Merge pull request #1279 from flatironinstitute/dev-fix_sphinx

Fix some sphinx issues
flatironinstitute · Feb 16, 2024 · 30aafba · 30aafba
2 parents 1cf9d71 + fb3e752
commit 30aafba
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diff --git a/caiman/base/movies.py b/caiman/base/movies.py
@@ -2203,23 +2203,24 @@ def load_iter(file_name: Union[str, list[str]], subindices=None, var_name_hdf5:
             logging.error(f"File request:[{file_name}] not found!")
             raise Exception('File not found!')
 
-def get_file_size(file_name, var_name_hdf5='mov') -> tuple[tuple, Union[int, tuple]]:
-    """ Computes the dimensions of a file or a list of files without loading
+def get_file_size(file_name, var_name_hdf5:str='mov') -> tuple[tuple, Union[int, tuple]]:
+    """
+    Computes the dimensions of a file or a list of files without loading
     it/them in memory. An exception is thrown if the files have FOVs with
     different sizes
-        Args:
-            file_name: str/filePath or various list types
-                locations of file(s)
 
-            var_name_hdf5: 'str'
-                if loading from hdf5 name of the dataset to load
+    Args:
+        file_name:
+            locations of file(s)
+        var_name_hdf5:
+            if loading from hdf5 name of the dataset to load
 
-        Returns:
-            dims: tuple
-                dimensions of FOV
+    Returns:
+        dims: tuple
+            dimensions of FOV
+        T: int or tuple of int
+            number of timesteps in each file
 
-            T: int or tuple of int
-                number of timesteps in each file
     """
     # TODO There is a lot of redundant code between this, load(), and load_iter() that should be unified somehow
     if isinstance(file_name, pathlib.Path):

diff --git a/caiman/cluster.py b/caiman/cluster.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 
-""" functions related to the creation and management of the "cluster",
+"""
+Functions related to the creation and management of the "cluster",
 meaning the framework for distributed computation.
 
 We put arrays on disk as raw bytes, extending along the first dimension.
@@ -182,32 +183,37 @@ def setup_cluster(backend:str = 'multiprocessing',
                   maxtasksperchild:int = None) -> tuple[Any, Any, Optional[int]]:
     """
     Setup and/or restart a parallel cluster.
+
     Args:
-        backend
+        backend:
             One of:
                 'multiprocessing' - Use multiprocessing library
                 'ipyparallel' - Use ipyparallel instead (better on Windows?)
                 'single' - Don't be parallel (good for debugging, slow)
+
             Most backends will try, by default, to stop a running cluster if
             it is running before setting up a new one, or throw an error if
             they find one.
-        n_processes
+        n_processes:
             Sets number of processes to use. If None, is set automatically. 
-        single_thread
+        single_thread:
             Deprecated alias for the 'single' backend.
-        ignore_preexisting
+        ignore_preexisting:
             If True, ignores the existence of an already running multiprocessing
             pool (which usually indicates a previously-started CaImAn cluster)
-        maxtasksperchild
+        maxtasksperchild:
             Only used for multiprocessing, default None (number of tasks a worker process can 
             complete before it will exit and be replaced with a fresh worker process).
             
     Returns:
-        c: ipyparallel.Client object; only used for ipyparallel backends, else None
-        dview: multicore processing engine that is used for parallel processing. 
+        c:
+            ipyparallel.Client object; only used for ipyparallel backends, else None
+        dview:
+            multicore processing engine that is used for parallel processing. 
             If backend is 'multiprocessing' then dview is Pool object.
             If backend is 'ipyparallel' then dview is a DirectView object. 
-        n_processes: number of workers in dview. None means single core mode in use. 
+        n_processes:
+            number of workers in dview. None means single core mode in use. 
     """
 
     sys.stdout.flush() # XXX Unsure why we do this

diff --git a/caiman/source_extraction/cnmf/deconvolution.py b/caiman/source_extraction/cnmf/deconvolution.py
@@ -677,7 +677,7 @@ def constrained_oasisAR2(y, g, sn, optimize_b=True, b_nonneg=True, optimize_g=0,
 
         s_min : float, optional, default 0
             Minimal non-zero activity within each bin (minimal 'spike size').
-            For negative values the threshold is |s_min| * sn * sqrt(1-decay_constant)
+            For negative values the threshold is abs(s_min) * sn * sqrt(1 - decay_constant)
             If 0 the threshold is determined automatically such that RSS <= sn^2 T
 
     Returns:

diff --git a/caiman/source_extraction/cnmf/estimates.py b/caiman/source_extraction/cnmf/estimates.py
@@ -1071,52 +1071,44 @@ def evaluate_components(self, imgs, params, dview=None):
             self.idx_components = np.intersect1d(self.idx_components, idx_ecc)
         return self
 
-    def filter_components(self, imgs, params, new_dict={}, dview=None, select_mode='All'):
-        """Filters components based on given thresholds without re-computing
+    def filter_components(self, imgs, params, new_dict={}, dview=None, select_mode:str='All'):
+        """
+        Filters components based on given thresholds without re-computing
         the quality metrics. If the quality metrics are not present then it
         calls self.evaluate components.
 
