Adding post_datapoints_frame (#112)

* Adding post_datapoints_frame

cognite/__init__.py

@@ -19,5 +19,6 @@
 
 from cognite.data_transfer_service import DataTransferService
 
-__all__ = ["v04", "v05", "v06", "preprocessing", "config", "data_transfer_service"]
-__version__ = "0.10.0"
+__all__ = ["v04", "v05", "v06", "preprocessing",
+           "config", "data_transfer_service"]
+__version__ = "0.10.1"

cognite/v05/timeseries.py

@@ -728,6 +728,35 @@ def _get_datapoints_frame_user_defined_limit(time_series, aggregates, granularit
     return df
 
 
+def post_datapoints_frame(data, **kwargs):
+    """Write a dataframe 
+
+    Args:
+        dataframe (DataFrame):  Pandas DataFrame Object containing the timeseries
+
+    Keyword Args:
+        api_key (str): Your api-key.
+
+        project (str): Project name.
+
+    Returns:
+        An empty response.
+    """
+    api_key, project = config.get_config_variables(kwargs.get("api_key"), kwargs.get("project"))
+
+    try:
+        timestamp = data.timestamp
+        names = data.drop(['timestamp'], axis=1).columns
+    except:
+        raise _utils.InputError('DataFrame not on a correct format') 
+
+    for name in names:
+        data_points = [Datapoint(int(timestamp[i]), data[name].iloc[i]) for i in range(0, len(data))]
+        res = post_datapoints(name, data_points, api_key=api_key, project=project)
+
+    return res
+
+
 def get_timeseries(prefix=None, description=None, include_metadata=False, asset_id=None, path=None, **kwargs):
     """Returns a TimeseriesObject containing the requested timeseries.
 

tests/v05/test_timeseries.py

@@ -25,7 +25,9 @@ def ts_name():
 class TestTimeseries:
     @pytest.fixture(scope="class", params=[True, False])
     def get_timeseries_response_obj(self, request):
-        yield timeseries.get_timeseries(prefix=TS_NAME, limit=1, include_metadata=request.param)
+        yield timeseries.get_timeseries(
+            prefix=TS_NAME, limit=1, include_metadata=request.param
+        )
 
     def test_post_timeseries(self):
         tso = dto.TimeSeries(TS_NAME)
@@ -58,9 +60,14 @@ def test_delete_timeseries(self):
         res = timeseries.delete_time_series(TS_NAME)
         assert res == {}
 
-    def test_get_timeseries_with_config_variables_from_argument(self, unset_config_variables):
+    def test_get_timeseries_with_config_variables_from_argument(
+        self, unset_config_variables
+    ):
         ts = timeseries.get_timeseries(
-            prefix=TS_NAME, limit=1, api_key=unset_config_variables[0], project=unset_config_variables[1]
+            prefix=TS_NAME,
+            limit=1,
+            api_key=unset_config_variables[0],
+            project=unset_config_variables[1],
         )
         assert ts
 
@@ -89,12 +96,28 @@ def test_post_datapoints(self):
         res = timeseries.post_datapoints(TS_NAME, datapoints=dps)
         assert res == {}
 
+    def test_post_datapoints_frame(self):
+        data = pd.DataFrame()
+        data["timestamp"] = [int(1537208777557 + 1000 * i) for i in range(0, 100)]
+        X = data["timestamp"].values.astype(float)
+        data["X"] = X ** 2
+        data["Y"] = 1.0 / (1 + X)
+
+        for name in data.drop(["timestamp"], axis=1).columns:
+            ts = dto.TimeSeries(name=name, description="To be deleted")
+            try:
+                timeseries.post_time_series([ts])
+            except:
+                pass
+
+        res = timeseries.post_datapoints_frame(data)
+        assert res == {}
+
     def test_get_datapoints(self, get_dps_response_obj):
         from cognite.v05.dto import DatapointsResponse
 
         assert isinstance(get_dps_response_obj, DatapointsResponse)
 
-
     def test_get_dps_output_formats(self, get_dps_response_obj):
         assert isinstance(get_dps_response_obj.to_ndarray(), np.ndarray)
         assert isinstance(get_dps_response_obj.to_pandas(), pd.DataFrame)
@@ -114,9 +137,15 @@ def test_get_dps_with_end_now(self):
         res = timeseries.get_datapoints(name="constant", start=0, end="now", limit=100)
         assert len(res.to_json().get("datapoints")) == 100
 
