def test_compute_angles():
path = pd.DataFrame({
't': [2, 4.5, 10.5],
'x': [0, 1, 1],
'y': [0, 0, 1],
'isHB': [True, True, False]
})
assert path_diversity.compute_angles(path) == [None, 90.0, None]
assert path_diversity.compute_angles(path, True) == [None, np.pi / 2, None]
def test_compute_angles_input():
# Check if function raises the correct type of errors.
path = pd.DataFrame({
't': [2, 1, -1],
'x': [5, 3, 8],
'y': [-3, 0, 0],
'isHB': [True, False, False]
})
# not pandas DataFrame
like_path = [1, 2, 3]
# not having keys 'x', 'y'
like_path2 = pd.DataFrame({
'x': [5, -2, 1],
't': [-2, 3, 1],
'isHB': [True, True, False]
})
# length is less than 2
like_path3 = pd.DataFrame({'t': [2], 'x': [5], 'y': [3], 'isHB': [True]})
with pytest.raises(TypeError) as excinfo:
path_diversity.compute_angles(path, 1)
assert excinfo.value.args[0] == 'radian must be bool'
with pytest.raises(TypeError) as excinfo:
path_diversity.compute_angles(like_path, True)
assert excinfo.value.args[0] == "path_obj must be pandas DataFrame"
with pytest.raises(ValueError) as excinfo:
path_diversity.compute_angles(like_path2, True)
assert excinfo.value.args[
0] == "the keys of path_obj must contain 'x', 'y'"
with pytest.raises(ValueError) as excinfo:
path_diversity.compute_angles(like_path3, True)
assert excinfo.value.args[0] == "path_obj must contain at least 3 rows"
def test_compute_angles_input():
# Check if function raises the correct type of errors.
path = pd.DataFrame({'t': [2, 1, -1], 'x': [5, 3, 8],
'y': [-3, 0, 0], 'isHB': [True, False, False]})
# not pandas DataFrame
like_path = [1, 2, 3]
# not having keys 'x', 'y'
like_path2 = pd.DataFrame(
{'x': [5, -2, 1], 't': [-2, 3, 1], 'isHB': [True, True, False]})
# length is less than 2
like_path3 = pd.DataFrame({'t': [2], 'x': [5], 'y': [3], 'isHB': [True]})
with pytest.raises(TypeError) as excinfo:
path_diversity.compute_angles(path, 1)
assert excinfo.value.args[0] == 'radian must be bool'
with pytest.raises(TypeError) as excinfo:
path_diversity.compute_angles(like_path, True)
assert excinfo.value.args[0] == "path_obj must be pandas DataFrame"
with pytest.raises(ValueError) as excinfo:
path_diversity.compute_angles(like_path2, True)
assert excinfo.value.args[
0] == "the keys of path_obj must contain 'x', 'y'"
with pytest.raises(ValueError) as excinfo:
path_diversity.compute_angles(like_path3, True)
assert excinfo.value.args[0] == "path_obj must contain at least 3 rows"
def test_compute_angles():
path = pd.DataFrame({'t': [2, 4.5, 10.5], 'x': [0, 1, 1], 'y': [
0, 0, 1], 'isHB': [True, True, False]})
assert path_diversity.compute_angles(path) == [None, 90.0, None]
assert path_diversity.compute_angles(path, True) == [None, np.pi / 2, None]
def detect_noise(movement, paths, angle_threshold, delta_t):
r"""
Return a list object containing boolean values at points
where measurement noise is detected and will be passed to
a smoothing function
Parameters
----------
movement : pandas.DataFrame
CT, CX, CY coordinates and homebase status
for the unique combination of strain, mouse and day
paths index : a list containing the indices for all paths
angle_threshold : float
positive number indicating the minimum turning angle to flag as noise
delta_t : float
positive number indicating the delta_time interval
Returns
-------
noise index : a pandas series containing the indices at which
noise, as defined by input parameters, is detected
Examples
--------
>>> movement = data.load_movement(1, 2, 1)
>>> paths = path_diversity.path_index(movement, 1, 1)
>>> noise = detect_noise(movement, paths, 135, .1)
"""
# check if all inputs are positive
conditions_value = [angle_threshold <= 0, delta_t <= 0]
if any(conditions_value):
raise ValueError("Input values need to be positive")
if not isinstance(movement, pd.core.frame.DataFrame):
raise TypeError("Movement must be pandas DataFrame")
if set(movement.keys()) != {'isHB', 't', 'x', 'y'}:
raise ValueError(
"The keys of movement must be 't', 'x', 'y', and 'isHB'")
if len(movement) <= 1:
raise ValueError("Movement must contain at least 2 rows")
noise_index = 1
noise_path = []
noise_path = pd.Series(noise_path)
current_noise = False
for path in paths:
path_obj = movement[path[0]:path[1] + 1]
if len(path_obj) > 3:
path_obj['angles'] = compute_angles(path_obj, False)
