add sampling and inspection docs

docs/tutorials/working_with_the_ensemble.ipynb

@@ -90,6 +90,7 @@
     "    err_col=\"error\",\n",
     "    band_col=\"band\",\n",
     "    npartitions=1,\n",
+    "    sort=True,\n",
     ")"
    ]
   },
@@ -130,7 +131,9 @@
     ")\n",
     "\n",
     "# Pass the ColumnMapper along to from_pandas\n",
-    "ens.from_pandas(source_frame=source_table, object_frame=object_table, column_mapper=col_map, npartitions=1)"
+    "ens.from_pandas(\n",
+    "    source_frame=source_table, object_frame=object_table, column_mapper=col_map, npartitions=1, sort=True\n",
+    ")"
    ]
   },
   {
@@ -201,10 +204,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Inspection, Filtering, and Selecting\n",
+    "## Inspection and Filtering\n",
     "\n",
     "The `Ensemble` contains an assortment of functions for inspecting and filtering your data."
    ]
@@ -290,6 +294,40 @@
     "ens.source.compute()"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Several methods exist to access individual lightcurves within the `Ensemble`. First of which is the `to_timeseries` function. This allows you to supply a given object ID, and returns a `TimeSeries` object (see <working_with_the_timeseries>)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ens.to_timeseries(8003).data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, if you aren't interested in a particular lightcurve, you can draw a random one from the `Ensemble` using `Ensemble.select_random_timeseries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ens.select_random_timeseries(seed=1).data"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -397,6 +435,28 @@
     "In the above operations, we remove any rows that have at least 1 NaN value present. And then filter such that only lightcurves which have at least 50 measurements are retained."
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sampling\n",
+    "\n",
+    "In addition to filtering by specific constraints, it's possible to select a subset of your data to work with. `Ensemble.sample` will randomly select a fraction of objects from the full object list. By default this will return a new ensemble."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset_ens = ens.sample(frac=0.5, overwrite=False)  # select ~half of the objects, don't overwrite ens\n",
+    "\n",
+    "print(\"Number of pre-sampled objects: \", len(ens.object))\n",
+    "print(\"Number of post-sampled objects: \", len(subset_ens.object))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

src/tape/ensemble.py

@@ -475,10 +475,13 @@ def persist(self, **kwargs):
     def sample(self, overwrite=False, **kwargs):
         """Selects a sample of objects.
 
+        This sampling will be lazily applied to the SourceFrame as well, but
+        will not affect any additional result frames.
+
         Parameters
         ----------
         overwrite: boolean, optional
-            Indicates whether to overwrite the current ensemble (set True), or 
+            Indicates whether to overwrite the current ensemble (set True), or
             create a new ensemble for the subset of objects (set False).
         **kwargs:
             keyword arguments passed along to
@@ -488,7 +491,7 @@ def sample(self, overwrite=False, **kwargs):
         ----------
         ensemble: `tape.ensemble.Ensemble`
             A new ensemble with the subset of data selected
-        
+
         """
 
         # first do an object sync, ensure object table is up to date
@@ -504,7 +507,7 @@ def sample(self, overwrite=False, **kwargs):
             # sync to source, removes all tied sources
             self._lazy_sync_tables(table="source")
 
-            return self # current in-place implementation
+            return self  # current in-place implementation
         else:
             # make a new ensemble
             # TODO: Investigate shared client warning