From c9ddef72c8e9903c5e6a6a93c072550f73005617 Mon Sep 17 00:00:00 2001
From: ngreenwald <noahfgreenwald@gmail.com>
Date: Tue, 9 Jun 2020 22:10:36 -0700
Subject: [PATCH] Validate inputs for cropping (#97)

* check crop_size arguments

* pep8

* check for floats

* lists or tuples
---
 caliban_toolbox/reshape_data.py      | 37 +++++++++++++++---
 caliban_toolbox/reshape_data_test.py | 56 ++++++++++++++++++++++++++++
 2 files changed, 88 insertions(+), 5 deletions(-)

diff --git a/caliban_toolbox/reshape_data.py b/caliban_toolbox/reshape_data.py
index 7a1eba5..77cb33b 100644
--- a/caliban_toolbox/reshape_data.py
+++ b/caliban_toolbox/reshape_data.py
@@ -54,12 +54,39 @@ def crop_multichannel_data(X_data, y_data, crop_size=None, crop_num=None, overla
     """
 
     # sanitize inputs
-    if len(crop_size) != 2:
-        raise ValueError('crop_size must be a tuple of (row_crop, col_crop), '
-                         'got {}'.format(crop_size))
+    if crop_size is None and crop_num is None:
+        raise ValueError('Either crop_size or crop_num must be specified')
 
-    if not crop_size[0] > 0 and crop_size[1] > 0:
-        raise ValueError('crop_size entries must be positive numbers')
+    if crop_size is not None and crop_num is not None:
+        raise ValueError('Only one of crop_size and crop_num should be provided')
+
+    if crop_size is not None:
+        if not isinstance(crop_size, (tuple, list)):
+            raise ValueError('crop_size must be a tuple or list')
+
+        if len(crop_size) != 2:
+            raise ValueError('crop_size must be a tuple of (row_crop, col_crop), '
+                             'got {}'.format(crop_size))
+
+        if not crop_size[0] > 0 and crop_size[1] > 0:
+            raise ValueError('crop_size entries must be positive')
+
+        if not isinstance(crop_size[0], int) and isinstance(crop_size[1], int):
+            raise ValueError('crop_size entries must be integers')
+
+    if crop_num is not None:
+        if not isinstance(crop_num, (tuple, list)):
+            raise ValueError('crop_num must be a tuple or list')
+
+        if len(crop_num) != 2:
+            raise ValueError('crop_num must be a tuple of (num_row, num_col), '
+                             'got {}'.format(crop_size))
+
+        if not crop_num[0] > 0 and crop_num[1] > 0:
+            raise ValueError('crop_num entries must be positive')
+
+        if not isinstance(crop_num[0], int) and isinstance(crop_num[1], int):
+            raise ValueError('crop_num entries must be integers')
 
     if overlap_frac < 0 or overlap_frac > 1:
         raise ValueError('overlap_frac must be between 0 and 1')
diff --git a/caliban_toolbox/reshape_data_test.py b/caliban_toolbox/reshape_data_test.py
index 0215cb2..9149abe 100644
--- a/caliban_toolbox/reshape_data_test.py
+++ b/caliban_toolbox/reshape_data_test.py
@@ -25,6 +25,7 @@
 # ==============================================================================
 import os
 import tempfile
+import pytest
 
 import numpy as np
 import xarray as xr
@@ -65,6 +66,61 @@ def test_crop_multichannel_data():
 
     assert log_data["num_crops"] == expected_crop_num
 
+    # invalid arguments
+
+    # no crop_size or crop_num
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data)
+
+    # both crop_size and crop_num
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_size=(20, 20), crop_num=(20, 20))
+    # bad crop_size dtype
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_size=5)
+    # bad crop_size shape
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_size=(10, 5, 2))
+    # bad crop_size values
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_size=(0, 5))
+    # bad crop_size values
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_size=(1.5, 5))
+    # bad crop_num dtype
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_num=5)
+    # bad crop_num shape
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_num=(10, 5, 2))
+    # bad crop_num values
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_num=(0, 5))
+    # bad crop_num values
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                crop_num=(1.5, 5))
+    # bad overlap_frac value
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data,
+                                                overlap_frac=1.2)
+    # bad X_data dims
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data[0], y_data=test_y_data,
+                                                crop_size=(5, 5))
+    # bad y_data dims
+    with pytest.raises(ValueError):
+        _ = reshape_data.crop_multichannel_data(X_data=test_X_data, y_data=test_y_data[0],
+                                                crop_num=(5, 5))
+
 
 def test_create_slice_data():
     # test output shape with even division of slice