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
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
|
%global _empty_manifest_terminate_build 0
Name: python-pydicom
Version: 2.3.1
Release: 1
Summary: A pure Python package for reading and writing DICOM data
License: MIT
URL: https://github.com/pydicom/pydicom
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0d/b6/4bb0f895db75ea0b1e0b925e2b0958ec0eb68b9bc19dc23277e13ba82d96/pydicom-2.3.1.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-numpydoc
Requires: python3-matplotlib
Requires: python3-pillow
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-sphinx-gallery
Requires: python3-sphinxcontrib-napoleon
Requires: python3-sphinx-copybutton
%description
[](https://github.com/pydicom/pydicom/actions?query=workflow%3Aunit-tests)
[](https://github.com/pydicom/pydicom/actions?query=workflow%3Atype-hints)
[](https://circleci.com/gh/pydicom/pydicom/tree/master)
[](https://codecov.io/gh/pydicom/pydicom)
[](https://img.shields.io/pypi/pyversions/pydicom.svg)
[](https://badge.fury.io/py/pydicom)
[](https://doi.org/10.5281/zenodo.6394735)
[](https://gitter.im/pydicom/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
# *pydicom*
*pydicom* is a pure Python package for working with [DICOM](https://www.dicomstandard.org/) files. It lets you read, modify and write DICOM data in an easy "pythonic" way.
As a pure Python package, *pydicom* can run anywhere Python runs without any other requirements, although if you're working with *Pixel Data* then we recommend you also install [NumPy](http://www.numpy.org).
If you're looking for a Python library for DICOM networking then you might be interested in another of our projects: [pynetdicom](https://github.com/pydicom/pynetdicom).
## Installation
Using [pip](https://pip.pypa.io/en/stable/):
```
pip install pydicom
```
Using [conda](https://docs.conda.io/en/latest/):
```
conda install -c conda-forge pydicom
```
For more information, including installation instructions for the development version, see the [installation guide](https://pydicom.github.io/pydicom/stable/tutorials/installation.html).
## Documentation
The *pydicom* [user guide](https://pydicom.github.io/pydicom/stable/old/pydicom_user_guide.html), [tutorials](https://pydicom.github.io/pydicom/stable/tutorials/index.html), [examples](https://pydicom.github.io/pydicom/stable/auto_examples/index.html) and [API reference](https://pydicom.github.io/pydicom/stable/reference/index.html) documentation is available for both the [current release](https://pydicom.github.io/pydicom/stable) and the [development version](https://pydicom.github.io/pydicom/dev) on GitHub Pages.
## *Pixel Data*
Compressed and uncompressed *Pixel Data* is always available to
be read, changed and written as [bytes](https://docs.python.org/3/library/stdtypes.html#bytes-objects):
```python
>>> from pydicom import dcmread
>>> from pydicom.data import get_testdata_file
>>> path = get_testdata_file("CT_small.dcm")
>>> ds = dcmread(path)
>>> type(ds.PixelData)
<class 'bytes'>
>>> len(ds.PixelData)
32768
>>> ds.PixelData[:2]
b'\xaf\x00'
```
If [NumPy](http://www.numpy.org) is installed, *Pixel Data* can be converted to an [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) using the [Dataset.pixel_array](https://pydicom.github.io/pydicom/stable/reference/generated/pydicom.dataset.Dataset.html#pydicom.dataset.Dataset.pixel_array) property:
```python
>>> arr = ds.pixel_array
>>> arr.shape
(128, 128)
>>> arr
array([[175, 180, 166, ..., 203, 207, 216],
[186, 183, 157, ..., 181, 190, 239],
[184, 180, 171, ..., 152, 164, 235],
...,
[906, 910, 923, ..., 922, 929, 927],
[914, 954, 938, ..., 942, 925, 905],
[959, 955, 916, ..., 911, 904, 909]], dtype=int16)
```
### Compressed *Pixel Data*
#### JPEG, JPEG-LS and JPEG 2000
Converting JPEG compressed *Pixel Data* to an ``ndarray`` requires installing one or more additional Python libraries. For information on which libraries are required, see the [pixel data handler documentation](https://pydicom.github.io/pydicom/stable/old/image_data_handlers.html#guide-compressed).
