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
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
|
%global _empty_manifest_terminate_build 0
Name: python-lbsntransform
Version: 0.25.1
Release: 1
Summary: Location based social network (LBSN) data structure format & transfer tool
License: GNU GPLv3 or any higher
URL: https://gitlab.vgiscience.de/lbsn/lbsntransform
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/18/61/fe7960bc1a74d44fd818f69d6373c755ec926cbde4bcb4549b7fd845d477/lbsntransform-0.25.1.tar.gz
BuildArch: noarch
Requires: python3-lbsnstructure
Requires: python3-protobuf
Requires: python3-psycopg2-binary
Requires: python3-ppygis3
Requires: python3-shapely
Requires: python3-emoji
Requires: python3-requests
Requires: python3-geos
Requires: python3-numpy
Requires: python3-regex
Requires: python3-nltk
%description
[](https://pypi.org/project/lbsntransform/) [](https://gitlab.vgiscience.de/lbsn/lbsntransform) [](https://gitlab.vgiscience.de/lbsn/lbsntransform/-/commits/master) [](https://lbsn.vgiscience.org/lbsntransform/docs/)
# LBSNTransform
A python package that uses the [common location based social network (LBSN) data structure][lbsnstructure]
(ProtoBuf) to import, transform and export Social Media data such as Twitter and Flickr.
## Motivation
The goal is to provide a common interface to handle Social Media Data,
without the need to individually adapt to the myriad API endpoints available.
As an example, consider the ProtoBuf spec [lbsn.Post][lbsnpost], which can be a Tweet on Twitter,
a Photo shared on Flickr, or a post on Reddit. However, all of these objects share
a common set of attributes, which is reflected in the lbsnstructure.
The tool is based on a 4-Facet conceptual framework for LBSN, introduced in a paper
by [Dunkel et al. (2018)](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1546390).
The GDPR directly requests Social Media Network operators to allow
users to transfer accounts and data in-between services.
While there are attempts by Google, Facebook etc. (e.g. see the [data-transfer-project][data-transfer-project]),
this is not currently possible. With the lbsnstructure, a primary motivation is to systematically
characterize LBSN data aspects in a common, cross-network data scheme that enables privacy-by-design
for connected software, data handling and database design.
## Description
This tool enables data import from a Postgres database, JSON, or CSV and export to CSV, [LBSN ProtoBuf][lbsnstructure]
or the [hll][hlldb] and [raw][rawdb] versions of the LBSN prepared Postgres Databases.
The tool will map Social Media endpoints (e.g. Twitter tweets) to a common [LBSN Interchange Structure][lbsnstructure]
format in ProtoBuf. LBSNTransform can be used using the command line (CLI) or imported to other Python projects with
`import lbsntransform`, for on-the-fly conversion.
## Quick Start
The recommended way to install lbsntransform, for both Linux and Windows,
is through the conda package manager.
1. Create a conda env using `environment.yml`
First, create an environment with the dependencies for lbsntransform using
the [environment.yml][environment.yml] that is provided in the root of the repository.
```bash
git clone https://github.com/Sieboldianus/lbsntransform.git
cd lbsntransform
# not necessary, but recommended:
conda config --env --set channel_priority strict
conda env create -f environment.yml
```
2. Install lbsntransform without dependencies
Afterwards, install lbsntransform using pip, without dependencies.
```bash
conda activate lbsntransform
pip install lbsntransform --no-deps --upgrade
# or locally, from the latest commits on master
# pip install . --no-deps --upgrade
```
3. Import data using a mapping
For each data source, a mapping must be provided that
defines how data is mapped to the [lbsnstructure][lbsnstructure].
The default mapping is [lbsnraw][lbsnraw].
Additional mappings can be dynamically loaded from a folder.
We have provided two [example mappings][mappings] for the [Flickr YFCC100M dataset][yfcc100m] (CSV)
and Twitter (json).
