thmosqueiro

import numpy as np
import pylab as pl
from lmfit import minimize, Parameters

# Creating some data with some strong random factor
x = np.linspace(0, 10, 50)
y = (x-3)**2 + np.random.rand( 50 )*5

# Defining residuals using an asymmetric loss function
def residual(params, x, data, eps_data):

thmosqueiro

thmosqueiro

def convert( image, theta0 = 0. ):
    
    numChannels = image.shape[2]
    numPixelX = image.shape[0]
    numPixelY = image.shape[1]
    
    newImage = np.zeros( (numPixelX, numPixelY, 3) )
    
    normSum = np.zeros( (3) )
    

thmosqueiro

thmosqueiro

import numpy as np

Nsamples = 10000
p_estimate = 0.
s_estimate = 0.

for j in range(Nsamples):

    ps = np.random.random(2)
    x = min(ps)

thmosqueiro

Keybase proof

I hereby claim:

• I am thmosqueiro on github.
• I am thmosqueiro (https://keybase.io/thmosqueiro) on keybase.
• I have a public key ASBkLAlSDFGMqYp3FCbOLsH9r6SPyhYSWwslaQl6fuIVGQo

To claim this, I am signing this object:

thmosqueiro

WeeChat through Docker

Docker setup

Creating a docker image created from the Docker file attached:

sudo docker build --tag=weechatcontainer .

Running in Docker attached to a screen:

thmosqueiro

thmosqueiro

class Malloc:
    """
    A very small educational simulation of a malloc/free memory allocator.

    The allocator manages a fixed-size contiguous block of memory represented
    by a Python bytearray. Memory is tracked using a free list containing
    tuples of (start, size).

    Pointers returned by malloc are simply integer indices into the bytearray.