#!/usr/bin/env python
# coding: utf-8

# # Introduction to scientific computing with Python

# ## Basics
# 
# We'll start with some basics in the interpreter, covering strings and maths, then come back to this notebook.
# 
# <hr />

# ## Lists

# Indexing, slicing, `append()`, `in`

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layers = [0.23, ] # Add some more


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# Play with lists
# e.g. indexing with layers[4]
# slicing with layers[2:4]


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uppers = layers[:-1]
lowers = layers# how shall we slice it?


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rcs = []
for pair in zip(uppers, lowers):
    rc = # Compute the contrast
    # Now what?


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rcs


# ## Functions

# Definition, inputs, side-effects, returning, scope, docstrings

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def add(a, b):
    return a + b


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# Exercise
def compute_rc(layers):
    """
    Computes reflection coefficients given
    a list of layer impedances.
    """
    
    # Put code here


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# This should give you what you got before
compute_rc(layers)


# Now we will put this function in a file and import into a new notebook.
# 
# * Open a text editor and copy the function definition into it
# * Save the file as `<something>.py` in your current working directory
# * Restart the kernel in this notebook (*Kernel* > *Restart*)
# 
# Run this:

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layers = [0.23, 0.34, 0.45, 0.25, 0.23, 0.35]
compute_rc(layers)


# Why didn't that work?
# 
# Change the name here to the name of your file:

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import something
something.compute_rc(layers)


# ## NumPy

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import numpy as np  # Just like importing file


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biglog = np.random.random(1000)
get_ipython().run_line_magic('timeit', 'compute_rc(biglog)')


# We need to learn the basics of NumPy arrays.

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layers + 1


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layers = np.array(layers)
layers + 1


# Etc.

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# Exercise
def compute_rc_vector(layers):

    # rewrite using NumPy arrays


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get_ipython().run_line_magic('timeit', 'compute_rc_vector(biglog)')


# Warning signs:
# 
# 
# * Writing loops on arrays
# * Writing complicated functions on arrays

# ## Plotting

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import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


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plt.plot(biglog)


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fig = plt.figure(figsize=(15,2))
ax = fig.add_subplot(111)
ax.plot(biglog)
ax.set_title("big log")
plt.show()


# ## Web

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url = "http://en.wikipedia.org/wiki/Jurassic"


# Use View Source in your browser to figure out where the age range is on the page, and what it looks like.
# 
# Try to find the same string here:

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import requests
r = requests.get(url)
r.text


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import re
# Use regex to extract the age range

# Make a function to get the start and end data of any geologic period