add load functino for coessentiality

#45

CanDI/candi/__init__.py

@@ -1,4 +1,5 @@
-from CanDI.candi import data
-data = data.Data() #Global object data instantiated on import required for access by GeneQuery Objects
-from CanDI.candi.candi import (Gene, CellLine, Organelle, Cancer, CellLineCluster, GeneCluster)
+from . import load
 
+from . import data
+data = data.Data() #Global object data instantiated on import required for access by GeneQuery Objects
+from .candi import (Gene, CellLine, Organelle, Cancer, CellLineCluster, GeneCluster)

CanDI/candi/load.py

@@ -0,0 +1,37 @@
+import numpy as np
+import pandas as pd
+import polars as pl
+from CanDI import candi
+from pathlib import Path
+
+
+def coessentiality(pvalue_threshold = 10**-3, data_dir='auto'):
+    if data_dir == 'auto':
+        data_dir=str(Path(candi.__path__[0]).parent.absolute()) + '/setup/data/coessentiality'
+    else:
+        # check if the path exists and it contains the necessary files
+        if not Path(data_dir).exists():
+            raise ValueError(f"Path {data_dir} does not exist")
+        if not Path(data_dir+'/genes.txt').exists():
+            raise ValueError(f"Path {data_dir}/genes.txt does not exist")
+        if not Path(data_dir+'/GLS_sign.npy').exists():
+            raise ValueError(f"Path {data_dir}/GLS_sign.npy does not exist")
+        if not Path(data_dir+'/GLS_p.npy').exists():
+            raise ValueError(f"Path {data_dir}/GLS_p.npy does not exist")
+
+    gene_names = pd.read_csv(f'{data_dir}/genes.txt',header=None,names=['gene_name'])['gene_name']
+
+    GLS_sign = np.load(f'{data_dir}/GLS_sign.npy')
+    GLS_p = np.load(f'{data_dir}/GLS_p.npy')
+
+    coessentiality_mat = pd.DataFrame((-1*np.log10(GLS_p)) * GLS_sign, columns = gene_names, index = gene_names).reset_index()
+    coessentiality_mat = pl.from_dataframe(coessentiality_mat)
+
+    coessentiality_df = coessentiality_mat.melt('gene_name')
+    coessentiality_df.columns = ['gene_1','gene_2','coessentiality']
+    coessentiality_df = coessentiality_df.filter(~(pl.col('gene_1') == pl.col('gene_2')))
+    coessentiality_df = coessentiality_df.filter(pl.col('coessentiality') > -np.log10(pvalue_threshold))
+
+    out = coessentiality_df.to_pandas()
+
+    return out