For example, to import the first 1000 records from json data from Twitter to the
[lbsn raw database][rawdb], clone [field_mapping_twitter.py][field_mapping_twitter]
to a local folder `./resources/mappings/`, startup the Docker [rawdb][rawdb] container,
and use:
```shell
lbsntransform --origin 3 \
--mappings_path ./resources/mappings/ \
--file_input \
--file_type "json" \
--mappings_path ./resources/mappings/ \
--dbpassword_output "sample-key" \
--dbuser_output "postgres" \
--dbserveraddress_output "127.0.0.1:5432" \
--dbname_output "rawdb" \
--dbformat_output "lbsn" \
--transferlimit 1000
```
.. with the above input args, the the tool will:
- read local json from `./01_Input/`
- and store lbsn records to the [lbsn rawdb][rawdb].
Vice versa, to import data directly to the privacy-aware
version of lbsnstructure, called [hlldb][hlldb], startup the
Docker container, and use:
```shell
lbsntransform --origin 3 \
--mappings_path ./resources/mappings/ \
--file_input \
--file_type "json" \
--mappings_path ./resources/mappings/ \
--dbpassword_output "sample-key" \
--dbuser_output "postgres" \
--dbserveraddress_output "127.0.0.1:25432" \
--dbname_output "hlldb" \
--dbformat_output "hll" \
--dbpassword_hllworker "sample-key" \
--dbuser_hllworker "postgres" \
--dbserveraddress_hllworker "127.0.0.1:25432" \
--dbname_hllworker "hlldb" \
--include_lbsn_objects "origin,post" \
--include_lbsn_bases hashtag,place,date,community \
--transferlimit 1000
```
.. with the above input args, the the tool will:
- read local json from `./01_Input/`
- and store lbsn records to the privacy-aware [lbsn hlldb][hlldb]
- by converting only lbsn objects of type [origin][lbsnorigin] and [post][lbsnpost]
- and updating the HyperLogLog (HLL) target tables `hashtag`, `place`, `date` and `community`
A full list of possible input and output args is available in the
[documentation](https://lbsn.vgiscience.org/lbsntransform/docs/).
## Built With
* [lbsnstructure](https://pypi.org/project/lbsnstructure/) - A common language independend and cross-network social-media datascheme
* [protobuf](https://github.com/google/protobuf) - Google's data interchange format
* [psycopg2](https://github.com/psycopg/psycopg2) - Python-PostgreSQL Database Adapter
* [ppygis3](https://github.com/AlexImmer/ppygis3) - A PPyGIS port for Python
* [shapely](https://github.com/Toblerity/Shapely) - Geometric objects processing in Python
* [emoji](https://github.com/carpedm20/emoji/) - Emoji handling in Python
## Authors
* **Alexander Dunkel** - Initial work
See also the list of [contributors](/../graphs/master).
## License
This project is licensed under the GNU GPLv3 or any higher -
see the [LICENSE.md](LICENSE.md) file for details.
[lbsnstructure]: https://lbsn.vgiscience.org/structure/
[lbsnpost]: https://lbsn.vgiscience.org/structure/#post
[lbsnorigin]: https://lbsn.vgiscience.org/structure/#origin
[data-transfer-project]: https://datatransferproject.dev/
[rawdb]: https://gitlab.vgiscience.de/lbsn/databases/rawdb
[hlldb]: https://gitlab.vgiscience.de/lbsn/databases/hlldb
[lbsnraw]: lbsntransform/input/mappings/field_mapping_lbsn.py
[mappings]: resources/mappings
[field_mapping_twitter]: resources/mappings/field_mapping_twitter.py
[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/
%package -n python3-lbsntransform
Summary: Location based social network (LBSN) data structure format & transfer tool
Provides: python-lbsntransform
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-lbsntransform
[](https://pypi.org/project/lbsntransform/) [](https://gitlab.vgiscience.de/lbsn/lbsntransform) [](https://gitlab.vgiscience.de/lbsn/lbsntransform/-/commits/master) [](https://lbsn.vgiscience.org/lbsntransform/docs/)
# LBSNTransform
A python package that uses the [common location based social network (LBSN) data structure][lbsnstructure]
(ProtoBuf) to import, transform and export Social Media data such as Twitter and Flickr.
## Motivation
The goal is to provide a common interface to handle Social Media Data,
without the need to individually adapt to the myriad API endpoints available.
As an example, consider the ProtoBuf spec [lbsn.Post][lbsnpost], which can be a Tweet on Twitter,
a Photo shared on Flickr, or a post on Reddit. However, all of these objects share
a common set of attributes, which is reflected in the lbsnstructure.
