binary wire format with flatbuffers (#509)

* first flatbuffer schema * do not lint auto-generated files * add flatbuffers package * add flatbuffer module * wire up /data/X/T route * use flatbuffers for matrix data fetc * clarity and comments * add flatbuffer layout route * clean up obsolete code * fix tests * move flake8 config to setup.cfg * add comments * lint * rework layout routes for fbs * add more type support to fbs * lint * add flatbuffer support for annotations * function name improvements * fix botched merge with master * remove unused import * route cleanup for flatbuffers * rename function for clarity * add missing globals to Jest tests * fix client JS tests * fix routes for Python tests * comments for clarity * non-finite floating point hardening * more non-finite number handling * lint * fix tests for summarizeAnnotations * harden diffexp calculation against FP errors * cleanup unused code * lint * add encoding tests for flatbuffers * application type specified as strings * fix spelling error * improve variable names * add note about documentation gap * rename FBS DataFrame to Matrix
2026-10-03 09:08:12 +08:00 · 2019-01-09 14:26:05 -08:00
parent 42e25a1a1f
commit b90447c387
41 changed files with 2317 additions and 266 deletions
@@ -0,0 +1,69 @@
+
+"""
+Code to decode, for testing purposes, the flatbuffer encoded blobs.
+This code will need to be updated if fbs/matrix.fbs changes.
+
+For more information, see fbs/matrix.fbs and server/app/util/fbs/
+"""
+import json
+
+import server.app.util.fbs.NetEncoding.TypedArray as TypedArray
+import server.app.util.fbs.NetEncoding.Matrix as Matrix
+import server.app.util.fbs.NetEncoding.Int32Array as Int32Array
+import server.app.util.fbs.NetEncoding.Uint32Array as Uint32Array
+import server.app.util.fbs.NetEncoding.Float32Array as Float32Array
+import server.app.util.fbs.NetEncoding.Float64Array as Float64Array
+import server.app.util.fbs.NetEncoding.JSONEncodedArray as JSONEncodedArray
+
+
+def decode_typed_array(tarr):
+    type_map = {
+        TypedArray.TypedArray.Uint32Array: Uint32Array.Uint32Array,
+        TypedArray.TypedArray.Int32Array: Int32Array.Int32Array,
+        TypedArray.TypedArray.Float32Array: Float32Array.Float32Array,
+        TypedArray.TypedArray.Float64Array: Float64Array.Float64Array,
+        TypedArray.TypedArray.JSONEncodedArray: JSONEncodedArray.JSONEncodedArray
+    }
+    (u_type, u) = tarr
+    if u_type == TypedArray.TypedArray.NONE:
+        return None
+
+    TarType = type_map.get(u_type, None)
+    assert(TarType is not None)
+
+    arr = TarType()
+    arr.Init(u.Bytes, u.Pos)
+    narr = arr.DataAsNumpy()
+    if u_type == TypedArray.TypedArray.JSONEncodedArray:
+        narr = json.loads(narr.tostring().decode('utf-8'))
+    return narr
+
+
+def decode_matrix_FBS(buf):
+    """
+    Given a FBS Matrix, return an decoded Python dict containing
+    same info in native format.
+
+    NOTE / TODO: row_idx not currently implemented
+    """
+    df = Matrix.Matrix.GetRootAsMatrix(buf, 0)
+    n_rows = df.NRows()
+    n_cols = df.NCols()
+
+    columns_length = df.ColumnsLength()
+
+    decoded_columns = []
+    for col_idx in range(0, columns_length):
+        col = df.Columns(col_idx)
+        tarr = (col.UType(), col.U())
+        decoded_columns.append(decode_typed_array(tarr))
+
+    cidx = decode_typed_array((df.ColIndexType(), df.ColIndex()))
+
+    return {
+        "n_rows": n_rows,
+        "n_cols": n_cols,
+        "columns": decoded_columns,
+        "col_idx": cidx,
+        "row_idx": None
+    }