Please see our overall guide on code & project formatting here.
- For experimental and ML projects, please use DataJoint/databases
- Be sure you work under a lab repo, typically called "https://github.com/AdaptiveMotorControlLab/YourName_workspace".
- For larger projects (DLC, CEBRA, ExperimentalPipelines, you should be sure your "final" work gets into a pipeline; talk to Mackenzie about this)
- Lab Specific: please check the CommonHelperCode for useful tips & scripts, and contribute your own!
- In the lab we use the Google Style Guide for code. Please review this.
- In the lab we use the semantic versioning of code. Please review this.
- In the lab we use the code formatting we outline in the main README here.
- As soon as you start a repo, start a JupterBook! Please see here, and this repo has the template.
Here is how to do so most efficiently with me.
- 🚨 First, I recommend having a one-on-one so we can lay out the paper sketch, authors, data and code sharing plan together if not already done.
- 📝 I ask that we use overleaf for manuscripts! Please ask me to start a template to share with you.
- 🗺 I ask we use figma for figures; you can start a free educational group (or ask me to start one). Please then link this in the paper basecamp group.
- If you have a deadline (ICCV/NeurIPS, etc), you must have everything ready 1 week before the deadline and schedule a meeting to go over it (see Timeline below).
- 🔗 Please link the development code repo code in basecamp
- 🔗 Please link the overleaf
- 🔗 Please link the figma file
Collectively, your basecamp project should look like this:
- We use
matplotlibandseabornin python. - Every plot in a paper must be reproducible in a Jupyter Notebook. This means it loads the data (from datajoint/ possible later figshare or zenodo), plots it, and saves it.
- By the time we are ready to submit a paper, it must have a
AMCL\PaperName-figuresrepo.- Here are examples from the lab:
- I really like the plots in CEBRA, which you can find here: https://github.com/AdaptiveMotorControlLab/cebra-figures
- Here is an example, be sure to note the font, size, broken axis, transparency output, etc:
plt.figure(figsize=(3.5, 3.5), dpi = 200)
ax = plt.subplot(111)
keys = ['cebra', 'pivae', 'autolfads', 'tsne', 'umap']
df = pd.DataFrame(synthetic_scores)
sns.stripplot(data=df[keys] * 100, color="black", s=3, zorder=1, jitter=0.15)
sns.scatterplot(data=df[keys].median() * 100, color="orange", s=50)
plt.ylabel("$R^2$", fontsize=20)
plt.yticks(
np.linspace(0, 100, 11, dtype=int), np.linspace(0, 100, 11, dtype=int), fontsize=20
)
plt.ylim(70, 100)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.tick_params(axis="both", which="major", labelsize=15)
ax.tick_params(axis = 'x', rotation = 45)
ax.set_xticklabels(
['CEBRA', 'piVAE', 'autoLFADS', 'tSNE', 'UMAP'],
)
sns.despine(
left=False,
right=True,
bottom=False,
top=True,
trim=True,
offset={"bottom": 40, "left": 15},
)
plt.savefig('figure1_synthetic_comparison.jpg', bbox_inches = "tight", transparent = True)
plt.savefig('figure1_synthetic_comparison.svg', bbox_inches = "tight", transparent = True)Modify by adding:
for spine in ax.spines.values():
spine.set_color("white")
ax.tick_params(colors="white")
ax.yaxis.label.set_color("white")
ax.xaxis.label.set_color("white")For conference submissions, all papers must be in final form 1 week prior to the deadline. There will be no exceptions going forward. This gives us needed time to reflect, refine writing, get some distance, and ask colleagues for feedback. It also gives us time to prepare the code submission, and potentially arXiv the work -- which is always preferred. We should not be submitting manuscripts to conferences we would not want publicly read!