--- a
+++ b/utils.py
@@ -0,0 +1,70 @@
+import anndata
+import scanpy as sc
+import scipy as s
+from scipy.sparse import csr_matrix, issparse
+
+def load_adata(adata_file, metadata_file = None, normalise = False, cells = None, cell_column = "cell", features = None, filter_lowly_expressed_genes = False, set_colors = False, keep_counts=False):
+
+	adata = sc.read(adata_file)
+
+	# Convert to sparse matrices
+	if not s.sparse.issparse(adata.X):
+		adata.X = csr_matrix(adata.X)
+	if len(adata.layers.keys())>0:
+		for i in list(adata.layers.keys()):
+			if not issparse(adata.layers[i]):
+				adata.layers[i] = csr_matrix(adata.layers[i])
+
+	if cells is not None:
+		tmp = np.mean(np.isin(cells,adata.obs.index.values)==False)
+		if tmp<1: print("%.2f%% of cells provided are not observed in the adata, taking the intersect..." % (100*tmp))
+		cells = np.intersect1d(cells,adata.obs.index.values)
+		adata = adata[cells,:]
+
+	if features is not None:
+		adata = adata[:,features]
+
+	if metadata_file is not None:
+		metadata = pd.read_table(metadata_file, delimiter="\t", header=0).set_index(cell_column, drop=False)
+		metadata = metadata.loc[cells]
+		assert np.all(adata.obs.index.isin(metadata[cell_column]))
+		# assert np.all(metadata.cell.isin(adata.obs.index))
+		assert metadata.shape[0] == adata.shape[0]
+		adata.obs = metadata#.reindex(adata.obs.index)
+
+	if filter_lowly_expressed_genes:
+		sc.pp.filter_genes(adata, min_counts=10)
+
+	if keep_counts:
+		adata.layers["raw"] = adata.X.copy()
+
+	if normalise:
+		sc.pp.normalize_total(adata, target_sum=None, exclude_highly_expressed=False)
+		sc.pp.log1p(adata)
+
+	if set_colors:
+		colPalette_celltypes = [opts["celltype_colors"][i.replace(" ","_").replace("/","_")] for i in sorted(np.unique(adata.obs['celltype']))]
+		adata.uns['celltype_colors'] = colPalette_celltypes
+		colPalette_stages = [opts["stage_colors"][i.replace(" ","_").replace("/","_")] for i in sorted(np.unique(adata.obs['stage']))]
+		adata.uns['stage_colors'] = colPalette_stages
+
+	return adata
+
+def scale(X, x_min, x_max):
+    nom = (X - X.min(axis=0)) * (x_max - x_min)
+    denom = X.max(axis=0) - X.min(axis=0)
+    denom[denom == 0] = 1
+    return x_min + nom / denom
+
+
+# cmap = custom_div_cmap(11, mincol='g', midcol='0.9' ,maxcol='CornflowerBlue')
+def custom_div_cmap(numcolors=11, name='custom_div_cmap',
+                    mincol='blue', midcol='white', maxcol='red'):
+    """ 
+    Default is blue to white to red with 11 colors.  
+    Colors can be specified in any way understandable by matplotlib.colors.ColorConverter.to_rgb()
+    """
+
+    from matplotlib.colors import LinearSegmentedColormap 
+    cmap = LinearSegmentedColormap.from_list(name=name, colors =[mincol, midcol, maxcol], N=numcolors)
+    return cmap
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