Thiago Mosqueiro thmosqueiro

## 0my_weechat.md

      
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                thmosqueiro
                / 0my_weechat.md
            
            
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              September 28, 2019 20:58
            
              
                Logging my weechat configuration before losing it by the 10th time.
              
          
    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:

  
## gitlab-from-source+viewing-ipython-notebooks.md

      
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                thmosqueiro
                / gitlab-from-source+viewing-ipython-notebooks.md
            
            
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              June 10, 2019 12:17
            
              
                This is what I needed to complete Gitlab installation + jupyter notebook rendering
              
          
    Gitlab from source + viewing ipython notebooks

This is what I needed to complete Gitlab installation by using @martijnvermaat's fork. Some guidelines are provided here. This is supposed to allow you to use a local installation of gitlab capable of rendering Ipython Notebooks. This was based on GitLab's documentation.
Pre-installation

cd


## SlowIntro2TensorFlow.ipynb

      
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                thmosqueiro
                / SlowIntro2TensorFlow.ipynb
            
            
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              May 16, 2019 02:08
            
              
                A slow introduction to TensorFlow
              
          
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## Missing FTDI kernel module on nvidia jetson.md

      
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                thmosqueiro
                / Missing FTDI kernel module on nvidia jetson.md
            
            
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              August 21, 2018 08:34
            
              
                Missing FTDI kernel module on nvidia jetson
              
          
    Quick view of the problem

I can see FTDI device plugged in:
user@nose:~$ lsusb 
Bus 002 Device 002: ID 0403:6001 Future Technology Devices International, Ltd FT232 Serial (UART) IC

But it won't mount as serial port:


## keybase.md

      
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                thmosqueiro
                / keybase.md
            
            
              Created
              January 18, 2018 07:11
            
          
    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:

  
## Estimating_TriangleProb.py
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)

## Example_Multiprocessing_ODEsolving.ipynb

      
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                thmosqueiro
                / Example_Multiprocessing_ODEsolving.ipynb
            
            
              Created
              September 21, 2017 21:23
            
              
                Example of using multiprocessing to solve in parallel a system of ODEs using different conditions
              
          
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## extendedColors.py
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) )


## RegressionAsymmetricLossFunction.py
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):

## Example.py
import numpy as np
import pylab as pl
from polarError import polarError

f, ax = pl.subplots(1, 1, figsize=(4,1.8) )

bsize  = 0.5
theta  = np.array( [np.pi*5./4. - bsize, np.pi/2. - bsize, np.pi*7.5/4. - bsize, 0] )
data   = np.array( [0.7, 0.3, 0.52, 0.8] )
edata  = np.array( [0.2, 0.1, 0.5, 0.2] )
	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)
	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) )
	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):
	import numpy as np
	import pylab as pl
	from polarError import polarError

	f, ax = pl.subplots(1, 1, figsize=(4,1.8) )

	bsize = 0.5
	theta = np.array( [np.pi5./4. - bsize, np.pi/2. - bsize, np.pi7.5/4. - bsize, 0] )
	data = np.array( [0.7, 0.3, 0.52, 0.8] )
	edata = np.array( [0.2, 0.1, 0.5, 0.2] )