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)
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
sns.set_style("ticks")
#%%
importlib.reload(ana)
# Get data
path = "C:\\Users\\Johannes\\Documents\\Uni\\Master's_Thesis\\compositionalDiff-johannes_tests_2\\data\\overall_benchmark"
#%%
# Find relations between numerical values and composition/increase vectors
b = []
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)
b.append(np.round(np.log(b_i / 5000), 2))
print(b)
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)
from scdcdm.util import data_generation as gen
np.random.seed(1234)
# General parameters
cases = [1]
K = [5]
n_samples = [[i + 1, j + 1] for i in range(10) for j in range(10)]
n_total = [5000]
num_results = [2e4]
# Get Parameter tuples: b: base composition; w: effect
b = []
for y1_0 in [115, 280, 1000]:
b.append(np.round(gen.counts_from_first(y1_0, 5000, 5), 3))
b_w_dict = {}
i = 0
for b_i in b:
b_t = np.round(np.log(b_i / 5000), 3)
w_t = []
for change in [-10, -50, -100]:
_, w = gen.b_w_from_abs_change(b_i, change, 5000)
w_t.append(np.round(w, 3))
b_w_dict[i] = (b_t, w_t)
i += 1
#%%
# Create bash script to execute run_one_job.py
count = 0