#数据类型
#在 Python 3.0之前是“语句”，在 Python 3.0中是“函数”，因此print需要加括号
x=3
print (type(x))
print (x)
print (x+12)
print(x**2)
print(12*19)
t = True
f = False
print (type(t)) # Prints "<type 'bool'>"
print (t and f) # Logical AND; prints "False"
print (t or f) # Logical OR; prints "True"
print (not t) # Logical NOT; prints "False"
print (t != f) # Logical XOR; prints "True"
print '
'
#字符串
hello = 'hello' # String literals can use single quotes
world = "world" # or double quotes; it does not matter.
print (hello) # Prints "hello"
print (len(hello)) # String length; prints "5"
hw = hello + ' ' + world # String concatenation
print (hw) # prints "hello world"
hw12 = '%s %s %d' % (hello, world, 12) # sprintf style string formatting
print (hw12) # prints "hello world 12"
#字符串函数
s = "hello"
print (s.capitalize()) # Capitalize a string; prints "Hello"
print (s.upper()) # Convert a string to uppercase; prints "HELLO"
print (s.rjust(7) ) # Right-justify a string, padding with spaces; prints " hello"
print (s.center(7)) # Center a string, padding with spaces; prints " hello "
print (s.replace('l', '(ell)')) # Replace all instances of one substring with another;
# prints "he(ell)(ell)o"
print (' world '.strip()) # Strip leading and trailing whitespace; prints "world"
#剥离前置后置空格
print '
'
#list
xs = [3, 1, 2] # Create a list
print(xs)
print (xs, xs[2] ) # Prints "[3, 1, 2] 2"
print (xs[-3]) # Negative indices count from the end of the list; prints "2"
xs[2] = 'foo' # Lists can contain elements of different types
print (xs) # Prints "[3, 1, 'foo']"
xs.append('bar') # Add a new element to the end of the list
print (xs) # Prints "[3, 1, 'foo', 'bar']"
x = xs.pop() # Remove and return the last element of the list
print (x, xs ) # Prints "bar [3, 1, 'foo']"
#Slicing
nums = range(5) # range is a built-in(内置) function that creates a list of integers
print (nums[-4]) # Prints "[0, 1, 2, 3, 4]"
print (nums[2:4]) # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]"
print (nums[2:]) # Get a slice from index 2 to the end; prints "[2, 3, 4]"
print (nums[:2])# Get a slice from the start to index 2 (exclusive); prints "[0, 1, 2]"
print (nums[:]) # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]"
print (nums[:-1]) # Slice indices can be negative; prints ["0, 1, 2, 3]"
nums[2:4] = [8, 9] # Assign a new sublist to a slice
print (nums) # Prints "[0, 1, 8, 9, 4]"
print '
'
#loops
animals = ['cat', 'dog', 'monkey']
for animal in animals:
print (animal)
# Prints "cat", "dog", "monkey", each on its own line.
animals = ['cat', 'dog', 'monkey']
for idx, animal in enumerate(animals):
print ('#%d: %s' % (idx + 1, animal))
# Prints "#1: cat", "#2: dog", "#3: monkey", each on its own line
print '
'
#list comprehension(列表推导式):
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
squares.append(x ** 2)
print (squares) # Prints [0, 1, 4, 9, 16]
nums = [0, 1, 2, 3, 4]
squares = [x ** 2 for x in nums]
print (squares) # Prints [0, 1, 4, 9, 16]
nums = [0, 1, 2, 3, 4]
even_squares = [x ** 2 for x in nums if x % 2 == 0]
print (even_squares) # Prints "[0, 4, 16]"
print '
'
#Dictionaries
d = {'cat': 'cute', 'dog': 'furry'} # Create a new dictionary with some data
print (d['cat']) # Get an entry from a dictionary; prints "cute"
print ('cat' in d) # Check if a dictionary has a given key; prints "True"
d['fish'] = 'wet' # Set an entry in a dictionary
print (d['fish']) # Prints "wet"
# print d['monkey'] # KeyError: 'monkey' not a key of d
print (d.get('monkey', 'N/A')) # Get an element with a default; prints "N/A"
print (d.get('fish', 'N/A') ) # Get an element with a default; prints "wet"
del d['fish'] # Remove an element from a dictionary
print (d.get('fish', 'N/A')) # "fish" is no longer a key; prints "N/A"
print '
'
