update documentation detection

bigfish/detection/snr.py

@@ -45,10 +45,11 @@ def compute_snr_spots(image, spots, voxel_size_z=None, voxel_size_yx=100,
     psf_yx : int or float
         Theoretical size of the PSF emitted by a spot in the yx plan, in
         nanometer.
+
     Returns
     -------
     snr : float
-        Median Signal-to-Noise ratio computed for every spots.
+        Median signal-to-noise ratio computed for every spots.
 
     """
     # check parameters

bigfish/detection/spot_detection.py

@@ -269,8 +269,7 @@ def local_maximum_detection(image, min_distance):
     local maximum is not unique.
 
     In order to make the detection robust, it should be applied to a
-    filtered image (using :func:`log_filter()<bigfish.stack.filter.log_filter>`
-    for example).
+    filtered image (using :func:`bigfish.stack.log_filter` for example).
 
     Parameters
     ----------
@@ -321,7 +320,7 @@ def spots_thresholding(image, mask_local_max, threshold,
     """Filter detected spots and get coordinates of the remaining spots.
 
     In order to make the thresholding robust, it should be applied to a
-    filtered image (using :func:`log_filter()<bigfish.stack.filter.log_filter>`
+    filtered image (using :func:`bigfish.stack.log_filter`
     for example). If the local maximum is not unique (it can happen if connected
     pixels have the same value), a connected component algorithm is applied to
     keep only one coordinate per spot.
@@ -407,10 +406,10 @@ def automated_threshold_setting(image, mask_local_max):
     """Automatically set the optimal threshold to detect spots.
 
     In order to make the thresholding robust, it should be applied to a
-    filtered image (using :func:`log_filter()<bigfish.stack.filter.log_filter>`
-    for example). The optimal threshold is selected based on the spots
-    distribution. The latter should have an elbow curve discriminating a fast
-    decreasing stage from a more stable one (a plateau).
+    filtered image (using :func:`bigfish.stack.log_filter` for example). The
+    optimal threshold is selected based on the spots distribution. The latter
+    should have an elbow curve discriminating a fast decreasing stage from a
+    more stable one (a plateau).
 
     Parameters
     ----------

docs/source/detection/cluster.rst

@@ -3,7 +3,7 @@
 Cluster detection
 ************************
 
-.. currentmodule:: bigfish.detection.cluster_detection
+.. currentmodule:: bigfish.detection
 
 Function to cluster spots in point cloud and detect relevant aggregated
 structures. A DBSCAN algorithm is performed.

docs/source/detection/dense.rst

@@ -3,22 +3,22 @@
 Dense regions decomposition
 ***************************
 
+.. currentmodule:: bigfish.detection
+
 Functions to detect dense and bright regions (with potential clustered spots),
 then use gaussian simulations to correct a misdetection in these regions.
 
 Decompose and simulate dense regions
 ====================================
 
-.. currentmodule:: bigfish.detection.dense_decomposition
-
 The main function to decompose dense regions is:
 
-* :func:`bigfish.detection.decompose_dense<decompose_dense>`
+* :func:`bigfish.detection.decompose_dense`
 
 It is also possible to perform the main steps of this decomposition separately:
 
-* :func:`bigfish.detection.get_dense_region<get_dense_region>`
-* :func:`bigfish.detection.simulate_gaussian_mixture<simulate_gaussian_mixture>`
+* :func:`bigfish.detection.get_dense_region`
+* :func:`bigfish.detection.simulate_gaussian_mixture`
 
 See an example of application `here <https://github.com/fish-quant/big-fish-
 examples/blob/master/notebooks/5%20-%20Detect%20spots.ipynb/>`_.
@@ -32,24 +32,22 @@ examples/blob/master/notebooks/5%20-%20Detect%20spots.ipynb/>`_.
 Modelize a reference spot
 =========================
 
-.. currentmodule:: bigfish.detection.spot_modeling
-
 To simulate additional spots in the dense regions it is necessary to:
 
 #. Build a reference spot.
 
-    * :func:`bigfish.detection.build_reference_spot<build_reference_spot>`
+    * :func:`bigfish.detection.build_reference_spot`
 
 #. Modelize this reference spot by fitting gaussian parameters.
 
-    * :func:`bigfish.detection.modelize_spot<modelize_spot>`
+    * :func:`bigfish.detection.modelize_spot`
 
 #. Simulate gaussian signal.
 
-    * :func:`bigfish.detection.precompute_erf<precompute_erf>`
-    * :func:`bigfish.detection.initialize_grid<initialize_grid>`
-    * :func:`bigfish.detection.gaussian_2d<gaussian_2d>`
-    * :func:`bigfish.detection.gaussian_3d<gaussian_3d>`
+    * :func:`bigfish.detection.precompute_erf`
+    * :func:`bigfish.detection.initialize_grid`
+    * :func:`bigfish.detection.gaussian_2d`
+    * :func:`bigfish.detection.gaussian_3d`
 
 .. autofunction:: build_reference_spot
 .. autofunction:: modelize_spot

docs/source/detection/spots.rst

@@ -3,7 +3,7 @@
 Automated spot detection
 ************************
 
-.. currentmodule:: bigfish.detection.spot_detection
+.. currentmodule:: bigfish.detection
 
 Functions used to detect spots in a 2D or 3D image. Detection is performed in
 three steps:
@@ -20,12 +20,12 @@ Detect spots
 
 The main function for spot detection is:
 
-* :func:`bigfish.detection.detect_spots<detect_spots>`
+* :func:`bigfish.detection.detect_spots`
 
 It is also possible to perform the main steps of the spot detection separately:
 
-* :func:`bigfish.detection.local_maximum_detection<local_maximum_detection>`
-* :func:`bigfish.detection.spots_thresholding<spots_thresholding>`
+* :func:`bigfish.detection.local_maximum_detection`
+* :func:`bigfish.detection.spots_thresholding`
 
 See an example of application `here <https://github.com/fish-quant/big-fish-
 examples/blob/master/notebooks/5%20-%20Detect%20spots.ipynb/>`_.
@@ -43,8 +43,8 @@ The need to set an appropriate threshold for each image is a real bottleneck
 that limits the possibility to scale a spot detection. Our method includes a
 heuristic function to to automatically set this threshold:
 
-* :func:`bigfish.detection.automated_threshold_setting<automated_threshold_setting>`
-* :func:`bigfish.detection.get_elbow_values<get_elbow_values>`
+* :func:`bigfish.detection.automated_threshold_setting`
+* :func:`bigfish.detection.get_elbow_values`
 
 .. autofunction:: automated_threshold_setting
 .. autofunction:: get_elbow_values
@@ -54,10 +54,8 @@ heuristic function to to automatically set this threshold:
 Compute signal-to-noise ratio
 =============================
 
-.. currentmodule:: bigfish.detection.snr
-
 Compute a signal-to-noise ratio (SNR) for the image:
 
-* :func:`bigfish.detection.compute_snr_spots<compute_snr_spots>`
+* :func:`bigfish.detection.compute_snr_spots`
 
 .. autofunction:: compute_snr_spots

docs/source/detection/subpixel.rst

@@ -3,7 +3,7 @@
 Subpixel fitting
 ****************
 
-.. currentmodule:: bigfish.detection.spot_modeling
+.. currentmodule:: bigfish.detection
 
 Function to detect individual spot coordinate with a subpixel accuracy,
 fitting a gaussian signal.