davipatti

#!/usr/bin/env python3

import time
import random

from typing import Generator, Iterable


def extend_loop(boxes: tuple[int, ...], loop: list[int], pointer: int) -> tuple[int, ...]:
    """

davipatti

> devtools::install_github("seroanalytics/serosolver")
Downloading GitHub repo seroanalytics/serosolver@HEAD
✔  checking for file ‘/tmp/Rtmpw3v9xS/remotes52ad50a06bda/seroanalytics-serosolver-4b632c4/DESCRIPTION’ ...
─  preparing ‘serosolver’:
✔  checking DESCRIPTION meta-information ...
─  cleaning src
─  checking for LF line-endings in source and make files and shell scripts
─  checking for empty or unneeded directories
─  building ‘serosolver_1.0.1.tar.gz’
   

davipatti

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

davipatti

Integrate executable programs with the system launcher in Ubuntu

Occasionally you download some pre-compiled software. It can be a pain to remember where you put it, navigate there in a shell and run it.

Better would be if it were integrated with the desktop launchers so you can launch it like a regular program.

Information for this behaviour (like where the executable is, what the icon is, etc...) is defined by a .desktop file, which has to be put in a directory the system monitors. See here for more details.

I was recently wanted to do this with Zotero, and their instructions had some recommendations on how to set this up properly:

davipatti

import pandas as pd


def remove_missing(df: pd.DataFrame) -> pd.DataFrame:
    """
    Remove missing data from a DataFrame.

    Iteratively remove the column or row that contains the highest proportion of
    missing data, until there is no missing data left.
    """

davipatti

library(pzfx)
path = 'data.pzfx'
for (table in pzfx_tables(path))
{
    filename = paste(sub(" ", "-", table), "csv", sep = ".")
    df = read_pzfx(path, table=table)
    write.csv(df, filename)
}

davipatti

Running any model in pymc3 spits out a lot of info, even if everything is running fine:

import pymc3 as pm

with pm.Model():
    pm.Normal(
        "obs",
        mu=pm.Normal("mu", 0, 1),
 sigma=pm.Exponential("sigma", 1),

davipatti

import sys
import csv
import networkx as nx
import matplotlib.pyplot as plt

"""
Expects CSV on stdin, containing weights of edges

,a,b,c
a,,2,3

davipatti

import os
from Bio.Blast.Applications import NcbiblastpCommandline
from Bio import SeqIO


# Grab any sequence to blast
with open("db.fasta", "r") as f:
    record = next(SeqIO.parse(f, "fasta"))

read, write = os.pipe()

davipatti

import os

read, write = os.pipe()

# Variable 'parent' is the process id of the parent fork
# It gets a value of 0 when the parent process exits
parent = os.fork()

if parent: