly_count(self, test_triplegs_modal_split):
"""Check counts per user and mode binned by week"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric='count', freq='W-MON', per_user=True)
w_1 = pd.Timestamp('1970-01-05 00:00:00', tz='utc') # data is aggregated to the next monday
w_2 = pd.Timestamp('1970-01-12 00:00:00', tz='utc')
assert modal_split.loc[[(0, w_1)], 'bike'][0] == 1
assert modal_split.loc[[(0, w_1)], 'car'][0] == 1
assert modal_split.loc[[(0, w_1)], 'walk'][0] == 1
assert modal_split.loc[[(0, w_2)], 'walk'][0] == 1
assert modal_split.loc[[(1, w_1)], 'walk'][0] == 2
def test_modal_split_weekly_count(self, test_triplegs_modal_split):
"""Check counts per user and mode binned by week"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric="count", freq="W-MON", per_user=True)
w_1 = pd.Timestamp("1970-01-05 00:00:00", tz="utc") # data is aggregated to the next monday
w_2 = pd.Timestamp("1970-01-12 00:00:00", tz="utc")
assert modal_split.loc[[(0, w_1)], "bike"][0] == 1
assert modal_split.loc[[(0, w_1)], "car"][0] == 1
assert modal_split.loc[[(0, w_1)], "walk"][0] == 1
assert modal_split.loc[[(0, w_2)], "walk"][0] == 1
assert modal_split.loc[[(1, w_1)], "walk"][0] == 2
def test_modal_split_daily_count(self, test_triplegs_modal_split):
"""Check counts per user and mode binned by day"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric='count', freq='D', per_user=True)
t_1 = pd.Timestamp('1970-01-01 00:00:00', tz='utc')
t_2 = pd.Timestamp('1970-01-02 00:00:00', tz='utc')
t_8 = pd.Timestamp('1970-01-08 00:00:00', tz='utc')
assert modal_split.loc[[(0, t_1)], 'bike'][0] == 1
assert modal_split.loc[[(0, t_1)], 'car'][0] == 1
assert modal_split.loc[[(0, t_2)], 'walk'][0] == 1
assert modal_split.loc[[(0, t_8)], 'walk'][0] == 1
assert modal_split.loc[[(1, t_1)], 'walk'][0] == 2
def test_modal_split_daily_count(self, test_triplegs_modal_split):
"""Check counts per user and mode binned by day"""
tpls = test_triplegs_modal_split
modal_split = calculate_modal_split(tpls, metric="count", freq="D", per_user=True)
t_1 = pd.Timestamp("1970-01-01 00:00:00", tz="utc")
t_2 = pd.Timestamp("1970-01-02 00:00:00", tz="utc")
t_8 = pd.Timestamp("1970-01-08 00:00:00", tz="utc")
assert modal_split.loc[[(0, t_1)], "bike"][0] == 1
assert modal_split.loc[[(0, t_1)], "car"][0] == 1
assert modal_split.loc[[(0, t_2)], "walk"][0] == 1
assert modal_split.loc[[(0, t_8)], "walk"][0] == 1
assert modal_split.loc[[(1, t_1)], "walk"][0] == 2
def test_create_plot_testdata(self, get_test_triplegs_with_modes):
"""Create a modal split plot based on randomly generated test data"""
tmp_file = os.path.join('tests', 'data', 'modal_split_plot.png')
modal_split = calculate_modal_split(get_test_triplegs_with_modes,
freq='d',
per_user=False,
norm=True)
modal_split = modal_split[['walk', 'bike', 'train', 'car', 'bus'
]] # change order for the looks of the plot
plot_modal_split(modal_split,
out_path=tmp_file,
date_fmt_x_axis='%d',
y_label='Percentage of daily count',
x_label='days')
assert os.path.exists(tmp_file)
os.remove(tmp_file)
os.remove(tmp_file.replace('png', 'pdf'))
def test_create_plot_testdata(self, get_test_triplegs_with_modes):
"""Create a modal split plot based on randomly generated test data"""
tmp_file = os.path.join("tests", "data", "modal_split_plot.png")
modal_split = calculate_modal_split(get_test_triplegs_with_modes,
freq="d",
per_user=False,
norm=True)
modal_split = modal_split[["walk", "bike", "train", "car", "bus"
]] # change order for the looks of the plot
plot_modal_split(modal_split,
out_path=tmp_file,
date_fmt_x_axis="%d",
y_label="Percentage of daily count",
x_label="days")
assert os.path.exists(tmp_file)
os.remove(tmp_file)
os.remove(tmp_file.replace("png", "pdf"))
