Searched defs:between (Results 1 - 15 of 15) sorted by relevance

/external/clang/test/Analysis/
H A Dlogical-ops.c30 int between(char *x) { function
/external/skia/src/core/
H A DSkGeometry.cpp169 Solve for t, only if it fits between 0 < t < 1
1205 static bool between(SkScalar a, SkScalar b, SkScalar c) { function
1220 if (between(startY, src.fPts[1].fY, endY)) {
1224 if (!between(startY, midY, endY)) {
1229 if (!between(startY, dst[0].fPts[1].fY, dst[0].fPts[2].fY)) {
1230 // If the 1st control is not between the start and end, put it at the start.
1234 if (!between(dst[1].fPts[0].fY, dst[1].fPts[1].fY, endY)) {
1235 // If the 2nd control is not between the start and end, put it at the end.
1240 SkASSERT(between(startY, dst[0].fPts[1].fY, dst[0].fPts[2].fY));
1241 SkASSERT(between(ds
[all...]
H A DSkPath.cpp2770 static bool between(SkScalar a, SkScalar b, SkScalar c) { function
2799 return between(start.fX, x, end.fX) && x != end.fX;
3063 if (!between(pts[0].fY, y, pts[1].fY) && !between(pts[1].fY, y, pts[2].fY)
3064 && !between(pts[2].fY, y, pts[3].fY)) {
3067 if (!between(pts[0].fX, x, pts[1].fX) && !between(pts[1].fX, x, pts[2].fX)
3068 && !between(pts[2].fX, x, pts[3].fX)) {
3091 if (!between(pts[0].fY, y, pts[1].fY) && !between(pt
[all...]
/external/skqp/src/core/
H A DSkGeometry.cpp169 Solve for t, only if it fits between 0 < t < 1
1198 static bool between(SkScalar a, SkScalar b, SkScalar c) { function
1213 if (between(startY, src.fPts[1].fY, endY)) {
1217 if (!between(startY, midY, endY)) {
1222 if (!between(startY, dst[0].fPts[1].fY, dst[0].fPts[2].fY)) {
1223 // If the 1st control is not between the start and end, put it at the start.
1227 if (!between(dst[1].fPts[0].fY, dst[1].fPts[1].fY, endY)) {
1228 // If the 2nd control is not between the start and end, put it at the end.
1233 SkASSERT(between(startY, dst[0].fPts[1].fY, dst[0].fPts[2].fY));
1234 SkASSERT(between(ds
[all...]
H A DSkPath.cpp2886 static bool between(SkScalar a, SkScalar b, SkScalar c) { function
2915 return between(start.fX, x, end.fX) && x != end.fX;
3179 if (!between(pts[0].fY, y, pts[1].fY) && !between(pts[1].fY, y, pts[2].fY)
3180 && !between(pts[2].fY, y, pts[3].fY)) {
3183 if (!between(pts[0].fX, x, pts[1].fX) && !between(pts[1].fX, x, pts[2].fX)
3184 && !between(pts[2].fX, x, pts[3].fX)) {
3207 if (!between(pts[0].fY, y, pts[1].fY) && !between(pt
[all...]
/external/mesa3d/src/compiler/nir/
H A Dnir.c1553 is_instr_between(nir_instr *start, nir_instr *end, nir_instr *between) argument
1557 if (between->block != start->block)
1560 /* Search backwards looking for "between" */
1562 if (between == end)
1591 * not be dominated by after_me is if it is between def and after_me in
1980 /* TODO: This does not include specific conversions between
/external/skia/src/pathops/
H A DSkPathOpsTypes.h270 // Use Almost Bequal when comparing coordinates in conjunction with between.
537 inline bool between(double a, double b, double c) { function
/external/skqp/src/pathops/
H A DSkPathOpsTypes.h270 // Use Almost Bequal when comparing coordinates in conjunction with between.
537 inline bool between(double a, double b, double c) { function
/external/python/cpython2/Lib/pydoc_data/
H A Dtopics.py4 'assignment': u'\nAssignment statements\n*********************\n\nAssignment statements are used to (re)bind names to values and to\nmodify attributes or items of mutable objects:\n\n assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)\n target_list ::= target ("," target)* [","]\n target ::= identifier\n | "(" target_list ")"\n | "[" [target_list] "]"\n | attributeref\n | subscription\n | slicing\n\n(See section Primaries for the syntax definitions for the last three\nsymbols.)\n\nAn assignment statement evaluates the expression list (remember that\nthis can be a single expression or a comma-separated list, the latter\nyielding a tuple) and assigns the single resulting object to each of\nthe target lists, from left to right.\n\nAssignment is defined recursively depending on the form of the target\n(list). When a target is part of a mutable object (an attribute\nreference, subscription or slicing), the mutable object must\nultimately perform the assignment and decide about its validity, and\nmay raise an exception if the assignment is unacceptable. The rules\nobserved by various types and the exceptions raised are given with the\ndefinition of the object types (see section The standard type\nhierarchy).\n\nAssignment of an object to a target list is recursively defined as\nfollows.\n\n* If the target list is a single target: The object is assigned to\n that target.\n\n* If the target list is a comma-separated list of targets: The\n object must be an iterable with the same number of items as there\n are targets in the target list, and the items are assigned, from\n left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n * If the name does not occur in a "global" statement in the\n current code block: the name is bound to the object in the current\n local namespace.\n\n * Otherwise: the name is bound to the object in the current global\n namespace.\n\n The name is rebound if it was already bound. This may cause the\n reference count for the object previously bound to the name to reach\n zero, causing the object to be deallocated and its destructor (if it\n has one) to be called.\n\n* If the target is a target list enclosed in parentheses or in\n square brackets: The object must be an iterable with the same number\n of items as there are targets in the target list, and its items are\n assigned, from left to right, to the corresponding targets.\n\n* If the target is an attribute reference: The primary expression in\n the reference is evaluated. It should yield an object with\n assignable attributes; if this is not the case, "TypeError" is\n raised. That object is then asked to assign the assigned object to\n the given attribute; if it cannot perform the assignment, it raises\n an exception (usually but not necessarily "AttributeError").\n\n Note: If the object is a class instance and the attribute reference\n occurs on both sides of the assignment operator, the RHS expression,\n "a.x" can access either an instance attribute or (if no instance\n attribute exists) a class attribute. The LHS target "a.x" is always\n set as an instance attribute, creating it if necessary. Thus, the\n two occurrences of "a.x" do not necessarily refer to the same\n attribute: if the RHS expression refers to a class attribute, the\n LHS creates a new instance attribute as the target of the\n assignment:\n\n class Cls:\n x = 3 # class variable\n inst = Cls()\n inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3\n\n This description does not necessarily apply to descriptor\n attributes, such as properties created with "property()".\n\n* If the target is a subscription: The primary expression in the\n reference is evaluated. It should yield either a mutable sequence\n object (such as a list) or a mapping object (such as a dictionary).