Remove asserts

python/tests/beagle_numba.py

@@ -22,7 +22,6 @@ def convert_to_genetic_map_position(pos):
     :return: Genetic map positions.
     :rtype: numpy.ndarray
     """
-    assert 0 < np.min(pos), "Site positions are not 1-based."
     return pos / 1e6
 
 
@@ -55,9 +54,6 @@ def get_weights(genotyped_pos, imputed_pos):
     marker_interval_start = np.repeat(-2, x, dtype=np.int64)
     idx_m = m - 1
     for idx_x in np.arange(x - 1, -1, -1):
-        assert (
-            imputed_cm[idx_x] != genotyped_cm[idx_m]
-        ), "Genotyped markers and imputed markers overlap."
         while imputed_cm[idx_x] < genotyped_cm[idx_m] and idx_m > -1:
             idx_m = max(idx_m - 1, -1)
         if idx_m == m - 1:
@@ -74,9 +70,6 @@ def get_weights(genotyped_pos, imputed_pos):
             cm_mP1_m = cm_mP1_m if cm_mP1_m > 0.005 else 0.005
             weights[idx_x] = cm_mP1_x / cm_mP1_m
         marker_interval_start[idx_x] = idx_m
-    assert 0 <= np.min(weights), "Some weights are negative."
-    assert np.max(weights) <= 1, "Some weights are greater than 1."
-    assert np.min(marker_interval_start) > -2, "Some marker indices are out of bounds."
     return (weights, marker_interval_start)
 
 
@@ -115,7 +108,6 @@ def get_switch_prob(pos, h, ne):
     :return: Switch probabilities.
     :rtype: numpy.ndarray
     """
-    assert 0 < np.min(pos), "Site positions are not 1-based."
     # Get genetic distances between adjacent markers.
     cm = convert_to_genetic_map_position(pos)
     d = np.zeros(len(pos), dtype=np.float64)
@@ -148,12 +140,6 @@ def compute_forward_probability_matrix(ref_h, query_h, rho, mu):
     """
     m = ref_h.shape[0]
     h = ref_h.shape[1]
-    assert m == len(query_h)
-    assert m == len(rho)
-    assert m == len(mu)
-    assert 0 <= np.min(rho) and np.max(rho) <= 1
-    # BEAGLE caps mismatch probabilities at 0.5.
-    assert 0 <= np.min(mu) and np.max(mu) <= 0.5
     fm = np.zeros((m, h), dtype=np.float64)
     last_sum = 1.0  # Part of normalization factor
     for i in np.arange(m):
@@ -202,11 +188,6 @@ def compute_backward_probability_matrix(ref_h, query_h, rho, mu):
     """
     m = ref_h.shape[0]
     h = ref_h.shape[1]
-    assert m == len(query_h)
-    assert m == len(rho)
-    assert m == len(mu)
-    assert 0 <= np.min(rho) and np.max(rho) <= 1
-    assert 0 <= np.min(mu) and np.max(mu) <= 0.5
     bm = np.zeros((m, h), dtype=np.float64)
     bm[-1, :] = 1.0 / h  # Initialise the last column
     for i in np.arange(m - 2, -1, -1):
@@ -252,9 +233,6 @@ def compute_state_probability_matrix(fm, bm, ref_h, query_h):
     """
     m = ref_h.shape[0]
     h = ref_h.shape[1]
-    assert m == len(query_h)
-    assert (m, h) == fm.shape
-    assert (m, h) == bm.shape
     # m + 1 columns to allow for imputed markers right of the last genotyped marker.
     sm = np.zeros((m + 1, h), dtype=np.float64)
     sm[:-1, :] = np.multiply(fm, bm)
@@ -284,14 +262,9 @@ def interpolate_allele_probabilities(sm, ref_h, genotyped_pos, imputed_pos):
     :return: Interpolated allele probabilities.
     :rtype: numpy.ndarray
     """
-    m = sm.shape[0] - 1
-    h = sm.shape[1]
     alleles = [0, 1]  # Assume all biallelic sites
-    assert m == len(genotyped_pos)
     x = len(imputed_pos)
-    assert (x, h) == ref_h.shape
     weights, marker_interval_start = get_weights(genotyped_pos, imputed_pos)
-    assert x == len(weights) == len(marker_interval_start)
     p = np.zeros((x, len(alleles)), dtype=np.float64)
     for a in alleles:
         for i in np.arange(x):
@@ -351,56 +324,25 @@ def run_beagle(ref_h, query_h, pos, miscall_rate=0.0001, ne=1e6):
     :return: Imputed alleles and interpolated allele probabilities.
     :rtype: list(numpy.ndarray, numpy.ndarray)
     """
-    assert ref_h.shape[0] == len(pos)
-    assert len(query_h) == len(pos)
-    # Indices of genotyped markers in the query haplotype
     genotyped_pos_idx = np.where(query_h != -1)[0]
-    assert len(genotyped_pos_idx) > 1
-    # Indices of imputed markers in the query haplotype
     imputed_pos_idx = np.where(query_h == -1)[0]
-    assert len(imputed_pos_idx) > 0
-    # Site positions of genotyped markers
     genotyped_pos = pos[genotyped_pos_idx]
-    # Site positions of imputed markers
     imputed_pos = pos[imputed_pos_idx]
     h = ref_h.shape[1]
-    m = len(genotyped_pos)
-    x = len(imputed_pos)
-    assert m + x == len(pos)
-    # Subset the reference haplotypes to genotyped markers
     ref_h_genotyped = ref_h[genotyped_pos_idx, :]
-    assert (m, h) == ref_h_genotyped.shape
-    # Subset the query haplotype to genotyped markers
     query_h_genotyped = query_h[genotyped_pos_idx]
-    assert m == len(query_h_genotyped)
-    # Set mismatch probabilities at genotyped markers
     mu = get_mismatch_prob(genotyped_pos, miscall_rate=miscall_rate)
-    assert m == len(mu)
-    # Set switch probabilities at genotyped markers
     rho = get_switch_prob(genotyped_pos, h, ne=ne)
-    assert m == len(rho)
-    # Compute forward probability matrix at genotyped markers
     fm = compute_forward_probability_matrix(ref_h_genotyped, query_h_genotyped, rho, mu)
-    assert (m, h) == fm.shape
-    # Compute backward probability matrix at genotyped markers
     bm = compute_backward_probability_matrix(
         ref_h_genotyped, query_h_genotyped, rho, mu
     )
-    assert (m, h) == bm.shape
-    # Compute HMM state probability matrix at genotyped markers
     sm = compute_state_probability_matrix(fm, bm, ref_h_genotyped, query_h_genotyped)
-    assert (m + 1, h) == sm.shape
-    # Subset the reference haplotypes to imputed markers
     ref_h_imputed = ref_h[imputed_pos_idx, :]
-    # Interpolate allele probabilities at imputed markers
     i_allele_probs = interpolate_allele_probabilities(
         sm, ref_h_imputed, genotyped_pos, imputed_pos
     )
-    # TODO: Allow for multiallelic sites.
-    assert (x, 2) == i_allele_probs.shape
-    # Get MAP alleles at imputed markers
     imputed_alleles = get_map_alleles(i_allele_probs)
-    assert x == len(imputed_alleles)
     return (imputed_alleles, i_allele_probs)