warmux(6) [debian man page]
WARMUX(6) Games Manual WARMUX(6) NAME
Warmux - A convivial mass murder game. SYNOPSIS
warmux [-h|--help] [-v|--version] [-p|--play] [-i|--internet] [-s|--server] [-c|--client [ip]] [-d|--debug debug_masks] DESCRIPTION
Have the mascots of your favorite free software battle in the Warmux arena using dynamite, grenades, baseball bat and other weapons ... Exterminate your opponent in a funny 2D toon-style scenery. Each player (2 minimum, on the same PC) controls the team of his choice (tux, gnu, firefox, wilber,...) and must destroy his adversaries using more or less casual weapons. Although a minimum of strategy is required to be victorious, Warmux is pre-eminently a "convivial mass murder" game where, turn by turn, each team attempts to produce maximum damage to their opponents. CONTROLS
* Key left/right: walk to the left / to the right * Key up/down: Aim up/down * Key shift: Smaller aim increment/decrement when used with up or down. Smaller step while walking * Key 'b': ("Backwards") jump * Key enter: ("Horizontal") jump * Key backspace: ("Vertical") jump * Key tab: Switch to next or previous (when Ctrl is pressed too) character * Key 'c': Recenter to active character * Key space: Shoot. Exceptions: the parachute is automatically deployed, and teleportation is made with a mouse click. Warning: The automatic bazooka and air strike first need a target to be selected (select it with a left click) * Mouse right click: Show/hide weapons menu * Mouse left click on the map: Select a target for teleportation, automatic bazooka, or air strike * Mouse left click on a character: Make it active (if it's allowed) * Mouse move next to screen borders: Move the map * Key F1, F2, ... F5: Change weapon in category 1, 2, ... 5 * Key F10: Toggle Fullscreen * Key 'p': Pause the game * Key escape: Leave the game (confirmation necessary) SEE ALSO
Have a look at the official website http://warmux.org On the website, you will find some useful help: * List of weapons: http://warmux.org/wiki/en/weapons.php * List of maps: http://warmux.org/wiki/en/maps.php * How to write your own map: http://warmux.org/wiki/Create_your_own_map.php * List of skins: http://warmux.org/wiki/en/skins.php * Forum: http://www.warmux.org/forum/ AUTHOR
Warmux was written by: - Jean-Christophe DUBERGA <jcduberga@gmx.de> - Laurent DEFERT SIMONNEAU <lodesi2@yahoo.fr> - Lawrence AZZOUG <lawrenceazzoug@wanadoo.fr> - Matthieu FERTRE <matthieu.fertre@free.fr> - Renaud LOTTIAUX <renaud.lottiaux@free.fr> - Victor STINNER <victor.stinner@haypocalc.com> This manual page was originally written by Jean Parpaillon <artefact@altern.org>, for the Debian project (but may be used by others). It is maintained by the Warmux team. October 28, 2007 WARMUX(6)
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gb_trees(3erl) Erlang Module Definition gb_trees(3erl) NAME
gb_trees - General Balanced Trees DESCRIPTION
An efficient implementation of Prof. Arne Andersson's General Balanced Trees. These have no storage overhead compared to unbalanced binary trees, and their performance is in general better than AVL trees. This module considers two keys as different if and only if they do not compare equal ( == ). DATA STRUCTURE
Data structure: - {Size, Tree}, where `Tree' is composed of nodes of the form: - {Key, Value, Smaller, Bigger}, and the "empty tree" node: - nil. There is no attempt to balance trees after deletions. Since deletions do not increase the height of a tree, this should be OK. Original balance condition h(T) <= ceil(c * log(|T|)) has been changed to the similar (but not quite equivalent) condition 2 ^ h(T) <= |T| ^ c . This should also be OK. Performance is comparable to the AVL trees in the Erlang book (and faster in general due to less overhead); the difference is that deletion works for these trees, but not for the book's trees. Behaviour is logarithmic (as it should be). DATA TYPES
gb_tree() = a GB tree EXPORTS