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.iteritems():
print 'A %s has %d legs' % (animal, legs)
# Prints "A person has 2 legs", "A spider has 8 legs", "A cat has 4 legs"
print '
'
#Dictionary comprehensions字典推导式:
nums = [0, 1, 2, 3, 4]
even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}
print even_num_to_square # Prints "{0: 0, 2: 4, 4: 16}"
print '
'
#Sets
animals = {'cat', 'dog'}
print 'cat' in animals # Check if an element is in a set; prints "True"
print 'fish' in animals # prints "False"
animals.add('fish') # Add an element to a set
print 'fish' in animals # Prints "True"
print len(animals) # Number of elements in a set; prints "3"
animals.add('cat') # Adding an element that is already in the set does nothing
print len(animals) # Prints "3"
animals.remove('cat') # Remove an element from a set
print len(animals) # Prints "2"
print '
'
#Set Loops:集合中的元素无序，不能确定访问的先后顺序
animals = {'cat', 'dog', 'fish'}
for idx, animal in enumerate(animals):
print '#%d: %s' % (idx + 1, animal)
# Prints "#1: fish", "#2: dog", "#3: cat"
print '
'
#Set comprehensions（集合推导式）:
from math import sqrt
nums = {int(sqrt(x)) for x in range(30)}
print nums # Prints "set([0, 1, 2, 3, 4, 5])"
print '
'
#Tuples(元组)
d = {(x, x + 1): x for x in range(10)} # Create a dictionary with tuple keys
t = (5, 6) # Create a tuple
print type(t) # Prints "<type 'tuple'>"
print d
print d[t] # Prints "5"
print d[(1, 2)] # Prints "1"
print '
'
#Functions
def sign(x):
if x > 0:
return 'positive'
elif x < 0:
return 'negative'
else:
return 'zero'
for x in [-1, 0, 1]:
print sign(x)
# Prints "negative", "zero", "positive"
print '
'
def hello(name, loud=False):
if loud:
print 'HELLO, %s!' % name.upper()
else:
print 'Hello, %s' % name
hello('Bob') # Prints "Hello, Bob"
hello('Fred', loud=True) # Prints "HELLO, FRED!"
print '
'
#Classes
class Greeter(object):
# Constructor
def __init__(self, name):
self.name = name # Create an instance variable
# Instance method
def greet(self, loud=False):
if loud:
print 'HELLO, %s!' % self.name.upper()
else:
print 'Hello, %s' % self.name
g = Greeter('Fred') # Construct an instance of the Greeter class
g.greet() # Call an instance method; prints "Hello, Fred"
g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!"
print '
'
#Numpy
#Arrays
import numpy as np
a = np.array([1, 2, 3]) # Create a rank 1 array
print type(a) # Prints "<type 'numpy.ndarray'>"
print a.shape # Prints "(3,)"
print a[0], a[1], a[2] # Prints "1 2 3"
a[0] = 5 # Change an element of the array
print a # Prints "[5, 2, 3]"
b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array
print b.shape # Prints "(2, 3)"
print b[0, 0], b[0, 1], b[1, 0] # Prints "1 2 4"
print '
'
import numpy as np
a = np.zeros((2,2)) # Create an array of all zeros
print a # Prints "[[ 0. 0.]
# [ 0. 0.]]"
b = np.ones((1,2)) # Create an array of all ones
print b # Prints "[[ 1. 1.]]"
c = np.full((2,2), 7) # Create a constant array
print c # Prints "[[ 7. 7.]
# [ 7. 7.]]"
d = np.eye(2) # Create a 2x2 identity matrix(单位矩阵)
print d # Prints "[[ 1. 0.]
# [ 0. 1.]]"
e = np.random.random((2,2)) # Create an array filled with random values
print e # Might print "[[ 0.91940167 0.08143941]
# [ 0.68744134 0.87236687]]"
print '
'
#Array indexing
import numpy as np
# Create the following rank 2 array with shape (3, 4)
# [[ 1 2 3 4]
# [ 5 6 7 8]
# [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print a
# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2; b is the following array of shape (2, 2):
# [[2 3]
# [6 7]]
b = a[:2, 1:3]
print b #b数组只是a数组的引用
# A slice of an array is a view into the same data, so modifying it
# will modify the original array.
print a[0, 1] # Prints "2"
b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]
print a[0, 1] # Prints "77"
print '
'
