aganders3

# To use me:
# Run `(venv1) > python shared_memory_hacking.py --server`
# this will print the name of a shared memory buffer like `psm_5bee30a5` and open a napari window
# copy that name of the shared memorry buffer into another terminal with:
# `(venv2) > python shared_memory_hacking.py --client --shared_memory_name psm_5bee30a5`
# you should see another napari window pop up
# paint into the labels layer in either viewer - changes in one will be reflected in the other
# you need to refresh the visual to see the changes - for example hide/show the labels layer

import argparse

alexmill

import numpy as np
import base64
import urllib
from PIL import Image

#Convert image to base64 thumbnail
def img2base64(img_link):
  with open("/tmp/img_file.jpg", "wb") as f:
      f.write(urllib.request.urlopen(img_link).read())
  tmp_img = np.asarray(Image.open("/tmp/img_file.jpg"))

astrofrog

tacaswell

import string
from itertools import cycle
from six.moves import zip

def label_axes(fig, labels=None, loc=None, **kwargs):
    """
    Walks through axes and labels each.

    kwargs are collected and passed to `annotate`

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD