def test_detect_noise():
movement = {
't': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'], index=[0, 1, 2, 3])
}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 2, 1)
# Check if function produces the correct outputs
noise = path_diversity.detect_noise(movement, paths, 120, 1)
noise = list(noise)
assert noise == [0, 1, 1, 0]
movement = {
't': pd.Series([0., 2., 4., 7.], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'], index=[0, 1, 2, 3])
}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 4, 1)
# Check if function produces the correct outputs
noise = path_diversity.detect_noise(movement, paths, 120, 1)
noise = list(noise)
assert noise == [0, 0, 0, 0]
def test_smooth_noise():
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = smooth_noise.smooth_noise(movement, paths, 120, 1)
assert len(smoothed) == 3
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = smooth_noise.smooth_noise(movement, paths, 120, 1)
assert smoothed['y'][1] == 0.5
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = smooth_noise.smooth_noise(movement, paths, 120, 1)
assert smoothed['x'][1] == 0.05
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = smooth_noise.smooth_noise(movement, paths, 120, 1)
assert smoothed['t'][1] == 0.03
def test_smooth_noise():
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = path_diversity.smooth_noise(movement, paths, 120, 1)
assert len(smoothed) == 3
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = path_diversity.smooth_noise(movement, paths, 120, 1)
assert smoothed['y'][1] == 0.5
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = path_diversity.smooth_noise(movement, paths, 120, 1)
assert smoothed['x'][1] == 0.05
movement = {'t': pd.Series([0., 0.02, 0.04, 0.06], index=[0, 1, 2, 3]),
'x': pd.Series([0., 0., 0.1, 0.2], index=[0, 1, 2, 3]),
'y': pd.Series([0., 1., 0., 1.], index=[0, 1, 2, 3]),
'isHB': pd.Series(['No', 'No', 'No', 'No'],
index=[0, 1, 2, 3])}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 1, 1)
# Check if function produces the correct outputs.
smoothed = path_diversity.smooth_noise(movement, paths, 120, 1)
assert smoothed['t'][1] == 0.03
def test_filter_path_input():
movement = data.load_movement(0, 0, 0)
paths = path_diversity.path_index(movement, 1, 1)
# checking functions raise the correct errors
# input negative number
with pytest.raises(ValueError) as excinfo:
path_diversity.filter_path(movement, paths, -1)
assert excinfo.value.args[0] == "Input values need to be positive"
# input zeros
with pytest.raises(ValueError) as excinfo:
path_diversity.filter_path(movement, paths, 0)
assert excinfo.value.args[0] == "Input values need to be positive"
def test_path():
movement = {
't': pd.Series([0.5, 0.6, 1.0, 2.5, 2.6, 2.7,
4.0, 4.5, 5.0, 6.0, 8.0, 8.5, 10.0]),
'x': pd.Series([1.42, 1.96, 1.79, 1.85, 1.80, 1.98,
1.19, 1.30, 1.85, 1.86, 1.90, 1.76, 1.65]),
'y': pd.Series([1.67, 1.60, 1.13, 1.95, 1.65, 1.11,
1.71, 1.37, 1.88, 1.97, 1.11, 1.15, 1.38])
}
movement = pd.DataFrame(movement)
# Checking functions output the correct path
paths = path_diversity.path_index(movement, 1, 1)
assert paths == [[0, 2], [3, 5], [6, 8]]
def test_filter_path():
movement = {
't': pd.Series([0.5, 0.6, 1.0, 2.5, 2.6, 2.7,
4.0, 4.5, 5.0, 6.0, 8.0, 8.5, 10.0]),
'x': pd.Series([1.42, 1.96, 1.79, 1.85, 1.80, 1.98,
1.19, 1.30, 1.85, 1.86, 1.90, 1.76, 1.65]),
'y': pd.Series([1.67, 1.60, 1.13, 1.95, 1.65, 1.11,
1.71, 1.37, 1.88, 1.97, 1.11, 1.15, 1.38])
}
movement = pd.DataFrame(movement)
paths = path_diversity.path_index(movement, 1, 1)
# Checking functions output the correct path
pass_paths = path_diversity.filter_path(movement, paths, 1)
assert pass_paths == [[6, 8]]
def test_detect_noise_input():
movement = data.load_movement(0, 0, 0)
paths = path_diversity.path_index(movement, 1, 1)
# Check if function raises the correct type of errors.
# Input negative angle_threshold
with pytest.raises(ValueError) as excinfo:
path_diversity.detect_noise(movement, paths, -1, 1)
assert excinfo.value.args[0] == "Input values need to be positive"
# Input negative delta_t
with pytest.raises(ValueError) as excinfo:
path_diversity.detect_noise(movement, paths, 1, -1)
assert excinfo.value.args[0] == "Input values need to be positive"
# Input zero angle_threshold
with pytest.raises(ValueError) as excinfo:
path_diversity.detect_noise(movement, paths, 0, 1)
assert excinfo.value.args[0] == "Input values need to be positive"
# Input zero delta_t
with pytest.raises(ValueError) as excinfo:
path_diversity.detect_noise(movement, paths, 1, 0)
assert excinfo.value.args[0] == "Input values need to be positive"
def test_path_input():
movement = data.load_movement(0, 0, 0)
# checking functions raise the correct errors
# input negative number
with pytest.raises(ValueError) as excinfo:
path_index(movement, -1, -1)
assert excinfo.value.args[0] == "Input values need to be positive"
# input zeros
with pytest.raises(ValueError) as excinfo:
path_index(movement, 0, 0)
assert excinfo.value.args[0] == "Input values need to be positive"
# min_path_length cannot be floating number
with pytest.raises(TypeError) as excinfo:
path_index(movement, 1, 1.5)
assert excinfo.value.args[0] == "min_path_length needs to be integer"
def test_path():
movement = data.load_movement(0, 0, 0)
# Checking functions output the correct path
paths = path_index(movement, 1, 1)
assert paths[:5] == [[22, 53], [55, 59], [67, 89], [91, 95], [96, 114]]
import numpy as np
import matplotlib.pyplot as plt
from mousestyles.data import load_movement
from mousestyles.path_diversity import path_index
movement = load_movement(0, 0, 0)
paths = path_index(movement, 1, 1)
xlim = [-16.25, 3.75]
ylim = [1.0, 43.0]
for sep in paths:
path = movement[sep[0]:sep[1] + 1]
plt.plot(path['x'], path['y'], 'b',
linewidth=1, alpha=.1)
plt.xlabel('x-coordinate')
plt.xlim(xlim[0], xlim[1])
plt.ylabel('y-coordinate')
plt.ylim(ylim[0], ylim[1])
plt.title("Example of path plot")
plt.show()
def test_filter_path():
movement = data.load_movement(0, 0, 0)
paths = path_index(movement, 1, 1)
# Checking functions output the correct path
pass_paths = filter_path.filter_paths(movement, paths, 20)
assert pass_paths == [[3082, 3181], [30835, 30970], [31346, 31557]]