import numpy as np
# Create the following rank 2 array with shape (3, 4)
# [[ 1 2 3 4]
# [ 5 6 7 8]
# [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
# Two ways of accessing the data in the middle row of the array.
# Mixing integer indexing with slices yields an array of lower rank,
# while using only slices yields an array of the same rank as the
# original array:
row_r1 = a[1, :] # Rank 1 view of the second row of a
row_r2 = a[1:2, :] # Rank 2 view of the second row of a
print row_r1, row_r1.shape # Prints "[5 6 7 8] (4,)"
print row_r2, row_r2.shape # Prints "[[5 6 7 8]] (1, 4)"
# We can make the same distinction when accessing columns of an array:
col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print col_r1, col_r1.shape # Prints "[ 2 6 10] (3,)"
print col_r2, col_r2.shape # Prints "[[ 2]
# [ 6]
# [10]] (3, 1)"
print '
'
import numpy as np
a = np.array([[1,2], [3, 4], [5, 6]])
# An example of integer array indexing.
# The returned array will have shape (3,) and
print a[[0, 1, 2], [0, 1, 0]] # Prints "[1 4 5]"
# The above example of integer array indexing is equivalent to this:
print np.array([a[0, 0], a[1, 1], a[2, 0]]) # Prints "[1 4 5]"
# When using integer array indexing, you can reuse the same
# element from the source array:
print a[[0, 0], [1, 1]] # Prints "[2 2]"
# Equivalent to the previous integer array indexing example
print np.array([a[0, 1], a[0, 1]]) # Prints "[2 2]"
# Create a new array from which we will select elements
a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
print a # prints "array([[ 1, 2, 3],
# [ 4, 5, 6],
# [ 7, 8, 9],
# [10, 11, 12]])"
# Create an array of indices
b = np.array([0, 2, 0, 1])
# Select one element from each row of a using the indices in b
print a[np.arange(4), b] # Prints "[ 1 6 7 11]"
# Mutate one element from each row of a using the indices in b
a[np.arange(4), b] += 10
print a # prints "array([[11, 2, 3],
# [ 4, 5, 16],
# [17, 8, 9],
# [10, 21, 12]])
a = np.array([[1,2], [3, 4], [5, 6]])
bool_idx = (a > 2) # Find the elements of a that are bigger than 2;
# this returns a numpy array of Booleans of the same
# shape as a, where each slot of bool_idx tells
# whether that element of a is > 2.
print bool_idx # Prints "[[False False]
# [ True True]
# [ True True]]"
# We use boolean array indexing to construct a rank 1 array
# consisting of the elements of a corresponding to the True values
# of bool_idx
print a[bool_idx] # Prints "[3 4 5 6]"
# We can do all of the above in a single concise statement:
print a[a > 2] # Prints "[3 4 5 6]"
print '
'
#Datatypes
import numpy as np
x = np.array([1, 2]) # Let numpy choose the datatype
print x.dtype # Prints "int64"
x = np.array([1.0, 2.0]) # Let numpy choose the datatype
print x.dtype # Prints "float64"
x = np.array([1, 2], dtype=np.int64) # Force a particular datatype
print x.dtype # Prints "int64"
#Array math
import numpy as np
x = np.array([[1,2],[3,4]], dtype=np.float64)
y = np.array([[5,6],[7,8]], dtype=np.float64)
# Elementwise sum; both produce the array
# [[ 6.0 8.0]
# [10.0 12.0]]
print x + y
print np.add(x, y)
# Elementwise difference; both produce the array
# [[-4.0 -4.0]
# [-4.0 -4.0]]
print x - y
print np.subtract(x, y)
# Elementwise product; both produce the array
# [[ 5.0 12.0]
# [21.0 32.0]]
print x * y
print np.multiply(x, y)
# Elementwise division; both produce the array
# [[ 0.2 0.33333333]
# [ 0.42857143 0.5 ]]
print x / y
print np.divide(x, y)
# Elementwise square root; produces the array
# [[ 1. 1.41421356]
# [ 1.73205081 2. ]]
print np.sqrt(x)
import numpy as np
x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])
v = np.array([9,10])
w = np.array([11, 12])
# Inner product of vectors; both produce 219
print v.dot(w)
print np.dot(v, w)
# Matrix / vector product; both produce the rank 1 array [29 67]
print x.dot(v)
print np.dot(x, v)
# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
# [43 50]]
print x.dot(y)
print np.dot(x, y)
import numpy as np
x = np.array([[1,2],[3,4]])
print np.sum(x) # Compute sum of all elements; prints "10"
print np.sum(x, axis=0) # Compute sum of each column; prints "[4 6]"
print np.sum(x, axis=1) # Compute sum of each row; prints "[3 7]"
import numpy as np
x = np.array([[1,2], [3,4]])
print x # Prints "[[1 2]
# [3 4]]"
print x.T # Prints "[[1 3]
# [2 4]]"
# Note that taking the transpose of a rank 1 array does nothing:
v = np.array([1,2,3])
print v # Prints "[1 2 3]"
