removing backslash sequences that raise SyntaxWarning in python 3.12+…

… when using sphinx-gallery to convert reST to markdown

examples/data_formatting/plot_format_tilefetcher.py

@@ -87,7 +87,7 @@
 # ---------------------------
 # The :py:class:`.FetchedTile` subclass defines the function you need for reading your images and
 # the other properties required by :py:func:`.write_experiment_json` to construct
-# :class:`slicedimage.Tile`\s.
+# :class:`slicedimage.Tile`.
 #
 # You can use any function to read images that returns a numpy array. We recommend using
 # :py:class:`imageio.volread` for 3D images and :py:class:`skimage.io.imread` for 2D images. For
@@ -153,7 +153,7 @@ def tile_data(self) -> np.ndarray:
 # Define TileFetcher subclass
 # ---------------------------
 # The :py:class:`.TileFetcher` subclass acts as the interface for :py:func:`.write_experiment_json`
-# to know where to get files to construct :class:`slicedimage.Tile`\s.
+# to know where to get files to construct :class:`slicedimage.Tile`.
 #
 # If you are not using structured data, you will need to extend :py:class:`TileFetcher` to reflect
 # your own file naming and organization schema.
@@ -252,4 +252,4 @@ def get_tile(
 from starfish import Experiment
 
 exp = Experiment.from_json(os.path.join(outputdir.name, "experiment.json"))
-print(exp.fovs())
+print(exp.fovs())

examples/data_loading/plot_tilefetcher_loader.py

@@ -4,7 +4,7 @@
 Loading Data through TileFetchers
 =================================
 
-:py:class:`.TileFetcher`\s provide a way for starfish's data
+:py:class:`.TileFetcher` provide a way for starfish's data
 formatting tooling to obtain image data and metadata for each tile that make up an image.  It is
 possible to use this interface to directly load data into starfish.  This could have performance
 implications as the TileFetcher's performance could potentially be highly sensitive to the order

examples/how_to/clip.py

@@ -10,7 +10,7 @@
 
 :py:class:`~starfish.image.Filter.Clip` is useful for normalizing images, removing background,
 and removing high-intensity outliers. If you want the values to start from zero after clipping see
-:py:class:`~starfish.image.Filter.ClipPercentileToZero`. Both :py:class:`FilterAlgorithm`\s use
+:py:class:`~starfish.image.Filter.ClipPercentileToZero`. Both :py:class:`FilterAlgorithm` use
 percentiles to set the ``p_min`` and ``p_max`` values to clip by.
 """
 
@@ -44,4 +44,4 @@ def plot_intensity_histograms(stack: starfish.ImageStack, r: int):
 clipper.run(stack, in_place=True)
 
 # View distribution of intensities after clipping
-plot_intensity_histograms(stack, r=0)
+plot_intensity_histograms(stack, r=0)

examples/how_to/plot_display.py

@@ -100,7 +100,7 @@
 # tweak your layer properties (see `napari`_).
 #
 # However, napari doesn't natively support starfish data structures, so to use napari methods
-# follow the example below. Adding :py:class:`.IntensityTable`\s is pretty complex,
+# follow the example below. Adding :py:class:`.IntensityTable` is pretty complex,
 # so we recommend sticking with :py:func:`.display` for that or using the method in the next
 # section.
 #

examples/how_to/tracebuildingstrategies.py

@@ -21,7 +21,7 @@
    :align: center
 
 The chosen ``TraceBuilder`` must also be compatible with how the :py:class:`.SpotFindingResults`
-was generated. :py:class:`.FindSpotsAlgorithm`\s can be run with or without a ``reference_image``.
+was generated. :py:class:`.FindSpotsAlgorithm` can be run with or without a ``reference_image``.
 If run with a ``reference_image`` then every :py:class:`.PerImageSliceSpotResults` in
 :py:class:`.SpotFindingResults` will have the same spots for every (round, channel) image volume.
 This is necessary for :py:func:`.build_spot_traces_exact_match` but not recommended for
@@ -88,7 +88,7 @@
 ####################################################################################################
 # Typical pipelines will set the ``trace_building_strategy`` as an argument in the
 # :py:class:`.DecodeSpotsAlgorithm` but here the underlying code is exposed to reveal what the
-# different :py:class:`.IntensityTable`\s look like depending on which ``TraceBuilder`` is used.
+# different :py:class:`.IntensityTable` look like depending on which ``TraceBuilder`` is used.
 
