neurots.morphmath.sample¶
Definition of distributions to sample from.
Functions
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Return N azimuth angles, depending on the input distribution. |
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Transform a distribution according to a selected function. |
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Return a number of neurites as sampled from a distribution plus some constraints. |
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Samples randomly a persistence diagram from the input distribution. |
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Return a random soma radius as sampled from a distribution plus some constraints. |
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Return N absolute angles, depending on the input distribution. |
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Return N relative angles, depending on the input distribution. |
Classes
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Class of custom distributions. |
- class neurots.morphmath.sample.Distr(params, random_generator=numpy.random)¶
Bases:
object
Class of custom distributions.
- Parameters
params (dict) – The parameters of the distribution.
random_generator (numpy.random.Generator) – The random number generator to use.
- draw()¶
Return a sampled number.
- draw_positive()¶
Return a positive sampled number.
- static expon(params)¶
Return loc, scale as expected from scipy from mean, std data.
- static norm(params)¶
Return loc, scale as expected from scipy from mean, std data.
- set_distribution(params)¶
Return a statistical distribution according to input parameters.
- static uniform(params)¶
Return loc, scale as expected from scipy from min, max of a uniform.
- neurots.morphmath.sample.azimuth_angles(distrib, N, random_generator=numpy.random)¶
Return N azimuth angles, depending on the input distribution.
- neurots.morphmath.sample.d_transform(distr, funct, **kwargs)¶
Transform a distribution according to a selected function.
- neurots.morphmath.sample.n_neurites(distrib, random_generator=numpy.random)¶
Return a number of neurites as sampled from a distribution plus some constraints.
It ensures the number will be an INT.
- neurots.morphmath.sample.ph(phs, random_generator=numpy.random)¶
Samples randomly a persistence diagram from the input distribution.
- neurots.morphmath.sample.soma_size(distrib, random_generator=numpy.random)¶
Return a random soma radius as sampled from a distribution plus some constraints.
- neurots.morphmath.sample.trunk_absolute_angles(distrib, N, random_generator=numpy.random)¶
Return N absolute angles, depending on the input distribution.
- neurots.morphmath.sample.trunk_angles(distrib, N, random_generator=numpy.random)¶
Return N relative angles, depending on the input distribution.