zerofind

zerofind(x, y, axis=0, diff=None, centered=True, which='both', **kwargs)[source]

Find the location of the zero value for a given data array.

Parameters

  • x (array-like) – The coordinates.

  • y (array-like) – The data for which we find zeros.

  • axis (int, optional) – The axis along which zeros are found and (optionally) derivatives are taken. These will be connected along the other axis with linetrack.

  • diff (int, optional) – How many times to differentiate along the axis.

  • centered (bool, optional) – Whether to use centered finite differencing or half level differencing.

  • which ({‘negpos’, ‘posneg’, ‘both’}, optional) – Whether to find values that go from negative to positive, positive to negative, or both (the 'min' and 'max' keys really only apply to when diff is 1).

  • **kwargs – Passed to linetrack and used to group the locations into coherent tracks.

Returns

  • zx (array-like) – The zero locations.

  • zy (array-like) – The zero values. If diff == 0 these should all be equal to zero up to floating point precision. Otherwise these are the minima and maxima corresponding to the zero derivative locations.

Example

>>> import xarray as xr
... import climopy as climo
... ureg = climo.ureg
... x = np.arange(100)
... y = np.sort(np.random.rand(50, 10) - 0.5, axis=0)
... y = np.concatenate((y, y[::-1, :]), axis=0)
... xarr = xr.DataArray(
...     x * ureg.s,
...     dims=('x',), attrs={'long_name': 'x coordinate'}
... )
... with climo.internals.warnings._unit_stripped_ignore():
...     yarr = xr.DataArray(
...         y * ureg.m, name='variable',
...         dims=('x', 'y'), coords={'x': xarr}
...     )
... zx, zy = climo.zerofind(xarr, yarr, ntrack=2)