warning/small fixes

docs/source/changelog.rst

@@ -152,7 +152,7 @@ Planar Imaging
   The default is 1000mm, but if you set it on your panel you can pass something like 1400mm.
 * The phantom-finding algorithm has been refactored to be more extensible. This does not affect normal users, but reduces the amount of duplicate code.
   It also makes adding new phantoms easier.
-* Generally speaking, the phantoms should all be centered along the CAX. Previously, the phantom could be offset from the CAX.
+* Generally speaking, the phantoms should all be roughly centered along the CAX. Previously, the phantom could be offset from the CAX.
   Due to general difficulty in finding the phantom reliably for the majority of clinics, I am enforcing this as a restriction.
   This shouldn't affect too many people but should make the ROI-finding algorithm better.
 * The low contrast background ROI (i.e. the base level of contrast) has been adjusted for some phantoms (QC-3 and Doselab). Previously, it

pylinac/core/image.py

@@ -83,7 +83,7 @@ def equate_images(image1: ImageLike, image2: ImageLike) -> Tuple[ImageLike, Imag
 
     # resize images to be of the same shape
     zoom_factor = image1.shape[1] / image2.shape[1]
-    image2_array = ndimage.interpolation.zoom(image2.as_type(np.float), zoom_factor)
+    image2_array = ndimage.interpolation.zoom(image2.as_type(float), zoom_factor)
     image2 = load(image2_array, dpi=image2.dpi * zoom_factor)
 
     return image1, image2
@@ -131,7 +131,7 @@ def load(path: Union[str, ImageLike, np.ndarray, BinaryIO], **kwargs) -> ImageLi
     Load an image from a file and then apply a filter::
 
         >>> from pylinac.core.image import load
-        >>> my_image = "C:\QA\image.tif"
+        >>> my_image = r"C:\QA\image.tif"
         >>> img = load(my_image)  # returns a FileImage
         >>> img.filter(5)
 
@@ -836,7 +836,7 @@ def _get_axis_value(self, axis_str: str, axis_dcm_attr: str) -> float:
                 axis_found = True
             # if it is, then make sure it follows the naming convention of <axis###>
             else:
-                match = re.search('(?<={})\d+'.format(axis_str.lower()), filename.lower())
+                match = re.search(r'(?<={})\d+'.format(axis_str.lower()), filename.lower())
                 if match is None:
                     raise ValueError(
                             f"The filename contains '{axis_str}' but could not read a number following it. Use the format '...{axis_str}<#>...'")

pylinac/core/io.py

@@ -229,9 +229,9 @@ def __init__(self, path: str, detector_row: int = 106, bias_row: int = 107, cali
         with open(path, encoding='cp437') as f:
             raw_data = f.read().splitlines()
             self.detectors = raw_data[detector_row].split('\t')[data_columns]
-            self.bias = np.array(raw_data[bias_row].split('\t')[data_columns]).astype(np.float)
-            self.calibration = np.array(raw_data[calibration_row].split('\t')[data_columns]).astype(np.float)
-            self.data = np.array(raw_data[data_row].split('\t')[data_columns]).astype(np.float)
+            self.bias = np.array(raw_data[bias_row].split('\t')[data_columns]).astype(float)
+            self.calibration = np.array(raw_data[calibration_row].split('\t')[data_columns]).astype(float)
+            self.data = np.array(raw_data[data_row].split('\t')[data_columns]).astype(float)
             self.timetic = float(raw_data[bias_row].split('\t')[2])
             self.integrated_dose = self.calibration * (self.data - self.bias * self.timetic)
 

pylinac/core/roi.py

@@ -80,7 +80,7 @@ def std(self) -> float:
         masked_img = self.circle_mask()
         return float(np.nanstd(masked_img))
 
-    @lru_cache(maxsize=1)
+    @lru_cache()
     def circle_mask(self) -> np.ndarray:
         """Return a mask of the image, only showing the circular ROI."""
         # http://scikit-image.org/docs/dev/auto_examples/plot_camera_numpy.html

pylinac/core/typing.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 
-NumberLike = Union[int, np.float, float]
+NumberLike = Union[int, float]
 
