[SymForce] A couple of random typos

Topic: chaoqu_symforce_typos
GitOrigin-RevId: 46d9645fc2ac203f80029269bccace65e1fc96e2

symforce/opt/barrier_functions.py

@@ -140,7 +140,7 @@ def symmetric_power_barrier(
     power: sf.Scalar,
 ) -> sf.Scalar:
     """
-    A symmetric barrier cenetered around x = 0, meaning the error at -x is equal to the error at x.
+    A symmetric barrier centered around x = 0, meaning the error at -x is equal to the error at x.
     The barrier passes through the points (x_nominal, error_nominal) and
     (x_nominal - dist_zero_to_nominal, 0) with a curve of the form x^power. For example, the
     barrier with power = 1 will look like:
@@ -282,7 +282,7 @@ def min_max_centering_power_barrier(
                     ------------------*******-------------------
                                    |
 
-    It may be easier to vizualize the bounding and centering barriers independently:
+    It may be easier to visualize the bounding and centering barriers independently:
 
     BOUNDING BARRIER:
                    **              |                          **

symforce/opt/noise_models.py

@@ -278,7 +278,7 @@ def pseudo_huber_error(self, x: sf.Scalar) -> sf.Scalar:
     def whiten_scalar(self, x: sf.Scalar, bounded_away_from_zero: bool = False) -> sf.Scalar:
         """
         Applies a whitening function to a single element of the unwhitened residual such that the
-        cost associated with the element in the least-sqaures cost function is the Pseudo-Huber
+        cost associated with the element in the least-squares cost function is the Pseudo-Huber
         loss function.
 
         Args:
@@ -329,7 +329,7 @@ class BarronNoiseModel(ScalarNoiseModel):
             by the pseudo-huber loss function.
         scalar_information: Scalar representing the inverse of the variance of an element of the
             unwhitened residual. Conceptually, we use "scalar_information" to whiten (in a
-            probabalistic sense) the unwhitened residual before passing it through the Barron loss.
+            probabilistic sense) the unwhitened residual before passing it through the Barron loss.
         x_epsilon: Small value used for handling the singularity at x == 0.
         alpha_epsilon: Small value used for handling singularities around alpha.
     """
@@ -386,7 +386,7 @@ def barron_error(self, x: sf.Scalar) -> sf.Scalar:
     def whiten_scalar(self, x: sf.Scalar, bounded_away_from_zero: bool = False) -> sf.Scalar:
         """
         Applies a whitening function to a single element of the unwhitened residual such that the
-        cost associated with the element in the least-sqaures cost function is the Barron loss
+        cost associated with the element in the least-squares cost function is the Barron loss
         function (weighted by self.weight).
 
         Args:

symforce/opt/optimization_problem.py

@@ -85,13 +85,13 @@ def optimized_keys(self) -> T.List[str]:
             subproblem.optimized_values() for subproblem in self.subproblems.values()
         )
 
-        content_addressible_inputs = {
+        content_addressable_inputs = {
             tuple(ops.StorageOps.to_storage(value)): key for key, value in inputs.items_recursive()
         }
 
         optimized_keys = []
         for value in optimized_values:
-            optimized_key = content_addressible_inputs[tuple(ops.StorageOps.to_storage(value))]
+            optimized_key = content_addressable_inputs[tuple(ops.StorageOps.to_storage(value))]
             if value != inputs[optimized_key]:
                 raise TypeError(
                     f"Variable returned by `optimized_values()` ({value}) in "

test/symforce_codegen_util_test.py

@@ -16,7 +16,7 @@
 
 class SymforceCodegenUtilTest(TestCase):
     """
-    Tests contents of symforce.codegen.codgen_util.
+    Tests contents of symforce.codegen.codegen_util.
     """
 
     def test_load_generated_package(self) -> None: