lazily load the low-hanging fruit

pyproject.toml

@@ -54,6 +54,7 @@ dependencies = [
     "setuptools>=68.0.0",
     "dask>=2024.4.1",
     "dask-expr>=1.1.0",
+    "lazy-loader>=0.4",
 ]
 description = "The Python version of LIGER package."
 keywords = [

src/pyliger/_utilities.py

@@ -1,7 +1,8 @@
 import os
 
-import h5sparse
-import numpy as np
+import lazy_loader as lazy
+h5sparse = lazy.load("h5sparse", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from anndata import AnnData
 
 

src/pyliger/clustering/_leiden.py

@@ -1,5 +1,6 @@
-import leidenalg
-import numpy as np
+import lazy_loader as lazy
+leidenalg = lazy.load("leidenalg", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 
 from pyliger.clustering._utilities import (
     _assign_cluster,

src/pyliger/clustering/_louvain.py

@@ -1,5 +1,6 @@
-import louvain
-import numpy as np
+import lazy_loader as lazy
+louvain = lazy.load("louvain", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 
 from pyliger.clustering._utilities import (
     _assign_cluster,

src/pyliger/clustering/_utilities.py

@@ -1,5 +1,6 @@
-import igraph as ig
-import numpy as np
+import lazy_loader as lazy
+ig = lazy.load("igraph", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from annoy import AnnoyIndex
 from numba import njit
 from pynndescent import NNDescent

src/pyliger/factorization/_iNMF_ANLS.py

@@ -1,4 +1,5 @@
-import numpy as np
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
 from tqdm import tqdm
 
 from pyliger.factorization._utilities import nnlsm_blockpivot

src/pyliger/factorization/_iNMF_HALS.py

@@ -1,6 +1,7 @@
+import lazy_loader as lazy
 import time
 
-import numpy as np
+np = lazy.load("numpy", error_on_import=True)
 
 from pyliger.factorization._utilities import (
     _init_H,

src/pyliger/factorization/_online_iNMF.py

@@ -1,5 +1,6 @@
-import h5sparse
-import numpy as np
+import lazy_loader as lazy
+h5sparse = np = lazy.load("h5sparse", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from scipy.sparse import vstack
 from tqdm import tqdm
 

src/pyliger/factorization/_utilities.py

@@ -1,4 +1,5 @@
-import numpy as np
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
 import numpy.linalg as nla
 import scipy.linalg as sla
 import scipy.sparse as sps

src/pyliger/plotting/_dataset_cluster.py

@@ -1,4 +1,5 @@
-import numpy as np
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
 from plotnine import (
     aes,
     geom_point,

src/pyliger/plotting/_gene.py

@@ -1,8 +1,9 @@
+import lazy_loader as lazy
 import warnings
 from typing import Optional
 
-import numpy as np
-import pandas as pd
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 from plotnine import (
     aes,
     element_blank,

src/pyliger/plotting/_gene_loadings.py

@@ -1,6 +1,7 @@
-import matplotlib as mpl
-import numpy as np
-import pandas as pd
+import lazy_loader as lazy
+mpl = lazy.load("matplotlib", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 from plotnine import (
     aes,
     annotate,

src/pyliger/plotting/_go.py

@@ -5,11 +5,13 @@
 from datetime import date
 from math import log, log10
 
-import matplotlib
+import lazy_loader as lazy
+
+matplotlib = lazy.load("matplotlib", error_on_import=True)
 import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
+sns = lazy.load("seaborn", error_on_import=True)
 from adjustText import (
     adjust_text,
 )  # # Python package which rearranges plot labels to ensure minimal overlap

src/pyliger/plotting/_spatial.py

@@ -1,5 +1,6 @@
+import lazy_loader as lazy
 import matplotlib.pyplot as plt
-import pandas as pd
+pd  = lazy.load("pandas", error_on_import=True)
 from plotnine import (
     aes,
     element_blank,

src/pyliger/plotting/_utilities.py

@@ -1,4 +1,5 @@
-import numpy as np
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
 
 
 def get_gene_values(

src/pyliger/preprocessing/_cal_feature.py

@@ -1,8 +1,9 @@
 from collections import Counter
 from pathlib import Path
+import lazy_loader as lazy
 
