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https://github.com/sudoxnym/habitica.git
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Merge remote-tracking branch 'origin/sabe/attributes' into develop
This commit is contained in:
Tyler Renelle
commit
f4f6dcebdc
2 changed files with 107 additions and 20 deletions
86
dist/habitrpg-shared.js
vendored
86
dist/habitrpg-shared.js
vendored
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@ -63,7 +63,7 @@ process.chdir = function (dir) {
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};
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},{}],3:[function(require,module,exports){
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var global=self;/**
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var global=typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {};/**
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* @license
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* Lo-Dash 2.4.1 (Custom Build) <http://lodash.com/>
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* Build: `lodash modern -o ./dist/lodash.js`
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@ -11777,6 +11777,9 @@ var process=require("__browserify_process");(function() {
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}
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}
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if (task.type !== 'reward') {
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if (user.preferences.automaticAllocation === true && user.preferences.allocationMode === 'taskbased') {
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user.stats.training[task.attribute] += nextDelta;
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}
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adjustAmt = nextDelta;
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if (direction === 'up' && task.type !== 'reward' && !(task.type === 'habit' && !task.down)) {
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adjustAmt = nextDelta * (1 + user._statsComputed.str * .004);
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@ -12055,8 +12058,70 @@ var process=require("__browserify_process");(function() {
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{update} if aggregated changes, pass in userObj as update. otherwise commits will be made immediately
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*/
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autoAllocate: function() {
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var diff, ideal, preference, suggested;
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switch (user.preferences.allocationMode) {
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case "flat":
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suggested = Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per);
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if (user.stats.int === suggested) {
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return "int";
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} else if (user.stats.per === suggested) {
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return "per";
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} else if (user.stats.str === suggested) {
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return "str";
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} else {
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return "con";
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}
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break;
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case "classbased":
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ideal = [user.stats.lvl / 7 * 3, user.stats.lvl / 7 * 2, user.stats.lvl / 7, user.stats.lvl / 7];
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switch (user.stats["class"]) {
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case "wizard":
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preference = ["int", "per", "con", "str"];
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break;
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case "rogue":
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preference = ["per", "str", "int", "con"];
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break;
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case "healer":
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preference = ["con", "int", "str", "per"];
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break;
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default:
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preference = ["str", "con", "per", "int"];
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}
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diff = [user.stats[preference[0]] - ideal[0], user.stats[preference[1]] - ideal[1], user.stats[preference[2]] - ideal[2], user.stats[preference[3]] - ideal[3]];
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suggested = _.findIndex(diff, (function(val) {
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if (val === _.min(diff)) {
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return true;
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}
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}));
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if (suggested === -1) {
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return "str";
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} else {
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return preference[suggested];
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}
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break;
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case "taskbased":
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suggested = _.findKey(user.stats.training, (function(val) {
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if (val === _.max(user.stats.training)) {
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return val;
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}
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}));
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user.stats.training.str = 0;
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user.stats.training.int = 0;
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user.stats.training.con = 0;
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user.stats.training.per = 0;
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if (suggested === void 0) {
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return "str";
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} else {
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return suggested;
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}
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break;
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default:
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return "str";
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}
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},
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updateStats: function(stats) {
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var suggested, tallies, tnl;
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var suggested, tnl;
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if (stats.hp <= 0) {
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return user.stats.hp = 0;
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}
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@ -12075,22 +12140,7 @@ var process=require("__browserify_process");(function() {
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user.stats.lvl++;
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tnl = api.tnl(user.stats.lvl);
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if (user.preferences.automaticAllocation) {
