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
|
FabIO: Fable Input/Output library
=================================
Main websites:
* https://github.com/silx-kit/fabio
* http://fable.sf.net (historical)
|Build Status| |Appveyor Status|
----
FabIO is an I/O library for images produced by 2D X-ray detectors and written in Python.
FabIO support images detectors from a dozen of companies (including Mar, Dectris, ADSC, Hamamatsu, Oxford, ...),
for a total of 30 different file formats (like CBF, EDF, TIFF, ...) and offers an unified interface to their
headers (as a python dictionary) and datasets (as a numpy ndarray of integers or floats)
Getting FabIO
-------------
FabIO is available from `PyPI <https://pypi.python.org/pypi/fabio>`_.
`Debian/Ubuntu packages <http://www.silx.org/pub/debian/binary/>`_, and
`wheels <http://www.silx.org/pub/wheelhouse/>`_ are available
for windows, linux and MacOSX from the silx repository:
Documentation is available at `silx <http://www.silx.org/doc/fabio/>`_.
Citation:
---------
The general philosophy of the library is described in:
`FabIO: easy access to two-dimensional X-ray detector images in Python; E. B. Knudsen, H. O. Sørensen, J. P. Wright, G. Goret and J. Kieffer Journal of Applied Crystallography, Volume 46, Part 2, pages 537-539. <http://dx.doi.org/10.1107/S0021889813000150>`_
Transparent handling of compressed files
----------------------------------------
FabIO is expected to handle gzip and bzip2 compressed files transparently.
Following a query about the performance of reading compressed data, some
benchmarking details have been collected at fabio_compressed_speed.
This means that when your python was configured and built you needed the
bzip and gzip modules to be present (eg libbz2-dev package for ubuntu)
Using fabio in your own python programs
Example::
>>> import fabio
>>> obj = fabio.edfimage.EdfImage("mydata0000.edf")
>>> obj.data.shape
(2048, 2048)
>>> obj.header["Omega"]
23.5
Design Specifications
---------------------
Name:
.....
FabIO = Fable Input/Output
Idea:
.....
Have a base class for all our 2D diffraction greyscale images.
This consists of a 2D array (numpy ndarray)
and a python dictionary (actually an ordered dict) of header information in (string key, string value) pairs.
Class FabioImage
................
Needs a name which will not to be confused with an RGB color image.
Class attributes, often exposed as properties:
* data -> 2D array
* header -> ordered dictionary
* rows, columns, dim1, dim2 -> data.shape (propertiy)
* header_keys -> property for list(header.keys()), formerly used to retain the order of the header
* bytecode -> data.typecode() (property)
* m, minval, maxval, stddev -> image statistics, could add others, eg roi[slice]
Class methods (functions):
* integrate_area() -> return sum(self.data) within slice
* rebin(fact) -> rebins data, adjusts dims
* toPIL16() -> returns a PILimage
* getheader() -> returns self.header
* resetvals() -> resets the statistics
* getmean() -> (computes) returns self.m
* getmin() -> (computes) returns self.minval
* getmax() -> (computes) returns self.maxval
* getstddev() -> (computes) returns self.stddev
* read() -> read image from file [or stream, or shared memory]
* write() -> write image to file [or stream, or shared memory]
* readheader() -> read only the header [much faster for scanning files]
Each individual file format would then inherit all the functionality of this class and just make new read and write methods.
There are also fileseries related methods (next(), previous(), ...) which returns a FabioImage instance of the next/previous frame in a fileserie
Other feature:
* possibility for using on-the-fly external compression - i.e. if files are
stored as something as .gz, .bz2 etc could decompress them, using an external
compression mechanism (if available).
Supported file formats
----------------------
* ADSC:
+ AdscImage
* Bruker:
+ BrukerImage
+ Bruker100Image
+ KcdImage: Nonius KappaCCD diffractometer
* D3M
+ D3mImage
* Dectris:
+ CbfImage (implements a fast byte offset de/compression scheme in python/cython)
+ PilatusImage (fileformat derived from Tiff)
+ EigerImage (derived from HDF5/NeXus format, depends on `h5py`)
* ESRF:
+ EdfImage: The ESRF data Format
+ XsdImage: XML serialized image from EDNA
+ Fit2dImage: Fit2d binary format
+ Fit2dmaskImage: Fit2d Mask format
+ Fit2dSpreadsheetImage: Fit2d ascii tables (spread-sheet)
+ LimaImage: image stacks written by the LImA aquisition system
+ SparseImage: single crystal diffractions images written by pyFAI
* General Electrics
+ GEimage (including support for variant used at APS)
* Hamamatsu
+ HiPiCImage
* HDF5: generic format for stack of images based on h5py
+ Hdf5Image
+ EigerImage
+ LimaImage
+ SparseImage
* JPEG image format:
+ JPEG using PIL
+ JPEG 2000 using Glymur
* Mar Research:
+ MarccdImage (fileformat derived from Tiff)
+ Mar345Image imaging plate with PCK compression
* MPA multiwire
+ MpaImage
* Medical Research Council file format for 3D electron density and 2D images
+ MrcImage
* Nonius -> now owned by Bruker
+ KcdImage
* Numpy: generic reader for 2D arrays saved
+ NumpyImage
* Oxford Diffraction Sapphire 3
+ OXDimage uncompressed or with TY1 or TY5 compression scheme
+ Esperanto format (with bitfield compression)
* Pixirad Imaging
+ PixiImage
* PNM
+ PnmImage
* Princeton Instrument SPE
+ SpeImage
* Raw Binary without compression
* Rigaku
+ RaxisImage
+ DtrekImage
* Tiff
+ TifImage using either:
- Pillow (external dependency)
- TiffIO taken from PyMca
Installation
------------
Please see doc/source/INSTALL.rst
Changelog
---------
Please see doc/source/Changelog.rst
.. |Build Status| image:: https://travis-ci.org/silx-kit/fabio.svg?branch=master
:target: https://travis-ci.org/silx-kit/fabio
.. |Appveyor Status| image:: https://ci.appveyor.com/api/projects/status/4k6lol1vq30qhf66/branch/master?svg=true
:target: https://ci.appveyor.com/project/ESRF/fabio/branch/master
|
