def test_fmap_with_stream_array():
x = StreamArray(dimension=2, dtype=int)
@fmap_e
def g(v): return 2*v
y = g(x)
A = np.array([[1, 10], [2, 20], [3, 30]])
x.extend(A)
run()
assert np.array_equal(
recent_values(y),
[[2, 20], [4, 40], [6, 60]])
x.append(np.array([4, 40]))
run()
assert np.array_equal(
recent_values(y),
[[2, 20], [4, 40], [6, 60], [8, 80]])
def stream_test():
# Numpy type for testing stream array.
txyz_dtype = np.dtype([('time', 'int'), ('data', '3float')])
#--------------------------------------------
# Testing StreamArray with positive dimension
s = StreamArray(name='s', dimension=3)
# Each element of s is a numpy array with with 3 elements
# Initially s is empty. So s.stop == 0
assert s.stop == 0
# If num_in_memory is not specified in the declaration for
# s, the default value, DEFAULT_NUM_IN_MEMORY, is used.
# The length of s.recent is twice num_in_memory
assert len(s.recent) == 2 * DEFAULT_NUM_IN_MEMORY
# Append a numpy array with 3 zeros to s
s.append(np.zeros(3))
assert (s.stop == 1)
# Thus s.recent[:s.stop] is an array with 1 row and 3 columns.
assert (np.array_equal(s.recent[:s.stop], np.array([[0.0, 0.0, 0.0]])))
# Extend s by an array with 2 rows and 3 columns. The number of
# columns must equal the dimension of the stream array.
s.extend(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]))
# s.stop is incremented to account for the addition of two elements.
assert s.stop == 3
# s.recent[:s.stop] includes all the elements added to s.
# Thus s.recent[:s.stop] is an array with 3 rows and 3 columns.
assert (np.array_equal(
s.recent[:s.stop],
np.array([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])))
# Extend s by an array with 1 row and 3 columns. The number of
# columns must equal the dimension of the stream array.
s.extend(np.array([[7.0, 8.0, 9.0]]))
# s.stop is incremented to account for the addition of a single row.
assert s.stop == 4
# Thus s.recent[:s.stop] is an array with 4 rows and 3 columns.
assert np.array_equal(
s.recent[:s.stop],
np.array([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]]))
# Note the difference between EXTENDING s with an array consisting
# of 1 row and 3 columns, versus APPENDING a rank-1 array consisting
# of 3 elements, as in the following example.
s.append(np.array([10.0, 11.0, 12.0]))
# s.stop is incremented to account for the addition of a single row.
assert s.stop == 5
# Thus s.recent[:s.stop] is an array with 5 rows and 3 columns.
assert np.array_equal(
s.recent[:s.stop],
np.array([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0],
[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]))
#---------------------------------------------------------------
# Testing StreamArray with zero dimension and user-defined dtype
t = StreamArray(name='t', dimension=0, dtype=txyz_dtype)
# Each element of t is an object of dtype txyz_dtype
# t[i]['time'] is an int.
# t[i]['data'] is a 3-tuple consisting of 3 floats.
# Nothing has been appended to t, and so t.stop == 0.
assert (t.stop == 0)
# Append an object with 'time' = 1, and 'data' = [0.0, 1.0, 2.0].
t.append(np.array((1, [0.0, 1.0, 2.0]), dtype=txyz_dtype))
# Increase t.stop to account for the element that has just been
# added to t.
assert t.stop == 1
# t.recent[:t.stop] contains all elements appended to t.
assert t.recent[:t.stop] == np.array([(1, [0.0, 1.0, 2.0])],
dtype=txyz_dtype)
assert t.recent[0]['time'] == np.array(1)
assert np.array_equal(t.recent[0]['data'], np.array([0., 1., 2.]))
