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Merge pull request #1083 from yarikoptic/enh-codespell
codespell: config, workflow (fail if typos introduced) and some typos fixed
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.codespellrc

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[codespell]
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skip = .git,*.pdf,*.svg
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# ignore embedded images or javascript in notebooks
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ignore-regex = (^\s*("image/\S+": ".*|"!function.*"|"[^\s]{30,100}\\\\n"|" <source src=\S+;base64.*"|"!\[.*\]\(data:.*;base64,.*))|'t Hart|Chater, N\.|\bChristina Savin\b
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# ignore all kinds of short up to 3 letter variables found to be used throughout
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ignore-words-list = 3nd,4rd,aci,acn,afe,ags,als,bu,bui,caf,cna,daa,ede,esy,fle,fo,fof,fwe,hax,hge,hte,hve,iif,inh,ist,ket,mis,mor,mot,nam,nax,nce,nd,ned,nin,nwe,nwo,oce,ofo,ois,oly,onl,onw,ot,oyu,siz,tbe,te,teh,tha,tht,thw,ue,wee,wew,wih,wll,wqs,wth,yau,yse,zar,

.github/workflows/codespell.yml

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---
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name: Codespell
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on:
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push:
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branches: [main]
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pull_request:
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branches: [main]
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permissions:
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contents: read
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jobs:
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codespell:
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name: Check for spelling errors
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Codespell
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uses: codespell-project/actions-codespell@v2

projects/ECoG/exploreAJILE12.ipynb

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},
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"source": [
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"### Access to data on cloud\n",
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"The data is hosted on [AMAZON AWS](https://aws.amazon.com) in **S3** buckets. The following steps guide you to locate the data based on the **dandiset** information, setup streaming and reading the data from the cloud. Alternatively, you can access the data on **[DANDI](https://dandiarchive.org/dandiset/000055?search=ajile12&pos=1)**. If you choose to directly download from DANDI, you will need a github account. The following code will be sufficient to programatically download/stream data (either for colab notebook or for your own personal machine)."
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"The data is hosted on [AMAZON AWS](https://aws.amazon.com) in **S3** buckets. The following steps guide you to locate the data based on the **dandiset** information, setup streaming and reading the data from the cloud. Alternatively, you can access the data on **[DANDI](https://dandiarchive.org/dandiset/000055?search=ajile12&pos=1)**. If you choose to directly download from DANDI, you will need a github account. The following code will be sufficient to programmatically download/stream data (either for colab notebook or for your own personal machine)."
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]
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},
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{
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"execution": {}
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},
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"source": [
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"Each subject has multiple experimental sessions. You can check that programatically."
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"Each subject has multiple experimental sessions. You can check that programmatically."
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]
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},
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{

projects/ECoG/load_ECoG_motor_imagery.ipynb

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"\n",
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"`dat1` and `dat2` are data from the two blocks performed in each subject. The first one was the actual movements, the second one was motor imagery. For the movement task, from the original dataset instructions:\n",
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"\n",
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"*Patients performed simple, repetitive, motor tasks of hand (synchronous flexion and extension of all fingers, i.e., clenching and releasing a fist at a self-paced rate of ~1-2 Hz) or tongue (opening of mouth with protrusion and retraction of the tongue, i.e., sticking the tongue in and out, also at ~1-2 Hz). These movements were performed in an interval-based manner, alternating between movement and rest, and the side of move- ment was always contralateral to the side of cortical grid placement.*\n",
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"*Patients performed simple, repetitive, motor tasks of hand (synchronous flexion and extension of all fingers, i.e., clenching and releasing a fist at a self-paced rate of ~1-2 Hz) or tongue (opening of mouth with protrusion and retraction of the tongue, i.e., sticking the tongue in and out, also at ~1-2 Hz). These movements were performed in an interval-based manner, alternating between movement and rest, and the side of move- meant was always contralateral to the side of cortical grid placement.*\n",
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"\n",
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"<br>\n",
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"\n",

projects/behavior/Loading_CalMS21_data.ipynb

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" stop_frame=5100,\n",
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" annotation_sequence=annotation_sequence)\n",
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"\n",
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"# Display the animaion on colab\n",
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"# Display the animation on colab\n",
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"ani"
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]
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},

