def operator_overload(self, another_stream, func):
from merge import merge_list
assert another_stream.dimension == self.dimension, \
'Both stream arrays must have the same dimension.' \
'The dimensions are {0} and {1}'.format(
self.dimension, another_stream.dimension)
assert another_stream.dtype == self.dtype, \
'Both stream arrays must have the same dtype.' \
'The dtypes are {0} and {1}'.format(
self.dtype, another_stream.dtype)
output_stream = StreamArray(dimension=self.dimension, dtype=self.dtype)
merge_list(func=func,
in_streams=[self, another_stream],
out_stream=output_stream)
return output_stream
def test_merge_list(self):
"""
Tests the merge_list(list) array
"""
# [1,3,5] & [2,4,6] => [1,2,3,4,5,6]
l1 = [1, 3, 5]
l2 = [2, 4, 6]
expected = [1, 2, 3, 4, 5, 6]
self.assertEqual(merge_list(l1, l2), expected)
# [1, 32, 49] & [2, 41, 83, 123] => [1, 2, 32, 41, 49, 83, 123]
l1 = [1, 32, 49]
l2 = [2, 41, 83, 123]
expected = [1, 2, 32, 41, 49, 83, 123]
self.assertEqual(merge_list(l1, l2), expected)
# Tests when numbers equal each other
# [1, 2, 3] & [1, 2, 3] => [1, 1, 2, 2, 3, 3]
l1 = [1, 2, 3]
l2 = [1, 2, 3]
expected = [1, 1, 2, 2, 3, 3]
self.assertEqual(merge_list(l1, l2), expected)
# Test empty lists
l1 = []
l2 = []
expected = []
self.assertEqual(merge_list(l1, l2), expected)
# Test l1 empty
l1 = [1, 2, 3]
l2 = []
expected = [1, 2, 3]
self.assertEqual(merge_list(l1, l2), expected)
# Test l2 empty
l1 = []
l2 = [1, 2, 3]
expected = [1, 2, 3]
self.assertEqual(merge_list(l1, l2), expected)
def test_list():
scheduler = Stream.scheduler
n = Stream('n')
o = Stream('o')
p = Stream('p')
q = Stream('q')
r = Stream('r')
s = Stream('s')
t = Stream('t')
u = Stream('u')
v = Stream('v')
w = Stream('w')
x = Stream('x')
y = Stream('y')
z = Stream('z')
#-------------------------------------------------------------------
# Test simple map
def simple(lst):
return [2 * v for v in lst]
a = map_list(func=simple, in_stream=x, out_stream=y, name='a')
yy = map_list_f(simple, x)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test map with state
# Function that operates on an element and state and returns an
# element and state.
def f(input_list, state):
output_list = [[]] * len(input_list)
for i in range(len(input_list)):
output_list[i] = input_list[i] + state
state += 2
return output_list, state
b = map_list(func=f, in_stream=x, out_stream=z, state=0, name='b')
zz = map_list_f(f, x, 0)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test map with call streams
c = map_list(func=f,
in_stream=x,
out_stream=v,
state=10,
call_streams=[w],
name='c')
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test sink with state
def sink_with_state(input_list, output_list):
# sink has no output stream.
# This function only returns the next state.
return output_list.extend(input_list)
out_list = []
# In this simple example, out_list will be the same as the input
# stream.
sink_agent = sink_list(func=sink_with_state,
in_stream=x,
name='sink_agent',
state=out_list)
out_list_stream = []
# Function version of the previous agent example
sink_list_f(sink_with_state, x, out_list_stream)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test merge
# Function that operates on a list of lists
def g(list_of_lists):
return [sum(snapshot) for snapshot in zip(*list_of_lists)]
d = merge_list(func=g, in_streams=[x, u], out_stream=s, name='d')
ss = merge_list_f(g, [x, u])
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test split
def h(input_list):
return [[element + 1 for element in input_list],
[element * 2 for element in input_list]]
e = split_list(func=h, in_stream=x, out_streams=[r, t], name='e')
rr, tt = split_list_f(h, x, num_out_streams=2)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test split with state
def h_state(input_list, state):
length = len(input_list)
output_list_0 = [[]] * length
output_list_1 = [[]] * length
for i in range(length):
output_list_0[i] = input_list[i] + state
output_list_1[i] = input_list[i] * state
state += 1
return ([output_list_0, output_list_1], state)
split_list(func=h_state, in_stream=x, out_streams=[p, q], state=0)
pp, qq = split_list_f(h_state, x, num_out_streams=2, state=0)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Test many
def f_many(list_of_lists):
snapshots = zip(*list_of_lists)
return [[max(snapshot) for snapshot in snapshots],
[min(snapshot) for snapshot in snapshots]]
multi_agent = multi_list(func=f_many,
in_streams=[x, u],
out_streams=[n, o],
name='multi_agent')
nn, oo = multi_list_f(func=f_many, in_streams=[x, u], num_out_streams=2)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
x.extend(range(5))
scheduler.step()
assert recent_values(x) == range(5)
assert recent_values(y) == [0, 2, 4, 6, 8]
assert recent_values(z) == [0, 3, 6, 9, 12]
assert recent_values(v) == []
assert out_list == range(5)
assert out_list == out_list_stream
assert recent_values(s) == []
assert recent_values(r) == [1, 2, 3, 4, 5]
assert recent_values(t) == [0, 2, 4, 6, 8]
assert recent_values(p) == [0, 2, 4, 6, 8]
assert recent_values(q) == [0, 1, 4, 9, 16]
assert recent_values(n) == []
assert recent_values(o) == []
assert recent_values(y) == recent_values(yy)
assert recent_values(z) == recent_values(zz)
assert recent_values(s) == recent_values(ss)
assert recent_values(r) == recent_values(rr)
assert recent_values(t) == recent_values(tt)
assert recent_values(p) == recent_values(pp)
assert recent_values(q) == recent_values(qq)
assert recent_values(n) == recent_values(nn)
assert recent_values(o) == recent_values(oo)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
w.append(0)
scheduler.step()
assert recent_values(x) == range(5)
assert recent_values(y) == [0, 2, 4, 6, 8]
assert recent_values(z) == [0, 3, 6, 9, 12]
assert recent_values(v) == [10, 13, 16, 19, 22]
assert out_list == range(5)
assert recent_values(s) == []
assert recent_values(r) == [1, 2, 3, 4, 5]
assert recent_values(t) == [0, 2, 4, 6, 8]
assert recent_values(p) == [0, 2, 4, 6, 8]
assert recent_values(q) == [0, 1, 4, 9, 16]
assert recent_values(n) == []
assert recent_values(o) == []
assert recent_values(y) == recent_values(yy)
assert recent_values(z) == recent_values(zz)
assert recent_values(s) == recent_values(ss)
assert recent_values(r) == recent_values(rr)
assert recent_values(t) == recent_values(tt)
assert recent_values(p) == recent_values(pp)
assert recent_values(q) == recent_values(qq)
assert recent_values(n) == recent_values(nn)
assert recent_values(o) == recent_values(oo)
#-------------------------------------------------------------------
#-------------------------------------------------------------------
u.extend([10, 15, 18])
scheduler.step()
assert recent_values(s) == [10, 16, 20]
assert recent_values(n) == [10, 15, 18]
assert recent_values(o) == [0, 1, 2]
u.append(37)
scheduler.step()
assert recent_values(s) == [10, 16, 20, 40]
assert recent_values(n) == [10, 15, 18, 37]
assert recent_values(o) == [0, 1, 2, 3]
u.extend([96, 95])
scheduler.step()
assert recent_values(x) == range(5)
assert recent_values(y) == [0, 2, 4, 6, 8]
assert recent_values(z) == [0, 3, 6, 9, 12]
assert recent_values(v) == [10, 13, 16, 19, 22]
assert out_list == range(5)
assert recent_values(s) == [10, 16, 20, 40, 100]
assert recent_values(r) == [1, 2, 3, 4, 5]
assert recent_values(t) == [0, 2, 4, 6, 8]
assert recent_values(p) == [0, 2, 4, 6, 8]
assert recent_values(q) == [0, 1, 4, 9, 16]
assert recent_values(n) == [10, 15, 18, 37, 96]
assert recent_values(o) == [0, 1, 2, 3, 4]
assert recent_values(y) == recent_values(yy)
assert recent_values(z) == recent_values(zz)
assert recent_values(s) == recent_values(ss)
assert recent_values(r) == recent_values(rr)
assert recent_values(t) == recent_values(tt)
assert recent_values(p) == recent_values(pp)
assert recent_values(q) == recent_values(qq)
assert recent_values(n) == recent_values(nn)
assert recent_values(o) == recent_values(oo)