\n Next, the subscript expression is evaluated.\n\n If the primary is a mutable sequence object (such as a list), the\n subscript must yield a plain integer. If it is negative, the\n sequence\'s length is added to it. The resulting value must be a\n nonnegative integer less than the sequence\'s length, and the\n sequence is asked to assign the assigned object to its item with\n that index. If the index is out of range, "IndexError" is raised\n (assignment to a subscripted sequence cannot add new items to a\n list).\n\n If the primary is a mapping object (such as a dictionary), the\n subscript must have a type compatible with the mapping\'s key type,\n and the mapping is then asked to create a key/datum pair which maps\n the subscript to the assigned object. This can either replace an\n existing key/value pair with the same key value, or insert a new\n key/value pair (if no key with the same value existed).\n\n* If the target is a slicing: The primary expression in the\n reference is evaluated. It should yield a mutable sequence object\n (such as a list). The assigned object should be a sequence object\n of the same type. Next, the lower and upper bound expressions are\n evaluated, insofar they are present; defaults are zero and the\n sequence\'s length. The bounds should evaluate to (small) integers.\n If either bound is negative, the sequence\'s length is added to it.\n The resulting bounds are clipped to lie between zero and the\n sequence\'s length, inclusive. Finally, the sequence object is asked\n to replace the slice with the items of the assigned sequence. The\n length of the slice may be different from the length of the assigned\n sequence, thus changing the length of the target sequence, if the\n object allows it.\n\n**CPython implementation detail:** In the current implementation, the\nsyntax for targets is taken to be the same as for expressions, and\ninvalid syntax is rejected during the code generation phase, causing\nless detailed error messages.\n\nWARNING: Although the definition of assignment implies that overlaps\nbetween the left-hand side and the right-hand side are \'safe\' (for\nexample "a, b = b, a" swaps two variables), overlaps *within* the\ncollection of assigned-to variables are not safe! For instance, the\nfollowing program prints "[0, 2]":\n\n x = [0, 1]\n i = 0\n i, x[i] = 1, 2\n print x\n\n\nAugmented assignment statements\n===============================\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)\n augtarget ::= identifier | attributeref | subscription | slicing\n augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section Primaries for the syntax definitions for the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like "x += 1" can be rewritten as\n"x = x + 1" to achieve a similar, but not exactly equal effect. In the\naugmented version, "x" is only evaluated once. Also, when possible,\nthe actual operation is performed *in-place*, meaning that rather than\ncreating a new object and assigning that to the target, the old object\nis modified instead.\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the same caveat about\nclass and instance attributes applies as for regular assignments.\n',
7 'attribute-access': u'\nCustomizing attribute access\n****************************\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of "x.name") for\nclass instances.\n\nobject.__getattr__(self, name)\n\n Called when an attribute lookup has not found the attribute in the\n usual places (i.e. it is not an instance attribute nor is it found\n in the class tree for "self"). "name" is the attribute name. This\n method should return the (computed) attribute value or raise an\n "AttributeError" exception.\n\n Note that if the attribute is found through the normal mechanism,\n "__getattr__()" is not called. (This is an intentional asymmetry\n between "__getattr__()" and "__setattr__()".) This is done both for\n efficiency reasons and because otherwise "__getattr__()" would have\n no way to access other attributes of the instance. Note that at\n least for instance variables, you can fake total control by not\n inserting any values in the instance attribute dictionary (but\n instead inserting them in another object). See the\n "__getattribute__()" method below for a way to actually get total\n control in new-style classes.\n\nobject.__setattr__(self, name, value)\n\n Called when an attribute assignment is attempted. This is called\n instead of the normal mechanism (i.e. store the value in the\n instance dictionary). *name* is the attribute name, *value* is the\n value to be assigned to it.\n\n If "__setattr__()" wants to assign to an instance attribute, it\n should not simply execute "self.name = value" --- this would cause\n a recursive call to itself. Instead, it should insert the value in\n the dictionary of instance attributes, e.g., "self.__dict__[name] =\n value". For new-style classes, rather than accessing the instance\n dictionary, it should call the base class method with the same\n name, for example, "object.__setattr__(self, name, value)".\n\nobject.__delattr__(self, name)\n\n Like "__setattr__()" but for attribute deletion instead of\n assignment. This should only be implemented if "del obj.name" is\n meaningful for the object.\n\n\nMore attribute access for new-style classes\n===========================================\n\nThe following methods only apply to new-style classes.\n\nobject.__getattribute__(self, name)\n\n Called unconditionally to implement attribute accesses for\n instances of the class. If the class also defines "__getattr__()",\n the latter will not be called unless "__getattribute__()" either\n calls it explicitly or raises an "AttributeError". This method\n should return the (computed) attribute value or raise an\n "AttributeError" exception. In order to avoid infinite recursion in\n this method, its implementation should always call the base class\n method with the same name to access any attributes it needs, for\n example, "object.__getattribute__(self, name)".\n\n Note: This method may still be bypassed when looking up special\n methods as the result of implicit invocation via language syntax\n or built-in functions. See Special method lookup for new-style\n classes.\n\n\nImplementing Descriptors\n========================\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in an\n*owner* class (the descriptor must be in either the owner\'s class\ndictionary or in the class dictionary for one of its parents). In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' "__dict__".\n\nobject.__get__(self, instance, owner)\n\n Called to get the attribute of the owner class (class attribute\n access) or of an instance of that class (instance attribute\n access). *owner* is always the owner class, while *instance* is the\n instance that the attribute was accessed through, or "None" when\n the attribute is accessed through the *owner*. This method should\n return the (computed) attribute value or raise an "AttributeError"\n exception.