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Rename modules and types (#860)

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Alexander Velikiy 5 years ago committed by GitHub
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  1. 2
      analytics/analytics/analytic_types/cache.py
  2. 0
      analytics/analytics/analytic_types/detector.py
  3. 2
      analytics/analytics/analytic_unit_worker.py
  4. 2
      analytics/analytics/detectors/anomaly_detector.py
  5. 2
      analytics/analytics/detectors/detector.py
  6. 2
      analytics/analytics/detectors/pattern_detector.py
  7. 2
      analytics/analytics/detectors/threshold_detector.py
  8. 12
      analytics/analytics/utils/common.py
  9. 4
      analytics/analytics/utils/meta.py
  10. 0
      analytics/scripts/build-dist.sh
  11. 2
      analytics/tests/test_detectors.py
  12. 2
      server/src/index.ts
  13. 2
      server/src/models/analytic_units/db.ts
  14. 4
      server/src/models/grafana_panel_model.ts
  15. 16
      server/src/routes/panel_router.ts
  16. 4
      server/src/services/data_service/index.ts
  17. 2
      server/src/services/data_service/migrations.ts
  18. 8
      server/src/services/grafana_service.ts

2
analytics/analytics/analytic_types/cache.py

@ -1,7 +1,7 @@
from typing import Optional, List, Dict from typing import Optional, List, Dict
from analytic_types.segment import AnomalyDetectorSegment from analytic_types.segment import AnomalyDetectorSegment
from analytic_types.detector_typing import Bound from analytic_types.detector import Bound
from utils.meta import JSONClass, SerializableList from utils.meta import JSONClass, SerializableList

0
analytics/analytics/analytic_types/detector_typing.py → analytics/analytics/analytic_types/detector.py

2
analytics/analytics/analytic_unit_worker.py

@ -9,7 +9,7 @@ import utils
from utils import get_intersected_chunks, get_chunks, prepare_data from utils import get_intersected_chunks, get_chunks, prepare_data
from analytic_types import ModelCache, TimeSeries from analytic_types import ModelCache, TimeSeries
from analytic_types.detector_typing import DetectionResult from analytic_types.detector import DetectionResult
logger = logging.getLogger('AnalyticUnitWorker') logger = logging.getLogger('AnalyticUnitWorker')

2
analytics/analytics/detectors/anomaly_detector.py

@ -7,7 +7,7 @@ from typing import Optional, Union, List, Tuple, Generator
import operator import operator
from analytic_types import AnalyticUnitId, ModelCache from analytic_types import AnalyticUnitId, ModelCache
from analytic_types.detector_typing import DetectionResult, ProcessingResult, Bound from analytic_types.detector import DetectionResult, ProcessingResult, Bound
from analytic_types.data_bucket import DataBucket from analytic_types.data_bucket import DataBucket
from analytic_types.segment import Segment, AnomalyDetectorSegment from analytic_types.segment import Segment, AnomalyDetectorSegment
from analytic_types.cache import AnomalyCache from analytic_types.cache import AnomalyCache

2
analytics/analytics/detectors/detector.py

@ -3,7 +3,7 @@ from pandas import DataFrame
from typing import Optional, Union, List from typing import Optional, Union, List
from analytic_types import ModelCache, TimeSeries, AnalyticUnitId from analytic_types import ModelCache, TimeSeries, AnalyticUnitId
from analytic_types.detector_typing import DetectionResult, ProcessingResult from analytic_types.detector import DetectionResult, ProcessingResult
from analytic_types.segment import Segment from analytic_types.segment import Segment

2
analytics/analytics/detectors/pattern_detector.py

@ -11,7 +11,7 @@ from detectors import Detector
from analytic_types.data_bucket import DataBucket from analytic_types.data_bucket import DataBucket
from utils import convert_pd_timestamp_to_ms from utils import convert_pd_timestamp_to_ms
from analytic_types import AnalyticUnitId, ModelCache from analytic_types import AnalyticUnitId, ModelCache
from analytic_types.detector_typing import DetectionResult from analytic_types.detector import DetectionResult
from analytic_types.segment import Segment from analytic_types.segment import Segment
import utils import utils

