serhii.net

TL;DR \newpage~\newpage~\newpage~\newpage for 3 empty pages

\newpage doesn’t always work for well me in, esp. not in the IEEE and LREC templates. Either only one column is cleared, or there are issues with images/tables/… positions.

\clearpage works for me in all cases I’ve tried.

EDIT: but only one page, not multiple! For multiple empty pages one after the other this¹ does the trick:

\newpage 
~\newpage 

ChatGPT thinks it works because ~ being a non-breaking space makes LaTex try to add both empty pages on the same page, leading to two empty pages. Somehow allowing a newline between new pages makes it interpret both pages as the same command, since it’s already a new page.

Seaborn saving with correct border

When saving seaborn images there was weirdness going on, with borders either cutting labels or benig too big.

Solution:

# bad: cut corners
ax.figure.savefig("inat_pnet_lorenz.png")
# good: no cut corners and OK bounding box
ax.figure.savefig("inat_pnet_lorenz.png", bbox_inches="tight")

Save as PDF/EPS for better picture quality in papers

EDIT 2023-12-14

Paper reviewer suggested exporting in PDF, which led me to graphics - Good quality images in pdflatex - TeX - LaTeX Stack Exchange:

Both gnuplot and matplotlib can export to vector graphics; file formats for vector graphics are e.g. eps or pdf or svg (there are many more). As you are using pdfLaTeX, you should choose pdf as output format, because it will be easy to include in your document using the graphicx package and the \includegraphics{} command.

Awesome! So I can save to PDF and then include using the usual code (edit - eps works as well). Wow!

Plotly

Static image export in Python:

fig.write_image("images/fig1.png")

PDF works as-is as well, EPS needs the poppler library but then works the same way

For excessive margins in the output PDFs:]

fig.update_layout( margin=dict(l=20, r=20, t=20, b=20), )

Overleaf antialiasing blurry when viewing

When including a PDF plot, I get this sometimes:

This is a problem only when viewing the PDF inside qutebrowser/Overleaf, in a normal PDF viewer it’s fine!

Didn’t find this in the documentation, but:

gg = ds.groupby(by=["species"])
lg = next(gg.groups)
# lg is the group name tuple (in this case of one string)
group_df = gg.get_group(lg)

Web zotero

Looking for a way to have vertical/tree tabs, I found a mention of the zotero web version being really good.

Then you can have multiple papers open (with all annotations etc.) in different browser tabs that can be easily navigated using whatever standard thing one uses.

You can read annotations but not edit them. Quite useful nonetheless!

Split view

PDF reader feature request: open the same pdf twice in split screen - Zotero Forums: View -> Split Horizontally/Vertically!

It’s especially nice for looking at citations in parallel to the text.

Shortcuts

vim!

EDIT 2023-12-05 Overleaf has Vim bindings! Enable-able in the project menu. There are unofficially supported ways to even make custom bindings through TamperMonkey

Shortcuts

Kurz und gut

• Ctrl+Enter compiles the project
• Bold/italic work as expected, <C-b/i>. Same for copypaste etc.
• Advanced reference search: is cool.
• Comments:
  • <C-/> for adding %-style LaTex comments.
  • <C-S-c> for adding Overleaf comments

Bible

Overleaf Keyboard Shortcuts - Overleaf, Online LaTeX Editor helpfully links to a PDF, screenshots here:

It seems to have cool multi-cursor functionality that might be worth learning sometime.

Templates

Overleaf has a lot of templates: Templates - Journals, CVs, Presentations, Reports and More - Overleaf, Online LaTeX Editor

If your conference’s is missing but it sends you a .zip, you can literally import it as-is in Overleaf, without even unpacking. Then you can “copy” it to somewhere else and start writing your paper.

Bits and pieces

• Renaming the main file to sth like 0paper.tex makes it appear on top, easier to find.

• https://extract.bbbike.org/: you pick an area and click

Difference between %time and %%time in Jupyter Notebook - Stack Overflow

• when measuring execution time, %time refers to the line after it, %%time refers to the entire cell
• As we remember¹:
  • real/wall the ‘consensus reality’ time
  • user: the process CPU time
    • time it did stuff
  • sys: the operating system CPU time due to system calls from the process
    • interactions with CPU system r/w etc.

TIL that for readability, x = 100000000 can be written as x = 100_000_000 etc.! Works for all kinds of numbers - ints, floats, hex etc.!¹

I have a larger-than-usual text-based dataset, need to do analysis, pandas is slow (hell, even wc -l takes 50 seconds…)

Vaex: Pandas but 1000x faster - KDnuggets - that’s a way to catch one’s attention.

Reading files

I/O Kung-Fu: get your data in and out of Vaex — vaex 4.16.0 documentation

• vx.from_csv() reads a CSV in memory, kwargs get passed to pandas’ read_csv()
• vx.open() reads stuff lazily, but I can’t find a way to tell it that my .txt file is a CSV, and more critically - how to pass params like sep etc
  • vx.from_ascii() has a parameter called sepe rator?! API documentation for vaex library — vaex 4.16.0 documentation
• the first two support convert= that converts stuff to things like HDFS, optionally chunk_size= is the chunk size in lines. It’ll create $N/chunk_size$ chunks and concat together at the end.
• Ways to limit stuff:
  • nrows= is the number of rows to read, works with convert etc.
  • usecols= limits to columns by name, id or callable, speeds up stuff too and by a lot

Writing files

• I can do df.export_hdf5() in vaex, but pandas can’t read that. It may be related to the opposite problem - vaex can’t open pandas HDF5 files directly, because one saves them as rows, other as columns. (See FAQ)
• When converting csv to hdf5, it breaks if one of the columns was detected as an object, in my case it was a boolean. Objects are not supported¹, and booleans are objects. Not trivial situation because converting that to, say, int, would have meant reading the entire file - which is just what I don’t want to do, I want to convert to hdf to make it manageable.

Doing stuff

Syntax is similar to pandas, but the documentation is somehow .. can’t put my finger on it, but I don’t enjoy it somehow.

Stupid way to find columns that are all NA

l_desc = df.describe()
# We find column names that have length_of_dataset NA values
not_empty_cols = list(l_desc.T[l_desc.T.NA!=df.count()].T.columns)
# Filter the description by them
interesting_desc = l_desc[not_empty_cols]

Use Virtual Environments Inside Jupyter Notebooks & Jupter Lab [Best Practices]

Create and activate it as usual, then:

python -m ipykernel install --user --name=myenv