\n\nobject.__set__(self, instance, value)\n\n Called to set the attribute on an instance *instance* of the owner\n class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n Called to delete the attribute on an instance *instance* of the\n owner class.\n\n\nInvoking Descriptors\n====================\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol: "__get__()", "__set__()", and\n"__delete__()". If any of those methods are defined for an object, it\nis said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, "a.x" has a\nlookup chain starting with "a.__dict__[\'x\']", then\n"type(a).__dict__[\'x\']", and continuing through the base classes of\n"type(a)" excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead. Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called. Note that descriptors are only invoked for new\nstyle objects or classes (ones that subclass "object()" or "type()").\n\nThe starting point for descriptor invocation is a binding, "a.x". How\nthe arguments are assembled depends on "a":\n\nDirect Call\n The simplest and least common call is when user code directly\n invokes a descriptor method: "x.__get__(a)".\n\nInstance Binding\n If binding to a new-style object instance, "a.x" is transformed\n into the call: "type(a).__dict__[\'x\'].__get__(a, type(a))".\n\nClass Binding\n If binding to a new-style class, "A.x" is transformed into the\n call: "A.__dict__[\'x\'].__get__(None, A)".\n\nSuper Binding\n If "a" is an instance of "super", then the binding "super(B,\n obj).m()" searches "obj.__class__.__mro__" for the base class "A"\n immediately preceding "B" and then invokes the descriptor with the\n call: "A.__dict__[\'m\'].__get__(obj, obj.__class__)".\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined. A descriptor can define\nany combination of "__get__()", "__set__()" and "__delete__()". If it\ndoes not define "__get__()", then accessing the attribute will return\nthe descriptor object itself unless there is a value in the object\'s\ninstance dictionary. If the descriptor defines "__set__()" and/or\n"__delete__()", it is a data descriptor; if it defines neither, it is\na non-data descriptor. Normally, data descriptors define both\n"__get__()" and "__set__()", while non-data descriptors have just the\n"__get__()" method. Data descriptors with "__set__()" and "__get__()"\ndefined always override a redefinition in an instance dictionary. In\ncontrast, non-data descriptors can be overridden by instances.\n\nPython methods (including "staticmethod()" and "classmethod()") are\nimplemented as non-data descriptors. Accordingly, instances can\nredefine and override methods. This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe "property()" function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n=========\n\nBy default, instances of both old and new-style classes have a\ndictionary for attribute storage. This wastes space for objects\nhaving very few instance variables. The space consumption can become\nacute when creating large numbers of instances.\n\nThe default can be overridden by defining *__slots__* in a new-style\nclass definition. The *__slots__* declaration takes a sequence of\ninstance variables and reserves just enough space in each instance to\nhold a value for each variable. Space is saved because *__dict__* is\nnot created for each instance.\n\n__slots__\n\n This class variable can be assigned a string, iterable, or sequence\n of strings with variable names used by instances. If defined in a\n new-style class, *__slots__* reserves space for the declared\n variables and prevents the automatic creation of *__dict__* and\n *__weakref__* for each instance.\n\n New in version 2.2.\n\nNotes on using *__slots__*\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n attribute of that class will always be accessible, so a *__slots__*\n definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n variables not listed in the *__slots__* definition. Attempts to\n assign to an unlisted variable name raises "AttributeError". If\n dynamic assignment of new variables is desired, then add\n "\'__dict__\'" to the sequence of strings in the *__slots__*\n declaration.\n\n Changed in version 2.3: Previously, adding "\'__dict__\'" to the\n *__slots__* declaration would not enable the assignment of new\n attributes not specifically listed in the sequence of instance\n variable names.\n\n* Without a *__weakref__* variable for each instance, classes\n defining *__slots__* do not support weak references to its\n instances. If weak reference support is needed, then add\n "\'__weakref__\'" to the sequence of strings in the *__slots__*\n declaration.\n\n Changed in version 2.3: Previously, adding "\'__weakref__\'" to the\n *__slots__* declaration would not enable support for weak\n references.\n\n* *__slots__* are implemented at the class level by creating\n descriptors (Implementing Descriptors) for each variable name. As a\n result, class attributes cannot be used to set default values for\n instance variables defined by *__slots__*; otherwise, the class\n attribute would overwrite the descriptor assignment.\n\n* The action of a *__slots__* declaration is limited to the class\n where it is defined. As a result, subclasses will have a *__dict__*\n unless they also define *__slots__* (which must only contain names\n of any *additional* slots).\n\n* If a class defines a slot also defined in a base class, the\n instance variable defined by the base class slot is inaccessible\n (except by retrieving its descriptor directly from the base class).\n This renders the meaning of the program undefined. In the future, a\n check may be added to prevent this.\n\n* Nonempty *__slots__* does not work for classes derived from\n "variable-length" built-in types such as "long", "str" and "tuple".\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings\n may also be used; however, in the future, special meaning may be\n assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n *__slots__*.\n\n Changed in version 2.6: Previously, *__class__* assignment raised an\n error if either new or old class had *__slots__*.\n',
