- Python Check For Illegal Characters Mac Os X File Names 2016
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- Python Check For Illegal Characters Mac Os X File Names 2017
Jul 26, 2017 The Unicode points are displayed next to the rendered characters, however, and the point will be - for invalid characters. $ utf8-viewer /bin/ls When a file is in an unknown encoding, one can inspect it byte-by-byte. The sort installed on Mac OS X does not have the -R flag. One can use awk to. $ hive -hiveconf mapred.job.tracker=local. This PEP describes a built-package format for Python called 'wheel'. A wheel is a ZIP-format archive with a specially formatted file name and the.whl extension. It contains a single distribution nearly as it would be installed according to PEP 376PEP 376. Return the longest path prefix (taken character-by-character) that is a prefix of all paths in list. If list is empty, return the empty string ('). Note that this may return invalid paths because it works a character at a time. Os.path.dirname(path)¶ Return the directory name of pathname path. When Mac OS X came out, rather than forcing everyone to go through an upgrade procedure to rename all their files with slashes in the names, it simply interpreted those files on the command line with colons instead of slashes. Similarly, if you create a file at the command line with a colon in it, it will show up as a slash in the Finder.
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- Everything Is a File
This chapter is from the book
This chapter is from the book
If you're a casual Mac user, or even if you're a hard-core Linux or Unix user, there are a few things about Mac OS X and the particular flavor of Unix under its candylike shell that might catch you off guard. Files and folders behave in rather different ways when you're addressing them with textual commands than when you're shoving them around with your mouse. Not only do they look different, they act different, too. You might even say they 'think different.'
The shell, which is what we call the command-line environment displayed by the Terminal application, is an austere and cryptic piece of software—about as un-Mac-like as it can possibly get. By the end of this book, you'll have found all kinds of uses for it—tricks that weren't otherwise possible using the graphical Aqua interface. But there's a steep learning curve, particularly for readers who have never dabbled in Unix before, and there are a few things you're going to have to know about how your files work in the shell before you can really start ordering them around.
Everything Is a File
Your Mac is designed primarily to show you your documents, folders, applications, and other items in neatly ordered windows, with pretty icons next to them to help you differentiate them based on their type. You can open Finder windows that show you each item's Kind in a column, distinguishing your Photoshop images from your Word documents and your folders and applications. Mac OS X even has 'bundles,' which are special folders full of executables and other items masquerading as single monolithic files in the Finder, which you'll learn more about in Chapter 5, 'Using the Finder.' At the graphical level, your Mac is full of all kinds of items that each get their own unique look and descriptive vocabulary.
Not much of that matters at the command-line level. Your shell doesn't see a folder differently from how it sees a Word document; they're both just 'streams of bits with names' as far as it's concerned, and in its 1970s-era worldview that's all that matters. The only thing distinguishing a folder (or directory) from a file is that the bits in it describe links to other files that the operating system should interpret as part of that folder, rather than the binary or textual data stream that make up a file's contents—but to Unix that's trivia. If you use the ls ('list') command in the shell to list the files in a folder, you'll just get a list of names—no icons, no turn-down arrows, no clues to help tell you that some of the things you're looking at are files and some are folders, applications, or what-have-you. (There are some options you can give to the ls command to make it smarter about how it lists the items, as you'll see later; but that's a courtesy that Unix only grudgingly grants.)
In the Unix world, everything's a file, including such oddities as running processes and network connections and attached devices, and you interact with them all in pretty much the same way, using the same commands for everything (with a few exceptions, like the mkdir command). I point this out to make you aware that if you see the command-line examples in this book refer to 'files,' it means 'files, folders, and any other discrete pieces of data.' If a command makes a distinction between regular data files and other kinds of items, I'll say so; but otherwise, you can generally expect that a command will work the same on one kind of item as on another, because it'll see 'files' with as little discrimination as Unix does.
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|Title:||The Theory of Type Hints|
|Author:||Guido van Rossum <guido at python.org>, Ivan Levkivskyi <levkivskyi at gmail.com>|
|Discussions-To:||Python-Ideas <python-ideas at python.org>|
- Summary of gradual typing
- Generic types
This PEP lays out the theory referenced by PEP 484.