2
analytics/analytics/detectors/threshold_detector.py

@ -6,7 +6,7 @@ import numpy as np
from typing import Optional, List from typing import Optional, List
from analytic_types import ModelCache, AnalyticUnitId from analytic_types import ModelCache, AnalyticUnitId
from analytic_types.detector_typing import DetectionResult, ProcessingResult from analytic_types.detector import DetectionResult, ProcessingResult
from analytic_types.segment import Segment from analytic_types.segment import Segment
from detectors import ProcessingDetector from detectors import ProcessingDetector
from time import time from time import time

12
analytics/analytics/utils/common.py

@ -17,6 +17,7 @@ from analytic_types.segment import Segment
SHIFT_FACTOR = 0.05 SHIFT_FACTOR = 0.05
CONFIDENCE_FACTOR = 0.5 CONFIDENCE_FACTOR = 0.5
SMOOTHING_FACTOR = 5 SMOOTHING_FACTOR = 5
MEASUREMENT_ERROR = 0.05
def exponential_smoothing(series: pd.Series, alpha: float, last_smoothed_value: Optional[float] = None) -> pd.Series: def exponential_smoothing(series: pd.Series, alpha: float, last_smoothed_value: Optional[float] = None) -> pd.Series:
@ -391,7 +392,7 @@ def find_parameters(segment_data: pd.Series, segment_from_index: int, pat_type:
segment = flat_segment.dropna() segment = flat_segment.dropna()
segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(segment) segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(segment)
height = 0.95 * (segment_max_line - segment_min_line) height = 0.95 * (segment_max_line - segment_min_line)
length = utils.find_length(segment_data, segment_min_line, segment_max_line, pat_type) length = utils.get_pattern_length(segment_data, segment_min_line, segment_max_line, pat_type)
return height, length return height, length
def find_pattern_center(segment_data: pd.Series, segment_from_index: int, pattern_type: str): def find_pattern_center(segment_data: pd.Series, segment_from_index: int, pattern_type: str):
@ -404,14 +405,15 @@ def find_pattern_center(segment_data: pd.Series, segment_from_index: int, patter
segment_cent_index = math.ceil((len(segment_data)) / 2) segment_cent_index = math.ceil((len(segment_data)) / 2)
return segment_cent_index return segment_cent_index
def find_length(segment_data: pd.Series, segment_min_line: float, segment_max_line: float, pat_type: str) -> int: def get_pattern_length(segment_data: pd.Series, segment_min_line: float, segment_max_line: float, pat_type: str) -> int:
x_abscissa = np.arange(0, len(segment_data)) # TODO: move function to jump & drop merged model
segment_max = max(segment_data) segment_max = max(segment_data)
segment_min = min(segment_data) segment_min = min(segment_data)
# TODO: use better way
if segment_min_line <= segment_min: if segment_min_line <= segment_min:
segment_min_line = segment_min * 1.05 segment_min_line = segment_min * (1 + MEASUREMENT_ERROR)
if segment_max_line >= segment_max: if segment_max_line >= segment_max:
segment_max_line = segment_max * 0.95 segment_max_line = segment_max * (1 - MEASUREMENT_ERROR)
min_line = [] min_line = []
max_line = [] max_line = []
for i in range(len(segment_data)): for i in range(len(segment_data)):

4
analytics/analytics/utils/meta.py

@ -1,6 +1,6 @@
from inspect import signature, Parameter from inspect import signature, Parameter
from functools import wraps from functools import wraps
from typing import Optional from typing import Optional, List
import re import re
@ -76,6 +76,6 @@ def JSONClass(target_class):
target_class.from_json = from_json target_class.from_json = from_json
return target_class return target_class
class SerializableList(list): class SerializableList(List[dict]):
def to_json(self): def to_json(self):
return list(map(lambda s: s.to_json(), self)) return list(map(lambda s: s.to_json(), self))