#------------------------------------------------------------------
#------------------------------------------------------------------
# Test NumPy arrays: StreamArray
#------------------------------------------------------------------
#------------------------------------------------------------------
# Test list map on StreamArray (dimension is 0).
a_stream_array = StreamArray(name='a_stream_array')
b_stream_array = StreamArray(name='b_stream_array')
def f_np(input_array):
return input_array + 1
a_np_agent = map_list(func=f_np,
in_stream=a_stream_array,
out_stream=b_stream_array,
name='a_np_agent')
bb_stream_array = map_array_f(f_np, a_stream_array)
scheduler.step()
assert np.array_equal(recent_values(b_stream_array),
np.array([], dtype=np.float64))
assert np.array_equal(recent_values(b_stream_array),
recent_values(bb_stream_array))
a_stream_array.extend(np.arange(5.0))
scheduler.step()
assert np.array_equal(recent_values(b_stream_array), np.arange(5.0) + 1)
assert np.array_equal(recent_values(b_stream_array),
recent_values(bb_stream_array))
a_stream_array.extend(np.arange(5.0, 10.0, 1.0))
scheduler.step()
assert np.array_equal(recent_values(b_stream_array), np.arange(10.0) + 1)
assert np.array_equal(recent_values(b_stream_array),
recent_values(bb_stream_array))
# Test list map with state on StreamArray (dimension is 0)
c_stream_array = StreamArray(name='c_stream_array')
d_stream_array = StreamArray(name='d_stream_array')
def f_np_state(input_array, state):
return np.cumsum(input_array) + state, np.sum(input_array)
b_np_agent = map_list(func=f_np_state,
in_stream=c_stream_array,
out_stream=d_stream_array,
state=0.0,
name='b_np_agent')
dd_stream_array = map_array_f(f_np_state, c_stream_array, state=0.0)
scheduler.step()
assert np.array_equal(recent_values(d_stream_array),
np.array([], dtype=np.float64))
assert np.array_equal(recent_values(d_stream_array),
recent_values(dd_stream_array))
c_stream_array.extend(np.arange(5.0))
scheduler.step()
assert np.array_equal(d_stream_array.recent[:d_stream_array.stop],
np.cumsum(np.arange(5.0)))
assert np.array_equal(recent_values(d_stream_array),
recent_values(dd_stream_array))
c_stream_array.extend(np.arange(5.0, 10.0, 1.0))
scheduler.step()
assert np.array_equal(d_stream_array.recent[:d_stream_array.stop],
np.cumsum(np.arange(10.0)))
assert np.array_equal(recent_values(d_stream_array),
recent_values(dd_stream_array))
# Test list map with positive integer dimension on StreamArray
e_stream_array = StreamArray(name='e_stream_array', dimension=3)
f_stream_array = StreamArray(name='f_stream_array', dimension=2)
def f_np_dimension(input_array):
output_array = np.zeros([len(input_array), 2])
output_array[:, 0] = input_array[:, 0] + input_array[:, 1]
output_array[:, 1] = input_array[:, 2]
return output_array
c_np_agent = map_list(func=f_np_dimension,
in_stream=e_stream_array,
out_stream=f_stream_array,
name='c_np_agent')
e_stream_array.extend(np.array([[1.0, 2.0, 3.0]]))
scheduler.step()
assert np.array_equal(f_stream_array.recent[:f_stream_array.stop],
np.array([[3.0, 3.0]]))
e_stream_array.extend(np.array([[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]))
scheduler.step()
assert np.array_equal(f_stream_array.recent[:f_stream_array.stop],
np.array([[3.0, 3.0], [9.0, 6.0], [15.0, 9.0]]))
# Test list map with a dimension which is a tuple of integers.
g_stream_array = StreamArray(name='g_stream_array', dimension=(2, 2))
h_stream_array = StreamArray(name='h_stream_array', dimension=(2, 2))
def f_np_tuple_dimension(input_array):
return input_array * 2
d_np_agent = map_list(func=f_np_tuple_dimension,
in_stream=g_stream_array,
out_stream=h_stream_array,
name='d_np_agent')
a_array = np.array([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]])
g_stream_array.extend(a_array)
scheduler.step()
assert np.array_equal(h_stream_array.recent[:h_stream_array.stop],
a_array * 2)
b_array = np.array([[[9.0, 10.0], [11.0, 12.0]]])
g_stream_array.extend(b_array)
scheduler.step()
assert np.array_equal(h_stream_array.recent[:h_stream_array.stop],
np.vstack((a_array, b_array)) * 2)
