1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237 | from enum import Enum
import h5py
import numpy
from typing import Union
class AlignmentAxis2(Enum):
"""Specific alignment named to help users orienting themself with specific name"""
CENTER = "center"
LEFT = "left"
RIGTH = "right"
class AlignmentAxis1(Enum):
"""Specific alignment named to help users orienting themself with specific name"""
FRONT = "front"
CENTER = "center"
BACK = "back"
class _Alignment(Enum):
"""Internal alignment to be used for 2D alignment"""
LOWER_BOUNDARY = "lower boundary"
HIGHER_BOUNDARY = "higher boundary"
CENTER = "center"
@classmethod
def from_value(cls, value):
# cast the AlignmentAxis1 and AlignmentAxis2 values to fit the generic definition.
if value in ("front", "left", AlignmentAxis1.FRONT, AlignmentAxis2.LEFT):
return _Alignment.LOWER_BOUNDARY
elif value in ("back", "right", AlignmentAxis1.BACK, AlignmentAxis2.RIGTH):
return _Alignment.HIGHER_BOUNDARY
elif value in (AlignmentAxis1.CENTER, AlignmentAxis2.CENTER):
return _Alignment.CENTER
else:
return super().__new__(cls, value)
def align_frame(
data: numpy.ndarray, alignment: _Alignment, alignment_axis: int, new_aligned_axis_size: int, pad_mode="constant"
):
"""
Align 2D array to extend if size along `alignment_axis` to `new_aligned_axis_size`.
:param numpy.ndarray data: data (frame) to align (2D numpy array)
:param alignment_axis: axis along which we want to align the frame. Must be in (0, 1)
:param HAlignment alignment: alignment strategy
:param int new_width: output data width
"""
if alignment_axis not in (0, 1):
raise ValueError(f"alignment_axis should be in (0, 1). Get {alignment_axis}")
alignment = _Alignment.from_value(alignment)
aligned_axis_size = data.shape[alignment_axis]
if aligned_axis_size > new_aligned_axis_size:
raise ValueError(
f"data.shape[alignment_axis] ({data.shape[alignment_axis]}) > new_aligned_axis_size ({new_aligned_axis_size}). Unable to crop data"
)
elif aligned_axis_size == new_aligned_axis_size:
return data
else:
if alignment is _Alignment.CENTER:
lower_boundary = (new_aligned_axis_size - aligned_axis_size) // 2
higher_boundary = (new_aligned_axis_size - aligned_axis_size) - lower_boundary
elif alignment is _Alignment.LOWER_BOUNDARY:
lower_boundary = 0
higher_boundary = new_aligned_axis_size - aligned_axis_size
elif alignment is _Alignment.HIGHER_BOUNDARY:
lower_boundary = new_aligned_axis_size - aligned_axis_size
higher_boundary = 0
else:
raise ValueError(f"alignment {alignment.value} is not handled")
assert lower_boundary >= 0, f"pad size must be positive - lower boundary isn't ({lower_boundary})"
assert higher_boundary >= 0, f"pad size must be positive - higher boundary isn't ({higher_boundary})"
if alignment_axis == 1:
return numpy.pad(
data,
pad_width=((0, 0), (lower_boundary, higher_boundary)),
mode=pad_mode,
)
elif alignment_axis == 0:
return numpy.pad(
data,
pad_width=((lower_boundary, higher_boundary), (0, 0)),
mode=pad_mode,
)
else:
raise ValueError("alignment_axis should be in (0, 1)")
def align_horizontally(data: numpy.ndarray, alignment: AlignmentAxis2, new_width: int, pad_mode="constant"):
"""
Align data horizontally to make sure new data width will ne `new_width`.
