remove initial benchmark

benchmarks/benchmarks.py

@@ -40,48 +40,3 @@ def time_prune_sync_workflow():
 
     ens.prune(50)  # calc nobs -> cut any object with nobs<50
     ens.source.head(5)  # should call sync implicitly
-
-
-def time_basic_workflow():
-    np.random.seed(1)
-
-    # Generate 10 astronomical objects
-    n_obj = 10
-    ids = 8000 + np.arange(n_obj)
-    names = ids.astype(str)
-    object_table = pd.DataFrame(
-        {
-            "id": ids,
-            "name": names,
-            "ddf_bool": np.random.randint(0, 2, n_obj), # 0 if from deep drilling field, 1 otherwise
-            "libid_cadence": np.random.randint(1, 130, n_obj),
-        }
-    )
-
-    # Create 1000 lightcurves with 100 measurements each
-    lc_len = 100
-    num_points = 1000
-    all_bands = np.array(["r", "g", "b", "i"])
-    source_table = pd.DataFrame(
-        {
-            "id": 8000 + (np.arange(num_points) % n_obj),
-            "time": np.arange(num_points),
-            "flux":  np.random.random_sample(size=num_points)*10,
-            "band": np.repeat(all_bands, num_points / len(all_bands)),
-            "error": np.random.random_sample(size=num_points),
-            "count": np.arange(num_points),
-        },
-    )
-
-    ens = Ensemble()  # initialize an ensemble object
-
-    # Read in the generated lightcurve data
-    ens.from_pandas(
-        source_frame=source_table,
-        object_frame=object_table,
-        id_col="id",
-        time_col="time",
-        flux_col="flux",
-        err_col="error",
-        band_col="band",
-        npartitions=1)