def build_time_index(
time: types_timeindex = None,
time_ref: pd.Timestamp = None,
) -> pd.TimedeltaIndex:
"""Build time index used for xarray objects.
Parameters
----------
time:
Datetime- or Timedelta-like time index.
time_ref:
Reference timestamp for Timedelta inputs.
Returns
-------
pandas.TimedeltaIndex
"""
if time is None:
return time, time_ref
time = util.to_pandas_time_index(time)
if isinstance(time, pd.DatetimeIndex):
if time_ref is None:
time_ref = time[0]
time = time - time_ref
return time, time_ref
def test_interp_time(ts, time, magnitude_exp, unit_exp):
"""Test the interp_time function."""
result = ts.interp_time(time)
assert np.all(np.isclose(result.data.magnitude, magnitude_exp))
assert Q_(1, str(result.data.units)) == Q_(1, unit_exp)
if isinstance(time, pint.Quantity):
assert np.all(result.time == ut.to_pandas_time_index(time))
else:
assert np.all(result.time == time)
def time(self) -> Union[None, pd.TimedeltaIndex]:
"""Return the data's timestamps.
Returns
-------
pandas.TimedeltaIndex:
Timestamps of the data
"""
if isinstance(self._data, xr.DataArray) and len(self._data.time) > 1:
return ut.to_pandas_time_index(self._data.time.data)
return None
def interp_time(
self,
time: types_time_and_lcs,
time_ref: Union[pd.Timestamp, None] = None,
) -> "LocalCoordinateSystem":
"""Interpolates the data in time.
Parameters
----------
time :
Series of times.
If passing "None" no interpolation will be performed.
time_ref:
The reference timestamp
Returns
-------
LocalCoordinateSystem
Coordinate system with interpolated data
"""
if (not self.is_time_dependent) or (time is None):
return self
# use LCS reference time if none provided
if isinstance(time, LocalCoordinateSystem) and time_ref is None:
time_ref = time.reference_time
time = ut.to_pandas_time_index(time)
if self.has_reference_time != (time_ref is not None
or isinstance(time, pd.DatetimeIndex)):
raise TypeError(
"Only 1 reference time provided for time dependent coordinate "
"system. Either the reference time of the coordinate system or the "
"one passed to the function is 'None'. Only cases where the "
"reference times are both 'None' or both contain a timestamp are "
"allowed. Also check that the reference time has the correct type."
)
if self.has_reference_time and (not isinstance(time,
pd.DatetimeIndex)):
time = time + time_ref
orientation = ut.xr_interp_orientation_in_time(self.orientation, time)
coordinates = ut.xr_interp_coordinates_in_time(self.coordinates, time)
return LocalCoordinateSystem(orientation,
coordinates,
time_ref=time_ref)
def interp_time(self,
time: Union[pd.TimedeltaIndex, pint.Quantity],
time_unit: str = "s") -> xr.DataArray:
"""Interpolate the TimeSeries in time.
If the internal data consists of discrete values, an interpolation with the
prescribed interpolation method is performed. In case of mathematical
expression, the expression is evaluated for the given timestamps.
Parameters
----------
time:
A set of timestamps.
time_unit:
Only important if the time series is described by an expression and a
'pandas.TimedeltaIndex' is passed to this function. In this case, time is
converted to a quantity with the provided unit. Even though pint handles
unit prefixes automatically, the accuracy of the results can be heavily
influenced if the provided unit results in extreme large or
small values when compared to the parameters of the expression.
Returns
-------
xarray.DataArray:
A data array containing the interpolated data.
"""
if isinstance(self._data, xr.DataArray):
if isinstance(time, pint.Quantity):
time = ut.to_pandas_time_index(time)
if not isinstance(time, pd.TimedeltaIndex):
raise ValueError(
'"time" must be a time quantity or a "pandas.TimedeltaIndex".'
