def test_potjans_diesmann():
from dipde.examples.cortical_column import example
result_dict = example(show=False)
true_ans_dict = {(23,'e'):0.373293157534,
(23,'i'):3.18198990435,
(4,'e'):4.84559227803,
(4,'i'):6.88123163289,
(5,'e'):12.4268759126,
(5,'i'):11.3345559791,
(6,'e'):2.15114563585,
(6,'i'):9.4090127714}
for layer in [23, 4, 5, 6]:
for celltype in ['e', 'i']:
print result_dict[layer, celltype], true_ans_dict[layer, celltype]
np.testing.assert_almost_equal(result_dict[layer, celltype], true_ans_dict[layer, celltype],3)
def test_potjans_diesmann():
from dipde.examples.cortical_column import example
result_dict = example(show=False)
true_ans_dict = {
(23, 'e'): 0.373293157534,
(23, 'i'): 3.18198990435,
(4, 'e'): 4.84559227803,
(4, 'i'): 6.88123163289,
(5, 'e'): 12.4268759126,
(5, 'i'): 11.3345559791,
(6, 'e'): 2.15114563585,
(6, 'i'): 9.4090127714
}
for layer in [23, 4, 5, 6]:
for celltype in ['e', 'i']:
print result_dict[layer, celltype], true_ans_dict[layer, celltype]
np.testing.assert_almost_equal(result_dict[layer, celltype],
true_ans_dict[layer, celltype], 3)
def test_singlepop():
from dipde.examples.singlepop import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[5], .0005)
np.testing.assert_almost_equal(y[5], .00039095499999999633)
def test_excitatory_inhibitory():
from dipde.examples.excitatory_inhibitory import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[-1], .1)
np.testing.assert_almost_equal(y[-1], 0.90731622281068436)
def test_singlepop_exponential_distribution():
from dipde.examples.singlepop_exponential_distribution import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[-1], .1)
np.testing.assert_almost_equal(y[-1], 8.6633532147226759)
def test_singlepop_recurrent():
from dipde.examples.singlepop_recurrent import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[10], .001)
np.testing.assert_almost_equal(y[100], 1.18954810997)
def test_singlepop_sine():
from dipde.examples.singlepop_sine import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[10], .001)
np.testing.assert_almost_equal(y[100], 1.8977443953614115)
def test_singlepop():
from dipde.examples.singlepop import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[5], .0005)
np.testing.assert_almost_equal(y[5], .00039095499999999633)
def test_excitatory_inhibitory():
from dipde.examples.excitatory_inhibitory import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[-1], .1)
np.testing.assert_almost_equal(y[-1], 0.90731622281068436)
def test_singlepop_exponential_distribution():
from dipde.examples.singlepop_exponential_distribution import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[-1], .1)
np.testing.assert_almost_equal(y[-1], 8.6633532147226759)
def test_singlepop_recurrent():
from dipde.examples.singlepop_recurrent import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[10], .001)
np.testing.assert_almost_equal(y[100], 1.18954810997)
def test_singlepop_sine():
from dipde.examples.singlepop_sine import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[10], .001)
np.testing.assert_almost_equal(y[100], 1.8977443953614115)
def test_excitatory_inhibitory():
from dipde.examples.excitatory_inhibitory import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[-1], .1)
np.testing.assert_almost_equal(y[-1], 0.90725590501763964)
def test_singlepop():
from dipde.examples.singlepop import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[5], .0005)
np.testing.assert_almost_equal(y[5], 0.00038097003623592236)
def test_excitatory_inhibitory():
from dipde.examples.excitatory_inhibitory import example
t, y = example(show=False)
np.testing.assert_almost_equal(t[-1], .1)
np.testing.assert_almost_equal(y[-1], 0.90725590501763964)
