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Notice that here we have called a method on a literal complex number In general, Python allows us to call methods or access attributes on any literal, as long as the literal s data type provides the called method or the attribute however, this does not apply to special methods, since these always have corresponding operators such as + that should be used instead For example, 4jreal produces 00, 4jimag produces 40, and 4j + 3+2j produces 3+6j The complex data type can be called as a function with no arguments it returns 0j, with a complex argument it returns a copy of the argument, and with any other argument it attempts to convert the given object to a complex When used for conversions complex() accepts either a single string argument, or one or two floats If just one float is given, the imaginary part is taken to be 0j The functions in the math module do not work with complex numbers This is a deliberate design decision that ensures that users of the math module get exceptions rather than silently getting complex numbers in some situations Users of complex numbers can import the cmath module, which provides complex number versions of most of the trigonometric and logarithmic functions that are in the math module, plus some complex number-speci c functions such as cmathphase(), cmathpolar(), and cmathrect(), and also the cmathpi and cmathe constants which hold the same float values as their math module counterparts

[ Team LiB ]

In many applications the numerical inaccuracies that can occur when using floats don t matter, and in any case are far outweighed by the speed of calculation that floats offer But in some cases we prefer the opposite trade-off, and want complete accuracy, even at the cost of speed The decimal module provides immutable Decimal numbers that are as accurate as we specify Calculations involving Decimals are slower than those involving floats, but whether this is noticeable will depend on the application To create a Decimal we must import the decimal module For example:

>>> import decimal >>> a = decimalDecimal(9876)

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Synchronous Request-Response Operations The synchronous request-response message exchange is the most common pattern for service operations This pattern is familiar to anyone who has programmed in a procedural or object-oriented language The requestresponse pattern is the prototypical local procedure call and is also quite common for remote procedure calls Figure 23 shows a request-response interaction, where a proxy running within a client sends a request to a service and the service responds synchronously back to the client

>>> b = decimalDecimal("54321012345678987654321") >>> a + b Decimal('64197012345678987654321')

Control objects are used for controlling content-type-specific media processing operations The set of operations are usually functionally related Thus a Control object provides a logical grouping of media-processing functions

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Decimal numbers are created using the decimalDecimal() function This function can take an integer or a string argument but not a float, since floats are held inexactly whereas decimals are represented exactly If a string is used it can use simple decimal notation or exponential notation In addition to providing accuracy, the exact representation of decimalDecimals means that they can be reliably compared for equality From Python 31 it is possible to convert floats to decimals using the decimalDecimalfrom_float() function This function takes a float as argument and returns the decimalDecimal that is closest to the number the float approximates All the numeric operators and functions listed in Table 22 (55 ), including the augmented assignment versions, can be used with decimalDecimals, but with a couple of caveats If the ** operator has a decimalDecimal left-hand operand, its right-hand operand must be an integer Similarly, if the pow() function s rst argument is a decimalDecimal, then its second and optional third arguments must be integers The math and cmath modules are not suitable for use with decimalDecimals, but some of the functions provided by the math module are provided as decimalDecimal methods For example, to calculate ex where x is a float, we write mathexp(x), but where x is a decimalDecimal, we write xexp() From the discussion in Piece #3 (20 ), we can see that xexp() is, in effect, syntactic sugar for decimalDecimalexp(x) The decimalDecimal data type also provides ln() which calculates the natural (base e) logarithm (just like mathlog() with one argument), log10(), and sqrt(), along with many other methods speci c to the decimalDecimal data type Numbers of type decimalDecimal work within the scope of a context; the context is a collection of settings that affect how decimalDecimals behave The context speci es the precision that should be used (the default is 28 decimal places), the rounding technique, and some other details In some situations the difference in accuracy between floats and decimal.

WCF makes request-response communication between client and service very easy At design time, you use Add Service Reference or to call the service s Metadata Exchange (MEX) endpoint and generate a client-side proxy that mimics the signature of the service operations This allows the client code to call methods on the proxy as local

Decimals becomes obvious: >>> 23 / 105 21904761904761905 >>> print(23 / 105) 219047619048 >>> print(decimalDecimal(23) / decimalDecimal("105")) 2190476190476190476190476190

All Control classes implement the interface Controllable, and the Player interface is a subinterface of Controllable Therefore, a Player implementation can use the Control interface to extend its media-processing functions For example, a Player can expose a VolumeControl to allow the volume level to be set

>>> decimalDecimal(23) / decimalDecimal("105") Decimal('2190476190476190476190476190')

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