Use Path.resolve() consistently to prevent UNC/drive letter ambiguity

.gitignore

@@ -135,3 +135,6 @@ dmypy.json
 tests/tmp
 tests/videos
 tests/ground_truths*
+
+# VSCode
+.vscode/

docs/source/conf.py

@@ -19,9 +19,9 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'mesmerize-core'
-copyright = '2023, Kushal Kolar, Caitlin Lewis, Arjun Putcha'
-author = 'Kushal Kolar, Caitlin Lewis, Arjun Putcha'
+project = "mesmerize-core"
+copyright = "2023, Kushal Kolar, Caitlin Lewis, Arjun Putcha"
+author = "Kushal Kolar, Caitlin Lewis, Arjun Putcha"
 
 # The full version, including alpha/beta/rc tags
 release = mesmerize_core.__version__
@@ -35,7 +35,7 @@
 autodoc_typehints = "description"
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -48,51 +48,56 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'pydata_sphinx_theme'
+html_theme = "pydata_sphinx_theme"
 html_theme_options = {"page_sidebar_items": ["class_page_toc"]}
 autoclass_content = "both"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 
-autodoc_member_order = 'bysource'
+autodoc_member_order = "bysource"
 
 
 def _setup_navbar_side_toctree(app: Any):
-    def add_class_toctree_function(app: Any, pagename: Any, templatename: Any, context: Any, doctree: Any):
+    def add_class_toctree_function(
+        app: Any, pagename: Any, templatename: Any, context: Any, doctree: Any
+    ):
         def get_class_toc() -> Any:
             soup = BeautifulSoup(context["body"], "html.parser")
 
-            matches = soup.find_all('dl')
+            matches = soup.find_all("dl")
             if matches is None or len(matches) == 0:
                 return ""
             items = []
-            deeper_depth = matches[0].find('dt').get('id').count(".")
+            deeper_depth = matches[0].find("dt").get("id").count(".")
             for match in matches:
-                match_dt = match.find('dt')
-                if match_dt is not None and match_dt.get('id') is not None:
-                    current_title = match_dt.get('id')
-                    current_depth = match_dt.get('id').count(".")
+                match_dt = match.find("dt")
+                if match_dt is not None and match_dt.get("id") is not None:
+                    current_title = match_dt.get("id")
+                    current_depth = match_dt.get("id").count(".")
                     current_link = match.find(class_="headerlink")
                     if current_link is not None:
                         if deeper_depth > current_depth:
                             deeper_depth = current_depth
                         if deeper_depth == current_depth:
-                            items.append({
-                                "title": current_title.split('.')[-1],
-                                "link": current_link["href"],
-                                "attributes_and_methods": []
-                            })
+                            items.append(
+                                {
+                                    "title": current_title.split(".")[-1],
+                                    "link": current_link["href"],
+                                    "attributes_and_methods": [],
+                                }
+                            )
                         if deeper_depth < current_depth:
                             items[-1]["attributes_and_methods"].append(
                                 {
-                                    "title": current_title.split('.')[-1],
+                                    "title": current_title.split(".")[-1],
                                     "link": current_link["href"],
                                 }
                             )
             return items
+
         context["get_class_toc"] = get_class_toc
 
     app.connect("html-page-context", add_class_toctree_function)

mesmerize_core/algorithms/cnmf.py

@@ -1,4 +1,5 @@
 """Performs CNMF in a separate process"""
+
 import click
 import caiman as cm
 from caiman.source_extraction.cnmf import cnmf as cnmf
@@ -32,7 +33,7 @@ def run_algo(batch_path, uuid, data_path: str = None):
     input_movie_path = str(df.paths.resolve(input_movie_path))
 
     # make output dir
-    output_dir = Path(batch_path).parent.joinpath(str(uuid))
+    output_dir = Path(batch_path).parent.joinpath(str(uuid)).resolve()
     output_dir.mkdir(parents=True, exist_ok=True)
 
     params = item["params"]
@@ -95,14 +96,14 @@ def run_algo(batch_path, uuid, data_path: str = None):
         print("performing eval")
         cnm.estimates.evaluate_components(images, cnm.params, dview=dview)
 
-        output_path = output_dir.joinpath(f"{uuid}.hdf5").resolve()
+        output_path = output_dir.joinpath(f"{uuid}.hdf5")
 
         cnm.save(str(output_path))
 
