Make flake8 compliant

examples/construct_graphs.ipynb

@@ -59,7 +59,7 @@
     "from pathml.preprocessing import StainNormalizationHE\n",
     "from pathml.graph import RAGGraphBuilder, KNNGraphBuilder\n",
     "from pathml.graph import ColorMergedSuperpixelExtractor\n",
-    "from pathml.graph.utils import _valid_image, _exists, plot_graph_on_image, get_full_instance_map, build_assignment_matrix"
+    "from pathml.graph.utils import get_full_instance_map, build_assignment_matrix"
    ]
   },
   {
@@ -256,6 +256,57 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "def plot_graph_on_image(graph, image):\n",
+    "    \"\"\"\n",
+    "    Plots a given graph on the original WSI image\n",
+    "\n",
+    "    Args:\n",
+    "        graph (torch.tensor): Graph as an sparse edge index\n",
+    "        image (numpy.array): Input image\n",
+    "    \"\"\"\n",
+    "\n",
+    "    from torch_geometric.utils.convert import to_networkx\n",
+    "\n",
+    "    pos = graph.node_centroids.numpy()\n",
+    "    G = to_networkx(graph, to_undirected=True)\n",
+    "    plt.imshow(image)\n",
+    "    nx.draw(G, pos, node_size=25)\n",
+    "    plt.show()\n",
+    "\n",
+    "def _valid_image(nr_pixels):\n",
+    "    \"\"\"\n",
+    "    Checks if image does not exceed maximum number of pixels or exceeds minimum number of pixels.\n",
+    "\n",
+    "    Args:\n",
+    "        nr_pixels (int): Number of pixels in given image\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if nr_pixels > MIN_NR_PIXELS and nr_pixels < MAX_NR_PIXELS:\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "def _exists(cg_out, tg_out, assign_out, overwrite):\n",
+    "    \"\"\"\n",
+    "    Checks if given input files exist or not\n",
+    "\n",
+    "    Args:\n",
+    "        cg_out (str): Cell graph file\n",
+    "        tg_out (str): Tissue graph file\n",
+    "        assign_out (str): Assignment matrix file\n",
+    "        overwrite (bool): Whether to overwrite files or not. If true, this function return false and files are\n",
+    "                          overwritten.\n",
+    "    \"\"\"\n",
+    "    if overwrite:\n",
+    "        return False\n",
+    "    else:\n",
+    "        if (\n",
+    "            os.path.isfile(cg_out)\n",
+    "            and os.path.isfile(tg_out)\n",
+    "            and os.path.isfile(assign_out)\n",
+    "        ):\n",
+    "            return True\n",
+    "        return False\n",
+    "\n",
     "def process(image_path, save_path, split, plot=True, overwrite=False):\n",
     "    # 1. get image path\n",
     "    subdirs = os.listdir(image_path)\n",

pathml/datasets/datasets.py

@@ -223,7 +223,7 @@ def __init__(
         )
 
         self.threshold = int(self.patch_size * self.patch_size * threshold)
-        self.warning_threshold = 0.75
+        self.warning_threshold = 0.50
 
         try:
             from torchvision import transforms
@@ -256,6 +256,8 @@ def __init__(
         elif self.entity == "tissue":
             self._precompute_tissue()
 
+        self._warning()
+
     def _add_patch(self, center_x, center_y, instance_index, region_count):
         """Extract and include patch information."""
 

pathml/graph/preprocessing.py

@@ -138,7 +138,7 @@ def process(  # type: ignore[override]
         node_features = self._compute_node_features(features, image_size)
 
         if annotation is not None:
-            node_labels = self._compute_node_labels(instance_map, annotation)
+            node_labels = self._set_node_labels(instance_map, annotation)
         else:
             node_labels = None
 
