I have a recarray containing various fields and I want to append an array of datetime objects on to it.
However, it seems like the append_fields function in numpy.lib.recfunctions won't let me add an array of objects.
Here's some example code:
import numpy as np
import datetime
import numpy.lib.recfunctions as recfun
dtype= np.dtype([('WIND_WAVE_HGHT', '<f4'), ('WIND_WAVE_PERD', '<f4')])
obs = np.array([(0.1,10.0),(0.2,11.0),(0.3,12.0)], dtype=dtype)
dates = np.array([datetime.datetime(2001,1,1,0),
datetime.datetime(2001,1,1,0),
datetime.datetime(2001,1,1,0)])
# This doesn't work:
recfun.append_fields(obs,'obdate',dates,dtypes=np.object)
I keep getting the error TypeError: Cannot change data-type for object array.
It seems to only be an issue with np.object arrays as I can append other fields ok. Am I missing something?
In [143]: recfun.append_fields(obs,'test',np.array([None,[],1]))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-143-5c3de23b09f7> in <module>()
----> 1 recfun.append_fields(obs,'test',np.array([None,[],1]))
/usr/local/lib/python3.5/dist-packages/numpy/lib/recfunctions.py in append_fields(base, names, data, dtypes, fill_value, usemask, asrecarray)
615 if dtypes is None:
616 data = [np.array(a, copy=False, subok=True) for a in data]
--> 617 data = [a.view([(name, a.dtype)]) for (name, a) in zip(names, data)]
618 else:
619 if not isinstance(dtypes, (tuple, list)):
/usr/local/lib/python3.5/dist-packages/numpy/lib/recfunctions.py in <listcomp>(.0)
615 if dtypes is None:
616 data = [np.array(a, copy=False, subok=True) for a in data]
--> 617 data = [a.view([(name, a.dtype)]) for (name, a) in zip(names, data)]
618 else:
619 if not isinstance(dtypes, (tuple, list)):
/usr/local/lib/python3.5/dist-packages/numpy/core/_internal.py in _view_is_safe(oldtype, newtype)
363
364 if newtype.hasobject or oldtype.hasobject:
--> 365 raise TypeError("Cannot change data-type for object array.")
366 return
367
TypeError: Cannot change data-type for object array.
So the problem is in this a.view([(name, a.dtype)]) expression. It tries to make a single field structured array from a. That works with dtypes like int and str, but fails with object. That failure is in the core view handling, so isn't likely to change.
In [148]: x=np.arange(3)
In [149]: x.view([('test', x.dtype)])
Out[149]:
array([(0,), (1,), (2,)],
dtype=[('test', '<i4')])
In [150]: x=np.array(['one','two'])
In [151]: x.view([('test', x.dtype)])
Out[151]:
array([('one',), ('two',)],
dtype=[('test', '<U3')])
In [152]: x=np.array([[1],[1,2]])
In [153]: x
Out[153]: array([[1], [1, 2]], dtype=object)
In [154]: x.view([('test', x.dtype)])
...
TypeError: Cannot change data-type for object array.
The fact that recfunctions requires a separate load indicates that it is somewhat of a backwater, that isn't used a lot, and not under active development. I haven't examined the code in detail, but I suspect a fix would be a kludge.
Here's a way of adding a new field from scratch. It performs the same basic actions as append_fields:
Define a new dtype, using the obs and the new field name and dtype:
In [158]: obs.dtype.descr
Out[158]: [('WIND_WAVE_HGHT', '<f4'), ('WIND_WAVE_PERD', '<f4')]
In [159]: obs.dtype.descr+[('TEST',object)]
Out[159]: [('WIND_WAVE_HGHT', '<f4'), ('WIND_WAVE_PERD', '<f4'), ('TEST', object)]
In [160]: dt1 =np.dtype(obs.dtype.descr+[('TEST',object)])
Make an empty target array, and fill it by copying data by field name:
In [161]: newobs = np.empty(obs.shape, dtype=dt1)
In [162]: for n in obs.dtype.names:
...: newobs[n]=obs[n]
In [167]: dates
Out[167]:
array([datetime.datetime(2001, 1, 1, 0, 0),
datetime.datetime(2001, 1, 1, 0, 0),
datetime.datetime(2001, 1, 1, 0, 0)], dtype=object)
In [168]: newobs['TEST']=dates
In [169]: newobs
Out[169]:
array([( 0.1 , 10., datetime.datetime(2001, 1, 1, 0, 0)),
( 0.2 , 11., datetime.datetime(2001, 1, 1, 0, 0)),
( 0.30000001, 12., datetime.datetime(2001, 1, 1, 0, 0))],
dtype=[('WIND_WAVE_HGHT', '<f4'), ('WIND_WAVE_PERD', '<f4'), ('TEST', 'O')])
With the native numpy datetimes, append works
In [179]: dates64 = dates.astype('datetime64[D]')
In [180]: recfun.append_fields(obs,'test',dates64,usemask=False)
Out[180]:
array([( 0.1 , 10., '2001-01-01'),
( 0.2 , 11., '2001-01-01'), ( 0.30000001, 12., '2001-01-01')],
dtype=[('WIND_WAVE_HGHT', '<f4'), ('WIND_WAVE_PERD', '<f4'), ('test', '<M8[D]')])
append_fields has some bells-n-whistles that my version doesn't - fill values, masked arrays, recarray, etc.
I could create a structured array with the dates
In [197]: sdates = np.array([(i,) for i in dates],dtype=[('test',object)])
In [198]: sdates
Out[198]:
array([(datetime.datetime(2001, 1, 1, 0, 0),),
(datetime.datetime(2001, 1, 1, 0, 0),),
(datetime.datetime(2001, 1, 1, 0, 0),)],
dtype=[('test', 'O')])
There must be a function that merges fields of existing arrays, but I'm not finding it.
This felt familiar:
https://github.com/numpy/numpy/issues/2346
TypeError when appending fields to a structured array of size ONE