)
# constant values are also treated by this branch
if self._data.attrs["interpolation"] == "linear" or self.shape[
0] == 1:
return ut.xr_interp_like(
self._data,
{"time": time},
assume_sorted=False,
broadcast_missing=False,
)
dax = self._data.reindex({"time": time}, method="ffill")
return dax.fillna(self._data[0])
# Transform time to both formats
if isinstance(time, pint.Quantity) and time.check(
UREG.get_dimensionality("s")):
time_q = time
time_pd = ut.to_pandas_time_index(time)
elif isinstance(time, pd.TimedeltaIndex):
time_q = ut.pandas_time_delta_to_quantity(time, time_unit)
time_pd = time
else:
raise ValueError(
'"time" must be a time quantity or a "pandas.TimedeltaIndex".')
if len(self.shape) > 1 and np.iterable(time_q):
while len(time_q.shape) < len(self.shape):
time_q = time_q[:, np.newaxis]
# evaluate expression
data = self._data.evaluate(**{self._time_var_name: time_q})
data = data.astype(
float).to_reduced_units() # float conversion before reduce!
# create data array
if not np.iterable(data): # make sure quantity is not scalar value
data = np.expand_dims(data, 0)
dax = xr.DataArray(data=data) # don't know exact dimensions so far
return dax.rename({"dim_0": "time"}).assign_coords({"time": time_pd})
def __init__(
self,
data: Union[pint.Quantity, MathematicalExpression],
time: Union[None, pd.TimedeltaIndex, pint.Quantity] = None,
interpolation: str = "linear",
):
"""Construct a TimSeries.
Parameters
----------
data:
Either a pint.Quantity or a weldx.MathematicalExpression. If a mathematical
expression is chosen, it is only allowed to have a single free variable,
which represents time.
time:
An instance of pandas.TimedeltaIndex if a quantity is passed and 'None'
otherwise.
interpolation:
A string defining the desired interpolation method. This is only relevant if
a quantity is passed as data. Currently supported interpolation methods are:
'step', 'linear'.
"""
self._data = None
self._time_var_name = None
self._shape = None
self._units = None
if isinstance(data, pint.Quantity):
if not np.iterable(data): # expand dim for scalar input
data = np.expand_dims(data, 0)
if time is None: # constant value case
time = pd.TimedeltaIndex([0])
interpolation = None
elif interpolation not in self._valid_interpolations:
raise ValueError(
"A valid interpolation method must be specified if discrete "
f'values are used. "{interpolation}" is not supported')
if isinstance(time, pint.Quantity):
time = ut.to_pandas_time_index(time)
if not isinstance(time, pd.TimedeltaIndex):
raise ValueError(
'"time" must be a time quantity or a "pandas.TimedeltaIndex".'
)
dax = xr.DataArray(
data=data,
attrs={"interpolation": interpolation},
)
self._data = dax.rename({
"dim_0": "time"
}).assign_coords({"time": time})
elif isinstance(data, MathematicalExpression):
if data.num_variables != 1:
raise Exception(
"The mathematical expression must have exactly 1 free "
"variable that represents time.")
time_var_name = data.get_variable_names()[0]
try:
eval_data = data.evaluate(**{time_var_name: Q_(1, "second")})
self._units = eval_data.units
if np.iterable(eval_data):
self._shape = eval_data.shape
else:
self._shape = (1, )
except pint.errors.DimensionalityError:
raise Exception(
"Expression can not be evaluated with "
'"weldx.Quantity(1, "seconds")"'
". Ensure that every parameter posses the correct unit.")
self._data = data
self._time_var_name = time_var_name
try:
self.interp_time(Q_([1, 2], "second"))
self.interp_time(Q_([1, 2, 3], "second"))
except Exception as e:
raise Exception(
"The expression can not be evaluated with arrays of time deltas. "
"Ensure that all parameters that are multiplied with the time "
"variable have an outer dimension of size 1. This dimension is "
"broadcasted during multiplication. The original error message was:"
f' "{str(e)}"')
else:
raise TypeError(f'The data type "{type(data)}" is not supported.')
def test_to_pandas_time_index_exceptions(arg, exception):
"""Test correct exceptions on invalid inputs."""
with pytest.raises(exception):
ut.to_pandas_time_index(arg)
def test_to_pandas_time_index(arg, expected):
"""Test conversion to appropriate pd.TimedeltaIndex or pd.DatetimeIndex."""
assert np.all(ut.to_pandas_time_index(arg) == expected)
