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For types 2 (byte string) and 3 (text string), the count is the length of the payload. For types 4 (array) and 5 (map), the count is the number of items (pairs) in the payload. For type 6 (tag), the payload is a single item and the count is a numeric tag number which describes the enclosed item.
A common modification allows reference counting to be made incremental: instead of destroying an object as soon as its reference count becomes zero, it is added to a list of unreferenced objects, and periodically (or as needed) one or more items from this list are destroyed. Simple reference counts require frequent updates.
In Python, non-innermost-local and not-declared-global accessible names are all aliases. Among dynamically-typed languages, Python is moderately type-checked. Implicit conversion is defined for numeric types (as well as booleans ), so one may validly multiply a complex number by an integer (for instance) without explicit casting .
In Python, if a name is intended to be "private", it is prefixed by one or two underscores. Private variables are enforced in Python only by convention. Names can also be suffixed with an underscore to prevent conflict with Python keywords. Prefixing with double underscores changes behaviour in classes with regard to name mangling.
If a directory is specified, the data files are in that directory and named after the input file name, without its extension. If a file is specified here, the data files are named after that file, without its extension.-s directory (--source-prefix directory): A prefix for source file names to remove when generating the output coverage files ...
Counting filters provide a way to implement a delete operation on a Bloom filter without recreating the filter afresh. In a counting filter, the array positions (buckets) are extended from being a single bit to being a multibit counter. In fact, regular Bloom filters can be considered as counting filters with a bucket size of one bit.
for item in iterable_collection: # Do something with item Python's tuple assignment, fully available in its foreach loop, also makes it trivial to iterate on (key, value) pairs in dictionaries : for key , value in some_dict . items (): # Direct iteration on a dict iterates on its keys # Do stuff
If each item to be sorted is itself an integer, and used as key as well, then the second and third loops of counting sort can be combined; in the second loop, instead of computing the position where items with key i should be placed in the output, simply append Count[i] copies of the number i to the output.