# Append another element to t.
t.append(np.array((2, [11.0, 12.0, 13.0]), dtype=txyz_dtype))
# Increase t.stop to account for the element that has just been
# added to t.
assert (t.stop == 2)
# t.recent[:t.stop] contains all elements appended to t.
a = np.array([(1, [0.0, 1.0, 2.0]), (2, [11.0, 12.0, 13.0])],
dtype=txyz_dtype)
assert np.array_equal(t.recent[:t.stop], a)
# Extend t by a list of 2 elements each of which consists of
# zeroes of txyz_dtype
t.extend(np.zeros(2, dtype=txyz_dtype))
# Increase t.stop to account for the element that has just been
# added to t.
assert (t.stop == 4)
# t.recent[:t.stop] contains all elements appended to t.
a = np.array([(1, [0.0, 1.0, 2.0]), (2, [11.0, 12.0, 13.0]),
(0, [0.0, 0.0, 0.0]), (0, [0.0, 0.0, 0.0])],
dtype=txyz_dtype)
assert np.array_equal(t.recent[:t.stop], a)
#---------------------------------------------------------------
# Testing simple Stream
u = Stream('u')
v = Stream('v')
# Add elements 0, 1, 2, 3 to stream u.
u.extend(list(range(4)))
# Increase u.stop to account for the element that has just been
# added to u.
assert u.stop == 4
# u.recent[:t.stop] contains all elements appended to u.
assert u.recent[:u.stop] == [0, 1, 2, 3]
# No change to v.
assert v.stop == 0
# Append element 10 to v and then append the list [40, 50]
v.append(10)
v.append([40, 50])
# Increase v.stop by 2 to account for the 2 new elements appended
# to v.
assert v.stop == 2
# v.recent[:v.stop] contains all elements appended to v.
assert v.recent[:v.stop] == [10, [40, 50]]
# Extend stream v
v.extend([60, 70, 80])
# Increase v.stop by 3 to account for the 3 new elements appended
# to v.
assert v.stop == 5
# v.recent[:v.stop] contains all elements appended to v.
assert v.recent[:v.stop] == [10, [40, 50], 60, 70, 80]
#------------------------------------------
# Test helper functions: get_contents_after_column_value()
# Also test StreamArray
y = StreamArray(name='y', dimension=0, dtype=txyz_dtype, num_in_memory=64)
# y[i]['time'] is a time (int).
# y[i]['data'] is 3-tuple usually with directional data
# for x, y, z.
# y.recent length is twice num_in_memory
assert len(y.recent) == 128
# y has no elements, so y.stop == 0
assert y.stop == 0
# Test data for StreamArray with user-defined data type.
test_data = np.zeros(128, dtype=txyz_dtype)
assert len(test_data) == 128
# Put random numbers for test_data[i]['time'] and
# test_data[i]['data'][xyx] for xyz in [0, 1, 2]
for i in range(len(test_data)):
test_data[i]['time'] = random.randint(0, 1000)
for j in range(3):
test_data[i]['data'][j] = random.randint(2000, 9999)
# ordered_test_data has time in increasing order.
ordered_test_data = np.copy(test_data)
for i in range(len(ordered_test_data)):
ordered_test_data[i]['time'] = i
y.extend(ordered_test_data[:60])
# extending y does not change length of y.recent
assert (len(y.recent) == 128)
# y.stop increases to accommodate the extension of y by 60.
assert (y.stop == 60)
