How to read fixed point numbers in python - python

I would like to read an 8 bit number that comes in a 2's complement fix8_7 (8 bit number and the binary point is in the 7 bit). How can I do this in Python?

I am assuming that you have (starting from the left end) one sign bit, the assumed binary point, and then seven bits that represent a fractional value. If that's the case then you can just take the signed integer value of the fixed-point number and divide by 128. You'll need to do this division using floating-point values, of course, because the result will be less than 1.
The range of values that can be represented in this fixed-point format is -1.0 to +(127/128).

Assuming that your input is s = '1101001.1' (for example), you can use:
d = int(s[0:7],2)+int(s[8])/2.0
This will give an unsigned result of course. If you want to get a negative value for an input that starts with '1', then I guess that you can use d when s[0] == '0' and 64-d when s[0] == '1'.

Related

In Python - how to retrieve the binary code of negative integer from the memory? [duplicate]

Integers in Python are stored in two's complement, correct?
Although:
>>> x = 5
>>> bin(x)
0b101
And:
>>> x = -5
>>> bin(x)
-0b101
That's pretty lame. How do I get python to give me the numbers in REAL binary bits, and without the 0b infront of it? So:
>>> x = 5
>>> bin(x)
0101
>>> y = -5
>>> bin(y)
1011
It works best if you provide a mask. That way you specify how far to sign extend.
>>> bin(-27 & 0b1111111111111111)
'0b1111111111100101'
Or perhaps more generally:
def bindigits(n, bits):
s = bin(n & int("1"*bits, 2))[2:]
return ("{0:0>%s}" % (bits)).format(s)
>>> print bindigits(-31337, 24)
111111111000010110010111
In basic theory, the actual width of the number is a function of the size of the storage. If it's a 32-bit number, then a negative number has a 1 in the MSB of a set of 32. If it's a 64-bit value, then there are 64 bits to display.
But in Python, integer precision is limited only to the constraints of your hardware. On my computer, this actually works, but it consumes 9GB of RAM just to store the value of x. Anything higher and I get a MemoryError. If I had more RAM, I could store larger numbers.
>>> x = 1 << (1 << 36)
So with that in mind, what binary number represents -1? Python is well-capable of interpreting literally millions (and even billions) of bits of precision, as the previous example shows. In 2's complement, the sign bit extends all the way to the left, but in Python there is no pre-defined number of bits; there are as many as you need.
But then you run into ambiguity: does binary 1 represent 1, or -1? Well, it could be either. Does 111 represent 7 or -1? Again, it could be either. So does 111111111 represent 511, or -1... well, both, depending on your precision.
Python needs a way to represent these numbers in binary so that there's no ambiguity of their meaning. The 0b prefix just says "this number is in binary". Just like 0x means "this number is in hex". So if I say 0b1111, how do I know if the user wants -1 or 15? There are two options:
Option A: The sign bit
You could declare that all numbers are signed, and the left-most bit is the sign bit. That means 0b1 is -1, while 0b01 is 1. That also means that 0b111 is also -1, while 0b0111 is 7. In the end, this is probably more confusing than helpful particularly because most binary arithmetic is going to be unsigned anyway, and people are more likely to run into mistakes by accidentally marking a number as negative because they didn't include an explicit sign bit.
Option B: The sign indication