22 'comparisons': u'\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation. Also unlike C, expressions like "a < b < c" have the\ninterpretation that is conventional in mathematics:\n\n comparison ::= or_expr ( comp_operator or_expr )*\n comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="\n | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: "True" or "False".\n\nComparisons can be chained arbitrarily, e.g., "x < y <= z" is\nequivalent to "x < y and y <= z", except that "y" is evaluated only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y\nopN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe forms "<>" and "!=" are equivalent; for consistency with C, "!="\nis preferred; where "!=" is mentioned below "<>" is also accepted.\nThe "<>" spelling is considered obsolescent.\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects. The objects need not have the same type. If both are\nnumbers, they are converted to a common type. Otherwise, objects of\ndifferent types *always* compare unequal, and are ordered consistently\nbut arbitrarily. You can control comparison behavior of objects of\nnon-built-in types by defining a "__cmp__" method or rich comparison\nmethods like "__gt__", described in section Special method names.\n\n(This unusual definition of comparison was used to simplify the\ndefinition of operations like sorting and the "in" and "not in"\noperators. In the future, the comparison rules for objects of\ndifferent types are likely to change.)\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Strings are compared lexicographically using the numeric\n equivalents (the result of the built-in function "ord()") of their\n characters. Unicode and 8-bit strings are fully interoperable in\n this behavior. [4]\n\n* Tuples and lists are compared lexicographically using comparison\n of corresponding elements. This means that to compare equal, each\n element must compare equal and the two sequences must be of the same\n type and have the same length.\n\n If not equal, the sequences are ordered the same as their first\n differing elements. For example, "cmp([1,2,x], [1,2,y])" returns\n the same as "cmp(x,y)". If the corresponding element does not\n exist, the shorter sequence is ordered first (for example, "[1,2] <\n [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if their sorted\n (key, value) lists compare equal. [5] Outcomes other than equality\n are resolved consistently, but are not otherwise defined. [6]\n\n* Most other objects of built-in types compare unequal unless they\n are the same object; the choice whether one object is considered\n smaller or larger than another one is made arbitrarily but\n consistently within one execution of a program.\n\nThe operators "in" and "not in" test for collection membership. "x in\ns" evaluates to true if *x* is a member of the collection *s*, and\nfalse otherwise. "x not in s" returns the negation of "x in s". The\ncollection membership test has traditionally been bound to sequences;\nan object is a member of a collection if the collection is a sequence\nand contains an element equal to that object. However, it make sense\nfor many other object types to support membership tests without being\na sequence. In particular, dictionaries (for keys) and sets support\nmembership testing.\n\nFor the list and tuple types, "x in y" is true if and only if there\nexists an index *i* such that either "x is y[i]" or "x == y[i]" is\ntrue.\n\nFor the Unicode and string types, "x in y" is true if and only if *x*\nis a substring of *y*. An equivalent test is "y.find(x) != -1".\nNote, *x* and *y* need not be the same type; consequently, "u\'ab\' in\n\'abc\'" will return "True". Empty strings are always considered to be a\nsubstring of any other string, so """ in "abc"" will return "True".\n\nChanged in version 2.3: Previously, *x* was required to be a string of\nlength "1".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y". If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative integer index *i* such that "x == y[i]", and all lower\ninteger indices do not raise "IndexError" exception. (If any other\nexception is raised, it is as if "in" raised that exception).\n\nThe operator "not in" is defined to have the inverse true value of\n"in".\n\nThe operators "is" and "is not" test for object identity: "x is y" is\ntrue if and only if *x* and *y* are the same object. "x is not y"\nyields the inverse truth value. [7]\n',
27 'customization': u'\nBasic customization\n*******************\n\nobject.__new__(cls[, ...])\n\n Called to create a new instance of class *cls*. "__new__()" is a\n static method (special-cased so you need not declare it as such)\n that takes the class of which an instance was requested as its\n first argument. The remaining arguments are those passed to the\n object constructor expression (the call to the class). The return\n value of "__new__()" should be the new object instance (usually an\n instance of *cls*).\n\n Typical implementations create a new instance of the class by\n invoking the superclass\'s "__new__()" method using\n "super(currentclass, cls).__new__(cls[, ...])" with appropriate\n arguments and then modifying the newly-created instance as\n necessary before returning it.\n\n If "__new__()" returns an instance of *cls*, then the new\n instance\'s "__init__()" method will be invoked like\n "__init__(self[, ...])", where *self* is the new instance and the\n remaining arguments are the same as were passed to "__new__()".\n\n If "__new__()" does not return an instance of *cls*, then the new\n instance\'s "__init__()" method will not be invoked.\n\n "__new__()" is intended mainly to allow subclasses of immutable\n types (like int, str, or tuple) to customize instance creation. It\n is also commonly overridden in custom metaclasses in order to\n customize class creation.\n\nobject.__init__(self[, ...])\n\n Called after the instance has been created (by "__new__()"), but\n before it is returned to the caller. The arguments are those\n passed to the class constructor expression. If a base class has an\n "__init__()" method, the derived class\'s "__init__()" method, if\n any, must explicitly call it to ensure proper initialization of the\n base class part of the instance; for example:\n "BaseClass.__init__(self, [args...])".\n\n Because "__new__()" and "__init__()" work together in constructing\n objects ("__new__()" to create it, and "__init__()" to customise\n it), no non-"None" value may be returned by "__init__()"; doing so\n will cause a "TypeError" to be raised at runtime.\n\nobject.__del__(self)\n\n Called when the instance is about to be destroyed. This is also\n called a destructor. If a base class has a "__del__()" method, the\n derived class\'s "__del__()" method, if any, must explicitly call it\n to ensure proper deletion of the base class part of the instance.\n Note that it is possible (though not recommended!) for the\n "__del__()" method to postpone destruction of the instance by\n creating a new reference to it. It may then be called at a later\n time when this new reference is deleted. It is not guaranteed that\n "__del__()" methods are called for objects that still exist when\n the interpreter exits.\n\n Note: "del x" doesn\'t directly call "x.