structured numpy arrays

structured numpy arrays

# #resource 
# https://docs.scipy.org/doc/numpy-1.14.0/user/basics.rec.html

conda install -c conda-forge traits=4.6.0
		traits: 4.6.0-py36_1 conda-forge


import numpy as np
from traits.api import Array, Tuple, List, String
from traitschema import Schema

class TrainingResults(Schema):
    training = List(Tuple(Array(dtype=np.float32), np.int64))


l = List((np.random.random(1), )
result = TrainingResults(training=l)


import numpy as np
x = np.array([('Rex', 9, 81.0), ('Fido', 3, 27.0)],
             dtype=[('name', 'U10'), ('age', 'i4'), ('weight', 'f4')])

x

x[1]

x['age']

np.dtype({'names': ['col1', 'col2'],
          'formats': ['i4','f4'],
          'offsets': [0, 4],
          'itemsize': 12})


dt_y = np.dtype([('x', 'f4'), ('y', np.float32), ('z', 'f4', (2,2))])

dt_y.names

dt_y.fields

y = np.zeros(1, dtype=dt_y)
>> array([(0., 0., [[0., 0.], [0., 0.]])],
      dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4', (2, 2))])

y = np.array((12, 3.4, 3), dtype=dt_y)
>> array((12., 3.4, [[3., 3.], [3., 3.]]),
      dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4', (2, 2))])

y['x']
>> array(12., dtype=float32)

y[0]
>> too many indices for array

---

dt_y = np.dtype([('x', 'f4'), ('y', np.float32), ('z', np.int64)])

y = np.array((12, 3.4, 3), dtype=dt_y)

y[0]
>> too many indices for array

y = np.array([(12, 3.4, 3)], dtype=dt_y)
>> array([(12., 3.4, 3)], dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<i8')])

scalar = y[0]
scalar
>> (12., 3.4, 3)

scalar.item()
(12.0, 3.4000000953674316, 3)


x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])

x.view(np.recarray)

>> rec.array([(1., 2), (3., 4)],
          dtype=[('x', '<f8'), ('y', '<i8')])

# ---
# http://docs.enthought.com/traits/traits_user_manual/defining.html
# http://docs.enthought.com/traits/traits_user_manual/index.html

t.trait( 'i' ).default
t.trait( 'i' ).default_kind              
t.trait( 'i' ).inner_traits
t.trait( 'i' ).is_trait_type(Int) 
t.trait( 'i' ).is_trait_type(Float)

t.trait( 'lf' ).inner_traits[0].is_trait_type(Float)

# ---
# https://traitschema.readthedocs.io/en/latest/

import numpy as np
from traits.api import Array
from traitschema import Schema

class Matrix(Schema):
    data = Array(dtype=dt_y)

matrix = Matrix(data=y)

matrix.data
>> array((12., 3.4, [[3., 3.], [3., 3.]]),
      dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4', (2, 2))])

matrix.to_json()
>> '{"data": [12.0, 3.4000000953674316, [[3.0, 3.0], [3.0, 3.0]]]}'