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nopython=False #255

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nopython=False #255

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4 changes: 2 additions & 2 deletions esda/crand.py
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Original file line number Diff line number Diff line change
Expand Up @@ -559,7 +559,7 @@ def parallel_crand(
#######################################################################


@njit(fastmath=False)
@njit(fastmath=False, nopython=True)
def _prepare_univariate(i, z, permuted_ids, weights_i):
mask = np.ones_like(z, dtype=boolean)
mask[i] = False
Expand All @@ -570,7 +570,7 @@ def _prepare_univariate(i, z, permuted_ids, weights_i):
return z[i], zrand


@njit(fastmath=False)
@njit(fastmath=False, nopython=True)
def _prepare_bivariate(i, z, permuted_ids, weights_i):
zx = z[:, 0]
zy = z[:, 1]
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2 changes: 1 addition & 1 deletion esda/gamma.py
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Expand Up @@ -275,7 +275,7 @@ def by_col(
# --------------------------------------------------------------
# Conditional Randomization Function Implementations
# --------------------------------------------------------------
@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _local_gamma_crand(i, z, permuted_ids, weights_i, scaling):
zi, zrand = _prepare_univariate(i, z, permuted_ids, weights_i)
return (zi * zrand) @ weights_i * scaling
2 changes: 1 addition & 1 deletion esda/geary_local.py
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Expand Up @@ -196,7 +196,7 @@ def _statistic(x, w, drop_islands):
# Note: does not using the scaling parameter


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _local_geary(i, z, permuted_ids, weights_i, scaling):
other_weights = weights_i[1:]
zi, zrand = _prepare_univariate(i, z, permuted_ids, other_weights)
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4 changes: 2 additions & 2 deletions esda/getisord.py
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Expand Up @@ -641,14 +641,14 @@ def _infer_star_and_structure_w(weights, star, transform):
# --------------------------------------------------------------


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _g_local_crand(i, z, permuted_ids, weights_i, scaling):
other_weights = weights_i[1:]
zi, zrand = _prepare_univariate(i, z, permuted_ids, other_weights)
return (zrand @ other_weights) / (scaling - zi)


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _g_local_star_crand(i, z, permuted_ids, weights_i, scaling):
self_weight = weights_i[0]
other_weights = weights_i[1:]
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2 changes: 1 addition & 1 deletion esda/join_counts_local.py
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Expand Up @@ -173,7 +173,7 @@ def _statistic(y, w, drop_islands):
# Note: scaling not used


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _ljc_uni(i, z, permuted_ids, weights_i, scaling):
# self_weight = weights_i[0]
other_weights = weights_i[1:]
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4 changes: 2 additions & 2 deletions esda/join_counts_local_bv.py
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Expand Up @@ -230,7 +230,7 @@ def _statistic(x, z, w, case, drop_islands):
# Note: scaling not used


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _ljc_bv_case1(i, z, permuted_ids, weights_i, scaling):
zx = z[:, 0]
zy = z[:, 1]
Expand All @@ -239,7 +239,7 @@ def _ljc_bv_case1(i, z, permuted_ids, weights_i, scaling):
return zx[i] * (zyrand @ other_weights)


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _ljc_bv_case2(i, z, permuted_ids, weights_i, scaling):
zy = z[:, 1]
other_weights = weights_i[1:]
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2 changes: 1 addition & 1 deletion esda/join_counts_local_mv.py
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Expand Up @@ -181,7 +181,7 @@ def _statistic(variables, w, drop_islands):
# Note: scaling not used


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _ljc_mv(i, z, permuted_ids, weights_i, scaling):
other_weights = weights_i[1:]
zi, zrand = _prepare_univariate(i, z, permuted_ids, other_weights)
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2 changes: 1 addition & 1 deletion esda/lee.py
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Expand Up @@ -245,7 +245,7 @@ def _statistic(Z, W):
# --------------------------------------------------------------


@_njit(fastmath=True)
@_njit(fastmath=True, nopython=False)
def _local_spatial_pearson_crand(i, z, permuted_ids, weights_i, scaling):
zxi, zxrand, zyi, zyrand = _prepare_bivariate(i, z, permuted_ids, weights_i)
return (zyrand @ weights_i) * (zxrand @ weights_i) * scaling
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