__del__()" --- the former\n decrements the reference count for "x" by one, and the latter is\n only called when "x"\'s reference count reaches zero. Some common\n situations that may prevent the reference count of an object from\n going to zero include: circular references between objects (e.g.,\n a doubly-linked list or a tree data structure with parent and\n child pointers); a reference to the object on the stack frame of\n a function that caught an exception (the traceback stored in\n "sys.exc_traceback" keeps the stack frame alive); or a reference\n to the object on the stack frame that raised an unhandled\n exception in interactive mode (the traceback stored in\n "sys.last_traceback" keeps the stack frame alive). The first\n situation can only be remedied by explicitly breaking the cycles;\n the latter two situations can be resolved by storing "None" in\n "sys.exc_traceback" or "sys.last_traceback". Circular references\n which are garbage are detected when the option cycle detector is\n enabled (it\'s on by default), but can only be cleaned up if there\n are no Python-level "__del__()" methods involved. Refer to the\n documentation for the "gc" module for more information about how\n "__del__()" methods are handled by the cycle detector,\n particularly the description of the "garbage" value.\n\n Warning: Due to the precarious circumstances under which\n "__del__()" methods are invoked, exceptions that occur during\n their execution are ignored, and a warning is printed to\n "sys.stderr" instead. Also, when "__del__()" is invoked in\n response to a module being deleted (e.g., when execution of the\n program is done), other globals referenced by the "__del__()"\n method may already have been deleted or in the process of being\n torn down (e.g. the import machinery shutting down). For this\n reason, "__del__()" methods should do the absolute minimum needed\n to maintain external invariants. Starting with version 1.5,\n Python guarantees that globals whose name begins with a single\n underscore are deleted from their module before other globals are\n deleted; if no other references to such globals exist, this may\n help in assuring that imported modules are still available at the\n time when the "__del__()" method is called.\n\n See also the "-R" command-line option.\n\nobject.__repr__(self)\n\n Called by the "repr()" built-in function and by string conversions\n (reverse quotes) to compute the "official" string representation of\n an object. If at all possible, this should look like a valid\n Python expression that could be used to recreate an object with the\n same value (given an appropriate environment). If this is not\n possible, a string of the form "<...some useful description...>"\n should be returned. The return value must be a string object. If a\n class defines "__repr__()" but not "__str__()", then "__repr__()"\n is also used when an "informal" string representation of instances\n of that class is required.\n\n This is typically used for debugging, so it is important that the\n representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n Called by the "str()" built-in function and by the "print"\n statement to compute the "informal" string representation of an\n object. This differs from "__repr__()" in that it does not have to\n be a valid Python expression: a more convenient or concise\n representation may be used instead. The return value must be a\n string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n New in version 2.1.\n\n These are the so-called "rich comparison" methods, and are called\n for comparison operators in preference to "__cmp__()" below. The\n correspondence between operator symbols and method names is as\n follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",\n "x==y" calls "x.__eq__(y)", "x!=y" and "x<>y" call "x.__ne__(y)",\n "x>y" calls "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".\n\n A rich comparison method may return the singleton "NotImplemented"\n if it does not implement the operation for a given pair of\n arguments. By convention, "False" and "True" are returned for a\n successful comparison. However, these methods can return any value,\n so if the comparison operator is used in a Boolean context (e.g.,\n in the condition of an "if" statement), Python will call "bool()"\n on the value to determine if the result is true or false.\n\n There are no implied relationships among the comparison operators.\n The truth of "x==y" does not imply that "x!=y" is false.\n Accordingly, when defining "__eq__()", one should also define\n "__ne__()" so that the operators will behave as expected. See the\n paragraph on "__hash__()" for some important notes on creating\n *hashable* objects which support custom comparison operations and\n are usable as dictionary keys.\n\n There are no swapped-argument versions of these methods (to be used\n when the left argument does not support the operation but the right\n argument does); rather, "__lt__()" and "__gt__()" are each other\'s\n reflection, "__le__()" and "__ge__()" are each other\'s reflection,\n and "__eq__()" and "__ne__()" are their own reflection.\n\n Arguments to rich comparison methods are never coerced.\n\n To automatically generate ordering operations from a single root\n operation, see "functools.total_ordering()".\n\nobject.__cmp__(self, other)\n\n Called by comparison operations if rich comparison (see above) is\n not defined. Should return a negative integer if "self < other",\n zero if "self == other", a positive integer if "self > other". If\n no "__cmp__()", "__eq__()" or "__ne__()" operation is defined,\n class instances are compared by object identity ("address"). See\n also the description of "__hash__()" for some important notes on\n creating *hashable* objects which support custom comparison\n operations and are usable as dictionary keys. (Note: the\n restriction that exceptions are not propagated by "__cmp__()" has\n been removed since Python 1.5.)\n\nobject.__rcmp__(self, other)\n\n Changed in version 2.1: No longer supported.\n\nobject.__hash__(self)\n\n Called by built-in function "hash()" and for operations on members\n of hashed collections including "set", "frozenset", and "dict".\n "__hash__()" should return an integer. The only required property\n is that objects which compare equal have the same hash value; it is\n advised to somehow mix together (e.g. using exclusive or) the hash\n values for the components of the object that also play a part in\n comparison of objects.\n\n If a class does not define a "__cmp__()" or "__eq__()" method it\n should not define a "__hash__()" operation either; if it defines\n "__cmp__()" or "__eq__()" but not "__hash__()", its instances will\n not be usable in hashed collections. If a class defines mutable\n objects and implements a "__cmp__()" or "__eq__()" method, it\n should not implement "__hash__()", since hashable collection\n implementations require that an object\'s hash value is immutable\n (if the object\'s hash value changes, it will be in the wrong hash\n bucket).\n\n User-defined classes have "__cmp__()" and "__hash__()" methods by\n default; with them, all objects compare unequal (except with\n themselves) and "x.__hash__()" returns a result derived from\n "id(x)".