def pos_and_ids(stir_data_root):
data = np.load(stir_data_root / 'position2detectorids.npz')
return {
'input':
List([PositionEvent(Point(r)) for r in data['position']]),
'expect':
List([DetectorIdEvent(r[0], r[1] // 10, r[1]) for r in data['ids']])
}
def parameters(self):
models = List()
if isinstance(self.init_op, Model):
models.append(self.init_op)
models += List(self.paths).filter(lambda m: isinstance(m, Model))
if isinstance(self.merge, Model):
models.append(self.merge)
return concat(models.fmap(lambda m: m.parameters))
def proj3to2(n: Vector) -> Matrix:
es = List([
Vector([1.0, 0.0, 0.0]),
Vector([0.0, 1.0, 0.0]),
Vector([0.0, 0.0, 1.0])
])
vs = es.fmap(lambda v: proj(v, n))
return transpose(Matrix([vs[0].unbox(), vs[1].unbox(), vs[2].unbox()]))
def incept_path(ipath, filters):
result = List([])
result.append(as_model(tf.nn.elu))
result.append(Conv2D(f'conv_in_{ipath}', filters, 1))
for i in range(ipath):
result.append(as_model(tf.nn.elu))
result.append(Conv2D(f"conv_{ipath}_{i}", filters, 3))
return Stack(result)
def _(xs, axis=0):
if xs is not None:
if all(map(lambda a: isinstance(a, tf.Tensor), xs)):
return tf.concat(xs.unbox(), axis=axis)
if all(map(lambda a: isinstance(a, np.ndarray), xs)):
return np.concatenate(xs.unbox(), axis=axis)
else:
res = List()
for item in xs:
res = res.extend(item)
return res
def generate_data_and_header(config, target):
with open(config, 'r') as fin:
c = json.load(fin)
scanner = get_scanner(c['scanner']['petscanner'])
path_data = Path(target)
gen_sino_script(c['scanner']['petscanner'], (path_data.abs + 'sinogram'))
path_file = c['input']['listmode']['path_file']
data = load_h5(path_file)
lor = List(
LoR(PositionEvent(data['fst'][i, ]),
PositionEvent(data['snd'][i, ]),
weight=data['weight'][i]) for i in range(0, data['weight'].size))
lm2sino(scanner, lor, (path_data.abs + '/sinogram.s'))
class TestAxisToAxis:
@pytest.mark.parametrize('src,tar',
List(
itertools.product(RANDOM_VECTORS,
AXIS_VECTORS)))
def test_random_axis(self, src, tar):
# data = np.concatenate([RANDOM_VECTORS, AXIS_VECTORS])
# axis = [Axis(np.array(data[i, :]))
# for i in range(data.shape[0])]
# for src in axis:
# for tar in axis:
# v_tar = tar.normal
# v_src = src.normal
# print(type(v_src))
# print(type(v_tar))
m = axis_to_axis(src, tar)
det = np.linalg.det(m.unbox())
assert abs(det) == pytest.approx(1.0)
assert all_close(m @ src, tar)
def test_getitem_int():
assert List([1, 2])[0] == 1
def test_add_type():
assert isinstance(List([1]) + List([2]), List)
def test_add_value():
assert List([1]) + List([2]) == [1, 2]
def parameters(ms):
return concat(List([m.parameters for m in ms if isinstance(m, Model)]))
def ndarray2listmode_positional(data: np.ndarray) -> List[LoR]:
return List([parse_positional_event(r) for r in data])
def test_filter():
assert List([1, 2, 3, 4, 5, 6]).filter(lambda x: x < 4) == [1, 2, 3]
def test_getitem_slice_class():
assert isinstance(List([1, 2, 3])[:2], List)
def __init__(self, sources: List[Sequence[T]]):
self.sources = List(sources)
from dxl.shape.data import Axis, AXIS3_X, AXIS3_Y, AXIS3_Z
