import numpy as np
import matplotlib.pyplot as plt
import mpld3

from IPython.display import HTML, display, YouTubeVideo

# Load CSS
display(HTML(open('style.css').read()))

YouTubeVideo('wjSsISiacEU', width=600, height=400)

def embed_url(url, width=800, height=450):
    src='<iframe src="{0}"  width="{1}" height="{2}"></iframe>'.format(url, 800, 450)
    display(HTML(src))
    
embed_url('http://worrydream.com/ExplorableExplanations/')

embed_url('https://en.wikipedia.org/wiki/Literate_programming')

embed_url('http://ipython.org/notebook.html')

import random

# generate a random number between 1000 and 9999
n = random.randint(1e3, 1e4-1)
n*2

if n < 5000:
    print('Smaller half')
else:
    print('Bigger half')

n = random.randint(1e3, 1e4-1)  # that's 10^3
print(n)

%%bash

#echo $PATH

py=$(which python)
echo "Current python executable: $py"

!ls ../data/

fls = !ls ../data/
fls

!head ../data/{fls[-3]}

import pandas as pd

df = pd.read_csv('../data/'+fls[-3])
df.head(4)

from sympy import init_printing, Symbol, cos, sin
init_printing(use_latex='mathjax')

x = Symbol('x')
print(cos(x).series(x, 0, 7))

display(cos(x).series(x, 0, 5))

from IPython.html.widgets import interact, ToggleButtonsWidget


def show_series(n):
    display(cos(x).series(x, 0, n))


interact(show_series, n=(3,25));

import string

def gen_passwd(length=6, punctuation=False):
    chars = string.ascii_letters + string.digits
    if punctuation:
        chars += string.punctuation
    passwd = [random.choice(chars) for _ in range(length)]
    return ''.join(passwd)


def print_passwd(length=6, punctuation=False):
    print("New password: " + gen_passwd(length, punctuation))


interact(print_passwd, 
         length=(6,20), 
         punctuation={"no": False, "yes":True});

from IPython.display import Audio, clear_output

duration = 3
sampling = 8000
ts = np.linspace(0, duration, sampling*duration)

def sig_gen(time, f0=220.0, f1=224.0):
    two_pi = 2*np.pi
    sig = np.sin(two_pi*f0*time) + np.sin(two_pi*f1*time)
    return sig

display(Audio(data=sig_gen(ts), rate=sampling))

def play_sig(f0=220.0, f1=224.0):
    sig = sig_gen(ts, f0, f1)
    display(Audio(data=sig, rate=sampling, autoplay=True))

    
interact(play_sig, f0=(200.0, 300.0), f1=(200.0, 300.0));

%matplotlib inline

def play_sig(f0=220.0, f1=224.0):
    sig = sig_gen(ts, f0, f1)
    display(Audio(data=sig, rate=sampling, autoplay=True))
    plt.plot(ts[:sampling/2], sig[:sampling/2])

    
interact(play_sig, f0=(200.0, 300.0), f1=(200.0, 300.0));

import mpld3
mpld3.enable_notebook()

t = ts[:4000]
s = sig_gen(t, f0=220.0, f1=224.0)

plt.plot(t, s);

npts = 50
rnd_err = np.random.randn(npts)*15

# simulate quadratic process
a, b, c = 0.5, -2.4, -8.0
x = np.linspace(-10, 20, npts)
y = a * x**2 + b * x + c

dat = y + rnd_err

# fit
p = np.polyfit(x, dat, 2)
y_fit = np.polyval(p, x)

#plt.plot(x, dat, 'ko', mfc='none');
plt.plot(x, dat, 'ko');
plt.plot(x, y, 'g');
plt.plot(x, y_fit, 'r');

mpld3.disable_notebook()
npts = 500
x1 = np.random.normal(-1, 1, size=npts)
x2 = np.random.normal(1, 1, size=npts)
x3 = np.random.normal(2, 1, size=npts)

plt.hist(x1, normed=True);
plt.hist(x2, normed=True);
plt.hist(x3, normed=True);

# better default plotting style
import seaborn as sb

plt.hist(x1, normed=True);
plt.hist(x2, normed=True);
plt.hist(x3, normed=True);

# works with d3
mpld3.enable_notebook()

plt.hist(x1, normed=True);
plt.hist(x2, normed=True);
plt.hist(x3, normed=True);

# lot's of styles and helper plots
sb.set_style('whitegrid')

sb.set_palette("deep")
#sb.set_palette(sb.color_palette("Set2"))

nbins = 10
sb.distplot(x1, bins=nbins);
sb.distplot(x2, bins=nbins);
sb.distplot(x3, bins=nbins);

sb.palplot(sb.color_palette())

mpld3.disable_notebook()