With this option, binary numbers are represented unsigned, and negative numbers have a "-" prefix, just like they do in decimal. This is (a) more consistent with decimal, (b) more compatible with the way binary values are most likely going to be used. You lose the ability to specify a negative number using its two's complement representation, but remember that two's complement is a storage implementation detail, not a proper indication of the underlying value itself. It shouldn't have to be something that the user has to understand.
In the end, Option B makes the most sense. There's less confusion and the user isn't required to understand the storage details.
To properly interpret a binary sequence as two's complement, there needs to a length associated with the sequence. When you are working low-level types that correspond directly to CPU registers, there is an implicit length. Since Python integers can have an arbitrary length, there really isn't an internal two's complement format. Since there isn't a length associated with a number, there is no way to distinguish between positive and negative numbers. To remove the ambiguity, bin() includes a minus sign when formatting a negative number.
Python's arbitrary length integer type actually uses a sign-magnitude internal format. The logical operations (bit shifting, and, or, etc.) are designed to mimic two's complement format. This is typical of multiple precision libraries.
Here is a little bit more readable version of Tylerl answer, for example let's say you want -2 in its 8-bits negative representation of "two's complement" :
bin(-2 & (2**8-1))
2**8 stands for the ninth bit (256), substract 1 to it and you have all the preceding bits set to one (255)
for 8 and 16 bits masks, you can replace (2**8-1) by 0xff, or 0xffff. The hexadecimal version becomes less readalbe after that point.
If this is unclear, here is a regular function of it:
def twosComplement (value, bitLength) :
return bin(value & (2**bitLength - 1))
The compliment of one minus number's meaning is mod value minus the positive value.
So I thinkļ¼Œthe brief way for the compliment of -27 is
bin((1<<32) - 27) // 32 bit length '0b11111111111111111111111111100101'
bin((1<<16) - 27)
bin((1<<8) - 27) // 8 bit length '0b11100101'
Not sure how to get what you want using the standard lib. There are a handful of scripts and packages out there that will do the conversion for you.
I just wanted to note the "why" , and why it's not lame.
bin() doesn't return binary bits. it converts the number to a binary string. the leading '0b' tells the interpreter that you're dealing with a binary number , as per the python language definition. this way you can directly work with binary numbers, like this
>>> 0b01
1
>>> 0b10
2
>>> 0b11
3
>>> 0b01 + 0b10
3
that's not lame. that's great.
http://docs.python.org/library/functions.html#bin
bin(x)
Convert an integer number to a binary string.
http://docs.python.org/reference/lexical_analysis.html#integers
Integer and long integer literals are described by the following lexical definitions:
bininteger ::= "0" ("b" | "B") bindigit+
bindigit ::= "0" | "1"
Use slices to get rid of unwanted '0b'.
bin(5)[2:]
'101'
or if you want digits,
tuple ( bin(5)[2:] )
('1', '0', '1')
or even
map( int, tuple( bin(5)[2:] ) )
[1, 0, 1]
tobin = lambda x, count=8: "".join(map(lambda y:str((x>>y)&1), range(count-1, -1, -1)))
e.g.
tobin(5) # => '00000101'
tobin(5, 4) # => '0101'
tobin(-5, 4) # => '1011'
Or as clear functions:
# Returns bit y of x (10 base). i.e.
# bit 2 of 5 is 1
# bit 1 of 5 is 0
# bit 0 of 5 is 1
def getBit(y, x):
return str((x>>y)&1)
