def test_create_collapse_intervals1():
# Checking intervals are being correctly loaded for a particular
# activity type and mouse-day
# More specifically this is testing 'Food' Consumption
# for strain=0, mouse=0, day=0
load_ints1 = metrics.create_intervals('F', strain=0, mouse=0, day=0)
# Collapse the intervals within a given threshold
cp = behavior.create_collapsed_intervals(load_ints1, bout_threshold=0.001)
# Calculate the sum of the loaded intervals
ints1_sum_collapsed = sum(cp)
# We now do the above calculations using a manual query
l_ints_manual = data.load_intervals('F')
l_ints_manual = l_ints_manual.query('strain == 0 and mouse == 0 and \
day == 0')[['start', 'stop']]
ints_manual = intervals.Intervals(l_ints_manual)
# Collapse the intervals within a given threshold
ints_man_collapsed = ints_manual.copy().connect_gaps(eps=0.001)
ints_man_sum_collapsed = sum(ints_man_collapsed)
# Check that the sum of total values from manual query and
# function output match for every value
assert (ints1_sum_collapsed == ints_man_sum_collapsed).all()
def test_process_raw_intervals():
ints_raw = np.array([[1, 2], [2.5, 3]])
ints = intervals.Intervals(ints_raw)
consump = 100
active_time = 4
total_time = 5
eps = 1
tree = behavior.process_raw_intervals('F', consump, ints, active_time,
total_time, eps)
assert len(tree.contents) == 9
assert tree['Consumption Rate'] == consump / total_time
assert tree['AS Prob'] == active_time / total_time
def test_create_intervals():
# Checking intervals are being correctly loaded for a particular
# activity type and mouse-day
load_ints1 = metrics.create_intervals('F', strain=0, mouse=0, day=0)
# Calculate the sum of the loaded intervals
ints1_sum = sum(load_ints1)
# We now do the above calculations using a manual query
l_ints_manual = data.load_intervals('F')
l_ints_manual = l_ints_manual.query('strain == 0 and mouse == 0 and \
day == 0')[['start', 'stop']]
ints_manual = intervals.Intervals(l_ints_manual)
ints_manual_sum = sum(ints_manual)
assert ints1_sum.all() == ints_manual_sum.all()
def create_intervals(activity_type, strain, mouse, day):
r"""Returns an interval object on a certain mouse-strain-day
for a given activity type.
Parameters
----------
activity_type : str
String specifying activity type {"AS", "F", "IS", "M_AS", "M_IS", "W"}
strain : int
Integer representing the strain of the mouse
mouse : int
Integer representing the specific mouse
day : int
Integer representing the day to produce the metrics for
bout_threshold: : float
Float representing the time threshold to use for collapsing separate
events into bouts
Returns
-------
An intervals object `intervals` for the given activity type and
mouse-strain-day within a bout threshold
Examples
--------
>>> ints=behavior.create_intervals(activity_type = 'F', strain = 0
, mouse = 0, day = 0
, bout_threshold = 0.001)
>>> print(sum(ints))
"""
# Load intervals by activity type
l_ints = data.load_intervals(activity_type)
# Subset to M*2 array of (start, stop) intervals based on
# specific mouse, strain and day
l_ints = _select_strain_mouse_day_in_data_frame(l_ints, strain, mouse, day)
l_ints = l_ints[['start', 'stop']]
# Create and return the intervals object
return intervals.Intervals(l_ints)
def test_create_intervals2():
# Checking intervals are being correctly loaded for a particular
# activity type and mouse-day
# More specifically this is testing 'Water' Consumption
# for strain=0, mouse=1, day=1
load_ints1 = metrics.create_intervals('W', strain=0, mouse=1, day=1)
# Calculate the sum of the loaded intervals
ints1_sum = sum(load_ints1)
# We now do the above calculations using a manual query
l_ints_manual = data.load_intervals('W')
l_ints_manual = l_ints_manual.query('strain == 0 and mouse == 1 and \
day == 1')[['start', 'stop']]
ints_manual = intervals.Intervals(l_ints_manual)
ints_manual_sum = sum(ints_manual)
# Check that the sum of total values from manual query and
# function output match for every value
assert (ints1_sum == ints_manual_sum).all()