balance(Tree1) -> Tree2 Types Tree1 = Tree2 = gb_tree() Rebalances Tree1 . Note that this is rarely necessary, but may be motivated when a large number of nodes have been deleted from the tree without further insertions. Rebalancing could then be forced in order to minimise lookup times, since deletion only does not rebalance the tree. delete(Key, Tree1) -> Tree2 Types Key = term() Tree1 = Tree2 = gb_tree() Removes the node with key Key from Tree1 ; returns new tree. Assumes that the key is present in the tree, crashes otherwise. delete_any(Key, Tree1) -> Tree2 Types Key = term() Tree1 = Tree2 = gb_tree() Removes the node with key Key from Tree1 if the key is present in the tree, otherwise does nothing; returns new tree. empty() -> Tree Types Tree = gb_tree() Returns a new empty tree enter(Key, Val, Tree1) -> Tree2 Types Key = Val = term() Tree1 = Tree2 = gb_tree() Inserts Key with value Val into Tree1 if the key is not present in the tree, otherwise updates Key to value Val in Tree1 . Returns the new tree. from_orddict(List) -> Tree Types List = [{Key, Val}] Key = Val = term() Tree = gb_tree() Turns an ordered list List of key-value tuples into a tree. The list must not contain duplicate keys. get(Key, Tree) -> Val Types Key = Val = term() Tree = gb_tree() Retrieves the value stored with Key in Tree . Assumes that the key is present in the tree, crashes otherwise. lookup(Key, Tree) -> {value, Val} | none Types Key = Val = term() Tree = gb_tree() Looks up Key in Tree ; returns {value, Val} , or none if Key is not present. insert(Key, Val, Tree1) -> Tree2 Types Key = Val = term() Tree1 = Tree2 = gb_tree() Inserts Key with value Val into Tree1 ; returns the new tree. Assumes that the key is not present in the tree, crashes otherwise. is_defined(Key, Tree) -> bool() Types Tree = gb_tree() Returns true if Key is present in Tree , otherwise false . is_empty(Tree) -> bool() Types Tree = gb_tree() Returns true if Tree is an empty tree, and false otherwise. iterator(Tree) -> Iter Types Tree = gb_tree() Iter = term() Returns an iterator that can be used for traversing the entries of Tree ; see next/1 . The implementation of this is very efficient; traversing the whole tree using next/1 is only slightly slower than getting the list of all elements using to_list/1 and traversing that. The main advantage of the iterator approach is that it does not require the complete list of all elements to be built in mem- ory at one time. keys(Tree) -> [Key] Types Tree = gb_tree() Key = term() Returns the keys in Tree as an ordered list. largest(Tree) -> {Key, Val} Types Tree = gb_tree() Key = Val = term() Returns {Key, Val} , where Key is the largest key in Tree , and Val is the value associated with this key. Assumes that the tree is nonempty. map(Function, Tree1) -> Tree2 Types Function = fun(K, V1) -> V2 Tree1 = Tree2 = gb_tree() maps the function F(K, V1) -> V2 to all key-value pairs of the tree Tree1 and returns a new tree Tree2 with the same set of keys as Tree1 and the new set of values V2. next(Iter1) -> {Key, Val, Iter2} | none Types Iter1 = Iter2 = Key = Val = term() Returns {Key, Val, Iter2} where Key is the smallest key referred to by the iterator Iter1 , and Iter2 is the new iterator to be used for traversing the remaining nodes, or the atom none if no nodes remain. size(Tree) -> int() Types Tree = gb_tree() Returns the number of nodes in Tree . smallest(Tree) -> {Key, Val} Types Tree = gb_tree() Key = Val = term() Returns {Key, Val} , where Key is the smallest key in Tree , and Val is the value associated with this key. Assumes that the tree is nonempty. take_largest(Tree1) -> {Key, Val, Tree2} Types Tree1 = Tree2 = gb_tree() Key = Val = term() Returns {Key, Val, Tree2} , where Key is the largest key in Tree1 , Val is the value associated with this key, and Tree2 is this tree with the corresponding node deleted. Assumes that the tree is nonempty. take_smallest(Tree1) -> {Key, Val, Tree2} Types Tree1 = Tree2 = gb_tree() Key = Val = term() Returns {Key, Val, Tree2} , where Key is the smallest key in Tree1 , Val is the value associated with this key, and Tree2 is this tree with the corresponding node deleted. Assumes that the tree is nonempty. to_list(Tree) -> [{Key, Val}] Types Tree = gb_tree() Key = Val = term() Converts a tree into an ordered list of key-value tuples. update(Key, Val, Tree1) -> Tree2 Types Key = Val = term() Tree1 = Tree2 = gb_tree() Updates Key to value Val in Tree1 ; returns the new tree. Assumes that the key is present in the tree. values(Tree) -> [Val] Types Tree = gb_tree() Val = term() Returns the values in Tree as an ordered list, sorted by their corresponding keys. Duplicates are not removed. SEE ALSO
gb_sets(3erl) , dict(3erl) Ericsson AB stdlib 1.17.3 gb_trees(3erl)