The tool is based on a 4-Facet conceptual framework for LBSN, introduced in a paper
by [Dunkel et al. (2018)](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1546390).
The GDPR directly requests Social Media Network operators to allow
users to transfer accounts and data in-between services.
While there are attempts by Google, Facebook etc. (e.g. see the [data-transfer-project][data-transfer-project]),
this is not currently possible. With the lbsnstructure, a primary motivation is to systematically
characterize LBSN data aspects in a common, cross-network data scheme that enables privacy-by-design
for connected software, data handling and database design.
## Description
This tool enables data import from a Postgres database, JSON, or CSV and export to CSV, [LBSN ProtoBuf][lbsnstructure]
or the [hll][hlldb] and [raw][rawdb] versions of the LBSN prepared Postgres Databases.
The tool will map Social Media endpoints (e.g. Twitter tweets) to a common [LBSN Interchange Structure][lbsnstructure]
format in ProtoBuf. LBSNTransform can be used using the command line (CLI) or imported to other Python projects with
`import lbsntransform`, for on-the-fly conversion.
## Quick Start
The recommended way to install lbsntransform, for both Linux and Windows,
is through the conda package manager.
1. Create a conda env using `environment.yml`
First, create an environment with the dependencies for lbsntransform using
the [environment.yml][environment.yml] that is provided in the root of the repository.
```bash
git clone https://github.com/Sieboldianus/lbsntransform.git
cd lbsntransform
# not necessary, but recommended:
conda config --env --set channel_priority strict
conda env create -f environment.yml
```
2. Install lbsntransform without dependencies
Afterwards, install lbsntransform using pip, without dependencies.
```bash
conda activate lbsntransform
pip install lbsntransform --no-deps --upgrade
# or locally, from the latest commits on master
# pip install . --no-deps --upgrade
```
3. Import data using a mapping
For each data source, a mapping must be provided that
defines how data is mapped to the [lbsnstructure][lbsnstructure].
The default mapping is [lbsnraw][lbsnraw].
Additional mappings can be dynamically loaded from a folder.
We have provided two [example mappings][mappings] for the [Flickr YFCC100M dataset][yfcc100m] (CSV)
and Twitter (json).
For example, to import the first 1000 records from json data from Twitter to the
[lbsn raw database][rawdb], clone [field_mapping_twitter.py][field_mapping_twitter]
to a local folder `./resources/mappings/`, startup the Docker [rawdb][rawdb] container,
and use:
```shell
lbsntransform --origin 3 \
--mappings_path ./resources/mappings/ \
--file_input \
--file_type "json" \
--mappings_path ./resources/mappings/ \
--dbpassword_output "sample-key" \
--dbuser_output "postgres" \
--dbserveraddress_output "127.0.0.1:5432" \
--dbname_output "rawdb" \
--dbformat_output "lbsn" \
--transferlimit 1000
```
.. with the above input args, the the tool will:
- read local json from `./01_Input/`
- and store lbsn records to the [lbsn rawdb][rawdb].
Vice versa, to import data directly to the privacy-aware
version of lbsnstructure, called [hlldb][hlldb], startup the
Docker container, and use:
```shell
lbsntransform --origin 3 \
--mappings_path ./resources/mappings/ \
--file_input \
--file_type "json" \
--mappings_path ./resources/mappings/ \
--dbpassword_output "sample-key" \
--dbuser_output "postgres" \
--dbserveraddress_output "127.0.0.1:25432" \
--dbname_output "hlldb" \
--dbformat_output "hll" \
--dbpassword_hllworker "sample-key" \
--dbuser_hllworker "postgres" \
--dbserveraddress_hllworker "127.0.0.1:25432" \
--dbname_hllworker "hlldb" \
--include_lbsn_objects "origin,post" \
--include_lbsn_bases hashtag,place,date,community \
--transferlimit 1000
```
.. with the above input args, the the tool will:
- read local json from `./01_Input/`
- and store lbsn records to the privacy-aware [lbsn hlldb][hlldb]
- by converting only lbsn objects of type [origin][lbsnorigin] and [post][lbsnpost]
- and updating the HyperLogLog (HLL) target tables `hashtag`, `place`, `date` and `community`
A full list of possible input and output args is available in the
[documentation](https://lbsn.vgiscience.org/lbsntransform/docs/).