This document lays out the theory of the new type hinting proposal forPython 3.5. It's not quite a full proposal or specification becausethere are many details that need to be worked out, but it lays out thetheory without which it is hard to discuss more detailed specifications.We start by recalling basic concepts of type theory; then we explaingradual typing; then we state some general rules anddefine the new special types (such as Union) that can be usedin annotations; and finally we define the approach to generic typesand pragmatic aspects of type hinting.
- t1, t2, etc. and u1, u2, etc. are types. Sometimes we writeti or tj to refer to 'any of t1, t2, etc.'
- T, U etc. are type variables (defined with TypeVar(), see below).
- Objects, classes defined with a class statement, and instances aredenoted using standard PEP 8 conventions.
- the symbol applied to types in the context of this PEP means thattwo expressions represent the same type.
- Note that PEP 484 makes a distinction between types and classes(a type is a concept for the type checker,while a class is a runtime concept). In this PEP we clarifythis distinction but avoid unnecessary strictness to allow moreflexibility in the implementation of type checkers.
Python Check For Illegal Characters Mac Os X File Names Free
There are many definitions of the concept of type in the literature.Here we assume that type is a set of values and a set of functions thatone can apply to these values.
There are several ways to define a particular type:
By explicitly listing all values. E.g., True and Falseform the type bool.
By specifying functions which can be used with variables ofa type. E.g. all objects that have a __len__ method formthe type Sized. Both [1, 2, 3] and 'abc' belong tothis type, since one can call len on them:
By a simple class definition, for example if one defines a class:
then all instances of this class also form a type.
There are also more complex types. E.g., one can define the typeFancyList as all lists containing only instances of int, stror their subclasses. The value [1, 'abc', UserID(42)] has this type.
It is important for the user to be able to define types in a formthat can be understood by type checkers.The goal of this PEP is to propose such a systematic way of defining typesfor type annotations of variables and functions using PEP 3107 syntax.These annotations can be used to avoid many kind of bugs, for documentationpurposes, or maybe even to increase speed of program execution.Here we only focus on avoiding bugs by using a static type checker.
A crucial notion for static type checker is the subtype relationship.It arises from the question: If first_var has type first_type, andsecond_var has type second_type, is it safe to assignfirst_var = second_var?
A strong criterion for when it should be safe is:
- every value from second_type is also in the set of valuesof first_type; and
- every function from first_type is also in the set of functionsof second_type.
The relation defined thus is called a subtype relation.
By this definition:
- Every type is a subtype of itself.
- The set of values becomes smaller in the process of subtyping,while the set of functions becomes larger.
An intuitive example: Every Dog is an Animal, also Doghas more functions, for example it can bark, therefore Dogis a subtype of Animal. Conversely, Animal is not a subtype of Dog.
A more formal example: Integers are subtype of real numbers.Indeed, every integer is of course also a real number, and integerssupport more operations, such as, e.g., bitwise shifts << and >>:
Let us also consider a tricky example: If List[int] denotes the typeformed by all lists containing only integer numbers,then it is not a subtype of List[float], formed by all lists that containonly real numbers. The first condition of subtyping holds,but appending a real number only works with List[float] so thatthe second condition fails:
There are two widespread approaches to declare subtype informationto type checker.
In nominal subtyping, the type tree is based on the class tree,i.e., UserID is considered a subtype of int.This approach should be used under control of the type checker,because in Python one can override attributes in an incompatible way:
In structural subtyping the subtype relation is deduced from thedeclared methods, i.e., UserID and int would be considered the same type.While this may occasionally cause confusion,structural subtyping is considered more flexible.We strive to provide support for both approaches, so thatstructural information can be used in addition to nominal subtyping.
Gradual typing allows one to annotate only part of a program,thus leverage desirable aspects of both dynamic and static typing.
We define a new relationship, is-consistent-with, which is similar tois-subtype-of, except it is not transitive when the new type Any isinvolved. (Neither relationship is symmetric.) Assigning a_valueto a_variable is OK if the type of a_value is consistent withthe type of a_variable. (Compare this to '.. if the type of a_valueis a subtype of the type of a_variable', which states one of thefundamentals of OO programming.) The is-consistent-with relationship isdefined by three rules:
- A type t1 is consistent with a type t2 if t1 is asubtype of t2. (But not the other way around.)
- Any is consistent with every type. (But Any is not a subtypeof every type.)
- Every type is consistent with Any. (But every type is not a subtypeof Any.)