projects/docs/project_guidance.md

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@@ -79,7 +79,7 @@ We have designed tutorials to help launch your projects. Once you're done with t
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(2h) Complete the intro/tutorial/outro for this day
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* You will need to use your group's project for some of this content. If you don’t have concrete ideas yet, or you haven’t done a research project before, use one of the provided project templates to walk through the four steps.
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* If you are using a project template, your goal is to translate the information from the slide and colab notebook into a 4-step format. Some information might not be readily available in the slide or notebook, and you might have to find it in your literature review later this day.
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* Try to write down a few sentences for each of the four steps applied to your project. You will re-use these in your proposal later today.
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* Try to write down a few sentences for each of the four steps applied to your project. You will reuse these in your proposal later today.
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(2.5h) Literature review: identify interesting papers
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The goal of this literature review is to situate your question in context and help you acquire some keywords that you will use in your proposal today.
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Project block task:
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(3h) Project proposal
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* Try to write a proposal for this project based on the way you understand it now. This should re-use some of the text you wrote down for the four steps, and should include keywords and concepts that you identified in your literature review. Don’t worry too much about the structure of this paragraph! The goal is to get as many words (200-300) on paper as possible. You have the entire day 10 to learn how to write a properly structured scientific abstract.
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* Try to write a proposal for this project based on the way you understand it now. This should reuse some of the text you wrote down for the four steps, and should include keywords and concepts that you identified in your literature review. Don’t worry too much about the structure of this paragraph! The goal is to get as many words (200-300) on paper as possible. You have the entire day 10 to learn how to write a properly structured scientific abstract.
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* It is important to include the concepts which you identified as relevant, and the keywords that go with them.
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* When you are ready, please submit your proposal [here](https://airtable.com/shrcYuFYMPh4jGIng). This is not mandatory and can be submitted at any time. We won't evaluate this, but we will use it to keep track of the overall progress of the groups.
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projects/fMRI/load_bonner_navigational_affordances.ipynb

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"execution": {}
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},
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"source": [
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"`Trajs.mat` contain data on the trajectories drawn by subjects during the evaluation phase before main experiment. The data is organised like `[n_images, heigth, width, n_evaluators]` There is a data on 173 images, of which 50 were presented to the participants. The filenames are stored as `dtype`.\n"
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"`Trajs.mat` contain data on the trajectories drawn by subjects during the evaluation phase before main experiment. The data is organised like `[n_images, height, width, n_evaluators]` There is a data on 173 images, of which 50 were presented to the participants. The filenames are stored as `dtype`.\n"
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]
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},
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],
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"source": [
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"trajs = loadmat('affordances/Trajs.mat')['Trajs']\n",
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"fnames = trajs.dtype.names # filenames get loaded as custom dtypes due and type of array is initialy np.void due to peculiarites of how it was saved in Matlab.\n",
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"fnames = trajs.dtype.names # filenames get loaded as custom dtypes due and type of array is initially np.void due to peculiarites of how it was saved in Matlab.\n",
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"trajs = np.asarray(trajs[0][0].tolist()) # turn np.void into float32\n",
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"trajs.shape"
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]

projects/fMRI/load_cichy_fMRI_MEG.ipynb

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downlading data...\n",
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"Downloading data...\n",
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"Download completed!\n"
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]
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}
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" if r.status_code != requests.codes.ok:\n",
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" print(\"!!! Failed to download data !!!\")\n",
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" else:\n",
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" print(\"Downlading data...\")\n",
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" print(\"Downloading data...\")\n",
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" with open(fname, \"wb\") as fid:\n",
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" fid.write(r.content)\n",
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" with zipfile.ZipFile(fname, 'r') as zip_ref:\n",

projects/fMRI/load_fslcourse.ipynb

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"execution": {}
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"Next we will convolve ouur regressors with the HRF. This is because the FMRI signal is a sluggish blood signal that lags behind neural signal. "
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"Next we will convolve our regressors with the HRF. This is because the FMRI signal is a sluggish blood signal that lags behind neural signal. "
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]
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},
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{

projects/fMRI/load_hcp.ipynb

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"N_RUNS_TASK = 2\n",
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"\n",
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"# Time series data are organized by experiment, with each experiment\n",
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"# having an LR and RL (phase-encode direction) acquistion\n",
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"# having an LR and RL (phase-encode direction) acquisition\n",
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"BOLD_NAMES = [\n",
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" \"rfMRI_REST1_LR\", \"rfMRI_REST1_RL\",\n",
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" \"rfMRI_REST2_LR\", \"rfMRI_REST2_RL\",\n",
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"outputs": [],
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"task = \"motor\"\n",
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"conditions = [\"lf\", \"rf\"] # Run a substraction analysis between two conditions\n",
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"conditions = [\"lf\", \"rf\"] # Run a subtraction analysis between two conditions\n",
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"\n",
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"contrast = []\n",
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"for subject in subjects:\n",

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