| import numpy as np | |
| from matplotlib import pyplot as plt | |
| ## DEFINE RE-USABLE METHODS | |
| def rotX(theta): # build a rotation matrix around the y-axis | |
| R = np.eye(3) | |
| R[1,1] = np.cos(theta) | |
| R[1,2] = -np.sin(theta) | |
| R[2,1] = np.sin(theta) |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| def drawellipse(center = [0,0], radius = 1, k=1, h=1, N=50): | |
| assert len(center) == 2 # good practice to do things like this. | |
| x0, y0 = center | |
| x = np.linspace(x0-h*radius , x0+h*radius, N) | |
| y = np.sqrt(radius**2 - ((x-x0)/h)**2 )*k # from the eq for an ellipse | |
| plt.plot(x, -y+y0, c='blue') | |
| plt.plot(x, y+y0, c='red') |
| a = 'trip distance' | |
| b = ' '.join([s.capitalize() for s in a.split(' ')]) | |
| print(b) |
mkdir /var/www/website.com
chown $SAFEUSER:$SAFEUSER /var/www/website.com
export SAFEUSER=yourname (if $SAFEUSER is not set)
This is where you keep the contents of your website.
Inside here should exist a public_html directory that will be what apache searches for.
(none, I think)
| myfile = 'pred.js' | |
| with open(myfile,'r') as inf: | |
| dict_from_file = eval(inf.read()) | |
| # each entry will be a dictionary, parse them as you wish. | |
| print('keys', dict_from_file[0].keys()) # will list available key-value pairs. | |
| # I'm going to extract the data that I personally care about. Class labels. | |
| # playing with these lines allowed me to find the numbers representing the class labels | |
| print(dict_from_file[0]['predicted'][3]) |
| # something that doesn't pass the vertical line test, comes to us unordered.... we want to piecewise-interpolate. | |
| import numpy as np | |
| # partition our data so that each segment DOES pass the vertical line test. | |
| c = np.where(a[:,1] >= 0.5)[0] | |
| d = np.where(a[:,1] < 0.5)[0] | |
| above = a[c,:] # copies of a | |
| below = a[d,:] |
| from mpl_toolkits.mplot3d import Axes3D | |
| import matplotlib.pyplot as plt | |
| from matplotlib import cm | |
| from matplotlib.ticker import LinearLocator, FormatStrFormatter | |
| import numpy as np | |
| def q1(lam): | |
| L1 = lam[:,0] # local column-vectors. | |
| L2 = lam[:,1] |
| ## | |
| # Create a figure space matrix consisting of 3 columns and 2 rows | |
| # | |
| # Here is a useful template to use for working with subplots. | |
| # | |
| ################################################################## | |
| fig, ax = plt.subplots(figsize=(10,5), ncols=3, nrows=2) | |
| left = 0.125 # the left side of the subplots of the figure | |
| right = 0.9 # the right side of the subplots of the figure |