snippets

may or may not be relevant to others

shell

personal O2 things

ssh me@o2.hms.harvard.edu
ssh login01
tmux
source ~/.bashrc_mark
ssh-add ~/.ssh/id_rsa_github

serve directory of files

ssh -L O2_PORT:127.0.0.1:LOCAL_PORT me@o2.hms.harvard.edu
ssh -L O2_PORT:127.0.0.1:O2_PORT login01
srun -t 0-3:00 --pty -p interactive --tunnel O2_PORT:O2_PORT /bin/bash
# cd to some directory with files
http-server --cors='*' --port O2_PORT .

start an interactive job with slurm on O2

srun -p interactive --pty -t 8:00:00 -n 4 --mem 16G bash

start an interactive job with slurm on O2 with 1 GPU

srun -p gpu --gres=gpu:1 --pty -t 8:00:00 -n 4 --mem 16G bash
module load gcc/9.2.0 cuda/11.7 # Needs same version of CUDA that was installed into Python environment

run snakemake with time-based rerun triggers

snakemake -j 1 --rerun-triggers mtime

run snakemake with time-based rerun triggers on a cluster filesystem

snakemake -j 1 --rerun-triggers mtime --latency-wait 30

copy a local directory to google cloud storage

This will create a copy at gs://{bucket}/{path}/local_dir

gcloud auth login
gsutil -m cp -r ./local_dir gs://{bucket}/{path}

copy from google cloud storage bucket to local

This will create a copy at ./remote_dir

gcloud auth login
gsutil -m cp -r gs://{bucket}/{path}/remote_dir .

download a file with curl

curl -L -o {output} {file_url}

# put quotes around file_url if it has characters that might be misinterpreted by the shell (e.g., '&')
curl -L -o {output} "{file_url}"

tar.gz a directory

tar -cvzf dir.tar.gz dir/

un-tar.gz a file

tar -xvzf dir.tar.gz
# or
tar -xvzf dir.tar.gz -C dest_dir

un-gz a file

gunzip -c {input} > {output}

serve a local directory over HTTP

# brew install http-server
http-server --cors='*' --port 8000 .

create conda env

mamba env create -f environment.yml

or

conda env create -f environment.yml

create conda env without environment file

conda create -n NAME python=3.11

create conda env in particular directory

conda create --prefix=/path/to/envs/env_name python=3.11
conda config --append envs_dirs /path/to/envs

configure mamba as conda solver

conda install -n base conda-libmamba-solver
conda config --set solver libmamba

create conda env for R

conda config --add channels r
conda create -n NAME
conda activate NAME
conda install r-base r-essentials r-irkernel

update conda env

mamba env update -f environment.yml

or

conda env update -f environment.yml

remove conda env

conda env remove -n NAME

update conda version

conda update -n base conda

symlink a file or directory

ln -s /path/to/reference-file symlink-file

revert to a particular git commit

git revert --no-commit b960c8e5..HEAD

change git case sensitivity

git config core.ignorecase false

authorize a direnv

direnv allow .

create a direnv for conda

in .envrc

use conda NAME

install a LaTeX package from CTAN with tlmgr

tlmgr install chemfig

convert mov to gif

ffmpeg -i input.mov -r 10 -pix_fmt rgb24 output.gif

checkout a single file from another git branch

# For example, if we want `some-file.js` from the `main` branch
git checkout main -- some-file.js

pip install from a github branch with extras_require

pip install 'vitessce[all] @ git+https://github.com/vitessce/vitessce-python@main'
pip install 'SomePackage[PDF] @ git+https://git.repo/SomePackage@main#subdirectory=subdir_path'

tmux

list

tmux ls

attach to existing session 0

tmux a -t 0

detach from session

CTRL+B D

altair

basics

plot = alt.Chart(df).mark_bar().encode(
    x=alt.X("x_colname:N"),
    y=alt.Y("y_colname:Q"),
    color=alt.Color("color_colname:N"),
).properties(
    width=200,
    height=200
)

plot.save("my_plot.png")
plot.save("my_plot.svg)

plot

title and subtitle

# ...
).properties(
    title={
        "text": "Title here",
        "subtitle": "Subtitle here",
        "fontSize": 16,
        "fontWeight": 500,
        "subtitleColor": "black",
        "subtitleFontSize": 12
    }
)

sorting/ordering things

    # x, y, color, etc.
    x=alt.X("x_colname:N", scale=alt.Scale(domain=sorted_x_vals)),
    # row, column
    row=alt.Row("row_colname:N", sort=sorted_row_vals),

axis titles

    # x, y, etc.
    x=alt.X("x_colname:N", axis=alt.Axis(title="Title Here")),
    # color
    color=alt.Color("color_colname:N", legend=alt.Legend(title="Title Here")),
    # row, column
    row=alt.Row("row_colname:N", header=alt.Header(title="Title Here")),

using row/column with errorbar/errorband

layer first, then facet

base = alt.Chart(df)

lines = base.mark_line(point=True).encode(
    x=alt.X("year:O", axis=alt.Axis(title="Year")),
    y=alt.Y("percent:Q"),
    color=alt.Color("method_or_other:N")
).properties(
    height=200,
    width=800
)

error_bands = base.mark_errorband().encode(
    x=alt.X("year:O"),
    y=alt.Y("pct_lower:Q", axis=alt.Axis(title="Percent")),
    y2=alt.Y2("pct_upper:Q"),
    color=alt.Color("method_or_other:N")
)

alt.layer(lines, error_bands, data=df).facet(
    row=alt.Row(
        "field:N",
        sort=sorted_field_df.index.tolist(),
        header=alt.Header(
            title="Subject Area",
            labelAngle=0,
            labelAlign="left",
        )
    )
)

choropleth: joining on state name

import altair as alt
from vega_datasets import data

state_df = pd.read_csv(data.population_engineers_hurricanes.url)
state_to_id = dict(zip(state_df["state"].values.tolist(), state_df["id"].values.tolist()))

# df is some dataframe with column 'my_field'
df["id"] = df["state"].apply(lambda x: state_to_id[x])

states = alt.topo_feature(data.us_10m.url, 'states')


plot = alt.Chart(states).mark_geoshape().encode(
    color=alt.Color("my_field:N")
).transform_lookup(
    lookup='id',
    from_=alt.LookupData(data=df, key='id', fields=['my_field'])
).properties(
    width=500,
    height=300
).project(
    type='albersUsa'
)
plot