 from starfish.core.spots.DecodeSpots.trace_builders import build_spot_traces_exact_match, \
     build_traces_sequential, build_traces_nearest_neighbors
@@ -122,4 +122,4 @@
 ####################################################################################################
 # The same :py:func:`.build_traces_nearest_neighbors` applied to spots found in
 # :py:class:`.ImageStack` *with* a ``reference image`` guarantees a spot to be found in every
-# round of :py:class:`.SpotFindingResults`.
+# round of :py:class:`.SpotFindingResults`.

examples/tutorials/normalize_intensity_distributions.py

@@ -11,7 +11,7 @@
 :ref:`section_normalizing_intensities`.
 
 Normalizing the distributions is done in starfish by matching the histograms of
-:py:class:`.ImageStack`\s to a reference histogram. The reference histogram is created by
+:py:class:`.ImageStack` to a reference histogram. The reference histogram is created by
 averaging the histograms from each group defined by the ``group_by`` parameter. These groups also
 determine along which :py:class:`.Axes` the intensities will be normalized.
 
@@ -128,4 +128,4 @@ def plot_intensity_histograms(
 # * normalizing with ``group_by={Axes.CH}`` has the effect of significantly rescaling histograms of channel 0 to match histograms of the other channels
 # * normalizing with ``group_by={Axes.R}`` does not scale histograms of channel 0 to match histograms of other channels
 # * normalizing with ``group_by={Axes.CH, Axes.ROUND}`` scales histograms from every round and channel to match each other
-plot_intensity_histograms(ref=stack, scaled_cr=scaled_cr, scaled_c=scaled_c, scaled_r=scaled_r, r=0)
+plot_intensity_histograms(ref=stack, scaled_cr=scaled_cr, scaled_c=scaled_c, scaled_r=scaled_r, r=0)

examples/tutorials/spotbased_decoding.py

@@ -146,10 +146,10 @@
 """
 
 ####################################################################################################
-# Comparison of :py:class:`.FindSpotsAlgorithm`\s
+# Comparison of :py:class:`.FindSpotsAlgorithm`
 # -----------------------------------------------
 # This tutorial demonstrates usage and characteristics of the three available
-# :py:class:`.FindSpotsAlgorithm`\s on 3-Dimensional STARmap images. Parameters were roughly
+# :py:class:`.FindSpotsAlgorithm` on 3-Dimensional STARmap images. Parameters were roughly
 # tuned, but these results are not reflective of the best possible performance of each
 # :py:class:`.FindSpotsAlgorithm`.
 
@@ -277,4 +277,4 @@ def get_cropped_coords(table, x_min, x_max, y_min, y_max):
 # display(stack=cropped_dots, spots=lmp_table)
 
 # TrackpyLocalMaxPeakFinder
-# display(stack=cropped_dots, spots=tlmpf_table)
+# display(stack=cropped_dots, spots=tlmpf_table)

examples/tutorials/watershed_segmentation.py

@@ -30,7 +30,7 @@
 classes and methods provided in :py:mod:`.morphology`. Then this tutorial will cover how to run
 the predefined segmentation pipeline.
 
-Inputs for this tutorial are :py:class:`.ImageStack`\s:
+Inputs for this tutorial are :py:class:`.ImageStack`:
 
 * registered primary images to mimic a cell stain
 * registered nuclei images to seed the water segmentation of cells