\n\n Classes which inherit a "__hash__()" method from a parent class but\n change the meaning of "__cmp__()" or "__eq__()" such that the hash\n value returned is no longer appropriate (e.g. by switching to a\n value-based concept of equality instead of the default identity\n based equality) can explicitly flag themselves as being unhashable\n by setting "__hash__ = None" in the class definition. Doing so\n means that not only will instances of the class raise an\n appropriate "TypeError" when a program attempts to retrieve their\n hash value, but they will also be correctly identified as\n unhashable when checking "isinstance(obj, collections.Hashable)"\n (unlike classes which define their own "__hash__()" to explicitly\n raise "TypeError").\n\n Changed in version 2.5: "__hash__()" may now also return a long\n integer object; the 32-bit integer is then derived from the hash of\n that object.\n\n Changed in version 2.6: "__hash__" may now be set to "None" to\n explicitly flag instances of a class as unhashable.\n\nobject.__nonzero__(self)\n\n Called to implement truth value testing and the built-in operation\n "bool()"; should return "False" or "True", or their integer\n equivalents "0" or "1". When this method is not defined,\n "__len__()" is called, if it is defined, and the object is\n considered true if its result is nonzero. If a class defines\n neither "__len__()" nor "__nonzero__()", all its instances are\n considered true.\n\nobject.__unicode__(self)\n\n Called to implement "unicode()" built-in; should return a Unicode\n object. When this method is not defined, string conversion is\n attempted, and the result of string conversion is converted to\n Unicode using the system default encoding.\n', namespace
42 'id-classes': u'\nReserved classes of identifiers\n*******************************\n\nCertain classes of identifiers (besides keywords) have special\nmeanings. These classes are identified by the patterns of leading and\ntrailing underscore characters:\n\n"_*"\n Not imported by "from module import *". The special identifier "_"\n is used in the interactive interpreter to store the result of the\n last evaluation; it is stored in the "__builtin__" module. When\n not in interactive mode, "_" has no special meaning and is not\n defined. See section The import statement.\n\n Note: The name "_" is often used in conjunction with\n internationalization; refer to the documentation for the\n "gettext" module for more information on this convention.\n\n"__*__"\n System-defined names. These names are defined by the interpreter\n and its implementation (including the standard library). Current\n system names are discussed in the Special method names section and\n elsewhere. More will likely be defined in future versions of\n Python. *Any* use of "__*__" names, in any context, that does not\n follow explicitly documented use, is subject to breakage without\n warning.\n\n"__*"\n Class-private names. Names in this category, when used within the\n context of a class definition, are re-written to use a mangled form\n to help avoid name clashes between "private" attributes of base and\n derived classes. See section Identifiers (Names).\n',
43 'identifiers': u'\nIdentifiers and keywords\n************************\n\nIdentifiers (also referred to as *names*) are described by the\nfollowing lexical definitions:\n\n identifier ::= (letter|"_") (letter | digit | "_")*\n letter ::= lowercase | uppercase\n lowercase ::= "a"..."z"\n uppercase ::= "A"..."Z"\n digit ::= "0"..."9"\n\nIdentifiers are unlimited in length. Case is significant.\n\n\nKeywords\n========\n\nThe following identifiers are used as reserved words, or *keywords* of\nthe language, and cannot be used as ordinary identifiers. They must\nbe spelled exactly as written here:\n\n and del from not while\n as elif global or with\n assert else if pass yield\n break except import print\n class exec in raise\n continue finally is return\n def for lambda try\n\nChanged in version 2.4: "None" became a constant and is now recognized\nby the compiler as a name for the built-in object "None". Although it\nis not a keyword, you cannot assign a different object to it.\n\nChanged in version 2.5: Using "as" and "with" as identifiers triggers\na warning. To use them as keywords, enable the "with_statement"\nfuture feature .\n\nChanged in version 2.6: "as" and "with" are full keywords.\n\n\nReserved classes of identifiers\n===============================\n\nCertain classes of identifiers (besides keywords) have special\nmeanings. These classes are identified by the patterns of leading and\ntrailing underscore characters:\n\n"_*"\n Not imported by "from module import *". The special identifier "_"\n is used in the interactive interpreter to store the result of the\n last evaluation; it is stored in the "__builtin__" module. When\n not in interactive mode, "_" has no special meaning and is not\n defined. See section The import statement.\n\n Note: The name "_" is often used in conjunction with\n internationalization; refer to the documentation for the\n "gettext" module for more information on this convention.\n\n"__*__"\n System-defined names. These names are defined by the interpreter\n and its implementation (including the standard library). Current\n system names are discussed in the Special method names section and\n elsewhere. More will likely be defined in future versions of\n Python. *Any* use of "__*__" names, in any context, that does not\n follow explicitly documented use, is subject to breakage without\n warning.\n\n"__*"\n Class-private names. Names in this category, when used within the\n context of a class definition, are re-written to use a mangled form\n to help avoid name clashes between "private" attributes of base and\n derived classes. See section Identifiers (Names).\n', namespace
47 'in': u'\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation. Also unlike C, expressions like "a < b < c" have the\ninterpretation that is conventional in mathematics:\n\n comparison ::= or_expr ( comp_operator or_expr )*\n comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "<>" | "!="\n | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: "True" or "False".\n\nComparisons can be chained arbitrarily, e.g., "x < y <= z" is\nequivalent to "x < y and y <= z", except that "y" is evaluated only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y\nopN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe forms "<>" and "!=" are equivalent; for consistency with C, "!="\nis preferred; where "!=" is mentioned below "<>" is also accepted.\nThe "<>" spelling is considered obsolescent.\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects. The objects need not have the same type. If both are\nnumbers, they are converted to a common type. Otherwise, objects of\ndifferent types *always* compare unequal, and are ordered consistently\nbut arbitrarily. You can control comparison behavior of objects of\nnon-built-in types by defining a "__cmp__" method or rich comparison\nmethods like "__gt__", described in section Special method names.