ddg.datastructures.nets.conversion module

ddg.datastructures.nets.conversion.sampling_decomposer(sampling, atol, anchor, dimension)[source]

Helper function for sample_smooth_domain. Given parameters of a sampling, this function returns a list of usable length for the sampling process of domains.

Parameters:

samplinglist, int or float

Stepsize/Amount of samples and additional options. To default pass an int/float. If not pass a list of the form [stepsize/samples, option].

atollist, int or float

Tolerance(s) used in the sampling process. To default pass an int/float. If not pass a list of tolerances for each direction.

If None is given, will return None again, i.e. won’t replace it with the global default.

anchorlist, tuple or None

Anchor point used in the sampling process.

dimensionint

Dimension of the domain you want to sample.

Returns:

list: List of the decomposed sampling.

[0] stepsize/samples

[1] options

[2] tolerances

[3] anchor

See also

sample_smooth_domain

ddg.datastructures.nets.conversion.sample_smooth_domain(domain, sampling, anchor=None, atol=1e-08)[source]

Samples a smooth domain.

Parameters:

domainddg.datastructures.nets.domain.SmoothDomain

SmoothDomain to sample.

samplinglist, int or float

Determines how the domain is sampled.
If a default stepsize/sample amount and option
should be used for all directions, either pass an int/float (will be
regarded as stepsize), or a list of length 2 where the first entry is
the stepsize/sample amount and the second is the option to use.
Else pass a list of length of the dimension of the domain where each
entry can be either an int/float or a list of length two (same rules as
above).

Options:
‘’  : stepsize given. The interval will be sampled with the given
stepsize starting from its lower bound.
‘t’ : use the number as total amount of samples. The interval will be
divided up into the given number of samples in an equal fashion.
Note that this option is only available for bounded intervals.
Will raise a warning, if the number is a float.
‘c’ : compound sampling. Behaves as option ‘t’ for bounded and ‘’ for
unbounded intervals. Additionally only the given amount of samples
will be returned for unbounded directions.
Sampling given as a list [stepsize, amount of samples, ‘c’]
‘s’ : symmetric sampling. This option tries to fit as many samples as
possible of the given stepsize in the interval in a symmetric fashion
around its center.

anchorlist, tuple or None

Anchor point to use for the sampling process, given as a list/tuple of anchors for each direction. Directions where the anchor is None will be sampled normally, while for the others the sampling process ensures that the given set of values appears as one of the samples. For example for the anchor (2, None, 3) the resulting discrete net will attain the value (2, x, 3) at some point of its domain, where x is some value depending on the sampling of the second direction. Note that this option is not available for the options ‘s’, ‘t’ and ‘c’.

atollist, int or float

Tolerance(s) used in the sampling process. Either pass a single value to use for all directions, or a list of length of the dimension of the domain of floats/ints.

This function uses the global tolerance defaults if atol or rtol are set to None. See ddg.nonexact for details.

Returns:

ddg.datastructures.nets.net.DiscreteNet