# Returns the first `count` bits of base 10 integer `x`
def tobin(x, count=8):
shift = range(count-1, -1, -1)
bits = map(lambda y: getBit(y, x), shift)
return "".join(bits)
(Adapted from W.J. Van de Laan's comment)
I'm not entirely certain what you ultimately want to do, but you might want to look at the bitarray package.
def tobin(data, width):
data_str = bin(data & (2**width-1))[2:].zfill(width)
return data_str
You can use the Binary fractions package. This package implements TwosComplement with binary integers and binary fractions. You can convert binary-fraction strings into their twos complement and vice-versa
Example:
>>> from binary_fractions import TwosComplement
>>> TwosComplement.to_float("11111111111") # TwosComplement --> float
-1.0
>>> TwosComplement.to_float("11111111100") # TwosComplement --> float
-4.0
>>> TwosComplement(-1.5) # float --> TwosComplement
'10.1'
>>> TwosComplement(1.5) # float --> TwosComplement
'01.1'
>>> TwosComplement(5) # int --> TwosComplement
'0101'
To use this with Binary's instead of float's you can use the Binary class inside the same package.
PS: Shameless plug, I'm the author of this package.
For positive numbers, just use:
bin(x)[2:].zfill(4)
For negative numbers, it's a little different:
bin((eval("0b"+str(int(bin(x)[3:].zfill(4).replace("0","2").replace("1","0").replace("2","1"))))+eval("0b1")))[2:].zfill(4)
As a whole script, this is how it should look:
def binary(number):
if number < 0:
return bin((eval("0b"+str(int(bin(number)[3:].zfill(4).replace("0","2").replace("1","0").replace("2","1"))))+eval("0b1")))[2:].zfill(4)
return bin(number)[2:].zfill(4)
x=input()
print binary(x)
A modification on tylerl's very helpful answer that provides sign extension for positive numbers as well as negative (no error checking).
def to2sCompStr(num, bitWidth):
num &= (2 << bitWidth-1)-1 # mask
formatStr = '{:0'+str(bitWidth)+'b}'
ret = formatStr.format(int(num))
return ret
Example:
In [11]: to2sCompStr(-24, 18)
Out[11]: '111111111111101000'
In [12]: to2sCompStr(24, 18)
Out[12]: '000000000000011000'
No need, it already is. It is just python choosing to represent it differently. If you start printing each nibble separately, it will show its true colours.
checkNIB = '{0:04b}'.format
checkBYT = lambda x: '-'.join( map( checkNIB, [ (x>>4)&0xf, x&0xf] ) )
checkBTS = lambda x: '-'.join( [ checkBYT( ( x>>(shift*8) )&0xff ) for shift in reversed( range(4) ) if ( x>>(shift*8) )&0xff ] )
print( checkBTS(-0x0002) )
Output is simple:
>>>1111-1111-1111-1111-1111-1111-1111-1110
Now it reverts to original representation when you want to display a twos complement of an nibble but it is still possible if you divide it into halves of nibble and so. Just have in mind that the best result is with negative hex and binary integer interpretations simple numbers not so much, also with hex you can set up the byte size.
We can leverage the property of bit-wise XOR. Use bit-wise XOR to flip the bits and then add 1. Then you can use the python inbuilt bin() function to get the binary representation of the 2's complement. Here's an example function:
def twos_complement(input_number):
print(bin(input_number)) # prints binary value of input
mask = 2**(1 + len(bin(input_number)[2:])) - 1 # Calculate mask to do bitwise XOR operation
twos_comp = (input_number ^ mask) + 1 # calculate 2's complement, for negative of input_number (-1 * input_number)
print(bin(twos_comp)) # print 2's complement representation of negative of input_number.
I hope this solves your problem`
num = input("Enter number : ")
bin_num=bin(num)
binary = '0' + binary_num[2:]
print binary