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tallies = _.reduce(user.tasks, (function(m, v) {
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m[v.attribute || 'str'] += v.value;
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return m;
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}), {
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str: 0,
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int: 0,
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con: 0,
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per: 0
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});
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suggested = _.reduce(tallies, (function(m, v, k) {
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if (v > tallies[m]) {
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return k;
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} else {
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return m;
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}
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}), 'str');
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suggested = user.fns.autoAllocate();
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user.stats[suggested]++;
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} else {
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user.stats.points = user.stats.lvl - (user.stats.con + user.stats.str + user.stats.per + user.stats.int);
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@ -747,6 +747,7 @@ api.wrap = (user) ->
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nextDelta *= task.checklist.length
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unless task.type is 'reward'
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if (user.preferences.automaticAllocation is true and user.preferences.allocationMode is 'taskbased') then user.stats.training[task.attribute] += nextDelta
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adjustAmt = nextDelta
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# ===== STRENGTH =====
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# (Only for up-scoring, ignore up-onlies and rewards)
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@ -1004,6 +1005,43 @@ api.wrap = (user) ->
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{stats} new stats
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{update} if aggregated changes, pass in userObj as update. otherwise commits will be made immediately
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###
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autoAllocate: ->
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switch user.preferences.allocationMode
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when "flat"
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suggested = Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per)
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if user.stats.int is suggested # In case of ties, favor INT first, to get the next point sooner
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return "int"
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else if user.stats.per is suggested # Then favor PER, it's a god stat
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return "per"
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else if user.stats.str is suggested # Then favor STR, everyone loves crits
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return "str"
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else
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return "con" # CON, the unsexiest of attributes
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when "classbased"
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# Attributes get 3:2:1:1 per 7 levels.
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ideal = [(user.stats.lvl / 7 * 3), (user.stats.lvl / 7 * 2), (user.stats.lvl / 7), (user.stats.lvl / 7)]
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# Primary, secondary etc. attributes aren't explicitly defined, so hardcode them. In order as above
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switch user.stats.class
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when "wizard" then preference = ["int", "per", "con", "str"]
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when "rogue" then preference = ["per", "str", "int", "con"]
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when "healer" then preference = ["con", "int", "str", "per"]
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else preference = ["str", "con", "per", "int"]
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# Get the difference between the ideal attribute spread according to level, and the user's current spread.
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diff = [(user.stats[preference[0]]-ideal[0]),(user.stats[preference[1]]-ideal[1]),(user.stats[preference[2]]-ideal[2]),(user.stats[preference[3]]-ideal[3])]
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suggested = _.findIndex(diff, ((val) -> if val is _.min(diff) then true)) # Returns the index of the first attribute that's furthest behind the ideal
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if suggested is -1 then return "str" else return preference[suggested] # If _.findIndex failed, we'd get a -1...
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when "taskbased"
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suggested = _.findKey(user.stats.training, ((val) -> if val is _.max(user.stats.training) then val)) # Returns the stat that's been trained up the most this level
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# FIXME Reset training for this level. Tried _.each but couldn't get it to take.
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user.stats.training.str = 0
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user.stats.training.int = 0
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user.stats.training.con = 0
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user.stats.training.per = 0
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if suggested is undefined then return "str" else return suggested # Failed _.findkey gives undefined
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# tallies = _.reduce user.tasks, ((m,v)-> m[v.attribute or 'str'] += v.value;m), {str:0,int:0,con:0,per:0}
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# suggested = _.reduce tallies, ((m,v,k)-> if v>tallies[m] then k else m), 'str'
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else return "str" # if all else fails, dump into STR
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updateStats: (stats) ->
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# Game Over
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return user.stats.hp=0 if stats.hp <= 0
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@ -1029,8 +1067,7 @@ api.wrap = (user) ->
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# Auto-allocate a point, or give them a new manual point
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if user.preferences.automaticAllocation
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tallies = _.reduce user.tasks, ((m,v)-> m[v.attribute or 'str'] += v.value;m), {str:0,int:0,con:0,per:0}
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suggested = _.reduce tallies, ((m,v,k)-> if v>tallies[m] then k else m), 'str'
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suggested = user.fns.autoAllocate()
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user.stats[suggested]++
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else
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# add new allocatable points. We could do user.stats.points++, but this does a fail-safe just in case
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