try disable jit to make coveralls work

.travis.yml

@@ -42,9 +42,10 @@ install:
   - pip install simpleutils
   - pip install coveralls
 script:
-  - python setup.py build_ext --inplace
-  - coverage run alphamind/tests/test_suite.py
-  - coverage report
-  - coverage html
+  - export NUMBA_DISABLE_JIT=1
+  - python setup.py build_ext --line_trace --inplace
+  - coverage run --rcfile=./.coveragerc alphamind/tests/test_suite.py
+  - coverage report --rcfile=./.coveragerc -i
+  - coverage html --rcfile=./.coveragerc -i
 after_success:
-  coveralls
+  - coveralls

alphamind/data/standardize.py

@@ -22,13 +22,3 @@ def standardize(x: np.ndarray, groups: np.ndarray=None, ddof=1) -> np.ndarray:
         return (x - mean_values) / std_values
     else:
         return (x - simple_mean(x, axis=0)) / simple_std(x, axis=0)
-
-
-if __name__ == '__main__':
-    import pandas as pd
-
-    df = pd.read_csv('d:/test_data.csv', index_col=0)
-
-    x = df.values
-    groups = df.index.values.astype(int)
-    standardize(x, groups)

alphamind/data/winsorize.py

@@ -63,15 +63,3 @@ def winsorize_normal(x: np.ndarray, num_stds: int = 3, groups: np.ndarray = None
         mean_values = simple_mean(x, axis=0)
         res = mask_values_1d(x, mean_values, std_values, num_stds)
     return res
-
-
-if __name__ == '__main__':
-    x = np.random.randn(3000, 10)
-    groups = np.random.randint(0, 20, size=3000)
-
-    import datetime as dt
-
-    start = dt.datetime.now()
-    for _ in range(3000):
-        winsorize_normal(x, 2, groups)
-    print(dt.datetime.now() - start)

alphamind/portfolio/linearbuilder.py

@@ -43,17 +43,3 @@ def linear_build(er: np.ndarray,
     prob = cvxpy.Problem(objective, constraints)
     prob.solve(solver=solver)
     return prob.status, prob.value, np.array(w.value).flatten()
-
-
-if __name__ == '__main__':
-
-    er = np.arange(300)
-    bm = np.ones(300) * 0.00333333333
-    risk_exposure = np.random.randn(300, 10)
-
-    s, v, x = linear_build(er, 0., 0.01, risk_exposure, bm, (-0.01*np.ones(10), 0.01*np.ones(10)))
-    print(s)
-    print(x.sum())
-    print(x.min(), ',', x.max())
-    print((x - bm) @ risk_exposure)
-

alphamind/portfolio/percentbuilder.py

@@ -46,4 +46,3 @@ def percent_build(er: np.ndarray, percent: float, groups: np.ndarray=None) -> np
             use_rank = int(percent * len(neg_er))
             set_value(weights, ordering[:use_rank], 1.)
         return weights
-

alphamind/portfolio/rankbuilder.py

@@ -42,14 +42,3 @@ def rank_build(er: np.ndarray, use_rank: int, groups: np.ndarray=None) -> np.nda
             ordering = neg_er.argsort(axis=0)
             set_value(weights, ordering[:use_rank], 1.)
         return weights
-
-
-if __name__ == '__main__':
-    n_sample = 6
-    n_groups = 3
-
-    x = np.random.randn(n_sample)
-    groups = np.array([1, 1, 2, 1, 0, 2])
-    print(groups)
-    print(groupby(groups))
-    print(rank_build(x, 1, groups))
\ No newline at end of file

alphamind/settlement/simplesettle.py

@@ -24,11 +24,3 @@ def simple_settle(weights: np.ndarray, ret_series: np.ndarray, groups: np.ndarra
         if ret_mat.ndim == 1:
             ret_mat = ret_mat.reshape((-1, 1))
         return simple_sum(ret_mat, axis=0)
-
-
-if __name__ == '__main__':
-    from alphamind.aggregate import group_mapping_test
-
-    s = np.random.randint(2, 5, size=6)
-    print(s)
-    print(group_mapping_test(s))
\ No newline at end of file