-import numpy as np
-import pandas as pd
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 from anndata import AnnData
 from scipy.sparse import csr_matrix
 

src/pyliger/preprocessing/_initialization.py

@@ -1,8 +1,9 @@
 from os import path
 from typing import List, Optional
 
-import h5sparse
-import numpy as np
+import lazy_loader as lazy
+h5sparse = lazy.load("h5sparse", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from scipy.sparse import csr_matrix
 
 from pyliger._utilities import _h5_idx_generator, _create_h5_using_adata, _merge_sparse_data_all, _remove_missing_obs

src/pyliger/preprocessing/_normalization.py

@@ -1,8 +1,9 @@
 from pathlib import Path
 from typing import Optional
 
-import h5sparse
-import numpy as np
+import lazy_loader as lazy
+h5sparse = lazy.load("h5sparse", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from sklearn.preprocessing import normalize as sp_normalize
 
 from pyliger._utilities import _h5_idx_generator, _remove_missing_obs

src/pyliger/preprocessing/_qc.py

@@ -1,5 +1,6 @@
-import numpy as np
-import pandas as pd
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 
 
 def calculate_qc(liger_object):

src/pyliger/preprocessing/_scale.py

@@ -1,7 +1,8 @@
 from typing import Optional
 
-import h5sparse
-import numpy as np
+import lazy_loader as lazy
+h5sparse = lazy.load("h5sparse", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from scipy.sparse import csr_matrix
 from sklearn.utils.sparsefuncs import inplace_column_scale
 

src/pyliger/preprocessing/_select_genes.py

@@ -1,9 +1,10 @@
 import warnings
 
-import h5sparse
+import lazy_loader as lazy
+h5sparse = lazy.load("h5sparse", error_on_import=True)
 import matplotlib.pyplot as plt
-import numexpr as ne
-import numpy as np
+ne = lazy.load("numexpr", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
 from scipy.optimize import minimize
 from scipy.stats import norm
 from sklearn.utils.sparsefuncs import mean_variance_axis

src/pyliger/pyliger.py

@@ -1,5 +1,6 @@
-import numpy as np
-import pandas as pd
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 from anndata import AnnData
 from scipy.sparse import vstack
 

src/pyliger/read_write.py

@@ -3,10 +3,12 @@
 import re
 from pathlib import Path
 
-import anndata
-import h5py
-import numpy as np
-import pandas as pd
+import lazy_loader as lazy
+
+anndata = lazy.load("anndata", error_on_import=True)
+h5py = lazy.load("h5py", error_on_import=True)
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 import scipy.io
 from anndata import AnnData
 from matplotlib.image import imread

src/pyliger/tools/_go.py

@@ -1,6 +1,6 @@
 from pathlib import Path
-
-import mygene
+import lazy_loader as lazy
+mygene = lazy.load("mygene", error_on_import=True)
 from goatools.anno.genetogo_reader import Gene2GoReader
 from goatools.base import download_go_basic_obo, download_ncbi_associations
 from goatools.goea.go_enrichment_ns import GOEnrichmentStudyNS

src/pyliger/tools/_marker.py

@@ -1,5 +1,6 @@
-import numpy as np
-import pandas as pd
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 from scipy.sparse import vstack
 from sklearn.preprocessing import scale
 

src/pyliger/tools/_metrics.py

@@ -1,4 +1,5 @@
-import numpy as np
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
 
 
 def calc_dataset_specificity(liger_object, dataset1=None, dataset2=None, do_plot=True):

src/pyliger/tools/_quantile_norm.py

@@ -1,4 +1,5 @@
-import numpy as np
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
 from scipy import interpolate
 from scipy.stats.mstats import mquantiles
 

src/pyliger/tools/_umap.py

@@ -1,6 +1,7 @@
-import numpy as np
-import pandas as pd
-import umap
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
+umap  = lazy.load("umap", error_on_import=True)
 import umap.plot
 
 

src/pyliger/tools/_wilcoxon.py

@@ -1,7 +1,7 @@
 from functools import reduce
-
-import numpy as np
-import pandas as pd
+import lazy_loader as lazy
+np = lazy.load("numpy", error_on_import=True)
+pd  = lazy.load("pandas", error_on_import=True)
 import statsmodels.stats.multitest as smt
 from scipy.sparse import vstack
 from scipy.stats import distributions