move misspellings

.vale.ini

@@ -3,3 +3,6 @@ Vocab = TBP
 
 [{docs/*.md,README.md}]
 BasedOnStyles = Docs
+
+[*.md]
+BlockIgnores = (?s) *\[block:embed\].*?\[/block\]

.vale/styles/config/vocabularies/TBP/accept.txt

@@ -1,6 +1,6 @@
-Bluesky
+\bBluesky\b
 neocortex
-Mountcastle
+\bMountcastle\b
 \bconfigs\b
 \bConfig\b
 \bconfig\b
@@ -12,7 +12,6 @@ hippocampal
 \bheterarchy\b
 saccading
 \bHTM\b
-html
 \bANNs*\b
 \bCNNs*\b
 \bGNNs*\b
@@ -69,20 +68,17 @@ fullscreen
 favicon
 href
 dataclass
-specifid
 Pretrained
 pretrained
 runtimes
 neurophysiologist
 mixin
-distingush
 Conda
 Miniconda
 zsh
-wanbd
+wandb
 utils
 matplotlib
-programatically
 timeseries
 pretrain
 misclassification
@@ -95,7 +91,6 @@ Leyman
 Sampath
 Shen
 Neuromorphic
-succesive
 interpretability
 Walkthrough
 presynaptic
@@ -143,9 +138,7 @@ prechecks
 repo
 rdme
 cli
-Exapmle
 semver
-attriubtes
 callouts
 discretized
 discretize

docs/contributing/documentation.md

@@ -52,7 +52,7 @@ title: 'New Placeholder Example Doc'
 > 🚧 Quotes
 >
 >Please put the title in single quotes and, if applicable, escape any single quotes using two single quotes in a row.
-Exapmle: `title: 'My New Doc''s'`
+Example: `title: 'My New Doc''s'`
 
 > 🚧 Your title must match the url-safe slug
 >

docs/contributing/style-guide.md

@@ -140,7 +140,7 @@ Images use `snake_case.ext`
 
 Images should generally be `png` or `svg` formats.  Use `jpg` if the file is actually a photograph.
 
-Upload high quality images as people can click on the image to see the larger version.  You can add style attriubtes after the image path with `#width=300px` or similar.
+Upload high quality images as people can click on the image to see the larger version.  You can add style attributes after the image path with `#width=300px` or similar.
 
 For example, the following markdown creates the image below:
 

docs/how-to-use-monty/customizing-monty.md

@@ -37,4 +37,4 @@ my_custom_config.update(
 )
 ```
 
-For simplicity we inherit all other default values from the `base_config_10distinctobj_dist_agent` config in `benchmarks/configs/ycb_experiments.py` and use the `monty_config` specifid in the `PatchAndViewSOTAMontyConfig` dataclass.
+For simplicity we inherit all other default values from the `base_config_10distinctobj_dist_agent` config in `benchmarks/configs/ycb_experiments.py` and use the `monty_config` specified in the `PatchAndViewSOTAMontyConfig` dataclass.

docs/how-to-use-monty/logging-and-analysis.md

@@ -14,13 +14,13 @@ To manage the logging for an experiment you can specify the handlers that should
 | **BasicCSVStatsHandler**          | Log a .csv file with one row per episode that contains the results and performance of this episode.                                                         |
 | **ReproduceEpisodeHandler**       | Logs action sequence and target such that an episode can be exactly reproduced.                                                                             |
 | **BasicWandbTableStatsHandler**   | Logs a table similar to the .csv table to wandb.                                                                                                            |
-| **BasicWandbChartStatsHandler**   | Logs episode stats to wandb charts. When running in parallel this is done at the end of a run. Otherwise one can follow the run stats live on wanbd.        |
+| **BasicWandbChartStatsHandler**   | Logs episode stats to wandb charts. When running in parallel this is done at the end of a run. Otherwise one can follow the run stats live on wandb.       |
 | **DetailedWandbHandler**          | Logs animations of raw observations to wandb.                                                                                                               |
 | **DetailedWandbMarkedObsHandler** | Same as previous but marks the view-finder observation with a square indicating where the patch is.                                                         |
 
 ## Logging to Wandb
 
-When logging to wanbd we recommend to run`export WANDB_DIR=~/tbp/results/monty/wandb`so the wandb logs are not stored in the repository folder.
+When logging to wandb we recommend to run`export WANDB_DIR=~/tbp/results/monty/wandb`so the wandb logs are not stored in the repository folder.
 
 The first time you run experiments that log to wandb you will need to set your WANDB_API key using `export WANDB_API_KEY=your_key`
 
@@ -122,7 +122,7 @@ plt.show()
 
 > 📘 Plotting in 3D
 >
-> Most plots shown here use the 3D projection feature of matplotlib. The plots can be viewed interactively by dragging the mouse over them to zoom and rotate. When you want to save figures with 3D plots programatically, it can be useful to set the `rotation` parameter in the `plot_graph` function such that the POV provides a good view of the 3D structure of the object.
+> Most plots shown here use the 3D projection feature of matplotlib. The plots can be viewed interactively by dragging the mouse over them to zoom and rotate. When you want to save figures with 3D plots programmatically, it can be useful to set the `rotation` parameter in the `plot_graph` function such that the POV provides a good view of the 3D structure of the object.
 
 ### Plotting Matching Animations
 

docs/how-to-use-monty/tutorials/multiple-learning-modules.md

@@ -2,7 +2,7 @@
 title: Multiple Learning Modules
 ---
 # Introduction
-Thus far, we have been working with models that use a single agent with a single sensor which connects to a single learning module. In the context of vision, this is analogous to a small patch of retina that picks up a small region of the visual field and relays its information to its downstream target--a single cortical column in the primary visual cortex (V1). In human terms, this is like looking through a straw. While sufficient to recognize objects, one would have to make many succesive eye movements to build up a picture of the environment. In reality, the retina contains many patches that tile the retinal surface, and they all send their information to their respective downstream target columns in V1. If, for example, a few neighboring retinal patches fall on different parts of the same object, then the object may be rapidly recognized once columns have communicated with each other about what they are seeing and where they are seeing it.
+Thus far, we have been working with models that use a single agent with a single sensor which connects to a single learning module. In the context of vision, this is analogous to a small patch of retina that picks up a small region of the visual field and relays its information to its downstream target--a single cortical column in the primary visual cortex (V1). In human terms, this is like looking through a straw. While sufficient to recognize objects, one would have to make many successive eye movements to build up a picture of the environment. In reality, the retina contains many patches that tile the retinal surface, and they all send their information to their respective downstream target columns in V1. If, for example, a few neighboring retinal patches fall on different parts of the same object, then the object may be rapidly recognized once columns have communicated with each other about what they are seeing and where they are seeing it.
 
 In this tutorial, we will show how Monty can be used to learn and recognize objects in a multiple sensor, multiple learning module setting. In this regime, we can perform object recognition with fewer steps than single-LM systems by allowing learning modules to communicate with one another through a process called [voting](../../overview/architecture-overview/other-aspects.md#votingconsensus). We will also introduce the distant agent, Monty's sensorimotor system that is most analogous to the human eye. Unlike the surface agent, the distant agent cannot move all around the object like a finger. Rather, it swivels left/right/up/down at a fixed distance from the object.