Fix typos

Author: tomwhite

cubed/core/ops.py

@@ -1710,7 +1710,7 @@ def scan(
 
     Notes
     -----
-    This method uses a variant of the Blelloch (1989) alogrithm.
+    This method uses a variant of the Blelloch (1989) algorithm.
 
     Returns
     -------

cubed/vendor/rechunker/algorithm.py

@@ -207,7 +207,7 @@ def multistage_rechunking_plan(
     consolidate_reads: bool = True,
     consolidate_writes: bool = True,
 ) -> _MultistagePlan:
-    """Caculate a rechunking plan that can use multiple split/consolidate steps.
+    """Calculate a rechunking plan that can use multiple split/consolidate steps.
 
     For best results, max_mem should be significantly larger than min_mem (e.g.,
     10x). Otherwise an excessive number of rechunking steps will be required.

docs/configuration.md

@@ -214,7 +214,7 @@ Note that `batch_size` is not currently supported for Lithops.
 | `region`                     | N/A     | The cloud region to run in. This must be set to match the region of your cloud store to avoid data transfer fees. See Modal's [Region selection](https://modal.com/docs/guide/region-selection) page for possible values. |
 | `retries`                    | 2       | The number of times to retry a task if it fails.                                    |
 | `timeout`                    | 180     | Tasks that take longer than the timeout will be automatically killed and retried.   |
-| `enable_output`              | False   | Print Modal output to stdout and stderr things for debuggging.                      |
+| `enable_output`              | False   | Print Modal output to stdout and stderr things for debugging.                      |
 | `use_backups`                | `True`  | Whether to use backup tasks for mitigating stragglers.                              |
 | `batch_size`                 | `None`  | Number of input tasks to submit to be run in parallel. The default is not to batch. |
 | `compute_arrays_in_parallel` | `False` | Whether arrays are computed one at a time or in parallel.                           |

docs/examples/how-to-run.md

@@ -2,7 +2,7 @@
 
 ## Local machine
 
-All the examples can be run on your laptop, so you can try them out in a familar environment before moving to the cloud.
+All the examples can be run on your laptop, so you can try them out in a familiar environment before moving to the cloud.
 No extra set up is necessary in this case.
 
 (cloud-set-up)=

docs/operations.md

@@ -92,7 +92,7 @@ The `reduction` operation reduces an array along one or more axes.
 * __Single__ input, __single__ output
 
 It is a core operation that is implemented using a `blockwise` operation called `partial_reduce` that reads multiple blocks and performs the reduction operation on them.
-The `partial_reduce` operations are arranged in a tree (`tree_reduce`) with multiple rounds until there's a single block in each reduction axis. Finally an aggregrate `blockwise` operation is applied to the results.
+The `partial_reduce` operations are arranged in a tree (`tree_reduce`) with multiple rounds until there's a single block in each reduction axis. Finally an aggregate `blockwise` operation is applied to the results.
 
 Here is an example of reducing over the first axis, with two rounds of `partial_reduce` operations:
 

docs/slides/intro/cubed-intro.ipynb

@@ -31,7 +31,7 @@
     "\n",
     "Tasks operate on Zarr chunks.\n",
     "\n",
-    "Tasks are embarassingly parallel, and their runtime memory can be tightly controlled."
+    "Tasks are embarrassingly parallel, and their runtime memory can be tightly controlled."
    ]
   },
   {