Near as I can tell the numpy.sort function doesn’t support requesting a
descending sort on specific columns. (Docs:
https://numpy.org/doc/stable/reference/generated/numpy.sort.html) I wanted to
check I’m not missing how this is properly done or if should I file this as a
feature request. There are many reasons one would need to choose sorting in
ascending/descending order on a column by column basis.
My first thought is another parameter to sort which is an array of bools to
indicate sort order. An alternative is to prepend ‘-‘ to column names that
should be sorted in descending order. However the second idea may conflict with
people's names for columns.
Thanks,
James Anderson
Example:
In my case I have an sweep line algorithm that needs to walk an ndarray with
the following dtype row-by-row:
active_dtype = np.dtype([('Value', 'f4'), ('ID', 'u8'), ('Active', 'u1')])
I need to sort one column (Value) in ascending order, but another column
(Active) in descending order. (To handle the degenerate case where a rectangle
has a zero width/height).
For full context the code below takes a rectangle array (rect_array) and
returns sorted arrays indicating where rectangle boundaries start and stop on
each axis. In this example one can invert the meaning of ‘Active’ to work
around the lack of API support. The intent is just to illustrate how I was
hoping I had expected to use numpy.sort.
x_sweep_array = np.empty(len(rect_array) * 2, dtype=cls.active_dtype)
y_sweep_array = np.empty(len(rect_array) * 2, dtype=cls.active_dtype)
for i, rect in enumerate(rect_array):
x_sweep_array[i * 2] = (rect['MinX'], rect['ID'], True)
x_sweep_array[(i * 2) + 1] = (rect['MaxX'], rect['ID'], False)
y_sweep_array[i * 2] = (rect['MinY'], rect['ID'], True)
y_sweep_array[(i * 2) + 1] = (rect['MaxY'], rect['ID'], False)
x_sweep_array = np.sort(x_sweep_array, order=('Value', 'Active')) # <- Cannot
reverse sort Active
y_sweep_array = np.sort(y_sweep_array, order=('Value', 'Active'))
return (x_sweep_array, y_sweep_array)
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