-    def test_get_dps_with_limit_with_config_variables_from_argument(self, unset_config_variables):
+    def test_get_dps_with_limit_with_config_variables_from_argument(
+        self, unset_config_variables
+    ):
         res = timeseries.get_datapoints(
-            name="constant", start=0, limit=1, api_key=unset_config_variables[0], project=unset_config_variables[1]
+            name="constant",
+            start=0,
+            limit=1,
+            api_key=unset_config_variables[0],
+            project=unset_config_variables[1],
         )
         assert len(res.to_json().get("datapoints")) == 1
 
@@ -164,11 +193,17 @@ def test_get_dps_frame_correctly_spaced(self, get_datapoints_frame_response_obj)
 
     def test_get_dps_frame_with_limit(self):
         df = timeseries.get_datapoints_frame(
-            time_series=["constant"], aggregates=["avg"], granularity="1m", start=0, limit=1
+            time_series=["constant"],
+            aggregates=["avg"],
+            granularity="1m",
+            start=0,
+            limit=1,
         )
         assert df.shape[0] == 1
 
-    def test_get_dps_frame_with_limit_with_config_values_from_argument(self, unset_config_variables):
+    def test_get_dps_frame_with_limit_with_config_values_from_argument(
+        self, unset_config_variables
+    ):
         df = timeseries.get_datapoints_frame(
             time_series=["constant"],
             aggregates=["avg"],
@@ -180,7 +215,9 @@ def test_get_dps_frame_with_limit_with_config_values_from_argument(self, unset_c
         )
         assert df.shape[0] == 1
 
-    def test_get_dps_frame_with_config_values_from_argument(self, unset_config_variables):
+    def test_get_dps_frame_with_config_values_from_argument(
+        self, unset_config_variables
+    ):
         res = timeseries.get_datapoints_frame(
             time_series=["constant"],
             start=1522188000000,
@@ -237,19 +274,25 @@ def test_post_multitag_datapoints(self):
             )
             assert post_request_mock.call_count == 2
 
-    def test_get_multi_time_series_dps_output_format(self, get_multi_time_series_dps_response_obj):
+    def test_get_multi_time_series_dps_output_format(
+        self, get_multi_time_series_dps_response_obj
+    ):
         from cognite.v05.dto import DatapointsResponse
 
         assert isinstance(get_multi_time_series_dps_response_obj, list)
 
         for dpr in get_multi_time_series_dps_response_obj:
             assert isinstance(dpr, DatapointsResponse)
 
-    def test_get_multi_time_series_dps_response_length(self, get_multi_time_series_dps_response_obj):
+    def test_get_multi_time_series_dps_response_length(
+        self, get_multi_time_series_dps_response_obj
+    ):
         assert len(list(get_multi_time_series_dps_response_obj)) == 2
 
     @pytest.mark.xfail(strict=True)
-    def test_get_multi_timeseries_dps_correctly_spaced(self, get_multi_time_series_dps_response_obj):
+    def test_get_multi_timeseries_dps_correctly_spaced(
+        self, get_multi_time_series_dps_response_obj
+    ):
         m = list(get_multi_time_series_dps_response_obj)
         timestamps = m[0].to_pandas().timestamp.values
         deltas = np.diff(timestamps, 1)
@@ -271,14 +314,18 @@ def test_split_TimeseriesWithDatapoints_if_over_limit():
         name="test", datapoints=[Datapoint(x, x) for x in range(1000)]
     )
 
-    result: List[TimeseriesWithDatapoints] = _split_TimeseriesWithDatapoints_if_over_limit(
+    result: List[
+        TimeseriesWithDatapoints
+    ] = _split_TimeseriesWithDatapoints_if_over_limit(
         timeseries_with_datapoints_over_limit, 100
     )
 
     assert isinstance(result[0], TimeseriesWithDatapoints)
     assert len(result) == 10
 
-    result = _split_TimeseriesWithDatapoints_if_over_limit(timeseries_with_datapoints_over_limit, 1000)
+    result = _split_TimeseriesWithDatapoints_if_over_limit(
+        timeseries_with_datapoints_over_limit, 1000
+    )
 
     assert isinstance(result[0], TimeseriesWithDatapoints)
     assert len(result) == 1