# Test list map with a datatype and dimension of 0.
dt_0 = np.dtype([('time', int), ('value', (float, 3))])
dt_1 = np.dtype([('time', int), ('value', float)])
i_stream_array = StreamArray(name='i_stream_array', dtype=dt_0)
j_stream_array = StreamArray(name='j_stream_array', dtype=dt_1)
def f_datatype(input_array):
output_array = np.zeros(len(input_array), dtype=dt_1)
output_array['time'] = input_array['time']
output_array['value'] = np.sum(input_array['value'], axis=1)
return output_array
e_np_agent = map_list(func=f_datatype,
in_stream=i_stream_array,
out_stream=j_stream_array,
name='e_np_agent')
c_array = np.array([(1, [2.0, 3.0, 4.0])], dtype=dt_0)
assert j_stream_array.stop == 0
i_stream_array.extend(c_array)
scheduler.step()
assert np.array_equal(j_stream_array.recent[:j_stream_array.stop],
f_datatype(c_array))
d_array = np.array([(10, [6.0, 7.0, 8.0]), (20, [10.0, 11.0, 12.0])],
dtype=dt_0)
i_stream_array.extend(d_array)
scheduler.step()
assert np.array_equal(j_stream_array.recent[:j_stream_array.stop],
f_datatype(np.hstack((c_array, d_array))))
# Test list map with a datatype and positive integer dimension.
k_stream_array = StreamArray(name='k_stream_array',
dtype=dt_0,
dimension=2)
l_stream_array = StreamArray(name='l_stream_array', dtype=dt_1)
def f_datatype_int_dimension(input_array):
m = len(input_array)
output_array = np.zeros(m, dtype=dt_1)
for i in range(m):
output_array[i]['time'] = np.max(input_array[i]['time'])
output_array[i]['value'] = np.sum(input_array[i]['value'])
return output_array
f_np_agent = map_list(func=f_datatype_int_dimension,
in_stream=k_stream_array,
out_stream=l_stream_array,
name='f_np_agent')
e_array = np.array([[(1, [2.0, 3.0, 4.0]), (2, [5.0, 6.0, 7.0])]],
dtype=dt_0)
assert l_stream_array.stop == 0
k_stream_array.extend(e_array)
scheduler.step()
assert np.array_equal(l_stream_array.recent[:l_stream_array.stop],
f_datatype_int_dimension(e_array))
f_array = np.array([[(3, [8.0, 9.0, 10.0]), (4, [11.0, 12.0, 13.0])],
[(5, [-1.0, 0.0, 1.0]), (6, [-2.0, 2.0, -2.0])]],
dtype=dt_0)
k_stream_array.extend(f_array)
scheduler.step()
assert np.array_equal(
l_stream_array.recent[:l_stream_array.stop],
f_datatype_int_dimension(np.vstack((e_array, f_array))))
# Test list map with a datatype and a dimension which is a tuple
m_stream_array = StreamArray(name='m_stream_array',
dtype=dt_0,
dimension=(2, 2))
n_stream_array = StreamArray(name='n_stream_array', dtype=dt_1)
g_np_agent = map_list(func=f_datatype_int_dimension,
in_stream=m_stream_array,
out_stream=n_stream_array,
name='g_np_agent')
assert n_stream_array.stop == 0
g_array = np.array(
[
# zeroth 2x2 array
[[(1, [2.0, 3.0, 4.0]), (2, [5.0, 6.0, 7.0])],
[(3, [8.0, 9.0, 10.0]), (4, [11.0, 12.0, 13.0])]],
# first 2x2 array
[[(5, [12.0, 13.0, 14.0]), (6, [15.0, 16.0, 17.0])],
[(7, [18.0, 19.0, 20.0]), (8, [21.0, 22.0, 23.0])]]
],
dtype=dt_0)
m_stream_array.extend(g_array)
scheduler.step()
assert np.array_equal(n_stream_array.recent[:n_stream_array.stop],
f_datatype_int_dimension(g_array))
h_array = np.array([[[(9, [0.0, 1.0, -1.0]), (10, [2.0, 2.0, -4.0])],
[(11, [80.0, -71.0, -9.0]), (15, [0.0, 0.0, 0.0])]]],
dtype=dt_0)
m_stream_array.extend(h_array)
scheduler.step()
assert np.array_equal(
n_stream_array.recent[:n_stream_array.stop],
f_datatype_int_dimension(np.vstack((g_array, h_array))))
# Test list merge with StreamArray and no dimension and no data type
a_in_0 = StreamArray(name='a_in_0')
a_in_1 = StreamArray(name='a_in_1')
a_out = StreamArray(name='a_out')
def a_merge(list_of_lists):
array_0, array_1 = list_of_lists
return array_0 + array_1
a_s_agent = merge_list(func=a_merge,
in_streams=[a_in_0, a_in_1],
out_stream=a_out,
name='a_s_agent')
assert a_out.stop == 0
#a_in_0.extend(np.array([1.0, 2.0, 3.0]))
a_in_0.extend(np.array([1.0, 2.0, 3.0]))
scheduler.step()
assert a_out.stop == 0
a_in_0.extend(np.array([4.0, 5.0, 6.0]))
scheduler.step()
assert a_out.stop == 0
a_in_1.extend(np.array([10.0, 20.0]))
scheduler.step()
assert np.array_equal(a_out.recent[:a_out.stop], np.array([11.0, 22.0]))
a_in_1.extend(np.array([30.0, 40.0]))
scheduler.step()
assert np.array_equal(a_out.recent[:a_out.stop],
np.array([11.0, 22.0, 33.0, 44.0]))
# Test list merge with StreamArray and no dimension and data type
a_in_dt_0 = StreamArray(name='a_in_dt_0', dtype=dt_0)
a_in_dt_1 = StreamArray(name='a_in_dt_1', dtype=dt_0)
a_out_dt = StreamArray(name='out', dtype=dt_0)
def a_merge_dtype(list_of_arrays):
input_array_0, input_array_1 = list_of_arrays
output_array = np.zeros(len(input_array_0), dtype=dt_0)
output_array['time'] = \
np.max((input_array_0['time'], input_array_1['time']), axis=0)
output_array['value'] = input_array_0['value'] + input_array_1['value']
return output_array
a_s_dt_agent = merge_list(func=a_merge_dtype,
in_streams=[a_in_dt_0, a_in_dt_1],
out_stream=a_out_dt,