Performing right shift and bit masking on binary fraction in python

I am looking for a way in python to perform right shift and bit masking on a binary number which has a fraction part as well. For e.g., if there are 1 integer and 2 fraction bits in the number, then number 0b101 corresponds to 1.25 in decimal. First, I want to know the pythonic way to represent this number in python.
Second, I want to perform 1 right shift on this number (0b101>>1) so that the resultant number will be 0b010 which will be 0.5 in decimal. Is there an intrinsic and pythonic way in python to perform this operation. Similarly, how to mask and get a specific bit from the binary number?
Presently, for shift I am multiplying the number by 2**-x, x is the number of right shifts. I cannot think a similar operation I can perform for bit mask.
If you really must get directly at the internal representation of a float you can use struct, like this:
>>> import struct
>>> a = 1.25
>>> b = struct.pack('>d',a)
>>> b
b'?\xf4\x00\x00\x00\x00\x00\x00' # the ? means \x3f, leftmost 7 bits of exponent
>>> a.hex()
'0x1.4000000000000p+0'
You can mask the bit you want out of the bytestring that struct.pack() returns.
[edit] The question mark representing \x3f is because the default output representation of a bytestring is a string and Python will where possible show an ascii character, not two hex digits.
[edit] This representation is in principle platform-dependent, but in practice it isn't, because virtually every computer (even IBM mainframes nowadays) has a floating-point processor that uses this format.
Finding out which bit you want may be something of a challenge.
>>> c = struct.pack('>d',a/2)
>>> c
b'?\xe4\x00\x00\x00\x00\x00\x00'
>>> (a/2).hex()
'0x1.4000000000000p-1'
As you can see, division by 2 is not quite the simple one-bit shift to the right that your question seems to suggest you are expecting. In this case, the division by 2 has decremented the exponent by 1 (from 0x3ff to 0x3fe; 1023 to 1022) and left the bit pattern of the fraction (0x4000) unchanged. The exponent appears large because it is biased by 1023.
The main difficulties are
Sign, exponent and fraction don't align to byte boundaries, but to nybble boundaries (sign plus exponent: 12 bits; fraction: 52 bits)
The number is normalized so that it has no leading zeroes (much as scientific notation in decimal is normalized so that it has no leading zeroes) and, since everyone knows it's there, the leading 1 is not stored.
I can recommend the Wikpedia article on this subject: it has lots of useful examples.
But I suspect that you don't really want to get at the internal representation of a float. Instead, you want a fixed-point binary class, without pesky binary exponents, that works much the same as you would do it on paper, and where division by a power of 2 really does reflect as a shift of so many bits to the right.
Depending on how much work you want to put into it, you could do this by defining a FixedBinary class as a subclass of numbers.Real, with the integer portion internally represented by one int and the fractional component by another int, and the sign by a third int, so that 1.25 would be represented as (1, int(0.25 * 65536), +1) (or some other power of 2).
This also shows you the simplest way to get a bit representation of your fraction.
[edit] I recommend storing the sign separately. You could store it in the integer portion, or the fraction, or both, but all have disadvantages.
If you store it in the sign of the fraction, the twos-complement
representation of negative integers will give you difficulty when
you want to mask your bits.
If you don't store it in the sign of the fraction there will be no way to
represent -0.5.
If you don't store it in the sign of the integer portion there will be no way to represent -1.0.
A multiplicand of 65536 will give you 4 decimal digits of accuracy. You can increase it if you want more. I also recommend that you store your fraction in the rightmost bits and simply ignore the leftmost bits. In other words, be content with the binary point being in the middle of the int, don't insist on it being on the left. That is because you will need headroom to the left of the binary point when you do multiplication.
Implementing your own numeric class is a considerable amount of work, though.
You can work using fxpmath.
Info about this package is at:
https://github.com/francof2a/fxpmath
For your example:
from fxpmath import Fxp
x = Fxp('0b0101', signed=True, n_word=4, n_frac=2)
print(x)
y = x >> 1
print(y)
# example of AND mask
z = x & Fxp('0b0110', signed=True, n_word=4, n_frac=2)
print(z.bin())
outputs:
1.25
0.5
0100

How to get the correct accuracy with big integer division in python

I have a big integer below, as 'max'. How come the value dividing max by '27' is not equivalent to just completely omiting the first number '27'. Technically they should be equal, but in python they are not. How can I get the same answer by dividing the max value with '27', in this example?
max = 27*37*47*30*17*6*20*17*21*43*5*49*49*50*20*42*45*1*22*44
no27 = 37*47*30*17*6*20*17*21*43*5*49*49*50*20*42*45*1*22*44
div27 = (max/27)
modno27 = no27%40
moddiv27 = div27%40
The values printed are:
no27 = 35882855955274315680000000
div27 = 3.5882855955274316e+25
modno27 = 0
moddiv27 = 8.0
Assuming this is Python 3, you used true division, which computes float results, but float (based on C double) has representation limitations that Python ints do not (above ~2**53, it can't represent every integer value).
When you know the number is evenly divisible, use // to preserve int-ness. If it's not evenly divisible, you'll round down, e.g. 5 // 3 == 1. If that's unacceptable, you can use divmod to compute both quotient and remainder at once (so no information is lost) or the fractions.Fraction type or decimal.Decimal type (with appropriate precision) to get more precise results in a single result type.

