Merge branch 'master' of github.com:hastic/hastic-server

Dockerfile

@@ -1,29 +0,0 @@
-FROM python:3.6.6
-
-EXPOSE 8000
-
-VOLUME [ "/var/www/data" ]
-
-COPY . /var/www
-
-WORKDIR /var/www/analytics
-
-RUN pip install -r requirements.txt
-
-RUN apt-get update && apt-get install -y \
-  apt-utils \
-  gnupg \
-  curl \ 
-  python \
-  make \
-  g++ \
-  git
-RUN curl -sL https://deb.nodesource.com/setup_8.x | bash -
-
-RUN apt-get update && apt-get install -y nodejs
-
-WORKDIR /var/www/server
-
-RUN npm install && npm run build
-
-CMD ["npm", "start"]

analytics/.dockerignore

@@ -0,0 +1,5 @@
+.git
+npm-debug
+node_modules
+__pycache__
+.vscode

analytics/Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.6.6
+
+WORKDIR /var/www/analytics
+
+COPY ./requirements.txt /var/www/analytics
+
+RUN pip install -r requirements.txt \
+ && apt-get update && apt-get install -y \
+    apt-utils \
+    gnupg \
+    curl \
+    make \
+    g++ \
+    git
+
+VOLUME [ "/var/www/data" ]
+
+COPY . /var/www/analytics/
+
+CMD ["python", "server.py"]

analytics/config.py

@@ -25,4 +25,4 @@ def get_config_field(field, default_val = None):
     raise Exception('Please configure {}'.format(field))
 
 ZMQ_DEV_PORT = get_config_field('ZMQ_DEV_PORT', '8002')
-ZMQ_CONNECTION_STRING = get_config_field('ZMQ_CONNECTION_STRING', 'tcp://*:%s' % ZMQ_DEV_PORT)
+ZMQ_CONNECTION_STRING = get_config_field('ZMQ_CONNECTION_STRING', 'tcp://0.0.0.0:%s' % ZMQ_DEV_PORT)

analytics/models/drop_model.py

@@ -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:
+                elif 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)

analytics/models/general_model.py

@@ -23,6 +23,8 @@ class GeneralModel(Model):
             'convolve_max': 240,
             'convolve_min': 200,
             'WINDOW_SIZE': 240,
+            'conv_del_min': 100,
+            'conv_del_max': 120,
         }
         self.all_conv = []
 
@@ -38,7 +40,7 @@ class GeneralModel(Model):
                 segment_data = data[segment_from_index: segment_to_index + 1]
                 if len(segment_data) == 0:
                     continue
-                x = segment_from_index + int((segment_to_index - segment_from_index) / 2)
+                x = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2)
                 self.ipats.append(x)
                 segment_data = data[x - self.state['WINDOW_SIZE'] : x + self.state['WINDOW_SIZE']]
                 segment_min = min(segment_data)
@@ -54,6 +56,20 @@ class GeneralModel(Model):
             convolve_list.append(max(auto_convolve))
             convolve_list.append(max(convolve_data))
 				
+        del_conv_list = []
+        for segment in segments:
+            if segment['deleted']:
+                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
+                del_mid_index = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2)
+                deleted_pat = data[del_mid_index - self.state['WINDOW_SIZE']: del_mid_index + self.state['WINDOW_SIZE'] + 1]
+                deleted_pat = deleted_pat - min(deleted_pat)
+                del_conv_pat = scipy.signal.fftconvolve(deleted_pat, self.model_gen)
+                del_conv_list.append(max(del_conv_pat))
+
         if len(convolve_list) > 0:
             self.state['convolve_max'] = float(max(convolve_list))
         else:
@@ -64,6 +80,16 @@ class GeneralModel(Model):
         else:
             self.state['convolve_min'] = self.state['WINDOW_SIZE'] / 3
 		
+        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:
+            self.state['conv_del_max'] = self.state['WINDOW_SIZE']
+
     def do_predict(self, dataframe: pd.DataFrame) -> list:
         data = dataframe['value']
         pat_data = self.model_gen
@@ -88,6 +114,8 @@ class GeneralModel(Model):
         for val in segments:
             if self.all_conv[val] < self.state['convolve_min'] * 0.8:
                 delete_list.append(val)
+            elif (self.all_conv[val] < self.state['conv_del_max'] * 1.02 and self.all_conv[val] > self.state['conv_del_min'] * 0.98):
+	            delete_list.append(val)
 
         for item in delete_list:
             segments.remove(item)

analytics/models/jump_model.py

@@ -173,7 +173,7 @@ class JumpModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < 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:
+                elif 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)