:param numpy.ndarray data: data to align
:param HAlignment alignment: alignment strategy
:param int new_width: output data width
"""
alignment = AlignmentAxis2(alignment).value
return align_frame(
data=data, alignment=alignment, new_aligned_axis_size=new_width, pad_mode=pad_mode, alignment_axis=1
)
class PaddedRawData:
"""
Util class to extend a data when necessary
Must to aplpy to a volume and to an hdf5dataset - array
The idea behind is to avoid loading all the data in memory
"""
def __init__(self, data: Union[numpy.ndarray, h5py.Dataset], axis_1_pad_width: tuple) -> None:
self._axis_1_pad_width = numpy.array(axis_1_pad_width)
if not (self._axis_1_pad_width.size == 2 and self._axis_1_pad_width[0] >= 0 and self._axis_1_pad_width[1] >= 0):
raise ValueError(f"'axis_1_pad_width' expects to positive elements. Get {axis_1_pad_width}")
self._raw_data = data
self._raw_data_end = None
# note: for now we return only frames with zeros for padded frames.
# in the future we could imagine having a method and miror existing volume or extend the closest frame, or get a mean value...
self._empty_frame = None
self._dtype = None
self._shape = None
self._raw_data_shape = self.raw_data.shape
@staticmethod
def get_empty_frame(shape, dtype):
return numpy.zeros(
shape=shape,
dtype=dtype,
)
@property
def empty_frame(self):
if self._empty_frame is None:
self._empty_frame = self.get_empty_frame(
shape=(self.shape[0], 1, self.shape[2]),
dtype=self.dtype,
)
return self._empty_frame
@property
def shape(self):
if self._shape is None:
self._shape = tuple( # noqa: C409
(
self._raw_data_shape[0],
numpy.sum(
numpy.array(self._axis_1_pad_width),
)
+ self._raw_data_shape[1],
self._raw_data_shape[2],
)
)
return self._shape
@property
def raw_data(self):
return self._raw_data
@property
def raw_data_start(self):
return self._axis_1_pad_width[0]
@property
def raw_data_end(self):
if self._raw_data_end is None:
self._raw_data_end = self._axis_1_pad_width[0] + self._raw_data_shape[1]
return self._raw_data_end
@property
def dtype(self):
if self._dtype is None:
self._dtype = self.raw_data.dtype
return self._dtype
def __getitem__(self, args):
if not isinstance(args, tuple) and len(args) == 3:
raise ValueError("only handles 3D slicing")
elif not (args[0] == slice(None, None, None) and args[2] == slice(None, None, None)):
raise ValueError(
"slicing only handled along axis 1. First and third tuple item are expected to be empty slice as slice(None, None, None)"
)
else:
if numpy.isscalar(args[1]):
args = (
args[0],
slice(args[1], args[1] + 1, 1),
args[2],
)
start = args[1].start
if start is None:
start = 0
stop = args[1].stop
if stop is None:
stop = self.shape[1]
step = args[1].step
# some test
if start < 0 or stop < 0:
raise ValueError("only positive position are handled")
if start >= stop:
raise ValueError("start >= stop")
if stop > self.shape[1]:
raise ValueError("stop > self.shape[1]")
if step not in (1, None):
raise ValueError("for now PaddedVolume only handles steps of 1")
first_part_array = None
if start < self.raw_data_start and (stop - start > 0):
stop_first_part = min(stop, self.raw_data_start)
first_part_array = numpy.repeat(self.empty_frame, repeats=stop_first_part - start, axis=1)
start = stop_first_part
third_part_array = None
if stop > self.raw_data_end and (stop - start > 0):
if stop > self.shape[1]:
raise ValueError("requested slice is out of boundaries")
start_third_part = max(start, self.raw_data_end)
third_part_array = numpy.repeat(self.empty_frame, repeats=stop - start_third_part, axis=1)
stop = self.raw_data_end
if start >= self.raw_data_start and stop >= self.raw_data_start and (stop - start > 0):
second_part_array = self.raw_data[:, start - self.raw_data_start : stop - self.raw_data_start, :]
else:
second_part_array = None
parts = tuple(filter(lambda a: a is not None, (first_part_array, second_part_array, third_part_array)))
return numpy.hstack(
parts,
)
|