         Cn = cm.local_correlations(images, swap_dim=False)
         Cn[np.isnan(Cn)] = 0
 
-        corr_img_path = output_dir.joinpath(f"{uuid}_cn.npy").resolve()
+        corr_img_path = output_dir.joinpath(f"{uuid}_cn.npy")
         np.save(str(corr_img_path), Cn, allow_pickle=False)
 
         # output dict for dataframe row (pd.Series)
@@ -115,12 +116,14 @@ def run_algo(batch_path, uuid, data_path: str = None):
 
         # save paths as relative path strings with forward slashes
         cnmf_hdf5_path = str(PurePosixPath(output_path.relative_to(output_dir.parent)))
-        cnmf_memmap_path = str(PurePosixPath(cnmf_memmap_path.relative_to(output_dir.parent)))
+        cnmf_memmap_path = str(
+            PurePosixPath(cnmf_memmap_path.relative_to(output_dir.parent))
+        )
         corr_img_path = str(PurePosixPath(corr_img_path.relative_to(output_dir.parent)))
         for proj_type in proj_paths.keys():
-            d[f"{proj_type}-projection-path"] = str(PurePosixPath(proj_paths[proj_type].relative_to(
-                output_dir.parent
-            )))
+            d[f"{proj_type}-projection-path"] = str(
+                PurePosixPath(proj_paths[proj_type].relative_to(output_dir.parent))
+            )
 
         d.update(
             {
@@ -136,10 +139,11 @@ def run_algo(batch_path, uuid, data_path: str = None):
         d = {"success": False, "traceback": traceback.format_exc()}
 
     cm.stop_server(dview=dview)
-    
+
     runtime = round(time.time() - algo_start, 2)
     df.caiman.update_item_with_results(uuid, d, runtime)
 
+
 @click.command()
 @click.option("--batch-path", type=str)
 @click.option("--uuid", type=str)

mesmerize_core/algorithms/cnmfe.py

@@ -29,7 +29,7 @@ def run_algo(batch_path, uuid, data_path: str = None):
     # resolve full path
     input_movie_path = str(df.paths.resolve(input_movie_path))
 
-    output_dir = Path(batch_path).parent.joinpath(str(uuid))
+    output_dir = Path(batch_path).parent.joinpath(str(uuid)).resolve()
     output_dir.mkdir(parents=True, exist_ok=True)
 
     params = item["params"]
@@ -81,15 +81,13 @@ def run_algo(batch_path, uuid, data_path: str = None):
         params_dict = {**cnmfe_params_dict, **params["main"]}
 
         cnmfe_params_dict = CNMFParams(params_dict=params_dict)
-        cnm = cnmf.CNMF(
-            n_processes=n_processes, dview=dview, params=cnmfe_params_dict
-        )
+        cnm = cnmf.CNMF(n_processes=n_processes, dview=dview, params=cnmfe_params_dict)
         print("Performing CNMFE")
         cnm = cnm.fit(images)
         print("evaluating components")
         cnm.estimates.evaluate_components(images, cnm.params, dview=dview)
 
-        cnmf_hdf5_path = output_dir.joinpath(f"{uuid}.hdf5").resolve()
+        cnmf_hdf5_path = output_dir.joinpath(f"{uuid}.hdf5")
         cnm.save(str(cnmf_hdf5_path))
 
         # save output paths to outputs dict
@@ -106,7 +104,9 @@ def run_algo(batch_path, uuid, data_path: str = None):
         move_file(fname_new, cnmf_memmap_path)
 
         # save path as relative path strings with forward slashes
-        cnmfe_memmap_path = str(PurePosixPath(cnmf_memmap_path.relative_to(output_dir.parent)))
+        cnmfe_memmap_path = str(
+            PurePosixPath(cnmf_memmap_path.relative_to(output_dir.parent))
+        )
 
         d.update(
             {

mesmerize_core/algorithms/mcorr.py

@@ -102,16 +102,15 @@ def run_algo(batch_path, uuid, data_path: str = None):
         Cn[np.isnan(Cn)] = 0
         cn_path = output_dir.joinpath(f"{uuid}_cn.npy")
         np.save(str(cn_path), Cn, allow_pickle=False)
-
-        print("finished computing correlation image")
 
+        print("finished computing correlation image")
 