\n\n(This unusual definition of comparison was used to simplify the\ndefinition of operations like sorting and the "in" and "not in"\noperators. In the future, the comparison rules for objects of\ndifferent types are likely to change.)\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Strings are compared lexicographically using the numeric\n equivalents (the result of the built-in function "ord()") of their\n characters. Unicode and 8-bit strings are fully interoperable in\n this behavior. [4]\n\n* Tuples and lists are compared lexicographically using comparison\n of corresponding elements. This means that to compare equal, each\n element must compare equal and the two sequences must be of the same\n type and have the same length.\n\n If not equal, the sequences are ordered the same as their first\n differing elements. For example, "cmp([1,2,x], [1,2,y])" returns\n the same as "cmp(x,y)". If the corresponding element does not\n exist, the shorter sequence is ordered first (for example, "[1,2] <\n [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if their sorted\n (key, value) lists compare equal. [5] Outcomes other than equality\n are resolved consistently, but are not otherwise defined. [6]\n\n* Most other objects of built-in types compare unequal unless they\n are the same object; the choice whether one object is considered\n smaller or larger than another one is made arbitrarily but\n consistently within one execution of a program.\n\nThe operators "in" and "not in" test for collection membership. "x in\ns" evaluates to true if *x* is a member of the collection *s*, and\nfalse otherwise. "x not in s" returns the negation of "x in s". The\ncollection membership test has traditionally been bound to sequences;\nan object is a member of a collection if the collection is a sequence\nand contains an element equal to that object. However, it make sense\nfor many other object types to support membership tests without being\na sequence. In particular, dictionaries (for keys) and sets support\nmembership testing.\n\nFor the list and tuple types, "x in y" is true if and only if there\nexists an index *i* such that either "x is y[i]" or "x == y[i]" is\ntrue.\n\nFor the Unicode and string types, "x in y" is true if and only if *x*\nis a substring of *y*. An equivalent test is "y.find(x) != -1".\nNote, *x* and *y* need not be the same type; consequently, "u\'ab\' in\n\'abc\'" will return "True". Empty strings are always considered to be a\nsubstring of any other string, so """ in "abc"" will return "True".\n\nChanged in version 2.3: Previously, *x* was required to be a string of\nlength "1".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y". If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative integer index *i* such that "x == y[i]", and all lower\ninteger indices do not raise "IndexError" exception. (If any other\nexception is raised, it is as if "in" raised that exception).\n\nThe operator "not in" is defined to have the inverse true value of\n"in".\n\nThe operators "is" and "is not" test for object identity: "x is y" is\ntrue if and only if *x* and *y* are the same object. "x is not y"\nyields the inverse truth value. [7]\n',
54 'objects': u'\nObjects, values and types\n*************************\n\n*Objects* are Python\'s abstraction for data. All data in a Python\nprogram is represented by objects or by relations between objects. (In\na sense, and in conformance to Von Neumann\'s model of a "stored\nprogram computer," code is also represented by objects.)\n\nEvery object has an identity, a type and a value. An object\'s\n*identity* never changes once it has been created; you may think of it\nas the object\'s address in memory. The \'"is"\' operator compares the\nidentity of two objects; the "id()" function returns an integer\nrepresenting its identity (currently implemented as its address). An\nobject\'s *type* is also unchangeable. [1] An object\'s type determines\nthe operations that the object supports (e.g., "does it have a\nlength?") and also defines the possible values for objects of that\ntype. The "type()" function returns an object\'s type (which is an\nobject itself). The *value* of some objects can change. Objects\nwhose value can change are said to be *mutable*; objects whose value\nis unchangeable once they are created are called *immutable*. (The\nvalue of an immutable container object that contains a reference to a\nmutable object can change when the latter\'s value is changed; however\nthe container is still considered immutable, because the collection of\nobjects it contains cannot be changed. So, immutability is not\nstrictly the same as having an unchangeable value, it is more subtle.)\nAn object\'s mutability is determined by its type; for instance,\nnumbers, strings and tuples are immutable, while dictionaries and\nlists are mutable.\n\nObjects are never explicitly destroyed; however, when they become\nunreachable they may be garbage-collected. An implementation is\nallowed to postpone garbage collection or omit it altogether --- it is\na matter of implementation quality how garbage collection is\nimplemented, as long as no objects are collected that are still\nreachable.\n\n**CPython implementation detail:** CPython currently uses a reference-\ncounting scheme with (optional) delayed detection of cyclically linked\ngarbage, which collects most objects as soon as they become\nunreachable, but is not guaranteed to collect garbage containing\ncircular references. See the documentation of the "gc" module for\ninformation on controlling the collection of cyclic garbage. Other\nimplementations act differently and CPython may change. Do not depend\non immediate finalization of objects when they become unreachable (ex:\nalways close files).\n\nNote that the use of the implementation\'s tracing or debugging\nfacilities may keep objects alive that would normally be collectable.\nAlso note that catching an exception with a \'"try"..."except"\'\nstatement may keep objects alive.\n\nSome objects contain references to "external" resources such as open\nfiles or windows. It is understood that these resources are freed\nwhen the object is garbage-collected, but since garbage collection is\nnot guaranteed to happen, such objects also provide an explicit way to\nrelease the external resource, usually a "close()" method. Programs\nare strongly recommended to explicitly close such objects. The\n\'"try"..."finally"\' statement provides a convenient way to do this.\n\nSome objects contain references to other objects; these are called\n*containers*. Examples of containers are tuples, lists and\ndictionaries. The references are part of a container\'s value. In\nmost cases, when we talk about the value of a container, we imply the\nvalues, not the identities of the contained objects; however, when we\ntalk about the mutability of a container, only the identities of the\nimmediately contained objects are implied. So, if an immutable\ncontainer (like a tuple) contains a reference to a mutable object, its\nvalue changes if that mutable object is changed.\n\nTypes affect almost all aspects of object behavior. Even the\nimportance of object identity is affected in some sense: for immutable\ntypes, operations that compute new values may actually return a\nreference to any existing object with the same type and value, while\nfor mutable objects this is not allowed. E.g., after "a = 1; b = 1",\n"a" and "b" may or may not refer to the same object with the value\none, depending on the implementation, but after "c = []; d = []", "c"\nand "d" are guaranteed to refer to two different, unique, newly\ncreated empty lists. (Note that "c = d = []" assigns the same object\nto both "c" and "d".)\n',
