1 files changed, 176 insertions, 0 deletions
diff --git a/gn2/wqflask/static/new/javascript/stats.js b/gn2/wqflask/static/new/javascript/stats.js
new file mode 100644
index 00000000..6c443ab3
--- /dev/null
+++ b/gn2/wqflask/static/new/javascript/stats.js
@@ -0,0 +1,176 @@
+// Generated by CoffeeScript 1.8.0
+var Stats, bxd_only;
+
+Stats = (function() {
+  function Stats(the_values) {
+    this.the_values = the_values;
+  }
+
+  Stats.prototype.add_value = function(value) {
+    return this.the_values.push(value);
+  };
+
+  Stats.prototype.n_of_samples = function() {
+    return this.the_values.length;
+  };
+
+  Stats.prototype.sum = function() {
+    var total, value, _i, _len, _ref;
+    total = 0;
+    _ref = this.the_values;
+    for (_i = 0, _len = _ref.length; _i < _len; _i++) {
+      value = _ref[_i];
+      total += value;
+    }
+    return total;
+  };
+
+  Stats.prototype.mean = function() {
+    return this.sum() / this.n_of_samples();
+  };
+
+  Stats.prototype.median = function() {
+    var is_odd, median_position, the_values_sorted;
+    is_odd = this.the_values.length % 2;
+    median_position = Math.floor(this.the_values.length / 2);
+    the_values_sorted = this.the_values.sort(function(a, b) {
+      return a - b;
+    });
+    if (is_odd) {
+      return the_values_sorted[median_position];
+    } else {
+      return (the_values_sorted[median_position] + the_values_sorted[median_position - 1]) / 2;
+    }
+  };
+
+  Stats.prototype.std_dev = function() {
+    var step_a, step_b, sum, value, _i, _len, _ref;
+    sum = 0;
+    _ref = this.the_values;
+    for (_i = 0, _len = _ref.length; _i < _len; _i++) {
+      value = _ref[_i];
+      step_a = Math.pow(value - this.mean(), 2);
+      sum += step_a;
+    }
+    step_b = sum / this.the_values.length;
+    return Math.sqrt(step_b);
+  };
+
+  Stats.prototype.std_error = function() {
+    return this.std_dev() / Math.sqrt(this.n_of_samples());
+  };
+
+  Stats.prototype.min = function() {
+    return Math.min.apply(Math, this.the_values);
+  };
+
+  Stats.prototype.max = function() {
+    return Math.max.apply(Math, this.the_values);
+  };
+
+  Stats.prototype.range = function() {
+    if (js_data.dataset_type == "ProbeSet"){
+      if (js_data.data_scale == "linear_positive"){
+        return Math.log2(this.max()) - Math.log2(this.min());
+      } else {
+        return this.max() - this.min()
+      }
+    } else {
+      return this.max() - this.min()
+    }
+  };
+
+  Stats.prototype.range_fold = function() {
+    if (js_data.dataset_type == "ProbeSet"){
+      return Math.pow(2, this.range());
+    } else {
+      return this.range()
+    }
+  };
+
+  Stats.prototype.interquartile = function() {
+    var iq, length, q1, q3;
+    length = this.the_values.length;
+    if (js_data.dataset_type == "ProbeSet" && js_data.data_scale == "linear_positive") {
+      q1 = Math.log2(this.the_values[Math.floor(length * .25)]);
+      q3 = Math.log2(this.the_values[Math.floor(length * .75)]);
+    } else {
+      q1 = this.the_values[Math.floor(length * .25)];
+      q3 = this.the_values[Math.floor(length * .75)];
+    }
+    iq = q3 - q1;
+    if (js_data.dataset_type == "ProbeSet") {
+        return Math.pow(2, iq);
+    } else {
+        return iq;
+    }
+  };
+
+  Stats.prototype.skewness = function() {
+    var len = this.the_values.length,
+        delta = 0,
+        delta_n = 0,
+        term1 = 0,
+        N = 0,
+        mean = 0,
+        M2 = 0,
+        M3 = 0,
+        g;
+
+    for ( var i = 0; i < len; i++ ) {
+      N += 1;
+
+      delta = this.the_values[ i ] - mean;
+      delta_n = delta / N;
+
+      term1 = delta * delta_n * (N-1);
+
+      M3 += term1*delta_n*(N-2) - 3*delta_n*M2;
+      M2 += term1;
+      mean += delta_n;
+    }
+    // Calculate the population skewness:
+    g = Math.sqrt( N )*M3 / Math.pow( M2, 3/2 );
+
+    // Return the corrected sample skewness:
+    return Math.sqrt( N*(N-1))*g / (N-2);
+  };
+
+  Stats.prototype.kurtosis = function() {
+    var len = this.the_values.length,
+        delta = 0,
+        delta_n = 0,
+        delta_n2 = 0,
+        term1 = 0,
+        N = 0,
+        mean = 0,
+        M2 = 0,
+        M3 = 0,
+        M4 = 0,
+        g;
+
+    for ( var i = 0; i < len; i++ ) {
+      N += 1;
+
+      delta = this.the_values[ i ] - mean;
+      delta_n = delta / N;
+      delta_n2 = delta_n * delta_n;
+
+      term1 = delta * delta_n * (N-1);
+
+      M4 += term1*delta_n2*(N*N - 3*N + 3) + 6*delta_n2*M2 - 4*delta_n*M3;
+      M3 += term1*delta_n*(N-2) - 3*delta_n*M2;
+      M2 += term1;
+      mean += delta_n;
+    }
+    // Calculate the population excess kurtosis:
+    g = N*M4 / (M2*M2) - 3;
+    //Return the corrected sample excess kurtosis:
+    return (N-1) / ( (N-2)*(N-3) ) * ( (N+1)*g + 6 );
+  };
+
+  return Stats;
+
+})();
+
+window.Stats = Stats;