Merge pull request #5 from brainglobe/tests

Add tests and fix bugs

README.md

@@ -1,2 +1,12 @@
+[![Python Version](https://img.shields.io/pypi/pyversions/brainreg-segment.svg)](https://pypi.org/project/brainreg-segment)
+[![PyPI](https://img.shields.io/pypi/v/brainreg-segment.svg)](https://pypi.org/project/brainreg-segment)
+[![Wheel](https://img.shields.io/pypi/wheel/brainreg-segment.svg)](https://pypi.org/project/brainreg-segment)
+[![Development Status](https://img.shields.io/pypi/status/brainreg-segment.svg)](https://github.com/brainglobe/brainreg-segment)
+[![Travis](https://img.shields.io/travis/com/brainglobe/brainreg-segment?label=Travis%20CI)](
+    https://travis-ci.com/brainglobe/brainreg-segment)
+[![Coverage Status](https://coveralls.io/repos/github/brainglobe/brainreg-segment/badge.svg?branch=master)](https://coveralls.io/github/brainglobe/brainreg-segment?branch=master)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
+[![Gitter](https://badges.gitter.im/cellfinder/brainreg.svg)](https://gitter.im/cellfinder/brainreg?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
 # brainreg-segment
 Segmentation of 3D brain structures in a common anatomical space

brainreg_segment/atlas/utils.py

@@ -2,14 +2,33 @@
 
 
 def lateralise_atlas_image(
-    atlas, hemispheres, left_hemisphere_value=1, right_hemisphere_value=2
+    masked_atlas_annotations,
+    hemispheres,
+    left_hemisphere_value=1,
+    right_hemisphere_value=2,
 ):
-    atlas_left = atlas[hemispheres == left_hemisphere_value]
-    atlas_right = atlas[hemispheres == right_hemisphere_value]
-    return atlas_left, atlas_right
+    """
+    :param masked_atlas_annotations: Masked image of atlas annotations
+    :param hemispheres: Hemispheres image
+    :param left_hemisphere_value: Value encoded in hemispheres image
+    :param right_hemisphere_value: Value encoded in hemispheres image
+    :return: Tuple (left, right) of numpy arrays of values within
+    each hemisphere
+    """
+    annotation_left = masked_atlas_annotations[
+        hemispheres == left_hemisphere_value
+    ]
+    annotation_right = masked_atlas_annotations[
+        hemispheres == right_hemisphere_value
+    ]
+    return annotation_left, annotation_right
 
 
 def get_available_atlases():
+    """
+    Get the available brainglobe atlases
+    :return: Dict of available atlases (["name":version])
+    """
     available_atlases = utils.conf_from_url(
         descriptors.remote_url_base.format("last_versions.conf")
     )

brainreg_segment/image/utils.py

@@ -4,7 +4,7 @@
 
 def create_KDTree_from_image(image, value=0):
     """
-    Create a KDTree of points equally a given value
+    Create a KDTree of points equalling a given value
     :param image: Image to be converted to points
     :param value: Value of image to be used
     :return: scipy.spatial.cKDTree object

brainreg_segment/regions/IO.py

@@ -1,5 +1,4 @@
-import imio
-
+import tifffile
 import numpy as np
 
 from pathlib import Path
@@ -90,11 +89,14 @@ def save_label_layers(regions_directory, label_layers):
         save_regions_to_file(label_layer, regions_directory)
 
 
-def export_label_layers(regions_directory, label_layers):
+def export_label_layers(
+    regions_directory, label_layers, voxel_size, obj_ext=".obj"
+):
     print(f"Exporting regions to: {regions_directory}")
     regions_directory.mkdir(parents=True, exist_ok=True)
     for label_layer in label_layers:
-        export_regions_to_file(label_layer, regions_directory)
+        filename = regions_directory / (label_layer.name + obj_ext)
+        export_regions_to_file(label_layer.data, filename, voxel_size)
 
 
 def save_regions_to_file(
@@ -118,28 +120,16 @@ def save_regions_to_file(
     name = label_layer.name
 
     filename = destination_directory / (name + image_extension)
-    imio.to_tiff(data.astype(np.int16), filename)
+    tifffile.imsave(filename, data.astype(np.int16))
 
 
-def export_regions_to_file(
-    label_layer, destination_directory, ignore_empty=True, obj_ext=".obj",
-):
+def export_regions_to_file(image, filename, voxel_size, ignore_empty=True):
     """
     Export regions as .obj for brainrender
-    :param label_layer: napari labels layer (with segmented regions)
-    :param destination_directory: Where to save files to
-    :param ignore_empty: If True, don't attempt to save empty images
-    :param obj_ext: File extension for the obj files
-    :param image_extension: File extension fo the image files
+
     """
-    data = label_layer.data
     if ignore_empty:
-        if data.sum() == 0:
+        if image.sum() == 0:
             return
 
