Reverting query_api.py to version in master branch. Updating README and quicstart with correct aggregateList parameter, and example illustrating how aggregation list works.

Author: mhavgar

README.md

@@ -48,14 +48,14 @@ client = tsi.TSIClient(
 You can check the docs at <https://raalabs-tsiclient.readthedocs.io/en/latest/authentication.html> for more information on authentication, and check
 the old way of authentication (these will be removed in a future version).
 
-You can query your timeseries data by timeseries id, timeseries name or timeseries description. The Microsoft TSI apis support aggregation, so you can specify a sampling freqency and an aggregation method. Refer to the documentation for detailed information.
+You can query your timeseries data by timeseries id, timeseries name or timeseries description. The Microsoft TSI apis support aggregation, so you can specify a sampling freqency and aggregation methods. Refer to the documentation for detailed information.
 
 ````python
 data = client.query.getDataById(
     timeseries=["timeseries_id1", "timeseries_id2"],
     timespan=["2019-12-12T15:35:11.68Z", "2019-12-12T17:02:05.958Z"],
     interval="PT5M",
-    aggregateList=["avg", "avg"],
+    aggregateList=["avg", "max"],
     useWarmStore=False
 )
 ````

TSIClient/query/query_api.py

@@ -669,7 +669,7 @@ def _getData(
                         if isinstance(aggregateList, list):
                             for idx, agg in enumerate(aggregateList):
                                 currColName  = colNames[i] + "/" + agg
-                                df[currColName] = response["properties"][0]["values"]
+                                df[currColName] = response["properties"][idx]["values"]
                         else:
                             df[colNames[i]] = response["properties"][0]["values"]
 
@@ -735,4 +735,4 @@ def _getData(
                         df = pd.merge_asof(df,df_temp,on=['timestamp'],direction='nearest',tolerance=pd.Timedelta(seconds=30))
                 finally:
                     logging.critical("Loaded data for tag: {tag}".format(tag=colNames[i]))
-        return df
+        return df

docs/source/quickstart.rst

@@ -167,21 +167,21 @@ is convenient for further statistical analysis.
     ...     timeseries=["timeseries_id1", "timeseries_id2"],
     ...     timespan=["2020-01-25T10:00:11.68Z", "2020-01-26T13:45:11.68Z"],
     ...     interval="PT5M",
-    ...     aggregateList=["avg", "avg"],
+    ...     aggregateList=["avg", "max"],
     ...     useWarmStore=False
     ... )
     >>> data
-    timestamp                   timeseries_id1  timeseries_id2
-    0    2020-01-25T10:00:00Z       360.272727      242.692308
-    1    2020-01-25T10:05:00Z       362.588235      244.523810
-    2    2020-01-25T10:10:00Z       369.280000      245.000000
-    3    2020-01-25T10:15:00Z       365.952381      242.962963
-    4    2020-01-25T10:20:00Z       367.962963      241.391304
-    ..                    ...              ...             ...
-    329  2020-01-26T13:25:00Z       315.210526      299.250000
-    330  2020-01-26T13:30:00Z       310.060606      569.776119
-    331  2020-01-26T13:35:00Z       300.961538      299.000000
-    332  2020-01-26T13:40:00Z       301.645161      293.421053
-    333  2020-01-26T13:45:00Z       300.000000             NaN
+    timestamp                        timeseries_id1/avg    timeseries_id1/max    timeseries_id2/avg    timeseries_id2/max           
+    0    2020-01-25T10:00:00Z            324.060000            325.1                  0.706804             0.882239   
+    1    2020-01-25T10:05:00Z            324.840000            325.1                  0.757573             0.882239   
+    2    2020-01-25T10:10:00Z            324.479310            325.1                  0.716411             0.744299   
+    3    2020-01-25T10:15:00Z            323.954545            324.9                  0.779331             1.156681   
+    4    2020-01-25T10:20:00Z            324.300000            325.1                  0.723073             1.205535   
+    ..          ...                         ...                   ...                    ...                  ...   
+    329  2020-01-26T13:25:00Z            325.468750            326.1                  0.871528             0.910976   
+    330  2020-01-26T13:30:00Z            325.135294            326.1                  0.858428             0.892297   
+    331  2020-01-26T13:35:00Z            323.957142            324.9                  0.804100             0.842006   
+    332  2020-01-26T13:40:00Z            324.666666            325.1                  0.788907             0.882239   
+    333  2020-01-26T13:45:00Z            324.969999            325.1                  0.756615             0.784532
 
     [334 rows x 3 columns]