print v.T # Prints "[1 2 3]"
#Broadcasting
import numpy as np
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x) # Create an empty matrix with the same shape as x
# Add the vector v to each row of the matrix x with an explicit loop
for i in range(4):
y[i, :] = x[i, :] + v
# Now y is the following
# [[ 2 2 4]
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]
print y
import numpy as np
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
vv = np.tile(v, (4, 1)) # Stack 4 copies of v on top of each other
print vv # Prints "[[1 0 1]
# [1 0 1]
# [1 0 1]
# [1 0 1]]"
y = x + vv # Add x and vv elementwise
print y # Prints "[[ 2 2 4
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]"
import numpy as np
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = x + v # Add v to each row of x using broadcasting
print y # Prints "[[ 2 2 4]
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]"
import numpy as np
# Compute outer product of vectors
v = np.array([1,2,3]) # v has shape (3,)
w = np.array([4,5]) # w has shape (2,)
# To compute an outer product, we first reshape v to be a column
# vector of shape (3, 1); we can then broadcast it against w to yield
# an output of shape (3, 2), which is the outer product of v and w:
# [[ 4 5]
# [ 8 10]
# [12 15]]
print np.reshape(v, (3, 1)) * w
# Add a vector to each row of a matrix
x = np.array([[1,2,3], [4,5,6]])
# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),
# giving the following matrix:
# [[2 4 6]
# [5 7 9]]
print x + v
# Add a vector to each column of a matrix
# x has shape (2, 3) and w has shape (2,).
# If we transpose x then it has shape (3, 2) and can be broadcast
# against w to yield a result of shape (3, 2); transposing this result
# yields the final result of shape (2, 3) which is the matrix x with
# the vector w added to each column. Gives the following matrix:
# [[ 5 6 7]
# [ 9 10 11]]
print (x.T + w).T
# Another solution is to reshape w to be a row vector of shape (2, 1);
# we can then broadcast it directly against x to produce the same
# output.
print x + np.reshape(w, (2, 1))
# Multiply a matrix by a constant:
# x has shape (2, 3). Numpy treats scalars as arrays of shape ();
# these can be broadcast together to shape (2, 3), producing the
# following array:
# [[ 2 4 6]
# [ 8 10 12]]
print x * 2
print '
'
#Numpy Documentation
#Image operations
from scipy.misc import imread, imsave, imresize
# Read an JPEG image into a numpy array
img = imread('cat.jpg')
print img.dtype, img.shape # Prints "uint8 (400, 248, 3)"
# We can tint the image by scaling each of the color channels
# by a different scalar constant. The image has shape (400, 248, 3);
# we multiply it by the array [1, 0.95, 0.9] of shape (3,);
# numpy broadcasting means that this leaves the red channel unchanged,
# and multiplies the green and blue channels by 0.95 and 0.9
# respectively.
img_tinted = img * [1, 0.95, 0.9]
# Resize the tinted image to be 300 by 300 pixels.
img_tinted = imresize(img_tinted, (300, 300))
# Write the tinted image back to disk
imsave('cat_tinted.jpg', img_tinted)
print '
'
#MATLAB files
#Distance between points
import numpy as np
from scipy.spatial.distance import pdist, squareform
# Create the following array where each row is a point in 2D space:
# [[0 1]
# [1 0]
# [2 0]]
x = np.array([[0, 1], [1, 0], [2, 0]])
print x
# Compute the Euclidean distance between all rows of x.
# d[i, j] is the Euclidean distance between x[i, :] and x[j, :],
# and d is the following array:
# [[ 0. 1.41421356 2.23606798]
# [ 1.41421356 0. 1. ]
# [ 2.23606798 1. 0. ]]
d = squareform(pdist(x, 'euclidean'))
print d
#Matplotlib
#Plotting
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)
# Plot the points using matplotlib
plt.plot(x, y)
plt.show() # You must call plt.show() to make graphics appear.
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
# Plot the points using matplotlib
plt.plot(x, y_sin)
plt.plot(x, y_cos)
plt.xlabel('x axis label')
plt.ylabel('y axis label')
plt.title('Sine and Cosine')
plt.legend(['Sine', 'Cosine'])
plt.show()
#Subplots
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
# Set up a subplot grid that has height 2 and width 1,
# and set the first such subplot as active.
plt.subplot(2, 1, 1)
# Make the first plot
plt.plot(x, y_sin)
plt.title('Sine')
# Set the second subplot as active, and make the second plot.
plt.subplot(2, 1, 2)
plt.plot(x, y_cos)
plt.title('Cosine')
