def create_frvect(timeseries):
"""Create a `~frameCPP.FrVect` from a `TimeSeries`
This method is primarily designed to make writing data to GWF files a
bit easier.
Parameters
----------
timeseries : `TimeSeries`
the input `TimeSeries`
Returns
-------
frvect : `~frameCPP.FrVect`
the output `FrVect`
"""
# create timing dimension
dims = frameCPP.Dimension(timeseries.size, timeseries.dx.value,
str(timeseries.dx.unit), 0)
# create FrVect
vect = frameCPP.FrVect(timeseries.name or '',
FRVECT_TYPE_FROM_NUMPY[timeseries.dtype.type], 1,
dims, str(timeseries.unit))
# populate FrVect and return
vect.GetDataArray()[:] = numpy.require(timeseries.value,
requirements=['C'])
return vect
def create_frvect(series):
"""Create a `~frameCPP.FrVect` from a `~gwpy.types.Series`
.. note::
Currently this method is restricted to 1-dimensional arrays.
Parameters
----------
series : `~gwpy.types.Series`
the input data array to store
Returns
-------
frvect : `~frameCPP.FrVect`
the newly created data vector
"""
from LDAStools import frameCPP
from ._framecpp import FrVectType
# create dimensions
dims = frameCPP.Dimension(
series.shape[0], # num elements
series.dx.value, # step size
str(series.dx.unit), # unit
0, # starting value
)
# create FrVect
vect = frameCPP.FrVect(
_series_name(series), # name
int(FrVectType.find(series.dtype)), # data type enum
series.ndim, # num dimensions
dims, # dimension object
str(series.unit), # unit
)
# populate FrVect
vect.GetDataArray()[:] = numpy.require(series.value, requirements=['C'])
return vect