from IPython.display import Image
Image(filename='confused-cat.jpg')

Image(filename='big_data.png')

a = 23
print("The answer is " + str(a))
b = a + len("confused kitty is confused")
print("The answer is now " + str(b))
a = "Goooooooooooooollllll"
print("The answer is now " + a)

len("This is a string")

len(23)

x = { "foo": [1,2,3], "bar": "star" }
x["foo"]

len(x)

x += 1

2 + "2"

int("2.3")

inval = raw_input("Think of a number: ")
try:
    ival = int(inval)
    print("Yay: an integer {}".format(ival))
except ValueError:
    try:
        fval = float(inval)
        print("Oh, a real-valued number {}".format(fval))
    except:
        print("Errrr: {}".format(inval))
        raise ValueError("I did not expect to be sent '{}'".format(inval))

def convert_value(inval):
    """Convert an input value (a string) into an integer or
    floating point value, returning the converted value. For
    other input a ValueError is raised."""
    
    try:
        val = int(inval)
        print("Yay: an integer {}".format(val))
    except ValueError:
        try:
            val = float(inval)
            print("Oh, a real-valued number {}".format(val))
        except:
            print("Errrr: {}".format(inval))
            raise ValueError("I did not expect to be sent '{}'".format(inval))
            
    return val

convert_value("23")

convert_value("23.0")

convert_value("23i")

import numpy as np

x = np.arange(1, 10, 2)
print(x)

type(x)

dir(x)

x.mean()

m = x.mean()
print("The mean is {0}".format(m))
print("         or {:.3f}".format(m))
print("         or {:13.6e}".format(m))
print("    or even %g" % (m,))

# Oh, don't forget that a # character starts a comment,
print("Unless # it is part of a string")
print('and that strings start and end with either a single or double quote') # this is back to being a comment

"""Although you can also make really long "comments"
using the triple-quote, as shown here.

This is normally used for providing in-line documentation to
your code, but can be useful to quickly comment out a 
region of recalcitrant code, since it creates a string
which is then ignored by the program.
"""

# Let's do something
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 45)      # a one-dimensional array of values
y = np.sin(x) * np.exp(-x/5)    # this calculation is done on each element of the array

plt.plot(x, y)

plt.show()

%matplotlib inline

plt.plot(x, y)

plt.plot(x, y, 'r--')
plt.xlabel("x")
plt.ylabel(r"$sin(x) * e^{-x/5}$") # use LaTeX; to avoid potential confusion with \ characters, use r"..."!

help(np.arange)

np.*lin*?

help(convert_value)

np.mean

np.pi

np.mean()

np.pi()

help(np.mean)

a = np.mean
print(x[0:5])
print(np.mean(x))
print(a(x))

help(range)

range(1, 10, 2)

range = 23.0

range(1, 10, 2)

help(range)

# Simple scalar types
a = 23            # integer
a = -2.3e-43      # floating-point
a = 4.2+2.3j      # complex

a = "A string"    # strings
a = True          # booleans, False is also acceptable

a = None          # None is used to indicate "does not exist"/"invalid"/"undefined"/"missing"

# Containers
a = [1,2,3,"foo",[1,2]]                       # list (elements do not need to have the same type)
a = { "foo": 1, "bar": { "foobar": True }}    # dictionary
a = (True, 23.4, "false")                     # tuples (like lists but are immutable)

# Access elements using [...] syntax
a = [1,2,3]; print(a[1]);                     # the first element of a list is numbered 0
a = { "foo": 1 }; print(a["foo"])
a = (True, 23.4, "false"); print(a[0])        # again, first element is numbered 0

print("-----")

# We can change elements in a list
a = [1,2,3]
print(a)
a[1] = 4
print(a)

# but not in tuples
a = (1, 2, 3)
print(a)
a[1] = 4

# np.arange is like Python's range
a = np.arange(12)
print(a)

# The resize method works "in place" (i.e. changes the object it is called on)
a.resize(3,4)
print(a)

# The reshape method creates a copy of the input array
b = np.arange(10, 34, 2)
c = b.reshape(3,4)
print(b)
print(c)

# By default many routines will "ignore" the dimensions of the data
print("b.sum = {}".format(b.sum()))
print("c.sum = {}".format(c.sum()))

# Some will work as "reducers" - i.e. here we sum along
# one axis, so converting a 2D array into a 1D one.
print("c.sum[axis=0] = {}".format(c.sum(axis=0)))
print("c.sum[axis=1] = {}".format(c.sum(axis=1)))