# Show the figure.
plt.show()
print'
'
#Images
import numpy as np
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt
img = imread('cat.jpg')
img_tinted = img * [1, 0.95, 0.9]
# Show the original image
plt.subplot(1, 2, 1)
plt.imshow(img)
# Show the tinted image
plt.subplot(1, 2, 2)
# A slight gotcha with imshow is that it might give strange results
# if presented with data that is not uint8. To work around this, we
# explicitly cast the image to uint8 before displaying it.
plt.imshow(np.uint8(img_tinted))
plt.show()
运行结果：
<type 'int'>
3
15
9
228
<type 'bool'>
False
True
False
True
hello
5
hello world
hello world 12
Hello
HELLO
hello
hello
he(ell)(ell)o
world
[3, 1, 2]
([3, 1, 2], 2)
3
[3, 1, 'foo']
[3, 1, 'foo', 'bar']
('bar', [3, 1, 'foo'])
1
[2, 3]
[2, 3, 4]
[0, 1]
[0, 1, 2, 3, 4]
[0, 1, 2, 3]
[0, 1, 8, 9, 4]
cat
dog
monkey
#1: cat
#2: dog
#3: monkey
[0, 1, 4, 9, 16]
[0, 1, 4, 9, 16]
[0, 4, 16]
cute
True
wet
N/A
wet
N/A
A person has 2 legs
A spider has 8 legs
A cat has 4 legs
{0: 0, 2: 4, 4: 16}
True
False
True
3
3
2
#1: fish
#2: dog
#3: cat
set([0, 1, 2, 3, 4, 5])
<type 'tuple'>
{(0, 1): 0, (1, 2): 1, (6, 7): 6, (5, 6): 5, (7, 8): 7, (8, 9): 8, (4, 5): 4, (2, 3): 2, (9, 10): 9, (3, 4): 3}
5
1
negative
zero
positive
Hello, Bob
HELLO, FRED!
Hello, Fred
HELLO, FRED!
<type 'numpy.ndarray'>
(3L,)
1 2 3
[5 2 3]
(2L, 3L)
1 2 4
[[ 0. 0.]
[ 0. 0.]]
[[ 1. 1.]]
[[ 7. 7.]
[ 7. 7.]]
[[ 1. 0.]
[ 0. 1.]]
[[ 0.17549336 0.2399925 ]
[ 0.5394008 0.44558024]]
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
[[2 3]
[6 7]]
2
77
[5 6 7 8] (4L,)
[[5 6 7 8]] (1L, 4L)
[ 2 6 10] (3L,)
[[ 2]
[ 6]
[10]] (3L, 1L)
[1 4 5]
[1 4 5]
[2 2]
[2 2]
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
[ 1 6 7 11]
[[11 2 3]
[ 4 5 16]
[17 8 9]
[10 21 12]]
[[False False]
[ True True]
[ True True]]
[3 4 5 6]
[3 4 5 6]
int32
float64
int64
[[ 6. 8.]
[ 10. 12.]]
[[ 6. 8.]
[ 10. 12.]]
[[-4. -4.]
[-4. -4.]]
[[-4. -4.]
[-4. -4.]]
[[ 5. 12.]
[ 21. 32.]]
[[ 5. 12.]
[ 21. 32.]]
[[ 0.2 0.33333333]
[ 0.42857143 0.5 ]]
[[ 0.2 0.33333333]
[ 0.42857143 0.5 ]]
[[ 1. 1.41421356]
[ 1.73205081 2. ]]
219
219
[29 67]
[29 67]
[[19 22]
[43 50]]
[[19 22]
[43 50]]
10
[4 6]
[3 7]
[[1 2]
[3 4]]
[[1 3]
[2 4]]
[1 2 3]
[1 2 3]
[[ 2 2 4]
[ 5 5 7]
[ 8 8 10]
[11 11 13]]
[[1 0 1]
[1 0 1]
[1 0 1]
[1 0 1]]
[[ 2 2 4]
[ 5 5 7]
[ 8 8 10]
[11 11 13]]
[[ 2 2 4]
[ 5 5 7]
[ 8 8 10]
[11 11 13]]
[[ 4 5]
[ 8 10]
[12 15]]
[[2 4 6]
[5 7 9]]
[[ 5 6 7]
[ 9 10 11]]
[[ 5 6 7]
[ 9 10 11]]
[[ 2 4 6]
[ 8 10 12]]
uint8 (334L, 500L, 3L)
[[0 1]
[1 0]
[2 0]]
[[ 0. 1.41421356 2.23606798]
[ 1.41421356 0. 1. ]
[ 2.23606798 1. 0. ]]
