mirror of
https://github.com/chanzuckerberg/cellxgene.git
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78c9d24ed4
* move local_server -> backend/server server-> backend/czi_hosted, pull common code into backend/common update imports, tests and make commands
248 lines
8.7 KiB
Python
248 lines
8.7 KiB
Python
import json
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import numpy as np
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import pandas as pd
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from flatbuffers import Builder
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from scipy import sparse
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import backend.common.fbs.NetEncoding.Column as Column
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import backend.common.fbs.NetEncoding.Float32Array as Float32Array
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import backend.common.fbs.NetEncoding.Float64Array as Float64Array
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import backend.common.fbs.NetEncoding.Int32Array as Int32Array
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import backend.common.fbs.NetEncoding.JSONEncodedArray as JSONEncodedArray
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import backend.common.fbs.NetEncoding.Matrix as Matrix
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import backend.common.fbs.NetEncoding.TypedArray as TypedArray
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import backend.common.fbs.NetEncoding.Uint32Array as Uint32Array
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# Serialization helper
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def serialize_column(builder, typed_arr):
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""" Serialize NetEncoding.Column """
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(u_type, u_value) = typed_arr
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Column.ColumnStart(builder)
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Column.ColumnAddUType(builder, u_type)
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Column.ColumnAddU(builder, u_value)
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return Column.ColumnEnd(builder)
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# Serialization helper
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def serialize_matrix(builder, n_rows, n_cols, columns, col_idx):
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""" Serialize NetEncoding.Matrix """
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Matrix.MatrixStart(builder)
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Matrix.MatrixAddNRows(builder, n_rows)
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Matrix.MatrixAddNCols(builder, n_cols)
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Matrix.MatrixAddColumns(builder, columns)
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if col_idx is not None:
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(u_type, u_val) = col_idx
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Matrix.MatrixAddColIndexType(builder, u_type)
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Matrix.MatrixAddColIndex(builder, u_val)
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return Matrix.MatrixEnd(builder)
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# Serialization helper
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def serialize_typed_array(builder, source_array, encoding_info):
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"""
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Serialize any of the various typed arrays, eg, Float32Array. Specific means of serialization and type conversion
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are provided by type_info.
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"""
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arr = source_array
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(array_type, as_type) = encoding_info(source_array)
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if isinstance(arr, pd.Index):
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arr = arr.to_series()
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# convert to a simple ndarray
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if as_type == "json":
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as_json = arr.to_json(orient="records")
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arr = np.array(bytearray(as_json, "utf-8"))
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else:
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if sparse.issparse(arr):
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arr = arr.toarray()
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elif isinstance(arr, pd.Series):
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arr = arr.to_numpy()
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if arr.dtype != as_type:
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arr = arr.astype(as_type)
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# serialize the ndarray into a vector
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if arr.ndim == 2:
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if arr.shape[0] == 1:
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arr = arr[0]
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elif arr.shape[1] == 1:
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arr = arr.T[0]
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vec = builder.CreateNumpyVector(arr)
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# serialize the typed array table
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builder.StartObject(1)
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builder.PrependUOffsetTRelativeSlot(0, vec, 0)
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array_value = builder.EndObject()
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return (array_type, array_value)
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def column_encoding(arr):
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column_encoding_type_map = {
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# array protocol string: ( array_type, as_type )
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np.dtype(np.float64).str: (TypedArray.TypedArray.Float64Array, np.float64),
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np.dtype(np.float32).str: (TypedArray.TypedArray.Float32Array, np.float32),
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np.dtype(np.float16).str: (TypedArray.TypedArray.Float32Array, np.float32),
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np.dtype(np.int8).str: (TypedArray.TypedArray.Int32Array, np.int32),
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np.dtype(np.int16).str: (TypedArray.TypedArray.Int32Array, np.int32),
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np.dtype(np.int32).str: (TypedArray.TypedArray.Int32Array, np.int32),
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np.dtype(np.int64).str: (TypedArray.TypedArray.Int32Array, np.int32),
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np.dtype(np.uint8).str: (TypedArray.TypedArray.Uint32Array, np.uint32),
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np.dtype(np.uint16).str: (TypedArray.TypedArray.Uint32Array, np.uint32),
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np.dtype(np.uint32).str: (TypedArray.TypedArray.Uint32Array, np.uint32),
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np.dtype(np.uint64).str: (TypedArray.TypedArray.Uint32Array, np.uint32),
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}
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column_encoding_default = (TypedArray.TypedArray.JSONEncodedArray, "json")
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return column_encoding_type_map.get(arr.dtype.str, column_encoding_default)
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def index_encoding(arr):
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index_encoding_type_map = {
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# array protocol string: ( array_type, as_type )
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np.dtype(np.int32).str: (TypedArray.TypedArray.Int32Array, np.int32),
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np.dtype(np.int64).str: (TypedArray.TypedArray.Int32Array, np.int32),
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np.dtype(np.uint32).str: (TypedArray.TypedArray.Uint32Array, np.uint32),
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np.dtype(np.uint64).str: (TypedArray.TypedArray.Uint32Array, np.uint32),
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}
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index_encoding_default = (TypedArray.TypedArray.JSONEncodedArray, "json")
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return index_encoding_type_map.get(arr.dtype.str, index_encoding_default)
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def guess_at_mem_needed(matrix):
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(n_rows, n_cols) = matrix.shape
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if isinstance(matrix, np.ndarray) or sparse.issparse(matrix):
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guess = (n_rows * n_cols * matrix.dtype.itemsize) + 1024
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elif isinstance(matrix, pd.DataFrame):
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# XXX TODO - DataFrame type estimate
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guess = 1
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else:
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guess = 1
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# round up to nearest 1024 bytes
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guess = (guess + 0x400) & (~0x3FF)
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return guess
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def encode_matrix_fbs(matrix, row_idx=None, col_idx=None):
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"""
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Given a 2D DataFrame, ndarray or sparse equivalent, create and return a Matrix flatbuffer.
