Many member functions have been changed to property attributes. There…

… is a new Zval model where one Scan has many Zvals. A particular scan model now has a slice_coords attribute which is an array (via the Zval model) of all the slice coordinates of the image volume. This will make the library more consistent so that coordinates are not given as indexes in-slice, while z-values are given between-slice.

README.md

@@ -92,7 +92,17 @@ You can engage an interactive slice view by calling:
 
     scan.visualize()
 
-Note that calling `visualize` on a scan object doesn't include its annotation information -- you must call the `visualize_in_scan` member function of an `Annotation` object to do this.
+By default, note that calling `visualize` on a scan object doesn't include its annotation information. However, if `scan.visualize` is supplied with a list of `Annotation` objects (say, grouped by the `scan.cluster_annotations()` function), then this annotation information *will* be displayed. For example
+
+    nodules = scan.cluster_annotations()
+    print(len(nodules))
+    # => 3
+
+    # Visualize the slices with the provided annotations indicated by arrows.
+    scan.visualize(annotations)
+
+`scan.cluster_annotations` returns a list. Each element of the list is a list of `Annotation` objects where each `Annotation` refers to the same nodule in the scan (probably). See [Clustering annotations](#clustering-annotations-to-identify-those-which-refer-to-the-same-physical-nodule) for more details on this function.
+
 
 #### The `Annotation` model
 
@@ -102,11 +112,11 @@ Let's grab the first annotation from the `Scan` object above:
     print(ann.scan.patient_id)
     # => LIDC-IDRI-0066
 
-    print(ann.spiculation, ann.Spiculation())
+    print(ann.spiculation, ann.Spiculation)
     # => 3, Medium Spiculation
 
-    print(ann.estimate_diameter(), ann.estimate_volume())
-    # => 15.4920358194, 888.052284241
+    print("%.2f, %.2f, %.2f" % (ann.diameter, ann.surface_area, ann.volume))
+    # => 15.49, 1041.37, 888.05
 
     ann.print_formatted_feature_table()
     # => Feature              Meaning                    # 
@@ -121,19 +131,29 @@ Let's grab the first annotation from the `Scan` object above:
     # => Texture            | Solid                    | 5 
     # => Malignancy         | Moderately Suspicious    | 4
 
-    from pylidc.Annotation import feature_names as fnames
     fvals, fstrings = ann.feature_vals(return_str=True)
-    print(fnames[0].title(), fstrings[0], fvals[0])
-    # => Subtlety, Obvious, 5
+
+    for fname,fval,fstr in zip(pl.annotation_feature_names, fvals, fstrings):
+        print(fname.title(), fval, fstr)
+    # => 'Subtlety', 5, 'Obvious'
+    # => 'Internalstructure', 1, 'Soft Tissue'
+    # => 'Calcification', 6, 'Absent'
+    # => 'Sphericity', 3, 'Ovoid'
+    # => 'Margin', 1, 'Poorly Defined'
+    # => 'Lobulation', 4, 'Near Marked Lobulation'
+    # => 'Spiculation', 3, 'Medium Spiculation'
+    # => 'Texture', 5, 'Solid'
+    # => 'Malignancy', 4, 'Moderately Suspicious'
 
 Let's try a different query on the annotations directly:
 
-    qu = pl.query(pl.Annotation).filter(pl.Annotation.lobulation > 3, pl.Annotation.malignancy == 5)
+    qu = pl.query(pl.Annotation).filter(pl.Annotation.lobulation > 3,
+                                        pl.Annotation.malignancy == 5)
     print(qu.count())
     # => 183
 
     ann = qu.first()
-    print(ann.lobulation, ann.Lobulation(), ann.malignancy, ann.Malignancy())
+    print(ann.lobulation, ann.Lobulation, ann.malignancy, ann.Malignancy)
     # => 4, Near Marked Lobulation, 5, Highly Suspicious
 
     print(len(ann.contours))
@@ -142,7 +162,7 @@ Let's try a different query on the annotations directly:
     print(ann.contours_to_matrix().shape)
     # => (671, 3)
 
-    print(ann.contours_to_matrix().mean(axis=0) - ann.centroid())
+    print(ann.contours_to_matrix().mean(axis=0) - ann.centroid)
     # => [ 0.  0.  0.]
 
 You can engage an interactive slice viewer that displays annotation values and the radiologist-drawn contours:
@@ -211,12 +231,12 @@ import pylidc as pl
 scan = pl.query(pl.Scan).first()
 nods = scan.cluster_annotations()
 
-print "Scan is estimated to have", len(nods), "nodules."
+print("Scan is estimated to have", len(nods), "nodules.")
 
 for i,nod in enumerate(nods):
-    print "Nodule", i+1, "has", len(nod), "annotations."
+    print("Nodule", i+1, "has", len(nod), "annotations.")
         for j,ann in enumerate(nod):
-            print "-- Annotation", j+1, "centroid:", ann.centroid()
+            print("-- Annotation", j+1, "centroid:", ann.centroid)
 ```
 
 Output:

_populate_db/add_zvals_to_scans.py

@@ -0,0 +1,19 @@
+import numpy as np
+import pylidc as pl
+
+
+scans = pl.query(pl.Scan)
+nscans = scans.count()
+for i,scan in enumerate(scans):
+    print i+1,"/",nscans
+
+    images = scan.load_all_dicom_images(verbose=0)
+    img_zs = [float(img.ImagePositionPatient[-1]) for img in images]
+    img_zs = np.unique(img_zs)
+
+    for zval in img_zs:
+        z = pl.Zval()
+        z.val = float(zval)
+        z.scan = scan
+
+pl._session.commit()

_populate_db/make_zvals.sql

@@ -0,0 +1,7 @@
+CREATE TABLE zvals (
+    id INTEGER NOT NULL, 
+    scan_id INTEGER, 
+    val FLOAT, 
+    PRIMARY KEY (id), 
+    FOREIGN KEY(scan_id) REFERENCES scans (id)
+);