name='a_s_dt_agent')
a_in_dt_0.extend(np.array([(1, [1.0, 2.0, 3.0])], dtype=dt_0))
scheduler.step()
assert a_out_dt.stop == 0
a_in_dt_1.extend(np.array([(2, [10.0, 20.0, 30.0])], dtype=dt_0))
scheduler.step()
assert np.array_equal(a_out_dt.recent[:a_out_dt.stop],
np.array([(2, [11.0, 22.0, 33.0])], dtype=dt_0))
a_in_dt_0.extend(
np.array([(5, [21.0, 23.0, 32.0]), (9, [27.0, 29.0, 31.0])],
dtype=dt_0))
scheduler.step()
assert np.array_equal(a_out_dt.recent[:a_out_dt.stop],
np.array([(2, [11.0, 22.0, 33.0])], dtype=dt_0))
a_in_dt_1.extend(
np.array([(6, [19.0, 17.0, 8.0]), (8, [13.0, 11.0, 9.0]),
(10, [3.0, 1.0, 5.0])],
dtype=dt_0))
scheduler.step()
assert np.array_equal(
a_out_dt.recent[:a_out_dt.stop],
np.array([(2, [11.0, 22.0, 33.0]), (6, [40.0, 40.0, 40.0]),
(9, [40.0, 40.0, 40.0])],
dtype=dt_0))
# Test list split with StreamArray and positive integer dimension and no data type
dim = 2
b_in = StreamArray(name='b_in', dimension=dim)
b_out_0 = StreamArray(name='b_out_0', dimension=dim)
b_out_1 = StreamArray(name='b_out_1')
def b_split(array_of_arrays):
length = len(array_of_arrays)
output_array_0 = np.zeros((
length,
dim,
))
output_array_1 = np.zeros(length)
for i in range(length):
input_array = array_of_arrays[i]
output_array_0[i] = np.array(
[[np.max(input_array),
np.min(input_array)]])
output_array_1[i] = np.array([np.sum(input_array)])
return output_array_0, output_array_1
b_split_agent = split_list(func=b_split,
in_stream=b_in,
out_streams=[b_out_0, b_out_1],
name='b_split_agent')
b_array_0 = np.array([[1.0, 9.0]])
b_in.extend(b_array_0)
scheduler.step()
assert np.array_equal(b_out_0.recent[:b_out_0.stop], np.array([[9.0,
1.0]]))
assert np.array_equal(b_out_1.recent[:b_out_1.stop], np.array([10.0]))
b_array_1 = np.array([[98.0, 2.0]])
b_in.extend(b_array_1)
scheduler.step()
assert np.array_equal(b_out_0.recent[:b_out_0.stop],
np.array([[9.0, 1.0], [98.0, 2.0]]))
assert np.array_equal(b_out_1.recent[:b_out_1.stop],
np.array([10.0, 100.0]))
b_array_3 = np.array([[10.0, 20.0], [3.0, 37.0], [55.0, 5.0]])
b_in.extend(b_array_3)
scheduler.step()
assert np.array_equal(
b_out_0.recent[:b_out_0.stop],
np.array([[9.0, 1.0], [98.0, 2.0], [20.0, 10.0], [37.0, 3.0],
[55.0, 5.0]]))
assert np.array_equal(b_out_1.recent[:b_out_1.stop],
np.array([10.0, 100.0, 30.0, 40.0, 60.0]))
# Test list many with StreamArray and no dimension and no data type
c_in_0 = StreamArray(name='c_in_0')
c_in_1 = StreamArray(name='c_in_1')
c_out_0 = StreamArray(name='c_out_0')
c_out_1 = StreamArray(name='c_out_1')
def c_many(list_of_arrays):
length = len(list_of_arrays)
input_array_0, input_array_1 = list_of_arrays
output_array_0 = np.zeros(length)
output_array_1 = np.zeros(length)
output_array_0 = input_array_0 + input_array_1
output_array_1 = input_array_0 - input_array_1
return [output_array_0, output_array_1]
c_multi_agent = multi_list(func=c_many,
in_streams=[c_in_0, c_in_1],
out_streams=[c_out_0, c_out_1],
name='c_multi_agent')
c_array_0_0 = np.arange(3.0) * 3
c_array_1_0 = np.arange(3.0)
c_in_0.extend(c_array_0_0)
scheduler.step()
c_in_1.extend(c_array_1_0)
scheduler.step()
assert np.array_equal(c_out_0.recent[:c_out_0.stop],
np.array([0.0, 4.0, 8.0]))
assert np.array_equal(c_out_1.recent[:c_out_1.stop],
np.array([0.0, 2.0, 4.0]))
c_array_0_1 = np.array([100.0])
c_array_1_1 = np.array([4.0, 5.0, 6.0])
c_in_0.extend(c_array_0_1)
c_in_1.extend(c_array_1_1)
scheduler.step()
assert np.array_equal(c_out_0.recent[:c_out_0.stop],
np.array([0.0, 4.0, 8.0, 104.0]))
assert np.array_equal(c_out_1.recent[:c_out_1.stop],
np.array([0.0, 2.0, 4.0, 96.0]))
## # Test list many with StreamArray and no dimension and no data type
## z_in_0 = StreamArray(name='z_in_0')
## z_in_1 = StreamArray(name='z_in_1')
## z_out_0 = StreamArray(name='z_out_0')
## z_out_1 = StreamArray(name='z_out_1')
## def execute_list_of_np_func(v, list_of_np_func):
## length = len(list_of_arrays)
## input_array_0, input_array_1 = list_of_arrays
## output_array_0 = np.zeros(length)
## output_array_1 = np.zeros(length)
## output_array_0 = input_array_0 + input_array_1
## output_array_1 = input_array_0 - input_array_1
## return [output_array_0, output_array_1]
# Test list many with StreamArray and positive integer dimension and no data type
dim = 2
d_in_0 = StreamArray(name='d_in_0', dimension=dim)
d_in_1 = StreamArray(name='d_in_1', dimension=dim)
d_out_0 = StreamArray(name='d_out_0', dimension=dim)
d_out_1 = StreamArray(name='d_out_1')
def d_many(list_of_arrays):
length = len(list_of_arrays)
input_array_0, input_array_1 = list_of_arrays
output_array_0 = input_array_0 + input_array_1
output_array_1 = np.array([np.sum(input_array_0 + input_array_1)])
return output_array_0, output_array_1
d_multi_agent = multi_list(func=d_many,
in_streams=[d_in_0, d_in_1],
out_streams=[d_out_0, d_out_1],