That's all! See Jeremy Siek's blog post What is GradualTypingfor a longer explanation and motivation. Any can be considered a typethat has all values and all methods. Combined with the definition ofsubtyping above, this places Any partially at the top (it has all values)and bottom (it has all methods) of the type hierarchy. Contrast this toobject -- it is not consistent withmost types (e.g. you can't use an object() instance where anint is expected). IOW both Any and object mean'any type is allowed' when used to annotate an argument, but only Anycan be passed no matter what type is expected (in essence, Anydeclares a fallback to dynamic typing and shuts up complaintsfrom the static checker).
Here's an example showing how these rules work out in practice:
Say we have an Employee class, and a subclass Manager:
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Let's say variable worker is declared with type Employee:
Now it's okay to assign a Manager instance to worker (rule 1):
It's not okay to assign an Employee instance to a variable declared withtype Manager:
Python Check For Illegal Characters Mac Os X File Names List
However, suppose we have a variable whose type is Any:
Now it's okay to assign something to worker (rule 2):
Of course it's also okay to assign worker to something (rule 3),but we didn't need the concept of consistency for that:
Types vs. Classes
In Python, classes are object factories defined by the class statement,and returned by the type(obj) built-in function. Class is a dynamic,runtime concept.
Type concept is described above, types appear in variableand function type annotations, can be constructedfrom building blocks described below, and are used by static type checkers.
Every class is a type as discussed above.But it is tricky and error prone to implement a class that exactly representssemantics of a given type, and it is not a goal of PEP 484.The static types described in PEP 484, should not be confused withthe runtime classes. Examples:
int is a class and a type.
UserID is a class and a type.
Union[str, int] is a type but not a proper class:
Typing interface is implemented with classes, i.e., at runtime it is possibleto evaluate, e.g., Generic[T].__bases__. But to emphasize the distinctionbetween classes and types the following general rules apply:
- No types defined below (i.e. Any, Union, etc.) can be instantiated,an attempt to do so will raise TypeError.(But non-abstract subclasses of Generic can be.)
- No types defined below can be subclassed, except for Generic andclasses derived from it.
- All of these will raise TypeError if they appearin isinstance or issubclass (except for unparameterized generics).
Fundamental building blocks
- Any. Every type is consistent with Any; andit is also consistent with every type (see above).
- Union[t1, t2, ..]. Types that are subtype of at least one oft1 etc. are subtypes of this.
- Unions whose components are all subtypes of t1 etc. are subtypesof this.Example: Union[int, str] is a subtype of Union[int, float, str].
- The order of the arguments doesn't matter.Example: Union[int, str] Union[str, int].
- If ti is itself a Union the result is flattened.Example: Union[int, Union[float, str]] Union[int, float, str].
- If ti and tj have a subtype relationship,the less specific type survives.Example: Union[Employee, Manager] Union[Employee].
- Union[t1] returns just t1. Union is illegal,so is Union[()]
- Corollary: Union[.., object, ..] returns object.
- Optional[t1]. Alias for Union[t1, None], i.e. Union[t1,type(None)].
- Tuple[t1, t2, .., tn]. A tuple whose items are instances of t1,etc. Example: Tuple[int, float] means a tuple of two items, thefirst is an int, the second is a float; e.g., (42, 3.14).
- Tuple[u1, u2, .., um] is a subtype of Tuple[t1, t2, .., tn]if they have the same length nm and each uiis a subtype of ti.
- To spell the type of the empty tuple, use Tuple[()].
- A variadic homogeneous tuple type can be written Tuple[t1, ..].(That's three dots, a literal ellipsis;and yes, that's a valid token in Python's syntax.)
- Callable[[t1, t2, .., tn], tr]. A function with positionalargument types t1 etc., and return type tr. The argument list may beempty n0. There is no way to indicate optional or keywordarguments, nor varargs, but you can say the argument list is entirelyunchecked by writing Callable[.., tr] (again, a literal ellipsis).
We might add:
- Intersection[t1, t2, ..]. Types that are subtype of each oft1, etc are subtypes of this. (Compare to Union, which has atleast one instead of each in its definition.)
- The order of the arguments doesn't matter. Nested intersectionsare flattened, e.g. Intersection[int, Intersection[float, str]] Intersection[int, float, str].
- An intersection of fewer types is a supertype of an intersection ofmore types, e.g. Intersection[int, str] is a supertypeof Intersection[int, float, str].
