lerp.
mesh2d
(x=[], d=None, x_label=None, x_unit=None, label=None, unit=None, clipboard=False, extrapolate=True, contiguous=False, step=False, **kwargs)¶Fundamental 2D object, strict monotonic
Instantiation by giving (x, d) parameters or by loading a csv-file.
Parameters: |
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Notes
print()
T
¶apply
(f, axis='d', inplace=False)¶Apply a function along axis
Parameters: |
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Returns: | Depends if inplace is set to False or True |
Return type: | Nothing or mesh1d |
d
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.
diff
(n=1)¶Checked
difff
¶dropnan
()¶Drop NaN values and return new mesh2d.
extrapolate
(x, *args, **kwargs)¶np.interp function with linear extrapolation np.polyfit np.poly1d
gradient
(x=None)¶interpolate
(x, assume_sorted=False, *args, **kwargs)¶Purpose of this method is to return a linear interpolation of a d vector for an unknown value x. If the targeted value is out of the x range, the returned d-value is the first, resp. the last d-value.
No interpolation is made out of the x definition range. For such a functionality, use:py:meth:extrapolate instead.
:param x:: iterable or single element,: :param kind: :type kind: str or int, optional :param Specifies the kind of interpolation as a string (‘linear’, ‘nearest’,: :param ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’ where ‘slinear’, ‘quadratic’: :param and ‘cubic’ refer to a spline interpolation of first, second or third: :param order) or as an integer specifying the order of the spline: :param interpolator to use. Default is ‘linear’.:
Returns: |
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plot
(data, *args, **kwargs)¶polyfit
(degree=2)¶push
(x=None, d=None)¶Pushes an element/array to the array
Notes
The element or the array is added and sorted inplace
Parameters: |
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read_clipboard
()¶resample
(x)¶steps
¶to_clipboard
(transpose=False, decimal=', ')¶to_csv
(fileName=None, nbreDecimales=2)¶Export CUR data into csv
Parameters: |
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lerp.
mesh3d
(x=[], y=[], d=None, x_label=None, x_unit=None, y_label=None, y_unit=None, label=None, unit=None, extrapolate=True, clipboard=False, sort=True, *pargs, **kwargs)¶Interpolate over a 2-D grid.
x, y and d are arrays of values used to approximate some function
f: d = f(x, y)
. This class returns a function whose call method uses
spline interpolation to find the value of new points.
Parameters: |
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Examples
Construct a 2-D grid and interpolate on it:
from scipy import interpolate
x = np.arange(-5.01, 5.01, 0.25)
y = np.arange(-5.01, 5.01, 0.25)
xx, yy = np.meshgrid(x, y)
z = np.sin(xx**2+yy**2)
T
¶apply
(f, inplace=False)¶d
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.
diff
(axis=0, n=1)¶extrapolate
(x, y)¶from_pandas
(obj)¶interpolate
(x=None, y=None)¶plot
(xy=False, filename=None, **kwargs)¶pop
(axis=0)¶push
(s=None, d=None, axis=0, inplace=False)¶read_clipboard
()¶reshape
(sort=True)¶sort
()¶to_gpt
(fileName=None)¶lerp.
mesh4d
(x=[], y=[], z=[], d=None, x_label=None, x_unit=None, y_label=None, y_unit=None, z_label=None, z_unit=None, label=None, unit=None, extrapolate=False, dtype='float64')¶d
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.
interpolate
(x=None, y=None, z=None)¶a
push
(s=None, d=None, axis=0)¶read_pickle
(fileName=None)¶reshape
()¶shape
¶sort
()¶to_pickle
(fileName=None)¶x
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.
lerp.
mesh5d
(x=[], y=[], z=[], v=[], d=None, x_label=None, x_unit=None, y_label=None, y_unit=None, z_label=None, z_unit=None, v_label=None, v_unit=None, label=None, unit=None, extrapolate=True, dtype='float64')¶d
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.
read_pickle
(fileName=None)¶reshape
()¶shape
¶sort
()¶to_pickle
(fileName=None)¶v
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.
x
¶partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.