init

client/src/components/pods/anomaly_pod.ts

@@ -94,7 +94,7 @@ export class AnomalyPod extends HasticPod<UpdateDataCallback> {
 
   public fetchData(): void {
     let to = Math.floor(Date.now() / 1000);
-    let from = to - 50000; // -50000 seconds
+    let from = to - 2592000; // -100000 seconds
 
     if(!(this.state.xValueRange[0] == 0 && this.state.xValueRange[1] == 1)) {
       [from, to] = this.state?.xValueRange;
@@ -163,4 +163,4 @@ export class AnomalyPod extends HasticPod<UpdateDataCallback> {
       .attr('opacity', head ? 0.5 : 0.3)
   }
 
-}
+}

client/src/components/pods/pattern_pod.ts

@@ -71,7 +71,7 @@ export class PatternPod extends HasticPod<UpdateDataCallback> {
 
   public fetchData(): void {
     let to = Math.floor(Date.now() / 1000);
-    let from = to - 50000; // -50000 seconds
+    let from = to - 2592000; // -100000 seconds
 
     if(!(this.state.xValueRange[0] == 0 && this.state.xValueRange[1] == 1)) {
       [from, to] = this.state?.xValueRange;
@@ -184,4 +184,4 @@ export class PatternPod extends HasticPod<UpdateDataCallback> {
     return this._tempIdCounted.toString();
   }
 
-}
+}

client/src/components/pods/threshold_pod.ts

@@ -27,7 +27,7 @@ export class ThresholdPod extends HasticPod<UpdateDataCallback> {
 
   public fetchData(): void {
     let to = Math.floor(Date.now() / 1000);
-    let from = to - 50000; // -50000 seconds
+    let from = to - 2592000; // -100000 seconds
 
     if(!(this.state.xValueRange[0] == 0 && this.state.xValueRange[1] == 1)) {
       [from, to] = this.state?.xValueRange;
@@ -41,4 +41,4 @@ export class ThresholdPod extends HasticPod<UpdateDataCallback> {
       .catch(() => { /* set "error" message */ })
   }
 
-}
+}

client/src/services/analytics.service.ts

@@ -68,7 +68,7 @@ export async function patchConfig(patchObj: any) {
 }
 
 export function getStatusGenerator(): AsyncIterableIterator<AnalyticStatus> {
-  return getGenerator<AnalyticStatus>(100, getStatus);
+  return getGenerator<AnalyticStatus>(10000, getStatus);
 }
 
 

server/src/services/analytic_service/analytic_unit/anomaly_analytic_unit.rs

@@ -56,31 +56,31 @@ impl SARIMA {
             ));
         }
         // TODO: ensure capacity with seasonality size
-        let mut res_ts = Vec::<(u64, f64)>::new();
-        let from = ts[0].0;
-        // TODO: unwrap -> ?
-        let to = ts.last().unwrap().0;
-        let iter_steps = (self.seasonality / DETECTION_STEP) as usize;
-
-        if to - from != self.seasonality_iterations * self.seasonality {
-            return Err(anyhow::format_err!(
-                "timeserie to learn from should be {} * sasonality",
-                self.seasonality_iterations
-            ));
-        }
-
-        for k in 0..iter_steps {
-            let mut vts = Vec::new();
-            for si in 0..self.seasonality_iterations {
-                vts.push(ts[k + iter_steps * si as usize].1);
-            }
-            let mut vt: f64 = vts.iter().sum();
-            vt /= self.seasonality_iterations as f64;
-            let t = ts[ts.len() - iter_steps + k].0;
-            res_ts.push((t, vt));
-        }
-
-        self.ts = res_ts;
+        // let mut res_ts = Vec::<(u64, f64)>::new();
+        // let from = ts[0].0;
+        // // TODO: unwrap -> ?
+        // let to = ts.last().unwrap().0;
+        // let iter_steps = 5 as usize;
+
+        // if to - from != self.seasonality_iterations * self.seasonality {
+        //     return Err(anyhow::format_err!(
+        //         "timeserie to learn from should be {} * seasonality",
+        //         self.seasonality_iterations
+        //     ));
+        // }
+
+        // for k in 0..iter_steps {
+        //     let mut vts = Vec::new();
+        //     for si in 0..self.seasonality_iterations {
+        //         vts.push(ts[k + iter_steps * si as usize].1);
+        //     }
+        //     let mut vt: f64 = vts.iter().sum();
+        //     vt /= self.seasonality_iterations as f64;
+        //     let t = ts[ts.len() - iter_steps + k].0;
+        //     res_ts.push((t, vt));
+        // }
+
+        self.ts = ts.to_vec();
 
         return Ok(());
     }
@@ -194,7 +194,7 @@ impl AnalyticUnit for AnomalyAnalyticUnit {
 
         let utc: DateTime<Utc> = Utc::now();
         let to = utc.timestamp() as u64;
-        let from = to - self.config.seasonality * self.config.seasonality_iterations;
+        let from = to - 86400;
 
         let mr = ms.query(from, to, DETECTION_STEP).await?;
         if mr.data.keys().len() == 0 {

server/src/services/metric_service.rs

@@ -22,14 +22,14 @@ impl MetricService {
         let mut mr = self.datasource.query(from, to, step).await?;
         // let keys: Vec<_> = mr.data.keys().into_iter().collect();
 
-        if mr.data.keys().len() > 0 {
-            // TODO: it's a hack, should replace all metrics
-            let key = mr.data.keys().nth(0).unwrap().clone();
-            let ts = mr.data.get_mut(&key).unwrap();
-            // TODO: make interpolate_nans_and_gaps_with_zeros configurable
-            *ts = subbeat::utils::interpolate_nans_and_gaps_with_zeros(&ts, from, to, step);
-            // mr.data.insert(*k, ts_interpolated);
-        }
+        // if mr.data.keys().len() > 0 {
+        //     // TODO: it's a hack, should replace all metrics
+        //     let key = mr.data.keys().nth(0).unwrap().clone();
+        //     let ts = mr.data.get_mut(&key).unwrap();
+        //     // TODO: make interpolate_nans_and_gaps_with_zeros configurable
+        //     *ts = subbeat::utils::interpolate_nans_and_gaps_with_zeros(&ts, from, to, step);
+        //     // mr.data.insert(*k, ts_interpolated);
+        // }
         return Ok(mr);
     }