Merge pull request #26 from niaid/ChangeJSONOutput

Change json output include floor and limit. Add prefix to field names.

docs/commandline.rst

@@ -1,7 +1,7 @@
 Command Line Interface
 ======================
 
-The Pytools packages contain a command line executables for varous tasks. They can be invoke directly as an executable:
+The Pytools packages contain a command line executables for various tasks. They can be invoke directly as an executable:
 
 .. code-block :: bash
 
@@ -13,7 +13,7 @@ Or the preferred way using the `python` executable to execute the module entry p
 
     python -m mrc_visual_min_max --help
 
-With either method of invoking the command line interface, the following sections descripts the sub-command available
+With either method of invoking the command line interface, the following sections describes the sub-commands available
 and the command line options available.
 
 .. click:: pytools.ng.mrc2nifti:main

docs/development.rst

@@ -68,7 +68,7 @@ line:
 
 .. code:: bash
 
-    python -m pytest tes
+    python -m pytest test
 
 
 Test Configuration

pytools/ng/build_histogram.py

@@ -169,17 +169,21 @@ def histogram_stats(hist, bin_edges):
 @click.option(
     "--output-json",
     type=click.Path(exists=False, dir_okay=False, resolve_path=True),
-    help='The output filename produced in JSON format with "min" and "max" data elements of a number '
-    "value (double).",
+    help='The output filename produced in JSON format with "neuroglancerPrecomputedMin", '
+    '"neuroglancerPrecomputedMax", "neuroglancerPrecomputedFloor" and "neuroglancerPrecomputedLimit" data '
+    "elements of a double numeric value.",
 )
 @click.version_option(__version__)
 def main(input_image, mad, sigma, percentile, output_json):
     """
-    Reads the INPUT_IMAGE to compute a estimated minimum and maximum range to be used for visualization of the
+    Reads the INPUT_IMAGE to compute am estimated minimum and maximum range to be used for visualization of the
     data set.
 
-    The optional OUTPUT_JSON filename will be created and contain "min" and "max" data elements with a number value
-     (double).
+    The optional OUTPUT_JSON filename will be created with the following data elements with a double numeric value:
+        "neuroglancerPrecomputedMin"
+        "neuroglancerPrecomputedMax"
+        "neuroglancerPrecomputedFloor"
+        "neuroglancerPrecomputedLimit"
     """
 
     logger = logging.getLogger()
@@ -213,6 +217,7 @@ def main(input_image, mad, sigma, percentile, output_json):
 
     logger.info(f'Building histogram for "{reader.GetFileName()}"...')
     h, bins = stream_build_histogram(input_image, list(bin_edges))
+    mids = 0.5 * (bins[1:] + bins[:-1])
 
     logger.info("Computing statistics...")
     if mad:
@@ -225,8 +230,6 @@ def main(input_image, mad, sigma, percentile, output_json):
         min_max = (stats["mean"] - stats["sigma"] * sigma, stats["mean"] + stats["sigma"] * sigma)
     elif percentile:
 
-        mids = 0.5 * (bins[1:] + bins[:-1])
-
         lower_quantile = (0.5 * (100 - percentile)) / 100.0
         upper_quantile = percentile / 100.0 + lower_quantile
         logger.debug(f"quantiles: {lower_quantile} {upper_quantile}")
@@ -241,8 +244,15 @@ def main(input_image, mad, sigma, percentile, output_json):
     else:
         raise RuntimeError("Missing expected argument")
 
-    output = {"min": float(min_max[0]), "max": float(min_max[1])}
+    floor_limit = weighted_quantile(mids, quantiles=[0.0, 1.0], sample_weight=h, values_sorted=True)
 
+    output = {
+        "neuroglancerPrecomputedMin": float(min_max[0]),
+        "neuroglancerPrecomputedMax": float(min_max[1]),
+        "neuroglancerPrecomputedFloor": float(floor_limit[0]),
+        "neuroglancerPrecomputedLimit": float(floor_limit[1]),
+    }
+    logger.debug(f"output: {output}")
     if output_json:
         import json
 

test/fixtures.py

@@ -13,18 +13,29 @@
 #
 import pytest
 import SimpleITK as sitk
+import numpy as np
 
