mirror of
https://github.com/chanzuckerberg/cellxgene.git
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f3015cb9df
* Fix Makefile whitespace and .PHONY use
* Fix Makefile filename
* Modularize Makefile into client and server Makefiles
Part of the reason that the Makefile in the root directory is a bit
complicated is that it tries to handle tasks that can be handled
separately in the client and server modules.
This commit pushes some of the make logic specific to each module into
their own makefiles and calls out to those makefiles from that in the
project root.
* Add auto-formatting to client and server modules
One thing that can make linting faster is auto-formatting. This commit
adds the yapf auto-formatting tool to the server module and uses
eslint's "fix" functionality to speed up the linting/formatting process.
* Add yapf for automatic code formatting
* Add a root test target that calls sub-tests
* Apply yapf to python files
* Do not duplicate npm commands, simply pass through
* Update documentation
* Do not shadow reserved word len
* Add general test target
* Fix make call in dev-env
* Use black instead of yapf
* Run flake8 from the root directory
* Revert "Apply yapf to python files"
This reverts commit cdca128a01.
* Apply black to python code
* Resolve lint errors resulting from black format
* Add explanation of server unit tests in dev guidelines
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
"""
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Code to decode, for testing purposes, the flatbuffer encoded blobs.
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This code will need to be updated if fbs/matrix.fbs changes.
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For more information, see fbs/matrix.fbs and server/app/util/fbs/
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"""
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import json
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import server.app.util.fbs.NetEncoding.TypedArray as TypedArray
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import server.app.util.fbs.NetEncoding.Matrix as Matrix
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import server.app.util.fbs.NetEncoding.Int32Array as Int32Array
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import server.app.util.fbs.NetEncoding.Uint32Array as Uint32Array
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import server.app.util.fbs.NetEncoding.Float32Array as Float32Array
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import server.app.util.fbs.NetEncoding.Float64Array as Float64Array
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import server.app.util.fbs.NetEncoding.JSONEncodedArray as JSONEncodedArray
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def decode_typed_array(tarr):
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type_map = {
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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 == TypedArray.TypedArray.NONE:
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return None
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TarType = type_map.get(u_type, None)
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assert TarType is not None
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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(buf):
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"""
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Given a FBS Matrix, return an decoded Python dict containing
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same info in native format.
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NOTE / TODO: row_idx not currently implemented
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"""
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df = Matrix.Matrix.GetRootAsMatrix(buf, 0)
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n_rows = df.NRows()
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n_cols = df.NCols()
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columns_length = df.ColumnsLength()
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decoded_columns = []
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for col_idx in range(0, columns_length):
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col = df.Columns(col_idx)
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tarr = (col.UType(), col.U())
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decoded_columns.append(decode_typed_array(tarr))
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cidx = decode_typed_array((df.ColIndexType(), df.ColIndex()))
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return {"n_rows": n_rows, "n_cols": n_cols, "columns": decoded_columns, "col_idx": cidx, "row_idx": None}
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