Do not calculate layout, used saved layout instead (#343)

* Do not calculate layout, used saved layout instead

See for rationale: https://docs.google.com/document/d/1HJFvbdDHxxgkCW0DZzdTZ9CUMgc2ef2rQFukATBQWvE/edit

* Error handling for when layout has not been precomputed

* Server error (500) not client error (400) for unprepared data

server/app/scanpy_engine/scanpy_engine.py

@@ -9,7 +9,7 @@ from scipy import stats
 from server.app.app import cache
 from server.app.driver.driver import CXGDriver
 from server.app.util.constants import Axis, DEFAULT_TOP_N, DiffExpMode
-from server.app.util.utils import FilterError, InteractiveError
+from server.app.util.utils import FilterError, InteractiveError, PrepareError
 
 """
 Sort order for methods
@@ -30,7 +30,6 @@ class ScanpyEngine(CXGDriver):
         self.cell_count = self.data.shape[0]
         self.gene_count = self.data.shape[1]
         self._create_schema()
-        self.layout({})
 
     def _create_schema(self):
         self.schema = {
@@ -340,8 +339,14 @@ class ScanpyEngine(CXGDriver):
         # TODO Filtering cells is fine, but filtering genes does nothing because the neighbors are
         # calculated using the original vars (geneset) and this doesn’t get updated when you use less.
         # Need to recalculate neighbors (long) if user requests new layout filtered by var
-        getattr(sc.tl, self.layout_method)(df, random_state=123)
-        df_layout = df.obsm[f"X_{self.layout_method}"]
+        # TODO for MVP we are pushing computation of layout to preprocessing and not allowing re-layout
+        # this will probably change after user feedback
+        # getattr(sc.tl, self.layout_method)(df, random_state=123)
+        try:
+            df_layout = df.obsm[f"X_{self.layout_method}"]
+        except ValueError as e:
+            raise PrepareError(f"Layout has not been calculated using {self.layout_method}, "
+                               f"please prepare your datafile and relaunch cellxgene") from e
         normalized_layout = DataFrame((df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
                                       index=df.obs.index)
         return {