name='d_multi_agent')
d_array_0_0 = np.array([[1.0, 2.0]])
d_array_1_0 = np.array([[0.0, 10.0]])
d_in_0.extend(d_array_0_0)
scheduler.step()
d_in_1.extend(d_array_1_0)
scheduler.step()
assert np.array_equal(d_out_0.recent[:d_out_0.stop], np.array([[1.0,
12.0]]))
assert np.array_equal(d_out_1.recent[:d_out_1.stop], np.array([13.0]))
d_array_0_1 = np.array([[4.0, 8.0]])
d_array_1_1 = np.array([[2.0, 4.0]])
d_in_0.extend(d_array_0_1)
d_in_1.extend(d_array_1_1)
scheduler.step()
assert np.array_equal(d_out_0.recent[:d_out_0.stop],
np.array([[1.0, 12.0], [6.0, 12.0]]))
assert np.array_equal(d_out_1.recent[:d_out_1.stop], np.array([13.0,
18.0]))
d_array_0_2 = np.array([[20.0, 30.0], [40.0, 50.0]])
d_array_1_2 = np.array([[-10.0, -20.0]])
d_in_0.extend(d_array_0_2)
d_in_1.extend(d_array_1_2)
scheduler.step()
assert np.array_equal(d_out_0.recent[:d_out_0.stop],
np.array([[1.0, 12.0], [6.0, 12.0], [10.0, 10.0]]))
assert np.array_equal(d_out_1.recent[:d_out_1.stop],
np.array([13.0, 18.0, 20.0]))
# Test list many with StreamArray and tuple dimension and no data type
dim = (2, 2)
e_in_0 = StreamArray(name='e_in_0', dimension=dim)
e_in_1 = StreamArray(name='e_in_1', dimension=dim)
e_out_0 = StreamArray(name='e_out_0', dimension=dim)
e_out_1 = StreamArray(name='e_out_1')
def e_many(list_of_arrays):
input_array_0, input_array_1 = list_of_arrays
output_array_0 = input_array_0 + input_array_1
output_array_1 = \
np.array([np.sum(input_array_0[i]+ input_array_1[i])
for i in range(len(input_array_0))])
return output_array_0, output_array_1
e_multi_agent = multi_list(func=e_many,
in_streams=[e_in_0, e_in_1],
out_streams=[e_out_0, e_out_1],
name='e_multi_agent')
e_array_0_0 = np.array([[[10.0, 20.0], [30.0, 40.0]]])
e_in_0.extend(e_array_0_0)
e_array_1_0 = np.array([[[1.0, 2.0], [3.0, 4.0]]])
e_in_1.extend(e_array_1_0)
scheduler.step()
assert np.array_equal(e_out_0.recent[:e_out_0.stop],
np.array([[[11.0, 22.0], [33.0, 44.0]]]))
assert np.array_equal(e_out_1.recent[:e_out_1.stop], np.array([110.0]))
e_array_0_1 = np.array([[[11.0, 13.0], [17.0, 19.0]],
[[2.0, 4.0], [6.0, 8.0]]])
e_in_0.extend(e_array_0_1)
scheduler.step()
assert np.array_equal(e_out_0.recent[:e_out_0.stop],
np.array([[[11.0, 22.0], [33.0, 44.0]]]))
assert np.array_equal(e_out_1.recent[:e_out_1.stop], np.array([110.0]))
e_array_1_1 = np.array([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0,
8.0]]])
e_in_1.extend(e_array_1_1)
scheduler.step()
assert np.array_equal(
e_out_0.recent[:e_out_0.stop],
np.array([[[11.0, 22.0], [33.0, 44.0]], [[12.0, 15.0], [20.0, 23.0]],
[[7.0, 10.0], [13.0, 16.0]]]))
assert np.array_equal(e_out_1.recent[:e_out_1.stop],
np.array([110.0, 70.0, 46.0]))
e_array_1_2 = np.array([[[11.0, 12.0], [13.0, 14.0]],
[[15.0, 16.0], [17.0, 18.0]]])
e_in_1.extend(e_array_1_2)
scheduler.step()
e_array_0_2 = np.array([[[-10.0, -11.0], [12.0, 16.0]],
[[-14.0, -15.0], [-16.0, -17.0]]])
e_in_0.extend(e_array_0_2)
scheduler.step()
assert np.array_equal(
e_out_0.recent[:e_out_0.stop],
np.array([[[11.0, 22.0], [33.0, 44.0]], [[12.0, 15.0], [20.0, 23.0]],
[[7.0, 10.0], [13.0, 16.0]], [[1.0, 1.0], [25.0, 30.0]],
[[1.0, 1.0], [1.0, 1.0]]]))
assert np.array_equal(e_out_1.recent[:e_out_1.stop],
np.array([110.0, 70.0, 46.0, 57.0, 4.0]))
#------------------------------------------------------------------
#------------------------------------------------------------------
# Test args and kwargs
#------------------------------------------------------------------
#------------------------------------------------------------------
# Test map
def map_args(lst, multiplicand):
return [multiplicand * element for element in lst]
in_stream_map_args_stream = Stream('in_stream_map_args_stream')
out_stream_map_args_stream = Stream('out_stream_map_args_stream')
out_stream_map_kwargs_stream = Stream('out_stream_map_kwargs_stream')
map_args_agent = map_list(map_args, in_stream_map_args_stream,
out_stream_map_args_stream, None, None,
'map_args_agent', 2)
map_kwargs_agent = map_list(func=map_args,
in_stream=in_stream_map_args_stream,
out_stream=out_stream_map_kwargs_stream,
name='map_args_agent',
multiplicand=2)
scheduler.step()
assert out_stream_map_args_stream.recent[:out_stream_map_args_stream.stop] == \
[]
assert out_stream_map_kwargs_stream.recent[:out_stream_map_kwargs_stream.stop] == \
[]
in_stream_map_args_stream.extend(range(5))
scheduler.step()
assert out_stream_map_args_stream.recent[:out_stream_map_args_stream.stop] == \
[0, 2, 4, 6, 8]
assert out_stream_map_kwargs_stream.recent[:out_stream_map_kwargs_stream.stop] == \
[0, 2, 4, 6, 8]
in_stream_map_args_stream.append(5)
scheduler.step()
assert out_stream_map_args_stream.recent[:out_stream_map_args_stream.stop] == \
[0, 2, 4, 6, 8, 10]
assert out_stream_map_kwargs_stream.recent[:out_stream_map_kwargs_stream.stop] == \
[0, 2, 4, 6, 8, 10]
