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Resolves deprecation error #161

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Resolves deprecation error #161

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2 changes: 1 addition & 1 deletion ncem/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -247,7 +247,7 @@ def plot_degree_vs_dist(
mean_d = [np.mean(degree) for degree in degrees]
print(np.mean(mean_d))
mean_degree += mean_d
distances += [np.int(dist * lateral_resolution)] * len(mean_d)
distances += [int(dist * lateral_resolution)] * len(mean_d)

sns_data = pd.DataFrame(
{
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6 changes: 3 additions & 3 deletions ncem/estimators/base_estimator.py
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Expand Up @@ -307,7 +307,7 @@ def get_data(
)
if robustness:
np.random.seed(robustness_seed)
n_images = np.int(len(self.data.img_celldata) * robustness)
n_images = int(len(self.data.img_celldata) * robustness)
print(n_images)
image_keys = list(
np.random.choice(
Expand All @@ -332,10 +332,10 @@ def get_data(
if segmentation_robustness:
node_fraction = segmentation_robustness[0]
overflow_fraction = segmentation_robustness[1]
total_size = np.int(self.data.celldata.shape[0] * node_fraction)
total_size = int(self.data.celldata.shape[0] * node_fraction)

for key, ad in self.data.img_celldata.items():
size = np.int(ad.shape[0] * node_fraction)
size = int(ad.shape[0] * node_fraction)
random_indices = np.random.choice(ad.shape[0], size=size, replace=False)
a = ad.obsp["adjacency_matrix_connectivities"].toarray()
err_ad = ad.copy()
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8 changes: 4 additions & 4 deletions ncem/interpretation/interpreter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -499,7 +499,7 @@ def _get_np_data(
for k, v in nodes_idx.items():
count = count + len(v)

with tqdm(total=np.int(count / self.n_eval_nodes_per_graph)) as pbar:
with tqdm(total=int(count / self.n_eval_nodes_per_graph)) as pbar:
for _step, (x_batch, y_batch) in enumerate(ds):
target_batch, interaction_batch, sf_batch, node_covar_batch, g_batch = x_batch
target.append(target_batch.numpy().squeeze())
Expand Down Expand Up @@ -919,7 +919,7 @@ def get_sender_receiver_effects(self, params_type: str = "ols", significance_thr
print("calculating inv fim.")
fim_inv = get_fim_inv(x_design, y)

interaction_shape = np.int(self.n_features_0**2)
interaction_shape = int(self.n_features_0**2)
params = params[:, self.n_features_0 : interaction_shape + self.n_features_0]
is_sign, pvalues, qvalues = wald_test(
params=params, fisher_inv=fim_inv, significance_threshold=significance_threshold
Expand Down Expand Up @@ -1902,7 +1902,7 @@ def _get_np_data(
def get_sender_receiver_effects(self, params_type: str = "ols", significance_threshold: float = 0.05):
data = {"target": self.data.celldata.obsm["node_types"], "proportions": self.data.celldata.obsm["proportions"]}
target = np.asarray(dmatrix("target-1", data))
interaction_shape = np.int(self.n_features_0**2)
interaction_shape = int(self.n_features_0**2)
interactions = np.asarray(dmatrix("target:proportions-1", data))

y = self.data.celldata.X
Expand All @@ -1922,7 +1922,7 @@ def get_sender_receiver_effects(self, params_type: str = "ols", significance_thr
is_sign, pvalues, qvalues = wald_test(
params=params, fisher_inv=fim_inv, significance_threshold=significance_threshold
)
interaction_shape = np.int(self.n_features_0**2)
interaction_shape = int(self.n_features_0**2)
# subset to interaction terms
is_sign = is_sign[self.n_features_0 : interaction_shape + self.n_features_0, :]
pvalues = pvalues[self.n_features_0 : interaction_shape + self.n_features_0, :]
Expand Down
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