Compressing data into one of the JPEG formats is not currently supported.
#### RLE
Encoding and decoding RLE *Pixel Data* only requires NumPy, however it can
be quite slow. You may want to consider [installing one or more additional
Python libraries](https://pydicom.github.io/pydicom/stable/old/image_data_compression.html) to speed up the process.
## Examples
More [examples](https://pydicom.github.io/pydicom/stable/auto_examples/index.html) are available in the documentation.
**Change a patient's ID**
```python
from pydicom import dcmread
ds = dcmread("/path/to/file.dcm")
# Edit the (0010,0020) 'Patient ID' element
ds.PatientID = "12345678"
ds.save_as("/path/to/file_updated.dcm")
```
**Display the Pixel Data**
With [NumPy](http://www.numpy.org) and [matplotlib](https://matplotlib.org/)
```python
import matplotlib.pyplot as plt
from pydicom import dcmread
from pydicom.data import get_testdata_file
# The path to a pydicom test dataset
path = get_testdata_file("CT_small.dcm")
ds = dcmread(path)
# `arr` is a numpy.ndarray
arr = ds.pixel_array
plt.imshow(arr, cmap="gray")
plt.show()
```
## Contributing
To contribute to *pydicom*, read our [contribution guide](https://github.com/pydicom/pydicom/blob/master/CONTRIBUTING.md).
To contribute an example or extension of *pydicom* that doesn't belong with the core software, see our contribution repository:
[contrib-pydicom](https://www.github.com/pydicom/contrib-pydicom).
%package -n python3-pydicom
Summary: A pure Python package for reading and writing DICOM data
Provides: python-pydicom
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pydicom
[](https://github.com/pydicom/pydicom/actions?query=workflow%3Aunit-tests)
[](https://github.com/pydicom/pydicom/actions?query=workflow%3Atype-hints)
[](https://circleci.com/gh/pydicom/pydicom/tree/master)
[](https://codecov.io/gh/pydicom/pydicom)
[](https://img.shields.io/pypi/pyversions/pydicom.svg)
[](https://badge.fury.io/py/pydicom)
[](https://doi.org/10.5281/zenodo.6394735)
[](https://gitter.im/pydicom/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
# *pydicom*
*pydicom* is a pure Python package for working with [DICOM](https://www.dicomstandard.org/) files. It lets you read, modify and write DICOM data in an easy "pythonic" way.
As a pure Python package, *pydicom* can run anywhere Python runs without any other requirements, although if you're working with *Pixel Data* then we recommend you also install [NumPy](http://www.numpy.org).
If you're looking for a Python library for DICOM networking then you might be interested in another of our projects: [pynetdicom](https://github.com/pydicom/pynetdicom).
## Installation
Using [pip](https://pip.pypa.io/en/stable/):
```
pip install pydicom
```
Using [conda](https://docs.conda.io/en/latest/):
```
conda install -c conda-forge pydicom
```
For more information, including installation instructions for the development version, see the [installation guide](https://pydicom.github.io/pydicom/stable/tutorials/installation.html).
## Documentation
The *pydicom* [user guide](https://pydicom.github.io/pydicom/stable/old/pydicom_user_guide.html), [tutorials](https://pydicom.github.io/pydicom/stable/tutorials/index.html), [examples](https://pydicom.github.io/pydicom/stable/auto_examples/index.html) and [API reference](https://pydicom.github.io/pydicom/stable/reference/index.html) documentation is available for both the [current release](https://pydicom.github.io/pydicom/stable) and the [development version](https://pydicom.github.io/pydicom/dev) on GitHub Pages.