0
analytics/scripts/build_dist.sh → analytics/scripts/build-dist.sh

2
analytics/tests/test_detectors.py

@ -2,7 +2,7 @@ import unittest
import pandas as pd import pandas as pd
from detectors import pattern_detector, threshold_detector, anomaly_detector from detectors import pattern_detector, threshold_detector, anomaly_detector
from analytic_types.detector_typing import DetectionResult, ProcessingResult, Bound from analytic_types.detector import DetectionResult, ProcessingResult, Bound
from analytic_types.segment import Segment from analytic_types.segment import Segment
from tests.test_dataset import create_dataframe, create_list_of_timestamps from tests.test_dataset import create_dataframe, create_list_of_timestamps
from utils import convert_pd_timestamp_to_ms from utils import convert_pd_timestamp_to_ms

2
server/src/index.ts

@ -10,7 +10,7 @@ import * as ProcessService from './services/process_service';
import { HASTIC_PORT, PACKAGE_VERSION, GIT_INFO, ZMQ_CONNECTION_STRING, HASTIC_INSTANCE_NAME } from './config'; import { HASTIC_PORT, PACKAGE_VERSION, GIT_INFO, ZMQ_CONNECTION_STRING, HASTIC_INSTANCE_NAME } from './config';
import { applyDBMigrations } from './migrations'; import { applyDBMigrations } from './services/data_service/migrations';
import * as Koa from 'koa'; import * as Koa from 'koa';
import * as Router from 'koa-router'; import * as Router from 'koa-router';

2
server/src/models/analytic_units/db.ts

@ -41,7 +41,7 @@ export async function create(unit: AnalyticUnit): Promise<AnalyticUnitId> {
return db.insertOne(obj); return db.insertOne(obj);
} }
export async function insertMany(analyticUnits: any[]): Promise<AnalyticUnitId[]> { export async function insertMany(analyticUnits: AnalyticUnit[]): Promise<AnalyticUnitId[]> {
return db.insertMany(analyticUnits); return db.insertMany(analyticUnits);
} }

4
server/src/models/panel_model.ts → server/src/models/grafana_panel_model.ts

@ -3,14 +3,14 @@ import * as AnalyticUnitCache from '../models/analytic_unit_cache_model';
import * as DetectionSpan from '../models/detection_model'; import * as DetectionSpan from '../models/detection_model';
import * as Segment from '../models/segment_model'; import * as Segment from '../models/segment_model';
export type PanelTemplate = { export type GrafanaPanelTemplate = {
analyticUnits: AnalyticUnit.AnalyticUnit[], analyticUnits: AnalyticUnit.AnalyticUnit[],
caches: AnalyticUnitCache.AnalyticUnitCache[], caches: AnalyticUnitCache.AnalyticUnitCache[],
detectionSpans: DetectionSpan.DetectionSpan[], detectionSpans: DetectionSpan.DetectionSpan[],
segments: Segment.Segment[] segments: Segment.Segment[]
} }
export type TemplateVariables = { export type GrafanaTemplateVariables = {
grafanaUrl: string, grafanaUrl: string,
panelId: string, panelId: string,
datasourceUrl: string datasourceUrl: string

