Comments (1)
There are a few ways that this can be accomplished -- let's assume we have a rs.DataSet
that looks like the following:
> ds
FMODEL PHIFMODEL
H K L
45 12 14 9.349418 -48.853035
37 10 13 17.603436 -126.121925
39 2 33 24.622107 -68.23137
14 1 37 214.67409 -46.876408
35 17 23 34.46986 86.572845
A simple solution would be this:
> ds2 = ds[[]].copy()
> ds2
Empty DataSet
Columns: []
Index: [(45, 12, 14), (37, 10, 13), (39, 2, 33), (14, 1, 37), (35, 17, 23)]
It is also possible to directly initialize one rs.DataSet
with another:
> ds2 = rs.DataSet(ds[[]])
> ds2
Empty DataSet
Columns: []
Index: [(45, 12, 14), (37, 10, 13), (39, 2, 33), (14, 1, 37), (35, 17, 23)]
rs.DataSet
objects are subclasses of pd.DataFrame
, so typically any pandas
solution should work in reciprocalspaceship
. In my mind, this is a significant advantage to the design choice in rs
, because it means that you can typically just follow the advice of stackoverflow results regarding pandas
.
However, I'd like to provide one cautionary example -- if you were to do it using the currently accepted answer, you will lose the attributes associated with rs.DataSet
(i.e. ds.cell
and ds.spacegroup
):
> ds2 = rs.DataSet(index=ds.index)
> ds2
Empty DataSet
Columns: []
Index: [(45, 12, 14), (37, 10, 13), (39, 2, 33), (14, 1, 37), (35, 17, 23)]
> ds.spacegroup
<gemmi.SpaceGroup("P 31 2 1")>
> print(ds2.spacegroup)
None
This is because the ds2
is only initialized with an index, which doesn't carry with it all the "metadata" of the rs.DataSet
object.
from reciprocalspaceship.
Related Issues (20)
- groupby apply drops cell and spacegroup HOT 2
- `stack_anomalous` inside `groupby` breaks `as_index=False` HOT 1
- Unstack anomalous taking into account Careless repeats HOT 10
- rs.utils.asu.in_asu() does not use the 'anomalous ASU' for stacked anomalous data. HOT 1
- function for cif file IO and possible support for multi-dataset files HOT 3
- `hkl_to_asu` does not annotate M/ISYM field correctly HOT 2
- support for read_precognition() for hkl without anomalous columns HOT 6
- unstack_anomalous makes data that phenix cannot interpret HOT 3
- API reference website display
- Return keys of dictionary in crystfel.py HOT 8
- add_rfree() does not consider Friedel mates HOT 2
- `rs.DataSet.reset_index()` call signature does not match pandas >1.5 HOT 1
- Used pandas.core.ops attribute does not appear to exist HOT 4
- A `rs.cifdump` utility? HOT 1
- No documentation of CrystFEL columns HOT 1
- mean_intensity_by_miller_index should use a grid HOT 1
- write_mtz SettingWithCopyWarning HOT 2
- Failure to correctly read Miller indices from cctbx-generated MTZ HOT 8
- RS requires set_index(["H","K","L"]) to create a multi_index using rs.DataSet HOT 5
- Refactor Ray contextmanager
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