# Test list map on StreamArray (dimension is 0).
a_stream_array_args = StreamArray(name='a_stream_array_args')
b_stream_array_args = StreamArray(name='b_stream_array_args')
c_stream_array_args_kwargs = StreamArray(name='c_stream_array_args_kwargs')
def f_np_args(input_array_args, addend):
return input_array_args + addend
def f_np_args_kwargs(input_array_args_kwargs, multiplicand, addend):
return input_array_args_kwargs * multiplicand + addend
a_np_agent_args = map_list(f_np_args, a_stream_array_args,
b_stream_array_args, None, None,
'a_np_agent_args', 1)
a_np_agent_args_kwargs = map_list(f_np_args_kwargs,
a_stream_array_args,
c_stream_array_args_kwargs,
None,
None,
'a_np_agent_args_kwargs',
2,
addend=10)
scheduler.step()
assert np.array_equal(
b_stream_array_args.recent[:b_stream_array_args.stop], np.array([]))
assert np.array_equal(
c_stream_array_args_kwargs.recent[:c_stream_array_args_kwargs.stop],
np.array([]))
a_stream_array_args.extend(np.arange(5.0))
scheduler.step()
assert np.array_equal(
b_stream_array_args.recent[:b_stream_array_args.stop],
np.arange(5.0) + 1)
assert np.array_equal(
c_stream_array_args_kwargs.recent[:c_stream_array_args_kwargs.stop],
np.arange(5.0) * 2 + 10)
a_stream_array_args.extend(np.arange(5.0, 10.0, 1.0))
scheduler.step()
assert np.array_equal(
b_stream_array_args.recent[:b_stream_array_args.stop],
np.arange(10.0) + 1)
assert np.array_equal(
c_stream_array_args_kwargs.recent[:c_stream_array_args_kwargs.stop],
np.arange(10.0) * 2 + 10)
print 'TEST OF OP (LISTS) IS SUCCESSFUL'
dict_csv = "../../Datasets/processed/word_count_dictionary" fw_csv = "../../Datasets/processed/filtered_words" aw_csv = "../../Datasets/processed/all_words" # Input data, no need to have output file name if test data input_file_name = "../../Datasets/train_input.csv" output_file_name = "../../Datasets/train_output.csv" """ Code, you shouldn't really need to touch this unless there's something very specific required """ merged =[] inputs = merge.read_csv(input_file_name) outputs = merge.read_csv(output_file_name) merged = merge.merge_list(inputs, outputs, 0, 0) # create the preprocessor pp = prp.PreProcessor(merged, accepts, rejects, tfms, n, stem=stemming, labelled = True) # save to csv if needed if save2csv: categories = pp.get_categories() worddict = pp.word_dict() ft_words = pp.ft_data allwords = pp.top_words labels = pp.labels ssuff = "" ssuff = ssuff + "_stemmed" if stemming else ssuff ssuff = ssuff + fsuffix
def test_merge(input, expected):
assert merge_list(input[0], input[1]) == expected
def test_some_merge_agents():
import numpy as np
scheduler = Stream.scheduler
#----------------------------------------------------
# Declare streams
s = Stream('s')
t = Stream('t')
u = Stream('u')
v_stream = Stream('v')
x = Stream('x')
#----------------------------------------------------
# Define functions
def g(lst):
return sum(lst)
def g_args(lst, multiplier):
return sum(lst) * multiplier
def general_f(lst, f):
return f(lst)
def fff(lst, f, addend):
return f(lst, addend)
def hhh(lst, addend):
return sum(lst) + addend
#----------------------------------------------------
# Define agents
d = zip_map(func=sum, in_streams=[x, u], out_stream=s, name='d')
def magnitude(vector):
return math.sqrt(sum([w * w for w in vector]))
ssssss = Stream()
ddd = zip_map(func=magnitude, in_streams=[x, u], out_stream=ssssss)
zipxu = zip_stream_f([x, u])
zip_map_xu = zip_map_f(sum, [x, u])
zip_map_xu_merge = Stream('zip map xu merge')
zip_map(sum, [x, u], zip_map_xu_merge)
zip_map_g_args = zip_map_f(g_args, [x, u], multiplier=2)
dd = zip_map(func=general_f,
in_streams=[x, u],
out_stream=t,
name='dd',
f=np.mean)
zip_map_ss = zip_map_f(np.mean, [x, u])
dddd = zip_map(func=fff,
in_streams=[x, u],
out_stream=v_stream,
name='dddd',
f=hhh,
addend=10)
#----------------------------------------------------
#----------------------------------------------------
# Append values to stream
x.extend(list(range(3)))
u.extend([10, 15, 18])
scheduler.step()
assert recent_values(s) == [10, 16, 20]
assert recent_values(zip_map_g_args) == [2 * v for v in recent_values(s)]
assert recent_values(zipxu) == [(0, 10), (1, 15), (2, 18)]
assert recent_values(t) == [5, 8, 10]
assert recent_values(zip_map_ss) == [5.0, 8.0, 10.0]
assert recent_values(v_stream) == [20, 26, 30]
assert recent_values(zip_map_xu) == s.recent[:s.stop]
assert recent_values(zip_map_xu) == recent_values(zip_map_xu_merge)
#----------------------------------------------------
u.append(37)
x.extend(list(range(3, 5, 1)))
scheduler.step()
assert recent_values(s) == [10, 16, 20, 40]
assert recent_values(zip_map_g_args) == [2 * v for v in recent_values(s)]
assert recent_values(zipxu) == [(0, 10), (1, 15), (2, 18), (3, 37)]
assert recent_values(t) == [5, 8, 10, 20]
assert recent_values(v_stream) == [20, 26, 30, 50]
assert recent_values(zip_map_xu) == recent_values(zip_map_xu_merge)
assert recent_values(ssssss) == [
10.0, 15.033296378372908, 18.110770276274835, 37.12142238654117
]
#----------------------------------------------------
u.extend([96, 95])
scheduler.step()
assert recent_values(s) == [10, 16, 20, 40, 100]
assert recent_values(zipxu) == [(0, 10), (1, 15), (2, 18), (3, 37),
(4, 96)]
assert recent_values(t) == [5, 8, 10, 20, 50]
assert recent_values(v_stream) == [20, 26, 30, 50, 110]
assert recent_values(zip_map_xu) == recent_values(zip_map_xu_merge)
#----------------------------------------------------
# TEST MERGE_ASYNCH AND MIX
#----------------------------------------------------
x = Stream('x')
y = Stream('y')
z = Stream('z')
w = Stream('w')
def g_asynch(pair):
index, value = pair
if index == 0:
return value * 10
elif index == 1:
return value * 2
else:
raise Exception()
merge_asynch(func=lambda v: v, in_streams=[x, y], out_stream=z)
merge_asynch(func=g_asynch, in_streams=[x, y], out_stream=w)
mix_z = mix_f([x, y])
scheduler.step()
assert recent_values(z) == []
assert recent_values(mix_z) == []
assert recent_values(w) == []
x.append(10)
scheduler.step()
assert recent_values(z) == [(0, 10)]
assert recent_values(mix_z) == recent_values(z)
assert recent_values(w) == [100]
y.append('A')
scheduler.step()
assert recent_values(z) == [(0, 10), (1, 'A')]
assert recent_values(mix_z) == recent_values(z)
assert recent_values(w) == [100, 'AA']
y.append('B')
scheduler.step()
assert recent_values(z) == [(0, 10), (1, 'A'), (1, 'B')]
assert recent_values(mix_z) == recent_values(z)
assert recent_values(w) == [100, 'AA', 'BB']
x.append(20)
scheduler.step()
assert recent_values(z) == [(0, 10), (1, 'A'), (1, 'B'), (0, 20)]
assert recent_values(z) == recent_values(mix_z)
assert recent_values(w) == [100, 'AA', 'BB', 200]
fahrenheit = Stream('fahrenheit')
celsius = Stream('celsius')
def fahrenheit_and_celsius(pair):
index, value = pair
if index == 0:
return (value - 32.0) / 1.8
elif index == 1:
return value
else:
raise Exception()
fahrenheit_stream = Stream('fahrenheit temperatures')
celsius_stream = Stream('celsius temperatures')
centigrade_stream = Stream('centigrade temperatures')
merge_asynch(func=fahrenheit_and_celsius,
in_streams=[fahrenheit_stream, celsius_stream],
out_stream=centigrade_stream)
fahrenheit_stream.append(32)
scheduler.step()
assert recent_values(centigrade_stream) == [0.0]
fahrenheit_stream.append(50)
scheduler.step()
assert recent_values(centigrade_stream) == [0.0, 10.0]
fahrenheit_stream.append(68)
scheduler.step()
assert recent_values(centigrade_stream) == [0.0, 10.0, 20.0]
celsius_stream.append(-10.0)
scheduler.step()
assert recent_values(centigrade_stream) == [0.0, 10.0, 20.0, -10.0]
#----------------------------------------------------
# TEST BLEND
#----------------------------------------------------
x = Stream('x')
y = Stream('y')
z = Stream('z')
z_addend = Stream('z_addend')
def double(v):
return 2 * v
def double_add(v, addend):
return 2 * v + addend
blend(func=double, in_streams=[x, y], out_stream=z)
blend(func=double, in_streams=[x, y], out_stream=z_addend)
blend_z = blend_f(double, [x, y])
blend_add_z = blend_f(double_add, [x, y], addend=10)
x.append(1)
scheduler.step()
assert recent_values(z) == [2]
assert recent_values(blend_z) == recent_values(z)
assert recent_values(blend_add_z) == [v + 10 for v in recent_values(z)]
x.extend(list(range(2, 4)))
scheduler.step()
assert recent_values(z) == [2, 4, 6]
assert recent_values(blend_z) == recent_values(z)
assert recent_values(blend_add_z) == [v + 10 for v in recent_values(z)]
y.extend(list(range(100, 102)))
scheduler.step()
assert recent_values(z) == [2, 4, 6, 200, 202]
assert recent_values(blend_z) == recent_values(z)
assert recent_values(blend_add_z) == [v + 10 for v in recent_values(z)]
x.extend([10, 20])
scheduler.step()
assert recent_values(z) == [2, 4, 6, 200, 202, 20, 40]
assert recent_values(blend_z) == recent_values(z)
assert recent_values(blend_add_z) == [v + 10 for v in recent_values(z)]
