Revert "Format loaded dataset"

.travis.yml

@@ -14,3 +14,6 @@ script:
   - set -eo pipefail
   - flake8 server/app/
   - pytest -s server/test/test_filter.py server/test/test_scanpy_engine.py
+  - cellxgene scanpy example-dataset/ &
+  - for i in {1..90}; do if http :5005/api/v0.1/initialize > /dev/null; then break; else echo "Waiting for server..."; sleep 1; fi; done
+  - pytest server/test/test_api.py

server/app/driver/driver.py

@@ -10,6 +10,10 @@ class CXGDriver(metaclass=ABCMeta):
     def _load_data(data):
         pass
 
+    @abstractmethod
+    def _load_or_infer_schema(data):
+        pass
+
     @abstractmethod
     def cells(self):
         pass

server/app/scanpy_engine/scanpy_engine.py

@@ -1,21 +1,20 @@
 import os
-import warnings
 
 import numpy as np
-from pandas import Series
 import scanpy.api as sc
 from scipy import stats
 
 from server.app.app import cache
 from server.app.driver.driver import CXGDriver
+from server.app.util.schema_parse import parse_schema
 
 
 class ScanpyEngine(CXGDriver):
 
-    def __init__(self, data, graph_method="umap", diffexp_method="ttest"):
+    def __init__(self, data, schema=None, graph_method="umap", diffexp_method="ttest"):
         self.data = self._load_data(data)
-        self._validatate_data_types()
-        self._add_mandatory_annotations()
+        self.schema = self._load_or_infer_schema(data, schema)
+        self._set_cell_names()
         self.cell_count = self.data.shape[0]
         self.gene_count = self.data.shape[1]
         self.graph_method = graph_method
@@ -40,18 +39,41 @@ class ScanpyEngine(CXGDriver):
     def _load_data(data):
         return sc.read(os.path.join(data, "data.h5ad"))
 
-    def _add_mandatory_annotations(self):
-        # ensure gene
-        self.data.var["name"] = list(self.data.var.index)
-        self.data.var.index = Series(list(range(self.data.var.shape[0])), dtype="int32")
-        # ensure cell name
-        self.data.obs["name"] = list(self.data.obs.index)
-        self.data.obs.index = Series(list(range(self.data.obs.shape[0])), dtype="int32")
-
-    def _validatate_data_types(self):
-        if self.data.X.dtype != "float32":
-            warnings.warn(f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
-                          f"Precision may be truncated.")
+    def _load_or_infer_schema(self, data, schema):
+        if not os.path.isfile(os.path.join(data, schema)):
+            # Initialize with cell name which is built off the index
+            data_schema = {
+                "CellName": {
+                    "type": "string",
+                    "variabletype": "categorical",
+                    "displayname": "Name",
+                    "include": True
+                }
+            }
+            metadata_fields = list(self.data.obs)
+            for m in metadata_fields:
+                # Since there are many type of float/int in numpy datatypes the kind attribute of a datatype object
+                # offers a decent insight into whether it can be lumped in with floats or ints, which is what we
+                # care about here.
+                data_kind = self.data.obs[m].dtype.kind
+                variable_type = "categorical"
+                data_type = "string"
+                if data_kind == 'f':
+                    variable_type = "continuous"
+                    data_type = "float"
+                elif data_kind in ['i', 'u']:
+                    data_type = "int"
+                    if self.data.obs[m].nunique() > 50:
+                        variable_type = "continuous"
+                data_schema[m] = {
+                    "type": data_type,
+                    "variabletype": variable_type,
+                    "displayname": m,
+                    "include": True
+                }
+        else:
+            data_schema = parse_schema(os.path.join(data, schema))
+        return data_schema
 
     def cells(self):
         return list(self.data.obs.index)

server/test/test_scanpy_engine.py

@@ -1,12 +1,11 @@
 import unittest
-import pytest
 
 from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
 
 
 class UtilTest(unittest.TestCase):
     def setUp(self):
-        self.data = ScanpyEngine("example-dataset/")
+        self.data = ScanpyEngine("example-dataset/", schema="data_schema.json")
 
     def test_init(self):
         self.assertEqual(self.data.cell_count, 2638)
@@ -14,16 +13,56 @@ class UtilTest(unittest.TestCase):
         epsilon = 0.000005
         self.assertTrue(self.data.data.X[0,0] - -0.17146951 < epsilon)
 
