hypney.utils package

Submodules

hypney.utils.all module

hypney.utils.eagerpy module

hypney.utils.eagerpy.astensor(x: Sequence, tensorlib=None, match_type=None)

Convert x to an eagerpy tensor specified by tensorlib or match_type.

Args:

  • tensorlib: name of module or ep.module to use

  • match_type: other tensor whose type to match.

hypney.utils.eagerpy.average(x, weights, axis=0)
hypney.utils.eagerpy.broadcast_to(x, shape)
hypney.utils.eagerpy.bucketize(x, p)
hypney.utils.eagerpy.cos(x)
hypney.utils.eagerpy.ensure_float(x)

Return a simple float or int from a 0-dimensional array, tensor, or float

hypney.utils.eagerpy.ensure_numpy(x)

Return numpy array from x, unless it already is a numpy array

hypney.utils.eagerpy.ensure_raw(x)

Return raw tensor from x, unless it already is a raw tensor

hypney.utils.eagerpy.logsumexp(tensor, axis=0)
hypney.utils.eagerpy.np64(x)
hypney.utils.eagerpy.sin(x)
hypney.utils.eagerpy.split(x, *args, **kwargs)
hypney.utils.eagerpy.tensorlib(x: TensorType)

hypney.utils.hashing module

class hypney.utils.hashing.NumpyJSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: JSONEncoder

Special json encoder for numpy types Edited from mpl3d: mpld3/_display.py

Methods

default(obj)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

encode(o)

Return a JSON string representation of a Python data structure.

iterencode(o[, _one_shot])

Encode the given object and yield each string representation as available.
default(obj)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)
hypney.utils.hashing.deterministic_hash(thing, length=10)

Return a base32 lowercase string of length determined from hashing the container hierarchy thing

hypney.utils.hashing.hashablize(obj)

Convert a container hierarchy into one that can be hashed. See http://stackoverflow.com/questions/985294

hypney.utils.interpolation module

class hypney.utils.interpolation.InterpolatorBuilder(anchors_per_parameter)

Bases: object

Methods

make_interpolator(f[, tensorlib])

Return interpolator of f between anchor points.
make_interpolator(f, tensorlib=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/hypney/envs/latest/lib/python3.8/site-packages/numpy/__init__.py'>)

Return interpolator of f between anchor points.

The interpolator is vectorized, so t will add one dimension for scalar inputs.

Args:

  • f: Function taking one argument, and returning an extra_dims shaped array.

class hypney.utils.interpolation.RegularGridInterpolator(points, values=None)

Bases: object

Eagerpy RegularGridInterpolator

Modified from Shane Barratt’s very nice torch interpolation code at https://github.com/sbarratt/torch_interpolations/blob/master/torch_interpolations/multilinear.py

Methods

__call__(points_to_interp)

Call self as a function.

get_values
get_values(list_of_indices)
hypney.utils.interpolation.interp1d_loglog(x, y)

Module contents