Merge pull request #23 from multipaths/diff_input_processing_recoding

Input processing updates for the recoding in DiffuPath cross-validations

Author: jmarinllao

src/diffupy/cli.py

@@ -6,11 +6,11 @@
 import logging
 import os
 import pickle
-import sys
 import time
 
 import click
-from diffupy.process_network import get_kernel_from_network_path
+
+from .process_network import get_kernel_from_network_path
 
 from .constants import OUTPUT, METHODS, EMOJI, RAW, CSV, JSON
 from .diffuse import diffuse as run_diffusion
@@ -93,7 +93,7 @@ def kernel(
     '-o', '--output',
     type=click.File('w'),
     help="Output file",
-    default=sys.stdout,
+    default=OUTPUT,
 )
 @click.option(
     '-m', '--method',
@@ -107,8 +107,8 @@ def kernel(
          '-1). For scoring methods that accept quantitative values (i.e., raw & z), node labels can also be codified '
          'with LogFC (in this case, set binarize==False).',
     type=bool,
-    default=True,
-    show_default=True,
+    default=False,
+    show_default=False,
 )
 @click.option(
     '-t', '--threshold',
@@ -121,8 +121,8 @@ def kernel(
     help='Codify node labels by applying threshold to | logFC | in input. If absolute_value is set to False,'
          'node labels will be signed.',
     type=bool,
-    default=True,
-    show_default=True,
+    default=False,
+    show_default=False,
 )
 @click.option(
     '-p', '--p_value',
@@ -141,11 +141,11 @@ def kernel(
 def diffuse(
         input: str,
         network: str,
-        output: str = sys.stdout,
+        output: str = OUTPUT,
         method: str = RAW,
-        binarize: bool = True,
+        binarize: bool = False,
         threshold: float = None,
-        absolute_value: bool = True,
+        absolute_value: bool = False,
         p_value: float = 0.05,
         output_format: str = CSV
 ):

src/diffupy/matrix.py

@@ -68,7 +68,7 @@ def __init__(
         if init_value is not None and self.rows_labels and list(self.cols_labels):
             mat = np.full((len(self.rows_labels), len(self.cols_labels)), init_value)
 
-        elif not list(mat):
+        elif mat is None:
             raise ValueError('A path matrix or initialization should be provided.')
 
         self.mat = np.array(mat)
@@ -114,7 +114,9 @@ def __next__(self):
 
         nxt = tuple()
         if len(self.rows_labels) == 1:
-            nxt += (self.mat[self.j],)
+            nxt += (self.mat[0, self.j],)
+        elif len(self.cols_labels) == 1:
+            nxt += (self.mat[self.i, 0],)
         else:
             nxt += (self.mat[self.i][self.j],)
 
@@ -284,11 +286,21 @@ def delete_col_from_label(self, label):
 
     def set_cell_from_labels(self, row_label, col_label, x):
         """Set cell from labels."""
-        self.mat[self.rows_labels_ix_mapping[row_label], self.cols_labels_ix_mapping[col_label]] = x
+        if len(self.rows_labels) == 1:
+            self.mat[0, self.cols_labels_ix_mapping[col_label]] = x
+        elif len(self.cols_labels) == 1:
+            self.mat[self.rows_labels_ix_mapping[row_label], 0] = x
+        else:
+            self.mat[self.rows_labels_ix_mapping[row_label], self.cols_labels_ix_mapping[col_label]] = x
 
     def get_cell_from_labels(self, row_label, col_label):
         """Get cell from labels."""
-        return self.mat[self.rows_labels_ix_mapping[row_label], self.cols_labels_ix_mapping[col_label]]
+        if len(self.rows_labels) == 1:
+            return self.mat[0, self.cols_labels_ix_mapping[col_label]]
+        elif len(self.cols_labels) == 1:
+            return self.mat[self.rows_labels_ix_mapping[row_label], 0]
+        else:
+            return self.mat[self.rows_labels_ix_mapping[row_label], self.cols_labels_ix_mapping[col_label]]
 
     """Methods"""