- An intersection of one argument is just that argument,e.g. Intersection[int] is int.
- When argument have a subtype relationship, the more specific typesurvives, e.g. Intersection[str, Employee, Manager] isIntersection[str, Manager].
- Intersection is illegal, so is Intersection[()].
- Corollary: Any disappears from the argument list, e.g.Intersection[int, str, Any] Intersection[int, str].Intersection[Any, object] is object.
- The interaction between Intersection and Union is complex butshould be no surprise if you understand the interaction betweenintersections and unions of regular sets (note that sets of types can beinfinite in size, since there is no limit on the numberof new subclasses).
The fundamental building blocks defined above allow to construct new typesin a generic manner. For example, Tuple can take a concrete type floatand make a concrete type Vector = Tuple[float, ..], or it can takeanother type UserID and make another concrete typeRegistry = Tuple[UserID, ..]. Such semantics is known as generic typeconstructor, it is similar to semantics of functions, but a function takesa value and returns a value, while generic type constructor takes a type and'returns' a type.
It is common when a particular class or a function behaves in such a typegeneric manner. Consider two examples:
Container classes, such as list or dict, typically contain onlyvalues of a particular type. Therefore, a user might want to type annotatethem as such:
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The following function can take two arguments of type int and returnan int, or take two arguments of type float and returna float, etc.:
To allow type annotations in situations from the first example, built-incontainers and container abstract base classes are extended with typeparameters, so that they behave as generic type constructors.Classes, that behave as generic type constructors are called generic types.Example:
Here Iterable is a generic type that takes a concrete type Taskand returns a concrete type Iterable[Task].
Functions that behave in the type generic manner (as in second example)are called generic functions.Type annotations of generic functions are allowed by type variables.Their semantics with respect to generic types is somewhat similarto semantics of parameters in functions. But one does not assignconcrete types to type variables, it is the task of a static type checkerto find their possible values and warn the user if it cannot find.Example:
Type variables are used extensively in type annotations, also internalmachinery of the type inference in type checkers is typically build ontype variables. Therefore, let us consider them in detail.
X = TypeVar('X') declares a unique type variable. The name must matchthe variable name. By default, a type variable rangesover all possible types. Example:
Y = TypeVar('Y', t1, t2, ..). Ditto, constrained to t1, etc. Behavessimilar to Union[t1, t2, ..]. A constrained type variable ranges onlyover constrains t1, etc. exactly; subclasses of the constrains arereplaced by the most-derived base class among t1, etc. Examples:
Function type annotation with a constrained type variable:
In this example, both arguments to longest() must have the same type(str or bytes), and moreover, even if the arguments are instancesof a common str subclass, the return type is still str, not thatsubclass (see next example).
For comparison, if the type variable was unconstrained, the commonsubclass would be chosen as the return type, e.g.:
The inferred type of result is MyStr (whereas in the AnyStr exampleit would be str).
Also for comparison, if a Union is used, the return type also has to bea Union:
The inferred type of result is still Union[str, bytes], even thoughboth arguments are str.
Note that the type checker will reject this function:
For such cases where parameters could change their types only simultaneouslyone should use constrained type variables.
Defining and using generic types
Users can declare their classes as generic types usingthe special building block Generic. The definitionclass MyGeneric(Generic[X, Y, ..]): .. defines a generic typeMyGeneric over type variables X, etc. MyGeneric itself becomesparameterizable, e.g. MyGeneric[int, str, ..] is a specific type withsubstitutions X -> int, etc. Example:
Classes that derive from generic types become generic.A class can subclass multiple generic types. However,classes derived from specific types returned by generics arenot generic. Examples:
Subclassing a generic type imposes the subtype relation on the correspondingspecific types, so that TodoList[t1] is a subtype of Iterable[t1]in the above example.
Generic types can be specialized (indexed) in several steps.Every type variable could be substituted by a specific typeor by another generic type. If Generic appears in the base class list,then it should contain all type variables, and the order of type parameters isdetermined by the order in which they appear in Generic. Examples:
If a generic type appears in a type annotation with a type variable omitted,it is assumed to be Any. Such form could be used as a fallbackto dynamic typing and is allowed for use with issubclassand isinstance. All type information in instances is erased at runtime.Examples:
Covariance and Contravariance
If t2 is a subtype of t1, then a generictype constructor GenType is called:
- Covariant, if GenType[t2] is a subtype of GenType[t1]for all such t1 and t2.