from doufo.tensor import Vector
from doufo.tensor import all_close
from doufo import List
import itertools
#from dxl.shape.utils.vector import Vector3
#from dxl.shape.utils.axes import Axis3, AXIS3_X, AXIS3_Z
import pytest
import math
import numpy as np
RANDOM_VECTORS = List([[0.40980446, 0.56395448, -0.71694885],
[-0.89215861, -0.28282346, 0.35222706],
[0.97295371, 0.16958264, -0.15685284],
[-0.79043812, -0.48951234, 0.36821901],
[-0.79298153, -0.55981545, 0.24038919],
[0.88280373, 0.38964822, -0.26235821],
[-0.65851061, 0.1799812, 0.73073288],
[0.3341779, -0.69191882, -0.63997928],
[-0.34021323, -0.75627046, -0.55884698],
[-0.0612565, -0.29257462, -0.95427865]]).fmap(Vector)
AXIS_VECTORS = List([[0., 0., 1.], [1., 0., 0.], [0., 1., 0.], [-1., 0., 0.],
[0., -1., 0.], [0., 0., -1.]]).fmap(Vector)
POINT_VECTORS = List([[1.0, 2.0, 0.0], [-2.0, 1.0, 0.0], [1.0, 0.0, 2.0],
[0.0, 2.0, -1.0]]).fmap(Vector)
class TestRotate:
def test_rotate2(self):
rot = rotate2(math.pi / 3)
def _(t) -> List[int]:
return List(t.shape.as_list())
def _(t) -> List[int]:
return List(t.shape)
def extend(self, x: 'Monoid[T]'):
return CompositeSequence(List(self.sources).extend([x]))
def embed2to3(n: Vector) -> Matrix:
es = List([Vector([1.0, 0.0]), Vector([0.0, 1.0])])
vs = es.fmap(lambda v: embed(v, n)).fmap(lambda v: v.unbox())
return transpose(Matrix(vs))
def test_eq():
assert List([1, 2]) == [1, 2]
def test_getitem_slice_value():
assert List([1, 2, 3])[:2] == [1, 2]
def moves(steps):
xs, ys, zs = steps
return List([Vector([x, y, z]) for x in xs for y in ys for z in zs])
def test_head():
assert head(List([1, 2])) == 1
import unittest
from unittest.mock import MagicMock
from jfs.api import Path
from doufo import List
import pytest
import itertools
INPUTS1 = List(['/','/tmp', '%2Ftmp', '%252Ftmp', './tmp', '/tmp/a/..'])
OUTPUTS1 = List(['/', '/tmp', '/tmp', '/tmp', 'tmp', '/tmp'])
INPUTS2 = ['/', 'tmp', '/tmp','/tmp/']
OUTPUTS2 = ['/', (Path('.')+'tmp').abs, '/tmp','/tmp']
OUTPUTS22 = ['', 'tmp', 'tmp', 'tmp']
OUTPUTS222 = [True,False,False,False]
OUTPUTS2222 = [True,False,True,True]
INPUTS3 = ['/', 'a', '/a/b', '/a/b/']
OUTPUTS3 = ['', 'a', 'b', 'b']
INPUTS4 = ['a/b', '/a/b', '/a/b/', 'a/b/']
OUTPUTS4 = [(Path('.')+'a').abs, '/a', '/a', (Path('.')+'a').abs]
INPUTS5 = ['a', 'a.txt', '/a/b.pdf', './b.zip']
OUTPUTS5 = ['','.txt','.pdf','.zip']
INPUTS6 = ['a', 'a.txt', '/a/b.tar.gz']
OUTPUTS6 = [[],['.txt'],['.tar','.gz']]
INPUTS7 = ['a', 'a/b', '/a/b', './a', '/a/b/c/..']
OUTPUTS7 = ['a', 'a/b', '/a/b', './a', '/a/b']
class TestPath:
@pytest.mark.parametrize('input, output',list(zip(INPUTS1,OUTPUTS1)))
def test_route(self,input,output):
assert Path(input).route ==output
def test_eq_List():
assert List([1, 2]) == List([1, 2])
def test_wrap_construct():
a = List([1, 2])
assert List(a) == [1, 2]
def test_wrap_construct_List():
a = List(List([1, 2]))
assert a == [1, 2]
def id_sinogram_matlab(stir_data_root):
data = np.load(stir_data_root / 'sinogramids.npz')
return {k: List((v - 1).tolist()) for k, v in data.items()}
