# First we create a list and then
# wrap it with the np.array() function.

import numpy as np

alist = [1, 2, 3]
arr = np.array(alist)

# Creating an array of zeros with five elements
arr = np.zeros(5)

# What if we want to create an array going from 0 to 100?
arr = np.arange(100)

# Or 10 to 100?
arr = np.arange(10, 100)

# If you want 100 steps from 0 to 1...
arr = np.linspace(0, 1, 100)

# Or if you want to generate an array from 1 to 10
# in log10 space in 100 steps...
arr = np.logspace(0, 1, 100, base=10.0)

# Creating a 5x5 array of zeros (an image)
image = np.zeros((5, 5))

# Creating a 5x5x5 cube of 1's
# The astype() method sets the array with integer elements.
cube = np.zeros((5, 5, 5)).astype(int) + 1

# Or even simpler with 16-bit floating-point precision...
cube = np.ones((5, 5, 5)).astype(np.float16)


# Data typing
# Array of zero integers
arr = np.zeros(2, dtype=int)

# Array of zero floats
arr = np.zeros(2, dtype=np.float32)


# Reshaping
# Creating an array with elements from 0 to 999
arr1d = np.arange(1000)

# Now reshaping the array to a 10x10x10 3D array
arr3d = arr1d.reshape((10, 10, 10))

# The reshape command can alternatively be called this way
arr3d = np.reshape(arr1d, (10, 10, 10))

# Inversely, we can flatten arrays
arr4d = np.zeros((10, 10, 10, 10))
arr1d = arr4d.ravel()

print arr1d.shape

# First we create a list and then
# wrap it with the np.array() function.

import numpy as np

alist = [1, 2, 3]
arr = np.array(alist)

# Creating an array of zeros with five elements
arr = np.zeros(5)

# What if we want to create an array going from 0 to 100?
arr = np.arange(100)

# Or 10 to 100?
arr = np.arange(10, 100)

# If you want 100 steps from 0 to 1...
arr = np.linspace(0, 1, 100)

# Or if you want to generate an array from 1 to 10
# in log10 space in 100 steps...
arr = np.logspace(0, 1, 100, base=10.0)

# Creating a 5x5 array of zeros (an image)
image = np.zeros((5, 5))

# Creating a 5x5x5 cube of 1's
# The astype() method sets the array with integer elements.
cube = np.zeros((5, 5, 5)).astype(int) + 1

# Or even simpler with 16-bit floating-point precision...
cube = np.ones((5, 5, 5)).astype(np.float16)


# Data typing
# Array of zero integers
arr = np.zeros(2, dtype=int)

# Array of zero floats
arr = np.zeros(2, dtype=np.float32)


# Reshaping
# Creating an array with elements from 0 to 999
arr1d = np.arange(1000)

# Now reshaping the array to a 10x10x10 3D array
arr3d = arr1d.reshape((10, 10, 10))

# The reshape command can alternatively be called this way
arr3d = np.reshape(arr1d, (10, 10, 10))

# Inversely, we can flatten arrays
arr4d = np.zeros((10, 10, 10, 10))
arr1d = arr4d.ravel()

print arr1d.shape

# First we create a list and then # wrap it with the np.array() function. # Creating an array of zeros with five elements # What if we want to create an array going from 0 to 100? # Or 10 to 100? # If you want 100 steps from 0 to 1... # Or if you want to generate an array from 1 to 10 # in log10 space in 100 steps... # Creating a 5x5 array of zeros (an image) # Creating a 5x5x5 cube of 1's # The astype() method sets the array with integer elements. # Or even simpler with 16-bit floating-point precision... # Data typing # Array of zero integers # Array of zero floats # Reshaping # Creating an array with elements from 0 to 999 # Now reshaping the array to a 10x10x10 3D array # The reshape command can alternatively be called this way # Inversely, we can flatten arrays