## Built With
* [lbsnstructure](https://pypi.org/project/lbsnstructure/) - A common language independend and cross-network social-media datascheme
* [protobuf](https://github.com/google/protobuf) - Google's data interchange format
* [psycopg2](https://github.com/psycopg/psycopg2) - Python-PostgreSQL Database Adapter
* [ppygis3](https://github.com/AlexImmer/ppygis3) - A PPyGIS port for Python
* [shapely](https://github.com/Toblerity/Shapely) - Geometric objects processing in Python
* [emoji](https://github.com/carpedm20/emoji/) - Emoji handling in Python
## Authors
* **Alexander Dunkel** - Initial work
See also the list of [contributors](/../graphs/master).
## License
This project is licensed under the GNU GPLv3 or any higher -
see the [LICENSE.md](LICENSE.md) file for details.
[lbsnstructure]: https://lbsn.vgiscience.org/structure/
[lbsnpost]: https://lbsn.vgiscience.org/structure/#post
[lbsnorigin]: https://lbsn.vgiscience.org/structure/#origin
[data-transfer-project]: https://datatransferproject.dev/
[rawdb]: https://gitlab.vgiscience.de/lbsn/databases/rawdb
[hlldb]: https://gitlab.vgiscience.de/lbsn/databases/hlldb
[lbsnraw]: lbsntransform/input/mappings/field_mapping_lbsn.py
[mappings]: resources/mappings
[field_mapping_twitter]: resources/mappings/field_mapping_twitter.py
[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/
%package help
Summary: Development documents and examples for lbsntransform
Provides: python3-lbsntransform-doc
%description help
[](https://pypi.org/project/lbsntransform/) [](https://gitlab.vgiscience.de/lbsn/lbsntransform) [](https://gitlab.vgiscience.de/lbsn/lbsntransform/-/commits/master) [](https://lbsn.vgiscience.org/lbsntransform/docs/)
# LBSNTransform
A python package that uses the [common location based social network (LBSN) data structure][lbsnstructure]
(ProtoBuf) to import, transform and export Social Media data such as Twitter and Flickr.
## Motivation
The goal is to provide a common interface to handle Social Media Data,
without the need to individually adapt to the myriad API endpoints available.
As an example, consider the ProtoBuf spec [lbsn.Post][lbsnpost], which can be a Tweet on Twitter,
a Photo shared on Flickr, or a post on Reddit. However, all of these objects share
a common set of attributes, which is reflected in the lbsnstructure.
The tool is based on a 4-Facet conceptual framework for LBSN, introduced in a paper
by [Dunkel et al. (2018)](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1546390).
The GDPR directly requests Social Media Network operators to allow
users to transfer accounts and data in-between services.
While there are attempts by Google, Facebook etc. (e.g. see the [data-transfer-project][data-transfer-project]),
this is not currently possible. With the lbsnstructure, a primary motivation is to systematically
characterize LBSN data aspects in a common, cross-network data scheme that enables privacy-by-design
for connected software, data handling and database design.
## Description
This tool enables data import from a Postgres database, JSON, or CSV and export to CSV, [LBSN ProtoBuf][lbsnstructure]
or the [hll][hlldb] and [raw][rawdb] versions of the LBSN prepared Postgres Databases.
The tool will map Social Media endpoints (e.g. Twitter tweets) to a common [LBSN Interchange Structure][lbsnstructure]
format in ProtoBuf. LBSNTransform can be used using the command line (CLI) or imported to other Python projects with
`import lbsntransform`, for on-the-fly conversion.
## Quick Start
The recommended way to install lbsntransform, for both Linux and Windows,
is through the conda package manager.
1. Create a conda env using `environment.yml`
First, create an environment with the dependencies for lbsntransform using
the [environment.yml][environment.yml] that is provided in the root of the repository.
```bash
git clone https://github.com/Sieboldianus/lbsntransform.git
cd lbsntransform
# not necessary, but recommended:
conda config --env --set channel_priority strict
conda env create -f environment.yml
```
2. Install lbsntransform without dependencies
Afterwards, install lbsntransform using pip, without dependencies.
```bash
conda activate lbsntransform
pip install lbsntransform --no-deps --upgrade
# or locally, from the latest commits on master
# pip install . --no-deps --upgrade
```
3. Import data using a mapping
For each data source, a mapping must be provided that
defines how data is mapped to the [lbsnstructure][lbsnstructure].
The default mapping is [lbsnraw][lbsnraw].
Additional mappings can be dynamically loaded from a folder.
We have provided two [example mappings][mappings] for the [Flickr YFCC100M dataset][yfcc100m] (CSV)
and Twitter (json).
For example, to import the first 1000 records from json data from Twitter to the
[lbsn raw database][rawdb], clone [field_mapping_twitter.py][field_mapping_twitter]
to a local folder `./resources/mappings/`, startup the Docker [rawdb][rawdb] container,
and use:
```shell
lbsntransform --origin 3 \
--mappings_path ./resources/mappings/ \
--file_input \
--file_type "json" \
--mappings_path ./resources/mappings/ \
--dbpassword_output "sample-key" \
--dbuser_output "postgres" \
--dbserveraddress_output "127.0.0.1:5432" \
--dbname_output "rawdb" \
--dbformat_output "lbsn" \
--transferlimit 1000
```
.. with the above input args, the the tool will:
- read local json from `./01_Input/`
- and store lbsn records to the [lbsn rawdb][rawdb].
Vice versa, to import data directly to the privacy-aware
version of lbsnstructure, called [hlldb][hlldb], startup the
Docker container, and use:
```shell
lbsntransform --origin 3 \
--mappings_path ./resources/mappings/ \
--file_input \
--file_type "json" \
--mappings_path ./resources/mappings/ \
--dbpassword_output "sample-key" \
--dbuser_output "postgres" \
--dbserveraddress_output "127.0.0.1:25432" \
--dbname_output "hlldb" \
--dbformat_output "hll" \
--dbpassword_hllworker "sample-key" \
--dbuser_hllworker "postgres" \
--dbserveraddress_hllworker "127.0.0.1:25432" \
--dbname_hllworker "hlldb" \
--include_lbsn_objects "origin,post" \
--include_lbsn_bases hashtag,place,date,community \
--transferlimit 1000
```
.. with the above input args, the the tool will:
- read local json from `./01_Input/`
- and store lbsn records to the privacy-aware [lbsn hlldb][hlldb]
- by converting only lbsn objects of type [origin][lbsnorigin] and [post][lbsnpost]
- and updating the HyperLogLog (HLL) target tables `hashtag`, `place`, `date` and `community`
A full list of possible input and output args is available in the
[documentation](https://lbsn.vgiscience.org/lbsntransform/docs/).
## Built With
* [lbsnstructure](https://pypi.org/project/lbsnstructure/) - A common language independend and cross-network social-media datascheme
* [protobuf](https://github.com/google/protobuf) - Google's data interchange format
* [psycopg2](https://github.com/psycopg/psycopg2) - Python-PostgreSQL Database Adapter
* [ppygis3](https://github.com/AlexImmer/ppygis3) - A PPyGIS port for Python
* [shapely](https://github.com/Toblerity/Shapely) - Geometric objects processing in Python
* [emoji](https://github.com/carpedm20/emoji/) - Emoji handling in Python
## Authors
* **Alexander Dunkel** - Initial work
See also the list of [contributors](/../graphs/master).
## License
This project is licensed under the GNU GPLv3 or any higher -
see the [LICENSE.md](LICENSE.md) file for details.
[lbsnstructure]: https://lbsn.vgiscience.org/structure/
[lbsnpost]: https://lbsn.vgiscience.org/structure/#post
[lbsnorigin]: https://lbsn.vgiscience.org/structure/#origin
[data-transfer-project]: https://datatransferproject.dev/
[rawdb]: https://gitlab.vgiscience.de/lbsn/databases/rawdb
[hlldb]: https://gitlab.vgiscience.de/lbsn/databases/hlldb
[lbsnraw]: lbsntransform/input/mappings/field_mapping_lbsn.py
[mappings]: resources/mappings
[field_mapping_twitter]: resources/mappings/field_mapping_twitter.py
[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/
%prep
%autosetup -n lbsntransform-0.25.1
%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-lbsntransform -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.25.1-1
- Package Spec generated
|