 ArrayLike = Union[list, tuple, np.ndarray]
 

pylinac/core/utilities.py

@@ -4,7 +4,7 @@
 import os.path as osp
 import struct
 import subprocess
-from collections import Iterable
+from collections.abc import Iterable
 from dataclasses import dataclass, field
 from datetime import datetime
 from enum import Enum

pylinac/ct.py

@@ -1263,7 +1263,7 @@ def _publish_pdf(self, filename: str, metadata: Optional[dict], notes: str, anal
 
     def _zip_images(self) -> None:
         """Compress the raw images into a ZIP archive and remove the uncompressed images."""
-        zip_name = f'{osp.dirname(self.dicom_stack[0].path)}\CBCT - {self.dicom_stack[0].date_created(format="%A, %I-%M-%S, %B %d, %Y")}.zip'
+        zip_name = fr'{osp.dirname(self.dicom_stack[0].path)}\CBCT - {self.dicom_stack[0].date_created(format="%A, %I-%M-%S, %B %d, %Y")}.zip'
         with zipfile.ZipFile(zip_name, 'w', compression=zipfile.ZIP_DEFLATED) as zfile:
             for image in self.dicom_stack:
                 zfile.write(image.path, arcname=osp.basename(image.path))

pylinac/picketfence.py

@@ -17,7 +17,7 @@
 import io
 import os.path as osp
 import warnings
-from collections import Sequence
+from collections.abc import Sequence
 from dataclasses import dataclass
 from itertools import cycle
 from tempfile import TemporaryDirectory

pylinac/planar_imaging.py

@@ -718,7 +718,7 @@ def _phantom_radius_calc(self) -> float:
         """
         return self.phantom_ski_region.major_axis_length / 14
 
-    @lru_cache(maxsize=1)
+    @lru_cache()
     def _phantom_angle_calc(self) -> float:
         """The angle of the phantom. This assumes the user is using the stand that comes with the phantom,
         which angles the phantom at 45 degrees.
@@ -930,7 +930,7 @@ def run_demo() -> None:
         leeds.analyze()
         leeds.plot_analyzed_image()
 
-    @lru_cache(maxsize=1)
+    @lru_cache()
     def _phantom_angle_calc(self) -> float:
         """Determine the angle of the phantom.
 

pylinac/starshot.py

@@ -301,7 +301,7 @@ def distance(p, lines):
             """Calculate the maximum distance to any line from the given point."""
             return max(line.distance_to(Point(p[0], p[1])) for line in lines)
 
-        res = optimize.minimize(distance, sp.as_array(), args=(self.lines,), method='Nelder-Mead', options={'ftol': 0.001})
+        res = optimize.minimize(distance, sp.as_array(), args=(self.lines,), method='Nelder-Mead', options={'fatol': 0.001})
         # res = optimize.least_squares(distance, sp.as_array(), args=(self.lines,), ftol=0.001)
 
         self.wobble.radius = res.fun

pylinac/winston_lutz.py

@@ -193,7 +193,7 @@ def analyze(self, bb_size_mm: float = 5):
             img.analyze(bb_size_mm)
         self._is_analyzed = True
 
-    @lru_cache(maxsize=1)
+    @lru_cache()
     def _minimize_axis(self, axes: Union[Axis, Tuple[Axis, ...]] = (Axis.GANTRY,)):
         """Return the minimization result of the given axis."""
         if isinstance(axes, Axis):

tests_basic/test_starshot.py

@@ -198,7 +198,7 @@ def test_demo_runs(self):
 
     def test_fails_with_tight_tol(self):
         star = Starshot.from_demo_image()
-        star.analyze()
+        star.analyze(tolerance=0.1)
         self.assertFalse(star.passed)
 
     def test_bad_inputs_still_recovers(self):

tests_basic/utils.py

@@ -112,7 +112,7 @@ class LocationMixin:
     @classmethod
     def get_filename(cls):
         """Return the canonical path to the file."""
-        if cls.dir_location is '':
+        if cls.dir_location == '':
             return get_file_from_cloud_test_repo([cls.cloud_dir, *cls.file_path])
         else:
             return osp.join(cls.dir_location, *cls.file_path)