[DATALAD RUNCMD] Do interactive fixing of some ambigous typos

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w -i 3 -C 2",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

Author: yarikoptic

docs/core_concepts.ipynb

@@ -12,7 +12,7 @@
     "\n",
     " - the `Atlas` class as the basic entry point for working with a brain atlas \n",
     " - the `Parcellation` class, giving access to a particular brain segregation scheme\n",
-    " - the `Space` class, givin access to a particular reference space\n",
+    " - the `Space` class, giving access to a particular reference space\n",
     " \n",
     "The above classes are all derived from the basic `AtlasConcept` class, and represent semantic concepts. They are special in the sense that `siibra` automatically builds a registry with predefined objects for each of them as soon as you import the package. To configure the predefined objects, siibra will retrieve configuration details from EBRAINS and some other online resources when you import the package: "
    ]
@@ -157,7 +157,7 @@
    "source": [
     "## Accessing parcellations and regions\n",
     "\n",
-    "While the registry `siibra.parcellations` gives access to all available parcellations, the recommended way is to access parcellations via an atlas object. Each atlas object provices a filtered registry of its supported parcellations."
+    "While the registry `siibra.parcellations` gives access to all available parcellations, the recommended way is to access parcellations via an atlas object. Each atlas object provides a filtered registry of its supported parcellations."
    ]
   },
   {

docs/create_preconfiguration.rst

@@ -14,7 +14,7 @@ How to structure jsons?
 =======================
 In ``config_schema`` folder within
 `siibra-python <https://github.com/FZJ-INM1-BDA/siibra-python>`_, the existing
-schemes are provied as json files. These are meant to be a guide, particularly
+schemes are provided as json files. These are meant to be a guide, particularly
 for the minimum requirements.
 
 Step-by-step

examples/02_maps_and_templates/007_adding_custom_parcellation.py

@@ -85,7 +85,7 @@
 
 # %%
 # On the other hand, some features are only anchored to a semantic region
-# object and the link to the custome region is not directly known to siibra.
+# object and the link to the custom region is not directly known to siibra.
 # However, siibra circumvents this by comparing volumes of these regions to
 # assign a link between them.
 volume = region.get_regional_mask('mni152')

examples/05_anatomical_assignment/002_activation_maps.py

@@ -66,7 +66,7 @@
 # assign cytoarchitectonic regions to this functional map. Since we are here
 # usually interested in correlations of the modes, we filter the result by
 # significant (positive) correlations.
-# To assigne an image, we first need to create Volume which has to have a
+# To assign an image, we first need to create Volume which has to have a
 # space defined.
 volume = siibra.volumes.volume.from_nifti(img, difumo_maps.space, "fusiform posterior")
 with siibra.QUIET:  # suppress progress output

siibra/commons.py

@@ -554,7 +554,7 @@ def connected_components(
 
     Note
     ----
-    `Uses skimage.measure.label()` to determine foreground compenents.
+    `Uses skimage.measure.label()` to determine foreground components.
 
     Parameters
     ----------
@@ -644,7 +644,7 @@ def MI(arr1, arr2, nbins=100, normalized=True):
     assert (all(arr.size > 0) for arr in [arr1, arr2])
 
     # compute the normalized joint 2D histogram as an
-    # empirical measure of the joint probabily of arr1 and arr2
+    # empirical measure of the joint probability of arr1 and arr2
     pxy, _, _ = np.histogram2d(arr1.ravel(), arr2.ravel(), bins=nbins)
     pxy /= pxy.sum()
 

siibra/features/tabular/cell_density_profile.py

@@ -60,7 +60,7 @@ def length(self):
         return sum(self.lengths)
 
     def sample(self, d: Union[Iterable[float], np.ndarray, float]):
-        # if d is interable, we assume a list of sample positions
+        # if d is iterable, we assume a list of sample positions
         try:
             iter(d)
         except TypeError:

siibra/features/tabular/cortical_profile.py

@@ -37,7 +37,7 @@ class CorticalProfile(tabular.Tabular, Compoundable):
     Optionally, the depth coordinates of layer boundaries can be specified.
 
     Most attributes are modelled as properties, so dervide classes are able
-    to implement lazy loading instead of direct initialiation.
+    to implement lazy loading instead of direct initialization.
 
     """