@@ -230,7 +230,7 @@ def _set_node_labels(self, instance_map, annotation):
         assert annotation.shape[0] == len(
             regions
         ), "Number of annotations do not match number of nodes"
-        return torch.FloatTensor(annotation.astype(float))
+        return annotation
 
     def _build_topology(self, instance_map):
         """Build topology using (thresholded) kNN"""
@@ -274,6 +274,14 @@ def __init__(self, kernel_size=3, hops=1, **kwargs):
         self.hops = hops
         super().__init__(**kwargs)
 
+    def _set_node_labels(self, instance_map, annotation):
+        """Set the node labels of the graphs using annotation"""
+        regions = regionprops(instance_map)
+        assert annotation.shape[0] == len(
+            regions
+        ), "Number of annotations do not match number of nodes"
+        return annotation
+
     def _build_topology(self, instance_map):
         """Create the graph topology from the instance connectivty in the instance_map"""
 

pathml/graph/utils.py

@@ -2,13 +2,8 @@
 Copyright 2021, Dana-Farber Cancer Institute and Weill Cornell Medicine
 License: GNU GPL 2.0
 """
-
-import importlib
 import math
-import os
 
-import matplotlib.pyplot as plt
-import networkx as nx
 import numpy as np
 import torch
 from skimage.measure import label, regionprops
@@ -87,74 +82,6 @@ def __inc__(self, key, value, *args, **kwargs):
             return super().__inc__(key, value, *args, **kwargs)
 
 
-def dynamic_import_from(source_file: str, class_name: str):
-    """Do a from source_file import class_name dynamically
-
-    Args:
-        source_file (str): Where to import from
-        class_name (str): What to import
-    Returns:
-        Any: The class to be imported
-    """
-    module = importlib.import_module(source_file)
-    return getattr(module, class_name)
-
-
-def _valid_image(nr_pixels):
-    """
-    Checks if image does not exceed maximum number of pixels or exceeds minimum number of pixels.
-
-    Args:
-        nr_pixels (int): Number of pixels in given image
-    """
-
-    if nr_pixels > MIN_NR_PIXELS and nr_pixels < MAX_NR_PIXELS:
-        return True
-    return False
-
-
-def plot_graph_on_image(graph, image):
-    """
-    Plots a given graph on the original WSI image
-
-    Args:
-        graph (torch.tensor): Graph as an sparse edge index
-        image (numpy.array): Input image
-    """
-
-    from torch_geometric.utils.convert import to_networkx
-
-    pos = graph.node_centroids.numpy()
-    G = to_networkx(graph, to_undirected=True)
-    plt.imshow(image)
-    nx.draw(G, pos, node_size=25)
-    plt.show()
-
-
-def _exists(cg_out, tg_out, assign_out, overwrite):
-    """
-    Checks if given input files exist or not
-
-    Args:
-        cg_out (str): Cell graph file
-        tg_out (str): Tissue graph file
-        assign_out (str): Assignment matrix file
-        overwrite (bool): Whether to overwrite files or not. If true, this function return false and files are
-                          overwritten.
-    """
-
-    if overwrite:
-        return False
-    else:
-        if (
-            os.path.isfile(cg_out)
-            and os.path.isfile(tg_out)
-            and os.path.isfile(assign_out)
-        ):
-            return True
-        return False
-
-
 def get_full_instance_map(wsi, patch_size, mask_name="cell"):
     """
     Generates and returns the normalized image, cell instance map and cell centroids from pathml SlideData object
@@ -223,21 +150,6 @@ def build_assignment_matrix(low_level_centroids, high_level_map, matrix=False):
     return assignment_matrix
 
 
-def compute_histogram(input_array: np.ndarray, nr_values: int) -> np.ndarray:
-    """Calculates a histogram of a matrix of the values from 0 up to (excluding) nr_values
-    Args:
-        x (np.array): Input tensor.
-        nr_values (int): Possible values. From 0 up to (exclusing) nr_values.
-
-    Returns:
-        np.array: Output tensor.
-    """
-    output_array = np.empty(nr_values, dtype=int)
-    for i in range(nr_values):
-        output_array[i] = (input_array == i).sum()
-    return output_array
-
-
 def two_hop(edge_index, num_nodes):
     """Calculates the two-hop graph.
     Args: