Remove numpy type aliases (#225)

* update python version to 3.10

* update numpy version to 1.20

* replace np.float with float per deprecation warning

* replace np.NaN with np.nan for consistency

---------

Co-authored-by: Solstice Cerasus <solst@izalith.wireless.yale.internal>

Author: dsm-72

.readthedocs.yml

@@ -1,4 +1,4 @@
 build:
   image: latest
 python:
-  version: 3.6
+  version: 3.10

diffxpy/testing/det.py

@@ -127,7 +127,7 @@ def log10_pval_clean(self, log10_threshold=-30):
         :param log10_threshold: minimal log10 p-value to return.
         :return: Cleaned log10 transformed p-values.
         """
-        pvals = np.reshape(self.pval, -1).astype(dtype=np.float)
+        pvals = np.reshape(self.pval, -1).astype(dtype=float)
         pvals = np.clip(
             pvals,
             np.nextafter(0, 1),
@@ -148,7 +148,7 @@ def log10_qval_clean(self, log10_threshold=-30):
         :param log10_threshold: minimal log10 q-value to return.
         :return: Cleaned log10 transformed q-values.
         """
-        qvals = np.reshape(self.qval, -1).astype(dtype=np.float)
+        qvals = np.reshape(self.qval, -1).astype(dtype=float)
         qvals = np.clip(
             qvals,
             np.nextafter(0, 1),
@@ -357,7 +357,7 @@ def plot_ma(
         plt.ioff()
 
         ave = np.log(np.clip(
-            self.mean.astype(dtype=np.float),
+            self.mean.astype(dtype=float),
             np.max(np.array([np.nextafter(0, 1), min_mean])),
             np.inf
         ))
@@ -1564,8 +1564,8 @@ def __init__(
         x0, x1 = split_x(data, grouping)
 
         # Only compute p-values for genes with non-zero observations and non-zero group-wise variance.
-        mean_x0 = np.asarray(np.mean(x0, axis=0)).flatten().astype(dtype=np.float)
-        mean_x1 = np.asarray(np.mean(x1, axis=0)).flatten().astype(dtype=np.float)
+        mean_x0 = np.asarray(np.mean(x0, axis=0)).flatten().astype(dtype=float)
+        mean_x1 = np.asarray(np.mean(x1, axis=0)).flatten().astype(dtype=float)
         # Avoid unnecessary mean computation:
         self._mean = np.asarray(np.average(
             a=np.vstack([mean_x0, mean_x1]),
@@ -1577,11 +1577,11 @@ def __init__(
         self._ave_nonzero = self._mean != 0  # omit all-zero features
         if isinstance(x0, scipy.sparse.csr_matrix):
             # Efficient analytic expression of variance without densification.
-            var_x0 = np.asarray(np.mean(x0.power(2), axis=0)).flatten().astype(dtype=np.float) - np.square(mean_x0)
-            var_x1 = np.asarray(np.mean(x1.power(2), axis=0)).flatten().astype(dtype=np.float) - np.square(mean_x1)
+            var_x0 = np.asarray(np.mean(x0.power(2), axis=0)).flatten().astype(dtype=float) - np.square(mean_x0)
+            var_x1 = np.asarray(np.mean(x1.power(2), axis=0)).flatten().astype(dtype=float) - np.square(mean_x1)
         else:
-            var_x0 = np.asarray(np.var(x0, axis=0)).flatten().astype(dtype=np.float)
-            var_x1 = np.asarray(np.var(x1, axis=0)).flatten().astype(dtype=np.float)
+            var_x0 = np.asarray(np.var(x0, axis=0)).flatten().astype(dtype=float)
+            var_x1 = np.asarray(np.var(x1, axis=0)).flatten().astype(dtype=float)
         self._var_geq_zero = np.logical_or(
             var_x0 > 0,
             var_x1 > 0
@@ -1690,8 +1690,8 @@ def __init__(
 
         x0, x1 = split_x(data, grouping)
 
-        mean_x0 = np.asarray(np.mean(x0, axis=0)).flatten().astype(dtype=np.float)
-        mean_x1 = np.asarray(np.mean(x1, axis=0)).flatten().astype(dtype=np.float)
+        mean_x0 = np.asarray(np.mean(x0, axis=0)).flatten().astype(dtype=float)
+        mean_x1 = np.asarray(np.mean(x1, axis=0)).flatten().astype(dtype=float)
         # Avoid unnecessary mean computation:
         self._mean = np.asarray(np.average(
             a=np.vstack([mean_x0, mean_x1]),
@@ -1702,11 +1702,11 @@ def __init__(
         )).flatten()
         if isinstance(x0, scipy.sparse.csr_matrix):
             # Efficient analytic expression of variance without densification.
-            var_x0 = np.asarray(np.mean(x0.power(2), axis=0)).flatten().astype(dtype=np.float) - np.square(mean_x0)
-            var_x1 = np.asarray(np.mean(x1.power(2), axis=0)).flatten().astype(dtype=np.float) - np.square(mean_x1)
+            var_x0 = np.asarray(np.mean(x0.power(2), axis=0)).flatten().astype(dtype=float) - np.square(mean_x0)
+            var_x1 = np.asarray(np.mean(x1.power(2), axis=0)).flatten().astype(dtype=float) - np.square(mean_x1)
         else:
-            var_x0 = np.asarray(np.var(x0, axis=0)).flatten().astype(dtype=np.float)
-            var_x1 = np.asarray(np.var(x1, axis=0)).flatten().astype(dtype=np.float)
+            var_x0 = np.asarray(np.var(x0, axis=0)).flatten().astype(dtype=float)
+            var_x1 = np.asarray(np.var(x1, axis=0)).flatten().astype(dtype=float)
         self._var_geq_zero = np.logical_or(
             var_x0 > 0,
             var_x1 > 0

diffxpy/testing/tests.py

@@ -1273,9 +1273,9 @@ def pairwise(
         elif isinstance(data, glm.typing.InputDataBase):
             data = data.x
         groups = np.unique(grouping)
-        pvals = np.tile(np.NaN, [len(groups), len(groups), data.shape[1]])
+        pvals = np.tile(np.nan, [len(groups), len(groups), data.shape[1]])
         pvals[np.eye(pvals.shape[0]).astype(bool)] = 0
-        logfc = np.tile(np.NaN, [len(groups), len(groups), data.shape[1]])
+        logfc = np.tile(np.nan, [len(groups), len(groups), data.shape[1]])
         logfc[np.eye(logfc.shape[0]).astype(bool)] = 0
 
         if keep_full_test_objs:

requirements.txt

@@ -1,4 +1,4 @@
-numpy>=1.14.0
+numpy>=1.20.0
 scipy
 pandas
 patsy>=0.5.0

setup.py

@@ -17,7 +17,7 @@
     long_description_content_type="text/markdown",
     packages=find_packages(),
     install_requires=[
-        'numpy>=1.16.4',
+        'numpy>=1.20',
         'scipy>=1.2.1',
         'pandas',
         'patsy>=0.5.0',