def fromarrays(arraylist, dates=None, start_date=None, freq='U',
fill_value=None, autosort=True,
dtype=None, shape=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None,):
"""
Creates a mrecarray from a (flat) list of masked arrays.
Parameters
----------
arraylist : array_like
A list of (masked) arrays. Each element of the sequence is first converted
to a masked array if needed. If a 2D array is passed as argument, it is
processed line by line
dates : {DateArray}, optional
Array of dates corresponding to each entry.
If None, a DateArray is constructed from `start_date` and the length
of the arrays in the input list.
start_date : {Date}, optional
First date of the output.
This parameter is inly needed if `dates` is None.
freq : {var}, optional
Frequency of the DateArray
fill_value : {var}, optional
Value used to fill in the masked values when necessary.
If None, a default based on the datatype is used.
autosort : {True, False}, optional
Whether the records should be sorted chronologically.
See Also
--------
numpy.core.records.fromarrays : equivalent function for ndarrays
The docstring of this function describes the additional optional
input parameters.
Notes
-----
* Lists of tuples should be preferred over lists of lists as inputs
for faster processing.
"""
_array = mrecfromarrays(arraylist, dtype=dtype, shape=shape, formats=formats,
names=names, titles=titles, aligned=aligned,
byteorder=byteorder, fill_value=fill_value)
_dates = _getdates(dates, length=len(_array), start_date=start_date,
freq=freq)
# if _dates._unsorted is not None:
# idx = _dates._unsorted
# _array = _array[idx]
# _dates._unsorted = None
result = _array.view(trecarray)
result._dates = _dates
if autosort:
result.sort_chronologically()
return result
def fromrecords(
reclist,
dates=None,
freq=None,
start_date=None,
fill_value=None,
mask=nomask,
autosort=True,
dtype=None,
shape=None,
formats=None,
names=None,
titles=None,
aligned=False,
byteorder=None,
):
"""
Creates a TimeSeriesRecords from a list of records.
The data in the same field can be heterogeneous, they will be promoted
to the highest data type. This method is intended for creating
smaller record arrays. If used to create large array without formats
defined, it can be slow.
If formats is None, then this will auto-detect formats. Use a list of
tuples rather than a list of lists for faster processing.
Parameters
----------
reclist : array_like
A list of records. Each element of the sequence is first converted
to a masked array if needed. If a 2D array is passed as argument, it is
processed line by line
dates : {DateArray}, optional
Array of dates corresponding to each entry.
If None, a DateArray is constructed from `start_date` and the length
of the arrays in the input list.
freq : {var}, optional
Frequency of the DateArray
start_date : {Date}, optional
First date of the output.
This parameter is inly needed if `dates` is None.
fill_value : {var}, optional
Value used to fill in the masked values when necessary.
If None, a default based on the datatype is used.
autosort : {True, False}, optional
Whether the records should be sorted chronologically.
See Also
--------
numpy.core.records.fromrecords : equivalent function for ndarrays
"""
_data = mrecfromrecords(
reclist,
dtype=dtype,
shape=shape,
formats=formats,
names=names,
titles=titles,
aligned=aligned,
byteorder=byteorder,
mask=mask,
)
_dtype = _data.dtype
# Check the names for a '_dates' .................
newdates = None
_names = list(_dtype.names)
reserved = [n for n in _names if n.lower() in ["dates", "_dates"]]
if len(reserved) > 0:
newdates = _data[reserved[-1]]
[_names.remove(n) for n in reserved]
_dtype = np.dtype([t for t in _dtype.descr if t[0] not in reserved])
_data = mrecfromarrays([_data[n] for n in _names], dtype=_dtype)
#
if dates is None:
dates = getattr(reclist, "_dates", None)
_dates = _getdates(dates=dates, newdates=newdates, length=len(_data), freq=freq, start_date=start_date)
#
result = _data.view(trecarray)
result._dates = _dates
if autosort:
result.sort_chronologically()
return result
def fromrecords(reclist, dates=None, freq=None, start_date=None,
fill_value=None, mask=nomask, autosort=True,
dtype=None, shape=None, formats=None, names=None,
titles=None, aligned=False, byteorder=None):
"""
Creates a TimeSeriesRecords from a list of records.
The data in the same field can be heterogeneous, they will be promoted
to the highest data type. This method is intended for creating
smaller record arrays. If used to create large array without formats
defined, it can be slow.
If formats is None, then this will auto-detect formats. Use a list of
tuples rather than a list of lists for faster processing.
Parameters
----------
reclist : array_like
A list of records. Each element of the sequence is first converted
to a masked array if needed. If a 2D array is passed as argument, it is
processed line by line
dates : {DateArray}, optional
Array of dates corresponding to each entry.
If None, a DateArray is constructed from `start_date` and the length
of the arrays in the input list.
freq : {var}, optional
Frequency of the DateArray
start_date : {Date}, optional
First date of the output.
This parameter is inly needed if `dates` is None.
fill_value : {var}, optional
Value used to fill in the masked values when necessary.
If None, a default based on the datatype is used.
autosort : {True, False}, optional
Whether the records should be sorted chronologically.
See Also
--------
numpy.core.records.fromrecords : equivalent function for ndarrays
"""
_data = mrecfromrecords(reclist, dtype=dtype, shape=shape, formats=formats,
names=names, titles=titles, aligned=aligned,
byteorder=byteorder, mask=mask)
_dtype = _data.dtype
# Check the names for a '_dates' .................
newdates = None
_names = list(_dtype.names)
reserved = [n for n in _names if n.lower() in ['dates', '_dates']]
if len(reserved) > 0:
newdates = _data[reserved[-1]]
[_names.remove(n) for n in reserved]
_dtype = np.dtype([t for t in _dtype.descr \
if t[0] not in reserved ])
_data = mrecfromarrays([_data[n] for n in _names], dtype=_dtype)
#
if dates is None:
dates = getattr(reclist, '_dates', None)
_dates = _getdates(dates=dates, newdates=newdates, length=len(_data),
freq=freq, start_date=start_date)
#
result = _data.view(trecarray)
result._dates = _dates
if autosort:
result.sort_chronologically()
return result