-    name = label_layer.name
-
-    filename = destination_directory / (name + obj_ext)
-    volume_to_vector_array_to_obj_file(
-        data, filename,
-    )
+    volume_to_vector_array_to_obj_file(image, filename, voxel_size=voxel_size)

brainreg_segment/regions/layers.py

@@ -1,4 +1,4 @@
-import imio
+import tifffile
 
 import numpy as np
 
@@ -61,7 +61,7 @@ def add_existing_label_layers(
     :return label_layer: napari labels layer
     """
     label_file = Path(label_file)
-    labels = imio.load_any(label_file)
+    labels = tifffile.imread(label_file)
     label_layer = viewer.add_labels(
         labels, num_colors=num_colors, name=label_file.stem
     )

brainreg_segment/segment.py

@@ -426,6 +426,7 @@ def initialise_track_tracing(self):
         self.track_panel.setVisible(True)
         self.region_panel.setVisible(True)
         self.splines = None
+        self.spline_names = None
 
     def add_track(self):
         print("Adding a new track\n")
@@ -545,7 +546,7 @@ def export_all(
     max_axis_2,
 ):
     if label_layers:
-        export_label_layers(regions_directory, label_layers)
+        export_label_layers(regions_directory, label_layers, resolution)
 
     if splines:
         export_splines(

brainreg_segment/tracks/IO.py

@@ -8,18 +8,19 @@ def save_track_layers(
     tracks_directory.mkdir(parents=True, exist_ok=True)
 
     for points_layer in points_layers:
-        save_single_track_layer(
-            points_layer,
+        save_single_track(
+            points_layer.data,
+            points_layer.name,
             tracks_directory,
             track_file_extension=track_file_extension,
         )
 
 
-def save_single_track_layer(
-    layer, output_directory, track_file_extension=".points",
+def save_single_track(
+    points, name, output_directory, track_file_extension=".points",
 ):
-    output_filename = output_directory / (layer.name + track_file_extension)
-    points = pd.DataFrame(layer.data)
+    output_filename = output_directory / (name + track_file_extension)
+    points = pd.DataFrame(points)
     points.to_hdf(output_filename, key="df", mode="w")
 
 

brainreg_segment/tracks/analysis.py

@@ -24,16 +24,18 @@ def track_analysis(
     spline_names = []
 
     for track_layer in track_layers:
-        spline = spline_fit(
+        spline = run_track_analysis(
             track_layer.data,
-            smoothing=spline_smoothing,
-            k=fit_degree,
-            n_points=spline_points,
+            track_layer.name,
+            tracks_directory,
+            atlas,
+            summarise_track=summarise_track,
+            spline_smoothing=spline_smoothing,
+            spline_points=spline_points,
+            fit_degree=fit_degree,
         )
+
         splines.append(spline)
-        if summarise_track:
-            summary_csv_file = tracks_directory / (track_layer.name + ".csv")
-            analyse_track_anatomy(atlas, spline, summary_csv_file)
 
         viewer.add_points(
             spline,
@@ -49,18 +51,55 @@ def track_analysis(
     return splines, spline_names
 
 
-def analyse_track_anatomy(atlas, spline, file_path, verbose=True):
+def run_track_analysis(
+    points,
+    track_name,
+    tracks_directory,
+    atlas,
+    spline_smoothing=0.05,
+    spline_points=100,
+    fit_degree=3,
+    summarise_track=True,
+):
+    """
+    For each set of points, run a spline fit, and (if required) determine which
+     atlas region each part of the spline fit is within
+    :param points: 3D numpy array of points
+    :param track_name: Name of the set of points (used for saving results)
+    :param tracks_directory: Where to save the results to
+    :param atlas: brainglobe atlas class
+    :param spline_smoothing: spline fit smoothing factor
+    :param spline_points: How many points used to define the resulting
+    interpolated path
+    :param fit_degree: spline fit degree
+    :param summarise_track: If True, save a csv with the atlas region for
+    all parts of the spline fit
+    :return np.array: spline fit
+    """
+
+    spline = spline_fit(
+        points,
+        smoothing=spline_smoothing,
+        k=fit_degree,
+        n_points=spline_points,
+    )
+    if summarise_track:
+        summary_csv_file = tracks_directory / (track_name + ".csv")
+        analyse_track_anatomy(atlas, spline, summary_csv_file)
+
+    return spline
+
+
+def analyse_track_anatomy(atlas, spline, file_path):
     """
-    For a given spline, and brainrender scene, find the brain region that each
+    For a given spline, find the atlas region that each
     "segment" is in, and save to csv.
 
-    :param scene: brainrender scene object
+    :param atlas: brainglobe atlas class
     :param spline: numpy array defining the spline interpolation
     :param file_path: path to save the results to
     :param bool verbose: Whether to print the progress
     """
-    if verbose:
-        print("Determining the brain region for each segment of the spline")
     spline_regions = []
     for p in spline.tolist():
         try:
@@ -94,6 +133,4 @@ def analyse_track_anatomy(atlas, spline, file_path, verbose=True):
                 },
                 ignore_index=True,
             )
-    if verbose:
-        print(f"Saving results to: {file_path}")
     df.to_csv(file_path, index=False)

setup.py

@@ -7,6 +7,7 @@
 
 requirements = [
     "numpy",
+    "tables",
     "scikit-image>=0.14.0,<0.17.0",
     "pandas>=0.25.1,<=0.25.3",
     "napari[pyqt5]",