# y.recent[:y.stop] now contains all the values put into y.
assert np.array_equal(y.recent[:y.stop], ordered_test_data[:60])
assert np.array_equal(
y.get_contents_after_column_value(column_number=0, value=50),
ordered_test_data[50:60])
assert np.array_equal(y.get_contents_after_time(start_time=50),
ordered_test_data[50:60])
assert (y.get_index_for_column_value(column_number=0, value=50) == 50)
yz = StreamArray(name='yz',
dimension=0,
dtype=txyz_dtype,
num_in_memory=64)
c = np.array((1, [0., 1., 2.]), dtype=txyz_dtype)
yz.append(c)
assert np.array_equal(yz.recent[:yz.stop],
np.array([(1, [0., 1., 2.])], dtype=txyz_dtype))
d = np.array([(2, [3., 4., 5.]), (3, [6., 7., 8.])], dtype=txyz_dtype)
yz.extend(d)
assert np.array_equal(
yz.recent[:yz.stop],
np.array([(1, [0., 1., 2.]), (2, [3., 4., 5.]), (3, [6., 7., 8.])],
dtype=txyz_dtype))
#------------------------------------------
# TESTING regular Stream class
x = Stream(name='x', num_in_memory=8)
# The length of x.recent is twice num_in_memory
assert (len(x.recent) == 16)
# No values have been appended to stream x; so x.stop == 0
assert (x.stop == 0)
# Test append
x.append(10)
# Appending values to x does not change len(x.recent)
assert (len(x.recent) == 16)
# x.stop increases to accomodate the value appended.
assert (x.stop == 1)
# x.recent[:x.stop] includes the latest append
assert (x.recent[:x.stop] == [10])
x.append(20)
assert (len(x.recent) == 16)
# x.stop increases to accomodate the value appended.
assert (x.stop == 2)
# x.recent[:x.stop] includes the latest append
assert (x.recent[:2] == [10, 20])
# Test extend
x.extend([30, 40])
assert (len(x.recent) == 16)
# x.stop increases to accomodate the values extended.
assert (x.stop == 4)
# x.recent[:x.stop] includes the latest extend
assert (x.recent[:x.stop] == [10, 20, 30, 40])
# Checking extension with the empty list.
x.extend([])
assert (len(x.recent) == 16)
# extending a stream with the empty list does not change
# the stream.
assert (x.stop == 4)
assert (x.recent[:4] == [10, 20, 30, 40])
# Checking extending a stream with a singleton list
x.extend([50])
assert (len(x.recent) == 16)
assert (x.stop == 5)
assert (x.recent[:5] == [10, 20, 30, 40, 50])
# Check registering a reader.
# Register a reader called 'a' for stream x starting
# to read from x[3] onwards.
x.register_reader('a', 3)
# Register a reader called 'b' for stream x starting
# to read from x[4] onwards.
x.register_reader('b', 4)
# x.start is a dict which identifies the readers of x
# and where they are starting to read from.
assert (x.start == {'a': 3, 'b': 4})
x.extend([1, 2, 3, 4, 5])
assert (len(x.recent) == 16)
assert (x.stop == 10)
assert (x.recent[:10] == [10, 20, 30, 40, 50, 1, 2, 3, 4, 5])
assert (x.start == {'a': 3, 'b': 4})
x.register_reader('a', 7)
x.register_reader('b', 7)
assert (x.start == {'a': 7, 'b': 7})
#------------------------------------------
# Test helper functions
assert (x.get_last_n(n=2) == [4, 5])
v = StreamArray(dimension=(3, 4), dtype=int)
v.append(np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]))
a = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])
np.array_equal(v.recent[:v.stop],
np.array([[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]))
v.extend(
np.array([[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
[[24, 25, 26, 27], [28, 29, 30, 31], [32, 33, 34, 35]]]))
np.array_equal(
v.recent[:v.stop],
np.array([[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]],
[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
[[24, 25, 26, 27], [28, 29, 30, 31], [32, 33, 34, 35]]]))
u = StreamArray(name='u', dimension=2, dtype=int)
a = np.array([0, 1])
u.append(a)
np.array_equal(u.recent[:u.stop], np.array([[0, 1]]))
u.extend(np.array([[2, 3], [4, 5], [6, 7]]))
np.array_equal(u.recent[:u.stop], np.array([[0, 1], [2, 3], [4, 5], [6,
7]]))
t = StreamArray('t')
t.append(np.array(1.0))
t.extend(np.array([2.0, 3.0]))
np.array_equal(t.recent[:t.stop], np.array([1.0, 2.0, 3.0]))
