jessicald

Class vs. Dictionary; or, An Example of Space–Time Tradeoff in Python

Conclusions; or, tl;dr

What wins?

• struct-like objects, i.e. ones that are largely the same as a C struct instance, win in the memory department.
  • These tests do not try to determine what is best for very large sets of values, however; I direct the reader to http://stackoverflow.com/a/2670191 for an insight into that scenario when using a dictionary.
• Dictionaries beat out objects in access times.
• According to Wikipedia, the Python dictionary is highly optimised because it is used internally to implement namespaces. [citation needed]

irazasyed

Homebrew Permissions Denied Issues Solution

sudo chown -R $(whoami) $(brew --prefix)/*

kevin-smets

Default

Powerlevel10k

bjmiller121

Sometimes you need to keep two upstreams in sync with eachother. For example, you might need to both push to your testing environment and your GitHub repo at the same time. In order to do this simultaneously in one git command, here's a little trick to add multiple push URLs to a single remote.

Once you have a remote set up for one of your upstreams, run these commands with:

git remote set-url --add --push [remote] [original repo URL]
git remote set-url --add --push [remote] [second repo URL]

Once set up, git remote -v should show two (push) URLs and one (fetch) URL. Something like this:

korakot

# CPU
!curl -L https://anaconda.org/pytorch/faiss-cpu/1.6.0/download/linux-64/faiss-cpu-1.6.0-py36h6bb024c_0.tar.bz2 | tar xj
# GPU, CUDA 10
# !curl -L https://anaconda.org/pytorch/faiss-gpu/1.6.0/download/linux-64/faiss-gpu-1.6.0-py36h1a5d453_0.tar.bz2 | tar xj
# install
!mv lib/python3.6/site-packages/* /usr/local/lib/python3.6/dist-packages/

import faiss

ChrisRackauckas

torchsde vs DifferentialEquations.jl / DiffEqFlux.jl (Julia)

This example is a 4-dimensional geometric brownian motion. The code for the torchsde version is pulled directly from the torchsde README so that it would be a fair comparison against the author's own code. The only change to that example is the addition of a dt choice so that the simulation method and time step matches between the two different programs.

The SDE is solved 100 times. The summary of the results is as follows:

ZosBHAI

### Find the kernel.json location 
jupyter kernelspec list

###Create Pyspark Folder and create Kernel.json file  with below content
{
  "display_name": "pyspark",
  "language": "python",
  "argv": [ "/opt/anaconda3/bin/python", "-m", "ipykernel", "-f", "{connection_file}" ],
  "env": {
    "JAVA_HOME": "/usr/lib/jvm/java-8-openjdk-amd64",

cgreer

import random
from typing import (
    List,
    Dict,
    Callable,
    Union,
)

import numpy

camriddell

from functools import partial
from textwrap import fill

from scipy.stats import norm, uniform, skewnorm, gaussian_kde, triang
from numpy import (
    array, linspace, quantile, histogram, atleast_2d, mean, std, add
)
from numpy.lib.stride_tricks import sliding_window_view

from matplotlib.pyplot import subplots, show, rc

mwaskom