Support datasource kit interface #176 (#177)

* Fix indentation in drop model

* Update datasource-kit version

* GrafanaMetric -> Metric

analytics/models/drop_model.py

@@ -39,7 +39,7 @@ class DropModel(Model):
                 segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms'))
                 segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms'))
                 segment_data = data[segment_from_index: segment_to_index + 1]
-                
+
                 if len(segment_data) == 0:
                     continue
                 segment_min = min(segment_data)
@@ -80,7 +80,7 @@ class DropModel(Model):
             convolve_drop = scipy.signal.fftconvolve(labeled_drop, self.model_drop)
             convolve_list.append(max(auto_convolve))
             convolve_list.append(max(convolve_drop))
-            
+
         del_conv_list = []
         for segment in segments:
             if segment['deleted']:
@@ -106,7 +106,7 @@ class DropModel(Model):
                 deleted_drop = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1]
                 deleted_drop = deleted_drop - min(labeled_drop)
                 del_conv_drop = scipy.signal.fftconvolve(deleted_drop, self.model_drop)
-                del_conv_list.append(max(del_conv_drop)) 
+                del_conv_list.append(max(del_conv_drop))
 
         if len(confidences) > 0:
             self.state['confidence'] = float(min(confidences))
@@ -117,7 +117,7 @@ class DropModel(Model):
             self.state['convolve_max'] = float(max(convolve_list))
         else:
             self.state['convolve_max'] = self.state['WINDOW_SIZE']
-            
+
         if len(convolve_list) > 0:
             self.state['convolve_min'] = float(min(convolve_list))
         else:
@@ -132,12 +132,12 @@ class DropModel(Model):
             self.state['DROP_LENGTH'] = int(max(drop_length_list))
         else:
             self.state['DROP_LENGTH'] = 1
-            
+
         if len(del_conv_list) > 0:
             self.state['conv_del_min'] = float(min(del_conv_list))
         else:
             self.state['conv_del_min'] = self.state['WINDOW_SIZE']
-            
+
         if len(del_conv_list) > 0:
             self.state['conv_del_max'] = float(max(del_conv_list))
         else:
@@ -172,7 +172,7 @@ class DropModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if conv[self.state['WINDOW_SIZE']*2] > self.state['convolve_max'] * 1.2 or conv[self.state['WINDOW_SIZE']*2] < self.state['convolve_min'] * 0.8:
                     delete_list.append(segment)
-                 if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
                     delete_list.append(segment)
             else:
                 delete_list.append(segment)

server/package.json

@@ -38,7 +38,7 @@
     "es6-promise": "^4.2.4",
     "event-stream": "^3.3.4",
     "file-loader": "^1.1.11",
-    "grafana-datasource-kit": "^0.0.3",
+    "grafana-datasource-kit": "^0.0.7",
     "jest": "^23.1.1",
     "koa": "^2.0.46",
     "koa-bodyparser": "^4.2.0",

server/src/models/analytic_unit_model.ts

@@ -1,6 +1,6 @@
 import { Collection, makeDBQ } from '../services/data_service';
 
-import { GrafanaMetric } from 'grafana-datasource-kit';
+import { Metric } from 'grafana-datasource-kit';
 
 
 let db = makeDBQ(Collection.ANALYTIC_UNITS);
@@ -20,7 +20,7 @@ export class AnalyticUnit {
     public name: string,
     public panelUrl: string,
     public type: string,
-    public metric: GrafanaMetric,
+    public metric: Metric,
     public id?: AnalyticUnitId,
     public lastPredictionTime?: number,
     public status?: AnalyticUnitStatus,
@@ -61,7 +61,7 @@ export class AnalyticUnit {
       obj.name,
       obj.panelUrl,
       obj.type,
-      GrafanaMetric.fromObject(obj.metric),
+      Metric.fromObject(obj.metric),
       obj._id,
       obj.lastPredictionTime,
       obj.status as AnalyticUnitStatus,