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:param matrix: 2D DataFrame, ndarray or sparse equivalent
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:param row_idx: index for row dimension, Index or ndarray
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:param col_idx: index for col dimension, Index or ndarray
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NOTE: row indices are (currently) unsupported and must be None
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"""
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if row_idx is not None:
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raise ValueError("row indexing not supported for FBS Matrix")
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if matrix.ndim != 2:
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raise ValueError("FBS Matrix must be 2D")
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(n_rows, n_cols) = matrix.shape
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# estimate size needed, so we don't unnecessarily realloc.
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builder = Builder(guess_at_mem_needed(matrix))
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columns = []
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for cidx in range(n_cols - 1, -1, -1):
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# serialize the typed array
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col = matrix.iloc[:, cidx] if isinstance(matrix, pd.DataFrame) else matrix[:, cidx]
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typed_arr = serialize_typed_array(builder, col, column_encoding)
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# serialize the Column union
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columns.append(serialize_column(builder, typed_arr))
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# Serialize Matrix.columns[]
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Matrix.MatrixStartColumnsVector(builder, n_cols)
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for c in columns:
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builder.PrependUOffsetTRelative(c)
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matrix_column_vec = builder.EndVector(n_cols)
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# serialize the colIndex if provided
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cidx = None
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if col_idx is not None:
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cidx = serialize_typed_array(builder, col_idx, index_encoding)
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# Serialize Matrix
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matrix = serialize_matrix(builder, n_rows, n_cols, matrix_column_vec, cidx)
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builder.Finish(matrix)
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return builder.Output()
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def deserialize_typed_array(tarr):
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type_map = {
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TypedArray.TypedArray.NONE: None,
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TypedArray.TypedArray.Uint32Array: Uint32Array.Uint32Array,
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TypedArray.TypedArray.Int32Array: Int32Array.Int32Array,
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TypedArray.TypedArray.Float32Array: Float32Array.Float32Array,
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TypedArray.TypedArray.Float64Array: Float64Array.Float64Array,
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TypedArray.TypedArray.JSONEncodedArray: JSONEncodedArray.JSONEncodedArray,
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}
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(u_type, u) = tarr
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if u_type is TypedArray.TypedArray.NONE:
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return None
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TarType = type_map.get(u_type, None)
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if TarType is None:
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raise TypeError(f"FBS contains unknown data type: {u_type}")
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arr = TarType()
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arr.Init(u.Bytes, u.Pos)
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narr = arr.DataAsNumpy()
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if u_type == TypedArray.TypedArray.JSONEncodedArray:
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narr = json.loads(narr.tostring().decode("utf-8"))
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return narr
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def decode_matrix_fbs(fbs):
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"""
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Given an FBS-encoded Matrix, return a Pandas DataFrame the contains the data and indices.
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"""
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matrix = Matrix.Matrix.GetRootAsMatrix(fbs, 0)
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n_rows = matrix.NRows()
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n_cols = matrix.NCols()
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if n_rows == 0 or n_cols == 0:
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return pd.DataFrame()
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if matrix.RowIndexType() is not TypedArray.TypedArray.NONE:
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raise ValueError("row indexing not supported for FBS Matrix")
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columns_length = matrix.ColumnsLength()
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columns_index = deserialize_typed_array((matrix.ColIndexType(), matrix.ColIndex()))
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if columns_index is None:
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columns_index = range(0, n_cols)
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# sanity checks
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if len(columns_index) != n_cols or columns_length != n_cols:
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raise ValueError("FBS column count does not match number of columns in underlying matrix")
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columns_data = {}
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columns_type = {}
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for col_idx in range(0, columns_length):
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col = matrix.Columns(col_idx)
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tarr = (col.UType(), col.U())
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data = deserialize_typed_array(tarr)
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columns_data[columns_index[col_idx]] = data
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if len(data) != n_rows:
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raise ValueError("FBS column length does not match number of rows")
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if col.UType() is TypedArray.TypedArray.JSONEncodedArray:
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columns_type[columns_index[col_idx]] = "category"
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df = pd.DataFrame.from_dict(data=columns_data).astype(columns_type, copy=False)
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# more sanity checks
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if not df.columns.is_unique or len(df.columns) != n_cols:
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raise KeyError("FBS column indices are not unique")
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return df
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