def test_change_functions(self):
"""
Tests gen.b_w_from_abs_change and gen.counts_from_first
Returns
-------
boolean -- all tests were passed or not
"""
np.random.seed(1234)
correct = True
counts_before = np.array([600, 400])
abs_change = 100
n_total = 1000
K = 2
b_0 = 600
b, w = gen.b_w_from_abs_change(counts_before, abs_change, n_total)
if any(np.abs(b - [-0.51082562, -0.91629073]) > 1e-5):
print("gen.b_w_from_abs_change: b not correct!")
correct = False
if any(np.abs(w - [0.44183275, 0.]) > 1e-5):
print("gen.b_w_from_abs_change: b not correct!")
correct = False
b_2 = gen.counts_from_first(b_0, n_total, K)
if not np.array_equal(b_2, [600., 400.]):
print("gen.counts_from_first not correct!")
correct = False
self.assertTrue(correct)
b_counts = dict(zip([b_i[0] for b_i in b], [20, 30, 50, 75, 115, 180, 280, 430, 667, 1000]))
b2 = []
for y1_0 in [20, 30, 50, 75, 115, 180, 280, 430, 667, 1000]:
b_i = np.round(gen.counts_from_first(y1_0, 5000, 5), 3)
b2.append(b_i)
b_w_dict = {}
i = 0
w_all = []
for b_i in b2:
b_t = np.round(np.log(b_i / 5000), 3)
print(b_t)
w_d = {}
for change in [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 400, 600, 800, 1000]:
_, w = gen.b_w_from_abs_change(b_i, change, 5000)
w_0 = np.round(w[0], 3)
w_d[w_0] = change
b_w_dict[b_t[0]] = w_d
i += 1
print(b_w_dict)
#%%
# all_study_params: One line per data point
# all_study_params_agg: Aggregate identical simulation parameters
p = []
params = []
for b_i in b:
print(b_i[0])
_, all_study_params, all_study_params_agg = ana.multi_run_study_analysis_prepare(path,
file_identifier=str(b_i[0]))