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set nohud
map h J
map l K
map c x
map C X
set cncpcompletion
let barposition = "bottom"
set typelinkhints
set noautofocus

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Left-Aligned	Center Aligned	Right Aligned
col 3 is	some wordy text	$1600
col 2 is	centered	$12
zebra stripes	are neat	$1

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# logging_example.py

import logging

# Create a custom logger
logger = logging.getLogger(__name__)

# Create handlers
c_handler = logging.StreamHandler()
f_handler = logging.FileHandler('file.log')

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# copyed from http://code.activestate.com/recipes/522995-priority-dict-a-priority-queue-with-updatable-prio/
from heapq import heapify, heappush, heappop

class priority_dict(dict):
    """Dictionary that can be used as a priority queue.

    Keys of the dictionary are items to be put into the queue, and values
    are their respective priorities. All dictionary methods work as expected.
    The advantage over a standard heapq-based priority queue is
    that priorities of items can be efficiently updated (amortized O(1))

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// from: https://stackoverflow.com/questions/12753805/type-converting-slices-of-interfaces
// make any slice to interface{} slice
func InterfaceSlice(slice interface{}) []interface{} {
    s := reflect.ValueOf(slice)
    if s.Kind() != reflect.Slice {
        panic("InterfaceSlice() given a non-slice type")
    }

    ret := make([]interface{}, s.Len())

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class Pipe(object):
    def __init__(self, func):
        self.func = func
 
    def __ror__(self, other):
        def generator():
            for obj in other:
                if obj is not None:
                    yield self.func(obj)
        return generator()

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# from: https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb
import scipy.stats as stats
from matplotlib import pyplot as plt
import numpy as np

a = np.linspace(0, 4, 100)
expo = stats.expon
lambda_ = [0.5, 1]

for l, c in zip(lambda_, colours):

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import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widget

from sklearn.datasets import make_blobs
from sklearn.svm import SVC

def plot_svc_decision_function(model, ax=None, plot_support=True):
    """Plot the decision function for a 2D SVC"""
    ax = ax or plt.gca()

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def f(x, y):
    return np.sin(x) ** 10 + np.cos(10+x*y) + np.cos(x)
  
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)

# draw contour
contour = plt.contour(X, Y, Z, 3, colors='black')

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"""
Add copy to clipboard from IPython!
To install, just copy it to your profile/startup directory, typically:

    ~/.ipython/profile_default/startup/

Example usage:

    %clip hello world
    # will store "hello world"