dwf

"""Demo of how to pop up plots asynchronously using separate processes."""
from multiprocessing import Process
import time
import sys
import matplotlib.pyplot as plt
import numpy as np

def demo():
    i = 0
    processes = []

CnrLwlss

import random, numpy

def genGuess(N,W,H):
    '''Generate sensible initial guess vector (random circle coordinates and radii)'''
    z=numpy.zeros(3*N)
    for i in xrange(0,N):
        z[i*3]=random.random()*W
        z[i*3+1]=random.random()*H
        z[i*3+2]=0.001*min(W,H)+random.random()*(0.009*min(W,H))
    return(z)

gtallen1187

"A little bit of slope makes up for a lot of y-intercept"

01/13/2012. From a lecture by Professor John Ousterhout at Stanford, class CS140

Here's today's thought for the weekend. A little bit of slope makes up for a lot of Y-intercept.

[Laughter]

albertomontesg

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

xiaolai

How to run Jupyterlab as a desktop app on Mac OSX

In Sep, 2021, Jupyterlab Desktop App (electron) was released by Mehmet Bektas (github repo).

brew install --cask jupyterlab

haampie

function concat_and_crop(mx::AbstractArray{T,4}, x::AbstractArray{T,4}) where T
    w, h = size(x)
    mw, mh = size(mx)
    rx = (1:mw) .+ ((w - mw) ÷ 2)
    ry = (1:mh) .+ ((h - mh) ÷ 2)
    return cat(x[rx, ry, :, :], mx, dims = 3)
end

create_model_2d_classes() = Chain(
    BatchNorm(1),

martinchapman

# Visualise a latex document git history

# loop through commits, create a PDF from your main file for each
# translate the pages of that PDF to a single image
# create GIF/mp4 from the folder of images created

# run within your local repository
# prerequisites: ImageMagick and FFmpeg

# create output folder

Emilycm-97