## *Pixel Data*
Compressed and uncompressed *Pixel Data* is always available to
be read, changed and written as [bytes](https://docs.python.org/3/library/stdtypes.html#bytes-objects):
```python
>>> from pydicom import dcmread
>>> from pydicom.data import get_testdata_file
>>> path = get_testdata_file("CT_small.dcm")
>>> ds = dcmread(path)
>>> type(ds.PixelData)
<class 'bytes'>
>>> len(ds.PixelData)
32768
>>> ds.PixelData[:2]
b'\xaf\x00'
```
If [NumPy](http://www.numpy.org) is installed, *Pixel Data* can be converted to an [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) using the [Dataset.pixel_array](https://pydicom.github.io/pydicom/stable/reference/generated/pydicom.dataset.Dataset.html#pydicom.dataset.Dataset.pixel_array) property:
```python
>>> arr = ds.pixel_array
>>> arr.shape
(128, 128)
>>> arr
array([[175, 180, 166, ..., 203, 207, 216],
[186, 183, 157, ..., 181, 190, 239],
[184, 180, 171, ..., 152, 164, 235],
...,
[906, 910, 923, ..., 922, 929, 927],
[914, 954, 938, ..., 942, 925, 905],
[959, 955, 916, ..., 911, 904, 909]], dtype=int16)
```
### Compressed *Pixel Data*
#### JPEG, JPEG-LS and JPEG 2000
Converting JPEG compressed *Pixel Data* to an ``ndarray`` requires installing one or more additional Python libraries. For information on which libraries are required, see the [pixel data handler documentation](https://pydicom.github.io/pydicom/stable/old/image_data_handlers.html#guide-compressed).
Compressing data into one of the JPEG formats is not currently supported.
#### RLE
Encoding and decoding RLE *Pixel Data* only requires NumPy, however it can
be quite slow. You may want to consider [installing one or more additional
Python libraries](https://pydicom.github.io/pydicom/stable/old/image_data_compression.html) to speed up the process.
## Examples
More [examples](https://pydicom.github.io/pydicom/stable/auto_examples/index.html) are available in the documentation.
**Change a patient's ID**
```python
from pydicom import dcmread
ds = dcmread("/path/to/file.dcm")
# Edit the (0010,0020) 'Patient ID' element
ds.PatientID = "12345678"
ds.save_as("/path/to/file_updated.dcm")
```
**Display the Pixel Data**
With [NumPy](http://www.numpy.org) and [matplotlib](https://matplotlib.org/)
```python
import matplotlib.pyplot as plt
from pydicom import dcmread
from pydicom.data import get_testdata_file
# The path to a pydicom test dataset
path = get_testdata_file("CT_small.dcm")
ds = dcmread(path)
# `arr` is a numpy.ndarray
arr = ds.pixel_array
plt.imshow(arr, cmap="gray")
plt.show()
```
## Contributing
To contribute to *pydicom*, read our [contribution guide](https://github.com/pydicom/pydicom/blob/master/CONTRIBUTING.md).
To contribute an example or extension of *pydicom* that doesn't belong with the core software, see our contribution repository:
[contrib-pydicom](https://www.github.com/pydicom/contrib-pydicom).
%package help
Summary: Development documents and examples for pydicom
Provides: python3-pydicom-doc
%description help
[](https://github.com/pydicom/pydicom/actions?query=workflow%3Aunit-tests)
[](https://github.com/pydicom/pydicom/actions?query=workflow%3Atype-hints)
[](https://circleci.com/gh/pydicom/pydicom/tree/master)
[](https://codecov.io/gh/pydicom/pydicom)
[](https://img.shields.io/pypi/pyversions/pydicom.svg)
[](https://badge.fury.io/py/pydicom)
[](https://doi.org/10.5281/zenodo.6394735)
[](https://gitter.im/pydicom/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
# *pydicom*
*pydicom* is a pure Python package for working with [DICOM](https://www.dicomstandard.org/) files. It lets you read, modify and write DICOM data in an easy "pythonic" way.
As a pure Python package, *pydicom* can run anywhere Python runs without any other requirements, although if you're working with *Pixel Data* then we recommend you also install [NumPy](http://www.numpy.org).
If you're looking for a Python library for DICOM networking then you might be interested in another of our projects: [pynetdicom](https://github.com/pydicom/pynetdicom).