#----------------------------------------------------
# TEST MANY
#----------------------------------------------------
# func operates on a list with one element for each input stream.
# func returns a list with one element for each output stream.
def f_many(lst):
return [sum(lst), sum(lst) + 1]
u_stream = Stream(name='u_stream')
v_stream = Stream(name='v_stream')
w_stream = Stream(name='w_stream')
x_stream = Stream(name='x_stream')
multi_agent = multi_element(func=f_many,
in_streams=[u_stream, v_stream],
out_streams=[w_stream, x_stream],
name='multi_agent')
ww_stream, xx_stream = multi_element_f(func=f_many,
in_streams=[u_stream, v_stream],
num_out_streams=2)
u_stream.extend(list(range(5)))
v_stream.extend(list(range(0, 40, 4)))
scheduler.step()
assert recent_values(w_stream) == [0, 5, 10, 15, 20]
assert recent_values(x_stream) == [1, 6, 11, 16, 21]
assert recent_values(ww_stream) == recent_values(w_stream)
assert recent_values(xx_stream) == recent_values(x_stream)
# ------------------------------------
# Test many with args and kwargs
# func operates on a list with one element for each input stream.
# func returns a list with one element for each output stream.
def f_multi_args_kwargs(lst, multiplicand, addend):
return sum(lst) * multiplicand, sum(lst) + addend
u_args_kwargs_stream = Stream(name='u_args_kwargs_stream')
v_args_kwargs_stream = Stream(name='v_args_kwargs_stream')
w_args_kwargs_stream = Stream(name='w_args_kwargs_stream')
x_args_kwargs_stream = Stream(name='x_args_kwargs_stream')
multi_args_kwargs_agent = multi_element(
func=f_multi_args_kwargs,
in_streams=[u_args_kwargs_stream, v_args_kwargs_stream],
out_streams=[w_args_kwargs_stream, x_args_kwargs_stream],
name='multi_args_kwargs_agent',
multiplicand=2,
addend=10)
ww_args_kwargs_stream, xx_args_kwargs_stream = multi_element_f(
func=f_multi_args_kwargs,
in_streams=[u_args_kwargs_stream, v_args_kwargs_stream],
num_out_streams=2,
multiplicand=2,
addend=10)
assert (recent_values(ww_args_kwargs_stream) == recent_values(
w_args_kwargs_stream))
assert (recent_values(xx_args_kwargs_stream) == recent_values(
x_args_kwargs_stream))
u_args_kwargs_stream.extend(list(range(5)))
v_args_kwargs_stream.extend(list(range(0, 40, 4)))
scheduler.step()
assert recent_values(w_args_kwargs_stream) == [0, 10, 20, 30, 40]
assert recent_values(x_args_kwargs_stream) == [10, 15, 20, 25, 30]
assert (recent_values(ww_args_kwargs_stream) == recent_values(
w_args_kwargs_stream))
assert (recent_values(xx_args_kwargs_stream) == recent_values(
x_args_kwargs_stream))
u_args_kwargs_stream.append(100)
v_args_kwargs_stream.extend(list(range(40, 80, 4)))
scheduler.step()
assert recent_values(w_args_kwargs_stream) == \
[0, 10, 20, 30, 40, 240]
assert recent_values(x_args_kwargs_stream) == \
[10, 15, 20, 25, 30, 130]
assert (recent_values(ww_args_kwargs_stream) == recent_values(
w_args_kwargs_stream))
assert (recent_values(xx_args_kwargs_stream) == recent_values(
x_args_kwargs_stream))
u_args_kwargs_stream.extend([200, 300])
scheduler.step()
v_args_kwargs_stream.append(100)
scheduler.step()
assert recent_values(w_args_kwargs_stream) == \
[0, 10, 20, 30, 40, 240, 448, 656]
assert recent_values(x_args_kwargs_stream) == \
[10, 15, 20, 25, 30, 130, 234, 338]
assert (recent_values(ww_args_kwargs_stream) == recent_values(
w_args_kwargs_stream))
assert (recent_values(xx_args_kwargs_stream) == recent_values(
x_args_kwargs_stream))
#----------------------------------------------------
#----------------------------------------------------
# TEST STREAM ARRAY
#----------------------------------------------------
#----------------------------------------------------
#----------------------------------------------------
# Test zip_map with StreamArray
#----------------------------------------------------
x = StreamArray('x')
y = StreamArray('y')
z = StreamArray('z')
a = StreamArray('a')
def sum_array_axis_0(a_list_of_arrays):
return np.sum(a_list_of_arrays, axis=0)
merge_list(func=sum_array_axis_0, in_streams=[x, y], out_stream=z)
def mean_array_axis_0(a_list_of_arrays):
return np.mean(a_list_of_arrays, axis=0)
zip_map_list(func=mean_array_axis_0, in_streams=[x, y], out_stream=a)
x.extend(np.linspace(0.0, 9.0, 10))
scheduler.step()
y.extend(np.linspace(0.0, 4.0, 5))
scheduler.step()
expected_array = np.sum(
[np.linspace(0.0, 4.0, 5),
np.linspace(0.0, 4.0, 5)], axis=0)
assert isinstance(z, StreamArray)
assert np.array_equal(recent_values(z), expected_array)
expected_means = np.linspace(0.0, 4.0, 5)
assert np.array_equal(recent_values(a), expected_means)
#----------------------------------------------------
# Test blend with StreamArray
#----------------------------------------------------
x = StreamArray('x')
y = StreamArray('y')
z = StreamArray('z')
a = StreamArray('a')
def double(v):
return 2 * v
def double_add(v, addend):
