You can change the input test numbers and get the converted ones. > from bitstring import BitArrayĬalculator with all neccessary functions for DEC,BIN,HEX: You can use numpy to turn Two's complement binary values into python integers: > import numpy as npīut I don't know an easy way to do the opposite with builtin functions. Python has no easy way to produce these binary representations. You can read the details in the link, but with an 8bit signed integer -37 would be 0b11011011 and 37 would be 0b00100101. More common is Two's complement notation, which seems more complicated and the result is very different from python's string formatting. This looks like what python produces, but just add a 0 or 1 in front for positive / negative numbers. One notation is sign-magnitude notation, where the first bit represents the negative sign, and the rest is the actual value. So if you're reading or producing binary streams of data to be processed by other software/hardware, you need to first know the notation being used. In computer/hardware binary data, negative signs don't exist. Python just adds a negative sign so the result for -37 would be this: > bin(-37) The question doesn't specify how you want to handle negative numbers. And in the opposite direction, use binary notation: > bin(37)īut with negative numbers, things get a bit more complicated. In addition to the answers above, you can also just use the bin and hex functions. The accepted answer didn't address negative numbers, which I'll cover. 1) Calculate sum 2) If both numbers are positive and sum is negative then return -1 Else If both numbers are negative and sum is positive then return -1 Else return 0 C++ C Java C Javascript Python3 include general idea is to use code from (in order of preference): It's really just meant as a guideline on how it could be done. Just keep in mind that's a generic bit of pseudo-code which may not be the most efficient way of doing it though, with the iterations you seem to be proposing, it won't make much difference. Which will construct your binary string based on the decimal value. Such as, for example, the pseudo-code: define intToBinString, receiving intVal: If you're working in an environment that doesn't provide exactly what you need, you should be collecting snippets of code as you develop to ensure you never have to write the same thing twice. The int64 data type can represent larger integers without overflow.Python actually does have something already built in for this, the ability to do operations such as ''.format(42), which will give you the bit pattern (in a string) for 42, or 101010.įor a more general philosophy, no language or library will give its user base everything that they desire. To avoid this behavior, you can use np.int64 instead of np.int32 if you need to handle larger values. Then, when you convert it back to an int32 using np.int32, the integer overflow occurs, and the value wraps around to -2^31. However, when you convert it to a float32 using np.float32, the floating-point representation of v exceeds the range of a float32 (which is approximately +/- 3.4e38), resulting in an approximation. In the above code, v is set to 2^31 - 65, which is the largest value that can be represented by an int32 without overflow. So, for int32, if you exceed 2^31-1, it wraps around to -2^31. In Python, integer overflow wraps around, meaning that if you exceed the maximum value that can be represented by the data type, it "wraps" back to the minimum value. When you try to convert a floating-point value that is greater than 2^31-1 into an int32, an integer overflow occurs. In numpy, the int32 data type represents signed 32-bit integers, which can hold values ranging from -2^31 to 2^31-1. The behavior you are observing is due to the way integer overflow is handled in numpy. You will still experience integer wrapping, but at least the operation will be accurate. If you want understandable behaviour, you should at least use float64 here. So you are using it outside of its acceptable precision zone: and here things are clear: everything going outside of the mantissa is silently dropped. This type only has a 24 bits mantissa (ref: IEEE 754). Long story made short: INT_MAX + 1 gives INT_MIN. In common implementations (specifically in the ones used by numpy) integer types use 2-complement for negative values, and process overflow by reinterpreting the value as if it was unsigned, do the operation as unsigned (meaning wrapping around 2**32 for a 32 bits types) and convert back the unsigned representation to the signed type. But for example np.int32 is a C-like 32 bits integer and has nothing to do with a multiprecision Python integer.Īnd C language clearly states that overflow over a signed type invokes a so called Undefined Behaviour (anything can be expected.). Numpy is not Python! Numpy is a bunch of C library interfaced with the Python language.
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