analytics/models/peak_model.py

@@ -134,7 +134,7 @@ class PeakModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95:
                     delete_list.append(segment)
-                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                elif 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)

analytics/models/trough_model.py

@@ -136,7 +136,7 @@ class TroughModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.1 or max(conv) < self.state['convolve_min'] * 0.9:
                     delete_list.append(segment)
-                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                elif 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)

docker-compose.yml

@@ -0,0 +1,19 @@
+version: '2'
+services:
+  server:
+    image: hastic/server:latest
+    build: server
+    environment:
+      HASTIC_API_KEY: ${HASTIC_API_KEY}
+      ZMQ_CONNECTION_STRING: tcp://analytics:8002
+    ports:
+      - 8000:8000
+    volumes:
+      - data-volume:/var/www/data
+
+  analytics:
+    image: hastic/analytics:latest
+    build: analytics
+
+volumes:
+  data-volume:

server/.dockerignore

@@ -0,0 +1,4 @@
+.git
+node_modules
+npm-debug
+.vscode

server/Dockerfile

@@ -0,0 +1,26 @@
+FROM python:3.6.6
+
+RUN apt-get install curl \
+                    bash \
+                    gnupg \
+                    make \
+                    g++ \
+ && curl -sL https://deb.nodesource.com/setup_8.x | bash - \
+ && apt-get update \
+ && apt-get install nodejs
+
+VOLUME [ "/var/www/data" ]
+
+WORKDIR /var/www/server
+
+COPY package.json /var/www/server
+
+RUN npm install
+
+COPY . /var/www/server
+
+RUN npm run build
+
+ENV INSIDE_DOCKER true
+
+CMD ["npm", "start"]

server/src/config.ts

@@ -20,6 +20,7 @@ export const HASTIC_PORT = getConfigField('HASTIC_PORT', '8000');
 export const ZMQ_CONNECTION_STRING = getConfigField('ZMQ_CONNECTION_STRING', null);
 export const ZMQ_IPC_PATH = getConfigField('ZMQ_IPC_PATH', path.join(os.tmpdir(), 'hastic'));
 export const ZMQ_DEV_PORT = getConfigField('ZMQ_DEV_PORT', '8002');
+export const ZMQ_HOST = getConfigField('ZMQ_HOST', '127.0.0.1');
 export const HASTIC_API_KEY = getConfigField('HASTIC_API_KEY');
 export const ANLYTICS_PING_INTERVAL = 500; // ms
 

server/src/services/analytics_service.ts

@@ -18,8 +18,12 @@ export class AnalyticsService {
   private _ipcPath: string = null;
   private _analyticsPinger: NodeJS.Timer = null;
   private _isClosed = false;
+  private _productionMode = false;
+  private _inDocker = false;
 
   constructor(private _onMessage: (message: AnalyticsMessage) => void) {
+    this._productionMode =  process.env.NODE_ENV !== 'development';
+    this._inDocker = process.env.INSIDE_DOCKER !== undefined;
     this._init();
   }
 
@@ -67,10 +71,12 @@ export class AnalyticsService {
 
   private async _init() {
     this._requester = zmq.socket('pair');
-    let productionMode = process.env.NODE_ENV !== 'development';
 
-    this._zmqConnectionString = `tcp://127.0.0.1:${config.ZMQ_DEV_PORT}`; // debug mode
+    this._zmqConnectionString = `tcp://${config.ZMQ_HOST}:${config.ZMQ_DEV_PORT}`; // debug mode
-    if(productionMode) {
+
+    if(this._inDocker) {
+      this._zmqConnectionString = config.ZMQ_CONNECTION_STRING;
+    } else if(this._productionMode && !this._inDocker) {
       this._zmqConnectionString = config.ZMQ_CONNECTION_STRING;
       if(this._zmqConnectionString === null) {
         var createResult = await AnalyticsService.createIPCAddress();
@@ -84,7 +90,7 @@ export class AnalyticsService {
     this._requester.on("message", this._onAnalyticsMessage.bind(this));
     console.log('Ok');
 
-    if(productionMode) {
+    if(this._productionMode && !this._inDocker) {
       console.log('Creating analytics process...');
       try {
         var cp = await AnalyticsService._runAnalyticsProcess(this._zmqConnectionString);
@@ -165,12 +171,13 @@ export class AnalyticsService {
 
   private async _onAnalyticsDown() {
     console.log('Analytics is down');
-    if(process.env.NODE_ENV !== 'development') {
+    if(this._productionMode && !this._inDocker) {
       await AnalyticsService._runAnalyticsProcess(this._zmqConnectionString);
     }
   }
 
   private _onAnalyticsMessage(data: any) {
+
     let text = data.toString();
     if(text === 'PONG') {
       this._pingResponded = true;