rlabbe

''' create object using numpy to read CSV. creates one variable per column
so you can use f.col_name instead of f.data['col_name']

f = NamedCSV(filename)
f.data # all data in the file in an np recarray
f.mass  
f.data['mass'] # same as previous line

f.extract(f.mass > 180)
'''

rlabbe

from inspect import getsource
import re
def psource(fun):
    print(re.sub('""".*"""', '', getsource(fun), flags=re.DOTALL))
    


#example
from somewhere import foo
psource(foo)

rlabbe

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

xs = np.linspace(-4, 4, n)
ys = np.linspace(-4, 4, n)

X, Y = np.meshgrid(xs, ys)

# mixture of Gaussians - based on Matlab peaks() function

rlabbe

void foo1(HANDLE start, HANDLE end, vector<Data>* data_queue, int index)
{
   int id = index;
   vector<Data>* data = data_queue;

   while (true) {
      WaitForSingleObject(start, INFINITE);
      ResetEvent(start);
      Data& d = (*data)[index++];
      if (index >= data->size())

rlabbe

dbeta (.7, 1, 2)
from scipy.stats import beta
beta(1, 2).pdf(.7) # frozen form, very slow if only calling once!!!!!!
beta.pdf(.7, 1, 2) 

rlabbe

# style the notebook
from IPython.core.display import HTML
import urllib.request
# this link is to my Kalman filter book CSS file.
response = urllib.request.urlopen('http://bit.ly/1LC7EI7')
HTML(response.read().decode("utf-8"))

rlabbe

import matplotlib.pyplot as plt

ax = plt.gca()
ax.set_aspect('equal')
plt.xlim(-4, 4)
plt.ylim(0, 8)

rlabbe

# draw the figure so the animations will work
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.show()
fig.canvas.draw()

while True:
    # compute something
    plt.plot([1, 2, 3, 4, 5], [2, 1, 2, 1, 2]) # plot something
    

rlabbe

from sympy.abc import x, y, z
from sympy.plotting import plot, plot3d
import sympy as sp
fron sympy import solve, Eq

def plot_line(e):
    e = solve(e, y)
    plot(e[0])

rlabbe

def triangle_angles(p0, p1, p2):
    A = p1 - p0
    B = p2 - p1
    C = p0 - p2
    angles = []
    for e1, e2 in ((A,-B), (B,-C), (C,-A)):
        num = np.dot(e1, e2)
        denom = np.linalg.norm(e1) * np.linalg.norm(e2)
        angles.append(np.arccos(num/denom))
    return angles