Creating a docker image created from the Docker file attached:
sudo docker build --tag=weechatcontainer .
Running in Docker attached to a screen:
Creating a docker image created from the Docker file attached:
sudo docker build --tag=weechatcontainer .
Running in Docker attached to a screen:
I hereby claim:
To claim this, I am signing this object:
| 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.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] ) |
| // Ping of the LED's positive leg | |
| int ledPin=7; | |
| int state = 0; | |
| int count; | |
| unsigned long previousMillis = 400L; | |
| double timer; | |
| unsigned long interval = 3L; | |
| unsigned long delta; |