16
server/src/routes/panel_router.ts

@ -1,10 +1,10 @@
import { PanelTemplate, TemplateVariables } from '../models/panel_model'; import { GrafanaPanelTemplate, GrafanaTemplateVariables } from '../models/grafana_panel_model';
import { exportPanel, importPanel } from '../services/export_service'; import { exportPanel, importPanel } from '../services/grafana_service';
import * as Router from 'koa-router'; import * as Router from 'koa-router';
async function exportPanelTemplate(ctx: Router.IRouterContext) { async function exportGrafanaPanelTemplate(ctx: Router.IRouterContext) {
let panelId = ctx.request.query.panelId; let panelId = ctx.request.query.panelId;
if(panelId === undefined) { if(panelId === undefined) {
throw new Error('Cannot export analytic units with undefined panelId'); throw new Error('Cannot export analytic units with undefined panelId');
@ -14,10 +14,10 @@ async function exportPanelTemplate(ctx: Router.IRouterContext) {
ctx.response.body = panelTemplate; ctx.response.body = panelTemplate;
} }
async function importPanelTemplate(ctx: Router.IRouterContext) { async function importGrafanaPanelTemplate(ctx: Router.IRouterContext) {
const { panelTemplate, templateVariables } = ctx.request.body as { const { panelTemplate, templateVariables } = ctx.request.body as {
panelTemplate: PanelTemplate, panelTemplate: GrafanaPanelTemplate,
templateVariables: TemplateVariables templateVariables: GrafanaTemplateVariables
}; };
// TODO: move to model // TODO: move to model
@ -50,5 +50,5 @@ async function importPanelTemplate(ctx: Router.IRouterContext) {
export var router = new Router(); export var router = new Router();
router.get('/template', exportPanelTemplate); router.get('/template', exportGrafanaPanelTemplate);
router.post('/template', importPanelTemplate); router.post('/template', importGrafanaPanelTemplate);

4
server/src/services/data_service.ts → server/src/services/data_service/index.ts

@ -1,5 +1,5 @@
import { getDbQueryWrapper, dbCollection, DBType } from './data_layer'; import { getDbQueryWrapper, dbCollection, DBType } from '../data_layer';
import * as config from '../config'; import * as config from '../../config';
import * as nedb from 'nedb'; import * as nedb from 'nedb';
import * as fs from 'fs'; import * as fs from 'fs';

2
server/src/migrations.ts → server/src/services/data_service/migrations.ts

@ -7,7 +7,7 @@
Note: do not import code from other modules here because it can be changed Note: do not import code from other modules here because it can be changed
*/ */
import { Collection, makeDBQ } from './services/data_service'; import { Collection, makeDBQ } from './index';
import * as _ from 'lodash'; import * as _ from 'lodash';

8
server/src/services/export_service.ts → server/src/services/grafana_service.ts

@ -1,4 +1,4 @@
import { PanelTemplate, TemplateVariables } from '../models/panel_model'; import { GrafanaPanelTemplate, GrafanaTemplateVariables } from '../models/grafana_panel_model';
import * as AnalyticUnit from '../models/analytic_units'; import * as AnalyticUnit from '../models/analytic_units';
import * as AnalyticUnitCache from '../models/analytic_unit_cache_model'; import * as AnalyticUnitCache from '../models/analytic_unit_cache_model';
@ -6,7 +6,7 @@ import * as DetectionSpan from '../models/detection_model';
import * as Segment from '../models/segment_model'; import * as Segment from '../models/segment_model';
export async function exportPanel(panelId: string): Promise<PanelTemplate> { export async function exportPanel(panelId: string): Promise<GrafanaPanelTemplate> {
const analyticUnits = await AnalyticUnit.findMany({ panelId }); const analyticUnits = await AnalyticUnit.findMany({ panelId });
const analyticUnitIds = analyticUnits.map(analyticUnit => analyticUnit.id); const analyticUnitIds = analyticUnits.map(analyticUnit => analyticUnit.id);
const analyticUnitTemplates = analyticUnits.map(analyticUnit => analyticUnit.toTemplate()); const analyticUnitTemplates = analyticUnits.map(analyticUnit => analyticUnit.toTemplate());
@ -26,8 +26,8 @@ export async function exportPanel(panelId: string): Promise<PanelTemplate> {
} }
export async function importPanel( export async function importPanel(
panelTemplate: PanelTemplate, panelTemplate: GrafanaPanelTemplate,
variables: TemplateVariables variables: GrafanaTemplateVariables
): Promise<void> { ): Promise<void> {
panelTemplate.analyticUnits.forEach(analyticUnit => { panelTemplate.analyticUnits.forEach(analyticUnit => {
analyticUnit.grafanaUrl = variables.grafanaUrl; analyticUnit.grafanaUrl = variables.grafanaUrl;
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