         Args:
             imgs: np.array (possibly memory mapped, t,x,y[,z])
                 Imaging data
-
             params: params object
                 Parameters of the algorithm
+            new_dict: dict
+                New dictionary with parameters to be called. The dictionary's keys are
+                used to modify the params.quality subdictionary:
+
+                min_SNR: float
+                    trace SNR threshold
+                SNR_lowest: float
+                    minimum required trace SNR
+                rval_thr: float
+                    space correlation threshold
+                rval_lowest: float
+                    minimum required space correlation
+                use_cnn: bool
+                    flag for using the CNN classifier
+                min_cnn_thr: float
+                    CNN classifier threshold
+                cnn_lowest: float
+                    minimum required CNN threshold
+                gSig_range: list
+                    gSig scale values for CNN classifier
 
-            select_mode: str
+            select_mode:
                 Can be 'All' (no subselection is made, but quality filtering is performed),
                 'Accepted' (subselection of accepted components, a field named self.accepted_list must exist),
                 'Rejected' (subselection of rejected components, a field named self.rejected_list must exist),
                 'Unassigned' (both fields above need to exist)
 
-            new_dict: dict
-                New dictionary with parameters to be called. The dictionary
-                modifies the params.quality subdictionary in the following
-                entries:
-                    min_SNR: float
-                        trace SNR threshold
-
-                    SNR_lowest: float
-                        minimum required trace SNR
-
-                    rval_thr: float
-                        space correlation threshold
-
-                    rval_lowest: float
-                        minimum required space correlation
-
-                    use_cnn: bool
-                        flag for using the CNN classifier
-
-                    min_cnn_thr: float
-                        CNN classifier threshold
-
-                    cnn_lowest: float
-                        minimum required CNN threshold
-
-                    gSig_range: list
-                        gSig scale values for CNN classifier
-
         Returns:
             self: estimates object
                 self.idx_components: np.array
@@ -1407,9 +1399,10 @@ def threshold_spatial_components(self, maxthr=0.25, dview=None):
 
     def remove_small_large_neurons(self, min_size_neuro, max_size_neuro,
                                    select_comp=False):
-        ''' remove neurons that are too large or too small
+        """
+        Remove neurons that are too large or too small
 
-    	Args:
+        Args:
             min_size_neuro: int
                 min size in pixels
             max_size_neuro: int
@@ -1421,7 +1414,9 @@ def remove_small_large_neurons(self, min_size_neuro, max_size_neuro,
         Returns:
             neurons_to_keep: np.array
                 indices of components with size within the acceptable range
-        '''
+
+        """
+
         if self.A_thr is None:
             raise Exception('You need to compute thresholded components before calling remove_duplicates: use the threshold_components method')
 

diff --git a/caiman/source_extraction/cnmf/initialization.py b/caiman/source_extraction/cnmf/initialization.py
@@ -153,10 +153,11 @@ def initialize_components(Y, K=30, gSig=[5, 5], gSiz=None, ssub=1, tsub=1, nIter
     Initialize components. This function initializes the spatial footprints, temporal components,
     and background which are then further refined by the CNMF iterations. There are four
     different initialization methods depending on the data you're processing:
-        'greedy_roi': GreedyROI method used in standard 2p processing (default)
-        'corr_pnr': GreedyCorr method used for processing 1p data
-        'sparse_nmf': Sparse NMF method suitable for dendritic/axonal imaging
-        'graph_nmf': Graph NMF method also suitable for dendritic/axonal imaging
+
+    greedy_roi: GreedyROI method used in standard 2p processing (default)
+    corr_pnr:   GreedyCorr method used for processing 1p data
+    sparse_nmf: Sparse NMF method suitable for dendritic/axonal imaging
+    graph_nmf:  Graph NMF method also suitable for dendritic/axonal imaging
 
     The GreedyROI method by default is not using the RollingGreedyROI method. This can
     be changed through the binary flag 'rolling_sum'.