return 2 * v + addend
## blend(func=double, in_streams=[x, y], out_stream=z)
## blend(func=double_add, in_streams=[x, y], out_stream=a, addend=10.0)
## x.append(np.array(1.0))
## scheduler.step()
## assert np.array_equal(recent_values(z), np.array([2.0]))
## assert np.array_equal(recent_values(a), recent_values(z)+10.0)
## x.extend(np.linspace(2.0, 3.0, 2))
## scheduler.step()
## assert np.array_equal(recent_values(z), np.array([2., 4., 6.]))
## assert np.array_equal(recent_values(a), recent_values(z)+10.0)
## y.extend(np.linspace(100.0, 101.0, 2))
## scheduler.step()
## assert np.array_equal(recent_values(z), [2., 4., 6., 200., 202.])
## assert np.array_equal(recent_values(a), recent_values(z)+10.0)
## x.extend([10., 20.])
## scheduler.step()
## assert np.array_equal(recent_values(z), [2., 4., 6., 200., 202., 20., 40.])
## assert np.array_equal(recent_values(a), recent_values(z)+10.0)
#----------------------------------------------------
# Test merge_asynch with StreamArray
#----------------------------------------------------
x = StreamArray('x')
y = StreamArray('y')
dt_0 = np.dtype([('time', int), ('value', float)])
z = StreamArray('z', dimension=2)
merge_asynch(func=lambda v: v, in_streams=[x, y], out_stream=z)
scheduler.step()
assert np.array_equal(recent_values(z), np.empty(shape=(0, 2)))
x.append(np.array(10.0))
scheduler.step()
assert np.array_equal(recent_values(z), np.array([(0, 10.0)]))
y.append(np.array(1.0))
scheduler.step()
assert np.array_equal(recent_values(z), [(0, 10.), (1, 1.0)])
y.append(np.array(2.0))
scheduler.step()
assert np.array_equal(recent_values(z), [(0, 10.), (1, 1.0), (1, 2.0)])
x.append(np.array(20.0))
scheduler.step()
assert np.array_equal(recent_values(z), [(0, 10.), (1, 1.), (1, 2.),
(0, 20.)])
#----------------------------------------------------------------
# Test window merge agent with no state
r = Stream('r')
w = Stream('w')
x = Stream('x')
a = Stream('a')
def h(list_of_windows):
return sum([sum(window) for window in list_of_windows])
merge_window(func=h,
in_streams=[r, w],
out_stream=x,
window_size=3,
step_size=3)
merge_stream = merge_window_f(func=h,
in_streams=[r, w],
window_size=3,
step_size=3)
#----------------------------------------------------------------
#----------------------------------------------------------------
# Test window merge agent with state
def h_with_state(list_of_windows, state):
return (sum([sum(window)
for window in list_of_windows]) + state, state + 1)
merge_window(func=h_with_state,
in_streams=[r, w],
out_stream=a,
window_size=3,
step_size=3,
state=0)
#----------------------------------------------------------------
#----------------------------------------------------------------
r.extend(list(range(16)))
scheduler.step()
assert recent_values(r) == list(range(16))
assert recent_values(x) == []
assert recent_values(merge_stream) == recent_values(x)
assert recent_values(a) == []
w.extend([10, 12, 14, 16, 18])
scheduler.step()
assert recent_values(r) == list(range(16))
assert recent_values(w) == [10, 12, 14, 16, 18]
assert recent_values(x) == [(0 + 1 + 2) + (10 + 12 + 14)]
assert recent_values(a) == [39]
#----------------------------------------------------------------
r.extend([10, -10, 21, -20])
scheduler.step()
assert recent_values(x) == [(0 + 1 + 2) + (10 + 12 + 14)]
assert recent_values(a) == [39]
#----------------------------------------------------------------
w.append(20)
scheduler.step()
assert recent_values(x) == [(0 + 1 + 2) + (10 + 12 + 14),
(3 + 4 + 5) + (16 + 18 + 20)]
assert recent_values(a) == [39, 67]
#----------------------------------------------------------------
r.extend([-1, 1, 0])
scheduler.step()
assert recent_values(x) == [(0 + 1 + 2) + (10 + 12 + 14),
(3 + 4 + 5) + (16 + 18 + 20)]
assert recent_values(a) == [39, 67]
#----------------------------------------------------------------
# TEST MERGE_WINDOW WITH STREAM ARRAY
#----------------------------------------------------------------
x = StreamArray('x', dimension=2)
b = StreamArray('b', dimension=2)
a = StreamArray('a', dimension=2)
#----------------------------------------------------------------
# Test window merge agent with state
def h_array(list_of_windows, state):
return (sum([sum(window)
for window in list_of_windows]) + state, state + 1)
merge_window(func=h_array,
in_streams=[x, a],
out_stream=b,
window_size=2,
step_size=2,
state=0)
#----------------------------------------------------------------
x.extend(np.array([[1., 5.], [7., 11.]]))
a.extend(np.array([[0., 1.], [2., 3.]]))
scheduler.step()
np.array_equal(recent_values(b), np.empty(shape=(0, 2)))
a.extend(np.array([[0., 1.], [1., 0.]]))
scheduler.step()
np.array_equal(recent_values(b), np.empty(shape=(0, 2)))
x.extend(np.array([[14., 18.], [18., 30.], [30., 38.], [34., 42.]]))
scheduler.step()
#-------------------------------------------------------------------
# TEST MERGE_LIST
#-------------------------------------------------------------------
# Function g operates on a list of lists, one list for each input
# stream, to return a single list for the output stream.
x = Stream('x list merge')
u = Stream('u list merge')
s = Stream('s list merge')
def g(list_of_lists):
return [sum(snapshot) for snapshot in list(zip(*list_of_lists))]
d = merge_list(func=g, in_streams=[x, u], out_stream=s, name='d')
ss = merge_list_f(g, [x, u])
x.extend(list(range(4)))
u.extend(list(range(10, 20, 2)))
scheduler.step()
assert recent_values(x) == [0, 1, 2, 3]
assert recent_values(u) == [10, 12, 14, 16, 18]
assert recent_values(s) == [10, 13, 16, 19]
x = StreamArray()
y = StreamArray()
z = StreamArray(dtype='bool')
def f(two_lists):
return np.array(two_lists[0]) > np.array(two_lists[1])
merge_list(f, [x, y], z)
x.extend(np.array([3.0, 5.0, 7.0]))
y.extend(np.array([4.0, 3.0, 10.0]))
run()