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2012-12-31 00:23:14.528+0000 INFO [o.n.k.i.DiagnosticsManager]: --- INITIALIZED diagnostics START --- | |
2012-12-31 00:23:14.529+0000 INFO [o.n.k.i.DiagnosticsManager]: Neo4j Kernel properties: | |
2012-12-31 00:23:14.532+0000 INFO [o.n.k.i.DiagnosticsManager]: neostore.propertystore.db.mapped_memory=3000M | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: cache_type=gcr | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: read_only=true | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: use_memory_mapped_buffers=true | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: neostore.nodestore.db.mapped_memory=50M | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: ha.pull_interval=10 | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: neostore.relationshipstore.db.mapped_memory=1200M | |
2012-12-31 00:23:14.533+0000 INFO [o.n.k.i.DiagnosticsManager]: neostore.propertystore.db.strings.mapped_memory=1000M |
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procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu----- | |
r b swpd free buff cache si so bi bo in cs us sy id wa st | |
0 0 175424 6648152 656812 17511332 0 0 38 20 0 0 1 1 98 0 0 | |
1 0 175424 6648136 656812 17511336 0 0 0 4 3436 21147 1 2 97 0 0 | |
0 0 175424 6648136 656816 17511336 0 0 0 11 3361 21589 1 2 97 0 0 | |
0 0 175424 6648136 656816 17511336 0 0 0 5 3418 21793 1 2 97 0 0 | |
0 0 175424 6648136 656816 17511336 0 0 0 4 3687 21588 1 2 97 0 0 | |
0 0 175424 6648136 656816 17511344 0 0 3 16 3599 21750 1 2 97 0 0 | |
0 0 175424 6647988 656820 17511348 0 0 0 21 3850 22033 1 2 97 0 0 | |
0 0 175424 6647988 656820 17511348 0 0 0 3 3926 21425 1 2 96 0 0 |
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procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu----- | |
r b swpd free buff cache si so bi bo in cs us sy id wa st | |
0 0 175424 7509264 656956 17513120 0 0 38 20 0 0 1 1 98 0 0 | |
1 0 175424 7487780 656968 17517780 0 0 1651 0 5169 22462 5 4 88 4 0 | |
1 0 175424 7450576 656976 17518080 0 0 5 156 7185 24458 21 3 76 0 0 | |
1 0 175424 7453236 656976 17518080 0 0 0 168 5435 22729 12 4 83 0 0 | |
1 0 175424 7428040 656984 17518080 0 0 0 4 5201 20206 13 3 84 0 0 | |
9 0 175424 7370016 656984 17518080 0 0 0 12 7874 21739 25 4 71 0 0 | |
3 0 175424 7312612 656988 17518084 0 0 1 11 7502 22395 22 4 73 0 1 | |
2 0 175424 7277488 656988 17518084 0 0 0 47 7545 23143 26 5 69 0 1 |
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/*! | |
* angular-packery | |
* Forked from angular-masonry (Pascal Hartig, weluse GmbH, http://weluse.de/) | |
*/ | |
(function () { | |
'use strict'; | |
angular.module('wu.packery', []).controller('PackeryCtrl', [ | |
'$scope', | |
'$element', | |
'$timeout', |
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var createSVG = function($scope, config) { | |
var getLabel = function(variable, circle) { | |
return variable + " (" + circle.name() + ")"; | |
}; | |
var getLabels = function() { | |
var ret = []; | |
_.each(config.groups, function(group, name) { | |
_.each( $scope.filterOnSet(group, name).all(), function(obj) { | |
var circle = obj.key.circle; |
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this.getReducedSTD = function(dimensionGroup, variable) { | |
// see https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Decremental_algorithm | |
var reduceAdd = function (p, v) { | |
var obj = p; | |
var value = +v.variables[variable]; | |
if( _.isNaN(value) ) { | |
//pass | |
} else { | |
obj.n = obj.n + 1; | |
var oldM2 = obj.M2; |
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this.getReducedSTD2 = function(dimensionGroup, variable) { | |
// see https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance | |
var reduceAdd = function (p, v) { | |
var value = +v.variables[variable]; | |
if( _.isNaN(value) ) { | |
//pass | |
} | |
else { | |
if( p.n === 0 ) { | |
p.k = value; |
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var t = beta / _sqrt; | |
console.log("degrees", degrees); | |
console.log("beta", beta, "sqrt", _sqrt); | |
console.log("t=", t, "tprob=", statDist.tprob(degrees, t)); | |
var pvalue = 2 * (1 - statDist.tprob(degrees, t)); | |
console.log("pvalue=", pvalue); | |
Tulosteet ylläolevasta: | |
degrees 82 (program):64 | |
beta 1.0097334538696747 sqrt 0.015688694906613447 (program):65 |
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// Specs: | |
// - As a default, the colour range should be scaled so, that at least 70% (..ish) of the colour range is available for the data shown. | |
// Example: we have a map with lowest positive correlation at r=0.85 and highest at r=0.99 while the negative correlations span from r=-0.2 to r=-0.35. The desired colour range would then be as follows: | |
// 100% red at r=0.99, | |
// 30% red at r=0.85, | |
// white at r=0, | |
// 30% blue at r=-0.2 and | |
// 100% blue at r=-0.35. | |
function CustomScale() { |
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