diff --git a/caiman/utils/nn_models.py b/caiman/utils/nn_models.py
@@ -487,35 +487,33 @@ def create_NL_model(Y=None, shape=(None, None, 1), n_channels=8, gSig=5, r_facto
 
 def fit_NL_model(model_NL, Y, patience=5, val_split=0.2, batch_size=32,
                  epochs=500, schedule=None):
-    """ Fit either the linear or the non-linear model. The model is fit for a
+    """
+    Fit either the linear or the non-linear model. The model is fit for a
     use specified maximum number of epochs and early stopping is used based on the 
     validation loss. A Tensorboard compatible log is also created.
+
     Args:
         model_LN: Keras Ring-CNN model
             see create_LN_model and create_NL_model above
-
         patience: int, default: 5
             patience value for early stopping criterion
-
         val_split: float, default: 0.2
             fraction of data to keep for validation (value between 0 and 1)
-
         batch_size: int, default: 32
             batch size during training
-
         epochs: int, default: 500
             maximum number of epochs
-
         schedule: keras learning rate scheduler
 
     Returns:
-        model_NL: Keras Ring-CNN model
+        model_NL: 
+            Keras Ring-CNN model
             trained model loaded with best weights according to validation loss
+        history_NL:
+            contains data related to the training history
+        path_to_model:
+            path to where the weights are stored
 
-        history_NL: contains data related to the training history
-
-        path_to_model: str
-            path to where the weights are stored.
     """
     if Y.ndim < 4:
         Y = np.expand_dims(Y, axis=-1)

diff --git a/docs/source/Installation.rst b/docs/source/Installation.rst
@@ -123,11 +123,9 @@ A couple of things to note:
    need to be removed. Use the Windows find-file utility
    (under the Start Menu) to look for ``vs2019_compiler_vars.bat`` under 
    your home directory. If a copy shows up, delete the version that has
-   conda:raw-latex:`\envs`:raw-latex:`\caiman` as part of its location.
+   your ``caiman`` environment name as part of its location.
    You may then continue the installation.
 
-.. code:: bash
-
 .. raw:: html
 
    </details>
@@ -171,7 +169,7 @@ and install the package file you will find in the folder that pops up
 
 
 Section 2: Set up demos with caimanmanager
------------------------
+------------------------------------------
 
 Once Caiman is installed, you will likely want to set up a working directory with code samples and datasets. 
 The installation step in Section 1 produced a command ``caimanmanager`` that handles this. caimanmanager will

diff --git a/docs/source/Overview.rst b/docs/source/Overview.rst
@@ -11,7 +11,7 @@ CaImAn implements a set of essential methods required in the analysis pipeline o
 <CaImAn_features_and_references.html>`_.
 
 Companion paper
---------------
+---------------
 
 A paper explaining most of the implementation details and benchmarking can be found `here
 <https://elifesciences.org/articles/38173>`_.
@@ -28,22 +28,8 @@ A paper explaining most of the implementation details and benchmarking can be fo
     publisher={eLife Sciences Publications Limited}
   }
 
-
-Developers/Contributors
-------------
-
-CaImAn is being developed at the `Flatiron Institute <https://www.simonsfoundation.org/flatiron/>`_ with numerous contributions from the broader community. The main developers are
-
-* Eftychios A. Pnevmatikakis, Flatiron Institute
-* Andrea Giovannucci, University of North Carolina at Chapel Hill, previously at Flatiron Institute
-* Johannes Friedrich, Flatiron Institute
-* Pat Gunn, Flatiron Institute
-
-A complete list of contributors can be found `here <https://github.com/flatironinstitute/CaImAn/graphs/contributors>`_.
-
-
 Questions, comments, issues
------------------------------
+---------------------------
 
 Please use our `gitter chat room <https://gitter.im/agiovann/Constrained_NMF>`_ for questions and comments and create an issue on our `repo page <https://github.com/flatironinstitute/CaImAn>`_ for any bugs you might encounter.
 

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -74,7 +74,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # There are two options for replacing |today|: either, you set today to some
 # non-false value, then it is used:

diff --git a/docs/source/core_functions.rst b/docs/source/core_functions.rst
@@ -53,8 +53,8 @@ Motion Correction
 .. automethod:: MotionCorrect.motion_correct_rigid
 .. automethod:: MotionCorrect.motion_correct_pwrigid
 .. automethod:: MotionCorrect.apply_shifts_movie
-.. automethod:: motion_correct_oneP_rigid
-.. automethod:: motion_correct_oneP_nonrigid
+.. autofunction:: motion_correct_oneP_rigid
+.. autofunction:: motion_correct_oneP_nonrigid
 
 
 Estimates
@@ -259,8 +259,4 @@ VolPy
 
 .. currentmodule:: caiman.source_extraction.volpy.spikepursuit
 .. autofunction:: volspike
-.. autofunction:: denoiseSpikes
-.. autofunction:: getThresh
-.. autofunction:: whitenedMathcedFilter
-.. autofunction:: highpassVideo
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -13,7 +13,6 @@ Contents:
 
    Overview
    Installation
-   news
    Getting_Started
    Handling_Movies
    CaImAn_Tips