path_obj['sharp_angle'] = path_obj['angles'] > angle_threshold
path_obj['noise'] = 0
# Note: The above DataFrame manipulations result in a
# SettingWithCopyWarning. The warning persists even after
# attempting the following format:
# .loc[row_indexer,col_indexer] = value. Despite this,
# the output of the function is working as intended.
for i in range(0, len(path_obj) - 1):
if path_obj['sharp_angle'].iloc[i]:
if path_obj['sharp_angle'].iloc[i + 1]:
if path_obj['t'].iloc[
i + 1] - path_obj['t'].iloc[i] < delta_t:
path_obj['noise'].iloc[i] = noise_index
path_obj['noise'].iloc[i + 1] = noise_index
current_noise = True
elif current_noise:
noise_index += 1
current_noise = False
elif current_noise:
noise_index += 1
current_noise = False
else:
path_obj['noise'] = 0
noise_path = noise_path.append(path_obj.noise)
return noise_path
def detect_noise(movement, paths, angle_threshold, delta_t):
r"""
Return a list object containing boolean values at points
where measurement noise is detected and will be passed to
a smoothing function
Parameters
----------
movement : pandas.DataFrame
CT, CX, CY coordinates and homebase status
for the unique combination of strain, mouse and day
paths index : a list containing the indices for all paths
angle_threshold : float
positive number indicating the minimum turning angle to flag as noise
delta_t : float
positive number indicating the delta_time interval
Returns
-------
noise index : a pandas series containing the indices at which
noise, as defined by input parameters, is detected
Examples
--------
>>> movement = data.load_movement(1, 2, 1)
>>> paths = path_diversity.path_index(movement, 1, 1)
>>> noise = detect_noise(movement, paths, 135, .1)
"""
# check if all inputs are positive
conditions_value = [angle_threshold <= 0, delta_t <= 0]
if any(conditions_value):
raise ValueError("Input values need to be positive")
if not isinstance(movement, pd.core.frame.DataFrame):
raise TypeError("Movement must be pandas DataFrame")
if set(movement.keys()) != {'isHB', 't', 'x', 'y'}:
raise ValueError(
"The keys of movement must be 't', 'x', 'y', and 'isHB'")
if len(movement) <= 1:
raise ValueError("Movement must contain at least 2 rows")
noise_index = 1
noise_path = []
noise_path = pd.Series(noise_path)
current_noise = False
for path in paths:
path_obj = movement[path[0]:path[1] + 1]
if len(path_obj) > 3:
path_obj['angles'] = compute_angles(path_obj, False)
path_obj['sharp_angle'] = path_obj['angles'] > angle_threshold
path_obj['noise'] = 0
# Note: The above DataFrame manipulations result in a
# SettingWithCopyWarning. The warning persists even after
# attempting the following format:
# .loc[row_indexer,col_indexer] = value. Despite this,
# the output of the function is working as intended.
for i in range(0, len(path_obj) - 1):
if path_obj['sharp_angle'].iloc[i]:
if path_obj['sharp_angle'].iloc[i + 1]:
if path_obj['t'].iloc[
i + 1] - path_obj['t'].iloc[i] < delta_t:
path_obj['noise'].iloc[i] = noise_index
path_obj['noise'].iloc[i + 1] = noise_index
current_noise = True
elif current_noise:
noise_index += 1
current_noise = False
elif current_noise:
noise_index += 1
current_noise = False
else:
path_obj['noise'] = 0
noise_path = noise_path.append(path_obj.noise)
return(noise_path)