pymgp.orbplot.rewrap#

pymgp.orbplot.rewrap(t, x, method='rewrap', wrapat=[0, 360], verbose=0)[source]#

Rewrap cyclic data for plotting line segments.

Re-wrap cyclic data and insert breaks in lines so that when plotted the lines are properly terminated at the borders of the wrapping region. This function solves two issues with plotting lines for cyclic data:

  • the near vertical (or horizontal) line segments at the wraps,

  • lines do not extend to the wrapping boundaries (instead they take the opposite direction)

If unsolved, line plots on cyclic data can be misleading or unclear at best, while a scatter plot may not provide all the information.

This version corrects for wraps the ordinates (x) only.

Parameters:
t, xarray_like, float

The absissae (time) and ordinates (data)

method{‘rewrap’, ‘unwrap’, split’, rewrap_nosplit’}, optional
The rewrapping method. Defines what action is taken at a data wrap
  • rewrapestimate time of wrap by interpolation from the data, and insert

    the upper/lower wrap limits and ‘np.nan’ to cause a split in the line. The output arrays are larger than the the input arrays (3 additional data points per wrap)

  • rewrap_noslitas with rewrap, but no ‘np.nan’ is inserted to split the

    lines. The output arrays are larger than the the input arrays (2 additional data points per wrap)

  • splitsplit the lines at a wrap by inserting ‘np.nan’. The output arrays

    are larger than the the input arrays (1 additional data point per wrap)

  • unwrap : make the data continuous. The length of the arrays is unchanged

wrapatlist, optional

List with the lower and upper wrap boundaries.

verboseint, optional

Vebosity level.

Returns:
tout, xoutndarray, float

The absissae (time) and rewrapped ordinates (data) for plotting

insndarray, int64

Index array for ‘np.insert’, can be applied to other arrays (‘z’) of similar shape with ‘zout = np.insert(z, ins, new_z_values_to_insert)’

See also

rewrap2d

Rewrap cyclic data in two dimensions (e.g. longitude and latitude).

Notes

The ouput should only be used for plotting purposes, as with some options interpolated data points and/or np.nan are inserted which could bias scientific analysis.

Examples

>>> x = np.array( [ 10., 100., 200, 300, 350, 45, 100, 180, 230, 160, 100, 10, 270, 150, 200, 300, 350, 45, 100, 200] )
>>> t = np.arange(x.size).astype(np.float64)
>>> rewrap(t, x, method='rewrap')
(array([ 0.        ,  1.        ,  2.        ,  3.        ,  4.        ,
         4.18181818,  4.18181818,  4.18181818,  5.        ,  6.        ,
         7.        ,  8.        ,  9.        , 10.        , 11.        ,
        11.1       , 11.1       , 11.1       , 12.        , 13.        ,
        14.        , 15.        , 16.        , 16.18181818, 16.18181818,
        16.18181818, 17.        , 18.        , 19.        ]),
 array([ 10., 100., 200., 300., 350., 360.,  nan,   0.,  45., 100., 180.,
        230., 160., 100.,  10.,   0.,  nan, 360., 270., 150., 200., 300.,
        350., 360.,  nan,   0.,  45., 100., 200.]),
array([ 5, 12, 17,  5, 12, 17,  5, 12, 17], dtype=int64))

>>> rewrap(t, x, method='rewrap', wrapat=[-180, 180])
(array([ 0.        ,  1.        ,  1.8       ,  1.8       ,  1.8       ,
         2.        ,  3.        ,  4.        ,  5.        ,  6.        ,
         7.        ,  7.        ,  7.        ,  7.        ,  8.        ,
         8.71428571,  8.71428571,  8.71428571,  9.        , 10.        ,
        11.        , 12.        , 12.75      , 12.75      , 12.75      ,
        13.        , 13.6       , 13.6       , 13.6       , 14.        ,
        15.        , 16.        , 17.        , 18.        , 18.8       ,
       18.8       , 18.8       , 19.        ]),
 array([  10.,  100.,  180.,   nan, -180., -160.,  -60.,  -10.,   45.,
         100.,  180.,   nan, -180., -180., -130., -180.,   nan,  180.,
         160.,  100.,   10.,  -90., -180.,   nan,  180.,  150.,  180.,
          nan, -180., -160.,  -60.,  -10.,   45.,  100.,  180.,   nan,
        -180., -160.]),
 array([ 2,  7,  9, 13, 14, 19,  2,  7,  9, 13, 14, 19,  2,  7,  9, 13, 14,
        19], dtype=int64))

>>> tout, xout, *_ = rewrap(t, x, method='rewrap')
>>> plt.figure
>>> plt.plot(t,x, linewidth='0.5', linestyle=':', color='r')
>>> plt.scatter(t,x)
>>> plt.plot(tout, xout)
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