-    def test_mandatory_annotations(self):
-        self.assertIn("name", self.data.data.obs)
-        self.assertEqual(list(self.data.data.obs.index), list(range(2638)))
-        self.assertIn("name", self.data.data.var)
-        self.assertEqual(list(self.data.data.var.index), list(range(1838)))
+    def test_schema(self):
+        self.assertEqual(self.data.schema, {'CellName': {'type': 'string', 'variabletype': 'categorical', 'displayname': 'Name', 'include': True}, 'n_genes': {'type': 'int', 'variabletype': 'continuous', 'displayname': 'Num Genes', 'include': True}, 'percent_mito': {'type': 'float', 'variabletype': 'continuous', 'displayname': 'Mitochondrial Percentage', 'include': True}, 'n_counts': {'type': 'float', 'variabletype': 'continuous', 'displayname': 'Num Counts', 'include': True}, 'louvain': {'type': 'string', 'variabletype': 'categorical', 'displayname': 'Louvain Cluster', 'include': True}})
 
-    @pytest.mark.filterwarnings("ignore:Scanpy data matrix")
-    def test_data_type(self):
-        self.data.data.X = self.data.data.X.astype("float64")
-        self.assertWarns(UserWarning, self.data._validatate_data_types())
+    def test_cells(self):
+        cells = self.data.cells()
+        self.assertIn("AAACATACAACCAC-1", cells)
+        self.assertEqual(len(cells), 2638)
+
+    def test_genes(self):
+        genes = self.data.genes()
+        self.assertIn("SEPT4", genes)
+        self.assertEqual(len(genes), 1838)
+
+    def test_filter_categorical(self):
+        filter = {"louvain": {"variable_type": "categorical", "value_type": "string", "query": ["B cells"]}}
+        filtered_data = self.data.filter_cells(filter)
+        self.assertEqual(filtered_data.shape, (342, 1838))
+        louvain_vals = filtered_data.obs['louvain'].tolist()
+        self.assertIn("B cells", louvain_vals)
+        self.assertNotIn("NK cells", louvain_vals)
+
+    def test_filter_continuous(self):
+        # print(self.data.data.obs["n_genes"].tolist())
+        filter = {"n_genes": {"variable_type": "continuous", "value_type": "int", "query": {"min": 300, "max": 400}}}
+        filtered_data = self.data.filter_cells(filter)
+        self.assertEqual(filtered_data.shape, (71, 1838))
+        n_genes_vals = filtered_data.obs['n_genes'].tolist()
+        for val in n_genes_vals:
+            self.assertTrue(300 <= val <= 400)
+
+    def test_metadata(self):
+        metadata = self.data.metadata(df=self.data.data)
+        self.assertEqual(len(metadata), 2638)
+        self.assertIn('louvain', metadata[0])
+
+    @unittest.skip("Umap not producing the same graph on different systems, even with the same seed. Skipping for now")
+    def test_create_graph(self):
+        graph = self.data.create_graph(df=self.data.data)
+        self.assertEqual(graph[0][1], 0.5545382653143183)
+        self.assertEqual(graph[0][2], 0.6021833809031731)
+
+    def test_diffexp(self):
+        diffexp = self.data.diffexp(["AAACATACAACCAC-1", "AACCGATGGTCATG-1"], ["CCGATAGACCTAAG-1", "GGTGGAGAAGTAGA-1"], 0.5, 7)
+        self.assertEqual(diffexp["celllist1"]["topgenes"], ['EBNA1BP2', 'DIAPH1', 'SLC25A11', 'SNRNP27', 'COMMD8', 'COTL1', 'GTF3A'])
+
+    def test_expression(self):
+        expression = self.data.expression(cells=["AAACATACAACCAC-1"])
+        data_exp = self.data.data[["AAACATACAACCAC-1"], :].X
+        for idx in range(len(expression["cells"][0]["e"])):
+            self.assertEqual(expression["cells"][0]["e"][idx], data_exp[idx])
 
 
 if __name__ == '__main__':