- Contravariant, if GenType[t1] is a subtype of GenType[t2]for all such t1 and t2.
- Invariant, if neither of the above is true.
To better understand this definition, let us make an analogy withordinary functions. Assume that we have:
If x1 < x2, then alwayscov(x1) < cov(x2), andcontra(x2) < contra(x1), while nothing could be said about inv.Replacing < with is-subtype-of, and functions with generic typeconstructor we get examples of covariant, contravariant,and invariant behavior. Let us now consider practical examples:
- Union behaves covariantly in all its arguments.Indeed, as discussed above, Union[t1, t2, ..] is a subtype ofUnion[u1, u2, ..], if t1 is a subtype of u1, etc.
- FrozenSet[T] is also covariant. Let us consider int andfloat in place of T. First, int is a subtype of float.Second, set of values of FrozenSet[int] isclearly a subset of values of FrozenSet[float], while set of functionsfrom FrozenSet[float] is a subset of set of functionsfrom FrozenSet[int]. Therefore, by definition FrozenSet[int]is a subtype of FrozenSet[float].
- List[T] is invariant. Indeed, although set of values of List[int]is a subset of values of List[float], only int could be appendedto a List[int], as discussed in section 'Background'. Therefore,List[int] is not a subtype of List[float]. This is a typicalsituation with mutable types, they are typically invariant.
One of the best examples to illustrate (somewhat counterintuitive)contravariant behavior is the callable type.It is covariant in the return type, but contravariant in thearguments. For two callable types thatdiffer only in the return type, the subtype relationship for thecallable types follows that of the return types. Examples:
- Callable[, int] is a subtype of Callable[, float].
- Callable[, Manager] is a subtype of Callable[, Employee].
While for two callable types that differonly in the type of one argument, the subtype relationship for thecallable types goes in the opposite direction as for the argumenttypes. Examples:
- Callable[[float], None] is a subtype of Callable[[int], None].
- Callable[[Employee], None] is a subtype of Callable[[Manager], None].
Yes, you read that right. Indeed, ifa function that can calculate the salary for a manager is expected:
then Callable[[Employee], Decimal] that can calculate a salary for anyemployee is also acceptable.
The example with Callable shows how to make more precise type annotationsfor functions: choose the most general type for every argument,and the most specific type for the return value.
It is possible to declare the variance for user defined generic types byusing special keywords covariant and contravariant in thedefinition of type variables used as parameters.Types are invariant by default. Examples:
Note, that although the variance is defined via type variables, it is nota property of type variables, but a property of generic types.In complex definitions of derived generics, variance onlydetermined from type variables used. A complex example:
A type checker finds from the second declaration that Derived[Manager]is a subtype of Derived[Employee], and Derived[t1]is a subtype of Base[t1].If we denote the is-subtype-of relationship with <, then thefull diagram of subtyping for this case will be:
so that a type checker will also find that, e.g., Derived[Manager] isa subtype of Base[Employee].
For more information on type variables, generic types, and variance,see PEP 484, the mypy docs ongenerics,and Wikipedia.
Some things are irrelevant to the theory but make practical use moreconvenient. (This is not a full list; I probably missed a few and someare still controversial or not fully specified.)
Where a type is expected, None can be substituted for type(None);e.g. Union[t1, None] Union[t1, type(None)].
Type aliases, e.g.:
Forward references via strings, e.g.:
Type variables can be declared in unconstrained, constrained,or bounded form. The variance of a generic type can alsobe indicated using a type variable declared with special keywordarguments, thus avoiding any special syntax, e.g.:
Type declaration in comments, e.g.:
Casts using cast(T, obj), e.g.:
Other things, e.g. overloading and stub modules, see PEP 484.
Predefined generic types and Protocols in typing.py
(See also the typing.py module.)
Python Check For Illegal Characters Mac Os X File Names 2017
- Everything from collections.abc (but Set renamed to AbstractSet).
- Dict, List, Set, FrozenSet, a few more.
- re.Pattern[AnyStr], re.Match[AnyStr].
- io.IO[AnyStr], io.TextIO ~ io.IO[str], io.BinaryIO ~ io.IO[bytes].
This document is licensed under the Open Publication License.Source: https://github.com/python/peps/blob/master/pep-0483.txt