Converting bitstring to 32-bit signed integer yields wrong result

I am trying to solve a challenge on this site. I have everything correct except I can't properly convert a bitstring to its 32-bit signed integer representation.
For example I have this bitstring:
block = '10101010001000101110101000101110'
My own way of converting this bitstring to 32-bit signed integer: I partially remember from school that first bit is the sign bit. If it is 1 we have negative number and vice versa.
when I do this, it gives me the number in base 10. It just converts it to base 10:
int(block, 2) #yields 2854414894
I have tried excluding the first bit and convert remaining 31 length bitstring, after that checked the first bit to decide whether this is negative number or not:
int(block[1:32], 2) #yields 706931246
But the correct answer is -1440552402. What operation should I do to this bitstring to get this integer? Is it relevant if the byte order of the system is little endian or big endian? My system is little endian.
In python there's no size for integers, so you'll never get a negative value with a high order 1 bit.
To "emulate" 32-bit behaviour just do this, since your 2854414894 value is > 2**31-1 aka 0x7FFFFFFF:
print(int(block[1:32], 2)-2**31)
you'll get
-1440552402
You're right that the upper bit determines sign, but it's not a simple flag. Instead, the whole character of negative numbers is inverted. This is a positive number 1 (in 8 bits):
00000001
This is a negative 1:
11111111
The upshot is that addition and subtraction "wrap around". So 4 - 1 would be:
0100 - 0001 = 0011
And so 0 - 1 is the same as 1_0000_0000 - 1. The "borrow" just goes off the top of the integer.
The general way to "negate" a number is "invert the bits, add 1". This works both ways, so you can go from positive to negative and back.
In your case, use the leading '1' to detect whether negation is needed, then convert to int, then maybe perform the negation steps. Note, however, that because python's int is not a fixed-width value, there's a separate internal flag (a Python int is not a "32-bit" number, it's an arbitrary-precision integer, with a dynamically allocated representation stored in some fashion other than simple 2's complement).
block = '10101010001000101110101000101110'
asnum = int(block, 2)
if block[0] == '1':
asnum ^= 0xFFFFFFFF
asnum += 1
asnum = -asnum
print(asnum)
You should check for when the input value is out of the positive range for 32 bit signed integers:
res = int(block, 2)
if res >= 2**31:
res -= 2**32
So first you interpret the number as an unsigned number, but when you notice the sign bit was set ( >= 2^31 ), you subtract 2^32 so to get the negative number.

How to describe the maths behind this function

This is a Decimal to Binary converter. I need help explaining the math behind is as I don't have a clue how to explain all the Shifts etc.
number = int(raw_input("Enter the Number:"))
binary = ''
while number > 0:
binary = str(number % 2) + binary
number >>=1
print(binary)
The loop builds up a string which represents a binary value.
str(number % 2) finds the lowest bit of the number (either 0 or 1).
binary = str(number % 2) + binary adds the bit to the left end of string binary
number >>=1 removes the low bit now that we're done with it
while number > 0 continues until the number is 0
Suppose you are converting 56 to binary.
When right shift by 1 bit, 56 (111000) becomes 28(011100)
Note: The right shift operator causes the bit pattern in the first operand to be shifted right the number of bits specified by the second operand. Bits vacated by the shift operation are zero-filled for unsigned quantities
Like this, the variable, number is right shifted by 1 till it is greater than 1 and every time, the remainder of number divided by 2 (this will be 0 or 1 always) is appended to the result variable, binary
Finally, the variable binary will have the binary equvalent

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