## Installation
Using [pip](https://pip.pypa.io/en/stable/):
```
pip install pydicom
```
Using [conda](https://docs.conda.io/en/latest/):
```
conda install -c conda-forge pydicom
```
For more information, including installation instructions for the development version, see the [installation guide](https://pydicom.github.io/pydicom/stable/tutorials/installation.html).
## Documentation
The *pydicom* [user guide](https://pydicom.github.io/pydicom/stable/old/pydicom_user_guide.html), [tutorials](https://pydicom.github.io/pydicom/stable/tutorials/index.html), [examples](https://pydicom.github.io/pydicom/stable/auto_examples/index.html) and [API reference](https://pydicom.github.io/pydicom/stable/reference/index.html) documentation is available for both the [current release](https://pydicom.github.io/pydicom/stable) and the [development version](https://pydicom.github.io/pydicom/dev) on GitHub Pages.
## *Pixel Data*
Compressed and uncompressed *Pixel Data* is always available to
be read, changed and written as [bytes](https://docs.python.org/3/library/stdtypes.html#bytes-objects):
```python
>>> from pydicom import dcmread
>>> from pydicom.data import get_testdata_file
>>> path = get_testdata_file("CT_small.dcm")
>>> ds = dcmread(path)
>>> type(ds.PixelData)
<class 'bytes'>
>>> len(ds.PixelData)
32768
>>> ds.PixelData[:2]
b'\xaf\x00'
```
If [NumPy](http://www.numpy.org) is installed, *Pixel Data* can be converted to an [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) using the [Dataset.pixel_array](https://pydicom.github.io/pydicom/stable/reference/generated/pydicom.dataset.Dataset.html#pydicom.dataset.Dataset.pixel_array) property:
```python
>>> arr = ds.pixel_array
>>> arr.shape
(128, 128)
>>> arr
array([[175, 180, 166, ..., 203, 207, 216],
[186, 183, 157, ..., 181, 190, 239],
[184, 180, 171, ..., 152, 164, 235],
...,
[906, 910, 923, ..., 922, 929, 927],
[914, 954, 938, ..., 942, 925, 905],
[959, 955, 916, ..., 911, 904, 909]], dtype=int16)
```
### Compressed *Pixel Data*
#### JPEG, JPEG-LS and JPEG 2000
Converting JPEG compressed *Pixel Data* to an ``ndarray`` requires installing one or more additional Python libraries. For information on which libraries are required, see the [pixel data handler documentation](https://pydicom.github.io/pydicom/stable/old/image_data_handlers.html#guide-compressed).
Compressing data into one of the JPEG formats is not currently supported.
#### RLE
Encoding and decoding RLE *Pixel Data* only requires NumPy, however it can
be quite slow. You may want to consider [installing one or more additional
Python libraries](https://pydicom.github.io/pydicom/stable/old/image_data_compression.html) to speed up the process.
## Examples
More [examples](https://pydicom.github.io/pydicom/stable/auto_examples/index.html) are available in the documentation.
**Change a patient's ID**
```python
from pydicom import dcmread
ds = dcmread("/path/to/file.dcm")
# Edit the (0010,0020) 'Patient ID' element
ds.PatientID = "12345678"
ds.save_as("/path/to/file_updated.dcm")
```
**Display the Pixel Data**
With [NumPy](http://www.numpy.org) and [matplotlib](https://matplotlib.org/)
```python
import matplotlib.pyplot as plt
from pydicom import dcmread
from pydicom.data import get_testdata_file
# The path to a pydicom test dataset
path = get_testdata_file("CT_small.dcm")
ds = dcmread(path)
# `arr` is a numpy.ndarray
arr = ds.pixel_array
plt.imshow(arr, cmap="gray")
plt.show()
```
## Contributing
To contribute to *pydicom*, read our [contribution guide](https://github.com/pydicom/pydicom/blob/master/CONTRIBUTING.md).
To contribute an example or extension of *pydicom* that doesn't belong with the core software, see our contribution repository:
[contrib-pydicom](https://www.github.com/pydicom/contrib-pydicom).
%prep
%autosetup -n pydicom-2.3.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pydicom -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.3.1-1
- Package Spec generated
|