55 'operator-summary': u'\nOperator precedence\n*******************\n\nThe following table summarizes the operator precedences in Python,\nfrom lowest precedence (least binding) to highest precedence (most\nbinding). Operators in the same box have the same precedence. Unless\nthe syntax is explicitly given, operators are binary. Operators in\nthe same box group left to right (except for comparisons, including\ntests, which all have the same precedence and chain from left to right\n--- see section Comparisons --- and exponentiation, which groups from\nright to left).\n\n+-------------------------------------------------+---------------------------------------+\n| Operator | Description |\n+=================================================+=======================================+\n| "lambda" | Lambda expression |\n+-------------------------------------------------+---------------------------------------+\n| "if" -- "else" | Conditional expression |\n+-------------------------------------------------+---------------------------------------+\n| "or" | Boolean OR |\n+-------------------------------------------------+---------------------------------------+\n| "and" | Boolean AND |\n+-------------------------------------------------+---------------------------------------+\n| "not" "x" | Boolean NOT |\n+-------------------------------------------------+---------------------------------------+\n| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership |\n| ">=", "<>", "!=", "==" | tests and identity tests |\n+-------------------------------------------------+---------------------------------------+\n| "|" | Bitwise OR |\n+-------------------------------------------------+---------------------------------------+\n| "^" | Bitwise XOR |\n+-------------------------------------------------+---------------------------------------+\n| "&" | Bitwise AND |\n+-------------------------------------------------+---------------------------------------+\n| "<<", ">>" | Shifts |\n+-------------------------------------------------+---------------------------------------+\n| "+", "-" | Addition and subtraction |\n+-------------------------------------------------+---------------------------------------+\n| "*", "/", "//", "%" | Multiplication, division, remainder |\n| | [8] |\n+-------------------------------------------------+---------------------------------------+\n| "+x", "-x", "~x" | Positive, negative, bitwise NOT |\n+-------------------------------------------------+---------------------------------------+\n| "**" | Exponentiation [9] |\n+-------------------------------------------------+---------------------------------------+\n| "x[index]", "x[index:index]", | Subscription, slicing, call, |\n| "x(arguments...)", "x.attribute" | attribute reference |\n+-------------------------------------------------+---------------------------------------+\n| "(expressions...)", "[expressions...]", "{key: | Binding or tuple display, list |\n| value...}", "`expressions...`" | display, dictionary display, string |\n| | conversion |\n+-------------------------------------------------+---------------------------------------+\n\n-[ Footnotes ]-\n\n[1] In Python 2.3 and later releases, a list comprehension "leaks"\n the control variables of each "for" it contains into the\n containing scope. However, this behavior is deprecated, and\n relying on it will not work in Python 3.\n\n[2] While "abs(x%y) < abs(y)" is true mathematically, for floats\n it may not be true numerically due to roundoff. For example, and\n assuming a platform on which a Python float is an IEEE 754 double-\n precision number, in order that "-1e-100 % 1e100" have the same\n sign as "1e100", the computed result is "-1e-100 + 1e100", which\n is numerically exactly equal to "1e100". The function\n "math.fmod()" returns a result whose sign matches the sign of the\n first argument instead, and so returns "-1e-100" in this case.\n Which approach is more appropriate depends on the application.\n\n[3] If x is very close to an exact integer multiple of y, it\'s\n possible for "floor(x/y)" to be one larger than "(x-x%y)/y" due to\n rounding. In such cases, Python returns the latter result, in\n order to preserve that "divmod(x,y)[0] * y + x % y" be very close\n to "x".\n\n[4] While comparisons between unicod
[all...]
/external/protobuf/php/ext/google/protobuf/
H A Dupb.c361 /* Def graph contains FieldDefs between each MessageDef, so double the
1219 static bool between(int32_t x, int32_t low, int32_t high) { function
1223 bool upb_fielddef_checklabel(int32_t label) { return between(label, 1, 3); }
1224 bool upb_fielddef_checktype(int32_t type) { return between(type, 1, 11); }
1225 bool upb_fielddef_checkintfmt(int32_t fmt) { return between(fmt, 1, 3); }
1228 return between(type, 1, 18);
7479 /* Error messages that are shared between the bytecode and JIT decoders. */
8564 * the middle. The seams between segments are where varints will be inserted
/external/protobuf/ruby/ext/google/protobuf_c/
H A Dupb.c425 /* Def graph contains FieldDefs between each MessageDef, so double the
1368 static bool between(int32_t x, int32_t low, int32_t high) { function
1372 bool upb_fielddef_checklabel(int32_t label) { return between(label, 1, 3); }
1373 bool upb_fielddef_checktype(int32_t type) { return between(type, 1, 11); }
1374 bool upb_fielddef_checkintfmt(int32_t fmt) { return between(fmt, 1, 3); }
1377 return between(type, 1, 18);
8163 /* Error messages that are shared between the bytecode and JIT decoders. */
9275 * the middle. The seams between segments are where varints will be inserted
/external/owasp/sanitizer/distrib/lib/
H A Dowasp-java-html-sanitizer.jarMETA-INF/ META-INF/MANIFEST.MF org/ org/owasp/ org/owasp/html/ org/owasp/html/AbstractTokenStream ...
/external/caliper/lib/
H A Djoda-time-2.1.jarMETA-INF/ META-INF/MANIFEST.MF org/ org/joda/ org/joda/time/ org/joda/time/base/ ...
/external/guice/extensions/persist/lib/
H A Dhibernate3.jarMETA-INF/ META-INF/MANIFEST.MF org/ org/hibernate/ org/hibernate/AssertionFailure.class AssertionFailure. ...
/external/conscrypt/benchmark-android/
H A Dvogar.jarMETA-INF/ META-INF/MANIFEST.MF vogar/ vogar/TestProperties.class TestProperties.java package vogar ...

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