         # Compute shifts
         if opts.motion["pw_rigid"] == True:
             x_shifts = mc.x_shifts_els
             y_shifts = mc.y_shifts_els
             shifts = [x_shifts, y_shifts]
-            if hasattr(mc, 'z_shifts_els'):
+            if hasattr(mc, "z_shifts_els"):
                 shifts.append(mc.z_shifts_els)
             shift_path = output_dir.joinpath(f"{uuid}_shifts.npy")
             np.save(str(shift_path), shifts)
@@ -125,12 +124,14 @@ def run_algo(batch_path, uuid, data_path: str = None):
 
         # save paths as relative path strings with forward slashes
         cn_path = str(PurePosixPath(cn_path.relative_to(output_dir.parent)))
-        mcorr_memmap_path = str(PurePosixPath(mcorr_memmap_path.relative_to(output_dir.parent)))
+        mcorr_memmap_path = str(
+            PurePosixPath(mcorr_memmap_path.relative_to(output_dir.parent))
+        )
         shift_path = str(PurePosixPath(shift_path.relative_to(output_dir.parent)))
         for proj_type in proj_paths.keys():
-            d[f"{proj_type}-projection-path"] = str(PurePosixPath(proj_paths[proj_type].relative_to(
-                output_dir.parent
-            )))
+            d[f"{proj_type}-projection-path"] = str(
+                PurePosixPath(proj_paths[proj_type].relative_to(output_dir.parent))
+            )
 
         d.update(
             {

mesmerize_core/arrays/__init__.py

@@ -7,5 +7,5 @@
     "LazyArrayRCB",
     "LazyArrayResiduals",
     "LazyTiff",
-    "LazyVideo"
+    "LazyVideo",
 ]

mesmerize_core/arrays/_base.py

@@ -12,6 +12,7 @@ class LazyArray(ABC):
     """
     Base class for arrays that exhibit lazy computation upon indexing
     """
+
     @property
     @abstractmethod
     def dtype(self) -> str:
@@ -122,10 +123,7 @@ def as_numpy(self):
     def save_hdf5(self, filename: Union[str, Path]):
         pass
 
-    def __getitem__(
-            self,
-            item: Union[int, Tuple[slice_or_int_or_range]]
-    ):
+    def __getitem__(self, item: Union[int, Tuple[slice_or_int_or_range]]):
         if isinstance(item, int):
             indexer = item
 
@@ -161,21 +159,25 @@ def __getitem__(
 
             if start is not None:
                 if start > self.n_frames:
-                    raise IndexError(f"Cannot index beyond `n_frames`.\n"
-                                     f"Desired frame start index of <{start}> "
-                                     f"lies beyond `n_frames` <{self.n_frames}>")
+                    raise IndexError(
+                        f"Cannot index beyond `n_frames`.\n"
+                        f"Desired frame start index of <{start}> "
+                        f"lies beyond `n_frames` <{self.n_frames}>"
+                    )
             if stop is not None:
                 if stop > self.n_frames:
-                    raise IndexError(f"Cannot index beyond `n_frames`.\n"
-                                     f"Desired frame stop index of <{stop}> "
-                                     f"lies beyond `n_frames` <{self.n_frames}>")
+                    raise IndexError(
+                        f"Cannot index beyond `n_frames`.\n"
+                        f"Desired frame stop index of <{stop}> "
+                        f"lies beyond `n_frames` <{self.n_frames}>"
+                    )
 
             if step is None:
                 step = 1
-            
+
             # convert indexer to slice if it was a range, allows things like decord.VideoReader slicing
             indexer = slice(start, stop, step)  # in case it was a range object
-            
+
             # dimension_0 is always time
             frames = self._compute_at_indices(indexer)
 
@@ -193,7 +195,9 @@ def __getitem__(
             return self._compute_at_indices(indexer)
 
     def __repr__(self):
-        return f"{self.__class__.__name__} @{hex(id(self))}\n" \
-               f"{self.__class__.__doc__}\n" \
-               f"Frames are computed only upon indexing\n" \
-               f"shape [frames, x, y]: {self.shape}\n"
+        return (
+            f"{self.__class__.__name__} @{hex(id(self))}\n"
+            f"{self.__class__.__doc__}\n"
+            f"Frames are computed only upon indexing\n"
+            f"shape [frames, x, y]: {self.shape}\n"
+        )