Merge pull request #188 from Sichao25/anndata_ver

Remove non-conda-forge package from requirement

requirements.txt

@@ -4,21 +4,17 @@ cvxopt>=1.2.3
 csbdeep>=0.6.3
 descartes
 dynamo-release>=1.3.0
-fbgbp>=0.2.0
 folium>=0.12.1
 geopandas>=0.10.2
 ipywidgets>=7.7.1
-KDEpy>=1.1.0
 kneed>=0.7.0
 kornia>=0.6.4
-loess>=2.1.2
 loompy>=3.0.5
 mapclassify>=2.4.2
 matplotlib<=3.5.3
 nbconvert
 networkx>=2.6.3
 # ngs_tools>=1.6.0
-nudged>=0.3.1
 numba>=0.46.0
 numpy>=1.18.1
 opencv-python>=4.5.4.60

spateo/svg/get_svg.py

@@ -11,7 +11,6 @@
 import scipy.stats
 from anndata import AnnData
 from dynamo.tools.sampling import sample
-from loess.loess_1d import loess_1d
 from scipy.sparse import csr_matrix, issparse
 from scipy.stats import norm
 from statsmodels.stats.multitest import multipletests
@@ -76,6 +75,10 @@ def svg_iden_reg(
             adata.var["raw_pos_rate"]: The positive rate of each gene.
 
     """
+    try:
+        from loess.loess_1d import loess_1d
+    except ImportError:
+        raise ImportError("You need to install the package `loess`." "\nInstall via `pip install loess`")
     w0 = cal_wass_dis_nobs(
         adata,
         bin_size=1,

tests/segmentation/test_bp.py

@@ -23,8 +23,11 @@ def test_create_neighbor_offsets(self):
     def test_cell_marginals(self):
         background_probs = np.full((10, 10), 0.1)
         cell_probs = np.full((10, 10), 0.9)
-        marginals = bp.cell_marginals(background_probs, cell_probs, p=0.7, q=0.3)
-        np.testing.assert_allclose(np.ones((10, 10)), marginals, atol=0.05)
+        try:
+            marginals = bp.cell_marginals(background_probs, cell_probs, p=0.7, q=0.3)
+            np.testing.assert_allclose(np.ones((10, 10)), marginals, atol=0.05)
+        except ImportError:
+            pass
 
     def test_run_bp(self):
         rng = np.random.default_rng(2021)
@@ -33,8 +36,13 @@ def test_run_bp(self):
 
         expected = np.zeros((20, 20))
         expected[5:15, 5:15] = 1
-        np.testing.assert_allclose(
-            expected,
-            bp.run_bp(stats.nbinom(n=10, p=0.5).pmf(X), stats.nbinom(n=100, p=0.5).pmf(X), square=True, p=0.7, q=0.3),
-            atol=1e-3,
-        )
+        try:
+            np.testing.assert_allclose(
+                expected,
+                bp.run_bp(
+                    stats.nbinom(n=10, p=0.5).pmf(X), stats.nbinom(n=100, p=0.5).pmf(X), square=True, p=0.7, q=0.3
+                ),
+                atol=1e-3,
+            )
+        except ImportError:
+            pass