 
 @pytest.fixture(
     scope="session",
-    params=[sitk.sitkUInt8, sitk.sitkInt16, sitk.sitkUInt16, sitk.sitkFloat32],
+    params=[sitk.sitkUInt8, sitk.sitkInt16, sitk.sitkUInt16, sitk.sitkFloat32, "uint16_uniform"],
 )
 def image_mrc(request, tmp_path_factory):
-    pixel_type = request.param
-    print(f"Calling image_mrc with {sitk.GetPixelIDValueAsString(pixel_type)}")
-    fn = f"image_mrc_{sitk.GetPixelIDValueAsString(pixel_type).replace(' ', '_')}.mrc"
-    img = sitk.Image([10, 9, 8], pixel_type)
-    img.SetSpacing([1.1, 1.2, 1.3])
+    if isinstance(request.param, str) and request.param == "uint16_uniform":
+
+        print(f"Calling image_mrc with {request.param}")
+        fn = f"image_mrc_{request.param.replace(' ', '_')}.mrc"
+
+        a = np.linspace(0, 2**16 - 1, num=2**16, dtype="uint16").reshape(16, 64, 64)
+        img = sitk.GetImageFromArray(a)
+        img.SetSpacing([1.23, 1.23, 4.96])
+    else:
+        pixel_type = request.param
+        print(f"Calling image_mrc with {sitk.GetPixelIDValueAsString(pixel_type)}")
+        fn = f"image_mrc_{sitk.GetPixelIDValueAsString(pixel_type).replace(' ', '_')}.mrc"
+        img = sitk.Image([10, 9, 8], pixel_type)
+        img.SetSpacing([1.1, 1.2, 1.3])
+
     fn = tmp_path_factory.mktemp("data").joinpath(fn)
     sitk.WriteImage(img, str(fn))
     return str(fn)

test/test_histogram.py

@@ -108,22 +108,31 @@ def test_histogram_mai_help(cli_args):
 
 
 @pytest.mark.parametrize(
-    "image_mrc,expected_min, expected_max",
-    [(sitk.sitkUInt8, 0, 0), (sitk.sitkInt16, 0, 0), (sitk.sitkUInt16, 0, 0)],
+    "image_mrc,expected_min, expected_max, expected_floor, expected_limit",
+    [
+        (sitk.sitkUInt8, 0, 0, 0, 0),
+        (sitk.sitkInt16, 0, 0, 0, 0),
+        (sitk.sitkUInt16, 0, 0, 0, 0),
+        ("uint16_uniform", 8191.5, 57343.5, 0, 65535),
+    ],
     indirect=["image_mrc"],
 )
-def test_build_histogram_main(image_mrc, expected_min, expected_max):
+def test_build_histogram_main(image_mrc, expected_min, expected_max, expected_floor, expected_limit):
     runner = CliRunner()
     output_filename = "out.json"
     with runner.isolated_filesystem():
         result = runner.invoke(
-            pytools.ng.build_histogram.main, [image_mrc, "--mad", "5", "--output-json", output_filename]
+            pytools.ng.build_histogram.main, [image_mrc, "--mad", "1.5", "--output-json", output_filename]
         )
         assert not result.exception
         with open(output_filename) as fp:
             res = json.load(fp)
 
-    assert "min" in res
-    assert "max" in res
-    assert res["min"] == expected_min
-    assert res["max"] == expected_max
+    assert "neuroglancerPrecomputedMin" in res
+    assert "neuroglancerPrecomputedMax" in res
+    assert "neuroglancerPrecomputedFloor" in res
+    assert "neuroglancerPrecomputedLimit" in res
+    assert res["neuroglancerPrecomputedMin"] == expected_min
+    assert res["neuroglancerPrecomputedMax"] == expected_max
+    assert res["neuroglancerPrecomputedFloor"] == expected_floor
+    assert res["neuroglancerPrecomputedLimit"] == expected_limit

test/test_mrc2ngpc.py

@@ -47,5 +47,7 @@ def test_mrc2ngpc(image_mrc, expected_pixel_type):
 
         with open("mrc2ngpc-output.json") as fp:
             mm = json.load(fp)
-            assert "min" in mm
-            assert "max" in mm
+            assert "neuroglancerPrecomputedMin" in mm
+            assert "neuroglancerPrecomputedMin" in mm
+            assert "neuroglancerPrecomputedFloor" in mm
+            assert "neuroglancerPrecomputedLimit" in mm