rlabbe

import os
import winreg
''' find all duplicate and invalid paths in your user account on windows'''

def expand_env_vars(paths: list[str]) -> list[str]:
    return [os.path.expandvars(path) for path in paths]

def find_invalid_paths():
    user_paths, system_paths = get_user_and_system_paths()
    combined_paths = user_paths + system_paths

rlabbe

Git diff on Jupyter notebook to ignore most of the irrelevant output cell changes

    $ git diff 5579009 -I image/png -I execution_count -I \"version\":

minimal log to just see commit, author, and one line comment

$ git log --pretty=format:"%h%x09%an%x09%ad%x09%s"


git config --global alias.jdiff "diff -I image/png -I execution_count -I \"version\"::

rlabbe

#include <stdarg.h>

// simple implementation of the proposed std::putf
// int i = 3;
// auto d = 3.1415926;
// cout << putf("%03di -> %.3f", i, d) << endl;

std::string putf(const char* format, ...)
{
    va_list args;

rlabbe

class AttrDict(dict):
    """
    dict that lets you access any key via attribute:
        x['hi'] or x.hi
    """
    def __init__(self, *args, **kwargs):
        super(AttrDict, self).__init__(*args, **kwargs)
        self.__dict__ = self

    def __repr__(self):

rlabbe

def as_dict(rec):
    """ turn a numpy recarray record into a dict. this is mostly useful
    just to have a human readable output of a record on the console.
    
    as_dict(my_data[234])
    """
    
    return {name:rec[name] for name in rec.dtype.names}

rlabbe

def camel_to_snake(name):
    s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
    return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()

# convert comma separated csv header
keys = header.split(',')
keys = [camel_to_snake(k) for k in keys]
header = ','.join(keys)

rlabbe

rom subprocess import call, Popen
import sys
import matplotlib as mpl
import numpy
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


def npp(precision=4):

rlabbe

''' create object using numpy to read CSV. creates one variable per column
so you can use f.col_name instead of f.data['col_name']

f = NamedCSV(filename)
f.data # all data in the file in an np recarray
f.mass  
f.data['mass'] # same as previous line

f.extract(f.mass > 180)
'''

rlabbe

from inspect import getsource
import re
def psource(fun):
    print(re.sub('""".*"""', '', getsource(fun), flags=re.DOTALL))
    


#example
from somewhere import foo
psource(foo)

rlabbe

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

xs = np.linspace(-4, 4, n)
ys = np.linspace(-4, 4, n)

X, Y = np.meshgrid(xs, ys)

# mixture of Gaussians - based on Matlab peaks() function