Normalize names for PipelineComponents (#1218)

The CLI name for PipelineComponent should match the API name.

Test plan: travis

docs/source/_static/data_processing_examples/3d_smFISH.py

@@ -48,7 +48,7 @@
 # local intensity maxima, and spots are matched to the gene they represent by looking them up in a
 # codebook that records which (round, channel) matches which gene target.
 
-tlmpf = starfish.spots.SpotFinder.TrackpyLocalMaxPeakFinder(
+tlmpf = starfish.spots.DetectSpots.TrackpyLocalMaxPeakFinder(
     spot_diameter=5,  # must be odd integer
     min_mass=0.02,
     max_size=2,  # this is max radius

docs/source/_static/data_processing_examples/iss_pipeline.py

@@ -6,8 +6,8 @@
 import os
 
 import starfish
-from starfish.image import ApplyTransform, Filter, LearnTransform, Segmentation
-from starfish.spots import SpotFinder, TargetAssignment
+from starfish.image import ApplyTransform, Filter, LearnTransform, Segment
+from starfish.spots import DetectSpots, AssignTargets
 from starfish.types import Axes
 
 test = os.getenv("TESTING") is not None
@@ -29,7 +29,7 @@ def iss_pipeline(fov, codebook):
     filtered = filt.run(registered, verbose=True, in_place=False)
 
     # detect spots using laplacian of gaussians approach
-    p = SpotFinder.BlobDetector(
+    p = DetectSpots.BlobDetector(
         min_sigma=1,
         max_sigma=10,
         num_sigma=30,
@@ -46,15 +46,15 @@ def iss_pipeline(fov, codebook):
     decoded = codebook.decode_per_round_max(intensities)
 
     # segment cells
-    seg = Segmentation.Watershed(
+    seg = Segment.Watershed(
         nuclei_threshold=.16,
         input_threshold=.22,
         min_distance=57,
     )
     label_image = seg.run(primary_image, fov.get_image('dots'))
 
     # assign spots to cells
-    ta = TargetAssignment.Label()
+    ta = AssignTargets.Label()
     assigned = ta.run(label_image, decoded)
 
     return assigned, label_image

docs/source/api/image/segmentation/index.rst

@@ -3,19 +3,19 @@
 Segmentation
 ============
 
-Segmentation can be imported using ``starfish.image.Segmentation``, which registers all classes
-that subclass ``SegmentationAlgorithmBase``:
+Segmentation can be imported using ``starfish.image.Segment``, which registers all classes that subclass
+``SegmentAlgorithmBase``:
 
 .. code-block:: python
 
-    from starfish.image import Segmentation
+    from starfish.image import Segment
 
 .. contents::
 
 
 Watershed
 ---------
 
-.. autoclass:: starfish.image._segmentation.watershed.Watershed
+.. autoclass:: starfish.image._segment.watershed.Watershed
    :members:
 

docs/source/api/spots/assign_targets/assign_targets.rst

@@ -0,0 +1,19 @@
+.. _assignment:
+
+Target Assignment
+=================
+
+AssignTargets can be imported using ``starfish.spots.AssignTargets``, which registers all classes that subclass
+``AssignTargetsAlgorithmBase``:
+
+.. code-block:: python
+
+    from starfish.spots import AssignTargets
+
+.. contents::
+
+Label
+-----
+
+.. autoclass:: starfish.spots._assign_targets.label.Label
+    :members:

docs/source/api/spots/decoding/decoding.rst

@@ -3,17 +3,17 @@
 Decoding
 ========
 
-Decoders can be imported using ``starfish.spots.Decoder``, which registers all classes that subclass
-``DecoderAlgorithmBase``:
+Decoders can be imported using ``starfish.spots.Decode``, which registers all classes that subclass
+``DecodeAlgorithmBase``:
 
 .. code-block:: python
 
-    from starfish.spots import Decoder
+    from starfish.spots import Decode
 
 .. contents::
 
 Per Round Max Channel Decoder
 -----------------------------
 
-.. autoclass:: starfish.spots._decoder.per_round_max_channel_decoder.PerRoundMaxChannelDecoder
+.. autoclass:: starfish.spots._decode.per_round_max_channel_decoder.PerRoundMaxChannelDecoder
     :members:

docs/source/api/spots/detection/detection.rst

@@ -3,29 +3,29 @@
 Detecting
 =========
 
-Spot Detectors can be imported using ``starfish.spots.SpotFinder``, which registers all classes that
-subclass ``SpotFinderAlgorithmBase``:
+Spot Detectors can be imported using ``starfish.spots.DetectSpots``, which registers all classes that
+subclass ``DetectSpotsAlgorithmBase``:
 
 .. code-block:: python
 
-    from starfish.spots import SpotFinder
+    from starfish.spots import DetectSpots
 
 .. contents::
 
 Blob Detector
 -------------
 
-.. autoclass:: starfish.spots._detector.blob.BlobDetector
+.. autoclass:: starfish.spots._detect_spots.blob.BlobDetector
     :members:
 
 Local Max Peak Finder
 ---------------------
 
-.. autoclass:: starfish.spots._detector.local_max_peak_finder.LocalMaxPeakFinder
+.. autoclass:: starfish.spots._detect_spots.local_max_peak_finder.LocalMaxPeakFinder
     :members:
 
 Pixel Spot Decoder
 -------------------
 
-.. autoclass:: starfish.spots._pixel_decoder.pixel_spot_decoder.PixelSpotDecoder
+.. autoclass:: starfish.spots._detect_pixels.pixel_spot_decoder.PixelSpotDecoder
     :members:

docs/source/api/spots/index.rst

@@ -14,4 +14,4 @@ Spots
    detection/detection.rst
 
 .. toctree::
-   target_assignment/target_assignment.rst
+   assign_targets/assign_targets.rst

notebooks/DARTFISH.ipynb

@@ -32,7 +32,7 @@
     "from starfish import IntensityTable\n",
     "\n",
     "from starfish.image import Filter\n",
-    "from starfish.spots import PixelSpotDecoder"
+    "from starfish.spots import DetectPixels"
    ]
   },
   {
@@ -195,7 +195,7 @@
     "# here, I set this to be a large number, so I can inspect the distribution of decoded distances below\n",
     "distance_threshold = 3\n",
     "\n",
-    "psd = PixelSpotDecoder.PixelSpotDecoder(\n",
+    "psd = DetectPixels.PixelSpotDecoder(\n",
     "    codebook=experiment.codebook,\n",
     "    metric='euclidean',\n",
     "    distance_threshold=distance_threshold,\n",
@@ -350,7 +350,7 @@
    "source": [
     "distance_threshold = min_dist\n",
     "\n",
-    "psd = PixelSpotDecoder.PixelSpotDecoder(\n",
+    "psd = DetectPixels.PixelSpotDecoder(\n",
     "    codebook=experiment.codebook,\n",
     "    metric='euclidean',\n",
     "    distance_threshold=distance_threshold,\n",

notebooks/MERFISH.ipynb

@@ -258,9 +258,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from starfish.spots import PixelSpotDecoder\n",
+    "from starfish.spots import DetectPixels\n",
     "\n",
-    "psd = PixelSpotDecoder.PixelSpotDecoder(\n",
+    "psd = DetectPixels.PixelSpotDecoder(\n",
     "    codebook=experiment.codebook,\n",
     "    metric='euclidean', # distance metric to use for computing distance between a pixel vector and a codeword\n",
     "    norm_order=2, # the L_n norm is taken of each pixel vector and codeword before computing the distance. this is n\n",

notebooks/py/BaristaSeq.py

@@ -38,7 +38,6 @@
 #Load Data
 #---------
 #Import starfish and extract a single field of view.
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -75,7 +74,6 @@
 #The first step in BaristaSeq is to do some rough registration. For this data,
 #the rough registration has been done for us by the authors, so it is omitted
 #from this notebook.
-#
 # EPY: END markdown
 
 # EPY: START markdown
@@ -84,7 +82,6 @@
 #BaristaSeq is typically processed in 2d. Starfish exposes
 #`ImageStack.max_proj` to enable a user to max-project any axes. Here
 #we max project Z for both the nissl images and the primary images.
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -103,7 +100,6 @@
 #There is a slight miss-alignment of the C channel in the microscope used to
 #acquire the data. This has been corrected for this data, but here is how it
 #could be transformed using python code for future datasets.
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -138,7 +134,6 @@
 #-----------------------------
 #There are some minor registration errors along the pixels for which y < 100
 #and x < 50. Those pixels are dropped from this analysis
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -153,7 +148,6 @@
 #The following matrix contains bleed correction factors for Illumina
 #sequencing-by-synthesis reagents. Starfish provides a LinearUnmixing method
 #that will unmix the fluorescence intensities
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -179,7 +173,6 @@
 #Channel 2 should look relative unchanged, as it only receives a bleed through
 #of 5% of channel 3. However, Channel 3 should look dramatically sparser after
 #spots from Channel 2 have been subtracted
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -212,7 +205,6 @@
 #To remove image background, BaristaSeq uses a White Tophat filter, which
 #measures the background with a rolling disk morphological element and
 #subtracts it from the image.
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -248,7 +240,6 @@
 #is identified and set as the maximum across channels, and the dynamic range is
 #extended to equalize the channel intensities. We first demonatrate what
 #scaling by the max value does.
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -262,7 +253,6 @@
 #should see that the histograms are better aligned in terms of intensities.
 #It gets most of what we want, but the histograms are still slightly shifted;
 #a result of high-value outliers.
-#
 # EPY: END markdown
 
 # EPY: START code
@@ -311,7 +301,7 @@ def plot_scaling_result(
 # EPY: END markdown
 
 # EPY: START code
-psd = starfish.spots.PixelSpotDecoder.PixelSpotDecoder(
+psd = starfish.spots.DetectPixels.PixelSpotDecoder(
     codebook=exp.codebook, metric='euclidean', distance_threshold=0.5,
     magnitude_threshold=0.1, min_area=7, max_area=50
 )

notebooks/py/DARTFISH.py

@@ -27,7 +27,7 @@
 from starfish import IntensityTable
 
 from starfish.image import Filter
-from starfish.spots import PixelSpotDecoder
+from starfish.spots import DetectPixels
 # EPY: END code
 
 # EPY: START markdown
@@ -134,7 +134,7 @@ def compute_magnitudes(stack, norm_order=2):
 # here, I set this to be a large number, so I can inspect the distribution of decoded distances below
 distance_threshold = 3
 
-psd = PixelSpotDecoder.PixelSpotDecoder(
+psd = DetectPixels.PixelSpotDecoder(
     codebook=experiment.codebook,
     metric='euclidean',
     distance_threshold=distance_threshold,
@@ -255,7 +255,7 @@ def compute_magnitudes(stack, norm_order=2):
 # EPY: START code
 distance_threshold = min_dist
 
-psd = PixelSpotDecoder.PixelSpotDecoder(
+psd = DetectPixels.PixelSpotDecoder(
     codebook=experiment.codebook,
     metric='euclidean',
     distance_threshold=distance_threshold,

notebooks/py/ISS.py

@@ -158,11 +158,11 @@
 # EPY: END markdown
 
 # EPY: START code
-from starfish.spots import SpotFinder
+from starfish.spots import DetectSpots
 import warnings
 
 # parameters to define the allowable gaussian sizes (parameter space)
-p = SpotFinder.BlobDetector(
+p = DetectSpots.BlobDetector(
     min_sigma=1,
     max_sigma=10,
     num_sigma=30,
@@ -205,7 +205,7 @@
 # EPY: END markdown
 
 # EPY: START code
-from starfish.image import Segmentation
+from starfish.image import Segment
 
 dapi_thresh = .18  # binary mask for cell (nuclear) locations
 stain_thresh = .22  # binary mask for overall cells // binarization of stain
@@ -217,7 +217,7 @@
 nuclei = nuclei.max_proj(Axes.ROUND, Axes.CH, Axes.ZPLANE)
 
 
-seg = Segmentation.Watershed(
+seg = Segment.Watershed(
     nuclei_threshold=dapi_thresh,
     input_threshold=stain_thresh,
     min_distance=min_dist
@@ -231,10 +231,10 @@
 # EPY: END markdown
 
 # EPY: START code
-from starfish.spots import TargetAssignment
+from starfish.spots import AssignTargets
 from starfish.expression_matrix.expression_matrix import ExpressionMatrix
 
-al = TargetAssignment.Label()
+al = AssignTargets.Label()
 labeled = al.run(masks, decoded)
 cg = labeled.to_expression_matrix()
 cg

notebooks/py/MERFISH.py

@@ -173,9 +173,9 @@
 # EPY: END markdown
 
 # EPY: START code
-from starfish.spots import PixelSpotDecoder
+from starfish.spots import DetectPixels
 
-psd = PixelSpotDecoder.PixelSpotDecoder(
+psd = DetectPixels.PixelSpotDecoder(
     codebook=experiment.codebook,
     metric='euclidean', # distance metric to use for computing distance between a pixel vector and a codeword
     norm_order=2, # the L_n norm is taken of each pixel vector and codeword before computing the distance. this is n